From b341f448a9c9f90fd135701d408edace5e293690 Mon Sep 17 00:00:00 2001 From: Thomas Gardos <3973626+trgardos@users.noreply.github.com> Date: Sat, 12 Apr 2025 18:24:37 -0400 Subject: [PATCH] notes from fall --- ds701_book/04-Linear-Algebra-Improvements.md | 12 + ds701_book/05-DTW-example.ipynb | 383 ++ ds701_book/09-GMM-EM-Convergence.qmd | 56 + ds701_book/09-GMM-EM.qmd | 213 + .../14ex-decision-tree-iris-dataset.qmd | 53 + ds701_book/15-Curse-of-Dimensionality.qmd | 388 ++ ds701_book/20-RecSys-GPTo1.qmd | 290 ++ ds701_book/20-Recommender-Systems-notes.md | 7 + ds701_book/20a-RecSys-with-Deep-Learning.qmd | 351 ++ .../21-ring-lattice-shortest-path-proof.qmd | 39 + ds701_book/23-25-NNs-outline.md | 29 + ds701_book/23-NN-I-Gradient-Descent-save.qmd | 1017 +++++ ds701_book/24-NN-Outline.md | 49 + ds701_book/26-TimeSeries-Notes.qmd | 279 ++ ds701_book/RNN-time-series-example.qmd | 232 + ds701_book/ch11.ipynb | 2710 ++++++++++++ ...de-on-time-series-analysis-in-python.ipynb | 1 + ds701_book/data/.gitignore | 4 + ds701_book/example.txt | 3 + .../figs/L06-k-means-on-uniform-data.png | Bin 0 -> 67868 bytes .../figs/L06-kmeans-dataset-comparison.png | Bin 0 -> 287155 bytes ds701_book/figs/L14-gain-ratio.png | Bin 0 -> 38134 bytes ds701_book/figs/L14-terrier-savings-logo.webp | Bin 0 -> 269032 bytes ds701_book/figs/L17-Daniel-Kahneman--NYT.webp | Bin 0 -> 31360 bytes .../figs/L17-Israeli-Airforce-Pirate.webp | Bin 0 -> 77158 bytes ds701_book/figs/RecSys-figs/dl-recsys-cnn.png | Bin 0 -> 76191 bytes ds701_book/figs/RecSys-figs/dl-recsys-mlp.png | Bin 0 -> 81717 bytes ds701_book/figs/RecSys-figs/dl-recsys-rnn.png | Bin 0 -> 82220 bytes ds701_book/figs/ada_lovelace.webp | Bin 0 -> 927978 bytes ds701_book/figs/decision_tree.png | Bin 102135 -> 102068 bytes .../how-random-forests-really-work.ipynb | 3724 +++++++++++++++++ ds701_book/img.jpg | Bin 0 -> 11780 bytes ds701_book/intro_dm_classification.ipynb | 1425 +++++++ ds701_book/iris | 37 + ds701_book/iris.png | Bin 0 -> 97383 bytes ds701_book/nvidia_data.csv | 251 ++ ds701_book/remove_dev_fences.py | 15 + ds701_book/sklearn-trees.ipynb | 1271 ++++++ ds701_book/sklearn-trees.qmd | 704 ++++ ds701_book/timeseries-chatgpt.ipynb | 1082 +++++ ds701_book/timeseries-chatgpt.qmd | 391 ++ ds701_book/yahoo_data.csv | 252 ++ 42 files changed, 15268 insertions(+) create mode 100644 ds701_book/04-Linear-Algebra-Improvements.md create mode 100644 ds701_book/05-DTW-example.ipynb create mode 100644 ds701_book/09-GMM-EM-Convergence.qmd create mode 100644 ds701_book/09-GMM-EM.qmd create mode 100644 ds701_book/14ex-decision-tree-iris-dataset.qmd create mode 100644 ds701_book/15-Curse-of-Dimensionality.qmd create mode 100644 ds701_book/20-RecSys-GPTo1.qmd create mode 100644 ds701_book/20-Recommender-Systems-notes.md create mode 100644 ds701_book/20a-RecSys-with-Deep-Learning.qmd create mode 100644 ds701_book/21-ring-lattice-shortest-path-proof.qmd create mode 100644 ds701_book/23-25-NNs-outline.md create mode 100644 ds701_book/23-NN-I-Gradient-Descent-save.qmd create mode 100644 ds701_book/24-NN-Outline.md create mode 100644 ds701_book/26-TimeSeries-Notes.qmd create mode 100644 ds701_book/RNN-time-series-example.qmd create mode 100644 ds701_book/ch11.ipynb create mode 100644 ds701_book/complete-guide-on-time-series-analysis-in-python.ipynb create mode 100644 ds701_book/data/.gitignore create mode 100644 ds701_book/example.txt create mode 100644 ds701_book/figs/L06-k-means-on-uniform-data.png create mode 100644 ds701_book/figs/L06-kmeans-dataset-comparison.png create mode 100644 ds701_book/figs/L14-gain-ratio.png create mode 100644 ds701_book/figs/L14-terrier-savings-logo.webp create mode 100644 ds701_book/figs/L17-Daniel-Kahneman--NYT.webp create mode 100644 ds701_book/figs/L17-Israeli-Airforce-Pirate.webp create mode 100644 ds701_book/figs/RecSys-figs/dl-recsys-cnn.png create mode 100644 ds701_book/figs/RecSys-figs/dl-recsys-mlp.png create mode 100644 ds701_book/figs/RecSys-figs/dl-recsys-rnn.png create mode 100644 ds701_book/figs/ada_lovelace.webp create mode 100644 ds701_book/how-random-forests-really-work.ipynb create mode 100644 ds701_book/img.jpg create mode 100644 ds701_book/intro_dm_classification.ipynb create mode 100644 ds701_book/iris create mode 100644 ds701_book/iris.png create mode 100644 ds701_book/nvidia_data.csv create mode 100644 ds701_book/remove_dev_fences.py create mode 100644 ds701_book/sklearn-trees.ipynb create mode 100644 ds701_book/sklearn-trees.qmd create mode 100644 ds701_book/timeseries-chatgpt.ipynb create mode 100644 ds701_book/timeseries-chatgpt.qmd create mode 100644 ds701_book/yahoo_data.csv diff --git a/ds701_book/04-Linear-Algebra-Improvements.md b/ds701_book/04-Linear-Algebra-Improvements.md new file mode 100644 index 00000000..06e7c740 --- /dev/null +++ b/ds701_book/04-Linear-Algebra-Improvements.md @@ -0,0 +1,12 @@ +# Notes on Improvements to make to 04-Linear-Algebra-Refresher + +Consider reordering the entire lecture to start with simple systems of linear +equations and how to represent them as vectors and matrices, and then how to +solve them, types of solutions, what the A matrices says about the solutions. +Then go into the geometry of linear algebra, etc. See Strang MIT OCW linear +algebra course for ideas. + +Make the figures interactive. +For example on scalar multiplications of vectors, have a slider to change scaler +values between -2 and 2. + diff --git a/ds701_book/05-DTW-example.ipynb b/ds701_book/05-DTW-example.ipynb new file mode 100644 index 00000000..dbf27ef4 --- /dev/null +++ b/ds701_book/05-DTW-example.ipynb @@ -0,0 +1,383 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# DTW Example\n", + "\n", + "Based on chatgpt prompt." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from ipywidgets import interact, FloatSlider\n", + "import ipywidgets as widgets" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def dtw(x, y):\n", + " \"\"\"\n", + " Computes the Dynamic Time Warping (DTW) distance between two time series x and y.\n", + " Returns the accumulated cost matrix and the warping path.\n", + " \"\"\"\n", + " n, m = len(x), len(y)\n", + " dtw_matrix = np.full((n+1, m+1), np.inf)\n", + " dtw_matrix[0, 0] = 0\n", + "\n", + " # Populate the DTW matrix.\n", + " for i in range(1, n+1):\n", + " for j in range(1, m+1):\n", + " cost = abs(x[i-1] - y[j-1])\n", + " last_min = min(\n", + " dtw_matrix[i-1, j], # Insertion\n", + " dtw_matrix[i, j-1], # Deletion\n", + " dtw_matrix[i-1, j-1] # Match\n", + " )\n", + " dtw_matrix[i, j] = cost + last_min\n", + "\n", + " dtw_matrix = dtw_matrix[1:, 1:] # Remove the extra padding.\n", + "\n", + " # Backtrack to find the optimal warping path.\n", + " i, j = n-1, m-1\n", + " warping_path = [(i, j)]\n", + "\n", + " while i > 0 or j > 0:\n", + " if i == 0:\n", + " j -= 1\n", + " elif j == 0:\n", + " i -= 1\n", + " else:\n", + " steps = [dtw_matrix[i-1, j], dtw_matrix[i, j-1], dtw_matrix[i-1, j-1]]\n", + " argmin = np.argmin(steps)\n", + " if argmin == 0:\n", + " i -= 1\n", + " elif argmin == 1:\n", + " j -= 1\n", + " else:\n", + " i -= 1\n", + " j -= 1\n", + " warping_path.append((i, j))\n", + "\n", + " warping_path.reverse()\n", + " return dtw_matrix, dtw_matrix[-1, -1], warping_path\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# Create two artificial time series.\n", + "t = np.linspace(0, 2 * np.pi, 100)\n", + "x = np.sin(t)\n", + "y = np.sin(t + 1.0) # Phase-shifted sine wave.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DTW distance between the two time series: 14.8877\n" + ] + } + ], + "source": [ + "\n", + "# Compute DTW.\n", + "dtw_matrix, distance, path = dtw(x, y)\n", + "print(f\"DTW distance between the two time series: {distance:.4f}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "L2 norm between the two time series: 6.7984\n" + ] + } + ], + "source": [ + "# also calculate the l2 norm between the two\n", + "l2_norm = np.linalg.norm(x - y)\n", + "print(f\"L2 norm between the two time series: {l2_norm:.4f}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "# Plot the time series.\n", + "plt.figure(figsize=(12, 6))\n", + "plt.subplot(2, 1, 1)\n", + "plt.plot(t, x, label='Time Series 1')\n", + "plt.plot(t, y, label='Time Series 2')\n", + "plt.title('Artificial Time Series')\n", + "plt.legend()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "# Plot the DTW matrix and warping path.\n", + "plt.subplot(2, 1, 2)\n", + "plt.imshow(dtw_matrix.T, origin='lower', cmap='gray', interpolation='nearest')\n", + "path_x, path_y = zip(*path)\n", + "plt.plot(path_x, path_y, 'r') # Warping path.\n", + "plt.title('DTW Matrix with Warping Path')\n", + "plt.xlabel('Time Series 1 Index')\n", + "plt.ylabel('Time Series 2 Index')\n", + "plt.colorbar()\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "83260a9f270044ac8dd8d0b83d91ca0c", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(FloatSlider(value=1.0, description='Shift:', max=3.2), Output()), _dom_classes=('widget-…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%matplotlib inline\n", + "\n", + "# Create two artificial time series.\n", + "t = np.linspace(0, 2 * np.pi, 100)\n", + "x = np.sin(t)\n", + "\n", + "def update_plot(shift):\n", + " y = np.sin(t + shift) # Phase-shifted sine wave.\n", + " \n", + " # Compute DTW.\n", + " dtw_matrix, distance, path = dtw(x, y)\n", + " l2_norm = np.linalg.norm(x - y)\n", + " \n", + " # Clear previous plots\n", + " plt.clf()\n", + " \n", + " # Plot the time series.\n", + " plt.figure(figsize=(12, 6))\n", + " plt.subplot(2, 1, 1)\n", + " plt.plot(t, x, label='Time Series 1')\n", + " plt.plot(t, y, label='Time Series 2')\n", + " plt.title(f'Artificial Time Series (Shift: {shift:.2f})')\n", + " plt.legend()\n", + " \n", + " # Plot the DTW matrix and warping path.\n", + " plt.subplot(2, 1, 2)\n", + " plt.imshow(dtw_matrix.T, origin='lower', cmap='gray', interpolation='nearest')\n", + " path_x, path_y = zip(*path)\n", + " plt.plot(path_x, path_y, 'r') # Warping path.\n", + " plt.title(f'DTW Matrix with Warping Path\\nDTW Distance: {distance:.4f}, L2 Norm: {l2_norm:.4f}')\n", + " plt.xlabel('Time Series 1 Index')\n", + " plt.ylabel('Time Series 2 Index')\n", + " plt.colorbar()\n", + " \n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "# Create an interactive slider\n", + "shift_slider = FloatSlider(value=1.0, min=0.0, max=3.2, step=0.1, description='Shift:')\n", + "interact(update_plot, shift=shift_slider)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create two artificial time series with Gaussian noise and additional Gaussian distributions.\n", + "t = np.linspace(0, 2 * np.pi, 100)\n", + "x = np.sin(t) + np.random.normal(0, 0.1, t.shape)\n", + "\n", + "# Add Gaussian distributions at different offsets\n", + "gaussian1 = np.exp(-0.5 * ((t - np.pi/2) / 0.1)**2)\n", + "gaussian2 = np.exp(-0.5 * ((t - np.pi) / 0.1)**2)\n", + "gaussian3 = np.exp(-0.5 * ((t - 3*np.pi/2) / 0.1)**2)\n", + "gaussian4 = np.exp(-0.5 * ((t - 2*np.pi) / 0.1)**2)\n", + "\n", + "x += gaussian1 + gaussian2 + gaussian3 + gaussian4\n", + "\n", + "# Create the second time series with a random offset between 0.1 and 0.5\n", + "offset = np.random.uniform(-0.5, 0.5)\n", + "y = np.sin(t + offset) + np.random.normal(0, 0.1, t.shape)\n", + "offset = np.random.uniform(-0.5, 0.5)\n", + "y += np.exp(-0.5 * ((t - np.pi/2 + offset) / 0.1)**2)\n", + "offset = np.random.uniform(-0.5, 0.5)\n", + "y += np.exp(-0.5 * ((t - np.pi + offset) / 0.1)**2)\n", + "offset = np.random.uniform(-0.5, 0.5)\n", + "y += np.exp(-0.5 * ((t - 3*np.pi/2 + offset) / 0.1)**2)\n", + "offset = np.random.uniform(-0.5, 0.5)\n", + "y += np.exp(-0.5 * ((t - 2*np.pi + offset) / 0.1)**2)\n", + "\n", + "# Plot the new time series\n", + "plt.figure(figsize=(12, 6))\n", + "plt.plot(t, x, label='Time Series 1')\n", + "plt.plot(t, y, label='Time Series 2')\n", + "plt.title('Artificial Time Series with Gaussian Noise and Distributions')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DTW distance between the two time series: 13.5916\n" + ] + } + ], + "source": [ + "\n", + "# Compute DTW.\n", + "dtw_matrix, distance, path = dtw(x, y)\n", + "print(f\"DTW distance between the two time series: {distance:.4f}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "L2 norm between the two time series: 4.4303\n" + ] + } + ], + "source": [ + "# also calculate the l2 norm between the two\n", + "l2_norm = np.linalg.norm(x - y)\n", + "print(f\"L2 norm between the two time series: {l2_norm:.4f}\")\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/ds701_book/09-GMM-EM-Convergence.qmd b/ds701_book/09-GMM-EM-Convergence.qmd new file mode 100644 index 00000000..6d3150f7 --- /dev/null +++ b/ds701_book/09-GMM-EM-Convergence.qmd @@ -0,0 +1,56 @@ +--- +title: "GMM EM Convergence" +--- + +The convergence criteria for the Expectation-Maximization (EM) algorithm generally revolve around assessing the change in either the model parameters or the likelihood function across iterations. Here are the common convergence criteria used: + +1. Log-Likelihood Convergence (Most Common) + +The EM algorithm seeks to maximize the log-likelihood of the observed data under the current model parameters. A common convergence criterion is based on the change in the log-likelihood value between successive iterations. The algorithm stops when the difference between the log-likelihood in two consecutive iterations is smaller than a predefined threshold (tolerance), typically denoted as tol. + +Convergence criterion: + +Where: + + •  is the log-likelihood at iteration , + •  is a small positive number (e.g., ). + +2. Parameter Convergence + +Instead of focusing on the log-likelihood, another approach is to check whether the model parameters (means, covariances, and mixture weights) have stabilized. This can be useful when the log-likelihood changes only marginally but the parameter values continue to evolve. + +Convergence criterion: + +Where: + + •  represents the model parameters (means, covariances, and weights) at iteration , + •  is the Euclidean (L2) norm, + •  is a small positive number. + +3. Responsibility Convergence + +This criterion checks whether the soft assignments or responsibilities (posterior probabilities of cluster membership) have stabilized across iterations. If the change in responsibilities between iterations is smaller than a threshold, the algorithm stops. + +Convergence criterion: + +Where: + + •  is the responsibility of data point  for cluster  at iteration , + •  is a small positive number. + +4. Maximum Number of Iterations + +The EM algorithm is typically capped at a maximum number of iterations to avoid long runtimes in cases where the log-likelihood or parameters converge very slowly or never fully stabilize. + +Criterion: + +Where: + + •  is a predefined limit (e.g., 100 or 500 iterations). + +Typical Setup in Practice: + + • The most commonly used criterion is log-likelihood convergence, combined with a maximum number of iterations as a safeguard. + • A typical tolerance value for the log-likelihood difference is  or , depending on the precision needed. + +In summary, the EM algorithm usually stops when the log-likelihood improvement between iterations falls below a small threshold or when the number of iterations exceeds a predefined limit. \ No newline at end of file diff --git a/ds701_book/09-GMM-EM.qmd b/ds701_book/09-GMM-EM.qmd new file mode 100644 index 00000000..82a934c5 --- /dev/null +++ b/ds701_book/09-GMM-EM.qmd @@ -0,0 +1,213 @@ +--- +title: "GMM EM Algorithm" +jupyter: python3 +--- + +## A GMM Example + +Imagine you’re running a coffee shop, and you have data on your customers’ preferences +for coffee. + +Each customer likes a different blend of beans, which you can represent +as a point in two dimensions: + +* sweetness (x-axis) and +* acidity (y-axis). + +Your goal is to identify three most popular blends (clusters) from a pile of +customer reviews that provide noisy measurements of these two characteristics. + +This data could be generated synthetically by sampling from three Gaussian +distributions, each representing a different coffee blend that your customers +might like. The task for the students would be to uncover these hidden coffee +blends using a GMM. + +For an intuitive way to explain Expectation-Maximization (EM): + +You can describe it as a two-step process that alternates between two roles: + +1. Expectation Step (E-step): The model takes a guess about the likelihood that each customer belongs to each blend. At this point, it might not be sure, so it assigns probabilities (soft assignments) based on how close the customers’ preferences are to the different blends. +2. Maximization Step (M-step): The model then updates its guess about the actual parameters of the coffee blends—essentially adjusting the mean, variance, and proportion of customers for each blend, based on the soft assignments from the previous step. + +The EM algorithm is like refining a recipe: each time you taste-test (E-step) and then tweak the ingredients (M-step), the blend becomes more representative of what customers want. + +Let’s continue with the Python code for generating synthetic data representing the coffee preferences of your customers. After generating the data, we’ll implement the EM algorithm step-by-step. + +## Step 1: Generate synthetic data + +```{python} +import numpy as np +import matplotlib.pyplot as plt + +# Set seed for reproducibility +np.random.seed(42) + +# Means and covariances for three Gaussian distributions (coffee blends) +means = np.array([[2, 3], [8, 7], [5, 10]]) # sweetness and acidity means +covariances = [np.array([[1, 0.5], [0.5, 1]]), # covariance matrix for blend 1 + np.array([[1, -0.3], [-0.3, 1]]), # covariance matrix for blend 2 + np.array([[1, 0], [0, 1]])] # covariance matrix for blend 3 + +# Number of points in each cluster (representing customers) +points_per_cluster = 100 + +# Generate points from each Gaussian distribution +X1 = np.random.multivariate_normal(means[0], covariances[0], points_per_cluster) +X2 = np.random.multivariate_normal(means[1], covariances[1], points_per_cluster) +X3 = np.random.multivariate_normal(means[2], covariances[2], points_per_cluster) + +# Combine all points into one dataset +X = np.vstack((X1, X2, X3)) +``` + + +## Plot the synthetic dataset + +```{python} +# Plot the synthetic dataset +plt.scatter(X[:, 0], X[:, 1], s=30, color='b', label="Customers' coffee preferences") +plt.title('Synthetic Coffee Preferences Dataset') +plt.xlabel('Sweetness') +plt.ylabel('Acidity') +plt.legend() +plt.show() +``` + +## Step 2: Implement the EM Algorithm + +Now that we have the data, we’ll implement the EM algorithm for a Gaussian Mixture Model. The algorithm involves two steps: + +1. Expectation (E-step): Estimate the probability that each data point belongs to each cluster based on current parameters (mean, covariance, and mixture weights). +2. Maximization (M-step): Update the parameters (means, covariances, and mixture weights) based on the probabilities from the E-step. + +Here is the Python code to implement this step-by-step: + +```{python} +from scipy.stats import multivariate_normal + +# Initialize parameters for the EM algorithm +# We'll randomly select data points as the initial means +# and initialize the covariances as identity matrices +# and the weights as equal. +def initialize_params(X, n_clusters): + np.random.seed(42) + n_samples, n_features = X.shape + + # Randomly initialize means from the data + means = X[np.random.choice(n_samples, n_clusters, False)] + + # Initialize covariances as identity matrices + covariances = [np.eye(n_features) for _ in range(n_clusters)] + + # Initialize equal weights for the mixture components + weights = np.ones(n_clusters) / n_clusters + + return means, covariances, weights + +# E-step: compute the responsibility (posterior probability that a point belongs to a cluster) +def expectation_step(X, means, covariances, weights): + n_samples, n_clusters = X.shape[0], len(means) + responsibilities = np.zeros((n_samples, n_clusters)) + + for k in range(n_clusters): + responsibilities[:, k] = weights[k] * multivariate_normal.pdf(X, means[k], covariances[k]) + + # Normalize the responsibilities + responsibilities /= responsibilities.sum(axis=1, keepdims=True) + + return responsibilities + +# M-step: update the parameters based on the current responsibilities +def maximization_step(X, responsibilities): + n_samples, n_clusters = responsibilities.shape + n_features = X.shape[1] + + # Initialize parameters + means = np.zeros((n_clusters, n_features)) + covariances = [] + weights = np.zeros(n_clusters) + + for k in range(n_clusters): + # Effective number of points assigned to cluster k + Nk = responsibilities[:, k].sum() + + # Update the means + means[k] = (X * responsibilities[:, k][:, np.newaxis]).sum(axis=0) / Nk + + # Update the covariance matrices + covariance_k = np.zeros((n_features, n_features)) + for i in range(n_samples): + diff = (X[i] - means[k]).reshape(-1, 1) + covariance_k += responsibilities[i, k] * (diff @ diff.T) + covariances.append(covariance_k / Nk) + + # Update the weights (mixture proportions) + weights[k] = Nk / n_samples + + return means, covariances, weights + +# Log-likelihood calculation +def log_likelihood(X, means, covariances, weights): + n_samples, n_clusters = X.shape[0], len(means) + log_likelihood = 0 + + for i in range(n_samples): + temp = 0 + for k in range(n_clusters): + temp += weights[k] * multivariate_normal.pdf(X[i], means[k], covariances[k]) + log_likelihood += np.log(temp) + + return log_likelihood + +# EM algorithm +def em_algorithm(X, n_clusters, n_iters=100, tol=1e-4): + # Initialize parameters + means, covariances, weights = initialize_params(X, n_clusters) + + log_likelihoods = [] + + for i in range(n_iters): + # E-step + responsibilities = expectation_step(X, means, covariances, weights) + + # M-step + means, covariances, weights = maximization_step(X, responsibilities) + + # Compute log-likelihood + log_likelihood_value = log_likelihood(X, means, covariances, weights) + log_likelihoods.append(log_likelihood_value) + + # Check for convergence + if i > 0 and np.abs(log_likelihoods[-1] - log_likelihoods[-2]) < tol: + break + + return means, covariances, weights, responsibilities, log_likelihoods +``` + +## Step 3: Run the EM algorithm + +```{python} +# Run the EM algorithm +n_clusters = 3 +means, covariances, weights, responsibilities, log_likelihoods = em_algorithm(X, n_clusters) + +# Plot the final clusters and means +plt.scatter(X[:, 0], X[:, 1], s=30, color='b', label="Data points") +plt.scatter(means[:, 0], means[:, 1], s=100, color='r', label="Estimated Means", marker='x') +plt.title('Clusters Found by Gaussian Mixture Model') +plt.xlabel('Sweetness') +plt.ylabel('Acidity') +plt.legend() +plt.show() +``` + +## Explanation + +1. Data Generation: We generated synthetic data by sampling points from three distinct Gaussian distributions, each representing a different coffee blend. +2. Expectation Step: The algorithm calculates the soft assignments (responsibilities) for each point to each cluster. +3. Maximization Step: The algorithm updates the parameters (means, covariances, and weights) to maximize the likelihood given the responsibilities. +4. Convergence: The algorithm stops when the log-likelihood improvement is below a certain threshold. + +This code should provide a clear step-by-step implementation of the EM algorithm, and the final plot will show the clusters found by the algorithm. + +Let me know if you need further clarifications! \ No newline at end of file diff --git a/ds701_book/14ex-decision-tree-iris-dataset.qmd b/ds701_book/14ex-decision-tree-iris-dataset.qmd new file mode 100644 index 00000000..7fb55f76 --- /dev/null +++ b/ds701_book/14ex-decision-tree-iris-dataset.qmd @@ -0,0 +1,53 @@ +--- +title: "14.1 Decision Trees on the Iris Dataset" +--- + +## Iris Data Set Example + +Let's look at the classic Iris data set which consists of 150 samples representing3 types of irises: + +1. Setosa, +2. Versicolor, and +3. Virginica + +The features for each sample are the petal and sepal length and width in cm. + +``` {python} +from sklearn.datasets import load_iris +from sklearn import tree +iris = load_iris() +X, y = iris.data, iris.target +clf = tree.DecisionTreeClassifier() +clf = clf.fit(X, y) +tree.plot_tree(clf, + filled=True, + max_depth=1, + impurity=False, + class_names=iris.target_names, + feature_names=iris.feature_names) +``` + +``` {.python} +# Render a PDF file of the tree +import graphviz +dot_data = tree.export_graphviz(clf, out_file=None) +graph = graphviz.Source(dot_data) +graph.render("iris") +``` + +``` {.python} +# Render a PNG file of the tree +graph.render("iris", format="png") +``` + +``` {python} +import graphviz + +dot_data = tree.export_graphviz(clf, out_file=None, + feature_names=iris.feature_names, + class_names=iris.target_names, + filled=True, rounded=True, + special_characters=True) +graph = graphviz.Source(dot_data) +graph +``` \ No newline at end of file diff --git a/ds701_book/15-Curse-of-Dimensionality.qmd b/ds701_book/15-Curse-of-Dimensionality.qmd new file mode 100644 index 00000000..cf7923e7 --- /dev/null +++ b/ds701_book/15-Curse-of-Dimensionality.qmd @@ -0,0 +1,388 @@ +--- +jupyter: python3 +--- + +Open In Colab + +From [notebook 8.4](https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap08/8_4_High_Dimensional_Spaces.ipynb) + +# **Notebook 8.4: High-dimensional spaces** + +This notebook investigates the strange properties of high-dimensional spaces as discussed in the notes at the end of chapter 8. + +Work through the cells below, running each cell in turn. In various places you will see the words "TO DO". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions. + +Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions. + +```{python} +import numpy as np +import matplotlib.pyplot as plt +import scipy.special as sci +``` + +# How close are points in high dimensions? + +In this part of the notebook, we investigate how close random points are in 2D, 100D, and 1000D. In each case, we generate 1000 points and calculate the Euclidean distance between each pair. + +```{python} +# Fix the random seed so we all have the same random numbers +np.random.seed(0) + +n_data = 1000 + +# Create 1000 data examples (columns) each with 2 dimensions (rows) +n_dim = 2 +x_2D = np.random.normal(size=(n_dim,n_data)) + +# Create 1000 data examples (columns) each with 3 dimensions (rows) +n_dim = 3 +x_3D = np.random.normal(size=(n_dim,n_data)) + +# Create 1000 data examples (columns) each with 100 dimensions (rows) +n_dim = 100 +x_100D = np.random.normal(size=(n_dim,n_data)) + +# Create 1000 data examples (columns) each with 1000 dimensions (rows) +n_dim = 1000 +x_1000D = np.random.normal(size=(n_dim,n_data)) +``` + +```{python} +print(x_2D.shape) +print(x_3D.shape) +print(x_100D.shape) +print(x_1000D.shape) +``` + +```{python} +# scatter plot of the 2D data +plt.scatter(x_2D[0,:], x_2D[1,:]) +plt.title('2D data') +plt.show() +``` + +```{python} +# make an interactive scatter plot of the 3D data +fig = plt.figure() +ax = fig.add_subplot(111, projection='3d') +ax.scatter(x_3D[0,:], x_3D[1,:], x_3D[2,:]) +plt.title('3D data') +plt.show() +``` + +```{python} +from scipy.spatial import distance +``` + +```{python} +def distance_ratio(x): + # TODO -- replace the two lines below to calculate the largest and smallest Euclidean distance between + # the data points in the columns of x. DO NOT include the distance between the data point + # and itself (which is obviously zero) + + ### BEGIN SOLUTION + smallest_dist = np.inf + largest_dist = 0 + for i in range(x.shape[1]): + for j in range(x.shape[1]): + if i != j: + dist = np.linalg.norm(x[:,i] - x[:,j], ord=2) # ord=2 for Euclidean, ord=1 for Manhattan + # dist = distance.cosine(x[:,i].flatten(), x[:,j].flatten()) + if dist < smallest_dist: + smallest_dist = dist + if dist > largest_dist: + largest_dist = dist + ### END SOLUTION + + print(f"smallest_dist = {smallest_dist}, largest_dist = {largest_dist}") + # Calculate the ratio and return + dist_ratio = largest_dist / smallest_dist + return dist_ratio +``` + +```{python} +dist_ratio_2d = distance_ratio(x_2D) +print('Ratio of largest to smallest distance 2D: %3.3f'%(dist_ratio_2d)) + +dist_ratio_3d = distance_ratio(x_3D) +print('Ratio of largest to smallest distance 3D: %3.3f'%(dist_ratio_3d)) + +dist_ratio_100d = distance_ratio(x_100D) +print('Ratio of largest to smallest distance 100D: %3.3f'%(dist_ratio_100d)) + +dist_ratio_1000d = distance_ratio(x_1000D) +print('Ratio of largest to smallest distance 1000D: %3.3f'%(dist_ratio_1000d)) +``` + +```{python} +plt.scatter([2,3,100,1000], [dist_ratio_2d, dist_ratio_3d, dist_ratio_100d, dist_ratio_1000d]) +plt.plot([2,3,100,1000], [dist_ratio_2d, dist_ratio_3d, dist_ratio_100d, dist_ratio_1000d], '--', color='lightgray') +plt.xscale('log') +plt.title('Euclidean Distance ratio') +plt.xlabel('Dimension') +plt.ylabel('Distance ratio') +plt.show() +``` + +If you did this right, you will see that the distance between the nearest and farthest two points in high dimensions is almost the same. + +```{python} +import numpy as np + +# Assuming x_1000D is your 2D array +# For example: +# x_1000D = np.random.rand(1000, 100) + +# Pick any two columns +col1 = x_2D[:, 0] +col2 = x_2D[:, 20] + +# Compute their dot product +dot_product = np.dot(col1, col2) + +print(dot_product) +``` + +# Volume of a hypersphere + +In the second part of this notebook we calculate the volume of a hypersphere of radius 0.5 (i.e., of diameter 1) as a function of the radius. Note that you you can check your answer by doing the calculation for 2D using the standard formula for the area of a circle and making sure it matches. + +The equation is + +$$ +V_n(R) = \frac{\pi^{n/2}}{\Gamma\bigl(\tfrac n2 + 1\bigr)}R^n, +$$ +where $\Gamma$ is Euler's gamma function, $\Gamma(n) = (n - 1)!$ for all positive integers $n$. + +```{python} +def volume_of_hypersphere(diameter, dimensions): + # Formula given in Problem 8.7 of the book or see https://en.wikipedia.org/wiki/Volume_of_an_n-ball#Formulas + # You will need sci.gamma() + # Check out: https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.gamma.html + # Also use this value for pi + pi = np.pi + + ### BEGIN SOLUTION + radius = diameter / 2 + #print("dimensions: ", dimensions) + #print("pi ** (dimensions / 2) = ", pi ** (dimensions / 2)) + #print("sci.gamma(dimensions / 2 + 1) = ", sci.gamma(dimensions / 2 + 1)) + #print("radius ** dimensions = ", radius ** dimensions) + volume = (pi ** (dimensions / 2)) / (sci.gamma(dimensions / 2 + 1)) * (radius ** dimensions) + ### END SOLUTION + + return volume +``` + +```{python} +diameter = 2.0 +vols = [] +for c_dim in range(1,21): + vols.append(volume_of_hypersphere(diameter, c_dim)) + print("Volume of unit radius hypersphere in %d dimensions is %3.3f"%(c_dim, volume_of_hypersphere(diameter, c_dim))) +``` + +```{python} +# plot vols +plt.scatter(range(1,21), vols) +plt.xlabel('Dimensions') +plt.ylabel('Volume') +plt.title('Volume of unit radius hypersphere') +plt.show() +``` + +```{python} +diameter = 1.0 +vols = [] +for c_dim in range(1,21): + vols.append(volume_of_hypersphere(diameter, c_dim)) + print("Volume of unit radius hypersphere in %d dimensions is %3.3f"%(c_dim, volume_of_hypersphere(diameter, c_dim))) +``` + +```{python} +# plot vols +plt.scatter(range(1,21), vols) +plt.xlabel('Dimensions') +plt.ylabel('Volume') +plt.title('Volume of unit diameter hypersphere') +plt.show() +``` + +You should see that the volume decreases to almost nothing in high dimensions. All of the volume is in the corners of the unit hypercube (which always has volume 1). + +# Proportion of hypersphere in outer shell + +In the third part of the notebook you will calculate what proportion of the volume of a hypersphere is in the outer 1% of the radius/diameter. Calculate the volume of a hypersphere and then the volume of a hypersphere with 0.99 of the radius and then figure out the ratio. + +```{python} +def get_prop_of_volume_in_outer_1_percent(dimension): + + ### BEGIN SOLUTION + outer_diameter = 1.0 + outer_volume = volume_of_hypersphere(outer_diameter, dimension) + inner_diameter = 0.99 + inner_volume = volume_of_hypersphere(inner_diameter, dimension) + proportion = (outer_volume - inner_volume) / outer_volume + ### END SOLUTION + + # print(f"Outer volume: {outer_volume}, Inner volume: {inner_volume}") + return proportion +``` + +```{python} +# While we're here, let's look at how much of the volume is in the outer 1% of the radius +propvols = [] +for c_dim in [1,2,10,20,50,100,150,200,250,300]: + propvols.append(get_prop_of_volume_in_outer_1_percent(c_dim)) + print('Proportion of volume in outer 1 percent of radius in %d dimensions =%3.3f'%(c_dim, get_prop_of_volume_in_outer_1_percent(c_dim))) +``` + +```{python} +# plot propvols +plt.scatter([1,2,10,20,50,100,150,200,250,300], propvols) +plt.xlabel('Dimensions') +plt.ylabel('Proportion of volume in outer 1%') +plt.title('Proportion of volume in outer 1% of diameter of hypersphere') +plt.show() +``` + +You should see see that by the time we get to 300 dimensions most of the volume is in the outer 1 percent.

+ +The conclusion of all of this is that in high dimensions you should be sceptical of your intuitions about how things work. I have tried to visualize many things in one or two dimensions in the book, but you should also be sceptical about these visualizations! + + + +# Distance ratios + +Some experiments comparing the closest/farthest distance ratios for different metrics. + +## Closest/farthest distance ratios + +Define a function to calculate the ratio of the largest to smallest distance between points in a dataset. + +```{python} +from scipy.spatial import distance +def distance_ratio(x, metric='euclidean'): + + if metric == 'euclidean': + ord = 2 + elif metric == 'manhattan': + ord = 1 + elif metric == 'cosine': + pass + else: + raise ValueError(f"Metric {metric} not supported") + + smallest_dist = np.inf + largest_dist = 0 + for i in range(x.shape[0]): + for j in range(i + 1, x.shape[0]): # start from i+1 to avoid redundant calcuations + if i != j: + if metric == 'euclidean' or metric == 'manhattan': + dist = np.linalg.norm(x[i,:] - x[j,:], ord=ord) + elif metric == 'cosine': + dist = distance.cosine(x[i,:].flatten(), x[j,:].flatten()) + if dist < smallest_dist: + smallest_dist = dist + if dist > largest_dist: + largest_dist = dist + + print(f"smallest_dist = {smallest_dist}, largest_dist = {largest_dist}") + + # Calculate the ratio and return + dist_ratio = largest_dist / smallest_dist + return dist_ratio +``` + +And then calculate the ratio for each dataset. + +```{python} +dist_ratio_2d = distance_ratio(x_2D) +print('Ratio of largest to smallest distance 2D: %3.3f'%(dist_ratio_2d)) + +dist_ratio_3d = distance_ratio(x_3D) +print('Ratio of largest to smallest distance 3D: %3.3f'%(dist_ratio_3d)) + +dist_ratio_100d = distance_ratio(x_100D) +print('Ratio of largest to smallest distance 100D: %3.3f'%(dist_ratio_100d)) + +dist_ratio_1000d = distance_ratio(x_1000D) +print('Ratio of largest to smallest distance 1000D: %3.3f'%(dist_ratio_1000d)) +``` + +--- + +```{python} +plt.scatter([2,3,100,1000], [dist_ratio_2d, dist_ratio_3d, dist_ratio_100d, dist_ratio_1000d]) +plt.plot([2,3,100,1000], [dist_ratio_2d, dist_ratio_3d, dist_ratio_100d, dist_ratio_1000d], '--', color='lightgray') +plt.xscale('log') +plt.title('Euclidean Distance ratio') +plt.xlabel('Dimension') +plt.ylabel('Distance ratio') +plt.show() +``` + + +--- +```{python} +euc_dist_ratios = [] +euc_dist_ratios.append(distance_ratio(x_2D)) +print('Ratio of largest to smallest distance 2D: %3.3f'%(euc_dist_ratios[0])) + +euc_dist_ratios.append(distance_ratio(x_3D)) +print('Ratio of largest to smallest distance 3D: %3.3f'%(euc_dist_ratios[1])) + +euc_dist_ratios.append(distance_ratio(x_100D)) +print('Ratio of largest to smallest distance 100D: %3.3f'%(euc_dist_ratios[2])) + +euc_dist_ratios.append(distance_ratio(x_1000D)) +print('Ratio of largest to smallest distance 1000D: %3.3f'%(euc_dist_ratios[3])) +``` + +```{python} +man_dist_ratios = [] +man_dist_ratios.append(distance_ratio(x_2D, metric='manhattan')) +print('Ratio of largest to smallest distance 2D: %3.3f'%(man_dist_ratios[0])) + +man_dist_ratios.append(distance_ratio(x_3D, metric='manhattan')) +print('Ratio of largest to smallest distance 3D: %3.3f'%(man_dist_ratios[1])) + +man_dist_ratios.append(distance_ratio(x_100D, metric='manhattan')) +print('Ratio of largest to smallest distance 100D: %3.3f'%(man_dist_ratios[2])) + +man_dist_ratios.append(distance_ratio(x_1000D, metric='manhattan')) +print('Ratio of largest to smallest distance 1000D: %3.3f'%(man_dist_ratios[3])) +``` + +```{python} +cos_dist_ratios = [] +cos_dist_ratios.append(distance_ratio(x_2D, metric='cosine')) +print('Ratio of largest to smallest distance 2D: %3.3f'%(cos_dist_ratios[0])) + +cos_dist_ratios.append(distance_ratio(x_3D, metric='cosine')) +print('Ratio of largest to smallest distance 3D: %3.3f'%(cos_dist_ratios[1])) + +cos_dist_ratios.append(distance_ratio(x_100D, metric='cosine')) +print('Ratio of largest to smallest distance 100D: %3.3f'%(cos_dist_ratios[2])) + +cos_dist_ratios.append(distance_ratio(x_1000D, metric='cosine')) +print('Ratio of largest to smallest distance 1000D: %3.3f'%(cos_dist_ratios[3])) +``` +--- + +```{python} +import matplotlib.pyplot as plt + +plt.scatter([2,3,100,1000], euc_dist_ratios, label='Euclidean') +plt.plot([2,3,100,1000], euc_dist_ratios, '--', color='lightgray') + +plt.scatter([2,3,100,1000], man_dist_ratios, label='Manhattan', color='orange') +plt.plot([2,3,100,1000], man_dist_ratios, '--', color='orange') + +plt.xscale('log') +plt.legend() +plt.title('Distance ratio') +plt.xlabel('Dimension') +plt.ylabel('Distance ratio') +plt.show() +``` diff --git a/ds701_book/20-RecSys-GPTo1.qmd b/ds701_book/20-RecSys-GPTo1.qmd new file mode 100644 index 00000000..bb193d1f --- /dev/null +++ b/ds701_book/20-RecSys-GPTo1.qmd @@ -0,0 +1,290 @@ +--- +title: "20-Recommender-Systems" +--- + +# Introduction to Recommender Systems + +## Motivating Examples + +• Netflix: Suggesting new movies or TV shows based on user’s watch history and ratings. +• Amazon: Recommending products based on user’s browsing and purchase history. +• YouTube: Suggesting videos a user might like based on their viewing history. +• Spotify: Creating playlists and recommending songs based on user’s music preferences. + +Discussion Prompt: Think about the recommender systems you encounter daily (e.g., social media feeds, shopping recommendations). How do they influence your decision-making? + +## Why Recommender Systems Matter + +• Information Overload: With vast amounts of content (products, movies, articles), recommender systems help users find what is relevant. +• Personalization: Tailor user experiences to individual preferences. +• Business Value: Increase engagement and sales. For instance, a large percentage of Netflix’s watched content is driven by recommendations. + +Question for Class: Can you name some other domains where recommender systems might be useful besides entertainment and shopping? + +## Goals of This Lecture + +1. Understand the basic types of recommender systems. +2. Learn about content-based filtering and collaborative filtering. +3. Explore example algorithms like Matrix Factorization and SVD. +4. Work through an example dataset. +5. Discuss advanced and hybrid approaches. +6. Encourage critical thinking on how to evaluate and improve recommenders. + +# Basic Approaches to Recommender Systems + +## Content-Based Filtering: Introduction + +• Definition: Recommend items similar to those a user has liked in the past. +• How It Works: Uses item features and user profiles. If a user liked a particular item, the system finds items with similar features. +• Example: If a user likes sci-fi movies, recommend other sci-fi movies. + +Example Dataset: Imagine a dataset with movies and their genres. If a user likes “Inception” (Sci-Fi/Thriller), the system might recommend “The Matrix” (Sci-Fi) based on overlapping genres. + +## Content-Based Filtering: Steps + +1. Profile each item using features (genres, actors, author, etc.). +2. Profile the user based on items they’ve liked. +3. Recommend items whose features match the user’s profile. + +Example: +• User’s watched and liked movies: “Inception” (Sci-Fi, Thriller), “Interstellar” (Sci-Fi, Drama). +• Derived user profile: Genre preference - Sci-Fi, Thriller. +• Recommend items with similar genres like “Matrix” (Sci-Fi) or “Shutter Island” (Thriller). + +Discussion Prompt: What are some limitations of content-based filtering? + +## Limitations of Content-Based Filtering + +• Feature Engineering: Requires carefully identified and extracted features. +• Lack of Diversity: Tends to recommend items similar to those already liked, possibly leading to a “filter bubble.” +• Cold Start Problem (Item): For new items with no features or feedback, it’s challenging to make recommendations. + +# Collaborative Filtering: Introduction + +## What is Collaborative Filtering? + +• Definition: Recommending items based on the past behavior of users who are similar to the target user. +• Key Idea: Similar users have similar preferences. If user A and user B have a high similarity in terms of preferences, items liked by user B might be recommended to user A. + +Example: In a movie recommendation scenario: + +• User A likes “Interstellar” and “Inception.” +• User B likes “Interstellar” and “The Martian.” +• Since both users like “Interstellar,” the system might recommend “Inception” to user B and “The Martian” to user A. + +## Collaborative Filtering Approaches + +1. User-Based Collaborative Filtering: + * Finds users similar to the target user and recommends items liked by those similar users. +2. Item-Based Collaborative Filtering: + * Finds items that are similar to the items the user likes, then recommends those similar items. + +Discussion Prompt: Which approach might work better in a situation with millions of users and thousands of items? + +## User-Based Collaborative Filtering + +* How It Works: + 1. Calculate similarity between users based on ratings or behavior. + 2. Select top N similar users. + 3. Aggregate items from those similar users and recommend them. + +Example: + +* Suppose we have user ratings for movies. For a target user who has rated some movies, we find other users with similar rating patterns. Items highly rated by these similar users are recommended. + +Class Exercise: Using a small movie ratings matrix, try to identify which users are similar and which movies would be recommended. + +## Item-Based Collaborative Filtering + +* How It Works: + 1. Calculate similarity between items based on user ratings. + 2. For an item the user has shown interest in, find similar items. + 3. Recommend items that are similar to the ones the user likes. + +Example: +* If a user has rated “Inception” highly, the system looks at other items that have been rated similarly by other users who liked “Inception.” + +Discussion Prompt: What might be the advantages or disadvantages of item-based vs. user-based collaborative filtering? + +## Similarity Measures + +* Common similarity measures used in collaborative filtering: + * Cosine Similarity + * Pearson Correlation + * Jaccard Similarity (for implicit feedback) + +Example: +* If we have vectors of user ratings for two items, we can compute the cosine similarity. This gives us how similar the items are in terms of user ratings patterns. + +Class Exercise: Given two rating vectors [5, 4, 0, 1] and [4, 5, 0, 2], compute the cosine similarity. + +# Advanced Collaborative Filtering Techniques + +## Matrix Factorization: Introduction + +* Definition: A technique that decomposes the user-item interaction matrix into lower-dimensional latent factors. +* Motivation: Exploit underlying structure in user-item interactions. +* Famous Example: Netflix Prize used matrix factorization methods for recommendation. + +## Matrix Factorization: How It Works + +* The user-item matrix R (where R_{u,i} is the rating of user u for item i) is approximated by two latent factor matrices P and Q: +* R ≈ P * Q^T +* P is a |U| x k matrix, representing k latent factors for each user. +* Q is a |I| x k matrix, representing k latent factors for each item. +* The ratings are predicted by the dot product of these latent factor vectors. + +Example: + +* If we have 1000 users and 500 movies, we might factor it into 20 latent factors. Each user and movie is represented in this 20-dimensional space, capturing aspects like genre preferences. + +## Singular Value Decomposition (SVD) + +* Definition: A factorization of a matrix into three matrices: R = U * Σ * V^T. +* In the context of recommender systems, SVD can be used to identify latent factors capturing underlying preferences. +* Truncated SVD: For recommendation, we often use a truncated version to reduce complexity. + +Class Exercise: + +* If the user-item matrix is: + +| User\Item | Item1 | Item2 | Item3 | +| ------- | ------- | ------- | ------- | +| User1 | 5 | 0 | 4 | +| User2 | 4 | 0 | 5 | +| User3 | 1 | 5 | 0 | + +Compute the truncated SVD for this small matrix. + +## Implementing Matrix Factorization + +* Optimization Problem: Minimize the squared error between actual ratings and predicted ratings. + +$$ +min_{P, Q} \sum_{(u,i) \in R} (R_{u,i} - P_u^T Q_i)^2 + \text{regularization terms} +$$ + +* Stochastic Gradient Descent (SGD): Commonly used method to learn P and Q. +* Regularization: Important to avoid overfitting. + +Discussion Prompt: Why might matrix factorization perform better than user-based or item-based collaborative filtering in some cases? + +# Practical Example with a Dataset + +## Example Dataset: MovieLens + +* MovieLens: A commonly used dataset for building and testing recommender systems. +* Dataset Details: + * Users, Movies, and Ratings from 1 to 5. + * Also includes movie metadata like genres. + +Plan: +* We’ll explore how to apply both content-based and collaborative filtering on this dataset. + +Class Discussion: If you were to recommend a movie to a user who only watched “Star Wars”, how would you proceed using each method? + +## Applying Content-Based Filtering to MovieLens + +* Step 1: Represent movies by their genres (and possibly actors, directors). +* Step 2: If a user liked a set of movies, compute the average feature vector of those movies. +* Step 3: Recommend movies whose feature vectors are most similar to the user’s profile vector. + +Example: +* If the user liked “The Empire Strikes Back” (Sci-Fi, Adventure), we recommend other Sci-Fi, Adventure movies like “Interstellar” or “Indiana Jones”. + +## Applying Collaborative Filtering to MovieLens + +* User-Based Approach: + 1. Find users similar to the target user based on ratings. + 2. Recommend movies highly rated by these similar users. +* Item-Based Approach: +1. Find movies similar to the movies the user has rated highly. +2. Recommend these similar movies. + +Example: +* If the user rated “Star Wars” and “The Matrix” highly, the system finds other movies that are commonly liked by users who liked these two movies, such as “Inception” or “Lord of the Rings.” + +Class Exercise: Use the user-item matrix from MovieLens to predict a missing rating using item-based collaborative filtering. + +## Evaluating Recommender Systems + +* Accuracy Metrics: + * Mean Absolute Error (MAE) + * Root Mean Square Error (RMSE) +* Rank-based Metrics: + * Precision@k + * Recall@k +* Mean Average Precision (MAP) +* Normalized Discounted Cumulative Gain (nDCG) + +Discussion Prompt: Besides accuracy, what other factors might be important when evaluating recommender systems? + +## Practical Concerns + +* Scalability: Large datasets with millions of users and items require efficient algorithms. +* Sparsity: Often user-item interaction matrices are very sparse. +* Cold Start: Difficulty making recommendations for new users (User Cold Start) or new items (Item Cold Start) with no history. + +Class Discussion: How can we tackle the cold start problem? + +# Advanced and Hybrid Approaches + +## Hybrid Recommender Systems + +* Definition: Combine content-based and collaborative filtering methods. +* Motivation: Overcome limitations of each approach. +* Content-based can handle new items (solves cold start for items). +* Collaborative filtering can learn more nuanced user preferences. + +Example: +* A hybrid approach may start by recommending items similar to those the user liked before (content-based), and refine recommendations by observing user feedback (collaborative filtering). + +Discussion Prompt: Can you think of ways to combine the two methods effectively? + +## Neural Network-based Approaches + +* Autoencoders: Used to learn latent representations of user-item interactions. +* Neural Collaborative Filtering: Deep neural networks to model complex user-item interactions. +* Graph Neural Networks: Model relationships in user-item graphs. + +Example: +* Using an autoencoder to predict missing ratings from known ratings by compressing user preferences into a latent space. + +Class Discussion: What might be the advantages of using neural networks over traditional matrix factorization? + +## Graph-based Approaches + +* Social Recommender Systems: Use relationships in a social graph. +* Trust-based Recommendations: If user A trusts user B, we can use user B’s preferences to inform recommendations for user A. + +Example: +* If your friend likes a certain product, the system might be more likely to recommend it to you in a social shopping platform. + +Discussion Prompt: How can network or graph analyses be integrated into recommender systems? + +# Summary and Wrap-up + +## Key Takeaways + +* Content-Based Filtering: Recommends items similar to those the user liked before. +* Collaborative Filtering: Uses preferences of similar users to make recommendations. +* Matrix Factorization: Finds latent factors in user-item interactions. +* Hybrid Approaches: Combine multiple methods to overcome individual limitations. + +## Final Thoughts + +* Recommender systems are crucial in helping users navigate large volumes of information and product choices. +* The choice of method depends on data availability, domain, and specific objectives (e.g., accuracy, diversity, or serendipity). +* Ongoing research in deep learning and graph-based methods is pushing the field forward. + +Question for Class: Reflect on the methods covered. Which method (or combination of methods) do you think would be most effective for a streaming music service and why? + +## Further Reading + +* Books: + * “Recommender Systems: The Textbook” by Charu C. Aggarwal. +* Papers: + * “Matrix Factorization Techniques for Recommender Systems” by Yehuda Koren, Robert Bell, and Chris Volinsky. + * “Deep Neural Networks for YouTube Recommendations” by Paul Covington, Jay Adams, Emre Sargin. + +Class Discussion: Any questions about the topics covered today or how these methods can be applied to real-world datasets? diff --git a/ds701_book/20-Recommender-Systems-notes.md b/ds701_book/20-Recommender-Systems-notes.md new file mode 100644 index 00000000..5f4b5c82 --- /dev/null +++ b/ds701_book/20-Recommender-Systems-notes.md @@ -0,0 +1,7 @@ +# Notes on Recommender Systems + +https://engineering.roku.com/personalised-retrieval-for-typed-text-queries + +Also look at Spotify playlist... + + diff --git a/ds701_book/20a-RecSys-with-Deep-Learning.qmd b/ds701_book/20a-RecSys-with-Deep-Learning.qmd new file mode 100644 index 00000000..3a9397e0 --- /dev/null +++ b/ds701_book/20a-RecSys-with-Deep-Learning.qmd @@ -0,0 +1,351 @@ +--- +title: Recommendation Systems Part II -- Deep Learning Based +bibliography: references.bib +jupyter: python3 +nocite: | + @ricci2022recommender +--- + +# Deep Learning for Recommender Systems + +Based on [@zhang2022deep]. + +## Introduction +- **Deep Learning in Recommender Systems**: + - Revolutionized AI applications across fields like computer vision and NLP. + - Reduces feature engineering effort and supports diverse data (e.g., text, images). + - Enhances tasks such as cold-start problems, temporal dynamics, and explainability. + +## Key Techniques +1. **Multi-layer Perceptrons (MLPs)**: + - Flexible, hierarchical networks for feature interaction. + - Universal approximators (@fig-dl-recsys-mlp). +2. **Convolutional Neural Networks (CNNs)**: + - Efficiently capture spatial patterns in grid-like data (@fig-dl-recsys-cnn). +3. **Recurrent Neural Networks (RNNs)**: + - Models sequential data with memory states (@fig-dl-recsys-rnn). +4. **Graph Neural Networks (GNNs)**: + - Handles graph-structured data like social and knowledge graphs. +5. **Autoencoders and GANs**: + - For representation learning and data generation. + +![Multi-layer Perceptrons](figs/RecSys-figs/dl-recsys-mlp.png){width=30% fig-align="center" #fig-dl-recsys-mlp} + +![Convolutional Neural Networks](figs/RecSys-figs/dl-recsys-cnn.png){width=30% fig-align="center" #fig-dl-recsys-cnn} + +![Recurrent Neural Networks](figs/RecSys-figs/dl-recsys-rnn.png){width=30% fig-align="center" #fig-dl-recsys-rnn} + +--- + +## Challenges in Recommender Systems +### Interaction Modeling +- Captures relationships in sparse user-item matrices. +- Approaches: + - **NeuMF**: Replaces dot product with MLPs. + - **Outer Product + CNNs**: Higher-order correlations (Figure 6). + +--- + +### User Modeling +1. **Temporal Dynamics**: + - Sequence-aware recommendations using RNNs, CNNs, and attention mechanisms (Figure 8). +2. **Diverse Interests**: + - Models multiple user preferences via clustering and disentanglement. + +--- + +## Content Representation Learning +1. **Text Features**: + - Leverages reviews and descriptions using CNNs, RNNs, and attention mechanisms. +2. **Image Features**: + - Integrates CNN-extracted visual data for applications like fashion and social media. +3. **Video/Audio Features**: + - Processes multimedia content for personalized music and video recommendations. + +--- + +## Advanced Applications +### Graph-Structured Data +- Incorporates graphs like user-item networks for collaborative filtering (Figure 10). +- Examples: + - **Session-based Recommendations**: Sequence learning from click patterns. + - **Knowledge Graphs**: Path-based reasoning for explainability. + +### Cold-Start Recommendations +- Tackles sparse data with side information and meta-learning. +- GNN-based approaches predict embeddings for new users/items. + +--- + +## Beyond Accuracy +### Explainability +- Enhances transparency and trust: + - Attention mechanisms highlight critical features. + - Knowledge graph paths provide reasoning for recommendations. + +### Robustness +- Defends against adversarial attacks with perturbation-based training. + +--- + +## Applications of Deep Learning in Recommendation +1. **E-commerce**: + - Amazon, eBay, Alibaba use deep learning for personalized shopping. +2. **Entertainment**: + - YouTube employs candidate generation and ranking modules. +3. **News**: + - Self-attention mechanisms capture user preferences across multiple views. +4. **Point-of-Interest**: + - Combines visual and textual data for location-based recommendations. + +--- + +## Conclusion +- **Deep learning transforms recommender systems**: + - Combines memorization and generalization. + - Supports diverse data types and complex challenges. +- Future directions include improving scalability and real-time adaptability. + +This presentation references key figures and tables from the document and is paced for a 15-minute delivery. Let me know if further adjustments are needed! + + +# Wide and Deep Learning for Recommender Systems + +Based on [@cheng2016wide]. + +## Introduction +- **Problem**: Balancing memorization and generalization in recommender systems. + - **Memorization**: Learns frequent co-occurrences of features for relevant recommendations. + - **Generalization**: Predicts unseen feature combinations for diverse recommendations. +- **Wide & Deep Learning Framework**: + - Combines linear models (memorization) and neural networks (generalization). + - Evaluated on Google Play, with over 1 billion users and 1 million apps. +- **Key Results**: + - Significant improvements in app acquisitions. + - Open-sourced implementation in TensorFlow. + +--- + +## Recommender System Overview +- Workflow (Figure 2): + - **Query**: User and contextual features (e.g., demographics, app usage). + - **Retrieval**: Filters 100 items based on relevance. + - **Ranking**: Scores and ranks items using Wide & Deep Learning. +- Challenges: + - High throughput with low latency (e.g., scoring over 10 million apps per second). + +--- + +## Wide & Deep Learning Framework +### Wide Component +- Linear model (Figure 1, left): + - Cross-product transformations capture interactions (e.g., "gender=female" AND "language=en"). + - Effective for memorization but limited in generalization. + +### Deep Component +- Feed-forward neural network (Figure 1, right): + - Converts sparse categorical features into dense embeddings. + - Layers compute activations using ReLU functions. + - Learns complex, nonlinear feature interactions. + +--- + +### Joint Training +- Combines wide and deep components (Figure 1, center): + - Jointly optimized via a shared logistic loss function. + - Uses: + - **FTRL** optimizer for wide part. + - **AdaGrad** for deep part. +- Distinction: + - Joint training integrates components during training, unlike ensembles. + +--- + +## System Implementation +### Data Generation +- **Training Data**: + - Generated from user-app interactions. + - Labels: 1 for app installs, 0 otherwise. +- **Feature Engineering**: + - Maps categorical strings to integer IDs (vocabulary generation). + - Normalizes continuous features using quantile-based scaling. + +### Model Training +- Structure (Figure 4): + - Cross-product transformations for wide component. + - Dense embeddings (32 dimensions each) for deep component. + - Three ReLU layers process embeddings and continuous features. +- **Warm-Starting**: + - Retrains models incrementally using weights from previous models. + +--- + +### Model Serving +- Scoring: + - Scores candidate apps using forward inference on Wide & Deep models. +- Optimization: + - Multithreading reduces latency from 31 ms to 14 ms (Table 2). + +--- + +## Experiment Results +### App Acquisitions +- A/B Testing (Table 1): + - **Wide-only**: Baseline model. + - **Deep-only**: +2.9% acquisition rate. + - **Wide & Deep**: +3.9% acquisition rate over baseline. +- Insights: + - Joint training enables exploratory recommendations for new user responses. + +### Serving Performance +- At peak, servers score over 10 million apps/second. +- Optimized serving reduces latency significantly. + +--- + +## Related Work +- **Factorization Machines**: + - Generalize linear models but lack nonlinear interaction modeling. +- **Collaborative Deep Learning**: + - Combines deep learning with collaborative filtering. +- Wide & Deep innovates by integrating linear models with deep networks. + +--- + +## Conclusion +- **Key Contributions**: + - Combines memorization (wide) and generalization (deep) in a single model. + - Scalable and effective for massive datasets (e.g., Google Play). +- **Impact**: + - Open-source implementation facilitates adoption in diverse applications. + +This structure references critical figures (Figure 1, 2, 4) and tables (Table 1, 2) and is timed for a detailed explanation within 25 minutes. Let me know if adjustments are needed! + + +# Deep Learning Recommender Model + +Besides the Collaborative Filtering and Matrix Factorization models, another popular approach to building recommender systems is to use Deep Learning. + +We'll look at the Deep Learning Recommender Model (DLRM) proposed by Facebook in 2019 [@naumov2019deep]. + +## Introduction + +- **Key Features**: + - Embeddings for categorical data. + - Multi-layer perceptrons (MLPs) for dense data processing. + - Combines statistical techniques like matrix factorization and factorization machines. + +## DLRM Architecture + + +:::: {.columns} +::: {.column width="50%"} + +- Components (@fig-dlrm-model): + 1. **Embeddings**: Dense representations for categorical data. + 2. **Bottom MLP**: Transforms dense continuous features. + 3. **Feature Interaction**: Dot-product of embeddings and dense features. + 4. **Top MLP**: Processes interactions and outputs probabilities. + +::: +::: {.column width="50%"} + +![DLRM Architecture](figs/RecSys-figs/dlrm-model.png){width=80% fig-align="center" #fig-dlrm-model} + +::: +:::: + +## Embeddings and Feature Interactions +1. **Embeddings**: + - Maps categorical inputs to latent factor space. + - Multi-hot vectors allow weighted combinations (Equation 2). + +```{python} +import torch +import torch.nn as nn + +# Example embedding matrix: 5 embeddings, each of dimension 3 +embedding_matrix = nn.EmbeddingBag(num_embeddings=5, embedding_dim=3, mode='mean') + +# Input: Indices into the embedding matrix +input_indices = torch.tensor([1, 2, 3, 4]) # Flat list of indices +offsets = torch.tensor([0, 2]) # Start new bag at position 0 and 2 in input_indices + +# Forward pass +output = embedding_matrix(input_indices, offsets) + +print("Embedding Matrix:\n", embedding_matrix.weight) +print("Output:\n", output) +``` + +## 2. **Feature Interaction**: + - Second-order interactions modeled via dot-products. + - Mimics Factorization Machines for efficiency (Equation 4). + + + +--- + +## Model Training and Parallelism +- **Training Challenges**: + - Large embeddings exceed single-device memory. + - Requires efficient parallelization of computations. +- **Parallelism Strategy**: + - **Model Parallelism**: Distributes embeddings across devices. + - **Data Parallelism**: Replicates MLPs for concurrent mini-batch processing. + - Butterfly shuffle for all-to-all communication (Figure 2). + +--- + +## Data Handling +1. **Random and Synthetic Data**: + - Facilitates system testing and preserves data privacy. + - Techniques for generating synthetic categorical data (Figure 3). +2. **Public Datasets**: + - Criteo AI Labs Ad Kaggle Dataset. + - Used for evaluating click-through rate (CTR) prediction models. + +--- + +## Experiments: Accuracy and Performance +1. **Accuracy**: + - Evaluated on Criteo dataset (Figure 5). + - Compared with Deep & Cross Network (DCN). + - DLRM shows superior training and validation accuracy with both SGD and Adagrad. +2. **Performance Profiling**: + - Tested on Big Basin AI platform (Figure 4). + - GPU significantly outperforms CPU, particularly in MLP computations (Figure 6). + +--- + +## Comparison with Prior Models +- DLRM vs Other Networks: + - Simplified interactions reduce dimensionality. + - Focuses on second-order interactions for computational efficiency. + - Outperforms alternatives like Wide & Deep, DeepFM, and xDeepFM. + +--- + +## Conclusion +- **Key Takeaways**: + - DLRM effectively combines embeddings, MLPs, and interaction layers for personalization tasks. + - Offers a scalable solution for large-scale recommendation systems. + - Open-source implementation fosters further research and system design. +- **Future Directions**: + - Optimization of communication primitives. + - Exploring higher-order interactions with minimal computational costs. + + +# Recap and References + + +## Recap + + +## References + +::: {#refs} +::: + + + diff --git a/ds701_book/21-ring-lattice-shortest-path-proof.qmd b/ds701_book/21-ring-lattice-shortest-path-proof.qmd new file mode 100644 index 00000000..c0f68527 --- /dev/null +++ b/ds701_book/21-ring-lattice-shortest-path-proof.qmd @@ -0,0 +1,39 @@ +From prompt to GPT-4o: + +Certainly! The simplification in step 5 arises from considering the distribution of distances in a ring lattice and how they contribute to the average shortest path length. + +### Detailed Explanation: + +1. **Understanding the Distance Distribution**: + - In a ring lattice, each node is connected to its nearest neighbors, and the distance between any two nodes $i$ and $j$ is given by: + +$$ +d(i, j) = \min(|i-j|, n - |i-j|) +$$ + + - This formula accounts for the circular nature of the lattice, where you can travel either clockwise or counterclockwise to reach another node. + +2. **Distance Contribution**: + + - For a node $i$, the distance to another node $j$ is determined by the smaller of the two possible paths (clockwise or counterclockwise). + - As you move further from node $i$, the distance initially increases linearly until you reach the halfway point around the circle, after which it starts decreasing symmetrically. + +3. **Summing Over All Pairs**: + + - The total distance for all pairs of nodes is calculated by summing the distances for each pair and then dividing by the number of pairs: + $$ + L = \frac{1}{n(n-1)} \sum_{i=0}^{n-1} \sum_{j=0, j \neq i}^{n-1} d(i, j) + $$ + - For each node $i$, the distances to other nodes range from 1 to $n/2$ (assuming $n$ is even for simplicity), and then symmetrically back to 1. + +4. **Approximation for Large $n$**: + + - For large $n$, the average distance from a node to all other nodes can be approximated by considering that, on average, you traverse about a quarter of the way around the circle to reach another node. + - This is because the average distance is roughly the midpoint of the maximum distance, which is $n/2$. Therefore, the average distance is approximately $n/4$. + + For skip connections of 2, then the average path length is $n/8$, or 1/2 of n/4. + +5. **Conclusion**: + - The approximation $L \approx n/4$ reflects the fact that in a ring lattice, the average path length grows linearly with the number of nodes $n$, but at a slower rate than the maximum possible distance, due to the circular structure and symmetry. + +This simplification is particularly useful for large $n$ where the exact calculation becomes cumbersome, and the approximation provides a good estimate of the average shortest path length. \ No newline at end of file diff --git a/ds701_book/23-25-NNs-outline.md b/ds701_book/23-25-NNs-outline.md new file mode 100644 index 00000000..9ba1cc1d --- /dev/null +++ b/ds701_book/23-25-NNs-outline.md @@ -0,0 +1,29 @@ +# NN Outline + +## The "Unreasonable" Effectiveness of Deep Neural Networks + +UDL examples of all the types of models... + +> Note: should we add more generative AI examples? chatgpt? NotebookLM? Suno.AI? + +### Emergent behavior in Pre-Trained LLMs + +Can we expand on this phenomenon a little bit? + +Is GPT scaling laws article relevant? + + +### Theory Sometimes Follows Invention + +Still relevant? Think so.... + +## Loss Functions for Model Fitting + +Relates a bit to models previously discussed... + +discusses simple convex loss functions with global minimum vs non-convex +loss functions without many local minima + +## Gradient Descent Intuitively + + diff --git a/ds701_book/23-NN-I-Gradient-Descent-save.qmd b/ds701_book/23-NN-I-Gradient-Descent-save.qmd new file mode 100644 index 00000000..6f0da79c --- /dev/null +++ b/ds701_book/23-NN-I-Gradient-Descent-save.qmd @@ -0,0 +1,1017 @@ +--- +title: Neural Networks I -- Gradient Descent +jupyter: python3 +--- + +## Introduction + +[![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/tools4ds/DS701-Course-Notes/blob/main/ds701_book/jupyter_notebooks/23-NN-I-Gradient-Descent.ipynb) + +```{python} +#| code-fold: true +import numpy as np +import scipy as sp +import matplotlib.pyplot as plt +import pandas as pd +import seaborn as sns +import matplotlib as mp +import sklearn +import networkx as nx +from IPython.display import Image, HTML + +import laUtilities as ut + +%matplotlib inline +``` + + +::: {.callout-note} +The content builds upon + +* Andrej Karpathy's excellent [video](https://youtu.be/VMj-3S1tku0?si=9HKPIq36EnHektSm) on building _micrograd_ and +* _Understanding Deep Learning_ [book preprint](https://udlbook.github.io/udlbook/) by Simone Prince +* as well as many other sources cited below. +::: + + +## The "Unreasonable" Effectiveness of Deep Neural Networks + +Deep Neural Networks have been effective in many applications. + +![](figs/NN-figs/IntroModels.svg){width="75%"} + +![](figs/NN-figs/IntroModels2a.svg){width="75%"} + +[Understanding Deep Learning, Simon J.D. Prince, MIT Press, 2023](http://udlbook.com) + +## Emergent Behavior in Pre-Trained Large Language Models + +![Emergence](./figs/NN-figs/EmergentAbilitiesFig2.png) + +[Emergent Abilities of Large Language Models.](https://arxiv.org/abs/2206.07682) J. Wei et al., Oct. 26, 2022. + +## Theory Sometimes Follows Invention + +| Invention | Theory | +| --------- | ------ | +| Telescope (1608) | Optics (1650-1700) | +| Steam Engine (1695-1715) | Thermodynamics (1824...) | +| Electromagnetism (1820) | Electrodynamics (1821) | +| Sailboat (??) | Aerodynamics (1757), Hydrodynamics (1738) | +| Airplane (1885-1905) | Wing Theory (1907-1918) | +| Computer (1941-1945) | Computer Science (1950-1960) | +| Teletype (1906) | Information Theory (1948) | + +* But then when theory is developed it can more quickly improve invention +* The same can be said for Neural Networks. The theory to make them work is well understood. The theory of why they work is still developing. +* We'll balance theory and application + +--- +[The Power and Limits of Deep Learning](https://learning.acm.org/techtalks/powerandlimitsdl), Yann LeCun, March 2019. + +Underlying all these techniques is the idea of applying optimization techniques +to minimize some kind of "loss" function. + +## Loss Functions for Model Fitting + +Most of the machine learning we have studied this semester is based on the idea +that we have a model that is _parameterized_, and our goal is to find good +settings for the parameters. + +We have seen example after example of this problem. + +* In $k$-means, our goal was to find $k$ cluster centroids, so that the $k$-means + objective was minimized. +* In linear regression, our goal was to find a parameter vector $\beta$ so that + sum of squared error $\Vert \mathbf{y} - \hat{\mathbf{y}}\Vert_2$ was minimized. +* In the support vector machine, our goal was to find a parameter vector $\theta$ + so that classification error was minimized. + +And similarly we'll want to find good parameter settings in neural networks. + +It's time now to talk about how, in general, one can find "good settings" for the +parameters in problems like these. + +What allows us to unify our approach to many such problems is the following: + +First, we start by defining an error function, generally called a __loss__ +function, to describe how well our method is doing. + +And second, we choose loss functions that are __differentiable__ with respect to +the parameters. + +These two requirements mean that we can think of the parameter tuning problem +using surfaces like these: + +![](figs/L23-convex_cost_function.jpeg){width="75%"} + +Imagine that the $x$ and $y$ axes in these pictures represent parameter settings. +That is, we have two parameters to set, corresponding to the values of $x$ and $y$. + +For each $(x, y)$ setting, the $z$-axis shows the value of the loss function. + +What we want to do is find the minimum of a surface, corresponding to the +parameter settings that minimize loss. + +Notice the difference between the two kinds of surfaces. + +The surface on the left corresponds to a __strictly convex__ loss function. +If we find a local minimum of this function, it is a global minimum. + +The surface on the right corresponds to a __non-convex__ loss function. +There are local minima that are not globally minimal. + +Both kinds of loss functions arise in machine learning. + +For example, convex loss functions arise in + +* Linear regression +* Logistic regression + +While non-convex loss functions arise in + +* $k$-means +* Gaussian Mixture Modeling +* and of course neural networks + +## Gradient Descent Intuitively + +The intuition of gradient descent is the following. + +Imagine you are lost in the mountains, and it is foggy out. You want to find a +valley. But since it is foggy, you can only see the local area around you. + +![](figs/L23-fog-in-the-mountains.jpeg){width="75%"} + + + +The natural thing to do is: + +1. Look around you 360 degrees. +2. Observe in which direction the ground is sloping downward most steeply. +3. Take a few steps in that direction. +4. Repeat the process ... until the ground seems to be level. + +The key to this intuitive idea is formalizing the idea of "direction of steepest +descent." + +This is where the differentiability of the loss function comes into play. + +As long as the loss function is _locally_ differentiable, we can define the +direction of steepest descent (really, ascent). + +That direction is called the __gradient.__ + +## Derivatives on Single Variable Functions + +```{python} +#| code-fold: true +import math +import numpy as np +import matplotlib.pyplot as plt +import ipywidgets as widgets +%matplotlib inline +``` + +We'll build up to concept of gradient by starting with derivatives on single +variable functions. + +Let's start with a simple quadratic function. + +$$ +f(x) = 3x^2 - 4x +5 +$$ + +Which we can write in python as well. + +```{python} +#| code-fold: true +def f(x): + return 3*x**2 - 4*x + 5 +``` + +And we can plot it. + +```{python} +#| code-fold: true +import numpy as np + +xs = np.arange(-5, 5, 0.25) +ys = f(xs) +plt.plot(xs, ys); +``` + +Let's assume for a minute that this is our loss function that we are minimizing. + + +__Question__ + +What do we know about where the minimum is in terms of the slope of the curve? + +__Answer__ + +It is necessary but _not sufficient_ that the slope be zero. + +__Question__ + +How do we calculate the slope? + +We take the derivative, denoted + +$$ +\frac{d f(x)}{dx} \hspace{10pt} \textrm{Leibniz' notation} +$$ + +or + +$$ +f'(x) \hspace{10pt} \textrm{Lagrange's notation} +$$ + +You may see both notations. The nice thing about Leibniz' notation is that it is +easy to express _partial derivatives_ when we get to multivariate differentiation, +which we'll get to shortly. + +We can take the derivate of the $f(x)$ + +$$ +f(x) = 3x^2 - 4x +5 +$$ + +By definition of the [derivative](https://en.wikipedia.org/wiki/Derivative), the +function $f(x)$ is differentiable at $x$ if + +$$ +\lim_{h\to 0} \frac{f(x+h)-f(x)}{h} +$$ + +exists at $x$. And in fact, that limit approaches the value of the derivative in the limit. + +```{python} +#| code-fold: true +import matplotlib.pyplot as plt +import numpy as np +import ipywidgets as widgets +from IPython.display import display + +# Define the function f(x) +def f(x): + return 3 * x ** 2 - 4 * x + 5 + +# Define the derivative f'(x) +def df(x): + return 6 * x - 4 + +# Function to plot f(x) and its tangent line at x = x_value +def plot_with_tangents(x_value, h_value): + # Generate x values for the function + x = np.linspace(-5, 5, 400) + y = f(x) + + #h_value = 10**(-e_value) + + # Compute the slope and function value at x = x_value + slope_at_x_value = df(x_value) + limit_at_x_value = (f(x_value + h_value) - f(x_value)) / h_value + f_at_x_value = f(x_value) + f_at_x_plus_h_value = f(x_value + h_value) + + # Generate x and y values for the tangent line near x = x_value + x_tangent = np.linspace(x_value - 2, x_value + 2, 400) + y_tangent = f_at_x_value + slope_at_x_value * (x_tangent - x_value) + y_limit_tangent = f_at_x_value + limit_at_x_value * (x_tangent - x_value) + + # Create the plot + plt.figure(figsize=(10, 6)) + plt.plot(x, y, label='f(x) = 3x^2 - 4x + 5') + plt.plot(x_tangent, y_tangent, linestyle='--', label=f'Asymptotic slope of {df(x_value):.2f} at x = {x_value:.2f}') + plt.plot(x_tangent, y_limit_tangent, linestyle='-.', label=f'Asymptotic limit of {limit_at_x_value:.2f} at x = {x_value:.2f}, h = {h_value:.3f}') + plt.scatter([x_value], [f_at_x_value], color='red') # point of tangency + plt.scatter([x_value+h_value], [f_at_x_plus_h_value], color='red') # point of tangency + plt.title('Plot of the function f(x) = 3x^2 - 4x + 5') + plt.xlabel('x') + plt.ylabel('f(x)') + plt.grid(True) + plt.legend() + plt.show() + +# Create an interactive widget +widgets.interact(plot_with_tangents, x_value=widgets.FloatSlider(value=-2, min=-5, max=5, step=0.1), h_value=widgets.FloatSlider(value=1, min=.001, max=2, step=.001)); +#widgets.interact(plot_with_tangents, h_value=widgets.FloatSlider(value=1, min=1, max=10, step=1)); +``` + +We use the rules of derivatives. See for example the derivative +[rules for basic functions](https://en.wikipedia.org/wiki/Derivative#Rules_for_basic_functions), +e.g. + +$$ +\frac{d}{dx} x^a = ax^{a-1}, + \quad \textrm{e.g.} \quad \frac{d}{dx} 3x^2 = 6x + \quad \textrm{,} \quad \frac{d}{dx} 6x = 6 + \quad \textrm{,} \quad \frac{d}{dx} 6 = 0 +$$ + +so + +$$ +\frac{d f(x)}{dx} = 6x - 4 +$$ + +```{python} +#| code-fold: true +# define the derivate of f as df +def df(x): + return 6*x - 4 +``` + +We can solve for where $\frac{d}{dx} f(x) = 0$ + +$$ +6x - 4 = 0 +$$ + +```{python} +#| code-fold: true +# Evaluate df and f for x where df = 0 +x_zero = 2/3 + +# Evaluate df +df(x_zero) +``` + +```{python} +#| code-fold: true +# And f at that value is +f(x_zero) +``` + +Which we can add to the plot of $f(x)$ to see if it indeed is at the minimum. + +```{python} +#| code-fold: true +xs = np.arange(-5, 5, 0.25) +ys = f(xs) +plt.plot(xs, ys) + +# Add a circle point at (2, 5) +plt.plot([x_zero], [f(x_zero)], 'o') + +# Show the plot +plt.show() +``` + +Now as Wikipedia [states](https://en.wikipedia.org/wiki/Derivative), + +> The derivative of a function of a single variable at a chosen input value, when +it exists, is the slope of the tangent line to the graph of the function at that point. + +## Slope of a Function + +We can explore the tangent at different x-values. + +```{python} +#| code-fold: true +import matplotlib.pyplot as plt +import numpy as np +import ipywidgets as widgets +from IPython.display import display + +# Define the function f(x) +def f(x): + return 3 * x ** 2 - 4 * x + 5 + +# Define the derivative f'(x) +def df(x): + return 6 * x - 4 + +# Function to plot f(x) and its tangent line at x = x_value +def plot_with_tangent(x_value): + # Generate x values for the function + x = np.linspace(-5, 5, 400) + y = f(x) + + # Compute the slope and function value at x = x_value + slope_at_x_value = df(x_value) + f_at_x_value = f(x_value) + + # Generate x and y values for the tangent line near x = x_value + x_tangent = np.linspace(x_value - 2, x_value + 2, 400) + y_tangent = f_at_x_value + slope_at_x_value * (x_tangent - x_value) + + # Create the plot + plt.figure(figsize=(10, 6)) + plt.plot(x, y, label='$f(x) = 3x^2 - 4x + 5$') + plt.plot(x_tangent, y_tangent, linestyle='--', label=f'Asymptotic slope of {df(x_value):.2f} at x = {x_value:.2f}') + plt.scatter([x_value], [f_at_x_value], color='red') # point of tangency + plt.title('Plot of the function $f(x) = 3x^2 - 4x + 5$') + plt.xlabel('$x$') + plt.ylabel('$f(x)$') + plt.grid(True) + plt.legend() + plt.show() + +# Create an interactive widget +widgets.interact(plot_with_tangent, x_value=widgets.FloatSlider(value=-2, min=-5, max=5, step=0.1)); +``` + +## Slope Shows Influence of $x$ on $f$ + +__Important Note:__ + +* if the slope is negative, then by increasing $x$, we will decrease $f(x)$. +* And if the slope is positive, then decreasing $x$ will decrease $f(x)$. + +## Interpretation of Slope + +Let's illustrate with this function $f(x)$ a useful way to interpret the slope. + +In the graph above, with $x=-2$, we see the slope, call it $m$, is -16. What that +means is that when we change the value of $x$, the impact on the ouptut will +roughly be _amplified_ by $m$, or -16 when $x=2$. + +Put another way, the slope (equivalently the derivative) of a function $f(x)$ at +an input $x$ indicates how sensitive the output is to changes in the input. + +> This will be key to understanding how we have to tweak the weights of our model +> to minimize our loss function. + +## Gradient Descent on a Linear Regression Model + +Now, in 2 or higher dimensions we can there many directions that will descend, +but we want to pick the direction of steepest descent. We'll formalize that idea. + +As long as the loss function is _locally_ differentiable, we can define the +direction of steepest descent. + +That direction is given by the _negative_ of the __gradient.__ + +The gradient is a generalization of the slope of a line. + +Let's say we have a loss function $\mathcal{L}(\mathbf{w})$. + +The components of $\mathbf{w}\in\mathbb{R}^n$ are the parameters we want to optimize. + +Just a reminder that $\mathbf{w} \in \mathbb{R}^n$ denotes an $n$-dimensional vector. + +For linear regression, the loss function could be squared loss: + +$$ +\mathcal{L}(\mathbf{w}) = \Vert\mathbf{y} - \hat{\mathbf{y}}\Vert^2 +$$ + +where $\hat{\mathbf{y}}$ is our estimate, ie, $\hat{\mathbf{y}} = X\mathbf{w}$ so that + +$$ +\mathcal{L}(\mathbf{w}) = \Vert\mathbf{y} - X\mathbf{w}\Vert^2 +$$ + +To find the gradient, we take the partial derivative of our loss function with respect to each parameter: + +$$ +\frac{\partial \mathcal{L}}{\partial w_i} +$$ + +and collect all the partial derivatives into a vector of the same shape as $\mathbf{w}$: + +$$ +\nabla_\mathbf{w}\mathcal{L} = \begin{bmatrix} + \frac{\partial \mathcal{L}}{\partial w_1}\\ + \frac{\partial \mathcal{L}}{\partial w_2}\\ + \vdots \\ + \frac{\partial \mathcal{L}}{\partial w_n} + \end{bmatrix} +$$ + +When you see the notation $\nabla_\mathbf{w}\mathcal{L},$ think of it as the +derivative with respect to the vector $\mathbf{w}$. + +The _nabla_ symbol, $\nabla$, denotes the _vector differentiator operator_ called _del_. + +It turns out that if we are going to take a small step of unit length, then the +gradient is the direction that maximizes the change in the loss function. + + + +![](figs/L23-gradient-of-convex.png){width="60%"} + +As you can see from the above figure, in general the gradient varies depending on +where you are in the parameter space. + +So we write: + +$$ +\nabla_\mathbf{w}\mathcal{L}(\mathbf{w}) = \begin{bmatrix} + \frac{\partial \mathcal{L}}{\partial w_1}(\mathbf{w})\\ + \frac{\partial \mathcal{L}}{\partial w_2}(\mathbf{w})\\ + \vdots \\ + \frac{\partial \mathcal{L}}{\partial w_n}(\mathbf{w}) + \end{bmatrix} +$$ + +Each time we seek to improve our parameter estimates $\mathbf{w}$, we will take +a step in the negative direction of the gradient. + +... "negative direction" because the gradient specifies the direction of maximum +increase -- and we want to decrease the loss function. + +How big a step should we take? + +For step size, will use a scalar value, here denoted by the greek letter "eta", +$\eta$, which we call the __learning rate.__ + +The learning rate is a hyperparameter that needs to be tuned for a given problem, +or even can be modified adaptively as the algorithm progresses as we will see later. + +Now we can write the __gradient descent__ algorithm formally: + +1. Start with an initial parameter estimate $\mathbf{w}^0$. +2. Update: $\mathbf{w}^{n+1} = \mathbf{w}^n - \eta \nabla_\mathbf{w}\mathcal{L}(\mathbf{w}^n)$ +3. If not converged, go to step 2. + +How do we know if we are "converged"? + +Typically we stop + +* after a certain number of iterations, or +* the loss has not improved by a fixed amount -- _early stopping_ + +## Example: Linear Regression + +```{python} +#| code-fold: true +import numpy as np +import scipy as sp +import matplotlib.pyplot as plt +import pandas as pd +import seaborn as sns +import matplotlib as mp +import sklearn +import networkx as nx +from IPython.display import Image, HTML + +import laUtilities as ut + +%matplotlib inline +``` + +Let's say we have this dataset. + +```{python} +#| code-fold: true +def centerAxes(ax): + ax.spines['left'].set_position('zero') + ax.spines['right'].set_color('none') + ax.spines['bottom'].set_position('zero') + ax.spines['top'].set_color('none') + ax.xaxis.set_ticks_position('bottom') + ax.yaxis.set_ticks_position('left') + bounds = np.array([ax.axes.get_xlim(), ax.axes.get_ylim()]) + ax.plot(bounds[0][0],bounds[1][0],'') + ax.plot(bounds[0][1],bounds[1][1],'') + +n = 10 +beta = np.array([1., 0.5]) +ax = plt.figure(figsize = (7, 7)).add_subplot() +centerAxes(ax) +np.random.seed(1) +xlin = -10.0 + 20.0 * np.random.random(n) +y = beta[0] + (beta[1] * xlin) + np.random.randn(n) +ax.plot(xlin, y, 'ro', markersize = 10); +``` + +Let's fit a least-squares line to this data. + +The loss function for this problem is the least-squares error: + +$$ +\mathcal{L}(\mathbf{\beta}) = \Vert\mathbf{y} - X\mathbf{\beta}\Vert^2 +$$ + +Of course, we know how to solve this problem using the normal equations, but let's do it using gradient descent instead. + +Here is the line we'd like to find: + +```{python} +#| code-fold: true +ax = plt.figure(figsize = (7, 7)).add_subplot() +centerAxes(ax) +ax.plot(xlin, y, 'ro', markersize = 10) +ax.plot(xlin, beta[0] + beta[1] * xlin, 'b-') +plt.text(-9, 3, r'$y = \beta_0 + \beta_1x$', size=20); +``` + +There are $n = 10$ data points, whose $x$ and $y$ values are stored in `xlin` and `y`. + +First, let's create our $X$ (design) matrix, and include a column of ones to model the intercept: + +```{python} +X = np.column_stack([np.ones((n, 1)), xlin]) +``` + +Now, let's visualize the loss function $\mathcal{L}(\mathbf{\beta}) = \Vert \mathbf{y}-X\mathbf{\beta}\Vert^2.$ + +```{python} +#| code-fold: true +fig = ut.three_d_figure((23, 1), '', + -12, 12, -4, 4, -1, 2000, + figsize = (7, 7)) +qf = np.array(X.T @ X) +fig.ax.view_init(azim = 60, elev = 22) +fig.plotGeneralQF(X.T @ X, -2 * (y.T @ X), y.T @ y, alpha = 0.5) +fig.ax.set_zlabel('$\mathcal{L}$') +fig.ax.set_xlabel(r'$\beta_0$') +fig.ax.set_ylabel(r'$\beta_1$') +fig.set_title(r'$\Vert \mathbf{y}-X\mathbf{\beta}\Vert^2$', '', + number_fig = False, size = 18) +# fig.save(); +``` + +We won't take you through computing the gradient for this problem (you can find it in the online text). + +We'll will just tell you that the gradient for a least squares problem is: + +$$ +\nabla_\beta \mathcal{L}(\mathbf{\beta}) = X^T X \beta - X^T\mathbf{y} +$$ + +::: {.callout-note} +For those interested in a little more insight into what these plots are showing, +here is the derivation. + +We start from the rule that $\Vert \mathbf{v}\Vert = \sqrt{\mathbf{v}^T\mathbf{v}}$. + +Applying this rule to our loss function: + +$$ +\mathcal{L}(\mathbf{\beta}) = \Vert \mathbf{y} - X\mathbf{\beta} \Vert^2 = \beta^T X^T X \beta - 2\mathbf{\beta}^TX^T\mathbf{y} + \mathbf{y}^T\mathbf{y} +$$ + +The first term, $\beta^T X^T X \beta$, is a quadratic form, and it is what makes +this surface curved. As long as $X$ has independent columns, $X^TX$ is positive +definite, so the overall shape is a paraboloid opening upward, and the surface +has a unique minimum point. + +To find the gradient, we can use standard calculus rules for derivates involving +vectors. The rules are not complicated, but the bottom line is that in this case, +you can almost use the same rules you would if $\beta$ were a scalar: + +$$ +\nabla_\beta \mathcal{L}(\mathbf{\beta}) = 2X^T X \beta - 2X^T\mathbf{y} +$$ + +And by the way -- since we've computed the derivative as a function of $\beta$, instead of using gradient descent, we could simply solve for the point where the gradient is zero. This is the optimal point which we know must exist: + +$$ +\nabla_\beta \mathcal{L}(\mathbf{\beta}) = 0 +$$ + +$$ +2X^T X \beta - 2X^T\mathbf{y} = 0 +$$ + +$$ +X^T X \beta = X^T\mathbf{y} +$$ + +Which of course, are the normal equations for this linear system. +::: + +So here is our code for gradient descent: + +```{python} +def loss(X, y, beta): + return np.linalg.norm(y - X @ beta) ** 2 + +def gradient(X, y, beta): + return X.T @ X @ beta - X.T @ y + +def gradient_descent(X, y, beta_hat, eta, nsteps = 1000): + losses = [loss(X, y, beta_hat)] + betas = [beta_hat] + # + for step in range(nsteps): + # + # the gradient step + new_beta_hat = beta_hat - eta * gradient(X, y, beta_hat) + beta_hat = new_beta_hat + # + # accumulate statistics + losses.append(loss(X, y, new_beta_hat)) + betas.append(new_beta_hat) + + return np.array(betas), np.array(losses) +``` + +We'll start at an arbitrary point, say, $(-8, -3.2)$. + +That is, $\beta_0 = -8$, and $\beta_1 = -3.2$. + +```{python} +beta_start = np.array([-8, -3.2]) +eta = 0.002 +betas, losses = gradient_descent(X, y, beta_start, eta) +``` + +What happens to our loss function per GD iteration? + +```{python} +#| code-fold: true +plt.plot(np.log(losses), '.-') +plt.ylabel(r'$\log\mathcal{L}$', size = 14) +plt.xlabel('Iteration', size = 14) +plt.title('Improvement in Loss Per Iteration of GD', size = 16); +``` + +And how do the parameter values $\beta$ evolve? + +```{python} +#| code-fold: true +plt.plot(betas[:, 0], betas[:, 1], '.-') +plt.xlabel(r'$\beta_0$', size = 14) +plt.ylabel(r'$\beta_1$', size = 14) +plt.title(r'Evolution of $\beta$', size = 16); +``` + +Notice that the improvement in loss decreases over time. Initially the gradient +is steep and loss improves fast, while later on the gradient is shallow and loss +doesn't improve much per step. + +Now remember that in reality we are like the person who is trying to find their +way down the mountain, in the fog. + +In general we cannot "see" the entire loss function surface. + +Nonetheless, since we know what the loss surface looks like in this case, we can +visualize the algorithm "moving" on that surface. + +This visualization combines the last two plots into a single view. + +```{.python} +#| code-fold: true +%matplotlib inline +# set up view +import matplotlib.animation as animation +mp.rcParams['animation.html'] = 'jshtml' + +anim_frames = np.array(list(range(10)) + [2 * x for x in range(5, 25)] + [5 * x for x in range(10, 100)]) + +fig = ut.three_d_figure((23, 1), 'z = 3 x1^2 + 7 x2 ^2', + -12, 12, -4, 4, -1, 2000, + figsize = (7, 7)) +plt.close() +fig.ax.view_init(azim = 60, elev = 22) +qf = np.array(X.T @ X) +fig.plotGeneralQF(X.T @ X, -2 * (y.T @ X), y.T @ y, alpha = 0.5) +fig.ax.set_zlabel('$\mathcal{L}$') +fig.ax.set_xlabel(r'$\beta_0$') +fig.ax.set_ylabel(r'$\beta_1$') +fig.set_title(r'$\Vert \mathbf{y}-X\mathbf{\beta}\Vert^2$', '', + number_fig = False, size = 18) +# +def anim(frame): + fig.ax.plot(betas[:frame, 0], betas[:frame, 1], 'o-', zs = losses[:frame], c = 'k', markersize = 5) + # fig.canvas.draw() +# +# create the animation +animation.FuncAnimation(fig.fig, anim, + frames = anim_frames, + fargs = None, + interval = 1, + repeat = False) +``` + +We can also see how evolution of the parameters translate to the line fitting to the data. + +```{.python} +#| code-fold: true +fig, ax = plt.subplots(figsize = (7, 7)) +plt.close() +centerAxes(ax) +ax.plot(xlin, y, 'ro', markersize = 10) +fit_line = ax.plot([], []) + +# +#to get additional args to animate: +#def animate(angle, *fargs): +# fargs[0].view_init(azim=angle) +def animate(frame): + fit_line[0].set_data(xlin, betas[frame, 0] + betas[frame, 1] * xlin) + fig.canvas.draw() +# +# create the animation +animation.FuncAnimation(fig, animate, + frames = anim_frames, + fargs=None, + interval=100, + repeat=False) +``` + +## Challenges in Gradient Descent + +Gradient Descent is a very general algorithm, one that can be applied to a huge +array of problem types. + +However, there are a variety of issues that arise in using gradient descent in +practice. + +## Learning Rate + +Setting the learning rate can be a challenge. + +Previously we had set the learning rate $\eta = 0.002$. + +Let set it a little higher and see what happens: $\eta = 0.0065.$ + +```{python} +beta_start = np.array([-8, -2]) +eta = 0.0065 +betas, losses = gradient_descent(X, y, beta_start, eta, nsteps = 100) +``` + +```{python} +#| code-fold: true +plt.plot(np.log(losses), '.-') +plt.ylabel(r'$\log\mathcal{L}$', size = 14) +plt.xlabel('Iteration', size = 14) +plt.title('Improvement in Loss Per Iteration of GD', size = 16); +``` + +```{python} +#| code-fold: true +plt.plot(betas[:, 0], betas[:, 1], '.-') +plt.xlabel(r'$\beta_0$', size = 14) +plt.ylabel(r'$\beta_1$', size = 14) +plt.title(r'Evolution of $\beta$', size = 16); +``` + +This is a total disaster. What is going on? + +It is helpful to look at the progress of the algorithm using the loss surface: + +```{python} +#| code-fold: true +%matplotlib inline +fig = ut.three_d_figure((23, 1), '', + -12, 2, -4, 4, -1, 2000, + figsize = (7, 7)) +qf = np.array(X.T @ X) +fig.ax.view_init(azim = 142, elev = 58) +fig.plotGeneralQF(X.T @ X, -2 * (y.T @ X), y.T @ y, alpha = 0.5) +fig.ax.set_zlabel('$\mathcal{L}$') +fig.ax.set_xlabel(r'$\beta_0$') +fig.ax.set_ylabel(r'$\beta_1$') +fig.set_title(r'$\Vert \mathbf{y}-X\mathbf{\beta}\Vert^2$', '', + number_fig = False, size = 18) +nplot = 18 +fig.ax.plot(betas[:nplot, 0], betas[:nplot, 1], 'o-', zs = losses[:nplot], markersize = 5); +# +``` + +We can see what is going on more clearly here. + +What is happening is that because the steps are __too large,__ each step +overshoots the local minimum. + +The next step then lands on a portion of the surface that steeper ... and in the +opposite direction. + +And so the process diverges. + +> For an interesting comparison, try setting $\eta = 0.0055$ and observe the +> evolution of $\beta$. + +Hence it is important to decrease the step size when divergence appears. + +Unfortunately, on a complicated loss surface, a given step size may diverge in +one location or starting point, but not in another. + +## Complex Loss Surfaces + +The loss surface for linear regression is the best possible kind: it is strictly +convex, so it has a single global minimum. + +For neural networks, the loss surface is more complex. + +In general, the larger the neural network, the more complex the loss surface. + +And deep neural networks, especially transformers have billions of parameters. + +Here's a visualization of the loss surface for the 56 layer neural network +[VGG-56](http://arxiv.org/abs/1409.1556), from +[Visualizing the Loss Landscape of Neural Networks](https://www.cs.umd.edu/~tomg/projects/landscapes/). + + + +![](figs/L23-complex-landscape.png){width="40%"} + +For a fun exploration, see https://losslandscape.com/explorer. + +## Recap + +So far we applied gradient descent on a simple linear regression model. + +As we'll soon see, deep neural networks are much more complicated multi-stage +models, with millions or billions of parameters to differentiate. + +Fortunately, the _Chain Rule_ from calculus gives us a relatively simple and +scalable algorithm, called _Back Propagation_, that solves this problem. + +## Neuron and Neural Networks + +Now let's switch gears a bit to define an _artificial neuron_. For better or worse +it is named after and loosely modeled on a biological neuron. + + + +![](figs/NN-figs/neuron.png){width="75%"} + + +From [cs231n](https://cs231n.github.io/neural-networks-1/) + +* The dendrites carry impulses from other neurons of different distances. +* Once the collective firing rate of the impulses exceed a certain threshold, + the neuron fires its own pulse through the axon to other neurons + +There are companies trying to mimic this impulse (i.e. spiking) based neuron in +silicon -- so called _neuromorphic computing_. + +See for example +[Neuromorphic Computing](https://en.wikipedia.org/wiki/Neuromorphic_engineering) +or [Spiking Neural Network](https://en.wikipedia.org/wiki/Spiking_neural_network) + +Some examples of companies and projects are Intel's +[Loihi](https://www.intel.com/content/www/us/en/research/neuromorphic-computing-loihi-2-technology-brief.html) +and startups such as GrAI Matter Labs [VIP processor](https://www.graimatterlabs.ai/product). + +## Artificial Neuron + + + + +![](figs/NN-figs/neuron_model.jpeg){width="75%"} + +From [cs231n](https://cs231n.github.io/neural-networks-1/) + +The more common artifical neuron + +* collects one or more inputs, +* each multiplied by a unique weight +* sums the weighted inputs +* adds a bias +* then finally usually applies a nonlinear activation function + +## Multi-Layer Perceptron (MLP) or Fully Connected Network (FCN) + +![](figs/NN-figs/neural_net2.jpeg){width="75%"} + +From [cs231n](https://cs231n.github.io/convolutional-networks/) + +Multiple artificial neurons can be acting on the same inputs, in what we call +a _layer_, and we can have more than one _layer_ until we produce one or more +outputs. + +The example above shows a network with _3 inputs_, two layers of neurons, each +with 4 neurons, followed by one layer that produces a single value output. + +E.g. a binary classifier. + +Activation function is typically some nonlinear function that compresses the input +in some way. Historically, it's been the sigmoid and $\tanh()$ functions. See for +example [Hyperbolic Functions](https://en.wikipedia.org/wiki/Hyperbolic_functions#Tanh). + +```{python} +#| code-fold: true +plt.plot(np.arange(-5,5,0.2), np.tanh(np.arange(-5,5,0.2))) +plt.title('tanh(x)') +plt.xlabel('x') +plt.ylabel('f(x)') +plt.grid() +``` + +A more common activation function these days and that is more efficient to implement is the _Rectified Linear Unit_ or _ReLU_. + +$$ +\textrm{ReLU}(x) = \mathrm{max}(0, x) +$$ + +```{python} +#| code-fold: true +plt.plot(np.arange(-5,5,0.2), np.maximum(0,np.arange(-5,5,0.2))) +plt.title('ReLU(x)') +plt.xlabel('x') +plt.ylabel('f(x)') +plt.grid() +``` + +There are many other variations. See for example +[PyTorch Non-linear Activations](https://pytorch.org/docs/stable/nn.html#non-linear-activations-weighted-sum-nonlinearity) + +## Next Lecture + +* We'll build out a Value class +* Visualize our compute graph +* Implement Backpropagation +* Build out our neural network +* Train and evaluate it +* Recreate and match it in PyTorch + diff --git a/ds701_book/24-NN-Outline.md b/ds701_book/24-NN-Outline.md new file mode 100644 index 00000000..f0c8c1eb --- /dev/null +++ b/ds701_book/24-NN-Outline.md @@ -0,0 +1,49 @@ +# NN II and III Outline + +## NN I Recap + +* Applications of NNs +* Intuition on loss functions for model fitting -- convex and nonconvex +* Gradient descent intuition +* Derivatives refresher (slope, gradient) +* Gradient descent on linear regression model +* Challenges in gradient descent -- learning rate... +* Complex loss surfaces of NNs +* biological and artificial neurons +* first glimpse at a MLP/FCN + + +## NN II Outline + +* Artificial Neuron -- picture +* Neuron -- scalar equation +* Neuron -- vector equation + +Individual weights are $\omega$ and matrices of weights are $\Omega$. +Each neuron has its own set of weights for each input. + +$\beta$ are vector of biases. Each neuron has it's own biases + +* Shallow network -- 1 hidden layer with 4 neurons, single regression output + * Show picture, + * then show matrix equation + * define loss function... + +* define as $loss(h_1(a_0(h_0(x))))$ + * linear part of hidden -- $h_0(x)$ + * activation function $a_0(.)$ + * linear part of output $h_1(.)$ + * loss function loss(.), say is just MSE + +We want to each parameter by the negative of the partial derivative + +* write the equations in terms of matrix values + +* then we want the partial derivative of each to update the parameters + +* Introduce the chain rule + +* show a single neuron with single input +* show as compute graph + +* show torchviz with numbers? diff --git a/ds701_book/26-TimeSeries-Notes.qmd b/ds701_book/26-TimeSeries-Notes.qmd new file mode 100644 index 00000000..60c84aec --- /dev/null +++ b/ds701_book/26-TimeSeries-Notes.qmd @@ -0,0 +1,279 @@ +--- +title: TS Notes +jupyter: python3 +--- + + +(gtp-4o, personal communications, Nov. 20, 2024) + +Classical decomposition is primarily used for breaking down a time series into its constituent components: trend, seasonality, and residuals. While it doesn't directly provide a forecasting method, you can use the decomposed components to make forecasts. Here's a general approach to forecasting using classical decomposition: + +1. **Decompose the Time Series**: Break down the time series into trend, seasonal, and residual components. + +2. **Forecast the Trend Component**: Use a suitable method (e.g., linear regression, moving average) to forecast the trend component. + +3. **Forecast the Seasonal Component**: Assume the seasonal component repeats itself and use the last observed seasonal pattern for future periods. + +4. **Combine the Forecasts**: Add the forecasted trend and seasonal components to get the final forecast. The residual component is often assumed to be zero for forecasting purposes. + +Here's a simplified example in Python using the `statsmodels` library for decomposition and a basic approach for forecasting: + +````{python} +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +from statsmodels.tsa.seasonal import seasonal_decompose + +# Load the dataset +data = pd.read_csv('data/air_passengers_1949_1960.csv', index_col='Date', parse_dates=True) +ts = data['Number of Passengers'] + +# Decompose the time series +decomposition = seasonal_decompose(ts, model='additive') +trend = decomposition.trend +seasonal = decomposition.seasonal +residual = decomposition.resid + +# Forecast the trend component using a simple linear extrapolation +trend_forecast = trend.dropna().iloc[-1] + np.arange(1, 25) * (trend.dropna().iloc[-1] - trend.dropna().iloc[-2]) + +# Forecast the seasonal component by repeating the last observed seasonal pattern +seasonal_forecast = np.tile(seasonal[-12:], 2) + +# Combine the forecasts +forecast = trend_forecast + seasonal_forecast + +# Plot the original data and the forecast +plt.figure(figsize=(10, 6)) +plt.plot(ts, label='Observed') +forecast_index = pd.date_range(ts.index[-1] + pd.DateOffset(months=1), periods=24, freq='M') +plt.plot(forecast_index, forecast, label='Forecast', color='red') +plt.legend() +plt.title('Forecast using Classical Decomposition') +plt.show() +```` + +### Explanation: +- **Trend Forecasting**: This example uses a simple linear extrapolation of the last two trend values. You might want to use a more sophisticated method depending on your data. +- **Seasonal Forecasting**: The seasonal component is assumed to repeat itself, so the last observed seasonal pattern is used for future periods. +- **Combining Components**: The final forecast is the sum of the trend and seasonal forecasts. The residual component is not used in the forecast. + +This approach is quite basic and may not be suitable for all datasets, especially those with complex patterns. For more accurate forecasting, consider using more advanced models like ARIMA or machine learning techniques. + +## MA Model Definition + +(gtp-4o, personal communications, Nov. 20, 2024) + +Certainly! Let's walk through an example of defining and training a Moving Average (MA) model using the Air Passengers dataset in Python. We'll use the `statsmodels` library, which provides tools for estimating MA models. + +### Step-by-Step Example + +1. **Import Libraries**: We'll need `pandas` for data manipulation and `statsmodels` for the MA model. + +2. **Load the Data**: We'll load the Air Passengers dataset. + +3. **Visualize the Data**: It's always a good idea to visualize the data to understand its structure. + +4. **Fit an MA Model**: We'll fit an MA model to the data. + +5. **Evaluate the Model**: We'll look at the model summary and plot the residuals. + +Here's how you can do it: + +```{python} +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +from statsmodels.tsa.arima.model import ARIMA + +# Load the Air Passengers dataset +data = pd.read_csv(os.path.join('data', 'air_passengers_1949_1960.csv'), + index_col='Date', parse_dates=True) + +# Visualize the data +plt.figure(figsize=(10, 6)) +plt.plot(data, label='Number of Passengers') +plt.title('Air Passengers Data') +plt.xlabel('Date') +plt.ylabel('Number of Passengers') +plt.legend() +plt.show() + +# Fit an MA model +# We use ARIMA with order (0, 0, q) to specify an MA(q) model +ma_order = 1 # You can experiment with different orders +model = ARIMA(data, order=(0, 0, ma_order)) +model_fit = model.fit() + +# Print the model summary +print(model_fit.summary()) + +# Plot the residuals +residuals = model_fit.resid +plt.figure(figsize=(10, 6)) +plt.plot(residuals, label='Residuals') +plt.title('Residuals of the MA Model') +plt.xlabel('Date') +plt.ylabel('Residuals') +plt.legend() +plt.show() + +# Plot the ACF of the residuals to check for remaining autocorrelation +from statsmodels.graphics.tsaplots import plot_acf +plot_acf(residuals, lags=20) +plt.show() +``` + +### Explanation + +- **Data Loading**: We load the dataset directly from a URL. The `parse_dates=True` argument ensures that the 'Month' column is treated as a datetime index. + +- **Visualization**: We plot the time series to get a sense of its structure. + +- **Model Fitting**: We use the `ARIMA` class from `statsmodels` with the order `(0, 0, ma_order)` to specify an MA model. The first two zeros indicate no autoregressive terms and no differencing, respectively. + +- **Model Summary**: The summary provides details about the estimated parameters and their statistical significance. + +- **Residuals**: We plot the residuals to check for any patterns. Ideally, they should resemble white noise. + +- **ACF Plot**: The autocorrelation function (ACF) plot of the residuals helps us check if there is any remaining autocorrelation. If the residuals are white noise, the ACF should show no significant lags. + +This example demonstrates how to define and train an MA model using the Air Passengers dataset. You can experiment with different orders of the MA model to see how it affects the fit. + +## Building MA from scratch + +(gtp-4o, personal communications, Nov. 20, 2024) + +Building a Moving Average (MA) model from scratch involves manually calculating the moving average of the error terms. Here's how you can implement an MA(1) model without using the `statsmodels` library: + +### Step-by-Step Implementation + +1. **Import Libraries**: We'll use `pandas` for data manipulation and `numpy` for numerical operations. + +2. **Load the Data**: Load the Air Passengers dataset. + +3. **Define the MA Model**: Implement the MA(1) model manually. + +4. **Calculate Residuals**: Compute the residuals and use them to predict future values. + +5. **Visualize the Results**: Plot the original data and the fitted values. + +Here's how you can do it: + +```{python} +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt + +# Load the Air Passengers dataset +data = pd.read_csv('data/air_passengers_1949_1960.csv', index_col='Date', parse_dates=True) +ts = data['Number of Passengers'] + +# Define the MA(1) model +def moving_average_forecast(series, window_size): + # Initialize the list to store the forecasted values + forecast = [np.nan] * window_size # First 'window_size' values are NaN + for t in range(window_size, len(series)): + # Calculate the average of the past 'window_size' residuals + forecast.append(np.mean(series[t-window_size:t])) + return np.array(forecast) + +# Calculate the residuals +residuals = ts - ts.shift(1) + +# Fit the MA(1) model +window_size = 1 # MA(1) model +ma_forecast = moving_average_forecast(residuals.dropna(), window_size) + +# Add the forecasted residuals to the original series to get the fitted values +fitted_values = ts.shift(1) + ma_forecast + +# Plot the original data and the fitted values +plt.figure(figsize=(10, 6)) +plt.plot(ts, label='Observed') +plt.plot(fitted_values, label='Fitted', color='red') +plt.title('MA(1) Model Fitting') +plt.xlabel('Date') +plt.ylabel('Number of Passengers') +plt.legend() +plt.show() +``` + +### Explanation + +- **Data Loading**: We load the dataset and parse the dates to use them as the index. + +- **MA Model Definition**: The `moving_average_forecast` function calculates the moving average of the past `window_size` residuals to forecast future values. + +- **Residual Calculation**: We calculate the residuals by subtracting the previous value from the current value. + +- **Model Fitting**: We use a window size of 1 for the MA(1) model, which means we use the last residual to predict the next value. + +- **Visualization**: We plot the original time series and the fitted values to visualize the model's performance. + +This implementation provides a basic example of how to manually fit an MA(1) model. You can extend this approach to higher-order MA models by adjusting the `window_size` and modifying the forecast calculation accordingly. + +## Parameterize the MA 1. + +(gtp-4o, personal communications, Nov. 20, 2024) + +Certainly! We can modify the function to accept the MA order as a parameter, allowing you to specify different orders for the Moving Average model. Here's how you can do it: + +```{python} +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt + +# Load the Air Passengers dataset +data = pd.read_csv('data/air_passengers_1949_1960.csv', index_col='Date', parse_dates=True) +ts = data['Number of Passengers'] + +# Define the MA model with a parameter for the order +def moving_average_forecast(series, ma_order): + # Initialize the list to store the forecasted values + forecast = [np.nan] * ma_order # First 'ma_order' values are NaN + for t in range(ma_order, len(series)): + # Calculate the average of the past 'ma_order' residuals + forecast.append(np.mean(series[t-ma_order:t])) + return np.array(forecast) + +# Calculate the residuals +residuals = ts - ts.shift(1) + +print(len(residuals)) +print(len(ts)) +print(len(ts.shift(1))) + +# Specify the MA order +ma_order = 3 # You can change this to experiment with different orders + +# Fit the MA model +ma_forecast = moving_average_forecast(residuals.dropna(), ma_order) +# Extend the forecast to be the same length as ts.shift(1) by repeating the last value +extended_ma_forecast = np.concatenate([ma_forecast, np.full(len(ts.shift(1)) - len(ma_forecast), ma_forecast[-1])]) + +print(len(ma_forecast)) + +# Add the forecasted residuals to the original series to get the fitted values +fitted_values = ts.shift(1) + ma_forecast + +# Plot the original data and the fitted values +plt.figure(figsize=(10, 6)) +plt.plot(ts, label='Observed') +plt.plot(fitted_values, label='Fitted', color='red') +plt.title(f'MA({ma_order}) Model Fitting') +plt.xlabel('Date') +plt.ylabel('Number of Passengers') +plt.legend() +plt.show() +``` + +### Explanation + +- **MA Order Parameter**: The `moving_average_forecast` function now takes `ma_order` as a parameter, allowing you to specify the order of the MA model. + +- **Forecast Calculation**: The function calculates the moving average of the past `ma_order` residuals to forecast future values. + +- **Experimentation**: You can change the `ma_order` variable to experiment with different orders of the MA model and observe how it affects the fit. + +This approach provides flexibility in choosing the order of the MA model, enabling you to tailor the model to better fit your data. diff --git a/ds701_book/RNN-time-series-example.qmd b/ds701_book/RNN-time-series-example.qmd new file mode 100644 index 00000000..38b4cde0 --- /dev/null +++ b/ds701_book/RNN-time-series-example.qmd @@ -0,0 +1,232 @@ +## Neural Network Example -- PyTorch + +Dataset: Energy Consumption Dataset (UCI Machine Learning Repository) + +```{python} +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +from sklearn.preprocessing import MinMaxScaler +import torch +import torch.nn as nn +from torch.utils.data import DataLoader, Dataset +``` + +Load dataset from https://archive.ics.uci.edu/dataset/374/appliances+energy+prediction + +```{python} +import os + +file_path = 'energydata_complete.csv' +url = "https://archive.ics.uci.edu/ml/machine-learning-databases/00374/energydata_complete.csv" + +if os.path.exists(file_path): + data = pd.read_csv(file_path) +else: + data = pd.read_csv(url) + data.to_csv(file_path, index=False) + +data.head() +``` + +```{python} +# Save the dataframe locally if it doesn't exist +if not os.path.exists(file_path): + data.to_csv(file_path, index=False) +``` + +
+ +**Column Descriptions** + + +| Column | Description | +| ---- | ----------- | +| date | time year-month-day hour:minute:second | +| Appliances | energy use in Wh | +| lights | energy use of light fixtures in the house in Wh | +| T1 | Temperature in kitchen area, in Celsius | +| RH_1 | Humidity in kitchen area, in % | +| T2 | Temperature in living room area, in Celsius | +| RH_2 | Humidity in living room area, in % | +| T3 | Temperature in laundry room area | +| RH_3 | Humidity in laundry room area, in % | +| T4 | Temperature in office room, in Celsius | +| RH_4 | Humidity in office room, in % | +| T5 | Temperature in bathroom, in Celsius | +| RH_5 | Humidity in bathroom, in % | +| T6 | Temperature outside the building (north side), in Celsius | +| RH_6 | Humidity outside the building (north side), in % | +| T7 | Temperature in ironing room , in Celsius | +| RH_7 | Humidity in ironing room, in % | +| T8 | Temperature in teenager room 2, in Celsius | +| RH_8 | Humidity in teenager room 2, in % | +| T9 | Temperature in parents room, in Celsius | +| RH_9 | Humidity in parents room, in % | +| To | Temperature outside (from Chievres weather station), in Celsius | +| Pressure | (from Chievres weather station), in mm Hg | +| RH_out | Humidity outside (from Chievres weather station), in % | +| Wind speed | (from Chievres weather station), in m/s | +| Visibility | (from Chievres weather station), in km | +| Tdewpoint | (from Chievres weather station), °C | +| rv1 | Random variable 1, nondimensional | +| rv2 | Random variable 2, nondimensional | + +
+ +Where indicated, hourly data (then interpolated) from the nearest airport weather station (Chievres Airport, Belgium) was downloaded from a public data set from Reliable Prognosis, rp5.ru. Permission was obtained from Reliable Prognosis for the distribution of the 4.5 months of weather data. + +```{python} +data.info() +``` + +```{python} + +data['date'] = pd.to_datetime(data['date']) +data.set_index('date', inplace=True) + +data.head() +``` + +We're interested in the `Appliances` column, which is the energy use of the appliances in Wh. + +First, we'll resample the data to hourly resolution and fill missing values using the forward fill method. + +```{python} +data = data['Appliances'].resample('h').mean().fillna(method='ffill') # Resample and fill missing + +data.head() +``` + +Scale the values to be between 0 and 1 and convert to a numpy array. + +```{python} +# Normalize data +scaler = MinMaxScaler() +data_scaled = scaler.fit_transform(data.values.reshape(-1, 1)) + +print(type(data_scaled)) +print(data_scaled.shape) +``` + +```{python} + +# Prepare data for LSTM +class TimeSeriesDataset(Dataset): + def __init__(self, data, seq_length): + self.data = data + self.seq_length = seq_length + + def __len__(self): + return len(self.data) - self.seq_length + + def __getitem__(self, index): + X = self.data[index:index + self.seq_length] + y = self.data[index + self.seq_length] + return torch.tensor(X, dtype=torch.float32), torch.tensor(y, dtype=torch.float32) +``` + +```{python} + +seq_length = 24 +dataset = TimeSeriesDataset(data_scaled, seq_length) + +print(len(dataset)) +``` + +```{python} + +# Split data into training and testing +train_size = int(len(dataset) * 0.8) +test_size = len(dataset) - train_size + +train_dataset, test_dataset = torch.utils.data.random_split(dataset, [train_size, test_size]) + +train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True) +test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False) + +print(len(train_loader)) +print(len(test_loader)) +``` + +```{python} +# let's look at the first batch +for X, y in train_loader: + print(X.shape) + print(y.shape) + break +``` + +```{python} + +# Define the LSTM model +class LSTMModel(nn.Module): + def __init__(self, input_size=1, hidden_size=50, output_size=1): + super(LSTMModel, self).__init__() + self.lstm = nn.LSTM(input_size, hidden_size, batch_first=True) + self.fc = nn.Linear(hidden_size, output_size) + + def forward(self, x): + x, _ = self.lstm(x) + x = self.fc(x[:, -1, :]) # Use the output of the last time step + return x +``` + +```{python} +model = LSTMModel() +criterion = nn.MSELoss() +optimizer = torch.optim.Adam(model.parameters(), lr=0.001) +``` + +```{python} +# Train the model +epochs = 20 +for epoch in range(epochs): + model.train() + train_loss = 0.0 + for X, y in train_loader: + X = X.unsqueeze(-1) # Add input dimension + y = y.unsqueeze(-1) # Add target dimension + + optimizer.zero_grad() + outputs = model(X) + loss = criterion(outputs, y) + loss.backward() + optimizer.step() + + train_loss += loss.item() + + print(f"Epoch {epoch+1}/{epochs}, Loss: {train_loss/len(train_loader):.4f}") +``` + +```{python} + +# Evaluate the model +model.eval() +predictions = [] +actuals = [] +with torch.no_grad(): + for X, y in test_loader: + X = X.unsqueeze(-1) + y = y.unsqueeze(-1) + preds = model(X) + predictions.extend(preds.numpy()) + actuals.extend(y.numpy()) +``` + +```{python} + +# Rescale predictions and actuals to original scale +predictions_rescaled = scaler.inverse_transform(predictions) +actuals_rescaled = scaler.inverse_transform(actuals) +``` + +```{python} + +# Plot results +plt.figure(figsize=(10, 6)) +plt.plot(actuals_rescaled, label='True Values') +plt.plot(predictions_rescaled, label='Predicted Values', alpha=0.7) +plt.legend() +plt.show() +``` \ No newline at end of file diff --git a/ds701_book/ch11.ipynb b/ds701_book/ch11.ipynb new file mode 100644 index 00000000..f02c25c1 --- /dev/null +++ b/ds701_book/ch11.ipynb @@ -0,0 +1,2710 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Chapter 11: Time Series -- Python for Data Analysis, 3rd Ed." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From [Python for Data Analysis, 3rd Ed.](https://wesmckinney.com/book/time-series) \n", + "and accompanying [notebook](https://github.com/wesm/pydata-book/blob/3rd-edition/ch11.ipynb). " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "np.random.seed(12345)\n", + "import matplotlib.pyplot as plt\n", + "plt.rc(\"figure\", figsize=(10, 6))\n", + "PREVIOUS_MAX_ROWS = pd.options.display.max_rows\n", + "pd.options.display.max_columns = 20\n", + "pd.options.display.max_rows = 20\n", + "pd.options.display.max_colwidth = 80\n", + "np.set_printoptions(precision=4, suppress=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Time series data is an important form of structured data in many different fields, such as finance, economics, ecology, neuroscience, and physics. Anything that is recorded repeatedly at many points in time forms a time series. Many time series are fixed frequency, which is to say that data points occur at regular intervals according to some rule, such as every 15 seconds, every 5 minutes, or once per month. Time series can also be irregular without a fixed unit of time or offset between units. How you mark and refer to time series data depends on the application, and you may have one of the following:\n", + "\n", + "**Timestamps**
\n", + "Specific instants in time.\n", + "\n", + "**Fixed periods**
\n", + "Such as the whole month of January 2017, or the whole year 2020.\n", + "\n", + "**Intervals of time**
\n", + "Indicated by a start and end timestamp. Periods can be thought of as special cases of intervals.\n", + "\n", + "**Experiment or elapsed time**
\n", + "Each timestamp is a measure of time relative to a particular start time (e.g., the diameter of a cookie baking each second since being placed in the oven), starting from 0.\n", + "\n", + "In this chapter, I am mainly concerned with time series in the first three categories, though many of the techniques can be applied to experimental time series where the index may be an integer or floating-point number indicating elapsed time from the start of the experiment. The simplest kind of time series is indexed by timestamp.\n", + "\n", + ">pandas also supports indexes based on timedeltas, which can be a useful way of\n", + "> representing experiment or elapsed time. We do not explore timedelta indexes\n", + "> in this book, but you can learn more in the\n", + "> [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#time-span-index).\n", + "\n", + "pandas provides many built-in time series tools and algorithms. You can\n", + "efficiently work with large time series, and slice and dice, aggregate, and\n", + "resample irregular- and fixed-frequency time series. Some of these tools are\n", + "useful for financial and economics applications, but you could certainly use them\n", + "to analyze server log data, too.\n", + "\n", + "As with the rest of the chapters, we start by importing NumPy and pandas:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 11.1 Date and Time Data Types and Tools" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Python standard library includes data types for date and time data, as well\n", + "as calendar-related functionality. \n", + "\n", + "The `datetime`, `time`, and `calendar` modules are the main places to start. The\n", + "`datetime.datetime` type, or simply `datetime`, is widely used:" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "datetime.datetime(2024, 11, 18, 16, 38, 16, 975792)" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from datetime import datetime\n", + "now = datetime.now()\n", + "now" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2024, 11, 18)" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "now.year, now.month, now.day" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`datetime` stores both the date and time down to the microsecond. `datetime.timedelta`,\n", + "or simply `timedelta`, represents the temporal difference between two `datetime` objects:" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "datetime.timedelta(days=926, seconds=56700)" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "delta = datetime(2011, 1, 7) - datetime(2008, 6, 24, 8, 15)\n", + "delta" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "926" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "delta.days" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "56700" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "delta.seconds" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can add (or subtract) a timedelta or multiple thereof to a datetime object to yield a new shifted object:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "datetime.datetime(2010, 12, 14, 0, 0)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from datetime import timedelta\n", + "start = datetime(2011, 1, 7)\n", + "start + timedelta(12)\n", + "start - 2 * timedelta(12)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Table 11.1 summarizes the data types in the datetime module. While this chapter is mainly concerned with the data types in pandas and higher-level time series manipulation, you may encounter the datetime-based types in many other places in Python in the wild.\n", + "\n", + "Table 11.1: Types in the datetime module\n", + "\n", + "| Type | Description |\n", + "| ---- | ----------- |\n", + "| date | Store calendar date (year, month, day) using the Gregorian calendar |\n", + "| time | Store time of day as hours, minutes, seconds, and microseconds |\n", + "| datetime | Store both date and time |\n", + "| timedelta | The difference between two datetime values (as days, seconds, and microseconds) |\n", + "| tzinfo | Base type for storing time zone information |\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Converting Between String and Datetime\n", + "\n", + "You can format datetime objects and pandas Timestamp objects, which I’ll introduce later, as strings using str or the strftime method, passing a format specification:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'2011-01-03'" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stamp = datetime(2011, 1, 3)\n", + "str(stamp)\n", + "stamp.strftime(\"%Y-%m-%d\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[datetime.datetime(2011, 7, 6, 0, 0), datetime.datetime(2011, 8, 6, 0, 0)]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "value = \"2011-01-03\"\n", + "datetime.strptime(value, \"%Y-%m-%d\")\n", + "datestrs = [\"7/6/2011\", \"8/6/2011\"]\n", + "[datetime.strptime(x, \"%m/%d/%Y\") for x in datestrs]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DatetimeIndex(['2011-07-06 12:00:00', '2011-08-06 00:00:00'], dtype='datetime64[ns]', freq=None)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "datestrs = [\"2011-07-06 12:00:00\", \"2011-08-06 00:00:00\"]\n", + "pd.to_datetime(datestrs)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([False, False, True])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "idx = pd.to_datetime(datestrs + [None])\n", + "idx\n", + "idx[2]\n", + "pd.isna(idx)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2011-01-02 -0.204708\n", + "2011-01-05 0.478943\n", + "2011-01-07 -0.519439\n", + "2011-01-08 -0.555730\n", + "2011-01-10 1.965781\n", + "2011-01-12 1.393406\n", + "dtype: float64" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dates = [datetime(2011, 1, 2), datetime(2011, 1, 5),\n", + " datetime(2011, 1, 7), datetime(2011, 1, 8),\n", + " datetime(2011, 1, 10), datetime(2011, 1, 12)]\n", + "ts = pd.Series(np.random.standard_normal(6), index=dates)\n", + "ts" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DatetimeIndex(['2011-01-02', '2011-01-05', '2011-01-07', '2011-01-08',\n", + " '2011-01-10', '2011-01-12'],\n", + " dtype='datetime64[ns]', freq=None)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ts.index" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2011-01-02 -0.409415\n", + "2011-01-05 NaN\n", + "2011-01-07 -1.038877\n", + "2011-01-08 NaN\n", + "2011-01-10 3.931561\n", + "2011-01-12 NaN\n", + "dtype: float64" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ts + ts[::2]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dtype('\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ColoradoTexasNew YorkOhio
2001-05-02-0.0060450.490094-0.277186-0.707213
2001-05-09-0.5601072.7355270.9273351.513906
2001-05-160.5386001.2737680.667876-0.969206
2001-05-231.676091-0.8176490.0501881.951312
2001-05-303.2603830.9633011.201206-1.852001
\n", + "" + ], + "text/plain": [ + " Colorado Texas New York Ohio\n", + "2001-05-02 -0.006045 0.490094 -0.277186 -0.707213\n", + "2001-05-09 -0.560107 2.735527 0.927335 1.513906\n", + "2001-05-16 0.538600 1.273768 0.667876 -0.969206\n", + "2001-05-23 1.676091 -0.817649 0.050188 1.951312\n", + "2001-05-30 3.260383 0.963301 1.201206 -1.852001" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dates = pd.date_range(\"2000-01-01\", periods=100, freq=\"W-WED\")\n", + "long_df = pd.DataFrame(np.random.standard_normal((100, 4)),\n", + " index=dates,\n", + " columns=[\"Colorado\", \"Texas\",\n", + " \"New York\", \"Ohio\"])\n", + "long_df.loc[\"2001-05\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2000-01-01 0\n", + "2000-01-02 1\n", + "2000-01-02 2\n", + "2000-01-02 3\n", + "2000-01-03 4\n", + "dtype: int64" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dates = pd.DatetimeIndex([\"2000-01-01\", \"2000-01-02\", \"2000-01-02\",\n", + " \"2000-01-02\", \"2000-01-03\"])\n", + "dup_ts = pd.Series(np.arange(5), index=dates)\n", + "dup_ts" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dup_ts.index.is_unique" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2000-01-02 1\n", + "2000-01-02 2\n", + "2000-01-02 3\n", + "dtype: int64" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dup_ts[\"2000-01-03\"] # not duplicated\n", + "dup_ts[\"2000-01-02\"] # duplicated" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2000-01-01 1\n", + "2000-01-02 3\n", + "2000-01-03 1\n", + "dtype: int64" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "grouped = dup_ts.groupby(level=0)\n", + "grouped.mean()\n", + "grouped.count()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ts\n", + "resampler = ts.resample(\"D\")\n", + "resampler" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DatetimeIndex(['2012-04-01', '2012-04-02', '2012-04-03', '2012-04-04',\n", + " '2012-04-05', '2012-04-06', '2012-04-07', '2012-04-08',\n", + " '2012-04-09', '2012-04-10', '2012-04-11', '2012-04-12',\n", + " '2012-04-13', '2012-04-14', '2012-04-15', '2012-04-16',\n", + " '2012-04-17', '2012-04-18', '2012-04-19', '2012-04-20',\n", + " '2012-04-21', '2012-04-22', '2012-04-23', '2012-04-24',\n", + " '2012-04-25', '2012-04-26', '2012-04-27', '2012-04-28',\n", + " '2012-04-29', '2012-04-30', '2012-05-01', '2012-05-02',\n", + " '2012-05-03', '2012-05-04', '2012-05-05', '2012-05-06',\n", + " '2012-05-07', '2012-05-08', '2012-05-09', '2012-05-10',\n", + " '2012-05-11', '2012-05-12', '2012-05-13', '2012-05-14',\n", + " '2012-05-15', '2012-05-16', '2012-05-17', '2012-05-18',\n", + " '2012-05-19', '2012-05-20', '2012-05-21', '2012-05-22',\n", + " '2012-05-23', '2012-05-24', '2012-05-25', '2012-05-26',\n", + " '2012-05-27', '2012-05-28', '2012-05-29', '2012-05-30',\n", + " '2012-05-31', '2012-06-01'],\n", + " dtype='datetime64[ns]', freq='D')" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "index = pd.date_range(\"2012-04-01\", \"2012-06-01\")\n", + "index" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DatetimeIndex(['2012-05-13', '2012-05-14', '2012-05-15', '2012-05-16',\n", + " '2012-05-17', '2012-05-18', '2012-05-19', '2012-05-20',\n", + " '2012-05-21', '2012-05-22', '2012-05-23', '2012-05-24',\n", + " '2012-05-25', '2012-05-26', '2012-05-27', '2012-05-28',\n", + " '2012-05-29', '2012-05-30', '2012-05-31', '2012-06-01'],\n", + " dtype='datetime64[ns]', freq='D')" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.date_range(start=\"2012-04-01\", periods=20)\n", + "pd.date_range(end=\"2012-06-01\", periods=20)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/ly/jkydg4dj2vs93b_ds7yp5t7r0000gn/T/ipykernel_24968/3742551278.py:1: FutureWarning: 'BM' is deprecated and will be removed in a future version, please use 'BME' instead.\n", + " pd.date_range(\"2000-01-01\", \"2000-12-01\", freq=\"BM\")\n" + ] + }, + { + "data": { + "text/plain": [ + "DatetimeIndex(['2000-01-31', '2000-02-29', '2000-03-31', '2000-04-28',\n", + " '2000-05-31', '2000-06-30', '2000-07-31', '2000-08-31',\n", + " '2000-09-29', '2000-10-31', '2000-11-30'],\n", + " dtype='datetime64[ns]', freq='BME')" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.date_range(\"2000-01-01\", \"2000-12-01\", freq=\"BM\")" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DatetimeIndex(['2012-05-02 12:56:31', '2012-05-03 12:56:31',\n", + " '2012-05-04 12:56:31', '2012-05-05 12:56:31',\n", + " '2012-05-06 12:56:31'],\n", + " dtype='datetime64[ns]', freq='D')" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.date_range(\"2012-05-02 12:56:31\", periods=5)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DatetimeIndex(['2012-05-02', '2012-05-03', '2012-05-04', '2012-05-05',\n", + " '2012-05-06'],\n", + " dtype='datetime64[ns]', freq='D')" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.date_range(\"2012-05-02 12:56:31\", periods=5, normalize=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from pandas.tseries.offsets import Hour, Minute\n", + "hour = Hour()\n", + "hour" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<4 * Hours>" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "four_hours = Hour(4)\n", + "four_hours" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/ly/jkydg4dj2vs93b_ds7yp5t7r0000gn/T/ipykernel_24968/3449897904.py:1: FutureWarning: 'H' is deprecated and will be removed in a future version, please use 'h' instead.\n", + " pd.date_range(\"2000-01-01\", \"2000-01-03 23:59\", freq=\"4H\")\n" + ] + }, + { + "data": { + "text/plain": [ + "DatetimeIndex(['2000-01-01 00:00:00', '2000-01-01 04:00:00',\n", + " '2000-01-01 08:00:00', '2000-01-01 12:00:00',\n", + " '2000-01-01 16:00:00', '2000-01-01 20:00:00',\n", + " '2000-01-02 00:00:00', '2000-01-02 04:00:00',\n", + " '2000-01-02 08:00:00', '2000-01-02 12:00:00',\n", + " '2000-01-02 16:00:00', '2000-01-02 20:00:00',\n", + " '2000-01-03 00:00:00', '2000-01-03 04:00:00',\n", + " '2000-01-03 08:00:00', '2000-01-03 12:00:00',\n", + " '2000-01-03 16:00:00', '2000-01-03 20:00:00'],\n", + " dtype='datetime64[ns]', freq='4h')" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.date_range(\"2000-01-01\", \"2000-01-03 23:59\", freq=\"4H\")" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<150 * Minutes>" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Hour(2) + Minute(30)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DatetimeIndex(['2000-01-01 00:00:00', '2000-01-01 01:30:00',\n", + " '2000-01-01 03:00:00', '2000-01-01 04:30:00',\n", + " '2000-01-01 06:00:00', '2000-01-01 07:30:00',\n", + " '2000-01-01 09:00:00', '2000-01-01 10:30:00',\n", + " '2000-01-01 12:00:00', '2000-01-01 13:30:00'],\n", + " dtype='datetime64[ns]', freq='90min')" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.date_range(\"2000-01-01\", periods=10, freq=\"1h30min\")" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[Timestamp('2012-01-20 00:00:00'),\n", + " Timestamp('2012-02-17 00:00:00'),\n", + " Timestamp('2012-03-16 00:00:00'),\n", + " Timestamp('2012-04-20 00:00:00'),\n", + " Timestamp('2012-05-18 00:00:00'),\n", + " Timestamp('2012-06-15 00:00:00'),\n", + " Timestamp('2012-07-20 00:00:00'),\n", + " Timestamp('2012-08-17 00:00:00')]" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "monthly_dates = pd.date_range(\"2012-01-01\", \"2012-09-01\", freq=\"WOM-3FRI\")\n", + "list(monthly_dates)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/ly/jkydg4dj2vs93b_ds7yp5t7r0000gn/T/ipykernel_24968/2994308253.py:2: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n", + " index=pd.date_range(\"2000-01-01\", periods=4, freq=\"M\"))\n" + ] + }, + { + "data": { + "text/plain": [ + "2000-01-31 -0.117388\n", + "2000-02-29 -0.517795\n", + "2000-03-31 NaN\n", + "2000-04-30 NaN\n", + "Freq: ME, dtype: float64" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ts = pd.Series(np.random.standard_normal(4),\n", + " index=pd.date_range(\"2000-01-01\", periods=4, freq=\"M\"))\n", + "ts\n", + "ts.shift(2)\n", + "ts.shift(-2)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/ly/jkydg4dj2vs93b_ds7yp5t7r0000gn/T/ipykernel_24968/903147437.py:1: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n", + " ts.shift(2, freq=\"M\")\n" + ] + }, + { + "data": { + "text/plain": [ + "2000-03-31 -0.066748\n", + "2000-04-30 0.838639\n", + "2000-05-31 -0.117388\n", + "2000-06-30 -0.517795\n", + "Freq: ME, dtype: float64" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ts.shift(2, freq=\"M\")" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/ly/jkydg4dj2vs93b_ds7yp5t7r0000gn/T/ipykernel_24968/4056683127.py:2: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", + " ts.shift(1, freq=\"90T\")\n" + ] + }, + { + "data": { + "text/plain": [ + "2000-01-31 01:30:00 -0.066748\n", + "2000-02-29 01:30:00 0.838639\n", + "2000-03-31 01:30:00 -0.117388\n", + "2000-04-30 01:30:00 -0.517795\n", + "dtype: float64" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ts.shift(3, freq=\"D\")\n", + "ts.shift(1, freq=\"90T\")" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Timestamp('2011-11-20 00:00:00')" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from pandas.tseries.offsets import Day, MonthEnd\n", + "now = datetime(2011, 11, 17)\n", + "now + 3 * Day()" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Timestamp('2011-12-31 00:00:00')" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "now + MonthEnd()\n", + "now + MonthEnd(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Timestamp('2011-10-31 00:00:00')" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "offset = MonthEnd()\n", + "offset.rollforward(now)\n", + "offset.rollback(now)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2000-01-31 -0.005833\n", + "2000-02-29 0.015894\n", + "2000-03-31 0.150209\n", + "dtype: float64" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ts = pd.Series(np.random.standard_normal(20),\n", + " index=pd.date_range(\"2000-01-15\", periods=20, freq=\"4D\"))\n", + "ts\n", + "ts.groupby(MonthEnd().rollforward).mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/ly/jkydg4dj2vs93b_ds7yp5t7r0000gn/T/ipykernel_24968/3978117481.py:1: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n", + " ts.resample(\"M\").mean()\n" + ] + }, + { + "data": { + "text/plain": [ + "2000-01-31 -0.005833\n", + "2000-02-29 0.015894\n", + "2000-03-31 0.150209\n", + "Freq: ME, dtype: float64" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ts.resample(\"M\").mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['US/Eastern', 'US/Hawaii', 'US/Mountain', 'US/Pacific', 'UTC']" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pytz\n", + "pytz.common_timezones[-5:]" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tz = pytz.timezone(\"America/New_York\")\n", + "tz" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2012-03-09 09:30:00 -0.202469\n", + "2012-03-10 09:30:00 0.050718\n", + "2012-03-11 09:30:00 0.639869\n", + "2012-03-12 09:30:00 0.597594\n", + "2012-03-13 09:30:00 -0.797246\n", + "2012-03-14 09:30:00 0.472879\n", + "Freq: D, dtype: float64" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dates = pd.date_range(\"2012-03-09 09:30\", periods=6)\n", + "ts = pd.Series(np.random.standard_normal(len(dates)), index=dates)\n", + "ts" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "None\n" + ] + } + ], + "source": [ + "print(ts.index.tz)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DatetimeIndex(['2012-03-09 09:30:00+00:00', '2012-03-10 09:30:00+00:00',\n", + " '2012-03-11 09:30:00+00:00', '2012-03-12 09:30:00+00:00',\n", + " '2012-03-13 09:30:00+00:00', '2012-03-14 09:30:00+00:00',\n", + " '2012-03-15 09:30:00+00:00', '2012-03-16 09:30:00+00:00',\n", + " '2012-03-17 09:30:00+00:00', '2012-03-18 09:30:00+00:00'],\n", + " dtype='datetime64[ns, UTC]', freq='D')" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.date_range(\"2012-03-09 09:30\", periods=10, tz=\"UTC\")" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DatetimeIndex(['2012-03-09 09:30:00+00:00', '2012-03-10 09:30:00+00:00',\n", + " '2012-03-11 09:30:00+00:00', '2012-03-12 09:30:00+00:00',\n", + " '2012-03-13 09:30:00+00:00', '2012-03-14 09:30:00+00:00'],\n", + " dtype='datetime64[ns, UTC]', freq='D')" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ts\n", + "ts_utc = ts.tz_localize(\"UTC\")\n", + "ts_utc\n", + "ts_utc.index" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2012-03-09 04:30:00-05:00 -0.202469\n", + "2012-03-10 04:30:00-05:00 0.050718\n", + "2012-03-11 05:30:00-04:00 0.639869\n", + "2012-03-12 05:30:00-04:00 0.597594\n", + "2012-03-13 05:30:00-04:00 -0.797246\n", + "2012-03-14 05:30:00-04:00 0.472879\n", + "Freq: D, dtype: float64" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ts_utc.tz_convert(\"America/New_York\")" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2012-03-09 15:30:00+01:00 -0.202469\n", + "2012-03-10 15:30:00+01:00 0.050718\n", + "2012-03-11 14:30:00+01:00 0.639869\n", + "2012-03-12 14:30:00+01:00 0.597594\n", + "2012-03-13 14:30:00+01:00 -0.797246\n", + "2012-03-14 14:30:00+01:00 0.472879\n", + "dtype: float64" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ts_eastern = ts.tz_localize(\"America/New_York\")\n", + "ts_eastern.tz_convert(\"UTC\")\n", + "ts_eastern.tz_convert(\"Europe/Berlin\")" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DatetimeIndex(['2012-03-09 09:30:00+08:00', '2012-03-10 09:30:00+08:00',\n", + " '2012-03-11 09:30:00+08:00', '2012-03-12 09:30:00+08:00',\n", + " '2012-03-13 09:30:00+08:00', '2012-03-14 09:30:00+08:00'],\n", + " dtype='datetime64[ns, Asia/Shanghai]', freq=None)" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ts.index.tz_localize(\"Asia/Shanghai\")" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Timestamp('2011-03-11 23:00:00-0500', tz='America/New_York')" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stamp = pd.Timestamp(\"2011-03-12 04:00\")\n", + "stamp_utc = stamp.tz_localize(\"utc\")\n", + "stamp_utc.tz_convert(\"America/New_York\")" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Timestamp('2011-03-12 04:00:00+0300', tz='Europe/Moscow')" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stamp_moscow = pd.Timestamp(\"2011-03-12 04:00\", tz=\"Europe/Moscow\")\n", + "stamp_moscow" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1299902400000000000" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stamp_utc.value\n", + "stamp_utc.tz_convert(\"America/New_York\").value" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Timestamp('2012-03-11 03:30:00-0400', tz='US/Eastern')" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stamp = pd.Timestamp(\"2012-03-11 01:30\", tz=\"US/Eastern\")\n", + "stamp\n", + "stamp + Hour()" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Timestamp('2012-11-04 01:30:00-0500', tz='US/Eastern')" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stamp = pd.Timestamp(\"2012-11-04 00:30\", tz=\"US/Eastern\")\n", + "stamp\n", + "stamp + 2 * Hour()" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DatetimeIndex(['2012-03-07 09:30:00+00:00', '2012-03-08 09:30:00+00:00',\n", + " '2012-03-09 09:30:00+00:00', '2012-03-12 09:30:00+00:00',\n", + " '2012-03-13 09:30:00+00:00', '2012-03-14 09:30:00+00:00',\n", + " '2012-03-15 09:30:00+00:00'],\n", + " dtype='datetime64[ns, UTC]', freq=None)" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dates = pd.date_range(\"2012-03-07 09:30\", periods=10, freq=\"B\")\n", + "ts = pd.Series(np.random.standard_normal(len(dates)), index=dates)\n", + "ts\n", + "ts1 = ts[:7].tz_localize(\"Europe/London\")\n", + "ts2 = ts1[2:].tz_convert(\"Europe/Moscow\")\n", + "result = ts1 + ts2\n", + "result.index" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/ly/jkydg4dj2vs93b_ds7yp5t7r0000gn/T/ipykernel_24968/520127536.py:1: FutureWarning: 'A-DEC' is deprecated and will be removed in a future version, please use 'Y-DEC' instead.\n", + " p = pd.Period(\"2011\", freq=\"A-DEC\")\n" + ] + }, + { + "data": { + "text/plain": [ + "Period('2011', 'Y-DEC')" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p = pd.Period(\"2011\", freq=\"A-DEC\")\n", + "p" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Period('2009', 'Y-DEC')" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p + 5\n", + "p - 2" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/ly/jkydg4dj2vs93b_ds7yp5t7r0000gn/T/ipykernel_24968/3816979827.py:1: FutureWarning: 'A-DEC' is deprecated and will be removed in a future version, please use 'Y-DEC' instead.\n", + " pd.Period(\"2014\", freq=\"A-DEC\") - p\n" + ] + }, + { + "data": { + "text/plain": [ + "<3 * YearEnds: month=12>" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.Period(\"2014\", freq=\"A-DEC\") - p" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "PeriodIndex(['2000-01', '2000-02', '2000-03', '2000-04', '2000-05', '2000-06'], dtype='period[M]')" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "periods = pd.period_range(\"2000-01-01\", \"2000-06-30\", freq=\"M\")\n", + "periods" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2000-01 -0.514551\n", + "2000-02 -0.559782\n", + "2000-03 -0.783408\n", + "2000-04 -1.797685\n", + "2000-05 -0.172670\n", + "2000-06 0.680215\n", + "Freq: M, dtype: float64" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.Series(np.random.standard_normal(6), index=periods)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "PeriodIndex(['2001Q3', '2002Q2', '2003Q1'], dtype='period[Q-DEC]')" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "values = [\"2001Q3\", \"2002Q2\", \"2003Q1\"]\n", + "index = pd.PeriodIndex(values, freq=\"Q-DEC\")\n", + "index" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/ly/jkydg4dj2vs93b_ds7yp5t7r0000gn/T/ipykernel_24968/3519524340.py:1: FutureWarning: 'A-DEC' is deprecated and will be removed in a future version, please use 'Y-DEC' instead.\n", + " p = pd.Period(\"2011\", freq=\"A-DEC\")\n" + ] + }, + { + "data": { + "text/plain": [ + "Period('2011-12', 'M')" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p = pd.Period(\"2011\", freq=\"A-DEC\")\n", + "p\n", + "p.asfreq(\"M\", how=\"start\")\n", + "p.asfreq(\"M\", how=\"end\")\n", + "p.asfreq(\"M\")" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/ly/jkydg4dj2vs93b_ds7yp5t7r0000gn/T/ipykernel_24968/1547579311.py:1: FutureWarning: 'A-JUN' is deprecated and will be removed in a future version, please use 'Y-JUN' instead.\n", + " p = pd.Period(\"2011\", freq=\"A-JUN\")\n" + ] + }, + { + "data": { + "text/plain": [ + "Period('2011-06', 'M')" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p = pd.Period(\"2011\", freq=\"A-JUN\")\n", + "p\n", + "p.asfreq(\"M\", how=\"start\")\n", + "p.asfreq(\"M\", how=\"end\")" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/ly/jkydg4dj2vs93b_ds7yp5t7r0000gn/T/ipykernel_24968/2271097004.py:2: FutureWarning: 'A-JUN' is deprecated and will be removed in a future version, please use 'Y-JUN' instead.\n", + " p.asfreq(\"A-JUN\")\n" + ] + }, + { + "data": { + "text/plain": [ + "Period('2012', 'Y-JUN')" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p = pd.Period(\"Aug-2011\", \"M\")\n", + "p.asfreq(\"A-JUN\")" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/ly/jkydg4dj2vs93b_ds7yp5t7r0000gn/T/ipykernel_24968/3858577534.py:1: FutureWarning: 'A-DEC' is deprecated and will be removed in a future version, please use 'Y-DEC' instead.\n", + " periods = pd.period_range(\"2006\", \"2009\", freq=\"A-DEC\")\n" + ] + }, + { + "data": { + "text/plain": [ + "2006-01 1.607578\n", + "2007-01 0.200381\n", + "2008-01 -0.834068\n", + "2009-01 -0.302988\n", + "Freq: M, dtype: float64" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "periods = pd.period_range(\"2006\", \"2009\", freq=\"A-DEC\")\n", + "ts = pd.Series(np.random.standard_normal(len(periods)), index=periods)\n", + "ts\n", + "ts.asfreq(\"M\", how=\"start\")" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/ly/jkydg4dj2vs93b_ds7yp5t7r0000gn/T/ipykernel_24968/2984082239.py:1: FutureWarning: PeriodDtype[B] is deprecated and will be removed in a future version. Use a DatetimeIndex with freq='B' instead\n", + " ts.asfreq(\"B\", how=\"end\")\n" + ] + }, + { + "data": { + "text/plain": [ + "2006-12-29 1.607578\n", + "2007-12-31 0.200381\n", + "2008-12-31 -0.834068\n", + "2009-12-31 -0.302988\n", + "Freq: B, dtype: float64" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ts.asfreq(\"B\", how=\"end\")" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Period('2012Q4', 'Q-JAN')" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p = pd.Period(\"2012Q4\", freq=\"Q-JAN\")\n", + "p" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Period('2012-01-31', 'D')" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p.asfreq(\"D\", how=\"start\")\n", + "p.asfreq(\"D\", how=\"end\")" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/ly/jkydg4dj2vs93b_ds7yp5t7r0000gn/T/ipykernel_24968/1108768030.py:1: FutureWarning: Period with BDay freq is deprecated and will be removed in a future version. Use a DatetimeIndex with BDay freq instead.\n", + " p4pm = (p.asfreq(\"B\", how=\"end\") - 1).asfreq(\"T\", how=\"start\") + 16 * 60\n", + "/var/folders/ly/jkydg4dj2vs93b_ds7yp5t7r0000gn/T/ipykernel_24968/1108768030.py:1: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", + " p4pm = (p.asfreq(\"B\", how=\"end\") - 1).asfreq(\"T\", how=\"start\") + 16 * 60\n" + ] + }, + { + "data": { + "text/plain": [ + "Timestamp('2012-01-30 16:00:00')" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p4pm = (p.asfreq(\"B\", how=\"end\") - 1).asfreq(\"T\", how=\"start\") + 16 * 60\n", + "p4pm\n", + "p4pm.to_timestamp()" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/ly/jkydg4dj2vs93b_ds7yp5t7r0000gn/T/ipykernel_24968/142431650.py:4: FutureWarning: PeriodDtype[B] is deprecated and will be removed in a future version. Use a DatetimeIndex with freq='B' instead\n", + " new_periods = (periods.asfreq(\"B\", \"end\") - 1).asfreq(\"H\", \"start\") + 16\n", + "/var/folders/ly/jkydg4dj2vs93b_ds7yp5t7r0000gn/T/ipykernel_24968/142431650.py:4: FutureWarning: 'H' is deprecated and will be removed in a future version, please use 'h' instead.\n", + " new_periods = (periods.asfreq(\"B\", \"end\") - 1).asfreq(\"H\", \"start\") + 16\n" + ] + }, + { + "data": { + "text/plain": [ + "2010-10-28 16:00:00 0\n", + "2011-01-28 16:00:00 1\n", + "2011-04-28 16:00:00 2\n", + "2011-07-28 16:00:00 3\n", + "2011-10-28 16:00:00 4\n", + "2012-01-30 16:00:00 5\n", + "dtype: int64" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "periods = pd.period_range(\"2011Q3\", \"2012Q4\", freq=\"Q-JAN\")\n", + "ts = pd.Series(np.arange(len(periods)), index=periods)\n", + "ts\n", + "new_periods = (periods.asfreq(\"B\", \"end\") - 1).asfreq(\"H\", \"start\") + 16\n", + "ts.index = new_periods.to_timestamp()\n", + "ts" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/ly/jkydg4dj2vs93b_ds7yp5t7r0000gn/T/ipykernel_24968/2638414699.py:1: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n", + " dates = pd.date_range(\"2000-01-01\", periods=3, freq=\"M\")\n" + ] + }, + { + "data": { + "text/plain": [ + "2000-01 1.663261\n", + "2000-02 -0.996206\n", + "2000-03 1.521760\n", + "Freq: M, dtype: float64" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dates = pd.date_range(\"2000-01-01\", periods=3, freq=\"M\")\n", + "ts = pd.Series(np.random.standard_normal(3), index=dates)\n", + "ts\n", + "pts = ts.to_period()\n", + "pts" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2000-01 0.244175\n", + "2000-01 0.423331\n", + "2000-01 -0.654040\n", + "2000-02 2.089154\n", + "2000-02 -0.060220\n", + "2000-02 -0.167933\n", + "Freq: M, dtype: float64" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dates = pd.date_range(\"2000-01-29\", periods=6)\n", + "ts2 = pd.Series(np.random.standard_normal(6), index=dates)\n", + "ts2\n", + "ts2.to_period(\"M\")" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2000-01-29 23:59:59.999999999 0.244175\n", + "2000-01-30 23:59:59.999999999 0.423331\n", + "2000-01-31 23:59:59.999999999 -0.654040\n", + "2000-02-01 23:59:59.999999999 2.089154\n", + "2000-02-02 23:59:59.999999999 -0.060220\n", + "2000-02-03 23:59:59.999999999 -0.167933\n", + "Freq: D, dtype: float64" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pts = ts2.to_period()\n", + "pts\n", + "pts.to_timestamp(how=\"end\")" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: 'examples/macrodata.csv'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[80], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mexamples/macrodata.csv\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m data\u001b[38;5;241m.\u001b[39mhead(\u001b[38;5;241m5\u001b[39m)\n\u001b[1;32m 3\u001b[0m data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myear\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n", + "File \u001b[0;32m~/Source/courses/tools4ds/DS701-Course-Notes/.venv/lib/python3.12/site-packages/pandas/io/parsers/readers.py:1026\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[1;32m 1013\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m 1014\u001b[0m dialect,\n\u001b[1;32m 1015\u001b[0m delimiter,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1022\u001b[0m dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[1;32m 1023\u001b[0m )\n\u001b[1;32m 1024\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m-> 1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Source/courses/tools4ds/DS701-Course-Notes/.venv/lib/python3.12/site-packages/pandas/io/parsers/readers.py:620\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 617\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[1;32m 619\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[0;32m--> 620\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 622\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[1;32m 623\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m parser\n", + "File \u001b[0;32m~/Source/courses/tools4ds/DS701-Course-Notes/.venv/lib/python3.12/site-packages/pandas/io/parsers/readers.py:1620\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m 1617\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 1619\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1620\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Source/courses/tools4ds/DS701-Course-Notes/.venv/lib/python3.12/site-packages/pandas/io/parsers/readers.py:1880\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m 1878\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[1;32m 1879\u001b[0m mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m-> 1880\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1881\u001b[0m \u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1882\u001b[0m \u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1883\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1884\u001b[0m \u001b[43m \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcompression\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m 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\u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1889\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1890\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1891\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n", + "File \u001b[0;32m~/Source/courses/tools4ds/DS701-Course-Notes/.venv/lib/python3.12/site-packages/pandas/io/common.py:873\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m 868\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m 869\u001b[0m \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[1;32m 870\u001b[0m \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[1;32m 871\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[1;32m 872\u001b[0m \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[0;32m--> 873\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m 874\u001b[0m \u001b[43m \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 875\u001b[0m \u001b[43m \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 876\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 877\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 878\u001b[0m \u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 879\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 880\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 881\u001b[0m \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[1;32m 882\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'examples/macrodata.csv'" + ] + } + ], + "source": [ + "data = pd.read_csv(\"examples/macrodata.csv\")\n", + "data.head(5)\n", + "data[\"year\"]\n", + "data[\"quarter\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [], + "source": [ + "index = pd.PeriodIndex(year=data[\"year\"], quarter=data[\"quarter\"],\n", + " freq=\"Q-DEC\")\n", + "index\n", + "data.index = index\n", + "data[\"infl\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [], + "source": [ + "dates = pd.date_range(\"2000-01-01\", periods=100)\n", + "ts = pd.Series(np.random.standard_normal(len(dates)), index=dates)\n", + "ts\n", + "ts.resample(\"M\").mean()\n", + "ts.resample(\"M\", kind=\"period\").mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [], + "source": [ + "dates = pd.date_range(\"2000-01-01\", periods=12, freq=\"T\")\n", + "ts = pd.Series(np.arange(len(dates)), index=dates)\n", + "ts" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [], + "source": [ + "ts.resample(\"5min\").sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [], + "source": [ + "ts.resample(\"5min\", closed=\"right\").sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [], + "source": [ + "ts.resample(\"5min\", closed=\"right\", label=\"right\").sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [], + "source": [ + "from pandas.tseries.frequencies import to_offset\n", + "result = ts.resample(\"5min\", closed=\"right\", label=\"right\").sum()\n", + "result.index = result.index + to_offset(\"-1s\")\n", + "result" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [], + "source": [ + "ts = pd.Series(np.random.permutation(np.arange(len(dates))), index=dates)\n", + "ts.resample(\"5min\").ohlc()" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [], + "source": [ + "frame = pd.DataFrame(np.random.standard_normal((2, 4)),\n", + " index=pd.date_range(\"2000-01-01\", periods=2,\n", + " freq=\"W-WED\"),\n", + " columns=[\"Colorado\", \"Texas\", \"New York\", \"Ohio\"])\n", + "frame" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [], + "source": [ + "df_daily = frame.resample(\"D\").asfreq()\n", + "df_daily" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [], + "source": [ + "frame.resample(\"D\").ffill()" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [], + "source": [ + "frame.resample(\"D\").ffill(limit=2)" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [], + "source": [ + "frame.resample(\"W-THU\").ffill()" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [], + "source": [ + "frame = pd.DataFrame(np.random.standard_normal((24, 4)),\n", + " index=pd.period_range(\"1-2000\", \"12-2001\",\n", + " freq=\"M\"),\n", + " columns=[\"Colorado\", \"Texas\", \"New York\", \"Ohio\"])\n", + "frame.head()\n", + "annual_frame = frame.resample(\"A-DEC\").mean()\n", + "annual_frame" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [], + "source": [ + "# Q-DEC: Quarterly, year ending in December\n", + "annual_frame.resample(\"Q-DEC\").ffill()\n", + "annual_frame.resample(\"Q-DEC\", convention=\"end\").asfreq()" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [], + "source": [ + "annual_frame.resample(\"Q-MAR\").ffill()" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [], + "source": [ + "N = 15\n", + "times = pd.date_range(\"2017-05-20 00:00\", freq=\"1min\", periods=N)\n", + "df = pd.DataFrame({\"time\": times,\n", + " \"value\": np.arange(N)})\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [], + "source": [ + "df.set_index(\"time\").resample(\"5min\").count()" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [], + "source": [ + "df2 = pd.DataFrame({\"time\": times.repeat(3),\n", + " \"key\": np.tile([\"a\", \"b\", \"c\"], N),\n", + " \"value\": np.arange(N * 3.)})\n", + "df2.head(7)" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [], + "source": [ + "time_key = pd.Grouper(freq=\"5min\")" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [], + "source": [ + "resampled = (df2.set_index(\"time\")\n", + " .groupby([\"key\", time_key])\n", + " .sum())\n", + "resampled\n", + "resampled.reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [], + "source": [ + "close_px_all = pd.read_csv(\"examples/stock_px.csv\",\n", + " parse_dates=True, index_col=0)\n", + "close_px = close_px_all[[\"AAPL\", \"MSFT\", \"XOM\"]]\n", + "close_px = close_px.resample(\"B\").ffill()" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [], + "source": [ + "close_px[\"AAPL\"].plot()\n", + "close_px[\"AAPL\"].rolling(250).mean().plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure()\n", + "std250 = close_px[\"AAPL\"].pct_change().rolling(250, min_periods=10).std()\n", + "std250[5:12]\n", + "std250.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [], + "source": [ + "expanding_mean = std250.expanding().mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure()" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [], + "source": [ + "plt.style.use('grayscale')\n", + "close_px.rolling(60).mean().plot(logy=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [], + "source": [ + "close_px.rolling(\"20D\").mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure()" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [], + "source": [ + "aapl_px = close_px[\"AAPL\"][\"2006\":\"2007\"]\n", + "\n", + "ma30 = aapl_px.rolling(30, min_periods=20).mean()\n", + "ewma30 = aapl_px.ewm(span=30).mean()\n", + "\n", + "aapl_px.plot(style=\"k-\", label=\"Price\")\n", + "ma30.plot(style=\"k--\", label=\"Simple Moving Avg\")\n", + "ewma30.plot(style=\"k-\", label=\"EW MA\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure()" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [], + "source": [ + "spx_px = close_px_all[\"SPX\"]\n", + "spx_rets = spx_px.pct_change()\n", + "returns = close_px.pct_change()" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [], + "source": [ + "corr = returns[\"AAPL\"].rolling(125, min_periods=100).corr(spx_rets)\n", + "corr.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure()" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [], + "source": [ + "corr = returns.rolling(125, min_periods=100).corr(spx_rets)\n", + "corr.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure()" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.stats import percentileofscore\n", + "def score_at_2percent(x):\n", + " return percentileofscore(x, 0.02)\n", + "\n", + "result = returns[\"AAPL\"].rolling(250).apply(score_at_2percent)\n", + "result.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": {}, + "outputs": [], + "source": [ + "pd.options.display.max_rows = PREVIOUS_MAX_ROWS" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/ds701_book/complete-guide-on-time-series-analysis-in-python.ipynb b/ds701_book/complete-guide-on-time-series-analysis-in-python.ipynb new file mode 100644 index 00000000..e1c21464 --- /dev/null +++ b/ds701_book/complete-guide-on-time-series-analysis-in-python.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{},"source":["\n","# **Complete Guide on Time Series Analysis in Python**\n","\n","From https://www.kaggle.com/code/prashant111/complete-guide-on-time-series-analysis-in-python/notebook.\n","\n","The dataset used is from https://www.kaggle.com/datasets/chirag19/air-passengers\n","and exhibits seasonality as well as long term trends.\n","\n","Hello friends,\n","\n","\n","As the name implies, this notebook is all about **Time Series Analysis**. A time series is a series of data points recorded at different time-intervals. The time series analysis means analyzing the time series data using various statistical tools and techniques. \n","\n","So, let's get started."]},{"cell_type":"markdown","metadata":{"trusted":true},"source":["### **I hope you find this notebook useful and your UPVOTES keep me motivated.**"]},{"cell_type":"markdown","metadata":{},"source":["\n","# **Table of Contents**\n","\n","\n","1.\t[Introduction to Time Series Analysis](#1)\n","2.\t[Types of data](#2)\n","3.\t[Time Series terminology](#3)\n","4.\t[Time Series Analysis](#4)\n","5.\t[Visualize the Time Series](#5)\n","6.\t[Patterns in a Time Series](#6)\n","7.\t[Additive and Multiplicative Time Series](#7)\n","8.\t[Decomposition of a Time Series](#8)\n","9.\t[Stationary and Non-Stationary Time Series](#9)\n","10.\t[How to make a time series stationary](#10)\n","11.\t[How to test for stationarity](#11)\n"," - 11.1\t[Augmented Dickey Fuller test (ADF Test)](#11.1)\n"," - 11.2\t[Kwiatkowski-Phillips-Schmidt-Shin – KPSS test (trend stationary)](#11.2)\n"," - 11.3\t[Philips Perron test (PP Test)](#11.3)\n","12.\t[Difference between white noise and a stationary series](#12)\n","13.\t[Detrend a Time Series](#13)\n","14.\t[Deseasonalize a Time Series](#14)\n","15.\t[How to test for seasonality of a time series](#15)\n","16.\t[Autocorrelation and Partial Autocorrelation Functions](#16)\n","17.\t[Computation of Partial Autocorrelation Function](#17)\n","18.\t[Lag Plots](#18)\n","19.\t[Granger Causality Test](#19)\n","20.\t[Smoothening a Time Series](#20)\n","21.\t[References](#21)\n"]},{"cell_type":"markdown","metadata":{},"source":["# **1. Introduction to Time-Series Analysis** \n","\n","\n","[Table of Contents](#0.1)\n","\n","\n","\n","- A **time-series** data is a series of data points or observations recorded at different or regular time intervals. In general, a time series is a sequence of data points taken at equally spaced time intervals. The frequency of recorded data points may be hourly, daily, weekly, monthly, quarterly or annually.\n","\n","\n","- **Time-Series Forecasting** is the process of using a statistical model to predict future values of a time-series based on past results.\n","\n","\n","- A time series analysis encompasses statistical methods for analyzing time series data. These methods enable us to extract meaningful statistics, patterns and other characteristics of the data. Time series are visualized with the help of line charts. So, time series analysis involves understanding inherent aspects of the time series data so that we can create meaningful and accurate forecasts.\n","\n","\n","- Applications of time series are used in statistics, finance or business applications. A very common example of time series data is the daily closing value of the stock index like NASDAQ or Dow Jones. Other common applications of time series are sales and demand forecasting, weather forecasting, econometrics, signal processing, pattern recognition and earthquake prediction.\n","\n","\n","\n","### **Components of a Time-Series**\n","\n","\n","- **Trend** - The trend shows a general direction of the time series data over a long period of time. A trend can be increasing(upward), decreasing(downward), or horizontal(stationary).\n","\n","\n","- **Seasonality** - The seasonality component exhibits a trend that repeats with respect to timing, direction, and magnitude. Some examples include an increase in water consumption in summer due to hot weather conditions.\n","\n","\n","- **Cyclical Component** - These are the trends with no set repetition over a particular period of time. A cycle refers to the period of ups and downs, booms and slums of a time series, mostly observed in business cycles. These cycles do not exhibit a seasonal variation but generally occur over a time period of 3 to 12 years depending on the nature of the time series.\n","\n","\n","- **Irregular Variation** - These are the fluctuations in the time series data which become evident when trend and cyclical variations are removed. These variations are unpredictable, erratic, and may or may not be random.\n","\n","\n","- **ETS Decomposition** - ETS Decomposition is used to separate different components of a time series. The term ETS stands for Error, Trend and Seasonality.\n","\n","\n","- In this notebook, I conduct time series analysis of video game sales over time."]},{"cell_type":"markdown","metadata":{},"source":["# **2. Types of data** \n","\n","[Table of Contents](#0.1)\n","\n","\n","As stated above, the time series analysis is the statistical analysis of the time series data. A time series data means that data is recorded at different time periods or intervals. The time series data may be of three types:-\n","\n","\n","1 **Time series data** - The observations of the values of a variable recorded at different points in time is called time series data. \n","\n","\n","2 **Cross sectional data** - It is the data of one or more variables recorded at the same point in time.\n","\n","\n","3 **Pooled data**- It is the combination of time series data and cross sectional data.\n"]},{"cell_type":"markdown","metadata":{},"source":["# **3. Time Series terminology** \n","\n","[Table of Contents](#0.1)\n","\n","\n","There are various terms and concepts in time series that we should know. These are as follows:-\n","\n","1\t**Dependence**- It refers to the association of two observations of the same variable at prior time periods.\n","\n","\n","2\t**Stationarity**- It shows the mean value of the series that remains constant over the time period. If past effects accumulate and the values increase towards infinity then stationarity is not met.\n","\n","\n","3\t**Differencing**- Differencing is used to make the series stationary and to control the auto-correlations. There may be some cases in time series analyses where we do not require differencing and over-differenced series can produce wrong estimates.\n","\n","\n","4\t**Specification** - It may involve the testing of the linear or non-linear relationships of dependent variables by using time series models such as ARIMA models. \n","\n","\n","5\t**Exponential Smoothing** - Exponential smoothing in time series analysis predicts the one next period value based on the past and current value. It involves averaging of data such that the non-systematic components of each individual case or observation cancel out each other. The exponential smoothing method is used to predict the short term prediction.\n","\n","\n","\n","6\t**Curve fitting** - Curve fitting regression in time series analysis is used when data is in a non-linear relationship.\n","\n","\n","7\t**ARIMA** - ARIMA stands for Auto Regressive Integrated Moving Average.\n"]},{"cell_type":"markdown","metadata":{},"source":["# **4. Time Series Analysis** \n","\n","[Table of Contents](#0.1)\n"]},{"cell_type":"markdown","metadata":{},"source":["## **4.1 Basic set up** \n","\n","[Table of Contents](#0.1)"]},{"cell_type":"code","execution_count":2,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Downloading from https://www.kaggle.com/api/v1/datasets/download/chirag19/air-passengers?dataset_version_number=1...\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 764/764 [00:00<00:00, 955kB/s]"]},{"name":"stdout","output_type":"stream","text":["Extracting files...\n","Path to dataset files: /Users/tomg/.cache/kagglehub/datasets/chirag19/air-passengers/versions/1\n"]},{"name":"stderr","output_type":"stream","text":["\n"]}],"source":["import kagglehub\n","\n","# Download latest version\n","path = kagglehub.dataset_download(\"chirag19/air-passengers\")\n","\n","print(\"Path to dataset files:\", path)"]},{"cell_type":"code","execution_count":3,"metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","trusted":true},"outputs":[],"source":["# This Python 3 environment comes with many helpful analytics libraries installed\n","# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n","# For example, here's several helpful packages to load\n","\n","\n","import numpy as np # linear algebra\n","import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n","\n","\n","import matplotlib as mpl\n","import matplotlib.pyplot as plt # data visualization\n","import seaborn as sns # statistical data visualization\n","\n","\n","# Input data files are available in the read-only \"../input/\" directory\n","# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n","\n","import os\n","for dirname, _, filenames in os.walk('~/.cache/kagglehub/datasets/chirag19/air-passengers/versions/1'):\n"," for filename in filenames:\n"," print(os.path.join(dirname, filename))\n","\n","# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n","# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"]},{"cell_type":"markdown","metadata":{},"source":["## **4.2 Import data** \n","\n","[Table of Contents](#0.1)"]},{"cell_type":"code","execution_count":4,"metadata":{"trusted":true},"outputs":[{"data":{"text/html":["
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Month#Passengers
01949-01112
11949-02118
21949-03132
31949-04129
41949-05121
\n","
"],"text/plain":[" Month #Passengers\n","0 1949-01 112\n","1 1949-02 118\n","2 1949-03 132\n","3 1949-04 129\n","4 1949-05 121"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["path = '~/.cache/kagglehub/datasets/chirag19/air-passengers/versions/1/AirPassengers.csv'\n","\n","df = pd.read_csv(path)\n","\n","df.head()"]},{"cell_type":"markdown","metadata":{},"source":["- We should rename the column names."]},{"cell_type":"code","execution_count":6,"metadata":{"trusted":true},"outputs":[{"data":{"text/html":["
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DateNumber of Passengers
01949-01112
11949-02118
21949-03132
31949-04129
41949-05121
\n","
"],"text/plain":[" Date Number of Passengers\n","0 1949-01 112\n","1 1949-02 118\n","2 1949-03 132\n","3 1949-04 129\n","4 1949-05 121"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["df.columns = ['Date','Number of Passengers']\n","\n","df.head()"]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["DataFrame written to air_passengers_1949_1960.csv\n"]}],"source":["# Write the dataframe to a local file\n","output_path = 'air_passengers_1949_1960.csv'\n","df.to_csv(output_path, index=False)\n","print(f\"DataFrame written to {output_path}\")\n"]},{"cell_type":"markdown","metadata":{},"source":["# **5. Visualize the Time Series** \n","\n","\n","[Table of Contents](#0.1)\n"]},{"cell_type":"code","execution_count":9,"metadata":{"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["def plot_df(df, x, y, title=\"\", xlabel='Date', ylabel='Number of Passengers', dpi=100):\n"," plt.figure(figsize=(15,4), dpi=dpi)\n"," plt.plot(x, y, color='tab:red')\n"," plt.gca().set(title=title, xlabel=xlabel, ylabel=ylabel)\n"," plt.show()\n"," \n","\n","plot_df(df, x=df['Date'], y=df['Number of Passengers'], title='Number of US Airline passengers from 1949 to 1960')"]},{"cell_type":"markdown","metadata":{},"source":["- Since all the values are positive, we can show this on both sides of the Y axis to emphasize the growth."]},{"cell_type":"code","execution_count":10,"metadata":{"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["x = df['Date'].values\n","y1 = df['Number of Passengers'].values\n","\n","# Plot\n","fig, ax = plt.subplots(1, 1, figsize=(16,5), dpi= 120)\n","plt.fill_between(x, y1=y1, y2=-y1, alpha=0.5, linewidth=2, color='seagreen')\n","plt.ylim(-800, 800)\n","plt.title('Air Passengers (Two Side View)', fontsize=16)\n","plt.hlines(y=0, xmin=np.min(df['Date']), xmax=np.max(df['Date']), linewidth=.5)\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["- It can be seen that its a monthly time series and follows a certain repetitive pattern every year. So, we can plot each year as a separate line in the same plot. This let us compare the year wise patterns side-by-side."]},{"cell_type":"markdown","metadata":{},"source":["# **6. Patterns in a Time Series** \n","\n","\n","[Table of Contents](#0.1)\n","\n","\n","- Any time series visualization may consist of the following components: **Base Level + Trend + Seasonality + Error**.\n","\n","\n","### **Trend**\n","\n","- A **trend** is observed when there is an increasing or decreasing slope observed in the time series. \n","\n","\n","### **Seasonality**\n","\n","- A **seasonality** is observed when there is a distinct repeated pattern observed between regular intervals due to seasonal factors. It could be because of the month of the year, the day of the month, weekdays or even time of the day.\n","\n","\n","However, It is not mandatory that all time series must have a trend and/or seasonality. A time series may not have a distinct trend but have a seasonality and vice-versa.\n"]},{"cell_type":"code","execution_count":11,"metadata":{"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["def plot_df(df, x, y, title=\"\", xlabel='Date', ylabel='Number of Passengers', dpi=100):\n"," plt.figure(figsize=(15,4), dpi=dpi)\n"," plt.plot(x, y, color='blue')\n"," plt.gca().set(title=title, xlabel=xlabel, ylabel=ylabel)\n"," plt.show()\n"," \n","\n","plot_df(df, x=df['Date'], y=df['Number of Passengers'], title='Trend and Seasonality')"]},{"cell_type":"markdown","metadata":{},"source":["### **Cyclic behaviour**\n","\n","- Another important thing to consider is the **cyclic behaviour**. It happens when the rise and fall pattern in the series does not happen in fixed calendar-based intervals. We should not confuse 'cyclic' effect with 'seasonal' effect.\n","\n","- If the patterns are not of fixed calendar based frequencies, then it is cyclic. Because, unlike the seasonality, cyclic effects are typically influenced by the business and other socio-economic factors."]},{"cell_type":"markdown","metadata":{},"source":["# **7. Additive and Multiplicative Time Series** \n","\n","\n","[Table of Contents](#0.1)\n","\n","\n","- We may have different combinations of trends and seasonality. Depending on the nature of the trends and seasonality, a time series can be modeled as an additive or multiplicative time series. Each observation in the series can be expressed as either a sum or a product of the components.\n","\n","\n","### **Additive time series:**\n","\n","Value = Base Level + Trend + Seasonality + Error\n","\n","\n","### **Multiplicative Time Series:**\n","\n","Value = Base Level x Trend x Seasonality x Error"]},{"cell_type":"markdown","metadata":{},"source":["# **8. Decomposition of a Time Series** \n","\n","\n","[Table of Contents](#0.1)\n","\n","\n","- Decomposition of a time series can be performed by considering the series as an additive or multiplicative combination of the base level, trend, seasonal index and the residual term.\n","\n","\n","- The seasonal_decompose in statsmodels implements this conveniently."]},{"cell_type":"code","execution_count":12,"metadata":{"trusted":true},"outputs":[{"data":{"image/png":"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yOHj161pJa/3QNjRo14oEHHuCBBx4AYMuWLYwbN44tW7bw2muv2fpVVIWa+v6LiIiIiJyLHq0RERERkUo1c+ZMLBYL3bp1wzCMc76eeOIJoHQT8ZLVHSV9KP7ubM24/3ret99+S2FhYbnP+yclDZuLioou+Fwovrk/YMAAMjIy+OGHH/jiiy8oKiri+uuvL1MWq27durRt25bMzExbf4rqYhgG999/P7m5ufj7+9v6QEBx/4fAwED27t3Lnj17yj3mlVdeCcBXX31Fdnb2eY/v06cPZrOZHTt2sHPnzjL7T506ZXtfrrjiinLHUVG//fYbiYmJZbYvWLCAlJQUfHx8bL1HnJ2d6dWrF8A5k0AlpdfKcw1dunThX//6FwA7duwoV7wX+50t6ZOyaNEiWzPzv9q+fTs7duzAbDaXaWwvIiIiImIPSmyIiIiISKUquXl7vobd48ePB/7sOwHFzZW9vb3ZsGED7733XqnjV65cyccff3zWsUaNGkW9evU4fvw4Tz/9dKm+BH/88QcvvfTSBV9HyZP4F3JD/+9KGoSXlBWCsmWoSpTEeNtttzFv3rwy+w3DYNOmTfz2228XHc/f7dq1i2HDhvHNN9/g5OTEF198Yes7AeDi4sKkSZMwDIMRI0awdu3aMmNYLBaWL1/Oxo0bbduuueYaOnToQFxcHKNHjyYlJaXUOXl5eSxcuND2c1hYGKNHj8YwDO6+++5Sx2dnZ3PXXXeRl5dHjx49bGW9qkNubi733nsvubm5tm1xcXE89thjANxzzz24u7vb9pVsnzJlCsuWLSs11syZM/nll19wcXHhoYcesm3/8ccfbc3T/6qwsNCWzGnYsGG54r3Y72yvXr3o1q0bubm53H333eTk5Nj2JScnc/fddwMwZswY2yoeERERERF7UikqEREREak0q1at4vDhw7i5uZUpt/R3rVq1omPHjmzbto3PP/+cxx57jLp16zJt2jTGjRvHQw89xKeffkrr1q2JjY1lzZo1PPzww7z99ttlxvLw8GDOnDkMGzaMN998k59++okuXbqQkpLCypUrufrqq9m6dautqXV5DBkyBC8vL3766Sd69epF06ZNcXJyomfPnudMTvzdddddR+3atW03uRs1akT//v3PeuzVV1/Nu+++y2OPPcY111xDkyZNaN68OX5+fiQlJbFz504SExN58sknS5WLKo+1a9dy6623AsVP86elpfHHH3/Y3o/w8HCmT59+1pUE999/P8ePH+f111+nd+/etGrViiZNmuDh4UF8fDw7duwgLS2NKVOm0L17d6C458KPP/7IkCFDWLhwIWFhYfTq1YuAgABiY2PZuXMntWrVKlU67MMPP2T//v1s2rSJiIgIrrjiCpydnVm1ahVJSUmEh4efcyVPVRk/fjzz58+ncePG9O7dm7y8PJYvX052djZRUVFlykMNHTqUZ599lpdeeolBgwbRs2dPwsLC2L9/P9u2bcPJyYmPP/6YVq1a2c5ZtWoV7777LoGBgXTo0IHg4GAyMzPZuHEjiYmJ1KtXz7a66XxGjhzJjBkzeOKJJ1i6dCnBwcGYTCZuv/328yaEvvzyS/r378/PP/9MeHg4ffr0obCwkBUrVpCRkUHHjh354IMPLvxNFBERERGpCoaIiIiISCW55ZZbDMAYNWpUuY5/5513DMBo0aJFqe1r1qwxhgwZYvj6+hqenp5Ghw4djE8++cQwDMMAjHP9NXb37t3G9ddfb/j7+xtubm5GixYtjMmTJxuFhYVGw4YNDcCIjo4udU7fvn0NwFixYkWZ8VavXm0MHDjQqF27tmE2mw3AmDBhQrnOLfGvf/3LFvOkSZPO+57s3r3buOuuu4ymTZsa7u7uhqenp9G4cWNjyJAhxnvvvWfExsaed4wSEyZMsM1d8nJzczOCg4ONTp06GXfeeafx888/G4WFhecda926dcbYsWONhg0bGm5uboaPj4/RrFkz47rrrjM+/fRTIzU1tcw5mZmZxquvvmp06dLF8PHxMdzc3IyGDRsa11xzjfH111+XOT47O9uYPHmy0b59e8PT09Nwd3c3WrRoYTzzzDNnHT86OtoAjIYNG5415hUrVhiA0bdv37PunzFjRpnP1DAMY9KkSbbP6+jRo8ZNN91khISEGK6urkaTJk2M5557zsjOzj7ne7Vw4UJj2LBhRkBAgOHs7GyEhoYao0ePNjZt2lTm2O3btxtPPfWU0atXL6NevXqGq6urERQUZHTq1Ml4+eWXjeTk5DLn/NPvwLRp04yOHTsanp6etuNmzJhRrnNTUlKMp59+2mjRooXtu9ehQwfjlVdeMXJycsocf77393zziYiIiIhcLJNhGEY15VBERERERERqvOeff54XXniBSZMm8fzzz9s7HBERERER+Rv12BAREREREREREREREYehxIaIiIiIiIiIiIiIiDgMJTZERERERERERERERMRhqMeGiIiIiIiIiIiIiIg4DK3YEBERERERERERERERh6HEhoiIiIiIiIiIiIiIOAwlNkRERERERERERERExGEosSEiIiIiIiIiIiIiIg5DiQ0REREREREREREREXEYSmyIiIiIiIiIiIiIiIjDUGJDREREREREREREREQchhIbIiIiIiIiIiIiIiLiMJTYEBERERERERERERERh6HEhoiIiIiIiIiIiIiIOAwlNkRERERERERERERExGEosSEiIiIiIiIiIiIiIg5DiQ0REREREREREREREXEYSmyIiIiIiIiIiIiIiIjDUGJDREREREREREREREQchhIbIiIiIiIiIiIiIiLiMJTYEBERERERERERERERh6HEhoiIiIiIiIiIiIiIOAwlNkRERERERERERERExGEosSEiIiIiIiIiIiIiIg5DiQ0REREREREREREREXEYSmyIiIiIiIiIiIiIiIjDUGJDREREREREREREREQchhIbIiIiIiIiIiIiIiLiMJTYEBERERERERERERERh6HEhoiIiIiIiIiIiIiIOAwlNkRERERERERERERExGEosSEiIiIiIiIiIiIiIg5DiQ0REREREREREREREXEYSmyIiIiIiIiIiIiIiIjDUGJDREREREREREREREQchhIbIiIiIiIiIiIiIiLiMJTYEBERERERERERERERh6HEhoiIiIiIiIiIiIiIOAwlNkRERERERERERERExGEosSEiIiIiIiIiIiIiIg5DiQ0REREREREREREREXEYSmyIiIiIiIiIiIiIiIjDUGJDREREREREREREREQchhIbIiIiIiIiIiIiIiLiMJTYEBERERERERERERERh6HEhoiIiIiIiIiIiIiIOAwlNkRERERERERERERExGEosSEiIiIiIiIiIiIiIg5DiQ0REREREREREREREXEYSmyIiIiIiIiIiIiIiIjDUGJDREREREREREREREQchhIbIiIiIiIiIiIiIiLiMJTYEBERERERERERERERh6HEhoiIiIiIiIiIiIiIOAwlNkRERERERERERERExGEosSEiIiIiIiIiIiIiIg5DiQ0REREREREREREREXEYSmyIiIiIiIiIiIiIiIjDUGJDREREREREREREREQchhIbIiIiIiIiIiIiIiLiMJTYEBERERERERERERERh6HEhoiIiIiIiIiIiIiIOAwlNkRERERERERERERExGEosSEiIiIiIiIiIiIiIg5DiQ0REREREREREREREXEYSmyIiIiIiIiIiIiIiIjDUGJDREREREREREREREQchhIbIiIiIiIiIiIiIiLiMJTYEBERERERERERERERh6HEhoiIiIjIZWrlypWYTCa+++47e4dSLgkJCYwaNYqAgABMJhPvvPOOvUMSERERERE7UGJDRERERKQKzZw5E5PJhLu7O7GxsWX29+vXj9atW9shMsfzyCOPsHjxYp5++mlmz57NlVdeec5jTSaT7WU2m6lbty6DBw9m5cqV1RewiIiIiIhUCWd7ByAiIiIicjnIz8/nlVde4f3337d3KA5r+fLlXHvttTz++OPlOn7QoEGMHz8ewzCIjo7mo48+on///vz6668MHTq0iqMVEREREZGqohUbIiIiIiLVoH379kybNo24uDh7h1LtsrOzK2WcxMREatWqVe7jmzVrxrhx47jlllt47rnnWLJkCYZhqITVBaqsz09EREREpLIosSEiIiIiUg2eeeYZLBYLr7zyyj8eFxMTg8lkYubMmWX2mUwmnn/+edvPzz//PCaTiYMHDzJu3Dj8/PwICgriP//5D4ZhcOLECa699lp8fX0JDQ3lzTffPOucFouFZ555htDQULy8vLjmmms4ceJEmeM2bdrElVdeiZ+fH56envTt25d169aVOqYkpr1793LzzTdTu3ZtevXq9Y/XfPToUUaPHo2/vz+enp50796dX3/91ba/pJyXYRh8+OGHthJTF6pNmzYEBgYSHR0NwJo1axg9ejRhYWG4ubnRoEEDHnnkEXJzc0udFx8fz2233Ub9+vVxc3OjTp06XHvttcTExNiO+f333xkyZAiBgYF4eHgQHh7O7bffXmocq9XKO++8Q6tWrXB3dyckJIS7776b06dPlzquUaNGDB8+nLVr19K1a1fc3d1p3Lgxn3/+eZlr2rVrF3379sXDw4P69evz0ksvMWPGDEwmU6n4ABYuXEjv3r3x8vLCx8eHq666ij179pQ65tZbb8Xb25sjR44wbNgwfHx8GDt2LACHDh1i5MiRhIaG4u7uTv369RkzZgzp6ekX9DmIiIiIiFSUSlGJiIiIiFSD8PBwxo8fz7Rp03jqqaeoW7dupY1944030qJFC1555RV+/fVXXnrpJfz9/fnkk0/o378/r776KnPmzOHxxx+nS5cu9OnTp9T5//vf/zCZTDz55JMkJibyzjvvMHDgQHbs2IGHhwdQXAZq6NChdOrUiUmTJmE2m5kxYwb9+/dnzZo1dO3atdSYo0ePpmnTprz88ssYhnHO2BMSEujRowc5OTk8+OCDBAQEMGvWLK655hq+++47RowYQZ8+fZg9eza33HKLrbzUxTh9+jSnT5+mSZMmAMydO5ecnBzuvfdeAgIC2Lx5M++//z4nT55k7ty5tvNGjhzJnj17eOCBB2jUqBGJiYksWbKE48eP234ePHgwQUFBPPXUU9SqVYuYmBh++OGHUvPffffdzJw5k9tuu40HH3yQ6OhoPvjgA7Zv3866detwcXGxHXv48GFGjRrFxIkTmTBhAp999hm33nornTp1olWrVgDExsZyxRVXYDKZePrpp/Hy8uLTTz/Fzc2tzLXPnj2bCRMmMGTIEF599VVycnKYMmUKvXr1Yvv27TRq1Mh2bFFREUOGDKFXr1688cYbeHp6UlBQwJAhQ8jPz+eBBx4gNDSU2NhY5s+fT1paGn5+fhf1mYiIiIiIXBRDRERERESqzIwZMwzA2LJli3HkyBHD2dnZePDBB237+/bta7Rq1cr2c3R0tAEYM2bMKDMWYEyaNMn286RJkwzAuOuuu2zbioqKjPr16xsmk8l45ZVXbNtPnz5teHh4GBMmTLBtW7FihQEY9erVMzIyMmzbv/32WwMw3n33XcMwDMNqtRpNmzY1hgwZYlitVttxOTk5Rnh4uDFo0KAyMd10003len8efvhhAzDWrFlj25aZmWmEh4cbjRo1MiwWS6nrv++++8o1LmBMnDjRSEpKMhITE41NmzYZAwYMMADjzTfftMX/d5MnTzZMJpNx7NgxwzCK3zfAeP311885148//mj7jM9lzZo1BmDMmTOn1PZFixaV2d6wYUMDMFavXm3blpiYaLi5uRmPPfaYbdsDDzxgmEwmY/v27bZtKSkphr+/vwEY0dHRhmEUv5+1atUy7rzzzlJzx8fHG35+fqW2T5gwwQCMp556qtSx27dvNwBj7ty557xGEREREZHqolJUIiIiIiLVpHHjxtxyyy1MnTqVU6dOVdq4d9xxh+3fnZyc6Ny5M4ZhMHHiRNv2WrVq0bx5c44ePVrm/PHjx+Pj42P7edSoUdSpU4cFCxYAsGPHDg4dOsTNN99MSkoKycnJJCcnk52dzYABA1i9ejVWq7XUmPfcc0+5Yl+wYAFdu3YtVa7K29ubu+66i5iYGPbu3Vu+N+Espk+fTlBQEMHBwXTr1o1169bx6KOP8vDDDwPYVqNAcR+J5ORkevTogWEYbN++3XaMq6srK1euLFMyqkRJ34/58+dTWFh41mPmzp2Ln58fgwYNsr1/ycnJdOrUCW9vb1asWFHq+JYtW9K7d2/bz0FBQWU+v0WLFhEVFUX79u1t2/z9/W2lo0osWbKEtLQ0brrpplJzOzk50a1btzJzA9x7772lfi5ZkbF48WJycnLOeo0iIiIiItVFiQ0RERERkWr07LPPUlRUdN5eGxciLCys1M9+fn64u7sTGBhYZvvZbs43bdq01M8mk4kmTZrYejQcOnQIgAkTJhAUFFTq9emnn5Kfn1+mz0J4eHi5Yj927BjNmzcvs71Fixa2/Rfr2muvZcmSJSxdupRNmzaRnJzMm2++idlc/L9Bx48f59Zbb8Xf3x9vb2+CgoLo27cvgO163NzcePXVV1m4cCEhISH06dOH1157jfj4eNs8ffv2ZeTIkbzwwgsEBgZy7bXXMmPGDPLz823HHDp0iPT0dIKDg8u8h1lZWSQmJpaK/e+fKUDt2rVLfX7Hjh2zldX6q79vK/n8+vfvX2bu3377rczczs7O1K9fv9S28PBwHn30UT799FMCAwMZMmQIH374ofpriIiIiIhdqMeGiIiIiEg1aty4MePGjWPq1Kk89dRTZfafqym2xWI555hOTk7l2gb8Y7+LcylZjfH666+XWh3wV97e3qV+/utqCHupX78+AwcOPOs+i8XCoEGDSE1N5cknnyQyMhIvLy9iY2O59dZbS61Aefjhh7n66qv56aefWLx4Mf/5z3+YPHkyy5cvp0OHDphMJr777js2btzIvHnzWLx4MbfffjtvvvkmGzduxNvbG6vVSnBwMHPmzDlrPEFBQaV+rorPb/bs2YSGhpbZ7+xc+n8L3dzcbMmfv3rzzTe59dZb+fnnn/ntt9948MEHmTx5Mhs3biyTCBERERERqUpKbIiIiIiIVLNnn32WL774gldffbXMvtq1awOQlpZWantFVi6cT8kT/SUMw+Dw4cO0bdsWgIiICAB8fX3PmSi4WA0bNuTAgQNltu/fv9+2vyrs3r2bgwcPMmvWrFLNyJcsWXLW4yMiInjsscd47LHHOHToEO3bt+fNN9/kiy++sB3TvXt3unfvzv/+9z++/PJLxo4dy9dff80dd9xBREQES5cupWfPnpWW9GnYsCGHDx8us/3v20o+v+Dg4Ap/fm3atKFNmzY8++yzrF+/np49e/Lxxx/z0ksvVWhcEREREZELoVJUIiIiIiLVLCIignHjxvHJJ5+UKmkExcmDwMBAVq9eXWr7Rx99VGXxfP7552RmZtp+/u677zh16hRDhw4FoFOnTkRERPDGG2+QlZVV5vykpKSLnnvYsGFs3ryZDRs22LZlZ2czdepUGjVqRMuWLS967H9SsiLirysgDMPg3XffLXVcTk4OeXl5pbZFRETg4+NjKzV1+vTpMispSla2lBxzww03YLFYePHFF8vEUlRUVCaRVR5Dhgxhw4YN7Nixw7YtNTW1zKqQIUOG4Ovry8svv3zWHiDl+fwyMjIoKioqta1NmzaYzeZSJbdERERERKqDVmyIiIiIiNjB//3f/zF79mwOHDhAq1atSu274447eOWVV7jjjjvo3Lkzq1ev5uDBg1UWi7+/P7169eK2224jISGBd955hyZNmnDnnXcCYDab+fTTTxk6dCitWrXitttuo169esTGxrJixQp8fX2ZN2/eRc391FNP8dVXXzF06FAefPBB/P39mTVrFtHR0Xz//fdnLYlUGSIjI4mIiODxxx8nNjYWX19fvv/++zI9SA4ePMiAAQO44YYbaNmyJc7Ozvz4448kJCQwZswYAGbNmsVHH33EiBEjiIiIIDMzk2nTpuHr68uwYcOA4j4cd999N5MnT2bHjh0MHjwYFxcXDh06xNy5c3n33XcZNWrUBV3DE088wRdffMGgQYN44IEH8PLy4tNPPyUsLIzU1FRbWTNfX1+mTJnCLbfcQseOHRkzZgxBQUEcP36cX3/9lZ49e/LBBx/841zLly/n/vvvZ/To0TRr1oyioiJmz56Nk5MTI0eOvKC4RUREREQqSokNERERERE7aNKkCePGjWPWrFll9j333HMkJSXx3Xff8e233zJ06FAWLlxIcHBwlcTyzDPPsGvXLiZPnkxmZiYDBgzgo48+wtPT03ZMv3792LBhAy+++CIffPABWVlZhIaG0q1bN+6+++6LnjskJIT169fz5JNP8v7775OXl0fbtm2ZN28eV111VWVc3lm5uLgwb948W58Id3d3RowYwf3330+7du1sxzVo0ICbbrqJZcuWMXv2bJydnYmMjOTbb7+13dDv27cvmzdv5uuvvyYhIQE/Pz+6du3KnDlzSjVR//jjj+nUqROffPIJzzzzDM7OzjRq1Ihx48bRs2fPC76GBg0asGLFCh588EFefvllgoKCuO+++/Dy8uLBBx/E3d3dduzNN99M3bp1eeWVV3j99dfJz8+nXr169O7dm9tuu+28c7Vr144hQ4Ywb948YmNj8fT0pF27dixcuJDu3btfcOwiIiIiIhVhMi6m+5yIiIiIiIjUSA8//DCffPIJWVlZ52xCLiIiIiLiyNRjQ0RERERExEHl5uaW+jklJYXZs2fTq1cvJTVERERE5JKlUlQiIiIiIiIOKioqin79+tGiRQsSEhKYPn06GRkZ/Oc//7F3aCIiIiIiVUaJDREREREREQc1bNgwvvvuO6ZOnYrJZKJjx45Mnz6dPn362Ds0EREREZEqox4bIiIiIiIiIiIiIiLiMNRjQ0REREREREREREREHIYSGyIiIiIiIiIiIiIi4jDUY+NvrFYrcXFx+Pj4YDKZ7B2OiIiIiIiIiIiIiMglzzAMMjMzqVu3LmbzP6/JUGLjb+Li4mjQoIG9wxARERERERERERERueycOHGC+vXr/+MxSmz8jY+PD1D85vn6+to5GhERERERERERERGRS19GRgYNGjSw3aP/J0ps/E1J+SlfX18lNkREREREREREREREqlF5WkSoebiIiIiIiIiIiIiIiDgMJTZERERERERERERERMRhKLEhIiIiIiIiIiIiIiIOQ4kNERERERERERERERFxGEpsiIiIiIiIiIiIiIiIw1BiQ0REREREREREREREHIYSGyIiIiIiIiIiIiIiDsRqNTAMw95h2I0SGyIiIiIiIiIiIiIiDuRfc7bR9vnfmLr6CIUWq73DqXZKbIiIiIiIiIiIiIiIOIj49DwW7YknM7+Ilxfs5+r317Lt+Gl7h1WtlNgQEREREREREREREXEQy/cnAlCvlge1PF3YH5/JyCnr+b8fd5OeU2jn6KqHEhsiIiIiIiIiIiIiIg5i+f4EAG7uFsbyx/oxqlN9DAPmbDrOgLdW8vOO2Eu+/4YSGyIiIiIiIiIiIiIiDiCv0MLaw8kA9I8Mxt/LlTdGt+Pru7oTEeRFclYBD329g1umbyY6OdvO0VadGpPYiI2NZdy4cQQEBODh4UGbNm34/fffbfsNw+C5556jTp06eHh4MHDgQA4dOlRqjNTUVMaOHYuvry+1atVi4sSJZGVlVfeliIiIiIiIiIiIiIhUug1HUsgrtFLXz53IUB/b9u6NA1jwUG8eH9wMN2czaw8nM+Sd1by79BD5RRY7Rlw1akRi4/Tp0/Ts2RMXFxcWLlzI3r17efPNN6ldu7btmNdee4333nuPjz/+mE2bNuHl5cWQIUPIy8uzHTN27Fj27NnDkiVLmD9/PqtXr+auu+6yxyWJiIiIiIiIiIiIiFSqZWfKUPVvEYzJZCq1z83Zifv7N+W3R/rQu2kgBUVW3l56kKHvrGFPXLo9wq0yJqMGFNt66qmnWLduHWvWrDnrfsMwqFu3Lo899hiPP/44AOnp6YSEhDBz5kzGjBnDvn37aNmyJVu2bKFz584ALFq0iGHDhnHy5Enq1q1brlgyMjLw8/MjPT0dX1/fyrlAEREREREREREREZEKMAyDnq8sJy49jxm3duGKyOB/PHberlP8d95ekrPyaVffj5/v71WN0V64C7k3XyNWbPzyyy907tyZ0aNHExwcTIcOHZg2bZptf3R0NPHx8QwcONC2zc/Pj27durFhwwYANmzYQK1atWxJDYCBAwdiNpvZtGlT9V2MiIiIiIiIiIiIiEgl2x+fSVx6Hu4uZqIiAv7xWJPJxDXt6vLL/T0B2BWbTlpOQXWEWS1qRGLj6NGjTJkyhaZNm7J48WLuvfdeHnzwQWbNmgVAfHw8ACEhIaXOCwkJse2Lj48nOLh0hsrZ2Rl/f3/bMWeTn59PRkZGqZeIiIiIiIiIiIiISE2yfH8iAL2aBOLu4lSuc+rW8qBZiDeGUdyf41JRIxIbVquVjh078vLLL9OhQwfuuusu7rzzTj7++OMqn3vy5Mn4+fnZXg0aNKjyOUVERERERERERERELsSyfWf6a0SGnOfI0npEBAKw9nBypcdkLzUisVGnTh1atmxZaluLFi04fvw4AKGhoQAkJCSUOiYhIcG2LzQ0lMTExFL7i4qKSE1NtR1zNk8//TTp6em214kTJyp8PSIiIiIiIiIiIiIilSUlK5/tJ9IA6P8PvTXOpmeT4sTGeq3YqFw9e/bkwIEDpbYdPHiQhg0bAhAeHk5oaCjLli2z7c/IyGDTpk1ERUUBEBUVRVpaGlu3brUds3z5cqxWK926dTvn3G5ubvj6+pZ6iYiIiIiIiIiIiIjUFCsPJGEY0KquL6F+7hd0brfG/jiZTUQnZxOblltFEVavGpHYeOSRR9i4cSMvv/wyhw8f5ssvv2Tq1Kncd999QHGjk4cffpiXXnqJX375hd27dzN+/Hjq1q3LddddBxSv8Ljyyiu588472bx5M+vWreP+++9nzJgx1K1b145XJyIiIiIiIiIiIiJy8Ur6a1zoag0AX3cX2tb3A2DdJVKOqkYkNrp06cKPP/7IV199RevWrXnxxRd55513GDt2rO2YJ554ggceeIC77rqLLl26kJWVxaJFi3B3/zM7NWfOHCIjIxkwYADDhg2jV69eTJ061R6XJCIiIiIiIiIiIiJSYQVFVlYfTAIuLrEBxQ3HAdZfIokNk2EYhr2DqEkyMjLw8/MjPT1dZalERERERERERERExK7WH07m5k83EeDlypb/G4jZbLrgMTYcSeGmaRsJ8nFj8zMDMJkufIyqdiH35mvEig0RERERERERERERESlr2ZkyVFdEBl9UUgOgY8NauLuYScrM51BiVmWGZxdKbIiIiIiIiIiIiIiI1FAl/TUGXGQZKgA3Zye6NPIHYO0hxy9HpcSGiIiIiIiIiIiIiEgNdDQpi+jkbFycTPRqGlihsXqW9Nk4osSGiIiIiIiIiIiIiIhUgZLVGt3CA/Bxd6nQWCUNxDceTaXIYq1wbPakxIaIiIiIiIiIiIiISA1UktjoX4EyVCVa1vGllqcLWflF7DyZXuHx7EmJDRERERERERERERGRGiYjr5DN0akADGhR8cSG2WyiR0QAAOsPO3Y5KiU2RERERERERERERERqmDUHkymyGkQEedEwwKtSxuwRUVyOaq0SGyIiIiIiIiIiIiIiUpmW7U8AYECLkEobs6TPxvbjaeQUFFXauNVNiQ0RERERERERERERkRrEYjVYeSAJqJz+GiUaBnhSr5YHBRYrW2JOV9q41U2JDREREREREREREZFLmGEYHIjPJDkr396hSDntOJFGanYBvu7OdGpYu9LGNZkujT4bzvYOQEREREREREREREQqX16hhV92xDFzfQx7T2XQJNibJY/0wWQy2Ts0OY/lZ8pQ9W0ejItT5a5P6NU0kLlbTzp0nw0lNkREREREREREREQuIbFpuczecIxvthzndE6hbfvhxCwOJWbRLMTHjtFJeSzblwhA/8igSh876syKjb2nMjidXUBtL9dKn6OqKbEhIiIiIiIiIiIi4uAMw2Dj0VRmrY/ht73xWI3i7fVqeXBLVEOW70tkc0wqaw4lK7FRw8Wm5bI/PhOzCfo2q7z+GiWCfdxpHuLDgYRMNhxNYVibOpU+R1VTYkNERERERERERETEQeUUFPHT9jg+3xDD/vhM2/aoxgHc2rMRA1uE4GQ2YTbB5phU1h5KYmKvcDtGLOezfH/xao2OYbXxr6LVFD2aBHAgIZO1h5OV2BARERERERERERGR6rFkbwKPfbuDjLwiADxcnBjRsR4TohrRPLT0qoxeTYKA/WyKTqWgyIqrc+X2bZDKs3xfcX+N/i0qf7VGiV5NApmxLsZhG4grsSEiIiIiIiIiIiLiYAzD4IV5e8jIKyLM35PxUQ0Z3akBfp4uZz0+MtSHAC9XUrIL2H78NN0aB1RzxFIeOQVFrDuSAsCAyJAqm6druD9OZhMxKTmcPJ1D/dqeVTZXVVBaTkRERERERERERMTB7DiRxsnTuXi6OrH44T7c0bvxOZMaAGaziZ5NAgFY66BP6V8O1h9OoaDISr1aHjQL8a6yeXzcXWhX3882p6NRYkNERERERERERETEwczfdQqAgS1C8HB1Ktc5vZoWJzbWHFJio6Zadqa/xoAWwZhMpiqdq5cDJ7qU2BARERERERERERFxIFarwa9nEhvD25a/8XPvM4mNXSfTSM8prJLY5OIZhsHy/Wf6a0RWXX+NEj3OJDbWH0nBMIwqn68yKbEhIiIiIiIiIiIi4kC2Hj9NfEYePm7O9G0eVO7z6vh5EBHkhdWADUcd7yn9S92euAwSMvLxcHGiezX0QOkQVgsPFyeSs/I5mJBV5fNVJiU2RERERERERERERBzI/J1xAAxqFYKbc/nKUJXo3bQ4EeKI5YcudQt2F6/C6dkkEHeXC/tcL4absxNdwv0Bx/s+KLEhIiIiIiIiIiIi4iAsVoMFf8QDcHXbuhd8vq2BuPps1CgFRVa+/f0EACM71qu2eXs1KV4Zsl6JDRERERERERERERGpCpuiU0jKzMfPw8WWpLgQ3Rv742Q2EZOSw4nUnCqIUC7G4j3xJGcVEOzjxsCWIdU2b4+I4u/QxqMpFFqs1TZvRSmxISIiIiIiIiIiIuIg5p9pGj6kVQiuzhd+e9fH3YUODWoBjld+6FL2xcZjAIzp0gAXp+q7bd+yji+1PV3ILrCw62Ratc1bUUpsiIiIiIiIiIiIiDiAIouVRWfKUA2/iDJUJXo1VTmqmuRQQiabolMxm2BM17BqndtsNtlWbaw9lFKtc1eEEhsiIiIiIiIiIiIiDmD9kRRSswvw93KlR0TARY/T+0xiY92RZCxWo7LCk4s0Z9NxAAa0CKFuLY9qn7/HmT4b6444TqJLiQ0RERERERERERERBzB/VxwAV7YOxbkC5Yra1q+Ft5szaTmF7I3LqKzw5CLkFBTx/baTAIzr3tAuMfQ606tl+/HT5BQU2SWGC6XEhoiIiIiIiIiIiEgNV1D01zJUdSo0louTme6Ni5/SX3M4qcKxycWbtzOOzLwiGgZ40vsimsFXhjB/T+rV8qDQYrA5OtUuMVwoJTZEREREREREREREari1h5PIyCsiyMeNbuEXX4aqRG/12agRvthYXIbq5q5hmM0mu8RgMplsqzbWH3GMPhtKbIiIiIiIiIiIiIjUcPN3nQJgWOtQnCrhBnhJA/HfY06TW2Cp8Hhy4XaeSGN3bDquzmZGd25g11hK+mw4SqKrxiQ2nn/+eUwmU6lXZGSkbX9eXh733XcfAQEBeHt7M3LkSBISEkqNcfz4ca666io8PT0JDg7m3//+N0VFjlETTERERERERERERORs8gotLNlTfC90eLu6lTJm40Av6vq5U2CxsjnGMcoPXWq+2HgMgKva1MHfy9WusfSIKE507T2VQWp2gV1jKY8ak9gAaNWqFadOnbK91q5da9v3yCOPMG/ePObOncuqVauIi4vj+uuvt+23WCxcddVVFBQUsH79embNmsXMmTN57rnn7HEpIiIiIiIiIiIiNU5ugYWsfD0I7GhWH0wiM7+IUF93OoXVrpQxTSaTbdXG2kPqs1Hd0nMKmXemGfzYbmF2jgaCfNyIDPUBivt+1HQ1KrHh7OxMaGio7RUYWPyLlZ6ezvTp03nrrbfo378/nTp1YsaMGaxfv56NGzcC8Ntvv7F3716++OIL2rdvz9ChQ3nxxRf58MMPKSio+RkmERERERERERGRqpKdX8Q7Sw/S+aUlDH5rFdlKbjiUkjJUV7WtU6l9GHqe6auw9rBj9FW4lHy37SR5hVYiQ33o1LByklUVVZJgeX/5oRqfAK1RiY1Dhw5Rt25dGjduzNixYzl+vLhxytatWyksLGTgwIG2YyMjIwkLC2PDhg0AbNiwgTZt2hASEmI7ZsiQIWRkZLBnz57qvRAREREREREREZEaoNBiZfaGGPq+voJ3lh4iu8BCXHqeSg85kNwCC0v3nSlD1bZOpY5dktjYdyqDpMz8Sh1bzs0wDOZsKi5DNbZ7Q0wm+zQN/7sxXcMID/QiOauAqauO2Ducf1RjEhvdunVj5syZLFq0iClTphAdHU3v3r3JzMwkPj4eV1dXatWqVeqckJAQ4uPjAYiPjy+V1CjZX7LvXPLz88nIyCj1EhERERERERERcWSGYbBg9ykGv72a//y8h+SsAhoFeNIhrBYAG47oCX1HseJAIjkFFurX9qB9g1qVOnagtxst6/gCsP6IYzSNvhRsOJrC0aRsvFydGNGhnr3DsXFxMvPklc0BmLYmmoSMPDtHdG41JrExdOhQRo8eTdu2bRkyZAgLFiwgLS2Nb7/9tkrnnTx5Mn5+frZXgwb27T4vIiIiIiIiIiJSEZuOpjDio/X8a842opOzCfBy5b/XtmLJo32ZENUIUGLDkcw/04fhqrZ1quTJ/t5n+mysOaTERnWZs7G4UtF1Herh7eZs52hKG9IqlE4Na5NbaOHtJQftHc451ZjExt/VqlWLZs2acfjwYUJDQykoKCAtLa3UMQkJCYSGhgIQGhpKQkJCmf0l+87l6aefJj093fY6ceJE5V6IiIiIiIiIiIhINTiYkMnEmVu4cepGdpxIw9PViYcGNGXVE1cwPqoRLk5moiICANgTl056TqGdI5bzyc4vYvn+RACublu3Sub4s4F4MoZhVMkc8qfEjDwW7ymuMDSue0M7R1OWyWTimWGRAHz7+wkOJmTaOaKzq7GJjaysLI4cOUKdOnXo1KkTLi4uLFu2zLb/wIEDHD9+nKioKACioqLYvXs3iYmJtmOWLFmCr68vLVu2POc8bm5u+Pr6lnqJiIiIiIiIiIg4ioy8Qp74bidXvrOaZfsTcTKbGNc9jJX/7scjg5qVeiI8xNedxkFeWA3YFK1VGzXd0n0J5BVaaRTgSau6VXPfsksjf1ydzcRn5HEkKatK5pA/fbPlBEVWg04Na9OiTs28F92poT9DW4diNWDygn32Duesakxi4/HHH2fVqlXExMSwfv16RowYgZOTEzfddBN+fn5MnDiRRx99lBUrVrB161Zuu+02oqKi6N69OwCDBw+mZcuW3HLLLezcuZPFixfz7LPPct999+Hm5mbnqxMREREREREREakaz/+yh29/P4nVgKGtQ1nySB9euq4NwT7uZz0+qnHxqo0NR5XYqOnm7zoFwPC2dauswbS7ixNdGtUGildtSNWxWA2+2lxchmpc9zA7R/PPnrgyEmeziRUHklh/uOZ9L2pMYuPkyZPcdNNNNG/enBtuuIGAgAA2btxIUFAQAG+//TbDhw9n5MiR9OnTh9DQUH744Qfb+U5OTsyfPx8nJyeioqIYN24c48eP57///a+9LklERERERERERKRKxabl8suO4h4MM2/rwpRxnWgc5P2P5/SIKC49pD4bNVtGXiGrDiQBMLxdnSqdq1eT4nuwa2vgDexLyfL9icSl51Hb04Whrav2M62o8EAvxnYrTr68vHAfVmvNKlNWYzqTfP311/+4393dnQ8//JAPP/zwnMc0bNiQBQsWVHZoIiIiIiIiIiIiNdJna6Mpshr0iAigX/Pgcp3TvbE/APvjM0nJyifAW9VOaqIlexIosFhpEuxN8xCfKp2rd9NAXl0EG4+mUmix4uJUY56Hv6R8sfEYAKM7N8DdxcnO0ZzfgwOa8v22WP6IzWDerjiubV/P3iHZ6BsqIiIiIiIiIiLigNJzCm1lbe7uG1Hu8wK83Ww3yjdFp1ZJbFJx83cVr8QZ3rZOlZWhKtGyji/+Xq5k5Rex40Ralc51uTqeksPqQ8UrcG7uWrPLUJUI8Hbj3n7Ff7a8tugAeYUWO0f0JyU2REREREREREREHNAXm46RU2AhMtSHPk0DL+jcqIjiPhvrj6j0UE2UllPAmjP9Loa3rVvl85nNJnqc+U6sUZ+NKjFn8zEMo3h1TKNAL3uHU2639wwn1Ned2LRcZm84Zu9wbJTYEBERERERERERcTB5hRZmrIsG4J6+ERf8RH9JYkN9NmqmxXviKbIaRIb60CT4n3umVJZeTYqTY+vUZ6PS5RdZmPv7SQDGdW9o52gujIerE48ObgbA+8sPkZZTYOeIiimxISIiIiIiIiIi4mB+2BZLclYB9Wp5cFXbC29C3D08AJMJjiRlk5CRVwURSkXM33UKKC5DVV16nVn1s+NEGhl5hdU27+Vg0R/xpGYXUMfPnQGR5euFU5OM7FifyFAfMvKK+HDFYXuHAyixISIiIiIiIiIi4lAsVoNpa44CcHuv8Itq9Ozn6UKrur4AbDyqVRs1SUpWPuvPrKSpjjJUJerX9iQ80AuL1WCjVvJUmmMp2UxdXfz7OqZLGM4O2JjdyWzi6WEtAJi1/hgnUnPsHJESGyIiIiIiIiIiIg5lyd54opOz8fNwYUyXBhc9To+I4if01x/WTeyaZM6m41isBm3q+VV7L4aSclRrVY6qQgzDYPXBJCbO3EK/N1ayJy4DV2czY7pe/O+rvfVpGkivJoEUWKy8vviAvcPB2d4BiIiIiIiIiIhI5csrtJCaXUBqdgGncwo4nVPI6b/8XMvDhQcHNHXIp4cvZ4ZhMGVV8dPft3RviJfbxd/ei2ocwNTVR9mgFRs1RmZeIdPXFvdOuaN3eLXP36tpILM3HmOtGohflKz8Ir7fepJZG2I4mpRt296nWRAPDWhCiK+7HaOrGJPJxFNDI7n6g7X8sjOOO3qH07Z+LbvFo8SGiIiIiIiIiMglICY5m39/t5O4tDxSswvILbSc95ywAC9GdapfDdFJZdkcncrOE2m4OpuZ0KNRhcbqEu6Pk9nE8dQcTp7OoX5tz8oJUi7arPUxpOcWEhHkVa1lqEpERQRgNsHR5Gxi03KpV8uj2mNwREeTsvh8wzG+23qSrPwiALzdnBnVqT63RDUkIqh6GsBXtdb1/BjRvh4/bI/l5QX7+OrO7phMJrvEosSGiIiIiIiIiIiDMwyDZ37czZaY06W2uziZqO3pWvzycsHfq/jfT6XnsXx/Il9sPKbEhoP55Eyt/lGd6hPk41ahsbzdnGlb34/tx9PYcCSF0Z2V2LCnrPwiPj2zWuPBAU1xMlf/DWNfdxfaNajF9uNprD6YxE1dw6o9BkdhGAYrDyQxc30Mqw4m2bY3DvJiQlQjRnaqj3cFVlTVVI8Nac783afYeDSVFQcS6R8ZYpc4Lr13VkRERERERETkMjN/1ynWH0nBzdnM9AldCPP3pLaXC95uzmd9mjY5K5+oycvYcSKNP2LTaV3Pzw5Ry4U6EJ/J8v2JmExwZ+/GlTJmVOOA4sTG0RRGd3bc+v+XglnrY0jLKaSxnVZrlBjYIoTtx9P4YuMxxnRpYLcn8mu61xYfYMrKIwCYTNC/eTATejSiV5NAzHZISlWXerU8uL1nOB+vOsJriw5wRfNgu3xHVERRRERERERERMSBZeUX8dKvewG4t18EvZoGEhbgiY+7yzlvNgV6u3Fl6zoAzNl0rNpilYqZema1xpWtQgmvpKbSJQ3ENxxJwTCMShlTLlx2fhGfrin+fB/o38QuqzVK3Nw1DA8XJ/bEZbBOjeXPak9cOp+sKk5q3NqjESsf78f0W7vQp1nQJZ3UKHFv3wjcnM3sj8/kj9gMu8SgxIaIiIiIiIiIiAN7f9khEjLyCfP35J6+EeU+b2y34hIzP22PIyOvsKrCk0pyKj2Xn3fEAnBXn8pZrQHQqWFtXJxMnErP41hKTqWNKxfm8w3HOJ1TSHigF1fbcbUGQG0vV27sUrx655PVR+waS01ktRo8+9MfWA24qk0dnr+mFQ0DKifR6Cj8PF0Y1LK4BNUP20/aJQYlNkREREREREREHNShhEymn6nJ//w1LXF3cSr3ud3C/Wka7E1uoYUft8VWVYhSST5bG02R1aBbuD8dwmpX2rgerk628dYf0dP59pCdX8S0M6s17r+iCc5O9r9lO7FXOE5mE2sOJfNHbLq9w6lRvt5ygu3H0/BydeI/w1vaOxy7ub5jPQDm7YyjyGKt9vnt/1siIiIiIiIiIiIXzDAMJv2yhyKrwcAWwRfcwNVkMtlWbXyx8ZjKENVg6bmFfLnpOMAFrcopr6jGAQBsOKrEhj18sfEYqdkFNArw5Nr29l2tUaKBvyfD2xaXqyspgSbF/YleWbgPgMcGNyfUz93OEdlP76ZBBHi5kpxVwJpDydU+vxIbIiIiIiIiIiIO6K8Nwydd3eqixri+U308XJw4lJjF5ujUSo5QKsucTcfILrDQPMSHfs2DKn38HhFnEhvqs1HtcgqKbImD+2rIao0SJSXPft19ihOpKlMG8PKCfWTkFdGyji/joxraOxy7cnEyc3W74kTcD9urf9VfzflNERERERERERGRcvlrw/B/9WtCA3/PixrH193F9oT4nDMrAqRmySu0MGNdDFB8o/lcDeEron1YLdyczSRn5XM4MavSx5dzm7PxOCnZBYT5ezKiQz17h1NKq7p+9G4aiMVq2EreXc42HEnhh22xmEzwvxGta1QSyl5KylH9tieezGru1aR3X0RERERERETEwfy1YfjdfSvWSHpc9+Knjhf+cYrkrPzKCE8q0U/bY0nKzKeOn7vt6ejK5ubsRJdG/oDKUVWn3AKLrTl3Temt8Xclpc++3nKc1OwCO0djPwVFVv7z8x8A3Nw1rFL73DiyNvX8iAjyIr/IysI/4qt17pr32yIiIiIiIiIiIudUkYbhZ9O6nh/tGtSi0GLw7e8nKiNEqSRWq2ErUzSxVziuzlV3Ky/qTDmq9YeV2KguczYdIzmrgAb+HozoWLNWa5ToERFA63q+5BVamb3hmL3DsZtpa45yODGLQG9XnhgSae9wagyTycT1HesD8OO26i1HpcSGiIiIiIiIiIiDKN0wPOSCG4afy7gzTcS/3HQci1U9FmqKJfsSOJqcjY+7M2O6hlXpXN3PNBDfGJ2CVd+BKpdbYOHjVWd6a/RrgksNXK0BxTeu7+5TvGpj1oYYcgssdo6o+p1IzeH95YcAeGZYC/w8XewcUc1SUs5wY3QKcWm51TZvzfyNERERERERERGRMko3DG9ZaeNe3a4uvu7OnDydy+qDSZU2rlw8wzD4eFVxmaJbujfE2825SudrW98PL1cn0nIK2RefUaVzCXy5+TjJWfnUq+Vhe+K9phraOpQG/h6kZhfw3dbLa1VXSTI5r9BK98b+Na4PSk1Qv7Yn3cL9MQz4aUf1rdpQYkNERERERERExAFUVsPws3F3cWJUpwYAfLHx8i03U5Psictg+/E0XJ3M3NqjUZXP5+Jkpkv4mT4bR1SOqirlFVpsSav7rmhSpSXGKoOzk5k7exf38pm2Jpoii9XOEVWfxXsSWL4/ERcnEy9d1xqTyWTvkGqkkibiP26LxTAufsXXr7viyn1szf6tERERERERERERoHIbhp/N2O7FpY6WH0jk5OmcSh9fLsyP24uffB7UMoRgX/dqmbPHmT4bG9VAvEp9tfk4SZnFqzVGdarZqzVKjO7UgNqeLhxPzWHRnuptEm0v2flFvDBvDwB39WlMk2AfO0dUcw1tUwc3ZzOHErPYE3dxK75Ons5h0i97yn28EhsiIiIiIiIiIjVcZTcMP5uIIG96RARgGMU3XsV+LFaDX3YWP7l8XTWWvolqHAjApqOpl9VT+dXpr6s17u0XUeNXa5TwcHViwpmVQ5+sOlqhp/IdxbvLDnEqPY/6tT24/4qm9g6nRvN1d2Fgy+KeTz9cZBPx53/ZS15h+f/ccYzfHBERERERERGRy1RVNQw/m3HdGwLwzZaTFBTpxra9rD+STFJmPrU8XejbLKja5m1Z1xdfd2cy84su+qlr+WffbDlBQkY+dfzcGd3ZMVZrlBgf1Qh3FzO7Y9Mv+XJl+05l2JLJL17bGg/Xyk8mX2quP5OE/WVn3AUnRn/bE8/SfQk4m8tf6kuJDRERERERERGRGqyqGoafzaCWIQT7uJGclc9vey+PcjM1UUkZqqva1KnWJ/qdzCa6NS4uR7X+Er9xbQ/5RRamrCxerfGvfhG4OTvWzXJ/L1du7Fzci+fj1UftHE3VsVoNnv3pDyxWgytbhXJFZLC9Q3IIfZoFEeDlSnJWPmsOJ5f7vJyCIl6YV9w/avwF9BNSYkNEREREREREpIYqslh5ecE+oPIbhp+Ni5OZMV3URNyecgqKWPxHcVJpRDWWoSoRdSaxsUF9Nirdt1tOEJ+RR6ivOzec+T1zNHf0bozZBKsPJrH3El3VM3frCbYeO42nqxPPVXEy+VLi4mTm6nZ1geIm4uX17rJDxKblUq+WB/dcQP8oJTZERERERERERGqolQeSOJWeR4CXa5U0DD+bMV3DMJtg49FUDidmVsuc8qclexPILrBQv7YHnRrWrvb5ezQpTmxsiU5VObJKtOloCq8tOgDAv65wvNUaJRr4e3JV2+Kb11NXH7FzNHA6uwCLtfL6fZxIzWHywv0APDKwGXVreVTa2JeD6zsWJ2MX74knM6/wvMcfiM9k+prikl8vXNMKT1fncs+lxIaIiIiIiIiISA31ze8ngOIn96uiYfjZ1K3lwYAWxX08vtioJuLV7ecdxU3DR3Soh8lU/nrzlaVZsA/+Xq7kFlrYdTKt2ue/FC3bl8D4zzaTmV9E98b+3OigqzVK3N2nOMk6b9cpTp7OsUsMW4+lcses3+nw4hJumrqRrPyiCo+ZlJnPLdM3kZZTSKu6vtzas1HFA73MtKnnR0SQF/lFVhb+8c/lDItLfu2myGowqGWIrfl4eSmxISIiIiIiInIJsVoNvv39BD/viCU99/xPS0rNlZiZx/L9iQDVfiO0pIn499tOklNQ8RuGUj4pWfmsOpgEwLXtq78MFYDZbPqzHJX6bFTYT9tjuWv2VvKLrAxsEczM27o67GqNEq3r+dGrSSAWq2FrsF0drFaDZfsSGP3xekZO2cDSfQkAbI5J5bYZm8muQHIjI6+QCZ9tJiYlh/q1Pfjs1i64OOnW+YUymUxc37E+cP5yVN9tO8mWmNN4uDjx/DWtLngufToiIiIiIiIil5BZG2J44rtdPPT1Djq9uIRxn25i1voY4tJy7R2aXKAft8VisRp0CKtF0xCfap27d5NAwvw9ycwrYt7OuGqd+3I2f9cpLFaDNvX8aBLsbbc4ukeogXhl+HxDDA9/swOL1WBEh3pMGdep2lZeVbWS0nhfbz7B6eyCKp2r0GLl+60nufLd1Uyc9TtbYk7j4mTihs71+WhsR3zcndkSc5rbZmy5qORGXqGFO2b9zt5TGQR6u/LFxG6E+LpXwZVcHq5tX1yqbGN0yjn/7nE6u4DJZ/pHPTywKfUuouRXjUxsvPLKK5hMJh5++GHbtry8PO677z4CAgLw9vZm5MiRJCQklDrv+PHjXHXVVXh6ehIcHMy///1vior0VIGIiIiIiIhcHo6lZPPqouLa4HX93CmyGqw9nMykX/bQ45XlDH9/De8uPcS+UxkYRuXVJJfKZxiGrQzVDZ2rv2yN2Wzi5m5hgMpRVacftxc/4XydHZqG/1WPM4mNrcdPk1dosWssjsgwDN5bdojnft4DwK09GvHm6HaX1AqAXk0CaVnHl9xCC19sPFYlc2TnFzF9bTR9X1vBY3N3cjAhC283Z+7u05i1T/bntVHtGNamDrMndsPHzZnNMancPnPLBa0yK7JYuf/LbWyOTsXHzZlZt3elUaBXlVzP5aJ+bU+6hftjGPDTjrOv2nh10X5O5xTSPMSH23uFX9Q8Ne63acuWLXzyySe0bdu21PZHHnmEefPmMXfuXFatWkVcXBzXX3+9bb/FYuGqq66ioKCA9evXM2vWLGbOnMlzzz1X3ZcgIiIiIiIiUu2sVoMnvttFXqGVqMYBrH2yPyse78czwyLp0qg2JhP8EZvB20sPMvTdNfR+bQUvzNvD1mOp9g5dzmLrsdMcTcrGw8WJ4W3r2CWG0Z3q4+pkZndsOjtPpNklhstJdHI2O06kYTbB1e3s85mXaBzoRbCPGwVFVrYdP23XWOzlu60nefO3AxxOzLyg86xWgxfn7+OtJQcBeGhAUyZd3RKzufr7pVQlk8lkW7Uxc31MhcpA/V1aTgFv/XaAnq8u58X5e4lLzyPQ240nrmzOuqf68/SwFqVWVLRvUIvPJ3bF282ZTdHFyY3cgvMn5KxWgye/383SfYm4OZv5dEJnWtX1q7TruJyVNBH/cVtsmQcpfo9J5estxYn7l0a0vuiEX41KbGRlZTF27FimTZtG7dq1bdvT09OZPn06b731Fv3796dTp07MmDGD9evXs3HjRgB+++039u7dyxdffEH79u0ZOnQoL774Ih9++CEFBVW7HEpERERERETE3uZsOsam6FQ8XJx4dWRbzGYT4YFe3NUngrn39GDL/w3ktZFtGdgiBDdnMydP5zJjXQwjp2zgx+0n7R2+/M23Z1ZrXNW2Dj7uLnaJIcDbjWFtQgGq7Ils+dPPZ55s7tU0iGAf+5bBMZlMtlUb6w9ffuWoYpKzeXzuTt5ffpiBb63m+o/W8c2W4+e9eV9ksfLE97v4bF1x34nnhrfkkUHN7NIEvjpc1aYOjQI8Scku4KOVhytlzPwiCzd8soH3lh8mLaeQRgGevDyiDWufvIJ/9WuCn8fZ/zzsEFbbltzYeDSVibP+OblhGAb/W7CP77edxMls4sObO9LtTG8Zqbihberg5mzmUGIWe+IybNsLLVae/ekPAG7oXJ8ujfwveo4aldi47777uOqqqxg4cGCp7Vu3bqWwsLDU9sjISMLCwtiwYQMAGzZsoE2bNoSE/Nk9fciQIWRkZLBnz57quQAREREREREROziRmsPkhcUlqJ68sjlhAZ5ljgn0duOGLg34dEJntj83iE9u6cTAFsX/D/36ogPkF6ncTE2RlV/E/F2nAPuUofqrW6KKm4j/vDOOpMx8u8ZyKTMMg59KylCdqU9vb32bBwHw5ebjpOcW2jma6vXVluLyawFerjiZTWw7nsaT3++m6/+W8uR3u9h67HSZp9DzCi38a842vttafKP8zdHtLrrEjqNwdjLz9LAWAExbE82J1JwKj/npmmgOJmQR6O3KR2M7suyxftzcLaxcvUk6htVm1u1d8XJ1Yv2RFO74fMs5S6l9tPKIrfH5ayPbMrBlyFmPk4vj6+5ie09/+EsT8ZnrYtgfn0ktTxeeGtqiQnPUmMTG119/zbZt25g8eXKZffHx8bi6ulKrVq1S20NCQoiPj7cd89ekRsn+kn3nkp+fT0ZGRqmXiIiIiIiIiKMwDIMnv99FToGFro38GR/V6LzneLo6M6RVKB/c3IEQXzfi0vP4evOJqg9WymXBrlPkFFhoHOhFl0a1z39CFeoYVpv2DWpRUGRl9oYYu8ZyKdtxIo2YlBw8XJwY0irU3uEAcHXbujQN9iY1u4D3lh2ydzjVJr/Iwtzfi1exTb6+DRue6s8TVzYnPNCL7AIL3/x+gpFT1jPo7dVMW32U5Kx8svKLuG3GFn7bm4Crs5kpYzsyslN9O19J9RjcMoQeEQEUFFl55UyC/WLFpuXywfLilR/PXtWSYW3q4HSBJbw6NfwzubHucAp3fv57meTGl5uO8/riAwD8Z3jLy+azqm7Xn+kV9MvOOIosVuLScnl7aXGJtmeGtsDfy7VC49eIxMaJEyd46KGHmDNnDu7u1bvUbvLkyfj5+dleDRrY90kIERERERERkQvx5ebjrD+SgruLmddGtb2gOu7uLk480L8pAB+sOFyumuRS9Uqaho/u3MDuJWxMJhN39Smuoz974zF9R6pIyWqNwa1C8HJztnM0xZydzPxneEsAZq2P4UhSlp0jqh6L9ySQml1AqK87/SODCfZ151/9mrD8sb58c1d3ru9YD3cXM4cTs/jfgn10f3kZg99axYajKXi5OjHzti4MriHJqepgMpl47uqWmE3w6+5TbDp68aXLXpq/l9xCC13D/bm2AiuXOjfyZ+btXfF0dWLNoeRSyY0Fu0/xfz/tBuD+K5ow8RJfVWNPfZoFEeDlSnJWPmsOJ/PCvD3kFFjo3LA2oyohmVQjEhtbt24lMTGRjh074uzsjLOzM6tWreK9997D2dmZkJAQCgoKSEtLK3VeQkICoaHFf1CEhoaSkJBQZn/JvnN5+umnSU9Pt71OnNATKiIiIiIiIuIYYtNymbyg+AnZfw+JpFGg1wWPcUPnBtSv7UFSZj6zN8ZUcoRyoQ4nZrL12GmczCZGnmm+am9DWoXSwN+D0zmFfLdN/VgqW6HFais9dl2HmvGZl+jTLIgBkcEUWQ3+9+s+e4dTLb7cVNxP5oYuDXD+S1Njk8lEt8YBvHVDezb/30D+N6I17er7UWQ1iEvPo7anC1/e2Z0eEYH2Ct1uIkN9ualrGAD/nb8Xi9U4zxllrT6YxMI/4nEym/jvta0qnNTt0sifmbf9mdy4a/ZWlu1L4KGvt2MYcHO3MB4b3KxCc8g/c3Eyc3W74gTVi/P2snhPAk5mEy+NaH1BD2GcS41IbAwYMIDdu3ezY8cO26tz586MHTvW9u8uLi4sW7bMds6BAwc4fvw4UVFRAERFRbF7924SExNtxyxZsgRfX19atmx5zrnd3Nzw9fUt9RIRERERERGp6QzD4Knvd5GVX0SnhrW5tUejixrH1dnMQwOKV21MWXmErPM0x5WqVVIC54rmQQT72reBdAkns4k7ehWv2pi+5uhF3bSUc1t7KJmU7AICvFzp3aTm3RR/5qoWOJtNLN+fyKqDSfYOp0odTsxi49FUzCYY0+XcVV183V0Y260hP9/fi4UP9eaJK5vz47960q5BreoLtoZ5dFAzfNyd2ROXwXdbL+zB8fwiC8//UtwjeUJUIyJDK+f+bNdwf2bc2gUPFydWH0xi4qzfKbQYDGsTyovXtrb7irjLwYgzydqjydkA3NErvNI+3xqR2PDx8aF169alXl5eXgQEBNC6dWv8/PyYOHEijz76KCtWrGDr1q3cdtttREVF0b17dwAGDx5My5YtueWWW9i5cyeLFy/m2Wef5b777sPNzc3OVygiIiIiIiJSub79/QRrDiXj5lxcgupC65D/1YgO9Wgc6MXpnEI+O9NMVapfocXK92dWRNi7afjfje5cHz8PF2JScliyN+H8J0i5/XimDNXV7eqWWiFQU0QEeTPhTOL0xfl7KbRY7RtQFfpqc3HT8P6RwdSt5VGuc1rU8eVf/Zpc1Iq5S0mAt5stSf764gNk5pW/4fyna6I5mpxNkI8bDw9qWqlxdWscwIzbipMbAL2aBPL2je0r9N9MKb+29f2ICCr+3ajr586DAyrv8615f1qew9tvv83w4cMZOXIkffr0ITQ0lB9++MG238nJifnz5+Pk5ERUVBTjxo1j/Pjx/Pe//7Vj1CIiIiIiIiKV71R6Li/NLy4L89jgZkQEeVdoPGcnMw8PKi7JMW31UdJyCioco1y4FfsTSc4qINDbjSsig+0dTimers7c0r0hANPWHLVzNJeOrPwiftsbD9S8MlR/9eCApvh7uXI4MYsvNx23dzhVIq/QYkss3twtzM7ROKbxUY1oHOhFclYBH6w4XK5zYtNyeX95cXP6/xvWAl93l0qPq3vjAObeE8XTQyP55JZOuDk7VfoccnYmk4kHBzQlxNeNV0e1rdQeQjU2sbFy5Ureeecd28/u7u58+OGHpKamkp2dzQ8//FCmd0bDhg1ZsGABOTk5JCUl8cYbb+DsXDMaLomIiIiIiIhUBsMweOaH3WTmF9G+QS0mnikRVFHD29SheYgPmflFunFtJ9+eaRo+smM9XGrgk/vjezTE1cnM1mOn2Xos1d7hXBIW/xFPXqGV8EAv2tX3s3c45+Tn4cKjZ5Kfby89eEkmPxf+cYq0nELq1fKgb7OalVh0FK7OZv7vqhYAzFgbw7GU7POe89L8veQVWivcMPx8Wtfz4+6+EZV6Y13K59r29dj0zEB6Nw2q1HFr3n8lRUREREREROScvt8Wy4oDSbg6m3ljdMVKUP2V2Wzi0TONVGesiyE5K79SxpXySczIY8WB4v4Fo2tYGaoSwT7uXNeh+MbjtNUqWVYZftpRXIbquvb1any9/zFdGhAZ6kNaTiHvLD1k73AqXclKlBu7NFCZogroHxlM76aBFFisvLzgnxvOr6rkhuFyeVFiQ0RERERERMRBJGTk8d95xQ1WHx7YlCbBPpU6/uCWIbSt70dOgYWPVx6p1LHln32/LRaL1aBTw9o0Ca5YabGqdEfv4hVCi/fGE5N8/qex5dwSM/JYdzgZoEqfVK8szk5m/jO8JQCzNx7jUEKmnSOqPAcTMtkScxons4kb/6FpuJyfyWTiueEtcTKbWLwngfVHks96XFU1DJfLhxIbIiIiIiIiIg7AMAz+78fdZOQV0ba+H3f1rpwSVH9lMpl4bHBzoPjGZXx6XqXPIWUZhsHcM2WobqyhqzVKNAvx4YrmQRgGTFej+Qr5ZWccVgM6hNVymMbTPZsEMqhlCBarwYu/7sMwDHuHVClKVmsMbBFMiK+7naNxfE1DfBh3pk/Jf+ftxWIt+z35dE000VXUMFwuD0psiIiIiIiIiDiAn3fEsXRfIi5OJl4f1Q7nKurB0KdpIF0a1Sa/yMqH5Wz+KhXz+7HTHE3OxtPViWFt69g7nPO6s09xUm3u1hOkZl96vRaqS0kZqhE1uGn42fzfsBa4OJlYfTCJFQcS7R1OheUW/LVpeEM7R3PpeHhgM/w8XNgfn8nXW0o3nK+OhuFy6VNiQ0RERERERKSGKyiy8srC/QA82L8pzUMrtwTVX/111cbXW45zIjWnyuaSYt9sKV6tMbxtHbwdoLFtVOMAWtfzJa/Qyhcbj9k7HId0ODGTP2IzcDabuKpNzU9m/VWjQC9u7xkOwEvz91FQZLVzRBUzf1ccmXlFNPD3oHeTQHuHc8mo7eXKIwOLV2K8+dtB0nMLbftenFc9DcPl0qbEhoiIiIiIiEgN9/OOWOIz8gjxdeOuvpVfgurvujcOoFeTQAotBu8tu/SaBNckmXmF/LrrFIDD1PY3mUzceaYU2qz1MeQVWuwckeP5aXscAH2bBRHg7WbnaC7c/f2bEOjtytHkbGY7eHJrzpkyVDd1DcOspuGVamz3hjQJ9iY1u4APzqzQWHUwiUV71DBcKk6JDREREREREZEazGo1mLr6KAC39wzHzdmpWuZ9dHAzAL7fdpKjSVnVMufl6Nddp8gttNA4yIuOYbXtHU65DWtTh3q1PEjJLuDH7bH2DsehWK2GrQzVtQ5WhqqEj7uLbWXXu0sPOmxJsr1xGew4kYaz2cToTo6RWHQkLk5mnr2qBQAz18dwID5TDcOl0iixISIiIiIiconaeiyVLTGp9g5DKmjFgUQOJWbh4+bMTWeasVaHjmG1GRAZjNWAd5Zq1UZV+eYvTcMd6cllFyczt/VsBMCna45iPUtzYDm7rcdPc/J0Ll6uTgxqEWLvcC7aDZ0b0KKOLxl5Rby15IC9w7koX24uXm0ypFUoQT6Ot3LGEfRrHswVzYMotBjcOHWDGoZLpVFiQ0RERERE5BJ0ID6T0R9vYPTHG3jo6+0O+zStwCerildr3Nw9rNobrD4yqHjVxrxdceyPz6jWuS8HhxIy2X48DSezies71rd3OBdsTNcwfNydOZKUfUk0kT6bNYeSeOu3A5xKz62U8Q4nZvLygn0AXNm6Dh6u1bMCqyo4mU1MurolAF9uOu5wf0Zk5xfZSoLdXI1J48vRs8Nb4mw2kZZT3GdDDcOlMiixISIiIiIicgl6ffEBSh6g/nlHHIPfXsWC3afsG5RcsK3HTrM5JhUXJ5OtWW91al3Pj2FtQjEMeHvJwWqf/1L37ZnVGv0jgx3yaXFvN2fbDeGScmmXklPpudz1+VbeW36Yfq+v5NVF+0s1QL4Q6bmF/HfeXq58Zw3bj6fh6mzm1h6NKjdgO+jeOIChrUOxGvDi/L0YRvWv3EnNLuCBr7bz/C97yMwr/+fzy844svKLaBTgSVTjgCqMUCKCvJlw5vuuhuFSWZTYEBERERERucRsO36apfsSMJvgnRvb0zTYm+SsAv41Zxv/mrOVpMx8e4co5TR19REARnSoR4ivu11ieHRQM8wmWLwngd0n0+0Sg71ZrAaJGXmVetO2oMjKD9uK+yzc2Nlxa/vf1iMcZ7OJTdGp7DyRZu9wKtXkBfvJLbTg4eJEfpGVKSuP0Pf1FXy65ij5ReVrmG6xGny1+Tj931jJZ+uiKbIaDGwRwm8P96FNfb8qvoLq8fTQFrg6mVl3OIWVB5Kqde4TqTmMnLKeeTvjmLk+hqHvril3CcYvzzQNv7mbmoZXhyevjOSN0e34ZFwnhyq7JzWXEhsiIiIiIiKXEMMweH1Rca3zUZ3qc12Hesx/sBcP9G+Ck9nEgt3xDH57FT/viLXLk7VSfkeSsvhtbwIAd/VpbLc4mgT7cF374gbHb/zmmHX0K+rF+Xvp+vIyer+2ghfm7WHDkRSKLNaLGiu/yML6w8lM+uUPUrILCPJxo1/zoEqOuPqE+rlzzZmnr6etuXRWbWyOTuWXnXGYTDD3niimje9Mk2Bv0nIKeenXfQx4cxU/bY/9x94iW2JSueaDtTz9w25SsguICPJi1u1d+XRCZxoFelXj1VStsABPbj3Tb+W1xQeqrd/KH7HpjPhoPdHJ2dT1c6d+bQ9Ons7lhk828Oqi/RQUnft3dPfJdHbHpuPqZGaUmoZXC1dnM6M61ae2l6u9Q5FLhLO9AxAREREREZHKs/ZwMhuOpuDqZOahgcX9EdycnXhscHOGtArl39/tYt+pDB76egfzdp7ifyNa220lgPyzT9ccxTBgYIsQmgT72DWWhwY25Zedcaw6mMTm6FS6hvvbNZ7qFJeWyxcbixsMnzydy4x1McxYF0MtTxf6RwYzuGUIfZoF4el69lsshmFwICGTtYeSWX0omc3RKeQV/nnD9cbODXB2cuznTu/s3ZgftsWyYPcpTqTm0MDf094hVYjFajDplz0A3NQ1jNb1/Ghdz48rmgfx3daTvL30ICdP5/LwNzuYtuYoTw2NpHfTP5NTcWm5TF64n3k7i/s3+Lg78/DAZoyPaoiLg3/W53Jv3wi+2nScfacymL/7FNe0q9pSQ6sPJnHvF1vJLrAQGerDzNu64uXmxAvz9vLd1pNMWXmEVQeSeGdMe5qFlP3zs6Rp+JWtQ/HXjXYRh2Qy9IhOKRkZGfj5+ZGeno6vr6+9wxERERERESk3wzC49sN17DqZzu09w3nuTFPXvyq0FJdTeX/5IQotBr7uzvxneEtGdaqv0hA1SGJmHr1eWUGBxcp390TRuZH9EwnP/LibLzcdp3PD2sy9J+qy+b78d95ePlsXTddwfyb2CmfJ3gSW7UvgdM6ftfxdnc30bhLIoJYhDGgRgoHBusPJrDmYzNrDyST+rfxbkI8bvZsG0rdZEENb18HV2fFvdt8yfRNrDiVzW89GTLq6lb3DqZAvNh7j2Z/+wM/DhRWP9ytz4zu3wMJn66L5eOURMvOLAOjdNJCHBzZj3eFkpqw8Qm6hBZMJxnRpwOODmxPg7Xg9VC7Ue8sO8daSgzQK8GTJo32rLInzw7aTPPHdLoqsBlGNA/hkfKdSjagX/XGKp3/YzemcQlydzTx1ZSS39mhkKzeVmVdIt5eXkVNg4eu7utNd/TVEaowLuTevxMbfKLEhIiIiIiKOauHuU9w7Zxterk6sfuKKf7yRdiA+k39/t5NdZ3om9G0WxBuj2zlkA+NL0WuL9vPRyiN0alib7+/tYe9wAIhPz6Pv6yvIL7Iy49YuXBEZbO+Qqtzp7AJ6vLKc3EILn9/elT7Nip/KL7JY2XrsNEv2JvDb3gSOp+bYzjGZ4O93WtxdzHQLD6B300B6Nw2iWYj3JZcYWn0wifGfbcbT1YkNTw3Az9Pl/CfVQKezC7jizZWk5RTy32tbMT6q0TmPTc0u4IPlh5m9MYZCS+kPvUuj2ky6uhWt610afTTKIyu/iL6vrSAlu4CXR7SxNZavLIZhMGXVEV47U27xmnZ1eX10W9ycncocm5iRxxPf77L1/OjVJJDXR7eljp8Hszce4z8//UFEkBdLH+17yf0uijiyC7k37/iPBIiIiIiIiAhFFqut/8HE3o3P+3Rw81Affri3B09eGYmrs5lVB5O4e/bvFF5k3wCpPFn5Rcw+U/robjv21vi7UD93JvRoBFRvHX17mrUhhtxCC63q+tK7aaBtu7OTmW6NA3h2eEtW/bsfix/uw+ODm9G2vh+GUZzcaF3Pl3v7RfDlHd3YOWkws27vyh29G9M81OeSvJHau2kgkaE+5BRY+GjlYXuHc9HeWnKQtJxCIkN9uLnrP9+Y9/dy5bmrW7L8sX5ce6bPSB0/d967qQPf3h11WSU1ALzdnLnviiYAvLvsIHmF5WuwXh4l5cFKkhp39WnMOze2P2tSAyDY150Zt3bhxeta4+5iZu3hZIa8vZpfdsb9pWl4w0vyd1HkcqEVG3+jFRsiIiIiIuKIvv39BE98t4tani6seeIKfNzL/7T0wYRMRk5ZT2ZeEff2i+DJKyOrMFI5n0/XHOWlX/fROMiLpY/0tZVPqQlOZxfQ57UVZOYX8d5NHaq8jr495RQU0eOV5aTlFPLBzR0Y3rZ815qclY+TyXRZNshdti+BibN+x9lsYv6DvYgMdaz7KnvjMhj+/hqsBnx1Z3eiIi6sRFFCRh61PF3OebP9cpBXaKH/GyuJS8/j/4a14M5KSM7mFVp4+OsdLNoTj8kEz17Vkom9wst9/pGkLB79Zgc7z6xQhOLycZufGUAtz8vv91SkJtOKDRERERERkctIfpGFd5ceAuBf/SIuKKkB0CzEh1dHtgVgysojrD6YVOkxSvkUFFmZvjYaKF6tUZOSGgC1vVxtNyrf+u3AJb3C5+vNJ0jLKaRhgCdDW9cp93mB3m6XZVIDYECLEAa3DKHIavDsj39U+aoewzBIycpnc3QqX28+zssL9vH5hhiKLuJ7aRgGz8/bg9WAq9rWueCkBkCIr/tlndQAcHdx4uGBzQD4aOVhMvMKz3PGP0vLKeCW6ZtYtCceVycz79/U4YKSGgARQd58d28PHhrQFKczf6YOb1NHSQ0RB+ds7wBERERERESkYr7cdJzYtFxCfd3/sR78PxnWpg5ju4UxZ9NxHv12Bwse6k2wj3vlBirnNW9nHKfS8wjyceO6DvXsHc5Z3d4rnFnrY4hJyWHu7ycrvY5+TVBQZGXamqMA3N0nwnYzVM5v0jWtWHs4md+Pnea7rSe5oUuDCo9ZaLFyPDWHI4lZHEnK5mhSFkeSsjianE1aTtkb5yv2J/L+zR3xdiv/ba/5u06xOToVdxczzwxrUeGYL2fXd6zHJ6uPcCQpm2lronl0ULOLGic2LZcJn23mcGIWPu7OTL2l80UlnABcnMw8MqgZV0QGs+iPeO7sfWHJERGpebRiQ0RERERExIFl5xfxwfLievYPDmiKu8vFPy38n+EtiQz1ITmrgEe+2XFZ9FCoSQzD4JPVRwC4vWd4jX3yuyrr6NcUP++ItSWYru9YMxNMNVW9Wh48PLApAJMX7iM1u6BC43239SRtn/+NAW+u4q7ZW3l10X7mbj3JtuNppOUUYjJB/doe9G0WxM3dwnB3MbPiQBKjpqwnNi23XHPkFBTx8oJ9ANzXrwn1anlUKObLnbOTmccGNwdg+pqjpGTlX/AYp9JzGTN1A4cTswj1dee7e3pcdFLjr9o3qMVTQyPP24dKRGo+JTZEREREREQc2Gdro0nJLqBRgCejO9ev0FjuLk58cHMHPFycWHc4hSmrjlRSlFIeKw8kcTAhC2835xq/CuLmbmHU9XMnISOfzzfE2DucSmW1Gnx85rs/sVd4hZKFl6vbeobTPMSH0zmFvLJw30WPs/5wMk9+v4vcQgseLk60rufLNe3q8sjAZnxwcwcWPtSbff+9krVP9mfW7V15eUQbvrkriiAfN/bHZ3Ldh+vYdTLtvPN8tOIIp9LzaODvUSk9IQSGtg6lTT0/sgssfLjiwv5bkpSZz9hPN3EiNZeGAZ788K8eNA/1qaJIRcRRKbEhIiIiIiLioE5nFzB1dXG5nEcHN8fFqeL/i9ck2IcXrm0FwFtLDvJ7TGqFx5TyKbmZfnO3MPw8LqxPSnUrXUf/SIXr6NckS/YlcCQpGx93Z8bW8ARTTeXiZOZ/I1oD8O3vJy/qz5Ho5GzunbMNi9Xg2vZ12fPCEOY/0Jv3burAQwObMrxtXVrU8S2TeGrXoBY/3deTyFAfkjLzueGTDSz6I/6c8xxLybb9OfrsVS2VyKokJpOJfw8pXrXxxcZj5V49U9JT42hSNnX93JlzRzfqagWNiJyFEhsiIiIiIiIO6uNVR8jML6JlHV+Gtyl/c+PzGd2pPte1r4vFavDgV9tJy6lYKRk5v+3HT7MpOhUXJxO39Wxk73DK5fqO9YgI8iItp5Bpa6LtHU6lMAyDj1YWJ5jGRzXEx71mJ5hqss6N/Lmxc3F/jf/78Y8LajSfnlvIxFlbSM8tpF2DWrw6si3mC+hzUq+WB3PviaJf8yDyCq3cO2crn6w6gmGULa/30q/7KLBY6d00kMEtQ8o9h5xf76aBdG/sT4HFyntLD533+My8QiZ8tpn98ZkE+bjx5Z3dqV/bsxoiFRFHpMSGiIiIiIiIA4pPz2Pm+hgA/j2k+QXd9Dsfk8nESyPaEB7oRVx6Ho/P3XXWG4JSeUqeGL+2fT3q+DnG08mVUUe/ptl4NJWdJ9JwczZzaw81F66op4ZGUtvThQMJmcxYV77kV5HFyv1fbuNoUjZ1/NyZdkuni1pF4ePuwqfjOzM+qiGGAZMX7ufpH3aXSrCsOpjEkr0JOJtNTLq6JSaTmsRXpuJVG5EAzN16giNJWec8NqegiIkzf2fnyXRqe7ow545uNAr0qq5QRcQBKbEhIiIiIiLigN5bfoj8IitdGtWmX/OgSh/f282Z92/qgKuTmaX7Eph1JokilS86OZtFe4pL5dzlYPX9K1JHvyYq6StzQ+cGBPmouXBF1fZy5elhLQB4e8mhcpUjeunXfaw5lIyHixPTxncm2Nf9oud3djLz32tb8/zVLTGb4OstJ7h1xmbScwopKLLywrw9AEzo0YgmwerhUBU6NazNwBbBWA1467eDZz0mr9DC3bO3sjkmFR93Z2ZP7EazEH0eIvLPlNgQERERERFxMDHJ2Xy75QQA/x4SWWVPGbeu58czw4qftn15wX7+iE2vknkud9PWHMUwYEBksMPdzLvYOvo10R+x6aw+mIST2eRwCaaabFTH+nRpVJvcQgsv/LLnH4+ds+mYbSXa2ze2o3U9v0qJ4dae4Xw6oTNerk6sO5zC9VPW8crC/RxNyibQ25WHBjatlHnk7B4f0hyTCX7dfarMf0cKz6zQWXMoGU9XJ2be1rXSPncRubQpsSEiIiIiIuJg3lpykCKrQb/mQXQN96/SuSb0aMSgliEUnLn5lJVfVKXz1WSGYXA0KYv0nIo3ys4rtLDjRBqzNx7ju60nAbi7b0SFx7WH3k0D6RZeXEf/3aVnfyLbEZSs1hjetg4N/FXXv7KYzSZeuq4NzmYTv+1NYOnehLMet/5IMpN+Lk58PDaoGVe2rry+QQD9I0OYe08P6vi5cyQpm8/OlMZ64spIfNVLpUpFhvpybbu6ALy2+IBtu8Vq8Mg3O1i6LxE3ZzOfTuhMp4a17RWmiDgYZ3sHICIiIiIiIuW3Ny6DX3bGAfD4mf4GVclkMvH6qLYMe3cNMSk5PPvjbt6+sf1lWYv+o5VHeP3MTblAb1caB3kTEeRNRJDXmX96U6+2B05/63eSkVfI3rgM9sRlsCc2nT1xGRxOysJi/bNvSYewWnRp5Jg39EwmE09cGcnIKev5butJ7uoTQZNgb7vEYhgGe+IyyMgtpFvjgDKfxblEJ2ezcPcpAO5x0ARTTdY81IeJvcP5ZNVRJv2yhx5NAvB0/fOWVExyNvd+sY0iq8E17epyf/8mVRJHy7q+/HRfT+6Y9Tu7Y9NpV9+PUR3rV8lcUtojg5oxf9cpVh9MYuPRFLo28uep73cxf9cpXJxMfHxLJ3pEBNo7TBFxIEpsiIiIiIiIOJBXF+0Hip8qr65yHbU8XXnvpg7cOHUjP+2Io0eTQG7o3KBa5q4pDiZk8s5fViMkZxWQnJXK5ujUUse5OpsJD/AiItgLEyb2xKUTk5Jz1jEDvV1pVdePVnV9ualrmEMni0rq6C/dl8jbSw7y4diO1Tr/0aQsftkZxy874jianA1AZKgPT14ZSb/mQed9b6euPorVgCuaB9Gijm91hHzZeWhAU+bvPEVsWi7vLz/Mk1cWl7lLzy1k4qwtpOcW0q5BLV4b1bZKfxdCfN355u7u/LYngb7NgjCXM/klFdMwwIsxXRvwxcbjvLZoP23q+TF360mczCbev6kDVzQPtneIIuJgTIZhGOc/7PKRkZGBn58f6enp+PrqLzMiIiIiIlJzrNifyG0zt+DiZGLJI31pFOhVrfN/uOIwry8+gIeLEwse6k14Nc9vLxarwcgp69lxIo2BLYJ5Z0wHopOyOZKUxdGkLI6U/HtyNgVF1rOOUa+WB63q+tKqrh+t6/nSup4fwT5uDp3M+Lv98RkMfXcNhgHzH+hV5Ym3U+m5zNsZxy874/gjNsO23c3ZjKuTmcwzZdO6hfvz9LAWtG9Q66zjJGbk0evVFRRYrHx7d1SVl3e7nP22J567Zm/F2WxiwUO9aRzoxe2zfmf1wSTq+Lnz8309K9QsXGq2xIw8+ry+grzC4j8nTSZ464Z2jOigVTMiUuxC7s3XmBUbU6ZMYcqUKcTExADQqlUrnnvuOYYOHQpAXl4ejz32GF9//TX5+fkMGTKEjz76iJCQENsYx48f595772XFihV4e3szYcIEJk+ejLNzjblMERERERGRi1JosfLir3sBuL1neLUnNQDu7RvBusPJrD+SwmPf7mDuPT3KXerHkc1aH8OOE2l4uznz4nWt8XZzpk19P9rUL33j3mI1iEvL5XBSFkcSs7AaBi3rFK/IqO3laqfoq09JHf2fdsTx2uIDfH5710qfIzW7gAW7T/HLzji2xKRS8qimk9lE76aBXNOuLoNahmCxGkxZeYQZ62PYFJ3KdR+u46o2dfj3kOZlfnemr4umwGKlc8PaSmpUscGtQhnYIoSl+xJ49qc/aFnHl9UHk3B3MTNtfGclNS5xwb7u3NojnI/P9LP533VtlNQQkYtWY1ZszJs3DycnJ5o2bYphGMyaNYvXX3+d7du306pVK+69915+/fVXZs6ciZ+fH/fffz9ms5l169YBYLFYaN++PaGhobz++uucOnWK8ePHc+edd/Lyyy+XOw6t2BARERERkZpo+tpoXpy/l0BvV1Y83g8fOzW7jU3LZcjbq8nKL+KpoZGXfD+CE6k5DH57NbmFFv43ojVjuzW0d0g12rGUbAa8uYoiq8GXd3artJr5iZl5/N+Pf7BifyJFf+lN0rWRP1e3r8uw1qEEeLuVOS82LZe3fjvID9tPYhjgbDZxc7cwHujflCAfN9JzC+n5ynKy8ouYPqEzA1qElBlDKtfJ0zkMeqv4d6rElLEdGdqmcpuFS82UmVfIcz/voUdEAKMvs5KGInJ+F3JvvsYkNs7G39+f119/nVGjRhEUFMSXX37JqFGjANi/fz8tWrRgw4YNdO/enYULFzJ8+HDi4uJsqzg+/vhjnnzySZKSknB1Ld/TMUpsiIiIiMilpuSv/JdSyZvLTWp2Af1eX0FGXhGTr2/DTV3D7BrPt7+f4InvduHqZGbeA71oHupj13iqimEYjP9sM2sOJdM13J+v7+yuevzl8OxPu/li43Ea+Hvw64O98a1gEs5iNbh52kY2neln0qquL9e2r8vwtnWpW8ujXGPsO5XBq4v2s/JAEgBerk7c2acxRRaDD1YcpnmIDwsf6q3Pt5pMWXnE1i/osUHNeGBAUztHJCIiNcGF3Js3V1NMF8RisfD111+TnZ1NVFQUW7dupbCwkIEDB9qOiYyMJCwsjA0bNgCwYcMG2rRpU6o01ZAhQ8jIyGDPnj3Vfg0iIiIiIjVBkcXK2E830WrSYh76ejvL9iWcsweA1FxvLzlIRl4RLer41oim3aM71WdAZDAFFiuPzd1BoeXS/E59vy2WNYeScXU288r1bXTTu5z+PSSS+rU9OJGay9M/7Kaiz1O+t+wQm6JT8XR1Yt79vfj1wd7c1Sei3EkNgBZ1fJl5W1e+vLMb7er7kV1g4Z2lh/hgxWEA7unXWJ9vNbqjdzg3dm7Avf0iuL9/E3uHIyIiDqhGJTZ2796Nt7c3bm5u3HPPPfz444+0bNmS+Ph4XF1dqVWrVqnjQ0JCiI+PByA+Pr5UUqNkf8m+c8nPzycjI6PUS0RERETkUvHxqiOsP5JCToGFn3fEMXHW73R9eSlP/7CbDUdSsFpr7AJuOeNAfCZzNh0DYNLVLWtETwuTycTk69vg5+HCH7EZfHjm5vClJCkznxfnF/c0eXhgUxoHeds5Isfh5+HC+zd1wNls4tddp/hq84mLHmvDkRTeX34IgP+NaF2mr8mF6hERyE/39eSDmzvQMMATgAb+HgxvW7dC48qFcXEy8+qotjx5ZaRWE4qIyEWpUYmN5s2bs2PHDjZt2sS9997LhAkT2Lt3b5XOOXnyZPz8/GyvBg3s//STiIiIiEhl2BOXzrvLim8IPjaoGbf1bESgtxtpOYV8tfk4N03bSI9XlvPS/L3sOplW4aeqpfIZhsGL8/diNWBo61C6Nw6wd0g2wb7uvHhdawA+WH6Y3SfT7RxR5Xp+3h7ScwtpWceXO3s3tnc4DqdDWG3+PaQ5AC/M28OB+MwLHiMlK5+Hv9mO1YBRnepXWpNhk8nE8LZ1WfJIX6be0okv7+iOi1ONuj0iIiIi51Gj/svt6upKkyZN6NSpE5MnT6Zdu3a8++67hIaGUlBQQFpaWqnjExISCA0NBSA0NJSEhIQy+0v2ncvTTz9Nenq67XXixMU/SSIiIiIiUlPkF1l47NudFFoMhrQK4f7+TZh0dSs2PTOAOXd048bODfBxdyY+I49P10ZzzQfr6P/mKt5acpCUrHx7hy9nLN2XyNrDybg6mXlmWAt7h1PG1W3rcFWbOhRZDR6bu4O8vzQDdmS/7Ynn112ncDKbeG1UW930vkh39m5M32ZB5BdZuf/LbeQWlP/7YbUaPD53JwkZ+UQEefHfa1tVenyuzmYGtwqlgb9npY8tIiIiVatG/+3MarWSn59Pp06dcHFxYdmyZbZ9Bw4c4Pjx40RFRQEQFRXF7t27SUxMtB2zZMkSfH19admy5TnncHNzw9fXt9RLRERERMTRvbfsEPvjM/H3cuV/I9rYSn04mU30bBLIq6Pa8vuzA5l6SyeGt62Du4uZ6ORs3lt2iBs+2UB2fpGdr0Dyiyz879fiFewTe4fXyJuvJpOJF69rTaC3KwcTsnh76UF7h1RhGXmF/OfnP4DiG/Ot61Ws9NHlzGw28eYN7Qj2ceNQYhbP/1L+/pfT10az4kASrs5mPri5I56uzlUYqYiIiDiaGpPYePrpp1m9ejUxMTHs3r2bp59+mpUrVzJ27Fj8/PyYOHEij/4/e/cdHlWdtnH8OzPJpPdKSEILLfROREFFQQUrrl3QtbLo2pflfXV3dVUsu6+66yqWtayKurq6KiqIDem995YC6W0mdep5/wiMRkGBlEnC/bmuc2XmnDPnPBOZmJz7/H7P3XfzzTffsHbtWq6//nqysrIYPXo0ABMmTCAzM5Nrr72WjRs3smDBAu6//35mzJhBUFCQn9+diIiIiEjrWZ9bwfPf7gXg0Yv7Ex9+5N+HgwIsTOiXzLNXDWXN/Wfz9OWDSYwIYm9JDf/7YdMb/krT/GtZDtlltSREBDHjjLbbXDc2zMrsSwYC8OJ3+1ibU+7nippm9mc7KLI76BYfxp1n9fR3Oe1efHgQT18+GJMJ3l2Tx0cbDv7iazbkVfL4/B0APDA5k76ddAOiiIiINNZmgo3i4mKmTp1K7969GT9+PKtXr2bBggWcffbZADz11FNMnjyZKVOmMHbsWJKTk/nggw98r7dYLMybNw+LxUJWVhbXXHMNU6dO5aGHHvLXWxIRERERaXV1zoYpqLwGXDQ4hXP6dzqm14UHBXDRkM78/cohWMwm/rshv0kNf6VpSqsd/O1Qf5T7JvYmPKht361+dmYSU4amYhhwz783UutsnyN+Vuwr4+1VuQDMvmQAwYEWP1fUMZySEc/th8K5//1wC9mlNUfd117v4va31+H2GpzbP5lrRqW3VpkiIiLSjpgM3YbViN1uJyoqCpvNpmmpRERERKTdeeiTbbyydD9JkUF8cec4okIDj/sYz3+7l8fn78AaYOaD6adoKh4/mPXBZt5elcuAzlF8NGMMZrPJ3yX9Iludi4lPfUehvZ5pWV148ML+fqnDXu9iXU4Fa3MqOFBRx+C0aE7tGU/3+DDflGxHUu/ycM7T35FdVsuVI9OZfcmAVqy643N7vFz10kpWZZczoHMU70/PIiigcXBkGAa3vb2eTzcVkBoTwqe/PY2okOP/GSYiIiLt0/Fcm2/bt/2IiIiIiMgxW763jFeW7gfgsSkDTyjUALhlbHfWZJfz1Y5iZsxdxye3n0pksC4utpZt+XbeXd0wauAP52e2i1ADICokkCcuHcjUV1bx+vIcJvZL5pSM+BY/78HKOtZkl7Mmu4LV2eXsLKrih7fvfbi+YeqjlKhgTuuZwKk94xmTEU9smLXRcZ76chfZZbUkRQYx67w+LV73ySbAYuaZKwdz7jOL2XzQxuOf7+QP5zfuh/n2qjw+3VRAgNnE368colBDREREjkojNn5EIzZEREREpD2qdrg55+nvOFBRx5Uj03w9D05UZa2TSX9bwsHKOs7pl8zz1wz92bvdpXkYhsGVL61gxb5yJg/sxLNXDfV3Scftfz/czFsrc+kcHcL8O08joplDsZyyGr7dWcKanArWZJdTYKv/yT7psaEM7xpDWkwoa3LKWb2/AqfH69tuMkH/lChO7RnPaRnxBAVauOyF5Xi8Bi9NHc7ZmUnNWrN878ttRdz4rzUAvDx1OGcd+l7vKLRz4bNLcbi9zDq3D7eM6+HPMkVERMQPjufavIKNH1GwISIiIiLt0eGpi1JjQph/59hm6cmwIa+SX81Zhstj8IfJmfz61G7NUKn8nPlbCrj1zXUEBZj56p5xpMaE+ruk41bjcHPOM9+RV17H5cPTePzSpoVsP7Q2p5wrX1zZKKSwmE30S4lkeJdYhneNYXiXGBIjgxu9rs7pYVV2OYt3lbBkTyk7CquOePxJAzvxj3YYJrU3h6fMiw4N5PM7GqabuuDZpewprmZsrwReu25EuxmpJCIiIs1HU1GJiIiIiJxEFu0q8TU8fvLSQc3WaHpwWjT/e15f/vTJNh79bDuD06MZmh7TLMeWn3K4PTzy2XYAbh7bvV2GGgBhQQH85dJBXPHSCt5dk8fE/kmc2afpIyCcbi+//89mnB4v/TtHMiEzmeFdYhicHk2o9ef/zYdYLYzrlcC4XgkAFNvrWbKnlCW7S1m8p5SSKgdxYVb+dH6/Jtcpv2zmub1ZnV3O5oM27nh7A13iQtlTXE1iRBD/d9kghRoiIiLyizRi40c0YkNEREROZsX2ehZuL+K7XSUMTY/RVCDtgK3WxcSnGxo2X3dKV/50QfNemDUMg9vmrufTzQWkRAXz6W9PI+ZHvQmkeRxu2p4UGcTX95xOWDMFVP7y8LxtvLxkP/HhQXxx19if9LQ4Xs9+vZu/fLGLuDArX90zjujQ5vl3aBgGe0uqiQwJJDEi+JdfIM0ip6yGSX9bQrXDDTRMD/bWDaNapS+LiIiItE0asSEiIiIix8QwDPYUV/PFtiIWbitiQ16lb9uCrUWkxoQyaWAn/xUov+jBT7ZSaK+nW3wYM89p/obHJpOJx6YMYGu+jeyyWu7+9wb+OU3TxPyYw+2hosZFRa2Tihon5Ye/1riw17uoc3mod3qocx1anB7qXT987qWi1gnAzHP6tPtQA+Deib1ZtKuE3cXVzPpgE3OuGXbCfVr2l9bwt6/3APDA5MxmCzWg4d94RmJEsx1Pjk2XuDAevWQAv317PQC3n5GhUENERESOWfv/bVlEREREjovHa7Aut4KFh8KM/aU1jbYPTosmPtzKl9uL+f1/NjEwNYq02PY5JU5HN39LIR+sP4jZBH/51SBCrJYWOU9EcCDPXT2Mi59byjc7S3h+0V5mnJHRIudqD0qqHMz+fDt7iqspr2kIMGqcnmY59shusVw0uHOzHMvfggMtPHX5YC5+bikLthbx/toD/Gp42nEfxzAM/vfDzTjdXk7rGc+Fg1NaoFrxhwsGpVBsr6e4ysFvx/f0dzkiIiLSjmgqqh/RVFQiIiLSUe0otPPKkv18tb2Yshqnb73VYuaUjDjOzkzirL5JJEUG4/J4ufyF5azLrWRQWjTv3ZKFNcDsx+rlx8qqHUx46jvKapxMP71Hi4zW+LF3V+cy8z+bMZtg7k2jGd09rsXP2da4PV6ufnklK/eX/2SbxWwiJjSQmFArMWFWYkIDiQ2zEhkSSGhgACFWMyGBFoIDLYRYLYQENizBP3icGhNCgKVjfdae+3YPT8zfSXhQAJ/fcdpxB6Xvrz3Ave9tJDjQzBd3jiM9TkGriIiISEekqahEREREpJGPN+bzu/c3Uu/yAhAZHMCZfRI5OzOZcb0TftJsOtBi5m9XDuG8ZxazMa+Sv36xk1nn9fVH6XIEDreH6W+to6zGSe+kCO48q3XudL5seBor95fzwbqD3P72ej777WkkRAS1yrnbiqe+3MXK/eWEWS08fulAUqJDiAm1EhtqJSI4QFN0HcEtY3vwzY5iVmdXcNe7G3j3liwsx/h9Kq9x8sin2wC4Y3wvhRoiIiIiAkDHuhVIRERERBrxeA0en7+D3769nnpXwzQub904irUPnM3TVwxh0sBOPwk1DkuNCeWJSwcC8MJ3+/hmZ3Frli5HYRgGv//PZlbtLyciKIC/XzWEoICWmYLqx0wmEw9f1J9eSeGUVDm44531eLwnzwDwb3YW849v9gLw2JSBTB6YwtD0GLrFhxEVGqhQ4ygsZhP/d9lgwqwW1uRU8MJ3e4/5tQ9/uo2KWhd9kiO48bRuLViliIiIiLQnCjZEREREOih7vYsbX1/N8982XES8dVwPXrt+JGMy4gk8xqluzunfialZXQC4598bKbLXt1i9cmye/nI3H64/iMVs4rlrhtIrqXWbHodaA3ju6qGEWi0s21vGw4fupu/oDlbWcde7GwCYmtWF8wepz8PxSIsN5Y8X9APgqYW72HLQ9ouvWbqnlA/WHcRkgtmXDDjmn1siIiIi0vHpN0MRERGRDmhvSTUX/aOh0XNQgJlnrhjM78/tc8zTv/zQ/5zXl8xOkZTXOLnznQ0n1R36bc1/1h7gma92A/DwRf05rWeCX+rISIzgL78aBMCrS7N5bel+v9TRWpxuL7fNXUdlrYsBnaP430malu1E/GpYKhP7JeHyGNz17gbqXUdvuF7v8vC/H24G4NrRXRiSHtNaZYqIiIhIO6BgQ0RERKSD+WZnMRf9Yyn7SmroFBXM+7eewoWDO5/w8YIDLTx71RBCrRaW7yvjH9/sacZq5Vit2FfG7z/YBDSMvrlyZLpf6zlvQCdfw/KH5m3jy21Ffq2nJT0+fwfrcyuJDG4YrdJaU391NCaTiUcvHkB8eBC7i6t5fP6Oo+777Nd7yC6rJSkyiPsm9m7FKkVERESkPVCwISIiItJBGIbBnEV7+fVrq6mqdzO8Swwf33YqA1Kjmnzs7gnhPHxRfwCe/nIXK/eVNfmYcuz2llRzyxtrcXkMzhuQzO/ayIXeW8d158qRaXgNuP3t9cc0vVB7M39LAf9c0jAi5a+XDSYtVs2rmyIuPIgnD/XueXVpNkt2l/5kn11FVcxZ1DCF3oMX9CMiOLBVaxQRERGRtk/BhoiIiEgHUO/ycOe7G3js8x0YBlw5Mo25N40mISKo2c5xydBULhnaGa8Bd7yzgfIaZ7MdW46urNrB9a+uxlbnYkh6NP932eA206TaZDLx0IX9Oa1nPHUuD79+bTX5lXX+LqvZ5JTVcN97DaNkbh7bnbMzk/xcUcdwRp9ErhndMOLo3vc2Uln7/c8Sr9dg1gebcXsNzs5MYmK/ZH+VKSIiIiJtmIINERERkXYuv7KOS+cs46MN+QSYTfz5wn48evEArAHN/6veny/sT/eEMArt9dz33kYMQ/02WlK9y8PNb6wlt7yWtNgQXpo6nODAtjUNUqDFzD+uHkqvpHCKqxz8+rXVVDvc/i6ryepdHn7z1jqqHA2jnzQdUvP6n/P60i2+4WfJ/f/d4vtZ8vbqXNbmVBBmtfDgBf0wmdpGiCciIiIibYuCDRERETkit8fL55sL+HZnsb9LkaPweA3+vSaPC55dwpaDdmLDrLxxwyiuzeraYhcDw4ICePbKoVgDzHy1o9g3RY80P6/X4N73NrI2p4LI4ABevW4E8eHNNwKnOUUGB/LKofp2FFYx4611uD1ef5cFNAR/b63MYV1uxXEFcQ/N28bW/IbP1d+vGkKgRX86NadQawBPXT4Yi9nEvE0FfLwxn2J7PY993tB3496JvUmJDvFzlSIiIiLSVpkM3WbXiN1uJyoqCpvNRmRkpL/LERERaXV1Tg/vrs7lpcX7OXhoSpkXrh2m6UDaEMMw+HpHMY/P38GuomoA+iRH8NLU4a02//8by7N54KOtBFpM/Gf6KQxMjW6V855Mnlywg398s5cAs4l/3TCSU3rE+7ukX7Qxr5LLX1xOvcvL1aPSefii/n67477AVsdz3+zl3dV5OA+FLGmxIZw/MIULB3emd3LEUV/73/UHufPdDZhM8Pr1IxnbK6G1yj7pPP3lLp7+cjcRwQEMTotm8e5SBqVG8cFvxmBpI1OuiYiIiEjrOJ5r8wo2fkTBhoiInKwqapy8vjyb15dlU1HrAsAaYMbp9hIRFMBHt42he0K4n6tsmwzDwOM1CGiFO7rX51Yw+/MdrNpfDkBUSCAzzujB1KyurTpFkWEYTH9zHfO3FpIeG8q8355KpBr8Npt/r87jd/9p6O3w5KUD+dXwND9XdOwWbC3k1jfXYhhw/6S+3Hha91Y9f6Gtnue/3cPbq74PNPqlRLK/tIZap8e3X++kCC4YnMIFg1IaBYJ7iqu44Nml1Do9/PbMDO6eoCmoWpLb42XKnOVszKsEwGI28fFtY+iXEuXfwkRERESk1SnYaAIFGyIicrI5UFHLy4v38+7qPOpcDRf90mNDuWlsdy4anMKvX1vN6uwKeiWF898ZYwi1Bvi54ralzunh0jnL2JpvJ9BiIjjQQkighRBrw9egQAshgWbfulBrAH07RTK8SwyZKZHHPL3N/tIanlywg882FwINodP1Y7rym3EZRIX6J1Cw1bo472+LOVhZx8iusbx6/QjCgk6efx8er8FLi/fhNQwmZCbRIyG8WUYnfLerhF+/thq31+D2MzO4px1eWH958T4e/nQ7JhM8f/Uwzunf8iO+iuz1PP/tXuauysXpbgg0RnWL5c6zepHVI446p4cvtxfx8cZ8Fu0s8YUeAEPSo7lgUApn9knkxtfXsLu4mlN6xPHGDaM0aqAV7C+t4bxnFlPn8nDz2O78z3l9/V2SiIiIiPiBgo0mULAhIiInix2Fdl5YtI+PN+bj8Tb8OtAvJZJbx/Xg3P7JvtEHxfZ6Jv19CSVVDs4flMLfrhisZq4/8Ohn23nxu30n9NqQQAuD06IZ0TWGYV1jGZoeTcSPRj2UVDl45qtdvLMqD7fXwGSCKUNTufvsXm1i/vktB21c+dIKqurdjOwWy2vXjzhpwq93V+cy8z+bfc+7xYdxdmYSEzKTGJIec8wXxKvqXazYV86S3SUs3l3KvtIaAC4cnMLTl7fPz5thGPzho628sSKH4EAz796cxaC06BY5V3HVoUBjZS6OQ4HGyK6x3Hl2z6NO32Wrc7FgSyEfb8xn2d5SvD/6iyghIojPfnsaCRFts6dJR/TdrhJW7Cvjt+N7turoMxERERFpOxRsNIGCDRER6cgMw2B1dgXPf7uHb3aW+NaPyYjj1nE9ODUj/ogXUVdnl3Pliytwew3+MDmTX5/arTXLbrM2Hajkon8sxWvAc1cPZUh6NHVOD3UuD/UuD/Uur+/54XUVNS42HqhkbU4FtjpXo+OZTdA7ObIh6OgSw76SGl5avM83fc4ZvROYeW4f+iS3rd9R1udWMPWfq6hyuBndPZZXruv44Uat080Zf/mWIruDPskR7CupaTQCIC7Myll9kzg7M4lTe8Y3ulDr9njZdNDG4l2lLNlTwvrcStw/uLJuMZs4p18yf71sULu+wOv2eLnxX2v4dmcJ8eFBfPibU5q1B0xJlYM5i/by5oocX6AxvEsMd53di1N6xB1zIFRcVc+nh5pXr8+tJMBs4s0bRzG6e1yz1SoiIiIiIr9MwUYTKNgQEZGOyOs1+HJ7EXMW7WVdbiUAJhOc178Tt4zrfkyNn19dup8HP9lGgNnE3JtGM7JbbMsW3ca5PF7O//sSdhRWceHgFJ65Yshxvd7rNdhTUs2a7ArWZJezOqecvPK6I+47KDWK35/bl6webfdC67pD4Ua1w01W9zheuW4EIdb2e1H+l/z9q938deEuUmNC+Oqecbg8Bot2lrBwWyFf7Simqt7t2zck0MLYXvEMSY9hQ24lS/eWNtoO0DUulNN6JnBqz3iyesR1mH4l1Q43lz6/jB2FVXRPCOOdm0eTGBHc5ONuOWjjmn+upPJQP6Ch6dHcdXavo4azxyqvvBaP16BrfFiTaxQRERERkeOjYKMJFGyIiEhH4nR7+e/6g7zw3V72ljRMb2MNMHPpsFRuPq37cV28MwyDO97ZwMcb80mICOLT208lMbLpFyjbq2e/3s1fvthFTGggX949jrjwpk9ZU2Svbwg6cspZk12B2QQ3j+3BeQOS28V0RGtzKpj2SkO4cUqPOP45rWOGG6XVDsY98Q01Tg/PXDGYCwd3brTd5fGyan85C7cV8cXWQvJt9T85RmRwAKf2jOfUjARO6xnfrCMZ2poCWx1TnltGvq2ejMRw3r5pdJOmeNqab+PqlxtCjT7JEcw6ry9jezYt0BAREREREf9TsNEECjZERKQjqHa4eXtlLi8v2UeR3QFARHAA147uwnVjup7wHdO1TjcX/2MZO4uqGNE1hrk3jT7m5tcdyZ7ias57ZjFOj5enLx/MRUM6//KLThJrc8qZ+s9V1Dg9nJoRz8vThrfr6ZSO5IH/buGNFTkM6BzFRzPGYP6ZXhqGYbA1387CbUVsL7AzoHMUp/VKYEDnqJOqKXVOWQ1XvLiCAls9PRPDefvm0cSfQBi4Ld/OVS+voLLWxZD0aP7165E/6UsjIiIiIiLtk4KNJlCwISIi7VlJlYPXlu3njeU52A9NdZMYEcQNp3bjqlHpzXIBcH9pDRf8fQlVDjfXj+nKH8/v1+Rjtider8HlLy5ndXYFp/dO4NXrRuhO8R9Zk13O1FdWUev0cFrPeF6a2nHCjb0l1Ux46js8XoO3bxrdpqcHa2uySxvCjUJ7Pb2Swpl70/GFG9sL7Fz10goqal0MSovmjRtGdpgpu0RERERE5PiuzZ98t1iKiIh0QB6vwcPztjHm8a/5xzd7sde76Z4QxhNTBrJ45hncMq5Hs93V3C0+jL9eNgiAV5dm89GGg81y3PbirZU5rM6uIMxq4ZGLByjUOILhXWN57fqRhFotLN5dys1vrKXe5fF3Wc3iifk78HgNxvdJVKhxnLrGh/H2zaNJigxiV1E1V7+0krJqxzG9dmdhFVe/vLIh1EiN4l+/VqghIiIiInIyU7AhIiLSAfz96928vGQ/TreXwWnRvHDtML68axyXjUgjKKD575Sf0C+ZGWf0AOD3/9nMzsKqZj9HW5RfWcdjn+8A4Hfn9KFzdIifK2q7RnaL5dXrRhASaOG7XSXc0gHCjTXZ5SzYWoTZBL8/t4+/y2mXusWH8fZNo0mMCGJnUUNYUV7j/NnX7Cqq4qqXVlBe42RgahT/umEUUSEKNURERERETmYKNkRERNq5FfvK+NtXuwF4fMoAPvzNKUzsl/yz8/43h7vP7s1pPeOpc3m49c212OtdLXo+fzMMg/v/u4Uap4dhXWK4dnQXf5fU5o3qHser1zeEG4t2lTD9zbU43O0z3DAMg0c/2w7A5SPS6JkU4eeK2q/uCQ09NhIjgthR2BBaVBwl3Nh9KNQoq3HSv3Mkb/xaoYaIiIiIiCjYEBERadfKqh3c8c56vAb8algql49Ib7WpkSxmE89cMYTO0SHsL63hnn9vxOvtuK27Pt6Yz9c7irFazDw+ZUCLB0cdxejucfzzuuEEB5r5ZmcJv3lzHW6P199lHbfPtxSyLreSkEALd53Vy9/ltHs9Ehp6bCQcCjeufnnlT8KNPcVVXPnSSkqrnfRLieTNG0YRFapQQ0RERERE2lCwMXv2bEaMGEFERASJiYlcdNFF7Ny5s9E+9fX1zJgxg7i4OMLDw5kyZQpFRUWN9snNzWXSpEmEhoaSmJjIfffdh9vtbs23IiIi0iq8XoN739tIkd1Bj4QwHryw9Zt4x4ZZef6aoVgtZhZuK+J//7uFOmf7vCP/55TXOHnwk20A3H5mBhmJulv/eJzSI55Xpo0gONDMVzuKefjT7f4u6bg43V6emN8wBdlNY7uTGBns54o6hozEcN6+aRTx4UFsK7BzzT9XUlnbEG7sKa7mihdXUlrtILNTJG/dOIroUKufKxYRERERkbaizQQbixYtYsaMGaxYsYKFCxficrmYMGECNTU1vn3uuusuPvnkE9577z0WLVpEfn4+l1xyiW+7x+Nh0qRJOJ1Oli1bxuuvv85rr73GH/7wB3+8JRERkRb1zyX7+WZnCdYAM89eNZRQa4Bf6hiYGs3DF/UH4O1VuUz++2I2H7D5pZaW8tAnWymvcdInOYJbxvXwdznt0ikZ8Tx9+WAAXluWzdyVuf4t6DjMXZlDdlkt8eFB3Dy2u7/L6VAyEiMOhRtWtuY3hBvrciu48qUVlFY76KtQQ0REREREjsBkGEabnDOipKSExMREFi1axNixY7HZbCQkJDB37lwuvfRSAHbs2EHfvn1Zvnw5o0eP5vPPP2fy5Mnk5+eTlJQEwJw5c5g5cyYlJSVYrb/8B5HdbicqKgqbzUZkZGSLvkcREZETtTGvkinPL8PtNXj4ov5c0wb6PSzaVcJ9722kuMpBgNnEnWf15NZxPQiwtJn7KE7INzuLuf7V1ZhN8OFvxjAoLdrfJbVrz369m798sYsAs4l/3TCSU3rEN9uxvV6D0moHByvrKLDVk19Zx8HKOvIPPS+tcnBazwRmntuH2LBju1Bur3dx+pPfUl7jbDOftY5od1EVV7zY0EvjsD7JEcy9afQx/7cSEREREZH27Xiuzfvn1s5jYLM13OkZGxsLwNq1a3G5XJx11lm+ffr06UN6erov2Fi+fDkDBgzwhRoAEydOZPr06WzdupUhQ4a07psQERFpAfZ6F7e9vQ631+C8AclcPSrd3yUBMK5XAgvuHMv//nczn20u5C9f7OKbnSX832WD6BIX5u/yTki1w83/frAZgF+P6aZQoxnMOCOD3cXVfLQhn+lvruOjGWPoGn9i/z4Mw+C1ZdnM31JIvq2OQls9Ls/P37Pz7po8Fmwr5H/O7culw1J/sVfKnG/3Ul7jpHtCGFeMSDuhOuWX9UxqCDEONwpXqCEiIiIiIj+nTQYbXq+XO++8kzFjxtC/f8PUFoWFhVitVqKjoxvtm5SURGFhoW+fH4Yah7cf3nYkDocDh8Phe26325vrbYiItGnf7izm8fk7sde5jmn/hIgg7jirJ2f0TmzhyuTnGIbBrA82k1deR2pMCLMvGdhqzcKPRUyYlX9cNZQP1x/kjx9tZW1OBec9s5g/nJ/JZcPT2lStx+LJ+TvIt9WTFhvC3RPUMLo5mEwmHp8ykOyyWjbmVXLD66v54DdjiAo5vqbQtU439723iU83FzRabzZBcmQwnaJDSIkOISU6mM7RIXSKCsFihifm72RHYRW/+88m3l97gIcv7k+vpCP3TCmw1fHPJfsB+P05fdr96KO2rndyBP+ZfgrztxZy2fA0hRoiIiIiInJUbTLYmDFjBlu2bGHJkiUtfq7Zs2fz4IMPtvh5RETaCsMweOG7fTw+fwfHMxnhwco6rn91NWf2SeSByZl0O8E7rKVp3l6Vx6ebCggwm/j7lUOO+2JwazCZTFwyNJWR3WK5+98bWbW/nJn/2cyX24uZfckA4sODWvT8bo8XW52Lilon5TUuymucVNQeWmoa1rk83l88jscw+OzQRfPZFw/0Ww+Tjig40MJL1w7jwn8sZW9JDbe/vZ5Xpg0/5uAgr7yWm99Yy/YCO4EWE/dM6M2wLjGkRIeQFBH0s8cZ2zOBV5dm838Ld7Equ5zznlnMzWO7c/uZPQmxWhrt+9cvduFwexnZNZazM5OOckRpTl3jw7hVfWxEREREROQXtLm/0G+77TbmzZvHd999R2pqqm99cnIyTqeTysrKRqM2ioqKSE5O9u2zatWqRscrKirybTuSWbNmcffdd/ue2+120tI0zYCIdEz1Lg8z/7OJjzbkA3DFiDSuHPnL0xgZwLyN+by2LJuvdxSzeHcJvx7TjdvOzCAiuO1dWPeXrfk2iu0OxmTEYw1o/ju7dxZW8eAnWwG4b2JvhqTHNPs5mlNqTChv3zSafy7Zx18W7GLhtiLW51bw2CUDOasZLxIbhsE/l+xn7qpcyqqd2I5xFNKx+tWwVE7t2Xx9IKRBYmQwL00dzqVzlvHdrhIe+Ww7fzy/3y++bsW+Mn7z1jrKa5zEh1t5/pphjOgae8znDbCYuWlsd84b2Ik/frSVL7cX8dy3e/lkUz4PXdjfNypte4Gd/6w7AMD/TOrb7kYbiYiIiIiIdGRtpnm4YRjcfvvtfPjhh3z77bf07Nmz0fbDzcPffvttpkyZAsDOnTvp06fPT5qHFxQUkJjY8Efpiy++yH333UdxcTFBQb98h6iah4tIR5VfWcctb6xl80EbAWYTfzw/k2tGdzmui3V7iqv587xtLNpVAkB8eBAzz+nNlKG/PE99R7bpQCVPf7mbr3cUA5AUGcSNp3bnylHphAc1zz0EdU4PFzy7hN3F1ZzeO4FXpo1oV9/z7QV27nxnAzuLqgC4cmQ6D0zu2+RREE63l1kfbPZdgP6hqJBAYsOsxIQe/molJsxKdGggwQGWIxztp0KsFi4e0pngwGPbX47f55sLmP7WOgAevXgAVx2lZ4xhGLy5IocHP9mG22vQv3MkL147nJTokCad/4uthfzp463k2+oBOG9AMn88vx/3vb+J73aVMGlgJ/5x1dAmnUNERERERER+2fFcm28zwcZvfvMb5s6dy0cffUTv3r1966OioggJafiDdfr06Xz22We89tprREZGcvvttwOwbNkyADweD4MHDyYlJYUnnniCwsJCrr32Wm688UYeffTRY6pDwYaIdERrssu59c21lFY7iQkN5Lmrh5HVI+6EjmUYBt/sLObP87azv7QGgEFp0fzp/Mw2P4KguW05aOPpL3fx5faGQMNiNhEdEkhZjROAyOAArs3qwnWndCMhomnTL818fxPvrskjMSKIz+44rcWnc2oJ9S4Pf/1iJy8v2Y9hQEZiOM9eNYQ+ySf2/9vKWie3vLGWlfvLsZhNzDq3D6f3TiAm1EpUSKD6IbQjf/9qN39duIsAs4l/3TCSU3o0HiHjdHv548dbeXtVLgAXDErh8SkDfzJ11Imqcbh5auEuXl2WjcdrEBJooc7lIdBi4su7x9ElTlPviYiIiIiItLR2GWwc7Y7hV199leuuuw6A+vp67rnnHt5++20cDgcTJ07kueeeazTNVE5ODtOnT+fbb78lLCyMadOm8dhjjxEQcGx3hCrYEJGO5p1VuTzw0RZcHoM+yRG8NHU4abGhTT6u0+3ltWX7+dtXe6h2uAG4ZEhnZp7bh6TI4CYfvy3bctDGM1/tZuG2hukOzSa4aEhnfntmTzpFB/PR+nzmfLeXfSUNwY81wMyvhqVy89juJ3SB9KMNB7njnQ2YTPDWDaM4JaN9T4u0bE8pd767geIqB0EBZv5wfiZXjUw/rtFD+0tr+PVrq9lfWkN4UADPXjWE09XYvt0yDIM73tnAxxvziQ4N5L+/GUPXQ318SqocTH9zLWtyKjCZYOY5fbhlbPcWmRpqa76N//1wCxvyKgG4fkzXY5oeS0RERERERJquXQYbbYWCDRHpKFweL3+et41/Lc8BGqZX+cuvBjV7A+TiqnqenL+T99Y2TAUUarUw69w+xz3NVXuwLd/O01/u4osfBBoXDu7M7Wdm0D0hvNG+Xq/Bwu1FPP/tXt9FUrMJzh3QienjetC/c9RRz1Pv8lBe46S8xkmBrZ673t1AtcPNb8/M4O4JvY/6uvaktNrBve9t5NudDdOaTRrQiUcvGXBMzdBX7ivjljfXUlnronN0CK9cN4LeyREtXbK0sHqXh8tfWM7GAzYyEsP54DenkFtWy83/WkO+rZ6IoAD+duUQzujTsgGWx2vw7zV57Ciwc8/E3kSqj5CIiIiIiEirULDRBAo2RKQjKK9x8pu31rJiXzkA907oxYwzMlo0aNiYV8mfPtnK+txKAKZmdeEPkzM7xHRAOwrtPL1wN/O3FgJgMjVMhXP7mT3JSAz/2dcahsHK/eXMWbTXdxEf4NSMeDISwymvcVJRe2ipcVFe46TO5fnJcUZ2jWXuTaM6xPfzMK+3oen34/N34PYapMaE8Pcrh/zslGb/WXuA33+wCZfHYFBaNC9NHUZiRMceIXQyKbbXc8GzSym019O/cyS7i6pxuL10jw/jpWnD6ZHw8583ERERERERab8UbDSBgg0RaSqH28Puomr6pUS2+ogFj9dgfW4Fd767gQMVdYRZLTx9xRDOzkxqlfN7vQYvLd7HY/N3YBgwrlcCz141hIh2eMdzSZWDTzfl89HGfF9YYzLB5IEp3DE+g4zE4x8hsL3AzguL9vLJpgI83p//32+gxURMqJXYMCs9EsP5w+TMDjvF14a8Sm5/ex155XUEmE3cN7E3N53WvVFzdK/X4Kkvd/H3r/cADSOQ/vqrwc3WY0Haji0HbVw6Zxn1Li8Ap/dO4JkrhhzTaB4RERERERFpvxRsNIGCDRFpimqHm6tfWsHGAzaGd4nhTxf0+9kph5qqzulhQ14la7LLWZNTwbqcCqoO9bvoEhfKS1OH0yup9afomb+lkDvfXU+9y0vvpAj+ed1wUmOa3tejpdnqXCzYUsjHG/NZtreUw9mDyQTnDejEHeN7Nsv3M6+8lvfXHsDt9frCi5hQKzFhVmJDrcSEBRIeFNDhpvL6OfZ6F7P+s5lPNxcADaHYXy8bRHx4EPUuD/e+t5F5mxq2/eb0Htw7oXej4EM6li+2FvLgJ9u4cHAK90zojUX/rUVERERERDo8BRtNoGBDRE6Uw+3h16+tZumeMt86kwkuH57GvRN7Ex8e1ORzlFQ5WJtTzursCtbkVLD1oA33j+78D7NaGN83iYcu7Ed0qLXJ5zxRmw5UcsPrayipchAfHsQ/pw1nUFq03+o5mjqnh692FPHxhny+3VmC0+P1bRucFs0Fg1KYPLATiR10tERbYhgG76zO408fb8Xh9pIQEcSDF/Tj5cX7WJdbSYDZxKOXDOCy4Wn+LlVEREREREREmpmCjSZQsCEno+P9MXAy3UV+rDxeg9vmruPzLYWEWS08c8UQ5m3K578b8gGICArgjrN6MjWrK9aAY++RYBgGW/PtfLwxn4XbithfWvOTfZIjgxneNYYRXWMZ1iWGPskRbaYPQ35lHb9+bTU7CqsIDjTz1GWDOXdAJ7/WZBgGueW1bD5o46vtxXyxtZAa5/c9LXolhXPBoBTOH5RCl7gwP1Z68tpZWMVtc9exu7jaty4qJJDnrxnKKT3i/ViZiIiIiIiIiLQUBRtNoGBDTjaLdpXwu/c3UmR3HNP+oVYLU4amctNp3UmPa/tTC7UGwzD4nw+38PaqXKwWM69eP4IxGQ0XX9fmlPOnj7ex+aANgO4JYfxhcian90782WPuK6nm4435fLwhn30/CDNMJuidFMHwrjEM7xLL8K4xdI4OadNhU7XDze1z1/HNocbZM8/pw63jurdKzW6Pl32lNWzNt7HloJ0tB21sK7BTVe9utF9qTAgXDErhgsEp9EnWz/62oM7p4cFPtvLO6jy6xIXyynUj1DhaREREREREpANTsNEECjbkZGEYh5o8f76DX+hhfERmE0wamMKt47rTL6Xleki0B08u2ME/vtmL2QTPXT2Uc/o3HpHg9Rq8v/YATyzYQWm1E4Az+yTywORMusV/PyKgwFbHJxvz+XhjPlsO2n3rgwLMnNU3ifMHdSKrR3y7bKDr9nh5+NPtvLYsG2iYnuvPF/U/rtErx6LG4ebTTQVsOljJ1nw72wvsvgbEP2S1mOnTKYKh6TGcPyiFoenRbTocOpntKa6ic3SomoSLiIiIiIiIdHAKNppAwYacDOpdHmZ9sJkP1x8E4LLhqdw7sTeWY7iwu6OwijmL9rJ4d6lv3dheCdw6tjtZPeJOuovDLy/ex8OfbgfgsUsGcMXI9KPua6938fevdvPq0mzcXoNAi4lfj+lGWmwoH2/MZ3V2OYd/IlvMJk7rGc8Fg1KY0C+Z8KCA1ng7Le61pft5aN42vAZkdY9jzjXDiAptnqBmd1EVt7y5ln0ljafrCrNayEyJpF9KFP1SIunfOYqMxHAC28h0XSIiIiIiIiIiomCjSRRsSEdXYKvjljfWsumADYvZxB8mZzI1q8txBxJbDtp44bt9fLop3zfiY1BqFLeM68HEfslYzB0/4Hh/7QHufW8j0DC90vTTexzT6/aWVPPnedv49tDUTD80smssFwxO4bwBnYgN81/j75b09Y4ibp+7nhqnh+4JYTxz+RAGpDZt1M/HG/P5/X82Uev0kBwZzIVDUuiXEkX/lEi6xoVhPgn+PYqIiIiIiIiItGcKNppAwYZ0ZGtzyrnljXWUVjuICQ3kH1cN5ZSMpjXizS2r5aXF+/j3mjwc7oYpf7rFh3Hz2O5cPKQzwYEdc/qYhduKuPXNtXi8BjeP7c6sc/scdzj0zY5invlqN17DYPLATkwemEJKdEgLVdy2bMu3c8Prqymw1WM2wdSsrtwzoRcRwcc3esPp9vLoZ99PcTUmI46/XTGEuPCgFqhaRERERERERERaioKNJlCwIR3Vv1fncf9/t+D0eOmTHMFLU4eTFtt8zb9Lqx28viybfy3PwVbnAqBHQhhv3Tia5KjgZjtPW7BiXxlTX1mF0+3lV8NSeeLSgSfdFFzNobTawZ/nbeOjDfkAJEUG8afz+3FO/+Rj+n4W2OqY8dY61uVWAnDbGRncdXavk2K0kIiIiIiIiIhIR6NgowkUbEhH4/J4eeQHTZvP6ZfMXy8bRFgL9Wyocbh5e1Uucxbto7TaQXpsKHNvGkVqTPOFKP605aCNK19cQZXDzdmZSTx/9VAC1KuhSRbvLuGB/24hu6wWgDN6J/DQhf1/NnhbtqeU299eT1mNk4jgAJ66bDBnZSa1VskiIiIiIiIiItLMFGw0gYIN6UjKa5zMeGsdy/eVAXDXWb24/cyMVuk3cKCilqteWklueS2do0N468ZRdI0Pa/HztqT9pTX8as4ySqudjOoWy+u/Htlhp9pqbfUuD899u5c53+7F6fESHGjmjvG9uPG0bo2afHu9BnO+28tfFuzEa0Bmp0jmXDOM9LiOEZyJiIiIiIiIiJysFGw0gYIN6Sh2FNq58fU1HKioI8xq4f8uH8zEfsmtWkOhrZ6rXlrBvtIakiKDeOvG0WQkhrdqDU3hcHvYVVjNlnwbW/NtfLG1iOIqB/1SInn75tFEHmc/CPlle4qruf+/m1mxrxyAXknhPHrxAIZ3jcVW5+Kef2/ky+1FAPxqWCp/vqi/wiURERERERERkQ5AwUYTnIzBhsvj5f21B5izaC+55bXH9JpAi5lz+ydzy9geZKacHN+n9mTV/nJueG01VQ436bGhvDR1OL2TI/xSS3FVPde8vJJdRdXEh1t568bRfqvl59Q43GwvsLPloI2t+Xa25NvZXVSF29v4R2S3+DDeuzWLeDWnbjGGYfDBuoM88tl2ymucAEwZmsqanHJyymqxBph56IJ+XD4iTb1NREREREREREQ6CAUbTXAyBRsuj5cP1h3g71/v4UBF3QkfZ1yvBG4d14PR3WN1kbEN+GZHMbe+uRaH28uIrjG8eO1wYsKsfq2pvMbJNS+vZFuBnZjQQN64YRT9O0f5rZ6KGidb8+1szbex5dDX/aU1HOmnYXRoIP1ToujXOZJ+KVGc2SeR8BbqTyKNVdQ4eXz+Dt5ZnedblxoTwvNXD2NAqv/+/YiIiIiIiIiISPNTsNEEJ0Ow4fZ4+WD9QZ79eo9vhEZ8eBDTT+/B5IGdMB9DOHGwso6XF+/js80FHL6hfVBaNNPHdWdCZnKr9HBoD6odbsKsllYLfD7acJB7/r0Rt9fgjN4JPHf1MEKsbWOaHluti6mvrGTjARuRwQG8/uuRDEmPadFzGoZBcZXj+1EYh74erDxykJccGUy/lEj6dY6iX0ok/TtHkRIVrMDOz1Znl/PY5ztIjgrmkYv6Ex3q36BORERERERERESan4KNJujIwYbb4+W/G/L5+9e7ySk7HGhYuXVcD64e1eWELoDnlNXw0uJ9vLfmAA63F4Du8WHcPLY7Fw/tTFBA27io3tr2llTz8LxtfLOzhNgwK0PTYxjRNYbhXWPo3zmqRb4vb6zI4Q8fbcEw4IJBKfz1skGNmi63BVX1Lq5/dTVrcioIDwrg1etHMKJrbLOfJ7esloc/3ca63ApKq51H3KdLXGhDiJESRf9DQYamlxIREREREREREfEPBRtN0BGDDbfHy8cb8/n713vYX1oDQFyYlVvGdeea0V0ItTZ9Wp3SagevLc3mX8uzsde7AUiMCOLXp3bj6lHpRJwkTZbt9S7+/tVuXl2a/ZPeDIdZA8wMSo1ieNdYhneJYViXmCbdgW4YBs99u5cnF+wE4NrRXXjwgn5tdtRMjcPNDa+vZsW+ckICLfxz2nBOyYhvtuPnlNVwxYsrKLDVA2A2QUZiOP1Tosg8NAojMyVSjb9FRERERERERETaEAUbTdCRgg2v1+DTzQU8tXAX+w4FGjGhgdwyrgfXju5CWAv0Cah2uHlnVS4vL95Pob3hwnJ0aCB/Or8fFw5O6bBT+ni9Bu+tzePJBTt9IwTO7JPI78/tQ43DzZrsCtbklLMmu4Kymp+OIOiZGM6YjHguHZZ6XL0nDMPg0c+289Li/QDcfmYGd5/dq81/n+ucHm5+Yw2Ld5cSFGDmhWuHcXrvxCYfN7esliteXE6+rZ4eCWE8celAMjtFtZnpuEREREREREREROTIFGw0QXMHG9UONwu2FGIxm0iJDiElOpikyOAWnyJo2Z5SHpu/g00HbEBDuHDz2O5My+raIoHGjzndXj7acJA5i/ayt6QhVDmrbyKPXDyApMjgFj9/a1qbU86fPt7G5oMN3+vuCWE8MDmTM45wod4wDLLLalmdXc7a7ApW55Sz79D357B+KZFcPiKNCwd1Jir06KMK3B4v//PhZv695gAA90/qy42ndW/Gd9ay6l0ebpu7ji+3F2O1mJl9yQCmDEs94ePllddyxYsrOFhZR/eEMN65aTSJHezfmoiIiIiIiIiISEelYKMJmivYcHm8vLMql2e+2v2TOf7NJkiMCCYlOpiU6BA6R4fQKarhcfeEMHokhJ/wHffbC+w89vkOFu0qASDMauHmsT244bRuhLdCoPFjLo+XFxbt5ZmvduPyGEQEB/DA5Ex+NSy1zY8q+CUFtjoe+3wHH23IByAiKIA7zurJ1KyuWAOOPbgqq3awOruCeZvy+WJrEU5PQ6+SoAAz5/RP5vLhaYzuHtdoaimH28Mdb29g/tZCzCZ4bMpALhue1rxvsBU43V7ueGc9n28pBGDK0FQeurDfcYdvBypqufyFQ6FGfBjv3KxQQ0REREREREREpD1RsNEETQ02DMPg8y2FPLlgp6+fRXpsKJ2jQ8i31VFQWe+7cH00qTEhnJ2ZxITMZEZ0jSHgGEZ3HKio5f8W7uLD9QcxDAgwm7h6VDq3j+/ZJhoi7yqq4r73NrLx0AiSsb0SmH3JADpHh/i5suNX7/Lw8uJ9/OObvdS5PJhMcPnwNO6Z0JuEiKZ9rytqnPx3w0HeXZ3HjsIq3/r02FAuG57KpcPSiAgO4JY31rJkTylWi5m/XTmEc/onN/Vt+Y3Ha/Ds13t45qtdeI2GES/PXjmUzJRj+/wdrKzj8heWc6Cijm6HQo2ONipIRERERERERESko1Ow0QRNCTZW7Ctj9uc72JhXCTQ06L7jrJ5cOTLdN/WU12tQWuOgoLKe/Mo6DlbWkV9ZT4GtjvzKOnYUVuFwfx98RIcGcmbvRCb0S+K0ngk/uZO9stbJP77Zw+vLc3Aeet2kgZ24b0JvusaHNeE70fzcHi8vL9nP/y3chdPtJTwogFnn9eGqkentYvRGeY2Td1bn8sbyHF9j6uFdYvjj+f0YkHrsfTGOhWEYbDpg4901eXyyIZ8qR0ND9sOjfQrt9YRaLbw0dThjmrHxtj+t2FfGne9soNBejzXAzP2T+nLt6C4/+28jv7KOy19cTl55HV3jQnnn5iySoxRqiIiIiIiIiIiItDcKNprgRIKNnYVVPD5/B1/vKAYg1GrhptO6c9PY7sc9/VOd08Pi3SV8sa2Ir7YXUVHr8m2zBpg5NSOeszOTODUjnk83F/DcN3uw1zdc9M7qHsfvz+3DoLTo4zpna9tbUs3v3t/E2pwKAE7pEcdjlwwkPS7Uz5Ud2ZaDNl5fls3HG/N9oVNyZDCzzuvDBYNaviF6ndPDZ5sLeHdNHqv2lwMNgddr149kcBv/b328ymuc3PfeRr469Fma2C+JJ6YMOmKvkQJbHZe/sILc8lq6xIXyzs2j6RTV/kYAiYiIiIiIiIiIiIKNJjmeb16BrY7/+2IX/1l3AK8BFrOJK0em8dvxPUmMaPpd426Pl7U5FSzcVsQX24rILa894n59kiOYeW4fTu+V0C5GPkDD9EOvL8vmiQU7qHd5CQm0MPOc3kzN6tqol4S/uDxeFmwt5PVl2azOrvCt7985kmlZXTl/UArBgZZWr2t/aQ1fbS9ifN8kurWxETnNxTAMXl2azezPt+PyGHSODuFvVw5mWJdY3z6Ftnouf3E5OWW1pMc2hBop7XBaMxEREREREREREWmgYKMJfu6bZxgGeeV1rM4uZ9X+cv674aDvDv5z+ydz38TedE8Ib5G6DMNgV1E1C7cV8sW2IjYdsJESFczdE3pz8ZDOWNpAGHAicspq+N37m1h5aCTCoLRo/nR+JkPSY5p87N1FVfxzyX6cbi8p0SF0+lGz9ojgn44CKK128PbKXN5amUuhvWG6qQCziXMHdOK6U7owND2m3YRH7d3mAzZue3sdOWW1WMwm7j67F9PH9aC4ysGVL61gf2kNabEhvHNzVrvs1SIiIiIiIiIiIiLfU7DRBD/85oWEhbO9wM7q7ArW5pSzOruCkipHo/1Hdo3l9+f1YWgzXIg/HlX1LkKtAe020Pghr9fgrVW5PP75DqoP9ZK4ZGhnfn9OHxJPoAl0ga2Opxbu4v21DSNpjiYiOIDO0SENoUdUMNUON59vLvQ1d48PD+KqUelcPSpdzaj9pKrexf9+uIWPN+YDcGpGPPmVdewrrSE1JoR3bh5NakzbnMJMREREREREREREjp2CjSY4/M279Jkv2VLios7labQ90GJiQOcoRnSN5bSeCYzJiNMd/M2kuKqeJ+bv5P21BwAIs1qYcWYGN5zajaCAX572yVbnYs6ivbyyZL9vJM05/ZIZlBbta85+8FDTdlud66jHGZwWzXWndOXcAcnHdF5pWYZh8N6aA/zh4y3Uuxr+u3aObgg10mIVaoiIiIiIiIiIiHQECjaa4PA3L+3Of2MOCiUqJJBhXWIY3jWG4V1iGZga5ZfeCieTjXmV/OmTrazPrQSgS1wo90/K5Ky+iUcMkRxuD28sz+HZb/ZQeajZ+oiuMfz+3L4M63LkkTQ1DjcFtu+DjoLKOupcHiYNTOlwDbk7ij3FVdzz3ibqnG7+OW2EQg0REREREREREZEOpF0GG9999x1PPvkka9eupaCggA8//JCLLrrIt90wDP74xz/y0ksvUVlZyZgxY3j++efp2bOnb5/y8nJuv/12PvnkE8xmM1OmTOGZZ54hPPzY+14c/ua99NUWxvZLJyMhvE00sz7ZeL0G/91wkMc+30Hxoem/TusZzx8mZ9IzKcK3z8cb8/nLFzs5UFEHQEZiODPP6XPUEETaP8Mw9N9WRERERERERESkgzmeYMPcSjX9opqaGgYNGsQ//vGPI25/4okn+Nvf/sacOXNYuXIlYWFhTJw4kfr6et8+V199NVu3bmXhwoXMmzeP7777jptvvvmE6rlseBq9kiIUaviJ2WzikqGpfH3v6Uw/vQdWi5nFu0s555nFPPjJVhZuK2Ly35dw57sbOFBRR1JkEI9dMoD5d5zG2ZlJuvDdgem/rYiIiIiIiIiIyMmtzYzY+CGTydRoxIZhGKSkpHDPPfdw7733AmCz2UhKSuK1117jiiuuYPv27WRmZrJ69WqGDx8OwPz58znvvPM4cOAAKSkpx3Tu40mFpPVkl9bwyGfbWbitqNH6iKAAbj29B78e040Qq6YIExEREREREREREWmP2uWIjZ+zf/9+CgsLOeuss3zroqKiGDVqFMuXLwdg+fLlREdH+0INgLPOOguz2czKlStbvWZpXl3jw3hp6nDeuGEkGYnhBFpMXD+mK4t+dwYzzshQqCEiIiIiIiIiIiJykgjwdwHHorCwEICkpKRG65OSknzbCgsLSUxMbLQ9ICCA2NhY3z5H4nA4cDgcvud2u725ypYWcFrPBL64cyz1bg+h1nbxz1dEREREREREREREmlG7GLHRkmbPnk1UVJRvSUtL83dJ8gvMZpNCDREREREREREREZGTVLsINpKTkwEoKmrcX6GoqMi3LTk5meLi4kbb3W435eXlvn2OZNasWdhsNt+Sl5fXzNWLiIiIiIiIiIiIiEhzaRfBRrdu3UhOTuarr77yrbPb7axcuZKsrCwAsrKyqKysZO3atb59vv76a7xeL6NGjTrqsYOCgoiMjGy0iIiIiIiIiIiIiIhI29Rm5vOprq5mz549vuf79+9nw4YNxMbGkp6ezp133snDDz9Mz5496datGw888AApKSlcdNFFAPTt25dzzjmHm266iTlz5uByubjtttu44oorSElJ8dO7EhERERERERERERGR5tRmgo01a9Zwxhln+J7ffffdAEybNo3XXnuN3/3ud9TU1HDzzTdTWVnJqaeeyvz58wkODva95q233uK2225j/PjxmM1mpkyZwt/+9rdWfy8iIiIiIiIiIiIiItIyTIZhGP4uoi2x2+1ERUVhs9k0LZWIiIiIiIiIiIiISCs4nmvz7aLHhoiIiIiIiIiIiIiICCjYEBERERERERERERGRdqTN9NhoKw7PzGW32/1ciYiIiIiIiIiIiIjIyeHwNflj6Z6hYONHysrKAEhLS/NzJSIiIiIiIiIiIiIiJ5eqqiqioqJ+dh8FGz8SGxsLQG5u7i9+80Tk6Ox2O2lpaeTl5f1isx8ROTp9lkSajz5PIs1DnyWR5qPPk0jz0GdJpHn4+7NkGAZVVVWkpKT84r4KNn7EbG5oOxIVFaUfhCLNIDIyUp8lkWagz5JI89HnSaR56LMk0nz0eRJpHvosiTQPf36WjnWwgZqHi4iIiIiIiIiIiIhIu6FgQ0RERERERERERERE2g0FGz8SFBTEH//4R4KCgvxdiki7ps+SSPPQZ0mk+ejzJNI89FkSaT76PIk0D32WRJpHe/osmQzDMPxdhIiIiIiIiIiIiIiIyLHQiA0REREREREREREREWk3FGyIiIiIiIiIiIiIiEi7oWBDRERERERERERERETaDQUbIiIiIiIiIiIiIiLSbijYEBERERERERERERGRdkPBhoiIiIiIiIiIiIiItBsKNkREREREREREREREpN1QsCEiIiIiIiIiIiIiIu2Ggg0REREREREREREREWk3FGyIiIiIiIiIiIiIiEi7oWBDRERERERERERERETaDQUbIiIiIiIiIiIiIiLSbgT4u4C2xuv1kp+fT0REBCaTyd/liIiIiIiIiIiIiIh0eIZhUFVVRUpKCmbzL4zJMNqg2bNnG4Bxxx13+NaNGzfOABott9xyS6PX5eTkGOedd54REhJiJCQkGPfee6/hcrmO69x5eXk/OY8WLVq0aNGiRYsWLVq0aNGiRYsWLVq0aNGipeWXvLy8X7yO3+ZGbKxevZoXXniBgQMH/mTbTTfdxEMPPeR7Hhoa6nvs8XiYNGkSycnJLFu2jIKCAqZOnUpgYCCPPvroMZ8/IiICgLy8PCIjI5vwTkRERERERERERERE5FjY7XbS0tJ81+h/TpsKNqqrq7n66qt56aWXePjhh3+yPTQ0lOTk5CO+9osvvmDbtm18+eWXJCUlMXjwYP785z8zc+ZM/vSnP2G1Wo+phsPTT0VGRirYEBERERERERERERFpRcfSIqJNNQ+fMWMGkyZN4qyzzjri9rfeeov4+Hj69+/PrFmzqK2t9W1bvnw5AwYMICkpybdu4sSJ2O12tm7d2uK1i4iIiIiIiIiIiIhIy2szIzbeeecd1q1bx+rVq4+4/aqrrqJLly6kpKSwadMmZs6cyc6dO/nggw8AKCwsbBRqAL7nhYWFRz2vw+HA4XD4ntvt9qa+FRERERERERERERERaSFtItjIy8vjjjvuYOHChQQHBx9xn5tvvtn3eMCAAXTq1Inx48ezd+9eevToccLnnj17Ng8++OAJv15ERERERERERERERFpPm5iKau3atRQXFzN06FACAgIICAhg0aJF/O1vfyMgIACPx/OT14waNQqAPXv2AJCcnExRUVGjfQ4/P1pfDoBZs2Zhs9l8S15eXnO9LRERERERERERERERaWZtYsTG+PHj2bx5c6N1119/PX369GHmzJlYLJafvGbDhg0AdOrUCYCsrCweeeQRiouLSUxMBGDhwoVERkaSmZl51HMHBQURFBTUTO9ERERERERERERERERaUpsINiIiIujfv3+jdWFhYcTFxdG/f3/27t3L3LlzOe+884iLi2PTpk3cddddjB07loEDBwIwYcIEMjMzufbaa3niiScoLCzk/vvvZ8aMGQouREREREREREREREQ6iDYRbPwSq9XKl19+ydNPP01NTQ1paWlMmTKF+++/37ePxWJh3rx5TJ8+naysLMLCwpg2bRoPPfSQHysXERERERERERERaX2GYVBor2dfSQ17S6rZV1KDrc6F0+3F4fbi9Hhxuj04fY+9uDyGb/ut47pz42nd/f02RI7IZBiG4e8i2hK73U5UVBQ2m43IyEh/lyMiIiIiIiIiIiJyVPUuD9llNewtPhxgVLO3pIZ9JdXUOH/au/hY/XZ8T+4+u1czViry847n2ny7GLEhIiIiIiIiIiIiIuD1Gmw6aOPbncV8s7OEzQcq8R7l1nWL2USXuFB6JITTPSGMhPAgAi1mrAFmrIe+BlrMBAUcWnfoudViJjFS0/tL26VgQ0RERERERERERKQNq6hx8t3uEr7dWcJ3u0ooq3E22h4RHEBGYrgvwOiR0PA4PTYUa4DZT1WLtBwFGyIiIiIiIiIiIiJtiNdrsDXffmhURjEb8hqPyogICuDUnvGc0TuRU3vG0ykqGJPJ5L+CRVqZgg0RERERERERERGRNqDG4eatlTm8siSbQnt9o219kiM4vXcip/dOYFiXGAItGokhJy8FGyIiIiIiIiIiIiJ+ZKt18dqybF5dtp/KWhcAYVYLYzLiOaNPIuN6JZASHeLnKkXaDgUbIiIiIiIiIiIiIn5QWu3gn0v288byHKodbgC6xYcx/fQeXDg4haAAi58rFGmbFGyIiIiIiIiIiIiItKICWx0vfrePt1flUu/yAg1TTf3mjAwmDeiExax+GSI/R8GGiIiIiIiIiIiISCvILavl+UV7eX9tHi5PQzfwQalR3HZmT8b3ScSsQEPkmCjYEBEREREREREREWlBlbVOHp+/g3+vOYDH2xBojOwWy+1nZnBqRjwmkwINkeOhYENERERERERERESkBRiGwbxNBTz4yVZKq50AjOuVwG1nZjCia6yfqxNpvxRsiIiIiIiIiIiIiDSzAxW1PPDfLXyzswSAjMRwHr14ACO7KdAQaSoFGyIiIiIiIiIiIiLNxO3x8tqybP76xS7qXB6sFjMzzsjg1tO7ExRg8Xd5Ih2Cgg0RERERERERERGRZrDloI1ZH2xm80Eb0NBH49GLB5CRGO7nykQ6FgUbIiIiIiIiIiIiIk1Q63Tz9Je7+eeS/Xi8BpHBAfzPeX25bHgaZrMag4s0NwUbIiIiIiIiIiIiIido0a4S7v/vZvLK6wCYPLATfzg/k8SIYD9XJtJxKdgQEREREREREREROU47Cu08OX8nX+0oBqBzdAh/vqgfZ/ZJ8nNlIh2fgg0RERERERERERGRY5RXXstTC3fx4YaDGAZYzCamZXXlngm9CAvS5VaR1qBPmoiIiIiIiIiIiMgvKKt28Pev9/DWyhxcHgOASQM7cc/ZveieoObgIq1JwYaIiIiIiIiIiIjIUVQ73Ly8eB8vfbePGqcHgFMz4vndOb0ZmBrt3+JETlIKNkRERERERERERER+xOH2MHdlLs9+vYeyGicAAzpHMfOcPpzaM97P1Ymc3BRsiIiIiIiIiIiIiBxS7/Lw8YZ8/vb1bg5U1AHQLT6Meyf05tz+yZjNJj9XKCIKNkREREREREREROSkt6e4mrkrc/nPugPY6lwAJEYEccdZPblseBqBFrOfKxSRwxRsiIiIiIiIiIiIyEnJ6fayYGshb63MYcW+ct/6ztEhTM3qwtSsroRYLX6sUESORMGGiIiIiIiIiIiInFTyymuZuyqX99bkUVrd0D/DbIIz+yRx9eh0xvZMwKIpp0TarDY5fuqxxx7DZDJx5513+tbV19czY8YM4uLiCA8PZ8qUKRQVFTV6XW5uLpMmTSI0NJTExETuu+8+3G53K1cvIiIiIiIiIiIibY3T7eWLrYVMe2UVY5/8hue/3UtptZPEiCB+O74nS2aeycvThnNG70SFGiJtXJsbsbF69WpeeOEFBg4c2Gj9XXfdxaeffsp7771HVFQUt912G5dccglLly4FwOPxMGnSJJKTk1m2bBkFBQVMnTqVwMBAHn30UX+8FREREREREREREfGjqnoXi3aV8MXWIr7ZUUyV4/uboE/rGc/Vo7owvm+i+meItDMmwzAMfxdxWHV1NUOHDuW5557j4YcfZvDgwTz99NPYbDYSEhKYO3cul156KQA7duygb9++LF++nNGjR/P5558zefJk8vPzSUpKAmDOnDnMnDmTkpISrFbrMdVgt9uJiorCZrMRGRnZYu9VRERERERERESkpRiGQY3TQ2Wtk8paFxWHvlbWOql3eTGbTVhMYDGbDj3+/uvhdQFmExHBAUSHWIkODSQ6NJDwoABMprY9mqG4qp4vtxXzxbZClu0pw+nx+rbFhwcxZVhnrhyRTtf4MD9WKSI/djzX5tvUiI0ZM2YwadIkzjrrLB5++GHf+rVr1+JyuTjrrLN86/r06UN6erov2Fi+fDkDBgzwhRoAEydOZPr06WzdupUhQ4a06nsRERERERERERFpKV6vQU55Ldvy7WwvsLOnuJryWieVtU4qal3Yal2NLug3lwCziejQQKJCAokJPRx4WIkLt9IlNowucaGkx4aSEh3SqtM57S2pZuG2Ir7YWsj6vEp+eCt3t/gwJvRLYkJmEoPTYjTNlEgH0GaCjXfeeYd169axevXqn2wrLCzEarUSHR3daH1SUhKFhYW+fX4Yahzefnjb0TgcDhwOh++53W4/0bcgIiIiIiIiIiLS7GocbnYUVrGtoCHE2F5gZ2dhFbVOzy++1moxEx36wxAikJBACx6jIRzxeA08hoFhHH78g/VeA3u9yzfiw+H24vYalFY7DzXcrjnqeQMtJtJiQkmPC6VLbChd4hpCjy5xoSSEBxMRHID5OAMGp9tLXkUtOWU17C+tJbu0huyyGvaV1HCwsq7RvoPSopmQmcTEfkn0SAhv86NMROT4tIlgIy8vjzvuuIOFCxcSHBzcqueePXs2Dz74YKueU0RERERERERE5GgMw2B1dgXvrM5lXU4FOeW1HGky+aAAM32SI+jbKZLeyREkRAQRE2ptGE0RZiXmUIjRXBf1610e35RWFbVObLUuKmpdVNY5KbY7yCmrIae8lgPldTg9XvaV1rCv9Mjhh8kEkcENIz98S+j3j6NDArGYTeSW17K/tIacsloOVNTiPcqk+oEWE1k94pmQmcTZmUkkRbbuNUYRaV1tIthYu3YtxcXFDB061LfO4/Hw3Xff8eyzz7JgwQKcTieVlZWNRm0UFRWRnJwMQHJyMqtWrWp03KKiIt+2o5k1axZ3332377ndbictLa053paIiIiIiIiIiMgxs9W5+GDdAd5amcue4upG25Iig+jbKdK3ZHaKoGtcGAGt2PQ6ONBCp6gQOkWF/Ox+Hq9Bga2O3LJacsprySmrJbe8IZzILaulyuHGMBrer63OdVw1hFotdIkLo1t8wyiQbodGgvRNiSQyOLApb09E2pE2EWyMHz+ezZs3N1p3/fXX06dPH2bOnElaWhqBgYF89dVXTJkyBYCdO3eSm5tLVlYWAFlZWTzyyCMUFxeTmJgIwMKFC4mMjCQzM/Oo5w4KCiIoKKiF3pmIiIiIiIiIiMjRGYbBhrxK5q7M5ZNN+dS7GvpihFotXDg4hfMGdCKzUyRx4e3n+pXFbCI1JpTUmFBOOcJ2p9vrCzVsdU7f48pa1/frD/UISYsNpWtcKF3jwugWH0ZCRJCmlRKRthFsRERE0L9//0brwsLCiIuL862/4YYbuPvuu4mNjSUyMpLbb7+drKwsRo8eDcCECRPIzMzk2muv5YknnqCwsJD777+fGTNmKLgQEREREREREZE2pdrh5qMNB3lrRS7bCr7v+donOYKrR3fhosEpRHTQEQjWADMJEUEkROianYicmDYRbByLp556CrPZzJQpU3A4HEycOJHnnnvOt91isTBv3jymT59OVlYWYWFhTJs2jYceesiPVYuIiIiIiIiIiHxvX0k1/1yyn/+uP0jNoebf1gAzkwd24upRXRiaHq0RCSIiv8BkGEdqPXTystvtREVFYbPZiIyM9Hc5IiIiIiIiIiLSAZRUOXjmq128vSoPz6EO2N0TwrhqZDqXDkslOtTq5wpFRPzreK7Nt5sRGyIiIiIiIiIiIu1NjcPNS4v38eJ3+6g9NELjzD6J3HRad0Z3j9XoDBGRE6BgQ0REREREREREpJm5PF7eXZ3H01/uprTaAcCg1ChmndeX0d3j/FydiEj7pmBDRERERERERESkmRiGwRfbinh8/g72ldQA0CUulN9N7MN5A5I1QkNEpBko2BAREREREREREWkGa3PKefSzHazNqQAgNszKHeN7cuXIdKwBZj9XJyLScSjYEBERERERERERaYL1uRU8/+1evthWBEBwoJkbT+3OLeO6ExEc6OfqREQ6HgUbIiIiIiIiIiIix8nt8bJgaxH/XLKPdbmVAJhNcNnwNO48qxfJUcH+LVBEpANTsCEiIiIiIiIiInKM7PUu/r06j1eXZnOwsg4Aq8XM+YNSuHVcd3omRfi5QhGRjk/BhoiIiIiIiIiIyC/IK6/l1aXZ/HtNHtUON9DQQ+OaUelck9WFxAiN0BARaS0KNkRERERERERERI7AMAzW5Vbw8uL9LNhaiNdoWJ+RGM4Np3bj4iGdCQ60+LdIEZGTkIINERERERERERGRHzhQUcsnGwv4aMNBdhRW+daf1jOeG07txrheCZhMJj9WKCJyclOwISIiIiIiIiIiJ72yagefbS7gow35rMmp8K23Bpi5eHBnfn1qN3onq3+GiEhboGBDREREREREREROStUON19sLeSjDfks2VOK59BcUyYTjO4WxwWDUzi3fzLRoVY/VyoiIj+kYENERERERERERE4aVfUulu4p5ZONBXy5vQiH2+vbNjA1igsGpTB5YArJUWoGLiLSVinYEBERERERERGRDsvp9rIhr5Ile0pZuqeUDXmVvpEZAN0TwrhwUGcuGJxCt/gwP1YqIiLHSsGGiIiIiIiIiEg7Vu/ykF1WQ3ZpDTlltdjrXdQ5vdS53NQ6PdQ6PdQ5PdS5Dj9uWO81DEKtAYQFBRAeZCHUGkB4UABhQZZD6xq2hQUFEBdmJTEiiKTIYBIigggOtPj7bR+VYRjsLKpiye6GIGPl/nJqnZ5G+3SJC2Viv2QuGJRCv5RINQIXEWlnFGyIiIiIiIiIiLRxbo+XAxV17C+tYV9pDftLq8kurWV/aQ0HK+uacGTnCb0qKiTQF3QkRgSRGBlMUmTD89SYENJiQokODWzxwMAwDIrsDvaWVLO3pJq1ORUs3VNGabWj0X5xYVZOyYjn1Iw4TukRT1psaIvWJSIiLUvBhoiIiIiIiIhIG1RcVc+nmwr4eGM+mw/YcP9g+qQfiwwOoHtCOF3jQokOtRJitRAaaGn4ag0gxGomJDCAUKuFUKuF4EALFrOJWqebGoeHGoebaoebGoebGqfn+8cOD9UOF2XVToqq6im2O3C4vdjqXNjqXOwurj5qTeFBAQ0hR2woaTGhpMWGHPra8DjUeuyXpZxuLzllNYcCjBr2Flezp6SavcXV1PxoNAZASKCFkd1iOTUjnjEZ8fRJjsBs1qgMEZGOQsGGiIiIiIiIiEgbYatzsWBLIR9vzGfZ3lJ+mGUEB5rpGhdG94QwusWH/eBxODGtMDoCGkZI2OvcFFfVU2R3+L4W2espqXJQYKsjr6KOkioH1Q43Owqr2FFYdcRjWS1mzGYIMJsxmyDAYsZsMhFgNmH5weL2esmvrG/UF+OHLGYTXeJC6ZEQTt/kCE7JiGdIejRBAW13uiwREWkaBRsiIiIiIiIiIn5U7/Lw1fZiPtpwkG93luD0eH3bhqRHc+GgFMb3TaJzdIjfRx2YTCaiQgOJCg2kZ1LEUferd3k4UFFLXnkdeRW15JU3fmyvdze8Tw+A96jH+aHwoAB6JIbTIyGMHgnh9EgIJyMxjPTYMKwB5uZ5gyIi0i4o2BARERERERERaWUer8Hi3SV8vCGfBVsLG02n1CspnAsHd+b8gSmkx7XPXhDBgRYyEiPISDxy+GGrc1HjcOPxGg2LYeD1GrgPP//BOoC02FASI4LU5FtERAAFGyIiIiIiIiIircbl8fLRhnye+3YP+0pqfOs7R4dwweAULhycQp/kSD9W2DqiQgKJCgn0dxkiItJOKdgQEREREREREWlhDreH99YcYM6ivRyoqAMaGn5fNKQzFw5OYWh6jEYjiIiIHCMFGyIiIiIiIiIiLaTW6ebtVXm8+N1eiuwOAOLCrNx4WneuGZ1ORLBGLYiIiBwvBRsiIiIiIiIiIs2sqt7Fv5bn8M8l+ymvcQKQHBnMLeO6c8WIdEKsFj9XKCIi0n6Z/V3AYc8//zwDBw4kMjKSyMhIsrKy+Pzzz33bTz/9dEwmU6Pl1ltvbXSM3NxcJk2aRGhoKImJidx333243e7WfisiIiIiIiIicpKqqHHyfwt3Meaxr3lywU7Ka5ykxYbw6MUDWPS707l+TDeFGiIiIk3UZkZspKam8thjj9GzZ08Mw+D111/nwgsvZP369fTr1w+Am266iYceesj3mtDQUN9jj8fDpEmTSE5OZtmyZRQUFDB16lQCAwN59NFHW/39iIiIiIiIiMjJwTAMNuRVMndlLp9syqfe5QWgR0IYM87I4IJBKQRY2sy9pSIiIu2eyTAMw99FHE1sbCxPPvkkN9xwA6effjqDBw/m6aefPuK+n3/+OZMnTyY/P5+kpCQA5syZw8yZMykpKcFqtR7TOe12O1FRUdhsNiIjI5vrrYiIiIiIiIhIB1PtcPPf9QeZuzKXbQV23/rMTpHcdmYG5/RLxmxWQ3AREZFjcTzX5tvMiI0f8ng8vPfee9TU1JCVleVb/9Zbb/Hmm2+SnJzM+eefzwMPPOAbtbF8+XIGDBjgCzUAJk6cyPTp09m6dStDhgxp9fchIiIiIiIiIh3PloM23lqZy8cbDlLj9ABgDTAzeUAnrh6dztD0GEwmBRoiIiItpU0FG5s3byYrK4v6+nrCw8P58MMPyczMBOCqq66iS5cupKSksGnTJmbOnMnOnTv54IMPACgsLGwUagC+54WFhUc9p8PhwOFw+J7b7faj7isiIiIiIiIiJ6dap5t5Gwt4a2UOGw/YfOu7J4Rx1ch0Lh2WSnTosc0WISIiIk3TpoKN3r17s2HDBmw2G++//z7Tpk1j0aJFZGZmcvPNN/v2GzBgAJ06dWL8+PHs3buXHj16nPA5Z8+ezYMPPtgc5YuIiIiIiIhIB1Lv8rB4dykLthayYGshVfVuAAItJs7p34mrRqYzunusRmeIiIi0sjYVbFitVjIyMgAYNmwYq1ev5plnnuGFF174yb6jRo0CYM+ePfTo0YPk5GRWrVrVaJ+ioiIAkpOTj3rOWbNmcffdd/ue2+120tLSmvxeRERERERERKT9qax18vWOYhZsLeS7XaXUuTy+bV3iQrny0OiM+PAgP1YpIiJycmtTwcaPeb3eRtNE/dCGDRsA6NSpEwBZWVk88sgjFBcXk5iYCMDChQuJjIz0TWd1JEFBQQQF6ZcRERERERERkZNVfmUdC7cVsWBrISv3l+PxGr5tnaNDmNAviQmZyYzqFqtm4CIiIm1Amwk2Zs2axbnnnkt6ejpVVVXMnTuXb7/9lgULFrB3717mzp3LeeedR1xcHJs2beKuu+5i7NixDBw4EIAJEyaQmZnJtddeyxNPPEFhYSH3338/M2bMUHAhIiIiIiIiIj7VDjcb8ypZk13BVzuK2PSDnhkAfZIjmJCZxIR+yfRLidRUUyIiIm1Mmwk2iouLmTp1KgUFBURFRTFw4EAWLFjA2WefTV5eHl9++SVPP/00NTU1pKWlMWXKFO6//37f6y0WC/PmzWP69OlkZWURFhbGtGnTeOihh/z4rkRERERERETEnwzDYF9pDetyKlifV8m6nAp2FVXxg0EZmEwwvEsMEzKTOTszia7xYf4rWERERH6RyTAM45d3O3nY7XaioqKw2WxERkb6uxwREREREREROUaGYVBW42RnYRXrcipYl9sQZlTWun6yb+foEIakRzMmI56z+iaREKHZHkRERPzpeK7Nt5kRGyIiIiIiIiLS9hiGQb3Li73ehdPtBRpGOJhMJkyHH2P6fj1gNpsIswYQHGhu9mmcPF6DAlsduWW1ZJfVklNeQ25ZLTllteSU1VDj9PzkNdYAMwM7RzG0SwxD06MZkh5DUmRws9YlIiIirUfBhoiIiIiIiMhJxOM1KLTXc6C8lryKOgptddjr3djrXNjrXdjr3NjrXVT9YJ3Lc2KTPQRaTEQEBxIRHNCwBB1+3PA1MjgAa4AZp8fA5fHicntxebzfPz+0ON0GDreHgxV1HKiow+nx/ux5U2NCGJwWzdD0GIZ2iSGzUyTWAPMJvQcRERFpexRsiIiIiIiIiHQwxVX1ZJfWcqCilgMVdeSVN3w9UFlLQWU9bu/xBxUWswmrxYyBgWGAAWDge37oKYZh+PpXuDwG5TVOymuczfXWgIbAJC0mlPS4ULrEhpIeF0bXuFC6xIWSGhNKcKClWc8nIiIibYuCDREREREREZF2zDAM9pfWsDq7nFX7K1idXU5uee3PvibQYqJzdAipMaF0igomJsxKZHAAkSGBRB4eTXHocWRIAJHBgYRaLcc8rZRhGNQ4PdjrGkZ+VB0eAVJ/+Pn365xuL9YAM4EWM4EBDeFJoG8x+bZZLWaSo4JJjw0lJToEi7l5p7gSERGR9kPBhoiIiIiIiEg74vEabC+wHwoyylmdXUFptaPRPmYTpMWGkhoTQmr0oa+xIaTFNIxoSIwIwtyCwYDJZCI8KIDwIF12EBERkean3zBERERERERE2rgah5v/bjjIF1uLWJdTQZXD3Wi7NcDM4NRoRnSLYUTXWIZ1iSEiONBP1YqIiIi0LAUbIiIiIiIiIm3UrqIq3lyRwwfrDlL9gzAjPCiAYV1iGNktlhFdYxmYGqW+EiIiInLSULAhIiIiIiIi0oY43V7mby3kzRU5rNpf7lvfPT6My0ekMSYjnr6dItVjQkRERE5aCjZERERERERE2oADFbW8vSqXd1fnUVrtBMBiNnF23ySuzerCKT3ijrl5t4iIiEhHpmBDRERERERExE8Mw2DRrhLeXJHD1zuK8RoN65Mig7hiRDpXjkwnOSrYv0WKiIiItDEKNkRERERERERamWEYfLOzmKcW7mbzQZtv/ZiMOK4Z1YWzMpMItJj9WKGIiIhI26VgQ0RERERERKSVHB6h8dSXu9mYVwlAqNXC5SPSuGZ0F3okhPu3QBEREZF2QMGGiIiIiIiISAszDIOle8r4v4U7WZdbCUBIoIWpp3Th5tO6Exce5N8CRURERNoRBRsiIiIiIiIiLWj53jKeWriLVdnlAAQFmLl2dBduGdeDhAgFGiIiIiLHS8GGiIiIiIiISAtYtb+cpxbuYvm+MgCsAWauHpXO9HE9SIxUQ3ARERGRE6VgQ0RERERERKSZGIbB4t2lzFm0l2V7DwUaFjNXjEzjN6dnkBylQENERESkqRRsiIiIiIiIiDSR2+Pl080FvLBoH9sK7AAEmE1cNiKNGWdk0Dk6xM8VioiIiHQcCjZERERERERETlCt082/V+fx8pL9HKioAxqagl8xMo0bTu1GakyonysUERER6XiOO9gYMmQIJpPpmPZdt27dcRckIiIiIiIi0taV1zh5fVk2/1qeTUWtC4C4MCvXndKVa0Z3ISbM6ucKRURERDqu4w42LrroIt/j+vp6nnvuOTIzM8nKygJgxYoVbN26ld/85jfNVqSIiIiIiIhIW5BXXsvLi/fx7po86l1eANJjQ7lpbHd+NSyV4ECLnysUERER6fiOO9j44x//6Ht844038tvf/pY///nPP9knLy+v6dWJiIiIiIiI+JnT7eXrHcW8tyaPb3YW4zUa1g/oHMWt43pwTv9kLOZjm9lARERERJrOZBiGcaIvjoqKYs2aNfTs2bPR+t27dzN8+HBsNluTC2xtdrudqKgobDYbkZGR/i5HRERERERE/GRXURX/Xp3Hh+sPUlbj9K0f2yuBW8d2J6tH3DFP1SwiIiIiP+94rs03qXl4SEgIS5cu/UmwsXTpUoKDg5tyaBEREREREZFWZ693MW9jAe+uyWNjXqVvfUJEEFOGpvKr4an0SAj3X4EiIiIi0rRg484772T69OmsW7eOkSNHArBy5UpeeeUVHnjggWYpUERERERERKQleb0GK/eX896aPD7bUuDrnRFgNjG+byKXDU9jXK8EAixmP1cqIiIiItDEYOP3v/893bt355lnnuHNN98EoG/fvrz66qtcdtllzVKgiIiIiIiISHNzuD2s2FfOV9uL+Gp7MQcr63zbeiaGc9nwNC4e2pn48CA/VikiIiIiR9KkHhvN6fnnn+f5558nOzsbgH79+vGHP/yBc889F4D6+nruuece3nnnHRwOBxMnTuS5554jKSnJd4zc3FymT5/ON998Q3h4ONOmTWP27NkEBBx7fqMeGyIiIiIiIh1TWbWDr3cU89X2YhbvLqHG6fFtCw8K4PxBKVw2PJXBadHqnSEiIiLSylqtx8ZhTqeT4uJivF5vo/Xp6enHfIzU1FQee+wxevbsiWEYvP7661x44YWsX7+efv36cdddd/Hpp5/y3nvvERUVxW233cYll1zC0qVLAfB4PEyaNInk5GSWLVtGQUEBU6dOJTAwkEcffbQ53qaIiIiIiIi0I4ZhsKuomi+3F/HV9iLW51Xyw1v7EiOCGN83kfF9khiTEU+I1eK/YkVERETkmDVpxMbu3bv59a9/zbJlyxqtNwwDk8mEx+M5yiuPTWxsLE8++SSXXnopCQkJzJ07l0svvRSAHTt20LdvX5YvX87o0aP5/PPPmTx5Mvn5+b5RHHPmzGHmzJmUlJRgtVqP6ZwasSEiIiIiItI+GYbBgYo6Vu0vZ3V2OUv3lpJXXtdon34pkYzvm8RZfRPpnxKF2ayRGSIiIiJtQauN2LjuuusICAhg3rx5dOrUqdmG6no8Ht577z1qamrIyspi7dq1uFwuzjrrLN8+ffr0IT093RdsLF++nAEDBjSammrixIlMnz6drVu3MmTIkCOey+Fw4HA4fM/tdnuzvAcRERERERFpWV6vwZ6Sal+QsWp/OQW2+kb7WAPMjOkRx/i+SYzvm0inqBA/VSsiIiIizaVJwcaGDRtYu3Ytffr0aZZiNm/eTFZWFvX19YSHh/Phhx+SmZnJhg0bsFqtREdHN9o/KSmJwsJCAAoLCxuFGoe3H952NLNnz+bBBx9slvpFRERERESkZXi8BiVVDg5U1LIhr5KV+8tZk11ORa2r0X4BZhMDUqMY2TWWkd1iyeoRR6i1WWZhFhEREZE2okm/3WVmZlJaWtpctdC7d282bNiAzWbj/fffZ9q0aSxatKjZjn8ks2bN4u677/Y9t9vtpKWlteg5RURERERE5HuGYVDlcFNQWU9+ZR0HK+vIr6yjwFbve1xoq8ft/elMysGBZoamxzCyWywju8YyOD1aQYaIiIhIB9ek3/Yef/xxfve73/Hoo48yYMAAAgMDG20/3h4VVquVjIwMAIYNG8bq1at55plnuPzyy3E6nVRWVjYatVFUVERycjIAycnJrFq1qtHxioqKfNuOJigoiKCgoOOqU0RERERE5GTk9RpU1rmoqHVir3NRVe/GXu/CXuemqt71o8cNXx1uL063F5fHi8tjHPp6eF3D8yMFFkcSYDaRFBlMn+QIRnaLZUS3WPqnRGENMLfwOxcRERGRtqRJwcbhnhfjx49vtL65mod7vV4cDgfDhg0jMDCQr776iilTpgCwc+dOcnNzycrKAiArK4tHHnmE4uJiEhMTAVi4cCGRkZFkZmY2qQ4REREREZGOyjAMKmtdHKyso6TKQWm1g9JqJ2XVDY/LapyUVDV8La9x4jnGEOJERIcGkhIVQkp0CJ2jg0mJDvEtnaNDSIgIwqJm3yIiIiInvSYFG998801z1cGsWbM499xzSU9Pp6qqirlz5/Ltt9+yYMECoqKiuOGGG7j77ruJjY0lMjKS22+/naysLEaPHg3AhAkTyMzM5Nprr+WJJ56gsLCQ+++/nxkzZmhEhoiIiIiInNRqHG7yKmrJK68jr7zW9/hARS0HKuqodriP63gRwQFEhQQSERxIZHBAw9eQACIPPY8MCSTi0PrgQDOBlu8Xq8VMYIDp+8cWM4EWEyFWi6aQEhEREZFj0qTfGseNG9dcdVBcXMzUqVMpKCggKiqKgQMHsmDBAs4++2wAnnrqKcxmM1OmTMHhcDBx4kSee+453+stFgvz5s1j+vTpZGVlERYWxrRp03jooYearUYREREREZG2rthez7rcCtblVrI+t4K9JTWU1zh/8XUJEUEkRgQRHx5EXLiVhENfG54HEX/ocWyYlUCLpn4SEREREf8xGYbRpHHEixcv5oUXXmDfvn289957dO7cmTfeeINu3bpx6qmnNledrcZutxMVFYXNZjvuHiEiIiIiIiKtyeH2sC3f7gsx1udWcrCy7oj7RocGkhYTSlpsCKkxoaTFhJAaG0paTCipMSEEB1pauXoRERERke8dz7X5Jo3Y+M9//sO1117L1Vdfzbp163A4HADYbDYeffRRPvvss6YcXkRERERERH7AMAxWZ1fwxdZC1uVWsCXfjtPtbbSP2QS9kyMZkh7N0PQY+naKIC02lMjgQD9VLSIiIiLSvJoUbDz88MPMmTOHqVOn8s477/jWjxkzhocffrjJxYmIiIiIiAgcqKjlg3UH+c+6A+SU1TbaFhMayND0GF+QMTAtmvAg9aoQERERkY6rSb/t7ty5k7Fjx/5kfVRUFJWVlU05tIiIiIiIyEmt1unm882FvL/2AMv3lfnWh1ktnNO/E2My4hiSHkPXuFBMJpMfKxURERERaV1NCjaSk5PZs2cPXbt2bbR+yZIldO/evSmHFhEREREROel4vQarsst5f+0BPt9cQI3T49t2So84Lh2Wyjn9kwm1akSGiIiIiJy8mvTb8E033cQdd9zBK6+8gslkIj8/n+XLl3PvvffywAMPNFeNIiIiIiIiHVp5jZM3V+Tw3to88sq/b/7dJS6US4emcvHQzqTGhPqxQhERERGRtqNJwcbvf/97vF4v48ePp7a2lrFjxxIUFMS9997L7bff3lw1ioiIiIiIdEgFtjpe+m4/b6/Kpc7VMDojPCiAyQM7cemwVIZ1idE0UyIiIiIiP2IyDMM4kRd6PB6WLl3KwIEDCQ0NZc+ePVRXV5OZmUl4eHhz19lq7HY7UVFR2Gw2IiMj/V2OiIiIiIh0QPtKqpmzaC8frj+Iy9PwJ1m/lEhuPK0b5/TrRIjV4ucKRURERERa1/Fcmz/hERsWi4UJEyawfft2oqOjyczMPNFDiYiIiIiInBS2HLTx3Ld7+HxLIYdvMRvVLZbfnJHB2J7xGp0hIiIiInIMmjQVVf/+/dm3bx/dunVrrnpEREREREQ6FMMwWLm/nH98s4fFu0t968/qm8j00zMY1iXGj9WJiIiIiLQ/TQo2Hn74Ye69917+/Oc/M2zYMMLCwhpt11ROIiIiIiJysnJ7vHy5vZgXv9vLutxKAMwmOH9QCtNP70GfZP29JCIiIiJyIk6ox8ZDDz3EPffcQ0RExPcH+sGQacMwMJlMeDye5qmyFanHhoiIiIiINEV5jZN3Vufy1opcDlbWAWANMPOrYancMrYH6XGhfq5QRERERKTtOZ5r8ycUbFgsFgoKCti+ffvP7jdu3LjjPbTfKdgQEREREZETseWgjdeWZfPxxnycbi8AMaGBXDEynetP6UpiZLCfKxQRERERabtavHn44SykPQYXIiIiIiIizcXp9vL5lgL+tTyHtTkVvvX9O0cyLasr5w9KITjQ4scKRUREREQ6nhPusfHDqadEREREREROJsX2et5amcvcVbmUVDkACLSYOG9AJ6ZmdWVoerT+ZhIRERERaSEnHGz06tXrF39RLy8vP9HDi4iIiIiItCn2ehdfbC3ik435LNlTisfbMJI9MSKIq0d14cqRaZpuSkRERESkFZxwsPHggw8SFRXVnLWIiIiIiIi0KXVOD1/taAgzvtlZ4uudATCsSwzTTunKOf2SsQaY/ViliIiIiMjJ5YSDjSuuuILExMTmrEVERERERMTvnG4vi3eX8PHGfBZuK6LW6fFt65EQxgWDOnP+oE50Twj3Y5UiIiIiIievEwo2NFesiIiIiIh0JDUON6v2lzN/SyHztxZiq3P5tqXGhHD+oBTOH5hC304R+ntIRERERMTPTijYMAyjuesQERERERFpNW6Pl00HbSzZXcqSPaWsz63A5fn+75yEiCAmD+zE+YNSGJKmRuAiIiIiIm3JCQUbXq/3l3cSERERERFpIwzDYF9pDUv3lLJ4dykr9pZR5XA32qdzdAjjeicweWAnRnWLw2JWmCEiIiIi0hadcI8NERERERGRtsrh9rCzsIpNB2xsyKtk2Z5S8m31jfaJCgnklB5xjMmI59SMeLrEhWpkhoiIiIhIO6BgQ0RERERE2jWn28uuoio2H7Sx6YCNzQcr2VlY1WhqKQCrxczwrjG+IKN/5yiNyhARERERaYcUbIiIiIiISJtX63RTUuXwLcVVDnYXV7H5gI3tBVU4PT+dLjc6NJABnaMY0DmK0d3jGNE1lhCrxQ/Vi4iIiIhIc1KwISIiIiIizc4wDOpcHqrr3VQ53NS7PDjd3obF4/U9dhz+emhdvcvTKMAoqXZQbK+nxun52fNFBgcwMDWa/p2jGJjaEGakxoRoaikRERERkQ6ozQQbs2fP5oMPPmDHjh2EhIRwyimn8Pjjj9O7d2/fPqeffjqLFi1q9LpbbrmFOXPm+J7n5uYyffp0vvnmG8LDw5k2bRqzZ88mIKDNvFURERERkTbL6fZS7XAfCiRc1Dg8VDtcVNW7fY8PhxU1DjfVDjdV9Q1fa3yva3jsNX75fMcjONBMYkQwCRFBJIQHkR4XyoBDQUZ6rPpjiIiIiIicLNrM1f5FixYxY8YMRowYgdvt5n/+53+YMGEC27ZtIywszLffTTfdxEMPPeR7Hhoa6nvs8XiYNGkSycnJLFu2jIKCAqZOnUpgYCCPPvpoq74fERERERF/crq95FfWkVdRy4GKOkqqHA2BxaHwodHXH6w/0pROTWEyQbg1gBCrBWuAuWGxmAk69DgowOJbZw0wExxoJj48iISIoO9DjENLmNWi8EJERERERDAZhtHM91E1j5KSEhITE1m0aBFjx44FGkZsDB48mKeffvqIr/n888+ZPHky+fn5JCUlATBnzhxmzpxJSUkJVqv1F89rt9uJiorCZrMRGRnZbO9HRERERKQ5GYZBob2enLJa8sobwou8iloOlDd8LbTX05Tf9EMCLYQHBxARFEBYUADhQQGNnwcfWneUbRGHvoYEKowQEREREZFfdjzX5tvMiI0fs9lsAMTGxjZa/9Zbb/Hmm2+SnJzM+eefzwMPPOAbtbF8+XIGDBjgCzUAJk6cyPTp09m6dStDhgz5yXkcDgcOh8P33G63t8TbERERERFpsrzyWpbvK2PF3jJW7Csj31b/s/sHB5pJjQklLSaEpMhgIoIDCA8KJCzI4nvcEFBYfvA4gDCrhQCLuZXelYiIiIiIyPFpk8GG1+vlzjvvZMyYMfTv39+3/qqrrqJLly6kpKSwadMmZs6cyc6dO/nggw8AKCwsbBRqAL7nhYWFRzzX7NmzefDBB1vonYiIiIiInLiDlXUsPxRiLN9bxsHKukbbA8wmUmNCGsKL2IavqTEhpMWGkhYTSny4VaMlRERERESkw2mTwcaMGTPYsmULS5YsabT+5ptv9j0eMGAAnTp1Yvz48ezdu5cePXqc0LlmzZrF3Xff7Xtut9tJS0s7scJFRERERJqgzunhqx1FfLerhOX7ysgr/2mQMTA1iqwecYzuHsewLjGEWtvkr/QiIiIiIiItps39FXTbbbcxb948vvvuO1JTU39231GjRgGwZ88eevToQXJyMqtWrWq0T1FREQDJyclHPEZQUBBBQUHNULmIiIiIyPFze7ws2VPKRxvyWbC1kFqnx7fNYjYxoPP3QcbwLjGEBbW5X+FFRERERERaVZv5q8gwDG6//XY+/PBDvv32W7p16/aLr9mwYQMAnTp1AiArK4tHHnmE4uJiEhMTAVi4cCGRkZFkZma2WO0iIiIiIsfDMAzW51Xy0fqDzNtUQFmN07ctLTaEc/t3IqtHHCO6xhKuIENERERERKSRNvNX0owZM5g7dy4fffQRERERvp4YUVFRhISEsHfvXubOnct5551HXFwcmzZt4q677mLs2LEMHDgQgAkTJpCZmcm1117LE088QWFhIffffz8zZszQqAwRERER8bs9xdV8vOEgH23MJ6es1rc+NszK5IGduHBwZ4amR6svhoiIiIiIyM8wGYZh+LsI4Kh/vL366qtcd9115OXlcc0117BlyxZqampIS0vj4osv5v777ycyMtK3f05ODtOnT+fbb78lLCyMadOm8dhjjxEQcGwZjt1uJyoqCpvN1ui4IiIiIiInYn9pDfO3FPLp5ny2HLT71odaLUzITOLCIZ05NSOeQIvZj1WKiIiIiIj41/Fcm28zwUZboWBDRERERJrCMAx2FFYxf0sh87cUsrOoyrctwGxibK8ELhycwtmZSWr8LSIiIiIicsjxXJvXX1IiIiIiIk3k9RpsOFDJgi2FzN9a2GiaqQCziawecUzsl8y5/ZOJC9cUqSIiIiIiIk2hYENERERE5ATUOT2szalg4bZCFmwtotBe79sWFGBmbK8EzumXzFl9k4gKDfRjpSIiIiIiIh2Lgg0RERERkWNgr3exNruClfvLWbW/jE0HbLi938/qGh4UwJl9EjmnfzLjeiUQFqRftUVERERERFqC/toSERERETmC8honq/aXNyzZZWzLt+P9UXe6TlHBnNYznnP7d+KUjDiCAiz+KVZEREREROQkomBDRERERE56dU4P2wvtbDloY8tBGxvyKtlVVP2T/brGhTKyWywju8UxqlssqTEhmEwmP1QsIiIiIiJy8lKwISIiIiInlap6F9vy7WzJt7P1oI0t+Tb2FFf/ZDQGQK+k8EZBRlJkcOsXLCIiIiIiIo0o2BARERGRDqmq3sX+0hr2ldSwr6SavSU1bCuws7+05oj7x/8/e/cdHtd1nQv/PdMxFb0RIAmAXRJlW4WiZKvYimU7SlzkksS23B07kvPZ8o0T5aY8zrWjuMRFiS057o67riUncq7lompZFCVRokiJBezoHZgZzGD6+f44s88MQBDADGbO2Qd8f8/DhwWFhwSwsPdea6/ld+OidUFcuC6EC9eFcOmGOjT43QY/NRERERERES2HiQ0iIiIisiRVVRFPZTEaSRQSGBOz+Z9jGI8mz/m262prcEG7SGIEcWF7CM28jUFERERERGQJTGwQERER0YplcypSmRySmWz+Z/Eji2QmV/izdBapbA7JdC7/59rLM/l+T6qqQlUBFYCabwGlovjPVMSSWcwm05hNZhBNZDCbzCCWzGA2kUE0/+vF2kcVawq40d3oQ3eTD92NfmxrC+CC9hDqfa6q/j8RERERERFR9TCxQURERHSeUFUVs8kMpmNpTMVTmI6nMBNPYSqWzv+cwkw8jen8r+fSWSTTuXyCYn5iQiY1Tju6Gn3oavKhp9GH7iY/upt82NjoQ9DjNPvxiIiIiIiIqMKY2CAiIiJaQ8JzaQxMx9E/NYeB6TgGpot/nsNsMlOxv8umAG6HHW6nDS67DW6nDW6HvejXNrgcdrgd4tc2OGwKFChQFEBRAIhf59+n9msFNgXwuh3wux0IeLSf/W4H/B4HAm4n/J7Cy9wOGxRFWeJJiYiIiIiIaC1hYoOIiIjIgiZnkzg4GMYLg2G8OBTBmck4+qfjiCaWT1zUOO2o97lQ63WizutCnc+FOq8TtV7tZ+1lLvjddrjsdj1J4XbY4XLY9ESFw24z4F9KRERERERENB8TG0RERESSG48m8cJgGAfzP14YDGM4nDjn6zf4XOio96Kjrgaddfmf879fV1sDj9Nu4NMTERERERERVRYTG0REREQSyeVUHBqO4PHjE3jm9DReGAxjJHJ2EkNRgK5GHy5sD+GidSH0NPvQWefFuroaeF1c4hEREREREdHaxV0vERERkcmGw3P43bEJPH5sAr8/PoHJWGreyxUF6G704aJ1IVy4Tktk7GgPIsDB2ERERERERHQeYmKDiIiIyGCxZAZ7T03isd4JPH58AsfHZue93Oey44ruBuzuacDFnbXY3haE381lGxERERERERHAxAYRERFR1SUzWTzfH8YTJybwxIlJPNc3jXRW1V9uU4CdHbV4xeZGvGJzE17SWQuXg4O5iYiIiIiIiBbDxAYRERFRhaWzORwYCOPJk5PYc2ISz5yZQiKdm/c6nfU1eMXmJrxiUyOu7GlEyMu2UkREREREREQrwcQGERER0SplcypeGAxjj0hknJ5CLJWd9zqNfhd2dTdgd3cDXrG5ERsafCY9LREREREREZG1MbFBREREVAJVVXFmMo4Dg2Ec6J/BgYEwXhgKI74gkVHrdeKKLm1Oxu6eBmxu9kNRFJOemoiIiIiIiGjtYGKDiIiIaAkj4QSeH5jBgQEtiXFgIIzwXPqs1wt4HNglEhndDdjWGoDNxkQGERERERERUaUxsUFEREQEIJbM4NjYLHpHo+gdiaJ3bBZHhiMYiybPel2X3Ybt7UFc3BHCzo5a7OwIoafJDzsTGURERERERERVx8QGERERnVcS6SyOj83i2FgUR0dmcWw0iqOjUQxMzy36+nabgs3NflzcUYuLOkK4uKMWW1sDcDlsBj85EREREREREQFMbBAREdEak8nmMBxOoH8qjv7pOPqn5vI/x9E/PYfxRW5gCI1+N7a2+rG5OYCtrQFsaQlgR1sQNS67gf8CIiIiIiIiIlqKNImNO+64A/feey+OHDmCmpoaXHnllfjMZz6DrVu36q+TSCTw8Y9/HD/+8Y+RTCZxww034Ktf/SpaWlr01+nr68OHP/xhPPzww/D7/XjXu96FO+64Aw6HNP9UIiKiNUtVVaSyOSQzOSTSWSTTOSQzWSTS2p8l01kkMzmksznYFAWKAv1nRVFgK/49tJ+TmRzmUhnEU1nEUln919qP/K+TWYTn0uifjmM4nEA2py75nHVeJza3BLC1JYAtLX5sadGSGHU+l0H/U0RERERERERULmlO+x999FHccsstuOyyy5DJZPC3f/u3ePWrX41Dhw7B5/MBAD72sY/hf/7nf3DPPfcgFArh1ltvxZve9Cb8/ve/BwBks1n84R/+IVpbW/HEE09geHgYN998M5xOJ/75n//ZzH8eERGRdJKZLCJzGUQSaUQTGUTm0ogk0vqfid/Hk1kkMiJJkU9YFP1cSFxov1eXzikYwuWwoaOuBp11XnTWi5+9+u9DNU4oCudhEBEREREREVmRoqoyHD+cbXx8HM3NzXj00Udx9dVXIxwOo6mpCT/84Q/x5je/GQBw5MgRbN++HXv27MEVV1yBX/7yl7jxxhsxNDSk3+K4++678dd//dcYHx+Hy7V8FWYkEkEoFEI4HEYwGKzqv5GIiKhakpkshmcSGJqZw8DMHIZm5jA4PYeh8ByGZhIYDs8hkc5V/Tk8ThvcDjvcDhs8zsLPdpsCFdoND1UFcqqKnLrw9ypUAG6HHV5X8Q+H/usalwO+/K8DHqeWzKj3osnvho2DvImIiIiIiIgso5SzeWlubCwUDocBAPX19QCAffv2IZ1O4/rrr9dfZ9u2bVi/fr2e2NizZw8uuuiiea2pbrjhBnz4wx/Giy++iJe+9KXG/iOIiIiqLJpI48WhCF4YDOOFwTBOT8YxOLP0HImFAh4Hgh4ngjVOBD2O/M9O7c9rnPC57PA47YsmKdx6siL/MmfhZS67jbciiIiIiIiIiKjipExs5HI5fPSjH8VVV12FCy+8EAAwMjICl8uF2traea/b0tKCkZER/XWKkxri5eJli0kmk0gmC4c/kUikUv8MIiKiippNZvDiYBgHi36cmoids/WTx2nDutoatNfWoKOuBu2hGqyr037fHqpBrc8Jv8vBmw1EREREREREZClSJjZuueUWvPDCC3j88cer/nfdcccd+OQnP1n1v4eIiKhUfZNxPH58Ak+fnsKBgRmcPEcSoz3kwYXrQtjZEcKmZj/W1Xqxrq4GdV7OkSAiIiIiIiKitUe6xMatt96KX/ziF3jsscfQ0dGh/3lraytSqRRmZmbm3doYHR1Fa2ur/jpPPfXUvPc3Ojqqv2wxt99+O2677Tb995FIBJ2dnZX65xAREa3YTDyFJ05M9JPJ+AABAABJREFU4nfHJvD74xPom4qf9TptIomxLoQLO0K4aF0IjX63CU9LRERERERERGQOaRIbqqriIx/5CO677z488sgj6OrqmvfySy65BE6nEw8++CBuuukmAMDRo0fR19eH3bt3AwB2796NT3/60xgbG0NzczMA4De/+Q2CwSB27Nix6N/rdrvhdvNAiIiIjJfMZLHv9DQePz6Bx49P4OBgeN6NDIdNwUvX12J3TyNe2lmLC9eF0BTg9ywiIiIiIiIiOr9Jk9i45ZZb8MMf/hD/9V//hUAgoM/ECIVCqKmpQSgUwvve9z7cdtttqK+vRzAYxEc+8hHs3r0bV1xxBQDg1a9+NXbs2IF3vvOd+OxnP4uRkRH83d/9HW655RYmL4iIyHTJTBYHBsJ46tQUnjw5iadPTyGRzs17nc3Nfrx8cyNevqkRu7ob4HdL862aiIiIiIiIiEgKiqqea+Sosc7VA/zb3/423v3udwMAEokEPv7xj+NHP/oRkskkbrjhBnz1q1+d12bqzJkz+PCHP4xHHnkEPp8P73rXu/Av//IvcDhWdjAUiUQQCoUQDocRDAZX/e8iIqLzVzyVwXN9M9h7agpPnZrEc30zSGbmJzKaAm68fJOWyHj55ka0BD0mPS0RERERERERkXlKOZuXJrEhCyY2iIioXJFEGvtOT+uJjAMDYWRy87/NNvpduLyrHpdvrMfunkZsafFzwDcRERERERERnfdKOZtnfwsiIqIyTcVSeOrUlPbj9CQODUWwII+BtpAHu7rqcXlXAy7vqkdPk4+JDCIiIiIiIiKiVWBig4iIaIVGIwn9NsZTp6bQOzp71utsbPBqNzK6GrCrqx4ddTVMZBARERERERERVRATG0RERItQVRUD03PzEhmnJ+Nnvd6WFr+eyLh8Yz1aQ5yRQURERERERERUTUxsEBERQUtknBiP5VtLaYmMoXBi3uvYFGBHexCXb9TaSl22sQ4NfrdJT0xEREREREREdH5iYoOIiM5LuZyKo6NR7D05iadOa3MyJmZT817HYVOwsyOkt5W6ZGMdgh6nSU9MREREREREREQAExtERHQeyOVUnJqM4YXBMA4OhHFgMIxDQxHMJjPzXs/tsOGl62v1RMZL19fC6+K3SiIiIiIiIiIimfC0hoiI1pSVJjEAwOey45KN9djVVY/Lu+qxsyMEt8NuwlMTEREREREREdFKMbFBRESWksrkMBpJYGhmDkPhOQzNaL8eDms/90/FEUtlz3o7j9OGHW1BXLQuhAvXhXBRRwibmvxw2G0m/CuIiIiIiIiIiKhcTGwQEZEpEuksZuJpRBNpRBIZRBNpRBMZzCYLv44mMojkfz0eTWJoZg7js0mo6tLvm0kMIiIiIiIiIqK1i4kNIiKqiLlUFmPRBEYjSUzFkpiMpTAdS2EqlsZ0PFX0+xSm4ynEF7lVsVIuhw3tIQ/aa2vQFqrBuloP2mpr0F6r/Xpjg49JDCIiIiIiIiKiNYqJDSIiWlI2p2IsmsBwOIGxiJa4GM3/PBZNYCScwGgkgUji7BkWy7HbFAQ8Du2H21n4tUf7td9d+HWj34X2fPKiweeCoihV+NcSEREREREREZHsmNggIjqPqaqKqVhKn08hfh4KJzCc//1IJIFsbpneT3kepw0tQQ8a/W7U+1yo97pQ53Oh3udEvc+Nep8TdV4X6n3anwfcDiYoiIiIiIiIiIioJExsEBGtcelsDoPTczgzFUffZAynJ+M4MxlH31QMfVNxJNK5Zd+Hw6agJehBS9Cd/9mD5qAbLQEPWkPanzcHPUxUEBERERERERFR1TGxQUS0RiTSWbw4FMHBgRkcG5tF35SWwBicmVv2xkWj363NqQjVoK3Wg3X52RVttR60h2rQFHDDbmPCgoiIiIiIiIiIzMfEBhGRBaUyORwdieL5gRkcHAjjwGAYvaPRcyYwPE4b1td7sb7eh40NXmxo8GJ9gw8b6r1oq/XA7bAb/C8gIiIiIiIiIiIqDxMbRESSU1UVZybjeOrUFA4MzuDAQBhHhqNIZc9uIdXod2FnRy22twWwIZ+42NjoQ5PfDRtvXBARERERERER0RrAxAYRkYSGZubwxIlJ7DkxiT0nJjAUTpz1OrVeJy5aF8LOjhAuWleLiztDaA16OOOCiIiIiIiIiIjWNCY2iIgkMB5NYs9JLYmx58QkTk/G573caVfwks5avHR9HXZ2hLBzXS0662uYxCAiIiIiIiIiovMOExtERCYYjSTw1KkpPH16CntOTOLY2Oy8l9sUYGdHLXb3NODKngZcuqEeNS7OwSAiIiIiIiIiImJig4ioylRVxenJOJ4+NYWnTk/hqVNT6JuKn/V6O9qCuLKnAbt7GnBZVz2CHqcJT0tERERERERERCQ3JjaIiCosk83h6GhUv5Hx1KlpTMwm572OTQG2twVx2cZ67OqqxxXdDajzuUx6YiIiIiIiIiIiIutgYoOIaBXS2RyOj83i4GAYLwyGcXAwjMPDESTSuXmv57LbcHFnCJdtrMdlXfW4ZEMdb2QQERERERERERGVgYkNIqIVSmdz6B2N6gmMg4MRHBmOIJnJnfW6frcDL9tQh11d9bhsYz12doTgcXJGBhERERERERER0WoxsUFEtEA0kcbJ8RiOj83ixLj4EcOZyRjSWfWs1/e7HbigPYiL1oVwUUcIF64LoavBB5tNMeHpiYiIiIiIiIiI1jZpEhuPPfYYPve5z2Hfvn0YHh7Gfffdhze84Q36y9/97nfju9/97ry3ueGGG/DAAw/ov5+amsJHPvIR3H///bDZbLjpppvw5S9/GX6/36h/BhFZRDKTxeD0HPqm4jg9EcOJ8ZiexBiNJM/5dgGPAxe2FxIYF60LYUO9l0kMIiIiIiIiIiIig0iT2IjFYrj44ovx3ve+F29605sWfZ3XvOY1+Pa3v63/3u12z3v529/+dgwPD+M3v/kN0uk03vOe9+CDH/wgfvjDH1b12YlIPqqqYnw2if6pOPqm4uif0pIY2q/jGIkkoJ59+ULXFHCjp8mHnia/9qPZj+5GH9bV1jCJQUREREREREREZCJpEhuvfe1r8drXvnbJ13G73WhtbV30ZYcPH8YDDzyAp59+GpdeeikA4N/+7d/wute9Dp///OfR3t5e8WcmIuOpqorwXBqjkSTGogmMRZIYzf88Hk1iNJLAWP7nxWZfFPO67Fhf70VnvRc9TX5savajp8mH7iY/QjUc7E1ERERERERERCQjaRIbK/HII4+gubkZdXV1eOUrX4lPfepTaGhoAADs2bMHtbW1elIDAK6//nrYbDbs3bsXb3zjG816bCJaIJXJIZJIIzKXRiSRQWQujWgiU/RnaUTmtN9H8y8XfzYVTyG1TMJCsClAW6gGnfU1WF/v1ZMYnflfN/hcUBTeviAiIiIiIiIiIrISyyQ2XvOa1+BNb3oTurq6cOLECfzt3/4tXvva12LPnj2w2+0YGRlBc3PzvLdxOByor6/HyMjIOd9vMplEMlnopx+JRKr2byBaC1RVRVJPTGTOSlDMT0YUJyUKyYtEemWJiaXUep1oDrjREvSgKeBGc8CDlqD2c3PQjZaAB60hD1wOWwX+1URERERERERERCQLyyQ2/uRP/kT/9UUXXYSdO3eip6cHjzzyCF71qleV/X7vuOMOfPKTn6zEIxJZSjanYjKW1Fo4zSa1BMTCZERRgiJadGsilV19YgIAAm4HgjVOBDzaz0GPA0GPs/DrGmf+9w4EPNqva71ONAXc8DjtFXkGIiIiIiIiIiIishbLJDYW6u7uRmNjI44fP45XvepVaG1txdjY2LzXyWQymJqaOudcDgC4/fbbcdttt+m/j0Qi6OzsrNpzExlhJp7CqYkYBqbnMBbVZk+MR7UEhvj1VCyJ3BLDs5ejKEDQk09K5JMPhaTEgmRFUYIimE9Q+D0O2DmEm4iIiIiIiIiIiEpk2cTGwMAAJicn0dbWBgDYvXs3ZmZmsG/fPlxyySUAgIceegi5XA67du065/txu91wu92GPDNRJc2lsjg9GcOpCe3HyfEYTk3M4tREDNPx9Ireh00BGvxuNPrdqPc5EXAvTFCIGxVFCYr8n/tcDtiYmCAiIiIiIiIiIiKDSZPYmJ2dxfHjx/Xfnzp1Cvv370d9fT3q6+vxyU9+EjfddBNaW1tx4sQJfOITn8CmTZtwww03AAC2b9+O17zmNfjABz6Au+++G+l0Grfeeiv+5E/+BO3t7Wb9s4hWLZ3N4fjYLA4PR3BoKILDIxGcGo9hKJxY8u1agx6sr/eiKehGc8CNpoAbTf78z/kfDT43b00QERERERERERGRpSiqqq6iGU3lPPLII7juuuvO+vN3vetduOuuu/CGN7wBzz33HGZmZtDe3o5Xv/rV+D//5/+gpaVFf92pqSnceuutuP/++2Gz2XDTTTfhzjvvhN/vX/FzRCIRhEIhhMNhBIPBivzbiFYqPJcuJDCGIzg0HMGx0dlzzrQI1TjR3eRDV6MPXQ0+dOV/vbHBB59bmrwlERERERERERER0ZJKOZuXJrEhCyY2yGjRRBof/+nzODQcwcD03KKvE3A7sL09iB1tQWxvC2BTcwDdjT7U+VwGPy0RERERERERERFR5ZVyNs+SbiKT+VwO7DkxiWgyAwBYV1uDHXoSI4gL2oPoqKuBorBlFBERERERERERERETG0Qms9kU3HHTRWj0u7G9NYiQ12n2IxERERERERERERFJi4kNIgncuJMD7omIiIiIiIiIiIhWwmb2AxAREREREREREREREa0UExtERERERERERERERGQZTGwQEREREREREREREZFlMLFBRERERERERERERESWwcQGERERERERERERERFZBhMbRERERERERERERERkGUxsEBERERERERERERGRZTjMfgDZqKoKAIhEIiY/CRERERERERERERHR+UGcyYsz+qUwsbHA5OQkAKCzs9PkJyEiIiIiIiIiIiIiOr9Eo1GEQqElX4eJjQXq6+sBAH19fcv+5xHRuUUiEXR2dqK/vx/BYNDsxyGyLH4tEVUOv56IKoNfS0SVw68nosrg1xJRZZj9taSqKqLRKNrb25d9XSY2FrDZtLEjoVCIgZCoAoLBIL+WiCqAX0tElcOvJ6LK4NcSUeXw64moMvi1RFQZZn4trfSyAYeHExERERERERERERGRZTCxQURERERERERERERElsHExgJutxv/+I//CLfbbfajEFkav5aIKoNfS0SVw68nosrg1xJR5fDriagy+LVEVBlW+lpSVFVVzX4IIiIiIiIiIiIiIiKileCNDSIiIiIiIiIiIiIisgwmNoiIiIiIiIiIiIiIyDKY2CAiIiIiIiIiIiIiIstgYoOIiIiIiIiIiIiIiCyDiQ0iIiIiIiIiIiIiIrIMJjaIiIiIiIiIiIiIiMgymNggIiIiIiIiIiIiIiLLYGKDiIiIiIiIiIiIiIgsg4kNIiIiIiIiIiIiIiKyDCY2iIiIiIiIiIiIiIjIMpjYICIiIiIiIiIiIiIiy2Big4iIiIiIiIiIiIiILMNh9gPIJpfLYWhoCIFAAIqimP04RERERERERERERERrnqqqiEajaG9vh8229J0MJjYWGBoaQmdnp9mPQURERERERERERER03unv70dHR8eSr8PExgKBQACA9p8XDAZNfhoiIiIiIiIiIiIiorUvEomgs7NTP6NfChMbC4j2U8FgkIkNIiIiIiIiIiIiIiIDrWREBIeHExERERERERERERGRZTCxQURERERERERERERElsHEBhERERERERERERERWQYTG0REREREREREREREZBlMbBARERERERERERERkWUwsUFERERERERERERERJbhMPsBiGTwXN80vvG7U3DYFfjdDvg9DgTcjvyvnfC7HQh4HPrL1td74bQzL0hE1nRsNIo7HzoOBVgQ7xxF8c4Jn9uOgMeBjjovPE672Y9NRFSWwZk5fO6BI8iqmL+mWxD7RDxcV1cDr4vbJCKyppl4Cp/6n8NIpLMIeBzwuc5e4xX/vi3kQcDjNPuxiYjKkkhn8cn7DyEyl15kT3t2/GsJulHrdZn92FQhXLETAfjSb4/h0d7xFb/+RetCuP8jL6/iExERVc/dj57E/c8Prfj1O+pq8NDHr4XLwYQuEVnPf+45g5/vX3nMq/M68chfXYdQDQ/6iMh6/u++AfzffQMrfv0apx2//fg1WFdbU8WnIiKqjl+9OIIfPdW34td32hXc9xdX4cJ1oSo+FRmFiQ0iAEdHogCA9728C363A7PJDGYTGcwmM4gmM4gV/X5wZg4HB8OYiqVQ72OWl4is5+hoBADwJ5d1ojXk0eOb/kPEv0QGw+E5DEzP4fRkDFtaAiY/ORFR6Y6OaDHvjy5ux6YmP2IpLb5p8S6tx7vZZAYj4QSm42m8MBjGVZsaTX5yIqLSib3tK7c146WdtfqeVl/vFa37RiIJzKWzeOb0FNa9ZJ3JT05EVDoR8y7vqsfVmxvnxbtY0RpvNpnBaCSBRDqHJ09OMrGxRjCxQee9cDyNkUgCAPDR6zcvew33qn95CIMzczgxPot6X70Rj0hEVDHZnIpjo7MAgD+/pgddjb4lX/8NX/k99vfP4MTYLBMbRGRJvfmYd/PuDbhs49Jrt/d/9xn89vAoTozPMrFBRJbUO6od8r35kg687qK2JV/39nsP4kdP9eHE2KwRj0ZEVHEi5t24sw0379645Ot+4ddHcedDx3FinDFvrWBPCTrv9Y5pQXBdbc2Keov2NPsBgIs/IrKkvqk4kpkc3A4b1td7l339nqZ8zOPij4gsKJpIY3BmDgCwpXn55GxPs5bs5TqPiKwol1P1ZO5KClJ6mvIxbzxW1eciIqqWo/nExopinn6ex5i3VjCxQec9cW1tS4t/Ra9fWPxxw0tE1iNi3uYWP+w2ZdnX1w/5uOElIgsSB3ytQQ9C3hUUsOjJXMY8IrKegek5zKWzcNlt2NiwggKWZhawEJF1xZIZ9E/lC1hWlMxlzFtrmNig8564traldWUtVrjhJSIr6y2hogXg4o+IrK38dR5jHhFZj6hc7mn2w2Ff/rhnUz7mnZyIIZtTq/psRESVdix/w7Yp4F7RDFzRhnkylsJ0LFXVZyNjMLFB5z1RvbyVh3xEdB4QG96SY97YLFSVG14ispbCOq+0m7nD4QRmk5mqPRcRUTX0jpYW89pra+B22JDK5DA4PVfNRyMiqrjeEs/zfG4H2kMeAMDJCZ7prQVMbNB5TVXV0quX821Z+qfiSKSzVXs2IqJqEIu/lVYvb2jwwmFTEEtlMRpJVvPRiIgqrtR1Xq3XhUa/VvF3irdzichijpa4zrPbFL2CmYV7RGQ1pczXEDhnY21hYoPOa+OzSUzH07ApwKbmlVW1NPndCHgcyKnAmcl4lZ+QiKhykpksTk1oC7iVVrU47Tasz/do5oaXiKxGr15e4SEfAHTzdi4RWVRviTdzAc7ZICLrKqzzVnaeB7ALy1pjmcTGXXfdhZ07dyIYDCIYDGL37t345S9/qb88kUjglltuQUNDA/x+P2666SaMjo6a+MRkBb0jWiDb2OCDx2lf0dsoisJASESWdGoihkxORcDtQFv+Cu5KMOYRkRVNzCYxMZuCUkIBC8CYR0TWlM7m9LhVUvUyYx4RWZR+S62kmMdbamuJZRIbHR0d+Jd/+Rfs27cPzzzzDF75ylfi9a9/PV588UUAwMc+9jHcf//9uOeee/Doo49iaGgIb3rTm0x+apJdOdfWgPk954mIrKK4PYGiKCt+O8Y8IrIiUcW3vt4Lr8ux4rfjhpeIrOjMZAzprAqfy451tTUrfjs95rEtCxFZyHQshbGo1ip5c1nJXMa8tWDlK3yT/dEf/dG833/605/GXXfdhSeffBIdHR345je/iR/+8Id45StfCQD49re/je3bt+PJJ5/EFVdcYcYjkwWU2mteEHM2uOElIisptde8UDjk4+KPiKyjt4wqPoC9l4nImo7muxFsbgnAZiujgIV7WyKyELG37airgd9dQgFLfp3XNxVHMpOF27Gy7i0kJ8vc2CiWzWbx4x//GLFYDLt378a+ffuQTqdx/fXX66+zbds2rF+/Hnv27FnyfSWTSUQikXk/6PxxtIwepAAzvERkTWLDu7Vl5S1ZAPZeJiJrOjoqYl5p67xN+XXeqYkYsjm14s9FRFQN5e5tu/MFLJOxFKZjqYo/FxFRNZQzUwgAmgNu+N0OZHMq+jg31/Isldg4ePAg/H4/3G43PvShD+G+++7Djh07MDIyApfLhdra2nmv39LSgpGRkSXf5x133IFQKKT/6OzsrOK/gGSiqiqOlTFoCJhf1ZLjhpeILOLYWJm31Bq1mDccTmA2man4cxERVYNY55Ua89pra+B22JDK5tA/xQ0vEVlDud0IvC6H3rqKRSxEZBW9+QKWUmOeNjdXS+geZ6tly7NUYmPr1q3Yv38/9u7diw9/+MN417vehUOHDq3qfd5+++0Ih8P6j/7+/go9rfV89oEj+NJve8+byrTBmTnEUlm47DZsaPCV9LYbGrxw2BTEU1mMRBJVekIiqqb/eOwEPv0/h5BIZ81+FEPEUxn05Q/oSq1qCXmdaPS7AQAnueElsqQfP9WHv/v5QUQTabMfxRCqqpZdvWy3KehqZNtRIiv7fweH8Vf3PI+J2aTZj2KYcquXgcKtDcY8Imv63bFx3PaT/edVQUa56zyALfjWEkslNlwuFzZt2oRLLrkEd9xxBy6++GJ8+ctfRmtrK1KpFGZmZua9/ujoKFpbW5d8n263G8FgcN6P81H/VBxffeQEvvTbY/jLHz2HVCZn9iNVnVj4dTf54LSX9qXgtNuwvsELgIGQyIrmUlnc8csj+PrvTuF9330asfPgFsLxsVmoKtDod6Ehn6QoBYfpElmXqqr49P87jO8/2Ye3f2Mvps6DViMjkQSiiQwcRUmKUrAFH5G1ff5XR3HPvgG89e49GJyZM/txqi6RzuL0pNYmeUuJ3QgAtlomsrp/e+g47n1uEG+++wn9xupapqpq2fMjgeJ1HmOe1VkqsbFQLpdDMpnEJZdcAqfTiQcffFB/2dGjR9HX14fdu3eb+ITWMTBdWOz9z8FhfOB7z2AutbarmPVe8yVeWxP0xR+vrhFZzuDMHNT85bTfH5/EO765FzPxtX3Qd7TMIboCh+kSWVckkUE0oSVwDwyE8dav7cFIeG3fOBUxr7vJB5ej9C1PYZ3HmEdkNbmcioF8MuPkRAxvueuJNX/j9PjYLHIqUOd1oqmcApZm7m2JrGwwf6Y3GknirV/bgwMDM+Y+UJWNR5OYiadhtyn6jbNSsGhv7bBMYuP222/HY489htOnT+PgwYO4/fbb8cgjj+Dtb387QqEQ3ve+9+G2227Dww8/jH379uE973kPdu/ejSuuuMLsR7cEUcWyrrYGNU47Hu0dx83f2ovIGm5XsJrsLsCqFiIrEzGv0e9GrdeJ5/pm8Cf/8STGomv3oK9yMY+LPyKrEZtdv9uBtpAHx8dm8ea7n8CZybW7hll9zOOGl8iqJmJJpDI52BQtuTkUTuCtX9uDF4fCZj9a1RTHPEVRSn57xjwi68pkc3qL9M3NfkzH0/izr+/FkycnTX6y6hFtqDY2eOFx2kt+++JCZVU9P9rxr1WWSWyMjY3h5ptvxtatW/GqV70KTz/9NH71q1/hD/7gDwAAX/ziF3HjjTfipptuwtVXX43W1lbce++9Jj+1dYgN78s3NeL7778cAY8DT5+exp/+x5Nrti/pqquXufgjsiwR83Z2hPCTD+5Gc8CNIyNRvOXuPWu2L+lRMVyNMY/ovCOSuV2NPtzzod3Y2ODFwPQc3nz3Hn09tNaIm7lM5hKdf8Q6ryXowT1/vhsXtAcxMZvCn/zHk3jm9JTJT1cdR1eZzN2Uj3l9U3EkM2u7cwPRWjMSSSCbU+G0K/jZX1yJ3d0NmE1m8K5vPYWHjoya/XhVsdrzvA0NPthtCmKpLEYja/PM83xhmcTGN7/5TZw+fRrJZBJjY2P47W9/qyc1AMDj8eArX/kKpqamEIvFcO+99y47X4MKhvIb3vbaGlyyoR4//uAVaPS78OJQBG+9e4/+8rUik83heH6jWs6gIYC9l4msrBDzPNjaGsA9H9qNzvoanJmM4y1378HxsbV30NebX/xtLaPvMlA45Ds9EUcmu/bnMBGtJUNFN3M76rz46Yd2Y1trAONRrV3Bc33TJj9h5a32xoZoazAdT58XM0mI1pKhGa1yub22Bg1+N370wStw2cY6RBMZvPObT+Gx3nGTn7DyxDpvS5ltlpsCbgTcDuRU4Mzk2izyIVqrRMxrC9Ug6HHi2++5DNdvb0Yyk8MHv7cP/7V/0OQnrLzVrvNcDhs21HNu7lpgmcQGVZfeiqquBgBwQXsIP/3z3WgPebS+pHfvWVN9Sc9MxZHK5FDjtKMj/28uVU+jdsg3GkkiuoZbdpFmODyHv7rnefzy4LDZj0IVUGi/py1mNjT4cM+fX4nNzX6MRBJ469eexMGBtdOuIBxPF64nl7n4W1dbA7fDhlQ2N28uE61N07EU/vd9B/GTp/vMfhSqgMGiAhYAaA548JMP7sbL1tciPJfG27+xF08cnzDzESsqm1NxbEwkc8uLeV6XA+vy/1/c8K59sWQG/+cXh/D1x06a/ShUAYMz2sG8+BoOepz43nt34dqtTZhLZ/G+7z6N/7fG1vS9o6sr2lMUBd2cs3HeSGay+MKvj+Lzvzpq9qNQBSyMeR6nHXe94xK84SXtyORUfPQn+/GDvWfMfMSKE90Iyl3nAUA3b+euCUxsEID5lXxCd5Mf//fDV6K7yYfBmTm89Wt7cGgoYtYjVpRe0dLih81Weg9SAAh5nWjMD2Y7yTkba9oTxydw452P4559A7jjl0fMfhyqgIXJXABoDXnw0z/fjZ0dIUzFUvjTrz+JvWukL2lv/oCvPeRB0OMs633YbAoXf+eJAwMzuPHfHscP9vbhn+4/hFyOfWetbrGYF/I68Z/v24WXb2pEPJXFu7/zNH794ohZj1hR/VNxJNI5uB02rM9X45VD3NrgId/adnxsFq//yu/xzcdP4Z9/eZgFS2uAqF4ujnk1Ljv+452X4g93tiGdVXHrD5/FT5/uN+sRKyqaSOtxfktLeTdzAbYdPV8MzczhrV97Enc+dBz//vBxnJ7gWYbVLRbznHYbvvDWl+CdV2yAqgL/+74XcNcjJ8x6xIrK5VQcW+WNDQDoaeY6by1gYoOgquq84eHF2mtr8NM/340dbaIv6R7sO2P9vqSr7UEqcPG3tqmqirsfPYF3fHMvJvNtKPqm4phNZkx+Mlot0Xt5Xa1n3p/X+Vz4wft3YVdXPWaTGdz8rafw8JExMx6xoo6usj2BwJi39v34qT68+a49+roglsryhs4acK6Y53M78M13X4obLmhBKpPDh3/wLO57bsCMR6wosc7b3OKHvcwCFoBzNs4HD7wwjDd85fc4nj/UUNVCewuyLvF9q33B3tblsOHOP3kp/vTyTuRU4BM/O4Bv/M76t3TEbY2WoBu1XlfZ76cQ83jQvVb9/vgEbvy3x/F8/4z+Z0dG1kbx6vnsXDHPZlPwT6+/ALdc1wMA+MwDR/CZB45Yflj24Mwc4qksXHYbNjaUX8DCmLc2MLFBmJhNIZnJQVG0iuWFGvN9SS/dUIdIIoN3fOMp/O6YtfuSig3Laq6tAZyzsZZFE2l86Pv78C+/PIKcCtz0sg40B7QbOke5+LO0TDant2USraiKBTxOfPe9l+NV27S+pB/43jP47+eHjH7MitJj3qqTuaJFARd/a00incVf/98D+Jt7DyKVzeH67S3YlP8ed2iYMc/qFrbfK+Z22PGVP3sZbnpZB7I5FR/7yfP43p7TBj9hZfWucqCkUFjnMeatNZlsDnf88jA+9P1nMZvMYFdXPV62vhYAcGiYiQ2rEzGvo/bslsN2m4J/fuNF+ODV3QCAT/3PYXzhN72WPuhbba95gcnctUtVVdz1yAm885t7MRVL4YL2IK7e0gSAMW8tWCrmKYqCv7phG25/7TYAwF2PnMDf/fwFS9/IFkV7Pc1+OOzlH2sz5q0NTGyQ3oaqJeCBy7H4p0SoRmtXcM0WrS/pe7/ztKVbtByt1IaXh3xrUu9oFK//99/jVy+OwmlX8Ok3XojPv2UndrQHAQCHufiztLFoEtmcCqdd0ZNVC3mcdtz9zkvw+nxf0v/vx8/hgRes26KlYjGPydw1aWA6jrfcvQc/eaYfNgX4qxu24j/eeQle0lkLgJV8VpfMZDEeTQKY36KgmMNuw+fevBPvvnIjAOAf/utF/HCvdeerHK1YMpe31Naiidkk3vnNp/C1R7VK/Q+8oku7rdndAAA4wmSu5Q0t0n6vmKIouP212/BXN2wFANz54DHc+eBxw56v0sQ6b7Uxb1NRWxYrJ3poPlGw95kHtIK9N1/SgZ99+Epck09sMOZZ33IxDwD+/Joe3PGmi6AowA/29uEf//tFox6v4grrvPJb7wGFdd5wOMGuHBbGxAYVDZQ8+7ZGsRqXHV+/+VJcv70Z6ayKH1u0J2kincXpSW240qpvbHDDu+bc//wQ3vCV3+PkRAxt+ZkLb9+1AYqiYFurltjgIZ+1iZjXGvIsOWPHabfhi299Cd58SQdUFZYduKaqauVuqTHmrTmP9o7jxn97HAcHw6jzareVbrluE2w2Bdvyny9HmMy1tOF832WP04Y677ln7NhsCv7xj3bgz6/RqpitfGtDr15eZczblC9g0WZ2ZFf9XGS+Z/umceOdj2PPyUl4Xdptpf/9hzvgsNsKMW+EMc/KZpMZhOe0OSkL27IUUxQFt1y3CX/3h9sBaDHPqof5x8YqE/PW1/tgtymIpbIYjSQr8WhksuKCPZfdhn9+40X43Jt3wuO0Yztj3pqgqqrecnSpmAcAf3r5enzpbS8BAPzoqT4kM9Zc21RqnVfrdaHRr7XvO8XbuZbFxAYVZXeX703nctjwZ7vWAwBeHApX9bmq5eR4DNmcilCN85zV2islbmycnowhk81V4vHIJOlsDv/nF4fwkR89h3gqiyt7GvCLj7wcL11fp7/O9jbtGydvbFjb0DlmCi3GZlNw8+4NAIAXhyKW3PCOzyYxHU9DUaC3FipXd6P29tPxNKbyc2fImnI5Ff/24DG8+9tPYSaexs6OEH7xl6/AKzY36a+zoy1/S43JXEsrjnmKsvS8CUVR9Fsbx8ZmLXmYn8rkcDK/OV1t9XJTwI2A24GcCpzJF8WQNamqiv988gze9rU9GIkk0NPkw3/fehX+cGeb/joi5h0Zjli6Rcf5TsS8UI0Tfrdj2dd/xxUbYLcpmIylLHuYf3REKzhZbcxzOWzYUK+dCbCIxfruf34Ir/93rWCvPeTBTz+0G3+2a72+FtiWj3l9U3FEE2kzH5VWYSaexlx+vda2SGv5hf744nbUep3I5FQcG7Xm13mlbqkBQDfbUVkeExtUNGho+SAIABe2hwAAx8dmMZey3oa3uNf8chv85ayrrYHbYUM6q6Kfw1UtayyawNu/vhfffPwUAODD1/bge++9HA3++Ymv7fnF39GRKDe8Fnau4WrnsqUlAIdNwVQsheFwopqPVhW9+c3uxgYfPE77qt5XjcuuJ4S4+LOu8FwaH/zPZ/Cvv+mFqmrVWz/9891nJfvEDZ8zk3HEeD3bsgZmSot5rUEPGnwuZHOqvnG0klMTMWRyKgJux4o2+EtRFAXdbMFneXOpLD5+z/P4+5+/gHRWxesuasV/3fpybGqefyDS1eiDy25DLJXV1wpkPSutXBY8Tjs257/OXxi0XuHe5GwSE7NaQmbzKtuyADzkWwvS2Rz+6X6tYG8uncVVmxpw/0derrcYFep9LrQEtf2uOCMh6xHdCBr97hXt9RRF0c/0rBjz0tlCActq2ywDnLOxFjCxQUsOGlpMc9CDpoAbOdWaVZxH9Wtrq1/42WxKYfE3xkBoRfv7Z3DjnY/jqdNTCLgd+No7L8Ffv2bbokOoxIZ3NpnRv27IekqNeR6nHZvziyYrLv70mFeBzS5QNGeDMc+Sjo/N4o///XH89vAYXA4bPnvTTtzxposW3Qg1+N36zcaj3PBaljjk61ii73IxRVFwwbr8hteCt3OPFrUnWG0BC1DUgo8xz5KGZubwpruewL3PDsJuU/C/X7cdX/mzly1aye+w2/SDYSvucUgzUMLNXOGCduvGvN58xfX6ei+8ruVvqCynp5kxz8qmYym8/et78a3fawV7f3FtD7733l1nFewJotUyOxJYl0jELzVfY6EL1mkfdyvGvDOTMaSyOfiKCu5Wg62WrY+JDVrRoKGFLswPUX7Rgod8vRW8tgYwEFrd3/zsAMaiSWxp8eO/br0KN1zQes7XdRZteA9xyJplrSbmvTBkvY87Yx4V++T9L+LMZBzramtw74evxFsv61zy9cVNtcOMeZZVSvs9QY95g9b7uIuYV4kqPoCVfFb3+V8dxeHhCBr9Lnz/fbvwgau7l0x4MeZZn4h5K03mAsCF6ywc80arFfPYb96K7n70xLyCvU+8ZhvsS8wUZMyzvqESi/YAFN3YsN7HXbTe29wSWHJe5koVivYY86yKiQ0qGh5eyuLPwoGwaos/bnitZi6V1T8fvvfeXfrtm6XoA8RZ1WJZpbYoAAoxz4rJ3KMVGq4mcMNrXaqq4vn+GQDA3e+4RP+8Xsq2Ng4Qt7rVrPOsOE/tqN5ytEK31BjzLG3/wAwA4HNvvhi7exqWfX19gDhjnmUNlthmGVgjMa8C3QgA7m2tbn9+nff3N+5YsmBPEDMkOUDcugrrvNJj3uHhiOVmxR4drWzR3qZ8zDs1oc3iJethYuM8F0tmMBPXBkWdD9d1Y8mMflWvYomNZm54rap3NApVBRr9LrSusA93YfFnvaQeaQe7ZVUvW/S6rqqqOFbhxR83vNY1HE4gksjAYVNW3I5xu0jmMuZZVnk3NrR13pHhKNIW2/D2VjiZu6m5cEtNVbnhtZK5VBanJ7T1uWi7sRxRvcyYZ12FmOdd8dtsbwtCUbTvk2JehVVU/paaFvOGwwnMcr6WpaiqqicoVhrzRNEeZ0haVznrvA31XvjdDiQzOcudY+kxr0LrvPb83NxUNoeB6XhF3icZi4mN85wIggGPAwGPc8VvJw75ekejSGasM0D8WL5XaHPAjTqfqyLvUyz+jo9xw2s1YtMqFnQrweu61haeSyOW0mJWKdXLYsM7GkliLGqdAeKDM3OIpbJw2hVsbPRV5H2K3sv9U3Ek0taJ/1SIeT1NfrgdKxskrx/yDUf5Pc6CcjkVQzNazCql/V5nfQ0CHgdS2RyOjVoniTmXyqJvStuUViqZu77eB7tNQTyVxUjEOvGfgGNjUeRUoMHnQtM5+ssvJG5snJmKI8ZDXUsqp3rZ73agK79OetFCbUdVVS26sVGZmFfrdaHRr+2TT1nswPN8NxJJIDyXht2mYFPzygpYupsKMyRFAShZSzk3c202BTv0tqPWKtzrHats0Z7dpujxn4V71sTExnmunOFq4vVrvU6ks6qlNrx6r/kKLfwAoLtRWzSE59KYiqUq9n6p+sSQtG0lfD5ww2ttYsHe6HctOiz5XLwuh35TwUobXlG53NPkh9NemW/5TX43Ah4HcipwZpJVLVaix7y2lce87iYfnHYFUW54LWliNolUNgebArQEV37IpyhKof+yhW6qaUUmWow/16DUUrkcNmyo1yq/2X/ZWo4UxbyVDpJv8LvRHHBDVQvtLsg60tkcRiOlJ3OB4p7z1ol5I5EEovmbmGJPWgndvJ1rSSLm9TT5VlzA4rTb9CTIYd5Us6TBMoaHA7DkOi+RLtzEXOnt85XgnA1rY2LjPFfOcDVgwYbXQou/Ss/XAIAal11PDFntGt/5Tr+x0bbyGxsNfjeauOG1rHKu6gpimK6V5myI4WqVjHmKorAdlUWJ9gSl3FLTNrza5w9vqlmPqOJrDXpKTm6K27mWinlVWOcBPOSzqsNl3MwFCutCxjzrGQknkFO1hGSjr7Tkph7zLHTIdzT/fb2r0QeXo3JHO1znWVP5MY+zhawqkc5iMl9c21FC+z2geJ1nne91J8ZnkVOBOq9zxTcxV4Ixz9qY2DjPlTNEV7jAgj3neyvca14ozNlgILSK4h6kpdzYAOa3ZiFrKeeqriCGrL1gocVfb4XbEwj64m+MMc9KjgyLZG6pMY+DJa2qIjHPgrfUKp3Y6GlmiwIrOlr2Oo+HfFalx7yQBzbbym7pCIWiPQvGvIqv8xjzrOhIGTdzAWAHk7mWJWKez2VHsMZR0tuKdd6LQ2HLzFcpXuet9CbmSjDmWRsTG+e5wVVVL1tv8Sc2OJtbKndtDSgKhDzks4zRSBIz8dJ6kArbWzlA3Kr0q7rlJHMteF1Xj3klfo4vh4d81pPMZHEyf3V7e4mVfBwgbl3lticACjHv0FAEWYtseI9WeIiuwEo+61FVVT+k217CzVyAMc/KKhHz+qbiCMfTFX2uatFv5jZXqWiPbVksRcSsUtd52xjzLKs45pV60N/d6IPHaUMslcXpSWt8rVejGwFQvM6zxv8DzcfExnluqAKVfIeHI8hkcxV9rmqYjqUwFk0CADZzw3veEws37Rv6ymctAIUqGFa1WM9QuPyYJwasDUzPYSYu/zydTDaH4/mYVLUbG1z8WcbxsVlkcypqvU60BEu7ul2IeaxetprVtN/ravTB67JjLp3FqQlrrG8Kt9QqXcDCQz6rGY8mMR1Pw6ag5AKW4rYsqmqNpB5p9L1tqPSYF/I60Vmvvd2Lw9YoYqlWzNuUj3mnJmKWSWyf75KZLE7m1+Xl3szlDEnrWc15nsNu0xP/VrmdW61bat35QuWpWIpzcy2IiY3z3GqqWjbUe+F3O5DM5CxxuCWCYEddDfzu0q7pLYeHfNajt6EqsYoPmN+Kihtea1lNzAvVOLGhQetdaoUB4mem4khlcvA4beisK63n6nKKk7n8GrAGvT1Ba+lXt0XMOz0ZQzzFDa+VrKYVld2m6O0prHA7NzyXxnBYGxpc6QIWccg3Eklgloc+lnA4v87rbvKXXMDS0+SH064gmsxgIL9uIGvQuxGUsc4DCh0JrNBzPpdTcWysOrfU2mtr4HbYkMrmMDAdr+j7puo4MRZDJqciVONEa9BT0ttyhqR1raYDC1Ac86yRzBU3cyvdWt7rcuj/hydZrGw5lkls3HHHHbjssssQCATQ3NyMN7zhDTh69Oi810kkErjlllvQ0NAAv9+Pm266CaOjoyY9sfwy2RxGItoGsKOMQGizKXoFsxUGiFdrvgZQqATrn44jkc5W/P1T5em95svI9nc3Fja8YjFB1jA4o8W81S7+LBHzilqylNpnejkbGrxw2BTEU1n9+wjJ7UiZAyUBoNHvRqNf2/D2jnKxbyV6zCv3kG+ddWLesfw6rz3kQdDjrOj7DnmdaMwPqeSG1xrEOq+cG4tOuw2bmjlbyIpWfci3zjptR7V9Zw4uhw0bGnwVfd92m4JudiSwFLHO21pGAQtQ2BNztpC1rDqZa6G5udFEWv/3bqlwa3mgcGuDMc96LJPYePTRR3HLLbfgySefxG9+8xuk02m8+tWvRixWqJD/2Mc+hvvvvx/33HMPHn30UQwNDeFNb3qTiU8tt5FIAjkVcNlt+matVBdaqOf80SpdWwOARr8LQY8DqgrL9Cc835U7OBwAXA6bXrHO1izWkUhnMTGrtaMrd8N7wTrrXNc9WqUhuoB26CNur7A1izWsJuYBhTYFbMFnLYP5StuyY167dTa81VznARwsaTUi5m0vN+a1MuZZ0WoTGxdYqGiveI6avcIFLEDxDEmu86xgtTFP3NDknA1rWc38SKBohuRgRPpb+Mfy82xbgm7Uel0Vf//swmJdlklsPPDAA3j3u9+NCy64ABdffDG+853voK+vD/v27QMAhMNhfPOb38QXvvAFvPKVr8Qll1yCb3/723jiiSfw5JNPmvz0chJBsK3WU3Y1r8jwWuG6bm9+0FA1bmwoisIhaxaSyuRwPP+NsZxWVEDR4o8bXssQPUi9LjtqveVV81rpum41b6kBnC1kNSIJW27M286YZznRRBqRhNY2abXVyy8ORpCTvM96b5XaEwhc51nL4eHyb6kBRTGPh3yWoapqYa5QmdXL4pDv5ERM+lkDXOdRMT3mlbnO4wxJa1ptMndLSwBOu4LwXFr61ovF3QiqobDOY8yzGsskNhYKh7VDpfr6egDAvn37kE6ncf311+uvs23bNqxfvx579uw55/tJJpOIRCLzfpwv9CG6ZQxXE/QN71BY6g2vqqpVrV4GuPizkhPjs8jkVAQ8DrSHSutBKuiDJdmiwDKG8i1Z2mtryrqiDRQq+U5OxBBNpCv2bNUgKvmqVr3czJhnFePRJCZmk1CU8q9ui5sehxnzLEPEvFqvE74yZ4ttavbD5bAhmsygX/I+61znkZDK5PSPU6lDdIXiAeJkDVOxFBLpHACgtcz1fVPAjdagB6oq/wHv0XxrSK7zCFj9zVyRBOYMSevI5lSMhAv723K4HDa9ZeOLkt/OPVr1ZC5v5lqVJRMbuVwOH/3oR3HVVVfhwgsvBACMjIzA5XKhtrZ23uu2tLRgZGTknO/rjjvuQCgU0n90dnZW89GlspohukJ3ow8epw2xVFbqFkxj0STCc+l8v9DK9iAVuOG1DlF9t701WPYBt1j8yb7poYLBmdW1ZAG04XoiGSZzG7JEOovTk9q/l5V8JJJcGxt88LrKO+Aujnnc8FqDiHmrKWBx2m16WwuZB4irqloYKMlWVOe9kxOzSGdVBNyOsr/ni5h3ajKGeEruyn3SiMrl5oAbbkdpA+OL6T3nJb+dW/VbanrMk3ePT5qJ2STGo6KApbzPh54mzpC0mrFoApmcCodNQUuJA+OLXVjUjkpmvVVuObopv7ftm4ojmeHcXCuxZGLjlltuwQsvvIAf//jHq35ft99+O8LhsP6jv7+/Ak9oDasdogsADrtNv6otc8/5wqGOFx5n+QvdpXDDax1H9JYs5X9TFJ/3pyZjmEvxG58VrHaIrnCBBYbpnhyPIZtTEfQ40BIsb4bScth72ToKg8PLj3mbmv1w2BREExkMhTkw3goqHvMkruSbmE1hOp6Gomifq9UgkrmnJ+LIZHNV+TuoMorXeeUWsDQF3Gj0u6GqQO8o1/ZWsNo2VILec17ivW3xraRqHfJ1N2oxbyqWwlQsVZW/gypDnHVsqPeWfUOTMyStR8S81pBnVXN2rLDOA4CjVWwtD2jf9wNuB3IqcGZS7lvKNJ/lEhu33norfvGLX+Dhhx9GR0eH/uetra1IpVKYmZmZ9/qjo6NobW095/tzu90IBoPzfpwvVtuPT7BCz3m9B2mVFn7A/N7LMrflokIrlXL7LgNiw+vKb3i5+LOC1Q5XE/SqFokXf8Uxr9xDneV05zc/I5EEZiXvQ32+0+drrCLmuRw2/cCYczasoeIxzwLrvI0NvqoVsKyrrYHbYUMqm5O+D/X57vDI6uZrCNv1dlSMeVYgvi7LbckiXGiBApbTkzFkcir87vLb6i6nxmXXv3+cZOGe1FY7U0jgPDVrqVjMay/cUpP1VvbkrNZWFwA2l9lWdzmKoqCbczYsyTKJDVVVceutt+K+++7DQw89hK6urnkvv+SSS+B0OvHggw/qf3b06FH09fVh9+7dRj+uJQzmeyWvtqpFv64r8SHf0SoPGgKA9fVeOGwK5tJZjERYzSqzI/pwtdV9PrAdlbVUohUVUIh5L0p8XbfaveYBIFTjRFNAuw3CDa/c9Bsbq455HCxpJRUrYBExb0jeNmSFdV51NrsAYLMpekKXt3PlVombuQBjntWImNdRoZh3bGwWibSct7KLY161ClgAztmwCn2+xipj3nbOkLSUSsW87W1B2G0KJmZTGIsmK/FoFSduTq6v95bdVncl2IXFmiyT2Ljlllvw/e9/Hz/84Q8RCAQwMjKCkZERzM1pX8yhUAjve9/7cNttt+Hhhx/Gvn378J73vAe7d+/GFVdcYfLTy0dV1XmDdFfjgqKefLJueHurPGgI0PpQb2jwAmAglNnkbFL/hr3azwcu/qylUjFPVPIdG4tK24ast8q95gUu/uSXyeZwLL8Z2F6hSj4OELeGSrVl2dISgMOmYCqWwrCkbciMWOcBjHlWcaRiNzYY86xExLzVrvNagx40+FzI5gqze2RjRDcCgHM2rKJSMY9Fe9ZSqZjncdr1+RKy3lTrNaBoDyieIcmYZyWWSWzcddddCIfDuPbaa9HW1qb/+MlPfqK/zhe/+EXceOONuOmmm3D11VejtbUV9957r4lPLa/peBpz+QqUtlVeX93SEoDTriA8l5byan4up+oZ3mr1IBX0QMira9LSe5A2lN+DVODizzpyORXD4coc8jXn+27n1MJGQjZG3NgAimMeF3+yOjURQyqbg89lR8cqP/e3sUWBpQxWqEWBx2nH5hYxQFzODe/RKg+UFBjz5DcVS2E0ki9gWeXng1jnHRmWt3iLCip1S01RFOl7zhvRjQDg3tYKMtmcftaxfbW31PJvzxmS1qC3HF3l+h4ALhBdWCTtSHBUT+ZW72YuUJzYYMyzEsskNlRVXfTHu9/9bv11PB4PvvKVr2BqagqxWAz33nvvkvM1zmciu9sUcK+6F7HLYdM3Di9KuPgbmJ7DXDoLl8OGDfXeqv5dheu63PDKqjBfY/UbAbH4O8wNr/TGZ5NIZ1XYbQpaAqsbpq0oSlELPvkWf7PJjJ5kNq6qhYs/WR0uur1jW8VgQQDYno+bpyZi0rbnIE06m8NoND88fJWHfEBR/2UJY56qqoVbatWOeWzLIj1RcLC+3gv/KgtYepp9cNgURBIZDEl6W4kKxM3cShzyFXrOyxfzACNvqTHmye70ZAypTA5elx2ddas762gOeDhD0kL0mFeRdZ7cydxeg5K5m5rzt9TGZnm+YyGWSWxQZVVq0JBwYVE7KtmI7O6mJj8c9up+ynPxJ78jFRquBgCbmv36hlfW9hykETGvNeipSBwQMe9FCauXj+VjXlPAjXqfq6p/Fw/55FeYKbT6mNcUcKPB50KOG17pjYQTUFWt+KTRv/o4IFrwyRjzBmfmEEtl4bQr2Njoq+rfxVZU8jtawQIWt8OOTfnvc7ypJrd4KoOpWApAZfa3esyT8JBvLpXFmSltblzVb6nlD/n6puJIZljQIKPDw5UrYAHYkcAqVFXVb6lVNOZJuM5TVbXoxkZ1Y976eh/sNgWxVFa//UnyY2LjPFWpQUOCzNd1jepBCnDDawViHsZqr+oC2oZXJLNkbUlEmkq1JxAKNzYkjnlVrmgBCjHv9EQcmWyu6n8flU6PeRX4Hqgoin5TTQznJTmJZO662pqKDJa1QszrafLDWeUClu5G7Xv+dDytH6KSXAqDw1efzAUKCRLOU5Ob6EYQcDsQqnGu+v2JApYjw1GkJVvfHB+bhaoCDT4XGv2ru4W8nCa/GwGPAzkVODMZr+rfReWp1HwNgTHPGiJzGcwmMwAqs7/dkb+lNhROYHJWrgP9kUgC0UQGDpuir8OqpbjLC8/0rIOJjfNUYdDQ6uZrCIXrumHprmwZNWgIALrzh9yjkSSiiXTV/z4qjdaDVFTyVWjxp7ej4uJPZpWOeRfkN7xHR6JIZeTa8OozhQyIee2hGnicNqSyOSlnLFFlb2wAhdh5iJV8UhuqcDJ3e1sQiqKtb8aict1QNDLm1bjs+v8pN7xyEod8lUjmAoXYyZgnt8F8S5ZKdSPorK9BwONAKpvDsVG5vtaNmqMGaAUNnLMhN5HMrUTRnvZ+eGPDCkTRXr3PhRrX6lrLA4Df7UB3/tbri5K1HRXrvK5GH1yO6h9hd7MLi+UwsXGeGpyu/IbXblMwMZvCWFSuDO/REWMGDQFAqMaJpnz//pOcsyGd05NxJDM51DjtWF+heStc/FlDJYerAUBHXQ1CNU6ks6p0LXl6DRquBgC2osoZLv7kE46n9b7wlbq1KGIeb6nJbbDCyVyvy6Efbkm34R0x7mYuUNSCj4d80snmCu0qKpXM1WMe13lSq/Q6T1EUaXvOG9mNAGCrZdkdGalW0R5nSMqs0t0IAHm7sOjzNQxb5xXmbJA1MLFxnhoKi8VfZQ53PU47NuUXPS9I1Jcvnc3pCYbNzUYt/tiOSlbiIK5SPUgBXte1ikL1cmViXvEAcdn6L4tk7mYDKvkAztmQmYh562prEPSsvjUHMD/mccMrr0rHPKBwO1e2/sviIHtzc/WTuQDXeTI7MxlDIl3hApZ8zDs1EUMizRkDsqr0LTWg0IJPtpgnEhubWwyKeeKQj0V70gnPpfUD7kolujhD0hqqEvP0dZ5ciXz9lpph53lib8uYZxVMbJynBqcrW8kHABeI/ssSBcIzkzGksjn4iloHVBurWuRV6au62vvSPu9Pjs9ywyuxSlcvA4X+yzLFvOlY4dac4Yd8Y1z8yaaSM4WETc1+2G0KZuJpjES44ZWVXslXoeploDBYUqaYl82pOJavqDO+epkxTzZHiqo67RUqYGkKuFHvcyGnQrobmlRQySG6gh7zZL2lZlQBC/e20hLFTOtqayoyWwbgDEmrqG7MkzOZa0Q3AoAxz4qY2DgPzaWymMwPPOyoaCWffIHw6IgWjDa3VK5CfzmFPqTc8MrmSBU2As1FG17ZevBSgUjmdlTwkE/G67pi4beutgaBClXoL4eLP3kdqUKLHo/TriezOEBcXlUpYJFwnXdmMoZUJgeP04bOCt1CXg5jnrz0mUIVXOcpiqInhxnz5FXpVlRAIeYdGoogm5PjhmIkUWgxadjN3KIZG7ypKZejRd0IKokzJOVXnZinFWyemYwjPCfHvNhcTjV0Zi4AvRPNcDihD2gnuTGxcR4Sbah8LjuCNY6KvV+R4ZXpuq64tmZURQvAtiwyE1Unleq7DGgbXtGa5TCrWqQUSaQRzS9KKlrV0l6Yr5LJyjFA3Oi+ywAP+WSmx7wK9V0WxPtjzJOTqqp6JV8lC1h25GPewPQcZuKpir3f1Sje7BpWwJJvy9I/FedNTckcFr3mK3hLDWDMs4JCv/nKJXO7Gn3wuuyYS2dxakKONc6xfMxrC3kqVqG/nA0NXjhsCmKpLEYjcs3SPN/pMa/C637OkJRfNWJerdelFwEekuSmWv90HIl0Di6HDRsafIb8nSGvE41+bW7uKd7OtQQmNs5DxdldRancJlBseIfCCUzOyrHoMXrQEFBoy3J6MibNYSdph9sD+c/9Si/+9A0vF39SEjGvzuuE11W5ZO7GBh98LjsS6RxOTsix6DlqcEULoG38FQWYjqcxFZPjsJO0CqejVWhFBbCST3aTsRSSmRwUBWgNVW7DG6pxYkODliiRZYC4uJlrZMxr8rsR8DiQU7WqRpJH9ZK5hWG6JJ9MNqe3RqzkXCG7TcGONrlaLZsR85x2G9bnYz+LWOSi31KrYNEewBmSVjBYhVlqQKELiywzJPXZkflWuEbhPDVrYWLjPDRYhUFDAOB3O9DVqAUAWTa8vSbc2GgP1cDjtCGdVdGfP1Al84kkV1vIg1qvq6Lvmy0K5FaNq7oAYLMphdYsktxU6x0RveaN6UEKADVFM4y4+JNH/3Qc8VQWLocNGytc4SQq+Y7wkE9KIuY1B9xwOSq71BdtCqSJeSas8xRF4U01CUUTafRPVaeARY95I1G24pHQaDSJbE6F066gOeCu6PuWNuYZWLQH8HaujOYVsFQp5nGGpJwS6SzG8zMVK72/lTbmGbjOA9iFxWqY2DgPDVVh0JCgB0IJMryJdBanJ7Uq6i0GHvLZbAq6Gwu9SEkO1bqqCxRveCPc8EpItN9rD1Uh5q2Tp5JPVVVTbmwA8/svkxzEbYotLX447JVd7m3PV0OfnIhxwyuhoSoVsADFczbMj3lA0S01sw75GPOkIQ4/WoMe1PkqW8CyKV8pOhNPsxWPhETMaw15Kt6STrZ5auIgm+s8GpieQyyVhctu04tLK4UzJOU2kp+z43HaUOetbEu6wgBxWdZ5+VtqTObSEpjYOA9Vq3oZKJ6zYX4gPD42i5yqtZ9p8le2emc5zPDKp1pXdYHChneaG14pVTXmSTRMdyyaRHguDZtSWIwZhYs/+VSrJQsAtATdqPU6kc2pOM5DDukMVrGARaZ5aqlMDqfybQCNr+RjiwLZiGRupedrAIDHaUd3/uCQ7ajko6/zqhHz2gt725wEA8RNq17W27LI0XqVCjN/NlehgIUzJOVW3IGlkq3lgULR3onxWcRT5g/OFl03TIt5Y4x5VsDExnmoWq2oALkO+Ybzmez19d6KB/zlsCeffI5U8cbGvA0vF3/SqWrMyx/yHRoyf8OrVywGPfA47Yb+3YVDPi7+ZCFa41Uj5imKot/a4CGffPSYV4VkrriZe3IihmgiXfH3X4qxaALZnAqX3YaWoMEFLHoylzFPFtVM5gJFw3S5zpNONZO5m1v8cNltiCYz6J82d6ZOIp3FZH6W2fr6yvbVXw6L9uRTWOdVJ+aJ98tWy/KpZsxrDnjQHHBDVeVY44vOC51Gx7z8Ou/URAxZCZLatDQmNs5D1TzkExveM5NxhOfM3fBO5AeYNxp8WwOw1oY3PJfGW7+2B3c9csLsR6ma+UN0q7T4a+Mhn6yqGfN6mnxwO2yYTWZwZsrcDe/ErLbZbaxwf+mVsNKNjUQ6i/d8+ync8f8Om/0oVSUO+aoX8zhAXFaiermjCjGv0e9GW34gudkfez3m+V0mFLAUYp7sLShzORUf+dFz+Kt7npf+WVdDHL5tr8KNDYAxT2ZinVeNmOe02/SPvdltR0VSw2W3IVjjMPTv7sm3WR4OJzCbNL+Kezl/9/OD+NB/7kM6mzP7UaqmsM6rTszbrsc87m1lo6/zqlDAAhS1ozI55iXSWUQTWrwxugPLutoauB02pLI5DJic1F6JL/z6KN75zb1S3LIxAxMb55lsTtV78lWjkq/O59IPDw+Z3JdvUoLExvEx+Te8Dx4exVOnpvBvDx1DMrM2e6UPzsxhNpmpSg9SQVRFs6pFPtVsReWw2/SDY7OHrMkQ8/qn4tLPXHj69BQePjqO//jdSUzlDwnWmlhRoq0aNzaAwpyNI6xelk41K/mAojkbssQ8E5K5Gxq8cNgUxFNZjEQShv/9pegdi+L+54dwz74BSySfy6GqhQKWqt3Y0KuXGfNkU811HlA8W0iOmNdgQjI35HXq68tTkhfujUeT+P6TfXjgxRE8c3ra7MepmiPVjnmcISmtahbtAcCFkgwQF/s0p10xPJlrsynotkjhXiqTw92PnsTvjk3gt4fHzH4cUzCxcZ4ZiyaQyalw2BQ0BzxV+TsuzPfle9HkxZ+o5GvwV3aA4Ep0NfqgKNptCNkPzp7vnwEAxFNZPH1qbS7+RKXJpmY/nBXuQSrsaOMhn4ySmSzGotpGsFqHfCLmmb3hFbfUGio8NHUlGv0uBD0O5FTtxp7MRMxTVeCx3nFzH6ZKekejUFWgKeBGQ5USXduLbqlxwyuXoSq2ogIY8wCtint9g9YWQfb+yyLmAcDDR9ZmzBucmUM0mYHTrqC7qToFLCLmnZyISZ/AP98MVTmZq8c8kw/5JooSG2awSqvlAwMz+q8fObo2D/niqQxOT2rfe6oxVwiYP0NS7KVIDtWOeRdIMkC8sM5zG57MBawzZ+PISASp/O20R46szZi3HCY2zjOioqU15IHdVp3gcKEklXzjJlYv17jsegZd9nZUzw8UPk5rdfGnV7RUaeFX/L5PjHPDKxNxQ83jtFXt8Kt4sKSZzGxFpSiKZfovn1cxr0q3NQCt77hNATe8komnMpiOa61Aq3bIJ1vMM2GdB1inBd+8mNe7RmNe/rbspuZA1QpYWoJu1HqdyOZUHB+T+2N+PlFV1YDq5XzMGzI3kT8RNTnmWXKdtzaTub2js1BV7XOhWp8PxTMkD/GmmlSqHvPyiY1jo1FTzzX01vIBs5K51ot5j/aOmz730wxMbJxnqh0EgaKefJK0ojKvqkX+QJjK5OYtVB5es4d8+R6kVbqqC2gDm0M13PDKprglS7UqPQoxL2zuhtfE6mWgKOZJ/vlfXMn3aO/4mhwIJ1qlVGu+BqBteEVrP/Zfloeo4gt4HAh6nFX5O/QN71gUcynzN7zVupW0HCus84D5Me+pU1OW6I9fqsI6r3rJXEVR9GQxY548wnNpxPNxqFrJ3K2tAdhtCqZiKQyHzWs9NxErVC+bwYox7+hoVN8LrCWFdV71Yh5QmCHJVsvyyOVUDM9Ur7U8ALSHPKjzOpHJqegdNe9jr3dgMSvmWSSZe6DoZu5kLIWDJheYm4GJjfOMEYmNC/LXdU+Mz5o6vEYEQqMHDQlWOOQ7OhJFKpOD3+2A3abgxHgMfZK3kSmHWIxV88aGoij64lJUS5P59L7LVYx5m1v8cNoVzMTTpm6exCFfkwk3NgBrbHhHwgmMRpKwKUDA7cB0PI3nizbAa8VhA25sAMX9lxnzZDFgQMxrCbrR6Hchp5rbfrF4eLgZrNCWJZHO6mugWq8T6ayK3x+fMPmpKu+wATdzAcY8GYmY1+h3weO0V+Xv8Djt2Jw/4DKzI4F+Y8O06mX527Koqqq336v1asn9tXg714ibucXvn62W5TExm0Qqm4NNAVqC1WktryiKFAPEJ0zswAIUr/PkjXkA9L2siHlrtVh5KZZKbDz22GP4oz/6I7S3t0NRFPz85z+f93JVVfEP//APaGtrQ01NDa6//nocO3bMnIeVVLWHqwFAc8CD5oAbqmpuRZOZQyUBoKtR670s+l/KSATBl66vxSUb6gCsvTYFc6ksTokepFW8sVH8/lnJJw8jkrluhx1bWrSFv5mLv0mT27KImHdK4uSoiHlbWgK4emsTgLXXi1RVVb2Sr9oxr3jOBsnBiJinKErRMF3z13lmJXPFjaXTE/LGvEPDEWRyKhr9Lrz+4nYAa/SQz6iYx3WedIyIeYAcHQn0AhbT1nn5mDcZk7bVycD0HKbjaTjtCm7evRHA2pwtdNigmLeD6zzpDORjXmvQU7XWiwCK1nnmJXP1va1JydyNDVrMm4qlEJ5Lm/IMy4klM3q3kPe/vAsA8PAabcG3FEslNmKxGC6++GJ85StfWfTln/3sZ3HnnXfi7rvvxt69e+Hz+XDDDTcgkTDvyqhsqj1oSDA7w5vO5vQe02a1ZdmYX/ydmpA3sSGu6l7cUYvrtjYDWHu9SMUQ3Ua/q+qHH4UbG1z8ycKwmKf3Xzaxks/k9nsb9UM+i8W8NTZAfDicQCSRgcOmoKe5OkN0Bb2Sjy0KpFHtweGCGKb7opnVy7PmtmURMW8oPCftbC3RnuDijlpct62wzjOzbWKlJdJZfa1d7Rsb4v0fHjZ31gIVGLfOMz/mTcbMXeetq62Bw6YgmclhJCLn+YooYNneFsQNF7QAAJ44MYFkRs4YXQ5VVQ2ZH1n8/k+Mx9bU/6GVGXeeZ37M029smLTO87kdaM6fH8m6v31hMIycqrUPe8ulnQC0/a4o/jlfWCqx8drXvhaf+tSn8MY3vvGsl6mqii996Uv4u7/7O7z+9a/Hzp078b3vfQ9DQ0Nn3ew4nxlW1ZJf/Jl1XXcqpmV37TYFdV5zM7z9U3PS9nB/vl/7+OzsCOHafPXyEycmpN2gl0MkGapd0QIUVy9HueGVhHGVfObGvOJkrlk3NjbUazEvPJfGdD4Gy0aPeZ0hXLNFi3kHBsIYX0PDr0XM62nyw+2oTlsOQcS8E+Oz3PBKwoj2e0AhmWtmJd+EyZV8DT4XAm4HVBXom5Lz1oYYKLmzoxZXdDfA7bBhOJzAURN7ZlfasdFZ5FTt41HtSvYtLQHYFGA6nl5T3zeszLCYt06CmGfy8HCH3Yb19fmOBJIe8ok2VDs7QtjRFkRzwI14KounT02b+2AVNBJJIDyXht2mYFO+RVq1cIakfIzowAIU1nmHR6JIZ3NV/bvOxezh4UBR4Z6kXVhEMndnRy1agh5sbwtCVYHHjq2twr3lWCqxsZRTp05hZGQE119/vf5noVAIu3btwp49e0x8MnmoqmpYILzA5Ou6IgjW+1yw2aozMHg57bU1cNltSGVzemZdJvFUBsfGtI3txZ212NYaQGvQg0Q6hydPTpr8dJVzeNiYHqQAsLlZ2/BOxVLc8EpiqMrD1QSzY55IJNgUmJbMrXHZ0Zrv9XpKwsWfqqrzbmw0Bdy4KP9xe3QN3do4bMBMIaEt5EHQ40CGG15piJhn1M1cMavLaJlsDtNxcw/5FEXBBtGCT9ZDPrHh7QzB47Tjyp4GAGurNcthUcDSFoCiVHfN73Ha9XY8h9iaRQpDYWP2ttvbglAUYDSSxFjUnNsK4saGWTEPADY0iLajssa8QjJXURS9cG8t9ZwXt2R7mnxVL2ApniF5mLdzpTBkUNHe+novAm4HUpmcaWt8s9ssA8DGBtnXeYWiPQC4TsS8NbTOW4k1k9gYGRkBALS0tMz785aWFv1li0kmk4hEIvN+rFWRuQxiKa2isj1kzIb32GjUlOp/UcVnVhsqQLst0lmv/T/LmOF9YTCCnKoNAW0JeqAoCq7blu85v4baUYnq5a0GJDZqXHY9q3+YgyVNl8upht3Y2N4ahE0BxqNJjJlwPX+8KJlrNymZCwAb84d8ZySMeacn44gkMnA5bHo8uG4tbnjzsceImKcoCraJYbrc8Eph0KBWVB11NQh6HEhnVfSaUP0/FU9BVc1N5gKF27kyxrxIIo2T+YGXF3fUAoDejmpNxbx87NnaUv2buQAKMY/rPCmIor1qJ3N9bge682v8F00oYsnmVL0jgVmtqIBC9fIZCeepZXOqfnNaj3lb117MO6zvbQ2Kea1inbd2z8msZNCgVlQ2m4IdJndhMbvlKCB3zAPmt1kGCuu8x46NS9s1phrWTGKjXHfccQdCoZD+o7Oz0+xHqpqBGe2LscHnQo2rutn99pAHdV4nMjlzNrwTUXMHSgpdEvecf76o77JwrT5nY20s/op7kIqWKdW2vY2LP1lMxJJIZXJQFKA15Knq31XjsuvXwc1oUzAhQUULUIh5pyQcpiti3gXtQX3Y3rX5xd/veseRMemadaWJ2LPdoA3vDv2QjzHPbJlsoe95tZO5iqLoRSxmzBYSLVnMTubKHPMO5qv4OutrUJ8v9Ll2ixbz9p2ZRiQh5yDMUh0purFhhB1c50nFqAIWoFC4Z0bP+alYCjkVUBSg3sRkbpfEMySPj80insrCW7Qmv2pzIxw2BSfHY+iT9GCyVEcM7EYAFK/zmMyVwYBBHViAophncjLXzFZUXQ3yxrzJ2ST6p7TPh4s6tI/VSztrEfQ4MBNPY39+73s+WDOJjdbWVgDA6OjovD8fHR3VX7aY22+/HeFwWP/R399f1ec0k1HtCYD5G14zBojLcFUXKFTyybjhFe0JLu6s1f/sqk2NcNoVnJ6MSxm8SzUWTWImnoZNQdV7kArbWwuDJclcIua1BDz6QXY16T3nzYh5s3LFPCmTuQsqWsSv67xORBIZPLcGFn/JTBYnDRqiK2xrZYsCWYxGk8jmVLjstqrPGgDAdR6sEfN2FsW89Q1edDf5kM2pePzYhDkPVkGqqurrLaOSuYx58kiks3phhyGJDTPXefmYV+91wWHAmvZcrBDzLlwX0hPeQY8Tl2yoAwA80rs2CveO6kV7Bq3z2gp7W86QNJ9RragAc2dITsflSObKPGPjQP7j0t3kQ9DjBKDNQnrFFtGFZW3EvJVYM4mNrq4utLa24sEHH9T/LBKJYO/evdi9e/c5387tdiMYDM77sVYNTmuH60YEQQC4wMTBkjK0ogIkD4R6D9KQ/md+twOXbawHADx8xPqBUGx2u5v88Dire0tJ0K/rsqrFdEbNFBL0ORsmLP70q7omticArBfz7DYFV28RvUitH/OOj80im1MRqnHq806qbRtvbEhDxLy2Wo8h88UuEC0KTFnnMeYt50C/aMkSmvfnemuWNRDzxqNJTOcLWDa3GFPAImLeifFZJDPGt9ulAnHA53XZUet1Vv3vu2CdiTEvan4bKqBwY+PMVBw5ydqcFFqyLIh529ZOzEtmsjgxrs072GZQMndLizZDcjKW0lvfkjmiiTQiiQwAY5O5h4Yjhrc1Euu8OpOTuWKu0Ew8jZn8bDdZFNZ5tfP+fC224FuOpRIbs7Oz2L9/P/bv3w9AGxi+f/9+9PX1QVEUfPSjH8WnPvUp/Pd//zcOHjyIm2++Ge3t7XjDG95g6nPLYihszBBdQWR4zbiuK1pRNbIV1aKmYin0TWmJrp3raue9bC0FQpFcMOqqLgBszx/0HB+bNWWgaimSmSy+8buT+jX+tcbIihYAuDD/sTfjuq4srag2Fl3XlamqK53NFfouF91SA4pjnvVnCxW3J6j2EF1ha0sAiqJ9Do5H5d7w5nIqvvvEaRwfW5uJZxHzqj1HTRA3Ng4PRwxv5SYO+cyOeWKdNxxOYC4l1yH3YrfUgELMe6R3XLqDyVKJeWZdjT7DCljaQx4EPQ5kcipOjMm1vl9IVVX89Ol+/cB3rRE3c9fV1hjyPU8U7Q1Mzxl+wDUhyc3ctpAHTruCVCanD26XxfP9S6/znjgxacrsz0o6MRZDJqci6HGgrcptdgWP065/r7PCPLX/fn4IT56cNPsxqkLEvFqvEz63o+p/n1YcakM8lTW8m0dhnWduMtfrcqAlqMVd2TqaPH+OZO41+aK9FwYjGIsaP/vTDCv+arjzzjtX/E7/8i//sqyHWc4zzzyD6667Tv/9bbfdBgB417vehe985zv4xCc+gVgshg9+8IOYmZnBy1/+cjzwwAPweIwJ+rIzariaIDK8h0eiSGdzhrSCESZicmx4RYa3fzqOTDZnara5mNjgdDX6EFpQ4XTdtiZ8+v8dxt5TU4inMvC6qv9Ns1r0XvMGzdcAtA1vwONANJHB8bFZfeiWjH60tw+f+p/DePLkFL7xrkvNfpyKM2q4miA+1oMzc5iOpVBn4I0xWTa8IuZFExlMxVJoMPl5hN7RKJKZHAJuh94rVbh6SxMURTucHQknqj6PpZrErQkjY16Ny46uBh9OTsRweDiCpkCTYX93qX75wgj+8b9fxEs6a/HzW64y+3EqzqjB4UJXgw8+lx2xlNYCbUuLcUUEE5K0oqrzOvXv+X1TcWw1sJBiKWPRBIbDCdiUQgJKuKyrDl6XHePRJA4NR856uZUc1edrGBfzFEXBtrYgnjo1hcPDEanXeU+fnsYnfnYA62pr8PhfX2dYwtsog/n5kUat80I1Tqyv96JvKo4XhyK4alOjIX8vIM86z2G3obPei5PjMZyeiKOjzmvq8wjJTFZfAy1M5m5p8aM95MFQOIEnT07qMyWt6EhRzDPy63lbWxAnxrV1nrjpLKMT47P4yx89B7/bgWf//g/gcshx9lIpeswzqIDFblOwoy2IZ/tm8OJQ2LDW3kCh/Z6Zg8OFjQ0+jEaSODMZx0vX15n9OAC0wgVxprdzQTK3KeDGzo4QDgyE8ejRcbzl0rU7R1pY8Vf6F7/4xRX9+NKXvlS1h7322muhqupZP77zne8A0Baa//RP/4SRkREkEgn89re/xZYtW6r2PFYzYHD18vp6L/xuB1KZnOE3FsSNDbOv67aHauBy2JDOqnqGXQaLtWQRepr8WFdbg1Qmhz0nyq92mJxNYjaZKfvtK8GMGxuKouh9nmVvzSI+D/acmEB6jQxOLmbkcDUACHic+sH+YYM/9nr7PZNjnsdpR3s+MXBaoiGN4nP9oo7QWS166n0ufRP86Cr6L8/EUwjHzR3Ga0bMAwr9l+WPeTMAtAonsz9W1TBgcAGLzaboB8pGz5WSpS2LoihSDtMV7Qk2NfvPqup0O+y4skc7kF1N/+VoIq3PdzKLqB7ebnDME3+fVWLe4MycVJ+flWJ0y1GgMEjZ8JgnyToPKAzTlakF3+HhKNJZFXVeJzoWfD4oioJrxE21VdzOnUtlMRYxdz8v1nnmxTy5b2wczK/3Z5OZNTk42ZSY1y5inrEf+3FJOrAAkHKdNxROYGI2BUc++bTQtfqcjfJjXjqb02+Dy27FiY1Tp06t6MfJkyer+byG6Z+S50CmUoxuy2KzKdjYqB3yGX51Lb/RMmJ45lJsNgUb6vP/BxIt/p7Pf6NfWNECaIu/67ble86XueHtn4rjus8/gjd99femtaNJZXI4PpbvQWpgJR8A9DSLBb/cceRQflMWS2X1z4m1pBDzjKvA79bbzxn7sRfJXLNjHlDUc16ixZ8e8xZUtAiFnvPlLf7C8TRu+NJjePWXHjW157rYdBge85q0Ci6rxDxVBfactP7g5IVEzOswaJ0HmBjzJKleBoqG6cq0zjtHGyqhsM4rL+Yl0lm84Su/x7WfewTTMfN6Th/Wk7lGr/OsFfMA4PfH117MGyxqRWWU7iZzvt6linmSr/MWu8lw3VYt5j10ZKysvWkup+Lt33gSL//Mw6aeE4mEmnnrPHk+5ospTjg+zphXEd2N+Y+94ed5crSiAuScpyZi3ra2wKKtOK/NzxZ67Nh42QWsf/GDZ3HVZx6yRDvLtXU3q4LWWl++RDqrZz2NzPCasdnL5VRMxeSpapFt8aeqKp4fED1IF28/UHzIV87i70u/PYZIIoPe0Vn9Y2G0E+OzyORUBDwOvYLcKBvyn/dnJPrmt1Aqk9OHzwHA74+vrZgHFLVlqTXumrxZCx/9uq5MMU+iz3895i1ySw0oHPI9fnyirNk4dz16AqORJEYjScMPeIXxaBITs0koitZ2wUhWiHnA/GqzNR3zjFznmRzzpNrwSrLOAwoxb2F7AkG0Ynmub7qsxMQP9vbhxHgM0WTGlLlSgFZJKObliFtjRmHMk4Noy2LkId9Gk5K5k7MSxjyJPv9FMnfnOZK5V21qhNOuoG8qXlbB5f0HhvBs3wxS2Rz2nZlexZOujlk3cwsxzzrJ3CfWZGLD2EJloGhmrNHrPJmSuQ0yrvNmAJw75l3cUYs6rxPRRAbPlhGznjgxgd8cGoWqAntPTq3iSY1RdmJjYGAAX/3qV/E3f/M3uO222+b9WAvWWmJjOD84vMZpR92CmQrVVLi2Zdw3wfBcGpn8MEQZevLJdnVtOJzAxGwy3zNx8UO+3T0NcDlsGJyZ0289rNSx0Sjue25A//2JcXP+3Xqv+VZje5ACwMZ8OyKZK/mOj80inS0krX5/Ym0t/maTGYTntFYz7Qbe2DDj6z2XUzEpyfBwoNCiQJaYN5fKondU2wiea/F3YXsIjX4XZpMZPHOmtMXbaCSB7zxxSv99ccLQSEfzm92NDT7DZyPpMc+kpM5KiMSPsNZinqqqhs9SA8xb48gyPBwAuky6nXwuxX2Xz5XMXVdbg60tAeRUrZqvFLPJDL7y8HH992bFvJPjMaSzKgJuh6GHPEAh5p2ZjEs7gF27uVxIbOw5OYmspM9aLjOSuabFPK7zlnRgmQIWn9uBXV0NAEq/qZbO5vCF3/Tqvzcr5k3MJjEeFQUsRic2tJg3FUshkpC3lWdxMnd//wxiJrfFrrTB6Xwy16QCFiO/301IlMwtjvtmdSNZSLQcPVfMs9sUfYh4qTFPVVV89oGj+u/NinmlKCux8eCDD2Lr1q2466678K//+q94+OGH8e1vfxvf+ta3sH///go/ojmeOjUl7UK1HEP6EF2PoYe8ZmQ3RRVfqMYpxcAo2VoUiM3u1pYAalxnX1sDAK/LgV1d9QBK78v3+V8fRfGXzkmTAuGRYXOq+ABgfb32Me+T5GO+GHFVV9xmea5vGvHU2ln8iZgX9DgQ8BiXzDUj5kUSRclcCRZ/G/TEnhyf/4eGw8jmVDQF3Gg7x+0tm03RhyE+WmLMu/PBY0ikC7c8TIt5YqCkCcOL1+c/5sPhOVNbcS1FxLyWoBs2RTsUHQnLM/tqtWbiacyltf/7c32eV4MZaxxVVYtubJh/yCfbOq9vKo6ZeBouu23JFk3Xbi0v5n3jdyfn3cY1Pea1BQwvYGmvrYHdpiCZyWEsau6ckXM5MT6rJ34CbgfCc2kcMul2TTVkc6oew41M5oqv96HwHBJp477fydSKSqzz+qfmpEiWRRNp/eDtXAUsQCHmlTpb6CdP98+7qXDSpKI9UcCyod571uykavO5HfrnXp+khXvFN5dbgx5kciqeOiV/pXkpxMxWI2NeR532/S6RzmE0aty6eTImTzJ3fb61fCSRwbQEM/pyORUHB0UHltpzvt61+myh0mLerw+NzptRY1bMK0VZp7633347/tf/+l84ePAgPB4Pfvazn6G/vx/XXHMN3vKWt1T6GU0xHU9LPxypFIVBQ8a1ZAHMuao6LslASaG4qksG+/uXbkMl6O2oSgiEz/VN41cvjsKmAK/YrA2mNCvDa1bfZaCw4J+Op6UdUCsO+V59QSvaQx6ksyqePm3e1epKMyvmiYqOM1PGVXGKzW7A44DbsXiy0khdRW0aZKhq2V9U0bLU4Vc5Me/MZAw/ebofAPSqGLNuqenzNUyIeU1+N7wuO3JqYYC1bETMu3RDPS5ap33/W0s950XlclPAvWiv3WoRs9Rm4mnMxI1pPRmZy+g3Dut95q/1xEHnaCQpRYGAaEO1vT24ZIGPvuHtHV/x96upWArf+J12Q+18jnlOu00fUCxLQmshEfO2twWxq1srVlpLN9XGo0mksyrsNgUtBg6XbfS74Hc7oKrGzeRU1cLNXBn2t+21NXDZbUhJMlj24GAYqqrdRGta4nNBxLy9J6dWHKvnUlnc+eAxAMUxz6S9rZivYULMA4o7Esgd87oafHoSay2t81KZQmLByFuKTrsNnfnvd0be0hLzIxskSGzUuOx60ZAMn/8nJ2Yxm8ygxmnHpqZztx++eksTFEVrYTccXlmszuZUfP5X2m0Ns2NeKcpKbBw+fBg333wzAMDhcGBubg5+vx//9E//hM985jMVfUAzPbGGFn+DJgzRBQoHXMPhBOZSxlS1yFTRAhSSO/1TcWTKHNxTSQeW6ccnXJcfOPT06SlEV3jl9HP5IPjGl3bg1Re0AjAvw3tkuFDJZzSf26EvrM9Mmf/NbzGHRauutgCu3KQlodZSL1KzYp6+2cvkMLTCBcRqiWSuDIPDAaCz3gtF0dqVTJo4VFZYacy7enMTbArQOzqLgemVHVZ84Te9yORUXLOlCX96eScA8xZ/xdXLRlMURfqe84VDvkLMW0uHfIMzxrehArQbni1BLfYYteEdL0rmGpnEOZc6nwuhGu1moAxFLPoQ3XO0JxAu3VgHv9uBqVgKB/KVf8v56sPHMZvM4IL2ID7yyk0A5LixYQZLxbyefMxbg+u81qAHDrtxN/QVRdETukbFvEgig1R+DynD/tZuU/SbmjIc8ok2VDuXiXk9TT501tcglc3hiRXOnPnuntMYiyaxrrYGf3/jdgDAyYmYKTdV9Pkapsc887/PLaY4mVtY562d9vKjkQRUFXA5bGgwuKjD6NlCqqpKNTwckGvOxvP5or0L1wWX/P5X73PhJfkbHSvtwvLz5wZxbGwWoRon/uWmiwBot2eMKl4qV1mrAJ/Ph1RK+4e1tbXhxIkT+ssmJtbOgmktLv6M7kFb53Ui6NGuShp1wCsGDclyyNca9MDtsCGTU02vZM3lVBzUe5DWLvm6XY0+bGzwIp1VVzRw8PFjE3jixCScdgUfvX4zepq04G/GId/kbFJvDbDV4B6kgsxzNlRV1Ssdt7cFcdUmrefsWjzkMzrm2W0KOuvzVZwGLf5kaskCAB6nHe0h8X9g/uJP77u8xFVdAAh5nbhkQx2AlS3+Dg1F8N/PDwEA/uqGrejJV8ycHDe+/2omm8OxUS3Wbje7kk/SORvzYl6PSOZOSnGrqBLELbUOg2MeYHwrJtnWeYBcA8QL8zVql3w9p92m3659+MjyN9WGZubwvSfPAJgf84bCCVP6mB8x8cYGIPc6D1gQ8/KHfE+fnpK2XWCpzFrnAcbHPP1mrluOZC4g1yGfHvOWWecpilLS7dzwXBp3PaKdc33sD7agq9FfKF4y4aZKoeWo2es88z/mi5mfzG3Q/2xyVs52gaUS50jramtgsxk9P9TYmBdNypXMBay5zgOKOhKsYJ2XzGTxxd9q84Q+dE0P2kI1+k0Vs27nrlRZiY0rrrgCjz/+OADgda97HT7+8Y/j05/+NN773vfiiiuuqOgDmumpU1NIS1BhXwmFtizGLv4URSlqS2LU4k+eq7qA1rtdfDM4ZXJVy8mJGKLJDDxOG7a0nPvamiCu7D7au3QgVFUVn/3VEQDA23dtQGe9V9/w9k3FDd9E6T1IG4zvQSroVS0SfPNbaCyaxFQsBVt++Jyo5HtxKCJ9Nn6lzIp5QNGAMaM2vPpVXTliHmDecM2FwvG0/gw71y1dyQcU9yJdPrHx+V8fhaoCN+5sw4XrQljf4IXdpmA2mTG85/qpiRhS2Rx8LrveHsVoMlcvJzNZPcm+vS2ISzfWweWwYSSSwEkJY3Q5BotmqRmt8PVuzAGvbOs8AOjKH/iYvc7LZHN4YVA73Fmu5ShQ1HO+d/mY9+XfHkMqk8Ournpcs6UJdT6X3grM6Fg/HUthJKK15NhqwlwhQO6YpxWwFKqXt7T40eh3I5HO4bm+GXMfrkKkWOcZVcAiY8zTb62Yn9gT1cvL3dgAiudsjC9b2PAfj51AeC6Nzc1+vPGl62C3Fc41jhtcuJfJ5tArCljMurHRKPuNjUIyt9Hv1mfO7Tm5Nm5tmJnMNXpfJ/a2fomSuXrMk+Dzf7+4pbZMMhfAvLZsqczSZ9s/2tuHgek5NAfcePeVGwEA3SYVK6uqir//+cEVv35ZiY0vfOEL2LVrFwDgk5/8JF71qlfhJz/5CTZu3IhvfvOb5bxL6dR5nYilsvp1bqsTLVFEFa2RNhq+4ZWrehko9KA2O8MrsrsXtIdWdG1bBMKHjyy9+HvghREcGAjD67Lj1nxrguaAG363AznV+CFjYrG5udmchR+gDXYDtFkLsjmU3+x2N/nhcdrREvRgc7MfqgrsWSNXdodMassCGF/FVriqK2HMM/nA58DgDABt6FvdCq5tFy/+lkrIPnN6Cg8dGYPdpuDjr94KAHA77HoPWqMXf8fHtL9vU0vA8CouQcwWkjHmHRudRSanIlTjRFvIA4/TjkvWa7dz1srt3CEzq5cNL2CRcZ0nRyXfsbFZzKWz8Lsd6G5ceQHLgYEZ/f91MSfGZ3HPPm2e0Cdes02fV2TW7Vzx962rrYHfrAIWsc6T4JBjofFoEpP5ApatrdpwdVHBvFbajg6ZmMw1fp0nccwzeZ03MZvE4MwcFAX6/Kyl7O5uhMthw+DMnL52WsxYNIFvPX4aAPC/btgKe35tJQ75jG61PDA9h1QmB4/Thk6D5wcKhb2tfMnchQUsANZcCz5TY55JhcqytKEC5LmllsrkcHgoX8CygmTuhe0hNPpdiKWyeOb01DlfL5bM4N8fPg4A+MtXbUaNS0soFXckMNJsMoP7nhta8euXldjo7u7Gzp07AWhtqe6++24cOHAAP/vZz7Bhw4Zy3qV0Lt+oDVl7fA0EwlxOxfBMftCQCVUtPOSTZ8N7YIVtqIQruhvgcWpVraKv50KZbA6f/7U2W+N9L+/S/98VRTFtwzsSFoO1jP/GLxSqWuRb/BVX8QlXrbGe86a2KDD46122VlRAcdw398BnpW2ohB1tQTQH3JhLZ/HUqcUXf6qq4jMPaDfU3npph17FBBQWf0Zf1xWVy6bGvAZ5D/mK2xOIA1m9Bd8aWOcBRTHPhAMPs1pRyRTzugzuP30uooDlonWhFSU5W4Ie7GgLQlWBx5a4tfGFX/cipwLXb2/WW/YBMsQ8c26oAYUE/pnJuHQt7UQBS1ejT692LbQdXRsFLIV1ngkxz+BDfSkTG5Ic8omY19PkR8DjXPb1a1x27O7WvhaWakf1lYeOYy6dxUs6a/HqHS36nxdinsF723zMawsZ34ZIEB/z0UhyxcPXjbKwgAUoXuetkZg3bV7M6xI3FKfiyBkwX0as82QYHC4U7+/N/J5/dCSKVDaHWq8T6+uX/1yw2RRcs2X5Fnzf/v0pTMymsKHBi7dd1qn/uVkxr9TuB6uatJVKpTAwMIC+vr55P9aCy3u0xMZKB0vJbGI2iVQ2B5uibWCMZnhbllkJ27Lom35zN7z7xUDJFbQnALRe+aLa4VyB8N7nBnFiPIZarxMfuLp73su6TdrwikDYbMLnuyBz7+XCVd3CjRZRybcWFn/pbA6jJh56GB3zxPBwmWKe3n7P5A3v/hUO0RXm9V8+svgh3yNHx/H06Wm4HDb85as2z3tZoZLPnMVfc8DMmKf92/un4shI1sazuD2BIJK5e05MmjIEtNLMrOQrblFgxGZvXMK2LEYnd87leb09wcpiHgBcty1/O/ccLfgODoTxPweHoSha5XIx02JeJD9nJWjeoUdHnReKolUVTsbkauO5VMzb3z+DaCJtynNVkgwxbzicwFyq+u12ZWy/Jw75+kz+nr+/hDZUwnVFHQkW0z8Vxw+f0s60PnHDVr0gAjB/ndcUMC/mhbxO1Hq15FGfZLdzFytg2dXdALtNQd9UHP2SPW859A4sJsS89loPnHZFmy8Trv58mUIyV56Yt75e+54fTWYwZeL3/P35ZO7Ojtp5sWkpy63zpmMpfO3RkwCA2/5gC5xFnV3MXuetVFmJjd7eXrziFa9ATU0NNmzYgK6uLnR1dWHjxo3o6uoq511KR2Tyn+ufli4jXaqB/MKvNeiZ90lqFLYoKPThNXPDm8rk9AqunSu8sQHM70W6UCKdxZd/ewwA8BfX9iC4oFLGrBsb4lC72cTF34Z67d8+Hk2aMlRzKYvd2NjV3QCboh1MmTEQr5JGwgnkVMBlt5kSB0TMM+qAV8aYV1zNaGZVy4Gixd9KFXrOn53MzeVUfPZX2g21d1+5EW0L2juaVb2sxzwTD/lagx64HDZkciqG8rdEZbFYzLtoXQgBtwORRAYvDoXNerSKSKSz+sFXhwmVfOK2TjRhzGZPypiXX+eNmfw9/3k9mVu74rcR7age6x1fNMknZqi94SXrzhpaa1rMi2oxpsXEZK7Haddb/Mp2O3exmNdR58WGBi+yOfWcNxKtRFQvmzFXqs7rRNCjtUAz4oBXxpjXFvTAnf+eP2jivqGUIbqCiHnPnJlaNMn3xd/0Ip1V8YrNjbgynxAUzIp5Y/l1nhlFqsU2SHIje6HFkrl+twMvyd/YfmINdCQwc66Qw25DZ71orV79j/24hB1Yir/nm3mmd6DEoj0AeMWmJtgUrXXxYkm+ux89gWgyg+1tQfzRzvZ5LxMx78xk3ND502PR0vaSZZ1yv+c974HNZsMvfvEL7Nu3D88++yyeffZZPPfcc3j22WfLeZfS6azzYl1tDdLZ1S3+hsNzeNe3nsKvXxyp4NOVxswgCBRuKxi12RMD1pokCoSismdges60gfS9o1GkMjkEPQ79NsFKXJu/urbvzDTCc/MXfz/Y24fBmTm0Bj24effGs96226SefOMS3NiQtaolkc7qGfcdRYu/UI0TF+U3BatpzTITT+ED33sG9zzTv6rnXI3iIbpmXNcWm7101pgDXtGKqikgV1WLTQHiqSzGl+jbXk0j4QRGI0nYFODCdcHl3yDvqs2NcNgUnByPnXVY9YuDwzg8HEHA7cCHr+k56231W2pL9G2uhnEJbmzYbIp+JdrsqvViqqrqSf3imOew27Cre/U31eZSWXzkR8/hG787uboHXQUR83wuO4I1xs8b0DZ72ueeER97GVtRhbxO1OW/55v1+Z9IZ3E03zZ0pe33AOClnbUIehwIz6Wxv3963sueODGB3x2bgNOu4GPXbznrbUXMOzUxa0h7CmE8ItZ55n4OrDfwoKcUesxrn/+9r9BzvvyYl8nm8Dc/O4B/zbehNUN4Lo1ofk9pxiw1RVEMHaarxzwTC7YWstkUPalt1u10VVVLbjkKaMU3XY0+pLPqWV8LR0eiuG//IADgrxbcUAMK1cvj0SQiBt58KtzMNfdzoDBbSJ51HgAcGtY+D4oTGwBwVQU6Eqiqik//zyH8w3+9YFqxlqoWEohmFLAAhTM9IzoSyNiKCii0oDRqdvBiSm0tD2hrVNFG9JEFbUdHwgl854nTALQbagvPTlqDHtQ47cjkVENvPhlyY2P//v342te+hte+9rV4yUtegosvvnjej7VAURS9L98Tq+hF+u8PHcejveP4xu9OVerRSmbmEF3A2M1eLJnBXFq7EizTdd2WoBs1TjuyBgeEYoU2VCu/tgYA6xu86GnyIZtT8fixwoH3bDKDr+QHDP1/12/We/gWK+7JZ+RCQJrFn+hHKdHi7+hIFDkVqPe5zvr/EYu/1cS87zxxGr85NIqvPnJiVc+5GmbHvOLNnhGLvwnRisonz+LP5bDpyXSzDnyez1fxbWkJwOta+WFv0OPEpRvzi7+im2rpbE4/yPnA1d2LDiMXt9SGwnOGtKcQxOLP7Ji3UcIB4sPhBMJzadhtCjY1zx+mXFjnlZ/M/dmzA7j/+SF8+bfHTNvw6oPD62pK+v5eSRv1Qz4jqpflGyoJFN9QNufz/9BwBJmcika/S080rYTDbsPVW85uzaKqKj77gBbz/vTy9Vi/SFFMZ10NnHYFibQx7SkEWdZ5+pwNiWLeuQpYgMrEvAePjOHHT/fj3x46buj3uWIi5tV5nSV9f68kI+ds6DFvkXWHmcyeszEwPYepWApOuzKvve5KFDoSzL+d+7lfHYWqAq+9sHXR274Bj1OPO0YW7o1J0I0AkHOdp6qqfmNjYcwTN26eODFZ9hrtuf4ZfP13p/C9PWcwMG3O7aTJWArJTA6KArSW8P29kozswiJuqTXJts4zOebFkhkcG9M+10tpOQoUbqo9cmR+zPvyg8eQzORw2cY6PS4Ws9kUPaFr5E010Y1gpcpKbOzYsQMTE9a/zrUcfZhumdXL0UQaP39Oy/ifmTLvYNPMIbqCUZs9cVujxmmHz23OQncxilJc1WLO50KhJUtpQRBAoed80eLvm787halYCl2NPrzlko5F325DQ74XYSJjWNV2KpPTW2GYfV1Xxjkbi/UgFYpjXjmLv3Q2hx/le9IOTMdN61tfGK5mYswzaOETTxWSuTJV8gHmL/4qHfN++kw/zkzG0eBz4X0vX7ztZr3PhVqvE6pq7HwRvS2LJC0Kzpg8W6WYiHk9Tb6zEvAi5j19egqJdOkHdKqq4vtPngGg9dydjpvTt16KmGfghlfGGxtA8Tw1cz7/RRuqUvouC4vFvN8cGsX+/hnUOO249ZWbFn07h92mf92bseGVJuZJVMDSO3ruAhbRavnISFQ/OCqViHmAeTeSze5GABi7xpmQ8MYGAENvrSxGFLBsaw3C7Ti7wG4pIuY9cnRc3/PsOzON3x4ehU0BPv7qs29rCD0m3M4dzRewMOadbakClpeur4XHacPEbBK9o+V9vGSKec0BN1wO41vLA0av8+RrRQUYP0dzoRcGw8ipQFvIU/ItfRHzfn9iQt/znJqI4af5LhufeM22c64du00YIG7I8PDPfOYz+MQnPoFHHnkEk5OTiEQi836sFbvz1cuHhiOYLqNn8M+fG0QsX8kyGkmWtWmuBLOrlwHjNnvjEg4OF7pMruQr59qacN02LRA+2juOXE7FVCyFr+fbbnz81VvgOMfsFo/Tjs467XDfqKoW8TngtCv6TSGzyLj40xMbrWe35rlkQx3cDhvGosmyvnE9eHhUX3hrbZjMqWopDFczMeYZtNkTtzXcDht8rtI2ddW20cAry4sppz2BIGLenhOTSKSzmEtlceeD2jyhW1+56ZyJc0VR0J3/2J+cMGbxl8xkMZM/UJelkk/OZO7ZMW9zsx9NATcS6Rye7Zs+6+XL2XdmGkfyrX8A82K9TOu8an+9z6Wy+tpatrWe0TPlFlrNOu+afJXei0MRjEUSyOZUfD5/Q+29L9+45Aa6x4TBktLc2JA65p1dwNLgd+uxsJzbuacmYvhd0e1t02KeWOeF1v46Dygc8jVIdmPD7BmShXVe6QUsl3fVo8Zpx0gkgSMj0fwNNW2e0Jsv6TjrgLyYPkzXoHUeUOg3b3rMa5Sv/d5SBSxuhx2XbawHADxeRrHyVCyFXxwY1n9/xqRYfz6t84BCMle2VlRmn+2sZp23vS2AlqC25xGjFr7wm15kcypeua1Z/zpZjDnrPANubFx//fV48skn8apXvQrNzc2oq6tDXV0damtrUVdXV867lFJzwIMtLX6oKrDnZGmLP1VV8Z9F2V0AprUgGpChqsWoQz5Jq/gAY68sLxRPZdA7WnrfZeHSjXXwuuwYjyZxaDiCrz58HLPJDC5oD+J1F7Yt+bZGDxAvXNX1mNaSQ9A3vFIt/s4eriZ4nHa9BU85vUgZ8wqM+nofL4p5Zn++L2TmIZ+qqmUN0RU2N/vRHvIgmclhz8lJfHfPaYxGklhXW4M/27V+ybctVPIZlMzNH/C57DZ9ro9ZzF7wL2apmKcoCq4ULfgqEPPMquQbmJEo5hm0znM7bPBLdDMXMHedBxSql0ttTwBo30PE7bZHesfx8+cG0Ts6i1CNEx+8+ux5QsV6DK7kS6Sz+sw3M2epAZLHvEUKWICitqNlHPL9QJKYJ8WNDYO+3hPpLGbz80Rku7FROOQ25/N/f9EttVJ5nHb9+//DR8fw2LEJ7D01BZfdhv9vkXlCxYxe5wFFyVxJYt5QeA7JjDlFuwstVcACFG7nlhPz7nmmH6lMYT6qaTFPig4s2td7/1QcmSrPjJW15WhXUWLPjPaz+1exzlMURZ+d+/DRMbwwGMb9zw8BAP7XEjfUgOJ1npHt9wy4sfHwww/j4YcfxkMPPTTvh/iztUQMWSs1w/v06Wn0js6ixmnXWxCZleEtDBqSYPFX5YWPrNfWgMIhtxnXdV8YjCCnarM+yrnC6nbY9UXBD/b24Xv5jc1fLTJgaCGjB4iLGwNNEiz+xde+LMPDVVXF4ZHFB0oKhcGSpcW8E+Oz+P3xSSgKsLVF63NrVv9VKWKeQS0KZBwoKXQ1mhfzTk/GEUlk4HLYsLW1tL7LQH7xl7+18d/7h3BXfmbMR6/fvGy7Az3mGVTJVxzzzE5ubSjqvWzkIOGlHF5kcHixq0TMK7Hn/MRsEr88OAKgsJnuM2udJ0Erqq6iA65qbvYmZE7m6us84z8PwnNpfZ1VTjIXKPRf/vWLo/jib3sBAB+6pgehmqUTpkav80Qy1+2wIegxN7klhofPxNMIm9SKbqFDKzzkKzXmJdJZ3LNvYN77Nj2ZK0H18mgkiXgqU7W/Ry9ecNgQkCyZK26t9E/PIV3lg86FsjkVLwyWX70MQF/nPXR4DJ/7lXZb4x1XbFj288roGxtzqSyiCe1zrDlo7nq/weeCz2WHqgL9U+bczF/oXPM1BLHO23tqqqQD+VxOxQ/2ai2WCzHPvHkygLnJ3PZQDVwOW74rQ2nV9KUoTubKdmOjs94Lm6LNmhXJFyOJNsvlxrzrtonZQuP6rdw/vrj9nOdCQreJN3NXqqzExjXXXLPkj7Wk3AyvqOJ7w0vb9SBrxiFfJJHWvxHKcHWt2lUthQ2vXNldoOig04SqrtUGQaDQl+9HT/UhlclhV1c9rtly9oChhYyu5BuX5KouML+qxaxWdMUGpucQTWTgtCv6x2UhEfOePDlZ0oyMHzypLfxeta0Zu7q1q4xmJHNVVZXjuq5Bmz1ZB0oChZh3ZtL4qhYR8y5oD8J5jlZ5yxEx777nBhGeS2Nzsx9vetni84SKGX1LTY95Jm92Ae2QyWFTkMrk9LkfZoqnMvqV+XMe8m3WYt6BgTCiiZUfTP70mX6ksjlc3BHC6y5sBWBeMle0ZTHzkE9s9mKpbFVnaslaxQcUingmZpMlfS5Vgjjg66yvQX2Z3w+uy7ej+u3hUQxMz6E54Ma7r9y47NsZfjO3KOaZndzyuR16IY2Z8xQFbYju0omNy7vq4bAp6J+aK+lm7f3PDyE8l0ZHXQ3ecYV2c9HstixmxryQ16m3vK3mzezJWGGdZ/bn+0ItAQ88ThuyOVVPsBvlxPgs4qksvC77km2jlnJtfh/7zJlpvDAYgc9lxy3XLX1DDSjsbU9PVL9yHSjEPI/T/OSWNjdUi/lmHfIvtFzM29EeRK3XidlkBs/nW/msxGPHxtE3FUfA48BfXKt9XpzPMc9mU7Ahn8yvZjsqcZ7nsptfvLCQ22HXzxeMPtObiqX0ZOJFZcyPBLRzHodNwamJGB45Og6HTcFtf7D0DTUA6G7UYt50PK3Psq2mWDKjJ7dWquzJMzMzM/jXf/1XvP/978f73/9+fPGLX0Q4vPJAUU1f+cpXsHHjRng8HuzatQtPPfVU2e9rV3c97DYFpyfjehXwcsajSTzwgtaL7x1XbMD6hsK1LaOJIFjrdZo6TFtcXZuYTVV1sydzKypx0Dk4PTfvSqMRxFXdctpQCddunZ/EWGrAULFChtfYGxtmD1cDtKoWv9sBVdWGaZtNLPw2NQfOOXjsonUhBDwORBIZ/aBkOfFUBvfs0wZPveOKDXoFoxkxbyqWQiKtfX211Zr3OdASdKPGaUc2p+pVNtUgc8zrqPPCblMwl86WXHWxWvtX0YZKuLKnAa6ipMjHX70V9mVuqAHzq5eNSOjoMa/EIXLV4LDb0JGvJpOhBd/RkShUVfv6ONctvnW1NdjY4EU2p2LvyakVvd9sTtWTucXrPDOql7M5FcP5yjkzK/nmbfaq+LGXOeYFPU69B77Rhx96G6pVxLydHbXzkiJ/+arNqFnB7CYR80YjyZI3ouWQKeYBcs3ZGJwpFLCc67DX53bgpetrAZR2O1cM0H37rg16wZpZLUdlaEUFGNOOaiIq781cm00xbZ6aWOdduC60orXZYjrrvdhc9HXy/ld0r6hCfF1tDdwOG1LZXFXX+ELx3laG5JZMczZWUsBitynY3V16Cz4R895ySSe25W9/95lQrAXI0YoKMKYLS3EBiwyf7wsZOV+pmFjndTf5EPSU13o44HHOm6Xxtss69Y/pUmpcdv1zz4hbG+LcoMa18nRFWYmNZ555Bj09PfjiF7+IqakpTE1N4Qtf+AJ6enrw7LPPlvMuK+YnP/kJbrvtNvzjP/4jnn32WVx88cW44YYbMDY2Vtb7C3qces/ZlS7+fvpMP9JZFS9bX4sL2kPYUG9e/1W9ctnE4WqA9kUkquuqWtUicSVfU8ANn8uOnGr84YcYNLSzzOwuoFW/ixZD129vxiUbVjZPR1S19E/HDbm1IMtwNUBUtciz+Cv0mj93ax67TcEV+cXfSlvw3f/8EKKJDNbXe3H15qZCz2kTKnnE1dimgHvZlkHVNP9jX73/h0IrKvlinsth0xdBRi/+KhHzfG4HLu/SFn8Xd9bihgtaVvR2Gxq8cNgUxFNZjESqf2thTKIbG4BcPedXEvMA4MoSW7M82juGwZk5hGqc+KOL2wvViyYcbI5Hk8jkVDhsypIDno3QZcCGd1LixAZg3Ey5hQozhcqPeXabgqvzN5g2NHjxtss6V/R2oRqn/vEwZMMbkTTmmTRnoJiIeT1N/nMWsAClt1o+MDCD5wfCcNlteOulHYWivel4Sbd7KyGZKRRLmHkzFygaplvVQz65Y54Ra93FFLoRlB/zgELhXp3Xife/omtFb2OzKfr3OyPaUcm0twXkWuetpIAFKH2dNzAdx4NHtPPDt1+xHp35or1oMoMZE9oOytCNADDmUH9S0sHhgmkxr391rfcEEfM8Thv+8lWbV/x23Qbezi2embtSZSU2Pvaxj+GP//iPcfr0adx777249957cerUKdx444346Ec/Ws67rJgvfOEL+MAHPoD3vOc92LFjB+6++254vV5861vfKvt9ir58K8nwalV8Wnb3HVdsAFDov2pGiwJZKloAGFLRMS5xICy+umnkQmA6ltITKTvX1a7qfX3sD7bgyp4G/P2NO1b8No1+FwIe7daCERWMYrMjw40NoKgdjwRzNpbrNS/ogyVXsPhTVVVvvff2XethsymFmGdCVcvgjPb/bHZFC2DM4k9UtTT45It5gDkDxNPZHF4cyi/+VnFLDQBuuW4TLu+qxz+/8cIVVw057Tb90MeIwZJjEt1SA+SqXl55zBPrvJUNEP/PPaKKrwMep12PeSORhOFtB0XMaw15yq5arRQj1nl6zJOwgAUobsFnTjJ3tRveD17dg8s21uEzN+0sqY2fkbdz9SG6vLFxlhXHvPwh354TkyuahyQql193USsa/G60hWrgtCtIZ1UMh41tQTQSLrTlaTC5DacRaxzRisrsf+u5mLHOA4pi3irXeTfv3ojLu+rxLzftRKCEKuieZuMGiIt1ntmDwwW5Yt7KCljE3vbZMzOYSy2/TvvRU31QVeCqTQ3oafLD47SjJSjaDhr7746nMpjOJ1PMPtMzorW6zK3lgfmtlo1UqWTuWy7txCs2N+KTf3xBSXvHHgPnqY1GS0/ol31j46//+q/hcBRaGzkcDnziE5/AM888U867rIhUKoV9+/bh+uuv1//MZrPh+uuvx549e8p+v1du0gLh709MLntI99CRMQyFE6jzOvG6i9oAFLJ6A1Nzhg/TlGG4mmDM1TW5q1rMuLomrq11NfoQ8pZ3bU14zYWt+OEHrtATNCuhKIqhczb0QbqSVPKJA04ZqlrE4PBzXdUVXp6v2Hzm9PSyh3TPD4TxwmAELocNb7lUq+4Uh3zRRAbhOWOrWmQYriYY0aJgXOLh4QDQJYbpGvj53zsaRSKdQ8Dt0Kspy7W7pwE//fPduKC9tEWk6EVqRCWfWPwtValmpPUS9V5eru+ysDu/4T06GtUrI8+lfyqOR3rHAQBvzxew1Hmdet9ro9sODkgwOFwwYp03Lv06z/gB4mORBIbDCdgUrS3LauxoD+KeD12p39xcKTPWebLc2LBizHtJZy1qnHZMxlI4Ohpd8nXD8TT+a/8QAOCdu7WYZ7cp6KgzpwWfKNprr60xvU2JIes8iVtRAUW3Vgw85Etmsvrn+mqTuZ31Xvz0z3fjhgtaS3q7HgNvbIxKdmNjfb2IeTIkNlaWzO1q9KEt5EEqm8MzZ5ZuO5rMZPGTp7UWy+/Mr/MA6F1YzIp5AY+j7BZElVJoQ2ZEKyo5Pt8XMuM8T1XVQsvRVSZz630u/Of7duFtl60v6e2MnKdWuLFR5cRGMBhEX1/fWX/e39+PQGDpbGk1TUxMIJvNoqVlfruIlpYWjIyMLPo2yWQSkUhk3o+FXra+Dm6HDePRJI6PLf2BFBUtb72sEx6n1galLeTRhmlmc4a0pSgm2rLIsOE1pkWBFgibJGzLAhRdXTPwkK8SLVlWS7+6tszXTyXINDwckKeqZTaZ0SsLltvw9jT50RxwI5nJ4dkz00u+rqhcvnFnm96bu8Zl1///ja5mkCrmGdCiYFL2qhYTKvlEzLuoIwSbSRXsPc3GxbxyFn/VtFGS9nu5nIojI6KSb+mYV+9z6ZviPSeWvrXxg71aFd8rNjfq6xpFUYqS2OdxzNMP9Y1ovyfH5/tCGwyoZlxIDEPd1Ow3bZ6eoRveaOktCqpJlnUesPLEhsth01stLtdq+Z59/UhmctjeFsTL1hfa0IoiFqNb8MnSax4oXuedn3OFAHPWeYeHo0hnVdR5nfpcL6MZeWNjPCLZLbXGwixFI4anL2WlMU9RlBW34HvghRFMzKbQEnTj+u2Fc0V9nprBxYpSxbz813v/9BzSVfrYT0jcgQWYn9A2qjPFUDiBidkUHDZl2SRetXTrBSwGxLwyEvplJTbe9ra34X3vex9+8pOfoL+/H/39/fjxj3+M97///fjTP/3Tct6lae644w6EQiH9R2fn2f1kPU67PmRlqUB4ZjKGR3vHoSjA2y8vZHeLh2kaveEdlqQfH1D9FgWpTE6vDpe/LYtxnweFa2u1hv2dC+lX16q86E1nc/qVbVnassjSh/Ro/rZGS9A9bzjoYhRF0dsULNWLdDqWwv0H8lV8RRUtQCGJZ/R1XdESoT1k/sffkKGSkle1iP8DI7/36TFvlRUtq9HTaEzMA+Rrv1cc88wYsCgMTM9hNpmBy27Tk+tLuUrczl1inZdIZ/HTZ86u4gOKYp7R67ywfOu8arYh1GOepG1ZjCjiWUiqdZ4Rraj09ntyfN8TVbzj0SRiBgxPP5dYMqOvuZZrywIUYt4TSyRzczkVP9irFTO+84oN825ImLfO0xJbZs+PBAoHvBOzSUQT1bmhLPP8SKAQ8wam40hljDnkLl7nmXVrx8ibuYV1nhwxryXggdthQyan6sUVZiilgAUoinnLtB0Vhcp/dvkGOIpaMm6oN2udl495EqzzWgIeeJw2ZHOqfmO40iYkj3mddV7YFCCeyuoH8NV2ID9HbVtbQC+eN5pY5/VNVT/Wj+aL9ppK+BwoK7Hx+c9/Hm9605tw8803Y+PGjdi4cSPe/e53481vfjM+85nPlPMuK6KxsRF2ux2jo6Pz/nx0dBStrYtfL7z99tsRDof1H/39/Yu+nt6OaolAKBZ+12xp0jO6ghg41G/S4q9VikO+6l5dm4xpgcVhUxCqMfea3rkYfXVNVVXsF4OGOs27sWFUi4KJ2SRUVfscqPfK8c1QHPQMVrGyYSUODa984QcAV/YsH/P+774BpDI5XNAexEsWHCKbFfOG9Jhn/uJPxLzB6bmqLACKk7myJja6iqqXjWrFqMc8E2+pGXVjI5XJYSqfzJXlxkZnfQ0UBYilsvrmxAyH8lV8m1v8K5oVoA+WPH7utqO/fGEYU7EU2kIevHJb87yXiZhndIsCcaggwzqvs94Lu03BXDqrtwuqtAnJb2yIZO5kLIVIlQ46FxI3NlbbnmA1igtYqj1MWrYbGyGvE3X5Vq9GH3gVO5IfotsccK+o0lVUL+89OXnO9ekTJyZxaiIGv9uB17+kfd7L1psU80QyV4aYF/A49cO3an3sZb+x0Rxwo8ZpR041rhXj/vwh304Tk7miYGJiNoWZeHXXOqNlDNKtJptNMaULxUKlF7BoMe+FofA5P2ZHRiJ4+vQ07DYFf3L5/ILn9WYlc2fkiXk2m1KYs1Gl86wJyVrsLuRy2PRWjEad6e0XbahMjHktQTd8LjuyObXqrTfLmaVWVmLD5XLhy1/+Mqanp7F//37s378fU1NT+OIXvwi327xPQJfLhUsuuQQPPvig/me5XA4PPvggdu/evejbuN1uBIPBeT8Wc1XR4m+xK3dLVfEBxVUtxgX/bE7VW1+115ofCEUQnI6nEY5XfrM3WTRQ0qz2I8sR/wdD4TlDBowOhxOYmE3CblOwo83MxEZhqGQ1K3hFFV9TwC3N50BzwA2PU1S1GDtgsdhKr+oKYvF3YGBm0cOZXE7F9/dqFS0Lq/iAQgWj0TdVCrfUzI95TX5tAZBTq7PxFwfadpuCWkmTuevqamC3KUikc3qP4GqaS2XRm+8XbuqGN1/JNxROIJ6qXgWvOPBw2hXUSZLMdTvseiWtmTfVSo15l2+sh8OmYHBm7pxfr6L13p9dvn5eFR9gXu/lwo0N82Oes+iGcjU2e+lsDjNxuZO5frdDfzYjbm2oqlqxgZKrsa6uBi6HDalMrqprnWQmqw9RlaV6GZDjdm6pMW9HWxB1Xidiqaz+ObTQfz55GgBw08vWndXmzKxWVCKZK0PMA4o6ElTrkE/yxIaiGH/IrQ8ONzHm+dwOtOZvyla7NYtsNzYAOWJeqQUsLUEPepp8UFXgyZOLF+6J2xo3XNBy1k3o9SYX7cnQjQCofswTxcqydmABjG8vf0CCoj1FUQxrR1W4sVHlVlSC1+vFRRddhA0bNuDXv/41Dh8+vJp3VxG33XYbvv71r+O73/0uDh8+jA9/+MOIxWJ4z3ves6r3e+G6EIIeB6LJDA4Ohs96+f8cGMZMPI11tTW4dmvzWS8vHPIZFwjHo0lkcyrsNkWKDL/P7dArSqvRjkoMlJQ5CDb6XfC7HVANqmoRG5WtLQHUuMy5tgZoFQ52m4LZZEZfnFXDqGS95gGtskEshMzsv1zqhre9tgZdjT7kVGDvybOHrP3u+ATOTMYR8Djwxwuq+ABz2rKkMjk9DrRJcGNDUZSq9h4Wm916n7zJXKfdhs4qHnQudGg4jGxORVPAjTYTNwB1Ppfe8q2arVmKF34yfQ6Y1ZapWKkxz+d26P3jF7up9uJQGM/2zcBhU/C2y89uW1r4NxuczM1veGWIeUBhw1uNzZ4VkrlAYdaIEd/z+6bimImn4bLbsK3VnL7LgPYxETf0jlfxdq5o++By2KS6nW1WW6ZipcY8m63Qc36xmDccnsNvDmldEN6xaNGeSQUs+WSuNDGviuu8TDanJ/IaJG3LAhR3JKj+5380kdY7AJhZwAIUbueerGLMS6Sz+u1sGc5zBLPaMhUrNeYBwMs3nTvmRRNp3PfsIIClY95IJGFIgaogbcyrUuzXW1FJOjMXKGo7asDnfy6n6ufPZrZZBoybpybOCxuDVW5F9da3vhX//u//DgCYm5vDpZdeire+9a3YuXMnfvazn5XzLivmbW97Gz7/+c/jH/7hH/CSl7wE+/fvxwMPPHDWQPFS2W0Kdvecuxfpf4pefLvWw77I4YIZbVmG8kGwJeBe9JnMUM3Fn96DVKJD7YW0g05xda36nwsytKECtApecbhZzUA4pl9dlGfhB5hf1ZLLqTia70G6YwV9l4VCO6qze86LyuWbXtYBr+vsYaVmxLzRSAKqCrjsNjRI0n+9mos/2av4BCNnCxW3oTKr77JgxOJPj3mSzNcQzI55AHB4RGx4S4h5ou3oIrOFvv+k1m70hgtbFz1c0Cv5pucMa7uWSGf1w34Z+s0D1Z0xYYVkLoCqt2koJlqybG8PwuVYVb3aqnUX3c6tFj3m+d2mx/hiUsS84VXEvEXWeT/a24ecCuzqqsfmlrPfZ2e9FnMiiUxVbuKfy7BkNzb0Q/0qJnNtCqS5lbkYIweIHxwMQ1W1Qcpmt6rpMaB6uTiZG6w5e79llg0GHuyeSzmJjSuXmCH58+cGEUtl0dPkw+7uhrNeXud1GlqgKoiY1yZNzKteGyYtmZvvwiJxsbKR67yTE7OYTWZQ47RjUz7mmKXbgHlqc6ksogmt20EpZ3plrYAfe+wxvOIVrwAA3HfffVBVFTMzM7jzzjvxqU99qpx3WVG33norzpw5g2Qyib1792LXrl0Veb/6MN0Fi78XBsPY3z8Dp13B2y47u4oPMKeSZ0RU8UkwaEjoquLVtcIhn7wLP6Cw+TEiEB6QoB+fYMTiT8arugCwUVxXNHBofLEzU3HEU1m4HTb9G/FKvPwcMW9wZg4PHTl3FR9QiHnDkQSSGWOqWkTrvdaQR5pDr+rGPLmHqwnVrOBeSKaYpw+WrGbMy3/Ot0iW0Ndjnkkb3mgijf4prbhjRwkbXrHO23Nicl5yIpJI4+fPaVV8i7UbBYC2kAcOm4JUxpi2a0DhtobXZZfmwEN87Ksb8+T6fF/IyEM+GVqyCEbMU9NjHtd58xQP0S0p5uVvbDzbNz2vbWI6m8OPns63WN69eMzzuhz6wbJRrZajiTSi+QHt0lQvV3FfJ2JevU+eIsXFdBm6zsvPFJIg5nU3Vv/Ghpgp1BKUK5m70aRbqsXKKWC5orsBNkVbm4uzMkBr6ygKWN6xSItlQCtQNXq2kKqqerGyLAUsVb2ZG09BVbVkbr0kRYqLMXJu7vP5or0L1wXPaoNrNEPWefmY53HaEHCvfG9T1v9MOBxGfX09AOCBBx7ATTfdBK/Xiz/8wz/EsWPHynmXliCu6z5zZnre9TPRi+91F7Wdc7MlguBMPK1fJ6y2IYkGDQlVrV6OWqN6WT/orPJCIJdTcVDf8NZW9e9aiUIlX/U3vDJd1QUKyaxqD1o6F1HRsrU1UNI3xN09DVAU4NjYrP5/CxSq+K7sacCm5sUrBxp8Lvhcdqgq9APGahMxz8wWRAvxxkZ1K7gX0g/5TL6qCxQNEDfgxkazZId8ZlcviwO+tpAHtSVUuV7cUQuvy46pWEp/HwBw37ODmEtnsbnZj11d9Yu+raNovoRRrRmKh+jKcuBhzDpP3s0uUNR/2sBk7nmzzitjoKQRzI55ffkCFpfDpn/PXYkNDV6sq63B/8/enYfJVdf54n+f2nutTu/dSXc2liRAIAQSgoggsogGUeSOICozijNcnfEC4wAzjoiOcnEZdUYHFUf0d8F1QBmcmQwoo8hMIkoIMSyBhJCll6TTne7qtaqr6vz+OPU9dXqvU3VO1/l+z/v1PHkeEyptdbrqU9/ls0xmdPzu9RPmnz/+wlH0DSfRVBPFZeta5/77i9yORhxE1sZCM2Z+lMsKF1vPyZK053ZrGisz5nlinef+Id/RhDdjnvicOzgwtmhVqlbFJrDEK8I4Y6lxKWZN3Pvd6yew9+gwKsJBvOvsZXP+/cVutTo0PomJSWO+r1fO9MRnTNeJcaTSM2cPl+L4sLjMjXj6MlfEvIP9Y67OjwW8us5zb25uPlHZ3t6mqIuNjo4ObN++HaOjo9i2bRsuu+wyAMCJEycQi3njDeeG1U1VaKmNIpXO4ve5xd/Q+CR+tmv+LD7A6N0sFiWL1Zqlx2ODhgBL32E3WlGNSpK9vEiHfK8dH8VwMo1YOIBTWspbtgYsbsWG9w75ypu9bJbq2uy/XVcZwWntxt8RLfhS6Sx++Dsjo2W+mKdp2qK3o8r3mvdizHP+36CfG94phsYmzcyZ9Uu9kMm3GBUb3tzweibm2djsAkarh025iwux4dV13Ww3+r4ts2fxCR2LPEzXbMnikSw+IL/hPdjv/GGHGCjp9cvcFS6uda3Smayl73L5Y96irPMS3l7n9Sxy73VBxLw1NhNYNE0z247+j+WQTwwNv+7cjnlbnC129nK3x2YKAfkD3oHRlOPJi7IksIiY58ZB53Qie9kTFRu5mHdoYAyTGXe+72MenB8JTK1S7U0sTpWqVbEJLIClHdWUmGes867e0D7v/KbORb7M7c6t8+qrIoiFyzcv1aqpJoqqSBBZ3fnYL8PgcABYtqQCwYCG8cmMefnoll2iSs0Dl7krG6ugacYZuDh7dVqxM3OLutj4P//n/+C9730vli1bhvb2dlx00UUAjBZVZ5xxRjFfUgqappklu6Iv38PPHsHEZBZrWmuwcfmSef/+YgdCrw0aAvIHXAeOO3/Ld1yC4eFA/qDT7deBuN09rT1e9rI1IL/423/MzawWr7YoyFVs9I8hU4aslmL6LgtmzMst/ra90IvjIyk010TxlnXzzy5a7GG6PaJiw0Pt98TPvnto3PHDDtGioMHrG15LdpObWV27uwYBGJ+1SzxQviwy+V47PuLa93102JsxT7z3h8YnMTjmzsJ3Po7EvNw6b8drA9h3bASVkSDeuWHpvH9XfN+LdciXX+d552JraV0FQgENyXQWPQ4fdpgxzwPv7/mIuH9ibNLV2QOvHhvBxGQW1dGQeZFaTiKTr284icSEO993fp3nndc8YLwmy9F7XSg2gQWwtFrOxbxXjw5jx2sDCGjAezZ1zvt3OxsW+zJXrPO88/OviobMAxinLzP7zXWet2NeU7V7B51Wx0eS6Boch6bBzLovp7baGCrCQUxmdNeSuI5aspe9JBQMmMkc5RggXmwCCzB1nafrOvqGk9i2pwcA8N7NcyftAfmYt3hJe95b52ma5lprdfMy18ODwwEgbKnSdjNxL5XO4qVu47XuhZajsXAQS3PnLG6d6RWbtFfUaef//t//Gzt27MB3vvMdPP300wgEjC+zatUqT8zYcJO44f2ffUYgfPC3xu3uXL34rMwy5UVqR9PtseFqALC83vg3SEykccLhzZ4YruXl4eGAuwedVrs91IYKyA/S7R4ax3jKne/bqy0K2uIxhIMaUpnyZLW81GNktRSz+DNj3v7+XP9RI+Zdt6kT4QUuzJZbypQXQ7cHq9TqqyKoyR12OL3ZkyWTz82DTisvtaECgI4lFQgHNUxMZs3+uE7zasVGZSR/0FOODe+LJcU8I3v5mQMDSKWz5jrv6g1LURObO4sPyK9xFjvmeekyNxQMmIk8jm94JVnnTTnodHHDKxJYzlga98RcqZpY2Py+3apUM4eHe+w1YBz0lG/ORj7m2b/MFRUbL3QnMDiWwkO/Napy37K2Be0LxJb8DMlF2tt6sGIDcK8yVZZ1npsHnVYi5q1uql7w83gxBAKaWaXoVqWaWOd5LeYBi5/AZlVKAss5K5YgEgrgaCKJ/X2j+PHvD2Myo2NDZx1OX+DCrGzrPI/FvJVuxbxhOWapAYszQHxv7zBSmSzqKsPm2rrcRHXuay5938V2YCk6jXvjxo145zvfierqfIbQ2972NrzhDW8o9ktK4Q25De8fuobwny/04rW+UVRHQ7h6gSw+oAwtCjxYsVERCZo3zk4P25GlFVV9VQQ1MXcOOq2eN3uQlv92FzC+77rKMHTdnUFL6UzWbM3jtRYFoWAAHUvKs/gbGptEVy7DbU0Rh3znrliCcFBD1+A4nnjxKJ45MIBgQMN1C2TxAYx5gLHZW+HSgDFZhoe7edBp9fzhQQDeyGgBjO9bbPTdO+TLlet6LOYBizs03iqT1bG3t/hMvrWttaivimAslcEvXjqK/9zTCwC4YYEsPsAa8xa3Ss1Ll7kA3It5oxJteBehBd/zZnsCb8Q8wNKOyqVMPq9WbADli3lAadnLzbUxnNxcDV0HfvnSMTz87BEAcw8Nt+pc9PZ73ox55vxEh2NenyQXG4B7B51WXmpDJZjVuS7N2cgPD/fWax6wxjy5Elhi4SA2dhqdVn7zah8eEu1G52mxLFjb7y3GbBEz5nkoURnIt59zfp0nRwcWwDJA3NV13iAAYP2yOs/M0nN7nVfszNyiLzaOHDmCf/qnf8Idd9yBW2+9dcovlbXFK7CqsQpZHfjEz/YAAN519lJUFzDAbPki9iGdzGTN2y4vlesC7txuZrM6BiTZ8Gqalh8s6eIhn8iSPbnZfiaDGzRNw6rcB8Brx50PhP2jKWR1IKB588NwsYeNCS/lDviW1lXM2zN0LpWREDbkFn9//dM/AAAuW9dS0ACzxYx5QH6opOdinktzdWTJ5APcO+i0MmNeizdiHpCvVHNjsGQ6kzUv9L1WsQGUL+a93j+KicksYuGA+VlrRyCgYcsqI4nlk4/uQTqr45zlS7CufeHNcz57eZHnCnmoYgNwL4tNVGx4vS0LkG/B527MM772KR5Z5wGWwZIurPOAfHW21/rNA+WLeUPjpSWwAPl2VPf8x0sYTqaxsrHKbNcyn85c9nJPYgLJtPuzRUTVs+dinkvrPFlaUQHuHXRamTHPJ+s8wFqZ6+WYJ1cCC5BPVv76f+1D99AEllSGceUZbQv+vfa6/GwR0Q7WTT0erdhw6yLfrNjweCsqIL/OczNpL7/OK3+7USG/znO3YsNum+WiLjZ++ctf4tRTT8V9992HL33pS/iv//ovPPDAA/jOd76DXbt2FfMlpSIWfyJb9oYCbneBxV3wHhtOQteBcFBDo8cOed3IYjsxljJnF9R7vPcy4P4A8bFU2rzoWbrEOx+E+Rte579vsfBrrI4i6IGWDNMtL1Mm34vdpS38AOCCEmPeYmS1JNMZ8/l5aZAuAKw0Byn78zIXyC+A3dr86Lpu9jVf6qEDDzFbyI2KjeMjKeg6EAxonpw5UK5DPhHzTm2tLfqzoNh1nsjkGxybdHyI7GzyG15vXWyJWWJutWVpkiHmubzOA4AjJ4zDbL+s8yanXOZ67zWw2BebgqjWKDaBBZgZ8967ubOg9maN1RFURoK52SLutFy06vZqxYY41Hf4806qmGeu89x7/ZsxzyfrPMDblbnlWucdOF5aAgswM+b9r3M6ChrOHQoGzM/cxahUE90IvFaxYVZoOdx6UcakPTfbT3p6nefSZe7RxazYuPPOO/GXf/mX+MMf/oBYLIaHH34Yhw8fxpve9CZce+21xXxJqYgbXgDYtLK+4KwBMWyoZ2gcqXTWlecmiLK1ltqYJ/ruWq10IaNDbHSWVIYX7PvvBW4cdFp15YJgTSxU9CbHDebiz4VMPi+X6gKWxd8i914WG951RfQgFawxb1VTldmPeSHtdRUI5mYriNt3t4hqjWgogLpK77zmAXeqFQbHJ6W6zM3HfXde/0PjkxjNze5Z5pPFn1j4NVVHPfc5D1hm7CzyZa7TMa++KoK3ntFa0N+riobMzZjbgyXHUmnz8sRrFxtuxDzZLnPN1jQuHXxkszp6crP0vBTz3KzYENUa4aCGJZXe+9wrd8wrJYFl86p6iI+RaCiAd29cVtDf0zRt0dpR6bpuXuYWUjW8mFiZa2nL4uJlrqhM8lLMc7NiI5nOmDNJWzxZmZuPebruflsmQcS8UhJYzlgaR02u44qmAddvXrjFsiBi3mJcYpsxz2PnGyLmOT0ztn9UxDzvfcZPZ22/51YCZz7meWO+BpCPeYcHxlyp1FzUGRsvvfQS3v/+9wMAQqEQxsfHUV1djU9/+tO49957i/mSUjlvVQNEi7NCevEJTdVRVISDyOr5F6lb8kN0vfPBL7hRupZvT+D9hR/g/g3vkUHvZbQA7i7+jnq4VBcoX+/ll0os1QWMvo5VESOL5YbNywvu8RgOBswME7fbUXXnDnja6yo804NScOP9Lja78YowIiHvX+a6XbEkMloaqyMFZVwtFvOQz4VMvmIXfoulXL2XnTjk66yvND8//+jcDkRDhb+mOuuNv+d2BqOIeTXRkCeGqFqJn/3hgXHzArZUQ+OTSEt0mSvivluH3H0jSaQyWQQDmqcOPMRl7uvHxxz72Qvm4HCPXuaK1/2RE+OYzLibwGblxGVubSyM9cvqAABXndmOOhsXR52L1HY0MZ7GWC6BwWttWcRA4aHxSZzIXcCWStd1qVpRiXWe0wedQiqdNRM6vJS9LA43T4xNmpfvThGXuZGg95K2AOOCKaABo6l81fxicCLmhYIBbM61HX3TKU3m67cQ5bjMbffYmU5DVQQ1Uednxso0PHxpXQVCuQROt9qSdXmwSq2pJoqaaAhZ3fm9zsRkxkzasnuZW9RpSFVVFVIp40XX1taG/fv3m//t+PHjxXxJqdRVRnDbpafg3RuX4fLTCsviA6ZmtbidzSMqNrzWax6YWrrm1O3+cUkGhwtuD5UUh3xeut0FppbrOp3ZkS/V9d5rHpjalmmxslrSmSxeOWpcIpVyyBcOBnDHW9fgbevb8Efndtj6u2Kz53rMMweHe+/nLzJ3exMTGE85s9nLZ/HJEfNE3D/U7/xhF2At1fVWzFvdaMS83sQERpJpR792saW6i0VUqR4fSTr+vc/npRIGSgqapuGvrjgVb1nbjA9dsNLW3xWbY7cP+cyY58F1XntdBSLBAFKZrNk6plQii0+Wy1xxyD04NonBMecPfETrvdbaGEIeqlReWleBaMj42Yvn6BQz5nl0nddcE0UsHEAmq5uHEYvBiZgHAP/nLSfjTac04S8uOdnW31usdjTduZi3pDKMioh3EhgAoCISNNefTg2StV7mynCx0VgdQXXuoNONisXeoQlkdaOiyEutuSojIfPQ0ekB4uZlbk3Uc0lbABANBc0D98WsVHMigQUAPnLxalxwUiPueOsaW3/Puqd3U/9oCql0FprmvY4UmqY5Xp2r67q51pMhWTkUDKCj3r3ZQmOptNmVxkuXuZqmWRL3nI155mVuKIDaioVnWFsVtRI+77zz8PTTTwMArrzyStx222347Gc/iz/5kz/BeeedV8yXlM5H33wyvnjtmbY3V52LFAi9OmgIADrqK6FpwEgy7djtvqjYkOF2F8gfdPYMOXfQadV1wnuluoCxEAgFNIylMuYAQKd4vWJj2ZJKBDRgLJVB34i7bZmE146PIpXOoioSNC9Vi/W+LSvw9evPRlXU3ocMYx6wpCpitoRz6jJTxE5ZYp4bB51WZqmuhzJaACBeGTYvn9za8Hq1YiNeETYz6xdrw3tiNGV+tqxpLW246DvOWopvf+Bc25urfPay2wks3o15wYBmxn6nNnt9w3IlsFREgmYlhRsbXi/2XQaAQEAzL7Kdrs495uHB4YDxvYtkjsWqzk1nsth71JmLjYtObcb3/mSTeVBTqE7zMnexEli89ZoXzCpFh97vIoGlNhayVTVYLsZBp3uHfEcG83PUvHbIv8qljgTHzMtcb8Y8oDzVuU5d5m7oXIIHP7QZa1rtfZ1OkbTn9t42t85rrI56MqHD6RZ8ifE0JjO5y1wJKnMB6wBx518LYr/stdbygLXVsrOx3kxULuIyt6h3yN///d9j8+bNAIC7774bl1xyCX70ox9hxYoV+Od//udivqRvLFbpmlcHDQFALBw0W2Q5d8gn18WG9aDzoAsbAS8O0QWMzH/xHnB6sGSfh4erAcbNcz6rZXEWf/kepDVla9uwWC0KvBzzAOcXf7Jd5gYDGjrqnY37VmbM89ghH+DeYMk+y+LPqxZrzSOImNdRX1G29kz5ytzFyV72bMxzuP2cWOfJkMUnrHBpiDpgqcz12DoPcG+AeJ8Eh3yLlcwhHHAwgaVYi7fOEy1ZPBrznF7nSZbAArjbdterl7lAPuY5vc7z+mUuYIl5EiawFCu/tnX3ezbXeR7sRgA4PzNWJH7WxEKeais8Hze7sBz2YBsqwbzMPeb0Za7xGiimQqmoi41Vq1Zh/fr1AIy2VN/4xjewe/duPPzww1i+vPCZE35klusu0uLPS313rZweMNY/IlcmH2C94XU+EHpxuJrg1gBxsfjz4nA1welsroW86FCpbimWL9Ihn5ezl4H84s+pFgUyDVcT8kPWnH8teLVKDXBvgPjREhZ/i8X8nFukiw0z5tnMvnPSYrUo6DXXed57zQPASoczd/tH8vMVZCE+8w+4kMnn5XXeapcGiJsxz9PrPPeyN2fzogcSWJZbLjbcbLXq+XWeiHkOfd7JlrQH+Hmd51bFBtd503kigSX3PZ8Ym0RiYtK1/59eD3cjAOB4K6p+iWOeG1VqXR5tLQ9Y9rYOf9/5Nsv2XwNF1zQNDg7i29/+Nu68804MDAwAAHbu3Imurq5iv6QvLFb2onWQrheZWWwOl+vKFAjzHwbOvxY8ndXS7M4N71EJMvkW68BLcKpUtxSLlb3YbS7+vLn4d75iQ75MvuUuXuwd8XBWy2qXBogfk6BiQ/zMF6sVlZdiXvfgOFJp9wYImzHPd9nL8lzmOv1vYNXl6XWeOxUbxzxemQv4M+YtzQ0QnpjMmklGbhDZy61eXec53YpKVObWyBPz/LrOc6syt5RDvsWy2DHPCwks1dGQuRZx80zP8zHPMjfXCTKu8xYj5nnyMje3znvt2IijCQ1movJiVWzs3r0bp5xyCu6991588YtfxODgIADgkUcewZ133lnMl/SNzkXIakmmM+ZBv2cP+diiwPw3cHohMDGZMQfvePKGt1FUbDj3fWeyuvlh6O2slsXtQ+rUcLVSiJg3MJrCsItZLV4epAtYstgcW/xJGPPcPOQblCCrxY+ZfC624pmNF2JeU3UUFeEgsnr+demGnkHRosB7Gx4gP0vMqc87KWOeiwc+ov2eF2PeqkZ3K3O9OjwccLcVz2y8EPPCwXyrVTeTWHrMpD1v/vxXWtY4TuzxxdDYhip5Yt5KhxMXrboGvRvzxDrv4MCYowkNcsU8/yTtATBnEfk65uV+9r0JZ2bGypioLP4NDg6MIZt19mzXy5W5yxuM2bHDybSjs2NFZW7TYlVs3Hrrrbjxxhvx6quvIhbLv9GuvPJKPPXUU8V8Sd8QA4THJ90bIHx0yPi60VDAHNzpNSsdrlaQ8YbXrdI10YasIhzEkkpvDRoC3KnYGBhNIZPVoWneHjYlMnkXI6vl+EgSfcNJaFr5epACQE0sP0DYrcXfeCqDwTHj0sSz5bqiJYlTl7mjEsY8h/8NhOGJSQyNGz9/L2Yviz6kB46PIuPQote4zJWg97IYsLgIG97JTBb7cp8r68q44dU0zTJnw71Y3yNJxcbhgTGkM6Uf9MjYb966znMymUnXdXPD683sZeP7Pj6SwtCYcwkNYsPr5ZgnKnMPD4w7Fu/n44WLDcDSatnFWO/14eEd9ZXQcgc94lKiFDIe8om1bvfQBCYmSz/otDJjngfXeS21UVRFgshkdUf3OlLM2Mitd4bGJzE4VvrrfiGeiXmL0GrZ6zHPOjPWicv8fjOBRZ69bXtdDOGghlQ6i55chZVTujw6MxcAoqGgebnnZHVuKd0IirrY+N3vfoc//dM/nfHnS5cuRW9vbzFf0jcioYAZnNwqXes2g2DM9jT5xSI2vAf7S9/s6bou5+LPpWFD+Sy+Ck/+/EUmX/fQBMZSaUe+pijVbaiKIhQsusOe6/L9tp095JiNWPgtr69EVTTk6v/XQtxuwSdiXlUkiNpYeb/XuYj3e99wEiPJ0l/3okWBVNnLjeLAx5mDTkFsdusqw6gu82t9NsuWVCISDCCZzqLboQz+/pEksjoQ0Lz9GhC9l3tcOOSYbn/fCFKZLKqjobJnN3U25F/rbkhMTJpxxKuVua21MURDAaSzullOX4rjEm54xWFvYiKNEw4e8PePpjAxacRQL15sVUVD5oy//Q5VbaQzWXO2VLOHZ2y0xXOHHJmsOeDWLf0jSRzzQAILkL/EdmuYrq7r+eHhHj3ki4WD5nNzomKhL9dyVKaYV18VQU1uHe7kgW8mq5vZ6+X+fJ+NpmlmOyonq3OPma2ovBvzKiJBtOTaA7qdxOKVBBYA6Mzt6d2s2Oj2eMUG4Gw1fp+ECSyhYMA84He6Us3LreUBYFWj8/PU+ha7FVU0GkUikZjx56+88gqampqK+ZK+0uly6Zo5UNKjm10A6MhVroylMiX3Yx1JppHMlX3KFAhF9vLRRNKxA37A232XAeN2X2TwO9WLNB8Evf3zF+/94Ym0WWHgFnGxsa69vAs/YPFiXludNy/zACBeka9cKXXhY73MlWmQbnu8ApFQAJMZ3VysO6HLw32XASAY0MzM7X0ObXhF5nJjdRTBMg2MLUR9VQQ1ucsmtw75hXwWX/mG6AqdLmfyiQOeeEUYlRHvXeYBQCCgOVqpJmMCi3HQaazFnazOFTGvpTaKaCjo2Nd1ktPVucdHUtB1I556uTI3FAygI9cq56AL7XisREsWTyWwuBTnT4xNmnu9lrh3Y4CT1fjiIk+mmKdpmisdCY4mJpDO6ggFNM8e8js9QHwykzUrf7y+v12+SC34RAJLjRcSWMyY5873nMnqZuKmVys2AGBlLoHDr+s8wNKRwMGYl0znz0i92H4PsLRadrBio5SZuUVdbFx11VX49Kc/jclJ42BO0zQcOnQIt99+O6655ppivqSvuF2uK7KXvZrRAhiVK+JNWmoQ6M/d7lZFgqiIeHODN5t4ZRh1uVZRTvXdB7w9XE3I3/A6EwhlGK4GGFktIovxoOuHfLkepGUcriaYMc+l71lkwXs1c1kQ2eulLvxHU5n8Za5EQyUDAc0s3XayHZUUMc/hAeIyDNEFjPXhcnPOxiLFvDJn8QHux7yeIUlinoP91sVaT6bLXMCdwZJebkMlrHJ4npqIeU3V0bJfXC5kecNixTxvtGQBFm+d11jt3cs8wNm5UmYCi0TrPMCdOTNinddWF/NsMofTA8RF0l4ooGFJpbdfAysWoRUdkI95azyQwOL2eV7/SBLprI6A5u3zDScrNvrNiw1vv96nc2OGpEgA9GprecAS8xyq2EimM2Z1czEX2EVdbHzpS1/CyMgImpubMT4+jje96U1YvXo1qqur8dnPfraYL+krokWBW1ktIpPPi+XpVk4FARkHSgpuLP68PERXyN/wOhMI8z1Ivf2aB6wLIbcz+byz4XW7FZXZa97zh3wOxbzc670iHPRstvZclrswTFeqmOdQJp9cMc+9AcpWXox5blWpmC1ZPHywDTgX80aTaYznWpnJ1JYFcKftqJcHhwtm9rJT6zwxX8Pjl7mAv2Oe79d55kVm6f8O4jJXpuHhgPWQ28l1Xi7m1Xk55rmzzmuqkeEyd3EqNjyVwJKLed2D45h0sL2u0D2Ub0Pm5TbbKxudi3kyzlIDnEtctBKVuV5tLQ84X6UmLnPDQa2oy5yi3iXxeBxPPPEEHnvsMfzDP/wDPvrRj2Lbtm146qmnUFVVVcyXXNBnP/tZnH/++aisrERdXd2sjzl06BDe9ra3obKyEs3Nzfj4xz+OdNq5Fj9OcXuopNcHDQlOla4dl/R2F7B8GLgQCL3aigqwtChwbPFnfPh7vVQXsGTyOVilM10ynTF7kK71UCuqgy6V68oT80Spamk/ezPmSZbFBwArG52p1LOSIeblKzacakUlUcyrd37BPxtPHvINjLkyT6lHkio1M+aVeNgpYl5FOFj2ljt2rXShYkmGmLe62dlDvqPD+UMer1vuwiHHbF4ULUe9EPNy33P/aMqROWLTyVKl5lQbprFUGmMp4zK30cPZ2rNZ4UIrKhlinrUy14nPfXO+RhG95heb29ULgpfWeU01UcTCAWT1/OvTSeY6z+uJyi60HJUtWTmfwOLgOi93mevlmCfWeUdOjDsyR9GatFfMZY6ti43t27fj5z//ufn7Cy64AFVVVfinf/onXHfddfjwhz+MZLK0eQlzSaVSuPbaa3HzzTfP+t8zmQze9ra3IZVK4X/+53/wve99D9/97nfxyU9+0pXnU4rlYsDagPNBEJBj0BDgZMWGnLe7gDWzx41MPu8GQrNFgUPluqLffJMUiz/3M/n2HRtBOqujNhYy+3uXk/ieuwcn3MlqkS3mlXyZK3HMc6FcV4aYl8/kc6oti8jk8/ZrHsh/zrm54T02PIHjIykENODUlvIO0QWMTHoxR6xvxPl1cbfPKjbMmCfhZa476zwJWlHlYt6hgTFHPvdlqthYjJiXSmfNSyMvJLDUxsJmhqUbVRv5dZ53X/PA1HVeKYfbx3ODw2PhAKokarMMWOO+822WvbzOW9lYBU0DhsYnzdkYpThqHvIx5gleutjQNM2SuOdCzBPrPI8n7Yn3e99wsqRL7SmXuZIlK4vX/6H+MWSyziQzybDOa6iKoDYWgq47k8hR6jrP1sXGpz/9abzwwgvm7//whz/gpptuwqWXXoo77rgDjz32GO65556inshC7r77btxyyy0444wzZv3vjz/+OF588UU8+OCDOOuss/DWt74Vn/nMZ/D1r38dqVTpHy5OElktx0eSGHUhq6VXgkFDgHMLH1lvdwFr/2lnPhAnM1nz57/Mw4FQ3PC+dnwEWQc+AMQhX4tMiz8XZ2xYS3W9UL7YXBNFNBRAJqubfZKdZA4P93jMW+l0+z3J2hMA+QxuZ7NavL/4E5l8fcNJJCYmS/56x2Sq2FiETD4R81Y0Vnli1lYkFDDjkRvtqGTLXj5yYgypdPGH21LHPEvcd6p6J99+z7sxr602hopwEJMZ3ZH3gFmZK8FlrjXmuVGxBRgJLJMZ7ySwAECnONxxoTq3V5KY12G91B4u/lL7+Gg+5nlhHW+HWOf1JiYwnio9ixeQY50XCwfN5+dE4l6fROs869mWGxVbgPcSWACg00xWdmGdJ0llbrwijPoq4yKilP2taL0XDQVQLVllbntdBSLBAFKZrGPnHPlWVN5tv6dpWr4614EB4ub8yCLP82xdbOzatQuXXHKJ+fsf/vCH2LRpE+6//37ceuut+Id/+Af8+Mc/LuqJlGr79u0444wz0NLSYv7Z5ZdfjkQiMeUyZrpkMolEIjHll9viFfmh0U4HwonJDAZyWQJeD4QrLf0YSznczg+UlOt2F7CULDuUvd87NIGsbhyqeDmbu2NJBcJBDROTWfTkFm6lkLNc172KDS9ltADG0Oh8Cz43slqMD39ZKjb6R1MlHW6bMU/G7OXcv8HhgTGkHcjiHU9lzGzuDg8v/mpiYXOh5sSGV6YZGyscOtyej9diHuDuhY6Ypdbq8XVec00UlZEgsjpw+ETx/w79ElepddRXQtOA4WTakSxeXden9F72qkBAM9e4jsQ8iSo2RMXW+GRph9vzscY8rxx8L3d1nSfmR3r3NQ8Y+y9xEFVKKyYxS022NlQAsKQqgniFcc7hVDu2IxIc8gHOztkQ3QhkWOfVxsJoyB1uu7W/9VoCC5Bf5x1y4XvukSTmAc7MmMi3lpfvMjcY0MzLPadjnpdbUQHWLiylx7xjJcY8WxcbJ06cmHJx8Otf/xpvfetbzd+fe+65OHz4cFFPpFS9vb1TnhsA8/e9vb1z/r177rkH8Xjc/NXR0eHq8xSs/ZedJIJgRThoLiq8atmSCoQCGpLpfJVBMfL95uVb/IkWPaWW7wnWsjUvDxoLBQPm917qYMlsVjc3jjKU6y43s1pSGHYgc3s2L3mo77LgVswbSaYxPGG8d1o9XrFRHQ2ZB3OlZLVYF3+yaa2NIRoKIJ3VzXhVCpHFVx0NobbC2xk+5obXgWG6+cWf918DzdY+xC5UbAHejnlOH/Lpup4fHu7xmKdpWn6oqAMxT8bL3Fg4aP6cnGhHlRhPYzi3Xlzq4UG6gLNzNo5JtM6LhALmYYST1YlWXrzMdWudB+Sr1LxSnTIfJ9qOHpc4aQ9wtu1oNqtLUaUGOLzOKzF7ebG5XZ3r5ZjnSgKLjDGvpHWeSGCRNOY53HZUmpjn4NzcUudH2rrYaGlpwYEDBwAYMy927tyJ8847z/zvw8PDCIcLP0y/4447oGnavL9efvllO0/RtjvvvBNDQ0Pmr8W6mDEXfw4HQuugIa/fdoaCAXSIoaIObHhlbFFgLd9zIsNB9Jr3cqmusMrM5CstEJ4YSyGdq/hpkmDxVzMlq8X5hZCu695c/DW4dJmbi3k1sZAUpatODM/Oxzz5Fn+BgOboYFVrzPP6Z545WPJ4aTEvk9XNuQ0tElSpaZpmzhZzL5NPxDxvtCcA8jHP6VZUQ+OTGM8N6fN6xQbgdMzz/mf8bFY4OED8SG6gZENVxDNZq3NZ5WDFRn7D6/3XPAD3Y16vBy9zXVrnZbO62XJUipjXIGJeKVVqkse8Budi3vGRJFLpLAKa93/++XWec5W50sQ8l+dseDKBxaWYB+STlb3+mgfyXVhKiXkyJ+0Bzsa8yUzWvNjycmt5wFKx4WDMW5SKjSuvvBJ33HEHfvOb3+DOO+9EZWUl3vjGN5r/fffu3Vi9enXBX++2227DSy+9NO+vVatWFfS1WltbcfTo0Sl/Jn7f2to659+LRqOora2d8msxmLfaDvchlWXQkCCCQCmtmPqlv+F1bs6GLLe7gDWTr7T3gCjVbaiKIBy0FdLKZrmLC6HBsUmcGDMqQU7K/Rt7Qb5Fgd9jXumzhcyYJ8FF3myczGqRKuY1OdOHdGA0hUxWh6bJ87nnZiZfJqub76eTm71zsWEebDoc58UQ3YaqCGJhbx9sA5b3u6/Xec7FPBmG6ApOVWxksrp56MHsZYO4LDqpxYvrPGe/5+OjSUxmjM88GQ55ncleFt0IGPOO5NZ5rbUxz+/z3GhFJUPSHuB+q2Uz5nlwb3towNl5SulM1rzMb/f4wTbgTJWaeZkr6zrPwSo1WVrLA8BJzfkOLKW+B8yLjSIrNmylt37mM5/Bu971LrzpTW9CdXU1vve97yESyb/4vvOd7+Cyyy4r+Os1NTWhqanJzlOY05YtW/DZz34Wx44dQ3NzMwDgiSeeQG1tLdatW+fI/4eT8uW6zrZlkGXQkLCisQrY21dSEOiTuBUVYPwb7Dw06Ej2cpelFZXXmZl8JWYvm6W6Emx2hBUNzv3MpxNfs6U26qlsznxWi0sxz+PzNQRnWhTIndViDtN14PCjS5IepIBzFRtis9NQFUXI45t8wYnX/Vx6hsaRymQRDmqe2gC61aLAHBwuW8wr4TJX9nWek/PUpIp5jc5kL/ePJJHVgYAGNEjyuefEhd5cJiYzZjav+P/xArHO6xocRzqTdezzScwUaq6Jev5gG3C2FZXs6zy/xbzVTfk5csl0BtFQcfuwdCaL/lG5KjbcjHm6rptf10sxb+mSCmgaMJab9+fUJdSxYeMzLxTQpIgBKx1tReX973c2jsa8QTlaywNAZ30VggENo6kMjg0nS4pX5szcIis2bF1sNDY24qmnnsLQ0BCqq6sRDE4N1j/5yU9QXe3OLeqhQ4cwMDCAQ4cOIZPJYNeuXQCAk046CdXV1bjsssuwbt06vO9978PnP/959Pb24hOf+AQ+8pGPIBr13hukM5fJ5/SwITGIWYZBQ4AlCBS54Z2YzJj99RslLdfNl+85mMlX7/2fv5nJV2L2skx9lwWzXNeBKp3pRBXIcg8t/ICpMU/XdcfaBpnD1SSp2MjHPAcucyXPanE05kmx4TVi3uvHx5DJ6ggWuViVaaaQ4Gb2smjp2bGksuh/Uzd0mvOUkhhNplHlUKu8bh/GPOlbUblQpSZFAkvukG9gNIWB0ZTZetUusc5rrI566j0+HzdjnmhvVxMLYUmld+YpttTEEAkFkEpn0T04YcbAUkm3zrMc8GazelEHU2bMk/SQz8nsZVkGhwNGdUVNNIThZBoH+8dwSktxVaT9oynoujGUWJa2s27GvMGxSfO8RySNeEE0ZMzQ6hocx6GBUccuNkQCS0ttTIrPPPF+7x9NITExidqY/c+lPsmT9sS/weGBsZIv9o9IlKgcCQXQWV+JA8dHsf/YSNEXG5OZLPpHjcutYis2ivoXj8fjMy41AKC+vn5KBYeTPvnJT2LDhg246667MDIygg0bNmDDhg34/e9/DwAIBoP4+c9/jmAwiC1btuCGG27A+9//fnz605925fmUSgT/IyeMrBanSFexUeLt/kDuDRAOap4fHDsXJxd/+Q2vdz7057I615OvNzFR0uD0/O2uPB+ETs4YmE5kxS730MIPMA6eNQ0YTWXMDy4nyDRcDSg95iXTlstcWRd/DmZ1yRTzltZVIBoKIJXJmrNBipGvUpPn5y/aMrlTpWb8Wzp1iOaUeEUYdblDx8Ml/Lynk3Wd1z00joncbBC7RCsqGYeHA1PXeaWW6ovYIcMhX2UkZG7MS5mnJmXMs3zOOdmixPiaIoGl0lOzpQIBLV+p5mCrZXOdJ0mV2rIlFQgFNExMZnE099q167jkCSzicufYsHGxX4quQXnmR2qalq/OLSHmicrcxuqI5zO2BRHzeoYmiv6sn4tXuxEA1i4szq3zRMtRWWJedTRk7kmLPc+SvRVVW20M0VAAkxnd/PkVq0uipD0gX6lWSgs+8ZkXCmioryzuNeD9es6c7373u9B1fcaviy66yHzM8uXL8e///u8YGxtDX18fvvjFLyIU8uZhd0ttDJFgAOmsbmaiOCGf1SJHIBSZfIf6jQxWu6xZfF5a3Nvh1CFfJquje1Cect14ZdhcsB8oYc6GbMPVAHezWsRmUhykeEUsHERr7mfk5OLPjHkSbHiA/BDZwbFJDI7Zv+ARB3yhgFZURowXiLh/5MQ4Jku82DeHh0sQ8wIBzfzeS1n8ib7LLUWW6paDmcwxMF7UZ/18zJjnsSo1wJ12VL2SZS83VkdQHQ1B14sbpJ5KZzE0bsyNkrVio6O+AoHcxb7ISiyWTBUbgKUFXwnrPBljnnjvD0+kMZibe+YU0cPea5W5gDuHfLJVbISCAXTUiwHixb3uZW/LEq8Mm9VEpe5vj0jUigqwztkoYW+bkG9vu6QyjJqYce5WzGf9fLzajQDgOk9Y2ehMzGuSNOYFApq51ym1HZW5t5VmnVd6zLPOFCr2Mleaiw3VBAOa2S7I2RtekdUixxuhva4CkaCRwSqeux2yD1cD8gedx0dSGJ4ofvNzbHgC6ayOUEBDiyTVC6scGLImslpkyuQTB3C9CeezWkRbFi+V6grmhtfBxV+3ZNnLlZEQWnKv1WIWf8ctGS2yZHFN11wTRSwcQCarl7T5SaWz5sWmNFktDrTgkzF7ub2uAuGghlQma2bfOsV3MU+y7GVN08x1TjExT/QZDwU0xCvkvMyNhoLmuryUWSOAXC1HAWeG6YpDPpliXkUkn8zhdKWaecjnl5gn2ToPAFaIyuwi3u/Wy1xZLzYAS9VSiTFPuuxlc51Xwt52WL5uBJqmWZI1nb3YEJcGnox5De6t8+SKeaW932VvvwdYY15pn/kigUWedV7pSXtOdGDhxUYZLXf4hnc0mUYi16JElkAYDGjoyL1pi1n4y57RAgA1sbD5Jn61hEWQWPi1xmPSDJRd7UC5bn7GhhyveQCoqwyjNpfV4uTFJjC1RYHXOF2pouu6dFVqQGlVWv0KxLxAQMOqXCu6UmJez9A4dB2IhQPS9CBebQ7TLb1io1miTD7js96dSjUZYp6fs5cBZ2KezJe5QP6A/9Vjw0V/jZFkPvtfnkw+seEtIZPPPOSTJ+YB7lXnyhDznPyepYx5JQwQF22WgwENdZJe5gLOxDxd1+WrUhOVuSUcbh6TcJ0HWN//zl7mvm5WqXk35jm6zhuUcG9bQsybzGTNtY2s7fcAZ2IeIFebZSCfqFxSZe5w6TFPjtNPRTldriuyIGuiIdRI1KJkZQkzJmQfKCmsaasFALzcU3wglGmIruBkua5MmXxGBqtzs1WE0WTafE+InvZe4nTMS4ynMZYyKl5k2vDmh+na/3foUyCjBQDWtBkDFZ2IeUvrKqRpRehMxYZ8w8MBZ2erCLqu45AEbVkOOhTzZL3MdSTmSb/OKz3miQSW2pg86/zV5obXXxUbgDsxD4AUMc/ZQ75c9rIkVWqANeYVv7etr5L7MnetAzHvxNikuc6XpRuFWOe9dmyk6Pk6XOdNdci8zPVuzHNqnQfkz/RkabMMlBbzxGVuQAOWFDlfwQuciHlZyVrLA/l1XtfgOMZTxXUi6WPFhtw6c8H5kEMD1sRmt1WizS6Q/xAsZsNrZi9L3IoKANa25gJhb6LoryHb7S5gzeQrbsOr6zr6JF38udGTU2wk6yrDiFd679DD8ZiXMF7zSyrDnhsmN59SLrXyFRuyx7zcZW4pMc/suyxRzMtVqpRSseHE4q8c3Ih5x0dSGE1loGkwqz+9pLNezBFzJub1j6aQSmehaXL13l5RQnl+fp0n1+t9Okdi3qA8g8MFsc47NDBW9EylPkkrNjpdqF5IZ7Lmpb7Xs5edGJqeyepmJme7RAkspcS8/OBwuWPeGgfXeU01UcTCcqzzlzdUIqABw8l00TOV8m1ZGPOA/KWBJ2Nebp3XN5zEWCrtyNfszp3pSRnziurAIi5zi5+v4AX5mDdc0qXmZEau1vL1VRHU5c6dip2x4sTMXF5slJHTrajMsjWJbneB0krXRCCUddCQYGby9ZaSvSw2vPL8/MUN74Hjo0UNlB0cm0Qqt1FukiT4C2IBcNChQ37A2wMlARdjnkQLP8CZxZ8qMW+vz2KeOOQ7PpLCUBEDZbNZ3ZHFXzmscKFFgbgkbY9XIBry3qGHOTT9hDND00XMa6yOIhKSZwnvxDpP9stc6zqv2A1vl2RDdAGgtTaGykgQ6axe9Ge/OTxc0ooNJ2Ne96AxTy8aCnhymPqyJZXQNKNtmsjCLUXfcBKZ3PxAmdb5Inv54MAYsjZj/3FFElhEzDs4MFb0ga+M67xoKGi23iy2Oje/zpPnNQ9YY55zFxujybSZxOjFbgTxyrA5/+vwQOkz5FLprLnukalKTcxSGxybxOCYvdivSsxb1VSFcFDDSDJtJiDYJWKeTK3lgdLnqR1lxYbclluGDTmR1WIOlJSsYsORVlSSB0LzhrcnUfRr4YiEG95lSyoRCQaQTBc3PF70XV5SGfbkodZ83OhD7OXhakD+ez42nCy6VNFKtiG6grVc1+77XbWYd6B/tOjXwhHJ+i4DQFU0ZA6U3V9E1caJsRTSuUMS2bI5lzc6v+E96OHB4YBxqBsJBpC2lJaXokfydV7P0ITt9/vxYTWyl1c1ViMc1DA8kTazMe2SseWopmklVedms7r5uSdb9rIr67zcZW5nfaUnM1tj4fzQdCfaUYl1XkttDEEPfr9zaa+rQCQYQCqdNb+HQvUrUrHRWB1FY3UUug68crS4wy7Z5msIq0qcp3ZU0oqNFWYyxxhS6eIq9KbzejcCwNnZIkcTE9B1IBKUZ34gAFRGQuZFnN2sfbHOk+nyejbhYAAnNZeWuGcODpdonQdYZgsVebFhtt8r4TKXFxtlJG7zhy3DAEshbfZyY75MPW2zTF2FQbqAccsZCmhITKTNlmJ2iUy+ZRIt/oIBzbzhLyYQmn2XJVv4AaVlsM7FywMlASBeEUZNbmj64ROlb3hFzJOt/Z74+QxP2M9oVCXmNdVE0VgdyW14i1v8yXjIBwCrm3OLvyIGp4uFX31VRKqMfWBqpZITyRzG1/J2zAsENCzLtchy4pBPxiG6gJF8UJuL/XarFPtH1cjki4QCZkbbyz3FtWaR8TIXsM7ZKKL3du4yV9Pkew2IuNQ/mkJiovR9HuD9mAfk97eOxDxJ13nBgGa2R3zdZqtlVarUAGvP+SJjnoRJe4Ale7mIio2M9TJXsoqNppooKsJBZPX8AW2pzG4EHk1gARyOeZbW8rLMDxSK7UjQPypmqSkQ80psL5+fH+nd1/tszNlCRc7NPerAmZ5cu2LFxMJB82bTiYFD3eagIbkWf221MURDRkaj3Q9BVfqQRkIBnJQLCMUEQl3XLTe8kgXCEgaIO3G7Wy5iU9p1YtzBrBZvt6LSNM3RDEYz5kl2yBcLB82Ma7uLP1ViHlB6/+UuWS82Soh5TpTqlsvSugoEAxomJrNm7C6Vl4foCk624JN1nadpWtHVuSrFvLVt+f7LxZD2MreEFgUi5jVURaVqywAANbGweTh9yKGqDb/FPHOIrmQXG4ClOtf2Ok+NBBYAWNNaWqvlfMyTbG/bXHzM6x9NIqsbg5RlO+i17vOcStw76OHB4QJjniHfkcDuZa5CMS93mftSiTFP2svcImJeOpM1L7dYsSEx0SvQidK1XgkHDQFGRqP4ELRTupbJ6ma2s+xtWYD84u+lHvuB8PhICsncQFHZsppObjG+7xe6h2z/XVlLdQFjRkJlxMhqOeJA9QJgXfx5dwPgSsyT7JAPyFfs2F/8qdGKCigt5qUzWfTm3v+yZbWUEvPyl7nyveYjoYD5XnWqNYsM2ctiM+5k9rJs6zyg+Jgnems3qLDhNWNeqZe53n29z+aUFmPD+0K3/e/bjHkSXuYC+TZ5/op5Th7yiXWehDGvyAHi+XWenK95K5HAUnTMG5SvGwEwNebZrVAV3QgaquW7zAWmtlp3gpcHhwvm9+xEovKgxDGv2AQW0XJU0s95K2t7+WLI2opKxLxXj44gmbbXcrZ/NAVdNyodG6p4sSEtUbp22OHSNdkUs/g7MZZCVgc0DaivlP+Q79TW4jP5zEFDtTHp2pOc3VkHAHj24Anbf7dP0uFqgJHVYm54HXj/pyxzSmQo13Uy5slWsQEUt/izXubKPjwcANa0FV+x0ZuYQCarIxzUpDvwEjHvuUODtgdKH8td5rRI9j0LxZapz0VsIr06YwOwtigo/XsWmXx+WecB6rSiAoBTS8henpjMmAeesrWiOrtzCQBgb28CwzZbMpkxT8J1HuBCzPP4XCHA6XWevNnLRR/yKTJIF8hnL7/cO1xUC0oZh4cDwGntcUSCARwfSdo+7D42zJhnJRLhZIh5zrSikjjmFfmzPy4SlSWrUJqNiHkHjo9iYtL+DEkz5km2zuusr0RDVQSpTBZ7uuwl7olE5cbqSEmztOQ6AVWQU1ktiYlJjCTTAOTMXjZbFNj4dxAbvPrKiJQZDdOtKaEPqazD1QDg7OVLoGnGe6DPZnsSsfiT7WBTEAuAgzY3PbM5cmIMWR2oCAc9PXzLjHklLv50PT+MV8bs5ZUN9lsUiMtcwJixIDtriwK7G16RudxeV+HJAarzWdNai+poCCPJtO3hcjK33wOcHbCYmJg0L/o8ncnnZCuqQXmr1Ippy5JV7DJXtKJ6rW/E9oZXrPOqIkHUeXSA6lyaa2PoqK9AVjcudO2QeZYakK/YciLm6bpuzqhZ4eW2LOJ7duAyt1vS+ZFAKa2o1Gm/d1JzNYIBDUPjk2aVbaGGxicxPGGcbcjWliUWDuL0pUa8//3r9hL31Il5znYjEBeFXiS+5yMnxmwnLE1nxjwJz3TyrajszdJTqWKjqTqK+qoIsrpRvWCHruvSVuZqmoaNy40klnLFPPlPgyXn1CGfaE8QrwijMhIq+XktNtGX7bnDgwX/nePD6rShAoC1uYqN146P2i7hkrXvMgDUxsI4Ndea5dmDA7b+rjloSMK2LACwvFH0IS198Wct1fXysDFxyFdqifKJsUkkc7NJWuLyLYTEAOldhwYLXvyJze6SyrASl7knNVcjoAGDY5Pme7lQMse8YEDDBrNSzV7Mk33Dm8/mKj3miRjSUBVBTcy7B73WtgylDE3PZnUzq0nGQz6xznupO1Hwof6JsZR5SLBEgcvc5poollSGkdWBfcfsbXi7LH2XvfwZP5dzltcDAH5vszpX9svcFQ6u844NJzExmUUwoHn6oFes844mkkVlrFqJ7GUZL3NFzDvYP2Ze0C7EepmrwsVGNBTEqtxh58s2246KmFdfFZHybOOcFcXFvPwQXTl//iscnLEhSzeC1toYIsEAJjO6GbOK1ZsQSXvyxbzlDZUIBTQMT6Txmo2kTfMyt4Q2RF6haVq+7ajNjgQyt5YHgHNW5C427MY8h6rU5D8ZkZxT5brdEpetAcBFa5oQDGh4/vAgXitw6IwYMqPCwg8w3sx1lWFksnpJG14ZFX3Dq0i5rhOZfKLqw8uZy0A+5h05MV5SVotY6DZWRxANBR15botpy6pGVEdD6BocL3gB0K/QcDXAyGhbldv4221HJXOVGmCJeWVa/JWLk9nLMswUAvIxbziZxuCYvTY8VsdHkkhndQQ0OQ88TmuvRXs8huFkGk++fKygvyPaUC2pDCOswGWuseEtrue8OVBS8phnP4ElV5krawKLCzFvaV2Fp98PdZVh1ESNg+hS9reTmax5sSXjIU9rPIYzlsaRyer4+e7ugv7O4PikuTZWoTIXsLYdtXmxocg6z3YCy7DkMS93kXV4oPTqBVm6EQQDmploVWrinkhWljHmxcJBXHhKEwDgZ891FfR3plzm1igS88w5G8XFvJYa+VrLA8DGXALLzoMnbCVyiaS9JlZsyE3cPvcmJkrKaumReNAQYGSfvvHkRgDATwsMhCoNlASm3vAWGwhlG6IrFHPDq+u6JatFvg9/wNKixIGenPmKDe+W6gJGjAoHNaQsw5+LIfN8DQCoiATx1tNbAQCP7DxS0N9RaXC4sKbInvPmZa6sMU9kLxdZrlvq4q9crO03S6leAPJtTrwe82LhoHkRVUqs7x4Sl1oxKSu2AgEN79iwFICNmKfYOg/Itx2124aua1D0mpc05uXWec8dGkQ6ky3478k+PNxavTCeKq16QVyOeP0yV9M0dDrQavloYgK6DoSDmrSZvO/MxbyHdxa2txXrvHhFWMqDrdnk13k2E1hO5C/yZCQuNl45OoIhG0kNsldsOFm9IEs3AgD5mFfCOm9iMmMmdMjYZhnIx7xHdnYhW8DF1tD4JNK5x5UyONpLzHXeUbsxT95uBABw+tJaREIB9I+mcMBGxY5T6zw1PjElVl8VQXU0BF3PD4spRq/EAyWFd529DIBxsVFIIFRpoKRg3vDaXPzJOlxNEId8L3QPFXzBlxhPI5VrReTlLI75iH6hh/rHbLcfm+6gBAMlAZHVUnqf/V7Jq9SAfMz7+e6egl73xxWr2ADyPeftzhY6Mih3zDursw4BzbiU7h0q7IJP13XzQl/Wio3O+koENGB4Im17ptJ0B4/LEfMAYHl9LtaXsOHtGVRgnZfb8P5qbx/6Rxb++R9XcJ23trXI7GXJK3NPaa5BTSyEsVTG1veeHx4u5+u+rjKMeIXRKm9/gRXpc5FlnQc402pZfDa2xmPSzdISrjqr3exIUMjPPz9fQ6GY11Zc0p7MLUcBY60uZg7sPFR4EkvfsNwxLxjIX2za7UAxnehGIEXMc2CAuIh5sXBAullawqXrWlCT60jwu9cXrlYSHVhqYyFlLnPXmpW59mZIivM8Wdd50VAQZy6LA7CXrOzUOk+NV4/ENE0z2xSUEghFJp+M/fiEy9a1oDoawpEThQVCc9CQUod89rOXrYOGZA2Ey5ZUoLkmismMjucLnLMiSnXjFWHEwvK1IgKMQ/naWAjpItqPTScuCbw8UFJwogWfGfMkzeQCgM0r67G0rgLDE2n88qWFW7OoNFBSKLliQ9KYVx0NmZc6vy+wTcHg2CRSGbkvc2PhoHmh+6LNy6zpzCG6jd7f8JrrvBIuc/PrPDlf8wBwcksNzlgaRzqr47HnF27NouI6b01bcdnLsh/yBQIazu4UbUcLi3m6rqNvRO7s5Sn9tkuOebkhuj5b58lamQsYsetNudYsPy2gakPFBBaRtLe/b8RWEpfZjUDSmAdY244W3o5K9io1AJaYZ29tP50Z8zw8OFzocGCGpGgt3x6Xc5YWYKzzrzyjDYBRtbGQvmHRhkre1/t0J7cYMyQHRlPmGqYQIubJus4D8u2onrXRkYAVGwox29GUEAh7zOxled8IsXC+NUsh7ahUzGo51XLDW6jBsUmM5srbZS3X1TTNdjsq2Ut1AeP7Xtdu/2c+XSar4/BAbriax1sUAA7FvEH5KzYCAQ3vOKsdAPDT5xZuzZI/5FMn5oney/uOjZgVWAvJZnV059ovyrz4O8fmbCGx8KurDEs5V0ZY11Z6zAOs2cve3/Aud6AtiwoxD8i3KbC3zpP3c366k5troGnGAaadqiXZ+80DlphX4DrvxNgkJjNGtqPMrwEn1nlAPoGlU4p1XumzRUTMkzlpD5ga8xbqSKDiZa41iWv/scJfD/nLXO+/3udid52XzeYrc5slrcwFrDGvtMvcQ1JVqeVi3kApMS93mVsnecw724h5//6HhTsSqLjOi4WDZrWWnUq1I5K3WQas6zw7l7ms2FCGMxteNQKhaM3ybwUEwnwrKnUC4Skt1bkNb7LgDW+XOUQ5Km3lAmC54S1ww+tUECw3kbX9Ynfxi7/exARSmSzCQU2KQy8nWhSYmXwSH/IAwLvOLrw1i4oxrz0eQ43Y8BbYpqNvJIlUJotgQEOrxO//jSvsxTwxRLdF0vkawtq20je8E5MZc0bPChkO+RyIeT2KxDyzNcuRoQUrFftH1GtFVREJYmXuAKTQqo1UOj+TSuZDvo0rxDBdezGvoSoidYsKJ2IekN8nylCxwZiXZ6c1i2jLolLM0zTNMkC88PeAEpe5uZj3/JFBTBYwW2hgLIV0Voemyb3WN/e2Jca81yXqRuDEDDkVEpUBYNOKXEeCZBq/eOnovI/tVzBRGUBxMU/yylwgX6W2v28UJ3LnFvPJOHiZK+8qUSEi86bYcl1d16eUrsnM2pploUCoYlZLZSRkfngXOlhS9n58grjhffbgiYJmrKhQqgs4s+EVGXHLllRKMVS204EWBT0KzNgAgJOaa7B+WWGtWVTMarG26Sh08SdiXqukQ5QFEfNe7ElgNJle8PHHFMjiA/IVG6VseI+cGIOuGy296qu8vxlyMubJnr08pTXLApVqKsY8wNKOqsBMvt4hY4hyNBSQevN/VkcdggENPUMT5qHlfETMk7X1nmCNecUeeA2OpTA0bgwgliF7WTzHIwPjBa3pZ9OtSJWandYsx4fVS2AB7LcdHUulMZA7FJN5f7uqsRp1lWFMTGbxQgEJbMdy3QgaqiIIS7y+FTHvtb6RgmdnTpfN6jh8Qp5uBB25pIPhibQZq+0yL3Mlj3mBgDZliPh8VGy/BwBrWuyt83RdV+JMb0lVBKubjLPMQpJY+keTyOqAphlxrxRSRMzXX38dH/zgB7Fy5UpUVFRg9erVuOuuu5BKTb0F2r17N974xjciFouho6MDn//858v0jO0Ri79is1oGxyYxMWlkAcg8VBIwAuHVG3KtWeYJhLqum4GwQeJN3mzsH/LJf7sLGGWrFeEghsYnC8rcFpl8zRJnbAOWtiy9xW94ZSrVBfKXucVWqWWzujlgTfbFH5AfqLtQaxZxmatezLM3TPeI5PM1hPa6CrTHY8hkC5stZMY8ySs2RIuCUja8r1sGh8vQh1jE5t7ERNHfsyrZy0C+Uu1nz3XPe+gpLjYaVNvwtuY/9wtxZDC32a2Tt+82YCTvnJZ7/xcyZ+Oo5IPDhZOaqxEKaBganzTfx3aJ9VJzTRQVEe9XZ7fFYwgFNKQy+Woju3oUmLEhvKvA1iyqx7xC13kic7kmFkK8Qs4hyoBxrrHRxmyho8NqrPOaa6JoqIogqwOvHC2uBV9vYgKpdBahgBzdCCoiQTPZstj9rUoxT7Sj+vUrfWZcm40Z86oUi3nm+U5hr/+hcflbywvn5LqwFNJ2VFzmNlZHS05WlOJi4+WXX0Y2m8U3v/lNvPDCC/jyl7+Mb3zjG/jrv/5r8zGJRAKXXXYZli9fjmeffRZf+MIX8KlPfQrf+ta3yvjMCyP6kB4aGCsqq0UEwfqqiNStiIR3bjDaUf1qnkA4nEybQ1SVu+G1u/gTg4YkD4LhYABndsQBFBgIFanYOLnF2PAOjhW/4X3dbE8gycVG7pBvaHwSQ2P2s1r6R1OYzBil2rIfeADA1jPbEVqgNYuu6ziuYCsqwH72sioxD8i3oyok5qnQdxkwYnZ9iRve/EBJOWJefVUE1dEQdD1fcWRHOpM1D3ll2OAv5C1r861Zfntg7sOe4wq2ogIsCSwFxjxVLnOBfJuCQjL5+hRZ58XCQaxuqgZQfHWuTIPDASAUDJgJV6Uf8skf8861tGZ54sW5OxLk13mKxTxznVfoZa78bagEOy34+hJqrPM0TSu51bJoQ9VRL0c3AqD0FnxmlZrkreUBYHVTNc7sqEMmq+Nfd83dkcBc59UoFvNy67x9x4YLakMn1nmyt5YHrDFv4cvcY+ZlbukxT4ooccUVV+CBBx7AZZddhlWrVuGqq67CX/7lX+KRRx4xH/PQQw8hlUrhO9/5Dk477TS85z3vwV/8xV/g7//+78v4zAvTXhdDMKAhlc6ah7V2qNKSRTipuRrrl8WRmac1i8hcromGpH/zT2cu/nxWsQFYbngLGLJ2zKzYkHvxFw2VvuE9NCAGSsqx4a2MhMzWEoeKWPyJmNdcE5W6VFtoKKA1y3AybQ7XVu5io9VeH1K1Yl7hw3SdXPyVk7HhNT7nit3wmkN0JRgcDhjfs7jQLSbmHRs2SrVDAU2J97+1NctcMc+ozFWzFZU48Nl3bKSgDa8KfZcFP67zAJQe847LMzhcEGvSYlrwJdMZ8/3frsDhtrU1y3zVuWabZck/56c7NdeW5dhwcsF5coAag8MFa/byQpX5+cpc+X/+IuYVvbeVrBsBkF+TFtt2VFzmyt5aXiikI4Gq67xlSypQHQ1hMqPjtb6FB8qrlMAi9rbPHxlCMj1/lbqo2PDNxcZshoaGUF9fb/5++/btuPDCCxGJ5G/7Lr/8cuzduxcnThQ2pK5cQsGAmZEgNut2dCtUtiYsFAhVbUMFAGtzh3yvHB1B2saGV4VAKG54f1/QDa8RCFXI2BetWYpd/Im2LMulWvyJrJYiYt6ggjHvbKNSba7WLGKzWxUJStGGwo5Tc1ktRxNJs6fyfJSKebnF33MHTyCzQMXm0YRCMa/E2UIiA1iGvsuCGfOKyF4Wl7kttUYijAryrVl6Z23NMpJMI6noZe7SOmPDm8pk8frxwje8Shzy5dZ5L/cmMDwxf8WmUjGv3V77selEBrBc67zc3raIdd7RIeNnHw0FsKRS3lZEVgu1ZplymatYW5aqaMj8vC5khqRKl7nrl8URDmroG04umNig5t62uMrc16Ve59mPeWOp/GwOFSo2gHxHgj90DeHVOSq0jys6PNzuDEmzG4ECMW9lYxUaqiJIpbPY0zU072OdXOdJebGxb98+/OM//iP+9E//1Pyz3t5etLS0THmc+H1vb++cXyuZTCKRSEz5VQ6llK715N4I7YoEQSAfCHcfGcK+YzMDYb+it7uAEdAqI0Gk0lmzDHM+oq2FChveszuXQNOMg5++eaqXdF139Ia33MxMviIO+XRdNxfKsrRlAfKb81IO+VSKeZesbUZNbO7WLP2iPYECr/fpqqMhczNQyOJPpZi3prUGVZEghpPpBdsyqVKxAeQz1ovd8IqYJ9OGd3kJs4XEZa5KMU+0ZhlJpvH4LK1Z+nMJLCpe5gYCGk5pyVVqFnLIZ5mxIbuW2hiWLalAVgeeOzQ472MZ8/JE9vLyRjmq1IB8q+WiYp65zpN7rozVQq1ZRlOZ/GWuYm1ZgHxrlkJiXn6dJ3/Mi4WDOH1prtXyApVqasa84mZIim4EyyXpRgA4s86rjoZQG1PjMre+KoKLTm0GADwyR7Jyv6LDwwFrFxYbMU+BdZ6maThbdCRYxJhX1ouNO+64A5qmzfvr5ZdfnvJ3urq6cMUVV+Daa6/FTTfdVPJzuOeeexCPx81fHR0dJX/NYpgtCoo65FMve7mhOoqLTjVaszwyyxBxVcvWAGPDKzKYF9oADU9MIjGRBqDGhjdeEcYpzcb3Pl9fvuFkGuO5DE/ZB6wBwLo2Y8FbzIZ3YDSFkWQamibXQa9op1BKzGutlf81L8TCQbx9/dytWcz2BArGPKDwnvO6rptZLSrEvFAwgA2dC7ej0nXdzGpRIua1F7/hTWeyZpm/TBteEfOKaVHQq+A6LxDQzKqNn+6cJeaJdZ4CBzyzEYMlC+k5b8Y8BQ75gMJb8ImY16RAzBOHfK/3j2I0mbb990Wik1QVGw3Ft98TCSytCmSuW11z9twdCcQ6rzISRGUktKjPazGYbUftxDwF1nmAP2Pe6qZqRIIBDCfTZtWhHWZlroQxr5R1XqsireUFsc772XNdMzoSjFrOc1Tc39qKeQp1IwDsx7xm2Ss2brvtNrz00kvz/lq1apX5+O7ublx88cU4//zzZwwFb21txdGjUzO+xO9bW1vnfA533nknhoaGzF+HDx928DssXCm9l1XMXgbyQ8RnC4R9CreiAgrvOS8WfnWVYVRF1VgEm+2o5rnhFdUaNbGQEpmcomKjmA2vKNVtq41JNW+mtJinXvYykI95//6HXoynprZmEYd8DVWKxry2wmLewGgKE5NGRqMqpdrmMN3X577MTYznZ6yo0G++lA1vz9AE0lkdkVAAbRIdeuXb7xWfvazKa14QPeefevX4jCpN1WPe2tbCMvkyWR09uUxOFbKXAWDjCqOV8HwJLLqum6+JFgViXmN1FM01Ueh6YdmbVmOptNmiRpbh4YAz6zzVYt7b18/dmqV/NBfzFN3brrWRvazaId/G5QvHPABKxbxwMICTc5WJdjsS6LpuXmzI1I1AxLyexMSCswWm61ZsZq7w5jVGR4KeoQnseK1/yn8T67xYOIBKBc5zprMV8xRqRQXk247uXGC2UJ8qFRtNTU1Ys2bNvL/EzIyuri5cdNFF2LhxIx544AEEAlOf+pYtW/DUU09hcjLfr/WJJ57AqaeeiiVLlsz5HKLRKGpra6f8KoeSWlGZ2ctqBULRmqV7aAI7DkwNhCq3ogIsgXCB7OUjA2oFQaCwG95jCg1XA4wKpWI3vPnB4fIs/IB8zCtqwzsoFn/qvO4B47W/bInRmuWJl6Ze1Iu5QspmLxd4yCcOwVtqo4iG1FgEn7OigJiXW/jVxkJSXWDOJRwM4KTm4ja8InO5Y0kFAhLNmxBtWQ4NjM06R2c+4mBbpoucQqyytmZ5fmprluMKtycACq/YOJowLvJCAU2Jai0gv8577tDgnLPkhsYnkcr9tyZFPvfWFjlbSKyT4hVhxCWaNyEO+QbHJs3e8YUSMU+VIbrCfK1Z+obVjnmnmjMkh+edKTYxmTEv8mSqRJ+PSGB55egIhsZmfy/oup5vy6LIZ72IeS9224t5snYjaKiKoCoShK4DhwdsJu0oGvOMjgTtAGbGPOs6T5WWg1antBh7256hCQyOzT9D0hweXifP630+py+NIxIKoH80hQPzzJITsV76io1CiUuNzs5OfPGLX0RfXx96e3unzM64/vrrEYlE8MEPfhAvvPACfvSjH+GrX/0qbr311jI+88J1ig2vzWFDuq5bspdVDIS51iw7pwdCNQcNCfmKjfkP+VQr1QWAc3JZLS90D806UBRQa7iaUOwA8XyprjxZfEA+5nUPjdvOalE1ky8Q0PCuDbO3ZskPlFQ15hmLv4U2vCrGvA2dSxDQjEXt0dyl7XQqDdEVRMyzu+HNDw6XK+a11xmDv1PpLI4Oz/5znkuPWbGhzuteyLdmmT3mNSh7yGfEvO6hiTkPuoD8Zre9rkKZwfGntNSgJhrCWCoz5zpXxLwllWFlLrHNmFfsOk+yBJaqaMg8pLfbdrRH0So1IB/zpnckyFepqRnzOusrUREOIrnADEmxxq8IB5UZHN9UE8WK3Pt356HZk1hOjE1iMmO8HpoU+dwr9jJXJPq2StaNQNM0dDaIJBZ7Z3oqxzzRjuo//tAzpSOByq3lAaAmFjaTj+c70xueyF/+q1KlFg0FsV7MFpojcS+bdbYyV4qLjSeeeAL79u3DL3/5SyxbtgxtbW3mLyEej+Pxxx/HgQMHsHHjRtx222345Cc/iQ9/+MNlfOaFE9nWJ8YmkZgoPKulfzSFVDoLTVPrwEPIt2aZGghVHjQE5De8XYPj874eVBqiK3TUV6CpJorJjI7nDw/O+hiVhqsJRS/+zIGScr0GGqsjqMxltXTZaEWTyeroTaiZ1QIA7zzbiHnTW7P0K16xsbyhCrFwABOTWRycZ8OrYsyrjobMy+y5WvDls/jU+fmXmr0s2yFfKBgwL+TsHvJ1D6kb80Rrlj1dCbxiac0iYl6TogkstbGw+XqYrwWfSoPDhWBAwwZzsOTsrVny6zx19jZFxzxJL3MBoLM+F/NsVud2K5q9DABvXtuM2llas5gxT8HB4YDxvj+lgHlq1sHhKmVxi3ZUv5+jHZWIefVVEURCUhzRLWidiHkLtJmdTuwDZFvnAZaYx3We6ZzlS9BRX4HRVAaPv5hPTM+f56kZ84DC5mxYW8tXK9JaHsi3l392jr3twFgK6awOTXPmTFeKqHnjjTdC1/VZf1mtX78ev/nNbzAxMYEjR47g9ttvL9Mztq86GjL7CNsJhKJsrbE6qsyHoNVcgVD1oZLxivyGd+88N7wqZi9rmrZgOyonBw15hVmua/tiQwyUlGvDq2laUT3n+4aTyGR1BAOaMu0prFY2VmFD58zWLKpntQQDGk5tWbgdlWp9l4V8O6q5NrzqDA4XRMtFuxve14/LN0RXKKbtaCqdNd//Kmby1VdFcPGaXGsWS3Wu6us8oLD+y8rGvAXWecfMdZ46P/91uZ/33t75KxOnk3FwuCAuYw4ye9kUDQXx9jNntmZRfZ0HWGcLzXPIp2rMW2CGpLm3VegzT1xsHB6YP1FzOlm7EQDWmGezSm1Q3ZinaZqZrDzrOk/lmGdnnafQeR6Q78Iy195WdCloqIogHCz9HFu9k3CJiSz9X7/SV/DfEYOG2hUbNCQEAhreeZZRvjY1EOaGhyvalgXIvx7mu+E9oujizxymO9eGd1jdxd/e3mFb/ddlbVEAWGLeXvsxr6UmqkxbjulEO6pHLO2oVB+kCxSW1XJE0cXfQjFPLP7UOuQrbsObr9iQb8MrLu/sxLyjiQnoOhAJBpR9/4uY97PnuswDX9XbsgCWdd48h3wi5qk0Sw3IX2zMGfMUrNhY2ViNWDiAsVRm3srE6UTMk22WGpB/jf/KRswbT2VwIteera1Wrde9IGKetTWLP9Z5uYSGeSs21FzniZj3/JFBTM4yW8icH6lQ0l68MmyeUS00N9RK7G2ljHmWdd58Q5On6xVtlhU903tnLub95tU+87WebzmqcswTVUsLxzzV1nlib7u/bxQnRmfOGBHneU0OrfN4seEh1+RakPzgmUMFH2yKINiqaBAE8q1ZfvNqH44NT2BiMoORZBqA2pl85uKvgBte1QLhOSuMG95nD56Y9b2g4uJvZWNVfsNbYJbHSDKN/twHhYyLPxHzHt55ZEqrufmYCz/FNjxWb1/fjnBQwwvdCbNiS/VWVACwpq2AmDeodsx7oTuBsVR6xn9XsWKjrjJie8Or67rUl7nvysW8/3yhd0qrufn0WNZ5KrXlsBKtWXoT+dYsvmpR4LPKXAA4q7MOwYCGnqEJ83u0UrFiw1qZON/B7nQi5q2Q8DL36rOWIhjQ8MyBAew7Vtj3LNqNVkaCqK1Qpy2H1cblS9BZXzmlI4E/1nki5i3clkWllqMAsLqpGvGKMCYms3hhltliKibtAcXNkBQXvzLGvMtPb0VVJIjXjo9ix2uzZ6pPNzwxieHc2Vabgq2oAOOc4+zOOmR1mB0JVG8tD+T3tq/Mk7iaX+epFfPqqyJY1WS8h2dLYulzuEqNFxse8rb1baiNhXDkxDieerWwzBaRvaxqEATyrVmyOvCvu7rN291IKIAahfrQTWcu/uZYCIynMuah9jLFAuFp7bWIhQMYGp/E/r6RGf/dHB6u0OLPuuEtdJiuWPjVV0VQG5NvwN4FJzWio74CwxNp/Hx398J/AUC3KNVV+DJ3SVUEF5+aa83y3BFMTGbMBa/Si7/W+Te8uq4re5m7tK4CbfEYMlkdu2aZLSQuc50YruYldnvO9w0nMT6ZQUCT89BjXXstzuqoQzqr4yfPHi7o75gtWRSOedbWLA/nKtX6fNSKar5KzXwmn3yv9/lURkJm1dZsczZEv3mV1nmA/Zg3mcmahx4yXua2xmN4c67V3Pd/W2DMs6zzVL3MNVqzGBnMD+c6EvihLYtI2jtyYu5KTVVbUQUCmpnBPGvMU3ydV+jeFpB3lhpgtJe/Ktdt5PvPHCro74gEltpYCFUKn22900xoNGJenw9i3oqGKkRDAYxPZuacNaXq3haYv+3oUYdjHi82PCQWDprZfD8oNBCK4WoK9uOzyrdm6TLbUDVWRZRd8AL5PqRzbXjFQMmaaEi5jKZwMIAzl9UBmD0QqlixAdjPapE5cxkwFvnvObcTgI2YJ4arKZa9Ot27zjZi3qPPdZuZ3ZFgALUxtd7rVmLDe3hg3KzKs0qMp80LHtWyWgBLO6pZ+i+rWLEB5GNewZe5uU1Be12FtHPFrt9kxLwfPnO4oOpcc4iu6jEvt87btqcXg2MpDE/kLnMVbkW1oqEKkZBRqXn4xMwNbzarK1ulBszfgu+YgrPUAEvMK3Cd13ViHJmsjlg4IG0mt4h5D+80EjUW0u2TdZ642Hg615olf8inbpVaXWXEvKR/ZY5KNevwcNXMG/MUXeettTlAfCSZNs96ZOxGAORj3n/u6UX/yMLVuSJpT/WY9/Yz2hAOanipJ4GXexO+aEUVDGgLth0VMU+1y1wgP2fj2VnmbDgd8+TcFSrsvZuNQPiLl46Zt1jz6fFBxQaQb83yYk8C/73vOAC1s/gAo1IlEgxgNJWZtUzfOl9DxQueuYasjSTTGM21LZJ1kzcXuwPE88PV5Fz4AcC15yxDKKBh56HBeUvTBT9kLwPAxWuaEa8Iozcxgcdy1SwN1Wpf5i6piphZG3tn2fAeyV3mNlRFUBEJLupzWwxzZbXoup4/5FM05hW64TUHh0u62QWAt5/ZhppoCIcGxvDf+48v+Hi/xDzRmmUslTGzHCPBgHKJG1ahYACntFQDmL010fHRJFLpLAKami1n5xumq2pbFrsVG/nB4VXSfv5feEoTltZVYGh8Ev+xp2fBx/f4oDIXAFY0VmHj8iXI6sBPnj2Sv8xVOHsZsM4WmhnzJjNZsxXZMgUPea3rvOnzF8xZaorFvHVt+ZaL6Vlmi0wnezcCADhjWRxnLI0jlcmaVajz6VF8voawpCpiVvD9dGeX2YqqSfWYt0ALSlVbjgLAxhVittAQkumpiQ2iMteplqO82PCYk1tqcO6KJchkdfz4dwuX7Hb7pGLD2prle//zOgD1F36hYAAnmxvemRsgVQcNCXPd8IpqjeqoeuWadje8hwaMxV+nhD1IheaaGC47rQUA8P3fLly1IWKe6pe50VAQb1/fBgD47n+/DkD9mAfM345K+ZiXm7Ox89DU2ULDyTTGc1muKvWbB/Ixb2+BG15ziG69vDGvMhLCO3MVWYVUqpkxT8ENj5W1NYuIeapf5gKFxbzW2hjCQfW2bGKd93JvYkqVnq7rlhYFau1vRGViz9DErMM0p5N5cLgQDGh4z7kdAApc5w35Y50H5Ks2xN42FNAQr5DzMLdQ88W83qEJZHWj3bSKa94zO+oQDmroG07i8MDUpEXzMlexmNdZX4mqSBCpdBYHcskp8zkkBodLnLQHANdvFh0JDi84RNy8zFV8nQcA79xgdKd55LkuDI0b7ehUfK9bzTdbaDyVMSuUOhRrOQoAqxqrUF8VQSqdxZ6uqd//0QQrNpR3nWhT8LvDyMzTpiCTzS/8/bD4E61ZxAd/Q5W6ZWvCfIMlVb7dBYCzO40b3tf7x6YMWVU1iw+wv+F9/bgYKCn3B6GIeT/d2bXgEHG/ZC8Ds8Q8hUt1BTFkbbZh0qr2XRbWtNagMhLE8EQar1iGrIpqjZpoCJURtS5zl9dXojISRLLADW9+iK4aMe/xF46aGUtz6U3kWhT4IOaJQz5fxbxW/8a81ngMS+sqkNWB5w7lqzYSE2kk08ZFZ5Nia72aWNg8sCskiUWVmPe/zu1AMKDhd6+fwKtH5x8i7qd13tvXtyESDEyJeapf5q6dZ51ndiOoq0AgoN6/QywcxGntcQDA7y2Je7quK7u/DVha8RTSkeB1RWLe1jPbURUJ4sDxUWx/rX/ex5oVG4pdas3m4jVNiFeEzbOdoA8uc9fOU6UmzvOqFWwtDxhJS+JMb3qycp95mcuKDWVdeUYb4hVhdA3OP0S8fySJdFZHQFPvQ3A2ojWLoHorKsCy4Z3lhlf1DW+8MmyW7ll7kZqluoplLgP2N7wyD1ezesPqRnTWV2I4mTbbLs1mMpM1F/5tilepAcblnvVnq3pGCwCsnSeTT/XL3FAwYC7+rK1Zjikc8wIBzfycK2TDK1oUyB7z1rbVYkNnboj47+dvUyBmqanYimg60ZpF8EPMK6xKTe7X+3zOnaUdlYh58YowYmH12g6Kg107MU/mylzAqLy5RAwRX6BSrXfIH1VqgDFzQrRmAfwW84ZnZLKrvs4DLDHPsrcdGp9EKneZq+Jaz85sIRW6EQDGQfU7cskaC1Wq9fgo5kVDQWw9s838fUNVRMlLTCtxsXewfwyj02ZIWueoqXqpPds6z7jMdbYylxcbHhQLB3FNboj4fIFQlOo218QQUrBEfTpraxbAJ4u/ebNaxHA1dTe8oi+f9Ya3T9HhakKhG95kOoPuXFbbcskXf4GAZmYwzxfzjg0noetAOKgpPVBWsLZmAfwX86ZveH0R82YZLKnqQEkh34Jv/ixeID88XPaYB1iGiP/u0JxDxCcmM+jPVe+1+6AyF4BvY97Bgdk2vLmBkgofdmxcIdqOzhbz1Pz524p5imQvA/nWLA8/O/8QcXOQrg8ucwGYrQkBf8S8VU1VCAc1jCTT5uWtoPLgcGGjaLX8+syYV1cZRjSk4mVu4TFPlW4EgGWI+AvzDxEX+3nfxLxcOyrAHzGvoTpqrmf2TqtYNAeHK7zOO2dFfm8r9vYnxiYxmTH+t1MzVtQ/DZfU9ZuNXqRPvnzMzFyZLt+Pzx9BEADedbY1EPqhRYGxEDjQPzqjRY8fslpmG6ar+oZ3XZtRorzQ4u/wwDh0HaiKBJVoy/bujcYQ8V2HB/Fi9+yXOiLmtcZjymd3CO/a4K+Yt6qxGuGghuFk2oxxgi9inpnJl7/MdXq4mtcUmsk3NDaJwTGjH6/svZcB4O3r21ETC+HwwDie3jf7EHGx/ouFA6irVLtUXxCtWQB/tKJqrI6isToKXQdembbh7VJ8rhCQX+c9d+iEOWdH+ZjXVljMy2b1/GWuxHOFhDeebAwRT0yk8e9/mH2I+GgyjURuiLYfspcB4OJTm8347oeYFw4GcFLz7K1Zuk6ov84TCSyvHBs2ZwwcS6i9t7UzQ1KVbgQAcPrSONYvi2Myo+Nfnp29OlfXdbMy1y8x7+zOOvPiyg8xD7DM2ejx3zrv9KVxREIB9I+mzFZzYp23pDKMSMiZKwlebHjUSc012LSi3hgi/vvZh4iLig2/ZPEBRiBc3WQs7lc2yr/IX0hTTRSN1ZEZG95kOmMO3FE5EIrBknu6hszsLlUHSgqFVmxYS3VVKF1sqoni8tNaAcw9UNdPAyWFzoZKbF5pvA/8EPMioQBWN1UDmLn4M9uy1Kv789/QuQQBzbi4FO1YRKxXN+YVtuE9mIt5jdVRVEXl70NbEQniXQu0Kchn8alboj5dXWUEl57WAsAYOugH4nN/b+/sMU/VlqMAcEpLDWqiIYymMuYhpxnzFK9S23ds2Gw/M5ujwxNIpbMIBTS0K5DEFgxouG7T/EPExXyNmlgI1QrE+UJEQgFcfZbxWeCbmGfOFpr6ue+HdV5TTRQrGiqh68DO3Gwh1fe2a1proGlG1wXr3MzprN0IOhW4zAXyVRs/eGb26tyh8UmM5844/DBXCDA6Elx7jvFZ4LeYt7d39pin8jovGgpi/dLcbKHXjcQ9N/a2vNjwMFGy+8NnDs06RNys2PBJEASMQPjAjZvw7fefg/XL6sr9dBbFbP2Xxc1+LBxAvQLZ+nPpqK9AU00Ukxkdu48MAbBktSiayVfohlelUl1BxLyfPdeFsVR6xn/v8Vl7AuEfr9+Ar12/YUofZpWJ94A15o0k02a2vsqZfNXRkBnzRaWa6lVqhW54VWrJIlyXi3m/eOmoeZFllc/i81fMu+ddZ+Arf3TWlHYFKlszy2BJXdd9UaUWDGjYMK0Fn1jnNSm6zlu2pAI1sRAmMzr2943M+TgR85YtqVCm5fD/OscYIv77gydmVCgBQPeg/5L2AOCOt67BV/7oLNz4hpXlfiqLwmw7Or1iw4x56nzOz2Z6OyqxzmtSdJ1XGQlhZa6F6HxJLNZuBKpUqW89sx3V0RBe7x/DjlmGiIuYV18VUXKm1Fz+9MJV+IfrNuBjbzml3E9lUYiY95JfY96K6es843XvZMxTY5WkqCtOb0VdZRjdQxN46pWZQ8T9NGjIqrOhEm9Z11Lup7FoZtvwWgdKqpzFqWmapR1V7oZ32PlA6CWFbnhFqW6nQod8W1Y1YHmDMUT858/PbFMgYl6rzza8zTUxvH19u4Se0IoAAGOQSURBVNLvdavZYp4o1Y1XhFETU7slzznThqwddWHx5yWFbnjzQ3TViXlrWmtxthgiPkubgt6E/6rUAKA2FsbVG5Y6Vp7udeIy0/r6PzE2ibFcC9J2xdf509uOinWeqnOFNE0zL/Dnar0JqDM43Kq5Noa3rM0NEZ+lakNUbLT6LIElFg7i6g1LfVOlMlvSXiarm/NVVO5GAMxsOyrWearGPKCw6lzVuhEAQFU0hHec1Q4AeGiWjgS9iVzMU7RaZy6hYABXndmudIKulRnzehJTZkj6Ya4QkO/CMj1pjxUbPmEdIv7QPIs/P1Vs+NFsPfn8MFBSMIfp5g75+hRvy2J3w6tC32XBOkR8tsWfiHkqtGSguZ0628WGH2NebsPb58Liz2sK2fCK7GWVYh4AXL95OYDZ2xR0+7Ay14+s2ctiwysuc5tqospncZ5jrvNyMc9c56l5mQvk52wUFvPUucwF8jHvkZ0zh4iLBBau89QmElgOHB81XwPHhieQzuoIBTSl1ztAPubtOjyIyUzWss5TN+YV0mpZ3Zhn7G0ff6EXx6cNETer1BjzlLa6qRqhgIbERNr8nEuls+YBv8qtqID83nbfsREMjqXMig0nuxHwYsPjxCHfky8fNQ/1BLNigxtepeWzlxMzNryqB0EAOGdFrlz30AmMJtMYThotilRtywLY2/Cq1JYFMIaIh4Manj88iBe6h6b8tx4fztjwI3HI/VrfiLnh9WPMe6E7gfFUxpXFn9eIDW9BMa9RrZj39vVtqI2FcOTEOH4zbYg4Y54/nNRcjWBAw9D4pFmlIy5zVc/iA4CzOusQDGjoHppA9+B4fni40tnLoi1FAYd8iq3z3nhSI5YtMYaI/3z31Opcs/0eY57SmmqiqK+KIKsDrx41qtPFOq81HkMwoEa2/lxWN1UjXhHGxGQWL3YnfBHz1rXbuMxVbJ13WnscZ3bUzTpEPJ+ozJinsikzJHOf+z1DRuu1WDiABsUrV+qrIliVm5P87METrrRZ5sWGx53UXI1NK+uR1YEf/y4fCNOZrFm2qHqJut+JDe+JsUkzCORbUan/sz+tvRaxcACDY5Nmb8rKSFDpcm3zYmOODW8mq+PwCfVaUQHGYODL5hgi3j3Iy1w/aK6JYkllGFndyOwA/BXzltZVoC0eQzqr43/2H8dorh1Ns8IZjGLDO28mn2hRoFgmXywcxLty1bnf/+3BKf/NrNhgJp/SoqGgOUBTVOeaAyV9sMavjITMdc/vD56wDJVU9zJ3XZsxSPPF7qltKaxEzFuuUCsqYGp17ox1HrsR+IKmaWbintjr+GmdFwhoZgazX2KeSFra3zc6o1JLULEbgXD9JmNY9vTqXL/OUvMjc87GLOs8VVqvzeecKTHPeN2zFZXPvDdXvvaj3+WHiB8bTiKrA6GAhsZqdT8EyTj0WNk4tf/4ER8MlBTCwQDOzA2K/7c/GJldzTVRpT8ArK2oZtvwdg+OYzKjIxIMKJnh8d5NYoh4N0ZzFTrJdMYs3+VlrtqMDe/UzC4/xTwgX7IrYl6V4pe5C214x1MZc+O/QrFDPiDfpuAXLx2bMkTcbMuiYJynqdZMS2g44qMqNSAf83619xjGczFA5ezlk1vySUsitlnpuq5sZS4AXHvOMoQCGp49eAJ7LW0n862o/PG697N8z3nj5++XIbqCte2oHyo2WmtjqKsMI5PVzSqd6VSOeVvPbEdNNISD/WPYbhkiLi5zuc5TX362UC7mmes89V7vsxFzNp593VKx4eBlLi82JHD5aa1Ykhsi/qu9xwDky9ZaatUv16SZw3S7LMPD/UAMWXvixaMA1F74AQtveMXg8GX1FUq+/7esbsCKhkqMJNN47PluAMDRIePfIRoKYEml2sOjaWrPecCayeeTmLd8WsxTuFoDmLrhFVU6ViLm1cRCqFPw/X9KSw3OWb4EmayOH//+MABgLJXG0PgkAGby+YG5zuvxacybts6riYZQEVF3tkgsnK/Sma01y4mxSQxPGIkdHYpVqQHGOv7SdS0A8pVquq6jh3OFfCO/zhOXuf5pvwfk13m/efU4JiazAJw95PMaTdPmbbWscjcCwKhMvHrDUgDA9y2zc9la3j/MmNfjz5i3MbfO23VkEMcSohUVKzZ8xTpEXJTsMgj6y1pzgHgC6UzW7MHsl0AobnjFJk/lhR9gvOdXN8294VV1uJqgaTPbFPRY2hOoXK1DhrVmVovx+u/yUYsCID9nw4x5Cs/XAKZueF/sni3miZYslcq+//Mx7zAyWd1c51VFgqhRuFqHDGunHfKJ7OVlPslc99s6D5i/BZ+Iea21MWWHx4uY98hzXRhPZTCcTJutF1WsRqap1lqyl3Vd912V2pkddQgHNTPm1cZCyr7XBbMjwSwxr2fI6EYQDmrKvv9FzPvPF3rRN5w0LnM5S803RMx77fgokumM77oRrGqsQn1VBKl0FqmMcZnb5McZG1dddRU6OzsRi8XQ1taG973vfeju7p7ymN27d+ONb3wjYrEYOjo68PnPf75Mz9Z5120WQ8SPoXtw3NKPzx9vBL+zVmz0JiaQyRptiJp80obs7M4lU36vesUGMP/iL3/Ip15LFuHdG5chEgzg+SND2NM1xIWfz4islr29w5iYzLch88vib01rDSot2cqqV2wA88c8UbGhcsx72/o2xCvC6Bocx29e7ZuyzlP1MofyRIuC1/pyG16fZfK1xmNT4rvf13n5mKdmAgsAXHBSIzrrKzE8kcbPd3ebMa+uMqx0tQ4ZTm6pRkADBkZT6BtJ+u4yNxYO4rT2uPl7v6/zRNJeR32lkt0IAOMy+6yOOqSzxhDxgdEUUmnjgLcl7o8zHT9rqY1OqU73W9KepmlTzvTiFWFHL3Oludi4+OKL8eMf/xh79+7Fww8/jP379+Pd7363+d8TiQQuu+wyLF++HM8++yy+8IUv4FOf+hS+9a1vlfFZO2d1UzXOW2UMEf/R7w5b+vGp/yFI+d7L+/tG8Ppx44O/vS6GgKIf/NPFK8M4paXa/L3Kw9WEQhZ/Km94G6qjuPx0Y4j49585lB8oyZYsvnBycw0CGnB8JIXnDw8CMDLXVWxDNJtQMIANnXXm71sUr9gA8jFvtiq1182BkurGPGOIeL5NAYfo+ktbPIbaWAjprI5dhwbNLF6/ZC8D+XZUgL/WebPGvOPqr/MCAQ3vyQ3UnbLOYwKLL0ydITnsuzbLQL4dFeCPmGdtRTV9hqTq3QgEMVPtB88cQnfuMrexOopoiJe5qjNmSObbjh7x2cUG4O46T5qLjVtuuQXnnXceli9fjvPPPx933HEHduzYgclJo//wQw89hFQqhe985zs47bTT8J73vAd/8Rd/gb//+78v8zN3jihf+/HvD5tvBG54/aE9HkNNLITJjI6nXu0D4K/NLgBszLUpAHzSokAs/mZryzIghqupm70MANflNryPPtdl9t3ncDV/qIgEzdf3L182ZkstXeKvzHW/xrwX59nwqh7zrs+t83758jHzQo8xzx80TTOTWETMW1IZRmXEP23IrId8fsheFjHvwPFRjKXSU/7bwQH1K3MBozo3FNDw3KFB/Cr3umfSnn+ISrX/3nccyXQWmmZUb/mF9ZDPD1VqJzVXm+23xFmW4JeY9/b1baiJhnBoYAz/8qwxU62dSXu+IWLenu4hs7X80jq1L/OspqzzHI550lxsWA0MDOChhx7C+eefj3DYyN7cvn07LrzwQkQiEfNxl19+Ofbu3YsTJ06U66k66orTjSHiPUMT+PVe43Cbraj8wXrD+4vcYMVlPgqCgLuB0ItEJt+B/qkbXl3XzVZUKg5Xs9qyqgErG6swmsrg58/3AGDFhp+IdlRmzPNRFh/gv5hn3fCKlhSCuNhQPead3FKDc1cYQ8R/8vsjABjz/GTGOs9nMW/KZa4PqtSaaqJorI5C1422i1Z+qMwFjM+2y04zhoj/4BnjkI8xzz+mx7zW2hgiISmPp4rit5gXCQWwusnowDC9Uu2gD6rUAGOI+Dtz1blmzPPRZZ7fiZj36719yGSNmTJ+eO8Lpy+NIxI0YrzT37dUnxy33347qqqq0NDQgEOHDuHRRx81/1tvby9aWlqmPF78vre3d86vmUwmkUgkpvzyqmgoiHdvNIaIi4ErzOTzjzWWgUOA/yo2/NaiYK4N7/GRFMZSGWia+qWLxhBxo2qDMc9/ZsQ8n13kb+isg+g26IeKjakb3nzMm8xkzYsO1Te8QL46lzHPf/we805trUFN1KhQ8UPFBpAfGm+NeYC1LYva2cvAzJjHVlT+IarU/Brzmmqi5rrGLzFvXbtoRzUt5vlgrpDAmOdf02Nee12Fb1rLA0YLwjOWGbOFnI55Zb3YuOOOO6Bp2ry/Xn75ZfPxH//4x/Hcc8/h8ccfRzAYxPvf//4Z7QrsuueeexCPx81fHR0dpX5brhKBUPBTuabfiexlQfVD7ek66yuxobMOnfWV6FC8/6YgFn/WORuiWqM9XuGLfpzv3thh3uwDjHl+IrJaBL/FvJpYGG9Z24KGqojZskR1ZsyztODrHhxHJqsjGgqgxQeVK1eeYQwRFxjz/MPv67xgQMPWs9pRFQliQ0dduZ/Oosiv84bMPxtNpnF8JAlA/So1AHjDamOIuMDsZf/w+zoPAN5x1lJEQwFsWlG/8IMVkG87mo951m4EqreiAoyuDNY5eox5/nFKSzWsXZX9GPOuPqsdkWAAW1Y3OPp1y9q49bbbbsONN94472NWrVpl/u/GxkY0NjbilFNOwdq1a9HR0YEdO3Zgy5YtaG1txdGjR6f8XfH71tbWOb/+nXfeiVtvvdX8fSKR8PTlxqqmamxZ1YDtr/UjEgygoSqy8F8iJYhMPsFvWS2apuGRm89HOqsjHJSq2Kxoa9tq8NQrfVPKdf3SnkCor4rgitNb8a/PdwNg9rKfrJ12mO+3KjUA+Ob7NmIyo/umNcO6tlo8gq4pMe910YaqvtIXWU2xcBDXnL0M3/nvAwDYe9lPTm2Zesjnx5j3uXeegU9tPc1XMQ+Ymr0s1nlLKsNTLjlVFQhouG5TJ+7dZiQzMnvZP5YtqUB1NISRpNFy148x79ZLT8FHLz7JNzFv7Swxz0/dCITrN3XiuUODANha3k8qIyGsaKjCAZ9WqQHA+7aswB+d2+l4zCtrBG1qasKaNWvm/WWdmWGVzRqlW8mkkdGyZcsWPPXUU+YwcQB44okncOqpp2LJkiWzfg0AiEajqK2tnfLL667fbFRtdDb4Y5NPhlNbueHVNM03lxrAHBteH5XqCiLm1VWGUVvhn0Gqfre0ztjwWn/vN5qm+WazC1g2vL35i41DZhafn2KekWATDmo85PORqmhoyuvcjzEPgC9j3ss9CWSzRheCQwNijpr6mcvCuzcuQzho7Gn9UKVCBusMScBfQ3St/BjzDg2MYXhiMve//dWNAADevr4dNTFjj7OCMc9XGPPciXlSnBD99re/xe9+9ztccMEFWLJkCfbv34+//du/xerVq7FlyxYAwPXXX4+7774bH/zgB3H77bdjz549+OpXv4ovf/nLZX72znvbGW0YGE3htHbvX8KQc6qjIXTWV+LQwBiCAQ2tPunF6Wf5iw1jwxsIaL4q1RU2r6zH59+9Hq21MWgaL3P9IhDQcGprDZ49eAKA/wbp+pHY8B7sNza8NbGwWbHhp5h3UnMNvnb9BoQCGqqiUizVySFrWmvMjH3GPPWtaqxCJBTAaCqDQwNjWNFYZcY8Px12NdVE8a33nYOB0ZRvL/T8ak1bDX5vrvP4s1ddfVUErbUx9CYm8HLvMM5dUY/XfTI43KoiEsS3338O9h4dxhlL4+V+OrSI1rTW4j/2GDOgGfOcI8X1cGVlJR555BFccsklOPXUU/HBD34Q69evx69//WtEo8ZAzXg8jscffxwHDhzAxo0bcdttt+GTn/wkPvzhD5f52TsvENDwgfNX4Byf9GKkPHHD21obQ8hHlQt+tTK34R3LbXgB60BJ/yz+NE3D/zqnAxee0lTup0KLTMS8aCiAxmq2XlSd2PACwN5eo1LNb+33hLevb8cVp7eV+2nQIrO2HfVjZa7fhIIBswWZaMHnx3UeAFy8phnXbFxW7qdBi4wxz3/Wtk2LeT7sRgAAm1c14P1bVjBpz2es89QY85wjRRrYGWecgSeffHLBx61fvx6/+c1vFuEZEZXHmtYaPP7iUd7u+oTY8P6hawgv9SSworHKvODwU/Yy+Ze42Fi6pIILf59Y21aD3sQEXupJ4JwV9WaLAsY88gMR82piIV/MVyAj5ol13lvPaGPMI1+Z2paF+1s/WNdei//am58heciH3QjIv6wxj2d6zmHKN5FELjutFbWxEC47rbXcT4UWiWhH9WJPAomJSQyMpgCwBzH5w0WnNhsD5BnzfGNdez7mZbO6b7OXyZ/OW9WA9ngMV7Jaxzes6zwAvmzLQv51+tI4Tm6uxpvXNCMW9sd8Bb8TbUdf7M7FPK7zyEc6llTinOVLcGZHHefoOUiKig0iMpy+NI7n77qMmcs+Yi3XPZRb+DVWR6YMVSZSVUd9JZ79xFsY83zE3PD2DOPYcBLJdBbBgMZybfKFJVUR/Pcdb2bM85G15jy1YSTTGfQMjQNgAgv5QywcxOO3XFjup0GLSMS8vUeHkcnqZjcCxjzyg0BAw0/+zJgTzbWec3gyRiQZBkB/WdduDBR7qWcYr7NUl3yIMc9fRPby3t4EXusbAWC0pwhzrhT5BGOev6zNVal1DY7jhe4EsjpQGQmiqTpa5mdGtDgY8/xlRUMVKsJBjE9msPvIoNmNgPtb8gvGPOdxl0hE5GFiwFTX4Dj+cGQIAEt1iUhdy3Mb3onJLH79al/uzxjziEhNtbGw2Wf7P1/oBQB01lfy4IOIlBQMaDg1N2dgWy7msRsBEZWCFxtERB5m3fCKxR8zWohIVVM2vHtEzOPFBhGpS7RmETFvBdd5RKSw6TGPe1siKgUvNoiIPE60ZjGH6PKQj4gUJgaI5weHc8NLROriOo+I/GTmOo8xj4iKx4sNIiKPE1ktAoerEZHKGPOIyE8Y84jIT9blWi0LjHlEVApebBARedz0DS9bFBCRyqZveBnziEhl67jOIyIfObWVMY+InMOLDSIijzutPb/4q4mGsKQyXMZnQ0TkrlNba2Gdm9vJFgVEpLBlSypQYxmcy5hHRCqrjoawwlKlwYoNIioFLzaIiDzOuuHtbKiEZj3xIyJSTHU0ZPZbbq6JoiISLPMzIiJyTyCgYU2uUi0c1NBeV1HmZ0RE5C5rRwLO2CCiUvBig4jI4zQtv+FlqS4R+YHY8DLmEZEfiJjXsaQSwQATWIhIbSLm1URDqK+KlPnZEJHMeLFBRCSBM5fVAQBObqku7xMhIloEZ3bUAWDMIyJ/EOu8k5oZ84hIfdZ1HrsREFEpQgs/hIiIyu1/X3wSOuorcfVZS8v9VIiIXHfDectRGQniitNay/1UiIhct/XMdoyl0njTKc3lfipERK5740mNuOddZ2Dj8iXlfipEJDlN13W93E/CSxKJBOLxOIaGhlBbW7vwXyAiIiIiIiIiIiIiopLYOZtnKyoiIiIiIiIiIiIiIpIGLzaIiIiIiIiIiIiIiEgavNggIiIiIiIiIiIiIiJp8GKDiIiIiIiIiIiIiIikwYsNIiIiIiIiIiIiIiKSBi82iIiIiIiIiIiIiIhIGrzYICIiIiIiIiIiIiIiaYTK/QS8Rtd1AEAikSjzMyEiIiIiIiIiIiIi8gdxJi/O6OfDi41p+vv7AQAdHR1lfiZERERERERERERERP4yPDyMeDw+72N4sTFNfX09AODQoUML/uMR0dwSiQQ6Ojpw+PBh1NbWlvvpEEmL7yUi5/D9ROQMvpeInMP3E5Ez+F4icka530u6rmN4eBjt7e0LPpYXG9MEAsbYkXg8zkBI5IDa2lq+l4gcwPcSkXP4fiJyBt9LRM7h+4nIGXwvETmjnO+lQosNODyciIiIiIiIiIiIiIikwYsNIiIiIiIiIiIiIiKSBi82polGo7jrrrsQjUbL/VSIpMb3EpEz+F4icg7fT0TO4HuJyDl8PxE5g+8lImfI9F7SdF3Xy/0kiIiIiIiIiIiIiIiICsGKDSIiIiIiIiIiIiIikgYvNoiIiIiIiIiIiIiISBq82CAiIiIiIiIiIiIiImnwYoOIiIiIiIiIiIiIiKTBiw0iIiIiIiIiIiIiIpIGLzaIiIiIiIiIiIiIiEgavNggIiIiIiIiIiIiIiJp8GKDiIiIiIiIiIiIiIikwYsNIiIiIiIiIiIiIiKSBi82iIiIiIiIiIiIiIhIGrzYICIiIiIiIiIiIiIiafBig4iIiIiIiIiIiIiIpBEq9xPwmmw2i+7ubtTU1EDTtHI/HSIiIiIiIiIiIiIi5em6juHhYbS3tyMQmL8mgxcb03R3d6Ojo6PcT4OIiIiIiIiIiIiIyHcOHz6MZcuWzfsYXmxMU1NTA8D4x6utrS3zsyEiIiIiIiIiIiIiUl8ikUBHR4d5Rj8fXmxMI9pP1dbW8mKDiIiIiIiIiIiIiGgRFTIigsPDiYiIiIiIiIiIiIhIGrzYICIiIiIiIiIiIiIiafBig4iIiIiIiIiIiIiIpMEZG0REREREREREREQKy2R1PHNgAMeGJ9BcE8OmlfUIBhaeY0DkVbzYICIiIiIiIiIiIlLUtj09uPuxF9EzNGH+WVs8hru2rsMVp7eV8ZkRFY+tqIiIiIiIiIiIiIgUtG1PD25+cOeUSw0A6B2awM0P7sS2PT1lemZEpeHFBhEREREREREREZFiMlkddz/2IvRZ/pv4s7sfexGZ7GyPIPI2XmwQERERERERERERKeaZAwMzKjWsdAA9QxN45sDA4j0pIofwYoOIiIiIiIiIiIhIMceG577UKOZxRF7Ciw0iIiIiIiIiIiIixTTXxBx9HJGX8GKDiIiIiIiIiIiISDGbVtajLR6DNsd/1wC0xWPYtLJ+MZ8WkSN4sUFERERERERERESkmGBAw11b1wHAjMsN8fu7tq5DMDDX1QeRd/Fig4iIiIiIiIiIiEhBV5zehvtuOBut8antplrjMdx3w9m44vS2Mj0zotKEyv0EiIiIiIiIiIiIiMgdV5zehkvXteKZAwM4NjyB5hqj/RQrNUhmvNggIiIiIiIiIiIiUlgwoGHL6oZyPw0ix7AVFRERERERERERERERSYMXG0REREREREREREREJA1ebBARERERERERERERkTQ4Y4OIiIiIiIiIiIhIMpmszoHg5Fu82CAiIiIiIiIiIiKSyLY9Pbj7sRfRMzRh/llbPIa7tq7DFae3lfGZES0OtqIiIiIiIiIiIiIiksS2PT24+cGdUy41AKB3aAI3P7gT2/b0lOmZES0eXmwQERERERERERERSSCT1XH3Yy9Cn+W/iT+7+7EXkcnO9ggidfBig4iIiIiIiIiIiEgCzxwYmFGpYaUD6BmawDMHBhbvSRGVAWdsEBERERERERER+QCHTcvv2PDclxrFPI5IVrzYICIiIiIiIiIiUhyHTauhuSbm6OOIZCVNK6oVK1ZA07QZvz7ykY8AAC666KIZ/+3P/uzPyvysiYiIiIiIiIiIyovDptWxaWU92uIxzFVno8G4sNq0sn4xnxbRopPmYuN3v/sdenp6zF9PPPEEAODaa681H3PTTTdNecznP//5cj1dIiIiIiIiIiKisuOwabUEAxru2roOAGZcbojf37V1HVuMkfKkudhoampCa2ur+evnP/85Vq9ejTe96U3mYyorK6c8pra2tozPmIiIiIiIiIiIqLw4bFo9V5zehvtuOBut8antplrjMdx3w9lsLUa+IOWMjVQqhQcffBC33norNC1/+/jQQw/hwQcfRGtrK7Zu3Yq//du/RWVl5bxfK5lMIplMmr9PJBKuPW8iIiIiIiIir+AQYSJ/4LBpNV1xehsuXdfKOE6+JeXFxs9+9jMMDg7ixhtvNP/s+uuvx/Lly9He3o7du3fj9ttvx969e/HII4/M+7Xuuece3H333S4/YyIiIiIiIiLv4BBhIv/gsGl1BQMatqxuKPfTICoLTdd16RroXX755YhEInjsscfmfMyTTz6JSy65BPv27cPq1avnfNxsFRsdHR0YGhpiKysiIiIiIiJSjhgiPP0wQOT4so0JkVoyWR0X3PskeocmZp2zocFoYfT07W9mtj8RlVUikUA8Hi/obF6aGRvCwYMH8Ytf/AIf+tCH5n3c5s2bAQD79u2b93HRaBS1tbVTfhERERERERGpiEOEifyHw6aJSEXSXWw88MADaG5uxtve9rZ5H7dr1y4AQFsbs0yIiIiIiIiIAA4RJvIrDpsmItVINWMjm83igQcewAc+8AGEQvmnvn//fnz/+9/HlVdeiYaGBuzevRu33HILLrzwQqxfv76Mz5iIiIiIiIhocc03FJxDhIn8i8OmiUglUl1s/OIXv8ChQ4fwJ3/yJ1P+PBKJ4Be/+AW+8pWvYHR0FB0dHbjmmmvwiU98okzPlIiIiIiIiGjxLTQUnEOEifyNw6aJSBVSDg93k50BJUREREREREReUchQ8EvXtXKIMBEREXmS0sPDiYiIiIiIiGiqQoeCA+AQYSLFZLI6tu/vx6O7urB9fz8yWeYwE5H6pGpFRUREREREREQz2RkKLoYIT29Z1WppWUVEclio/RwRkap4sUFEREREREQkObtDwTlEmEh+c7Wf6x2awM0P7sR9N5zNyw0iUhYvNoiIiIiIiIgkV8xQcA4RJpLXQu3nNBjt5y5d18oLSyJSEmdsEBEREREREUlu08p6tMVjM+ZmCBqM9jSbVtYv5tMij+JMBvnZaT9HRKQiVmwQERERERERSS4Y0HDX1nW4+cGd0IApWdwcCk5WnMmgBrvt54iIVMOKDSIiIiIiIiIFiKHgrfGpbala4zH22veJhSoxxEyG6Zn+YibDtj09i/l0qQTFtJ8jIlIJKzaIiIiIiIiIFMGh4P61UCUGZzKoRbSf6x2amPVnqsG41GT7OSJSFSs2iIiIiIiIiBQihoK/46yl2LK6gYfUPlBIJQZnMqhFtJ8DMGO2DtvPEZEf8GKDiIiIiIhIQRwOTOQPC1ViAEYlRm+CMxlUw/ZzRORnbEVFRERERESkGA4HJvKPQisxBkaSBX09zmSQC9vPEZFf8WKDiIiIiIhIIaIlzfTsbdGShlm8RGoptMKivirCmQyKEu3niIj8hK2oiIiIiIiIFFFoSxq2pSJSR6EVFq3xCs5kICIiZfBig4iIiIiISBEcDkzkP5tW1qMtHptxWSFoMFrRbVpZz5kMRESkDLaiIiIiIiIiUkShLWk4HJhIHcGAhru2rsPND+6EBkyp2JqtEoMzGYiISAW82CAiIiIiIlJEoS1pOByYSC2iEuPux16cUrXVGo/hrq3rZlRicCYDERHJjhcbREREREREihAtaTgcmMh/WIlBRER+wosNIiIiIiIiRdhtSUNEamElBhER+QWHhxMRERERESmkmOHAmayO7fv78eiuLmzf349MdrZ6Dyon/oyIiIiI8lixQUREREREpBg7LWm27emZ0Ze/bY6+/FQe/BkRERERTaXpus40D4tEIoF4PI6hoSHU1taW++kQERERERG5ZtueHtz84M4Z8zjE9cdcFR60ePgzIiIiIr+wczbPVlREREREREQ+lMnquPuxF2cdMi7+7O7HXmTLozLiz4iIiIhodtJcbHzqU5+CpmlTfq1Zs8b87xMTE/jIRz6ChoYGVFdX45prrsHRo0fL+IyJiIiIiIi865kDA1NaG02nA+gZmsAzBwYW70nRFPwZEREREc1OmosNADjttNPQ09Nj/nr66afN/3bLLbfgsccew09+8hP8+te/Rnd3N971rneV8dkSERERERF517HhuQ/Mi3kcOY8/IyIiIqLZSTU8PBQKobW1dcafDw0N4Z//+Z/x/e9/H29+85sBAA888ADWrl2LHTt24Lzzzlvsp0pERERERORpzTUxRx9HzuPPiIiIiGh2UlVsvPrqq2hvb8eqVavw3ve+F4cOHQIAPPvss5icnMRb3vIW87Fr1qxBZ2cntm/fPu/XTCaTSCQSU34RERERERGpbtPKerTFY+YQ6uk0AG3xGDatrF/Mp0UW/BkRERERzU6ai43Nmzfju9/9LrZt24b77rsPBw4cwBvf+EYMDw+jt7cXkUgEdXV1U/5OS0sLent75/2699xzD+LxuPmro6PDxe+CiIiIiIjIG4IBDXdtXQcAMw7Oxe/v2roOwcBcx+rkNv6MiIiIiGYnzcXGW9/6Vlx77bVYv349Lr/8cvz7v/87BgcH8eMf/7ikr3vnnXdiaGjI/HX48GGHnjEREREREZG3XXF6G+674Wy0xqe2MmqNx3DfDWfjitPbyvTMSODPiIiIiGgmqWZsWNXV1eGUU07Bvn37cOmllyKVSmFwcHBK1cbRo0dnnclhFY1GEY1GXX62RERERERE3nTF6W24dF0rnjkwgGPDE2iuMVobsQrAO/gzIiIiIppK2ouNkZER7N+/H+973/uwceNGhMNh/PKXv8Q111wDANi7dy8OHTqELVu2lPmZEhEREREReVswoGHL6oZyPw2aB39GRERERHnSXGz85V/+JbZu3Yrly5eju7sbd911F4LBIK677jrE43F88IMfxK233or6+nrU1tbiz//8z7Flyxacd9555X7qRERERERERERERETkEGkuNo4cOYLrrrsO/f39aGpqwgUXXIAdO3agqakJAPDlL38ZgUAA11xzDZLJJC6//HL80z/9U5mfNREREREREREREREROUnTdV0v95PwkkQigXg8jqGhIdTW1pb76RARERERERERERERKc/O2bw0FRtERERERERERH6TyeocHE9ERDQNLzaIiDyOGxkiIiIiIn/atqcHdz/2InqGJsw/a4vHcNfWdbji9LYyPjMiIqLy4sUGEZGHcSNDRERERORP2/b04OYHd2J6//DeoQnc/OBO3HfD2dwTEBGRbwXK/QSIiGh2YiNjvdQA8huZbXt6yvTMiMhtmayO7fv78eiuLmzf349MliPRiIiI/CST1XH3Yy/OuNQAYP7Z3Y+9yDUCERH5Fis2iIg8aKGNjAZjI3Ppula2pSJSDCu1iIiI6JkDAzMSnKx0AD1DE3jmwAC2rG5YvCdGRETkEazYICLyIDsbGSJSByu1iIiICACODc+9FyjmcURERKrhxQYRkQdxI0PkP2w5QUREREJzTczRxxGRXNialmhhbEVFRORB3MgQ+Q9bThAREZGwaWU92uIx9A5NzJr0oAFojcewaWX9Yj81InIZW9MSFYYVG0REHiQ2MnNNz9BgLGy4kSFSByu1iIiISAgGNNy1dR0AzNgTiN/ftXUd5+0RKYataYkKx4sNIiIP4kaGyH9YqUVERERWV5zehvtuOBut8amf/a3xGO674WxmbhMphq1piexhKyoiIo8SG5npJaitLEElklYmq+OZAwM4NjyB5hqj6kpcULLlBBEREU13xeltuHRd65zrByJSB1vTEtnDiw0iIg/jRoZIHQv1yhWVWjc/uBMaMOVyg5VaRERE/hUMaDzEJPIBtqYlsoetqIiIPE5sZN5x1lJsWd3AQ00iCRXaK5ctJ4iIiIhIyGR1bN/fj0d3dWH7/n62IFIcW9MS2cOKDSIiIiIXLdQrV4PRK/fSda0IBjRWahERERHRgtW+pB62piWyhxUbRERERC6y0ytXYKUWEc2F2btEROortNqX1CJa0wL5VrQCW9MSzcSKDSIimtd8w46JaGHslUtETmH2LhGR+uxW+5JaRGva6Z/3rfy8J5qBFxtERDQnHqAQlY69conICSJ7d/pBl8je5RweIiI12Kn25VB5NbE1LVFh2IqKiIhmxfJnImeIXrlzbUM0GBeG7JVLRHNZKHsXMLJ32ZaKiEh+rPYlgK1piQrBiw0iIpqBByhEzmGvXCIqVTGzekgenJtCRFas9iUiKgxbURER0QwsfyZyFnvlElEpmL2rLrb9JKLpRLVv79DErIlmGow1JKt9icjveLFBREQz8ACFyHnslUtExWL2rpo4N4WIZiOqfW9+cCc0YEqMYLUvEVEeW1EREdEMPEAhcgd75RJRMTirRz1s+0lE8xHVvq3xqfut1niMl55ERDnSXGzcc889OPfcc1FTU4Pm5mZcffXV2Lt375THXHTRRdA0bcqvP/uzPyvTMyYikhcPUIiIiLyDs3rUw7kpRLSQK05vw9O3vxk/uOk8fPU9Z+EHN52Hp29/My81iIhypLnY+PWvf42PfOQj2LFjB5544glMTk7isssuw+jo6JTH3XTTTejp6TF/ff7zny/TMyYikhcPUIiIiLyF2btqYdtPIioEq32JiOYmzYyNbdu2Tfn9d7/7XTQ3N+PZZ5/FhRdeaP55ZWUlWltbF/vpEREph8OOiYiIvIWzetTBtp8kk0xWZ9whcgDfS0TOkuZiY7qhoSEAQH391DYoDz30EB588EG0trZi69at+Nu//VtUVlaW4ykSEUmPByhERETeIrJ3SW6i7Wfv0MSsczY0GMkkbPtJ5bZtT8+MRKc2JjoR2cb3EpHzNF3XpZtGls1mcdVVV2FwcBBPP/20+eff+ta3sHz5crS3t2P37t24/fbbsWnTJjzyyCNzfq1kMolkMmn+PpFIoKOjA0NDQ6itrXX1+yAiIiIiIiJ/2ranBzc/uBMAplxuiPQRthijchOv0emHRnyNEtnD9xJR4RKJBOLxeEFn81JebNx88834j//4Dzz99NNYtmzZnI978skncckll2Dfvn1YvXr1rI/51Kc+hbvvvnvGn/Nig2gqlkwSERERETmLGbzkVZmsjgvufXLOIfeiqujp29/MfSHRPPheIrLHzsWGdK2oPvrRj+LnP/85nnrqqXkvNQBg8+bNADDvxcadd96JW2+91fy9qNggojxuuIiIiOTF5AQi72LbT/KqZw4MzHkQCxhVRj1DE3jmwADb4xHNg+8lIvfYuthIJBIFP9bpagdd1/Hnf/7n+OlPf4pf/epXWLly5YJ/Z9euXQCAtra5D16j0Sii0ahTT5NIOXOVTPYOTeDmB3eyZJKIiMjDmJwgD15A+RfnppAXHRue+yC2mMcR+RXfS0TusXWxUVdXB00rbHGdyWSKekJz+chHPoLvf//7ePTRR1FTU4Pe3l4AQDweR0VFBfbv34/vf//7uPLKK9HQ0IDdu3fjlltuwYUXXoj169c7+lyI/CKT1XH3Yy/OOtBQh1EyefdjL+LSda3ceBMRKYQHrGpgcoI8eAFF5E9e/rxtrok5+jgiv+J7icg9ti42/uu//sv836+//jruuOMO3HjjjdiyZQsAYPv27fje976He+65x9lnCeC+++4DAFx00UVT/vyBBx7AjTfeiEgkgl/84hf4yle+gtHRUXR0dOCaa67BJz7xCcefC5FfsGRSPnY2R17eSBFR+fCAtbycis1MTpAHL6CI/Mnrn7ebVtajLR5D79DErJ8lYi7AppX1i/3UiKTC9xKRe4oeHn7JJZfgQx/6EK677ropf/79738f3/rWt/CrX/3Kiee36OwMKCFS3aO7uvCxH+5a8HFffc9ZeMdZS91/QjQvO5sjr2+kiKg85jpgFcfePGB1l5Oxefv+flx3/44FH/eDm85jckIZcaAokT/J8nkrnieAKc/Va8+TyOv4XiIqnJ2z+UCx/yfbt2/HOeecM+PPzznnHDzzzDPFflki8hCWTLork9WxfX8/Ht3Vhe37+5HJFnXPDCC/UJp+MCKyPbft6SnqsUS0MCffy+W0UIY/YGT4y/r9eZ3TsZn9nOVgpzqWiNQg0+ftFae34b4bzkZrfOp+rzUe40EskQ18LxG5w1YrKquOjg7cf//9+PznPz/lz7/97W+jo6Oj5CdGROXHkkn3OJmVa6fdCHL/m61JiJyhUvUT2w+Wjxtto5icIAdeQBH5j2yft1ec3oZL17WyhS1RifheInJe0RcbX/7yl3HNNdfgP/7jP7B582YAwDPPPINXX30VDz/8sGNPkIjKJxjQcNfWdbj5wZ3QMHvJ5F1b1/GD2Cane2nbzfaUaSNF5GVe6Yvv1EwGHrCWjxuHXExOkAMvoIj8R8bP22BA496AyAF8LxE5q+hWVFdeeSVeeeUVbN26FQMDAxgYGMDWrVvxyiuv4Morr3TyORJRGbFk0llulJ7b2RzJuJEi8iKvtJHYtqcHF9z7JK67fwc+9sNduO7+Hbjg3idnbVu0UMssHrCWjxuxWSQnAPlkBIHJCd4hLqDm+iloMKrAeAFFpA5+3hIRETmj6IoNwGhH9bnPfc6p50JEC3AqK9culkw6x42sXDc2R9xIEc3PC20k7FSMFNIyixn+5VNsHF9oXSCSE6b/7FvnaZdWrrWGX7E6lsh/+HlLRETkDFsXG7t378bpp5+OQCCA3bt3z/vY9evXl/TEiGiqcvdxZ8mkM9zIyrW7OeJGiqh05a5+sjOT4YkXewu6AOEBa/kUc8hV6LrATnJCudcaflXMBRRROfDi0xn8vCUir2O8J1louq4X3CMhEAigt7cXzc3NCAQC0DQNs/11TdOQyWQcfaKLJZFIIB6PY2hoCLW1teV+OkQA5s7KFR8rbAklj+37+3Hd/TsWfNwPbjrP1kWSeI0As2+OpmduF/pYIpqdW+9lp///H/rgZvzlvzw/Z3WJODB/+vY3m5sVHm6XRzFx3Ml1Adca5cdDBPIyfjY4j/+mRORFjE1UbnbO5m1dbBw8eBCdnZ3QNA0HDx6c97HLly8v9Mt6Ci82yGsyWR0X3PukrUMp8i7x81woK7eYn6edBQgXK3LgIZd3ufleLsSju7rwsR/uWvBxH714Nb72X/sXfNz0Cxi+9sqjkNjsxrqAaw0i56kUR3nx6R6VXidEJD/Ge/ICO2fztlpRWS8rZL24IJKNF/q4U+EW2py4WXpup90I56Z4Hy+fvK3cbSQKn4NT2P//9JZZbD9YHoXEZjfWBVxrEDlLpc9wO60PuY60j5+3ROQVjPcko0Cxf/F73/se/u3f/s38/V/91V+hrq4O559//oLVHERUOLf7uGeyOrbv78eju7qwfX8/MtmCi7homm17enDBvU/iuvt34GM/3IXr7t+BC+59Etv29Ex5nOil3RqfejDZGo+VnAEhNkfvOGsptqxumHfBYeextLhEpsz0g0YxE2H6a4rKw8338kLETIa53rUajEO0Qg9LCr8oIbctFJvdWBeUe2YMyYVrx/mp9hlu5+KTDHyPEJGMGO9JRrYqNqw+97nP4b777gMAbN++HV/72tfwla98BT//+c9xyy234JFHHnHsSRL5WaGHTcUcSqmUTVZuc5VsTh/OK7BigubCTBm5lOu9XGjFyHmrGmwPpSZvc2Nd4OZag9TCteP8VPwM58WnPXyPEJGsGO9JRkVXbBw+fBgnnXQSAOBnP/sZ3v3ud+PDH/4w7rnnHvzmN79x7AkS+V2hWbl2D6VUyyYrp4U2sYCxiZ2ercWKCZoNM2XkU673ciEVI+ICBJjZlGoxWmaR89xYF7i11iC1cO24MBU/w3nxWTi+R4hIZoz3JKOiLzaqq6vR398PAHj88cdx6aWXAgBisRjGx8edeXZE5MqhVLEH8TQ7FTexbmOJ/tyYKUN2XHF6G56+/c34wU3n4avvOQs/uOk8PH37m2dUiJWrZRYZnIx5bqwLeAFGC+HasTAqfobz4rMwfI8QkewY70lGRbeiuvTSS/GhD30IGzZswCuvvIIrr7wSAPDCCy9gxYoVTj0/IuUtNGwayB9KTS9rbp2jrHmhr8khoc5ScRPrJpboz4+ZMmRXIYNH2f6ufNyIeXbXBeX6mqQOrh0Lo+JneKGtD/3+ecL3CBHJjvGeZFT0xcbXv/51fOITn8Dhw4fx8MMPo6HB+HB+9tlncd111zn2BIlUZuewo9BDqUK+Jg/inaXiJtYtdmeR+JHIlOFMBHJaIRcg5Cw3Y54bl1W8AHNeIQksMuDasTCqfobz4nNhfI8QkQoY70k2RV9s1NXV4Wtf+9qMP7/77rtLekJEflHMYcdCh1KFfk0exDtL1U1sMeY7wFFxoKYbvJQpo8qBHFE5LEbMc+OyihdgzlGpQpFrx8J46TPcabz4nB/fI0SkCsZ7kknRMzYA4De/+Q1uuOEGnH/++ejq6gIA/L//9//w9NNPO/LkiFTlRg9WO1+TvROdxd7khm17enDBvU/iuvt34GM/3IXr7t+BC+590hyUyFkkhfPCTISFfp5END/GPH9TbYgw146F88JnuFvExec7zlqKLasblF/b2sH3CBGphPGeZFH0xcbDDz+Myy+/HBUVFdi5cyeSySQAYGhoCJ/73Occe4JEKnLjsMPO1+RBvPNU3sQWopADHJbo5xUySLiQodB2v2ahVDuQIyoHxjz/UnGIMNeO9tj9DCf58T1CRES0+IpuRfV3f/d3+MY3voH3v//9+OEPf2j++Rve8Ab83d/9nSNPjkhVbhx22P2a7J3oPL+WbBbabuWL155Z0NdTvUTfTmuSQlvCONnuhC3DiJzBtiT+peoQYa4d7WFbN//he4SIiGhxFX2xsXfvXlx44YUz/jwej2NwcLCU50SkPDcOO4r5mn49iHeTHzexhR7gQIfvZ5G4MUjY6a+p6oEcuYNzWObG+Uv+pXK1DteORPPje4SIiGjxFH2x0drain379mHFihVT/vzpp5/GqlWrSn1eREpz47Cj2K/pxkE8D7r8pdCDmeOjSWUHahbCjUoIN76mygdy5CyVBiO7QeUhwjQ/1at1/JjEQWQH3yO0EO6XiYicUfSMjZtuugkf+9jH8Nvf/haapqG7uxsPPfQQbrvtNtx8881OPkci5bjRg9UrfV05cNh/7Bzg+HkWSbln6xRK9QM5ckYxc1icnAMjCz/HPD/jEGEiIpoL98tERM4pumLjjjvuQDabxSWXXIKxsTFceOGFiEaj+PjHP44PfehDTj5H277+9a/jC1/4Anp7e3HmmWfiH//xH7Fp06ayPiei6dzowVruvq5utNkh77NbLeTXEn0vzNYphNvtc+xkqDGbzZuKqRTyc3WHX2Oen7Fah0g9XJOQE7hfVhdjBFF5FH2xoWka/uZv/gYf//jHsW/fPoyMjGDdunX45je/iZUrV6K3t9fJ51mwH/3oR7j11lvxjW98A5s3b8ZXvvIVXH755di7dy+am5vL8pyI5uLGYUe5DlA4cNi/ijnAsVOir8oi0SuzdRbi5oGcncNtPx+Ee53dOSzcxLMtiR+VO9mEiJzDNQk5gftldTFGEJWPpuu6rT4AyWQSn/rUp/DEE0+YFRpXX301HnjgAXziE59AMBjERz7yEdx+++1uPed5bd68Geeeey6+9rWvAQCy2Sw6Ojrw53/+57jjjjsW/PuJRALxeBxDQ0Oora11++kSKWP7/n5cd/+OBR/3g5vO4+GOotxY0Km0SMxkdVxw75MLVkI8ffubbc3YcPprCk7/2891uC2elfVw285jafE9uqsLH/vhrgUf99X3nIW3r2/HBfc+OedFSCmvUSIZqHI5T+RXXJOQU7hfVhNjBJHz7JzN267Y+OQnP4lvfvObeMtb3oL/+Z//wbXXXos//uM/xo4dO/ClL30J1157LYLBYNFPvhSpVArPPvss7rzzTvPPAoEA3vKWt2D79u22vtbo6GjZvg8iGR0+PlTw49a3GtnjmayOZw8Nom8khabqCDZ21nGzL7E3rqzFf35086w/09HRUdtf74mX+nDLv7wwZ5b3l999Gi5d2+TMk18kt1+6Grf8ywtzVkLcfulqTIyPlf1rAs7+PDNZHXc9OvNnCeQz1D71ry/g/OU1AFDwYxkvyqM2XFhOTG1Yx1MvdRVU3fHUS13YtGKJQ8+QyFvWt8aA3NqnmHhM3sL1q3/YWb/wNUALKWa/TN7GGEHkDjvnDbYvNn7yk5/g//v//j9cddVV2LNnD9avX490Oo3nn38emlbeN+rx48eRyWTQ0tIy5c9bWlrw8ssvz/p3kskkksmk+ftEIgEAaG9vd++JEiko2nEGWq+/Z8HHvf/aq5E8/AdUnLIF9Zd8GKHa/MF0OtGHgV9+C+Ov2LuIJAVpASz9s39GsKZxxmeLDkDXs/jod36Frm98ENCz5XmORZrttT+Ze+2/897iXvtufE0nLRQfdAC9iSSaTzsfAAp+bPLwHxx+plQQ8/3ZAE0LzPjPup5FZrgfbz5jOSrXXICmq/5qwS/59mvfi7GXnnLj2ZJbtACiy05DsHoJMiMnkDzygnTxmMgurl/9xc76hWsSWojd/TJ5H2MEUfnZvtg4cuQINm7cCAA4/fTTEY1Gccstt5T9UqNY99xzD+6+++5yPw0i6SWPvIB0om/Bg67kkRdQccoWNF391zMeE6xpQNPVf42+n32Om0Ofiy47bcqhwXSaFkCotgnRZadJt0gcf2U7ul79raMHgm58TScFqwvLxC/0cXYfSw7Tsxj45bfQdPVfQ9ezU2K+rmcBaBj45bcAPYvMyImCvmShjyNv4OEu+RHXr/7jxvqF/MvOfpnkwBhBVH62Z2wEg0H09vaiqcnYyNTU1GD37t1YuXKlK0/QjlQqhcrKSvzLv/wLrr76avPPP/CBD2BwcBCPPvrojL8zW8VGR0cHuru7OWODyCbROgiYvSXOl999Gt58aiMu/YcdODqcnPH3xWNbaqN4/M/PY7mmj/3bnqP4q5++tODjPv/OtXjb6S0LPm4xsDXF3J55/QT++P89v+DjHnjfmQBQ8GPZuqi8nnipD/f8574p8by1Noo7LjvJbBOXyeq49B924Nhwcs45MIz5cpmrTaD1s162NoFECxGxjOtXf7GzfuGahApRyH6Zn6HyYIwgckcikUB7e7s7MzZ0XceNN96IaDQKAJiYmMCf/dmfoaqqasrjHnnkEbtfumSRSAQbN27EL3/5S/NiI5vN4pe//CU++tGPzvp3otGo+b1YVVVVzfieiGh+V59ThVgsOmPgcKtl4PD2/f1zbgqBfLnmC8eSHJrmYx2N8YIf54VYrdKQczdcuLYSbfG9Cw45v3DtUgAo+LE8PCqvq8+pwtazly84GPnud5yGmx/cOeccmE9ddRpqa6oX62k7ym+DoTNZHfc+sX/eXtL3PrEfW89ervS/A/kP16/+ZGf9wphHhShkv+w1flvr2MEYQeSOTCZT8GNtX2x84AMfmPL7G264we6XcNWtt96KD3zgAzjnnHOwadMmfOUrX8Ho6Cj++I//uNxPjcgXrji9DZeua51z8XNseO4hslaFPo7UtGllPdrisQUXiZtW1i/2U5th254e3PzgzjmHnN93w9me2qS4tTmZ7+sGAxru2rpu3sPtu7auMx9v57FUXsGAtuAh3hWnt+G+G86WahNfCD9eaD5zYKCgYfDPHBjg4S4phetXf7K7fiEqxEL7ZS/x41rHDsYIovKzfbHxwAMPuPE8HPNHf/RH6Ovrwyc/+Un09vbirLPOwrZt22YMFCci98x30NVcEyvoa0x/HDNF/EWWRWImq+Pux16cN3v57sdexKXrWsv+XAH3NieFfF07h9t2D8IZH7xPpk18IWS70HQKD3fJLlXic7HrV5KfqpfzVF6FJIaUm1/XOnYxRhCVl+0ZG6pLJBKIx+MF9fEiIvsyWR0X3Pvkgpn4T9/+ZnPjy0wR//L6z377/n5cd/+OBR/3g5vOK/vmZa7NiTheKnZzYvfr2jnkKuSxXn+NkHrE59hclQuzfY6pQqaYR+WnUnwuZv1KalHlks5t/HdSg5/XOlZO71uIqDB2zuZtV2wQEZXCbiY+M0X8zetZ3rJkL7tVWVLM17WTobbQYxkfqBz83I5JpjaBVF6qxWdZKknJPTJk2JebSpeZfufntY5g9/XMGEFUHoFyPwEi8h9Rrtkan1qu3xqPTdnoLnRoChiHppksC89UJhaJ7zhrKbasbvDUoYEsrSnsbE688HULwfhA5SLLhaYbxOEukD/MFXi4S4Kq8bnQ9SuRH4nLzOnrQnGZuW1PT5meGRXDz2sdgK9nIpmwYoOIyqKQTHxmipDXyZK97NbmpJybHsYHKhdZLjTdUkwvabZn8BeV47PXK0mJykG2mXO0MD+vdfh6JpILLzaIqGwWKtf0e6YIeZ8srSnc2pyUc9PD+EDlIsuFppvsHO6yNYn/qB6f2W6kMLzQ9A+VLzP9ys9rHb6eieTCVlRE5Fl+zhQhecjQmkJsTuY6TtBgHDTa3Zy49XULwfigtkxWx/b9/Xh0Vxe27+/3VMsatmMyFNImkK0c/Inxmbbt6cEF9z6J6+7fgY/9cBeuu38HLrj3Sb7nFaX6ZaYf+Xmtw9czkVxYsUFEnuXnTBGSi9dbU7hVWVLOihXGB3XJkOFfTDsmv2ErB/9ifPY31QbH08J4makmv651+Homkoum67p3UuA8IJFIIB6PY2hoCLW1teV+OkS+JzZHwOyHptwcERXOrQPjch1EMz6oZ64DMa/+TNlqZW7b9/fjuvt3LPi4H9x0Hls5KIjx2Z8yWR0X3PvknG1cxKXW07e/mbFSIeLnvtBlJn/ucvLbWoevZ6Lys3M2z4uNaXixQeQ9MmTvUvn5bdFdLLf+ncr178/4oA4eiKnl0V1d+NgPdy34uK++5yy846yl7j8hh/EzZ2GMz/7DC03/4mUmqYSvZ6LysnM2z1ZUROR5Xm/zQ+XHw5PCuTX0tFzDVN2KDzy0XHwc1qgWlVs58DOnMFy/+Q970/uXX9sWkZr4eiaSBy82iEgK5To0Je9jL2dyOj7w0LI8eCCmFlXnLPAzxx6u3/xF5QtNWhgvM0klfD0TySFQ7idARERUrIWG0wLGcNpMll0XqTDi0HJ65YA4tNy2p6dMz0x9xRyIZbI6tu/vx6O7urB9fz/f6x4SDGi4a+s6APnWDYL4/V1b10l1QMDPHKL5iQvNud7VGoxEAdkuNKlw4jLzHWctxZbVDVLFeKLp+Hom8j5ebBARkbTstK4hWggPLcvL7oHYtj09uODeJ3Hd/TvwsR/uwnX378AF9z7JyycPEa0cWuNTL61a4zEpKxv4mUM0PxUvNImIiMi72IqKiJTD3vj+wdY15CTOeCgvcSB284M7oWH2YY3iQIztgOShUisHfuYQLYy96YmIiGix8GKDiJTC3vj+wl7O5CQeWpZfIQdiC1XWaDAqay5d1yrl4bmKVJmzwM8cosKodKFJRERE3sWLDSJSBjN4/UfV4bRUHjy09IaFDsRYWUPlws8cosKpcqFJRERE3sUZG0SkBPbG9yf2ciYnceipd8w3rJGVNVQu/MwhIiIiIvIOXmwQkRI40NO/VBtOS+XDQ0s5sLKGyomfOURENJdMVsf2/f14dFcXtu/vZ1IdEZHL2IqKiJTADF5/Yy9ncgqHnnof2wFRufEzh4iIpuOsRyKixceLDSJSAjN4ib2cySk8tPQ2UVlz84M7oQFTLjdYWUOLhZ85REQkcNYjEVF5sBUVESmBvfGJyEnzzXig8mM7ICIiIloMC7WX4qxHIqLyYcUGESmBGbxEVIhMVmclhiJYWUNERERuKqS9lJ1Zj6z0IyJyFi82iEgZ7I1PRPNh72P1sB0QERERuaHQ9lKc9Whg8hARlYMUFxuvv/46PvOZz+DJJ59Eb28v2tvbccMNN+Bv/uZvEIlEzMesXLlyxt/dvn07zjvvvMV+ykRUJszgJaLZsPcxEZEaeHhGRG5bqL2UBqO91KXrWjnrEUweIqLykeJi4+WXX0Y2m8U3v/lNnHTSSdizZw9uuukmjI6O4otf/OKUx/7iF7/AaaedZv6+oYFZfER+wwxeIrKysznl4RgRkXfx8IzIf8pxmWmnvZSY9dg7NDHrWlOD0UFA1VmPTB4ionKS4mLjiiuuwBVXXGH+ftWqVdi7dy/uu+++GRcbDQ0NaG1tXeynSERERB7F3sdEcmAmPs2Hh2dE/lOuy0w77aX8POuRyUNEVG6Bcj+BYg0NDaG+fuaN91VXXYXm5mZccMEF+Nd//dcyPDMiIiLyEvY+JvK+bXt6cMG9T+K6+3fgYz/chevu34EL7n0S2/b0lPupkQcsdHgGGIdnmexsjyAiGYnLzOnJKeIy083PB7vtpcSsx9b41L/XGo8pfelqJ3mIiMgNUlRsTLdv3z784z/+45RqjerqanzpS1/CG97wBgQCATz88MO4+uqr8bOf/QxXXXXVnF8rmUwimUyav08kEq4+dyIiIlpc7H1M5G3MxKeFsPKOyF/KXQlQTHspP856ZPIQEZVbWSs27rjjDmiaNu+vl19+ecrf6erqwhVXXIFrr70WN910k/nnjY2NuPXWW7F582ace+65+L//9//ihhtuwBe+8IV5n8M999yDeDxu/uro6HDleyUiIqLyEJvTubaVGoy2Bqr2PibyMmbiE2C8Drbv78eju7qwfX//jJ83D8+I1DPf+77clQCivRSAGevH+dpLiVmP7zhrKbasblD6UgNg8hARlV9ZKzZuu+023HjjjfM+ZtWqVeb/7u7uxsUXX4zzzz8f3/rWtxb8+ps3b8YTTzwx72PuvPNO3HrrrebvE4kELzeIiIgU4ufex0Rex0x8KqSHPg/PiNSy0PveC5eZor3U9OfZuggzPmTh98HpRFR+Zb3YaGpqQlNTU0GP7erqwsUXX4yNGzfigQceQCCwcLHJrl270NY2/4dNNBpFNBot6DkQERGRnLg5JfImLxxeUfkU2oaMh2dE6ijkfe+Vy0w/tpeyg8lDRFRuUszY6OrqwkUXXYTly5fji1/8Ivr6+sz/1traCgD43ve+h0gkgg0bNgAAHnnkEXznO9/Bt7/97bI8ZyIiIvIWbk6JvMcrh1e0+Oz20OfhGZH8Cn3f//rjF3vmMlO0l6LZMXmIiMpJiouNJ554Avv27cO+ffuwbNmyKf9N1/Mfc5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"]},"metadata":{},"output_type":"display_data"}],"source":["from statsmodels.tsa.seasonal import seasonal_decompose\n","from dateutil.parser import parse\n","\n","\n","# Multiplicative Decomposition \n","multiplicative_decomposition = seasonal_decompose(df['Number of Passengers'], model='multiplicative', period=30)\n","\n","# Additive Decomposition\n","additive_decomposition = seasonal_decompose(df['Number of Passengers'], model='additive', period=30)\n","\n","# Plot\n","plt.rcParams.update({'figure.figsize': (16,12)})\n","multiplicative_decomposition.plot().suptitle('Multiplicative Decomposition', fontsize=16)\n","plt.tight_layout(rect=[0, 0.03, 1, 0.95])\n","\n","additive_decomposition.plot().suptitle('Additive Decomposition', fontsize=16)\n","plt.tight_layout(rect=[0, 0.03, 1, 0.95])\n","\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["- If we look at the residuals of the additive decomposition closely, it has some pattern left over. \n","\n","- The multiplicative decomposition, looks quite random which is good. So ideally, multiplicative decomposition should be preferred for this particular series."]},{"cell_type":"markdown","metadata":{},"source":["# **9. Stationary and Non-Stationary Time Series** \n","\n","\n","[Table of Contents](#0.1)\n","\n","\n","- Now, we wil discuss **Stationary and Non-Stationary Time Series**. **Stationarity** is a property of a time series. A stationary series is one where the values of the series is not a function of time. So, the values are independent of time.\n","\n","\n","- Hence the statistical properties of the series like mean, variance and autocorrelation are constant over time. Autocorrelation of the series is nothing but the correlation of the series with its previous values.\n","\n","\n","- A stationary time series is independent of seasonal effects as well.\n","\n","\n","- Now, we will plot some examples of stationary and non-stationary time series for clarity."]},{"cell_type":"markdown","metadata":{},"source":["![Stationary and Non-Stationary Time Series](https://www.machinelearningplus.com/wp-content/uploads/2019/02/stationary-and-non-stationary-time-series-865x569.png?ezimgfmt=ng:webp/ngcb1)\n","\n","image source : https://www.machinelearningplus.com/wp-content/uploads/2019/02/stationary-and-non-stationary-time-series-865x569.png?ezimgfmt=ng:webp/ngcb1"]},{"cell_type":"markdown","metadata":{},"source":["- We can covert any non-stationary time series into a stationary one by applying a suitable transformation. Mostly statistical forecasting methods are designed to work on a stationary time series. The first step in the forecasting process is typically to do some transformation to convert a non-stationary series to stationary."]},{"cell_type":"markdown","metadata":{},"source":["\n","\n","# **10. How to make a time series stationary?** \n","\n","\n","[Table of Contents](#0.1)\n","\n","\n","- We can apply some sort of transformation to make the time-series stationary. These transformation may include:\n","\n","\n","1. Differencing the Series (once or more)\n","2. Take the log of the series\n","3. Take the nth root of the series\n","4. Combination of the above\n","\n","\n","- The most commonly used and convenient method to stationarize the series is by differencing the series at least once until it becomes approximately stationary."]},{"cell_type":"markdown","metadata":{},"source":["## **10.1 Introduction to Differencing** \n","\n","\n","[Table of Contents](#0.1)\n","\n","\n","- If Y_t is the value at time t, then the first difference of Y = Yt – Yt-1. In simpler terms, differencing the series is nothing but subtracting the next value by the current value.\n","\n","\n","- If the first difference doesn’t make a series stationary, we can go for the second differencing and so on.\n","\n","\n"," - For example, consider the following series: [1, 5, 2, 12, 20]\n","\n","\n"," - First differencing gives: [5-1, 2-5, 12-2, 20-12] = [4, -3, 10, 8]\n","\n","\n"," - Second differencing gives: [-3-4, -10-3, 8-10] = [-7, -13, -2]"]},{"cell_type":"markdown","metadata":{},"source":["## **10.2 Reasons to convert a non-stationary series into stationary one before forecasting** \n","\n","\n","[Table of Contents](#0.1)\n","\n","\n","There are reasons why we want to convert a non-stationary series into a stationary one. These are given below:\n","\n","\n","- Forecasting a stationary series is relatively easy and the forecasts are more reliable.\n","\n","\n","- An important reason is, autoregressive forecasting models are essentially linear regression models that utilize the lag(s) of the series itself as predictors.\n","\n","\n","- We know that linear regression works best if the predictors (X variables) are not correlated against each other. So, stationarizing the series solves this problem since it removes any persistent autocorrelation, thereby making the predictors(lags of the series) in the forecasting models nearly independent."]},{"cell_type":"markdown","metadata":{},"source":["# **11. How to test for stationarity?** \n","\n","\n","[Table of Contents](#0.1)\n","\n","\n","- The stationarity of a series can be checked by looking at the plot of the series.\n","\n","\n","- Another method is to split the series into 2 or more contiguous parts and computing the summary statistics like the mean, variance and the autocorrelation. If the stats are quite different, then the series is not likely to be stationary.\n","\n","\n","- There are several quantitative methods we can use to determine if a given series is stationary or not. This can be done using statistical tests called [Unit Root Tests](https://en.wikipedia.org/wiki/Unit_root). This test checks if a time series is non-stationary and possess a unit root. \n","\n","\n","- There are multiple implementations of Unit Root tests like:\n","\n","\n","**1. Augmented Dickey Fuller test (ADF Test)**\n","\n","**2. Kwiatkowski-Phillips-Schmidt-Shin – KPSS test (trend stationary)**\n","\n","**3. Philips Perron test (PP Test)**\n","\n","\n"]},{"cell_type":"markdown","metadata":{},"source":["## **11.1 Augmented Dickey Fuller test (ADF Test)** \n","\n","\n","[Table of Contents](#0.1)\n","\n","\n","- **Augmented Dickey Fuller test or (ADF Test)** is the most commonly used test to detect stationarity. Here, we assume that the null hypothesis is the time series possesses a unit root and is non-stationary. Then, we collect evidence to support or reject the null hypothesis. So, if we find that the p-value in ADF test is less than the significance level (0.05), we reject the null hypothesis.\n","\n","\n","- Feel free to check the following links to learn more about the ADF Test.\n","\n","\n","https://en.wikipedia.org/wiki/Augmented_Dickey%E2%80%93Fuller_test\n","\n","https://www.machinelearningplus.com/time-series/augmented-dickey-fuller-test/\n","\n","https://machinelearningmastery.com/time-series-data-stationary-python/\n","\n","http://www.insightsbot.com/augmented-dickey-fuller-test-in-python/\n","\n","https://nwfsc-timeseries.github.io/atsa-labs/sec-boxjenkins-aug-dickey-fuller.html\n","\n","https://www.statisticshowto.com/adf-augmented-dickey-fuller-test/\n","\n"]},{"cell_type":"markdown","metadata":{},"source":["\n","## **11.2 Kwiatkowski-Phillips-Schmidt-Shin – KPSS test (trend stationary)** \n","\n","\n","[Table of Contents](#0.1)\n","\n","\n","- The KPSS test, on the other hand, is used to test for trend stationarity. The null hypothesis and the P-Value interpretation is just the opposite of ADH test.\n","\n","- Interested readers can learn more about the KPSS test from the below links:\n","\n","\n","https://en.wikipedia.org/wiki/KPSS_test\n","\n","https://www.machinelearningplus.com/time-series/kpss-test-for-stationarity/\n","\n","https://www.statisticshowto.com/kpss-test/\n","\n","https://nwfsc-timeseries.github.io/atsa-labs/sec-boxjenkins-kpss.html\n","\n"]},{"cell_type":"markdown","metadata":{},"source":["## **11.3 Philips Perron test (PP Test)** \n","\n","\n","[Table of Contents](#0.1)\n","\n","\n","- The Philips Perron or PP test is a [unit root test](https://en.wikipedia.org/wiki/Unit_root). It is used in the [time series analysis](https://en.wikipedia.org/wiki/Time_series) to test the [null hypothesis](https://en.wikipedia.org/wiki/Null_hypothesis) that a time series is integrated of order 1. It is built on the ADF test discussed above.\n","\n","\n","- For more information on PP test, please visit the following links:\n","\n","\n","https://en.wikipedia.org/wiki/Phillips%E2%80%93Perron_test\n","\n","https://www.mathworks.com/help/econ/pptest.html\n","\n","https://people.bath.ac.uk/hssjrh/Phillips%20Perron.pdf\n","\n","https://www.stata.com/manuals13/tspperron.pdf"]},{"cell_type":"markdown","metadata":{},"source":["# **12. Difference between white noise and a stationary series** \n","\n","\n","[Table of Contents](#0.1)\n","\n","\n","- Like a stationary series, the white noise is also not a function of time. So, its mean and variance does not change over time. But the difference is that, the white noise is completely random with a mean of 0. In white noise there is no pattern.\n","\n","- Mathematically, a sequence of completely random numbers with mean zero is a white noise."]},{"cell_type":"code","execution_count":13,"metadata":{"trusted":true},"outputs":[{"data":{"text/plain":[""]},"execution_count":13,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["rand_numbers = np.random.randn(1000)\n","pd.Series(rand_numbers).plot(title='Random White Noise', color='b')"]},{"cell_type":"markdown","metadata":{},"source":["# **13. Detrend a Time Series** \n","\n","\n","[Table of Contents](#0.1)\n","\n","\n","- Detrending a time series means to remove the trend component from the time series. There are multiple approaches of doing this as listed below:\n","\n","\n","1. Subtract the line of best fit from the time series. The line of best fit may be obtained from a linear regression model with the time steps as the predictor. For more complex trends, we may want to use quadratic terms (x^2) in the model.\n","\n","2. We subtract the trend component obtained from time series decomposition.\n","\n","3. Subtract the mean.\n","\n","4. Apply a filter like Baxter-King filter(statsmodels.tsa.filters.bkfilter) or the Hodrick-Prescott Filter (statsmodels.tsa.filters.hpfilter) to remove the moving average trend lines or the cyclical components.\n","\n","\n","Now, we will implement the first two methods to detrend a time series."]},{"cell_type":"code","execution_count":14,"metadata":{"trusted":true},"outputs":[{"data":{"text/plain":["Text(0.5, 1.0, 'Air Passengers detrended by subtracting the least squares fit')"]},"execution_count":14,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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"]},"metadata":{},"output_type":"display_data"}],"source":["# Using scipy: Subtract the line of best fit\n","from scipy import signal\n","detrended = signal.detrend(df['Number of Passengers'].values)\n","plt.plot(detrended)\n","plt.title('Air Passengers detrended by subtracting the least squares fit', fontsize=16)"]},{"cell_type":"code","execution_count":15,"metadata":{"trusted":true},"outputs":[{"data":{"text/plain":["Text(0.5, 1.0, 'Air Passengers detrended by subtracting the trend component')"]},"execution_count":15,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["# Using statmodels: Subtracting the Trend Component\n","from statsmodels.tsa.seasonal import seasonal_decompose\n","result_mul = seasonal_decompose(df['Number of Passengers'], model='multiplicative', period=30)\n","detrended = df['Number of Passengers'].values - result_mul.trend\n","plt.plot(detrended)\n","plt.title('Air Passengers detrended by subtracting the trend component', fontsize=16)"]},{"cell_type":"markdown","metadata":{},"source":["# **14. Deseasonalize a Time Series** \n","\n","\n","[Table of Contents](#0.1)\n","\n","\n","There are multiple approaches to deseasonalize a time series. These approaches are listed below:\n","\n","\n","- 1. Take a moving average with length as the seasonal window. This will smoothen in series in the process.\n","\n","- 2. Seasonal difference the series (subtract the value of previous season from the current value).\n","\n","- 3. Divide the series by the seasonal index obtained from STL decomposition.\n","\n","\n","\n","If dividing by the seasonal index does not work well, we will take a log of the series and then do the deseasonalizing. We will later restore to the original scale by taking an exponential.\n"]},{"cell_type":"code","execution_count":16,"metadata":{"trusted":true},"outputs":[{"data":{"text/plain":["[]"]},"execution_count":16,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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"]},"metadata":{},"output_type":"display_data"}],"source":["# Subtracting the Trend Component\n","\n","\n","# Time Series Decomposition\n","result_mul = seasonal_decompose(df['Number of Passengers'], model='multiplicative', period=30)\n","\n","\n","# Deseasonalize\n","deseasonalized = df['Number of Passengers'].values / result_mul.seasonal\n","\n","\n","# Plot\n","plt.plot(deseasonalized)\n","plt.title('Air Passengers Deseasonalized', fontsize=16)\n","plt.plot()"]},{"cell_type":"markdown","metadata":{},"source":["# **15. How to test for seasonality of a time series?** \n","\n","\n","[Table of Contents](#0.1)\n","\n","\n","\n","The common way to test for seasonality of a time series is to plot the series and check for repeatable patterns in fixed time intervals. So, the types of seasonality is determined by the clock or the calendar.\n","\n","\n","1. Hour of day\n","2. Day of month\n","3. Weekly\n","4. Monthly\n","5. Yearly\n","\n","However, if we want a more definitive inspection of the seasonality, use the **Autocorrelation Function (ACF) plot**. There is a strong seasonal pattern, the ACF plot usually reveals definitive repeated spikes at the multiples of the seasonal window."]},{"cell_type":"code","execution_count":17,"metadata":{"trusted":true},"outputs":[{"data":{"text/plain":[""]},"execution_count":17,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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"]},"metadata":{},"output_type":"display_data"}],"source":["# Test for seasonality\n","from pandas.plotting import autocorrelation_plot\n","\n","# Draw Plot\n","plt.rcParams.update({'figure.figsize':(10,6), 'figure.dpi':120})\n","autocorrelation_plot(df['Number of Passengers'].tolist())"]},{"cell_type":"markdown","metadata":{},"source":["Alternately, if we want a statistical test, the [CHTest](https://alkaline-ml.com/pmdarima/modules/generated/pmdarima.arima.CHTest.html#pmdarima.arima.CHTest) can determine if seasonal differencing is required to stationarize the series."]},{"cell_type":"markdown","metadata":{},"source":["# **16. Autocorrelation and Partial Autocorrelation Functions** \n","\n","\n","[Table of Contents](#0.1)\n","\n","\n","- **Autocorrelation** is simply the correlation of a series with its own lags. If a series is significantly autocorrelated, that means, the previous values of the series (lags) may be helpful in predicting the current value.\n","\n","\n","- **Partial Autocorrelation** also conveys similar information but it conveys the pure correlation of a series and its lag, excluding the correlation contributions from the intermediate lags."]},{"cell_type":"code","execution_count":18,"metadata":{"trusted":true},"outputs":[{"data":{"image/png":"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"]},"execution_count":18,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABSAAAAEpCAYAAACDVYzKAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/TGe4hAAAACXBIWXMAAA9hAAAPYQGoP6dpAABqxUlEQVR4nO3deXhU5d3/8c9MlskCWYAsBCIBVBYRUJAUXECJBLRaFBEsPiy1YC3RIliFPgoCtpRqKS4o2grSn1gQt7o9KIJIVQqCRVyAsgRZE9bs+8z5/REyZMgkOQMzmUnyfl3XXMmcc8997nPODHzznXuxGIZhCAAAAAAAAAB8wOrvBgAAAAAAAABoukhAAgAAAAAAAPAZEpAAAAAAAAAAfIYEJAAAAAAAAACfIQEJAAAAAAAAwGdIQAIAAAAAAADwGRKQAAAAAAAAAHyGBCQAAAAAAAAAnyEBCQAAAAAAAMBnSEACQABbv369LBaL1q9f79V6LRaLHn/8ca/WCQAA0Fg8/vjjslgs5/XaQYMGadCgQd5tENzyxbW+kHsP4PyRgATQYJ5//nlZLBalpqZeUD0ffvghyTMTuE4AACAQvfLKK7JYLM5HWFiYLr30UmVkZCg7O9trxykqKtLjjz/u9S9yPWW325WUlCSLxaL/+7//u6C6AuWcAh3XCQg8JCABNJjly5crJSVFmzdv1p49e867ng8//FCzZ8/2YsuaprquU3FxsR599NEGbhEAAMBZc+bM0f/7f/9Pzz33nAYMGKAXXnhB/fv3V1FRkVfqLyoq0uzZs90moR599FEVFxd75Tj1WbdunY4ePaqUlBQtX778guqq65xwVqDcewBnkYAE0CAyMzP15ZdfasGCBYqLi7vg4CvQ1RY4V1RUqKysrIFbU1NYWJiCg4P93QwAANCMDRs2THfffbd++ctf6pVXXtGUKVOUmZmpf/7znxdUr8PhUElJSZ1lgoODFRYWdkHHMevVV1/VlVdeqQcffFDvvPOOCgsLG+S4/lJSUiKHw+F2XyCce0PeewBnkYAE0CCWL1+u2NhY3XzzzbrjjjtqJCBrm+tw//79slgseuWVVyRJ48eP16JFiyTJZehOlcLCQk2bNk3Jycmy2Wzq0qWLnnrqKRmGUaNNr776qvr166eIiAjFxsbquuuu08cff+xS5vnnn9dll10mm82mpKQkTZ48WTk5OS5lBg0apB49emjr1q267rrrFBERod/97nfOtj/11FNauHChOnfuLJvNph9++EGStHPnTt1xxx1q1aqVwsLC1LdvX7377rv1Xst//etfGjlypC666CLZbDYlJyfrwQcfdPkmt77r5G4OyP/85z8aNmyYoqKi1KJFCw0ePFj//ve/XcpUDZn64osvNHXqVMXFxSkyMlK33Xabjh8/Xm/bAQAAanPDDTdIqvziWpKeeuopDRgwQK1bt1Z4eLj69OmjN954o8brLBaLMjIytHz5cmfctnjxYsXFxUmSZs+e7YyFquIfd/MALl26VDfccIPi4+Nls9nUvXt3vfDCCxd0TsXFxXr77bc1evRo3XnnnSouLnabYK1trsPx48crJSVFUmVcXNc5SZW9La+99lpFRkYqJiZGP/vZz7Rjx44a9R4+fFj33HOPkpKSZLPZ1LFjR913330uX5Tv27dPI0eOVKtWrRQREaGf/OQn+uCDD1zqqYrhV6xYoUcffVTt2rVTRESE8vLyNH78eLVo0UJ79+7VTTfdpJYtW2rMmDGSKpPECxcu1GWXXaawsDAlJCTo3nvv1enTp+u8nmVlZZo5c6b69Omj6OhoRUZG6tprr9Wnn37qLFPfdXJ37ysqKjR37lxnvJ6SkqLf/e53Ki0tdSmXkpKin/70p/r888/Vr18/hYWFqVOnTvr73/9eZ7sBSHR/AdAgli9frttvv12hoaG666679MILL+irr77SVVdd5VE99957r44cOaI1a9bo//2//+eyzzAM3Xrrrfr00091zz33qHfv3vroo4/029/+VocPH9Zf/vIXZ9nZs2fr8ccf14ABAzRnzhyFhoZq06ZNWrdunYYMGSKpMjiZPXu20tLSdN9992nXrl3Odn/xxRcKCQlx1nfy5EkNGzZMo0eP1t13362EhATnvqVLl6qkpESTJk2SzWZTq1at9P333+vqq69Wu3btNH36dEVGRur111/X8OHD9eabb+q2226r9RqsWrVKRUVFuu+++9S6dWtt3rxZzz77rA4dOqRVq1bVe53c+f7773XttdcqKipKDz/8sEJCQvTiiy9q0KBB+uyzz2rM23n//fcrNjZWs2bN0v79+7Vw4UJlZGRo5cqV9R4LAADAnb1790qSWrduLUl6+umndeutt2rMmDEqKyvTihUrNHLkSL3//vu6+eabXV67bt06vf7668rIyFCbNm3Uq1cvvfDCC7rvvvt022236fbbb5ck9ezZs9bjv/DCC7rssst06623Kjg4WO+9955+/etfy+FwaPLkyed1Tu+++64KCgo0evRoJSYmatCgQVq+fLl+/vOfe1xXXFxcnef0ySefaNiwYerUqZMef/xxFRcX69lnn9XVV1+tr7/+2pnIPHLkiPr166ecnBxNmjRJXbt21eHDh/XGG2+oqKhIoaGhys7O1oABA1RUVKQHHnhArVu31rJly3TrrbfqjTfeqBGrzp07V6GhoXrooYdUWlqq0NBQSZWJvfT0dF1zzTV66qmnFBERIakyVn3llVc0YcIEPfDAA8rMzNRzzz2n//znPzXi7Ory8vL0t7/9TXfddZcmTpyo/Px8vfzyy0pPT9fmzZvVu3fveq+TO7/85S+1bNky3XHHHZo2bZo2bdqkefPmaceOHXr77bddyu7Zs0d33HGH7rnnHo0bN05LlizR+PHj1adPH1122WUe3lWgGTEAwMe2bNliSDLWrFljGIZhOBwOo3379sZvfvMbZ5lPP/3UkGR8+umnLq/NzMw0JBlLly51bps8ebLh7p+vd955x5BkPPHEEy7b77jjDsNisRh79uwxDMMwdu/ebVitVuO2224z7Ha7S1mHw2EYhmEcO3bMCA0NNYYMGeJS5rnnnjMkGUuWLHFuGzhwoCHJWLx4sdu2R0VFGceOHXPZN3jwYOPyyy83SkpKXI49YMAA45JLLqnzuhQVFdU493nz5hkWi8X48ccf671OhmEYkoxZs2Y5nw8fPtwIDQ019u7d69x25MgRo2XLlsZ1113n3LZ06VJDkpGWlua8VoZhGA8++KARFBRk5OTkuD0eAABAlap44pNPPjGOHz9uHDx40FixYoXRunVrIzw83Dh06JBhGDVjnrKyMqNHjx7GDTfc4LJdkmG1Wo3vv//eZfvx48drxDxVZs2aVSNOchdjpaenG506dXLZNnDgQGPgwIGmzvWnP/2pcfXVVzufv/TSS0ZwcHCN2LC2OseNG2d06NDB+byuc+rdu7cRHx9vnDx50rntm2++MaxWqzF27FjntrFjxxpWq9X46quvatRRFd9NmTLFkGT861//cu7Lz883OnbsaKSkpDjj46pYtVOnTjWu37hx4wxJxvTp0122/+tf/zIkGcuXL3fZvnr16hrbz70uFRUVRmlpqcvrTp8+bSQkJBi/+MUvTF2nc+/9tm3bDEnGL3/5S5dyDz30kCHJWLdunXNbhw4dDEnGhg0bnNuOHTtm2Gw2Y9q0aTWOBeAshmAD8Lnly5crISFB119/vaTKYTKjRo3SihUrZLfbvXacDz/8UEFBQXrggQdctk+bNk2GYThXHXznnXfkcDg0c+ZMWa2u/wxWDcf45JNPVFZWpilTpriUmThxoqKiomoMP7HZbJowYYLbdo0YMcI5DESSTp06pXXr1unOO+9Ufn6+Tpw4oRMnTujkyZNKT0/X7t27dfjw4VrPMzw83Pl7YWGhTpw4oQEDBsgwDP3nP/+p6xK5Zbfb9fHHH2v48OHq1KmTc3vbtm3185//XJ9//rny8vJcXjNp0iSXoSvXXnut7Ha7fvzxR4+PDwAAmqe0tDTFxcUpOTlZo0ePVosWLfT222+rXbt2klxjntOnTys3N1fXXnutvv766xp1DRw4UN27d7+g9lQ/Xm5urk6cOKGBAwdq3759ys3N9bi+kydP6qOPPtJdd93l3DZixAhZLBa9/vrrF9TWcx09elTbtm3T+PHj1apVK+f2nj176sYbb9SHH34oqXLo8zvvvKNbbrlFffv2rVFPVXz34Ycfql+/frrmmmuc+1q0aKFJkyZp//79zimFqowbN87l+lV33333uTxftWqVoqOjdeONNzrj4BMnTqhPnz5q0aKFy3DqcwUFBTl7VzocDp06dUoVFRXq27ev2/eFGVXXZurUqS7bp02bJkk14v7u3bvr2muvdT6Pi4tTly5dtG/fvvM6PtBcMAQbgE/Z7XatWLFC119/vXM+H0lKTU3Vn//8Z61du9Y55PlC/fjjj0pKSlLLli1dtnfr1s25X6oc3mO1WusMUqvKdunSxWV7aGioOnXqVCPR1q5dO2cwdK6OHTu6PN+zZ48Mw9Bjjz2mxx57zO1rjh075gy+z3XgwAHNnDlT7777bo15cs4nOD5+/LiKiopqnKtUee0cDocOHjzoMqTkoosucikXGxsrSfXO2wMAAFBl0aJFuvTSSxUcHKyEhAR16dLF5Yvf999/X0888YS2bdvmMhffufP3STXjrfPxxRdfaNasWdq4cWONBQVzc3MVHR3tUX0rV65UeXm5rrjiCu3Zs8e5PTU1VcuXLz/vYd3u1Ba7SpXx3EcffaTCwkIVFBQoLy9PPXr0qLe+c6fgqaqran/1Omq7/sHBwWrfvr3Ltt27dys3N1fx8fFuX3Ps2LE627Zs2TL9+c9/1s6dO1VeXl5vG+rz448/ymq16uKLL3bZnpiYqJiYmBpx/7lxsFQZCxMHA3UjAQnAp9atW6ejR49qxYoVWrFiRY39y5cv15AhQ9wGkpK82kPSl2r7xtfdvqpVAR966CGlp6e7fc25AVAVu92uG2+8UadOndIjjzyirl27KjIyUocPH9b48eNrXXHQ24KCgtxuN9ws9gMAAOBOv3793PbCkyoX3bv11lt13XXX6fnnn1fbtm0VEhKipUuX6rXXXqtRvq5YzIy9e/dq8ODB6tq1qxYsWKDk5GSFhobqww8/1F/+8pfzirGqFl28+uqr3e7ft2+fc/SJxWJxG0c19ljYZrPVGHHkcDgUHx9fY1HKKtVHDp3r1Vdf1fjx4zV8+HD99re/VXx8vIKCgjRv3jznHKLnq7a/R85FHAycHxKQAHxq+fLlio+Pd67IXN1bb72lt99+W4sXL3b2oDt3hWl3Q3prCw46dOigTz75RPn5+S69IHfu3OncL0mdO3eWw+HQDz/8oN69e9dalyTt2rXLZVhyWVmZMjMzlZaWVssZ16+qvpCQEI/r+fbbb/Xf//5Xy5Yt09ixY53b16xZU6Os2SAqLi5OERER2rVrV419O3fulNVqVXJyskftBAAAuBBvvvmmwsLC9NFHH8lmszm3L1261HQdZmMhSXrvvfdUWlqqd99916WHW13DgeuSmZmpL7/8UhkZGRo4cKDLPofDof/5n//Ra6+9pkcffVRSZQ86d0N4z42F64qDJdUaz7Vp00aRkZEKDw9XVFSUvvvuuzrb36FDh1rrqn6889G5c2d98sknuvrqqz1OHL/xxhvq1KmT3nrrLZdrMWvWLJdyntz7Dh06yOFwaPfu3c4enpKUnZ2tnJycCzpXAGcxByQAnykuLtZbb72ln/70p7rjjjtqPDIyMpSfn693331XHTp0UFBQkDZs2OBSx/PPP1+j3sjISEk1k5U33XST7Ha7nnvuOZftf/nLX2SxWDRs2DBJ0vDhw2W1WjVnzpwa32ZXfXOZlpam0NBQPfPMMy7fZr788svKzc2tsfKiJ+Lj4zVo0CC9+OKLOnr0aI39x48fr/W1Vd+4Vm+TYRh6+umna5St7Tq5q3PIkCH65z//qf379zu3Z2dn67XXXtM111yjqKioOusAAADwpqCgIFksFpcegPv379c777xjuo6qFZfri4Wqjie5xli5ubkeJTyrq+rd9/DDD9eIge+8804NHDjQpQdg586dtXPnTpc48JtvvtEXX3xh6pzatm2r3r17a9myZS77vvvuO3388ce66aabJElWq1XDhw/Xe++9py1bttRod9X533TTTdq8ebM2btzo3FdYWKiXXnpJKSkpFzTf5p133im73a65c+fW2FdRUVHn/XJ3nzZt2uTSTsmze191bRYuXOiyfcGCBZJ0QXE/gLPoAQnAZ959913l5+fr1ltvdbv/Jz/5ieLi4rR8+XKNGjVKI0eO1LPPPiuLxaLOnTvr/fffdzsHTJ8+fSRJDzzwgNLT0xUUFKTRo0frlltu0fXXX6///d//1f79+9WrVy99/PHH+uc//6kpU6aoc+fOkiqHN//v//6v5s6dq2uvvVa33367bDabvvrqKyUlJWnevHmKi4vTjBkzNHv2bA0dOlS33nqrdu3apeeff15XXXWV7r777gu6NosWLdI111yjyy+/XBMnTlSnTp2UnZ2tjRs36tChQ/rmm2/cvq5r167q3LmzHnroIR0+fFhRUVF688033c45U9t1cueJJ57QmjVrdM011+jXv/61goOD9eKLL6q0tFR/+tOfLuhcAQAAPHXzzTdrwYIFGjp0qH7+85/r2LFjWrRokS6++GJt377dVB3h4eHq3r27Vq5cqUsvvVStWrVSjx493M5/OGTIEIWGhuqWW27Rvffeq4KCAv31r39VfHy82y+M67N8+XL17t271lEkt956q+6//359/fXXuvLKK/WLX/xCCxYsUHp6uu655x4dO3ZMixcv1mWXXeayGGBd5/Tkk09q2LBh6t+/v+655x4VFxfr2WefVXR0tB5//HFnHX/4wx/08ccfa+DAgZo0aZK6deumo0ePatWqVfr8888VExOj6dOn6x//+IeGDRumBx54QK1atdKyZcuUmZmpN998s8awak8MHDhQ9957r+bNm6dt27ZpyJAhCgkJ0e7du7Vq1So9/fTTuuOOO9y+9qc//aneeust3Xbbbbr55puVmZmpxYsXq3v37iooKDB1nc7Vq1cvjRs3Ti+99JJycnI0cOBAbd68WcuWLdPw4cOdC2kCuEB+WXsbQLNwyy23GGFhYUZhYWGtZcaPH2+EhIQYJ06cMI4fP26MGDHCiIiIMGJjY417773X+O677wxJxtKlS52vqaioMO6//34jLi7OsFgsRvV/yvLz840HH3zQSEpKMkJCQoxLLrnEePLJJw2Hw1Hj2EuWLDGuuOIKw2azGbGxscbAgQONNWvWuJR57rnnjK5duxohISFGQkKCcd999xmnT592KTNw4EDjsssuq1F/ZmamIcl48skn3Z773r17jbFjxxqJiYlGSEiI0a5dO+OnP/2p8cYbbzjLfPrpp4Yk49NPP3Vu++GHH4y0tDSjRYsWRps2bYyJEyca33zzjUfXSZIxa9Ysl/Z8/fXXRnp6utGiRQsjIiLCuP76640vv/zSpczSpUsNScZXX33lst1dOwEAANypLZ4418svv2xccsklhs1mM7p27WosXbrUmDVrlnHun7GSjMmTJ7ut48svvzT69OljhIaGusQ/7up59913jZ49exphYWFGSkqKMX/+fGPJkiWGJCMzM9NZbuDAgcbAgQNrbffWrVsNScZjjz1Wa5n9+/cbkowHH3zQue3VV181OnXqZISGhhq9e/c2PvroI2PcuHFGhw4dTJ2TYRjGJ598Ylx99dVGeHi4ERUVZdxyyy3GDz/8UOP4P/74ozF27FgjLi7OsNlsRqdOnYzJkycbpaWlzjJ79+417rjjDiMmJsYICwsz+vXrZ7z//vsu9VTFgKtWrapxjHHjxhmRkZG1XoOXXnrJ6NOnjxEeHm60bNnSuPzyy42HH37YOHLkiLPMudfa4XAYf/jDH4wOHToYNpvNuOKKK4z333/fo+vk7t6Xl5cbs2fPNjp27GiEhIQYycnJxowZM4ySkhKXch06dDBuvvnmGudS33sCgGFYDIOZUgEAAAAAAAD4BnNAAgAAAAAAAPAZEpAAAAAAAAAAfIYEJAAAAAAAAACf8WkCcsOGDbrllluUlJQki8Wid955p97XrF+/XldeeaVsNpsuvvhivfLKKzXKLFq0SCkpKQoLC1Nqaqo2b97s/cYDAACgSSAmBQAA8C+fJiALCwvVq1cvLVq0yFT5zMxM3Xzzzbr++uu1bds2TZkyRb/85S/10UcfOcusXLlSU6dO1axZs/T111+rV69eSk9P17Fjx3x1GgAAAGjEiEkBAAD8q8FWwbZYLHr77bc1fPjwWss88sgj+uCDD/Tdd985t40ePVo5OTlavXq1JCk1NVVXXXWVnnvuOUmSw+FQcnKy7r//fk2fPt2n5wAAAIDGjZgUAACg4QX7uwHVbdy4UWlpaS7b0tPTNWXKFElSWVmZtm7dqhkzZjj3W61WpaWlaePGjbXWW1paqtLSUudzh8OhU6dOqXXr1rJYLN49CQAAgAZgGIby8/OVlJQkq5Vpvb3JFzEp8SgAAGhqPIlHAyoBmZWVpYSEBJdtCQkJysvLU3FxsU6fPi273e62zM6dO2utd968eZo9e7ZP2gwAAOBPBw8eVPv27f3djCbFFzEp8SgAAGiqzMSjAZWA9JUZM2Zo6tSpzue5ubm66KKLdPDgQUVFRXn1WH9Z81+98uV+2R01R7YHWS0aPyBFD954qVePCQAAmp+8vDwlJyerZcuW/m4KTCAeBQAATY0n8WhAJSATExOVnZ3tsi07O1tRUVEKDw9XUFCQgoKC3JZJTEystV6bzSabzVZje1RUlNcDvrEDu2nZlmxZ3cysabFI4wZ2U1RUpHNb5olCvb7loA6dLlb72HDd2TdZHdtE1nwxAACAGwzf9T5fxKTEowAAoKkyE48G1IRB/fv319q1a122rVmzRv3795ckhYaGqk+fPi5lHA6H1q5d6yzjbx3bRGr+iJ6yVrv2QRaLrBZp/oieSqkWzL2+5aAG/3m9XtqwTx9sP6KXNuzT4D+v16otB/3QcgAAAEiNPyYlHgUAAIHGpwnIgoICbdu2Tdu2bZMkZWZmatu2bTpw4ICkyqEoY8eOdZb/1a9+pX379unhhx/Wzp079fzzz+v111/Xgw8+6CwzdepU/fWvf9WyZcu0Y8cO3XfffSosLNSECRN8eSoeGdk3WR88cI3z+YRrUrRu2iCN7Jvs3JZ5olDT39wuhyHZHYbLz0fe3K79Jwr90XQAAIAmpznGpMSjAAAgkPh0CPaWLVt0/fXXO59XzXszbtw4vfLKKzp69Kgz8JOkjh076oMPPtCDDz6op59+Wu3bt9ff/vY3paenO8uMGjVKx48f18yZM5WVlaXevXtr9erVNSYB97cOrc9+szz1xksVEep6qV/fcrCyi6pRc2yMxWLRyi0H9cjQrj5vJwAAQFPXXGNS4lEAABAofJqAHDRokAw3AU2VV155xe1r/vOf/9RZb0ZGhjIyMi60eX516HRxrdfGMAwdOl3cwC0CAABomohJ3SMeBQAADSWg5oBsTtrHhtc6SafFYlH72PAGbhEAAACaE+JRAADQUEhA+smdfZPr/MZ5VLX5eQAAAABvIx4FAAANhQSkn3iyOiEAAADgbcSjAACgofh0DkjUbWTfZPVoF6VhT38uqXJ1wrtTOxDsAQAAoEEQjwIAgIZAAtLP6ludsErmiUK9vuWgDp0uVvvYcN3ZN1kdCQwBAABwgczGowAAAOeL6KIReH3LQU1/c7ssFosMw5DFYtGLn+3V/BE9NZK5eQAAAAAAABDAmAMywGWeKNT0N7fLYUh2h+Hy85E3t2v/iUJ/NxEAAAAAAACoFQnIAPf6loOyWCxu91ksFq3ccrCBWwQAAAAAAACYRwIywB06XSzDMNzuMwxDh04XN3CLAAAAAAAAAPNIQAa49rHhdfaAbB8b3sAtAgAAAAAAAMwjARng7uybXGcPyFEsQgMAAAAAAIAARgIywHVsE6n5I3rKWq0TZJDFIqtFmj+ip1LaRPqvcQAAAAAAAEA9gv3dANRvZN9k9WgXpWFPfy5JmnBNiu5O7eA2+Zh5olCvbzmoQ6eL1T42XHf2TVZHkpQAAAAAAADwExKQjUSH1meTiFNvvFQRoTVv3etbDmr6m9tlsVhkGIYsFote/Gyv5o/oqZEM1QYAAAAAAIAfkIBsIjJPFGr6m9vlMCRVzRl55ucjb27XVSmtGK4NAE1Y1XzBhiEZ525zPq9WXkaNbTXrPOe5ahZ293p3VdY6n7GJ47oreG5bXM+t5nENN2Vd6jBUa7m6jltb2YSoMAVZ3S8iBwAAADQ3JCCbiNe3HKxcLdvNX0sWi0UrtxzUI0O7+qFlAND4GYYhhyHZHYYcRuWj8nfXfVW/V5UxnL+fLSdVPXctV3mcygRXVRLRMIyzP6slyAzDNbFWVxIR/tG6RaiCrEH+bgYAAAAQEEhANhGHThfXuVr2odPFDdwiAGgYDochu5uEX42EoLNc5WuqJxFdkooOOetzOM6WAQAAAACcHxKQTUT72PA6e0C2jw33Q6uaNhb8QXPmcPYElEviz9CZn45qvfskt70BDaOW7apKGJ67/2xvwLNJRP9eBwAAAABA/RokAblo0SI9+eSTysrKUq9evfTss8+qX79+bssOGjRIn332WY3tN910kz744ANJ0vjx47Vs2TKX/enp6Vq9erX3G99I3Nk3WS9+ttftPsMwNOqcRWhInl0YFvxBILM7e+1V/rRX68lnN6r18HNUJveq9/arPsy4+j6SfgAaO+JRAAAA//F5AnLlypWaOnWqFi9erNTUVC1cuFDp6enatWuX4uPja5R/6623VFZW5nx+8uRJ9erVSyNHjnQpN3ToUC1dutT53Gaz+e4kGoGObSI1f0RPPVK1EI2kIItFhgzNH9HTZQEakmd1qy85y4I/8CXDMFRur0wWljscqrAbqnA4ZHcYqrBXJgMrziQTXZ87ZHdUJg8BAK6IRwEAAPzL5wnIBQsWaOLEiZowYYIkafHixfrggw+0ZMkSTZ8+vUb5Vq1auTxfsWKFIiIiagR8NptNiYmJvmt4IzSyb7J6tIvSsKc/lyRNuCZFd6d2cEmGkTyrm5nkLAv+wFMVdofK7A6VVxiVP52PyuRihd1Qud2hijNJRQCAdxGPAgAA+JdPE5BlZWXaunWrZsyY4dxmtVqVlpamjRs3mqrj5Zdf1ujRoxUZ6ZoUW79+veLj4xUbG6sbbrhBTzzxhFq3bu22jtLSUpWWljqf5+XlncfZNA4dWp+9TlNvvFQRoa63mORZ7cwmZ1nwB1UMw1BpRWVysbS88mdZRWVysayiKunoYMgyAPgR8SgAAID/+TQBeeLECdntdiUkJLhsT0hI0M6dO+t9/ebNm/Xdd9/p5Zdfdtk+dOhQ3X777erYsaP27t2r3/3udxo2bJg2btyooKCgGvXMmzdPs2fPvrCTaSI8TZ41p7kizSZnz2fBn+Z0HZsKwzCcCcWqZGJZhaMy4XjmZ7nd4e5tAAAIIMSjAAAA/hfQq2C//PLLuvzyy2tMED569Gjn75dffrl69uypzp07a/369Ro8eHCNembMmKGpU6c6n+fl5Sk5uXnOdehJ8qy5zRVpNjnr6YI/ze06NgZ2R+WQZ5cE4zlJRpKLAACJeBQAAMAbfJqAbNOmjYKCgpSdne2yPTs7u975cgoLC7VixQrNmTOn3uN06tRJbdq00Z49e9wGfDabjUnBzzCbPGuKc0XW1wvRbHLWkwV/muJ1DFRVPRar5lMsOzPHYnnVkGjn8GiDhVoAoBkhHgUAAPA/qy8rDw0NVZ8+fbR27VrnNofDobVr16p///51vnbVqlUqLS3V3XffXe9xDh06pJMnT6pt27YX3Oamrip5ZrWc3RZkschqkUvyzDkc2Y2q4ciNyetbDmrwn9frpQ379MH2I3ppwz4N/vN6rap2Hnf2Ta6zB2T1no0j+ybrgweucT6fcE2K1k0bVKNHY1O7jg2t3O5QcZldeSXlOlVYpuy8Eh06XaTME4XanZ2v74/katvBHG3Zf0r/3ndKX/+Yo+2HcrXjaL72HivUgZNFOppbohMFZcorrlBJuYPkIwA0M8SjAAAA/ufzIdhTp07VuHHj1LdvX/Xr108LFy5UYWGhcxXCsWPHql27dpo3b57L615++WUNHz68xkTeBQUFmj17tkaMGKHExETt3btXDz/8sC6++GKlp6f7+nSaBDOrZZ/PQiuBOs+h2V6InvRslOpf8Ec6v+vYlFWcWem5ek9F52rQVT0W7Q5VOCp/Zwg0AMAbiEcBAAD8y+cJyFGjRun48eOaOXOmsrKy1Lt3b61evdo5EfiBAwdktbp2xNy1a5c+//xzffzxxzXqCwoK0vbt27Vs2TLl5OQoKSlJQ4YM0dy5cxnW4oH6kmeeLrTiz3kO60t8erLyt5nkrCfOZ8Gaxqb6fIrlFWeTiGVVycWKqoQjq0EDAPyDeBQAAMC/GmQRmoyMDGVkZLjdt379+hrbunTpUmuvsfDwcH300UfebB7c8GShFU/nOfRmT0kziU9PeyGa6dlolqcL1gSS2laBrv478ykCABoL4lEAAAD/CehVsOE/ngxH9qSHoSc9JetLVJpNfPqzF6Knw7obksNhqLTCodIKu0orHCoptztXgGYVaAAAAAAA4C0kIFErs8ORzfYw9KSnpJlEpdnEp797IXoyrNvb82iW2x0qLrerpNyu0vLKZGNJeWWCsazCcd71AgAAAAAAmEUCEnUyMxzZbA9DswlDs4lKs4nPQOiFaOY6nu88moZR2ZOxuMyu4vIzj7LKpGO5nS6MAAAAAADAv0hA4oKZ7WFoNmFoNlHpydBqby8u421mk67ldocKSytUWGZX0ZmfpeV2FncBAAAAAAABy1p/EaBuVT0MrZaz24IsFlktculh6EwYulE9YWg2UXln3+Q6y507tPrcXoiBknyUqiVda/H8+j3a+uNpbdl/WjuO5uvAySKdKChTcRnJRwAAAAAAENhIQMIrRvZN1gcPXON8PuGaFK2bNshl6LDZhKHZRKXZxGdjcOhUUe3XRtLB08XM2QgAAAAAABolEpDwmvp6GJpNGHrSs9FM4jMQORyGcovLdfBUkX44kqcga+29Hy2S4lrYGq5xAAAAAAAAXkQCEg3KTMLQ056NgTy0uoqj2jjpH47k6av9p/TDkTwdOl2s3OJyXXdpnGobSW1Iur5LfIO0EwAAAAAAwNtYhAYNzsyK0IG+aIwZJeV25RSVK6e4TMfySp3b80sqFBYS5FK2bXS47r2uk17csM+5Bo3VUpl8vPe6TkqMDnMpfzS3WOt3HdfxglLFtbBpUJc4tY0OFwAAAAAAQKAhAYmAZSZRGUgcDkP5JRXKKS7T6aJyFZfZnfvsJlaKGXhpvFJaR2r6W99Kkob2SNSN3RJrJB/X7zqml/61TxZVJigtkt7bfkT3XtdJAy+lpyQAAAAAAAgsgZ3RARqJ3dkFKq1wmEo01iUh6myycWSf5Bo9JY/mFuulf1X2kqw6UtXPFzfsU5eEqBoJSwAAAAAAAH9iDkjAAw6Hodyicv14slDfHsp1bj9VWHbByUcz1u86rtqWq7FI+nTXMZ+3AQAAAAAAwBP0gATqUVpxZi7HonLlFpc7E40l5fZ6Xul9xwtK61ys5nhBaS17AQAAAAAA/IMEJHCO6itWf3soVw3QsdG0uBY259yP57Kc2Q8AAAAAABBISECi2TMMQ4VlduUWlyu3qFzH80uc+4rK7DXmYfSnQV3i9N72I273GZKu7+K6CA2rZV84riEAAAAAABeGBCSapZJyu/JLKpRbXK684nKV28/2KQykHo/nahsdrnuv66QXN1QuRCNJVktl8vHe6zq5LEDDatkXjmsIAAAAAMCFIwGJZqG0wq4T+WXO598czA2ono2eGHhpvFJaR2r6W99Kkob2SNSN3RJdko+sll2/+no2cg0BAAAAAPAOEpBoksoqHMorqezdmFtcrpJyh18WjfGVhKizia+RfZJrJFOrVsuuba7IT3cd0139LvJpGwOZmZ6NXEMAAAAAALzD2hAHWbRokVJSUhQWFqbU1FRt3ry51rKvvPKKLBaLyyMszLWXkWEYmjlzptq2bavw8HClpaVp9+7dvj4NNAL7TxRp28Ecbf3xtHZnFyg7r1Ql5Q5/N6vBebpa9tHcYv1j8wE9s263/rH5gI7mFvu8jf5SvWejw5DLzxc37FNWbuUcoKw4DgBNC/EoAACA//g8Ably5UpNnTpVs2bN0tdff61evXopPT1dx44dq/U1UVFROnr0qPPx448/uuz/05/+pGeeeUaLFy/Wpk2bFBkZqfT0dJWUlNRSI5qacrtDJwtKlXmiUN8eynVuz84rUXFZ0+npeL6qVst259zVstfvOqZpq77R+9uP6N/7Tur97Uc0bdU3+uy/tX9GG7Oqno3uVPVslDy7hlLzSuICQGNDPAoAAOBfPk9ALliwQBMnTtSECRPUvXt3LV68WBEREVqyZEmtr7FYLEpMTHQ+EhISnPsMw9DChQv16KOP6mc/+5l69uypv//97zpy5IjeeecdX58O/KTC7tCpwjLtP1Go7YdytGX/af03u0BZuSUqIuFYw6AucXX23qtaLdtsb8DGpL5EoNmejWavodT8krgA0NgQjwIAAPiXTxOQZWVl2rp1q9LS0s4e0GpVWlqaNm7cWOvrCgoK1KFDByUnJ+tnP/uZvv/+e+e+zMxMZWVludQZHR2t1NTUWussLS1VXl6eywOBzeEwlFtU7nz+9YEc7crK19HcEhWWknCsT9Vq2ZZqXfisFslicV0t22xvwMbCTCLQbM9Gs9ewKSZxAaApIR4FAADwP58mIE+cOCG73e7yjbEkJSQkKCsry+1runTpoiVLluif//ynXn31VTkcDg0YMECHDh2SJOfrPKlz3rx5io6Odj6Sk5Mv9NTgAwWlFTqcU6wfjuTpq/2ntDMr37nPqK0rGmo18NJ4zbvtcufzoT0StWBkb+ciK1LTmivSbCLQk56NZq5hU0viAkBTQzwKAADgfwG3Cnb//v3Vv39/5/MBAwaoW7duevHFFzV37tzzqnPGjBmaOnWq83leXh5BX4A4nleqMnuxcovLVW4ny+ht9a2WXdUbsLaVns+dK7K+laN96WhusdbvOq7jBaWKa2HToC5xahsdXq195latrurZ+OKGfc7EttVS+brqPRur1HcNWawGAJoe4lEAAADv8mkCsk2bNgoKClJ2drbL9uzsbCUmJpqqIyQkRFdccYX27NkjSc7XZWdnq23bti519u7d220dNptNNpvN7T40HMMwlF9aoaOnzw5J3XeisEZCBw1nUJc4vbf9iNt9tc0VaVTbL1X2LuySEOWSuKsvWegpM8lPTxKBAy+NV0rrSE1/61tJlT0bb+yWWCP5aIYnSVwAQMMjHgUAAPA/nw7BDg0NVZ8+fbR27VrnNofDobVr17p8q1wXu92ub7/91hncdezYUYmJiS515uXladOmTabrRMOpOLNa9Z5j+dr642l9fzhPRwJo2G5z54u5Ij1dkKW+Yd1mh1Z7umr1uT0bzyf5KHk2pLtKIA9lB4CmhngUAADA/3w+BHvq1KkaN26c+vbtq379+mnhwoUqLCzUhAkTJEljx45Vu3btNG/ePEnSnDlz9JOf/EQXX3yxcnJy9OSTT+rHH3/UL3/5S0mVKxJOmTJFTzzxhC655BJ17NhRjz32mJKSkjR8+HBfnw5MysotUUm5Q3kl5czfGODM9AY027vQ056SZno2mh1abbY3p7d5OqTb30PZAaA5Ih4FAADwL58nIEeNGqXjx49r5syZysrKUu/evbV69WrnpN0HDhyQ1Xq2I+bp06c1ceJEZWVlKTY2Vn369NGXX36p7t27O8s8/PDDKiws1KRJk5STk6NrrrlGq1evVljY+fVgwoUrKbfrSM7ZodU/nixiaHUj4q25Is0mCyXzyUqzyU9PE4HeZHZIt6cJWgCAdxCPAgAA+FeDLEKTkZGhjIwMt/vWr1/v8vwvf/mL/vKXv9RZn8Vi0Zw5czRnzhxvNRHnodzu0MmCMp0oKFV+SYVKyu3+bhJ8xGzvQk/mYTSbrPRkjkVvzu3oqfqSuJJnCVoAgHcRjwIAAPiPT+eARNNjdxg6nl+qHUfztPXH08o8Uaj8kgp/Nws+ZnauSE/mYTSbrPR0jkVvze3oC6yYDQAAAABojkhAwrQ9xwq0Zf8p7TlWoJwi5nZsbgZeGq95t13ufD60R6IWjOztMm+hJ8lCs8lKs8nPxsDThXIAAAAAAGgKSECiVuV2h8u8jicLyuQg6dis1de70JNkoSfJSjPJz8bA096crJYNAAAAAGgKGmQOSDQuhaUVOppbopMFpSoqY15HeMbsPIyeLhpjZo7FQOfJObNaNgAAAACgqSABCUmSYRg6WVimrNwS5nTEBTObLPTnojH+YuacWS0bAAAAANCUkICEDp0qVn5pucoqGF+NhtcUejZ6qr5zZrVsAAAAAEBTwhyQzVS53eH8/XBOMclHIICwWjYAAAAAoCmhB2Qz43AYOppXon3HC/zdFAC1qFotu7YekOeuln00t1jrdx3X8YJSxbWwaVCXOLWNDm+IpgIAAAAAUC8SkM2EYRg6nl+qg6eLVVbhUIWdHo9AoBrUJU7vbT/idt+5q2WzWI13kMQFAAAAAN8hAdkMnC4s04FTRaxoDTQSZlfLZrGa+plJLJLEBQAAAADfIgHZhOWXlCvzRKHyilnVGmhszKyW7eliNc2tl5+ZxCJJXAAAAADwPRKQTdgPR/KbxYrCQFNV32rZnixW09x6+ZlNLLLiOAAAAAD4HgnIJiavpNzfTQDQQMwuVtMUe/nV15vTbGLR0xXHm1svUgAAAADwBhKQTcjhnGLtzs73dzMANBCzi9U0taHaZnpzmk0serLieHPrRQoAAAAA3mL1dwNw4SrsDu3MytOBk0XOBSsANH1Vi9VYLGe3WS2SxeK6WI2nQ7WnrfpG728/on/vO6n3tx/RtFXf6LP/HvPdiXigem9OhyGXny9u2Kes3BJJZxOL7lRPLA7qElfntalK4po9LgAAAACgJhKQjVxBaYW2H87V6UKGXgPN0cBL4zXvtsudz4f2SNSCkb1deuSZTcYFQpLtaG6x/rH5gJ5Zt1v/2HxAR3OLXfZX9eZ0p6o3p2Q+sWg2iWv2uAAAAACAmhiC3Yhl5Zbox5OFctDrEWjW6lusxt9Dtc2W8+bQ6qrE4osb9jl7hlstlWWqJxYlcyuOezpXJAAAAADgrAbpAblo0SKlpKQoLCxMqamp2rx5c61l//rXv+raa69VbGysYmNjlZaWVqP8+PHjZbFYXB5Dhw719WkEDLvD0O7sfGWeIPkIoH7+HKpttpy3h1ZL5nqHVjk3iXvuojyeHLfqfOrqyQmg4RGPAgAA+I/PE5ArV67U1KlTNWvWLH399dfq1auX0tPTdeyY++Fq69ev11133aVPP/1UGzduVHJysoYMGaLDhw+7lBs6dKiOHj3qfPzjH//w9akEjO8O5+pEQZm/mwGgEfHHUG1PhnR7e2h1lfoSi2Z5ctxAn0cTaI6IRwEAAPzL5wnIBQsWaOLEiZowYYK6d++uxYsXKyIiQkuWLHFbfvny5fr1r3+t3r17q2vXrvrb3/4mh8OhtWvXupSz2WxKTEx0PmJjY319KgGjpNzh7yYAaITqS8aZTbKZTRZ6Mm+ip0Or6+vN6W1mjxsI82gCqIl4FAAAwL98moAsKyvT1q1blZaWdvaAVqvS0tK0ceNGU3UUFRWpvLxcrVq1ctm+fv16xcfHq0uXLrrvvvt08uRJr7Y90Bw6xfA9AL7l7aHangzp9tXQam8yc1wWqwECD/EoGkLmiULNX71T9//jP5q/eqcyTxT6u0kAAAQUny5Cc+LECdntdiUkJLhsT0hI0M6dO03V8cgjjygpKcklaBw6dKhuv/12dezYUXv37tXvfvc7DRs2TBs3blRQUFCNOkpLS1VaevYP3by8vPM8o4Zndxjae7xAh3NIQALwPTMLslQlC2tbrKYqWWi2nGR+oZwq9S284yv1HZfFaoDAQzwKX3t9y0FNf3O7LBaLDMOQxWLRi5/t1fwRPTWyb7K/mwcAQEAI6FWw//jHP2rFihVav369wsLO/tE3evRo5++XX365evbsqc6dO2v9+vUaPHhwjXrmzZun2bNnN0ibvam0wq5dWfkqLLX7uykAmhFvrartSVLRk1WrA5knSVfJ/ArhAPynucejqFvmiUJNf3N75cKQVf+Bnfn5yJvbdVVKK6W0ifRfAwEACBA+HYLdpk0bBQUFKTs722V7dna2EhMT63ztU089pT/+8Y/6+OOP1bNnzzrLdurUSW3atNGePXvc7p8xY4Zyc3Odj4MHD3p2In6QX1Ku7w7nknwEEHDMDtX2dL5Gfw2t9iYWqwECD/EofOn1LQdlsbiffMNisWjlFu4zAACSjxOQoaGh6tOnj8uE3VUTePfv37/W1/3pT3/S3LlztXr1avXt27fe4xw6dEgnT55U27Zt3e632WyKiopyeQSyEwWl+uFInsoqavszFgD8y2yy0NOkordWrfYXFqsBAg/xKHzp0OliGYb7mN0wDB06zTRKAABIDbAK9tSpU/XXv/5Vy5Yt044dO3TfffepsLBQEyZMkCSNHTtWM2bMcJafP3++HnvsMS1ZskQpKSnKyspSVlaWCgoKJEkFBQX67W9/q3//+9/av3+/1q5dq5/97Ge6+OKLlZ6e7uvT8bmDp4q0O7ugchgHAAQws8nCxp5U9BSL1QCBh3gUvtI+NrzOHpDtY5lWAwAAqQHmgBw1apSOHz+umTNnKisrS71799bq1audE4EfOHBAVuvZPOgLL7ygsrIy3XHHHS71zJo1S48//riCgoK0fft2LVu2TDk5OUpKStKQIUM0d+5c2Wyuc2s1NruPFaiIIdcA0OixWA0QWIhH4St39k3Wi5/tdbvPMAyNYhEaAAAkNdAiNBkZGcrIyHC7b/369S7P9+/fX2dd4eHh+uijj7zUssByqqCswVZyBQD4j6eL1UgsWANcKOJR+ELHNpGaP6KnHqlaiEZSkMUiQ4bmj+h5QQvQZJ4o1OtbDurQ6WK1jw3XnX2T1ZEFbQAAjVRAr4LdHOSXlPu7CQCABubJCuFS5YI1L/1rnzNpaZH03vYjuve6To1qkR4AaIpG9k1Wj3ZRGvb055KkCdek6O7UDrUmH80kFl/fclDT39wui8UiwzBksVj04md7NX9ET42kVyUAoBEiAelHJwpKtfNovr+bAQBoYFWL1by4oXIhGqlysRpDNVcIr75gTVWPyaqfL27Ypy4JUU1+Xk0ACHQdWp9NIE698VJFhLr/M8tMYjHzRKGmV/WorPpP4szPR97crqtSWl1Qz0oAAPzB54vQwD0WmwGA5s3sCuEsWAMATUP1xKLdYbj8fOTN7dp/olBSZZKyroVtVm452JDNBgDAK+gB2cAcDkN7jhfoZEGZv5sCAPCz+harkTxfsIa5ImvHtQHgT87EolHzX/WqxOIjQ7vq0OliGW7KSJUL2xw6XezrpgIA4HUkIBtYYVkFyUcAgGmeLFjTXOeKNJNYbK7XBqjNxr0nnb+XlNudv2/ad6pZLIro7S8kzFzDbQdz5KglsegwDG07mKONe0/W2uu9ikWu9w8AAHf6d27t7ya4IAEJAEAAM7tgTXOdK9JMYrG5XhsA7vnrCwmzXyh5ulAZAACNAXNAAgAQwKoWrKk+HZjVIlksrgvWeDpX5NHcYv1j8wE9s263/rH5gI7mNr4hfdUTiw5DLj9f3LBPWbklkphHE8BZZv/d8IVBXeLqnFKjKrFo9t99AAAaE3pAAgAQ4AZeGq+U1pGa/ta3kioXrLmxW6LLH6GezBXZGIYjmxtWfbzO3kSf7jqmu/pdxDyaAJzM/rvhC1WJxRc37HNOA2m1VLbl3MSimX/3AQBoTEhAAgDQCNS3YI3ZoX2eDkf2RzLObILUbGKReTSBwNXQ/8b4+wsJTxKLZhYq8ye+rAEAeIIEJAAATYDZOcM86f3jSTLO7B+i9ZXzJEHq7fnUmCsSaFj+SPgHwhcSgZ5YNIMvawAAnmIOSAAAmgCzc4aZ7f3jyTxp63cd07RV3+j97Uf0730n9f72I5q26ht99l/XuRXNlPNkvkZvz6fGXJFAw/F0LkZvzVtr9t8Nf84VGegC4do0hXmMAaC5IQEJAEATMfDSeM277XLn86E9ErVgZG+X3ihVvX/cqd77x2wyzuwfombLeTI80pOFGsxcG0+HZgI4f54k/M1+yWEGX0hcOH9fG2++H1A/kr0AvIUh2AAANCH1De0zOxzZbDLO7JBus+U8GR4peXc+NU+PzfxnwPk7n97Y3poawdsLezU3/rw2TJXRsBhqD8CbSEACANCMmF2F1WwyzuwfombLmU2QVuet+dQ8OTZ/lAEXxuy/Mb5atdrbX0g0J/68Np6+H7z9RZE/62voL71I9jY8vthEU0cCEgCAZsZM7x+zyTizf4iaLWc2QeoLZo9t5o8yoLmr7w9pb/fG9rbz+TKkufDntfHk/eDtL4r8WZ8/vvTyVfIf7vHFJpoD5oAEAKAZOrf3z7mJPbPzpJld0MFsOcncfI2+YubY/p7/DAh0ZuboM/tvjNl5a73NkzlmmxtfXpv65hs0+37w9kI5/qzPX4v+MA1BwwmEhZ2AhkACEgAAuGUmGWf2D1FP/2CtL0HqS/Udmz/KgNp58oe0mX9jPPnywtv8+WVIoPPk2phdxMRM4trs+8HbXxT5sz5/fenlr+R/c8QXm81Lc17YiSHYAACgVmbmVzS7EIwnC8YEMuaGA2rn6bDN+v6N8ee0DGba15yZuTZmh5WanW/Q7PvB218U+bO+5joNQXOaDzEQvtj01/VuTvdZYqh9g/SAXLRokVJSUhQWFqbU1FRt3ry5zvKrVq1S165dFRYWpssvv1wffvihy37DMDRz5ky1bdtW4eHhSktL0+7du315CgAAoA5meyz6s2ejt/izRxbOH/Fow/DFH9K+6G0H3/OkN6wnPcDMvB+83XvPn/U1lmkIzH72zJQz0xu2KfF3b1N/Xe/mdp99NdS+Mf2/5/ME5MqVKzV16lTNmjVLX3/9tXr16qX09HQdO+b+TfXll1/qrrvu0j333KP//Oc/Gj58uIYPH67vvvvOWeZPf/qTnnnmGS1evFibNm1SZGSk0tPTVVLC3AgAAMC3mBuu8SEebTi++kPazJcXze2P2UDnSVLR08R1fe8Hb39R5M/6GsM0BGY/e2bKNcf5EH15j+tLTvkrKebL++zthJy36vPFUPvG9v+ezxOQCxYs0MSJEzVhwgR1795dixcvVkREhJYsWeK2/NNPP62hQ4fqt7/9rbp166a5c+fqyiuv1HPPPSep8tvmhQsX6tFHH9XPfvYz9ezZU3//+9915MgRvfPOO74+HQAAAOaGa2SIRxuOv5IlzTFpEeg8SSp6O3Ht7S+K/Fmfv7/0qi/Za/azZ7bc+SRp/NUDzFvH9dU9NpOc8ldSzFfzXno7IedJffW9H7w9QqAx/r/n0zkgy8rKtHXrVs2YMcO5zWq1Ki0tTRs3bnT7mo0bN2rq1Kku29LT053BXGZmprKyspSWlubcHx0drdTUVG3cuFGjR4823b6isgoFl1V4cEbmFVWr99zfS8rtzueltfx+rkAv1xjayLUJvHKNoY2cM9cmEMo1hjY2x2sTEx7i/P3WnkmyhQQ5/48vKquQ3VFbmOkdRT6KYZoa4tGav0s6r3jUjNiIUP1iQEct+SLT+YdW1Rx9vxjQUTERIT6JhT/ZkV3n3JNrdmRpZJ9krx/X03K+qtMf6mtfbERInfckttp7oX/n1nXONzigc2uP3zepHVsrKTpMM9/9QZJ0Y7cEXd81XglRYS51meXP+rx9bE9467Nntlx2XkmdSZrsvBKXc/7X7uNa+uX+GnPq/WJAR11zSRuX12fllehfu4/rZEGZWrcI1bWXxCkx6vySe54c1wxv3+OsvJI651VNaR2phKgwj693oB7Xk2P7oj4z7wdP/k00w8xnKr1Hgun6zpcn8ajFMAyfRcdHjhxRu3bt9OWXX6p///7O7Q8//LA+++wzbdq0qcZrQkNDtWzZMt11113Obc8//7xmz56t7Oxsffnll7r66qt15MgRtW3b1lnmzjvvlMVi0cqVK2vUWVpaqtLSs9nkvLw8JScnK3nK67LaIrx1ugAAAA3GUVqkgwvvVG5urqKiovzdnIBFPAoAAOAbnsSjDbIIjb/NmzdP0dHRzkdycnL9LwIAAAC8hHgUAAA0Zz4dgt2mTRsFBQUpOzvbZXt2drYSExPdviYxMbHO8lU/s7OzXb5xzs7OVu/evd3WOWPGDJdhNFXfOG/+38EN3mMgv6RcPxzJb9BjAgCAhtX7omjZgoN8eoy8vDy1XejTQzQJxKPubdp3yuPXlJbb9avlX0uSFo+5UrYQ377HPZGVV6Lfvf2t3I3tslikebdd7tHQu8bE7H2pr5yvrmF2Xok2VBv2et0lcQF3Lw6cLNSs9yqHvqZ3T9CgrvHnPTRX8u9nxcyx/7X7uJZ+sd/tNAmeDh82+77x5P31+e4TWvJlpstwVnftW/zZXm3ef6rWOvultNKvBnbWqq0Htfq7LLmbGcVqqZzHeWSfZNNtNHvcKt5+f0n132ez5yyZv97+Oq7Z96snxzZTp9n6PH0/mP030Rv/Zt/aO6nmTi/zJB71aQIyNDRUffr00dq1azV8+HBJksPh0Nq1a5WRkeH2Nf3799fatWs1ZcoU57Y1a9Y4h8x07NhRiYmJWrt2rTPAy8vL06ZNm3Tfffe5rdNms8lmqzlxcURosCJCfXoJarA7DIUFULAGAAC8LyI02OcJyIoGjmEaK+JR9y40HrWFBAVUTJvSOlL3XtdJL27YV+OP2Xuv66QOrSP928AGYva+uCu3ce/JOucT+3LvSd3V7yKP29ShdaT+J4Cv//pdx/TSv/Y5n6/Zka2Pd2Tr3us6eWVhMX9+Vtwd+2husZZ+ud/lPlclWJZ8make7aI9WvTE7GfPk89oWvcE9WgXrU93HdPxglLFtbDp+i7xNdqVEBVW53s2ISpMYSFBOl1UXud8g6eLyhUWEmT6M2D2uJLv31+S+/uc1i1B//ddltvyhqQbuyU6X2P2evvjuJ68X80e22ydZuvz5P0gmf83MSvv7CIy724/orRuCWobHe7cZuYz1RDxhSfxqM9bM3XqVI0bN059+/ZVv379tHDhQhUWFmrChAmSpLFjx6pdu3aaN2+eJOk3v/mNBg4cqD//+c+6+eabtWLFCm3ZskUvvfSSJMlisWjKlCl64okndMkll6hjx4567LHHlJSU5AwqAQAAgCrEo83DwEvj1SUhyuM/olHJ2yu0NgbVV5GtUpWIeHHDPnVJiGpy75+q1YdrS5Z8uuuYx4lms589Tz6jidFh9bZjUJe4Ohcvur5LZYKvapX12s65apV1s58Bs8f15furenJq1daDNZJTVStr15acOve4Zq63Gd4+rifvV7PHNlun2frMvh88cW7ievV3Wfq/77JqJK4b2/97Pk9Ajho1SsePH9fMmTOVlZWl3r17a/Xq1UpIqFyN58CBA7Jaz05FOWDAAL322mt69NFH9bvf/U6XXHKJ3nnnHfXo0cNZ5uGHH1ZhYaEmTZqknJwcXXPNNVq9erXCwgLzIgMAAMB/iEebD2/9Ed0cmU3SNCW+SMYFOl8lms1+9rz5GfV2gsjsZ8DbiS5PBXpyypvH9fT9aubYntRppj5Pk6718TRx3Zj+32uQ8R4ZGRm1DnFZv359jW0jR47UyJEja63PYrFozpw5mjNnjreaCAAAgCaMeBSomy968QS65tjrs6klmr2ZIPLkM+DtRJdZjSU55a3jns/7tb5je1qnmXPxZtK1KX8xwuRBAAAAANDMebsXT2PQ1JJxZjTFRLO3EkTeHj7si/eXv5NT9Q399jZfvF999RnwVtK1KX8xQgISAAAAANDo5hO7UL5KRDR0ksYTzTHRXKWhe7L54v3lz+SU2aHf3uSL92ugfwaa8hcjJCABAAAAAJIa13xiF8oXiQh/JGk81dwSzZ7y14IsZvgrOeXPBZt88X4N5M9AU+ylXIUEJAAAAACgWfJmIqIxrardnBLN/uTtRJe/klP+Hvrti/droH4GAr2H5oUgAQkAAAAAaLa8lYjwd5IGgckfK397W1OelzAQBXIPzQtBAhIAAAAAGqlAnm+wuSFJg4bgj+RUU56XMFAFag/NC0ECEgAAAAAaocYw32BzQpIGDaWhk1NNeV5CNByrvxsAAAAAAPBMbfMNGkblfINZuSW1vxg+MahLXJ09IEnSoLGqGvptsUhWi1x+NvZ5CdFw6AEJAAAAAI0M8w0Gnqa8eATQVOclRMMhAQkAAAAAjQzzDQYmfydpmBMUvtQU5yVEwyEBCQAAAACNDPMNBi5/JWmYExRAIGMOSAAAAABoZJhvENUxJyiAQEcCsoEFW62yWPzdCgAAAACNGYtCoLqqOUHdqZoTFAD8iSHYDSw8NEjd2kZpd3a+yu21fWcJAAAA+E7/zq09fk1RWYXz99ROrRQRyp8S/ta/c2uNvuoirdxyUIdOF6t9bLhG9U1WSpvIGmW5f03ba5sP1Lnf0Pl97gHAW/hfxw+iw0PUo120dmblq7jM7u/mAAAAAGikUtpE6pGhXf3dDPhZ+9hwWSwWuYzBPsNisah9LAvRAPAvhmD7SVhIkHokRSkmIsTfTQEAAAAANGJ39k2W4Sb5KEmGYWhU3+QGbhEAuCIB6UfBQVZ1TWyptszPAgAAgAC3/2Sh8/cFa/6rzBOFdZQG0JA6tonU/BE9ZbVIQVaLy8/5I3q6HZYPAA2JIdh+ZrFYlNImUuGhQco8UeiuxzwAAADgV69vOajpb253Pl/6+X4t+TxT80f01Eh6VgEBYWTfZF2V0srUnKAA0NB82gPy1KlTGjNmjKKiohQTE6N77rlHBQUFdZa///771aVLF4WHh+uiiy7SAw88oNzcXJdyFoulxmPFihW+PBWfS4gKU7e2UQoJYolsAAAAbyEevXCZJwo1/c3tclT7otxuGHIY0iNvbtd+ekICAaNqTtBn77pCjwztSvIRQMDwaQ/IMWPG6OjRo1qzZo3Ky8s1YcIETZo0Sa+99prb8keOHNGRI0f01FNPqXv37vrxxx/1q1/9SkeOHNEbb7zhUnbp0qUaOnSo83lMTIwvT6VBsDgNAACAdxGPXrjXtxysc3GLlVsOsggKAACok88SkDt27NDq1av11VdfqW/fvpKkZ599VjfddJOeeuopJSUl1XhNjx499Oabbzqfd+7cWb///e919913q6KiQsHBZ5sbExOjxMREXzXfb6oWp9l9rEA5ReX+bg4AAECjRTzqHYdOF9e5uMWh08UN3CIAANDY+GwI9saNGxUTE+MM9iQpLS1NVqtVmzZtMl1Pbm6uoqKiXII9SZo8ebLatGmjfv36acmSJbUGRY1R1eI0iSxOAwAAcN6IR72jfWx4ZQ9INywWi9rHhjdwiwAAQGPjsx6QWVlZio+Pdz1YcLBatWqlrKwsU3WcOHFCc+fO1aRJk1y2z5kzRzfccIMiIiL08ccf69e//rUKCgr0wAMPuK2ntLRUpaWlzud5eXkenk3Ds1gs6tgmUhEsTgMAAHBeiEe9486+yXrxs71u9xmGoVEsQgMAAOrhcQ/I6dOnu510u/pj586dF9ywvLw83Xzzzerevbsef/xxl32PPfaYrr76al1xxRV65JFH9PDDD+vJJ5+sta558+YpOjra+UhObjxBUkJUmLolsjgNAABAFeLRhtWxTaTmj+gpq0UKslpcfs4f0ZNFLgAAQL087gE5bdo0jR8/vs4ynTp1UmJioo4dO+ayvaKiQqdOnap3rpz8/HwNHTpULVu21Ntvv62QkJA6y6empmru3LkqLS2VzWarsX/GjBmaOnWq83leXl6jCvqiI1icBgAAoArxaMMb2TdZV6W00sotB3XodLHax4ZrVN9kko8AAMAUjxOQcXFxiouLq7dc//79lZOTo61bt6pPnz6SpHXr1snhcCg1NbXW1+Xl5Sk9PV02m03vvvuuwsLqnwdx27Ztio2NdRvsSZLNZqt1X2NRtTjNnuMFOl3I4jQAAKD5Ih71j5Q2kax2DQAAzovP5oDs1q2bhg4dqokTJ2rx4sUqLy9XRkaGRo8e7Vxx8PDhwxo8eLD+/ve/q1+/fsrLy9OQIUNUVFSkV199VXl5ec75ceLi4hQUFKT33ntP2dnZ+slPfqKwsDCtWbNGf/jDH/TQQw/56lQCRuXiNFE6nFOsg6eKmBcSAACgDsSjAAAAgcFnCUhJWr58uTIyMjR48GBZrVaNGDFCzzzzjHN/eXm5du3apaKiIknS119/7VyR8OKLL3apKzMzUykpKQoJCdGiRYv04IMPyjAMXXzxxVqwYIEmTpzoy1MJKO1iwtUyLFi7swtUVuHwd3MAAAACFvEoAACA/1kMo/n1o8vLy1N0dLRyc3MVFRXl7+act3K7Q7uzC5RbzJBsAAACyZUdYmQLDvLpMZpKPNNccf/Q0IrKKtR95keSpB/mpCsi1Kd9UQAAzYAn8YzHq2AjcIQEWdWtbUu1jw2XhUWyAQAAAAAAEIBIQDZyFotFya0i1C0xSiFBZCEBAAAAAAAQWEhANhHRESG6vH20WoYxlAIAAAAAAACBgwRkE2ILDtJlSVFqFxPu76YAAAAAAAAAkkhANjkWi0UXtY5Qj3ZR9IYEAAAAAACA35GAbKJahoWoR7todUlsqfBQ367CCQAAAAAAANSGLnJNXKvIUMVGhOh4fqkOni5WWYXD300CAAAAAABAM0ICshmwWCyKjwpT6xY2Hc0t1tHcElXYDX83CwAAAAAAAM0AQ7CbkSCrRe1jI9Q7OUZto8Nktfi7RQAAAAAAAGjqSEA2QyFBVqW0iVSv5BjFtbSRiAQAAAAAAIDPMAS7GQsLCdLF8S3UoXWEsvNKlJ1XyhyRAAAAAAAA8CoSkFBIkFXtYyPULiZcpwrLlJVXorziCn83CwAAAAAAAE0ACUg4WSwWtW5hU+sWNhWVVSgrt0QnCspkd7BgDQAAAAAAAM4PCUi4FREarE5xLXRRK4eOF5QqK7dEJeUMzwYAAAAAAIBnSECiTsFBVrWNDlfb6HDll5TrZEGZThaWqqyCXpEAAAAAAACoHwlImNYyLEQtw0LUoXWE8oordLygVKeLylRhJxkJAAAAAAAA90hAwmMWi0XRESGKjgiRw2HodFGZThaW6XRhmZguEgAAAAAAANWRgMQFsVrPLlxTYXfoVFGZTheWK7e4nMVrAAAAAAAAIKsvKz916pTGjBmjqKgoxcTE6J577lFBQUGdrxk0aJAsFovL41e/+pVLmQMHDujmm29WRESE4uPj9dvf/lYVFRW+PBWYEBxkVXzLMHVJbKm+HWLVrW1LJUaHKTTYp28zAACAWhGPAgAA+J9Pe0COGTNGR48e1Zo1a1ReXq4JEyZo0qRJeu211+p83cSJEzVnzhzn84iICOfvdrtdN998sxITE/Xll1/q6NGjGjt2rEJCQvSHP/zBZ+cCz1itFsVEhComIlQd20SqsLRCp4vKlFNUrvwSgnMAANAwiEcBAAD8z2cJyB07dmj16tX66quv1LdvX0nSs88+q5tuuklPPfWUkpKSan1tRESEEhMT3e77+OOP9cMPP+iTTz5RQkKCevfurblz5+qRRx7R448/rtDQUJ+cDy5MpC1YkbZgtY+VyiocyikqU05xufKKy1XOIjYAAMAHiEcBAAACg8/Gxm7cuFExMTHOYE+S0tLSZLVatWnTpjpfu3z5crVp00Y9evTQjBkzVFRU5FLv5ZdfroSEBOe29PR05eXl6fvvv/f+icDrQoOtio8K06UJLdWnQ6wubx+t5FbhigoPltXi79YBAICmgngUAAAgMPisB2RWVpbi4+NdDxYcrFatWikrK6vW1/385z9Xhw4dlJSUpO3bt+uRRx7Rrl279NZbbznrrR7sSXI+r63e0tJSlZaWOp/n5eWd1znB+ywWi1rYgtXiTO9Iu8NQXnHlIja5xeUqKrP7u4kAAKCRIh4FAAAIDB4nIKdPn6758+fXWWbHjh3n3aBJkyY5f7/88svVtm1bDR48WHv37lXnzp3Pq8558+Zp9uzZ590mNJwgq0WxkaGKjawculRaYVdecYXySiqHa5eUO/zcQgAA4G/Eo4Dn9p8sdP6+YM1/NSa1gzq2ifRjiwAAzYnHCchp06Zp/PjxdZbp1KmTEhMTdezYMZftFRUVOnXqVK3z6biTmpoqSdqzZ486d+6sxMREbd682aVMdna2JNVa74wZMzR16lTn87y8PCUnJ5tuA/zHFhykuJZBimtpkySVlNvPJCMrk5KlJCQBAGh2iEcBz7y+5aCmv7nd+Xzp5/u15PNMzR/RUyP78j4EAPiexwnIuLg4xcXF1Vuuf//+ysnJ0datW9WnTx9J0rp16+RwOJxBnBnbtm2TJLVt29ZZ7+9//3sdO3bMOaRmzZo1ioqKUvfu3d3WYbPZZLPZTB8TgSssJEhhIUGKb1n5vHpCMre4XGUVJCQBAGjqiEcB8zJPFGr6m9vlqLbuo92ofPLIm9t1VUorpdATEgDgYz5bhKZbt24aOnSoJk6cqM2bN+uLL75QRkaGRo8e7Vxx8PDhw+ratavzG+S9e/dq7ty52rp1q/bv3693331XY8eO1XXXXaeePXtKkoYMGaLu3bvrf/7nf/TNN9/oo48+0qOPPqrJkycT1DVDlcnIMF0c30J9OsTqioti1DkuUnEtQxUa7LO3NwAAaASIR4HK3o8Wi/uVHi0Wi1ZuOdjALQIANEc+W4RGqlw9MCMjQ4MHD5bVatWIESP0zDPPOPeXl5dr165dzlUFQ0ND9cknn2jhwoUqLCxUcnKyRowYoUcffdT5mqCgIL3//vu677771L9/f0VGRmrcuHGaM2eOL08FjYSzh2RUmCSpuMzunD8yr6RcZRVGPTUAAICmhHgUzd2h08UyDPcxsGEYOnS6uIFbBABojixGbf8bNWF5eXmKjo5Wbm6uoqKi/N0cNKCisgrncO28knJV2Jvd2x8A0ACu7BAjW3CQT49BPNO4cf/QUOav3qmXNuyT3VEz7g2yWjTpuk56ZGhXP7QMANDYeRLP+LQHJBBoIkKDFREarMToMBmGoYLSymRkTlG5Ckor1PzS8QAAAGjK7uybrBc/2+t2n2EYGsUiNACABsAkeWi2LBaLWoaFqH1shHq0i9ZVKa3UNbGl2kaHKSLUt71WAAAAgIbQsU2k5o/oKaulssdj9Z/zR/RkARoAQIOgByRwRpDVotjIUMVGhkqSyiocyikuU05RuXKLGa4NAACAxmlk32RdldJKK7cc1KHTxWofG65RfZNJPgIAGgwJSKAWocFWxbcMU3zLyuHa+aUVyiksV05xmQpL7f5uHgAAAGBaSptI5noEAPgNCUjABIvFoqiwEEWFhegiRdA7EgAAAAAAwCQSkMB5OLd3ZF5JhXKL6B0JAAAAAABwLhKQwAWyWCyKDg9RdHhl78jSCvuZZCS9IwEAAAAAAEhAAl5mCw5SfFSQ4qPO9o7MKaocrl1URu9IAAAAAADQvJCABHyoeu/IDq2lcrtD+SUVyisuV15JZULSoIMkAAAAAABowkhAAg0oJMiqVpGhahUZKkmqOJOQzC+pUF5JuQpKK0hIAgAAAACAJoUEJOB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"]},"metadata":{},"output_type":"display_data"}],"source":["from statsmodels.tsa.stattools import acf, pacf\n","from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n","\n","# Draw Plot\n","fig, axes = plt.subplots(1,2,figsize=(16,3), dpi= 100)\n","plot_acf(df['Number of Passengers'].tolist(), lags=50, ax=axes[0])\n","plot_pacf(df['Number of Passengers'].tolist(), lags=50, ax=axes[1])"]},{"cell_type":"markdown","metadata":{},"source":["# **17. Computation of Partial Autocorrelation Function** \n","\n","\n","[Table of Contents](#0.1)\n","\n","\n","- The partial autocorrelation function of lag (k) of a series is the coefficient of that lag in the autoregression equation of Y. The autoregressive equation of Y is nothing but the linear regression of Y with its own lags as predictors.\n","\n","\n","- For example, if **Y_t** is the current series and **Y_t-1** is the lag 1 of Y, then the partial autocorrelation of **lag 3 (Y_t-3)** is the coefficient $\\alpha_3$ of Y_t-3 in the following equation:"]},{"cell_type":"markdown","metadata":{},"source":["![Partial Autocorrelation Function](https://www.machinelearningplus.com/wp-content/uploads/2019/02/12_5_Autoregression_Equation-min.png?ezimgfmt=ng:webp/ngcb1)\n","\n","image source : https://www.machinelearningplus.com/wp-content/uploads/2019/02/12_5_Autoregression_Equation-min.png?ezimgfmt=ng:webp/ngcb1"]},{"cell_type":"markdown","metadata":{},"source":["# **18. Lag Plots** \n","\n","\n","[Table of Contents](#0.1)\n","\n","\n","- A **Lag plot** is a scatter plot of a time series against a lag of itself. It is normally used to check for autocorrelation. If there is any pattern existing in the series, the series is autocorrelated. If there is no such pattern, the series is likely to be random white noise.\n"]},{"cell_type":"code","execution_count":19,"metadata":{"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["from pandas.plotting import lag_plot\n","plt.rcParams.update({'ytick.left' : False, 'axes.titlepad':10})\n","\n","# Plot\n","fig, axes = plt.subplots(1, 4, figsize=(10,3), sharex=True, sharey=True, dpi=100)\n","for i, ax in enumerate(axes.flatten()[:4]):\n"," lag_plot(df['Number of Passengers'], lag=i+1, ax=ax, c='firebrick')\n"," ax.set_title('Lag ' + str(i+1))\n","\n","fig.suptitle('Lag Plots of Air Passengers', y=1.05) \n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["# **19. Granger Causality Test** \n","\n","\n","[Table of Contents](#0.1)\n","\n","\n","- **Granger causality test** is used to determine if one time series will be useful to forecast another. It is based on the idea that if X causes Y, then the forecast of Y based on previous values of Y AND the previous values of X should outperform the forecast of Y based on previous values of Y alone.\n","\n","\n","- So, **Granger causality test** should not be used to test if a lag of Y causes Y. Instead, it is generally used on exogenous (not Y lag) variables only. It is implemented in the statsmodel package.\n","\n","\n","- It accepts a 2D array with 2 columns as the main argument. The values are in the first column and the predictor (X) is in the second column. The Null hypothesis is that the series in the second column, does not Granger cause the series in the first. If the P-Values are less than a significance level (0.05) then we reject the null hypothesis and conclude that the said lag of X is indeed useful. The second argument maxlag says till how many lags of Y should be included in the test."]},{"cell_type":"code","execution_count":20,"metadata":{"trusted":true},"outputs":[{"ename":"FileNotFoundError","evalue":"[Errno 2] No such file or directory: '/kaggle/input/dataset/dataset.txt'","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)","Cell \u001b[0;32mIn[20], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mstatsmodels\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtsa\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mstattools\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m grangercausalitytests\n\u001b[0;32m----> 2\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m/kaggle/input/dataset/dataset.txt\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdate\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mto_datetime(data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdate\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 4\u001b[0m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmonth\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mdate\u001b[38;5;241m.\u001b[39mdt\u001b[38;5;241m.\u001b[39mmonth\n","File \u001b[0;32m~/Source/courses/tools4ds/DS701-Course-Notes/.venv/lib/python3.12/site-packages/pandas/io/parsers/readers.py:1026\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[1;32m 1013\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m 1014\u001b[0m dialect,\n\u001b[1;32m 1015\u001b[0m delimiter,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1022\u001b[0m dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[1;32m 1023\u001b[0m )\n\u001b[1;32m 1024\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m-> 1026\u001b[0m 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'/kaggle/input/dataset/dataset.txt'"]}],"source":["from statsmodels.tsa.stattools import grangercausalitytests\n","data = pd.read_csv('/kaggle/input/dataset/dataset.txt')\n","data['date'] = pd.to_datetime(data['date'])\n","data['month'] = data.date.dt.month\n","grangercausalitytests(data[['value', 'month']], maxlag=2)"]},{"cell_type":"markdown","metadata":{},"source":["- In the above case, the p-values are zero for all tests. So the ‘month’ indeed can be used to forecast the values."]},{"cell_type":"markdown","metadata":{},"source":["# **20. Smoothening a Time Series** \n","\n","\n","[Table of Contents](#0.1)\n","\n","\n","\n","Smoothening of a time series may be useful in the following circumstances:\n","\n","\n","- Reducing the effect of noise in a signal get a fair approximation of the noise-filtered series.\n","- The smoothed version of series can be used as a feature to explain the original series itself.\n","- Visualize the underlying trend better.\n","\n","\n","We can smoothen a time series using the following methods:\n","\n","\n","- Take a moving average\n","- Do a LOESS smoothing (Localized Regression)\n","- Do a LOWESS smoothing (Locally Weighted Regression)"]},{"cell_type":"markdown","metadata":{},"source":["## **Moving Average** \n","\n","\n","- **Moving average** is the average of a rolling window of defined width. We must choose the window-width wisely, because, large window-size will over-smooth the series. For example, a window-size equal to the seasonal duration (ex: 12 for a month-wise series), will effectively nullify the seasonal effect.\n"," "]},{"cell_type":"markdown","metadata":{},"source":["## **Localized Regression**\n","\n","\n","- LOESS, short for ‘Localized Regression’ fits multiple regressions in the local neighborhood of each point. It is implemented in the statsmodels package, where you can control the degree of smoothing using frac argument which specifies the percentage of data points nearby that should be considered to fit a regression model."]},{"cell_type":"markdown","metadata":{},"source":["# **21. References** \n","\n","[Table of Contents](#0.1)\n","\n","The concepts and code in this notebook is taken from the following websites:-\n","\n","1.\thttps://www.machinelearningplus.com/time-series/time-series-analysis-python/\n","2.\thttps://towardsdatascience.com/an-end-to-end-project-on-time-series-analysis-and-forecasting-with-python-4835e6bf050b\n","3.\thttps://towardsdatascience.com/time-series-analysis-in-python-an-introduction-70d5a5b1d52a\n","4.\thttps://www.digitalocean.com/community/tutorials/a-guide-to-time-series-visualization-with-python-3\n","\n"]},{"cell_type":"markdown","metadata":{"trusted":true},"source":["So, now we will come to the end of this notebook.\n","\n","I hope you find this notebook useful and enjoyable.\n","\n","Your comments and feedback are most welcome.\n","\n","Thank you\n"]},{"cell_type":"markdown","metadata":{},"source":["[Go to Top](#0)"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.12.4"}},"nbformat":4,"nbformat_minor":4} diff --git a/ds701_book/data/.gitignore b/ds701_book/data/.gitignore new file mode 100644 index 00000000..e7dd2674 --- /dev/null +++ b/ds701_book/data/.gitignore @@ -0,0 +1,4 @@ +dogs-vs-cats/ +dogs-vs-cats.zip +cifar-10-batches-py/ +cifar-10-python.tar.gz diff --git a/ds701_book/example.txt b/ds701_book/example.txt new file mode 100644 index 00000000..6a092d59 --- /dev/null +++ b/ds701_book/example.txt @@ -0,0 +1,3 @@ +Hello World! +How are you? +I'm fine. 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"duration": 0.088578, + "end_time": "2022-05-23T23:52:03.886920", + "exception": false, + "start_time": "2022-05-23T23:52:03.798342", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Introduction" + ] + }, + { + "cell_type": "markdown", + "id": "f058b15f", + "metadata": { + "papermill": { + "duration": 0.075585, + "end_time": "2022-05-23T23:52:04.041562", + "exception": false, + "start_time": "2022-05-23T23:52:03.965977", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Previously I've shown how to create a [linear model and neural net from scratch](https://www.kaggle.com/code/jhoward/linear-model-and-neural-net-from-scratch), and used it to create a solid submission to Kaggle's [Titanic](https://www.kaggle.com/competitions/titanic/) competition. However, for *tabular* data (i.e data that looks like spreadsheet or database tables, such as the data for the Titanic competition) it's more common to see good results by using ensembles of decision trees, such as Random Forests and Gradient Boosting Machines.\n", + "\n", + "In this notebook, we're going to learn all about Random Forests, by building one from scratch, and using it to submit to the Titanic competition! That might sound like a pretty big stretch, but I think you'll be surprised to discover how straightforward it actually is.\n", + "\n", + "We'll start by importing the basic set of libraries we normally need for data science work, and setting numpy to use our display space more efficiently:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "d64452b3", + "metadata": { + "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", + "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5", + "execution": { + "iopub.execute_input": "2022-05-23T23:52:04.194966Z", + "iopub.status.busy": "2022-05-23T23:52:04.194414Z", + "iopub.status.idle": "2022-05-23T23:52:04.615833Z", + "shell.execute_reply": "2022-05-23T23:52:04.614881Z" + }, + "papermill": { + "duration": 0.501423, + "end_time": "2022-05-23T23:52:04.618761", + "exception": false, + "start_time": "2022-05-23T23:52:04.117338", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from fastai.imports import *\n", + "np.set_printoptions(linewidth=130)" + ] + }, + { + "cell_type": "markdown", + "id": "963b3840", + "metadata": { + "papermill": { + "duration": 0.076015, + "end_time": "2022-05-23T23:52:04.770832", + "exception": false, + "start_time": "2022-05-23T23:52:04.694817", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Data preprocessing" + ] + }, + { + "cell_type": "markdown", + "id": "c582e910", + "metadata": { + "papermill": { + "duration": 0.07709, + "end_time": "2022-05-23T23:52:04.924481", + "exception": false, + "start_time": "2022-05-23T23:52:04.847391", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "We'll create `DataFrame`s from the CSV files just like we did in the \"*linear model and neural net from scratch*\" notebook, and do much the same preprocessing (so go back and check that out if you're not already familiar with the dataset):" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "8c16f049", + "metadata": { + "execution": { + "iopub.execute_input": "2022-05-23T23:52:05.080498Z", + "iopub.status.busy": "2022-05-23T23:52:05.079868Z", + "iopub.status.idle": "2022-05-23T23:52:05.132162Z", + "shell.execute_reply": "2022-05-23T23:52:05.131303Z" + }, + "papermill": { + "duration": 0.131266, + "end_time": "2022-05-23T23:52:05.134686", + "exception": false, + "start_time": "2022-05-23T23:52:05.003420", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: Your Kaggle API key is readable by other users on this system! To fix this, you can run 'chmod 600 /Users/tomg/.kaggle/kaggle.json'\n", + "titanic.zip: Skipping, found more recently modified local copy (use --force to force download)\n" + ] + } + ], + "source": [ + "import os\n", + "iskaggle = os.environ.get('KAGGLE_KERNEL_RUN_TYPE', '')\n", + "\n", + "if iskaggle: path = Path('../input/titanic')\n", + "else:\n", + " import zipfile,kaggle\n", + " path = Path('titanic')\n", + " kaggle.api.competition_download_cli(str(path))\n", + " zipfile.ZipFile(f'{path}.zip').extractall(path)\n", + "\n", + "df = pd.read_csv(path/'train.csv')\n", + "tst_df = pd.read_csv(path/'test.csv')\n", + "modes = df.mode().iloc[0]" + ] + }, + { + "cell_type": "markdown", + "id": "c0a0c35b", + "metadata": { + "papermill": { + "duration": 0.074326, + "end_time": "2022-05-23T23:52:05.283755", + "exception": false, + "start_time": "2022-05-23T23:52:05.209429", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "One difference with Random Forests however is that we don't generally have to create *dummy variables* like we did for non-numeric columns in the linear models and neural network. Instead, we can just convert those fields to *categorical variables*, which internally in Pandas makes a list of all the unique values in the column, and replaces each value with a number. The number is just an index for looking up the value in the list of all unique values." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "0e222580", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "PassengerId 1\n", + "Survived 0.0\n", + "Pclass 3.0\n", + "Name Abbing, Mr. Anthony\n", + "Sex male\n", + "Age 24.0\n", + "SibSp 0.0\n", + "Parch 0.0\n", + "Ticket 1601\n", + "Fare 8.05\n", + "Cabin B96 B98\n", + "Embarked S\n", + "Name: 0, dtype: object\n" + ] + } + ], + "source": [ + "print(modes)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "1071c1a3", + "metadata": { + "execution": { + "iopub.execute_input": "2022-05-23T23:52:05.438310Z", + "iopub.status.busy": "2022-05-23T23:52:05.437730Z", + "iopub.status.idle": "2022-05-23T23:52:05.458998Z", + "shell.execute_reply": "2022-05-23T23:52:05.458205Z" + }, + "papermill": { + "duration": 0.099983, + "end_time": "2022-05-23T23:52:05.461215", + "exception": false, + "start_time": "2022-05-23T23:52:05.361232", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def proc_data(df):\n", + " df['Fare'] = df.Fare.fillna(0)\n", + " df.fillna(modes, inplace=True)\n", + " df['LogFare'] = np.log1p(df['Fare'])\n", + " df['Embarked'] = pd.Categorical(df.Embarked)\n", + " df['Sex'] = pd.Categorical(df.Sex)\n", + "\n", + "proc_data(df)\n", + "proc_data(tst_df)" + ] + }, + { + "cell_type": "markdown", + "id": "64f0196d", + "metadata": { + "papermill": { + "duration": 0.075622, + "end_time": "2022-05-23T23:52:05.612873", + "exception": false, + "start_time": "2022-05-23T23:52:05.537251", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "We'll make a list of the continuous, categorical, and dependent variables. Note that we no longer consider `Pclass` a categorical variable. That's because it's *ordered* (i.e 1st, 2nd, and 3rd class have an order), and decision trees, as we'll see, only care about order, not about absolute value." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "98336680", + "metadata": { + "execution": { + "iopub.execute_input": "2022-05-23T23:52:05.767333Z", + "iopub.status.busy": "2022-05-23T23:52:05.766770Z", + "iopub.status.idle": "2022-05-23T23:52:05.771007Z", + "shell.execute_reply": "2022-05-23T23:52:05.770353Z" + }, + "papermill": { + "duration": 0.082835, + "end_time": "2022-05-23T23:52:05.772965", + "exception": false, + "start_time": "2022-05-23T23:52:05.690130", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "cats=[\"Sex\",\"Embarked\"]\n", + "conts=['Age', 'SibSp', 'Parch', 'LogFare',\"Pclass\"]\n", + "dep=\"Survived\"" + ] + }, + { + "cell_type": "markdown", + "id": "1d73a083", + "metadata": { + "papermill": { + "duration": 0.074566, + "end_time": "2022-05-23T23:52:05.923051", + "exception": false, + "start_time": "2022-05-23T23:52:05.848485", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Even although we've made the `cats` columns categorical, they are still shown by Pandas as their original values:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d986270f", + "metadata": { + "execution": { + "iopub.execute_input": "2022-05-23T23:52:06.075283Z", + "iopub.status.busy": "2022-05-23T23:52:06.074927Z", + "iopub.status.idle": "2022-05-23T23:52:06.084866Z", + "shell.execute_reply": "2022-05-23T23:52:06.084138Z" + }, + "papermill": { + "duration": 0.088602, + "end_time": "2022-05-23T23:52:06.087425", + "exception": false, + "start_time": "2022-05-23T23:52:05.998823", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 male\n", + "1 female\n", + "2 female\n", + "3 female\n", + "4 male\n", + "Name: Sex, dtype: category\n", + "Categories (2, object): ['female', 'male']" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.Sex.head()" + ] + }, + { + "cell_type": "markdown", + "id": "f3213c9b", + "metadata": { + "papermill": { + "duration": 0.075127, + "end_time": "2022-05-23T23:52:06.239653", + "exception": false, + "start_time": "2022-05-23T23:52:06.164526", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "However behind the scenes they're now stored as integers, with indices that are looked up in the `Categories` list shown in the output above. We can view the stored values by looking in the `cat.codes` attribute:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "52d11e29", + "metadata": { + "execution": { + "iopub.execute_input": "2022-05-23T23:52:06.393628Z", + "iopub.status.busy": "2022-05-23T23:52:06.392869Z", + "iopub.status.idle": "2022-05-23T23:52:06.399858Z", + "shell.execute_reply": "2022-05-23T23:52:06.398821Z" + }, + "papermill": { + "duration": 0.085298, + "end_time": "2022-05-23T23:52:06.401867", + "exception": false, + "start_time": "2022-05-23T23:52:06.316569", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 1\n", + "1 0\n", + "2 0\n", + "3 0\n", + "4 1\n", + "dtype: int8" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.Sex.cat.codes.head()" + ] + }, + { + "cell_type": "markdown", + "id": "41672065", + "metadata": { + "papermill": { + "duration": 0.07507, + "end_time": "2022-05-23T23:52:06.552765", + "exception": false, + "start_time": "2022-05-23T23:52:06.477695", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Binary splits" + ] + }, + { + "cell_type": "markdown", + "id": "d9a0be88", + "metadata": { + "papermill": { + "duration": 0.078022, + "end_time": "2022-05-23T23:52:06.707522", + "exception": false, + "start_time": "2022-05-23T23:52:06.629500", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Before we create a Random Forest or Gradient Boosting Machine, we'll first need to learn how to create a *decision tree*, from which both of these models are built.\n", + "\n", + "And to create a decision tree, we'll first need to create a *binary split*, since that's what a decision tree is built from.\n", + "\n", + "A binary split is where all rows are placed into one of two groups, based on whether they're above or below some threshold of some column. For example, we could split the rows of our dataset into males and females, by using the threshold `0.5` and the column `Sex` (since the values in the column are `0` for `female` and `1` for `male`). We can use a plot to see how that would split up our data -- we'll use the [Seaborn](https://seaborn.pydata.org/) library, which is a layer on top of [matplotlib](https://matplotlib.org/) that makes some useful charts easier to create, and more aesthetically pleasing by default:" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "55f4f7a9", + "metadata": { + "execution": { + "iopub.execute_input": "2022-05-23T23:52:06.861689Z", + "iopub.status.busy": "2022-05-23T23:52:06.861126Z", + "iopub.status.idle": "2022-05-23T23:52:07.884110Z", + "shell.execute_reply": "2022-05-23T23:52:07.883114Z" + }, + "papermill": { + "duration": 1.102355, + "end_time": "2022-05-23T23:52:07.886349", + "exception": false, + "start_time": "2022-05-23T23:52:06.783994", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "

    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn as sns\n", + "\n", + "fig,axs = plt.subplots(1,2, figsize=(11,5))\n", + "sns.barplot(data=df, y=dep, x=\"Sex\", ax=axs[0], hue=\"Sex\", palette=[\"#3374a1\",\"#e1812d\"]).set(title=\"Survival rate\")\n", + "sns.countplot(data=df, x=\"Sex\", ax=axs[1], hue=\"Sex\", palette=[\"#3374a1\",\"#e1812d\"]).set(title=\"Histogram\");" + ] + }, + { + "cell_type": "markdown", + "id": "7e9411ae", + "metadata": { + "papermill": { + "duration": 0.076048, + "end_time": "2022-05-23T23:52:08.041349", + "exception": false, + "start_time": "2022-05-23T23:52:07.965301", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Here we see that (on the left) if we split the data into males and females, we'd have groups that have very different survival rates: >70% for females, and <20% for males. We can also see (on the right) that the split would be reasonably even, with over 300 passengers (out of around 900) in each group.\n", + "\n", + "We could create a very simple \"model\" which simply says that all females survive, and no males do. To do so, we better first split our data into a training and validation set, to see how accurate this approach turns out to be:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "7b21423f", + "metadata": { + "execution": { + "iopub.execute_input": "2022-05-23T23:52:08.197669Z", + "iopub.status.busy": "2022-05-23T23:52:08.197358Z", + "iopub.status.idle": "2022-05-23T23:52:08.381950Z", + "shell.execute_reply": "2022-05-23T23:52:08.381258Z" + }, + "papermill": { + "duration": 0.266668, + "end_time": "2022-05-23T23:52:08.384358", + "exception": false, + "start_time": "2022-05-23T23:52:08.117690", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from numpy import random\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "random.seed(42)\n", + "trn_df,val_df = train_test_split(df, test_size=0.25)\n", + "trn_df[cats] = trn_df[cats].apply(lambda x: x.cat.codes)\n", + "val_df[cats] = val_df[cats].apply(lambda x: x.cat.codes)" + ] + }, + { + "cell_type": "markdown", + "id": "1ca3d1b4", + "metadata": { + "papermill": { + "duration": 0.076211, + "end_time": "2022-05-23T23:52:08.538389", + "exception": false, + "start_time": "2022-05-23T23:52:08.462178", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "(In the previous step we also replaced the categorical variables with their integer codes, since some of the models we'll be building in a moment require that.)\n", + "\n", + "Now we can create our independent variables (the `x` variables) and dependent (the `y` variable):" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "a1bdbf46", + "metadata": { + "execution": { + "iopub.execute_input": "2022-05-23T23:52:08.693767Z", + "iopub.status.busy": "2022-05-23T23:52:08.692988Z", + "iopub.status.idle": "2022-05-23T23:52:08.701823Z", + "shell.execute_reply": "2022-05-23T23:52:08.700843Z" + }, + "papermill": { + "duration": 0.08891, + "end_time": "2022-05-23T23:52:08.704290", + "exception": false, + "start_time": "2022-05-23T23:52:08.615380", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def xs_y(df):\n", + " xs = df[cats+conts].copy()\n", + " return xs,df[dep] if dep in df else None\n", + "\n", + "trn_xs,trn_y = xs_y(trn_df)\n", + "val_xs,val_y = xs_y(val_df)" + ] + }, + { + "cell_type": "markdown", + "id": "7f1503e1", + "metadata": { + "papermill": { + "duration": 0.075886, + "end_time": "2022-05-23T23:52:08.857760", + "exception": false, + "start_time": "2022-05-23T23:52:08.781874", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Here's the predictions for our extremely simple model, where `female` is coded as `0`:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "4c717040", + "metadata": { + "execution": { + "iopub.execute_input": "2022-05-23T23:52:09.014276Z", + "iopub.status.busy": "2022-05-23T23:52:09.013681Z", + "iopub.status.idle": "2022-05-23T23:52:09.018107Z", + "shell.execute_reply": "2022-05-23T23:52:09.017344Z" + }, + "papermill": { + "duration": 0.085919, + "end_time": "2022-05-23T23:52:09.020308", + "exception": false, + "start_time": "2022-05-23T23:52:08.934389", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "preds = val_xs.Sex==0" + ] + }, + { + "cell_type": "markdown", + "id": "b4a11532", + "metadata": { + "papermill": { + "duration": 0.078424, + "end_time": "2022-05-23T23:52:09.175518", + "exception": false, + "start_time": "2022-05-23T23:52:09.097094", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "We'll use mean absolute error to measure how good this model is:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "0c0d9955", + "metadata": { + "execution": { + "iopub.execute_input": "2022-05-23T23:52:09.331784Z", + "iopub.status.busy": "2022-05-23T23:52:09.331490Z", + "iopub.status.idle": "2022-05-23T23:52:09.338210Z", + "shell.execute_reply": "2022-05-23T23:52:09.337243Z" + }, + "papermill": { + "duration": 0.087547, + "end_time": "2022-05-23T23:52:09.340660", + "exception": false, + "start_time": "2022-05-23T23:52:09.253113", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(0.21524663677130046)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics import mean_absolute_error\n", + "mean_absolute_error(val_y, preds)" + ] + }, + { + "cell_type": "markdown", + "id": "f1c9360b", + "metadata": { + "papermill": { + "duration": 0.076347, + "end_time": "2022-05-23T23:52:09.494011", + "exception": false, + "start_time": "2022-05-23T23:52:09.417664", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Alternatively, we could try splitting on a continuous column. We have to use a somewhat different chart to see how this might work -- here's an example of how we could look at `LogFare`:" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "acbdf71a", + "metadata": { + "execution": { + "iopub.execute_input": "2022-05-23T23:52:09.650478Z", + "iopub.status.busy": "2022-05-23T23:52:09.650134Z", + "iopub.status.idle": "2022-05-23T23:52:09.932886Z", + "shell.execute_reply": "2022-05-23T23:52:09.931944Z" + }, + "papermill": { + "duration": 0.363754, + "end_time": "2022-05-23T23:52:09.935270", + "exception": false, + "start_time": "2022-05-23T23:52:09.571516", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df_fare = trn_df[trn_df.LogFare>0]\n", + "fig,axs = plt.subplots(1,2, figsize=(11,5))\n", + "sns.boxenplot(data=df_fare, x=dep, y=\"LogFare\", ax=axs[0], hue=dep, palette=[\"#3374a1\",\"#e1812d\"])\n", + "sns.kdeplot(data=df_fare, x=\"LogFare\", ax=axs[1]);" + ] + }, + { + "cell_type": "markdown", + "id": "79a5316d", + "metadata": { + "papermill": { + "duration": 0.080153, + "end_time": "2022-05-23T23:52:10.094741", + "exception": false, + "start_time": "2022-05-23T23:52:10.014588", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "The [boxenplot](https://seaborn.pydata.org/generated/seaborn.boxenplot.html) above shows quantiles of `LogFare` for each group of `Survived==0` and `Survived==1`. It shows that the average `LogFare` for passengers that didn't survive is around `2.5`, and for those that did it's around `3.2`. So it seems that people that paid more for their tickets were more likely to get put on a lifeboat.\n", + "\n", + "Let's create a simple model based on this observation:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "1ba429ff", + "metadata": { + "execution": { + "iopub.execute_input": "2022-05-23T23:52:10.253568Z", + "iopub.status.busy": "2022-05-23T23:52:10.253277Z", + "iopub.status.idle": "2022-05-23T23:52:10.258015Z", + "shell.execute_reply": "2022-05-23T23:52:10.257122Z" + }, + "papermill": { + "duration": 0.086971, + "end_time": "2022-05-23T23:52:10.260566", + "exception": false, + "start_time": "2022-05-23T23:52:10.173595", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "preds = val_xs.LogFare>2.7" + ] + }, + { + "cell_type": "markdown", + "id": "f6d760f2", + "metadata": { + "papermill": { + "duration": 0.078029, + "end_time": "2022-05-23T23:52:10.418725", + "exception": false, + "start_time": "2022-05-23T23:52:10.340696", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "...and test it out:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "faa47c50", + "metadata": { + "execution": { + "iopub.execute_input": "2022-05-23T23:52:10.577363Z", + "iopub.status.busy": "2022-05-23T23:52:10.576760Z", + "iopub.status.idle": "2022-05-23T23:52:10.583341Z", + "shell.execute_reply": "2022-05-23T23:52:10.582517Z" + }, + "papermill": { + "duration": 0.088187, + "end_time": "2022-05-23T23:52:10.585326", + "exception": false, + "start_time": "2022-05-23T23:52:10.497139", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(0.336322869955157)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mean_absolute_error(val_y, preds)" + ] + }, + { + "cell_type": "markdown", + "id": "b1fb2616", + "metadata": { + "papermill": { + "duration": 0.078849, + "end_time": "2022-05-23T23:52:10.742895", + "exception": false, + "start_time": "2022-05-23T23:52:10.664046", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "This is quite a bit less accurate than our model that used `Sex` as the single binary split.\n", + "\n", + "Ideally, we'd like some way to try more columns and breakpoints more easily. We could create a function that returns how good our model is, in order to more quickly try out a few different splits. We'll create a `score` function to do this. Instead of returning the mean absolute error, we'll calculate a measure of *impurity* -- that is, how much the binary split creates two groups where the rows in a group are each similar to each other, or dissimilar.\n", + "\n", + "We can measure the similarity of rows inside a group by taking the standard deviation of the dependent variable. If it's higher, then it means the rows are more different to each other. We'll then multiply this by the number of rows, since a bigger group as more impact than a smaller group:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "b651bfd0", + "metadata": { + "execution": { + "iopub.execute_input": "2022-05-23T23:52:10.902098Z", + "iopub.status.busy": "2022-05-23T23:52:10.901650Z", + "iopub.status.idle": "2022-05-23T23:52:10.906471Z", + "shell.execute_reply": "2022-05-23T23:52:10.905688Z" + }, + "papermill": { + "duration": 0.087027, + "end_time": "2022-05-23T23:52:10.908542", + "exception": false, + "start_time": "2022-05-23T23:52:10.821515", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def _side_score(side, y):\n", + " tot = side.sum()\n", + " if tot<=1: return 0\n", + " return y[side].std()*tot" + ] + }, + { + "cell_type": "markdown", + "id": "2e592ee2", + "metadata": { + "papermill": { + "duration": 0.078184, + "end_time": "2022-05-23T23:52:11.065440", + "exception": false, + "start_time": "2022-05-23T23:52:10.987256", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Now we've got that written, we can calculate the score for a split by adding up the scores for the \"left hand side\" (lhs) and \"right hand side\" (rhs):" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "516a5e14", + "metadata": { + "execution": { + "iopub.execute_input": "2022-05-23T23:52:11.227933Z", + "iopub.status.busy": "2022-05-23T23:52:11.227382Z", + "iopub.status.idle": "2022-05-23T23:52:11.231832Z", + "shell.execute_reply": "2022-05-23T23:52:11.231159Z" + }, + "papermill": { + "duration": 0.089432, + "end_time": "2022-05-23T23:52:11.233859", + "exception": false, + "start_time": "2022-05-23T23:52:11.144427", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + " \n", + "def score(col, y, split):\n", + " lhs = col<=split\n", + " return (_side_score(lhs,y) + _side_score(~lhs,y))/len(y)" + ] + }, + { + "cell_type": "markdown", + "id": "9709bc86", + "metadata": { + "papermill": { + "duration": 0.078311, + "end_time": "2022-05-23T23:52:11.393682", + "exception": false, + "start_time": "2022-05-23T23:52:11.315371", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "For instance, here's the impurity score for the split on `Sex`:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "fae1fdbc", + "metadata": { + "execution": { + "iopub.execute_input": "2022-05-23T23:52:11.552734Z", + "iopub.status.busy": "2022-05-23T23:52:11.552167Z", + "iopub.status.idle": "2022-05-23T23:52:11.559158Z", + "shell.execute_reply": "2022-05-23T23:52:11.558604Z" + }, + "papermill": { + "duration": 0.088961, + "end_time": "2022-05-23T23:52:11.561023", + "exception": false, + "start_time": "2022-05-23T23:52:11.472062", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(0.40787530982063946)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "score(trn_xs[\"Sex\"], trn_y, 0.5)" + ] + }, + { + "cell_type": "markdown", + "id": "364e9f82", + "metadata": { + "papermill": { + "duration": 0.07955, + "end_time": "2022-05-23T23:52:11.720650", + "exception": false, + "start_time": "2022-05-23T23:52:11.641100", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "...and for `LogFare`:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "e6549176", + "metadata": { + "execution": { + "iopub.execute_input": "2022-05-23T23:52:11.885615Z", + "iopub.status.busy": "2022-05-23T23:52:11.885055Z", + "iopub.status.idle": "2022-05-23T23:52:11.892268Z", + "shell.execute_reply": "2022-05-23T23:52:11.891417Z" + }, + "papermill": { + "duration": 0.09165, + "end_time": "2022-05-23T23:52:11.894535", + "exception": false, + "start_time": "2022-05-23T23:52:11.802885", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(0.47180873952099694)" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "score(trn_xs[\"LogFare\"], trn_y, 2.7)" + ] + }, + { + "cell_type": "markdown", + "id": "356238b3", + "metadata": { + "papermill": { + "duration": 0.079081, + "end_time": "2022-05-23T23:52:12.055373", + "exception": false, + "start_time": "2022-05-23T23:52:11.976292", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "As we'd expect from our earlier tests, `Sex` appears to be a better split.\n", + "\n", + "To make it easier to find the best binary split, we can create a simple interactive tool (note that this only works in Kaggle if you click \"Copy and Edit\" in the top right to open the notebook editor):" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "8c928d3b", + "metadata": { + "execution": { + "iopub.execute_input": "2022-05-23T23:52:12.216334Z", + "iopub.status.busy": "2022-05-23T23:52:12.215970Z", + "iopub.status.idle": "2022-05-23T23:52:12.265156Z", + "shell.execute_reply": "2022-05-23T23:52:12.264332Z" + }, + "papermill": { + "duration": 0.132469, + "end_time": "2022-05-23T23:52:12.267078", + "exception": false, + "start_time": "2022-05-23T23:52:12.134609", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "cab1dd9c53a64059a45ac29ce03e807f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(Dropdown(description='nm', options=('Age', 'SibSp', 'Parch', 'LogFare', 'Pclass'), value…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def iscore(nm, split):\n", + " col = trn_xs[nm]\n", + " return score(col, trn_y, split)\n", + "\n", + "from ipywidgets import interact\n", + "interact(nm=conts, split=15.5)(iscore);" + ] + }, + { + "cell_type": "markdown", + "id": "815eb4ff", + "metadata": { + "papermill": { + "duration": 0.079567, + "end_time": "2022-05-23T23:52:12.429393", + "exception": false, + "start_time": "2022-05-23T23:52:12.349826", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Try selecting different columns and split points using the dropdown and slider above. What splits can you find that increase the purity of the data?\n", + "\n", + "We can do the same thing for the categorical variables:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "7a01bf2f", + "metadata": { + "execution": { + "iopub.execute_input": "2022-05-23T23:52:12.590541Z", + "iopub.status.busy": "2022-05-23T23:52:12.589936Z", + "iopub.status.idle": "2022-05-23T23:52:12.629855Z", + "shell.execute_reply": "2022-05-23T23:52:12.629042Z" + }, + "papermill": { + "duration": 0.12315, + "end_time": "2022-05-23T23:52:12.632048", + "exception": false, + "start_time": "2022-05-23T23:52:12.508898", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "2194cd3ce3a348ee939d7de2c3c4948c", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(Dropdown(description='nm', options=('Sex', 'Embarked'), value='Sex'), IntSlider(value=2,…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "interact(nm=cats, split=2)(iscore);" + ] + }, + { + "cell_type": "markdown", + "id": "2ca21219", + "metadata": { + "papermill": { + "duration": 0.083889, + "end_time": "2022-05-23T23:52:12.798280", + "exception": false, + "start_time": "2022-05-23T23:52:12.714391", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "That works well enough, but it's rather slow and fiddly. Perhaps we could get the computer to automatically find the best split point for a column for us? For example, to find the best split point for `age` we'd first need to make a list of all the possible split points (i.e all the unique values of that field)...:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "e874abe7", + "metadata": { + "execution": { + "iopub.execute_input": "2022-05-23T23:52:12.966711Z", + "iopub.status.busy": "2022-05-23T23:52:12.966335Z", + "iopub.status.idle": "2022-05-23T23:52:12.974377Z", + "shell.execute_reply": "2022-05-23T23:52:12.973469Z" + }, + "papermill": { + "duration": 0.094016, + "end_time": "2022-05-23T23:52:12.976340", + "exception": false, + "start_time": "2022-05-23T23:52:12.882324", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.42, 0.67, 0.75, 0.83, 0.92, 1. , 2. , 3. , 4. , 5. , 6. , 7. , 8. , 9. , 10. , 11. , 12. ,\n", + " 13. , 14. , 14.5 , 15. , 16. , 17. , 18. , 19. , 20. , 21. , 22. , 23. , 24. , 24.5 , 25. , 26. , 27. ,\n", + " 28. , 28.5 , 29. , 30. , 31. , 32. , 32.5 , 33. , 34. , 34.5 , 35. , 36. , 36.5 , 37. , 38. , 39. , 40. ,\n", + " 40.5 , 41. , 42. , 43. , 44. , 45. , 45.5 , 46. , 47. , 48. , 49. , 50. , 51. , 52. , 53. , 54. , 55. ,\n", + " 55.5 , 56. , 57. , 58. , 59. , 60. , 61. , 62. , 64. , 65. , 70. , 70.5 , 74. , 80. ])" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nm = \"Age\"\n", + "col = trn_xs[nm]\n", + "unq = col.unique()\n", + "unq.sort()\n", + "unq" + ] + }, + { + "cell_type": "markdown", + "id": "89155c82", + "metadata": { + "papermill": { + "duration": 0.082301, + "end_time": "2022-05-23T23:52:13.139089", + "exception": false, + "start_time": "2022-05-23T23:52:13.056788", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "...and find which index of those values is where `score()` is the lowest:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "46695da8", + "metadata": { + "execution": { + "iopub.execute_input": "2022-05-23T23:52:13.304274Z", + "iopub.status.busy": "2022-05-23T23:52:13.303102Z", + "iopub.status.idle": "2022-05-23T23:52:13.380984Z", + "shell.execute_reply": "2022-05-23T23:52:13.380401Z" + }, + "papermill": { + "duration": 0.162462, + "end_time": "2022-05-23T23:52:13.382996", + "exception": false, + "start_time": "2022-05-23T23:52:13.220534", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(6.0)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scores = np.array([score(col, trn_y, o) for o in unq if not np.isnan(o)])\n", + "unq[scores.argmin()]" + ] + }, + { + "cell_type": "markdown", + "id": "a16194f3", + "metadata": { + "papermill": { + "duration": 0.081418, + "end_time": "2022-05-23T23:52:13.547416", + "exception": false, + "start_time": "2022-05-23T23:52:13.465998", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Based on this, it looks like, for instance, that for the `Age` column, `6` is the optimal cutoff according to our training set.\n", + "\n", + "We can write a little function that implements this idea:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "dbfea30b", + "metadata": { + "execution": { + "iopub.execute_input": "2022-05-23T23:52:13.713714Z", + "iopub.status.busy": "2022-05-23T23:52:13.713139Z", + "iopub.status.idle": "2022-05-23T23:52:13.796796Z", + "shell.execute_reply": "2022-05-23T23:52:13.795637Z" + }, + "papermill": { + "duration": 0.169239, + "end_time": "2022-05-23T23:52:13.799146", + "exception": false, + "start_time": "2022-05-23T23:52:13.629907", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(np.float64(6.0), np.float64(0.478316717508991))" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def min_col(df, nm):\n", + " col,y = df[nm],df[dep]\n", + " unq = col.dropna().unique()\n", + " scores = np.array([score(col, y, o) for o in unq if not np.isnan(o)])\n", + " idx = scores.argmin()\n", + " return unq[idx],scores[idx]\n", + "\n", + "min_col(trn_df, \"Age\")" + ] + }, + { + "cell_type": "markdown", + "id": "4982e304", + "metadata": { + "papermill": { + "duration": 0.082152, + "end_time": "2022-05-23T23:52:13.965025", + "exception": false, + "start_time": "2022-05-23T23:52:13.882873", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Let's try all the columns:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "ea0dd416", + "metadata": { + "execution": { + "iopub.execute_input": "2022-05-23T23:52:14.132925Z", + "iopub.status.busy": "2022-05-23T23:52:14.132393Z", + "iopub.status.idle": "2022-05-23T23:52:14.415875Z", + "shell.execute_reply": "2022-05-23T23:52:14.414970Z" + }, + "papermill": { + "duration": 0.368983, + "end_time": "2022-05-23T23:52:14.418270", + "exception": false, + "start_time": "2022-05-23T23:52:14.049287", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Sex': (np.int8(0), np.float64(0.40787530982063946)),\n", + " 'Embarked': (np.int8(0), np.float64(0.47883342573147836)),\n", + " 'Age': (np.float64(6.0), np.float64(0.478316717508991)),\n", + " 'SibSp': (np.int64(4), np.float64(0.4783740258817434)),\n", + " 'Parch': (np.int64(0), np.float64(0.4805296527841601)),\n", + " 'LogFare': (np.float64(2.4390808375825834), np.float64(0.4620823937736597)),\n", + " 'Pclass': (np.int64(2), np.float64(0.46048261885806596))}" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cols = cats+conts\n", + "{o:min_col(trn_df, o) for o in cols}" + ] + }, + { + "cell_type": "markdown", + "id": "b9efce23", + "metadata": { + "papermill": { + "duration": 0.084939, + "end_time": "2022-05-23T23:52:14.586316", + "exception": false, + "start_time": "2022-05-23T23:52:14.501377", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "According to this, `Sex<=0` is the best split we can use.\n", + "\n", + "We've just re-invented the [OneR](https://link.springer.com/article/10.1023/A:1022631118932) classifier (or at least, a minor variant of it), which was found to be one of the most effective classifiers in real-world datasets, compared to the algorithms in use in 1993. Since it's so simple and surprisingly effective, it makes for a great *baseline* -- that is, a starting point that you can use to compare your more sophisticated models to.\n", + "\n", + "We found earlier that out OneR rule had an error of around `0.215`, so we'll keep that in mind as we try out more sophisticated approaches." + ] + }, + { + "cell_type": "markdown", + "id": "188dce42", + "metadata": { + "papermill": { + "duration": 0.08557, + "end_time": "2022-05-23T23:52:14.754376", + "exception": false, + "start_time": "2022-05-23T23:52:14.668806", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Creating a decision tree" + ] + }, + { + "cell_type": "markdown", + "id": "4b46f3c0", + "metadata": { + "papermill": { + "duration": 0.082087, + "end_time": "2022-05-23T23:52:14.936889", + "exception": false, + "start_time": "2022-05-23T23:52:14.854802", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "How can we improve our OneR classifier, which predicts survival based only on `Sex`?\n", + "\n", + "How about we take each of our two groups, `female` and `male`, and create one more binary split for each of them. That is: fine the single best split for females, and the single best split for males. To do this, all we have to do is repeat the previous section's steps, once for males, and once for females.\n", + "\n", + "First, we'll remove `Sex` from the list of possible splits (since we've already used it, and there's only one possible split for that binary column), and create our two groups:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "c763fa2d", + "metadata": { + "execution": { + "iopub.execute_input": "2022-05-23T23:52:15.108874Z", + "iopub.status.busy": "2022-05-23T23:52:15.107962Z", + "iopub.status.idle": "2022-05-23T23:52:15.113883Z", + "shell.execute_reply": "2022-05-23T23:52:15.113202Z" + }, + "papermill": { + "duration": 0.096166, + "end_time": "2022-05-23T23:52:15.116020", + "exception": false, + "start_time": "2022-05-23T23:52:15.019854", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "cols.remove(\"Sex\")\n", + "ismale = trn_df.Sex==1\n", + "males,females = trn_df[ismale],trn_df[~ismale]" + ] + }, + { + "cell_type": "markdown", + "id": "6fe0016a", + "metadata": { + "papermill": { + "duration": 0.083992, + "end_time": "2022-05-23T23:52:15.291509", + "exception": false, + "start_time": "2022-05-23T23:52:15.207517", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Now let's find the single best binary split for males...:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "7e2ddff4", + "metadata": { + "execution": { + "iopub.execute_input": "2022-05-23T23:52:15.458872Z", + "iopub.status.busy": "2022-05-23T23:52:15.458593Z", + "iopub.status.idle": "2022-05-23T23:52:15.681368Z", + "shell.execute_reply": "2022-05-23T23:52:15.680415Z" + }, + "papermill": { + "duration": 0.308595, + "end_time": "2022-05-23T23:52:15.683697", + "exception": false, + "start_time": "2022-05-23T23:52:15.375102", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Embarked': (np.int8(0), np.float64(0.3875581870410906)),\n", + " 'Age': (np.float64(6.0), np.float64(0.3739828371010595)),\n", + " 'SibSp': (np.int64(4), np.float64(0.3875864227586273)),\n", + " 'Parch': (np.int64(0), np.float64(0.3874704821461959)),\n", + " 'LogFare': (np.float64(2.803360380906535), np.float64(0.3804856231758151)),\n", + " 'Pclass': (np.int64(1), np.float64(0.38155442004360934))}" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "{o:min_col(males, o) for o in cols}" + ] + }, + { + "cell_type": "markdown", + "id": "9a2311f4", + "metadata": { + "papermill": { + "duration": 0.082834, + "end_time": "2022-05-23T23:52:15.849096", + "exception": false, + "start_time": "2022-05-23T23:52:15.766262", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "...and for females:" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "9bf7b509", + "metadata": { + "execution": { + "iopub.execute_input": "2022-05-23T23:52:16.018822Z", + "iopub.status.busy": "2022-05-23T23:52:16.017551Z", + "iopub.status.idle": "2022-05-23T23:52:16.192436Z", + "shell.execute_reply": "2022-05-23T23:52:16.191481Z" + }, + "papermill": { + "duration": 0.262921, + "end_time": "2022-05-23T23:52:16.194640", + "exception": false, + "start_time": "2022-05-23T23:52:15.931719", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Embarked': (np.int8(0), np.float64(0.4295252982857327)),\n", + " 'Age': (np.float64(50.0), np.float64(0.4225927658431649)),\n", + " 'SibSp': (np.int64(4), np.float64(0.42319212059713535)),\n", + " 'Parch': (np.int64(3), np.float64(0.4193314500446158)),\n", + " 'LogFare': (np.float64(4.256321678298823), np.float64(0.41350598332911376)),\n", + " 'Pclass': (np.int64(2), np.float64(0.3335388911567601))}" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "{o:min_col(females, o) for o in cols}" + ] + }, + { + "cell_type": "markdown", + "id": "aff7ee6c", + "metadata": { + "papermill": { + "duration": 0.082127, + "end_time": "2022-05-23T23:52:16.360962", + "exception": false, + "start_time": "2022-05-23T23:52:16.278835", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "We can see that the best next binary split for males is `Age<=6`, and for females is `Pclass<=2`.\n", + "\n", + "By adding these rules, we have created a *decision tree*, where our model will first check whether `Sex` is female or male, and depending on the result will then check either the above `Age` or `Pclass` rules, as appropriate. We could then repeat the process, creating new additional rules for each of the four groups we've now created.\n", + "\n", + "Rather than writing that code manually, we can use `DecisionTreeClassifier`, from *sklearn*, which does exactly that for us:" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "ec097da2", + "metadata": { + "execution": { + "iopub.execute_input": "2022-05-23T23:52:16.529664Z", + "iopub.status.busy": "2022-05-23T23:52:16.529398Z", + "iopub.status.idle": "2022-05-23T23:52:16.695471Z", + "shell.execute_reply": "2022-05-23T23:52:16.694497Z" + }, + "papermill": { + "duration": 0.252427, + "end_time": "2022-05-23T23:52:16.697904", + "exception": false, + "start_time": "2022-05-23T23:52:16.445477", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from sklearn.tree import DecisionTreeClassifier, export_graphviz\n", + "\n", + "m = DecisionTreeClassifier(max_leaf_nodes=4).fit(trn_xs, trn_y);" + ] + }, + { + "cell_type": "markdown", + "id": "d99cb625", + "metadata": { + "papermill": { + "duration": 0.083074, + "end_time": "2022-05-23T23:52:16.867207", + "exception": false, + "start_time": "2022-05-23T23:52:16.784133", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "One handy feature or this class is that it provides a function for drawing a tree representing the rules:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "aecbdf2c", + "metadata": { + "execution": { + "iopub.execute_input": "2022-05-23T23:52:17.035987Z", + "iopub.status.busy": "2022-05-23T23:52:17.035668Z", + "iopub.status.idle": "2022-05-23T23:52:17.053247Z", + "shell.execute_reply": "2022-05-23T23:52:17.052446Z" + }, + "papermill": { + "duration": 0.104803, + "end_time": "2022-05-23T23:52:17.055420", + "exception": false, + "start_time": "2022-05-23T23:52:16.950617", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import graphviz\n", + "\n", + "def draw_tree(t, df, size=10, ratio=0.6, precision=2, **kwargs):\n", + " s=export_graphviz(t, out_file=None, feature_names=df.columns, filled=True, rounded=True,\n", + " special_characters=True, rotate=False, precision=precision, **kwargs)\n", + " return graphviz.Source(re.sub('Tree {', f'Tree {{ size={size}; ratio={ratio}', s))" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "1bdc0cef", + "metadata": { + "execution": { + "iopub.execute_input": "2022-05-23T23:52:17.223303Z", + "iopub.status.busy": "2022-05-23T23:52:17.222716Z", + "iopub.status.idle": "2022-05-23T23:52:18.363065Z", + "shell.execute_reply": "2022-05-23T23:52:18.362266Z" + }, + "papermill": { + "duration": 1.226978, + "end_time": "2022-05-23T23:52:18.365861", + "exception": false, + "start_time": "2022-05-23T23:52:17.138883", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Tree\n", + "\n", + "\n", + "\n", + "0\n", + "\n", + "Sex ≤ 0.5\n", + "gini = 0.47\n", + "samples = 668\n", + "value = [415, 253]\n", + "\n", + "\n", + "\n", + "1\n", + "\n", + "Pclass ≤ 2.5\n", + "gini = 0.38\n", + "samples = 229\n", + "value = [59, 170]\n", + "\n", + "\n", + "\n", + "0->1\n", + "\n", + "\n", + "True\n", + "\n", + "\n", + "\n", + "2\n", + "\n", + "Age ≤ 6.5\n", + "gini = 0.31\n", + "samples = 439\n", + "value = [356, 83]\n", + "\n", + "\n", + "\n", + "0->2\n", + "\n", + "\n", + "False\n", + "\n", + "\n", + "\n", + "3\n", + "\n", + "gini = 0.06\n", + "samples = 120\n", + "value = [4, 116]\n", + "\n", + "\n", + "\n", + "1->3\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "4\n", + "\n", + "gini = 0.5\n", + "samples = 109\n", + "value = [55, 54]\n", + "\n", + "\n", + "\n", + "1->4\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "5\n", + "\n", + "gini = 0.41\n", + "samples = 21\n", + "value = [6, 15]\n", + "\n", + "\n", + "\n", + "2->5\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "6\n", + "\n", + "gini = 0.27\n", + "samples = 418\n", + "value = [350, 68]\n", + "\n", + "\n", + "\n", + "2->6\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "draw_tree(m, trn_xs, size=10)" + ] + }, + { + "cell_type": "markdown", + "id": "227b4580", + "metadata": { + "papermill": { + "duration": 0.088127, + "end_time": "2022-05-23T23:52:18.559846", + "exception": false, + "start_time": "2022-05-23T23:52:18.471719", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "We can see that it's found exactly the same splits as we did!\n", + "\n", + "In this picture, the more orange nodes have a lower survival rate, and blue have higher survival. Each node shows how many rows (\"*samples*\") match that set of rules, and shows how many perish or survive (\"*values*\"). There's also something called \"*gini*\". That's another measure of impurity, and it's very similar to the `score()` we created earlier. It's defined as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "2fa894c2", + "metadata": { + "execution": { + "iopub.execute_input": "2022-05-23T23:52:18.752681Z", + "iopub.status.busy": "2022-05-23T23:52:18.751920Z", + "iopub.status.idle": "2022-05-23T23:52:18.758546Z", + "shell.execute_reply": "2022-05-23T23:52:18.757602Z" + }, + "papermill": { + "duration": 0.097829, + "end_time": "2022-05-23T23:52:18.760992", + "exception": false, + "start_time": "2022-05-23T23:52:18.663163", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def gini(cond):\n", + " act = df.loc[cond, dep]\n", + " return 1 - act.mean()**2 - (1-act).mean()**2" + ] + }, + { + "cell_type": "markdown", + "id": "7dfd7abc", + "metadata": { + "papermill": { + "duration": 0.084105, + "end_time": "2022-05-23T23:52:18.931050", + "exception": false, + "start_time": "2022-05-23T23:52:18.846945", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "What this calculates is the probability that, if you pick two rows from a group, you'll get the same `Survived` result each time. If the group is all the same, the probability is `1.0`, and `0.0` if they're all different:" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "4dc6dc0d", + "metadata": { + "execution": { + "iopub.execute_input": "2022-05-23T23:52:19.104861Z", + "iopub.status.busy": "2022-05-23T23:52:19.104134Z", + "iopub.status.idle": "2022-05-23T23:52:19.116417Z", + "shell.execute_reply": "2022-05-23T23:52:19.115262Z" + }, + "papermill": { + "duration": 0.103004, + "end_time": "2022-05-23T23:52:19.118690", + "exception": false, + "start_time": "2022-05-23T23:52:19.015686", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(np.float64(0.3828350034484158), np.float64(0.3064437162277842))" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gini(df.Sex=='female'), gini(df.Sex=='male')" + ] + }, + { + "cell_type": "markdown", + "id": "fd8824b5", + "metadata": { + "papermill": { + "duration": 0.084331, + "end_time": "2022-05-23T23:52:19.288293", + "exception": false, + "start_time": "2022-05-23T23:52:19.203962", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Let's see how this model compares to our OneR version:" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "176028cc", + "metadata": { + "execution": { + "iopub.execute_input": "2022-05-23T23:52:19.457789Z", + "iopub.status.busy": "2022-05-23T23:52:19.457491Z", + "iopub.status.idle": "2022-05-23T23:52:19.466175Z", + "shell.execute_reply": "2022-05-23T23:52:19.465462Z" + }, + "papermill": { + "duration": 0.095697, + "end_time": "2022-05-23T23:52:19.468099", + "exception": false, + "start_time": "2022-05-23T23:52:19.372402", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(0.2242152466367713)" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mean_absolute_error(val_y, m.predict(val_xs))" + ] + }, + { + "cell_type": "markdown", + "id": "a4ca0406", + "metadata": { + "papermill": { + "duration": 0.083894, + "end_time": "2022-05-23T23:52:19.636103", + "exception": false, + "start_time": "2022-05-23T23:52:19.552209", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "It's a tiny bit worse. Since this is such a small dataset (we've only got around 200 rows in our validation set) this small difference isn't really meaningful. Perhaps we'll see better results if we create a bigger tree:" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "b81bfa65", + "metadata": { + "execution": { + "iopub.execute_input": "2022-05-23T23:52:19.807239Z", + "iopub.status.busy": "2022-05-23T23:52:19.806662Z", + "iopub.status.idle": "2022-05-23T23:52:19.855092Z", + "shell.execute_reply": "2022-05-23T23:52:19.854063Z" + }, + "papermill": { + "duration": 0.136732, + "end_time": "2022-05-23T23:52:19.857341", + "exception": false, + "start_time": "2022-05-23T23:52:19.720609", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Tree\n", + "\n", + "\n", + "\n", + "0\n", + "\n", + "Sex ≤ 0.5\n", + "gini = 0.47\n", + "samples = 668\n", + "value = [415, 253]\n", + "\n", + "\n", + "\n", + "1\n", + "\n", + "Pclass ≤ 2.5\n", + "gini = 0.38\n", + "samples = 229\n", + "value = [59, 170]\n", + "\n", + "\n", + "\n", + "0->1\n", + "\n", + "\n", + "True\n", + "\n", + "\n", + "\n", + "8\n", + "\n", + "LogFare ≤ 3.31\n", + "gini = 0.31\n", + "samples = 439\n", + "value = [356, 83]\n", + "\n", + "\n", + "\n", + "0->8\n", + "\n", + "\n", + "False\n", + "\n", + "\n", + "\n", + "2\n", + "\n", + "SibSp ≤ 0.5\n", + "gini = 0.06\n", + "samples = 120\n", + "value = [4, 116]\n", + "\n", + "\n", + "\n", + "1->2\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "5\n", + "\n", + "LogFare ≤ 2.7\n", + "gini = 0.5\n", + "samples = 109\n", + "value = [55, 54]\n", + "\n", + "\n", + "\n", + "1->5\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "3\n", + "\n", + "gini = 0.03\n", + "samples = 67\n", + "value = [1, 66]\n", + "\n", + "\n", + "\n", + "2->3\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "4\n", + "\n", + "gini = 0.11\n", + "samples = 53\n", + "value = [3, 50]\n", + "\n", + "\n", + "\n", + "2->4\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "6\n", + "\n", + "gini = 0.49\n", + "samples = 59\n", + "value = [25, 34]\n", + "\n", + "\n", + "\n", + "5->6\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "7\n", + "\n", + "gini = 0.48\n", + "samples = 50\n", + "value = [30, 20]\n", + "\n", + "\n", + "\n", + "5->7\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "9\n", + "\n", + "Age ≤ 20.5\n", + "gini = 0.24\n", + "samples = 320\n", + "value = [275, 45]\n", + "\n", + "\n", + "\n", + "8->9\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "18\n", + "\n", + "SibSp ≤ 0.5\n", + "gini = 0.43\n", + "samples = 119\n", + "value = [81, 38]\n", + "\n", + "\n", + "\n", + "8->18\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "10\n", + "\n", + "gini = 0.43\n", + "samples = 55\n", + "value = [38, 17]\n", + "\n", + "\n", + "\n", + "9->10\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "11\n", + "\n", + "Age ≤ 32.5\n", + "gini = 0.19\n", + "samples = 265\n", + "value = [237, 28]\n", + "\n", + "\n", + "\n", + "9->11\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "12\n", + "\n", + "Age ≤ 24.75\n", + "gini = 0.22\n", + "samples = 181\n", + "value = [158, 23]\n", + "\n", + "\n", + "\n", + "11->12\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "17\n", + "\n", + "gini = 0.11\n", + "samples = 84\n", + "value = [79, 5]\n", + "\n", + "\n", + "\n", + "11->17\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "13\n", + "\n", + "LogFare ≤ 2.18\n", + "gini = 0.16\n", + "samples = 114\n", + "value = [104, 10]\n", + "\n", + "\n", + "\n", + "12->13\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "16\n", + "\n", + "gini = 0.31\n", + "samples = 67\n", + "value = [54, 13]\n", + "\n", + "\n", + "\n", + "12->16\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "14\n", + "\n", + "gini = 0.21\n", + "samples = 50\n", + "value = [44, 6]\n", + "\n", + "\n", + "\n", + "13->14\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "15\n", + "\n", + "gini = 0.12\n", + "samples = 64\n", + "value = [60, 4]\n", + "\n", + "\n", + "\n", + "13->15\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "19\n", + "\n", + "gini = 0.48\n", + "samples = 60\n", + "value = [36, 24]\n", + "\n", + "\n", + "\n", + "18->19\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "20\n", + "\n", + "gini = 0.36\n", + "samples = 59\n", + "value = [45, 14]\n", + "\n", + "\n", + "\n", + "18->20\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m = DecisionTreeClassifier(min_samples_leaf=50)\n", + "m.fit(trn_xs, trn_y)\n", + "draw_tree(m, trn_xs, size=12)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "5d82961e", + "metadata": { + "execution": { + "iopub.execute_input": "2022-05-23T23:52:20.034875Z", + "iopub.status.busy": "2022-05-23T23:52:20.034551Z", + "iopub.status.idle": "2022-05-23T23:52:20.044976Z", + "shell.execute_reply": "2022-05-23T23:52:20.044315Z" + }, + "papermill": { + "duration": 0.10291, + "end_time": "2022-05-23T23:52:20.046905", + "exception": false, + "start_time": "2022-05-23T23:52:19.943995", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(0.18385650224215247)" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mean_absolute_error(val_y, m.predict(val_xs))" + ] + }, + { + "cell_type": "markdown", + "id": "ccfb9c35", + "metadata": { + "papermill": { + "duration": 0.085949, + "end_time": "2022-05-23T23:52:20.220973", + "exception": false, + "start_time": "2022-05-23T23:52:20.135024", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "It looks like this is an improvement, although again it's a bit hard to tell with small datasets like this. Let's try submitting it to Kaggle:" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "061feaf1", + "metadata": { + "execution": { + "iopub.execute_input": "2022-05-23T23:52:20.396929Z", + "iopub.status.busy": "2022-05-23T23:52:20.396607Z", + "iopub.status.idle": "2022-05-23T23:52:20.418077Z", + "shell.execute_reply": "2022-05-23T23:52:20.417266Z" + }, + "papermill": { + "duration": 0.112139, + "end_time": "2022-05-23T23:52:20.420499", + "exception": false, + "start_time": "2022-05-23T23:52:20.308360", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "tst_df[cats] = tst_df[cats].apply(lambda x: x.cat.codes)\n", + "tst_xs,_ = xs_y(tst_df)\n", + "\n", + "def subm(preds, suff):\n", + " tst_df['Survived'] = preds\n", + " sub_df = tst_df[['PassengerId','Survived']]\n", + " sub_df.to_csv(f'sub-{suff}.csv', index=False)\n", + "\n", + "subm(m.predict(tst_xs), 'tree')" + ] + }, + { + "cell_type": "markdown", + "id": "d66f526e", + "metadata": { + "papermill": { + "duration": 0.085412, + "end_time": "2022-05-23T23:52:20.593822", + "exception": false, + "start_time": "2022-05-23T23:52:20.508410", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "When I submitted this, I got a score of 0.765, which isn't as good as our linear models or most of our neural nets, but it's pretty close to those results.\n", + "\n", + "Hopefully you can now see why we didn't really need to create dummy variables, but instead just converted the labels into numbers using some (potentially arbitary) ordering of categories. For instance, here's how the first few items of `Embarked` are labeled:" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "44582a9f", + "metadata": { + "execution": { + "iopub.execute_input": "2022-05-23T23:52:20.770788Z", + "iopub.status.busy": "2022-05-23T23:52:20.770281Z", + "iopub.status.idle": "2022-05-23T23:52:20.778767Z", + "shell.execute_reply": "2022-05-23T23:52:20.777390Z" + }, + "papermill": { + "duration": 0.102015, + "end_time": "2022-05-23T23:52:20.781428", + "exception": false, + "start_time": "2022-05-23T23:52:20.679413", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 S\n", + "1 C\n", + "2 S\n", + "3 S\n", + "4 S\n", + "Name: Embarked, dtype: category\n", + "Categories (3, object): ['C', 'Q', 'S']" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.Embarked.head()" + ] + }, + { + "cell_type": "markdown", + "id": "dac18715", + "metadata": { + "papermill": { + "duration": 0.087676, + "end_time": "2022-05-23T23:52:20.957609", + "exception": false, + "start_time": "2022-05-23T23:52:20.869933", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "...resulting in these integer codes:" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "70dcfd2b", + "metadata": { + "execution": { + "iopub.execute_input": "2022-05-23T23:52:21.134608Z", + "iopub.status.busy": "2022-05-23T23:52:21.133972Z", + "iopub.status.idle": "2022-05-23T23:52:21.141145Z", + "shell.execute_reply": "2022-05-23T23:52:21.140254Z" + }, + "papermill": { + "duration": 0.09824, + "end_time": "2022-05-23T23:52:21.143476", + "exception": false, + "start_time": "2022-05-23T23:52:21.045236", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 2\n", + "1 0\n", + "2 2\n", + "3 2\n", + "4 2\n", + "dtype: int8" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.Embarked.cat.codes.head()" + ] + }, + { + "cell_type": "markdown", + "id": "3daae03b", + "metadata": { + "papermill": { + "duration": 0.08905, + "end_time": "2022-05-23T23:52:21.320942", + "exception": false, + "start_time": "2022-05-23T23:52:21.231892", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "So let's say we wanted to split into \"C\" in one group, vs \"Q\" or \"S\" in the other group. Then we just have to split on codes `<=0` (since `C` is mapped to category `0`). Note that if we wanted to split into \"Q\" in one group, we'd need to use two binary splits, first to separate \"C\" from \"Q\" and \"S\", and then a second split to separate \"Q\" from \"S\". For this reason, sometimes it can still be helpful to use dummy variables for categorical variables with few levels (like this one).\n", + "\n", + "In practice, I often use dummy variables for <4 levels, and numeric codes for >=4 levels." + ] + }, + { + "cell_type": "markdown", + "id": "2b45887e", + "metadata": { + "papermill": { + "duration": 0.086779, + "end_time": "2022-05-23T23:52:21.494320", + "exception": false, + "start_time": "2022-05-23T23:52:21.407541", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## The random forest" + ] + }, + { + "cell_type": "markdown", + "id": "1976d0fb", + "metadata": { + "papermill": { + "duration": 0.086362, + "end_time": "2022-05-23T23:52:21.667495", + "exception": false, + "start_time": "2022-05-23T23:52:21.581133", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "We can't make the decision tree much bigger than the example above, since some leaf nodes already have only 50 rows in them. That's not a lot of data to make a prediction.\n", + "\n", + "So how could we use bigger trees? One big insight came from Leo Breiman: what if we create lots of bigger trees, and take the average of their predictions? Taking the average prediction of a bunch of models in this way is known as [bagging](https://link.springer.com/article/10.1007/BF00058655).\n", + "\n", + "The idea is that we want each model's predictions in the averaged ensemble to be uncorrelated with each other model. That way, if we average the predictions, the average will be equal to the true target value -- that's because the average of lots of uncorrelated random errors is zero. That's quite an amazing insight!\n", + "\n", + "One way we can create a bunch of uncorrelated models is to train each of them on a different random subset of the data. Here's how we can create a tree on a random subset of the data:" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "08aa92ef", + "metadata": { + "execution": { + "iopub.execute_input": "2022-05-23T23:52:21.850413Z", + "iopub.status.busy": "2022-05-23T23:52:21.849900Z", + "iopub.status.idle": "2022-05-23T23:52:21.854882Z", + "shell.execute_reply": "2022-05-23T23:52:21.854244Z" + }, + "papermill": { + "duration": 0.096075, + "end_time": "2022-05-23T23:52:21.856770", + "exception": false, + "start_time": "2022-05-23T23:52:21.760695", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def get_tree(prop=0.75):\n", + " n = len(trn_y)\n", + " idxs = random.choice(n, int(n*prop))\n", + " return DecisionTreeClassifier(min_samples_leaf=5).fit(trn_xs.iloc[idxs], trn_y.iloc[idxs])" + ] + }, + { + "cell_type": "markdown", + "id": "2325174f", + "metadata": { + "papermill": { + "duration": 0.087327, + "end_time": "2022-05-23T23:52:22.032957", + "exception": false, + "start_time": "2022-05-23T23:52:21.945630", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Now we can create as many trees as we want:" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "0fcc811f", + "metadata": { + "execution": { + "iopub.execute_input": "2022-05-23T23:52:22.211835Z", + "iopub.status.busy": "2022-05-23T23:52:22.211276Z", + "iopub.status.idle": "2022-05-23T23:52:22.483121Z", + "shell.execute_reply": "2022-05-23T23:52:22.482418Z" + }, + "papermill": { + "duration": 0.364166, + "end_time": "2022-05-23T23:52:22.485456", + "exception": false, + "start_time": "2022-05-23T23:52:22.121290", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "trees = [get_tree() for t in range(100)]" + ] + }, + { + "cell_type": "markdown", + "id": "4171fcfb", + "metadata": { + "papermill": { + "duration": 0.086759, + "end_time": "2022-05-23T23:52:22.659496", + "exception": false, + "start_time": "2022-05-23T23:52:22.572737", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Our prediction will be the average of these trees' predictions:" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "69a26910", + "metadata": { + "execution": { + "iopub.execute_input": "2022-05-23T23:52:22.835922Z", + "iopub.status.busy": "2022-05-23T23:52:22.835355Z", + "iopub.status.idle": "2022-05-23T23:52:22.976514Z", + "shell.execute_reply": "2022-05-23T23:52:22.975799Z" + }, + "papermill": { + "duration": 0.231614, + "end_time": "2022-05-23T23:52:22.978449", + "exception": false, + "start_time": "2022-05-23T23:52:22.746835", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(0.22748878923766816)" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_probs = [t.predict(val_xs) for t in trees]\n", + "avg_probs = np.stack(all_probs).mean(0)\n", + "\n", + "mean_absolute_error(val_y, avg_probs)" + ] + }, + { + "cell_type": "markdown", + "id": "2ab6c8b4", + "metadata": { + "papermill": { + "duration": 0.086497, + "end_time": "2022-05-23T23:52:23.151915", + "exception": false, + "start_time": "2022-05-23T23:52:23.065418", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "This is nearly identical to what `sklearn`'s `RandomForestClassifier` does. The main extra piece in a \"real\" random forest is that as well as choosing a random sample of data for each tree, it also picks a random subset of columns for each split. Here's how we repeat the above process with a random forest:" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "3ec0ada5", + "metadata": { + "execution": { + "iopub.execute_input": "2022-05-23T23:52:23.328862Z", + "iopub.status.busy": "2022-05-23T23:52:23.328403Z", + "iopub.status.idle": "2022-05-23T23:52:23.622038Z", + "shell.execute_reply": "2022-05-23T23:52:23.620968Z" + }, + "papermill": { + "duration": 0.385511, + "end_time": "2022-05-23T23:52:23.624308", + "exception": false, + "start_time": "2022-05-23T23:52:23.238797", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(0.18834080717488788)" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "rf = RandomForestClassifier(100, min_samples_leaf=5)\n", + "rf.fit(trn_xs, trn_y);\n", + "mean_absolute_error(val_y, rf.predict(val_xs))" + ] + }, + { + "cell_type": "markdown", + "id": "2495c6d7", + "metadata": { + "papermill": { + "duration": 0.086944, + "end_time": "2022-05-23T23:52:23.799357", + "exception": false, + "start_time": "2022-05-23T23:52:23.712413", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "We can submit that to Kaggle too:" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "83c6187f", + "metadata": { + "execution": { + "iopub.execute_input": "2022-05-23T23:52:23.979091Z", + "iopub.status.busy": "2022-05-23T23:52:23.978811Z", + "iopub.status.idle": "2022-05-23T23:52:24.005874Z", + "shell.execute_reply": "2022-05-23T23:52:24.005221Z" + }, + "papermill": { + "duration": 0.120066, + "end_time": "2022-05-23T23:52:24.008236", + "exception": false, + "start_time": "2022-05-23T23:52:23.888170", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "subm(rf.predict(tst_xs), 'rf')" + ] + }, + { + "cell_type": "markdown", + "id": "1afe6ee9", + "metadata": { + "papermill": { + "duration": 0.086793, + "end_time": "2022-05-23T23:52:24.182155", + "exception": false, + "start_time": "2022-05-23T23:52:24.095362", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "I found that gave nearly an identical result as our single tree (which, in turn, was slightly lower than our linear and neural net models in the previous notebook)." + ] + }, + { + "cell_type": "markdown", + "id": "86e64d72", + "metadata": { + "papermill": { + "duration": 0.087065, + "end_time": "2022-05-23T23:52:24.357493", + "exception": false, + "start_time": "2022-05-23T23:52:24.270428", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "One particularly nice feature of random forests is they can tell us which independent variables were the most important in the model, using `feature_importances_`:" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "3af99972", + "metadata": { + "execution": { + "iopub.execute_input": "2022-05-23T23:52:24.533906Z", + "iopub.status.busy": "2022-05-23T23:52:24.533390Z", + "iopub.status.idle": "2022-05-23T23:52:24.766416Z", + "shell.execute_reply": "2022-05-23T23:52:24.765411Z" + }, + "papermill": { + "duration": 0.324146, + "end_time": "2022-05-23T23:52:24.768794", + "exception": false, + "start_time": "2022-05-23T23:52:24.444648", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pd.DataFrame(dict(cols=trn_xs.columns, imp=m.feature_importances_)).plot('cols', 'imp', 'barh');" + ] + }, + { + "cell_type": "markdown", + "id": "288344e4", + "metadata": { + "papermill": { + "duration": 0.088243, + "end_time": "2022-05-23T23:52:24.947669", + "exception": false, + "start_time": "2022-05-23T23:52:24.859426", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "We can see that `Sex` is by far the most important predictor, with `Pclass` a distant second, and `LogFare` and `Age` behind that. In datasets with many columns, I generally recommend creating a feature importance plot as soon as possible, in order to find which columns are worth studying more closely. (Note also that we didn't really need to take the `log()` of `Fare`, since random forests only care about order, and `log()` doesn't change the order -- we only did it to make our graphs earlier easier to read.)\n", + "\n", + "For details about deriving and understanding feature importances, and the many other important diagnostic tools provided by random forests, take a look at [chapter 8](https://github.com/fastai/fastbook/blob/master/08_collab.ipynb) of [our book](https://www.amazon.com/Deep-Learning-Coders-fastai-PyTorch/dp/1492045527)." + ] + }, + { + "cell_type": "markdown", + "id": "2721efdd", + "metadata": { + "papermill": { + "duration": 0.089775, + "end_time": "2022-05-23T23:52:25.126465", + "exception": false, + "start_time": "2022-05-23T23:52:25.036690", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Conclusion" + ] + }, + { + "cell_type": "markdown", + "id": "3c6d7087", + "metadata": { + "papermill": { + "duration": 0.090308, + "end_time": "2022-05-23T23:52:25.305738", + "exception": false, + "start_time": "2022-05-23T23:52:25.215430", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "So what can we take away from all this?\n", + "\n", + "I think the first thing I'd note from this is that, clearly, more complex models aren't always better. Our \"OneR\" model, consisting of a single binary split, was nearly as good as our more complex models. Perhaps in practice a simple model like this might be much easier to use, and could be worth considering. Our random forest wasn't an improvement on the single decision tree at all.\n", + "\n", + "So we should always be careful to benchmark simple models, as see if they're good enough for our needs. In practice, you will often find that simple models will have trouble providing adequate accuracy for more complex tasks, such as recommendation systems, NLP, computer vision, or multivariate time series. But there's no need to guess -- it's so easy to try a few different models, there's no reason not to give the simpler ones a go too!\n", + "\n", + "Another thing I think we can take away is that random forests aren't actually that complicated at all. We were able to implement the key features of them in a notebook quite quickly. And they aren't sensitive to issues like normalization, interactions, or non-linear transformations, which make them extremely easy to work with, and hard to mess up!" + ] + }, + { + "cell_type": "markdown", + "id": "7b4df4a4", + "metadata": { + "papermill": { + "duration": 0.087358, + "end_time": "2022-05-23T23:52:25.482959", + "exception": false, + "start_time": "2022-05-23T23:52:25.395601", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "If you found this notebook useful, please remember to click the little up-arrow at the top to upvote it, since I like to know when people have found my work useful, and it helps others find it too. (BTW, be sure you're looking at my [original notebook here](https://www.kaggle.com/jhoward/how-random-forests-work) when you do that, and are not on your own copy of it, otherwise your upvote won't get counted!) And if you have any questions or comments, please pop them below -- I read every comment I receive!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4fecbc09", + "metadata": { + "papermill": { + "duration": 0.088049, + "end_time": "2022-05-23T23:52:25.659455", + "exception": false, + "start_time": "2022-05-23T23:52:25.571406", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + }, + "papermill": { + "default_parameters": {}, + "duration": 33.243357, + "end_time": "2022-05-23T23:52:26.679734", + "environment_variables": {}, + "exception": null, + "input_path": 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You should refer to the Chapters 3 \n", + "and 4 of the **\"Introduction to Data Mining\"** book to understand some of the concepts introduced in this tutorial. \n", + "This notebook can be downloaded from http://www.cse.msu.edu/~ptan/dmbook/tutorials/tutorial6/tutorial6.ipynb.\n", + "\n", + "The dataset can be downloaded from [here](http://www.cse.msu.edu/~ptan/dmbook/tutorials/tutorial6/vertebrate.csv).\n", + "\n", + "Classification is the task of predicting a nominal-valued attribute (known as class label) based on the values of other\n", + "attributes (known as predictor variables). The goals for this tutorial are as follows:\n", + "1. To provide examples of using different classification techniques from the scikit-learn library package.\n", + "2. To demonstrate the problem of model overfitting.\n", + "\n", + "Read the step-by-step instructions below carefully. To execute the code, click on the corresponding cell and press the SHIFT-ENTER keys simultaneously.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6.1 Vertebrate Dataset\n", + "\n", + "We use a variation of the vertebrate data described in Example 3.1 of Chapter 3. Each vertebrate is classified into one of 5 categories: mammals, reptiles, birds, fishes, and amphibians, based on a set of explanatory attributes (predictor variables). Except for \"name\", the rest of the attributes have been converted into a *one hot encoding* binary representation. To illustrate this, we will first load the data into a Pandas DataFrame object and display its content." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    NameWarm-bloodedGives BirthAquatic CreatureAerial CreatureHas LegsHibernatesClass
    0human110010mammals
    1python000001reptiles
    2salmon001000fishes
    3whale111000mammals
    4frog001011amphibians
    5komodo000010reptiles
    6bat110111mammals
    7pigeon100110birds
    8cat110010mammals
    9leopard shark011000fishes
    10turtle001010reptiles
    11penguin101010birds
    12porcupine110011mammals
    13eel001000fishes
    14salamander001011amphibians
    \n", + "
    " + ], + "text/plain": [ + " Name Warm-blooded Gives Birth Aquatic Creature \\\n", + "0 human 1 1 0 \n", + "1 python 0 0 0 \n", + "2 salmon 0 0 1 \n", + "3 whale 1 1 1 \n", + "4 frog 0 0 1 \n", + "5 komodo 0 0 0 \n", + "6 bat 1 1 0 \n", + "7 pigeon 1 0 0 \n", + "8 cat 1 1 0 \n", + "9 leopard shark 0 1 1 \n", + "10 turtle 0 0 1 \n", + "11 penguin 1 0 1 \n", + "12 porcupine 1 1 0 \n", + "13 eel 0 0 1 \n", + "14 salamander 0 0 1 \n", + "\n", + " Aerial Creature Has Legs Hibernates Class \n", + "0 0 1 0 mammals \n", + "1 0 0 1 reptiles \n", + "2 0 0 0 fishes \n", + "3 0 0 0 mammals \n", + "4 0 1 1 amphibians \n", + "5 0 1 0 reptiles \n", + "6 1 1 1 mammals \n", + "7 1 1 0 birds \n", + "8 0 1 0 mammals \n", + "9 0 0 0 fishes \n", + "10 0 1 0 reptiles \n", + "11 0 1 0 birds \n", + "12 0 1 1 mammals \n", + "13 0 0 0 fishes \n", + "14 0 1 1 amphibians " + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "data = pd.read_csv('data/vertebrate.csv',header='infer')\n", + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Given the limited number of training examples, suppose we convert the problem into a binary classification task (mammals versus non-mammals). We can do so by replacing the class labels of the instances to *non-mammals* except for those that belong to the *mammals* class." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    NameWarm-bloodedGives BirthAquatic CreatureAerial CreatureHas LegsHibernatesClass
    0human110010mammals
    1python000001non-mammals
    2salmon001000non-mammals
    3whale111000mammals
    4frog001011non-mammals
    5komodo000010non-mammals
    6bat110111mammals
    7pigeon100110non-mammals
    8cat110010mammals
    9leopard shark011000non-mammals
    10turtle001010non-mammals
    11penguin101010non-mammals
    12porcupine110011mammals
    13eel001000non-mammals
    14salamander001011non-mammals
    \n", + "
    " + ], + "text/plain": [ + " Name Warm-blooded Gives Birth Aquatic Creature \\\n", + "0 human 1 1 0 \n", + "1 python 0 0 0 \n", + "2 salmon 0 0 1 \n", + "3 whale 1 1 1 \n", + "4 frog 0 0 1 \n", + "5 komodo 0 0 0 \n", + "6 bat 1 1 0 \n", + "7 pigeon 1 0 0 \n", + "8 cat 1 1 0 \n", + "9 leopard shark 0 1 1 \n", + "10 turtle 0 0 1 \n", + "11 penguin 1 0 1 \n", + "12 porcupine 1 1 0 \n", + "13 eel 0 0 1 \n", + "14 salamander 0 0 1 \n", + "\n", + " Aerial Creature Has Legs Hibernates Class \n", + "0 0 1 0 mammals \n", + "1 0 0 1 non-mammals \n", + "2 0 0 0 non-mammals \n", + "3 0 0 0 mammals \n", + "4 0 1 1 non-mammals \n", + "5 0 1 0 non-mammals \n", + "6 1 1 1 mammals \n", + "7 1 1 0 non-mammals \n", + "8 0 1 0 mammals \n", + "9 0 0 0 non-mammals \n", + "10 0 1 0 non-mammals \n", + "11 0 1 0 non-mammals \n", + "12 0 1 1 mammals \n", + "13 0 0 0 non-mammals \n", + "14 0 1 1 non-mammals " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['Class'] = data['Class'].replace(['fishes','birds','amphibians','reptiles'],'non-mammals')\n", + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can apply Pandas cross-tabulation to examine the relationship between the Warm-blooded and Gives Birth attributes with respect to the class. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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    " + ], + "text/plain": [ + "Class mammals non-mammals\n", + "Warm-blooded Gives Birth \n", + "0 0 0 7\n", + " 1 0 1\n", + "1 0 0 2\n", + " 1 5 0" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.crosstab([data['Warm-blooded'],data['Gives Birth']],data['Class'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The results above show that it is possible to distinguish mammals from non-mammals using these two attributes alone since each combination of their attribute values would yield only instances that belong to the same class. For example, mammals can be identified as warm-blooded vertebrates that give birth to their young. Such a relationship can also be derived using a decision tree classifier, as shown by the example given in the next subsection." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.2 Decision Tree Classifier\n", + "\n", + "In this section, we apply a decision tree classifier to the vertebrate dataset described in the previous subsection." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn import tree\n", + "\n", + "Y = data['Class']\n", + "X = data.drop(['Name','Class'],axis=1)\n", + "\n", + "clf = tree.DecisionTreeClassifier(criterion='entropy',max_depth=3)\n", + "clf = clf.fit(X, Y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The preceding commands will extract the predictor (X) and target class (Y) attributes from the vertebrate dataset and create a decision tree classifier object using entropy as its impurity measure for splitting criterion. The decision tree class in Python sklearn library also supports using 'gini' as impurity measure. The classifier above is also constrained to generate trees with a maximum depth equals to 3. Next, the classifier is trained on the labeled data using the fit() function. \n", + "\n", + "We can plot the resulting decision tree obtained after training the classifier. To do this, you must first install both graphviz (http://www.graphviz.org) and its Python interface called pydotplus (http://pydotplus.readthedocs.io/)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pydotplus \n", + "from IPython.display import Image\n", + "\n", + "dot_data = tree.export_graphviz(clf, feature_names=X.columns, class_names=['mammals','non-mammals'], filled=True, \n", + " out_file=None) \n", + "graph = pydotplus.graph_from_dot_data(dot_data) \n", + "Image(graph.create_png())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, suppose we apply the decision tree to classify the following test examples." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    NameWarm-bloodedGives BirthAquatic CreatureAerial CreatureHas LegsHibernatesClass
    0gila monster000011non-mammals
    1platypus100011mammals
    2owl100110non-mammals
    3dolphin111000mammals
    \n", + "
    " + ], + "text/plain": [ + " Name Warm-blooded Gives Birth Aquatic Creature Aerial Creature \\\n", + "0 gila monster 0 0 0 0 \n", + "1 platypus 1 0 0 0 \n", + "2 owl 1 0 0 1 \n", + "3 dolphin 1 1 1 0 \n", + "\n", + " Has Legs Hibernates Class \n", + "0 1 1 non-mammals \n", + "1 1 1 mammals \n", + "2 1 0 non-mammals \n", + "3 0 0 mammals " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "testData = [['gila monster',0,0,0,0,1,1,'non-mammals'],\n", + " ['platypus',1,0,0,0,1,1,'mammals'],\n", + " ['owl',1,0,0,1,1,0,'non-mammals'],\n", + " ['dolphin',1,1,1,0,0,0,'mammals']]\n", + "testData = pd.DataFrame(testData, columns=data.columns)\n", + "testData" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We first extract the predictor and target class attributes from the test data and then apply the decision tree classifier to predict their classes." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    NamePredicted Class
    0gila monsternon-mammals
    1platypusnon-mammals
    2owlnon-mammals
    3dolphinmammals
    \n", + "
    " + ], + "text/plain": [ + " Name Predicted Class\n", + "0 gila monster non-mammals\n", + "1 platypus non-mammals\n", + "2 owl non-mammals\n", + "3 dolphin mammals" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "testY = testData['Class']\n", + "testX = testData.drop(['Name','Class'],axis=1)\n", + "\n", + "predY = clf.predict(testX)\n", + "predictions = pd.concat([testData['Name'],pd.Series(predY,name='Predicted Class')], axis=1)\n", + "predictions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Except for platypus, which is an egg-laying mammal, the classifier correctly predicts the class label of the test examples. We can calculate the accuracy of the classifier on the test data as shown by the example given below." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy on test data is 0.75\n" + ] + } + ], + "source": [ + "from sklearn.metrics import accuracy_score\n", + "\n", + "print('Accuracy on test data is %.2f' % (accuracy_score(testY, predY)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.3 Model Overfitting\n", + "\n", + "To illustrate the problem of model overfitting, we consider a two-dimensional dataset containing 1500 labeled instances, each of which is assigned to one of two classes, 0 or 1. Instances from each class are generated as follows:\n", + "1. Instances from class 1 are generated from a mixture of 3 Gaussian distributions, centered at [6,14], [10,6], and [14 14], respectively. \n", + "2. Instances from class 0 are generated from a uniform distribution in a square region, whose sides have a length equals to 20.\n", + "\n", + "For simplicity, both classes have equal number of labeled instances. The code for generating and plotting the data is shown below. All instances from class 1 are shown in red while those from class 0 are shown in black." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from numpy.random import random\n", + "\n", + "%matplotlib inline\n", + "\n", + "N = 1500\n", + "\n", + "mean1 = [6, 14]\n", + "mean2 = [10, 6]\n", + "mean3 = [14, 14]\n", + "cov = [[3.5, 0], [0, 3.5]] # diagonal covariance\n", + "\n", + "np.random.seed(50)\n", + "X = np.random.multivariate_normal(mean1, cov, int(N/6))\n", + "X = np.concatenate((X, np.random.multivariate_normal(mean2, cov, int(N/6))))\n", + "X = np.concatenate((X, np.random.multivariate_normal(mean3, cov, int(N/6))))\n", + "X = np.concatenate((X, 20*np.random.rand(int(N/2),2)))\n", + "Y = np.concatenate((np.ones(int(N/2)),np.zeros(int(N/2))))\n", + "\n", + "plt.plot(X[:int(N/2),0],X[:int(N/2),1],'r+',X[int(N/2):,0],X[int(N/2):,1],'k.',ms=4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this example, we reserve 80% of the labeled data for training and the remaining 20% for testing. We then fit decision trees of different maximum depths (from 2 to 50) to the training set and plot their respective accuracies when applied to the training and test sets. " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Accuracy')" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#########################################\n", + "# Training and Test set creation\n", + "#########################################\n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.8, random_state=1)\n", + "\n", + "from sklearn import tree\n", + "from sklearn.metrics import accuracy_score\n", + "\n", + "#########################################\n", + "# Model fitting and evaluation\n", + "#########################################\n", + "\n", + "maxdepths = [2,3,4,5,6,7,8,9,10,15,20,25,30,35,40,45,50]\n", + "\n", + "trainAcc = np.zeros(len(maxdepths))\n", + "testAcc = np.zeros(len(maxdepths))\n", + "\n", + "index = 0\n", + "for depth in maxdepths:\n", + " clf = tree.DecisionTreeClassifier(max_depth=depth)\n", + " clf = clf.fit(X_train, Y_train)\n", + " Y_predTrain = clf.predict(X_train)\n", + " Y_predTest = clf.predict(X_test)\n", + " trainAcc[index] = accuracy_score(Y_train, Y_predTrain)\n", + " testAcc[index] = accuracy_score(Y_test, Y_predTest)\n", + " index += 1\n", + " \n", + "#########################################\n", + "# Plot of training and test accuracies\n", + "#########################################\n", + " \n", + "plt.plot(maxdepths,trainAcc,'ro-',maxdepths,testAcc,'bv--')\n", + "plt.legend(['Training Accuracy','Test Accuracy'])\n", + "plt.xlabel('Max depth')\n", + "plt.ylabel('Accuracy')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The plot above shows that training accuracy will continue to improve as the maximum depth of the tree increases (i.e., as the model becomes more complex). However, the test accuracy initially improves up to a maximum depth of 5, before it gradually decreases due to model overfitting." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.4 Alternative Classification Techniques\n", + "\n", + "Besides decision tree classifier, the Python sklearn library also supports other classification techniques. In this section, we provide examples to illustrate how to apply the k-nearest neighbor classifier, linear classifiers (logistic regression and support vector machine), as well as ensemble methods (boosting, bagging, and random forest) to the 2-dimensional data given in the previous section." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.4.1 K-Nearest neighbor classifier\n", + "\n", + "In this approach, the class label of a test instance is predicted based on the majority class of its *k* closest training instances. The number of nearest neighbors, *k*, is a hyperparameter that must be provided by the user, along with the distance metric. By default, we can use Euclidean distance (which is equivalent to Minkowski distance with an exponent factor equals to p=2):\n", + "\n", + "\\begin{equation*}\n", + "\\textrm{Minkowski distance}(x,y) = \\bigg[\\sum_{i=1}^N |x_i-y_i|^p \\bigg]^{\\frac{1}{p}}\n", + "\\end{equation*}" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Accuracy')" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.neighbors import KNeighborsClassifier\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "numNeighbors = [1, 5, 10, 15, 20, 25, 30]\n", + "trainAcc = []\n", + "testAcc = []\n", + "\n", + "for k in numNeighbors:\n", + " clf = KNeighborsClassifier(n_neighbors=k, metric='minkowski', p=2)\n", + " clf.fit(X_train, Y_train)\n", + " Y_predTrain = clf.predict(X_train)\n", + " Y_predTest = clf.predict(X_test)\n", + " trainAcc.append(accuracy_score(Y_train, Y_predTrain))\n", + " testAcc.append(accuracy_score(Y_test, Y_predTest))\n", + "\n", + "plt.plot(numNeighbors, trainAcc, 'ro-', numNeighbors, testAcc,'bv--')\n", + "plt.legend(['Training Accuracy','Test Accuracy'])\n", + "plt.xlabel('Number of neighbors')\n", + "plt.ylabel('Accuracy')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.4.2 Linear Classifiers\n", + "\n", + "Linear classifiers such as logistic regression and support vector machine (SVM) constructs a linear separating hyperplane to distinguish instances from different classes. \n", + "\n", + "For logistic regression, the model can be described by the following equation:\n", + "\\begin{equation*}\n", + "P(y=1|x) = \\frac{1}{1 + \\exp^{-w^Tx - b}} = \\sigma(w^Tx + b)\n", + "\\end{equation*}\n", + "The model parameters (w,b) are estimated by optimizing the following regularized negative log-likelihood function:\n", + "\\begin{equation*}\n", + "(w^*,b^*) = \\arg\\min_{w,b} - \\sum_{i=1}^N y_i \\log\\bigg[\\sigma(w^Tx_i + b)\\bigg] + (1-y_i) \\log\\bigg[\\sigma(-w^Tx_i - b)\\bigg] + \\frac{1}{C} \\Omega([w,b])\n", + "\\end{equation*}\n", + "where $C$ is a hyperparameter that controls the inverse of model complexity (smaller values imply stronger regularization) while $\\Omega(\\cdot)$ is the regularization term, which by default, is assumed to be an $l_2$-norm in sklearn.\n", + "\n", + "For support vector machine, the model parameters $(w^*,b^*)$ are estimated by solving the following constrained optimization problem:\n", + "\\begin{eqnarray*}\n", + "&&\\min_{w^*,b^*,\\{\\xi_i\\}} \\frac{\\|w\\|^2}{2} + \\frac{1}{C} \\sum_i \\xi_i \\\\\n", + "\\textrm{s.t.} && \\forall i: y_i\\bigg[w^T \\phi(x_i) + b\\bigg] \\ge 1 - \\xi_i, \\ \\ \\xi_i \\ge 0 \n", + "\\end{eqnarray*}" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Accuracy')" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn import linear_model\n", + "from sklearn.svm import SVC\n", + "\n", + "C = [0.01, 0.1, 0.2, 0.5, 0.8, 1, 5, 10, 20, 50]\n", + "LRtrainAcc = []\n", + "LRtestAcc = []\n", + "SVMtrainAcc = []\n", + "SVMtestAcc = []\n", + "\n", + "for param in C:\n", + " clf = linear_model.LogisticRegression(C=param)\n", + " clf.fit(X_train, Y_train)\n", + " Y_predTrain = clf.predict(X_train)\n", + " Y_predTest = clf.predict(X_test)\n", + " LRtrainAcc.append(accuracy_score(Y_train, Y_predTrain))\n", + " LRtestAcc.append(accuracy_score(Y_test, Y_predTest))\n", + "\n", + " clf = SVC(C=param,kernel='linear')\n", + " clf.fit(X_train, Y_train)\n", + " Y_predTrain = clf.predict(X_train)\n", + " Y_predTest = clf.predict(X_test)\n", + " SVMtrainAcc.append(accuracy_score(Y_train, Y_predTrain))\n", + " SVMtestAcc.append(accuracy_score(Y_test, Y_predTest))\n", + "\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12,6))\n", + "ax1.plot(C, LRtrainAcc, 'ro-', C, LRtestAcc,'bv--')\n", + "ax1.legend(['Training Accuracy','Test Accuracy'])\n", + "ax1.set_xlabel('C')\n", + "ax1.set_xscale('log')\n", + "ax1.set_ylabel('Accuracy')\n", + "\n", + "ax2.plot(C, SVMtrainAcc, 'ro-', C, SVMtestAcc,'bv--')\n", + "ax2.legend(['Training Accuracy','Test Accuracy'])\n", + "ax2.set_xlabel('C')\n", + "ax2.set_xscale('log')\n", + "ax2.set_ylabel('Accuracy')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that linear classifiers perform poorly on the data since the true decision boundaries between classes are nonlinear for the given 2-dimensional dataset." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.4.3 Nonlinear Support Vector Machine\n", + "\n", + "The code below shows an example of using nonlinear support vector machine with a Gaussian radial basis function kernel to fit the 2-dimensional dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Accuracy')" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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cHIzp06cDAMqWLQsA6NSpEzQaTdrtzCxbtgzVqlXDe++9h23btuHy5ctG96ekpGDs2LEICgqCu7s7KlasiPnz56fd/88//6Bdu3bw9vZGkSJF0LBhQ5w9exaANLd7++23jZ6vY8eO6N+/f9rtsmXLYurUqejbty+8vb0xZMgQALI557PPPgtPT0+UL18eEydOxOPHj42e67fffkPdunXh4eEBX1/ftI05Te0+DgAhISGYOHFilj8PIqJsbd6c/ZC+ViubWjKxyTGO3OTQ/fuZ3+fqCnh4SAuBqVOBVq1Mn6fTARMmGLcayOx5vbzyHmt6ixcvxqRJkzB79mzUqlULBw8exODBg+Hl5YV+/fph1qxZWL16NX755ReUKVMGly9fTktK9u3bh5IlS2LBggVo1apV2o7amZk/fz569+4NHx8ftG7dGgsXLjRKAPr27Yvdu3dj1qxZqFmzJs6fP4+bN28CAK5evYpGjRqhSZMm2Lx5M7y9vbFz5048efIkV9/vZ599hkmTJmHy5Mlpx4oUKYKFCxeiVKlSOHr0KAYPHowiRYpgzJgxAIA1a9agU6dOGD9+PH744QekpqZi7dq1AICBAwdiypQp2LdvH+rWrQsAOHjwII4cOYKYmJhcxUZEBECSlT//BJYuBZYsydlj0o3QUw4oTiYxMVEBoCQmJma47+HDh8rx48eVhw8fZrhPxgxNX9q0MZyn0ymKRpP5uY0aGT+vr6/p8/JqwYIFio+PT9rtChUqKEuWLDE6Z+rUqUp4eLiiKIoyYsQIpWnTpopOpzP5fACUlStXZvu6p0+fVgoWLKjcuHFDURRFWblypVKuXLm05z116pQCQPnjjz9MPn7cuHFKuXLllNTUVJP3N27cWBk5cqTRsQ4dOij9+vVLux0cHKx07Ngx21hnzJihhIaGpt0ODw9XevXqlen5rVu3VoYOHZp2e8SIEUqTJk2yfR1ryOp3lohsiFarKDt3KsqIEYri75/1h4qpy5Ytan8Hqsvq8/tpnJYyM40m62knazaIvH//Ps6ePYvXXnsNhQsXTrv873//S5vu6d+/Pw4dOoTKlSvjrbfewsaNG/P0WlFRUWjZsiV8/xs2bdOmDRITE7F582YAwKFDh+Dq6orGjRubfPyhQ4fQsGFDFCxYME+vr1enTp0Mx6Kjo9GgQQP4+/ujcOHCmDBhAi5dumT02s2aNcv0OQcPHoyff/4Zjx49QmpqKpYsWYKBAwfmK04icgKKAhw8CIwdC5QrBzRoAHz1FRAfL0u4Bw8GNm6UVVGZfThoNFLP0LChdWO3c5yWyqF79zK/7+nZmps3gcaNgcOHZfTR1RWoWVNGIZ8+N33/JXO791/Q8+bNQ7169Z6KWQKpXbs2zp8/j3Xr1mHTpk3o1q0bIiIicrUjtVarxaJFixAfH48CBQoYHY+KikKzZs1QKJuK/uzud3FxybCh5NN1MwDg9dR83u7du9GrVy9MmTIFLVu2hI+PD5YuXYrPP/88x6/dvn17uLu7Y+XKlXBzc8Pjx4/RtWvXLB9DRE7sxAmZclq6FDh92nC8cGGgY0dZvt28ueEv4S+/lNVSGo1xYbE+4YmMdPqmfLnF5CaHclMDU7gwMG2aofZGq5XbphYtmau2xhQ/Pz+UKlUK586dQ69evTI9z9vbG927d0f37t3RtWtXtGrVCrdv30bx4sVRsGBBaLXaLF9n7dq1SE5OxsGDB43qco4dO4YBAwbg7t27qF69OnQ6Hf78809ERERkeI4aNWpg0aJFePz4scnRmxIlShitCtNqtTh27BheeumlLGPbtWsXgoODMX78+LRjFy9ezPDasbGxGDBggMnnKFCgAPr164cFCxbAzc0NPXr0yDYhIiInc+6c7L69dClw5IjhuIcH0K6dJDRt2pheut25M7B8uek+N5GRcj/lCpMbC2nRQpaF79snX1u0UCeOKVOm4K233oKPjw9atWqFlJQU/P3337hz5w5GjRqFmTNnIiAgALVq1YKLiwuWLVsGf39/FC1aFICsQIqNjUWDBg3g7u6OYsWKZXiN+fPno23btqhZs6bR8WrVquGdd97B4sWLMWzYMPTr1w8DBw5MKyi+ePEirl+/jm7dumH48OH46quv0KNHD4wbNw4+Pj7466+/EBYWhsqVK6Np06YYNWoU1qxZgwoVKmDmzJm4e/dutt9/pUqVcOnSJSxduhR169bFmjVrsHLlSqNzJk+ejGbNmqFChQro0aMHnjx5grVr12Ls2LFp5wwaNAhVq1YFAOzcuTOX7wIROaSrV6Vb8NKl0j1Yr2BBoGVLSWhefhkoUiT75+rcGejQIfMOxZQ7li8Bytrs2bOV4OBgxd3dXQkLC1P27NmT6bmpqanKlClTlPLlyyvu7u5KjRo1lHXr1uXq9fJaUJwXf/yhKFWryldrebqgWFEUZfHixUpISIji5uamFCtWTGnUqJESExOjKIqifPfdd0pISIji5eWleHt7K82aNVMOHDiQ9tjVq1crFStWVAoUKKAEBwdneL34+HilQIECyi+//GIynqFDhyq1atVSFEV+vu+8844SEBCguLm5KRUrVlSioqLSzj18+LDSokULxdPTUylSpIjSsGFD5ezZs4qiyHs/dOhQpXjx4krJkiWV6dOnmywo/uKLLzLE8H//93/KM888oxQuXFjp3r278sUXX2T4Ga1YsSLtZ+Tr66t07tw5w/M0bNhQee6550x+n2phQTGRlV2/rijffKMojRsbrx5xcVGUZs0UZd48Rbl1S+0oHVJuCoo1ipJV5yDLio6ORt++fTF37lzUq1cPkZGRWLZsGU6dOoWSJUtmOH/s2LH46aefMG/ePFSpUgUbNmzAqFGjsGvXLtSqVStHr5mUlAQfHx8kJibC29vb6L5Hjx7h/PnzKFeuHDw8PMzyPZJjUBQFlSpVwptvvolRo0apHU4a/s6S3chq3yRbj+nuXWDVKhmh2bRJHqfXoIGM0HTtKl1ayWKy+vzOwNKZVlbCwsKUYcOGpd3WarVKqVKllOnTp5s8PyAgQJk9e7bRsc6dO2e5jPdp1hy5Icdw/fp1ZdasWYqXl5dy+/ZttcMxwt9ZsgsrVihK6dLGS5tLl5bjthrTvXuK8vPPitKhg6K4uRmfFxqqKDNmKMrFi+rF74RyM3KjWs1Namoq9u/fj3HjxqUdc3FxQUREBHbv3m3yMSkpKRn+Oi1UqBB27NiR6eukpKQgRb97KiTzI8qNkiVLwtfXF999953JmiMiyoJ+36SnJwmuXpXjy5dbv2A2q5i6dJHRmIMHgQcPDPdVqwb07Al07w5UqmTdeCnXVEtubt68Ca1WCz8/P6Pjfn5+OHnypMnHtGzZEjNnzkSjRo1QoUIFxMbGIiYmJsvVPNOnT8eUKVPMGjs5F0W9mVsi+5bVvkmKIkud335bCmmtNUWVXUwAoF80UL68TDn16AE8/7x1G5VRvtjVaqkvv/wSgwcPRpUqVaDRaFChQgUMGDAAUVFRmT5m3LhxRjUSSUlJCAoKska4RETObfv2rPdNUhTg8mWgVClZMm0Njx4B169nf9433wCvv86Exk6pltz4+vrC1dUVCU9toZ2QkAD/TIqySpQogVWrVuHRo0e4desWSpUqhffeew/ly5fP9HXc3d3hbs4tuImIKGfSL4/OSk6SDWvz8WFiY8dUS27c3NwQGhqK2NhYdOzYEQCg0+kQGxuL4cOHZ/lYDw8PBAYG4vHjx1ixYgW6detm1tg4DUH2gr+rZHNSU2Vl0Zw5wLZtOXvMN98AoaEWDSvN/v3A0KHZnxcQYPlYyGJUnZYaNWoU+vXrhzp16iAsLAyRkZG4f/9+WqfYvn37IjAwENOnTwcA7NmzB1evXkVISAiuXr2KDz74ADqdLm135/zSd8Z98OABO9CSXXjwX8FjfvfkIsq3K1eA774D5s2TvZMAwMUFcHcHHj40/RiNRrrwDh5svZqb2rWBjz6S4mFTfxzoY+JeTnZN1eSme/fuuHHjBiZNmoT4+HiEhIRg/fr1aUXGly5dgouLYW/PR48eYcKECTh37hwKFy6MNm3a4Mcff0zrpptfrq6uKFq0KK7/N0Tq6ekJDYclyQYpioIHDx7g+vXrKFq0qNG2F0RWoyjA5s3A118Dv/5q6P/i7w8MGSJJy969sjJJf76eWvsmubpyLycnoGoTPzVk1wRIURTEx8fnqLU/kdqKFi0Kf39/JuFkXXfvAosWyXTSqVOG440bA8OGyeaQ6UcTY2Iy7psUFKTuvkm2GBNlKTdN/JjcZEKr1ZrcdZrIVhQsWJAjNmRdhw7JKM3ixYYeMEWKAH37Sh3Lc89l/lh77lBMNiE3yY1dLQW3JldXV35wEBGlpEijvTlzgPQNVp97TkZpevfO2caQrq5AkyYWCzNPbDEmMgsmN0RElNHFi8DcucD8+cCNG3KsQAHp4DtsGPDii1wqTTaLyQ0REQmdDti4Uaaefv/dUGxburQ0tBs0iJtDkl1gckNE5Oxu3QIWLJCRmrNnDccjIoA33wTat5dRGyI7wd9WIiJntW+fjNIsXSrbEgDSmXfAAOCNN4DKldWNjyiPmNwQETmThw+B6GhJavbtMxwPCZFamp49AS8v1cIjMgcmN0REzuDsWelLExUF3Lkjx9zcgG7dJKmpV48FwuQwmNwQETkqrRZYu1ZGadavNxwPDpa+NAMHAiVKqBcfkYUwuSEicjQ3bsgS7rlzZUk3IKMyrVpJgXDr1mxWRw6NyQ0RkSNQFOCvv6TZ3rJlsjs3ABQvLiM0b7wBVKigboxEVsLkhojInt2/DyxZIlNPhw4ZjoeFyShNt25AoUKqhUekBiY3RET26NQpSWgWLQISE+WYh4esdnrzTaBOHXXjI1IRkxsiInvx5AmwerUkNbGxhuMVK0qBcP/+Mg1F5OSY3BAR2bq4OOD774FvvwWuXpVjLi5Au3YyStO8udwmIgBMboiIbJOiANu3S4FwTIyM2gCydHvQINnrKThY3RiJbBSTGyIiW5KUBPz0k0w9/fOP4XiDBjJK06UL4O6uXnxEdoDJDRGRLTh2TDoI//ADcO+eHPP0BHr3lqSmZk114yOyI0xuiIjUkpoKrFolU0/bthmOV6kiCU3fvrKRJRHlCpMbIiJru3IF+O47YN48ID5ejrm6Ah07SlLz0kvc54koH5jcEBFZg6IAmzfLKM3q1bLvEwD4+wNDhsglMFDdGIkcBJMbIiJLuntXGu1984003tNr0kRGaTp2BAoWVCk4IsfE5IaIyBIOHZIVT4sXAw8eyLEiRaSOZuhQ4LnnVA2PyJExuSEiMpeUFGD5cpl62r3bcPz554Fhw4BevSTBISKLYnJDRJRfFy5I9+Dvvwdu3pRjBQoAXbvK1NOLL7JAmMiKmNwQEeWFTgds3CijNGvWSMEwAJQuLd2DBw2SYmEisjomN0RET9NqZeuDuDggIABo2FCWagPArVvAggXA3LnA2bOGxzRvLqM07drJqA0RqYb/AomI0ouJAUaOlF40eqVLAyNGACdOAEuXAo8eyXEfH2DAAOCNN4DKldWJl4gyYHJDRKQXEyN1MvopJr0rV4CxYw23a9WSAuEePQAvL+vGSETZYnJDRATIVNTIkRkTm/Q8PaXOpn59FggT2TAXtQMgIrIJ27cbT0WZ8uAB8PgxExsiG8fkhogIkOJhc55HRKphckNEBMiqKHOeR0SqYXJDRATIcu9nnsn8fo0GCAqS84jIpjG5ISICgNRUwCWT/xL1NTaRkYZ+N0Rks5jcEBEBwOefAzduyOhNYKDxfaVLy55RnTurExsR5QqXghMRXb4MTJsm12fPBl55JfMOxURk85jcEBH93/8BDx9KEtO9u0xDNWmidlRElEecliIi57ZtGxAdLfU2s2axhw2RA2ByQ0TO68kT2TMKAIYMAUJCVA2HiMyDyQ0ROa9584AjR4BixYCpU9WOhojMhMkNETmn27eBCRPk+tSpgK+vuvEQkdkwuSEi5zRpkiQ41asDr7+udjREZEZMbojI+Rw5AnzzjVz/8kugABeOEjkSJjdE5FwUBRg5EtDpgK5dgZdeUjsiIjIzJjdE5FyWLwe2bgU8PIDPPlM7GiKyAI7FEpHj02ql4/CFC8DYsXLsvfeA4GBVwyIiy2ByQ0SOLSZGpqGuXDEcc3UFnn1WvZiIyKKY3BCR44qJkboaRTE+rtUCvXoB7u7cDJPIAbHmhogck1YrIzZPJzbpvf22nEdEDoXJDRE5luRk2S9qxAjjqainKYrsBr59u/ViIyKr4LQUEalLX+wbFwcEBMjO3K6uOXvsgwfAoUPA338bLidPZj1a87S4uDyFTUS2i8kNEanHVLFv6dLSWO/pWphHj4DDh4H9+w2JzD//SL+apwUFyUqoHTuyjyEgIH/fAxHZHCY3RKSOzIp9r16V4598Anh7GxKZY8dkF++n+fsDdesCderIJTQU8POTEaGyZeX5TI3kaDSSSDVsaJFvj4jUw+SGiKwvq2Jf/bExYzLe5+trnMjUqQOUKmX6NVxdZQSoa1dJZNK/lkYjXyMjcz4FRkR2g8kNEVnf9u1ZF/vqhYYCLVoYEpmgIENikhOdO0tHYlNTX5GRXAZO5KCY3BCR9eW0iPfdd4GePfP3Wp07Ax065L1omYjsDpMbIrI+U0XAppir2NfVFWjSxDzPRUQ2j8kNEVnXmjXAsGFZn8NiXyLKBzbxIyLr0GqByZOBdu2AxETZ20mjyVhDw2JfIsonJjdEZHm3bgFt2wIffii3hw0Djh6VYt/AQONzS5eW4yz2JaI84rQUEVnWgQNAly7AhQtAoULAt98CffrIfSz2JSILYHJDRJazYAEwdCiQkgKULy+N+2rWND6Hxb5EZGacliIi83v0CBgyBBg4UBKbdu2ky/DTiQ0RkQWontzMmTMHZcuWhYeHB+rVq4e9e/dmeX5kZCQqV66MQoUKISgoCO+88w4ePXpkpWiJKFuXLsnU0rx5Uhw8dSrw669AsWJqR0ZETkLVaano6GiMGjUKc+fORb169RAZGYmWLVvi1KlTKFmyZIbzlyxZgvfeew9RUVGoX78+Tp8+jf79+0Oj0WDmzJkqfAdEZGTTJqBHDykgLl4cWLIEaNlS7aiIyMmoOnIzc+ZMDB48GAMGDEC1atUwd+5ceHp6IioqyuT5u3btQoMGDfDqq6+ibNmyaNGiBXr27JntaA8RWZhOB0ybJonMrVuybcL+/UxsiEgVqiU3qamp2L9/PyIiIgzBuLggIiICu3fvNvmY+vXrY//+/WnJzLlz57B27Vq0adMm09dJSUlBUlKS0YWIzOjuXaBTJ2D8eElyXnsN2LFDduQmIlKBatNSN2/ehFarhZ+fn9FxPz8/nDx50uRjXn31Vdy8eRMvvvgiFEXBkydP8MYbb+D999/P9HWmT5+OKVOmmDV2IvrP0aOynPvMGcDdHZg9Gxg0SO2oiMjJqV5QnBtbt27FtGnT8PXXX+PAgQOIiYnBmjVrMHXq1EwfM27cOCQmJqZdLl++bMWIiRzYkiXACy9IYlOmjIzWMLEhIhug2siNr68vXF1dkZCQYHQ8ISEB/v7+Jh8zceJE9OnTB4P++w+0evXquH//PoYMGYLx48fDxSVjrubu7g53d3fzfwNEzio1FRg9GvjqK7ndvLkkOr6+6sZFRPQf1UZu3NzcEBoaitjY2LRjOp0OsbGxCA8PN/mYBw8eZEhgXP/rZKooiuWCJSJx7Rrw0kuGxGb8eGDdOiY2RGRTVF0KPmrUKPTr1w916tRBWFgYIiMjcf/+fQwYMAAA0LdvXwQGBmL69OkAgPbt22PmzJmoVasW6tWrhzNnzmDixIlo3759WpJDRBby559A9+5AQgLg4wP8+CPQvr3aURERZaBqctO9e3fcuHEDkyZNQnx8PEJCQrB+/fq0IuNLly4ZjdRMmDABGo0GEyZMwNWrV1GiRAm0b98eH330kVrfApHjUxTgiy+AMWNkZ+/q1WUbhYoV1Y6MiMgkjeJk8zlJSUnw8fFBYmIivL291Q6HyLYlJ8vS7mXL5HavXsB33wGenurGRUROJzef39w4k4hMO3lSlnmfOAEUKABERgJvvilbKhAR2TAmN0SU0YoVQP/+wL17QKlSMnJTv77aURER5Yhd9bkhIgt78kRqa7p2lcSmcWPgwAEmNkRkV5jcEJFISJCeNTNmyO3Ro2UjzKe6iBMR2TpOSxER8NdfMlpz9SpQuDCwYIHcJiKyQxy5IXJmigJ8/TXQqJEkNlWqAHv3MrEhIrvG5IbIWT14APTrBwwbBjx+LAnN3r1A1apqR0ZElC+cliJyRmfPyjLvI0cAV1fgk0+AUaO4zJuIHAKTGyJn8/vvQO/eQGIiULIkEB0NNGmidlRERGbDaSkiZ6HVApMmyX5QiYlAeLgs82ZiQ0QOhiM3RM7g1i3ZOmHDBrk9fDjw+eeAm5u6cRERWQCTGyJHt38/0KULcPEiUKiQ7A3Vu7faURERWQynpYgcWVQU0KCBJDYVKkg/GyY2ROTgmNwQOaJHj4AhQ2RH75QUqbP5+2+gRg21IyMisjhOSxHZM60W2L4diIsDAgKAhg2BK1dkGmr/flnaPXUqMG4c4MK/ZYjIOTC5IbJXMTHAyJGSzOj5+sqozb17QPHiwM8/Ay1aqBcjEZEKmNwQ2aOYGOkorCjGx2/elK8VKgCxsUBwsPVjIyJSGcepieyNVisjNk8nNumlpAClS1svJiIiG8LkhsjebN9uPBVlypUrch4RkRNickNkb+LizHseEZGDYXJDZG8CAsx7HhGRg2FyQ2RvGjaUeprMdvDWaICgIDmPiMgJMbkhsjeursCXX5q+T5/wREbKeURETojJDZE96twZmDMn4/HSpYHly+V+IiInxT43RPbq8WP5WrMmMHasoUMxR2yIyMkxuSGyV7/9Jl/79gV69lQ3FiIiG8JpKSJ7lJgIbN0q19u3VzUUIiJbk+vkpmzZsvjwww9x6dIlS8RDRDmxYQPw5AlQpQpQqZLa0RAR2ZRcJzdvv/02YmJiUL58eTRv3hxLly5FSkqKJWIjosysXi1fOWpDRJRBnpKbQ4cOYe/evahatSpGjBiBgIAADB8+HAcOHLBEjESU3pMnwNq1cv3ll9WNhYjIBuW55qZ27dqYNWsWrl27hsmTJ+P7779H3bp1ERISgqioKChZbepHRHm3cydw5w7wzDNAeLja0RAR2Zw8r5Z6/PgxVq5ciQULFuCPP/7ACy+8gNdeew1XrlzB+++/j02bNmHJkiXmjJWIAMMqqbZtueybiMiEXCc3Bw4cwIIFC/Dzzz/DxcUFffv2xRdffIEqVaqkndOpUyfUrVvXrIES0X9Yb0NElKVcJzd169ZF8+bN8c0336Bjx44oWLBghnPKlSuHHj16mCVAIkrn1Cng338BNzegZUu1oyEiskm5Tm7OnTuH4ODgLM/x8vLCggUL8hwUEWVCP2rTpAlQpIiqoRAR2apcFxRfv34de/bsyXB8z549+Pvvv80SFBFlQl9vwykpIqJM5Tq5GTZsGC5fvpzh+NWrVzFs2DCzBEVEJty6JSulACY3RERZyHVyc/z4cdSuXTvD8Vq1auH48eNmCYqITFi7FtDpgBo1gGymhomInFmukxt3d3ckJCRkOB4XF4cCBbgPJ5HF6Ott2LiPiChLuU5uWrRogXHjxiExMTHt2N27d/H++++jefPmZg2OiP6Tmir7SQGckiIiykauh1o+++wzNGrUCMHBwahVqxYA4NChQ/Dz88OPP/5o9gCJCMCffwLJyYC/P1CnjtrREBHZtFwnN4GBgThy5AgWL16Mw4cPo1ChQhgwYAB69uxpsucNEZmBfkqqXTvAJc+7phAROYU8Fcl4eXlhyJAh5o6FiExRFMMScNbbEBFlK88VwMePH8elS5eQmppqdPxl/udLZF5HjwIXLwIeHkCzZmpHQ0Rk8/LUobhTp044evQoNBpN2u7fGo0GAKDVas0bIZGz04/aNG8OeHqqGwsRkR3I9eT9yJEjUa5cOVy/fh2enp74559/sG3bNtSpUwdbt261QIhETo4bZRIR5UquR252796NzZs3w9fXFy4uLnBxccGLL76I6dOn46233sLBgwctESeRc4qPB/bulevt2qkbCxGRncj1yI1Wq0WR/zbs8/X1xbVr1wAAwcHBOHXqlHmjI3J2v/8uX+vWBQIC1I2FiMhO5Hrk5vnnn8fhw4dRrlw51KtXD59++inc3Nzw3XffoXz58paIkch5cZUUEVGu5Tq5mTBhAu7fvw8A+PDDD9GuXTs0bNgQzzzzDKKjo80eIJHTevgQ+OMPuc56GyKiHMt1ctOyZcu06xUrVsTJkydx+/ZtFCtWLG3FFBGZQWysJDhBQbJZJhER5Uiuam4eP36MAgUK4NixY0bHixcvzsSGyNzSb5TJf19ERDmWq+SmYMGCKFOmDHvZEFmaTmcoJuaUFBFRruR6Wmr8+PF4//338eOPP6J48eKWiImI9u8H4uKAwoWBJk3UjsYiLl8GbtzI/P6SJYHSpW3/NYjI9uQ6uZk9ezbOnDmDUqVKITg4GF5eXkb3HzhwwGzBETkt/Sqpli0Bd3d1Y7GAlBRZ3Z6QkPk5/v7AhQt5//at8RpEZJtyndx07NjRAmEQkREHXwLu5gaUKSOjKjpdxvtdXKSO2s3Nuq/BkR4ix6BR9JtDOYmkpCT4+PggMTER3t7eaodDlNGlS0BwsHz6JiQAvr5qR2QRGzYArVplfv/69TJwlRuKYqi9fvQIWL4c6NMnZ6+RkiI/do70ENmm3Hx+57pDMRFZmL6QuH59h01sAKBFC5k2cnU1Pq7RAMWKAXPnyo4T27cb7tuwAXjuOaBiRRmV8fOTcz09gQIFgB9+MJy7aVPWiU2FChIDAGi1EkeZMpJTmmKO0SQiso5cT0u5uLhkueybK6mI8slJNsrUaICpUzOO3igKcOcOsGqV3H71VcN9Dx4Ax49n/pwpKYbrHh6SiLi6Srugp3XqZBjl2bpVEqmAANNTWIAcnzqVq/KJ7EGuk5uVK1ca3X78+DEOHjyIRYsWYcqUKWYLjMgpJScDW7bIdQettwEk2ShUSEZOatUC9PvtajRAYCAwerRM/bi7A/XqGR734ovA5s1y3M0t49dixQznRkRIsqMo8hwHDsgIjYsLUK4c8NZbhnNPnpRprPPnTcfr6grUrm0Y6SEi22a2mpslS5YgOjoav/76qzmezmJYc0M2bcUKoGtXmXc5fdrhhgn+/RcYO1aKdrdtk2/v6dqbvNTaZCe719BqpZbmxAkZMZo/P+NzWCIuIso5VWpuXnjhBcTGxprr6YicU/pVUg6U2Ny+DbzzjtTLrFwJ7NplGK3R194A8tUSoyPZvYarq9TgtGsHzJtnXAuk0QB16hgek37qi4hsk1mSm4cPH2LWrFkIDAw0x9MROSetFlizRq47SL1NaioQGSkDUZGRwOPHMoJy+LBM8wCSPEybBlStKl8tkdPl5jX0tUD68kFFAf73Pzl+44YkQVOnmq7jISLbkOtpqac3yFQUBcnJyfD09MRPP/2El228ToDTUmSzdu6UopJixWQ9csGCakeULxcuAM2bA2fOyO3nnwc+/9w+6lb0dTr79skozp49ktzMmAGMGSPnBAfL7a5dHWqQjchm5ebzO9cFxV988YVRcuPi4oISJUqgXr16KJa+mo+Icke/Sqp1a7tPbABZNu3hIcu1p04FBg7MuOzbVulHet56y3ikZ/Ro+b7+7/+AixeBbt2ARo2AL78EQkJUDZmI0rGJJn5z5szBjBkzEB8fj5o1a+Krr75CWFiYyXObNGmCP//8M8PxNm3aYI1+SD8LHLkhm1WtmlS0/vwz0KOH2tHk2qVLMjLz8ceyEgqQVUiBgUCRIurGZm4PHgCffgp88omssnJxAd54A5g9m6M4RJZi0YLiBQsWYNmyZRmOL1u2DIsWLcrt0yE6OhqjRo3C5MmTceDAAdSsWRMtW7bE9evXTZ4fExODuLi4tMuxY8fg6uqKV155JdevTWQzzpyRxKZAgazb9tqg5GRg/HigcmVg1iwZxdCrUsXxEhtAmgZ+8AFw6hTQvbv0wNFomNgQ2YpcJzfTp0+Hr4muqSVLlsS0adNyHcDMmTMxePBgDBgwANWqVcPcuXPh6emJqKgok+cXL14c/v7+aZc//vgDnp6eTG7IvulXSTVqBBQtqmooOfXkCfDtt1IsPG2ajGA0bix1Ns6iTBlg6VJZ1p6+zdeJE8C6derFReTscp3cXLp0CeXKlctwPDg4GJcuXcrVc6WmpmL//v2IiIgwBOTigoiICOzevTtHzzF//nz06NEjw+7keikpKUhKSjK6ENkcO9soc8MGqTF54w3g+nWgUiXpD7NlCxAaqnZ01tewIfDMM3JdUYCRI4E2bYC2baVdERFZV66Tm5IlS+LIkSMZjh8+fBjP6P9159DNmzeh1Wrh5+dndNzPzw/x8fHZPn7v3r04duwYBg0alOk506dPh4+PT9olKCgoVzESWdydO/KnP2A3S8C/+Qb45x+geHGZhjp2DOjQgdMygIxo1aghM4xr10pvn9GjgcREtSMjch65Tm569uyJt956C1u2bIFWq4VWq8XmzZsxcuRI9LByEeT8+fNRvXr1TIuPAWDcuHFITExMu1y+fNmKERLlwPr10lTlueeA8uXVjsakhAQZodH79FNg1CgpFXrrLW4mmV7BgsBnn0nC17atJDuffy6jW99/b+ifQ0SWk+vkZurUqahXrx6aNWuGQoUKoVChQmjRogWaNm2a65obX19fuLq6IiEhweh4QkIC/P39s3zs/fv3sXTpUrz22mtZnufu7g5vb2+jC5FNseGNMh8+BD76SOpqxo0zHH/2WfnAZveHzFWuLBu8r10r12/cAAYPNt65nIgsI9fJjZubG6Kjo3Hq1CksXrwYMTExOHv2LKKiouCWyz/f3NzcEBoaarRtg06nQ2xsLMLDw7N87LJly5CSkoLevXvn9lsgsh2PHxsqT22o3kanA376SZKYCROAe/ekSDY1Ve3I7E/r1sDRo8AXX0hjwF69DPdxFIfIMlTvcxMdHY1+/frh22+/RVhYGCIjI/HLL7/g5MmT8PPzQ9++fREYGIjp06cbPa5hw4YIDAzE0qVLc/V67HNDNmXzZqBZM6BECSAuzia63G3bBrz7LvD333K7TBnpXdO9u/RzobxTFENdUmqqJDsdOkjXY09PdWMjsnUW7VDcpUsXhIWFYezYsUbHP/30U+zbt89kD5ysdO/eHTdu3MCkSZMQHx+PkJAQrF+/Pq3I+NKlS3B56n/UU6dOYceOHdi4cWNuwyeyLfpVUu3a2URis2gR0L+/XC9SBHj/fVn5o2/KR/mTvuB6xQrg0CG5zJ8vWzl0786ibCJzyPXITYkSJbB582ZUr17d6PjRo0cRERGRoX7G1nDkhmyGokgxy7lzQEwM0KmT2hHh7l1pvNe5szSpK1lS7Ygcl6JIgjN6tGzlAAANGsjqM2dcTk+UHYt2KL53757J2pqCBQuyhwxRbpw4IYmNu7sqne9SU6UOpGNH+aAFpH/gmTPA118zsbE0jUY23TxxQvbe8vSUvVPr1gUGDQJSUtSOkMh+5Tq5qV69OqKjozMcX7p0KapVq2aWoIicgn6VVNOmQOHCVntZ/YhBtWqynPvXX2U1up4VQyHIlN+ECbKVQ69e8v5cusTl9UT5keuam4kTJ6Jz5844e/YsmjZtCgCIjY3FkiVLsHz5crMHSOSw9PU2VlwCvm+fJDQ7dshtf38ZNWjRwmohUCZKl5YVasOGSXNEfe3NrVvA7t3SM4f1OEQ5k+uRm/bt22PVqlU4c+YM3nzzTbz77ru4evUqNm/ejIoVK1oiRiLHc/26fGIBVkluEhOB3r2BsDBJbAoVAiZOBP79V6ZAbKCWmf4THi59cfQmT5ZfkdatZQqLiLKXp4Wdbdu2xc6dO3H//n2cO3cO3bp1w+jRo1GzZk1zx0fkmNaulfmHWrXkT3YL8/KSVTkA0Lev7Hf04YecgrJ1iiJ1UG5usp9X9erA22/Ljh1ElLlcT0vpbdu2DfPnz8eKFStQqlQpdO7cGXPmzDFnbESOS19vk8fGfZcvS8fbzBQvLptY9uwJeHjIPkfffy9bA3Aljv3QaID//Q8YMEB6D/36q6ym+uknmU4cPFjeWyIylqt/FvHx8Vi4cCHmz5+PpKQkdOvWDSkpKVi1ahWLiYly6tEjQN+jKQ9TUikpsqImq64Lrq7S/fb6dUDfkuqFF/IQK9mEChVk1/U//pCRm+PHgTfflAR30iS1oyOyPTmelmrfvj0qV66MI0eOIDIyEteuXcNXX31lydiIHNPWrcD9+0CpUkDt2rl+uJubdA3OqluwViv7Pj3zTN7DJNvTvDlw+DDw1Veyx+rQoYb71O01T2RbcpzcrFu3Dq+99hqmTJmCtm3bwpUViER5k36jzDwsf9FoZEpCp8v8nC5dgLNnpViYHEuBAsDw4VI3VaKEHFMUabyo3weMyNnlOLnZsWMHkpOTERoainr16mH27Nm4efOmJWMjcjyKYlgCno+NMlu0kKmpp//G0Gik6HTZMu7Y7ejSv/e7dsm01UcfyUqrn37KOvklcnQ5Tm5eeOEFzJs3D3FxcXj99dexdOlSlCpVCjqdDn/88QeSk5MtGSeRYzh0CLhyRdrR/tcnKi/0ozdP7yqtKLJHEfuhOJf69YGVK2Wq6to1oE8f2cph7161IyNSR66Xgnt5eWHgwIHYsWMHjh49infffRcff/wxSpYsiZfz8ZcokVPQj9q0aCHLmPLh6dEbV1e5zYZ8zkejkW00/vkHmD5dlv7/9ZfsOt6/v+wZRuRM8tTnRq9y5cr49NNPceXKFfz888/mionIcaWvt8mnp0dvtFq5zVEb5+XhAbz3ntTj9Osnx3btkoFCImeS613B7R13BSfVXL0qDfs0GiAuDvDzy9fT/fsvMHs2EBsrf7HXrQvs2cPkhgz27pXOA40aye3Hj4FNm4BWrfh7QvbHoruCE1Ee/f67fK1XL9+JDQAsXAjMmiV/lVetCkybxg8sMhYWZkhsAOCbb4A2bWRJ+bFj6sVFZGlMboisxQyrpPS0WuCHH+T66NHS1C0iIt9PSw7u4UPA3V1G+2rWlCXlt26pHRWR+TG5IbKG+/dlPgAwS73Nli2y6KpoUbPkSuQkxo6VzTe7dJGl4nPmAJUqyfTmkydqR0dkPkxuiKxh0ybZN6FsWeC55/L9dAsXylf93lFEOVWuHLB8ObB5s/REunMHGDFCtnMgchRMboisIf1GmfksjElMBGJi5Hr//vkLi5zXSy8BBw5IHU7JkpLgEDkKJjdElqbTGYqJzTAltWyZ1E5UrSorpIjyqkAB4I03gEuXZBRHb8wYWVLO3qxkr5jcEFna3r2yPbe3t/HSlTx68kT+0u7fn6ujyDzc3Q3XL1wAZs4EPvkEePZZmQLlVg5kb5jcEFmafpVUq1aypXc+vfGGFBMPH57vpyLKIDhY9qmqWBGIjwcGDJDuBbt3qx0ZUc4xuSGyNDMuAdcrWJBdZ8kyNBqgXTvpgzNjBlCkCPD337J/Ve/eQEKC2hESZY/JDZElXbgAHD0qGz+1bp2vp9JqZYXL05tlElmCu7v0UPr3X+C11yTp0efpRLaOyQ2RJek/DV58EShePF9PtWUL0KwZULu27P5NZA1+fsD33wP79gHz5hk31962jb+LZJuY3BBZkhk3ytT3tmnQgIXEZH2hoUC3bobb69YBjRvLkvLDh9WLi8gUJjdElpKYCPz5p1zPZ70Ne9uQrbl4URpI/vmnjCa+8QZw44baUREJJjdElrJhg2zDXLmy9LjPB/a2IVvzxhvAyZMymqPTAd9+K7/mkZHya2/LLl+WBoaZXa5cUTtCyq8CagdA5LDMuEpq0SL5yt42ZEuCg4HoaGDYMGDkSODQIeCdd4A//gDWrJFzLl/OekSnZEmgdGmrhAtAdkGpWzfrVV/+/rIWIH3/H7IvTG6ILOHJE8P/7vmstzlzBtixA3BxkaW4RLamUSNZLh4VBYwfDwweLMdtMZFwcwPKlJGEy1RzQhcXICjILC2pSEVMbogsYdcu2ZGweHEgPDxfT/Xrr/K1ZUugVCkzxEZkAa6uktT06AEULizH3Nyy3tjVGomETgckJQGPHsnU7sOHQN++svors/OnTuUIqb1jckNkCfpVUm3bygY++TBqlKwkd2GFHNmBIkUM15OTgVu3Mj83fSJx546MUOoTEP1Fn5Q0aQI0bSqPu3BBpr/S35/++tChwPvvy7lnz8o2Ejnh6iq7poeF5eU7J1vC5IbIEvT1NmZYAq7RSPt7Invj7Q0sXQp06SJTVE8rXRpo0UKunzmTfXmaPrl5+FC2iMjMzZuG6/qRI40GKFRILh4ekljFxRk/TquVOHx9Zel706ZyadAA8PLKOjayLUxuiMzt1Cng9GnZI6Fly3w9lVYrf00S2au2bYHly03n+S1aGKZ/ihWTEZP0CYj+eqFCxqMppUoBc+can5P+ekCA4dzAQBnRcXMznmpSFPmj4cABw7+zihXl+OnTMm21b59sIFqwIPDCC8CkSUBEhGV+TmReTG6IzE0/atOkifzpmkdJSUCVKvKh8MUX3EuK7FfbtlJYnD6RqF1bOh/rVawI7NmTs+fz8QFefz1n57q4mC5W1mhkSqxVK7mt1QJffil/j1y9Kh3BN28GYmOBS5eA7duNuzHv2SPnNG0q30s+Z5/JzPh2EJmbvt4mn0vAly2TYfPt2+WvUSJ7ZSqRsIWi3RYtJOnat0++6qfIAgNlZWLv3pLQnD8viU6DBobHLlsGfP65XPf2lm7NzZpJsvPcc6yRUxuTGyJzunUL2LlTruez3mbBAvnK3jbkCDJLJNSk0QDTpgFvvSVfTf0702iA8uXlkl5YGNCxI7B1K3D3rgzY6gdtS5SQLSnST49lxdZ6ATkCjaI417ZnSUlJ8PHxQWJiIrzzMWVAZNJPPwF9+gA1auRrw51//5UVHi4u8h8fl4CTI9i0SRKJWbMcp3ZFq5XmhZs3y2XbNpk2u3rVkCyNHCkJkL5AOSjI8PiUFGmGaEu9gGxVbj6/OXJDZE5m2ihT35GYvW3IkUREAMePqx2Febm6ysqq0FDg//4PSE2VRESf2Oh0wM8/y8jMDz/IsYoVJclp1kyms9hU0Pw4ckNkLqmpsoY0OVmqDfPYLEOrBcqWlf1toqONd2ImIvui0xlGdTZvlmm59ElM3brG9UimrF+f74WXDoEjN0Rq+PNPSWz8/YE6dfL8NFu2SGJTtKhZtqUiIhW5uMiIlX4aLjFRFgnExkqy06yZoR5Jv5os/WOffx5o3lyd2O0Zkxsic9FXE7Zrl6+lEpUrAxMnynB3Vq3ricj++PjIfxHt2sltnS7jajI9nQ44ckSmpsPDZXuLNm2sH7M9YnJDZA6KYrZ6m6Ag4MMPzRATEdk8/d9BT4/eaDTS2yolRYqNV60yXmF2+rQUZoeHS4PB8uW5qjI9JjdE5nDsGHDxogy1OMoyECKymqdHbxQFWLFCdlw/eBDYvds4udm6FZgzRy6ALBd/4QVDshMW5tyNP9lmiMgc9KM2ERH5+h9l/Hh5qsePzRQXEdkN/egNYOgFVKgQUL8+8O67QIUKhnNDQoC335ZExs0NuH5d/u8YNw546SWp69G7dEnaSzjT8iGO3BCZgxk2yjxzRhqJsbcNkXPKSVNBvbAww4LMR49kdOevv2SEZ88e481258wBPv1UFnO+8IJhhKduXeNd3B0Jkxui/IqPN2yKo68SzAN9b5sWLZjYEDmrvPQC8vCQZCU8HHjnnYz337snozs3bwK//y4XwLAaa/Nm4Jln8h+7LWFyQ5Rfa9bI1zp18pyV6HSG5KZ/f/OERUQEyMjNzJnSSVk/urN7t0xXXbsGFC9uOPf116UVhX50JywsZ/v/2toWEkxuiPLLDBtlbtki/zn4+AAdOpgpLiKi/7i7y1RVvXqyHQQgiU36bsoAsHatJDdr18ptjUY2Ag0PBxo2lN1lnpaSIlNctrSFBJMbovx4+BD44w+5no96m4UL5WvPnuxtQ0TWUaqU8WCzogAxMTKqox/huXBBFoMeOyYjP+mTm7lzZQl6WJjtbSHB5IYoPzZvlgQnKAioWTNPT5GUJEs+AU5JEZF6NBoZgalbV4qaASkp1Cc66Tf8vH8fGD7c0JMnKMh0YgPI8alTrduHh8kNUX6kb9yXx3+5V64AVasCDx7keTsqIiKL8PcHOnaUS3rJyUCPHpL0nDsn9TumuLoCtWsb9+ixBiY3RHml0xmWHeSj3qZaNWD/fuDOHXYYJSL74O8P/PSTXL9+XUZ3Fi8GfvnF+Dyt1vqjNgB3BVc7HLJnf/8t47eFC8saS2tVyhER2SBFkYJl/RYS+lGbPXvMk9zk5vObHYqJ8krfuK9lyzwnNvv3S80NEZG9028hod/ZXK1RG4DJDVHe5XOjTJ0O6NxZhnd37zZjXEREKjG1hYQamNwQ5cXly7Iu0sUFaNMmT0+xdasU4bm5AbVqmTU6IiJV6LeQqFo1+y0kLIkFxUR5oZ+SCg8HSpTI01Owtw0ROaK8bCFhbhy5IcoLfXKTx1VSSUnA8uVynb1tiIjMiyM3RLmh1QIbNhi6EudxSmr5cun9V6UKe9sQEZkbR26IciomBihbFmjb1rAcoHVrOZ5L+imp/v3Z24aIyNyY3BDlREwM0LWrtBNO7+pVOZ6LBCcuDti1S2qRe/c2c5xERMTkhihbWq1so2uq36X+2NtvG0ZzshEQAFy8KN08AwPNFyYREQkmN0TZ2b4944hNeooiS8O3b8/xUwYGyr4sRERkfkxuiLJz9WrOzouLy/aUzHbNJSIi81E9uZkzZw7Kli0LDw8P1KtXD3v37s3y/Lt372LYsGEICAiAu7s7nn32Waxdu9ZK0ZLT2bUL+PDDnJ0bEJDtKQMHym4N2fyaExFRPqia3ERHR2PUqFGYPHkyDhw4gJo1a6Jly5a4fv26yfNTU1PRvHlzXLhwAcuXL8epU6cwb948BLJwgcztyhWgVy+gQQPg9OmslzRpNEBQENCwYZZPmZQkO+Zu3Gi6fIeIiMxD1T43M2fOxODBgzFgwAAAwNy5c7FmzRpERUXhvffey3B+VFQUbt++jV27dqFgwYIAgLJly1ozZHJ0Dx8CM2dK3/AHDyRxee016UQ8aJCckz4z0Sc9kZGyBW4W2NuGiMg6VBu5SU1Nxf79+xEREWEIxsUFERER2J3JLoKrV69GeHg4hg0bBj8/Pzz//POYNm0atFmsUklJSUFSUpLRhSgDRQFWrACqVQMmTJDEpkEDYN8+YN48mU9avjzj8qbSpeV4587ZvgR72xARWYdqIzc3b96EVquFn5+f0XE/Pz+cPHnS5GPOnTuHzZs3o1evXli7di3OnDmDN998E48fP8bkyZNNPmb69OmYMmWK2eMnB3L0qCz13rJFbgcGAjNmyHKm9FlI585Ahw6yKiouTmpsGjbMdsQGAM6ckYextw0RkeXZ1fYLOp0OJUuWxHfffQdXV1eEhobi6tWrmDFjRqbJzbhx4zBq1Ki020lJSQgKCrJWyGTLbt0CJk0C5s6VZUzu7sCYMcDYsYCXl+nHuLoCTZrk+qV++EG+tmjB3jZERJamWnLj6+sLV1dXJCQkGB1PSEiAv7+/yccEBASgYMGCcE33l3LVqlURHx+P1NRUuLm5ZXiMu7s73N3dzRs82bcnTyShmTQJuHNHjnXpAnz2mWyvYGY6HbBokVznJplERJanWs2Nm5sbQkNDERsbm3ZMp9MhNjYW4eHhJh/ToEEDnDlzBrp0zUJOnz6NgIAAk4kNUQabNwO1agEjRkhiU726HFu+3CKJDSCNiydPBlq1klktIiKyLFWXgo8aNQrz5s3DokWLcOLECQwdOhT3799PWz3Vt29fjBs3Lu38oUOH4vbt2xg5ciROnz6NNWvWYNq0aRg2bJha3wLZi3PnpGamWTPg2DGgeHHg66+BAweAl16y6EsXLCj1yOvWAR4eFn0pIiKCyjU33bt3x40bNzBp0iTEx8cjJCQE69evTysyvnTpElxcDPlXUFAQNmzYgHfeeQc1atRAYGAgRo4cibFjx6r1LZCtu3cPmD4d+PxzICVFambefBP44ANJcIiIyOFoFMW52oklJSXBx8cHiYmJ8Pb2VjscshRFkZ0px44Frl2TY82aST+a55+3Whi//QacPw+8+irg62u1lyUicji5+fy2q9VSRDmyb58s7db3SypXThrzdehg9QYzM2bIEvD794F0M6xERGRBqu8tRWQ28fFS3BIWJomNl5d0Gj5+HOjY0eqJTfreNn37WvWliYicGkduyP6lpgJffglMnQokJ8uxPn2Ajz8GSpVSLSx9b5vmzdnbhojImpjckP1SFGDNGmDUKODff+VY3brArFnACy+oGhp72xARqYfTUmSfTp4EWrcG2reXxMbPD1iwAPjrL9UTGwDYuhW4dAnw8WFvGyIia2NyQ/bl7l0ZqaleHdiwQZrIjBkDnD4tQyQutvErrR+16dEDKFRI3ViIiJwNp6XIPmi1QFQUMH48cOOGHGvfXvrXVKqkbmwm6HRAgQKckiIiUoNt/JlLlJXt26WWZsgQSWyqVgXWrwdWr7bJxAYAfvxRNg6vV0/tSIiInA+TG7Jdly8DPXsCjRoBBw9KAUtkJHD4MNCypdrRZcvX1+qrz4mICJyWIlv04IHs0P3xx8DDh5IhDBkiS71LlFA7uiwlJMhq9IoV1Y6EiMh5ceSGbIeiAL/8ItNOkydLYtOwoWxuOXeuzSc2ADBnjsyUjR6tdiRERM6LIzdkGw4fli0T/vxTbgcFyejNK6/YzdyOTmdo3Bcaqm4sRETOjCM3pK6bN4E33gBq15bExsNDRm1OngS6dbObxAaQ8C9eBLy9ZbcHIiJSB0duSB2PHwNffw188IH0rgGA7t2BTz8FypRRM7I8W7hQvrK3DRGRupjckPX98Qfw9tuyoSUAhITI3lCNGqkZVb4kJwPLl8t19rYhIlIXp6XIes6ckb0IWrSQxMbXF/j2W+Dvv+06sQEksXnwAHj2WZvY/YGIyKlx5IYsLzkZ+Ogj4IsvZAfvAgWA4cOBSZOAYsXUjs4sli2Tr/3721WZEBGRQ2JyQ5aj00mr3vfeA+Lj5ViLFtKIr2pVVUMztxUrgFWrgMaN1Y6EiIiY3JBl7NkDvPUWsHev3K5QQUZu2rVzyKGNQoWkmTIREamPNTdkXnFxQL9+Uniydy9QuDDwySfAP//IRpcOltgoilyIiMh2MLkh80hJke0Snn3W0Mmuf3/g33+BMWMAd3dVw7OUrVtlhm32bLUjISIiPU5LUf4oiuzO/e67wNmzcuyFF4BZs2Qnbwe3cCFw6hRw7JjakRARkR5Hbijvjh+X3bk7dpTEJiBARm127nSKxIa9bYiIbBOTG8q9O3dkH6gaNaQhn5sbMG4ccPo00KcP4OIcv1bpe9vUq6d2NEREpMdpKco5rRaYNw+YMAG4dUuOdewoG1xWqKBqaGrQb7fA3jZERLaFyQ3lzJ9/ytLuI0fk9nPPSb+aiAhVw1LL2bPAtm2S1PTpo3Y0RESUnnPMH1DeXbwou3M3aSKJTbFiwFdfAYcOOW1iAxgWhDVvDpQurW4sRERkjCM3ZNr9+7JD96efAo8eSR3N668DH34oe0I5mcuXgRs3DLeDg4HWrYGXXgIOHABKlmSSQ0RkK5jckDFFAaKjgf/7P+DKFTnWpIns2l2jhqqhqSUlRRZ/JSRkvG/dOvnq7w9cuOCw7XyIiOwKp6XI4MAB2Z27Z09JbIKDZUnQ5s1Om9gAshisTJnMF4G5uABBQXIeERGpj8kNAdevA4MHA3XqADt2AJ6ewNSpwIkTQJcuTr8USKORH4dOZ/p+nU7ud/IfExGRzeC0lDNLTQXmzAGmTAESE+XYq6/KXlAsIAEgs3SHDwPbtwMeHlJ+lJ6rK1C7tmx2TkREtoHJjbNavx54+23ZOwCQT+hZs4AGDVQNy1YoCjB+PPDLL4ZdJUzRajlqQ0Rkazgt5WxOnwbatZOlPqdOyTKf77+XHbydOLHR6Yz3h9JoZLTm7FkZsenUCfjpJ6BWLRmtAeRr3boctSEisjUcuXEWSUkyxPDll8Djx0CBArKFwsSJgI+P2tGpQquVBGb5cmDlSlkNFR9vWOn+/vvAvXuSBxYuLMd8fYFWrQyP56gNEZHtYXLj6HQ62Sdg3DgpHAbk0/qLL4DKlVUNTQ2PHwNbt0pCs2qV4UcCAN7ewNGj0rsGkB/T01q0kNGaffs4akNEZKuY3DiyXbtky4T9++X2s89KUtOmjbpxqWjRIlkYplesmGyP1aWLNFzOrk+NRgNMmyY/1mnTOGpDRGSLNIqiKGoHYU1JSUnw8fFBYmIivL291Q7HMq5eBcaOBRYvltve3sCkScCIEU7TjOXhQ2DDBmDFChmJGThQjl+/DoSEAO3bA127Sn/CggXVjJSIiHIiN5/fHLlxJI8eAZ9/LkMKDx7IsMLAgcBHHwF+fmpHZ3H37knH4OXLgTVrZAcJQLZO0Cc3JUtKf8LMGvIREZH9Y3LjCBRFKmLffVf2AACA+vVlaXdoqKqhWYOiSFPlX3817kMTFCSjM127Gp/PxIaIyLExubF3R49Kv5rNm+V2YKBsdtmzp8MWhNy+LaucOnSQ2xqNjNo8egRUqCD1M127SsNlB/0REBFRFlhzY69u3QImTwa++UZWRLm7y2aX770HeHmpHZ3Z3bghq5v0W109eSKDVMHBcv+BAzIiU7MmExoiIkfEmhtH9uQJ8O23UiB8+7Yc69IFmDEDKFdO3djMLCFBkpkVK4A//zTe26l6deDaNUNyU7u2OjESEZHtYXJjTzZvlsZ7+la61atLUz59YxYHoCiGkZetW4Hhww33hYZKHteli6xqJyIiMoXJjT04fx4YPRqIiZHbxYsD//ufNGwpYP9v4blzMjqzfLnU0bz/vhxv2xZo1EiWbXfp4nADU0REZCH2/8noyO7dAz7+GPjsMyAlRTYzGjpUdvEuXlzt6PLl1CnDlNPBg4bjT54YkpvChWU6ioiIKDeY3NgiRQGWLAHGjJHCEgBo1gyIjASef17V0PJLUWR/zt27DcdcXaWZXpcuskElERFRfjC5sTV//y29/fWf/uXKATNnynyNnS0DUhQZlYmNlVk1jUYuwcHybTZrJku2O3QwbFZJRESUX1wKbivi42U+ZuFCyQq8vIDx44F33gE8PNSOLscUBdi71zDldP68HD961DDodPGi7AhRrJh6cRIRkX3hUnB7kpoqnYQ//BBITpZjffoA06dLQz47cfIkMHeuJDRXrhiOFyok+3SmT6H1y7eJiIgsgcmNmtaskZGZf/+V23XrytLu8HB148qBJ09kc8oiReT2+fMSOiCFwO3ayZRTq1YO2VOQiIhsGHfZUcPJkzKc0a6dJDZ+fsCCBcBff9l0YvP4sey0PWQIEBAgq9H1mjUDBg2S/Z1u3AB+/lkKhJnYEBGRtXHkxpru3pXpp6++kqGPggVl5Gb8eClCsUEpKcAff0gNzerVwJ07hvu2bDFcd3MD5s2zfnxERERPY3JjDVotEBUlScyNG3KsXTtZBVWpkrqxZUFRpAj4zBnDMT8/Wa7dtSvQuLF6sREREWWGyY25aLWyVXVcnMzZNGwoDVx27JCl3fpOdVWqAF98IcUoNuTePSkB2rRJtq5ycZFl282aSW2NftuDBg3k2yIiIrJVXApuDjExsudT+mVCAQFA+fLAzp1y28cH+OADYNgwmY6yAYmJwG+/yQqn9euBR4/k+M6dQP36cj05WepmXFidRUREKuJScGuKiZE5mqdzxLg4uQBSgfu//wElSlg/PhP27AGmTgU2bpQiYb2KFeVbCQgwHNOvhiIiIrIXTG7yQ6uVEZusBr/8/ICvv1Z1LichQdrpBAXJ7SdPZAoKAKpVk+mmrl1lk3E7a4JMRESUAScb8mP7duOpKFMSEuQ8K7t2DZg9W/ZsKlUKmDbNcF94OPDJJ8A//8jlww+BGjWY2BARkWPgyE1+6KedzHVePl28KLNky5cDu3YZ33f5suG6i4vsyUlEROSImNzkR/riFHOclw+KAjRtCpw7ZzhWv75MOXXuDJQta/EQiIiIbAKTm/xo2BAoXRq4etV03Y1GI/c3bGjWlz1xQlY4bdwoS7fd3OSlXnlFmhzrExo72pqKiIjIbJjc5Ierq2yo1LWrZBfpExx9AUtkZL6LiRVFdtVesUKmnI4fN9y3ebOhZc706aybISIiYkFxfnXuLBnH08MkpUvL8c6d8/X0mzYBlSsDNWtK4e/x49Imp00bYP58oF49w7lMbIiIiDhyYx6dOwMdOpjuUJwLOh2wdy/g6SmrlwBZSf7vv4C7u4zQdOkCtG8PFC1q/m+DiIjIEdjEyM2cOXNQtmxZeHh4oF69eti7d2+m5y5cuBAajcbo4uHhYcVoM+HqKuuue/aUrzlMbPS7NowcCZQpY1imrff888DKlbIl1apVQJ8+TGyIiIiyovrITXR0NEaNGoW5c+eiXr16iIyMRMuWLXHq1CmULFnS5GO8vb1x6tSptNsaFedjLl827IVpSsmSMkOVnqJIrcyKFbJ0OyHBcF/hwnLR02iAjh3NGjIREZFDUz25mTlzJgYPHowBAwYAAObOnYs1a9YgKioK7733nsnHaDQa+Pv7WzNMk1JSgLp1jZOTp/n7AxcuSJ2Mfn8mjQZ4913g8GG5XbQo8PLLUpfcvDlgCwNRRERE9krVaanU1FTs378fERERacdcXFwQERGB3bt3Z/q4e/fuITg4GEFBQejQoQP++eefTM9NSUlBUlKS0cVc3NxkKimzTSVdXGQUZvBgGb25d89w32uvAYMGAevWSXK0aJHU0jCxISIiyh9Vk5ubN29Cq9XCz8/P6Lifnx/i4+NNPqZy5cqIiorCr7/+ip9++gk6nQ7169fHlUy2QZg+fTp8fHzSLkH6DZbMQKORDSh1OtP363TAmTPAjz9KnfH69Yb7RowA5s2TImE3N7OFRERE5PRsoqA4N8LDw9G3b1+EhISgcePGiImJQYkSJfDtt9+aPH/cuHFITExMu1xOvw+BGbRoIVNTmdUPBwZKsfC2bUCnTmZ9aSIiIjJB1ZobX19fuLq6IuGpopWEhIQc19QULFgQtWrVwpkzZ0ze7+7uDnd393zHmhn96I2+kV56kZEyQpPZtBURERGZn6ofu25ubggNDUVsbGzaMZ1Oh9jYWISHh+foObRaLY4ePYoAK+zflJmnR29cXeX2W28xsSEiIrI21T96R40ahXnz5mHRokU4ceIEhg4divv376etnurbty/GjRuXdv6HH36IjRs34ty5czhw4AB69+6NixcvYtCgQWp9C2mjN1qt3NZq5TY7BhMREVmf6kvBu3fvjhs3bmDSpEmIj49HSEgI1q9fn1ZkfOnSJbikG/64c+cOBg8ejPj4eBQrVgyhoaHYtWsXqlWrpta3AMAwerNvn3xt0ULVcIiIiJyWRlFMbWftuJKSkuDj44PExER4e3ub9bk3bZKpqFmzgHSr24mIiCifcvP5rfrIjSOJiDDesZuIiIisT/WaGyIiIiJzYnJDREREDoXJDRERETkUJjdERETkUJjcEBERkUNhckNEREQOhckNERERORQmN0RERORQmNwQERGRQ2FyQ0RERA7F6bZf0G+llZSUpHIkRERElFP6z+2cbInpdMlNcnIyACAoKEjlSIiIiCi3kpOT4ePjk+U5TrcruE6nw7Vr11CkSBFoNBoAQN26dbFv3z6T52d2n6njSUlJCAoKwuXLl82+43h+ZPX9qfGcuX1sTs/P7jxHfp8t8R7n93n5Ppufs7zP+T2H77P5n9cW3mdFUZCcnIxSpUrBxSXrqhqnG7lxcXFB6dKljY65urpm+kud2X1ZPcbb29tm/pEAWceqxnPm9rE5PT+78xz5fbbEe5zf5+X7bH7O8j7n9xy+z+Z/Xlt5n7MbsdFjQTGAYcOG5fq+rB5jaywRa36eM7ePzen52Z3nyO+zpeLk+2xbnOV9zu85fJ/N/7y2+D5nxemmpSwpKSkJPj4+SExMtJm/AMj8+D47B77PzoHvs2PiyI0Zubu7Y/LkyXB3d1c7FLIgvs/Oge+zc+D77Jg4ckNEREQOhSM3RERE5FCY3BAREZFDYXJDREREDoXJDRERETkUJjdERETkUJjcqODy5cto0qQJqlWrhho1amDZsmVqh0QW0qlTJxQrVgxdu3ZVOxQyo99//x2VK1dGpUqV8P3336sdDlkI//3aLy4FV0FcXBwSEhIQEhKC+Ph4hIaG4vTp0/Dy8lI7NDKzrVu3Ijk5GYsWLcLy5cvVDofM4MmTJ6hWrRq2bNkCHx8fhIaGYteuXXjmmWfUDo3MjP9+7RdHblQQEBCAkJAQAIC/vz98fX1x+/ZtdYMii2jSpAmKFCmidhhkRnv37sVzzz2HwMBAFC5cGK1bt8bGjRvVDossgP9+7ReTGxO2bduG9u3bo1SpUtBoNFi1alWGc+bMmYOyZcvCw8MD9erVw969e/P0Wvv374dWq0VQUFA+o6bcsub7TLYjv+/7tWvXEBgYmHY7MDAQV69etUbolAv89+3cmNyYcP/+fdSsWRNz5swxeX90dDRGjRqFyZMn48CBA6hZsyZatmyJ69evp50TEhKC559/PsPl2rVraefcvn0bffv2xXfffWfx74kystb7TLbFHO872T6+z05OoSwBUFauXGl0LCwsTBk2bFjaba1Wq5QqVUqZPn16jp/30aNHSsOGDZUffvjBXKFSPljqfVYURdmyZYvSpUsXc4RJZpaX933nzp1Kx44d0+4fOXKksnjxYqvES3mTn3/f/Pdrnzhyk0upqanYv38/IiIi0o65uLggIiICu3fvztFzKIqC/v37o2nTpujTp4+lQqV8MMf7TPYnJ+97WFgYjh07hqtXr+LevXtYt24dWrZsqVbIlAf89+34mNzk0s2bN6HVauHn52d03M/PD/Hx8Tl6jp07dyI6OhqrVq1CSEgIQkJCcPToUUuES3lkjvcZACIiIvDKK69g7dq1KF26NP/jtHE5ed8LFCiAzz//HC+99BJCQkLw7rvvcqWUncnpv2/++7VfBdQOwBm9+OKL0Ol0aodBVrBp0ya1QyALePnll/Hyyy+rHQZZGP/92i+O3OSSr68vXF1dkZCQYHQ8ISEB/v7+KkVF5sb32TnxfXcOfJ8dH5ObXHJzc0NoaChiY2PTjul0OsTGxiI8PFzFyMic+D47J77vzoHvs+PjtJQJ9+7dw5kzZ9Junz9/HocOHULx4sVRpkwZjBo1Cv369UOdOnUQFhaGyMhI3L9/HwMGDFAxasotvs/Oie+7c+D77OTUXq5li7Zs2aIAyHDp169f2jlfffWVUqZMGcXNzU0JCwtT/vrrL/UCpjzh++yc+L47B77Pzo17SxEREZFDYc0NERERORQmN0RERORQmNwQERGRQ2FyQ0RERA6FyQ0RERE5FCY3RERE5FCY3BAREZFDYXJDREREDoXJDRERETkUJjdE5BDi4+MxYsQIlC9fHu7u7ggKCkL79u2NNkckIufAjTOJyO5duHABDRo0QNGiRTFjxgxUr14djx8/xoYNGzBs2DCcPHlS7RCJyIq4txQR2b02bdrgyJEjOHXqFLy8vIzuu3v3LooWLapOYESkCk5LEZFdu337NtavX49hw4ZlSGwAMLEhckJMbojIrp05cwaKoqBKlSpqh0JENoLJDRHZNc6sE9HTmNwQkV2rVKkSNBoNi4aJKA0LionI7rVu3RpHjx5lQTERAeDIDRE5gDlz5kCr1SIsLAwrVqzAv//+ixMnTmDWrFkIDw9XOzwisjKO3BCRQ4iLi8NHH32E33//HXFxcShRogRCQ0PxzjvvoEmTJmqHR0RWxOSGiIiIHAqnpYiIiMihMLkhIiIih8LkhoiIiBwKkxsiIiJyKExuiIiIyKEwuSEiIiKHwuSGiIiIHAqTGyIiInIoTG6IiIjIoTC5ISIiIofC5IaIiIgcCpMbIiIicij/DzuTj6tDkweLAAAAAElFTkSuQmCC", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.svm import SVC\n", + "\n", + "C = [0.01, 0.1, 0.2, 0.5, 0.8, 1, 5, 10, 20, 50]\n", + "SVMtrainAcc = []\n", + "SVMtestAcc = []\n", + "\n", + "for param in C:\n", + " clf = SVC(C=param,kernel='rbf',gamma='auto')\n", + " clf.fit(X_train, Y_train)\n", + " Y_predTrain = clf.predict(X_train)\n", + " Y_predTest = clf.predict(X_test)\n", + " SVMtrainAcc.append(accuracy_score(Y_train, Y_predTrain))\n", + " SVMtestAcc.append(accuracy_score(Y_test, Y_predTest))\n", + "\n", + "plt.plot(C, SVMtrainAcc, 'ro-', C, SVMtestAcc,'bv--')\n", + "plt.legend(['Training Accuracy','Test Accuracy'])\n", + "plt.xlabel('C')\n", + "plt.xscale('log')\n", + "plt.ylabel('Accuracy')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Observe that the nonlinear SVM can achieve a higher test accuracy compared to linear SVM." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.4.4 Ensemble Methods\n", + "\n", + "An ensemble classifier constructs a set of base classifiers from the training data and performs classification by taking a vote on the predictions made by each base classifier. We consider 3 types of ensemble classifiers in this example: bagging, boosting, and random forest. Detailed explanation about these classifiers can be found in Section 4.10 of the book.\n", + "\n", + "In the example below, we fit 500 base classifiers to the 2-dimensional dataset using each ensemble method. The base classifier corresponds to a decision tree with maximum depth equals to 10." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/tomg/Source/courses/tools4ds/DS701-Course-Notes/.venv/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/plain": [ + "[Text(1.5, 0, 'Random Forest'),\n", + " Text(2.5, 0, 'Bagging'),\n", + " Text(3.5, 0, 'AdaBoost')]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn import ensemble\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "\n", + "numBaseClassifiers = 500\n", + "maxdepth = 10\n", + "trainAcc = []\n", + "testAcc = []\n", + "\n", + "clf = ensemble.RandomForestClassifier(n_estimators=numBaseClassifiers)\n", + "clf.fit(X_train, Y_train)\n", + "Y_predTrain = clf.predict(X_train)\n", + "Y_predTest = clf.predict(X_test)\n", + "trainAcc.append(accuracy_score(Y_train, Y_predTrain))\n", + "testAcc.append(accuracy_score(Y_test, Y_predTest))\n", + "\n", + "clf = ensemble.BaggingClassifier(DecisionTreeClassifier(max_depth=maxdepth),n_estimators=numBaseClassifiers)\n", + "clf.fit(X_train, Y_train)\n", + "Y_predTrain = clf.predict(X_train)\n", + "Y_predTest = clf.predict(X_test)\n", + "trainAcc.append(accuracy_score(Y_train, Y_predTrain))\n", + "testAcc.append(accuracy_score(Y_test, Y_predTest))\n", + "\n", + "clf = ensemble.AdaBoostClassifier(DecisionTreeClassifier(max_depth=maxdepth),n_estimators=numBaseClassifiers)\n", + "clf.fit(X_train, Y_train)\n", + "Y_predTrain = clf.predict(X_train)\n", + "Y_predTest = clf.predict(X_test)\n", + "trainAcc.append(accuracy_score(Y_train, Y_predTrain))\n", + "testAcc.append(accuracy_score(Y_test, Y_predTest))\n", + "\n", + "methods = ['Random Forest', 'Bagging', 'AdaBoost']\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12,6))\n", + "ax1.bar([1.5,2.5,3.5], trainAcc)\n", + "ax1.set_xticks([1.5,2.5,3.5])\n", + "ax1.set_xticklabels(methods)\n", + "ax2.bar([1.5,2.5,3.5], testAcc)\n", + "ax2.set_xticks([1.5,2.5,3.5])\n", + "ax2.set_xticklabels(methods)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.5 Summary\n", + "\n", + "This section provides several examples of using Python sklearn library to build classification models from a given input data. We also illustrate the problem of model overfitting and show how to apply different classification methods to the given dataset." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/ds701_book/iris b/ds701_book/iris new file mode 100644 index 00000000..864ab2b1 --- /dev/null +++ b/ds701_book/iris @@ -0,0 +1,37 @@ +digraph Tree { +node [shape=box, fontname="helvetica"] ; +edge [fontname="helvetica"] ; +0 [label="x[2] <= 2.45\ngini = 0.667\nsamples = 150\nvalue = [50, 50, 50]"] ; +1 [label="gini = 0.0\nsamples = 50\nvalue = [50, 0, 0]"] ; +0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ; +2 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100644 index 00000000..6a8d9330 --- /dev/null +++ b/ds701_book/remove_dev_fences.py @@ -0,0 +1,15 @@ +import sys +import json + +def remove_div_fences(cell): + if cell['cell_type'] == 'markdown': + cell['source'] = ''.join(line for line in cell['source'] if not line.startswith(':::')) + return cell + +def main(): + notebook = json.load(sys.stdin) + notebook['cells'] = [remove_div_fences(cell) for cell in notebook['cells']] + json.dump(notebook, sys.stdout) + +if __name__ == '__main__': + main() diff --git a/ds701_book/sklearn-trees.ipynb b/ds701_book/sklearn-trees.ipynb new file mode 100644 index 00000000..92ee15f7 --- /dev/null +++ b/ds701_book/sklearn-trees.ipynb @@ -0,0 +1,1271 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "title: \"14b. Decision Trees in sklearn\"\n", + "---\n", + "\n", + "## Decision Trees\n", + "\n", + "**Decision Trees (DTs)** are a non-parametric supervised learning method used\n", + "for :ref:`classification ` and :ref:`regression\n", + "`. The goal is to create a model that predicts the value of a\n", + "target variable by learning simple decision rules inferred from the data\n", + "features. A tree can be seen as a piecewise constant approximation.\n", + "\n", + "For instance, in the example below, decision trees learn from data to\n", + "approximate a sine curve with a set of if-then-else decision rules. The deeper\n", + "the tree, the more complex the decision rules and the fitter the model.\n", + "\n", + ".. figure:: ../auto_examples/tree/images/sphx_glr_plot_tree_regression_001.png\n", + " :target: ../auto_examples/tree/plot_tree_regression.html\n", + " :scale: 75\n", + " :align: center\n", + "\n", + "Some advantages of decision trees are:\n", + "\n", + "- Simple to understand and to interpret. Trees can be visualized.\n", + "\n", + "- Requires little data preparation. Other techniques often require data\n", + " normalization, dummy variables need to be created and blank values to\n", + " be removed. Some tree and algorithm combinations support\n", + " :ref:`missing values `.\n", + "\n", + "- The cost of using the tree (i.e., predicting data) is logarithmic in the\n", + " number of data points used to train the tree.\n", + "\n", + "- Able to handle both numerical and categorical data. However, the scikit-learn\n", + " implementation does not support categorical variables for now. Other\n", + " techniques are usually specialized in analyzing datasets that have only one type\n", + " of variable. See :ref:`algorithms ` for more\n", + " information.\n", + "\n", + "- Able to handle multi-output problems.\n", + "\n", + "- Uses a white box model. If a given situation is observable in a model,\n", + " the explanation for the condition is easily explained by boolean logic.\n", + " By contrast, in a black box model (e.g., in an artificial neural\n", + " network), results may be more difficult to interpret.\n", + "\n", + "- Possible to validate a model using statistical tests. That makes it\n", + " possible to account for the reliability of the model.\n", + "\n", + "- Performs well even if its assumptions are somewhat violated by\n", + " the true model from which the data were generated.\n", + "\n", + "\n", + "The disadvantages of decision trees include:\n", + "\n", + "- Decision-tree learners can create over-complex trees that do not\n", + " generalize the data well. This is called overfitting. Mechanisms\n", + " such as pruning, setting the minimum number of samples required\n", + " at a leaf node or setting the maximum depth of the tree are\n", + " necessary to avoid this problem.\n", + "\n", + "- Decision trees can be unstable because small variations in the\n", + " data might result in a completely different tree being generated.\n", + " This problem is mitigated by using decision trees within an\n", + " ensemble.\n", + "\n", + "- Predictions of decision trees are neither smooth nor continuous, but\n", + " piecewise constant approximations as seen in the above figure. Therefore,\n", + " they are not good at extrapolation.\n", + "\n", + "- The problem of learning an optimal decision tree is known to be\n", + " NP-complete under several aspects of optimality and even for simple\n", + " concepts. Consequently, practical decision-tree learning algorithms\n", + " are based on heuristic algorithms such as the greedy algorithm where\n", + " locally optimal decisions are made at each node. Such algorithms\n", + " cannot guarantee to return the globally optimal decision tree. This\n", + " can be mitigated by training multiple trees in an ensemble learner,\n", + " where the features and samples are randomly sampled with replacement.\n", + "\n", + "- There are concepts that are hard to learn because decision trees\n", + " do not express them easily, such as XOR, parity or multiplexer problems.\n", + "\n", + "- Decision tree learners create biased trees if some classes dominate.\n", + " It is therefore recommended to balance the dataset prior to fitting\n", + " with the decision tree.\n", + "\n", + "\n", + "## Classification\n", + "\n", + "`DecisionTreeClassifier` is a class capable of performing multi-class\n", + "classification on a dataset.\n", + "\n", + "As with other classifiers, :class:`DecisionTreeClassifier` takes as input two arrays:\n", + "an array X, sparse or dense, of shape ``(n_samples, n_features)`` holding the\n", + "training samples, and an array Y of integer values, shape ``(n_samples,)``,\n", + "holding the class labels for the training samples::" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn import tree\n", + "X = [[0, 0], [1, 1]]\n", + "Y = [0, 1]\n", + "clf = tree.DecisionTreeClassifier()\n", + "clf = clf.fit(X, Y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After being fitted, the model can then be used to predict the class of samples::" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1])" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clf.predict([[2., 2.]])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In case that there are multiple classes with the same and highest\n", + "probability, the classifier will predict the class with the lowest index\n", + "amongst those classes.\n", + "\n", + "As an alternative to outputting a specific class, the probability of each class\n", + "can be predicted, which is the fraction of training samples of the class in a\n", + "leaf::" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0., 1.]])" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clf.predict_proba([[2., 2.]])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`DecisionTreeClassifier` is capable of both binary (where the\n", + "labels are [-1, 1]) classification and multiclass (where the labels are\n", + "[0, ..., K-1]) classification.\n", + "\n", + "Using the Iris dataset, we can construct a tree as follows::" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.datasets import load_iris\n", + "from sklearn import tree\n", + "iris = load_iris()\n", + "X, y = iris.data, iris.target\n", + "clf = tree.DecisionTreeClassifier()\n", + "clf = clf.fit(X, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once trained, you can plot the tree with the :func:`plot_tree` function::" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[Text(0.5, 0.9166666666666666, 'x[2] <= 2.45\\ngini = 0.667\\nsamples = 150\\nvalue = [50, 50, 50]'),\n", + " Text(0.4230769230769231, 0.75, 'gini = 0.0\\nsamples = 50\\nvalue = [50, 0, 0]'),\n", + " Text(0.46153846153846156, 0.8333333333333333, 'True '),\n", + " Text(0.5769230769230769, 0.75, 'x[3] <= 1.75\\ngini = 0.5\\nsamples = 100\\nvalue = [0, 50, 50]'),\n", + " Text(0.5384615384615384, 0.8333333333333333, ' False'),\n", + " Text(0.3076923076923077, 0.5833333333333334, 'x[2] <= 4.95\\ngini = 0.168\\nsamples = 54\\nvalue = [0, 49, 5]'),\n", + " Text(0.15384615384615385, 0.4166666666666667, 'x[3] <= 1.65\\ngini = 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "tree.plot_tree(clf)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + ".. figure:: ../auto_examples/tree/images/sphx_glr_plot_iris_dtc_002.png\n", + " :target: ../auto_examples/tree/plot_iris_dtc.html\n", + " :scale: 75\n", + " :align: center\n", + "\n", + ".. dropdown:: Alternative ways to export trees\n", + "\n", + " We can also export the tree in `Graphviz\n", + " `_ format using the :func:`export_graphviz`\n", + " exporter. If you use the `conda `_ package manager, the graphviz binaries\n", + " and the python package can be installed with `conda install python-graphviz`.\n", + "\n", + " Alternatively binaries for graphviz can be downloaded from the graphviz project homepage,\n", + " and the Python wrapper installed from pypi with `pip install graphviz`.\n", + "\n", + " Below is an example graphviz export of the above tree trained on the entire\n", + " iris dataset; the results are saved in an output file `iris.pdf`::" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'iris.pdf'" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import graphviz # doctest: +SKIP\n", + "dot_data = tree.export_graphviz(clf, out_file=None) # doctest: +SKIP\n", + "graph = graphviz.Source(dot_data) # doctest: +SKIP\n", + "graph.render(\"iris\") # doctest: +SKIP" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " The :func:`export_graphviz` exporter also supports a variety of aesthetic\n", + " options, including coloring nodes by their class (or value for regression) and\n", + " using explicit variable and class names if desired. Jupyter notebooks also\n", + " render these plots inline automatically::" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Tree\n", + "\n", + "\n", + "\n", + "0\n", + "\n", + "petal length (cm) ≤ 2.45\n", + "gini = 0.667\n", + "samples = 150\n", + "value = [50, 50, 50]\n", + "class = setosa\n", + "\n", + "\n", + "\n", + "1\n", + "\n", + "gini = 0.0\n", + "samples = 50\n", + "value = [50, 0, 0]\n", + "class = setosa\n", + "\n", + "\n", + "\n", + "0->1\n", + "\n", + "\n", + "True\n", + "\n", + "\n", + "\n", + "2\n", + "\n", + "petal width (cm) ≤ 1.75\n", + "gini = 0.5\n", + "samples = 100\n", + "value = [0, 50, 50]\n", + "class = versicolor\n", + "\n", + "\n", + "\n", + "0->2\n", + "\n", + "\n", + "False\n", + "\n", + "\n", + "\n", + "3\n", + "\n", + "petal length (cm) ≤ 4.95\n", + "gini = 0.168\n", + "samples = 54\n", + "value = [0, 49, 5]\n", + "class = versicolor\n", + "\n", + "\n", + "\n", + "2->3\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "12\n", + "\n", + "petal length (cm) ≤ 4.85\n", + "gini = 0.043\n", + "samples = 46\n", + "value = [0, 1, 45]\n", + "class = virginica\n", + "\n", + "\n", + "\n", + "2->12\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "4\n", + "\n", + "petal width (cm) ≤ 1.65\n", + "gini = 0.041\n", + "samples = 48\n", + "value = [0, 47, 1]\n", + "class = versicolor\n", + "\n", + "\n", + "\n", + "3->4\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "7\n", + "\n", + "petal width (cm) ≤ 1.55\n", + "gini = 0.444\n", + "samples = 6\n", + "value = [0, 2, 4]\n", + "class = virginica\n", + "\n", + "\n", + "\n", + "3->7\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "5\n", + "\n", + "gini = 0.0\n", + "samples = 47\n", + "value = [0, 47, 0]\n", + "class = versicolor\n", + "\n", + "\n", + "\n", + "4->5\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "6\n", + "\n", + "gini = 0.0\n", + "samples = 1\n", + "value = [0, 0, 1]\n", + "class = virginica\n", + "\n", + "\n", + "\n", + "4->6\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "8\n", + "\n", + "gini = 0.0\n", + "samples = 3\n", + "value = [0, 0, 3]\n", + "class = virginica\n", + "\n", + "\n", + "\n", + "7->8\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "9\n", + "\n", + "petal length (cm) ≤ 5.45\n", + "gini = 0.444\n", + "samples = 3\n", + "value = [0, 2, 1]\n", + "class = versicolor\n", + "\n", + "\n", + "\n", + "7->9\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "10\n", + "\n", + "gini = 0.0\n", + "samples = 2\n", + "value = [0, 2, 0]\n", + "class = versicolor\n", + "\n", + "\n", + "\n", + "9->10\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "11\n", + "\n", + "gini = 0.0\n", + "samples = 1\n", + "value = [0, 0, 1]\n", + "class = virginica\n", + "\n", + "\n", + "\n", + "9->11\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "13\n", + "\n", + "sepal width (cm) ≤ 3.1\n", + "gini = 0.444\n", + "samples = 3\n", + "value = [0, 1, 2]\n", + "class = virginica\n", + "\n", + "\n", + "\n", + "12->13\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "16\n", + "\n", + "gini = 0.0\n", + "samples = 43\n", + "value = [0, 0, 43]\n", + "class = virginica\n", + "\n", + "\n", + "\n", + "12->16\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "14\n", + "\n", + "gini = 0.0\n", + "samples = 2\n", + "value = [0, 0, 2]\n", + "class = virginica\n", + "\n", + "\n", + "\n", + "13->14\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "15\n", + "\n", + "gini = 0.0\n", + "samples = 1\n", + "value = [0, 1, 0]\n", + "class = versicolor\n", + "\n", + "\n", + "\n", + "13->15\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dot_data = tree.export_graphviz(clf, out_file=None, # doctest: +SKIP\n", + " feature_names=iris.feature_names, # doctest: +SKIP\n", + " class_names=iris.target_names, # doctest: +SKIP\n", + " filled=True, rounded=True, # doctest: +SKIP\n", + " special_characters=True) # doctest: +SKIP\n", + "graph = graphviz.Source(dot_data) # doctest: +SKIP\n", + "graph # doctest: +SKIP" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " .. only:: html\n", + "\n", + " .. figure:: ../images/iris.svg\n", + " :align: center\n", + "\n", + " .. only:: latex\n", + "\n", + " .. figure:: ../images/iris.pdf\n", + " :align: center\n", + "\n", + " .. figure:: ../auto_examples/tree/images/sphx_glr_plot_iris_dtc_001.png\n", + " :target: ../auto_examples/tree/plot_iris_dtc.html\n", + " :align: center\n", + " :scale: 75\n", + "\n", + " Alternatively, the tree can also be exported in textual format with the\n", + " function :func:`export_text`. This method doesn't require the installation\n", + " of external libraries and is more compact:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "|--- petal width (cm) <= 0.80\n", + "| |--- class: 0\n", + "|--- petal width (cm) > 0.80\n", + "| |--- petal width (cm) <= 1.75\n", + "| | |--- class: 1\n", + "| |--- petal width (cm) > 1.75\n", + "| | |--- class: 2\n", + "\n" + ] + } + ], + "source": [ + "from sklearn.datasets import load_iris\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.tree import export_text\n", + "iris = load_iris()\n", + "decision_tree = DecisionTreeClassifier(random_state=0, max_depth=2)\n", + "decision_tree = decision_tree.fit(iris.data, iris.target)\n", + "r = export_text(decision_tree, feature_names=iris['feature_names'])\n", + "print(r)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " |--- petal width (cm) <= 0.80\n", + " | |--- class: 0\n", + " |--- petal width (cm) > 0.80\n", + " | |--- petal width (cm) <= 1.75\n", + " | | |--- class: 1\n", + " | |--- petal width (cm) > 1.75\n", + " | | |--- class: 2\n", + " \n", + "\n", + ".. rubric:: Examples\n", + "\n", + "* :ref:`sphx_glr_auto_examples_tree_plot_iris_dtc.py`\n", + "* :ref:`sphx_glr_auto_examples_tree_plot_unveil_tree_structure.py`\n", + "\n", + ".. _tree_regression:\n", + "\n", + "## Regression\n", + "\n", + ".. figure:: ../auto_examples/tree/images/sphx_glr_plot_tree_regression_001.png\n", + " :target: ../auto_examples/tree/plot_tree_regression.html\n", + " :scale: 75\n", + " :align: center\n", + "\n", + "Decision trees can also be applied to regression problems, using the\n", + ":class:`DecisionTreeRegressor` class.\n", + "\n", + "As in the classification setting, the fit method will take as argument arrays X\n", + "and y, only that in this case y is expected to have floating point values\n", + "instead of integer values::" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.5])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn import tree\n", + "X = [[0, 0], [2, 2]]\n", + "y = [0.5, 2.5]\n", + "clf = tree.DecisionTreeRegressor()\n", + "clf = clf.fit(X, y)\n", + "clf.predict([[1, 1]])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " array([0.5])\n", + "\n", + ".. rubric:: Examples\n", + "\n", + "* :ref:`sphx_glr_auto_examples_tree_plot_tree_regression.py`\n", + "\n", + "\n", + ".. _tree_multioutput:\n", + "\n", + "## Multi-output problems\n", + "\n", + "A multi-output problem is a supervised learning problem with several outputs\n", + "to predict, that is when Y is a 2d array of shape ``(n_samples, n_outputs)``.\n", + "\n", + "When there is no correlation between the outputs, a very simple way to solve\n", + "this kind of problem is to build n independent models, i.e. one for each\n", + "output, and then to use those models to independently predict each one of the n\n", + "outputs. However, because it is likely that the output values related to the\n", + "same input are themselves correlated, an often better way is to build a single\n", + "model capable of predicting simultaneously all n outputs. First, it requires\n", + "lower training time since only a single estimator is built. Second, the\n", + "generalization accuracy of the resulting estimator may often be increased.\n", + "\n", + "With regard to decision trees, this strategy can readily be used to support\n", + "multi-output problems. This requires the following changes:\n", + "\n", + "- Store n output values in leaves, instead of 1;\n", + "- Use splitting criteria that compute the average reduction across all\n", + " n outputs.\n", + "\n", + "This module offers support for multi-output problems by implementing this\n", + "strategy in both :class:`DecisionTreeClassifier` and\n", + ":class:`DecisionTreeRegressor`. If a decision tree is fit on an output array Y\n", + "of shape ``(n_samples, n_outputs)`` then the resulting estimator will:\n", + "\n", + "* Output n_output values upon ``predict``;\n", + "\n", + "* Output a list of n_output arrays of class probabilities upon\n", + " ``predict_proba``.\n", + "\n", + "The use of multi-output trees for regression is demonstrated in\n", + ":ref:`sphx_glr_auto_examples_tree_plot_tree_regression_multioutput.py`. In this example, the input\n", + "X is a single real value and the outputs Y are the sine and cosine of X.\n", + "\n", + ".. figure:: ../auto_examples/tree/images/sphx_glr_plot_tree_regression_multioutput_001.png\n", + " :target: ../auto_examples/tree/plot_tree_regression_multioutput.html\n", + " :scale: 75\n", + " :align: center\n", + "\n", + "The use of multi-output trees for classification is demonstrated in\n", + ":ref:`sphx_glr_auto_examples_miscellaneous_plot_multioutput_face_completion.py`. In this example, the inputs\n", + "X are the pixels of the upper half of faces and the outputs Y are the pixels of\n", + "the lower half of those faces.\n", + "\n", + ".. figure:: ../auto_examples/miscellaneous/images/sphx_glr_plot_multioutput_face_completion_001.png\n", + " :target: ../auto_examples/miscellaneous/plot_multioutput_face_completion.html\n", + " :scale: 75\n", + " :align: center\n", + "\n", + ".. rubric:: Examples\n", + "\n", + "* :ref:`sphx_glr_auto_examples_tree_plot_tree_regression_multioutput.py`\n", + "* :ref:`sphx_glr_auto_examples_miscellaneous_plot_multioutput_face_completion.py`\n", + "\n", + ".. rubric:: References\n", + "\n", + "* M. Dumont et al, `Fast multi-class image annotation with random subwindows\n", + " and multiple output randomized trees\n", + " `_,\n", + " International Conference on Computer Vision Theory and Applications 2009\n", + "\n", + ".. _tree_complexity:\n", + "\n", + "## Complexity\n", + "\n", + "In general, the run time cost to construct a balanced binary tree is\n", + ":math:`O(n_{samples}n_{features}\\log(n_{samples}))` and query time\n", + ":math:`O(\\log(n_{samples}))`. Although the tree construction algorithm attempts\n", + "to generate balanced trees, they will not always be balanced. Assuming that the\n", + "subtrees remain approximately balanced, the cost at each node consists of\n", + "searching through :math:`O(n_{features})` to find the feature that offers the\n", + "largest reduction in the impurity criterion, e.g. log loss (which is equivalent to an\n", + "information gain). This has a cost of\n", + ":math:`O(n_{features}n_{samples}\\log(n_{samples}))` at each node, leading to a\n", + "total cost over the entire trees (by summing the cost at each node) of\n", + ":math:`O(n_{features}n_{samples}^{2}\\log(n_{samples}))`.\n", + "\n", + "\n", + "## Tips on practical use\n", + "\n", + "* Decision trees tend to overfit on data with a large number of features.\n", + " Getting the right ratio of samples to number of features is important, since\n", + " a tree with few samples in high dimensional space is very likely to overfit.\n", + "\n", + "* Consider performing dimensionality reduction (:ref:`PCA `,\n", + " :ref:`ICA `, or :ref:`feature_selection`) beforehand to\n", + " give your tree a better chance of finding features that are discriminative.\n", + "\n", + "* :ref:`sphx_glr_auto_examples_tree_plot_unveil_tree_structure.py` will help\n", + " in gaining more insights about how the decision tree makes predictions, which is\n", + " important for understanding the important features in the data.\n", + "\n", + "* Visualize your tree as you are training by using the ``export``\n", + " function. Use ``max_depth=3`` as an initial tree depth to get a feel for\n", + " how the tree is fitting to your data, and then increase the depth.\n", + "\n", + "* Remember that the number of samples required to populate the tree doubles\n", + " for each additional level the tree grows to. Use ``max_depth`` to control\n", + " the size of the tree to prevent overfitting.\n", + "\n", + "* Use ``min_samples_split`` or ``min_samples_leaf`` to ensure that multiple\n", + " samples inform every decision in the tree, by controlling which splits will\n", + " be considered. A very small number will usually mean the tree will overfit,\n", + " whereas a large number will prevent the tree from learning the data. Try\n", + " ``min_samples_leaf=5`` as an initial value. If the sample size varies\n", + " greatly, a float number can be used as percentage in these two parameters.\n", + " While ``min_samples_split`` can create arbitrarily small leaves,\n", + " ``min_samples_leaf`` guarantees that each leaf has a minimum size, avoiding\n", + " low-variance, over-fit leaf nodes in regression problems. For\n", + " classification with few classes, ``min_samples_leaf=1`` is often the best\n", + " choice.\n", + "\n", + " Note that ``min_samples_split`` considers samples directly and independent of\n", + " ``sample_weight``, if provided (e.g. a node with m weighted samples is still\n", + " treated as having exactly m samples). Consider ``min_weight_fraction_leaf`` or\n", + " ``min_impurity_decrease`` if accounting for sample weights is required at splits.\n", + "\n", + "* Balance your dataset before training to prevent the tree from being biased\n", + " toward the classes that are dominant. Class balancing can be done by\n", + " sampling an equal number of samples from each class, or preferably by\n", + " normalizing the sum of the sample weights (``sample_weight``) for each\n", + " class to the same value. Also note that weight-based pre-pruning criteria,\n", + " such as ``min_weight_fraction_leaf``, will then be less biased toward\n", + " dominant classes than criteria that are not aware of the sample weights,\n", + " like ``min_samples_leaf``.\n", + "\n", + "* If the samples are weighted, it will be easier to optimize the tree\n", + " structure using weight-based pre-pruning criterion such as\n", + " ``min_weight_fraction_leaf``, which ensure that leaf nodes contain at least\n", + " a fraction of the overall sum of the sample weights.\n", + "\n", + "* All decision trees use ``np.float32`` arrays internally.\n", + " If training data is not in this format, a copy of the dataset will be made.\n", + "\n", + "* If the input matrix X is very sparse, it is recommended to convert to sparse\n", + " ``csc_matrix`` before calling fit and sparse ``csr_matrix`` before calling\n", + " predict. Training time can be orders of magnitude faster for a sparse\n", + " matrix input compared to a dense matrix when features have zero values in\n", + " most of the samples.\n", + "\n", + "\n", + ".. _tree_algorithms:\n", + "\n", + "## Tree algorithms: ID3, C4.5, C5.0 and CART\n", + "\n", + "What are all the various decision tree algorithms and how do they differ\n", + "from each other? Which one is implemented in scikit-learn?\n", + "\n", + ".. dropdown:: Various decision tree algorithms\n", + "\n", + " ID3_ (Iterative Dichotomiser 3) was developed in 1986 by Ross Quinlan.\n", + " The algorithm creates a multiway tree, finding for each node (i.e. in\n", + " a greedy manner) the categorical feature that will yield the largest\n", + " information gain for categorical targets. Trees are grown to their\n", + " maximum size and then a pruning step is usually applied to improve the\n", + " ability of the tree to generalize to unseen data.\n", + "\n", + " C4.5 is the successor to ID3 and removed the restriction that features\n", + " must be categorical by dynamically defining a discrete attribute (based\n", + " on numerical variables) that partitions the continuous attribute value\n", + " into a discrete set of intervals. C4.5 converts the trained trees\n", + " (i.e. the output of the ID3 algorithm) into sets of if-then rules.\n", + " The accuracy of each rule is then evaluated to determine the order\n", + " in which they should be applied. Pruning is done by removing a rule's\n", + " precondition if the accuracy of the rule improves without it.\n", + "\n", + " C5.0 is Quinlan's latest version release under a proprietary license.\n", + " It uses less memory and builds smaller rulesets than C4.5 while being\n", + " more accurate.\n", + "\n", + " CART (Classification and Regression Trees) is very similar to C4.5, but\n", + " it differs in that it supports numerical target variables (regression) and\n", + " does not compute rule sets. CART constructs binary trees using the feature\n", + " and threshold that yield the largest information gain at each node.\n", + "\n", + "scikit-learn uses an optimized version of the CART algorithm; however, the\n", + "scikit-learn implementation does not support categorical variables for now.\n", + "\n", + ".. _ID3: https://en.wikipedia.org/wiki/ID3_algorithm\n", + "\n", + "\n", + ".. _tree_mathematical_formulation:\n", + "\n", + "## Mathematical formulation\n", + "\n", + "Given training vectors :math:`x_i \\in R^n`, i=1,..., l and a label vector\n", + ":math:`y \\in R^l`, a decision tree recursively partitions the feature space\n", + "such that the samples with the same labels or similar target values are grouped\n", + "together.\n", + "\n", + "Let the data at node :math:`m` be represented by :math:`Q_m` with :math:`n_m`\n", + "samples. For each candidate split :math:`\\theta = (j, t_m)` consisting of a\n", + "feature :math:`j` and threshold :math:`t_m`, partition the data into\n", + ":math:`Q_m^{left}(\\theta)` and :math:`Q_m^{right}(\\theta)` subsets\n", + "\n", + "$$\n", + "\\begin{aligned}\n", + "Q_m^{left}(\\theta) &= \\{(x, y) | x_j \\leq t_m\\} \\\\\n", + "Q_m^{right}(\\theta) &= Q_m \\setminus Q_m^{left}(\\theta)\n", + "\\end{aligned}\n", + "$$\n", + "\n", + "The quality of a candidate split of node :math:`m` is then computed using an\n", + "impurity function or loss function :math:`H()`, the choice of which depends on\n", + "the task being solved (classification or regression)\n", + "\n", + "$$\n", + "G(Q_m, \\theta) = \\frac{n_m^{left}}{n_m} H(Q_m^{left}(\\theta))\n", + "+ \\frac{n_m^{right}}{n_m} H(Q_m^{right}(\\theta))\n", + "$$\n", + "\n", + "Select the parameters that minimises the impurity\n", + "\n", + "$$\n", + "\\theta^* = \\operatorname{argmin}_\\theta G(Q_m, \\theta)\n", + "$$\n", + "\n", + "Recurse for subsets :math:`Q_m^{left}(\\theta^*)` and\n", + ":math:`Q_m^{right}(\\theta^*)` until the maximum allowable depth is reached,\n", + ":math:`n_m < \\min_{samples}` or :math:`n_m = 1`.\n", + "\n", + "## Classification criteria\n", + "\n", + "If a target is a classification outcome taking on values 0,1,...,K-1,\n", + "for node :math:`m`, let\n", + "\n", + "$$\n", + "p_{mk} = \\frac{1}{n_m} \\sum_{y \\in Q_m} I(y = k)\n", + "$$\n", + "\n", + "be the proportion of class k observations in node :math:`m`. If :math:`m` is a\n", + "terminal node, `predict_proba` for this region is set to :math:`p_{mk}`.\n", + "Common measures of impurity are the following.\n", + "\n", + "Gini:\n", + "\n", + "$$\n", + "H(Q_m) = \\sum_k p_{mk} (1 - p_{mk})\n", + "$$\n", + "\n", + "Log Loss or Entropy:\n", + "\n", + "$$\n", + "H(Q_m) = - \\sum_k p_{mk} \\log(p_{mk})\n", + "$$\n", + "\n", + ".. dropdown:: Shannon entropy\n", + "\n", + " The entropy criterion computes the Shannon entropy of the possible classes. It\n", + " takes the class frequencies of the training data points that reached a given\n", + " leaf :math:`m` as their probability. Using the **Shannon entropy as tree node\n", + " splitting criterion is equivalent to minimizing the log loss** (also known as\n", + " cross-entropy and multinomial deviance) between the true labels :math:`y_i`\n", + " and the probabilistic predictions :math:`T_k(x_i)` of the tree model :math:`T` for class :math:`k`.\n", + "\n", + " To see this, first recall that the log loss of a tree model :math:`T`\n", + " computed on a dataset :math:`D` is defined as follows:\n", + "\n", + "$$\n", + "\\mathrm{LL}(D, T) = -\\frac{1}{n} \\sum_{(x_i, y_i) \\in D} \\sum_k I(y_i = k) \\log(T_k(x_i))\n", + "$$\n", + "\n", + " where :math:`D` is a training dataset of :math:`n` pairs :math:`(x_i, y_i)`.\n", + "\n", + " In a classification tree, the predicted class probabilities within leaf nodes\n", + " are constant, that is: for all :math:`(x_i, y_i) \\in Q_m`, one has:\n", + " :math:`T_k(x_i) = p_{mk}` for each class :math:`k`.\n", + "\n", + " This property makes it possible to rewrite :math:`\\mathrm{LL}(D, T)` as the\n", + " sum of the Shannon entropies computed for each leaf of :math:`T` weighted by\n", + " the number of training data points that reached each leaf:\n", + "\n", + "$$\n", + "\\mathrm{LL}(D, T) = \\sum_{m \\in T} \\frac{n_m}{n} H(Q_m)\n", + "$$\n", + "\n", + "## Regression criteria\n", + "\n", + "If the target is a continuous value, then for node :math:`m`, common\n", + "criteria to minimize as for determining locations for future splits are Mean\n", + "Squared Error (MSE or L2 error), Poisson deviance as well as Mean Absolute\n", + "Error (MAE or L1 error). MSE and Poisson deviance both set the predicted value\n", + "of terminal nodes to the learned mean value :math:`\\bar{y}_m` of the node\n", + "whereas the MAE sets the predicted value of terminal nodes to the median\n", + ":math:`median(y)_m`.\n", + "\n", + "Mean Squared Error:\n", + "\n", + "$$\n", + "\\bar{y}_m = \\frac{1}{n_m} \\sum_{y \\in Q_m} y\n", + "$$\n", + "\n", + "$$\n", + "H(Q_m) = \\frac{1}{n_m} \\sum_{y \\in Q_m} (y - \\bar{y}_m)^2\n", + "$$\n", + "\n", + "Mean Poisson deviance:\n", + "\n", + "$$\n", + "H(Q_m) = \\frac{2}{n_m} \\sum_{y \\in Q_m} (y \\log\\frac{y}{\\bar{y}_m} - y + \\bar{y}_m)\n", + "$$\n", + "\n", + "Setting `criterion=\"poisson\"` might be a good choice if your target is a count\n", + "or a frequency (count per some unit). In any case, :math:`y >= 0` is a\n", + "necessary condition to use this criterion. Note that it fits much slower than\n", + "the MSE criterion. For performance reasons the actual implementation minimizes\n", + "the half mean poisson deviance, i.e. the mean poisson deviance divided by 2.\n", + "\n", + "Mean Absolute Error:\n", + "\n", + "$$\n", + "median(y)_m = \\underset{y \\in Q_m}{\\mathrm{median}}(y)\n", + "$$\n", + "\n", + "$$\n", + "H(Q_m) = \\frac{1}{n_m} \\sum_{y \\in Q_m} |y - median(y)_m|\n", + "$$\n", + "\n", + "Note that it fits much slower than the MSE criterion.\n", + "\n", + ".. _tree_missing_value_support:\n", + "\n", + "## Missing Values Support\n", + "\n", + ":class:`DecisionTreeClassifier` and :class:`DecisionTreeRegressor`\n", + "have built-in support for missing values when `splitter='best'` and criterion is\n", + "`'gini'`, `'entropy`', or `'log_loss'`, for classification or\n", + "`'squared_error'`, `'friedman_mse'`, or `'poisson'` for regression.\n", + "\n", + "For each potential threshold on the non-missing data, the splitter will evaluate\n", + "the split with all the missing values going to the left node or the right node.\n", + "\n", + "Decisions are made as follows:\n", + "\n", + "- By default when predicting, the samples with missing values are classified\n", + " with the class used in the split found during training::" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 0, 1, 1])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.tree import DecisionTreeClassifier\n", + "import numpy as np\n", + "\n", + "X = np.array([0, 1, 6, np.nan]).reshape(-1, 1)\n", + "y = [0, 0, 1, 1]\n", + "\n", + "tree = DecisionTreeClassifier(random_state=0).fit(X, y)\n", + "tree.predict(X)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " array([0, 0, 1, 1])\n", + "\n", + "- If the criterion evaluation is the same for both nodes,\n", + " then the tie for missing value at predict time is broken by going to the\n", + " right node. The splitter also checks the split where all the missing\n", + " values go to one child and non-missing values go to the other::" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.tree import DecisionTreeClassifier\n", + "import numpy as np\n", + "\n", + "X = np.array([np.nan, -1, np.nan, 1]).reshape(-1, 1)\n", + "y = [0, 0, 1, 1]\n", + "\n", + "tree = DecisionTreeClassifier(random_state=0).fit(X, y)\n", + "\n", + "X_test = np.array([np.nan]).reshape(-1, 1)\n", + "tree.predict(X_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " array([1])\n", + "\n", + "- If no missing values are seen during training for a given feature, then during\n", + " prediction missing values are mapped to the child with the most samples::" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.tree import DecisionTreeClassifier\n", + "import numpy as np\n", + "\n", + "X = np.array([0, 1, 2, 3]).reshape(-1, 1)\n", + "y = [0, 1, 1, 1]\n", + "\n", + "tree = DecisionTreeClassifier(random_state=0).fit(X, y)\n", + "\n", + "X_test = np.array([np.nan]).reshape(-1, 1)\n", + "tree.predict(X_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " array([1])\n", + "\n", + ".. _minimal_cost_complexity_pruning:\n", + "\n", + "## Minimal Cost-Complexity Pruning\n", + "\n", + "Minimal cost-complexity pruning is an algorithm used to prune a tree to avoid\n", + "over-fitting, described in Chapter 3 of [BRE]_. This algorithm is parameterized\n", + "by :math:`\\alpha\\ge0` known as the complexity parameter. The complexity\n", + "parameter is used to define the cost-complexity measure, :math:`R_\\alpha(T)` of\n", + "a given tree :math:`T`:\n", + "\n", + "$$\n", + "R_\\alpha(T) = R(T) + \\alpha|\\widetilde{T}|\n", + "$$\n", + "\n", + "where :math:`|\\widetilde{T}|` is the number of terminal nodes in :math:`T` and :math:`R(T)`\n", + "is traditionally defined as the total misclassification rate of the terminal\n", + "nodes. Alternatively, scikit-learn uses the total sample weighted impurity of\n", + "the terminal nodes for :math:`R(T)`. As shown above, the impurity of a node\n", + "depends on the criterion. Minimal cost-complexity pruning finds the subtree of\n", + ":math:`T` that minimizes :math:`R_\\alpha(T)`.\n", + "\n", + "The cost complexity measure of a single node is\n", + ":math:`R_\\alpha(t)=R(t)+\\alpha`. The branch, :math:`T_t`, is defined to be a\n", + "tree where node :math:`t` is its root. In general, the impurity of a node\n", + "is greater than the sum of impurities of its terminal nodes,\n", + ":math:`R(T_t)` and :ref:`regression +`. The goal is to create a model that predicts the value of a +target variable by learning simple decision rules inferred from the data +features. A tree can be seen as a piecewise constant approximation. + +For instance, in the example below, decision trees learn from data to +approximate a sine curve with a set of if-then-else decision rules. The deeper +the tree, the more complex the decision rules and the fitter the model. + +.. figure:: ../auto_examples/tree/images/sphx_glr_plot_tree_regression_001.png + :target: ../auto_examples/tree/plot_tree_regression.html + :scale: 75 + :align: center + +Some advantages of decision trees are: + +- Simple to understand and to interpret. Trees can be visualized. + +- Requires little data preparation. Other techniques often require data + normalization, dummy variables need to be created and blank values to + be removed. Some tree and algorithm combinations support + :ref:`missing values `. + +- The cost of using the tree (i.e., predicting data) is logarithmic in the + number of data points used to train the tree. + +- Able to handle both numerical and categorical data. However, the scikit-learn + implementation does not support categorical variables for now. Other + techniques are usually specialized in analyzing datasets that have only one type + of variable. See :ref:`algorithms ` for more + information. + +- Able to handle multi-output problems. + +- Uses a white box model. If a given situation is observable in a model, + the explanation for the condition is easily explained by boolean logic. + By contrast, in a black box model (e.g., in an artificial neural + network), results may be more difficult to interpret. + +- Possible to validate a model using statistical tests. That makes it + possible to account for the reliability of the model. + +- Performs well even if its assumptions are somewhat violated by + the true model from which the data were generated. + + +The disadvantages of decision trees include: + +- Decision-tree learners can create over-complex trees that do not + generalize the data well. This is called overfitting. Mechanisms + such as pruning, setting the minimum number of samples required + at a leaf node or setting the maximum depth of the tree are + necessary to avoid this problem. + +- Decision trees can be unstable because small variations in the + data might result in a completely different tree being generated. + This problem is mitigated by using decision trees within an + ensemble. + +- Predictions of decision trees are neither smooth nor continuous, but + piecewise constant approximations as seen in the above figure. Therefore, + they are not good at extrapolation. + +- The problem of learning an optimal decision tree is known to be + NP-complete under several aspects of optimality and even for simple + concepts. Consequently, practical decision-tree learning algorithms + are based on heuristic algorithms such as the greedy algorithm where + locally optimal decisions are made at each node. Such algorithms + cannot guarantee to return the globally optimal decision tree. This + can be mitigated by training multiple trees in an ensemble learner, + where the features and samples are randomly sampled with replacement. + +- There are concepts that are hard to learn because decision trees + do not express them easily, such as XOR, parity or multiplexer problems. + +- Decision tree learners create biased trees if some classes dominate. + It is therefore recommended to balance the dataset prior to fitting + with the decision tree. + + +## Classification + +`DecisionTreeClassifier` is a class capable of performing multi-class +classification on a dataset. + +As with other classifiers, :class:`DecisionTreeClassifier` takes as input two arrays: +an array X, sparse or dense, of shape ``(n_samples, n_features)`` holding the +training samples, and an array Y of integer values, shape ``(n_samples,)``, +holding the class labels for the training samples:: + +```{python} +from sklearn import tree +X = [[0, 0], [1, 1]] +Y = [0, 1] +clf = tree.DecisionTreeClassifier() +clf = clf.fit(X, Y) +``` + +After being fitted, the model can then be used to predict the class of samples:: + +```{python} +clf.predict([[2., 2.]]) +``` + +In case that there are multiple classes with the same and highest +probability, the classifier will predict the class with the lowest index +amongst those classes. + +As an alternative to outputting a specific class, the probability of each class +can be predicted, which is the fraction of training samples of the class in a +leaf:: + +```{python} +clf.predict_proba([[2., 2.]]) +``` + +`DecisionTreeClassifier` is capable of both binary (where the +labels are [-1, 1]) classification and multiclass (where the labels are +[0, ..., K-1]) classification. + +Using the Iris dataset, we can construct a tree as follows:: + +```{python} +from sklearn.datasets import load_iris +from sklearn import tree +iris = load_iris() +X, y = iris.data, iris.target +clf = tree.DecisionTreeClassifier() +clf = clf.fit(X, y) +``` + +Once trained, you can plot the tree with the :func:`plot_tree` function:: + +```{python} +tree.plot_tree(clf) +``` + +.. figure:: ../auto_examples/tree/images/sphx_glr_plot_iris_dtc_002.png + :target: ../auto_examples/tree/plot_iris_dtc.html + :scale: 75 + :align: center + +.. dropdown:: Alternative ways to export trees + + We can also export the tree in `Graphviz + `_ format using the :func:`export_graphviz` + exporter. If you use the `conda `_ package manager, the graphviz binaries + and the python package can be installed with `conda install python-graphviz`. + + Alternatively binaries for graphviz can be downloaded from the graphviz project homepage, + and the Python wrapper installed from pypi with `pip install graphviz`. + + Below is an example graphviz export of the above tree trained on the entire + iris dataset; the results are saved in an output file `iris.pdf`:: + +```{python} +import graphviz # doctest: +SKIP +dot_data = tree.export_graphviz(clf, out_file=None) # doctest: +SKIP +graph = graphviz.Source(dot_data) # doctest: +SKIP +graph.render("iris") # doctest: +SKIP +``` + + The :func:`export_graphviz` exporter also supports a variety of aesthetic + options, including coloring nodes by their class (or value for regression) and + using explicit variable and class names if desired. Jupyter notebooks also + render these plots inline automatically:: + +```{python} +dot_data = tree.export_graphviz(clf, out_file=None, # doctest: +SKIP + feature_names=iris.feature_names, # doctest: +SKIP + class_names=iris.target_names, # doctest: +SKIP + filled=True, rounded=True, # doctest: +SKIP + special_characters=True) # doctest: +SKIP +graph = graphviz.Source(dot_data) # doctest: +SKIP +graph # doctest: +SKIP +``` + + .. only:: html + + .. figure:: ../images/iris.svg + :align: center + + .. only:: latex + + .. figure:: ../images/iris.pdf + :align: center + + .. figure:: ../auto_examples/tree/images/sphx_glr_plot_iris_dtc_001.png + :target: ../auto_examples/tree/plot_iris_dtc.html + :align: center + :scale: 75 + + Alternatively, the tree can also be exported in textual format with the + function :func:`export_text`. This method doesn't require the installation + of external libraries and is more compact: + +```{python} +from sklearn.datasets import load_iris +from sklearn.tree import DecisionTreeClassifier +from sklearn.tree import export_text +iris = load_iris() +decision_tree = DecisionTreeClassifier(random_state=0, max_depth=2) +decision_tree = decision_tree.fit(iris.data, iris.target) +r = export_text(decision_tree, feature_names=iris['feature_names']) +print(r) +``` + + + |--- petal width (cm) <= 0.80 + | |--- class: 0 + |--- petal width (cm) > 0.80 + | |--- petal width (cm) <= 1.75 + | | |--- class: 1 + | |--- petal width (cm) > 1.75 + | | |--- class: 2 + + +.. rubric:: Examples + +* :ref:`sphx_glr_auto_examples_tree_plot_iris_dtc.py` +* :ref:`sphx_glr_auto_examples_tree_plot_unveil_tree_structure.py` + +.. _tree_regression: + +## Regression + +.. figure:: ../auto_examples/tree/images/sphx_glr_plot_tree_regression_001.png + :target: ../auto_examples/tree/plot_tree_regression.html + :scale: 75 + :align: center + +Decision trees can also be applied to regression problems, using the +:class:`DecisionTreeRegressor` class. + +As in the classification setting, the fit method will take as argument arrays X +and y, only that in this case y is expected to have floating point values +instead of integer values:: + +```{python} +from sklearn import tree +X = [[0, 0], [2, 2]] +y = [0.5, 2.5] +clf = tree.DecisionTreeRegressor() +clf = clf.fit(X, y) +clf.predict([[1, 1]]) +``` + + array([0.5]) + +.. rubric:: Examples + +* :ref:`sphx_glr_auto_examples_tree_plot_tree_regression.py` + + +.. _tree_multioutput: + +## Multi-output problems + +A multi-output problem is a supervised learning problem with several outputs +to predict, that is when Y is a 2d array of shape ``(n_samples, n_outputs)``. + +When there is no correlation between the outputs, a very simple way to solve +this kind of problem is to build n independent models, i.e. one for each +output, and then to use those models to independently predict each one of the n +outputs. However, because it is likely that the output values related to the +same input are themselves correlated, an often better way is to build a single +model capable of predicting simultaneously all n outputs. First, it requires +lower training time since only a single estimator is built. Second, the +generalization accuracy of the resulting estimator may often be increased. + +With regard to decision trees, this strategy can readily be used to support +multi-output problems. This requires the following changes: + +- Store n output values in leaves, instead of 1; +- Use splitting criteria that compute the average reduction across all + n outputs. + +This module offers support for multi-output problems by implementing this +strategy in both :class:`DecisionTreeClassifier` and +:class:`DecisionTreeRegressor`. If a decision tree is fit on an output array Y +of shape ``(n_samples, n_outputs)`` then the resulting estimator will: + +* Output n_output values upon ``predict``; + +* Output a list of n_output arrays of class probabilities upon + ``predict_proba``. + +The use of multi-output trees for regression is demonstrated in +:ref:`sphx_glr_auto_examples_tree_plot_tree_regression_multioutput.py`. In this example, the input +X is a single real value and the outputs Y are the sine and cosine of X. + +.. figure:: ../auto_examples/tree/images/sphx_glr_plot_tree_regression_multioutput_001.png + :target: ../auto_examples/tree/plot_tree_regression_multioutput.html + :scale: 75 + :align: center + +The use of multi-output trees for classification is demonstrated in +:ref:`sphx_glr_auto_examples_miscellaneous_plot_multioutput_face_completion.py`. In this example, the inputs +X are the pixels of the upper half of faces and the outputs Y are the pixels of +the lower half of those faces. + +.. figure:: ../auto_examples/miscellaneous/images/sphx_glr_plot_multioutput_face_completion_001.png + :target: ../auto_examples/miscellaneous/plot_multioutput_face_completion.html + :scale: 75 + :align: center + +.. rubric:: Examples + +* :ref:`sphx_glr_auto_examples_tree_plot_tree_regression_multioutput.py` +* :ref:`sphx_glr_auto_examples_miscellaneous_plot_multioutput_face_completion.py` + +.. rubric:: References + +* M. Dumont et al, `Fast multi-class image annotation with random subwindows + and multiple output randomized trees + `_, + International Conference on Computer Vision Theory and Applications 2009 + +.. _tree_complexity: + +## Complexity + +In general, the run time cost to construct a balanced binary tree is +:math:`O(n_{samples}n_{features}\log(n_{samples}))` and query time +:math:`O(\log(n_{samples}))`. Although the tree construction algorithm attempts +to generate balanced trees, they will not always be balanced. Assuming that the +subtrees remain approximately balanced, the cost at each node consists of +searching through :math:`O(n_{features})` to find the feature that offers the +largest reduction in the impurity criterion, e.g. log loss (which is equivalent to an +information gain). This has a cost of +:math:`O(n_{features}n_{samples}\log(n_{samples}))` at each node, leading to a +total cost over the entire trees (by summing the cost at each node) of +:math:`O(n_{features}n_{samples}^{2}\log(n_{samples}))`. + + +## Tips on practical use + +* Decision trees tend to overfit on data with a large number of features. + Getting the right ratio of samples to number of features is important, since + a tree with few samples in high dimensional space is very likely to overfit. + +* Consider performing dimensionality reduction (:ref:`PCA `, + :ref:`ICA `, or :ref:`feature_selection`) beforehand to + give your tree a better chance of finding features that are discriminative. + +* :ref:`sphx_glr_auto_examples_tree_plot_unveil_tree_structure.py` will help + in gaining more insights about how the decision tree makes predictions, which is + important for understanding the important features in the data. + +* Visualize your tree as you are training by using the ``export`` + function. Use ``max_depth=3`` as an initial tree depth to get a feel for + how the tree is fitting to your data, and then increase the depth. + +* Remember that the number of samples required to populate the tree doubles + for each additional level the tree grows to. Use ``max_depth`` to control + the size of the tree to prevent overfitting. + +* Use ``min_samples_split`` or ``min_samples_leaf`` to ensure that multiple + samples inform every decision in the tree, by controlling which splits will + be considered. A very small number will usually mean the tree will overfit, + whereas a large number will prevent the tree from learning the data. Try + ``min_samples_leaf=5`` as an initial value. If the sample size varies + greatly, a float number can be used as percentage in these two parameters. + While ``min_samples_split`` can create arbitrarily small leaves, + ``min_samples_leaf`` guarantees that each leaf has a minimum size, avoiding + low-variance, over-fit leaf nodes in regression problems. For + classification with few classes, ``min_samples_leaf=1`` is often the best + choice. + + Note that ``min_samples_split`` considers samples directly and independent of + ``sample_weight``, if provided (e.g. a node with m weighted samples is still + treated as having exactly m samples). Consider ``min_weight_fraction_leaf`` or + ``min_impurity_decrease`` if accounting for sample weights is required at splits. + +* Balance your dataset before training to prevent the tree from being biased + toward the classes that are dominant. Class balancing can be done by + sampling an equal number of samples from each class, or preferably by + normalizing the sum of the sample weights (``sample_weight``) for each + class to the same value. Also note that weight-based pre-pruning criteria, + such as ``min_weight_fraction_leaf``, will then be less biased toward + dominant classes than criteria that are not aware of the sample weights, + like ``min_samples_leaf``. + +* If the samples are weighted, it will be easier to optimize the tree + structure using weight-based pre-pruning criterion such as + ``min_weight_fraction_leaf``, which ensure that leaf nodes contain at least + a fraction of the overall sum of the sample weights. + +* All decision trees use ``np.float32`` arrays internally. + If training data is not in this format, a copy of the dataset will be made. + +* If the input matrix X is very sparse, it is recommended to convert to sparse + ``csc_matrix`` before calling fit and sparse ``csr_matrix`` before calling + predict. Training time can be orders of magnitude faster for a sparse + matrix input compared to a dense matrix when features have zero values in + most of the samples. + + +.. _tree_algorithms: + +## Tree algorithms: ID3, C4.5, C5.0 and CART + +What are all the various decision tree algorithms and how do they differ +from each other? Which one is implemented in scikit-learn? + +.. dropdown:: Various decision tree algorithms + + ID3_ (Iterative Dichotomiser 3) was developed in 1986 by Ross Quinlan. + The algorithm creates a multiway tree, finding for each node (i.e. in + a greedy manner) the categorical feature that will yield the largest + information gain for categorical targets. Trees are grown to their + maximum size and then a pruning step is usually applied to improve the + ability of the tree to generalize to unseen data. + + C4.5 is the successor to ID3 and removed the restriction that features + must be categorical by dynamically defining a discrete attribute (based + on numerical variables) that partitions the continuous attribute value + into a discrete set of intervals. C4.5 converts the trained trees + (i.e. the output of the ID3 algorithm) into sets of if-then rules. + The accuracy of each rule is then evaluated to determine the order + in which they should be applied. Pruning is done by removing a rule's + precondition if the accuracy of the rule improves without it. + + C5.0 is Quinlan's latest version release under a proprietary license. + It uses less memory and builds smaller rulesets than C4.5 while being + more accurate. + + CART (Classification and Regression Trees) is very similar to C4.5, but + it differs in that it supports numerical target variables (regression) and + does not compute rule sets. CART constructs binary trees using the feature + and threshold that yield the largest information gain at each node. + +scikit-learn uses an optimized version of the CART algorithm; however, the +scikit-learn implementation does not support categorical variables for now. + +.. _ID3: https://en.wikipedia.org/wiki/ID3_algorithm + + +.. _tree_mathematical_formulation: + +## Mathematical formulation + +Given training vectors :math:`x_i \in R^n`, i=1,..., l and a label vector +:math:`y \in R^l`, a decision tree recursively partitions the feature space +such that the samples with the same labels or similar target values are grouped +together. + +Let the data at node :math:`m` be represented by :math:`Q_m` with :math:`n_m` +samples. For each candidate split :math:`\theta = (j, t_m)` consisting of a +feature :math:`j` and threshold :math:`t_m`, partition the data into +:math:`Q_m^{left}(\theta)` and :math:`Q_m^{right}(\theta)` subsets + +$$ +\begin{aligned} +Q_m^{left}(\theta) &= \{(x, y) | x_j \leq t_m\} \\ +Q_m^{right}(\theta) &= Q_m \setminus Q_m^{left}(\theta) +\end{aligned} +$$ + +The quality of a candidate split of node :math:`m` is then computed using an +impurity function or loss function :math:`H()`, the choice of which depends on +the task being solved (classification or regression) + +$$ +G(Q_m, \theta) = \frac{n_m^{left}}{n_m} H(Q_m^{left}(\theta)) ++ \frac{n_m^{right}}{n_m} H(Q_m^{right}(\theta)) +$$ + +Select the parameters that minimises the impurity + +$$ +\theta^* = \operatorname{argmin}_\theta G(Q_m, \theta) +$$ + +Recurse for subsets :math:`Q_m^{left}(\theta^*)` and +:math:`Q_m^{right}(\theta^*)` until the maximum allowable depth is reached, +:math:`n_m < \min_{samples}` or :math:`n_m = 1`. + +## Classification criteria + +If a target is a classification outcome taking on values 0,1,...,K-1, +for node :math:`m`, let + +$$ +p_{mk} = \frac{1}{n_m} \sum_{y \in Q_m} I(y = k) +$$ + +be the proportion of class k observations in node :math:`m`. If :math:`m` is a +terminal node, `predict_proba` for this region is set to :math:`p_{mk}`. +Common measures of impurity are the following. + +Gini: + +$$ +H(Q_m) = \sum_k p_{mk} (1 - p_{mk}) +$$ + +Log Loss or Entropy: + +$$ +H(Q_m) = - \sum_k p_{mk} \log(p_{mk}) +$$ + +.. dropdown:: Shannon entropy + + The entropy criterion computes the Shannon entropy of the possible classes. It + takes the class frequencies of the training data points that reached a given + leaf :math:`m` as their probability. Using the **Shannon entropy as tree node + splitting criterion is equivalent to minimizing the log loss** (also known as + cross-entropy and multinomial deviance) between the true labels :math:`y_i` + and the probabilistic predictions :math:`T_k(x_i)` of the tree model :math:`T` for class :math:`k`. + + To see this, first recall that the log loss of a tree model :math:`T` + computed on a dataset :math:`D` is defined as follows: + +$$ +\mathrm{LL}(D, T) = -\frac{1}{n} \sum_{(x_i, y_i) \in D} \sum_k I(y_i = k) \log(T_k(x_i)) +$$ + + where :math:`D` is a training dataset of :math:`n` pairs :math:`(x_i, y_i)`. + + In a classification tree, the predicted class probabilities within leaf nodes + are constant, that is: for all :math:`(x_i, y_i) \in Q_m`, one has: + :math:`T_k(x_i) = p_{mk}` for each class :math:`k`. + + This property makes it possible to rewrite :math:`\mathrm{LL}(D, T)` as the + sum of the Shannon entropies computed for each leaf of :math:`T` weighted by + the number of training data points that reached each leaf: + +$$ +\mathrm{LL}(D, T) = \sum_{m \in T} \frac{n_m}{n} H(Q_m) +$$ + +## Regression criteria + +If the target is a continuous value, then for node :math:`m`, common +criteria to minimize as for determining locations for future splits are Mean +Squared Error (MSE or L2 error), Poisson deviance as well as Mean Absolute +Error (MAE or L1 error). MSE and Poisson deviance both set the predicted value +of terminal nodes to the learned mean value :math:`\bar{y}_m` of the node +whereas the MAE sets the predicted value of terminal nodes to the median +:math:`median(y)_m`. + +Mean Squared Error: + +$$ +\bar{y}_m = \frac{1}{n_m} \sum_{y \in Q_m} y +$$ + +$$ +H(Q_m) = \frac{1}{n_m} \sum_{y \in Q_m} (y - \bar{y}_m)^2 +$$ + +Mean Poisson deviance: + +$$ +H(Q_m) = \frac{2}{n_m} \sum_{y \in Q_m} (y \log\frac{y}{\bar{y}_m} - y + \bar{y}_m) +$$ + +Setting `criterion="poisson"` might be a good choice if your target is a count +or a frequency (count per some unit). In any case, :math:`y >= 0` is a +necessary condition to use this criterion. Note that it fits much slower than +the MSE criterion. For performance reasons the actual implementation minimizes +the half mean poisson deviance, i.e. the mean poisson deviance divided by 2. + +Mean Absolute Error: + +$$ +median(y)_m = \underset{y \in Q_m}{\mathrm{median}}(y) +$$ + +$$ +H(Q_m) = \frac{1}{n_m} \sum_{y \in Q_m} |y - median(y)_m| +$$ + +Note that it fits much slower than the MSE criterion. + +.. _tree_missing_value_support: + +## Missing Values Support + +:class:`DecisionTreeClassifier` and :class:`DecisionTreeRegressor` +have built-in support for missing values when `splitter='best'` and criterion is +`'gini'`, `'entropy`', or `'log_loss'`, for classification or +`'squared_error'`, `'friedman_mse'`, or `'poisson'` for regression. + +For each potential threshold on the non-missing data, the splitter will evaluate +the split with all the missing values going to the left node or the right node. + +Decisions are made as follows: + +- By default when predicting, the samples with missing values are classified + with the class used in the split found during training:: + +```{python} +from sklearn.tree import DecisionTreeClassifier +import numpy as np + +X = np.array([0, 1, 6, np.nan]).reshape(-1, 1) +y = [0, 0, 1, 1] + +tree = DecisionTreeClassifier(random_state=0).fit(X, y) +tree.predict(X) +``` + + array([0, 0, 1, 1]) + +- If the criterion evaluation is the same for both nodes, + then the tie for missing value at predict time is broken by going to the + right node. The splitter also checks the split where all the missing + values go to one child and non-missing values go to the other:: + +```{python} +from sklearn.tree import DecisionTreeClassifier +import numpy as np + +X = np.array([np.nan, -1, np.nan, 1]).reshape(-1, 1) +y = [0, 0, 1, 1] + +tree = DecisionTreeClassifier(random_state=0).fit(X, y) + +X_test = np.array([np.nan]).reshape(-1, 1) +tree.predict(X_test) +``` + + array([1]) + +- If no missing values are seen during training for a given feature, then during + prediction missing values are mapped to the child with the most samples:: + +```{python} +from sklearn.tree import DecisionTreeClassifier +import numpy as np + +X = np.array([0, 1, 2, 3]).reshape(-1, 1) +y = [0, 1, 1, 1] + +tree = DecisionTreeClassifier(random_state=0).fit(X, y) + +X_test = np.array([np.nan]).reshape(-1, 1) +tree.predict(X_test) +``` + + array([1]) + +.. _minimal_cost_complexity_pruning: + +## Minimal Cost-Complexity Pruning + +Minimal cost-complexity pruning is an algorithm used to prune a tree to avoid +over-fitting, described in Chapter 3 of [BRE]_. This algorithm is parameterized +by :math:`\alpha\ge0` known as the complexity parameter. The complexity +parameter is used to define the cost-complexity measure, :math:`R_\alpha(T)` of +a given tree :math:`T`: + +$$ +R_\alpha(T) = R(T) + \alpha|\widetilde{T}| +$$ + +where :math:`|\widetilde{T}|` is the number of terminal nodes in :math:`T` and :math:`R(T)` +is traditionally defined as the total misclassification rate of the terminal +nodes. Alternatively, scikit-learn uses the total sample weighted impurity of +the terminal nodes for :math:`R(T)`. As shown above, the impurity of a node +depends on the criterion. Minimal cost-complexity pruning finds the subtree of +:math:`T` that minimizes :math:`R_\alpha(T)`. + +The cost complexity measure of a single node is +:math:`R_\alpha(t)=R(t)+\alpha`. The branch, :math:`T_t`, is defined to be a +tree where node :math:`t` is its root. In general, the impurity of a node +is greater than the sum of impurities of its terminal nodes, +:math:`R(T_t)\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    dateApplianceslightsT1RH_1T2RH_2T3RH_3T4...T9RH_9T_outPress_mm_hgRH_outWindspeedVisibilityTdewpointrv1rv2
    02016-01-11 17:00:00603019.8947.59666719.244.79000019.7944.73000019.000000...17.03333345.536.600000733.592.07.00000063.0000005.313.27543313.275433
    12016-01-11 17:10:00603019.8946.69333319.244.72250019.7944.79000019.000000...17.06666745.566.483333733.692.06.66666759.1666675.218.60619518.606195
    22016-01-11 17:20:00503019.8946.30000019.244.62666719.7944.93333318.926667...17.00000045.506.366667733.792.06.33333355.3333335.128.64266828.642668
    32016-01-11 17:30:00504019.8946.06666719.244.59000019.7945.00000018.890000...17.00000045.406.250000733.892.06.00000051.5000005.045.41038945.410389
    42016-01-11 17:40:00604019.8946.33333319.244.53000019.7945.00000018.890000...17.00000045.406.133333733.992.05.66666747.6666674.910.08409710.084097
    \n", + "

    5 rows × 29 columns

    \n", + "" + ], + "text/plain": [ + " date Appliances lights T1 RH_1 T2 RH_2 \\\n", + "0 2016-01-11 17:00:00 60 30 19.89 47.596667 19.2 44.790000 \n", + "1 2016-01-11 17:10:00 60 30 19.89 46.693333 19.2 44.722500 \n", + "2 2016-01-11 17:20:00 50 30 19.89 46.300000 19.2 44.626667 \n", + "3 2016-01-11 17:30:00 50 40 19.89 46.066667 19.2 44.590000 \n", + "4 2016-01-11 17:40:00 60 40 19.89 46.333333 19.2 44.530000 \n", + "\n", + " T3 RH_3 T4 ... T9 RH_9 T_out Press_mm_hg \\\n", + "0 19.79 44.730000 19.000000 ... 17.033333 45.53 6.600000 733.5 \n", + "1 19.79 44.790000 19.000000 ... 17.066667 45.56 6.483333 733.6 \n", + "2 19.79 44.933333 18.926667 ... 17.000000 45.50 6.366667 733.7 \n", + "3 19.79 45.000000 18.890000 ... 17.000000 45.40 6.250000 733.8 \n", + "4 19.79 45.000000 18.890000 ... 17.000000 45.40 6.133333 733.9 \n", + "\n", + " RH_out Windspeed Visibility Tdewpoint rv1 rv2 \n", + "0 92.0 7.000000 63.000000 5.3 13.275433 13.275433 \n", + "1 92.0 6.666667 59.166667 5.2 18.606195 18.606195 \n", + "2 92.0 6.333333 55.333333 5.1 28.642668 28.642668 \n", + "3 92.0 6.000000 51.500000 5.0 45.410389 45.410389 \n", + "4 92.0 5.666667 47.666667 4.9 10.084097 10.084097 \n", + "\n", + "[5 rows x 29 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "\n", + "file_path = 'energydata_complete.csv'\n", + "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/00374/energydata_complete.csv\"\n", + "\n", + "if os.path.exists(file_path):\n", + " data = pd.read_csv(file_path)\n", + "else:\n", + " data = pd.read_csv(url)\n", + " data.to_csv(file_path, index=False)\n", + "\n", + "data.head()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "# Save the dataframe locally if it doesn't exist\n", + "if not os.path.exists(file_path):\n", + " data.to_csv(file_path, index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Column Descriptions\n", + "\n", + "| Column | Description |\n", + "| ---- | ----------- |\n", + "| date | time year-month-day hour:minute:second |\n", + "| Appliances | energy use in Wh |\n", + "| lights | energy use of light fixtures in the house in Wh |\n", + "| T1 | Temperature in kitchen area, in Celsius |\n", + "| RH_1 | Humidity in kitchen area, in % |\n", + "| T2 | Temperature in living room area, in Celsius |\n", + "| RH_2 | Humidity in living room area, in % |\n", + "| T3 | Temperature in laundry room area |\n", + "| RH_3 | Humidity in laundry room area, in % |\n", + "| T4 | Temperature in office room, in Celsius |\n", + "| RH_4 | Humidity in office room, in % |\n", + "| T5 | Temperature in bathroom, in Celsius |\n", + "| RH_5 | Humidity in bathroom, in % |\n", + "| T6 | Temperature outside the building (north side), in Celsius |\n", + "| RH_6 | Humidity outside the building (north side), in % |\n", + "| T7 | Temperature in ironing room , in Celsius |\n", + "| RH_7 | Humidity in ironing room, in % |\n", + "| T8 | Temperature in teenager room 2, in Celsius |\n", + "| RH_8 | Humidity in teenager room 2, in % |\n", + "| T9 | Temperature in parents room, in Celsius |\n", + "| RH_9 | Humidity in parents room, in % |\n", + "| To | Temperature outside (from Chievres weather station), in Celsius |\n", + "| Pressure | (from Chievres weather station), in mm Hg |\n", + "| RH_out | Humidity outside (from Chievres weather station), in % |\n", + "| Wind speed | (from Chievres weather station), in m/s |\n", + "| Visibility | (from Chievres weather station), in km |\n", + "| Tdewpoint | (from Chievres weather station), °C |\n", + "| rv1 | Random variable 1, nondimensional |\n", + "| rv2 | Random variable 2, nondimensional |\n", + "\n", + "Where indicated, hourly data (then interpolated) from the nearest airport weather station (Chievres Airport, Belgium) was downloaded from a public data set from Reliable Prognosis, rp5.ru. Permission was obtained from Reliable Prognosis for the distribution of the 4.5 months of weather data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    ApplianceslightsT1RH_1T2RH_2T3RH_3T4RH_4...T9RH_9T_outPress_mm_hgRH_outWindspeedVisibilityTdewpointrv1rv2
    date
    2016-01-11 17:00:00603019.8947.59666719.244.79000019.7944.73000019.00000045.566667...17.03333345.536.600000733.592.07.00000063.0000005.313.27543313.275433
    2016-01-11 17:10:00603019.8946.69333319.244.72250019.7944.79000019.00000045.992500...17.06666745.566.483333733.692.06.66666759.1666675.218.60619518.606195
    2016-01-11 17:20:00503019.8946.30000019.244.62666719.7944.93333318.92666745.890000...17.00000045.506.366667733.792.06.33333355.3333335.128.64266828.642668
    2016-01-11 17:30:00504019.8946.06666719.244.59000019.7945.00000018.89000045.723333...17.00000045.406.250000733.892.06.00000051.5000005.045.41038945.410389
    2016-01-11 17:40:00604019.8946.33333319.244.53000019.7945.00000018.89000045.530000...17.00000045.406.133333733.992.05.66666747.6666674.910.08409710.084097
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    5 rows × 28 columns

    \n", + "
    " + ], + "text/plain": [ + " Appliances lights T1 RH_1 T2 RH_2 \\\n", + "date \n", + "2016-01-11 17:00:00 60 30 19.89 47.596667 19.2 44.790000 \n", + "2016-01-11 17:10:00 60 30 19.89 46.693333 19.2 44.722500 \n", + "2016-01-11 17:20:00 50 30 19.89 46.300000 19.2 44.626667 \n", + "2016-01-11 17:30:00 50 40 19.89 46.066667 19.2 44.590000 \n", + "2016-01-11 17:40:00 60 40 19.89 46.333333 19.2 44.530000 \n", + "\n", + " T3 RH_3 T4 RH_4 ... T9 \\\n", + "date ... \n", + "2016-01-11 17:00:00 19.79 44.730000 19.000000 45.566667 ... 17.033333 \n", + "2016-01-11 17:10:00 19.79 44.790000 19.000000 45.992500 ... 17.066667 \n", + "2016-01-11 17:20:00 19.79 44.933333 18.926667 45.890000 ... 17.000000 \n", + "2016-01-11 17:30:00 19.79 45.000000 18.890000 45.723333 ... 17.000000 \n", + "2016-01-11 17:40:00 19.79 45.000000 18.890000 45.530000 ... 17.000000 \n", + "\n", + " RH_9 T_out Press_mm_hg RH_out Windspeed \\\n", + "date \n", + "2016-01-11 17:00:00 45.53 6.600000 733.5 92.0 7.000000 \n", + "2016-01-11 17:10:00 45.56 6.483333 733.6 92.0 6.666667 \n", + "2016-01-11 17:20:00 45.50 6.366667 733.7 92.0 6.333333 \n", + "2016-01-11 17:30:00 45.40 6.250000 733.8 92.0 6.000000 \n", + "2016-01-11 17:40:00 45.40 6.133333 733.9 92.0 5.666667 \n", + "\n", + " Visibility Tdewpoint rv1 rv2 \n", + "date \n", + "2016-01-11 17:00:00 63.000000 5.3 13.275433 13.275433 \n", + "2016-01-11 17:10:00 59.166667 5.2 18.606195 18.606195 \n", + "2016-01-11 17:20:00 55.333333 5.1 28.642668 28.642668 \n", + "2016-01-11 17:30:00 51.500000 5.0 45.410389 45.410389 \n", + "2016-01-11 17:40:00 47.666667 4.9 10.084097 10.084097 \n", + "\n", + "[5 rows x 28 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "data['date'] = pd.to_datetime(data['date'])\n", + "data.set_index('date', inplace=True)\n", + "\n", + "data.head()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We're interested in the `Appliances` column, which is the energy use of the appliances in Wh. \n", + "\n", + "First, we'll resample the data to hourly resolution and fill missing values using the forward fill method." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/ly/jkydg4dj2vs93b_ds7yp5t7r0000gn/T/ipykernel_38583/973969212.py:1: FutureWarning: Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n", + " data = data['Appliances'].resample('h').mean().fillna(method='ffill') # Resample and fill missing\n" + ] + }, + { + "data": { + "text/plain": [ + "date\n", + "2016-01-11 17:00:00 55.000000\n", + "2016-01-11 18:00:00 176.666667\n", + "2016-01-11 19:00:00 173.333333\n", + "2016-01-11 20:00:00 125.000000\n", + "2016-01-11 21:00:00 103.333333\n", + "Freq: h, Name: Appliances, dtype: float64" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = data['Appliances'].resample('h').mean().fillna(method='ffill') # Resample and fill missing\n", + "\n", + "data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Scale the values to be between 0 and 1 and convert to a numpy array." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "(3290, 1)\n" + ] + } + ], + "source": [ + "# Normalize data\n", + "scaler = MinMaxScaler()\n", + "data_scaled = scaler.fit_transform(data.values.reshape(-1, 1))\n", + "\n", + "print(type(data_scaled))\n", + "print(data_scaled.shape)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# Prepare data for LSTM\n", + "class TimeSeriesDataset(Dataset):\n", + " def __init__(self, data, seq_length):\n", + " self.data = data\n", + " self.seq_length = seq_length\n", + "\n", + " def __len__(self):\n", + " return len(self.data) - self.seq_length\n", + "\n", + " def __getitem__(self, index):\n", + " X = self.data[index:index + self.seq_length]\n", + " y = self.data[index + self.seq_length]\n", + " return torch.tensor(X, dtype=torch.float32), torch.tensor(y, dtype=torch.float32)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3266\n" + ] + } + ], + "source": [ + "\n", + "seq_length = 24\n", + "dataset = TimeSeriesDataset(data_scaled, seq_length)\n", + "\n", + "print(len(dataset))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "82\n", + "21\n" + ] + } + ], + "source": [ + "\n", + "# Split data into training and testing\n", + "train_size = int(len(dataset) * 0.8)\n", + "test_size = len(dataset) - train_size\n", + "\n", + "train_dataset, test_dataset = torch.utils.data.random_split(dataset, [train_size, test_size])\n", + "\n", + "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n", + "test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)\n", + "\n", + "print(len(train_loader))\n", + "print(len(test_loader))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# let's look at the first batch\n", + "for X, y in train_loader:\n", + " print(X.shape)\n", + " print(y.shape)\n", + " break\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# Define the LSTM model\n", + "class LSTMModel(nn.Module):\n", + " def __init__(self, input_size=1, hidden_size=50, output_size=1):\n", + " super(LSTMModel, self).__init__()\n", + " self.lstm = nn.LSTM(input_size, hidden_size, batch_first=True)\n", + " self.fc = nn.Linear(hidden_size, output_size)\n", + "\n", + " def forward(self, x):\n", + " x, _ = self.lstm(x)\n", + " x = self.fc(x[:, -1, :]) # Use the output of the last time step\n", + " return x\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "model = LSTMModel()\n", + "criterion = nn.MSELoss()\n", + "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Train the model\n", + "epochs = 20\n", + "for epoch in range(epochs):\n", + " model.train()\n", + " train_loss = 0.0\n", + " for X, y in train_loader:\n", + " X = X.unsqueeze(-1) # Add input dimension\n", + " y = y.unsqueeze(-1) # Add target dimension\n", + "\n", + " optimizer.zero_grad()\n", + " outputs = model(X)\n", + " loss = criterion(outputs, y)\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " train_loss += loss.item()\n", + "\n", + " print(f\"Epoch {epoch+1}/{epochs}, Loss: {train_loss/len(train_loader):.4f}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# Evaluate the model\n", + "model.eval()\n", + "predictions = []\n", + "actuals = []\n", + "with torch.no_grad():\n", + " for X, y in test_loader:\n", + " X = X.unsqueeze(-1)\n", + " y = y.unsqueeze(-1)\n", + " preds = model(X)\n", + " predictions.extend(preds.numpy())\n", + " actuals.extend(y.numpy())\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# Rescale predictions and actuals to original scale\n", + "predictions_rescaled = scaler.inverse_transform(predictions)\n", + "actuals_rescaled = scaler.inverse_transform(actuals)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# Plot results\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(actuals_rescaled, label='True Values')\n", + "plt.plot(predictions_rescaled, label='Predicted Values', alpha=0.7)\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Case Study and Discussion\n", + "\n", + "## Real-world case study: Application of time series analysis\n", + "\n", + "- **Case Study**: Let's explore a real-world case study where time series analysis is applied.\n", + " - **Industry**: Choose an industry (e.g., finance, healthcare, retail).\n", + " - **Problem Statement**: Define the problem that needs to be addressed using time series analysis.\n", + " - **Data Collection**: Describe the data collection process and the type of data used.\n", + " - **Model Selection**: Select appropriate time series models for the analysis.\n", + " - **Analysis**: Perform the time series analysis and interpret the results.\n", + " - **Outcome**: Discuss the outcomes and how the analysis helped in decision-making.\n", + " \n", + "## Group discussion on potential projects or applications\n", + "\n", + "- **Group Discussion**: Let's engage in a group discussion to brainstorm potential projects or applications of time series analysis.\n", + " - **Project Ideas**: Share and discuss various project ideas that can benefit from time series analysis.\n", + " - **Application Areas**: Identify different application areas such as finance, healthcare, retail, and more.\n", + " - **Challenges**: Discuss the potential challenges and limitations of applying time series analysis in these projects.\n", + " - **Collaboration**: Explore opportunities for collaboration and knowledge sharing within the group." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/ds701_book/timeseries-chatgpt.qmd b/ds701_book/timeseries-chatgpt.qmd new file mode 100644 index 00000000..baed192c --- /dev/null +++ b/ds701_book/timeseries-chatgpt.qmd @@ -0,0 +1,391 @@ +--- +title: Classic and Deep Learning Time Series Forecasting +jupyter: python3 +--- + + + +## ARIMA/SARIMA Example + +```{python} +import pandas as pd +import numpy as np +from statsmodels.tsa.seasonal import seasonal_decompose +from statsmodels.tsa.statespace.sarimax import SARIMAX +import matplotlib.pyplot as plt +``` + +```{python} +path = '~/.cache/kagglehub/datasets/chirag19/air-passengers/versions/1/AirPassengers.csv' + +data = pd.read_csv(path) + +data.head() +``` + +```{python} +# Load the dataset +# from statsmodels.datasets.airline import load_pandas +#data = load_pandas().data +data['Month'] = pd.date_range(start='1949-01', periods=len(data), freq='ME') +data.set_index('Month', inplace=True) + +data.head() +``` + +```{python} + +# Log transform to stabilize variance +data['Log_Passengers'] = np.log(data['#Passengers']) + +data.head() +``` + +```{python} +# Seasonal decomposition +decomposition = seasonal_decompose(data['Log_Passengers'], model='additive') +decomposition.plot() +plt.show() +``` + +```{python} +# SARIMA model +model = SARIMAX(data['Log_Passengers'], + order=(1, 1, 1), + seasonal_order=(1, 1, 1, 12), + freq='ME') +results = model.fit() + +# Summary and diagnostics +print(results.summary()) +results.plot_diagnostics(figsize=(15, 10)) +plt.show() +``` + +```{python} + +# Forecasting +forecast = results.get_forecast(steps=24) +forecast_index = pd.date_range(data.index[-1] + pd.DateOffset(months=1), periods=24, freq='ME') +forecast_values = np.exp(forecast.predicted_mean) # Convert back from log +confidence_intervals = np.exp(forecast.conf_int()) + +# Plot +plt.figure(figsize=(10, 6)) +plt.plot(data['#Passengers'], label='Observed') +plt.plot(forecast_index, forecast_values, label='Forecast', color='red') +plt.fill_between(forecast_index, confidence_intervals.iloc[:, 0], confidence_intervals.iloc[:, 1], color='pink', alpha=0.3) +plt.legend() +plt.show() +``` + +## Neural Network Example -- TensorFlow/Keras + +Dataset: Energy Consumption Dataset (available via UCI Machine Learning Repository) + +```{python} +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +from sklearn.preprocessing import MinMaxScaler +from tensorflow.keras.models import Sequential +from tensorflow.keras.layers import Dense, LSTM +``` + +```{python} + +# Load dataset +url = "https://archive.ics.uci.edu/ml/machine-learning-databases/00374/energydata_complete.csv" +data = pd.read_csv(url) +data['date'] = pd.to_datetime(data['date']) +data.set_index('date', inplace=True) +data = data['Appliances'].resample('H').mean().fillna(method='ffill') # Resample and fill missing + +# Normalize data +scaler = MinMaxScaler() +data_scaled = scaler.fit_transform(data.values.reshape(-1, 1)) + +# Prepare data for LSTM +def create_sequences(data, seq_length): + X, y = [], [] + for i in range(len(data) - seq_length): + X.append(data[i:i + seq_length]) + y.append(data[i + seq_length]) + return np.array(X), np.array(y) + +seq_length = 24 +X, y = create_sequences(data_scaled, seq_length) +X_train, X_test = X[:int(len(X) * 0.8)], X[int(len(X) * 0.8):] +y_train, y_test = y[:int(len(y) * 0.8)], y[int(len(y) * 0.8):] + +# LSTM model +model = Sequential([ + LSTM(50, activation='relu', input_shape=(seq_length, 1)), + Dense(1) +]) +model.compile(optimizer='adam', loss='mse') + +# Train model +history = model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=20, batch_size=32, verbose=1) + +# Evaluate and predict +predictions = model.predict(X_test) +predictions_rescaled = scaler.inverse_transform(predictions) +y_test_rescaled = scaler.inverse_transform(y_test.reshape(-1, 1)) + +# Plot results +plt.figure(figsize=(10, 6)) +plt.plot(y_test_rescaled, label='True Values') +plt.plot(predictions_rescaled, label='Predicted Values', alpha=0.7) +plt.legend() +plt.show() +``` + +## Neural Network Example -- PyTorch + +Dataset: Energy Consumption Dataset (UCI Machine Learning Repository) + +```{python} +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +from sklearn.preprocessing import MinMaxScaler +import torch +import torch.nn as nn +from torch.utils.data import DataLoader, Dataset +``` + +Load dataset from https://archive.ics.uci.edu/dataset/374/appliances+energy+prediction + +```{python} +import os + +file_path = 'energydata_complete.csv' +url = "https://archive.ics.uci.edu/ml/machine-learning-databases/00374/energydata_complete.csv" + +if os.path.exists(file_path): + data = pd.read_csv(file_path) +else: + data = pd.read_csv(url) + data.to_csv(file_path, index=False) + +data.head() +``` + +```{python} +# Save the dataframe locally if it doesn't exist +if not os.path.exists(file_path): + data.to_csv(file_path, index=False) +``` + +### Column Descriptions + +| Column | Description | +| ---- | ----------- | +| date | time year-month-day hour:minute:second | +| Appliances | energy use in Wh | +| lights | energy use of light fixtures in the house in Wh | +| T1 | Temperature in kitchen area, in Celsius | +| RH_1 | Humidity in kitchen area, in % | +| T2 | Temperature in living room area, in Celsius | +| RH_2 | Humidity in living room area, in % | +| T3 | Temperature in laundry room area | +| RH_3 | Humidity in laundry room area, in % | +| T4 | Temperature in office room, in Celsius | +| RH_4 | Humidity in office room, in % | +| T5 | Temperature in bathroom, in Celsius | +| RH_5 | Humidity in bathroom, in % | +| T6 | Temperature outside the building (north side), in Celsius | +| RH_6 | Humidity outside the building (north side), in % | +| T7 | Temperature in ironing room , in Celsius | +| RH_7 | Humidity in ironing room, in % | +| T8 | Temperature in teenager room 2, in Celsius | +| RH_8 | Humidity in teenager room 2, in % | +| T9 | Temperature in parents room, in Celsius | +| RH_9 | Humidity in parents room, in % | +| To | Temperature outside (from Chievres weather station), in Celsius | +| Pressure | (from Chievres weather station), in mm Hg | +| RH_out | Humidity outside (from Chievres weather station), in % | +| Wind speed | (from Chievres weather station), in m/s | +| Visibility | (from Chievres weather station), in km | +| Tdewpoint | (from Chievres weather station), °C | +| rv1 | Random variable 1, nondimensional | +| rv2 | Random variable 2, nondimensional | + +Where indicated, hourly data (then interpolated) from the nearest airport weather station (Chievres Airport, Belgium) was downloaded from a public data set from Reliable Prognosis, rp5.ru. Permission was obtained from Reliable Prognosis for the distribution of the 4.5 months of weather data. + +```{python} +data.info() +``` + +```{python} + +data['date'] = pd.to_datetime(data['date']) +data.set_index('date', inplace=True) + +data.head() +``` + +We're interested in the `Appliances` column, which is the energy use of the appliances in Wh. + +First, we'll resample the data to hourly resolution and fill missing values using the forward fill method. + +```{python} +data = data['Appliances'].resample('h').mean().fillna(method='ffill') # Resample and fill missing + +data.head() +``` + +Scale the values to be between 0 and 1 and convert to a numpy array. + +```{python} +# Normalize data +scaler = MinMaxScaler() +data_scaled = scaler.fit_transform(data.values.reshape(-1, 1)) + +print(type(data_scaled)) +print(data_scaled.shape) +``` + +```{python} + +# Prepare data for LSTM +class TimeSeriesDataset(Dataset): + def __init__(self, data, seq_length): + self.data = data + self.seq_length = seq_length + + def __len__(self): + return len(self.data) - self.seq_length + + def __getitem__(self, index): + X = self.data[index:index + self.seq_length] + y = self.data[index + self.seq_length] + return torch.tensor(X, dtype=torch.float32), torch.tensor(y, dtype=torch.float32) +``` + +```{python} + +seq_length = 24 +dataset = TimeSeriesDataset(data_scaled, seq_length) + +print(len(dataset)) +``` + +```{python} + +# Split data into training and testing +train_size = int(len(dataset) * 0.8) +test_size = len(dataset) - train_size + +train_dataset, test_dataset = torch.utils.data.random_split(dataset, [train_size, test_size]) + +train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True) +test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False) + +print(len(train_loader)) +print(len(test_loader)) +``` + +```{python} +# let's look at the first batch +for X, y in train_loader: + print(X.shape) + print(y.shape) + break +``` + +```{python} + +# Define the LSTM model +class LSTMModel(nn.Module): + def __init__(self, input_size=1, hidden_size=50, output_size=1): + super(LSTMModel, self).__init__() + self.lstm = nn.LSTM(input_size, hidden_size, batch_first=True) + self.fc = nn.Linear(hidden_size, output_size) + + def forward(self, x): + x, _ = self.lstm(x) + x = self.fc(x[:, -1, :]) # Use the output of the last time step + return x +``` + +```{python} +model = LSTMModel() +criterion = nn.MSELoss() +optimizer = torch.optim.Adam(model.parameters(), lr=0.001) +``` + +```{python} +# Train the model +epochs = 20 +for epoch in range(epochs): + model.train() + train_loss = 0.0 + for X, y in train_loader: + X = X.unsqueeze(-1) # Add input dimension + y = y.unsqueeze(-1) # Add target dimension + + optimizer.zero_grad() + outputs = model(X) + loss = criterion(outputs, y) + loss.backward() + optimizer.step() + + train_loss += loss.item() + + print(f"Epoch {epoch+1}/{epochs}, Loss: {train_loss/len(train_loader):.4f}") +``` + +```{python} + +# Evaluate the model +model.eval() +predictions = [] +actuals = [] +with torch.no_grad(): + for X, y in test_loader: + X = X.unsqueeze(-1) + y = y.unsqueeze(-1) + preds = model(X) + predictions.extend(preds.numpy()) + actuals.extend(y.numpy()) +``` + +```{python} + +# Rescale predictions and actuals to original scale +predictions_rescaled = scaler.inverse_transform(predictions) +actuals_rescaled = scaler.inverse_transform(actuals) +``` + +```{python} + +# Plot results +plt.figure(figsize=(10, 6)) +plt.plot(actuals_rescaled, label='True Values') +plt.plot(predictions_rescaled, label='Predicted Values', alpha=0.7) +plt.legend() +plt.show() +``` + +# Case Study and Discussion + +## Real-world case study: Application of time series analysis + +- **Case Study**: Let's explore a real-world case study where time series analysis is applied. + - **Industry**: Choose an industry (e.g., finance, healthcare, retail). + - **Problem Statement**: Define the problem that needs to be addressed using time series analysis. + - **Data Collection**: Describe the data collection process and the type of data used. + - **Model Selection**: Select appropriate time series models for the analysis. + - **Analysis**: Perform the time series analysis and interpret the results. + - **Outcome**: Discuss the outcomes and how the analysis helped in decision-making. + +## Group discussion on potential projects or applications + +- **Group Discussion**: Let's engage in a group discussion to brainstorm potential projects or applications of time series analysis. + - **Project Ideas**: Share and discuss various project ideas that can benefit from time series analysis. + - **Application Areas**: Identify different application areas such as finance, healthcare, retail, and more. + - **Challenges**: Discuss the potential challenges and limitations of applying time series analysis in these projects. + - **Collaboration**: Explore opportunities for collaboration and knowledge sharing within the group. + diff --git a/ds701_book/yahoo_data.csv b/ds701_book/yahoo_data.csv new file mode 100644 index 00000000..3b4f22b7 --- /dev/null +++ b/ds701_book/yahoo_data.csv @@ -0,0 +1,252 @@ +Date,Open,High,Low,Close,Adj Close,Volume +2015-01-02,55.459999084472656,55.599998474121094,54.2400016784668,55.150001525878906,55.150001525878906,1664500 +2015-01-05,54.540000915527344,54.95000076293945,52.33000183105469,52.529998779296875,52.529998779296875,2023000 +2015-01-06,52.54999923706055,53.93000030517578,50.75,52.439998626708984,52.439998626708984,3762800 +2015-01-07,53.31999969482422,53.75,51.7599983215332,52.209999084472656,52.209999084472656,1548200 +2015-01-08,52.59000015258789,54.13999938964844,51.7599983215332,53.83000183105469,53.83000183105469,2015300 +2015-01-09,55.959999084472656,56.9900016784668,54.720001220703125,56.06999969482422,56.06999969482422,6224200 +2015-01-12,56.0,56.060001373291016,53.43000030517578,54.02000045776367,54.02000045776367,2407700 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