From 8c4ddd8f303ecb7eefc8a94ac550f7c9c6a95a98 Mon Sep 17 00:00:00 2001 From: Munish Patel <53735021+munishpatel@users.noreply.github.com> Date: Wed, 9 Oct 2024 23:33:12 -0500 Subject: [PATCH 01/12] Delete regularized_discriminant_analysis directory --- .../models/RegularizedDiscriminantAnalysis.py | 17 ----------------- .../test_rdamodel.py | 19 ------------------- 2 files changed, 36 deletions(-) delete mode 100644 regularized_discriminant_analysis/models/RegularizedDiscriminantAnalysis.py delete mode 100644 regularized_discriminant_analysis/test_rdamodel.py diff --git a/regularized_discriminant_analysis/models/RegularizedDiscriminantAnalysis.py b/regularized_discriminant_analysis/models/RegularizedDiscriminantAnalysis.py deleted file mode 100644 index 089f9ad..0000000 --- a/regularized_discriminant_analysis/models/RegularizedDiscriminantAnalysis.py +++ /dev/null @@ -1,17 +0,0 @@ - - -class RDAModel(): - def __init__(self): - pass - - - def fit(self, X, y): - return RDAModelResults() - - -class RDAModelResults(): - def __init__(self): - pass - - def predict(self, x): - return 0.5 diff --git a/regularized_discriminant_analysis/test_rdamodel.py b/regularized_discriminant_analysis/test_rdamodel.py deleted file mode 100644 index 095725b..0000000 --- a/regularized_discriminant_analysis/test_rdamodel.py +++ /dev/null @@ -1,19 +0,0 @@ -import csv - -import numpy - -from regularized_discriminant_analysis.models.RegularizedDiscriminantAnalysis import RDAModel - -def test_predict(): - model = ElasticNetModel() - data = [] - with open("small_sample.csv", "r") as file: - reader = csv.DictReader(file) - for row in reader: - data.append(row) - - X = numpy.array([[v for k,v in datum.items() if k.startswith('x')] for datum in data]) - y = numpy.array([[v for k,v in datum.items() if k=='y'] for datum in data]) - results = model.fit(X,y) - preds = results.predict(X) - assert preds == 0.5 From ca8797322deee20c377d0e0644d59d419e2b8373 Mon Sep 17 00:00:00 2001 From: Munish Patel <53735021+munishpatel@users.noreply.github.com> Date: Wed, 9 Oct 2024 23:34:55 -0500 Subject: [PATCH 02/12] Updated ElasticNet.py --- elasticnet/models/ElasticNet.py | 55 +++++++++++++++++++++++++++------ 1 file changed, 45 insertions(+), 10 deletions(-) diff --git a/elasticnet/models/ElasticNet.py b/elasticnet/models/ElasticNet.py index 017e925..241183e 100644 --- a/elasticnet/models/ElasticNet.py +++ b/elasticnet/models/ElasticNet.py @@ -1,17 +1,52 @@ +class ElasticNetModel: + + def __init__(self, lambdas=1.0, l1_ratio=0.5, iterations=10000, learning_rate=0.001): + self.lambdas = lambdas + self.l1_ratio = l1_ratio + self.iterations = iterations + self.learning_rate = learning_rate + self.coef_ = None + self.intercept_ = 0 -class ElasticNetModel(): - def __init__(self): - pass + def fit(self, X, y): + + n_samples, n_features = X.shape + self.coef_ = np.zeros(n_features) + self.intercept_ = 0 + # Performing gradient descent + for _ in range(self.iterations): + current_predictions = np.dot(X, self.coef_) + self.intercept_ + residuals = current_predictions - y + + # Computing gradients for coefficients + # First, we calculate the gradient from the residuals + residual_gradient = np.dot(X.T, residuals) / n_samples + + # Computing the L1 regularization term + l1_term = self.l1_ratio * self.lambdas * np.sign(self.coef_) + + # Computing the L2 regularization term + l2_term = (1 - self.l1_ratio) * self.lambdas * 2 * self.coef_ + + # Combining the gradients from residuals, L1, and L2 terms + coef_gradient = residual_gradient + l1_term + l2_term + + # Computing the gradient for the intercept + intercept_gradient = np.sum(residuals) / n_samples - def fit(self, X, y): - return ElasticNetModelResults() + # Updating the model parameters + self.coef_ -= self.learning_rate * coef_gradient + self.intercept_ -= self.learning_rate * intercept_gradient + return ElasticNetModelResults(self.coef_, self.intercept_) -class ElasticNetModelResults(): - def __init__(self): - pass +class ElasticNetModelResults: + def __init__(self, coef, intercept): + self.coef_ = coef + self.intercept_ = intercept - def predict(self, x): - return 0.5 + def predict(self, X): + + return np.dot(X, self.coef_) + self.intercept_ From 10db65f7c5b63a6196118d9b39546cb124e06a77 Mon Sep 17 00:00:00 2001 From: Munish Patel <53735021+munishpatel@users.noreply.github.com> Date: Wed, 9 Oct 2024 23:36:01 -0500 Subject: [PATCH 03/12] Updated test_ElasticNetModel.py --- elasticnet/tests/test_ElasticNetModel.py | 49 ++++++++++++++++++------ 1 file changed, 38 insertions(+), 11 deletions(-) diff --git a/elasticnet/tests/test_ElasticNetModel.py b/elasticnet/tests/test_ElasticNetModel.py index 5022c3c..73657b2 100644 --- a/elasticnet/tests/test_ElasticNetModel.py +++ b/elasticnet/tests/test_ElasticNetModel.py @@ -1,19 +1,46 @@ +import numpy as np import csv +from sklearn.preprocessing import StandardScaler # To standardize the features -import numpy - -from elasticnet.models.ElasticNet import ElasticNetModel +# Assuming the ElasticNetModel is defined as provided above def test_predict(): - model = ElasticNetModel() + model = ElasticNetModel(lambdas=1.0, l1_ratio=0.5, iterations=1000, learning_rate=0.01) data = [] - with open("small_test.csv", "r") as file: + + # Load data from the CSV file + with open("/content/small_test.csv", "r") as file: reader = csv.DictReader(file) for row in reader: - data.append(row) + # Convert all values to float for consistency + data.append({k: float(v) for k, v in row.items()}) + + # Extract features and targets + X = np.array([[v for k, v in datum.items() if k.startswith('x')] for datum in data]) + y = np.array([datum['y'] for datum in data if 'y' in datum]) + + # Normalize the feature data + scaler = StandardScaler() + X_scaled = scaler.fit_transform(X) + + # Fit the model + results = model.fit(X_scaled, y) + + # Make predictions + preds = results.predict(X_scaled) + + # Print predictions to verify outputs + print("Predictions:", preds) + + # Plotting the results + plt.figure(figsize=(10, 6)) + plt.scatter(y, preds, alpha=0.5, color='blue') # Plot predictions vs actual values + plt.title('Actual vs. Predicted Values') + plt.xlabel('Actual Values') + plt.ylabel('Predicted Values') + plt.plot([y.min(), y.max()], [y.min(), y.max()], 'k--', lw=2) # Diagonal line + plt.grid(True) + plt.show() - X = numpy.array([[v for k,v in datum.items() if k.startswith('x')] for datum in data]) - y = numpy.array([[v for k,v in datum.items() if k=='y'] for datum in data]) - results = model.fit(X,y) - preds = results.predict(X) - assert preds == 0.5 +# Run the test function +test_predict() From f69d5521b17428de4574914eaa42da54c8f727f0 Mon Sep 17 00:00:00 2001 From: Munish Patel <53735021+munishpatel@users.noreply.github.com> Date: Wed, 9 Oct 2024 23:36:54 -0500 Subject: [PATCH 04/12] Updated README.md --- README.md | 86 +++++++++++++++++++++++++++++++++++++++++++++++++++---- 1 file changed, 81 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index c1e8359..62de0af 100644 --- a/README.md +++ b/README.md @@ -1,8 +1,84 @@ # Project 1 +Course: CS584 - Machine Learning +Instructor: Steve Avsec -Put your README here. Answer the following questions. +## Team Members +1. Munish Patel - mpatel176@hawk.iit.edu (A20544034) +2. Jaya Karthik Muppinidi - jmuppinidi@hawk.iit.edu (A20551726) +3. Meghana Mahamkali - mmahamkali@hawk.iit.edu (A20564182) +4. Nirusha Mantralaya Ramesh - nmantralayaramesh@hawk.iit.edu (A20600814) -* What does the model you have implemented do and when should it be used? -* How did you test your model to determine if it is working reasonably correctly? -* What parameters have you exposed to users of your implementation in order to tune performance? (Also perhaps provide some basic usage examples.) -* Are there specific inputs that your implementation has trouble with? Given more time, could you work around these or is it fundamental? +## Linear Regression with ElasticNet Regularization +### Project Overview +Linear regression with ElasticNet regularization (combination of L2 and L1 regularization) +This project implements a ElasticNet Model, which combines L1 and L2 penalties to enhance model generalization and prevent overfitting. The model, developed from scratch in Python using NumPy, optimizes its parameters via gradient descent. + +### Usage + ```bash + # Fit and predict using the model + model = ElasticNetModel(lambdas=0.1, l1_ratio=0.5, iterations=1000, learning_rate=0.001) + results = model.fit(x_train_scaled, y_train) + predictions = results.predict(x_test_scaled) + + predicted_categories = np.clip(np.round(predictions), 0, len(label_encoder.classes_) - 1).astype(int) + # Converting numeric predictions back to job role labels + predicted_job_roles = label_encoder.inverse_transform(predicted_categories) + + print("Numerical Predictions:", predictions) + print("Predicted Job Roles:", predicted_job_roles) + ``` +### Explanation of the Model +1. What does the model you have implemented do and when should it be used? + + * The ElasticNetModel implemented is a type of regularized linear regression that combines both L1 and L2 regularization. + * L1 Regularization (Lasso) helps in feature selection by shrinking some coefficients to zero, which is beneficial in models with high dimensionality. + * L2 Regularization (Ridge) tends to shrink coefficients evenly and helps in dealing with multicollinearity and model stability by keeping the coefficients small. + * The main reason behind using ElasticNet is to build a model with least complexity while excelling in occasions where features seem to relate or when there are more variables than cases. When it is desirable to decrease the model’s complexity with regards to the features contributing to collinearity, then ElasticNet can prove effective. + * ElasticNet is used when we suspect or know there is multicollinearity in your data, have a large number of features, some of which might be irrelevant, need a model that can perform feature selection to improve prediction accuracy. + + +2. How did you test your model to determine if it is working reasonably correctly? + + * We evaluated our model by training it on a dataset that predicts suggested job roles. + * To verify the models ability to generalize, we have divided the data into training sets and testing sets. + * Fit the model on the training data using results = model.fit(x_train_scaled, y_train). + * Make predictions on the testing data using results.predict(x_test_scaled). + * We tested tthe model in test.py using small_data.csv and also tested it in generate_regression_data.py where we generated random data and stored it in data.csv + + +3. What parameters have you exposed to users of your implementation in order to tune performance? + + * ```lambdas```: Controls the strength of the regularization. A higher value means more regularization. + * ```l1_ratio```: Balances between L1 and L2 regularization. + * ```iterations```: Determines the number of iterations in the gradient descent algorithm. + * ```learning_rate```: Controls the step size at each iteration while moving toward a minimum of the loss function. + * Example Usage for random generated data: + ```bash + model = ElasticNetModel(lambdas=1.0, l1_ratio=0.5, iterations=1000, learning_rate=0.01) + results = model.fit(X_scaled, y) + + predictions = results.predict(X_scaled) + + # Plotting the results + plt.figure(figsize=(10, 6)) + plt.scatter(y, predictions, alpha=0.5) + plt.title('Comparison of Actual and Predicted Values') + plt.xlabel('Actual Values') + plt.ylabel('Predicted Values') + plt.plot([y.min(), y.max()], [y.min(), y.max()], 'k--', lw=4) # Diagonal line for reference + plt.grid(True) + plt.show() + + print("Predictions:", predictions) + print("Actuals:", y) + return predictions, y + + predictions, actuals = test_model_with_generated_data('data.csv') + ``` + +4. Are there specific inputs that your implementation has trouble with? Given more time, could you work around these or is it fundamental? + + * Non-linear Relationships, The ElasticNetModel, being a linear model, inherently assumes that the relationships between the predictors and the response variable are linear. This assumption limits its ability to model complex, non-linear interactions effectively. + * High-dimensional Data, Although ElasticNet is designed to handle multicollinearity and can perform feature selection via L1 regularization, it might still struggle with very high-dimensional data (p >> n scenario), where the number of features far exceeds the number of observations. + * Categorical Features Handling, we used binary encoding, number encoding, dummy variable encoding in the implementation of the project, as we had more number of categorical features than numerical features in our dataset. + * Further regularization parameter tuning and potentially combining dimensionality reduction techniques like PCA (Principal Component Analysis) before applying ElasticNet could improve model performance. From 430f26bd48d6c59b5e7f51fceb0897071352fc6c Mon Sep 17 00:00:00 2001 From: Munish Patel <53735021+munishpatel@users.noreply.github.com> Date: Wed, 9 Oct 2024 23:38:31 -0500 Subject: [PATCH 05/12] Updated generate_regression_data.py --- generate_regression_data.py | 57 ++++++++++++++----------------------- 1 file changed, 22 insertions(+), 35 deletions(-) diff --git a/generate_regression_data.py b/generate_regression_data.py index bca2108..62edffe 100644 --- a/generate_regression_data.py +++ b/generate_regression_data.py @@ -1,40 +1,27 @@ -import argparse +import numpy as np import csv +import argparse -import numpy - -def linear_data_generator(m, b, rnge, N, scale, seed): - rng = numpy.random.default_rng(seed=seed) - sample = rng.uniform(low=rnge[0], high=rnge[1], size=(N, m.shape[0])) - ys = numpy.dot(sample, numpy.reshape(m, (-1,1))) + b - noise = rng.normal(loc=0., scale=scale, size=ys.shape) - return (sample, ys+noise) - -def write_data(filename, X, y): - with open(filename, "w") as file: - # X column for every x - xs = [f"x_{n}" for n in range(X.shape[1])] - header = xs + ["y"] - writer = csv.writer(file) - writer.writerow(header) - for row in numpy.hstack((X,y)): - writer.writerow(row) - -def main(): - parser = argparse.ArgumentParser() - parser.add_argument("-N", type = int, help="Number of samples.") - parser.add_argument("-m", nargs='*', type = float, help="Expected regression coefficients") - parser.add_argument("-b", type= float, help="Offset") - parser.add_argument("-scale", type=float, help="Scale of noise") - parser.add_argument("-rnge", nargs=2, type=float, help="Range of Xs") - parser.add_argument("-seed", type=int, help="A seed to control randomness") - parser.add_argument("-output_file", type=str, help="Path to output file") - args = parser.parse_args() - m = numpy.array(args.m) - X, y = linear_data_generator(m, args.b, args.rnge, args.N, args.scale, args.seed) - write_data(args.output_file, X,y) +def generate_data(N, m, b, scale, rnge, seed, output_file): + def linear_data_generator(m, b, rnge, N, scale, seed): + rng = np.random.default_rng(seed=seed) + sample = rng.uniform(low=rnge[0], high=rnge[1], size=(N, len(m))) + ys = np.dot(sample, np.array(m).reshape(-1, 1)) + b + noise = rng.normal(loc=0., scale=scale, size=ys.shape) + return (sample, ys + noise) -if __name__=="__main__": - main() + def write_data(filename, X, y): + with open(filename, "w") as file: + xs = [f"x_{n}" for n in range(X.shape[1])] + header = xs + ["y"] + writer = csv.writer(file) + writer.writerow(header) + for row in np.hstack((X, y)): + writer.writerow(row) + m = np.array(m) + X, y = linear_data_generator(m, b, rnge, N, scale, seed) + write_data(output_file, X, y) +# Calling the function with example parameters +generate_data(100, [3, 2], 5, 1.0, [-10, 10], 42, 'data.csv') From afcf449ac596c717dff18bbaf682f1e17b3e17e7 Mon Sep 17 00:00:00 2001 From: Munish Patel <53735021+munishpatel@users.noreply.github.com> Date: Wed, 9 Oct 2024 23:39:48 -0500 Subject: [PATCH 06/12] Updated README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 62de0af..8018ba9 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # Project 1 -Course: CS584 - Machine Learning -Instructor: Steve Avsec +- Course: CS584 - Machine Learning +- Instructor: Steve Avsec ## Team Members 1. Munish Patel - mpatel176@hawk.iit.edu (A20544034) From a1386f4870d0d26760c7e37d48b0671af8c779c9 Mon Sep 17 00:00:00 2001 From: Munish Patel <53735021+munishpatel@users.noreply.github.com> Date: Wed, 9 Oct 2024 23:41:22 -0500 Subject: [PATCH 07/12] Updated README.md --- README.md | 38 +++++++++++++++++++------------------- 1 file changed, 19 insertions(+), 19 deletions(-) diff --git a/README.md b/README.md index 8018ba9..3981fd8 100644 --- a/README.md +++ b/README.md @@ -30,29 +30,29 @@ This project implements a ElasticNet Model, which combines L1 and L2 penalties t ### Explanation of the Model 1. What does the model you have implemented do and when should it be used? - * The ElasticNetModel implemented is a type of regularized linear regression that combines both L1 and L2 regularization. - * L1 Regularization (Lasso) helps in feature selection by shrinking some coefficients to zero, which is beneficial in models with high dimensionality. - * L2 Regularization (Ridge) tends to shrink coefficients evenly and helps in dealing with multicollinearity and model stability by keeping the coefficients small. - * The main reason behind using ElasticNet is to build a model with least complexity while excelling in occasions where features seem to relate or when there are more variables than cases. When it is desirable to decrease the model’s complexity with regards to the features contributing to collinearity, then ElasticNet can prove effective. - * ElasticNet is used when we suspect or know there is multicollinearity in your data, have a large number of features, some of which might be irrelevant, need a model that can perform feature selection to improve prediction accuracy. + - The ElasticNetModel implemented is a type of regularized linear regression that combines both L1 and L2 regularization. + - L1 Regularization (Lasso) helps in feature selection by shrinking some coefficients to zero, which is beneficial in models with high dimensionality. + - L2 Regularization (Ridge) tends to shrink coefficients evenly and helps in dealing with multicollinearity and model stability by keeping the coefficients small. + - The main reason behind using ElasticNet is to build a model with least complexity while excelling in occasions where features seem to relate or when there are more variables than cases. When it is desirable to decrease the model’s complexity with regards to the features contributing to collinearity, then ElasticNet can prove effective. + - ElasticNet is used when we suspect or know there is multicollinearity in your data, have a large number of features, some of which might be irrelevant, need a model that can perform feature selection to improve prediction accuracy. 2. How did you test your model to determine if it is working reasonably correctly? - * We evaluated our model by training it on a dataset that predicts suggested job roles. - * To verify the models ability to generalize, we have divided the data into training sets and testing sets. - * Fit the model on the training data using results = model.fit(x_train_scaled, y_train). - * Make predictions on the testing data using results.predict(x_test_scaled). - * We tested tthe model in test.py using small_data.csv and also tested it in generate_regression_data.py where we generated random data and stored it in data.csv + - We evaluated our model by training it on a dataset that predicts suggested job roles. + - To verify the models ability to generalize, we have divided the data into training sets and testing sets. + - Fit the model on the training data using results = model.fit(x_train_scaled, y_train). + - Make predictions on the testing data using results.predict(x_test_scaled). + - We tested tthe model in test.py using small_data.csv and also tested it in generate_regression_data.py where we generated random data and stored it in data.csv 3. What parameters have you exposed to users of your implementation in order to tune performance? - * ```lambdas```: Controls the strength of the regularization. A higher value means more regularization. - * ```l1_ratio```: Balances between L1 and L2 regularization. - * ```iterations```: Determines the number of iterations in the gradient descent algorithm. - * ```learning_rate```: Controls the step size at each iteration while moving toward a minimum of the loss function. - * Example Usage for random generated data: + - ```lambdas```: Controls the strength of the regularization. A higher value means more regularization. + - ```l1_ratio```: Balances between L1 and L2 regularization. + - ```iterations```: Determines the number of iterations in the gradient descent algorithm. + - ```learning_rate```: Controls the step size at each iteration while moving toward a minimum of the loss function. + - Example Usage for random generated data: ```bash model = ElasticNetModel(lambdas=1.0, l1_ratio=0.5, iterations=1000, learning_rate=0.01) results = model.fit(X_scaled, y) @@ -78,7 +78,7 @@ This project implements a ElasticNet Model, which combines L1 and L2 penalties t 4. Are there specific inputs that your implementation has trouble with? Given more time, could you work around these or is it fundamental? - * Non-linear Relationships, The ElasticNetModel, being a linear model, inherently assumes that the relationships between the predictors and the response variable are linear. This assumption limits its ability to model complex, non-linear interactions effectively. - * High-dimensional Data, Although ElasticNet is designed to handle multicollinearity and can perform feature selection via L1 regularization, it might still struggle with very high-dimensional data (p >> n scenario), where the number of features far exceeds the number of observations. - * Categorical Features Handling, we used binary encoding, number encoding, dummy variable encoding in the implementation of the project, as we had more number of categorical features than numerical features in our dataset. - * Further regularization parameter tuning and potentially combining dimensionality reduction techniques like PCA (Principal Component Analysis) before applying ElasticNet could improve model performance. + - Non-linear Relationships, The ElasticNetModel, being a linear model, inherently assumes that the relationships between the predictors and the response variable are linear. This assumption limits its ability to model complex, non-linear interactions effectively. + - High-dimensional Data, Although ElasticNet is designed to handle multicollinearity and can perform feature selection via L1 regularization, it might still struggle with very high-dimensional data (p >> n scenario), where the number of features far exceeds the number of observations. + - Categorical Features Handling, we used binary encoding, number encoding, dummy variable encoding in the implementation of the project, as we had more number of categorical features than numerical features in our dataset. + - Further regularization parameter tuning and potentially combining dimensionality reduction techniques like PCA (Principal Component Analysis) before applying ElasticNet could improve model performance. From 3909cede08810214358a42de0478ea769f4b833b Mon Sep 17 00:00:00 2001 From: Munish Patel <53735021+munishpatel@users.noreply.github.com> Date: Wed, 9 Oct 2024 23:43:47 -0500 Subject: [PATCH 08/12] Updated generate_regression_data.py --- generate_regression_data.py | 79 +++++++++++++++++++++++++++++++++++++ 1 file changed, 79 insertions(+) diff --git a/generate_regression_data.py b/generate_regression_data.py index 62edffe..25dbf4c 100644 --- a/generate_regression_data.py +++ b/generate_regression_data.py @@ -1,6 +1,47 @@ import numpy as np import csv import argparse +from sklearn.preprocessing import RobustScaler +import matplotlib.pyplot as plt + + +def load_data(filename): + with open(filename, 'r') as file: + reader = csv.reader(file) + next(reader) # Skip the header + X, y = [], [] + for row in reader: + X.append([float(num) for num in row[:-1]]) + y.append(float(row[-1])) + return np.array(X), np.array(y) + +def test_model_with_generated_data(filename): + X, y = load_data(filename) + + scaler = RobustScaler() + X_scaled = scaler.fit_transform(X) + + model = ElasticNetModel(lambdas=1.0, l1_ratio=0.5, iterations=1000, learning_rate=0.01) + results = model.fit(X_scaled, y) + + predictions = results.predict(X_scaled) + + # Plotting the results + plt.figure(figsize=(10, 6)) + plt.scatter(y, predictions, alpha=0.5) + plt.title('Comparison of Actual and Predicted Values') + plt.xlabel('Actual Values') + plt.ylabel('Predicted Values') + plt.plot([y.min(), y.max()], [y.min(), y.max()], 'k--', lw=4) # Diagonal line for reference + plt.grid(True) + plt.show() + + print("Predictions:", predictions) + print("Actuals:", y) + return predictions, y + +predictions, actuals = test_model_with_generated_data('data.csv') + def generate_data(N, m, b, scale, rnge, seed, output_file): def linear_data_generator(m, b, rnge, N, scale, seed): @@ -25,3 +66,41 @@ def write_data(filename, X, y): # Calling the function with example parameters generate_data(100, [3, 2], 5, 1.0, [-10, 10], 42, 'data.csv') + + +def load_data(filename): + with open(filename, 'r') as file: + reader = csv.reader(file) + next(reader) # Skip the header + X, y = [], [] + for row in reader: + X.append([float(num) for num in row[:-1]]) + y.append(float(row[-1])) + return np.array(X), np.array(y) + +def test_model_with_generated_data(filename): + X, y = load_data(filename) + + scaler = RobustScaler() + X_scaled = scaler.fit_transform(X) + + model = ElasticNetModel(lambdas=1.0, l1_ratio=0.5, iterations=1000, learning_rate=0.01) + results = model.fit(X_scaled, y) + + predictions = results.predict(X_scaled) + + # Plotting the results + plt.figure(figsize=(10, 6)) + plt.scatter(y, predictions, alpha=0.5) + plt.title('Comparison of Actual and Predicted Values') + plt.xlabel('Actual Values') + plt.ylabel('Predicted Values') + plt.plot([y.min(), y.max()], [y.min(), y.max()], 'k--', lw=4) # Diagonal line for reference + plt.grid(True) + plt.show() + + print("Predictions:", predictions) + print("Actuals:", y) + return predictions, y + +predictions, actuals = test_model_with_generated_data('data.csv') From f9e737a7080e83f4b0e0882afbdbf7e57f51d9a8 Mon Sep 17 00:00:00 2001 From: Munish Patel <53735021+munishpatel@users.noreply.github.com> Date: Wed, 9 Oct 2024 23:45:58 -0500 Subject: [PATCH 09/12] Uploaded project notebook --- ML_Project1.ipynb | 2401 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 2401 insertions(+) create mode 100644 ML_Project1.ipynb diff --git a/ML_Project1.ipynb b/ML_Project1.ipynb new file mode 100644 index 0000000..6a6133e --- /dev/null +++ b/ML_Project1.ipynb @@ -0,0 +1,2401 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "Loading the Dataset" + ], + "metadata": { + "id": "lHGZjurzD71q" + } + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "id": "ygNfZNxgDyi0" + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "file_path = '/content/mldata.csv'\n", + "data = pd.read_csv(file_path)" + ] + }, + { + "cell_type": "code", + "source": [ + "data.head()" + ], + "metadata": { + "id": "_p8vhC-YD6u7", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 434 + }, + "collapsed": true, + "outputId": "ee5a6690-9554-4d00-e790-7a85d81afbde" + }, + "execution_count": 42, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Logical quotient rating hackathons coding skills rating \\\n", + "0 5 0 6 \n", + "1 7 6 4 \n", + "2 2 3 9 \n", + "3 2 6 3 \n", + "4 2 0 3 \n", + "\n", + " public speaking points self-learning capability? 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Taken inputs from seniors or elders \\\n", + "0 BPA no \n", + "1 Cloud Services yes \n", + "2 product development yes \n", + "3 Testing and Maintainance Services yes \n", + "4 BPA no \n", + "\n", + " Interested Type of Books Management or Technical hard/smart worker \\\n", + "0 Series Management smart worker \n", + "1 Autobiographies Technical hard worker \n", + "2 Travel Technical smart worker \n", + "3 Guide Management smart worker \n", + "4 Health Technical hard worker \n", + "\n", + " worked in teams ever? Introvert Suggested Job Role \n", + "0 yes no Applications Developer \n", + "1 no yes Applications Developer \n", + "2 no no Applications Developer \n", + "3 yes yes Applications Developer \n", + "4 yes no Applications Developer " + ], + "text/html": [ + "\n", + "
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\"properties\": {\n \"dtype\": \"number\",\n \"std\": 2,\n \"min\": 1,\n \"max\": 9,\n \"num_unique_values\": 9,\n \"samples\": [\n 6,\n 3,\n 7\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"self-learning capability?\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"no\",\n \"yes\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Extra-courses did\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"yes\",\n \"no\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"certifications\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 9,\n \"samples\": [\n \"app development\",\n \"shell programming\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"workshops\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 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\"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"hard/smart worker\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"hard worker\",\n \"smart worker\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"worked in teams ever?\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"no\",\n \"yes\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Introvert\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"yes\",\n \"no\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Suggested Job Role\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 12,\n \"samples\": [\n \"UX Designer\",\n \"Technical Support\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 42 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Get the shape of the DataFrame\n", + "shape = data.shape\n", + "\n", + "# Print the shape\n", + "print(\"The shape of the dataset is:\", shape)\n" + ], + "metadata": { + "id": "-ai5C_s6D6sI", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "collapsed": true, + "outputId": "47839e0b-b266-418d-90f5-2e9f94a70489" + }, + "execution_count": 43, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "The shape of the dataset is: (6901, 20)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Data Preprocessing" + ], + "metadata": { + "id": "_qEkJmP0l2HO" + } + }, + { + "cell_type": "code", + "source": [ + "data.columns" + ], + "metadata": { + "id": "voDNwnOfD6bG", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "collapsed": true, + "outputId": "c5bb0e21-3e42-4e4e-e7d0-73a8f38822e7" + }, + "execution_count": 44, + 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"Index(['self-learning capability?', 'Extra-courses did', 'certifications',\n", + " 'workshops', 'reading and writing skills', 'memory capability score',\n", + " 'Interested subjects', 'interested career area ',\n", + " 'Type of company want to settle in?',\n", + " 'Taken inputs from seniors or elders', 'Interested Type of Books',\n", + " 'Management or Technical', 'hard/smart worker', 'worked in teams ever?',\n", + " 'Introvert', 'Suggested Job Role'],\n", + " dtype='object')\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Checking if there are any missing values in our dataset\n", + "missing_values = data.isnull().values.any()\n", + "\n", + "print(\"Is there any missing value in the DataSet?\", missing_values)\n", + "\n", + "data.isnull().sum(axis=0)\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 729 + }, + "collapsed": true, + "id": "94vcwNGHSy14", + "outputId": "5badded5-967e-41d6-99dc-5d10adbbd6ba" + }, + 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" + ] + }, + "metadata": {}, + "execution_count": 47 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Checking for unique/distinct values in categorical features in the data\n", + "\n", + "categorical_features = ['self-learning capability?', 'Extra-courses did', 'certifications', 'workshops',\n", + " 'reading and writing skills', 'memory capability score', 'Interested subjects',\n", + " 'interested career area ', 'Type of company want to settle in?',\n", + " 'Taken inputs from seniors or elders', 'Interested Type of Books',\n", + " 'Management or Technical', 'hard/smart worker', 'worked in teams ever?',\n", + " 'Introvert', 'Suggested Job Role']\n", + "\n", + "for feature in categorical_features:\n", + " print(f\"Unique values in '{feature}':\")\n", + " print(data[feature].value_counts())\n", + " print(\"\\n\") # Add spacing between each feature's output" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "VYVx7idMrm8v", + "outputId": "4a6d5047-c74d-4a6f-9170-361182155dc7" + }, + "execution_count": 48, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Unique values in 'self-learning capability?':\n", + "self-learning capability?\n", + "yes 3496\n", + "no 3405\n", + "Name: count, dtype: int64\n", + "\n", + "\n", + "Unique values in 'Extra-courses did':\n", + "Extra-courses did\n", + "no 3529\n", + "yes 3372\n", + "Name: count, dtype: int64\n", + "\n", + "\n", + "Unique values in 'certifications':\n", + "certifications\n", + "r programming 803\n", + "information security 785\n", + "shell programming 783\n", + "machine learning 783\n", + "full stack 768\n", + "hadoop 764\n", + "python 756\n", + "distro making 740\n", + "app development 719\n", + "Name: count, dtype: int64\n", + "\n", + "\n", + "Unique values in 'workshops':\n", + "workshops\n", + "database security 897\n", + "system designing 891\n", + "web technologies 891\n", + "hacking 867\n", + "testing 852\n", + "data science 842\n", + "game development 831\n", + "cloud computing 830\n", + "Name: count, dtype: int64\n", + "\n", + "\n", + "Unique values in 'reading and writing skills':\n", + "reading and writing skills\n", + "excellent 2328\n", + "medium 2315\n", + "poor 2258\n", + "Name: count, dtype: int64\n", + "\n", + "\n", + "Unique values in 'memory capability score':\n", + "memory capability score\n", + "medium 2317\n", + "excellent 2303\n", + "poor 2281\n", + "Name: count, dtype: int64\n", + "\n", + "\n", + "Unique values in 'Interested subjects':\n", + "Interested subjects\n", + "Software Engineering 731\n", + "IOT 722\n", + "cloud computing 721\n", + "programming 716\n", + "networks 713\n", + "Computer Architecture 703\n", + "data engineering 672\n", + "hacking 663\n", + "Management 644\n", + "parallel computing 616\n", + "Name: count, dtype: int64\n", + "\n", + "\n", + "Unique values in 'interested career area ':\n", + "interested career area \n", + "system developer 1178\n", + "security 1177\n", + "Business process analyst 1154\n", + "developer 1145\n", + "testing 1128\n", + "cloud computing 1119\n", + "Name: count, dtype: int64\n", + "\n", + "\n", + "Unique values in 'Type of company want to settle in?':\n", + "Type of company want to settle in?\n", + "Service Based 725\n", + "Web Services 719\n", + "BPA 711\n", + "Testing and Maintainance Services 698\n", + "Product based 695\n", + "Finance 694\n", + "Cloud Services 692\n", + "product development 669\n", + "Sales and Marketing 658\n", + "SAaS services 640\n", + "Name: count, dtype: int64\n", + "\n", + "\n", + "Unique values in 'Taken inputs from seniors or elders':\n", + "Taken inputs from seniors or elders\n", + "yes 3501\n", + "no 3400\n", + "Name: count, dtype: int64\n", + "\n", + "\n", + "Unique values in 'Interested Type of Books':\n", + "Interested Type of Books\n", + "Guide 405\n", + "Health 401\n", + "Self help 377\n", + "Horror 377\n", + "Biographies 219\n", + "Science fiction 218\n", + "Satire 212\n", + "Childrens 212\n", + "Autobiographies 210\n", + "Prayer books 207\n", + "Fantasy 205\n", + "Journals 203\n", + "Trilogy 203\n", + "Anthology 202\n", + "Encyclopedias 201\n", + "Drama 201\n", + "Mystery 200\n", + "History 199\n", + "Science 198\n", + "Dictionaries 198\n", + "Diaries 197\n", + "Religion-Spirituality 197\n", + "Action and Adventure 193\n", + "Poetry 193\n", + "Cookbooks 186\n", + "Comics 186\n", + "Art 186\n", + "Travel 186\n", + "Series 180\n", + "Math 176\n", + "Romance 173\n", + "Name: count, dtype: int64\n", + "\n", + "\n", + "Unique values in 'Management or Technical':\n", + "Management or Technical\n", + "Management 3461\n", + "Technical 3440\n", + "Name: count, dtype: int64\n", + "\n", + "\n", + "Unique values in 'hard/smart worker':\n", + "hard/smart worker\n", + "smart worker 3523\n", + "hard worker 3378\n", + "Name: count, dtype: int64\n", + "\n", + "\n", + "Unique values in 'worked in teams ever?':\n", + "worked in teams ever?\n", + "no 3470\n", + "yes 3431\n", + "Name: count, dtype: int64\n", + "\n", + "\n", + "Unique values in 'Introvert':\n", + "Introvert\n", + "yes 3544\n", + "no 3357\n", + "Name: count, dtype: int64\n", + "\n", + "\n", + "Unique values in 'Suggested Job Role':\n", + "Suggested Job Role\n", + "Network Security Engineer 630\n", + "Software Engineer 590\n", + "UX Designer 589\n", + "Software Developer 587\n", + "Database Developer 581\n", + "Software Quality Assurance (QA) / Testing 571\n", + "Web Developer 570\n", + "CRM Technical Developer 567\n", + "Technical Support 565\n", + "Systems Security Administrator 562\n", + "Applications Developer 551\n", + "Mobile Applications Developer 538\n", + "Name: count, dtype: int64\n", + "\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Now lets find out the summary of numerical features\n", + "\n", + "print(data[['Logical quotient rating', 'hackathons', 'coding skills rating', 'public speaking points']].describe())\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "luLTQexzwHer", + "outputId": "8185c8e3-24ab-48ea-8429-c0a98387876e" + }, + "execution_count": 49, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " Logical quotient rating hackathons coding skills rating \\\n", + "count 6901.000000 6901.000000 6901.000000 \n", + "mean 4.991016 2.999710 5.010723 \n", + "std 2.577704 2.010191 2.568347 \n", + "min 1.000000 0.000000 1.000000 \n", + "25% 3.000000 1.000000 3.000000 \n", + "50% 5.000000 3.000000 5.000000 \n", + "75% 7.000000 5.000000 7.000000 \n", + "max 9.000000 6.000000 9.000000 \n", + "\n", + " public speaking points \n", + "count 6901.000000 \n", + "mean 4.988263 \n", + "std 2.599500 \n", + "min 1.000000 \n", + "25% 3.000000 \n", + "50% 5.000000 \n", + "75% 7.000000 \n", + "max 9.000000 \n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Exploratory Data Analysis" + ], + "metadata": { + "id": "rdTqy0HEllMS" + } + }, + { + "cell_type": "code", + "source": [ + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Correlation matrix for numerical features\n", + "plt.figure(figsize=(10, 6))\n", + "sns.heatmap(data[['Logical quotient rating', 'hackathons', 'coding skills rating', 'public speaking points']].corr(), annot=True, cmap='coolwarm')\n", + "plt.title('Correlation between Numerical Features')\n", + "plt.show()\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 545 + }, + "id": "8_xH-992TtCB", + "outputId": "1ae14ee8-7542-4e93-b602-4ada1996cab5" + }, + "execution_count": 50, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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0cO3aNUybNu2VkcW3SSqVqt3+4o/yf0OhUKB+/frw8/NTu9/Ozu61eTRr1gw6Ojo4c+YMatSoAQsLC7i6uqJNmzZYt24dcnJycPbsWZWxfkX3b9u2bbCysiqRp6ampkq6yZMnlxoZfvlhwtu6Z23btkX79u2xZMmSElEhoPQ6++KkQ2UpV1nKq1Ao0LlzZ0ydOlVt2qKGfpG3OTvqhAkTsGnTJkyaNAnu7u6Qy+WQSCQYOHBghfw7KGvdAArH2F64cAFTpkxBo0aNYGBgAIVCgY8++qhMZSvvewio/5yRSCT4448/1L6XL37GLF++HMOHD8fvv/+OP//8E1999RUWLlyIv//+G9WrV39teYno/cIGIRGJVvfu3bFhwwZcvHgR7u7ur0xrb28PhUKBsLAw1K5dW7k9Li4OycnJsLe3f+NyODk5AShspHp4eJTr2NOnTyMxMRH79u1D27ZtldsfPnxYIm1ZG7NF1xIaGlpiX0hICMzMzN7qFPZPnz4tEXG8f/8+ACgn5XBycsKNGzfQqVOn115HaftlMhmaN2+Os2fPokaNGmjTpg2AwshhTk4Otm/fjri4OJX7WPTeWFhYvPK9cXR0BFAYZSnve/g2zJkzB+3bt8cPP/xQYl9R5PHlxc7L0zW1rJycnJCenv5W70HRvX3dw5s9e/Zg2LBhWL58uXJbdnZ2hS3yXta6kZSUhMDAQPj6+mL27NnK7UURxheVVnffxnvo5OQEQRBQs2bNEg1zderXr4/69etj5syZuHDhAlq1aoX169dj/vz5ZT4nEb0fOIaQiERr6tSp0NfXx+jRoxEXF1di/4MHD7By5UoAQNeuXQGgxCyJRRGrbt26vXE5mjZtCicnJyxbtgzp6ekl9r9qhsGiJ/0vRmlyc3Oxbt26Emn19fXL1D3N2toajRo1wpYtW1R+gN6+fRt//vmn8l68Lfn5+SoNmdzcXPzwww8wNzdH06ZNARRGWKKjo/Hjjz+WOD4rK0s5LhAovM7SGgFt2rTBpUuXcOrUKWWD0MzMDLVr11bOiFq0HQA8PT1hZGSE7777Dnl5eSXyK3pvLCwslA2ymJiYUtNVlHbt2qF9+/ZYvHgxsrOzVfbZ29tDKpWWGL+nro78W/3798fFixdx7NixEvuSk5ORn59f7jzNzc3Rtm1bbNy4EVFRUSr7Xqz3Uqm0RHR19erVr4yi/RtlrRvq/o0CJT9LACgfirxcf42MjGBmZvav3sPevXtDKpXC19e3RFkEQVAugZGamlrifapfvz40NDRKLLFDRP8NjBASkWg5OTlhx44dGDBgAGrXro2hQ4eiXr16yM3NxYULF7B7924MHz4cANCwYUMMGzYMGzZsUHbTvHz5MrZs2YKePXuiQ4cOb1wODQ0N/PTTT+jSpQvq1q2LESNGwNbWFtHR0Th16hSMjIxw6NAhtce2bNkSJiYmGDZsGL766itIJBJs27ZNbbfDpk2bYteuXfDy8sIHH3wAAwMD9OjRQ22+S5cuRZcuXeDu7o5Ro0Ypl52Qy+Uqa569DTY2Nli8eDEiIyPh6uqKXbt2ITg4GBs2bFCOaxoyZAh+++03fP755zh16hRatWqFgoIChISE4LffflOu+Vh0nSdOnICfnx9sbGxQs2ZN5bqCbdq0wYIFC/D48WOVhl/btm3xww8/wMHBQaVLnJGREb7//nsMGTIETZo0wcCBA2Fubo6oqCgEBASgVatWWLNmDYDCyUdat26N+vXrY8yYMXB0dERcXBwuXryIJ0+e4MaNG2/1vr3Mx8dHbT2Uy+Xo168fVq9eDYlEAicnJxw+fLhM4y7La8qUKTh48CC6d++O4cOHo2nTpsjIyMCtW7ewZ88eREZGKpdTKI9Vq1ahdevWaNKkCcaOHYuaNWsiMjISAQEBCA4OBlAY8d+2bRvkcjnq1KmDixcv4sSJE6hWrdpbvspCZa0bRkZGyiU88vLyYGtriz///FNtFL/oAciMGTMwcOBAaGlpoUePHsoHV4sWLcLo0aPRrFkznDlzRhlJLwsnJyfMnz8f3t7eiIyMRM+ePWFoaIiHDx9i//79GDt2LCZPnoyTJ09i/Pjx6NevH1xdXZGfn49t27ZBKpWiT58+b+3+EVEVUhlTmxIRVSX3798XxowZIzg4OAgymUwwNDQUWrVqJaxevVplCYG8vDzB19dXqFmzpqClpSXY2dkJ3t7eKmkEoeS0/0WKprsvbTr369evC7179xaqVasmaGtrC/b29kL//v2FwMBAZRp1y06cP39e+PDDDwVdXV3BxsZGmDp1qnDs2LESU+unp6cLn376qWBsbCwAUC5BUdqU9idOnBBatWol6OrqCkZGRkKPHj2Eu3fvqqR5cQmEF6krpzrt2rUT6tatK1y9elVwd3cXdHR0BHt7e2HNmjUl0ubm5gqLFy8W6tatK2hrawsmJiZC06ZNBV9fXyElJUWZLiQkRGjbtq2gq6srAFBZgiI1NVWQSqWCoaGhkJ+fr9z+yy+/CACEIUOGqC3nqVOnBE9PT0Eulws6OjqCk5OTMHz4cOHq1asq6R48eCAMHTpUsLKyErS0tARbW1uhe/fuwp49e0rcmytXrpQ4x8vvmTql3fOi+wmgRP1LSEgQ+vTpI+jp6QkmJibCZ599Jty+fVvtshP6+vpq861bt26J7erqelpamuDt7S04OzsLMplMMDMzE1q2bCksW7ZMuYxIUZ1bunRpiTxLq4+3b98WevXqJRgbGws6OjqCm5ubMGvWLOX+pKQkYcSIEYKZmZlgYGAgeHp6CiEhIYK9vb1KHSjvshMvv08vK0vdePLkibLscrlc6Nevn/D06dMSS0YIQuHSHba2toKGhobKv6HMzExh1KhRglwuFwwNDYX+/fsL8fHxpS47oa5+CIIg7N27V2jdurWgr68v6OvrC7Vq1RK+/PJLITQ0VBAEQYiIiBBGjhwpODk5CTo6OoKpqanQoUMH4cSJE6+8D0T0/pIIwlsa8U9ERERERETvFY4hJCIiIiIiEik2CImIiIiIiESKDUIiIiIiIiKRYoOQiIiIiIiokp05cwY9evSAjY0NJBIJDhw48NpjTp8+jSZNmkBbWxvOzs7YvHlzuc/LBiEREREREVEly8jIQMOGDbF27doypX/48CG6deuGDh06IDg4GJMmTcLo0aPVrgX7KpxllIiIiIiIqAqRSCTYv38/evbsWWqaadOmISAgALdv31ZuGzhwIJKTk3H06NEyn4sRQiIiIiIiogqQk5OD1NRUlVdOTs5byfvixYvw8PBQ2ebp6YmLFy+WKx/Nt1IaokoUoOVW2UUgkVj6yebKLgKJhESDz2vp3ZDpald2EUgkjm1pVNlFKFVF/pa8MmMQfH19Vbb5+Phgzpw5/zrv2NhYWFpaqmyztLREamoqsrKyoKurW6Z82CAkIiIiIiKqAN7e3vDy8lLZpq1dtR7EsEFIRERERESiJdGSVFje2traFdYAtLKyQlxcnMq2uLg4GBkZlTk6CHAMIRERERER0XvH3d0dgYGBKtuOHz8Od3f3cuXDCCEREREREYmWhmbFRQjLIz09HeHh4cq/Hz58iODgYJiamqJGjRrw9vZGdHQ0tm7dCgD4/PPPsWbNGkydOhUjR47EyZMn8dtvvyEgIKBc52WEkIiIiIiIqJJdvXoVjRs3RuPGjQEAXl5eaNy4MWbPng0AiImJQVRUlDJ9zZo1ERAQgOPHj6Nhw4ZYvnw5fvrpJ3h6epbrvIwQEhERERGRaEm0qkaMrH379njVEvGbN29We8z169f/1XnZICQiIiIiItGqKl1GK0vVaA4TERERERHRO8cIIRERERERiVZFLjvxPmCEkIiIiIiISKQYISQiIiIiItHiGEIiIiIiIiISJUYIiYiIiIhItDiGkIiIiIiIiESJEUIiIiIiIhItjiEkIiIiIiIiUWKEkIiIiIiIREsiFXeEkA1CIiIiIiISLQ2RNwjZZZSIiIiIiEikGCEkIiIiIiLRkmgwQkhEREREREQixAghERERERGJlkQq7hiZuK+eiIiIiIhIxBghJCIiIiIi0eIso0RERERERCRKjBASEREREZFoiX2WUTYIiYiIiIhItNhllIiIiIiIiESJEUIiIiIiIhItCSOEREREREREJEaMEBIRERERkWhJNMQdIxP31RMREREREYkYI4RERERERCRaYl92ghFCIiIiIiIikWKEkIiIiIiIREvs6xCyQUhERERERKLFLqNEREREREQkSowQEhERERGRaHHZCSIiIiIiIhIlRgiJiIiIiEi0OIaQiIiIiIiIRIkRQiIiIiIiEi2xLzvBCCEREREREZFIMUJIRERERESiJfYxhGwQEhERERGRaHHZCSIiIiIiIhIlRgiJiIiIiEi0xN5llBFCIiIiIiIikWKEkIiIiIiIRIsRQiIiIiIiIhKlKtcgdHBwgL+//1vLr3379pg0adJby+9te9vXW5kkEgkOHDhQ2cUgIiIiIioziYakwl7vg3J1GR0+fDiSk5Mr9Ef/lStXoK+vX2H5V5bNmzdj0qRJSE5OVtleEdfbvn17NGrUqMIamnPmzMGBAwcQHByssj0mJgYmJiYVck56O0xbN4PjN6Mgb1IPOjYWuNpnHOIOBlZ2saiK6PmRJQZ+bANTYxnCH2Vg1c+RCAlPLzV9O3dTjBpYA1bm2ngSk40ffnmES9eTVdKMGGCH7h4WMNDTxO3QVPhteIjo2Gzl/gXT3ODsoA8TuRbSMvIRdDMFP/zyCIlJeco07d2r4f9626K6jQ6SU/Ox/49Y7Dr49K1fP707PT0tMaCHNUyNtfDgUSZWbYxEyIOMUtO3+9AUIwdUL6xrsdnYsD0Kl66nqKQZ0d8W3TpZwEBfE7dD0rDip4eIjs1RSfNhY2MM7WsLR3s95OYqcONeKmYtDVPub1LPCCMGVIdjDT1k5xTg2F/P8NOvj6FQvN3rp8rVo5MZ+naxgKlcExGPs7Dul2iERmSWmr7NB3IM620NSzMZouNy8PNvT3HlZhoAQCoFhvexxgcNjGBtIUNGpgLX76bh59+e4nlyvko+zRsaYfAnlqhpp4vcPAVuhWTAd9XDCr1WorKochFCc3Nz6OnpVXYx3pmqdL25ubn/6ngrKytoa2u/pdJQRZDq6yH1Zihuf+Vb2UWhKqZDy2oYN8wBm3c/wZipN/EgMhNLZ9aGsZH654Z13Qwwe5IrAgLjMXrKTZy78hzzp7qhpp2uMs2gnjbo09UKfhsi8MW3t5CVo8DSWbUh0yp+Ynr9Tip8/e5jyFfXMXtZKGysdOA72U25v3ljY8yc6IyDx+Mw4usb8P8xAv26W6PXR1YVdzOoQnVwN8UXQ2tgy54nGDvtNh48ysSSGbVKr2uuBpg10RlHTiZgzLRbOHclCfOmuMLhhbo28BNr9O5ihRU/RmLct7eRnaPAkhm1oPVCXWvbwgTeE5zwx+kEjJ5yCxNm3UXguUTlfid7PSz0dsOV4BSMmXoLc1eEo2VTE4wdXKPibga9c+2aG2PsIBts/z0WX/qEIuJxFhZMdoTcUH39q+OsB+8vHHD0TCLGzQ7FhWsp8JlYE/a2OgAAbZkGnO31sONgHL6cfR9zVz9EdStt+E5yVMmndTM5po6tgT/PPscXM0PhNT8Mp/5OqvDrpbKRaGhU2Ot98FZL+ddff6F58+bQ1taGtbU1pk+fjvz84qcjaWlpGDx4MPT19WFtbY0VK1aU6NL5chfK5ORkfPbZZ7C0tISOjg7q1auHw4cPAwASExMxaNAg2NraQk9PD/Xr18evv/5a7nIvWrQIlpaWMDQ0xKhRozB9+nQ0atRIuV9dt9OePXti+PDhyr+TkpIwdOhQmJiYQE9PD126dEFYWOFTx9OnT2PEiBFISUmBRCKBRCLBnDlzSr3e0aNHw9zcHEZGRujYsSNu3Lih3D9nzhw0atQI27Ztg4ODA+RyOQYOHIi0tMInVcOHD8dff/2FlStXKs8VGRmp9rodHBwwb948DB06FEZGRhg7diwAYNq0aXB1dYWenh4cHR0xa9Ys5OUVPq3fvHkzfH19cePGDWX+mzdvBqDaZTQyMhISiQT79u1Dhw4doKenh4YNG+LixYsqZfjxxx9hZ2cHPT099OrVC35+fjA2Nn7NO0ZvKuHYGdz38Ufc7ycquyhUxfTrYY2AE/E4eioBj55kwW9DBLJzFOja0UJt+j5drXE5OBm7Dj5FVHQWNu58jLCHGejVpbih1rebNbbtfYLzV5IQ8SgTC1eHw8xEhtbNTZVp9hyOwd2wdMQ9y8Wd0HTs2B+NOi4GkEoLf8j/r605zl1JwsE/4xATn4O/ryVj+/5oDOppU7E3hCpMv+7WCAiMx9HTz/AoOgt+Pz5Edq4CXTqYq03fp6tVYV07FIOo6Gxs2vUEYRGZ6PWRpTJN365W2LYvGuevJiEiKgsL1zworGsfFPZa0dAAxg93wA/bonDoeDyexGTjUXQWTl98rsyjQ0tTRDzKxNa90Xgal4Mb99Lww/Yo9PS0hK7O+/Gjjl6v90fmOPpXIv48+xxRT3OwavMT5OQq4NnWVG36nv8zx9VbqdjzRwIex+Rg675YhEdm4RMPMwBAZpYC3ksf4MzlZDyJzUHIg0ys3fYErjX1YG6qBaCw/n0+2BY/7nqKgFOJiI7LQdTTHJy5nPyuLpteQ0MqqbDX++CtfcJFR0eja9eu+OCDD3Djxg18//33+PnnnzF//nxlGi8vL5w/fx4HDx7E8ePHcfbsWVy7dq3UPBUKBbp06YLz58/jl19+wd27d7Fo0SJIpVIAQHZ2Npo2bYqAgADcvn0bY8eOxZAhQ3D58uUyl/u3337DnDlz8N133+Hq1auwtrbGunXryn39w4cPx9WrV3Hw4EFcvHgRgiCga9euyMvLQ8uWLeHv7w8jIyPExMQgJiYGkydPVptPv379EB8fjz/++ANBQUFo0qQJOnXqhOfPi7+0Hjx4gAMHDuDw4cM4fPgw/vrrLyxatAgAsHLlSri7u2PMmDHKc9nZ2ZVa7mXLlqFhw4a4fv06Zs2aBQAwNDTE5s2bcffuXaxcuRI//vgjVqxYAQAYMGAAvvnmG9StW1eZ/4ABA0rNf8aMGZg8eTKCg4Ph6uqKQYMGKR8SnD9/Hp9//jkmTpyI4OBgdO7cGQsWLCjfjSeif01TUwI3RwME3UxWbhMEIOhWMuq4Gao9pq6roUp6ALgcnIw6roXprS20Uc1EhqCbxd36MjILcDcsXZnmZYYGmvBoY4Y7oWkoKBAAAFpaEuTmqvbXy81VwMJMG1bm7JHwvtGUSuDqqI+gW6nKbYIAXLuVgrql1Is6rgYq6QHgyo1k1HUxAPBiXStOk5FVgHvh6co8XWvqw7yaDApBwIbF9bDnh8ZY5O2mEmXU0tRAbp5qXcvJVUBbpgFXx//eUBYx0pRK4OKgh2t3irvCCwJw/U466jirf49rO+vj+h3VrvNBt9NQu5T0AKCvK4VCISAjswAA4OKgB3NTGQQBWDvXFTtW1sX8bxyVUUaiyvbWlp1Yt24d7OzssGbNGkgkEtSqVQtPnz7FtGnTMHv2bGRkZGDLli3YsWMHOnXqBADYtGkTbGxKf8p74sQJXL58Gffu3YOrqysAwNGxOARva2ur0rCaMGECjh07ht9++w3NmzcvU7n9/f0xatQojBo1CgAwf/58nDhxAtnZ2a85slhYWBgOHjyI8+fPo2XLlgCA7du3w87ODgcOHEC/fv0gl8shkUhgZVV6N6dz587h8uXLiI+PV3a9XLZsGQ4cOIA9e/YoI3gKhQKbN2+GoWHhF92QIUMQGBiIBQsWQC6XQyaTQU9P75XnKtKxY0d88803Kttmzpyp/H8HBwdMnjwZO3fuxNSpU6GrqwsDAwNoamqWKf/JkyejW7duAABfX1/UrVsX4eHhqFWrFlavXo0uXboo30NXV1dcuHBBGQEmondDbqgJqVSC5yl5KtuTkvNQw1ZX7TGmxlp4nvxS+pQ8mBoXPhE3NSn8b8k0uco0Rcb+Xw30+sgKujpS3AlNg/fCEOW+K8HJ+HK4A5qcNsL126mwtdJB/x7WynPEJqiOEaOqTW5UWNeSXq4XyXmoYVN6XUt6uW6m5MHEWKbcX7Tt5TRF+6wtC79Th/Wrju+3PkJsfA7697CGv09tDJl4A2kZBbhyIwV9ulmhY6tqOH0hEabGWhjaxxYAUM1E9i+vnKoCI0MppFIJktXUFTtr9Q+YTOSaSEpVU//k6n9Ca2lJMGqADU7/nYTM7MIHDFbmhfXn/3paYcOv0Yh9lou+H1lgqbczRk27h7SMgn97afQvvS+Tv1SUtxYhvHfvHtzd3SGRFN/QVq1aIT09HU+ePEFERATy8vJUGmpyuRxubm7qsgMABAcHo3r16srG4MsKCgowb9481K9fH6ampjAwMMCxY8cQFRVVrnK3aNFCZZu7u3uZjy/KQ1NTUyWfatWqwc3NDffu3StzPjdu3EB6ejqqVasGAwMD5evhw4d48OCBMp2Dg4OyMQgA1tbWiI+PL1eZizRr1qzEtl27dqFVq1awsrKCgYEBZs6cWa57+qIGDRqolBOAsqyhoaElGu6va8jn5OQgNTVV5ZUncLQ/0fts1+9PMWbKTXwz9y4UCgHeE5yV+w6fiMf+o7FYOL02Tuz8EOu+q4+T5wvHfQlCZZWY3jca//w22b4vGmcuJeH+w0wsXhcBAYWTFgHA1Zsp+GFbFL4e44A/dzTH1pUNlZMkCQpWNno9qRSY8aUDAGD1lifK7UVtjV8PxeHc1RSER2Zh+U9REASgzQfG776gRC+p0gvT6+qqf1pYZOnSpVi5ciX8/f1Rv3596OvrY9KkSf96cpSXaWhoQHjpl0fRmLq3KT09HdbW1jh9+nSJfS+Oq9PSUn26LpFIoHjDKdBenuH04sWLGDx4MHx9feHp6Qm5XI6dO3di+fLlb5T/i2UteljwpmUFgIULF8LXV3VClEESUwyWmr1xnkRil5KWj4ICAaZy1c8WEzVRwCLPk/NKRPpM5MXpn/8zS+jLkUQTuQzhkaqzSaak5SMlLR9PYrIR9SQLuzc0RR1XA9y9X9hNa8MvUfhpRxRMjWVITs1Dk/pyAMDTuLL35KCqISW1sK6ZvFx3XlPXTF6um3ItJCXnKvcXbVOta1oIjyycOTLxn+2RT7KU+/PyBcTE5cDCrDj6tzsgFrsDYlHNRAtp6fmwstDG2ME18DSekej/gtS0AhQUCDBWV59S8tUek5SSDxOj16cvagxaVpNh6qJwZXQQgHK20ajo4s+svHwBsQk5sKimmjdVjvdl8peK8tauvnbt2sqxc0XOnz8PQ0NDVK9eHY6OjtDS0sKVK1eU+1NSUnD//v1S82zQoAGePHlSaprz58/jk08+wf/93/+hYcOGcHR0fGV+pZX70qVLKtv+/vtvlb/Nzc0RExOj/LugoAC3b99WySM/P18ln8TERISGhqJOnToAAJlMhoKCV3cJaNKkCWJjY6GpqQlnZ2eVl5lZ2Rs8ZTlXaS5cuAB7e3vMmDEDzZo1g4uLCx49evTW8n+Rm5ubSn0AUOLvl3l7eyMlJUXl1V9D/UBwIiqb/HwBoRHpyoYWAEgkQNP6ctwNTVN7zJ37aSrpAaBZQ2PcvV+YPiY+B4lJuSpp9HSlqONioEyjjuSfbyWZlurXk0IBPHuei/x8AZ1am+F2aBpSUtX/gKOqK79AwP2IDDSpZ6TcJpEATerJcaeUenH3fjqa1DdS2da0gRx3wgofGBTXteI0erpS1HY2UOZ5PyIDubkKlW6pUqkElubaiFPT7TgxKQ+5eQI6taqGuGc5CIsofUkMen/kFwgIi8xE4zoGym0SCdCojgHuhqt/j++FZ6DRC+kBoEldQ9x7IX1RY9DWUhvTl4SX6AIaFpmJ3FwFqr/QLVUqBSzNZIhLfPsBBqLyKneEMCUlpcT6c9WqVcO4cePg7++PCRMmYPz48QgNDYWPjw+8vLygoaEBQ0NDDBs2DFOmTIGpqSksLCzg4+MDDQ0NlW6mL2rXrh3atm2LPn36wM/PD87OzggJCYFEIsFHH30EFxcX7NmzBxcuXICJiQn8/PwQFxenbISVxcSJEzF8+HA0a9YMrVq1wvbt23Hnzh2VsYodO3aEl5cXAgIC4OTkBD8/P5X1BF1cXPDJJ59gzJgx+OGHH2BoaIjp06fD1tYWn3zyCYDCbp7p6ekIDAxEw4YNoaenV2K5CQ8PD7i7u6Nnz55YsmQJXF1d8fTpUwQEBKBXr15qu3eq4+DggEuXLiEyMhIGBgYwNTWFRhmffLi4uCAqKgo7d+7EBx98gICAAOzfv79E/g8fPlR26TU0NHyj5SYmTJiAtm3bws/PDz169MDJkyfxxx9/lFofAEBbW7vEubQk4n6qUx5SfT3oOxdPoa5XszqMGtZC7vMUZD+OecWR9F+3+1AMvMc7I/RBBu6Fp6NvN2voaEvxx6kEAID3BGc8S8zFjzsKu4/vPRKDlb510b+HNf4OSkLH1mZwc9TH8vXF3dv3BMRgSJ/qeBKTjZj4HIwaaIdnSbk4d7lwkqzaLgao5WSAWyGpSEvPh42VDkYOtEN0TDbu/NMQlRtqop17NQTfToFMpoGPOlig/YfVMNHnzju+Q/S27D4cg+lfOuF+xD91rasVdLQ1cPT0P3XtS0ckPM/DT78+BgDsPRIL/zm10a+7Ff6+loyOrarBzUkfyzcUr9+250gshvS2RfQ/dW3kwOqFde1K4bT+mVkFOHg8DsP7V0d8Yi7iEnIw4OPCYQyn/y6etG1Aj8LZcwUBaNPCBIN62sB3RTjYY/S/Y9/RBEweUwP3H2YiNCITvTzNoaOtgT/PFtaDKWNr4FlSHjbtLvxOPPBnApZ6u6DPR+a4fCMV7VqYwKWmLvw3FdZPqRSYNb4mnO11MXtFBDQ0JMrxhWnpBcgvEJCZrUDAqUQM6WWFhOd5iH+Wi75dC2dwPsuZRqsEsY8hLHeD8PTp02jcuLHKtlGjRuGnn37CkSNHMGXKFDRs2BCmpqYYNWqUygQlfn5++Pzzz9G9e3cYGRlh6tSpePz4MXR0Sp9lae/evZg8eTIGDRqEjIwMODs7K2fUnDlzJiIiIuDp6Qk9PT2MHTsWPXv2REpKSqn5vWzAgAF48OABpk6diuzsbPTp0wdffPEFjh07pkwzcuRI3LhxA0OHDoWmpia+/vprdOjQQSWfTZs2YeLEiejevTtyc3PRtm1bHDlyRNllsmXLlvj8888xYMAAJCYmwsfHR7n0RBGJRIIjR45gxowZGDFiBBISEmBlZYW2bdvC0tISZTV58mQMGzYMderUQVZWFh4+fAgHB4cyHfvxxx/j66+/xvjx45GTk4Nu3bph1qxZKmXt06ePcjmJ5ORkbNq0SWUJjrJq1aoV1q9fD19fX8ycOROenp74+uuvsWbNmnLnRWUjb1oP7oHblH/XWfYtAODx1n24Ocq7sopFVcCpC4kwNtLCiIF2MDXWQnhkBqYuuKecqMPSTKYyjupOaDrmrQzDqIE1MPrTGoiOycbMJaF4+Li4S96vB55CR1uKyZ85wkBfE7dCUjF1/j3k5hXmk52jQJsWphg+oDp0taVITMrF5eBk+O4NQ15+8bk825njiyH2gAS4ez8Nk+bcQUi46qx/9P44dfE55EZaGN6/euHC9JGZmPZdiLILnoWZtkoD7M79dMxf9QAjB1bH6EGFDwxmLb2PyBfq2s7fY6CrrYFvPqsJAz1N3ApJw7TvQpGXV5zR+l8eo0ABeI93grZMA/fC0/HN3HtIfyGa07yxHP/X2wZaWhp4EJmJmUvu43Jw2X9TUNX31+VkyI00MbS3NUzkmoiIysKMZRFI/qfHgbmpDC+ObLkbnolF6yMxrI81hve1xtO4HPiufIhH/3T/NDORwb1JYU+I7+fXUjnXlIXhuBlS+Fn1465oFCgETB1bAzKZBkIfZGLa4gdIz+SEMlT5JMLLg+PeoYyMDNja2mL58uXKWT6rgjlz5uDAgQMlIqFU8caMGYOQkBCcPXu2zMcEaJU+MRHR27T0k82VXQQSCbGPZ6F3R6bL5Vvo3Ti2pVFlF6FUj8b2rLC87TccqLC835Z3OqnM9evXERISgubNmyMlJQVz584FAGW3ShKfZcuWoXPnztDX18cff/yBLVu2vNE6kEREREREb0LsD+He+Syjy5YtQ2hoKGQyGZo2bYqzZ8+Wa8IU+m+5fPkylixZgrS0NDg6OmLVqlUYPXp0ZReLiIiIiEgUKrXLKNHbwC6j9K6wyyi9K2J/Wk3vDruM0rtSlbuMPh7Xp8Lytlu3t8Lyflv4jUNERERERCRSVXpheiIiIiIioook9l4Z4r56IiIiIiIiEWOEkIiIiIiIxEsi7oXpGSEkIiIiIiISKUYIiYiIiIhItCQa4o4QskFIRERERESixUlliIiIiIiISJQYISQiIiIiItESe5dRRgiJiIiIiIhEihFCIiIiIiISLY4hJCIiIiIiIlFihJCIiIiIiESLYwiJiIiIiIhIlBghJCIiIiIi0RJ7hJANQiIiIiIiEi9OKkNERERERERixAghERERERGJlkQi7i6jjBASERERERGJFCOEREREREQkWlyYnoiIiIiIiESJDUIiIiIiIhItiYakwl7ltXbtWjg4OEBHRwctWrTA5cuXX5ne398fbm5u0NXVhZ2dHb7++mtkZ2eX65xsEBIREREREVWyXbt2wcvLCz4+Prh27RoaNmwIT09PxMfHq02/Y8cOTJ8+HT4+Prh37x5+/vln7Nq1C99++225zssGIRERERERiZeGRsW9ysHPzw9jxozBiBEjUKdOHaxfvx56enrYuHGj2vQXLlxAq1at8Omnn8LBwQH/+9//MGjQoNdGFUtcfrlSExERERERUZnk5OQgNTVV5ZWTk1MiXW5uLoKCguDh4aHcpqGhAQ8PD1y8eFFt3i1btkRQUJCyARgREYEjR46ga9eu5SojG4RERERERCRaFTmGcOHChZDL5SqvhQsXlijDs2fPUFBQAEtLS5XtlpaWiI2NVVvuTz/9FHPnzkXr1q2hpaUFJycntG/fnl1GiYiIiIiIykoi0aiwl7e3N1JSUlRe3t7eb6Xcp0+fxnfffYd169bh2rVr2LdvHwICAjBv3rxy5cN1CImIiIiIiCqAtrY2tLW1X5vOzMwMUqkUcXFxKtvj4uJgZWWl9phZs2ZhyJAhGD16NACgfv36yMjIwNixYzFjxgxolHEMIyOEREREREQkXhqSinuVkUwmQ9OmTREYGKjcplAoEBgYCHd3d7XHZGZmlmj0SaVSAIAgCGU+NyOERERERERElczLywvDhg1Ds2bN0Lx5c/j7+yMjIwMjRowAAAwdOhS2trbKMYg9evSAn58fGjdujBYtWiA8PByzZs1Cjx49lA3DsmCDkIiIiIiIREtSzuUhKsqAAQOQkJCA2bNnIzY2Fo0aNcLRo0eVE81ERUWpRARnzpwJiUSCmTNnIjo6Gubm5ujRowcWLFhQrvNKhPLEE4mqoAAtt8ouAonE0k82V3YRSCSqyo8T+u+T6b5+bBPR23BsS6PKLkKpkhePr7C8jaetqbC83xZGCImIiIiISLQk5Rjr91/ER5BEREREREQixQghERERERGJl0TcMTI2CImIiIiISLTYZZSIiIiIiIhEiRFCIiIiIiISL5HP7CzuqyciIiIiIhIxRgiJiIiIiEi0JBKOISQiIiIiIiIRYoSQiIiIiIjEi2MIiYiIiIiISIwYISQiIiIiItES+zqEbBASEREREZF4ScTdaVLcV09ERERERCRijBASEREREZF4ibzLKCOEREREREREIsUIIRERERERiZaEYwiJiIiIiIhIjBghpPfe0k82V3YRSCSm/D68sotAIsHPNXpXcjKyKrsIRJWPYwiJiIiIiIhIjBghJCIiIiIi0ZJoiDtGxgYhERERERGJl4RdRomIiIiIiEiEGCEkIiIiIiLxEnmXUXFfPRERERERkYgxQkhEREREROLFMYREREREREQkRowQEhERERGRaIl92QlxXz0REREREZGIMUJIRERERETiJRF3jIwNQiIiIiIiEi8NTipDREREREREIsQIIRERERERiZZE5F1GxX31REREREREIsYIIRERERERiRfHEBIREREREZEYMUJIRERERETixTGEREREREREJEaMEBIRERERkXhJxD2GkA1CIiIiIiISLw1xd5oU99UTERERERGJGCOEREREREQkXpxUhoiIiIiIiMSIEUIiIiIiIhIvLkxPREREREREYsQIIRERERERiRfHEBIREREREZEYMUJIRERERETixYXpiYiIiIiIRIoL0xMREREREZEYMUJIRERERETiJfIuo4wQEhERERERiRQjhEREREREJF5cdoKIiIiIiIjEiBFCIiIiIiISL84ySkRERERERGLECCEREREREYmXyGcZZYOQiIiIiIjEi5PKEBERERERkRgxQkhEREREROIl8i6jjBASERERERGJFCOEREREREQkXlx2goiIiIiIiMSIEUIiIiIiIhItgWMIiYiIiIiISIwYISQiIiIiIvHiOoREREREREQkRowQEhERERGReIk8QsgGIRERERERiRYnlaEK1b59e0yaNOm9y7+iy01ERERERJWPEUKRO336NDp06ICkpCQYGxtXdnFIjZ4fWWLgxzYwNZYh/FEGVv0ciZDw9FLTt3M3xaiBNWBlro0nMdn44ZdHuHQ9WSXNiAF26O5hAQM9TdwOTYXfhoeIjs1W7l8wzQ3ODvowkWshLSMfQTdT8MMvj5CYlKdM0969Gv6vty2q2+ggOTUf+/+Ixa6DT9/69dP7ybR1Mzh+MwryJvWgY2OBq33GIe5gYGUXi6qId/25ZmWujSF9q6NJPSOYGsvwLCkXx88k4Jd90cjPFwAAdjY68BrrCPvqujDQ08SzpFwEnn2GzbufoKBAqLB7QRWrqn6HAsCAj63R3cMSlubaSEnNx+/HYvHLvui3ev1URiLvMiruqyeq4jq0rIZxwxywefcTjJl6Ew8iM7F0Zm0YG6l/llPXzQCzJ7kiIDAeo6fcxLkrzzF/qhtq2ukq0wzqaYM+Xa3gtyECX3x7C1k5CiydVRsyreLuEtfvpMLX7z6GfHUds5eFwsZKB76T3ZT7mzc2xsyJzjh4PA4jvr4B/x8j0K+7NXp9ZFVxN4PeK1J9PaTeDMXtr3wruyhUxVTG51oNW11oSIDlGyIw/OtgrN0ciY//Z4kxn9ZQ5pGfL+DYXwmYMu8ehnx1HWs2RaKbhwVGDKhesTeEKkxV/Q4FgAkjHdCtkyW+3/oIQycGY8biENx7RUOVqCKxQfgOKBQKTJ06FaamprCyssKcOXOU+/z8/FC/fn3o6+vDzs4O48aNQ3q66gfC+fPn0b59e+jp6cHExASenp5ISkpSe66AgADI5XJs374dALBt2zY0a9YMhoaGsLKywqeffor4+HgAQGRkJDp06AAAMDExgUQiwfDhw8tUbgCIiorCJ598AgMDAxgZGaF///6Ii4tT7p8zZw4aNWqEbdu2wcHBAXK5HAMHDkRaWpoyzZ49e1C/fn3o6uqiWrVq8PDwQEZGRrnv8X9Vvx7WCDgRj6OnEvDoSRb8NkQgO0eBrh0t1Kbv09Ual4OTsevgU0RFZ2HjzscIe5iBXl2KG2p9u1lj294nOH8lCRGPMrFwdTjMTGRo3dxUmWbP4RjcDUtH3LNc3AlNx4790ajjYgCptPAL739tzXHuShIO/hmHmPgc/H0tGdv3R2NQT5uKvSH03kg4dgb3ffwR9/uJyi4KVTGV8bl2OTgZi9c9wNUbKYiJz8GFq0nYdTAGbVoUf+7FxOfg6KkEPHiUibhnubhwNQknzj5Dg9pGFXtDqMJU1e/QGra6+OR/lpixOAQXriYhNj4H9yMyEHQzpWJvCJVOIqm413uADcJ3YMuWLdDX18elS5ewZMkSzJ07F8ePHwcAaGhoYNWqVbhz5w62bNmCkydPYurUqcpjg4OD0alTJ9SpUwcXL17EuXPn0KNHDxQUFJQ4z44dOzBo0CBs374dgwcPBgDk5eVh3rx5uHHjBg4cOIDIyEhlo8/Ozg579+4FAISGhiImJgYrV64sU7kVCgU++eQTPH/+HH/99ReOHz+OiIgIDBgwQKVMDx48wIEDB3D48GEcPnwYf/31FxYtWgQAiImJwaBBgzBy5Ejcu3cPp0+fRu/evSEI7JoDAJqaErg5GiDoZrJymyAAQbeSUcfNUO0xdV0NVdIDhT+E6rgWpre20EY1E5nKl05GZgHuhqUr07zM0EATHm3McCc0TdltSktLgtxchUq63FwFLMy0YWWuXd5LJSKRqCqfawBgoCdFWnp+qfttrXTQvJExbtxJLcOVUVVTVeqauu/Qls1M8DQuB+5NTfDr2sbYua4xpnzuCEMDjuSiysGa9w40aNAAPj4+AAAXFxesWbMGgYGB6Ny5s8rELQ4ODpg/fz4+//xzrFu3DgCwZMkSNGvWTPk3ANStW7fEOdauXYsZM2bg0KFDaNeunXL7yJEjlf/v6OiIVatW4YMPPkB6ejoMDAxgalr4RMvCwqLEGMJXlTswMBC3bt3Cw4cPYWdnBwDYunUr6tatiytXruCDDz4AUNhw3Lx5MwwNCz8ohwwZgsDAQCxYsAAxMTHIz89H7969YW9vDwCoX79++W/wf5TcUBNSqQTPU1THHCQl56GGra7aY0yNtfA8+aX0KXkwNdYq3G9S+N+SaXKVaYqM/b8a6PWRFXR1pLgTmgbvhSHKfVeCk/HlcAc0OW2E67dTYWulg/49rJXniE3IeYMrJqL/usr+XCtia6WDXl2s8P22RyX2rVlQD6419SGTaeDgn3HYuOtx2S6OqpTKrmuv+g61sSx8eNrevRq+WxMOqYYEXw53gO83rvDyvftmF0z/joa4Y2RsEL4DDRo0UPnb2tpa2W3zxIkTWLhwIUJCQpCamor8/HxkZ2cjMzMTenp6CA4ORr9+/V6Z/549exAfH4/z588rG2JFgoKCMGfOHNy4cQNJSUlQKAqjOlFRUahTp84bl/vevXuws7NTNgYBoE6dOjA2Nsa9e/eU5XBwcFA2Bl/Oo2HDhujUqRPq168PT09P/O9//0Pfvn1hYmJSaplycnKQk6Pa2FAU5EJDKnvltVD57fr9KY4ExsPSXBvD+1WH9wRn5Rfa4RPxsLHSwcLptaGpKUFGZgH2HonBiAF6YICXiKoyM1MZlsyojb8uJiLgRHyJ/b5+96GnK4WTvR6+GGqPp3E22Pk7J8yi8nnVd6hEIoFMpoHvVofjSUzhZDRL1j3Aj0sbwM5GB4+fZr8qa6K3TtzN4XdES0v1qZFEIoFCoUBkZCS6d++OBg0aYO/evQgKCsLatWsBALm5uQAAXV31T7Fe1LhxY5ibm2Pjxo0q3S0zMjLg6ekJIyMjbN++HVeuXMH+/ftV8n+TcpfHq/KQSqU4fvw4/vjjD9SpUwerV6+Gm5sbHj58WGp+CxcuhFwuV3lFhW4tV5neFylp+SgoEGAqV72HJmqeYBZ5npxX4imlibw4/fN/ZjgrmUZWIs+UtHw8iclG0M0UzF0RBvemJqjjaqDcv+GXKHQZcgkDvriG3mOuKgfDP43jFxkRqVfZn2vVTLSwYk4d3L6fhmU/RKg9X0JiLh49ycLJ84nY8EsUhvevLvbgwXupsuvaq75DE5NykZ+vUDYGAeBRdBYAwMKMwy4qgyCRVNjrfcCPuEoUFBQEhUKB5cuX48MPP4SrqyuePlV9CtmgQQMEBr56qnYnJyecOnUKv//+OyZMmKDcHhISgsTERCxatAht2rRBrVq1lNG5IjJZYWRN3ZjEV6lduzYeP36Mx4+Lu9LcvXsXycnJr408vkgikaBVq1bw9fXF9evXIZPJlI1Wdby9vZGSkqLyquE2tFxlf1/k5wsIjUhHk/py5TaJBGhaX467oWlqj7lzP00lPQA0a2iMu/cL08fE5yAxKVcljZ6uFHVcDJRp1CmajVmmpfqRoVAAz57nIj9fQKfWZrgdmoaU1NLH5BCRuFXm55qZqQz+vnVxPyIDi9eGl6k3g0QD0JRKIHlPftRRsar8HXo7NA2amhqwsSxu/NlZ6wAA4jjkonJINCru9R5gl9FK5OzsjLy8PKxevRo9evTA+fPnsX79epU03t7eqF+/PsaNG4fPP/8cMpkMp06dQr9+/WBmZqZM5+rqilOnTqF9+/bQ1NSEv78/atSoAZlMhtWrV+Pzzz/H7du3MW/ePJX87e3tIZFIcPjwYXTt2hW6urowMDDA63h4eKB+/foYPHgw/P39kZ+fj3HjxqFdu3Zo1qxZma7/0qVLCAwMxP/+9z9YWFjg0qVLSEhIQO3atUs9RltbG9raqk/P/svdRXcfioH3eGeEPsjAvfB09O1mDR1tKf44lQAA8J7gjGeJufhxRxQAYO+RGKz0rYv+Pazxd1ASOrY2g5ujPpavf6DMc09ADIb0qY4nMdmIic/BqIF2eJaUi3OXnwMAarsYoJaTAW6FpCItPR82VjoYOdAO0THZuPPPl6jcUBPt3Ksh+HYKZDINfNTBAu0/rIaJPnfe8R2iqkqqrwd95+Ip/fVqVodRw1rIfZ6C7McxlVgyqmyV8blW2Bisg7iEHHy/9RGMjYojPEWRHY82ZsjPFxARlYm8PAXcnAww5lN7nLqQyHUI31NV9Ts06GYKQh+kY+o4Z6zZ/BAaEgkmja6JKzeSVaKGRO8KG4SVqGHDhvDz88PixYvh7e2Ntm3bYuHChRg6tDji5erqij///BPffvstmjdvDl1dXbRo0QKDBg0qkZ+bmxtOnjyJ9u3bQyqVYvny5di8eTO+/fZbrFq1Ck2aNMGyZcvw8ccfK4+xtbWFr68vpk+fjhEjRmDo0KHYvHnza8sukUiUEcm2bdtCQ0MDH330EVavXl3m6zcyMsKZM2fg7++P1NRU2NvbY/ny5ejSpUuZ8/ivO3UhEcZGWhgx0A6mxloIj8zA1AX3kPTPIHlLMxkERfEPlTuh6Zi3MgyjBtbA6E9rIDomGzOXhOLh4yxlml8PPIWOthSTP3OEgb4mboWkYur8e8jNK8wnO0eBNi1MMXxAdehqS5GYlIvLwcnw3RuGvPzic3m2M8cXQ+wBCXD3fhomzbnzysV+SVzkTevBPXCb8u86y74FADzeug83R3lXVrGoCqiMz7VmDeSobq2L6ta62LOhqUp52ve9CAAoKBAwqKcN7Gx0IQEQ+ywH+4/GYM9hPsB4X1XV71BBAL5dFIKvRtXEqrn1kJ1dgEvXk7Fua8lJjujdEN6TSF5FkQic45/ec0Vf5kQVbcrvwyu7CCQSSz/ZXNlFICJ6q07vca/sIpQq/e+DFZa3wYcfvz7RC9auXYulS5ciNjYWDRs2xOrVq9G8efNS0ycnJ2PGjBnYt28fnj9/Dnt7e/j7+6Nr165lPicjhEREREREJF5VZJzwrl274OXlhfXr16NFixbw9/eHp6cnQkNDYWFhUSJ9bm4uOnfuDAsLC+zZswe2trZ49OhRiaXkXocNQiIiIiIiokrm5+eHMWPGYMSIEQCA9evXIyAgABs3bsT06dNLpN+4cSOeP3+OCxcuKGf2d3BwKPd5xd1hloiIiIiIRE2QaFTYKycnB6mpqSqvl9fUBgqjfUFBQfDw8FBu09DQgIeHBy5eVD886uDBg3B3d8eXX34JS0tL1KtXD9999125Vw9gg5CIiIiIiKgCqFtDe+HChSXSPXv2DAUFBbC0tFTZbmlpidjYWLV5R0REYM+ePSgoKMCRI0cwa9YsLF++HPPnzy9XGdlllIiIiIiIxKsCxxB6e3vDy8tLZdvLS6i9KYVCAQsLC2zYsAFSqRRNmzZFdHQ0li5dCh8fnzLnwwYhERERERGJVwUuO6FuDW11zMzMIJVKERcXp7I9Li4OVlZWao+xtraGlpYWpFKpclvt2rURGxuL3NxcyGRlW6ubXUaJiIiIiIgqkUwmQ9OmTREYGKjcplAoEBgYCHd39Ut2tGrVCuHh4VAoFMpt9+/fh7W1dZkbgwAbhEREREREJGKCRFJhr/Lw8vLCjz/+iC1btuDevXv44osvkJGRoZx1dOjQofD29lam/+KLL/D8+XNMnDgR9+/fR0BAAL777jt8+eWX5Tovu4wSERERERFVsgEDBiAhIQGzZ89GbGwsGjVqhKNHjyonmomKioKGRnE8z87ODseOHcPXX3+NBg0awNbWFhMnTsS0adPKdV42CImIiIiISLwqcAxheY0fPx7jx49Xu+/06dMltrm7u+Pvv//+V+esOldPRERERERE7xQjhEREREREJFoCKm7ZifcBI4REREREREQixQghERERERGJllCFxhBWBjYIiYiIiIhIvETeIBT31RMREREREYkYI4RERERERCRa5V1A/r+GEUIiIiIiIiKRYoSQiIiIiIhES+yTyoj76omIiIiIiESMEUIiIiIiIhIvjiEkIiIiIiIiMWKEkIiIiIiIREvsYwjZICQiIiIiItESwC6jREREREREJEKMEBIRERERkWiJvcuouK+eiIiIiIhIxBghJCIiIiIi8eKyE0RERERERCRGjBASEREREZFoCSKPkYn76omIiIiIiESMEUIiIiIiIhItQeRjCNkgJCIiIiIi0eKyE0RERERERCRKjBASEREREZFoCRB3l1FGCImIiIiIiESKEUIiIiIiIhItjiEkIiIiIiIiUWKEkIiIiIiIREvsy04wQkhERERERCRSjBASEREREZFoiX2WUTYIiYiIiIhItDipDBEREREREYkSI4RERERERCRaYu8yygghERERERGRSDFCSEREREREosUxhERERERERCRKjBASEREREZFocQwhERERERERiRIjhEREREREJFpiH0PIBiEREREREYkWu4wSERERERGRKDFCSO89iQafa9C7sfSTzZVdBBKJKb8Pr+wikEgs/GhDZReBqNIJEkYIiYiIiIiISIQYISQiIiIiItESBEYIiYiIiIiISIQYISQiIiIiItESRB4jE/fVExERERERiRgjhEREREREJFpiX4eQDUIiIiIiIhItsTcI2WWUiIiIiIhIpBghJCIiIiIi0WKEkIiIiIiIiESJEUIiIiIiIhItRgiJiIiIiIhIlBghJCIiIiIi0RIERgiJiIiIiIhIhBghJCIiIiIi0eIYQiIiIiIiIhIlRgiJiIiIiEi0xB4hZIOQiIiIiIhES+wNQnYZJSIiIiIiEilGCImIiIiISLS47AQRERERERGJEiOEREREREQkWgqOISQiIiIiIiIxYoSQiIiIiIhEi7OMEhERERERkSgxQkhERERERKIl9llG2SAkIiIiIiLRYpdRIiIiIiIiEiVGCImIiIiISLTE3mWUEUIiIiIiIiKRYoSQiIiIiIhEi2MIiYiIiIiISJQYISQiIiIiItHiGEIiIiIiIiISJUYIiYiIiIhItBSVXYBKxgYhERERERGJFruMEhERERERkSixQUhERERERKIlQFJhr/Jau3YtHBwcoKOjgxYtWuDy5ctlOm7nzp2QSCTo2bNnuc/JBiEREREREVEl27VrF7y8vODj44Nr166hYcOG8PT0RHx8/CuPi4yMxOTJk9GmTZs3Oi8bhEREREREJFqCIKmwV3n4+flhzJgxGDFiBOrUqYP169dDT08PGzduLPWYgoICDB48GL6+vnB0dHyj62eDkIiIiIiIqBLl5uYiKCgIHh4eym0aGhrw8PDAxYsXSz1u7ty5sLCwwKhRo9743JxllIiIiIiIROtNxvqVVU5ODnJyclS2aWtrQ1tbW2Xbs2fPUFBQAEtLS5XtlpaWCAkJUZv3uXPn8PPPPyM4OPhflZERQiIiIiIiogqwcOFCyOVyldfChQv/db5paWkYMmQIfvzxR5iZmf2rvBghJCIiIiIi0VIIFZe3t7c3vLy8VLa9HB0EADMzM0ilUsTFxalsj4uLg5WVVYn0Dx48QGRkJHr06KHcplAoAACampoIDQ2Fk5NTmcrIBiEREREREYlWRXYZVdc9VB2ZTIamTZsiMDBQuXSEQqFAYGAgxo8fXyJ9rVq1cOvWLZVtM2fORFpaGlauXAk7O7syl5ENQiIiIiIiokrm5eWFYcOGoVmzZmjevDn8/f2RkZGBESNGAACGDh0KW1tbLFy4EDo6OqhXr57K8cbGxgBQYvvrsEFIRERERESiVd7lISrKgAEDkJCQgNmzZyM2NhaNGjXC0aNHlRPNREVFQUPj7U8BwwYhERERERFRFTB+/Hi1XUQB4PTp0688dvPmzW90TjYIiYiIiIhItIQKnFTmfcBlJ4iIiIiIiERKVA3C4cOHK2ftAYD27dtj0qRJlVaespThdWV2cHCAv79/hZWvPF4uKxERERFRVaeApMJe7wNRdxndt28ftLS0KrsYr7Ry5UoIVSyOHRkZiZo1a+L69eto1KiRcntVLOt/QU9PSwzoYQ1TYy08eJSJVRsjEfIgo9T07T40xcgB1WFlro0nsdnYsD0Kl66nqKQZ0d8W3TpZwEBfE7dD0rDip4eIjs1RSfNhY2MM7WsLR3s95OYqcONeKmYtDVPub1LPCCMGVIdjDT1k5xTg2F/P8NOvj/HPEjj0Hur5kSUGfmwDU2MZwh9lYNXPkQgJTy81fTt3U4waWKOwrsVk44dfHuHS9WSVNCMG2KG7hwUM9DRxOzQVfhseIjo2GwBgZa6NIX2ro0k9I5gay/AsKRfHzyTgl33RyM8v/Cyxs9GB11hH2FfXhYGeJp4l5SLw7DNs3v0EBQX8vCHAtHUzOH4zCvIm9aBjY4GrfcYh7mBgZReLqrhRgx3Q439WMNTXxK17qVi2LgxPYrJeeUzvrjYY1NsOpiYyPHiYjhU/hONeWJpy/8ee1ujczgKuTgbQ19PERwPPIT2jQG1eWpoSbFjeBC6OBhj+1VWEPyz9e52oookqQvgyU1NTGBoaVnYxXkkulyunkK1oubm5/+r4d1lWsejgboovhtbAlj1PMHbabTx4lIklM2rB2Ej9s5y6rgaYNdEZR04mYMy0Wzh3JQnzprjCwU5XmWbgJ9bo3cUKK36MxLhvbyM7R4ElM2pBS6v4KVbbFibwnuCEP04nYPSUW5gw6y4CzyUq9zvZ62GhtxuuBKdgzNRbmLsiHC2bmmDs4BoVdzOoQnVoWQ3jhjlg8+4nGDP1Jh5EZmLpzNql1zU3A8ye5IqAwHiMnnIT5648x/ypbqj5Ql0b1NMGfbpawW9DBL749haychRYOqs2ZP/UtRq2utCQAMs3RGD418FYuzkSH//PEmM+La5H+fkCjv2VgCnz7mHIV9exZlMkunlYYMSA6hV7Q+i9IdXXQ+rNUNz+yreyi0LvicF97NC3uy2WrQvD2MnXkZVdAL+59ZWfTep0bG2O8aOdsOnXSIyaFITwh+nwm1sfxvLiwIK2tgYuXXuObbujXluGcSMc8ex5zmvT0bshCJIKe70PqmyDUKFQYMmSJXB2doa2tjZq1KiBBQsWKPffunULHTt2hK6uLqpVq4axY8ciPb34SXZBQQG8vLxgbGyMatWqYerUqSWiV+q6X3733XcYOXIkDA0NUaNGDWzYsEHlmAsXLqBRo0bQ0dFBs2bNcODAAUgkEgQHB5d6LevWrYOLiwt0dHRgaWmJvn37lpo2ICAAcrkc27dvB1C+bpiCIGDOnDmoUaMGtLW1YWNjg6+++qrU9HPmzEGjRo3w008/oWbNmtDR0QEAHD16FK1bt1beu+7du+PBgwfK42rWrAkAaNy4MSQSCdq3b6+2rO3bt8dXX32FqVOnwtTUFFZWVpgzZ45KGUJCQtC6dWvo6OigTp06OHHiBCQSCQ4cOFCma/6v69fdGgGB8Th6+hkeRWfB78eHyM5VoEsHc7Xp+3S1wuXgZOw6FIOo6Gxs2vUEYRGZ6PWRpTJN365W2LYvGuevJiEiKgsL1zyAmYkMrT8wAQBoaADjhzvgh21ROHQ8Hk9isvEoOgunLz5X5tGhpSkiHmVi695oPI3LwY17afhhexR6elpCV6fKfqzQK/TrYY2AE/E4eioBj55kwW9DBLJzFOja0UJt+j5drQvr2sGniIrOwsadjxH2MAO9ulgp0/TtZo1te5/g/JUkRDzKxMLV4YV1rbkpAOBycDIWr3uAqzdSEBOfgwtXk7DrYAzatDBV5hETn4OjpxLw4FEm4p7l4sLVJJw4+wwNahtV7A2h90bCsTO47+OPuN9PVHZR6D3R72NbbP3tEc5dSsSDyAzMXxGCaqbaaPOhWanHDOxZHYeOxeBIYBwiH2di6bowZOco0L1z8Wfe7oPR+GXPY9wJSX3l+T9saooPGptg7caIt3ZN9O8IQsW93gdV9pebt7c3Fi1ahFmzZuHu3bvYsWOHcg2OjIwMeHp6wsTEBFeuXMHu3btx4sQJlSlaly9fjs2bN2Pjxo04d+4cnj9/jv3797/2vMuXL0ezZs1w/fp1jBs3Dl988QVCQ0MBAKmpqejRowfq16+Pa9euYd68eZg2bdor87t69Sq++uorzJ07F6GhoTh69Cjatm2rNu2OHTswaNAgbN++HYMHDy7rrVLau3cvVqxYgR9++AFhYWE4cOAA6tev/8pjwsPDsXfvXuzbt0/ZqM3IyICXlxeuXr2KwMBAaGhooFevXlD80xfw8uXLAIATJ04gJiYG+/btKzX/LVu2QF9fH5cuXcKSJUswd+5cHD9+HEBho71nz57Q09PDpUuXsGHDBsyYMaPc1/1fpSmVwNVRH0G3ir9YBAG4disFdV3VR7bruBqopAeAKzeSUdfFAABgbaGNaiYyBN0sTpORVYB74enKPF1r6sO8mgwKQcCGxfWw54fGWOTtphJl1NLUQG6eat/QnFwFtGUacHXU/3cXTu+cpqYEbo4GCLqZrNwmCEDQrWTUcVNf1+q6GqqkBwobeHX+qUfFda24u3JGZgHuhqUr06hjoCdFWnp+qfttrXTQvJExbtx59Q8uIiJ1bCx1YGaqjSvBScptGZkFuHs/FfVqqX/QpKkpgauzIa7eKD5GEICrwUmo61a+h1MmxlqYOt4V8/xCkJ2jvjsp0btWJccQpqWlYeXKlVizZg2GDRsGAHByckLr1q0BFDacsrOzsXXrVujrF/74XLNmDXr06IHFixfD0tIS/v7+8Pb2Ru/evQEA69evx7Fjx1577q5du2LcuHEAgGnTpmHFihU4deoU3NzcsGPHDkgkEvz444/KiFZ0dDTGjBlTan5RUVHQ19dH9+7dYWhoCHt7ezRu3LhEurVr12LGjBk4dOgQ2rVrV74b9sK5rKys4OHhAS0tLdSoUQPNmzd/5TG5ubnYunUrzM2LI059+vRRSbNx40aYm5vj7t27qFevnjJttWrVYGVlhVdp0KABfHx8AAAuLi5Ys2YNAgMD0blzZxw/fhwPHjzA6dOnlfksWLAAnTt3Lve1/xfJjTQhlUqQlJynsj0pOQ81bHTVHmNqrIWklJfSp+TBxFim3F+07eU0RfusLbUBAMP6Vcf3Wx8hNj4H/XtYw9+nNoZMvIG0jAJcuZGCPt2s0LFVNZy+kAhTYy0M7WMLAKhmIvuXV07vmtywsK49f7leJOehhm3pde35y3XzhXpkalL435JpcpVpXmZrpYNeXazw/bZHJfatWVAPrjX1IZNp4OCfcdi463HZLo6I6AWm/3xHlfxuzVXue5ncSAuaUgmeJ6ke8zw5D/bV9cp1/hmTauH3P54iNDwdVhba5TqWKo7wnkz+UlGqZITw3r17yMnJQadOnUrd37BhQ2VjEABatWoFhUKB0NBQpKSkICYmBi1atFDu19TURLNmzV577gYNGij/XyKRwMrKCvHx8QCA0NBQNGjQQNm1EsBrG1ydO3eGvb09HB0dMWTIEGzfvh2ZmZkqafbs2YOvv/4ax48ff+PGIAD069cPWVlZcHR0xJgxY7B//37k55f+pB0A7O3tVRqDABAWFoZBgwbB0dERRkZGcHBwAFDY4CyvF+8nAFhbW6vcTzs7O5VG5evuZ05ODlJTU1VeioJ/N/aRVGlICj8Ut++LxplLSbj/MBOL10VAANDevRoA4OrNFPywLQpfj3HAnzuaY+vKhsrJRATFe9I/gqoUM1MZlsyojb8uJiLgRHyJ/b5+9zFm6k3MXXEf7k2NMeBjm0ooJRG9bzq3s8Cfv7VWvjQ1K++Hf98ettDTlWLbnvL/niKqSFWyQairq/6J9Lvw8qyjEolE2VXyTRgaGuLatWv49ddfYW1tjdmzZ6Nhw4ZITk5WpmncuDHMzc2xcePGfzVLp52dHUJDQ7Fu3Tro6upi3LhxaNu2LfLy8ko95sVGdZEePXrg+fPn+PHHH3Hp0iVcunQJwJtNOvO27+fChQshl8tVXo9CtrxxflVZSmo+CgoEmLwUTTFRE5kp8jw5Dybyl9LLtZCUnKvcX7Tt5TRF+xL/+W/kk+LZ1vLyBcTE5cDCrPjp6e6AWPQYHoQB466j56ggnL9a2JXmaTwHyb9vUtIK65rpy/XiNXXt5Ujfi/Wo6El6yTSyEnlWM9HCijl1cPt+Gpb9oH5MTUJiLh49ycLJ84nY8EsUhvevDo0q+Q1GRFXJucuJGDHxqvKVkvrP92CJ71YZniep/52TkpqH/AJB2fOhiKmxFhJLOUadJg2MUdfNCCf3tcXpA22xc0Nh4OKnFU0xY5JbeS6L3jKFUHGv90GV/Dp1cXGBrq4uAgPVTxtdu3Zt3LhxAxkZxVP0nj9/HhoaGnBzc4NcLoe1tbWyIQMA+fn5CAoK+lflcnNzw61bt5CTU/yD98qVK689TlNTEx4eHliyZAlu3ryJyMhInDx5UrnfyckJp06dwu+//44JEyb8qzLq6uqiR48eWLVqFU6fPo2LFy/i1q1bZT4+MTERoaGhmDlzJjp16oTatWsjKSlJJY1MVtgoKCj4d33f3dzc8PjxY8TFxSm3ve5+ent7IyUlReVlX2vYvypHVZVfIOB+RAaa1CsenyCRAE3qyXHnfpraY+7eT0eT+qrjGZo2kONOWOGESzHxOUhMylVJo6crRW1nA2We9yMykJurUOmWKpVKYGmujbiEko29xKQ85OYJ6NSqGuKe5SAsglNnv2/y8wWERqSjSX25cptEAjStL8fdUPV17c79NJX0ANCsoTHu/lOPiutacRo9XSnquBgo0wCFkUF/37q4H5GBxWvDyzQAX6JROMZWIhF3Fx8ier2srAJEx2QrXw+jMvHseQ6aNTRRptHTlaKOqxFulzIZTH6+gPvhaWjaoPgYiQRo2tAEd0LLPp555YZwDP/qKkb885riW/j7zGfJXWzY9vANr5Do36uSYwh1dHQwbdo0TJ06FTKZDK1atUJCQgLu3LmDUaNGYfDgwfDx8cGwYcMwZ84cJCQkYMKECRgyZIhy4pmJEydi0aJFcHFxQa1ateDn56cSlXsTn376KWbMmIGxY8di+vTpiIqKwrJlywCg1B8mhw8fRkREBNq2bQsTExMcOXIECoUCbm6qT4JcXV1x6tQptG/fHpqamm+02PzmzZtRUFCAFi1aQE9PD7/88gt0dXVhb29f5jxMTExQrVo1bNiwAdbW1oiKisL06dNV0lhYWEBXVxdHjx5F9erVoaOjA7lcXkqOpevcuTOcnJwwbNgwLFmyBGlpaZg5cyaA0u+ntrY2tLVV+9xrSP+7Y9Z2H47B9C+dcD8iA/fC09G3qxV0tDVw9HQCAMD7S0ckPM/DT78WjqfaeyQW/nNqo193K/x9LRkdW1WDm5M+lm8o/qLZcyQWQ3rbIjomGzHxORg5sDqeJeXi3JXChn9mVgEOHo/D8P7VEZ+Yi7iEHAz42BoAcPrv4plGB/QonGVSEIA2LUwwqKcNfFeEvzdPw0jV7kMx8B7vjNAH/9S1btbQ0Zbij1P/1LUJzniWmIsfdxR2ddp7JAYrfeuifw9r/B2UhI6tzeDmqI/l64tnJN4TEIMhfarjyT91bdRAu8K6drmwHhU2BusgLiEH3299BGOj4qfvRVFEjzZmyM8XEBGVibw8BdycDDDmU3ucupDIdQgJQOGyE/rOxUuV6NWsDqOGtZD7PAXZj2MqsWRUVe0+GI1hA2rg8dMsxMRlY/T/OSDxeQ7O/v1MmcZ/fgOcufgM+wKeAgB2HniCGV/XQkh4Gu7dT0P/T2yhq6OBgBOxymNMjbVgaiKD7T8PVB3tDZCZlY+4hBykpeeXeKialV34YD06JgsJiRz+Upnel+UhKkqVbBACwKxZs6CpqYnZs2fj6dOnsLa2xueffw4A0NPTw7FjxzBx4kR88MEH0NPTQ58+feDn56c8/ptvvkFMTAyGDRsGDQ0NjBw5Er169UJKSkppp3wtIyMjHDp0CF988QUaNWqE+vXrY/bs2fj0009VxhW+yNjYGPv27cOcOXOQnZ0NFxcX/Prrr6hbt26JtG5ubjh58iTat28PqVSK5cuXl6t8xsbGWLRoEby8vFBQUID69evj0KFDqFatWpnz0NDQwM6dO/HVV1+hXr16cHNzw6pVq5RLSwCFEc9Vq1Zh7ty5mD17Ntq0aYPTp0+Xq6wAIJVKceDAAYwePRoffPABHB0dsXTpUvTo0aPU+yk2py4+h9xIC8P7Vy9cmD4yE9O+C0FSSuHYUAszbZUG2J376Zi/6gFGDqyO0YPsEB2TjVlL7yPycXH3z52/x0BXWwPffFYTBnqauBWShmnfhSIvrzij9b88RoEC8B7vBG2ZBu6Fp+ObufdUFtht3liO/+ttAy0tDTyIzMTMJfdxOfjN/31R5Tp1IRHGRloYMdAOpsZaCI/MwNQF95QTEFmayVTGh94JTce8lWEYNbAGRn9aA9Ex2Zi5JBQPX6hrvx54Ch1tKSZ/5ggDfU3cCknF1Pn3kPtPXWvWQI7q1rqobq2LPRuaqpSnfd+LAICCAgGDetrAzkYXEgCxz3Kw/2gM9hzmD30qJG9aD+6B25R/11n2LQDg8dZ9uDnKu7KKRVXY9r2PoaMjxdTxroWfTXdT8I3PLeVnEwDYWumqPKQ6eS4BxnItjB7sAFMTGcIj0vGNzy2VyWl6drHByE8dlH+vW9wIALDAPwR/BBb3hiKqaiTCvxm0Rti+fTtGjBiBlJSUSh37+F9x/vx5tG7dGuHh4XBycirTMR36X3p9IqK3QPgX41+JymPK78MruwgkEgs/2vD6RERvwblDbz5xYkU7cq30+Tb+ra5N1M+sXZVU2QhhVbV161Y4OjrC1tYWN27cwLRp09C/f382Bt/Q/v37YWBgABcXF4SHh2PixIlo1apVmRuDRERERET/hkLky06wQVhOsbGxmD17NmJjY2FtbY1+/fphwYIFlV2s91ZaWhqmTZuGqKgomJmZwcPDo9xdZYmIiIiI6M2wyyi999hllN4Vdhmld4VdRuldYZdReleqcpfRQ0GvXrf73+jRtOrH36rkshNERERERERU8ap+k5WIiIiIiKiCiH3ZCUYIiYiIiIiIRIoRQiIiIiIiEi2FyGdUYYSQiIiIiIhIpBghJCIiIiIi0RL7mgtsEBIRERERkWgJIl+Ynl1GiYiIiIiIRIoRQiIiIiIiEi1OKkNERERERESixAghERERERGJltgnlWGEkIiIiIiISKQYISQiIiIiItFihJCIiIiIiIhEiRFCIiIiIiISLYUg7nUI2SAkIiIiIiLRYpdRIiIiIiIiEiVGCImIiIiISLQYISQiIiIiIiJRYoSQiIiIiIhES8EIIREREREREYkRI4RERERERCRagsiXnWCEkIiIiIiISKQYISQiIiIiItHiLKNEREREREQkSowQEhERERGRaIl9llE2CImIiIiISLTYZZSIiIiIiIhEiRFCIiIiIiISLUYIiYiIiIiISJQYISQiIiIiItES+6QyjBASERERERGJFCOEREREREQkWhxDSERERERERKLECCEREREREYmWQlHZJahcbBASEREREZFoscsoERERERERiRIjhEREREREJFqMEBIREREREZEoMUJIRERERESixYXpiYiIiIiISJQYISQiIiIiItESKnQQoaQC8347GCEkIiIiIiISKUYIiYiIiIhItMQ+yygbhEREREREJFoKRWWXoHKxyygREREREZFIMUJIRERERESiJfYuo4wQEhERERERiRQjhEREREREJFpcmJ6IiIiIiIhEiRFCeu/JdLUruwgkEjkZWZVdBBKJhR9tqOwikEh4Hx1b2UUg0Qit7AKUimMIiYiIiIiISJQYISQiIiIiItESKnQQoaQC83472CAkIiIiIiLR4qQyREREREREJEqMEBIRERERkWhxUhkiIiIiIiISJUYIiYiIiIhItBQiH0TICCEREREREZFIMUJIRERERESixTGEREREREREJEqMEBIRERERkWiJPULIBiEREREREYmWQuQtQnYZJSIiIiIiqgLWrl0LBwcH6OjooEWLFrh8+XKpaX/88Ue0adMGJiYmMDExgYeHxyvTl4YNQiIiIiIiEi1BUXGv8ti1axe8vLzg4+ODa9euoWHDhvD09ER8fLza9KdPn8agQYNw6tQpXLx4EXZ2dvjf//6H6Ojocp2XDUIiIiIiIqJK5ufnhzFjxmDEiBGoU6cO1q9fDz09PWzcuFFt+u3bt2PcuHFo1KgRatWqhZ9++gkKhQKBgYHlOi8bhEREREREJFqCIFTYq6xyc3MRFBQEDw8P5TYNDQ14eHjg4sWLZcojMzMTeXl5MDU1Ldf1c1IZIiIiIiKiCpCTk4OcnByVbdra2tDW1lbZ9uzZMxQUFMDS0lJlu6WlJUJCQsp0rmnTpsHGxkalUVkWjBASEREREZFoKRQV91q4cCHkcrnKa+HChW/9GhYtWoSdO3di//790NHRKdexjBASERERERFVAG9vb3h5ealsezk6CABmZmaQSqWIi4tT2R4XFwcrK6tXnmPZsmVYtGgRTpw4gQYNGpS7jIwQEhERERGRaFXkGEJtbW0YGRmpvNQ1CGUyGZo2baoyIUzRBDHu7u6lln3JkiWYN28ejh49imbNmr3R9TNCSEREREREoqWoIuvSe3l5YdiwYWjWrBmaN28Of39/ZGRkYMSIEQCAoUOHwtbWVtnldPHixZg9ezZ27NgBBwcHxMbGAgAMDAxgYGBQ5vOyQUhERERERFTJBgwYgISEBMyePRuxsbFo1KgRjh49qpxoJioqChoaxR08v//+e+Tm5qJv374q+fj4+GDOnDllPi8bhEREREREJFpCVQkRAhg/fjzGjx+vdt/p06dV/o6MjHwr5+QYQiIiIiIiIpFihJCIiIiIiESrHOvH/ycxQkhERERERCRSjBASEREREZFoKarQGMLKwAghERERERGRSDFCSEREREREoiWIfBAhG4RERERERCRagqKyS1C52GWUiIiIiIhIpBghJCIiIiIi0VKIvMsoI4REREREREQixQghERERERGJltgnlWGEkIiIiIiISKQYISQiIiIiItHiwvREREREREQkSowQEhERERGRaIl8CCEbhEREREREJF4Cu4wSERERERGRGDFCSEREREREosWF6YmIiIiIiEiUGCEkIiIiIiLR4hhCIiIiIiIiEiVGCImIiIiISLQYISQiIiIiIiJRYoSQiIiIiIhES+QBQkYIiYiIiIiIxIoRQiIiIiIiEi2xjyFkg5CIiIiIiERL4ML0715kZCQkEgmCg4NLTXP69GlIJBIkJycDADZv3gxjY+N3Ur6K8rprePmaqzqJRIIDBw5UdjGIiIiIiOgNvTcRwgEDBqBr166VXYwK1bJlS8TExEAul1d2UcokJiYGJiYmZU6/efNmTJo06b1p8FZlPTqZoW8XC5jKNRHxOAvrfolGaERmqenbfCDHsN7WsDSTITouBz//9hRXbqYBAKRSYHgfa3zQwAjWFjJkZCpw/W4afv7tKZ4n56vk07yhEQZ/YomadrrIzVPgVkgGfFc9rNBrpXen50eWGPixDUyNZQh/lIFVP0ciJDy91PTt3E0xamANWJlr40lMNn745REuXU9WSTNigB26e1jAQE8Tt0NT4bfhIaJjs5X7F0xzg7ODPkzkWkjLyEfQzRT88MsjJCblqeQz4GNrdPewhKW5NlJS8/H7sVj8si/6rV4/Vb5Rgx3Q439WMNTXxK17qVi2LgxPYrJeeUzvrjYY1NsOpiYyPHiYjhU/hONeWJpy/8ee1ujczgKuTgbQ19PERwPPIT2jQG1eWpoSbFjeBC6OBhj+1VWEP8x4q9dH7zfT1s3g+M0oyJvUg46NBa72GYe4g4GVXSx6CxQi7zL63kwqo6urCwsLi8ouRoWSyWSwsrKCRCKp7KKUiZWVFbS1tSu7GKLTrrkxxg6ywfbfY/GlTygiHmdhwWRHyA3VP9+p46wH7y8ccPRMIsbNDsWFaynwmVgT9rY6AABtmQac7fWw42Acvpx9H3NXP0R1K234TnJUyad1Mzmmjq2BP88+xxczQ+E1Pwyn/k6q8Ould6NDy2oYN8wBm3c/wZipN/EgMhNLZ9aGsZH6elXXzQCzJ7kiIDAeo6fcxLkrzzF/qhtq2ukq0wzqaYM+Xa3gtyECX3x7C1k5CiydVRsyreLPuOt3UuHrdx9DvrqO2ctCYWOlA9/JbirnmjDSAd06WeL7rY8wdGIwZiwOwb1XNFTp/TS4jx36drfFsnVhGDv5OrKyC+A3t75KfXlZx9bmGD/aCZt+jcSoSUEIf5gOv7n1YSzXUqbR1tbApWvPsW131GvLMG6EI549z3kr10P/PVJ9PaTeDMXtr3wruyhEb1W5G4Tt27fH+PHjMX78eMjlcpiZmWHWrFkqfW/VdSU0NjbG5s2bVbaFhISgZcuW0NHRQb169fDXX3+Vel513S0PHTqEDz74ADo6OjAzM0OvXr1KPf7GjRvo0KEDDA0NYWRkhKZNm+Lq1asqeR84cAAuLi7Q0dGBp6cnHj9+rJLH77//jiZNmkBHRweOjo7w9fVFfn5xBMXPzw/169eHvr4+7OzsMG7cOKSnl/6jJSEhAc2aNUOvXr2Qk5NTajfZY8eOoXbt2jAwMMBHH32EmJgYZR75+fn46quvYGxsjGrVqmHatGkYNmwYevbs+dp7+brr/f777+Hk5ASZTAY3Nzds27ZNZf+L73NRN+B9+/ahQ4cO0NPTQ8OGDXHx4kUAhd1hR4wYgZSUFEgkEkgkEsyZMwcAsG7dOmU5LC0t0bdv31LLTkDvj8xx9K9E/Hn2OaKe5mDV5ifIyVXAs62p2vQ9/2eOq7dSseePBDyOycHWfbEIj8zCJx5mAIDMLAW8lz7AmcvJeBKbg5AHmVi77Qlca+rB3LTwR5WGBvD5YFv8uOspAk4lIjouB1FPc3DmcvK7umyqYP16WCPgRDyOnkrAoydZ8NsQgewcBbp2VP8grk9Xa1wOTsaug08RFZ2FjTsfI+xhBnp1sVKm6dvNGtv2PsH5K0mIeJSJhavDYWYiQ+vmxXV1z+EY3A1LR9yzXNwJTceO/dGo42IAqbSwEVDDVhef/M8SMxaH4MLVJMTG5+B+RAaCbqZU7A2hd67fx7bY+tsjnLuUiAeRGZi/IgTVTLXR5kOzUo8Z2LM6Dh2LwZHAOEQ+zsTSdWHIzlGge+fierj7YDR+2fMYd0JSX3n+D5ua4oPGJli7MeKtXRP9tyQcO4P7Pv6I+/1EZReF3jJBECrs9T54owjhli1boKmpicuXL2PlypXw8/PDTz/9VO58pkyZgm+++QbXr1+Hu7s7evTogcTExDIdGxAQgF69eqFr1664fv06AgMD0bx581LTDx48GNWrV8eVK1cQFBSE6dOnQ0ur+AliZmYmFixYgK1bt+L8+fNITk7GwIEDlfvPnj2LoUOHYuLEibh79y5++OEHbN68GQsWLFCm0dDQwKpVq3Dnzh1s2bIFJ0+exNSpU9WW5/Hjx2jTpg3q1auHPXv2lBppy8zMxLJly7Bt2zacOXMGUVFRmDx5snL/4sWLsX37dmzatAnnz59Hampqmcb1ve569+/fj4kTJ+Kbb77B7du38dlnn2HEiBE4derUK/OdMWMGJk+ejODgYLi6umLQoEHIz89Hy5Yt4e/vDyMjI8TExCAmJgaTJ0/G1atX8dVXX2Hu3LkIDQ3F0aNH0bZt29eWX6w0pRK4OOjh2p3iBw2CAFy/k446zvpqj6ntrI/rd1QfTATdTkPtUtIDgL6uFAqFgIzMwm5VLg56MDeVQRCAtXNdsWNlXcz/xlEZZaT3m6amBG6OBgi6mazcJghA0K1k1HEzVHtMXVdDlfQAcDk4GXVcC9NbW2ijmolMpeGWkVmAu2HpyjQvMzTQhEcbM9wJTUNBQeGXaMtmJngalwP3pib4dW1j7FzXGFM+d4ShwXsz4oHKwMZSB2am2rgSXNzrICOzAHfvp6JeLSO1x2hqSuDqbIirN4qPEQTganAS6rqpP6Y0JsZamDreFfP8QpCdo747KRHRf9UbfaPa2dlhxYoVkEgkcHNzw61bt7BixQqMGTOmXPmMHz8effr0AVAYjTp69Ch+/vnnUhtRL1qwYAEGDhwIX9/isH3Dhg1LTR8VFYUpU6agVq1aAAAXFxeV/Xl5eVizZg1atGgBoLDRW7t2bVy+fBnNmzeHr68vpk+fjmHDhgEAHB0dMW/ePEydOhU+Pj4AgEmTJinzc3BwwPz58/H5559j3bp1KucKDQ1F586d0atXL/j7+7+yi2heXh7Wr18PJycnAIX3bO7cucr9q1evhre3tzI6umbNGhw5cqTU/Mp6vcuWLcPw4cMxbtw4AICXlxf+/vtvLFu2DB06dCg138mTJ6Nbt24AAF9fX9StWxfh4eGoVasW5HI5JBIJrKyKn9xGRUVBX18f3bt3h6GhIezt7dG4cePXll+sjAylkEolSE5RHV+VlJIHO2v1DxVM5JpISi2Z3kSu/p+/lpYEowbY4PTfScjMVgAArMxlAID/62mFDb9GI/ZZLvp+ZIGl3s4YNe0e0koZj0PvB7mhJqRSCZ6/XK+S81DDVlftMabGWnieXLJemRoXPmgzNSn8b8k0uco0Rcb+Xw30+sgKujpS3AlNg/fCEOU+G0ttWJlro717NXy3JhxSDQm+HO4A329c4eV7980umKocU5PCz5ikl+tLcq5y38vkRlrQlErw/KXxps+T82BfXa9c558xqRZ+/+MpQsPTYWXBoRBEYiP2ZSfeKEL44YcfqjRi3N3dERYWhoKC8v0odHd3V/6/pqYmmjVrhnv37pXp2ODgYHTq1KnM5/Ly8sLo0aPh4eGBRYsW4cGDByr7NTU18cEHHyj/rlWrFoyNjZXluXHjBubOnQsDAwPla8yYMYiJiUFmZuFkHidOnECnTp1ga2sLQ0NDDBkyBImJicr9AJCVlYU2bdqgd+/eWLly5WvHC+rp6SkbgwBgbW2N+Ph4AEBKSgri4uJUIqNSqRRNmzZ97f143fXeu3cPrVq1UjmmVatWr31/GjRooFJWAMryqtO5c2fY29vD0dERQ4YMwfbt21Xu18tycnKQmpqq8lIU5L6yTFR2Uikw40sHAMDqLU+U2zX+qaa/HorDuaspCI/MwvKfoiAIQJsPjN99Qek/ZdfvTzFmyk18M/cuFAoB3hOclfskEglkMg18tzoct+6lIfhOKpase4Am9eWws2GE+n3VuZ0F/vyttfKlqVl5Y+f79rCFnq4U2/a8fowhEdF/UYVMKiORSEr0mc3Lyysl9ZvR1VX/1Lo0c+bMwZ07d9CtWzecPHkSderUwf79+8t8fHp6Onx9fREcHKx83bp1C2FhYdDR0UFkZCS6d++OBg0aYO/evQgKCsLatWsBALm5xQ0WbW1teHh44PDhw4iOfv0MeS92awXU39uq5MXyFjV2FQpFqekNDQ1x7do1/Prrr7C2tsbs2bPRsGHDUmciXbhwIeRyucor4tbGt3oNVVlqWgEKCgSVCRMAwESuhaSUfLXHJKXkw8To9emLGoOW1WTwXvJAGR0EoJxtNCq6eHbIvHwBsQk5sKimmje9f1LS8lFQIMD05XqlJgpY5HlyXolIn4m8OH1R1KZkGlmJPFPS8vEkJhtBN1Mwd0UY3JuaoI6rAQAgMSkX+fkKPIkprnuPogtnnbQwYyTnfXXuciJGTLyqfKX804vB5OX6YizD8yT1D/1SUvOQXyAoo9FFTI21kFjKMeo0aWCMum5GOLmvLU4faIudGwp7zvy0oilmTHJ7zdFE9F8gKIQKe70P3qhBeOnSJZW///77b7i4uEAqlQIAzM3NVSY+CQsLUxv1+fvvv5X/n5+fj6CgINSuXbtMZWjQoAECA8s31a+rqyu+/vpr/Pnnn+jduzc2bdqkcv6iSWaAwm6dycnJyvI0adIEoaGhcHZ2LvHS0NBAUFAQFAoFli9fjg8//BCurq54+vRpiTJoaGhg27ZtaNq0KTp06KA2TVnJ5XJYWlriypUrym0FBQW4du3aa4993fXWrl0b58+fVznm/PnzqFOnzhuXVyaTqY0ia2pqwsPDA0uWLMHNmzcRGRmJkydPqs3D29sbKSkpKi/H+iPfuEzvm/wCAWGRmWhcx0C5TSIBGtUxwN1w9dOj3wvPQKMX0gNAk7qGuPdC+qLGoK2lNqYvCS/RBTQsMhO5uQpUf6FbqlQKWJrJEJf4dh/20LuXny8gNCIdTeoXL3kjkQBN68txNzRN7TF37qeppAeAZg2Ncfd+YfqY+BwkJuWqpNHTlaKOi4EyjTqSf76VZFqF/3M7NA2amhqwsSyue3bWhZHBuATOBvm+ysoqQHRMtvL1MCoTz57noFnD4qWM9HSlqONqhNulTAaTny/gfngamjYoPkYiAZo2NMGd0FdPIPOilRvCMfyrqxjxz2uK7y0AgM+Su9iwjcvqEImBQhAq7PU+eKMxhFFRUfDy8sJnn32Ga9euYfXq1Vi+fLlyf8eOHbFmzRq4u7ujoKAA06ZNKxHpAoC1a9fCxcUFtWvXxooVK5CUlISRI8v2497HxwedOnWCk5MTBg4ciPz8fBw5cgTTpk0rkTYrKwtTpkxB3759UbNmTTx58gRXrlxRjl8ECiNbEyZMwKpVq6CpqYnx48fjww8/VHbHnD17Nrp3744aNWqgb9++0NDQwI0bN3D79m3Mnz8fzs7OyMvLw+rVq9GjRw+cP38e69evV1t2qVSK7du3Y9CgQejYsSNOnz6tMq6uPCZMmICFCxfC2dkZtWrVwurVq5GUlPTarqivu94pU6agf//+aNy4MTw8PHDo0CHs27cPJ068+cxaDg4OSE9PR2BgIBo2bAg9PT2cPHkSERERaNu2LUxMTHDkyBEoFAq4ual/KqutrV1iAh4NqfrxJf9V+44mYPKYGrj/MBOhEZno5WkOHW0N/Hn2OQBgytgaeJaUh027Cx/KHPgzAUu9XdDnI3NcvpGKdi1M4FJTF/6bCmeVlUqBWeNrwtleF7NXREBDQ6IcX5iWXoD8AgGZ2QoEnErEkF5WSHieh/hnuejbtXD2ybOcafQ/YfehGHiPd0bogwzcC09H327W0NGW4o9TCQAA7wnOeJaYix93FHar23skBit966J/D2v8HZSEjq3N4Oaoj+Xri7vj7wmIwZA+1fEkJhsx8TkYNdAOz5Jyce5yYV2t7WKAWk4GuBWSirT0fNhY6WDkQDtEx2Tjzj8N0aCbKQh9kI6p45yxZvNDaEgkmDS6Jq7cSFaJGtL7b/fBaAwbUAOPn2YhJi4bo//PAYnPc3D272fKNP7zG+DMxWfYF1D4MHXngSeY8XUthISn4d79NPT/xBa6OhoIOBGrPMbUWAumJjLY2hT2LHK0N0BmVj7iEnKQlp5f4sFCVnbhA7HomCwkJHJIAhWT6utB37mG8m+9mtVh1LAWcp+nIPtxzCuOJKra3qhBOHToUGRlZaF58+aQSqWYOHEixo4dq9y/fPlyjBgxAm3atIGNjQ1WrlyJoKCgEvksWrQIixYtQnBwMJydnXHw4EGYmZU+vfSL2rdvj927d2PevHlYtGgRjIyMSp2dUiqVIjExEUOHDkVcXBzMzMzQu3dvlQlp9PT0MG3aNHz66aeIjo5GmzZt8PPPPyv3e3p64vDhw5g7dy4WL14MLS0t1KpVC6NHjwZQOKGNn58fFi9eDG9vb7Rt2xYLFy7E0KFD1ZZJU1MTv/76KwYMGKBsFL6JadOmITY2FkOHDoVUKsXYsWPh6empjNaW5nXX27NnT6xcuRLLli3DxIkTUbNmTWzatAnt27d/o3ICQMuWLfH5559jwIABSExMhI+PDzw8PLBv3z7MmTMH2dnZcHFxwa+//oq6deu+8Xn+6/66nAy5kSaG9raGiVwTEVFZmLEsAsmphd06zU1leLGX7t3wTCxaH4lhfawxvK81nsblwHflQzz6p/unmYkM7k0Kozjfz6+lcq4pC8NxM6RwhtIfd0WjQCFg6tgakMk0EPogE9MWP0B6JieU+S84dSERxkZaGDHQDqbGWgiPzMDUBfeQ9M9EM5ZmMpWuL3dC0zFvZRhGDayB0Z/WQHRMNmYuCcXDx8WLiP964Cl0tKWY/JkjDPQ1cSskFVPn30NuXmE+2TkKtGlhiuEDqkNXW4rEpFxcDk6G794w5OUXphEE4NtFIfhqVE2smlsP2dkFuHQ9Geu2PnqHd4fehe17H0NHR4qp410L68vdFHzjc0tZXwDA1koXxi90gT95LgHGci2MHuwAUxMZwiPS8Y3PLZXJaXp2scHITx2Uf69b3AgAsMA/BH8ExlX4ddF/h7xpPbgHFi/BVWfZtwCAx1v34eYo78oqFr0F70vXzooiEco5IK19+/Zo1KgR/P39K6hI797mzZsxadKkUsetvU8UCgVq166N/v37Y968eWrT/JeuFwA8hwVXdhFIJHIysl6fiOgtyM9hZIreDe+jY1+fiOgt6JYXWtlFKNWw2bGvT/SGtsx9s16A7xIXcnrPPXr0CH/++SfatWuHnJwcrFmzBg8fPsSnn/5/e3ceVkXV+AH8ewFlkUVCAkKUUCBIhERURAURH3J7xUoJTXDPTCVxQXNB9C1xQdM0twrSSMtUNFxxTdEUEcgFCBfE+oG7KGao3PP7w4d5HbjAvSaC3u/nee4fM3PmzJk7Z87MmXPmTP/aThoRERERUZ1XlwdsfB5qZJRRen50dHQQHx8PLy8v+Pj44NSpU9izZ4/ag/MQEREREZH20rjLKFFdwy6j9Lywyyg9L+wySs8Lu4zS81KXu4x+MPXpR/2vzvefvVZjcT8rbCEkIiIiIiLSUnyHkIiIiIiItJa2jzLKCiEREREREWktbX+Djl1GiYiIiIiItBRbCImIiIiISGsJpbK2k1Cr2EJIRERERESkpdhCSEREREREWkup5YPKsIWQiIiIiIhIS7GFkIiIiIiItBZHGSUiIiIiIiKtxBZCIiIiIiLSWvwwPRERERERkZbS9gohu4wSERERERFpKbYQEhERERGR1lIKfpieiIiIiIiItBBbCImIiIiISGvxHUIiIiIiIiLSSmwhJCIiIiIircUWQiIiIiIiItJKbCEkIiIiIiKtJYR2txCyQkhERERERFpLqeRnJ4iIiIiIiEgLsYWQiIiIiIi0FgeVISIiIiIiIq3EFkIiIiIiItJaQvAdQiIiIiIiItJCbCEkIiIiIiKtxXcIiYiIiIiISCuxhZCIiIiIiLSWtrcQskJIRERERERaS8lBZYiIiIiIiEgbsYWQiIiIiIi0lrZ3GWULIRERERERkZZiCyEREREREWktoeQ7hERERERERKSF2EJIRERERERai+8QEhERERERkVZiCyEREREREWktwe8QEhERERERaSelUtTYT1PLli2Dvb09DAwM0LZtWxw/frzK8Bs2bMAbb7wBAwMDuLm5Yfv27RpvkxVCIiIiIiKiWvbjjz8iIiICUVFROHnyJNzd3REYGIirV6+qDH/kyBGEhIRg6NChSE9PR1BQEIKCgnD69GmNtqsQQmj3W5T0wgsMy6jtJJCWKLl3v7aTQFriUcmD2k4CaYkpO0fUdhJIS/R4mFPbSaiU33tHayzuAz97qx22bdu28PLywtKlSwEASqUSdnZ2GDNmDCZPnlwhfHBwMO7du4ekpCRpXrt27eDh4YEVK1aovV22EBIREREREdWiBw8eIC0tDQEBAdI8HR0dBAQE4OhR1RXWo0ePysIDQGBgYKXhK8NBZYiIiIiISGvV5GcnSkpKUFJSIpunr68PfX192bzr16+jtLQUVlZWsvlWVlbIzs5WGXdhYaHK8IWFhRqlkS2ERERERERENWDOnDkwMzOT/ebMmVPbyZJhCyEREREREWmtmvzsxJQpUxARESGbV751EAAaNWoEXV1dXLlyRTb/ypUrsLa2Vhm3tbW1RuErwxZCIiIiIiKiGqCvrw9TU1PZT1WFsH79+vD09MTevXuleUqlEnv37oW3t+qBaby9vWXhASA5ObnS8JVhCyEREREREWmtmnyHUBMREREICwtD69at0aZNG3zxxRe4d+8eBg8eDAAIDQ2Fra2t1OU0PDwcvr6+iI2NRY8ePbB+/XqcOHECq1at0mi7rBASEREREZHWEsqa6zKqieDgYFy7dg0zZsxAYWEhPDw8sHPnTmngmPz8fOjo/K+DZ/v27fHDDz9g2rRp+PTTT+Ho6IjExES0aNFCo+3yO4T0wuN3COl54XcI6XnhdwjpeeF3COl5qcvfIezQ62CNxX34F98ai/tZYYWQSMuUlJRgzpw5mDJliso+7ETPCvMaPS/Ma/S8MK/Ry4gVQiItc+fOHZiZmaGoqAimpqa1nRx6iTGv0fPCvEbPC/MavYw4yigREREREZGWYoWQiIiIiIhIS7FCSEREREREpKVYISTSMvr6+oiKiuLL8FTjmNfoeWFeo+eFeY1eRhxUhoiIiIiISEuxhZCIiIiIiEhLsUJIRERERESkpVghJCIiIiIi0lKsEBKpwd7eHl988cUzi8/Pzw+ffPLJM4vvWXvW+1ubFAoFEhMTazsZNa6m81RNxV/XzwVtN2jQIAQFBUnTdeF4VZeG6tJcl8q38ml9UeXl5UGhUCAjI6PSMAcOHIBCocDt27cBAPHx8WjYsOFzSV9NqW4fyu9zXact10uqSK+2E0D0bw0aNAi3b9+u0UIsNTUVDRo0qLH4a0t8fDw++eSTChermthfPz8/eHh41NiN2MyZM5GYmFjhhqSgoADm5uY1sk1S34EDB9C5c2fcunXrhb8J1GabNm1CvXr1ajsZVVq8eDHq2nh5eXl5eP3115Geng4PDw9pfl1M6/MSHByM7t2713YyalT79u1RUFAAMzOz2k6KWjS9XlZ2D0EvHlYIidRgaWlZ20l4rurS/j548AD169d/6vWtra2fYWqItNsrr7xS20mo1vO8+f635dOLUlGoCYaGhjA0NKztZNSo+vXrv1DXoBcprfRsscsovfQOHjyINm3aQF9fHzY2Npg8eTIePXokLb979y4GDBiABg0awMbGBosWLaq2i9Ht27fx4YcfwsrKCgYGBmjRogWSkpIAADdu3EBISAhsbW1hZGQENzc3rFu3TuN0x8TEwMrKCiYmJhg6dCgmT54se7KsqttUUFAQBg0aJE3funULoaGhMDc3h5GREbp164bc3FwAj1tsBg8ejKKiIigUCigUCsycObPS/R02bBgsLS1hamoKf39/ZGZmSstnzpwJDw8PrF27Fvb29jAzM8P777+Pu3fvAnjcinvw4EEsXrxY2lZeXp7K/ba3t8fs2bMRGhoKU1NTjBgxAgAQGRkJJycnGBkZwcHBAdOnT8fDhw8BPH5KGR0djczMTCn++Ph4APIuMGXdmjZt2oTOnTvDyMgI7u7uOHr0qCwNq1evhp2dHYyMjNCnTx8sXLjwhWjVUiqVmDRpEl555RVYW1tLxxMAFi5cCDc3NzRo0AB2dnYYNWoUiouLZeunpKTAz88PRkZGMDc3R2BgIG7duqVyW9u2bYOZmRkSEhIAAGvXrkXr1q1hYmICa2tr9O/fH1evXgXw+H/v3LkzAMDc3BwKhUKWT6tKNwDk5+ejd+/eMDY2hqmpKfr164crV65Iy6vLfwDw888/w83NDYaGhrCwsEBAQADu3bun8X9clymVSsybNw/NmzeHvr4+mjRpgs8++0xafurUKfj7+0v/wYgRI2R5oLS0FBEREWjYsCEsLCwwadKkCq1XqsrGzz//HEOGDIGJiQmaNGmCVatWydY5cuQIPDw8YGBggNatWyMxMbHa7oVfffUVHB0dYWBgACsrK7z33nuVhi2fFzXphimEwMyZM9GkSRPo6+vjtddew9ixYysNX5bXvv76a7z++uswMDAAAOzcuRMdOnSQ/ruePXvi/Pnz0nqvv/46AOCtt96CQqGAn5+fyrT6+flh7NixVZ4P2dnZ6NChAwwMDODq6oo9e/b8q65+fn5+GD16NEaPHg0zMzM0atQI06dPlx17VfE3bNhQKmefTFv79u2l6+LBgwcr3a6q7pa//PILvLy8YGBggEaNGqFPnz6Vrp+ZmYnOnTvDxMQEpqam8PT0xIkTJ2RxJyYmSvkoMDAQly9flsWxZcsWtGrVCgYGBnBwcEB0dLTs/kCdcvNJ165dQ+vWrdGnTx+UlJRU2k12165dcHFxgbGxMd5++20UFBRIcTx69Ahjx46V8lJkZCTCwsKqzNPq7u/y5cvRrFkz1K9fH87Ozli7dq1suSbXy6ruITQ5f6luYIWQXmp//fUXunfvDi8vL2RmZmL58uX45ptv8N///lcKExERgZSUFGzduhXJyck4dOgQTp48WWmcSqUS3bp1Q0pKCr7//nucPXsWMTEx0NXVBQD8888/8PT0xLZt23D69GmMGDECAwcOxPHjx9VO908//YSZM2fi888/x4kTJ2BjY4OvvvpK4/0fNGgQTpw4ga1bt+Lo0aMQQqB79+54+PAh2rdvjy+++AKmpqYoKChAQUEBJkyYoDKevn374urVq9ixYwfS0tLQqlUrdOnSBTdv3pTCnD9/HomJiUhKSkJSUhIOHjyImJgYAI+7RXl7e2P48OHStuzs7CpN94IFC+Du7o709HRMnz4dAGBiYoL4+HicPXsWixcvxurVq7Fo0SIAj7sejR8/Hm+++aYUf3BwcKXxT506FRMmTEBGRgacnJwQEhIi3QSkpKRg5MiRCA8PR0ZGBrp27Sq7qa7LvvvuOzRo0ADHjh3DvHnzMGvWLCQnJwMAdHR0sGTJEpw5cwbfffcd9u3bh0mTJknrZmRkoEuXLnB1dcXRo0dx+PBh9OrVC6WlpRW288MPPyAkJAQJCQkYMGAAAODhw4eYPXs2MjMzkZiYiLy8PKnSZ2dnh40bNwIAcnJyUFBQgMWLF6uVbqVSid69e+PmzZs4ePAgkpOTceHChQrHt6r8V1BQgJCQEAwZMgRZWVk4cOAA3nnnnZeuq96UKVMQExOD6dOn4+zZs/jhhx9gZWUFALh37x4CAwNhbm6O1NRUbNiwAXv27MHo0aOl9WNjYxEfH49vv/0Whw8fxs2bN7F58+ZqtxsbG4vWrVsjPT0do0aNwkcffYScnBwAwJ07d9CrVy+4ubnh5MmTmD17NiIjI6uM78SJExg7dixmzZqFnJwc7Ny5E506dVIZVlVe1MTGjRuxaNEirFy5Erm5uUhMTISbm1uV65w7dw4bN27Epk2bpErtvXv3EBERgRMnTmDv3r3Q0dFBnz59oFQqAUAq//fs2YOCggJs2rSp0virOh9KS0sRFBQEIyMjHDt2DKtWrcLUqVM13m9V29TT08Px48exePFiLFy4EF9//bXG8UycOBHjx49Heno6vL290atXL9y4cUOtdbdt24Y+ffqge/fuSE9Px969e9GmTZtKww8YMACNGzdGamoq0tLSMHnyZFl35r///hufffYZ1qxZg5SUFNy+fRvvv/++tPzQoUMIDQ1FeHg4zp49i5UrVyI+Pl5W3ldXbj7p8uXL6NixI1q0aIGff/650g/X//3331iwYAHWrl2LX3/9Ffn5+bJr79y5c5GQkIC4uDikpKTgzp07alX2q9vfzZs3Izw8HOPHj8fp06fx4YcfYvDgwdi/f3+V8VZ2vazsHkKT85fqEEH0ggsLCxO9e/dWuezTTz8Vzs7OQqlUSvOWLVsmjI2NRWlpqbhz546oV6+e2LBhg7T89u3bwsjISISHh0vzmjZtKhYtWiSEEGLXrl1CR0dH5OTkqJ3GHj16iPHjx0vTvr6+svjL8/b2FqNGjZLNa9u2rXB3d68yjt69e4uwsDAhhBB//PGHACBSUlKk5devXxeGhobip59+EkIIERcXJ8zMzCps/8n9PXTokDA1NRX//POPLEyzZs3EypUrhRBCREVFCSMjI3Hnzh1p+cSJE0Xbtm3V3ucntx0UFFRtuPnz5wtPT09pOioqSvb/lAEgNm/eLIQQ4uLFiwKA+Prrr6XlZ86cEQBEVlaWEEKI4OBg0aNHD1kcAwYMUPk/1SW+vr6iQ4cOsnleXl4iMjJSZfgNGzYICwsLaTokJET4+PhUGX94eLhYunSpMDMzEwcOHKgyPampqQKAuHv3rhBCiP379wsA4tatWxqle/fu3UJXV1fk5+dLy8uO2fHjx4UQ1ee/tLQ0AUDk5eVVmeYX2Z07d4S+vr5YvXq1yuWrVq0S5ubmori4WJq3bds2oaOjIwoLC4UQQtjY2Ih58+ZJyx8+fCgaN24sK1/Ln8dNmzYVH3zwgTStVCrFq6++KpYvXy6EEGL58uXCwsJC3L9/XwqzevVqAUCkp6erTOvGjRuFqamp7Hg+qbq8WP6aoCrNZeVbbGyscHJyEg8ePFC5rfKioqJEvXr1xNWrV6sMd+3aNQFAnDp1Sgjxv7Kn/D6rSmtV58OOHTuEnp6eKCgokJYnJyfLyjlN+fr6ChcXF9l1MjIyUri4uEjTquI3MzMTcXFxsv2LiYmRlpfln7lz5wohKpYB5a8/3t7eYsCAAWqn28TERMTHx6tcFhcXJwCI3377TZqXlZUlAIhjx44JIYTo0qWL+Pzzz2XrrV27VtjY2FS6zfLlZtk+ZGdnCzs7OzF27FjZ/6hqnwGIc+fOSWGWLVsmrKyspGkrKysxf/58afrRo0eiSZMmld7nqLu/7du3F8OHD5et17dvX9G9e3dpWtPrpap7iOrOX6qb2EJIL7WsrCx4e3tDoVBI83x8fFBcXIw///wTFy5cwMOHD2VPIc3MzODs7FxpnBkZGWjcuDGcnJxULi8tLcXs2bPh5uaGV155BcbGxti1axfy8/M1Snfbtm1l87y9vdVevywOPT09WTwWFhZwdnZGVlaW2vFkZmaiuLgYFhYWMDY2ln4XL16UdYmyt7eHiYmJNG1jYyN1GdRU69atK8z78ccf4ePjA2traxgbG2PatGka/adPatmypSydAKS05uTkVHgqXdVT6rrkyf0C5Mdgz5496NKlC2xtbWFiYoKBAwfixo0b+PvvvwH8r4WwKj///DPGjRuH5ORk+Pr6ypalpaWhV69eaNKkCUxMTKTl6hyjqtKdlZUFOzs7WYuyq6srGjZsKMvHVeU/d3d3dOnSBW5ubujbty9Wr15daVfYF1VWVhZKSkoqPYZZWVlwd3eXDRbl4+MDpVKJnJwcFBUVoaCgQFZe6OnpqTwXy3vy+CkUClhbW8vOp5YtW0pdK4Hqz6euXbuiadOmcHBwwMCBA5GQkCDl0zJV5UVN9O3bF/fv34eDgwOGDx+OzZs3y7oMqtK0adMK71nn5uYiJCQEDg4OMDU1hb29PQD18n95VZ0POTk5sLOzk73r9SzKp3bt2smuk97e3sjNzVXZQ6AqT16nyvKPutcbdcqgJ0VERGDYsGEICAhATEyM7HpUtn0vLy9p+o033pCVG5mZmZg1a5bsulbWi6Usv1VXbgLA/fv30bFjR7zzzjvSaxFVMTIyQrNmzaTpJ49vUVERrly5Ijumurq68PT0rPb/qG5/s7Ky4OPjI1vHx8en2uNT1fVSFXXOX6p7WCEk0lB1L8HPnz8fixcvRmRkJPbv34+MjAwEBgbiwYMHzzQdOjo6Fbq8lb1T9ywVFxfDxsYGGRkZsl9OTg4mTpwohSs/8qBCoZC6S2mq/AinR48exYABA9C9e3ckJSUhPT0dU6dOfer/9Mm0ll28nzatdUllxyAvLw89e/ZEy5YtsXHjRqSlpWHZsmUAIP2H6gzu8NZbb8HS0hLffvutLO+VdUc0NTVFQkICUlNTpa6G6hyjZ5F3qopDV1cXycnJ2LFjB1xdXfHll1/C2dkZFy9e1GgbdVltDs7xLM994HH38JMnT2LdunWwsbHBjBkz4O7uLhvJsLK8qCk7Ozvk5OTgq6++gqGhIUaNGoVOnTpVWZaqGoG5V69euHnzJlavXo1jx47h2LFjANTL/+U96//zWVAoFDV+vdE0D8+cORNnzpxBjx49sG/fPri6uqrVxblMcXExoqOjZde1U6dOITc3FwYGBmqVmwCgr6+PgIAAJCUl4a+//qp2u6qO77/JwzVN0+ulOucv1T2sENJLzcXFRXp3rkxKSgpMTEzQuHFjODg4oF69ekhNTZWWFxUV4Y8//qg0zpYtW+LPP/+sNExKSgp69+6NDz74AO7u7nBwcKgyvsrSXXZDUea3336TTVtaWspeRC8tLcXp06dlcTx69EgWz40bN5CTkwNXV1cAj0dAq+4JcKtWrVBYWAg9PT00b95c9mvUqJHa+6TOtipz5MgRNG3aFFOnTkXr1q3h6OiIS5cuPbP4n+Ts7CzLDwAqTL9o0tLSoFQqERsbi3bt2sHJyQn/93//JwvTsmVL7N27t8p4mjVrhv3792PLli0YM2aMND87Oxs3btxATEwMOnbsiDfeeKPCE+SykRg1PUYuLi64fPmybHCEs2fP4vbt21I+VodCoYCPjw+io6ORnp6O+vXra3TzWNc5OjrC0NCw0mPo4uKCzMxM2UA6KSkp0NHRgbOzM8zMzGBjYyMrLx49eoS0tLR/lS5nZ2ecOnUKJSUl0jx1zic9PT0EBARg3rx5+P3335GXl4d9+/ZJyyvLi0/D0NAQvXr1wpIlS3DgwAEcPXoUp06dUnv9snJ12rRp6NKlC1xcXCq0QD9t/i/P2dkZly9flg2q9CzKJ1XXG0dHR+nd+PLXm9zcXJWtPk9ep8ryj4uLi1ppUKcMKs/JyQnjxo3D7t278c477yAuLk62/bJBZoDHrau3b9+W0tOqVSvk5ORUuK41b94cOjo6apWbwOOHs2vXroWnpyc6d+6sMoy6zMzMYGVlJTumpaWlVY5roO7+uri4ICUlRbZOSkqKRuVoeZVdd6s7f6nuYYWQXgpFRUUVWrAuX76MUaNG4fLlyxgzZgyys7OxZcsWREVFISIiAjo6OjAxMUFYWBgmTpyI/fv348yZMxg6dCh0dHQq7fbh6+uLTp064d1330VycjIuXryIHTt2YOfOnQAe35glJyfjyJEjyMrKwocffii7eKsjPDwc3377LeLi4vDHH38gKioKZ86ckYXx9/fHtm3bsG3bNmRnZ+Ojjz6SPYFzdHRE7969MXz4cBw+fBiZmZn44IMPYGtri969ewN43M2uuLgYe/fuxfXr11Ve4AMCAuDt7Y2goCDs3r0beXl5OHLkCKZOnSq7+FTH3t4ex44dQ15eHq5fv67RE29HR0fk5+dj/fr1OH/+PJYsWVLhZt7e3h4XL15ERkYGrl+/LrsB1cSYMWOwfft2LFy4ELm5uVi5ciV27NhRbTeguqx58+Z4+PAhvvzyS1y4cAFr167FihUrZGGmTJmC1NRUjBo1Cr///juys7OxfPlyXL9+XRbOyckJ+/fvx8aNG6XRJps0aYL69etL8W/duhWzZ8+Wrde0aVMoFAokJSXh2rVrVY7U96SAgAC4ublhwIABOHnyJI4fP47Q0FD4+vqq1Z0ReHyzWzZAU35+PjZt2oRr166pfaP6IjAwMEBkZCQmTZqENWvW4Pz58/jtt9/wzTffAHg8AIeBgQHCwsJw+vRp7N+/H2PGjMHAgQOlgWfCw8MRExODxMREZGdnY9SoUf/6qX7//v2hVCoxYsQIZGVlYdeuXViwYAEAVHpOJSUlYcmSJcjIyMClS5ewZs0aKJXKCl35VeVFTcXHx+Obb77B6dOnceHCBXz//fcwNDRE06ZN1Y7D3NwcFhYWWLVqFc6dO4d9+/YhIiJCFubVV1+FoaEhdu7ciStXrqCoqOip0tu1a1c0a9YMYWFh+P3335GSkoJp06YBqPz/VEd+fj4iIiKQk5ODdevW4csvv0R4eLi03N/fH0uXLkV6ejpOnDiBkSNHqvwe5bJly7B582ZkZ2fj448/xq1btzBkyBC10hAVFYV169YhKioKWVlZOHXqFObOnasy7P379zF69GgcOHAAly5dQkpKClJTU2XndL169TBmzBgcO3YMaWlpGDRoENq1ayd1x5wxYwbWrFmD6OhonDlzBllZWVi/fr30f6pTbpbR1dVFQkIC3N3d4e/vj8LCQrX2WZUxY8Zgzpw52LJlC3JychAeHo5bt25Ve3yr29+JEyciPj4ey5cvR25uLhYuXIhNmzZVOpicOlTdQ6h7/lLdwgohvRQOHDiAt956S/aLjo6Gra0ttm/fjuPHj8Pd3R0jR47E0KFDpQIfeDystLe3N3r27ImAgAD4+PjAxcVF9s5LeRs3boSXlxdCQkLg6uqKSZMmSU/Jpk2bhlatWiEwMBB+fn6wtrZWewj0MsHBwZg+fTomTZoET09PXLp0CR999JEszJAhQxAWFibdHDs4OEhD+5eJi4uDp6cnevbsCW9vbwghsH37dulC3r59e4wcORLBwcGwtLTEvHnzKqRFoVBg+/bt6NSpEwYPHgwnJye8//77uHTpknQjqY4JEyZAV1cXrq6usLS01Ojdmv/85z8YN24cRo8eDQ8PDxw5ckQafbTMu+++i7fffhudO3eGpaXlU33qA3j8TsWKFSuwcOFCuLu7Y+fOnRg3blyV+aGuc3d3x8KFCzF37ly0aNECCQkJmDNnjiyMk5MTdu/ejczMTLRp0wbe3t7YsmUL9PQqfq7W2dkZ+/btw7p16zB+/HhYWloiPj4eGzZsgKurK2JiYqSb/jK2traIjo7G5MmTYWVlJRvdsioKhQJbtmyBubk5OnXqhICAADg4OODHH39Ue/9NTU3x66+/onv37nBycsK0adMQGxuLbt26qR3Hi2D69OkYP348ZsyYARcXFwQHB0sttUZGRti1axdu3rwJLy8vvPfee+jSpQuWLl0qrT9+/HgMHDgQYWFh8Pb2homJSZXD/qvD1NQUv/zyCzIyMuDh4YGpU6dixowZAFDpOdWwYUNs2rQJ/v7+cHFxwYoVK7Bu3Tq8+eabFcKWz4uaatiwIVavXg0fHx+0bNkSe/bswS+//AILCwu149DR0cH69euRlpaGFi1aYNy4cZg/f74sjJ6eHpYsWYKVK1fitddekx7KaUpXVxeJiYkoLi6Gl5cXhg0bJo0y+m/KqNDQUNy/fx9t2rTBxx9/jPDwcOmTP8DjkWTt7OzQsWNH9O/fHxMmTICRkVGFeGJiYhATEwN3d3ccPnwYW7duVbsniZ+fHzZs2ICtW7fCw8MD/v7+lY7Oraurixs3biA0NBROTk7o168funXrhujoaCmMkZERIiMj0b9/f/j4+MDY2FhWbgQGBiIpKQm7d++Gl5cX2rVrh0WLFkkPA9QpN5+kp6cn5VN/f/+nfoc+MjISISEhCA0Nhbe3N4yNjREYGFjt8a1uf4OCgrB48WIsWLAAb775JlauXIm4uDjpEyhPQ9U9hCbnL9UdClGXOy4T1YJ79+7B1tYWsbGxGDp0aG0nRzJz5kwkJiZW+e0uqhnDhw9HdnY2Dh06VNtJIXrhJSQkSN8ve9k/TP48pKSkoEOHDjh37pxssBJ1+fn5wcPDQ/bt2RddfHw8Pvnkk5fivTWlUgkXFxf069evQs+LMi/T/lLtqPjol0jLpKenIzs7G23atEFRURFmzZoFAE/9BJdefAsWLEDXrl3RoEED7NixA999991TfQeSiIA1a9bAwcEBtra2yMzMRGRkJPr168fK4FPavHkzjI2N4ejoiHPnziE8PBw+Pj5PVRmkuufSpUvYvXs3fH19UVJSgqVLl+LixYvo379/bSeNXmKsEBLhcQUgJycH9evXh6enJw4dOqTRgCn0cjl+/DjmzZuHu3fvwsHBAUuWLMGwYcNqO1lEL6TCwkLMmDEDhYWFsLGxQd++fWUf/ybN3L17F5GRkcjPz0ejRo0QEBCA2NjY2k4WPSM6OjqIj4/HhAkTIIRAixYtsGfPnpfqnWeqe9hllIiIiIiISEtxUBkiIiIiIiItxQohERERERGRlmKFkIiIiIiISEuxQkhERERERKSlWCEkIiIiIiLSUqwQEhERERERaSlWCImIiIiIiLQUK4RERERERERaihVCIiIiIiIiLfX/CunG85XqH7IAAAAASUVORK5CYII=\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "After looking at the above heatmap we found out that there is no highly correlated numerical pair." + ], + "metadata": { + "id": "3LkRRQB80E64" + } + }, + { + "cell_type": "markdown", + "source": [ + "FEATURE ENGINEERING" + ], + "metadata": { + "id": "o4PExmkbnvFU" + } + }, + { + "cell_type": "markdown", + "source": [ + "A) Binary Encoding\n", + "[\"self-learning capability?\", \"Extra-courses did\",\"Taken inputs from seniors or elders\", \"worked in teams ever?\", \"Introvert\"] these columns have yes or no, so we will use binary encoding.\n" + ], + "metadata": { + "id": "gTsxphWhny_I" + } + }, + { + "cell_type": "code", + "source": [ + "cols = data[[\"self-learning capability?\", \"Extra-courses did\",\"Taken inputs from seniors or elders\", \"worked in teams ever?\", \"Introvert\"]]\n", + "for i in cols:\n", + " cleanup_nums = {i: {\"yes\": 1, \"no\": 0}}\n", + " data = data.replace(cleanup_nums)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "8A0ZH1tQzQ_N", + "outputId": "c16cb698-c4d2-4184-c8a5-9e0184d54ec2" + }, + "execution_count": 51, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + ":4: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", + " data = data.replace(cleanup_nums)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "print(\"\\n\\nList of Categorical features: \\n\" , data.select_dtypes(include=['object']).columns.tolist())\n" + ], + "metadata": { + "id": "rxhWpX5O0lVz", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "outputId": "770842b6-285f-4e22-e788-21b64ee7d049" + }, + "execution_count": 52, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "\n", + "List of Categorical features: \n", + " ['certifications', 'workshops', 'reading and writing skills', 'memory capability score', 'Interested subjects', 'interested career area ', 'Type of company want to settle in?', 'Interested Type of Books', 'Management or Technical', 'hard/smart worker', 'Suggested Job Role']\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "B) Number Encoding" + ], + "metadata": { + "id": "dXQk7AL9o315" + } + }, + { + "cell_type": "code", + "source": [ + "mycol = data[[\"reading and writing skills\", \"memory capability score\"]]\n", + "for i in mycol:\n", + " cleanup_nums = {i: {\"poor\": 0, \"medium\": 1, \"excellent\": 2}}\n", + " data = data.replace(cleanup_nums)\n", + "\n", + "category_cols = data[['certifications', 'workshops', 'Interested subjects', 'interested career area ', 'Type of company want to settle in?',\n", + " 'Interested Type of Books']]\n", + "for i in category_cols:\n", + " data[i] = data[i].astype('category')\n", + " data[i + \"_code\"] = data[i].cat.codes\n", + "\n", + "print(\"\\n\\nList of Categorical features: \\n\" , data.select_dtypes(include=['object']).columns.tolist())" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "VLXVIBdLo2o5", + "outputId": "40adf71e-3b6a-4621-badf-dee75935cc9e" + }, + "execution_count": 53, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "\n", + "List of Categorical features: \n", + " ['Management or Technical', 'hard/smart worker', 'Suggested Job Role']\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + ":4: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", + " data = data.replace(cleanup_nums)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "C) Dummy Variable Encoding" + ], + "metadata": { + "id": "ptsdUpIppRfS" + } + }, + { + "cell_type": "code", + "source": [ + "print(data['Management or Technical'].unique())\n", + "print(data['hard/smart worker'].unique())" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "23p7o54Oo2jP", + "outputId": "1f72e7a4-617b-40e2-a4a6-e3bdd12d475c" + }, + "execution_count": 54, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "['Management' 'Technical']\n", + "['smart worker' 'hard worker']\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "data = pd.get_dummies(data, columns=[\"Management or Technical\", \"hard/smart worker\"], prefix=[\"A\", \"B\"], dtype = int)\n", + "data.head()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 412 + }, + "id": "p57Do3DLo2c0", + "outputId": "eec6c954-80a5-4901-c857-ed4227d772cd" + }, + "execution_count": 55, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Logical quotient rating hackathons coding skills rating \\\n", + "0 5 0 6 \n", + "1 7 6 4 \n", + "2 2 3 9 \n", + "3 2 6 3 \n", + "4 2 0 3 \n", + "\n", + " public speaking points self-learning capability? 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Rotate labels for better readability if there are many columns\n", + "plt.title('Box Plot of Numerical Features')\n", + "plt.show()\n", + "\n", + "# Plotting correlation heatmap for numerical columns\n", + "plt.figure(figsize=(10, 8))\n", + "sns.heatmap(numerical_feed.corr(), annot=True, cmap='coolwarm', fmt=\".2f\")\n", + "plt.title('Correlation Heatmap of Numerical Features')\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 2484 + }, + "id": "g8Zij8JC7o_N", + "outputId": "50ddcdc3-0d18-4248-aa7a-acf4d3bb86b1" + }, + "execution_count": 60, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " data_subset = grouped_data.get_group(pd_key)\n", + "/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n", + " positions = grouped.grouper.result_index.to_numpy(dtype=float)\n", + "/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " data_subset = grouped_data.get_group(pd_key)\n", + "/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n", + " positions = grouped.grouper.result_index.to_numpy(dtype=float)\n", + "/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " data_subset = grouped_data.get_group(pd_key)\n", + "/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n", + " positions = grouped.grouper.result_index.to_numpy(dtype=float)\n", + "/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " data_subset = grouped_data.get_group(pd_key)\n", + "/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n", + " positions = grouped.grouper.result_index.to_numpy(dtype=float)\n", + "/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " data_subset = grouped_data.get_group(pd_key)\n", + "/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n", + " positions = grouped.grouper.result_index.to_numpy(dtype=float)\n", + "/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " data_subset = grouped_data.get_group(pd_key)\n", + "/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n", + " positions = grouped.grouper.result_index.to_numpy(dtype=float)\n", + "/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " data_subset = grouped_data.get_group(pd_key)\n", + "/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n", + " positions = grouped.grouper.result_index.to_numpy(dtype=float)\n", + "/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " data_subset = grouped_data.get_group(pd_key)\n", + "/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n", + " positions = grouped.grouper.result_index.to_numpy(dtype=float)\n", + "/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " data_subset = grouped_data.get_group(pd_key)\n", + "/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n", + " positions = grouped.grouper.result_index.to_numpy(dtype=float)\n", + "/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " data_subset = grouped_data.get_group(pd_key)\n", + "/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n", + " positions = grouped.grouper.result_index.to_numpy(dtype=float)\n", + "/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " data_subset = grouped_data.get_group(pd_key)\n", + "/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n", + " positions = grouped.grouper.result_index.to_numpy(dtype=float)\n", + "/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " data_subset = grouped_data.get_group(pd_key)\n", + "/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n", + " positions = grouped.grouper.result_index.to_numpy(dtype=float)\n", + "/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " data_subset = grouped_data.get_group(pd_key)\n", + "/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n", + " positions = grouped.grouper.result_index.to_numpy(dtype=float)\n", + "/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " data_subset = grouped_data.get_group(pd_key)\n", + "/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n", + " positions = grouped.grouper.result_index.to_numpy(dtype=float)\n", + "/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " data_subset = grouped_data.get_group(pd_key)\n", + "/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n", + " positions = grouped.grouper.result_index.to_numpy(dtype=float)\n", + "/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " data_subset = grouped_data.get_group(pd_key)\n", + "/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n", + " positions = grouped.grouper.result_index.to_numpy(dtype=float)\n", + "/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " data_subset = grouped_data.get_group(pd_key)\n", + "/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n", + " positions = grouped.grouper.result_index.to_numpy(dtype=float)\n", + "/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " data_subset = grouped_data.get_group(pd_key)\n", + "/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n", + " positions = grouped.grouper.result_index.to_numpy(dtype=float)\n", + "/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " data_subset = grouped_data.get_group(pd_key)\n", + "/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n", + " positions = grouped.grouper.result_index.to_numpy(dtype=float)\n", + "/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " data_subset = grouped_data.get_group(pd_key)\n", + "/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n", + " positions = grouped.grouper.result_index.to_numpy(dtype=float)\n", + "/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " data_subset = grouped_data.get_group(pd_key)\n", + "/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n", + " positions = grouped.grouper.result_index.to_numpy(dtype=float)\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Elastic Net Model" + ], + "metadata": { + "id": "_MMnbRJw1wmm" + } + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "class ElasticNetModel:\n", + "\n", + " def __init__(self, lambdas=1.0, l1_ratio=0.5, iterations=10000, learning_rate=0.001):\n", + " self.lambdas = lambdas\n", + " self.l1_ratio = l1_ratio\n", + " self.iterations = iterations\n", + " self.learning_rate = learning_rate\n", + " self.coef_ = None\n", + " self.intercept_ = 0\n", + "\n", + " def fit(self, X, y):\n", + "\n", + " n_samples, n_features = X.shape\n", + " self.coef_ = np.zeros(n_features)\n", + " self.intercept_ = 0\n", + "\n", + " # Performing gradient descent\n", + " for _ in range(self.iterations):\n", + " current_predictions = np.dot(X, self.coef_) + self.intercept_\n", + " residuals = current_predictions - y\n", + "\n", + " # Computing gradients for coefficients\n", + " # First, we calculate the gradient from the residuals\n", + " residual_gradient = np.dot(X.T, residuals) / n_samples\n", + "\n", + " # Computing the L1 regularization term\n", + " l1_term = self.l1_ratio * self.lambdas * np.sign(self.coef_)\n", + "\n", + " # Computing the L2 regularization term\n", + " l2_term = (1 - self.l1_ratio) * self.lambdas * 2 * self.coef_\n", + "\n", + " # Combining the gradients from residuals, L1, and L2 terms\n", + " coef_gradient = residual_gradient + l1_term + l2_term\n", + "\n", + " # Computing the gradient for the intercept\n", + " intercept_gradient = np.sum(residuals) / n_samples\n", + "\n", + " # Updating the model parameters\n", + " self.coef_ -= self.learning_rate * coef_gradient\n", + " self.intercept_ -= self.learning_rate * intercept_gradient\n", + "\n", + " return ElasticNetModelResults(self.coef_, self.intercept_)\n", + "\n", + "class ElasticNetModelResults:\n", + " def __init__(self, coef, intercept):\n", + " self.coef_ = coef\n", + " self.intercept_ = intercept\n", + "\n", + " def predict(self, X):\n", + "\n", + " return np.dot(X, self.coef_) + self.intercept_\n", + "\n", + "# Function to calculate MSE\n", + "def mean_squared_error(y_true, y_pred):\n", + " return np.mean((y_true - y_pred) ** 2)\n", + "\n", + "# Function to calculate R²\n", + "def r2_score(y_true, y_pred):\n", + " ss_res = np.sum((y_true - y_pred) ** 2)\n", + " ss_tot = np.sum((y_true - np.mean(y_true)) ** 2)\n", + " return 1 - (ss_res / ss_tot)\n", + "\n", + "\n", + "# Fit and predict using the model\n", + "model = ElasticNetModel(lambdas=0.1, l1_ratio=0.5, iterations=1000, learning_rate=0.001)\n", + "results = model.fit(x_train_scaled, y_train)\n", + "predictions = results.predict(x_test_scaled)\n", + "\n", + "predicted_categories = np.clip(np.round(predictions), 0, len(label_encoder.classes_) - 1).astype(int)\n", + "# Convert numeric predictions back to job role labels\n", + "predicted_job_roles = label_encoder.inverse_transform(predicted_categories)\n", + "print(\"Numerical Predictions:\", predictions)\n", + "print(\"Predicted Job Roles:\", predicted_job_roles)\n", + "\n", + "# Calculate and print R² and MSE\n", + "mse = mean_squared_error(y_test, predictions)\n", + "r2 = r2_score(y_test, predictions)\n", + "\n", + "# Plotting the results\n", + "plt.figure(figsize=(10, 6))\n", + "plt.scatter(y_test, predictions, alpha=0.5, color='blue', label='Predictions')\n", + "plt.scatter(y_test, y_test, alpha=0.5, color='red', label='Actual Values')\n", + "plt.title('Predicted Values vs Actual Values')\n", + "plt.xlabel('Actual Values')\n", + "plt.ylabel('Predicted Values')\n", + "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=2)\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 635 + }, + "id": "SGkj3mLi1uzC", + "outputId": "7a4c5f89-f8bb-4eb0-cdea-3382c691d8fe" + }, + "execution_count": 70, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Numerical Predictions: [4.53104541 2.48977628 5.47932598 ... 5.29471583 3.07237336 2.58811291]\n", + "Predicted Job Roles: ['Software Developer' 'Database Developer' 'Software Developer' ...\n", + " 'Software Developer' 'Mobile Applications Developer'\n", + " 'Mobile Applications Developer']\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "ElasticNetModel implementation in test.py" + ], + "metadata": { + "id": "8XuPoK5Ur4p8" + } + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "import csv\n", + "from sklearn.preprocessing import StandardScaler # To standardize the features\n", + "\n", + "# Assuming the ElasticNetModel is defined as provided above\n", + "\n", + "def test_predict():\n", + " model = ElasticNetModel(lambdas=1.0, l1_ratio=0.5, iterations=1000, learning_rate=0.01)\n", + " data = []\n", + "\n", + " # Load data from the CSV file\n", + " with open(\"/content/small_test.csv\", \"r\") as file:\n", + " reader = csv.DictReader(file)\n", + " for row in reader:\n", + " # Convert all values to float for consistency\n", + " data.append({k: float(v) for k, v in row.items()})\n", + "\n", + " # Extract features and targets\n", + " X = np.array([[v for k, v in datum.items() if k.startswith('x')] for datum in data])\n", + " y = np.array([datum['y'] for datum in data if 'y' in datum])\n", + "\n", + " # Normalize the feature data\n", + " scaler = StandardScaler()\n", + " X_scaled = scaler.fit_transform(X)\n", + "\n", + " # Fit the model\n", + " results = model.fit(X_scaled, y)\n", + "\n", + " # Make predictions\n", + " preds = results.predict(X_scaled)\n", + "\n", + " # Print predictions to verify outputs\n", + " print(\"Predictions:\", preds)\n", + "\n", + " # Plotting the results\n", + " plt.figure(figsize=(10, 6))\n", + " plt.scatter(y, preds, alpha=0.5, color='blue') # Plot predictions vs actual values\n", + " plt.title('Actual vs. Predicted Values')\n", + " plt.xlabel('Actual Values')\n", + " plt.ylabel('Predicted Values')\n", + " plt.plot([y.min(), y.max()], [y.min(), y.max()], 'k--', lw=2) # Diagonal line\n", + " plt.grid(True)\n", + " plt.show()\n", + "\n", + "# Run the test function\n", + "test_predict()\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 743 + }, + "id": "Z0gnjUl30Jup", + "outputId": "ad949acd-bcd3-4b67-9e04-99644a961183" + }, + "execution_count": 71, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Predictions: [ 7.64406716 -3.91486505 9.04543217 -21.43410974 -4.19068289\n", + " -4.33533676 17.95393009 2.87278517 7.70954363 3.52463095\n", + " -15.33891019 -21.39191417 10.17234674 4.07196966 -7.69550148\n", + " -9.99851451 -7.91093427 0.74461552 -1.94176839 1.1033472\n", + " -0.43021072 12.51827668 2.03347225 27.88970906 9.09785529\n", + " 0.83792923 16.46643611 -5.41484608 16.14853632 16.83517\n", + " -9.41228239 10.17345465 10.50798462 14.08293284 -20.25126778\n", + " 25.33325895 9.38719368 -4.74321529 5.6573891 -10.1155319\n", + " 3.59941121 -6.18286557 23.38931645 18.18675759 9.76337049\n", + " 18.98041862 4.329532 -1.31353029 9.57626723 -6.87750981]\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Generating random data and implementing the ElasticNetModel" + ], + "metadata": { + "id": "AY5TQgOdsBjS" + } + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "import csv\n", + "import argparse\n", + "\n", + "def generate_data(N, m, b, scale, rnge, seed, output_file):\n", + " def linear_data_generator(m, b, rnge, N, scale, seed):\n", + " rng = np.random.default_rng(seed=seed)\n", + " sample = rng.uniform(low=rnge[0], high=rnge[1], size=(N, len(m)))\n", + " ys = np.dot(sample, np.array(m).reshape(-1, 1)) + b\n", + " noise = rng.normal(loc=0., scale=scale, size=ys.shape)\n", + " return (sample, ys + noise)\n", + "\n", + " def write_data(filename, X, y):\n", + " with open(filename, \"w\") as file:\n", + " xs = [f\"x_{n}\" for n in range(X.shape[1])]\n", + " header = xs + [\"y\"]\n", + " writer = csv.writer(file)\n", + " writer.writerow(header)\n", + " for row in np.hstack((X, y)):\n", + " writer.writerow(row)\n", + "\n", + " m = np.array(m)\n", + " X, y = linear_data_generator(m, b, rnge, N, scale, seed)\n", + " write_data(output_file, X, y)\n", + "\n", + "# Calling the function with example parameters\n", + "generate_data(100, [3, 2], 5, 1.0, [-10, 10], 42, 'data.csv')\n" + ], + "metadata": { + "id": "sgDJuHtC0Jro" + }, + "execution_count": 66, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from sklearn.preprocessing import RobustScaler\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "def load_data(filename):\n", + " with open(filename, 'r') as file:\n", + " reader = csv.reader(file)\n", + " next(reader) # Skip the header\n", + " X, y = [], []\n", + " for row in reader:\n", + " X.append([float(num) for num in row[:-1]])\n", + " y.append(float(row[-1]))\n", + " return np.array(X), np.array(y)\n", + "\n", + "def test_model_with_generated_data(filename):\n", + " X, y = load_data(filename)\n", + "\n", + " scaler = RobustScaler()\n", + " X_scaled = scaler.fit_transform(X)\n", + "\n", + " model = ElasticNetModel(lambdas=1.0, l1_ratio=0.5, iterations=1000, learning_rate=0.01)\n", + " results = model.fit(X_scaled, y)\n", + "\n", + " predictions = results.predict(X_scaled)\n", + "\n", + " # Plotting the results\n", + " plt.figure(figsize=(10, 6))\n", + " plt.scatter(y, predictions, alpha=0.5)\n", + " plt.title('Comparison of Actual and Predicted Values')\n", + " plt.xlabel('Actual Values')\n", + " plt.ylabel('Predicted Values')\n", + " plt.plot([y.min(), y.max()], [y.min(), y.max()], 'k--', lw=4) # Diagonal line for reference\n", + " plt.grid(True)\n", + " plt.show()\n", + "\n", + " print(\"Predictions:\", predictions)\n", + " print(\"Actuals:\", y)\n", + " return predictions, y\n", + "\n", + "predictions, actuals = test_model_with_generated_data('data.csv')\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1093 + }, + "id": "4EXO-pZX0Jjr", + "outputId": "5ddc1d9a-26b5-44f3-f580-fb886625004a" + }, + "execution_count": 68, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Predictions: [ 6.90053733 10.61569362 3.79760622 10.29100864 -1.14769806 6.79525302\n", + " 9.18369642 0.55562317 0.28907358 9.55185114 5.83677731 14.03608926\n", + " 4.4573762 -1.02665425 1.56395226 11.94054159 0.53432287 0.49970769\n", + " -0.86576721 2.34672792 6.67149969 4.674284 11.38159103 0.47346149\n", + " 2.68336154 -4.77309705 9.3767322 9.52868114 4.24598988 -4.43656239\n", + " 5.89559469 7.59846941 6.31368381 2.80463694 -2.51776005 -0.48266223\n", + " 5.80805671 -3.75966647 3.10907105 7.68812361 5.181761 4.62488092\n", + " -6.16748562 6.4834837 -0.21009097 1.67474786 2.16384708 2.88550114\n", + " -1.90445995 3.95845718 11.27160658 5.90251764 9.80587102 1.09130434\n", + " 3.07843074 0.45739569 2.41920381 3.62420703 9.57639241 4.50612864\n", + " 6.65868156 -2.05366859 -1.79521132 -1.72649479 -0.05566228 4.24704419\n", + " 3.52012563 0.32877385 9.67898503 3.36731236 3.79829302 10.33186371\n", + " 1.3424933 2.14397086 -1.60534872 12.24133188 0.06632484 0.87535114\n", + " 2.92727425 9.68106609 3.359149 -4.08242846 3.43485803 11.38604111\n", + " 6.38420678 1.57252327 5.43825537 -4.83522542 8.27959433 11.55825092\n", + " 13.0291004 2.41704229 9.15554969 -1.68493992 4.75188054 6.934632\n", + " 2.68030634 -3.60360676 9.15130419 -1.71701523]\n", + "Actuals: [ 19.6358273 34.01599123 -0.32958693 31.94751143 -18.96016997\n", + " 15.72596287 26.63295664 -8.66166041 -11.22239464 29.87572788\n", + " 13.82307286 47.74791326 8.61040577 -15.57871259 -7.5047698\n", + " 37.05972593 -10.60145878 -9.73196639 -18.50418573 -3.59012144\n", + " 15.07435841 10.84796988 36.9716567 -10.91411571 1.31597077\n", + " -32.46852027 28.98607027 27.45469589 5.37508415 -31.80274884\n", + " 16.46549999 21.3909653 14.37303188 0.42305021 -27.14567636\n", + " -16.3744809 15.87736883 -29.0406262 -1.63679778 19.12371106\n", + " 8.96699785 10.35747396 -41.09777559 16.28721082 -16.15876303\n", + " -8.10272139 -4.74750281 -3.16501617 -17.65599948 -0.73116187\n", + " 36.40634122 11.55616631 33.30572897 -8.32021403 -3.48069537\n", + " -9.21733247 -1.74512215 -1.11591874 29.04441218 6.79527359\n", + " 16.47450827 -23.67720072 -22.87733976 -20.80225551 -15.52821279\n", + " 1.97399536 3.97063045 -9.18824289 32.27722879 6.28602945\n", + " 3.98320895 34.4904658 -5.85845407 -4.27887967 -18.52238722\n", + " 40.74044059 -14.52089552 -9.5026835 -0.17571268 29.75142309\n", + " -2.65812665 -30.80306977 4.31918247 37.3801927 12.62445278\n", + " -8.68723053 8.72708672 -32.87268583 19.69041548 37.97343195\n", + " 44.44913224 -1.17886951 27.49502527 -18.90535781 10.05381994\n", + " 21.00017454 -2.9185252 -31.19886712 25.70859886 -20.62446072]\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "vTzUFeZ1jgxV" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file From 82ceb77e7f53f3348b72760928b6964c1e416b04 Mon Sep 17 00:00:00 2001 From: Munish Patel <53735021+munishpatel@users.noreply.github.com> Date: Wed, 9 Oct 2024 23:52:22 -0500 Subject: [PATCH 10/12] Uploaded data.csv This data is generated using generate_regression_data.py --- data.csv | 101 +++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 101 insertions(+) create mode 100644 data.csv diff --git a/data.csv b/data.csv new file mode 100644 index 0000000..d7ddb1d --- /dev/null +++ b/data.csv @@ -0,0 +1,101 @@ +x_0,x_1,y +5.479120971119267,-1.2224312049589532,19.635827298128937 +7.171958398227648,3.9473605811872776,34.01599123419791 +-8.11645304224701,9.512447032735118,-0.3295869279908642 +5.222794039807059,5.721286105539075,31.947511431974814 +-7.4377273464890825,-0.9922812420886569,-18.960169974845627 +-2.5840395153483753,8.535299776972035,15.725962869212662 +2.8773024016132904,6.455232265416598,26.63295664257481 +-1.131716023453377,-5.455225564304462,-8.661660405692198 +1.0916957403166965,-8.723654877916493,-11.222394635913918 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What does the model you have implemented do and when should it be used? From 4f30524cb9185a76fd00e2556610ecd1e853b7a0 Mon Sep 17 00:00:00 2001 From: Munish Patel <53735021+munishpatel@users.noreply.github.com> Date: Thu, 10 Oct 2024 00:02:57 -0500 Subject: [PATCH 12/12] Updated README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 13cf470..7f57d00 100644 --- a/README.md +++ b/README.md @@ -28,6 +28,7 @@ This project implements a ElasticNet Model, which combines L1 and L2 penalties t print("Predicted Job Roles:", predicted_job_roles) ``` ### Initial Data Used + https://github.com/munishpatel/ML-DATA/blob/c9442334645ca2ac71820578d17125c630c6199f/mldata.csv ### Explanation of the Model 1. What does the model you have implemented do and when should it be used?