From e55296a2321b6a447b5e74e6214f7b1b68020654 Mon Sep 17 00:00:00 2001 From: Amit Rathi Date: Tue, 11 Sep 2018 17:53:26 +0530 Subject: [PATCH 01/19] Deleted Notebook --- notebooks/00.00-Preface.ipynb | 198 ---------------------------------- 1 file changed, 198 deletions(-) delete mode 100644 notebooks/00.00-Preface.ipynb diff --git a/notebooks/00.00-Preface.ipynb b/notebooks/00.00-Preface.ipynb deleted file mode 100644 index e9e8d99a8..000000000 --- a/notebooks/00.00-Preface.ipynb +++ /dev/null @@ -1,198 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", - "\n", - "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "| [Contents](Index.ipynb) | [IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb) >" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Preface" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## What Is Data Science?\n", - "\n", - "This is a book about doing data science with Python, which immediately begs the question: what is *data science*?\n", - "It's a surprisingly hard definition to nail down, especially given how ubiquitous the term has become.\n", - "Vocal critics have variously dismissed the term as a superfluous label (after all, what science doesn't involve data?) or a simple buzzword that only exists to salt resumes and catch the eye of overzealous tech recruiters.\n", - "\n", - "In my mind, these critiques miss something important.\n", - "Data science, despite its hype-laden veneer, is perhaps the best label we have for the cross-disciplinary set of skills that are becoming increasingly important in many applications across industry and academia.\n", - "This cross-disciplinary piece is key: in my mind, the best extisting definition of data science is illustrated by Drew Conway's Data Science Venn Diagram, first published on his blog in September 2010:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Data Science Venn Diagram](figures/Data_Science_VD.png)\n", - "\n", - "(Source: [Drew Conway](http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram). Used by permission.)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "While some of the intersection labels are a bit tongue-in-cheek, this diagram captures the essence of what I think people mean when they say \"data science\": it is fundamentally an *interdisciplinary* subject.\n", - "Data science comprises three distinct and overlapping areas: the skills of a *statistician* who knows how to model and summarize datasets (which are growing ever larger); the skills of a *computer scientist* who can design and use algorithms to efficiently store, process, and visualize this data; and the *domain expertise*—what we might think of as \"classical\" training in a subject—necessary both to formulate the right questions and to put their answers in context.\n", - "\n", - "With this in mind, I would encourage you to think of data science not as a new domain of knowledge to learn, but a new set of skills that you can apply within your current area of expertise.\n", - "Whether you are reporting election results, forecasting stock returns, optimizing online ad clicks, identifying microorganisms in microscope photos, seeking new classes of astronomical objects, or working with data in any other field, the goal of this book is to give you the ability to ask and answer new questions about your chosen subject area." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Who Is This Book For?\n", - "\n", - "In my teaching both at the University of Washington and at various tech-focused conferences and meetups, one of the most common questions I have heard is this: \"how should I learn Python?\"\n", - "The people asking are generally technically minded students, developers, or researchers, often with an already strong background in writing code and using computational and numerical tools.\n", - "Most of these folks don't want to learn Python *per se*, but want to learn the language with the aim of using it as a tool for data-intensive and computational science.\n", - "While a large patchwork of videos, blog posts, and tutorials for this audience is available online, I've long been frustrated by the lack of a single good answer to this question; that is what inspired this book.\n", - "\n", - "The book is not meant to be an introduction to Python or to programming in general; I assume the reader has familiarity with the Python language, including defining functions, assigning variables, calling methods of objects, controlling the flow of a program, and other basic tasks.\n", - "Instead it is meant to help Python users learn to use Python's data science stack–libraries such as IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related tools–to effectively store, manipulate, and gain insight from data." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Why Python?\n", - "\n", - "Python has emerged over the last couple decades as a first-class tool for scientific computing tasks, including the analysis and visualization of large datasets.\n", - "This may have come as a surprise to early proponents of the Python language: the language itself was not specifically designed with data analysis or scientific computing in mind.\n", - "The usefulness of Python for data science stems primarily from the large and active ecosystem of third-party packages: *NumPy* for manipulation of homogeneous array-based data, *Pandas* for manipulation of heterogeneous and labeled data, *SciPy* for common scientific computing tasks, *Matplotlib* for publication-quality visualizations, *IPython* for interactive execution and sharing of code, *Scikit-Learn* for machine learning, and many more tools that will be mentioned in the following pages.\n", - "\n", - "If you are looking for a guide to the Python language itself, I would suggest the sister project to this book, \"[A Whirlwind Tour of the Python Language](https://github.com/jakevdp/WhirlwindTourOfPython)\".\n", - "This short report provides a tour of the essential features of the Python language, aimed at data scientists who already are familiar with one or more other programming languages." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Python 2 vs Python 3\n", - "\n", - "This book uses the syntax of Python 3, which contains language enhancements that are not compatible with the 2.x series of Python.\n", - "Though Python 3.0 was first released in 2008, adoption has been relatively slow, particularly in the scientific and web development communities.\n", - "This is primarily because it took some time for many of the essential third-party packages and toolkits to be made compatible with the new language internals.\n", - "Since early 2014, however, stable releases of the most important tools in the data science ecosystem have been fully compatible with both Python 2 and 3, and so this book will use the newer Python 3 syntax.\n", - "However, the vast majority of code snippets in this book will also work without modification in Python 2: in cases where a Py2-incompatible syntax is used, I will make every effort to note it explicitly." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Outline of the Book\n", - "\n", - "Each chapter of this book focuses on a particular package or tool that contributes a fundamental piece of the Python Data Sciece story.\n", - "\n", - "1. IPython and Jupyter: these packages provide the computational environment in which many Python-using data scientists work.\n", - "2. NumPy: this library provides the ``ndarray`` for efficient storage and manipulation of dense data arrays in Python.\n", - "3. Pandas: this library provides the ``DataFrame`` for efficient storage and manipulation of labeled/columnar data in Python.\n", - "4. Matplotlib: this library provides capabilities for a flexible range of data visualizations in Python.\n", - "5. Scikit-Learn: this library provides efficient & clean Python implementations of the most important and established machine learning algorithms.\n", - "\n", - "The PyData world is certainly much larger than these five packages, and is growing every day.\n", - "With this in mind, I make every attempt through these pages to provide references to other interesting efforts, projects, and packages that are pushing the boundaries of what can be done in Python.\n", - "Nevertheless, these five are currently fundamental to much of the work being done in the Python data science space, and I expect they will remain important even as the ecosystem continues growing around them." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Using Code Examples\n", - "\n", - "Supplemental material (code examples, figures, etc.) is available for download at http://github.com/jakevdp/PythonDataScienceHandbook/. This book is here to help you get your job done. In general, if example code is offered with this book, you may use it in your programs and documentation. You do not need to contact us for permission unless you’re reproducing a significant portion of the code. For example, writing a program that uses several chunks of code from this book does not require permission. Selling or distributing a CD-ROM of examples from O’Reilly books does require permission. Answering a question by citing this book and quoting example code does not require permission. Incorporating a significant amount of example code from this book into your product’s documentation does require permission.\n", - "\n", - "We appreciate, but do not require, attribution. An attribution usually includes the title, author, publisher, and ISBN. For example:\n", - "\n", - "> *The Python Data Science Handbook* by Jake VanderPlas (O’Reilly). Copyright 2016 Jake VanderPlas, 978-1-491-91205-8.\n", - "\n", - "If you feel your use of code examples falls outside fair use or the per‐ mission given above, feel free to contact us at permissions@oreilly.com." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Installation Considerations\n", - "\n", - "Installing Python and the suite of libraries that enable scientific computing is straightforward . This section will outline some of the considerations when setting up your computer.\n", - "\n", - "Though there are various ways to install Python, the one I would suggest for use in data science is the Anaconda distribution, which works similarly whether you use Windows, Linux, or Mac OS X.\n", - "The Anaconda distribution comes in two flavors:\n", - "\n", - "- [Miniconda](http://conda.pydata.org/miniconda.html) gives you the Python interpreter itself, along with a command-line tool called ``conda`` which operates as a cross-platform package manager geared toward Python packages, similar in spirit to the apt or yum tools that Linux users might be familiar with.\n", - "\n", - "- [Anaconda](https://www.continuum.io/downloads) includes both Python and conda, and additionally bundles a suite of other pre-installed packages geared toward scientific computing. Because of the size of this bundle, expect the installation to consume several gigabytes of disk space.\n", - "\n", - "Any of the packages included with Anaconda can also be installed manually on top of Miniconda; for this reason I suggest starting with Miniconda.\n", - "\n", - "To get started, download and install the Miniconda package–make sure to choose a version with Python 3–and then install the core packages used in this book:\n", - "\n", - "```\n", - "[~]$ conda install numpy pandas scikit-learn matplotlib seaborn jupyter\n", - "```\n", - "\n", - "Throughout the text, we will also make use of other more specialized tools in Python's scientific ecosystem; installation is usually as easy as typing **``conda install packagename``**.\n", - "For more information on conda, including information about creating and using conda environments (which I would *highly* recommend), refer to [conda's online documentation](http://conda.pydata.org/docs/)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "| [Contents](Index.ipynb) | [IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb) >" - ] - } - ], - "metadata": { - "anaconda-cloud": {}, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.1" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} From 9289d8532875949362c6f1805a274372750ff5ed Mon Sep 17 00:00:00 2001 From: Amit Rathi Date: Tue, 11 Sep 2018 23:27:06 +0530 Subject: [PATCH 02/19] Renamed a Notebook --- {notebooks => nb2}/03.07-Merge-and-Join.ipynb | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename {notebooks => nb2}/03.07-Merge-and-Join.ipynb (100%) diff --git a/notebooks/03.07-Merge-and-Join.ipynb b/nb2/03.07-Merge-and-Join.ipynb similarity index 100% rename from notebooks/03.07-Merge-and-Join.ipynb rename to nb2/03.07-Merge-and-Join.ipynb From ad5839fd12140018e2e638d4097965d030587be0 Mon Sep 17 00:00:00 2001 From: Amit Rathi Date: Wed, 12 Sep 2018 00:00:41 +0530 Subject: [PATCH 03/19] 3 renamed and edits --- .../05.02-V2-Introducing-Scikit-Learn.ipynb | 495 +++--------- .../05.04-V2-Feature-Engineering.ipynb | 0 nb2/05.08-V2-Random-Forests.ipynb | 728 +++++++++++++++++ notebooks/05.08-Random-Forests.ipynb | 751 ------------------ 4 files changed, 839 insertions(+), 1135 deletions(-) rename notebooks/05.02-Introducing-Scikit-Learn.ipynb => nb2/05.02-V2-Introducing-Scikit-Learn.ipynb (65%) rename notebooks/05.04-Feature-Engineering.ipynb => nb2/05.04-V2-Feature-Engineering.ipynb (100%) create mode 100644 nb2/05.08-V2-Random-Forests.ipynb delete mode 100644 notebooks/05.08-Random-Forests.ipynb diff --git a/notebooks/05.02-Introducing-Scikit-Learn.ipynb b/nb2/05.02-V2-Introducing-Scikit-Learn.ipynb similarity index 65% rename from notebooks/05.02-Introducing-Scikit-Learn.ipynb rename to nb2/05.02-V2-Introducing-Scikit-Learn.ipynb index e05db813d..526881760 100644 --- a/notebooks/05.02-Introducing-Scikit-Learn.ipynb +++ b/nb2/05.02-V2-Introducing-Scikit-Learn.ipynb @@ -2,10 +2,7 @@ "cells": [ { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "\n", "\n", @@ -16,10 +13,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "\n", "< [What Is Machine Learning?](05.01-What-Is-Machine-Learning.ipynb) | [Contents](Index.ipynb) | [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) >" @@ -34,10 +28,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "There are several Python libraries which provide solid implementations of a range of machine learning algorithms.\n", "One of the best known is [Scikit-Learn](http://scikit-learn.org), a package that provides efficient versions of a large number of common algorithms.\n", @@ -51,20 +42,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Data Representation in Scikit-Learn" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Machine learning is about creating models from data: for that reason, we'll start by discussing how data can be represented in order to be understood by the computer.\n", "The best way to think about data within Scikit-Learn is in terms of tables of data." @@ -72,10 +57,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Data as table\n", "\n", @@ -87,16 +69,25 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", + "\n", "\n", " \n", " \n", @@ -168,17 +159,15 @@ } ], "source": [ - "import seaborn as sns\n", - "iris = sns.load_dataset('iris')\n", + "import seaborn as sns_v2\n", + "import os\n", + "iris = sns_v2.load_dataset('iris')\n", "iris.head()" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Here each row of the data refers to a single observed flower, and the number of rows is the total number of flowers in the dataset.\n", "In general, we will refer to the rows of the matrix as *samples*, and the number of rows as ``n_samples``.\n", @@ -189,10 +178,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "#### Features matrix\n", "\n", @@ -209,10 +195,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "#### Target array\n", "\n", @@ -229,18 +212,14 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 3, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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1ICc4Ty+XT+7pp2qjJejc1eV0Y3d24+r28OQbR4lSyFidMnxDL0NOWnQqn5gO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3/+E4B169axaNGiVucvLS1l4cKFrFq1iiuuuKJFedpTTOZEdoXvf7PV0ehaSUUt2YU1JMXqCQkM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\nK2P16tVs27YNSZJ48cUXOX78OA0NDTz77LOMHz++K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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -250,15 +229,12 @@ "source": [ "%matplotlib inline\n", "import seaborn as sns; sns.set()\n", - "sns.pairplot(iris, hue='species', size=1.5);" + "sns.pairplot(iris, hue='species', size=4);" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "For use in Scikit-Learn, we will extract the features matrix and target array from the ``DataFrame``, which we can do using some of the Pandas ``DataFrame`` operations discussed in the [Chapter 3](03.00-Introduction-to-Pandas.ipynb):" ] @@ -266,11 +242,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -291,11 +263,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -315,20 +283,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "To summarize, the expected layout of features and target values is visualized in the following diagram:" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "![](figures/05.02-samples-features.png)\n", "[figure source in Appendix](06.00-Figure-Code.ipynb#Features-and-Labels-Grid)" @@ -336,30 +298,21 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "With this data properly formatted, we can move on to consider the *estimator* API of Scikit-Learn:" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Scikit-Learn's Estimator API" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The Scikit-Learn API is designed with the following guiding principles in mind, as outlined in the [Scikit-Learn API paper](http://arxiv.org/abs/1309.0238):\n", "\n", @@ -382,10 +335,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Basics of the API\n", "\n", @@ -405,10 +355,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Supervised learning example: Simple linear regression\n", "\n", @@ -419,11 +366,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -448,20 +391,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "With this data in place, we can use the recipe outlined earlier. Let's walk through the process: " ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "#### 1. Choose a class of model\n", "\n", @@ -473,9 +410,7 @@ "cell_type": "code", "execution_count": 6, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -484,20 +419,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Note that other more general linear regression models exist as well; you can read more about them in the [``sklearn.linear_model`` module documentation](http://Scikit-Learn.org/stable/modules/linear_model.html)." ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "#### 2. Choose model hyperparameters\n", "\n", @@ -523,11 +452,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -547,10 +472,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Keep in mind that when the model is instantiated, the only action is the storing of these hyperparameter values.\n", "In particular, we have not yet applied the model to any data: the Scikit-Learn API makes very clear the distinction between *choice of model* and *application of model to data*." @@ -558,10 +480,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "#### 3. Arrange data into a features matrix and target vector\n", "\n", @@ -573,11 +492,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -597,10 +512,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "#### 4. Fit the model to your data\n", "\n", @@ -611,11 +523,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -634,10 +542,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This ``fit()`` command causes a number of model-dependent internal computations to take place, and the results of these computations are stored in model-specific attributes that the user can explore.\n", "In Scikit-Learn, by convention all model parameters that were learned during the ``fit()`` process have trailing underscores; for example in this linear model, we have the following:" @@ -646,11 +551,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -670,11 +571,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -693,10 +590,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "These two parameters represent the slope and intercept of the simple linear fit to the data.\n", "Comparing to the data definition, we see that they are very close to the input slope of 2 and intercept of -1.\n", @@ -709,10 +603,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "#### 5. Predict labels for unknown data\n", "\n", @@ -725,9 +616,7 @@ "cell_type": "code", "execution_count": 12, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -736,10 +625,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "As before, we need to coerce these *x* values into a ``[n_samples, n_features]`` features matrix, after which we can feed it to the model:" ] @@ -747,11 +633,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "Xfit = xfit[:, np.newaxis]\n", @@ -760,10 +642,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Finally, let's visualize the results by plotting first the raw data, and then this model fit:" ] @@ -771,11 +650,7 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -795,20 +670,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Typically the efficacy of the model is evaluated by comparing its results to some known baseline, as we will see in the next example" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Supervised learning example: Iris classification\n", "\n", @@ -826,9 +695,7 @@ "cell_type": "code", "execution_count": 15, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -839,10 +706,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "With the data arranged, we can follow our recipe to predict the labels:" ] @@ -850,11 +714,7 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "from sklearn.naive_bayes import GaussianNB # 1. choose model class\n", @@ -865,10 +725,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Finally, we can use the ``accuracy_score`` utility to see the fraction of predicted labels that match their true value:" ] @@ -876,11 +733,7 @@ { "cell_type": "code", "execution_count": 17, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -900,20 +753,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "With an accuracy topping 97%, we see that even this very naive classification algorithm is effective for this particular dataset!" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Unsupervised learning example: Iris dimensionality\n", "\n", @@ -933,9 +780,7 @@ "cell_type": "code", "execution_count": 18, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -947,10 +792,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Now let's plot the results. A quick way to do this is to insert the results into the original Iris ``DataFrame``, and use Seaborn's ``lmplot`` to show the results:" ] @@ -958,11 +800,7 @@ { "cell_type": "code", "execution_count": 19, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -983,10 +821,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We see that in the two-dimensional representation, the species are fairly well separated, even though the PCA algorithm had no knowledge of the species labels!\n", "This indicates to us that a relatively straightforward classification will probably be effective on the dataset, as we saw before." @@ -994,10 +829,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Unsupervised learning: Iris clustering\n", "\n", @@ -1012,11 +844,7 @@ { "cell_type": "code", "execution_count": 20, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "from sklearn.mixture import GMM # 1. Choose the model class\n", @@ -1028,10 +856,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "As before, we will add the cluster label to the Iris ``DataFrame`` and use Seaborn to plot the results:" ] @@ -1039,11 +864,7 @@ { "cell_type": "code", "execution_count": 21, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -1064,10 +885,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "By splitting the data by cluster number, we see exactly how well the GMM algorithm has recovered the underlying label: the *setosa* species is separated perfectly within cluster 0, while there remains a small amount of mixing between *versicolor* and *virginica*.\n", "This means that even without an expert to tell us the species labels of the individual flowers, the measurements of these flowers are distinct enough that we could *automatically* identify the presence of these different groups of species with a simple clustering algorithm!\n", @@ -1076,20 +894,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Application: Exploring Hand-written Digits" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "To demonstrate these principles on a more interesting problem, let's consider one piece of the optical character recognition problem: the identification of hand-written digits.\n", "In the wild, this problem involves both locating and identifying characters in an image. Here we'll take a shortcut and use Scikit-Learn's set of pre-formatted digits, which is built into the library." @@ -1097,10 +909,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Loading and visualizing the digits data\n", "\n", @@ -1110,11 +919,7 @@ { "cell_type": "code", "execution_count": 22, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -1135,10 +940,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The images data is a three-dimensional array: 1,797 samples each consisting of an 8 × 8 grid of pixels.\n", "Let's visualize the first hundred of these:" @@ -1147,11 +949,7 @@ { "cell_type": "code", "execution_count": 23, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -1179,10 +977,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "In order to work with this data within Scikit-Learn, we need a two-dimensional, ``[n_samples, n_features]`` representation.\n", "We can accomplish this by treating each pixel in the image as a feature: that is, by flattening out the pixel arrays so that we have a length-64 array of pixel values representing each digit.\n", @@ -1193,11 +988,7 @@ { "cell_type": "code", "execution_count": 24, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -1218,11 +1009,7 @@ { "cell_type": "code", "execution_count": 25, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -1242,20 +1029,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We see here that there are 1,797 samples and 64 features." ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Unsupervised learning: Dimensionality reduction\n", "\n", @@ -1267,11 +1048,7 @@ { "cell_type": "code", "execution_count": 26, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -1294,10 +1071,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We see that the projected data is now two-dimensional.\n", "Let's plot this data to see if we can learn anything from its structure:" @@ -1306,11 +1080,7 @@ { "cell_type": "code", "execution_count": 27, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -1333,10 +1103,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This plot gives us some good intuition into how well various numbers are separated in the larger 64-dimensional space. For example, zeros (in black) and ones (in purple) have very little overlap in parameter space.\n", "Intuitively, this makes sense: a zero is empty in the middle of the image, while a one will generally have ink in the middle.\n", @@ -1348,10 +1115,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Classification on digits\n", "\n", @@ -1363,9 +1127,7 @@ "cell_type": "code", "execution_count": 28, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -1376,9 +1138,7 @@ "cell_type": "code", "execution_count": 29, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -1390,10 +1150,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Now that we have predicted our model, we can gauge its accuracy by comparing the true values of the test set to the predictions:" ] @@ -1401,11 +1158,7 @@ { "cell_type": "code", "execution_count": 30, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -1425,10 +1178,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "With even this extremely simple model, we find about 80% accuracy for classification of the digits!\n", "However, this single number doesn't tell us *where* we've gone wrong—one nice way to do this is to use the *confusion matrix*, which we can compute with Scikit-Learn and plot with Seaborn:" @@ -1437,11 +1187,7 @@ { "cell_type": "code", "execution_count": 31, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -1466,10 +1212,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This shows us where the mis-labeled points tend to be: for example, a large number of twos here are mis-classified as either ones or eights.\n", "Another way to gain intuition into the characteristics of the model is to plot the inputs again, with their predicted labels.\n", @@ -1479,11 +1222,7 @@ { "cell_type": "code", "execution_count": 32, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -1512,10 +1251,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Examining this subset of the data, we can gain insight regarding where the algorithm might be not performing optimally.\n", "To go beyond our 80% classification rate, we might move to a more sophisticated algorithm such as support vector machines (see [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)), random forests (see [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb)) or another classification approach." @@ -1523,20 +1259,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Summary" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "In this section we have covered the essential features of the Scikit-Learn data representation, and the estimator API.\n", "Regardless of the type of estimator, the same import/instantiate/fit/predict pattern holds.\n", @@ -1547,10 +1277,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "\n", "< [What Is Machine Learning?](05.01-What-Is-Machine-Learning.ipynb) | [Contents](Index.ipynb) | [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) >" @@ -1574,9 +1301,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.6.4" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/notebooks/05.04-Feature-Engineering.ipynb b/nb2/05.04-V2-Feature-Engineering.ipynb similarity index 100% rename from notebooks/05.04-Feature-Engineering.ipynb rename to nb2/05.04-V2-Feature-Engineering.ipynb diff --git a/nb2/05.08-V2-Random-Forests.ipynb b/nb2/05.08-V2-Random-Forests.ipynb new file mode 100644 index 000000000..cebb236dc --- /dev/null +++ b/nb2/05.08-V2-Random-Forests.ipynb @@ -0,0 +1,728 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb) | [Contents](Index.ipynb) | [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb) >" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# In-Depth: Decision Trees and Random Forests" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Previously we have looked in depth at a simple generative classifier (naive Bayes; see [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb)) and a powerful discriminative classifier (support vector machines; see [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)).\n", + "Here we'll take a look at motivating another powerful algorithm—a non-parametric algorithm called *random forests*.\n", + "Random forests are an example of an *ensemble* method, meaning that it relies on aggregating the results of an ensemble of simpler estimators.\n", + "The somewhat surprising result with such ensemble methods is that the sum can be greater than the parts: that is, a majority vote among a number of estimators can end up being better than any of the individual estimators doing the voting!\n", + "We will see examples of this in the following sections.\n", + "We begin with the standard imports:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plotting\n", + "import os\n", + "import seaborn as snsv2; sns.set()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Motivating Random Forests: Decision Trees" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Random forests are an example of an *ensemble learner* built on decision trees.\n", + "For this reason we'll start by discussing decision trees themselves.\n", + "\n", + "Decision trees are extremely intuitive ways to classify or label objects: you simply ask a series of questions designed to zero-in on the classification.\n", + "For example, if you wanted to build a decision tree to classify an animal you come across while on a hike, you might construct the one shown here:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](figures/05.08-decision-tree.png)\n", + "[figure source in Appendix](06.00-Figure-Code.ipynb#Decision-Tree-Example)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The binary splitting makes this extremely efficient: in a well-constructed tree, each question will cut the number of options by approximately half, very quickly narrowing the options even among a large number of classes.\n", + "The trick, of course, comes in deciding which questions to ask at each step.\n", + "In machine learning implementations of decision trees, the questions generally take the form of axis-aligned splits in the data: that is, each node in the tree splits the data into two groups using a cutoff value within one of the features.\n", + "Let's now look at an example of this." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating a decision tree\n", + "\n", + "Consider the following two-dimensional data, which has one of four class labels:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.datasets import make_blobs\n", + "\n", + "X, y = make_blobs(n_samples=200, centers=4,\n", + " random_state=0, cluster_std=1.0)\n", + "plt.scatter(X[:, 0], X[:, 1], c=y, s=80, cmap='rainbow');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A simple decision tree built on this data will iteratively split the data along one or the other axis according to some quantitative criterion, and at each level assign the label of the new region according to a majority vote of points within it.\n", + "This figure presents a visualization of the first four levels of a decision tree classifier for this data:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](figures/05.08-decision-tree-levels.png)\n", + "[figure source in Appendix](06.00-Figure-Code.ipynb#Decision-Tree-Levels)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that after the first split, every point in the upper branch remains unchanged, so there is no need to further subdivide this branch.\n", + "Except for nodes that contain all of one color, at each level *every* region is again split along one of the two features." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This process of fitting a decision tree to our data can be done in Scikit-Learn with the ``DecisionTreeClassifier`` estimator:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.tree import DecisionTreeClassifier\n", + "tree = DecisionTreeClassifier().fit(X, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's write a quick utility function to help us visualize the output of the classifier:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "def visualize_classifier(model, X, y, ax=None, cmap='rainbow'):\n", + " ax = ax or plt.gca()\n", + " \n", + " # Plot the training points\n", + " ax.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=cmap,\n", + " clim=(y.min(), y.max()), zorder=3)\n", + " ax.axis('tight')\n", + " ax.axis('off')\n", + " xlim = ax.get_xlim()\n", + " ylim = ax.get_ylim()\n", + " \n", + " # fit the estimator\n", + " model.fit(X, y)\n", + " xx, yy = np.meshgrid(np.linspace(*xlim, num=200),\n", + " np.linspace(*ylim, num=200))\n", + " Z = model.predict(np.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)\n", + "\n", + " # Create a color plot with the results\n", + " n_classes = len(np.unique(y))\n", + " contours = ax.contourf(xx, yy, Z, alpha=0.3,\n", + " levels=np.arange(n_classes + 1) - 0.5,\n", + " cmap=cmap, clim=(y.min(), y.max()),\n", + " zorder=1)\n", + "\n", + " ax.set(xlim=xlim, ylim=ylim)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can examine what the decision tree classification looks like:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:967: UserWarning: The following kwargs were not used by contour: 'clim'\n", + " s)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "visualize_classifier(DecisionTreeClassifier(), X, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you're running this notebook live, you can use the helpers script included in [The Online Appendix](06.00-Figure-Code.ipynb#Helper-Code) to bring up an interactive visualization of the decision tree building process:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'helpers_05_08'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# helpers_05_08 is found in the online appendix\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mhelpers_05_08\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mhelpers_05_08\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_tree_interactive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'helpers_05_08'" + ] + } + ], + "source": [ + "# helpers_05_08 is found in the online appendix\n", + "import helpers_05_08\n", + "helpers_05_08.plot_tree_interactive(X, y);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that as the depth increases, we tend to get very strangely shaped classification regions; for example, at a depth of five, there is a tall and skinny purple region between the yellow and blue regions.\n", + "It's clear that this is less a result of the true, intrinsic data distribution, and more a result of the particular sampling or noise properties of the data.\n", + "That is, this decision tree, even at only five levels deep, is clearly over-fitting our data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Decision trees and over-fitting\n", + "\n", + "Such over-fitting turns out to be a general property of decision trees: it is very easy to go too deep in the tree, and thus to fit details of the particular data rather than the overall properties of the distributions they are drawn from.\n", + "Another way to see this over-fitting is to look at models trained on different subsets of the data—for example, in this figure we train two different trees, each on half of the original data:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](figures/05.08-decision-tree-overfitting.png)\n", + "[figure source in Appendix](06.00-Figure-Code.ipynb#Decision-Tree-Overfitting)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is clear that in some places, the two trees produce consistent results (e.g., in the four corners), while in other places, the two trees give very different classifications (e.g., in the regions between any two clusters).\n", + "The key observation is that the inconsistencies tend to happen where the classification is less certain, and thus by using information from *both* of these trees, we might come up with a better result!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you are running this notebook live, the following function will allow you to interactively display the fits of trees trained on a random subset of the data:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "invalid syntax (, line 2)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m import ../notebooks/helpers_05_08\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" + ] + } + ], + "source": [ + "# helpers_05_08 is found in the online appendix\n", + "import ../notebooks/helpers_05_08\n", + "helpers_05_08.randomized_tree_interactive(X, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Just as using information from two trees improves our results, we might expect that using information from many trees would improve our results even further." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Ensembles of Estimators: Random Forests\n", + "\n", + "This notion—that multiple overfitting estimators can be combined to reduce the effect of this overfitting—is what underlies an ensemble method called *bagging*.\n", + "Bagging makes use of an ensemble (a grab bag, perhaps) of parallel estimators, each of which over-fits the data, and averages the results to find a better classification.\n", + "An ensemble of randomized decision trees is known as a *random forest*.\n", + "\n", + "This type of bagging classification can be done manually using Scikit-Learn's ``BaggingClassifier`` meta-estimator, as shown here:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'visualize_classifier' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mbag\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mvisualize_classifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbag\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'visualize_classifier' is not defined" + ] + } + ], + "source": [ + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.ensemble import BaggingClassifier\n", + "\n", + "tree = DecisionTreeClassifier()\n", + "bag = BaggingClassifier(tree, n_estimators=100, max_samples=0.8,\n", + " random_state=1)\n", + "\n", + "bag.fit(X, y)\n", + "visualize_classifier(bag, X, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this example, we have randomized the data by fitting each estimator with a random subset of 80% of the training points.\n", + "In practice, decision trees are more effectively randomized by injecting some stochasticity in how the splits are chosen: this way all the data contributes to the fit each time, but the results of the fit still have the desired randomness.\n", + "For example, when determining which feature to split on, the randomized tree might select from among the top several features.\n", + "You can read more technical details about these randomization strategies in the [Scikit-Learn documentation](http://scikit-learn.org/stable/modules/ensemble.html#forest) and references within.\n", + "\n", + "In Scikit-Learn, such an optimized ensemble of randomized decision trees is implemented in the ``RandomForestClassifier`` estimator, which takes care of all the randomization automatically.\n", + "All you need to do is select a number of estimators, and it will very quickly (in parallel, if desired) fit the ensemble of trees:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'visualize_classifier' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRandomForestClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_estimators\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mvisualize_classifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'visualize_classifier' is not defined" + ] + } + ], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "model = RandomForestClassifier(n_estimators=100, random_state=0)\n", + "visualize_classifier(model, X, y);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that by averaging over 100 randomly perturbed models, we end up with an overall model that is much closer to our intuition about how the parameter space should be split." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Random Forest Regression\n", + "\n", + "In the previous section we considered random forests within the context of classification.\n", + "Random forests can also be made to work in the case of regression (that is, continuous rather than categorical variables). The estimator to use for this is the ``RandomForestRegressor``, and the syntax is very similar to what we saw earlier.\n", + "\n", + "Consider the following data, drawn from the combination of a fast and slow oscillation:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Od48kVn2+ZclvCJsfeSKAeJ4yqGsI1XK7frklduULRZRMKHvdCy0gb79pRWC7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\nAZmIiEgCDMhEREQS+P8p5hEpezc9PwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rng = np.random.RandomState(42)\n", + "x = 10 * rng.rand(200)\n", + "\n", + "def model(x, sigma=0.3):\n", + " fast_oscillation = np.sin(5 * x)\n", + " slow_oscillation = np.sin(0.5 * x)\n", + " noise = sigma * rng.randn(len(x))\n", + "\n", + " return slow_oscillation + fast_oscillation + noise\n", + "\n", + "y = model(x)\n", + "plt.errorbar(x, y, 0.3, fmt='o');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using the random forest regressor, we can find the best fit curve as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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lef4veKfTSXdHC3YpjtFqRpFlLPkMl21sxmIvaJEV1pCn5kk/8ryPyMQ4Nkcd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W0LsZgM9Id6AFBdbnSDg6V60GP3Xd6iza0OhULE4zEWGwxgMECFK3RNlb/QBx6OlH8xhlxymECd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\nQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.ensemble import RandomForestRegressor\n", + "forest = RandomForestRegressor(200)\n", + "forest.fit(x[:, None], y)\n", + "\n", + "xfit = np.linspace(0, 10, 1000)\n", + "yfit = forest.predict(xfit[:, None])\n", + "ytrue = model(xfit, sigma=0)\n", + "\n", + "plt.errorbar(x, y, 0.3, fmt='o', alpha=0.5)\n", + "plt.plot(xfit, yfit, '-r');\n", + "plt.plot(xfit, ytrue, '-k', alpha=0.5);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here the true model is shown in the smooth gray curve, while the random forest model is shown by the jagged red curve.\n", + "As you can see, the non-parametric random forest model is flexible enough to fit the multi-period data, without us needing to specifying a multi-period model!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example: Random Forest for Classifying Digits\n", + "\n", + "Earlier we took a quick look at the hand-written digits data (see [Introducing Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb)).\n", + "Let's use that again here to see how the random forest classifier can be used in this context." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['data', 'target', 'target_names', 'images', 'DESCR'])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.datasets import load_digits\n", + "digits = load_digits()\n", + "digits.keys()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To remind us what we're looking at, we'll visualize the first few data points:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# set up the figure\n", + "fig = plt.figure(figsize=(6, 6)) # figure size in inches\n", + "fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)\n", + "\n", + "# plot the digits: each image is 8x8 pixels\n", + "for i in range(64):\n", + " ax = fig.add_subplot(8, 8, i + 1, xticks=[], yticks=[])\n", + " ax.imshow(digits.images[i], cmap=plt.cm.binary, interpolation='nearest')\n", + " \n", + " # label the image with the target value\n", + " ax.text(0, 7, str(digits.target[i]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can quickly classify the digits using a random forest as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'RandomForestClassifier' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m Xtrain, Xtest, ytrain, ytest = train_test_split(digits.data, digits.target,\n\u001b[1;32m 4\u001b[0m random_state=0)\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRandomForestClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_estimators\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mytrain\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mypred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXtest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'RandomForestClassifier' is not defined" + ] + } + ], + "source": [ + "from sklearn.cross_validation import train_test_split\n", + "\n", + "Xtrain, Xtest, ytrain, ytest = train_test_split(digits.data, digits.target,\n", + " random_state=0)\n", + "model = RandomForestClassifier(n_estimators=1000)\n", + "model.fit(Xtrain, ytrain)\n", + "ypred = model.predict(Xtest)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can take a look at the classification report for this classifier:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'ypred' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclassification_report\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mypred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mytest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'ypred' is not defined" + ] + } + ], + "source": [ + "from sklearn import metrics\n", + "print(metrics.classification_report(ypred, ytest))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And for good measure, plot the confusion matrix:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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IC5iISJBHuidcRbJnXzoWLVuJ4uJiNPD3x2ydFi4uLjaby2zbf68nRoahY9d2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\nShVHRFTh2NT3gImIKhIWMBG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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.metrics import confusion_matrix\n", + "mat = confusion_matrix(ytest, ypred)\n", + "sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False)\n", + "plt.xlabel('true label')\n", + "plt.ylabel('predicted label');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We find that a simple, untuned random forest results in a very accurate classification of the digits data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary of Random Forests\n", + "\n", + "This section contained a brief introduction to the concept of *ensemble estimators*, and in particular the random forest – an ensemble of randomized decision trees.\n", + "Random forests are a powerful method with several advantages:\n", + "\n", + "- Both training and prediction are very fast, because of the simplicity of the underlying decision trees. In addition, both tasks can be straightforwardly parallelized, because the individual trees are entirely independent entities.\n", + "- The multiple trees allow for a probabilistic classification: a majority vote among estimators gives an estimate of the probability (accessed in Scikit-Learn with the ``predict_proba()`` method).\n", + "- The nonparametric model is extremely flexible, and can thus perform well on tasks that are under-fit by other estimators.\n", + "\n", + "A primary disadvantage of random forests is that the results are not easily interpretable: that is, if you would like to draw conclusions about the *meaning* of the classification model, random forests may not be the best choice." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb) | [Contents](Index.ipynb) | [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb) >" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/notebooks/05.08-Random-Forests.ipynb b/notebooks/05.08-Random-Forests.ipynb deleted file mode 100644 index 74da6a281..000000000 --- a/notebooks/05.08-Random-Forests.ipynb +++ /dev/null @@ -1,751 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", - "\n", - "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "< [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb) | [Contents](Index.ipynb) | [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb) >" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# In-Depth: Decision Trees and Random Forests" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Previously we have looked in depth at a simple generative classifier (naive Bayes; see [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb)) and a powerful discriminative classifier (support vector machines; see [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)).\n", - "Here we'll take a look at motivating another powerful algorithm—a non-parametric algorithm called *random forests*.\n", - "Random forests are an example of an *ensemble* method, meaning that it relies on aggregating the results of an ensemble of simpler estimators.\n", - "The somewhat surprising result with such ensemble methods is that the sum can be greater than the parts: that is, a majority vote among a number of estimators can end up being better than any of the individual estimators doing the voting!\n", - "We will see examples of this in the following sections.\n", - "We begin with the standard imports:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns; sns.set()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Motivating Random Forests: Decision Trees" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Random forests are an example of an *ensemble learner* built on decision trees.\n", - "For this reason we'll start by discussing decision trees themselves.\n", - "\n", - "Decision trees are extremely intuitive ways to classify or label objects: you simply ask a series of questions designed to zero-in on the classification.\n", - "For example, if you wanted to build a decision tree to classify an animal you come across while on a hike, you might construct the one shown here:" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": false - }, - "source": [ - "![](figures/05.08-decision-tree.png)\n", - "[figure source in Appendix](06.00-Figure-Code.ipynb#Decision-Tree-Example)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The binary splitting makes this extremely efficient: in a well-constructed tree, each question will cut the number of options by approximately half, very quickly narrowing the options even among a large number of classes.\n", - "The trick, of course, comes in deciding which questions to ask at each step.\n", - "In machine learning implementations of decision trees, the questions generally take the form of axis-aligned splits in the data: that is, each node in the tree splits the data into two groups using a cutoff value within one of the features.\n", - "Let's now look at an example of this." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Creating a decision tree\n", - "\n", - "Consider the following two-dimensional data, which has one of four class labels:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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jQJ0sHpMKry8RiF9j3b6ayX4jI4z27sYpLJQoVzfSe/Si+xczSrprerOzs+di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\nx7fzqr/V11bxzvBMfMrHmrU3CuX0fOsIc568/7Ib7hU2icuOcdckLm3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s7KmtVrN6RSaL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VODe/cl6vOFZHTqVO/arvsPMKdny9T63bgnnb+fOkb+8GEoBgNeWzdwt0bP45NubYnVO4HsI25l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\nZiA+eeokxooKIsMj0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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from sklearn.datasets import make_blobs\n", - "\n", - "X, y = make_blobs(n_samples=300, centers=4,\n", - " random_state=0, cluster_std=1.0)\n", - "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='rainbow');" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A simple decision tree built on this data will iteratively split the data along one or the other axis according to some quantitative criterion, and at each level assign the label of the new region according to a majority vote of points within it.\n", - "This figure presents a visualization of the first four levels of a decision tree classifier for this data:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![](figures/05.08-decision-tree-levels.png)\n", - "[figure source in Appendix](06.00-Figure-Code.ipynb#Decision-Tree-Levels)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notice that after the first split, every point in the upper branch remains unchanged, so there is no need to further subdivide this branch.\n", - "Except for nodes that contain all of one color, at each level *every* region is again split along one of the two features." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This process of fitting a decision tree to our data can be done in Scikit-Learn with the ``DecisionTreeClassifier`` estimator:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "from sklearn.tree import DecisionTreeClassifier\n", - "tree = DecisionTreeClassifier().fit(X, y)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's write a quick utility function to help us visualize the output of the classifier:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def visualize_classifier(model, X, y, ax=None, cmap='rainbow'):\n", - " ax = ax or plt.gca()\n", - " \n", - " # Plot the training points\n", - " ax.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=cmap,\n", - " clim=(y.min(), y.max()), zorder=3)\n", - " ax.axis('tight')\n", - " ax.axis('off')\n", - " xlim = ax.get_xlim()\n", - " ylim = ax.get_ylim()\n", - " \n", - " # fit the estimator\n", - " model.fit(X, y)\n", - " xx, yy = np.meshgrid(np.linspace(*xlim, num=200),\n", - " np.linspace(*ylim, num=200))\n", - " Z = model.predict(np.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)\n", - "\n", - " # Create a color plot with the results\n", - " n_classes = len(np.unique(y))\n", - " contours = ax.contourf(xx, yy, Z, alpha=0.3,\n", - " levels=np.arange(n_classes + 1) - 0.5,\n", - " cmap=cmap, clim=(y.min(), y.max()),\n", - " zorder=1)\n", - "\n", - " ax.set(xlim=xlim, ylim=ylim)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can examine what the decision tree classification looks like:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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LE2ruPffcCJgBWhnkrVxPcJcgHBooUWksMpmMmf/+C5u+W4FZajoqdzfCn1lg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IZbN/FatgK9d3ttXL2vL5Zlyzjz7WlmP7MY6WkFly5j8fbG+VHiBXD298erpYX//dO/7LoUZlwM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\nTzdzeWwIb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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "visualize_classifier(DecisionTreeClassifier(), X, y)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you're running this notebook live, you can use the helpers script included in [The Online Appendix](06.00-Figure-Code.ipynb#Helper-Code) to bring up an interactive visualization of the decision tree building process:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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QuXrzxPNTKC4s4cDxg0yf2Lx2cu9RDVGxU1tt4/j+bayYW9K0PMnDTUGgVwEK\nhfVyj5CAOnILivDwdEVWuGI0yiiVsG5bNRq1hMUikVu1jUFDh+Dq3nZtaHuSJImERd/loz1rcVQW\nUmd2I2rGwl5dXiMIwqNDJN8e5KS8YbUxg06nwFR9noyLYwkfNbxHbkPv2fABiyYfxknfOHq8nZ3N\nsVQl5QVZrEhqvhAoLN7Dwd06ps56os22AoMDCAxe0vS1r78Pd7IWsW73TkL9yrmd546DbzJerewa\nBCBZKlssI/J2l8kvMuPn3Tz6P53hzdTFjQX3Y6cn8+G603g43mH2dH3TWl5ZvsPKbe+S/PR/d+Gn\n0jucXZ2Ytbj36j0X5BVwPu1j9Op8GsyuBITNITwyqtf6FwSh60Ty7UEWuWUpSqUpmxDp9+z4MJBx\nSd+w+dZ0OvlSU+IFGBAMx6+cxllZYHUh4OMlYTl3EWg7+bYmZloiRuN0igpLmRjrgUrV9q+Qm+8o\nbmcfZUBwc79GyZdtJ0cy0P0cwf51nLnijU/Y00234vVOOqYt+im7Vn8PZ6eGptdJkoSr+hYlReW4\neTj3+Vv3PU2WZU7v/gsvLfryln8Juw//izyvn+B//4NnQRD6JJF8e5CsiyavcAf+Po3J5/otA8EB\nSgaHKhkcms+q7R8xa6mNR3Jyy12XJMmCpZVVZbLctQSmVqsICGz/omHMhBj2bLhK1p3jDAmu5cwV\nT1wGPMnUmEmUllSQV1DE1MUDWyRSJ2cdbl6hQJbV8cryUsoufZfLJe7o/OZ0ag1tf3PhbDozY/O4\nf8OHxMkNrN63F/+FK+wXmCAIHSKSbw+KT36SI3t1GNPTKc65wujhZuInNc9+1avzbd5ntTwMg+F0\n0+3egiITt6/fxGhSUTnZgssXOwFduw1OvjGdajv3XiEWs5mgUP/2T/5C4sLnqChfSHZOPhMXDESt\nbvyV8/B0xcPTtc3XeYVO5VzGDcaMMAOQX2jCw83C5HEAZew9spbC/Ige3/e3z2pjgeAjMqFeEB57\nYp1vL9n/+Zs8Pct6pvDHu0JJWPxjm/ZjMBjZt/F99IprlBUVEOBrJGWmHlmGv71fh7t3CGoHJ3Re\nE4idNqNDbVZX1rB/w5+JHHQTldLC2esDmDTn63h6e9g09gddPHOaotuHKC64y7DgAhKnNZcIlGWZ\njw/OYcYTXS8r+SiTZZkdH/6YFxc2zzTfc0SD16gfExDU8YsjQRB6zsPW+Yrk20uupJ/HnP8vEiY2\nIEkSaSfJoo65AAAgAElEQVTVGNxeICK6c6PPjsrMyMLP+FvCBlnfbl69bzIzF7zQqbZ2rH2b52ad\nblpCI8syq7aPYvay/7RVuA914tAhJgavxNuz+b2UVVhIzXqGyQkJvRJDX5Sfm0f6oS8nXLnhP3Q2\nI6Kal2NVVdZQX9eAt2/PXiQJgtA6UWSjDwiPjOKe2w9Ys38/smwhLDKBYYNCHvqa6soa0ra9i4v6\nFmZZg8VxHPEpSzo0S1qSFFalCLvDWX3Pam9fSZJw1tyzSdsdMX7yZNav3MNXnsxFoZCoqjbzh3+q\nGDG+jIqyKlzdnbvdh8ViYd+mj9CYLqDATJVpCAkLX8HBoe/WTvYL8MevlTkDZrOZnZ+9Q6DLJZwc\nDZzZG8zoaV8hIKj9jSEEQegdIvn2oqDQIIJCn+vw+Wnb3uH55IwvEl8N+UW7ObLXmbjEOe2+dujw\nwWxdFcywwTlNyTr1uIawyM6PFA3mloXpDebe25pNoVAwc/H3Wb13HZUlN3CUsvmfb1lQKHawZf9B\nvIa/StjIUd3q48C2dcyNOdi0sYHReI6PNr1H8tL/sMVb6FUHtq1n6fQz6HUKQGIy9/hgy0oClv/Q\n3qEJgvAFsbFCH2UymfBwuGE14vTzljBVXezQ6yVJYlLyt1i1YzSf7/Zkzc6ByF4vEzLw4aPt1ngP\nnMmxc83Lps5lKHAJ7N5MY1mW2b9lLQc//xFpn/+AXes+wGw2t3m+k4uepEXP4ebmwktLlWg0EiqV\nxMKkOrIzNncrFgCV8YrVjkJqtYSL6nq327UHlenmF4m3mac+m4YGg50iEgThQWLk20cpFArM5pZL\ngSydWB7k6e3B7KXf7HYsEdHjuZHpxpp9B0E2Ezg0juhR3dtK8MD2DSSO3o2XR+PFRU1tGus3QdKi\n5x/6OgdVactjypbHOssst/xTMFsezbXERtmxxbF6o2PTTHNBEOxP/DX2UQqFgip5NDW1x5pGMZcy\nFXgEtb6BQU8bPGwog4cNtVl7ivoLTYkXQK9T4GC53O7r6kxegHUt6Tqzd7fj0XnFcu32LYYOaPy6\npEzGqHk0q0WFjkgk9XgW0yc0Fim5lydjcpjQqZ2nBEHoWSL59mGJi15k41YnNOYszLIWt8A4xkyY\nYO+wWijIyyf96GYclBU0yAFMmbO43YlKstzapLH2k4PPoOn87f1TvPCUI0oFfLalhnrH7ld0ip02\ng5NpEmevnQRMSLpRzJjXd/YL7oyh4eHcUv0Xa/bsRaIBvddops+Nt3dYgiDcRyTfPkypVHZ4L1h7\nqSyv4tKB3/Ps3EoAjMZM3v/kLvNf+MFDX6dwHkN+UTZ+3o1JuKLKgkHV/qSpgtsnefUZHWnH6zCb\nZZ6ap2fLwctYLJZuj+xipiYA/WPp0sChgxk4dLC9wxAEoQ0i+QrdciptB0/NrgAak6haLREXcYOs\nK1mEhYe1+bopSU9waLcF+dw5JCwY1SNJmPdUu/05KCrQaCRmTm2uFObhVE5tTT1Ozh3fO/d+sixz\neM8uTLXXMZj1jI2bi7dv929lC4IgtEUkX6FbZEsdKpX1LWRfLwvXsx8+CUqSJKbOWgAs6FR/9bIf\nJlOmVZ8FFb6MdGo5yaijdn72L56YcAJPdwWyLPPZzouMjP8hXj6PaelKQRB6nJiBIXRLyLAJnLts\nnXz3HHdjTEx0j/Q3Zc5TvLdxANduyVRUmvl0uzOB4Yu7vD1jWWklIa7n8HRv/FOQJIklsys5e3ib\nLcMWBEGwIka+QreEhQ/j6L4FXNu5Hy+XSvLKfAga+RQaTcvtFG3B0VHL/Bd/zJWLGVy6VsLEBbFo\ntZout1daXIafVx3QHK8kSagVNTaIVhAEoXUi+QrdNmlGMmbzLGqq6xjhou/yKLQzwiNG2KSdgUOC\n2f+pHyPCSpqO5RXKOLgNt0n7giAIrRHJV7AJpVKJi2vvlZy0FYVCQcjoFazeuppRg/LJK9ZR1DCe\nxIWP717BgiD0PLGrkSDQOOM5+04+Hp6uXZ41LQiCcD+xq5EgtEOSJEIGiH1wBUHoHSL5PiLMZjMn\nDh2ivqaK6EnxNtlGT7ANg8GIyWhCp+/6cidBEB4vIvk+AspKyjm06XcsTirAWS+xI2032oAVRI6P\ntXdojzVZlvnLj9dyZvNtjNVmgqPd+M//W0JAsG0LdJw7dpW1/3eQkrvVeA924dk3EhkeMaDDr6+p\nrqW8vJqAQO9emQwnCEL7xDPfR8Dude+yIvG41QfnJzt8iV/yC/Fhep/C/GLu3blD+OhRODo+vLa0\nLax5axdbv5+BSm5epuQ3W8HvPnvdZn2Ul1XxrWl/R77VPJlNM6qOvx/4VrtLrGRZ5s8/+pTTn9+m\nodSC7xgdr/32CUaNFWUnHwU11bW8/fON3LtYhrO3Awtem8zYSWIW/qPkYc98ey35Bod2v/j942p+\nkpm//MLd6tjqddX84LdmkXxpTDIjhhj5xks6xkdp+GRjLSs/M1FS3rM3dtRFLgyuj7Q6li/dJdfn\nWof/XWoqawHQu7Q+yUuqVhFZNcWqPbNsIt01Dam9eWG1CkZUxKKVmm+HX1ddpNKzkNqquof22xaL\nSUZbo0dr1lGvrMbgVItCKWr19ASHUheGN0Q3/dvnS9nkeGahUIm/+UdFcW5Jm98Tt50fATdvW5Bl\n2eoDOOOaiS/rKT/unHUm3vmdK6FBjSPQb77iAlTyh3dkJEXP/YyMkrHlMYzIRjV0MPka6ho3uNc5\nurZ+gkVCRka6799aRkY2q8D48D4c6/VWiRfAyxhAWUNJ+/22QpZl3Mo9GWwZ2fR1liGdatfKbl8E\nWswWMAIqUKhEMjebzPg3DLD6ufrJwRRV5WJ0MtgxMsFWxG3nR0BJUSnHtv2Op2YVoddJ7D6sRfJ6\nhqjYSfYOrU9I3fRvliUctzpWWGzidMHXiJ4wrt3X37uTw9XzaSjVOmKnJXV44tRnuw7x3pKdeMuN\ns6RNkpGRS4ex4tUXOxz7wmUzANjwyb5Wv19TU82vXvsZyhyHpmPSEBM//cfPUakeXkVs5d//xY11\nd62OGb1q+cnKn7PipXkP7bc12zdt5vD/O4ZSar5mN0oG5v1vCpOmTO1wOy3a3bCZtDUHURRrMDsb\nGJ4Uxgtfe/Wxvqtz40YWb/3H2zia9FbHBy0K5sWvv2qnqITOmhDf9mMCMfJ9BHh6ezDrmV+y+2Aq\nDfXVRMVMw9PHw95h9Rmy0o2GBgtabfOIKeu2lqDhoe2+9vThVJwbPmX5dBMGg8xnGw8zOuE7+Pr7\ntvmaxpnnh6mvyuGOaxZFtbnET5jJ4DFDmDPvCZu8py/p9U688OOX2fHxVirzK3ELdGP+c4vaTbwA\nSQvm8Pfjf0GR0/j82yQZCY8fjl6nb+eVraurrkWB0uqY0qKiory8022dOHKEC8fSMWHk5pFbOFQ4\ngwTKahVXN1/n9PgTjI/te3tX95ZBg4biFu5Mw0VL0zGjvp6Y+Mf3Z9LfiOT7iFCrVcTNTLR3GH3S\nhIQUPvz0LM8+kY9WqyC3QOZ60VjmTH/4rGNZlqnO3U3KHDMgodVKrJhXzkd7NpG4qPXRRUlhCce2\n/5GFMwpIHgXSNxX8fWUl3/jf/+6Bd9YobNhwwv638xNtAgKD+Nqb32T3xh3UV9UzKGIQicnJXY4j\nLjGeUxtOoS5tfk4sBxiYNnNGp9rZ/Nl6jv77OGqDlhI5H3e8rZ6gaEwOXLuQ+VgnX0mSeOn7X2Ht\nO2sovFGE3l1HwhNJjBw12t6hCTYikq/wyNPpHJix9CdsOLAdjGXoPYcxe8nkdl/X0GDAVVfW4rhW\nans7xNNpn/HSokIkqXGU/e3XXLh7r2UbfUVAYBAvfO0rNmnL18ePuV+fR+pn+6jMq8QtyI3ZzyxB\n59jxkbQsy5zedQq1oXE0rsOZaipxoXlCoUk24h3kY5OYH2UBAUH858++Z+8whB4iku9joLamjvy8\nEoJD/VCr++c/uU7nwPTkRZ16jVarobTGB8hrOmaxyNTLbd9y1quKWjyLHD5U2cbZ/U9c/DQmT5uK\nyWREpVJ3+rmsxWKhvqIeLY1LpxwlPRVyKRoccMARk2zEJcaR6UniLo/Qv/XPT2KhyYHtn6M3pTEo\nsIJjG71wDl7ImAlx9g6rT5AkiYDhi/hs5wc8EV9JSTlsPRTEzMVPtfmaOpMHYD2J6fotM9GPUb0T\nSZJQq7u2jaNSqcRniDcVJXVNx7zxx326ngDfILyDfEiYlYRKKT6ahP5N/Ib3Y1cvXWaEz24ihsmA\nhhFhlWza+yk11WPRO4nNAwDCI6MYEBbO5kOHcHFzZ/6LYx86mhs1cREfbrrFklllaDQSqz6rZute\nmaXLejHoR9yy11ewqv49Si6Xo3BQEDIhiP/43jc6NInscfbgcsOuunv3Nvfu3GXs+PE4OIiSqPYi\nkm8/lnPjLNMSrFeSJU6qZfvx40yZmWCnqPoeR0ct05Jmduhc/0B/3J78FZsO7iW7PJ83f70TySL+\njDojKDiEH/zpf8gvyMXBwRF3t7Zn7h87fJj0w+dQqBRMSoxjVGRkm+f2VyWlxXz4l/fIzyxAq9cS\nOT2SRc8s7XQ7FouFt//wV26n3UVRo2Jr4BZSXnmCyfFdXyYmdJ341OjHlBo36uosODo2L8G5dU/C\nLzDYjlE9+hwdtcTPTuFCaQ7Sj3eDpf3XCNYkScLfL/Ch5+zYtIWDbx1qmpy1+shqFn2vjvETH69Z\n0O//8Z+UH61FLemwACfvnsXdx4PpiZ17Lr5nxw6yd+ahRdc4uzwXdn6wnZjJE7r8GEHoOlFKph+b\nMD2JNdt9MJsbR7+1tRaOXB7O0PChdo5MENp3ZvfppsQLoKrQcnh7mh0j6n3V1VUUXLKe5Kc2acg4\ncbnTbd3LzEaF9a392tsN3Lp9o9txCp0nRr79mIODlmkLf8QnqZtRSaXI6mBSlqfYOyxB6JC6qnoU\nWI/IGqoamv4/6+pVUjfvpb6qnuARwcx/ajFKZf+aea5Sq1FqFVD1wHFN5z+6nb2cscgWFFLzmEvt\npcDf/+F3IISeIZJvP+fs6sTMBcvtHYYgdJp/mC/52aVNoz6LbCEgPACAWzdvsPJ/3kNZ1DgyLjhc\nQnFeMa/+99fsFm9PcNA6MGjCIG5vuddU1tPk2sDkWVM63Vbyk/PJOHEZQ4YZpaTEoK5jzJwonJ1c\nbB220AEi+QqC0GlGo4HDBw+gVKqYNHVqjywNWvG1F3i37m0KLhYhqSRCxgWz7MVnAUjdsrcp8QIo\nJRU3jt6k+tUqnJycbR6LPb38rddY5/0J2Zey0eg1TEmZ1qWJZ056J97440/YtWUblSWVjIgeRfT4\nmB6IWOgIkXwFQeiUWzdv8P5v/oXxugTI7Bu2h6/8+HWCgmw7kc/N3YPv/OqHVFSUoVSqrJKqoa7l\njlKmWjN19XV9NvlmXr3Cjo+3UlVQhXuQGwueX0xQcEi7r1MqlTz17DM2icHBwZH5SxbbpC2he8SE\nK0EQOmXTB+uRb6hRSSpUkhpLlopNq9b1WH+uru4tEmr4uHCMKuut9bzC3fH26ptlKauqKvngl+9R\ncqgSQ5ZMwf4y/vnLtzCZWl5ECI+HXhv5Rg0XV1tC/1NeWIpG69D+ib2orq6WD996n7wreWj0GsYl\njicxZY7N2i+7VwYP7G5Umt12PeyeMG3mDApzC0jfl05DlQHvMC+Wf/25Xo2hM/Zu34mUo7HaQMKY\nJXP44EHiZ3RsjbnQv/Ra8pUQ68iE/sVoMtutb5PJyL6duykrLGV0TBQjRkU0fe+ff/gHRfvLkSQJ\nE0b2Zabi5OLExCmdn6TTGhcfF8pv1Vgdc/Xt/Uk7S55bzqJnlmI0GXHoYxdAD5ItMhIPVqeSsJjF\nIvHHVa8l349P7u+trgShV1y8m8+vFz2D0WDq1X7rG+r5wxu/oua8CZWk4uxn6Yx7eixPPbecurpa\ncs/nopGadxpSN2g5l3bWZsk3adkc1tz+EGW+AzIycqCBWcvsM6NeqVQ+EsuLZqTM4tSWU6jym8s5\nqobITJkeb7+gBLsSE64EoY/JvJrBqYMn0Og0zJqfgquLG7Isc+nybszGCxzck0vdeR2qL5aeaBsc\nObv1LHMWzUWtUkMr9X8lRfdrAn8pIjKSN94OYe/2XSgUCmYmz8bZuedHvscPH+HcoTNISIxLiGFc\n7KOzm4Wrixsr3niO3Z/upDy/HI9gD+Y9t1BUlnqMieQrCH3Iri3bSP1nKuoaR2RZ5sK+C7z2y6+T\nm7uJhUm7CPSTyD4vUSxZb6puLpLJzr7DiPAIgscGkbenuKmYgsmxgej48TaN09XVnSef7r3dJPZu\n28m+v+9HVd+4vOjOkXXU/lcNU2d0rUa5yWRk/+49VJSUMylhCoG9UHJ15OjRjBw9uv0Te0hdXS0a\njbZH7hRs+HgtF9MuYmowERwRxLOvv9znHwXYm0i+Qp9hNpu5fD4dnV7PkOHD7B1Or1EpjZw6tRpv\nnyiObT6Cuqbx1qQkSXBXw7oPPkE2HWHf+8446M24BJRhko2opOZSgdogFYMGNZYN/cp3XmeN80py\nruSi1WuJmRVPzMSJdnlvtnJqz8mmxAugqtVyYtfxLiXfysoK/u9Hv6P+khklKk6vO8PMr8y06aS0\nviQr8yrr//kZJTdLcHR3ZHxyDE8sXmiz9ndt2cbJ986gMmsAFbdv5fK+8R3+43vfslkf/ZFIvkKf\ncOvGTVbt+5yKwXrIN+J3cBtfW/4qemcne4fWo0aENfCL7+uYEP0p6ZfX4uQLFTesR0cZZy7jXTYK\nSZKoBQovVuEcfZGyi0PQGJww+9ST9MysppGGVqPlxW981Q7vpuc01DTw4MrIxmOdt/mT9RgvSU0X\nL5pKHWmfH2T6rJn9bltDi8XCmv9bhTlLiQPOyBVw+N9HCR4UQtTYaJv0kXHi8heJt5FCUnD3fLbN\ntkDsr0TyFfqEdYe2Uxvr21T2vThA5vNt63l+Wd9dPtJdt+9e4tc/0DN+TOOILnKkzA9+auQ7C8tQ\n1bkDYJJNaOodrD7EVEZn/Ly0jP76cNTKYCZNmdorz1w7oriokK1rN1FdUo3PQB8WLF2CRtP955r+\nw/zIvl5gVWoycHhQl9qqKKhokRTqChooryjDy7NvrhPuqqzMK9Rca8CB5v27NQ0OnDtyxmbJV6Fo\nWS5CUoqk2x5RZEPoE4rM1pXjJYVEwQPH+pvCgrNNifdLY0ar0Q4vwCA3UC1XUKC90+pfaXaeNylz\nX2NW8tw+k3hramv48w//xPXP71BwoJT09zL46y//aJO2V7z+Il7TXWhwrcHgXoP/TE+efvX5LrXl\nGeyJRbZe4uMUpMPdzdMWofYpzq6u4GC9p7csy2gcbDfRa+z0aEwOzQVPzLKJoTFDxKi3HWLkK/QJ\nzgrtgxu34KLQtnpuf+HtFcn5S6uJGtX8Pq9eh4YaD2qpQo2GQMNgcoy3cJY9UEqNE2WMjvWkLHmu\nz3247d68Dct1JYov4lJICgpPlpCVdZWwsOHdaluvc+I/f/o9amprkCTQOeqtvl9SXMS+HbsBmDEn\nCU8v7zbbWrBsMbcyfkf52RpUZjVmn3pmLZ/3SCxZ6qzAgCCCYv0pTC1D8cXvjxxoIGmh7Z5vT5k+\nHZPRxJl9pzE2GBkYGcaS58RmLu0RyVfoE6YMGcvWG2dRDPZElmWU6YUkTl6ILMtknExHtlgYOWFM\nn0s43TFwYCQ/+FkNv/8pjArXknlD5pPNIzBlmXCTmm8T+ltCMYZV4+zgidpRTUzidCbETbZpLLIs\ns33jJjJPZiIpJMbEjyU+sXOVl2ora622qwOQGpQUFRR0O/l+Sa/Ttzh2KT2d1W82rjsGOLf9HM+8\n8Wybmw84ODjyxm9/yukTxyksKGBKwnRcnF1tEl9f9PoPvsX60LXkXc9D76YnafEcvL18bdrH9KRE\npicl2rTN/k4kX6FPmDYlnqCsAI5fOINKoWBm8ovIRgu/f/In1JyuA2Db2HW8+Lev4xPk3+n2b924\nyfbje6mw1OGpcmLBtGR8Azrfjq1duKJl3vN1fOeby3F3H0V0dCCp+t/BfQWkJCSiYsay/OWu3Wbt\niHWrP+XMynOoLI23I3ee2Y3FbCFhdlKH2xgbN54Lmy6hqbuvkEQojI/t+kxrWZbZum4jV45nABA+\nIZy5Ty60ugjb89kuVAWOTaUbVQWO7F6786E7/0iSxPgJj/YM8I5SqzUsfX6FvcMQHiCe+Qp9xuCw\nMJ5Z/DRLFy3F09ubjW9+jPG4jNbsiNbsiPmUxMbffNzpdmurq3k3dS13R2mpGO3GzREq3tr0AWZT\n71amakuDQc24cc8zeHA0vr5+DJgcjFm+L7ZQI7MWpPRoDBmHLjUlXgC1QcvZ1DOdaiN8xEimvhwH\nIUbqHKtRh8Oiry3u1oSrzWvXc+Tt41SdrafqbD1H3j7Bpk8/tzqnsrCyxeuqivr3fAHh0SdGvkKf\nVZJV3OI2c0lWcafb2XdgH4YxPlZXmtW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WaMBPi2ZoKHVerjdHo2LAIJlocFhb/IHOhXjd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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# helpers_05_08 is found in the online appendix\n", - "import helpers_05_08\n", - "helpers_05_08.plot_tree_interactive(X, y);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notice that as the depth increases, we tend to get very strangely shaped classification regions; for example, at a depth of five, there is a tall and skinny purple region between the yellow and blue regions.\n", - "It's clear that this is less a result of the true, intrinsic data distribution, and more a result of the particular sampling or noise properties of the data.\n", - "That is, this decision tree, even at only five levels deep, is clearly over-fitting our data." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Decision trees and over-fitting\n", - "\n", - "Such over-fitting turns out to be a general property of decision trees: it is very easy to go too deep in the tree, and thus to fit details of the particular data rather than the overall properties of the distributions they are drawn from.\n", - "Another way to see this over-fitting is to look at models trained on different subsets of the data—for example, in this figure we train two different trees, each on half of the original data:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![](figures/05.08-decision-tree-overfitting.png)\n", - "[figure source in Appendix](06.00-Figure-Code.ipynb#Decision-Tree-Overfitting)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It is clear that in some places, the two trees produce consistent results (e.g., in the four corners), while in other places, the two trees give very different classifications (e.g., in the regions between any two clusters).\n", - "The key observation is that the inconsistencies tend to happen where the classification is less certain, and thus by using information from *both* of these trees, we might come up with a better result!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you are running this notebook live, the following function will allow you to interactively display the fits of trees trained on a random subset of the data:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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xItv2fMKidZvve6+C1mtoXHQ2jI3GNq5+cKPGTeBU+m6mT2wZYr9VaMPbP6Jb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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# helpers_05_08 is found in the online appendix\n", - "import helpers_05_08\n", - "helpers_05_08.randomized_tree_interactive(X, y)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Just as using information from two trees improves our results, we might expect that using information from many trees would improve our results even further." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Ensembles of Estimators: Random Forests\n", - "\n", - "This notion—that multiple overfitting estimators can be combined to reduce the effect of this overfitting—is what underlies an ensemble method called *bagging*.\n", - "Bagging makes use of an ensemble (a grab bag, perhaps) of parallel estimators, each of which over-fits the data, and averages the results to find a better classification.\n", - "An ensemble of randomized decision trees is known as a *random forest*.\n", - "\n", - "This type of bagging classification can be done manually using Scikit-Learn's ``BaggingClassifier`` meta-estimator, as shown here:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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bZE4OqCl77rkVMAG0Mshes5nAoADsGihR2VZkMhlz//1Xtn33MyZJKahcXRj9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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from sklearn.tree import DecisionTreeClassifier\n", - "from sklearn.ensemble import BaggingClassifier\n", - "\n", - "tree = DecisionTreeClassifier()\n", - "bag = BaggingClassifier(tree, n_estimators=100, max_samples=0.8,\n", - " random_state=1)\n", - "\n", - "bag.fit(X, y)\n", - "visualize_classifier(bag, X, y)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this example, we have randomized the data by fitting each estimator with a random subset of 80% of the training points.\n", - "In practice, decision trees are more effectively randomized by injecting some stochasticity in how the splits are chosen: this way all the data contributes to the fit each time, but the results of the fit still have the desired randomness.\n", - "For example, when determining which feature to split on, the randomized tree might select from among the top several features.\n", - "You can read more technical details about these randomization strategies in the [Scikit-Learn documentation](http://scikit-learn.org/stable/modules/ensemble.html#forest) and references within.\n", - "\n", - "In Scikit-Learn, such an optimized ensemble of randomized decision trees is implemented in the ``RandomForestClassifier`` estimator, which takes care of all the randomization automatically.\n", - "All you need to do is select a number of estimators, and it will very quickly (in parallel, if desired) fit the ensemble of trees:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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bZE4OqKh477kVMAE0MshZu5mg4EDsmihRaSgymYx5//4r2775EZPEFJSuLox5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\nH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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from sklearn.ensemble import RandomForestClassifier\n", - "\n", - "model = RandomForestClassifier(n_estimators=100, random_state=0)\n", - "visualize_classifier(model, X, y);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We see that by averaging over 100 randomly perturbed models, we end up with an overall model that is much closer to our intuition about how the parameter space should be split." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Random Forest Regression\n", - "\n", - "In the previous section we considered random forests within the context of classification.\n", - "Random forests can also be made to work in the case of regression (that is, continuous rather than categorical variables). The estimator to use for this is the ``RandomForestRegressor``, and the syntax is very similar to what we saw earlier.\n", - "\n", - "Consider the following data, drawn from the combination of a fast and slow oscillation:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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Od48kVn2+ZclvCJsfeSKAeJ4yqGsI1XK7frklduULRZRMKHvdCy0gb79pRWC7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\nLrsMa9euxT/90z/52Z7IRJl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L4+PjePfdd7Fnzx688847uO222/Dv//7vUTdLKwsWLMDR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\nAZmIiEgCDMhEREQS+P8p5hEpezc9PwAAAABJRU5ErkJggg==\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "rng = np.random.RandomState(42)\n", - "x = 10 * rng.rand(200)\n", - "\n", - "def model(x, sigma=0.3):\n", - " fast_oscillation = np.sin(5 * x)\n", - " slow_oscillation = np.sin(0.5 * x)\n", - " noise = sigma * rng.randn(len(x))\n", - "\n", - " return slow_oscillation + fast_oscillation + noise\n", - "\n", - "y = model(x)\n", - "plt.errorbar(x, y, 0.3, fmt='o');" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Using the random forest regressor, we can find the best fit curve as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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lef4veKfTSXdHC3YpjtFqRpFlLPkMl21sxmIvaJEV1pCn5kk/8ryPyMQ4Nkcd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W0LsZgM9Id6AFBdbnSDg6V60GP3Xd6iza0OhULE4zEWGwxgMECFK3RNlb/QBx6OlH8xhlxymECd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\nSia0OFMpA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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from sklearn.ensemble import RandomForestRegressor\n", - "forest = RandomForestRegressor(200)\n", - "forest.fit(x[:, None], y)\n", - "\n", - "xfit = np.linspace(0, 10, 1000)\n", - "yfit = forest.predict(xfit[:, None])\n", - "ytrue = model(xfit, sigma=0)\n", - "\n", - "plt.errorbar(x, y, 0.3, fmt='o', alpha=0.5)\n", - "plt.plot(xfit, yfit, '-r');\n", - "plt.plot(xfit, ytrue, '-k', alpha=0.5);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here the true model is shown in the smooth gray curve, while the random forest model is shown by the jagged red curve.\n", - "As you can see, the non-parametric random forest model is flexible enough to fit the multi-period data, without us needing to specifying a multi-period model!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Example: Random Forest for Classifying Digits\n", - "\n", - "Earlier we took a quick look at the hand-written digits data (see [Introducing Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb)).\n", - "Let's use that again here to see how the random forest classifier can be used in this context." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "dict_keys(['target', 'data', 'target_names', 'DESCR', 'images'])" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.datasets import load_digits\n", - "digits = load_digits()\n", - "digits.keys()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To remind us what we're looking at, we'll visualize the first few data points:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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Hl946Ie3vrVjRTqXjaOSeNH36dC+r8R6f+IiIyFHY8RERkaOw4yMiIkdhx0dERI7Cjo+I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\nXIjFixfbXYqpXn/9daSnp+P++++3uxT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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# set up the figure\n", - "fig = plt.figure(figsize=(6, 6)) # figure size in inches\n", - "fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)\n", - "\n", - "# plot the digits: each image is 8x8 pixels\n", - "for i in range(64):\n", - " ax = fig.add_subplot(8, 8, i + 1, xticks=[], yticks=[])\n", - " ax.imshow(digits.images[i], cmap=plt.cm.binary, interpolation='nearest')\n", - " \n", - " # label the image with the target value\n", - " ax.text(0, 7, str(digits.target[i]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can quickly classify the digits using a random forest as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from sklearn.cross_validation import train_test_split\n", - "\n", - "Xtrain, Xtest, ytrain, ytest = train_test_split(digits.data, digits.target,\n", - " random_state=0)\n", - "model = RandomForestClassifier(n_estimators=1000)\n", - "model.fit(Xtrain, ytrain)\n", - "ypred = model.predict(Xtest)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can take a look at the classification report for this classifier:" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 1.00 0.97 0.99 38\n", - " 1 1.00 0.98 0.99 44\n", - " 2 0.95 1.00 0.98 42\n", - " 3 0.98 0.96 0.97 46\n", - " 4 0.97 1.00 0.99 37\n", - " 5 0.98 0.96 0.97 49\n", - " 6 1.00 1.00 1.00 52\n", - " 7 1.00 0.96 0.98 50\n", - " 8 0.94 0.98 0.96 46\n", - " 9 0.96 0.98 0.97 46\n", - "\n", - "avg / total 0.98 0.98 0.98 450\n", - "\n" - ] - } - ], - "source": [ - "from sklearn import metrics\n", - "print(metrics.classification_report(ypred, ytest))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And for good measure, plot the confusion matrix:" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from sklearn.metrics import confusion_matrix\n", - "mat = confusion_matrix(ytest, ypred)\n", - "sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False)\n", - "plt.xlabel('true label')\n", - "plt.ylabel('predicted label');" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We find that a simple, untuned random forest results in a very accurate classification of the digits data." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summary of Random Forests\n", - "\n", - "This section contained a brief introduction to the concept of *ensemble estimators*, and in particular the random forest – an ensemble of randomized decision trees.\n", - "Random forests are a powerful method with several advantages:\n", - "\n", - "- Both training and prediction are very fast, because of the simplicity of the underlying decision trees. In addition, both tasks can be straightforwardly parallelized, because the individual trees are entirely independent entities.\n", - "- The multiple trees allow for a probabilistic classification: a majority vote among estimators gives an estimate of the probability (accessed in Scikit-Learn with the ``predict_proba()`` method).\n", - "- The nonparametric model is extremely flexible, and can thus perform well on tasks that are under-fit by other estimators.\n", - "\n", - "A primary disadvantage of random forests is that the results are not easily interpretable: that is, if you would like to draw conclusions about the *meaning* of the classification model, random forests may not be the best choice." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "< [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb) | [Contents](Index.ipynb) | [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb) >" - ] - } - ], - "metadata": { - "anaconda-cloud": {}, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.1" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} From d620f7d85ece9d5a73bf23598524fb008316b0dc Mon Sep 17 00:00:00 2001 From: Amit Rathi Date: Fri, 14 Sep 2018 17:21:58 +0530 Subject: [PATCH 04/19] misc file types updated --- README.md | 2 +- .../02.02-The-Basics-Of-NumPy-Arrays.ipynb | 224 +++++------------- notebooks/02.08-Sorting.ipynb | 138 ++++------- 3 files changed, 107 insertions(+), 257 deletions(-) diff --git a/README.md b/README.md index 3717b8371..708168d4e 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/jakevdp/PythonDataScienceHandbook/master?filepath=notebooks%2FIndex.ipynb) -This repository contains the entire [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do), in the form of (free!) Jupyter notebooks. +This is where I say goodbye repository contnaoins the entire [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do), in the form of (free!) Jupyter notebooks. ![cover image](notebooks/figures/PDSH-cover.png) diff --git a/notebooks/02.02-The-Basics-Of-NumPy-Arrays.ipynb b/notebooks/02.02-The-Basics-Of-NumPy-Arrays.ipynb index c8bde4cb7..39f6becb8 100644 --- a/notebooks/02.02-The-Basics-Of-NumPy-Arrays.ipynb +++ b/notebooks/02.02-The-Basics-Of-NumPy-Arrays.ipynb @@ -63,17 +63,15 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ - "import numpy as np\n", + "import numpy as np2\n", "np.random.seed(0) # seed for reproducibility\n", "\n", - "x1 = np.random.randint(10, size=6) # One-dimensional array\n", - "x2 = np.random.randint(10, size=(3, 4)) # Two-dimensional array\n", - "x3 = np.random.randint(10, size=(3, 4, 5)) # Three-dimensional array" + "x1 = np2.random.randint(10, size=6) # One-dimensional array\n", + "x2 = np2.random.randint(10, size=(3, 4)) # Two-dimensional array\n", + "x3 = np2.random.randint(10, size=(3, 4, 5)) # Three-dimensional array" ] }, { @@ -86,9 +84,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -116,9 +112,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -142,9 +136,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -185,9 +177,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -207,9 +197,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -229,9 +217,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -258,9 +244,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -280,9 +264,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -309,9 +291,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -333,9 +313,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -355,9 +333,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -377,9 +353,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -406,9 +380,7 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -439,9 +411,7 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -489,9 +459,7 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -512,9 +480,7 @@ { "cell_type": "code", "execution_count": 17, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -534,9 +500,7 @@ { "cell_type": "code", "execution_count": 18, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -556,9 +520,7 @@ { "cell_type": "code", "execution_count": 19, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -578,9 +540,7 @@ { "cell_type": "code", "execution_count": 20, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -600,9 +560,7 @@ { "cell_type": "code", "execution_count": 21, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -631,9 +589,7 @@ { "cell_type": "code", "execution_count": 22, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -653,9 +609,7 @@ { "cell_type": "code", "execution_count": 23, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -685,9 +639,7 @@ { "cell_type": "code", "execution_count": 24, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -709,9 +661,7 @@ { "cell_type": "code", "execution_count": 25, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -732,9 +682,7 @@ { "cell_type": "code", "execution_count": 26, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -763,9 +711,7 @@ { "cell_type": "code", "execution_count": 27, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -797,9 +743,7 @@ { "cell_type": "code", "execution_count": 28, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -816,9 +760,7 @@ { "cell_type": "code", "execution_count": 29, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -842,9 +784,7 @@ { "cell_type": "code", "execution_count": 30, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -872,9 +812,7 @@ { "cell_type": "code", "execution_count": 31, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -900,9 +838,7 @@ { "cell_type": "code", "execution_count": 32, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -928,9 +864,7 @@ { "cell_type": "code", "execution_count": 33, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -949,9 +883,7 @@ { "cell_type": "code", "execution_count": 34, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -986,9 +918,7 @@ { "cell_type": "code", "execution_count": 35, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1014,9 +944,7 @@ { "cell_type": "code", "execution_count": 36, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1035,9 +963,7 @@ { "cell_type": "code", "execution_count": 37, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1067,9 +993,7 @@ { "cell_type": "code", "execution_count": 38, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1100,9 +1024,7 @@ { "cell_type": "code", "execution_count": 39, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1125,9 +1047,7 @@ { "cell_type": "code", "execution_count": 40, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1148,9 +1068,7 @@ { "cell_type": "code", "execution_count": 41, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1173,9 +1091,7 @@ { "cell_type": "code", "execution_count": 42, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1224,9 +1140,7 @@ { "cell_type": "code", "execution_count": 43, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1255,9 +1169,7 @@ { "cell_type": "code", "execution_count": 44, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1282,9 +1194,7 @@ { "cell_type": "code", "execution_count": 45, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "grid = np.array([[1, 2, 3],\n", @@ -1294,9 +1204,7 @@ { "cell_type": "code", "execution_count": 46, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1320,9 +1228,7 @@ { "cell_type": "code", "execution_count": 47, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1351,9 +1257,7 @@ { "cell_type": "code", "execution_count": 48, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1380,9 +1284,7 @@ { "cell_type": "code", "execution_count": 49, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1422,9 +1324,7 @@ { "cell_type": "code", "execution_count": 50, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1451,9 +1351,7 @@ { "cell_type": "code", "execution_count": 51, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1477,9 +1375,7 @@ { "cell_type": "code", "execution_count": 52, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1501,9 +1397,7 @@ { "cell_type": "code", "execution_count": 53, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1559,9 +1453,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.6.4" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/notebooks/02.08-Sorting.ipynb b/notebooks/02.08-Sorting.ipynb index 1a82f745a..fdb02d56c 100644 --- a/notebooks/02.08-Sorting.ipynb +++ b/notebooks/02.08-Sorting.ipynb @@ -41,9 +41,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -58,9 +56,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -92,9 +88,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "def bogosort(x):\n", @@ -106,9 +100,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -140,9 +132,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Fast Sorting in NumPy: ``np.sort`` and ``np.argsort``\n", + "## Fast Sorting in NumPy: ```np.sort``` and ``np.argsort``\n", "\n", - "Although Python has built-in ``sort`` and ``sorted`` functions to work with lists, we won't discuss them here because NumPy's ``np.sort`` function turns out to be much more efficient and useful for our purposes.\n", + "Although Python has built-in `sort` and ``sorted`` functions to work with lists, we won't discuss them here because NumPy's ``np.sort`` function turns out to be much more efficient and useful for our purposes.\n", "By default ``np.sort`` uses an $\\mathcal{O}[N\\log N]$, *quicksort* algorithm, though *mergesort* and *heapsort* are also available. For most applications, the default quicksort is more than sufficient.\n", "\n", "To return a sorted version of the array without modifying the input, you can use ``np.sort``:" @@ -150,20 +142,19 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, + "execution_count": 1, + "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "array([1, 2, 3, 4, 5])" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" + "ename": "NameError", + "evalue": "name 'np' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined" + ] } ], "source": [ @@ -181,9 +172,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -208,9 +197,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -237,9 +224,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -273,9 +258,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -297,9 +280,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -323,9 +304,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -365,9 +344,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -398,9 +375,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -444,9 +419,7 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "X = rand.rand(10, 2)" @@ -462,9 +435,7 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -496,9 +467,7 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "dist_sq = np.sum((X[:, np.newaxis, :] - X[np.newaxis, :, :]) ** 2, axis=-1)" @@ -514,9 +483,7 @@ { "cell_type": "code", "execution_count": 17, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -538,9 +505,7 @@ { "cell_type": "code", "execution_count": 18, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -562,9 +527,7 @@ { "cell_type": "code", "execution_count": 19, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -593,9 +556,7 @@ { "cell_type": "code", "execution_count": 20, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -623,9 +584,7 @@ { "cell_type": "code", "execution_count": 21, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -661,9 +620,7 @@ { "cell_type": "code", "execution_count": 22, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "K = 2\n", @@ -679,20 +636,19 @@ }, { "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": false - }, + "execution_count": 2, + "metadata": {}, "outputs": [ { - "data": { - "image/png": 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RaLCxqUVMzFoSEp4dvjE3P4eDw358fJywsLDI1mfqU//5TR7rKYQeUalUfPbZ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\naNmyla5LEULkAQluoRMJCQlERp4mPPw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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" + "ename": "NameError", + "evalue": "name 'plt' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m# draw lines from each point to its two nearest neighbors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mK\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined" + ] } ], "source": [ @@ -773,9 +729,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.6.4" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } From d1127b172bc2835e106d92d9797214ce88a74be6 Mon Sep 17 00:00:00 2001 From: Amit Rathi Date: Tue, 25 Sep 2018 03:07:30 +0530 Subject: [PATCH 05/19] Markdown changes - v2 --- notebooks/02.00-Introduction-to-NumPy.ipynb | 68 +-- notebooks/Executable Runbook Demo.ipynb | 618 ++++++++++---------- 2 files changed, 333 insertions(+), 353 deletions(-) diff --git a/notebooks/02.00-Introduction-to-NumPy.ipynb b/notebooks/02.00-Introduction-to-NumPy.ipynb index 177fa9342..3ab2a4cf8 100644 --- a/notebooks/02.00-Introduction-to-NumPy.ipynb +++ b/notebooks/02.00-Introduction-to-NumPy.ipynb @@ -2,13 +2,10 @@ "cells": [ { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "\n", - "\n", + "\n", "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", "\n", "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" @@ -16,35 +13,28 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "\n", - "< [More IPython Resources](01.08-More-IPython-Resources.ipynb) | [Contents](Index.ipynb) | [Understanding Data Types in Python](02.01-Understanding-Data-Types.ipynb) >" + "< [More Jupyter Resources](01.08-More-IPython-Resources.ipynb) | [Contents](Index.ipynb) | [Understanding Data Types in Python](02.01-Understanding-Data-Types.ipynb) >" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ - "# Introduction to NumPy" + "# Introduction to NumPy & Pandas" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This chapter, along with chapter 3, outlines techniques for effectively loading, storing, and manipulating in-memory data in Python.\n", - "The topic is very broad: datasets can come from a wide range of sources and a wide range of formats, including be collections of documents, collections of images, collections of sound clips, collections of numerical measurements, or nearly anything else.\n", - "Despite this apparent heterogeneity, it will help us to think of all data fundamentally as arrays of numbers.\n", + "The topic is very broad: datasets can come from a wide range of sources and a wide range of formats, including be collections of documents, collections of images, collections of sound clips, collections of numerical measurements, or nearly anything else. Let us look at following data formats:\n", + "\n", + "* Illustration of main Pandas methods\n", + "* Plotting DataFrame\n", "\n", "For example, images–particularly digital images–can be thought of as simply two-dimensional arrays of numbers representing pixel brightness across the area.\n", "Sound clips can be thought of as one-dimensional arrays of intensity versus time.\n", @@ -67,11 +57,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -91,10 +77,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "For the pieces of the package discussed here, I'd recommend NumPy version 1.8 or later.\n", "By convention, you'll find that most people in the SciPy/PyData world will import NumPy using ``np`` as an alias:" @@ -103,11 +86,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np" @@ -115,20 +94,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Throughout this chapter, and indeed the rest of the book, you'll find that this is the way we will import and use NumPy." ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Reminder about Built In Documentation\n", "\n", @@ -151,10 +124,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "\n", "< [More IPython Resources](01.08-More-IPython-Resources.ipynb) | [Contents](Index.ipynb) | [Understanding Data Types in Python](02.01-Understanding-Data-Types.ipynb) >" @@ -178,9 +148,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.6.4" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/notebooks/Executable Runbook Demo.ipynb b/notebooks/Executable Runbook Demo.ipynb index d486ca40d..03122d192 100644 --- a/notebooks/Executable Runbook Demo.ipynb +++ b/notebooks/Executable Runbook Demo.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -20,27 +20,15 @@ "text": [ "env: DB_USER=tankman\n", "env: DB_PASSWORD=abcd1234\n", - "env: DB_ENDPOINT=xyz-api-prod-db.cxvlabuzou3z.ap-south-1.rds.amazonaws.com\n" + "env: DB_ENDPOINT=xyz-api-prod-db.cxvlabuzou3z.ap-south-1.rds.amazonaws.com\n", + "The sql module is not an IPython extension.\n" ] }, { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'sql'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'env'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'DB_ENDPOINT=xyz-api-prod-db.cxvlabuzou3z.ap-south-1.rds.amazonaws.com'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'load_ext'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'sql'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'sql'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'postgres://$DB_IDE:$DB_PASSWORD@$DB_POINT_BEND:5432/xyz_api_db'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - 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"\u001b[0;32m/anaconda3/lib/python3.6/importlib/__init__.py\u001b[0m in \u001b[0;36mimport_module\u001b[0;34m(name, package)\u001b[0m\n\u001b[1;32m 124\u001b[0m \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 125\u001b[0m \u001b[0mlevel\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 126\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_bootstrap\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_gcd_import\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpackage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 127\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 128\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/anaconda3/lib/python3.6/importlib/_bootstrap.py\u001b[0m in \u001b[0;36m_gcd_import\u001b[0;34m(name, package, level)\u001b[0m\n", - "\u001b[0;32m/anaconda3/lib/python3.6/importlib/_bootstrap.py\u001b[0m in \u001b[0;36m_find_and_load\u001b[0;34m(name, import_)\u001b[0m\n", - "\u001b[0;32m/anaconda3/lib/python3.6/importlib/_bootstrap.py\u001b[0m in \u001b[0;36m_find_and_load_unlocked\u001b[0;34m(name, import_)\u001b[0m\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'sql'" + "name": "stderr", + "output_type": "stream", + "text": [ + "UsageError: Line magic function `%sql` not found.\n" ] } ], @@ -68,7 +56,29 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: SQL in /anaconda3/lib/python3.6/site-packages (0.4.0)\n", + "\u001b[31mgithub-email-explorer 0.2.8 has requirement Jinja2==2.8, but you'll have jinja2 2.10 which is incompatible.\u001b[0m\n", + "\u001b[31mecs 0.1 has requirement six==1.5.2, but you'll have six 1.10.0 which is incompatible.\u001b[0m\n", + "\u001b[31mawscli 1.15.4 has requirement botocore==1.10.4, but you'll have botocore 1.8.50 which is incompatible.\u001b[0m\n", + "\u001b[33mYou are using pip version 10.0.1, however version 18.0 is available.\n", + "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n" + ] + } + ], + "source": [ + "!pip install SQL" + ] + }, + { + "cell_type": "code", + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -100,294 +110,294 @@ "name": "AWS/EC2 CPUUtilization", "type": "scatter", "x": [ - "2018-09-06 08:33:00", - "2018-09-06 08:38:00", - "2018-09-06 08:43:00", - "2018-09-06 08:48:00", - "2018-09-06 08:53:00", - "2018-09-06 08:58:00", - "2018-09-06 09:03:00", - "2018-09-06 09:08:00", - "2018-09-06 09:13:00", - "2018-09-06 09:18:00", - "2018-09-06 09:23:00", - "2018-09-06 09:28:00", - "2018-09-06 09:33:00", - "2018-09-06 09:38:00", - "2018-09-06 09:43:00", - "2018-09-06 09:48:00", - "2018-09-06 09:53:00", - "2018-09-06 09:58:00", - "2018-09-06 10:03:00", - "2018-09-06 10:08:00", - "2018-09-06 10:13:00", - "2018-09-06 10:18:00", - "2018-09-06 10:23:00", - "2018-09-06 10:28:00", - "2018-09-06 10:33:00", - "2018-09-06 10:38:00", - "2018-09-06 10:43:00", - "2018-09-06 10:48:00", - "2018-09-06 10:53:00", - "2018-09-06 10:58:00", - "2018-09-06 11:03:00", - "2018-09-06 11:08:00", - "2018-09-06 11:13:00", - "2018-09-06 11:18:00", - "2018-09-06 11:23:00", - "2018-09-06 11:28:00", - "2018-09-06 11:33:00", - "2018-09-06 11:38:00", - "2018-09-06 11:43:00", - "2018-09-06 11:48:00", - 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" ] }, "metadata": {}, From f6186a6590fe555c264f489cfedc8f28891a9a9b Mon Sep 17 00:00:00 2001 From: Amit Rathi Date: Sat, 29 Sep 2018 20:25:01 +0530 Subject: [PATCH 06/19] The ultimate test --- .../01.00-IPython-Beyond-Normal-Python.ipynb | 0 notebooks/01.01-Help-And-Documentation.ipynb | 350 --- notebooks/02.00-Introduction-to-NumPy.ipynb | 41 +- .../02.01-Understanding-Data-Types.ipynb | 341 +-- notebooks/03.09-Pivot-Tables.ipynb | 428 ++-- .../04.14-Visualization-With-Seaborn.ipynb | 313 +-- ...uction_to_artificial_neural_networks.ipynb | 2236 +++++++++++++++++ notebooks/helpers_05_08.py | 1 + 8 files changed, 2718 insertions(+), 992 deletions(-) rename {notebooks => nb3}/01.00-IPython-Beyond-Normal-Python.ipynb (100%) delete mode 100644 notebooks/01.01-Help-And-Documentation.ipynb create mode 100644 notebooks/10_introduction_to_artificial_neural_networks.ipynb diff --git a/notebooks/01.00-IPython-Beyond-Normal-Python.ipynb b/nb3/01.00-IPython-Beyond-Normal-Python.ipynb similarity index 100% rename from notebooks/01.00-IPython-Beyond-Normal-Python.ipynb rename to nb3/01.00-IPython-Beyond-Normal-Python.ipynb diff --git a/notebooks/01.01-Help-And-Documentation.ipynb b/notebooks/01.01-Help-And-Documentation.ipynb deleted file mode 100644 index 47af64c88..000000000 --- a/notebooks/01.01-Help-And-Documentation.ipynb +++ /dev/null @@ -1,350 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", - "\n", - "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "< [IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb) | [Contents](Index.ipynb) | [Keyboard Shortcuts in the IPython Shell](01.02-Shell-Keyboard-Shortcuts.ipynb) >" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Help and Documentation in IPython" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you read no other section in this chapter, read this one: I find the tools discussed here to be the most transformative contributions of IPython to my daily workflow.\n", - "\n", - "When a technologically-minded person is asked to help a friend, family member, or colleague with a computer problem, most of the time it's less a matter of knowing the answer as much as knowing how to quickly find an unknown answer.\n", - "In data science it's the same: searchable web resources such as online documentation, mailing-list threads, and StackOverflow answers contain a wealth of information, even (especially?) if it is a topic you've found yourself searching before.\n", - "Being an effective practitioner of data science is less about memorizing the tool or command you should use for every possible situation, and more about learning to effectively find the information you don't know, whether through a web search engine or another means.\n", - "\n", - "One of the most useful functions of IPython/Jupyter is to shorten the gap between the user and the type of documentation and search that will help them do their work effectively.\n", - "While web searches still play a role in answering complicated questions, an amazing amount of information can be found through IPython alone.\n", - "Some examples of the questions IPython can help answer in a few keystrokes:\n", - "\n", - "- How do I call this function? What arguments and options does it have?\n", - "- What does the source code of this Python object look like?\n", - "- What is in this package I imported? What attributes or methods does this object have?\n", - "\n", - "Here we'll discuss IPython's tools to quickly access this information, namely the ``?`` character to explore documentation, the ``??`` characters to explore source code, and the Tab key for auto-completion." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Accessing Documentation with ``?``\n", - "\n", - "The Python language and its data science ecosystem is built with the user in mind, and one big part of that is access to documentation.\n", - "Every Python object contains the reference to a string, known as a *doc string*, which in most cases will contain a concise summary of the object and how to use it.\n", - "Python has a built-in ``help()`` function that can access this information and prints the results.\n", - "For example, to see the documentation of the built-in ``len`` function, you can do the following:\n", - "\n", - "```ipython\n", - "In [1]: help(len)\n", - "Help on built-in function len in module builtins:\n", - "\n", - "len(...)\n", - " len(object) -> integer\n", - " \n", - " Return the number of items of a sequence or mapping.\n", - "```\n", - "\n", - "Depending on your interpreter, this information may be displayed as inline text, or in some separate pop-up window." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Because finding help on an object is so common and useful, IPython introduces the ``?`` character as a shorthand for accessing this documentation and other relevant information:\n", - "\n", - "```ipython\n", - "In [2]: len?\n", - "Type: builtin_function_or_method\n", - "String form: \n", - "Namespace: Python builtin\n", - "Docstring:\n", - "len(object) -> integer\n", - "\n", - "Return the number of items of a sequence or mapping.\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This notation works for just about anything, including object methods:\n", - "\n", - "```ipython\n", - "In [3]: L = [1, 2, 3]\n", - "In [4]: L.insert?\n", - "Type: builtin_function_or_method\n", - "String form: \n", - "Docstring: L.insert(index, object) -- insert object before index\n", - "```\n", - "\n", - "or even objects themselves, with the documentation from their type:\n", - "\n", - "```ipython\n", - "In [5]: L?\n", - "Type: list\n", - "String form: [1, 2, 3]\n", - "Length: 3\n", - "Docstring:\n", - "list() -> new empty list\n", - "list(iterable) -> new list initialized from iterable's items\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Importantly, this will even work for functions or other objects you create yourself!\n", - "Here we'll define a small function with a docstring:\n", - "\n", - "```ipython\n", - "In [6]: def square(a):\n", - " ....: \"\"\"Return the square of a.\"\"\"\n", - " ....: return a ** 2\n", - " ....:\n", - "```\n", - "\n", - "Note that to create a docstring for our function, we simply placed a string literal in the first line.\n", - "Because doc strings are usually multiple lines, by convention we used Python's triple-quote notation for multi-line strings." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we'll use the ``?`` mark to find this doc string:\n", - "\n", - "```ipython\n", - "In [7]: square?\n", - "Type: function\n", - "String form: \n", - "Definition: square(a)\n", - "Docstring: Return the square of a.\n", - "```\n", - "\n", - "This quick access to documentation via docstrings is one reason you should get in the habit of always adding such inline documentation to the code you write!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Accessing Source Code with ``??``\n", - "Because the Python language is so easily readable, another level of insight can usually be gained by reading the source code of the object you're curious about.\n", - "IPython provides a shortcut to the source code with the double question mark (``??``):\n", - "\n", - "```ipython\n", - "In [8]: square??\n", - "Type: function\n", - "String form: \n", - "Definition: square(a)\n", - "Source:\n", - "def square(a):\n", - " \"Return the square of a\"\n", - " return a ** 2\n", - "```\n", - "\n", - "For simple functions like this, the double question-mark can give quick insight into the under-the-hood details." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you play with this much, you'll notice that sometimes the ``??`` suffix doesn't display any source code: this is generally because the object in question is not implemented in Python, but in C or some other compiled extension language.\n", - "If this is the case, the ``??`` suffix gives the same output as the ``?`` suffix.\n", - "You'll find this particularly with many of Python's built-in objects and types, for example ``len`` from above:\n", - "\n", - "```ipython\n", - "In [9]: len??\n", - "Type: builtin_function_or_method\n", - "String form: \n", - "Namespace: Python builtin\n", - "Docstring:\n", - "len(object) -> integer\n", - "\n", - "Return the number of items of a sequence or mapping.\n", - "```\n", - "\n", - "Using ``?`` and/or ``??`` gives a powerful and quick interface for finding information about what any Python function or module does." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exploring Modules with Tab-Completion\n", - "\n", - "IPython's other useful interface is the use of the tab key for auto-completion and exploration of the contents of objects, modules, and name-spaces.\n", - "In the examples that follow, we'll use ```` to indicate when the Tab key should be pressed." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Tab-completion of object contents\n", - "\n", - "Every Python object has various attributes and methods associated with it.\n", - "Like with the ``help`` function discussed before, Python has a built-in ``dir`` function that returns a list of these, but the tab-completion interface is much easier to use in practice.\n", - "To see a list of all available attributes of an object, you can type the name of the object followed by a period (\"``.``\") character and the Tab key:\n", - "\n", - "```ipython\n", - "In [10]: L.\n", - "L.append L.copy L.extend L.insert L.remove L.sort \n", - "L.clear L.count L.index L.pop L.reverse \n", - "```\n", - "\n", - "To narrow-down the list, you can type the first character or several characters of the name, and the Tab key will find the matching attributes and methods:\n", - "\n", - "```ipython\n", - "In [10]: L.c\n", - "L.clear L.copy L.count \n", - "\n", - "In [10]: L.co\n", - "L.copy L.count \n", - "```\n", - "\n", - "If there is only a single option, pressing the Tab key will complete the line for you.\n", - "For example, the following will instantly be replaced with ``L.count``:\n", - "\n", - "```ipython\n", - "In [10]: L.cou\n", - "\n", - "```\n", - "\n", - "Though Python has no strictly-enforced distinction between public/external attributes and private/internal attributes, by convention a preceding underscore is used to denote such methods.\n", - "For clarity, these private methods and special methods are omitted from the list by default, but it's possible to list them by explicitly typing the underscore:\n", - "\n", - "```ipython\n", - "In [10]: L._\n", - "L.__add__ L.__gt__ L.__reduce__\n", - "L.__class__ L.__hash__ L.__reduce_ex__\n", - "```\n", - "\n", - "For brevity, we've only shown the first couple lines of the output.\n", - "Most of these are Python's special double-underscore methods (often nicknamed \"dunder\" methods)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Tab completion when importing\n", - "\n", - "Tab completion is also useful when importing objects from packages.\n", - "Here we'll use it to find all possible imports in the ``itertools`` package that start with ``co``:\n", - "```\n", - "In [10]: from itertools import co\n", - "combinations compress\n", - "combinations_with_replacement count\n", - "```\n", - "Similarly, you can use tab-completion to see which imports are available on your system (this will change depending on which third-party scripts and modules are visible to your Python session):\n", - "```\n", - "In [10]: import \n", - "Display all 399 possibilities? (y or n)\n", - "Crypto dis py_compile\n", - "Cython distutils pyclbr\n", - "... ... ...\n", - "difflib pwd zmq\n", - "\n", - "In [10]: import h\n", - "hashlib hmac http \n", - "heapq html husl \n", - "```\n", - "(Note that for brevity, I did not print here all 399 importable packages and modules on my system.)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Beyond tab completion: wildcard matching\n", - "\n", - "Tab completion is useful if you know the first few characters of the object or attribute you're looking for, but is little help if you'd like to match characters at the middle or end of the word.\n", - "For this use-case, IPython provides a means of wildcard matching for names using the ``*`` character.\n", - "\n", - "For example, we can use this to list every object in the namespace that ends with ``Warning``:\n", - "\n", - "```ipython\n", - "In [10]: *Warning?\n", - "BytesWarning RuntimeWarning\n", - "DeprecationWarning SyntaxWarning\n", - "FutureWarning UnicodeWarning\n", - "ImportWarning UserWarning\n", - "PendingDeprecationWarning Warning\n", - "ResourceWarning\n", - "```\n", - "\n", - "Notice that the ``*`` character matches any string, including the empty string.\n", - "\n", - "Similarly, suppose we are looking for a string method that contains the word ``find`` somewhere in its name.\n", - "We can search for it this way:\n", - "\n", - "```ipython\n", - "In [10]: str.*find*?\n", - "str.find\n", - "str.rfind\n", - "```\n", - "\n", - "I find this type of flexible wildcard search can be very useful for finding a particular command when getting to know a new package or reacquainting myself with a familiar one." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "< [IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb) | [Contents](Index.ipynb) | [Keyboard Shortcuts in the IPython Shell](01.02-Shell-Keyboard-Shortcuts.ipynb) >" - ] - } - ], - "metadata": { - "anaconda-cloud": {}, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.1" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/02.00-Introduction-to-NumPy.ipynb b/notebooks/02.00-Introduction-to-NumPy.ipynb index 3ab2a4cf8..813b85ab4 100644 --- a/notebooks/02.00-Introduction-to-NumPy.ipynb +++ b/notebooks/02.00-Introduction-to-NumPy.ipynb @@ -5,8 +5,8 @@ "metadata": {}, "source": [ "\n", - "\n", - "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*This image is aligned to left notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", "\n", "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" ] @@ -16,14 +16,14 @@ "metadata": {}, "source": [ "\n", - "< [More Jupyter Resources](01.08-More-IPython-Resources.ipynb) | [Contents](Index.ipynb) | [Understanding Data Types in Python](02.01-Understanding-Data-Types.ipynb) >" + "< [More Jupyter Resources](01.08-More-IPython-Resources.ipynb) | [Contents](Index.ipynb) | [Understanding Data Types in Python](https://app.reviewnb.com/amit1rrr/PythonDataScienceHandbook/commit/d620f7d85ece9d5a73bf23598524fb008316b0dc) > LAST LINK CHANGED TO REVIEWNB" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# Introduction to NumPy & Pandas" + "### Introduction to modifying header and it's size & Pandas" ] }, { @@ -34,7 +34,12 @@ "The topic is very broad: datasets can come from a wide range of sources and a wide range of formats, including be collections of documents, collections of images, collections of sound clips, collections of numerical measurements, or nearly anything else. Let us look at following data formats:\n", "\n", "* Illustration of main Pandas methods\n", - "* Plotting DataFrame\n", + "* Plotting \n", + "* DataFrame splitting bullets\n", + "* adding new bullets\n", + "\n", + "\n", + "\n", "\n", "For example, images–particularly digital images–can be thought of as simply two-dimensional arrays of numbers representing pixel brightness across the area.\n", "Sound clips can be thought of as one-dimensional arrays of intensity versus time.\n", @@ -42,6 +47,10 @@ "No matter what the data are, the first step in making it analyzable will be to transform them into arrays of numbers.\n", "(We will discuss some specific examples of this process later in [Feature Engineering](05.04-Feature-Engineering.ipynb))\n", "\n", + "1. Adding numbered bullet 1\n", + "\n", + "1. Adding numbered bullet 2\n", + "\n", "For this reason, efficient storage and manipulation of numerical arrays is absolutely fundamental to the process of doing data science.\n", "We'll now take a look at the specialized tools that Python has for handling such numerical arrays: the NumPy package, and the Pandas package (discussed in Chapter 3).\n", "\n", @@ -56,23 +65,23 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "'1.11.1'" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "4\n" + ] } ], "source": [ - "import numpy\n", - "numpy.__version__" + "import numpy # modifying code cell and output\n", + "numpy.__version__\n", + "a = 7\n", + "b = 5\n", + "print(b-1)" ] }, { diff --git a/notebooks/02.01-Understanding-Data-Types.ipynb b/notebooks/02.01-Understanding-Data-Types.ipynb index 2f053aae2..c5765cbe8 100644 --- a/notebooks/02.01-Understanding-Data-Types.ipynb +++ b/notebooks/02.01-Understanding-Data-Types.ipynb @@ -137,14 +137,12 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]" + "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]" ] }, "execution_count": 1, @@ -153,111 +151,43 @@ } ], "source": [ - "L = list(range(10))\n", + "L = list(range(20)) # modifying code\n", "L" ] }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "int" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(L[0])" - ] - }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Or, similarly, a list of strings:" + "Deleted multiple code cells Because of Python's dynamic typing, we can even create heterogeneous lists:" ] }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, + "execution_count": 5, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']" + "[bool, str, float, int]" ] }, - "execution_count": 3, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "L2 = [str(c) for c in L]\n", - "L2" + "L3 = [True, \"2\", 3.0, 4]" ] }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "str" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(L2[0])" - ] - }, - { - "cell_type": "markdown", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ - "Because of Python's dynamic typing, we can even create heterogeneous lists:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[bool, str, float, int]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "L3 = [True, \"2\", 3.0, 4]\n", "[type(item) for item in L3]" ] }, @@ -266,7 +196,8 @@ "metadata": {}, "source": [ "But this flexibility comes at a cost: to allow these flexible types, each item in the list must contain its own type info, reference count, and other information–that is, each item is a complete Python object.\n", - "In the special case that all variables are of the same type, much of this information is redundant: it can be much more efficient to store data in a fixed-type array.\n", + "\n", + "Adding extra line In the special case that all variables are of the same type, much of this information is redundant: it can be much more efficient to store data in a fixed-type array.\n", "The difference between a dynamic-type list and a fixed-type (NumPy-style) array is illustrated in the following figure:" ] }, @@ -282,8 +213,14 @@ "metadata": {}, "source": [ "At the implementation level, the array essentially contains a single pointer to one contiguous block of data.\n", - "The Python list, on the other hand, contains a pointer to a block of pointers, each of which in turn points to a full Python object like the Python integer we saw earlier.\n", - "Again, the advantage of the list is flexibility: because each list element is a full structure containing both data and type information, the list can be filled with data of any desired type.\n", + "The Python list, on the other hand, contains a pointer to a block of pointers, each of which in turn points to a full Python object like the Python integer we saw earlier." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Splitting markdown cell Again, the advantage of the list is flexibility: because each list element is a full structure containing both data and type information, the list can be filled with data of any desired type.\n", "Fixed-type NumPy-style arrays lack this flexibility, but are much more efficient for storing and manipulating data." ] }, @@ -299,18 +236,16 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, + "execution_count": 2, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array('i', [0, 1, 2, 3, 4, 5, 6, 7, 8, 9])" + "array('i', [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39])" ] }, - "execution_count": 6, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -318,7 +253,10 @@ "source": [ "import array\n", "L = list(range(10))\n", - "A = array.array('i', L)\n", + "L2 = list(range(20)) \n", + "L3 = list(range(30)) \n", + "L4 = list(range(40)) \n", + "A = array.array('i', L4)\n", "A" ] }, @@ -329,6 +267,10 @@ "Here ``'i'`` is a type code indicating the contents are integers.\n", "\n", "Much more useful, however, is the ``ndarray`` object of the NumPy package.\n", + "Adding line 1 md\n", + "Adding line 2 md\n", + "Adding line 3 md\n", + "Adding line 4 md\n", "While Python's ``array`` object provides efficient storage of array-based data, NumPy adds to this efficient *operations* on that data.\n", "We will explore these operations in later sections; here we'll demonstrate several ways of creating a NumPy array.\n", "\n", @@ -338,9 +280,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np" @@ -358,9 +298,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -389,9 +327,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -418,9 +354,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -447,9 +381,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -489,9 +421,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -512,9 +442,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -537,9 +465,7 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -562,9 +488,7 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -587,9 +511,7 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -604,92 +526,82 @@ ], "source": [ "# Create an array of five values evenly spaced between 0 and 1\n", - "np.linspace(0, 1, 5)" + "np.linspace(0, 1, 5) # added new cells below" ] }, { "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, + "execution_count": 20, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[ 0.99844933, 0.52183819, 0.22421193],\n", - " [ 0.08007488, 0.45429293, 0.20941444],\n", - " [ 0.14360941, 0.96910973, 0.946117 ]])" + "array([[ 1., 0., 0.],\n", + " [ 0., 1., 0.],\n", + " [ 0., 0., 1.]])" ] }, - "execution_count": 17, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Create a 3x3 array of uniformly distributed\n", - "# random values between 0 and 1\n", - "np.random.random((3, 3))" + "# Create a 3x3 identity matrix\n", + "np.eye(3)" ] }, { "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, + "execution_count": 20, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[ 1.51772646, 0.39614948, -0.10634696],\n", - " [ 0.25671348, 0.00732722, 0.37783601],\n", - " [ 0.68446945, 0.15926039, -0.70744073]])" + "array([[ 1., 0., 0.],\n", + " [ 0., 1., 0.],\n", + " [ 0., 0., 1.]])" ] }, - "execution_count": 18, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Create a 3x3 array of normally distributed random values\n", - "# with mean 0 and standard deviation 1\n", - "np.random.normal(0, 1, (3, 3))" + "# Create a 3x3 identity matrix\n", + "np.eye(3)" ] }, { "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, + "execution_count": 20, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[2, 3, 4],\n", - " [5, 7, 8],\n", - " [0, 5, 0]])" + "array([[ 1., 0., 0.],\n", + " [ 0., 1., 0.],\n", + " [ 0., 0., 1.]])" ] }, - "execution_count": 19, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Create a 3x3 array of random integers in the interval [0, 10)\n", - "np.random.randint(0, 10, (3, 3))" + "# Create a 3x3 identity matrix\n", + "np.eye(3)" ] }, { "cell_type": "code", "execution_count": 20, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -711,92 +623,73 @@ }, { "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": false - }, + "execution_count": 17, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([ 1., 1., 1.])" + "array([[ 0.99844933, 0.52183819, 0.22421193],\n", + " [ 0.08007488, 0.45429293, 0.20941444],\n", + " [ 0.14360941, 0.96910973, 0.946117 ]])" ] }, - "execution_count": 21, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Create an uninitialized array of three integers\n", - "# The values will be whatever happens to already exist at that memory location\n", - "np.empty(3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## NumPy Standard Data Types\n", - "\n", - "NumPy arrays contain values of a single type, so it is important to have detailed knowledge of those types and their limitations.\n", - "Because NumPy is built in C, the types will be familiar to users of C, Fortran, and other related languages.\n", - "\n", - "The standard NumPy data types are listed in the following table.\n", - "Note that when constructing an array, they can be specified using a string:\n", - "\n", - "```python\n", - "np.zeros(10, dtype='int16')\n", - "```\n", - "\n", - "Or using the associated NumPy object:\n", - "\n", - "```python\n", - "np.zeros(10, dtype=np.int16)\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "| Data type\t | Description |\n", - "|---------------|-------------|\n", - "| ``bool_`` | Boolean (True or False) stored as a byte |\n", - "| ``int_`` | Default integer type (same as C ``long``; normally either ``int64`` or ``int32``)| \n", - "| ``intc`` | Identical to C ``int`` (normally ``int32`` or ``int64``)| \n", - "| ``intp`` | Integer used for indexing (same as C ``ssize_t``; normally either ``int32`` or ``int64``)| \n", - "| ``int8`` | Byte (-128 to 127)| \n", - "| ``int16`` | Integer (-32768 to 32767)|\n", - "| ``int32`` | Integer (-2147483648 to 2147483647)|\n", - "| ``int64`` | Integer (-9223372036854775808 to 9223372036854775807)| \n", - "| ``uint8`` | Unsigned integer (0 to 255)| \n", - "| ``uint16`` | Unsigned integer (0 to 65535)| \n", - "| ``uint32`` | Unsigned integer (0 to 4294967295)| \n", - "| ``uint64`` | Unsigned integer (0 to 18446744073709551615)| \n", - "| ``float_`` | Shorthand for ``float64``.| \n", - "| ``float16`` | Half precision float: sign bit, 5 bits exponent, 10 bits mantissa| \n", - "| ``float32`` | Single precision float: sign bit, 8 bits exponent, 23 bits mantissa| \n", - "| ``float64`` | Double precision float: sign bit, 11 bits exponent, 52 bits mantissa| \n", - "| ``complex_`` | Shorthand for ``complex128``.| \n", - "| ``complex64`` | Complex number, represented by two 32-bit floats| \n", - "| ``complex128``| Complex number, represented by two 64-bit floats| " + "# Create a 3x3 array of uniformly distributed\n", + "# random values between 0 and 1\n", + "np.random.random((3, 3))" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 18, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.51772646, 0.39614948, -0.10634696],\n", + " [ 0.25671348, 0.00732722, 0.37783601],\n", + " [ 0.68446945, 0.15926039, -0.70744073]])" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "More advanced type specification is possible, such as specifying big or little endian numbers; for more information, refer to the [NumPy documentation](http://numpy.org/).\n", - "NumPy also supports compound data types, which will be covered in [Structured Data: NumPy's Structured Arrays](02.09-Structured-Data-NumPy.ipynb)." + "# Create a 3x3 array of normally distributed random values\n", + "# with mean 0 and standard deviation 1\n", + "np.random.normal(0, 1, (3, 3))" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 19, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[2, 3, 4],\n", + " [5, 7, 8],\n", + " [0, 5, 0]])" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "\n", - "< [Introduction to NumPy](02.00-Introduction-to-NumPy.ipynb) | [Contents](Index.ipynb) | [The Basics of NumPy Arrays](02.02-The-Basics-Of-NumPy-Arrays.ipynb) >" + "# Create a 3x3 array of random integers in the interval [0, 10)\n", + "np.random.randint(0, 10, (3, 3)) # deleted a bunch of cells at the end" ] } ], @@ -817,9 +710,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.6.4" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/notebooks/03.09-Pivot-Tables.ipynb b/notebooks/03.09-Pivot-Tables.ipynb index 726d2019f..1f2d9e279 100644 --- a/notebooks/03.09-Pivot-Tables.ipynb +++ b/notebooks/03.09-Pivot-Tables.ipynb @@ -60,11 +60,165 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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survivedpclasssexagesibspparchfareembarkedclasswhoadult_maledeckembark_townalivealone
003male22.0107.2500SThirdmanTrueNaNSouthamptonnoFalse
111female38.01071.2833CFirstwomanFalseCCherbourgyesFalse
213female26.0007.9250SThirdwomanFalseNaNSouthamptonyesTrue
311female35.01053.1000SFirstwomanFalseCSouthamptonyesFalse
403male35.0008.0500SThirdmanTrueNaNSouthamptonnoTrue
\n", + "
" + ], + "text/plain": [ + " survived pclass sex age sibsp parch fare embarked class \\\n", + "0 0 3 male 22.0 1 0 7.2500 S Third \n", + "1 1 1 female 38.0 1 0 71.2833 C First \n", + "2 1 3 female 26.0 0 0 7.9250 S Third \n", + "3 1 1 female 35.0 1 0 53.1000 S First \n", + "4 0 3 male 35.0 0 0 8.0500 S Third \n", + "\n", + " who adult_male deck embark_town alive alone \n", + "0 man True NaN Southampton no False \n", + "1 woman False C Cherbourg yes False \n", + "2 woman False NaN Southampton yes True \n", + "3 woman False C Southampton yes False \n", + "4 man True NaN Southampton no True " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "titanic.head()" + "titanic.head() # added a table" ] }, { @@ -86,7 +240,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -102,112 +256,73 @@ " vertical-align: top;\n", " }\n", "\n", - " .dataframe thead tr th {\n", - " text-align: left;\n", - " }\n", - "\n", - " .dataframe thead tr:last-of-type th {\n", + " .dataframe thead th {\n", " text-align: right;\n", " }\n", "\n", "\n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
fare(-0.001, 14.454](14.454, 512.329]
classFirstSecondThirdFirstSecondThirdsurvived
sexagefareclass
female(0, 18]NaN1.0000000.7142860.9090911.0000000.318182(-0.001, 14.454]First0.000000
(18, 80]NaN0.8800000.4444440.9729730.9142860.391304Second0.358696
male(0, 18]NaN0.0000000.2608700.8000000.8181820.178571Third0.226361
(18, 80]0.00.0980390.1250000.3913040.0303030.192308(14.454, 512.329]First0.647619
Second0.586957
Third0.281690
\n", "" ], "text/plain": [ - "fare (-0.001, 14.454] (14.454, 512.329] \\\n", - "class First Second Third First \n", - "sex age \n", - "female (0, 18] NaN 1.000000 0.714286 0.909091 \n", - " (18, 80] NaN 0.880000 0.444444 0.972973 \n", - "male (0, 18] NaN 0.000000 0.260870 0.800000 \n", - " (18, 80] 0.0 0.098039 0.125000 0.391304 \n", - "\n", - "fare \n", - "class Second Third \n", - "sex age \n", - "female (0, 18] 1.000000 0.318182 \n", - " (18, 80] 0.914286 0.391304 \n", - "male (0, 18] 0.818182 0.178571 \n", - " (18, 80] 0.030303 0.192308 " + " survived\n", + "fare class \n", + "(-0.001, 14.454] First 0.000000\n", + " Second 0.358696\n", + " Third 0.226361\n", + "(14.454, 512.329] First 0.647619\n", + " Second 0.586957\n", + " Third 0.281690" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fare = pd.qcut(titanic['fare'], 2)\n", - "titanic.pivot_table('survived', ['sex', age], [fare, 'class'])" + "titanic.pivot_table('survived', [fare, 'class']) # modified a table" ] }, { @@ -216,66 +331,110 @@ "source": [ "This immediately gives us some insight: overall, three of every four females on board survived, while only one in five males survived!\n", "\n", - "This is useful, but we might like to go one step deeper and look at survival by both sex and, say, class.\n", + "This is useful, \n", + "but \n", + "we\n", + "might\n", + "like\n", + "split into different lines to go one step deeper and look at survival by both sex and, say, class.\n", "Using the vocabulary of ``GroupBy``, we might proceed using something like this:\n", "we *group by* class and gender, *select* survival, *apply* a mean aggregate, *combine* the resulting groups, and then *unstack* the hierarchical index to reveal the hidden multidimensionality. In code:" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 12, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Multiple output with mixed types\n" + ] + }, { "data": { "text/html": [ "
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survived
fareclass
(-0.001, 14.454]First0.000000
Second0.358696
Third0.226361
sex(14.454, 512.329]First0.647619
female0.9680850.9210530.500000Second0.586957
male0.3688520.1574070.135447Third0.281690
\n", "
" ], "text/plain": [ - "class First Second Third\n", - "sex \n", - "female 0.968085 0.921053 0.500000\n", - "male 0.368852 0.157407 0.135447" + " survived\n", + "fare class \n", + "(-0.001, 14.454] First 0.000000\n", + " Second 0.358696\n", + " Third 0.226361\n", + "(14.454, 512.329] First 0.647619\n", + " Second 0.586957\n", + " Third 0.281690" ] }, - "execution_count": 4, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "titanic.groupby(['sex', 'class'])['survived'].aggregate('mean').unstack()" + "titanic.pivot_table('survived', [fare, 'class'])\n", + "titanic.pivot_table('survived', [fare, 'class'])\n", + "print('Multiple output with mixed types')\n", + "titanic.pivot_table('survived', [fare, 'class']) ## multiple tables in one output\n" ] }, { @@ -910,74 +1069,25 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 7, "metadata": {}, "outputs": [ { - "data": { - "text/html": [ - "
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\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mbirths\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpivot_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'births'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'decade'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'gender'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maggfunc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'sum'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mbirths\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpivot_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'births'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'decade'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'gender'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maggfunc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'sum'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'births' is not defined" + ] } ], "source": [ "births['decade'] = 10 * (births['year'] // 10)\n", - "births.pivot_table('births', index='decade', columns='gender', aggfunc='sum')" + "births.pivot_table('births', index='decade', columns='gender', aggfunc='sum')\n", + "births.pivot_table('births', index='decade', columns='gender', aggfunc='sum') # converted table to error" ] }, { diff --git a/notebooks/04.14-Visualization-With-Seaborn.ipynb b/notebooks/04.14-Visualization-With-Seaborn.ipynb index a16416ef1..652627b2a 100644 --- a/notebooks/04.14-Visualization-With-Seaborn.ipynb +++ b/notebooks/04.14-Visualization-With-Seaborn.ipynb @@ -57,9 +57,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", @@ -79,9 +77,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# Create some data\n", @@ -100,15 +96,13 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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H3PzFzWwq28SW8i2UmEoIVASSGCx5ESgVSqakTOFPa/5EdnW2X/pXairF7DLz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\nSKoIUuBLJBJJFeH/OyKinWS1qMsAAAAASUVORK5CYII=\n", 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -118,7 +112,7 @@ "source": [ "# Plot the data with Matplotlib defaults\n", "plt.plot(x, y)\n", - "plt.legend('ABCDEF', ncol=2, loc='upper left');" + "plt.legend('ABCDEF', ncol=2, loc='upper left'); # recreated same plot" ] }, { @@ -136,9 +130,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import seaborn as sns\n", @@ -154,16 +146,14 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, + "execution_count": 8, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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hJGWVk9eYtpea8HujHDvQm7fdUT692tTOSitqjYq25kGe+fcPee2FY+OOa28e\nYs/O1jEuf1dvYEyhl6xnYumqci420skwUf8pAPz9u8ikY1McAfFwN4moi3i4m0wqSsj9Ea5TP8PV\n/BiJSP98i1wwLoyplUAguKCRZZlXnjkC5K/LVtXauev+9Xlj9QYtn/78WvbubKO2oZjaxSWUOqdn\ndY/GUW6hqNhI++khEvEUrt4Ag/0hGpY6cFYoyjarvAf6FOs8G4U9EeVVNvq7/STiaZatKqerzYvF\npufyzXWTHpdFrVZRXmWjt9NHOJhgyBUi4IvmXPdZfvvsUQCc5Zace739jJvfPXsUe4mRO++7DL1B\nq0TVt3ooq7JOut7+cSXiPTrqm0wq4UdnnLiTWiadwHX60dx3vWURKvXI+GjgDDpTxXyIWnCE5S0Q\nCOYdvzdK0B+jepGdT3xq+ZTjyypt3H7PWtZtrJuV4oaRNeZUKkPLyUFOHFJc3ZdtqssFpY0o7+Dw\ndSe3oEd7ANZurOXer2/kzvsuw2SZfL17NJs+sZj1Vy1i/dX1ALSeGszbPzqYbd+77TnrO9vtzOeJ\n8v5brQDs3ak0Yll/Vf20r/9xIhZqB8BcehkA6eTk6/OxYGve93iog3ioE0mlHd7fUngh5wmhvAUC\nwbyTjYaeKAd5vlg6nAp16mg/Q64QeoMGZ4U1F1x2tvLOWuQTYTLruPnOS9i8pZFSpwWNRj3j+ymr\ntHHFNQ2suqwKSVLKlI52hY/OI/cORXJV07JR7nqDhhOH+mg7PUjHGTcV1TbqFk8dif9xQ5Zl4qFO\n1Do7enM1oASvTUbENzZ1MJOOYixagc5YSTzcRSZT2EyA+UIob4FAMO90tSnKu3rR9FKZCoW1yEBV\nnZ2+Lj9+bxRHuQVJklCrVZiteoL+GJmMzGB/kOJS07RymhuWOlm7YeZr8GdjNOlYua4KnyfKkQ97\nctuzjVmWr1Hct+1nlOYgnqGwEidwnZK+9bvnlPXydRtrL6oo6yzJ2ACZdBSDZRFqrTLp8nT+hsHW\np0nGxpahTUT6z3Kzj2CwLkJrqgA5Qzoxf/XnC4lQ3gKBYF4J+mN0nHFT7DBNmIY1n2SVIICjbMQF\nby0yEArG8Q6FSSbSU7rM54MN1zYgSdB2esR13tnqQaNVccU1yr6OFjeZTAa/J0pxqYmK6qLcWItN\nP60Uuo8j0WErWm+pzylvgKj/JK7Tj+YFn8WC7bjO/ALI4Gj4XN54AJN9JRqtEu+QEsr748+hwWPs\n6ftwocVTUqAUAAAgAElEQVQQCM5rPnyvnUxGZt2GhbEQG5eP1OYudphzn21FBmQZWk4qinOqYLX5\nwGDUYi81MTQQQpZl/N4IPneEmvpiLFY9pU4LQ/0hfJ4omYxMcakp7x4al5fl6q9fTMhyhpD7AJJK\nh8m+PF8ZS2oy6Sj+vrdzm4KDe5HTcYprP4XJvpyqlX+Ks/H3ATBYG1Cp9ah1yt9/qnXz8wWhvOfA\nI0d+wRMnniaZSS20KALBeUnLyUFOHu6nxGnOK8V5LtFo1Fy+eREqlZTnts8GrWUD2aZbnazQOMut\npJIZ/N4oHWeUPPRFw/XES8rMpFIZdg93NauoKUKlktAMNz5ZvOzitLrj4S7SyQDm4lWo1HpU6hGP\nTkntp1Bri4hHunPBfsm4G0ltwDIc2CapNBisjTgXfx7H4nsAUA9b3heK21ykis2SeDqR+9wX7qfO\nOnFNY4HgYuXYAWUt98Y7Vo7bsONcccU19Vx6ZS1a3cgrL6u8I+EEZquOYsfCpFqVllngmIsXf3Uw\nVyCmtl4JQHOUWTiNi642Lza7gWXDbUrvvO8yXL0Byqsuzj7bqeE1bZ1Zee+O9ujoTJXozdVEfMdJ\nJ/yodVZScQ86Y2XeOEmSMBYtyX3XDFveKWF5f7xxRUaaGnQFeyYZKRBcnEQjCXo7fZRX2SguNU99\nwDwiSVKe4ob8Gua1DSULFvSVrRwXCSVyZWKz0fClo9bo119dn5sAlZZZxtQmv5hIxhUPhUY/EmWv\n0iiTL63Bic6kFOEZaHmSeKgL5Axaw+ReCmF5XyS4wiMBJl3B3klGCgQXJyeP9CPL569r11FuwWLT\no9WpueTSqgWTo6quiMs21fHR+505ubJKubRsZNJzdsvOi410MoQsp9Do7KSGlbd2lPKuXP515EwS\nSVKhNyvZAKm4m4EzjwOg0U9ePlal1iOp9RfMmrdQ3rOkf5Tl3RG4sGriCgTzTcAXZd877RiMGpau\nOj8rVhmMWr74x5sWWgxUKhUbr1uM3xul5eRgXkS80aRj623LsRUbF3TZYaFJxoZwNT+GJGmpXvUt\nUnEvkkqLSjMyuVFrRz7rzDWU1t+Jr+f1nDKeyvIG0GiLSCV8yJk0kko9K1lTCT+ZdJzgwG60xgps\nZVfO6jxTIZT3LOkPK8q73FRGZ7CHoagHh/HiK5QgEIxH+xk36VSGq7Y2TdjoQ5DP9bcsw1lpZdVl\n1Xnbz9fJz7kilQgwcOZJMimlSE06FSWV8KDRT7zUIUkS5uJVGKyN+PveJpXwYbDWT3ktg7WB4OBe\nwp5DWByXTUs+WZaJhzvRm2uRJBX9p/4tJyscnjflffFO5eZIe6ATi9bMJ+uuA+CNzp2kznHUuS/u\n5+jQCYaintxkQiA4H8iW8qyqLZpipCCLTq/h0o11aLWzs/g+rni7f0s66c+tScdDHciZZN5690So\nNUZKam+hrPHzeTXMJ8JWvhlJ0uDv3zmtJicAUd8JBpp/gbvjBeRMapTiVpDl9LTOM1OE8p4FnpgX\nX9xPY1E9l5atxqI1s6vnfR46/BjpzPz8ocbjJwd/xv87/O/8f+//L/5+7z8RTUXP2bUFgolIJtMM\n9gfRaFUUXYTNMgSFIxX3EvWfQmeqwlZ+FQDBwQ8A0JuqJzt0Vqi1VmzlV5FOBvH1vAGQ19ltPGJh\nZdk04j1KyH1wzP5U3FtwOUEo72khyzKdwW4SaaXmbauvHYDF9nqMGgPfu+JbrChZygnPaXb1vH/O\nZOoLu/K2vdf7wTm5tuD8JRHpQz6HE8iziceSPPp/3sU7FMFeYrooC4gICkdwaD8AVufGnKUdH25G\nYrKvmJdr2iquRmNwEHJ/RMR3iu7DPyTsHb91K0A64ct99vZsB0BvqcsFyCXjY0u1FoKPvfKOpWI8\n+NFDHBk6DsDevv388/6f5uVpT0ZGzvCzo7/kf+/7Mds73gSgNdABwOKiegCKDXa+sOIuAE56mgt8\nB+MzOmAui6j2dnGTiA7Qf+rfhstALgwDfUEyGcVSqawRLnPB7JFlmYj3CJJaj8m+Au2oaHGtsRyN\nvnheritJaooqrgNkhtqeQs7Ecbc/S/osd3iWZGwISTUc1zHsIi+pvQ171VYAfD07pmyYMhsKory/\n//3vs3nzZm6//fbcNr/fz/33389NN93EV77yFYLBhQm/P+45TbOvlYcOP0ZGzvD4iado8bfT6m+f\n8ti9ffv52ZEnODio9CHuH7Z0OwLdqCQVtZaR9BK7vohivZ2j7hO82PJbEtOcHMyWZu9I6zqtSkO1\npZK+sItIUrjOL1ZSMaWBRSLcTTzcvSAyDA0oL6m6xSVctml6Pa4FgvGIhzpIJ4OY7CuRVJpc+VIA\ne+WWeb22yb5yTP1z16mfExzYSyzUkduWySRJxb3oTBWYilcBYCxahkZfmptspOJu/P3vFFzGgijv\nO++8k5///Od52x555BE2bdrE9u3b2bhxIw8//HAhLjVj/PFA7vOJUVZxT6gv9zkjy7xzuJfdR/tI\nDbfmS2fSPH7iKQ4NjbhLwskI6UyanlAv1eYKtGpt3rUaipSX1Wsdb7G3/6N5uZ8s7YEuAP7s0q/x\nvSu+xSWlSo/kjmDXvF5XMDcSkX4G254hFuoYtz3hXBidnzrY+hSerleRJwmiTCfDRANnCiqDe1h5\nX33Dkhn1uBZcvCQifTn3eDLuIR7uJuB6j7DnMACmomUASJKK0kV34Kj/vbzKaPOBJEkYbSPXsJVf\nTSrhxduznYEzT5BJx4FspTcZrcFJcc0tOOp/D0fDXUiShMbgwOrcOHyPha8FUhDlvX79emy2/DJ9\nb7zxBtu2bQNg27Zt7NixoxCXmjGuyEgxlZ8eGplgjK6KdrjFzb+/epKfvXyC/3y7hXAykueCXlrc\nhEVrJpAI0Rd2kcykqLONLYdaZR5J6Rg9OSgkhwaP0ervoDvUi06lpdFeT4W5nAabMnFo9wvlfT7j\n7f5tLjp1qO1p0skwoDRa8PfvylWOmgnpVISAazdh38hEM5MKExr6kFiwlVTcy2DLr/H37cw7ztP1\nEoMtvyroJGLIFUKrU2OzTx3ZKxDIskz/qX/D2/UKiahLWfY5/Si+3jcIe5TgL62pMjfeXLIGU/HK\ncyKbcXhN3WRfib1qC87Ge4eFzpCMKl7YrIdLZ6xArTFiKl6JJClqVZIkimtuQmsoIxkbQJYzY66R\nSSfG3T4d5m3N2+Px4HAoSfFOpxOPZ+YvpUIwWnkD3FK/FYNaz4eug3QEunjwo4f4bdvIxOKN/d38\n/PB/8KtTzwJwz7JtfH3NH2DTWQkkAnQOK/3acWqZX1uzmaurlJnWOz3v82LLb8nM8g8zHh2BLh45\n8gt+tP//0hPqo9pSiWr4h1I/bPULy/v85ux2g6nhYJeI9xj+vrcZaH580uNlOUMi6kKWZWRZJpOO\n03/qZ/h6d5AYfpEU134qNz4e6WWg5UmigdP4+3fmpa1E/acBCA7uKci9pVMZfO4IpU7zRVu282Ik\n4j+ViwCfKdngM4DgwPvIwxZtFpXaiFpjYSEw2hopW/IlSupuz30vqfsMoMSXAMRCbQDorQ0Tnkdr\nLEcedq+PJpUM0nP0n/F0vTIr+c5ZkZbp/mN2OufeU7fT18Oe7gM0ldQzFBvCbrDx6eU3UFtUxZry\nFXSEOzk+2MwPP/xX5QA1mI238+VPL+Wht1/llP9U7lzXL91AicmOw1JMb7ifvoTi/lhTuwRnab6s\nTqx8s+rLHP/NKTxRH691vMXKqsVsrltPIp3kl4ee4/r6TSwuGVkLzMgZ/vc7P6Xc7OT+y++e9L4+\n9OZHl1dYKjnY6uGGjYtwyBb0Gj2hVHBaz7AQz1kwOWc/40TMR2cykLfNbIhT7LSSDikvrXQyMOHf\nRpYztB1+Eq/rMJKkRqXWYi1pzIt2BWhYdg01Des4susfCPTvyttn0vmw2OvJZFJ0SiqQM8RDnRRZ\n0+gMc+uq5eoLIMtQVWs/Z78v8Tuef6Z6xvsPPAVA3ZJNaLQzSw1s6x+1LDnsJl9y+QP0t71J0HMG\nrd5MWdkCNl9xrsr7atY34OkENR4cDjM9RzvQGuxUVtdNqOMy4UVEvEcwaP2UOOtz29uP/RY5kyDs\nPgD8/oxFmzflXVpaytDQEA6Hg8HBQUpKpld9bHBw7oFtP93/BK3+DiQkZGSWFy9hY8lGovEUr+9u\n49P1t3J88F/yjllZX8SRyJvoFp0AoEhnY33FOtJhNYPhIAZJaTl3qFfZb0haJ5T1irLLcpHpD77/\ncz5oP0y1pYrfNb/Nns4D/MNVf00gEaQr2IssZzjQp/yAb6+7ddL72t2sRMyr0gYy6hi79nh5s/8g\nTpueaoeZIq2VoYh3ymfodE4su6AwjPeMQ24l8NFedQMafQlDbU/hGeonpV6M39OfGzcwEBj3RRAN\nnMHrUl5wspwmnUrjGziWa8QQ9hwCYMgdQZZVSJIGWU4BKuzVW/H1vE5/13GKkqXKGtwor1Bvx3HM\nJavndM8tpxVrxGjWnZPfl/gdzz9TPePREdh9Xc3oLQ2E3QcwWBej0U8+GZQzabwDx1BpTLnCJpJK\nSyzlIC0rk4BMRjqv/sZyxgSoCHi76e1qI52MYLA2MTQ0cTR5IqM8B7erjbR6sXIeWcbTe2BOshTM\nbX52IvuWLVt47rnnAHj++efZunVroS41JdmmITKKTPU2pUj9v796gn997gg9XWruXf57eceUNfjZ\n51IeppzUcnPJPdzZdFtuv02nzD7dMQ8OQwl69cQlH2+p38rfXvmX3NZwEwC7+/bxTPOLAAQTIRLp\nJP99zz/y00M/54kTT+eOm6oYQE+4BzmtJnxoM5dbryE1sAiAviFl3bRIbyOUCJ/TQjGCEVJx36R/\nw4j3KAAm+3I0OiWNKus2Tw5Hiivbxi/qkBx21RmLlo+qFiVRUnd7rjViFkmScmOsZRsxl6wFJCK+\nE0o5x4gSk5HtbxwfFUE7W7xDygu4xCkKs1wsJMIjsUOJSB/xUDuerpfpPf7jXDzHRMSCrcjpOObi\n1WiG647byq9BUmmwV30Cnakq57I+X5BUGrRGJ8lIf87lr5uiWIzWqMRCJaIjntN0Mjg8sZ49BbG8\n/+Iv/oK9e/fi8/m4/vrr+dM//VMeeOABvvWtb/Hss89SXV3Ngw8+WIhLTcqRoeP8puV3hM/Kx1tR\nqkQrfnhKUeqnu/zcsGhR3pg3h14C4DOV9/DrF724dLAXF0++fpp1SxwsWjXiOqq0TF5rWKvWUm5y\nckvDVjZVred/7P3nXPUzrUrLzu73iKaU0nuhUT/wcCqCQWVk38kB1i8rQ6sZmVuFkxFCGS+ZcDGk\ndHQdLYWMMiPt8yj3W6S3ISMTSAQpnqMLVDAzvD07CA7sRm+pw7l4xAWWbaAAMrFgGzpzDRp9MZnh\nv38q4UeWZZKxkbz9ZKQ/r1tSbntM+f3aq7agNThIJ8Okk0F0pgrkzNjUxJJFnyHqO4G98hNIKg3G\nomVE/ScJew4TGtwHSFjLriTsPZqX/jJTMqkY6XQEz/AkcqHbfwrOHfFIz6jPvXmWeMh9gKKKq8c9\nTpZl/P1KAKWp+BLMJWtIJfyY7ErWjEZXRMWyr86j5LPHZF+Bv+9tfL2Kd1U3KqBuPNQaE2qtLRfk\nBiMTdJ2pmkRkdi2lC6K8f/SjH427/bHHHivE6afNQ4dHrrfGcQmHh9O8Gmx1uRQwgLa+AOWmsakG\n11Zv4pr6NTwl7eJ0t4+3D/YSjafYd2KA1ZeNrLtUmsunLZNdX8SfrvsqDx9+DH8iSCwdy1n4WXQq\nLYlMkkA8yL7TXp547TTBSJIbr6jNjdnZtRskyPiUtoAdriAqSSIjy/S7RyxvAF88IJT3OSQR6SM4\nsBuAeKiTqP8kVFyFLMv0HlfiKhwNnwNkjLalAKg0BqX9YMKHu+MFMukYKo2FTCpEwPUeRvvyXNRq\nlmRsECRVrtKUWmvOdVLSW+oxl6zLvfxACbAx2hpz363OK4j6T+LpVLxA5pK1aA0O9JZFxALNpBI+\nNLqZ/276T/+cVNxNwLMVnV6DySIakVwMZNIxwu6DgLLEEw+2k1CNpM8GXO8Qdh+gtH4b+mHPUCad\nYKDll0iSlkSkF1Pxqty+qZTg+YK5eDX+vrdzE2atcWp9oDWWEws003XwB1Su/GNSccXjZi5Zg24a\nx4/Hx6bCmi+eH8W7vGQJl5at4db6T6JWqekeHFmT6HQF8YeS3Ff7NWLHlI4vxXo7dzbdhlGvodZp\noaUnQDSuuDXiyTRl+irUktIwIJuWNV0W2Wr5h6v+hpsXKYUFekJ9lJkcXFW1gWK9netqlJq9gUSQ\n/acHAZnDXaMKAcgZ3up+FzmlodEwsi553boqNGqJPrcy27UPFzHwJ/KDogTzS9YiNhYpijMbVZpJ\njfzmsvnUo6tCaXTFJGODw1WkDDgXfw5zyRoS0T7c7c+TinuR5czwfzLJ2BBavWOMUodsDuynMRYt\nnVBOg7UBR8Nd2MqvweK4IlcBKpszG/XPvDqgLMukhss/phIeShymSYNTZTnNYOtThIbmtw6CYGKi\n/tP0HP0XEsNLJ1Mt101EwLWbdDKAreIaiiqvJ5OOkk4GMFgblXiLTJJUwovr9KM5izwe7lKKCA1H\naRdVXl+QezqXaPTFmOyX5L6rVNpJRitk25HKcoqQ+wDJmGt4eykldbdNdujEcszqqPOQI0Mn8r6X\nGR1cV7OZUCBGMpmmuUtR7ktqimju9vOzl49z5SXlyGE7Nxd9kZvWLc8VXbmkoYTO4WITRr2GaDxF\nIqznn679O/rCLurGSRObCkmSKDeX5b4vLqrn88s+i4zM7uGa5INhH6c6Q2hqTtNmb+OUu4JlpU10\nBXuIpCKkPTWsbajgxsuMPLnjNNetq+J0t48+TwRZlnOW9+jCNIL5J5ubbbAtJuo/mXOJJWMjNY3D\nbkVZabQjHhyDtZ5kVAlUK676JHpzDVqDk2TcQ8R3jGigGbXWikptwNHwWeRMYlo9iSfDZF8xpia0\n0bYEL8pL3eq8YkbnS4+Knm9a3InRGqDv5IdYHevHbakYD3UR9Z8i6j81ZcvFufRUFoyPLKfxdm8n\nnfTj6f4taq2VdDJIedN9SKqZqYOI/ySSSout/CokVMTD3UiSiuLqmxhsezr32wYlDcxetTWvWIla\nZx93eehCoKTuNjKZRF4hl8kwl6wdrnzYRWBUtTWNbvb3/7GxvLuC+eUgHcZSopEET/x0D7/9zyMc\nbfNQAtTH0qysLuJEh5eTHcpLdnFpNbpR1dKuWzdS9vS6tcrnnqEwOrWORbbaWeewrnaswKhRgoia\n7IuVoCJJlQuGO9XbTzojo61SZqX7e5Q83FNexWrLBEqpKTNz6VIn//THV1FXbqXMbiSeSBOMJinS\nK0FQZ3shBPNL1tI2WBoAKecSS41TcGV0icfRStRgU6JQVWo9ZU1fxFS8CjmTIBV3k4j0EPEqmQY6\ncy2FRqMrQmusIBZqz1WOmi6jX8YV5W6KTC0ko/0EXO+NOz42Kq93IotPzqQYbH2arkM/wNf39ozk\nEUxO1N+sTC4lNYlwN1HfCRLhbtydL5FOhif8m2RS0bzfRiruJRUbwmBpQKXSIqnUlDV+Hufiu9Ho\n7aiGa31r9KWoNRaCg/vIpOM5a99gbcTZ8Ln5v+F5QqXWU9b4eazO9dMarzOWUb70D4YDR0cY/T6Y\nsQyzPvIcEIom+W///gEfnHBNObY31I9KUvGdy/+Eu5fegdNUSt+wtd3T4aOrw0sjKnxDEWp1ygzz\n/WPKeR22/GpQZcUmNq4sp7LUxOXLnMr5hyaPnJwORo2Rv9/8V3xp5T2sL1+X2561mA+E30G/9u3c\n9uOdijv2lEdR3ulACdWO/IIFJVZFdm8gjtPoQCWpeK93r+jvfQ5JxT3Da9HFqLW2kQjycboJja6X\nrDPVIEka1Fpb3lqzSqWluPrGvOP8LmW2brIvm49bUCwIOU3Ye2RGXcliwfb8DapStMYKUgkvsVAH\nIffBvApS8WBb7vPocq6jCXuPKnEDyAT6d80pmE6QTzYwsrTu01idG9FblMDdiPcIPUd/RO+xBwkM\njC3a033kn+g9NpJeGw0oSyyGCcqUSsPZOFqDE4vzCiWf2XOERKQXlcaCs/H30ZkmD/z9OFJceyuV\nK/4YS+nlmEsvG3cJbLqc18p773EXna4QD704cTs2UNaEe8L9VJjKaCiq49qazQD094xYoHXpkRll\nZCA/J6/ENrYG8x/evpL/8dWNVDmUgKB+z/gdZWaKUWNkQ8VlaEe5qLKWN4BKP9IAfjDi4cCZflr8\n7ajiNswaM/azgoGysnuDcYr0Vu5eegfhZITXOt4qiLyCqUnFPWh0xUiSCo3eTjoZIJNJ5daCDdaR\noLHR/1glSaJq1Z9RufyPxpxTrbVgsC7OfZfTcbTGilkFlE2H7Fq5t+tV+k78NC91bSKSMTch90d5\nExJL+SdyrveB5l/g6fzNcBEKpcjM6OjkVGxojKUnyzLBwb2ARHGtUvcgFmyd072dT8hyOueZWQiS\nMcUbpDNXU1xzE+VLvoSz8V7UWsVrl04G8fW+kbfko1jcMpl0jPRw1kw2hsNoaxr3OiU1t2AsWkpx\nzc1YStcBEt7uV5U1ccvEBU0+7qhUWrQGByV1n6J0lmvduXMVSKZ5we2PTT0I8MS8JNIJqs5K4err\nHlHeeiTUWjVNK8uIhZPYtcqt28w6tJqx62oqSVKK0+s1WIxaBr3z163LprNiVlvIhIrYzH38xeXf\nUGTQx/iP3ftIZpIkvMXUlVvH/OiLrYry9gSVZ3VV1UZKDMXs7d/PE8efzqWoCeYHxZ0YzUWAa3RK\nQFoi6iERHUClNuSsm/HYv9vFwX3je0mUJgcjrkVzyZoCSp6PzlSFsWgpap2dVMI7rXKXwaF9IKex\nj/ISFDkWYypakTfx8HS9Qs+xf6H36IN5hWECA+/TdfDviYc6c9sivuMkoy5M9pW59cRUbGFKK88H\nAdf79B7/Ma7mx4kGzpCZ5+6DZ6NMKFV5k0CjrZHyZfejUhvR6B0gp3M1CZRjRuoOxIKtZNIJ4sF2\ntIayXL2Cs9Hoi3EuvgeNzoZaa6Wo4lokSYPWWEFxzS3zdn8XE+e18m7tGwmGSWcmrhHePdwEpNo8\nkmrgHggx2BekvNpGWqcoZ2eFhZJhS3pjo5NSi44Ny8vGnvAsyoqNDPljZDIysUSKjCyTTBWuEIpa\npWZ14veIH7+SyxqrWVy0iGK9Hb05jk9S1hTTgVIuXTI2WKlk2OXvDcZz/w/1Ka7+Pf0fcsx9aswx\ngsKRDVbLBt5oDUobwKHuD0gnfBhsTai149dmHuwPsn93B3t3tuF1j/XsqDQGDKNSvSwl68aMKRSS\nJOFcfA+VK74O5BeNCQ59iKfzlTwrWZZlYv5mJJUOo20Z+w6s41Trpag1BlQaA2VNX6Du0r/NWeXp\nhJ/0cPS9YVgpx4JKW9vRE4VA/y6Q1NirtqDW2pAkzbixAxcq2frz8VA7gy2/ou/k/yOV8JPOyLzX\n7yVSwPfKeKTiHjT64jHuWo3WSvWqb1PWpNQoiPhO4un+HelULK9okLv9WQbOPI4sp2bU2auo8jpq\n1vwXKpZ9NZfeKJgb563y7nOHae8fUd5Dvomt8NPDAV0NRSMWzntvnEGWwVhl5WgiSUyr4uqtTViH\n3cyek4MsDqVYXTx1Nagyu5F0Rua3ezv4xj/v4ke/Psgf//MuntvVMuWx0+VYqx+DTsOSGmUmW2yw\nk1RF0Di7kWUJOVjM+nEmGjnLO6Ao7/2nBvG3V6HJKEp9MDJ23VVQOLJWSTYFLBtQ5uoYKUBhsq9A\nZ6qmtP7OvGMP7h2xOA/u6WQ8VCot9qobKKn7DCrN/HfqUqm0qHV2ksMu7VQigK9nByH3/rwqbKm4\nh1TCi8G6mGRSZmDARjIz1sNgK78KSaVFPyrQzmhrUiy8LMOKRJYzJGND6E1VwwpGQqMvIRl3zzqd\nab7IpvDNlGTcjUptpHzJlzEVryad8DNw5pfs7HXxStcQz7W5OOYN8djpHhLpwjU1Akif5SU6G0ml\nQa0tQlLpSMZchAY/4OCb/5WhtmfyxilBitKMPUGSSj2nNV5BPuflk5RlmbffeYEVzj70w1Zzr3vi\ngLGTnmZ0al2un7bXHaanw0d5jY1n9nWRAEovKcNZYcVyVnDauzvOcGCCF2cWh12pa/7szlZk4ESH\nl3RG5uXdHXgC03PtT4bLE2HAG2VlfQkatfInyQaxSbo4qe4l1JYWYx+nP3J2m3fYbX6iwwtJA+bu\n6wAYjE69dvnBrjae+fcPiYRmFmksUHKbgdwLUW8ayVRQacwYrU2o1Hoqln0Fc3F+kwNXTwCjWUtx\nqYnTx1wEJ1gmspVvwlK6dtx984FWX0omFcLf/za9xx7MFaMIDOwmk0kCIwFLxqIlBP3K78ZSNHZy\nYXVuoGb1d7E4Ls9t0+iKMJeMPIvU8PpqOhUG5Lw1dI2+BDmTIJOae8BooUgnQ/SdeIiBM0/M6Dg5\nk1YsX0MpeksdjvptWMs2k4q7ae5XPGS9kThPnunjtD/CUe/E9bJnQ7b2/WTpWZIkTZiOWL7sq5Qu\nugPIFvhxFlQ+wcw4L5V3MBziqkUtbFvdzNdvVizR/nHciqCsd7sigyy1N6IZDgI7cUhxo5cvVn6k\nFqOWO65R1uAso4LTqursGIxaDu7tIj3JLLdsWHmPR1vf3HOqD7cqL681jaW5bZeULMOoMpNoXU2q\nbzEVpeN7CLQaFTazDk8wTjqT4VSXYgm6XDIqScVAZGrlvX93B0OuEDteOjHlWEE+WZduVnmPzpW1\nOjdMmKecTmcIBeMUFZtYd2UdmYzMkQ+7xx17rsm+vHP5qJIaraGMWOAM7vbnAYhlo41tTYSGJ7BW\n2/ieAUmlRqMf+W2rdUXYyq7CXn0jao2FZFyx8tPDxYXOVt4wfuR+Ici2Vk1E+pVgssTUwWTujhdJ\nxVPVei8AACAASURBVIeIhzpmZH0r7mcZ7Sivg71qK7aKa/HJytLK6IiWgagyacrIMpk5eh7C3mP4\nel5DpTHlTaTGQ6UeayQAaPUOTMWrcTbeS3GtWLdeaM5L5e319OU+F6M0ZO+bQHl3BZU14UZ7PQB+\nb5RjH/ViNGvRDlsCt2+ux2ZWorTN1pEfZl1jCUtWlhGLJuluH78ZBChr3lnuv3UFjiIDX7pZSdlp\n65t7x5sjLcqLaVXDyIx4U9UVfGvln5MeUoreV5RM7N532g24/THOdPuJxpU1M1lWYdMUTWl5x2Mj\nxfH7uvxkMueXe3I6hGPJBbu2suYt5QXulC7ahq10GVbnhgmPCwViyDLY7AaWrCxDq1PTfub8WOIY\nbVHpLfXUrPku5Uu/jNbgJBpoJp0MEwt1KNHvWiuh4SUbyzhZGyPnHFHeGp0dSaXGVnYleksdciZJ\nOhnMpY6pRxWyyaYTFaJxytmu92TMTfeRH9J77F/oP/UIPUd+RO+xfx3Td3k0qYQvt1YPylo+QMR3\ngr6Tj5CMDk54bC6CW1eKL678ZiVJQiq5Cj/DpY0TqZwCbw9FSWVk/ulwO8+0Tp0uOxFyJoWn8yUk\nlY6ypvumLPQjDTe0MdpXcOkn/ydqnR21zo5KrVOCeG2N06oqJphfzkvlHQ6OVOYh0YtOLdM3gdt8\nIKL8Yyk3KS+c/e+1k0pluGprE/6I8g+keJTCVqtHbrnEYWbJJUpd2SP7Jy4O31ht4+YNdfztl9dz\n9ZpKfvj1zVyxXDnubMvbPRDid88dxTfN1LJ4Ms3JTh81Tksu+CxLqW1k0jCZ8q6vsJHOyLy2rwuA\ntcMWvCppIZQME06ML0vAF+W1F0bS8DIZmWj43Ea/ns2AL0rPUJhILMnjvzvJ6a7JLaGjrW6++eA7\nvLqng30nB3j01RMT/lYKjSxnhtPE7EjSiIVtLlnNksu/OqEFAxAYjuEoshtRq1VUL7Lj90YJ+BY+\nO2B07q7R1oRKpUWlNmCwNYGcxtv9O5AzuWjw4BSWNzCqC1q+ZZftJpWM9o8o71Gpk0bbEpDURLzH\nSSdDZNKzW6YKuHbTffh/Ewt1kIi6iIU68HS9jJyO56rEKeeWiQbGj2VJxoYYOPNLRcbhCUYy7iER\n6Weo7RmS0X6iwTMTypAto/u8x8kPD7cTTCoT5zOBkX+f8vB/AN2hKG3BCL5EikOeIF2h2d17Mu5B\nziQwFV+Czjh1gG5x9U2YitdQUnMLKpWGyhVfp3L512Z1bcH8cV4q72whgYTkRJZTXFKToM8dGTdo\nJesWLhtW3v29AXR6DU0rynIR2MUTWAQlDjNllVaqF9npavXw0q8P8fJTh3JuwCxqlYrPbWmivmJU\ndSyDhooSE+39gZxLKxFPceJQH22nh/iPRz4gEZ+65dvJDi+pdIbVjWPXoUyGERfsRG5zgPoK5WV3\noFl5Fp/b0oRaJTHQr/x5d545gjs6NmJ33zvtOY+DeTh/PBScet17PoOHfvyfh/mvP9vLn//kPd4+\n2Mvzu8bm+PYMhuhzK9Wg/n/23jM+jvM8+/3PbO+7ABa9gygEeyfFIkpUtbrkJtmxY8WOk/hYceL0\nxDk+r98Uvzk+aY6d4irLsmxLsmyrUJ2iKFEkxd4JoncsdrG9z8z5MFuJQoASJco/Xl9IzM7Ozu7M\nPHe77uv++a5uFODxXd18+6kT7Dk2yt//6CCJ1OUfi+ob+BVyOjptHOd8kDXSdqdq1OoyWZfB3vef\nWa3V2ahs/10sJSuwlOYZ7kZbEwBR/0kQNPjDTTzy7bdy99BckTdAWfPHKGv6SNG2bEtZLHAuZ0QL\n0+aixoDR1kQqPsHwif+P0VPfWtD9J8sppoZfwj/yEoqcZKLrh4yd+S8mun44azQfD/fOuD04sZd0\nwoeoMWKvUOcRpJNTRZH4XFF71nj3RNXYejiiPmtnAqrxXimcKj53BF4ezmfO3hiffuxTU2GO++bO\n/mX1BnQFpYu5oNXbKWu8O9cloTpvV4fNXGm4Io23IKkLmGhVF4628jDRRJrgDFGhJzaJgIBZsLP7\n0BABX4ykRiCZlnO9z1kVsixuuruT5etqsdoNCILA5h2LMJq0DPVNMdg7xVuvzU8UoqnKTiwhMe5T\nxyF+95/3FEXwhX3ms+F4tt7dPPeDVTEHK76pKu9U1LqtVJVaqC23IofVXs5nxp7gb/f+47RFr9BQ\nN7Wpzs+FjkshJEnmse/s56VfqbXxZCI950I6MB7iq9/fz8D47ItLz0iQr35/Pz0jQfzhRE7JLplW\na4neGc7nK9/dz1//zz6OdE0yOBHGZTPQVGVncYOLjnonkXian716npN9sxvCbz11gv/85YlZX78Y\npHSUiO84OmM5JRkxkYUgG3nbM3yKqkyXweT4u0tSulTozZWUNtyFRpu/7wyWekSt2uZjc69n3+se\nQoE4k+NhHC5TUUlqJpgd7dN01Q2WWkStmWjgLOlMzbtQ/x3AUbE1J3QjpcMLIq+FJw/mJr4JogFR\nY8TsWoatfCOljfdSu+IvcTffT+2Kv6Ru5d+g0TtIhHpnrGUno2pGsGbpl3OToNKJKRKRPFdhrra2\nVHwCmXy6+eGuEb51aoDzgShObZrFYt4JqEQ19AOZNU8nCpwLRJEKnre0LPPI+VF+0j2GNz57xizb\n9qc1zs94X8UHA1ec8VYUGbPoZSpqwJGZw11lU43g9549UzTa8/WjI4yEJigxOnnj2DhPv6BqgY9G\nkzyztw9fKIEoCDgsxV5jS0c5m3csygmelJZb+a0vbOKBz6+nxG2h6+TEvNKXTVVqhNA7GmSwZ/pD\n6524+EJ8ZsCPQa+hpWZmsYMHP7SY2zY1YDLMPjSgssSMOfP6XVsaAfjEjW3UXaCDHb8g5Rj0xxAE\nuP1jy6muVw19tn4JIMsyEwVlgYFuL1OTUc6fnuD1F7r47j/voefs7DW+f3viGAPjYX79Rh+9o0Fe\nentwmrF/+s0+BsbD/N3Db3PkvLrI3L21iW9+aSsd9U68gTjxZD6DEYrmF6mHXziLIMCXP7aSr3x6\nLX96/yru2KxGh68eGuYbjx3hmb19084rkZJ4+8wE+09PMH6JynnxUC+gYHYtmTM9PhsujLyzRjw4\nR0vk+w1Ro6d6yUNUL/kSzuobivgSa65puCTVLEEQMTnakdORjCRqceQNYLDWUb7oE9jcG4DZZVVn\nQlZspKz5Y9Qu/zNqlv0pZY334Kq5CYtrKaKow+RoVfW5BRGTvQ1Zik+bsKYoEqn4BHpzdYaAlyHS\nxcaJh3rR6OyIWsuskXe2DS5iKG6nG4okSMgynTYRO/n1olnMd8BUmw2sLrMTl2QGwnG88SR7xqbo\nDubXqFdGZnca8pH3OxtqcxVXFq44452IDKLXpOj2unA5XCozVa+yNI/3eNl7UvV+Q9Ek33/+BBEp\nTIdoYLRrhKbM14misHPfIIMTYRxWPaJ48UVFq9XgcJlp7VRrQlOTMy/qiXianrMezh4fI57RTu8d\nCRURvbLzjL0Tc0cIkiwz7otSXWrJtYhdiC3Lq7jv2pYZX8tCFAX+5P6VfO131rOmXT3/RTUO/vYT\n20ARECUtKBBKhkmnJRRFIeiPEQ4mqK53UtdUkkt5Fkbjb77czRM/PERvJh1/+liei3DikJphOHu8\ngJ9QgKGJcK73vG8sxNd/fIhHX+rieIGTk0hKnOpX/1aAh3eq7TJLGkswG3XUldtQgCFP/ncsJC4G\nwkk2L6vKSdgCuT75LH6xu5c9x0Y53OXhj765h3FftEinPns/zRepuBf/yCv4h18AKBJRWQiC/jha\nrYgp41jq9BrMFv27VvNWFOWykA9FUYdWbyccTBAJJXCUmNi4vZnWJRevpc4Gs0N10hU5hUbvnHW6\nVdaop1MX7/CQUmGS0VGS0RGMthbMjnaVHHYRB8NaugqAkGdfUS93KuYBRUJnUgl0otaCVu8iHupB\nkZPqRDhDCemkP6cNL6WjjJ39Dv6RV9SIXJHwa2aeWb21plolgwmqc6onxRbxbZYYp/jEoioWO9V7\n/KVhL0/0jvPs4CSP9eTv3VP+CNIs11slyqnSvVfxm4MrbiTo8OAxdEDCW0bQH0Nvrkbyn+avHmjj\n7x/t4vVjo2xdXs2QJ4JoDlGnFdliCJNo28VLw5sQNSLXbWzkiTf6SEvQUGG72EcWwZZhqAcDMy+i\nu58/y/nT+WjTIQj0jgWpL2jycDhNpFPSRSPvyUAcSVbmJKPNF4X1+EIsHt6GMGokrUvyfPc5EmGZ\ndEoiGwA7M7X0nPEuiLyzJYCxoQBNrWWMDgZwlJhoaC7lWKatSaefuRWqeyRfMihMff/6zV6Wt5Ry\nfjjA8/sGSKZkblxbxyuHhpBkBadVT0Omhl9bri5YQxNhFmUyE4WGV6sRuSsTaRdu+9TN7UTiKWrd\nVv718WN879l8C9zBcx6spnzq8sj5yVwb4XzgH36RWFDN8Gj1LvSmmRfjuZB1nmxOY5ExsTuNjI8E\nkSS5iFh5KXjtyAiPvdLF135nA+45Wh0vFeMjqgHtXFHNyg3vbNJZoZSqtXT2MaFZkpg0w7x6RZGQ\n0zGS0RES0WFCE/ty/emWkmXzPhe9uRKDtYFEuI/Bo/8AioSj6rqc46DPGG9BEChv/RTB8TeQUiFs\n5RsJTx4kERkkHu5DEDQEx98gGR0hGR3J1Y+nUCP2OosRg0ak0WaixKDFYbKRsDVzT3AnJ+VW2nUT\nCFIEnTaAy7Aeh15Lp9PCKX/+/k9IMhathk6XhQOeIAOROE224mutKAqpxCRaQ0kRqfIqPvi4ooy3\noigEvOdxGQWCHiev7TzHdTtqiPlPU2ULsqSphJO9PiYDMYY8YQRLgBJRXeQM+jQllV7ue+Amoikj\nT7zRB6htYgtB1njPJJgRiyaLDDdAvUFL91gIn7agv9dhRAHGhwMM9fnQG7SUV003rtne9dnIaJPj\nIcaGgyxZVX3JQv6aEdUA6pMmwr7pLVWWjMiL2aJmKLI178L0djKRJhFPk0ykqay1s2F7E3qjlrf3\n9BHKGPtTR0cYHw6y/VY1wslGy7df08jTb/bljtU9HORY9yT/+vgxFAUW1Tq4a0sTTVU29hwf5bdu\nas9lIerL1QVzsMAJGs4Y77ZaBxuXVlI6gzDI9lU1uf9/6SPL+ZefH8v9fWZgqog/MOyJkJbkWTMf\nhZDTMZVNLIiUt3xSTaEu8LpM+mNY9BqSCYmquuKF1u40MTYcJBJK5NLol4oDZyZIpmSO93i5fvXC\nCXUXQ/b5cM1BpJwvBFGLpXTVRWd8Z1noM6XNA6OvERzfM+P7TI6FTWJzN32MqeGdJGMe0nEP4cmD\nmJwdAEWTsLR6RxHfIUuA83T/eNoxp4Z2AgI+nECST7ZWYdMVL7/W0lXEQ4+zzTJCWdOn8A08TSIy\nhCwlEDUGPtZSyS96Jzg+FabOamQ0kuCTi6qISRIHPEHOBSLTjLeU9KNICfS2mQeIXMUHF1eU8e4b\nDeA0hgmHLciyyNRkBIO1EYCI9whtdZsxpU/Sf/oxntzTiNjgx1Ww6G5acYqRk6ep7PhdPnVLO+m0\nzJKmhQ07zy6aMxnvbPrYajeg02uIR1MQTbEEgZEBtaVp1cZ6Vm6o4+ThEcaGAvz6sWOIosBn/3gr\nR/YP0tLhxlliRlGUXBRZNUvk/dwTJwgHE1is+hyhbCEoJJ+ltQm0TQrtZW1ERkKEFIXBgSneHPDl\n6pWOEhO+yQiyrDBVEOFOeaN5IQ6HEa1Ww7otjZw9Ppbb/tpzajS6bksjcUWheziAANy2sYEjXR6G\nPBHu3tLEU3t6+eHOsygKLGkq4Q8/vBytRmTjkko2LikeLFNdZkYUBAY9qvHedWSYlw+qEf8ffmTF\nnDyALJa3lFHmMDKZuZ7nBvx4A3EEATZ2VrL35Bgnen3sPjJC71iQ379rKW11M6cXo4GzoMg4qq7H\naGucxxUoxu6jI/zguTNsz8jcZuvdWWT/DkzF3pHxTktyLvPRNRS4LMY7kimvmK3vDgu5pO42lJqb\n52Q1Z4ls0gxp83BmclkWosaELMUwOdoXzEkQtcackthk31NEp44RmTwECOgyRLWZYLDWT9tWUnc7\nvsGnATC7ljIRVDBrNVhnGIZkdnVSY/1jRK0FQRDUDEBkkER4AJOjFZ0o8tGWSu6VZTSCQFpR0AoC\nSUlCLwoc9Ya4oaYUTYFDmZ2frTcvPEN0FVc2rqia92uvHESrUYjE1fatWDSFqKvAYG0iHuqmwenn\n9s5u3IZB1tUNUO0IUKa9UCxAIew9zPaVNdywduHpPJNZh1YrzkgcCmYmi91wx2I+/tn13Pup1Tiq\n8ml5s0XPxu3NGE06lq+tzaWUZVnh6Z8dY//uXt54We0D/dUbffx8l8ourSwxE40kefbx4/Sey0f2\n2QXyyL7BBX8PgLFhdZFLl0t0LdvN4cggj+zt48GHtuAzajiLwuG+KXoyKVB3pY10Sibgi3L0QJ5B\n6/dFCQam9/Ja7QYioSRSOk8iHB8L8df/s4++sRAldgMGvYa//OQa/vyBVdx2TQNOqz7XwrdjTe2c\nEa9Oq6Gy1MzQRJhzg35+9PxZREHgxrV18zLcWdx7rZqWrXCZSKZlRr1RNiyuoLVOTcX/2+PHOHJ+\nkkA4yfeePU1yljaz7FCJ2cYgzoW0JPPU62oXw9EzaiukP55m74mx3Lz6rMEOXOIEu6A/xvf/dQ9v\n7ukjmVKvyblB/2Vp7YtkpHQvxjCfLwRBvGg7Uq7mnSyOvBVFyU0rM7uWUbfyr6lZ9idUtv8upfV3\nvaPzyrLjFSWN1lg6pziJSmZTv4OgMeCo2o61bDXu5vtx1d6KpepGphIpKk36WTM2Gp0195oxE7jE\nw31F+2hFUZUxFUU83Y8wcfIbLLEk8CfTnPUX82ySsavG+zcVV5TxToyqDMvalkU4MtFo0B/HUbkV\nAGfy2dy+N7T18xm3hjKtjrSkYXwiH2HHpk6RToXwDe0k4ju+oHMQBAGbwzhj5J016LbMImt3mtj2\noXZGMrIK0YJWNp1ew4d/ew0tHWrEnI3Mw8EEsqLw0tuqQdZrRVwWPc/+/Bj9571FrWaGTG12bDh4\nSUSm4X6V+bp+XS2SLoWgS6Ct6ub0RDfDnnwqevfREaLxNPZMCvTAnj7OHh+jrMJKbaOLWCSFN9PC\nZCtIU2cNebb+CTA4GCCVMeYWo3r+JoOW9noXGlFkTVue2DQfPkKt20I8KfGTl7tQFPjT+1dy/w3z\nn2YEaoT9jS9s5iufXseadjdVpWY+fkNr0effvL6OHatrmZiKcWKWXutkbAwE8ZI0nY93e/GHk6xu\nc2PPRF0vnxjlf54+xX/+8iSKolDiVksc3gKC3uk+H//wyMEZ+90vxKmjo8RjabqOZRZsnchUKMG5\nQf+73vceCSURRQGT+b1T2hJELaLWPC3yltORTJTdQVnjPQiCBkEQ0Jsr3/Ewl0JHLTvuddbzEzS5\n6Nvd9FEcldvUYzhasbnX4U1pUYAK0/yyFXprHQgi8WD3jA6YIqeJh3pR5ATtaTXzsGdsip92j+UU\n3HKRt6ly2vuv4oONK8p4O62qR11aUZ9LIQb9MQzWBnXBVKaLnjhIk0xaOHGqFaxrMbuWIqXDjJz4\nZ8Ke/fiGnlvwedgcRhLxNF5PMeEsFIgjaoScoAlAdakFQ0Y+VbbpCYQTuQfNWWJm9ab6acfoHg4Q\niadZ3ebmS3cu4YUnT+AZUz9rYlRlrktpWU3LZ9DfvTDpTFmW6Tk7icmso22R+uDqy0fQ1XXxyKFf\nMT4Vo6PeSYndwP7T4/z9Iwf5yR5VnKL7jAeNRmDH7YspzRiUrHhIofHOktwKhUU8BX3KMxnZlW0F\nus7zSLnWlatEn/6xEC6bYdaU9sXgshkwG7V84Z5l/N3nNmI366mvsLJ+cTn372jlo9ctYm3G0Toz\nML3dR1FkUrEJdMbyWfXKL0QskSYcU6/hqYyQyU3r6lhRrxqBQjkcbyBOSZkFURQY7J/in35ymHAs\nxXeeOU3XUIBn9vaz68gwkzMQKfe82MXeV7s5d0KN4JORFGbgw5kuha8/epj/9yeHp73vnSASTmC2\nzh5BXi5o9U6V0V3Qh52KqZkMnendH5QhiBosGRLdfNTJnFXXYa/cNuMM9/GYesUrTPPLVoiiDpO9\njVR8gmR0ugJkod67Sx7DpBHpC8c56gvxyoiPE74Qu4IuNDoXsmjgka4RnuobJy5dfgGjq7j8uKKM\nd0vzILIiojdVYncU972mnMuIo+PZSS2vh4sFFKIxI6LWRn3rh7CWrS16TZHiSOmFRa0dy1Vj9/yT\nJ4s83mAgjs1ezBAWRYG/+fxGeowih0Nx/uibb3CwoPe5xG3FZjdQVeegpcNNKilx4LjqDTfpNLz6\n5EnGhoO0dLhpW1pBKinh80RyacmaBtVY9XV5c0MU5oPhfj/xWIrmDjd2g2oAZUF1fgb8mc+vtrOh\ns4J4UmJkMkJQkklnsghbbmylxG3JsdGzKfiiyDvjYBVqcmdLC1+8bxnt9dMjlfY6Jy6bgc1LK+e1\n8Hc25jMqHfXOBRsLRVHo7/aSSk5fsDSiyO/dtZQb19UhCALN1XZ0WpGzA9MlWdMJL4qSzolzzIZR\nb4SfvXqe/SfHeOhfX+cff3wIRVE41e9DrxNprrYTnYqh0YoU5nb6xkJotCKuMgsBX5TT/VM8u7c/\nV2KQFYWHd57lP35RLCwTj6U4fnCYI/sGiYQSOYfKCVy7spoNner5do8E37X0uaIoRMPJd63evRCo\nTrxUJIaSjI9nXrv0drW5UFJ7C66627BXbL3ovnpzFc6q7TOOvhzPDBqpNM//d7O51fUsNLEP/+iu\noj7yrGIbgCJFqbXknYLD3iCPdo9xRGpjWN/JUCTBKX+E/Z4gu0ZmV4G7ig8Orijj3e11oS+/F1Fj\nykXeYx4ff7L7/+afjj/L97r1HBf9vJmK8rqQTwMNDJTm9i+aG5ypV6Wio8yFdGIqN+oQVBGXptYy\nAlOxXN05lUwTj6aKjFchWppKyLoUbxzPf54oCnz8c+u54+MrcmnRs11ejDqR8W4fRrOOO+9fwU13\nL6E6E1WODQdy/dYV1XYqa+0M9U3xn19/jUe+/RY7nzxx0bpodrJaa2cFGlGDJaOUpaS1JIQQxjUv\nUlIVKlJ2k4HjKBxCZvEKtUbmLCDTaXUiBqOWHzx3hrfPTFCRYdD7CtK8yUgCASifRRFOqxH5P7+/\niQdvU69NMpHOkd5i0WTRsUBVj8sOlWmunlnIZi6MDPh59ufHc61tc0Gn1dBSbWdwIpyLmLPIph/n\nIiwBfPPJ4+zcN8DXvrcPSVZJiT2jQZLeKO1VdpLxNIGpGCUV1qL39WdU6OIiiAiYgJ371TJSVv4W\n1AyEJOed19HBYhW/ilY1s1EuihzdN8hnb1vMykXqttg85Hrng1g0hSwruU6F9xLZkkVWNQwgntEi\nN1yCTO18IIhabGVr3rFE6FjGeJfPM20OYLA2odE5iPpPEhzbjX/01dxr2SEoola9lxzavHMmFfhp\ne6I1HPXmeQL94fdfP/8q3jmuKON9611fRnnyefq/+hXMDgOxGgtdfUEEv5H2o9fRcH4N5pAaie31\nnac3lUY2L2N03J0j+wiCQEXbg5Q1fSQ3PzkemX1ed8R3jJFT/45/+KWi7WWV6gNx6ugogalobmbx\nhQzhLGoKhEIuFIXR6jRoNCIlmX1S0STLqxwk4mlaF5dT06BGqFUZAtVw/1TOabDYDGzeka+7hYMJ\nes9NziqOkkqmefbnx+g+46HUbaGyRjWw22o3saVyM5JPNcqCRuLNqZeLlN1uWV+P3qBFIt+e5Sxo\nBaqotjPkibD76AjfeuoEKVFAq1NvIaNJp2YsZLAA5bP8TqBGvIIgMDke5vv/9gY/+tZbHHt7iEe+\n/RaP/+BtEhdMCfvr31rDzevr2LJs4aSbycz3mI/aHUBjxiEZvqBkEguqRMMsiWg2TMzgVD37SjfN\niJhHI4wPq8a2vtGFWJBF6B4O8PLBIU6OqRmOuoIF/pYN9VgKdO67h/M13+ELUvw/OzhIHAWdrGrX\njwz4sVvUunQw+u5MX8vdm++L8c4MMslEnXI6RjzUh95cXTTZ7UrEeCyBU6/FqJl/v7UgCJgcbbm/\nY4FzyHIKWU6RyBDZsn3yK+yqU7fdOo4xk9fRkmYypWGfJ+/kDUcSswq6zIbvnBniR10jpOX5j0C9\nisuLy268d+/ezS233MLNN9/Mf//3f8+57/EHHiBy7CiJkWEe7h5hssOJr9lJy+lr0EjqAnRL6S2A\nqsj183CC0bHlALmoFlQP3OxcjN5cDUBwbDcR3zEuhCwl8A78GoDw5NtFacVSt2q8D77Rz6P/tZ++\njHTnbC086xdX5JjT4zMs4KFoEnuJ+l4TAvbM6M7m9nydzuEyYbMb6Dk7yZ6XVGNhsRkor7Jz5/0r\n+Mhn1vKp/2sTAL7JmdXb+rt99HerKcUVG+pyaebbm2/m/s67uLYjvxAYtAa0GpEb1tRSVWrm3mub\n+cRN6utff/QQw5ORIkJSdZ2T3rG84dh5YJB0htVc2+TKSay6DVp0M7TCXIjxkSByJkR446XzpFMy\nkqQw2FtskNxOEx+7vhXDLIIwc8Gf6aWfmmWk7IWoyjgrowWyqYoiEw+eR6Ozzxl5S7JclNbfvFTN\nDvUPqWl4OSXxxstqlFjXVEJFiQmNKFBfYeXMgJ8fv3iOlEmLVqehTM7Pdu6od/H137uGL9yjio28\nciifRTjflSlZaEUmUZCBwvg6Fk1hy6RpZ5oNcCnI/pY2x/sZeavGOxbqAeQF93K/14imJUIpad5k\ntUIUkuYUOUnMfxb/8IuqGqWlNtd7XqWN8FdLy2mPv8YmfTdV+jS/V3aelaX5zM3qUhtpRWEsdvEB\nRFmEUml6QjFO+yP87cFuXp1DivUq3jtcVuMtyzJf+9rX+O53v8vTTz/NM888Q3f3zOP2ALBZJ7Dn\nFwAAIABJREFUiZqtxEwWBjNpy6RLR8KuQ8msZPq4GWOmb7OEMo7tG8JqN7BkVfW0w6kiCrcTFpz8\nR49Et1/1PqV0lFR8Uh0ooGRroQrpglRcabml6Fj7XuvFaNLSvmxm1mZFiZlvf3kbTVU2JqaiRdKU\nwUiSv/ivvfw/jxxERqEMgcB4mJoGZy7aBtXLrmlUo/AsWc2RIcPVNLgoq7BitugxGLUzGu+hvinO\nn1bJO9fd1kHbkumGprM6/ztNxtQ6+gM3tvF3n9uIViOyrLkUAYglJPYcGykyRpW1dvrH8um3E71e\ntt6kktJWrq8jlTGuZQk5J586F7KjJJtay9BqRZrb1agq6yhdiGgkydjwxYe9FCJrvAO+6LzkQrNS\nq6MF8riJ8ACyFMfkaJuz5j7mi+W09y0mHXdtaUIjChTmIEKBOGuuaaCq1sEnb2rnc3d0snlpPqPw\nR/evZvnaGlKJNPctr+bmtnIGz01iNmpZ1VpGc7Wd/acnON0/hSTJRAIxIigcSKeZNGnY0FmBryDz\nEw0nsL/LxnsgM0yntnFu9vXlgEbvRNAYiId6kFKxXA34SmdTZ+vd8yWrFcJob8FWvomS+jsAEf/o\nK0T9pxE1JsoX/Vau/93b/xSB899FQGJTTR1fXLGY6qY7iox3k111TgdmGS+qKAqD4XhRZD4cKd73\njH/+g2Gu4vLhsoq0HDt2jIaGBmpqVMWr2267jZdffpmWlpn1oH/88TtIic3U9Z3LbZM1GibWlbPe\nZmb0qS4Cvhg71m/jpPcsndE1DMgJlq+tRT9L36+1bDWnPSL+lIUfnR/li+5dRKaOgyLnBh2YnZ1E\n/aeIhbpzjNULa9tWu4Htt7ZjtszuOWtEkYoSM72jISaDccozUfqR85PEMpF2HIFsIvq6D3VMMwYt\nHW7OHBujqa2MJauqc6n2LARBwFVmYXw4QDotoc1EuIl4il8/djS3X3Nb2YyGprO0gy0N6zkycpJw\nKkIwGcJhyKu/WU06/vmLW/jSv+9h74kxzg36uX5TPWN9U1TWOuh7tRutRmRFSykHz3lw1jn47B9v\nQafX8tyRYYIo2BF4/YUuLDYDTa2zD0PItuNtvmERFpsBQYAf/cdehnpnJtS8/sI5es5OsvmGRSxf\nO3d9MxZNotGITGUiaElSCAfjRZmTyfEwRpMWa0HvelWJ+nu/+PYgNW4L21ZU5+RQs/OrZ0N2etrH\nr1/EndsXcfztQe5aVcPRQyP5Ic3A2i0qE3lxplwSCCd4ak8Pa9vLqSu3UmrRc/TAEP0ZLfnd5yZZ\ntNiN3qBl+6Iy+kaC/NNPDuPQibQhEEXBatLxmUx9W7lDoefsJC88dZJIKIm9Wl28g9F3ZrwPvzWA\n2aJnsMeHxaantNx68Te9yxAEAXv5NQRGX2Wk5yWktHoPXTjM5ErDWDTLNF945C0IIq6aGwG1zh3y\nvAWAydGGKOpy0quKkkZKh9EZy4tGubbYzDTZTCyym6mzqPf6YCTOphk+qzsY43vnhmmwGvlcRy2+\nWJLdY2rmaFuli91jU8SustWvCFxW4z0+Pk5VVT6qqKio4Pjx2fuuk1QgAIONaurWbdThydQ/z8aT\nVFr1+L1RPtl0I2uNG3KjKWubZo4Awqk0OlFEb3JDOEpSEYn48gYu5NkHgKPyWqL+U8SD3djLNwLq\nInHtLW3IskLnymoEgXkxnbM65Y+91IVGIyBJSm5a1pc/tpKuvQOMDvgxGLUzkt/qm0v57YeuwTQH\nI7WkzMzYUAC/N0ZZhvjkvYDoNZszo9foeGjjZ/j+vsd5ru9lRsJjOeOtKAqvD79FrbUKg15DMJoi\nGE3x3dEQm5dVkkjLDHnC1FfYWLGojIPnPBw4PcE925pJpiT2nZ7AaNXxmftW8MQPD3Lm2Oicxjuc\nUTqz2PLDY0orrAx0+0jEUxiM+ZS9oqgGCWDvq90sWVmNRjtz4miob4rnnjiOLCu5tDyo6d6s8U4l\n0/z8+28DcOt9S6msdWA06YpmqP/guTN01DtJB7oQRF1unvVsOJupPy+qdfLMT49y7uQ4oijg1ohI\naRmrXeUviGLxeTusBv7p9zdj0KvbzRY9qzbU8fYb+XnTAz0+dHoNx3f30Y7IaWTElAyIdLSWcc9d\nnblShSAIOa5DJJygzqzyRGaKvB/eeYbXj43yrT/eNmepIxpJ8taufK9558qq97xNLAt7+SYi3iNM\nDOzJTcrKGrArFbnI2/zOSg32imtyxttgUdtQCx0Xk6MdR9V1RTrmGlHgcx2qsysrCkaNmIuu45KM\nRZffN0tm6w/H+Un3KL6UxGgmSt9W5aI7GGUslkRWlCLOxlW897ii5FE1GguFmc3VVQ6e71UXbEmA\nsgobA91e9r/Wy/FDwyQTabQ6kfbF09uO4mmJf9h1khKjHrdFD0SB6TebyVZDdX0zU0OVxEM9mHQ+\nrE41Mlq2NAYClFTMPPRjJly7pp6nXu/NGewsmmscbF/fQKDbx+iAn5b2ctzuS4sW6hpLOHVklImR\nEPFIipXr6+jvyrdr1TWVXPTYnTUtPNf3Ml7Zg9u9BoD9Q0f46blfAKBbYiPV24rsV9tv3jg+xog3\niiQrLG4q4ZYtzTyxu5uXDg5x46ZGBsbCxBJpbt/SypLl1TxvO4HPE5nzPCLhJHaniYqKfOmgqtrB\nQLcPQRGK3lsoUiNLCoqk4C5QtwuHEnSdGmfZ6hoeefatXC0eIGLzYgmV8pP9T/Plzo/Q9VaA4wfz\ndePnnjjB2msa+dB9ak15w5JK9mWmjT26cx/3dnhxli+hvGJmJzGWSPPTF8+y++goVpOOxgo7z588\npJ6rrICs4Co188W/2jHXJSnCh+5dzsZtLXjGQ/z0ewcY6Q/Q0KwaYSvqnWzO3M/r19RSXVXc/15a\nakUQIJmQaKhTzzuckCgrsxKPpXLO4a4jIwBIoobqOa7ViaHiMsjqDQ2XfP++GxiP3k7i3GMQnwBB\npKKqYsb2rCsFvvMjiAJ01page0dDZ2wER1sJebuorOvE4rChKBYS/rXYS9soqVp10SM0uyycmgzx\ng55RhoIxvnZtJ2adlifODLMrU8+usBg4OZUPCDZWl9BQ5aRqzMdwNIHebsRlfO9bBa8ij8tqvCsq\nKhgZGcn9PT4+Tnn57L2YsgKteoWupLoolQlBFCWBIBgIJ9MYys3Q7eXg3nxEsm5LI5OTxczgtz0B\nftk/gaRAKJmmP1hQv9S4KC1pJezZD4De2obHE0JnbiQeHuPs/m9isNQhpcKkk2oklaIGUTM/pSa7\nYfqDecv6eq5fXYPHE2LlpnpkRWHtlkY8nvnPJS6EJsPwfu15dYSmIij096rGe92WRhavqJrz2G63\njRLU8sDJ0fNscav7/ujwL3L7SLoQhrZDxA5vxyBYsJt19GaU1CqcRsLBGLdvauTHL57jj/75Ncoy\nEe3K5hI8nhAl5RYGun0M9HunZRGktMyvHztKKBCnus5RdK56k3pL9vV4c/8HcnPDrXYD4WCCrjMT\naA35iOGVp09z9sQ4z/3yGOmEgt89RFpMIZni+FzDLD58A+KUmZ/9cg/BI9MXnfNnJ3Ln8YkbWrlr\ncyPfefoUpTo1U3TeU459lt/0sZe7eOHAIGagXYJnH1ezOzfd3YnPE+HtN/qpaXBd0vV2uc3YnUa6\nTo+jN+a/7/3r6xnr8hKailHutsx4bLNFj98XJZ1R23rl7UHSoTixs14qauw4XSaaEehFobvfh3EO\nm3L6eHG7pcVhuOT7951gOBLngCfAfk+aGmErd2heQaO1MDkLgfNKgKIoDAVjlBp0+H3v/DwdtR/G\nXDpGNOkkmrkGlooPIcG8rkmlXscpoDtjnP/slROIArnAyaQR+UJHHU8PTDAQTfDbi6qx67V4PCHM\nGfJR14h/2hCUq7h0XIojfFld1WXLljEwMMDw8DDJZJJnnnmGHTvmjj7aXHbWvvUyTadfYzzSTzD8\nMEudatQlNzvYemO+7njHx1ewckNewSyWlnhrws+TfRNFfY6FkKvuw1Vzc+5vs2sJAJaSFbltA+Eo\nB+MVubGZUf9p5gtBEPjCPctoq3XQUmOn1G7knm3NOeNmtujZcmMrRtOly0pe2K729E+PcfKQ6iSt\n3FA3L71pp8GBQ2+nLziAJEt4Yz7GoxPT9jOt2sWfPdjETevyv3NTZvzo9atruGdbM5KsMO6L0lhp\no6pUrRm7M9KjY0PTCWajQ35GM9vFC6KQLEHP7ytmh09kiHKdK1XC3WSBkpssy3SfUY17OpGRqq2Y\n4PqbO/mLDz9Is7uWuDGMOewi0/GVw6qN6veKhpO5bgOrSUdliZm/+MQqtrYGSKZFXjw5c5Tx6zf7\nePGAKnVbg4CYlBjsncJqN9DYWsa6rU187stb2XzDpc39FgSBxtYyUkmJsyfy7YFd+4cITcVoW1Ix\nKw/DYjMQDSeK2sz6M6WH8eEgZ0+MU4qAi+KxrYVIpyW6To1z/vQEOr2GuuYStt7YOq0d8r2Aoig8\ncn6U/R7ViRxWKvAp9iu63p2QZP7hSC9xSb4kstpMEEVdkZ7FQrGx3EHNBen7woxnTJLRigJ3N1bw\ntWuXYNfn7x+XQV23phLvTtvhVVw6LmvkrdFo+MpXvsKDDz6Ioih8+MMfnpWslkWN00bZsX0IisIr\nrjGwwJaKCgYjUXaPT/G77bXIL4MoU8TUBtg5NMmBzINdYtBxbZWLU1NhzgbyhqA7pqNdEHBW70BK\nhdEZ1FSk3lRB/aq/ZbLvV/zCowqItFqTmOM9RLxHsJZOT0dFp06RinvQmSqZGn4em3s99vKNrGl3\ns6bdTVqSkWUF3Sy12UIoikJoYi+ixoildOWcKcDZhGJcpWa0uvm3UzU66jnqOcFDu/4yt82kNRJL\nFy/ko9Ex1rav4McvqsStqjK1ri8IArduqOeF/QNE4mnWL86z27O1+J1PnmTzjkVM+aKUlVtYsqqG\nob68gllWDCaLrCjMhSI0w/1TCIK6/4HXe5kcDxGPpTi0tx+700Q6LRMsGSOpiyEoIvetv4mV5Wqf\n/y2NO3ji8F5KPPUQhup6B2aLHp1ey8btzQSmYvSc9RAOJop+W0GOIcpBxiIVnB+OEk+mMRYsZN5A\nnF/s7qHUbuCL9y3nrWfO4J1Qo5kVa+tyM7kXck1mQlNrGccODBGLqAtmVa2D0aEARrOOa3bM/jyZ\nrXomRhWS8TQ72tzsP+chm/ifQCGlFalJK5QizCi7CrDnxfM5wZ8b7+pk0eLLo2I2H0wl0wSSaSxa\nDTtqSvhVv4c35dXcKZ+7+JvfJwyEY4TTmbZQ+5URqVp1Wn6vsw5/IsU3jvdPe/1DdbPzVEozxnt8\nAa1mV3F5cNlr3tu2bWPbtm3z2vdTy+ppMOg5VepAN+ln9cu9dN9TQ63NzX1Ncb53bpj/PDOE5YY6\nPlNdnlscQTV+2RaGpS4rH2muQCeK2HQazgaiLC+x0h2MsWfcj12vZXPFNaQVhX853o+Cwp0N5bTY\nzURKrwdPJkVYdQ/GyaeIh7pJRkdzk3nioT7C3kNEp4qlKv3DL2Fzr88ZXq1GhIus28HxN0lEhzFY\n6vGPqEIx6VQQZ9X2ov3C3qNo9XaMtqYcwzwLQVDlTKtqFyZSsaZ8OUc9xd9hfeUaXht6o2hbJBXF\nYTVw+zUNaEURjSgST8f5dc/z3FB/LZuWVvL60VHWFyzs9S0lNLWV0XtuMjdJDdTJZcP9U4iiwINf\n2oxOf8FMY7sBjVZkqoCAl4in8IyGqKixY7boqai2MzYc5MmHDxUZ+anyQUJ2D/e338sK95Lc9o6S\nVj5+rZ0XHlczKKZygRtvyL9eVm6h56wH70S4yHhnB2CIRjOSkubcYIDlLXlFusNdarR/y/p6+o+O\n5Qx3Za2DDduaicXfndasyloHeoOWZCKNyazjutvaOf72MCs31M1JbHSVmunr8nL66CjBc146Mom2\ncRQGUCAt4UTAAXhmkIRVFCXXtnf3J1ZSdYm68u8W+kPqtd5e5WK928GJsR56ElUcTgS4PNpq7xzD\nEdXI3dtYzuqy+XNnLjc0gkCpUU+dxchgQSvYX65smnFcaRYNNiNGjcgRb4ibasuKxo9exXuLK4rh\nsbVObW/SfeYBkloBZ1iiU3YjCiKLHGa2VqpxQ0SS6VJSxCWJ/zo9yKHJIOOxJKGUxIoSGw8sqkKX\nYfR2OK18vqOWuxvKWedWH55nBycZjyU54QszEU/iiad4rHuMaFpid4EIiSeexFautpP5R17ODUOY\nGtqZM9wanQ1LyXJEjQmQiYd65/19FUXBP/oKMf9p/MPP57YHx3Yz2fsE6ZSaKk6nQvgGfsnE+R9N\nO8bS1dV86gubWLq6ZsGtO2sqVvLn6x5iY1VeD35D5Wo+v+zT/P3mv+HP1n5RPZ/MCMZ7t7Vw5xaV\ncX1o4hi7ht7glcHX+eh1i/inP7iGkoKWK61Ww833LJmWXn3ih4eYGA1RWeuYZrghz5T2eiL88tEj\nDPb6GOrzoygqUQ9gVWbYS2AqhqhRj68ziISsk2yp3sCWmo3TCIzNLXnHYsxQHG2UZVL8Pz+wk7O+\nvKOR/f27pDPoGk7z+K7zRRHq4cx8d7dWk+trX7Wxjns+uQrruzQqE1TFvqzGvSTJOFxmttzYWtTi\nNhMqqtX7fd9rxfekr6BvrR8FCZCGQoRC8Vzp4MyxUf7z668Ri6RoW1LxvhtugL4ME7rRZkIUBO5v\nEtAgcVZuuCxjTxeKlCzTE4wWnctQxjC2OsxXJDv7wfYa/nBpviRm02nn7CLQiSIrS22EUhKnpuan\nWngVlwdXlPHOorZ5Ka+tUQ3RNf48w/fWujK+sqoZgyiybyLAockQ/eE4j/eO050hpbU5zMjxOFIo\nT9xosJkwajVcX13KUpd63PFYkgMZycD1bjuRtMT/PtzDaX8ES8bz9MSSGG0tGG0txEM9BMffVA9Y\ncHNXtP0OpQ13U9Z0HwCJGYy3nI4xcvLfCYy9fsH2SG4OcRZZpaio/ySBkVcAiAfyacFUwkdgbA86\nnZpCNRh1JAOvM9H9KOnkwgRMAOpttXykNT/zuNJSwXL3EhwGO3Z9pj84Y7wVRWHf6EHeHj9Ct78P\ngK4pte/bOkMNXxAEHBlVuSWrqnNRrU6vyYm7zISGRWp0OzLg57XnzuaEWaozBqyhpZTl62rZdF1L\nrmZtqxVBVKixziyhKggCH//cOgJNvRyRDpCS8zpklbUOQEHwmfm3I/+d+77phPq5YUVBWz7E0NQU\nf/M/+xj3RZEVhe7hALVuC/5M/b2swsqyNZcnBnRnyhDJxPx7bMur85Fetq0uiUIY+NQt6n0WAYZR\nEID/8629/HDnGTxjIXY9dzb33rrmEq4EjEQSaAWByky9tqFpCy1mGZ9kYjh6aWncpCTzzIDnHddw\nFUXhx+dH+c7ZYc4E1AxMOJVmMBLHqtVg111RjT05GDQiFSYDO6pL+EjT3Lr9WWwsdyIALw57Fyyz\nehXvHq5I423Wmalbvx1FFNDtPYQUzdesTVoNTTYT/mSatybyqb7JTD94pdnA8Df/ld6//gvkeHEd\nTysKrHerqeXzwSh94ThNNhN3NJTTmGFO6kWB32mvQUSNvAVBoKzxPgRRT3jyAIoiI6ejantK+2fR\n6tUFUqt3EVWM7PTZmEqkSEbHCGUkV8Pew6STUwQKhgpAfpShzb0Bg6UOR+W12Nzrcq9HfMdIJ4NE\nC4y3p/snBEZfYft2lSTV3FFKcOJN4sHzjJ/7vvpZqYWxgI1aA5ur17PSvQxDwfAFq141GMGkapx+\n3vVLHj79U75/8lHeGlN7pIfCo4RTszNob7hjMY2LSlm7pZFb71vK1htbue/Ta6aJzxSicVHBsBRZ\nYTJDVssaMEEQ2LxjESs31LFkZTX1LSXQrGZMqmcx3gCuUgv1K2wk5CSeaL6VT2/QkLCGMUeciJKG\nwxMqw9wfVQli4cwCtWxNjGRa5lSfD18gTjItUyXDycMjaHUi935q9bzIgpeC1oxa3vptc/eaF6JQ\ne/zmu5fQtqSC9dua+JeHtnDtirzSXtykJY1CqQJHz08y1DeFokDnqmpaOtxF1+P9gqIoeBMpSoy6\nXKpWEEQ21ahtnd86Ncg3jvURTS9MQOTNcT9vjPt59Pz04UWxtMSj50cvGmHG0hI/7BrhXIZb89Z4\ngKQk8+1Tg4RSEu1Oy/vWEz9f7KgpZdU80/rlJj1r3XYm4ym6glcuy/83HZqvfvWrX32/T6IQ0YwK\nVHtlJ8gKkaNHUBIJLMuW5/YZiyboD8eJpvNRa0KWiaZlrtem8D/+U5RUCm1pGcbG4sVOKwrsGfcz\nmvHUt1a6qLeaaHOY0SBwd2M55SYDh70h/Mk0WytdiBodUipIItyHN57mrZCNRosOV9VWhsJxomkJ\nm8HIcyMRTqZreHPcT3DqNOnAcZ6atNMdCNIkqKnasO8oosaALMWZ6FbT4Lbyjbhqb8Joa0RrcGGr\nuAaNzko8eB6twUXEeygXocuS6pDotCmuvevj6DUBwt6DAChygljgHIqULBpmcCEsFkPud85iWVkn\naypWFG3TCCKvDb7BWHQcb8zHmyMHsOmsGDR6knL+/U32eiotM3vtZquB1s4KdHoNZoue8mp7kV76\nTDCadNjsBgZ7fCQSEpFwEmeJmRXrpzNs9QYtbUsq2DXxGpMxL/ctugOdZvbjj0c9nPF10VHSSqVF\nTaUPhUfZ13MUS6iUiM1HTB9iQ9Uahkf3oE+HUFzLOR8aodbpZrjbSondiMWko/fUOI6YaizqW0po\nW5KX6JzpN34nMBh1rN5YT/UCx6IaDFoMJi2rNtbT3O6mrs6JQadBEAR+mZnf/tCHl6NJyoS8MYZT\nEjU6DX5fjJvu7qRzDjGc9xLRtMyroz7qrUZWZOQ+LRYDJllBAXpDMWKSjE4UMGk12GaIdA9PBtk5\nOEmjzYQpk107OBlkNJoglJLYUZN3UrLM9jOBCAPhOJsrZ5eC3TvuZ58niNuoRyOo08MSkkJXMMo6\nt507Gtwf2NrwbPexABz1hSk3Ga62jL0LsFgW7vS//0/lHCi97Q50bjeB3btIefMiJBUFJB1ThrQ2\nGU9h0WqIvrQz91pg92vTjmnTadAUPEedmTS6Tafl5royyjLCA812EzFJ5nSGBGcr34gg6nly0sFR\nZTEnpCYkReG/zwzxbycH6A4l6VbytaND6RZ+Id3EcELDOamamKIeV0r68Q38ionzD+f2zQ5byEIU\ndRhtKos4OPEmipzCUroajT5fd1TkFBqNQDKqtoi5am/B5OgAIJWYWRv8UmDLRN/7xg6ioLCuchX3\nd9wLQINdNaYvD77Oi/273tW6Y8fyKjoyTHRZVnJT3mbDSHgMl8GJWTf3QlJuUpm0E1EPiqLwfN8r\n/OOBfyFsV++vqngjXf4eXuzfxURQnUa3pHw1AHFCCAIMecKMToapQkDUCGy5cVFRC+PlgkYrLjiC\nW76ulhvv7JzxfV/++Eo2dFbQWuekNiPVuhSBvi4vesPMCoDvF7wJ1YBk2c6FuKGmlD/ozNyLIz7+\n/eQA8gX3oqIoPNU/QVcwyn+cGsxl7UYL0u2xgqh9KpmmK1OKC6WkWadpKYrCIW8IjSDwe4trWe92\noAB7J/wYRJFba8ty/JvfJGTb3hYy4OQq3l1c0XeVoNVScvudKOk0gd27ctsL+yW3FnjEzlSc4J7X\n0VdXY2hsIjE4gJwqrmUJgpDzyleW2nDMQJoC2JxR03pjTBVq0RlKqGj9NFOoBnRccnDCFyadWSS+\nd26YFDo6xD6WOqYfc9I0s/KRRmdHZ5yeltQaStDonUiZOrbRWo/Z2Zl7XVHSpGITJKIqUcpgqcfd\n/FFErRlpjjT2QnGhOV7kbGKFeyl/se4P+eLKz6ITdfQE+niq+1lGIjOPKS3Ew6d+yg9OPgZAOBWZ\n0+AXptbdlbP38oZTEQLJINXWiw+nKDerjtLxyVMc8ZzgVz2qs1dfX4ooCthCbiRF4pddOylN2Eim\nReodjdj1NnxxH5UlZoY9YUbGQugQqKhzsmxN7UXJY1ciljSW8Pk7l6DViDlCnJhRbdMZtdOu/fsJ\nb6YsVmqcOatSfUHf8uQFY2WHIwlSmfKHpCj8qt/DqyO+IuPTG4rN+H9JUXKs8QvhiaeYiCXpcJox\naTVF2uU1FgPGeUzX+yDCqddiEMWc7OtVvPe4oo03gG3NOgStlsixvCZ5WcEDfE1FPho1T6h1q+ov\n/CHGhgaQZZKjeYW3LO5uLGd7lYv7GmcnaJSb9NRaDAxE4jmvW9HnGctn4hZ+2jPdWC0RzrAl8iPs\nqHXauxoyqVl5eu+ks3oH1Z1fLNIhzkIQBCyufDuTwVKHvXwTBktdLsKOBc+RCPUhiAZ0JvVzNDo7\nUir4rkXBFwq3tDjUMkSdrQaT1oS+IEU9HvXMeSxJltg3dpAD44fYP3aIv9rzv/nZuadm3T/Lli4t\nt9AxyzQ3UKNuYFayWiHKTCr5qifQz3dOqGWLNmcLn1l+PxU1dpJTAk2nNtJ8ahMmY5Jk3IAoiJSZ\nSvAl/FS7zcQSEscy09uqa66c9p93AmepmaqCGQHdgRh7T1zcGXuv4M0QykoNM7fGiYLAtQWO/IWT\nsE751br1JxdV8dCSeuw6LS8Oe5EVcnyXwmlZvZmo+7oq9X7JMt0vRJZN3mxT9QnKCwKLCx2K3yQI\ngkCFSc9EAfH3Kt5bXPHGWzQaMbW1kxgcYOx730FOpdCJItdWuri5thS9RqQkk0pLRmMY6urRV1Rg\nqFFZv8nhoWnHbHNY1B7Fi6hEVZuNyApMZLzLkVm8zI3l+f7qMtRI/U7NK3yyVmGt245eFBhJmXP7\nlLd+GqN9EZbSVQji7J65o+o6LCUrMdoXodE70egsVLR9htKGuxBEHYHRXaSTUxhtTfnecp0dRU6h\nSGrbjyKnZz3+fHBj/XYAbm3cwX2td2DVFxPNPtx6Z+7/Y5HxOY/ljefnAP/w1GNIisSdmkM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KqqJJ3z+NReVWDndvyvvYL/1Zen3Cc+NDjn2rlBqeCra2tZadEzGI3jDsdIi2JOXjHbdgLkGJop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kbItFUwDoDRoAlq2aikarYtrsc68dwWSgEEVRnMwByMukSchLxmWR70UW+V5kcTgsdLR72PhmE80NTmbMK6GkworPHWXfdimzZsnKKSxbXT3JIz3zjHUpwXFZ9Mlkkm9/+9t0dXWRSCT4/Oc/T11dHd/85jdRKBRMmzaNBx98EKVSnjLJyJwOXneE7g7fOV11ebYQRZE/PbadaCSJ3qDh8utnoFAoSKXSzFlURtAfzVSpyuQyLiV++eWXsdvtPPPMMzz22GM89NBDPPzww9x7770888wziKLIhg0bJnqsMjIXHI/81wZeenov8VjqrF43Ek5weF8PkzzhzyESShCNSEt8zphXnFnrWK1WYcszUDE1H41W9kaPxLiE/tprr+XLX/5y5rVKpaK+vp5ly5YBsHr1aj744IOJGaGMzAWKb0jxj9sZOsGeE8+LT+/h3dcbM+uengt43NLKTWarjqUXT53cwfyTMS6hN5lMmM1mQqEQ99xzD/feey+iKGaesCaTiWBQ9ivKyJwO7c1ZkXX3jV/oX/nTPt59vfGU99/01hH8nigAniHL4k02Xpc0lsUfmnLBVriOl3HfrZ6eHu6++27WrVvHjTfeyA9+8IPMe+FwGKv11HxlYw0qnM/I9yKLfC/IWZqu9Yib+UsqKSwyj+kcsWiSzmNeAG5fvyRjjI2GkBZy8tODvtiEfhcdrR7++Ph21n1mORVT8sZ0bP0uaVxVU/Pl38cYGZfQu1wuPvWpT/HAAw/woQ99CIDZs2ezbds2li9fzsaNG1mxYsUpnUvOKJCQsyuyyPdCoqvdi1KpwJpnoKvNyy9/8C53fGopeYWmUz5HT6c/8++2Y25MZt0J9/e6wgiCyIx5JRw74qLhYC+Nh3rJd5z6NU/EW387RCya5IWnd7P2M8sy2939ITa9fZRVV09DrVHidUeGNSDr7w0AoFDJujHWB924XDe//OUvCQQCPProo6xfv57169dz77338sgjj3DnnXeSTCZZs2bNeE4tIyOD5JN39YUoKbdy89oFVE8rRBBEdmw6NqZzvPjUnszrU3HDuPolF1FBkYmll0wlnRLYsal1zOMfjcHiJZ87wtb3WohFk4iiyHNP7KS73UfjwV6efXwHr/35AAFfNHOcIIgcO+rGYtNjspz4YSUznHFZ9Pfffz/333//sO1PPfXUaQ9IRuZCob8nwJZ/NHPpdTOw5xsz20VR5K0XDwGw4KIqjGYd1942l9/+ZBOuMfjqO44LpO7f0YnBqKGweGRrMB5LUb9bco8UFpkpq7JzcFcX7S0eUqk0avXpLdiRiKfoaM2Oac+Wdlx9ISy27KpMe7d1ZP7d3e4j4ItiMGpJJtPEoklqZhSe1P0kMxw50V1G5iywc9Mx3nsjNyC6d1sH3R1+/vbs/pztLY1OvO4I02YXcdGQ7BJ7gZGAL0o6dWpdTuPx3JTM9hYPf/ndrlFTJl//ywF6Ov1UTyuktNKOQqFg6rQCUkmBrjbfKV3zRLz513oEQaSw2MySlVMA6WF0aEhMYCjvvNbIK3/az3NP7KS1yQVAVU3+aY/jQkQWehmZs8COTcc4tLcnJx9+MCc86I+RTKYBaDzQy4ZXDqNSK1k8IIaD5BWYEEXwe6OMhLs/RCgQy7wO+aVg7q0fX0z1dGllJVGEvz65h6b6vsx+giDy1kuH6On0UzE1j2tumYNSKVnN1dOk4waFdrx4XOFMUHjZ6mqWra7mY59fwZU3zqKkwoZWl50tTJ1WMCyrZu+2DrQ6FZXVstCPB1noZWTOMEPXGXjix5tw9gYRRRFXXzagGPBGEUWRre+1kE6LXH3zbPKPC7oOuneefXwHzz2xA3d/1o0T8EV57omdvPrnA5ltQX8MhQIKi81cdt0MFq2oAqCvO8AHG45m9us85uHo4X4A5i4uy4g8QHG5Db1RQ+sR1ynPJEaiucEJwNU3z84EWS02PdPnFHPT2gWs+9xyyqqk6t9VV0/jk19eOewcM+eVTuh6rxcSstDLyJxhwsF4zutNbx9h56ZjJOLpzDafJ4qzN0gklGD63OKMJT2U6ukFGExS0y53f5gDu7oy7+0Z8G0PDbgGAzFMFh0qlRK9QcPyS6u5/iPzUGuURCNJYtEke7a103ZU8ptX1eYz9bjrKpUKZswtIRZJZh4G46HzmAeFAiqrh6dUqlRKDEYt1946h7WfXYbZqkepVHK8K37pyqnjvv6Fjiz0MjJnmKA/lvO6tzPAzs1tmMxaLr6qDgC/N5LJXx9J5AFseUbWfXZ5Jng5KOqiKNLa5Mzsl0qmScRThAJxrPbsymgKhbRm6qyBFr7v/72Jre+0cHC39MBYdfW0EQOdcxZJ+4/XfROLJunvDuIotaDTa0bdT6fX5ASl7/rMMq6+eTYf/bflrLllzpjz7ieKZMyNu+0lUgn/yXc+R5GFXkbmDDMYyJy9qIzrbp+b2b74Q1MyPudt77XSsL8Xs1XHlLrRF7DW6tR87PMrKCq14OwNkoin6O8JEg0nM/sEAzH6eyS3UHHZ8Awbe4EkpkcPZx8Oao0yJ/tlKLY8Ixabnq52H1veaR6W+eNxhunpyAZr02mBZCIbi6jf3YUgiNTNGlvLYHu+kbpZRVjtBmpmnNoSmWcCX/dbhD376G9+etR9hHScVCJAOhkiGffQ2/g4/UefRBTTox5zNpHriGVkziABXzSzQMb0OcWUVti49ra5REIJZs4vQalUMH1OMU31fZSUW1m8csopLZQxdVoh/T1BjhzqyzxISiqs9HYGCPhiGTEuKh1eoV4wQnXttNnFJ0xbLKuy03igl73bOnD1hbjxrgWZ9/7y+12kUwLrPrcMW56Rt146REeLh2tvm0tppY0Du7rQ6lSZmcQ/E0IqSjTQDEAq5kJIJ1CqtMP26z/yBxLRHgA0+iKSMcnNFQ93ojdPGbb/2UYWehmZM8hgU7C5i8soKZdE93jXzJU3zuKSq+tO6NY4npnzStjxfisHdnbh80QoKDIxZ2EZvZ0Bgv4Yfd1SFelIbXtLyq2YLFrCwQTX/MtsDEYtpZW2E15vwbIKREGkrydAV5uXSDiB0aQlHIpngrR7tnZw2XUzMi6et18+zMIVlUQjSRYur/yn7E8TC7XBEKs8nQyiVOXOuIRUNCPyQEbkAWKBo+eE0MuuGxmZM8hgSuH8iypPaDGPReQBTBYdU+sK8bojiCJUTMnL+OOD/hj93QHMVt2IVaQKhYJb1y/myhtnUTPDQVmV/aRFSAUOM1feOIt5S8oRRdjxvlQt292eddm0NrmIhLKB51g0ydZ3WlAqFcxbUj6mz3euEAsdA0BnljKWXMeeJxHJzfsPuXcPO06tleIJ8XDnmR3gKSILvYzMGSIUjNPW7Maeb8CWZzj5AWNk1sKsK6SwxILFLvnYu9t9RCPJEd02g5itUmrjWKtMZy8sw2rX03igl3RayHTYdJRYiEWTHN7fC8CU2my+e81MB2bryP7/cxlRFIn6m1AoNRhsMwFIRnvpbfwN0YCUnhpy7cLXvUFqwDMEU8FCVBrLORPAvaCFPpqK8Z0PHuattncneygy5xmiKLL57SMIaZEFyyrPyDUqq/MxWyWLvbDYjNGkRaVWDgnETvxqSyqVkvIpeaTTIh5nmLZmNyaLloXLpc94eJ/kwhgaPJ0xt3jCx3E2iAWbSSd8GPPmotblZvwE+jYDEHTtBKB42v+hbPY9GGwzADAXLEalsZJOBs6JxVsuaKFv8bfhiXl5sfm1yR6KzHlGT4eflkYXpRU2ZswrOSPXUCoVrLpmGgsuqiCvwIhCocBizbpqRspZnwgcJVIw9y+/20U8lmJKbQGFxdK2oD+GWqOkqraA2QtLyXeYKJ+ktMjTJR6SguimvDmoNNnsJY3eQSLSjSikScXcaAwl6EwVqHV2CqfeRvm8r6PSmFBprSAKCKmzu2jMSPzzRUcmEHfUnfl3WkijUspVdzITQ3ODFJBbcvGpZdGMl6l1hUytywZ3LXYDPk8UvUE9Ya2Fj8dRkpuyWVZlx2o3oNYoSSUF5i+twGjScum1M87I9c8WybjkllLrHSgGbGKN3oHWVEEy5qTz4A8RxRQaffb+K5RqVEpJVtUaKcCdSvhzHhSTwQVt0feEs9Hx3sj4q/5kZIbi90ZoONCL3qihfMrZXdR71vxSyiptrLpm+hnr8ugosWSakgGUVtpRKhWUVtrRGzRnzFV1Nkgl/PQd+QNhbz2puAeFUoNKbUalMVE66/MUT/8kBus0AMS0VAg3VOiHotJKrrN0InDK1xfFNKlkkED/VtLJiZsJXNAWfW8429jpoOsw5eZ/vjxfmXOP+t3dpJICq6+ZjlJ5dm2p2pkOamee2eIihULBstXVJOIpwqE45oHMnmtunk0qJaA3jC2D6FwhnQzT1/QE6WQQIR0jFfeg1hVkHpgavXRfDbYZlM66G1frn0nG+tGNkj6pNUixiYjvEMa82aNeVxRFIt56DLY6Ar2bCPRL623Hg8dw1N41IZ/tghV6QRRoD3Zi1VqIpKK8fmwDNbapTMurmdDrPH/kFdwxL1athdun3YhaecHe8guG/p4gCgWTWs15Nrjk6mk5r7U6Ndp/4jVB/D3vkE5KgexktB8Q0OiGd8tUKBRo9AWUzPwcopBAqRr5Q+vMU9Eay4j4DhELtqC3jKwtscAR3G0voNLaSSey6aqDrqOJ4IJz3biiblJCis5QN7F0nLkFs7h2ypUkhSS/2P8ErujE3VxPzMs/Ot5nn/Mg73dtYXvvnpMfJHPGEIQkqTFMo8fD9o2t9HT6sdj0aLRyzOefhXQqQtizH5XWjtZYDkhFYDpL9ajHKBSKUUV+8P28yusBBZ6O1/B2vpnJ0hlKIip5FoaKvMT4u4Uez2kJ/b59+1i/fj0AbW1trF27lnXr1vHggw8iCBM3yNF46vCfebbxRbwxH/+x7YfsdR4cdV9RFHm5+Q0e3PI9/tbyd5p9xwCos1dzXfWVfGTazcTTCQ64Dk3Y+Bq9zTmvN3dvm7Bzy4wdb+cbdNf/OPOHNdGIophpd1BcPvGpjTJnjoj3IKKYwlJ4EWpttkrYaDu9gLLOWIa5cDGpuIegcxvejteGFVElY86c18a8eWj0DlJxD2HP6Jo2FsYt9I899hj3338/8bhUCffwww9z77338swzzyCKIhs2bJiQAY5GSkixpWcHG7s+4IWjf6Mn3McfG57PvO8LxQlFpUZPgUSQL75zH2+2/QOArlAPHUGpY98UqxQ4mlsoFUT85cjL/L3tnQkZ45HjhL492EkinRxlb5kTEQu2kor7EFKxk+88CmG3NKPytL9CPDLyqkaikEIUxteIamiXymWrRrcEZSYXURRIxlwD/xZJxj2EPfsBBab8eWhNFQCY8heg0gzvCzRWLI4VOa/7mp6g+9DPiIclDUpGnaBQotJYUKr02EpWZfz+7ra/nvb14TSEvqqqikceeSTzur6+nmXLpFXdV69ezQcffHD6ozsBziGpkbv7paXYVAoloijS4w7zfx/9gK//fDOHO5y80vxGzrGRVJSOYBdapYYioxQxL9BnfXEvNb9+WmPzxnzs6ttLk7cZnUrL91d9l8srL8nEBWTGRjLmpv/ok3Qf+ik9Db9AFEWEdIxA32ZEIXXS44V0ImfKnIh009f4G5IxJ56O1wm59wHSH31Pw6/oO/K7cRW5DLYNXra6Oqc9sMy5hafjVXoOP4q380069j5Ez6GfkYh0ozEUo9KYMRcuoahuPflVN03I9TT6bG8cW+kVAKTiHiLegwhCkmTcidZQQsmMz1I258to9IXYSi4dOELMxA1AekiNp9p23EK/Zs0a1OpsYFEUxUx02mQyEQwGRzv0tEkLaXrDw9Mh/Ykg393yPf5j378jqMMkUgJP7HqJD3p2AHBJ2XJsWivumIfeSD/l5jKUCukWKBQKbq37cOZcv9r/ewRRcj+FkuGc6wQSwcyMYCT+cuQVnqh/Bm/cR4W5DJPGSLVV6pXRHugY9TiZkYkFWzL/TieDCKkInvZX8XVvwN+7cdj+oigQ7N9G2LOfaKAZZ8szeDuGF8X1HP4FIdcOPO0vI4oi6YSfVNxNItJFfKDHyVhwDwh9wRnKX5cZmYivgf6jTyOkEycGcn/qAAAgAElEQVTdV0gnMjO7oDPXlao1SIVtSqUGvaV6QtNTi6d/EnvZVViLL6Zi/n2A5JuPh9pAFNCZp6LSmDI+f5XGlBH7RCTbMM3d9hLd9T8Z8/UnLAVkaBpZOBzGaj01H6XDMbZCgg3Nm/j1rmcyFteKisVcVrWaJs8RXmh4FVdsoMghvx9bSYiwNmtBf/Hij3P/hh9w1HMMgGlFU3Kuf5fjBkJigL83b2S/q54nGp5kXvFM/rD3eR647MvUFVQTSUb51ssPAfDkbT9Bp85tWSqKIg3urDB19MZ59t1mrruyCuohqoyM+pnHei/OZ4bei0BX7sPRYkrgSkh+dkXaPey+dTb9DW/XeyOe1140j/zSRbTs+8OQrSIWQ5iY4M1sEWJNOGrmj2nM4YDkxpw2szhnAY3TRf5dZBnpXuza8xwAWrowmitRKJRo9SN34/T05CZEGC3lRAaMNost/8zda8dsIJti6TxaSCrejzIlXbukcg7Wgtxra8Qa/L3voVF6cTgsxCMe2r0HGA8TJvSzZ89m27ZtLF++nI0bN7JixYqTHwQ4nadu+aeEFJtbd+dMq2+oXMO3f7YPlQqmX1ZHk1dqNmQo6yGslqY4lnQpX1h2B4eOOjEqs9ZWocox7PorHMtpdrXT7D/Gnp569vTUA/DqoXfpjTxHVyj7dD3a1ZVx/QzijfmICWGEmAGtWk2wtZI3Q21c+yEp1a7H5xrxMzscljHdi/MNIZ0gHu5Ab6mmqMiWuRdCOo7f2YBal4/FsQxv5xs4+7pJp6XfQCwaGnbfPL3DA+pKtQkhFSYWDRNPZ6fS5oIlhNy76Dq2GyGd7bzo7W/AUBgYk1XX3elDq1ORSKUm7Lu80H8XQxnpXgzVAld/G+HDL5JOBimd/cURUyOd7btyXmutc9CYp+HveRdRM/Ws3Wul1kE6chhn1w5AQTRZQPy4a6eS0sPK62xDbQkS8R0d4UynxoQJ/X333cd3vvMdfvSjH1FTU8OaNWsm6tSAlPf+X9t/TN9xFaypuJ5ESoAUTDXWZIQ+qfajVWpItMwjGi7hfxtaCYQbWbEm20WvwlI27DrFRgdfXfIF/PEgj+57nM6QFLTb1b9v2L6+eIDXN7qoLbNxycCiCnv7GgBIOysI9tRm9lUJepQKJb7YudHN7lxCFNL0Hfk9yWgPBttMioo+TTLmJujcikbvQBRTmPLno9ZKVabxUDupuBSjSUT7cxaDEIU0yZgbrbGcgik3k4j2otHlE3LvJeTaiUprQ6WxYC5cglqbj7lwMWHvAYL92wARhVKHzlRBLNhMKu7J8a+ORiLaT9C5G79HS1HpyVv+ykwcyWhv5t8h106EVAQAd9uL5JVfg24gsJqI9hEPdxL1N6IxlGArvoR0Koy5cCkw2ITs9AOvp4rRNoOo7zBCKoJGXzTiYiYqjQWl2kjU30A83EXqNPLqT0voKyoqeO45adpUXV3NU089dTqnOyGt/vZhIr/QMY/Gtux02xCZii5tI66SxPSjM29nk1PJ/n43IPnvkpFs3muZafRmUzadhS8v+hwvtbzOpq6tI+5ztK+P9/ZGea++hYsHGle93bYRUVSQ9uRW2fZ5o9i0VnxxWeiPJx7pIjmwcEPU34gopPF2vp7jm9dbqlEope8u5NqR2S4KcdxtL5JfeQNKtZ5k3A0IaAxFaPSFmfJ0u96BSmPB4rgIgPzKGzLnsBStIDDg67eXXYVSpScWbCYWbD4loe9regJRSFBSPAN7UcWI+0R8hxHSMcwFi8ZwZy4cUgk/vu5/YCu9FLU275Qflt6uv2f+PSjyAIlwJ31NT1A07RPoTJX0Nvwq815exZphi4GcTZEHMNhnQduLAKhH+Y0pFApUGitCKkJf0+MY7XMAsJeP3Yj+pynT3DckR96kMbJ26h0YgnY2tnoZLEt5fVMveu0VhNXNrFtTw9KSRbiq2tnfnM3QMYRquX6aEavOikZ14lJto8bA2hm30h7ooD3YhQIFn5yzlt5wP68de5uG7l5UBSG0tQd4v62Y6Y5KfCkXgreYJdVT2dnQz/QKG02dfnpcEew6K23BTgRRyASBZTjOUhFJxLyIQm4aqkprR6XK+r3VugJKZnyG/uaniPob6PJLMylzoSTkg+XqgyiVGmwlq0a8vr30MqyOFaRTYdS6fNJJ6WEcDTRjcSw76fhFQTIiFs1vJC3242rdT2H17dn3RRFX658BMOXNR3Fc87x0MkQi2oveUoPiAv1deDpeJRY4SsR7AKXaSEHVTRhs0094TCruJR5qQ2euxlZyCf3Nz6AzVWS6ToJUO+GouTPzWqnSnxsrPik1FFZ/BNexFzANCPhImAsW4e2UsgAjPsmNbC5cPPbrjW+YE0+vJ8LW+t5R328LZgNyS4oW0vpujHdebqK3xc18lFxk0OIPJ+jzRslP1XJpxUoAVh7XIrbPmeCGmmtYVX5qMQSAK6uk6PfS4oUsKV7IrAKpiKI1Xo+2VgqObGzbSYP3CABpfyFrr5zGT7+8ipsvkfKpnb4oNp0NQRRy/PwyZNwwOvNUAOJRD+kh1hmASm1CoVSh1knWj95Sg1Klpaj2Y5njIGvtD64IdKoo1Xo0eqmviVprR6N3EA+2Iggnrns4Pq9fpfAS8R0iGXUiDDS9Sg5dZi7uztk/lQjQc/hRnM3P4Ov+x5jGfL4gpOPEAln/s5CK4Gz5E+72vxF07sjxwwf6PhjIeYeovwkAY95s9JZqKub9X4rqPp5z7mS0Nyczy1H70TP5UcaE0T6LygXfPGEfHIvjIkpmfi5nm1I59l5C54zQf/vXW/n1K4fo80SGvSeKIl2hXoqNDr625AvcWncDfV1SKbsjJqAG0tEUgzHrwiGr2VuNWj55/Uw+ds10ivONdLnCw85/MpYWL+Shld9i3czbALBppSsJumw5fY8zzsH+RgD08RLsZi1mg4b8gbF4gjEWF80DpIpemSyDPT0MVimmEQs7ScW9OfsMWrqF1bejt9ZhLbkEAKVKS175NTn7qrQ2tIbTa1BnsM1AFFN07nuYiL9x1P2Cru05r7Vm6TP0NPyC3sbHEYQk0UDWBZWI9OSkAYac2zMPhGD/llOqC5gMkjF3TrB6Qs898PAzFSwiv+pmdGbJOAq7d+PtfD2TDikIKXzdb+Nue1Fa/SkgCf1gN0mlSiv15HcsByC/6uaB80jHF9WtR2c6t5Y0VChO3iZDayimcsG3Kay+A0ftunFd55wQ+qFP7B73cKH3xn1EU1HKzaXU2KYSC41cuWhH8usV2HKLVVbNL+OKxRWU5BkIx1KZitmxkK/PQzsQMLHprGjQI8T1zDNJP6q0JkSjtxkhZmRKflHGx5g/0NnPE4izpHgh060z6Ax1842N3yVxCnm/FwKpmNQOVmeSqpR9/QcZrc9HX6+Gt96qwefJ+nC1xpKc4hZT3rzTDoia8uejUEkPaV+n5AcWhXTOA0gU0wT6t6BUZzO5HFNvzqwXmoq76dz3MP6erKXuaX+J7kOPIIoiqUSAoGsHSrUJY958QJzQRlYTRTLuoefwo3TV/wRv55vDHsKnSyomCb3WUIy5YAHF09ZTWP2RzPtB1w6EdJzEkD5Uvu63iQXb0BhKUWtzU7nt5VdRPvcrmAsWSL5wwGCbmTPz+2dDoVRjtM/EYK0b1/HnhNC39GQt4273cIv72ECRUYVZypLZvUXywSUHRl9cJWVjlKBgoVnH0rqRgxtFeZKP99kNR3j0xYP88qWDNLaP/UerVqrJ67yW5P5L+fiCm7BpragsPgRFEsFfwJLpWf+wRq3CYtTgCcRIpgQO7JTGEE5FMhk9FzKS4HlQ6/LRGIoACHqkabylSHK/qTTWzL5v/rWegC/G5reP5JzHXLAwY8UPZlKcDhp9IZXzv4HOPIVUwouQTuDtepPuQ49kCljioXbEdByDbRZbti+gqXUlKo2ZsjlfonCIX/h4hFQYIRWW+qsISWyll6Id+Oyp41w75wLSYtgiYjpG0LmNvqbfsrevh3/f1UxbMMr7vV7SwviXyxu06AfdciC5NcrnfhWNoYRktI/O/d+jfvMPMu8H+7cAAsYR/PgKhSqz0Efh1Nspm/sVCqs/ckFnQ0260IuiyJ5dr7CgTCqA6RnBtXLYLU3RZuTXEQ7FObyvB5VBzT5BoFkJ190yB6VyoGd0KMnet5uJjWC1Fw0s0Lz5YC87G/rZfrif7z+zh1R6bA3Y/OEEbd0xplfmYdRrMGmyQcK0u4wlM4py9s+36vEE4zR3+RF8xaSc0gOrL+Ia8fwtjU7+/NudeEd46J1vpJNBRCGJWpePUqXPWMMKpQZ76WUUTr2d4umfAMDvjZJMSLO57g4/zt7cvGNH7TpKZ35+mIV3OgwGdTsP/IDQQBsFX/fbiEIq4yNGPRWP14ZCk3ULGKzTsJVcis6UjRUMFbJkzEUqKRk4OmNFJvMiGTs7Qp9ORehtegL3QObHiUhGpaZbhTV3Yiu9jHQqxHPtIeKCwK8aOnm9w8V+z/jyzwUhSdQnucY0ulwDTaUxDwuqH495IItqNBQKBWqN5YIWeTgHhN4fDLCsooVb5h1hSl5wRIv+sKcJk9pIlaWC+t3diCIkzFpE4JbrZmAwaBCGWBRBf4ym+uEdCh0j9B8RgS7n2AR1f7Mk0PNrpdS9RUXzUKeNxBuXoEkUYjXl5sTmW3QkUwJbDw20I3VJgtAfye1aN8i+HZ24+kK8/peJ6Vx3LjOYcTNY3KIeSIeUslPUGPNmZ/Ln/d4oAKWVUiHJ/h25fYNUGhMaw8T2gM8IjZh1F8aCrQT6PyAaOIJCqSGakB7sZls2dVehUGIrvRRj3tzMNkft2ow7KBlzZVYeUmksaHSFme2nQzLmJNC/NdMWd7T4gq/rbRLhTsKe/VL7h2SYqP/IiPsOdlfUGsuwlazGVnbNsH2OBiK0BaMcGKPge9peJhnrw2Cdjko7vJpVqRyeX148418prP4IRXXrUaknrgL5fGbShd7rzfaMWVbtodsdyfHZR1NRvHEfVdYKQv44e7a1YzRp8QBatZKVc3OzauYsKkOhgIZ9PcMaU1UWmVEooK7CxpVLKjLHDrqORFFk/45O+rpP3LN8MF1zwYCL6Prqq5kTux3B70CnHn5LywolH+7GfZKrRohJrzv9wx9GvV1+ejul9L5wMH7GV5Dv9UQIhBO8sa2daHx4IFAQRB5/9RCvbW1j0/4edjU6J3RMyZhUGzFo7eZXXEtJ9RXYy68etm/AJwn9nEVlmCxa2lvcOQ/4M4HWmA3q5ld+mPK5X5HG0ruJVNyD3lJLKCDNHi1DkgAGGSpEGl0+RQNZH8mYk3QyAAoVSrURtS4PpdpELHiUVNx3yvc44j2Eu/0VkjEXiUgPvY2/wdf1d0KunQSd23C1PDvsXFH/EcKevZnXqYSXroM/xNnyRxKR4ZlvyZiTTkUlv2jyEkymCJgWDNvniD/Erxo6+WNzL92RUwvappNhIr56NIbSUV0repvkk7aWrKZ63joMtulo9cUY7bPQn6BXvEwuk55HHwn2ZrJlpth9xBNpvME4+Vbpj6Z/wL1RbHTQ1eZDSIssXlnFrk2t5Fn12WW+tCqSiTSFxWZqZxVx9FA/Tz66hcrqfC67boa0CIBFx/984WJsJi1KpYIuZ4gPDvbS0u1n1bwSDu3pYfMGyT+89rPLRuxXkkoLHGz1UGQ3UDLk/dSAwafVDI+iV5dmXQmFNj0uv4iYVnHI08S2nl0sL10CSA+avz6Z7cWRSgnEokkMxlyrJpVK4+oLUVI+cj8PgGg8RSotYDEOt4i8wThmg4bdTU5+9XJ9ZrtGreTKJVLBT1tvkLa+ICqlgs0Hcv/4//XDs1g5d3hWiycQQ61SDpvRjEY6FcXf+z6QbSil1uXhqLhuxFL0gFfKTrHaDVTVFHB4Xw/O3iDFZWeu97vWWI61ZJW01Jt9Jiq1EZ25inioHYCmJjv790pWs8U6XOj11jp05qlYBuIGWkMxCqWWWOCo5LLSWAd+wwqM9lmEXDvpPvRTzI5l5Fdce8Kx+br/QaBvE5DNLBmJVMyVmemIooir7YWc94PObOZQMuZEa8waT6KQIhX38HrqKtLJOH9p6aPIMPz7DaWyD5P3e7zcWSudwxNLsqHbzTUVhdi0uXIzOFMwWGuG1RYMYrTNoHTW51HrCskvspJWjy8YeaEz6UKfiveDEtIKI2ZNELM2wZ4jrozgNPRJ0/NIQMdzbzdQhIKDvQFC0SSVRdlqtpvWLuDQ3h6mzy2mqiYfd18IrztCw/5eaqY7mDJgfedZstPr0gITeq2Klu4A295rzXEFtLd4RhT6xg7pYTR/fkGOBVJZZGZnQz+zp+YNO6ZmiBCtml/K9sP9OEN2VDY3fzj8LJWWchwOC9HI8LhCKBAfJvTbNx5j3/YOFiyrRBAEVlxWg1qd/UMRRJH/enIXoWiS735qGem0kHlw+sMJvv3rrVQWmbGbc8/b3pcV13//nZSPbtBJ5y0vNCEC3a4wv/nbYXY3ufjEdTMxD6wPKooiX39Uak39+H2Xn5JPNOpvQkiFsTiW54jLaPgHLHpbnoGSChuH9/Xg6gudUaFXKBTYSy/HXnp5ZpspfyHxcCda41T2783mNBeVDh+HUqWleFo2t1uhVGOwTssUvwzN97cUrSCdDBH1N5KMnLjWIhHty4j84Hk0OgdaU9lAX3MDscBR/L3vEQ00ZYQ+FXcjpuNoTRWY8uYNpC9mrfvj8/wlV5JIemDyfyQQ4UggghKROsUxmsThVnWjP0xKEPHEk/yj281+Twh3PMm/zcpdNDwbhB15ce1BTuanlzk5kyr0oihiEDuJJVVo7fMgvI1yW5Cn32piXm0BvmCc57fuZ2qVkmB9mCIksXp7wMLMt2ZFu6jUmvlDM1tV3PWZZfR2+fnrk3toaXJmhH6QtmY3fm+U6SYd+90ROpW52TfuvpFXYD824OaZWZUr6NevqCLPrGP57KJhx9jNOhbWFaLVKLn6okoumlXMTz+oJxKPkFLH8UUDhINx2gdcQtPnFpNfaGLruy2EAjEcJRZeemYvCgVc/5F57NsuZSEN/r+k3EbdrOx19x11ZeoFvvKIJAY//tIlWE1ath/qI55Mc7RroOGbUcMP776YL/xoIx39wz9zNJ7m2mVV3HGFZEl96r+lVMHdTU5C0STLZhVR3+rh9suyfX2auwPUnWC2IYoiIdfOTCGLueDUKv0CvihanQqdXo1tILA+6LcfC6m0QDyZxqQf3yLW5oKFmPIX0NbsBqQ4ysoratHpT+3PyWCfmRF6lSZ7nzS6fBw1d9B18MeZQO2I4497CTqlB3F+5Q2YC5eMuJ9Gl0+gbzNB506M9tkoVfpMxpDRPicjoIOVvdK5pZhJxN9IyLULg3UagqhAgYhI9uE9w5SiJNqXEXo1KfQqFVOsZuq9YX52qJ3+aPa87aEYwWQKiyZ7j1ID8YhTaTMhc3pMqtD39LSiV0Xo7HdQYCjAAFxUm6bRCRv3dqPXqlAaA3zMaoT59bztXk5Kb0UMSH/cRSdZ3KGwWLL4A77c6sXudh+v/VmqaNUN/Ccocy1Q1yhC3zuQ5z/odx9EqVBw8bySUS3Ze27PtrwtyVfzLzUXs+tlabHg93Z18x7ZVMuKqXmoVJIFFQzEiceSdLdL60n2dw93aUQjCWLRJOm0gMms4/Cx4Smjb25vR6NW8uaO3Ja/C2oLUauUlDtMdDrDpAWBaDwbeDTo1Fz/oWzJ+Cevm8nmg72EY0maOnw0dUjjGiqae5qcJxT6aKApU9at1hVmArAnQhRFAt4o+Q4TCoUiI/SDfvux8MRrh9la38dPv7wqMyMZKwqFIlO0d8Md86mqGd4pcTQGC3wATHnDy99VGguJSG6MKRHpJeI7RMRbTyohfb8KlQ5T/nB/+SBKtQFTwQJCrl10H3oEhUKdaSugNZagM1VhLb6YiPcQBtt0gq6dRLyH6E/HMpWqscBRAlhyRL7SpOf6KUX4G//KMUU50xWtlChc5BcthoJLaQ5Ec0R+kCP+CIsLs7OeQdeN5hS+f5nTY1KFfvee3ZRqwOXKo9eZZOl8mFkSxKgtJubZxgFvGdYqPyC5GK66fBumoss58Gc18aTApYtOXOWmVqswmbUEjxOD/TtzszVmo8A3RNiLSi309wT5xX+/y+2fWEIsmqR8Sh5KpYIeTwSVUpFTfSuKIn98bDsFDjNrbhm9b8VQRNcJBEanxjLg5w54o/R0ZhuhdRyTLK6Lr6rD1Ruk8WAfQX+cp3+5lUQ8zWe/sZrWnuHW4Ovb2jP//vQNs9BpVDR3+zMtGiqLzLT1Bul1R3h3r/TQUSkVfObG2TliuGpBGasWlBGNp7jvl1syxWebDmRdDa09Ad7Z04XbH+PmS6rRHBegjgysg2krvRxL4UUndfO8sa2d7p4A6bSYWbnJYNSg1igJjNGiF0WRrQMZWU0dPhZPH79bYHDpwLyCsWV+KFVarMUXk4y50A8R/UHUWhuJSBdCKgRIwug69vywHHu9eQoK5Yn/hPPK16BUGYmFWkmEO4n4pBbOWkOp5JYquxJ72ZUAxELtJKM9Oe0IALyqMkjD9ZWFXFyc7c6pLFrKh0NHsBZfgvvYX0mH2yip0PLpGeW80u7EG09SYtBxWVk+jzV0Uu8NZYReFEUSkR7U2jyUquGxDZmJZVKFPuJshzIIBMzEkykM1ulEA018cnkEh9HP6uqjbIzmZgyE+9/h/o/djVpjwjpCoPF4LHYDfV1+0mkhYyX39wRQqZUsWzWV5kZnjpW8/gsr6G73seFvUpOsv/xO6l99zb/MZndvgJbuAHazFk9/iB3vH+Piq+rQaFT4PVH8nijO3iCOkpMvXjBoDfoKuujQhrmm/HL2NfThDyXofq+ZB/7PUhQKcPYFCQWzWQztzZLQ2+wGamc4BoQ+RmLACv/ln/fT3B3AoFPztTsX8uKmFiwGLVsG+gh95LJaLp4nBVKXzsy6e6oG4h3PvdPMgRZJUL546zwW1I1sbRl0av5lVTVP/b0ps02rVqJUKmho99EwMANRKOC2S7NuHVEUiQWbUWntWIsvOanI93oiPPfOUSzATJS4Ikme/nsTt15agy3PgN8bzaxulkoLiCLDHiyD7NnaTuOhPpRIdbenK/Thge/FaD614PNQBsV1JAaLfVKJAFAmFZUNiLxKY8WUN5dkzIW9bHhm0vEolGrsZZeTTi6j6+APASnDaaS2uHrzlExfHq2xDJ15ClF/E2HtAkhAsUGb833lVWS7KIac24mHOxFSMcpN+ow/PhZqw9nyc4rUa2jwgTeeJE+nIZ3wIaSjcubMWWJS0yvzjSEEQUGeo5xEPI0hX+ou6DBmLdjVBt2w4wTnn4m2/5yIr+Gk17Da9IiiFNQEKWMlHExQXGZl4fIqbl2/GI8uG8g0W/VMm1PM0kum5pynq93H61slq3h+hZ1Xn9tPe4uHxgO9OULceHD0xmyDJBMp+roCmPO0dNbuQ1F5lPeEv+AU4viALleYFze1orPo6O0M0NqUza0edClZbHqMZi1KlYKeTl/m/SOt0oNgSrGZmjIrX71jIdcuzwb8akdxqVQVS+JyoMWNSqng/318yagiP8jli8q5+5Z5fHzNDKxGDevXzGButeTCKBgI/r6zuysnBTKd8COkY+iM5acUsH1/vzS7GPwV7G33sGF3J399v4X8QhOppMDO/T10u8J8/eeb+eavtgxrcREJxenu8LH13Ra8/WHsA9v/vqODv29vZ7yEgnEMJk3GgJgoVAMFX+kBP/3gmqF66zTK5nwZe/lVOGrvGpNvW6UxodEXSy9Gue+DgWGtsZzi6Z8mr/xqymbfjVuUxlNiHP63mDnWUg2IRAO5ufhRfxNiOsJMYT8i8IP9x3ijw0Wvv5u4qEFjKOOoP0I0Nb4F2WVOjUkV+jx7kCR27ANLaIUiZhSG4YuBAOzeOwvRKHWRTMb6MyXkJ6OgSPKl79p8DMhOt612SYgUCgWrLqshiEgrAo+9cgiFQsFFxwl954AFvtJhJtboIhaVcs672n2ZhwjAsSPuk+ZAtza5SKUEamdmrcmE2ktM7Uarkb6S17e10xHIxhZuXrcw5xwWm5RaarboiIazwjboRFi/ZkZmW8WQNUyHZioNZej2mjIrtWWj+9gHCfpjzCq3ctmicn58zyounlfKzatquHV1Df/xr8tZNb+USDxFe392xpTIWIwjZ9lsOdjL+3u6eOj3O9l2qI/6Vg9qlYKrB9I5Bx01/d5oZub07OsNfO+Z3QQiSbzBOPf85H3qW7N9UV54cg8vPZ3NLClAQe1Aps6f/nEUtz83hnMqiKJIOJTAbBld/MbLYIXoYPFUMiq5mnTGstOq8MyvugEUKmzFI7drNthmUDDlFhw1d+Vcpzcax6RW5QRSj8eUJzXsC/R9QMi9D3FgveVBP3y5IlszsrHXy6NtWl5OX8mRdAlPNHXx/X3HSApjq1CXOXUmVOgFQeCBBx7gzjvvZP369bS1tZ1w/0hCjUU1HatVmkY+tfNFfnO0l7c6rPzUG+JQIitgTlceFbWrUGntmW2JUVLQYqE2YgN5znOXlGPPN3DkUD/ptJAJzFqH+NhXLyrHX2DABWyplwKNALesX8QVH56J0azF745gApLOCFa7gWtvnUtRmQVnTxDfQDWvSqUg6I/xy++9x5OPbqF+z/AFxAVB5OAeyUqdNTf3oaabuZMPrcz+2HsQ6UFk1mXVlFbaUA88BAxGDa9tb+d3rzdQcJxwG1Fw1dIKSguy4q5QKHjgE0v50m3zEJJpGvb3EPBFaTjQi3sg08agU1NXIYn7lOKTu55EUeTpX27jyUdzF2UpLzTx4ZVT0WlVzBpINT08ZHEYqW8KaAzDhb65289jfzvE95/aSWtPgL++10yqL8T0QjPe/hAKpYLBlne+YBxRp8585uBxqal/Hwg6p/aa5g0AACAASURBVFNC5uE+iB0FywvMmeI2X3jsXRnjsRTplBT8nmg0esmlloxKxWSxUCsguVNOB52pgsoF38SUP2/E9xUKBab8eag02d9OPC3gjacoOYmbVKMvQG+pIRnrw9P+EsH+rTmfwcrw5AY3ebzQLVnycUGg8xS+B3csQTB5bnb4PJeZUKF/++23SSQSPPvss3zta1/jv//7v0+4v+rJdgI/+yMdoV56VhSh7qqj5OAlJA4tJB018U4kQWcyzZ4DCzGYTKjUSopq12EvvwaduZpUwkvEl1viHQu20H/k9/Qf/QOiKKBWqyirsiMIIr/76Wa6BkTHclzGTsEQ4R/MrCkptzFjbgn2AiOkBCoHCj5WXlFL9fRCyqvyEASR3Vukh8rlN8xEo1WhVCqIhBLDSvRFUeRPj22nrytAZXUeeQUmrp96FeWqrPW9N/4P9FrJlRQHOhF5c1838WQ64yIoKrXy0qZWNu7rJnycgWdh5GykqSVWFk1z8O5rjbzzWiNP/3Ib77zawFsvZ9dXvfuWeVy9tJIPr5x6oq8NyM6MBEEcdQZTMzAr6BySthn1HwWFKtOpcijHp3eG/THKUWLqC+PuD2MtMDJ4pfb+ED97/TBpRPIhkxNSPFD7cKw3QDIl0NOVu6KXe+AMRw70cv1SaQyB8Ni7iA76501nwKJXaW0olBqSMakKOeI7jEKpnRB/9qm0xR1KX1T6nCUjuFCPZ7BTJEDE30DYsz/jflIoYIUtSYF6dDHvCJ04sJ4WRH54oI2H97byZqfrjFeNn09MqNDv2rWLVaukaeHChQs5ePDErpWgWkvYZGGTJY+USUOg2kpaKw2pUjGVkCjyXstUurutmTx4jb4Qa9EKjHmzSYhqnj7awzGfB0FIEg93EHLtlk4uCsTDktAOWr2JeJp92zv5/+ydd5hkZZX/P/dWzqmrU3WOkzMTYIYZYMg5CoigsOiaWF3U1d2fCXNAFJE14SooQQQkShgYwYlMzj3TOYfqUF05398ft7rCdJieBOMu3+fp5+mqeu+tWzec97znfM/3iKIwrsjmtosyxrYvpYkfiSX45u+3sWtscogm0RvVlKbi0K5yeXURj8teeHGplVs+vpSP/ds5FBSbGR0JEY/JHoskSex5tzPN+162WqZWXl51Efeekymo0at0fOW2xVyyrIxffWENBq2S/pEQL29uJxKWPRlRm3lYG4fl1YTBqEZh1aJGINA2eQn9yFGicSODQYJ++eGzGNTcsraW0Gh4nBc8bj9ZctLZoats5Jm1qJVimtMfj4wQC/ejNVVOmAzsyGI+KUSBo03LvMUutGoFytSElwScFTY0CJQqRM4pNPON2xdz8dJSfMEYr2xpZ1tq9RREookkATLnRZOaHUZPwND3pGilY6HBUwlBEFBpncQig4T8vSSio6lWiu89d6I3KJ+bqeLzY9BbZiAo5HHRQFdaMM2cChUtCPyFG3mW6zRbuMwZ44bSXIek3T/+nusPRfBG5fu+J2sieLt3hP4JKJwfYGKcUkPv9/sxGjOhBIVCQTw++TLr2Q/dzdO33ZN+Hc7T0r9cXraWiZUYlHqcngpEhcDSc6tytjXlLabDfBmtkovfNrrp2vM9+o/8T5o+BqRpYvYszrsoCpx9QXWahz2GfKuO/7hV7ufZ3D3KoCfEk2820t7nyzEO515chyK15C90WVBrFDKD59xKDCYNBqMGtUaJ3WlAkjIGsbnBzeb1cgOK5Wuqcpg5GqWK8yplSV5v1EdRnpabzqtBpRT59HWyKNbLm9sJObQIooA66/c0ugOce1k9V922gPaUwe5pGhpHIQU5tOb3RXAWmrjomtnpPERXFu8+Gonz3GM7+eN/b5mwGEmSJA7v7+PArgzvf0xlMx5LsOmtpvR2oihQYNfT0e/nyTcb0wJbesuMcfuFTDHanRfUcnmZnaKstnqucitzFhTz8L+v5gf/ugKNSsHieieXXz4TUSFQkIBon59927tZ7LJQoVTw/IZWDjTIoYMmJC6+oJZbb5iHKZWfGTOb2R69JEnsODxAJDpxcjAYiPLMH3aw4Q353iqvPj3FPgbHApCStO59HGBCwa/3An1pj/7YzCKFyoBr9ucpqP1o+j2duQ59lqcPMLN0ESsrZjE3vxi7RsU5BVYsKiUd/nCOgxJPSjx8sJPv72mlwx/ie5tyV+/DkePvK/F/FafURTAajQQCGY8xmUyiVE7+FUnleP5sQqUgKQq4dydZaL8UjyeEq9ySU3Xoj8X5S2s/kuQAwiSPmq905jpCvmZCo4cxF66iqNTCyvN0FJRWYcuzoVJPfEzFeQYE4O+7e9JccoDzV5TTtlmO+ZZXZwpjVGq5AlejUaI8SuPGnkqAth4ZxGBUM+TOeKsT0S8/ufQjRCMJNvZspTfQT6nJxattb/K31nVoqoqI9lSwP0Wj3rdFzn0smZHP9oYBDg4HCKhEeuMJ5swvwrOnn9Yjg8w/Kzc84vdGkCSZ9109w0l3u3weRrK6ern7fIw9a00H+1l8TgUgTwABf4SWw4O8+05rzn43NOziEsty/v7yEfp7vOx5t4tZC4tZfXEdhtR1e31bJ4vszWhhXC9QSZLY1jBAW5+PGpeZps0dhIKxdGLZkW/g/CsyxsJm0vD9TyxHr1WhUoosX13FpreaAWhrGkLRNoIzLjEE6IAEEt/51NlpGQhjEl59dj9CTF6JeQPRtIHZuK+P371yiCUz8vnUNRnlyTEc3tfHQK8vfR2NE+jbnAoYHYsYcu8l4O9GIYBSdey8yelAfzCCABPq20wEUaFGYyxDVOhIJkKodE6UWZOUzXUxeqvcOk8litw7txxBEBiNxtk/4uf3R3qYazey0GHmlU43sRRj65eHMo7LWpeDdd1DDH1g6KeNU2roFy1axPr167nsssvYvXs3dXVTN/fNhkKIkZDkwpyYVY1mOIJnWPYMF5xVhtMp3+gNQz7u3906bntJyrDGCsoWMdSTxDfcRNee7wFgUYMmOYti18cmPQYnUOw00p1llFctcHHL1XP4TfMI9XMLKSjI9azGjutoVFTn8Q8a2bGpnR2b2tMMm/xCE3MWuFBNIH4211XLxp6tDCYHqDAU8mLLawCIeV1o87oI71mFFDGkVSY/d8siPvXDt9K0T4BrLprBy30Bhgb85DmMCKmK30QiySMPyHIIBUVmnE4TWrV8vkP+WPp3HN6boYd6PeH0+888toMDWZMfgM8ygGk0n94WH0/t3Z7z2cFdPVx/6yI+euUcvv/oNpIxLxqpF2/CyeLiXEG0de+288vnD1AM2HsChCQpLVKn06v41BfPS/+Oic772stnsfbyWTz2y820NmaoqDVqFYpoAq1FS311huEUC8veujalD/TWzm6E9lECo2GUZg1OZBmJia6tJytkNWeha9LrfzIIxhK83NTLW4FVVNLGBYrNWO1OHKfhu6ZCJJGk3R+mwKChuOD4VhTGpZ+ku/FvlNWfh1prQa/9JFp9HirNxLpEs/0h9o/401o6z7YNpD+rthloHw0ST0p8fEEFBUYt67qHCAmTP38fIBen1NBfeOGFbNy4kZtvvhlJkvjud797zG2qm/eSQMk+5zZ0uvkoVQswLi8m+korAnKYo7Tahtvt47WuQd7unbgjlKb2HqJNDwIQF8vRWBL4hptoTxazNTmfKxTrwX2Q/j53TiVePOZDVGgJe1vQGFx85KI6/vuv+xkNRLlwSSm3rK3F4wlx452y+mC2qmI86kWhMqb7mWZDIjdG3twg08yuu2MRHs/4dolOpwmHIIetXjz0Jn87/PdxY/Rzt7IgfCsbU1z9aCjK526cx32/l41sRaEJnULA5jTg7vez7pWDlFc7MJg0uPt8RFMThKgUcLt9SCmDOtDrTf+uliMZjfyu9hH6ekfxecM5Rn4ov4155xbhDkfxvx5E7x8v5AbQ1DhAvl3PTz59Ds2H30QIwjuHLVTPG0GXYszsbhrkoWf2IQAuxLRnffO/nEUkHEcQBAaHJpajOBollbYcQ69MhV9WnF2ec92iqXCidySECBgBb8qAJ4ZDlCIwFE/mbBOLJdjweiMNKZ0lZ6GJ8lr7hCqbJ4t3eod5vUtevjVSwRJpH8GwiuRp+K6p8MC+NiQgT606gd9pxFJ6I6M+wOcDnIS9ABPvp0ajpkCnnjDufnGhHVNZPkX5JoKeEOFU3qDbEzwt5/+fAcc7wZ1SQy+KIvfdd9+0x5vVShZ17sdwuJWyYjVN13pRa7W0+MMI57uoPDLKrAVyqbY3Gk8beYdGxVqXg6daMt7nXzq83FJ2E4NRiT+1DLHEWUrtnM/zy93ymC5FLXXJffiH9mDOX0Yi5me07238gzvS+9AYK6irvZ0HPruSeCKZ7lqVjWQ8jHdgIwDe/o2odIUU1t2ZTpSFfW0oVEYMpvGx2xlTaOEAFBryMSj19AcHJvw8KUZZOa+Ijfv7WD6rAH8sQHfyELWlFho7R1lcL3utrnIbR/b3s3l9C5vXt6A3qqlNiZ7pDCoq6+RCqDHNmJGhYJo9093hwWzVYnXo6Wge5qlHtqVj7h57DxGdnxXL6rm4ZjWjER+/3/EG9MpBltmLiil0WQgFomx6q5nBfn9aAdSq9RMIQovHwOFODwtSxVjrtneSlCSurnXS0ygbt6o6J0azFuNxilJW1uXxj9flgh2tXkU4GKN+TgEz5uVSOXV6NQqFwECvl+VaNbFUknuMkeNAIP+oibr1yGDayF/7kYVTSkSfLFp98vleWWBlQ7+HVxPnUqucuP7hdCEcT+BO0YyXOE+fOugYDCoFn51dxosdbrYOZJhSM6wGivQaFKKAQaUkCGgVCqxqOaYfSSTRnOKCtf+NeF/P0A/On4v53JUAlPdFqdEVcWNVIQpBQBIgsbiA3d4AP9/fzv4R2aubbTPwb3PKme8wcXlpHleWOVEI0BOMsC2Yx3qPkUOeAI819vL2QMY7SNpXIAhKfP0bScR8ePs35hh5gIi/jXhUZlMoFSKiIBALu0nGM0lJ//BuvP0b8fbLxj4W6qNzz3eJBntJxAIMND1K76GHcyYJq13DrXfPYc1l9UwFURC5a85tKLPYFeeVruTCsjVoUmyG4kIVX//oWdx+ST2PHnyKxxueYdHyINeuquSiVEz+aOMW9EfZs60LhULg1o8vy5E9zi8ykYgn+dUP3+bwvj5i0QRlVXZmzZc529kJWXdxMytX13NxnVy4ZtGY+PAlmW5Ds1bmUTe7IC0m99yOdXT75VqHaKqhdKRyN8++00zvUAB/KEZDu4dahyFt5OcsKuaG2ydWYzwWsjntNTOcfORTyznv8hnjJldRFHAWmvB7I2kjD9CCRCcSMSRKEdm0vZNINM6mt5p588VDANzw0cWn1cgnJYk2fxiHRsWlpXnUCq2MYKEtdGLia8eDQx4/R0blHFtXqnnI6kIbdZZTzyyaCKIgcElJHoscGW/19tpiFBM4XIvzzESSSXYNTd0k6APIeF8NvVIUKF2yii1zDSiSML9fhU2j4kvzKyg1aGn3h3m+3U1vKMrfe+RKx5UxH0N/epRkLMo5hTZWFFi5s17Wrt8z5KPDH05zqt/syVRHDkYkLEWrScT9jHS/QSKeSRqXzv9PrK6L8Es6uoe7SSbCxKNeYuFBeg/9N4Ptz6XHjlUpHo3hjpcIZbVt6z30S2bWySybc1e7Ge14GG//BpKJqWmL9fYafnxuZlV0fc2VXFNzGcsKZSlfb9THwdBWvr3thxwYkiUgesPdXHlOJapUzFkQBOYsygi+iQr5jKw4vxq1JncRV5Elc/D2q7JujavcRkWtgwXLSjlrVUX684jOR601l/2UV2Ck7Bw1bXXvssu9F4CCYjOSkEQxbOC3+x9DkiR8oQGCSYmkLkBPrJmfP7OPtj4vSBJ5KWNrtetYcV412hNUlAQodJnT58CY1ZjmaFiPEiIbRqIs30gM2eADvLWuiZffak7LQas1yvQkdrowEokRSSQpNcrHfsVc+bo/3TbEjsFjGzVJkvjFgQ6ebc29T73ROA2eyVtm7hz08lhjL78/0oM3GueVjlRFq+G9FRzTKERuqCpkrcvOLdWT9yhYmm9BLQq82T1M9Dh7Pv9fxPveeEStULPkktvg0CMEn3uBxOIVmIxGZtsMdAYyRtEfTyAKEPrJ94lIEpqycqyr1wBQadJhVikZTVXM3VRVyGA4ytu9I1xV7uTljkEGQlFM1WcTGN5LYHg/+6Q6SgQzdbPu5IAnTLnKyouJ8xntVDOzfyeOeDudihpWSXrwNjHY9hx62ywCw3tAUGAtvgC1vojwaCPegU1EQ714BzanjzcWHqCqEtTWVRCTu/+M9q5ntHc9rrlfmLLXpUpUctec21AIYtpQmdWyl/Pddx8YN77NO74CWaZQzsJZaEqzaI6mlEKmFiAb+UVyM+UV51Wn9/Vs5/MISYEiw/iH76zFtbyy5a/0BmTjEpSCBEzDGL15tI166Qn0oUhE8KZK3K1FXvoOBjnS4aEagXhKwuH6OxaPYy8dLy66ZjZb325h0dnlU46rqnfSsLePhctLMZi1VNTlEUvKzVO8QFIpYo0n6O30ICK3L5wxb+rQ26nAYCpckqeVV10zXRXUt0c4PBrkmdZ+ookkS52WtJcrSRLruocxqhQsz7cwFInRHYzQHYxwVXk+SlEgnEjw/T0ygeGe2WXjOPFN3iB/yZoYHjzQQTCeoNSgpc7y/vRkPb94atqqSaVkidPCpn4P3cEIlaapJcv/r+OMCG7Nql9B3lXXkPD7GH5N1ikvmKASzyIlEVNWy7vxndzPsiiTM6wGLnA5+NqiapY4LZQZtQyEo/QEI5gLV9GHk03JxfwlcQlbh+M83tzLD5sVjKYkYQ9FnWxILqE9ZuVwqrFCcGQfgy1PASAgYM5fjtZYjtW1FnvZVQDjZGQBFi/VkIj5EMTM74lPowH0ovx5zHdm6H1mTW7yZVlhJrzhDg1x35Yf44/memzVM/IxW3UYLCr66ZmwiEqpUvCv/7E6XRugM6jGVXuWVdnpFtvI1ztRK8Z72w6tHVEQGQi62es+wFc2fAu/WT4XBq+DH299AJUgEUmxqrRGOSywe38f1tT6a9VFteNWGycCg0nD+VfMRH+Mdobl1Q4+dNdZLFtdxdxFLkxGDXazVubmryhn7txCFAiIQynm17LSCTtInWoMhuVwY15K318QBG6tKaLcKHvWL3a42TaYiWH3haKs7x3mxQ43v2noyvHau1OO0sGRzHstvvG1Edvd8v4uLZVXd8F4Ar1SwZ31LtRncPzblZqw+qbZo/b/Ms6Yq2hdexEKownvxn8gJZM52hpjPpS2rxtBrQZBINLRgZRVjFVtlmf01UW2dHJGmfJ6zimUvdbtbi8G2xxGdbIBTaLgjW7ZICVSNrBayPWOuxiveW91rc15rTVnZHj11tlZRwyjqVi+KX8ptlQP0Km6B00Gkyo3ZHB19aV8+azPcWWVvM/+4AB73BNXIm/p3c7Pdv2KZxpf5Nd7/8BA0J3zuSAIaZG34lLrOK91OOwhFA9TYhzfJxZAISqwa220ejv41b4/AKAtkL33suaF1LfLzTEknx2T2khMIedbIikZgXMvrssJNb1XGGtiko1PXzuX61dXp6ufx9DnO37hsxPBGDc8T5OZUFWiyI1VmZVUa5ax3juUYZ20+cO80plxIhq9qWI9b3DCbUHOCRz2BNOFS2OoNGnP+CTn2Mpk84DnA0G0Y+CMuZKiWo1h3nwSXi89D/0Mk0LEoFRQatBSlTLiYaUa84pzMJ+zCikeJ9qfWW6uKbLzqVmlXOQav+SrMulRCNAdlB/WfmnyZeFacRPLxV3M1odx6TX0J23Epcxpclbfism5NGcbpcqEybkMkIuBimd9JmXUBcIp2VatqSpd3ZiIeolHR0nEJo+ZHg1LFv94hq0Wi8ZMqamYhfkZgaqh8MTU0w6fLK62vmsDewYP8OPtvyAp5T4Y51xQw4JlpZx78fjah06/vL1rEkMP5KwWtAoN9573L2j1srEqSIkZJAJWnDoHwYQPtVIWIwMoKD7zuNAVtQ7yZ+UzjEQXSbYempgJdaox1pnJoc1dkdg1Kq6rkJlT+4b99KS82PaAnJP6+qJq5tllZ6BIr0EU4FCKwNDqC6FXihiVCrqOkhkYCseIJJOUGbWIWZPeRCvqMw3O1DkaDMdymDofYDzOGEMPYDpLNqCBvXsINzfxpfkV3D2jhGvK87HEIyx+9y0Mc+ehKZHZJZGuTKGQWiFSYpg4+aYUBQp0GvqCURJJia5oynvVJBEFWJEvezJVQgeCAAvEBj5UU4rLoCWJiAczGmMlJfO+hM48cRd6q+siimZ9Br1tDkqNDZNzaU4FqMZQglIlG+tY2E3PgZ/R3/SHaZ+bcnMpn5z3MX606ht8duHd6fcL9E4+u0B+3ReYOFHcF8g1UoF4kO39u3PeK620T5oIPTQkJ5lrbVXjPhvDdbVXZL0SUCtVXH3LAlZcWo6rVp7QhoaMWHpKKT28iMtm2ig1axFFAVvee8PqOB4IgsD1V83i3z+/ij4But3yb5AkiY37etPNWY4HSUkiEEvQF4wQmSCBOFagVKTXTOhNj4UhAR460MG67iHcoSg2jQqNQuTGykI+UlvEx2eUUGvW0xuKsnvIiycap8SgpUivYTQWz9F+H3N+xsIghlRC3zZJ9fiZBKUocG6hXMMxJtXwASbGGWXoDXPnkXfdDQD4d+1EJYooRQFLOMCt77xASWcLuppaNGVyg4Rwc/O0912s1xCXJL66owlfLMF8u4nPzKvn20tqubLcyRfnVXCeKEuruuZ+AaXGSkGq7HuEPGyutVO2PBMEAZXGnjPR2EsvR2+dhbV4LYKgSHv0geE9gByrPx4Fvjl5M9GrxifH6m016JU6eoOTGPpgPya1EYNSz9w8ufz83b6dx/y+eDLO31rfZEPPVgxKPRXmsknHLnDO4WvLv4hRZeCjVecTCXRjy9OzYH4l5YVy5fLQoJ7YATOm0Xy69nmIeCPYnYZT3rjjVEKnUeK06ugZDCBJEkc6PTzy8iF+va2VFxqP3WRmDImkxP8c6eY7u1t48EAHf24Zv23jaJCEJDHTOvnEd2mJ3M7PqlbyVs8wgXgCZyqerxAFZlqNaBQia10OFAL8uUW+Jwp1mnSooy+rKKk7JQ1cnGLX3FXvYmWBlfmO05+POBVY67IjAMORD6SLp8IZ94RZL7wYUavFu+Edgg2HGHjyT7R84fOEGo8g6g0ojEZ0VdWIWi2BfXumbSgX5pmxZ8U9x8JBY7BpVLhqb8FednWaETNm6OPOi1DrJw9bTAaFykhe5Q2YC2TBMlGhQxBzl+TJ+Pgq2eOFIAiUmUoYCA7iDsqepiRJvN6+np0DewnEglSYS/n2Of/Fx+feTp7WToev65jnbo97Py+1yjIMZxcvRZygAjgb+Vob/15YjmXwHfqPPILPvSXVG7QPRCvxhBK1RkG8YBQhFbY53XTFUwFXngF/KIY3GKNjwA+igKnKwhbP9KsyuwJhmr2Z+PghT4CkJBGMJ9LXoSflXU/FICk36bi8zMnHZ5Sk39MpxjOVXAYtVaaMU1CgU6eFybKTl62+EApBoMQgTwKFeg2XlTnT+a0zHUpRxKxSMpKle7NnyMfDBzsIJ96frlU9wQg/3NPK/uHpVXS/FzjjDL2oUqGfPYdkKETXj3+AZ90b6c+UVtkjFpRK9LPnEHO7ifaMb+4xESpNOr4wr4J7Zpex1Glhnn18XFhrqsLomJ9+PeYBjS1vJ0IiKbFr0Et8GskgQRDIr76Vwvq7MeWvACAeGT7GVtPD0hTP/htbfsDW3h10+Xt4vvlvPLL/jwDMzZuFWqFCFERKzSUEYkFaJ6BlZmM4LBePLXDO4arqS455DGF/GxF/B0q1vJwOjhwgEfMiJSMYLMXc+LHFXPeRRTirMiujDlpO6PceL97u2sShoSPHHngUeoMRYkU6FDolXW4/3e4AKlPGYWj3hWhIGe0p9zNBaOHVzkG+vauF/z7UyXb3KFtSceY87bEFxKwaFdemYvazbBNPljWWXENfkZpAXuxw0+QNsm/YR28wQplRi0o840zBtGHTKBmNxoknJeLJJE+19NEViNDlzz3nkUSSxtFjX6uTxaERP56ozOY7Uzj+Z+TVNZ21LOe1aJCXsoksZUzTEjme792ymeNBoV7DNRX502IU6JUKCnVqmryhdPLraGzsH+Hp1n5eaHdP+PnR0BjLUOuLUGpkYxg7RYZ+YRYd88nDz9Lmze2FelbBwvT/5SbZG7x/x8NEEpNrenujssd6Ufl5x/TmARIx2YMxF65Ea6okGuwhOHIAAJXWSV6BCVuegdqaIhIK2QPbm9hJPHl6l90jYQ9/PvJXHtrzW0bCnmNvkIIkyTK5gyowlJu4/8ndbGwcwFCWcRJ+1dDFo409PHSgIyf2/Ub3EC+0y7mRWDLJOyn5jn+pd3FZisa4oV8+lq5AhGfbBginjIJ5mrUEZzktfGVBJbNtE4d65ttNVJp0rMi3UqjXYNOo0jH+3x3u5onmPiSg2vz+cOVPFeyp0NUzrf00jmZWyN6sTlR/bu7jmzub+Z8jPfxgTysNntPnbbvD0Qn/fz9xRhp64+Il5F1/U/p13tXXAnIMfwyG+QsQtVr827dNuA8pmSThP/mLOfYQPHSgg0AsQTie4KUON3/rlLv/jBW47JxG1WI21Dq5UfNo71tEgj3HGD2N/SlUfHzu7SwpWEA0GWP3QIZq+bHZt6LOavSxougslKlOQ82e8UqgYxiNyL/JMoni4NEYC0OJSj3GFDPJ07MOAJUuPz1udkE9/pWHaJzzDmGDl/7g9CbJE8X+oUPp/19qfX3a2wXiCRIp709l0aArMpC3vBBtfq5hVAoCfaEojzb2MBKJsc09yvqeYbYMjHLYE+CNriE8qeYZ1k3f5QAAIABJREFUZUbdMSUFjqcoy6RSTjrerFZy94wSrix3phk1V5Y5x43Llhz4Z8TKAht6pYI9wz62ujPsG0+qFWmHP8Tu4UyYzRdLsGeaQnlTYTQa45nWfrzROFsHPDze1EtSktLMKfjA0E8JQRCwXSyHCpR2O5bzLqD4ns+Rf8tt6TGiWo2uto6Ye4D46HgvbfilF2j+3GcIt7flvC9JEtJxcG6X5Wd0TfaN+Ng97GNTv4d/9HkYicbTetlJmJBJMRk0hlKsxReQiPkYanv2lLVFK9TLBrVhpBG1qOI75/wXSwpyG4sb1Qb+db4s19ww3DjpvrxRHwLCOA7/ZBiTlVAoDegt9WiMmeRtdo5Dq9TwpeWf4YLZcvjqu+8+gC96ejysZ5te4snDGQmLrb07CMSmlxcZCmfiviqjClONBQGJBaYQ1tGMt7g8dY+0+8P8aG8bz2VJ7P6hsSftud9QWYBSFMjTqlBPEgOfaTUQiSZInCZeuMugTa8oAM4rsmPVnH4dndOJQr2Gy1O/6UiWR7+ue5jRaDzthF1Xkc+3FtegEASGIidvgN/p9bBj0MujjT083+5m/4ifvcO+ozz6M0Mz/4w09ACCKFJ1/08p//q3ZN2SeQsQNbncXm21THUMTcC+GXpBbmPm35Grkz76j7dp/PidRDo7p3UceVo1X54vV8ceHPHnzNY9gUhOl5uDI8dnrMwF56CzzCAeGSZyirzafH3mIb6o/DysmokFuKotlWgVGnYM7CGRTDAYGuLwcFPOmNGIF6PKgEKcXighmWXoAbSmTCGZUp0rtSAKIrXWTA/U/YOy1+0ODhGNnxovqC8wwJsd76BT6ji/dBUXlZ+HhESXb3orqDHJ3EIdICUR1Qoi0X283fNHnNaMIY7E9h5zXxcU21mUJ6+MREGYUEPmC3MruLzYwSd/8jZ/+NvhcZ+fKizLt3BuoY1755ZzYcnp6ZD1XiM7T1GflZt4sX2AZm8IjUJkYZ4ZhShg1ygZDMdO2rkKpkJ12WHdP7f0k5BI1/O4z5B2h2esoQdQWqwoDJMvc3U1tQD0/uphPG+ty/lMUMuhiqPDNwOPydx175aN0z4Os1pJvk5Nhz+co5f9dGtfjh7Pa12Dxx3C0Vnk3zA6eGoe7Hx9JkSytnzNpOPUChXLipbgiYyyb/AgP935Kx7c/Wv6szj3o1HvOOmFqTBWACamDL3RsRC13oWz+tYJx1dbKqm2yMb+jw1Ps61vF9/Y8gN+vePxaX/nVNjrlvMDN9ZexfW1V1JilBU5u/zHNvT9oQh/TcXYuz1vMOr/PRW6w4Sjcqhwp09eJSiCXta1v0Y0/AY3VxWyyGGi1KBlnt3IZ2dnVjRLs1aGsWScm6sKmG01MF+hpkBUcFNVAXatio4eOcSwYV/vKTgDE0MlilxSmjeuKOufGRqFiCmV21iYRQ096AkwFIlRZdKhSIWvHFo14UQSX+zkWDmThWWcWjWri2wYlQra/SGC8QRD4ej7ysI5ow39saCtTBXwJBIMPP5HEsFMslbUyh5T8EgDI2+8RmwkVTV6grN4pVFHNCnllJCPhW3m2IwU6zV4Ywn+0tqPLzb95KLGKItvBUY7jjFyenAZCzm/dBX/tvDjqI7RTHp5kayXs8u9j5GIHF7Y1CsbMn8sQCQRxaYZL3o2GRLxIIKoSjf+VqiMFNbfNWmRmUJU8LlFn0i//v3BJwB4p21rzrg97v1s7pk4FzMV9g4eQBRE5uTJbQhLTLKhH5NOngrZIQB/tBNIsGfgHUgpWyYlL5HWfQx1yjURoVgb9VYNN1QV8slZpdxcXUSRXsMnZ5ZyZ70Lk0q+FsFYiK9suI+nDj+JtifAa683s+eNNqp08v16pDMThkwmTy875H8b7qx3cZHLwWy7kY/VFed8tiQvY/zH6g5+sKc1rfNzvDg6Fp8Nh0aFIAhUmXX4Ygm+vauF+/e183hzL0PvU8z+n9rQHx3K8e+SVSKlZDLN0In19eF+6glav/h52u/7enpsfPT4LvAce2ZpWGPW8aGqQi4ucfAv9S6urchnQVZCa6xr/XSgVNsQRA2haYYTjgVRELm+9krqbBMb12yUGl3YtTZ2DmRCDwdTVbBj1bSFhvwJtz0aYX87sVAv4hSqnJMd7421V+e8l51c3NG/m1/ve5Q/Njx9XOyc0YiXVm8HNZZKDKkiM6fOgVqhzvHo1/cMs2tw/L3QmZIKuLIkBuR6fpdUXACA2eZBsmZ6mbaMto3bT6lRS00Wq6Un0EcoHmaXex+vH870QzjYOszfd3ezbkcmpNjef+Z0T2poH6F7cPqSHe8HCnQa1hTbUQgCtRZDmk66It/KzKzQzop8K0udFiRyJ/TjwWg0TlySmJO137l2IzaNkgtcsk7SjAkK37JrKd5LnJShf+ONN7j33nvTr3fv3s2NN97IzTffzEMPPXTSBzcdmM9emf7fu1kOx8Q9HpigWCLSkeGN+7ZsJnBgP8HDDbTf93Ui3V3jxmej2qznW0tquKmqgMvLnMx3mFhdZKfKrEenVLCiwEpxinfvPQ6PXhAE1PoCwgE3ySmojqcDgiBwWeWFObo3vYF+grFQ2mgV6o9t6CVJYqRTVh3VmiaXSZgMa0rP4cE130tPTgICiWSCdm8n/3PgifS4wdD0ZAeSUjIt8TDTMYc3uofwx+KIgojLUERvoJ9YMk5fMMIb3UM83TrArv596e29UT9NXi8qIcFwSK7TyF4dLStcLKt1KhoQ9Znl+HRWCkOhLDqtyU3RnA7U9dvYuL+Xx149jFatZFaFTL391h+28+Qbh4mcZIjhWBiTdfAGJ77/mrpG+eETu/jqb7fS1PXPoylzdbmTNUU2LinNzUNYNSquLneiFIScHNtEeKXDza8busZx78dYVA6Nis/PKWehw8RlpU6+OK8ynX+ZZzdxZ52Lry+q5pISOXfW4pOrn7cMeN7Tgq4TNvTf/va3uf/++0lmsQO+/vWvc//99/PEE0+wZ88eDhw4cEoOcirk33Y75d/8Drq6ekINh+j97a/wbpKbYHMMmlr3Az+m60ffJ9LRTvfPHsC3fVtaJnkiKASBBQ7zhIJPCkFgZUol0xvNvYDD4Rid/vCkhRoqXSEgEQufXprhRFhRtCRdpQpyr9vX2t/i+Wb5PBzLo4+GBhhsfZpYeAC9dRaOsitP6DgUooJ7FtzN0sJFJKUkG3ve5U8Nf0FCYpZd7szVN82E9RMNz/Js00vy8VHO+p5hHm3soS8YwWUsJiklafa08scjmUT9i21bSEoSg+Eo39/TSzghEog08kqbXLBXb6tNj83X51GftWIye+X6hS5fxtCv63ibJxqeIRTP9eDahjPSB6JlEI/+IArLEAf75f6sly4r4+4rZqXH/OnVBv62ZerCtpPFziODPPLyIX729J5xn/UNB/nxU7vSr1/fPj0Sw5mAAp2Gi0ryJiwGEwQBu0bFcGTqpOyGfg9tvhAtKU98u3uUx5t62Zeia1o1Spw6NTdWFeZIpYOcdK+x6NEoRFYVWjGpFLR4Q2zu9/BCu5unWyaWLJkMkiSlv/94ccKGftGiRXzjG99Iv/b7/USjUcrKyhAEgZUrV7J58/EVM50IRLUajcuFZZXc3s63ZTNDf30WAOsFGTlh5y0fTv9vWX3euP3Eh4fo/eUvGHz6KSLd06u2PRpjcdjsGH2HP8T9+9r470OdPNbYQ+8EglYqrTzbH61nLyXfmxn/+lrZOC/Ol6uCt/ZlQgqFhoIpt3U3P05oVO50ZbDPn3LssSAIAgV6mef91JHn6Pb3UmOt5NwSmYbZH5ieguSm3ncB0CjUjMbkW7wrEOHBAx10heVJ4+e7H2Ewkgm3hcU1/Kahi79mUSMj0YzwW7FRlgkemxQ/OusWVrlW4Oi7kMEjJahFdY6sxHNNL7OhZyvPN7+a3sfGfb28dUCmsyaDJkRtZhJQWOVJrLLIjMWo4bPXZVRJX9jYxoa9py852z8ihy9ae8eHil7d2k40lsRm0lBo17OnaZD4GVLtebKwaZSEE0l+05BZzY9GY+meANnSx692DfLrhi6ebRtg/4g/XcVsVU+PmioIApUmHf54It2ucay4q8Mf4rcNXWwZmLqYb++wP/39x4tjGvqnn36aK664Iudv7969XHbZZTmxVL/fj9GYiVcZDAZ8vvcuxmhcvCTntaasHMdV16KtqaXoU5/FdsGFOG++Fdull1PwkTuofuDn5F1/I46rrsmZBIBxDJ7pwpwy9N5onIQk8UL7AL881JVuM314NMjPD3Twp6bceLxKIxv6WFZDkpC3ia59P8TT89YJHcvx4LzSlXxt+Re5sFyeAMc47Z+Yewc65dSt5BJZ2vpjieWTQbbnDLDAOTdt/HsD/YyEPcQSUy+3x7pxXV1zJwdGcuPKIzEVLlMtSoULUdQRiR4imZR/b7s/TIsvRDzRT574d/J1Gc2ZC8tWMzdvJvcu/hQg1yLcXH8tC0qqSCShSFNOf3CARk9zTm3CXvf+tPFv6/UhagNIEsTdufr7olF+yMsL5WOvL8tNgv/ulUOcKgx6Qoz4MpTA/uFMnProMFFDh3xc3717OTMrbMTiSTr6zxwNl5PBWFOVNn+YRCrx/WhjL786JIdqsusoeoIR2nwhKk26HNE563E0yxkrvmxKrQ7ikkRSknissZcWX4gX2t3p3NDRkCSJ17oGUQoCd2fpHE0XxzzKG2+8kRtvvPGYOzIajQSyJAoCgQBm83ungCeq1ZR99RsggcrpRNRoEJRKyr78X+kxtrWZRtYKkwn7pZcD8kmMDw3h27aV+MgIo+/8Hev5F6BxHd8JNaeWbgMpKtWWLI3su+pdrO8ZpsUXoisQQZKk9EQ55tF7+zegVFsx2Ocx3PESUjKGt38DWnM12lNgRKdCgd5JKJ57k1Vapv5OSUqCoAApQYf+XP52pJ876orRTiCyNV2UmzPn/KLy81hZvAyFqECr0HBkpIn/t+m7KEUlV1bdyvKiGRhVubdwIpnAF/VTY63kkFfF0YlUeb9X8fgRuUNZLN7CBS4Hr3dsR69bI78Xa6HCUUQgbqEvOIBVY0Gv0vOv8z42bl+zyu28tKkds38GKBr52a5f53w+GvXR5e/FrDbRFmxCNI+S8NpJevKhvCE9TtAGMepU6FKGQ69V8dN7VvK5Bzekx0RiCTTTkEcYGAmi1Sg50uHhSKeHi5eW4bDIE7YkSXzpl/JK+4efXMEDf95D71DG0G892M+5qcbwI74IAyMhFtTkoVErqCm2sH5nN83do1QV/3OoW06F1UV29qUoj95YHAFZ2whkZ22MPnmhy4FTq6LUqMOiVhKKJ/jWLlmjaboePcBsm5GXO9xEs9hUvcEIgXgClSgQS0rsHPJSahzvXHUHIniicRY6TCfUNvGUiU4bjUZUKhUdHR2UlpayYcMGPvOZzxxzO6fzFJZfO+cee8wkyP/03cDdDG3eQsP3f0R0+xZKFox/sI+FeoeRw0N+3EfF25dV5bO8uoBf7Ghmd/8oCpMGRyrWL0lGeg4qkZJxhjtfwtPzOslEFI3OQSQ0jK/3dUpWfI4tPSM8caCLr62cQZ7+dDSGMGHSGPFF/KgUKiqLp+6RGg64QUpgL1rELztdQJitHj/X1btymlhMB/GkhDsYodcf4sur7kUhRplfmIlVV9nLOOiWPeWEpOKtfg0bB9t56OLcqt/hoAcJCafJQas/il2r5sNzSvn59kxRnagzUec8i05vjM8vv5Ylrlkc9uyhJ9SBSlnGDIeWDy28jCQShY0OauwVk96nVpsezTN76enSwFHzok1tZyQ6TFewlzcPPU6vWQ4L2SMzufLSpRxkhJ09chJY0ASpq7DkfI8T2cNv75NXxt5IglnFU9NdE4kkn/3pOwTCmfCh2xtGq1aiUopcuyaTW1i3sydt5EsLjAyMhHh5SzvXXVCHIAh0ptoozqrOw+k0sXy+gt+8dJAn3mxEEkU+fMmMKY/ldOBU2gsnJi4LR3mluQ9Jp6I3qyYmoBLZl+rMNafYxixn7sT2BXUt7mCEksKJCxInw8rhPN7K0sX6xUE553FlbRFvtA7QMBrgrjzjuOdu04h8Dywvd57QOTil3QW++c1v8oUvfIFEIsHKlSuZP//YMVu3+8yhkAFIFfWIBgMD72zAeNX1x90M+mpXHj8ZDqQFrnQKkRqLnsHBlOBXShzsy+sP8JlZpWkd8JqFH2NooBf/0C6iQTlHYHCejcLXTnBkL62H3uZ3bfJNte5ILxdM0EnrVMCuseGL+NEptOljngh9viGSPc8DEJEybfdeaxlAFZc4u2Big5SUJPaP+FGLYg797O3eYV7rknMUFRY9SknkzZbDzLQaWOK0UKQt4iBN6LTnIkmyEYskkmxp7s8R5Xqt7W0AFEkToXiSapMaRTiXBXWgz0OPL06hTk2lppyhwQB3z76DNzs2UmXVMtdxEyl9Ni4skqmUU92ndSVW9rUMoS8XkMh4a+4eLco8eObgK4QkeXtFxMp3broKQRBYmryV84o6eaNlI3tH9nDhCvu47/nup1byl3WHee6dFjbt7qa9y8OC2jyUk4jydbv9OUYeYE9jJiS4YU8mbPj61na0agX/7/Yl5Nt0/O6VQ2w50M9nf7QebzCaDu8YNSJutw8BMBvUeANRnnzjMO/s6mJWhY1b147vSnY64HSaTrm90KTyDW0DXg5kxb5/sUP22CtNOhzS+OtvB+xazXEfT41Ow1gwdkW+lc2puLwVgWqTjt1DPvZ1DFF0lCPXmtLmsSYl3G7fcRv7k6JXLlu2jAceeCD9esGCBfz5z3/mmWee4fOf//zJ7Pp9g6BUoq+bQWLUQ8J7/L1drRoVq4tkely+Vs1/LqjiQ1n9PrNjeg8d7OTVzkESkoTZUYcxbxHO6lvQmqpQqm3ozLVYCs4BYKgzwwYaOgH9jKQk0eEP8eeWvnHt5LIx2yF7aeXmEqRUUcjRrIS9wz4ebBjm996FPCddxe5ELmd/LNl0NKKJJK92DvJkcx+PNvbkSEb8oy+TiGobDdLkDXLIE+DZtgHiSYkycwkKRT5qVR0adcbTf7nDnRYe80X9vNAiJz8l5EmxQKfGnjrnOoUot9jzBIhLEmdlVasaVQaurr6IuY7SSc/NZFg6U2YmFflXkqe18/G5t7Mofz7xblkCYszIR9tmURW6MO08KEQFVZYKKm1yqCQsjKcumg1qzlvoQhDg+Q2tPPzX/bz27uTFdUfHzzVqOdRzw5pqygvGG4drVlVRnGdAqRBZUCOHENv7fTkx/AJbZiK9fnWGPtszGGDd9i4GPRNzw599p4WfPj39nhHvB6ypcOtgOEqzN4gmi6Ezw2rgtpqi416dToUyo5YSg4azC6xcWe5kVaHcRKbEoKE25bA0TsDtHw5HUYsCRuWJhUXP/H5h7wNUBTLTpOPb36Tg9o+inzkLQTn9U3V+sZ08rRqHRoXiKPGqGRYDGzUeRiIxksA7fSPUWvQU5stLQ4VST35NRrxNkRIUGyZjlHYP+7Ap46zOV6NUW3E3P47RuQSDbc6kx/RMaz+7Uo2kD3kC/OeCyglpZ5dVrGWGrRaHzsaR0SB/aOxhuUPD5WV56YYs73TLtDA/RvwJ6O+VDdRd9S6ebe3nyGiQUDyB7qib8jcNXXRn6YLsTemh7xv2p3VDJsJgOEqZqQSFaMt5X8EIfSEbL7e34FQPY1JnyADdYTmBW2XWoxRF7p1bjk6p4PdHuukKRFAIAkvyjm/ZPRnOnlPI33d103wQvv+Je8i36bEly9kYeRcpoUBQJJAiOhIDpcxbML6BzRijp9vfw3zn7HGfG3Uq6kut6cToM2+3sOVgP2sWuOgdCtDW5+NLtyxErVLQMSBf43uun4dRr8KgVdLQPsLqhXI4bawI696bF+ANRFk2K8OqWlTnzAkVjSHflokJr5pXzOK6fH7z4gF8oRgtPV5+9OQuZlXYuWZVFRZDSnokmeSlTW0A+IIxzIYzU25hTAZi37CfaFJiqdNEoV6DL5rg3CLbKW+QLgoCn5qVkca4tNTJJSV5qUpa+flq84c4l8y9LkkSQ5EY9lTF7Ql978kd9v9OqFOGPj4yTPfPfsLAE386ru0FQWC+w0TJBEkVq0bFvfMquCsrc54tqzAGSZI47JFDQAb7PHySHOaw4EUrxFg/EOLdg88T9rUQCXQw1PbslMfUnuXFRxJJugIRnm8fSPOBs4+92lqBVWOhMRWj3DIUoWHvw7R5AzzS0EVPeLyHZlQqqDTp0omk3x7OpaiORuNpI39LdSFqUWDvsJ83e4YZSCW9Li5xpAtLLi/NSysS9oeitPqVaFS5IYIR/yskpTCbB6I8fvj1dJHUDXW3MBKFWVZDOnHl0KrRKxVpCqxGIZxUF6W4Z4S2b3yVju99G9+7W1izUGbRjGnUNLSPAAJJvxzCkkbzue2ies5b6Bq3r1KT/N5u937+58Dj6T4A2bjr8lmsXZK5Z7rdAf70xhHe2tlNS4+XfS1yIdaYR19fZqXGZaHIYeC8RSWIgsC584tYMbuQr3/0LGZX2FkxuzDHW1UqRO65fh5Hw6DNTTjqtUr+7cb5aQqo2xPm7d09PPVmhm2UTdXsGz75LmqnCza1ErNKwVCqcCpPq2Z5vpULSxyn3MhPhjHjbVErsWmUtPtCRBPJtFjagZEA0aSEQ3viKqMfePQTQFVQmPM61Nw0ycgTR6VJx+fmlPOz/e1sc49ycZZuRiyZ5KUON9vcXhY6TFxffhmC0A39cc4S92EVvPwlcSk7k7OZEzp239KkJDEajVNi0LCq0MYTzX38o3eEhtEAWwdGmTtBty2AgayuSH9KXA2Hx8s0VJt1CMD5xQ5EQWCty8HeYT/9oQixZDK9amj1yQ/7JSV5zLWb2NDnSQvCfaKmALtSgclkIJ6UmKOOohsepKG9CywlPJXqryoqMt5nkTbEqC9IOLwVvW41SmUZ2/rlwh6D2gWMTsheWJ5v4ZAnwKUleeM+Ox6MrHuDaJecSOtrbmLxw7/l8XVHeGlTO//Y28uoX76eK/RX0tTfydWL57LkKP2VMVg0ZsxqE93+Xrr9vYxGvHxi3h3olBlP2mHRcuvaOi5dVk7fUIAfPZnb3P2Rlw+y5YCdQ+0j5Nt0afZONvRaFXdfOWvc+9mwmTKx4S/cvGDKbkwWo4ZPXjOHlze10THgZ3/rMPtbh/jtiwfTISOQefp1pdPXTHovIQgCFSYde1PsG/v7LNlcadSxc8jHN3bK5IEPVRWm73/nSYjQfWDoJ4C6SF5eqwuLENRqor09SMkkwilut5avU7Ms38KWgVGebujiuhI53LC+Z5htbjk/sGvIxw2VBQQFEzCCQQiSJ3goEXrpkoroH97ImDmQpCTCBJ2g/DG5gYZVraLcKBvmhqw4ujcaT1NDxyBJUrpx9NGoEjppkeRY9soCG/VZSdU8rZplTgtb3aO82jnIFWVOmcGRautWYZKNr1WjpDMg5zGEh3/KoN+H8VvfQ6lUMvzVr5KMRlGqNahv+TRRbVbooNCKRa1ikcNAV+kniSc1PNocQKdyoRQFnIa5vN4tP7SFE1Qw11oMfGVBZdqzP1H4tm7Jea3weVg+q5D1u7rTRr7IoeeOi2cBUxtXgApzGXsH5UryRk8LX930ff5jyT04yZ2EbSYNNpOGf7thHs+83UyXW76O4WiCHUdkNkeh/eQ6Rt1311KQoCT/2H0IzpqRz1kz8vndy4fYsK+XnzyVqq4NZvJI/cPvj77LdFFj1p8xhn5VkY2dQ5nV0JiRX5JnZmWhbbLNjokPQjcTQGkyU37fdyj9z6+iLipGisWIDQ4Sbm875YmlK8ucFOnU7Oj1MJrSz2g7KpQzEI6mu+UYkT3jKkH2JhvDGSMY9o7X5YdMpx2bRolZrcxppgLQnArRHBzx0xeUPfEXOtyEEknKdeOrIC83NnJVuZPCrD6k2RhjDGweGKU59VvGqg3H5CPWFNlZ5rRwh1kg3NpCzO3Gv3MHiUCAZFQeq4lGuPUPP+G8hu2sIcRnZ5dxSUkeZxdY0SpV1FgrqbcVYVAqMGgqUKmX4Ynp0/H+Qv3EHtDJGvlkOER8ZBj9rNk4rr0egEhnJ9etruKcufJqUK0U+dilM6e9zzl5uVTFUDyU0xnraMyvyeO+u5bx0OdWUe3Kpf6VOE+u4XqJ0zgtI5+NVfPH5x7G0Ng1/faN7weyHZX329AX6DTcVpN7LvO1aq6rLEB/golY+MDQTwpNsQuFXp/27vt//wgd3/oG3n+8c0q/R47nm5GQS6GTkkRPMEK+Vs0t1bLReHfAy2hULuioqr+FgrqPsbTuQkCiNZmJ27pbniAaGiQQS9A0GsSfkmJ4tVOm140Vd1xW6uQjtUXcVS/Hhpu8cvL0j029PHigg7d7R9iaKvaq1ARZJOynQBVnjtjEFeJbqHVFLM+3cs+c8gnjmNXmjPHfPSSvTNzhKGaVIj2+SK/h6op8knt2pscG9u8l3Ca3NtRUVFLx7e8BUP72a1T86ic445FxyShBEKi16Akclcy90OVIVyqfakQHZD68qqAATam8sol0dqCNR7jzspn87svn8/C/r6amZPrJ3jkOeVJwGYu4vPJCAAaCg1NtAsjhGKtBnjwtRjUfubiey5af3uK6iVBbYuXLH1407v05VXYau0Zp6zt+Btt7BZNKSaVJh0uvSVfLvp+YZTPyrcUZJtuivJMvTvsgdHMMjGneh47I8r3+XTuwnLv6lH5HSepBfaK5jxsqC4gmJVwGDbOsRgxKBftHfCQkOVmjM8jGWQMUie30Jp0onKtRJYNsdAdp2N+FD1l736RScPeMEtpSiVhX6nuUosBMq5GkJGFQKmjyBnOaKAxn0TedwigzFPsorFtONFTIcMc2tKYVU/4eh1bNfYtruH9fGzsHffiiCTzROFUTeP+RroxIVrS/P23o7ZddgbpdBIlPAAAgAElEQVSwCPuVVzP8oszX929/F+v5a8ft45ryfHanlrtmlZK7Z7hOeVONgScfJ9rdjaasDFIaKOr8AjSlslEdev45hp5/DsfV1+K48mrE40z0WjRmvr/ya2gVGhJSgpdb32BgmiJuY2tMtVKcMNk7BrmrmoSmtGzSMSeDulIrn79pPkUOPX/b2sH8ageBcJz9LcO09HipKDxzq2nvqndx6kiUJw+FKHBbTRHhRDJHAv1E8YGhPwZ0tXUIKhVSLGX8TqK8fzJkt5X7S6tMXZxpNaIQBYr06rQ2xsyjZvY5BS56ewMMaudzxBtmV1I2dk5VDHdMlWqCLL+3It9CmTHX0IqCwAyrgR2DXtZ1ZwTVxtg2AA5JPh6F2opRX4TWVI1CdewbTykK3FFbzCOHu9P7yzvK+Eb7+/Hv3oWo16MwGIkN9BNNCcppU8bIceXVaMvK6fnFg4Qaj0xo6NUKkXqLnsOjQZY4zafUyA8+94x8nNtlobTgoYwiq6qgAKXViqDRIkXkydTz5jrsl195QvmcMXqoChUWtYmB0LE9eoCLzipl5xE3N6ypmXSMFI/T+aPvkQwGKf70PRgXjve+TwXmVsmFfB+5SBaPa+6RV4YDI2d2nP5UcuVPFbLbI54s3v91yhkOUa1GPzOTTIsNTu/hOx5oFCLnlDhQCHI8bnWRjdk2OW7o0GSMVp0lN8lWYZGTM02+CHuHfTg1Sm5TvsDN2o3cVCUzVMZCMFXmiRN0a4psKISM0BJAIJ7AqlbyhXkVKMLdKFRmFCkGiFJtnjaXt1Cv4RMzM6GlWbZMLDR45DBt//UfkEyiMBhRFRSQ8PkINTciarUoHbLBEEQRw7z5IAhTNou5qaqQtS47555EwupoJMNhhl9+MW3kj4ampBRBENJGHiDh9+X0PThRFOjzGQl7cAeOrcFfV2rl119cw1kzJpeUDre2kgzKE65v+/F36zpROK3yfbOtYQBv4Mzon/p/ER8Y+mnAefOHUZhkbzrW34eUPPUyrR+dV863ltTyubnlXJwqoADS3FkRcjoVARTrNQjAjkEvCQkW5llwGOzEQr1UGlWIgD8Vu86bhIPr0Kq5vXY87e/sAisWMUIi7ketL5xgy+khT6tmnt3IArspXfkHMPrO39P/q10u1PmykYoPDaEvLc3xiAWFAoXRRHx08qSeTqng/GLHKY2xRjpzK1Dzrr8Jx9XXpl+r7PJkZFq2HADjkrMACLVMnBQ/HiwvWoKExGO7nyUxDbnqySQRxhBsOJj+37d1M9G+Y9NyxxDt7ckJsR0PTDr5vhvxRfjKr7fgD02/qruhfYSn1zeRmOR5i8QSPPiXvTyZxd//ABPjA0M/Dajz86n60U8wLVshM3Dc712DkDEDXWbUjqs01ShESlNhH61CZIHDhEone/K6xChrimUNGqUg4JiCTVBrMfChCgdn5+n4yoJKPjmzlBUONUGPzPqQG6OcOG6uLuKm6lyBtHBzM6JOR8HtHyP/ltswLlma/sxQOT6ZqLRaiHve2+5G4fa2nNcqZx6mpcsQlMocaeuCj9xByRe/jOOqawCItLae9HcvKVhApbmMLV07+dGOh6Zl7KdC8HADCALqlCJr94MPHGMLGXGfl/Zvfo32b36NuGeEqHsA9zNPkwxPLxSTfc1DkTjbD0+vpwDAy5vb+NvWDjbtHz8pdQ74+d3Lh9jdNMjr2zoZ9k4u6/EBPjD004agVMqJOMC3/d33TL+jzKgjP1WtNxFuqSliTZGNO+tdWDWqtORx3+FfszpPwR21xXy0rhjlMWLGrsDbzPc+hl6IYg3tpXv//2/vvMOjKtM+fJ/pLT2T3kmA0JuAdLuwthVBxMVldd3V1VUXxC7q7mdbRd1iW1fXCgpiXdeCBZVepIbQAum9TmYmmf79cZKZDAmQhGACvPd1eTlz5pwz73kzPOc9T/k9S6gr/hxJqcUY0X1V0I5wN1pwVVWi65dJ2JSpqKOiMPQfQPK9DxA962qS51zd7hhlWDg+RzPe5p/vH3TTgf2AHKcB0KWmo4mNo98/XgiKFSh0egwDBsp1F1odlvVrqf/u2xP6jSgVSv4w/AYGRPejqLGEvXVdX7XWfPoxJX9/lubCApoPHkCblEz0lVcB4KqswOs6viulavm7+Nxu8Pmo++Zraj5cSd3nn1Hx5uudHsd1Fw0gySy77bbuq+KFD3cd1zB/t62EnHw5qeCTNfn+ZifWJhePvbWVh17bxOa9gZvG+pzOP6GciQhD3wV0KS0ZFh+uxLZj+3H27hkMKiV3DE1l2FEi72EaFRcmRZPUsrJX68z+zxrKviMrVBPkn/f5fB0aIFdzFT6vC3vdbupKvvJvN6fPRq3rOaVMZ3kZzXmya0OXlh70mb5fJpEXTUcbFdnuOFWYfKM7lvumJ/E6HNh27UQdF0fSwrvIWPI31GZ5bhVqTYdxCkmhIHS87MapfOdN7HtOrJWmQa1n3vArAdhcvu04ewfjqq2h5uMPse3cQeU7b+FzuzEMzMY0fARh084FwFl27K5VzvIyGtevQ9ky966qKjwtPScaN23s9FimjUzkgevGIAE5h2vZsq+KLzcd3RW0dV8lb325z/++xtLM8m8P4vP5WJ9TzsGSBr+mzoQhcUgS7MjrXD/hMxWRddMF2qalNeUdxDRiZC+OpmM0+ngkhbrFaO+iqWEvsf2vR9Pi0qkp+BhXcxVxA34bZKw8Ljk7p770O/B5MUaOIDT27KAbx4nSlHeQosf/z/++NQe9M6jC5Jx0d20tmtgTcyUdDZ/XS9FfH0cTF49p+Ah8Ticho89CUqn83388zFfPxb5vL67ychzFRRgHH11o7kgcpaVUvbeU8PMuwDRMlvjOikonWhfJjuocHB4nWqWGmqZaDGrDMbt/2bYHbgzNLRIe+mw5V7+1oY59Tw7qyCiUpo6zO5oL5KByxIUXUf3+cjyWBr+hB/A0NaHUd64JhkatpO3y4lh59as2B24Cg9MiqLM6+XprMWqtirwi+Ub/4K/HUFDeSHZaBJV1TeSVNNBodxJylCK5Mx2xou8CSpOJmHnzAfAcIwOkN1GodCQNu5uIpBl+g++wyv9g7Q37sNftxNVUhtcdSKH0eT143baW17JUgS4kvUeNPLTP9tAmdt7Q69Ll1X/NJx/5t3msVtz1dT0zOMCeu4fmgwewrPkB2y65lN/QBUMNcpZW4q23A+AsKT7O3sE0rP4Ge85uSv/+rFwh3NxE2WefMzZiCE6Pkz01+3B6XPx5w1MsXvf4Mf32liMkGlAo0GfJKY/aVPnJtPr95RQ98ehRz+Esk1NdtckpKE0hNO3fF3RN7traLl3flVMyyEoKIzpMx6FSC00Od7t9mp1u8kotRIRoOWugLAT3m+ly1fDn6/LZX1RPcoyJyFAdI/ub0WlUjBkYg88Hq7e312ISyAhD30VCJ0wEScJV/fMFZLuKJCkIMY8hNms+IPeibazcSPWh9/z7tK7gATzu9mqJGkPHAlwnQtO+vUHv1TFHTwc8EuOIUegHZtN0YD/uRgt1337N4XvuJP/B+2jcshmf14uzQvbT+rzebmVGte0V3PDD90hqtb9griuozTFIKhXNhYXUfPoxtZ//r1PH2dq4emw7t1O/+jsOv/IqWct+BKCwsZhKexVunwe7u4n1ZYEbZ72jwe+Sc5SW0Jx3EMPgISh0LcH6tHT/6ruty8xZfnT3jbNENpzahERU4e2faNx1XXOXXDIhjXt/NZrxg2PxeH08/e527M0uCisasbcU6e0rrMfj9TFhSBw3XzGE2EgD/RLDuGJyYMyThwVLBEweFo9WrWRDGz/9w69t4om3t3Ikbo+Xpav2c7is71bqngyE66aLKNRqVOHhx82nd5aXodAbOv3IfzJQtQRmrdVb/NuU6hA8rsYWQy+7QDwtsrjGyBEgKVAo1Ki07f3kJ4LX4QhKVww759wuFRVJkoQ+M5OmvbkUPfkYrjbpgWUvPY+uXybNeQeJmTefpoP7adq3j/THnux0HwHb7l3t4i6mESNRqLuufSIplWjiE3AUFVLTkpYYNnkKPrcLR0kJhkGD2/n47bl7cJWXo46NxVVRgWXjBpTGFpdKaQVR9ZEUR5aSZAoYudXFa5mYMI7dNbm8tPN1ru7/S6YknY1tp/w0Enr2BOw5u4HgJxNJoSB65iyqV64AwOfxIB1RCOiqr8d+YB8KgxFlWBgoA/MYMnY8jZs24Oriir6V1j67h8ssPLVsOwUVjRh1Kp65dSJ7WgKwg9KCf3+XTUwnOT6MVRvymTg02NDrtSpiI/WU19jx+Xx4vD4KK2WRsra9mQF+2l/F11uL+W5bCa/cdU63xn8q0i1D39jYyKJFi7BarbhcLu655x5GjhzJ9u3befTRR1EqlUyaNKlTPWNPRdTRZpoOHjiqj9JRVETBIw+iH5hN8p1398IIZRSKgJFSaSIw97sGh62E2sKPg1f0La/V+lhCY8adlLE4CgvB5yPsnPMImzylW2X4rXIDrUY++qrZVL+/HAj4oSvfet2/v6u6Ck3c0cW2Wqn57FNqPlwJSiVJCxbhrCjHvieHmF/9ustjbEWTlBR0Y7Ns2kDV0rcBSFywCOOg4AYjVStXgCQRf+NNVL67FPvuXUGfDyuGrTGllNtkd5dRZaDMVsGB+kNsKJNXrqsKVzPWE++fE8PAQf4boHFIcOZUxMUzaD58SBaSszb6g91FjaVsLN9C/OoczFYruktnIEkS7jrZqIeMO5uwyVNo3LShy66bVvolhiFJ4PPhb4Ria3ZTUdfEnoJaNCoFmYntF0gXjktlZEbHC5CoUB2FFXIbRVubXH27w41Rp8be7ObZ5dtp7cvt8fbdrlcng265bv7zn/8wfvx43n77bR5//HH+/Oc/A/DQQw+xZMkSli1bxo4dO8jJObGsg76KYfAQ8PloWP2df1vbTJbaL+RH9aa9ub3eRi0sbioaVSIxKdeh1kWjapEvcLsCj64Om7zqbE3N7Eka1vxI3p13YN0hBwf1mZnoUlK71SlHl5bmfx173W+IOP9CjMco5e9sUVDrqjfh5lsxDBhI+JRpJNx0C0qj8ThHHh1tQlLQ+5oP3ve/duQH59m7amtw5B/GkD0IXVo6UZde7v/MkJaKpNWRlW/H4rCQWyunfM7uL++zpmSDfy69Pi/VH64EAvIMCX/4I0l33o2+X7A8giRJ/mwajyVw038t522+K1qDtUge4/4BcqFgawBXn5mFOkr+nbgqO58T3xa1StHhanpTbgUlVTYGpkagVnXNNEWGtjS8+e8eyto0OqlpkNM4dxysJq/UEuSyaehkpa7L7WXbgapjavP3dbpl6OfPn8+cOXMA8Hg8aLVarFYrTqeTlJQUJEli0qRJrF+/vkcH21cIP/c8JLUay4Z1eOw2Sv72DPkP3OPPSHDVBNw6HkvvBm0N6kFY/vYjxUueBkCpkQ192xV9k+UgkkKNztTzqocVr7+Kp76eupab35EGpyuoo6JJXLCIjCXPETZlKpJKReItt5HU8tSkTUlFodOhDJWNU6vP/ng4K8pRRUf3aBaVKjKw8lSaQoLy/x0lwd23bC2r99bvNw4eQtQvZ6JNSSXpqpmYRo1Cb2lmzpd1lFTnE6YJYXTsCExqIwWNxTQ4ZOPl8rr8TxEJN90ijyMsDMPAjuWSVS3z5KqrwetyUWgpRn24lMtW1xNX48Kuk9jXLI817re/J2befMKmTkMVFYVCrz8hqQeFJPlTJFt97v9dJ5/vorO63rc3qsXQ78yrYfW2wPxWNzSzbncZ1Q3tC7z2FR47kN9gc/LRj4d45r3t/GPlLn44hYO9x3XdrFixgjfeeCNo22OPPcawYcOoqqpi0aJF3HfffVitVkxt0rSMRiNFRd0rm+7rKA1G9P0HYM/ZTfmrr2DbtROQV/LmmbPwtMn1dpaW+h+LO4vP5wOvt53ftCNcVVX43C408Ql47DasP/2EITubhh+/J3LGpdj3yKXvzuIivA4HSnVLmqJDHqPLUYvbUYM+rD+SoudDNpJKJRfcAPr+A1BHn1gmz5EuDwDDwGxSFj+CKiICSVLgqq6m8P8exlVRcdzzeex2PA0NGIb0bFGYcchQ1LFxRE6fgX3PHho3BbJgGjeuJ+LCi9ClpgGBfHZtmyBp1C8uJeoXl2I2h9CkMdG4eROxtW5mflNP5YWZFDz8IAPHh6Pcd4j0UicFF0TgcVjxNDRgHDqsU66x1hti6d+fQx0bR+7scVz6QwNal7xyrYwzcNhSgM/nQxUaSvjUaf5jtckpNB3Yj9fhQKFt3+ClM9wxazgl1VaSY0L4cac8B+eMTGRgatf1inTawL+VnW1y6j9bnx/U1hDkhjBlNXa2H6xmbHYsR+OD7/P84wJZoG3aMdRB+zLH/Zc9a9YsZs2a1W77vn37WLBgAXfddRdjx47FarVia5Nja7PZCA09viyp2XziEpy9gXPcGPJzdmPbsR1dXCzNlVU49+cSHW3iYJvUS3VDVaevsXW/kg8/pnDZe4x6/u9ozcd2p6z97XwAJnywnAPvvE7Vd6v9n4UnxOI+EGheYf10JYa0VBSSBq+rBrM5hMpC+SZlThhyUv4Wh40GXA3yijPj2qsJ7+JcdBpzINjoTYyiUKHAcWAv+sYqTBlHz5zZ/+xrAISlp/Ts9ZtDiPvX8wBYBmSQ31BL9OSJ1GzYhGV3Do3/+4TkxfcDUN0g+7rjszNQh7YfQ8q44SS+9zaf3Xc7MfsqiHlvA05g4spAqmOExY3OKRvoiAGZbGvYxo/5G5kx4FwGRmfy8pZ3UCARpgslOSyeizKnokiMpdX54qooR/nNGjwKiVbhY19sJE3uZlQhXiL1wYuVxgFZNO3fh95SReigzjdYCZoicwhjgOY2aZa3XTPqmLo9R/sbTRmdwptf7Gu3/UgjDzD3ooG88dkecgvqiI42dehGtDW52Jgb7Jpye09de9WtJdzBgwe5/fbbee655xg4UM5xNZlMqNVqCgsLSU5OZs2aNZ0KxlZVtf9DnAooh58FyrfA4yFq7nVULXsHW94hNsy9Dq/TiTo2DldFOdW79+JNzECTkOBfoTuKCqlb9RVqsxnTyFEoDAYSBqRRVdWIz+cj//U3ASj8bg3eZgeSWkXE+RfKKYOS1OEPc//by6luY+QBytdukKszlUrweCj/Qq54VV8ShzfVQFlBMbVlchzFrUju8b+F1+nE1WBB0mpJ+tOduBIzOvUdZnPICY9Fm5hEc1EhO/60iH7/eLHDoLnX4aBqtdxIRkrp3Ni6hTmJ+EX3yS+Hj8Vy2x+o2/oT+d9vwDhoMNbiEhR6PXXNIDmCx9B2LkZf9yfy77+nw6+YHTaRjXvlmFFTRBT/3roMgH3rDqFX6WhyB0sOlNRUMV0X/BQTf6AWl0kHDtnN4RyUBeSwuzCP7MjgxuwkyW6+0vVbcJiD4xHd4a5rRhIRqqWu1nbUfY71u1AAr959Dg+9tomKuiZumzmM77aV8NP+4DTom68YwqDkMFJjQ9i6v4oDh2uC+uS2snVfJU6XhxGZ0Ww/KLtiN+aU87elW7licjqGE2jU3RN09YbTLUO/ZMkSnE4njz4qF1uYTCZefPFFHnnkEe688048Hg+TJk1i+PDh3Tn9KYHSaCTtz4/hLC/DOGgw9WYzzrJSvxSsYcAAGhvqadywnsYN64m5bj7hU6bhsVopfvZpPBZ5lVvz8YcoDEZiX3sZkOVkW6l85y3/a9OoMRy+awFRV1xJ1CWXAQT5fatbgn3GocNQmkKwrF/rDzLG3/A7LOvX+l1MvjonpBoo+uRRlBmhKDRGVJqeTwNtTUENGTsOfWZWj5//WKgiIvz+amdZKfqMfgBBvX9dlbJrJ2TceEwjR/8s41Lq9YSePQHLurWUPPMU6U89i6uqCk18wnED1GpzDAq9Hm+TbIiNMy4mtzKXlC0FRNS7iauWV8Yfu3YGHddq5OcPuoZEUzz/2P4K68s2c/mk6STftxh3TTWluzejW7sZXYtufNg559E4ZCjk5FBuq2xn6A3Zg0ChwLZ7FxHTZ4BPLhbrLt1x1xyJJEncN0/+O+o0KrKSwsgvb8Tl8bLk3e0kmo1+KeekGBNb91dRVNnYoaHffVh+yppxdioXnJXMBz/kkVdi4eutxcRHG4/Z4KUv0i1D/+KLL3a4fcSIESxfvvyEBnQqoYmNRRMr+/iUR7ipVOERaFNS/Z2pHIWy0bHv3YPHYiF00mQsa+RCGK/dRvkXXyENGkHF6692+F3Wn+Rc+JqPPvAb+iOrQltvJiArLzpL5aCUfsBAjCNHUfGff9O4aSPecrn6VTnABHhxHazEnVaHKvzE/rHZc/fg8/kwZA9CkiRsO+W8dG1S14NrJ4pp1Bh/PnnRY38h4ZbbaD58CMv6tSQtuhdNTIw/WHuk5s7JJmzKNCzr1gJQ/cEKfC4X6pij+4pbkRQKtKlpNO3NxTB4CIlXziG2oZ5DW+7A+sUXZAJWvYKtjkMgSYRpQmlwBrJMkkMSiTPG0C88nW2VO6ltriMqIwNPWiq7D3xF6/pem5pG7LXzcFtl/3SZrX2sQ2kwos/MounAfg4tWoBCrSb9r8/4b1b2vbmU/+ffJP7xjp/1769r0+Reo1bSP1l2OT37x0lB+yW39MQtqrQyrF+we9Tn87H7UA1GnYr0+BCUCgW/vWQQ974sx1nqGk89pUxRGdtD+DtQASiV6PplEnHBRRhagoetYlytKWmmEaNIvGMhhiHDAKhZv5Gqd9/BWVpC2JRpxM6/HvM11xI6cTIQuFEANG7dLJf/18mG3jhiJKkP/cVv5CG4QEYVFoZCrSZ2/g3E3XAjUpUSzwGr/3NvZXOXRKo6wlFcRPGzT1PyzFMUPPwg1p3bqf/2GySNhtDxE07o3N0hdOIkomcGYkulz/+d2v/9F3ddHVXLZbdGa/plZ3LtexJ9ZhaZ/3wRSaWicf06AIxDOxcMjrnmWsyzryH2178BQBkahjYlkC1VMTgBWoztrSN+y++HBmoBovRyJlBqiOxqKWgsxulxsfLgp+xRBQKYrdk4MQYzCknRoaGHlipxnw9vy2+xNcPM4/VQ+uLzuGtqqGlpA9nbhBk1/iwfCDb0R1Jea6fG4iA7LRJly9NfbISBJ34vC9ZV1DZRa2mmoPzUcTsLQ99DRF1yGWpzDMn3LSbrhX9hHDwE08hRJP7pTiSNBndNDW6LhYa18ipeHRODcchQku5YgC6jH4379mH9aSuaxCRi5v2asElTiDjvAr80smXdGv93lb34PJVL3/JXJpqGjWgnEGYcKt9A2rY+VGg0hJ49EdPoMcGGvtSJZcN6mg4e4MAtv8e+v31QC8BRUkzxs0/TdKC9ZG7d16v8vVSdJcWU/v053LU1hJ498YTy0buLJEmEjB3f4WetLptW3Rb1SRJJOxYKnR5Nm5WuaUTnWvtpE5OIuPAif9MTSZKIv/kWQsafTcoDD5M48xr/vlH6SNLCAtk36pasqvQw+caQW7Of/x1exffF66gNDfxOlC3V3GqFCrM+ikMN+fz30Ff+HrY/Ve5kdfFaQtr0EAA5e+hAXR73f3UfXpv8+2rat6+dHIXL66bSXoXX1/MNfDpLdJgOvVbJptxKXvssN+izVr/+kPTg4qzocD1qlYLKuibufGEdj7y+mR93lvLBD3m9Xi9zPIQEQg+hiU8g/fG/ttsuSRLqyChcNdWUvfS8P+WvbZph+HkXUGdrxN3sJOqyy4N8ta3SuEecFOu2n/zuIlVke5eLYWA20TNnY8hunxFhnj0H7eZELMjuA31sf+w7dlD05GPg81H9wfuk3HN/0DE+n4/SF/+Jq7yc5vzDZDz9LAp1YIXkLC0GpZL0J54m//678TmdLdfWvsfrz4UqIoKQcePR98vE5/VR9e47ALjrG/C53dhydqOKiOx4jn8GQsdPoCr/MJG/uPSEboYacwzxv/09AHH2QA2HVqlBq9QwPm4MkfrAbyQjLJUIbThbK7cTrpWN+i8GXYoq8mNZHTQ+oHOkb2kh+Xn+19Q113Nt9lW8uluu8A3XhDLo8b9SsuQpXNVVVHz2MV+bSghtc9PwWBtxVVX6FUfrmut57qeXqG6uJS00hVuGX49B3XGby5OJJEmEGDQ0OZpYs6uMK6dmEG7SUm91sGpLMTqNktEDgn8XCkkiJlzvr+YF+M//ZP2mswfHER/18y9oOosw9D8DqqgonOVlfn89BAeuQseNp98lF3SYUXCkqFbiHQtpPpRHzScfUf/1KtlN1BJobIukUBA5fUaH41GoNYRPOA91bQxqnRmHqwD7jh1yTTpAB6qIXpvNLz3gtdmw7dpFyCg58OXz+XCWlqKJjUMdEYF59hwq334TSaNBm9B7QStJoSD+xpv8743DhlP51uvYc/dg270Lr91O6NkTu1Wl2xOETzsHfWYm2pZ8+p4gusU9Y1AFsozmDZodtI9CUnBW3Ei+KviOCnsVGWFpnJ8yFeeC/rjr69H3H+Dfd7h5MPkW2W14sP4Qd6wOLADWlm1ixPAbiL/pDxT+3yO4cvdyXpvvkRLi8JWW01yQ7zf0a0s3Ud1ci16lJ99SyPfF65ie3juLgRC92t+0fNehGiYPS+C1z3Kx2JzMnJqBsYPMmqzkcEqq22cG7cmv69OGXrhufgZaOxS1EtbGl348VCGh9P/364Sfez7h51+AcchQQs4O+LyNg4egNHTvB2aMHIrGEIdx+Iig7Y6iIn+RUyuuKjm20HotbRtmu+vr8TY3o4mXfd2h4ycQOnkKKfc92K1xnSw0MTF+t0T96m8BMI36ebJtOkJSqdClpffojUYhKfjLhHt5aPxdx9xvUGTAmPcLSwPkWIVhYHaQ2Nw5yZN5cNxCEk3xVDfX4vEFFgGH6vPxeD1ok1M6VPlsHipXQTtaWjI6PS7WlW5CISm4f+yfUClUfHZ4FaXWzkukUgwAAB7ySURBVFUwrynYzIs7XsPh6Zkm4/NnZJOZJP8eln59gILyRvLLG4kK1TJjfMdV4ueN6njh8uOOUh58dSMf/HDi/YJPBsqHH3744d4cgN1++neG12dm4bFYcBTkY77mWqJ/ObPdPkaj9phzYRw6DGNL4FZpNOJoyaiJnTffHzzrLpJKhc/jwdPUhD4zC2dJMaaRo1CFB4pkmg7sx7p1CxEXXoyztARXeQURF01HkiSa9u2lcdNGTGPOkg2FSoVpxEhUod1L2TzeXJwIzXkHac47iKuyEmVICDHX/KrXVvSdoTtzoVfp0SiPneoYpg3hi/xvAPjN4Gv8LpojUUoKTBoTefX5lNrK0So1TEueiFkfRUFjMcOiBxOuDydk/Nnsqt9PaFEgqFsx/SxCN+VS47XygmozH+V9hsPjIC00hfNSplBuq6DUVk6JtZQJCbK/3+fzsa/uIBHacBRS4IbzdeH3vLFzOZVN1YRoTKSHdV0U70hCDRomD0sg1Khh2/4q1uWU0+TwkJUUztmDO47bhBo1ZKdG8MspGdQ1OrjwrGTcXi8HihtotLvYX9RAUaWVBquTfh0Is/UURmPXqpHFiv5nQFIoiPnVdXJmzNSekUZNuOkW0v78mF9s6kSJ/uVM0h7+C8aWzkbNhw4Ffe5sWdGrY8zos/rjsTZSveI9fD6fP2PHOLTv1020laMwjRnbJank0wmVQsXC0X9g0ZhbidQdP602O7I/GqWGP474HVdmXkJmuJySWmyV9V8UGg3bB4dyIEWLFGtmf4qWcp0Tm0GFt6KS2uZAKvBV/S8F4Lrsq4nQhpNvKaLJ3YzT42JN6Qb+sf0V/ns40M6ywFLEhwc/879vq8PfE5wzMpE552fhdMnBYXP4sbtm9U8OJyJEy81XDGHK8ASuPb9/kNrmT/urWPbNAVzu3gs2H8mZ+SvvBSRJQpuc3Gl99N5Cly77+yvfeZPi55b4XTiulpxzdXQM+kzZfVP31Rc05x3EumMb6phYfxeovowqMtD/NnzamaNH3hEZYWmkhXZuZTwufjRPT37Ev5KON8or3tbUS6/PS3FTOVsvzCL1z4+xakoUG8q2UB0qEWL3omkxoneN+aP/O5UKJePiR+P1eXlv30cs+P4B3t//CQAbywI9FL4rWhs0lhJrGRZnz6Y2XjAmkAHVUQHVsYiNNHDfvNHMPidYsK8vNSwXhl4QhCY+HnWc/I/YvnsXtj05eF0urDu2owwJRRMTQ8iYs/xpm0VPPIrP4cAwuH0zjb6IcfgIYub9moTb/tRjT0NnCkpFIJsm3igXeLUa+ip7Nc0eBykhSWiUajLD0nH7PNSGygub6Do3jyT+ihRTsI+7NVawueInfPhwt8QAHB4nPp8Pt9fN7po9ROoieG/2C1zebzoAe2uDU3wLLEWd9vUfjeyW6txWJcyuMnVEApdNTGPB1cPRa5Us/Xq/v3NWbyMMvSAISaEgacFd/syL8n//i/JX/4XXZiN0wkQklQplSAj9lvwt6DhD1oCOTtfnUKjVhE89x998W9A99CodEdpwSqxleH1eChvlmFFqqHzzHBwta2CVR8uZK7O+rqf2qWfadfE6mq+92eOgwWnhYP1hmtzNDI+WFxLDogcBcnet1tx1t9fNX7f8g0c3PXNCufm3XjmUGy8ZxFnZnW9x2Ra9VsUVkzMYkh7FRWel4HR52X24tk/k2AtDL2iHOjKSpEX3YBg0GK/dhnXLZtRmM5EXB9I1lSYTsfNvQNJokLQ69EfRPBecvgyIyMTibORA3SF/CmaroT87fgwA+1O1hF12mf+Y5pamKx6rleqPVuKpq2dm5iX8cofEtXUZhGpCUEnyk0OJtZyilhtIZoSc1RNnjGW4eQgFliLyGvLJtxSypjRQ1X2ooYAmdxMH64Obu3QGvVbF2UPiUPTAk+nwTFlW4aWPc7jzhXU4nEdv5P5zILJu+ggnM9OkO0iShGnMWdR+9ikAUZdeLgtZtUGXkkrkjEuIOP/CHq1+7Wtz0Zv05bkwqY3+wGhRYwk2l41ZWZejVChRK9RkhWcwICKTAWPOQ5/VH8v6tagiIwkZPQbL2h+pXrmC+lVfMiB7AsrPv8NwsJgrrlhAYngyP9XtIdEUT7G1lFJbOZdlXIw5PBy73UmIxsTG8q24PC7eP/Ape2oC9Sk6pZZN5T/xUd7/cHlcpIYm+yuCW/H5fLhra6j+8H3sObtRx8ah0Gp7NDAfZtRQXmunpNpGs9PDgJRwYiJ6rjCsq1k3fTsyKOhVFGoNmoREnKUlGI/SfUmSJKRuNp4QnNpkhKUSpgllV/Uemj0OUkOSUSsDRUb9IwKFfPoBA5FUKpwtleGtOk0A5f9+2f+6+JmnMFosDBwfQmlcOaXWcjRKDVFtKnuzwjOI1EWwtXKHf5uEhEqhYlvlLr+Q26rC1Xh8HmZmyVk+rro6HEUFuGtqqXznTf+x9d+sIuKi6ZhnXd1TU4MkSdx0+RAmDKnhuRU7eOer/Txy/Vg06kCco9HuZPvBas4aGBMkxnYyEIZecEyS7rwbd001GnP3/JaC0xdJkhgane13nWSEHb0VpaRQoI6JwVlSTNWKd/0GH8DndqPP6o/b0uCXCBmXY+ezQfnUOupJDUkOyqmXJIn+4f3YUC5n5oRrw7hl+A18U/QDG9pk6wB8W/QjOTV7mWWehvrZV4OkvXWZWTQflIO6dV9+TvRVs3s8oSArKQylQqKiron3vjvIvAvlWJbX5+OR1zdTa3Gw+1AtdY0OrpiczqC0jpufnyjCRy84JqrQ0A6rHgUCgKHRAXfesQw9gHHYCHwuF3VffoFtu9wsPuHW21EYDERddkVAiA9QSUqqm2vx+rxkR/Vvd67WPH6AB8ctJMEUx6iYwPGTE8/2v66wV5G/5ssgIw+Qcs/9hJwVEGZzFvd861O9VsXCq0egVStZva0ES4sbrqzGTq1FlgvfvLeSgyUNPP3udpyuk+PLF4ZeIBB0m/4RmagVsrsmvUVK4WiYr5pNyoMP+98rQ0IwjRhJ5t9fwJA9KCjdVe0LmKah0e0D/SNjhjE1aQL3j12ATiWnQw6IyERCXpFf3u9iZve/ApNXjdHuwXBAViptlXcOaylcjP/9H4hr0UOytskIOlJx80QYmBrBRWOT8fmgoLyRgvJGHvy3/BSk0wT3hc4tOHbD8u4iXDcCgaDbaJRqLkydRm1zPWHa47e30yanIGm1+BwOVBHBbgpNG0OvsTmRvD58ColkU3t9GZ1Ky+z+VwRtUylUPDBuId49+6h+9m+MvuAiUtcqcbbIMiiTEgmbPBVtSqpfZA3kRu4oFNh2bCfqksuo/2E1Ve8uJeWBh9EmJNATpMbKc1NQ3kiDLRBcv+nyITy3IhBr2H241p+x05N0y9Db7XYWLlxIQ0MDer2ep556isjISLZv386jjz6KUqlk0qRJneoZKxAITm1mpF/Q6X0lhQJdegZNe3PRxAXrybRVOpW8XhI9Jq4YdU2X/OaxBjP5Hz2Dq6Kcpn17gz6zJssGVHeEWqjSaESXlkbzoUMcvv8efxV4077cnjP0cS2GvqKR4pZmJ9PHpTAkI5JHbxyHSqlg8Wub2H2o5lin6Tbdct0sX76cwYMHs3TpUn7xi1/wwgsvAPDQQw+xZMkSli1bxo4dO8jJyenRwQoEglOf2F//hrjf3UTMvPlB2xU6Hcl33+9XFL0j89r2TcmPQqurxVFYgKuiHGUbQb5Wyo6hMaZpucm0GnnA39inJ4gI0RIRomXrvioq6poYMzCGWedkopAk4qOMmMP1ZKdEUFHXRFV9U499byvdMvTz58/n5ptvBqC0tJTo6GisVitOp5OUlBQkSWLSpEmsX7++RwcrEAhOfTTmGELHjkepby8eps/K8jeSdzc0dOp81R+uJG/BbbgtFpwlcoFVa19lgLBzz2NfPyPfRlRRbqvseExtmq2kP/EUEJDm7gkkSQpyyUwd3v5JYWiG7Mr6fGMhG3LKqfjkE+q/+6ZHvv+4hn7FihVccsklQf/t3LkTpVLJddddx9tvv83UqVOxWq2YTCb/cUajkcbGU6enokAg6Bu0rsY99ccPTDpKS6j97FO8VitNe3NxVrb0AY6Nk3syDxpM9C+vQjd3FjbJzbdFP3R4HnWb9GFVZBSSWo2rqqoHribAhCFxKBUSk4bGk53WXjF09MAYIkK0rN5Wwn8+3kHDJx9Q+c5bPfLdx/XRz5o1i1mzZnX42ZtvvkleXh6///3v+eijj7DZAp1XbDYboZ3QSTebjx/AOVMQcxFAzEWAM20uNKkJlAMaV1O7az/yfcWO0sCbskIU9fUAxGX3Q2uOJu0cuUnP7MTpfHroCyodVR3OZ+iYoZQBMeefR0xsGMVxsTirK4mONvVYbr3ZHMKKIfGoVcGZNk1lZey69wEyb/0DT902hfdW7WP/6oCsQ4ReQtVmEd0duhWMffnll4mNjeWKK67AYDCgVCoxmUyo1WoKCwtJTk5mzZo1nQrGdtQ+70zEbA4Rc9GCmIsAZ+JcOJGbppR/9z3682cgtSildjQXNQcCmja1u/aA14ukVtPgVSMdsW+MwUxhfQkVlQ1BBVgyGvo9908Uej1VVY0oY+LwFBVTtr8QdeTJKWJqpWLp+7jq6tn75NNkvfAvZp+dyIerA0HZ0t0H0PcLlkDu6s2/W4Z+5syZ3H333axcuRKPx8Njjz0GwCOPPMKdd96Jx+Nh0qRJDB8uFAIFAkHXaG0O466upmHtj4Qfo/WmsyV4qgwPx1lchM/nk1M4O9CtSTDGUWaroLa5jmh9VLvPlW1WzdqkZKxbt+AoLjrpht5jt8svfD68TicFD93PsDaBYEdpSTtD31W6Zeijo6N59dVX220fMWIEy5cvP6EBCQSCMxuFTocuox/Nh/JwFBa2+7y5sABtUjKSQoGrohyFXo8hexCN69cBEDZhYofnzQhPY2vlDrZUbOfitPM63KcVbZLciMTy4w9ISiXGwUNO8KqOjqOwAACfy0XlO2/hbjHy5bpo4pqrsRzII3zy1BP6DlEZKxAI+hxJC+Xm5k3792FZvxZXdRX24mIKH/sLhX9+iJpPPsTn9eKqrEQTF++vqpU0GkLGT+jwnOPjRqNX6VhTspG9tQfIqdnb4X4A2lRZzsG6bSslzz5Nw5ofe/gKZdz19bgqA7o/lrXy98TdcCOWmTfQrFBjz9l1wpr2wtALBII+h0KrRdLqcJaWUP7qK+Q/9AA777qX5kN5ANT+91McxUX43G7UcXFoU2TDHDJ2PEpDx3LAOpWOrPB+1DnqeXHHa/wnZ9lRDag6Moq4G36HKkp28dR99flJuEqw78sFIHrW1QEZcIWC0LMnkpJi5rAhAUVDHQdu/A27/vYCPk/3tHCEoRcIBH0Sn1MW/VJFReFzOPDYZF+2MkQORNZ89AEgp1IasgcRd/2NmGcfW2o4NVR2ybh9HprcTf5WiB0RevYEMp5cgnHkKJylpThKS4+6b3dp3LwJAMOAbExjZIG1kDFnATAoLRJHXEAoTrtrEzvv+JM/LtEVROORPkJfbjDxcyPmIsCZPBfquDjw+khaeBduiwWVWkncTbegzxqAdctmv6Rx+LRz0SYmok1OQaHWHPOcbq+bTeU/+d//WLKeodHZhGmPkQru8WDdthV3TTV1X36BYdDgHmm0Y9+bS80H76Prl0nkJZeiS0lFEx9PxAUXo1CrUSgkks1GrOsCbiO124Ft1w4SL7ukS98lRM0EAkGfJHTseELHjgcgbv71/vRK5xEVq22rWo9HVngG5yVPocndzLoyeTW9qzqXlJCjN4ovTQlBoVRi2ymLj1l+/IHoK6/q6uW0o7V7m/nquXKuviT5r7cVU3qa//WbidO5uGoDMd0o5BKuG4FAcEqhjjajaJFPiJ1/A9rE9uqWR0OpUHJl1iUMaWleDlDbfPQK3Ep7Nf/Y9xYHUwNyDU0H9uOx2456TGdw1VRjz92DfsBA9BlH7/eg0GiIvORSon45k8zxw/hvbMcZRcdDrOgFAsEphSRJpD/2V1Aqjxp4PR79wtMJUZtodFn9Dcg7osRaBsDnY/X8ZdIsKt/8D00H9lP+6isk/vEOfG43RU8/iSo0FOPwEYRNnNyp77dulTthhYwbf5w9IfqKmQAkbC7iB00EnvD2NQDHQxh6gUBwytEakO0uJrWRJyYvZsnW58m3FNHsdqBTaalpqsPpdRJvjAWgxCoHYL0KiT/7VnFTy/G2Hdvx+Xw0Hz7kb0do/WkranMMhv4Djvq9DWvXyOmiLQFV0/ARnR5zdJgOJImiX/ymy9crXDcCgeCMpV9YOl6fl3xLIU6Pk8XrH+f/Ni7hLxuepqapjoLGYv++Do+Tr8cGbjDOsjLsR2je1331xVG/y+fxULXiXZr25uKuq0MZFu6vAu4MSTFy5e67G7uuqikMvUAgOGPpF54GwMH6w6wr2+zfXm6v5I09y9hTs484QwznJE1Cq9SQk6mn5hdyP9qajz/Asm5t0Pma9uYeNdfdvjcXr9Xqf2/TwdrSjR3u2xEx4XqSY7onbiYMvUAgOGPpF5aOQlLwef7XrNj/cdBneQ35AFwzcCZX9b+MBaP+AEBFrByYtW7dgquyAk1iEv3//TphU6fhbW7m8L13ddhz1p6zGwCHTvaYN7isLN27ErfX3enx/mbGQM4bdfQMoaMhDL1AIDhjMaj1ROqCteHvOet2RsUMA2Biwlgyw9MBiNTJbpYyQ7BhVrdUz4a0FDy5a2s61Oix783Fo5B49/xQSs1qvm1xA+2vy+v0eNPiQrn2ws513WqLMPQCgeCMZkba+UHvk0MSuWHIr3hq8iPMGXClf7tepUen1FLnbIBpAT0dxVT5tSF7ELHXyYHSpv3BvnuP1YqjqJAys5qQhBSS7r4PY1o/AA41FLC5fBvN7uaTcn0gsm4EAsEZzrj40QyNHsS/d7/FxISx/u0GdXCrQ0mSiNCFU2ar4N8pOmxzY1C7vEwz1nBF6zEtKpf2A/uJuPBi/7G1e3aAz0dRjJrUsBQyw9O5btDVPLLhKT7P/xqA0THDuX7ItSflGsWKXiAQnPEY1HpuG/k7RsceO90x0RSP1+fF5pZ1d1xqBasKV/Npnpxto46KQhkSStPeXBylJXgdDly1tezZKH9eHKtmdItbKOIIl9HB+kM9fVl+hKEXCASCTjI2bpT/9dNTHuHseFmAbFvVLv92bVIS3qYmChbfT96Sxzh81wLMu4pwaRTcM/Ov9I+Qm4ioFSrCNIF0TY3y2Do9J4Jw3QgEAkEnGRiRxcSEcQyMzEKv0jN34EzyGg7T6AykTSrDwvyvfYfkpiIqL3gHDUR5hOhapC6CBufJbxV5Qiv6vLw8Ro8ejcMhy4lu376dWbNmMWfOHP75z3/2yAAFAoGgr6BUKJk7cKY/K0chKYjRR2N3N2F3ye4cfUa/Do9NPv/SdtvatjSsdzSccIORo9FtQ2+1WnnyySfRaAJ3qIceeoglS5awbNkyduzYQU5OTo8MUiAQCPoqUS3GurpJbgFomjIVxyXn+D9fd3k2yY8/gXFgdrtjZ6Sfz8ysS0kJScLldWN1nZhY2tHolqH3+Xw8+OCDLFiwAH2LipzVasXpdJKSkoIkSUyaNIn169f36GAFAoGgr2FuMfQFjUUArCr6gZdCdlOcEc6PI4xEDRqO3hzX4bExBjPnJk8mK0JWsKywd12CuDMc10e/YsUK3njjjaBtCQkJzJgxg4EDA1KfVqsVU5su6kajkaKioh4cqkAgEPQ9hkYP4pO8z/k070tGx4xgd00uSBIrx2sADbdHZh33HAlG+UZQZiv3F2j1JMc19LNmzWLWrFlB2y644AJWrlzJypUrqaqq4vrrr+fll1/GZgs8dthsNkJDj9G1pQWz+cRU6E4nxFwEEHMRQMxFgL44F2ZCuMr2C5bu/Ii11euoaq72fzYhZQwT+x9foXKQMgNyod5Td1KusVtZN6tWrfK/Pvfcc3nttdfQarWo1WoKCwtJTk5mzZo13Hrrrcc9V1XVyY84nwq0ds8RiLloi5iLAH15LsZEjGGF4r98lPslACEaEwMiMpmZdnmnxqz1mJCQ2Fd5iMpKi9xx6hh09WbQo+mVjzzyCHfeeScej4dJkyYxfPjwnjy9QCAQ9Em0Sg3JIYkcapDTKe8ecxsRus5LEGuVGgZEZLK37gA7qnMYYR7So+M7YUP/7bff+l+PGDGC5cuXn+gpBQKB4JRDr9K3/F/XJSPfyhWZM3hi89/YWdXzhl5UxgoEAkEPcG6y3EZw7sDuNQ5PNMWjV+nJqdnLO7nvU2ot77GxCUMvEAgEPcDAyCyenvJnfzFVV1FICtLDUrC6bKwr28Q7e9/vsbEJQy8QCAQ9hF6lO6Hjx8WN9r+ua64/0eH4EYZeIBAI+gijY4Zzcdp5ADQ4LdhaZBUAdlTlsLs6t1vnFaJmAoFA0EeQJIlLMy7C5/PxZcG3FDYW4/S4cHqcvL5nGQCPTrwfM72YXikQCASCEycpJAGAnyp2BDUtB7h/7aMsv/rFLp1PuG4EAoGgj5FsSgRoZ+S7izD0AoFA0MeI1keiUsgOF6PKwM3DfkOiKb7b5xOGXiAQCPoYkiQxd8BMJCQuSJ3GkOhs7j3rjm6fT/joBQKBoA8yLn402VH9MamNAMfVvzkWYkUvEAgEfZRQTQgKKWCmjWpDt84jVvQCgUBwinDn6FvYWrGzy8eJFb1AIBCcIsQYzExPP6/LxwlDLxAIBKc5wtALBALBaY4w9AKBQHCa061grM/nY8qUKaSlpQFyw5GFCxfy7bff8vzzz6NSqZg5cyazZ8/uybEKBAKBoBt0y9AXFhYyePBgXnrpJf82l8vF448/zvvvv49er+eaa67hnHPOwWw299hgBQKBQNB1uuW6ycnJoaKignnz5nHjjTdy6NAh8vLySElJISwsDI1Gw+jRo9myZUtPj1cgEAgEXeS4K/oVK1bwxhtvBG1bvHgxv/vd75g+fTpbtmxh0aJF3HvvvYSEBKQzjUYjVqu150csEAgEgi5xXEM/a9YsZs2aFbStqakJpVIJwJgxY6ioqMBkMmGz2fz72Gy2IMMvEAgEgt6hWz76f/7zn4SHh3PjjTeyd+9eEhIS6NevHwUFBdTX12MwGNiyZQs33HDDcc9lNoubQStiLgKIuQgg5iKAmIvuIfl8Pl9XD2poaGDRokXY7XaUSiWLFy+mX79+/qwbn8/HzJkzufbaa0/GmAUCgUDQBbpl6AUCgUBw6iAKpgQCgeA0Rxh6gUAgOM0Rhl4gEAhOc4ShFwgEgtOcXjH0Xq+XxYsXc/XVVzNv3jwKCgp6Yxh9ApfLxaJFi5g7dy5XXXUV33zzTW8PqVepqalh6tSp5OXl9fZQep2XX36Zq6++miuvvJIVK1b09nB6DZfLxcKFC5kzZw5z5849Y38bO3bsYN68eQAUFBRwzTXXMHfuXB566CG8Xu8xj+0VQ//111/jdDp57733WLhwIU888URvDKNP8MknnxAeHs7SpUt55ZVX+Mtf/tLbQ+o1XC4XixcvRqfT9fZQep2NGzeybds2li1bxltvvUV5eXlvD6nX+P7773G73bz77rvccsstPPfcc709pJ+dV155hQceeACHwwHA448/zh133MHSpUvx+XzHXSD2iqHfunUrkydPBmTly927d/fGMPoEF198Mbfffrv/fWvF8ZnIk08+yZw5c4iJientofQ6a9asoX///txyyy3cdNNNTJs2rbeH1Gukp6fj8Xjwer1YrVZUqjOvA2pKSgr/+Mc//O9zcnIYO3YsAFOmTGHdunXHPL5XZsxqtWIymfzvlUolbrf7jPwDGo1yh3er1cptt93GHXfc0csj6h0++OADIiMjmTx5Mv/61796ezi9Tl1dHaWlpbz00ksUFxdz880388UXXyBJUm8P7WfHYDBQUlLC9OnTqaurC1LNPVO46KKLKC4u9r/3+Xz+34LRaKSxsfGYx/fKiv5IXRyv13tGGvlWysrKuO6667j88su59NJLe3s4vcLKlStZt24d8+bNIzc3l7vvvpuqqqreHlavER4ezqRJk9BoNGRkZKDVaqmtre3tYfUKr7/+OpMmTeLLL7/k448/5p577vG7MM5UFIqA6bbZbISGhh57/5M9oI4YNWoUP/zwAwDbt2+nf//+vTGMPkF1dTXXX389ixYt4qqrrurt4fQa77zzDm+//TZvvfUW2dnZPPnkk2d0L4PRo0fz448/4vP5qKiooKmpifDw8N4eVq8QGhrqF0gMCwvD7Xbj8Xh6eVS9y6BBg9i4cSMAP/zwA2PGjDnm/r2yjL7gggtYu3Ytc+bMwefz8dhjj/XGMPoEL730EhaLhRdeeIEXXngBkAMvIiB5ZnPOOeewefNmrrrqKnw+H4sXLz5j4zfz58/nvvvuY+7cubhcLv70pz9hMBh6e1i9yt13382DDz7IM888Q0ZGBhdddNEx9xdaNwKBQHCaIwqmBAKB4DRHGHqBQCA4zRGGXiAQCE5zhKEXCASC0xxh6AUCgeA0Rxh6gUAgOM0Rhl4gEAhOc4ShFwgEgtOc/weIZhGrcq8NvwAAAABJRU5ErkJggg==\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -173,7 +163,7 @@ "source": [ "# same plotting code as above!\n", "plt.plot(x, y)\n", - "plt.legend('ABCDEF', ncol=2, loc='upper left');" + "plt.legend('TUVXYZ', ncol=2, loc='upper left'); ## legend changes" ] }, { @@ -206,28 +196,22 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, + "execution_count": 9, + "metadata": {}, "outputs": [ { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "I am image destroyer\n" + ] } ], "source": [ "data = np.random.multivariate_normal([0, 0], [[5, 2], [2, 2]], size=2000)\n", "data = pd.DataFrame(data, columns=['x', 'y'])\n", "\n", - "for col in 'xy':\n", - " plt.hist(data[col], normed=True, alpha=0.5)" + "print('I am image destroyer')" ] }, { @@ -240,9 +224,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -257,6 +239,11 @@ ], "source": [ "for col in 'xy':\n", + " a = List(range(10))\n", + " b = List(range(10))\n", + " # Going to pretend like I am writing some serious code\n", + " data = np.random.multivariate_normal([0, 0], [[5, 2], [2, 2]], size=2000)\n", + " data = pd.DataFrame(data, columns=['x', 'y'])\n", " sns.kdeplot(data[col], shade=True)" ] }, @@ -270,9 +257,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -300,9 +285,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -330,9 +313,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -360,9 +341,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -383,128 +362,26 @@ { "cell_type": "markdown", "metadata": {}, - "source": [ - "### Pair plots\n", - "\n", - "When you generalize joint plots to datasets of larger dimensions, you end up with *pair plots*. This is very useful for exploring correlations between multidimensional data, when you'd like to plot all pairs of values against each other.\n", - "\n", - "We'll demo this with the well-known Iris dataset, which lists measurements of petals and sepals of three iris species:" - ] + "source": [] }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
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\n", - "
" - ], - "text/plain": [ - " sepal_length sepal_width petal_length petal_width species\n", - "0 5.1 3.5 1.4 0.2 setosa\n", - "1 4.9 3.0 1.4 0.2 setosa\n", - "2 4.7 3.2 1.3 0.2 setosa\n", - "3 4.6 3.1 1.5 0.2 setosa\n", - "4 5.0 3.6 1.4 0.2 setosa" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "iris = sns.load_dataset(\"iris\")\n", - "iris.head()" - ] + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] }, { "cell_type": "markdown", "metadata": {}, - "source": [ - "Visualizing the multidimensional relationships among the samples is as easy as calling ``sns.pairplot``:" - ] + "source": [] }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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VAW1dnR5yXyjixd90ovtLdSEBDBcMpWiFW+gw3LZYuIUMwy98KGrrYc6niUeS\nAYlSqRRMZk9PT+cie0RJLlwMMQB4PC60Hz2ApoaRxahyZl8Ohd+j+7SyCthuX49+ez10NhvSL1sA\nm2fkSYnOWoj0+Yvw82Pp6LfboS22IccgzJAnXvwqrWwWcjMvHG+zYdLs+Th3ZN/o+ZcswNDJ40wN\nSUlvpnE67p53Oxy9LXAOdMOiN6EwswCWTDNmGKfhZNdnaHQ2o9hYiElft2Cw2QGDMQe9fQNQZeox\n1NOL9IIC2H74A/SfOj2yMOi8K1GSY8KA3Y40rQb9bW2w3bwO2qorRt/4wrwuu6MR6vxC3iMU1Azj\nNNxSeSMau5pRlDUZM4zCvnmGoRT/MulaDDY0IN1mRZ5hqq/NBvu9SJs5ayQct6ERuqJCpF92OWyA\n77dBU1YhKD/4wodZYy6EOHj86GjfX1aBwZPH2M4nEMnmkPz7v/87+vr68Pbbb+Pll1/G5ZdPvJXM\nIl31PCenMuwxRHILF0MMAO1HD+Dcr0ZSP3YDwF1Arl8I1eDJYwGpHO0vvDS6rU7DuQv7ewAYNhmE\nj92DLH6VW7nQF6Z17sg+3/v3AEhfP4Cm50az9zGumJKVAkp4PB7sPPa67zXvPXay6zPfvXePvhrn\nfvOHkTS/r/y379i8xYvQtH07SjZtEoRjaWbNgburU3jfaTS+FKpMn0qR+LzrS7xw5A++bUOVQdD/\ndxz9EF3/+3kAQD8Az50e/KrjTd9+8e9F/0f7hX0/INguMWaF7fvH2h48flQ4X/H29ZxjMsFIMiDZ\nvHkzdu7ciZkzZ+LVV19FdXW1IOxqooh01fOcF57DpEmRPZInkku4GGIA6LfbA7f9BiTiOSRSxwGL\n338gIHUk44opeYW6x/xfVzWfgxtB0vxe2A7WxoPdZ9oLfwMU35O8RyiYcP2/uO8drG8A9GMdH24+\nYWztMNxvDdt56otpQNLU1OT795IlS7BkyegiN62trZg8eXKw01IaVz2nVBVuFfVgMcLaYht6/LdF\ncb4BccDFtjG3xXNEwgl4/5JiQWrI9JLikOcSyckDNwzaTFw2eTa0ai0ON38KtVqFk12fwWosRKZK\ni+96pkPvUkK1ZBEUKuHPsXd+SLrVGpBeWzdliuBY/zkkgfOyGHtPgYqMk/2yaGlhNQrnJ4n73nRr\nEdBx1Lct/r3QlZQI+madqK+Puh2K2rxW1NdzjsnEE9OAZN26dVAoFPB4PAAAhWIkx7rH44FCocDu\n3btjr2GbP5SLAAAgAElEQVQSiXRFdZfLlaAaEUUu3CrqQVOQzr4cuAsYaKhHepEVOXOEoZjiOGAo\nFaM/SlotejPT0L5uGfRne9BtyoSiKAOh87QE8r6/d05JmlqPRr/UkPrLwqcXJ5LDZ11fCEJivl12\nDf7787fRO9SHu6rW4/7s5Tj3q9+i48L+/HVrMXn9zVD2DUCZmYnhnr6RMJTySwLDsO65xzd3S2uz\nQjt/9Kml9550ORqhyi9kGmAKyuNxC7JoXWqeLdjfOSUfyjtuhKr5HFwFueieVoS7PKF/LxSZekHa\n3pL5C4RzRKJsh8HafOCck2y28wkkpgHJO++8E/aYl19+GWvWrInlbZKIJ7IV1RNYI6JIiR/R13c1\nYlnh0pDpRwFAoVAht3IhTN8MsfiUKA7YuesNwY9SZk4mXsr8BMgF4Aa+67RiepCBT7j394aJiVNF\nDjQ0QFPBOVuUfAJCYrqa0TvUd2GfA4WOHsF+xYAL+qVXB13oLSAMq6EBhuXX+cK0hAWN3JOm6oWS\nLxhHE0ej0xGwXWYs822f7qrHn7rrAAOAbuC7XZlj/l6I0/560/yON4wqVJsXzzFhO584JJlDMpYd\nO3ZMmAGJSqWKaEV1ZhijZBRtGsegRI/RxZlNAlI72qzAucO+bauhAOeO7AuZtSschqNQsvGGQta1\ntsCitfiyDwVL+5uRpsPcggr0ufrQbRmZi6jKzMCkSy+Fp78XQ8ePwrP4ioD3CGj3RUXCjEPMMERh\nBIbs5gv2Ww0FcBzeg8H6BqQXW1EyVdjmwqb9LbZJGk7Lvv7iE/cBiTeci4jkNdM4PWyIVjjhMvh8\nahnGoF+IVnehDgszR1f7zf7agXO//h2A4Fm7wglIFcnH9CSzUNnqhPdbPpQKFQoNBb6sW3VqLX54\nx40wdgyi7Q+vXTj7DaSnbwZKZwneIyA0UqXE6f/1uG8/MwxROOJ2uv4bawUrsRu/ahZk1cq68/u4\nq+o2tPSPDrTHIl6pPdZwWvb1F5+4D0i880pCcTqdeOCBB/DFF19AqVTi0UcfRWWlMATj4Ycfxt69\ne6HT6fDYY4+hvLw8nlUmmpAUUKLMOHPMEK1wwmXwOdPVgLfdoyFaV3XpBXHKi7rF2YKiXIk9SKpI\nIjmFylYU7H6r7xrNPNQ73I+9xnbMP9MDjd/5PWfOQCcakAQLjfTHDEMUjrid2s83jNk3D9Q3oGzu\nUiwurYooJCogZCvWcFr29ReduA9IwnnkkUdQXV2Np59+GsPDw+gXpT6sq6uD3W7HW2+9hSNHjuDB\nBx/Ezp07Zaot0cUt3GP0oixhZr3JorCA9GIr/O9wrsROqS6aUMhgYVw9JoVgQJJZXAx3mPdkOAtF\nK6CdGoXbabZCQd+siTYjItskxUjWAUl3dzcOHTqExx57bKQyajX0er3gmN27d2PVqlUAgMrKSjid\nTrS1tSEvLy/h9SVKZd5V132rnkc5fwMIsrJ6eYVg9d5Lc74BT6UHjV3NKDQW4NLcb0BdqfatBpyX\nPQeZ610YaGhAelERMmcviO4zRLC6PFEi+Va87m5Gob4gYMVrAHDDhUPn/o7W7rOonfMd9A70wajJ\nhOVMB7S9Xci+/TYM9fRCnalDT0Mjens6cCzPDbPeHLSNM5yF/IWax+RPHLI7wzgNxiqjb9tsmArF\nnQoM1jdAYy2CqfJKwXy/7Nnz8bf2j319+WU5c6HE6O9HWlmFL/NbsJXZicKJ+4DEYAi9ZkdDQwMm\nTZqEn/70pzh58iQuueQSPPDAA9BeyL8OjKxnkp8/+ldWi8WClpYWDkiIouS/6noPEPX8DQABj9H9\nV5wGRuLn5+fOGwnZurDfP/Xp1NwBnPNfaT07L6pH8pGsLk+USOIVr41VxoA2eejc3wXH3FJ5I77R\nko7Tz/xfDAA4j8CVpzXrluFX7teDt3GGs5CfSPrFYCGE4u38uUuBuSP/Pndkn+/3ohvA4J0DeKHj\nr75jPZWekb7+gsGTx4Qrp4tXZicKI6YBya9//esx92/cuBEvvvhiyP3Dw8M4fvw4tmzZgtmzZ+OR\nRx7Bb37zG9x9992xVMvHZBr/AobBzu3o0Ac5Mvb3jrTcnBx9xOV6j+vo0ONUlGXHct0mwvlykLrO\nwcprahCnUayH6ZuRv2+wMutaWwTbLf0tWFxaFXL/gKgOLkcjTNWRD4rGer9EXMNkLDPRpPoME6Wc\ncPcAADSeEc0z6W7GbIcwffxAvTAGX3+2B8gNXl6kpLg2qd5mk71fkKK8SNpgtMS/F0P1TcKV2rub\nYSobrbvdIVyZfay+PRmvIclP1pCt/Px85OfnY/bskQV5li9fjmeffVZwjNlshsMxmi/b4XDAYrFE\nVP54c1MHywMPAO3t3RGXEc17R1pue3t3ROX61z/askN99khNhPPlIGUe9VDXIN1qhX9rSC+yRvy+\nocq0aC0B2/7Hifdri4uh9UsNqSosEhwfLiQr1PvF+r2LSV1ePMpM5bYq1bVIhnIC26QZ7311SNCG\niwzCuVWF+gKo89MFr2kvxOx70wAPKDS4WTkHOdqCcdVNimsj5fWVSzL3C1KVF64fHg/x74XGOlm4\nUrte2C7Vk4sEaX/FfbtXsl5DcZmUeDENSDZu3Bj0dY/HgwZRxoVg8vLyUFBQgFOnTmHKlCk4cOAA\nSktLBccsW7YM27Ztw7XXXouPP/4YRqNRtnAtl8uNnjANv+eskyu1U1ISr3ouXnV9PMKlEhbvV5w6\nh7N+qSFRWY4cv+PDhR5IkbqYSEreNulNj6pUKPHUR7/17b+r6jZcljNXMLeqKvdSfJX2Ndr9UmSr\nZhWiZNMmuFuaYd+2HQAwCUCxeTYwK8SbEyGwDUrRL4pXau+fMRW39N8oaMP+nANdgrS/4r6dKBxJ\nnpD8/ve/xxNPPIG+vj7fa0VFRfif//mfsOf+7Gc/w3333Yfh4WFYrVZs3boVO3bsgEKhwJo1a1Bd\nXY26ujrU1NRAp9Nh69atUlR5nDzoPDQFA4bQt1mfsx24KYFVIoqQeNVzScoMk0pYvL/Rvl2wv0+U\n9jdUCtVI348o0bxt0psedXfjHsF+bxv2n1sFAHZnI/7klyL7u04rSmctRZ8o9GWgvgGaWTGkT6UJ\nT9wGpRCwUnvnyErt/m3YX5/dHrgt4W8NTXySDEiee+45vPbaa/jlL3+Je+65BwcPHsS+ffsiOres\nrAx//OMfBa+tXbtWsL1lyxYpqhkzlUqF3KJy6CcVhjymu6ORK7XThCUOqZqun4LeD/ZisKEJGmsh\nMhcsxvBnJ0OuIK0tto1MqPdui9L+SrKafAw8Hg+O2zvhONyIgpwMlBdnQ4Gx11Ki8fNe7/qWbtgs\n+pS63t57oaWnFTqNFr0tfchMy8CwZwgLbfNwuPlT9A71hWzDodp6RlHgquyU3ORux5Fk2fJmeguV\nJUss2r5Y3Ldn2GzoP7DXl3Urff6VgDKG/zfyuDF44tOQvy2U+iQZkOTm5sJqtWLmzJn4/PPP8d3v\nfhe///3vpSiaEsjlcuHzzz8PO++kpGQqB10XKXFI1db0a+D43ei9bh12o/7Fbb5t8QrS3rCxgYZ6\npBdZA8LG5A7JOm7vxH9sP+zbvvcf56KieFJC63AxSeXr7b0XFtqqsO/E6AJzC21V2Gc/hNUVK2DR\nmUO24VBtvb+9fTQWX6uFu6szIZ+Hxk/udhxJli1xpjdxliyxaMPAxCHBukEIsm7Z4IH28iXRfjSf\nwROf4vQTT/i2xb8tlPokGZDodDocOHAAM2fOxNtvv43Zs2ejq6tLiqIpgU6f/hr777kbBRkZIY9p\n7u0FnnwapaWM3b8YiUOqhhqaBNv9jcJt8QrS3rAx0zeDT0SUOySrvqU7YDtV/gc5FaXy9fbeC/3D\nA4LXvdvDw64x23Gott57+rQgFt+k0UB7hVS1pniQux2HC3UFgMau5sDtEOFXQPRhYOKQ4M6d2wT7\n++310MYwbXGgvj5gmwOSiUWS513/8i//gnfeeQeLFy9GZ2cnvvWtb2HdunVSFE0JVpCRAZveEPK/\nsQYrNPGJH9unWYXZg7SFwu1UW63XZhGm4LZaIk/1TdFL5evtvRe0aq3gda06XbA/WhmiEC1dUegQ\nYUoOcrfjSMKrirJEmd6M8Q2H1YnCcbW22H4LuBL8xCfJE5Lp06dj8+bNOHHiBO6880489dRTUCoZ\n20eU8i7E7dodjVDnF2JG+ayRVakvZFrJmDQHBR6MzCEpmoy0yxfBaEjDYH0D0m1FUJWXC1Zyn2Gc\nhs+7vhwz1jmuHydMrHd5cTbu/ce5cLT3Ij8nA7OKsyV/Dxrlvd71Ld2wWvSYVZwtuH4l+Xq4PJA9\nNj9YGmpfSEtPK26pvBGDrkEoFUqc7TmHf5q9Cl92fAXnsBOX5cyFAoox01mPvNnIveZWKGC7qRZ9\nzc3QFRZCe2V1wj4vjY+4HZfbsnDsTIev3ZbZsnDCfj5u7XiGcdpIv9zdjCLDZMwwTgs45tKcb2Bo\n9hCanA5MNubjG7lzsP/sB2hyOlBozMf8vHlQSbgSRPr8K2GDZ+TJiM0K7bwrMXj8qO+3JNo5IGnl\nl6Bk0yYM1I+sHK8pv0SyulJykKT17du3D/fffz/MZjPcbje6urrwy1/+EnPm8HEaUSoTx+3m3vUD\nvHDuNd+2scqIsoXf9G0fPPcRXuh4c2QBrfajqG1Lx7ZP/uzbf0vljYI45kSvtB4u1lsBBSqKJ2Fp\nlW3c2WrkjidPJd7r7X99jtk7fNdvydxC7D08mnUqmWLzxSFXpwa+wuP7/l/fsQttVfjLx3vgqfTA\nmGYMG+PPGPnUJW7Hx850CPqAH6yswG9fO+bblrodf971paBfNVQZAtrXF11fCfpi92w3tn8y2pd7\nZgNXmiSMDVSqoL18iS9Ma/D40djat0IJzaw5vCcmMEn+NLl161Y8++yz+NOf/oRXX30VTz31FP71\nX/9ViqKJSEbiuN1+UWrHgNhlUZxyk9Mx5n7x+fEWLNY7Fd9jIvO/Xn0DwyH3JUKw2PxQ7OeF6Xq9\nc0kau5ojKidYjDylJnE7tTvi2ydE0r7ErzU7WwXb4r5aamzfFI4kT0g0Gg3Kysp8296V14kotYnj\ndrU2G3Bu9C9/RcbJgpCskqwi3KScDf3ZHvSY9FAYhfHv4rjlyYb8+FU+iETEessdT57q/K9fRrrw\nJ0ru2Hy1WoWTXZ/5Mg75h2FZjaL5VN65JMYCZGmysNBWhf7hAWjVWlj974sLoVqe/l7kLVmEjr/9\nHa6eXsbIpzBxH2DLj2+fEGwOiTjcUHzMZKNwdfdCo7AvjiSVcDS0xTbBSu7pJcXjLosmJkkGJHPm\nzMEDDzyA1atXQ6VS4Y033kBhYSE++ugjAMC8eaFTy1199dXQ6/VQKpVQq9V45ZVXBPsPHjyIDRs2\nwHqhc66pqcGGDRukqHbKcrlcOH3665D7Ozr0aG/vRknJ1ATWiiai00U64WrSU/Nx15TRVKUejxu/\nOjSa2vHfclei7fe7AQAaAKa7p/j9j1g6VFAKtnuHexP6eYLNWUjF95jI/K9fSYEeVWVm2a6ld57I\nl51fo2vQif/+/G30DvXhrqrbAEAQhvWDS/8JC21V0Kg0yMvIwbneDny77BrkpOdg2D2EffbR1MCX\nmkf/aCcO1bKt+0cozQWMkU9hAXNKirNgzIhfnxAsRa843PDH834gSDPdP9yHb5ddg46+TkzSZUOj\n0AjKjCSVcDQ8Lrcge5z+stD/X0gXJ0kGJF999RUA4PHHHxe8/vTTT0OhUODFF18Mea5CocBLL72E\nrKyskMdUVVXhmWeekaKqE0K49LynMJqelygWAatJd1mxrHCp74dJvCq1eLXe3jN27MscfaKiUaYJ\n/sdMp9LispxL41Z/sWBzFlLxPSayYNdPrmvpnSfS6GzGX+zv+l4PFhJz6rwd++yHcNnk2Xj31H7f\n69+deW3AsY1OB8qMI1EF4tAVj9vNOPkUF6oNx6sdB0vRK26j9V2Ngr77la//jHdPf+Dbf1XJFbgs\n9zLfdiSphKMx0NAQsK2pqBx3eTTxSDIgeemll8Z9rsfjgdvtlqIaFxVvel6i8QpYdd1Yir+dOyxY\nybfIOFkQamLLKhKEaInDAIKuxO4X4iUOC4g19WSiV1ZnBi3p+V/TLEM6enoHMTkvM6murW8V9TQd\n5hZUoM/Vh5yMbFxRdCm0ai1cHhcUUGChbR7UosxBBm0mWnvaQq7eLg6LzCwuBn8RU4vb7caHn52F\n3dENW74BC8rzoExk9sAg4VXh+u7ANMCivjnKldrDYdpeCkeSAUljYyN+9rOfobGxEdu2bcO9996L\nRx99FEWifOrBKBQKrF+/HkqlEmvWrMHq1asDjjl8+DBWrlwJi8WCzZs3Y9q0wJR2RBQd8SP52tnf\nEWRhGckOZBA80Zg2qUSQzUUcBpBjmAbDJoMvNWNaeQXucuYJBj2qSjUau5tRqC9AVW5sT0cSndGK\nGbSkJ76mS+YW4j//5/Okura+kJi+Vuw89rrv9YW2Kgy6BwX3yNqKFbil8kY4+7th0Orxh+P/jd6h\nPgAIunq7OJ1pzvx5aDvnP6ynZPfhZ2cFWbSAClxRbgl5vNSChVcBnjH77lsqvycIn9WqdIIyo12p\nPRxvO3c5GqHKL2RIIgWQZECyZcsW3HbbbXj88ceRl5eH66+/Hvfffz+2bdsW9tzt27fDbDajvb0d\nt956K6ZOnYqqqirf/oqKCuzZswc6nQ51dXW48847sWvXrojqZTKN/wlCsHM7OiKfiBbNe0dabk6O\nHiaTAR0depyK8HgAcTl2rM8Xy3VPhvPlIHWdIymvrrVFsN3ULcqI1d2MPl1fwGv+mvua8b2K64QF\nmxcKN81Vgu3rTFeHrVukHIeFmY0c7b1YWmULcXR0gl3DWN8vFdummFSfwVuO+Jp6M2tFem2lrk8o\nZlMVXjn2huA18SrtANDW34E7qmoBAK8ce8M3GAEAKDxYXFoVcI74nknUZ0pUGXJKRN9aX/eVcLu1\nG99eEtkfTqWon7gvb+lvCThG3HfbuxqF4bNpWlwzY7HgGLMpSFuNhaidSyXV2yiNkGRA0tHRgUWL\nFuHxxx+HQqHA6tWrIxqMAIDZbAYA5OTkoKamBp988olgQJKZmen7d3V1NR566CF0dnYiOzv8pLDx\nriNgMhmCntveHnmqvmjeO9Jy29u7cfasM6rjpa6Dfz2CCXXtIpUM58shljqLRXoNLFpRlhVRxqtC\nfQGMaUbBa0UG4WN+i9YSVd29oQX+f3WLJnOLODQiP0c4jyo/J0NQn3AhVqFCLUJdw4Iw7zeWWNtm\nsPLkIMVn8L8W4muqu5BZa2jYjdff+woqBfB1kzNoKIxU13S898xIJi1hWFmhvsBXlvh4eBR476tD\nY7b7RH+meJfhLUcuiehbrWaDaFsvOM7lcmPf8RY0tPagyKLHwkvMUI3Rz0RL3M4C2h0C+25xhkP/\ndgvE3leHEo9+UMryvGVS4kkyINFqtXA4HFAoRjrmQ4cOQaPRhDkL6Ovrg9vtRmZmJnp7e/H+++9j\n48aNgmPa2tqQl5cHADh69CgARDQYkZvL5cLeve+GPW7JkqsSUBuiQN5H8gHhVBdWYa/KvRQKKASP\n7WcYp8FQZRCsOB2NWDO3iEMjfvSdS8ZcWT1ciFW0oRbMoCU9pXIkTGtw0IVCsx5Dw24smVuIN/ad\nQk//sGhxxMSGwoiN3jNNMGj16BvsR4HegumTpqChuykgDHGsLF2JXBCU4kujVuCGq6bh3Pl+5GZp\noVEL/8d93/EWPP/GidEXPB4smR3bnAx/ocKr/Pt3cd893VgKtai/9yd1li2icCQZkPz0pz/FD3/4\nQ9jtdqxcuRLnz5/HU089Ffa8trY2bNy4EQqFAi6XCytWrMCiRYuwY8cOKBQKrFmzBrt27cL27duh\nVquh1Wrx5JNPSlHluDt9+mv8YvdTyMjJDHlMb3sPbDbm4iZ5iFeaBoD5ufNGMmr5EWdvEZ8TjVgz\nt4gXGDvV5MSaq0pDrqwebJFC/wGJuDy7o3vM/+FlBi3pnW7uHh1wHAO+Oc8mWJ3df3HEcN9PvAW7\nZ7yuLQv8S+1YWbr4P3cTx5cNXdj14Rnf9vIFxbhsusm33dAqnBMk3o5VsCxbQGBfHUl/7yV1li2i\ncCQZkHg8HqxYsQLV1dX4t3/7NzQ3N8PhcKCycuyUblarFa+99lrA62vXrvX9u7a2FrW1tVJUM+FM\nZQUwTA79F1RnU2cCa0Mkv1gzt9jyDaLtsedfhVukMNrySHri76hItK3zWxwxVb8fqTMWUXIJ14+I\n23SROfQfKpMF2ywlmiQDkocffhg/+clPcPLkSej1erz22mvYuHEjli9fLkXxRDRBxJq5ZUF5HoCK\nC3M+9FhQbhrz+DJbFn6wssI3R6S8WLjeUbTlkfTEYXAzbVmAx4OG1h5YLXpkpqug06hT+vsRh0fG\nmrGIksv8sjwMDZf75ojMF7XThZeYfW26yJyJhbPle8oXKamzbBGFI8mAxO12Y968ebj33ntxzTXX\noKCgAC6XS4qiiWgCCRVaECkllLii3BJx2M4J+3nBHBFjhnAOSbTlkfTEYXDHznQI4u3v/ce5WHNV\nqVzVk8RYoV6U+k7azwvabK4hXdDPqKCUdM5IIsTaVxNFS5KVe3Q6HZ577jl8+OGHuOqqq/DCCy8I\nsmMREckh2BwSSm78zijVsM0SxU6SJySPP/44/vCHP+Dpp59GVlYWWltb8R//8R9SFE1ESUy82rtU\nqSFDvp8ojW+ZLQsn7OdDrtQebg5JtO+XTKuHTzTea61JVwlej/Y7SxRx28/Ni22RT0od4n5BPEck\nWdvsWBLdlxOJSTIgsVgsgnS9P/nJT6QoNqW5XG70hHnM2XPWCZfLDZWKNz2lpkSnhhSn8f3BygpB\nSJY4rW+saXq5MnvieK/1N+dZsWRuIfoGhqFLV6Onf0juqgUlbvvp6WpMSU/t0DKKjLhfuP3bs1Ki\nzY6FaX5JbpIMSCgYDzoPTcGAISfkEX3OduA6TwLrRCStRKeGFIdCiNP2itP6xpqmN1zaYJKO91qf\n7xnER8dHV5rWadSYP9MsV7VCErd9+/lGTDFzQHIxEPcLgtTVSN42Oxam+SW5cUASJyqVCrlF5dBP\nKgx5THdHI1QqVcj9RHLzPsavaw2+Wm+8U0OKQyNK8vW+v0RmpKsxZbJohWRRqIT4/BlFWdgfZMXk\nUGIN+aLIea9tdqYGS+YWwuV2Iz8nE30Dw9h9uAlFeTpMLxoJ0fN+n4tzY/s+wrXvsYjbui0rdF9P\nE4u4X5haZMAN+tGFEfNzdPjgRIsvu9+8mXn46LOzvu35ZXk4OUaoqRSiDcFiml+SGwckE5zL5UZz\nb++YxzT39sLG0DEKItxj/HinMxWHRtyxskLwl8jSoqwxQyXE5998bTlefDPyFZO5Mnvi9PQPYcnc\nQuRm67Djfz7HkrmF+OO7X/r2L5lbiPbuQUGIniY9DdNiWJskljAVcduvKpyDc23SLnhHyUncL5xz\n9gvaqrifGRBtDw2XB2SSk/rJa7Rtm6mpSW6yD0iuvvpq6PV6KJVKqNVqvPLKKwHHPPzww9i7dy90\nOh0ee+wxlJeXy1DTVOXBf85RIyMnLeQRve1qLABDxyhQuMf48U5nKg6NOBNkZfWxQiXE5zeeFW6H\nWzGZK7MnzqkmJ/YebsSyKisA4Qrt3m1xiN6Z5vMxDUhiCVMRt32lgn/QuViI+4X/3P2lYL+4nwnX\n78QjFDTats3U1CQ32QckCoUCL730ErKysoLur6urg91ux1tvvYUjR47gwQcfxM6dOxNcy9SlUqki\nWjGeoWMUjNyP8cWhEeIVkcUrHotXSA5YBdyUeismXyy8360lNwMAkJEu/HnSpasDvv/iguC/G5GS\nu33TxCDOslWYF12/E49QULZtSjWyD0g8Hg/cbnfI/bt378aqVasAAJWVlXA6nWhra0NeXl6iqkiU\nEqKNh48kxljq1Xq9czpCxU6L53zMFK20XjUzD0PXlftWPF5QYYHbM/IXyCKTHlVlJhw70zGaFrg4\nS7gKeHEWFAqk1IrJE5nH48HJ+k40netF38AQbr62HK3tPbj52nL09Q/i5n8oR2NbNwrzMmGz6FCS\nnwVjxuj3uaAiH+fOjX/Nh2DtW3xfKBVK1Hc1MhUqhTR/phlul2ekrZr0WDDbAqUSvrlql1eYBf3O\nlbMtyDVq4WjvRX5OBsptWYJ+K9o5JW64cOjc39F4phlFhsm4LGcuQ7Ao5cg+IFEoFFi/fj2USiXW\nrFmD1atXC/a3trYiPz/ft22xWNDS0nJRD0g4L4SCiTZmOJLjpV6tN1wa3XBpfYeuE8Zeuz0QxGYr\nFAgam+3/Hqm2YvJEdtzeiY9OtmLv4UYsmVuIP+352rfvpm+V4cW/jH6X37+uHFPzJwm+T6UytonA\nwdr3ya7PBPfFQlsV9tkPAWAqVAruwPEWQVuFB4LtNJVCuJK7UYuK4klYWmXD2bNOHDvTEVN68UPn\n/o4Xjvxh9O0rPZifO48hWJRSZB+QbN++HWazGe3t7bj11lsxdepUVFVVSVK2yWQIf1AU53Z0RP5Y\nNScnsmMjPc57rMlkQFtbZkTzQv4hJzPiUCxvPU5FUY9QYrnuyXC+HKSoc11ri2C7pb8Fi0tD30vR\nHi9FHR1+8z0AwNHei6VVtpD761tFsddnhbHXAbHZov3i8qMRj3aUim1TTKrPYDIZ4Djc6JsrIp4z\n0nRO+F02nO0J+t5S1gcIvC/6hwd8/07EPZJs5aR6m5W6/sHKa2z7QrQtShcu6sf8+yXvfRBqfyQa\nz4jmi3Q3w1Qm3edOxDVMpvJIHrIPSMzmkQmoOTk5qKmpwSeffCIYkJjNZjgcDt+2w+GAxRJZmMV4\n/6JrMhmCntveHnloQKTHRlvm2bNOnD/fF9G8kPPn+6IqO9p6BBPq2kUqGc6XgxRPHyxaS8D2WOUG\nHnfOMZUAACAASURBVG/Ge18dEoZweYDBE5/C5WiEOr8QaeWXADFM3i3IyRBsT87NwH/t/dIXkpUv\n2m81i+aMmDJF2+JY7UxBWuDCvIxxXdtY21Eiykzltuq9FgU5GWi48D9r4jkj+bnCtlBkygx4b6mu\nqX854vtCq073/Xuse2rMunjcGDzxKQbq66G1Wse8j+LxmeQsw1uOXKS+586edcLtduNDvzS+Vovw\n84nnkFjNwu38nJF+yf8+CLY/UkWGycL31xdI9rlDtoEo2nRE5UldvxjLpMSTdUDS19cHt9uNzMxM\n9Pb24v333xes+A4Ay5Ytw7Zt23Dttdfi448/htFolDRc65ln/g86OjoEr2VkatDbM+jbrrnmW6i6\n7DLJ3lMsmlXdiUKJdr6HOMZYqVDiqY9+69t/V9VtmNowgNNPPOF7rWTTJmhmzRl3HZVKCNL0tnX1\n43f/PRrK8KPvXCKY81FeLJwzUFachTS18sL/COgxr9wETZoS9a3dsJr1yDVqBKERVWWptTjZxaa8\nOBtKJVBk1sPZO4QbrpqGxtZuaDQqdJzvx5K5hdClq1GQm5Gw+T5KhRILbVXoHx5AZloGpk2aAovO\nHFMc/uCJTyW9j0heH352VhBKevu3Z+GGq0bXIckxpo3Zj4nTh8eaXnySZhK+XXYNOvo6MUmXjZz0\n0AsyS4VtmqQm64Ckra0NGzduhEKhgMvlwooVK7Bo0SLs2LEDCoUCa9asQXV1Nerq6lBTUwOdToet\nW7dKWoe/fvgVXOnBFrTS+f6V9v7f4jog4aruJIVo53uI0zzubtwj2N/obEZhvTBsZqC+PqYfHfGK\nxhq1MKTwVJMTa64qFcRPi+eAXFFuwRXlFsH2t5dMw9mzTvz1YL2gPK6sntwUUKDMOgll1kl4+d2v\n8F/vjc4hmTfLgo+Ot2D5guKEzvup72r0zRkBgDxtLpYVLo2pzIH6+oBt/s9b6hKnnz7V7MTuj0a/\n4+ULisP2Y/5iTS9+5nw9/uuzt3zb3515LabpS8dVVqTYpklqsg5IrFYrXnvttYDX165dK9jesmVL\n3OqQay6GZ9IlYx6TaWiN2/sDXNWdkkOwNJFa64DgtXSrNab3mFKgF/wlMTdLC3w0ur84zJoS4lCJ\nBeV5UPplPeLK6qlrSqFREG6Xph75Xo16DT440YoF5XlQeBSCLGyxrtQeTDzSpWpF902s9xHJq7hA\nnH48cOX2WLJmRUuOFL9s0yQ12eeQEFFyCJYmUlE+8ije5WiEKr8QmvKxB+/htHcPClY0vvX6ckEI\nV7ZeM+b54lAJoELwtIQrq6cut8steHq2tmYGlswtxJv7TqGnfxhABYwZGkE2olhXag8mHulS08ov\nQcmmTRior0e61RrzfUTyys5ME/Rb6WlKwXZfvwv/94+f+o6Px0rs/qROzx4JtmmSGgckRAQgxEq9\nCkAzaw5M1QslmTgoDnWob+kR/E9o/qQMlFlD/3CLz7c7ugUDEq6snrrOiL7bs519grZhd3QjK1M4\nYI11pfZg4rJitUIJzaw5DGmZIMShp2lqZcC2v3iHjkqdnj2yN2WbJmlxQEJECRNupfVwIVbi88Ur\ns1PqEn+3haIMarZ8PbIyhAOSWFdqJxoPcWhoYLY/ho4SRYsDEiJKmAXleQAqfFmx5pebkGvUjmbR\nsmbhgxMtIeeIeM/3ZtlaUG6S7bOQNDweD47bOzEwMITvX1eO5rZe2PL1qCozQem3uvX8chOUUAhC\n8mJdqZ1oPMShodOtWfB4RtYfKczT44o5FuRlaRk6ShQFDkjIJ5oV4InGQwmlICsWIMw+88GJljHn\niHjP93+NUttxe2fQVaqPnekIurq1lCu1E42HODT0gxPCldrTNSP9FENHiSLHAQn58US0AvwCMP0w\nxUe4OSI08dS3iOcVjcTbh3qdKNmw3yKKHQck5KNSqSJaAZ7ph0kq3nAdb3rMqaLUr1MmG8Y8XpxO\nM9x+kpf3+3EcbkRBTgbKi7N98fh5WemovtSKzu4B7P3UgcwM4c8T4/ApWYn7rdIEp/0lmgiSYkDi\ndrtxww03wGKx4JlnnhHsO3jwIDZs2ADrhRzXNTU12LBhgxzVJBGXy4XTp78WvNbRoUd7u/CvRSUl\nU6MaxAQrN5Roy6bkIg7XuWNlhSBbjXil9VDhPZHuJ3kF+35mXYjHb+3sw0t/Oenbd+PV07FkbiGy\nMjWYYc1mHD4lrf7BYUG/NWWyEf8ngWl/iSaCpBiQvPjiiygtLUV3d/DJiVVVVQEDFZLf6dNfY/89\nd6MgI8P32inRMc29vcCTT6O0NPK86MHKDWY8ZVNyEYfliFO/isN0woXxMMwnuYX6fiqKJ+HTr9sF\n+7xpf1dfPZ3fISW1+pYe4XYr+yGiaMk+IHE4HKirq8OPfvQj/O53v5O7OhSlgowM2PSG8AcmSbk0\nKhnCm8TpM8WpX8VhOuFWYudK7ckt2PfjbYeTjOmCfblZWt8xRMmsSJwG2KwXhHCVFLANE4Uj+4Dk\n0UcfxebNm+F0hl7M5/Dhw1i5ciUsFgs2b96MadOmJbCGJIVIw7BycioTUBsCkiO8SZw+s7w4C8aM\n0Cuth1uJnSu1Jzfv9+No70V+TgZmFWfj+JmRdpipVWPJ3EJkaNXIz8mA2+X2hXQRJbOFl5gBj8eX\notqUrcULfhnixKGnRBRI1gHJnj17kJeXh/Lycnz44YdBj6moqMCePXug0+lQV1eHO++8E7t27Yqo\nfJMp/F/YVWolhsMco8vUwGQyoKMj8r9y5OREdmykx3mPjaYe0ZY9nnqIQ7RCHd/V1Ro2DKu5txc5\nLzyHnJzIyvWvi79IvvdkI3WdIynP4RfzDACO9l4srbLFVGY0vOWZTUbB6xbT2IvdiY8PV954xaMd\npWLbFIv1M4i/n3cONwEAevpH4vBrl8/EDVfPSFh9pCwnmeoiVTmp3mYT1bfecPVov7XjrZOCfWP1\nrXL0/XKXmezlkTxkHZD8/e9/xzvvvIO6ujoMDAygp6cHmzdvxi9+8QvfMZmZoys5V1dX46GHHkJn\nZyeys8P/1cy7zsFYXMPh19To6xnE2bPOgMnaY4n02GjLjKYe8ahvLPWINAxrPHXxMpkMEX3vocjV\nscVSZ7FIr0FBjnBwmJ+TEfK88V5XcVhYmS0LJ+zn4Wjv9WVZkiJLVqzfe7zLi0eZqdxW/a9FYW6G\nILxFo1ag7pA9ou9eqmsqRTnJVBepypGyLnJJRL/gcrmx73jLyBMSix6FeZH1rRdrv5XM5XnLpMST\ndUCyadMmbNq0CcBINq3nnntOMBgBgLa2NuTl5QEAjh49CgARDUaIaGyJCG8Sh4X9YGWFYOFDZsmi\nzp5BQYYi86Tp+N0bh/ndU8rYd7xFsIjnrdeXM3SUKEqyzyEJZseOHVAoFFizZg127dqF7du3Q61W\nQ6vV4sknn5S7ekQTgni14XgQZ1USLyDGLFkkzqx2trMPAL97Sh0NraIsWy09WHxJAdsvURSSZkAy\nf/58zJ8/HwCwdu1a3+u1tbWora2Vq1pEFAOps2jRxCNuE8yuRakmMMtWZogjiSiUpBmQENHEIw4L\nK7NlAahAfWs3rOaRrFpjHc9Qh4nJ5fb4VrIuLdTjBysrYHd0oyCP2bUo+Ynnul0pyrK1cLZF7ioS\npRwOSIgobsRhYcfOdAjmkBgzhPMEEhFGRvI7eMwRMFdozVWlMtaIKHLB5rotmV0gY42IUp9S7goQ\n0cUj2BwRuvicaT4v2GY7oFTCfoxIehyQEFHCcI4IAUBJgTBUj+2AUgn7MSLpMWSLxs3lcqO5t3fM\nY5p7e2FzuaFScexLwVfqpovP/Ip8zhWilMW5bkTS44CEYuDBf85RIyMnLeQRve1qLIAngXWiZOad\nI7K0yib5YlaUOpRKzhWi1MW5bkTS44CExk2lUsFUVgDD5NB/HXI2dUKlUiWwVkRERESUSjggSUEu\nlxs9Yf663HPWCRdDpYiIiIgoySXFgMTtduOGG26AxWLBM888E7D/4Ycfxt69e6HT6fDYY4+hvLxc\nhlomEw86D03BgCEn5BF9znbgOoZKEREREVFyS4oByYsvvojS0lJ0dwemzqurq4Pdbsdbb72FI0eO\n4MEHH8TOnTtlqGXyUKlUyC0qh35SYchjujsaGSpFRERERElP9ngeh8OBuro63HjjjUH37969G6tW\nrQIAVFZWwul0oq2tLZFVJCIiIiKiOJH9Ccmjjz6KzZs3w+kMPieitbUV+fn5vm2LxYKWlhbk5eVJ\n8v7GtF4oh78QvKZJU2NwaNi3nZM9+iSi93zrmOX574/XsdEeH8l8k/EcG+3xkaQIjvRY7zFTwh5F\nRERERMlM4fF4ZJtosGfPHuzduxdbtmzBhx9+iN/97ncBc0h+9KMf4Y477sCll14KAPj+97+Pn/zk\nJ6ioqJCjykREREREJCFZn5D8/e9/xzvvvIO6ujoMDAygp6cHmzdvxi9+8QvfMWazGQ6Hw7ftcDhg\nsVjkqC4REREREUlM1jkkmzZtwp49e7B792488cQTWLBggWAwAgDLli3Dq6++CgD4+OOPYTQaJQvX\nIiIiIiIieck+hySYHTt2QKFQYM2aNaiurkZdXR1qamqg0+mwdetWuatHREREREQSkXUOCRERERER\nXdxkT/tLREREREQXLw5IiIiIiIhINhyQEBERERGRbDggISIiIiIi2XBAQkREREREsuGAhIiIiIiI\nZMMBCRERERERyYYDEiIiIiIikg0HJEREREREJBsOSIiIiIiISDYckBARERERkWw4ICEiIiIiItlw\nQEJERERERLJRy12Bq6++Gnq9HkqlEmq1Gq+88krAMQ8//DD27t0LnU6Hxx57DOXl5TLUlIiIiIiI\npCb7gEShUOCll15CVlZW0P11dXWw2+146623cOTIETz44IPYuXNngmtJRERERETxIHvIlsfjgdvt\nDrl/9+7dWLVqFQCgsrISTqcTbW1tiaoeERERERHFkewDEoVCgfXr1+OGG24I+uSjtbUV+fn5vm2L\nxYKWlpZEVpGIiIiIiOJE9pCt7du3w2w2o729HbfeeiumTp2KqqqqmMv1eDxQKBQS1JAovthWKVWw\nrVIqYXslSh2yD0jMZjMAICcnBzU1Nfjkk08EAxKz2QyHw+HbdjgcsFgsYctVKBQ4e9Y5rjqZTIZx\nn5vq56dy3aU6P9H+f/buPDyq8u4f/3tmMslMlklIMplsMwmEJSGGSA2LRAkWsBWLgH0UbBRbXL4W\ngUvxV1RasbUUl0trXdra9qkLygMuj/tS7IMaXEEURWUrIGTfSEKSyT4zvz/CTOacObMkc2ZL3q/r\n6lXPnHPu3BNvP3PuzP25P/6MVSn+/g6C0eZYay8QbUbyWJXrdxFO7YRTX+RqR86+hEK4x9Zwby8Q\nbYZ7e/Y2KfhCumSru7sbZrMZANDV1YWPPvoIkyZNElwzf/58vPrqqwCAr776CjqdDqmpqUHvKxER\nERERyS+k35A0NzdjzZo1UCgUsFgsWLx4MS644ALs2LEDCoUCy5cvR1lZGSoqKrBw4UJotVrce++9\noewyERERERHJKKQTEqPRiNdee83l9RUrVgiON23aFKwuERERERFREIV8ly0iIiIiIhq7OCEhIiIi\nIqKQ4YSEiIiIiIhChhMSIiIiIiIKGU5IiIiIiIgoZDghISIiIiKikOGEhIiIiIiIQoYTEiIiIiIi\nChlOSIiIiIiIKGQ4ISEiIiIiopDhhISIiIiIiEKGExIiIiIiIgqZsJiQWK1WLFu2DDfddJPLub17\n96KkpATLli3DsmXL8Je//CUEPSQiIiIiokCICnUHAGDr1q3Iy8tDZ2en5PmSkhI88cQTQe4VERER\nEREFWsi/Iamvr0dFRQWuuOKKUHeFiIiIiIiCLOQTki1btmDDhg1QKBRur9m/fz+WLFmCG2+8EceO\nHQti74iIiIiIKJAUNpvNFqof/sEHH2D37t3YtGkT9uzZg6eeesplaZbZbIZSqYRWq0VFRQW2bNmC\nnTt3hqjHREREREQkp5BOSP74xz/i9ddfh0qlQm9vL8xmMxYuXIgHHnjA7T0//OEP8fLLLyMpKclr\n+01NHSPql16fMOJ7I/3+SO67XPeHgj99FvP3dxCMNsdae4FoM5LHqly/i3BqJ5z6Ilc7cvYlVMI5\nLoR7e4FoM9zbs7dJwRfSpPb169dj/fr1AAZ303ryySddJiPNzc1ITU0FABw4cAAAfJqMjCY2mw0H\nK9tQ1dAJkyEeBTlJUMD9EjciokjFeEejDcc0kXdhscuW2I4dO6BQKLB8+XLs3LkT27dvR1RUFDQa\nDR5++OFQdy/oDla24aHt+x3Ht101HYU540LYIyKiwGC8o9GGY5rIu7CZkMycORMzZ84EAKxYscLx\nenl5OcrLy0PVrbBQ1dDpcsxgRpHIYrHg5MkTaG2NR0uL9DbfubkToFKpgtwzCheMdzTacEwTeRc2\nExJyz2SIFxwbRcdEkeLkyRP45NZ1yIiNlTxf19UFPPwo8vImBblnFC4Y72i04Zgm8o4TkghQkJOE\n266ajqqGThgN8ZiaM7ZyaGh0yYiNhSmeSYMkjfGORhuOaSLvOCGJAAooUJgzjl/xEtGox3hHow3H\nNJF3IS+MSEREREREYxcnJEREREREFDKckBARERERUcgwhySM2Isn1e+vQUZyLIsnEdGYweJxFKk4\ndon8xwlJGGHxJCIaqxj/KFJx7BL5j0u2wohU8SQiorGA8Y8iFccukf84IQkjLJ5ERGMV4x9FKo5d\nIv9xyVYQ+Lq+1F48qb6lC+nJsSyeRERjhnPxuMSEaNQ1m6E4+zrX41M4EX+m5+cksvAhkZ84IQkC\nX9eX2osnzSsxoampI5hdJCIKKXv8A8D1+BTW3H2mc5wSjVxYLNmyWq1YtmwZbrrpJsnzmzdvxsUX\nX4wlS5bg0KFDQe6d/7i+lIjIN4yXFO44RonkFxYTkq1btyIvL0/yXEVFBSorK/Huu+/innvuwd13\n3x3k3vmP60uJiHzDeEnhjmOUSH4hX7JVX1+PiooK3HTTTXjqqadczu/atQtLly4FABQXF6OjowPN\nzc1ITU0NdldHzHlttL/rS7nfORGNFlLxTM54SRQI4jFaYErEd6da+blM5IeQT0i2bNmCDRs2oKND\nOmeisbER6enpjmODwYCGhoaImpDY10bLsb6U+50T0WjhaS0+4xqFK/Fn+nenWvm5TOSnkE5IPvjg\nA6SmpqKgoAB79uyRvX29PiEk9wby/vr9NcLjli7MKzHJ+vPD9b0H6/5QkLvPgfgdyNFma2s8vvdy\nTXJy/Ih+1lj5HYaaXO9Br0/wOZ4Fqz/h0Ea4tRPpYzYYccGfcTwW41a4t0ehEdIJyZdffon33nsP\nFRUV6O3thdlsxoYNG/DAAw84rklLS0N9fb3juL6+HgaDwaf2R7pTlV6f4NcuV4G8PyM5VnCcnhzr\ncq0/Pz+c33uw7g8FOXdV8/d3EMg2W1q8J3+2tHQO+2fJ/Z7D+Xfo3F4oyPEe7L8LX+KZL+3I1Z9Q\ntxFu7cjZl1AJRlwY6Tgeq3ErnNuzt0nBF9IJyfr167F+/XoAwN69e/Hkk08KJiMAMH/+fGzbtg2L\nFi3CV199BZ1OF1HLtbyxWq3Yc6QJlfWdMKUnYFaB5/fG9dVENFoU5CThVz+bjtrTXWg390EBwAab\nY/09c+YoEnj7XJb6nFeGx55CRGEj5DkkUnbs2AGFQoHly5ejrKwMFRUVWLhwIbRaLe69995Qd09W\ne4404R+vfef0SiEu0ye6vV7OfBSicGWxWHDy5AmP1+TmToBKpQpSjygQFFDAagO27TwCAHgDwvX3\nzJmjSODtc1nqc/78At9WehCNFWEzIZk5cyZmzpwJAFixYoXg3KZNm0LRpaCorO/0eEw0Fp08eQKf\n3LoOGbGxkufrurqAhx9FXt6kIPeM5CZV08H+YOfpHFGkkPqc54SESChsJiRjlSk9QXTM/cyJACAj\nNhameK7lHe081XRgvQcaDfg5T+SdLBOSM2fO4K233kJraytsNpvj9TVr1sjRfESyWKz4+GADqhvN\nyDbEo/ScNKgk1owO5owUnl1bGo9ZBXqXa8b0OmqbFX2HvkVvVRU0RiPUBecACqX7c0QUUdzVdKht\nNiM+Vo1LS3MRr4nGOF0M8kX1Hi5M4YPdsLiJmW5jLPlE/Bk9xZiIvU45IyX5qegfKHA8D8yU+Jwf\n9c6Ovcr6GkSlZ7mOM0+f9TQmyDIhufnmm5GcnIxJkyZBoRgjD8pefHywAU+/dWjoBZsNc4syXK5T\nQonzCwwev74dy+uo+w59i5N//KPjOHf9ekRPneb2HNJKg95HIho5dzUd5k7Pwm6n7VTnTs+CxWoV\nrMWPjlFjIv/a7DPJmAm4jbHkG/Fn9M8vLRB8/vcPCI9TEmLGzGe4nafPcl/O0+gn2zckzz33nBxN\njRrVjWaPx8MxltdR91ZVuRzbg5TUOSKKbPZ41907IHi9u3fAZS3+qboznJAMgy8x0znGkm/En9He\nPv/H0me4nafPcl/O0+gny/dhkydPxrfffitHU6NGtmitc3Za3IjbGsvrqDVGo+A4xunY0zkiikz2\neBcbI/x7mTYmymUtfk6G+x0JyZVUzGQc9Z/4M1r8eS9+HhhLn+F23sYZxyH59Q3JD3/4QygUCvT0\n9ODtt9+GwWCASqWCzWaDQqHArl275OpnxCk9Jw2w2QbXjKbFobRo5DtqjOXaI+qCc5C7fj16q6oQ\nYzQi2ilPxNM5IopM9nhX12zGDUsK0dDShYTYaGSlxmKyMRG62KFYOKswHadPc2dCX7mLmYyj/hF/\nRufnJEIdpXTkhs4s0CMlIWZMfobb2ceepb4GqvQsl3HGz3Pya0Ly7LPPytWPUUdpUyBFp0FX9wBS\ndRoonZLQxQlwSiVwsm4oYV1sTNceUSgRPXWa9Fe3ns4RUUSyWmw43d6DxrYeZMdEYXFpjmBDEOdY\nqFQyZ3FY3MRMxlH/2Kw2tHf14Yy5D4ld/VAAgtxQ581+xuyIPTv29GWl0pXV+Xk+5vk1IcnKygIA\nrF27Fo899pjg3LXXXotnnnnGn+YjmqdEdPE55+TN266ajjS9LridJSIKE75uCEIULrwVPhzLG9MQ\n+cqvCcnNN9+MQ4cOobGxEfPnz3e8brFYkJ6e7nfnItlwin05J2+KzxERjSVybghCFAzeCh+O5Y1p\niHzl14Tk/vvvR1tbG/7whz/gN7/5zVCjUVFISUnxu3ORbDjFvrROyZtjMdmNiMhOzg1BiILBW+HD\nsbwxDZGv/JqQHDo0+LX6qlWrUFtbKzhXWVmJGTNm+NN8yPlSkFAqH2TX/hpkp8a6TUQXJ8CplED6\nuNjRm+zGgkdEhKF4Wb+/BhnJsYKYarVasedIE1rOdGPlJQWoO21Glt6/DUHGMpvFgr6DBxh3A0D8\nuV8yJRW9iwpQ09SJLH08ZogKH47ljWkCxluhRYo4fk1IHn30UQBAW1sbKisr8YMf/ABKpRL79+/H\n5MmTsWPHDlk6GSq+rPv0lg/y45muW9dJJannG0fv17cseEREgOeYKl6Hf8OSQo8FY8mzls/3Me4G\niFQhxK1vD+U9xaiVgrE7pjemCRA+V4w+fk0nn332WTz77LNIT0/H66+/jqeeegr//Oc/8cYbbyAu\nzvvX7H19fbjiiiuwdOlSLF68GI8//rjLNXv37kVJSQmWLVuGZcuW4S9/+Ys/XR4WqXWf3q5hPogr\nFjAkIsBzTJVah08jZz51SnDMuCsfb4UQOXYDj88Vo48sldpra2uRk5PjOM7MzHRZwiUlOjoaW7du\nhVarhcViwVVXXYW5c+di2jThLLekpARPPPGEHF0dFl/WfTIfxDsWPCIiwEtunZd1+DQ8cTm5gmPG\nXfl4K4TIsRt4fK4YfWSZkBQWFuL222/HJZdcAqvVijfffBMlJSU+3avVagEMflsyMDDg5erg8mXd\np/M14zPi0dDWg2i1Csa0ePT2DeD5949jfKYOcZooj7kogOf11ZGMBY+ICBiKl/UtXUhPjsXUnCRH\n7khVQydWLirA6TPdSNFp0dM7gE8PNeBMR5/HuEnSkmeWMO4GiPjZYFJ2IqxWoKZ5MIfkB1P0+PRQ\nw9nCiAmYVZAKpYcFKb7kq5KQt0KLFHlkmZBs3rwZzz33nCNnZM6cOfjZz37m071WqxWXX345Kisr\nUV5e7vLtCADs378fS5YsgcFgwIYNGzBx4kQ5uu2VL+s+na/59JBw//yfXjQRO/ecEuSVAO73IB+1\ne5Wz4BERYShezisxOYqjfXakUZA78rOLp2DrO4d8jpskTaFk3A0U8bPB7m/qsPUd59o5EB7Dcz7U\nqP3sDyRvhRYp4vg1IWlqaoJer0dzczN+/OMf48c//rHjXGNjIzIzM722oVQq8eqrr6KzsxOrV6/G\nsWPHBBOOwsJCfPDBB9BqtaioqMDNN9+MnTt3+tQ/vT7B+0Uy3ltVcVxwfPpMDwBhXgkA1Ld0YV6J\nyeX+eqcPX0/X+SLY73003R8Kcvc5EL8DOdpsbY3H916uSU4eXO7gy3XOfRorv8NQk+s92NsRx82G\nli4AvsdNufsT6jbCrZ1IH7PBiAvVTccExzXNolypxk5cNlf6D6l6fULYfPYHq81wb49Cw68JyW9+\n8xv87W9/w9VXXw2FQgGbzSb4/127dvncVnx8PGbNmoUPP/xQMCFxTo4vKyvD7373O7S1tSEpyfu2\neSOdNev1CSO615gm/I8iJVEDAIiNEf6a05NjJdvPSI716TpvRtp/f+8dLfeHgpx/4fH3dxDINlta\nvCd7+nKN/Tp7n+R+z+H8O3RuLxTkeA/Ovwtx3DScjYO+xE25fqdytBNOfZGrHTn7EirBiAvZacKc\nkSy9KFcqLV7yPnt74fDZH6w2w709e5sUfH5NSP72t78BAF588cURFUJsaWmBWq1GQkICenp6AsxU\nawAAIABJREFU8Mknn+DGG28UXNPc3IzU1FQAwIEDBwDAp8mIHNyt67SveRavD50h2os8NkaJBTNM\nMKXHoyB3HL6v7YApPQEFOYmOn+Hc1vhMncv66mH1F1Ycaf8PKhobYNAYMEU3CQooXeqA2FRK9J48\nxb3piSjkZhWkAihEZX0nDMmx6B/ox8pLCtDU1oWVlxSgsdWMlCQtoqMAq82KQ5VnHDH5wpTISB62\nx+aajjpkJWQMxWafGxDVcsovRN/h71hjJEzMmJwG6yU2Rw7JrCIDlIrB3bey0+JQ4iWnJBLqlPg9\nhof9AznmxxpZckhWrlyJ+Ph4lJWV4aKLLkJBQYFP9zU1NeGOO+6A1WqF1WrFokWLUFZWhh07dkCh\nUGD58uXYuXMntm/fjqioKGg0Gjz88MNydNkn7tZ1ivfLt68P/fxIk2MvcvH6Z+djXaznvfdXXJw/\nohn/kfb/4LF9/3Qcry25Dvm6KS77dadeeAGaP/wIAPfuJqLQUmKoZsM/XvsOP71oIrb/W5iLt+1f\nRzB3ehaa2/sE8TI6Ro2JEbCjkbvY7CtxDDddvwqV//2k45hxPLT2HGrwkkMCQX6pOKckEuqU+DuG\nh4tjfuyRZULy1ltvobq6Grt378ajjz6KkydPYubMmfjd737n8b4pU6bglVdecXl9xYoVjn8uLy9H\neXm5HN0cNqk98wtzxknul39+gUHwunj9s7g+iT3wyLn3fk1Hnctxvm6Ky/7clp4exz/3VlXxP2oi\nCjl77LPn3tk55+KJ4+OpujMRMSFxF5t9JY7hPZWuNRgYx0NHnDMiPpaqUxJpRT/9HcPDxTE/9sgy\nIbFarWhtbUV3dzdsNhv6+/vR2toqR9Mh5W7PfHf75Tu/Ll7/7K4+iZx772clZEgei/frVmk0jn/m\n3t0kB4vFgpMnT3i8Jjd3QpB6Q5HIHgvtuXd29mNtTJRLvMzJSEQkcBebfSWO4VoTazCEE3HOSFaq\nqE6J6FkiEuuU+DuGh4tjfuyRZUJSUlKC2NhYlJeX45ZbbkF+fr4czYacu3WdzmueTenxmFWgF7xe\n1diJ3PQElOSnOe5VKYH0cbEu60PdtTUSU3STsLbkOjT0DOWQAKI6INnZQJQK6vQM7k1Psjl58gQ+\nuXUdMmJjJc/XdXUBDz8a5F5RJLHHwtNnurHykgLUnTYjIyUOnT29WLFwMkxpcZhsTIQudigmzypM\nx+nT4V8V2x6bndffD4dLLaf8QuTqklhjJEzMmWYAbGfrkKTG4/xiA/RJGsc4zc9JhFqlkOVzPlT8\nHcPDxTE/9sgyIXnsscfw6aefYvfu3fjoo49QUlKCmTNnorS0VI7mQ8bduk77mmfxV64KmwK62Gik\n6DSI16ihVNrbAaYYk5BvdF0f6q6tkfVXiXzdFFyYVyLMQXGqA2KzWdBy4DP09LVDM9CBZNi8l1+y\nWtCz92P0VFZBazIhZuYcQKnyu780umTExsIUz91JaGTs8fNMRx/SkjQoK04HbHBsLGK1usZkpTIy\nisfZY7N9iYsNVhxuP+J7grBELafo/EJY29vQ9e03sLWfGYrLZ5OBK+trEJWexeTfIFDbFNAnadDT\nO4C0JA2iJZ4dnD/nbTYbvqtsHXVFkIdFnLQuHqdSY9752GpBz57dOFZVDY3RyOeSUUCWCUlpaSlK\nS0vR3t6Of//73/jb3/6GrVu3Yv/+/d5vHkXESfDOiezhUuio5cBnOP3YPwAAZgBYC6QUe5449uz9\nWJBMZoINmtlzA9hLIhprpDYRATAqC8bJkSDsLi6Lk4GZ/Bt4wy1sGImFEOVOavd3nPK5ZPSR5c8m\nDz74IP7rv/4LV1xxBQ4dOoS77roLe/bskaPpiCJOghcnsoeDnspKj8fS93hOLiMi8pfUJiJSr40G\nUgnCw+UuLouTgcXHJL/hjtNIHNdyjFln/o5TPpeMPrJ8Q5KSkoIHHngAEya4Jq0+//zzWL58uRw/\nJuyJk+DdJbKHkibHBOf9PjQm79VgtaJrNCYmkxGRvKQ2EREvYgmXOOovORKE3cVlcTIwk38Dz90G\nOHJdHw7kTmr3d5zyuWT0kWVC8otf/MLtuR07doT1hESq+KHUOaMhHuaefkdxQ3FhIwDINyXihiWD\nSe3GtHik6KIlE9ll7b9EsSJPkotmA2sHvxnR5uZAYQVq3tgObY4J8TE6VL5X7bLuOGbmHJhgQ09l\nFTQmE5RJSejY+ZZLkcWT2VpUNNYJizIC3teKUlizWCw4evSo2wrq3D2LRspms+FwVRuqmszoGxjA\nykUFaDjdBVN6PPJNiThceQaLLxgPXVwMslK1mGIMv4JxvhDH6Um6PPzi3BU409uGzv5uADbYYIUC\nStgsFvQdPDAYL3NMsFms6K2udomdMTPOh6m/D93VNdBmZ0FTcr7jPtPPr0Ffcwti0tMRnV8Y2jc/\nBkzMTMTKSwocSe2Tcjzv/mbfMGekRZADQTxGJ+sm4mj7MUeh5cm6iR6T2h35qZWV0OSYkFw0GwqF\nU06HOLcpv1CYtC5OUrcMoOeTCnRX1yDWmIWY88sA1dAjq/25pLeqGjHGbGhmRnbOMsk0IfHEZrMF\n+kf4RWotZ5peJ3lOWOyw0CUR/VDlGUHRrtuumo4fzwzsrF1qXWeavsTt9QqFajBnpLgUp7/+GM3O\n+STuCiYqVdDMngvNbKDv4AGcfPAhR3vORRZbrp6PF6zfOPphX1/KNc2RzdMOWtw9i/xxsLINnx9u\nxO79NZg7PQsvv++8dXShSzyN1MRfcZy+tvgKHGv9Hh9X7gMAvIsKR8xs+XyfI146x1dAGDv7jhxE\n5TPPOs6Z1GrBmvrUCy9A/ZtvIVeXyHgbYJ8cbHAphDiv2P03CPbNGeaVmEZUBDkQpMboM1+/6Di2\nj093eSPe8lPdPQe4G5s9n1QIx7cN0Fw4f+iCs88lxsUJYfM7JP8E/M/UCkV4f4B4WsvpKSdEqoBh\nKNaF+rOuU5w/Ii6YKMVTkcX4pqHFYM794JrmyGffQUv8P3fb/BL5oqqh0xFXxcVkxTE2EtbZu+MS\np9vr0DPQK3mN+dQpx2vO8RUQxk5vhePs9zLeBp63woiRQGqMejov5i0/dbjPAd3VNR6PafQZ8+tm\nPK3l9JQTIlXYKBTrQv1Z16nJEa7B9KVgoqcii536OMl+cE0zEUkxGeIdRWTFxWTFMTYS1tm74xKn\ndRnQRGkkr4nLyXW8ptIKr3GOna6F46TjOeNt4HkrjBgJxGM0OzHT43kx8fOEOD91uM8BscYswbE2\nO8vNlTRaBHzJVrhzV/wQGMoJqazvRE56PFRKBbTRURifmYA4jRr/2luF3PR4tJn7cKq+E+MzdUFf\nF+pPsaLkc2YhZlUvequqoTFmQ603QGvMgio9S7Ce02obQONXH6GvqhqaCbnIvfVW9FZXIyY7C5Yz\nbdBHq6HJzkLMuRNxZXeOoCgjIFHgiAWNxiyLxTq4zMuNuq4umCxWxz/7ch1FroKcJCiVQEZqHMw9\n/fj5pQUwdw9Aq4lCbbMZKxcVoOVMDzJS41DgZV1+OBpal1+Ln597Jbp6u2GIS8MkXR4UUEAbpUGS\nJgG6aB0azI0AgPN/cC5M168arPuUY0L8zFnoq6lFVJwWvVVVsLWfwUBPL6I00UhffCnUCTqosrIQ\nPakAubpE9FZVQZ2YAFtfL3JnzGS8DQBx7umsQmFhxNnF/tcVC7bJuom4tvgK1LTXITsxE+cmT0N5\nUT9qO+uRlZCOSbo8j/c7P0/EGLMRVzRLcN7+HGCprxl8xsgvHMqVksgtjZk9FyaLFd21tdBmZUIz\n+0KP11PkC/iEJCEhvAuluSt+CEjnhCy/KA/fnWp15JYI80qAG5YUYsXF+UFb0yguuDUc/YcPovbJ\nrY7j3PXrYVp+pUvfG7/6CO1/fhoA0APAevPPkf6jS9H5yXuC+zNVK1GyZInre5cocERjlQ3/My0K\nsclqybNdLVGYhcG8M1+vo8ilgAL5xnGCorG7v6nD028NrcefOz0Lb772PXSx4V+rQcxd7YbD7Ufw\n9NcvOF4vNZU48kkMp5aiySkXJHf9ekRnZAjW32ctW4pTz70quAZKlSDO6vVcWx8o4vzSlYsKBDkk\nSiUwt8i/XaiC7Wj7MUHOSHlRP7Z984rjWFUchZkpM9ze7/I8kZQq/Mw/+xygLytFU1PHYD6qh9zS\nvqOHUPnsNsexKTpGkCPFXNTRx68JyeOPP+7x/Jo1a7B161a35/v6+lBeXo7+/n5YLBb86Ec/wpo1\na1yu27x5M3bv3g2tVov77rsPBQUF/nTbZ1I5IYU54wSve1v3HM58XdPZV1Xtejwd6BW9Lj4mElOp\nVNDnZyAhU/rbw47aNqhUgzuz+HodjS7VjWbBsT3G2uNvJJHK8cvXTXF53TmfpNvL2nsA6GtpcbmG\nD2fBI342qGkSHovHcCQQj8najnrh+fY6IMX9/VLPE57GpLfrveVIccyPPiFdshUdHY2tW7dCq9XC\nYrHgqquuwty5czFt2tAgq6ioQGVlJd599118/fXXuPvuu/HCCy94aFU+7nJCnF/3tu45nPm6pjMm\nxwjn1MpoY/bg66Zs0f3CYyKi4cp2k7sXiTkk7nL8xK9romIc/xybkwPnx9sYo9Flb7HoFOGTIfNE\ngkv8bJAtyiHJTotDpBGPyUxduvC8zksOyTBzRLxd75ojxVzU0c6vCYnUtxnA4PrK6mrf/lqu1WoB\nDH5bMjAw4HJ+165dWLp0KQCguLgYHR0daG5uRmpq6gh7LVz/mZseD4sNknVI3OWXTDEm4ueXFqC6\n0QyTIR5TcpJwoqYDRkMcxsVHY8e7h5GRHIuCnCSv21S6qyNihQX7Tn+JmvY6mJKyEauKRW1HveMa\ne40P572/xbVEOqxd6P7+e+k9wSHK7TAZYT3djKOP/Rmx2VmIPn8ujphPoKajDhPycpD+i6vRX12L\n6OxMxMTrB+uQTBiPrFUr0WNfMzp77tk3Jao7kl+IvsPf+b720/l+D/vwE1Fkslht+O5UqyAG1zab\noVarUH/anjvSjWSdFi3tPbhhSWHY55BYrVYcbj+CBnMD1Go1GjqbYdRlYtW5K1DX2YAkrQ7H2o6j\npa8FXb09uP7cFUj7vhXRDW2IbRyHH/bPAgwpOKFXYfKqlbDUNiAmTY/uI4ehychA7u0b0Hv8BNTa\nGPS2nYHpmnL0d/cixmiE1dyBthe2QZuZjgELEK3Xwzpnpuc196wPNWLOzwDZhnjMKEiDzZ5Doo/H\nrHMMjvFtf64I9ZbV7uqM2I8n6iagvGjZ4HOGLh3TU8+FtciKuo5GZCSkYXryNMF4iiooxJGOofun\nTJoC09U/Q3ddHbRZWYielC/qgKgOyaR8mK4pd+SIRE/KF47XyQUwXXvNUB2SkvORq0tiLuooJss3\nJM899xz++Mc/oru72/FadnY2/v3vf3u912q14vLLL0dlZSXKy8sF344AQGNjI9LTh2bqBoMBDQ0N\nfk1InNd/inNAnOuQuMsv2XukSbDG+acXTcT/fV6JudOz8NSbhwRteVti4K6OyL7TXzrWczqvL7Zf\nY88Zcdn722nf+tQLL4D5w48k9wQffINDuR09H+4S7PlttNrw2MC7AIBb48tQ/9TQ2tJUUb2SuDk/\nFDQr3m/cdP2qYa39dL7f0z78RBSZ9n5X7xKDxbF45aICbH17KJ6Gew7JvtoDeGzfP13idalpsC7U\nO8feR6mpBO8c+wAAcI2yCObndsEMoBVnY92LbyDz6p+i9rn/ddyfeuEFOPXW2zBdvwrqJJ0glpqu\nXwVrxxnBa1nLluL7Z58FOq/Hyb//t+N1lzX6rA81YuJnAKvF5lKHxHns+vIsEGje6oyUFy0T5IxY\niqzY/s1rjuO8cb3o+PPQEvyUtTfgsdND5x9SLUTlc//jODbZbNCULXQcuzwXXFMuzBGxQXh87TXC\nOiRRamhmz+UYHcVkmZA8+eSTeO211/CnP/0Jt956K/bu3YuPP/7Yp3uVSiVeffVVdHZ2YvXq1Th2\n7BgmTpwoR7eg10sn1Nc7feiJc0DqW7o83gsAVRXHBcenz/S4bWteiXDrO7GKxgbBcUPP4HFN59B6\nTvF+9Q09Dbgwb/BDrrbafV0QQV2R6iroF7h/T0drhHt899TUAGc3ClHVnYbzfkaCn1FfA32ZcKJj\nqRe2Jc4tkbrH3f3iffi93Qt4/ncXruTus5zttbbG43sP55OTB5creLrG+TpvhnOd8/sM599hINsM\nNjnewy6JGCyOn+J1+e7iqVy/U3/bqfhu8D2J47XzsfM/O9dtAoZinbK+WfJ1qRw9qdfs+SVdp0R1\npkSxs1IUp93F1kgfs4GIC+JnAJc6JD6O3UD1T4r4WcP5GQMAajuFOSN1HY2C4/6qWsFxb3UVoB06\n7q4Vnu+urYXRqS/i8SZ1veC4xvU5wrhY+r1F+hilQbJMSFJSUmA0GjFlyhQcPXoUl19+OZ577rlh\ntREfH49Zs2bhww8/FExI0tLSUF8/9B9KfX09DAbfttRzt8NIRvJQMTdxDkj62XOedicxpgkHf0qi\nxm1b3nY5MWgMksfZCUN7gIv3qzdoDI52Y4xGwXpj57oggroi2UaPfYkV7fGtycoCBgZ3GLNkCL+N\nEvyM9CxBu3p9AqLSRW2Ja5eI7nEmvl+8D7+ne+33+7OzTKgCm5y74ci9u05Li+eNGrydD+R19vcp\n93sOxA5FgehjKMjxHnIzhpZfuatDIq7tIBVP5fqdytGOKXEwbonj9WB+iMLlnFkfj2in6+xx1eom\n3sYYs12W/Ui9Fp2cDACIzRXVJRHFTnGcloqtcv5+QyUQcUH8DCAeq+KcEnfPAsGMW+Jnjax4Uc5I\ngjBnJCMhTXCsNmYJckljso3A6S8dx9osUd2QzEyP402blelyvTPxM0mMMTsov0N7mxR8skxItFot\nPvvsM0yZMgX/93//h6KiIrS3t3u9r6WlBWq1GgkJCejp6cEnn3yCG2+8UXDN/PnzsW3bNixatAhf\nffUVdDqdX8u1AGFuSG5GPEry0yTrkLgzqyAVwGB9ElN6PFJ00bjyh5McbQ2nDom7OiLnJU+Hrdg2\nmEOSmI3paUWCHBK75KLZwNrBqqhakwnxGt1gLRFDJjpt3YhLjoPGZELytNke+xFzfhlMtsG/Smiz\nshAzZy7Wmo2o6aiDQmdEytrrB/fFP/sz1OkZbtdxutQdyS907I/vy9pPwf25OYg/b8Zg3ROuGyUa\nFWYWprvE4LqzdUfqms3I0sdhTpEB+kTNsGJzKJVkTcPakuvQYG5AedEyNJibkZ2QgcRoHeo661Fe\ntAwt3W0oL1qG7r4eJCdkIyetCL1V1VAnJmDA3I2UtTfgeIYKeWtvQHR9K9SxWvQ2n4bp+lXQzBz8\n9sIEG3oqq6AxGV1fy0iHxTq4/Cq9dBZscTq3cZf1oUZO/AxQUqCHUjG4u1Z22uDYTQ2zsSt+1pis\nmwhdic5xPEmXh6jiKNS01yFLl4HpKcVQFilR21mPzPh0pKWWIGV9imO8qAsKsbYj1XF/TOx4mGy2\nwZyQzExoSucJfr5LHZLJBTAplOiuroE2Owua8+ciV28YGo9TpsIUpXYZ6zR6yTIhueuuu/Diiy/i\njjvuwEsvvYQf//jHWLt2rdf7mpqacMcdd8BqtcJqtWLRokUoKyvDjh07oFAosHz5cpSVlaGiogIL\nFy6EVqvFvffe63d/nXND7Anug6/7Rgklzi8w4PyCob84TM4aWh86r8Tk84zdXR0RBRTQqXXoiDYj\nXh0Pm026CJxCoRrMDSkudSStNYyLg0GjgVIRi6pxPcjW6dHU8R/UdNTDlJCJnOrus8UQjTiZrUVl\nR83gROfCi2DUJ6KpqQM2p0Va/bYBJBfPgaJ4KOExekqRhzflWnfEcexLIqXU/YXFvvw6iSgCKJWu\n+XlTTUk4WNmGnp4BpOo0UHmoERVsUpuP2DcWsVMqBmP5FN0kHGn/D/oH+tFv68d3p48gW5eBRLUO\n3X09UKvUsKlsaO1vQ2ViJ7KyJ2KKbhI0UCIeQL4+AU1pHcDZkKcR9UUzey40swbjaMe//wWN0QjN\nrAugmS3qT1SU17jL+lAjI34GsNlsSNFp0NUdfmPXTvys0Y8+NPc2o6W3FZoYNSYg1/HMoVProIIK\nyTHJ6Lf1IzkmGUqFCirReBE8u1gtQEwMFKooKGI0g8VYBB0Q1iEBAM2F8wXjWzweNbPnQuP5b6k0\nisgyIZk0aRI2bNiAQ4cO4eabb8YjjzwCpXgwSpgyZQpeeeUVl9dXrFghON60aZMc3ZQkLnDknNQe\nSs4JaJ6S2t3d43yf8/3XKItge26X45qWq+fjZes3jnbT9CWSbbn7mcPFREoikiIVi8PlgW448dB+\nrVRyu/34svyL8frX7/rUnpThxlHG3cAK57Hrzp6mzwVJ67YiCI7FSe/exmjP3o+FGy7ABo19500i\nH8iyx9/HH3+MefPm4a677sIdd9yBBQsW4MCBA3I0HXBSxQ/DgXORInGSpLiAkbvX7fd5SqR0Pna+\nX6qglxx8LcZIRGNLuMZiYHjx0H7OU3J7a3ebz+1JGW4cZdwNrHAeu+6Ik9bFxzXtw3sGEBcuFB8T\neSPLNyT33nsv/vu//xv5+YP7Tn/zzTe4++678fLLL8vRfEC5K34Yas5FisRJkuICRu5etxfb8pRI\n2amPg311lvP97gp6+Wu4xZOIaGwI11gMDC8e2s9JJ7cPGqdNkrzHV3IXoSP/hPPYdSdTJ0xaFyex\nZycKk8y9jVGtSbiJgsbEMUbDI8uEJDo62jEZAYCiIg/5BWHGXfHDUJuSMBH3pCwZTFbvysLEohxU\ndtTApMvEhOoedFS95ZKDYU9aa+hpQJomDR197YhWqjEh0YgfmTPQU1mF2PHjob0hBz2nBgspZits\nuOd7G2JM2WhSROOl796CQWPAZN1EyWR7X9ZSeyJIpMwxARbrYJFF+3sRY/EuojEhXGMx4H7zEWAo\nJn7U1IxoZTQ6ejrx83OvRF9fL3KdkttVChW0URro41LQ29+Hn0/7L2ScagdqG5H0bQ06u08iJiMD\nllkl6Plst2MTkZiZcwClqKjtlKmConHRU6a69NlmsQgKzeX+6v9D78lTrgnsjLF+C+exayf+7P5B\n6nTYiuAofHie/lxEF0ejpr0O2YmZmD5uGozjOtFXXY1oYzbSEvI8th8z43yY+vsGk9SNWdCUnC8s\ndDhlKno+/wTHzuavxsw4H31HDvpeQJnjdNSTZUIybdo0/PrXv8aVV14JlUqFt956C1lZWfj8888B\nADNmzJDjxwSEu+KHodZ/6DtBwUPr1fPxvvUbXKMswkmnHBDntcD2pLUL80rw1uH38MyBlwAA2cpO\nnD57j/bCC1DpVGgw9cILcPrsse7qn+IF64cAhtaLiteM+p1b4pRI2XfwAE4+/LDgvSBNuJMG1z4T\njQ3hGosB95uPAO5zRhyxUQ8cbj8iiJuX5V+McSea0PX3wTX6XRiMxbXbt8P685WofHqoAJ3UWvye\nzz+RLBrnrOXzfS6xM+FHl7r0nzHWf+E8du3En91XFS0R5IxAlENiHNeJ9j8/DQDoAaBeq3Ytruyk\n78hBlzEpyCkRFzrs7xMeeymgzHE6+skyvTx+/DgqKyvx4IMP4v7778e3336LtrY2PProo3jsscfk\n+BFjjniNrz3XQ5wD4m4tsPP6T+d7XAoNOh07F+TyNU/Fn9wSX9Y1c+0zEYUzdzkjnnLyWrvboKo7\nLXjNHou7qoXFDqXW4vuyXt986pTg2F3sZIwdG8Rj0FsOSV+VeBwKC22KiceNeEx2V9d4PBZf721c\ncpyOPrJ8Q/Lss896v4iGRbzm157rIc4BcbcW2Hn9p/M9LoUGnYocWtNTJfNJnMmZW+LLumaufR6b\nLBYrzB62zjY3dcBikd4KmyiY3OWMeMrJS9YmwZIRK3jNHotjs7MFr0utxfdlvX5cTq7g2F3sZIwd\nG8RjMFMnLJQoziGJNmULCiFqTNKV5h3nReNGPEa1okKHWqPoWHS9eBxynI5+skxIampq8Jvf/AY1\nNTXYtm0bbrvtNmzZsgXZosA6Vo0k7yKqoBApa29wFDw0Z8diQXsidIm5MK40oKemFprsLERNGcrd\nsf+cisYGGDRpWHXuClSeqYZal43c9YMFuGJyTIidMmUwhyQ3BzaLBfroaGizs1BXPAFX9iTCoDEI\n1kg787SWerh8KczF4l1jlQ1t+8ajNyFZ8mx3RwtwqS3IfSIaYoUF+05/iZqOepRPWwalTYHcomw0\nmpuRFpeKBnMDgMGYORQ3a5GgiUd/fz/ME/VIXX0NUNOI5EQ9+k63IOf665A6txQ2i2WwwFxWFpSp\nemGenUKJmJlzJAskOkueWeJT7GSMjXzCz36D5DPGZN1EXFt8hSNHpDi5CLYimyOHZKa+xJFDkqXL\ngD75XKjXqtFbXYWYbCPGTZuFw+1H3D7HqPMLYbp+1dncJyNifjATpmt6HONYM+sCmNTqwecQYzY0\nM+YgN1nvcwFljtPRT5YJyaZNm3DdddfhwQcfRGpqKn7yk5/g9ttvx7Zt2+RoPuKNJO/iSMcxPHb6\nNSAOwOn9WDv+Oiwbfxk6Pv4/VG0d+r1mKIGE0gVuf86y8ZcNHqQA0VOL0XfwACr/MXRN6oUXoPls\nDknu+vWYWXapx6KOntZSD5svhblYvGtMUqlUSMkuQPy4LMnzna01UKlUkueIgmHf6S8FdRqunrYM\n2w68glJTCbZ9M1RfyzkfzyVuJgN9mgOCtfHWvl5UPTsU48UxOnrqNECp8lo0TqH0MXYyxkY8X54x\njrYfE4zXa4ttgpyR6OJowXldiQ75xaXQL0hAU1OHSx6U+Gf0Hf5OmDNyTS8qncaxSaGA5sL5MC5O\ncDxjuC2gLIXjdNSTJYektbUVF1xwAQBAoVDgyiuvRGdn+O/DHSwjybtwd09fda3gdedPywOZAAAg\nAElEQVRjX36OeN2lcw4J12QSEfnGtU5DPQDf60bZuay9rxGurWeMJm98+ex3uUY8fr3UHfF2LB6b\n3bXCZxVxzgiRmCwTEo1Gg/r6eigUCgDAvn37EB0d7eWusWMkeRfu7okWrbuMzs70eo8z8TpM5xwS\nrskkIvKNS50GXToA3+tG2Yljska01p4xmrzx5bNf/Jrr+PXchrdjlxySLFGOSLb0t91EdrIs2brz\nzjvx//7f/0NlZSWWLFmCM2fO4JFHHpGj6VHB17wL53WgGdoMrC1ZhZqOemQlpEOpUGJXzQeYMC0P\n6b+4Gv3VtVBnZyL2/LkuP6ehpwGZmnTkVHe71CsRrMPMzgaiVFCnZ4TvmkzuPU5EIeIp/++85Omw\nFdvQ2NmE5Nhx6B3oO1t/pA8Ti3PR0dOJrIQMR+x2lz8oXhufNvs8WFRK9FZVQ2MyQj0u1TVGBzIu\nOrWtnDgBmDCFMTfMOfJDOuuQnZCJybqJLtdM0uWhvGgZajvqkaVLx/TkYiSUJDjG9uSEPOSl9KGn\nshKaHBOSE4RteHuOccnxmFwAk0IxWJckOwuaOWXDe1P87B9zZJmQ2Gw2LF68GGVlZfj973+Puro6\n1NfXo7i4WI7mI56veRdS60DnZ83D4fYjeOTzwZokpaYSfNy7D9AD6P0WazuNjnad65DUVHyMk38U\n1viInjpNch1m9JQwnIicxb3HiShUPK3NV0KFmSkzcFjtfm29c+wWn3MQxWR1jAbxc34I51rf0fnC\nYsOBjIvObdfJ3DYFhjg/JKEkwWWcfXF6vyC3SVUchZkpMxzX9R08IKh9lrA+QfDv3etzjMSzhebC\n+dBIX+0VP/vHHlmmm5s3b0ZxcTEOHz6M+Ph4vPbaa/j73/8uR9Njirs1ms6v+7o+ebTs2T1a3sdo\nZ7FYUdfVhcrODsn/1XV1cZteijgjWpvvof6IP3WbnAUyLjLmRh6fxqmXnJFw+/cebv2hwJPlGxKr\n1YoZM2bgtttuw8UXX4yMjAxYLBav99XX12PDhg04ffo0lEolrrjiCqxcuVJwzd69e7F69WoYz65P\nXLhwIVavXi1Ht8OOuzWazq/7uj55tOzZPVrex+hnw/9Mi0JsslrybFdLFGaB2/RSZBnJ2nxP9Uf8\nqdvkLJBxkTE38vgyzrzljITbv/dw6w8FniwTEq1WiyeffBJ79uzBpk2b8MwzzyAuLs7rfSqVCnfe\neScKCgpgNptx+eWXo7S0FHl5eYLrSkpK8MQTT8jR1bDmnAPiXAvEee2mUZeFH6QVnc0tcZ+PMlr2\n7B4t72O0U6lU0OdnICEzSfJ8R20bt+mliONL/p+7uO3r/SMRyLjo3HbixPGwTsj3fhOFlKcxaGfP\nebLXGSlJ+YHgfLh91oZbfyjwZJmQPPjgg3jxxRfx6KOPIjExEY2NjXjooYe83qfX66HX6wEAcXFx\nyMvLQ2Njo8uEJKxJJV5hZMUQxf7TcRxV7TUwJWRiQnUPsqrM0Bi7oS44B/k6Lx8SbvbsttksaDnw\n2VDiWtFsKBReHhRDmVzGvceJKEC8FZTztG7efm+DuRHaaA2kvgB03J8wCX2HvkVn1TsuMdTeTqu5\nCUXf9+FMTT00RiNiZs4BlG5is3NclDs+O7Wdok/wWJeKwoNz/qj935ejcOfZQojnJU/HzJQZQMrg\nPTZYXQodOn/WDo7LI+6LLYrHXX4h+g5/5/W4sr4GUelZ3scpP/vHHFkmJAaDAWvWrHEc/+pXvxp2\nG9XV1Th8+DCmTXMdfPv378eSJUtgMBiwYcMGTJzouoNEqEglXiGtdGTFEEX3lJpK8HHlPlyjLILt\nuV2CnzHS/0hbDnwmSFzDWiCl2LXKrzMmlxHRaDSSOC2+t9RUgo8P7fPYhqcYam/nLuWFqH3ufx3X\nmGCDZvZceMP4TFLEhTttxbbBCclZ3sa+t/PicWe6fpWwMKKXY45TEpNlQuIvs9mMdevWYePGjS5L\nvQoLC/HBBx9Aq9WioqICN998M3bu3OlTu3p9woj75Ou9lfWiIlZnjxt6GgSvN/Q04MK8Eo9tVTQK\n77EnsMc3mV1+hr7M8yTCXf9rq0WJYtVV0C8QXiu+V+o9evr5/vzew+H+UJC7z3K219oaj+89nE9O\njvdwNrDXOb/PcP4dBrLNYJPrPYRDO+KY60ucFt8r3mhEqg1PMdTejrK+WXBNb1U1jIu9vzdPbcvx\nO470MRvucSFQ7dWcEiWxd9ZBnz/0s7yNfW/nxeOut6p6WMe+PMf4KtLHKA0K+YRkYGAA69atw5Il\nS7BgwQKX884TlLKyMvzud79DW1sbkpKk16o7G+lXzfphfE0dlS4qYnX22KAxCF43aAxe2xTfo4mK\nAQCY9fFwLjOpSs/y2Jan/scYjeh0Ps42Cq6VulfqPbprfzi/u3C9PxTkXBbh7+9ArKmpHXVdXZLn\n6rq60NTUDpXK+xKRlpZOr9cM9zr7+5T7PcvdXiDajOSxKtfvwt92RhKnxfeKNxqRasNTDLW3Y81I\nFVwTY8z2qS/u2pbjdyznv6dQCee4EMj2shNESezxGYKf5W3sezsvHncuBT5djrMFx96eY3wVqFhN\nwRfyCcnGjRsxceJEXHvttZLnm5ubkZo6GKgPHDgAAD5NRoIlqqAQKWtvGMzJMJmgLigEMLJkRkfh\nos56ZCakI12bDoM2DckJWchJK0JvVbXfyV3JRbOBtXD0N3nabK/3MLlsrHO/gxZ3z6JIJpUM7Cn/\nb+hcLXTaBFw2eSESouMwqXg8uga6YNBKJxR7iqH2PnxvPo2iVSthqalHjDEbmpm+/fWY8Zkc+SKn\nBgsjnpc8HT9IPhf9Rf2o7ahHpi4d56VMF9zj7RnFW6K8y7jLL0SuLtHLcRIs9TVQpWdxnJKLkE5I\nvvjiC7zxxhuYPHkyli5dCoVCgVtvvRW1tbVQKBRYvnw5du7cie3btyMqKgoajQYPP/yw94aD6EjH\nMTx2+jUgDsDp/VjbkYq0tBKfiyE6Excuurb4CszPmjd4MBWInup/oUmFQjWYM+Ilb0R4E5PLxjJP\nO2hx9yyKZFLJwIfb3Rc6lMrze/3ov7G25Dosyr/I/V9qPcRQex+gA5Axgr/4Mj6PeVL5Ijq1TvA8\nkVySLHge8faMIvXfhvACiSLLPhzry0q5UQJJCumE5LzzzsOhQ4c8XlNeXo7y8vIg9Wj45Cx8JVm4\nKGXEzRER0TBJxXT7Q5v4nD1/RK6Ch0QjIfXs0BEtzD11HsdE4SjkS7YinZyFr7wVLiKi4bNYLDh5\n8oTHa3JzJ/CbHgIwvEKH9jw/uQoeEo2E1LODTq0TvsYxSmGOExInNpsNByvbUL+/BhnJsSjISYIC\nCo/3OK/DzNZlwmaz4qXv3pLet9sLx5rPznpkxruu+ZSDHPVRiCLJyZMn8Mmt65ARGyt5vq6rC3j4\nUeTlyVO0jobHHnerGjphMsT7FHcDabJuIq4tvsJRv2Gybmib+aF4X4sETTy6+3qwtmQVlAqlZNxn\nvB2bgj2mBc8OCYPPDkooPeaISNUpUYJ/lKHQ4YTEycHKNjy0fb/j+LarpqMwZ5zHe5zXYQ6uPR7a\nZ3s4e9oDwH/aj3tc8ykHf/bdJ4pUGbGxMMVz55RwNJK4G0hH248J1uMnlCQ4YqTUuvvD7UfwyOf/\ncBx7yjlhvB0bgj2m3T07eMoR8VanhCjY+KcaJ1UNnR6PvfE3n0TOfJRQ/gwidywWK8xNHeiobZP8\nn7mpAxaLdRjtWXD8+H9w9OhRHD/+H5f/WSyWAL4bkoO/cVduw42Rnq5nvB2bgj2mRzLOJHNWiUKI\n35A4MRmEBdmMBt8KtNn5m08iZz5KKH8GkXs2tO0bj96EZMmz3R0twKW+byPsaTmWfSkWhTd/467c\nhhsjh5Nzwng7NgR7TI9knDFnlcINJyROCnKScNtV01Hf0oX05FhMzRlevRNv+3YH+n6bzYKWA5+h\ntnpw3+/kotlQKFSCdcxGXRbWlqxCTUe9z/VRiOSiUqmQkl2A+HFZkuc7W2uGnVzO5ViRzR53qxo6\nYTTEDzvuym24NaQcOSedgzUgPOWcNJgbHa+75JLYrOg79C0q62sQlZ4FdcE5gIKLGCJRsMe08xjM\nis8QjEF3zkueDluxDTXtdcjSZaAk5QeC8+6eJ4gChRMSJwooUJgzDvNKTCPaJ9vrvt0Bvr/lwGc4\n/djgWuZOAFgLpBSXSq5jdtQ3IYpgFovVYxV509nlX+6uEV9HwWePu6HMG3E23BpSvuScAPCaS9J3\n6Fuc/OMfHce569eztkiECvaYFo9BXYnO6/hVQjWYM+KmtIC75wmiQOGEZBTpqax0PS4u9bivPlFk\ns+FvylzEqFz/AtmrbHNUkXd3jfg6ouHyJb76ck1vVZXLMSck5ItAfMa7e54gChROSEYRTY4JzqWQ\nNCYTAK5jptFLpVIhc8ocySVgzsu/3F0jvo5ouHyJr75cozEaBccxomMidwLxGe/ueYIoUDghGUWS\ni2YDa4He6irEZBuRPG02gOGviSYiIt/4kvvnSwxWF5yD3PXrYamvgSo9C9EF5wSj+zQK+Jt/KsXd\n8wRRoHBCMoooFCqkFJdCvyBBkIMy3DXRRETkG19y/3yKwQoloqdOg76sdEQ5hDR2+Zt/Ktmmm+cJ\nokDhFh5ERERERBQyIZ2Q1NfXY+XKlbj00kuxePFibN26VfK6zZs34+KLL8aSJUtw6NChIPeSiIiI\niIgCJaRLtlQqFe68804UFBTAbDbj8ssvR2lpKfLy8hzXVFRUoLKyEu+++y6+/vpr3H333XjhhRdC\n2Gui0cFisWD37vc9XjN37kVB6g0RERGNVSGdkOj1euj1egBAXFwc8vLy0NjYKJiQ7Nq1C0uXLgUA\nFBcXo6OjA83NzUhNTQ1Jnz2xFyCsaBxKLHMpfkUUJk6ePIEHdj2C2OQ4yfNdLWaYTDlB7hVR+HAu\nKmtPRmdMp3DDZw8aDcImqb26uhqHDx/GtGnCfdcbGxuRnp7uODYYDGhoaAjLCYlUAUImklM40+dn\nICFTuj5HR21bkHtDFF4Y0ykScJzSaBAWExKz2Yx169Zh48aNiIuT/mvtSOj1CUG9t6KxQXDc0NOA\nC/NKgvbz5bo/lD87HO4PBbn77Et7ra3xXq9JTpbnmlBdN5y2xL+zQIyjSBybYnK9h0hoZ7gxPRLe\nUyjaCKVQxNZgtyfns4eUcHzPgWyPQiPkE5KBgQGsW7cOS5YswYIFC1zOp6Wlob6+3nFcX18Pg8Hg\nU9sj3apOrx/ZNncGjcHleCTtjPTny3F/KH92uNwfCnJuq+jr76ClpTNo14TquuG05fw783ccSZG7\nzUgeq3L9LgLdznBieqS8p1D1JVRCEVuD3Z5czx5SwvU9B6o9e5sUfCGfkGzcuBETJ07EtddeK3l+\n/vz52LZtGxYtWoSvvvoKOp0uLJdrAYEpTkRERKHBorIUCfjsQaNBSCckX3zxBd544w1MnjwZS5cu\nhUKhwK233ora2looFAosX74cZWVlqKiowMKFC6HVanHvvfeGssseBaI4ERERhQaLylIk4LMHjQYh\nnZCcd955PtUV2bRpUxB6Q0REREREwcZ94YiIiIiIKGQ4ISEiIiIiopAJeVI7EcnvXy8+j/7eHslz\nNpsNC356ZZB7RERERCSNExKiUajpnTcxS6mSPHeqswP1pRcEuUdERERE0rhki4iIiIiIQobfkBCN\nQr39A+hR2aTPWaywSZ8iIiIiCjpOSIhGobeVLXg3KVryXI+yGw/090Ot5n/+REREFHp8IiEahVJn\nTIB6aoLkuY6aNkRHq2Hj1yREREQUBjghISICYLFYsGPHNgBAQoIGHR2uu5StWFEOAI7r3Fmxohwq\nlfSmAkRERCTECQkREYCTJ0/gry9+ipi4JMnzveY2zJ59PgD4dF1e3qSA9ZWIiGg04YSEiOiszClz\nED8uS/JcZ2vNsK8jIiIi77jtLxERERERhUzIvyHZuHEjPvjgA6SkpOCNN95wOb93716sXr0aRqMR\nALBw4UKsXr062N0kGnUsFivMTR1uz5ubOmCxWKFSef+7hZxtERER0dgS8gnJ5ZdfjmuuuQYbNmxw\ne01JSQmeeOKJIPaKaCywoW3fePQmJEue7e5oAS71dScuOdsiIiKisSTkE5KSkhLU1HDNNVGwqVQq\npGQXeMyF8HWnKDnbIiIiIv+98soryMzMxKxZs0LdFa9CPiHxxf79+7FkyRIYDAZs2LABEydODHWX\niMLamaYziKmzSJ4zN7U7/rnrTKPbNpzPuVuO5fy6r20F67pQ/Exv54iIiIJl2bJloe6CzxS2MKiO\nVlNTg5tuukkyh8RsNkOpVEKr1aKiogJbtmzBzp07Q9BLIiIiIqLA+fzzz/HQQw9BoVBgxowZ2L9/\nP8aPH4+jR48iJycH999/P1pbW7Fx40Z0dXUhLi4O9913H+Lj4/HrX/8aJ06cAADcd999eOuttzBh\nwgQsWLAAGzduRGNjI6KiorB582bExMTg1ltvhc1mg06nw8MPP4zo6OiQve+wzzCNi4uDVqsFAJSV\nlaG/vx9tbW0h7hURERERkbzee+89XH311di+fbtjQ6cFCxZgx44dUKvVeP/99/H3v/8dl112GZ55\n5hlcdtll+Mc//oGdO3dCq9Xi+eefx29/+1scOnTI0eYLL7yA/Px8bN26FbfeeisefPBBfPPNN8jL\ny8MzzzyDK664Au3t7e66FBRhsWTL05c0zc3NSE1NBQAcOHAAAJCUJF2QjIiIiIgoUt14443461//\nipdeegnTpk2DzWbDjBkzAADnnHMOTp06hePHj2P//v3Yvn07LBYLTCYTqqurMW3aNABAQUEBCgoK\n8PjjjwMAjh8/jq+//hq7d+8GAERFRaGsrAzHjx/H9ddfj9TUVBQXF4fmDZ8V8gnJbbfdhj179qCt\nrQ3z5s3D2rVr0d/fD4VCgeXLl2Pnzp3Yvn07oqKioNFo8PDDD4e6y0REREREsnvzzTexfPly5OXl\n4Ze//CWOHz+OgwcP4rzzzsOBAwdwySWXoK6uDnPnzkVpaSkOHjyIU6dOQa1WY8+ePVi6dCm+/vpr\nvPfee1Cr1QCA8ePHo6CgAFdeeSVqa2tRUVGBzz77DFlZWXjyySfx9NNP4+2330Z5eXnI3ndY5JAQ\nEREREY11X3zxhSMnxGAwoLq6GikpKWhsbMTUqVNx1113oaWlBRs3boTZbMbAwAA2b96MCRMmYNOm\nTTh58iQAYMuWLXjttdccOSR33HEHmpqa0N3djTvuuAMTJkzALbfcAoVCAbVajT/84Q8wGAwhe9+c\nkBARERERhaFrrrkGf/rTn5CSkhLqrgRU2Ce1ExERERGNRQqFItRdCAp+Q0JERERERCHDb0iIiIiI\niChkOCEhIiIiIqKQ4YSEiIiIiIhChhMSIiIiIiIKGU5IiIiIiIhGiVdeeQVNTU2h7sawcEJCRERE\nRDRKvPzyy2hoaAh1N4aF2/4SEREREcnEYrXhZO0Z2ACMz0yESul/LZHu7m7ccsstaGhogMViwerV\nq2EymXDfffehq6sL48aNw7333osvv/wSd9xxB9LT06HRaPD888/jiy++wAMPPACLxYKioiL89re/\nhVqtxoMPPogPPvgAKpUKpaWl2LBhA95//3389a9/xcDAAJKSkvDggw8iOTnZ/1+KF5yQEBERERHJ\nwGq14fXdx/HPN74DAPz8J1OxrGwilH5OSt5991189NFHuOeeewAAnZ2duP766/HXv/4V48aNw9tv\nv42PPvoIW7ZswTXXXIM777wTU6dORV9fHy6++GJs3boVJpMJt99+OwoLC3HZZZdhxYoV+Ne//uVo\nLz4+Hh0dHUhISAAAvPjiizhx4gRuv/12v/rui6iA/wQiIiIiojGgua0bT735neP46TcPYk5RBjJS\n4/1qd/Lkybj//vvx0EMPoaysDImJifjPf/6DVatWwWazwWq1Ii0tzXG9/fuGEydOwGg0wmQyAQCW\nLl2K7du3o7y8HBqNBr/+9a8xb948zJs3DwBQV1eHW265BY2NjRgYGEB2drZf/fYVJyRERERERDKI\nilIiJjoK3b0DAICYaBWi1Sq/283NzcUrr7yCiooKPPLII5g1axYmTZqEHTt2eL1XajGUSqXCiy++\niE8//RT/+te/8Nxzz+GZZ57B73//e1x33XWYN28e9u7di8cff9zvvvsiYpLan376afzkJz/B4sWL\ncdttt6Gvry/UXSIiIiIickjWafCrq8+DPkmD1KTBf05J1PrdbmNjIzQaDRYvXozrrrsOBw4cQGtr\nK7766isAwMDAAI4dOwYAiI+PR2dnJwBgwoQJqK2tRVVVFQDg9ddfx4wZM9Dd3Y2Ojg7MnTsXd955\nJ44cOQIAMJvNjm9aXnnlFb/77auI+IakoaEBzz77LN555x1ER0fjlltuwdtvv42lS5eGumtERERE\nRA4zpqajaGIqYAM0MfI8ah89ehQPPPAAlEol1Go1fvvb30KlUmHz5s3o6OiA1WrFypUrMXHiRCxb\ntgx33303tFotnn/+efzhD3/AunXrHEntK1asQFtbG1avXo3e3l4AwJ133gkAuPnmm7Fu3TokJiZi\n9uzZqKmpkaX/3kREUntDQwNWrFiBV199FXFxcVizZg1WrlyJOXPmhLprRERERETkh4j4hsRgMOAX\nv/gF5s2bB61Wi9LSUk5GiIiIiIhGgYjIIWlvb8euXbvw/vvv48MPP0RXVxfeeOMNj/dEwBc/RAA4\nVilycKxSJOF4JYocEfENySeffAKj0YikpCQAwMKFC7F//34sXrzY7T0KhQJNTR0j+nl6fcKI7430\n+yO573LdH2z+jFUp/v4OgtHmWGsvEG1G8liV63cRTu2EU1/kakfOvoRCuMfWcG8vEG2Ge3v2Nin4\nIuIbkszMTHz99dfo7e2FzWbDZ599hry8vFB3i4iIiIiI/BQR35BMmzYNP/rRj7B06VJERUVh6tSp\nuPLKK0PdLSIiIiIi8lNETEgAYM2aNVizZk2ou0FERERERDKKiCVbREREREQkj0cffRSffvrpsO/b\nu3cvbrrpJtn7EzHfkBARERERke9sNhsUCoXL6+vWrQvKz7dYLFCpVF6v44SEiIiIiEgmVqsVJ9uq\noQCQk5QNpdK/BUkPPfQQ0tPTUV5eDgB4/PHHERsbC5vNhnfeeQf9/f1YuHAh1qxZg5qaGlx33XUo\nLi7GwYMH8fe//x2PPvoovv32WygUCvz0pz/FtddeizvvvBMXXXQRLr74Yhw4cABbtmxBd3c3YmJi\n8PTTTyMqKgp33303vv32W6jVatx+++2YNWuWoF9nzpzBxo0bUVVVhdjYWNxzzz2YPHkyHn/8cVRW\nVqKqqgqZmZl46KGHvL5HTkiIiIiIiGRgtVnx9tH3sPXr/wUAlE9bhsX5C6BUjHxSsmjRImzZssUx\nIXnnnXdwww034Msvv8RLL70Em82GX/7yl9i3bx8yMjJw6tQpPPDAA5g2bRq+++47NDQ0OOr3dXZ2\nCtru7+/H+vXr8cgjj6CwsBBmsxkxMTHYunUrlEol3njjDZw4cQLXXXcddu7cKbj3sccew9SpU/Hn\nP/8Zn332GTZs2IBXX30VAHD8+HFs374d0dHRPr1H5pAQEREREcngtLkVzx542XG87cAraDSf9qvN\ngoICtLS0oKmpCYcPH/7/2bvz+CirQw/4v9mSmUlmQpbJwpAJkAgJEagQdoFWsSCKgLulglKtvW69\nYi9vpS5vXdprNz9qb19rtytotd72olKseqUKKlXcKspmUUL2fZvMZDLJzLx/hEzmPLMmszwzye/7\n+fiRJ8855zkz8zxn5uzIysrCiRMn8M4772Djxo3YuHEjTp06hdOnTwMAzGYz5syZAwAoLi5GXV0d\nHnzwQbz11lvIyMgQ0j516hTy8/NRWVkJAMjIyIBKpcKHH36ISy65BAAwffp0mM1mVFdXC3E//PBD\nrF+/HgCwePFidHd3w2azAQDOO++8iCsjAHtIiIiIiIhiQqNSI12VBsdgPwAgXZWGNKUm6nTXrFmD\nV155BW1tbVi7di3q6+tx0003+W2DUV9fD51O5z02Go148cUX8fbbb+O5557DK6+8goceekiI4/F4\nwl4/kjC+9Hr9qMKzh4SIiIiIKAYm6bLw70u+hTxdDnJ12fjukm8hRz8p6nQvvPBC7N27F6+++irW\nrFmDc889F3/5y19gt9sBAM3Nzejo6PCL19nZCZfLhQsuuAD//u//jqNHjwrnp02bhra2Nnz22WcA\nAJvNBpfLhaqqKu8wr1OnTqGxsRHTpk0T4s6fPx8vvfQSAOC9995Ddna2Xw9MpNhDQkREREQUI/Mm\nz8YjF86ARwFo1ekxSbOsrAw2mw2FhYXIy8tDXl4evvzyS1x11VUAhoZa/fSnP/WbQN/c3IwdO3bA\n7XZDoVDgzjvvFM5rNBo88sgjeOCBB+BwOKDT6fCHP/wB3/jGN3Dfffdh3bp10Gg0ePjhh6HRiD09\nt912G3bs2IFLLrkEer0eDz/88Jhfn8Iz2j6YFNLaah1TPJPJMOa4qR4/lfMeq/hyiCbPUtG+B4lI\nc6KlF480U/lejdV7kUzpJFNeYpVOLPMil2QuF5I9vXikmezpDadJicchW0REREREJJuUGLJ16tQp\n3HHHHVAoFPB4PKitrcV3v/tdbN68We6sERERERFRFFKiQjJt2jTvusZutxsrVqzABRdcIHOuiIiI\niIgoWik3ZOvgwYOwWCwoKiqSOytERERERBSllOgh8fXyyy/joosukjsblCgeN5zHPkN/bS20xcXQ\nVJwNRLHbKUWJnwcREcUbv2smnJRaZWtgYADLly/Hyy+/jJycHLmzQwnQ/u57OP7jn3iPy+/ajtzF\ni2TM0cTGz4OIiOKN3zUTT0r1kBw4cACVlZURV0a49G1qXTtQfOvJU8L57pOn4IlPhm8AACAASURB\nVC6dFdfryyEVlkFsbbWO+vMIl16sJHt68Ugzle/VZFraNlbpJFNeYpUOl/0VJXs5kyrlViTpRfpd\nM5GX/W1pacFDDz2ERx99dFTx7rnnHlx33XUoLS0NGua5556DTqfD+vXro81mxFKqQrJ3715cfPHF\ncmeDEkhbXCwcp0uOKbH4eRARUbzxuya8/Pz8gJURl8sFlUoVNN4DDzwQNu2rr746qryNRcpUSPr6\n+nDw4EHcf//9cmeFEkgzcxYsW65FX1099MVmpM0cfWs8xY6m4mxM3bYN/bW1SC8uRlrF2WKAZB/3\nm+z5IyIaj86UvTVN9VAXmv3LXmnZXF4Z+rsmyXlcLtiqTwMAMqaWQBGighCJn//85ygsLMSmTZsA\nAL/85S+h1+uxe/du7NmzB7t378Zrr70Gu90Ot9uNnTt34oc//CEOHTqEoqIiqFQqXH755fj617+O\na6+9Ft///vdRWVmJc845B5s3b8abb74JnU6HX/3qV8jJycEvf/lLZGRk4Prrr0dNTQ3uu+8+dHR0\nQKVS4dFHH0Vubi5uvvlm9PT0YHBwEN/97ndx/vnnR/UaU6ZCotPp8O6778qdDUowx/sHUfPULu+x\nRa2BdvEKGXM0wSmUSJs1B2mz5gQ87Tz2Gap/8Qvv8dRt24KGlUOy54+IaDwKV/YGO5+K5bPH7UbD\nnr2o/sNTAICSLdfCvOESKJRjb/xau3YtfvSjH3krJH/7299w//33Y/fu3d4wx44dw549e2AwGPDq\nq6+isbERL7/8Mtra2rB27Vpcfvnlfun29fVh3rx5uOOOO/DTn/4Uzz//PL7zne8IYb73ve/hpptu\nwvnnnw+n0wmPxwONRoP/+q//QkZGBjo7O3HVVVdNnAoJTSA+LSWurg7hlKOmFtrFMuWLwuqvrfU7\nFr5QwrWSyZ0/IiKKuUBl7/D/tcXF46ps7m9rR7VPQ+rpp3Yhd+li6AoLx5xmRUUFOjo60Nraivb2\ndmRlZaFQkt7SpUthMAzNf/nwww+xZs0aAEBeXh4WLQq8IEBaWhpWrlwJAKisrMQ//vEP4bzNZkNL\nS4u3spGWlgYAGBwcxC9+8Qu8//77UCqVaGlpQXt7O3Jzc8f8GlkhoaTj21JivmyjcE5r4TjSZBZu\n3K/cPRQcl0xElHjSsleTZRC+C0pu+JZwPpXLZqVGDVV6Glx9jqHj9HQoNZqo012zZg1eeeUVb4+H\nlF6vH3WaavVINUClUmFwcNAvTKDFePfs2YPOzk688MILUCqVOO+889Df3z/q6wt5iSo2URz4tpS0\n7D8AyzevgaOlDVpLMbQLl8mYMwon3BwTuVvBws6BoYTo6enByq9fBKU6PWiYeXPPxs9/HH7yJREl\nv+Gy19VUD1WhGc7GRuH8gM0+bsrmtOxszLhzG7749ZOAx4PSm25EehQ9B8MuvPBC3H333ejq6sLT\nTz8dsgIwb948vPDCC9iwYQPa29tx6NAhrFu3zi9cuJ0/MjIyUFRUhNdffx2rVq2C0+mE2+2G1WpF\nTk4OlEol3n33XTQ0NET9+lghoaTj25Iy0NYOZX4RJn11tYw5ooiFmWMiew9FmPxRYjidTkyesx66\nvLOChsnLqEtgjogors6UvaaVy9DaaoVCcjqtqGhclc05C+Yja/aj8ABQa7UxSbOsrAw2mw2FhYXI\ny8tDfX190LCrV6/Gu+++i4suughFRUWorKz0DudSKEbefd9/B/Pwww/j3nvvxWOPPQaNRoNHH30U\n69atw7/927/hkksuwdlnnx1yCeFIsUJCSSfiVuxAKyZRUpO2kiW8FYyrbBERJZ50/mCKr6IVCVWM\nKiK+9uzZ4/232Wz2Hm/cuBEbN44McVcoFNi+fTv0ej26urpw5ZVXYsaMGQCAnTt3esN99NFH3n+v\nXr0aq1cPNf7eeuut3r+XlJTgqaee8svLc889F6NXNYQVEko+EbZiB5qPgHwO6UpqklayRJN7DgsR\n0UQ0nlbRShU33XQTrFYrBgcHcfPNN0c14TwRWCGhxIphC3WwVTsoiSRZj4Tcc1iIiMalMGU9y97E\n27VrV/hASYQVEkqoWLZQyz4fgcJKth4J3jNERLEXrqxn2UvhsEJCCRXLVhKumJT8kq1VjPcMEVHs\nhSvrZZ8/SEkvZSokVqsVP/jBD/Cvf/0LSqUSP/rRjzB37ly5s0WR8OnKTcsyQpWhh8tmhypDD02W\nAdZX9waflB6qG5grJiUfyeelnVoinE4vscB59HD8NkYMN0SM9wwRUcz59YCcKet9y+JRzR8MV5bL\nvMkuxV7KVEgeeughrFy5Eo899hgGBwfhcDjkzhJFSNqVa7lhKwa6rdBkGVDz2997/x5oUnqyDfmh\n0Pw+rzvuEHok4HKj+pFHRs7H+PPk/UJElHjS3udoy/pwZTnL+vEnJaqTvb29+OCDD3DZZZcBGNpZ\nMjMzU+ZcpSiPG86jh2F9dS8Gjh4GPO64X9LZ2Ii85ecie0EV8laci0FbHwyrL8JAt9hKEmhSOieu\npxa/z6uuDmmz5sCw+iKkzZqD/rq6kOH97k+3a1T3K+8XIiJ5KYDwZX0Y4cpylvXjT0r0kNTV1SE7\nOxt33XUXjh8/jrPPPhs/+MEPoI3DGs/jnRytCpoMHRreett7bLlhK4DIJrlxIlxqCfd5hTsfqDdN\n2osW6n7l/UJElHjSsrvkhm8J50dbFocry9OyjMKxJsswqvQp+aREhWRwcBBHjx7Fvffei9mzZ+Oh\nhx7Ck08+idtvvz1kPJNp7DdoNHGTOX5Nk7izp6upHqaV4jCpUNf2uFzoeP8D2E6fRkbJVOQsrIJC\nKXa05eXohTBum01Mw2aDyWSAZ/kSpKdvPxOuBDkLF/hdP1AY6fUife3JLNZ5jsd7EEma4T4v99KF\nQO8NsJ+ugb7EgsJli6BUjxRDNa1NyFt+LlwOB1Q6LZxNzUL6ge5X3/yN5X6J5vUmQ5qJFovX0NbW\nH3aHYK1WHdG1YvWexiKdZMpLrNJJ9Xs22cvWZE8v0jSlvy3cTgfK7wpcFptMhrC/JYTviqkBviuc\njpHvCq0WHmd/yt+rE11KVEgKCwtRWFiI2bNnAxjaTfK3v/1t2Hhj3XjNZDJEtWlbMsdXF5qFY1Wh\nWQgb7trOo4dD9rCYTAY0vP1uyJYSVZHPNUtnQVc6C24Abe22wNeXhAklFu+dHGK5SWC070HUaYb4\nvJxHD6P6yZFn15NpFO4fZboWbb69aVuuFeJL79eA+RvF/RKM7O9hhOnJIRavQaEAPB5PyDAOx2DY\na8XqPY1FOsmUl1ilE8u8yCWZy9ZkT280afr9tsgrgDtAWTycXrjfEn7fFRnid4U6rwBtO58Zib9g\nYcxeOys28kiJCkleXh6Kiopw6tQpTJs2De+++y5KS0vlzlZKCrrsaagVK3xWu/A47EJ6gZZxlY7l\nHHT0w3LDVjhqaqGzWJBWXhm310cxFuOVTAZaWmDeuAHOjg6k5eVioLUVaT7nnZJ5RQN2B5fpJSKS\nW5hVrzTllT7f88Vhv+elvxOcjY3ev2uLi7mM8ASUEhUSALj77rvxve99D4ODgyguLsaPf/xjubOU\nmoIsexpqbonvubwV5wrxIpn3odKmifMAjFlcDSNFxHrOkUqlQM3uF7zH0h6QQOOGuUwvEZG8wq56\ndfyI5Ht+0qjm+6kzdKObg3Lmt0zEywhT0kuZCkl5eTn+8pe/yJ2NcStUa4TQqp2fh+ItmzFo70P6\nlCmASum3j4i0FybZNsejyMX6s3M0Nvkd+y5N4dfKNqMCjncPeHvX0hcuBZSqMV+fiIhGL9x3wWi/\nK8Sy3oIBmzj6YrDfGbrHhfuQjDspUyGhUQrWvep2wXHonaGHvMQCj1IJx6lq6CYXQpOXi4G2dgBi\na4RKrRRbta/bDMPqi4bGeP70Z96/e/cRkfTCSKeucuWj1BHJZlfCl4Dv/WWxIH3BEjhPHPWG11ks\nYvpTS4T0PCql0MpmGRhAzVO7Ro4BKI1Zwa9PRESjF2ZI1qhXUJwyJeTlnJ8fhf3E53A5HHA7+pBR\nWSksaKLSa1Hz6994w0/NzoG7s8P73aIwGOO6pxUlHisk41Sw7lXHoXeEH3x5y8/1TiK2bLkWngGn\n33jMfkmrdv+ZVu1I1wEPOm+Fkp50nG64za6k95dlwClUKKb+P9thuWEr+mvrkF48BcoMg3CfFn/j\nauH6fXXiyi39tbVofvV3Qa9PRESjF25IVrjvcY9KKax6BXXonmxXQ72wgIneUiwcF0sqNO7GetQ8\n/az3uODCNcJ5jrxIfWxaHKeCVRYcNSN/V2XoocnJ9m5Y2N/aCsC/R0OTPUk8zjLC+upev3XAg/Z8\nnOkxGd4cjy3aKeTMZ2e56sqINjb0vb8A/wrFQH3DSNJQ+MUfsIpjgfXF4sotmklZIa9PRESjF7aB\nMcz3+EBjE9Lz8qDW65Geb8KAZMl2KWlZ72hqCX2+WTyvlmyOzZEXqY89JONUsO5V3yEz2fPmofHF\nPd7j4muuQu0f/wRAbB3RFFuElg9nVxdaXnsdqgw9LDdsxUC3lT0fE0S4bnvpkCzdFMlSkCqF2IOy\n+ZvC+bSCArEVbuYsWNQaOGpqobUUQ5WdE/L6REQ0etFuKhtuwRKp9MJC8fpTJovHZTMwddtM73eB\np6dbvF5ONlfZGmdYIRmnhO7VEgvgcnsnn1tuuhGOU9VQSLpUbaeqvf/27f7UzJiFTJcb/bW1UGtU\naHjxJW84j31oIlroLc5ovAjXbZ++cCks8HgrENoFSzE11+QNb//sUyF8f2fXyIIJuTlw9Q9AK1lV\nS7t4BbSLzxy4XVxCmogoxqIdWh1uwRKpwb5+n7I/F26FWrx+eSWcx48AGPp9kbZgifjdMn8xoFRx\nla1xJKEVku7ubuzduxednZ3Cpli33nprIrMxMfhMLHcePew37n/SlZswcPQw8PIr3r8rNRrvv4XW\nEZ+0Bo4ehuvMahjZ8+ah9o/PCekiP/Au2jROBFk22kupEisQgBBe2sqVnp2FGp/NrSw3bA15ef+l\nJbmENBFR1MKV7WH4LVhiCd3Dotam4fTTPj0qN2wVrh9o40TpdwuNLwmtkNxyyy3IycnBWWedBYWC\nbeqJEmw5Ps3MWbBsuRZ9dfXQFU+BMs8EXbE5ZPenbytKoE0SaYIJszKL1HAPyvCk9oGuHuH8QLcV\nyhCreHEJaSKi5BOodzzUiox+m+B2W4UeFZb1E0/Ce0iefvrpRF6SEHxsqOP9g+KSqjdsheWqK0N3\nf0p6S4C9funSxDHqjRPP9KAUrzOgtdUK1dHDwmlNliFketGOcyYiojiQ9I4H6uEYTVkuXTRHk2WI\ncYYp2SS0QjJjxgx89tlnOPtsTj5KpGBjQ6Urbrm6u1Hzp+f9NxkK0grO5Xxp1K1YZ/YpOVlbB21x\nMdIXLBHuIWdjY8j0eM8RESU/6XfDcNnu3ciwvDJkWT7Y7/SbX0jjW0IqJOeddx4UCgUcDgdefvll\nFBQUQKVSwePxQKFQYN++fYnIxsQVZGyodMWt+v/5i/fYtzUjaCt4lGNOKfWNtsfCb58SeKBdvCLy\nTTR5zxERJT3pd4M6Qxfwd0SwslydrsHpXeIcExrfElIh2bVrV/hAYZx33nnIzMyEUqmEWq3Gn//8\n5xjkLAW5BuE4uB99dfXQF5uRvngFnJ8f8/ZeVE/RYX9LIwq0BZhpPAsK6VYzkt4O74pbKnHFLd+W\n6aCt4IF6TmhCUc+sQPHmb8JRXw/tFDM0Z82E490DIzu1L1wKKEfurf7GJmE33v7GJnHOyMxZPqto\nFXMVLRp3XC4Xqqu/DHq+szMTRmM+VKrQG8sRxdWZ7/fhHg11RSVOWE+i3toIs6HI//eF9PfAjArv\nHFV9sRlOyXzBcL3pfnNMunpCzi+k1JeQConZPLQXwW233YbHH39cOLdlyxY89dRTYdNQKBTYtWsX\nsrKywoYdzxwH94vzPlxu1OwaWaWo45vn43n30NKqt1V9C+XGmUL8QL0dk67chP533xLC+Y7XDNYK\nHigtrrI1sdjeewsNO0fmhVncHuF+HO4BGZael4uavS+PnL92k3APWW7YKllFaxJ7Q2hcqa7+Egfv\nuB1Fen3A8wftdix95DGUlp6V4JwRjZB+v+fediMeb3/Reyz9fSENb9lyrfhbRbLnVLg5IX5zSIL0\nsND4kZAKyS233ILjx4+jubkZ559/vvfvLpcLhZLNcYLxeDxwu93xymJy82l5GOxsF071NTQIxyVt\nHvybZzpspky09Lb4V0gaG4UWamdj49AEdZtd2Pxw0NbnjaOuqETubTfCUVMDrcUCTcVQq3XYnV0p\nuYxyRSwA/nM+pD0eteLO7dL70VFTKyzTOGDvE873t7b5hRfOc2UVGoeK9HpYMjlJl5KX3/d7TS2Q\nMXJcb2048/+hHhOzJHxffYPYG97VPao5IdLfJP1t4m8ffjeMPwmpkDz88MPo6urCQw89hLvvvnvk\n4mo1cnNzI0pDoVBg69atUCqVuOqqq3DllVfGK7vJwefHY1qWEXXPPguXzQ7zZRuFYDqzuBN2us2J\ntLfehS5Dj4KrJsP66V5xInqGDg1vve0NPzwuM72oCA3PPuv9+9Rt27z/PmE9OdQykgGg/WPcZs1D\nuXFmgJ6TKbF69RQHo14RC8HnfAyTrjUvvR+1JeLa9ANF2cKxenKBGN/CVbSIiOQm7aEw5uYDjpFj\ngzYTj3/wO+/xg0UbhPC6yUWo8e09v3YT7NWn4XI44PG4oTeZQl5f+pukRDKHhN8N409CKiSZmZnI\nzMzE9ddfjwafFlSFQoGWlhaUlJTAaDSGSAF49tlnkZ+fj46ODlx//fWYPn06qqqqQsYxmcbeAhVN\n3FjEV355XPjxmLf8XLS99TZa9h+AZdPV6GtugX7KFBReuBp682TYTp+GQqFE/QtDXarZ8+ah4fc7\nvfHL79qO3MWL8HmzuIpRX3Mjik0GeJYvQXr6dthOn0ZGSQlyFi6AQjnUer6/pVmI0+xoxvLSKnww\n3YCOb56PzFYbek0ZSJ9uhDkGr13u+HKIdZ4DpVfTVC8cu5rqYVoZeojd5zU1wnFfTQ2K142krSgo\nFFqxlEaDcKw3mZDrk5dGDArnuzNVwj2km2tB+V2B78OxvOZoxOM+SsV7UyoWr6GtrT/sXlRarTqi\na8XqPY1FOpGk0dmZiVNhwuTkZCYsP4lIQ07JXi4ka3pHrG1CWW239eB7K25CTXc9LFlmNPSIO7Or\n+vqF8AM9kjkjrW1o820MLS4OmVfpb5LsqvnQmfICfjek+j1KQxK67O+vfvUrfPbZZ1iyZAk8Hg8O\nHToEs9mM3t5efPe738XFF18cNG5+fj4AICcnBxdccAE+/fTTsBWSkPtphGAyGcYcN1bxu0+KX1ku\nx1DTxEBbO5QFk5H9tTUAgA5rP06YVKjX6nBOZzqy582Dy+GAJidnaCnfM7uqd312BN0nTyHdKM7B\nUWZkjOS1dBYsixehtdWKtnabN0yBVtKKrdLhuX/ugUatwh7VSdhz+wA3cGl7MaqK58j+3kUbXw7R\n5Fkq2HugLhR7L1SF5rDXVZnFIZXKyQVCHOu/vhS+ZFS+DQsKoOfUabinl3v/pGnuFs4rG9uwS/Mp\nkAvADVzZNRU2Uz7qtTqYDSrMbLf6L8wQQLSfe7zTi0eaqXyvKhRDw3BDcTgGw14rVu9pLNKJNI2O\njt6IwiQqP/FOYzgduSRzuZBM6Xngxomef3mHYOmy9ej/08icEc1NV2Faeimm5ZcCAPrTB4X47saW\nkQMFoErXCuc1kh4XZ09P+LyWzoKudBbcANo7+4Tj4d8o8SqrKfESWiHxeDx46aWXMHnyZABAc3Mz\nduzYgV27duHaa68NWiHp6+uD2+1GRkYG7HY73n77bdx6662JzHrCSYdDGebOgXbqNL/1uk/0/Mvb\nbZql+gqMPj8Oh3tVAGCwqxttb+2FKkMP88YNsNfVQaXVwlWYFzYvM41n4baqb6He2giDNgP/c/Sv\nsA8MzQVYZqnCOzUfAADMhqLoXjTF1Vj28GidPQ3Gb14GZVMb3IV5aJszHb5VWul9mpabg1qfSeuT\nt24WzhsMk3D6rf/1Hpu3bhaGAejStMIwgEALMxARUWz5/pYAgC1zLofbp/c6bXoBpvmE9/1dYDYU\nQftxDWr++jfvecv1W4TvG4VKbFjSls2I90uiFJPQCklLS4u3MgIABQUFaGlpQWZmZsjWsra2Ntx6\n661QKBRwuVxYt24dzj333ERkWTaBfjxqA0xArreODMHStohdpDBmQHvJKmRnZKPlpaEd1V02O/qd\nfegrmoS04inIrwzdyyTlGHQIx8Y0Ay6duRZmQyGUCiX+fGRv8CWHSV5j2MNjunEaTswbRLMj68zn\nOk04L71P2788JpzvaW9BXc+JkYmPkqUcXVY7tlRdgfqeRkzJmgx7v104L504yfuKiGj0pD0g0rJ0\nuKwdZh9wIK2yDMd7mzDZUIg5ubNx3Kcsn2k8C+XGmd4Go47m98X49fXIWfa1ke8bjxtTt22Dq6ke\nqkIzN7UlPwmtkMybNw933nkn1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f+kx2Mh3fuE9zERUWwFmqd6nnlFyB4Pls2UbFghiaFA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\nU1M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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sns.pairplot(iris, hue='species', size=2.5);" - ] + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] }, { "cell_type": "markdown", @@ -512,16 +389,14 @@ "source": [ "### Faceted histograms\n", "\n", - "Sometimes the best way to view data is via histograms of subsets. Seaborn's ``FacetGrid`` makes this extremely simple.\n", + "Emptied above cells Sometimes the best way to view data is via histograms of subsets. Seaborn's ``FacetGrid`` makes this extremely simple.\n", "We'll take a look at some data that shows the amount that restaurant staff receive in tips based on various indicator data:" ] }, { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -617,9 +492,7 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -651,9 +524,7 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -684,9 +555,7 @@ { "cell_type": "code", "execution_count": 17, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -714,9 +583,7 @@ { "cell_type": "code", "execution_count": 18, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -745,9 +612,7 @@ { "cell_type": "code", "execution_count": 19, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -837,9 +702,7 @@ { "cell_type": "code", "execution_count": 20, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -869,9 +732,7 @@ { "cell_type": "code", "execution_count": 21, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -915,9 +776,7 @@ { "cell_type": "code", "execution_count": 22, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# !curl -O https://raw.githubusercontent.com/jakevdp/marathon-data/master/marathon-data.csv" @@ -926,9 +785,7 @@ { "cell_type": "code", "execution_count": 23, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1013,9 +870,7 @@ { "cell_type": "code", "execution_count": 24, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1046,9 +901,7 @@ { "cell_type": "code", "execution_count": 25, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1131,9 +984,7 @@ { "cell_type": "code", "execution_count": 26, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1164,9 +1015,7 @@ { "cell_type": "code", "execution_count": 27, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1264,9 +1113,7 @@ { "cell_type": "code", "execution_count": 28, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1299,9 +1146,7 @@ { "cell_type": "code", "execution_count": 29, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1405,9 +1250,7 @@ { "cell_type": "code", "execution_count": 30, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1428,9 +1271,7 @@ { "cell_type": "code", "execution_count": 31, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1459,9 +1300,7 @@ { "cell_type": "code", "execution_count": 32, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1494,9 +1333,7 @@ { "cell_type": "code", "execution_count": 33, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1528,9 +1365,7 @@ { "cell_type": "code", "execution_count": 34, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1560,9 +1395,7 @@ { "cell_type": "code", "execution_count": 35, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1664,9 +1497,7 @@ { "cell_type": "code", "execution_count": 36, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1693,17 +1524,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Looking at this, we can see where the distributions of men and women differ: the split distributions of men in their 20s to 50s show a pronounced over-density toward lower splits when compared to women of the same age (or of any age, for that matter).\n", + "Removed 2 lines and adde crap 1\n", "\n", - "Also surprisingly, the 80-year-old women seem to outperform *everyone* in terms of their split time. This is probably due to the fact that we're estimating the distribution from small numbers, as there are only a handful of runners in that range:" + "and here is crap 2" ] }, { "cell_type": "code", "execution_count": 38, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1730,9 +1559,7 @@ { "cell_type": "code", "execution_count": 37, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1784,9 +1611,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.6.4" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/notebooks/10_introduction_to_artificial_neural_networks.ipynb b/notebooks/10_introduction_to_artificial_neural_networks.ipynb new file mode 100644 index 000000000..d017def96 --- /dev/null +++ b/notebooks/10_introduction_to_artificial_neural_networks.ipynb @@ -0,0 +1,2236 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Chapter 10 – Introduction to Artificial Neural Networks**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "_This notebook contains all the sample code and solutions to the exercises in chapter 10._" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Setup" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# To support both python 2 and python 3\n", + "from __future__ import division, print_function, unicode_literals\n", + "\n", + "# Common imports\n", + "import numpy as np\n", + "import os\n", + "\n", + "# to make this notebook's output stable across runs\n", + "def reset_graph(seed=42):\n", + " tf.reset_default_graph()\n", + " tf.set_random_seed(seed)\n", + " np.random.seed(seed)\n", + "\n", + "# To plot pretty figures\n", + "%matplotlib inline\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "plt.rcParams['axes.labelsize'] = 14\n", + "plt.rcParams['xtick.labelsize'] = 12\n", + "plt.rcParams['ytick.labelsize'] = 12\n", + "\n", + "# Where to save the figures\n", + "PROJECT_ROOT_DIR = \".\"\n", + "CHAPTER_ID = \"ann\"\n", + "\n", + "def save_fig(fig_id, tight_layout=True):\n", + " path = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id + \".png\")\n", + " print(\"Saving figure\", fig_id)\n", + " if tight_layout:\n", + " plt.tight_layout()\n", + " plt.savefig(path, format='png', dpi=300)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Perceptrons" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from sklearn.datasets import load_iris\n", + "from sklearn.linear_model import Perceptron\n", + "\n", + "iris = load_iris()\n", + "X = iris.data[:, (2, 3)] # petal length, petal width\n", + "y = (iris.target == 0).astype(np.int)\n", + "\n", + "per_clf = Perceptron(max_iter=100, random_state=42)\n", + "per_clf.fit(X, y)\n", + "\n", + "y_pred = per_clf.predict([[2, 0.5]])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1])" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_pred" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure perceptron_iris_plot\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "a = -per_clf.coef_[0][0] / per_clf.coef_[0][1]\n", + "b = -per_clf.intercept_ / per_clf.coef_[0][1]\n", + "\n", + "axes = [0, 5, 0, 2]\n", + "\n", + "x0, x1 = np.meshgrid(\n", + " np.linspace(axes[0], axes[1], 500).reshape(-1, 1),\n", + " np.linspace(axes[2], axes[3], 200).reshape(-1, 1),\n", + " )\n", + "X_new = np.c_[x0.ravel(), x1.ravel()]\n", + "y_predict = per_clf.predict(X_new)\n", + "zz = y_predict.reshape(x0.shape)\n", + "\n", + "plt.figure(figsize=(10, 4))\n", + "plt.plot(X[y==0, 0], X[y==0, 1], \"bs\", label=\"Not Iris-Setosa\")\n", + "plt.plot(X[y==1, 0], X[y==1, 1], \"yo\", label=\"Iris-Setosa\")\n", + "\n", + "plt.plot([axes[0], axes[1]], [a * axes[0] + b, a * axes[1] + b], \"k-\", linewidth=3)\n", + "from matplotlib.colors import ListedColormap\n", + "custom_cmap = ListedColormap(['#9898ff', '#fafab0'])\n", + "\n", + "plt.contourf(x0, x1, zz, cmap=custom_cmap)\n", + "plt.xlabel(\"Petal length\", fontsize=14)\n", + "plt.ylabel(\"Petal width\", fontsize=14)\n", + "plt.legend(loc=\"lower right\", fontsize=14)\n", + "plt.axis(axes)\n", + "\n", + "save_fig(\"perceptron_iris_plot\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Activation functions" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def logit(z):\n", + " return 1 / (1 + np.exp(-z))\n", + "\n", + "def relu(z):\n", + " return np.maximum(0, z)\n", + "\n", + "def derivative(f, z, eps=0.000001):\n", + " return (f(z + eps) - f(z - eps))/(2 * eps)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure activation_functions_plot\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "z = np.linspace(-5, 5, 200)\n", + "\n", + "plt.figure(figsize=(11,4))\n", + "\n", + "plt.subplot(121)\n", + "plt.plot(z, np.sign(z), \"r-\", linewidth=2, label=\"Step\")\n", + "plt.plot(z, logit(z), \"g--\", linewidth=2, label=\"Logit\")\n", + "plt.plot(z, np.tanh(z), \"b-\", linewidth=2, label=\"Tanh\")\n", + "plt.plot(z, relu(z), \"m-.\", linewidth=2, label=\"ReLU\")\n", + "plt.grid(True)\n", + "plt.legend(loc=\"center right\", fontsize=14)\n", + "plt.title(\"Activation functions\", fontsize=14)\n", + "plt.axis([-5, 5, -1.2, 1.2])\n", + "\n", + "plt.subplot(122)\n", + "plt.plot(z, derivative(np.sign, z), \"r-\", linewidth=2, label=\"Step\")\n", + "plt.plot(0, 0, \"ro\", markersize=5)\n", + "plt.plot(0, 0, \"rx\", markersize=10)\n", + "plt.plot(z, derivative(logit, z), \"g--\", linewidth=2, label=\"Logit\")\n", + "plt.plot(z, derivative(np.tanh, z), \"b-\", linewidth=2, label=\"Tanh\")\n", + "plt.plot(z, derivative(relu, z), \"m-.\", linewidth=2, label=\"ReLU\")\n", + "plt.grid(True)\n", + "#plt.legend(loc=\"center right\", fontsize=14)\n", + "plt.title(\"Derivatives\", fontsize=14)\n", + "plt.axis([-5, 5, -0.2, 1.2])\n", + "\n", + "save_fig(\"activation_functions_plot\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def heaviside(z):\n", + " return (z >= 0).astype(z.dtype)\n", + "\n", + "def sigmoid(z):\n", + " return 1/(1+np.exp(-z))\n", + "\n", + "def mlp_xor(x1, x2, activation=heaviside):\n", + " return activation(-activation(x1 + x2 - 1.5) + activation(x1 + x2 - 0.5) - 0.5)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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4e/Yd3Lzi89y/ajE7n3dA3uWIFILGsGTKFtQgCmt5WnLgk5mHtaoENaD0QS3kITh6zGwq0ekXJpvZ1GZt6Wb2CqKjn58en+dNSuDmFZ9n819vV1CTytIYlg0FtWQU1JIrc1ALOZP2UaJzrl0AvCn+/0fNbJGZDZjZoni5C4lOhfHP8fUDZvaDgHVIStYsv5bNf72dx08/OO9SRNKgMSwjCmrJ9PVmu0+jglrr0gpqwUKau1/k7tZwucjdH3b3me7+cLzcy929J76udnlNqDokXWuWX8vJb/uFgppUjsawbJ0x6+7ShbUiBDXNqCWXRVALHdZ0gnVpW62hQEFNRDqloNa+PIJaFmFt22LLpKEgbSGDmkKaJKLOTxEJpYxBLe+wps7P5MrU+amQJomp81NEQilbUIP8Z9XU+dmZMgQ1hTTpmDo/RSSE2ZN35F1C2/IOaqD91DqRxdefnVBIkyDU+SkiIWhGLRkFteSKHNQU0iQYdX6KSAgKaskoqCVX1KCmkCZBqfNTRELQITqSyTqojfaOqvMzRQppEpw6P0UklDIGtbzDmjo/k8ui87MdCmmSilrn5+CCXjUUiEhHyhbUIP9ZNXV+dqYoQU0hTVK1dL8n1PkpIh1TUEtGQS25IgQ1hTRJnTo/RSQEBbVkFNSSyzuoKaRJJtT5KSIhKKglo6CWXJ5BTSFNMqPOTxEJQZ2fyeicn8nlFdQU0iRT6vwUkVDKGNTyDmvq/Ewuj87Pnky3ViGjwyP09vwDo8MjTOqZnHc5pXL27Ds4e8UdnMr7WfTPw0y797G8S6q8n5x1NUPTJz7lzg/7gXdMvL7eHdN5xdfP7bwwyc3I8AhTJl/GyPAIk0s8hp0x626+tfXovMtoy8kL1nLrxmW5bb8W1NY/Oi+zbQ4uGqLv4d7E9183+nFG2Dbhcu9ZDxw08fp6RvZh+eMXJaply9I+9l03mOi+7dJMWkI7+7cyyR5kZ//WvEspLZ3zMzutBLQ81yfZG+gfwOxBBvoH8i6lY2WbUYPu/fozqVYCWjuGJ3e2vqxm1BTSEhgdHmFoYDtmztDAdkaHR/IuqbTU+SmSvZHhEXZs24GZs2PbDkYqMIYpqCVTpqBWNFkENYW0BHb2bwWPf3A0m9YhdX6KZGugf2CPMawKs2mgoJaUglpyaQc1hbQ21WbR6mk2rXPq/BTJRm0WrV5VZtNAnZ9JVbXzMwtpBjWFtDbtMYtWo9m0INT5KZK+PWbRaio0m1ZTxqCWd1ircudn2tLq/FRIa0OzWbQazaaFUTvn5/2rFquhQCSwZrNoNVWaTaspW1CD/GfVqnzOzyyEDmpBQ5qZnWdmvzKzQTO7boJlP2BmG81sq5ldY2b5nyRrAk1n0Wo0mxaUOj8la1Ufv2CMWbSaCs6mgYJaUgpqyYUMaqFn0h4HPglcM95CZvYq4ALglcBi4DnAJwLXEtR4s2g1mk0LS52fkrHKjl8w/ixaTRVn00BBLSkFteRCBbWgIc3db3L3W4CnJlj0zcDV7n6fu/cDfwO8JWQtoY07i1aj2bTg1PkpWany+AUTzKLVVHQ2DRTUklJQSy5EUMvrjANHAt+u+/nXwHwz29/d9xggzWwlsBJg3ry5PL72Y9lV+YwtTJ1yMdbCGScGtw6x5al3AbNSr6qZ3bvm8/jaVblsu5kQ9bwSOOrVM9h67EuY0t/ZUZ5nL5jBaauO62gdIWVVzw/7w68zq+fxh+/LZDPtSDx+rV97YXZV7mELvT1/09IYtn3Lbvqfeg95jGGDuxawfu0Fqa3/2Pjf/pHpLS3fN7QfSx9ZkVo9rVgKbN09FYA5o9M5c8dR2RcxBwaHmseF+ZP7+NDMwM1ez43+mTT0p3mk8x4IuwmAt77ywHg7E316Se6uqzu7f14hbSawpe7n2v/3oeFTrLtfBVwFcOhhi3zhstWZFFhv+5P9DG3b3dKyZrvZd/9PM2Pe7JSrau7xtavI4zkaS6h6FgJr+l/MTf/7hRy+ekPi9Zy26jhuWn1nx/WEklk9LZzqqV1Feh4zlmj8OuywRb5k2SWZFNhoy5Nb2LG19TFs9v6fZt95+6Zc1d7Wr72ALJ6jJdDSqaSWPrKCdQfdkno9LXvwDVw//Z58tj29+WmkPjRzMZcOJB+TJ9LJqaQmctkf/nRKwn02pBfUOpFXd+cAe35Mq/0/7HkfAmhlX7RG2jctHer8lIIozfgFre2L1qiq+6bVK+PXn7Om7Mp1+1Xu/Ez75OxJ5RXS7gOW1/28HNjU+FVBEbS0L1oj7ZuWKnV+Ss5KM35Bi/uiNarwvmn1yhjUtJ9aeooY1EIfgqPHzKYCk4HJZjbVzJp9pfpV4Fwze66Z7Qd8FLguZC0hJJlFq9FsWrrU+SmhVW38gmSzaDXdMJsGCmpJ5XHg2ywULaiFnkn7KLCTqD39TfH/P2pmi8xswMwWAbj7D4HVwE+Bh4ENwMcD19KxRLNoNZpNS506PyWwSo1fkHAWraZLZtNAQS0pBbX0BW0ccPeLgIvGuHlmw7KfAz4XcvshdTKLVjM0sJ1ps2cxqWdyoKqk0dmz74C3wa0cz8IbH8q7HCmxKo1f0NksWs2ObTuYOXsmk7tgDKsFtVYaCori5AVruXXjslxr6OsdznX7aakFtbwbCnRaqDF0NItWo9m0TOicnyJ762gWraaLZtNqyjar1q3n/MxK3rNqCmlNhJhFq9G+adlQ56fIn4SYRavpln3T6pUtqEH+X3/m0fmZlTyDmkJaE0Fm0Wo0m5YpdX6KBJpFq+nC2TRQUEtKQS0shbQmRgbDtvuGXp+MT52fe+vd0doR1vNan4Q1tCvsmBN6fWUxe3KY2cgsVTWoTZo0c+KF2jCZfdq+Tx5BLa8zDhTarAPnt7Rc0Y7uL3+yZvm1rFn0YjUUxF7x9XNbWq5oZ2SQZOYdtPeR4ZvJ6gj/ZXbGrLtL1UwAxWgoWHLgk03PUJDUQYs+OuEyfQ/38t5DDtjjTAKhbVtsmTYTaCZNKqvWUKAZNRHpxBmz7i7d159VnVEbz+CiIUZ7R1PfzrbFltmsmkKaVJo6P0UklDIGtbzDWh77qFXpDAUKaVJ59Z2fPm1K3uWISImVLahB/rNqOudncgpp0jVuXvF5hp81qs5PEemIgloyCmrtU0iTrnLwtKfU+SkiHVNQS0ZBrT0KadJ1dM5PEQlBQS0ZBbXWKaRJV1Lnp4iEoM7PZPIIalmEtdCdnwpp0rXU+SkioZQxqOUd1tT5OTGFNOlqOueniIRStqAG+c+qqfNzfAppIuicnyIShoJaMlUNap1SSBOJ6ZyfkqZhDbddQ0EtGQW1vWnUEKmjzk9JU9nOASnJKaglo6C2J4U0kQbq/JQ0Kah1D3V+JlPVzs8kFNJEmlDnp6RJQa27lDGo5R3Wqtz52Q6FNJExqPNT0qSg1l3KFtQg/1m1Knd+tkohTWQC6vyUtCiodRcFtWS6OagFDWlmNsfMbjaz7Wa2wczOHGO5PjP7kpltMrPNZvZdM9M7oBSWOj+rL6/x61tbj1ZY6yIKasl0a1ALPZN2BTAEzAfOAq40syObLPc+4M+A5wMLgX7g8sC1iASlzs/Ky3X8UlDrHgpqyXRjUAsW0sxsBnA6cKG7D7j77cB3gLObLH4w8CN33+Tuu4BvAM0GQ5FCUednNRVl/FJQ6x7q/Eym2zo/zd3DrMjsBcDP3X163XXnAy9z91Malj0W+AfgvwBPA18BnnD39zdZ70pgJcC8eXOP+aevfSxIvSHs3jWfKVM35V3GHopWU1XreWpkBk9vm0Hfxs7+eGcvmEH/xu0d1xNK0eoBWPm+c+5y92PT3EYW49fceXOPufyrn26pntmTdyR8JO0Z3LWAvqkbM9lWK7q1nv6R6RMvBPQN7cdg79MpV9OarbunAjBndDqbJ2Xzeq03ONTT9Pr5k/vYNDKYyjYnDbU/r3XemX/V0fjV/FEmMxPY2nDdFmCfJsv+HngEeAwYAX4LnNdspe5+FXAVwKGHLfKFy1aHqrdjj69dRZHqgeLVVNV6Fsb/nnrL+1n0z8NMu/exROs5bdVx3LT6zo7rCaVo9WQo9fHrOYct8XUH3dJyQVnMsqxfewFLll2S+nZa1a31LKG1WdSlj6ygnddQ2m7duIwzdxzF9dPvyX7jca5d/+i8Pa7+0MzFXDqwIbXN9j3cm9q6mwm5T9oAMKvhulnAtibLXgH0AfsDM4CbgB8ErEUkE+r8rIzCjV/66rO7lO2rT+jerz+zFDKkPQD0mNmhddctB+5rsuxRwHXuvtndB4l2un2hmc0NWI9IJtT5WQmFHL/U+dldyhjUZk3ZlXcJlQ5qwUKau28n+kR5sZnNMLMTgNcDa5osfidwjpnta2ZTgPcAj7v7H0PVI5IldX6WW9HHLwW17lHGoKYZtfSEPgTHe4BpwBPADcC73f0+MzvRzAbqljsf2EW0b8eTwGuBUwPXIpIpdX6WXqHHLwW17qHOz2Sq2PkZsnEAd98MrGhy/W1EO+bWfn6K6DhEIpVy9uw74G1w09IXcvjq9HZelfDKMH59a+vRpXvzluTOmHV3qcJ5LajdunFZbjX09Q5nvs3BRUOpNRTotFAigemcn5KmMr1pS+fKGMrznlWr0jk/FdJEUqLOT0mLglp3UVBLpgpBTSFNJEXq/JS0qPOzuyioJVP2oKaQJpIydX5KmhTUukdWZ6IISUGtMwppIhlQ56ekSUGte6jzM5mydn4qpIlkpBbU7l+1OO9SpIIU1LpLGYNa3mEt66AWgkKaSIbU+SlpUlDrLmULapD/rFoenZ+dUEgTyUGt89OnTcm7FKkYBbXuoqCWTFmCmkKaSE7WLL+W4WeNaj81CU6dn91FQS2ZMgQ1hTSRHB087Sk1FEhqFNS6h4JaMkUPaqUKaYMj+mpIqkedn5ImBbXuoc7PZIoc1EoV0ibtHuXUW97Pmv4X512KSFDq/JQ0Kah1lzIGtbzDWlGDWqlCGsDhqzdw6zXHK6hJ5ajzU9KkoNZdyhbUIP9ZtSJ2fpYupAEsvPEhbr3meM7+9VvzLkUkOJ3zU9KioNZdFNSSKVJQK2VIgyiozblihoKaVJLO+SlpUednd1FQS6YoQa20IQ1g2r2PMeeKGZx6y/vzLkUkOJ3zU9KkoNY9FNSSKUJQK3VIgyioHb56g4KaVJI6PyVN/SPT8y5BMqLOz2TyDmqlD2k1taCmhgKpGnV+Spo0o9ZdyhjU8g5reQa1yoQ0UOenVJc6PyVNCmrdpWxBDfKfVcur87NSIQ3U+SnVps5PSYuCWndRUEsm66BWuZAG6vyUalPnp6RFnZ/dRUEtmSyDWtCQZmZzzOxmM9tuZhvM7Mxxlj3azP7NzAbMbJOZvS9kLer8lCpT52d4RRq/8qag1j0U1JLJKqiFnkm7AhgC5gNnAVea2ZGNC5nZXOCHwJeB/YFDgB8HrkWdn1Jp6vwMrlDjV94U1LqHOj+TySKoBQtpZjYDOB240N0H3P124DvA2U0W/yDwI3f/ursPuvs2d/9dqFoaqfNTqkqdn2FkMX6NePn2LlFQ6y5lDGp5h7W0g1rIUeMwYNjdH6i77tfAXp9EgeOBzWZ2h5k9YWbfNbNFAWvZizo/parU+RlEJuPXrRuXBSg1Wwpq3aVsQQ3yn1VLs/PT3D3MisxOBL7p7gvqrnsHcJa7n9Sw7APAs4CTgd8Cq4Fj3P2EJutdCawEmDt37jGfuvBzHdW5e3YfzBrm4GlPdbQegN275jNl6qaO1xNS0WpSPeMLXc9DO/en54lJ2M7die4/e8EM+jduD1ZPCCvfd85d7n5smtvIZPyaN/eYv73675+5bdaUXeEfSJv6hvZjsPfplpadPXlHytXA4K4F9E3dmPp2WtXN9bR6oON2XkNp27p7KnNGp7N5Uvqv1bEMDvXs8fN7z3hjR+NXz8SLtGwAmNVw3SxgW5NldwI3u/udAGb2CeCPZravu2+pX9DdrwKuAliy6GC/afWdHRe683kHsPmvt7Nm+bUdrefxtatYuGx1x/WEVLSaVM/4QtezEDj712/F/mU2C298qO37n7bqOEL8jZVQ6uPX4kOf49dPv2ePFeU9A7D0kRWsO+iWtu6T5kzL+rUXsGTZJamtv13dXM+XhkxWAAAb4ElEQVQSWptFTfIaStWDb6Dx7yxT02H9o/OCrS7k150PAD1mdmjddcuB+5os+xugfgovzHRei9T5KVWmzs9Echm/9PWnFFkZv/oswgx1yK8+g4U0d98O3ARcbGYzzOwE4PXAmiaLXwucamZHmdkU4ELg9sZPoWlS56dUmTo/25Pn+KWgJkWmzs9kQgW10O1G7wGmAU8ANwDvdvf7zOxEMxuoLeTuPwE+Anw/XvYQYMxjEqVJnZ9SVer8bFtu45eCmhRdGYNa3mEtRFALGtLcfbO7r3D3Ge6+yN2vj6+/zd1nNix7pbsf4O6z3f0Ud38kZC3tUOenVJU6P1uX9/iloCZFV7agBsWYVetE+Q7ckxKd81OqTOf8LIdbNy4rXVhTUOsuCmrZUkiro3N+SpXVzvmpoFZ8ZQxqCmvdQ0EtOwppDdT5KVW2Zvm1HPuZu9VQUAJlC2qgWbVuoqCWDYW0JtT5KVWmzs/yUFCTIjtj1t2ZHOQ4pLIFNYW0cajzU6pKnZ/loaAmRVe2WbUidH62SiFtAur8lKpS52d5KKhJ0ZUtqEE5ZtUU0lqgzk+pMnV+loM6P6XoFNTCU0hrkTo/pcpqnZ+7Z/flXYpMoIxBTWGteyiohaWQ1gZ1fkqVrVl+LbPmDqihoATKFtRAs2rdREEtHIW0NqnzU6ps/8nb1flZEgpqUmQ652cYCmkJHb56A+uefpYaCqRy1PlZHgpqUnRlDGpFCmsKaR3o2zikzk+pJHV+loeCmhRd2YIaFGdWTSGtQ+r8lCpT52c5qPNTik5BLRmFtADU+SlVpnN+lkcZg5rCWvdQUGufQlog6vyUKtM5P8ujbEENNKvWTRTU2qOQFpA6P6XKdM7P8lBQkyJT52frFNJSoHN+SlWp87M8yhjU+kem512CZKiMQS3rsKaQlhKd81OqSp2f5VHGoKYZte5StqAG2c6qKaSlSJ2fUmXq/CwHdX5K0SmojU0hLWXq/JQqU+dneZQxqCmsdQ8FteYU0jKgzk+pMnV+tsZH866gfEENNKvWTRTU9hY0pJnZHDO72cy2m9kGMztzguV7zex3ZvZoyDqKSJ2fUmVV6PzMYvxa/+i8zgvtkIKaFJk6P/cUeibtCmAImA+cBVxpZkeOs/yHgScD11Bo6vyUqqpA52cm45eCWjIKat2ljEEtjbAWLKSZ2QzgdOBCdx9w99uB7wBnj7H8wcCbgE+HqqEs1PkpVVXWzs+sx6/1j87LPawpqEnRlS2oQfhZtZAzaYcBw+7+QN11vwbG+iR6OfARYGfAGkpDnZ9SZSXs/Mxl/Mo7qG3dPbV0YU1Brbt0e1Azdw+zIrMTgW+6+4K6694BnOXuJzUseyqw0t1fY2YnAV9z9wPHWO9KYCXA3Llzj/nUhZ8LUm8IsxfMoH/j9o7W4dOmMPysUQ6e9lSQmnbvms+UqZuCrCsE1TO+qtfz0M796XliErZzd+J1rHzfOXe5+7HBimoik/Fr3txjPnbl5U2339c7HOJhtG3O6HQ2T9oBwKwpu3KpoV7f0H4M9j7d8vKzJ+9IsRoY3LWAvqkbU91GO4pWD2RXU6sHOm73NZSmrbun8s6/PLuj8asnYD0DwKyG62YB2+qviL9WWA28tpWVuvtVwFUASxYd7DetvrPzSgM5bdVxhKhn5/MO4OHX9nDzis93vK7H165i4bLVHa8nFNUzvqrXsxBY0/9ibr3meBbe+FCw9aYg9fFr0dLn+KUDG8ZcdsmB2e+ee+aOo7h++j3P/Jz3yaSXPrKCdQfd0tZ90pxpWb/2ApYsuyS19beraPVAdjUtobVZ1CSvoSIL+XXnA0CPmR1ad91y4L6G5Q4ler5vM7ONwE3As81so5ktCVhPaajzU6qsJJ2fuY9feX/1CdpPTYqtjJ2fnQoW0tx9O9GAdbGZzTCzE4DXA2saFr0XOAg4Kr68HdgU//+RUPWUkTo/paqK3vlZlPFLQS0ZBbXu0k1BLfQhON4DTAOeAG4A3u3u95nZiWY2AODuw+6+sXYBNgOj8c8jgespHXV+SlWVoPOzEOOXOj+TUVDrLt0S1IKGNHff7O4r3H2Guy9y9+vj629z95lj3OdnY+10263U+SlVVtTOz6KNX0UIamULawpq3aUbgppOC1VQOuenVJnO+dmavIMalG9WTef87C5VD2oKaQWmc35Klemcn61RUEtGQa17VDmoKaQVnDo/pcpK0vmZOwW1ZBTUukdVOz8V0kpCnZ9SVUXv/CwKBbVkFNS6S9oHOM6aQlqJqPNTqqoEnZ+FoM7PZBTUukuVZtQU0kpGnZ9SZUXt/CyaIgS1soU1BbXuUpWgppBWQur8lCpT52dr8g5qUL5ZNXV+dpcqBDWFtJJS56dUmTo/W6OgloyCWvcoe1BTSCsxdX5KldUaCmR8CmrJKKh1jzJ3fiqkVYA6P6Wqzp59R94llIKCWjIKat2ljEFNIa0iap2fT43MyLsUEcmBOj+TUVDrLmULagppFbLwxofY+seZaigQ6WJFCGplC2sKat2lTEFNIa1ipvQPqvNTpMvlHdSgfLNq6vzsLmUJagppFaTOT5ECcst0cwpqySiodY8yBDWFtIpS56dI8fQ93Jvp9hTUklFQ6x5F7/xUSKs4dX6KFIuCWjkoqHWXogY1hbQuoHN+ihRLHkEt77BWxqDWPzI97xIkQ0UMagppXULn/BQplqyDGuQ/q6bOTym6ogU1hbQuonN+ihRL38O9+vqzBNT52V2KFNQU0rqMOj9FikdBrRwU1LpHUYKaQloXUuenSPEoqJWDglr3KELnp0JaF1Pnp0ixZB3UBod6Mt1eMwpqUnR5BrWgIc3M5pjZzWa23cw2mNmZYyz3YTO718y2mdlDZvbhkHVI69T5KRIpyvilzs9yUFDrLnkFtdAzaVcAQ8B84CzgSjM7sslyBpwDzAZeDZxnZm8IXIu0SJ2fIkCBxq9u7fzcuntqrjW0S0Gtu+QR1IKFNDObAZwOXOjuA+5+O/Ad4OzGZd19tbvf7e7D7n4/8G3ghFC1SPvU+SndrIjjlzo/y0Gdn90l66Bm7h5mRWYvAH7u7tPrrjsfeJm7nzLO/Qy4G/iyu3+pye0rgZUAc+fOPeZTF34uSL0hzF4wg/6N2/MuYw+d1uTTpjC0r7F0vyeC1LN713ymTN0UZF0hqJ7xFa0egNe+6r13ufuxaW4jm/Fr3jEXXfaFRPWN9o4mut9E5k/uY9PI4F7X9/UOp7K9icwZnc7mSTsAmDVlVy411Osb2o/B3qdbXn725B0pVgODuxbQN3VjqttoV9FqyqqeVg90/MbXvKOj8SvkXqMzga0N120B9pngfhcRzehd2+xGd78KuApgyaKD/abVd3ZWZUCnrTqOItUD4Wq6f9Vibl7x+Y7X8/jaVSxctrrj9YSiesZXtHoylPr4teg5S/2yPzyWuMDBRUOJ7zuWD81czKUDG5retuTAJ4NvbyJn7jiK66ff88zPJy9Ym3kN9ZY+soJ1B93S1n3SnGlZv/YCliy7JLX1J1G0mrKqZ0n8b9qzqCH3SRsAZjVcNwvYNtYdzOw8on07Xufue3+ck9yo81O6TOHHL331WQ766rO7pP31Z8iQ9gDQY2aH1l23HLiv2cJm9jbgAuCV7v5owDokEHV+Shcpxfilzs9yUFDrLmkGtWAhzd23AzcBF5vZDDM7AXg9sKZxWTM7C/gUcLK7PxiqBglPnZ/SDco0fnVr52fZwpqCWndJK6iFPgTHe4BpwBPADcC73f0+MzvRzAbqlvsksD9wp5kNxJe9drqVYlDnp3SJ0oxf6vwsB3V+dpc0glrQkObum919hbvPcPdF7n59fP1t7j6zbrmD3X2Ku8+su7wrZC0Sls75KVVXxvFLQa0cFNS6R+igptNCSct0zk+R4lFQKwcFte4R8pyfCmnSNnV+ihSLglo5KKh1lxBBTSFNElHnp0ixqPOzHBTUpB0KaZKYOj9FikWdn+WgoCatUkiTjqjzU6Q15rDPhjCn4RuPOj/LQUFNWqGQJh1T56dI67IIaqD91MpAh+iQiSikSRDq/BRpnYJaesoW1ECzajI2hTQJSp2fIq1RUEuPgppUhUKaBKfOT5HWVDmo5R3WFNSkChTSJBW1zs+Hdu6fdykihVbVoAb5z6qp81PKTiFNUrPwxofoeWKSOj9FJrDPBlfnZ4oU1KSsFNIkVbZztzo/RVpU1Vk1BbX2qfNTQCFNMqDOT5HWKailp2xBDTSr1u0U0iQz6vwUaU1WQW3SULZvAQpqySioda+evAsoop+cdTVD03dMuNwP+4F3TLy+3h3TecXXz+28sAo4fPUGbl13PLwNzp59R97lSJ3R4RF6e/6B0eERJvVMzrucrrfPBmfbYkt033WjH2eEbRMud94Dra1v0qSZHLToo4lqaVQLaksOfDLI+pK4deMyTl6wNrftJ/GtrUdzbN5FFNzI8AhTJl/GyPAIkysyhmkmrYlWAlqe6ys7nfOzmHb2b2WSPcjO/q15lyKxpDNqrQS0doyODgRdH+Q/q1bGzs/+kel5l1BoA/0DmD3IQH/412teFNIkFzrnZ7GMDo8wNLAdM2doYDujwyN5lySxrDo/85B3UIPyff2prz6bGxkeYce2HZg5O7btYKQiY5hCmuRG5/wsjp39W6GWAxzNphWQglp6yhjUFNb2NNA/sMcYVpXZNIU0yZU6P/NXm0Wrp9m0YlJQS0/ZghpoVq2mNotWryqzaQppUgjq/MzPHrNoNZpNKywFtfQoqJXTHrNoNRWZTVNIk8LQOT+z12wWrUazacVV1aA2ONSTe1hTUCuXZrNoNVWYTQsa0sxsjpndbGbbzWyDmZ05xnJmZp8xs6fiy2fMLFmvuVSKOj+z1XQWrabLZtPKNn5VNahB/rNqZez87Nag1nQWraYCs2mhZ9KuAIaA+cBZwJVmdmST5VYCK4DlwPOBU4B3Bq5FSkqdn9kYbxatpstm00o3fmXZ+dmNZyjYuntq3iW0pduC2nizaDVln00LFtLMbAZwOnChuw+4++3Ad4Czmyz+ZuBSd3/U3R8DLgXeEqoWKT91fqZv3Fm0mi6ZTSv7+KWglp4yzqh1S1gbdxatpuSzaeYe5o/bzF4A/Nzdp9dddz7wMnc/pWHZLcBfuPv/iX8+Fvipu+/TZL0riT65Mnfu3GM+deHngtQ7npX95wRf51Wzvxp8nc3MXjCD/o3jz45kKUQ9gwt6WbrfE0Hq2b1rPlOmbgqyrhDyq2cLU6dcjNnuCZd0n8Ku3R8HZqVfVhOvfdV773L3VA+2ntX4dfFnv5Dio4DR3uhb1/Me+Kvg6/7CYd+o285o8PUDzJ/cx6aRwb2u7+sdTmV7E5kzOp3Nk6KZmllTduVSQ72+of0Y7H265eVnT07/QOqDuxbQN3Vj6tvZ2xZ6e/6m5TFsaPhj5DGGvfov3tfR+BXytFAzgcaP3FuAvQaueNktDcvNNDPzhtTo7lcBVwEsWXSw37T6znAVj6WFUz21K5O6gdNWHZfZtloRqp77Vy3mtJf9suNTST2+dhULl63uuJ5Q8qpn+5P9DG2beHADMNvNvvt/mhnzZqdcVa5SH78WL36OX33bY+EqHkPSU0lN5LI/7Fn74KKh4Nv40MzFXDqwoelteZxG6swdR3H99Hue+TnvU0ktfWQF6w66pa37nDHr7pSqiaxfewFLll2S6jaa2fLkFnZsbX0Mm73/p9l33r4pVxVeyH3SBtg7ps6CpucnaVx2FjDQOMCJ1KjzM5xW9kVr1AX7pmUyfu27bu9ZotCq/NVn3l9/lu2rT6jmfmqt7IvWqKz7poUMaQ8APWZ2aN11y4H7mix7X3zbRMuJPEOdn2G0tC9ao+rvm5bZ+LXvusFMwloWsg5qkP9+aur8zF9L+6I1Kum+acFCmrtvB24CLjazGWZ2AvB6YE2Txb8KfNDMDjCzhcCHgOtC1SLVpc7PziSZRaup8mxaHuNXlYKaGgqKrypBLcksWk0ZZ9NCH4LjPcA04AngBuDd7n6fmZ1oZvUR9svAd4HfAvcC34+vE5mQOj+TSzSLVlP92bTMx6+qBDVQ52cZVKHzM9EsWk0JZ9OChjR33+zuK9x9hrsvcvfr4+tvc/eZdcu5u69y9znxZZX2R5N26Jyf7etkFq2m4rNpuYxfCmrJKaglU9ag1sksWk3ZZtN0WigpNZ3zs3UdzaLVVH82LRcKaskpqCVTxqDW0SxaTclm0xTSpPTU+TmxELNoNVWeTcuTglpy6vxMpkxBLcQsWk2ZZtMU0qQS1Pk5viCzaDWaTUuNOj87U4SgVrawVpagFmQWraZEs2kKaU307pg+8UI5rk+aU+fn2EYGwx54NPT6ZE+dBrWekWbH4E1uctNj+k5MnZ/lUIagNrQr7JgTen1pCXnGgcp4xdfPbWm5oh3dX2qdnwdw6mvfz80rPp93OYUx68D5LS1XtDMydLN91w2yZWlfovsuf/yilpY798QD+PzDjyfaRjv6Hu5N5QwFY1n/6LxczlBQ79aNy3I/Q0E7akEt7TMUJDXvoNbCd15nQEiLZtKkctT5KVWhMxQkpxm1ZMowq9ZNFNKkstT5KVWgoJacgloyCmrFoZAmlabOT6kCBbXk1PmZjIJaMSikSeWp81OqIIvOz6oGNch/Vk2dn5KEQpp0hVrn50M798+7FJGOZBHUsghr6vwsBwW1fCmkSdeYdu9j9DwxSQ0FUnr6+jM5BbX2VeGcn2WlkCZdxXbuVuenVIKCWnIKaskoqGVPIU26kjo/pQoU1JJTUEtGQS1bCmnStdT5KVVQpaA2aSjbtyR1fiajoJYdhTTpaur8lCpQ52dnihDUyhbWFNSyoZAmXU/n/JQsmKcfctT5mVzeQQ3KN6umoJY+hTQRauf8nKGGAklV79pH6V37aKrbqNLXnwpqxdc/Ml1hLUUKaSIxnfNTsqKg1joFtXJQUEuHQppIA3V+ShYU1FqnoFYOCmrhKaSJNKHOT8mCglrr8ghqg0M9mW6zkYKaKKSJjEGdn5KFLIKaOj+Ty3tWTZ2f3S1ISDOzOWZ2s5ltN7MNZnbmOMt+2MzuNbNtZvaQmX04RA0iaVDnZ3fIewxLO6iBOj87kXdQg/LNqimohRFqJu0KYAiYD5wFXGlmR46xrAHnALOBVwPnmdkbAtUhEpw6P7tC7mOYOj/b041BbevuqXmX0Bad87NzHYc0M5sBnA5c6O4D7n478B3g7GbLu/tqd7/b3Yfd/X7g28AJndYhkiZ1flZX0cYwBbXWdWNQK9uMGmhWrRPmHR5g0cxeAPzc3afXXXc+8DJ3P2WC+xpwN/Bld//SGMusBFbGPz4PuLejgsOaC/wx7yIaFK0m1TM+1TOxw919n7RWnuYYVvDxC4r3+1Y94ytaPVC8mopWT0fjV4jWlZnA1obrtgCtFHUR0WzetWMt4O5XAVcBmNmv3P3YZGWGV7R6oHg1qZ7xqZ6JmdmvUt5EamNYkccvKF5Nqmd8RasHildTEevp5P4Tft1pZj8zMx/jcjswAMxquNssYNsE6z2PaL+O17l7+vPvItKVNIaJSFlNOJPm7ieNd3u8P0ePmR3q7r+Pr14O3DfOfd4GXAC81N3Tb2sSka6lMUxEyqrjxgF33w7cBFxsZjPM7ATg9cCaZsub2VnAp4CT3f3BNjd3VUfFhle0eqB4Name8ameiaVaU4ZjWNc9twmonvEVrR4oXk2VqqfjxgGIjjEEXAOcDDwFXODu18e3nQj8wN1nxj8/BBwI1H898DV3f1fHhYiIJKAxTESKKEhIExEREZGwdFooERERkQJSSBMREREpoEKHtLzPp9dODRb5jJk9FV8+Ex/oMqg26snkHKnt/I7i5XvN7HdmlkpHXJuvmaPN7N/MbMDMNpnZ+/Kqx8z6zOxLcR2bzey7ZnZACvWcZ2a/MrNBM7tugmU/YGYbzWyrmV1jZn2h62mnJjN7s5ndFdfzqJmtNrMQx3pMhcavjurJ7BzPGsPC1NOtY1ja41ehQxoFOJ9eGzWsBFYQte4/HzgFeGeA7SetJ6tzpLbzOwL4MPBkCnW0VY+ZzQV+CHwZ2B84BPhxXvUA7wP+jOi1sxDoBy5PoZ7HgU8S7SQ/JjN7FdEhJl4JLAaeA3wihXpargmYDryf6IjiL4prOz+lmkLQ+JW8nizP8awxLEA9dO8Ylu745e6FvAAziF4Yh9Vdtwa4pMX7XwZcnlUNwB3AyrqfzwV+UZTnJMTz0Wk9wMHA74DXAI/m+ZohOoTCmtA1dFDPlcDqup9fB9yfYm2fBK4b5/brgU/V/fxKYGPKz9e4NTVZ/oPAd9OsKYvf/Rj31/gV+PkIUZPGMI1hSetpsnxL41eRZ9IOA4bd/YG6634NjPcJB3jmfHonMs7BKFOo4cj4tomWy6qeZwR8Pjqt53LgI8DOwHUkqed4YLOZ3WFmT8RT84tyrOdq4AQzW2hm04k+sf4gcD3taPZ6nm9m++dUTzMvJfxrOhSNX53V84wUx68kNWkM0xgWSkvjV5FDWqrnBE2hhpnxbfXLzQy8X0fS5+Qiwjwfiesxs1OBye5+c+AaEtVDdJyrNxNN0S8CHgJuyLGe3wOPAI/F9zkCuDhwPe1o9nqG1v7+UmfREf+PBT6bdy1j0PjVWT31LiKd8autmjSGTViPxrAWtTN+5RbSrBzn02unhsZlZwEDHs9rBtL2cxL4+UhUj0Wn3VkNvDfw9hPVE9sJ3Ozud7r7LqJ9FV5sZvvmVM8VQB/RviUziI6An+en0GavZ5jg7y8LZrYC+DTwGnf/Y041aPxKtx4gk/OjagwLV4/GsBa0O37lFtLc/SR3tzEuLwEeID6fXt3dWj2f3is9zPn02qnhvvi2lmrNoJ40no+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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x1s = np.linspace(-0.2, 1.2, 100)\n", + "x2s = np.linspace(-0.2, 1.2, 100)\n", + "x1, x2 = np.meshgrid(x1s, x2s)\n", + "\n", + "z1 = mlp_xor(x1, x2, activation=heaviside)\n", + "z2 = mlp_xor(x1, x2, activation=sigmoid)\n", + "\n", + "plt.figure(figsize=(10,4))\n", + "\n", + "plt.subplot(121)\n", + "plt.contourf(x1, x2, z1)\n", + "plt.plot([0, 1], [0, 1], \"gs\", markersize=20)\n", + "plt.plot([0, 1], [1, 0], \"y^\", markersize=20)\n", + "plt.title(\"Activation function: heaviside\", fontsize=14)\n", + "plt.grid(True)\n", + "\n", + "plt.subplot(122)\n", + "plt.contourf(x1, x2, z2)\n", + "plt.plot([0, 1], [0, 1], \"gs\", markersize=20)\n", + "plt.plot([0, 1], [1, 0], \"y^\", markersize=20)\n", + "plt.title(\"Activation function: sigmoid\", fontsize=14)\n", + "plt.grid(True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# FNN for MNIST" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using the Estimator API (formerly `tf.contrib.learn`)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "import tensorflow as tf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Warning**: `tf.examples.tutorials.mnist` is deprecated. We will use `tf.keras.datasets.mnist` instead. Moreover, the `tf.contrib.learn` API was promoted to `tf.estimators` and `tf.feature_columns`, and it has changed considerably. In particular, there is no `infer_real_valued_columns_from_input()` function or `SKCompat` class." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "(X_train, y_train), (X_test, y_test) = tf.keras.datasets.mnist.load_data()\n", + "X_train = X_train.astype(np.float32).reshape(-1, 28*28) / 255.0\n", + "X_test = X_test.astype(np.float32).reshape(-1, 28*28) / 255.0\n", + "y_train = y_train.astype(np.int32)\n", + "y_test = y_test.astype(np.int32)\n", + "X_valid, X_train = X_train[:5000], X_train[5000:]\n", + "y_valid, y_train = y_train[:5000], y_train[5000:]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Using default config.\n", + "WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpuflzeb_h\n", + "INFO:tensorflow:Using config: {'_evaluation_master': '', '_session_config': None, '_model_dir': '/tmp/tmpuflzeb_h', '_task_type': 'worker', '_cluster_spec': , '_save_summary_steps': 100, '_is_chief': True, '_save_checkpoints_steps': None, '_log_step_count_steps': 100, '_master': '', '_service': None, '_keep_checkpoint_every_n_hours': 10000, '_task_id': 0, '_tf_random_seed': None, '_num_ps_replicas': 0, '_global_id_in_cluster': 0, '_train_distribute': None, '_num_worker_replicas': 1, '_save_checkpoints_secs': 600, '_keep_checkpoint_max': 5}\n", + "INFO:tensorflow:Calling model_fn.\n", + "INFO:tensorflow:Done calling model_fn.\n", + "INFO:tensorflow:Create CheckpointSaverHook.\n", + "INFO:tensorflow:Graph was finalized.\n", + "INFO:tensorflow:Running local_init_op.\n", + "INFO:tensorflow:Done running local_init_op.\n", + "INFO:tensorflow:Saving checkpoints for 1 into /tmp/tmpuflzeb_h/model.ckpt.\n", + "INFO:tensorflow:loss = 122.883514, step = 0\n", + "INFO:tensorflow:global_step/sec: 480.267\n", + "INFO:tensorflow:loss = 9.599711, step = 100 (0.209 sec)\n", + 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0.015441139, step = 41200 (0.141 sec)\n", + "INFO:tensorflow:global_step/sec: 797.458\n", + "INFO:tensorflow:loss = 0.010338774, step = 41300 (0.125 sec)\n", + "INFO:tensorflow:global_step/sec: 747.132\n", + "INFO:tensorflow:loss = 0.021103038, step = 41400 (0.134 sec)\n", + "INFO:tensorflow:global_step/sec: 750.845\n", + "INFO:tensorflow:loss = 0.025617313, step = 41500 (0.133 sec)\n", + "INFO:tensorflow:global_step/sec: 754.583\n", + "INFO:tensorflow:loss = 0.013572226, step = 41600 (0.133 sec)\n", + "INFO:tensorflow:global_step/sec: 756.386\n", + "INFO:tensorflow:loss = 0.0033336552, step = 41700 (0.132 sec)\n", + "INFO:tensorflow:global_step/sec: 782.906\n", + "INFO:tensorflow:loss = 0.053168457, step = 41800 (0.128 sec)\n", + "INFO:tensorflow:global_step/sec: 719.6\n", + "INFO:tensorflow:loss = 0.029688315, step = 41900 (0.139 sec)\n", + "INFO:tensorflow:global_step/sec: 788.136\n", + "INFO:tensorflow:loss = 0.01654516, step = 42000 (0.127 sec)\n", + "INFO:tensorflow:global_step/sec: 738.526\n", + "INFO:tensorflow:loss = 0.015659308, step = 42100 (0.135 sec)\n", + "INFO:tensorflow:global_step/sec: 777.578\n", + "INFO:tensorflow:loss = 0.01840079, step = 42200 (0.129 sec)\n", + "INFO:tensorflow:global_step/sec: 786.416\n", + "INFO:tensorflow:loss = 0.021663003, step = 42300 (0.127 sec)\n", + "INFO:tensorflow:global_step/sec: 763.347\n", + "INFO:tensorflow:loss = 0.011599571, step = 42400 (0.131 sec)\n", + "INFO:tensorflow:global_step/sec: 762.321\n", + "INFO:tensorflow:loss = 0.0044469903, step = 42500 (0.131 sec)\n", + "INFO:tensorflow:global_step/sec: 768.549\n", + "INFO:tensorflow:loss = 0.0019147585, step = 42600 (0.130 sec)\n", + "INFO:tensorflow:global_step/sec: 771.429\n", + "INFO:tensorflow:loss = 0.0054854164, step = 42700 (0.130 sec)\n", + "INFO:tensorflow:global_step/sec: 793.871\n", + "INFO:tensorflow:loss = 0.0017117725, step = 42800 (0.126 sec)\n", + "INFO:tensorflow:global_step/sec: 770.1\n", + "INFO:tensorflow:loss = 0.012048513, step = 42900 (0.130 sec)\n", + "INFO:tensorflow:global_step/sec: 744.636\n", + "INFO:tensorflow:loss = 0.06634566, step = 43000 (0.134 sec)\n", + "INFO:tensorflow:global_step/sec: 696.882\n", + "INFO:tensorflow:loss = 0.0003919307, step = 43100 (0.144 sec)\n", + "INFO:tensorflow:global_step/sec: 705.516\n", + "INFO:tensorflow:loss = 0.06582007, step = 43200 (0.141 sec)\n", + "INFO:tensorflow:global_step/sec: 699.244\n", + "INFO:tensorflow:loss = 0.0038124803, step = 43300 (0.143 sec)\n", + "INFO:tensorflow:global_step/sec: 792.079\n", + "INFO:tensorflow:loss = 0.003364585, step = 43400 (0.126 sec)\n", + "INFO:tensorflow:global_step/sec: 753.586\n", + "INFO:tensorflow:loss = 0.00725976, step = 43500 (0.133 sec)\n", + "INFO:tensorflow:global_step/sec: 720.951\n", + "INFO:tensorflow:loss = 0.024148291, step = 43600 (0.139 sec)\n", + "INFO:tensorflow:global_step/sec: 770.384\n", + "INFO:tensorflow:loss = 0.013779048, step = 43700 (0.130 sec)\n", + "INFO:tensorflow:global_step/sec: 799.363\n", + "INFO:tensorflow:loss = 0.014951154, step = 43800 (0.125 sec)\n", + "INFO:tensorflow:global_step/sec: 791.774\n", + "INFO:tensorflow:loss = 0.0015594304, step = 43900 (0.126 sec)\n", + "INFO:tensorflow:Saving checkpoints for 44000 into /tmp/tmpuflzeb_h/model.ckpt.\n", + "INFO:tensorflow:Loss for final step: 0.0012097486.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "feature_cols = [tf.feature_column.numeric_column(\"X\", shape=[28 * 28])]\n", + "dnn_clf = tf.estimator.DNNClassifier(hidden_units=[300,100], n_classes=10,\n", + " feature_columns=feature_cols)\n", + "\n", + "input_fn = tf.estimator.inputs.numpy_input_fn(\n", + " x={\"X\": X_train}, y=y_train, num_epochs=40, batch_size=50, shuffle=True)\n", + "dnn_clf.train(input_fn=input_fn)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Calling model_fn.\n", + "INFO:tensorflow:Done calling model_fn.\n", + "INFO:tensorflow:Starting evaluation at 2018-05-18-19:12:49\n", + "INFO:tensorflow:Graph was finalized.\n", + "INFO:tensorflow:Restoring parameters from /tmp/tmpuflzeb_h/model.ckpt-44000\n", + "INFO:tensorflow:Running local_init_op.\n", + "INFO:tensorflow:Done running local_init_op.\n", + "INFO:tensorflow:Finished evaluation at 2018-05-18-19:12:50\n", + "INFO:tensorflow:Saving dict for global step 44000: accuracy = 0.9798, average_loss = 0.10096103, global_step = 44000, loss = 12.779877\n" + ] + } + ], + "source": [ + "test_input_fn = tf.estimator.inputs.numpy_input_fn(\n", + " x={\"X\": X_test}, y=y_test, shuffle=False)\n", + "eval_results = dnn_clf.evaluate(input_fn=test_input_fn)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'accuracy': 0.9798,\n", + " 'average_loss': 0.10096103,\n", + " 'global_step': 44000,\n", + " 'loss': 12.779877}" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "eval_results" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Calling model_fn.\n", + "INFO:tensorflow:Done calling model_fn.\n", + "INFO:tensorflow:Graph was finalized.\n", + "INFO:tensorflow:Restoring parameters from /tmp/tmpuflzeb_h/model.ckpt-44000\n", + "INFO:tensorflow:Running local_init_op.\n", + "INFO:tensorflow:Done running local_init_op.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'class_ids': array([7]),\n", + " 'classes': array([b'7'], dtype=object),\n", + " 'logits': array([ -3.809414 , -4.1564407, -0.426081 , 3.2636993, -11.065331 ,\n", + " -8.790985 , -10.436305 , 19.935707 , -6.9282775, 2.2807484],\n", + " dtype=float32),\n", + " 'probabilities': array([4.8710768e-11, 3.4428106e-11, 1.4354495e-09, 5.7469666e-08,\n", + " 3.4389070e-14, 3.3431518e-13, 6.4506329e-14, 1.0000000e+00,\n", + " 2.1533745e-12, 2.1505466e-08], dtype=float32)}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_pred_iter = dnn_clf.predict(input_fn=test_input_fn)\n", + "y_pred = list(y_pred_iter)\n", + "y_pred[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## Using plain TensorFlow" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "import tensorflow as tf\n", + "\n", + "n_inputs = 28*28 # MNIST\n", + "n_hidden1 = 300\n", + "n_hidden2 = 100\n", + "n_outputs = 10" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "reset_graph()\n", + "\n", + "X = tf.placeholder(tf.float32, shape=(None, n_inputs), name=\"X\")\n", + "y = tf.placeholder(tf.int32, shape=(None), name=\"y\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "def neuron_layer(X, n_neurons, name, activation=None):\n", + " with tf.name_scope(name):\n", + " n_inputs = int(X.get_shape()[1])\n", + " stddev = 2 / np.sqrt(n_inputs)\n", + " init = tf.truncated_normal((n_inputs, n_neurons), stddev=stddev)\n", + " W = tf.Variable(init, name=\"kernel\")\n", + " b = tf.Variable(tf.zeros([n_neurons]), name=\"bias\")\n", + " Z = tf.matmul(X, W) + b\n", + " if activation is not None:\n", + " return activation(Z)\n", + " else:\n", + " return Z" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "with tf.name_scope(\"dnn\"):\n", + " hidden1 = neuron_layer(X, n_hidden1, name=\"hidden1\",\n", + " activation=tf.nn.relu)\n", + " hidden2 = neuron_layer(hidden1, n_hidden2, name=\"hidden2\",\n", + " activation=tf.nn.relu)\n", + " logits = neuron_layer(hidden2, n_outputs, name=\"outputs\")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "with tf.name_scope(\"loss\"):\n", + " xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y,\n", + " logits=logits)\n", + " loss = tf.reduce_mean(xentropy, name=\"loss\")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "learning_rate = 0.01\n", + "\n", + "with tf.name_scope(\"train\"):\n", + " optimizer = tf.train.GradientDescentOptimizer(learning_rate)\n", + " training_op = optimizer.minimize(loss)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "with tf.name_scope(\"eval\"):\n", + " correct = tf.nn.in_top_k(logits, y, 1)\n", + " accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "init = tf.global_variables_initializer()\n", + "saver = tf.train.Saver()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "n_epochs = 40\n", + "batch_size = 50" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "def shuffle_batch(X, y, batch_size):\n", + " rnd_idx = np.random.permutation(len(X))\n", + " n_batches = len(X) // batch_size\n", + " for batch_idx in np.array_split(rnd_idx, n_batches):\n", + " X_batch, y_batch = X[batch_idx], y[batch_idx]\n", + " yield X_batch, y_batch" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 Batch accuracy: 0.9 Val accuracy: 0.9146\n", + "1 Batch accuracy: 0.92 Val accuracy: 0.936\n", + "2 Batch accuracy: 0.96 Val accuracy: 0.945\n", + "3 Batch accuracy: 0.92 Val accuracy: 0.9512\n", + "4 Batch accuracy: 0.98 Val accuracy: 0.9558\n", + "5 Batch accuracy: 0.96 Val accuracy: 0.9566\n", + "6 Batch accuracy: 1.0 Val accuracy: 0.9612\n", + "7 Batch accuracy: 0.94 Val accuracy: 0.963\n", + "8 Batch accuracy: 0.98 Val accuracy: 0.9652\n", + "9 Batch accuracy: 0.96 Val accuracy: 0.966\n", + "10 Batch accuracy: 0.92 Val accuracy: 0.9688\n", + "11 Batch accuracy: 0.98 Val accuracy: 0.969\n", + "12 Batch accuracy: 0.98 Val accuracy: 0.967\n", + "13 Batch accuracy: 0.98 Val accuracy: 0.9706\n", + "14 Batch accuracy: 1.0 Val accuracy: 0.9714\n", + "15 Batch accuracy: 0.94 Val accuracy: 0.9732\n", + "16 Batch accuracy: 1.0 Val accuracy: 0.9736\n", + "17 Batch accuracy: 1.0 Val accuracy: 0.9742\n", + "18 Batch accuracy: 1.0 Val accuracy: 0.9746\n", + "19 Batch accuracy: 0.98 Val accuracy: 0.9748\n", + "20 Batch accuracy: 1.0 Val accuracy: 0.9752\n", + "21 Batch accuracy: 1.0 Val accuracy: 0.9752\n", + "22 Batch accuracy: 0.98 Val accuracy: 0.9764\n", + "23 Batch accuracy: 0.98 Val accuracy: 0.9752\n", + "24 Batch accuracy: 0.98 Val accuracy: 0.9772\n", + "25 Batch accuracy: 1.0 Val accuracy: 0.977\n", + "26 Batch accuracy: 0.98 Val accuracy: 0.9778\n", + "27 Batch accuracy: 1.0 Val accuracy: 0.9774\n", + "28 Batch accuracy: 0.96 Val accuracy: 0.9754\n", + "29 Batch accuracy: 0.98 Val accuracy: 0.9776\n", + "30 Batch accuracy: 1.0 Val accuracy: 0.9756\n", + "31 Batch accuracy: 0.98 Val accuracy: 0.9772\n", + "32 Batch accuracy: 0.98 Val accuracy: 0.9772\n", + "33 Batch accuracy: 0.98 Val accuracy: 0.979\n", + "34 Batch accuracy: 1.0 Val accuracy: 0.9784\n", + "35 Batch accuracy: 1.0 Val accuracy: 0.9778\n", + "36 Batch accuracy: 0.98 Val accuracy: 0.978\n", + "37 Batch accuracy: 1.0 Val accuracy: 0.9776\n", + "38 Batch accuracy: 1.0 Val accuracy: 0.9792\n", + "39 Batch accuracy: 1.0 Val accuracy: 0.9776\n" + ] + } + ], + "source": [ + "with tf.Session() as sess:\n", + " init.run()\n", + " for epoch in range(n_epochs):\n", + " for X_batch, y_batch in shuffle_batch(X_train, y_train, batch_size):\n", + " sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n", + " acc_batch = accuracy.eval(feed_dict={X: X_batch, y: y_batch})\n", + " acc_val = accuracy.eval(feed_dict={X: X_valid, y: y_valid})\n", + " print(epoch, \"Batch accuracy:\", acc_batch, \"Val accuracy:\", acc_val)\n", + "\n", + " save_path = saver.save(sess, \"./my_model_final.ckpt\")" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Restoring parameters from ./my_model_final.ckpt\n" + ] + } + ], + "source": [ + "with tf.Session() as sess:\n", + " saver.restore(sess, \"./my_model_final.ckpt\") # or better, use save_path\n", + " X_new_scaled = X_test[:20]\n", + " Z = logits.eval(feed_dict={X: X_new_scaled})\n", + " y_pred = np.argmax(Z, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicted classes: [7 2 1 0 4 1 4 9 5 9 0 6 9 0 1 5 9 7 3 4]\n", + "Actual classes: [7 2 1 0 4 1 4 9 5 9 0 6 9 0 1 5 9 7 3 4]\n" + ] + } + ], + "source": [ + "print(\"Predicted classes:\", y_pred)\n", + "print(\"Actual classes: \", y_test[:20])" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow_graph_in_jupyter import show_graph" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "show_graph(tf.get_default_graph())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using `dense()` instead of `neuron_layer()`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note: previous releases of the book used `tensorflow.contrib.layers.fully_connected()` rather than `tf.layers.dense()` (which did not exist when this chapter was written). It is now preferable to use `tf.layers.dense()`, because anything in the contrib module may change or be deleted without notice. The `dense()` function is almost identical to the `fully_connected()` function, except for a few minor differences:\n", + "* several parameters are renamed: `scope` becomes `name`, `activation_fn` becomes `activation` (and similarly the `_fn` suffix is removed from other parameters such as `normalizer_fn`), `weights_initializer` becomes `kernel_initializer`, etc.\n", + "* the default `activation` is now `None` rather than `tf.nn.relu`.\n", + "* a few more differences are presented in chapter 11." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "n_inputs = 28*28 # MNIST\n", + "n_hidden1 = 300\n", + "n_hidden2 = 100\n", + "n_outputs = 10" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "reset_graph()\n", + "\n", + "X = tf.placeholder(tf.float32, shape=(None, n_inputs), name=\"X\")\n", + "y = tf.placeholder(tf.int32, shape=(None), name=\"y\") " + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "with tf.name_scope(\"dnn\"):\n", + " hidden1 = tf.layers.dense(X, n_hidden1, name=\"hidden1\",\n", + " activation=tf.nn.relu)\n", + " hidden2 = tf.layers.dense(hidden1, n_hidden2, name=\"hidden2\",\n", + " activation=tf.nn.relu)\n", + " logits = tf.layers.dense(hidden2, n_outputs, name=\"outputs\")\n", + " y_proba = tf.nn.softmax(logits)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "with tf.name_scope(\"loss\"):\n", + " xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)\n", + " loss = tf.reduce_mean(xentropy, name=\"loss\")" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "learning_rate = 0.01\n", + "\n", + "with tf.name_scope(\"train\"):\n", + " optimizer = tf.train.GradientDescentOptimizer(learning_rate)\n", + " training_op = optimizer.minimize(loss)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "with tf.name_scope(\"eval\"):\n", + " correct = tf.nn.in_top_k(logits, y, 1)\n", + " accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "init = tf.global_variables_initializer()\n", + "saver = tf.train.Saver()" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 Batch accuracy: 0.9 Validation accuracy: 0.9024\n", + "1 Batch accuracy: 0.92 Validation accuracy: 0.9254\n", + "2 Batch accuracy: 0.94 Validation accuracy: 0.9372\n", + "3 Batch accuracy: 0.9 Validation accuracy: 0.9416\n", + "4 Batch accuracy: 0.94 Validation accuracy: 0.9472\n", + "5 Batch accuracy: 0.94 Validation accuracy: 0.9512\n", + "6 Batch accuracy: 1.0 Validation accuracy: 0.9548\n", + "7 Batch accuracy: 0.94 Validation accuracy: 0.961\n", + "8 Batch accuracy: 0.96 Validation accuracy: 0.962\n", + "9 Batch accuracy: 0.94 Validation accuracy: 0.9648\n", + "10 Batch accuracy: 0.92 Validation accuracy: 0.9656\n", + "11 Batch accuracy: 0.98 Validation accuracy: 0.9668\n", + "12 Batch accuracy: 0.98 Validation accuracy: 0.9684\n", + "13 Batch accuracy: 0.98 Validation accuracy: 0.9702\n", + "14 Batch accuracy: 1.0 Validation accuracy: 0.9696\n", + "15 Batch accuracy: 0.94 Validation accuracy: 0.9718\n", + "16 Batch accuracy: 0.98 Validation accuracy: 0.9728\n", + "17 Batch accuracy: 1.0 Validation accuracy: 0.973\n", + "18 Batch accuracy: 0.98 Validation accuracy: 0.9748\n", + "19 Batch accuracy: 0.98 Validation accuracy: 0.9756\n" + ] + } + ], + "source": [ + "n_epochs = 20\n", + "n_batches = 50\n", + "\n", + "with tf.Session() as sess:\n", + " init.run()\n", + " for epoch in range(n_epochs):\n", + " for X_batch, y_batch in shuffle_batch(X_train, y_train, batch_size):\n", + " sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n", + " acc_batch = accuracy.eval(feed_dict={X: X_batch, y: y_batch})\n", + " acc_valid = accuracy.eval(feed_dict={X: X_valid, y: y_valid})\n", + " print(epoch, \"Batch accuracy:\", acc_batch, \"Validation accuracy:\", acc_valid)\n", + "\n", + " save_path = saver.save(sess, \"./my_model_final.ckpt\")" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "show_graph(tf.get_default_graph())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Exercise solutions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. to 8." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "See appendix A." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 9." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "_Train a deep MLP on the MNIST dataset and see if you can get over 98% precision. Just like in the last exercise of chapter 9, try adding all the bells and whistles (i.e., save checkpoints, restore the last checkpoint in case of an interruption, add summaries, plot learning curves using TensorBoard, and so on)._" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First let's create the deep net. It's exactly the same as earlier, with just one addition: we add a `tf.summary.scalar()` to track the loss and the accuracy during training, so we can view nice learning curves using TensorBoard." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "n_inputs = 28*28 # MNIST\n", + "n_hidden1 = 300\n", + "n_hidden2 = 100\n", + "n_outputs = 10" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "reset_graph()\n", + "\n", + "X = tf.placeholder(tf.float32, shape=(None, n_inputs), name=\"X\")\n", + "y = tf.placeholder(tf.int32, shape=(None), name=\"y\") " + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "with tf.name_scope(\"dnn\"):\n", + " hidden1 = tf.layers.dense(X, n_hidden1, name=\"hidden1\",\n", + " activation=tf.nn.relu)\n", + " hidden2 = tf.layers.dense(hidden1, n_hidden2, name=\"hidden2\",\n", + " activation=tf.nn.relu)\n", + " logits = tf.layers.dense(hidden2, n_outputs, name=\"outputs\")" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "with tf.name_scope(\"loss\"):\n", + " xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)\n", + " loss = tf.reduce_mean(xentropy, name=\"loss\")\n", + " loss_summary = tf.summary.scalar('log_loss', loss)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "learning_rate = 0.01\n", + "\n", + "with tf.name_scope(\"train\"):\n", + " optimizer = tf.train.GradientDescentOptimizer(learning_rate)\n", + " training_op = optimizer.minimize(loss)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "with tf.name_scope(\"eval\"):\n", + " correct = tf.nn.in_top_k(logits, y, 1)\n", + " accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))\n", + " accuracy_summary = tf.summary.scalar('accuracy', accuracy)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "init = tf.global_variables_initializer()\n", + "saver = tf.train.Saver()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we need to define the directory to write the TensorBoard logs to:" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "from datetime import datetime\n", + "\n", + "def log_dir(prefix=\"\"):\n", + " now = datetime.utcnow().strftime(\"%Y%m%d%H%M%S\")\n", + " root_logdir = \"tf_logs\"\n", + " if prefix:\n", + " prefix += \"-\"\n", + " name = prefix + \"run-\" + now\n", + " return \"{}/{}/\".format(root_logdir, name)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "logdir = log_dir(\"mnist_dnn\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can create the `FileWriter` that we will use to write the TensorBoard logs:" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "file_writer = tf.summary.FileWriter(logdir, tf.get_default_graph())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Hey! Why don't we implement early stopping? For this, we are going to need to use the validation set." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "m, n = X_train.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0 \tValidation accuracy: 92.180% \tLoss: 0.30208\n", + "Epoch: 5 \tValidation accuracy: 95.980% \tLoss: 0.15037\n", + "Epoch: 10 \tValidation accuracy: 97.100% \tLoss: 0.11160\n", + "Epoch: 15 \tValidation accuracy: 97.700% \tLoss: 0.09562\n", + "Epoch: 20 \tValidation accuracy: 97.840% \tLoss: 0.08309\n", + "Epoch: 25 \tValidation accuracy: 98.040% \tLoss: 0.07706\n", + "Epoch: 30 \tValidation accuracy: 98.140% \tLoss: 0.07287\n", + "Epoch: 35 \tValidation accuracy: 98.280% \tLoss: 0.07133\n", + "Epoch: 40 \tValidation accuracy: 98.220% \tLoss: 0.06968\n", + "Epoch: 45 \tValidation accuracy: 98.220% \tLoss: 0.06993\n", + "Epoch: 50 \tValidation accuracy: 98.160% \tLoss: 0.07093\n", + "Epoch: 55 \tValidation accuracy: 98.280% \tLoss: 0.06994\n", + "Epoch: 60 \tValidation accuracy: 98.200% \tLoss: 0.06894\n", + "Epoch: 65 \tValidation accuracy: 98.260% \tLoss: 0.06906\n", + "Epoch: 70 \tValidation accuracy: 98.220% \tLoss: 0.07057\n", + "Epoch: 75 \tValidation accuracy: 98.280% \tLoss: 0.06963\n", + "Epoch: 80 \tValidation accuracy: 98.320% \tLoss: 0.07264\n", + "Epoch: 85 \tValidation accuracy: 98.200% \tLoss: 0.07403\n", + "Epoch: 90 \tValidation accuracy: 98.300% \tLoss: 0.07332\n", + "Epoch: 95 \tValidation accuracy: 98.180% \tLoss: 0.07535\n", + "Epoch: 100 \tValidation accuracy: 98.260% \tLoss: 0.07542\n", + "Early stopping\n" + ] + } + ], + "source": [ + "n_epochs = 10001\n", + "batch_size = 50\n", + "n_batches = int(np.ceil(m / batch_size))\n", + "\n", + "checkpoint_path = \"/tmp/my_deep_mnist_model.ckpt\"\n", + "checkpoint_epoch_path = checkpoint_path + \".epoch\"\n", + "final_model_path = \"./my_deep_mnist_model\"\n", + "\n", + "best_loss = np.infty\n", + "epochs_without_progress = 0\n", + "max_epochs_without_progress = 50\n", + "\n", + "with tf.Session() as sess:\n", + " if os.path.isfile(checkpoint_epoch_path):\n", + " # if the checkpoint file exists, restore the model and load the epoch number\n", + " with open(checkpoint_epoch_path, \"rb\") as f:\n", + " start_epoch = int(f.read())\n", + " print(\"Training was interrupted. Continuing at epoch\", start_epoch)\n", + " saver.restore(sess, checkpoint_path)\n", + " else:\n", + " start_epoch = 0\n", + " sess.run(init)\n", + "\n", + " for epoch in range(start_epoch, n_epochs):\n", + " for X_batch, y_batch in shuffle_batch(X_train, y_train, batch_size):\n", + " sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n", + " accuracy_val, loss_val, accuracy_summary_str, loss_summary_str = sess.run([accuracy, loss, accuracy_summary, loss_summary], feed_dict={X: X_valid, y: y_valid})\n", + " file_writer.add_summary(accuracy_summary_str, epoch)\n", + " file_writer.add_summary(loss_summary_str, epoch)\n", + " if epoch % 5 == 0:\n", + " print(\"Epoch:\", epoch,\n", + " \"\\tValidation accuracy: {:.3f}%\".format(accuracy_val * 100),\n", + " \"\\tLoss: {:.5f}\".format(loss_val))\n", + " saver.save(sess, checkpoint_path)\n", + " with open(checkpoint_epoch_path, \"wb\") as f:\n", + " f.write(b\"%d\" % (epoch + 1))\n", + " if loss_val < best_loss:\n", + " saver.save(sess, final_model_path)\n", + " best_loss = loss_val\n", + " else:\n", + " epochs_without_progress += 5\n", + " if epochs_without_progress > max_epochs_without_progress:\n", + " print(\"Early stopping\")\n", + " break" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "os.remove(checkpoint_epoch_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Restoring parameters from ./my_deep_mnist_model\n" + ] + } + ], + "source": [ + "with tf.Session() as sess:\n", + " saver.restore(sess, final_model_path)\n", + " accuracy_val = accuracy.eval(feed_dict={X: X_test, y: y_test})" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9796" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "accuracy_val" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + }, + "nav_menu": { + "height": "264px", + "width": "369px" + }, + "toc": { + "navigate_menu": true, + "number_sections": true, + "sideBar": true, + "threshold": 6, + "toc_cell": false, + "toc_section_display": "block", + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/notebooks/helpers_05_08.py b/notebooks/helpers_05_08.py index 0f3b15aa9..72cc3df4e 100644 --- a/notebooks/helpers_05_08.py +++ b/notebooks/helpers_05_08.py @@ -2,6 +2,7 @@ import numpy as np import matplotlib.pyplot as plt from sklearn.tree import DecisionTreeClassifier +# random change to see this is last from ipywidgets import interact From f2c5ea80e1893e6f532267cee69710c3c5c802f0 Mon Sep 17 00:00:00 2001 From: Amit Rathi Date: Wed, 26 Dec 2018 12:11:37 +0530 Subject: [PATCH 07/19] Output type changes --- .../01.02-Shell-Keyboard-Shortcuts.ipynb | 202 ------------- notebooks/03.09-Pivot-Tables.ipynb | 267 ++---------------- 2 files changed, 26 insertions(+), 443 deletions(-) delete mode 100644 notebooks/01.02-Shell-Keyboard-Shortcuts.ipynb diff --git a/notebooks/01.02-Shell-Keyboard-Shortcuts.ipynb b/notebooks/01.02-Shell-Keyboard-Shortcuts.ipynb deleted file mode 100644 index a13f196c9..000000000 --- a/notebooks/01.02-Shell-Keyboard-Shortcuts.ipynb +++ /dev/null @@ -1,202 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", - "\n", - "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "< [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb) | [Contents](Index.ipynb) | [IPython Magic Commands](01.03-Magic-Commands.ipynb) >" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Keyboard Shortcuts in the IPython Shell" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you spend any amount of time on the computer, you've probably found a use for keyboard shortcuts in your workflow.\n", - "Most familiar perhaps are the Cmd-C and Cmd-V (or Ctrl-C and Ctrl-V) for copying and pasting in a wide variety of programs and systems.\n", - "Power-users tend to go even further: popular text editors like Emacs, Vim, and others provide users an incredible range of operations through intricate combinations of keystrokes.\n", - "\n", - "The IPython shell doesn't go this far, but does provide a number of keyboard shortcuts for fast navigation while typing commands.\n", - "These shortcuts are not in fact provided by IPython itself, but through its dependency on the GNU Readline library: as such, some of the following shortcuts may differ depending on your system configuration.\n", - "Also, while some of these shortcuts do work in the browser-based notebook, this section is primarily about shortcuts in the IPython shell.\n", - "\n", - "Once you get accustomed to these, they can be very useful for quickly performing certain commands without moving your hands from the \"home\" keyboard position.\n", - "If you're an Emacs user or if you have experience with Linux-style shells, the following will be very familiar.\n", - "We'll group these shortcuts into a few categories: *navigation shortcuts*, *text entry shortcuts*, *command history shortcuts*, and *miscellaneous shortcuts*." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Navigation shortcuts\n", - "\n", - "While the use of the left and right arrow keys to move backward and forward in the line is quite obvious, there are other options that don't require moving your hands from the \"home\" keyboard position:\n", - "\n", - "| Keystroke | Action |\n", - "|-----------------------------------|--------------------------------------------|\n", - "| ``Ctrl-a`` | Move cursor to the beginning of the line |\n", - "| ``Ctrl-e`` | Move cursor to the end of the line |\n", - "| ``Ctrl-b`` or the left arrow key | Move cursor back one character |\n", - "| ``Ctrl-f`` or the right arrow key | Move cursor forward one character |" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Text Entry Shortcuts\n", - "\n", - "While everyone is familiar with using the Backspace key to delete the previous character, reaching for the key often requires some minor finger gymnastics, and it only deletes a single character at a time.\n", - "In IPython there are several shortcuts for removing some portion of the text you're typing.\n", - "The most immediately useful of these are the commands to delete entire lines of text.\n", - "You'll know these have become second-nature if you find yourself using a combination of Ctrl-b and Ctrl-d instead of reaching for Backspace to delete the previous character!\n", - "\n", - "| Keystroke | Action |\n", - "|-------------------------------|--------------------------------------------------|\n", - "| Backspace key | Delete previous character in line |\n", - "| ``Ctrl-d`` | Delete next character in line |\n", - "| ``Ctrl-k`` | Cut text from cursor to end of line |\n", - "| ``Ctrl-u`` | Cut text from beginning of line to cursor |\n", - "| ``Ctrl-y`` | Yank (i.e. paste) text that was previously cut |\n", - "| ``Ctrl-t`` | Transpose (i.e., switch) previous two characters |" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Command History Shortcuts\n", - "\n", - "Perhaps the most impactful shortcuts discussed here are the ones IPython provides for navigating the command history.\n", - "This command history goes beyond your current IPython session: your entire command history is stored in a SQLite database in your IPython profile directory.\n", - "The most straightforward way to access these is with the up and down arrow keys to step through the history, but other options exist as well:\n", - "\n", - "| Keystroke | Action |\n", - "|-------------------------------------|--------------------------------------------|\n", - "| ``Ctrl-p`` (or the up arrow key) | Access previous command in history |\n", - "| ``Ctrl-n`` (or the down arrow key) | Access next command in history |\n", - "| ``Ctrl-r`` | Reverse-search through command history |" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The reverse-search can be particularly useful.\n", - "Recall that in the previous section we defined a function called ``square``.\n", - "Let's reverse-search our Python history from a new IPython shell and find this definition again.\n", - "When you press Ctrl-r in the IPython terminal, you'll see the following prompt:\n", - "\n", - "```ipython\n", - "In [1]:\n", - "(reverse-i-search)`': \n", - "```\n", - "\n", - "If you start typing characters at this prompt, IPython will auto-fill the most recent command, if any, that matches those characters:\n", - "\n", - "```ipython\n", - "In [1]: \n", - "(reverse-i-search)`sqa': square??\n", - "```\n", - "\n", - "At any point, you can add more characters to refine the search, or press Ctrl-r again to search further for another command that matches the query. If you followed along in the previous section, pressing Ctrl-r twice more gives:\n", - "\n", - "```ipython\n", - "In [1]: \n", - "(reverse-i-search)`sqa': def square(a):\n", - " \"\"\"Return the square of a\"\"\"\n", - " return a ** 2\n", - "```\n", - "\n", - "Once you have found the command you're looking for, press Return and the search will end.\n", - "We can then use the retrieved command, and carry-on with our session:\n", - "\n", - "```ipython\n", - "In [1]: def square(a):\n", - " \"\"\"Return the square of a\"\"\"\n", - " return a ** 2\n", - "\n", - "In [2]: square(2)\n", - "Out[2]: 4\n", - "```\n", - "\n", - "Note that Ctrl-p/Ctrl-n or the up/down arrow keys can also be used to search through history, but only by matching characters at the beginning of the line.\n", - "That is, if you type **``def``** and then press Ctrl-p, it would find the most recent command (if any) in your history that begins with the characters ``def``." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Miscellaneous Shortcuts\n", - "\n", - "Finally, there are a few miscellaneous shortcuts that don't fit into any of the preceding categories, but are nevertheless useful to know:\n", - "\n", - "| Keystroke | Action |\n", - "|-------------------------------|--------------------------------------------|\n", - "| ``Ctrl-l`` | Clear terminal screen |\n", - "| ``Ctrl-c`` | Interrupt current Python command |\n", - "| ``Ctrl-d`` | Exit IPython session |\n", - "\n", - "The Ctrl-c in particular can be useful when you inadvertently start a very long-running job." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "While some of the shortcuts discussed here may seem a bit tedious at first, they quickly become automatic with practice.\n", - "Once you develop that muscle memory, I suspect you will even find yourself wishing they were available in other contexts." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "< [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb) | [Contents](Index.ipynb) | [IPython Magic Commands](01.03-Magic-Commands.ipynb) >" - ] - } - ], - "metadata": { - "anaconda-cloud": {}, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.1" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/03.09-Pivot-Tables.ipynb b/notebooks/03.09-Pivot-Tables.ipynb index 1f2d9e279..1f336a28d 100644 --- a/notebooks/03.09-Pivot-Tables.ipynb +++ b/notebooks/03.09-Pivot-Tables.ipynb @@ -48,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -60,165 +60,20 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 5, "metadata": {}, "outputs": [ { - "data": { - "text/html": [ - "
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Third0.281690
\n", - "
" - ], - "text/plain": [ - " survived\n", - "fare class \n", - "(-0.001, 14.454] First 0.000000\n", - " Second 0.358696\n", - " Third 0.226361\n", - "(14.454, 512.329] First 0.647619\n", - " Second 0.586957\n", - " Third 0.281690" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" + "ename": "NameError", + "evalue": "name 'pd' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# converting pivot table to error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfare\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqcut\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtitanic\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'fare'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mtitanic\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpivot_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'survived'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mfare\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'class'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# modified a table\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'pd' is not defined" + ] } ], "source": [ + "# converting pivot table to error\n", "fare = pd.qcut(titanic['fare'], 2)\n", "titanic.pivot_table('survived', [fare, 'class']) # modified a table" ] @@ -1069,25 +860,19 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 1, "metadata": {}, "outputs": [ { - "ename": "NameError", - "evalue": "name 'births' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mbirths\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'decade'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m10\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mbirths\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'year'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m//\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mbirths\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpivot_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'births'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'decade'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'gender'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maggfunc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'sum'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mbirths\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpivot_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'births'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'decade'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'gender'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maggfunc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'sum'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mNameError\u001b[0m: name 'births' is not defined" + "name": "stdout", + "output_type": "stream", + "text": [ + "I am changing error to text output\n" ] } ], "source": [ - "births['decade'] = 10 * (births['year'] // 10)\n", - "births.pivot_table('births', index='decade', columns='gender', aggfunc='sum')\n", - "births.pivot_table('births', index='decade', columns='gender', aggfunc='sum') # converted table to error" + "print('I am changing error to text output')" ] }, { From 68e9203bf54a59dd129a8f25167a760c874fa0fe Mon Sep 17 00:00:00 2001 From: Amit Rathi Date: Sun, 23 Jun 2019 08:35:21 +0530 Subject: [PATCH 08/19] Modify global container styling --- notebooks/05.12-Gaussian-Mixtures.ipynb | 30 ++++++++++++++++++++++++- 1 file changed, 29 insertions(+), 1 deletion(-) diff --git a/notebooks/05.12-Gaussian-Mixtures.ipynb b/notebooks/05.12-Gaussian-Mixtures.ipynb index 22d1fb7a5..c30880071 100644 --- a/notebooks/05.12-Gaussian-Mixtures.ipynb +++ b/notebooks/05.12-Gaussian-Mixtures.ipynb @@ -54,7 +54,35 @@ "deletable": true, "editable": true }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "This library uses services provided by ipstack. In this section we will take a look at Gaussian mixture models (GMMs), which can be viewed as \n", + "https://ipstack.com" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + + ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", From 837f5a07a8af22b98764ae6d39474be4b41fcc22 Mon Sep 17 00:00:00 2001 From: Amit Rathi Date: Sun, 23 Jun 2019 08:37:02 +0530 Subject: [PATCH 09/19] Fix JSON formatting --- notebooks/05.12-Gaussian-Mixtures.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/05.12-Gaussian-Mixtures.ipynb b/notebooks/05.12-Gaussian-Mixtures.ipynb index c30880071..12f2dcab5 100644 --- a/notebooks/05.12-Gaussian-Mixtures.ipynb +++ b/notebooks/05.12-Gaussian-Mixtures.ipynb @@ -59,7 +59,7 @@ "data": { "text/html": [ "\n", - "This library uses services provided by ipstack. In this section we will take a look at Gaussian mixture models (GMMs), which can be viewed as \n", + "This library uses services provided by ipstack. In this section we will take a look at Gaussian mixture models (GMMs), which can be viewed as \n", "https://ipstack.com" ], "text/plain": [ From 4a9758ed51e9fad112d2d63f7f0ad3c2b53ed878 Mon Sep 17 00:00:00 2001 From: Amit Rathi Date: Sun, 23 Jun 2019 08:39:24 +0530 Subject: [PATCH 10/19] update global style --- notebooks/05.12-Gaussian-Mixtures.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/05.12-Gaussian-Mixtures.ipynb b/notebooks/05.12-Gaussian-Mixtures.ipynb index 12f2dcab5..c3eec0bc9 100644 --- a/notebooks/05.12-Gaussian-Mixtures.ipynb +++ b/notebooks/05.12-Gaussian-Mixtures.ipynb @@ -72,7 +72,7 @@ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" From 6cc691a6dce7daa9005ad4755a587a31add51d0b Mon Sep 17 00:00:00 2001 From: Amit Rathi Date: Sun, 23 Jun 2019 08:45:45 +0530 Subject: [PATCH 11/19] Random addition --- notebooks/05.12-Gaussian-Mixtures.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/05.12-Gaussian-Mixtures.ipynb b/notebooks/05.12-Gaussian-Mixtures.ipynb index c3eec0bc9..32df4a6cb 100644 --- a/notebooks/05.12-Gaussian-Mixtures.ipynb +++ b/notebooks/05.12-Gaussian-Mixtures.ipynb @@ -41,7 +41,7 @@ "source": [ "The *k*-means clustering model explored in the previous section is simple and relatively easy to understand, but its simplicity leads to practical challenges in its application.\n", "In particular, the non-probabilistic nature of *k*-means and its use of simple distance-from-cluster-center to assign cluster membership leads to poor performance for many real-world situations.\n", - "In this section we will take a look at Gaussian mixture models (GMMs), which can be viewed as an extension of the ideas behind *k*-means, but can also be a powerful tool for estimation beyond simple clustering.\n", + "In this section we will take a random addition look at Gaussian mixture models (GMMs), which can be viewed as an extension of the ideas behind *k*-means, but can also be a powerful tool for estimation beyond simple clustering.\n", "\n", "We begin with the standard imports:" ] From 69e6c34e7d900475037934c8b84c50fca06b1bdd Mon Sep 17 00:00:00 2001 From: Amit Rathi Date: Wed, 1 Jan 2020 12:30:22 +0530 Subject: [PATCH 12/19] Python version change --- notebooks/02.00-Introduction-to-NumPy.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/02.00-Introduction-to-NumPy.ipynb b/notebooks/02.00-Introduction-to-NumPy.ipynb index 813b85ab4..d2f4e92c9 100644 --- a/notebooks/02.00-Introduction-to-NumPy.ipynb +++ b/notebooks/02.00-Introduction-to-NumPy.ipynb @@ -157,7 +157,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.4" + "version": "3.6.8" } }, "nbformat": 4, From 4a247d6bb3fd9544c5735733c0cc243606a6b93d Mon Sep 17 00:00:00 2001 From: Amit Rathi Date: Wed, 1 Jan 2020 12:55:49 +0530 Subject: [PATCH 13/19] Add markdown tables for testing --- .../04.14-Visualization-With-Seaborn.ipynb | 126 +++++++++++++++++- 1 file changed, 119 insertions(+), 7 deletions(-) diff --git a/notebooks/04.14-Visualization-With-Seaborn.ipynb b/notebooks/04.14-Visualization-With-Seaborn.ipynb index 652627b2a..92cd76860 100644 --- a/notebooks/04.14-Visualization-With-Seaborn.ipynb +++ b/notebooks/04.14-Visualization-With-Seaborn.ipynb @@ -8,6 +8,15 @@ "\n", "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", "\n", + "| Tables | Are | Cool | Let us | test them | properly, shall we? |\n", + "|:----------:|:-------------:|:------:|:----------:|:-------------:|:------:|\n", + "| col 1 is | left aligned | 1600 | Still some | work to | be done |\n", + "| col 2 is | centered | 12 | to fix | all the changes | in the table properly |\n", + "| col 3 is | right aligned | 1 | hence, we are testing | the table changes | here |\n", + "| col 1 is | left aligned | 1600 | It's weird | to write random | text\n", + "| col 2 is | centered | 12 | but it's 2020 | and I got | to do this | \n", + "| col 3 is | right aligned | 1 | Let's make | world a better place | peace\n", + "\n", "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" ] }, @@ -16,7 +25,16 @@ "metadata": {}, "source": [ "\n", - "< [Geographic Data with Basemap](04.13-Geographic-Data-With-Basemap.ipynb) | [Contents](Index.ipynb) | [Further Resources](04.15-Further-Resources.ipynb) >" + "< [Geographic Data with Basemap](04.13-Geographic-Data-With-Basemap.ipynb) | [Contents](Index.ipynb) | [Further Resources](04.15-Further-Resources.ipynb) >\n", + "\n", + "| Tables | Are | Cool | Let us | test them | properly, shall we? |\n", + "|:----------:|:-------------:|:------:|:----------:|:-------------:|:------:|\n", + "| col 1 is | left aligned | 1600 | Still some | work to | be done |\n", + "| col 2 is | centered | 12 | to fix | all the changes | in the table properly |\n", + "| col 3 is | right aligned | 1 | hence, we are testing | the table changes | here |\n", + "| col 1 is | left aligned | 1600 | It's weird | to write random | text\n", + "| col 2 is | centered | 12 | but it's 2020 | and I got | to do this | \n", + "| col 3 is | right aligned | 1 | Let's make | world a better place | peace" ] }, { @@ -33,6 +51,15 @@ "Matplotlib has proven to be an incredibly useful and popular visualization tool, but even avid users will admit it often leaves much to be desired.\n", "There are several valid complaints about Matplotlib that often come up:\n", "\n", + "| Tables | Are | Cool | Let us | test them | properly, shall we? |\n", + "|:----------:|:-------------:|:------:|:----------:|:-------------:|:------:|\n", + "| col 1 is | left aligned | 1600 | Still some | work to | be done |\n", + "| col 2 is | centered | 12 | to fix | all the changes | in the table properly |\n", + "| col 3 is | right aligned | 1 | hence, we are testing | the table changes | here |\n", + "| col 1 is | left aligned | 1600 | It's weird | to write random | text\n", + "| col 2 is | centered | 12 | but it's 2020 | and I got | to do this | \n", + "| col 3 is | right aligned | 1 | Let's make | world a better place | peace\n", + "\n", "- Prior to version 2.0, Matplotlib's defaults are not exactly the best choices. It was based off of MATLAB circa 1999, and this often shows.\n", "- Matplotlib's API is relatively low level. Doing sophisticated statistical visualization is possible, but often requires a *lot* of boilerplate code.\n", "- Matplotlib predated Pandas by more than a decade, and thus is not designed for use with Pandas ``DataFrame``s. In order to visualize data from a Pandas ``DataFrame``, you must extract each ``Series`` and often concatenate them together into the right format. It would be nicer to have a plotting library that can intelligently use the ``DataFrame`` labels in a plot.\n", @@ -51,7 +78,16 @@ "## Seaborn Versus Matplotlib\n", "\n", "Here is an example of a simple random-walk plot in Matplotlib, using its classic plot formatting and colors.\n", - "We start with the typical imports:" + "We start with the typical imports:\n", + "\n", + "| Tables | Are | Cool | Let us | test them | properly, shall we? |\n", + "|:----------:|:-------------:|:------:|:----------:|:-------------:|:------:|\n", + "| col 1 is | left aligned | 1600 | Still some | work to | be done |\n", + "| col 2 is | centered | 12 | to fix | all the changes | in the table properly |\n", + "| col 3 is | right aligned | 1 | hence, we are testing | the table changes | here |\n", + "| col 1 is | left aligned | 1600 | It's weird | to write random | text\n", + "| col 2 is | centered | 12 | but it's 2020 | and I got | to do this | \n", + "| col 3 is | right aligned | 1 | Let's make | world a better place | peace" ] }, { @@ -71,7 +107,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now we create some random walk data:" + "| Tables | Are | Cool | Let us | test them | properly, shall we? |\n", + "|:----------:|:-------------:|:------:|:----------:|:-------------:|:------:|\n", + "| col 1 is | left aligned | 1600 | Still some | work to | be done |\n", + "| col 2 is | centered | 12 | to fix | all the changes | in the table properly |\n", + "| col 3 is | right aligned | 1 | hence, we are testing | the table changes | here |\n", + "| col 1 is | left aligned | 1600 | It's weird | to write random | text\n", + "| col 2 is | centered | 12 | but it's 2020 | and I got | to do this | \n", + "| col 3 is | right aligned | 1 | Let's make | world a better place | peace" ] }, { @@ -121,6 +164,15 @@ "source": [ "Although the result contains all the information we'd like it to convey, it does so in a way that is not all that aesthetically pleasing, and even looks a bit old-fashioned in the context of 21st-century data visualization.\n", "\n", + "| Tables | Are | Cool | Let us | test them | properly, shall we? |\n", + "|:----------:|:-------------:|:------:|:----------:|:-------------:|:------:|\n", + "| col 1 is | left aligned | 1600 | Still some | work to | be done |\n", + "| col 2 is | centered | 12 | to fix | all the changes | in the table properly |\n", + "| col 3 is | right aligned | 1 | hence, we are testing | the table changes | here |\n", + "| col 1 is | left aligned | 1600 | It's weird | to write random | text\n", + "| col 2 is | centered | 12 | but it's 2020 | and I got | to do this | \n", + "| col 3 is | right aligned | 1 | Let's make | world a better place | peace\n", + "\n", "Now let's take a look at how it works with Seaborn.\n", "As we will see, Seaborn has many of its own high-level plotting routines, but it can also overwrite Matplotlib's default parameters and in turn get even simple Matplotlib scripts to produce vastly superior output.\n", "We can set the style by calling Seaborn's ``set()`` method.\n", @@ -173,6 +225,20 @@ "Ah, much better!" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "| Tables | Are | Cool | Let us | test them | properly, shall we? |\n", + "|:----------:|:-------------:|:------:|:----------:|:-------------:|:------:|\n", + "| col 1 is | left aligned | 1600 | Still some | work to | be done |\n", + "| col 2 is | centered | 12 | to fix | all the changes | in the table properly |\n", + "| col 3 is | right aligned | 1 | hence, we are testing | the table changes | here |\n", + "| col 1 is | left aligned | 1600 | It's weird | to write random | text\n", + "| col 2 is | centered | 12 | but it's 2020 | and I got | to do this | \n", + "| col 3 is | right aligned | 1 | Let's make | world a better place | peace" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -181,6 +247,15 @@ "\n", "The main idea of Seaborn is that it provides high-level commands to create a variety of plot types useful for statistical data exploration, and even some statistical model fitting.\n", "\n", + "| Tables | Are | Cool | Let us | test them | properly, shall we? |\n", + "|:----------:|:-------------:|:------:|:----------:|:-------------:|:------:|\n", + "| col 1 is | left aligned | 1600 | Still some | work to | be done |\n", + "| col 2 is | centered | 12 | to fix | all the changes | in the table properly |\n", + "| col 3 is | right aligned | 1 | hence, we are testing | the table changes | here |\n", + "| col 1 is | left aligned | 1600 | It's weird | to write random | text\n", + "| col 2 is | centered | 12 | but it's 2020 | and I got | to do this | \n", + "| col 3 is | right aligned | 1 | Let's make | world a better place | peace\n", + "\n", "Let's take a look at a few of the datasets and plot types available in Seaborn. Note that all of the following *could* be done using raw Matplotlib commands (this is, in fact, what Seaborn does under the hood) but the Seaborn API is much more convenient." ] }, @@ -191,7 +266,16 @@ "### Histograms, KDE, and densities\n", "\n", "Often in statistical data visualization, all you want is to plot histograms and joint distributions of variables.\n", - "We have seen that this is relatively straightforward in Matplotlib:" + "We have seen that this is relatively straightforward in Matplotlib:\n", + "\n", + "| Tables | Are | Cool | Let us | test them | properly, shall we? |\n", + "|:----------:|:-------------:|:------:|:----------:|:-------------:|:------:|\n", + "| col 1 is | left aligned | 1600 | Still some | work to | be done |\n", + "| col 2 is | centered | 12 | to fix | all the changes | in the table properly |\n", + "| col 3 is | right aligned | 1 | hence, we are testing | the table changes | here |\n", + "| col 1 is | left aligned | 1600 | It's weird | to write random | text\n", + "| col 2 is | centered | 12 | but it's 2020 | and I got | to do this | \n", + "| col 3 is | right aligned | 1 | Let's make | world a better place | peace" ] }, { @@ -251,7 +335,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Histograms and KDE can be combined using ``distplot``:" + "Histograms and KDE can be combined using ``distplot``:\n", + "\n", + "| Tables | Are | Cool | Let us | test them | properly, shall we? |\n", + "|:----------:|:-------------:|:------:|:----------:|:-------------:|:------:|\n", + "| col 1 is | left aligned | 1600 | Still some | work to | be done |\n", + "| col 2 is | centered | 12 | to fix | all the changes | in the table properly |\n", + "| col 3 is | right aligned | 1 | hence, we are testing | the table changes | here |\n", + "| col 1 is | left aligned | 1600 | It's weird | to write random | text\n", + "| col 2 is | centered | 12 | but it's 2020 | and I got | to do this | \n", + "| col 3 is | right aligned | 1 | Let's make | world a better place | peace" ] }, { @@ -279,6 +372,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "| Tables | Are | Cool | Let us | test them | properly, shall we? |\n", + "|:----------:|:-------------:|:------:|:----------:|:-------------:|:------:|\n", + "| col 1 is | left aligned | 1600 | Still some | work to | be done |\n", + "| col 2 is | centered | 12 | to fix | all the changes | in the table properly |\n", + "| col 3 is | right aligned | 1 | hence, we are testing | the table changes | here |\n", + "| col 1 is | left aligned | 1600 | It's weird | to write random | text\n", + "| col 2 is | centered | 12 | but it's 2020 | and I got | to do this | \n", + "| col 3 is | right aligned | 1 | Let's make | world a better place | peace\n", + "\n", + "\n", "If we pass the full two-dimensional dataset to ``kdeplot``, we will get a two-dimensional visualization of the data:" ] }, @@ -307,7 +410,16 @@ "metadata": {}, "source": [ "We can see the joint distribution and the marginal distributions together using ``sns.jointplot``.\n", - "For this plot, we'll set the style to a white background:" + "For this plot, we'll set the style to a white background:\n", + "\n", + "| Tables | Are | Cool | Let us | test them | properly, shall we? |\n", + "|:----------:|:-------------:|:------:|:----------:|:-------------:|:------:|\n", + "| col 1 is | left aligned | 1600 | Still some | work to | be done |\n", + "| col 2 is | centered | 12 | to fix | all the changes | in the table properly |\n", + "| col 3 is | right aligned | 1 | hence, we are testing | the table changes | here |\n", + "| col 1 is | left aligned | 1600 | It's weird | to write random | text\n", + "| col 2 is | centered | 12 | but it's 2020 | and I got | to do this | \n", + "| col 3 is | right aligned | 1 | Let's make | world a better place | peace" ] }, { @@ -1611,7 +1723,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.4" + "version": "3.6.8" } }, "nbformat": 4, From aea7b3ed1afa7cc83acd0e6bd0b45a0aed08cdc4 Mon Sep 17 00:00:00 2001 From: Amit Rathi Date: Wed, 1 Jan 2020 13:22:23 +0530 Subject: [PATCH 14/19] Markdown table changes for testing --- .../04.14-Visualization-With-Seaborn.ipynb | 186 ++++++++++++------ 1 file changed, 127 insertions(+), 59 deletions(-) diff --git a/notebooks/04.14-Visualization-With-Seaborn.ipynb b/notebooks/04.14-Visualization-With-Seaborn.ipynb index 92cd76860..ad54504d7 100644 --- a/notebooks/04.14-Visualization-With-Seaborn.ipynb +++ b/notebooks/04.14-Visualization-With-Seaborn.ipynb @@ -8,9 +8,11 @@ "\n", "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", "\n", - "| Tables | Are | Cool | Let us | test them | properly, shall we? |\n", - "|:----------:|:-------------:|:------:|:----------:|:-------------:|:------:|\n", - "| col 1 is | left aligned | 1600 | Still some | work to | be done |\n", + "**Changing Header and first row completely**\n", + "\n", + "| Change | header and | 1st | row | completely | for testing |\n", + "|----------|-------------|------|----------|-------------|------|\n", + "| the | colons | after | the header | row are | removed |\n", "| col 2 is | centered | 12 | to fix | all the changes | in the table properly |\n", "| col 3 is | right aligned | 1 | hence, we are testing | the table changes | here |\n", "| col 1 is | left aligned | 1600 | It's weird | to write random | text\n", @@ -27,14 +29,16 @@ "\n", "< [Geographic Data with Basemap](04.13-Geographic-Data-With-Basemap.ipynb) | [Contents](Index.ipynb) | [Further Resources](04.15-Further-Resources.ipynb) >\n", "\n", - "| Tables | Are | Cool | Let us | test them | properly, shall we? |\n", - "|:----------:|:-------------:|:------:|:----------:|:-------------:|:------:|\n", - "| col 1 is | left aligned | 1600 | Still some | work to | be done |\n", - "| col 2 is | centered | 12 | to fix | all the changes | in the table properly |\n", - "| col 3 is | right aligned | 1 | hence, we are testing | the table changes | here |\n", - "| col 1 is | left aligned | 1600 | It's weird | to write random | text\n", - "| col 2 is | centered | 12 | but it's 2020 | and I got | to do this | \n", - "| col 3 is | right aligned | 1 | Let's make | world a better place | peace" + "**Additional Column in the middle**\n", + "\n", + "| Tables | Are | Cool | NEW COLUMN | Let us | test them | properly, shall we? |\n", + "|:----------:|:-------------:|:-------------:|:------:|:----------:|:-------------:|:------:|\n", + "| col 1 is | left aligned | 1600 | ADDING A | Still some | work to | be done |\n", + "| col 2 is | centered | 12 | COLUMN | to fix | all the changes | in the table properly |\n", + "| col 3 is | right aligned | 1 | FOR THE | hence, we are testing | the table changes | here |\n", + "| col 1 is | left aligned | 1600 | TESTING | It's weird | to write random | text\n", + "| col 2 is | centered | 12 | PURPOSE | but it's 2020 | and I got | to do this | \n", + "| col 3 is | right aligned | 1 | HERE ON OUT | Let's make | world a better place | peace" ] }, { @@ -51,14 +55,16 @@ "Matplotlib has proven to be an incredibly useful and popular visualization tool, but even avid users will admit it often leaves much to be desired.\n", "There are several valid complaints about Matplotlib that often come up:\n", "\n", + "**Small changes at multiple places**\n", + "\n", "| Tables | Are | Cool | Let us | test them | properly, shall we? |\n", "|:----------:|:-------------:|:------:|:----------:|:-------------:|:------:|\n", - "| col 1 is | left aligned | 1600 | Still some | work to | be done |\n", + "| col 1 is | right aligned | 1800 | Still some | work got to | be done |\n", "| col 2 is | centered | 12 | to fix | all the changes | in the table properly |\n", "| col 3 is | right aligned | 1 | hence, we are testing | the table changes | here |\n", "| col 1 is | left aligned | 1600 | It's weird | to write random | text\n", - "| col 2 is | centered | 12 | but it's 2020 | and I got | to do this | \n", - "| col 3 is | right aligned | 1 | Let's make | world a better place | peace\n", + "| row 2 is | centered | 12 | but it is 2220 | and I got | to do this | \n", + "| col 3 is | right aligned | 1 | Let's make | world A better place | apple pie\n", "\n", "- Prior to version 2.0, Matplotlib's defaults are not exactly the best choices. It was based off of MATLAB circa 1999, and this often shows.\n", "- Matplotlib's API is relatively low level. Doing sophisticated statistical visualization is possible, but often requires a *lot* of boilerplate code.\n", @@ -80,14 +86,16 @@ "Here is an example of a simple random-walk plot in Matplotlib, using its classic plot formatting and colors.\n", "We start with the typical imports:\n", "\n", - "| Tables | Are | Cool | Let us | test them | properly, shall we? |\n", - "|:----------:|:-------------:|:------:|:----------:|:-------------:|:------:|\n", - "| col 1 is | left aligned | 1600 | Still some | work to | be done |\n", - "| col 2 is | centered | 12 | to fix | all the changes | in the table properly |\n", - "| col 3 is | right aligned | 1 | hence, we are testing | the table changes | here |\n", - "| col 1 is | left aligned | 1600 | It's weird | to write random | text\n", - "| col 2 is | centered | 12 | but it's 2020 | and I got | to do this | \n", - "| col 3 is | right aligned | 1 | Let's make | world a better place | peace" + "**Remove 1st Column**\n", + "\n", + "| Are | Cool | Let us | test them | properly, shall we? |\n", + "|:-------------:|:------:|:----------:|:-------------:|:------:|\n", + "| left aligned | 1600 | Still some | work to | be done |\n", + "| centered | 12 | to fix | all the changes | in the table properly |\n", + "| right aligned | 1 | hence, we are testing | the table changes | here |\n", + "| left aligned | 1600 | It's weird | to write random | text\n", + "| centered | 12 | but it's 2020 | and I got | to do this | \n", + "| right aligned | 1 | Let's make | world a better place | peace" ] }, { @@ -107,14 +115,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "**First and last row removed**\n", + "\n", "| Tables | Are | Cool | Let us | test them | properly, shall we? |\n", "|:----------:|:-------------:|:------:|:----------:|:-------------:|:------:|\n", - "| col 1 is | left aligned | 1600 | Still some | work to | be done |\n", "| col 2 is | centered | 12 | to fix | all the changes | in the table properly |\n", "| col 3 is | right aligned | 1 | hence, we are testing | the table changes | here |\n", "| col 1 is | left aligned | 1600 | It's weird | to write random | text\n", - "| col 2 is | centered | 12 | but it's 2020 | and I got | to do this | \n", - "| col 3 is | right aligned | 1 | Let's make | world a better place | peace" + "| col 2 is | centered | 12 | but it's 2020 | and I got | to do this | " ] }, { @@ -164,11 +172,14 @@ "source": [ "Although the result contains all the information we'd like it to convey, it does so in a way that is not all that aesthetically pleasing, and even looks a bit old-fashioned in the context of 21st-century data visualization.\n", "\n", + "**ADD ROW IN MIDDLE**\n", + "\n", "| Tables | Are | Cool | Let us | test them | properly, shall we? |\n", "|:----------:|:-------------:|:------:|:----------:|:-------------:|:------:|\n", "| col 1 is | left aligned | 1600 | Still some | work to | be done |\n", "| col 2 is | centered | 12 | to fix | all the changes | in the table properly |\n", "| col 3 is | right aligned | 1 | hence, we are testing | the table changes | here |\n", + "| COL NEW | NEWLY ADDED | COLUMN | 2200 | TO THIS TABLE | IN THE MIDDLE |\n", "| col 1 is | left aligned | 1600 | It's weird | to write random | text\n", "| col 2 is | centered | 12 | but it's 2020 | and I got | to do this | \n", "| col 3 is | right aligned | 1 | Let's make | world a better place | peace\n", @@ -229,14 +240,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "**DELETE TOP 2 AND BOTTOM 2 ROWS**\n", + "\n", "| Tables | Are | Cool | Let us | test them | properly, shall we? |\n", "|:----------:|:-------------:|:------:|:----------:|:-------------:|:------:|\n", - "| col 1 is | left aligned | 1600 | Still some | work to | be done |\n", - "| col 2 is | centered | 12 | to fix | all the changes | in the table properly |\n", "| col 3 is | right aligned | 1 | hence, we are testing | the table changes | here |\n", - "| col 1 is | left aligned | 1600 | It's weird | to write random | text\n", - "| col 2 is | centered | 12 | but it's 2020 | and I got | to do this | \n", - "| col 3 is | right aligned | 1 | Let's make | world a better place | peace" + "| col 1 is | left aligned | 1600 | It's weird | to write random | text" ] }, { @@ -247,14 +256,16 @@ "\n", "The main idea of Seaborn is that it provides high-level commands to create a variety of plot types useful for statistical data exploration, and even some statistical model fitting.\n", "\n", - "| Tables | Are | Cool | Let us | test them | properly, shall we? |\n", - "|:----------:|:-------------:|:------:|:----------:|:-------------:|:------:|\n", - "| col 1 is | left aligned | 1600 | Still some | work to | be done |\n", - "| col 2 is | centered | 12 | to fix | all the changes | in the table properly |\n", - "| col 3 is | right aligned | 1 | hence, we are testing | the table changes | here |\n", - "| col 1 is | left aligned | 1600 | It's weird | to write random | text\n", - "| col 2 is | centered | 12 | but it's 2020 | and I got | to do this | \n", - "| col 3 is | right aligned | 1 | Let's make | world a better place | peace\n", + "**DELETE FIRST 2 AND LAST 2 COLUMNS**\n", + "\n", + "| Cool | Let us |\n", + "|:------:|:----------:|\n", + "| 1600 | Still some |\n", + "| 12 | to fix |\n", + "| 1 | hence, we are testing |\n", + "| 1600 | It's weird |\n", + "| 12 | but it's 2020 |\n", + "| 1 | Let's make |\n", "\n", "Let's take a look at a few of the datasets and plot types available in Seaborn. Note that all of the following *could* be done using raw Matplotlib commands (this is, in fact, what Seaborn does under the hood) but the Seaborn API is much more convenient." ] @@ -268,14 +279,12 @@ "Often in statistical data visualization, all you want is to plot histograms and joint distributions of variables.\n", "We have seen that this is relatively straightforward in Matplotlib:\n", "\n", - "| Tables | Are | Cool | Let us | test them | properly, shall we? |\n", - "|:----------:|:-------------:|:------:|:----------:|:-------------:|:------:|\n", - "| col 1 is | left aligned | 1600 | Still some | work to | be done |\n", - "| col 2 is | centered | 12 | to fix | all the changes | in the table properly |\n", - "| col 3 is | right aligned | 1 | hence, we are testing | the table changes | here |\n", - "| col 1 is | left aligned | 1600 | It's weird | to write random | text\n", - "| col 2 is | centered | 12 | but it's 2020 | and I got | to do this | \n", - "| col 3 is | right aligned | 1 | Let's make | world a better place | peace" + "**REPLACE ENTIRE TABLE**\n", + "\n", + "Markdown | Less | Pretty\n", + "--- | --- | ---\n", + "*Still* | `renders` | **nicely**\n", + "1 | 2 | 3" ] }, { @@ -337,6 +346,8 @@ "source": [ "Histograms and KDE can be combined using ``distplot``:\n", "\n", + "**NEW ROW AT END**\n", + "\n", "| Tables | Are | Cool | Let us | test them | properly, shall we? |\n", "|:----------:|:-------------:|:------:|:----------:|:-------------:|:------:|\n", "| col 1 is | left aligned | 1600 | Still some | work to | be done |\n", @@ -344,7 +355,8 @@ "| col 3 is | right aligned | 1 | hence, we are testing | the table changes | here |\n", "| col 1 is | left aligned | 1600 | It's weird | to write random | text\n", "| col 2 is | centered | 12 | but it's 2020 | and I got | to do this | \n", - "| col 3 is | right aligned | 1 | Let's make | world a better place | peace" + "| col 3 is | right aligned | 1 | Let's make | world a better place | peace\n", + "| NEWLY | ADDED | ROW | AT THE | END OF THE | TABLE" ] }, { @@ -372,14 +384,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "| Tables | Are | Cool | Let us | test them | properly, shall we? |\n", - "|:----------:|:-------------:|:------:|:----------:|:-------------:|:------:|\n", - "| col 1 is | left aligned | 1600 | Still some | work to | be done |\n", - "| col 2 is | centered | 12 | to fix | all the changes | in the table properly |\n", - "| col 3 is | right aligned | 1 | hence, we are testing | the table changes | here |\n", - "| col 1 is | left aligned | 1600 | It's weird | to write random | text\n", - "| col 2 is | centered | 12 | but it's 2020 | and I got | to do this | \n", - "| col 3 is | right aligned | 1 | Let's make | world a better place | peace\n", + "**REMOVE LAST COLUMN**\n", + "\n", + "| Tables | Are | Cool | Let us | test them |\n", + "|:----------:|:-------------:|:------:|:----------:|:-------------:|\n", + "| col 1 is | left aligned | 1600 | Still some | work to |\n", + "| col 2 is | centered | 12 | to fix | all the changes |\n", + "| col 3 is | right aligned | 1 | hence, we are testing | the table changes |\n", + "| col 1 is | left aligned | 1600 | It's weird | to write random |\n", + "| col 2 is | centered | 12 | but it's 2020 | and I got |\n", + "| col 3 is | right aligned | 1 | Let's make | world a better place |\n", "\n", "\n", "If we pass the full two-dimensional dataset to ``kdeplot``, we will get a two-dimensional visualization of the data:" @@ -447,7 +461,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "There are other parameters that can be passed to ``jointplot``—for example, we can use a hexagonally based histogram instead:" + "There are other parameters that can be passed to ``jointplot``—for example, we can use a hexagonally based histogram instead:\n", + "\n", + "| Tables | Are | Cool | Let us | test them | properly, shall we? |\n", + "|:----------:|:-------------:|:------:|:----------:|:-------------:|:------:|\n", + "| col 1 is | left aligned | 1600 | Still some | work to | be done |\n", + "| col 2 is | centered | 12 | to fix | all the changes | in the table properly |\n", + "| col 3 is | right aligned | 1 | hence, we are testing | the table changes | here |\n", + "| col 1 is | left aligned | 1600 | It's weird | to write random | text\n", + "| col 2 is | centered | 12 | but it's 2020 | and I got | to do this | \n", + "| col 3 is | right aligned | 1 | Let's make | world a better place | peace" ] }, { @@ -486,7 +509,16 @@ { "cell_type": "markdown", "metadata": {}, - "source": [] + "source": [ + "| Tables | Are | Cool | Let us | test them | properly, shall we? |\n", + "|:----------:|:-------------:|:------:|:----------:|:-------------:|:------:|\n", + "| col 1 is | left aligned | 1600 | Still some | work to | be done |\n", + "| col 2 is | centered | 12 | to fix | all the changes | in the table properly |\n", + "| col 3 is | right aligned | 1 | hence, we are testing | the table changes | here |\n", + "| col 1 is | left aligned | 1600 | It's weird | to write random | text\n", + "| col 2 is | centered | 12 | but it's 2020 | and I got | to do this | \n", + "| col 3 is | right aligned | 1 | Let's make | world a better place | peace" + ] }, { "cell_type": "code", @@ -501,6 +533,15 @@ "source": [ "### Faceted histograms\n", "\n", + "| Tables | Are | Cool | Let us | test them | properly, shall we? |\n", + "|:----------:|:-------------:|:------:|:----------:|:-------------:|:------:|\n", + "| col 1 is | left aligned | 1600 | Still some | work to | be done |\n", + "| col 2 is | centered | 12 | to fix | all the changes | in the table properly |\n", + "| col 3 is | right aligned | 1 | hence, we are testing | the table changes | here |\n", + "| col 1 is | left aligned | 1600 | It's weird | to write random | text\n", + "| col 2 is | centered | 12 | but it's 2020 | and I got | to do this | \n", + "| col 3 is | right aligned | 1 | Let's make | world a better place | peace\n", + "\n", "Emptied above cells Sometimes the best way to view data is via histograms of subsets. Seaborn's ``FacetGrid`` makes this extremely simple.\n", "We'll take a look at some data that shows the amount that restaurant staff receive in tips based on various indicator data:" ] @@ -630,7 +671,16 @@ "source": [ "### Factor plots\n", "\n", - "Factor plots can be useful for this kind of visualization as well. This allows you to view the distribution of a parameter within bins defined by any other parameter:" + "Factor plots can be useful for this kind of visualization as well. This allows you to view the distribution of a parameter within bins defined by any other parameter:\n", + "\n", + "| Tables | Are | Cool | Let us | test them | properly, shall we? |\n", + "|:----------:|:-------------:|:------:|:----------:|:-------------:|:------:|\n", + "| col 1 is | left aligned | 1600 | Still some | work to | be done |\n", + "| col 2 is | centered | 12 | to fix | all the changes | in the table properly |\n", + "| col 3 is | right aligned | 1 | hence, we are testing | the table changes | here |\n", + "| col 1 is | left aligned | 1600 | It's weird | to write random | text\n", + "| col 2 is | centered | 12 | but it's 2020 | and I got | to do this | \n", + "| col 3 is | right aligned | 1 | Let's make | world a better place | peace" ] }, { @@ -661,6 +711,15 @@ "source": [ "### Joint distributions\n", "\n", + "| Tables | Are | Cool | Let us | test them | properly, shall we? |\n", + "|:----------:|:-------------:|:------:|:----------:|:-------------:|:------:|\n", + "| col 1 is | left aligned | 1600 | Still some | work to | be done |\n", + "| col 2 is | centered | 12 | to fix | all the changes | in the table properly |\n", + "| col 3 is | right aligned | 1 | hence, we are testing | the table changes | here |\n", + "| col 1 is | left aligned | 1600 | It's weird | to write random | text\n", + "| col 2 is | centered | 12 | but it's 2020 | and I got | to do this | \n", + "| col 3 is | right aligned | 1 | Let's make | world a better place | peace\n", + "\n", "Similar to the pairplot we saw earlier, we can use ``sns.jointplot`` to show the joint distribution between different datasets, along with the associated marginal distributions:" ] }, @@ -718,7 +777,16 @@ "source": [ "### Bar plots\n", "\n", - "Time series can be plotted using ``sns.factorplot``. In the following example, we'll use the Planets data that we first saw in [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb):" + "Time series can be plotted using ``sns.factorplot``. In the following example, we'll use the Planets data that we first saw in [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb):\n", + "\n", + "| Tables | Are | Cool | Let us | test them | properly, shall we? |\n", + "|:----------:|:-------------:|:------:|:----------:|:-------------:|:------:|\n", + "| col 1 is | left aligned | 1600 | Still some | work to | be done |\n", + "| col 2 is | centered | 12 | to fix | all the changes | in the table properly |\n", + "| col 3 is | right aligned | 1 | hence, we are testing | the table changes | here |\n", + "| col 1 is | left aligned | 1600 | It's weird | to write random | text\n", + "| col 2 is | centered | 12 | but it's 2020 | and I got | to do this | \n", + "| col 3 is | right aligned | 1 | Let's make | world a better place | peace" ] }, { From fbc8c812c1e07a655531bc1096f131d2a9a6b0d5 Mon Sep 17 00:00:00 2001 From: Amit Rathi Date: Sat, 4 Jan 2020 12:07:09 +0530 Subject: [PATCH 15/19] Random tiny change --- .../04.14-Visualization-With-Seaborn.ipynb | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/notebooks/04.14-Visualization-With-Seaborn.ipynb b/notebooks/04.14-Visualization-With-Seaborn.ipynb index ad54504d7..d10e5a99d 100644 --- a/notebooks/04.14-Visualization-With-Seaborn.ipynb +++ b/notebooks/04.14-Visualization-With-Seaborn.ipynb @@ -6,7 +6,7 @@ "source": [ "\n", "\n", - "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook] (random addition) (http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", "\n", "**Changing Header and first row completely**\n", "\n", @@ -88,14 +88,14 @@ "\n", "**Remove 1st Column**\n", "\n", - "| Are | Cool | Let us | test them | properly, shall we? |\n", - "|:-------------:|:------:|:----------:|:-------------:|:------:|\n", - "| left aligned | 1600 | Still some | work to | be done |\n", - "| centered | 12 | to fix | all the changes | in the table properly |\n", - "| right aligned | 1 | hence, we are testing | the table changes | here |\n", - "| left aligned | 1600 | It's weird | to write random | text\n", - "| centered | 12 | but it's 2020 | and I got | to do this | \n", - "| right aligned | 1 | Let's make | world a better place | peace" + "Are | Cool | Let us | test them | properly, shall we? |\n", + "-------------:|:------:|:----------:|:-------------:|:------:|\n", + " left aligned | 1600 | Still some | work to | be done |\n", + " centered | 12 | to fix | all the changes | in the table properly |\n", + " right aligned | 1 | hence, we are testing | the table changes | here |\n", + " left aligned | 1600 | It's weird | to write random | text\n", + " centered | 12 | but it's 2020 | and I got | to do this | \n", + " right aligned | 1 | Let's make | world a better place | peace" ] }, { From 097acae0374954067f1f475c40ca28d5722e6753 Mon Sep 17 00:00:00 2001 From: Amit Rathi Date: Tue, 28 Jan 2020 12:30:51 +0530 Subject: [PATCH 16/19] Add audio example of IPython.display.Audio --- Add Audio.ipynb | 68 +++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 68 insertions(+) create mode 100644 Add Audio.ipynb diff --git a/Add Audio.ipynb b/Add Audio.ipynb new file mode 100644 index 000000000..f61681028 --- /dev/null +++ b/Add Audio.ipynb @@ -0,0 +1,68 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Generate a sound\n", + "import numpy as np\n", + "import IPython.display as ipd\n", + "from IPython.display import Audio\n", + "framerate = 44100\n", + "t = np.linspace(0,5,framerate*5)\n", + "data = np.sin(2*np.pi*220*t) + np.sin(2*np.pi*224*t)\n", + "Audio(data,rate=framerate)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Raw Cell Format", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 7ba18cfaedd327263d3afcf7e043b022e03127e3 Mon Sep 17 00:00:00 2001 From: Amit Rathi Date: Fri, 7 Feb 2020 21:11:59 +0530 Subject: [PATCH 17/19] Shap JS visualization test --- notebooks/Shap Trial.ipynb | 280 +++++++++++++++++++++++++++++++++++++ 1 file changed, 280 insertions(+) create mode 100644 notebooks/Shap Trial.ipynb diff --git a/notebooks/Shap Trial.ipynb b/notebooks/Shap Trial.ipynb new file mode 100644 index 000000000..bcb6f9da7 --- /dev/null +++ b/notebooks/Shap Trial.ipynb @@ -0,0 +1,280 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "
\n", + "
\n", + " Visualization omitted, Javascript library not loaded!
\n", + " Have you run `initjs()` in this notebook? If this notebook was from another\n", + " user you must also trust this notebook (File -> Trust notebook). If you are viewing\n", + " this notebook on github the Javascript has been stripped for security. If you are using\n", + " JupyterLab this error is because a JupyterLab extension has not yet been written.\n", + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import xgboost\n", + "import shap\n", + "\n", + "# load JS visualization code to notebook\n", + "shap.initjs()\n", + "\n", + "# train XGBoost model\n", + "X,y = shap.datasets.boston()\n", + "model = xgboost.train({\"learning_rate\": 0.01}, xgboost.DMatrix(X, label=y), 100)\n", + "\n", + "# explain the model's predictions using SHAP\n", + "# (same syntax works for LightGBM, CatBoost, scikit-learn and spark models)\n", + "explainer = shap.TreeExplainer(model)\n", + "shap_values = explainer.shap_values(X)\n", + "\n", + "# visualize the first prediction's explanation (use matplotlib=True to avoid Javascript)\n", + "shap.force_plot(explainer.expected_value, shap_values[0,:], X.iloc[0,:])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a\n" + ] + } + ], + "source": [ + "print('a')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + "
\n", + " Visualization omitted, Javascript library not loaded!
\n", + " Have you run `initjs()` in this notebook? If this notebook was from another\n", + " user you must also trust this notebook (File -> Trust notebook). If you are viewing\n", + " this notebook on github the Javascript has been stripped for security. If you are using\n", + " JupyterLab this error is because a JupyterLab extension has not yet been written.\n", + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# visualize the training set predictions\n", + "shap.force_plot(explainer.expected_value, shap_values, X)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# create a dependence plot to show the effect of a single feature across the whole dataset\n", + "shap.dependence_plot(\"RM\", shap_values, X)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import shap\n", + "shap.initjs()\n", + "# summarize the effects of all the features\n", + "shap.summary_plot(shap_values, X)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# summarize the effects of all the features\n", + "shap.summary_plot(shap_values, X)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From aad6fb7272dede2fbc9b708d8cac1932950fb03a Mon Sep 17 00:00:00 2001 From: Amit Rathi Date: Sat, 8 Feb 2020 11:48:30 +0530 Subject: [PATCH 18/19] Test Lime & Shap visualizations --- .../Lime - basic usage, two class case.ipynb | 74665 ++++++++++++++++ notebooks/Shap Trial.ipynb | 20 +- 2 files changed, 74672 insertions(+), 13 deletions(-) create mode 100644 notebooks/Lime - basic usage, two class case.ipynb diff --git a/notebooks/Lime - basic usage, two class case.ipynb b/notebooks/Lime - basic usage, two class case.ipynb new file mode 100644 index 000000000..04e2e635e --- /dev/null +++ b/notebooks/Lime - basic usage, two class case.ipynb @@ -0,0 +1,74665 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import print_function\n", + "import lime\n", + "import sklearn\n", + "import numpy as np\n", + "import sklearn\n", + "import sklearn.ensemble\n", + "import sklearn.metrics" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fetching data, training a classifier" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this tutorial, we'll be using the [20 newsgroups dataset](http://scikit-learn.org/stable/datasets/#the-20-newsgroups-text-dataset). In particular, for simplicity, we'll use a 2-class subset: atheism and christianity." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Downloading 20news dataset. This may take a few minutes.\n", + "Downloading dataset from https://ndownloader.figshare.com/files/5975967 (14 MB)\n" + ] + } + ], + "source": [ + "from sklearn.datasets import fetch_20newsgroups\n", + "categories = ['alt.atheism', 'soc.religion.christian']\n", + "newsgroups_train = fetch_20newsgroups(subset='train', categories=categories)\n", + "newsgroups_test = fetch_20newsgroups(subset='test', categories=categories)\n", + "class_names = ['atheism', 'christian']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's use the tfidf vectorizer, commonly used for text." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "vectorizer = sklearn.feature_extraction.text.TfidfVectorizer(lowercase=False)\n", + "train_vectors = vectorizer.fit_transform(newsgroups_train.data)\n", + "test_vectors = vectorizer.transform(newsgroups_test.data)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's say we want to use random forests for classification. It's usually hard to understand what random forests are doing, especially with many trees." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n", + " criterion='gini', max_depth=None, max_features='auto',\n", + " max_leaf_nodes=None, max_samples=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, n_estimators=500,\n", + " n_jobs=None, oob_score=False, random_state=None,\n", + " verbose=0, warm_start=False)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rf = sklearn.ensemble.RandomForestClassifier(n_estimators=500)\n", + "rf.fit(train_vectors, newsgroups_train.target)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9241540256709451" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pred = rf.predict(test_vectors)\n", + "sklearn.metrics.f1_score(newsgroups_test.target, pred, average='binary')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that this classifier achieves a very high F score. [The sklearn guide to 20 newsgroups](http://scikit-learn.org/stable/datasets/#filtering-text-for-more-realistic-training) indicates that Multinomial Naive Bayes overfits this dataset by learning irrelevant stuff, such as headers. Let's see if random forests do the same." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Explaining predictions using lime" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lime explainers assume that classifiers act on raw text, but sklearn classifiers act on vectorized representation of texts. For this purpose, we use sklearn's pipeline, and implements predict_proba on raw_text lists." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "from lime import lime_text\n", + "from sklearn.pipeline import make_pipeline\n", + "c = make_pipeline(vectorizer, rf)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.252 0.748]]\n" + ] + } + ], + "source": [ + "print(c.predict_proba([newsgroups_test.data[0]]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we create an explainer object. We pass the class_names as an argument for prettier display." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "from lime.lime_text import LimeTextExplainer\n", + "explainer = LimeTextExplainer(class_names=class_names)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We then generate an explanation with at most 6 features for an arbitrary document in the test set." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/amitrathi/.virtualenvs/loaded2/lib/python3.6/site-packages/lime/lime_text.py:116: FutureWarning: split() requires a non-empty pattern match.\n", + " self.as_list = [s for s in splitter.split(self.raw) if s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Document id: 83\n", + "Probability(christian) = 0.492\n", + "True class: atheism\n" + ] + } + ], + "source": [ + "idx = 83\n", + "exp = explainer.explain_instance(newsgroups_test.data[idx], c.predict_proba, num_features=6)\n", + "print('Document id: %d' % idx)\n", + "print('Probability(christian) =', c.predict_proba([newsgroups_test.data[idx]])[0,1])\n", + "print('True class: %s' % class_names[newsgroups_test.target[idx]])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The classifier got this example right (it predicted atheism). \n", + "The explanation is presented below as a list of weighted features. " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('Host', -0.13158254377478837),\n", + " ('Posting', -0.12755833682065656),\n", + " ('NNTP', -0.1091239484180258),\n", + " ('edu', -0.02540690605359643),\n", + " ('post', -0.0130522416154676),\n", + " ('Thanks', 0.009196448838747762)]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "exp.as_list()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These weighted features are a linear model, which approximates the behaviour of the random forest classifier in the vicinity of the test example. Roughly, if we remove 'Posting' and 'Host' from the document , the prediction should move towards the opposite class (Christianity) by about 0.27 (the sum of the weights for both features). Let's see if this is the case." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original prediction: 0.492\n", + "Prediction removing some features: 0.734\n", + "Difference: 0.242\n" + ] + } + ], + "source": [ + "print('Original prediction:', rf.predict_proba(test_vectors[idx])[0,1])\n", + "tmp = test_vectors[idx].copy()\n", + "tmp[0,vectorizer.vocabulary_['Posting']] = 0\n", + "tmp[0,vectorizer.vocabulary_['Host']] = 0\n", + "print('Prediction removing some features:', rf.predict_proba(tmp)[0,1])\n", + "print('Difference:', rf.predict_proba(tmp)[0,1] - rf.predict_proba(test_vectors[idx])[0,1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pretty close! \n", + "The words that explain the model around this document seem very arbitrary - not much to do with either Christianity or Atheism. \n", + "In fact, these are words that appear in the email headers (you will see this clearly soon), which make distinguishing between the classes much easier." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualizing explanations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The explanations can be returned as a matplotlib barplot:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "fig = exp.as_pyplot_figure()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The explanations can also be exported as an html page (which we can render here in this notebook), using D3.js to render graphs. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + "
\n", + " \n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "exp.show_in_notebook(text=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, we can save the fully contained html page to a file:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "exp.save_to_file('/tmp/oi.html')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we can also include a visualization of the original document, with the words in the explanations highlighted. Notice how the words that affect the classifier the most are all in the email header." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + "
\n", + " \n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "exp.show_in_notebook(text=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That's it for this tutorial. Random forests were just an example, this explainer works for any classifier you may want to use, as long as it implements predict_proba." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/notebooks/Shap Trial.ipynb b/notebooks/Shap Trial.ipynb index bcb6f9da7..2d681f0ca 100644 --- a/notebooks/Shap Trial.ipynb +++ b/notebooks/Shap Trial.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -36,18 +36,11 @@ "metadata": {}, "output_type": "display_data" }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n" - ] - }, { "data": { "text/html": [ "\n", - "
\n", + "
\n", "
\n", " Visualization omitted, Javascript library not loaded!
\n", " Have you run `initjs()` in this notebook? If this notebook was from another\n", @@ -57,8 +50,8 @@ "
\n", " " ], @@ -66,7 +59,7 @@ "" ] }, - "execution_count": 1, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -80,7 +73,7 @@ "\n", "# train XGBoost model\n", "X,y = shap.datasets.boston()\n", - "model = xgboost.train({\"learning_rate\": 0.01}, xgboost.DMatrix(X, label=y), 100)\n", + "model = xgboost.train({\"learning_rate\": 0.02}, xgboost.DMatrix(X, label=y), 90)\n", "\n", "# explain the model's predictions using SHAP\n", "# (same syntax works for LightGBM, CatBoost, scikit-learn and spark models)\n", @@ -257,6 +250,7 @@ } ], "metadata": { + "celltoolbar": "Edit Metadata", "kernelspec": { "display_name": "Python 3", "language": "python", From 85afecc501e50faac5e869763602715cfad34693 Mon Sep 17 00:00:00 2001 From: Amit Rathi Date: Wed, 8 Oct 2025 11:29:18 +0530 Subject: [PATCH 19/19] Update 01.03-Magic-Commands.ipynb --- notebooks/01.03-Magic-Commands.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/01.03-Magic-Commands.ipynb b/notebooks/01.03-Magic-Commands.ipynb index 4a47b373a..b2dd79cd4 100644 --- a/notebooks/01.03-Magic-Commands.ipynb +++ b/notebooks/01.03-Magic-Commands.ipynb @@ -30,7 +30,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The previous two sections showed how IPython lets you use and explore Python efficiently and interactively.\n", + "This is a test for the special character ñ in markdown cell. The previous two sections showed how IPython lets you use and explore Python efficiently and interactively.\n", "Here we'll begin discussing some of the enhancements that IPython adds on top of the normal Python syntax.\n", "These are known in IPython as *magic commands*, and are prefixed by the ``%`` character.\n", "These magic commands are designed to succinctly solve various common problems in standard data analysis.\n",