From de9e582e186c1384df5ba1672de5a26d6eb533a4 Mon Sep 17 00:00:00 2001 From: cif2cif Date: Tue, 15 Mar 2016 13:55:14 +0100 Subject: [PATCH] Add ml1 --- ml1/2_0_0_Intro_ML.ipynb | 110 ++ ml1/2_0_1_Objectives.ipynb | 103 ++ ml1/2_1_Intro_ScikitLearn.ipynb | 184 ++++ ml1/2_2_Read_Data.ipynb | 633 ++++++++++++ ml1/2_3_0_Visualisation.ipynb | 389 +++++++ ml1/2_3_1_Advanced_Visualisation.ipynb | 763 ++++++++++++++ ml1/2_4_Preprocessing.ipynb | 314 ++++++ ml1/2_5_0_Machine_Learning.ipynb | 201 ++++ ml1/2_5_1_kNN_Model.ipynb | 567 +++++++++++ ml1/2_5_2_Decision_Tree_Model.ipynb | 677 +++++++++++++ ml1/2_6_Model_Tuning.ipynb | 1290 ++++++++++++++++++++++++ ml1/2_7_Model_Persistence.ipynb | 204 ++++ ml1/2_8_Conclusions.ipynb | 130 +++ ml1/images/EscUpmPolit_p.gif | Bin 0 -> 3171 bytes ml1/images/cart.png | Bin 0 -> 96968 bytes ml1/images/data-chart-type.png | Bin 0 -> 35071 bytes ml1/images/iris-dataset.jpg | Bin 0 -> 44614 bytes ml1/images/plot_ML_flow_chart_1.png | Bin 0 -> 57683 bytes ml1/images/plot_ML_flow_chart_2.png | Bin 0 -> 89437 bytes ml1/images/plot_ML_flow_chart_3.png | Bin 0 -> 58937 bytes ml1/iris-tree.png | Bin 0 -> 21793 bytes ml1/plotml.py | 112 ++ ml1/util_ds.py | 117 +++ ml1/util_knn.py | 51 + 24 files changed, 5845 insertions(+) create mode 100644 ml1/2_0_0_Intro_ML.ipynb create mode 100644 ml1/2_0_1_Objectives.ipynb create mode 100644 ml1/2_1_Intro_ScikitLearn.ipynb create mode 100644 ml1/2_2_Read_Data.ipynb create mode 100644 ml1/2_3_0_Visualisation.ipynb create mode 100644 ml1/2_3_1_Advanced_Visualisation.ipynb create mode 100644 ml1/2_4_Preprocessing.ipynb create mode 100644 ml1/2_5_0_Machine_Learning.ipynb create mode 100644 ml1/2_5_1_kNN_Model.ipynb create mode 100644 ml1/2_5_2_Decision_Tree_Model.ipynb create mode 100644 ml1/2_6_Model_Tuning.ipynb create mode 100644 ml1/2_7_Model_Persistence.ipynb create mode 100644 ml1/2_8_Conclusions.ipynb create mode 100644 ml1/images/EscUpmPolit_p.gif create mode 100644 ml1/images/cart.png create mode 100644 ml1/images/data-chart-type.png create mode 100644 ml1/images/iris-dataset.jpg create mode 100644 ml1/images/plot_ML_flow_chart_1.png create mode 100644 ml1/images/plot_ML_flow_chart_2.png create mode 100644 ml1/images/plot_ML_flow_chart_3.png create mode 100644 ml1/iris-tree.png create mode 100644 ml1/plotml.py create mode 100644 ml1/util_ds.py create mode 100644 ml1/util_knn.py diff --git a/ml1/2_0_0_Intro_ML.ipynb b/ml1/2_0_0_Intro_ML.ipynb new file mode 100644 index 0000000..a6794b3 --- /dev/null +++ b/ml1/2_0_0_Intro_ML.ipynb @@ -0,0 +1,110 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](files/images/EscUpmPolit_p.gif \"UPM\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Course Notes for Learning Intelligent Systems" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © 2016 Carlos A. Iglesias" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Introduction to Machine Learning\n", + "\n", + "This lecture provides a quick introduction to Machine Learning in Python using the Iris dataset as an example. \n", + "\n", + "In this session we will focus on applying multiclass classification algorithms." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Table of Contents" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. [Home](2_0_0_Intro_ML.ipynb)\n", + " 1. [Objectives](2_0_1_Objectives.ipynb)\n", + "1. [What is scikit-learn](2_1_Intro_ScikitLearn.ipynb)\n", + "1. [Reading Data](2_2_Read_Data.ipynb)\n", + "2. [Visualisation](2_3_0_Visualisation.ipynb)\n", + " 1. [Advanced visualisation](2_3_1_Advanced_Visualisation.ipynb)\n", + "3. [Preprocessing](2_4_Preprocessing.ipynb)\n", + "4. [Machine learning](2_5_0_Machine_Learning.ipynb)\n", + " 1. [kNN Model](2_5_1_kNN_Model.ipynb)\n", + " 1. [Decision Tree Learning Model](2_5_2_Decision_Tree_Model.ipynb)\n", + "4. [Model tuning](2_6_Model_Tuning.ipynb)\n", + "4. [Model persistence](2_7_Model_Persistence.ipynb)\n", + "4. [Conclusions](2_8_Conclusions.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* [Scikit-learn web page](http://scikit-learn.org/stable/)\n", + "* [Scikit-learn videos](http://blog.kaggle.com/author/kevin-markham/) and [notebooks](https://github.com/justmarkham/scikit-learn-videos) by Kevin Marham\n", + "* [Learning scikit-learn: Machine Learning in Python](http://proquest.safaribooksonline.com/book/programming/python/9781783281930/1dot-machine-learning-a-gentle-introduction/ch01s02_html), Raúl Garreta; Guillermo Moncecchi, Packt Publishing, 2013.\n", + "* [Python Machine Learning](http://proquest.safaribooksonline.com/book/programming/python/9781783555130), Sebastian Raschka, Packt Publishing, 2015." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Licence\n", + "The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n", + "\n", + "© 2016 Carlos A. Iglesias, Universidad Politécnica de Madrid." + ] + } + ], + "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": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/ml1/2_0_1_Objectives.ipynb b/ml1/2_0_1_Objectives.ipynb new file mode 100644 index 0000000..d950824 --- /dev/null +++ b/ml1/2_0_1_Objectives.ipynb @@ -0,0 +1,103 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](files/images/EscUpmPolit_p.gif \"UPM\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Course Notes for Learning Intelligent Systems" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © 2016 Carlos A. Iglesias" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## [Introduction to Machine Learning](2_0_0_Intro_ML.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Introduction to Machine Learning\n", + "\n", + "This lecture provides a quick introduction to Machine Learning in Python using the Iris dataset as an example. In this session we will focus on applying multiclass classification algorithms.\n", + "\n", + "The main objectives of this session are:\n", + "\n", + "* Learn to use scikit-learn\n", + "* Learn the basic steps to apply machine learning techniques: dataset analysis, load, preprocessing, training, validation, optimization and persistence.\n", + "* Learn how to do a exploratory data analysis\n", + "* Learn how to visualise a dataset\n", + "* Learn how to load a bundled dataset\n", + "* Learn how to separate the dataset into traning and testing datasets\n", + "* Learn how to train a classifier\n", + "* Learn how to predict with a trained classifier\n", + "* Learn how to evaluate the predictions\n", + "* Learn how to optimize the configuration of a classifier\n", + "* Learn how to save a model\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* [Scikit-learn web page](http://scikit-learn.org/stable/)\n", + "* [Scikit-learn videos](http://blog.kaggle.com/author/kevin-markham/) and [notebooks](https://github.com/justmarkham/scikit-learn-videos) by Kevin Marham\n", + "* [Learning scikit-learn: Machine Learning in Python](http://proquest.safaribooksonline.com/book/programming/python/9781783281930/1dot-machine-learning-a-gentle-introduction/ch01s02_html), Raúl Garreta; Guillermo Moncecchi, Packt Publishing, 2013.\n", + "* [Python Machine Learning](http://proquest.safaribooksonline.com/book/programming/python/9781783555130), Sebastian Raschka, Packt Publishing, 2015." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## LIcence\n", + "The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n", + "\n", + "© 2016 Carlos A. Iglesias, Universidad Politécnica de Madrid." + ] + } + ], + "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": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/ml1/2_1_Intro_ScikitLearn.ipynb b/ml1/2_1_Intro_ScikitLearn.ipynb new file mode 100644 index 0000000..d354580 --- /dev/null +++ b/ml1/2_1_Intro_ScikitLearn.ipynb @@ -0,0 +1,184 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](files/images/EscUpmPolit_p.gif \"UPM\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Course Notes for Learning Intelligent Systems" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © 2016 Carlos A. Iglesias" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## [Introduction to Machine Learning](2_0_0_Intro_ML.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Table of Contents\n", + "* [Introduction to scikit-learn](#Introduction-to-scikit-learn)\n", + "* [What is scikit-learn?](#What-is-scikit-learn?)\n", + "* [Problems that scikit-learn can solve](#Problems-that-scikit-learn-can-solve)\n", + "* [Helpers for Machine Learning](#Helpers-for-Machine-Learning)\n", + "* [How to install scikit-learn](#How-to-install-scikit-learn)\n", + "* [References](#References)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Introduction to scikit-learn" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This lecture provides a quick introduction to [scikit-learn](http://scikit-learn.org/stable/), a Python library for machine learning." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What is scikit-learn?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Scikit-Learn is a Python library that provides a wealth of machine learning algorithms. \n", + "\n", + "The library is built upon SciPy (Scientific Python) that should be installed before using scikit-learn.\n", + "\n", + "In particular, scikit-learn uses:\n", + "* **NumPy**: package for managing n-dimensional arrays (http://www.numpy.org/)\n", + "* **pandas**: data analysis toolkit (http://pandas.pydata.org/pandas-docs/stable/index.html)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Problems that scikit-learn can solve" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Scikit-learn provides algorithms for solving the following problems:\n", + "* **Classification**: Identifying to which category an object belongs to. Some of the available [classification algorithms](http://scikit-learn.org/stable/supervised_learning.html#supervised-learning) are decision trees (ID3, kNN, ...), SVM, Random forest, Perceptron, etc. \n", + "* **Clustering**: Automatic grouping of similar objects into sets. Some of the available [clustering algorithms](http://scikit-learn.org/stable/modules/clustering.html#clustering) are k-Means, Affinity propagation, etc.\n", + "* **Regression**: Predicting a continuous-valued attribute associated with an object. Some of the available [regression algorithms](http://scikit-learn.org/stable/supervised_learning.html#supervised-learning) are linear regression, logistic regression, etc.\n", + "* ** Dimensionality reduction**: Reducing the number of random variables to consider. Some of the available [dimensionality reduction algorithms](http://scikit-learn.org/stable/modules/decomposition.html#decompositions) are SVD, PCA, etc." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Helpers for Machine Learning" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In addition, scikit-learn helps in several tasks:\n", + "* ** Model selection**: Comparing, validating, choosing parameters and models, and persisting models. Some of the [available functionalities](http://scikit-learn.org/stable/model_selection.html#model-selection) are cross-validation or grid search for optimizing the parameters. \n", + "* ** Preprocessing**: Several common utility functions and transformer classes to change raw feature vectors into a representation that is more suitable for the downstream estimators. Some of the available [preprocessing functions](http://scikit-learn.org/stable/modules/preprocessing.html#preprocessing) are scaling and normalizing data, or imputing missing values." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## How to install scikit-learn" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you installed the conda distribution, scikit-learn is already installed! This is the best option.\n", + "\n", + "In case it is an old installation, you can updated it using conda: `conda update scikit-learn`.\n", + "\n", + "If it is not installed, install it with conda: `conda install scikit-learn`.\n", + "\n", + "If you have installed scipy and numpy, you can also installed using pip: `pip install -U scikit-learn`.\n", + "\n", + "It is not recommended to use pip for installing scipy and numpy. Instead, use conda or install the linux package *python-sklearn*." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* [Scikit-learn site](http://scikit-learn.org/stable/index.html)\n", + "* [How to install Scikit-learn](http://scikit-learn.org/stable/install.html/)\n", + "* [An introduction to NumPy and Scipy](http://www.engr.ucsb.edu/~shell/che210d/numpy.pdf)\n", + "* [NumPy tutorial](https://docs.scipy.org/doc/numpy-dev/user/quickstart.html)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Licence\n", + "The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n", + "\n", + "© 2016 Carlos A. Iglesias, Universidad Politécnica de Madrid." + ] + } + ], + "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": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/ml1/2_2_Read_Data.ipynb b/ml1/2_2_Read_Data.ipynb new file mode 100644 index 0000000..f25c9a6 --- /dev/null +++ b/ml1/2_2_Read_Data.ipynb @@ -0,0 +1,633 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](files/images/EscUpmPolit_p.gif \"UPM\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Course Notes for Learning Intelligent Systems" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © 2016 Carlos A. Iglesias" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## [Introduction to Machine Learning](2_0_0_Intro_ML.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Table of Contents\n", + "* [Reading Data](#Reading-Data)\n", + "* [Iris flower dataset](#Iris-flower-dataset)\n", + "* [References](#References)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Reading Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The goal of this notebook is to learn how to read and load a sample dataset.\n", + "\n", + "Scikit-learn come with some bundled [datasets](http://scikit-learn.org/stable/datasets/): iris, digits, boston, etc.\n", + "\n", + "In this notebook we are going to use the Iris dataset." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Iris flower dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The [Iris flower dataset](https://en.wikipedia.org/wiki/Iris_flower_data_set), available at [UCI dataset repository](https://archive.ics.uci.edu/ml/datasets/Iris), is a classic dataset for classification.\n", + "\n", + "The dataset consists of 50 samples from each of three species of Iris (Iris setosa, Iris virginica and Iris versicolor). Four features were measured from each sample: the length and the width of the sepals and petals, in centimetres. Based on the combination of these four features.\n", + "\n", + "![Iris](files/images/iris-dataset.jpg)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In ordert to read the dataset, we import the bundle datasets and then load the Iris dataset. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# import datasets from scikit-learn\n", + "from sklearn import datasets\n", + "\n", + "# load iris dataset\n", + "iris = datasets.load_iris()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A dataset is a dictionary-like object that holds all the data and some metadata about the data. This data is stored in the `.data` member, which is a 2D (`n_samples`, `n_features`) array. In the case of supervised problem, one or more response variables are stored in the `.target` member." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "sklearn.datasets.base.Bunch" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#type 'bunch' of a dataset\n", + "type(iris)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iris Plants Database\n", + "\n", + "Notes\n", + "-----\n", + "Data Set Characteristics:\n", + " :Number of Instances: 150 (50 in each of three classes)\n", + " :Number of Attributes: 4 numeric, predictive attributes and the class\n", + " :Attribute Information:\n", + " - sepal length in cm\n", + " - sepal width in cm\n", + " - petal length in cm\n", + " - petal width in cm\n", + " - class:\n", + " - Iris-Setosa\n", + " - Iris-Versicolour\n", + " - Iris-Virginica\n", + " :Summary Statistics:\n", + "\n", + " ============== ==== ==== ======= ===== ====================\n", + " Min Max Mean SD Class Correlation\n", + " ============== ==== ==== ======= ===== ====================\n", + " sepal length: 4.3 7.9 5.84 0.83 0.7826\n", + " sepal width: 2.0 4.4 3.05 0.43 -0.4194\n", + " petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)\n", + " petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)\n", + " ============== ==== ==== ======= ===== ====================\n", + "\n", + " :Missing Attribute Values: None\n", + " :Class Distribution: 33.3% for each of 3 classes.\n", + " :Creator: R.A. Fisher\n", + " :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n", + " :Date: July, 1988\n", + "\n", + "This is a copy of UCI ML iris datasets.\n", + "http://archive.ics.uci.edu/ml/datasets/Iris\n", + "\n", + "The famous Iris database, first used by Sir R.A Fisher\n", + "\n", + "This is perhaps the best known database to be found in the\n", + "pattern recognition literature. Fisher's paper is a classic in the field and\n", + "is referenced frequently to this day. (See Duda & Hart, for example.) The\n", + "data set contains 3 classes of 50 instances each, where each class refers to a\n", + "type of iris plant. One class is linearly separable from the other 2; the\n", + "latter are NOT linearly separable from each other.\n", + "\n", + "References\n", + "----------\n", + " - Fisher,R.A. \"The use of multiple measurements in taxonomic problems\"\n", + " Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n", + " Mathematical Statistics\" (John Wiley, NY, 1950).\n", + " - Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.\n", + " (Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.\n", + " - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n", + " Structure and Classification Rule for Recognition in Partially Exposed\n", + " Environments\". IEEE Transactions on Pattern Analysis and Machine\n", + " Intelligence, Vol. PAMI-2, No. 1, 67-71.\n", + " - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\". IEEE Transactions\n", + " on Information Theory, May 1972, 431-433.\n", + " - See also: 1988 MLC Proceedings, 54-64. Cheeseman et al\"s AUTOCLASS II\n", + " conceptual clustering system finds 3 classes in the data.\n", + " - Many, many more ...\n", + "\n" + ] + } + ], + "source": [ + "# print descrition of the dataset\n", + "print (iris.DESCR)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n" + ] + } + ], + "source": [ + "# names of the features (attributes of the entities)\n", + "print(iris.feature_names)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['setosa' 'versicolor' 'virginica']\n" + ] + } + ], + "source": [ + "#names of the targets(classes of the classifier)\n", + "print(iris.target_names)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "numpy.ndarray" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#type numpy array\n", + "type(iris.data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we are going to inspect the dataset. You can consult the NumPy tutorial listed in the references." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 5.1 3.5 1.4 0.2]\n", + " [ 4.9 3. 1.4 0.2]\n", + " [ 4.7 3.2 1.3 0.2]\n", + " [ 4.6 3.1 1.5 0.2]\n", + " [ 5. 3.6 1.4 0.2]\n", + " [ 5.4 3.9 1.7 0.4]\n", + " [ 4.6 3.4 1.4 0.3]\n", + " [ 5. 3.4 1.5 0.2]\n", + " [ 4.4 2.9 1.4 0.2]\n", + " [ 4.9 3.1 1.5 0.1]\n", + " [ 5.4 3.7 1.5 0.2]\n", + " [ 4.8 3.4 1.6 0.2]\n", + " [ 4.8 3. 1.4 0.1]\n", + " [ 4.3 3. 1.1 0.1]\n", + " [ 5.8 4. 1.2 0.2]\n", + " [ 5.7 4.4 1.5 0.4]\n", + " [ 5.4 3.9 1.3 0.4]\n", + " [ 5.1 3.5 1.4 0.3]\n", + " [ 5.7 3.8 1.7 0.3]\n", + " [ 5.1 3.8 1.5 0.3]\n", + " [ 5.4 3.4 1.7 0.2]\n", + " [ 5.1 3.7 1.5 0.4]\n", + " [ 4.6 3.6 1. 0.2]\n", + " [ 5.1 3.3 1.7 0.5]\n", + " [ 4.8 3.4 1.9 0.2]\n", + " [ 5. 3. 1.6 0.2]\n", + " [ 5. 3.4 1.6 0.4]\n", + " [ 5.2 3.5 1.5 0.2]\n", + " [ 5.2 3.4 1.4 0.2]\n", + " [ 4.7 3.2 1.6 0.2]\n", + " [ 4.8 3.1 1.6 0.2]\n", + " [ 5.4 3.4 1.5 0.4]\n", + " [ 5.2 4.1 1.5 0.1]\n", + " [ 5.5 4.2 1.4 0.2]\n", + " [ 4.9 3.1 1.5 0.1]\n", + " [ 5. 3.2 1.2 0.2]\n", + " [ 5.5 3.5 1.3 0.2]\n", + " [ 4.9 3.1 1.5 0.1]\n", + " [ 4.4 3. 1.3 0.2]\n", + " [ 5.1 3.4 1.5 0.2]\n", + " [ 5. 3.5 1.3 0.3]\n", + " [ 4.5 2.3 1.3 0.3]\n", + " [ 4.4 3.2 1.3 0.2]\n", + " [ 5. 3.5 1.6 0.6]\n", + " [ 5.1 3.8 1.9 0.4]\n", + " [ 4.8 3. 1.4 0.3]\n", + " [ 5.1 3.8 1.6 0.2]\n", + " [ 4.6 3.2 1.4 0.2]\n", + " [ 5.3 3.7 1.5 0.2]\n", + " [ 5. 3.3 1.4 0.2]\n", + " [ 7. 3.2 4.7 1.4]\n", + " [ 6.4 3.2 4.5 1.5]\n", + " [ 6.9 3.1 4.9 1.5]\n", + " [ 5.5 2.3 4. 1.3]\n", + " [ 6.5 2.8 4.6 1.5]\n", + " [ 5.7 2.8 4.5 1.3]\n", + " [ 6.3 3.3 4.7 1.6]\n", + " [ 4.9 2.4 3.3 1. ]\n", + " [ 6.6 2.9 4.6 1.3]\n", + " [ 5.2 2.7 3.9 1.4]\n", + " [ 5. 2. 3.5 1. ]\n", + " [ 5.9 3. 4.2 1.5]\n", + " [ 6. 2.2 4. 1. ]\n", + " [ 6.1 2.9 4.7 1.4]\n", + " [ 5.6 2.9 3.6 1.3]\n", + " [ 6.7 3.1 4.4 1.4]\n", + " [ 5.6 3. 4.5 1.5]\n", + " [ 5.8 2.7 4.1 1. ]\n", + " [ 6.2 2.2 4.5 1.5]\n", + " [ 5.6 2.5 3.9 1.1]\n", + " [ 5.9 3.2 4.8 1.8]\n", + " [ 6.1 2.8 4. 1.3]\n", + " [ 6.3 2.5 4.9 1.5]\n", + " [ 6.1 2.8 4.7 1.2]\n", + " [ 6.4 2.9 4.3 1.3]\n", + " [ 6.6 3. 4.4 1.4]\n", + " [ 6.8 2.8 4.8 1.4]\n", + " [ 6.7 3. 5. 1.7]\n", + " [ 6. 2.9 4.5 1.5]\n", + " [ 5.7 2.6 3.5 1. ]\n", + " [ 5.5 2.4 3.8 1.1]\n", + " [ 5.5 2.4 3.7 1. ]\n", + " [ 5.8 2.7 3.9 1.2]\n", + " [ 6. 2.7 5.1 1.6]\n", + " [ 5.4 3. 4.5 1.5]\n", + " [ 6. 3.4 4.5 1.6]\n", + " [ 6.7 3.1 4.7 1.5]\n", + " [ 6.3 2.3 4.4 1.3]\n", + " [ 5.6 3. 4.1 1.3]\n", + " [ 5.5 2.5 4. 1.3]\n", + " [ 5.5 2.6 4.4 1.2]\n", + " [ 6.1 3. 4.6 1.4]\n", + " [ 5.8 2.6 4. 1.2]\n", + " [ 5. 2.3 3.3 1. ]\n", + " [ 5.6 2.7 4.2 1.3]\n", + " [ 5.7 3. 4.2 1.2]\n", + " [ 5.7 2.9 4.2 1.3]\n", + " [ 6.2 2.9 4.3 1.3]\n", + " [ 5.1 2.5 3. 1.1]\n", + " [ 5.7 2.8 4.1 1.3]\n", + " [ 6.3 3.3 6. 2.5]\n", + " [ 5.8 2.7 5.1 1.9]\n", + " [ 7.1 3. 5.9 2.1]\n", + " [ 6.3 2.9 5.6 1.8]\n", + " [ 6.5 3. 5.8 2.2]\n", + " [ 7.6 3. 6.6 2.1]\n", + " [ 4.9 2.5 4.5 1.7]\n", + " [ 7.3 2.9 6.3 1.8]\n", + " [ 6.7 2.5 5.8 1.8]\n", + " [ 7.2 3.6 6.1 2.5]\n", + " [ 6.5 3.2 5.1 2. ]\n", + " [ 6.4 2.7 5.3 1.9]\n", + " [ 6.8 3. 5.5 2.1]\n", + " [ 5.7 2.5 5. 2. ]\n", + " [ 5.8 2.8 5.1 2.4]\n", + " [ 6.4 3.2 5.3 2.3]\n", + " [ 6.5 3. 5.5 1.8]\n", + " [ 7.7 3.8 6.7 2.2]\n", + " [ 7.7 2.6 6.9 2.3]\n", + " [ 6. 2.2 5. 1.5]\n", + " [ 6.9 3.2 5.7 2.3]\n", + " [ 5.6 2.8 4.9 2. ]\n", + " [ 7.7 2.8 6.7 2. ]\n", + " [ 6.3 2.7 4.9 1.8]\n", + " [ 6.7 3.3 5.7 2.1]\n", + " [ 7.2 3.2 6. 1.8]\n", + " [ 6.2 2.8 4.8 1.8]\n", + " [ 6.1 3. 4.9 1.8]\n", + " [ 6.4 2.8 5.6 2.1]\n", + " [ 7.2 3. 5.8 1.6]\n", + " [ 7.4 2.8 6.1 1.9]\n", + " [ 7.9 3.8 6.4 2. ]\n", + " [ 6.4 2.8 5.6 2.2]\n", + " [ 6.3 2.8 5.1 1.5]\n", + " [ 6.1 2.6 5.6 1.4]\n", + " [ 7.7 3. 6.1 2.3]\n", + " [ 6.3 3.4 5.6 2.4]\n", + " [ 6.4 3.1 5.5 1.8]\n", + " [ 6. 3. 4.8 1.8]\n", + " [ 6.9 3.1 5.4 2.1]\n", + " [ 6.7 3.1 5.6 2.4]\n", + " [ 6.9 3.1 5.1 2.3]\n", + " [ 5.8 2.7 5.1 1.9]\n", + " [ 6.8 3.2 5.9 2.3]\n", + " [ 6.7 3.3 5.7 2.5]\n", + " [ 6.7 3. 5.2 2.3]\n", + " [ 6.3 2.5 5. 1.9]\n", + " [ 6.5 3. 5.2 2. ]\n", + " [ 6.2 3.4 5.4 2.3]\n", + " [ 5.9 3. 5.1 1.8]]\n" + ] + } + ], + "source": [ + "#Data in the iris dataset. The value of the features of the samples.\n", + "print(iris.data)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", + " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n", + " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n", + " 2 2]\n" + ] + } + ], + "source": [ + "# Target. Category of every sample\n", + "print(iris.target)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(150, 4)\n" + ] + } + ], + "source": [ + "# Iris data is a numpy array\n", + "# We can inspect its shape (rows, columns). In our case, (n_samples, n_features)\n", + "print(iris.data.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2\n" + ] + } + ], + "source": [ + "#Using numpy, I can print the dimensions (here we are working with 2D matriz)\n", + "print(iris.data.ndim)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "150\n" + ] + } + ], + "source": [ + "# I can print n_samples\n", + "print(iris.data.shape[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4\n" + ] + } + ], + "source": [ + "# ... n_features\n", + "print(iris.data.shape[1])" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n" + ] + } + ], + "source": [ + "# names of the features\n", + "print(iris.feature_names)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In another session, we will learn how to load a dataset from a file (csv, excel, ...). We will use the library pandas for this purpose." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* [Iris flower data set](https://en.wikipedia.org/wiki/Iris_flower_data_set)\n", + "* [How to load an example dataset with scikit-learn](http://scikit-learn.org/stable/tutorial/basic/tutorial.html#loading-example-dataset)\n", + "* [Dataset loading utilities in scikit-learn](http://scikit-learn.org/stable/datasets/)\n", + "* [How to plot the Iris dataset](http://scikit-learn.org/stable/auto_examples/datasets/plot_iris_dataset.html)\n", + "* [An introduction to NumPy and Scipy](http://www.engr.ucsb.edu/~shell/che210d/numpy.pdf)\n", + "* [NumPy tutorial](https://docs.scipy.org/doc/numpy-dev/user/quickstart.html)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Licence\n", + "\n", + "The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n", + "\n", + "© 2016 Carlos A. Iglesias, Universidad Politécnica de Madrid." + ] + } + ], + "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": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/ml1/2_3_0_Visualisation.ipynb b/ml1/2_3_0_Visualisation.ipynb new file mode 100644 index 0000000..e804c0a --- /dev/null +++ b/ml1/2_3_0_Visualisation.ipynb @@ -0,0 +1,389 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](files/images/EscUpmPolit_p.gif \"UPM\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Course Notes for Learning Intelligent Systems" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © 2016 Carlos A. Iglesias" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## [Introduction to Machine Learning](2_0_0_Intro_ML.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Table of Contents\n", + "* [Visualisation](#Visualisation)\n", + "* [Exploratory visualisation](#Exploratory-visualisation)\n", + "* [References](#References)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Visualisation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The goal of this notebook is to learn how to analyse a dataset. We will cover other tasks such as cleaning or munging (changing the format) the dataset in other sessions." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exploratory visualisation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, we are going to inspect the distribution of the samples per feature." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from sklearn import datasets\n", + "iris = datasets.load_iris()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# library for displaying plots\n", + "import matplotlib.pyplot as plt\n", + "# display plots in the notebook\n", + "# if this is not set, you will not see the graphic here\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First we are going to analyse the [histogram](https://en.wikipedia.org/wiki/Histogram). \n", + "\n", + "A histogram is a graphical representation of the distribution of numerical data. It is an estimate of the probability distribution of a continuous variable (quantitative variable). \n", + "\n", + "For building a histogram, we need first to 'bin' the range of values—that is, divide the entire range of values into a series of intervals—and then count how many values fall into each interval. \n", + "\n", + "In our case, since the values are not continuous and we have only three values, we do not need to bin them." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot histogram, the default is 10 bins\n", + "plt.hist(iris.target, bins=10)\n", + "plt.xlabel('iris class')\n", + "plt.ylabel('Number of species')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see we have the same distribution of samples for each class.\n", + "Now we are going to see the distribution of the features" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n" + ] + } + ], + "source": [ + "# We remember the name of the features to see its index\n", + "print(iris.feature_names)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['setosa' 'versicolor' 'virginica']\n" + ] + } + ], + "source": [ + "# We remember the name of target names\n", + "print(iris.target_names)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A [**scatter plot**](https://en.wikipedia.org/wiki/Scatter_plot) (*gráfico de dispersión*) display values for typically two variables for a set of data." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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r+ypJd0h6IHtdlC0/SdLfZfX3S/rdbPnjQ88akrQ2a7NP0o0qOUGlJ/I+rNKX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\nRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# scatter makes a plot of x vs y\n", + "plt.scatter(iris.data[:,0], iris.target)\n", + "plt.ylabel(iris.feature_names[0])\n", + "plt.xlabel('species')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the distribution of the dataset\n", + "names = set(iris.target)\n", + "\n", + "# x and y are all the samples from column 0 (sepal_length) and 1 (sepal_width) respectively\n", + "x,y = iris.data[:,0], iris.data[:,1]\n", + "\n", + "for name in names:\n", + " cond = iris.target == name\n", + " plt.plot(x[cond], y[cond], linestyle='none', marker='o', label=iris.target_names[name])\n", + "\n", + "plt.legend(numpoints=1)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see, the Setosa class seems to be linear separable with these two features.\n", + "\n", + "Another nice visualisation is given below." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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Kd14c+2KA/PTp00nft4/+OTlEYMSXdQTaud2cPnSIfrm5hPvKE4CWmZm8/cYb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\nsko2UcDk/02irVwwGGCvXtfZpvz4nfXyrYXx+fTptM5jMMA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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x_index = 0\n", + "y_index = 1\n", + "formatter = plt.FuncFormatter(lambda i, *args: iris.target_names[int(i)])\n", + "plt.scatter(iris.data[:, x_index], iris.data[:, y_index], s=40,\n", + "c=iris.target)\n", + "plt.colorbar(ticks=[0, 1, 2], format=formatter)\n", + "plt.xlabel(iris.feature_names[x_index])\n", + "plt.ylabel(iris.feature_names[y_index])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we can also check that the Setosa class seems to be linear separable.\n", + "\n", + "Students interested in practicing advanced visualisations can check [Advanced visualisation notebook](2_3_1_Advanced_Visualisation.ipynb).\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# References" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* [Feature selection](http://scikit-learn.org/stable/modules/feature_selection.html)\n", + "* [Classification probability](http://scikit-learn.org/stable/auto_examples/classification/plot_classification_probability.html)\n", + "* [Mastering Pandas](http://proquest.safaribooksonline.com/book/programming/python/9781783981960), Femi Anthony, Packt Publishing, 2015.\n", + "* [Matplotlib web page](http://matplotlib.org/index.html)\n", + "* [Using matlibplot in IPython](http://ipython.readthedocs.org/en/stable/interactive/plotting.html)\n", + "* [Seaborn Tutorial](https://stanford.edu/~mwaskom/software/seaborn/tutorial.html)\n", + "* [Iris dataset visualisation notebook](https://www.kaggle.com/benhamner/d/uciml/iris/python-data-visualizations/notebook)\n", + "* [Tutorial plotting with Seaborn](https://stanford.edu/~mwaskom/software/seaborn/tutorial/axis_grids.html)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Licence\n", + "\n", + "The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n", + "\n", + "© 2016 Carlos A. Iglesias, Universidad Politécnica de Madrid." + ] + } + ], + "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": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/ml1/2_3_1_Advanced_Visualisation.ipynb b/ml1/2_3_1_Advanced_Visualisation.ipynb new file mode 100644 index 0000000..3383f00 --- /dev/null +++ b/ml1/2_3_1_Advanced_Visualisation.ipynb @@ -0,0 +1,763 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](files/images/EscUpmPolit_p.gif \"UPM\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Course Notes for Learning Intelligent Systems" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © 2016 Carlos A. Iglesias" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## [Introduction to Machine Learning](2_0_0_Intro_ML.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Table of Contents\n", + "\n", + "* [Advanced Visualisation](#Advanced-Visualisation)\n", + "* [Install seaborn](#Install-seaborn)\n", + "* [Transform Data into Dataframe](#Transform-Data-into-Dataframe)\n", + "* [Visualisation with seaborn](#Visualisation-with-seaborn)\n", + "* [References](#References)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Advanced Visualisation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the previous notebook we developed plots with the [matplotlib](http://matplotlib.org/) plotting library.\n", + "\n", + "This notebook introduces another plotting library, [**seaborn**](https://stanford.edu/~mwaskom/software/seaborn/), that provides advanced facilities for data visualization.\n", + "\n", + "*Seaborn* is a library for making attractive and informative statistical graphics in Python. It is built on top of *matplotlib* and tightly integrated with the *PyData* stack, including support for *numpy* and *pandas* data structures and statistical routines from *scipy* and *statsmodels*.\n", + "\n", + "*Seaborn* requires that the data is provides as *DataFrames* (a structure created with the library *pandas*)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Install seaborn" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You should install the SeaBorn package. Use `conda install seaborn`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Transform Data into Dataframe" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Seaborn* requires that data is represented as a *DataFrame* object from the library *pandas*. \n", + "\n", + "A *DataFrame* is a 2-dimensional labeled data structure with columns of potentially different types. We will not go into the details of DataFrames in this session." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)\n", + "0 5.1 3.5 1.4 0.2\n", + "1 4.9 3.0 1.4 0.2\n", + "2 4.7 3.2 1.3 0.2\n", + "3 4.6 3.1 1.5 0.2\n", + "4 5.0 3.6 1.4 0.2" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn import datasets\n", + "from pandas import DataFrame\n", + "\n", + "# iris data set from scikit learn (it is a Bunch object)\n", + "iris = datasets.load_iris()\n", + "\n", + "# transform into dataframe\n", + "iris_df = DataFrame(iris.data)\n", + "iris_df.columns = iris.feature_names\n", + "\n", + "iris_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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sepal length (cm)sepal width (cm)petal length (cm)petal width (cm)species
05.13.51.40.20
14.93.01.40.20
24.73.21.30.20
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" + ], + "text/plain": [ + " sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) \\\n", + "0 5.1 3.5 1.4 0.2 \n", + "1 4.9 3.0 1.4 0.2 \n", + "2 4.7 3.2 1.3 0.2 \n", + "3 4.6 3.1 1.5 0.2 \n", + "4 5.0 3.6 1.4 0.2 \n", + "\n", + " species \n", + "0 0 \n", + "1 0 \n", + "2 0 \n", + "3 0 \n", + "4 0 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iris_df['species'] = iris.target\n", + "iris_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Visualisation with seaborn" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following examples are taken from [a kaggle tutorial](https://www.kaggle.com/benhamner/d/uciml/iris/python-data-visualizations/notebook) and [the seaborn tutorial](https://stanford.edu/~mwaskom/software/seaborn/tutorial/axis_grids.html).\n", + "\n", + "To plot multiple pairwise bivariate distributions in a dataset, you can use the *pairplot()* function and *PairGrid()*.\n", + "\n", + "A **scatterplot matrix** (*matriz de diagramas de dispersión*) presents every pairwise relationship between a set of variables." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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fX3CtRKQg7hCLfG5xZXuKf+b8RUeIxsz5i7Q01jteOzl1nnu/nvxUtjODRymO\nQ8Qv6a6JVCFNve1NjjCNG6/uZWiwm66NjXztOy8CsYkRGlbXL02SkpBcXmtzA1/9ll2KeS53lgEJ\nNncb/fDPv2FZn5ycGWPP7kjO4RDZQkFyVYn9fCGD54fj/+XMGPMxYjMU1gN/Ya29N2ndzcAngIvA\nvdbavQXUUaQqpUoNlKtst/teee2sowNd21BHa8gZo9xQ54y9d2cKyMaP4xDxS7prIlU7ffWEc9vJ\nmTmeOTTKjVf3OV4/cmJy2eA5ubyHnz66NHBOfk+RVNxt1N1P19c57/wdG5thaFtXTuEQfmd4qcR+\nPsfHeS6x1n4ZeAw4SWw67f3x1zIyxlwHXGOtfQtwPdCbtK4OuAe4Mb7ug8aYcIpiRKTI8snO4WUW\nQpFKlcvt5XSzDrqvif4u54QShbynSLbZ/3rCmWd5zec9qrFNFpJt413A/wesBd4CPGmM+X+ttX+T\nZde3Ac8ZYx4EmoHfSloXAV601k7F3+NxYIjYhCwi4lFitsDEjE+5xhpDilmhXLM+uWdD2xkJg2uG\nwaVZCOOzV+2I5Pa3cLa4a5FSSszWlshI455JDS5deydOzrJnd4TZc/M0rq1jcvpCbHn2Infc+gZm\nZucINdbzwvBpjpyYoqdtLVf0Lm/flXhLW4rDy2yS2WbzM/2t1NSwNDvgNVs7HM+lXG3aeCbLd0e2\nWQurQSFhG79DbNC831o7ZowZBL4DZBs8twF9wDuA1wH/CAzE17UAk0nbngWq71MpgNfUdsl6evqy\nbyQVJdtsgV64b6WlmvXJPRvaweGJrLMQ5nJrTrOnSZBkm60Nll97d9wau/bc188dt251bJeYYdBd\nXiXe0pbi8NIfepnNb2hb19K/3bO6Xrgpwle+kfk5FS/XwUpXyOB5wVp71hgDgLX2hDFm0cN+p4DD\n1tp54AVjzHljTJu19iQwRWwAndAMnPFSmXC4OftGHvefmsr9Nsb69SHPdcinfK/ySW3313/wTmCd\nr+ewEvcvhWLUMVWZRx972bk8Ns0tQ1sKKnMkKW4OYOT0LNdv78u4zdExZ2xcqn0yyfaefp/PUn0+\nlcCveq+kcrxcA+muvWzXxrkL8zlfH25BOEelVinXrB9leml/uXK31+PjrnjmFN8dudQjqOeyUIUM\nng8aY34DqDfGvAn498C/etjvceA/AH9qjNkENBIbUAMcBrYYY9YBs8RCNj7lpTKF5P1z59lMTDaS\ni4mJGc9YhuUWAAAgAElEQVR1yKf8XOSa2i5RHz/PYaXtnyij2PzOT5nuuHvbXbP/tTd5fu90ZXZt\naHQsd25oXLade5vLOp0zUXW3OffJFpaR6T39+MyT+V1eMcssBT/q7dfxB6WcVO3xe88OO9pvumvP\nvW9ve9OltHUX5unraF52feQiKOcouZxSqJRr1o8yvfTBuXK3125XTHSq747ujY0Z+/WEIJ/L5PLy\nUcjg+SPEYp7PAX8N/DNwV7adrLX7jDFvNcY8TSxP9EeAXzbGhKy1e40xdwKPxNfttdaeKKCOIlXJ\nPVOfe1ayfHiJvXRvc/bcnONJb9Pn3CfbbUjFe0qQuGfzW1ULf3Kfs/2mu/ZqXTNybmxp4N1vM0u3\nv585NMpdtw+W47CkQhRjNkn3DIN97WuS4plTf3ecmcncr1eDvAfP1toZ4O74f7nu+7EM6/YB+/Kt\nl4gsn6nPD15iL93bfO1R5y3BIyPTvDmpTtlSHineU4LEPZtfuhkwU117r56YTjkjZ6r9RVIpxmyS\n6WYYzPTdcWRketnym338rqkEOQ+e43HN0RSraoCotXZVinUiUoVSpbNzLCvlkVSwQlLXpZqRU+1f\nSi2fPjhbv14Nch48W2vzzg0tIpXDHY98eXcrTxwa5fj4ND3hJt6yrYMXhiczppFL3MJOpPZy3wIM\nQliGl/RPkr+Vkm4wGo3y/NEzvHZqlulzF+nY0Mj0zNxS2rmutlDG9puqrS8sRHnfTRGOj0/THY6l\nEZNgCkI79tJX5ZqmNJ9QEHdo0tUmzJOHRwtKjZrqOIPcZxQS8ywrQCK13fr1IU8PMvb09LFqlW4u\nVAN3PPL7fjbCV755KYVRNIpjOVXapET4yC1DW1LeZgxCWIbS4RXXSjm/h4bP8MzzY47Qi6HBbvYf\neMHTMaVq6z84NOJIC1Zb40wjJsERhHbspQ65pinNJxTEHRboTneXT2rUZEE419lo8FzlllLbfT17\naru5mVN85s7d9PdvLkHNpNzc8cjHT2ZertR4Tb+nmhWnlXJ+j45Oc+7CvOO1xHK+x3RsbCbjsgRH\nENqxlzoMu+KRh0emfX32JRW/3zMI5zobDZ4l59R2sjK5bwlu7nLGsblTGHW3LY+Vc99uG+hr5fDw\nZFlDIrLdAvQj7roSbjOWS7rzm66tlOscemknoxOzAEsp5lbXr+LdbzNMnr3Ak4fH2BlpoyZa46kt\nRKNRusPOnP/5TJUspeFux/2dTY4ZV0vRfi/rbHKkiLusa3lftXmTMz3o5X2t7P/RidiMgh1NXPuG\ndlYVEFKRijsGOnFu8u33K+FZmHweGPzPmdZba38//+qISLm4b5V98NatjtRafe1rHPGZ65vqHetn\nzl9cVoZ7FrUg3ur0I/1TJdxmLJd0ce1Bayte2kltLfS0N7G4GOX+b7+wtG5osJsvPPQcsJWWxgZP\nbeHQ8Bn2PfHjpTRhfZ3NXLtNP2IElbsdL0YpeftdiOIIG9o+0L5sm9CaOsc2/V0tzhkDo1HfQ4Pc\nMdDrmhqWpXDM5VwE4VmYbPL55Vk/p4isQO5bZUdGlqfWevuO3qXlrz36smP92oY6WkMNjjLct/OC\neKvTj/RPlXCbsVzSxbW7z1m524qXdjLQu56B3vV81zXDWiJ8Y3hketk1kO44jo5Oc3LywlKasHfu\nutz3XwTFP+527E5TWIr266WfWRZu55oxsBihQe4Y6HQpHL0KwrMw2eSTbeP3Ur1ujKkBFAwrUqHc\nt8rc6Yfctylf1+28Pbh5UzNNa+od+/R2lv/2WyluAVbCbcagWd7eXDPzlfgcuutTX1/LoSMTS7ec\nk8M6Nqxb49h27erYV2lfZxPrQw0Zb60nyjk3N891g908e3iUmfPzajMVJlt/Wap+JrlPjvSvW7ZN\njyvcrqfDGRpUjGxDXsJLKl3eMc/xqbn/CEj+JF4BtqTeQ0SCLNUMaJluU37w1q3LbiG6y6hzLc+c\nv1jy4yrFLcBKuM0YNO5zFulvpaWxfOcwUZ8Xjp5hcmaOBx97mZnz80u3nJPDOm68upehwW5W168i\nvG4tJ8+c47YbtrCxpYGL85lvrbvDQ977swO0r1urNlNhytF+vcxweaWrXufnLjpmEFxT77y7UYyQ\nMy/hJZWukAcG7wLeCPwh8HHgeuCnve5sjPkhMBlffMVa++tJ6z4KfAAYi7/0IWvtiwXUVUSySDUD\n2tt39Ka9TemeZSpxuzC5jIa6VctCO3aY0nakpbgFWAm3GYMm1Tkr5zlM1Ofo6DT/9PgrS68nbjkn\n3w6fnJnjmUOjXH1lB99+enjp9XfuunxZue5b1u7b6ouLUbWbClSO9ut1hsvkenz1uy/xnWcutdEb\nr+5jh/E+y2s+qiGMrZDB85i19hVjzP8FtllrvxT/NTorY8xqAGvtrjSbXAW811p7IM16EfFZttAD\nL7fZ3Tf7erLc2hQJmnTXQfLrjfEwjcT/k7fNNmvgsqwNXZoYRfLjJVzM3Qe7M7oUI+SsGsLYChk8\nzxhjbgD+L/BzxphnAK9/WrwRCBljvgWsAn7XWvtU0vqrgLuNMV3APmvtJwuop4iwPBWX6W3l6aSZ\nqK4eaOOOWxOzATYz0NfqTMXU3+rpNmXyNgP9rdSvqkk7w2Ahx1HK9HdKRVe45HPY2ryamdk5NrWF\nAncuE7fGXzs5Q1NjPUdHp5mavcjM7Bwf/vltTM5cYPLsHL/2jitZmF/gjlu3Mnl2LnZNxNOV3fyT\nm2kJraa7bS2md13K8hPXyM6tnZw6NZ2mNhIUuc7cVwzuvs/0tXLHrVuX6hTpb13WV735ynYWF6Ic\nPzlNd1sT17gyuviRbcitGGUGTSGD598kFlpxF/DrgAX+i8d9Z4FPWWu/aIy5HPimMeYKa+1ifP39\nwOeBKeBBY8xN1tpvFFBXkarnjm3bszvCl/ZdSmF0cd65DKlTL2W7Tel+LdMMg34cRylSmikVXeHc\n53BosJuvftvb7HyllLg1DiyrLydnHGFId9zqnEnt4JGJZe3E/YeB+3Z/bW1w/nCQ9HKdua8YsqV3\nbGkcBFjWzyfPBLu6odZRbz+yDbkVo8ygyXvwbK09aIz5LeBNwO8Bv5Q0+M3mBeCleDkvGmNOAV1A\nolf6rLV2CsAYsw8YBDIOnsPh5kyrs0ref2oq90T169eHPNchn/KDItNx+vkZlGP/UihGHb2WOeJK\nr3VsfCbj8tEx569hI6dnuX57Xx41jPHr2N3HUWi9kqWrYyHvWQntMhW/6p0ox30OE+ndvJ5Lv+uT\nTbr6Jjs6Ns0tQ5eekc+3nZT62EpVTimUqk89+tjLzmXXZ59Pmblyt69UfbRbqn49Xb3L+f1U7jJz\nVUi2jZ8Gvgy8Riz0Yp0x5p3W2mc87P5+YBvwEWPMJqAZOBEvtwV4zhgzAJwDdgFfzFZgIX/dhMPN\njv0nJnLPgzgxMeO5DvmUHxTpjtN9DnNV7v0TZRSb33+F53LcXRsaHcs97e5YOFecWrvzfHRuaMy5\n/ku3GU/P5hVikepWqfs43PXKFmKR7vZrpnOZ7T3T8aNdpiqzFPyod/Lxu89hIr3bxflF/nH/y1xt\n2ngmzW1xv85jIdfL2tV1y+OZ25sc5bn3aVxTx9j4VMY2X45jK1U5pVCqPtXdH7o/+4WFRZ44NJpy\nJj+/zqm7ffW6+uzODY3LnztxzWLprnehfXQmxer//Cwz33ZaSNjGnwI/a639NwBjzHbgr4DtHvb9\nInCvMeb7wCKxwfS7jDEha+1eY8zdwPeA88B3rbUPF1DPootGFzl+/Jjn7XPZVsQv7ljLRDxyYlao\nHZEwG5tXL8Wp+ZF6qdBwh1S3St8cac8YT5ftPfO5/apUdIVLpDGcm1ugu72Ji/OLDA12s++JV5g5\nP8+FmyLOmdDKcFs8WfJn3trcwMzsRXraQ5i+dQynieGP9K/jjlu38q8vnmTt6jq++i1LS2NDoMJS\nJD8NdTWOlG8Ndc545ycOjTrD3oowk587ltjzcyd1tUv9vLvNKiQtP4UMni8kBs4A1tpn4xOlZGWt\nvQj8iuvlf0lafx9wXwF1K6m5mQnu+doEDSFvg+Lp8ZdoCisdtpRWqtRKybNCAcvi1ApNvVRoyiL3\nrF3DI9NcE+nIGE+X7T3TlZmJUtEVzpEK8WAsZVZy/LB7JjQvn0sxZfrMb04Tw19DDZNnY2nsElZi\nmq5q9NKxKb711JGl5bft7Oeqyy8NRN0z9xVjJr9UscRenztJdy1VQ1q5Yihk8PyUMWYv8AVgHvhl\n4FVjzBCAtXa/D/WrGA2hjaxp8dbRX5g+VeTaiARDoSmL3OnwvKS6yza7VT5lSuGWpWjb1Oz4nPpX\nyOdSDWm6qtHmTctnVE2WLSVcUKm95qeQwXMk/n93GrnfA6LEYpVFpIoVmrJoZ6QN2Jr2lmMq2Wa3\nyqdMKZw79CWK83O6aqA9Ke1W5X4uCvFZmUJr6jL2K9e+oR2i0VjMc3uIa7eV765JLqohrVwxFJJt\n4wY/KyIiK0+hKYtqqc14yzGVbLch8ylTCucOg3DPjnZsdJq37+it+M9FIT4rU7Z+ZRW1vsc4l0I1\npJUrhkKybfQDe4HLgLcCXwXeb6191ZeaiYjkQbchK4M+J6kkaq+SrJCwjf8OfAr4Y2CU2MQmXwGG\nfKiXiARMOWbZS/WeRMk4w2Cht801m2BxJc7v2MQs77spwmvjsdvcA/3BnqY6uV1c3ree13WG1C5W\nMHc/cEVvK3t2R5bCMoLeXtNR/+aPQgbPbdbaR4wxf2ytjQJfMMZ8xK+KiUiwBGVmPyBjPQq9ba7U\nTcWVOL9Dg92OGNL6utpAh2yoXVSXbDOyBr29pqN27I9CJmY/Z4zpIfZwIMaYnwQu+FIrEQmcVDF/\n5XjPYtejHMdZTRLn0z1jnzuFYNCoXVQX9+frTj0X9PaajtqxPwr55fk/AV8HXm+M+VdgA/BLvtRK\nREpuaaapNOEQpYj5c99STJV2bmHRuY+7Hstut/a08oM0M3+lotjG4kqcz3WhBoYGu1lYXKRzQ4jz\nc/M8eXiMmdk5esIhFqIsfYZv3VjYZ5CtbXuhdlFd3J/363qaua3p0iQpnRvW8uTh0aUZMVPNkFkT\nrSm43WWSTwiG2rE/Csm28awx5mrgCmLTcx+OT34iIhUo2+28UqTgctfhg7duXZYeaub8xaUB9drV\ndcycv5ixjPe5Z67LMvOXUo0VV+Lz27huLX/77RcYGuzmgUdfWlo/NNjNsZMzjs+9YXU9WwrI++zH\nrerkdrGlbz2v76yMPL6SH3c/cOrseUc7dfcrqWbIbGlsKGqIRD7tWv2bPwrJtrED+Engz4n9Aj1o\njPmwtfYBj/v/EJiML75irf31pHU3A58ALgL3Wmv35ltPEfEmWyqmUqTgctfhyMjyOk3OzDkGVmsb\n6thh2h3bJHPPXJdt5i+lGiuuV147y/4Dx/mp7b3A8vAN9zLAkROTBQ2e/ZhFLbldhMPNSuu1wrn7\nga9+9yXHene/kmqGzNZQg+M1v2fvy6ddq3/zRyFhG58Dfgf4RWAWuAp4IP5fRsaY1QDW2mUTqRhj\n6oB74uWdA54wxjxkrR0voK4iK0o+t6Gz3eIrxu28bPV01+l1m5xhGq/rbllWp7a5BW5rvHT7tGvD\nWg4emVgqw30cPeHKnPlrpUh8xq+dnKGpsZ61a1Zx2w1bmD1/kesGu1m72vk1tLF5NefmFhyv9XcV\nltkgXdt2t7/a2tg04spCIG6XuWbE3NzlXO7rcM442N/ZREtjw7LXkvuqXNvY4uIiT9lxjj72Mr3t\nzcv6aIVglE4hg+daa+1jxpj7gAestcPxga8XbwRCxphvEQv5+F1r7VPxdRHgRWvtFIAx5nFi6e88\n/aItUg3yuV3nNSzDz5mmsr2ne/0drjCN13W3LAvRWFiMOm6f7tkd4c8f+NHS8m+/Z9BxW9L0t1JT\nQ8XN/LVSuLNruLNsvPdnjeMz7gqHCLeuYftA+9JnuHNrJ6dO5f9gU7q27W5/yXVTFgJJNj+/6Gi3\nl21qcSxHLlvvaMfrmhowvc52txjNnCkom6fsOF946ODS8od//g0KwSiTQgbPs8aYu4hNw/0bxpj/\nCHi9jzULfMpa+0VjzOXAN40xV1hrF4EWLoVzEC8z688O4XBztk087z81pV+m0lm/PpT2XPv5GZRj\n/1Lwq44jSZ02wMjpWa7f3lfwPu1h5y+9hcr2nu71R8dctyHHnLGvjWuWd1nHxp1hGMdPzvLLPzPg\neO22Xd5+uSxGG6qEdpmKb2319CxwKRzDHZbx2slZZ3xz/Sp+4YYrfK9Pqrbtbn/Jdct2Tfn5ufpV\nVtDKKYVSXbNHx150LB9z9VWvjpx1tOPe9ibe+hN9jnb3t48879jHS7/tqMNjLy97z4/84ps8759N\npfR/QWifhQye3wP8OnCbtXbCGLMJeLfHfV8AXgKw1r5ojDkFdAHHgSliA+iEZuBMtgILiT9zx69N\nTGSOiaxmExMzKc91oTGA5d4/UUax+RUn2bWh0bHcuaExa9le9/EznjPbe7rX97Y7P4OecMi1vglc\ntznd23g5F6kUI461WGWWgh/1Doeblz7jxnh4RqMrTGNTm/Pz6wmHlr23X+fRXY67/SWHkGRqR35+\nrsU6tiCUUwqluma7XeFf3W2ukIl253Jy+0mUmU+/7XyPZtdyU+DaYaWVmW87LSTbxnHg95OWfyeH\n3d8PbAM+Eh90NwMn4usOA1uMMeuI/UI9RGwmQxGJyyfEwv2UdaSv1RF/N9DXyuHhSV/TKg30tXLH\nrVs5OjZNb3ssbdz+H51YShv3lje0O+vU30pL46Xlgf5W6utq4+mfmtgZCcdLvlTmjkiYjS1rdOsy\noBLt7sTJGe64dSszsxd5300RxiZmaV/XyJnp87zvZyOMnJ5hU1tpw2oS7XN4ZJr+zibWNTXQub6x\noHakGdxWhoWFRZ5ISnG5c1sHROH4yWm625q45o0dhNetSdt3pWo/hWa62DHQxsX5CMfGZ+gJh9ix\n1B8Wj9pzaoX88lyILwL3GmO+DywSG0y/yxgTstbuNcbcCTxC7CemvdbaExnK8mT42HF+8MyBlOua\nQquZnrk0v8vpU+PAmkLfUqRoEk9MX7+9z/Nf4e6nrA8emVgWb5wcT+dHzOfh4UlHmcvSOcXTxiW/\nj/tJ8GsiHctm8rom0sEtQ1uWjl1PjwdXunb37p8xfOWbl9rCnt2RjCkEi8HdPu+6fZC37+gtqEzN\n4LYyPHFo1DGj4OJi1NFew+vWLOt3svVDhWa6eH540lGnjS1ryjLLq9pzmQbP8XzQv+J6+V+S1u8D\n9vn5ns/88N94xDZm2OLSuvNTGjjLyudOc+SeMcuPtEqFpo2TlSfRJkbjsdAJ5WgLfqSwK0WZUnru\n9ujuu8rxuZajbak9p1auX55FpMzc6bv6Ol3xdD6kPdrc1cRtN1xKK7e+ebVjfba0cYnUTMmzdtVm\nmB1Qgi/RJmbiqeqePTzKzPl5Ojas5cnDY46Z2fyaYTCdYqRn1AxuK4P7c+tpb+LdP2MYPT1Lx8ZG\nNnc1FpR2Lh/laFtqz6lp8CxSpZbFQMdj9vxMVXd6es6RVu79N1/pGEx3bsh8l8edmgm2LgvhkMri\nbhO/9FOXs7gY5Vv/8ionJy+Qama2QmcYTKcYs61pBreVoWP9akdftbq+li8nhUy4ZxgsRThDMdKJ\nen1PtWcnDZ5FqlSq+Ltc46izcYeCDI9M851nhpeW37nrcq7oTv+Fk2p/DZ4rm/szPT11nu8+c9Sx\n3j0zW6EzDKZTjNnWNIPbyvDj49OOP/J+6mpnLHw5wjjyedbFr/dUe3bS4FlEisYdCuIO08h2C9C9\nf18RBlBSWu7P1J0CrK+ziVb3zGwFzjAokqusM5WGFc5QzTR4FpGi2RlpI1NauYHeVp48PJo2pjmx\n//JUdVJpEimvLly4yJ7dEU6cnKWvs4ntA2Fqk2aA3BEJU0uN41ZxoTMMiuTKHa5weW8r0eRUdf9P\nB22tSpFZrTR4FpGiqaU2Y1q5Jw+PZoxpTuyvUI3Kly7l1cEjEynTbyW3k9pa5ZWV0nKHKzx5eNSR\nqm51Q6xvUjhDddJj6yJSNqlimmVlSpXyKtPrIkGivkqS6ZdnESkZ92xVr+tuYWiwm3MX5mlcXcfm\nTc0Zt0+VDkozYAVP4jNJnq0yEUPa1rqa636ilzPTF9j/3AihRufXkGJHJYjcfdXre5pLnqpOgkOD\nZ/EsGl3k+PFjKddNTYWYmFg+yUFPTx+rVq3yVP7CwgLHjg1n3zCPsiUY3LfuP3jrVvYfOL60vH2g\nPeP2qdJBaQas4En1mVwZjyEdO3OO//nN55fW/dKuyxka7KY11MAVvesUOyqBdH5u3tFXbd7Uwl88\n8NzSsvqd6lK2wbMxph14FrjRWvtC0usfBT4AjMVf+pC19sUyVFFc5mYmuOdrEzSEUg+gl29/is/c\nuZv+/s2etj92bJi77tlHQ2ij72VLMLhvyR/JMquhl9mtNANW8KT7TLb2r+e5H592rBs/c479B47z\nzl2X63OTwDo66vxx6OiY+p1qVpbBszGmDvgrYDbF6quA91prD6RYJ2XWENrImpbiPbxV7PKrWRDC\nG3Kd1dDL7FaaASt4Un0mifa3vsU5y+TG1jVL24gEVU+KGQeTwzgu61L7rSbl+uX508BfAnenWHcV\ncLcxpgvYZ639ZElrJr7JFObhNjUV8ryt5CcI4Q3pZjVMl+7Jy+xWmgEreFLNhHboSKz9hdbUMTTY\nTeOaOjo3NLK4sLgU1iESVNe+oR2i0aWUiuF1axwzDrpDzmRlK/ng2RizBxiz1n7bGPPxFJvcD3we\nmAIeNMbcZK39RrZyw+HmjOubmjNPAyz+yzXMY3r8JZrCWzyXv359yPG5Z2sDQVCMOnotcyQpXg9g\n5PQs12/vK6jMXCTKbA+3OF7vCGeeAMO9vZcy81XOzydoCq23+zP55wOvATBzPhY7+p63GW7bdUXJ\n6hPUcvwsK2jllEIpr9nbdl3qq/72kecd6zL1p5nKzFel9FWVUmauyvHL868Bi8aYnwbeBHzFGHOL\ntTYR4/xZa+0UgDFmHzAIZB08Z5uqcvrseUAD6FLLJQzjwvSpnMqemJhZ+tzD4eaCpystxQXp95Sq\nuRx314ZGx3LnhsaU+/pxLiuxzEqoY6LMUvCj3snH77X9ZSvHr/oEoRw/ywpiOaVQrms2l/ZczX1V\n0MvMt52WfPBsrb0u8W9jzKPEHggciy+3AM8ZYwaAc8Au4IulrqPISlSK8AZ3XPVAXyuHhycdKcuS\n46yDEIctpWF6W9mzO8KxsRm6wyEmps5x6Aj6zKUiLCws8sSh0VjYRkcTb3lDu8LFqli5U9VFAYwx\ntwMha+1eY8zdwPeA88B3rbUPl7F+IiuGe8asYnDHVd9x61bHDILuOOsgxGFLaTxtxx0zCd52wxb+\net8BfeZSEZ44NOpov0SjDG3rUtutUmUdPFtrd8X/+ULSa/cB95WnRiJSCHeKMvcsXPmkopOVwd0W\nTk2eB/SZS2U4NjaTcVmqS7l/eRaRFcSdoqy/q/BUdFK5FhajS7OwdYVDjnVKUSdB5g4pc7fTnvZQ\nmj2lGmjwLCK+ccdVr6plKRfq2tV1rKrNvL3iBleWpw+OLIXltLWuZs/uCCdOztLVphR1EmzukLKP\nvXdwKWa/pz3Etds0H0E10+BZRHzjjqt++OmjjiltO9c3MtC7Pu32srIcOTG59O+TkxeYPTfPu254\nfRlrJOKNO6Tsx8enefuO3jLVRoKmNvsmIiL5UVhGdbusy5nDW5+/VAr1XZKJfnkWkaJJNdOcVI8d\nWzsVliMVSSFlkokGzyJSNImwjOu39/meLF+Cr7ZWYTlSmRRSJpkobENERERExCMNnkVEREREPCpb\n2IYxph14FrjRWvtC0us3A58ALgL3Wmv3lqmKIiIiIiIOZfnl2RhTB/wVMJvi9XuAG4HrgQ8aY8Il\nr6CIiIiISArlCtv4NPCXwGuu1yPAi9baKWvtReBxYKjUlRMRERERSaXkYRvGmD3AmLX228aYj7tW\ntwCTSctngVZ8sBhd5PzZMU/bXpg5RU0OZV88N0FNDjvksn0xy67kuszNnPJesIiIiIhPaqLRaEnf\n0BjzGLAYX3wTYIFbrLVjxphtwCettbvj294DPG6t/YeSVlJEREREJIWSD56TGWMeBT6UeGAwHvN8\nENhJLB76B8DN1toTZaukiIiIiEhcuSdJiQIYY24HQtbavcaYO4FHgBpgrwbOIiIiIhIUZf3lWURE\nRESkkmiSFBERERERjzR4FhERERHxSINnERERERGPNHgWEREREfFIg2cREREREY80eBYRERER8UiD\nZxERERERjzR4FhERERHxSINnERERERGPNHgWEREREfFIg2cREREREY80eBYRERER8aiuXG9sjPkh\nMBlffMVa++tJ624GPgFcBO611u4tQxVFRERERBxqotFoyd/UGLMa+IG19qoU6+qAw8BVwDngCWC3\ntXa8tLUUEREREXEqV9jGG4GQMeZbxpjvGGN2Jq2LAC9aa6estReBx4GhstRSRERERCRJuQbPs8Cn\nrLVvA/4dcJ8xJlGXFi6FcwCcBVpLXD8RERERkWXKFfP8AvASgLX2RWPMKaALOA5MERtAJzQDZzIV\nFo1GozU1NUWqqlSRojYitVPxSdEbkdqq+EDtVCpBXg2oXIPn9wPbgI8YYzYRGyCfiK87DGwxxqwj\n9gv1EPCpTIXV1NQwPn4278qEw81l3T8Idaj2/RNlFFOh7TQVP45bZRanvGKWWWx+tVW/jn+lluNn\nWUEsp9iqtU+thDpWSpn5ttNyhW18EWg1xnwfuJ/YYPpdxpgPWGvngTuBR4g9LLjXWnsifVEiIiIi\nIqVRll+e4w8C/orr5X9JWr8P2FfSSomIiIiIZKFJUkREREREPNLgWURERETEIw2eRUREREQ80uBZ\nRNTWlU8AACAASURBVERERMQjDZ5FRERERDzS4FlERERExCMNnkVEREREPNLgWURERETEIw2eRURE\nREQ80uBZRERERMQjDZ5FRERERDzS4FlERERExCMNnkVEREREPNLgWURERETEo7pyvbExph14FrjR\nWvtC0usfBT4AjMVf+pC19sUyVFFERERExKEsg2djTB3wV8BsitVXAe+11h4oba1ERERERDIrV9jG\np4G/BF5Lse4q4G5jzPeNMR8rbbVERERERNKriUajJX1DY8weYJO19o+MMY8SC8tIDtv4BPB5YAp4\nEPgLa+03shRb2oOQlaqmyOWrnYofit1OQW1VCqd2KpUgr3ZajsHzY8BifPFNgAVusdaOxde3WGun\n4v/+d8AGa+0fZik2Oj5+Nu86hcPNlHP/INSh2vePl1H0wXOhdXTz47iLVebCwgLHjg0DsH59iImJ\nmbTb9vT0sWrVqpzK9/vYg3wuXWWWZFDiR739Ov6VWo6fZQWwnIppp8kqoR+ohDpWSpn5ttOSxzxb\na69L/Dvpl+elgTPwnDFmADgH7AK+WOo6ikjhjh0b5q579tEQ2phxu7mZU3zmzt30928uUc1ERETy\nV7ZsG3FRAGPM7UDIWrvXGHM38D3gPPBda+3DZaxf2USjUQ4Nn+Ho6DR9HU1E+tdRU5K7YCL+aQht\nZE1LR7mrIQGkPk5WErXn6lLWwbO1dlf8ny8kvXYfcF95ahQch4bP8Jn7LyUcuev2Qbb2ry9jjURE\n/KM+TlYStefqoklSAuro6HTGZRGRSqY+TlYStefqosFzQPV1NDmWe13LIiKVTH2crCRqz9Wl3DHP\nkkakfx133T7I0dFpejuauLJ/XbmrJCLiG/VxspKoPVcXDZ4DqoYatvavV8yUiKxI6uNkJVF7ri4K\n2xARERER8UiDZxERERERjxS2UWaJ3JAjB47TtaFRuSFFZEVTPlypRGq3kkyD5zJTbkgRqSbq86QS\nqd1KMoVtlJlyQ4pINVGfJ5VI7VaSafBcZsoNKSLVRH2eVCK1W0mmsI0yS+SGHDk9S+eGRuWGFJEV\nTflwpRKp3UqyvAfPxpjrgFuAy4FF4CXgIWvt932qW0Xz+nBBIjfk9dv7GB8/W4aaioiUTqLPu7Jv\nHYeGz/Ctp4/pASwJnFTf4crjLAk5D56NMW8C/gwYA74PPAZcBDYD/8EY84fAR621/8fPilYaPVwg\nIpKe+kgJMrVPySSfX57fA9xmrT2VYt1fGGPagY8BGQfP8e2eBW601r6Q9PrNwCeIDcjvtdbuzaOO\nZZfq4QJdeCIiMeojJcjUPiWTnAfP1trfyrJ+DLgz0zbGmDrgr4DZFK/fA1wFnAOeMMY8ZK0dz7We\n5aaHC0RE0lMfKUGm9imZFBLz/Fbgo4DjTzFr7S4Pu38a+EvgbtfrEeBFa+1U/D0eB4aAB/KtZ7n4\n+XCBkrOLSKVK13/pASwJMnf7jPS1cvDIhL6HBSgs28aXgN8DjuSykzFmDzBmrf22MebjrtUtwGTS\n8lmgtYA6lk3ioRg/bvMo9kpEKlW6/svPPlLEb+72efDIhL6HZUkhg+fj1tqv5LHfrwGLxpifBt4E\nfMUYc0s83GOK2AA6oRk446XQcLg5j6oEZ/9MZYwcOO5cPj3L9dv7fK9Dte9fCsWoY1DLnJoKed52\n/fpQXu/p97EH9VyWg1/1Hjk9u2w5Vf9VqvoErRw/ywpaOaVQqmvW6/dwLmUWolL6qkopM1eFDJ4/\nZ4z5G+CfgfnEi9kG1Nba6xL/NsY8CnwoPnAGOAxsMcasIxYPPQR8yktlCknzFg43l3X/bGV0bWh0\nLHduaFy2bbmPodL3T5RRbH6nI/TjuItV5sTETE7b5vqefh97kM+lu8xS8KPe4XCzp/7LSzl+1SdI\n5fhZVhDLKYVSXbOFtONq7quCXma+7bSQwfO/j///rUmvRYFcfo2OAhhjbgdC1tq9xpg7gUeAGmCv\ntfZEAXUMnMXFRZ6y4wyPTNPX2czOSBu1WSZ6VGygiFSqRP/12skZmhrrOTo6TU389eSYUT3bIUGW\n7Xs4n+92qVyFDJ67rLWRQt486eHCF5Je2wfsK6TcIHvKjvOFhw4mvbKVayIdGfdRbKCsZNHoIseP\nH/O0bU9PH6tWrSpyjcRPif4LyBgzqmc7JMiyfQ/n890ulauQwfP3jTHvAB621s5n3VoAGB6ZXras\nC0yq2dzMBPd8bYKGUOYB9NzMKT5z5276+zeXqGbip2x5c5VXVyqZvturSyGD55uBDwBRYwzEwiyi\n1lr9LJRBX2eza1m5I0UaQhtZ06IvmpUsW95c5dWVSqbv9uqS9+DZWtuV+LcxpsZaG/WnSivbzkgb\nsDUeF9XEzki43FUSESm6bDGjerZDKpm+26tLIZOkXA/8obX2WuAKY8w3gV+x1v7Ar8pVmoWFRZ44\nNMqxsRl6Opq49g3trHI9MFBLLddEOrLeztHDM+nPQarXRSTY3DGj0WiUg8MTSw8STp6do7V5NXWr\nalhVA4eOXLrG37pRv+LlI2VfGaXqv1vy4T6XpreVp5MeENwx0EZLYwOtoQZaGxuq+pwmztXIgeN0\nbWhckQ8HFxK2cQ/wPgBrrTXG3AT8T+BqPypWiZ44NMqX9h2+9EI0ytC2rvQ7ZKCHZ9Kfg1Svt4db\nUhUhIgGVuI6HBrvZn5RDd2iwm2MnZxyvNayuZ4tug+csVV8JmR/clNTc53LP7ojj+/7ivHO5ms9r\ntvHLShjfFJJHZY219rnEgrX2eaC+8CpVrmNjMxmXc5Hq4Zlqk+4c6NyIVL7EdXvugvN583MX5pe9\nduTEJJK7VH2l+s/8uM9Ttu/7aj6v2drYSmiDhfzy/Lwx5o+J/doM8MskpZyrRj2uB1x62r3PsOam\nh2fSnwOdG5HKl7iOG1c7v4bWrq5bdgO3v6u1RLVaWVL1le5zq/7TG/e5dH+/u7//q/m8VsPDwYUM\nnn8d+APgfuAi8Bhwhx+VqlRv2drO4mKU4+PTdIebeMu2WFyzO76nthZePZE51kcPz6Q/Bzo3IpXv\nip5W3ndThBMnZ3jfTRHmLy4QaqxnZvYiPe0htg+0L13jO7d2cupU5f06VW7p+kr1n7kzva3s2R1Z\neqbpmje0U19Xu/SA4NUDYepX1SwtR/qr9w++RLsbOT1L54bGFflwcM6DZ2NMp7V2xFo7AfxGpm0K\nrl2FscOTfOUbl2Kewq1rUsboJsf4pYv10cQo6c+Bzo1I5fvBoVFHf7lnd2TZg9SJa7y2trIeJgqK\ndH2l+s/cPW3HHTHN9atqHA//Hzwy4ZgkpaWx8uJ4/ZJod9dv70s5lfZK+A7PJ+b5k8aYPzTGXOFe\nYYwZMMZ8CvhU4VWrPF5jdJPj+Sox1kdEpFB+PiMiUmypJkFJthLieMW7nH95ttbuMcbsBr5gjLkc\neA2YB3qAl4FPWWu/7m81K4PXGN21STF+lRjrIyJSKD+fEREptmyToKyEOF7xLq+YZ2vtPmCfMWY9\n8HpgEXglHspRsXLNPejOZTjQ3+opRndVLXSub6zYWB+vVkIuRxHJXaY8r4l1tdFF3ndThNfGZ+hp\nD3HtNs0wWYiFxSgHj0yovy2Sq00bF26KLD3TdLVrEpSVEMcbZNlyR5daIQ8MEh8sP+tTXcou19yD\n6bb3EqM70Fu5sT5erYRcjiKSu0zXvvqF4nj64IjOaxE9Y8cdMfqr62sdMforIY43yILWbxSS53nF\nyTVmSTFOmen8iFSnTNe++oXicOfC1nn1V7aYZymuoPUbBf3ynC9jTC3wBcAQC/n4sLX2UNL6jwIf\nAMbiL33IWvtiseuVa8ySYpwy0/kRqU6Zrn31C8VxmSsXts6rv7LFPEtxBa3fyHvwbIypB24E2uBS\n4Im19isedr8ZiFprf9IYcx3wR8DPJa2/CnivtfZAyr2LJNeYpWy5DKudYsBEqlOmvlH9QnHs2Nqp\n81pEOyNtwNalPM47XTHPUlxBG28V8svz/wK6gMNANP5aFMg6eLbWPmSM+af44mWA+0HDq4C7jTFd\nwD5r7ScLqKdnucYsRRejTM3OcWrqPI1r6llYjPKMHWN4ZJrNm1oIranL+vBG0ILg/aQYMJHqlCrP\n6+LiIk/ZcYZHpunc2AgscmrqPN/54XGaGuuZPDunB90KUFur/raYogtwcX6RhcUoFxeiLCzCU3Y0\nPphuZmekjdoskbB6iD5/2XJHl1ohg+cBa+1AvjtbaxeNMV8i9ovzL7pW3w98HpgCHjTG3GSt/Ube\nNS2Sp+y4Iyn6hZsiSw8UJE+EAumD24MWBC8iUgzu/vLdP2P40r7DnvtKkXJ64tCoY5KUxYUoX/nm\n4aQtti6b5MdN3/crRyGD55eNMX3W2uF8C4jnjG4HnjbGRKy15+KrPmutnQIwxuwDBoGMg+dwuDnT\n6qzy2f/oYy87lo+PXwpgT54IBWDk9CzXb+9bVsZI0pdGpu28KMc5WEn7l0Ix6hjUMqem/M/bu359\nyFE3v489qOeyHPyqd6Icd385enoW8N5X+l2foJTjZ1lBK6cUSnXNHht/ybF8/KTrAbaxaW4Z2pKx\nzCB936vMwuQzPfejxMIz2oEfGWP+jdgkKQBYa3d5KONXgJ54OMZ5YIHYg4MYY1qA54wxA8A5YBfw\nxWxlFvIzfjjcnNf+ve3OD7A7fCmAvXG189R2bmhM+R5dGxo9bZdNvseg/Z1lFJvft5v8OO5ilTkx\n4f+McRMTM0t18/vYg3wu3WWWgh/1Tj5+d3/ZEe/7vPSVfp3HoJXjZ1lBLKcUSnXN9rQ7H1BL/r4H\n6G1vSluXRJlB+b5Xmc7y8pHPL8//Na93cvoH4F5jzGPxOnwU+AVjTMhau9cYczfwPWID6+9aax/2\n4T2zShePlByrlxzb5E6afs22DmprYtPM9nU2EblsPa+8dpa+zmYi/bEnod1l7Yi0FRQEny5m2n0s\ntbXw6gnFWYlIeSQ/cNWxofH/Z+/Nw+S4ynv/7/Ss6u6Z1mimZ5FmkbHsM+2xIIplCdkgbEIItsAK\n17mAWRSRRIHchHv54SeLkyfJJfeSH0nAIdu9SWwCmJ9ZkkuA64UtYGwwjmWDwqLleMHSjKTZNZqZ\n7tas3b8/eqqn6nR1VU13VXfVzPfzPHo01XXOqbeqT7311ul3wdLyEo7clsDE5TSO3JbA+HQKHdsi\nmJhO40wNsJJFXn+9ui04mQ3c8Gul/vYfN17bgcxtWVyYzD3v9+ue9z0dEewVcTx12toHOgjBstXw\ny1aPOdAXw+mhGV/7hpdSnvtxABBC/I2U8n36fUKITwF43MEYaQBvtdj/IIAH1ytbuRTzR1J99TTf\nJjVpeqgGBp8ovS9fS9h6rFKd4IvJrH6ul4V+VoSQShPCWlGJ+758Enfeuguf/caavrzz1l144NGc\nD/TZsaTBD7qhsR67ApIazA2/Vupv//H06TGjj3MWis+z8flv5gMdhCD6avhlq8c8dnjQYCf5cc6X\n4rZxP4CXAdgrhBhUxvLfa9Q6MEvCPdjfapoc/UCis+Dz8+PGn6n1vnx2Y7kts/q5mSyElMLKygrO\nn7cPdbhw4XwFpCFBQ9OBUzPzhs+1bdUHGsgVAAmK8VxMJ5czBvV39VF9nNVt9flf7rO9Wrgxf8s9\npmon+XHOl+K28T+RSy/3VwA+qPt8Gbm0dYGlWBLuYsnR1c97lP5bdL58dmOVSlGZHchCSCmcPz+E\nu+99BA2RNst2yYkXEI0XD6AhmxNNB7bFmgyfa9tbGusKfqDtVwqA+Bk3ijlQf/sP1cd5R7txW33+\nB7WISjWKkajHVK+dH+d8KcZzBsBPkSt0ohIFcKksiapIMX+kYsnR9w20Y2k5gfMTKfR0RHFgsAP1\ntTUYGk2ivyuKrdEGdLWGHY1Vrsyqz7T+XHo6ophNL6ChrhY9HREM9Ns/iIr5eRMCAA2RNjS1WK+q\nLCSnKiQNCRKa3rw4mcKR2xOYvHwF7bEtSM4v4MjtCVyamUdPRxR7Bzryunj/YBempoJRDll9jiT6\nYjh5bnpd/pvqGLUhYEd7BI0NtTj50iXMppfyOnkj1wrwEwd2dwJZ5H2eX/nyTsS3NuW/o4H+WP75\nrz3bg/jdDPTFcOzwmo2ScGAv2GHnR11wz/TH0BI22mKaTTL8+Ivo7ai+TVKK8fw4ctk2mgB0ImdI\nrwDYBeBF5EpuB5Ji/kiar576E8yZoRmDj1N9bU2Bn84b9vU6GqtcmVWfaf25PHV6DPf/33z1c9TX\nhWyPX8w3mxBCykHVm5o/48lz0wW+lpr+DIX8bXDoUZ8jZudl9xO02bNoOrloqpOZO7gyvDA0Y/Bx\njseaCr4j9dl+cmj93321OT00Y5hnWrxWOdjNUbP5rm7/uxz3lU2ybrNdSnmVlPJlAJ4AcIuU8prV\nYikHAPzIbQH9jBM/HT9g5mftRR9CCLHDzKfS6vOg49Z5FdPJG/W6+Y1SrnMQvxsvZHZjTL/ZJOWs\neSeklN/RNqSUzwAoueJgEAmKn04pftZu+2YTQgjgPE7DL/qzXNw6r6KxNxv0uvmNUq5zEL8bL2R2\nJQ7AZzZJORUGzwsh/gTA55Ezwt8J4DlXpAoIqr+xmZ+OHyjFz9pt32xCCAGKx5YEIQduKbh1XsV0\ncrG4F+IupXyPQfxuvLgP3RhTm//D40n0dlTfJinHeH4ngD8B8DnkfKD/DcBRF2TyFDPHdbN9vZ1R\npOaX8kVOTJ3Ts2t/1qByORytzsEM1c86k8ngKZlL5t7f3YytkXp8UwloUPtks1mcHFoLetEn7Q+F\nUNC/mJx+D5bYzGgp6GZnI5bVAZmCjqwHTQ9cnEyhsaEWUzPz+cwaNSjUE7+wryfQesKs4AMA1NbW\nYDa9iK8dP7+mt7PAUz8ewQtD07bFIWqyNWgJNyAWaUAs3ABkkdfJseZG1NUG+ar5n+WlLCYuz2Nq\nbh5NTXVYRhb1Nle8WExSNSlWkEQf1Ghnx9glFDALlLQac2UlgydPjeUKznRGcfP1HahV7C3NJrnj\n4C5fXMuSjWcp5TSA99k29Blmjusd8RbTffrE9GbO6dUK1LA6ByeowYBOEvBbXZti/RnIEiyYgo54\ngaYHND1xcM8OPPTdl/L7g1AQYT0UK/hgfJ7kzhOA4+IQdoUkDu7ZgU89eibw18+vPHmqsEjKLa/o\nrp5AJeJGQRK7hALrffY/eWrMWGAmm8XB3f6+tuv2eRZC/GD1/4wQYkX3LyOEWHFfRHexcly3Skxv\n5pxerWCAco+rnouagN/JMfV9ivUPYrDEZkdLQWf1r34LH8zEOdp9r+kJtQiKXwOtS6VYILl63sNj\nyXUFndu11cYP+vXzK3ZFUoKCG4kO7IL31vvsVwvMqNt+pJTy3D+7+meDlLKwFJTPsXJct0pMb+ac\nXq1ggHKPqzreO0nAb3VtivUPYrAEIcRdND0QXtUT4UbjY8evgdalUiyQXD3v3s5owY/+qm620qfF\n9HjQr59fsSuSEhTs5pGjQEib4L31PvvVAjM9HRFbGapNOT7PPxVCfA/AwwAelVIGojiKleO6Pjl4\nf1cUtaEabGmow1XbmxFpqsdXjw/jqu4oxi7P4/x4Cn2dUfzOO/bgwmRlgwHKdb7PF3cZT6G3M4rO\n1kb0dkQN56D6IN10fYeh6Mrl1ALq60LoiUfR29FU0N8NOQkhwUfTAyOTKRw9lMDUzDyOHkogdWUZ\nWxrrcH48iSO3JzCXWkD71rArRRmqRTabRRbAm151FVoijdjRvgXX9MQADGJ4LImjb7wOiwvLiITr\nMTKZQl9nBO95826cvTiLvq5m7Eu0oyW8BxcnU4iG6zE8lkRqfhmpK4u4nFzEr//i9UimFtHdHjEE\nqMeaG7C4tIIbB/ZQz7qE+gzcf72uSEp7FAdeEcy6B8YiKM3YK9qxdGit2JuTImp6G6KnM4p9SvBe\nQTIFm0JBNw12IJPJ4sJErgDNges7111YqNKUYzy/DMCrANwG4ANCiBSAh6WUf+aKZB5hFdSnJge/\n+649eOutVxuS3N956y584bEX8m2OHkrgba8fqKgDe7mBiWZFCtRzKOaDNNjfiid+PGLYV+waVCqA\nkhDiX4rpgW//aAQPPLqmR+68dRfu+/JPXCnKUC3MfD3PKM8VvY+p6getP3dtHDNfaa2N/rrG482+\nCKTaKKjPwMxK1uDz3NhgX2zMj6h2ztKhREGxN7vzUm2ItuZG06InWqCkXaEgOTRj0AWN9SHfx0GU\nnOd51WXjJIBnADwJoB/ALznpK4QICSE+LoT4rhDiCSHEdcr+NwkhjgshnhRC/FqpMq4XJ8n7p2bm\nDW2C4Juj4sQfycoHKYj+SYQQf3Fhwqh3NN0aZJ9dM91q5WNq5getjlOsDfEW9bmm+jhXu0hHqajz\nRz1PJ+e1Xp9mu/ZBKTinp2TjWQhxCrmKgjcjl6bu5VLKGx12fxOArJTyVQD+EMCf6satA3AvgNcB\nuAXArwshKpLQz0nyfi3FkkYQfHNUnPgjWfkgBdE/iRDiL3oUH1JNtwbZZ9dMt1oV0zLzg1bHKdaG\neIv6nFN9nKtdpKNU1PmonqejImrr9Gm2ax+UgnN6ynHb+EsAP4ecgdsJoFMI8ZiU8nm7jlLKLwsh\nHlrd3AlgWrc7AeB5KeUsAAghvgvgIIAvlCqoPq/hzq4oVrIwzZFczEdX9MZw9FDOvycWbcCv3nEd\nzo7MYUc8iq5tTfjc188U5Dh2Iovqy6PPnXjV9hZEmups2+nzNHdtC9vnplbOs78zism5efzV50+g\npyOK7W2NeGE4iWt6ozhyWyLn3xWP4pU6H6Srd0Tz16OnI4Kbd3eanptVzlIn12a9/Qkh/mIlk83r\nDU33XpxMob6+FuOXUjhyewKT02m0bw1jLr2AY4cHA+HzrJ2Xlrt6dCqN3s5m3DjQjve++XpcmlvA\nlfllTM3OI31lGccOX4/5hSU0NdZhZm4RR994HaYuX8H2eAT9Xc0Yn06jc1sEcugyZtNL2Jdoz+vo\naKQeXW1hzKaWkNjZioHeGJ46ncvT3x2PILO8go7WMG5qjdj6iTL3vnMOXNeBzEo27+O8//rcc057\nJt4wEPelX67dc/RanT2T81c2nqd6XmbP4Wt2xAz2wbXKPavmeb6mJ4YjtyfyPs3XKj7Qqkw3JuK+\nLDinp5w8z/cBuE8IEQLwDgB/BOB/A6h12D8jhPgkgF+E0d2jBcCMbnsOQFnaVO+HZuY/puVILuab\nd1xOGPx77rx1F775zDAO7tlh8NNx4pdjlf9QnzvRys/NKk+zXW5q9TxV/+W3v17gn7/1fIFvd6gG\nBX7Sah7GcvNHupF/khDiH46fHC3QvapuO3J7wqBHg+DzrJ2Xei5Lywn89OKso3zWB/fswExq0dD/\n4J4dePjLL0HT3bPpRUOfno4Ijiv6/85bd+Ejnz2B98wv4x+++OP852b6krn3nfOsnDDmdQYKt9f5\n/K8Eds/Ro4qPs+rLjRrjeZk9hydm5guuxS0vX7MHVBnUexwwHkOVSfO79sP1LEbJxrMQ4j3IrTzv\nA/BDAB8B8Mh6xpBSHhVCdAA4LoRISCmvAJhFzoDWaAZw2W6seLy56L5RnXJS/cdGL6Vt+w8//qJh\nW/PNMxvrlr19lnLqZVH7DI8X93MztFPkKZpzeTyJOw5aF7M4P/GCYXts9XoU+HZPGP2izM5Vu5b6\n49v1cbO/1XfoF7yQ0a0xZ2f9737T2hoxnK/b19PP30+lcUPub5roXlW3qb7Pxe5zt66jm+elnsv5\niVTR81T1mbpf/5mmu1Vdr44BrOnqc6Ozhs9NdbTF80dPkOasV/eseu0L8jw7nLf6Md2k2Hjqd6zO\nGfVZbndeZs9h9Vl9YSJpkEeVQR1T3VZlsrNd/DA/y3HbGATwcQDvklIurKejEOKdAHqklB8GMA9g\nBUBmdfdpALuEEFsBpJFz2fgLuzGtooy7t4Xzf6v+Y12r+6z693YYvyjNN89sLLtoZ70s+j7xeLPh\nOFZjq/IUzbncEbWVp6fD6EvUuSpfgW933GhYqecajzcXnJsqp9X1caN/uZHmlbgh3Y6GdzPC3qok\nt1+Ynk7lz9ft7AJeZCvwasxK4IbcO7vXfjQsludZzZ9rdp+7dR3dGkc7L/VceuIRLC2tmO4z09vq\nj/z5XM2rulvt09sRBZRemq7u7zZWmTW7jsWeP3rcvNaVwKt7Vr326jxVffa9fj45Ha/wOarGKFn7\ncqvnpfbv2hZGXa3RHXRH3GhrqDIU5MhWr2VH4TEreS1LoSabzbomhFOEEGEAnwDQhZwB/2EAUQAR\nKeX9QohDAP4YOS3xcSnl39sMmbW6mFlkcercqs9zdxSXk4s4N5pEX1cU+xNxdMZjll9GBhk8fVqr\n4x5FW0sDfnohN9ZKBvlchtc58XnWyaL58tSgBvF4M0bHZ/DkT3J5Ja/uacZKBhg2qR2vl6e/O4pQ\nTQ3Ojs6hvzOKlUwWZ0eS6O2MIB5rwtmRQj9vvQ/TNb1RXJicz+V4jEfQEq6HHJrBrp4WLC5n8ue8\nLxHHmXMzBXJrxOPNGJ+YNZxboj+G07o+tSHg7Ii5f5iT/lbX1yXj2WuHNct5WgpuKpJz517CPf/w\n72hqsU5TNHPxFBqjbRVvNz87hv/3Pa9Ef/9VADa18VwJx0pX5mpbWxRP/GA4r3tXMsDIZAp1dbUY\nmUqhuz2Cm1/eieds7vNKGs9O/IK18xqfTqMmFMLk5Stoj23B5OU0utojWMlkMDE9n/ts5go6Wrcg\ns5JB46rPc6y5Aan0ElqiDUjPL+NycgHbWpoweXke3e1h7E/EEUIor+vPjcyhsy2MnvYteNmOGI6v\n6v/u9jAyKxnEW8N41c/04Lv/cd5S3xZ7/nh0rQMzT/Vo568+928YiON7Px7L++06mbfqmG7LaIb6\nHe/aHsNTp8by/sn7d3fimVU7o6cjgv3Xd+Ipi/Myew4vZbK5a7HqJ33TKzrRoIuv0mTQbCPRCuLR\nqQAAIABJREFUH8P3frx2zJt2d0LqxhzoX5vTml1mFq/l0bUsaZ6Ws/JcMlLKNIC3Wux/BOt0AbFC\n7+N78tw0/lHnv9MS3oPOuLVLdQi5fI56/+Frd6z54mi5DNcri4o+d+LicvH8n3p51PyJer/CTzx8\n2vCZhpkP052vvRbffnYoP9bXnl7Lc61hl7PZ7Nz01/3PH7T2tbPqTwgJHqGQub7T66x4rMlX97kT\nv2DtvADkfZ8f/d7Z/H69zr3z1l0F8SL68ayMgRBCaAk34OvHhwz91ecRANTVhWz1LXPvO0d97p88\nN23w0/XbvNVQv+Nv/3DE6J+cNfpu19eFbM9L3f6+HLfMea3meQZQECOljmk2p/1Myanqgsp68xNW\nklJye6qfm/nbqWMVy6Ho5bXx83UnhFQOv+uC9cin7VN1rH5bjR9Z7/mWcr38fo2DSFCvaYFPs02+\naifnpfYJas7rclj3yrMQ4o+s9ksp/6R0cbxnvfkJK0kpuT3V89li4leojlUsh6KX18bP150QUjn8\nrgvWI5/WVtWx+tgTNX5kvedbyvXy+zUOIkG9pgX+xgX5qhW/eifzS+kT1JzX5VCK20b1ExmWQbFc\nzn5AL9vO7ij2DnTYyqnWkK8NAV2tYUN/daxEf8w0h6KX18bP150QUjn8rgvWI5/WdmQyhaOHEhid\nSqOvM4qt0QZ0tYYRa27AwsIyjh0exMzcYknnW8r18vs1DiJBvaY3v7wTyMLgnxzf2lRgD+hjt+zY\nn2gHMGjwUd5srNt4llJ+0OxzIUQNgKvKlshjfO3zpYvdXMkAqfklzKQWsW1+OR8AoAaw6H2LxsZm\n8LScwExqEbPpZUSa6grGrQGQzQCz6UXMpBYRSy/h+QuX8a0TF9G9euOYXZtyk+vrr3s2my04H9PL\nwYT+hGw4fK2DYS2fppMmfnQRjfW1mJlbRF9nFD93w468btLaaKNp6rehIYSp2Xn8y7d/iq72CBYX\nltETz+VtfunibNHCVtlM1qCvs8ia6kF9QZq+VePOC12+WfH7vNVQv99re2IIhXLyh2prECpiD0zN\nziPcVF90fumpydagJdyAWKQBsXADkAVODq3NPdEbw3E5geHHX0RvRzP2DbTjjK7Qil0BtCDM0XLy\nPP8WcmW19TnMXgJgnViYFEUNVClWVKBYMvZiRVbU/mpCcn1BlGJju5lc32wsrVCNV8ckhJBy0XSS\nlU5W9dadt+7Cp78qV/WszH9+cM8OnJ9MGcYxK2ylFsUqVvxKX5BGlcnsHOzakWBiV6Akk8laFigp\nNr+sjmFXiGVp2bhtVwAtCHO0nIDBuwG8AsDnAVwN4FcBPO2GUJsVJ8F/Zu009E77VgGD58eN+Xz1\nAS1OAxPLCZZwOlZQAzQIIRuTYgGCet2k6ilNv6qBg1cWlm2Duc0+KxacdW5kxrBNvbo5Ub9P2wIl\nij3gJPhPPYbaRx3T7hh2c9KPc7Qc43lcSvkSgB8B2C2l/CQA4YpUmxQnwX+ARfBgl3mRlYJE/h3G\ngif6gBangYnlBEs4HSuoARqEkI1JsQBBvW5S9ZamX9XAwS2NdbbB3LnPnAVn6QvSqDIZ+lOvbmjU\n71ctelJQoKRTDSBcf0CqOkdVG6PwGNZBikGYo+XkeU4JIW5Fznj+RSHEMwD8ta5eRUrx2Rnoi+HY\n4ZwTfn9XFLWhGmxpqMO1fTH0d7Xkk5xf27+mJLXjjJ64gK5tYbz3zdfjpYtzuGp7syFgUPRtzReG\n2TuQc+7XEpZvb2/CO35BWAYLuBks4XSsoAZoEEI2HplMBrPpRbzhQD/iW7fg3W9MYGQqF2Q1MplC\nDXI6S6+39AGDCwvLuaDCS2l0tIZxaWYefV3N6O9uyReouGEgbvBbTvRvdRyctW+wi3p1g6N/3ndv\nC5vaFXo7oq+rGTcMtCML5OfYTbs70Vgfys+nGxNx1NfWYHg8id6OKPaZzEHrY0Rxw7XtWLg9gQsT\nSfTEo3jl7k7U14XWxkzE0dbcaJu0QCMIc7Qc4/l9AH4NOfeNXwUgkasKSFCaz87poZkCP6C33no1\nvv0jJck5gFte3l30OGphE7UwDDBYkLT/ba8fsCz04mawhNOxghKgQQjZ+Ki+xwf37AAA0wIoVnpL\nX9hK9Z0GYPBH1cZzUkCiWEEaFerV4OLErlDtCGDQMKca60OG/S3hXNGdOw7uwsTEXEHhNSfHWFD8\nqmtqckVRtDGBwqIoVnMwCHO0ZLcNKeVJAL8N4GcAfBBAq5TyY24JFnTcTGxv5bPk5Dh2/kl+9Cci\nhBA/oepNM5/l9ep5tb+q66mbiR43nvdu+Bvb+VWrPs4bkZKNZyHEzwMYAvCPAD4F4EUhxI1uCRZ0\n3Exsb+Wz5OQ4hf5J/vcnIoQQP6H6aZr5LK9Xz6v9VX9U6maip7TnvbXPfCn+xnZ+1arP80akHLeN\nvwRwm5TyhwAghNgL4O8B7HVDsKDj1GdH78PU0x429KkNAV89Poxr+6I4clsin+T85pd3Fhxn9FIa\n29vCyGRzffS+Sqosdv5GfiEIuR4JIRsDO32zP9GO2tD1uDS7gNT8EnbEo5ifXzIUQEn0xWz9RdVi\nWFf3bMXQ6Bx6OiK4aXcn4rEmg272Wg+q47+6jQa7X9F8jXO+xM1I9McK2ojeGI4eSuRimjqjuDHR\nbnjeD/TFsOZDXziGE9tFbSP6Y6ipWYujunm3tYuRShCf9eUYzwua4QwAUspnVwulEDj32THzYXrD\nvl6cPDeNP3/Q3C8uvrUpP66+SMq3nx0y9VUyk8Xv/kRAMHI9EkI2Bnb6JoQQIk31+Psv/qRoGyf+\noqo+jsebDfEmqm4+OWQ/Zjmo593QWI9dm7DcchBQfY1bwoVz4bicMPjh19fW4ECiM9/u5LlpyzGc\n2C5mbQ7u7i75vIL4rC8nVd3TQoj7hRD7hRA3CCH+AsBZIcRBIcRBtwTc6BTzL7Lyi9ss+Ts32vkQ\nQvxLKb6elchP67UeVMdT80UT/+BkLpTr41wN/CiTHeWsPCdW//+w8vkHkSsI/VqzTkKIOgD/BGAn\ngAYAH5JSPqTb/37ksniMr370Hinl82XI6WuK+RdZ+cVtlvydG+18CCH+pRRfz0rkp/VaD6rj93cX\nugIQf+Bojtr4OPvxuepHmewo2XiWUt5aYtd3ApiUUh4RQrQC+A8AD+n23wDgXVLKE6a9Nxh6n2V9\nnmXVL07L2byZ8ndutPMhhPiX9fh6qvp6PWN4IZeb4+8f7MLUlP9X/jYjdvMPgG1ecD8+V/0okx0l\nG89CiH4A9yO3gvxqAJ8B8CtSyrM2Xf8ZwL+s/h0CsKTsvwHAPUKIbgCPSCnVle2qYubYbrXP1uk9\nu/ZnDQA5fBlnR5LYqXtbzGSA6/q3lpy/M5PJ4Gk5kQ8Q2J9oR8jGY8cPDvxByPVICPE/TopL2Omb\nbDaLM8OXMXopjeSVZVNtWIMaXNeXeyYMjyXzhVP0x9JkGZtOo642hNFLL6K3o7heVuXKZrM4OWQd\nlLge1PFDIYYu+RV9jNPExBwymQz+XY4XPNv1ecHN5ovZfCp2b5jaPFkYPhvoi+H00EzBttX9ZnZe\nQXrWl+O28Q8A/gLAnwEYA/BZAA8AsPR3llKmAUAI0YycEf0HSpPPAvg7ALMAviSEuF1K+WgZcrqK\nmWN7R7yl6L71BgxqwYFqkGA5DvRqcn9g0DbhfhAd+AkhxAw39Nmpoct45sy4rV62O5a2/85bd+EL\nj0ldT3u97Na5kI2Bk2e70/m4nv0ADJ8dOzxokEPd3ohztBzjuV1K+XUhxJ9JKbMA7hNC/KaTjkKI\nXgD/CuBvpZSfV3b/lZRydrXdIwD2ALA1nuPxZrsmrvQfVapBjV5K5/ub7btlb9+6xtOCA9UgQSdj\nFTuH4cdfNG6PJ3HHwV2W/Us5l0p9B171rwReyOjWmLOz/s/N2doaMZyv29fTz99PpXFLbj+MU4o+\nMxvDiV62O5a2f2pm3tCumF42k6PY+H641pUmKPesF7rKybPd6Xxcz36V4fGk5XYp95sVfpif5RjP\nV4QQPVh1PBBCvArAgl0nIUQngK8B+E0p5WPKvhYAPxFCDAC4glzQ4cedCGNVWtoONVWQFd3bwobt\nrtXtiYk5031246p9tqwGB6pBgnZjWZ1Db0ezsh0taKv2X++5rOca+rG/NobXlCujihvnrTE97f+q\nUNPTqfz5unnuXozn5ZiVwA253Tr/cscpRTebjXFeMQrMxrE7lra/LdZkaGeml4vJYTa+X661fpxK\nEJR71gtd5eTZ7nQ+rme/6oBhJofVmOXgxbUshXKM5/8HwMMArhZC/AeAbQD+s4N+9wDYCuAPhRB/\nhJzxfR+AiJTyfiHEPQC+DWAewDellF8tQ0bX0ZKUa874+gTjpTi95xOaT6TQE49ge3sTulrDjoME\nnWAXQGBGEB34CSHEjGKBVlaxHfp9seZGpNKLSOzchr7OKGbTy9i1o8VRAYliQYUT02kcPZTA6KU0\nejuc6WUn45ONixa/NPx4zk9+70A7lpYT+eIk+0zmkNP5uN4gWKvCa9q2VWBj0Ckn28azq+W4rwVQ\nC+C0lFIN/jPr934A77fY/yCAB0uVy2vMkpR3xnMGdClO72pC82OHB/GGfb35bTf8hNQAAicE0YGf\nEELMUAOtNKz8PU3jUb7xHO6+aw/e8vN9RVe/7HSnth9FiqQ4PRfq5s2H6uO8tJww2A9tLU0F88Lp\nfFTvDbv+doXXrMbcCJRcJEUIsQ/A+wA8D+AjAC4KIe50SzC/4nYyb7uE5oQQQrzBSp+r+zR/5yAU\ncCAbE9U+OD9udLXj3Kwc5bht/DWA3wXwSwDSyKWY+8Lqvw2L28m87RKaE0LWx8rKCs6fH3LUtqen\nD7W1tR5LRPyKlT5X92nxKEEo4EA2Jqq90NNhDPLm3Kwc5RjPISnl40KIBwF8QUo5tFo9MDA4yf2p\novf/6e+KIpMFPvf1M477q+zTfJZWfZ7NfJbKxQ85mwmpFOfPD+Huex9BQ6TNst1iagof/cAh9Pdf\nVSHJNjd+1EPGGJbmojEsseYGpNJLuPuuPagNFdf5fjxH4g3V+K4N9kJHFDdd34G2liZL//dS6jwQ\ne8oxdtNCiLuRy4jxW0KI/wYgUM4tpeTL1Pv/nDw3XXa+zTNDM7Y+S+XCvKBks9EQaUNTi3Mff+I9\nftRDZjEsmkxmvp4nz03jzx8sPWcu2ThU47susBeaG23930up80DsKef14x0AIgDulFJOA9gO4O2u\nSFUhyvVfdsP/2W0f6modgxBCrPCjHlqvTHbt/XiOxBuq8V2XckzGVXlDOdk2LgD4E93277oiUQUp\n13/ZDf9nt32oq3UMQgixwo96aL0y2bX34zkSb6jGd13KMRlX5Q2B8lF2G7v8hl73d2sMp8dgXlBC\nSLXwox5ar0yl5sQlG49qfNel2Aul1Hkg9mxq49kuv6HX/d0YQ02argUDqMEM1/Vvpe8dIaRq+DE/\n8bplyur7mo93XV/OoBkeS6IGKBpIVkrAOvEPVZnPNvPPDLs6D8VsCGLNpjaeNwLFggEYuEI2Gtls\nBhcunM9vz85GTEuK69sQ4iZO9KpT3UsdTdaLF3OGAYWlQeM54JgFAxxIdJoGFlAxkyCzmJrGvZ+f\nRkPE2jhOTryAaHxXhaQimwknetWp7qWOJuvFizlTzIYg1tB4DjjFggEYuEI2Ik5S0C0kpyokDdls\nONGrTnUvdTRZL17MGQYUlgaN54CjBQMMjyfR27EWDMDAFUIIcRcnAVtOdW8lgsXJxsKLOVPMhiDW\n0HgOOFowwB0HdxkCDv0YnEMIIUHGSYC3U93rRsA52Vx4MWeK2RDEGoZUEkIIIYQQ4pCKrzwLIeoA\n/BOAnQAaAHxISvmQbv+bAPwhgCUAn5BS3l9pGQnZSKysrOD8+SHbdsxSQQghhNhTDbeNdwKYlFIe\nEUK0AvgPAA8BecP6XgA3ALgC4EkhxJellBNVkNMW5ukkQeD8+SHcfe8jaIi0WbZjlgqymVFz41Of\nE79Bm8M/VMN4/mcA/7L6dwi5FWaNBIDnpZSzACCE+C6AgwC+UFEJHcI8nSQoMEsFIdZQnxO/wznq\nHypuPEsp0wAghGhGzoj+A93uFgAzuu05ADEn48bjzfaNXO4/euKCcftSGrfs7auoDOzvXv9K4IWM\ndmPOzkZcP+ZGobU1Yrh+1fh+/IpbcgdlnPXqcze/16BcIz8SlHvWjTHdtjlU/HrelRhzvVQl24YQ\nohfAvwL4Wynl53W7ZpEzoDWaAVx2MmY5UaLxeHNJ/bu3hQ3bXdvCJctRqgzs705/bQyvcTua2cl5\nm1XhIzmmp1P56+fGHFLxasxK4Ibcbp1/JcZZjz5383sN0jVa7ziVICj3rBtjumlzqPj5vL0cs9R5\nWo2AwU4AXwPwm1LKx5TdpwHsEkJsBZBGzmXjLyosomOYp5MQQjYGzI1P/A5tDv9QjZXnewBsBfCH\nQog/ApAFcB+AiJTyfiHEBwB8HUANgPullCNVkNERzNNJCCEbA+bGJ36HNod/qIbP8/sBvN9i/yMA\nHqmcRIQQQgghhDiDRVIIIYQQQghxCI1nQgghhBBCHELjmRBCCCGEEIdUJVUdIcQd7vmTv8ZStsmy\nzezlKQDW1QUJIYQQ4gwaz4QEmOdGFrEY7rdsM5+skDCEEELIJoBuG4QQQgghhDiExjMhhBBCCCEO\nofFMCCGEEEKIQ2g8E0IIIYQQ4hAGDBJCNiXZbAYXLpzPb8/ORjA9nSpot7KyAqAGtbX2aw09PX2o\nra11U0xCCCE+g8YzIWRTspiaxr2fn0ZD5Lxlu+TEC2gIt6IhYp3ubzE1hY9+4BD6+69yU0xCCCE+\ng8YzIWTT0hBpQ1NLp2WbheSUo3aEEEI2B/R5JoQQQgghxCFVW3kWQuwH8GEp5a3K5+8H8GsAxlc/\neo+U8vlKy0cIIYQQQohKVYxnIcRvA3gXALPaZzcAeJeU8kRlpSKEEEIIIcSaarltvADgzUX23QDg\nHiHEd4QQv1dBmQghhBBCCLGkKivPUsovCiH6i+z+LIC/AzAL4EtCiNullI9WTjpCgsNichwryw2W\nbTLJS1iuabQda+nKNGpq7I/JduYspqbsGxFCCAk8NdlstioHXjWePyulvEn5vEVKObv6928A2Cal\n/FA1ZCSEEEIIIURPtVPVGdZzhBAtAH4ihBgAcAXAawF8vBqCEUIIIYQQolJt4zkLAEKIuwBEpJT3\nCyHuAfBtAPMAviml/GoV5SOEEEIIISRP1dw2CCGEEEIICRoskkIIIYQQQohDaDwTQgghhBDiEBrP\nhBBCCCGEOITGMyGEEEIIIQ6h8UwIIYQQQohDaDwTQgghhBDiEBrPhBBCCCGEOITGMyGEEEIIIQ6h\n8UwIIYQQQohDaDwTQgghhBDiEBrPhBBCCCGEOITGMyGEEEIIIQ6pq7YAZggh6gB8CsBOAMsAjkkp\nn6uqUIQQQgghZNPj15Xn2wHUSilvBvA/APxpleUhhBBCCCHEt8bzcwDqhBA1AGIAFqssDyGEEEII\nIf502wCQBHAVgDMA2gC8sbriEEIIIYQQAtRks9lqy1CAEOKjAOallH8ghNgB4DEA10spTVegs9ls\ntqampqIykg2Jp5OI85S4hOeTiHOVuADnKQkCJU0gv648XwKwtPr3ZeTkrC3WuKamBhMTcyUfLB5v\nrmp/P8iw2ftrY3hJufPUDDfOm2N6M56XY3qNW3PVrfPfqOO4OZYfx/GazapTgyBjUMYsdZ761Xj+\nGIB/EkI8AaAewD1SyitVlokQQgghhGxyfGk8SylTAN5abTkIIYQQQgjR49dsG4QQQgghhPgOGs+E\nEEIIIYQ4hMYzIYQQQgghDqHxTAghhBBCiENoPBNCCCGEEOIQGs+EEEIIIYQ4hMYzIYQQQgghDqHx\nTAghhBBCiENoPBNCCCGEEOIQGs+EEEIIIYQ4hMYzIYQQQgghDqmrtgBmCCF+GcBRAFkAWwC8AkCX\nlHK2mnIRQgghhJDNjS+NZynlpwB8CgCEEH8L4H4azoQQQgghpNr42m1DCLEXwHVSyo9XWxZCCCGE\nEEJ8bTwDuAfAB6stBCGEEEIIIQBQk81mqy2DKUKIGIDvSil3O2juz5MgZbGSyeL4yVGcG5nBzu4Y\n9g12IRSq8fKQng6OgM3TKlx/4oxKfAmBmqvEl3CeEkdU+VlT0oF86fO8ykEA33TaeGJiruQDxePN\nVe3vBxn82P/kuWl89LMn8tt337UHg/2tnhxfG8NrypVRxY3zLjbmeq6/0zHdxO0xgyCjNmYlcENu\nt85/o47j5lh+HKcSBOWe3ay6ysmYlX7Wq+OVgp/dNgSAn1ZbCFI9hseSltvEW3j9CSGEeE0QnzW+\nXXmWUn6k2jKQ6rKzK4qDe3bgysIywo112NkdrbZIm4q+TuP17lW2s9ksTg1dxvBYEn2dUST6t6Km\nIr/Uro+gyEkIIRsBTeeOnriA7m3hAp2r6mS7Z40f8a3xTMjl1CKeOHEhvy36tlZRms1Hon8r7r5r\nD4bHkujtjOK6fuP1PzV02TW3Di8JipyEELIRsNO56v7feccey2eNH6HxTHyD+jZ6btT408250SRe\nmeisknSbjxrUYLC/taihafZTm9rWbgWiEjiRkxBCiDsUc8PQnu3q/rMjSbxhX2+g9DKNZ+Ib1LfR\no2+8zrC/r8v/P+VsJpz81OaHVd8g/iRICCFBRdW5seYGw3Pg2OHrDfuDqJNpPBPfoL6NZpZXcOzw\nIIZGk+jrimJ/Il4lyYgZdm4dgD9WfZ3ISdznzHPP477PnMT8/JJt2/jWMN72nw5VQCpCiNdoOnf0\nUhpd28IYmUwZ9qfSi4HXyTSeiW9Q31bjrWEM9rfiAF01fImdWwfgj1VfJ3IS93nhxZfwvbNR1ITs\nkzr1zVywbUMICQaazr1lbx8mJuYKHPW62yOB18k0nolvcLpCaJY9gfgTdQWiGisMzLZBCCGVQ411\nGeiPBX6lWYXGM/ENTlcIzfxoO+ItXotHSkBdgagGfvC7JoSQzUIxnbuR9K6fi6SQDUQ2m8XJc9P4\n6vFhnDo3jWwZVVWDmFB9s+Dm9+wWnC+EEOIednp+M+hcrjyTiuDm6p8f/GiJOX5c5eV8IYQQ97DT\n85tB59J4JhXBzawLzJ7gX/yQXUOF84UQQtzDTs/7IdbFa2g8k4rg5psosyf4Fz+uOHC+EEKIe9jp\neT/EuniNb41nIcTvAbgDQD2A/yWl/ESVRSLrRM1y8Dvv2IOzI0n0d0WRyQJfPT5smS2DWRL8jdn3\no67yJvpiOHlu2tMKg5wnhBBSOYrp+VJ1sBMd7odqtXp8aTwLIV4D4ICU8iYhRATA3dWWiawfM7+o\nN+zrxclz046yZfjRf5asYRVRrX1PZt+1298h5wkhhFQO9de8cvW8Ex3uNz3vmfG8agDfAeAaABkA\nLwD4spTyOw66/wKAnwghvgSgGcBveyXnZqBaK3MXJ1M4uGcHriwsI9xYh5HJFAb7Wx1H4vrRf5as\n4eT7sWujzs2BvhhOD82sa65ynhBCSOVQ9Xa5OtiNZ0mlcd14FkL8DICPARgH8B0AjwNYAnAVgP8q\nhPgQgPdLKX9gMUw7gD4AbwTwMgD/F8CA27JuFtx4Y1vPzypam2i4Hk+cWKscduzwIADnfrF+9J8l\nazj5fuzanBm+jGfOjOPKwjLGptNIzi/h77/4k/x+J3OV84QQQiqHalMcO3y9Yb+qg+3sh51dUcNC\n287uQh0ea25UthvcOJWS8WLl+R0A7pRSTpns+19CiA4AvwfAynieAnBaSrkM4DkhxLwQol1KOVms\nQzzeXJbQ1e7vpQyjJ4ylb0cvpXHL3r51Hf+pH48YbpbfP7oPB3Z3G9q8OJYytDl88GrD/vT8MuLx\nZry6LYqGxnqcG5lBf3cM+we7TI9v1i4UKr4K6cZ34DVeyFitMZ18P01jybxS3NJYhy1N9YaxH/vh\nRcMLVmdb2NC/2FzVy7neeWJFUL6fSlCu3NHmJgCLjtrWN9TZHs+t6+i3cdwcy2/jVIKg3LNuj1lN\nGVWbYnFpGb9/dJ+pDo7Hm23thxdG5wzPgZtfsb1AlsUfXTQ8SxaXVqo6T103nqWUli4WUspxAB+w\nGea7AP4rgL8UQmwHEEbOoC5KORGd8XhzVft7LUP3NqNB0rUtnG+bd8K/lDY44atvikPKTyYvDE1j\nV9fa22E83owXhqYNbWLRBsPb5I72tePu6orm+09NJYvKr7Yr5fydUokb0e3IYzfOW6PYXLDC7vs5\nOzKD+NYtmJqZR1usCUMjM3iZblXi8tyCoX0yvWTY1s9VFf25O50nVrh5Lb0esxKUK3dybh5O63At\nLS5bHs+t6+i3cdwcy4/jVIKg3LNujumljE5+ZVZtivbYFlMdrI2p2gYvXbiMhYUlC/viMnZ1GedP\nPLYFn3r0TH77xoE9VZ2nXvo8vxrA+wEYfnOVUr7Wrq+U8hEhxKuFEMcB1AD4L1LK6pcqCyhWeW6L\nuXSs92cZoPDn86b6kOFtcu9AR9nnQrzDi4CMutoQvvCYzG8fPZQw7Be9W/GQbjvR34rEql88czIT\nQkhlcfIcCIVgWAWutXlHVm2DaLh+3faF33JHe5lt45MAPgjgXCmdpZS/56o0mxirPLfFnPDHptO4\n89Zd+RXDleVlgwFeGypMNaca6X5z8CfWePF9jUymLbcH+mI4dngQQ6NJ9HVFIXpjeEZOYCa1iFh6\nCVlkmXaOEEIqhJPnwNmRpGFhrKs1jIHe4s8Ko55vRiptdOeaX1gyPAcS/bHCQXTLp354InhpPF+Q\nUj7g4fjEBYoFW5mtGGoG+Mlz0/jzBwtTzalGujrBGcjlb7wIvOtTfnrr6zKOeXpoBvd9+WR+e+lQ\nAp985LSuxSAOJDrLloMQQog9bgSCq6h6XkseoNHUWGfY3xLexKnqAPy1EOL/A/AtAMs/mtd9AAAg\nAElEQVTahzSovaOYr1Imk8HTciL/1nejaMczuu3fe9ceDI8bfwoZnTKuEOq3naaaY1nkYKH+LGaX\n+F6dV/sT7ajJ1hjm4L5EO4BBDI8n0dsRxb6BuGHMkcmUQYbz48bt4bEkWsINLIBCCCEuYOfT7OS5\nrf5iaLpSrENNWzu/sGy5Ej02ncZsetHwbPHbL9leGs//ZfX/V+s+ywKg8ewRxd7MnpYThre6hdsT\neODRtdW9Y4cH8bbXDxic77vaI4axu3RZEJy+dbIscrBQS6raJb5X5xUwiJZwQ0GfA4lO3HFwl+mY\n6gpEjzKXutrCvlptIISQIGO3guvkua2uJJutFOtR09YePZSwXImuqw0VPFv8lpLUS+O5W0qZsG9G\n3KLYm9nQqPHziem04S3w4kQKn/v6GUOGhcWFZWNamMWV/IrhVd1RrihvAuze9NV5NTSaRCxizL2p\nrSAMP/4iejuaMaNk15iZWzTMpYH+GOpra/IrGikl+0a1VxsIISTIuLGCq8ZETUynAYsxZuaMK8tq\n7Iv6HPjJTy8Z9g+NJvGWW1+2aQIGvyOEeCOAr67mayYeU+zNTPU7jW8N4ytfWVt5fvvrBR78Ws6/\nWXsL3d4ewWe+8Vy+zbHDg6altsnGxe5N38yfORY2Gs/qCsIv3258n441NxSschxIdOb9nE+dM6Y4\nqvZqAyGEBBk3VnDtsiipqAVOuuPGX7Z7O6OG58CssmjS1xUt+GW02nhpPL8JwK8ByAohgFz8WFZK\nWevhMTc1el+l/q4oMtlcRozezije++br8dLFOfR1RQveAl8amc3/rb2Fqn5Pet/USFMdRi+lDT5T\nZONh5/u2f9WfWVsl3p+IowY1lisIl+cWDCsWCwvW79VqlLadbx0hhJDiuBGLZJdFSWV+Ycmg90PZ\njEEGNb5mn8mzxW94ZjxLKfPlY4QQNczT7D16XyUzf9W33pqr+Keu5tXXrSVp1N5CrTJn3JDozK9U\na2N3xFvcPh1SZex830IIGVaJNaxWEGLNjQX+9las17eOEEJIcdyIRbLLoqTS1FiHT391zWY4dnjQ\nIIOZvWL2bPETXhZJuQXAh6SUNwO4VgjxFQDvlFJ+z6tjkjWs/JpEbwxHDyVwfjyF3s4o4rFG9HZE\nLf2I9G+rVxaNq4XFsm2QjYWTylMq+5VsG+qvHjNzi5YZPfwWYU0IIZsd9VdHNYuSqsfN9L6eIOp5\nL9027gVwBACklFIIcTuATwO40cNjklWs/JqOywlDLl2zbBsq+rfVU+emDVXh6Ie6OSglz6a2Oq1l\n21B/9Yg1F2bn0I/ptwhrQgjZ7Ki/OtplZrLT46pPdKzZGDvjR7w0npuklD/RNqSUZ4QQ9R4ej+iw\n8mvSZ0mINNVhJrlYkG3DapWR+Zs3J6WsDmi5oLVsG/sS7UV96c3G5FwjhBB/oz4bLq7q9dETF9C9\nLYyB/pilHld9ou1iYfyAl8bzGSHEnyG32gwAbwPwnEV74iJWfk16f6UbEp34528+n9/W3hitVhmZ\nv3lzUsoqsFku6AOJTsdVKDnXSFBZWVnB+fNDtu1mZyOYnk6hp6cPtbWMpyfBQ302RMP1pvZDMT1u\n5hPtd7w0nn8VwP8A8FkASwAeB3DMaWchxPcBzKxuviSl/FXXJQwIKysZPHlqDOfHU+jpjOKmwQ7I\noZn8qnAoBHxz9Q3PzA9VXUXWR7LW1hrbait/VquMZqvSZONzbU8MR25P4MJEEj3xKK7pjeGp02OG\nKlAhhAx9RiaNOcVHJtMG3zixzkpVhASF8+eHcPe9j6Ah0mbbdjE1hY9+4BD6+6+qgGSErKE9z/Or\nxH0xnNbZF05simt1cVQ9nYWxLXa/Uq43FsYPuG48CyG6pJSjUsppAL9l1cZijEYAkFK+1m35goJ+\ncjY11RkyFGQyWcP2nbfuwtDYHM6PJxEKAQO9xkl6ZvgynjkzjisLyxibTiMUWsul+9TpcUNbzdfI\napXRbFWa2Tb8RSnBfaqLhWoMf+/UmGHeZbPAA7p84dqqsp72rU14+MmX8ttHbk8UVBhkNg2yUWmI\ntKGpxb8ZAwhRn+e/fngQ/6jTyXfftQcADM8Stc+735jATy/O4srCMpYurOCqHUZ7YKuND7Pq8xze\nUuf7yrJerDx/WAhxAcCnpJQGNw0hxAByK9JdAN5lMcYrAESEEF8DUAvgD6SUT3sgq2/RT85bfrbH\nsO/ChFLZbWwOz5waAwD0dEQLjOeLU2lDaUx9m1R60VBJUKvoFgrB8HmtbkHRbFWa+ItSgvuKuVho\nnB83+idfmCysMKgaz5dm5w3b45eMY6hVCoMQZU0IIRuF54YvG7aHxpMF+x/67toCiOa7rOfipNHG\n2NERMdgPC0srljKodsjkZeNzw4/PBdeNZynlUSHEIQD3CSGuAXARwDKAHgAvAvgLKeXDNsOkV9t9\nfHWMrwghrpVSZop1iMebi+1yRLX7A8C2tiiOnxzFuZEZhEI1iDTVITW/jLZYk6FdT9y4KrylMfc1\nRprqkMnkXDh2dsewb7ALoVANkleMuXaTV5by8l61Y6uhkuDvH92HeLwZ3zxxwXAz9HZE8eqf7QMA\n7Oozumlo29W+hm58B17jhYxmY47qvjsAGL2Uxi17+yzHGX78ReP2eBJ3HNyV31Zze6rz8KrtLQWy\nbFVWFDpaw4btnduNKxS7+lrXdY3cvp6V+n6CQLlyR5ubACzatgOA+oY62+O5dR0rNc7sbMRyv0pr\na8Q3OjBIczYo96xfdZW66tsaNW6rOnz0UhpX9xptgK1R48rypZmF/N81AFLzy5byqnbIe9+827Bf\nfS74YX564vMspXwEwCNCiFYAVwPIIOe3PG3dM89zAF5YHet5IcQUgG4AF4p1KKdcYzzeXNX+2hjf\n+cGwYbXw4J4deOLEBTz+g2EcuT2BixMp9HREcNPuTrTHmjA8lkSsuQGfWS1YckOiE5/9hrF4yWB/\nK9pajMb3tpbGvLwv64oY6sVf3RXBxMQcurcZjZyubeF8n+WlZcNb4vJSLjJ2I3wHXuN2WdFi5231\n/RWjq834sFf7tLc0GL736JY6w3ZLuK7gGOFGY5twU61huzVaZ4jC1uZfOedeKm6P5+WYlaBcuZNz\n84DiA1+MpcVly+O5dR0rOc70dMpyv1n7autAt8epBEG5Z/2qq5oajDo5Gjbq5DrlFu7aFsb07BVD\nn1jEaDx3t4fxiYeN6XCt5NXskHzFQSU7h/654MW1LAUvAwaxaiw/W0LXXwGwG8BvCiG2A2gGMOKm\nbH5E/SkkFmnAW157TT61i+azms2uFWtsa27A239BYHg8ia3RxvxqNZD7uWV4LIn6uhBuvaEHyStL\n2NJYh4nptZ9EitWLV1OE6ctn1tfX4vunx/LH6WoN49WeXRVSCqWkeMssrxjSBWVWjD/0nB1JGn6N\naAmvKcwa5Nw6VJehmeSioc2FCeXnvfYItrdH8vsJIYR4hxoPM3X5imH/yOQVHHh111qCAGQLniVf\n+u7ZfPsaAJMzVyxTkKoBgSpmWZX8nmXJU+O5DD4O4BNCiO8gt2r9K1YuGxsFNUjv2t6tppNH78+q\nrU5r6LdnUot5X6WDe3bk/aKdpIFRJ7OaBF1/HBau8B+lpHjraA3jI4qftB51fm6LGYMBjx5KFIwZ\nDdcb5qfaplhKI0IIIe6jxsMcPZTAFx//aX5btQ/MniWtzU0GP+gjtyUMbexSkG4EfGk8SymXALyz\n2nJUGqerhfoV6itKMvFIUz1ed2Mf2mKNeFg3ubXPezoi2JeI28qivp2qb5L6VfHaEAqKrJDgoc0/\nzYVHnX/q/Dx19pJh/+hUumDeqCsO6SvLutR0zUilzcu0+jlFESGE+BW7TEvqL9wLiyu5NHMTKfR0\nRHGjTaltABidSllu2z1LNgKeGc+r1QRfB6AduhcRKeUDXh0z6DhdLdSvAIYbjV9han4JT5y4gNfs\n2ZF3q9B/DgBtLU22x1DfTtW3UW1V/OS5afz5g1w53AgUc+FR92vf79TcgmF/V1vYZN5cb2gTa24w\nZPRQV6KDkKKIEEL8il2mpXDYWOi5vr4Wn3xkzT+5vrbGoKPNdPD2DuNK8va4MV7G7lmyEfBy5flf\nkAvyOw1Ac9LNAqDxXCb6FcCd3VHsHejA6KU0lleymJq5ghuv60RDXQhvfd21yGayqK8P4Uu6TApm\nBU9GT1zAjrYwVrK5/VcWjSvaM3OLpqvipZRsJhsDMx9pdT7MLyzh2OFBDI8n0dsRLSi7OjqVDlyK\nIkII8SvFUslqK8mzyXmD3lZ9np2kD735+g4gm80VRemI4Obdmy+XuZfG84CUcsDD8TctZivUt+zt\nwxe+9Rz+7Znh/GdHDyVwcHc3Tp2bNqxCFyt4ovdjfs2eHYZj9nZGTVfFSynZTDYGjY31eEApqdrY\naPx5r7Y2ZFjFUH/B6O2MKvuvL9hPiF/QSm5rJbWtuHDhfIWkImQN9Zkca24wrEQfuT1hKHZ15Dbj\nr3/d7cZMTWY6uBYhHNzd7Ya4gcVL4/lFIUSflHLIw2NUnHJLU1v5I6n7QqFchgMr3099VbhQjXH/\npdl5fPX4MPo6o/idd+zB2ZHCzBn6FWa9//Szp8fwjl8QWFrKWPpfbwbfpiBSSoVBtUyrXZ/5hSXD\nCsbCwjKygOGz8Wnjqob6C8ZAXwxaqfi+rmbsS7SjJWye5YU+0KTarKfkdnLiBUTju2zbEeImamyK\nGq80filt2J6avYJ33TaAi5MpbG+PoCVca4hLGaAONsWL8tyPIeee0QHgx0KIHyJXJAVA8Etul1ua\n2sofSd2nXwku5vuprwqnrhZfTi7iiRMv5fu/YV8vAGPmDH0fvf90an4ZXdvCtj+ZbwbfpiBSSoXB\n9fZpaqzDp5WVZwCGz47cXujTrGZxMSvPXSzLC32gSbVxWnJ7ITlVAWkIMaL+Mj2bNhZJ62o3+ie3\ntWzBA18xrkTrt4FBWx/ozYgXK8//3YMxfUO5pakLy1rm3gpzFeFqDHma9SvBxXw/9f5Jz54ew1t+\n7hogC4Ofc6SpDqOX0qaZM7QVZgDoiYdxbd9WDI0m0d/djNoQ8ivXfNsMFmbz9Lq+reuKwrbLfKFm\n0jDL5Tk5bUymPzUzb1jFUFdF1HlOn3pCCHGO+qvj8vKy4dfApvqcS6fmrzymrERfmDTqXCc+0JsR\nL8pzPw4AQoi/kVK+T79PCPEpAI+7fcxKUq6Pr9pfzXOrX23eolsJLnYcfcnk1PwyYtEGHEh0Gvyc\nb0h04sGvFa4Qan26toVxy94+fPvZIcMbppOVb+JPzOap3cqyna+cXfvezmjB61W8dQseffRsfvvI\n7QnLLC7qPKdPPSGEOMcsj/MXHnshv13g86z8OrgjrujgLmMFPurgHF64bdwP4GUA9goh9E/GOgCB\nd4gtpXKbVX+rqoK1oVz1Pqvj7E+0A1jLZrB/NYfzQF8s77fU2mKsPDgzt2jwaUr0xwAUrvI5Wfkm\n/sRsnn7tuDGASf1OVf91u1Vhs2NkMtm1VY3OKELZjGHleXrWmE2jWBYXq2MQQggxR32OT16ez+vg\ncGMdJpU4lNSVBRy5LYELk0nsaI/ippd3Ih5rMpTK1sehUAfn8MJt438C2AngrwB8UPf5MnJp6wJN\nKZXbrPqrK3VqVUG13LFKCCEcSHTijoO7DD7Hp4dmiq4iq7l2W8J70BmPFazyOVn5Jv7EbJ7areKq\n/ut2VaLMjnFcjhtyhh49lDBUGDRbaba6n8q93wghZDNhVwlWza6xrWULDiQ6EY83522IoJXKrgZe\nuG2cBXBWCHEH1vI7Y/XvkNvHCzr61b7tbWFksjk/46u6o7iUXMyvDu9PtCO0evmcZPywWtE2W1HU\ny6K9YTpZ+SbBYb2ruGp7J5kv1Hk3filtrF6ViHMVgxBCPEL/q3NfVzMuKj7MkzNXdPuj2LdaUdBp\nliWSw8tUdV8EsBvAj5BbYB0EMCqEWAbw61LKb3p47MCgX+379rNDeV+lt73uGlycSuPKwjLmF5eR\nzWYwm1zKp7Czy/ihvn3qV7SLrSiarfLZrXyT4LDeVVy1vZr54rffvgeZrDGgUI3kbt8aNqxEN9TW\n4JWJTq5iEELIOnGSTlT91fndb7rO4LbR1RbGgUQnDiRyGWOY0ag0vDSezwM4JqX8PgAIIXYjl4nj\n/QC+AGCf3QBCiA4AzwJ4nZTyOe9E9QfPDV/O/72UyRp+7m5v3YIvfCvn9K9lx9Awy/hhtcpIP1JS\nCvr5CQDDEyl87htrt+Xdd+3B4sKysWLgjFK9ajyJVyY2XzUqQggpFyfpRFV7IH1lyWBLdLWFLdsz\nvskZXhrPV2mGMwBIKX8shLhaSjkshLA97mqbvweQtmsbZPRFTtq3bsl/Ppcypv1KpZdw43WdCDfW\n4crCWt7GSFMdYs0N+NzXzxjKa/etGsVmNwH9SDc+pq49WVimqlNXNQb6Yjg9NJNvv7W50XCMy8kF\nw7bW7jM6g/odbzC+6G2NGscghBDiDCeGrvqr86xiS8wo28xoVBpeVxj8MIBPI+fr/HYALwghDgBY\ncdD/IwD+N4B7vBOx+uiLnESa6nDnrbswNDaH1uYmQ7vllQyeOTUGIBd0pa0c64P/9EGBAH9+2cyY\nrVAAhe4++vmh9jl22Jgc/71vvt6wqry9zeiiEWtuKPhVo6EOhj59HcY+hBBCnBFTFjBizQ0FbUIh\no87tVvR01zbjyjOrBJeGl8bzEQB/DOAzyBnL3wDwbgB3AHivVUchxFEA41LKbwghft/JweLxZvtG\nFey/ksni+MlRnBuZwc7uGPYNdiEUKnTCH14tZALkci6n5pfQFmtCe6wJ9/zyjRganUUoVIN/1eVp\nTM0v446DubKvn/v6mfzn+tRyADB6KY1b9vaVfA7rJej9K4EXMpqNOap7iQJyc6GgjTI/RpQ+w+PG\nVY6Z1BJetiOG8+NJ9HVEgZqsQUkvLq2gI95i8L/PZLKoqa3HuZEZ9HfHsL/IfVAqbl/PSn0/QaBc\nuaPNTQAKC+eYUd9QZ3s8t65jOePMznr38tfaGvGNDgzSnA3KPevGmIs/umjQuSuZDF4YTRrsjPMn\nLhgW0SJNdYY+E5evFPS5ZR1VkteLX69luXhmPEspZwHcbbLrQQfd3w0gI4T4eQA/A+ABIcQdUsrx\nYh3KKQ2tT9HiVn+nTvi9HcZJkJ5fNhQm+bk9O/DU6fF8jmYgdzNox+vWvUXqy2sDuTdMp+flxTUI\nUn9tDK9xu4R5sfPuVlYXuraFCwJF1fkRaao37ldWLBrrQ/jkw6fy22oauhsH9pjKsqsrigO7uzEx\nMYepqfVV5LTCje/cy/G8HLMSlCt3cm4eThMsLS0uWx7PretY7jjT0yn7RmWMXW0d6PY4lSAo96wr\n1zS2BZ96dG3BTPRtxZ9+8nh+++679hTo8fjWML7yFWNRFLXPYH+rr8/byzFLnaeeGc+rq8cfAaBZ\njDUAslLKWru+UsrX6MZ5DMB7rAxnP+LUCX/fQDuWlnOpvDq3bcHXnjpb0CeVXjS8OaZ0ter1P7ns\naA9j70AHAwFJ0aBQq9RzCwtLhnmWWV4xtJdDxoDB0ak0A08JIaRCqC4WFydThkwaWhpavR5HNmMo\nx51ZyRjGZIBgaXjptvFHAG6RUv6kzHGy9k38h1Mn/DNDM4ZUXnq/Za3P9vaIIQhL818FCgtbAOCN\nQIoGhVqlnjt2eNC4kry6IqG1n9W9tAH2BU4IIYS4h/q8n00vFhShioUbDPbCjWpsy7lpw5gMECwN\nL43nCy4YzpBSvtYNYSqN03Rw6ptjW6wxX8xE68PUcsQL1LmXSi9ZBo5opeC15PpaKXhCCCHeo2ZE\nSikLGjNzi3hlosPSXqA94Q5eGs/fF0L8HwBfBzCvfSilfMDDY/oGp+ngouH6gjfHA0oeXKaWI15g\nNvfUXzH0aKXg1flJCCHEe8wyIunp7Yza2gu0J9zBS+M5BmAOwAHdZ1kAm8J4dsrMnDEafexSGl89\nPly0/DEhbqHOPXW7FMzyS3MOE0JI+aixVDNzi7aryNTJ3uBlto13A4AQolVKOW3XfrOi+kZfTi7i\niRMvAWCeZuItXiTHd1IBixBCyPox09l2q8jUyd7gZbaNVwD4PICwEOKVAJ4A8BYp5Q+8OmalcVJn\n3g599CwAfGk173OkqQ6jl9J8WySmuDn3iq1alLJiwVKvhBDiDaI3lsucMZFCTzyCgf6YbR/qZG/w\n0m3jbwC8GcBnpJQXhRC/gVy57X0eHrOiuPFGp4+effzZoXw+5xsSnXjwa7KsscnGxc25V6xfKcdg\nqVdCCPGG43LCkJ2rvi5kG4NCnewNzjLYl0ZYSpn/lqWU3wDQaNE+cJi90ZWDthL4ltdeg1jEWHaz\n3LHJxsLtuefWMfRz+O679jCSmxBCXGJoNGm5bQZ1sjd4ufJ8adV1IwsAQoh3ALjk4fEqjttvdPqV\nwFPnpvGQi2OTjUUlVhNKOQYjuQkhxBv6upqVberkauGl8fwbAD4FYFAIcRnA8wDe6eHxKoLeD/Rl\n26M4dngQw+NJ9HY0I+HA/8gpzMVIrFArTTmZH6oP80BfDKeHZor6NHMOEkKIf7hRtGPh9gQuTCTR\nE4/iRubarxpeZtt4EcCrhBARALVSylmvjlVJ9H6g+mqAANASds8vmW+LxAqzypJ2mOUIve/LJ/Pb\nqk8z5yAhhPiHZ+QEHnh0zee5od7e55l4g+vGsxDiMZiU1BZCAAhuxUANvd/nlYXlgn00NIhfUX2W\nVX85zl9CqkM2m8GFC+cdt+/p6UNtba2HEhE/YubzTOO5Onix8vzfyx1ACBECcB8AASAD4L1SylPl\njusGej/QcKPx8tEvmfgZ1YdZ9Zfj/CWkOiympnHv56fRELE3oBdTU/joBw6hv/+qCkhG/EQpPs/E\nG1w3nqWUj7swzJsAZKWUrxJCvAbAnwL4RRfGLRu9H+jO7ij2DnSsy++UkGqh+jAn+mNoCdOnmRA/\n0BBpQ1MLVxFJcfYn2gFocVZR7KfPc9XwMmCwZKSUXxZCaMkmdgLwTYVCMz9Qze80m83i5NA0C5sQ\nX2I2d/Xb2vwtp/AKIYQQbwgh5+N8x8FdtDmqjC+NZwCQUmaEEJ9EbsX5l6osjiNYBpMEGc5fQggJ\nDtTZ1cOLgMGDVvullE84HUtKeVQI0QHguBAiIaW8UqxtPN5cbJcj3Og/qsu8AQCjl9K4ZW9fRWVg\nf3/jhYxujVnu/LXDz+fu1XhejVkJypU72twEYNFR2/qGOtvjuXUdyxlndjbiigzl0toaMT0PP1yj\nShOUe9YLXeW2zg7CeXs15nrxYuX5gxb7sgBss20IId4JoEdK+WEA8wBWkAscLIrTdF1mxOPNrvTv\n3hY2fN61Lex4XLdkYP/SqcQNWa6MKm6ct0Y589cON+X0aswgyKiNWQnKlTs5Nw+nRWyXFpctj+fW\ndSx3nOnpVNkyuMH0dKrgPPxyjfTjVIKg3LNe6Co3dXYQztuLMUudp14EDN7qwjD/CuATQojHkZPx\nv0kpF1wY11NYVIIEmVIKrxBCCKkOtDmqh2c+z0KIVwH4bQBRADUAagH0Syl32vWVUqYBvNUr2byC\nRSVIkCml8AohhJDqQJujejj7Ta007gfwJeQM9L9Drjz3Fz08HiGEEEIIIZ7ipfF8RUr5CQDfRi7V\n3DEAr/HweIQQQgghhHiKl8bzvBBiGwAJ4JVSyiwAf4QqE0IIIYQQUgJeGs/3Avg8gIcAHBFCnATw\nrIfHI4QQQgghxFO8LJLybwD+j5QyK4S4AcC1AC57eDxCCCGEEEI8xYsiKb3IZdd4FMBtQgitVuQM\ngK8AGHD7mIQQQgghhFQCr4qk3ApgOwB9NcFlAA97cDxCCCGEEEIqghdFUn4FAIQQvyul/DO3xyeE\nEEIIIaRaeOnz/DEhxO8DEADeB+D9AD4spVz08JiEEEICRjaTwblzLxXdPzsbyZfG7unpQ21tbaVE\nI4SQArw0nv8WwASAG5Bz2dgF4OMA3uXhMQkhhASM1Nw07r73ETRE2izbLaam8NEPHEJ//1UVkowQ\nQgrx0ni+QUr5s0KI26SUaSHELwP4sYfHI4QQElAaIm1oaumsthiEEGKLl8ZzVgjRACC7ut2u+9sS\nIUQdgH8CsBNAA4APSSkf8kJIQgghhBBCnOJlkZSPIZfruVsI8THkCqT8pcO+7wQwKaU8COA25FxA\nCCGEEEIIqSqerTxLKT8thPg+cmnrQgDeJKX8kcPu/wzgX1b/DgFY8kBEQgghhBBC1oVnxrMQoh7A\n6wH8HHLG77wQ4sdSSlvXDSllenWMZuSM6D/wSk5CCCGEEEKc4qXP8/0AtgD4R+RWj48AGEQuZZ0t\nq5UK/xXA30opP2/XPh5vLl1Sm/4rmSyOnxzFuZEZ7OyOYd9gF0KhGkObeLzZUbtSZWB/7/tXAi9k\n3Mhj2t1TTsZcz33pl/P2A+XKHW1uAuAsM2ldfZ3TpmhtjZQlWzl9Z2cjJfd1i2w2g2TyUoEss7Pj\nBW137txZUlq/IM3ZSt2zdnrEDV1Vrowcs3J4aTzvl1LmS3ELIR4C8BMnHYUQnQC+BuA3pZSPOekz\nMTFXkpBA7ouw6n/y3DQ++tkT+e2779qDwf7Wgv527cqRgf297a+N4TXlyqjixnn7eUyre8rpmE7v\nSz+dt92YlaBcuZNz83AaVrO8tOx43OnpVMmylft9aLmmq8liahp//I9PoSHynE270tL6uTVngzJP\nVYqdv50ecUNXlSsjxyxtvFLwMmBwWAixS7fdCeCCw773ANgK4A+FEI8JIb4lhGh0XUKHDI8lLbfX\n244Q4gw37inel2SjoaX1s/pnlzObrA87PUI9s7nwcuW5HsAPhRBPIFck5VUARoQQ3wIAKeVri3WU\nUr4fDt07KkFfZ9Sw3atsr7cdIcQZbtxTvC8JIeVip0eoZzYXXhrPf6xsf8TDYzk5JB8AACAASURB\nVHlKon8r7r5rD4bHkujtjOK6/q2m7Qb6Yjh2eBBDo0n0dUWR6I8VtMlkMnhaTqy2acb+RDtCJj8A\nZLNZnBq6jOGxJPo6o0j0b0UNnPtPExJE1HkvHNxTdqj35UBfDCfPTfPeIoQ4xk6PmOkqTZ+NnriA\n7m1h6poNhJep6h73auxKU4MaDPa32vovnx6awX1fPpnfbgkX+lY+LScMbYBBHEgUVtU6NXS5ZP9p\nQoKKOu+PHR60vafsUO9LwDgm7y1CiB12esRMVwHgc3yD4uXKc6DRr4Dt7IricmoR55TV4pWVDJ48\nNYbzEy+gpyOK+lAWB/fswJWFZYQb6zAymSq4UYZGkwXbZsazmf8UbzoSNNSVl4G+GE4PzeRXa9Tt\nkUljQNbIZLrgnrqub+u6VnMuTqaUMdKG/by3CCF2TKfm8a43DODiVArb2yNIpRcM+8101fKKMTPv\nxVX9xl+9gg+N5yLoV8AO7tmBJ07oYx1zq8VPnhrDJx85nf/0yG0JQ7tjhwcLxu3rala26T9NNi52\nK8lm23q2xZrw8JMv5bePHkqs+1eZ6P/P3r3H2VXV9/9/zTWTmTOXZObMTJgbYGDNENCmQCKIKXiH\noGhpvy1V0mhB2/ptf1QetqV92Ivfb/u19Sutvfj9qlQRSy2tiBYCaKsUJfKF2tKKJCwBJcmEZDJJ\nJnO/z/n9cS7Ze885Z+9zP2fm/Xw8eDD77L3WXnP2WmtWzvmstRrrXO1y7+4h13m1LRHxs7QIX3z0\n+cTxnmvd/UhHm7uvuvWGbbQ21ruuCTXW6ZPoNUKDZwfnp82zC2eXTpqdP/tzR+sGZuaW+Ltvvkht\nrftfjEdPuj8tfuXkNPc99hLnndNCU0MtR0am6O0K8b63X8Sh45P0hkNcPhROWpYg8dMi5c77DcrJ\nM7PceM1WTo3P0d7awMkzs67z45MLrvkFLx8fd10/MT3PzKx7WTPvJ8eJb4ROTNPbFWJh3n39zOxS\n2jkM2cw30BwFkbVlaWmFJw6McHR0it5wiLHJWdcnyyfHZx1/o5uZmllwnZ+eWeS1Q53cftN2jp+e\noXtzo75RXkPW3eA52R+5uB8On+Hl45OcGp/jnHATTQ21TM8t0dZUn2gUF/S18dXHX2J6bok3X97n\naiwDnk+VG+pruX//i6s+uXYeb9xQQ1ND3aqvoIPET4uUG2/76u8OudrIpuYGPvfQgcT1773+Itf5\n884JsbQcPVcFtIYa+NyDZ6/fc90QG2rdmz60NNe7Ju6cnJhb9Y2QU2tzfdo5DMk+2Y6HiqQaHGuO\ngsja8sSBEe55+Gw/8t53XMT49HjieEtHI/bwGWbnl5hbWOJVPa2uv/N7rhtKzJe6+rJ+RkcnV/1z\nWt96Va51N3hO9keuM9wCwCunZ7n/sRcT526+dpD5+WWaGmv5/EPRRvRvB0YSg9+llYirsZzT0ZQY\nCGzcUMvx09H4plnPJ1/O49MT8/zfB87uHRP/o6t/oUol8ravvbvdoUydmxtd10/NLLrOX9jf5vpH\n48++8QLX9a+cnKapodbVzk6cnuUuR5o3Xt7nSnPs1LTr+umZxbS/Q6r1WtMNjtVeK9vy8jLDw4d9\nrzt6dLgIpZFycHTU3aanPX1VV3tj2r7NO38Dgq/cJeVv3Q2eR8ZmEl8Dd27ayOmJWf7+G8+zZXMj\nI6fdE4mOn5phU2gDR0+4G0F88Ds75x4Uj08trPqEGaBxg/tt3ug4nl9cTjrJUDHPkm/FCC3wDiKP\njrrbzpRn4Do95z72TqidnHHv2dy1aSPV1VXU1lQnQjkaN7g/ie4Ju7ct3tLR5PoE6fabtqddqi5Z\n2/MbHKu9Vrbh4cPcfuc+341FpkZfJBTemvYaWRu8/cjElLsvGvccT8+6+zJvegi+cpeUv3U3eK6t\nqeb+x2zi2BlCsff6i1zXtoTq+YdvvcCN17g7y8GBTbQ01tPT2cT/e+544vXw5o38wlsMI6dn6OkM\n8croFJdf1EVdbTXvv2EbZyYXaGuu50evTHD5RV1s3FDLpuYNfO3bP0rkEZ8wpX+hSr4VI7TAO4jc\n0uH+A9K5eSN7rhvi6OgUPeEQtdXuwXt3u/vTm3M6Qq6Y502heiZml1zfEO25zh2W0RveyN7dQ9GY\n584mrryki3BrQ6It1VTDn96b+n1I1vb8vm5Ve6188V370pmfOlWk0kip9YXP/j3v2txIfZ17P4Zu\nzyfNHW0bXX1V9+aGYhZXimzdDZ69y1Q5QygW5pcSEwA6N2/kke9GZ84+/h9HuPnaQU6cnqW/O0R7\nSz3z88tMzy64vg5eXFimqbGO2ppqGjfU8JqtHZ4/wFU8+vQR/uXfjiTu6f1Uenwy+q9Z579QI5EI\nBw5pMpLkphihBd5B5LGT06vayJsu7U1c/8ATP3adn5yZd6UfHGjl6YOjTM0ssrllAz9xQTv/+NiP\nXfccOTXjSvOqLW1s3eJuH85Pex59+ojrXPx9cc478H465Dc41idKImvL+VvamFuIThzc0t7IC8Nn\nXIPjqdl5V991cmyWh598OZH+v73hAi7sUX+wVq27wbN3qThnCMWWjia2DWziiqEuDhwa4+R4dB3H\nk+PzdLZt5JrXnJO49sKeTTx58AT3P+b+1PiKoS7Xus3eP6arv95t9hyv/rpXk5EkH4oRWuAdRFYB\nf/fPP0ycv/2m7a7r29s28uAT7qXovINQb5tKttxjJgNX7/vQ2lzv2740OBZZX7xtfnR8zhX+tefa\nIe5/5uzffy2Bub6U9eDZGLMT+Ji19ppc8nHGer6qJ5T4dHmgO0RbqJ6+zhDdmxtdnyYF+Rp2emYh\no4lIyfIdGmilpfHsUjbJ7qPJSJIPpQgtiC+5eOTEFH2dq5dcXJhfcrWhhYVl3zx3DnUAZ/PcmWK5\nx1SSfTrupPYlIt45InNzC64wDiJLruVkdwyFaW9pUOjWOlG2g2djzIeBm4Epv2v9JPvk9ueueVXi\n+PU/GV1GxinIJ03ndDSl/VQtmWT5OpeySUaTkSQfSvHpqd+Si9m0oWqquWKoi3fs2pqyzaST7NNx\nJ7UvEVm1ctD1F3G3Y5nNVN806x/e60PZDp6BF4F3AV/MNaOgn9xmuhpB/BOsdJ8a54MmI0ml8mt7\n+ajbua4iUqx2LCKVw9t3VUVWXBORd2T4jZesLWU7eLbWPmCMGchHXkE/uc00tti7AHqhKN5SKpVf\n28tH3c51TkCx2rGIVA5v31VbW+P6Fq29pUF/k9exsh08Zyocbk557vXtIeo31HHo2DgDW1rZua2b\nas8SWeFwM8cdazQDHD89w9WX9ed8/6ByzUPpc38GhVaIMpZznkHaXi5ybbfJ8su3SqiXyeRa7lBz\nA7Dgex1AbV1t0EvZtKkpp7IlSzsxsXpN3rUg2/eqkupsodqst+86dGzcdU2m/Uy+y1kpfVWl5Jmp\nShg8B/pL6/eJ0dbuEFu7o/+SPHXK/XVMONzM6OgkWzzrNnZvbgz0SVQ8fS5yzUPp8/MMCi3fn2zm\n4/cudJ5bu0NccckWRkcnV7W9XOTablPll0+FyrMYci331OQcUO17HcDS4pL/RTFjY9NZly3V8xgb\nW70bXKWLRFb4wQ9s4N+tt7efmpqavNXZSqmnXs7f3zluWJh3LwiQST+T736gkvqqcs8z23paCYPn\nSLFupNhikcqjdiuy2sL0GHfeN0Z9k/+W4gvTp/jEh3YzMHBeEUpWmdTPiFNZD56ttYeAK4t1P8UW\ni1QetVuR5ILsmijBqJ8Rp2DfqYmIiIiIiAbPIiIiIiJBafAsIiIiIhKQBs8iIiIiIgGV9YRBERGR\nbCwvLzM8fNj12sREU9Kl244e9V+RQkQkToNnERFZc4aHD3P7nfuob2r3vXZq9EVC4a1FKJWIrAUa\nPIuIyJoUdKm2+alTRSiNiKwVinkWEREREQlIg2cRERERkYA0eBYRERERCUiDZxERERGRgMpywqAx\npgr4FPAaYA64xVr7o9KWSkRERETWu3L95PmdwAZr7ZXAHcCdJS6PiIiIiEjZDp6vAh4FsNY+BVxW\n2uKIiIiIiJRp2AbQAow7jpeMMdXW2pVSFUhERIKrq6uldtoSifhfuxiZYGHG/8/RwvSpwLsBHj06\nzMJ0sPWbF2fHqKrK33XlcG0meQZ9n0QkqioSpGcrMmPMJ4AnrbVfjh0fttb2l7hYIiIiIrLOlWvY\nxn7gOgBjzGuBZ0tbHBERERGR8g3beAB4szFmf+z4vaUsjIiIiIgIlGnYhoiIiIhIOSrXsA0RERER\nkbKjwbOIiIiISEAaPIuIiIiIBKTBs4iIiIhIQBo8i4iIiIgEpMGziIiIiEhAGjyLiIiIiASkwbOI\niIiISEAaPIuIiIiIBKTBs4iIiIhIQBo8i4iIiIgEpMGziIiIiEhAtaW4qTGmGvgsYIAV4JettQcc\n598OfARYBD5vrb2rFOUUEREREXEq1SfPbwci1tqriA6S/zh+whhTC9wJvAm4Gni/MSZcikKKiIiI\niDiVZPBsrf0a8P7Y4bnAmOP0EPCCtXbCWrsIPAHsKm4JRURERERWK0nYBoC1dsUYczfwTuBnHKda\ngHHH8STQWsSiiYiIiIgkVbLBM4C1dq8xphN42hgzZK2dBSaIDqDjmoEz6fKJRCKRqqqqApZU1omC\nViLVU8mTglci1VXJA9VTqQRZVaBSTRh8D9Brrf0YMAcsE504CHAQ2GqMaQNmiIZsfDxdflVVVYyO\nTmZdnnC4uaTpy6EM6z19PI9CyrWeJpOP31t5Fia/QuZZaPmqq/n6/ddqPvnMqxzzKbT12qdWQhkr\nJc9s62mpJgx+BdhujHkceAS4DfhpY8wt1tol4EPAN4D9wF3W2mMlKqeIiIiISEJJPnm21s4AP5fm\n/D5gX/FKJCIiIiLiT5ukiIiIiIgEpMGziIiIiEhAGjyLiIiIiASkwbOIiIiISEAaPIuIiIiIBKTB\ns4iIiIhIQBo8i4iIiIgEpMGziIiIiEhAJdkkRURERKScLS8vMzEx4XtdVRW0tW0qQomkXGjwLCIi\nIuLx7f3f5W8eOkBNbX3a65Ynj3D3n/0OtbUaUq0XetIiIiIiHpEI1DT3UVPXkPa6mshCkUok5UIx\nzyIiIiIiAWnwLCIiIiISkAbPIiIiIiIBFT3m2RhTC3wOOBeoB/7IWvug4/xtwC3AidhLH7DWvlDs\ncoqIiIiIeJViwuB7gJPW2j3GmE3AfwIPOs5fCtxsrX2mBGVbkyKRCAcOn+HIyBT9XSGGBtqooirv\naQqRh6wvKysrPGVHOXx8iv7uZnYOdVDt8wWZ6pmsB6nqefz10e+/woa6GsYnF9QORAqsFIPnfwD+\nMfZzNbDoOX8pcIcxZguwz1r7sWIWbi06cPgMn/jS2X+L3H7TdrYNpF+TMps0hchD1pen7Cif/dpz\njle2ccVQV9o0qmeyHqSq5/HXd23v4dvPHF11XkTyr+iDZ2vtDIAxppnoIPp3PZd8CfhrYAL4qjHm\nOmvtw375hsPNOZWr1OkLWYbjjg4V4PjpGa6+rD9t+qBp0t0/0zzK4RkUWiHKuJbyPPL4S+7jE1O8\nY9fWtHlmU1dzKWM55FkM+Sq38slPXqnqefz12fmlpOcLVZ5yUcg229LSAEz6Xl9dU0U43Jx2ned8\nl7NS+qpKyTNTJVnn2RjTB3wF+Ctr7X2e05+01k7ErtsHbAd8B8+jo/4VPJVwuLmk6Qtdhi2bG13H\n3ZsbV13rTR8kjd/9M8mjXJ5BoeVaRq98/N7llGdfZ7PnOORbZzKtq7mWsRzyLIZ8lDtfv/9azSeT\nvFLV8/jrjRtqk54vVHmC5FMMhWyzExNzgdKsLEcYHZ1MOXjOdz9QSX1VueeZbT0txYTBLuDrwAet\ntY95zrUAPzDGDAKzwBuAvyl2GdeaoYE2br9pO0dGpujrCnHRQFtB0hQiD1lfdg51ANtiMc8hdg6F\nfdOonsl6kKqex18/OT7Lhf3bGJ9cUDsQKbBSfPJ8B9AGfMQY83tABPgs0GStvcsYcwfwr8Ac8E1r\n7aMlKGNZyXlCVOTsj4FTZZPGo4oqtg1sUtzdGlWIiajVVHPFUFcizjkSifDc4bG091A9k0oViUR4\n8tljvHh4LGX99k6ifcuOHtck2nj9D4f78/4pn4gkV4qY59uA29Kcvxe4t3glKn+5Togq1YRBWduK\nUa9UD2UtC1K/s5lEKyKFpU1SKsCRkam0x4VIn+s9Ze0rRr1SPZS1LEj9Pnx8Ku2xiBSfBs8VoL8r\n5Dru8xwXIn2u95S1rxj1SvVQ1rIg9bu/u9lzrDYgUmolWW1DMpPNhChvbOlvvns7Lx+Lph/qb+W5\nQ+44Uq/B/lZuvWFbIs5u0JGmryvE9NwiP35lMrGRRSFks2GG5I9ffHKQehl/hkcef4m+zmYuH+xw\n1asLe1v59rPHGD4xTW9XiNdd3EmN4xl76+HQQGtRfneRYhjsb+UD77qEoyem6Ghr4MjIFBMzi8zN\nL9KwoTYx+e+X33UxP35lki0djczPL3Hg0BiD/a0cPDyeaJ9Xbmpa1a8HmRvjbeevb9fgXMSPBs8V\nIJsJUcli6d62ow+A5w6NrTrXGW5xpT94eHxVnJ3z2L0g/zbeEc7/oEaxfqXlF48ZpF56n+Hi0hB3\n7zuYOJ6/boh7Hj57TCTCrku2JA699bClUTHPsnYcPDzOpx94ll3be3ho/48Tr994zVa++KhNHN9+\n03YuPn+zqz3eeoO7T/7A3BKffuBZV5ogbcXbzus31LFVn26LpKWP8daodLF0QeLsvK954+ycC/IX\nKgZPsX6llY94Y+8zGz4x7To+Opr+vGKeZS2L12fvBienxudWXefXJx86PpE076BlSORzbDxQOpH1\nTJ88r1HpYukCxdl5XvPG2W10LMhfqBg8xfqVVj7ijb3PsLezyX0cDqU9r5hnWcvi9du7wUl7a4Pr\nuK8rtCoAw9u2zu12f3sYtK1429jAFoVGifjR4HmNSheP6o0jrauFv//G82zZ3JiIk/OmHxpopaVx\nuyvmeWN9beCNLLKxY7CDxaWhaDxsZxM7CnQfSc4vpjnIOs+JZzg6TW+4iSsv6aK9pSGRpxlopaqK\nxDN+3SXusJx8bICS8zrpIgUyNNDG7+zdwctHz3DrDdENTlqb65mfX0oc93WFqKmGl49NcesNFzM9\ns0BTYx3z84uua676iV6aGmp924q3PQwOtLra2M5t3Zw6pW94RNLR4HmNSheP6o0jdcYvx+PkkqX3\nHu8wnQX8DeD5w+Ou+Nj2lgbFuxaRX0xzkDVqUz1D53XOGOdMyxCE1oqWclVFFVdcsiVtjPFzh8b4\n03tTxzrH63NtbXWgtpKqPcTTVVfrH5YifhTzvA55Y9yc8XblFFOqeNfylk3sfCmeYTmUQSRbfrHO\nxVj3X0TcNHheh7wxbs745XKKKVW8a3nLJna+FM+wHMogki2/+SfFWPdfRNwUtrEOuWOeQ7S31NPX\nGaJ7c2MiTm5VXJxjTdFzu0MsR1i1TnSusaV+sXjZxLtK4XjrUbI1mC/sbWXPdUMcHZ2iNxziwiRr\njDvrSCHik/MRNy1STM52sKllA++9foijJ6Y5J9wUi4c+G/t87OQ0VRBofeZIJEJ1Nbz7rYaJ6QUu\n7GtTexDJggbP65A35vn2m7bz828ZZHR0MvGaNy7OGWfnXuP57DrRucaW+sXiSXkJsgbzdw+MuNZx\njoDr2FtHChGfnI+4aZFi8rYDZ58b/9kb+xxkfeZk7UuTZ0Uy5zt4NsaEgGuAC4AV4EXgX6y1c2kT\nStnKdZ1n75qk8WuT5ZvJgCXX9FJcQZ6X37rO3jSqAyLp56XEf161zvOxcd/Bs9qXSH6kHDwbYxqB\n3wd+Gvg+cAhYBK4E/swY8xXgf1hrM5ptYIypBT4HnAvUA39krX3Qcf7twEdi9/q8tfauTPIXf7mu\n8+xdkzSePtdYOsXiVZYgz6vX85p3XWdvGtUBkfTzUuI/e2Ofg6zPrPa1Pi0vLzM8fDjtNRMTTYyN\nTdPb209NTU2RSla50n3y/LfAZ4A7rLUrzhPGmGrg+tg178zwnu8BTlpr9xhjNgH/CTwYy7cWuBO4\nFJgF9htjvmatHc3wHpJGkBjQdOs8n7slxGWDnavS5xpbqtjUyhLkeb3u4k6IRFzrPHe0NqRMozog\n4m4Hbc31zC8u0xY6j67NjUzPLHL7TdtXrb0fZH1mta/1aXj4MLffuY/6pva01y1Mn+ITH9rNwMB5\nRSpZ5Uo3eL7RWhtJdiI2mP4nY8yDyc77+AfgH2M/VxP9hDluCHjBWjsBYIx5AtgF3J/FfSqOd7JU\ndWxh/Hxv7OCMAV1ZWeH/HTzBkcd/RF9nMzuHOqimOtA6z96v+3KNLfWmj0QiPHd49eSyZJPKJHfx\n9/X4M0ddG+akvH4lwsTMAuPTC7TOLBIhsur66kgV7S0NLC6t0NHSQE2AZ5zv+GRtkiLlxFkfW5s3\nMPb0YRob6mioq+bM5EJG7cBvfeZkdf+i/mh/eWRkiipQe1gn6pvaaWjp8r9QAkk5eI4PnI0xYeDn\ngU2e8x9NNbhOx1o7E8u3megg+ncdp1uAccfxJLBu9gpNN0mkUBs7PGVHXZNOYBtXDJVHA0s1eSzZ\n653hlmRZSAYynawXpO745VmMDUy0SYqUk2T9PMyumoSdjzqarO4Dag8iOQqy2sbDwLNEY57zwhjT\nB3wF+Ctr7X2OUxNEB9BxzcCZIHmGw805lanU6QGOn55xHTsniRw/PcPVl/XnvQxHHn/JfXxiinfs\n2ppxPtneP136444/JnD2PUj2ej7uXwyFKGO+8kz1fqcSpO745ZnpPb2C/O6Z3KOcn0+x5avcysfN\nWx+9E7Ah83aQqkyp+kq/e1VSnS1km21paSD6GV561TVVhMPN1NamHlLlu5yZ5Dcx0RT42k2bmvJa\n1rXapwZaqs5a+7583dAY0wV8HfigtfYxz+mDwFZjTBswQzRk4+NB8nUus5apcLi5pOnjeWzZ3Oh6\nzTlJpHtzY9p7ZFuGvs5mz3Eoq3wK8R5634/4e5DsdcitDsTLUGi5ltErH3UvLtX7nUqQuuOXZ6b3\ndAr6uwe9Rz7fy0LnWQz5KHe+fv+1lE+yft4bNJFJO0hXpmR13+9e+XyPiqGQbXZiItiiYivLEUZH\nJ1MOnvPdD2Sa39jYtP9FjmvzVdZK6FOzradBBs9fNcbcAnwLSPwT2VqbfupmancAbcBHjDG/R3Tp\n188CTdbau4wxHwK+AVQBd1lrj2V5n4rjncxRUw3dmxoZ6A4xPr3AfY+9RH/32bjkoNJteHLelhDv\nv2Ebh09M0dcZYudQuIC/YfLypIpZTjW5RZNeCiO+6cmRE1P0dTav2vRkeXmF/QdGGD4xTW9XiNdu\n62RxaShxvCNJ3Yk/q+OnZ1yb8HjPF/JZqr5IOYnXx1dOTtOwoYZjJ2dob21g7+4hZmaXaG2uT2x8\nkkk88vJKZNUGRKnqvtqDSG6CDJ5bgd8GTjpeiwDnZ3NDa+1twG1pzu8D9mWTd6VLNuFusG8TTx4c\nySkuOd2GJxDtSD/4Mz+R938hBi1PqpjlVBMQtelFYfhterL/wAh37zu7wcnKSsS14Ul784aUz+rq\ny/qT1q9iPEvVFykn8foIrIp9Pv+cllV9c9B6+/Rzx1NuMuXNQ+1BJDdBPr68Eei01p7n+C+rgbNk\nx7sYvvfYT7oNT5KdL7Qgm7RI8fk9lyAbnohIMMk2QvG2sUza1KFj465jtUeRwgkyeP4RnpU2pLj6\nu5s9x7ltPuJNX+yF8rVQf3nyey7eDU96fDY8EZHUkm2E0tvpntiVSZs617NJitqjSOEECduIAAeM\nMT8AFojGIkestW8oaMkkYedQB7CNw8en6O8OcbkJ8+TBEQ4fn2JgSzNtTXV8M7Y2r3Nt6PjP524J\n8d7rhzgyMk1vZxOXDYYT+Q1saaamGv7+G8+71vbNx9q4qdatPm9LSDF3Zcgd8xxaFfN85bZOVlYi\nHB2doicc4spLuthQV52ol0MDraysrPCUHY291sylF3Tw3QMjHB19gd5Ymh/G4u37u0KYvlaedlyf\naTy/SKWIzxl4ZXSa7o4mpmbmueWGbUxOL3BmaoGejiZee3En7S1nNxEa7GvlyYMjHDo2SVd7I70d\nG3nVOWfnrDjnsJwcn+XWG7YxPrmQmDPz6NNHtLa5SAEEGTz/UcFLIWlVU80VQ12JOGdvDLRzPegg\nPwOu2NVk60nnY21cv3Wr37ajL6P8pLD8Yp7t4XFXjPOGuupV10/MLLhem792iHseOZsmEsF1vHf3\nkKsultM64yL55J0zsGt7DyfH51398qbYvIF4u/P29Tdes5WTE+42lmwOC8Cf3qu1nEUKJchHPC8C\n11lrHwcOA78EPF/QUkla3phl5zqhQX72xtU5z8Xj5PIRl5wspi+X/KSw/J55kNh572tHT6Y/9tbF\nTOP5RSpFsn7Xu8azXxs7NT7nOwfmyMiU5pWIFFiQT57/Fvj72M+vAN8Bvgi8pVCFkvS8MdDO9aCD\n/OyNXXWei8fJ5SMuOVlMXy75SWH5PfPVsfOedZ67QrTOLLpe88ZF93R44qg9MZ6ZxvOLVIpk/a43\nkGJVm/O0sfbWBja3NKS9pq8r5JuviOQmyOB5s7X20wDW2nngs8aYXylssSQdZwz0QHeItlA9fZ0h\nujc3JtaGdq4T3dcVYnpukY31tfR3R9fjbW/e4FpPOp4+n2spp1q3WnHO5SnTNZmHBlppaXTXkQgR\nnPH5l8bWfo7HSb/u1V2E2xwxnQOt1NWejZsuxjrjIqXwuos7IRKJxjy3NzE2OUdPuIkrX30OLx05\nk7RfjPf18Zjnno6NbO1xt7t4O/S2W80rESmcIIPnWWPMtdbaRwCMMW8Egm9Xs8YE3eCjkLwx0ACv\n/8mz6+gO9rnXiY7bYToTP3vX+XSmB6LTRGOynWaSat1qKVM+zzzZ8/QeXnNfhwAAIABJREFUV1Hl\nqpuRSIRwawPLyyt0tjZQmyQPb10WqWSpJlvXUM2uS7asuu7w8YmUk/qS9fWwut0lW0tdazmLFE6Q\nwfMHgHuNMV+MHR8B3lO4IpW3oBt8VLp8TBiUylKIZ656JOtN0DqvtiFSuXwnDFpr/8taezFggPOt\ntduttc/5pVur1stEjPXye8pZhXjmqkey3gSt82obIpUr5eDZGPNlY8yb48fW2lPW2gnH+d3GmPsL\nXcBys142+Fgvv6ecVYhnrnok603QOq+2IVK50oVt7AV+3xjzF8B/AcPAEnAucBnwVeC9BS5f2cnH\nRLp8im9KceTxl+jrTL7JRDYbnuTj98zHRitSPH4TBrN5nu6NV5pXbbzi5b1HfAMI1SGpFPE6H58E\nO9jfynOHxlbV4aGBNj78C9s5PjbLmcn56O5jRHzrt/pVkdJLOXi21k4BHzbGfBR4A3ABsAI8CfyS\ntXZdThpMNmmqlJ6yo64F8pNtMpFNbF0+fk/F9FWW+DP3TjyKy+Z5+m284nePZBtAqA5JOfPWeUhe\nh6uoYiUCX3wkum3CgwSr3+pXRUrPd8KgtXYS+FoRyiJZSLZgvnfwnCy2rhidbanuK4WRzfPMNE2Q\njVhUh6ScZVKHi9GmRCT/gqy2URDGmJ3Ax6y113hevw24BTgRe+kD1toXil2+SuFdID/ZJhOliq1T\nTN/aks3zzDRNkI1YRMrZ6jqcug0Uo02JSP6VZPBsjPkwcDOQbHrxpcDN1tpnkpyreOni1YLGsjmv\nu6AvxJ7rhjg6OkVvOMTlsU0mnNectyXE3t1DDJ+YprcrhHHE4J3bHWI5AsefOcqWzY2Je2ZazmTK\nLT58PYnHwkfjLpPHwnvFn6u3LsR5n+dgXytPHhxx3aMqUuWqGxf0tibqZ084xAWeNDsGO3jeEdM8\nONDquxGLSLlI1hduPaeVPdcOcfTkFD0dITbWV7F39xDHTs7Q392MibWBYydn6O5o5KY3X8jY1Dz9\nXf5zAkD9qkg5CDR4NsY0AZtx7J1grT2cw31fBN5FdJtvr0uBO4wxW4B91tqP5XCfspMuXi2b9UFv\nvGYr9z/2YuJcfV10Uf101xCJcPe+gwDs2t7Dt585uuqemZYz2VrX5RYfvp4EiYX38qt/3uf55MGR\nVfdoaax35bHnuiHuefjg2UsicM8jZ48Xl4YSddF5z3QbsYiUi2RtZvTMnKuO33ztYCKuGWBxd7TO\n79rew8ThBVf/21yk+SgikhvfwbMx5veBDwOjjpcjwPnZ3tRa+4AxZiDF6S8Bfw1MAF81xlxnrX3Y\nL89wuNnvkrJIf9zRUQIcPz3D1Zf1J35OdS5VHqfG51znjpyY4h27tqa9Znj07FzP2fmlpPdMW84k\n56BynkEpFaKMyfI88vhL7uNYvUgn3TNPJtk92lsaXK8dHXV/uXT0pPvYWReD3NMr3+9nsZ5PJchX\nuddyPsnajLcff+Wku47H67y3742nz6T+JytTPlRSnS1km21paQBWT572qq6pIhxuprY29ZCqlH3V\nxERT4Gs3bWrKa1nXap8a5JPnvcCAtfZUgcsS98n4etLGmH3AdsB38JxsdYCgwuHmoqXfsrnRddy9\nuZHR0UnC4eaU59Ll0d7qHqz0dYYYHZ1Me01v+GxDatzgrgLxe6YrS7JzUDnPIF0ehZZrGb1S/d59\nnZ5Y4Vi9SCdo/Ut3j9bGetdrvWF3PGaP59hZF4Pc0ykfz7yQ+RUyz2LIR7nz9fuXaz7J2kxNjTs8\nqqfDXcfjdd7b98bTZ1u+cnyPiqGQbXZiYs7n6qiV5Qijo5MpB8+l7qvGxoIvjjY2Np23slZCn5pt\nPQ0yeH4FGM8qd3+ugF5jTAvwA2PMIDBLdIm8vynQvUsiXbxa0Fg253Xn94Qc6+iG2BmLeU52TXzd\n0R1DYdpbGqIxz1tCXDbYuWpt33yUU0pn51AHcPaZx+tFOn7rPAe5RxVVrrphBlqpqop+2tYbbuKK\nS7rYUFedtC6qLkmlSdYXLhGBCImY56aGKm69YRvjkwvRuQIDrdTVVkdjnts30t/dzJnJeS7sa1P9\nF6kQKQfPxpjfi/14BnjSGPMI0U1SALDWfjQP94/E7nUT0GStvcsYcwfwr8Ac8E1r7aN5uE/RpZpw\nly5eLWgs26rreoiGahwf54lnRxITA193cafrGmfMq/c+V1/Wz4kTExw4dLbM8Q9QqpL8PhcNtLFt\nYFP09UNn+NYzrySdZCalUU009t0vztnJu85zJBLhucNnN3fwblgy2N9KS2M9rU31tDbWR597xJkf\nVK1AXW01tTVV1NXWUB3BncZzvUi5ck3U7t/E+d1Nq/rilZUVvmdHGTk9wwW9bTQ11HJkZIrGxjoW\nl5c5NTHHPz8dbT+vfX0nVVT5fpKmTVFEyk+6T57jrfPpJK9FyJG19hBwZeznLzlevxe4N9f8S60U\nC9nvPzDimnxFJMKuS7YETu8ts3MyYarNKrRg/9rlt2FJsjoBuNLs3e2eEOidIKhNUKRSBOnrnBN1\nvZOxb7xma9LJsfm4r4gUV7odBv8QwBjzi9baLzjPGWM+WOiCVbpSLGQ/fGI67bEfb5mdE1pSLfSv\nBfvXLr/NHpLVCS+/OqlNUKRSBOnrnPXZOyFw1eTugHVdfaxI+UkXtnEb0AL8smdljFrg3URXxJAU\nSrGQfa/nHr2dwWfYwuoyb3RMaEm1WYUW7F+7/DZ7SFYnvF8m+9XJdBtIiJSTIH2ds014JwSumtwd\nsK6rjxUpP+nCNl4kuuZyFe5wxHmiK3BIGkEn1cU3szjy+Euct6WFuYUljoxMc35vCwuLywyPnI1f\nrvHZ5OLKbZ2srESiG6Z0hgi3NfDo00fo7wpRXQ0vH0seuxrf5GSwvzUxsXCgO0RbqJ7uTY1pN6vI\ndJKZlI7fxineTVK8G5YM9rdydoJgM5de2MG8Y4MeM9DKyiJnN4gIh7h8WydEIokJg1de0uWaIKhN\nUKTcpIoxHhpo47fevZ3hUzOcmZxn9Mwc//jyS3RuamRiep7mpg2cPDPLnuuGGB2bYUtHI1t72zg6\nOkXn5kamZubZu3uImdmljOq6JmiLlJ90YRsPAQ8ZY+6z1j6f6jpJLujkv1QxcjeGVm9s4he/bA+P\nJzak2LW9hy844uv84pc7wy0cPDy+6vW37ehLHCf7fbyTzKR8+W2ckiq2Mv7Mnzs05ko/79kApaoK\nVlbcm6B4N0Vpb2nQJihS1lK1gyqqGDkzx72PWnZt72Hf/pcT19x4zVZXW9i1vYfPPXjQ1e9mG6us\nTVFEyk+6sI0fc3Y1jFXnrbVZb5IiZ6WKkVu1sUmA+GVnbJw33s4vftmbPn6sDnvtSBaz7Bw8+z1/\n73nvBijDJ6aJeOYSezdFUZ2ScpeuHcT7Yb945vh553Wq+7JWLC8vMzzsv8n0xEQTTU3t1NTUFKFU\nxZUubONqouEavwf8CLib6FJ17wbOK3TB1otUMXKrNjYJEL/sjI3zxtu545eTx9Aptm5t88Yor4ph\n9nn+3vPeDVB6O5tY8azD09OhOiWVJV07iMfw+8Uzx/tbZ7+rui9rxfDwYW6/cx/1Te1pr1uYPsUn\nPrSbgYG1N2RMF7ZxCMAY82pr7fscpz5hjPn3gpdsndgx2MHi0hDDo9Oct6WZV/W2cvj4FF2bG9gT\niyftCYdoaazjvsdeShqrGueMWT7vnBYuG+xMxMnVVBM4flmxdWuT38Yp3vj1wb5Wnjw4kohxvnyw\nw7XZzqWDYSKQqKNXXtIV/dzZsUHEFa/uYkN9dWwTn2aGBlpL8JuLBJeqH4xEInS1beDdbzWMTc6z\n59ohTk3M0t6ykfEpx3HrRk6Nz7Ln2kGaG+vo6Wgi1FjHkZEpqmL5a51mqXT1Te00tATfR2CtCbLD\nYJUx5hpr7WMAxphrcWyWIrl5/vD4qrU/r3pjN88dGuNT9/8g8bp7zdBtSTe/8ItZHuxLH2eq2Lq1\nzW/jFG/8+pMHR1z1ybtGM2xzxXmGW6PxzFe/5mxsvjdOuqVRa9RKeUvVDx44fIZ/e/6Ea+3mIGuf\nn9NRq3WaRdaY9Ms3RN0CfNIYM2qMOQX8T+C9hS3W+pEsvi7Z6+lilv3yEsmGt54FWaPZS3VS1ooj\nI1OrYp2DrH2uNiCy9vh+8mytfQZ4tTGmHYhYa08XvljrR6r4uvRrLiePnVPMsuSTN0Y6mzWaVSdl\nrejvCjEyNuN+zWceQbK1z9UGRCpfutU2PmOtfb8x5jEc23HHV96w1r6h8MVb+1Ktk+yNu5ueW2Rj\nfW3SWFVvXopZlnzwxkjvGApnvEaz1gGXtWJooI3qaujtDDE1u8TWnpZVbSBVm1C/LLK2pPvk+dOx\n//9BEcqxJqRaXD99orM/VgH2yJnEZiYXDbS5YuN2mM60WSlmWTLhra+mr5WnYxv29HVGJ6Z6Y6Qz\nXaNZ64BLOfBuAJTJpD1nO2lt3kBkJcLF57dzfndT0j43WZtQvyyytqRbbSO+osZvAg8CD1lrh4tS\nqgqVanH9TNLkY1F9kSC8dW/v7tUTAlNNLhSpJNn0zanS7trew9/98w/VP4usY0EmDH4U6AbuN8b8\nhzHmfxpjduZ6Y2PMzlhIiPf1txtjnjbG7DfG3JLrfYopm4kh6SYGamKJFJK3fvlNCBSpVLlM2kvV\nR6t/Flm/fAfP1tqnrLV/AFwPfBbYC3wnl5saYz4cy2uD5/Va4E7gTUQ3aXm/MSZ5gG8ZymZyVLqJ\ngZpYIoW0atMT76YoKSamilSaXCaupuqj1T+LrF++q20YY/4auApYBh4HfjX2/1y8CLwL+KLn9SHg\nBWvtROzeTwC7gPtzvF/eOOPfztsS4vTUQiJGdMdQR8YTQ+Ibm8Q3kWhvqaN7UyMD3SFWIvDo00fo\n7wpRXU0iFtoZr5dLLJ9ULr/nniz+ngiu1y7sa2Xv7iGGT0zT2xXiios7qaupitXFEDsGwzx3aCxx\n/WB/KwcPj2cW0y9SBlJNXF1ZWeEpO8rh41Ocu6WZqir48SuT9Hc3s2Owg+dj9f3WGy5memaBpsa6\nxAYo03OLif5ZbUFkfQmySUobsblswEHgeWvteC43tdY+YIwZSHKqBXDmPQmU1ZZkzvi3G6/Zyv2P\nveg4G40RzSQOLtXGJs8dGgsUC51LLJ9ULr/nnuw8kDbGua6miiuGunjHrq2Mjk6uqoPJNoBQXZNK\nkGri6lN21FWnnf2sd1OgeBv67Nee82xapbYgst4EWef53QDGmCHgjcBDxpgma21PAcozQXQAHdcM\nnAmSMBxu9r8oD+mPOzrMU+NzrnNHTkzxjl1bM7qvMz+A46dnuPqy/lWvO2Oh49ekS5+NYr2H5Zq+\nGPJVRr/nnuy81/CoO8bZWX/D4eZVeRw54Y7xzLSuFeL55DvPSihjseSr3OWcz5HHX3Kdc/az3vbh\nbEPezVJy6Xe9ZcpFueVTDIVssy0tDUQ/w0uvuqaKcLiZ2trUQ6pS9lUTE03+F8Vs2tTkm3e+88tU\nOdTPIGEbhuig+U3ATwBPAfvydH/v91wHga3GmDZghmjIxseDZJTLMljhcHPg9Fs2NyZ+bm9tcJ3r\n6wxlXA5nfgDdmxsZHZ1c9bozFjp+Tbr0mcrkPViL6eN5FFq+lmvze+7JznsbW2/Y3QHG62/8vfTm\n0dfpjvHMpK7l4/kUOs9KKGM8z2LIR7nz9fsXKp++Tvd76exne5PU93gbatxQu+pctuUr9/col3yK\noZBtdmJizufqqJXlCKOjkykHz6Xuq8bGpv0vclzrl3e+88tEId7LbAQJ2/hH4CGiE/m+a61dyepO\nyUUAjDE3AU3W2ruMMR8CvkF0YH2XtfZYHu+XM+dGJOf3hBzxyqk3LwmSn98mKTXV0L2pcVUstTah\nWJ/8nnuqDXOcrw0OtFJXW53YBMVbf715BNkURaSSODcCGtgSorqqKrEZ1Y6hMO3NG5K2oZPjs1zY\nv43xyQW1BZF1KEjYxqsLcWNr7SHgytjPX3K8vo/8fbKdd6sWxe8hESOaS37eWLxki+8P9q2OqdMm\nFOuT33NPtWGO9zXvJih+eWizB1lLqqle1Qacm1GlakPhsPpbkfUsyDrPIiIiIiKCBs8iIiIiIoGl\nDNswxuxKl9Ba++38F0dEREREpHyli3n+wzTnIsAb8lwWEREREZGylnLwbK29ppgFEREREREpd0HW\neb4K+DAQIrp8XA0wYK09t7BFExEREREpL0EmDN4FfJXoQPuvgReABwpZKBERERGRchRk8Dxrrf08\n8K/AGHAr8FOFLJSIiIiISDkKMnieM8ZsBizwWmttBAi+sbmIiIiIyBoRZPB8J3Af8CCwxxjzHPC9\ngpZKRERERKQM+U4YBP4F+LK1NmKMuRS4EDhT2GKJiIiIiJSfdJuk9BFdXeNh4FpjTFXs1DjwCDBY\n+OKVt0gkwoHDZzj+zFG2bG5kaKCNKqr8E4pI1tTu1pb48zwyMkV/V0jPU0TKnt8mKdcA5wDO3QSX\ngIcKWahKceDwGT7xpWcSx7fftJ1tA5tKWCKRtU/tbm3R8xSRSpNuk5T3ARhjfsta+yfFK1LlODIy\ntepYnb5IYandrS16niLr2/LyMsPDhwNdu3nztgKXJpggMc9/boz5HcAAvwbcBnzMWruQzQ1j4R+f\nAl4DzAG3WGt/5Dh/G3ALcCL20gestS9kc69C6+8KuY77PMcikn9qd2uLnqfI+jY8fJjb79xHfVN7\n2usWpk/xuf/RREtLZ5FKllqQwfNfAaPApURDNrYCfwPcnOU93wlssNZeaYzZSXQ1j3c6zl8K3Gyt\nfSZp6jIyNNDG7Tdt5/jpGbo3N3LRQFupiySy5qndrS3x53lkZIq+rpCep8g6VN/UTkNLV6mLEViQ\npeoutdb+DrBorZ0BfhHYnsM9rwIeBbDWPgVc5r0fcIcx5jvGmN/O4T4FV0UV2wY28fNvGYx+zRiB\n5w6N8ejTRzhwaIwIkVIXUaSiRCIR3zbkbXeaXFbZ4s/zrZf3AvD1p4fVf4pIWQvyyXPEGFMPiZ6s\nw/FzNlqIrtgRt2SMqbbWrsSOv0R0G/AJ4KvGmOustQ/ncL+i0cQXkdyoDa1fevYiUikCxTwTXet5\nizHmz4F3EV2JI1sTQLPj2DlwBviktXYCwBizj+in3L6D53C42e+Sgqc//sxR12vHT89w9WX9RS2D\n0pe3QpRxLeWZaRvKdzkr5b0shnyVO2g+fs++2OUpVj75zKvc8imGQrbZlpYGYNL3+uqaKsLhZmpr\nUw+pStlXTUwE3xR606Ym37xLmR+UR/30HTxba79ojPl3osvWVQNvt9Z+P4d77geuB75sjHkt8Gz8\nhDGmBfiBMWYQmAXeQDS+2tfoqH8FTyUcbs5L+i2bG12vd29uDJxvvsqg9NkrRoPMtYxe+fi9yynP\nTNpQvstZSe9lMeSj3Jn8/umefb7ex3LLJ595lWM+xVDINjsxMRcozcpyhNHRyZSD51L3VWNj0xld\n65d3KfOD/D7zbOup7+DZGFMHvAV4I7AIzBljnrXWZhu68QDwZmPM/tjxe40xNwFN1tq7jDF3AP9K\ndCWOb1prH83yPgXn3azhwr5W9u4eYvjENL1dIQYHWlelWV5eYf+BkcQ1r7u4k5okoefaOEDWI+/k\nscG+Vp48OMLh41P0dzezc6iDap+pGt62M9jfysHD44lj09fK03Y0ozz9qL36875HF/a28t0DIxwd\nneacjiYWF5fZe/1FnBybpbcz+knUo08fob8rxOvbtQKHiJSPIGEbdwEbgc8Q/eR5D7CN6JJ1GYsN\nun/F8/IPHefvBe7NJu9i88bo7d09xN37DiaO62qquGLIPXt0/4ER1zVEIuy6ZItv3or/k/UgPnks\nXtefPDjCZ7/2nOOKbavalJe37dx6wzZXHt52GiRPP2qv/rzv0Z7rhrjn4bPPYdf2Hr79zFF2be/h\nwMsLfNsRxlG/oY6t3RpAi0h5CPJxy05r7X+z1j5orf0a8LNEP4le97yL+w+fcH/1cPi4+3yya7zH\nqfL2HousB942lKxNeXnbijdNkHaaKbVXf9735Oio+3h2finx//jPcYeOjSMiUi6CDJ6PGGO2Oo67\ngKOpLl5PvIv793qO+5N8UuK9Jv71pF/e2jhA1qP+7mbPsX878LYdbx7eNhckz0zvqfa62qr+Muw+\n3rihNvH/xg3uL0UHtqwOgRMRKZUgYRt1wH8ZY75NdJOUq4BjxphvAVhr31DA8hWUN2bZGxsZj1tM\nFc842N/KrTds48iJKfo6m7nUdLCyEuHo6BQ94RCXD4VX3fN1F3dCJBKNee5s4nWXJP+6OJ53PC5z\nKEn8tEgmShGX621jmd5zx2AHi0tDifayYyjMysoKT9lRjjz+En2dq2OWvXHTpq+Vxd1n87jiki7q\naqtjbSvEziTtNFPa6MOfic0JOXF6lo62jZwYm2HPdUOcPDNLR+tGFpeW2bt7iJNn5ujtDHHZYGfi\n/dy5rZtTp/RpvoiUhyCD59/3HP/vQhSkFPxiI+Nxi6niGQ8eHnddv7jbHcO3oa56VSxlDdVJY5y9\nvHm3NCqGUnJTirjcXO/5/OFxV3xye0sDEzMLaeOgk8VNu+Yi1EbbZa5xzk7ee8pqT9tR7t53kF3b\ne3j4kZcTr+/a3sPD3305af/7th19AFRXa/KliJSPIEvVPV6MgpSCX2zkkZEptg1sShrPmOz1ZLGU\n2f6BTnVPkWyVok7les9k6cenF1yv+bWzZHHT+Rw4SzDx5+CNZ44fp+p/RUTKTZBPntes1bGRyeMW\nU8Uzrorhy2MspWIoJd9KUadyvWey9K0zi+5rfNpZNnHTkn/x5+CNZ47HOqfqf0VEys26HjzH4xSP\nn56he3MjQwOttDSujltMFc/oTT840Jq3WErFUEq+laJOedtIpvdMVuYIESA+18C/ne0c6gC25TXG\nWTIXfw7HTs6w59ohRk5P09XexNLiMrfftD1l/ysiUm7W3eA52SYlV1/Wz+joJMvLK5yamOP05Dyh\nxjq+98NRfnR0kvN7WphbWOL05DyNjXW8dOwMLx6ZYmtviNEzcxw/PUNNTTWvWm5lcWmF5ZUIS8sR\nnnnhFC8OT3DeOS00NdQm/ihMzy3y41cm6e9u5nLTwb8lmfzkjKGMRCIcOKQNGCQ3xYjLTbYRxqmJ\nOUbGZqmrq2FpJcL37InERNif3NrBkwdGOHoyOsn2yku6eMExafdVW1oZPTPHqck5GhpqWSRCzTIs\nLq2wtBxhcTnC8go8Zc9upBJvU/HjHYMdtDTW09pUT2tj/aq2E2RSozZByYzzPe1pb+TM9AJnJudo\n2FBPdXX8muj/x2cWqB2v4eC3x+ho20hbqJaaKlx9njZJEZFysu4Gz8k2KbnxDa1JzzkX7Xcu2P8L\nbzH8w7de4OZrB/niI8878oJ7Hjmb/sZrtvL1pw6tSu88nvdsFJBswwZtwCCVwm8jjJXliKuN7Ll2\nyHXsbUPJzldX42qn3jy9bWpxyb0pirf9BGlfaoOZcb5f8f7uF95iuOeRg9x4zVbX89q1vYd9+19O\nTBy8+W2DPH/whDZJEalwkcgKR48O+14X5Jpys+4Gz+k2KfGecy7a7zRyegaAV066rz960j3h5dT4\nXNL0zmPvRgHJJjNp8qBUCr+NMLxtJJtj7ye+q64ZTT+R19t+grQvtcHMON+veH8X7zfj/aL3fPz/\nr5yaTrpJigbPIpVlYXqMO+8bo74p/eB4avRFQuGtaa8pN+tu8JxukxLvufhEFu8El67NjQD0hN0T\nBHs63OnbWxuSpt/oOPZuFJBsMpMmD0ql8NsIw9tGerzn/a7vCFFdU5X2Gu89vRN5ve0nSPtSG8yM\n8/2K939d7dF+M94vxjk3RwE4p72JpaUV1zXaJEWkMtU3tdPQkn51o/mpU0UqTf6si8GzM17xVT0h\n9jo2THBuUuLcwKSvq4mG+ho21tfyqt5mzjunhSMnpjivp4WlxRXeeHkfG+pq2Lt7iCMnpujpCPHa\n13RRXU0snrqJhrpq3nR5P+ee08ylg50MO2KeN9bX0t8d3Uilvq467eQnTR6USuGtqxf0tRKJRD8d\n7ukIsfPiaHuLxzjvfHUXOM5f+eouwq0NifRbY5sDJc6/posaiLbT0Wl6w9FNTzbUn52oG29T8eMd\nQ2HaWxpStp9VE4f7W3nu0JgrvlltMDPx9+vY6RlaG+sY6G5mdn6RPdcOcWZqjj3XDnH89DTdm5s4\nNTHLnmuHGJuM/r+hDnZe1KlNUqRiRCIrHDr0MrW1NUnPT0w0MTYW/Qast7efmprk12VqeXmZ4eHD\nvtdVYlhEuVsXg+dk8YrJNipJtoHJ5Rd28tyhMT51/w8AuDG0lfsfexGAbwJ7dw9x28//JKOjkwCJ\n9M8dGlt1z/iC/wA7TGfi5yuGunjHrq2JPLy0AYNUimQblLhilnHHNFdXwdWvcbc5b133nodoOwuH\nmxNtxrvpifc4XfuJlzk+cThZ242nVxsMJv6ebthQxx/f/XTi9dtv2k64rYFPfOmZRAx03J7rhrj6\n1avrAmiTFClv8zPj/NZfPEp9U3va6xamT/GJD+1mYOC8vNx3ePgwt9+5z/e+lRgWUe6KPng2xlQB\nnwJeA8wBt1hrf+Q4/3bgI8Ai8Hlr7V253jOfGzV44/W88ZT5uqfIWuDd+MIbn5yq/ZSS2m7+HDo2\n7jp2vrfxGOg4b6y6SCUJEp5QqvtWYlhEuSvFJ8/vBDZYa680xuwE7oy9hjGmNnZ8KTAL7DfGfM1a\nO5rLDfO5UYM3Xs8bT5mve4qsBd4NSrwxzanaTymp7ebPuZ5Y5b6uUGK6ZzwGOs5bN0TWK79wjHgY\niMIxSqcUg+ergEcBrLVPGWMuc5wbAl6w1k4AGGOeAHYB9+dyw1zjFZ3pz08TM53Pe4qsBfGNMeIx\n/ZcNhamuwrf9lJLabv7s2Nad9L28/abtjMVinePx71e9uvzqgkjL71GwAAAWWklEQVQpKByj/JVi\n8NwCOL/LWzLGVFtrV5KcmwRynmada8ywN/2FPYW/p8haUE31qpj+ZPMNyonabv5UVyd/L6PHen9F\nUlE4RnkrxeB5AnB+lxsfOMfPtTjONQNngmQaDjf7X1TG6cuhDOs9fTEUoozKs3zzK1SexZCvciuf\n4uVVbvkUQyHbbEtLA9HP8NKrrq5ixfeqqE2bmnzLPDFRunC2ci8flEf9LMXgeT9wPfBlY8xrgWcd\n5w4CW40xbcAM0ZCNjwfJNNVKFUE4Z+2XIn05lGG9p4/nUWi5ltErH7+38ixMfoXMsxjyUe58/f5r\nNZ985lWO+RRDIdvsxMScz9VRKyuRwPmPjU37ljm+rF2xRSIr/OAH1vf+pY61zne/n41SDJ4fAN5s\njNkfO36vMeYmoMlae5cx5kPAN4Aq4C5r7bESlFFERERk3VjLOwLmW9EHz9baCPArnpd/6Di/D9hX\n1EKJiIiIrHOKtQ6mutQFEBERERGpFBo8i4iIiIgEpMGziIiIiEhAGjyLiIiIiASkwbOIiIiISEAa\nPIuIiIiIBKTBs4iIiIhIQBo8i4iIiIgEpMGziIiIiEhAGjyLiIiIiASkwbOIiIiISEAaPIuIiIiI\nBKTBs4iIiIhIQLWlLoCIiIjIWheJrHD06LDvdUGukdIq+uDZGNMA/C3QCUwAv2itPeW55s+B1wGT\nsZdusNZOIiIiIlKBFqbHuPO+Meqb0g+Op0ZfJBTeWqRSSTZK8cnzrwDft9Z+1Bjzc8BHgNs811wK\nvNVae7ropRMREREpgPqmdhpautJeMz91Ku15Kb1SxDxfBTwa+/kR4E3Ok8aYKuAC4DPGmCeMMe8t\ncvlERERERJIq6CfPxpj3Ab8BRGIvVQHHgfHY8STQ4knWBPwFcGesfI8ZY/7NWvuDQpZVREREJK5x\nYwNNi89Ss1KX9rqlxVPMTId881ucHaOqyv++ui65heny+US+KhKJ+F+VR8aY+4H/Za39njGmBXjC\nWvtqx/lqoNFaOxU7/hOiYR73FrWgIiIiIiIepQjb2A9cF/v5OuA7nvMXAvuNMVXGmDqiYR7/UcTy\niYiIiIgkVYoJg/8H+IIx5jvAPPALAMaY3wBesNY+ZIy5B3gKWAC+YK09WIJyioiIiIi4FD1sQ0RE\nRESkUmmHQRERERGRgDR4FhEREREJSINnEREREZGANHgWEREREQmoFKtt5MQY0wl8D3iTtfaHjtff\nTnSr70Xg89bauzJMfxtwC3Ai9tIHrLUvJEn/75zd5OXH1tpfy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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "sns.set(color_codes=True)\n", + "\n", + "# if matplotlib is not set inline, you will not see plots\n", + "%matplotlib inline \n", + "\n", + "sns.pairplot(iris_df)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**PairGrid** allows you to quickly draw a grid of small subplots using the same plot type to visualize data in each. In a PairGrid, each row and column is assigned to a different variable, so the resulting plot shows each pairwise relationship in the dataset. This style of plot is sometimes called a “scatterplot matrix”, as this is the most common way to show each relationship" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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S8f3O3j56BkYYGh4jkYCTju7MTNmfif1F87hzzcv09A2RSCSYfXgbff0jtLfU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\nFQkiTmKBGAdHqgXtKnFC0TODUsrrhBCXA3hACDEHwF4AaQC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25+C0jUgYJx091dDhmn3s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CiEWADgHgAHALwCYC2AFIDZAP5eCPFVALdJKf/XppmDADZLKdMAtgohRoUQ06WUfVY7dHdHy5K7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\ncCc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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# PairGrid\n", + "g = sns.PairGrid(iris_df)\n", + "g.map(plt.scatter);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A very common way to use this plot colors the observations by a separate categorical variable. For example, the iris dataset has four measurements for each of three different species of iris flowers so you can see how they differ.\n", + "\n", + "We are going to color each class, so that we can identify easily **clustering** and **linear relationships**." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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RU/W9DzjHgraJEkUiEbZt25rzXyTNr/hC5MM9u8f0gDH8N9JsTC6eLg2DuQ7z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\nTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "g = sns.PairGrid(iris_df, hue=\"species\")\n", + "g.map_diag(plt.hist)\n", + "g.map_offdiag(plt.scatter)\n", + "#names = {i: name for i,name in enumerate(iris.target_names)}\n", + "#g.add_legend(legend_data=names)\n", + "g.add_legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By default every numeric column in the dataset is used, but you can focus on particular relationships if you want." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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AMeZqEfl/Yxz/eeBbwM+NMVsDjViOGWNMK1aoYhes8kaHAbfkGrDcNIHHS0941XOPMXTb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\n1gNkrwmUYDxrl+VbV65Y8xazf6X1HQ8Kta9Y11U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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "g = sns.PairGrid(iris_df, vars=['sepal length (cm)', 'sepal width (cm)'], hue=\"species\")\n", + "g.map(plt.scatter);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It’s also possible to use a different function in the upper and lower triangles to emphasize different aspects of the relationship." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/cif/anaconda3/lib/python3.5/site-packages/matplotlib/axes/_axes.py:519: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n", + " warnings.warn(\"No labelled objects found. \"\n" + ] + }, + { + "data": { + "image/png": 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Pvw8S/wY4Z6UTKQUdXUEOOdbFSjUbo9OhziALlqwa9NzG+sCgz5vGVolbzvYz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ysx2317c9TqegpKmdQ42+9VuRGhlHKsx4vdAzUMneYhNSsYh+wVJ25VUS6a9C5zaTUd3AhJQE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ny9PDrO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\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DIgNQebnwwrItrD2Uho3MillDYgDoF6UiyNuDgyczqKprwMXBjlGDB7FqwyY279xLbOSZ\nvF8CgYB7732YlJRkfvzxe3r37kt4eGS3tu1/gbQ0c2CrHj16cjJDDUCAXwAHdp8gIjSEjDotgYFB\nnGoChUyMRmcAgQCJECLdbcipa0MsFBBoL8VKJKS0uZ38Bi02VkJ87WUU1rcR6GRNXk0rjgoJhU1m\nd7naNrPRl5pXjK+XJ5m5eR3jJNARgt+ChUvBaDJxKO1MYLj+PS7OpdzV3preYa4cOb1lZN3ePGaP\nDLkiXQxGY8deRIAxvX2vOK/gpdI3wo3MwrqOCKPLt2UT5uuAm6P8quphwYKFP+eCo4Jarf4OuAf4\nPyAXmKRWq5d0t2L/JFJSTqLVaklIGIRAIKCiuob07Fyiw0Oxlis4kJaDn5sj1e1Cmtp0TIz2ZUdO\nHQorEW5KKY1aAwP87cmp16Izmhjka4uHUkpiVStFzTpaDUZsrEQYTFCm0ZNYqaFZb2JEgD1ioYCk\nsib6+5j3G6SUNxPnpaRRa0BuLUMogJzKRjydzJHBKuvMq4Fya+tOAWT0enN4a4nE6iq3noVrnYYG\n8wy2nZ25Dx5PSUMoEODn48uRVDUhvp4U1LdRWFXP2LhQAny8eHyl2RCc2NObxf9L4MER4RgEIhbu\nKmDBxhxOljYT6abgtXHB3D/AB2eFFYcLG/j0YDFv7Mzn4wNF7DllfsYeH+JHjI8dScWN7MypY97o\nCAKcbdiaXkZycR0ArvY2vHHreJxt5fy45wRF1WadBQIBo/vFYjSZSEwzz0n5envh4+nJkWPJtLd3\n3j+hUCh44omnMZlMfPrphxiNnaObWrh08vPzkMvleHl5k5lrTjmrF5rHKYncPO7VtIuwtxbTpDUQ\n6qKg3WAi0t2GsmYdTtZiJoc40MNVQaCjjHFB9kQ4W9PcbsRRboUAsLYyu8772lvT0m7EycGe/OpG\nZFIrTpVW4OnuitFopLKmFrPjvgULl4e6oK4jAIeNtYQel5A3cHTvM6kn9hwvoaFZ+xdXX5jEjEqq\nG06nmpKJGRzteYE7uodZI0LwcjHn3tTpjSzemIHRaJltsWDhn8IFjUGVSiUBRgNjMecd7HtWAvr/\nBMnJJwCIizNnwUhMNs9kD4iP42BaLnqDkVHxkWxJL0EiEqJUKtHojIwKdeR4WRNOcglKmZjmdgM9\nXRUoZWIy6s17XYLtpAxwVzBK5UJ/Nzlh9lKEAsisawMBRLnK0RpMaAwmXBQSsmtaCXE2z6gV1Wtx\nt5NTXNeCo615oK1pagFAJrWiTXvmRVavN894i8UXsxhswcLFc7YxqNPpyM47RVCAP8ezTmE0mRja\nqyc/70/B1lrKmN6RPLMiEQHw3MSe3D0klMoWHf+3LY+Xt+ZxorSJUGc584f58/SIAPwdraluaeej\nA0WsTKkkr7YVrcFIQV0b69KrWZ1aiUgoYP6YEKzFQlanViIQCHhwuNk1afnhvA497RQy7hzdB4PR\nxNZj2R3HY1RBAGTmF3Uc69WzB21aLdmn8s+pb48ePRk6dDjZ2VkcOnSgy9vzv4TRaKS4uBh/f3+E\nQiG5+QVYy6RU1JtXhWtb9UikMhq1BrwdzG7I7UYTComQhnYDMpGA/t5K0uq0HKtuJbmmjSOVrfjb\ny/BUWlHTpifISU6j1oBSJqb99Auora0dzW06PF1cKK6sxcXR/MJeVVvboZvJsjRo4TI420W0b7jb\nJa3EBXraEuRlngDRG4ydIoBeKiaTiY2HCjo+j4z3QWp1cRGiuxoriYi5EyMQnXa9ziluYHtS8d+i\ni4W/Jjqwc8TXmCBLBNj/AhczSn0FJABfAEsxG4XvdadS/zRyc80vjqGhKgDSssybsWPCw0jKMg+2\n/t4eFNa00MvPiWOlTYiEAhRWYowm6Otji7qmFYVESJizNep6LSIBxDhb46WQdCSYFQgEuMklhNnL\nMAKnmtoJPP0CVNKkxcdOht5oQn56QK9o0uKokNLYqkMqMRt5racNQKFQiMl0ZtWi7fQqoZWVJWm2\nha6lqcm86VmpVFJQXILeYCAkwJ+kDPNzYpDIaWlrZ0KfcJYcyKNFq+fBEWHE+zux/FgZfiQYAgAA\nIABJREFUL27JI72ihZ4eNrw4OpAXxwQR7alEIBBQ3qTl/X1FFDdo6eWl5NkRAbwwMpDnRgTgaSvl\nUGEjBwsbcLaxYnCQA/Wtek6WNhHiZkuMjwNppQ2U1J3JlRgf6oNYJCSl4MwLm5uTPQKBgMraMxlv\ngvzNSZLzCs64WJ3NjTeagyn/nijdwuVRX1+PXq/D3d0do9FIaWUl3h4eFFZUYyURU1rXgpODAwBG\nEyilItoNJnzsrdEbIczJmsx6LS16I84yEV4KCXqjibS6NkIdzWPn7+Olh62U5nYDQgGYROaVR2uF\nHIPRiPj0uNjY1Nwxblr2DFq4VHR6I0nqqo7PF+siejaj4n06/t91vASd3vAXV/85SZmVFFeZJ4et\nJEJG9PK+LDldha+bkgn9zySfX7U7l8r61r9RIwvn42Re5+BCJ3IvLtiQhX83F2MM9lWr1TPVavV6\ntVq9Frge80rhf4aSkmJcXV1RKMwzJPnFJcitZXi6uZBVXIGTrYLqFvOAHeHlSEFdGypnOQX1bQgF\nYCM1G4URLnLKW/UYTRBoK8VGcv5ZOmdrMdYiAfVaAzYSITKxgAatAXvrzqt6rTojVmIhJsz7FM5G\np9cjFp25vqXF/KOgUCi6qlksWADO9C0bGxtKys1Glo+nB+qCEjycHckorgYg0Mud1JJ6+gW7MiTU\njSVHSvk1oxoPWynPjwpkwfAAQl3O9M82vZGlSWXmIExRrsyKccdOZu7TtjIxt8ebgyYll5l/rOK8\nzFG0TtWaXzB6+TkBUHBW5DyZRIzSWkpL25lVc7FIhAA6uXy6Opv34NbWn50S9Qx+fv6Eh0eSnp6K\nRqM57zUWLszvq8pOTk40Nbeg0+lxcXKkur4JR3sHjCYTUqk56FVLuwEnhdmIE4rMhpqttYQ2gwlP\nuYQIBxnBdlKC7aQYTNBqNGItFtKqN3+v0tPjrUgoQGcy3y8+7TYvOB2BWdve3uFSLxJZvCgsXBrp\n+bUdKRzcneQEeFx6ZL9eKhccbU9PTmh0HD4dhfxS+XnHGe+HIdFe2FxiSofuYGKCP94u5veodr2R\nH7dlX+AOCxYsXA0uxhgsUqlU/8/efQZGcZwNHP9f0xVJp3qo93ISvTcBoplqimnuvZc4OK/jEndc\n4iSO7Th24sQlNhCIC5hiekeA6CAkBKcC6r2Xk66/H04SYIMNRsWg+X3S3e3OzSx7yz47M89En/fa\nD/jlYxeuMc71+crb1hVzOBwUlZYT6NeDJrOFyrpGwvx8yGm54VSrnBfxCB81RXUmArVKylsWng3R\nKilvsiKTgL/mp280XBVSbA7nkCgXmRST1Y685Um1rWWokwPnUBIAc8vTQ5eWHsJGYxMa9bnMoa03\nXa1JPgShvTQ3O4MvlUpFeaVzmJ3G1Y0GYxMRgX6k5Zbg7+XOiSJnD+LNIyJJL21ke1YV4V4qXrkh\nkrgeP35IcaSgjvJGC6PDPRke+uPz1lOtwFstp6TeGdh5a5w3O1UtmfPcWjL41ZusbfuYrVYamsxo\nlOdujBqamrE7HG2/XTj3O25qvnQmtV69emO32zlzJuuS2wg/7fwHCXUNzmuou6srdcamtuy0DqkM\nmVSCA1DKnf9lmW3OoaINLYFeoKuiLV29v0aOBKgz2/FUyWi2OZAAjpYsoWqFDGtr3C9xltc6f8lu\nt7fNE3VxEfOrhStzfq/gyD6Bv2hhd5lUyoSB53rxthzOv+Ihy5kFNZw8U9lSnoTJQ0N+Zo/OIZdJ\nuWdqXNus3ONZFRzPrOjSOgmCcHnBoAJI0ev1G/R6/VogHQjS6/Xb9Xr99o6tXtdrbGzEbrfj6elM\njmFsasZiteLt4UFtg7NHwMvdlapG50RvScvNhVYpxwF4qRUYzXYkOAM8k82BRi5tGxp6KbaWa79M\nIsFss6OUS2lseeJobZ33opRRbTSjVSmoa6mLt7srDoeDqpoafDzP3UCXlzv/k/L11V39QRGE87Rm\nYJTLFW0ZbK0tPS+eHloams1E+nuTUlCNUi5lcISONenO8/G+oUFoVRd/MGIod57ToyI8L/q53eHA\naLHj2jIMsPX34dbyuqTWGaT2cD/3UOTE2RIsNhu9w84N38rKd2a5iwg8l3K9qdn5e1b/xFIsPj7O\n3sOamupLbiP8tNa5zAqFAnPredTyQEsmd/47OpDQOmKz9bJpbXlIZmm5Fqpk566nUokEmRRsDgey\ntiH4tC2IdP6lt/Um29bSKyyXy9uWStFoxCgK4fLZ7HaOZZ4XDPYN+MVlje4X2Da/L7+sgfTcK7vG\nrN5ztu3v4b388Nb+epaUigzUMqb/uUQ2y7ZmYLb8sqGwgiC0j8sJBl8BpgFvA+8As3EuDfEaV7HE\nxLWidX2+1qfUJovzqbFK6dI2ll+pkLf10LU+CWydMy6TcEFyOqkErD/zlM/mcFBntqGUSWiy2Gi2\nOvBQnusBae3p8HFVUFRjJMBTQ26Zs0cm0MeTiqpqTGYL/rpzgV9BgXPuU0BA12QTE65frTfUEomE\nZpMziLK1vCeVO3tXvN3UFNcYCfNxQyqVcLqskVBPFdG+l04vrlU5b4ZOlTVe9PO9ObU0W+1EeDtv\ndNJLnduFeqlwOBzsyypDLpUQ3cO9rZ5fJaUAMLp3RFs5ScdOAtAvNrLtvfwiZ4Dop/O97OMgXJ22\nudOtl8fW84ofL/Mgk0qw2h0oWqJE03mZCS12B1Y7KKTOIaISnPMNW6/DzRY7rbGj3ea8lrYGpa5q\nNXUt67O6uYnECcLly8irobHZeT55uSuJCfH6xWW5qRWMP2/u4Jo9Zy+7d/BUbjXpOc7gUSqRMG14\n2M/s0fnmJkbh2vIQsKK2mU2H8n9mD0EQOtLPToowGAy7OqMiv1Y/vP623rDY7HZkUmfEZ7XZcFE6\n/2596mxp6dqrN9nwcnPBATRY7LgrZNSYbdSbbbhfIrNXXr0ZmwMC1XJya5031z00CpLPVqNzVZBd\n4ezxkNqt2OwOYv08OJ1bDEB0kI6sXOeFNTwkqK3M3Nwc5HI5QUFdO4lcuP5Ipa1D7ext8+7sP3g4\n4qJQYLU34aaUU9NkwWJz4Of+08PwJsX4cLyogVUny0kraWBUuCchniqaLDYOFdSRdLYGjULK5Fgf\nzFY7O7KqkEslDAjSciinkvxqI2NieuDeMlx07cFTnMovY0RcKPog54OSipo6dh4+gc7Lg76x5wLE\n4yfTAdBHRf64Yi2qWjJPti6pIVy51nVPLRYLSqXzfDCZzchl0pYeZxlS7LTGeq0P3VykEhotNtzk\nUsqAokYLUVoXJBIJuS0PzbQKKaebrWgUzvOzdXi91e5A3hJxtg4JbW7pDfRwd6coLxsAL69ffjMv\ndD+HM871Cg6M1V11AqJ542LYvD8Xm91BZkEt6TnV9PqZZSocDgcrd2e3vR7Zx58An19fD7ebWsHc\nxCgWb3KuK7phfy6J/QLRuoqh2YLQFTp39dFrkEbj7BFsTRLh1jJ0qK6+AS93Z69GVV0jPbTO7WQ4\nb1ZK6814q+UU1pkIaLnAna1uJtTdefNzsroZk+3CNcocDgeFjRbyGiyoZBL8VHLSyhtRSCXUN1uw\n2B3E6Vw5XlRPgFbJ6SLnzejgcB8OGXJQuSiIDw1oW7i5T6xzqqfFYuHMmWxiYmLE0hJCu1MqnfPr\nzGYTLi03963zs+wtmRltNhsSwGS14alWoJBKKGswX7S8VlqVnEeGB6HXaciqbOKLI8W8vu0s7+zO\nY9eZGtxcZNw3JBB3pZwl+/Mpb7QwWe+DTAKf7clEKpGwYEg4AClni/hs80E8XVU8PHW4s44OB/9a\nsR6T2cKtUxLbHu7U1Nax/8gxIsKCCQ+59MOTrCxn8oPw8EsHjMJPa+19q6urw1PrTKlfU1ePl7sb\nDS1zCO1WC3YHqORSapqcPS9SnL19zRYbapmEwkYLh8qMpFU1UdhoQSOXYrPZsDlA3nJPXtVgRimX\nOoNCm/Pcq66pxtNNQ0Wlc35VgJ+OsjLnguE63blhw4LwU+wOB0fPCwYHxV79dIwe3poL1gVclXTm\nZ3sHU7IryS509mzLZVJmJoRfdT06yuh+AQT6Ou+nms02Vu89+zN7CILQUUQw+DPUag1yuZyaGmcC\nFrlchrenB6UVlWhUSjxc1eSVVRHu67ypqaxrRKOQklrSQLSPhmarHZPVjotMwslyI0qphCBXBWab\ng0NlRrJrTZQaLWSUNXC0oomsWueyEz29lBwoqqfZ6iDeV8Pe3FoUUgm1RjMWu4MRoVp2ni7GQ61A\nI7VSWFHDoNhQ5DIpyUePo1GriIty9nSkp6dhtVro169flx1H4frVmmW3oaGh7eGJpOWmxdoyrLq6\nsQlfdyUF1UakEojr4UpudTOnSi8+BLRVsIeKB4cG8duEEG6I8aavvxtDg7XM79OD58eFE+6lZpOh\ngv8dLkTnpmB2bx0f7ThNUU0TN/YLJszHjZN5pSxavg2JRMIz88bi27Im5zdbktiXcopekaHcMGxA\n23cu+XYlVquV+TOmXjIBhNFoJC0thbCwcNzdrzxjoODk6ens6aisrEStUuGqUVNSXo6/jwdVtbW4\nq1yoa1m6RC2XUmm0IJVASb0zU3NauZFe3ip8VDKabA4qm224yqXEaF1IK29CKoH8mibkLQ8fWucW\n1tTW4a5yobyymjB/HXmFRbi5avBwd6ew0DmyIjAw6OKVFoQfOFNUR23Lwy03tYKYkPZJ1DZ9RBjy\nlnM2u6iOlOzKS25rsdr4avu5ZFZTRoTh66Ful3p0BJlUyvyxUW2vdx0rorjyp/8/EAShY3R6MKjX\n6yV6vf6fer1+X0sSmsgffD5Dr9cf1Ov1e/V6/QOdXb8fkkql+PrqKGqZQwQQGuhPWWUVDUYj+hA/\nymrqCWpJBX3obAUDgrRUGi14tYyJ35NTQ38/Nyx2B9vO1hCkkRPt4dy+oNHC6RoTJ4rrabDY0ank\nDPBVc7S4kbxaE/6uCrLLG2kw2xgU5M6WzCpcXWSYm4w0mKxM6RPCxoNpAEwc1JMTpzMor6omYdCA\ntiFYrQtjDx8+vNOOm9B9eHg4b3xqaqrxbkm0hN3Zg1NbW4dSISe3rJqeAZ7UNlkwFNcyv5+z1+Wj\nvXkczKttS4p0KSGeKibH+nDXoAAW9PNjWKgHlUYLH+zJ48vDxXhqFDw3NpwVR3LZaShF76flnpFR\nHM4s4JWlm7HabDw/fxx9w51JHVZs28uX32/D11PLc/ctaBvqeuh4Cht37CIsOIibpk28ZH2SknZg\nsVgYNSrxqo5dd6fValEqVRQVFSGRSAgNDKSotIxQP18cDgj00lBTU4NMAjVNFhwO0LrIqDRa8VXL\nMVrs7M2vJ9LdhaE9NAzRadB7uLC/sB6jxY6vSk5tS4DocEBlowUXmYS62lqCvZwjO6KC/CgqLSM8\nOBiJRMKZM9kolUr8/X95AhChezlqOH+IqG/bKIOr5a1Vkdj/3EOJZVsyMJkvnmxl7b4cSqucI5jU\nShkLJsa2Sx06Ut8oH+JCnf9n2B0Ovt2Z/TN7CILQEbpizOBsQGkwGEbq9fphwLst76HX6+UtrwcB\nTcBevV6/2mA470rbBUJDwzh4cD81NTV4enrSMzqK4+kGUk9nMiQugoOnc0jNymFAqA/H8iqZPSiS\nvTmwOaOSAcHuHCtqILfKSIy3isyqZtZnVTPA341hfhrqzXaabQ68PNRImk2UNlrYnF1NrcmGTiNH\nhoO0skaCtUrSip03OAv69GDJ7jTclHKGhnjw9PenCfPzZnBsOG98+C8ApiSOAsBqtbJz5za0Wi3D\nhg2jpubSqfIF4Zfw9e0BQFlZKdHxfQGoqqpE66rhbGEJ8VE9OX6miHljvdiVUco3B87wyKgYbhvg\nz1cppbyflIdSLsXXVYFcKmmb2yWXSXBXynF1keGulOHqIkMqkdBgspFT3URmhRGHA6J91Tw/Tc+S\nHadYm1JAgIea56f1YXfaGT5YsweZVMrz88cxTB+K2WLhk+82sX7PIXw9tbz+2F14a1vWJ8zL508f\nfoxCLuephx+45JBqm83GN998hVwuZ/Lk6Z1whK9fEomE0NBQcnJysNlsxESGcyorGx8358MyV6kN\ncBCilZNb1USMnxs5lUZ83JWcKm2gd4A7xQ0WVmdU4amUo5RLKG10DisNcFNwvLAOhVRCZnkj7io5\nJwvriNBKOYMDpaNlbqFKisPhID4misbGRnJzc+jZszcy2cXndAvC+RwOB0cyzq0FODC2R7uWP2Nk\nOPtPltDYbKWitpnl2zK4e0rcBaMW0nOqWLcvt+313MQovNxVlDdb2rUu7U0ikbBgfDSLvjgMwLHM\nCjLya4gNEfOwBaEzdcUw0VHARgCDwXAAGHzeZ/FApsFgqDMYDBZgDzCm86t4odjYOABOnXL2wA3s\n3ROAfUeOMbJXFHKZlPUH0pje1zm/aOvJPIaGaDlT1QR28NEo2J9fR73RQp8eGhrMNnbn1fJtegVH\ni+rJrjRyMKea705XsiOnllqTDb23GovZxp6cWjxVchRSCakljfT2d2W/IZ9Gs5U7hkezeNMe7HYH\nt08cTlZOLvuPpaCPDG8bIpqcvJeammrGjZvY1lMoCO2pdThdYWEBEaHODHjZOXnERYRQWlVDbIBz\nKGBNTRVBnho2HM/neF4VN/bU8fa0aCbEeKNzVVDbbKWs3kxNk5WaJivFtSZSixvYn1vLlowqVqWV\nszK1jM0ZlWRWGIn20fDkqBDuG+zPq98cYm1KASFeGt6Y3Z9V+1J4b1USKoWCRXdMYnhcGDlFpfzu\nr5+wfs8hwgP9+MvC+wn1d87tSc/I5NnX/4ixqYnfPngfsZERF28ssHLl1xQW5jNp0lR0OrFUy9WK\niorBbDZz9mw2PWOc85ybG2qRy2SUlRQhlUBzfbVznUGphHqzHa2LDKPFzunSBvTeKrxVcqqarRQ3\nWHBVyIj1cj48M9scSAGTzYG1ZXHBqvJSVAoZZ3PO4qpWUlpUAED/nvGkpZ3AbrfTq1efLjoawrUm\nv6yB8paHrGqljPiw9k08pHV14ebxMW2vd6cU8/2+nLb5g1kFtXy4MrV15RTiQj0Z2//aGeIc7q9l\neK9z83O/2Zl1xesqCu3n3mkxP/lauD51Rc+gFqg977VVr9dLDQaD/SKf1QNdvkp6v34DWbr0Cw4f\nPsiIEaOIj47EX+fDnsPHeOjW+YwfEMfmw+k01lTSJ9iLQ2creCLSj8wKOd+dLOPuQQGcLDOSnF9H\niIeSUeGeNFjsZFc3U1B/LomGm4uUcE8VMmB/Xi2VRgv+bi6o5VK2Z1UR5KHES2pmd34lg8N9kZlq\nOZ6Vz6DYMIbFR/Ds2+8CcM+82UgkEmdmsZVfAzB9+qyuOHRCNxAcHIJcLic7OwsPd3eCAvxJz8jk\nnrtHcTDNgNzWhItcxpoD6SycO4HX1qby1vpUfjM+jlExPbh/6KVvXKw2O3UmG/UmK0azHbvD0dJT\nKCW7rJ7t6QUkZ5dhd8BYvR9zBwTx7sodpOWWEuzrwUu3TKCHhyvLNuzgq81JWG02powcxINzpqBq\nWVR8887d/OOLJVhtNv7vkQcZlzDikvU5eTKVJUv+g6enF/fc0+Wj2K8LvXv3ZePGdRw/fozJU29E\nKpFwLDWVwXHx7D+ZSd+B4aTmlxES5UNqcT0DQrScKG5gRJgHBfVmtmZVEeqpIkTrgt0BhXUm0kvq\nkUpAp5ZztLCeHq4KDufVEumpIDuzjsHhvhw6lMG0Ef3Zt2szWjc3+sbr+fTTjwHo12/Az9RaEJzO\nTxzTL8oXhbz9n7En9PEnPbeK/SedyY2+SzrLiexKtK4upGRVYm8JnrSuLjw0s9dVZzLtbHPGRHL4\ndBlWm4PswjqOZ1UwIEY8aOsKo/uGMLpvCDqdO+Xl9V1dHaGTdEUwWAecn3GhNRBs/Ux73mfuQM3l\nFKrTdVwSh9Gjh+Lj48Pevbt56aU/oFAouHnGZP72+TK270/msXnj2HUigy82JfPOwtv5zZf7+DzJ\nwAtzh/LPPfl8ebiY24cGE2y2cbygluUppQR7qojSueLnrkQqlWCy2SmpNbH3bA31JitSCYyM9OZU\ncR3JOTWEeqvp18OFL3edIsTHjTtHhvP0B8vRalS8fP8Mdu5J5lTWGSYkDGXiGGdn686dOzl9Op1x\n48YxcGCvDj9OnVF+Z31HR+uINnRlmfHx8aSnp+PqKmPU0IF8tXo9YX7uKOQy9h5LZUHiKJZuO4oh\nv5BF8wfx8jdH+POmk6w4nk/PIE9CfNzA4aDBZKW60USN0UxDswVzS2+OBOeyLWarnRqjmdKWBeUB\nYvy1PDBOT2VlJc/9Zx0NTWbG9Y/ixdsnkpVbyO/eXcaZghJ6eHvw7P3zGN3yW6ipq+edjz5l0449\nuLu58ufnFpIwdOAl25+RkcHrr7+E3W7n9dcXERn5y56+X6vnb0fV+4YbxvLXv77NsWMHefTRBxg2\nqB/Jh4/z3OyZ7D+ZicbhnAclaaxAKtFSWN2Mn6eK5NxahoZ7oVHKyC5vJO+8IfDhPmrqmqwcLahD\n5+ZCWlEdbkoZlSUFKBUyassLAegZ5su62jrmz5iMn58HBw/uw9XVlbFjR+LicuVp7q+H69+1en7+\n0NW243L3Pz+py9ghoRfs1551ePrOISz6dD8nsioAZ0KZ82ldXXjr0QTCArQX3b896tBR++t07kxL\niGDN7jMArN6bw4ThEe3y/e1VRkdrrzq2Z1tFnTq3rK48T7siGNwL3Ah8q9frhwOp5312CojW6/We\ngBHnENG/XE6hHf0EY+rUqSxdupRVq9Yzdux4Rg8ewn++Xs3iFd+TMGgwt4wbypeb9vHRV5t4YsJQ\n/rIhlbe/O8RjE3rz7clKlh4sIMJbzdgoL0oazGRUGCmsaeaHgyE0Cin9A9wwWeysOFpEs9VOH383\nVDYjX+7KxtdNyUMJEbz0rxVYbDZeWDCdM1l5fPCf/6F1c+W++XMpL6+nubmZv/zlHWQyGbfccjfl\n5fUd/qSnM54kdUYbOkN7t6EjjsuVlBkf34fU1FS2bUuif68+fLV6Pdt2JTNmYG+2HUxhpsyOzsOV\nxVuO8Ky3lg9vG8ri5DMcOFNObnn9JRPIyKWStl5ucKZL16oU9A32ItZPy8BQL0rKK/jo223kllaj\ndlHw5IwEhkYH8sd/fc2WA8cAmDZqCPfOmIhGraKktJYtO3fzxdcrqKuvRx8VybNPPIp/D90F7T2/\n/bt37+C99/5Mc3MzCxf+nujo3r/oeHfUv1Nn6LjfnQs9e/bk+PHjZGbmMW7ECJIPH+fEsRQiAntw\n4HgaQ4aO4NCZMgb09OJ4uXOJCD+tkoM51ajkUuJ7uOKplmN3QGWjmeTsaqx2B8FaJamFdTRb7PTy\ntJFW0MDo6B7sTs5gZJ9YNmzeDkDisOHs3JlMUVERY8dOoLbWBJiuqBXXy/WvM9rQGa6mHZd7HEqq\njOSWOLdTyKWE+Wja9rvaY3mx/R+f3Zuvt2ex83hh29xqAH2IJ/dOj0cjl7Tb97dHGVey//j+gWza\nn4vJbCOvpJ61OzOZPT72V9GGztAev7v2/P22V1miTp1fzi/RFcHgd8ANer1+b8vre/V6/a2Aq8Fg\n+FSv1/8O2IyzM+BTg8FQ3AV1/JG5c+eydOlSVq78msTEcWjUKu6ZN5sPvvgvf//yv7z4xMOknS3k\nSEYu/t5afjOhJx9uT+e9jSncMTKGUpOU5NxazlY14a1RMDREi5dGgQRwACq1gsqaZnKrm1h7shyr\n3YFWKWNSjDd7T+WRWVpHuI8bdw8L5Z1l66htbOKJm8YT5qvl/958B5vNxjMP34eXh/Op4JIln1Na\nWsK8ebcQGhrWpcdOuP4NHTqcr79exqFD+3n88YX46XzZvf8A77z2CruOpPHf9dt54aE7WPS/7fzp\nqx2M7xfFvWMHsHBiPAXVRqobTUgkEjQuMjzULmhVciQSB8ZmC01mCxarDYvNRrPZSk1jM0VVdWTm\nnGHNzhJMFisyqYRJA2K4eXRfko+f5OG3VtJgbCIiyJ/HF9xIfEQIDoeDg8eO88VX35KTX4BapeKB\n225m1pRJl0wWkpeXyxdffEpy8h6UShUvvvgaCQldPo35ujN58mROnjzJjh1bmTlzDn6+vmzbm8zC\nRx/lz8vWUV2cRw+tN8fTMxjSrzeHipqoabIwOsqb/FoTx4ou/E/Uz80FrVLGrqwq7A4Hg3VSDqVn\nExfozYm0E8hlMobFhvDu9vUM6deXyNAQ/vKXJQBMmSKSAgmX5/whor0jvFG6dGzSIYVcyu2TYpky\nLBRDfjUms43IQA9C/dwuuQzOtUKrcWHK0FBW73GuN7gq6SzTx0T9zF6CILSHTg8GDQaDA3j0B29n\nnPf5OmBdp1bqMoSEhDB6dCJJSbtIStrJmDHjmDR6JLsPHuFQShrfbdrG72+ZzLP/WsG6/alMtFj5\nw7S+vLc1nc+TDOj9PbitTygFDTb259Wy0XDp9YKCPJQMCHCjqLySxbvSsDtgXFwAfXzk/HHJGsxW\nK4/OHMuw2BCeeftdKqqruW/BTW2JbQ4e3M/Kld8QFBTC7bff1VmHSOjG4uJ64unpxd69STz22G+Z\nMWkin/73f+xJ3s/Nk0bz3w07WbZuC4vumMLfv9/H9pRsdqRkExukI8hXi4tMRpPZSnVjExW1jVTW\nNWKyXjyF+vlCfD0YGR/O3MQ+7D6QwvN/+4zSqhpc1SoemjOFG0cPRSqVcuj4Cf67chUZ2WeQSCRM\nHDOKuxfMxcfr4skesrIyeP/9lWzZsqUtocjChU8THBza3odOwDny4sMPP2TNmu+YOXMON8+Yxgf/\nWcyRI4cY3S+OpJTTTBzhz75mM4dPpDFmQC8OFptZn16Op1pOTz831AopMqmUaqOZtKI6mix2PNVy\nYlytHEzPJMRXi7yxgpr6Ru6ZlsiKtWuRSCTcNXc2lZUV7Nq1nZCQUPr0EeuxCpcxuo2iAAAgAElE\nQVTnyAVLSnTeHDcfDxUjPa6/pU8mDQlh25ECGposVNY1s2FfDiPi2zc7qyAIP9YVPYPXrHvueZDk\n5L38+9//YODAIbi5ufHMw/fym1f/yBffrsJT687bD87h5f+sZuuRU+SVVvHyjHGsTStmT2YphpJU\ngr00jIrsgYfWFZtDisUONrsDdzcXLE0mmpqbOVlQxeJdzqdjwV4abh0SzqETJ/nbTgNqFwUv3Xkj\nQZ4ann7rL5SUVzJ/2mTmTZ0EOHsy/vKXN5HLFTz33EuoVL/eRWeF64dMJmP06LGsXfsdycl7mDZh\nHCvXbWTl+g188OYiDLmFHE7PRCKR8MFvbmfH0TN8f/AUhoJyDIUXrhzjoVERrPPEy02Nu1qJSiHH\nRSFHIZOiVMjxcFXh5+mGv6crZRVV7E87zf0vbaGuwYhCLmdW4nBumZKIm1rFgaPH+d/qtWSecf6e\nRg4ZxB1zbyI8JPhHbbDZbCQn72H16pWkpZ0AICIikjvvvJfhwxOu+Sfvv2ZeXl5MmDCJDRu+Z9eu\n7dwwdgLfb9vB1j37eP7Jx8kqKGFr8hHmThzN1owKdh09Sb+IQLx0/hwtavjRWpX+7i6MCFaSeTaH\ng3k1hPlqiXC3sePgWYbER9FcU0puYRFTxo4hKjyUf/zjA2w2G3PmnFtzUhB+SlVdM2eLnfP2ZFIJ\n/aJ9u7hG1z61Us6MkeEs35YJwFdbM+gf6Y1aKW5VBaEjiV/YFQgMDOK22+5i8eLP+eij93nmmRfw\n1GpZ9NQTPPen9/jbf5ZiMpt5+6G5fLRqB9uPneaFT75hVkJ/3pjVn82nStifXca3R3J+8nskQO8g\nLybGB1BRVsxHX6+jsdlEbLAfTy+YRHFJEb978yMaGo3cNms6t89yDmsqLy/jpZeepaGhgaeffp7o\naJESWOg8M2bMZu3a7/j2269ISBjDI3ffzlt/+4gPPvmcV59+ij99uYJDJzOY89s3uWfGRF6+ZTwq\npQuVdUYsNhtqFwVajRKrxUppVQ3lNXVU19ZTb2zC2FxPU4OJqqZm0usaKamspqSiqi2LnpfWjfkT\nRzEjcRie7m7sOXCQr1Z/T05+ARKJhIQhg7n1pplEhv24Z6+xsYFNm9azevVKysqc2foGDRrC3Xff\nSXR0bxEEdpKbb76dLVs2snTpF4walchTD97LU6++ycdfLuGZJx7nz8vWsWJrEjMTh5PTAClni+Bs\nEUE+7owIC0DhosThsGM0NnMqP49tZxqRAON7hVJbmseOg9nEhQUyZZCeN97/Ozofb+69eS4FBXls\n2LCWgIBAJk6c3NWHQbhGHDp9bm1BfagnbmqxdFN7GDsgiM2H8qisM1HXaGbzoXxmjbr0Uj+CIFw9\nEQxeofnzb+XAgWR27txGbKyem26aT0RIEG8+/SQvv/ch/1z6FTkFRTx281yGxUfw7+938+2uI6zd\nl8Ko3tH837hocFFztqKR4hojDSYLCpkUjdoFT5WCUC8NMmszJ7Jy+PjrwzSZLbiqlDx04xgmDtSz\nfPV6vtu8DblMxlP338UNo5xp8EtKinnuud9RVlbK3Xffz4QJk7r4SAndTUhIKCNGjCI5eQ9Hjx4i\nYcgQEkcMY1fyAf755RJeefgB1uw6wDdbk/jnt+v514oNBPXwxdPdFYAGYxMVNXXUG5t+5pvAXaMm\nPjKUuPBgBsRFMX54H3JyS9i8aw/rtm6jrKISqUTCuIQR3DzzRkKDf5z5Mz8/jzVrVrJt22aamppQ\nKlXceOMsZs6cQ0hIqEit3cn8/PyZPn0Wq1evYMWKr7j11jt54NYFfLx0Of9avJiXH3qQt5esYc2u\n/fQMD+aBxAEcya0ko7iKTUczLihLpZAxrlcoIVoF3+9KprK2nqG9opg9oi+L3vs7UpmM5x57GFe1\nmtdfexer1cr99z+CXC7+SxQuT/LJkra/h8X7/cSWwpVQyKXMHh3JZ+tOAbDxYB7jBgah1Vx5dl9B\nEC6P+J/vCsnlcl588TWefPIRPvnkn2i1HkyYMIno8FDeffEZFn3wMRt2JpFyysAjty/gk6fvZsOB\nVNYmp7Dt2Gm2HTsNQGSAL35eWjzVSiRWCeYmK0dLqlhVWoXN7kynr/N0Z97Ywdw4vA8pJ0/xm1fe\noqS8gsAeOp599H5iwp2JYU6fTmfRopeorq7ijjvu4eabb++y4yN0b7fffjfJyXv49NOP+fvfB7Lw\nofspq6hk5779mMxm/u/hB1kwNYH/rt3F0dPZ5BWXkV/qHCaqUSnx1roTExqEv48nOi8PvDzc0Wo0\nuGpUqJUuuKpVeLhpUCuVOBwOSsrLSU0/zbOvb2TfoWNYrVaUShemTRjH3OlTCfC7cL6JzWbj4MFk\n1q1bw5EjhwDw9dVxyy13MGXKdLTaLl/WtFu78857SEraybJlSxg6dAQzbhhPYUkpa7du54N/f8or\njz3Msq372XvCQHpOAX2iQpnfPxqlxhUkMuwOB1KHjerqKvakpLC9vAq5TMrtk0YxJC6A5996H5PJ\nxHOPP0x8TBTffLOc1NQURowYRULC6K5uvnCNKCxvIK+0AXBmOB6kF/Pa2tOIXv5sPJBHYUUjJrON\ndftyuXWiGOkkCB1FBIO/gK+vjkWL/sjzzz/NX//6Ns3NzUyfPhN/nS/vvfQMX65Yw5ot23n53Q/p\nGxfLgumT+ddTd2IoKOVoZh7HM/M4W1LBmeKKC8pVKuREB+noGR7I8PhIooN07D+awvNvv0t2Xj4y\nmZS5UyZy++wZqJQtC2Zv3sCHH76HzWbjkUeeYNasuV1xSAQBgKioaKZMmc7GjetYvnwJd955L68+\nvZC3PviI5MNHefTsCzxw+3zmjh/JLZMTAbDZ7UjggrlaNpuNiqoqyiurqK6tpaK0kLr6BuobG6mu\nqaWsooL8wiIajMa2fcJDgpk0dgwTRifg7up6Qb3Ky8vYtGk9Gzeuo7LS+bvr3bsvM2fOYeTIUZfM\nJip0LldXNxYu/D0vv/wcb731Ku+//08evuMWHDj4fusOnn/rzzz14L3MGj2YZZv3cjwzh9TsvIuW\n5SKXM2Fwb+aPG8bu5GR+98piJFIpv3/0QUYNHUxKyjG++OJTvL19ePLJ33VyS4Vr2f700ra/+8f4\nolGJW6n2JJVKmJMYyd9XOFce23GsgBuGBOPrIXIgCEJHEFewXyg6OpY//vEdXnjhGT788D3y8nJ4\n4IFHUbq48NCt87hh1HA+/+Y7jqSmc+J0Bn6+PowY2J8BveK4KeFGXNVq6hqbMJot4HAQGuSDuclK\nVU0taRlZbN2xnTeOpVDX0IhEIiFx2GBunzWd4AB/AOrr6/j44w/Zvn2LM5HNMy8yZMiwLj4qggD3\n3/8IR48eZvnyJcTH92Lw4KG88ezTLPtuNd9+v54/fvAv3DRLiI+NISjAH41KhcVqpa6+nvLKKorL\nyiirqMRmu3Q2UZlMhn8PHQP69KZnbAwTE4fiqr6wV89isZCcvIctWzZy9Ohh7HY7Go0rM2bMZurU\nG4mIEGnLf42GDBnGzTffxldfLWPRohd5/fU/8eidtxHk78dny7/h1Xc/YEi/PiyYOpknF0zlRFYu\neaUV1Dc2IZFI8PZwIzrIn/iwQA6npPDaX9+nqLQUP50Pv3/kQXrGRHP2bDaLFr2EVCrl+edfxtPz\n4lllBeGH7A4H+88bIjqilxgi2hH6R/sSF+bF6dxqrDYHq5POcv+NPbu6WoJwXRLB4FWIjo7l/ff/\nwauv/oE1a77j5Mk0Fi58mujoWCJCgnn9d78h42wu67bvYs/ho6zavI1Vm7cBoPP2wsfLE1XLcDeT\n2URRaQV1DQ1t5Xt7enDT5AlMHzeGwJbhbg6Hgx07tvLppx9TXV1FbGwczz33EgEBgV1yDAThh9zc\n3PjDH17l6aef5M03X+GNN/5Mr159uHPeHKaOH8eOfUms27KLQ8dTOHQ85Uf7e2q1xEZG4N9Dh87H\nBy8PDzy07ri7ueLu5oaXhxYvD48L5ne1zu9zOBxkZ2eyffsWtm/fSm1tDQB6fTxTp05nzJjxqNXi\n6fKv3V133U9RUSFJSbt49dU/8OKLi5g1aSL9esbzjy//y6GUVA6lpBLo14MBvXoSFhxEtE6H3e6g\nsrqaXbt38F7qSRqMRmQyGdPHj+V3j9yJqdlBZqaBF198BqOxkWeeeYHevft2dXOFa0hmfg2VdSYA\nXFVy+kT6dHGNrk8SiYS7p/fk+X84l6Tel1bC5GGhBOvcurhmgnD9EcHgVQoICORvf/snH3/8IZs2\nrefJJx9h8uRp3HnnvXh7+xAbEUbs/XfxxF23kpqRRXpmNumZ2RQUl5KZk4vN5pwfqHRR4OvtRc+Y\nKOKiIugXrycmPLRt6JzD4eDYsSN88cWnZGYacHFx4Z57HmDevFvEEDfhV0evj+P551/mzTdf4cUX\nn+Hpp58nIWEMvt5ePHbv7cy/cSY1tXWUVVTQ1GxCoZCjdXPDx9sLtUp1Rd9ltVo5fvw4mzZtIylp\nFyUlRQBotVrmzJnP5MnTCQ0N64hmCh1EKpXy+9+/gM1mZ9++JH73u8d58cVFhIeG8ac//B5D9hnW\nbt3BvsNHWbd950XL0Pl4M2XcGKZPGIefrw9adze+3vAdf/vbO5jNZhYu/D3jxk3s3IYJ17zdKUVt\nfw+N90MuE0uRdJTeUb70ifQh9UwlDuC73Wf4zVzx8EYQ2psIBtuBSqVm4cLfk5g4no8//jsbN65j\n+/YtjB9/A1Om3EhsrB6FQsHAXvEM7BXftp/D4WgLBgMCPC+aubCxsZHdu3ewYcNaMjOdGfPGjBnL\nvfc+hL//9bforHD9GDlyFC+88Bp//vObvPHGK0yYMIk77rgHnc4dAE8PLZ4e2p8tx2q1Ul1dRVVV\nFbW11dTU1FBZWUFpaQl5eblkZ2diNpsBUKvVjBkzjsTE8QwZMgyFQqR7v1YpFAr+8IdX+Pzzf7Ny\n5dc8+eQj3HHH3cyaNZe46CjioqOwWK2czcunsKSMRqMRiVSCl1ZLWHAQgX492pYFKSsr5Z133mDb\ntm2o1Rpefvl1hg9P6OIWCtea2gYTB0+dW1JidD/xf3BHm5sYSeqZSgCOZVZwKqeK+HDvLq6VIFxf\nRDDYjgYMGMQ//vEZmzdv4JtvlrNx4zo2blxHaGgYCQlj6N9/IDEx+rZhahKJBLn8wl49h8NBcXER\nJ0+mcujQfg4e3I/JZEIqlZKQMJpbbrmD6OjYrmieIFyxkSNH8d57H/HOO39k+/YtbNu2mUmTJtGv\n32CioqLx9vZBJpPT3NxMTU0VpaUlFBYWUlCQR2FhAcXFRVRVVeJwOC5avlQqJTw8gkGDBtKzZz8G\nDBiMUqns5FYKHUUmk/Hgg48SF9eTv//9XT777F98//1q5s27mbFjJ+Lm5kZsZASxkRdfhywvL5f1\n69ewYcP3mM1mevbsxVNPPUtwcEgnt0S4Huw6XoTN7rwWRQVpCff/+YdZwtUJ9XNneC8/9p90Ju1Z\nuiWD1+4bKnpkBaEdiWCwnclkMqZOvZFJk6Zy9OhhNm1az8GDySxfvoTly5cgkUjQ6Xqg0/XA1dUV\nV1c37HY7FkszxcUlFBcX0dzc3FZeYGAQEydOZuLEKeh0ui5smSD8MhERkXzwwcfs2bOLZcsWs3nz\nZjZv3vyz+7X+Vnr16oOvrw4vLy88Pb3x8vLCy8ubHj38CAgIRKFQiDUBr3OjRyfSv/9Ali1bzLp1\nq/noo7/x8ccfEhERSb9+AwkODmm5ljqXlTh79gypqSkUFzuH9Ol0PXj88ccYPFhkjhV+GavNzo5j\nhW2vJwwK7sLadC/zx0ZzPLOCZrON4kojmw7mMX1EeFdXSxCuG50eDOr1ehWwFOgB1AF3GwyGyh9s\n8z6QALTe3c0yGAzX1J2eTCZjyJBhDBkyDKPRSErKUdLSTpCdnUVhYQGnTp0EwN6ypiCAWq0hICCQ\n0NBw9Pp4Bg4cRGhoeNtQJ0G4VslkMhITxzNmzDiqqorYuXMvBQV51NbWYLVaUalUeHp6odP1IDAw\nmODgEAICAnFxEQsNC07u7u48/PDjzJt3M9u2bWbfviRycnLIysq86PYajSsjRowiMXEcCQljCAjw\nEg8MhF/ssKGM2kbncHQPNxcGi7UFO42Xu5LZoyP53zbnb33tvhyGxvuh8xTJwAShPXRFz+CjwAmD\nwbBIr9ffDLwELPzBNoOAyQaDoarTa9cBNBoNI0aMYsSIUW3v2Ww2GhsbMJnMSKUSQkP9aGy8dCp9\nQbgeSCQS4uLi8PEJ6uqqCNcoHx9fFiy4jQULbsNoNJKdnUlJSTFGYyNSqRQPD09CQsIIDQ0TvYBC\nu7A7HKxPPree5bj+QWKYYiebMCiIPSeKKShvwGyx8+n36Tx720CkUvGwXBCuVlcEg6OAP7X8vQFn\nMNhGr9dLgBjg33q93h/4zGAw/Kdzq9jxZDIZWu25ddE0Gg2NjeKptSAIwuXSaDT06dOPPn36dXVV\nhOvYUUM5BeXOZZ9cFFLGDhAPszqbTCrl3mlxvLn4CHaHg8yCWjYezGPacJEpWhCuluRSiRnag16v\nvw94Cmj9EglQAjxhMBgMLYFfrsFgCD1vHzfgSeBdnMHqDuBeg8GQ9hNf1XGNELqbznjMKM5XoT2J\nc1a4llxT56vFaueJv2ynqKIRgLnjornnxl7tVbxwhZZvOs2yzQYA5DIJf/7NaGJCvDryK6+p81Xo\n9n7R+dqhPYMGg+Fz4PPz39Pr9SsA95aX7kDND3YzAh8YDIbmlu23A/2AnwoGO3wuSEcnqOiMBBii\nDZdXfmdo7zZ0xHHpqGN9rdT1WiqzM4hrR9eW3xnf0Vlt6AxX047zj8PGA3ltgaBaKWdMH//LKvtq\nj2VX7/9rqMPF9h/bL4Dk1GLOFtdhtTl4/bMDvHT3YDzdLp5Fuj3q0Bna43fXnr/f9ipL1Knzy/kl\numLQ+15gWsvf04CkH3weC+zV6/USvV6vwDms9Ggn1k8QBEEQhG6utMrIqqQzba9njYrAXSOSWnUl\nuUzKQzN6olY6+zKq6038fcUJzBaRc0EQfqmuCAb/CfTW6/VJwAPAawB6vf4pvV5/o8FgOA0sBg7g\nHCL6pcFgONUF9RQEQRAEoRuyWO188n06Zqsz43ewzpXxA8VcwV8DP28Nj87uhbQl0/rZ4no+/C5V\nBISC8At1egIZg8HQBCy4yPvvnff3X4G/dma9BEEQBEEQHA4H/91i4ExRHQAyqYT7p/cUGUR/RXpH\n+HDLhGiWbXUuN5F2por3v0nhyXl9UbmIJbQF4UqIK5sgCIIgCALOQPA/36ezO6W47b25iVGE+XfO\n3DHh8k0YFMzMhPC216fzanhz8REKWzK/CoJweUQwKAiCIAhCt9dksvLJ2nS+25nV9t6IXn5MHhrS\nhbUSLkUikTB7dCTzxka1vVdY0ciiLw+z8UAelpYhvoIg/DTRly4IgiAIQrdltzs4dLqMb3dmU1nX\n3PZ+/2hf7p0Wj0QiFjb/NZs2PAx3jYL/bs7AbLVjsdr5ekcW244UMH9iLL1CPXBVKbq6moLwqyWC\nQUEQBEEQug2Hw0Fto5m80npSs6s4mllOdb3pgm0S+wdy+w2xYp7gNWJ030AiAz34eHUaheXOpUAq\n65r5eOUJ5DIpvcK9iAvzIirQA38fDW5qERwKQisRDAqCIAiCcF0rKG/gu91nKKkyUttgxmiyXnQ7\nN7WCh2/qQ69Qz06uoXC1gnxdeeWeIew8VsjafTnUGy0AWG12UrIrScmubNtWrZTjrlbgqlagUcmR\nSECCBKVCytgBQfQM9+6qZghCpxPBoCAIgiAI17UVO7MvCAZ+yE2tYOyAICYPDSE8xLvdFqUWOpdc\nJmXi4BAS+gSwL62EA6dKySqo/dF2TSYrTSYr1DT96DNDfg3vPTEKqVQMDxa6B4nD4ejqOgiCIAiC\nIAiCIAidTAyGFwRBEARBEARB6IZEMCgIgiAIgiAIgtANiWBQEARBEARBEAShGxLBoCAIgiAIgiAI\nQjckgkFBEARBEARBEIRuSASDgiAIgiAIgiAI3ZAIBgVBEARBEARBELohEQwKgiAIgiAIgiB0QyIY\nFARBEARBEARB6IZEMCgIgiAIgiAIgtANiWBQEARBEARBEAShGxLBoCAIgiAIgiAIQjckgkFBEARB\nEARBEIRuSASDgiAIgiAIgiAI3ZAIBgVBEARBEARBELohEQwKgiAIgiAIgiB0QyIYFARBEARBEARB\n6IZEMCgIgiAIgiAIgtANiWBQEARBEARBEAShG5J3dQXag9Vqc1RXGzv0O7y8NHTkd3R0+Z3xHddD\nG3Q6d0mHFd6iI87XjjguHXWsr5W6XitlXqvn7Pmuh2uHaMPluRbO1/Y4DldbRlfv/2uow6+hDdfC\n+dqqPX+/7VWWqFPnlvNLz9fromdQLpdd898h2vDr+Y6O1hFtuFbK7Khyu3OZnUFcO7q+/M74jmv1\n/Pyhq21HexyHrq6DaEP7ldHR2quO7dlWUafOLaurz9PrIhgUBEEQBEEQBEEQrowIBgVBEARBEARB\nELohEQwKgiAIgiAIgiB0QyIYFARBEARBEARB6IZEMCgIgiAIgiAIgtANiWBQEARBEARBEAShGxLB\noCAIgiAIgiAIQjd0XSw6LwjCr4fJZCI/P4+8vByKi4uoqCinvr4ei8WMVCrFy8sDV1cPQkPDiI6O\nJTQ0DJns178WlCAIgiAIwvVGBIOCIFyV+vo6jhw5RErKMU6fTicvLxe73X7Z+6vVGgYMGERCwmhG\njhyNSqXqwNoKgiAIHcVut1NXV0tjYyO1tQpqaoy4uChxc3PHzc0NiUTS1VUUBOEHRDAoCMIVq6+v\nZ8+eXSQl7SIl5Whb8KdUqoiP70V4eAShoeEEBQWj0/VAq9Xi4qLEbrejUsHp02fIyTlDRoaBtLQT\n7NuXxL59Sbi6ujJ58jRmzpyDn59/F7dSEDqOw+EgPz+PU6fSyMnJoby8lLq6Omw2G0qlEq3WAz8/\nf8LCwunVq4/4PQi/OvX1dZw8mcrp06c4e/YM+fm5lJWVYrPZLrq9QqHA3z+A0NAwIiKiiImJpVev\nPri6unVyzQVBOJ8IBruxqqoqsrIM5OU5L+C1tTU0N5sAUKmUuLtr0el6EBQUTEREFAEBgUilYppp\nd3b27BlWrPiK3bt3YLFYAIiNjWPEiAQGDBhMdHTMzw751Onckcvd6N27b9t7BQV5bNu2hU2b1rFy\n5TesXr2SiRMnc9dd9+Pt7d2hbRKEzlRdXcX69WvZunUzJSVFP/pcKpVetGc9KCiEadOmMHr0RHS6\nHp1RVUH4kcbGBnbt2s6OHdtIT0+74Fz19PQiNjYOb28f3Nzc0GpdMRpNmM1m6uvrqKysoKiokPz8\nPPbuTQKc53vPnr0ZM2YsY8dOwN1d21VNE4RuSwSD3YjFYmH//n3s37+XlJRjlJQUX9H+arWGuLie\n9O3bj8GDhxIVFSOGfHQTWVkZLFnyBQcPJgPOG9NJk6aSmDjukj0WNrud8opKyioqqK2vx2y2IJVK\nCQzwQanQEBTgj0LuvAQFB4dy9933c+utd5KUtJOvvlrGpk3r2b17B3fccQ+zZs0V8wqFa1pdXS3L\nli1m/fq1WCwWVCoVY8aMpV+/AURGRuPvH4C7uxaZTIbZbKa2tobi4iKys7NITU3h6NFDfPLJJ3z2\n2WeMHDmaW265g6io6K5ultBNFBYW8O23/2PHjm2YTM1IJBLi43sycOAQ4uN7ERkZjaen5wX76HTu\nlJfXX/Cew+GgqqqS7OwsTp9O59ixI5w8mUpa2gk++eSfjB49ljlzFohzWxA6UacHg3q9Xg58DoQD\nLsCbBoNh7XmfLwQeAMpa3nrYYDBkdnY9ryelpSWsW7eGrVs3Ul1dDYCbmztDh44gNlZPeHgkAQEB\neHp6oVQ652uZTCZqa2soKyslPz+X7OwssrIyOHbsMMeOHebLLz9Dp+tBQsIYEhLG0LNnr65sotBB\n6uvr+Pzzf7Np03ocDgc9e/Zi/vzbGDp0+I96iRuNRk6kn+akIYNTmVmcyc3DZDZfsmyFQk5sZCRD\n+vdl1NAhBPr74eLiwoQJkxg7dgIbN65j8eLP+OSTf7J7906effZFAgICO7rJgtDukpJ28eGH71JX\nV4e/fwBz5sxnwoTJaDSai27v4uKCTtcDna4Hffv256ab5tHc3MSRI/tYvvwr9uzZxd69u7nhhinc\ne+9DP7oJF4T2UlVVxZIln7N58wbsdjv+/gFMnjyNCRMmo9Pprrg8iUSCj48vPj6+DB06nLvuuo+q\nqkq2b9/Cpk3r2b59C9u3b2H06ET+7/+eQqn06IBWCYJwvq7oGbwDqDAYDHfp9Xov4Diw9rzPBwF3\nGgyGY11Qt+tKSUkxS5f+hx07tmG32/Hw8GDWrLkkJo4jNjbuJ3taXF1d8fb2JiIikmHDRrS9X1NT\nQ0rKUQ4cSObgwWRWrfqWVau+xcfHl6lTpzB8eCKRkVGix/A6sHv3bl57bRG1tTWEh0fw0EOPM2DA\noAu2aW42sfvAQXbu28+J9FNtQ4akUinhwUGEhQTjp/PFU6tFqXTBZrNjd1jJPptPVk4u6RmZnDRk\n8MVX39InXs/c6VMZ0r8fMpmM6dNnMmpUIh9//AE7d27niSce4rnnXmLIkGFdcTgE4YpZrVY++eQf\nrFnzHS4uLjzwwKPMnHkTCoXiotvb7HbsNhtyufxH11CVSs3s2bMZOXI8R48e4rPP/sXmzRvYv38v\nv/3t7xk5clRnNEnoJhwOB6tWreK9996jsbGRkJAw7rzzXhISRl90uojdbqe0vIL8oiLKK6uob2hA\nLpdgbDKjUirx0Lqj8/EhNDAQH2+vC85vb28f5s27hblzb+bIkUMsXfofkpJ2sX//Pm677S7mz79V\njAwRhA7UFcHg18A3LX9LAcsPPh8EPK/X6wOAdQaD4e3OrNz1wGQysXz5Ekk9+owAACAASURBVFau\n/BqLxUJ4eARz597MnDkzqKu7dE/N5fD09CQxcTyJieMxm82kpBxjz55d7NuXxNKlS1m6dCmhoWGM\nGTOO0aPHEhoa1k6tEjqLw+Fg8eLP+d//luLi4sL99z/M7NnzkMvPXS6KSkpZtXEz25L20tTcDEBM\nZARD+vWlb884YiMjUSpdKK+upbiimur6BkxmMy5SGUH+XvTt1Y+gHj40GI0cOpbC9j37OH4yndRT\nBvRRkTx0523Ex0Tj4eHBs8++xKBBQ/ngg7/y6qt/4PHHFzJt2oyuOjyCcFlMJhNvvfUqBw/uJyws\nnBdffI3g4NC2z212Oynppzickooh+yyFJaXUNTQAzocpXh5aggP8iYuKZHC/PsRHRwHOnpVBg4bS\nv/8g1qxZyRdffMrrr7/EnDnzue++h8VNs3DV6uvreO+9P5OcvBe1WsNjjz3JtGkzf3RuFZWUsu/w\nEY6mnsSQ9f/snXdgXMW1xn/bm3alVe/V0sqWLBfJcpeNu2MMpoYaehIgQCghEIoNJPTQCSXhBV7I\nAwIEY2Pjbsu9SbYlS/LKkqxeVmW1q+31/XFl2QbT3dH3j625956ZOXvu3PnmnDlTO/At+C7oQkIw\nDMlgZO4wJhSMJqbfwygSiSgoKCQ/fwzFxet55503eO+9d9i9eycPPvgoERGRJ7yvgxjEIEAUDAZP\nS8UGg0ELfA68ZTQaPzqq/BHgdcAKLAb+ZjQal3+HuOBX49JPNI4X+34myjcaq3j22b/Q2tpCVFQ0\n119/M1OnTkcsFn+tDn8gQH1zC3WNzbR2mOjpteA8nEBGIUcfGkpcdCQpCfGkJyeikMu/sV6Px0N1\ndRmLFy9h164dePrDA1NSUpk4sYjCwnEMGZL1kycqp+B3OBUuzRNurydKL36/n5deeo41a1aSmJjI\ngw8uJD39yN6NbrOZf33yGWs2biYQCBCh1zN7ahEzp0wiOjKShjYTJVU17Kuu40B9M3bnN08OVAo5\nwzPTmDwyhwkjh9LW3sG//7uYrbtKAJg7bSo3X30Fqv6jJozGKhYu/BMWSy833vhrLrvsyhPe/6Nx\nFsk8K232aJzs9/pU1HG0fK/Xy6JFf6K0dDf5+WP4058WDYSEuj0evlxXzH9XrKKrRwjbl0gkxEVH\noQ/VIZVKcbnddPWY6ezuGZAfGxXJFQvmMnnM2IF3AqCh4RB//vNCmpubKCwcx4MPLvzRx7Ocot/h\njLfXE6GHnyrjdD3f1NTIwoUP0tbWSkFBAXfeef8x4aB+v59tu0v5fOVqKozVA+WJcbEMSUslJTGR\n6MgIdNoQYqLDMJvtuNxuei1WOjq7aGhuobahgXZT58Czw4camDdjOhPH5B8zR5DLAyxa9DibNhUT\nFqZn0aK/YDAMPSV6OOr5M95eD+NEvr8nStZgm065nB9lr6eFDBoMhiTgv8BrRqPxva9c0xmNRmv/\n/28Fwo1G41++Q+TpYbRnGD766CNeeOEFAoEAV155JbfeeisqleqYe/z+AFtL9rGyeBtbd++lz+74\nXrKlUgl52ZlMLhzN9EmFxEV/8wqd3W5n48aNrFmzhm3btg0Qw9DQUAoLCxk7dizjxo0jNvaMTJV+\nSgb+U1DHD0YgEOCJJ55g6dKl5OTk8NJLL6HX6wFhAvDR4uW88e4HuNxu0lISufmqy5hWNB6zxcaS\nDTtZvmk3Te1HPvBJsZEYUhNJjosiUq9D1R8mau6z0djaSdnBehpaha3BWo2Ky2ZN4przp1J7qIGn\nXn6L2vpGkuJjeebRP5CZngpAQ0MDt912Gx0dHdxzzz1cddVVp1xPZyB+tjZ7JiIYDLJo0SKWLVvG\n5MmTeeaZZ5D3L6RtK9nL06/+g9aOTlRKBXOnTWbG5PHkDcs67mJbn83O3ooDrN28gzWbtuF2ewjV\nabntuitYMGfaQLiezWbjgQceYPv27RQUFPDSSy+dyed1DtrrGYoDBw5w++23Y7FYuOGGG/jtb387\nQM6CwSAbt+3itXfep76pBYDCUXnMmT6ZiWPyCdeHDtzXbenDbLHh8ngQiUSolQoiw3RoNaqB8FBT\nVzdbdpayasMWdu8tByA+NprbbriKWVMnDdwXDAb58MMPefHFF1EoFLzwwguMGTPmVKpl0F4HcTbh\n7CCDBoMhBlgP3G40Gtd/5ZoO2A9kA06EkNJ3jEbjiu8Q+7P2DAYCAd5++298/vmnhIXpuf/+h762\nt8vt8bC1tIT//XQZHV3dAERHhDNyWDZZaakkxsUQqQ9DrRImEE6Xm+7eXlo7OqlrbKayppbahiYO\n20tedhaziyYyqWDUwP6X4/XB4XBQUrKL3bt3UFq6m66uI2QhMTGJ/PxCxo4dT17eyO/lNRz0DB4f\nJ0Iv7777Dz766N9kZhp46qm/kpoaS2dnH13dPTzz+ptUGKvRabVcf/klzJxaREe3mQ9XbqS4pByf\n349CLmPMsCwKc7II1etp7LJS09ZNS5eF7j4nbq8XiVhMmFZFbJiWoYnRJEeEUFNfz4qtJfT22dFp\n1Ny0YDZFo4bxr08+49NlX6JUKHjwztsYM3IEAK2tLfzhD3fR09PNffc9yPTps84mL96gZ/A4OJc8\ng0uXfsbf/vYKBkM2Tz/9IkqlkkAgwP9+upj/LF2ORCLhwlnTuez8uYRqtfRYbZTVNFDXaqLHasPr\n86OSy4jWh5KeEMPwjCQ0KiV9Nhtrt27mX58sxelyMzw7i3t/fRPRkRGA4I18+ukn2Lp1E4WF43j0\n0T//4EiMQc+ggJ+jZ7C2toY//vH3OBwO7rrrXmbPnjcgo6e3l9feeY/tpXsQi8XMLJrEJfPmkhgf\nh8frZa+xjtIDtRgbmmloM+H2fHX3jwCNSklqfAzZqYmMys5g+JBUpBIJzW3tfL5iFSs3bMTn8zF8\nqIG7br6REcOHDPRh69bNPPXU40gkEp566nmGDv1+SesGPYOnV9Zgm065nLOGDL4EXA4cQGCwQeDv\ngMZoNP7DYDBcDdwFuIC1RqPxse8h9mdLBgOBAK+//jLLly8hJSWVxx576mup/reW7OXtDz7G1N2D\nQi5j2vixzC6aSGZayteSFAQCQUQijpsAptdqZVvpPtZv38X+/gSvep2O+TOmMm9aEen95OGbEAwG\naW5uorR0F6Wluykr24urf4+BXh/OrFlzmD//om/dFzBIBo+Pn6qXLVs28ec/P0p8fAIvvPA6oaGh\nREVpKd5SyuMvvIK1r49JhQXcfsOvkMrk/GvZOpZt3kUgECAxJpIFU8eRFJ/ApqoGtlY1YLY5B2Qr\npBLCdWrUchm+QACrw33M9fTYcC4oHEqnqY2PV2/C7fFSmJPF3ddcRHllJc//7W18fj9/vP23TBor\nrAg3NNRz33134HK5ePLJ55k2bdLZQtwGyeBxcK6QwZKS/dxxx69RKlW8/vrfiYyMwu/388Lf/4f1\nW3cQHxPNg7f/lvSUJEoO1PHfDTspq23g2z7DErGYwpwhzJ+Uz/RxORgPNvO3//0320r2EKrV8vCd\nt5FjyASODU+9+OLLuOWW235wHwbJ4M+PDHZ0tHP33bfT22vmvvseZNq0mQMy1hbv5JnX3sBssZI3\nNJvbb/wVSfHxtHX1sHjDNtbvKhvYDiCVSEiKjSI+MpwwrYZwfQh2uxun20N3r5XWzh5aO7sJ9Bu8\nTqNmeuEIzi8aS2yEnjaTib//6wO2l+5BoZDz6L2/Y1TOkfNot2/fyhNPPEJISAgvv/wmsbFxJ1QP\n3/D8GW+vh3Euk5wTKetcbtNZQwZPEn62ZPC9997hww/fJz19CE8++TyhoUfSMPfZ7Lz+rw/YuLME\nqUTCFRfO5hdTphCmEw517emzs6PqEPsPtVDf3oXJ3IfDLYR0qhQyIkNDSImJIDs5jtGZySRHhw+Q\nxNYOE8vXb2LFxs04nC5USgWXnz+LOUVFhGpDvlfbvV4v+/eXsXnzRjZuXIfNZkMqlXHhhRdz5ZXX\noNF8Xc4gGTw+fopeOjs7ufXWG/D5/Lz00t9ITU0DoLahlnsXPo3P7+e3v7qaeTOmsa/6EC+8/xnd\nFivxURFcO28aCrWWj7eUc6BZ8Prq1AoKMpOIjgjHi4wuu4dOmxunx4dUIiYqVEWEUopaEuBQSxu7\nq5sIBINkxEVw1eThLFm3kb3GOmIi9Dxx6zWYe7pZ+NyLeLxeHrvvbkbn5QKwd28pDz98PxqNhvff\nfx+ZTHtilNmPQTJ4DAbJ4HcgMjKEG264if37y3j44ceZOHEywWCQl995j1UbNzN0SAaL7rkDm9vH\nax+vYO/BegBy0hIZl5tJVnI8UWFaZFIpTreHtu5ejA0tbCuv5lCb8G7lD03j5vnTSYqO4Iu163nr\n/Q+RSiQ8ctft5Pe/F3a7jbvvvp2mpkYefvgxJk4s+t59GCSDAn5OZNDtdnPPPb+jrq6G3/zmdhYs\nuHTg2q59pTz+19cBuPHKy1kwZxZWm4P3l69jxbZSYd94qJYp+cMZm2tgSFI8XX1Omrt66bE5kcol\n2O1uNAo5EToNSVGhhCikHDjUzI4KI5tKK7DY7IjFYmYUjuS6+dMJDdFQvG0Hr77zLk6Xi+suv5Rf\nXnj+QJuWL1/Kq6++QGZmFs8//+pACPZP1cO3PH/G2+thnMsk50TKOpfbNEgGz/KJxI+Rv27dap57\n7kni4uJ58cW/HUME65tbePyVN2jv7GbokHTuvvFaRuVlYjJZ2VvbxOLNeymprh9YkVbKZcSG69Ao\nFQA4XB46zNYBcgiQFK1nxuhhzCwYRqhG2IvocDpZvmETn61ci9liRaVUcMGM87h49gy0IZrv3Re3\n283atav46KN/YzJ1EBUVzf33P0Rubt4x9w2SwePjx+olGAzy6KMPsHv3Tu68817mzhU+uhXGgzz8\n9HMEg0Ee+v3vKBiRxwcrivn3l+uRiMVcMXsKhXk5/H3VLioaOwAoyExieEYyzX0+ttV1YXf7BuoR\ni0SoZBJ8gQBuX2CgPFQlo2hIFObuDjbtr0MsEnH11FG4rZ18tGoTOo2aJ393HfY+Cw89/RxSiZQX\nH3uE5MQEAJYvX8Krr75IVlYWzzzz8gndJzVIBo/BIBn8Duzbt4MHHniAceMmsHChsM394y++5J//\n+ZTMtFSeeuA+KutbePb9JdhdbvKz07nuF1MI1enYWdOKsaWbDosDr9+PWiEjXh/C0IRICjJiaWrv\n5MPVW9h9oA6pRMxN86cxf1I+JeUVPPHyawSD8Jf772Z4tgGAxsYG7rjj1ygUSt5885+Eh4d/rz4M\nkkEBPycy+OqrL7B8+VLmzJnHXXfdN1C+Yn0xr/zjn4So1Txyz50MH5rNtrIqXvlgCVa7g8ToSK6c\nM4XC4dnsOtjMlsp69h1qw+769ozl+hAVI9PjmTQslbyUWLaVV/Hx6k00tneiUSm59dJ5nDcmj8aW\nVhY9/wLtpi6uuuhCrrn0ogEZL7zwDKtXr+CXv7yK66+/5YTo4VueP+Pt9TDOZZJzImWdy20aJINn\n+UTih8pvbm7kjjt+g1gs4eWX3yAxMWngWlVNHQtffA2bw8kV8+dy9YLzkYjFWD0unnpvOWV1zQBk\nJ8UyZUQWozKTSYzSfy00NBgMYurto7yuhR1Vdew21uPx+VHIpMwtzOWyqQWEhRzJkLdp9y7++Z8l\nmC1W1ColC2ZOY8Hs6YR8w8HKx4PH4+Gjj/7Nhx++j1gs5p57/sh558340Xr6oTibBv6j8WP1sm3b\nZh5//BFGjSrgL395FpFIRFd3D3c8vBCbzc6j997F6OG5vPzB56zduY+Y8DD+eP1l7G3s5v+K9+AP\nBCnMSqJg2BDWGruo7rACEBmiYHhSBBqVEpcfel1+XL4AUrGIcK0CRdCP0+li1yETdrePMLWc2dmR\nrN6xj+4+B1Ny0xkareKNj5cRpg3hud/fyMGaGp557Q1SEhN48fFHUSoUBINBXnnlr6xYsYyiovN4\n4IFHTtgZl4Nk8BgMksFvgd/v5447bqGhoYG3336PhIREqusOce8TTxOm0/LK449QUd/Gs+8vQSoR\nc9sls8lOT+FfGyvYVt1M4KjPsFQswndUgUwiZlpuCr+cMJQuSw9/fuczLDYHs8eO4PZLZ7OvsopF\nf30FlVLJi4v+RHxMDABLlnzGG2+8wnnnzeD++x/6Xv0YJIMCfi5ksLR0Nw899AfS0tJ56aU3Brxs\nW3eX8ORLr6HTaXnywftJTojn319u4MOVxchlUn41bzqzJuSzbJeRz7dXYHEIYaIxYSEMS44hJVpP\nhFZNbJQOi8WB3eXBZLFTbzJT2dgxsE0gPETFRRNymT0qk7U79/Le0jU43R5mT8jntsvmIRJ7+fW9\nj9LWYeKOm65n7rSpADidTm699Ua6ujp5/fV/kJKSejL1eMbb62GcyyTnRMo6l9v0Y+1VsmjRop9c\n+RmARQ7HTzs/77ug0Sg4mXX8EPmBQIDHHnuY9vY27r33AfLyRg5cq21o4sHnXsLl9nDfLdezYNY0\nRMDiLXtZ9D9LaOu2UJCVwv1XzOHK6WNJj4/mUI+D9QfaWFHezIr9zaw/0Mau+i4OdfYhlcrIH5LA\n9NHZzBufR1iImkNtXZQebOTLneVIxBKyEqORy2SMHZ3D1LHj0IZoOFB7iN3lFSxfvwmP10taYsK3\nHk1xGBKJhBEjRpGbm8eWLRspLl5HUlIyKSlpP1hPPwYajeL77FH9qTjh9vpj9OL3+3nssYdxOBws\nXPgXwsLChIyiL71KY3ML9956IxMKCnjp/z5n3a59GFISWfibq3l3wz6+LDESoVVzyy8m0OiQsqSs\nlR67m8K0SMZlxeMWK6nq8tDQ66HF6qHH4cXtC9Dj9NFsdtFo8dBmD5AWo2dEYig1HRbKWqyMz0lH\nEfRSUtOMRK5kbsEwtu6rZK+xjpsumYfL6WLnnn04XW4KRuT1n7k2hqqqcnbs2I5EImH48BGnTaen\nSeZZabNH42S/1ye7jm3bNrNkyWfMnDmHWbPm4vf7WfjCy5h7LTxy1+04vEGe+OenyGVSHrv5Mjpd\nIv7y3600dFlJjwnjlxOGctO0EVw3NY+Lxw3lsrEGJhgSiNKp6ei1s7fBxIp9dQxNjef6meMor21k\nV1UtJrOFi6ZNJFKvZ+OOXVQerGXm5ImIxWKGDMli9+6dlJTsJD+/kKOPBzgdOjqqjjPeXk+EHn6q\njJP9vNfr5dFHH8DhsPP4408TFRUNQHNbO48881ckEgmvP7OQ+Og43vh4GZ+t30ZshJ4nf3c9Gl0Y\nC99fzdYDDUglYi4oHMpt8yYwIjMNe1BGdbebkuY+ttT1UNJkpcHixSdWMCQhhksn5jJn1BDkUgnV\nLZ3srG5mU2U9M/JzuGzaOCrrGtlVUU1tcxsXzZjIcMMwNmzbwZaduxmdl0tkeDgymYzY2DjWr19L\ne3vbwB7Hk6THM95eD+NEvr8nStZgm065nB9lr4Nk8HviTCKDK1cu54svPqeoaCrXXHPDQHmXuZcH\nnnkRm93BH397E1PHjcHn9/Pix6v576ZS9Fo1f/jlbK6ZOZ6gSMq/ttfw/MpyVlW0sL/FTEO3jXaL\nk3aLk8ZuGxWtvRRXt7N4TwN1nVbiwzQU5aRx/rg89Fo1FQ2tbK+sY3tlHdnJcSTG6vG4/Qwdks75\n06agVio5UCeQwmXri7HZ7aQmJhxzRtY3ITY2jvz8QtavX8OWLRvJzy8kIiJykAx+A36MXtauXcWq\nVV8yd+58Zs2aA8DK9cUsWbWGcaNHcd/tN/KPT1exZOMODCmJPHjTFTz16UbKG9oZlRHPhZMLeHtz\nPY09dkYk6jkvN4WyLi9VnU5sHj95cSGMStCSHqEmSisnTCUjLlRBboKOzEg1WrkEY6eD5j4f+alR\niAI+9jaZSYmLIUolprSmhcjwcHISI9lVWU1Xr5XfXnERm3fuYve+csaMyCMiXI9EImHmzPNYtWo1\nW7duJiYmloyMId/R+5Oj09Mk86y02aNxtpPB119/iY6Odu6//2FCQ8P4cn0xqzduYVbRJKZMnMCD\nb36Ay+Nl0c2Xsam2iw+3VqFVKbhnXiFTR2RS0uHhw30m3tvdyidlHSyu6ORAl5sYfSg3Tc1hSEwo\nZQ0mivc30m1zc/9l0ymva2R3VR2BYJCLZ0ymo7OL3WX7hQWRbANisZiEhETWrFlJZ6eJ6dNnnVYd\nHVXHGW+vPwcy+MUXn7Nhw1rmz1/A7Nm/AMDn8/Hos3/F1N3NvbfewpQJY3jtgy9YUryDtIRYnrnz\nBoorm3hp8SacHi8XT8jljgsmU2fx8/bmGlZUtFLRaqG114nb50culeDzB+nsc1HfbWNfs5nVlW3s\nabYwPDWe2+YUIAZKa1tYV1aLSqHgzstmU9vUSklVDQ2tJmaMy8eQkc6aTVsoq6xi9pQipFIpiYnJ\n7N9fxp49JYwYMeprifNOoB7PeHs9jHOZ5JxIWedymwbJ4Fk8kfgh8j0eD0888SiBgJ/HHnsKtVrY\nlxcIBHji1Tepb27l5l9ewpwpk/D5/Tz74Qo2lh0kOymWt+6/ltiwUD7edYhnvtxHZWsvWqWM2bmJ\n/LIwnesnZnH9hEyuKExn7vBE8lMiidGp6HW42d/Sy+rKVipbzWTGhFJoSGZ2QQ59Thcl1Q2sLqlE\nIZeRHhOFSCRCKpWSkzWEedOK0IWEUNPQSOn+Kpas3YCpu4fEuBh0Id+eaCY8PJz09CGsXbuKsrJ9\nzJkzD51OPUgGj4Mfap+BQIDnn3+Svj4rDz20CI0mBJfLzRMvvgLAEw/cS317J8+++ykxEXoe++01\nPL94MweaO5k+YgiZ6em8tbEGqUTMVeMy6HBL2dHUh1QsYlpmOKnhKposbtptXkx2L31uP0FE2D1+\n2qxuTDYPTl+A/EQdUhFUmRyE6TTEh8jY19RDWnw08qCH3TXNzCzMo9dsZnfVQYalpzB+VB5rNm6m\nzWRixuRJAERF6cnOHsH69avZvLmY8PAIMjOzTqlOT6PMs9Jmj8bZTAbb2lp5++2/UVBQwEUXXYbX\n5+PJ197E7w+w8O7f8Y8l6znQ0MoN886j3hpg6e4a0qPD+NMlk1lW08c7O1uo7XYgFoEhWkOKXolG\nLqGp10V5u43lVZ2EaTXcMSOXZrOVnQfbaOmxc+8l57FtfzXb9x8kJy2RWZPGsnbzVvbsr2TaxPFo\n1GpiYmIpL9/H3r2lTJxYNHBu6KnW0VfqOOPt9Vwngx6PhyefXEQwGOThh58Y2Gu9+MuVrNm0hRlF\nk7hywQVs2VfJ6x9+QWJ0JE/97jr+s7WCjzeXERWqYeFVM3CLVTy7soJ9zWaUUgkzh8Vz1dg0rh6b\nwZSh8UwdkUJ+UgQXjEziwpGJ5CaEoZCJqTXZKGnsYb2xg6m5qfxyQjZlh9rYUd2E2e7i95fNoqqu\nkR3lRmRSKdPHFeByudm5Zx+IRIzMGQZAfHwCq1Z9ic1mY8qUaSdLj2e8vR7GuUxyTqSsc7lNg2Tw\nLJ1I/FD5K1cuY8OGdSxYcCmTJk0ZKF+2fiNfrCtm3Mg8fnP15YhEIt5cWsza0gMMT0vgiRsXoA5R\n88BHO1hd2YpOKePXU7L5/cwcCtKi0KkVNFnc1HQ7abG48QQgNVJDYVoUv8hLYkRSBF19LvY29bCq\nogWpRMyI5EjGDUvHkBRD6cFGivdW09DRzRhDKjKpcL6VTCoVPIXTpxAVrqehpZW9lQdYtq4YU3cP\nQ1KSUatU39jfhIRE+vqs7Nq1A7lcwdixYwbJ4HHwQ+1z9+6dLF78CdOmzWDmzLkALFm5mq27S7ns\ngnmMzhvOA6+8i93p5olbr+W/O4zsMDYyOSeV1JQU/ndbHZEhCq6dZGBxlZkuu5cJKaFkx4RQ1m6n\n0+4lOkROQaKWrCg1MToFYWoZ8aFKchNDSQmV4/QGqO91IZWKyYnRUNlhRyZXEKORsK/JzJTcNJra\nOiipaeF3F51H8e4yjPXN3HjRXKprD7FnfwXDhxqIiYpCo1Egk6kYOTKfLVs2snHjepqaGkhLS0en\nC/0ObZwYnZ5GmWelzR6Ns5kMLl++hL17S7nllltITEyleNsO1mzeyvwZ0wiPjOHvS9aSlRTH2NEj\neHvNXhLDtfzu/PE8ua6B6k4H2dEa7piUwq/HJZEXryU3TsvMrEh+OTKW5DAVjWYnpS19lLbYePzy\nMdQ0dVFyqB1/EK6ZVsCaXeXsq2nggqIxhIfq2LyrBIfTxbjRwvYBrVZLcfE6AAoLx58WHX2ljjPe\nXs91Mrhu3WrWrVvNhRdewoQJwoKapa+PP7/4KmqVioX3/h6rw8kDL7+LVCLhyTuuY0NFIx9vLiM5\nKozHrp7NByXNfL63GYVUwnUTM7hpcibdblh9sJf/lJlYV2Nm7YFONh/qZW1ND2tre+l1BxiVHMEt\nkzPQKWTsb+llS00ndi/cf2EhFQ0d7D7YjMvr49YF09i0p4LtZQcozM1iUsFo1mzawt6KCmZPLUKl\nVBIZGcXOndsoL9/H7Nm/GFgcP8F6POPt9TDOZZJzImWdy236sfYq/sk1D+KUIRAI8NlnnyCVSrn4\n4ssGym0OB//671LUKiW/u/4qRCIR6/ccYNn2clJjI1h43Xzc/iC3vlNMebOZ8RnRvPGricwcFk9p\nSx+Pra7l9s8O8HxxA//Y2cLfd7bw140N3Pm5kUdW1rC+1kx2bChPXJTPo/NHoVPJeXfLQZ5YsgeX\n10eBIZXX77qKfEMKWytq+cObn9BlsR3TdrlMxtypk3n7qUU8eNvNJMXHsmrTVm55cCGffLkKfyDw\n1e4O4Nprb0Sn0/Hppx/S13dykxv8XLBq1ZcAzJ9/MSDsH/x85RoUCjkL5sxi6cadtJp6uKBoLCa7\nlzV7DzIkLoIJeUN5b6tABK8cn8X7ezoIBoNcMzqOXrefvW024rRyzh8aSZROzkGzi4pOB/W9Ljrt\nHpqtbspbrRzodqJTy5iSHobHH6Sh10VRRhiddi/KEB0RIQqWlrUyf+skLQAAIABJREFUf+IIXF4f\na8obmDVuFK2d3RSX7OfqSxYA8NHnXxzTr6wsAy+88BpDh+awceMGbrnlOm666RqefvoJli1bQk9P\nz6lV9CDOeezatQOxWMzkyZMBWLVxMwDzZ07jg1VbALhm7hT+tqoUmUTMb2YX8PT6BrodXn5VEM8f\nzkulwmTnj18e5Im1h3i2uIGHV9Xy+NpD9Hn8PHt+FhcPj6bV6ub2D8q5ZVYByZE6lpbUYHYHuXhq\nIZ29Vj4r3sm0ieNJiI1h3ZZtdJt7ARgzZhx6vZ5Nmzbg9R7/MPBB/LywYsUyRCIR8+cvGCj7bPlK\nnC4XVyyYj04bwtv//RKHy81vLvkFnTYP768vJUqn4dGrZvLyumq213WRlxjGS1cWYg9I+cMXNXxS\nZqLN6iYnRsNsQwTXT0jm0rxopg8JJ0WvpLrTwXu723hgWQ0qtYbXri4kJz6UzTUmnl5RycNXzCAl\nOoylO6sorWvn0d9eQSAY5PX/fIFCIeeKBfPxen18vmI1IJyHPHv2PAKBAOvWrTld6hzEIM56DJLB\nswhVVRU0NzdRVHQe4eERA+XL1hXTZ7dz+bw5hIeGYrE7eWvpRlRyGQ9dPQ+ZVMqTX+yltsPK+SOS\neHDeCFy+IM9sqOe1rU3UdTsZGq1hQU4UNxUmcFNhAhfnRjM8NoTmXhfv7m7lgS9rOGCyU5gexatX\nj2dUcgS76rt4dHEpDo+PsBA1r959Jb8YO5xD7V3c/9YntHX3fq0PErGYyWPyee3xh/n9DdeiVqr4\nn/98xuOvvIHD6fza/QAajYaLL74cu93OsmXLTpp+fy5wuZzs3LmNpKRksrKEVPT7Kqvo7O5m2sQJ\nKBQK/rtuCyFqJRdNn8jfV+xAJpFw85zxvL6hGqVMwo2TDfx7TwcKiZjrx8SzpaEXi8vHtAw96ZFq\nSttt9Lp8GCLVzB4SztyscKZnhDMnM5xLRsUxMlaDw+vnoNnF5PQwpBIxDWY3oxO01HY7GZuZSCAY\npNzkJiM2gk37D1FUMAqxSMTnxdvJHpJBbraBPfsraDd1HtO/hIREnnvuZf74x4cpLByPxdJLcfE6\nXnvtRW644Ur+8Y83ByfFgzghsNvtVFVVkJlpICwsDLPFQvmBanKyMgmIJJRWH2J4RhLVnQ567W4u\nH5/NR/t76HF4uakwgbQINU9vaGB7owWtQsq45FCmZegZFa/F6w+worqbp4sbmJwezi3jEjE7vPy1\nuIG75xUilYh5c/UeFkwpRKdR8fnGXXh8fhbMmYnP72ft5q2AkJRr8uSpWK1W9u8vO80aG8TpRnt7\nG5WV+4/ZZ+fxeFi+dj2hOi1zp03lYGMLW/dVkZeVyqTRuby2dCtisYg/XX4e722rp7LNwqQh0dw1\nI4dXNjeztLKLMJWUmwrjefPSodw3NZWidD3ZMSFkRWmYkx3BY7MyeP3ibC4fEYNELOKDve28tb2N\n++cOpygrhgPtVt7aWMODl09DJZfx5pfbyUxNZNLIHKobWthVeZCZRZPQabWs3LARr084uqio6Dwk\nEglbt248fUodxCDOcgySwbMImzYVAxxz1ILf72fJmg2oVUrOnyYcLvzhup30OV1cPXMc8ZFhfLCj\nlorWXmbkJvKbKdl09Hl4bHUdlR12RsRreWZeJg9OS+Pi4TFMSdczJV3Pgtxo/jA1lRcvMDAjM5xO\nu4en19ezurqbUJWchReMYnJWLJWtvTy1bB/+QBCpVMJtF07l6hlj6TBb+ePbn2LqPb4nTyIWM6to\nAn/78yOMyhnKrn37eej5V/B8wyR99uxfIJVKWbJkyQnW6s8Pu3btwOPxMHFi0cAxDJt37AJg6oTx\nbNlbRW+fnQXTxrO5spHuPgcLxufwZWUHdrePa8dn8PH+LnzBINeNiWfVwR58gSCXDo+mxebhUK+L\n5FAFC4ZGopJLKO9ysM/kYH+Xg7JOB9sOmbH5g8zKjCBUIaG628nYZB3eQBBvEMKUUrY39zEqJZLK\nVguFOZkEgU0HmijMNVDT1EptUxszioTwpuJt27/WR4lEwtSp03nssSf5+OOl/P3v7/Gb39xOWJie\nTz/9iGee+TN+v/+U6XwQ5yYqKsoJBAKMHDkagJ17ywgGg4zPH8W63fsBmFaQx+e7q9Gp5IToI6lo\ntzE+NYwkvYp/7m5FBFw7Oo5FM9O5dnQclwyP4ebCBP4yZwi/yI7E6vLx6tYmsqM1XDwqnnqziy3N\nDi7IH0Kn1cHGAy38YsIo+hwuiksrmTK2EJlMyobtOwbaeTg8dPfunadcR4M4s7B5s0Cajt5jt61k\nDza7nVlTJqOQy/l0reDR/vWlc1i6oxKTxcaCcTk0WH1srjExLC6Um4uy+MvaQxzqcTIlXc9z52eR\nHa3h03ITj66q5fVtzbyyvo53S9p4cXMTj62pY32tmfOGhPP8/CzGJOmoMtl5el09vy7KYnhCGNvq\nOtnb3Me100Zjd3l458udXDFbmNd8tm4rcrmc8yaOw9rXR2lZOSCEQQ8dmkN1tZG+Pusp1uYgBnFu\nYJAMnkXYvXsHSqWSESNGDZTtqzJitlg5b1whapWKPoeLlbsqiAoNYf74PFrMdj4tqSdKq+Shi/Lp\nc/t5ZkM9FpePK0fGcs/kZGK1igF5Tq//mEPBw1QyfpUfz6Mz0tEppfyrtI3V1d1IJWLum53LmNRI\n9jR283/bawAhbOOq6WO5ce5Euq12Hv3nYvr6zyA6HkK1ITx+9+1MHTcGY109r733f8e9LyxMz8iR\no6mursZk6vipqvxZY+dOgTxNnCiEtQWDQfbsryBEo2GYIZONpcJHdv7UQpbtqkIulVA4NJ1NB01k\nRGlxBqV02b3My45kZ7MVjz/IxcOj2d9pp8/jZ2yClqHRGra32jA5vMRoZBTEhTA1OZRJiTqGx+tw\n+wLsM9kZnaAjRC6h1uwiP0FLt8NLfrIOrz9IdLiw16+u10O4Vs3mikNMHp0LwPbyA4wvGC2sCO8u\n/db+ikQiEhOTWbDgUt56612GDx/Bli0b+fTT/5wsFQ/iZ4KDB40ADBuWA0BZlfB3/vAcdlQcRCqR\nIFaGYHN5mZGXxuL9ncglIi7Li+H9PW0opGLumZzM2CQdDWYXy4xdfFphYn2dGavLx7zsSG4dl0gw\nGOSdXa38alwSkRoZSytMnJeXgUwi5ouSGmYV5gGwvrSCEI2avKHZ1De10NVjBiAnJxexWMyBAxWn\nQUuDOJOwd28JcOz+0W27hbKpE8ZjtTvYVnaAlLho8gxpLNlRiUYp54JxOby3pQaFVMzds4bxxrZm\nOu1eFuREcfPYeFYf7OGlzU3sbbMRqZFTlBbGlWMSOD87knHJOqRiEVsaLDy9vp6KDju/n5zMbEME\nzRY3b25v5g+zc9DIpfx7Rx2TcjOICtWwdHslkeF6cjNSKDt4CFNPL5MKCwGEZDL9yM3NIxgMUl19\n4BRqchCDOHcwSAbPEphMHbS0NDNy5GhkMtlA+bb+AbFobAEAG8uqcXt9zJ8wAqlEwmelDfgCQW6c\nlIVaIeWDve30OLxcnBvN3OxIRCIRbl+A4jozL25u5OniBp7cUM9fNzWwtqYHp1fwnmREqPnTtDRC\nlVL+vaeN+h4nErGYe+cMJ0an4uPd9dSZjqzKXVKUz0WTRtFkMvP64vUEg0edqvwVSCQSfn/jtWSm\nprBmy3b29U+ovor8/DEA7Nu356cp82eO8vJ96HQ60tOF4xe6enro6OwiNzuLQCDAvupDJMdGYfME\naDP3MSknlQ3VJoLAglHJrDB2o1dJiQ9T0t7nYVyyDpPDi8XtZ2yijlC1jPJOB2qZmPNSQpmaHEq4\nSoZfBDKpmLGpemam6dHIxBh7nIxN0hEE/CJQSES0WT2EKqWUtdlJiwihpKGHQkMyNpcHtUaLWCRi\nb3UdWo2G3OwsDtYdotfy/VaElUoljzzyOFqtjk8//QiP5+QmyxjEuY1Dh+oABt4lY20dIRo1oaFh\nHGrrJDc9iT31wuJVXFQUJpuHaZkRbG6w4PEHuWpkLNEhcpYc6OKj/SbKO+wc7Hayo9nKOyV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r3fu25zeEAswusP0N5hRSEGs8NHS7sFueTrgTo1/REZYr+fQy29BBE+2m3dwnvsc3mxOT3IpRJa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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "g = sns.PairGrid(iris_df)\n", + "g.map_upper(plt.scatter)\n", + "g.map_lower(sns.kdeplot, cmap=\"Blues_d\")\n", + "g.map_diag(sns.kdeplot, lw=3, legend=True);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "PairGrid is flexible, but to take a quick look at a dataset, it can be easier to use pairplot(). This function uses scatterplots and histograms by default, although a few other kinds will be added (currently, you can also plot regression plots on the off-diagonals and KDEs on the diagonal)." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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RqYRj87HJCTptnRX7OUFS3bOXSwrxB4G9AFrr00qpXcDTQNo3sMA9wF+bfuYALke1p4Hm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\n9ucE701hZqdz5C79IQp7iiT5HmLSgynrMZvgzkK2L2o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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.pairplot(iris_df, hue=\"species\", size=2.5);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can also control the aesthetics of the plot with keyword arguments, and it returns the PairGrid instance for further tweaking." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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UJpvv+nFStz6A4/oFfF97goHn/iuxH/z7mANDLZReiNZZQ9HMGmFrQvQba/3G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bZCJRAvaj+K682JT6VDMxGuLBe2Y5HB3k0vwmH//cT1hYibf0mCIiIs3WlQNYY4xl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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "g = sns.pairplot(iris_df, hue=\"species\", palette=\"Set2\", diag_kind=\"kde\", size=2.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we are going to show other representions.\n", + "\n", + "[**Box plots** or **boxplot** ](https://en.wikipedia.org/wiki/Box_plot) (*diagramas de caja*) are a convenient way of graphically depicting groups of numerical data through their quartiles." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# We can look at an individual feature in Seaborn through a boxplot\n", + "sns.boxplot(x=\"species\", y=\"sepal length (cm)\", data=iris_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# One way we can extend this plot is adding a layer of individual points on top of\n", + "# it through Seaborn's striplot\n", + "# \n", + "# We'll use jitter=True so that all the points don't fall in single vertical lines\n", + "# above the species\n", + "#\n", + "# Saving the resulting axes as ax each time causes the resulting plot to be shown\n", + "# on top of the previous axes\n", + "ax = sns.boxplot(x=\"species\", y=\"petal length (cm)\", data=iris_df)\n", + "ax = sns.stripplot(x=\"species\", y=\"petal length (cm)\", data=iris_df, jitter=True, edgecolor=\"gray\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[**Violin plots**](https://en.wikipedia.org/wiki/Violin_plot) (*diagramas de violín*) are a method of plotting numeric data. A violin plot is a box plot with a rotated kernel density plot on each side. A violin plot is just a histogram (or more often a smoothed variant like a kernel density) turned on its side and mirrored." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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shgLcbYz5S9IjYf6E9JZ9X7PWxrN470+A/zTGPJO51p9aawdfok9kHJo+fTqQ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\nfNchMhyNgxcR8SnNZBVfM8b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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# A violin plot combines the benefits of the previous two plots and simplifies them\n", + "# Denser regions of the data are fatter, and sparser thiner in a violin plot\n", + "sns.violinplot(x=\"species\", y=\"petal length (cm)\", data=iris_df, size=6)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Another useful representation is the kde (kernel density estimate) plot. [Kernel density estimation (KDE)](https://en.wikipedia.org/wiki/Kernel_density_estimation) is a non-parametric way to estimate the probability density function of a random variable. The kdeplot represents the shape of a distribution. Like the histogram, the KDE plots encodes the density of observations on one axis with height along the other axis:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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XNvHykddoG2h3oEoRAYVqVmVzTBWSk5XUUh3fnj+wjt/ueISqYCVfOuULVATK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\neLVlE5NLmzCrZ7tdzpj1vv4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8ynaOq2w/JekQ8FtgAWVbwXuB62tMV9neY/uw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\nERHRJUmqERERXZKkGhER0SX/A5I8YztC2tw0AAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# A final seaborn plot useful for looking at univariate relations is the kdeplot,\n", + "# which creates and visualizes a kernel density estimate of the underlying feature\n", + "sns.FacetGrid(iris_df, hue=\"species\", size=6) \\\n", + " .map(sns.kdeplot, \"petal length (cm)\") \\\n", + " .add_legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Depending on the data, we can choose which visualisation suits better. the following [diagram](http://www.labnol.org/software/find-right-chart-type-for-your-data/6523/) guides this selection.\n", + "\n", + "\n", + "![](files/images/data-chart-type.png \"Graphs\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* [Feature selection](http://scikit-learn.org/stable/modules/feature_selection.html)\n", + "* [Classification probability](http://scikit-learn.org/stable/auto_examples/classification/plot_classification_probability.html)\n", + "* [Mastering Pandas](http://proquest.safaribooksonline.com/book/programming/python/9781783981960), Femi Anthony, Packt Publishing, 2015.\n", + "* [Matplotlib web page](http://matplotlib.org/index.html)\n", + "* [Using matlibplot in IPython](http://ipython.readthedocs.org/en/stable/interactive/plotting.html)\n", + "* [Seaborn Tutorial](https://stanford.edu/~mwaskom/software/seaborn/tutorial.html)\n", + "* [Iris dataset visualisation notebook](https://www.kaggle.com/benhamner/d/uciml/iris/python-data-visualizations/notebook)\n", + "* [Tutorial plotting with Seaborn](https://stanford.edu/~mwaskom/software/seaborn/tutorial/axis_grids.html)\n", + "* [Choose the Right Chart Type for your Data](http://www.labnol.org/software/find-right-chart-type-for-your-data/6523/)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Licence\n", + "The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n", + "\n", + "© 2016 Carlos A. Iglesias, Universidad Politécnica de Madrid." + ] + } + ], + "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": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/ml1/2_4_Preprocessing.ipynb b/ml1/2_4_Preprocessing.ipynb new file mode 100644 index 0000000..7cd9238 --- /dev/null +++ b/ml1/2_4_Preprocessing.ipynb @@ -0,0 +1,314 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](files/images/EscUpmPolit_p.gif \"UPM\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Course Notes for Learning Intelligent Systems" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © 2016 Carlos A. Iglesias" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## [Introduction to Machine Learning](2_0_0_Intro_ML.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Table of Contents\n", + "* [Preprocessing](#Preprocessing)\n", + "* [Training set and Test set](#Training-set-and-Test-set)\n", + "* [Preprocessing](#Preprocessing)\n", + "* [References](#References)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Preprocessing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The goal of this notebook is to learn how separate the dataset into training and test datasets and then preprocess the data." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from sklearn import datasets\n", + "iris = datasets.load_iris()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Training set and Test set" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A common practice in machine learning to evaluate an algorithm is to split the data at hand into two sets, one that we call the **training set** on which we learn data properties and one that we call the **testing set** on which we test these properties. \n", + "\n", + "We are going to use *scikit-learn* to split the data into random training and testing sets. We follow the ration 75% for training and 25% for testing. We use `random_state` to ensure that the result is always the same and it is reproducible. (Otherwise, we would get different training and testing sets every time)." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from sklearn.cross_validation import train_test_split\n", + "x_iris, y_iris = iris.data, iris.target\n", + "# Test set will be the 25% taken randomly\n", + "x_train, x_test, y_train, y_test = train_test_split(x_iris, y_iris, test_size=0.25, random_state=33)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(112, 4) (38, 4)\n" + ] + } + ], + "source": [ + "# Dimensions of train and testing\n", + "print(x_train.shape, x_test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 5.7 2.9 4.2 1.3]\n", + " [ 6.7 3.1 4.4 1.4]\n", + " [ 4.7 3.2 1.6 0.2]\n", + " [ 6.5 2.8 4.6 1.5]\n", + " [ 6.1 2.6 5.6 1.4]\n", + " [ 6.3 3.3 6. 2.5]\n", + " [ 4.8 3.4 1.9 0.2]\n", + " [ 5.1 3.5 1.4 0.3]\n", + " [ 6.4 3.1 5.5 1.8]\n", + " [ 6.9 3.2 5.7 2.3]\n", + " [ 6.8 3.2 5.9 2.3]\n", + " [ 4.4 3. 1.3 0.2]\n", + " [ 6.3 3.4 5.6 2.4]\n", + " [ 6.1 2.9 4.7 1.4]\n", + " [ 6.9 3.1 5.1 2.3]\n", + " [ 6.4 2.9 4.3 1.3]\n", + " [ 6. 3. 4.8 1.8]\n", + " [ 5.2 3.5 1.5 0.2]\n", + " [ 6.3 3.3 4.7 1.6]\n", + " [ 7.2 3.2 6. 1.8]\n", + " [ 4.9 3.1 1.5 0.1]\n", + " [ 5.7 3.8 1.7 0.3]\n", + " [ 6.5 3. 5.8 2.2]\n", + " [ 4.8 3. 1.4 0.1]\n", + " [ 6. 2.2 5. 1.5]\n", + " [ 6.2 2.8 4.8 1.8]\n", + " [ 6.1 3. 4.6 1.4]\n", + " [ 6.1 2.8 4. 1.3]\n", + " [ 6.5 3. 5.2 2. ]\n", + " [ 5.9 3. 5.1 1.8]\n", + " [ 5.6 2.7 4.2 1.3]\n", + " [ 6.7 3.1 4.7 1.5]\n", + " [ 5.6 2.8 4.9 2. ]\n", + " [ 6.4 3.2 5.3 2.3]\n", + " [ 6.7 3.1 5.6 2.4]\n", + " [ 6.7 3. 5.2 2.3]\n", + " [ 5.8 2.7 5.1 1.9]\n", + " [ 5.7 3. 4.2 1.2]]\n" + ] + } + ], + "source": [ + "#Test set\n", + "print (x_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Preprocessing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Standardization of datasets is a common requirement for many machine learning estimators implemented in the scikit; they might behave badly if the individual features do not more or less look like standard normally distributed data: Gaussian with zero mean and unit variance.\n", + "\n", + "The preprocessing module further provides a utility class `StandardScaler` to compute the mean and standard deviation on a training set so as to be able to later reapply the same transformation on the testing set." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Standardize the features\n", + "from sklearn import preprocessing\n", + "scaler = preprocessing.StandardScaler().fit(x_train)\n", + "x_train = scaler.transform(x_train)\n", + "x_test = scaler.transform(x_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-0.09752318 -0.32858743 0.34599443 0.25682671]\n", + " [ 1.06445511 0.09442168 0.45718919 0.39124069]\n", + " [-1.25950146 0.30592623 -1.09953753 -1.22172707]\n", + " [ 0.83205945 -0.54009199 0.56838396 0.52565467]\n", + " [ 0.36726814 -0.9631011 1.12435779 0.39124069]\n", + " [ 0.59966379 0.51743079 1.34674732 1.86979447]\n", + " [-1.14330363 0.72893534 -0.93274538 -1.22172707]\n", + " [-0.79471015 0.9404399 -1.2107323 -1.08731309]\n", + " [ 0.71586162 0.09442168 1.06876041 0.92889661]\n", + " [ 1.29685076 0.30592623 1.17995517 1.60096651]\n", + " [ 1.18065293 0.30592623 1.29114994 1.60096651]\n", + " [-1.60809495 -0.11708288 -1.26632968 -1.22172707]\n", + " [ 0.59966379 0.72893534 1.12435779 1.73538049]\n", + " [ 0.36726814 -0.32858743 0.62398134 0.39124069]\n", + " [ 1.29685076 0.09442168 0.84637087 1.60096651]\n", + " [ 0.71586162 -0.32858743 0.40159181 0.25682671]\n", + " [ 0.25107031 -0.11708288 0.67957873 0.92889661]\n", + " [-0.67851232 0.9404399 -1.15513491 -1.22172707]\n", + " [ 0.59966379 0.51743079 0.62398134 0.66006865]\n", + " [ 1.64544425 0.30592623 1.34674732 0.92889661]\n", + " [-1.0271058 0.09442168 -1.15513491 -1.35614105]\n", + " [-0.09752318 1.57495356 -1.04394015 -1.08731309]\n", + " [ 0.83205945 -0.11708288 1.23555256 1.46655253]\n", + " [-1.14330363 -0.11708288 -1.2107323 -1.35614105]\n", + " [ 0.25107031 -1.80911932 0.79077349 0.52565467]\n", + " [ 0.48346596 -0.54009199 0.67957873 0.92889661]\n", + " [ 0.36726814 -0.11708288 0.56838396 0.39124069]\n", + " [ 0.36726814 -0.54009199 0.23479966 0.25682671]\n", + " [ 0.83205945 -0.11708288 0.90196826 1.19772457]\n", + " [ 0.13487248 -0.11708288 0.84637087 0.92889661]\n", + " [-0.21372101 -0.75159654 0.34599443 0.25682671]\n", + " [ 1.06445511 0.09442168 0.62398134 0.52565467]\n", + " [-0.21372101 -0.54009199 0.73517611 1.19772457]\n", + " [ 0.71586162 0.30592623 0.95756564 1.60096651]\n", + " [ 1.06445511 0.09442168 1.12435779 1.73538049]\n", + " [ 1.06445511 -0.11708288 0.90196826 1.60096651]\n", + " [ 0.01867465 -0.75159654 0.84637087 1.06331059]\n", + " [-0.09752318 -0.11708288 0.34599443 0.12241273]]\n" + ] + } + ], + "source": [ + "# As we see, the iris dataset is now normalized\n", + "print(x_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* [Feature selection](http://scikit-learn.org/stable/modules/feature_selection.html)\n", + "* [Classification probability](http://scikit-learn.org/stable/auto_examples/classification/plot_classification_probability.html)\n", + "* [Mastering Pandas](http://proquest.safaribooksonline.com/book/programming/python/9781783981960), Femi Anthony, Packt Publishing, 2015.\n", + "* [Matplotlib web page](http://matplotlib.org/index.html)\n", + "* [Using matlibplot in IPython](http://ipython.readthedocs.org/en/stable/interactive/plotting.html)\n", + "* [Seaborn Tutorial](https://stanford.edu/~mwaskom/software/seaborn/tutorial.html)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Licences\n", + "The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n", + "\n", + "© 2016 Carlos A. Iglesias, Universidad Politécnica de Madrid." + ] + } + ], + "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": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/ml1/2_5_0_Machine_Learning.ipynb b/ml1/2_5_0_Machine_Learning.ipynb new file mode 100644 index 0000000..aebefc7 --- /dev/null +++ b/ml1/2_5_0_Machine_Learning.ipynb @@ -0,0 +1,201 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](files/images/EscUpmPolit_p.gif \"UPM\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Course Notes for Learning Intelligent Systems" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © 2016 Carlos A. Iglesias" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## [Introduction to Machine Learning](2_0_0_Intro_ML.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Table of Contents\n", + "\n", + "* [Machine Learning](#Machine-Learning)\n", + "* [Machine learning algorithms](#Machine-learning-algorithms)\n", + "\t\t* [Supervised machine learning model](#Supervised-machine-learning-model)\n", + "\t\t* [Unsupervised machine learning model](#Unsupervised-machine-learning-model)\n", + "* [sklearn interface](#sklearn-interface)\n", + "* [References](#References)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Machine Learning" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is an introduction of general ideas about machine learning and the general interface of scikit-learn, taken from the [scikit-learn tutorial](http://www.astroml.org/sklearn_tutorial/general_concepts.html). \n", + "\n", + "You can skip it during the lab session and read it later," + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Machine learning algorithms" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Machine learning algorithms are programs that learn a model from a dataset with the aim of making predictions or learning structures to organize the data.\n", + "\n", + "In scikit-learn, machine learning algorithms take as an input a *numpy* array (n_samples, n_features), where\n", + "* **n_samples**: number of samples. Each sample is an item to process (i.e. classify). A sample can be a document, a picture, a sound, a video, a row in database or CSV file, or whatever you can describe with a fixed set of quantitative traits.\n", + "* **n_features**: The number of features or distinct traits that can be used to describe each item in a quantitative manner.\n", + "\n", + "The number of features should be defined in advanced and it can be very high dimensional (e.g. millions of features) with most of them being zeros for a given sample. In this case we may use (scipy.sparse) sparse matrices instead of (numpy) arrays so as to make the data fit in memory.\n", + "\n", + "The first step in machine learning is **identifying the relevant features** from the input data, and the second step is **extracting the features** from the input data. \n", + "\n", + "[Machine learning algorithms](http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/) can be classified according to learning style into:\n", + "* **Supervised learning**: input data (training dataset) has a known label or result. Example problems are classification and regression. A model is prepared through a training process where it is required to make predictions and is corrected when those predictions are wrong. The training process continues until the model achieves a desired level of accuracy on the training data.\n", + "* **Unsupervised learning**: input data is not labeled. A model is prepared by deducing structures present in the input data. This may be to extract general rules. Example problems are clustering, dimensionality reduction and association rule learning.\n", + "* **Semi-supervised learning**:i nput data is a mixture of labeled and unlabeled examples. There is a desired prediction problem but the model must learn the structures to organize the data as well as make predictions. Example problems are classification and regression." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Supervised machine learning model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In *supervised machine learning models*, the machine learning algorithm takes as an input a training dataset, composed of feature vectors and labels, and produces a predictive model which is used for make prediction on new data.\n", + "![](files/images/plot_ML_flow_chart_1.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Unsupervised machine learning model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In *unsupervised machine learning models*, the machine learning model algorithm takes as an input the feature vectors and produces a predictive model that is used to fit its parameters so as to best summarize regularities found in the data.\n", + "![](files/images/plot_ML_flow_chart_3.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## sklearn interface" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "scikit-learn has a uniform interface for all the estimators, some methods are only available is the estimator is supervised or unsupervised:\n", + "\n", + "* Available in *all estimators*:\n", + " * **model.fit()**: fit training data. For supervised learning applications, this accepts two arguments: the data X and the labels y (e.g. model.fit(X, y)). For unsupervised learning applications, this accepts only a single argument, the data X (e.g. model.fit(X)).\n", + "\n", + "* Available in *supervised estimators*:\n", + " * **model.predict()**: given a trained model, predict the label of a new set of data. This method accepts one argument, the new data X_new (e.g. model.predict(X_new)), and returns the learned label for each object in the array.\n", + " * **model.predict_proba()**: For classification problems, some estimators also provide this method, which returns the probability that a new observation has each categorical label. In this case, the label with the highest probability is returned by model.predict().\n", + "\n", + "* Available in *unsupervised estimators*:\n", + " * **model.transform()**: given an unsupervised model, transform new data into the new basis. This also accepts one argument X_new, and returns the new representation of the data based on the unsupervised model.\n", + " * **model.fit_transform()**: some estimators implement this method, which performs a fit and a transform on the same input data.\n", + "\n", + "\n", + "![](files/images/plot_ML_flow_chart_2.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "## References" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* [General concepts of machine learning with scikit-learn](http://www.astroml.org/sklearn_tutorial/general_concepts.html)\n", + "* [A Tour of Machine Learning Algorithms](http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Licence" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n", + "\n", + "© 2016 Carlos A. Iglesias, Universidad Politécnica de Madrid." + ] + } + ], + "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": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/ml1/2_5_1_kNN_Model.ipynb b/ml1/2_5_1_kNN_Model.ipynb new file mode 100644 index 0000000..00015d8 --- /dev/null +++ b/ml1/2_5_1_kNN_Model.ipynb @@ -0,0 +1,567 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](files/images/EscUpmPolit_p.gif \"UPM\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Course Notes for Learning Intelligent Systems" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © 2016 Carlos A. Iglesias" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## [Introduction to Machine Learning](2_0_0_Intro_ML.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Table of Contents\n", + "* [kNN Model](#kNN-Model)\n", + "* [Load data and preprocessing](#Load-data-and-preprocessing)\n", + "* [Train classifier](#Train-classifier)\n", + "* [Evaluating the algorithm](#Evaluating-the-algorithm)\n", + " * [Precision, recall and f-score](#Precision,-recall-and-f-score)\n", + "\t* [Confusion matrix](#Confusion-matrix)\n", + "\t* [K-Fold validation](#K-Fold-validation)\n", + "* [Tuning the algorithm](#Tuning-the-algorithm)\n", + "* [References](#References)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# kNN Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The goal of this notebook is to learn how to train a model and make and evaluate predictions.\n", + "\n", + "The notebook uses the [kNN (k nearest neighbors) algorithm](https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load data and preprocessing\n", + "\n", + "The first step is loading and preprocessing the data as explained in the previous notebooks." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# library for displaying plots\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# display plots in the notebook \n", + "%matplotlib inline\n", + "\n", + "## First, we repeat the load and preprocessing steps\n", + "\n", + "# Load data\n", + "from sklearn import datasets\n", + "iris = datasets.load_iris()\n", + "\n", + "# Training and test spliting\n", + "from sklearn.cross_validation import train_test_split\n", + "\n", + "x_iris, y_iris = iris.data, iris.target\n", + "\n", + "# Test set will be the 25% taken randomly\n", + "x_train, x_test, y_train, y_test = train_test_split(x_iris, y_iris, test_size=0.25, random_state=33)\n", + "\n", + "# Preprocess: normalize\n", + "from sklearn import preprocessing\n", + "scaler = preprocessing.StandardScaler().fit(x_train)\n", + "x_train = scaler.transform(x_train)\n", + "x_test = scaler.transform(x_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## Train classifier" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The usual steps for creating a classifier are:\n", + "1. Create classifier object\n", + "2. Call *fit* to train the classifier\n", + "3. Call *predict* to obtain predictions\n", + "\n", + "Once the model is created, the most relevant methods are:\n", + "* model.fit(x_train, y_train): train the model\n", + "* model.predict(x): predict\n", + "* model.score(x, y): evaluate the prediction" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n", + " metric_params=None, n_jobs=1, n_neighbors=15, p=2,\n", + " weights='uniform')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.neighbors import KNeighborsClassifier\n", + "import numpy as np\n", + "\n", + "# Create kNN model\n", + "model = KNeighborsClassifier(n_neighbors=15)\n", + "\n", + "# Train the model using the training sets\n", + "model.fit(x_train, y_train) " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Prediction [1 0 1 1 1 0 0 1 0 2 0 0 1 2 0 1 2 2 1 1 0 0 1 0 0 2 1 1 2 2 2 2 0 0 1 1 0\n", + " 1 2 1 2 0 2 0 1 0 2 1 0 2 2 0 0 2 0 0 0 2 2 0 1 0 1 0 1 1 1 1 1 0 1 0 1 2\n", + " 0 0 0 0 2 2 0 1 1 2 1 0 0 2 1 1 0 1 1 0 2 1 2 1 2 0 2 0 0 0 2 1 2 1 2 1 2\n", + " 0]\n", + "Expected [1 0 1 1 1 0 0 1 0 2 0 0 1 2 0 1 2 2 1 1 0 0 2 0 0 2 1 1 2 2 2 2 0 0 1 1 0\n", + " 1 2 1 2 0 2 0 1 0 2 1 0 2 2 0 0 2 0 0 0 2 2 0 1 0 1 0 1 1 1 1 1 0 1 0 1 2\n", + " 0 0 0 0 2 2 0 1 1 2 1 0 0 1 1 1 0 1 1 0 2 2 2 1 2 0 1 0 0 0 2 1 2 1 2 1 2\n", + " 0]\n" + ] + } + ], + "source": [ + "print(\"Prediction \", model.predict(x_train))\n", + "print(\"Expected \", y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy in training 0.964285714286\n" + ] + } + ], + "source": [ + "# Evaluate Accuracy in training\n", + "\n", + "from sklearn import metrics\n", + "y_train_pred = model.predict(x_train)\n", + "print(\"Accuracy in training\", metrics.accuracy_score(y_train, y_train_pred))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy in testing 0.921052631579\n" + ] + } + ], + "source": [ + "# Now we evaluate error in testing\n", + "y_test_pred = model.predict(x_test)\n", + "print(\"Accuracy in testing \", metrics.accuracy_score(y_test, y_test_pred))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we are going to visualize the Nearest Neighbors classification. It will plot the decision boundaries for each class.\n", + "\n", + "We are going to import a function defined in the file [util_knn.py](files/util_knn.py) using the *magic command* **%run**." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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SmCVxY4xJYhEncRFJEZElIvJuiNf6iMh29/UlInJnbMM0pu6woRQTS9GMiY8EVgGNwry+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\nkyjaEaDbGUdx+Xinnc/e+Iy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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%run util_knn.py\n", + "plot_classification_iris()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Evaluating the algorithm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Precision, recall and f-score" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For evaluating classification algorithms, we usually calculate three metrics: precision, recall and F1-score\n", + "\n", + "* **Precision**: This computes the proportion of instances predicted as positives that were correctly evaluated (it measures how right our classifier is when it says that an instance is positive).\n", + "* **Recall**: This counts the proportion of positive instances that were correctly evaluated (measuring how right our classifier is when faced with a positive instance).\n", + "* **F1-score**: This is the harmonic mean of precision and recall, and tries to combine both in a single number." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " setosa 1.00 1.00 1.00 8\n", + " versicolor 0.79 1.00 0.88 11\n", + " virginica 1.00 0.84 0.91 19\n", + "\n", + "avg / total 0.94 0.92 0.92 38\n", + "\n" + ] + } + ], + "source": [ + "print(metrics.classification_report(y_test, y_test_pred,target_names=iris.target_names))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Confusion matrix" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Another useful metric is the confusion matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 8 0 0]\n", + " [ 0 11 0]\n", + " [ 0 3 16]]\n" + ] + } + ], + "source": [ + "print(metrics.confusion_matrix(y_test, y_test_pred))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "We see we classify well all the 'setosa' and 'versicolor' samples. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### K-Fold validation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to avoid bias in the training and testing dataset partition, it is recommended to use **k-fold validation**." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0.93333333 0.8 1. 0.93333333 0.93333333 0.93333333\n", + " 1. 1. 0.86666667 1. ]\n" + ] + } + ], + "source": [ + "from sklearn.cross_validation import cross_val_score, KFold\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "# create a composite estimator made by a pipeline of preprocessing and the KNN model\n", + "model = Pipeline([\n", + " ('scaler', StandardScaler()),\n", + " ('kNN', KNeighborsClassifier())\n", + "])\n", + "\n", + "# create a k-fold cross validation iterator of k=10 folds\n", + "\n", + "cv = KFold(x_iris.shape[0], 10, shuffle=True, random_state=33)\n", + "\n", + "# by default the score used is the one returned by score method of the estimator (accuracy)\n", + "scores = cross_val_score(model, x_iris, y_iris, cv=cv)\n", + "print(scores)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "We get an array of k scores. We can calculate the mean and the standard error to obtain a final figure" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean score: 0.940 (+/- 0.021)\n" + ] + } + ], + "source": [ + "from scipy.stats import sem\n", + "def mean_score(scores):\n", + " return (\"Mean score: {0:.3f} (+/- {1:.3f})\").format(np.mean(scores), sem(scores))\n", + "print(mean_score(scores))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So, we get an average accuracy of 0.940." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tuning the algorithm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are going to tune the algorithm, and calculate which is the best value for the k parameter." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "k_range = range(1, 21)\n", + "accuracy = []\n", + "for k in k_range:\n", + " m = KNeighborsClassifier(k)\n", + " m.fit(x_train, y_train)\n", + " y_test_pred = m.predict(x_test)\n", + " accuracy.append(metrics.accuracy_score(y_test, y_test_pred))\n", + "plt.plot(k_range, accuracy)\n", + "plt.xlabel('k value')\n", + "plt.ylabel('Accuracy')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The result is very dependent of the input data. Execute again the train_test_split and test again how the result changes with k." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* [KNeighborsClassifier API scikit-learn](http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html)\n", + "* [Learning scikit-learn: Machine Learning in Python](http://proquest.safaribooksonline.com/book/programming/python/9781783281930/1dot-machine-learning-a-gentle-introduction/ch01s02_html), Raúl Garreta; Guillermo Moncecchi, Packt Publishing, 2013.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Licence\n", + "The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n", + "\n", + "© 2016 Carlos A. Iglesias, Universidad Politécnica de Madrid." + ] + } + ], + "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": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/ml1/2_5_2_Decision_Tree_Model.ipynb b/ml1/2_5_2_Decision_Tree_Model.ipynb new file mode 100644 index 0000000..a7b0bb1 --- /dev/null +++ b/ml1/2_5_2_Decision_Tree_Model.ipynb @@ -0,0 +1,677 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](files/images/EscUpmPolit_p.gif \"UPM\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Course Notes for Learning Intelligent Systems" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © 2016 Carlos A. Iglesias" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## [Introduction to Machine Learning](2_0_0_Intro_ML.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Table of Contents\n", + "* [Decision Tree Learning](#Decision-Tree-Learning)\n", + "* [Load data and preprocessing](#Load-data-and-preprocessing)\n", + "* [Train classifier](#Train-classifier)\n", + "* [Evaluating the algorithm](#Evaluating-the-algorithm)\n", + "\t* [Precision, recall and f-score](#Precision,-recall-and-f-score)\n", + "\t* [Confusion matrix](#Confusion-matrix)\n", + "\t* [K-Fold cross validation](#K-Fold-cross-validation)\n", + "* [References](#References)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Decision Tree Learning" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The goal of this notebook is to learn how to learn how create a classification object using a [decision tree learning algorithm](https://en.wikipedia.org/wiki/Decision_tree_learning). \n", + "\n", + "There are a number of well known machine learning algorithms for decision tree learning, such as ID3, C4.5, C5.0 and CART. The scikit-learn uses an optimised version of the [CART (Classification and Regression Trees) algorithm](https://en.wikipedia.org/wiki/Predictive_analytics#Classification_and_regression_trees).\n", + "\n", + "This notebook will follow the same steps that the previous notebook for learning using the [kNN Model](2_5_1_kNN_Model.ipynb), and details some pecualiarities of the decision tree algorithms." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load data and preprocessing\n", + "\n", + "Here we repeat the same operations for loading data and preprocessing than in the previous notebooks." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# library for displaying plots\n", + "import matplotlib.pyplot as plt\n", + "# display plots in the notebook \n", + "%matplotlib inline\n", + "\n", + "## First, we repeat the load and preprocessing steps\n", + "\n", + "# Load data\n", + "from sklearn import datasets\n", + "iris = datasets.load_iris()\n", + "\n", + "# Training and test spliting\n", + "from sklearn.cross_validation import train_test_split\n", + "\n", + "x_iris, y_iris = iris.data, iris.target\n", + "# Test set will be the 25% taken randomly\n", + "x_train, x_test, y_train, y_test = train_test_split(x_iris, y_iris, test_size=0.25, random_state=33)\n", + "\n", + "# Preprocess: normalize\n", + "from sklearn import preprocessing\n", + "scaler = preprocessing.StandardScaler().fit(x_train)\n", + "x_train = scaler.transform(x_train)\n", + "x_test = scaler.transform(x_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## Train classifier" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The usual steps for creating a classifier are:\n", + "1. Create classifier object\n", + "2. Call *fit* to train the classifier\n", + "3. Call *predict* to obtain predictions\n", + "\n", + "*DecisionTreeClassifier* is capable of both binary (where the labels are [-1, 1]) classification and multiclass (where the labels are [0, ..., K-1]) classification." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=3,\n", + " max_features=None, max_leaf_nodes=None, min_samples_leaf=1,\n", + " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", + " presort=False, random_state=1, splitter='best')" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.tree import DecisionTreeClassifier\n", + "import numpy as np\n", + "\n", + "from sklearn import tree\n", + "\n", + "max_depth=3\n", + "random_state=1\n", + "\n", + "# Create decision tree model\n", + "model = tree.DecisionTreeClassifier(max_depth=max_depth, random_state=random_state)\n", + "\n", + "# Train the model using the training sets\n", + "model.fit(x_train, y_train) " + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Prediction [1 0 1 1 1 0 0 1 0 2 0 0 1 2 0 1 2 2 1 1 0 0 2 0 0 2 1 1 2 2 2 2 0 0 1 1 0\n", + " 1 2 1 2 0 2 0 1 0 2 1 0 2 2 0 0 2 0 0 0 2 2 0 1 0 1 0 1 1 1 1 1 0 1 0 1 2\n", + " 0 0 0 0 2 2 0 1 1 2 1 0 0 2 1 1 0 1 1 0 2 1 2 1 2 0 1 0 0 0 2 1 2 1 2 1 2\n", + " 0]\n", + "Expected [1 0 1 1 1 0 0 1 0 2 0 0 1 2 0 1 2 2 1 1 0 0 2 0 0 2 1 1 2 2 2 2 0 0 1 1 0\n", + " 1 2 1 2 0 2 0 1 0 2 1 0 2 2 0 0 2 0 0 0 2 2 0 1 0 1 0 1 1 1 1 1 0 1 0 1 2\n", + " 0 0 0 0 2 2 0 1 1 2 1 0 0 1 1 1 0 1 1 0 2 2 2 1 2 0 1 0 0 0 2 1 2 1 2 1 2\n", + " 0]\n" + ] + } + ], + "source": [ + "print(\"Prediction \", model.predict(x_train))\n", + "print(\"Expected \", y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, the probability of each class can be predicted, which is the fraction of training samples of the same class in a leaf:" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicted probabilities [[ 0. 0.97368421 0.02631579]\n", + " [ 1. 0. 0. ]\n", + " [ 0. 0.97368421 0.02631579]\n", + " [ 0. 0.97368421 0.02631579]\n", + " [ 0. 0.97368421 0.02631579]\n", + " [ 1. 0. 0. ]\n", + " [ 1. 0. 0. ]\n", + " [ 0. 0.97368421 0.02631579]\n", + " [ 1. 0. 0. ]\n", + " [ 0. 0. 1. ]]\n" + ] + } + ], + "source": [ + "# Print the \n", + "print(\"Predicted probabilities\", model.predict_proba(x_train[:10]))" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy in training 0.982142857143\n" + ] + } + ], + "source": [ + "# Evaluate Accuracy in training\n", + "\n", + "from sklearn import metrics\n", + "y_train_pred = model.predict(x_train)\n", + "print(\"Accuracy in training\", metrics.accuracy_score(y_train, y_train_pred))" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy in testing 0.921052631579\n" + ] + } + ], + "source": [ + "# Now we evaluate error in testing\n", + "y_test_pred = model.predict(x_test)\n", + "print(\"Accuracy in testing \", metrics.accuracy_score(y_test, y_test_pred))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we are going to visualize the DecisionTree classification. It will plot the decision boundaries for each class.\n", + "\n", + "The current version of pydot does not work well in Python 3.\n", + "For obtaining an image, you need to install `pip install pydotplus` and then `conda install graphviz`.\n", + "\n", + "You can skip this example. Since it can require installing additional packages, we include here the result.\n", + "![Decision Tree](files/images/cart.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: Not built with libexpat. Table formatting is not available.\n", + "in label of node 0\n", + "in label of node 1\n", + "in label of node 2\n", + "in label of node 3\n", + "in label of node 4\n", + "in label of node 5\n", + "in label of node 6\n", + "in label of node 7\n", + "in label of node 8\n", + "\n", + "Warning: Not built with libexpat. Table formatting is not available.\n", + "in label of node 0\n", + "in label of node 1\n", + "in label of node 2\n", + "in label of node 3\n", + "in label of node 4\n", + "in label of node 5\n", + "in label of node 6\n", + "in label of node 7\n", + "in label of node 8\n", + "\n" + ] + }, + { + "data": { + "image/png": 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G18wBIDgkBMev5+N6rop3FFH5+cx92NnaICAgQKfjBAcH4+TR43Sq5V9+2fQz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\nmDJlCjQaDaZOnco7lsmgZq4HxcXF6NO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+ "text/plain": [ + "" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image \n", + "from sklearn.externals.six import StringIO\n", + "import pydotplus as pydot\n", + "\n", + "dot_data = StringIO() \n", + "tree.export_graphviz(model, out_file=dot_data, \n", + " feature_names=iris.feature_names, \n", + " class_names=iris.target_names, \n", + " filled=True, rounded=True, \n", + " special_characters=True) \n", + "\n", + "\n", + "graph = pydot.graph_from_dot_data(dot_data.getvalue()) \n", + "graph.write_png('iris-tree.png')\n", + "Image(graph.create_png()) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we show a graph of the decision tree boundaries. For each pair of iris features, the decision tree learns decision boundaries made of combinations of simple thresholding rules inferred from the training samples.\n", + "\n", + "We are going to import a function defined in the file [util_ds.py](files/util_ds.py) using the *magic command* **%run**." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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YVMYmcLdsgaefVn/1rKBpEB0NOYqYw/Dh8OGHSjhERCg3kd4dNGWy4K/XDuav\n1w5u17zzrSwq0yw6Y2Ux27jV1ioB/NRTjsHcfSxf+Qe9UqMJCfJj14ESZjyxknvutfL002rs/PyU\nALApWQMy4vHz83E6bvp1BwdyjjBj4SrmzbPUW4tTp2xGCHsrMD9fCZmWKm0tGddmi8p0BrRlURlX\naR6Kd25k8ysvYTEuBjahWEH/BGJRcXIFH//+nHXv5Prsom1dVOZ4i1i4Ov6macsJCw1gYEY8BoOh\nfvs/bjiz/nNrispox/cAPpVSDnCxv8sUldm0LY91G7MY1DeB2OgQft6ay+JX1xMdY+XttxuOu/lm\nOHIEbrwR3nxTrTI1GhWTJDJSfV+6tOH4CX/358kHrmZQ3/azHFtTVEY7tgfHMa6daUxt2rivr4WA\nQGk3FjfdpBZshYTAsWMqlhMbCyUlahHXUY23GBenCAApCeGUVhxrtp6ALf4QF6fWftisxQl/9+fy\nMUN4+b2NJCYYyDtsxmqVdE/xq1fajjcm0CZFZYQQAcDVQA/98VLKR4+rd50QrtI8qO1PYTXFAbcA\nL6IyWxQBVehdP1LmOk0h0RZwZ0GSo9/YlR85v/go7yy+qs36KoRYBmQCMUKIbGCOlPL1NrtgG+L6\nyR+waXsecXHw4jIID1Mvc7iE4iJHip/yNaelwZo1cMklajGQ8j8rIaE/Pu+wuU1cdBaLleIjx7CY\nG6y8ZA9cp6uPq436ueG3HKY+9oXdWFRVwcKFiv4ZEGBvJUydqgT4vHkqzlZSAvPmVdplEXVGA9XH\nHxytRWWtD2D8pQPqFTWgXS1CcM8dtAKoQKm/dc0ce9zoKN6xqzQPYcnp2vbvaIgFDEf4+tJ9+CVk\nr/8SrMOBJIRPEQPG39curh9PF6kY0j+JnftL6Jse2wa9BSlll89JXXrkGAv+s55N2/PsVnVOngx+\n/hAQAUeLFKUzRtMYb7tNCYD9+5VWmZSkNEdbwH7qVLj7bujWTWmTVqvnLfHXP9zMU6//RFxUMEJb\nGa4vL9kadMZxPd50HTFRwVw6NoNN2/OYNGkrsbFKAEyerAL7kZEQFtaY8mkyqf2gAvv6cXVFA7XF\nH/THhYfDQw/5sGB6g3vWUXFrT7gjBFKklG2ZWaxDeMeu0jxUZO3Utieh3EChCJ94Tr34EqJ7DyR1\n5OXUlqtkXIGR8ViMNdRVHWlzQeCpIhXn3fIWQggsFisffPEHqd0inNahPdmx4utdzHpyFbV1FlJT\n7V/iiAgRghe2AAAgAElEQVTFDjnvPDXZT5igJvwhQ5QL6LPPlKUwdaryIRfprIW0NKVRTpgAvXvD\nfdN8PB78e/W/m1m37DaiIk78rLCtKeH46JRzGTU0jb/P/oQndMyu8nLlDnIkWVitDdtsqSFcWXX6\n+ENhoQO9tNyHT1++sUPSRjuDO0JggxBigFYK0uPoKN6xqzQPEWl9Mdc9BWSgKKL7kJZwdq94FQwx\nGHyOMmD8FAB++88/2y1jqKeKVLzxxBVt1MMTBzar697JFpYta/yyl5aqCR8Uw8THR/mOd+5UKR2W\nLgV/f3jvPRsDCKZMhPAYKClV1sLQodrEkWf2eB2HbvFhhIUEeLTN1sD37rVt0m6J2cTsfb+z6DnZ\nYB3f/RWjP8wh1tc9hsEFwK2RMcycodZ4FJdAoD/UaJTPmJiGmEBlZcO2ylIBRsm0idAtBg6XgjRZ\n8Zm1Hl9fPxJQaw9mPbyKgADJxIlWUpIVffTx+8d1GgEATaeN2I7i7fsCtwshDqDcQQKQXX3JuLPs\noLbJHQk2BpASEmOAL8B6NVbr/9i29HKEEFhN37VbxtCmUt8eD1I0oTH5n1/y9MMX2u1ztu1khM3q\nGjJEMXmuv175cePiFOvHalWCABTDxNHfe/31SgBUVQmkFBisVswWmJAPrwAfvAHffQoFpRBthFsn\nf8T8B8/nsnGtWxj07/cUYSG1WwTX3vNfzj27p13hoL9ff6arU7skco1GkuIE6enKpZaeDomxglyj\n0W0hAPDPlFRuro3j4ZwcSozVRBrVRDcmLIxTTUG8aiwitExtuzMqnuGBoZAMTxw6xEqjlUP5KmB6\nvsHQ6NpCQFSkD0aj4JpxZzH+0gGdjv7blCVwSbv1wg20Re6gbkPGEpacTkXWTiLS+gJwcM37QDz2\ni8TSgBCUZWAEopAy2O4YZwvEHJlH7i5XdwVXqW9bgj0Hy+y+WyxWtu+2zznf1jRCZ+hWerjN2i6u\nqOVQUTU94kOIiwh0ub9noC+F+RZKS5Wf+KmnFGMkOwuuN8MXoQ2JwyIi7F1FYWGKEiqtYCqSWKyS\nBFS+lQnAEuB/RgjJh2oU4+ITk4WrH1/F1en+TvvlLgyaZOoTDn36R0Flef0+gWjT37YjkOLvT362\ntLeOSyQppzROi15iNpFrNJLir/bZPtsm7HKzmU3V1XyFetOrgYuqqvihqop3UEFRgHuKi+gXHESf\noCAOScnv2vG/AwetVsrNZkrMivffUJPAosXwNjL+UqeEqg5FsxRRIcTbUsqbm9vWqk4od9CnrqyL\ntqKI6tlB5trdKCNHpYiGK4GlKEtgFPAxcDFgQOkEfsCP2KyFQDGcTf161T9UK8rKmJWTTQ8hOCQl\nV8dE819jBbGxkJtnJibah+pq0W5Lw2147u2NPPfWRmqNZoICVF+llPj5+XDjZQOYqcsg2lqKaHNw\nRhEtOGOwp5q3w8flZcwsyCYpTpBfLJmfmMoVkdH1+x3HKz0okO3mGuJioagYgkxq1L8ELvGHq66D\njz5Srh77OrMgLYBZTfwrUGrDtcB3qJwrdwHRwBHgBeA64AyDgcdPOYVBwa1L6wDwafkRLo2Mcrkt\ncfNvjc7x5Ng6jmtbjSk0jGtirKCgpPG4gv3Y7rVaMQhBujbOj3dPBWBadhYRAmr8ICkW8ksg0AhV\nAsx+EKe5iqKMUImaKf4UEsKP1dV0B7JQM0Jvg4FDUvKP+AT+F1HGSy83JOxrDzpwm1BEgX4OjfoA\nnrYrhfbXbrBnB/mhbqkhT5BaJJaBooXWAleg0hz1RTHkFmj/owkQ+SzuHl8vAErMJmblZLNWSgZK\nyVrggspSnn9B7zawMHcuzJ7TwPBpj1qjd998FnfffBbzX/rBbsI/kVFiNjGzIFvzHUvNd5zNOaFh\nxPr6OR2vi47VKK3wcIPGPh24ELAa4YP3ITlS+YInT4SIGOUiusmoJvwDwKM0cKsBzgJ6oxJw5QJf\noZ6gbUCWlPVaamvxTGFhIyHgbNuJgCsiozknNExp9qf4N3ID6cc2SUoyoH6ctwGjsrMwCMELwD/8\nsHtHJ00Eq4QXddvunwifG5WKuKG6mp+BJNRMsRYYaLWqdgsLkMd8unaheSHELOBBIEgIUWnbjFJs\n/u2pDnQU79ieHfQmjVNEJ9NgBEZpx9hqtPZAlT7YDQzm4W5RXBnd8ILlGo30EIKBmjYUAsTH2rsN\nEhIU3zgy0kpOQSVPv/lTu9YavXhMb7bvLrTbFhYaQEpCOL6+BhdndU005zt2Nl7dUdrPIZSGFwr0\nByK1zw8Y4f4ilVJ3vwVS8tX68v8Bm4GDKOGxCXu14gDKlpybnMJVh/NIE4IsTSM9Hj+2M3xTWcG3\nlZUUmEw8lJtbv73KYsFXtKuO1a6I9fVzOvnnGo2Um831Y7sJ6In9W54gBIFCcJqUJDm8o/ExKkBs\nx8iLUa68bkApSgBsBlId2k01GBh9+RncN21Lq2J47YGmsog+DjwuhHhcSjmrrTrQUbxje3bQWTRO\nEZ2HWhphQr2+x7Qzt6Gmhh5APlDKyDB7clOKvz+HNE1jIGoyKHLIQlhYqGhm+QVWDmSXsezTrQ5u\nhbatNTp70bfs2FNEn/RYkLDrQAkZvWKpPFrHY/ePZfRZXTZRZCM05zt2Nl5ZKK09ClX6Lgi4CZXo\nqhL4E7APlVlxIrAYGIe9LalWkygMRE1ATwEzDAb6hwTzXb9+jXzTrUGinx8Dg4JZWVHBwOAGemiI\nwYe5oSdPpThH155RG9seKOGsf8sLpcSAGvNyh3e0uAwsVnj3XcX62r9fBfM/1drphlICLCguib7d\n/VYr2R9sZta9o+mXkdCui7+OF01ZAjZH3n91n+shpWzsWOwC0AdrFTtoFJIEpMmEerW7o4z1v6Fc\nP6CG+3q1T+SDNKGya+dye0wopwba87Fjff14MDmZ0Xl5pApBjpTcHB7NlMlHiIhUeeSjouDhhyEk\nCD5ds7fRwpPYWPjqu31U15jsAsngmRWFCbGhLJw5joxearHYnoOlLHp1Aw/eNZK/z/7shBICsb5+\nzE9M5b677X3Htok31tePx7unkpmTTbSUFKBMXgPKd1+BmiR8UZP8TmyOQDisHXcHynbUa4NJKCEx\nHjUxFAD+NLh+nGmwrUG/oGD6BQVzVXQ0fiew5t8UHF1724CRQjBKSpJQ4ziMhpjMmIgI+gYGclVR\nEWEmWb/wr7ISpj+g1nVMnAiffAxHyiHYCAtR5Q9tE34meuewshBeA/oaLWQ++x1rPryz0woAaDom\nsEj7HwgMAbai3o2BwC8oRadLwTFNRMqwcVhNdahJPw04TGSvEMoPCpB3amdtQ9XTeRnhczcjZrwE\nwICI5Vz/uk8jAQBKE3ksL48UFGPg/5JTuCkujov+Ppbr7v2AK65UGQjj4uDwYfjt9wMcrbbXQoqK\n4Ok3N/DByp/Iz7eSJHwosVrBX5Cc7NvqfOMHco7UCwBQlaj2ZR0hLdkz9U2FEBegyDAG4FUp5QKP\nNNxCuPId29wGI8LD+K5fP+7Yt5+c2hp8sScJD0MlDknStlUC92jfS4FrUEFevTZYLAQTpeQxITgg\nFUvoKiE84vpxhjG7djYZWPu2T98m9rqPzja2eji69gYCvYQgKTSUtVVVhKAifMFAGbChooJvKyo4\nMziY2SkpvFFUxBpjOW+/rVYOA8TGQEm+utkEIBulCAzU/nqgdw7D+zQ4jlN9O3/69qbcQWMAhBDL\ngcG2xWJCiP7AI+3SOw+icZqItWR/fyHK0F+L7dUtPzBMC1Fnoob3EFCH8L2XgTfeT1hSDwAyLxjM\nqe8eanQdO00ETVM4nMcFUZEMHZjMJWNOZcWKPXZpCKZNg+uuUxpHbKwqWOHvb8tOqRaI3TdRLSha\n8rQkPd3Y6rQRp/aMYdaT33DZ2AwAPv1mN6f2iKbOaG51TEAIYQCeA8ailOVNQogVUspdrWq4lXDU\nvB3dBvcmJrKjtoanURqQTavfiXokfFEBwAXADGAj9q4fgHOAngYDOVLyRPdURoQrwRNsMHDMavWY\n68cZ3uqlTMk3SooBuCZKsWQ+OlLmMdZFZx1bGxxde9tQitjuqiqWoawyR/rHMmD8sWMYrVYujori\n47xySksbsoWWlioB4Kj9n4tyCB9C7xxW1h7acdnmzhkM1sMddlCGfrWwlHKHEMIzKgXtp1U0ThMR\nAsRpfw4BYVEDcgZK7o/Bx38MZ/z1DuL6ntXsdZxpImlCBSEjgXOG9mDTH3saBYnPPhtWfiWoq5Xc\nd5/yQ+qLj8THqNwlrU0bYcPi2X/mreVbefUD5dUbMqAbD989Cj9fHz545prjbs8BZwF7pZRZAEKI\n94DLgU4xUYBzt8GI/HxSUIyemaiXOAlF6WwgA6sJPwH7p6anEDzUsyf9g4Mb+fnbatJ3RHeNXfRd\nVRWrMxosxIeCkhm3exezPXOZTj22eteeLeh+RXQ0P5aWUkFj+kciKrCbCKyrqmJcRASxRrhvonrn\nirQFfaEO50WjBH4hyrIYjrL8boyO5qqyMnVtPwPzZnbOYLAe7giBbUKIV4B3tO83ot6FVqM9tYrG\naSKqUW6eozQKCFul1i21XkDKPMJTert1HWeaiJ7+d8ZpiY3SEBQWwoYNUF4hiY2FRYsU/1xffMRU\nC34OucdbQzkLCvDlHzecaZc62gZbsfJWIBn149mQi5o8Og2cCevumkWQD7yMKqQdgnrhHX39hdg/\nNYeB/sHBHvfztwRSwsajRzkrNBSATdVH8WDG+E4/tpdHR9dbYCn+/pSZzXxQWkoE9vSPJ1Bxmv9p\n//ceO8btcXFUAD5GMOSr9iqAcuzHuxTl9vFHScDpaWmMCFOU48lJSYqRtmhMpxcA4J4QuB2lDE3W\nvn+Hyq3sCbSbVuGYJsJiPoS0CJBGGngch1GxfntiX4+xN7idDsKZJmLzAZuB6IgghFDlB20xgcBA\neP996l1EW7aodLb6VLYTJ4LZDFMmG0ju5ttqytmmbXk89dpPjYrKrP/vnU2c5Xk4FpVpj2VzJWYT\n5WZzI2GdJyVWGhyBEqUm+OPAKAGsNDw1xULwRBv5+VuCRampTMvOotJiRSKJ9PFlcWqq3TEdsRq8\nPaBfGaxfeGdFuYICUS6gRNTEb+caqqrir7W1GITgO507dxgqhjAMFV/IASxSMsNgIEtz+10e1bBA\nzaYImLuAAAA3hICUshbFbHuqDa7frlpFTMZgBtw0FWNlGf7h0fz+7nLMtatRBiHAzUAE6tXehJoK\nkhFY7dqpqzrCvm25nGY2Eevrx57aGr6vrCTOz4+zw8K4PDqaJH8/vqyo4MaAAJL8/Xi/rJSBh0qp\nrjGRlubP/AVGCgqgrAwee0ytSLS5f0ymxsXH4+JgaN8MbrnqdA7mljtdUFZ65Bg79hSBgP69G9eo\n1WP6/NXMuWc0A/ok4GPwOJMkD0WdtiFF29YIeiEA6sVsS+jjAEYpGSkEvTRhfVtcHF8UFbGBBj/v\n6SitTz/hP6n5+nccU7RhmwXQWXB6cDDf9OlLpUXFkcJ9GldwzczMJDMzs/773Llz3W3erbHVj+vA\nqirODgtzt/0WwzHG83j3VC6PjibXaKzn9fug1nOU0JjNlYJyCaU7WIinGAzcmZLCGcHB9XEdaJx6\nojOgJcK9KYroB1LKa3WJ5OzQ3gnkWps76PAv37D17YWaq6cbwqcQKa2ocM75KOOwEmX8ZaBY3WrJ\nj2ISKxT8spo9yxZQHABPVhs5IziYH6ur6aa1JIHhYSGsr6tWVYmKlWsnMR6K73iLq87vR0GBldJS\nWL0aPv9cFZgoKm5w/xw+rASBYyrbDaY9fPn9blKSfSkpwY4dtOLrXcxcuJKISNW2QQiemHGBS/ZQ\nWEgAY5opSN4KbXETcIqWDiQfxa+9oSUNeRJ7amuYnp3FJ0CmbcWolMzQfPkAbxQXky8lQ1F0gXLU\nzdjonhOlZES4MvszwyM65kZc4MOyMq6JjualoiKn+yfEx3viMm6Nrf59LVjxiSeu6xIlZhM7jh1j\nRk620uC1sc3MyWZEeBjBBgMFqADwrcAqVEznTOwtvFxgUHAwrzlYiLlSMjY8vNFk35kmfxtaItyb\nsgRs7p+2TCTXIo1x43HmDqqrOsK2pYvB6o++PrDwOQd8RyIM3bAas1CZYa7Anhw4goNff0iPUSoF\n855lC9hgqmOgSWUTGl9dbWdSjgR+qKu2Y/9MmQLPPq9YBpMm/s4Dfx/J5Ht/wGSWvPCCsgBuvLGx\n+8eWpKy4WGWm/PhjqS0oM9uxgwAeXLSKJU9bddeUzHpylUv20NmDU5j3/HdcOPoUu0yTAzIS6j+3\nVFuUUlqEEHej3jdbwH+nWye3EVaUlTEzJ5tEVODXlrMnRQgifX3rX+gnuqcyOiebGCkpRC0Gsmk7\n44GFTjJFdhYcsyqLtdrq2dTUenS2sbVp/xFSEotD2keNkAGK/nE71I+/LfGZ3hGc7u/P5EOHiJSy\n3vVzGNqM0ttZ0BRFVAuLcB7wnZRybxtcv100xpqyAgyGBCyE4Zj584y/3sGRA9vZ/5UFFQZMx/5R\n6o3BUEVNmXJUpPj4MtCkCqxVoKSW/uh4oMZh+XlSEhQUQJ8+yu1TZ7Qwamgvtu/fT3q6cgElJ9uf\nk5amVik++4ygW5KB4cMt/Pijo4tIsGNPERHhgSTEG0hPt9hds65WuGQPbf5D3c+2XQ2pI4QQvN96\nZhAAUsqvUCZVh8PGBFqn8/OOQWmDB6QkWFdj2RZUXFpcwvOFBewHl0H+zoZbYtW6j0nxCQQaWkfz\nbQqdZWzt8gJpHdKP1SFtrMrMZopp8P+vBS7SvtssvAnAPqOxnga6FrhMSj7r08fpWqATCe4EhlOB\nl7Xi0r+iAsPfSym3tPbi7aVVBEUnYrUWojyB9kVkwlN6ExgZz/6v3kcxhg7h8ChhtVoIilaZ/3It\n5vq9ESgTUn90EVDrsPw8P1/RPPfvV26fp9/YQEQElFeobYmJ6hhHxlBUFJjNBsrKcFqhKDfPxF1z\nPuHBu0ZTWGRtdE2BdMke+uDZv3j6Z+60cMYEikCZuLE0aNA22BgeN8bFsrSkhMzCQo/m+GlrjNm1\nkzg/P/4UEsKfQkI5KzTUaVygq8NxXF9EafanoFJ6XBMdXZ8bSu/ntyWF11t4C1CuP9u2TCBdW9tx\nosOdwPAcACFEECqXwnQUr98jT1V7aBUBYVH0vfof/PH+0+hCfFpMAMKSepA68mKyv78IlSJsGNi8\n/AbBwBun17ODeo+fydnL5tMzAPKqjZg009EWExAowpFt4VdpKVgtKiNhZQn4C1iiuYrmzVPHxcWp\nSX7SRK2SUSlERggemePD4/ePA1S20dAQyaRJFlXZqBKmT4e0NAv3TfuOB+/KZMrkNYRHWCkrs8UE\nzncZHC4uq2bBy+spLKnm7UVXsudgKb/9ns/1l/Rvw5HoGDjSdm3UQNtksePYMacpnGN9/ZicmMSN\nsbGdMgjoCj+epnIS/Xz0KF9XVjIrN5cIHx++7tN+KcvbA47j2hfF5LoLmArcGRdXf1we9uRwR+Ut\nG8X37ypWnyfRrBAQQjyESpQTiqLR3A9838b98jgiuvfGJ6A3ljpbvu0z8PU/r74QTL9rJ5M68nIq\nsnYSHJuMxVQLQHhKbzt6aNKQ84jOOJOxGZ/Ta/pq5hw8iEHKeh/jaOAWwGiE6w7DR8C9wIJ8lVp4\nicYCKi+Ha66B3ZuhIk9NTP0A8uGuAAOXXjyUy8/LqGcA2YrJ5ORXMP+VVbz9trl+WXtCvKBf73h+\neO9vbPgth+Ijxxg5JLXJ5HPT/rWKay86jWff2ghAr+5RTJzz+QkpBPS03XgpycaeGpiZl8cFkZEu\nJ/jOwP0/Hhw2GtlUfZSfq4/yR00NGUGBnBXS+joFnQ36cU2QkiyUCjcNuCkmpt6N44y2fWN0NCPL\nyojT1oUI7ZxM20KvLmL1eQLuuIOuQrGqPgfWAT9KKevatFdtgKDoRKQ1DxUaanAH2dw8oCwCW1qI\nphAQFsUpA1M4LTiYPCRmf3ghVtUitVoVvbOkRCWbKgRmA5ECZvlBrL8KAksJ3bpB0VG1MuEMlAn6\nBJBfZ2XtB7/x1ru/Mm/mOC4b14eYqGBiooLpnhhORTl2y9qzckzs2FNIVl55fdHtxa/90GRuobKK\nGi4dm8Hz72wCwNfXgE8b+pE7GjZf/zeVlbyWm8tAzczXBxBPlBd+yB+/Myg4mHsTEniie2rzJ3Rh\n6BeGGa1WDhoVY8/Rj++4gMzm8nOk+doWenUVq88TcMcdNFgIEY6yBsYB/xZCFEkpu1RFElc1hW1a\n/muv3tvk+fpFKLG+fvCqijDgb19ZavJkmDwF/Pxg5gwQRsU5utKhYMWUKfDkkzbGEFxmhBiU0PgJ\nGHjMpLTU+asZMaSB4RMTFcysCaOZNOkbUlIUc+jWW+GxF9chBFo5O5rNLRQc6MeRihqElm3ytx35\nhIWe2KZvrK8fY8PDmdvEiu4TAaszMth4tJr/HTnCc4WF9AwIYHhoGONjOk9xc09Cb6k1tcjI0aJz\nRvPtalafJ+COO6g/ivk4GpVNNAcPuIOEENegEtH1BYa2R2rqbkPGEpMx2K7urztoahFKnI4JlJWl\n1gS8/LKanINDINSoAlGOBSv0jKHYWJhxGBb6+3CKQTCw1gxoxSmcZCHs1zuelGQ/7r/fRGKisgi+\n+lIQ4C/sGEJN5Rb6v3tGccfMFWTllXPlXe9ReqSGl+d1qrLSbYKmVnSfKOgXFEwP/wDSAgL4ufoo\nH5WV8ePRox0mBO6485kOuW6HY3Pzh3gaX4w4/nPccQfNRzGCngE2SSlNx38Zp9iOqtL2sofacwsB\nYVFuT/7gPNGYfhFKvpYHKCZGFSR/7jl7rv9RVCAqvwnGUHEJhKOWqh212mupzrIQdk8Mp6RE4ufX\n4BI6ckQihHQ7t9CAjAT+++y17M8uQwLpqVH4+Z54DBJncOYaOJHw5927MErJEI0d9L/ep9Ynl/PC\nC0e44w5qE/VQSrkbQNj8EZ0UTWUFLTebSTCpuqPBYRAe7sDjj4Vuh1Vd2hAHxpDBAA88oGifoSFw\nj/Rh/iyVhTxz/mpSfQ1km61OsxDGRAXzr2njuG/a6vrSdTYW0bSpq4iPN1BUZOWx+xqf++U658s9\nDuYcAeDC0e4lyuvqOJHN/qXp6SfsvTUFfcGo41H0Tna4Ywmc1HCVFXRH9THm5eViAu4zwqJSMPs7\npHooUZzaXf4QGAu15YLSEomvn6KHBgYqauj/PezDitdvrGfzjBiS2mz1sMvP61PPGLIdt+LrXUgJ\nRpN0mTVy9foDLu9VIFotBFrr5jtpXQftiC86ugNtAMeCUQPGT6HbkLEd3a0ugTYVAkKI1aiFmfWb\nUOl1ZkspP23La3sKznzIDyYn81heHt+hCk1MRgVzZxgbeP/FxWA2wnx/eK4+ICyZMtmAxSKZNUsS\nHQ2VFQbmTz/fjs5pYwI1B/1xpUeOMXvxap5a0nRgePGDf/bsD9QYHeLm86Lt0RFxPHfQuGDUNrYv\nG0VMxmCvReAG2lQISCnHeaqt1iaQOx7omUAAaYEBfJyRUZ9B0OYiSpISf+BU1PKzR4FJRvhrHjwP\nPA4QL0hPV2p5ejokd/PljitHsS+7jFNSo/nzyHS7ibyl9YNzCipJTDR4rOgMtCyBXGd08zlzE3hd\nBy1CpxTwjQtGDcRqiiVn/aeccsEtHdq3roCmsoh+ipPsoTZIKS/zYD+anTBak0DuePBxeRkzC7JJ\nihPkZkmkUXKqMNSzgmwrS/darWSgVh0cEHCKHyTHQl4JWI1qNWopIB0Cwjm5Jh5+6msS4lUKid37\ni5k79VxWfL2rnuPfkvrB3RPDKSiwuh0YdgetSDfcaeDMTQCckK6Dgi3rmtyfOGh0q9rvjAIebAWj\nDqEy/oSgqBhl7Fv5Pt1HXEpAWBRl+7ZRsnsTsRlDiT5FCQuvIqDQlCXwZFteWAhxBfAsKn3LZ0KI\nLVLKC9vyms2hxGxiZkE2i56TpKcrps20ibDSaCWfBlYQqLQMtsRVPf1gsW4NwKSJcIURFqSmIQ1w\n393ZJMYK8isEJpOFF1/SH7uVS8ZmMHvxahYtNrvF8XcGZ8Hi1hSdaQqedPM5FpXxpHHqyk0gpUSa\nV2A1qQlj+7IrickYjPFoBRVZO4lI62u3aLCrTBZFO9a73imEnRA4kYrKBIRFEddvCIVbLkIVkMwB\n/gZyJTVlBWx98zFKd28FUtj/1fvEZJxOyrALTkhFoCVoKoto02pFKyGl/BiVjbnTINdoJCnO3n3T\nLQYO5cNQ7FPT2hJSbQJSHdYAJMcJ/hXQs34hyjmhio648epuvPLh2kYMonUbszziynEWLHaEK3aQ\nDe4EhtvKzQfwxA7PWXmVuXsRwj7PqxApWE35KM5Wd6AUKcP5/YOnKdyyAdskkjryYvpdO7lLBRwH\n3vSg28e6svK6YhyvruoIxTs24ZAMBGmpofzQTk0ANOwr3T2M0r3bwbpBpxyMPmljCO4sFuuNcm+f\nhqrOBoCUslcb9qtDkOLvT26WPdf+cKmqMOW4snSv1co21L48B5dPSSn0P6VhArbREQOGpvLYi40Z\nRKPPSuON5b96xJXTXFC5rdlBjZrsIGT/8Am///cFlcdDx+2yGA+gsqDEAVlABtK8i8ItxSjejLIO\nsr+/iKTBY5xYEl1jsijasYGjBQexmIz123pfeHuz53lKwHs6hmezxnz8g7AYa+rTvdSUFWA6VgUi\nBRWZW6WdEQEYKNm9icYJ35MRohKpqyBo8EmtzyPWleHRymI6vA7MQZWXHIOqzXDCJpmRRsm0icoC\nyCkFkxHOMxjI0a0s3VNbg5GGWrQW2xqAGKgsEyxMcr4CtXePGG689HQmTdxKXKwSADdeejpDBya3\nmyunrdlBHeXm07tsCrZ+zx/vP4NKxFFCw0jZBIABqEMlwt2NqkAbiSo30gOVTjyCwm3fofLD6ieQ\nbghS9m0AACAASURBVJ1+stjx3pNYjLWU7d1MyvBLKNiylsi0vp6+TJMC3pMxPJs1JmUk0lyIwa8n\nVksWQhjw8euF1XIIq6kWFYmLR5WIMQMWEk/PpHj7Iuzzg+YhLRJ9BUGz0WiXR6yrwtOVxWwIklJ+\nI4QQWkH4R4QQvwL/19KOdgY48/PmGo2cKgysNFo5lK+mg0whuCMlxa683OZjx+gB/EBDLdo/GeHP\nplgm9U5scqHO3KnncsnYDNZtzGL0WWkMHZgMuOfK8TS+2XCAPQdLqTM2VKKacvuwVrXZEW6+w798\nw9Z3nsQgorBaSkGaUI+2QGn8q4H/AYtQL30Ban12IEogCFQ2+QZLAC7i0JqPtf26GhSmg/j4d+4i\nI+UHt3POrDf54fFb6X3R7fQcex2/vDC91e12hICvqzrCtncWIS1PoFIxfoHVZASuQfIwZovNOTEd\nWIES+Nu0/zUAhKemU5k9DFVVOI+w7r04ejgbaVmLbVyF6FKp0DwKd4RAnRDCAOzVCsDkodJKtwpC\niCeAS1Eq2X7gdillZWvbdQeuGCO2hWH5qBjANuCglJgsVruJ/YzgYHLA7rgC4IbY2GZXan6yehcP\nzV9Nmq/BLksouL8+wBOYtfBramrNbNicww2X9OfztXsZ1LfraUJ1VUfY+uZ8QGAlEKXV+6Lo7POB\naOBslGa4ngZtcLjWghn1OAfhaAkoQeGPMoBTgUMInxgsxpp2uLOWw+AXoP77B1JbUYJfcDh1laWt\nbrcjBPwvL81GWiyoEiYBqDEKR43byyit34IaH33h0ESgiu3vLESNocQmFKpy9oIhHr2FJy3xJy2l\n1B23zmQgGJUW/0xUec5bPXDtVUA/KeUgYC8wywNtNgs9Y8RcuwWraR3bly2hxGwi1tePB5OTGQac\njnr1HwEeO5xHibkhZVK0r5KdmcBg7b9+uyuUHjnGQ/NXs7bOzOZqI2vrzDw0fzWlR455+C6bxy87\n8lny8AVEhAUy9Y7hrHjpeg5oqSO6Ekr3bkZp8j8Be4BXUXHNBSjK4D5UBnQrymcM6uXPAF5CuYQq\nUITeNajieWu0bUtROsptKFdSMtJSSkVOW1Ra9Rzi+5+N6VgVvcbewPoFd7Lukb+QdOZ5Hd2t40bh\ntvVUZu8DfkRZcuWoCh0VqPHep/0PATagxm0iatwPoer8vYwSGD+jSslox1tLtONAKQWKUlpX1fXe\ngdbCndxBmwA0a+BeKWWVJy4spfxa9/UnlBhvczhbWGLwSSXXWEusrx/9g4NJF4JXpKQHypnwrkO+\n+VyjkT4GAyut1np30PlOCpDvqa1h87Fj9fnNd+wtIk7YT0XOsoS2BwL91dAHBfhSUHKUqPBAikqr\n27UPnkDxjp+w99uPA+5ATfJ6X76tmux4tDC/dmwSilLY0+H4dJT7oBtq6d9GbFbEruWjSTx9ZKeN\nC/QcOx4fP38SB2US1+9srGYjBt+ulUDu8C/fsPXtJ2gI6m5C+fxDUOOiH6seqEl/KCoOdDnKkrMp\nbo6B4R4oAXEBiu+SBbyIj+9jnT7e0xZo1hIQQgwRQmxHvTnbhRBbhRBnergfd6DS7rc51MKSLNTt\ngK24jI31YytFF4ASAM7yzTu6jfKdHPNwbjbnH9jFIrP6P37/Hu6Z+QnWWjMZwPu4zhLaHhg7oicV\nVbVMGD+Ei+5Yytl/efW4Fqd1BtRVHSF/8w9AGQ3jmY8y/feiH2PlNrgTNZFkotwG+dr2cOCgk+Or\nEYZCDP690E8iNiZJZ8VPiyfUf/bx88cvKNRuW2eHzVrH+jnKQrPx8HJR8RpbsUiw1QFv4PDlohIe\nV6CEgL4SuP74UvDxRRVK3AX0bVRk6mSBOzGB14CJUsrvAYSKoLxOw1vhEu5wjoUQswGTlHLZcfa9\nRXBVXCZ26wrAvXzzzR2zp7aGtytL7YrITJpYzXtGuIIGj7Sfvw+POckS2h64a/wQAvx9uSizN2PP\n7kmd0UKAf9dKJV1TVoCPby/M5lko5103lIvAB+XIG4PSAvfS7axMEk8fzW+vPALSH/gXkAXCypC7\nZnGstIBdy0cD3bCaDiJ8YhGGK+hz1V3sWv4f7ILDnXSyqKsspba8BIvJSEXOHmwL/s21x7AYu04x\nwAZrPRMlrMegYjsqYK8mdltlb1tMYDSqJFME8A9tWzjK8jNgVzccM6kjLyOqV3+2L5vktMjUyQR3\nhIDFJgAApJQ/CCHM7jTeHOdYCHEbalTPba4tT/KOnRaX0YQAuJdvvqljNh87RrzDArK4GKjIV98H\nAumBfsz41yWM/lOPFt9Ha3DFhPf58rUbAQjw9yXA35cL71havw06/6rSBquuL0qbWw2GOzH49sJq\nfADFZj6ET8BtpI26ksi0vpx+y0y2Lf1/9s47vsmqe+Dfm3QXWrpooaUFylYqICKIMvR9edGfgAOV\nqSKvvoiL4UBQmYriAJw4UEHEgVtEFBVQUEEFZAiCjO4CHbR0pknu74+bpEln2ibp4Pl+Pvm0efKM\nm5znuefcc8895zl0uiLMZkgY/zAR3VU9qqgLLqsQi+7bMgRvv8AqK9I1Jk4f3Enqjq8pPnOKQ5++\naNvu5RdIlxF3NGDLaofjaP0mIBJ0/0efyQvwDmjJyb0/cmLzF0AvlCvnDZRbx6okDMBylBIAob+b\n3rfN5Wz6MUASmTDItiK8LkWmmhvOKIGtQohXgfdQpsVNwBYhRB+AumYSFEIMR8V1DXKmZrGrcwfV\nVFzGmXzzVe3TOyCAU8fKLQrLUjYKWAatUnJ+l9b1+AZ141RWARmn8ykuMbL/8CmkJed0foGBomLH\nekGNPXdQZaO67tfdw0EHyz0daU6zWe7VVZer6p6oa0U6TxNz8ZXEXHwlGXu2ENVrSEM3p85UPlp/\nkMgEVTYrtFMCwbHdLQEe/sCdWMM/y1JCPGZ37P1EJgy0HV/+Wo1Vnp7CGSVwgeXv3HLbe6OUQo1W\nfBW8gIrd2mTJR/WrlHJqHc/lcirUFK4FXfz8mRgUxl1Ts2yLwi7xDeQ2UcK8aorFeIKtO0+wbsNf\npJ86y4IXyjKDtAj04aH/1aE2XQNTWQftVYPlXpcHvyl1FiEde7Lv3Scpzs3koqnPcDb9OGdOHKDd\ngKZTPrQmxWv/eUluNrnJhxySwzUFpd1YcCY6aKg7LiylbLQlrKqqKVwbFsbEMrE4QkUHdVTRQScX\n9vPoYrDKuOHK87jhyvPYsOUIVw1xvQgaYv1H+Q66qVju7mLvmsXE9L+Ko9+sBiCwdTv2vDWvSSkB\nqFnx2n9e3spvSkq7oXEmOihSCLFSCPG15X0PIcRk9zetYbCvKbzLbGaLlDycnOSwTsBZuvj5c1No\nGF381ArTsJAAenWPajAFYE/fnm25f/G3TJz5KQCHj2fx/vr9rjh1g6z/KI9vyxBaxXU/JzuC0oJc\n2vS5XNUwBXR6L4Su2WZ60agnztwZbwPfoKbWQa3ImeauBjU0tprClvf2NYWbEzOf+JbB/eI4mZkP\nQMd2IbzxYf0LRUkpv5NSmi1vf0WF52h4EL2PH4aCXFtyn5zjB/DyD6z3eYUQS4QQB4UQe4QQHwsh\nPB/brOFynFEC4VLKD1FLLpFSqsxMzRT7msJQ+TqB5kB2bhEjruiKTqe6Ci8vHXrXW4seW/+hUUa3\n6+7mj1dnUZiZxi/P3cnedxbRY7RL7LZGMcrTcC3OTAwXCCHCsAQdCyH6o1ZiNEucWSfQHAjw8yYn\ntwhrkahd+9Np2cI5RdcY139olBHcrisX3/cCBaeSQUoCI2PR6etfrKehVvlruBdn7owZwBdAvBBi\nO2oh7ej6XlgIsQC1vtuMWuVxq5SyUSzDdGadQFPnsXsGcdusz0lMPcO1d75PVk4Rry5ybuLQXes/\nXF1Z7FzFVFpC0k+fknN0HwhBSHwCsZeOQm9JLAcuWQNyG/B+PZuq0QgQ1jjxancSwguVjEUAf0sp\naz9LWvGcLaSU+Zb/7wF6SCnvrGJfad/Oq9xQY/jNlfe6/JzlMb44xO3XqA1Go5mjSdlIID42BG8v\nxxXDMQOfc3gvhEBKWW0eecv6j2dR6z+qTV1ZXq7gHtmea+x+8zG8fANoe9EwANJ+34SxKJ/ekxcC\nsOHuimmTrbKtxSivj5Sy0pGAEELOnVsWUf61C4rKaDjHg+cbHZT7/Pnza3xmnaksdgOwUUp5QAjx\nCNBHCLGorovErFgVgIVALHMOGp6huMTI6k//5Le9aQgB/S6IZsKoBPx8622JN+r1H+cCZ9OPMWjO\nGtv7sC59+PHxCU4d66pRnqsXd2o4R10WeDozE/iolPKsJWfQFahcva/UsY0OCCEWCSGSUAk+mnSR\nmqbG9EUbOXw8i0mje3Hr9b04fDyLaQs31vu8UsrOUso4KWUfy0tTAB4mOKYLOccP2N6fOXGA4Hb1\nTw5ot8p/pDOr/DWaBk7lDrL8/T/gdSnlV0KIRc6cvKahpZTyEeARIcRDwD2orF+V4uqapec6fx/P\n4oc1ZWUhLunTjssnrHLYp7HnDtKonNzkw/y69E78Q9SjV5RzksDWsfz0xC0IAdx9tK6n1kZ5zRBn\nlECqJXfQv4GnhBC+OFljuBZFq9eiavvNq2oHbXjpWs7v0ppd+9Ppc76qbrD7QDoJXSMd9mnsuYM0\nKueiqc+45byNeZW/Rt1xRgnciKq+8IyU8owQog1qSFgvhBCdpJT/WN5eAxys7zk1nGff36e45s73\niY5U631ST+YRHxvKv25ejRCCTasmNnALNepKY0xzrdF4cSZ3UCHwid37dFRS7vrypBCiC2pCOBFo\nOlUvmgHvPHttQzdBQ0OjEdBgQdlSynqvNdCoOzENUM1MQ0Oj8aGtzNFoVFQWw66hoeFGpJSN/qWa\nqdi8ebOsC9px9T/OIgdNrs3wOFfKVpNr4znOGbk2ufyydQ1Z1I5z7XGupql87+Z+nKtpKt+7uR9X\nHU1OCWhoaGhouA5NCWhoaGicwziVQK6hEUI0/kaeI8gaklHVBk2ujQtXyVaTa+OiJrk2CSWgoaGh\noeEeNHeQhoaGxjmMpgQ0NDQ0zmE0JaChoaFxDtPklIAQYoEQ4k8hxG4hxEYhhFPZsoQQS4QQB4UQ\ne4QQHwshnMqbIIQYLYTYL4QwCSH6OLH/cCHEISHEYUuKbKcQQqwUQpwUQuyteW+H42KEED8IIQ4I\nIfYJIZwqkSaE8BVC7LD8jvuEEHNrPsp91FWulmPdLltNrnWnOT6zzUquNa0ma2wvoIXd//cArzh5\n3L8AneX/J4HFTh7XFegM/IAqqVfdvjrgHyAO8Ab2AN2cvM6lQC9gby1/jyigl/W3Af6uxTUDLH/1\nqMLh/ZqaXD0hW02uDSPbxvzMNie5NrmRgKxjWUop5XdSSuu+vwIxTh73t5TyCKogTk30A45IKROl\nqsP8PjDKyetsA3Kc2bfccRlSyj2W//NRKbmjnTy20PKvLyqPVIOFitVVrpZj3S1bTa71oDk+s81J\nrk1OCYBLylLeBnzt2lYBSpjJdu9TcFLArkAI0R5lnexwcn+dEGI3kAFsklL+5r7WOdUeV5QbdYds\nNbnWE+2ZrUhjkWujVAJCiE1CiL12r32WvyMApJSPSCljgXdRw0unjrPsMwcolVKurc1xjR0hRAvg\nI+C+cpZXlUgpzVLK3igL62IhRA83t7FOcnXmWMs+zU62TUGuoD2ztaUxybVRppKWdSxLWdNxQohb\ngauAy+t4vZpIBWLt3sdYtrkVIYQX6oZ6R0r5eW2Pl1LmCSE2oyrI/eXq9tldp87lRhtYtppca76W\n9sw6SWOTa6McCVSHEKKT3Vuny1IKIYajymKOlFKW1PXyNXz+G9BJCBEnhPABxgBf1PL8dVm6/ybw\nl5RyudMXEiJcCBFs+d8fVUP6UB2u7RLqKlfLse6WrSbXetCMn9nmIVdXzC578oXSoHtRs/ifA22c\nPO4IqozlLsvrZSePuwblMyxCldX8uob9h6Nm/I8As2rxvdYCaUAJkARMcvK4gYDJ8nvstny34U4c\n19Oy7x7L7zmnKcrVU7LV5Op52TbmZ7Y5yVXLHaShoaFxDtPk3EEaGhoaGq5DUwIaGhoa5zCaEtDQ\n0NA4h2nQEFEhhC/wI+BjactHUsr5DdkmDQ0NjXOJBp8YFkIESCkLhRB6YDtwr5RyZ4M2SkNDQ+Mc\nocHdQbKR5TnR0NDQOJdo8BXDQggd8AcQD7wkK8mHIbSapY0GqdUYbra4SraaXBsXNcm1MYwEnMqH\nMXfuXNtr8+bNlS56mDt3bq0WSdRmf3eeu7Huv3nzZoff3U3ybxTftTGcuyH3bwi51ve7NOdzuKoN\nztDgIwErsoZ8GPPmzfN4m851hgwZwpAhQ2zv58/X5uw1NJobDToSaIx5TjQ0NDTOJRp6JNAGWGWZ\nF9ABH0gpN9T1ZPZWq6v3d+e5m8P+7qQxfdfG1BZP7O8KUrbPcGq/88KSnd63uZ+jrsfHDHzO9v+Q\nIUOcGr03eIioMwghpH076ysgDeewv6EAhBBIF08Ml7//NNm6n/JyBdfKVnteG466PLMNPRLQ0NBo\nQvj7+2cUFxdH1rSfEC6zFTScoF3bUH7+8JY6HaspAQ0NDacpLi6ObAreg3ON+ijdBg8R1dDQ0NBo\nODQloKGhoXEOU6M7SAjRF7gMaIuq1LMfVek+x81t09DQ0NBwM1WOBIQQk4QQu4CHAX9U+bVTwKXA\nd0KIVUKI2KqO19DQ0NBo/FQ3EggABkopiyr7UAjRC+iMqq+p0QQ5k1fMycx8/Hy9aNcmGJ1Oi+ho\nLmiy1XCWKpWAlPKl6g6UUu5xfXM03E1efgmrPvmTz787RGmpmbBW/hQbjGTmFNKnRxtuvu4CLunT\nrl7XEELEAKuBSMAMvC6lfN4FzdeoBk/ItrmRmJhIhw4dMBqN6HTn5hSpM3MCHYB7gPb2+0spR9b3\n4lpn4XmmPLKe64d35+OXbiS4pZ/DZ3sPneSTbw6SlJbLmKvPr89ljMAMKeUeIUQL4A8hxLdSSi0l\niBtxVrb3D/R824xGI3PnPs769d8TFRXB0qUL6dGj0lyRHkVKaV1Q1dBNaTCcUX2fASeAF4Bn7V6u\nwNpZnAcMAO4SQnRz0bk1KmHtsuu5fniPCp0EQEK3SObdN6S+CgApZYZ1pCilzAcOAtH1OqlGjXhC\ntlWRnJzM2LGTufTS/2PBgsUYjUaHz6dMmcayZT+yd+88Nm0axIABl5OSkuKwj5SSY8eOsX//fkpL\nS+vUjqeeeoqYmBiCgoLo3r27LePwk08+SadOnYiIiGDMmDGcOXMGgMGDBwPQqlUrgoKC2LFjB1JK\nFi1aRPv27YmKiuLWW28lLy8PgJKSEiZOnEh4eDghISFcfPHFnD59GoC3336bHj16EBQURKdOnXjt\ntdfq9B08jTOLxYrdZZ1LKTOADMv/+UIIa2ehWYwe4OA/p0nOyMNkMtu2XTm4s0uvIYRoD/QCdrj0\nxBrV4gnZWsnOzqZv38vIyroZk2kUu3cv49ixRN5+ewWgOvfVq9+itDQRCEfKyykt3cX69euZMmUK\nACaTieuvn8i33/6AXt+SyMgAtm37hqioKKfbcfjwYV566SX++OMPIiMjSUpKwmQy8fzzz/PFF1/w\n008/ER4ezr333svUqVNZu3YtP/74Ix07diQvL8+24OrNN99k9erVbN26lYiICCZOnMg999zDqlWr\nWLVqFXl5eaSmpuLj48OePXvw9/cHIDIykg0bNtC+fXt++uknhg8fTr9+/ejVq5drf3AX44wSWC6E\nmAt8C5RYN0opd7myIVpn4VlmPvEtB4+epmuHMIRl0lAgXNpRWFxBHwH3WUYEFbBPET5kyBA6ebvs\n8ucszsh2y5YtbNmyxSXX27hxI4WFF2AyLQCgsHAIa9ZE8MYbL+LlpboYvd6L0tKyGBMhimyfAaxY\n8SqbNqVTVHQC8KW4eDaTJ9/LV1996HQ79Ho9BoOB/fv3ExYWRmysCl589dVXeemll2jTpg0Ajz32\nGHFxcaxZs8bmBrK6hQDWrl3LjBkziIuLA2Dx4sX07NmTt956C29vb7Kysjh8+DA9e/akd+/etutf\neeWVtv8vu+wyhg0bxk8//eRRJVAXuTqjBHoCE4HLUX57UCUgL6/Vlaqhtp3FeWHJDNAmuOrF7r/S\n+WFN9blG6tNRCCG8UDJ9R0r5eVX7la8TkbL9izpdT6MMZ2TryloRqvO096nLCp9Pnz6d5ctHUFg4\nAy+vfbRosZNrr33Fts+uXQcoLLwWUK4so3Ese/eOrVU74uPjWbZsGfPmzePAgQMMHz6cZ599lsTE\nRK699lrbxK+UEm9vb06ePFlpuoW0tDSbAgCIi4ujtLSUkydPMnHiRFJSUhgzZgy5ublMmDCBxx9/\nHL1ez9dff82CBQs4fPgwZrOZoqIiEhISavUd6ktd5OqMErgB6CilNNS5ZdVQl87C01kJs3IKSc7I\no11UEGEhAR69trvoc14bDh/PokuHsCr3qWdH8Sbwl5RyeV3bqFE3nJGtKxk+fDgBAbMpKpqDyXQR\nAQHLuemmyQ6W/uOPz6VDh3asX/8NbdqE89hjPxMWVta+hISu+Puvp6hoCuCDl9endO/etdZtGTNm\nDGPGjCE/P5877riDhx56iNjYWN58800GDBhQYf+kpIoR7m3btiUxMdH2PjExEW9vbyIjI9HpdDz6\n6KM8+uijJCUlceWVV9K1a1fGjx/P6NGjWbNmDaNGjUKn03Httdc2iQlnZ5TAfqAVaqGYO2jUncXn\n3x1iznObiIrSkZFh5vEZ/2bUv5r+3PX1w3twzZT3iQgNxMdHbxsOb1o1sd7nFkIMBMYD+4QQu1Gm\n4Wwp5cZ6n7yRcuREFrv/yqB3jyg6t6/Y+XrSkHCnbCsjJCSEXbu2MWvWfJKSVjJs2JXMmjXTYR8h\nBLffPpnbb59c6TmmTr2TDRs2s21bF7y8WhEcXMLKld/Wqh2HDx8mNTWVgQMH4uPjg7+/P2azmSlT\npjB79mxWrVpFbGwsp0+f5pdffmHkyJFERESg0+k4evQonTsrd9nYsWNZsmQJw4cPJzw8nDlz5jBm\nzBh0Oh1btmwhPDycHj160KJFC7y9vW1uKIPBQHh4ODqdjq+//ppvv/2Wnj171u1H9SDOKIFWwCEh\nxG84zgm4IkS0UXcWWTmFzHluE88+ZyQ+Ho4ehZkzNnHphcrX6MnRgas7kQee/JZljw6nW8dwly8k\nklJuB/QuPWkj5rFlP7D2yz+JiIDTp2HciAtYMK3MW+ppQ8Kdsq2K6Oho3nmn7tEw3t7ebNz4CQcO\nHKCwsJCEhAT8/CpGOVVHSUkJs2bN4tChQ3h7e3PJJZfw2muvERkZiZSSYcOGkZ6eTuvWrbnpppsY\nOXIk/v7+zJkzh4EDB2I0Gtm4cSO33XYb6enpDBo0iJKSEoYPH87zz6vYmIyMDKZMmUJqaiotWrRg\nzJgxTJgwAZ1Ox/PPP88NN9yAwWBgxIgRjBo1qs6/hyepsaiMEGJwZdullFvd0qLK29AgRSr2HMzg\n/qc/ZsWrZZ6wKXf4MGrohbz6/m+VPtTusPjc0YmM+t/7fP7qmGr3OReLyjgjP/t9snOLuHLyal56\nCZuhMHUqLJtzlW1h1uDxK8sZEl5sfXey24yHmmRbn6IylclMo+ERQpC8bbrbisokAelSymLLSf1R\ni7uaPe2igsjIMHP0aNkDnpZuYsV7O3luqanC6GDbH0ku76yrG43UpxM5r3MEd8/bwL8GdsTXp8xo\nd1cYYVPAGWVbfp8Rl3cnIkLdHwCJiSAEPPP2N2Q9K/jfmH5ERelsn8fHQ1SkjuSMPLcpAU22GrXB\nGSWwDrjE7r3Jsu0it7SoEREWEsDjM/7NzBmbiIrUkXHSzJSxF/HF1j+IjzcBZQ/1/sOn3NJZJ2fk\nuaUTKTYY8fHR8+NvZRNgrg4RbezYW/RAtfI7ciKLn35P4unXf2TZcjPx8bBtGyxauA+TWe0fFgZL\nl8KTT4Kfn4niYpj72A6kxMGQyDhptl3THWiy1agNzigBL/vIICmlQQjh48Y2NSpG/asbl14Y69BZ\nvPbBbw4PdXqGiaNJOURGCpd31u2igkhNNTpcLzXNWO9O5LnZ/6nX8U2d8hZ9dRb78lW/2nz+pUZl\n7W/YAF99BRGt4dQp+N//lBLw9oa5cyEyEk6ehBYtYPzV/Zg54zebIfH4jH+7dR7pXJetRu1wRgmc\nFkKMlFJ+ASCEGAVkurdZjYuwkACHh9Z+dJCaZsRsNrP6y+2kpJa6xeIzmyXTpkGbNpCert7Xl+mL\nNjLvviG2FANn8opZ+OKPPDt7WL3P3dipzMU2Y3rlFntpqYm1X/7p4PO/914wmagwD/Cv/ufz8Tf7\nHbbfdZeJKwd3ZvzIBI8FEpzLstWoPc4ogSnAu0KIFy3vU1CLx85ZRv2rGz06RdjcA8+/IImPL+W9\n9+CuuyAm2ovTpyVPzKy9xVd+YjI5I4927bx58ikDGRkQFQWzHvSu9wjj4NFMhxwzrYL8OHDEXVHA\njYvKXGxtovSMHHIh06ftJCREkJMjWXz/vzmecsbB5x8fr6z90FDHbRERYDJLYqK9iI832rZHt/Wi\noKiUzu3DKpWXOwIJzmXZatSeGpWAlPIo0N+yqpeqVvSeS1hdCcGtILiV2dYZjB0Ln38O+QUS+wCK\n8r7nqh76yiYmL70wlowMM1lZ0K2b60YYZrPkTF4xrYJUZ5GTV4zRLs9Mc6ayCf+Mk2bCQvwRAnx9\nBEIoAfbuEcXp044jBINBuYDst50+DcMvi2fjj387bM/KokpZuSt09FyWrUbtqVIJCCEmAGullGao\n2PkLIeKBNlLKbe5tYuPC3pUQFgYTJzp2Bvn5MHeuCW9vmDN3E/mFJTz+8lZCQgWnT5vQ6wTR0V6V\nhpZWNjG59d3JFSanXeFTvmPMhVwz5X3+b2gXAL7afJh7bu5X79+nKVDZhP+s/w1i8Yofmb/AYmqr\nogAAIABJREFUZJvUnTN3E5++PA4B3HefsvZPnoTSUvDygjvvhNatlQLoe3404aGBPDxlMDNnbK1R\nVu6K+oJzW7Yatae6kUAYsFsI8QfwB3AaldijEzAYNS8wy+0tbCCqGqaXdyVMnw533w1t2+hJSTXh\n5QXLl8OZM9Ai0Mz8Fzaj10v0ejCb4YUXJPHxhgoPfXVRQOUnp13hNhh9ZQ8SukXy865kAF57fITH\n0gw0Bsr/pskZeQQGSsdJ3UDJ7r8y8PWDoiIoLASjEfR6Je9TpyQJnTrQbXg4b677g/uf/piMDKVQ\nzu8SWa2s3BX1BZpsNWpHdZXFllvmAS4HBgIJqELzB4GJUkqXlJUUQqwErgZOSik9m22pCqobppd3\nJcTFgbeXnv/dMIRHln6PTgcBAZCbC1nZZvR6eOIJyMiADz5QESSHDinfvv1DX5WLwupKKD85XVcK\nCg0EBqjgri4dwirtHOz3qSuNUa5QUblbf9Ps3CKysk2W8E4oLoZZs0wUF5dSVAQvv4xt5LdsGcTH\nmzh6FKZPO8FPvx+3hY0q5f5jjYvBapJ3XXBWthr1o2XLluzbt4/27dvX+RwdOnRg5cqVXH65y/Jw\n1plq5wSklCZgk+XlLt5CFaxZ7cZrOE3lkSPf0irIj/M7t7a5Eu677xtaBAryCySPTB1MVk4ROp21\ng7BGhiir8bHHlHWZnq7mDdq1U/+XGgzk5hWTlVNYqYvCHaGEkx/+gh6dIhh2WTwJXSMJ8Fe5mxNT\nz/DL7hS+/OEw40acb3Ml1INGJVeoXrkXFJUSFOQY3hnUEvYePmWbGD50SEVo2VvvQcEmDAZqbdGH\nhQRw3bDzuOsu+3QT59VL3s7Ktuu/63wJDeDs2bMN3QSX4kx0kFuRUm4TQsTVvKdnqGyY3qKlidnL\nviT3jAoP/WN/GkajGW8fMObC/Od/IDRUT1iYY2fQqpVyCz3/fJlimDYNliyBr7+Gt9+G+a+s59Qp\naeuQXO32Kc/7y0fzwy/HeffzfUzf9w1n8orx8tIRHxvC5QM6sHTOf2gdFljv6zQ2udbkgy8tNZGb\nqyx++7DP+JhWfPqteh8VpZS3vfWel6fcfLW16LNyCvnk2wMOI495cw9w3y396yx3T8m2LhiNRh6f\nO5fv168nIiqKhUuXNorykpVhMpnQ6xtn6it3tK3BlUBjo7Jhel4evPOOkawsmHbfN5QazQ6x4NOm\nwROLTdx5p2NnkJurJhOtGXPj45UleeQIvP8+PPUU+PmV2iYhrR2Su+PILx/QgcsHdHDrNRobNfng\nf/jlOMHBjrKKiICCYiPBwTBjhhohmExqhBceDmfPKtmDmheKifbm9Gnp1AjO2h77eiOumBNoKNkm\nJycz78EHyUhO5tJhw3hg9myHVNLTpkzh4HvvMa+wkH379nH5gAH8fuAAMTExtn2klBw/fpzCwkK6\ndu2Kt3ftKgwtWbKE3377jXXr1tm23XfffQghWLBgAdOnT+frr79Gr9dz6623smDBAoQQrFq1itdf\nf51+/fqxevVqpk6dyi233MLkyZPZs2cPPj4+XHHFFbz33nsA6HQ6/vnnHzp27EhxcTFz5szh448/\nJjc3l549e7Jp0yZ8fX354osvmD17NmlpafTq1YuXX36Zbt0qRn8ZDAYefPBB1q1bhxCCG264gSVL\nluDt7c3WrVuZMGEC99xzD0uXLmXYsGGsWrWqtuKpFk0JlMPeLRMRIUhJLeWBB5RV36oVBAUJhM7R\n4m/TRoUMguoUwsMhNRV8fEBK5UeePl3NH6Snq/38/akwCenOfDKNFU9VFqtu5fXE+z/m511JhIc7\nyiozEwb3i+PtT/5g/nyjzWJ/9BEdOdnw+BNmevVS5/L20jNv6tWc36W1UzJ0x5xAbXBlZbHs7Gwu\n69uXm7OyGGUysWz3bhKPHWPF228DqnN/a/VqEktLCQcul5JdpaUVyktOvP56fvj2W1rq9QRERvLN\ntm21Ki85ZswYFixYQEFBAYGBgZjNZtatW8dnn33GrbfeSlRUFMeOHSM/P5+rr76a2NhYbr/9dgB2\n7NjBuHHjOHXqFAaDgdtuu43//Oc/bNmyBYPBwO+//267jn0hmpkzZ3Lw4EF+/fVXIiMj2bFjBzqd\njsOHDzNu3Di++OILBg8ezHPPPceIESM4ePCgg3IEWLRoETt37mTv3r0AjBw5kkWLFtnqd2RkZHDm\nzBmSkpIwm6sP9XVLZTEhhC9wPdDefn8p5YJaXameeLKymNUts//wKe6c+wVxcSpP0HvvQWaWCSEc\nLX5rx962rQol/O47tW35csf5ASmVJfnMM8pNZO96uOsuE4H+jau2ois7iqrwZGUx68rr1pZUD2az\nZPdf6fy8K8kmiz174MEHy47Z9/cp7r15AI8+sp3QMMjOgkfuGkr2mSIee3QHERFKWSy+fxiDL27v\ndFs8NQdUFa6sLLZx40YuKCxkgUk9J0MKC4lYs4YX33jD1uF56fUU2RWPLxLCoTN8dcUK0jdt4kRR\nEb7A7OJi7p08mQ+/+srpdsTGxtKnTx8+/fRTJkyYwPfff09gYCDt27dnw4YN5Obm4uvri5+fH9Om\nTeO1116zKYHo6GimTp0KgJ+fH97e3iQmJpKamkp0dDSXXFKWPs2+JOVbb73Fzp07bcqqf//+AHz4\n4YdcffXVtonf+++/n+XLl/Pzzz8zaNAgh3avXbuWl156yVZkZ+7cuUyZMsUmE71ez/z5850aGbmr\nstjnQC4qTLSkhn3rirC8qsTTlcXCQgIYfHF7Ft8/jJkzNhEeJkhJK+Wpp2D/ftXZBwdDVqbKGrni\nFS+Sko088AA2t4L9aCE6GkaNgpVv6Llm6AVs2vGnQxI668rSxoQLOooa5eopkjPyaNVKR95ZE9a+\nqFUrHRt/Okrr1koGP/ygFHdkpOrYTSZ4/eMtZGQoGRcXQ4kBHn3ue0JDBcUlkvyzavuufWm1Xujl\niTkgTyCEqKa4ZFl5yRHLlzOjsJB9Xl7sbNGCV6691rbPgV27uLawEOs657FGI2MtlnFtGDt2LO+9\n9x4TJkzgvffeY9y4cSQmJlJaWmqrMSylREppq0EM0K6do1H59NNP88gjj9CvXz9CQ0OZMWMGkyZN\nctgnMzOTkpISOnbsWKEd5UtUCiFo164dqample5r35a4uDjS0tJs7yMiImrtGqsNOif2iZFS3iSl\nXCKlfNb6clUDhBBrgZ+BLkKIJCHEpJqOcQVZOYXsOZhBVk5hpZ8fOZHFhxsO0KNTBFvfnczUsUMJ\naqlcOD/+CDqdWhhmNMG1w87j/knD0OtVPplXX1XzCEePqnMdPao6lUGDIDxcMOjiOLKzhcPn1a0s\ndRcmk5mMzHxSM/JsL1fRUHKtikB/b7KyTSxbBqtXqyiurGwTCV1ac+qUGgEsXw7PPQfvvAMvvgi+\nvmpUoNfDK6+oEN9XXgGdXq0Kf+UV+GAdvPwKvPvlnxw5kVXrdoWFBNCre5TLFYA7ZVue4cOHsy8g\ngDl6PZ8B1wQEMPnmmx0s/bmPP849y5bxzciRGCZP5ufdux3KS3ZNSGC9vz/WANZPvbzo2r17rdty\nww03sGXLFlJTU/n0008ZP3487dq1w8/Pj6ysLLKzs8nJyeHMmTM29ws4ungAWrduzWuvvUZqaior\nVqxg6tSpHDt2zGGf8PBw/Pz8OGp9kO0oX6IS1LyJ/RxIVfsmJibStm3bKtvmapwZCfwshOgppdzn\njgZIKce547zVUdNy/coqRQ26KI7cvPIuHHjgAVi69ABhrQIccsxMn64+DwtTCmH6dNXRp6YZadu6\nZYO6AgDe+mg3S9/6lYiQAISl+pQrSxA2hFyro6ColDZROuLjlU/VOjHs5+eNv7/q7CMjK871/P47\nFXIHhYerVcMOuYPCYfdfGZWWlvQ07pZteUJCQti2axfzZ81iZVISVw4bxsxZjutIhRBMvv12Jlvc\nL+W5c+pUNm/YQJdt22jl5UVJcDDfrlxZ67aEh4czePBgJk2aRMeOHenSRYU6Dxs2jOnTp7Nw4UJa\ntGjB8ePHSUlJqeCasfLRRx8xYMAAoqOjadWqFTqdzlao3v47TZo0iRkzZrB69WoiIyPZuXMnF154\nITfeeCNPPfUUmzdv5rLLLmPZsmX4+flVWud47NixLFq0iL59+wKwcOFCJk70XHq26tJG7EON7LyA\nSUKIYyh3kABkY1oAVBtqChU8ciKrQtbIu+76E6PR7NAZhIVBUJAK/2zRAk6eznfIJxMXp0IHc3OV\nK+H999UEcFionoKi0gZ3Baxct5uta28lJNjfo9etCa+7t7jlvEHFRaRXMhHb6cNjUCKYOVPywgsV\n53r69lUjAPvtmZkqbYRD7qBM6PtJEl4bTrul/bXhzb8OsK1LV0LLTUBi/W13u/6a0dHRvPbOO3U+\n3tvbm082bqxXeUkr48aN45ZbbuHpp5+2bVu9ejUPPfQQPXr0ID8/n44dO/LQQw9VeY7ffvuNadOm\nkZeXR2RkJM8//7xtcZi9Zf7MM88we/ZsLrroIgoKCrjgggv45ptv6NKlC2vWrOHuu++2RQd9+eWX\nttGR/TkeeeQRzp49S0JCAkIIbrzxRubMmVOn714XqiwvWVOMt5QysbrPXYkry0tWVTLymQevp1f3\nKN5ct5vXP96C/f08YQIMvvB8Ptq4n5dfVvnkly5Vvv/cXOUTvvHK8/lgw368vLCNIMxm5VJYuLAs\nFvyRR3S8unCUbeFZQ3HjPetYu/R6vLyq9gg2RHnJjN59XHV6B/YUFvDfrMOcLbWLyPKGlWFdOGEo\n4aGMJAICJTn5EB0uyMiUlJohsjWkZyj3n3Xdh9kMocGQm69GAJmZMLpFCBPCI4jxUSt2UwwGYnx8\nCPfy/GT/9f8c4YP4TnhV4UaI2r2rwjatvGTTxi3lJa2dvBDiHSmlw9hECPEOTTSddE2heRFhAZwu\nlyEy8zTERAYRHKwmhI1Gyo0U4Kv1+wlqpTJMGgyq0zCbVUcz6yEID4PMLDAZzDz9yHqSTJJFs/7N\nyH+7r+B4Zbz2/h8AxLYN5sZ71nH5JR3w8S5bfHLHmAs92h5PEePjQ36BYP5iWbY462FBTBsfEotL\nEMWSoBJBgZTcaIxkfOdwso1GfsrL43FzGno9BHpDkQCzCZ5v1ZGAUB3HDQZywkp5PiODv87kcsRs\nRicE8UJwQkoWt4tlVGioR77jCkuccpyPD9f9c4R/BQXhI8qU/JTWrT3SDo2mhTNzAufZvxFC6IEG\n7Snq4zKIBJ4MjWbG3Um0DhecypQ8FRVN5KM7AbjMWAqlcNdUCI9QCoBS+L+fsnn1rGDCLZL16x39\nwWFhUJSmEow55J55CGbnQDtgTDo8DcwFNhWWkg4Mmv81oe/8zfkBAR6zGIsyVDxrO8vL9PlRiiyf\nCcBrm92SeDe4DdzN4eIidhcW0jsggC5+Za6ucC9vnoyK5aGH7eWuIjIeTk7iRyBBSrYAI05mMKBl\nCzr6+RHv54feR7DsJWlT+vfdBUdLSujp70+klxdzk9TxbaSkK7BFShKkZC8wJDmJgUEtPSLfArOK\nNov28SHax4dSKSmValujCNHSaJRUNyfwMDAb8BdCWEMLBGAAXvNA29yGMIMoluhSQUhJvtHEnsIC\n2/B9YlgYq3OzKC1Rsf03hIZQaDYzJyKa+W+lYCy3TiArE14E7jXBo4/aVwCDl4FEIBR4DGgFnACO\nAaXA4hMnSPagxTgzSoXJfXkmhxGtQhw++/JMjtuv704eTUninbwsWofDqWMwMSiMhTFloXfl5S7M\nym3TXggSpOR9AXd6Q6twuDHpHzBAnBAEtZEOSj84FF5NTSUTlWq3FJVVsQTogMq0iOVvnBCkGAwe\nUQLNWbYa7qM6d9BiYLEQYrGU8mEPtsmtZBpLHSy/vUD/lBTihSAVmN02mnX52bz8ip27Z2oOf2af\nIRUYHtyKL86cYfpUiAqDk1mgM0AsMMEIq0ogNxsMJTDMCD8BG4AhoK6F0qJ3Ar8ACWazxy1GgOdP\nnqzQUVS2ralwuLiId/KyeMk+emtqFhOLI+ji51+p3IckJ/FZ166csIwA7vSGZxyOh2kGyfTMcjUj\nsuAfIB0YCnwFXAdsB46j5Jxg+ZsopW2ewFM0N9lquBdn3EHrhBDlZ+tygUQppdENbXIr9pYfqIe1\nM/CGlPgCl6am0CZGR3y8+jw+HuLC4M10yWZg3pkzhAK5BjCkQzGqyMIs4BDwUCmMyIIC4CqgDUoB\nWK/VFhiHsiAbwmL8Pi+XH/LyyCgt5ZGUFNv2syZTlROJTYHdhYW0Dq8kbLOwkC5+/pXKPdpy3Oy2\n0VydmkJI+ePD4L10CDXAzKkQGgZpWfC2ASJQrzggEDXSu0YIDFJymRB0FIJEywjPU4q9ucpWw704\nowReBvqgDBsB9AT2A8FCiDullN+6sX0uJ8bHx2b5BaI66yQgB+iN6owTM6WD5XcyS1l4jwO/ojqQ\nz4AbAG+UtW+1/IYC96A6iLbASRwtw2wheDI2lllJSeyV0uMWY5S3Nwn+AXyTm0tCQJnPPFCnZ36L\naLdf3130Dgjg1LGKYZu9O6oILKvcrbJYIuCIt+RlcwppmRIzan/7409lKflmA34G8E5XD4C1kMZe\nlKuvADgtBK926MD5Aep6DREd1Fxlq+FenFECacBkKeUBACFED2AB8CDwCVAvJSCEGA4sQ61eXiml\nfKo+56uJcC9vrg8N5aqsLNoByZbtD6M6eoOUXBMYwl1Tc4gIVx0DBngU1bG3AZ5ATfK2BXxwtOhj\nUD7/dNQPdwdqJBCKUgCL28UyMiQUKZU7Is7DFuN5/gGc5x/AdaGheDcj67CLnz8Tg8K4a2qWTW5X\nBwazu7DQ9vnidrEMSkokDEj1xuI6UpFi06eCyQAzpkJbi8UvDMr6OQnKjUSZS28FkAEEAdcCJikd\nJvgbIjS0ucpWw704owS6WBUAgJTyLyFENynlsfouZxZC6FBzqleg+szfhBCfSykP1evE1ZBpLOXj\n7GybRb8X1UlvRHXclwBfZOWwEQhMU1bedSjLvy/QBQhHWYSPAA/haOkfAW6zfJkg4AvADNwQGcX4\niHBb5zAqNJSBQS09bjEOPXSw2kiRH7rVfql+ZXhauQMsjIllYnEEuwsL2eafy1c5uezLySUZGB8W\nRt/AFuiEQAeEhztO9rYMA306/GGAE+mwB5gG/Gk590GUjBNQk7+jgWEoWb8APKTTeWwCuCo8JVuN\n5oUzSuCAEOIV4H3L+5uAvyzZReub8awfcMRuTcL7wCiUe90tVOYbbo+y3tujXEQtKfPjY9luXT69\nFUfXz1PAANSoIBW4HxiBUh4jgGUdO1YZAhru5e3xTmN1R9XzvZ2pVraODlERSR/nZLssjLAhlLsV\na1jow0lJDor+4qwsPsrOZquUtAE6lXP95GWpCfvtQABqmPsLjobC5ShD4TgwBnXzZ6JGgw0xAVwe\nT8i2qXLVVVcxduzYOqdjqM3x9b2Wp3FGCdwKTEUZRqCek/tRz8DQel4/mjKPDEAKSjG4jfK+4b0o\nBbAH+A8qhDMNKnx+EhVX7zCZi1IaAkho1Yph3t68kJnJJyiF8Ey7WIYEBbvz69SadpaO6sezZ9nU\ntWyh2iP+0fz770O4aLG6x5W7PbsLCyvIKgI1MrNue9UAk6aqNR6ZWeBlifAah5Jp+Yn7UNQoMdty\nnqtRCiESuM7i5mvIUQB4TLZNkg0bNnjs+Ppey9PUqASklEXAs5ZXefJd3iIXk2ksdXC5hHt5s7hd\nrM0ff0xKSqVkGmWW3xKU37crKp6/CHgGOI2jcvgb5fMHOJ6Xx2YpmdM2mvMDAxosZYCzSAk78/Pp\n16IFAL8V5OPCbAAeV+729A4IIBlHWZ0Gztpt6wH4GmBeuprI/xnHEV5muePTgLeB1qiorwVxcXTz\n96fQbG50snazbOuE0Whk4cK5bNq0ntato3jiicZTXrIxl5P0BM4UlRkIzEMZvvZFZSom0a49qSgD\nzEqMZVsF7OsJJJw9yyUtW9Z48s+zs3k4OYn25Zbwl/fHbz97lmcSE22W34PAK6iOwAT4C0ErITht\nNtPf0sgUYEjLlvx89qyaNLTG+6el8uN55zWqTqEyno2NZUZSInkmMxJJK70Xz8XGOuzj6aIyQ4YM\nwRVJNLr4+TM+LIz+WVk2Wf0nKJhLg1oyKCWFENTI7i2gIypEuPwIbzSObj4JPImKBooEYn19HVYk\nNyYaQrbJyck8+uiDpKcnM2jQMB56yLG85L33TuG3395j7NhCjh3bx5AhA9i1y/3lJadNm4aUkr17\n9zJx4kRuu+22SstJzps3jwceeIDVq1cTFBTEjBkzuOeeezAajeh0OoYOHepw/BtvvEH//v1ZuXIl\nISEhvPTSSwwfPhzAYV+A119/naVLl5KSkkJsbCxr1qyhV69ePPXUU7z++uucOnWK2NhYFi1axDXX\nXFMfMbinshiwEpiOKipjqn2zquU3oJMlWV06ytU6trId7TuLjM9rrj5lXRxU1RJ+e398d3//SkM5\nn7aEctqfYxBwS9u2XBYURKHZTEZBAQmWkm+eXiFaHy4ICOD7bt3Js1SDCqrEEqpHUZk6KXdQETeu\nYGG7WCZGRPDm6dN8lpVFUv5ZHsvLBVTYJ6gO/XKUknBY4IVy/eiByMBA0gsKWE9ZSPF1QjS4/786\n3CzbCmRnZzNwYF+GDs1i0CATn366mxMnjvH6628DqnN/++3VvPdeKcHB0KeP5OjRiuUlx427nh9+\n+JaAAD0tW0by3Xf1Ly/54Ycf8tlnnznUDgDHcpKlpaW89tprfPPNN+zdu5eAgABGjx5dbR7/nTt3\nMmnSJLKysnj11VeZPHlypQVj1q1bx4IFC/j888/p06cPx44dsym3Tp06sX37diIjI1m3bh0TJkzg\n6NGjREZGOv2dy1MXuTpTVCZXSvm1lPKUlDLL+qpzK+2QUpqAu1FhpgeA96WUB11xbtsEsOW9fQdt\nz+fZ2Vzz99/4o1xAXSx/bwwNJdbXt8I5Ouh0XNiiBV38/B3mF6DhVojWho+yswGVbGzFqVOszcpi\nbVaW7b2LsCl3IYQPSrm7tG5kprGUPYUFZBpLq93+eXY2bwOjLYr6V+Cw5e88oBfKpzkQ6CEE/VE+\n/+uE4OnYOD7q3IVnY+O4Tgju0Okajf+/Mjwk2wps3LiR9u0LmTTJxMCBMH9+IatXr8FoLFtL6uWl\np8SuLqHB4FhecsWKFfzzzybefbeIt9/Op1evRO66a3Kt2mFfXhKwlZfs16+iJ9JaTlKn0+Hr68u6\ndeu47777aNOmDcHBwcwqVw+hPHFxcdx2220IIbjllltIT0/nVCW/8cqVK3nwwQfp00ett+3YsaOt\nitn1119v6/BvuOEGOnfuzM6dO2v1nV2BMyOBzUKIp1FrAmxilFJWzEdbB6SUG1Hud5dS2QRw+Q7a\nOlr4REquR6V3sFl72dlMjIio9hzl5xc8vUK0LhRaOkNrsjF3IKU0CSGsyt0aIuoS5Q5Vu/nstx+1\nzPX4oyZ7rYv37BV6W1T4rh9qiGtCPRBjGkk4b23xhGwro7zFXH7+QQjBtGnTefTR5Vx3XSEnTnhx\n5EgLrrUrL7l//y4uuaQQ6+M5dKiRp5+uf3nJ8ePHV7pf+XKSaWlpDtvKf14e+xGKv79yC+bn59O6\nXKbW5ORk4q2xyOVYvXo1S5cu5cSJEwAUFBSQmZlZ7XXdgTNK4GLL37522yRqJN1ocaaDto4WAqWk\nPY5hofYpBYakpVZ5jqbSQVi5OTwcgLtaR+Knc2YgWDfcpdyrcvN1D/CvsH0QUAjsQC3y60o5lx9q\ncj8dJfttUqr/T51kfES4w3UbIpy3tnhKtuUZPnw4s2YFsHJlEV27mvj00wAmTbrJwdKfP/9xYmM7\n8N1364mIaMOvvz7mUF6yW7cE1q71Z+TIIry9Yft2L7p2rVt5yfvvv99WXnLHjh2V7ldecbVp04YU\nu1QbSUlJ5Q+pE+3atau0/GRSUhJ33HEHmzdvtlUb6927t62IvSdxJjqovmGgDUZNHbR1tFCACgO1\npRQAjkrJWykpJEnJ7Ohozg+oOuKnKXQQ5Rl66CAR3t5cHBjIxYEt6NeiRaW+48ZGZes84oRgd2Fh\nhe3tgDzKrP9XKJvsTQPepCwHUHvUPXARTWdepyo8LduQkBB+/nkXjz02i+3bk7jhhmE88EDF8pL/\n/e/t/Pe/lZeXnDp1Kt9/v4Fbb91Gy5ZemM3BfP+968pL1sSNN97I8uXLueqqqwgICGDJkiW1vnZl\n/Pe//2XmzJkMHDiQPn36cPToUXx8fCgoKECn0xEeHo7ZbGbVqlXs37/fJdesLc5EB0WiMiW0lVJe\naUkbMUBKWXsJNQDVddDW0cJ1yUkES0l/1GxmEpYcQdaIn9SmEfFTG37pcR4pBgM78vP5Li+Ph1NS\nCNbr+a6bZ4vc1Jaq3Hy9AwKYX257Msp/ad3WHeX+uap1a1adOoXVzrSuBWlP05jXqYmGkG10dDQr\nV9avvOTnn7uvvGRN2Q1uv/12jhw5QkJCAsHBwdx7771s3brVVle4puPtP7f/f/To0WRnZzNu3DjS\n0tJo374977zzDhdccAEzZ86kf//+6PV6br75Zi699NK6fN16U2V5SdsOQnyNiqabI6W8QAjhBeyW\nUvb0RAMtbXAoaefqEoTWtQQBOh27Cwt5KyWFPRb/KkBvnY7FnTrRKyDQpddtSNIMBnYU5PNLfj5/\nFRXRysuLfoGB3BtZ5ussX4awsZSXtPr+7V109nMC1vUfJikJQE38WkNFx4eFsbBdbKX7dtbpHM7X\nVKlJtlp5yZrZuHEjd955J8ePH2/opjiFW8pL2hEupfzQUmQGKaVRCOHZmSc3Yz9aCPXyqmBRNnXL\nsDL6/nWAXgEB3BsZyZJ2sTUf0Iioys1Xfjso95HBbOa4weBQbayqfZvCvE5NNGXZNhQlfpD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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%run util_ds\n", + "\n", + "# display plots in the notebook \n", + "%matplotlib inline\n", + "plot_tree_iris()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we are going to export the pseudocode of the the learnt decision tree." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "if ( petal width (cm) <= -0.415243178606 ) {\n", + " return setosa ( 42 examples )\n", + "}\n", + "else {\n", + " if ( petal width (cm) <= 0.861689627171 ) {\n", + " if ( petal length (cm) <= 0.818572163582 ) {\n", + " return versicolor ( 37 examples )\n", + " return virginica ( 1 examples )\n", + " }\n", + " else {\n", + " return versicolor ( 1 examples )\n", + " return virginica ( 2 examples )\n", + " }\n", + " }\n", + " else {\n", + " if ( petal length (cm) <= 0.707377433777 ) {\n", + " return versicolor ( 1 examples )\n", + " }\n", + " else {\n", + " return virginica ( 28 examples )\n", + " }\n", + " }\n", + "}\n" + ] + } + ], + "source": [ + "%run util_ds\n", + "get_code(model, iris.feature_names, iris.target_names)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also obtain the feature importance of the fitted model as follows." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n", + "[ 0. 0. 0.05947455 0.94052545]\n" + ] + } + ], + "source": [ + "print(iris.feature_names)\n", + "print(model.feature_importances_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that the most important feature for this classifier is `petal width`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Evaluating the algorithm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Precision, recall and f-score" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For evaluating classification algorithms, we usually calculate three metrics: precision, recall and F1-score\n", + "\n", + "* **Precision**: This computes the proportion of instances predicted as positives that were correctly evaluated (it measures how right our classifier is when it says that an instance is positive).\n", + "* **Recall**: This counts the proportion of positive instances that were correctly evaluated (measuring how right our classifier is when faced with a positive instance).\n", + "* **F1-score**: This is the harmonic mean of precision and recall, and tries to combine both in a single number." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "print(metrics.classification_report(y_test, y_test_pred,target_names=iris.target_names))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Confusion matrix" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Another useful metric is the confusion matrix" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "print(metrics.confusion_matrix(y_test, y_test_pred))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see we classify well all the 'setosa' and 'versicolor' samples. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### K-Fold cross validation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to avoid bias in the training and testing dataset partition, it is recommended to use **k-fold validation**.\n", + "\n", + "Sklearn comes with other strategies for [cross validation](http://scikit-learn.org/stable/modules/cross_validation.html#cross-validation), such as stratified K-fold, label k-fold, Leave-One-Out, Leave-P-Out, Leave-One-Label-Out, Leave-P-Label-Out or Shuffle & Split." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 1. 0.8 1. 0.93333333 0.93333333 1. 1.\n", + " 1. 0.86666667 0.93333333]\n" + ] + } + ], + "source": [ + "from sklearn.cross_validation import cross_val_score, KFold\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "# create a composite estimator made by a pipeline of preprocessing and the KNN model\n", + "model = Pipeline([\n", + " ('scaler', StandardScaler()),\n", + " ('DecisionTree', DecisionTreeClassifier())\n", + "])\n", + "\n", + "# create a k-fold cross validation iterator of k=10 folds\n", + "\n", + "cv = KFold(x_iris.shape[0], 10, shuffle=True, random_state=33)\n", + "\n", + "# by default the score used is the one returned by score method of the estimator (accuracy)\n", + "scores = cross_val_score(model, x_iris, y_iris, cv=cv)\n", + "print(scores)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "We get an array of k scores. We can calculate the mean and the standard error to obtain a final figure" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean score: 0.947 (+/- 0.022)\n" + ] + } + ], + "source": [ + "from scipy.stats import sem\n", + "def mean_score(scores):\n", + " return (\"Mean score: {0:.3f} (+/- {1:.3f})\").format(np.mean(scores), sem(scores))\n", + "print(mean_score(scores))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So, we get an average accuracy of 0.947." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* [Plot the decision surface of a decision tree on the iris dataset](http://scikit-learn.org/stable/auto_examples/tree/plot_iris.html)\n", + "* [Learning scikit-learn: Machine Learning in Python](http://proquest.safaribooksonline.com/book/programming/python/9781783281930/1dot-machine-learning-a-gentle-introduction/ch01s02_html), Raúl Garreta; Guillermo Moncecchi, Packt Publishing, 2013.\n", + "* [Python Machine Learning](http://proquest.safaribooksonline.com/book/programming/python/9781783555130), Sebastian Raschka, Packt Publishing, 2015.\n", + "* [Parameter estimation using grid search with cross-validation](http://scikit-learn.org/stable/auto_examples/model_selection/grid_search_digits.html)\n", + "* [Decision trees in python with scikit-learn and pandas](http://chrisstrelioff.ws/sandbox/2015/06/08/decision_trees_in_python_with_scikit_learn_and_pandas.html)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Licence\n", + "The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n", + "\n", + "© 2016 Carlos A. Iglesias, Universidad Politécnica de Madrid." + ] + } + ], + "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": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/ml1/2_6_Model_Tuning.ipynb b/ml1/2_6_Model_Tuning.ipynb new file mode 100644 index 0000000..79f4908 --- /dev/null +++ b/ml1/2_6_Model_Tuning.ipynb @@ -0,0 +1,1290 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](files/images/EscUpmPolit_p.gif \"UPM\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Course Notes for Learning Intelligent Systems" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © 2016 Carlos A. Iglesias" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## [Introduction to Machine Learning](2_0_0_Intro_ML.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Table of Contents\n", + "\n", + "* [Model Tuning](#Model-Tuning)\n", + "* [Load data and preprocessing](#Load-data-and-preprocessing)\n", + "* [Train classifier](#Train-classifier)\n", + "* [More about Pipelines](#More-about-Pipelines)\n", + "* [Tuning the algorithm](#Tuning-the-algorithm)\n", + "\t* [Grid Search for Parameter optimization](#Grid-Search-for-Parameter-optimization)\n", + "* [Evaluating the algorithm](#Evaluating-the-algorithm)\n", + "\t* [K-Fold validation](#K-Fold-validation)\n", + "* [References](#References)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model Tuning" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the previous [notebook](2_5_2_Decision_Tree_Model.ipynb), we got an accuracy of 9.47. Could we get a better accuracy if we tune the parameters of the estimator?\n", + "\n", + "The goal of this notebook is to learn how to learn how to tune an algorithm by opimizing their parameters using grid search." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load data and preprocessing" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# library for displaying plots\n", + "import matplotlib.pyplot as plt\n", + "# display plots in the notebook \n", + "%matplotlib inline\n", + "\n", + "## First, we repeat the load and preprocessing steps\n", + "\n", + "# Load data\n", + "from sklearn import datasets\n", + "iris = datasets.load_iris()\n", + "\n", + "# Training and test spliting\n", + "from sklearn.cross_validation import train_test_split\n", + "\n", + "x_iris, y_iris = iris.data, iris.target\n", + "# Test set will be the 25% taken randomly\n", + "x_train, x_test, y_train, y_test = train_test_split(x_iris, y_iris, test_size=0.25, random_state=33)\n", + "\n", + "# Preprocess: normalize\n", + "from sklearn import preprocessing\n", + "scaler = preprocessing.StandardScaler().fit(x_train)\n", + "x_train = scaler.transform(x_train)\n", + "x_test = scaler.transform(x_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## Train classifier" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As previously, we train the model and evaluate the result." + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1. , 0.8 , 1. , 0.93333333, 0.93333333,\n", + " 0.93333333, 1. , 1. , 0.86666667, 0.93333333])" + ] + }, + "execution_count": 128, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.cross_validation import cross_val_score, KFold\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "# create a composite estimator made by a pipeline of preprocessing and the KNN model\n", + "model = Pipeline([\n", + " ('scaler', StandardScaler()),\n", + " ('ds', DecisionTreeClassifier())\n", + "])\n", + "\n", + "# Fit the model\n", + "model.fit(x_train, y_train) \n", + "\n", + "# create a k-fold cross validation iterator of k=10 folds\n", + "cv = KFold(x_iris.shape[0], 10, shuffle=True, random_state=33)\n", + "\n", + "# by default the score used is the one returned by score method of the estimator (accuracy)\n", + "scores = cross_val_score(model, x_iris, y_iris, cv=cv)\n", + "\n", + "from scipy.stats import sem\n", + "def mean_score(scores):\n", + " return (\"Mean score: {0:.3f} (+/- {1:.3f})\").format(np.mean(scores), sem(scores))\n", + "print(mean_score(scores))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We obtain an accuracy of 0.947." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## More about Pipelines" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When we use a Pipeline, every chained estimator is stored in the dictionary *named_steps* and as a list in *steps*." + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'ds': DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n", + " max_features=None, max_leaf_nodes=None, min_samples_leaf=1,\n", + " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", + " presort=False, random_state=None, splitter='best'),\n", + " 'scaler': StandardScaler(copy=True, with_mean=True, with_std=True)}" + ] + }, + "execution_count": 115, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.named_steps" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)),\n", + " ('ds',\n", + " DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n", + " max_features=None, max_leaf_nodes=None, min_samples_leaf=1,\n", + " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", + " presort=False, random_state=None, splitter='best'))]" + ] + }, + "execution_count": 116, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.steps" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can get the list of parameters of the model. As you will observe, the parameters of the estimators in the pipeline can be accessed using the <estimator>__<parameter> syntax. We will use this for tuning the parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['ds__min_samples_split', 'ds__max_features', 'ds__max_leaf_nodes', 'scaler__copy', 'scaler__with_mean', 'ds__class_weight', 'ds__splitter', 'ds__min_weight_fraction_leaf', 'ds', 'ds__max_depth', 'ds__min_samples_leaf', 'scaler', 'scaler__with_std', 'ds__random_state', 'ds__presort', 'ds__criterion', 'steps'])" + ] + }, + "execution_count": 117, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.get_params().keys()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we are going to change a parameter" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Pipeline(steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('ds', DecisionTreeClassifier(class_weight='balanced', criterion='gini',\n", + " max_depth=None, max_features=None, max_leaf_nodes=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n", + " splitter='best'))])" + ] + }, + "execution_count": 118, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.set_params(ds__class_weight='balanced')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Another alternative is to create the pipeline with the values we want to set, but it can be useful to access the estimators of the Pipeline." + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Pipeline(steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('ds', DecisionTreeClassifier(class_weight='balanced', criterion='gini',\n", + " max_depth=None, max_features=None, max_leaf_nodes=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n", + " splitter='best'))])" + ] + }, + "execution_count": 119, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = Pipeline([\n", + " ('scaler', StandardScaler()),\n", + " ('ds', DecisionTreeClassifier(class_weight='balanced'))\n", + "])\n", + "model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The same approach can be used for accessing attributes such as *feature_importances_* we saw in the previous notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0.01428571 0.03745715 0.55176597 0.39649117]\n" + ] + } + ], + "source": [ + "# Fit the model\n", + "model.fit(x_train, y_train) \n", + "# Using named_steps\n", + "my_decision_tree = model.named_steps['ds']\n", + "print(my_decision_tree.feature_importances_)" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0.01428571 0.03745715 0.55176597 0.39649117]\n" + ] + } + ], + "source": [ + "#Using steps, we take the last step (-1) or the second step (1)\n", + "#name, my_desision_tree = model.steps[1]\n", + "name, my_desision_tree = model.steps[-1]\n", + "print(my_decision_tree.feature_importances_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tuning the algorithm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that the most important feature for this classifier is `petal width`.\n", + "\n", + "Look at the [API](http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html) of *scikit-learn* to understand better the algorithm, as well as which parameters can be tuned. As you see, we can change several ones, such as *criterion*, *splitter*, *max_features*, *max_depth*, *min_samples_split*, *class_weight*, etc.\n", + "\n", + "We can get the full list parameters of an estimator with the method *get_params()*. " + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'ds': DecisionTreeClassifier(class_weight='balanced', criterion='gini',\n", + " max_depth=None, max_features=None, max_leaf_nodes=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n", + " splitter='best'),\n", + " 'ds__class_weight': 'balanced',\n", + " 'ds__criterion': 'gini',\n", + " 'ds__max_depth': None,\n", + " 'ds__max_features': None,\n", + " 'ds__max_leaf_nodes': None,\n", + " 'ds__min_samples_leaf': 1,\n", + " 'ds__min_samples_split': 2,\n", + " 'ds__min_weight_fraction_leaf': 0.0,\n", + " 'ds__presort': False,\n", + " 'ds__random_state': None,\n", + " 'ds__splitter': 'best',\n", + " 'scaler': StandardScaler(copy=True, with_mean=True, with_std=True),\n", + " 'scaler__copy': True,\n", + " 'scaler__with_mean': True,\n", + " 'scaler__with_std': True,\n", + " 'steps': [('scaler',\n", + " StandardScaler(copy=True, with_mean=True, with_std=True)),\n", + " ('ds', DecisionTreeClassifier(class_weight='balanced', criterion='gini',\n", + " max_depth=None, max_features=None, max_leaf_nodes=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n", + " splitter='best'))]}" + ] + }, + "execution_count": 123, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.get_params()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can try different values for these parameters and observe the results." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Grid Search for Parameter optimization" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Changing manually the parameters to find their optimal values is not practical. Instead, we can consider to find the optimal value of the parameters as an *optimization problem*. \n", + "\n", + "The sklearn comes with several optimization techniques for this purpose, such as [**grid search**](https://en.wikipedia.org/wiki/Hyperparameter_optimization#Grid_search) and **randomized search**. In this notebook we are going to introduce the former one." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The sklearn provides an object that, given data, computes the score during the fit of an estimator on a parameter grid and chooses the parameters to maximize the cross-validation score. " + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best score: 0.946428571429\n", + "Best params: {'max_depth': 3}\n" + ] + } + ], + "source": [ + "from sklearn.grid_search import GridSearchCV\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "import numpy as np\n", + "\n", + "param_grid = {'max_depth': np.arange(3, 10)} \n", + "\n", + "gs = GridSearchCV(DecisionTreeClassifier(), param_grid)\n", + "\n", + "gs.fit(x_train, y_train)\n", + "\n", + "# summarize the results of the grid search\n", + "print(\"Best score: \", gs.best_score_)\n", + "print(\"Best params: \", gs.best_params_)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "Now we are going to show the results of grid search" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.946 (+/-0.075) for {'max_depth': 3}\n", + "0.929 (+/-0.025) for {'max_depth': 4}\n", + "0.929 (+/-0.025) for {'max_depth': 5}\n", + "0.938 (+/-0.050) for {'max_depth': 6}\n", + "0.929 (+/-0.025) for {'max_depth': 7}\n", + "0.946 (+/-0.075) for {'max_depth': 8}\n", + "0.946 (+/-0.075) for {'max_depth': 9}\n" + ] + } + ], + "source": [ + "# We print the score for each value of max_depth\n", + "for params, mean_score, scores in gs.grid_scores_:\n", + " print(\"%0.3f (+/-%0.03f) for %r\" % (mean_score, scores.std() * 2, params))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now evaluate the KFold with this optimized parameter as follows." + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean score: 0.953 (+/- 0.020)\n" + ] + } + ], + "source": [ + "# create a composite estimator made by a pipeline of preprocessing and the KNN model\n", + "model = Pipeline([\n", + " ('scaler', StandardScaler()),\n", + " ('ds', DecisionTreeClassifier(max_depth=3))\n", + "])\n", + "\n", + "# Fit the model\n", + "model.fit(x_train, y_train) \n", + "\n", + "# create a k-fold cross validation iterator of k=10 folds\n", + "cv = KFold(x_iris.shape[0], 10, shuffle=True, random_state=33)\n", + "\n", + "# by default the score used is the one returned by score method of the estimator (accuracy)\n", + "scores = cross_val_score(model, x_iris, y_iris, cv=cv)\n", + "def mean_score(scores):\n", + " return (\"Mean score: {0:.3f} (+/- {1:.3f})\").format(np.mean(scores), sem(scores))\n", + "print(mean_score(scores))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have got an *improvement* from 0.947 to 0.953 with k-fold.\n", + "\n", + "We are now to try to fit the best combination of the parameters of the algorithm. It can take some time to compute it." + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# Tuning hyper-parameters for precision\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/cif/anaconda3/lib/python3.5/site-packages/sklearn/metrics/classification.py:1074: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.\n", + " 'precision', 'predicted', average, warn_for)\n", + "/home/cif/anaconda3/lib/python3.5/site-packages/sklearn/metrics/classification.py:1074: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.\n", + " 'precision', 'predicted', average, warn_for)\n", + "/home/cif/anaconda3/lib/python3.5/site-packages/sklearn/metrics/classification.py:1074: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.\n", + " 'precision', 'predicted', average, warn_for)\n", + "/home/cif/anaconda3/lib/python3.5/site-packages/sklearn/metrics/classification.py:1074: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.\n", + " 'precision', 'predicted', average, warn_for)\n", + "/home/cif/anaconda3/lib/python3.5/site-packages/sklearn/metrics/classification.py:1074: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.\n", + " 'precision', 'predicted', average, warn_for)\n", + "/home/cif/anaconda3/lib/python3.5/site-packages/sklearn/metrics/classification.py:1074: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.\n", + " 'precision', 'predicted', average, warn_for)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best parameters set found on development set:\n", + "\n", + "{'max_leaf_nodes': 20, 'criterion': 'gini', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 3}\n", + "\n", + "Grid scores on development set:\n", + "\n", + "0.964 (+/-0.092) for {'max_leaf_nodes': None, 'criterion': 'gini', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 3}\n", + "0.955 (+/-0.078) for {'max_leaf_nodes': None, 'criterion': 'gini', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 3}\n", + "0.946 (+/-0.123) for {'max_leaf_nodes': 5, 'criterion': 'gini', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 3}\n", + "0.945 (+/-0.126) for {'max_leaf_nodes': 5, 'criterion': 'gini', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 3}\n", + "0.957 (+/-0.120) for {'max_leaf_nodes': 10, 'criterion': 'gini', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 3}\n", + "0.962 (+/-0.080) for {'max_leaf_nodes': 10, 'criterion': 'gini', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 3}\n", + "0.950 (+/-0.118) for {'max_leaf_nodes': 20, 'criterion': 'gini', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 3}\n", + "0.976 (+/-0.078) for {'max_leaf_nodes': 20, 'criterion': 'gini', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 3}\n", + "0.950 (+/-0.118) for {'max_leaf_nodes': None, 'criterion': 'gini', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 4}\n", + "0.974 (+/-0.080) for {'max_leaf_nodes': None, 'criterion': 'gini', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 4}\n", + "0.953 (+/-0.126) for {'max_leaf_nodes': 5, 'criterion': 'gini', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 4}\n", + "0.957 (+/-0.090) for {'max_leaf_nodes': 5, 'criterion': 'gini', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 4}\n", + "0.957 (+/-0.120) for {'max_leaf_nodes': 10, 'criterion': 'gini', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 4}\n", + "0.968 (+/-0.082) for {'max_leaf_nodes': 10, 'criterion': 'gini', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 4}\n", + "0.957 (+/-0.120) for {'max_leaf_nodes': 20, 'criterion': 'gini', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 4}\n", + "0.972 (+/-0.068) for {'max_leaf_nodes': 20, 'criterion': 'gini', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 4}\n", + "0.957 (+/-0.120) for {'max_leaf_nodes': None, 'criterion': 'gini', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 5}\n", + "0.945 (+/-0.162) for {'max_leaf_nodes': None, 'criterion': 'gini', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 5}\n", + "0.953 (+/-0.126) for {'max_leaf_nodes': 5, 'criterion': 'gini', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 5}\n", + "0.964 (+/-0.092) for {'max_leaf_nodes': 5, 'criterion': 'gini', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 5}\n", + "0.953 (+/-0.126) for {'max_leaf_nodes': 10, 'criterion': 'gini', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 5}\n", + "0.934 (+/-0.123) for {'max_leaf_nodes': 10, 'criterion': 'gini', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 5}\n", + "0.957 (+/-0.120) for {'max_leaf_nodes': 20, 'criterion': 'gini', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 5}\n", + "0.940 (+/-0.146) for {'max_leaf_nodes': 20, 'criterion': 'gini', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 5}\n", + "0.957 (+/-0.120) for {'max_leaf_nodes': None, 'criterion': 'gini', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 6}\n", + "0.936 (+/-0.165) for {'max_leaf_nodes': None, 'criterion': 'gini', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 6}\n", + "0.946 (+/-0.123) for {'max_leaf_nodes': 5, 'criterion': 'gini', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 6}\n", + "0.950 (+/-0.118) for {'max_leaf_nodes': 5, 'criterion': 'gini', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 6}\n", + "0.957 (+/-0.120) for {'max_leaf_nodes': 10, 'criterion': 'gini', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 6}\n", + "0.952 (+/-0.105) for {'max_leaf_nodes': 10, 'criterion': 'gini', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 6}\n", + "0.953 (+/-0.126) for {'max_leaf_nodes': 20, 'criterion': 'gini', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 6}\n", + "0.956 (+/-0.142) for {'max_leaf_nodes': 20, 'criterion': 'gini', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 6}\n", + "0.957 (+/-0.120) for {'max_leaf_nodes': None, 'criterion': 'gini', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 7}\n", + "0.947 (+/-0.109) for {'max_leaf_nodes': None, 'criterion': 'gini', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 7}\n", + "0.946 (+/-0.123) for {'max_leaf_nodes': 5, 'criterion': 'gini', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 7}\n", + "0.941 (+/-0.167) for {'max_leaf_nodes': 5, 'criterion': 'gini', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 7}\n", + "0.957 (+/-0.120) for {'max_leaf_nodes': 10, 'criterion': 'gini', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 7}\n", + "0.953 (+/-0.126) for {'max_leaf_nodes': 10, 'criterion': 'gini', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 7}\n", + "0.957 (+/-0.120) for {'max_leaf_nodes': 20, 'criterion': 'gini', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 7}\n", + "0.949 (+/-0.140) for {'max_leaf_nodes': 20, 'criterion': 'gini', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 7}\n", + "0.957 (+/-0.120) for {'max_leaf_nodes': None, 'criterion': 'gini', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 8}\n", + "0.941 (+/-0.135) for {'max_leaf_nodes': None, 'criterion': 'gini', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 8}\n", + "0.953 (+/-0.126) for {'max_leaf_nodes': 5, 'criterion': 'gini', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 8}\n", + "0.956 (+/-0.098) for {'max_leaf_nodes': 5, 'criterion': 'gini', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 8}\n", + "0.957 (+/-0.120) for {'max_leaf_nodes': 10, 'criterion': 'gini', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 8}\n", + "0.972 (+/-0.068) for {'max_leaf_nodes': 10, 'criterion': 'gini', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 8}\n", + "0.957 (+/-0.120) for {'max_leaf_nodes': 20, 'criterion': 'gini', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 8}\n", + "0.947 (+/-0.073) for {'max_leaf_nodes': 20, 'criterion': 'gini', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 8}\n", + "0.953 (+/-0.126) for {'max_leaf_nodes': None, 'criterion': 'gini', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 9}\n", + "0.940 (+/-0.131) for {'max_leaf_nodes': None, 'criterion': 'gini', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 9}\n", + "0.953 (+/-0.126) for {'max_leaf_nodes': 5, 'criterion': 'gini', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 9}\n", + "0.924 (+/-0.148) for {'max_leaf_nodes': 5, 'criterion': 'gini', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 9}\n", + "0.957 (+/-0.120) for {'max_leaf_nodes': 10, 'criterion': 'gini', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 9}\n", + "0.950 (+/-0.118) for {'max_leaf_nodes': 10, 'criterion': 'gini', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 9}\n", + "0.953 (+/-0.126) for {'max_leaf_nodes': 20, 'criterion': 'gini', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 9}\n", + "0.943 (+/-0.113) for {'max_leaf_nodes': 20, 'criterion': 'gini', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 9}\n", + "0.968 (+/-0.082) for {'max_leaf_nodes': None, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 3}\n", + "0.878 (+/-0.325) for {'max_leaf_nodes': None, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 3}\n", + "0.957 (+/-0.120) for {'max_leaf_nodes': 5, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 3}\n", + "0.959 (+/-0.068) for {'max_leaf_nodes': 5, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 3}\n", + "0.950 (+/-0.118) for {'max_leaf_nodes': 10, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 3}\n", + "0.950 (+/-0.087) for {'max_leaf_nodes': 10, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 3}\n", + "0.957 (+/-0.120) for {'max_leaf_nodes': 20, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 3}\n", + "0.962 (+/-0.113) for {'max_leaf_nodes': 20, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 3}\n", + "0.950 (+/-0.118) for {'max_leaf_nodes': None, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 4}\n", + "0.966 (+/-0.070) for {'max_leaf_nodes': None, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 4}\n", + "0.957 (+/-0.120) for {'max_leaf_nodes': 5, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 4}\n", + "0.968 (+/-0.082) for {'max_leaf_nodes': 5, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 4}\n", + "0.957 (+/-0.120) for {'max_leaf_nodes': 10, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 4}\n", + "0.953 (+/-0.134) for {'max_leaf_nodes': 10, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 4}\n", + "0.957 (+/-0.120) for {'max_leaf_nodes': 20, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 4}\n", + "0.957 (+/-0.102) for {'max_leaf_nodes': 20, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 4}\n", + "0.957 (+/-0.120) for {'max_leaf_nodes': None, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 5}\n", + "0.946 (+/-0.130) for {'max_leaf_nodes': None, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 5}\n", + "0.957 (+/-0.120) for {'max_leaf_nodes': 5, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 5}\n", + "0.935 (+/-0.102) for {'max_leaf_nodes': 5, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 5}\n", + "0.957 (+/-0.120) for {'max_leaf_nodes': 10, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 5}\n", + "0.964 (+/-0.092) for {'max_leaf_nodes': 10, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 5}\n", + "0.957 (+/-0.120) for {'max_leaf_nodes': 20, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 5}\n", + "0.959 (+/-0.066) for {'max_leaf_nodes': 20, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 5}\n", + "0.957 (+/-0.120) for {'max_leaf_nodes': None, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 6}\n", + "0.907 (+/-0.168) for {'max_leaf_nodes': None, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 6}\n", + "0.957 (+/-0.120) for {'max_leaf_nodes': 5, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 6}\n", + "0.910 (+/-0.090) for {'max_leaf_nodes': 5, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 6}\n", + "0.957 (+/-0.120) for {'max_leaf_nodes': 10, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 6}\n", + "0.933 (+/-0.136) for {'max_leaf_nodes': 10, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 6}\n", + "0.957 (+/-0.120) for {'max_leaf_nodes': 20, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 6}\n", + "0.967 (+/-0.087) for {'max_leaf_nodes': 20, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 6}\n", + "0.957 (+/-0.120) for {'max_leaf_nodes': None, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 7}\n", + "0.912 (+/-0.160) for {'max_leaf_nodes': None, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 7}\n", + "0.957 (+/-0.120) for {'max_leaf_nodes': 5, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 7}\n", + "0.913 (+/-0.140) for {'max_leaf_nodes': 5, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 7}\n", + "0.957 (+/-0.120) for {'max_leaf_nodes': 10, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 7}\n", + "0.944 (+/-0.138) for {'max_leaf_nodes': 10, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 7}\n", + "0.957 (+/-0.120) for {'max_leaf_nodes': 20, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 7}\n", + "0.944 (+/-0.146) for {'max_leaf_nodes': 20, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 7}\n", + "0.957 (+/-0.120) for {'max_leaf_nodes': None, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 8}\n", + "0.944 (+/-0.138) for {'max_leaf_nodes': None, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 8}\n", + "0.957 (+/-0.120) for {'max_leaf_nodes': 5, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 8}\n", + "0.936 (+/-0.121) for {'max_leaf_nodes': 5, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 8}\n", + "0.957 (+/-0.120) for {'max_leaf_nodes': 10, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 8}\n", + "0.975 (+/-0.078) for {'max_leaf_nodes': 10, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 8}\n", + "0.957 (+/-0.120) for {'max_leaf_nodes': 20, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 8}\n", + "0.950 (+/-0.118) for {'max_leaf_nodes': 20, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 8}\n", + "0.957 (+/-0.120) for {'max_leaf_nodes': None, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 9}\n", + "0.946 (+/-0.123) for {'max_leaf_nodes': None, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 9}\n", + "0.957 (+/-0.120) for {'max_leaf_nodes': 5, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 9}\n", + "0.942 (+/-0.168) for {'max_leaf_nodes': 5, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 9}\n", + "0.957 (+/-0.120) for {'max_leaf_nodes': 10, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 9}\n", + "0.957 (+/-0.120) for {'max_leaf_nodes': 10, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 9}\n", + "0.957 (+/-0.120) for {'max_leaf_nodes': 20, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 9}\n", + "0.957 (+/-0.120) for {'max_leaf_nodes': 20, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 9}\n", + "0.968 (+/-0.082) for {'max_leaf_nodes': None, 'criterion': 'gini', 'splitter': 'best', 'class_weight': None, 'max_depth': 3}\n", + "0.950 (+/-0.086) for {'max_leaf_nodes': None, 'criterion': 'gini', 'splitter': 'random', 'class_weight': None, 'max_depth': 3}\n", + "0.950 (+/-0.118) for {'max_leaf_nodes': 5, 'criterion': 'gini', 'splitter': 'best', 'class_weight': None, 'max_depth': 3}\n", + "0.949 (+/-0.116) for {'max_leaf_nodes': 5, 'criterion': 'gini', 'splitter': 'random', 'class_weight': None, 'max_depth': 3}\n", + "0.943 (+/-0.113) for {'max_leaf_nodes': 10, 'criterion': 'gini', 'splitter': 'best', 'class_weight': None, 'max_depth': 3}\n", + "0.950 (+/-0.118) for {'max_leaf_nodes': 10, 'criterion': 'gini', 'splitter': 'random', 'class_weight': None, 'max_depth': 3}\n", + "0.950 (+/-0.118) for {'max_leaf_nodes': 20, 'criterion': 'gini', 'splitter': 'best', 'class_weight': None, 'max_depth': 3}\n", + "0.972 (+/-0.069) for {'max_leaf_nodes': 20, 'criterion': 'gini', 'splitter': 'random', 'class_weight': None, 'max_depth': 3}\n", + "0.943 (+/-0.113) for {'max_leaf_nodes': None, 'criterion': 'gini', 'splitter': 'best', 'class_weight': None, 'max_depth': 4}\n", + "0.938 (+/-0.089) for {'max_leaf_nodes': None, 'criterion': 'gini', 'splitter': 'random', 'class_weight': None, 'max_depth': 4}\n", + "0.943 (+/-0.113) for {'max_leaf_nodes': 5, 'criterion': 'gini', 'splitter': 'best', 'class_weight': None, 'max_depth': 4}\n", + "0.962 (+/-0.084) for {'max_leaf_nodes': 5, 'criterion': 'gini', 'splitter': 'random', 'class_weight': None, 'max_depth': 4}\n", + "0.950 (+/-0.118) for {'max_leaf_nodes': 10, 'criterion': 'gini', 'splitter': 'best', 'class_weight': None, 'max_depth': 4}\n", + "0.924 (+/-0.151) for {'max_leaf_nodes': 10, 'criterion': 'gini', 'splitter': 'random', 'class_weight': None, 'max_depth': 4}\n", + "0.950 (+/-0.118) for {'max_leaf_nodes': 20, 'criterion': 'gini', 'splitter': 'best', 'class_weight': None, 'max_depth': 4}\n", + "0.960 (+/-0.082) for {'max_leaf_nodes': 20, 'criterion': 'gini', 'splitter': 'random', 'class_weight': None, 'max_depth': 4}\n", + "0.943 (+/-0.113) for {'max_leaf_nodes': None, 'criterion': 'gini', 'splitter': 'best', 'class_weight': None, 'max_depth': 5}\n", + "0.950 (+/-0.118) for {'max_leaf_nodes': None, 'criterion': 'gini', 'splitter': 'random', 'class_weight': None, 'max_depth': 5}\n", + "0.950 (+/-0.118) for {'max_leaf_nodes': 5, 'criterion': 'gini', 'splitter': 'best', 'class_weight': None, 'max_depth': 5}\n", + "0.959 (+/-0.110) for {'max_leaf_nodes': 5, 'criterion': 'gini', 'splitter': 'random', 'class_weight': None, 'max_depth': 5}\n", + "0.943 (+/-0.113) for {'max_leaf_nodes': 10, 'criterion': 'gini', 'splitter': 'best', 'class_weight': None, 'max_depth': 5}\n", + "0.970 (+/-0.099) for {'max_leaf_nodes': 10, 'criterion': 'gini', 'splitter': 'random', 'class_weight': None, 'max_depth': 5}\n", + "0.943 (+/-0.113) for {'max_leaf_nodes': 20, 'criterion': 'gini', 'splitter': 'best', 'class_weight': None, 'max_depth': 5}\n", + "0.939 (+/-0.118) for {'max_leaf_nodes': 20, 'criterion': 'gini', 'splitter': 'random', 'class_weight': None, 'max_depth': 5}\n", + "0.950 (+/-0.118) for {'max_leaf_nodes': None, 'criterion': 'gini', 'splitter': 'best', 'class_weight': None, 'max_depth': 6}\n", + "0.925 (+/-0.128) for {'max_leaf_nodes': None, 'criterion': 'gini', 'splitter': 'random', 'class_weight': None, 'max_depth': 6}\n", + "0.943 (+/-0.113) for {'max_leaf_nodes': 5, 'criterion': 'gini', 'splitter': 'best', 'class_weight': None, 'max_depth': 6}\n", + "0.973 (+/-0.068) for {'max_leaf_nodes': 5, 'criterion': 'gini', 'splitter': 'random', 'class_weight': None, 'max_depth': 6}\n", + "0.950 (+/-0.118) for {'max_leaf_nodes': 10, 'criterion': 'gini', 'splitter': 'best', 'class_weight': None, 'max_depth': 6}\n", + "0.941 (+/-0.155) for {'max_leaf_nodes': 10, 'criterion': 'gini', 'splitter': 'random', 'class_weight': None, 'max_depth': 6}\n", + "0.950 (+/-0.118) for {'max_leaf_nodes': 20, 'criterion': 'gini', 'splitter': 'best', 'class_weight': None, 'max_depth': 6}\n", + "0.945 (+/-0.177) for {'max_leaf_nodes': 20, 'criterion': 'gini', 'splitter': 'random', 'class_weight': None, 'max_depth': 6}\n", + "0.943 (+/-0.113) for {'max_leaf_nodes': None, 'criterion': 'gini', 'splitter': 'best', 'class_weight': None, 'max_depth': 7}\n", + "0.922 (+/-0.157) for {'max_leaf_nodes': None, 'criterion': 'gini', 'splitter': 'random', 'class_weight': None, 'max_depth': 7}\n", + "0.943 (+/-0.113) for {'max_leaf_nodes': 5, 'criterion': 'gini', 'splitter': 'best', 'class_weight': None, 'max_depth': 7}\n", + "0.904 (+/-0.091) for {'max_leaf_nodes': 5, 'criterion': 'gini', 'splitter': 'random', 'class_weight': None, 'max_depth': 7}\n", + "0.950 (+/-0.118) for {'max_leaf_nodes': 10, 'criterion': 'gini', 'splitter': 'best', 'class_weight': None, 'max_depth': 7}\n", + "0.915 (+/-0.150) for {'max_leaf_nodes': 10, 'criterion': 'gini', 'splitter': 'random', 'class_weight': None, 'max_depth': 7}\n", + "0.950 (+/-0.118) for {'max_leaf_nodes': 20, 'criterion': 'gini', 'splitter': 'best', 'class_weight': None, 'max_depth': 7}\n", + "0.949 (+/-0.118) for {'max_leaf_nodes': 20, 'criterion': 'gini', 'splitter': 'random', 'class_weight': None, 'max_depth': 7}\n", + "0.950 (+/-0.118) for {'max_leaf_nodes': None, 'criterion': 'gini', 'splitter': 'best', 'class_weight': None, 'max_depth': 8}\n", + "0.943 (+/-0.081) for {'max_leaf_nodes': None, 'criterion': 'gini', 'splitter': 'random', 'class_weight': None, 'max_depth': 8}\n", + "0.950 (+/-0.118) for {'max_leaf_nodes': 5, 'criterion': 'gini', 'splitter': 'best', 'class_weight': None, 'max_depth': 8}\n", + "0.962 (+/-0.115) for {'max_leaf_nodes': 5, 'criterion': 'gini', 'splitter': 'random', 'class_weight': None, 'max_depth': 8}\n", + "0.943 (+/-0.113) for {'max_leaf_nodes': 10, 'criterion': 'gini', 'splitter': 'best', 'class_weight': None, 'max_depth': 8}\n", + "0.946 (+/-0.146) for {'max_leaf_nodes': 10, 'criterion': 'gini', 'splitter': 'random', 'class_weight': None, 'max_depth': 8}\n", + "0.950 (+/-0.118) for {'max_leaf_nodes': 20, 'criterion': 'gini', 'splitter': 'best', 'class_weight': None, 'max_depth': 8}\n", + "0.913 (+/-0.163) for {'max_leaf_nodes': 20, 'criterion': 'gini', 'splitter': 'random', 'class_weight': None, 'max_depth': 8}\n", + "0.943 (+/-0.113) for {'max_leaf_nodes': None, 'criterion': 'gini', 'splitter': 'best', 'class_weight': None, 'max_depth': 9}\n", + "0.953 (+/-0.080) for {'max_leaf_nodes': None, 'criterion': 'gini', 'splitter': 'random', 'class_weight': None, 'max_depth': 9}\n", + "0.950 (+/-0.118) for {'max_leaf_nodes': 5, 'criterion': 'gini', 'splitter': 'best', 'class_weight': None, 'max_depth': 9}\n", + "0.915 (+/-0.153) for {'max_leaf_nodes': 5, 'criterion': 'gini', 'splitter': 'random', 'class_weight': None, 'max_depth': 9}\n", + "0.943 (+/-0.113) for {'max_leaf_nodes': 10, 'criterion': 'gini', 'splitter': 'best', 'class_weight': None, 'max_depth': 9}\n", + "0.936 (+/-0.170) for {'max_leaf_nodes': 10, 'criterion': 'gini', 'splitter': 'random', 'class_weight': None, 'max_depth': 9}\n", + "0.943 (+/-0.113) for {'max_leaf_nodes': 20, 'criterion': 'gini', 'splitter': 'best', 'class_weight': None, 'max_depth': 9}\n", + "0.934 (+/-0.160) for {'max_leaf_nodes': 20, 'criterion': 'gini', 'splitter': 'random', 'class_weight': None, 'max_depth': 9}\n", + "0.968 (+/-0.082) for {'max_leaf_nodes': None, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 3}\n", + "0.879 (+/-0.258) for {'max_leaf_nodes': None, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 3}\n", + "0.957 (+/-0.120) for {'max_leaf_nodes': 5, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 3}\n", + "0.902 (+/-0.257) for {'max_leaf_nodes': 5, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 3}\n", + "0.950 (+/-0.118) for {'max_leaf_nodes': 10, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 3}\n", + "0.946 (+/-0.130) for {'max_leaf_nodes': 10, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 3}\n", + "0.943 (+/-0.113) for {'max_leaf_nodes': 20, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 3}\n", + "0.960 (+/-0.083) for {'max_leaf_nodes': 20, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 3}\n", + "0.950 (+/-0.118) for {'max_leaf_nodes': None, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 4}\n", + "0.943 (+/-0.138) for {'max_leaf_nodes': None, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 4}\n", + "0.957 (+/-0.120) for {'max_leaf_nodes': 5, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 4}\n", + "0.950 (+/-0.089) for {'max_leaf_nodes': 5, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 4}\n", + "0.950 (+/-0.118) for {'max_leaf_nodes': 10, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 4}\n", + "0.936 (+/-0.133) for {'max_leaf_nodes': 10, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 4}\n", + "0.950 (+/-0.118) for {'max_leaf_nodes': 20, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 4}\n", + "0.945 (+/-0.123) for {'max_leaf_nodes': 20, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 4}\n", + "0.950 (+/-0.118) for {'max_leaf_nodes': None, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 5}\n", + "0.953 (+/-0.134) for {'max_leaf_nodes': None, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 5}\n", + "0.957 (+/-0.120) for {'max_leaf_nodes': 5, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 5}\n", + "0.900 (+/-0.248) for {'max_leaf_nodes': 5, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 5}\n", + "0.950 (+/-0.118) for {'max_leaf_nodes': 10, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 5}\n", + "0.949 (+/-0.091) for {'max_leaf_nodes': 10, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 5}\n", + "0.943 (+/-0.113) for {'max_leaf_nodes': 20, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 5}\n", + "0.972 (+/-0.068) for {'max_leaf_nodes': 20, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 5}\n", + "0.950 (+/-0.118) for {'max_leaf_nodes': None, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 6}\n", + "0.950 (+/-0.118) for {'max_leaf_nodes': None, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 6}\n", + "0.957 (+/-0.120) for {'max_leaf_nodes': 5, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 6}\n", + "0.909 (+/-0.296) for {'max_leaf_nodes': 5, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 6}\n", + "0.950 (+/-0.118) for {'max_leaf_nodes': 10, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 6}\n", + "0.952 (+/-0.105) for {'max_leaf_nodes': 10, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 6}\n", + "0.950 (+/-0.118) for {'max_leaf_nodes': 20, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 6}\n", + "0.930 (+/-0.133) for {'max_leaf_nodes': 20, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 6}\n", + "0.950 (+/-0.118) for {'max_leaf_nodes': None, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 7}\n", + "0.950 (+/-0.118) for {'max_leaf_nodes': None, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 7}\n", + "0.957 (+/-0.120) for {'max_leaf_nodes': 5, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 7}\n", + "0.970 (+/-0.099) for {'max_leaf_nodes': 5, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 7}\n", + "0.950 (+/-0.118) for {'max_leaf_nodes': 10, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 7}\n", + "0.965 (+/-0.115) for {'max_leaf_nodes': 10, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 7}\n", + "0.943 (+/-0.113) for {'max_leaf_nodes': 20, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 7}\n", + "0.926 (+/-0.138) for {'max_leaf_nodes': 20, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 7}\n", + "0.950 (+/-0.118) for {'max_leaf_nodes': None, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 8}\n", + "0.950 (+/-0.087) for {'max_leaf_nodes': None, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 8}\n", + "0.957 (+/-0.120) for {'max_leaf_nodes': 5, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 8}\n", + "0.956 (+/-0.094) for {'max_leaf_nodes': 5, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 8}\n", + "0.950 (+/-0.118) for {'max_leaf_nodes': 10, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 8}\n", + "0.934 (+/-0.141) for {'max_leaf_nodes': 10, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 8}\n", + "0.943 (+/-0.113) for {'max_leaf_nodes': 20, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 8}\n", + "0.959 (+/-0.069) for {'max_leaf_nodes': 20, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 8}\n", + "0.950 (+/-0.118) for {'max_leaf_nodes': None, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 9}\n", + "0.950 (+/-0.118) for {'max_leaf_nodes': None, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 9}\n", + "0.957 (+/-0.120) for {'max_leaf_nodes': 5, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 9}\n", + "0.961 (+/-0.081) for {'max_leaf_nodes': 5, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 9}\n", + "0.950 (+/-0.118) for {'max_leaf_nodes': 10, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 9}\n", + "0.965 (+/-0.070) for {'max_leaf_nodes': 10, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 9}\n", + "0.950 (+/-0.118) for {'max_leaf_nodes': 20, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 9}\n", + "0.938 (+/-0.160) for {'max_leaf_nodes': 20, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 9}\n", + "\n", + "Detailed classification report:\n", + "\n", + "The model is trained on the full development set.\n", + "The scores are computed on the full evaluation set.\n", + "\n", + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 8\n", + " 1 0.85 1.00 0.92 11\n", + " 2 1.00 0.89 0.94 19\n", + "\n", + "avg / total 0.96 0.95 0.95 38\n", + "\n", + "\n", + "# Tuning hyper-parameters for recall\n", + "\n", + "Best parameters set found on development set:\n", + "\n", + "{'max_leaf_nodes': 10, 'criterion': 'gini', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 4}\n", + "\n", + "Grid scores on development set:\n", + "\n", + "0.946 (+/-0.140) for {'max_leaf_nodes': None, 'criterion': 'gini', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 3}\n", + "0.920 (+/-0.243) for {'max_leaf_nodes': None, 'criterion': 'gini', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 3}\n", + "0.938 (+/-0.159) for {'max_leaf_nodes': 5, 'criterion': 'gini', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 3}\n", + "0.902 (+/-0.164) for {'max_leaf_nodes': 5, 'criterion': 'gini', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 3}\n", + "0.946 (+/-0.140) for {'max_leaf_nodes': 10, 'criterion': 'gini', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 3}\n", + "0.955 (+/-0.146) for {'max_leaf_nodes': 10, 'criterion': 'gini', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 3}\n", + "0.946 (+/-0.140) for {'max_leaf_nodes': 20, 'criterion': 'gini', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 3}\n", + "0.955 (+/-0.117) for {'max_leaf_nodes': 20, 'criterion': 'gini', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 3}\n", + "0.938 (+/-0.137) for {'max_leaf_nodes': None, 'criterion': 'gini', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 4}\n", + "0.929 (+/-0.155) for {'max_leaf_nodes': None, 'criterion': 'gini', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 4}\n", + "0.938 (+/-0.159) for {'max_leaf_nodes': 5, 'criterion': 'gini', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 4}\n", + "0.911 (+/-0.146) for {'max_leaf_nodes': 5, 'criterion': 'gini', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 4}\n", + "0.938 (+/-0.159) for {'max_leaf_nodes': 10, 'criterion': 'gini', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 4}\n", + "0.973 (+/-0.108) for {'max_leaf_nodes': 10, 'criterion': 'gini', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 4}\n", + "0.946 (+/-0.140) for {'max_leaf_nodes': 20, 'criterion': 'gini', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 4}\n", + "0.955 (+/-0.121) for {'max_leaf_nodes': 20, 'criterion': 'gini', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 4}\n", + "0.946 (+/-0.140) for {'max_leaf_nodes': None, 'criterion': 'gini', 'splitter': 'best', 'class_weight': 'balanced', 'max_depth': 5}\n", + "0.964 (+/-0.119) for {'max_leaf_nodes': None, 'criterion': 'gini', 'splitter': 'random', 'class_weight': 'balanced', 'max_depth': 5}\n", + "0.929 (+/-0.155) for {'max_leaf_nodes': 5, 'criterion': 'gini', 'splitter': 'best', 'class_weight': 'balanced', 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None, 'max_depth': 5}\n", + "0.911 (+/-0.159) for {'max_leaf_nodes': 20, 'criterion': 'gini', 'splitter': 'random', 'class_weight': None, 'max_depth': 5}\n", + "0.929 (+/-0.132) for {'max_leaf_nodes': None, 'criterion': 'gini', 'splitter': 'best', 'class_weight': None, 'max_depth': 6}\n", + "0.964 (+/-0.119) for {'max_leaf_nodes': None, 'criterion': 'gini', 'splitter': 'random', 'class_weight': None, 'max_depth': 6}\n", + "0.938 (+/-0.138) for {'max_leaf_nodes': 5, 'criterion': 'gini', 'splitter': 'best', 'class_weight': None, 'max_depth': 6}\n", + "0.929 (+/-0.212) for {'max_leaf_nodes': 5, 'criterion': 'gini', 'splitter': 'random', 'class_weight': None, 'max_depth': 6}\n", + "0.929 (+/-0.132) for {'max_leaf_nodes': 10, 'criterion': 'gini', 'splitter': 'best', 'class_weight': None, 'max_depth': 6}\n", + "0.938 (+/-0.159) for {'max_leaf_nodes': 10, 'criterion': 'gini', 'splitter': 'random', 'class_weight': None, 'max_depth': 6}\n", + "0.938 (+/-0.138) for {'max_leaf_nodes': 20, 'criterion': 'gini', 'splitter': 'best', 'class_weight': None, 'max_depth': 6}\n", + "0.920 (+/-0.187) for {'max_leaf_nodes': 20, 'criterion': 'gini', 'splitter': 'random', 'class_weight': None, 'max_depth': 6}\n", + "0.929 (+/-0.132) for {'max_leaf_nodes': None, 'criterion': 'gini', 'splitter': 'best', 'class_weight': None, 'max_depth': 7}\n", + "0.946 (+/-0.140) for {'max_leaf_nodes': None, 'criterion': 'gini', 'splitter': 'random', 'class_weight': None, 'max_depth': 7}\n", + "0.938 (+/-0.138) for {'max_leaf_nodes': 5, 'criterion': 'gini', 'splitter': 'best', 'class_weight': None, 'max_depth': 7}\n", + "0.929 (+/-0.131) for {'max_leaf_nodes': 5, 'criterion': 'gini', 'splitter': 'random', 'class_weight': None, 'max_depth': 7}\n", + "0.938 (+/-0.138) for {'max_leaf_nodes': 10, 'criterion': 'gini', 'splitter': 'best', 'class_weight': None, 'max_depth': 7}\n", + "0.938 (+/-0.109) for {'max_leaf_nodes': 10, 'criterion': 'gini', 'splitter': 'random', 'class_weight': None, 'max_depth': 7}\n", + "0.938 (+/-0.138) for {'max_leaf_nodes': 20, 'criterion': 'gini', 'splitter': 'best', 'class_weight': None, 'max_depth': 7}\n", + "0.938 (+/-0.159) for {'max_leaf_nodes': 20, 'criterion': 'gini', 'splitter': 'random', 'class_weight': None, 'max_depth': 7}\n", + "0.938 (+/-0.138) for {'max_leaf_nodes': None, 'criterion': 'gini', 'splitter': 'best', 'class_weight': None, 'max_depth': 8}\n", + "0.946 (+/-0.120) for {'max_leaf_nodes': None, 'criterion': 'gini', 'splitter': 'random', 'class_weight': None, 'max_depth': 8}\n", + "0.938 (+/-0.138) for {'max_leaf_nodes': 5, 'criterion': 'gini', 'splitter': 'best', 'class_weight': None, 'max_depth': 8}\n", + "0.929 (+/-0.155) for {'max_leaf_nodes': 5, 'criterion': 'gini', 'splitter': 'random', 'class_weight': None, 'max_depth': 8}\n", + "0.929 (+/-0.132) for {'max_leaf_nodes': 10, 'criterion': 'gini', 'splitter': 'best', 'class_weight': None, 'max_depth': 8}\n", + "0.946 (+/-0.140) for {'max_leaf_nodes': 10, 'criterion': 'gini', 'splitter': 'random', 'class_weight': None, 'max_depth': 8}\n", + "0.938 (+/-0.138) for {'max_leaf_nodes': 20, 'criterion': 'gini', 'splitter': 'best', 'class_weight': None, 'max_depth': 8}\n", + "0.938 (+/-0.164) for {'max_leaf_nodes': 20, 'criterion': 'gini', 'splitter': 'random', 'class_weight': None, 'max_depth': 8}\n", + "0.929 (+/-0.132) for {'max_leaf_nodes': None, 'criterion': 'gini', 'splitter': 'best', 'class_weight': None, 'max_depth': 9}\n", + "0.920 (+/-0.171) for {'max_leaf_nodes': None, 'criterion': 'gini', 'splitter': 'random', 'class_weight': None, 'max_depth': 9}\n", + "0.938 (+/-0.138) for {'max_leaf_nodes': 5, 'criterion': 'gini', 'splitter': 'best', 'class_weight': None, 'max_depth': 9}\n", + "0.893 (+/-0.227) for {'max_leaf_nodes': 5, 'criterion': 'gini', 'splitter': 'random', 'class_weight': None, 'max_depth': 9}\n", + "0.929 (+/-0.132) for {'max_leaf_nodes': 10, 'criterion': 'gini', 'splitter': 'best', 'class_weight': None, 'max_depth': 9}\n", + "0.902 (+/-0.169) for {'max_leaf_nodes': 10, 'criterion': 'gini', 'splitter': 'random', 'class_weight': None, 'max_depth': 9}\n", + "0.938 (+/-0.138) for {'max_leaf_nodes': 20, 'criterion': 'gini', 'splitter': 'best', 'class_weight': None, 'max_depth': 9}\n", + "0.902 (+/-0.148) for {'max_leaf_nodes': 20, 'criterion': 'gini', 'splitter': 'random', 'class_weight': None, 'max_depth': 9}\n", + "0.955 (+/-0.115) for {'max_leaf_nodes': None, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 3}\n", + "0.875 (+/-0.228) for {'max_leaf_nodes': None, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 3}\n", + "0.946 (+/-0.140) for {'max_leaf_nodes': 5, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 3}\n", + "0.920 (+/-0.147) for {'max_leaf_nodes': 5, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 3}\n", + "0.929 (+/-0.132) for {'max_leaf_nodes': 10, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 3}\n", + "0.938 (+/-0.137) for {'max_leaf_nodes': 10, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 3}\n", + "0.938 (+/-0.138) for {'max_leaf_nodes': 20, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 3}\n", + "0.929 (+/-0.116) for {'max_leaf_nodes': 20, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 3}\n", + "0.929 (+/-0.132) for {'max_leaf_nodes': None, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 4}\n", + "0.911 (+/-0.160) for {'max_leaf_nodes': None, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 4}\n", + "0.946 (+/-0.140) for {'max_leaf_nodes': 5, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 4}\n", + "0.866 (+/-0.234) for {'max_leaf_nodes': 5, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 4}\n", + "0.938 (+/-0.138) for {'max_leaf_nodes': 10, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 4}\n", + "0.911 (+/-0.183) for {'max_leaf_nodes': 10, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 4}\n", + "0.938 (+/-0.138) for {'max_leaf_nodes': 20, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 4}\n", + "0.938 (+/-0.109) for {'max_leaf_nodes': 20, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 4}\n", + "0.938 (+/-0.138) for {'max_leaf_nodes': None, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 5}\n", + "0.946 (+/-0.088) for {'max_leaf_nodes': None, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 5}\n", + "0.946 (+/-0.140) for {'max_leaf_nodes': 5, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 5}\n", + "0.929 (+/-0.174) for {'max_leaf_nodes': 5, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 5}\n", + "0.929 (+/-0.132) for {'max_leaf_nodes': 10, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 5}\n", + "0.938 (+/-0.152) for {'max_leaf_nodes': 10, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 5}\n", + "0.938 (+/-0.138) for {'max_leaf_nodes': 20, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 5}\n", + "0.938 (+/-0.116) for {'max_leaf_nodes': 20, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 5}\n", + "0.938 (+/-0.138) for {'max_leaf_nodes': None, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 6}\n", + "0.946 (+/-0.120) for {'max_leaf_nodes': None, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 6}\n", + "0.946 (+/-0.140) for {'max_leaf_nodes': 5, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 6}\n", + "0.938 (+/-0.164) for {'max_leaf_nodes': 5, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 6}\n", + "0.938 (+/-0.138) for {'max_leaf_nodes': 10, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 6}\n", + "0.946 (+/-0.140) for {'max_leaf_nodes': 10, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 6}\n", + "0.929 (+/-0.132) for {'max_leaf_nodes': 20, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 6}\n", + "0.929 (+/-0.187) for {'max_leaf_nodes': 20, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 6}\n", + "0.938 (+/-0.138) for {'max_leaf_nodes': None, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 7}\n", + "0.920 (+/-0.147) for {'max_leaf_nodes': None, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 7}\n", + "0.946 (+/-0.140) for {'max_leaf_nodes': 5, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 7}\n", + "0.920 (+/-0.153) for {'max_leaf_nodes': 5, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 7}\n", + "0.938 (+/-0.138) for {'max_leaf_nodes': 10, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 7}\n", + "0.911 (+/-0.137) for {'max_leaf_nodes': 10, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 7}\n", + "0.929 (+/-0.132) for {'max_leaf_nodes': 20, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 7}\n", + "0.929 (+/-0.158) for {'max_leaf_nodes': 20, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 7}\n", + "0.929 (+/-0.132) for {'max_leaf_nodes': None, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 8}\n", + "0.920 (+/-0.168) for {'max_leaf_nodes': None, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 8}\n", + "0.946 (+/-0.140) for {'max_leaf_nodes': 5, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 8}\n", + "0.920 (+/-0.162) for {'max_leaf_nodes': 5, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 8}\n", + "0.938 (+/-0.138) for {'max_leaf_nodes': 10, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 8}\n", + "0.946 (+/-0.140) for {'max_leaf_nodes': 10, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 8}\n", + "0.938 (+/-0.138) for {'max_leaf_nodes': 20, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 8}\n", + "0.938 (+/-0.159) for {'max_leaf_nodes': 20, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 8}\n", + "0.938 (+/-0.138) for {'max_leaf_nodes': None, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 9}\n", + "0.929 (+/-0.158) for {'max_leaf_nodes': None, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 9}\n", + "0.946 (+/-0.140) for {'max_leaf_nodes': 5, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 9}\n", + "0.893 (+/-0.194) for {'max_leaf_nodes': 5, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 9}\n", + "0.938 (+/-0.138) for {'max_leaf_nodes': 10, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 9}\n", + "0.946 (+/-0.140) for {'max_leaf_nodes': 10, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 9}\n", + "0.929 (+/-0.132) for {'max_leaf_nodes': 20, 'criterion': 'entropy', 'splitter': 'best', 'class_weight': None, 'max_depth': 9}\n", + "0.929 (+/-0.155) for {'max_leaf_nodes': 20, 'criterion': 'entropy', 'splitter': 'random', 'class_weight': None, 'max_depth': 9}\n", + "\n", + "Detailed classification report:\n", + "\n", + "The model is trained on the full development set.\n", + "The scores are computed on the full evaluation set.\n", + "\n", + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 8\n", + " 1 0.91 0.91 0.91 11\n", + " 2 0.95 0.95 0.95 19\n", + "\n", + "avg / total 0.95 0.95 0.95 38\n", + "\n", + "\n" + ] + } + ], + "source": [ + "# Set the parameters by cross-validation\n", + "\n", + "from sklearn.metrics import classification_report\n", + "\n", + "# set of parameters to test\n", + "tuned_parameters = [{'max_depth': np.arange(3, 10),\n", + "# 'max_weights': [1, 10, 100, 1000]},\n", + " 'criterion': ['gini', 'entropy'], \n", + " 'splitter': ['best', 'random'],\n", + " # 'min_samples_leaf': [2, 5, 10],\n", + " 'class_weight':['balanced', None],\n", + " 'max_leaf_nodes': [None, 5, 10, 20]\n", + " }]\n", + "\n", + "scores = ['precision', 'recall']\n", + "\n", + "for score in scores:\n", + " print(\"# Tuning hyper-parameters for %s\" % score)\n", + " print()\n", + "\n", + " # cv = the fold of the cross-validation cv, defaulted to 5\n", + " gs = GridSearchCV(DecisionTreeClassifier(), tuned_parameters, cv=10, scoring='%s_weighted' % score)\n", + " gs.fit(x_train, y_train)\n", + "\n", + " print(\"Best parameters set found on development set:\")\n", + " print()\n", + " print(gs.best_params_)\n", + " print()\n", + " print(\"Grid scores on development set:\")\n", + " print()\n", + " for params, mean_score, scores in gs.grid_scores_:\n", + " print(\"%0.3f (+/-%0.03f) for %r\" % (mean_score, scores.std() * 2, params))\n", + " print()\n", + "\n", + " print(\"Detailed classification report:\")\n", + " print()\n", + " print(\"The model is trained on the full development set.\")\n", + " print(\"The scores are computed on the full evaluation set.\")\n", + " print()\n", + " y_true, y_pred = y_test, gs.predict(x_test)\n", + " print(classification_report(y_true, y_pred))\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "New we evaluate the resulting tuning." + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean score: 0.960 (+/- 0.018)\n" + ] + } + ], + "source": [ + "# create a composite estimator made by a pipeline of preprocessing and the KNN model\n", + "model = Pipeline([\n", + " ('scaler', StandardScaler()),\n", + " ('ds', DecisionTreeClassifier(max_leaf_nodes=20, criterion='gini', \n", + " splitter='random', class_weight='balanced', max_depth=3))\n", + "])\n", + "\n", + "# Fit the model\n", + "model.fit(x_train, y_train) \n", + "\n", + "# create a k-fold cross validation iterator of k=10 folds\n", + "cv = KFold(x_iris.shape[0], 10, shuffle=True, random_state=33)\n", + "\n", + "# by default the score used is the one returned by score method of the estimator (accuracy)\n", + "scores = cross_val_score(model, x_iris, y_iris, cv=cv)\n", + "def mean_score(scores):\n", + " return (\"Mean score: {0:.3f} (+/- {1:.3f})\").format(np.mean(scores), sem(scores))\n", + "print(mean_score(scores))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So, we get an average accuracy of 0.96!! Better than 0.947 (without tuning) and 0.953 (tuning only *max_depth*)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* [Plot the decision surface of a decision tree on the iris dataset](http://scikit-learn.org/stable/auto_examples/tree/plot_iris.html)\n", + "* [Learning scikit-learn: Machine Learning in Python](http://proquest.safaribooksonline.com/book/programming/python/9781783281930/1dot-machine-learning-a-gentle-introduction/ch01s02_html), Raúl Garreta; Guillermo Moncecchi, Packt Publishing, 2013.\n", + "* [Python Machine Learning](http://proquest.safaribooksonline.com/book/programming/python/9781783555130), Sebastian Raschka, Packt Publishing, 2015.\n", + "* [Parameter estimation using grid search with cross-validation](http://scikit-learn.org/stable/auto_examples/model_selection/grid_search_digits.html)\n", + "* [Decision trees in python with scikit-learn and pandas](http://chrisstrelioff.ws/sandbox/2015/06/08/decision_trees_in_python_with_scikit_learn_and_pandas.html)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Licence" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n", + "\n", + "© 2016 Carlos A. Iglesias, Universidad Politécnica de Madrid." + ] + } + ], + "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": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/ml1/2_7_Model_Persistence.ipynb b/ml1/2_7_Model_Persistence.ipynb new file mode 100644 index 0000000..56285c0 --- /dev/null +++ b/ml1/2_7_Model_Persistence.ipynb @@ -0,0 +1,204 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](files/images/EscUpmPolit_p.gif \"UPM\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Course Notes for Learning Intelligent Systems" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © 2016 Carlos A. Iglesias" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## [Introduction to Machine Learning](2_0_0_Intro_ML.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Table of Contents\n", + "* [Model Persistence](#Model-Persistence)\n", + "* [References](#References)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model Persistence" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The goal of this notebook is to learn how to save a model in the the scikit by using Python’s built-in persistence model, namely pickle\n", + "\n", + "First we recap the previous tasks: load data, preprocess and train the model." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Pipeline(steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('KNN', KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n", + " metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n", + " weights='uniform'))])" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# load iris\n", + "from sklearn import datasets\n", + "iris = datasets.load_iris()\n", + "\n", + "# Training and test spliting\n", + "from sklearn.cross_validation import train_test_split\n", + "x_iris, y_iris = iris.data, iris.target\n", + "# Test set will be the 25% taken randomly\n", + "x_train, x_test, y_train, y_test = train_test_split(x_iris, y_iris, test_size=0.25, random_state=33)\n", + "\n", + "# Create the model using the pipeline\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "\n", + "# create a composite estimator made by a pipeline of preprocessing and the KNN model\n", + "model = Pipeline([\n", + " ('scaler', StandardScaler()),\n", + " ('KNN', KNeighborsClassifier())\n", + "])\n", + "\n", + "# Train the model\n", + "model.fit(x_train, y_train) \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we are going to save the model to a data structure called *pickle*. A pickle is a dictionary and can be used as a file or a string." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pickle\n", + "s = pickle.dumps(model)\n", + "model2 = pickle.loads(s)\n", + "model2.predict(x_iris[0:1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A more efficient alternative to pickle is joblib, especially for big data problems. In this case the model can only be saved to a file and not to a string." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# save model\n", + "from sklearn.externals import joblib\n", + "joblib.dump(model, 'filename.pkl') \n", + "\n", + "#load model\n", + "model2 = joblib.load('filename.pkl') " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* [Tutorial scikit-learn](http://scikit-learn.org/stable/tutorial/basic/tutorial.html)\n", + "* [Model persistence in scikit-learn](http://scikit-learn.org/stable/modules/model_persistence.html#model-persistence)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Licence\n", + "The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n", + "\n", + "© 2016 Carlos A. Iglesias, Universidad Politécnica de Madrid." + ] + } + ], + "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": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/ml1/2_8_Conclusions.ipynb b/ml1/2_8_Conclusions.ipynb new file mode 100644 index 0000000..36fc7a0 --- /dev/null +++ b/ml1/2_8_Conclusions.ipynb @@ -0,0 +1,130 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](files/images/EscUpmPolit_p.gif \"UPM\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Course Notes for Learning Intelligent Systems" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © 2016 Carlos A. Iglesias" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## [Introduction to Machine Learning](2_0_0_Intro_ML.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Table of Contents\n", + "* [Conclusions](#Conclusions)\n", + "* [References](#References)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Conclusions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this chapter, we have introduced the essentials of machine learning in a practical way. \n", + "\n", + "We have gone through some of the most interesting features offered by scikit-learn. They essentially concern the machine learning features, and the visualisation features brought by the matplotlib and seaborn libraries. In the following session we will analyse other machine learning algorithms, such as SVM and Perceptron.\n", + "\n", + "Before concluding this session, we include a comparison of the algorithms reviewed in this session on synthetic datasets, based on the sample code of [sklearn](http://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html#example-classification-plot-classifier-comparison-py).\n", + "\n", + "Particularly in high-dimensional spaces, data can more easily be separated linearly and the simplicity of classifiers such as naive Bayes and linear SVMs might lead to better generalization than is achieved by other classifiers.\n", + "\n", + "The plots show training points in solid colors and testing points semi-transparent. The lower right shows the classification accuracy on the test set.\n", + "\n", + "The [DummyClassifier](http://scikit-learn.org/stable/modules/generated/sklearn.dummy.DummyClassifier.html#sklearn.dummy.DummyClassifier) is a classifier that makes predictions using simple rules. It is useful as a simple baseline to compare with other (real) classifiers. \n", + "\n", + "As previosly, we import a function defined in the file [plotml.py](files/plotml.py) using the *magic command* **%run**." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# display plots in the notebook \n", + "#%matplotlib inline\n", + "\n", + "# Run in a separate window to make it bigger\n", + "%matplotlib qt\n", + "%run plotml\n", + "plot_classifiers()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* [Classifier comparison¶](http://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html#example-classification-plot-classifier-comparison-py)\n", + "* [DummyClassifier ](http://scikit-learn.org/stable/modules/generated/sklearn.dummy.DummyClassifier.html)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Licence\n", + "The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n", + "\n", + "© 2016 Carlos A. Iglesias, Universidad Politécnica de Madrid." + ] + } + ], + "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": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/ml1/images/EscUpmPolit_p.gif b/ml1/images/EscUpmPolit_p.gif new file mode 100644 index 0000000000000000000000000000000000000000..a821282d01d9828217973d170df5311b92f3fe6b GIT binary patch literal 3171 zcmV-p44m^vNk%w1VOju50QUd@0000!VRu48K2A+XSyoD3T1{3}P*q-9M_OS-Uv5lX zVnJnlOK5pfXmVL^a$RzFWnoTdWK&>cT54ocYiL?=YF2V?TYGd{VQ6A+Y+!P3V{ma{ zcyeNYduDroZFO~XdV6|BZ-+#2i%oQiLw1uze4I>tm{N9%Tz!s2f1+G~k41o^Mun+K zgr`V}uStrrNRGHllD$=ptzVF=VS|@uiJfnamvD`hbC8y0jHGUwvv884aG9}tpR{_U zy-b|UU4p<{pTWNcsoYPj;!v#OQLyJxvg%=m!exrY zYmLTapTTaB$8M6ybd?mzbHEn3kHG znuMLJlAWWPpP`MczLlh{o1mhbq^hK%o2sRhx~zk|uZX>{ioUXpqobptrl_N;t*WP> zsHmr^tE;4~u&lJXvazkQva+_fxUsptySu!Bp3a1z&xE1SiKNkvsMCe8$ceGXlB?C9 zz0Q@e*O#%_n6THKwAr4z+@Q7Eqq*FwzTUCBz`4J|sKL&oz|f}1)2YSLt-#;9!Nsn@ z;IqWyx5(qV%H+Vaj=;2!!L^aXx0J)Ul*G7}#k!csyqm|ooXEYM%Dz@p5;qs+pk z&BUh8#i-E6tI^1<(#o*Z%d*tWvenJC*Uz}u(74&ryTZf6&CSck&Ct)#(A(6&+||L} z*Tmk~#njZ))z{hA*x1dV;c)7|dZ;PBhz@!;9V;M&RI+{)tJ z%;etAgwR<^W*9C z>FxIH@%ZrV}00000A^8LW00930EC2ui09pV^000R80RIUbNU)&6g9sBUT*$DY!-o(fN}Ncs zqQ#3CGiuy;5!)|ZAaeoJ#<8TuT&iI4(iScpIdZ^u&0@97q)mq2TEWuxYa23T?o`FP z_v)OlciLvbB6qVX!C0`a_3Nii*gaO<(5P`2Yu~GGxUeaERmlm*TxjFmQX zz=T=##cEbEck}Muv*+%bEm*sRd7HJZpFCB`He1a0#~dth_kQ(Sr7PalsrZghoyRK{ zvRhN{A@k!+H$^_e+}(?n?3b!@%MlU(OZSCMzh}mdyKD8!m$z5K!tt>#5f(OWtiqYQ zH5A`Be8gO!!5$Y_Fvu7bJcG|X{oq53FtF713oobtOWu zC+u%g#-#Z0op2qK_x z0}eN!FhU3_^k4@w%ALcCXx#|^GvA~IX(LNE~}#IH*z@xl*3#PI8{N(ka7 zP3OF`Po1<9lnbA%Oxx?RO_V@_2qe6~!m&yi;wPW5*@8^81NothJNx9c>2bdP%7P0B z6u7{!$cpQxE6Ma@jxV{OE07x3bc2pCelVz9yhOmTzy(}vfk6pE0PBgL_WWU$GTE>q z@IQR)@(M2q^Kl> zqde{$(~6>@{2D|dZ@jSxBZ?GqN92<$2b(a-bdx!{e0=1~U?wY9$*(*V!UhO{1pkI2 zNGQFCpZur-PB+NN`Y|8mgoDg43H!Nw6Ot$bLK}rBk_aG&5TSG_e#XNJFjHm|%g0fg zBMUiqS^ujKg>27A@P*_NIT7S5=gnRG@C7b@@iAT;0~@yhhc0{}zb0rQ5rjy@{R)u? zIxInMpYYSPz`+k}sG=Jmo6Rkh5e{(l!B3%Uf)R9Jf*CMi1UWcC4es`XVJIhg*@*`? za3-)mcmo^RKn5%SEO(w~qyq~|h+Eu>K!axxLt)*}MK~CUjaPh3Ah!^PJo=%GU---! z%NRyEo{@+;1OgCqNP-OL5Qsp?K@iV~BdhlDDq&<}7p)-46~zH3TFhb|uh>R8;fC+aECjjr;Aze!X2z&kSy+zjBAecAL5{n zFm%z6Sp8xjirGakZlMcd%n26#2*)nuF+D&0G$F6hg*k-rlUfv281KN8esa-|KiFd* zv#4TOu9^^p{o)?*@Iyc1(+`#Sq88NLg)-ie3qE9ntqk$SADa4(U4SDSRmui9a-j`m zz~T?*0<1&2aSK+wVsFA=%uHw^*@}?E8`{_gILy)6(wde&>#znjror0Qx^^1Uhz2xL zLEGB@+V-}$g+?@X%Uj+`!?o0~hH$O1j^dhR3P4D%a+k~8<~sMeKyU(dpZf$SSl0

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--git a/ml1/plotml.py b/ml1/plotml.py new file mode 100644 index 0000000..5ba3d1b --- /dev/null +++ b/ml1/plotml.py @@ -0,0 +1,112 @@ +import numpy as np +import matplotlib.pyplot as plt +from matplotlib.colors import ListedColormap +from sklearn.cross_validation import train_test_split +from sklearn.preprocessing import StandardScaler +from sklearn.datasets import make_moons, make_circles, make_classification +from sklearn.neighbors import KNeighborsClassifier +from sklearn.svm import SVC +from sklearn.tree import DecisionTreeClassifier +from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier +from sklearn.naive_bayes import GaussianNB +from sklearn.discriminant_analysis import LinearDiscriminantAnalysis +from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis +from sklearn.dummy import DummyClassifier + +# Taken from http://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html#example-classification-plot-classifier-comparison-py + +def plot_classifiers(): + """ + Plot classifiers in synthetic datasets, taken from http://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html + A comparison of a several classifiers in scikit-learn on synthetic datasets. The point of this example is to illustrate the nature of decision boundaries of different classifiers. This should be taken with a grain of salt, as the intuition conveyed by these examples does not necessarily carry over to real datasets. + Particularly in high-dimensional spaces, data can more easily be separated linearly and the simplicity of classifiers such as naive Bayes and linear SVMs might lead to better generalization than is achieved by other classifiers. + The plots show training points in solid colors and testing points semi-transparent. The lower right shows the classification accuracy on the test set. + """ + h = .02 # step size in the mesh + + names = ["DummyClassifier", "Nearest Neighbors", "Decision Tree", "Naive Bayes", "Linear SVM", "RBF SVM", + "Random Forest"] + classifiers = [ + DummyClassifier(strategy="prior"), + KNeighborsClassifier(3), + DecisionTreeClassifier(max_depth=5), + GaussianNB(), + SVC(kernel="linear", C=0.025), + SVC(gamma=2, C=1), + RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1) + ] + + X, y = make_classification(n_features=2, n_redundant=0, n_informative=2, + random_state=1, n_clusters_per_class=1) + rng = np.random.RandomState(2) + X += 2 * rng.uniform(size=X.shape) + linearly_separable = (X, y) + + datasets = [make_moons(noise=0.3, random_state=0), + make_circles(noise=0.2, factor=0.5, random_state=1), linearly_separable] + ds_names = ["Dataset moons", "Dataset circles", "Dataset linearly_separable"] + + figure = plt.figure(figsize=(27, 9)) + i = 1 + # iterate over datasets + for ds_name, ds in zip(ds_names, datasets): + # preprocess dataset, split into training and test part + X, y = ds + X = StandardScaler().fit_transform(X) + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.4) + + x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5 + y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5 + xx, yy = np.meshgrid(np.arange(x_min, x_max, h), + np.arange(y_min, y_max, h)) + + # just plot the dataset first + cm = plt.cm.RdBu + cm_bright = ListedColormap(['#FF0000', '#0000FF']) + ax = plt.subplot(len(datasets), len(classifiers) + 1, i) + ax.set_title(ds_name) + # Plot the training points + ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright) + # and testing points + ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6) + ax.set_xlim(xx.min(), xx.max()) + ax.set_ylim(yy.min(), yy.max()) + ax.set_xticks(()) + ax.set_yticks(()) + i += 1 + + # iterate over classifiers + for name, clf in zip(names, classifiers): + ax = plt.subplot(len(datasets), len(classifiers) + 1, i) + clf.fit(X_train, y_train) + score = clf.score(X_test, y_test) + + # Plot the decision boundary. For that, we will assign a color to each + # point in the mesh [x_min, m_max]x[y_min, y_max]. + if hasattr(clf, "decision_function"): + Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()]) + else: + Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1] + + # Put the result into a color plot + Z = Z.reshape(xx.shape) + ax.contourf(xx, yy, Z, cmap=cm, alpha=.8) + + # Plot also the training points + ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright) + # and testing points + ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, + alpha=0.6) + + ax.set_xlim(xx.min(), xx.max()) + ax.set_ylim(yy.min(), yy.max()) + ax.set_xticks(()) + ax.set_yticks(()) + ax.set_title(name) + ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'), + size=15, horizontalalignment='right') + i += 1 + + figure.subplots_adjust(left=.02, right=.98) + plt.suptitle("Comparison of Classifiers in synthetic datasets", fontsize=18) + plt.show() diff --git a/ml1/util_ds.py b/ml1/util_ds.py new file mode 100644 index 0000000..a4721f2 --- /dev/null +++ b/ml1/util_ds.py @@ -0,0 +1,117 @@ +import numpy as np + +# Taken from http://chrisstrelioff.ws/sandbox/2015/06/25/decision_trees_in_python_again_cross_validation.html + +def get_code(tree, feature_names, target_names, + spacer_base=" "): + """Produce psuedo-code for decision tree. + + Args + ---- + tree -- scikit-leant DescisionTree. + feature_names -- list of feature names. + target_names -- list of target (class) names. + spacer_base -- used for spacing code (default: " "). + + Notes + ----- + based on http://stackoverflow.com/a/30104792. + """ + left = tree.tree_.children_left + right = tree.tree_.children_right + threshold = tree.tree_.threshold + features = [feature_names[i] for i in tree.tree_.feature] + value = tree.tree_.value + + def recurse(left, right, threshold, features, node, depth): + spacer = spacer_base * depth + if (threshold[node] != -2): + print(spacer + "if ( " + features[node] + " <= " + \ + str(threshold[node]) + " ) {") + if left[node] != -1: + recurse(left, right, threshold, features, + left[node], depth+1) + print(spacer + "}\n" + spacer +"else {") + if right[node] != -1: + recurse(left, right, threshold, features, + right[node], depth+1) + print(spacer + "}") + else: + target = value[node] + for i, v in zip(np.nonzero(target)[1], + target[np.nonzero(target)]): + target_name = target_names[i] + target_count = int(v) + print(spacer + "return " + str(target_name) + \ + " ( " + str(target_count) + " examples )") + + recurse(left, right, threshold, features, 0, 0) + +# Taken from http://scikit-learn.org/stable/auto_examples/tree/plot_iris.html#example-tree-plot-iris-py +import numpy as np +import matplotlib.pyplot as plt + +from sklearn.datasets import load_iris +from sklearn.tree import DecisionTreeClassifier + +def plot_tree_iris(): + """ + + Taken fromhttp://scikit-learn.org/stable/auto_examples/tree/plot_iris.html + """ + # Parameters + n_classes = 3 + plot_colors = "bry" + plot_step = 0.02 + + # Load data + iris = load_iris() + + for pairidx, pair in enumerate([[0, 1], [0, 2], [0, 3], + [1, 2], [1, 3], [2, 3]]): + # We only take the two corresponding features + X = iris.data[:, pair] + y = iris.target + + # Shuffle + idx = np.arange(X.shape[0]) + np.random.seed(13) + np.random.shuffle(idx) + X = X[idx] + y = y[idx] + + # Standardize + mean = X.mean(axis=0) + std = X.std(axis=0) + X = (X - mean) / std + + # Train + model = DecisionTreeClassifier(max_depth=3, random_state=1).fit(X, y) + + # Plot the decision boundary + plt.subplot(2, 3, pairidx + 1) + + x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1 + y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1 + xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step), + np.arange(y_min, y_max, plot_step)) + + Z = model.predict(np.c_[xx.ravel(), yy.ravel()]) + Z = Z.reshape(xx.shape) + cs = plt.contourf(xx, yy, Z, cmap=plt.cm.Paired) + + plt.xlabel(iris.feature_names[pair[0]]) + plt.ylabel(iris.feature_names[pair[1]]) + plt.axis("tight") + + # Plot the training points + for i, color in zip(range(n_classes), plot_colors): + idx = np.where(y == i) + plt.scatter(X[idx, 0], X[idx, 1], c=color, label=iris.target_names[i], + cmap=plt.cm.Paired) + + plt.axis("tight") + + plt.suptitle("Decision surface of a decision tree using paired features") + plt.legend() + plt.show() \ No newline at end of file diff --git a/ml1/util_knn.py b/ml1/util_knn.py new file mode 100644 index 0000000..1f71f94 --- /dev/null +++ b/ml1/util_knn.py @@ -0,0 +1,51 @@ +import numpy as np +import matplotlib.pyplot as plt +from matplotlib.colors import ListedColormap +from sklearn import neighbors, datasets +from sklearn.neighbors import KNeighborsClassifier + +# Taken from http://scikit-learn.org/stable/auto_examples/neighbors/plot_classification.html +def plot_classification_iris(): + """ + Plot knn classification of the iris dataset + """ + # import some data to play with + iris = datasets.load_iris() + + X = iris.data[:, :2] # we only take the first two features. We could + # avoid this ugly slicing by using a two-dim dataset + y = iris.target + + h = .02 # step size in the mesh + n_neighbors = 15 + + # Create color maps + cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF']) + cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF']) + + for weights in ['uniform', 'distance']: + # we create an instance of Neighbours Classifier and fit the data. + clf = KNeighborsClassifier(n_neighbors, weights=weights) + clf.fit(X, y) + + # Plot the decision boundary. For that, we will assign a color to each + # point in the mesh [x_min, m_max]x[y_min, y_max]. + x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1 + y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1 + xx, yy = np.meshgrid(np.arange(x_min, x_max, h), + np.arange(y_min, y_max, h)) + Z = clf.predict(np.c_[xx.ravel(), yy.ravel()]) + + # Put the result into a color plot + Z = Z.reshape(xx.shape) + plt.figure() + plt.pcolormesh(xx, yy, Z, cmap=cmap_light) + + # Plot also the training points + plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold) + plt.xlim(xx.min(), xx.max()) + plt.ylim(yy.min(), yy.max()) + plt.title("3-Class classification (k = %i, weights = '%s')" + % (n_neighbors, weights)) + + plt.show() \ No newline at end of file