From 907b0ab9f3361d7bcfb596678733d9da6157099c Mon Sep 17 00:00:00 2001 From: Petra Bosilj Date: Fri, 13 Dec 2024 16:10:57 +0000 Subject: [PATCH] Workshop W16 and solutions to Workshop W10 --- .../Week 10/Week10_workshop_solutions.ipynb | 1062 +++++++++++++++++ .../workshops/Week 16/Week16_workshop.ipynb | 137 +++ 2 files changed, 1199 insertions(+) create mode 100644 CMP3751 Machine Learning/workshops/Week 10/Week10_workshop_solutions.ipynb create mode 100644 CMP3751 Machine Learning/workshops/Week 16/Week16_workshop.ipynb diff --git a/CMP3751 Machine Learning/workshops/Week 10/Week10_workshop_solutions.ipynb b/CMP3751 Machine Learning/workshops/Week 10/Week10_workshop_solutions.ipynb new file mode 100644 index 0000000..6aadd11 --- /dev/null +++ b/CMP3751 Machine Learning/workshops/Week 10/Week10_workshop_solutions.ipynb @@ -0,0 +1,1062 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Week 10 - Introduction to Artificial Neural Networks\n", + "\n", + "\n", + "Today in the lecture you got the introduction to Artificial neural networks (ANN). You learned about the following topics:\n", + "- perceptron: single layer [[1](###References)]\n", + "- activation functions (Adaptive Linear Neuron: AdaLine) [[2](#References)]\n", + "- gradient descent\n", + "- multilayer perceptron\n", + "\n", + "This workshop is going to focus on the very basics behind neural networks: **the perceptron**. The workshop will be a combination of **examples** and **four (4) excercises**. We will go through the _examples_ together, and you will be given some time to solve each _excercise_ as we work through this notebook.\n", + "\n", + "We will show how to implement our own perceptron, as well as how to use the `sklearn` implementations. At the end, we will also showcase the multi-layer perceptron (from `sklearn`) as an introduction for the coming lesson and workshop.\n", + "\n", + "The workshop will demonstrate the following concepts:\n", + "- [Linear functions](#Linear-functions)\n", + " - [Exercise 1](#Exercise-1)\n", + "- [Single layer perceptron](#Single-layer-perceptron)\n", + " - [Convergence of the perceptron](#Convergence-of-the-perceptron)\n", + " - [Exercise 2](#Exercise-2)\n", + "- [Linear separability](#Linear-separability)\n", + " - [Exercise 3](#Exercise-3)\n", + " - [Example-XOR-function](#Example-XOR-function) - kernel trick and showcase of multi-layer perceptron\n", + "- [Exercise 4](#Exercise-4)\n", + "- [References](#References)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Linear functions\n", + "\n", + "Let's revise some maths about linear functions. A linear function $f(x)$ in 2D can be expressed as:\n", + "\\begin{equation*}\n", + "f(x) = y = ax + b\n", + "\\end{equation*}\n", + "Here, $a$ is the _slope_ of the line, and $b$ is the _shift_ _(intercept)_.\n", + "\n", + "-----------\n", + "\n", + "But, when we talk about (2 class) classification for 2D samples (2 features - e.g. sepal length and petal length), we often talk about a **decision surface** instead. We evaluate it on a sample $\\textbf{x} = [x_1, x_2]$:\n", + "\\begin{equation*}\n", + "w_0 + w_1 x_1 + w_2 x_2 = 0\n", + "\\end{equation*}\n", + "\n", + "-----------\n", + "\n", + "We classify as positive all the samples that fall \"above\" the decision surface:\n", + "\\begin{equation*}\n", + "w_0 + w_1 x_1 + w_2 x_2 > 0,\n", + "\\end{equation*}\n", + "and as negative all the samples that fall \"under\":\n", + "\\begin{equation*}\n", + "w_0 + w_1 x_1 + w_2 x_2 < 0,\n", + "\\end{equation*}\n", + "\n", + "------------\n", + "\n", + "A linear decision function for _nD_ samples will be a hyperplane:\n", + "\\begin{equation*}\n", + "w_0 + w_1 x_1 + \\dots + w_n x_n = 0,\n", + "\\end{equation*}\n", + "\n", + "------------\n", + "\n", + "\n", + "\"Maths trick\": extend our input samples with a leading component $x_0 = 1$ for all samples:\n", + "\\begin{equation*}\n", + " \\mathbf{x}' =\n", + " \\begin{bmatrix}\n", + " 1 \\\\\n", + " \\mathbf{x}\n", + " \\end{bmatrix},\n", + "\\end{equation*}\n", + "and we unify all the weights and the bias in a weights vector $\\mathbf{w}$:\n", + "\\begin{equation*}\n", + " \\mathbf{w} = \n", + " \\begin{bmatrix}\n", + " w_0 \\\\\n", + " w_1 \\\\\n", + " \\cdots \\\\\n", + " w_n\n", + " \\end{bmatrix},\n", + "\\end{equation*}\n", + "we end with a short notation of an _nD_ decision function in vector form:\n", + "\\begin{equation}\n", + "\\mathbf{w}^\\intercal \\mathbf{x}'\\ = 0\n", + "\\end{equation}\n", + "(here $\\mathbf{w}^\\intercal \\mathbf{x}'$ is a _dot product_ between the weights $\\mathbf{w}$ and the sample $\\mathbf{x}'$)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's remind ourselves of the influence of the weights and bias for 2D linear functions." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise 1\n", + "\n", + "Below is the code that plots the samples of setosa and versicolor irises. The below code classifies them according to the decision surface $\\mathbf{w}^\\intercal \\mathbf{x}'\\ = 0$ defined by the vector $\\mathbf{w}$.\n", + "\n", + "More specifically, the samples will be classified as `setosa` if:\n", + "\\begin{equation*}\n", + "w_0 + w_1 x_1 + w_2 x_2 > 0,\n", + "\\end{equation*}\n", + "otherwise they will be classified as `versicolor`.\n", + "\n", + "The current _weights_ and _biases_ do not classify the samples correctly. Change the weights and biases (stored together in `w`):\n", + "- Check what happens when you double all the weights and biases. Try to double them. How does the decision surface change?\n", + "- Find the weights and biases which classify the samples correctly. Are these unique?\n", + "- (The left and right subplot showing samples classified by the linear decision surface defined by $\\mathbf{w}$ and the ground truth should be the same when successful)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from sklearn import datasets\n", + "\n", + "plt.rcParams.update({'font.size': 20})\n", + "\n", + "iris = datasets.load_iris()\n", + "# get length and width features\n", + "X_length = iris.data[:, [0,2]]\n", + "X_width = iris.data[:, [1,3]]\n", + "\n", + "# We will work with sepal and petal area for 2D - multipy length with width\n", + "X_area_full = X_length * X_width\n", + "y_full = iris.target\n", + "\n", + "class_names = iris.target_names\n", + "feature_names = [\"sepal area\", \"petal area\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(100, 3)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib.colors import ListedColormap\n", + "\n", + "# Select the data for this example:\n", + "# select samples with class == 0 or == 1\n", + "select = np.any([y_full == 0, y_full==1], axis = 0)\n", + "\n", + "# construct the data to work with\n", + "X = X_area_full[select, :]\n", + "y = y_full[select]\n", + "y = np.where(y == 0, -1, 1) # change between classes: (0, 1) to classes: (-1, 1)\n", + "\n", + "\n", + "# set some nice plotting limits\n", + "x_min, x_max = X[:, 0].min(), X[:, 0].max()\n", + "y_min, y_max = X[:, 1].min(), X[:, 1].max()\n", + "x_min = min(x_min, 0)\n", + "y_min = min(y_min, 0)\n", + "x_max += 1\n", + "y_max += 1\n", + "\n", + "# Adding x_0 = 1 for every sample (so we can do the dot product)\n", + "X_prime = np.hstack((np.ones((X.shape[0], 1)), X))\n", + "print(X_prime.shape)\n", + "#####################################################################################################\n", + "#####################################################################################################\n", + "#####################################################################################################\n", + "# Change the following bit of code:\n", + "# Find the correct weights for the decision surface. What happens if you double all the weights?\n", + "w = np.array([-1, #w0\n", + " 1, #w1\n", + " -6 #w2\n", + " ])[:, np.newaxis]\n", + "\n", + "#####################################################################################################\n", + "#####################################################################################################\n", + "#####################################################################################################\n", + "\n", + "\n", + "# Classifying using the decision surface defined with the weights above. \n", + "# Calculate the dot product:\n", + "# - all points \"above\" the surface = positive class\n", + "# - all points \"below\" the surface = negative class\n", + "#y_prediction = np.dot(X_prime, w) < 0\n", + "#print(w.shape, X_prime.shape, X_prime.transpose().shape)\n", + "w=w.transpose()\n", + "y_prediction = np.dot(w, X_prime.transpose()) < 0\n", + "\n", + "\n", + "# Plot different things:\n", + "fig, axs = plt.subplots(1, 2, figsize=(12, 4))\n", + "\n", + "# Plot decision surface\n", + "xx, yy = np.meshgrid(np.linspace(x_min, x_max, 200), np.linspace(y_min, y_max, 200))\n", + "surface = np.c_[xx.ravel(), yy.ravel()]\n", + "surface_prime = np.hstack((np.ones((surface.shape[0], 1)), surface))\n", + "#Z = np.dot(surface_prime, w) < 0\n", + "Z = np.dot(w, surface_prime.transpose()) < 0 \n", + "Z = Z.reshape(xx.shape)\n", + "cmap_light = ListedColormap(['pink', 'yellow'])\n", + "axs[0].pcolormesh(xx, yy, Z, cmap = cmap_light, shading = 'auto')\n", + "axs[1].pcolormesh(xx, yy, Z, cmap = cmap_light, shading = 'auto')\n", + "\n", + "# Currently, the below two scatter plots will give you different outputs\n", + "# If you find the correct weights and biases, the below two scatter plots\n", + "# will have the same input regardless of which is used.\n", + "\n", + "# Plot the output of linear classifier for training points\n", + "scatter0 = axs[0].scatter(X_prime[:, 1], X_prime[:, 2], c=y_prediction, cmap=plt.cm.Set1,\n", + " edgecolor='k', s=80)\n", + "\n", + "# Plot the training points with ground truth\n", + "scatter1 = axs[1].scatter(X_prime[:, 1], X_prime[:, 2], c=y, cmap=plt.cm.Set1,\n", + " edgecolor='k', s=80)\n", + "\n", + "# Plot the legend, label your axis:\n", + "handles0, labels0 = scatter0.legend_elements()\n", + "legend0 = axs[0].legend(handles0, class_names, loc = \"upper left\", title = \"Species\", fontsize='x-small')\n", + "axs[0].add_artist(legend0)\n", + "axs[0].set_title('Samples - prediction')\n", + "handles1, labels1 = scatter1.legend_elements()\n", + "legend1 = axs[1].legend(handles1, class_names, loc = \"upper left\", title = \"Species\", fontsize='x-small')\n", + "axs[1].add_artist(legend1)\n", + "axs[1].set_title('Samples - ground truth')\n", + "for ax in axs:\n", + " ax.set_xlabel(feature_names[0])\n", + " ax.set_ylabel(feature_names[1])\n", + "\n", + " ax.set_xlim(x_min, x_max)\n", + " ax.set_ylim(y_min, y_max)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Single-layer perceptron\n", + "\n", + "Rosenblatt's perceptron [[1](###References)] is a **linear classifier**, just like the SVM (with a linear kernel). \n", + "\n", + "Below, we directly implement this learning algorithm. The training of a single perceptron (the `fit` function) is implemented in the following steps:\n", + "- randomly initialise the weights $\\mathbf{w}$\n", + "- repeat for `n_iter` (maximum number of iterations)\n", + " - repeat for every sample $\\textbf{x}_i$ in the training set `X`:\n", + " - evaluate if $\\textbf{x}_i$ falls \"above\" or \"below\" the decision surface\n", + " - compare to the expected `target` for `xi`\n", + " - if $\\textbf{x}_i$ is correctly classified, do nothing\n", + " - if $\\textbf{x}_i$ is not correctly classified:\n", + " - for positive samples $\\textbf{x}_i$ add $\\eta*\\textbf{x}_i$ to $\\textbf{w}$\n", + " - for negative samples $\\textbf{x}_i$ subtract $\\eta*\\textbf{x}_i$ from $\\textbf{w}$\n", + " - (for all samples, add $\\eta*\\textbf{x}_i*y_i$ to $\\textbf{w}$)\n", + " \n", + "In this implementation, we will always run `n_iter` epochs. Instead, one can add different **stopping criteria**: for example, we can stop the update when the number of incorrect classifications in a single iteration (also sometimes: _epoch_) reaches 0, or stops improving.\n", + "\n", + "The below examples are heavily influenced by the implementations provided through [this free book](https://github.com/rasbt/python-machine-learning-book-3rd-edition) (it comes with public jupyter notebooks for all examples in the book so feel free to check it out). It has been changed to reflect the maths at the beginning of the workshop (i.e. adding $x_0=1$ to every sample)." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "class Perceptron(object):\n", + " \"\"\"Perceptron classifier.\n", + "\n", + " Parameters\n", + " ------------\n", + " eta : float\n", + " Learning rate (between 0.0 and 1.0)\n", + " n_iter : int\n", + " Passes over the training dataset.\n", + " random_state : int\n", + " Random number generator seed for random weight\n", + " initialization.\n", + "\n", + " Attributes\n", + " -----------\n", + " w_ : 1d-array\n", + " Weights after fitting.\n", + " errors_ : list\n", + " Number of misclassifications (updates) in each epoch.\n", + "\n", + " \"\"\"\n", + " def __init__(self, eta=0.01, n_iter=50, random_state=1):\n", + " self.eta = eta\n", + " self.n_iter = n_iter\n", + " self.random_state = random_state\n", + "\n", + " def fit(self, X, y):\n", + " \"\"\"Fit training data.\n", + "\n", + " Parameters\n", + " ----------\n", + " X : {array-like}, shape = [n_examples, n_features]\n", + " Training vectors, where n_examples is the number of examples and\n", + " n_features is the number of features.\n", + " y : array-like, shape = [n_examples]\n", + " Target values.\n", + "\n", + " Returns\n", + " -------\n", + " self : object\n", + "\n", + " \"\"\"\n", + " \n", + " # we add the \"1\" -> X = [2, 7, -1] --> X_prime = [1, 2, 7, -1]\n", + " X_prime = np.hstack((np.ones((X.shape[0], 1)), X))\n", + " \n", + " rgen = np.random.RandomState(self.random_state)\n", + " self.w_ = rgen.normal(loc=0.0, scale=0.01, size=1 + X.shape[1])\n", + " self.errors_ = []\n", + "\n", + " for _ in range(self.n_iter):\n", + " errors = 0\n", + " for xi, target in zip(X_prime, y):\n", + " # is our sample missclassified\n", + " if target * self.net_output(xi) < 0:\n", + " #if (self.net_output(xi) > 0 and target == -1) or (self.net_output(xi) <= 0 and target == 1):\n", + " update = self.eta * target # target has sign\n", + " else:\n", + " update = 0\n", + " #errors += 1\n", + " self.w_ += update * xi\n", + " errors += int(update != 0.0)\n", + " self.errors_.append(errors)\n", + " return self\n", + "\n", + " def net_output(self, X_prime):\n", + " \"\"\"Calculate net input\"\"\"\n", + " return np.dot(X_prime, self.w_)\n", + "\n", + " def predict(self, X):\n", + " \"\"\"Return class label after unit step\"\"\"\n", + "\n", + " # X = [2, 7, 4] --> X_prime = [1, 2, 7, 4]\n", + " X_prime = np.hstack((np.ones((X.shape[0], 1)), X))\n", + " return np.where(self.net_output(X_prime) >= 0.0, 1, -1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Convergence of the perceptron\n", + "\n", + "The speed of convergence depends on the learning rate, $\\eta$ (`eta`). Continuing with _setosa_ and _versicolor_ iris classification, train a single perceptron given above for 10 iterations with learning rates $\\eta = \\{0.1, 0.0001, 0.00001\\}$.\n", + "\n", + "The code snipped below also prints out the weights $\\textbf{w}$. Try using these values in [Exercise 1](#Exercise-1). Does the decision surface defined by them classify the samples correctly?" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[-0.08375655 -0.11111756 1.85671828]\n" + ] + }, + { + "data": { + "image/png": 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dIiMjsXz5cowbN67BY8aOHQtRFHHixAkpXZOOqZOg3i1cJLE+Hg7WmNSjalsWbqVBRET6ICkJ+umnnyAIAhYsWIAePXpodUz37t0BVN1GI8OQV1yOq9n3AMi7PP7vwkOriifuv5CJlDuFMkdDRESmRlISpN4HbPTo0Vof4+bmBgDIzc2V0jXp0Nm0XIgi4O9qC3d7a7nD0ejQ2gFDO3hAFKtWihEREemSpCTo3r2q0QN7e3utjyktLQUAWFqyCJ6h+Gu/MGd5A6nD3NCq5fLbYtKQV1QuczRERGRKJCVB6lGd5ORkrY9JSEgAwF3kDYlmPpAB3QpTG/yAGzp5OaCorBLfRafKHQ4REZkQyRuoAsAff/yh9TGbN2+GIAgYOHCglK5JR1QqEWfvF0nsaSCToqsTBAFz7o8GbTyehLIKFk8kIiLdkJQEPf744xBFEWvWrEFqauN/pa9cuVKTME2bNk1K16QjV7PvoaC0ArZWSnTycpA7nDpN6O4NDwdrZOWXYm98htzhEBGRiZCUBD3zzDPo1q0bSkpKMGzYMPz66681CtsJggBRFHH69Gk89dRTePXVVyEIAkJDQzF27FjJwZN06lth3X2dYSFzkcT6WFsoMXNQWwDA2j9YPJGIiHRD0i7yCoUCu3btQkhICJKTk/HII4/A1tYWgiAAAIYNG4aCggLNZGhRFNGuXTts27ZNeuSkE5pJ0QZSJLE+0/v5Y9Xhq7hwMx8nEu9gUDt3uUMiIiIjJ/lPf39/f5w9exbTpk2DQqFAYWGhZuuMW7duoaSkRPOX+9SpUxEdHQ1PT0/JgZNu/LVzvOHNB6rOxc4KT/T2A8DiiUREpBuSRoLUXF1d8e233+Kjjz7C3r17ERMTg+zsbFRWVsLNzQ09e/bEhAkT0KFDB110RzqSW1SG67eqihAa4qTov5sdEogtp1Jw+FI2rmXfwwOe2pdmICIi+judJEFqAQEBeOGFF3R5StKjuPurwoLc7eBqZyVvMFoIdLfDyM6t8duFLKyLSsKyyV3lDomIiIyYpNthqampSE1NRWVlpdbHqFQqzXEkL82mqUYwCqQ2J6RqK42fYm/gzr1SmaMhIiJjJikJatu2LYKCgnD58mWtj0lKStIcR/I6YySToqvrF+iKbr5OKK1QYctJJtJERNR8kidGN3e5Mpc5y6tSJeLPtFwAhj8pujpBEBB+fzTom5PJKCnXfhSSiIiouhYvDKNOfhQKw6xJYy4uZxagsKwS9tYW6NDaMIsk1mdcV2/4ONng9r0y/Hw2Xe5wiIjISLV4JnLz5k0AgIODcX3wmhr1fKAefs5QKgSZo2kaS6UCMwe3BVC1XJ6jikRE1Bw6SYLUxREbUl5ejkuXLuHDDz8EAHTs2FEXXVMzGfLO8dr4f/38YWelxNXsezh65Zbc4RARkRFq0hJ5pVJZ6zVRFBEcHNykTgVBwOOPP96kY0i3NCvDDHDneG042ljiyb7+WH8sCRGRSRjWkQU4iYioaZo0EqSuBK1+1Pd6Y48nnngCCxcu1PW1kJbu3CtF8p0iAEAvP+NMggBg1uC2UAhA1LXbuHgzX+5wiIjIyDRpJGjJkiU1vl66dCkEQcBzzz3X4FYYgiDAxsYG3t7eGDRoENq1a9e8aEkn1EUSH/C0h5OtpbzBSODnaouxwd7YG38TEZFJ+HRqd7lDIiIiIyKIEmaVKhQKCIKA+Ph4dOnSRZdxyS4/Px9OTk7Iy8uDo6Oj3OHo1Ip9l7D69+uY2scXHz9u3IlDXOpdTPq/47BUCjj2xgh4OtrIHRIREcmoKZ/fkiZGb9iwAevXr4evr6+U01AL+2tStPHeClPr6e+C3gEuKK8UselEstzhEBGREZG0d1hYWJiu4qAWUlGpwrkbeQCA3kY6Kfrv5oYG4kzKXXx7KhUvDn8AtlY63RKPiIhMFCsWmplLmQUoLq+Eo40F2nmYxi7so7p4wd/VFrlF5fjfmRtyh0NEREZCp38y3717F3/++Sdu376N4uLiRovYzZgxQ5fdkxbU+4X18HeBwsiKJNZHqRAwe3BbvLv7AtZFJeGp/gEmc21ERKQ/OkmCfv/9dyxZsgRRUVFaHyMIApMgGajrAxlrkcT6PNHHD5/9dgXJd4pw8GIWRj/oJXdIRERk4CTfDlu9ejVGjhyJqKioJtcLopanToJMZT6Qmp21Bab3DwAAREQlyRwNEREZA0lJ0MWLF/HSSy9BFEV07doVO3fuxN69ewFUjfRcv34dp0+fxurVq9GrVy8AQEhICBISEpCYmCg9emqS7IISpOUUQxCq9gwzNTMHtYWFQkB0Ug7O3ciVOxwiIjJwkpKgVatWobKyEu7u7oiMjMSjjz4Kf39/zfuBgYHo3bs3nn32WZw+fRqvvfYaoqKiMH/+fAQEBEgOnpomNiUXANDB0wEONsZbJLE+Xk42mNDdB0DVxqpEREQNkZQEHT16FIIg4KWXXmp0V3hBELBixQqMGDECR44cwfr166V0Tc0Qp54PFOAsbyB6FB4SCADYG38T6bnFMkdDRESGTFISdONG1XJk9a0uoOaO8uXl5bWOmTdvHkRRxJYtW6R0Tc3w16Ro05oPVF1wGycMDHJDpUrEpuPJcodDREQGTFISVFJSAgDw8fHRvGZnZ6d5fvfu3VrHPPDAAwCACxcuSOmamqis4q8iib1MbFL0380dUjUa9P2pVBSU1E7EiYiIAIlJkKurKwCgsLBQ85qHh4dmNOjKlSu1jrl9+zYAIDc3V0rX1EQXbuajtEIFZ1tLBLnbNX6AERvWwRNBHnYoKK3AthgWTyQiorpJSoI6deoEALh69armNVtbW7Rv3x4AsGvXrlrH7NixA0BVskQtR71fWE8/5xq3LE2RQiFgTkgQAGB9VBIqKlUyR0RERIZIUhIUEhICURQRGRlZ4/XJkydDFEX897//xYYNG1BYWIjs7Gx8/PHHiIiIgCAIGDFihKTAqWlMtT5QfSb3agNXOyuk5xZjf0KW3OEQEZEBkpQEPfLIIwCAnTt3auYHAcCrr74KV1dXlJeXY86cOXB0dIS3tzcWLVqEyspK2NjY4M0335QWOTVJXGouANOeFF2djaUSTw+oKsOwNjKRxTmJiKgWSUlQ//79sWHDBqxYsaLGJGg3Nzfs378fbdu2rVUl2tPTEzt27EDnzp0lB0/aycwrQXpuMRQC0N0EiyTW55kBAbCyUOBsWq5mJIyIiEhN8t5hYWFhdb7eu3dvXLp0CYcPH0ZCQgIqKirQvn17jBkzBra2tlK7pSZQJwAdvRxhZ63TPXMNmoeDNSb1aIMfYtKw9o8k9H7GVe6QiIjIgOj1E9HS0hJjxozBmDFj9NkNNUI9Kbq3CRdJrE94aCB+iEnD/guZSLlTiAA3014ZR0RE2pO8gSoZPnMoklifDq0dMLSDB0QR2HAsWe5wiIjIgDAJMnGlFZU4n54PwDyTIACYE1pVPHFbTBryilg8kYiIqmh1O2zz5s166XzGjBl6OS/95Xx6PsoqVXC1s0KAm3nOxQp5wB2dvBxwKbMA30Wn4vlh7eQOiYiIDIBWSdDMmTN1XmBPEAQmQS0grtqtMFMvklgfQRAQHhKI17afw8bjSQgPCYSVBQdBiYjMndafBH9f6q6LB+nfmRTT3zleG4/28IGHgzWy8kuxNz5D7nCIiMgAaDUSlJSUVO97d+/exbPPPovTp08jODgYYWFh6NevH1q3bg0AyMrKwunTp7Fp0ybEx8ejb9+++Prrr+HiYp7zU1qSKIpmPSm6OmsLJcIGBuDfB65g7R9JeKxHG7MdGSMioiqCKGFIpqysDIMGDUJcXByWLl2Kt956q94PFlEU8dFHH+Gdd95B7969cezYMVhZWTU7cH3Lz8+Hk5MT8vLy4OjoKHc4zZKeW4zByw9DqRAQ/+5o2FqZT42gutwtLMPA5YdQUq7Cd3P7Y1A7d7lDIiIiHWvK57ekiRGrVq1CbGwsnnjiCbz99tsN/mUtCALeeustTJ06FbGxsfjPf/4jpWvSgro+UBdvR7NPgADAxc4Kj/f2BQBERNY/uklEROZBUhL03XffQRAEzJw5U+tjZs2aBVEUsXXrVildAwBiYmLw3nvvYfTo0fD19YW1tTXs7e3RoUMHzJo1C1FRUZL7MGaa+UD+zvIGYkDCQ4IgCMDhS9m4ln1P7nCIiEhGkoYHrl+/DgCa+T/a8PT0rHFscw0ZMqTW7vVA1S26q1ev4urVq9i4cSNmzJiBtWvXGvStN33RrAwzk53jtRHoboeRnVvjtwtZWBeVhGWTu8odEhERyUTSSJB6OtHVq1e1PkbdVurqsIyMqhU+Pj4+WLBgAbZv347o6GicOHECn332Gdq0aQOgqsZRU0aqTEVJeSUSMsy7SGJ95oRUFU/8KfYG7twrlTkaIiKSi6QkSL0T/MqVK6FSqRptr1Kp8Pnnn9c4trk6deqEH374AampqVi5ciWmTJmCvn37YsCAAXj55Zdx9uxZdOjQAQDw/fff448//pDUn7GJT89DhUqEh4M1fF1ayR2OQekX6Ipuvk4orVBhy8lUucMhIiKZSEqCZsyYAVEUcerUKTz22GPIzMyst21WVhYmT56MU6dO6aRQ4p49ezB16lQolco633d3d8enn36q+Xr79u2S+jM21ecDcSl4TeriiQCw+UQSjl7Jxs9n03Hi+h1Uqli/iojIXEiaE/Tcc89h69atiIqKwt69exEUFITRo0ejb9++8PT0hCAImjpBBw4cQGlp1a2HwYMH47nnntPJBTRk+PDhmudS5yAZm9gU1gdqyLiu3nh3VwLuFJYjbP1pzeveTjZYMqELHg72ljE6IiJqCZKSIIVCgV9//RVPPfUUdu3ahZKSEuzevRu7d++u1VY9B2jChAn49ttvoVDof9sCddIFoN4RI1NUVSQxFwDQm5Oi63ToYhbu1rGZamZeCZ7fEovVT/diIkREZOIkZyJ2dnbYuXMndu/ejXHjxqFVq1a1tsewsbHB2LFjsWvXLvz888+wt7fXReyNOnr0qOa51DlIxuTG3WLcvlcKS6WA4DZOcodjcCpVIpbuvlDne+qbYUt3X+CtMSIiE6ezCnrjx4/H+PHjoVKpcP36deTk5AAAXFxc0K5duxYfiVGpVFi+fLnm66lTpzbYvrS0tMbIUX5+vt5i0zf1fKAuPk6wsTSfETBtRSfl4GZeSb3viwBu5pUgOikHA9u5tVxgRETUonReRlihUKB9+/a6Pm2Tff7554iOjgYATJ48Gb17926w/bJly7B06dKWCE3v/tovzFneQAxUdkH9CVBz2hERkXHS/8QcGRw9ehRvvvkmgKrijKtXr270mEWLFiEvL0/zSEtL03eYeqNOgjgfqG6eDjY6bUdERMZJpyNBsbGxOHjwIOLj4zW3w1xdXREcHIyRI0c2OhqjCwkJCZg0aRIqKipgY2ODH3/8UVOluiHW1tawtrbWe3z6VlRWgYs3CwBwZVh9+gW6wtvJBpl5Jahr1o8AwMvJBv0CXVs6NCIiakE6SYJiY2Pxwgsv4PTp0/W2+de//oU+ffrgyy+/RJ8+fXTRbS1JSUkYPXo07t69C6VSia1bt2LIkCF66ctQ/ZmWh0qVCC9HG/g4s0hiXZQKAUsmdMHzW2IhAHUmQksmdIFSwfpKRESmTPLtsO3bt2PQoEE4ffq0ZjWYpaUlWrdujdatW8PS0lLz+unTpzF48GD8+OOPuoi9hoyMDIwcORIZGRkQBAHr16/HxIkTdd6PodPMBwpwljcQA/dwsDdWP90LXk41b3kJArDy//Xg8ngiIjMgKQm6fPkynnnmGZSVlUGpVOL555/H6dOnUVhYiIyMDGRkZKCwsBAxMTF4/vnnYWFhgfLycsyYMQOXLl3S1TXg9u3bGDVqFBITEwEAq1atklyR2lhpNk3lrbBGPRzsjag3RuD7uQPw+ZM94OlgDVEE8ksq5A6NiIhagKQkaMWKFSgtLYWNjQ0OHDiAL7/8Er17966xHF6pVKJXr1748ssv8dtvv8HGxgZlZWX4+OOPJQcPAHl5eRgzZgwuXKiq+7J8+XK8+OKLOjm3saleJJE7x2tHqRAwsJ0bJvVsgxeGtQMArI9Kgoo1goiITJ6kJOjgwYMQBAELFy7EsGHDGm0/dOhQLFy4EKIo4uDBg1K6BgAUFRVh/PjxiI2NBQC89dZbeOONNySf11gl3ylCTmEZrJQKPOjjKHc4RueJPn5wtLFA0u1CHLqULXc4RESkZ5KSoFu3bgEAxo0bp/Ux48ePr3Fsc5WVlWHSpEk4duwYAGDBggX44IMPJJ3T2Kn3Cwtu4whrCxZJbCo7awtM7x8AAFgbmShzNEREpG+SVod5eHggPT0dNjba11NRL0N3d3eX0jWmTZuGAwcOAABGjBiB8PBwnD9/vt72VlZW6NChg6Q+DR3rA0kXNigAEZGJiE7Kwbkbuejm6yx3SEREpCeSkqDBgwdj27ZtOH36tNbL3tVVnENCQqR0jZ9++knz/PDhw+jWrVuD7QMCApCcnCypT0OnmQ/ESdHN5u3UChO6+2BHXDoiIpPw32k95Q6JiIj0RNLtsFdeeQVKpRIfffSRVre3srOzsWzZMlhaWuLll1+W0jX9zb3SClzOrNrvjJOipQkPCQQA7I2/iYzcYpmjISIifZGUBPXt2xdff/01srOz0b9/f+zcuRMqlapWO5VKhZ9//hkDBw7ErVu3sHr1avTr109K17V2qm/sYeqjQH+m5UIlAm2cW6G1I7d7kCK4jRMGBrmhUiVi4/FkucMhIiI9kXQ7bPbs2QCALl264M8//8SUKVPg4uKCnj17wtPTE4IgICsrC2fPntVso9G9e3dERUUhKiqqznMKgoB169ZJCcssqSdFcxRIN+aEBuJE4h18fyoVLz3UHvbWOt9rmIiIZCaIotjsgigKhQKC8NfWAupTVX+todf/ThRFCIKAysrK5oakM/n5+XByckJeXh4cHQ1/ufmsDdE4cvkWlkzoglmDA+UOx+ipVCJGfn4UibcK8c4jXTS3yIiIyLA15fNb0p+3/v7+jSY2pH8qlchJ0TqmUAgIDwnEWzvOY8OxJIQNDICFUvIuM0REZEAkJUGmPs/GWCTeLkRecTmsLRTo7G34o1bGYkovX/x7/2XcuFuM/QlZGN+N+4kREZkS/mlrAtT1gbr7OsPKgt9SXbGxVOKZAX8VT5Rw55iIiAwQPzFNgHpSdE/uHK9zzwxsCysLBc6m5WqSTSIiMg1MgkxALHeO1xsPB2tM6tEGALD2jySZoyEiIl2SNCcoNTVVUuf+/v6Sjicgv6QcV7PvAWASpC/hoYH4ISYN+y9kIuVOIQLc7OQOiYiIdEBSEhQY2Pxlw4IgoKKiQkr3BOBsai5EEfB3tYWHg7Xc4ZikDq0dMLSDB45euYUNx5Lx7qMPyh0SERHpgKTbYU2t2vz3B0l3Rl0k0d9Z3kBM3JzQqoR/W0wa8orKZY6GiIh0QdJI0IYNGxptU1hYiCtXruB///sf0tPTMXjwYMyZM0dKt1SNZj4QK0XrVcgD7ujk5YBLmQX4LjoVzw9rJ3dIREQkkaQkKCwsTOu2n3zyCV5++WWsXr0agwcPxvLly6V0Tagqkng2LRcA5wPpmyBUFU98bfs5bDyehPCQQJYjICIyci32W9zS0hJffPEFhg0bhk8++QT79+9vqa5N1rVb91BQUoFWlkp08nKQOxyT92gPH3g4WCMrvxR74zPkDoeIiCRq8T9ln332WYiiiFWrVrV01yZHPR+ou58Tt3RoAdYWSoQNrCqeGBGZxHltRERGrsU/Odu3bw8AiImJaemuTY5m53jeCmsxT/UPgI2lAgkZ+TiReEfucIiISIIWT4Ly8vJq/Jeaj0USW56LnRUe7+0LAFgXyeKJRETGrMWToE2bNgEAvL25GaUUuUVluH6rEABXhrW02YMDIQjAoUvZuHa/UCURERmfFkuCrl69iueeew6bNm2CIAgYN25cS3VtkuJScwEAge52cLWzkjcYMxPkYY+HOrUGAKw/xtEgIiJjJWmJfFBQUKNtVCoVcnNzUVBQoHnN09MTb731lpSuzZ76VlhPFkmUxdzQQBy8mIX/nbmBV0d1gJs9q3UTERkbSUlQcnJyk48ZOHAg1q9fz9thEnE+kLz6BbqiaxsnxKfn4dtTqXjpofZyh0RERE2k92KJCoUCDg4OCAwMxNChQ9GjRw8pXRKASpWIs/dvh/XmfCBZCIKAOaGBWLD1LDafSMa8IUGwsVTKHRYRETWB3rfNIN27nFmAwrJK2FtboENrFkmUy7iu3lj+6yXczCvBrrMZmNrXT+6QiIioCVhhzwipb4V193OCUiHIHI35slQqMGtwWwBARFQiiycSERkZJkFGiPOBDMeTff1hZ6XElax7+OPqbbnDISKiJmASZITUy+NZH0h+Tq0s8WRffwBARGSizNEQEVFTMAkyMnfulSLp9v0iiX5MggzBrMFtoRCAyKu3cSkzX+5wiIhIS0yCjIx6FKidhx2cbC3lDYYAAH6uthgbXFXyIYJbaRARGQ0mQUaG84EMU3hoIADg57PpyM4vkTkaIiLSBpMgI6NOglgfyLD08ndB7wAXlFeK2HwiRe5wiIhIC0yCjEhFpQp/puUB4KRoQzQnpGo0aMupFBSXVcocDRERNYZJkBG5lFmA4vJKONhY4AEPe7nDob8Z/aAX/FxbIbeoHNtjb8gdDhERNUKrJGjy5MmYMmUKbtzgL3Y5/bVpqgsULJJocJQKAbMHV40GrY9KgkrF4olERIZMqyRo586d2LlzJ/Lzay7/VSgUsLCwwIULF/QSHNUUm6KeFO0sbyBUr6l9/OBoY4Gk24U4dClb7nCIiKgBTbodVte2ANwqoOWc4cowg2dnbYHp/QMAAGtZPJGIyKBplQQ5OFRt0pmVlaXXYKh+twpKkZZTDEEAenAkyKCFDQqAhUJAdFIOzt3IlTscIiKqh1ZJUKdOnQAA//nPf3Dv3r1a7wsC56fom3o+UAdPBzjasEiiIfN2aoUJ3X0AsHgiEZEhs9Cm0fTp03H69Gns2bMHrq6uaN26NSwt//ogHj16dI2vtSEIAq5fv960aM2YZj5QgLO8gZBWwkMCsSMuHXvjb+LNsZ3g49xK7pCIiOhvtEqC5s+fj2PHjmH79u2oqKhAenq65j1RFGt8rS2OHjVN9ZVhZPiC2zhhYJAbTiTewcbjyfjXuM5yh0RERH+jVRKkUCiwbds2nDhxAgcPHkR6ejpKS0uxadMmCIKARx99FM7OznoO1XyVVahw7sb9IolMgozGnNBAnEi8g+9PpeKlh9rD3lqrf25ERNRCmvRbeeDAgRg4cKDm602bNgEAPvzwQ3Tp0kW3kZHGxZv5KK1QwdnWEkHudnKHQ1oa3tETQR52SLxViB9OpyH8fkVpIiIyDKwYbQTO3J8P1NPPmUUSjYhCIWgSnw3HklBRqZI5IiIiqk5SEqRSqVBZWclRID3jzvHGa0ovX7jYWuLG3WLsT2CJCSIiQ8KRICMQl5oLgJumGiMbSyWeGVBVPDEiisUTiYgMic5malZWVmLnzp04ePAgzp8/j5ycHACAq6srgoODMXLkSDz22GNQKpW66tIsZOaVID23GAoB6O7nLHc41AzPDGyLr44mIi41F2dSctA7wFXukIiICDpKgvbt24d58+bVWjoPVC2FP378ONasWQNfX1+sWbMGY8aM0UW3ZkF9K6yjlyNXFxkpDwdrPNbTB9tibiAiMolJEBGRgZB8O+ybb77BI488gvT0dIiiCFEUERAQgAEDBmDAgAEICKi6FSCKItLS0jB+/Hh8++23kgM3F9w01TTMCQ0CAOxPyETqnSKZoyEiIkBiEpSSkoJ58+ZBpVLB1tYWH3zwATIzM5GYmIjjx4/j+PHjSExMRGZmJj788EPY29tDpVJh7ty5SE1N1dU1mDROijYNHVo7YEgHD6hEYP0xbqVBRGQIJCVB//nPf1BaWgp7e3tERkbiX//6Fzw9PWu18/DwwKJFixAZGQl7e3uUlpbiP//5j5SuzUJpRSXOp+cDAHpzUrTRmxtatVx+W0wa8orKZY6GiIgkJUEHDhyAIAh47bXX0KNHj0bbd+/eHf/85z8hiiL2798vpWuzcD49H2WVKrjaWSHAzVbucEiikAfc0cnLAUVllfj+NEdCiYjkJikJUt/SGjlypNbHjBo1qsaxVL+41L/mA3GvNeMnCH8VT9x4LBllFSyeSEQkJ0lJUGVlJQA0adm7hUXVCieVih8AjeGmqabn0R4+8HCwRmZ+CX6Jvyl3OEREZk1SEtSmTRsAwPHjx7U+Rt3Wx8dHStcmTxRFzXYZnA9kOqwtlAgbWLVicm1koqaUBBERtTxJSdDw4cMhiiKWL1+OjIyMRttnZGRg+fLlEAQBI0aMkNK1ycvIK0FWfimUCgHdfJ3kDod06Kn+AbCxVCAhIx8nE3PkDoeIyGxJSoLmz58PhUKBW7duoX///ti+fbvmFll1KpUK27dvx8CBA5GVlQWFQoF//OMfUro2eer6QJ29HWBrxSKJpsTFzgqP9/YFAEREcisNIiK5SPp0DQ4Oxvvvv4+33noLGRkZePLJJ+Hs7IxevXrB09MTgiAgKysLsbGxyM3N1Qz9v//++wgODtbJBZgq1gcybbMHB+LbU6k4dCkb12/dQzsPe7lDIiIyO5KHGBYtWgQnJye8/vrrKCoqwt27d3H48OEabdTJj62tLT755BM8//zzUrs1ebH3N03lfCDTFORhj4c6tcbBi1lYF5WEjyZ1lTskIiKzo5Nd5F944QUkJydjxYoVGDVqFLy8vGBlZQUrKyt4eXlh1KhRWLFiBZKTk5kAaaGkvBIJ6XkAOBJkyubcL574vzM3kFNYJnM0RETmR2eTTdzd3fHaa6/htdde09UpzVZ8eh4qVCLc7a3h69JK7nBIT/oHuqJrGyfEp+dhy8kUvPRQe7lDIiIyKzoZCSLdqr5pKoskmi5BEDSjQZtPJKOkvPaiAiIi0h8mQQZIPSma84FM37iu3vB2ssHte2XYdbbxMhNERKQ7TIIMTFWRxFwAQC8mQSbPUqnAzEFtAQARUSyeSETUkpgEGZgbd4tx+14pLBQCurZhkURz8P/6+cPOSokrWffwx9XbcodDRGQ2mAQZGPWtsAd9HGFjqf2ebGS8nFpZ4sm+/gBYPJGIqCUxCTIwmknRvBVmVmYNbguFAERevY1Lmflyh0NEZBaYBBmYM6wUbZb8XG0xNtgbABARmSRzNERE5oFJkAEpKqvAxZsFADgSZI7C7y+X//lsOrLzS2SOhojI9DEJMiDnbuShUiWitaM1fJxs5A6HWlgvfxf0DnBBeaWIzSdS5A6HiMjkSUqC3nvvPbz33nvYv3+/ruIxa2dS/qoPxCKJ5mlOSNVo0JZTKSguY/FEIiJ9kpQEvfvuu1i6dClKS0t1FY9Zi+N8ILM3+kEv+Lm2Qm5RObbH3pA7HCIikyYpCXJzcwMA+Pv76yQYcyaKombn+J5MgsyWUiFg9uCq0aD1UUlQqVg8kYhIXyQlQQ888AAAIDMzUyfBmLOUO0XIKSyDlVKB4DaOcodDMpraxw8ONhZIul2IQ5ey5Q6HiMhkSUqCnnzySYiiiG3btukqHrOlng8U3MYR1hYskmjO7KwtML0/iycSEembpCTohRdeQPfu3bF582Zs3LhRRyGZp1jOB6JqZg5qCwuFgFNJOYi/kSd3OEREJslCysGZmZmIiIhAeHg4wsPD8d1332H69Ono1q0bXFxcoFQ2PKLBuUR/Uc8HYn0gAgBvp1Z4pJs3dp7NQERUIv7z/3rKHRIRkcmRlAS1bdtWs5RbFEUcOnQIhw4d0upYQRBQUVEhpXuTca+0Apfvb5XAkSBSmxMahJ1nM7Dn3E288XAn+Di3kjskIiKTIrlYoiiKEEWxxnNtH1Tlz7RcqESgjXMreLFIIt0X3MYJA4JcUakSsel4stzhEBGZHEkjQRs2bNBVHGZNvWlqT39neQMhgzM3NAgnE3PwXXQq5j/UHvbWkv7JEhFRNZJ+o4aFhekqDrPGSdFUn+EdPRHkYYfEW4XYdjoNs+9XlCYiIum4d5jMVCoRcWm5AKq2yyCqTqEQEH4/8Vl/LAkVlSqZIyIiMh1MgmSWeLsQuUXlsLZQoLM3iyRSbZN7+sLF1hI37hbjwIUsucMhIjIZOptgoFKpcOTIEZw4cQKZmZkoKirChx9+CG9vb02bsrIyVFRUQKlUwtraWlddGzX1rbBuvk6wsmBOSrW1slLimQEB+O/ha1gbmYhxXb0bP4iIiBqlk0/dPXv24IEHHsDo0aOxZMkSrF69Gps2bcLdu3drtIuIiICDgwM8PT1RWFioi66NHjdNJW08PTAAVkoF4lJzNdXFiYhIGslJ0Nq1azFx4kQkJydDFEW4ubnVu/x9zpw5cHJywr1797Bjxw6pXZuE2JRcACySSA3zdLDBYz19AHArDSIiXZGUBF29ehUvvvgiAGDEiBG4cOECsrPr3/DRysoKU6ZMgSiKOHDggJSuTUJ+STmuZBcA4EgQNS48JAgAsD8hE6l3imSOhojI+ElKgj7//HNUVFTgwQcfxC+//IJOnTo1ekxoaCgAIC4uTkrXJuFsai5EEfBzbQUPB86RooZ19HLAkA4eUIlVK8WIiEgaSUnQ4cOHIQgCFi5cCCsrK62OeeCBBwAAaWlpUro2CawPRE015/5y+W0xacgrLpc5GiIi4yYpCbpx4wYAoHv37lofY2dnBwAoKuJwvnrTVNYHIm2FtndHx9YOKCqrxPfRqXKHQ0Rk1CQlQerNU5uS0Ny5cwcA4OTkJKVro6dSiVwZRk0mCALCQ6tGgzYeS0ZZBYsnEhE1l6QkqE2bNgCAxETtV6tERUUBAIKCgqR0bfSu3bqHgpIKtLJUopOXg9zhkBGZ2MMHHg7WyMwvwS/xN+UOh4jIaElKgoYNGwZRFLFp0yat2ufl5eGrr76CIAgYMWKElK6NnnrT1G6+TrBQskgiac/aQomwgQEAgLWRifWWpCAiooZJ+vR99tlnIQgCjh49io0bNzbY9s6dO3jssceQmZkJCwsLPPfcc1K6BgBkZ2djz549WLx4McaOHQt3d3cIggBBEDBz5kzJ59cn9aRozgei5niqfwBsLBVIyMjHycQcucMhIjJKkrbN6NmzJxYsWICVK1ciPDwcv/76K6ZMmaJ5//jx4zh79iyOHTuG7777Dvn5+RAEAe+88w4CAgIkB9+6dWvJ55CLuuov5wNRc7jYWeHx3r7YcjIVEZGJGNjOTe6QiIiMjuS9wz799FOUlpZi9erV2L59O7Zv366ZMP3ss89q2qmH7BcuXIi3335bare1+Pv7o1OnTkZRhDG3qAzXb1VtG9LT31neYMhozR4ciG9PpeLQpWxcv3UP7Tzs5Q6JiMioSJ6MIggCvvzyS+zfvx/Dhg2DIAgQRbHGAwAGDhyIvXv34rPPPpMctNrixYuxe/duZGZmIiUlBV9//bXOzq1PcWm5AIC2brZws2eRRGqeIA97PNSpajR0XRSLJxIRNZXOdpEfNWoURo0ahYKCAsTFxSE7OxuVlZVwc3NDjx494O7urquuNJYuXarzc+pbpUrErrPpAAA/V1tUqkQoFYLMUZGxmhMaiIMXs7A9Jg3DOniguLwSng426Bfoyp8rIqJG6CwJUnNwcMCQIUN0fVqTsO/8TSzdfQE380oAAJFXbyNkxWEsmdAFDwd7yxwdGaP+ga7wd7VFak4R5n1zRvO6t5MNf66IiBrBtdktZN/5m3h+S6wmAVLLzCvB81tise88671Q0+1PyERqTu1ipfy5IiJqnE5HgrKzs/H7778jPj4eOTlVy3ZdXV0RHByMYcOGGfVqLikqVSKW7r6Auqq5iAAEAEt3X8CoLl68hUFaU/9c1YU/V0REjdNJEpSeno5XX30VO3bsQEVFRZ1tlEolJk2ahE8++QT+/v666FanSktLUVpaqvk6Pz9fZ+eOTsqpNQJUnQjgZl4JopNyuNSZtMafKyIiaSTfDouKikJwcDB+/PFHlJeX11oZpn5UVFRg+/bt6Natm2brDEOybNkyODk5aR5+fn46O3d2Qf0fVM1pRwTw54qISCpJSVBGRgYmTJiAvLw8iKKIsWPH4scff0RKSgpKSkpQUlKClJQUbN++HePGjYMoisjPz8eECROQkZGhq2vQiUWLFiEvL0/zSEtL09m5PR1sdNqOCODPFRGRVJKSoGXLliEvLw9KpRKbN2/G3r17MWXKFPj5+cHKygpWVlbw8/PD5MmTsWfPHmzZsgUKhQL5+flYvny5rq5BJ6ytreHo6FjjoSv9Al3h7WSD+mZlCKhazdMv0FVnfZLp488VEZE0kpKgX375BYIgYO7cuXj66acbbT99+nTMmzcPoihi7969Uro2KkqFgCUTugBArQ8s9ddLJnTh5FVqkoZ+rtT4c0VEVD/Jt8MA4IknntD6GHVbQ7sdpm8PB3tj9dO94OVU89aEl5MNVj/di/VcqFnq+7kCgOVTuvLnioioAZJWh7m4uCArKwtOTk5aH6Nu6+JifhuHPhzsjVFdvBCdlIPsghJW9iWd+PvP1ReHr+JqdiFu3C2WOzQiIoMmaSSoT58+AID4+Hitj1G3VR9rbpQKAQPbuWFijzYY2M6NCRDpRPWfq1dGdQQAbDmZguKySpkjIyIyXJKSoJdeegmiKOLjjz9GUVHtqrV/V1RUhBUrVkAQBMyfP19K10RUj9EPesHPtRXuFpXjf7E35A6HiMhgSUqCRo4ciSVLluDixYsYNmwYzp49W2/bP//8E8OHD8fly5exZMkSjBo1SkrXRFQPpULA7MGBAID1UUlQqeqqVU5ERFrNCXrvvffqfU8QBPTp0wcxMTHo3bs3unbtir59+8LT0xOCICArKwunT5+udRvsvffew+LFiyUFHxUVhWvXrmm+vn37tub5tWvXsHHjxhrtZ86cKak/ImPxRB8/fPbbFSTeLsThS9kY2cU8t6whImqIIIpio38mKhQKCELjc1dEUay3XV3vVVZKm68wc+ZMbNq0Sev2WlyqRn5+PpycnJCXl6fTmkFELWXZrxfx9dFE9A90xQ/PDpQ7HCKiFtGUz2+tb4fVtx1G9UdD7ep6j4j0Z+agtrBQCDiVlIP4G3lyh0NEZHC0SoJUKpVeHlJt3LhRq+SMSReZI2+nVnikW1WdoIioRJmjISIyPJI3UCUiwzUnNAgAsPfcTWTksm4QEVF1TIKITFhwGycMCHJFhUrEpuPJcodDRGRQmAQRmbg5IVWjQd9Fp+JeaYXM0RARGQ5J22bUJT8/HwUFBVqt/PL399d190T0NyM6eSLI3Q6Jtwux7XQaZocEyh0SEZFB0EkSdODAAaxevRqRkZG4e/euVscIgoCKCv5VSqRvCoWA2SGBeHvneaw/loSwQW25XQsREXRwO+y5557D2LFjsWvXLuTk5HC1FpEBmtLLFy62lrhxtxj7EzLlDoeIyCBIGgn6+uuvsWbNGgCAg4MDJk2ahO7du8PZ2RkKBacbERmKVlZKPDMgAP89fA1rIxMxrqu33CEREclOUhKkToA6d+6Mw4cPo3VrluYnMlRPDwzAV0cTEZeaizMpd9E7wEXukIiIZCVpuObSpUsQBAHvvvsuEyAiA+fpYIPHevoAACIiWTyRiEhSEmRvbw8AaN++vU6CISL9Cr+/XH5/QiZS7xTJHA0RkbwkJUGdO3cGANy8eVMnwRCRfnX0csCQDh5QicD6Y0lyh0NEJCtJSdC8efMgiiK+//57XcVDRHo2536doG0xacgrLpc5GiIi+UhKgqZPn44pU6bg22+/xRdffKGrmIhIj0Lbu6NjawcUlVXi++hUucMhIpKNIEos2FNWVoZ//vOf+L//+z8MGDAAU6dORYcOHWBra9vosUOGDJHStV7l5+fDyckJeXl5cHR0lDscIp3aFpOG17efg5ejDSLfGA5LJUtaEJFpaMrnt+SK0ZaWlujevTtcXFxw4sQJnDhxQqvjWDGaSD4Te/jg432XkZlfgr3nbuKxnm3kDomIqMVJ+vOvoqICU6dOxbx585pcLZoVo4nkY22hRNjAAABARFQi/z0SkVmSNBL01Vdf4X//+x8AICAgAGFhYawYTWQknhoQgC9/v4bz6fk4mZiDge3c5A6JiKhFSUqCIiIiAAADBgzAoUOH0KpVK50ERUT652pnhSm9fPHtqVSsi0pkEkREZkfScM21a9cgCAIWLVrEBIjICIXfXy5/8GI2rt+6J3M0REQtS1ISZGdnBwDw8/PTSTBE1LKCPOwxsrMnAGB9FIsnEpF5kZQEde/eHQCQkpKik2CIqOXNCa3aSmP7mRvIKSyTORoiopYjKQl67rnnIIoi1q1bp6t4iKiF9Q90RXAbR5RWqPDtSf5BQ0TmQ1ISNHnyZDz33HPYs2cP/vnPf6KyslJXcRFRCxEEAXPvjwZtOpGCknL+OyYi8yCpYvTmzZsBAKtXr0Z0dDT8/PwwefJkrStGz5gxo7ld6x0rRpM5Ka9UYcjHR3AzrwQfP94NU/twnh8RGaemfH5LSoIUCgUEQWjWsYZeMZpJEJmbr49ex7JfL6FjawfsWxja7H/bRERyasrnt+SKhk2tEs2K0USG6f/184edlRKXswoQefW23OEQEemdpGKJSUlcUktkKpxaWWJqXz9sOJaMtZGJGNLBQ+6QiIj0SlISFBAQoKs4iMgAzB4ciE3HkxF59TYuZxago5eD3CEREekNN/giIg0/V1s8HOwFAIiITJQ5GiIi/WISREQ1hIdULZf/+WwGsgtKZI6GiEh/mAQRUQ29A1zQy98ZZZUqfHOCxROJyHRJmhMUFBTU7GMFQcD169eldE9EejInNAgvfBuLLSdT8MKwB9DKSil3SEREOicpCUpOTta6rSAINZbFswYJkeEa86AX/FxbIS2nGP+LvYGnB3ARBBGZHklJUFhYWKNtCgsLceXKFZw7dw6CIKBnz57o2rWrlG6JSM+UCgGzBwdi6e4LWB+VhOn9/KFQ8A8XIjItkipGN0VCQgLCw8MRHx+Pb775BpMnT26JbpuNFaPJ3N0rrcDAZYdQUFKBiBl9MLJLa7lDIiJqVItWjNbWgw8+iIMHD8LHxwczZszApUuXWqprImoGe2sLTO/vDwCIiOJyeSIyPS26Osze3h6vvPIKioqK8Mknn7Rk10TUDDMHtYWFQsDJxBycT8+TOxwiIp1q8SXyffr0AQAcOnSopbsmoibydmqFR7p5A2DxRCIyPS2eBKmnIGVlZbV010TUDHNCq0ph7Dl3EzfzimWOhohId1o8Cdq/fz8AwMnJqaW7JqJmCG7jhAFBrqhQidh4PFnucIiIdKZFk6CtW7di2bJlEAQBISEhLdk1EUkw5/5WGt+dSsW90gqZoyEi0g1JdYJmz57daBuVSoW7d+8iNjYWGRkZEEURFhYWePPNN6V0TUQtaEQnTwS52yHxdiG2nU7D7JBAuUMiIpJMUhK0ceNGrSs/q+cCOTo6IiIiQjNBmogMn0IhYHZIIN7eeR7rjyUhbFBbKFk8kYiMnKQkyN/fv9EkSKFQwMHBAYGBgRg6dCiefvppuLu7S+mWiGQwpZcvPj1wGTfuFmN/QibGdfWWOyQiIklabO8wIjJurayUeHpAAFYdvoaIyEQmQURk9Fp8dRgRGa9nBgbASqlAbGouzqTclTscIiJJmAQRkdY8HWwwsYcPAGAdt9IgIiPHJIiImkRdPHHf+Uyk5RTJHA0RUfMxCSKiJuno5YDQ9u5QicD6Y0lyh0NE1GxaJ0FKpVKnDwsLSXOyiUhGc++PBm07nYa84nKZoyEiah6tkyBRFHX+ICLjFNreHR1bO6CwrBJbo1PlDoeIqFm0Ho4JCwuT1JEoivjll19w584dJkBERk4QBISHBuL17eew8XgyZocEwlLJu+tEZFy0ToI2bNjQ7E527tyJJUuW4M6dO5rX/Pz8mn0+IpLfxB4++HjfZdzMK8Ev8TcxsUcbuUMiImoSvf7p9ssvv6Bv376YMmUKzp8/D1EU4eXlhVWrVuHKlSv67JqI9MzaQomwgQEAgLWRiRzhJSKjo5ck6LfffsOgQYMwYcIExMbGQhRFeHh44NNPP8X169fx4osvwsrKSh9dE1ELempAAGwsFTifno9TSTlyh0NE1CQ6TYKOHj2KIUOG4OGHH8apU6cgiiJcXV2xfPlyJCYm4uWXX4aNjY0uuyQiGbnaWWFKL18AQEQkiycSkXHRSRJ0/PhxjBw5EiNGjMCxY8cgiiKcnJzw3nvvISkpCa+//jpsbW110RURGZjZIYEAgIMXs5F4657M0RARaU9SEhQTE4OxY8ciNDQUR44cgSiKsLe3xzvvvIOkpCS8/fbbsLe311WsRGSA2nnYY2RnTwDAuigWTyQi49GsJOjPP//ExIkT0b9/fxw4cACiKMLW1hZvvPEGkpKSsHTpUjg5Oek6ViIyUOEhVcUT/xd7AzmFZTJHQ0SknSYlQQkJCXj88cfRq1cv7NmzB6IowsbGBq+88goSExOxbNkyuLq66itWIjJQA4JcEdzGESXlKnx7MkXucIiItKJ1EjRt2jR0794dO3bsgCiKsLKywksvvYTr16/j3//+Nzw8PPQZJxEZMEEQMOf+aNCmEykoraiUOSIiosYJopbFPRSKv/IlDw8PvPTSS5ILHs6YMUPS8fqUn58PJycn5OXlwdHRUe5wiAxeeaUKQz4+gpt5Jfj48W6Y2ocFUYmo5TXl87tJSZAgCDoJEKj6y7GiokJn59M1JkFETff10etY9usldGztgH0LQ3X6O4OISBtN+fxu0pwgbqBKRA35f/38YWelxOWsAkRevS13OEREDdJ677AjR47oMw4iMgFOrSwxta8fNhxLRkRUEoZ04FxBIjJcWidBQ4cO1WccRGQiZg8OxKbjyfjjyi1czixARy8HuUMiIqqTXjdQJSLz4+dqi4eDvQAA66K4lQYRGS4mQUSkc+riiTvjMpBdUCJzNEREdWMSREQ61zvABb38nVFWqcKWEyyeSESGiUkQEenFnNCq0aBvTqaguIzFE4nI8DAJIiK9GPOgF/xcW+FuUTl+irshdzhERLUwCSIivVAqBMwaFAgAWBeZBJWKtcGIyLAwCSIivZna1w8ONhZIvF2II5ez5Q6HiKgGJkFEpDf21haY3s8fALA2ksvliciwMAkiIr2aObgtLBQCTibm4Hx6ntzhEBFpMAkiIr3ydmqF8d28AQARHA0iIgPCJIiI9G7O/eKJe87dxM28YpmjISKqwiSIiPSuq68T+ge6okIlYuPxZLnDISICwCSIiFrI3PvFE787lYp7pRUyR0NExCSIiFrIiE6eCHK3Q0FJBX6MSZM7HCIiJkFE1DIUCgGzQ6qKJ64/loRKFk8kIpkxCSKiFjOlly9cbC2RllOMAwmZcodDRGaOSRARtZhWVko8PSAAAIsnEpH8mAQRUYt6ZmAArJQKxKbm4kzKXbnDISIzxiSIiFqUp4MNJvbwAQCsi+JoEBHJh0kQEbW48NCqCdL7zmciLadI5miIyFwxCSKiFtfJyxGh7d2hEqtWihERyYFJEBHJYs794onbTqchr7hc5miIyBwxCSIiWQxp746OrR1QWFaJrdGpcodDRGaISRARyUIQBM3coI3Hk1FeqZI5IiIyNyaRBKWkpODVV19Fp06dYGdnB1dXV/Tt2xeffPIJioo46ZLIUE3s4QN3e2vczCvBL/E35Q6HiMyM0SdBu3fvRrdu3fDZZ5/h8uXLKCoqwt27dxETE4PXX38dPXv2xLVr1+QOk4jqYG2hRNjAv4oniiK30iCilmPUSVBcXByefPJJ5Ofnw97eHh9++CGOHz+OQ4cOYe7cuQCAK1euYPz48SgoKJA5WiKqy1MDAmBjqcD59HycSsqROxwiMiNGnQQtWLAAxcXFsLCwwIEDB/Cvf/0LAwcOxIgRI7BmzRp8/PHHAKoSoU8//VTmaImoLq52VpjSyxcAEBHJ5fJE1HIE0UjHn6Ojo9G/f38AwLPPPouvvvqqVhuVSoXg4GBcvHgRzs7OyM7OhqWlpVbnz8/Ph5OTE/Ly8uDo6KjT2Imopuu37uGhT48CAD6b2h1KhQBPBxv0C3SFUiHIHF3TVapERCflILughNdhAHgdhkXf19GUz28LnfXawnbu3Kl5PmvWrDrbKBQKzJgxA4sWLUJubi6OHDmC0aNHt1CERKStdh726NbGEefS8/HKtj81r3s72WDJhC54ONhbxuiaZt/5m1i6+wJu5pVoXuN1yIfXYVgM7TqM9nZYVFQUAMDOzg69e/eut93QoUM1z48dO6b3uIio6fadv4lz6fm1Xs/MK8HzW2Kx77xxrBzbd/4mnt8SW+MXPMDrkAuvw7AY4nUY7UjQxYsXAQAPPPAALCzqv4xOnTrVOoaIDEelSsTS3RfqfE99r37xzwno7O1o0EP/lSoR7/ycgLrmF/A6Wh6vw7A0dh0CgKW7L2BUF68WvQ6jnBNUUlKCVq1aAQDGjx+PPXv2NNje3t4ehYWFGDBgAE6cOFFnm9LSUpSWlmq+zs/Ph5+fH+cEEenZiet3MG3tSbnDICID8P3cARjYzk3SOUx+TlD15e729vaNtrezs0NhYSHu3btXb5tly5Zh6dKlOomPiLSXXVDSeCMAFgrB4P/SrVA1/jclr6Nl8DoMi7bXoe3vA10xyiSopOSv/0lWVlaNtre2tgYAFBcX19tm0aJFeOWVVzRfq0eCiEi/PB1stGr3TXh/yX8h6pO2I1q8jpbB6zAs2l6Htr8PdMUoJ0bb2Pz1P6msrKzR9urbXOpbaHWxtraGo6NjjQcR6V+/QFd4O9mgvr9hBVStHukX6NqSYTUZr8Ow8DoMi6Feh1EmQQ4ODprnDd3iUissLASg3a0zImpZSoWAJRO6AECtX5Dqr5dM6GLQQ/0Ar8PQ8DoMi6Feh1EmQTY2NnBzqxr2u3HjRoNt7969q0mCeHuLyDA9HOyN1U/3gpdTzaFwLycbrH66l9HUQeF1GBZeh2ExxOswytVhADBkyBBERkbCzs4Oubm59S6TP3HiBAYNGgQAWLx4sdaTn1kxmqjlsSKuYeF1GBZeh3ZMfnUYAISEhCAyMhKFhYU4c+aMZguNvzt69Kjm+eDBg1sqPCJqBqVCMOjJndridRgWXodhMaTrMMrbYQDw2GOPaZ5v2LChzjYqlQqbN28GADg7O2P48OEtERoREREZAaNNgvr164fQ0FAAwLp16+osgvjpp59qqkQvWLBA681TiYiIyPQZ7ZwgAIiLi8PgwYNRXFwMe3t7/Otf/8Lw4cNRXFyMrVu3Ys2aNQCADh06ICYmpsaqssZwThAREZHxacrnt1EnQQCwe/duPP3008jPr735IlCVAO3duxcPPPBAk87LJIiIiMj4NOXz22hvh6lNmDAB586dw8svv4wOHTrA1tYWzs7O6NOnD1asWIG4uLgmJ0BERERk+ox+JEhfOBJERERkfMxqJIiIiIioOZgEERERkVliEkRERERmyWgrRuubeqpUfavOiIiIyPCoP7e1mfLMJKgeBQUFALjpKhERkTEqKCiAk5NTg224OqweKpUKGRkZcHBwgCAY3wZ1LSE/Px9+fn5IS0vjCjoDwO+HYeH3w7Dw+2FY9Pn9EEURBQUF8PHxgULR8KwfjgTVQ6FQwNfXV+4wjIKjoyN/qRgQfj8MC78fhoXfD8Oir+9HYyNAapwYTURERGaJSRARERGZJSZB1GzW1tZYsmQJrK2t5Q6FwO+HoeH3w7Dw+2FYDOX7wYnRREREZJY4EkRERERmiUkQERERmSUmQURERGSWmAQRERGRWWISRFqJiYnBe++9h9GjR8PX1xfW1tawt7dHhw4dMGvWLERFRckdIgF44403IAiC5vH777/LHZJZSk1NxZIlS9CnTx94eHjAxsYGfn5+CA0NxeLFi3H+/Hm5QzQLZWVliIiIwJgxY+Dt7a35vdWxY0fMmjULx48flztEk5CdnY09e/Zg8eLFGDt2LNzd3TW/g2bOnNnk8/3666+YNGmS5rPG19cXkyZNwq+//qr74EWiRoSGhooAGn3MmDFDLC0tlTtcsxUXFydaWFjU+J4cOXJE7rDMzn//+1/Rzs6uwX8rCxYskDtMk5ecnCw++OCDjf7emj9/vqhSqeQO16g19P83LCxM6/NUVlaK4eHhDZ5vzpw5YmVlpc5i57YZ1KiMjAwAgI+PD5544gmEhobC398flZWVOHHiBD799FOkp6dj8+bNKC8vx3fffSdzxOZHpVJh3rx5qKiogKenJ7Kzs+UOySx98MEHeOeddwAAHTp0wNy5c9G3b184OTnhzp07iIuLw44dOxrdz4ikKS8vx/jx45GQkAAA6NatG1555RV07NgRBQUFiIqKwqefforCwkKsWrUKPj4+ePPNN2WO2jT4+/ujU6dOOHDgQJOPfeutt7Bu3ToAQM+ePfH666+jXbt2uH79Oj7++GPExcUhIiICHh4e+Oijj3QTsM7SKTJZ48ePF3/44QexoqKizvdv3boldujQQZOpHz16tIUjpM8//1wEIHbq1ElctGgRR4JkcPDgwRqjomVlZfW25Yipfv3444+a78XAgQPr/N0VExMjWlpaigBEZ2dnsby8XIZITcPixYvF3bt3i5mZmaIoimJSUlKTR4IuX76sGcnu06ePWFRUVOP9wsJCsU+fPiIA0cLCQrx69apOYuefI9SoPXv2YOrUqVAqlXW+7+7ujk8//VTz9fbt21sqNELV/BP16MNXX30FKysrmSMyPyqVCs8//zwAoHv37li3bh0sLS3rbc/vkX5Vn+uzaNGiOn939e7dG4888ggAIDc3FxcvXmyx+EzN0qVL8cgjj6B169bNPsfKlStRUVEBAFi1ahVatWpV431bW1usWrUKAFBRUYHPP/+8+QFXwySIdGL48OGa59evX5cxEvPz4osv4t69ewgLC8PQoUPlDscsHThwAFevXgVQNTndwoIzDeRUVlameR4UFFRvu3bt2tV5DLUsURTx888/AwA6deqEAQMG1NluwIAB6NixIwDg559/hqiDDS+YBJFOlJaWap7XN2JEurdt2zbs2bMHrq6u+Pe//y13OGbrxx9/BAAIgqAZXQCAnJwcXL16FTk5OXKFZpbUH5QAkJiYWG879R9sgiCgffv2eo+L6paUlKSZe9rYH3Lq99PT05GcnCy5byZBpBNHjx7VPO/cubOMkZiP3NxcLFiwAACwYsUKuLu7yxyR+Tp58iQAoG3btnBwcMB3332Hrl27ws3NDR06dICbmxs6duyIf//73zX+YCD9mDZtGhwdHQFU/duorKys1SYuLg579+4FAEyfPl3TnlrehQsXNM87derUYNvq7+viFiaTIJJMpVJh+fLlmq+nTp0qYzTm4/XXX0dmZiYGDx6M8PBwucMxWyqVCpcuXQJQNT9uwYIFeOqpp2rVArpy5Qpee+01jBgxArm5uTJEaj7c3d3xzTffwNbWFseOHUPfvn2xefNmnDx5EgcPHsTSpUsxdOhQlJWVoVevXjXmNFLLu3Hjhua5r69vg239/Pw0z9PS0iT3zSSIJPv8888RHR0NAJg8eTJ69+4tc0SmLzIyEhEREbCwsMBXX30FQRDkDsls5eXlQaVSAQDi4+Px3//+F97e3tiyZQtycnJQVFSEo0ePauY5HD9+HLNnz5YzZLPw6KOP4syZM5gzZw7Onj2LsLAwDBw4EKNGjcK7774LW1tbrFy5EpGRkZIm9JJ0BQUFmuf29vYNtrWzs9M8v3fvnuS+mQSRJEePHtXU1/D09MTq1atljsj0lZWVYd68eRBFES+//DKCg4PlDsmsFRYWap6XlJTA1tYWR44cwVNPPQUXFxe0atUKQ4YMweHDh9G9e3cAwI4dO3Dq1Cm5QjYLZWVl2Lx5c70TaLOysrBlyxYcPHhQhuioupKSEs3zxlZOWltba54XFxdL7ptJEDVbQkICJk2ahIqKCtjY2ODHH3+Ep6en3GGZvI8++giXLl2Cv78/lixZInc4Zs/GxqbG13PmzKkxMVetVatW+PDDDzVf//DDD3qPzVwVFhZi5MiRWLZsGXJycvD666/j4sWLKC0tRV5eHg4cOICQkBDExMTgsccew2effSZ3yGat+r+hxlbpVZ9T9/dl9M3BJIiaJSkpCaNHj8bdu3ehVCqxdetWDBkyRO6wTN6lS5ewbNkyAFW1NKoPDZM8HBwcanw9evToets+9NBDmuXzp0+f1mtc5uzdd99FZGQkAGDdunVYsWIFOnXqBCsrKzg6OmLUqFE4cuQIhg8fDlEU8dprr+HPP/+UOWrzVf3fUGO3uKqPvDZ260wbLGZBTZaRkYGRI0ciIyMDgiBg/fr1mDhxotxhmYXPP/8cZWVlCAoKQlFREbZu3VqrTfUJuYcPH0ZmZiYAYMKECUya9MDa2hoeHh64desWgJoTN//OxsYG7u7uyMzM1LQn3RJFEevXrwdQtXVJWFhYne0sLCzw/vvvIyQkBCqVChs3btRZAT5qmuqToatPkq5L9cnQDf1b0xaTIGqS27dvY9SoUZraG6tWrcKMGTNkjsp8qIeCExMTMW3atEbbv//++5rnSUlJTIL05MEHH8Tvv/8OAHUux65O/T4LKupHVlaWpi5Tz549G2xbfRGHeoUftbwuXbponjf2faj+vi7KsfB2GGktLy8PY8aM0dR0WL58OV588UWZoyKSX/VbwQ0V58vPz8ft27cBAG3atNF7XOaoenKp3oahPuXl5XUeRy0rMDAQPj4+AGrWnKvLH3/8AaDq30/btm0l980kiLRSVFSE8ePHIzY2FkDVbr9vvPGGzFGZn40bN0IUxQYf1SdLHzlyRPO6Ln5hUN2mTJmieb5jx4562+3YsUOzUik0NFTvcZkjV1dXTeHDEydONJgIVf/ADQwM1HtsVDdBEDRTKi5duqQpPvp3J0+e1IwETZw4USelQZgEUaPKysowadIkHDt2DACwYMECfPDBBzJHRWQ4unXrhrFjxwIAvv/+exw6dKhWm8zMTLz99tsAqpYBz5o1q0VjNBcKhQLjx48HUDV/sfqKvOru3r1b4w+56tudUMtbuHChZsul+fPn11r+XlxcjPnz5wOoGrVbuHChTvrl+B81atq0aThw4AAAYMSIEQgPD69VDbc6KysrdOjQoaXCIzIIK1euxIkTJ5Cbm4tHHnkECxcuxLhx49CqVStER0dj2bJlmkmf77//Pm+H6dHixYvx888/o6ioCO+++y7OnDmDsLAwBAUFoaSkBCdPnsTKlSuRmpoKoGrVXkOr+qhhUVFRuHbtmuZr9S1fALh27Ro2btxYo/3MmTNrnaNDhw547bXXsHz5csTExGDw4MF444030K5dO1y/fh0rVqxAXFwcAOC1117T3V5vIlEjADTpERAQIHfIZm3JkiWa78WRI0fkDsesREZGiq1bt67334YgCOLbb78td5hm4bfffhPd3d0b/X01YsQIMScnR+5wjVpYWFiTPiPqU1lZKc6ePbvBY8PDw8XKykqdxc6RICIiHQkJCUFCQgJWrVqFnTt3IikpCWVlZfD29sawYcMwf/78RlcskW6MHDkSly5dwrp16/Drr78iISEBubm5sLCwgJeXF/r27Yvp06fj0Ucf5bYzBkKhUGDdunWYMmUK1qxZg9OnT+P27dtwd3dH37598eyzz2puO+uKIIp11BMnIiIiMnGcGE1ERERmiUkQERERmSUmQURERGSWmAQRERGRWWISRERERGaJSRARERGZJSZBREREZJaYBBEREZFZYhJEREREZolJEBEREZklJkFERERklpgEERFJ1LZtWwiCgJkzZ8odChE1AZMgIqrT77//DkEQIAgC3n33XbnDISLSOSZBRNRkycnJmgRp48aNcoejF8OGDYMgCBg2bJjcoRCRnljIHQARkbFLTk6WOwQiagaOBBEREZFZYhJEREREZolJEBE1iSAICAwM1Hw9a9YszfygxiZSx8bG4rnnnkPHjh1hb28POzs7dOzYEc8//zyuXLlSb58bN27UnDs5ORmlpaVYuXIlBgwYAHd391p9lpWVYffu3fjHP/6Bvn37wsXFBZaWlnBzc0P//v3x7rvv4vbt23X2NXPmTAiCgKNHjwIAjh49Wuv62rZtW+MYbVeH7d69G48//jh8fX1hbW0NNzc3DBw4EMuXL8e9e/e0vn6VSoU1a9Zg0KBBcHFxgZ2dHbp164YPP/wQRUVFDcZARH/hnCAi0juVSoV//vOfWLlyJURRrPHelStXcOXKFURERODLL7/EvHnzGjzX7du3MWnSJJw9e7beNvPmzcOmTZtqvZ6Tk4Po6GhER0fjiy++wM8//4zBgwc365qaoqSkBNOnT8eOHTtqxXPy5EmcPHkSq1atwt69e9GjR48Gz1VUVITRo0fj0KFDNV6Pj49HfHw8du3ahcOHD8POzk7Xl0FkcpgEEVGTxMfHIyMjA2PGjAEAfPDBB5g4cWKNNp6enjW+nj9/Pv7v//4PADBkyBDMnDkTQUFBsLW1xZ9//omVK1ciISEBzz77LLy8vPDoo4/W2394eDji4+MxY8YMPPnkk/Dy8kJqaiqsra01bSoqKhAUFIRJkyahX79+8Pf3h4WFBVJSUnDw4EGsX78ed+7cwaRJk3D+/Pka8X744Yf45z//iVmzZiEmJgZ9+vTBhg0basRgZWXVpP9nYWFhmgSoe/fuePXVV9G5c2fk5ORg69at2LhxIzIyMvDQQw/h3LlzaNOmTb3nmjt3Lk6ePImwsDBMnTpVc/0ff/wxTpw4gejoaHzwwQdYtmxZk2IkMksiEVEdjhw5IgIQAYhLliyp8V5SUpLmvQ0bNjR4ngMHDmjaRkRE1NmmuLhYHDFihAhADAgIEMvLy2u8v2HDBs05GjqP2rVr10SVSlXv++fOnRPt7e1FAOLbb79dZ5uhQ4eKAMShQ4c22JcoimJAQIAIQAwLC6v13p49ezRxP/TQQ2JpaWmtNmvWrNG0mTp1aq33/37933zzTa02JSUlYnBwsAhAdHNzq/X/kIhq45wgItKr5cuXAwCmTJmC8PDwOtvY2Njgiy++AACkpKTgyJEj9Z5vxIgR9Z5HrV27dhAEod73u3btijlz5gAAdu7c2eC5pPryyy8BAJaWltiwYUOdo0hz587FyJEjAQA//fQTbt68We/5Jk+ejKeffrrW69bW1vjHP/4BALhz5w4uXLigi/CJTBqTICLSm/z8fPz+++8AgMcff7zBtp07d4a7uzsA4MSJE/W2e+qpp5ocx927d3H9+nUkJCTg/PnzOH/+PJydnQEAFy5cQHl5eZPPqY2KigrNBOvRo0fDz8+v3rZz587VHKP+f1aXhq6/d+/emueJiYlNjJbI/HBOEBHpTVxcHFQqFQBg2rRpmDZtmlbHZWZm1vtet27dtDpHfHw8Pv/8c/z6668Nnk+lUuHu3bu15jHpQmJioma1Vv/+/RtsW/398+fP19uuU6dO9b7n6uqqeV5QUKBtmERmi0kQEelNdnZ2s45raJm3i4tLo8evW7cOzz33HCoqKrTqr7i4WOvYmiInJ0fzvLEky8vLq87j/s7W1rbe9xSKvwb3KysrtQmRyKwxCSIivan+Qfz1119j0KBBWh3XUKKjVCobPPbSpUuaBMjT0xOvvfYaRowYgbZt28LBwQGWlpYAgPXr12vmFol/W7avDw3NUSIieTAJIiK9cXNz0zy3tbVFcHCw3vvcuHEjKioqoFQqcfTo0XpvHzU02qIr1W9PZWVlNdi2+i276scRkf5wYjQRNZm2oxo9evTQtD127Jg+Q9JISEgAUFWPp6H5MzExMQ2eRxcjN+paSABw6tSpBttGR0drnrdEskhETIKIqBlsbGw0z0tLS+tt5+HhgQEDBgAAvvvuO9y6dUvvsannARUWFtbb5ubNm9i1a1eD51FfY0PX1xgLCwsMHToUAPDbb7/hxo0b9baNiIjQHDNs2LBm90lE2mMSRERN5ubmpql3c/369Qbbvv322wCqlss//vjjyM3NrbdtaWkpvvzyS5SUlDQ7tvbt2wMArl69iuPHj9d6v6ioCNOnT290MrS3tzeAqhVeUuYMvfjiiwCq9jMLDw+vczn++vXrceDAAQBVdYDUfRORfjEJIqIms7CwQN++fQFUfYB///33uHjxIq5du4Zr167VmG8zbtw4LFiwAADwxx9/oHPnzli6dCkOHTqEs2fP4tixY9i0aRPmzJkDb29v/OMf/9B6VVddnnnmGQBVS9/Hjx+Pjz76CH/88Qeio6OxevVq9OjRA7///nuje4apJ3FnZ2fjlVdewZkzZzTXl5KSonU848ePxxNPPAEAOHDgAAYMGIBvv/0WZ86cwcGDBzFnzhxN4UZXV1d89tlnzblsImoOuUtWE5FhamjbDFGs2g5CEIQa2znU116lUolLly4VLSws6mxf/WFnZycWFRXVOL76thFJSUmNxr506dIG+3j11VcbPWdBQYEYFBRU5/EBAQE12ja0bYYoVm0LMmnSpAZj8vHxEePi4uo8Xtvrb8p2JkTEbTOIqJnGjx+PQ4cOYeLEifDx8dEsPa+LIAhYvHgxrly5gtdffx19+vSBq6srlEolHBwc0KVLFzz11FPYtGkTbt68iVatWkmKbfHixdi7dy9Gjx4NFxcXWFlZwdfXF5MnT8aBAwfw73//u9Fz2Nvb4/jx41iwYAE6d+7cYH2extjY2OCnn37Crl27MHnyZPj4+MDKygouLi7o378/li1bhsuXLze6gzwR6ZYgii1QIIOIiIjIwHAkiIiIiMwSkyAiIiIyS0yCiIiIyCwxCSIiIiKzxCSIiIiIzBKTICIiIjJLTIKIiIjILDEJIiIiIrPEJIiIiIjMEpMgIiIiMktMgoiIiMgsMQkiIiIis8QkiIiIiMwSkyAiIiIyS/8f+Vx7Q5qV9ZUAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Initialise model\n", + "ppn = Perceptron(eta=0.1, n_iter=10)\n", + "\n", + "# Fit the model to the data\n", + "ppn.fit(X, y)\n", + "\n", + "# Plot the error vs the number of iterations\n", + "plt.plot(range(1, len(ppn.errors_) + 1), ppn.errors_, marker='o')\n", + "\n", + "# Label the axis\n", + "plt.xlabel('Iteration')\n", + "plt.ylabel('Number of updates')\n", + "\n", + "print(ppn.w_)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise 2\n", + "\n", + "**Comparison to other linear classifiers.** Let's compare the properties of the decision surface learned by the perceptron to the decision surface learned by another linear classifier: _SVM_ (with a linear kernel). We continue using the _setosa_ and _virginica_ iris data.\n", + "\n", + "----------\n", + "\n", + "Fill the gaps in the code below.\n", + "\n", + "Fill the **first gap** such that:\n", + "- you import the appropriate `sklearn` packages. Careful for the name clash with our own implementation of `Perceptron`.\n", + "- `sklearn` implementation of perceptron is located at [`sklearn.linear_model.Perceptron`](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Perceptron.html)\n", + "- `sklearn` implementation is SVM located at [`sklearn.svm.SVC`](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html) \n", + "\n", + "Fill the **second gap** such that:\n", + "- `c_our_perceptron` initialises a `Perceptron` classifier of our own implementation with $\\eta=0.1$ and 20 iterations (we called the argument `n_iter`)\n", + "- `c_perceptron` initialises a perceptron classifier from `sklearn`, for 20 iterations and with $\\eta=0.001$. The arguments are called `eta0` and `max_iter`\n", + "- `c_svm` initialises a _SVM_ classifier with a linear kernel (parameter `kernel`)\n", + "\n", + "Fill the **third gap** such that:\n", + "- you fit each of the three classifiers to our data (stored in `X` and `y`)\n", + "\n", + "Fill the **fourth gap** such that:\n", + "- use each of the fitted classifiers to classify the input data again. Since it is linearly separable, we expect perfect classification in all cases.\n", + "- store the classification output of `c_our_perceptron` in `y_our_perceptron`. Classification output and decision surface will be shown in first subplot.\n", + "- store the classification output of `c_perceptron` in `y_perceptron`. This will be shown in the second subplot.\n", + "- store the classification output of `c_svm` in `y_svm`. This will be shown in the third subplot.\n", + "\n", + "----------\n", + "\n", + "When you are done, **compare the resulting decision surfaces**:\n", + "- Our \"bare bones\" implementation of the _perceptron_ stops as soon as it finds a decision surface which correctly classifies the training samples.\n", + "- The `sklearn` implementation has a more sophisticated _stopping criterion_: keeps improving the decision surface beyond \"first good find\"\n", + "- The _SVM_ algorithm is formulated such that it finds \"support vectors\" that limit the distance to the training samples of different classes. This forces a \"gap\" between the decision surface and the samples.\n", + "- How do the three decision surfaces compare?" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#####################################################################################################\n", + "#####################################################################################################\n", + "#####################################################################################################\n", + "# Required imports go here\n", + "from sklearn import linear_model\n", + "from sklearn import svm\n", + "\n", + "\n", + "#####################################################################################################\n", + "#####################################################################################################\n", + "#####################################################################################################\n", + "# Initialise the ML models\n", + "c_our_perceptron = Perceptron(eta = 0.1, n_iter = 20)\n", + "c_perceptron = linear_model.Perceptron(eta0 = 0.001, max_iter = 20)\n", + "c_svm = svm.SVC(kernel = 'linear')\n", + "\n", + "#####################################################################################################\n", + "#####################################################################################################\n", + "#####################################################################################################\n", + "# Fit models to the data (using the 'fit' function)\n", + "c_our_perceptron.fit(X, y)\n", + "c_perceptron.fit(X, y)\n", + "c_svm.fit(X, y)\n", + "\n", + "\n", + "#####################################################################################################\n", + "#####################################################################################################\n", + "#####################################################################################################\n", + "# Predict the training samples again\n", + "y_our_perceptron = c_our_perceptron.predict(X)\n", + "y_perceptron = c_perceptron.predict(X)\n", + "y_svm = c_svm.predict(X)\n", + "\n", + "\n", + "#####################################################################################################\n", + "#####################################################################################################\n", + "#####################################################################################################\n", + "\n", + "\n", + "# Plot different things:\n", + "fig, axs = plt.subplots(1, 3, figsize=(22, 6))\n", + "\n", + "clfs = [c_our_perceptron, c_perceptron, c_svm]\n", + "outputs = [y_our_perceptron, y_perceptron, y_svm]\n", + "\n", + "# \"run\" across all different outputs, classifiers and axis\n", + "# that's why I use .subfigure :)\n", + "for y, clf, ax in zip(outputs, clfs, axs):\n", + " # Plot decision surface\n", + " xx, yy = np.meshgrid(np.linspace(x_min, x_max, 200), np.linspace(y_min, y_max, 200))\n", + " surface = np.c_[xx.ravel(), yy.ravel()]\n", + " #surface_prime = np.hstack((np.ones((surface.shape[0], 1)), surface))\n", + " #Z = np.dot(surface_prime, w) > 0\n", + " Z = clf.predict(surface)\n", + " Z = Z.reshape(xx.shape)\n", + " cmap_light = ListedColormap(['pink', 'yellow'])\n", + " ax.pcolormesh(xx, yy, Z, cmap = cmap_light, shading = 'auto')\n", + "\n", + " # Plot the output\n", + " scatter = ax.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Set1,\n", + " edgecolor='k', s=80)\n", + "\n", + " # Plot the legend, label your axis:\n", + " handles, labels = scatter.legend_elements()\n", + " legend = ax.legend(handles, class_names, loc = \"upper left\", title = \"Species\", fontsize='x-small')\n", + " ax.add_artist(legend)\n", + "\n", + " ax.set_xlabel(feature_names[0])\n", + " ax.set_ylabel(feature_names[1])\n", + "\n", + " ax.set_xlim(x_min, x_max)\n", + " ax.set_ylim(y_min, y_max)\n", + " \n", + "axs[0].set_title('Our perceptron')\n", + "axs[1].set_title('sklearn perceptron')\n", + "axs[2].set_title('SVM')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Linear separability\n", + "\n", + "Until now, we worked with _setosa_ and _versicolor_ iris flowers. These classes are linearly separable (we were able to fit a line between them in [Excercise 1](#Excercise-1). Now, let's work with _versicolor_ and _virginica_ iris instead." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The data looks like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Select the data for this example:\n", + "# select samples with class == 1 or == 2\n", + "select = np.any([y_full == 1, y_full==2], axis = 0)\n", + "\n", + "# construct the data to work with\n", + "X = X_area_full[select, :]\n", + "y = y_full[select]\n", + "y = np.where(y == 1, -1, 1) # change between classes: (1, 2) to classes: (-1, 1)\n", + "\n", + "# set some nice plotting limits\n", + "x_min, x_max = X[:, 0].min(), X[:, 0].max()\n", + "y_min, y_max = X[:, 1].min(), X[:, 1].max()\n", + "x_min = min(x_min, 0)\n", + "y_min = min(y_min, 0)\n", + "x_max += 1\n", + "y_max += 1\n", + "\n", + "fig, ax = plt.subplots(1, figsize=(8, 6))\n", + "\n", + "# plot our samples\n", + "scatter = ax.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Set1,\n", + " edgecolor='k', s=80)\n", + "handles, labels = scatter.legend_elements()\n", + "# add the legend\n", + "legend = ax.legend(handles, class_names[1:], loc = \"lower right\", title = \"Species\", fontsize='x-small')\n", + "ax.add_artist(legend)\n", + "# and label the axes\n", + "ax.set_xlabel(feature_names[0])\n", + "ax.set_ylabel(feature_names[1])\n", + "\n", + "ax.set_xlim(x_min, x_max)\n", + "ax.set_ylim(y_min, y_max)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's try and train our \"bare bones\" perceptron implementation on the data above instead, and plot the results. Experiment with replacing the classifier with a `sklearn` implementation of _perceptron_ or _SVM_.\n", + "\n", + "Notice that our implementation of the perceptron allows setting the random state (the seed which will be used to initialise the weights before training). If we change the random states (i.e. randomly re-initialise the weights and re-train the perceptron), does the _decision surface_ change?\n", + "\n", + "Did we succeed in classifying the training samples?" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Initialise the ML model\n", + "#clf = linear_model.Perceptron(eta0=0.01, max_iter=500)#, random_state = 50)\n", + "clf = Perceptron(eta=0.1, n_iter = 100)\n", + "# Fit models to the data\n", + "clf.fit(X, y)\n", + "# Predict the training samples again\n", + "y_prediction = clf.predict(X)\n", + "\n", + "# Plot different things:\n", + "fig, axs = plt.subplots(1, 2, figsize=(16, 6))\n", + "\n", + "clfs = [clf, clf]\n", + "outputs = [y_prediction, y]\n", + "\n", + "for y, clf, ax in zip(outputs, clfs, axs):\n", + " # Plot decision surface\n", + " xx, yy = np.meshgrid(np.linspace(x_min, x_max, 200), np.linspace(y_min, y_max, 200))\n", + " surface = np.c_[xx.ravel(), yy.ravel()]\n", + " Z = clf.predict(surface)\n", + " Z = Z.reshape(xx.shape)\n", + " cmap_light = ListedColormap(['pink', 'yellow'])\n", + " ax.pcolormesh(xx, yy, Z, cmap = cmap_light, shading = 'auto')\n", + "\n", + " # Plot the output\n", + " scatter = ax.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Set1,\n", + " edgecolor='k', s=80)\n", + "\n", + " # Plot the legend, label your axis:\n", + " handles, labels = scatter.legend_elements()\n", + " legend = ax.legend(handles, class_names[1:], loc = \"upper left\", title = \"Species\", fontsize='x-small')\n", + " ax.add_artist(legend)\n", + "\n", + " ax.set_xlabel(feature_names[0])\n", + " ax.set_ylabel(feature_names[1])\n", + "\n", + " ax.set_xlim(x_min, x_max)\n", + " ax.set_ylim(y_min, y_max)\n", + " \n", + "axs[0].set_title('Samples - prediction')\n", + "axs[1].set_title('Samples - truth')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise 3\n", + "\n", + "Observe how the number of samples/errors per \"epoch\" changes when training a perceptron for _versicolor_ and _virginica_ iris classification. As mentioned in the example above, these two classes are not **linearly separable**.\n", + "\n", + "Train our own perceptron model with $\\eta=0.1$ for 10 iterations (you can use the training samples `X` and `y` initialised above), and plot the number of wrongly classified samples in each iteration (corresponding to the number of updates). Also try the following:\n", + "- Change the value of $\\eta$.\n", + "- Increase the number of iterations to 100.\n", + "\n", + "Does the number of errors converge? Decrease?" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Initialise model:\n", + "iter = 100\n", + "model = Perceptron(eta = 0.01, n_iter = iter)\n", + "# Fit the model to the data:\n", + "model.fit(X, y)\n", + "# Plot the error vs the number of iterations:\n", + "#print(model.errors_)\n", + "plt.plot(range(1, iter+1), model.errors_, marker = 'X')\n", + "# Label the axis\n", + "plt.xlabel('Iteration')\n", + "plt.ylabel('Number of updates')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Example - XOR function\n", + "\n", + "We don't need to look to complex real data to find linearly inseparable classes. One of the basic logical functions, _XOR_, is also not linearly separable.\n", + "\n", + "Let's apply our `Perceptron` implementation to it. We will plot the number of updates done in every iteration, and the decision surface resulting from 10 iterations.\n", + "\n", + "Can you observe the similarity in the \"number of updates\" plot between the XOR example and the example with _versicolor_ and _virginica_ iris?" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "X_xor = np.array([[0, 0],\n", + " [0, 1],\n", + " [1, 0],\n", + " [1, 1]])\n", + "y_xor = np.array([0, 1, 1, 0])\n", + "\n", + "X, y = X_xor, y_xor\n", + "y = np.where(y == 0, -1, 1) # change between classes: (0, 1) to classes: (-1, 1)\n", + "x_min = y_min = -0.5\n", + "x_max = y_max = 1.5\n", + "\n", + "clf = Perceptron(eta=0.01, n_iter=100)\n", + "# Fit the data\n", + "clf.fit(X, y)\n", + "\n", + "y_predicted = clf.predict(X)\n", + "\n", + "fig, axs = plt.subplots(1, 2, figsize=(16, 6))\n", + "\n", + "# Plot the errors\n", + "axs[0].plot(range(1, len(clf.errors_) + 1), clf.errors_, marker='o')\n", + "\n", + "# Label the axis\n", + "axs[0].set_xlabel('Epochs')\n", + "axs[0].set_ylabel('Number of updates')\n", + "axs[0].set_title('Training')\n", + "\n", + "\n", + "# Plot the decision surface\n", + "xx, yy = np.meshgrid(np.linspace(x_min, x_max, 200), np.linspace(y_min, y_max, 200))\n", + "surface = np.c_[xx.ravel(), yy.ravel()]\n", + "Z = clf.predict(surface)\n", + "Z = Z.reshape(xx.shape)\n", + "cmap_light = ListedColormap(['pink', 'yellow'])\n", + "axs[1].pcolormesh(xx, yy, Z, cmap = cmap_light, shading = 'auto')\n", + "\n", + "# Plot the XOR function (training data)\n", + "scatter = axs[1].scatter(X[:, 0], X[:, 1], c=y_predicted, cmap=plt.cm.Set1,\n", + " edgecolor='k', s=80)\n", + "axs[1].set_xlim(x_min, x_max)\n", + "axs[1].set_ylim(y_min, y_max)\n", + "axs[1].set_xlabel('x')\n", + "axs[1].set_ylabel('y')\n", + "axs[1].set_title('Perceptron - XOR')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Kernel trick**. One way to get around this limitation is using a similar approach as with the _SVM_. The XOR function is not _linearly separable_ in 2D space. However, if we artificially increase the dimensionality of our samples, we may end up in a hyperspace in which the samples _are_ linearly separable.\n", + "\n", + "If our samples are\n", + "\\begin{equation*}\n", + " \\mathbf{x} =\n", + " \\begin{bmatrix}\n", + " x_0 \\\\\n", + " x_1 \\\\\n", + " x_2\n", + " \\end{bmatrix}.\n", + "\\end{equation*}\n", + "\n", + "\n", + "We can represent our samples as:\n", + "\\begin{equation*}\n", + " \\mathbf{x}_{\\texttt{ext}} =\n", + " \\begin{bmatrix}\n", + " x_0 \\\\\n", + " x_1 \\\\\n", + " x_2 \\\\\n", + " x_0^2 \\\\\n", + " x_1^2 \\\\\n", + " x_2^2 \\\\\n", + " x_0 x_1 \\\\\n", + " x_1 x_2 \\\\\n", + " x_0 x_2\n", + " \\end{bmatrix}.\n", + "\\end{equation*}\n", + "\n", + "Then, we can solve the _XOR_ problem with our perceptron in 5D (using the extended data) instead of in 2D." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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pkuPYL7/8YiSZO++8s0h1F5T8/Lf8kp9ZSfEHHngg13M3btyYa6+3jRs3Gh8fH9O8eXNz5syZbMcOHDhgatSoYSpVqmT+/PNPm+PctGmTkWR69epl8zm5Kc77Zkd9ThhTuORnfnJ73v7880/j4+NjateuneNLip9//tm4ubmZa6+91qSkpBT5PWCr4k5+Tpo0yUgy8+bNy3Hs/vvvN5Js6oFZlN9ZT0/PXP9GTUtLMy4uLoX6Pb0SMQEbgDLD29vbpnIpKSm67bbbrIsXVapUSc8884zWrVunqKgoJSQkKDMzU+bSF0SaM2eO9dzCzGmUn6wFhZzBEW3Hx8dbt318fGw6x8/Pz+52AQC2Wb9+vSSpW7duOY51795dkhQREVFgPfv379eJEyfUrl07+fr6Zjvm5eWlLl266Pjx4zpw4IAk6fz580pPT1eNGjVy/bypVauWJGndunWSLn0mzZs3TyEhIbr33nt1+vRpa9mnn35a27Zt07PPPqs2bdrYcNXFY/369fL19VW7du1yHOvRo4ck257Lwr4mp06dkvTPc3a5rH2bNm2yLig4evRoSdKnn36ao3xCQoIWLFigxo0bq23btgXGWhw2b96slJQUde3aNcd7o2rVqmrWrJm2bNli03x/hX1NbHku165da903efJktW/fXjNmzNAPP/xg3b9ixQq99957uvHGG/XEE09Y98fGxqp79+6KjY3V+vXrdf3112drw8fHR8uWLVP//v119913a/Xq1QVeo3Tp98Pd3T3HApUlLb/3bqtWrRQSEmLT70Bx69y5s+rVq6cvv/wy1/fR7NmzJUmjRo2y7tuxY4d69eqlZs2aad26dTkWTa1Tp442bNigkJAQde3aVUePHrUplqyFVp392uXHUZ8Tl1u+fLmmTZumN998U2vXri303MS5PW8NGjTQG2+8ob/++ksPPfSQdX9cXJyGDBkid3d3zZ8/Xx4eHkV6D1xJ8ntNHPV5k9fv7NVXX63jx4/rjz/+yLb/+++/V2Zmpm688UZbLuGKRfITQLnz7bff6tChQ5Kkq666Sr///rv+85//qHPnzqpWrZp8fHyy3ZTHxcU5K9Qr1uWJzMTERJvOSUhIKK5wAAD/krXoS7169XIcCwgIUOXKlW1arCe/ei7fn1UuKChIrq6uOnLkiIwxOcpnff7u27fPui8sLEyffPKJzpw5oxEjRki6lIh6/fXX1aZNGz3zzDMFxllcEhISdPLkSdWqVUuurq45jv/7+vNT2NekYsWKkv55zi6XtS89PV1//fWXJKl9+/Zq3LixvvrqKyUlJWUr/8033yguLk4jR44sMM7iYst7KSMjw3o9eSnKa2LLc3n5e9LV1VWff/65AgICNGzYMEVHR+v06dO69957FRQUpHnz5mW7VwwICNC2bdt06tQpXXPNNbnG7eHhoW+//VYpKSm5JiRys2jRIsXGxtpcvrjk99pZLBbVrVtXJ06csPmesLhYLBaNGjVKFy9e1OLFi7Mdi4+P18KFC9W0aVO1bt3aur9p06a6cOGCNm3apAoVKuRab7Vq1bRnzx4dPXpU1atXtymWrC8hnP3a5cdRnxOXe/jhhzV58mQ9/vjjuummm9S4cWNt27bN5vPzet7Gjh2rfv366bPPPtPXX38tSXrooYd08OBBvf7662rUqJGkor0HriT79++Xn5+fqlSpkuOYoz5v8vqdnTFjhry9vdW2bVsNHTpUkyZN0sCBA3Xbbbepf//+evHFF4t6WVcEkp8Ayp3Lv8EfN26cqlatmm/5I0eOFHdIpU7WHxHSpRV9bWFrOQCA/WJiYiRJgYGBuR4PDAy0lrG3nsvL+fj4qGPHjjp9+rQ++OCDbGUXL16s3377TZJ08eLFbMcGDBigUaNG6fvvv9eUKVM0dOhQ+fn56Ysvvsg1wVVSCnv99tZ1eT3dunWTq6urZs+eraioKOv+9PR0TZ061frz5c/l6NGjFRMTo2+//TZb3bNnz5aHh4fuueeeAuMsLo56LotST1aPqRdeeCFbT7RDhw5ZR/j8+z1Zs2ZNvf/++zp+/LhGjRqlESNG6MyZM5o5c6aqVauWb4yOEBUVpUceeUTe3t76z3/+U+zt5ceRvwfFbfjw4XJ3d8/RA/rrr79WfHx8iXwB8P333+vjjz9Wo0aNnPqFQ0Ec9TkhSZ06ddK3336rqKgoJSUlac+ePXrsscd08OBBdevWTSdPniywjoKet1mzZqlq1aq677779Prrr+uzzz5Tr1699MADD2QrdyW8B4oqJiamxD5v/l1X586d9eOPP6py5cr6/PPP9corr2jRokWqXbu2hg8froCAgEJdy5WG5CeAcufEiRPW7aZNmxZY/scffyzOcEql5s2bW7d/+eUXm87ZsmVLMUUDALiSvPnmm/Lz89NDDz2k7t27a8KECRowYIAGDhxo7RXn4pLzz5C33npL9evX19SpU3X8+HG9++67ql27ts3tTpkyRRaLJdujS5cukqQRI0bkODZlyhSHXG9xqVWrliZPnqzo6Ghdc801GjlypB577DFdd911Wrt2rbUH2uXP5dChQ+Xl5WUd2ilJe/fu1YYNG9S3b98cQ3rzUrNmzRzP17x58yQpx36LxaLDhw877sKLwWOPPabGjRvr66+/VosWLfT444/r3nvvVfPmzVWjRg1Jub8nhwwZorvuukvfffedvv/+ew0fPly33357scd7/vx59ezZU2fOnNEnn3yiBg0a2Hxubq9PRESEjhw5kuuxsiY0NFT9+/fX2rVrs/X0nT17tjw9PYv9C4DIyEgNHjxYFSpU0DfffCNPT0+bzlu/fn2er09WT+PLH507dy6wzosXL2rKlCk5HsVhxIgRuvXWW1WtWjV5eXmpYcOGeuONNzRx4kSdP39e77zzTr7n2/K8VaxYUXPnzlVMTIyefPJJhYaG5jrNh6PeA8XxmfLbb7/leD3mzp1rUzzFbcWKFerUqZPat2+vvXv3KjExUb/99ptq166t/v376/3333d2iHa5ciegAIBicvnNbUHDc3799VdFRkYWd0ilzuU3XFu2bNGBAwdUt27dPMsfPXpUP/30UwlEBgCQCu4hkl/vksLWc3k5SWrWrJkiIyP1/PPPa926dVq3bp3q1q2rjz/+WBcvXrT+0fpvvr6+uvnmm7Vv3z6FhIRo8ODBBcZ3udySAYcPH9a8efPUr1+/bF/c5VX+34py/fbU9e96XnjhBdWvX1/vvvuuvvrqK7m5ual9+/b66quvdMcdd0hStucyODhYt912m7788ksdPHhQderUsSYHCtPbady4cTl6Qi5ZskS///67nn/++Rzl8xoufDlHPZdFqcff318bN27UCy+8oO+++07vvfeeQkNDdd9996l3797q2LFjru9JSerfv7++/PJLSdKDDz6Yb2yOcP78ed10003atWuXPvnkEw0ZMqRQ5+f2+sydO1cXL17UuHHjihSTrc/5ldIzbMyYMfrmm280Z84cvfDCC9qzZ482b96swYMHKzg4uNja3bp1q7XH9urVq3X11VfbfG7NmjVzfe2mTp2qZs2aqX///jnKF+TixYvZeolnyUrSOepzIj8jR47Uyy+/rI0bN+ZZpjDPW9u2bRUaGqrTp09r4MCBef7eOuI9UByfKb/99luO16RTp04aPny4pPx72zr680b653f2/PnzGjJkiBo0aKC5c+daE+/NmjXTt99+q3r16mny5MkaNWqUzQn9K46TF1wCgDwVdrX33FYlz03WKp2SzPDhw/Msl5CQYK677rpsq73ntdpiYVd7t3XFzeJY7X3dunUFtmvL89qqVStrmV69euW6qm+WW2+91abnEQDgGFkrxn711Vc5jsXExBhJpm3btgXWk7Vi7C233JLr8az/3/ft22dTXFkrAi9dujTHsbVr1xoXFxcTEhJiJJmHH37Ypjrz44iVeatWrWp8fX1Nenp6jmPvvPOOkWRmzpxZYD2Oek2MMSY5Odl4enqakJCQHMciIiKMJPP000+btLQ0U6VKFVO9enWTkZFhU915sXe196yVpceOHZvr8euuu864uLiYpKSkAuty1GtijDFz5swxkszjjz+e49jp06dNaGioqVChgnF1dTUtWrQwqampNtVbFOfOnTPNmjUzFovFfPjhhw6r197V3j/++GMjyUybNi3HsczMTBMSEmLCwsKKVLccuNr75THVqVPH+r4fP368kWT+97//FSlGW0RGRpoKFSqYgIAAs2XLFofVW5z3zY78PykvWfW0aNEi1+OFfd6GDx9uJJmQkBDj5uZmfvnll1zLFdd7oLhXe2/Tpo2RZE6ePJnj2NKlS40kM3ny5ALrKezvbFbduf0/aMw/n/V79uwpxNVcWRj2DqDc6dOnj3V73rx5mjFjRo6VCA8cOKBu3bpp27ZtOVa3xSUvvfSSdXvFihUaNmyYYmNjs5WJj4/XqFGjtHjx4tL7LSEAlEKdOnWSpFxXlV65cmW2MvmpV6+ewsLCtHHjxhwL1yUnJ2v9+vUKCwvLt/d/lri4OC1btsy6avLlLly4YJ3nc8uWLerUqZPee+89a6zO1KlTJyUkJOTac+n777+3lrGlHsn+10S6tBBOSkpKrr1jO3bsqIYNG2ru3LlaunSpTp06pREjRuQ6rLsk3XDDDfLw8ND//ve/HIthnTx5Ur///ruuv/56eXl5FViXo14TSZo/f74kWXvSXu7ee+/VmTNn9Omnn2ry5Mn69ddf9eyzz9pUb2Fl9fj8/fff9e677+q+++4rlnaKIr/3bmRkpM6fP2/z810Ssha9OXr0qP773//q888/V61atXTTTTcVS3tbt25V165dlZGRoVWrVqlVq1bF0o6jOfL/pLxkTXuVW0/Vwj5vixYt0ty5c9W3b19FRETIzc1NQ4YMUXx8fI6yJf0ecJT8XhNHfd7k9jubkpIiSTp37lyu9Z09e1aSSvffc87OvgJAXoqr56cxxnTs2DFbT8RatWqZ2267zdx7772mQ4cOxtXV1UgyV111lXn11Vfp+ZmHRx55JNvz6O/vb/r27WtGjx5t+vXrZwICAowkExwcbF544QVruREjRhR88QCAIktLSzO1a9c2np6eZvv27db9cXFx5uqrrzZubm45emseOXLE7NmzxyQkJGTb/9xzzxlJZsKECbnuf+6557LtT0xMNGlpadn2JScnm4EDBxpJ5u23384Rb9axzz//3BpLhQoVTOXKlc2ZM2cKff1ZHNFLZ+3atUaS6dChg0lJSbHuX7NmjbFYLKZjx47Zyqemppo9e/aYAwcOZNtflNckJiYmRzy7d+82lStXNgEBAebo0aO5xjxjxgwjyYSFhRkXFxdz+PDhwl52Dvb2/DTGmKFDhxpJ5oMPPsi17k8//TTb/rNnz5o9e/bk6OVX2NfEmNyfyzfeeMNIMgMGDMhx7P333zeSzMiRI40xl16/1q1bGxcXF7N+/XrbL9oG58+fN82bN8/z98Ne9vb8NOafe+cVK1ZY96Wmppqbb77ZSDJr167NVv7EiRNmz5495uLFi/nWq2Lo+WmMMadOnTLu7u4mLCzMSDIvvPCCzW0UxtatW02FChWMn5+f2bhxo8Prz+/vD3s56nPi/Pnzuf4fc/z4cdO4cWMjyXzzzTfZjhX2eYuKijJBQUGmSpUq1s+ErF7e9957b67nFMd7oLh7fu7du9e4ubmZ+vXrZ/vd2blzp/Hx8TF16tTJ8fm6Z8+eXHtkFuZ39ujRo8bV1dX4+PiYnTt3Zqtn1apVxmKx2P1/iLOR/ARwxSrO5OepU6dyDGn/96Nx48Zm165d1uFQJD9zyszMNI899pixWCx5Po9hYWFm8+bNZubMmdZ9jz76aIExACifTp8+bZYtW2aeffZZ0717d+sQaEf/AXj5/3MFPWz5f/Ps2bPm2WefNU2bNjX+/v7G39/fNG3a1Dz77LPm3LlzDou7MNauXWvc3d2Nn5+fGT16tBk/frypVauWkWRefPHFHOWzPiv+fb3x8fHWxEzXrl3NU089ZXr06GEkmebNm5v4+Phs5X/66SdTqVIlc9ddd5mJEyea+++/31SvXt1IMqNHj84xTUrW5+wdd9yRbf9XX31lJJk+ffoU+Tlw1B+qo0aNst4bPPnkk2bo0KHG09PTBAYGml27dmUrm/V5n9tnfWFfk1GjRpnrrrvOjB071kyaNMncfvvtxtPT03h7e5tVq1blGe/Zs2eNp6enkWS6detm17VncUTy88SJEyY8PNxYLBZz6623mqeeesq0a9fOSJemVvj30Pys39Pc7kUK85oYY4yfn5/p06ePefzxx8348eNNixYtjCTTsmVLEx0dna3s7t27jbe3t6lbt66Ji4uz7t+/f7/x9fU14eHh5sKFC3Y9F5fL+t1r2LChef7553N92Fu/vYmLXbt2mcDAQOPh4WHuvvtuM2HCBHPNNdcYSWbUqFE5yme9X/79u3f27FkzbNgw60OSqVChQrZ9/zZ+/HjrscqVK1v/v8jal9dQ3Kyhui4uLiYqKsqu68/N+fPnTVBQkJFkunfvnuvrZu//PcWZ/DTGMZ8T27dvN66urqZDhw5m5MiRZuLEieaOO+4wfn5+Rro0zdjl/+8X9nnLzMw0N954o5Fkvv/++2z7u3fvbiSZb7/9Ntfrc/R7oLiTn8YY8+KLLxpJpnr16ubxxx83Y8eONQEBAcbd3T3HlwzGGOu9yr8V9nd24sSJRpLx8vIyd955p5kwYYLp37+/cXFxMa6urmbJkiXFcr0lheQngCtWcSY/jbnUC+W9994z7du3NxUqVDAeHh6mWrVq5qabbjIzZ860fqNJ8vP5Asv//PPPZvjw4aZmzZrWOchatmxppk2bZv2j/5VXXinyawWg/MgvCXmlJj9//vlnU6VKlTzPr1q1ap7zkhW3X375xXTv3t0EBgYab29v07JlS/PFF1/kWjav5Kcxxly8eNE89thjJjw83Li7u5vw8HDz2GOP5dqr68iRI2bgwIEmPDzceHh4mAoVKpgbb7zRLFq0KEfZgwcPGn9//zyTSXfffbeRcvYUtJWj/lDNyMgw77zzjrn66qutn3O333672bt3b46y+SU/jSnca7Jw4ULTvn17ExwcbH3e77333hy9SnMzYMAAI8l8/fXXhbrWvDgi+WnMpQTovffeaypXrmw8PDxM3bp1zdSpU01ycnKOsvklPwvzmhhjzH333WcaNGhgfHx8jK+vr7n22mvNa6+9lqPdlJQUc+211xo3Nzfz888/56hn1qxZRpIZPHhw0Z6AXFx+L5fXwx6OSH4ac6lX2u23325CQkKMp6enufrqq80777yT63yyeSU/L78ftvVaC3p+8vo/+v/+7/+MJNOjRw+7rz03tlxLXn8X2Kq4k5/G2P85cfr0aTN27FjTvHlzExwcbNzc3ExwcLC56aabcp1PtLDPW9YovNzmgT558qSpVKmSCQ4ONseOHctx3NHvgZJIfhpjzBdffGFatmxpvL29TWBgoOnevXuec6Lm939EYX5njTFm/vz5plOnTiYgIMC4urqa0NBQ079/f7Np0yaHXZuzWIz514QrAAAUgyFDhlhXS12wYEGhV/EFUD5krTAqSdWrV1fDhg2tc1YNGzZMc+fOdUg7U6ZMsa64umPHjnzL1qpVK8/5n6OiotSiRQudPXtWbm5uevzxx9W7d29J0vLly/XGG28oPT1doaGh+vXXX1WtWjWHxA/kxxij+vXr68KFCzpx4oQ8PDycHRJQ4v7zn//oueee07fffqtbb73V2eHACXgPIIubswMAAJR98fHxWrFihfXn0jIRPICS99xzz6lVq1Zq1aqVKleurMOHD6tWrVrF2maTJk2KfO7TTz9tXQjgyy+/1MCBA63HOnTooBYtWmjw4ME6c+aMnnnmGYclb4H8LF26VAcOHNCTTz5J4hPlUmJioj766COFhYWpb9++zg4HTsB7AJcj+QkAKHaTJ09WTEyMJOn6669X7dq1nRwRgCtVVm/M0uDUqVPWlaJvueWWbInPLIMGDdKnn36qVatW6fPPP9crr7yiKlWqlHSoKCc+/PBDHT16VJ988on8/f312GOPOTskoERt2LBBERER+v7773XixAm99957cnMj7VGe8B5AblycHQAAoPR677339J///EfHjh3L9fiZM2c0ZswYvfvuu9Z9EydOLKnwAKBYLV26VJmZmZKkESNG5Flu+PDhkqTMzEwtXbq0JEJDOTV9+nS9/vrrCgsL0+LFi1W1alVnhwSUqDVr1uiZZ57R/v379dRTT+n+++93dkgoYbwHkBvS3wCAIjt37pymTp2q559/Xo0bN9bVV1+toKAgJScn68CBA4qMjFRqaqq1/LBhwzRgwAAnRgwAjrNhwwbrdqdOnfIsd/mxjRs3asyYMcUaF8qvw4cPOzsEwKmmTJmiKVOmODsMOBHvAeSG5CcAwG7GGO3atUu7du3K9bibm5seffRRvfrqqyUcGQAUrFu3bvrtt9908eJFVahQQY0bN1b37t01duxYBQUF5Xne7t27JUmBgYH5DmWvWrWqAgICFBsbqz179jg8fgAAAOSN5CcAoMiefPJJNW7cWGvWrNEff/yhM2fO6Ny5c0pOTlZwcLBq166tzp07695771XdunWdHS4A5Op///ufdfvs2bOKiIhQRESEpk+frrlz56pfv365npc15YctK7iHh4dr165dioqKckzQAAAAsAnJzzxkZmbqxIkT8vf3l8VicXY4AHDF6t69u7p3715gudjY2BKIBsDljDGKi4tTWFiYXFyY6v3fmjZtqv79+6t169YKCwtTWlqa9u7dq/nz52v16tW6ePGibrvtNi1btkw9evTIcX5cXJwkyc/Pr8C2fH19JUnx8fF5lklJSVFKSor158zMTEVHRyskJIT7UQAAUCpdCfejJD/zcOLECYWHhzs7DAAAALtFRUXZ1DuxPBk3blyuc4Jdf/31Gjp0qD7++GPdd999ysjI0KhRo3Tw4EF5eXllK5ucnCxJ8vDwKLA9T09PSVJSUlKeZaZNm1aqVrsHAACwlTPvR0l+5sHf31/SpRcnICDAydEAAAAUXmxsrMLDw633NfhHhQoV8j0+duxYRUZGavbs2Tpx4oS+/fZbDRkyJFsZLy8vJSYmZlvYLS9ZPTq9vb3zLDNp0iQ9/vjj1p9jYmJUvXp1RUVJ3I4CKJQN65wdAQBIkmITExQ+sLdT70dJfuYha2hRQEAAyU8AAFCqMWS6aMaOHavZs2dLkiIiInIkP/39/ZWYmJjvUPYsCQkJkvIfIu/p6WntIXq5gACSnwAKybfg6TgAoCQ5836UyZ8AAACAXDRu3Ni6ffz48RzHs4ZuZS18lJ+shY6YVgkAAKBkkfwEAAAAclFQD4Ws5GhMTIxOnTqVZ7mTJ09aF31r1KiR4wIEAABAgUh+AgAAALnYvXu3dTssLCzH8fbt21u3IyIi8qzn8mPt2rVzUHQAAACwBclPAAAAIBcff/yxdbtTp045jvft21cuLpdup+fMmZNnPXPnzpUkubi4qG/fvo4NEgAAAPki+QkAAIAyZe7cubJYLLJYLJoyZUqO4zt27NCBAwfyrWPmzJmaNWuWJKlKlSoaMGBAjjJVqlSxLoK0atUqLVq0KEeZb775RqtWrZIk3XPPPapSpUphLwcAAAB2YLV3AAAAXDE2bNiQLTF57tw56/aBAwesvSizDB8+vNBt/Prrrxo1apS6dOmiHj16qGnTpgoJCVF6err+/PNPzZ8/X6tXr5Ykubq6aubMmfL19c21rpdeekkrV67U2bNndeedd2rr1q3q3bu3JGn58uWaMWOGJKlSpUp68cUXCx0rANgsItLZEQDAFYnkJwAAAK4Ys2bN0rx583I9tnHjRm3cuDHbvqIkPyUpIyNDa9as0Zo1a/IsExISotmzZ6tPnz55lgkPD9eyZcvUv39/nTp1StOnT9f06dOzlalSpYqWLFliXR0eAAAAJYfkJwA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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Extend the training data\n", + "X_extended = np.array([[x[0], x[1], x[0]*x[0], x[1]*x[1], x[0]*x[1]] for x in X])\n", + "\n", + "clf = Perceptron(eta=0.1, n_iter=20)\n", + "# Fit the data -- with extended samples\n", + "clf.fit(X_extended, y)\n", + "\n", + "fig, axs = plt.subplots(1, 2, figsize=(16, 6))\n", + "\n", + "# Plot the errors\n", + "axs[0].plot(range(1, len(clf.errors_) + 1), clf.errors_, marker='o')\n", + "\n", + "# Label the axis\n", + "axs[0].set_xlabel('Epochs')\n", + "axs[0].set_ylabel('Number of updates')\n", + "axs[0].set_title('Training')\n", + "\n", + "\n", + "# Plot the decision surface\n", + "\n", + "xx, yy = np.meshgrid(np.linspace(x_min, x_max, 200), np.linspace(y_min, y_max, 200))\n", + "# Careful, now we need to extend all the samples in the \"surface\"\n", + "# used for plotting the decision function and surface\n", + "#surface = np.c_[xx.ravel(), yy.ravel()]\n", + "surface = []\n", + "for x_, y_ in zip(xx.ravel(), yy.ravel()):\n", + " surface.append([x_, y_, x_*x_, y_*y_, x_*y_])\n", + "surface = np.array(surface)\n", + "Z = clf.predict(surface)\n", + "Z = Z.reshape(xx.shape)\n", + "cmap_light = ListedColormap(['pink', 'yellow'])\n", + "axs[1].pcolormesh(xx, yy, Z, cmap = cmap_light, shading = 'auto')\n", + "\n", + "# Plot the output of linear classifier for training points\n", + "scatter = axs[1].scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Set1,\n", + " edgecolor='k', s=80)\n", + "axs[1].set_xlim(x_min, x_max)\n", + "axs[1].set_ylim(y_min, y_max)\n", + "axs[1].set_xlabel('x')\n", + "axs[1].set_ylabel('y')\n", + "axs[1].set_title('{:.2f}x + {:.2f}y + {:.2f}x^2 + {:.2f}y^2 + {:.2f}xy + {:.2f}'.\n", + " format(clf.w_[1], clf.w_[2], clf.w_[3], clf.w_[4], clf.w_[5], clf.w_[0]), fontsize = \"x-small\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise 4\n", + "\n", + "**Multi-layer perceptron.** This is just a gentle introduction to the next workshop. Instead of forcing our samples in a higher-dimension hyperplane (and hoping they are linearly separable there), we can _chain_ single-layer perceptrons in a _network_: **artificial neural networks (ANN)**.\n", + "\n", + "ANN can predict non-linear functions directly. But, stochastic gradient descent and other _weight optimisation approaches_ require differentiability... Which leads to a choice of _activation functions_.\n", + "\n", + "For now, let's just make the appropriate imports and initialise a `sklearn` multi-layer perceptron classifier `clf`, with 2 hidden layers with 4 neurons each, and observe the output:\n", + "- `sklearn` implementation of multi-layer perceptron is located at [`sklearn.neural_network.MLPClassifier`](https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html)\n", + "- vary the learning rate $\\eta \\in \\{0.1, 0.01, 0.001\\}$ (argument `learning_rate_init`)\n", + "- hidden layer sizes are set with the parameter `hidden_layer_sizes`\n", + "- try different activation fuctions (start with `'tanh'`; lecture:`'logistic'` is the sigmoid, `'relu'` is the ReLU discussed in the lecture)\n", + "- use stochastic gradient descent (`solver` parameter)\n", + "- start with the maximum number of iterations `10000`, and then increase as needed\n", + "- parameter `tol` has to do with the tolerance: if the improvements are smaller than this number, the learning will stop -- try $\\mathtt{tol} \\in \\{1e^{-4}, 1e^{-6}, 1e^{-8}, 1e^{-10}\\}$\n", + "- parameter `alpha` has to do with regularisation, out of scope of today's workshop. use `0`\n", + "- momentum is also out of scope today, use `0`\n", + "\n", + "_(Note: sklearn only has the support for very rudimentary neural networks; not fit beyond demonstration purposes. If you want to run your ANN models on your GPU or try out deep learning for images, you can look into frameworks such as `pytorch` or `TensorFlow` (and others)...)_" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/pbosilj/Programming/env2324/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "###########################\n", + "# make appropriate imports:\n", + "###########################\n", + "from sklearn.neural_network import MLPClassifier\n", + "\n", + "###########################\n", + "# initialise the classifier:\n", + "###########################\n", + "clf = MLPClassifier(learning_rate_init = 0.1, hidden_layer_sizes = (4, 4), activation = 'relu',\n", + " solver = 'sgd', max_iter = 1000, tol = 1e-8, alpha =0, momentum = 0)\n", + " \n", + "\n", + "###########################\n", + "# below code fits the model\n", + "# to the data, and plots\n", + "# the results nothing needs\n", + "# to be changed\n", + "###########################\n", + "clf = clf.fit(X, y)\n", + "\n", + "fig, axs = plt.subplots(1, 2, figsize=(16, 6))\n", + "\n", + "# Plot the decision surface\n", + "xx, yy = np.meshgrid(np.linspace(x_min, x_max, 200), np.linspace(y_min, y_max, 200))\n", + "surface = np.c_[xx.ravel(), yy.ravel()]\n", + "Z = clf.predict(surface)\n", + "Z = Z.reshape(xx.shape)\n", + "cmap_light = ListedColormap(['pink', 'yellow'])\n", + "axs[0].pcolormesh(xx, yy, Z, cmap = cmap_light, shading = 'auto')\n", + "\n", + "# Plot the ground truth\n", + "scatter = axs[0].scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Set1,\n", + " edgecolor='k', s=80)\n", + "axs[0].set_xlim(x_min, x_max)\n", + "axs[0].set_ylim(y_min, y_max)\n", + "axs[0].set_xlabel('x')\n", + "axs[0].set_ylabel('y')\n", + "axs[0].set_title('Multi Layer Perceptron - XOR')\n", + "\n", + "axs[1].plot(clf.loss_curve_, '-')\n", + "axs[1].set_xlabel(\"number of iterations\")\n", + "axs[1].set_ylabel(\"loss\")\n", + "axs[1].set_title(\"MLP XOR - Training loss\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### References\n", + "\n", + "[1] F. Rosenblatt. The perceptron, a perceiving and recognizing automaton Project Para. Cornell Aeronautical Laboratory, 1957.\n", + "\n", + "[2] B. Widrow et al. Adaptive ”Adaline” neuron using chemical ”memistors”. Number Technical Report 1553-2. Stanford Electron. Labs., Stanford, CA, October 1960." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/CMP3751 Machine Learning/workshops/Week 16/Week16_workshop.ipynb b/CMP3751 Machine Learning/workshops/Week 16/Week16_workshop.ipynb new file mode 100644 index 0000000..b4f01b0 --- /dev/null +++ b/CMP3751 Machine Learning/workshops/Week 16/Week16_workshop.ipynb @@ -0,0 +1,137 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Week 16 - Learning from a dataset\n", + "\n", + "As the final workshop in this module, this week's workshop will be a free-form exercise.\n", + "\n", + "The goal of Machine Learning is to construct predictive models from data (= samples represented by their features), in a way that:\n", + "- groups the data by discovering the structure of the data (**clustering**)\n", + "- we are able to predict an unknown feature for a new, unseen sample (**supervised learning**)\n", + " - if this feature is a real-valued, numerical feature, we talk about **regression**\n", + " - if this feature is a categorical feature, describing some _type_ of our sample, we talk about **classification**\n", + "- if the model is able to predict actions of an agent in a dynamic environment, we talk about **reinforcement learning**." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The type of ML approaches mostly covered in this module all fall under the **supervised learning** paradigm. Supervised learning approaches to ML are **data driven**, in the sense that the predictive models are constructed by learning from (many) examples.\n", + "\n", + "Your task for today's workshop will therefore be to pick and download a supervised learning (classification or regression) dataset from [a dataset repository](https://archive.ics.uci.edu/), find the best performing ML approach for the dataset, and train a predictive model. The suggested workflow consists of three parts:\n", + "- [Chosing and cleaning a dataset](#Chosing-and-cleaning-a-dataset)\n", + "- [Chosing the model and the parameters](#Chosing-the-model-and-the-parameters)\n", + "- [Evaluating the model](#Evaluating-the-model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Chosing and cleaning a dataset\n", + "\n", + "Take care that you may need to \"clean\" the dataset of your choice: check for missing values, make sure it has been loaded in the correct format (i.e. some datasets use different delimiters, some have header lines and some do not, can rely on different file types...).\n", + "\n", + "You will also need to **handle the different types of data** if they are present in the dataset. As most algorithms implemented in `sklearn` are implemented for _numerical_ data, you should represent all of your features as numerical:\n", + "- **real-valued** numerical values can be left as-is\n", + "- **categorical** values should be one-hot encoded. This will transform a single feature column in a dataset with $n$ categorical values (e.g. blood type can be A, B, AB or O (n=4)) into $n$ indicator features (one column indicating if the blood type of the patient is A; one column for B, one column for AB and one for O. For this, you can check out [`pandas.get_dummies()`](https://pandas.pydata.org/docs/reference/api/pandas.get_dummies.html) or [`sklearn.preprocessing.OneHotEncoder()`](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html)\n", + "- **ordinal** values fall between the above two categories. They might sometimes be expressed as numbers (user rating 1-5) or as categories ('low' 'mid' 'high'); however they _can be ordered and compared_. These should be left as-is if they are already represented as numbers, or encoded as numbers ('low' -> 0, 'mid' -> 1, 'high' -> 2) if expressed as categories.\n", + "\n", + "You should also check the **scale** of all the features. Some ML approaches benefit from normalised data, while some do not care about the scale. Check the scale of all your features (the difference between the lowest and highest value for each feature). Especially if the scales are vastly different, consider:\n", + "- **normalising** the data (i.e. forcing the feature into a 0-1 range)\n", + "- **zero-centering** the data (i.e. after normalisation, forcing the data to have a median at zero)\n", + "- for these purposes you can check out [`sklearn.preprocessing.StandardScaler()`](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html)\n", + "\n", + "Here is a list of some datasets I have used this year, but feel free to pick a different one from the repository (or anywhere else):\n", + "- presence of [amphibian species](https://archive.ics.uci.edu/ml/datasets/Amphibians) along planned roadways (multi class classification)\n", + "- quality of [Portugeese wines](https://archive.ics.uci.edu/ml/datasets/Wine+Quality) (classification)\n", + "- presence of [forest fires in Algeria](https://archive.ics.uci.edu/ml/datasets/Algerian+Forest+Fires+Dataset++) (classification)\n", + "- distinguishing between varieties of [dried beans](https://archive.ics.uci.edu/ml/datasets/dry+bean+dataset) (classification)\n", + "- [heart desease](https://archive.ics.uci.edu/ml/datasets/Statlog+%28Heart%29) dataset (classification)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Chosing the model and the parameters\n", + "\n", + "Compare the performance of different ML models on your dataset (e.g. for classification, you could compare [`sklearn.neigbors.KNeigborsClassifier()`](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html), [`sklearn.ensemble.RandomForestClassifier()`](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html), [`sklearn.svm.SVC()`](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html) (the Support Vector Machine classifier), [`sklearn.neural_network.MLPClassifier()`](https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html)).\n", + "\n", + "Be careful how you perform the comparison. When **comparing performance**, be careful to use a _separate set_ for training (the training set), and a separate set for testing - your performance evaluation should be done on samples unseen during training. For this purpose, check out [`sklearn.model_selection.train_test_split()`](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html).\n", + "\n", + "When chosing your performance **metrics**, you should take into account how _balanced_ your dataset is:\n", + "- **accuracy** is good for a balanced set (e.g. around the same number of instances for all the classes)\n", + "- but **F1** or **Cohen's Kappa** might be better for a dataset with a class imbalance.\n", + "\n", + "You will also notice that the above classifiers (and many more ML approaches) have different **parameters** to chose from. Their performance could differ substantially depending on the parameter choice. To chose between different parameters of the same algorithm (e.g. to chose K for [`sklearn.neigbors.KNeigborsClassifier()`](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html)), you can perform **hyperparameter search**. This means comparing the performance for different values of K. However, there are some things to be careful about:\n", + "- the _test set_ (used to measure the final performance) **should not** be used to determine the best parameters. Doing that would mean that the \"unseen\" data from the test set was actually not completely unseen, as it was used to chose the best model (which is considered part of training).\n", + "- therefore, the _training set_ should be further split, by separating a smaller _validation set_ (while the remainder of the _training set_ with the validation part removed should be used for training).\n", + "- to achieve this, you can call [`sklearn.model_selection.train_test_split()`](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) _again_ on your training set.\n", + "- to quickly compare the classifier performance when using different combinations of parameters, you can look into [`sklearn.model_selection.GridSearchCV()`](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Evaluating the model\n", + "\n", + "Our goal so far has been to select the best performing model on our data. For this purpose, we trained different models on the _training set_, compared different parameters of the same model on the _validation set_, and finally compared the performance of different models on the _testing set_.\n", + "\n", + "However, this means that the our models were evaluated only on the small portion of the dataset (the size of the training set is usually selected to be around 0.2-0.3). To get a better sense of the model performance, it would be good to test how it performs on _all the available data_. However, we still should not test the model on the data seen during training.\n", + "- To get around this, one can use k-fold cross validation. (Unless your chosen dataset is very small, say less than 100 samples, in which case leave-one-out might be better).\n", + "- For k-fold cross validation, check [`sklearn.model_selection.KFold()`](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html).\n", + "- Alternatively, for leave-one-out, check [`sklearn.model_selection.LeaveOneOut()`](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.LeaveOneOut.html).\n", + "\n", + "If you are training a classifier model, try visualising the [confusion matrix](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html) (using [`sklearn.metrics.ConfusionMatrixDisplay()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.ConfusionMatrixDisplay.html)) _on the whole dataset_ by _aggregating the results for different folds_." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}