From f48a22555d51ded0361f37902859065466f10574 Mon Sep 17 00:00:00 2001 From: Duhmariya Date: Mon, 18 May 2026 13:58:00 +0800 Subject: [PATCH] Improve ML basics notebook: fix scaling leakage, add regression and clustering --- .../machine_learning.ipynb | 1704 +++++++++-------- 1 file changed, 874 insertions(+), 830 deletions(-) diff --git a/content/en/modules/machine_learning_basics/machine_learning.ipynb b/content/en/modules/machine_learning_basics/machine_learning.ipynb index 3ad2d197..ced2fe72 100644 --- a/content/en/modules/machine_learning_basics/machine_learning.ipynb +++ b/content/en/modules/machine_learning_basics/machine_learning.ipynb @@ -1,862 +1,906 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Machine Learning in Python for Neuroimaging\n", - "\n", - "This notebook, prepared by Désirée Lussier, is to provide an introduction to machine learning for BrainHack School 2022. It has been adapted from Brainhack Global MTL 2019 traintrack tutorial (https://github.com/BrainhackMTL/global2019-traintrack/blob/master/machine_learning.ipynb) by Désirée Lussier which was modified from the MAIN 2019 training given by Alexadre Hutton (https://github.com/main-training/main-training-nilearn-ml/blob/master/01_intro_ml.ipynb; https://github.com/main-training/main-training-nilearn-ml/blob/master/01_intro_ml_slides.odp) and sklearn tutorial (http://scipy-lectures.org/packages/scikit-learn/index.html). \n", - "\n", - "This training is meant to give a brief overview of the basics of machine learning in Python3. For more complete training or other specific examples please see the additional BrainHack School modules (https://school.brainhackmtl.org/modules/) above MAIN courses (https://github.com/main-training) and documentation for Scikit Learn (https://scikit-learn.org/stable/)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Training Outline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "__Machine Learning with Scikit Learn__
\n", - "
  • Machine learning classification examples
  • \n", - "
  • Model evaluation
  • \n", - "
  • Model complexity
  • \n", - "\n", - "\n", - "# What is machine learning?" - ] - }, - { - "attachments": { - "machine_learning.png": { - "image/png": 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" - } - }, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![machine_learning.png](attachment:machine_learning.png)" - ] - }, - { - "attachments": { - "Picture1.png": { - "image/png": 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+ "cells": [ + { + "cell_type": "markdown", + "id": "aa2c40c6", + "metadata": { + "id": "aa2c40c6" + }, + "source": [ + "# Machine Learning Basics – Solved Notebook\n", + "**Brainhack School 2020 | Assignment 4**\n", + "\n", + "Presenters: Estefany Suarez & Jacob Vogel \n", + "Dataset: Iris (sklearn built-in) \n", + "Library: scikit-learn\n", + "\n", + "---\n" + ] }, - "Picture2.png": { - "image/png": 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+ { + "cell_type": "markdown", + "id": "17105ff5", + "metadata": { + "id": "17105ff5" + }, + "source": [ + "## 0. Imports & Setup" + ] }, - "Picture3.png": { - "image/png": "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" - } - }, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In __machine learning__ models with parameters which are __optimized__ according to previously-seen data.\n", - "\n", - "Machine learning (ML) can be divided into subcategories:\n", - "\n", - "## Supervised Learning\n", - "We have observations __X__ we want to use __to predict Y__

    \n", - "__X__: data, features, inputs
    \n", - "__Y__: target, labels, outputs
    \n", - "The goal is to find a model which best predicts __Y based on X__:
    \n", - "Y = f(X)
    \n", - "\n", - "![Picture1.png](attachment:Picture1.png)\n", - "\n", - "Models are further divided:
    \n", - "
  • Classification: Predicting ordinal numbers; determining classes for inputs
  • \n", - "
  • Regression: Predicting continuous values
  • \n", - "\n", - "## Unsupervised Learning\n", - "We have observations __X__
    \n", - "The goal is to __extract information about X__
    \n", - "E.g.: finding a representation, cluster the data\n", - "\n", - "![Picture2.png](attachment:Picture2.png) ![Picture3.png](attachment:Picture3.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "ML models are typically developed in some variation of:
    \n", - "
  • Parameter training
  • \n", - "
  • Model evaluation
  • \n", - "
  • Model selection
  • \n", - "
  • Model generalization
  • \n", - "\n", - "Let's start off by taking a look at some classification examples:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Scikit-learn: Classification" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Import relevant packages:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import sklearn\n", - "import numpy as np\n", - "from matplotlib import pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Scikit-learn (\"sklearn\") uses a common interface for its estimators (models). You create an instance of a model, fit it to your data by using model.fit(data, target), and you can then use it to make predictions using model.predict(new_data)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Classification with Iris Dataset" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Sklearn contains a number of example datasets. In this section, we'll look at the iris dataset, which contains information about different flowers and we will try to predict its species based on a few of the plant's features. Load the iris dataset and print its shape:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "(150, 4)\n" - ] - } - ], - "source": [ - "from sklearn.datasets import load_iris\n", - "iris = load_iris()\n", - "print(iris.data.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Sklearn uses the data structure convention of (n_samples, n_features). The iris dataset has 150 samples, with each sample having 4 features. \n", - "Let's look at the first few samples:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": null, + "id": "c3c7e2e1", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-16T07:46:05.598692Z", + "iopub.status.busy": "2026-05-16T07:46:05.598450Z", + "iopub.status.idle": "2026-05-16T07:46:07.467806Z", + "shell.execute_reply": "2026-05-16T07:46:07.466588Z" + }, + "id": "c3c7e2e1", + "outputId": "c10d3b86-3154-4677-ac47-8f475131b826", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "All imports OK ✓\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from sklearn import datasets\n", + "from sklearn.model_selection import (train_test_split, cross_val_score,\n", + " GridSearchCV, learning_curve)\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.svm import SVC\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n", + "import warnings; warnings.filterwarnings('ignore')\n", + "\n", + "%matplotlib inline\n", + "plt.rcParams['figure.dpi'] = 110\n", + "print(\"All imports OK ✓\")\n" + ] + }, { - "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", - "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n" - ] - } - ], - "source": [ - "print(iris.data[0:5])\n", - "print(iris.feature_names)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's also look at the target, or label of the data:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "id": "d7a6a1b6", + "metadata": { + "id": "d7a6a1b6" + }, + "source": [ + "## 1. Load & Explore the Iris Dataset\n", + "The Iris dataset contains **150 samples** of iris flowers across **3 species** (setosa, versicolor, virginica). \n", + "Each sample has **4 features**: sepal length, sepal width, petal length, petal width (all in cm).\n" + ] + }, { - "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", - "['setosa' 'versicolor' 'virginica']\n" - ] - } - ], - "source": [ - "print(iris.target)\n", - "print(iris.target_names)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": true - }, - "outputs": [ + "cell_type": "code", + "execution_count": null, + "id": "ee02d52e", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-16T07:46:07.470800Z", + "iopub.status.busy": "2026-05-16T07:46:07.469682Z", + "iopub.status.idle": "2026-05-16T07:46:07.479143Z", + "shell.execute_reply": "2026-05-16T07:46:07.478181Z" + }, + "id": "ee02d52e", + "outputId": "26b8c603-1807-4a3e-b000-e4bfb5d6706c", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Feature matrix shape : (150, 4)\n", + "Target vector shape : (150,)\n", + "Feature names : ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n", + "Target names : ['setosa' 'versicolor' 'virginica']\n", + "\n", + "First 5 samples:\n", + "[[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", + "\n", + "Class distribution : [50 50 50] (balanced – 50 each)\n" + ] + } + ], + "source": [ + "iris = datasets.load_iris()\n", + "X = iris.data # shape (150, 4)\n", + "y = iris.target # 0=setosa, 1=versicolor, 2=virginica\n", + "\n", + "print(f\"Feature matrix shape : {X.shape}\")\n", + "print(f\"Target vector shape : {y.shape}\")\n", + "print(f\"Feature names : {iris.feature_names}\")\n", + "print(f\"Target names : {iris.target_names}\")\n", + "print(f\"\\nFirst 5 samples:\\n{X[:5]}\")\n", + "print(f\"\\nClass distribution : {np.bincount(y)} (balanced – 50 each)\")\n" + ] + }, { - "data": { - "image/png": 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    " + "cell_type": "markdown", + "id": "e3731cab", + "metadata": { + "id": "e3731cab" + }, + "source": [ + "## 2. Visualise the Data\n", + "Understanding your data before modelling is essential in machine learning.\n" ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# From: http://scipy-lectures.org/packages/scikit-learn/auto_examples/plot_iris_scatter.html\n", - "# The indices of the features that we are plotting\n", - "x_index = 1\n", - "y_index = 3\n", - "\n", - "# this formatter will label the colorbar with the correct target names\n", - "formatter = plt.FuncFormatter(lambda i, *args: iris.target_names[int(i)])\n", - "\n", - "plt.figure(figsize=(5, 4))\n", - "plt.scatter(iris.data[:, x_index], iris.data[:, y_index], 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])\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Classification: k nearest neighbours: kNN" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "K nearest neighbors (kNN) is one of the simplest learning strategies: given a new, unknown observation, look up in your reference database which ones have the closest features and assign the predominant class. Let’s try it out on our iris classification problem:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Get the data and target\n", - "from sklearn import neighbors\n", - "iris = load_iris()\n", - "X, y = iris.data, iris.target" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "['virginica']\n" - ] - } - ], - "source": [ - "# Instantiate the kNN model\n", - "knn = neighbors.KNeighborsClassifier(n_neighbors=1)\n", - "knn.fit(X, y)\n", - "# What kind of iris has 3cm x 5cm sepal and 4cm x 2cm petal?\n", - "print(iris.target_names[knn.predict([[3, 5, 4, 2]])])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And that's it! Our model, \"knn\" is now a trained classifier." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can plot our class boundaries for our kNN classifier (from http://scipy-lectures.org/packages/scikit-learn/auto_examples/plot_iris_knn.html)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": null, + "id": "3ff033d4", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-16T07:46:07.480949Z", + "iopub.status.busy": "2026-05-16T07:46:07.480775Z", + "iopub.status.idle": "2026-05-16T07:46:08.483400Z", + "shell.execute_reply": "2026-05-16T07:46:08.482361Z" + }, + "id": "3ff033d4", + "outputId": "9ce53ac3-c2e7-432f-f363-ed8804359bab", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + } + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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7djTZbubMmRg9ejS2b9+OAwcO4JdffsG3336L7t27Y//+/bVKZiufw6wJUwmlsX2m/qI2t+97+5g7dy4+/vhjjB8/Hq+99pruhYdr165h6tSpBgljTQ0YMABBQUG6BLGkpATx8fHo27ev3rNileN88803CAoKqrKv1q1b1ymWe8fq2LEjPv/8c6NtKq/aJSUlYdCgQQgJCcGiRYsQEhICJycnCIKAf/7znwYv/ZhiLBExVVaqqu9V5VwtWrQI3bt3r/K4yn9QAHde/Jg2bRq2b9+OgwcPIiEhAatWrcKYMWPwww8/mEyQKv+fNfUPE0dHR5MJeWW8MTExNb7K/neW/Dwqz8nHx6dOMVHTwQSRmr2nn35adwvz6aefNtqu8irU+fPnDfaVlJQgJSUF7du319vu7u6OCRMmYMKECQCAL7/8Es8++yy++OIL3du29fEvdrVaje+//x4tWrTA8OHDq23v6+uLqVOnYurUqRBFEW+88QaWLl2KDRs2YOrUqborN0qlEl27drV4vLbi+++/R9++fREfH6+3ffv27QZta/O5CYKAJ598Eu+99x6OHDmCq1evoqioyKBkSuV8e3h4mEw2LKFDhw7IyMhAREREtSWQ1q1bh/Lycmzfvt3gqmxubq7BPyZMzZGnp2eVBd1NPepQlcq5cnBwMHuugoKCMHv2bMyePRvl5eWYOnUq1q1bh/3795tc/SYgIACurq74888/axTjvdq3bw87OzuUlJSYFa+pOazp52HKn3/+CS8vL7Rq1crsY6hp4zOI1Ow9+eSTWLBgAT766CM88MADRtvJ5XL07dsXO3fuxPHjx/X2rVixAsXFxRg3bhwA6ApX/114eDgA/SsQLi4uyM/PN/tqWHVu3ryJSZMmIT09HS+88AICAgKMtr19+7ZBGRBBEHS3kivjjI6Ohkwmw3vvvVflbda6XlmzFRKJxOBzKCsrw5IlSwzaVr6xXNPbzpWr4KxZswZr1qyBs7MzoqOj9drExMTAwcEBCxcuRHFxsUEfJSUluHnzZo3GNebJJ59Efn4+Fi1aVOX+e29lV16J/fscffnll1Xe8jb13e7YsSNu3rxp8P/Shx9+WKP4hw0bBl9fX3z44YdVPldYXl6u+4wKCwtRVlamt79Fixbo1q0bgOofWZBIJOjbty+OHz9e61JDXl5eiIyMxNatW7F3794q29w7l6a+ZzX9PIwpKyvDyZMndSvvEAG8gkgET09Ps9fv/fTTT9GvXz8MHDgQzz77rK7Mzfr169GtWzfMmTMHwJ0krVWrVnj00UcRGhqKVq1a4a+//sJ//vMftGjRQq/MSc+ePbFlyxa88MIL6N27NyQSCQYOHGjWrZ4ffvhBt5JKQUEBTp8+jR9++EG3kkp1f9leunQJ/fr1Q1RUFB544AHI5XKkpKTgyy+/RMuWLXXlW9q0aYNPP/0Us2fPxgMPPIBp06ahbdu2yM7Oxo4dOzB37lyjz242JtHR0fj3v/+N8ePHY+jQocjLy8O6devg6Oho0NbLywvt27fHhg0b0K5dO/j6+sLZ2RmPPvqoyTFCQkLQt29fxMbG4vbt23jiiScMyuO0adMGX331FaZPn46OHTviqaeeQkhICPLy8nDx4kVs3rwZiYmJFlnr+Z///Cf27NmDhQsX4sCBAxg6dCg8PT2RkZGBI0eOICUlRXdVb+zYsVi5ciVGjBiBWbNmwcnJCYcOHcLOnTvRrl07g9vDpr7bzzzzjK5c0T//+U84OTlh69atJl+qqoqTkxP+97//YcyYMejcuTOmTZuGTp064ebNm7hy5Qo2b96MpUuXYurUqdi7dy9mzpyJxx57DB07doS7uzt+//13fPnll2jTpo1ZV/QmTJiArVu3Yt++fRg0aFCNYq305Zdfok+fPhgyZAgmT56Mhx9+GHZ2dkhLS8O2bdvw0EMPYc2aNQCA+++/Hy1btsSqVavg5OQEd3d3+Pj4YODAgTX+PIz59ddfUVJSwiLZpK9B3p0maiD3lrmpTlVlbkRRFH///XcxJiZG9Pb2Fu3t7cWgoCBxzpw5YkFBga5NaWmpOH/+fLFHjx6it7e3KJVKRX9/f3H8+PHib7/9ptffrVu3xOnTp4s+Pj6inZ2dCEDcu3evydgqy9xU/tjb24teXl7iww8/LL744oviiRMnqjzu72VucnJyxJdffllUKBSih4eHKJPJxKCgIHHq1KnihQsXDI7fs2ePOHz4cNHDw0OUSqViYGCgOGXKFL1yHX8v73IvU6VoqioXU3mete3HVN+rV682mOvbt2+Lr732mhgUFCRKpVIxODhYnD9/vnjhwoUq+/ntt9/E3r17i05OTiIAvRIipuahcuzqPutjx46J48ePF319fUV7e3vR19dX7NWrl/jee++Jubm5Ro+rZOw7/Hfl5eXiqlWrxB49eoguLi6ig4ODGBwcLI4dO1bcuHGjXtuffvpJfOihh0QnJyfRw8NDfPTRR8Xz589XWUKluu/2zp07xfDwcFEqlYpyuVycPXu2rvRSVWVujJUUEkVRvHDhgvjUU0+J/v7+or29vejt7S2Gh4eL8+fPF9PT00VRFMWUlBRx9uzZ4v333y+6urqKjo6OYvv27cUXX3zRZMmoe6nValEul4tPPPGEwb6goCCxXbt2ZvWTl5cnvv7662KnTp1EmUwmtmzZUuzUqZM4c+ZM8dixY3ptt27dKioUClEmk4kA9L5XNfk8jJW5mTx5sujn5ydqNBqzYqfmgWsxExER1cBHH32E+fPn4/LlywZLXTY2aWlp6NChA1auXInnn3++ocMhG8IEkYiIqAbKysrQpUsX9O/fH1999VVDh1MnTz/9NI4dO4YzZ87UuDQVNW1MEImIiIhID99iJiIiIiI9TBCJiIiISA8TRCIiIiLSwwSRiIiIiPSwUPZdarUaZ8+ehVwur3a5KSIiIqLGpry8HCqVCl26dKl2KUZmQnedPXvW6ELvRERERE3F8ePH8fDDD5tswwTxLrlcDuDOpHGxciIiImpqrl+/ju7du+tyHlOYIN5VeVu5VatWjb4yPhEREZEx5jxKx5dUiIiIiEgPE0QiIiIi0sMEkYiIiIj0MEEkIiIiIj18SaUGRFFETk4O1Go1tFptQ4dDViSRSODg4ABvb28IgtDQ4RAREdUrJohmEkUR165dw82bNyGVSiGRSBo6JLIijUaD4uJilJaWok2bNkwSiYioSWOCaKacnBzcvHkTPj4+8PLyauhwqAHk5uYiOzsbOTk5ZtWQIiIiaqz4DKKZ1Go1pFIpk8NmzMvLC1KpFGq1uqFDISIiqldMEM2k1Wp5W5kgkUj4/CkRETV5TBCJiIiISA8TRCIiIiLSwwSRiIiIiPTYRIIYHx+PMWPGwN/fH87OzggNDcV3330HURRNHhccHAxBEAx++BJB/SooKMDChQvx+++/N3QoRERkhFarxYmTJ7F1x3acOHmyXp+fNmcsa8ZDdWcTZW5WrlyJ4OBgrFixAnK5HLt378bMmTORkZGBBQsWmDx2/PjxeOWVV/S2yWSy+gy32SsoKMA777yDBx98EPfff39Dh0NERH+zffdOrN+WAI2XPSQuMmiLSyFd9zUmR47DiCHDrD6WNeMhy7CJBPHnn3+Gt7e37veBAwciNzcXK1euxNtvvw07O+MXOn19fdGzZ09rhGlRWq0Wp5TJUOXkQu7thTBFKN+SJiKiOtu+eydW790Enz7t9Yr6i6KI1Xs3AYDFkjJzxgJgtXjIcmziFvO9yWElhUKBoqIi3Lp1qwEiql87du/BzDlv499bTuCns3n4cssJzHrlbezYvccq458/fx6RkZHw8vKCk5MTOnbsiGXLlun2Hz16FAMHDoSzszPc3NwwefJkZGdnAwBSU1PRtm1bAEB0dLTutn5qaioAIC8vD9OnT4e3tzccHR3Ru3dvHDhwQG/8w4cPo1+/fnBzc0PLli3RpUsXfP/997r9W7duxZAhQ+Dj4wNXV1f06NEDO3bsqOdZISJq/LRaLdZvS4BPeHuDFZ8EQYBPeHus35Zgkdu75oy1dks81m3dZJV4yLJsIkGsyqFDh9CmTRu0bNnSZLt169ZBJpPBxcUFkZGROHv2rFn9FxUVITMzU/dz/fp1S4RdrR279yD2lyS06RkF/07h8AlohzadwtG6RxRif0mySpL46KOPIj8/H99++y22bt2KuXPn6hLxo0ePIiIiAm5ubti4cSO+/vprnDhxAmPGjAEAtGrVCps3bwYALF68GEePHsXRo0fRqlUraLVajBgxAj///DM++OADxMfHw8XFBUOGDEFSUhKAO/M+cuRIuLq6IjY2FomJiZg1axYKCgp08V29ehWPPvoo/ve//yEhIQGPPPIIIiMjsW/fvnqfGyKixuyUUgmNl73R5UAFQYDGyx7KZKVVxsouK0SeQ6lV4iHLsolbzH936NAhbNiwAStWrDDZbvTo0ejRowcCAwORkpKCRYsWoU+fPlAqlQgJCTF57MqVK/HOO+9YMuxqabVaxG3Zg8CeUVX+SyowNALxWxMxZGBEvd1uzsnJwdWrV/HJJ5/g0UcfBQAMGDBAt//111/HQw89hM2bN+ti7NKlCx588EFs27YNkZGRUCgUAID77rtP7/b+Tz/9hOPHj2PHjh0YNuzO7YJhw4ahffv2WLx4MRISEnDp0iUUFhZiyZIl6NKlCwBg0KBBejG+8MILuv+uqKjAgAEDcP78eXz99deIiIiw/KQQETUR2TkqSFxMP4cvcZEhS6WyylhlohYSmelrUZaKhyzL5q4gZmZmYsKECRgwYAD+8Y9/mGz76aefYsqUKejbty+eeuop7N+/HwCwfPnyaseZM2cOMjIydD/Hjx+3SPymnFImAx6BJv8lBfdAKJOT6y0GLy8vBAUFYf78+fj++++RmZmp23f79m0cPnwY0dHR0Gq1KC8vR3l5OTp06ICAgACcOHHCZN8HDx6Eq6urLjkEAHt7e4wdOxaHDh0CALRr1w6urq549tlnERcXB1UVfyhkZmbiqaeeQps2bdCiRQvY29tj165duHTpkoVmgYioafLxlkNbXGqyjba4FL4WWE/enLHsBQnsSiusEg9Zlk0liAUFBRgxYgS8vLyQkJBg8uWUqrRq1Qp9+vTR3c40xdXVFf7+/rqfVq1a1TZss6lyciF1djfZxt7ZHSpVbr3FIAgCdu3ahc6dO+P5559HQEAAHnroIRw4cAD5+fnQarV4+eWXYW9vr/eTnp6OjIwMk33n5+fDx8fHYLuvry/y8vIAAB4eHti9ezdatmyJJ554An5+foiIiNA9GlBRUYHRo0fj0KFDePfdd7F3716cOHECI0aMYPkiIqJqhCkUkOaWGS0TJ4oipLllUIQqrDKWj70bPNUyq8RDlmUzt5hLSkowatQoFBYW4ujRo3Bzc2vokCxO7u2FslspJtuU3SqAXH5fvcbRoUMHxMfHo6ysDEeOHMEbb7yBRx99FOnp6RAEAW+88QaioqIMjqvqZaJ7eXp66l5muVdWVhY8PT11v3fv3h3bt29HSUkJ9u7di7lz5yIqKgpXrlzB5cuXoVQqkZiYqHvuEbjz/SAiItMkEgkmR46789ZwuOFbw9lJlzEtcrxFHmMya6xR0QBglXjIsmwiQSwvL0dMTAwuXLiAgwcPok2bNrXq56+//sKhQ4fwxBNPWDhCywhThEL8XwJEMazK28yiKAIF6VCEzrRKPPb29ujfvz9ef/11jB49GllZWejVqxcuXLiA999/3+hxUqkUAAyu6PXp0wcffvghdu3ahaFDhwK489n+8MMP6NOnj0E/jo6OiIyMxJUrV/DPf/4TarValwhWjgEAaWlpOHz4MDp06FDncyYiauoqS8YY1B3MLcO0yPEWLSlTk7GsEQ9ZjiBWt1yJFcyaNQv/+c9/sGLFCvTu3Vtvn0KhgEwmw6BBg5CWlobLly8DAGJjY7FlyxZERkaidevWSElJwZIlS5CXl4ekpCRdKRZzZWZmIiAgABkZGfD39zfYX1nGJTg4uFbnWKnyLebA0AiDf0mlJ+/DpMHhGD5kkIke6ubMmTN45ZVXMGHCBLRr1073wkh2djYuX76M3377DQMHDsSYMWMwceJEeHh4IDMzE7t378a0adMQERGBiooKeHl5oXfv3njjjTcgk8nQtWtXSCQS9O7dG1evXsXSpUvh6+uLzz77DHv37sWRI0cQHh6OrVu34ttvv8Vjjz2GwMBA3LhxA2+//TZ8fX1x8OBBlJaWon379vD09MSiRYtQXFyMBQsWQKPRwNnZGefOnau3uTGHpb4HRET1TavVQpmsRJZKBV+5HIpQRb1dqTNnLGvGQ1WrLte5l01cQdy1axcAGKyIAtwpeRIcHKx7aaJS27Zt8ddff+Gll15CQUEB3N3dMXDgQLz77rs1Tg6tqTL5i9+aCLgHwt7ZHWW3CoCCdEwaOahek0MA8PPzg5+fH5YsWYJr167Bzc0Nffv2xdq1a3UJ3qFDh7BgwQJMmzYNGo0G/v7+GDRoENq3bw8AsLOzw+rVq/HGG29g0KBBKC0t1X1O27Ztw9y5c/Hqq6/i1q1bCAsLw65duxAeHg4AaN++Pezs7PDmm28iOzsbXl5eGDp0KJYsWQLgzio4mzdvxvPPP4/o6GgEBATgrbfewq+//oqTJ0/W69wQETUlEokED4U/ZDNjWTMeqjubuIJoC6x1BbHSnX9JJUOlyoVc7gVFKFdSaQx4BZGIiBqrRncFsTm68y+p8IYOg4iIiMiATZW5ISIiIqKGxwSRiIiIiPQwQSQiIiIiPXwGkYiICHdeHjylVCI7RwUfbznCFLZdhkWj0SB24wZcTU9D28AgTJowUa+GLFFdMEEkIqJmb/vunYaFnNd9jcmR42yykPP7y5Zg7c4EtAjxgL27E8pSD2DJmk/x+LBxeGve/IYOj5oAJohERNSsbd+9885ScH0Ml4JbvXcTANhUkvj+siVYl7QNfo8pINjdE2+3YKw7uA1YBiaJVGd8BpGIiJotrVaL9dsSDNYJBgBBEOAT3h7rtyVAq9U2UIT6NBoN1u5MgG/fTnrJIQAIdgJ8+3bC2p0J0Gg0DRQhNRVMEImIqNk6pVRC42VvkBxWEgQBGi97KJOVVo6sarEbN6BFiIdBclhJsBPQIsQDG+I2WjkyamqYIBIRUbOVnaOCxEVmso3ERYYslcpKEZl2NT0N9u5OJtu0cHPE1fRU6wRETRYTRLK6iIgIjBo1yuL9Tp06FQ8++KDF+yWipsvHWw5tcanJNtriUvjK5VaKyLS2gUEoK7htsk15YQnaBgZbJyBqspggktWtWrUKK1asaOgwiIgQplBAmlsGURSr3C+KIqS5ZVCEKqwcWdUmTZiI8pR8iBVG4q0QUZ6Sj4kxE6wcGTU1TBAbiFarxamTJ7Bn+zacOnnCZh6ArouSkhKz2t1///3o2LFjPUdTe+aeBxE1fhKJBJMjxyE76bJBkiiKIrKTLmNy5DibqYcolUrx+LBxyDp40SBJFCtEZB28iMeHjWM9RKozJogNYP/OHVj5wkwUrP0Sfgd+RsG6r7DyhVnYv3NHvY+9Zs0atGjRAllZWXrb8/LyIJVK8dVXXwEAjh49ioEDB8LZ2Rlubm6YPHkysrOzde1TU1MhCALWrFmDmTNnwsvLC927dwcAHD58GP369YObmxtatmyJLl264Pvvv9cdW9Ut5gsXLmDs2LHw9PSEk5MTunXrhtjYWN1+tVqNOXPmoHXr1nBwcEBoaCh++OGHas/37NmzGDZsmO48xo8fj/T0dL02giBg6dKleO211+Dn5wcfHx8zZ5OImoIRQ4Zh2oDxKDx0Bdm/pyE3/Qayf09D4aErmDZgvE2VuAHulLCZEh6JGz8ooVJeRX7KDaiUV3HjByWmhEeyxA1ZBOsgWtn+nTtw46dYzO0UpHtr7gEAA0QRcT/FYj+A/sOG19v4jz32GGbPno34+Hi88MILuu0JCQkAgOjoaBw9ehQRERGIjIzExo0bcevWLbz11lsYM2YMjh49qtff/PnzMXLkSMTGxqKiogJFRUUYOXIk+vTpg9jYWMhkMvz+++8oKCgwGtOff/6JXr16ISAgAJ9++in8/Pxw7tw5vURuypQp2LFjBxYtWoROnTrhv//9L8aNG4fExESMHj26yn4zMjLQr18/tGvXDmvXroVarcabb76J/v3748yZM2jZsqWu7SeffIKePXvi22+/RXl5eW2mlogasRFDhmHowMFQJiuRpVLBVy6HItR2V1J5a958zHvpFWyI24ir6aloGxqMiV9M4JVDshhBNPbgRTOTmZmJgIAAZGRkwN/f32B/amoqACA4OLjWY2i1Wqx8YSbmdvKvsqSCKIpYfvEa5nz+db3+oTR27FhkZWXh8OHDum0DBw6Ek5MTtmzZgv79+6O8vByHDh3Sxfn777/jwQcfxJYtWxAZGYnU1FS0bdsWw4cPx/bt23X9nDx5Eg8//DDOnDmDLl26VDl+REQEXFxcsGXLFgB3kr/du3fj8uXLcHV1NWh/5swZdOvWDV9++SWeeeYZ3fbevXujtLQUSUlJAO68pHLy5EmcO3cOADBnzhx8/fXXSE9Ph6enJwDg4sWLuP/++/HJJ5/gxRdfBHDnCuL999+Pc+fOGS11UckS3wMiIqKGUF2ucy/eYrai08pTCJcJJutthTsIOK2s33pbkyZNwtGjR3VX6K5fv479+/dj0qRJuH37Ng4fPozo6GhotVqUl5ejvLwcHTp0QEBAAE6cOKHX18iRI/V+b9euHVxdXfHss88iLi4OKjNKQ+zZswfjx4+vMjkEgIMHDwK4c3XzXhMmTIBSqcStW7eMHjdw4EBdcggAnTp1Qrdu3XDo0CG9tiNGjKg2OSQiImoumCBaUb5KBV9H05f/fWT2yK/nelujRo2Cs7MzNmzYAACIi4uDg4MDoqKikJ+fD61Wi5dffhn29vZ6P+np6cjIyNDry9fXV+93Dw8P7N69Gy1btsQTTzwBPz8/RERE4OzZs0bjyc3NRevWrY3uz8/Ph729vV6iVzm2KIpGb1/n5+cbxFd5XF5ensnzICIias74DKIVecjlyFKX4QETbbJLy+BRz/W2HB0dERUVhQ0bNmDevHnYsGEDHn30UTg7OwO4cyXzjTfeQFRUlMGx3t7eer9XddWte/fu2L59O0pKSrB3717MnTsXUVFRuHLlSpXxeHl54a+//jIar6enJ8rKypCfnw8PDw/d9qysLAiCAHd3d6PH3ftizb3HdejQodrzICIiaq54BdGKuinCkKSuMFlvK0ktopui/uttTZo0CUqlEjt37sSxY8cwadIkAICzszN69eqFCxcu4KGHHjL4qcmzd46OjoiMjMSzzz6Lq1evQq1WV9lu8ODB2LRpE27evFnl/j59+gAA4uPj9bbHx8dDoVDoEtuqjtuzZw/y8/N12/744w+cOXNG1ycREREZ4hVEK5JIJOgeFYO4n2IR0yFI76qVKIqIu5SG7lGTrPLW3JAhQ+Dl5YXp06fD3d0dI0aM0O378MMPMXDgQEyYMAETJ06Eh4cHMjMzsXv3bkybNg0RERFG+926dSu+/fZbPPbYYwgMDMSNGzfw2Wef4ZFHHoGDg0OVxyxYsABbtmxBnz59MG/ePLRq1Qq///47bt++jXnz5qFr164YO3Ys5syZg5KSEnTs2BFr167FkSNH8OOPPxqN5eWXX8bq1asxdOhQvPnmm1Cr1XjrrbcQGBiIqVOn1nbqiIiImjwmiFbWf9hw7AewPDEe4Q4CfGT2yC4tQ5JaRPeoSfVa4uZe9vb2GD9+PL766is8/fTTeqURevfujUOHDmHBggWYNm0aNBoN/P39MWjQILRv395kv+3bt4ednR3efPNNZGdnw8vLC0OHDsWSJUuMHnPffffhyJEjmD9/Pp577jndSzGvv/66rs3atWvxxhtvYOnSpcjLy0OnTp2wadMmPProo0b7DQgIwP79+zF37lxMmTIFEokEQ4YMwcqVK/VK3BARAXcXMFAqkZ2jgo+3HGEK/TI31e23ZBtrnE9j7aepsdV5YZmbu6xR5uZeWq0Wp5VK5KtU8JDL0c1GvhBkGsvcEDVN23fvxPptCdB42UPiIoO2uBTS3DJMjhyHEUOGVbvfnD7MbWON82ms/TQ11p6XmpS54RXEBiKRSBD20EMNHQYRUbO3ffdOrN67CT592hs8+rN67yYkKU/hTEGK0f2VTPVhbhtLJAXVnY+549haP02Nrc8LX1IhIqJmS6vVYv22BPiEtzeoZiAIArxDQ7B2ZwLkYe2q3O8T3h7rtm7C2i3xRvvwCW+PtVvisW7rJpNt1m9LgFarrdfzMXccW+unqWkM88IEkYiImq1TSiU0XvZGS13lpl1HixAP3LxZVOV+QRCQJytFdlmhyUUQsssKkedQarKNxsseyuS6LZRQ3fmYO46t9dPUNIZ5YYJIRETNVnaOChIXmdH9JcW3YO/uhFKNxmibCpkdykXTV3rKRC0qZKb/ypW4yJBVx4USqjsfc8extX6amsYwL0wQiYio2fLxlkNbXGp0v6OLM8oKbkMmNb4Kll1pBVoIpl8ytBcksCutMNlGW1wK3zoulFDd+Zg7jq3109Q0hnlhgkhERM1WmEIBaW6Z0QUMvIJaoTwlHy1bVr1WvCiK8CyVwcfezeQiCD72bvBUy0y2keaWQRFat4USqjsfc8extX6amsYwL0wQiYio2ZJIJJgcOQ7ZSZcN/rIWRRE5ySl4fNg4qE5dqXJ/dtJlTBk5Ho+PijbaR3bSZTw+KhpTRo432WZy5Lg6lzur7nzMHcfW+mlqGsO8sMwNERE1a5WlRKqqRzctcrzJOoiV+ytZqk19n09j7KepsfV5YaHsu6xdKJsaJ34PiJourVYLZbISWSoVfOVyKEINV1Ixtd+SbaxxPo21n6bGmvNSk0LZTBDvYoL4/6ZOnYqTJ0/i3LlzFulv4cKFWL58OYqLixs0DktoTt8DIiJqWriSCtXJ22+/jVu3blmsvxkzZmDkyJENHgcRERGZhwliA7HVxbkBoF27dtW2KSkpgaOjo1n9+fv7V/svldrGQURERJbHt5gbwPbdOzF17rNYvmM11l3cjeU7VmPq3GexfffOeh97zZo1aNGiBbKysvS25+XlQSqV4quvvsLUqVPx4IMP6h0jCAKOHj2KIUOGwNnZGa+++ioA4Pz58+jXrx8cHBxw3333Yd26dYiKikJERITu+IULF8LFxUX3+759+yAIAnbv3o3JkyejZcuWCAoKwrJly/Ri+nscAHDt2jU8+eST8PX1haOjIzp16oRPPvlEt/+///0v+vTpA09PT3h4eCAiIgLHjx+v87wRERE1J7yCaGUNvTj3Y489htmzZyM+Ph4vvPCCbntCQgIAIDo6GkePHq3y2MmTJ2PWrFl444034OTkhJKSEgwdOhTu7u5Yu3YtAOCdd95BQUGBWVf/Zs+ejSeeeAI//PADEhMT8dprr6Fr164YPnx4le1zc3PRq1cvAMCiRYsQEhKCP//8E1euXNG1SU1NxZNPPol27dpBo9EgNjYW/fr1w5kzZ9ChQwfzJomIiKiZY4JoRbrFufuYXpx76MDB9Xa72c3NDZGRkYiNjdVLEGNjYzF06FB4enoaPXb27Nl47bXXdL+vWrUKWVlZOHz4sO6ljYceegjt27c3K0EcN24cFi5cCAAYNGgQtm7dik2bNhlNEFeuXIns7GxcvHhRN97AgQP12vzrX//S/XdFRQWGDBmC48ePY82aNVi8eHG1MRERERFvMVuVrSzOPWnSJBw9ehTp6ekAgOvXr2P//v2YNGmSyeP+/qLJiRMn0KVLF703eoODg9GtWzez4hg6dKjuvwVBQOfOnZGZmWm0/Z49ezBw4ECTbxBfuHABjz32GHx9fSGRSGBvb48//vgDly5dMismIqLGQqvV4sTJk9i6YztOnDwJrdb0etD11Udz1dTnjlcQrchWFuceNWoUnJ2dsWHDBsybNw9xcXFwcHBAVFSUyeN8fX31fr9+/TrkVawT6ePjg5KSkmrjcHd31/tdKpWioKDAaPvc3FyDZxLvdfPmTQwdOhRyuRwrV65EUFAQHBwcMGPGDKjV6mrjISJqLKos3L3ua0yOHGf2Y0qW6KO5ag5zxwTRimxlcW5HR0dERUXpEsQNGzbg0UcfhbOzs8nj/n7ls1WrVkhOTjZol52djZYtW1oyZACAl5cX/vrrL6P7jx49iszMTGzZskXvKmZhYWGt3qImIrJFlniWvaGfh2/Mmsvc8RazFdnS4tyTJk2CUqnEzp07cezYsWpvL1fl4YcfxpkzZ3D16lXdttTUVJw+fdqSoeoMHjwYv/76q+7W+N9VXrWUSqW6bUeOHNEVtyYiaux0z7KHm36W3dTtTkv00Vw1p7ljgmhFtrQ495AhQ+Dl5YXp06fD3d0dI0aMqHEf06ZNg5+fH0aNGoVNmzZh06ZNGDVqFPz8/GBnZ/mv1ssvvwwfHx/069cP3377Lfbu3Ytvv/1W9+JMz5494eLigueffx67du3C6tWrMXHiRLRp08bisRARNQRLPMtuK8/DN0bNae6YIFrZiCHDMG3AeBQeuoLs39OQm34D2b+nofDQFUwbYL3Fue3t7TF+/Hj89ddfGDdunN5VN3M5Ojpi165d8PT0xJQpUzBv3jy88soruO++++Dm5mbxmL28vHD48GH06dMH8+bNQ2RkJJYvX667fezr64v4+HhkZ2djzJgx+Pjjj/HVV1+hffv2Fo+FiKghWOJZdlt5Hr4xak5zx7WY77L2WsxNddHyvLw8hISE4OWXX8aCBQsaOhyL41rMRNSQTpw8ieU7VsPn/iCjbbJ/T8OrI6bhofCH6q2P5qqxzx3XYm4EJBKJTX55auqDDz6Ar68vgoODcf36dSxfvhxarRbTp09v6NCIiJqcMIUC0nVfQxTFKm9zmvMsuyX6aK6a09zxFjPViZ2dHd5//32MGDECs2bNgoeHB3799VcEBAQ0dGhERE2OJZ5lt6Xn4Rub5jR3vIJIdfLqq6/q1mUmIqL6V/msukEdvtwyTIs071l2S/TRXDWXueMziHdZ+xlEapz4PSAiW2GJZ9mb6vPw1tAY547PINYDiUQCjUbT0GFQA9NqtbV645uIyNIs8Sx7U3keviE09bnjM4hmcnBwgEajQW5ubkOHQg0kNzcXGo0GDg4ODR0KERFRveIVRDN5e3ujtLQU2dnZKCgosPnLyGRZWq0WGo0GLVu2hLe3d0OHQ0REVK+YIJpJEAS0adMGOTk5UKvVTWIZHTKfVCqFq6srvL29jVbQJyIiaiqYINaAIAiQy+UNHQYRERFRveIziERERESkh1cQiYiIrEir1eKUUonsHBV8vOUIUxiWR7FEG3P6sFS8TVFzPe9KrIN4V01qAxEREdXG9t07qyywPDlynK7AsiXamNOHpeJtiprqebMOIhERkY3ZvnsnVu/dBJ8+7fVedhNFEav3btL9Xtc2ScpTOFOQYrIPc5Icc+JtzMmSMc31vP+OzyASERHVM61Wi/XbEuAT3t6gEoIgCPAJb491Wzdh7ZZ4k23WbonHuq2bjLaRh7XD2p0J8A4NMdrH+m0J1VbiMCdec/ppbJrreVeFCSIREVE9O6VUQuNlb7RMliAIyJOVIrus0GSb7LJC5DmUGm1TdLMILUI8kJt23WgfGi97KJOVdY7XnH4am+Z63lVhgkhERFTPsnNUkLjITLapkNmhXDR9ZapM1KJCZvyvbo1GgxZujigpvmW0jcRFhiyVqs7xmtNPY9Ncz7sqTBCJiIjqmY+3HNriUpNt7Eor0EIw/ZasvSCBXWmF0f1SqRTlhSVwdHE22kZbXArfamr6mhOvOf00Ns31vKvCBJGIiKiehSkUkOaWwVjhEFEU4Vkqg4+9m8k2PvZu8FTLjLZxbemK8pR8eAW1MtqHNLcMilBFneM1p5/Gprmed1WYIBIREdUziUSCyZHjkJ102SD5EEUR2UmXMWXkeDw+Ktpkm8dHRWPKyPFG26hOXcHjw8YhJznFaB+TI8dVW8/PnHjN6aexaa7nXRWWuSEiIrKCytIoVdXXmxY5Xq90Sl3bGKvj9/c+LBVvU9Jcz/vvWCj7LhbKJiIia9BqtVAmK5GlUsFXLocitOpVUuraxpw+LBVvU9QUz7smuQ4TxLuYIBIREVFTVpNch88gEhEREZEeJohEREREpIcJIhERERHpYYJIRERERHqYIBIRERGRHptIEOPj4zFmzBj4+/vD2dkZoaGh+O6774xWMq8kiiKWLl2KwMBAODo6olevXjh27JiVoiYiIlug1Wpx4uRJbN2xHSdOnoRWa7iesSXamNMHNTxrfU5N/ftgE2VuevXqheDgYERFRUEul2P37t1YtmwZ/vWvf2HBggVGj1u6dCkWLFiApUuXomvXrvjiiy/wyy+/IDk5GSEhITWKgWVuiIgaH2MFoSdHjtMVNLZEG3P6oIZnrc+psX4fGl0dxJycHHh7e+ttmzVrFjZu3Ij8/HzY2Rle6FSr1fD19cXzzz+PxYsXAwA0Gg06dOiAyMhIrFq1qkYxMEEkImpctu/eidV7N8EnvD0EQdBtr1wSbdqA8QBQ5zZd3UNwpiDFZB+2nBQ0F+Z8HyzxOVlrnPrQ6Oog/j05BACFQoGioiLcunWrymOOHDmCoqIixMTE6LZJpVKMHTsW27Ztq7dYiYio4Wm1WqzflmDwlzQACIIAn/D2WLd1E9Zuia9TG+/QEKzdmQB5WDujfazfltDkbi82NuZ8HyzxOVlrHFtgEwliVQ4dOoQ2bdqgZcuWVe6/ePEiAKBTp0562zt37oz09HSUlJSY7L+oqAiZmZm6n+vXr1smcCIiqnenlEpovOwN/pKuJAgC8mSlyC4rrFOb3LTraBHigZs3i4z2ofGyhzJZWbsTIYsw5/tgic/JWuPYAptMEA8dOoQNGzZg7ty5Rtvk5+dDJpPBwcFBb7uHhwdEUUR+fr7JMVauXImAgADdT/fu3S0SOxER1b/sHBUkLjKTbSpkdigXTV/Jqa5NSfEt2Ls7oVSjMdpG4iJDlkplOmCqV+Z8HyzxOVlrHFtgcwliZmYmJkyYgAEDBuAf//hHvY0zZ84cZGRk6H6OHz9eb2MREZFl+XjLoS0uNdnGrrQCLQRJndo4ujijrOA2ZFKp0Tba4lL4yuWmA6Z6Zc73wRKfk7XGsQU2lSAWFBRgxIgR8PLyQkJCQpUvp1Ty8PBAaWkp1Gq13vb8/HwIggAPDw+TY7m6usLf31/306pVK4ucAxER1b8whQLS3DKj5dBEUYRnqQw+9m51auMV1ArlKflo2dLVaB/S3DIoQhW1OxGyCHO+D5b4nKw1ji2wmQSxpKQEo0aNQmFhIbZv3w43NzeT7SufPfzjjz/0tl+8eFFXF5GIiJomiUSCyZHjkJ102eAv68q3SaeMHI/HR0XXqU1OcgoeHzYOqlNXjPYxOXIcJBLTVyqpfpnzfbDE52StcWxBi4YOAADKy8sRExODCxcu4ODBg2jTpk21x/Tu3Ruurq6Ij49Ht27dAABlZWXYvHkzIiMj6ztkIiJqYJWlRKqqRzctUr/USF3bGKt79/c+qOHU5PvQGMZpaDZRB3HWrFn4z3/+gxUrVqB37956+xQKBWQyGQYNGoS0tDRcvnxZt2/p0qVYuHAhPvjgA3Tp0gWrVq3Crl27WCibiKgZ0Wq1UCYrkaVSwVcuhyJUYXAFxxJtzOmDGp61PqfG+H1odIWyg4ODkZaWVuW+q1evIjg4GBEREUhNTUVqaqpuX+VSe6tWrYJKpUJoaCg++ugj9OrVq8YxMEEkIiKipqzRJYi2gAkiERERNWWNbiUVIiIiIrIdTBCJiIiISA8TRCIiIiLSwwSRiIiIiPTYRB1EIiKi+qTVanFKqUR2jgo+3nKEKQxLkmg0GsRu3ICr6WloGxiESRMmQmpiib3ajtNccW4aF77FfBffYiYiapqMFbmeHDlOV9T4/WVLsHZnAlqEeMDe3QllBbdRnpKPx4eNw1vz5ltsnOaKc2MbapLr8AoiERE1Wdt378TqvZvg06c9BEHQbRdFEav3bgIAJClPYV3SNvg9poBgd0+bbsFYd3AbsAzVJonmjNNcEyHOTePEZxCJiKhJ0mq1WL8tAT7h+okJAAiCAJ/w9li7NR7/27EJvn076SWHACDYCfDt2wlrdyZAo9HUaZz12xKg1Wotd3KNBOem8WKCSERETdIppRIaL3uDxKSSIAi4pi1AuVsLg+RQ18ZOQIsQD2yI21incTRe9lAmK2t+Eo0c56bxYoJIRERNUnaOChIXmck2mhYVgL3pFyVauDnianpqncaRuMiQpVKZbNMUcW4aLyaIRETUJPl4y6EtLjXZRlpuB5SZvr1ZXliCtoHBdRpHW1wKX7ncZJumiHPTeDFBJCKiJilMoYA0twzGinWIoog2Ene0KCyHWGGkTYWI8pR8TIyZUKdxpLllUIQqan4SjRznpvFigkhERE2SRCLB5MhxyE66bJCgiKKI7KTLeHxkNJ4YPh5ZBy8aJIlihYisgxfx+LBxJushmjPO5MhxzbLmH+em8WKZGyIiarIqy6dUVYNvWuR4jBgy7E6bZcDaH+7UQWzh5ojywpIa1UE0Z5zminPTOLFQ9l0slE1E1HRptVook5XIUqngK5dDEVr1Siob4jbianoq2gYGY2LMhFqtpFLdOM0V56bh1STXYYJ4FxNEIiIiaspqkuvwGUQiIiIi0sMEkYiIiIj0MEEkIiIiIj1MEImIiIhIDxNEIiIiItLDOohERGSzSkpK8OGHH+BKagraBYfg1Vdfg6Ojo14bjUaD2I0bcDU9DW0DgzBpwsQal6cB7pRhOaVUIjtHBR9vOcIUDVeGxZxzskS8ljpna82dLX1GTR3L3NzFMjdERLbl6WemY+fJ/XDp7AephzPK8m/j5oXrGPZQf3z71XcAgPeXLcHanXcKXNu7O6Gs4HaNClxX2r57Z5WFnCdHjrN6IWdzzskS8VrqnK01d7b0GTVWNcl1eAWRiIhsztPPTMfev5LRfmJ3CMLdp6GCAN9uAdh7IBlPPzMdbdvdh3VJ2+D3mAKCnaA7VuwWjHUHtwHLYFaSuH33Tqzeuwk+fdpDEO7pRxSxeu8mALBaAvL+siXVnlO4IqzO8VrqnK01d7b0GTUXfAaRiIhsSklJCXae3I/Afh3/Pzm8SxDsENivI3ae2I//bo+Hb99OeokUAAh2Anz7dsLanQnQaDQmx9JqtVi/LQE+4fqJx52xBPiEt8f6bQnQarWWOTkTNBoN1u5MqOacNuG/P22sU7yWOmdrzZ0tfUbNCRNEIiKyKR9++AFcOvsZJIeVBMEOsvu8UeIoGiRSujZ2AlqEeGBD3EaTY51SKqHxsjdIPP5/LAEaL3sok5U1O4laiN24AS1CPEyekxDkhis30usUr6XO2VpzZ0ufUXPCBJGIiGzKldQUSD2cTbaRuDgAkqoThkot3BxxNT3VZJvsHBUkLrJqxpIhS6Uy2cYSrqanwd7dyXQsLR1QWqo23aaaeC11ztaaO1v6jJoTJohERGRT2gWHoCz/tsk22mI1BK3pdyzLC0vQNjDYZBsfbzm0xaXVjFUKX7ncZBtLaBsYhLKCas77phoymYPpNtXEa6lzttbc2dJn1JwwQSQiIpvy6quv4eaF6xDFiir3i2IFSv/MgUOJALGi6iRRrBBRnpKPiTETTI4VplBAmlsGYwU9RFGENLcMilBFzU6iFiZNmIjylHyT5ySmFaKdX2Cd4rXUOVtr7mzpM2pOmCASEZFNcXR0xLCH+iP9wB8GSaIoViD9wB8Y9nB/PDkiGlkHLxokVGKFiKyDF/H4sHHV1kOUSCSYHDkO2UmXDRIQURSRnXQZkyPHWaXWnlQqxePDxlVzTuPx5OgJdYrXUudsrbmzpc+oOWGZGyIisjnffvXdnTqIG/ajZedWsHd3RFlBiUEdRCwD1v5wp2ZgCzdHlBeW1LgOYmV5lKpq7E2LHG/V8ilvzZtv9jnVJV5LnbO15s6WPqPmgoWy72KhbCIi21NSUoLlH36IK6lX0C64Hea++mqVK6lsiNuIq+mpaBsYjIkxE2q9kooyWYkslQq+cjkUoQ27kkp152SJeC11ztaaO1v6jBqjmuQ6TBDvYoJIRERETVlNch0+g0hEREREepggEhEREZEeJohEREREpIcJIhERERHpYYJIRERERHpYB5GIiGpMq9XilFKJ7BwVfLzlCFPYdrkRrVaL08pTyFep4CGXo5sizKbjJWpoTBCJiKhGtu/eaViweN3XmBw5ziYLFu/fuQPHE+MQLhPg5yhFlroMK78V0T0qGv2HDW/o8IhsEhNEIiIy2/bdO7F67yb49GkPQRB020VRxOq9mwDAppLE/Tt34MZPsZjbKUgX7wMABogi4n6KxX6ASSJRFfgMIhERmUWr1WL9tgT4hOsnhwAgCAJ8wttj/bYEaLXaBopQn1arxfHEOMR0CKoy3pgOQTieGG8z8RLZEiaIRERkllNKJTRe9gbJViVBEKDxsocyWWnlyKp2WnkK4TLBZLzhDgJOK20jXiJbwgSRiIjMkp2jgsRFZrKNxEWGLJXKShGZlq9SwdfR9JrMPjJ75NtIvES2hAkiERGZxcdbDm1xqck22uJS+MrlVorINA+5HFnqMpNtskvL4GEj8RLZEiaIRERkljCFAtLcMoiiWOV+URQhzS2DIlRh5ciq1k0RhiR1hcl4k9QiuilsI14iW8IEkYiIzCKRSDA5chyyky4bJF2iKCI76TImR46zmfqCEokE3aNiEHcprcp44y6loXtUtM3ES2RLWOaGiIjMVlnCxqAOYm4ZpkWOt6kSN8CdEjb7ASxPjEe4gwAfmT2yS8uQpBbRPWoSS9wQGSGIxq69NzOZmZkICAhARkYG/P39GzocIiKbptVqoUxWIkulgq9cDkVoY1hJRXnPSiq2HS9RfahJrsMriEREVGMSiQQPhT/U0GGYTSKRIOyhxhMvUUPjM4hEREREpKfWVxArKiqQnJyM3377DdevX0dJSQm8vLzQsWNH9OnTB3KWDSAiIiJqlGqcIF65cgVffPEF1q1bB5VKBYlEAnd3d8hkMhQUFOD27dsQBAF9+/bFzJkzMWnSJNjZ8UIlERERUWNRo8xt1qxZeOCBB3D69Gm88847SE5OhlqthkqlQmZmJoqLi5GdnY0tW7agW7dumDdvHu6//34cOXKkvuInIiIiIgur8RXE8+fPo127dkb3e3t7Y8SIERgxYgRWrlyJ9evXIzU1Fb17965ToERERERkHTVKEL/++usadS6RSPDEE0/U6BgiIrJ9d8rGnLqnbExYjcvGWKIPS7K1eKxBq9XilFKJ7BwVfLzlCGP5H7qLZW6IiKhG9u/cgeOJcQiXCfBzlCJLXYaV34roHhVtduFpS/RhSbYWjzVs373TsOD5uq8xOXKczRU8J+urU4J47NgxxMfHIyMjA2q1Wm+fIAj48ccf6xQcERHZlv07d+DGT7GY2ykIgiAAAB4AMEAUEfdTLPYD1SZUlujDkmwtHmvYvnsnVu/dBJ8+7XXnDNxZgnD13k0AwCSxmav168WffPIJevfujXXr1uHatWu4efOm3k9RUZEl4yQiogam1WpxPDEOMR2C9JIK4M5FgZgOQTieGA+tVluvfViSrcVjDVqtFuu3JcAnvH2V5+wT3h7rtyU0qXOmmqv1FcTly5fjhRdewMcff8wyNkREzcBp5SmEywSDpKKSIAgIdxBwWqk0umqJJfqwJFuLxxpOKZXQeNmbPGeNlz2UycpGtVoOWVatM7tbt25hzJgxTA6JiJqJfJUKvo5Sk218ZPbIV6nqtQ9LsrV4rCE7RwWJi8xkG4mLDFlN6Jyp5mqd3U2YMAHbt2+3ZCxERGTDPORyZKnLTLbJLi2Dh4mVtCzRhyXZWjzW4OMth7a41GQbbXEpfJvQOVPN1TpB/Pjjj5GVlYXJkyfju+++w+bNmw1+iIio6eimCEOSugKiKFa5XxRFJKlFdFMo6rUPS7K1eKwhTKGANLfM5DlLc8ugCG0650w1V+tnEC9evIjDhw8jNTUVGzZsMNgvCAIfcCUiakIkEgm6R8Ug7qdYg5c6RFFE3KU0dI+aZLKOniX6sCRbi8caJBIJJkeOu/MWc7jhW8zZSZcxLXJ8kzpnqrlaJ4jTp0+Hi4sLfv75Z3To0AFSqelnOIiIqPHrP2w49gNYnhiPcAcBPjJ7ZJeWIUktonvUJLPKwViiD0uytXisobKEjUEdxNwyTIsczxI3BEE0do25Gk5OTti8eTOGD28a/+NkZmYiICAAGRkZ8Pf3b+hwiIhs2p1VR5T3rDpS8xU4LNGHJdlaPNag1WqhTFYiS6WCr1wORWjTP+fmrCa5Tq2vIIaGhiIrK6u2hxMRUSMmkUjqXPbFEn1Ykq3FYw0SiYSlbKhKtX5JZdWqVfjoo4+wa9culJeXWzImIiIiImpAtb6C2LdvX5SVlWHEiBGws7ODo6Oj3n5BEFBYWFjnAImIiIjIumqdIL7yyitGq7ATERERUeNV6wRx4cKFFgyDiIiIiGxFrZ9BvHnzJq5fv17lvuvXr6O4uLjWQRERERFRw6n1FcQZM2agZcuW+Oabbwz2LViwAMXFxVi/fr3Z/V2+fBnLly/HsWPHcO7cOXTq1Annzp2r9rjg4GCkpaUZbC8pKYGDg4PZ4xMRNXZ3yrScuqdMS1i9lSwpLCzErGlTkZuRBq+AIHy9eg3c3NxqFI+l4tVoNIjfEIsb6WnwCwxC9MRJBrV5tVotTimVyM5RwcdbjrAqStiY04aouah1gnjgwAGsWrWqyn2RkZF4/vnna9Tf+fPnsXXrVvTo0QMVFRWoqKgw+9jx48fjlVde0dsmk5leiJyIqCnZv3MHjifGIVwmwM9Riix1GVZ+K6J7VLTFCz2P6vkwZFnpGB/siyA/R6TlX8G0bh2g8QvElmMnzIrHUvF+tmQRziTGYbhPS/R1liHjt1I8t+ojdBsTjRfnvwkA2L57p2FB6HVfY3LkOF1BaHPaEDUntU4Q8/Pz0bJlyyr3OTs7Izc3t0b9PfrooxgzZgwAYOrUqTh58qTZx/r6+qJnz541Go+IqKnYv3MHbvwUi7md/n+puAcADBBFxP0Ui/2AxZLEUT0fRj9tIeYO6AY7uztPKfX29cCEtn5YfjYVo3o+jFffec9kPJ+dSoJP5qU6x/vZkkUQf0nEV706we5uP90BPNZOxOe/JOIzAO0feujOknJ9DJeUW713k+736towSaTmptbPIIaEhOCXX36pct+ePXsQHBxcs0Dsah0KEVGzpdVqcTwxzmAdYeBOubGYDkE4nhgPrVZb57EKCwshy0rH3C7BBn9m29nZYW6XYEhvpGP/+v8ajWdc+wCcSYxH9H2BdYpXo9HgTGIcXugSoksOdbEIAl7oEgJlYhzWbok3WG+4ciyf8PZYuyUe67Yarkl8b5v12xIsMn9EjUmts7IZM2Zg5cqVWLZsGXJycgAAOTk5+PDDD/HRRx9h5syZFguyOuvWrYNMJoOLiwsiIyNx9uzZao8pKipCZmam7sfYCzdERLbstPIUwmWC0bJjgiAg3EHAaaWyzmPNmjYV44N9jf6D3s7ODtHBvjh2/JjReM5cy8JwHxfcvFlUp3jjN8RiuE9Lg+RQF4sgoKNMwLWKApNzk11WiDyHUpNtNF72UCbXff6IGpNa32J++eWXceXKFcyfPx/z589HixYtdCuqzJ492+CZwPoyevRo9OjRA4GBgUhJScGiRYvQp08fKJVKhISEGD1u5cqVeOedd6wSIxFRfclXqeDnKDXZxkdmjyyVqs5j5WakIcjP0WSbwJYO0KTmGN2ff+s2/J1k0JRqjLYxJ94b6Wno62z6WfMWEgEV9qLJNmWiFhKZ6WslEheZReaPqDGpdYIoCAK++OILvPTSS/j111+Rm5sLLy8vDBw4EPfdd58lYzTp008/1f133759MXToUHTq1AnLly83+hINAMyZMwczZszQ/X79+nV07969XmMlIrI0D7kcWeoyPGCiTXZpGTzk8jqP5RUQhLT8K+jt62G0TfpNNaSOxpNID2cnXM0sRSeZ8aTWnHj9AoOQ8VspTP2pXa4VYVdmekEHe0ECu1LTL0Vqi0vha4H5I2pMap0gVrrvvvusmhBWp1WrVujTpw+SkpJMtnN1dYWrq6uVoiIiqh/dFGFY+W0FBohilbdJRVFEklrEHIWizmN9vXoNpod2wIS2flXeZq6oqEB8ahZ69h8E0Ug8Xdv44t8HT+CxllX/+WtuvNETJ+H5L1bisXZilbeZK0QRf5SKaGPnbjQWURThY+8GQS2YbCPNLYMitO7zR9SY1OgZxN9//73GA5SVlSElJaXGxxERUfUkEgm6R8Ug7lIaRFH/dqooioi7lIbuUdEWqefn5uaGUt9ALD+balCKrKKiAsvPpkLjF4j+k580Gk/C5Qx0jYpG/J/pdYpXKpWia1QMPj+bgoq/9VMhivj8bAoUUTF4fFQ0spMuVzlWdtJlPD4qGlNGjjfZZnLkONZDpGanRlcQe/bsiQEDBuDpp5/G8OHDDQqR3uvKlSuIjY3Fl19+iTlz5mDOnDl1DtYcf/31Fw4dOoQnnnjCKuMRETW0/sOGYz+A5YnxCHcQ4COzR3ZpGZLUIrpHTbJoHcQtx05gVM+HcezX04hu64tAFwekF6sRfzVLvw6iiXgm3K2DWNd4X5z/Jj4DMOvHeIyQu6CNkwzXbpdiu6pYrw4iAMMah7llmBY5Xq98jTltiJoLQfz7P5lMyM7OxqJFi/Ddd99BEASEhYWha9eukMvlkMlkKCgowNWrV5GUlIQrV64gLCwMb7/9NkaPHl1t37dv38a2bdsAAF988QWuXLmClStXAgD69+8PuVyOQYMGIS0tDZcvXwYAxMbGYsuWLYiMjETr1q2RkpKCJUuWIC8vD0lJSWjbtq3ZE5GZmYmAgABkZGTA39/f7OOIiGzFnZVJlPesTFJ/K4EUFhZi5rRpyMlIhXdAMP6zerWRlVSMx2OpeDUaDeI3bsCNtDT4BQUhesLEKldSUSYrkaVSwVcuhyK06pVUqmtD1JjVJNepUYJY6datW4iPj8eePXtw4sQJXL9+HWq1Gp6enujYsSMeeeQRjB8/HooaPPOSmppqNKHbu3cvIiIiEBERgdTUVKSmpgIAjh07hvnz5+PcuXMoKCiAu7s7Bg4ciHfffRcdO3as0TkxQSQiIqKmrN4TxKaICSIRERE1ZTXJdbh8CRERERHpYYJIRERERHqYIBIRERGRHiaIRERERKSnziupEBGRaXfKuZy6p5xLmE2XT9FoNIjfEIsb6WnwCwxC9MRJJuveGlPdeZszTmObO2vSarU4pVQiO0cFH285wuqxrBE1P3yL+S6+xUxE9WH/zh04nhiHcJkAX0cpstSVBaGjLVrA2lI+W7IIZxLjMNynJQKcZci4VXXh6epUd97mjNPY5s6atu/eWWVh78mR41jYm4yyWpmbP/74AwkJCcjMzIRardbvWBDw7bff1rZrq2OCSESWtn/nDtz4KRYxHYL01vmtXFLOb7RlVzmpq8+WLIL4SyJe6BKit75x5dJ1wuAos5LE6s77uNoOQannTI7TNSy8Uc2dNW3fvROr926CT3h7g7nJTrqMaQO4+gtVzSplbv73v//hgQcewOLFi7F//34olUqDHyKi5kqr1eJ4YpxBggPc+Qd0TIcgHE+Mh1arbaAI9Wk0GpxJjDNI2gDAThDwQpcQnP4xHhqNxmQ/1Z33+PYBuHZoD559INjoOMrEOBxL2NBo5s6atFot1m9LMEgOgTtz4xPeHuu3JTTLuSHLqnWC+N5772H8+PHIzs7G+fPnDZLDU6dOWTJOIqJG5bTyFMJlgsFf4pUEQUC4g4DTNvKP6fgNsRju09IgaatkJwgYIXdB/MYNJvup7ryvX/8LUQFeOJtTaHSczjIBgTdVjWburOmUUgmNl73JudF42UOZ3Pzmhiyr1gniX3/9hZkzZ8LJycmS8RARNQn5KhV8HU2/2OEjs0e+SmWliEy7kZ6GAGeZyTZtnGS4kZZmsk11511aUoIAZ0fkq41fiXSyE+AumH76yZbmzpqyc1SQuJj+nCQuMmQ1w7khy6p1gtivXz+cO3fOkrEQETUZHnI5stRlJttkl5bBQy63UkSm+QUGIeNWqck2126Xwi8oyGSb6s5b5uiIjFsl8HAwnkTerhCRL1Z9haySLc2dNfl4y6EtNv05aYtL4dsM54Ysq0YJYl5enu5n8eLF+M9//oOvvvoKKSkpevsqf4iImqtuijAkqStg7D1AURSRpBbRTaGwcmRVi544CTuyb6LCSLwVoojtqmJET5hosp/qzrtVq9ZIzMhFF283o+NcKBWR0VLeaObOmsIUCkhzy0zOjTS3DIrQ5jc3ZFk1ShC9vb0hl8shl8sRHh6O33//Hc8++yzuu+8+3fZ7f4iImiuJRILuUTGIu5Rm8Jd55Zu43aOibaZunVQqRdeoGHx+NsUgSax8u7jbmOhq6yFWd96bLmegTZ9B+Pf5VKPjKKJi0HPcxEYzd9YkkUgwOXIcspMuVzk32UmXMTlyXLOcG7KsGhXK/u6774w+GEtERPr6DxuO/QCWJ8Yj3EGAj8we2aWVtfxsr0zLi/PfxGcAZv0YjxFyF7RxkuHa7ZrXQazuvFfcrYNY3TiNae6sqbKETVV1EKdFssQNWQYLZd/FOohEVF/urAaivGc1ENte8UKj0SB+4wbcSEuDX1AQoidMrMNKKsbP25xxGtvcWZNWq4UyWYkslQq+cjkUoZwbMs0qhbJDQkLwww8/oFu3bgb7zp07h9GjRyMlJaU2XTcIJohERETUlFmlUHZqaipKS6t+k+r27dvIyMiobddERERE1IBq9AyiWq3G7du3dQ/GFhUVGbytrFarkZiYiNatW1suSiIiIiKymholiB988AHeffddAHeqtQ8bZvxB2IULF9YpMCIiIiJqGDVKEKOiohAcHAxRFDF9+nS89dZbaNeunV4bqVSKzp07IzQ01JJxEhEREZGV1ChB7Natm+6lFEEQMHLkSHh7e9dLYERERETUMGqUIN7rqaeesmQcRERWd6eEyql7SqiENViZkJKSEqxc9gH+unoFrdu2w5x5r8HR0VGvjUajQfyGWNxIT4NfYBCiJ07SKwtT3X5z25gzL5ZqQ0S2qUZlbtq2bVujQtksc0NEtmr/zh04nhiHcJkAX0cpstSVRZijrV6E+ZWnp+Ha4V8R5e+JQGdHpN8qQWJmHto8MhArvl0NAPhsySKcSYzDcJ+WCHCWIeOWfmHp6vab04e582KpNkRkXfVWB/Gll17SSxA3bdqEoqIiDB48GL6+vsjKysIvv/wCNzc3jB8/HitXrqz9WVgZE0Si5mP/zh248VMsYjoE6f2ZVrmMm99o663U8crT09Dq998wp0tb2N0TS4UoYuXZq7h+fw8Et28P8ZdEvNAlxKDN52dTcFzmie6leUb3C4Oj7pyfiT6EwVHoGhZe7bwAsEgbJolE1meVQtkffvghfvjhB+zYsQOurq667YWFhRgxYgTGjBmD1157rTZdNwgmiETNg1arxcoXZmJuJ/8q74iIoojlF69hzudf1/vt0JKSEkxTdML6fg/qJW2VKkQRE/adgau7B/7Tu3OVbcortJj48yHEjuoD+yrirRBFzDhyAQJgtI8KUcSsoxfRvqsCrz0YaHRePvw9EyIqMO9+422W/Z4BAQJevb/h55eI9FmlUPann36K+fPn6yWHAODm5obXX38dn332WW27JiKqN6eVpxAuE4w+LiMIAsIdBJxWKus9lpXLPkCUv2eVSRsA2AkCHmjpgEdcJEbb3Ci6hXHBPlBm5Rrto4O9iJ4uLUyO09/dAVJVpsl56aAthltRnsk2bjfz0FFbbBPzS0S1V+sEMS8vD4WFhVXuKywsRH5+fq2DIiKqL/kqFXwdTa8r7COzR75KVe+x/HX1CgKdHU22cZLYoZWD8XhLy8oR6OyA3JKqV7YCACdBgK+Dvclx/BzsISkrM9nG0w6QihUm20grtPCUmH5W3VrzS0S1V+sEcdCgQXjttdewf/9+ve379u3D66+/jkGDBtU5OCIiS/OQy5GlNp0IZZeWwUMur/dYWrdth/RbJSbb3NZW4LpaY3S/zL4F0m+p4eUoM96HKFZ7zjfUZSi3N50451UAGjvTt4U1dhLkak02sdr8ElHt1TpB/Oqrr9C6dWsMHDgQnp6e6NixIzw9PTFo0CC0atUKX375pSXjJCKyiG6KMCSpK2Ds8WtRFJGkFtFNoaj3WObMew2JmXmoMBJLhSji/E01Dhdrjbbxc3VGQmo2FL5eRvu4VCbgWHG5yXH2F6hRJm9jcl4uSVxQ2NLDZJvClp64JHG2ifklotqrdYLYqlUrnDhxAlu3bsVLL72EgQMH4qWXXsLWrVtx8uRJrsVMRDZJIpGge1QM4i6lGSQxlW/Zdo+KtsoLFI6OjmjzyECsPHvVIHmrfIs5sO9ghD42AZ+fTamyzapzqXDo1BX/Pp9a5f7Pz6ZA8dgEk318fjYF3aJi0Gv8RJPz0mNsDHqONd2m59gJ6DHWNuaXiGqv1oWyKw0fPhzDh7NcARE1Hv2HDcd+AMsT4xHuIMBHZo/s0so6fdYtwbLi29V45elpmHzgTh3EAGdHZFRVBxHArB/jMULugjZOMly7/f81DNferYNobL+uDqKJPnR1EM2YF0u1ISLbVaMyN3l5eXB3d4ednR3y8vKqbe/p6Vmn4KyJZW6Imp87K30o71npQ9GgK6ms+HAZrqdcQauQdnjl1XlVr6SycQNupKXBLygI0RMmGq6kYmK/uW3MmRdLtSEi66m3OogSiQRHjx5F9+7dYWdnV+2qKlptNU8q2xAmiERERNSU1STXqdEt5u+++w7t2rXT/XdNlt0jIiIiosahRgniU089pfvvqVOnWjoWIiIiIrIBtX6LecWKFTh58iQqKkwXTSUiIiKixqVOCWKPHj3g7u6OYcOGYdGiRThw4AA0GuMFXYmIiIjI9tU6Qfzrr79w8eJFrFy5En5+fvjmm28QEREBNzc39OvXD2+//bYl4yQiIiIiK6nRW8zV2bt3L959913s378fgiDwLWYiIlSWezl1T7mXMCMlYUy3scQ41uzHVsYhojvq7S3mv/vjjz+wf/9+7N+/HwcOHMD169fRuXNnPPvss+jfv39duiYiahL279yB44lxCJcJ8HOUIktdhpXfiugeFf3/RaXNaGOJcazZj62MQ0S1U+sriH5+fsjNzUW3bt3Qv39/9O3bF3379oWXV9Xrgdo6XkEkIkvbv3MHbvwUi5gOQXplwSqXnPMbPQkAqm1TXcJkzjjmJF2W6sdWxiEifTXJdWr9DKJarYadnR0cHR3h6OgIJycnODg41LY7IqImRavV4nhinEESBACCICCmQxCObd6I3zabbnM8Md7k4zrmjFNdH5bspzrWGoeI6qbWCWJ+fj6OHTuG8ePH48KFC3jiiSfg4eGBHj164NVXX8XPP/9syTiJiBqV08pTCJcJRhcUEAQBbjfz0FFbbLJNuIOA00plncaprg9L9lMda41DRHVT6wRREAQoFAr885//REJCArKysrBjxw44OTlhxYoViIqKsmCYRESNS75KBV9Hqck20gotPCWmV6TykdkjX6Wq0zjV9WHJfqpjrXGIqG7q9JJKWloaDhw4oPu5fPkyJBIJHn74Yb6kQkTNmodcjix1GR4w0UZjJ0FuNXdSs0vL4CGX12mc6vqwZD/VsdY4RFQ3tb6CGBQUhJCQEMyaNQt//PEHYmJisGPHDuTn5+O3337DsmXLLBknEVGj0k0RhiR1BYy9ByiKIgpbeuKSxNlkmyS1iG4KRZ3Gqa4PS/ZTHWuNQ0R1U+sEcebMmdi7dy8KCgpw4MABvPfeexgyZAicnZ0tGR8RUaMkkUjQPSoGcZfSDJKhyrd1e46dgB5jTbfpHhVtsjagOeNU14cl+6mOtcYhorqxaKHsxoxlboioPtyp9xePcAcBPjJ7ZJeWIUldVR1E020sMY41+7GVcYjo/9Uk12GCeBcTRCKqL3dWDFHes2KIwshKKqbbWGIca/ZjK+MQ0R1MEGuBCSIRERE1ZVYplE1ERERETRMTRCIiIiLSwwSRiIiIiPTUqFD2ypUrzW4rCAJefvnlGgdERERERA2rRgni3LlzzW7LBJGIiIiocapRglhRUVFfcRCRjbtTkuTUPSVJwliSBObNC+eOiBqbOq3FTETNw52ixnEIlwnwc5QiS12Gld+yqLE588K5I6LGqM4JolqtRkpKCtRqtcG+sLCwunZPRA1s/84duPFTLOZ2CoIgCACABwAMEEXE/RSL/UCzTHTMmRcAnDsiapRqnSBqNBo8++yzWLt2LcrLy6tso9Vqax0YETU8rVaL44lxeglOJUEQENMhCMsT49Fn8JBmdcvUnHn5cHMcRFRg3v2cOyJqfGpd5uadd97Brl27sGbNGoiiiM8//xyrV6/GoEGDEBwcjJ9//tmScRJRAzitPIVwmWCQ4FQSBAHhDgJOK5VWjqxhmTMvHbTFcCvK49wRUaNU6wQxPj4eCxcuRExMDACge/fuePLJJ7Fr1y706dOHCSJRE5CvUsHXUWqyjY/MHvkqlZUisg3mzIunHSAVTb/Y1xznjogah1oniJmZmejQoQMkEgkcHByQn5+v2/f4448jPj7eIgESUcPxkMuRpS4z2Sa7tAwecrmVIrIN5sxLXgWgsTN967g5zh0RNQ61ThBbtWqFgoICAEDbtm2xb98+3b5Lly7VNS4isgHdFGFIUldAFMUq94uiiCS1iG4KhZUja1jmzMsliQsKW3pw7oioUap1ghgREYGDBw8CAGbOnImlS5di3LhxmDRpEubOnYsxY8ZYLEgiahgSiQTdo2IQdynNINERRRFxl9LQPSq62b1kYc689Bgbg55jJ3LuiKhRqvVbzIsWLUJOTg4A4KWXXoIoiti0aRNKSkrwj3/8A//6178sFiQRNZz+w4ZjP4DlifEIdxDgI7NHdmkZktQiukdNarZlWsydF84dETVGgmjs/kczk5mZiYCAAGRkZMDf37+hwyGyOXdWA1HesxqIgle/YN68cO6IyBbUJNep9RXEkJAQ/PDDD+jWrZvBvnPnzmH06NFISUmpbfdEZGMkEgnCHnqoocOwOebMC+eOiBqbWj+DmJqaitLS0ir33b59GxkZGbUOioiIiIgaTo2uIKrVaty+fVv3wHVRURHy8vIM2iQmJqJ169aWi5KIiIiIrKZGCeIHH3yAd999F8CdVQCGDRtmtO3ChQvrFBgRERERNYwaJYhRUVEIDg6GKIqYPn063nrrLbRr106vjVQqRefOnREaGmrJOImIiIjISmqUIHbr1k33UoogCBg5ciS8vb0tEsjly5exfPlyHDt2DOfOnUOnTp1w7ty5ao8TRREffPABVq1aBZVKhdDQUHz00Ufo2bOnReIiIuvSaDSI3xCLG+lp8AsMQvTESZBKTS9rV1/93Hn7+NQ9bx+H1dvbx+aMZc14iKh5q3OZm/z8fJw7dw4ZGRkYMWIEPDw8oFarIZVKYWdn/jswP/74I1544QX06NEDly5dQkVFhVkJ4tKlS7FgwQIsXboUXbt2xRdffIFffvkFycnJCAkJMXt8lrkhanifLVmEM4lxGO7TEgHOMmTcKsV2VTG6jYnGi/PftGo/+3fuwPHEOITLBPg6SpGlrqxfGG3x+oXmjGXNeIioaapJrlPrBFEURbz55pv49NNPcfv2bQiCgBMnTiAsLAyRkZHo0aMHFixYYHZ/FRUVuoRy6tSpOHnyZLUJolqthq+vL55//nksXrwYwJ2rBh06dEBkZCRWrVpl9vhMEIka1mdLFkH8JREvdAmBnSDotleIIj4/mwJhcJRZyZ0l+tm/cwdu/BSLmA5BEO7po3IFFL/Rlitybc5YAKwWDxE1XTXJdWpd5ubtt9/G559/jhUrVuDSpUt6S0mNHj0aP//8c436q8nVxkpHjhxBUVERYmJidNukUinGjh2Lbdu21bg/ImoYGo0GZxLjDJI6ALATBLzQJQSnf4yHRqOp9360Wi2OJ8YZJGPAnUdrYjoE4XhiPLRabQ3PsnZjHdu8Eb9ttk48RESVap0grlmzBosXL8YzzzyDtm3b6u1r164drly5UufgqnPx4kUAQKdOnfS2d+7cGenp6SgpKTF6bFFRETIzM3U/169fr9dYici4+A2xGO7T0iCpq2QnCBghd0H8xg313s9p5SmEywSDZKySIAgIdxBwWqk0GYs5zBnL7WYeOmqLrRIPEVGlWieIubm56Ny5c5X7tFotysrKah2UufLz8yGTyeDg4KC33cPDA6IoIj8/3+ixK1euREBAgO6ne/fu9R0uERlxIz0NAc4yk23aOMlwIy2t3vvJV6ng62j6ZRYfmT3yVSqTbcxhzljSCi08JVUnh5aOh4ioUq0TxA4dOmD37t1V7tu3bx8efPDBWgdlDXPmzEFGRobu5/jx4w0dElGz5RcYhIxbVa/MVOna7VL4BQXVez8ecjmy1Kb/gZtdWgYPudxkG3OYM5bGToLcau4eWyoeIqJKtU4QX375ZaxYsQJvv/227mWSzMxMfPHFF/j0008xZ84ciwVpjIeHB0pLS6FWq/W25+fnQxAEeHh4GD3W1dUV/v7+up9WrVrVd7hEZET0xEnYkX0TFUbemasQRWxXFSN6wsR676ebIgxJ6goYe39PFEUkqUV0UyhMxmIOc8YqbOmJSxJnq8RDRFSp1gni1KlT8cEHH+CTTz5BWFgYgDuFtF9//XW8//77ei+O1JfKZw//+OMPve0XL15EYGAgHB0d6z0GIqo7qVSKrlEx+PxsikFyV/n2cbcx0dXWMbREPxKJBN2jYhB3Kc0gKat8a7h7VLRF6g+aM1bPsRPQY6x14iEiqlSjQtl/N2fOHMyaNQuHDx9Gbm4uPD090atXL7i5uVkqPpN69+4NV1dXxMfH6wp4l5WVYfPmzYiMjLRKDERkGS/OfxOfAZj1YzxGyF3QxkmGa7drXr/QEv30HzYc+wEsT4xHuIMAH5k9sksr6w5atqSMuWNZKx4iIqAWdRB///13fPnll7h69SratGmD8ePHY/DgwXUO5Pbt27rSNF988QWuXLmClStXAgD69+8PuVyOQYMGIS0tDZcvX9Ydt3TpUixcuBAffPABunTpglWrVmHXrl0slE3USGk0GsRv3IAbaWnwCwpC9ISJtV9JpY793Fm5RHnPyiWKel5JxfRY1oyHiJqeeiuUfejQIQwePBhlZWWQy+XIzc1FRUUFvvjiC8yePbtOQaemphqUy6m0d+9eREREICIiAqmpqUhNTdXtE0URS5cuNVhqr1evXjUanwkiERERNWX1liAOGjQIubm5+PnnnxEQEICioiJMmzYN+/fvR05OTp0Db0hMEImIiKgpq7eVVM6ePYt//etfCAgIAHDnTeAVK1YgLy8PGRkZtY+YiIiIiGxGjRLEnJwcg4yzMlls7FcQiYiIiOiOGpe5MbbcExERERE1DTUuczNgwADY2RnmlX379tXbLggCCgsL6xYdEREREVldjRLEBQsW1FccRERERGQjmCASERERkZ5aL7VHRERERE0TE0QiIiIi0sMEkYiIiIj0MEEkIiIiIj1MEImIiIhIDxNEIiIiItLDBJGIiIiI9DBBJCIiIiI9TBCJiIiISA8TRCIiIiLSwwSRiIiIiPQwQSQiIiIiPUwQiYiIiEgPE0QiIiIi0sMEkYiIiIj0MEEkIiIiIj1MEImIiIhIDxNEIiIiItLDBJGIiIiI9DBBJCIiIiI9TBCJiIiISA8TRCIiIiLSwwSRiIiIiPQwQSQiIiIiPUwQiYiIiEgPE0QiIiIi0sMEkYiIiIj0MEEkIiIiIj1MEImIiIhIDxNEIiIiItLDBJGIiIiI9DBBJCIiIiI9TBCJiIiISA8TRCIiIiLSwwSRiIiIiPQwQSQiIiIiPUwQiYiIiEgPE0QiIiIi0sMEkYiIiIj0MEEkIiIiIj1MEImIiIhIDxNEIiIiItLDBJGIiIiI9DBBJCIiIiI9TBCJiIiISA8TRCIiIiLSwwSRiIiIiPQwQSQiIiIiPUwQiYiIiEhPi4YOgBo3rVaLU8pkqHJyIff2QpgiFBKJpKHDIiIiojpggki1tmP3HsRt2QN4BELq7I6yWynA2gREjxyE4UMGNXR4REREVEtMEKlWduzeg9hfkhDYMwqCIOi2i2IYYn/ZBwBMEomIiBopPoNINabVahG3ZQ8CQyP0kkMAEAQBgaERiN+6B1qttoEiJCIiorpggkg1dkqZDHgEGiSHlQRBANwDoUxOtmpcREREZBlMEKnGVDm5kDq7m2xj7+wOlSrXOgERERGRRTFBpBqTe3uh7FaByTZltwogl3tZJyAiIiKyKCaIVGNhilCI+ekQRbHK/aIoAgXpUISGWjcwIiIisggmiFRjEokEMaMGIT15n0GSKIoi0pP3IXrkINZDJCIiaqRY5oZqpbKETfzWRMA9EPbO7nduOxekYxLrIBIRETVqTBCp1oYPGYQhAyOgTE6GSpULufw+KEJn8sohERFRI8cEkepEIpHgofDwhg6DiIiILIjPIBIRERGRHiaIRERERKSHCSIRERER6WGCSERERER6mCASERERkR6bSRAvXryIIUOGwNnZGX5+fpg3bx40Gk21xwUHB0MQBIMftVpthaipOlqtFidOJmHbjl04cTIJWq22oUMiIiKiathEmZv8/HwMHDgQ9913HzZv3oxr165hzpw5uH37Nj7//PNqjx8/fjxeeeUVvW0ymay+wiUz7di9B3Fb9gAegZA6u6PsVgqwNgHRLKRNRERk02wiQfzyyy9RVFSEH374AZ6engCA8vJyPPfcc3jjjTfQunVrk8f7+vqiZ8+e1giVzLRj9x7E/pKEwJ5REARBt10UwxD7yz4AYJJIRERko2ziFvP27dsxePBgXXIIADExMaioqMCuXbsaMDKqDa1Wi7gtexAYGqGXHAKAIAgIDI1A/NY9vN1MRERko2wiQbx48SI6deqkt83d3R2tWrXCxYsXqz1+3bp1kMlkcHFxQWRkJM6ePVvtMUVFRcjMzNT9XL9+vdbxk75TymTAI9AgOawkCALgHghlcrJV4yIiIiLz2MQt5vz8fLi7uxts9/DwQF5ensljR48ejR49eiAwMBApKSlYtGgR+vTpA6VSiZCQEKPHrVy5Eu+8805dQ6cqqHJyIXV2N9nG3tkdKlWudQIiIiKiGrGJK4h18emnn2LKlCno27cvnnrqKezfvx8AsHz5cpPHzZkzBxkZGbqf48ePWyPcZkHu7YWyWwUm25TdKoBc7mWdgIiIiKhGbOIKooeHBwoLCw225+fn6z2XaI5WrVqhT58+SEpKMtnO1dUVrq6uNeqbzBOmCIX4vwSIYliVt5lFUQQK0qEIndkA0REREVF1bOIKYqdOnQyeNSwsLMT169cNnk0k2yeRSBAzahDSk/fdSQbvIYoi0pP3IXrkIEgkkgaKkIiIiEyxiSuII0aMwOLFi1FQUKB7FjE+Ph52dnYYOnRojfr666+/cOjQITzxxBP1ECmZq7KETfzWRMA9EPbO7nduOxekYxLrIBIREdk0Qfz7JZ4GkJ+fjwceeAAdOnTAG2+8oSuUPWXKFL1C2YMGDUJaWhouX74MAIiNjcWWLVsQGRmJ1q1bIyUlBUuWLEFeXh6SkpLQtm1bs2PIzMxEQEAAMjIy4O/vb/FzbK60Wi2UyclQqXIhl3tBERrKK4dEREQNoCa5jk1cQfTw8MCePXvw4osvIioqCi1btsSMGTOwaNEivXZarRbl5eW639u2bYu//voLL730ku7q48CBA/Huu+/WKDmk+iORSPBQeHhDh0FEREQ1YBNXEG0BryASERFRU1aTXMcmXlIhIiIiItvBBJGIiIiI9DBBJCIiIiI9TBCJiIiISI9NvMVMlqXVanFKmQxVTi7k3l4IU9SutExJSQk++HAFrlxNR7u2gXjt1Vfg6OhYo7EsFYul+iEiIqLq8S3mu5rKW8w7du9B3JY9gEcgpPcUp46uYXHqac88jz3Hf4dXhx5wcJdDXaBC7qVjGNT9Aaz+6guzxrJULJbqh4iIqDlrdHUQyTJ27N6D2F+SENgzSm8NZFEMQ+wv+wDArIRq2jPP41i6Gg+OmwvB7m4/QZ3RqktfHDu8GdOeeR4Txo81OVaSMhmXcrV1jsVS50RERETm4zOITYRWq0Xclj0IDI3QS6QAQBAEBIZGIH7rHmi1WpP9lJSUYM/x39H2kbH/nxxW9mMnoO0jY7Hn+HmsT9xpdKyAbv0Rt+0A/Lv2rVMsljonIiIiqhkmiE3EKWUy4BFokEhVEgQBcA+EMjnZZD8ffLgCXh16GCSHun7sBLiHKHAlq8joWEVFN+ESFIqstD/rFIulzomIiIhqhgliE6HKyYXU2d1kG3tnd6hUuSbbXLmaDgd3uck2spae0JQbf3RVo9FA5uaF28U36xSLpc6JiIiIaoYJYhMh9/a68/KGCWW3CiCXe5ls065tINQFKpNtSm/mQdqi6qt6ACCVSlFamAsnl5Z1isVS50REREQ1wwSxiQhThELMT4exl9JFUQQK0qEIDTXZz2uvvoLcS79BrDDST4WIghQl2vm6Gh3L1bUlitOS4Rt0X51isdQ5ERERUc0wQWwiJBIJYkYNQnryPoOEShRFpCfvQ/TIQdXWDnR0dMSg7vfj6uHNBkmiWCHi6uHNGNT9AUyOGmZ0rIzT+xET2Q+ZZw7WKRZLnRMRERHVDMvcNCGV5V7ityYC7oGwv6dm4KQa1Axc/dUXd+ogJnwIrw494eDmDXVhjkEdxOrG2rF7T51jsdQ5ERERkflYKPuuplIoG7hTHkaZnAyVKhdyuRcUobVfSWXZ8hW4kpKGdiFBmDe36pVUTI1lqVgs1Q8REVFzVZNchwniXU0pQSQiIiL6u5rkOnwGkYiIiIj0MEEkIiIiIj1MEImIiIhIDxNEIiIiItLDBJGIiIiI9LAOYhOk0WgQuzEOaemZCAr0x6QJMZBKpXptSkpK8MGHK3DlajratQ3Ea69WXcLmlDIZqpxcyL29EKaoeWkZS/RBRERE1sUyN3c1lTI3i5atQNy2A3AJCoXMzRulhTkoTktGTGQ/vDnvFQC4UwT7+O/w6tADDu5yqAtUBkWwd+zeg7gtewCPQEjvKU4dXYPi1Jbog4iIiCyjJrkOryA2IYuWrUDibynoOOp5CHaCbrvY5REkHv0RWLYCl6+k4Fi6Gg+Om/v/bYI6o1WXvjh2eDOmPfM8Jowfi9hfkhDYMwqCcE8/Yhhif9kHANUmeDt276lzH0RERNQw+AxiE6HRaBC37QDa9hqjlxwCgGAnoG2vMdi4ZR/2/HYebR8ZW3WbR8Ziz/HzWJ+4E4GhEXqJHQAIgoDA0AjEb90DrVZrNBatVou4LXvq1AcRERE1HCaITUTsxji4BIUaJH6VBDsB5Y7ecA3qYrKNe4gCV7KKDBI7XRtBANwDoUxONhrLKWUy4BFYpz6IiIio4TBBbCLS0jMhc/M22Uawl0Lq4m6yjaylJzTlph9LtXd2h0qVa3S/KicXUmfT41TXBxERETUcJohNRFCgP0oLc0y2Ecs00NwsMNmm9GYepC2qvvJXqexWAeRyL6P75d5ed15IqUMfRERE1HCYIDYRkybEoDgtGWJF1Vf/xAoRLUpyUJR+1mSbghQl2vm6wtjL7aIoAgXpUISGGo0lTBEKMT+9Tn0QERFRw2GC2ERIpVLERPbD1aM/GiSAYoWIq0d/xIRRERjU4wFcPby56jaHN2NQ9wcwOWoY0pP3GSR4oigiPXkfokcOMlnLUCKRIGbUoDr1QURERA2HZW6akDfnvQIsW4G4LV/crYPohdLC3KrrICZ8CK8OPeHg5g11YY5BHUQAiN+aCLgHwv6eGoaTzKxhWNmmLn0QERFRw2Ch7LuaSqFs4E7Jmw1xcUhLy0RQkD8mxlS9ksqy5StwJSUN7UKCMG9u1SupKJOToVLlQi73giK0diup1LUPIiIiqrua5DpMEO9qSgkiERER0d/VJNfhM4hEREREpIcJIhERERHpYYJIRERERHqYIBIRERGRHiaIRERERKSHdRCtSKvV4pQyGaqcXMi9vRCmqHnJF41Gg9iNcUhLz0RQoD8mTTAsYVNcXIw5c+fhSmoG2gUHYOXyZXBxcdFrc+PGDQwYOBS5RcXwcnXB3l93wc/PT6+NSqXC8MhHkZVbAF8vd+zY9jPkcrlem8LCQkydPgOpGdcRHNAKa777Bm5ubhY9Z0v2Q0RERNVjmZu76rvMzY7dexC3ZQ/gEQjpPUWjo2tQNHrRshWI23bgbhFsb5QW5hgUwR42ehzOpGRBfn9vOLr5oKQgG6oLR9A1xBc7f0oAALQK6YQKB0/4PtAHju5ylBSokHX+EOzUebiechEAENSpK9R2LgZtHCqKkXbxDADg4b4DkZ6nhu/9+m0CvRxw4uCvFjlnS80dERFRc8c6iLVQnwnijt17EPtLEgJDIyAIgm575bJzkwaHV5voLFq2Aom/paBtrzEQ7O7p4+4yelE9QnDg0BGkql0R1Gcc7O4Zp0IUkXYoAcEORThz7jxc2vdC8CNjYWf3/08YVFRUIPXwZhRfPgqpVAppYLjRNpr0JPjIvVHoGISgKtqkHd4MO9V59Bg6vk7nbKm5IyIiItZBtClarRZxW/YYJDgAIAgCAkMjEL91D7RardE+NBoN4rYdMEgOAUCwE9C21xjE/vwrzqRkGSSHAGAnCAjqMw7Ky9ehdfA0SPwAwM7ODsGPjIVW5oHbgrPJNiWCM9JySgySw8o2QY+MRX5pC8g7htf6nC01d0RERFRzTBDr2SllMuARaJDgVBIEAXAPhDI52WgfsRvj4BIUapAc6vqwE1BYdAvyzr0MksNKdoIAmYsHfDr3NEjqdG3s7ODd4SG09A022UbesTuc5QEm2gjwfaAPdqz5uOp4zThnwDJzR0RERDXHBLGeqXJyIXV2N9nG3tkdKlWu0f1p6ZmQuXmb7ENboYWDm4/pYOzs4OBquh9ZS09IpLJq2njBroWJNiIgc5ejqMD4OVV3zoBl5o6IiIhqjgliPZN7e915qcKEslsFkMu9jO4PCvRHaWGOyT4kdhKoC7JNB1NRAXU1/ZTezINWU1pNm1xUlJtoIwClBSq4uhs/p+rOGbDM3BEREVHNMUGsZ2GKUIj56TD2LpAoikBBOhShoUb7mDQhBsVpyRArjPRRIcLN1Rmqi0dRYWScClFEaXE+si8eQ0VFRdVtKiqQc+kkbmalmmyj+uM4bqkyTLQRkXX+EIZPfanqeM04Z8Ayc0dEREQ1xwSxnkkkEsSMGoT05H0GiU7lm7jRIweZrOknlUoRE9kPV4/+aJAkVr7FPOnRgega4ou0QwkGSWLlW8yK9q0gUech9fBmg+Su8g1lSWk+nMRbJts4ircQ5O2INCNt0g5vhoesHKo/kmp9zpaaOyIiIqo5lrm5yxp1EOO37gHcA2FvkTqIXigtzDVeB7Fzbzi4y6EuUJmsgyhzl6O0mjqI97YxqIOYqzZoc28dxLqes6XmjoiIqLljHcRaqO8EEbhTtkWZnAyVKhdyuRcUobVbSWVDXBzS0jIRFOSPiTFVr6TyyqvzcOVqOtq1DcSKD6teSWXgoEFQFdyC3N0Zv+7ZU+VKKiNGjsQNVQH85O7YvnVrlSupTHt6BlLT/0JwYGus/tZwJZW6nrMl+yEiImqumCDWgjUSRCIiIqKGwkLZRERERFRrTBCJiIiISA8TRCIiIiLSwwSRiIiIiPQwQSQiIiIiPS0aOgDSp9VqcUqZDFVOLuTeXghT1E85F41Gg9iNcUhLz0RQoD8mTTAsl2NOGyIiImp6mCDakB279yBuyx7AIxBSZ3eU3UoB1iZYvCC0fsFtb5Re+hMrv43WK7htThsiIiJqmpgg2ogdu/cg9pckBPaMgiAIuu2iGIbYX/YBgEWSxEXLViDxtxR0HPU8BLt7xunyCBKP/ggsWwEA1bZhkkhERNR08RlEG6DVahG3ZQ8CQyP0kkMAEAQBgaERiN+6B1qttk7jaDQaxG07gLa9xuglfgAg2Alo22sM4rbuw4Yt+0y32XYAGo2mTrEQERGR7WKCaANOKZMBj0CD5LCSIAiAeyCUycl1Gid2YxxcgkINEj/dOHYCWvh1RIWjp8k2LkGh2BAXV6dYiIiIyHYxQbQBqpxcSJ3dTbaxd3aHSpVbp3HS0jMhc/M2PY6TO4QW9ibbyNy8kJaWWadYiIiIyHYxQbQBcm8vlN0qMNmm7FYB5HKvOo0TFOiP0sIc0+PcLoBYXmayTWlhLoKCuF41ERFRU8UE0QaEKUIh5qdDFMUq94uiCBSkQxEaWqdxJk2IQXFaMsQKI+NUiCi/8QfsSvJMtilOS8bEmJg6xUJERES2iwmiDZBIJIgZNQjpyfsMkkRRFJGevA/RIwfVuR6iVCpFTGQ/XD36o0ECKFaIuHr0R8SMjMDEURGm20T2Yz1EIiKiJoxlbmxEZQmb+K2JgHsg7J3d79x2LkjHJAvWQXxz3ivAshWI2/LF3RqHXigtzEVxWrJ+jUNz2hAREVGTJIjG7ms2M5mZmQgICEBGRgb8/Rvu+TqtVgtlcjJUqlzI5V5QhNbfSiob4uKQlpaJoCB/TIypeiWV6toQERFR41CTXIcJ4l22kiASERER1Yea5Dp8BpGIiIiI9DBBJCIiIiI9TBCJiIiISA8TRCIiIiLSwwSRiIiIiPTYTIJ48eJFDBkyBM7OzvDz88O8efOg0WiqPU4URSxduhSBgYFwdHREr169cOzYMStETERERNQ02USCmJ+fj4EDB0Kj0WDz5s1YvHgxvv76a8yZM6faYz/44AMsWLAAL7/8MrZs2YJWrVph6NChSElJsULkRERERE2PTayk8uWXX6KoqAg//PADPD09AQDl5eV47rnn8MYbb6B169ZVHqdWq7FkyRK88sorePnllwEAffv2RYcOHbB8+XKsWrXKaudARERE1FTYxBXE7du3Y/DgwbrkEABiYmJQUVGBXbt2GT3uyJEjKCoqQkxMjG6bVCrF2LFjsW3btnqNmYiIiKipsokriBcvXsT06dP1trm7u6NVq1a4ePGiyeMAoFOnTnrbO3fujPT0dJSUlMDR0bHKY4uKilBUVKT7/fr167UNn4iIiKhJsYkEMT8/H+7u7gbbPTw8kJeXZ/I4mUwGBwcHg+NEUUR+fr7RBHHlypV455136hQ3ERERUVNkE7eYG8KcOXOQkZGh+zl+/HhDh0RERERkE2ziCqKHhwcKCwsNtufn5+s9l1jVcaWlpVCr1XpXEfPz8yEIAjw8PIwe6+rqCldXV93v5eXlAHirmYiIiJqmyhynMucxxSYSxE6dOhk8a1hYWIjr168bPF/49+MA4I8//kC3bt102y9evKiri2gulUoFAOjevXtNQiciIiJqVFQqFYKDg022sYkEccSIEVi8eDEKCgp0zyLGx8fDzs4OQ4cONXpc79694erqivj4eF2CWFZWhs2bNyMyMrJGMXTp0gXHjx+HXC5Hixb1Ny3Xr19H9+7dcfz4cbRq1arexmmuOL/1i/Nbvzi/9YvzW784v/XLEvNbXl4OlUqFLl26VNvWJhLE2bNn47PPPkNUVBTeeOMNXLt2Da+++ipmz56tVwNx0KBBSEtLw+XLlwEADg4OmD9/PhYuXAi5XI4uXbpg1apVyM3Nxdy5c2sUg4ODAx5++GGLnpcprVq1gr+/v9XGa244v/WL81u/OL/1i/Nbvzi/9auu81vdlcNKNpEgenh4YM+ePXjxxRcRFRWFli1bYsaMGVi0aJFeO61Wa3Df/LXXXoMoili+fDlUKhVCQ0Oxc+dOhISEWPMUiIiIiJoMm0gQgTu1C3/55ReTbfbt22ewTRAEzJ8/H/Pnz6+nyIiIiIial2Zb5qahuLq6YsGCBXpvUJPlcH7rF+e3fnF+6xfnt35xfuuXtedXEEVRtMpIRERERNQo8AoiEREREelhgkhEREREepggEhEREZEeJohEREREpIcJIhERERHpYYJoJZcvX8bs2bMRGhqKFi1a4MEHH2zokJqM+Ph4jBkzBv7+/nB2dkZoaCi+++478AV9y9i2bRv69+8PuVwOmUyGkJAQzJkzB4WFhQ0dWpNUXFwMf39/CIKAkydPNnQ4jd6aNWsgCILBz+uvv97QoTUp33//PRQKBRwcHODt7Y0RI0agpKSkocNq9CIiIqr8/gqCgA0bNtTr2DZTKLupO3/+PLZu3YoePXqgoqICFRUVDR1Sk7Fy5UoEBwdjxYoVkMvl2L17N2bOnImMjAwsWLCgocNr9PLy8tCjRw/84x//gJeXF86dO4eFCxfi3Llz2LVrV0OH1+S89957BitGUd3t2LEDbm5uut/btGnTgNE0LYsWLcIHH3yAN954A7169UJOTg727NkDrVbb0KE1eqtWrUJRUZHeto8//hgJCQkYPHhwvY7NOohWUlFRATu7Oxdsp06dipMnT+LcuXMNHFXTkJOTA29vb71ts2bNwsaNG5Gfn6+bd7Kc//znP5g1axauXbumt1461c3Fixfx0EMPYcWKFZg9ezZOnDiBhx56qKHDatTWrFmDadOmQaVSGfw5QXX3xx9/4MEHH8RPP/2EESNGNHQ4zUJISAg6d+6MrVu31us4/JvTSpik1J+q/tBXKBQoKirCrVu3GiCips/LywsAoNFoGjiSpuXFF1/E7Nmz0bFjx4YOhcgsq1evRtu2bZkcWsmRI0dw9epVTJkypd7HYtZCTdKhQ4fQpk0btGzZsqFDaTK0Wi3UajVOnTqFd999F6NHj0ZwcHBDh9VkbNq0CWfPnsW//vWvhg6lSXrggQcgkUgQEhKCJUuW8PanhRw7dgxdunTB+++/Dx8fH0ilUjzyyCP47bffGjq0Jmn9+vVwdnbGmDFj6n0sPoNITc6hQ4ewYcMGrFixoqFDaVKCgoJw7do1AMDw4cOxfv36Bo6o6bh9+zbmzJmDxYsXcx1bC2vVqhXeeecd9OjRA4Ig4KeffsJbb72Fa9eu4fPPP2/o8Bq9GzduICkpCWfPnsWqVavg5OSExYsXY+jQofjzzz/h4+PT0CE2GeXl5YiLi8Po0aPh7Oxc7+MxQaQmJTMzExMmTMCAAQPwj3/8o6HDaVK2bduGW7du4fz583j//ffx6KOPYvfu3ZBIJA0dWqP3/vvvw9fXF9OmTWvoUJqcYcOGYdiwYbrfhw4dCkdHR3z00Ud488030apVqwaMrvGrqKhAcXExNm3ahK5duwIAevbsieDgYHz++ed49913GzjCpmP37t1QqVSYPHmyVcbjLWZqMgoKCjBixAh4eXkhISGBz31aWNeuXdGrVy/MmDEDP/74I/bu3YsffvihocNq9NLS0rBixQq88847KCwsREFBAYqLiwHcKXlT+d9kOTExMdBqtUhOTm7oUBo9Dw8PeHl56ZJDAPD09IRCocD58+cbMLKmZ/369fDy8tL7B0994hVEahJKSkowatQoFBYW4ujRo3rlLMjyunbtCnt7e1y+fLmhQ2n0rl69Co1Gg5EjRxrsGzBgAHr06IFjx441QGRE1XvggQdw5cqVKvep1WorR9N0lZSUIDExEY8//jjs7e2tMiYTRGr0ysvLERMTgwsXLuDgwYOsb2YFv/32G8rKyhASEtLQoTR6oaGh2Lt3r9625ORkvPzyy/jyyy/x8MMPN1BkTdeGDRsgkUigUCgaOpRGb9SoUVi9ejWSk5MRGhoKAP/X3r3HVF3/fwB/HpLL4bLDdQrJdQTolIUJAzQ5HSgLNUA405Fj4dZlKKuDjBINT4aT4hI2y2I1oNANswshymaOouD8gRlW4mbF0TzGQhgOuURc3t8/HJ+fHxGVi+egv+djO398Xu/P5/16fc7GeO3z/nzOB93d3Th9+jR0Op1li3uAfPPNN+jr6zPb8jLABtFsBgYGcOzYMQDXl5R6e3tx5MgRAJDeUkHTk5GRgaNHj6K4uBi9vb2yqy1hYWGwtbW1YHX3v/Xr12P58uUIDQ2FUqnEmTNnUFhYiNDQUCQmJlq6vPues7Mz1Gr1Lccee+wxLFu2zLwFPWBWr14NjUaDpUuXArj+j7asrAyvvPIKFixYYOHq7n+JiYkIDw9HSkoK9uzZA6VSib1798LW1hYZGRmWLu+BcejQIfj4+GDlypVmy8kG0Uw6Ozuh1WplsfHthoaGSf9B0J2Nv81j27ZtE8aMRiN/imWGIiIiUF1djYKCAoyNjcHPzw8vvPACsrOzYWNjY+nyiG4rJCQEn3zyCUwmE8bGxhAUFITS0lJkZmZaurQHgpWVFY4dOwadToeXXnoJ//33Hx5//HE0NjayAZ8lPT09qK+vx6uvvgqFQmG2vHyTChERERHJ8DFPIiIiIpJhg0hEREREMmwQiYiIiEiGDSIRERERybBBJCIiIiIZNohEREREJMMGkYiIiIhk2CASERERkQwbRCKyGL1eD4VCIX08PDyg0Wjwww8/TGme1tZW6PV6DAwMTKsOhUKBoqKi2+7j5+eHrVu3Tmv+2VJRUYFDhw5NiKvVaqxdu3ba877//vtme+fztWvX4OrqiqamJrPkI6LpYYNIRBalVCphMBhgMBhw4MABdHd3IzY2Fr/99ttdz9Ha2oo333xz2g3i/WKyBnEmBgYGkJ+fj9dff31W552Mk5MTMjMzkZuba5Z8RDQ9bBCJyKKsrKwQGRmJyMhIpKSkoLa2FiMjI/jwww8tXdr/C9XV1RgeHkZCQoLZcm7evBmNjY04c+aM2XIS0dSwQSSiOcXHxwceHh4wGo1SrKKiAqGhobCzs8PDDz+MHTt2YHR0VBpLT08HAHh4eEChUMDPzw8A0NHRgc2bNyMgIABKpRKPPPIIcnNzMTQ0NCu1GgwGaDQaODg4QKVSITU1FZ2dndL4hQsXoFAoUFVVha1bt8LFxQWenp7Izs7GyMiIbK6vvvoKwcHBsLOzQ2RkJE6fPg1nZ2fo9XoA15eRv//+e9TV1UlL8uNj444cOYLg4GA4OjpCo9Hgzz//vOM5VFZWIiEhAfPmzZPFL1++jLS0NMyfPx9KpRIhISHYt2+fND6+5F5aWgpvb284OTnh+eefx9DQEFpbW7FixQo4ODggIiICv/76q2xuX19fREREoKKi4i6+ZSKyhHl33oWIyHx6e3vR3d0NLy8vAEBJSQlycnKg0+lQXFyMc+fOSQ1iQUEB1qxZg507dyI/Px/19fVQqVSwtbUFAHR1dcHV1RUlJSVwcXHB+fPnodfr0dHRgfLy8hnVaTAYoFarER8fj+rqavT392Pnzp1ISEiAwWCQ7btjxw4kJCTg8OHDaG5uhl6vR2BgIF5++WUAwM8//wytVot169bh3XffxcWLF7FhwwZZI/vBBx9g06ZNsLe3l+6XXLhwoTTe2tqKwsJCFBQUYHR0FFlZWdi0adOEWm40ODiI5uZmpKWlyeLd3d2IiooCAOzZswcBAQH4/fffJzScNTU1WLJkCT766CO0t7cjKysLNjY2MBgMyMrKwvz58/Haa69Bq9Wira0NVlb/d00iOjoaJ06cmMpXTkTmJIiILGTXrl3CwcFBDA8Pi+HhYWE0GsX69esFAFFfXy96e3uFo6Oj2L59u+y4AwcOCKVSKbq6uoQQQpSXlwsA4sqVK7fNNzw8LA4ePCjmzZsn+vv7pTgAUVhYeNtjfX19xZYtW6TtVatWiejoaDE2NibFzp49KxQKhairqxNCCGE0GgUAodVqZXPFxMSI2NhYaVur1YrAwEAxOjoqxT777DMBQOzatUt23Jo1aybUFhMTIxwcHERnZ6cUG/9OLl26NOk5NTc3CwCipaVFFs/NzRW2trbCaDROeqyvr69YuHChGBoakmLJyckCgDh+/LgUq62tFQBEa2ur7Pjy8nKhUChEb2/vpDmIyHK4xExEFtXf3w9ra2tYW1vD398fDQ0N2L9/P1avXo3m5mb09fVBq9ViZGRE+sTFxWFwcPCOD7IIIVBaWorFixdDqVTC2toazz33HEZGRtDe3j7tmgcGBtDU1AStVovR0VGprqCgIHh7e6OlpUW2/1NPPSXbXrx4MUwmk7Td0tKCtWvXyq6wTfWewEcffRQeHh6yHABkeW7W0dEBALLjAODkyZPQaDTSUv1kYmJiYGNjI20HBQXBysoKGo1GFgOAS5cuyY51d3eHEAL//PPPbXMQkWVwiZmILEqpVKKxsREKhQLu7u7w9vaWGqWuri4AwLJly2557M1Nx81KS0uRnZ2NnJwcPPHEE3BxcUFLSwu2bNmCf//9d9o19/T0YHR0FDqdDjqd7o51OTs7y7ZtbGxk+Ts6OiY0aU5OTrCzs7vrmm6VA8Btz3N8bHxJflx3dzeWLFkyrZxKpVLWNE5Wx3jOwcHBO+YhIvNjg0hEFmVlZYXly5ffcszV1RUA8OWXX8Lb23vCuL+//23n/vzzz/Hss89i7969UqytrW0G1V7n7OwMhUKB3NxcJCYmThh3d3ef0nyenp64cuWKLHbt2rUZNbF3Y/z7vXr1KhYsWCDF3dzc8Pfff9/T3FevXpVyEdHcwwaRiOasqKgo2Nvbw2QyISkpadL9JrtKNTg4KLuaBQAHDx6ccV0ODg6IiorCuXPnkJ+fP+P5wsPDcfToURQXF0tXT7/++usJ+9185XGmgoODAQBGoxEhISFSPC4uDkVFRfjrr7/g4+Mza/ludOHCBahUKlljSkRzBxtEIpqznJ2dsXv3buTk5MBkMkGtVuOhhx5Ce3s7ampq8MUXX8De3h6LFi0CcP2NIImJibC3t8fSpUvx5JNPYt++fdi/fz+CgoJQVVWFP/74Y1ZqKywshEajwYYNG7Bx40a4uLjAZDLhxIkTSE9Ph1qtvuu5tm/fjvDwcCQnJ+PFF1/ExYsXUVRUBDs7O9l9iYsWLUJlZSVqa2vh6ekJLy8v6Wnv6fD394enpyd++uknPPPMM1Jcp9Ph008/xapVq/DGG28gICAA7e3tOH/+PN5+++1p57vRqVOnEB0dLTs/Ipo7+JdJRHPatm3bUF5ejoaGBiQnJ0Or1aKsrAzh4eHS1cGwsDDo9XpUVVUhOjoa69atAwDk5eUhNTUVeXl52LhxI+zs7PDee+/NSl3R0dH48ccf0dfXh/T0dMTHx2P37t2wt7dHYGDglOYKCwvD4cOH0dbWhqSkJHz88ceorKzEyMgIVCqVtF9OTg5WrFiBtLQ0hIeHo6ysbMbnkZKSguPHj8tibm5uaGpqwsqVK5GTk4P4+HgUFRXJflZnJoaHh/Htt98iJSVlVuYjotmnEEIISxdBRERyJ0+eRFxcHL777jvExMTcszy//PILwsLC0N7eDl9f33uW50Z1dXVITU3F5cuX4ejoaJacRDQ1bBCJiOaAjIwMxMbGws3NDWfPnsVbb70FLy8vnDp16p4vwyYlJcHf3x8lJSX3NM84jUYDtVqNvLw8s+QjoqnjPYhERHNAT08PMjMz0dXVBZVKhaeffhpFRUVmuUfvnXfeQU1NzT3PAwB9fX2IiYm55c8DEdHcwSuIRERERCTDh1SIiIiISIYNIhERERHJsEEkIiIiIhk2iEREREQkwwaRiIiIiGTYIBIRERGRDBtEIiIiIpJhg0hEREREMmwQiYiIiEjmfyGRwdzWt3W5AAAAAElFTkSuQmCC\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Observation: Setosa is perfectly linearly separable.\n", + "Versicolor & Virginica overlap slightly in petal width.\n" + ] + } + ], + "source": [ + "colors = ['steelblue', 'tomato', 'seagreen']\n", + "\n", + "# Histograms\n", + "fig, axes = plt.subplots(2, 2, figsize=(10, 7))\n", + "fig.suptitle(\"Iris – Feature Distributions by Class\", fontsize=14, fontweight='bold')\n", + "for ax, feat_idx, feat_name in zip(axes.ravel(), range(4), iris.feature_names):\n", + " for cls in range(3):\n", + " ax.hist(X[y==cls, feat_idx], bins=15, alpha=0.6,\n", + " color=colors[cls], label=iris.target_names[cls])\n", + " ax.set_title(feat_name); ax.set_xlabel(\"cm\"); ax.legend(fontsize=8)\n", + "plt.tight_layout(); plt.show()\n", + "\n", + "# Best 2-feature scatter (petal features are most discriminative)\n", + "fig, ax = plt.subplots(figsize=(6, 5))\n", + "for cls in range(3):\n", + " ax.scatter(X[y==cls, 2], X[y==cls, 3], alpha=0.7,\n", + " color=colors[cls], label=iris.target_names[cls],\n", + " edgecolors='k', linewidths=0.4)\n", + "ax.set_xlabel(\"Petal length (cm)\"); ax.set_ylabel(\"Petal width (cm)\")\n", + "ax.set_title(\"Most Discriminative Features (Petal)\"); ax.legend()\n", + "plt.tight_layout(); plt.show()\n", + "\n", + "print(\"Observation: Setosa is perfectly linearly separable.\")\n", + "print(\"Versicolor & Virginica overlap slightly in petal width.\")\n" + ] + }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\desir\\AppData\\Local\\Temp/ipykernel_7628/1884370761.py:25: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3. Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading']. This will become an error two minor releases later.\n", - " plt.pcolormesh(xx, yy, Z, cmap=cmap_light)\n" - ] + "cell_type": "markdown", + "id": "855e4fb5", + "metadata": { + "id": "855e4fb5" + }, + "source": [ + "## 3. Train / Test Split & Feature Scaling\n", + "**Key rule:** fit the scaler ONLY on training data, then transform both sets. \n", + "This prevents *data leakage* from the test set into the model.\n" + ] }, { - "data": { - "text/plain": [ - "(4.2, 8.0, 1.9, 4.5)" + "cell_type": "code", + "execution_count": null, + "id": "04d64fb8", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-16T07:46:08.485416Z", + "iopub.status.busy": "2026-05-16T07:46:08.485160Z", + "iopub.status.idle": "2026-05-16T07:46:08.495502Z", + "shell.execute_reply": "2026-05-16T07:46:08.494548Z" + }, + "id": "04d64fb8", + "outputId": "b957dce1-78e2-446a-840d-1351ff2110ba", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Train: 120 samples | Test: 30 samples\n", + "Train class dist: [40 40 40]\n", + "Test class dist: [10 10 10]\n", + "\n", + "After scaling – train mean: [-0. -0. 0. 0.] (≈ 0)\n", + "After scaling – train std : [1. 1. 1. 1.] (≈ 1)\n" + ] + } + ], + "source": [ + "# 80 / 20 split, stratified to keep class balance\n", + "X_train, X_test, y_train, y_test = train_test_split(\n", + " X, y, test_size=0.2, random_state=42, stratify=y)\n", + "\n", + "print(f\"Train: {X_train.shape[0]} samples | Test: {X_test.shape[0]} samples\")\n", + "print(f\"Train class dist: {np.bincount(y_train)}\")\n", + "print(f\"Test class dist: {np.bincount(y_test)}\")\n", + "\n", + "# StandardScaler: (x - mean) / std\n", + "scaler = StandardScaler()\n", + "X_train_sc = scaler.fit_transform(X_train) # fit + transform on train\n", + "X_test_sc = scaler.transform(X_test) # transform only on test\n", + "\n", + "print(f\"\\nAfter scaling – train mean: {X_train_sc.mean(axis=0).round(4)} (≈ 0)\")\n", + "print(f\"After scaling – train std : {X_train_sc.std(axis=0).round(4)} (≈ 1)\")\n" ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" }, { - "data": { - "image/png": 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8MLECVJQGUFSUnUuyNZKKougAfGuM+ZK6Poqkhp0xphj4EPCE9jPNWntdzDoTgTuBULwnD1hrH09V+FZPVRWcf37dSrAffQQXXwwHHyyvly6VfIxwdNQ338Cbb8Idd0SiopYskb4RwaBMYRUXQ9++8Kc/pXdoFghI86NFi8QiKyqCp5+W/aQzB0VRlLSSSkzAdcBJwC3A3VGP+vACh1tr9waGAEcbY0Y4rPectXZI6KFKoiHcd59zufB//COiGG69NT6ENhiU5SAj/Lvvlu2EvYHV1fDDDxE/TLp4/31REmF/is8n+7r7bucwX0VR8oJUFMVy4Atr7QfW2g+AmcCy+j5khcrQy6LQI7lDRGkY8+Y5Lw8EYOHCiO/CiY0b5f+aNc79Inw+aUaUTmbMcM4Nqa6WaTFFUfKSVBTFVOom2gVCy+rFGOMyxnyF+DfesdZ+4bDaKcaYecaYacaYnRJsZ5IxZpYxZtZ6bYKTfhIFNGTCR5GIbO5LUZQGkYqiKIyOeAo9dydZvxZrbcBaOwToBexnjNkjZpVXgD7W2r2AdwDHpHtr7aPW2uHW2uFdy8pS2XXrYO+9nZe7XDB4cCTvwomw169HD2dnudstPSjSyeGH1+1pEaakRLPIFSWPSUVRrDfG1DqujTHjgA0N2Ym1djPwPnB0zPKN1trwXMTjwD4N2W6r59JLJSQ2losvjqSk/vGP8empLpc4kEFG8r//PZSWRhoceTziXB4zJr3yHnqo5H5E76ekBK64QlNoWyvjp0YeSt6SStTTecAzxpgHQq9XIgl4STHGdAVqrLWbjTElwJHA7THr9LDWrgm9PAFYlLLkikQnTZ4Mb7wBc+eKlTBhguR2hNl5Z6maNm0a/PSTRDONH1+3E90uu8DDD0tl3E2bREnsvnv6p4NcLrjqKqmA+803EvA+YoQoKUVR8pZ6FYW19kdghDGmbeh1ZT0fCdMDeNIY40Isl+etta8aY24AZllrpwOXhKwVP1AOTGzEd8gN1dWS3dOpU9NCSJctk0yhvn0bN6ouKJBOcrvsIrWfopVEmOJiOO00UQIdO9ZVEtHr7LcfVFZKVnhjlUQwKBFTxcXO00nGiCJqRuGwQYJsYAOllNKWxud8pHLKVFZK1HOXLq3QyEpmVWgSY05JqCiMMWcC/7HWBiFeQRhj+gE9rLUfO33eWjsPiKsGb639S9Tza4BrGid6jggGpX7x//4nNz1jpObSiSc27Oa6cKGEqIbrNLlcUo6joaVAHn4Y3nsv8rpLFynOH/blBIPSx+L11yPyHXecdHYJv66ulu3MnClyFBXBxIlwyCENk2XGDHjkkUhpdI9H+mU04w4yc5jDwzxMFVUECbIHe3AJlzRIYfj9YtS9/77c/I0Ro+744yPrVFbC/ffD/PmyTmkpTJoEwxuVR6so6SWZRdEZmGuMmY20Ql2PtELtDxyK+CmuzriE+caUKXJjji7E98ILcmMePTq1bVRXww031I02CgSkrtOAAeJgToXp0+sqCYANG2R65+GH5fUrr8jUVLS8r70mvo3wneqBB2Tqyu+Xh9cLjz0mQ989YuMPErBqleRvROP1SiLfU085WzFpJt2DzuUs5x7uwUfk2M1nPrdyKzdzc8rbefpp0aE1NZFlzz8vxt3IkfL69tuleny4BqTPB/feCzfdJMn0rZ5oa0Oti6yT0Li11v4dGAY8C3QFRoderwLOstaeYq39PitS5gvBoGQ1x+YCeL3w4oupb+eFFxKHpDakAVKifZaXR5obTZ/uLO/LL8vzLVtESUTfxcLrvPRS6rIkktvahh2bPOI1XsNP3TLvAQIsYxkrSdAEIQa/H95917nX1X//K89XrRL3UWxF+Zoa0emKkmuS+iistQEkbPWd7IiT5/h8iTvHbdmS+nbWrk383vr1qW/HqS9GmNWrpXlwZQKXUnj55s1S1DBWUTRUlmTrrlmT+L08Zi1rCTr06iqkkI1spBe96t1GVVXipPNwLuTGjfITxJ5a1jq3G1GUbNPa3GVNw+NJnJewyy6pbydZI9/Bg1PfTrIKaOGopZ49nd/vFbrJ9ejhfCcrKJBtpEoyP8SwYalvJ48YzGCKiPc611BDH/qktI22bRPXPOzfX/736eOsp4uKGnY6KEqmUEXREIwRJ2/sfLvbDWeemfp2Dj3UubFQYaGEt6bKeec5Lx8+PHJ3SiTvxImR56edVjcRzhiJWjr55NRl+eUvncutl5U13CmeJxzN0ZRSigtX7TIPHo7kSNrTPqVtFBTAr38d/xN4PPCLX8jzsjI46qi6P4HLJSkmxxzT1G+hKE2n3n4U+UZe9KNYuBCmTpUppD595Ebbt2/DtlFdDffcI2Eu1kK/fpL41rlzw7Yzfz489JDMX7jd0sXll7+su85334n3dOVK2GknkXfAgLrrfP65+CQ2bxbr4LTToHv3hsmycaN8px9/FGWz117wf/+X0R4ZmfZrllPONKYxl7m0oQ1jGcuhHIohSYSbg1Dz5kkqy7p1cqqcfjr07h1531r44AOpGL9tGwwZIpFRiQzYFkNjEu3Umd0omtKPIpXGRR7gFKAPUT4Na+0NjdlhU8kLRaHkDXl5z8hLofIUVRRZI9ONi14GtiAhsg6lP5WcUl4uEUwLFog1csIJ8SGty5dL5FG4cdFJJ4lloeQVU5bM5M1XivBtakPfg1Zz4cF70aM4NybFp59KIFs4QfCcc5qtq0lJA6koil7W2qPrX03JOuXlUidp+3bJw1ixQkpjnHOOFOADmXa66SYJqbFWYjG//BKuvTZ++knJGTd88D4L/rUfVEntrsU/7sz/vbGR+2/bQld3av6QdPHaa5JTGmbdOsnhjO6HpbQuUnFmf2qM2TPjkigN54UXIkoijM8H//53JCj/n/+MNAoC+e/1wr/+lX1508TU8ZFHS+Bn72YWTB5RqyQA8BYT3NCJR2bOybo8zzzjvPxxbSvWakmoKIwx840x84CRwBxjzHehvhHh5Uqu+frrukoiTDAouQvWSitUJ376KaOiKanzxbol4HbIz6ku4fsvsjv1VF0dn/gXxqmZotI6SDb1NDZrUiiNo2NH54ysQEDCb42RGMuqqvh1tGJr3rBDm3bgd7gUTYA23VKtwZkeklVa0d5SrZdkJTyWWWuXATeFn0cvy56ISkLGjYtvBFRYKM7scBXZY45xzqPQAP28Yf9Ou1LUbwUUxmTdlVQzfkyHrMpSUCCR2k4MGZJVUZQ8IhUfRZ3c0FDZcG0wlA/ss08kWa6kRFJ5Bw2ShkZhTj1VPJBFRZF1DjkETjkld3Ircdx8eUc8e38HxduhXQWUVTD6ijmM2iH7qdnXXQc77FB3We/ecOWVWRdFyROSlRm/BvgjUGKMCTeqNoAPeDQLsimpcPzx0o9i9WppBBSbsOdywe9+J2nA69bJHSBRTYk8oqU4qlOlT+kOPPWHHVi0dSUbqreyT6e+lLpyE2JUXCwlz1etktYiAwfGKw6ldZFQUVhrbwVuNcbcGuob0TL4+WfJZi4tlRG5Uw/n+ggGJW9h7VoZag0YED+BW1kpmc7l5VJLOpNB6D//LNnQHTuKsnAqpdGunXPZkFbOFrYwhzkYDPuwD+3I7THavV0vkomwprqc6T9+QzBoOa7/QHYu6Rq3zrp1kgleUiKneGMT43v2TFwqDKQ+1dy5kmux++6R8mHRbNsGs2eLg3zIEOdM841s5Gu+pogi9mEfSmm4/8xaWLRIig/suKPUyFKfSvpIZlGE72xTo57XYq3NftxeU3n6aenNEO4eY4z0lG5It7WKCvjLX0QBBIOyjd694c9/jlyRn30Gf/tb5DMffyzlMO69N71ty4JB2eac0E/hconiu/761Hta5BHZtiLe4z0mM5kCCjAYHuMxzud8RjIyu4KkyBPffsLrtwwD1xAA3g+4OPj3H3LxkEgtrSlTpAVJ+BQH6R3VkPqOqbBiBfz1r6IAwoF3I0bABRdETvE5c+QyMEZu5MEgnHEGjI0Kk3mJl5jK1Nrf4FEe5XIuZwhDUpalqkrau6xeLfsoKJBmj3/9a7MwnpsFye5ad4ceDwJfINNNj4WeP5h50dLMvHnw1lsyDPJ6JQ5w+3bpGJMoHtCJhx+WEXx1teQseL0Savrcc/J+MAh//3v859aurZvFlA7efVeGdOHy59u3y/Du7rvTu58WyDrWMZnJ1FCDFy/VVFNDDQ/zMJvZnGvx4li+fT2v3zoMqktgW1t5VJfw0T37srBiOSAlyF57re4pXl0tp7hTddrGYi3ccQds3SqnXPj0++ILGROB3Lz/9reIHF6vyDBlihQIAFjSYQ7TmFbnN/Di5W7uppokJfRjeOopKT4QviSrq0VpNONUobwj2dTTKABjzAvAMGvt/NDrPYC/ZkW6dPK//8U38AEZDn37bWqd3Px+uTHH5i7U1MCHH0qZ0M8/T9yA4IMP4OyzGy57It55J/47WSs5FOvWybAqQzR3H8JnfIbFuc7Z53zO0TShGEFs/aI0HKwXFi0C4xBDEnDx4rwfGTxyZ95/3/kUt1aUSMpRS4nqL4W+x8qVUjsyFq9XTslDDpHpJqepH79fLpWzzoIPej9BDfEazGCYy1wO4ICUxP344/ixnt8vhv1FF+kUVDpIZR5kt7CSALDWLgDSbMhmgWRDqlSHW8Fg4s504TPV6UqN/nw6SWQJFRQ0zEpqhdRQ49iUKEgwrqtdPuD3Wwg4XK6BAmpq5E6YqKcWpNei8PsT33zD+6mpcb5UgsGodVzehMraSYEkwinnNLwvJT2koijmGWMeN8YcFno8BjS/zOyRI50d18Fg6hO4bjfsumv8cpcL9t1Xnh90UOLPpzsQ/aCDJNw1lrZtM+KjaEmlM4YznEIHgzrs1E4r46dGHo3kqIF9cKxs7vFx5J5SDv6gg5xP8UCgngZI0fIlkzH0fu/Lp+JuFz815HZHeoAPHep8o/Z4xJcBMGLleDzECxwg0CAfxdCh8YrLGNhzT7Um0kUqiuJsYCFwaejxTWhZ82LECMkxCDucXS45s887r2FhIeedB23aRJLYPB6JNAo3LnK7pdlALMXFiRsNNZaxYyXEIyx/UZHIc8kleoXUQx/6MIYxePBgQn9u3IxjHD3Iv0CAPct6M/jcz6GkCgr8UBCA0m30OW0mI7sMBGSsstde8af4pEnpTcQvcMEl//kCjycSYFdcDDvvDGPGyOuOHSUi2+2OOLc9HjjwwMi4bM91o9mP/WqVRQEFuHHzK35FGWUpy3P22dL8KawkPR4ZK/3mN+n4tgq0tsZFwaA4tWfPlpv9YYc1vDkPSOjrhx9KoHm/fs5DucWL4dlnJUpq2DBRHk5hq03F74eZM2USuksX+U4dO6Zt8y3BekjG93zPp3yKwTCSkfSlAQ2oog9OlvoqfLR+Ea/N/hkbgDH7dGF097q+NWsl+vvLL+UUP+QQGUskpZGWzsaHxzNjhgQA7rWXNFZ0uequs3w5fPSRTIvtv3+kQ2+tvFgWdp3Blzu+hGfAcg7hkPp7kTsct+3bZT/LlonCOvhgrVITS0YaFxljnrfWnmaMmQ/xE4nW2r0as8OmkjeNi4JBOfs9nqaN3v1+ubqdppDCVFaKkslgp7hEtHRF0SRyoCggtVOmQSSQ3Rf0s3m7l86lJbiMw+RDivJXVvuoCQbpWFrP+ZvqMdSTslFkqnFRuA6EFgeMxloJVH/hBYnDa9dOWo8edljDtrN5MzzyCHz1lWxzwACZmooe/n32GfzjHxEHeadOkiOhabKtkvJyic4Od8/dfXc4//z0B7cFbJAr/raKVTdNhK3toFM5u978JDf/ZpcGbee7nzdy/Q0G/3qxcE3bbfzmynKO3F2bZjU3UmmFei7wobX2++yIlJycWxTTp0u/7OjoJrdb4vDCXrr6CAbhsstg/fpIyIYxMlfwwANiMy9fLk2JYnG7pd9EOhP3kqCDtyiSHYwMWxR+v7iewnmeIKdMWZmU22iwsZlE3svvXc6KP/0OqqKy1Uq3sedj93PtLxJUDIz5Lj5/kDN/7YeaIiJeeAvGcvtDW9mlk0MzpmTHUE/EJtMUiyKVu83OwCPGmCXGmKnGmIuNMUMas7NmTzAoLUVjQ2B9vkjCXSrMmycWRXRcn7USNxjOWHriCefP+nzw/vsNkVppAcyZIzOQ0ZFE4R5Un3+evv0EbJAVN/26rpIAqGrD/L+cmvJ2/vXBjzFKAnluDQ88ty4tsirZo17vqrX2OgBjTAnwW+BK4F7AleRjLZNw2qcT69envp01a5yDv71eyWYKr5OIJUtg9OjU96fk54i0AYl5a9c650KEs5DTxfYaP5QnaJa0shcwN6XtLF2W6B3DxuVtnN/Kx99IAVKwKIwxfzbGvAG8DfQHroD6whJaKOG4OyeSVU+LpXfv+PAQkPmDcDOAXZLMB6eSRa60KHr3dnZeFxdDnz7p209JUSHsmEDzDEh99nmP3RPdWiw9d8tuMyal6aQy9XQy0Bl4F3gBeNlam2S424IxRhzXTo2AfvnL1LcTLrUZfeW7XKKEDgiVLTj7bOdoquh1lFbDnntKDEN0hLXLJSk8++2Xvv24TAF73P40lG6r+0bpNg6549W6y5JkYE7Yvy+mbRV1AyYtFAS59JQGDKqUvKBeRWGtHQYcAcwEjgTmG2M+zrRgecthh8GFF0p0ktstI/+rrpJA8lQxRirQHnmk3PhLSiQX45ZbIkqoa1e48cZIfwljJCv8vvvS/pWU/KegQALeRo+WmIfSUsmRuPnm9Kfn/OWX/dj36ftg929EYQz5isNevI+Lju6f8jZcBYYH7jeUDVoJJghYPL3Wc+09m+jWLsHUk5K3pBL1tAdwMHAoMBxYAXxkrf1L5sWLJ+dRT62MFjNtnOkv0oTyHLVk82BrhFGrI1N5FGFuAz4E7gO+tNamsbxYGrFW0lFnzJDnhx4qNnl0GKnPJxVcZ86UkfyYMekv1J8qwSBMmyalwoNBmU769a/rDg/9fomC+uQTsTqOPFLmIKLZtk22MW+eWCHHHCMT2jkgQIDP+IyP+IgiijicwxnKUIxjkaLELGUpj/M4q1hFV7oykYkMYlCddVayktd5nbWsZQ/2YAxjaEvDmg/4g0Ge+eELPprhwhRYDjsMzui3HwVRU35er5xSX34poahHHy0pL9Gs2r6BR/9qWf7xznTabR1nXbqJIQNykxa8ZQvceaf0sXK54PDD4Zxz6q5TWVTO27zAQhbSjW4cy7HsRN3chkVbV/LEhz+wbkkZOw7cwrkH7k7fNnWrGCxZIu1dysul3tLo0XKappuqKin+/NVXUnzgmGPi/TIbN4osS5bIe8ceK+vmgupqeO89iVTr0EHOmf4xxtjmzfDmm1LAoVcvkbcxRSKyRcZKeBhjihEF40EU0rRwBFXUOh7g30gP7o3A6dbapcm2m9CieOABKYgfDl31eKS916WXyrSNzwfXXishItHrnH563U4q2eKKKyRXIpqyMsmoKiyUqKjrr5deF9HyHn+89MkGKQ/yhz9IYwCfT5RiYaEE3Kdp4jrVwWWQILdxG4tYhBeR14OH0YxmIhNT3t9XfMUt3BK3/HzOZxSjate5m7trK8AWUUQppdzBHXQkQfmSmC8StJYL/v0Z5TP2kt4OAG0q2eGYudx/mrQg9Xqlr9W6dfLcGHErnXUWHHWUfOTbypX85coS2FYqvSIKa8DtY+LrUzn20AZOsTRxJF9ZCeeeG1+1tXv3yIzlZs9arjpyGNtKNlBDDQUUUEghl3M5QxkKwAcPDubBq3cGrxtq3OCphmIvV9+5kWEdpMTJxx/LqRquEut2S+WY226TqbF0UVkpM7sVFXKKh3+DCy+MuOpWrJC+YTU1MrYqLJTHDTek19GfCtu3S6OoDRvqynvOOaK0QSLYrrlGzim/XxR6YaHcnmIHIekk03kUjcULHG6t3RsYAhxtjInNSDsX2GSt7Q/8Dbi9UXtasqSukgB5Pnu2NP0FObOjlUR4nWeflbMxm8ydG68kQK6GV0MOwy++qKskQJ6//LIM4UCeb9kSqS8dLivyyCNZr7E8j3l8y7e1SgLAi5d3eIe1rE15Ow/wgOPyyUwGRCE9xEN48daWCa+hhkoqmca0lPfzztr5lM/YO6IkALa15efXh/Hphu8AGRX+/HPkJ7BWDu9TT8kNAeD+l5fB5vaiJAD8RVDVhqd+cyjBLNdRu+su59Lea9fCd/KV+O+gG9nq3lBbxjtIEB8+HuKh2uP52GNAZVtREgDeYqhox4PPbgTkhvzYY3Iswvvz+eS0fOON9H6nV16R0Xf4FA//Bo8+Gqmk/69/ye8Rfu33y6j+8cfTK0sqvP12RElEy/vEE5FlTz8tVlJY3kBAzrFHHsm+vKmSMUVhhfAduCj0iD2NxwHhtm/TgNHGNKJw0rx5zv0XfD74+mt5HqtIwhQWRq6ibDFjRuL3PvtM/s+a5SyvywXffCPPv/zS+XvX1KQ3uD4F5jDHsSuZwTCf+Q6fcKaCCsflPnxUUcVGNrKNbXHvBwgwm9kp7+fTbzdClUM6c00hHyyWoL6ZM517PBQWwvehSNH1Hw2EQPwMbmD5jqzZmKRBRAZYvDjxey+/LP9n9XiFgCt+9riKKjawgeqaAL75Dq2BbQFbP5Ow7HCHulhqauQySyeJTvFAIJJytGiR82e//z77PSkSnTPGyHgWIiVYYlm1KnGaVq7JaB0IY4zLGPMVsA54x1obexr1RJzjWGv9wBYkFDd2O5OMMbOMMbPWVzjcSEpLnUM/iooidnBZmXO4qbXptZVToSxJCeWwLO3aOZfpMCZSFjNRTkcgkPXSmW1pi8shB7OAAtqQ+vFN5s9w46aEEseGQwClpP6d27YpAI/DFe2uoW2pHPd27Zw/GwxGfqaCdlUJ99GuNEM5qQl6R8RGbUfToYP8b1PTwfH9IEGKKabQZaAogRuyjXzXNm0SNwtKdMwaS6JLMxCIvOfUgwPkeGS72n6iSzIYjFySicqthGeO85GEisIY84oxZnqiRyobt9YGrLVDkAS9/UIRVA3GWvuotXa4tXZ4V6ebbKK8AmOkAD6I49opY6m0NLMTg06cckri98L+h9Gjnc+awsJIKO6xx8ZfJS6XJO11SpBdmyEO4zBHRdHQRkADGei4vAc9KKSQtrRlMIPj9uXBw7Ecm/J+TttjdzDOU0On7S7H9+ij42++4fpKfUPVyIed8mN8zoG7mo7Hf0JZaXav+mSutokT5f+x31+Gx1/37uvCxSAGUUYZhQUF7HjiTCjeXncDJVXsdqr0K+vRQ6LDY8cxHo+ckunkuOPiT/GCAonX6NpVXh9xRPzvVFQEo0ZlX1E4XZLGiGN9p1C8wFFHxctbWCi3sWanKIC7gLuTPFLGWrsZeB/iGhGvAgm3MMYUAu0Rp3bDaNdOPF6lpRJ2EX5cfrlkJIHkIJx5pvxCpaWi1jt3Fi9Ylgrs1dKhA/z2t/HLjz8eBoZulDvvLJ1X3O7I9+nQQTxe4bPpoIMiCrC0VM7QXr3g97/P1jeppRvduIiL8OChJPTXjnb8iT85djFLxDVcQ+cYo7ItbbmBG2pfX8zF9KEPHjyUUkoRRRzKoYwm9bImvUu7cdr1C6FdBZRtgbIK6LCZs278nh08HQDpc3X66XJ4S0rklOnSRU6Z8A3o9/sdTPdj54jDt/0WKN1G8X4LuHly6n6ZJhFlXZz63NS46BqQYLrwjWnU0rMZ9dPZFFFECSV48NCb3lzCJbXr3zBuOG33Xwgl2+U7FW+n06iv+dNhI2vX+cMfRGF4PHLqFRXJ6Tu8Ua7SxOy/v0Q5RZ/iPXvWrZd5+umw996RdYqKpHhBuJdYNtl7bzjppLrnzA47iPM6fM6MGyexJmF53W7Ybbf8brSUyainrkCNtXZzqE7U28Dt1tpXo9a5ENjTWnueMWYCcLK19rRk202aR+H3w7ffynTSwIHOFkRVlUzmlpSI8si2koimulri/mpqZFjkZLdWV8t3Ki4Wy8dJ3s2bZQK0Y0cJ80jjMKqhgThevHzHdxRSyG7s5mhlpMIiFjGf+ezKrrXROLEsYxkb2Ugf+tCJeiyoBF+kKuDl7VULcRnDkT0GU1wYP39TWSnz3W3ayCnjdHgXj/g3X88P0qe3Yd/BjZz2S/Vg15MDsXq1VMEvK4MJE5ynpDaNf5Sf+InOdKY3veO2AfDV5p9YvGkDe3TuzqCy+NLg1sppt2WLhH8mm1FtKlu2SMhvhw6S4+r0G6xdK665Hj0y0gm4QWzdKnE07dqJge8k7/r1ErG1ww4NqwDUWDLSuKh2BWN2BW4FBgG1s2vW2qStwIwxeyGOahdiuTxvrb3BGHMDMMtaOz0UQvsUMBQoByZYa5ck226TE+4qK+XGW1IiORS5VBTNgFzlXq1hDStYQXe6szM7x71vsSxhCRvZSF/60oUGBs0n+mION+EKKviO72hDGwYykAIHQ3wd61jKUrrRjT70cdz0UpayjnX0pjc7EN9TJPjceL77Tk7R3XZzvvH6Cqr55pQbCRJkMIOTW2tJGittYANLWEInOtGPfol9Qy0o+c5aUTbl5XLz7hznDW3ZZDrh7l/AdUj46iikX3YqpT/mQfxQMDqj21pbDWTvTHz1VQmHLSyUs6a4WOYRdo6/ESm5wY+fv/E3vuIrCikkQIB+9ONqrqYECUHdwhZu4ibWspYCCvDj52AOZhKTHG/iTeFFXmQa0yikEIullFL+wl/YEWkwFSDAgzzIF3xBIYUECdKLXvyJP9UmAFZSyS3cwgpW1Mq7H/txERfVWlyrv2vLjRdK/qQxYhyfcgqcfHJElnnd3uXuA0+BqNDWi7mY/Ug9ZyZIkMd4jA/5sFbeHdiBP/NnOtAhLccsH9m0SSribNgQOb6jRkneibaXr59ULIrZ1tp9jDHzrbV7Ri/LioQxNNqi+O47OVNiY9c6doSHHlLLIkSuB5BTmMKrvIqPyO9USCEHcRAXciEAN3Ij3/ANASKhNx48nMVZjGGM84YbMa2zgAXczu11ckMAutKVB3gAg2E603me5+vI68LFMIZxJVcCcDd3M5vZ+InEebpxcwqncBInYS1c3O8Y1v1Ud+rR44Err5TYhcqiTZw/die8hXUd527c3Md99U+9hXiXd3mSJ+t8JxcuBjKQ67guySeJP4bNqGzJtdfGh8t6PKIoGtqcsrmS6YQ7rzGmAPjeGHORMeYkaGCthHzg7bedA5zDPgAlL3iXd+vcdEGsjE/4hCBBtrKVRSyqoyRAfCNvkN5sr7d4K05JAGxlKz/yY+06sfIGCDCXuVRTjRdvnJIAyQt5m7cB+GlOByrWx08heb3w1lvy/Ite/3XoXC9TcB+Teo3ON3gj7jsFCLCYxQlzWJo7mzaJLyU2p8LrTX+CYEsllamnS4FS4BLgRuBw4NeZFCojJMu+3r498XutgFxbEdE4Je2B3MwCBPDiTTi9FPfZppbEwPmcMRiqqHLeZxQ11GAwWKc7fNRnqyqKMAXO64RP2+2FFQQK4vMbaqiplSUVtuN8rhsM1VRTRhKPdDosiGTbzNCJuH174gmDqtQPXasmFV/Dl6EM6wrgEmvtydbaNDZfzBIjRjhn5vj9kZBUJefsxV6OjtVd2IUiiuhMZ9oRn9XlwsVw0hubOYIRjs7iIEEGILk3QxnqqLi60pW2ob/uxFd7K6CAYQwDoP9+5QT98dtwuyMpQnv/fBQFNj6CzIMnYVSYE8MZ7hiJ1pa2dKVryttpTnTv7pzkVliY3l4eLZlUOtwNN8bMB+YhvSi+NsbkxD/RJA4+WJzWYWVhjFyJZ56Z/cxsJSG/4le1uREg/oliipnEJEBGvhdwAR48tTc8N27KKOMUkiQyNoJRjGJHdqxVFgaDGzcTmUhxKADwDM6gLW1x466V14OH8zm/VuGdz/l48FAYMuCLKKINbTiDMwAobhPg7Pvn1Mkk9ngkxDNcSG6nisGMWnpOnWQ5Dx6GM7xWaaXCqZxKe9rXyuvChRs3F3BBg6v8NhcKCuCCC+SYhi0Lt1tCbU88MZeSNR9ScWbPAy601n4Uej0S+Ie1tgGdetJHk8Jj/X4p2f3FF5KzcOSREhjfysjpVFMKO9/iWcc7fR/h+06f03vLXoz58QK6bI+K4x8/lTWs4S3eYg1r2IM9GM3o+BIejfmiMdMrPnx8zMd8yZe0pz1jGENf6kaGV1LJu7zLIhbRk54czdF0o1sdOdaVLuWt/g+yst0iBm44iCN+mkQ7X934zCVL4J13JC1m331h5Mi6ORAWy9c7vMWMPk8QMH4OWX4W+6w+vq5Fk8L0UBVVvMd7zGc+3enOURxVG8WVUzJ8Yq5aJT6fdeukWv+oUVmvdJNTMp1HMddaOzRm2ZxQ57uso42Lmk6+K4p6SXWuPA2KIi3kS0OifCefnGUtkEznUXxgjHkEeBaJuzgdmGGMGQZgrZ3TmB0r2SVn12AjdjxnjpRiXrtWSlZNmCCj6zBBgvydv/M5n2OxuHFzJmdydFSFmBpvAc89LYnvPp+UdJg4MTMZu9OZznM8V9vf4WAO5nzOrzPSf+89+O9/JQKnZ0/paRHdPdfvl2zqt96SQLyBA0XenaIMqZ9/lm65a0Id67t2hauvrrvOWtbyBE8wn/m4cXM4hzOBCbVTeekiSJCXeZnXeZ1tbKM//ZnIxDhrq0FkwbGdDJ9P0qxmzJDne+0lreu7dav3ow3mq6+kXP2aNXKOjx8vvdbylVQsiveTvG2ttYenV6TkqEXROJqLopg7F+6+u24ks8cjF2x4vv768XuwkIVxn41ubnT7CQcy782e1IQChcJFd//2t0glVUcaOCJ/ndd5gifilg9nOFdxFQBvnDOe//ynbtV4t1vq/wweLK/vuy++RHVJiRyLLl1EkfzqV/EltwsK4J//lO9W4V7PpeN2oYqq2kirIorYgz24hmsa9L3q43EeZwYz6oQGe/BwO7enZxorByfsjTdKpHz0OdOmjfw2iarCNoZ58+COO+LP8bPOktJtmSKjeRTW2lFJHllVEkrDmDo+8mguPPNMfLqL1wtTpkgyfWXhZkclAfA0TwOwenFb5r+7Q+0FD5EGMu+8k155pzDFcfksZuHDRzAAzz8f31okPHoFaeP5xRfx37umBl57TZ6/9JJzX4ZgUI4NwNv9HsaHr044bg01LGQhK1nZiG/nzFa28j7vx+WP1FDDS7yUtv1kk2XLpASc0znzv/+ld1+JzvHnnst+/4xUSSXqaQdjzGRjzBuh14OMMedmXjSlNRKeVomlokIu4uXt5yX8bDjvYcWC9riK4i3lmppIw8N0kSyP4md+pnKT2zHPEyKNd1atcq5f6fdH5A03SnLixx9D/zvOqu1cF40LV1oVxVrWOk5lBQnWJiI2N1ascC7l4fNFjm+6SHSOV1U178ZFTwBvQa09uRi4LEPyKE0g2oJoTlZENOEeA7G0aSM3015bByX8bLgW1I67bSXgj7/qCwvT30M5WVG+rnSlTYeahD0GdgjVBezeve5INozLJX0XIPLfiXCpst5b9kp4A+9B+pwz3ejmqJAMJr4SbTOhRw/nrnNFRcmPfWNIdI57PImbGuWaVBRFF2vt8yBtxUKd6BL0t1KUpuFUFtvjkQJ5xkCZrwv96Of42dOQCvU7Da5g1/3L40bpRUXSNCadnMRJjssHM5hiinEVWsaNi8/1dLvlu4I4S/fe27mZTbgZ0amnJm54+MtfyvMxP55fm6sRpogi+tI3rTfw9rTnAA6ozcUI48bNiZyYtv1kk759RSHEKvWiIukhlk7OOMP5HD/55PwtOZeKM3sGcArSynSYMWYE0lciJz76lurMntrYIrrNORwyAR8+vRP/uWYvyleV0K6Ll1P/8g1HX/hj7dSAHz+3cAsLWADI1MpJnFSrKACqK108cdkQPnq6N35fAf37G3772xQsikYcz6d4itd4rbZF6xCGcDVX10Y92efH8+qr8OKLUpKja1dxXI4YEdmGzydRMO+/L9ZFnz7SyCY6zWf5crjpJsmzAOl18Ic/1G3QuGz8XTzGY3zP9xRSyEhGcjZn1yYIpgs/fp7lWd7mbXz46EUvzuVcBpHY4msSWTCRq6rgX/+SVKtAQI7rpEl1o8rSxSefiK9iwwb5HU8+WbrjZbKSbabzKIYB9wN7AAuArsCpoTLiWUcVRQwtUFGE8dcYCh18DWGCBPHhS3oTtBaCAYPrxVNT22kTjmc11bhxx5f0CN3krJUbULJ2l9aKQ9OVpN9T2KntuJ2Q/AECFFCQ8WxriyVIsNENqlImi3OpwaD8Dsl+g3Th98t+slHqPKN5FNbaOcaYQ4HdAAN8Z61N0H1daa5sYAPv8R4b2MAe7MEBHJD22PtU2c52PuADfiz6kZ3YiVGMcqzvVEBBUiXx7PRKXrthGIGtbeh32Pv8+bCDKW5gU+IAAWYxiznMoYyy2rIeTtQ3ajcmuZJYs0byLSoqYOhQyc6OvVktXgwffywK56CDpPeW000m4zfuEAaTtX1li2xO/+Rrj+xYUrEoxgNvWmu3GmP+DAwDbspVol1zsygabSk0hkaOhsN9FwIE8OPHg4dudONmbk77lEV9bGQj13AN29mOFy9u3BRRxI3cSC96pbydS35TytrJx4ZeGcBC53L+eX8b2hYm+U5Rx9CPnxu5kZ/4iWqqcYX+LuACDuTA1L9UCqPhzz+HBx4QBRAIiFOzd2+47rrIzWTKFOm9VVMjI16PR5K0EvZabknWZnONzsgjMt2P4tqQkhgJjAYmAw81ZmdK/hEkyH3chxdvbc8EL17WspZXebWeT6eff/NvKqio7Zngw8c2tvEIj6S8jR9Wbg8pCRN6IP83duL6N1MvfPwRH7GEJbUhsAEC+PDxMA/H5RA0BZ8P/vEP+R8IhYlUV8PSpfDBB/J6zRp45RVZJzy283rl/XSHbypKLKkYPuEIp+OAx6y1rxljbsqgTM2erFoRTWQ1qx17FNRQw6d8yqmkOLefJuYwp9YpHM33fI8ff1xUjxNPPlKCVJuJnZMxLH97IIxNTZaP+dixcZHBsJjF7MEezh9s4Oj3hx+cp4+8XplmGj1aypo4Gf8+H8yaJT2gk8rR3K2LHJf3aO2kYlGsCtV6Oh143RjjSfFzSjPAjTthY53Y8MdskMgvYkJ/qVDSroZ4JQFgMZ7ULYFEORLh+lLpoqjIWQlAJKzW7XaeO3e5nNusKEo6ScWiOA04GrjLWrvZGNMDQs2AFaB5WRCxdKMb3enOClbUURgePIn7T2eQwziMt3irTkKXCxf7sm/KTtMLLrJMutqCjVcWw8cvAXZOaTtHciTzmR9nVRRTTH/61125CaPcfv2kVlNsVq7HI5XwAfbfH558Mv6zxsCBDXCXONKIfuJK6yKVWk9V1toXrLXfh16vsda+nXnRlGxxBVfQkY6UUIIHD0UUsT/7cxiHZV2W0zmd/vTHE/orppie9KxtXJQKHUrdHPLoM0iOaBCZhrKUHPAVV+x3WMrbGcIQxjCGIoooppgSSmhLW67hmoTtWBtDQYFUgW3bVgoBFheLlXHEETAsVMy/rAwuuUQsi+LiyDq//W1mqpsqSjT1Rj3lG/kY9ZQ3FkUTRnwBAsxnPpvYxG7slvNGNj/yI8tYxo7syG7s1qh8gHWbvdx7awnbyos5cdI6Ru3buBKg61nPQhbSlrYMYUjET5LmuXK/X8pPV1bCoEHOCqCqSirsBoMSQpvOqqZNIpvWhvooGkWm+1EorQAXLoYwJCv7qqaa5SynjDLHftIA/UJ/yVjPesopZyd2iu9uB3Tt4OaC25dTRRW7sEuj5e1K13qtq8pKKfLXtSt07px01YQUFsLwei7j0lLJn1CaP1u3SkHIbt2kJ0U+o4pCySpv8Ab/4T8UUECAAL3pzZVcSQc6pLyNKqq4h3tYxCKKKKKGGk7gBE7jtFrLYx3ruI3bWM/62mmiSUziINJ7l7VWmiy9+aZMBdXUSMObyy5rZU7mZKN89W3UIRiEJ56Q8uXhc2bYMLj44vgaUPmCRi8pWWMe8/gP/8GLl+1sx4ePJSzhTu5s0HYe5EEWsYgaaqiiihpqeJVX+YRPAMkNuYEbWMWq2n1tZzsP8RDLWJbW7/TOO/D223KxV1XJ/3nz4LHH0robpQXxxhuSgR99zsyZI8ojX1GLQskar/JqXARRgADLWMZa1iachopmG9uYy9za5MAwXry8zMuMZCSLWUwFFXFhv378vMVbDXKM18Fh1Pzqq/FNiWpq4NNPpaBc0hFiY0fazW2OXq2NOrz6qnOTqg8+gHPOyc+yHmpRKFljM5sdl7twUUFFStvYxraEEUdb2QpABRWOzu8gQTaxKTVhU6SyMvF7+dqERskt27Y5Lw8EnPuS5AOqKJSsMZShCRvrpNovoQtdHOtPFVDAnuwJwAAGxFkcILkhQxnaQKmTM3iwc1Z1hw5SPlpRYhk40Hl5t24SHp2P5KGRo7RUxjKWD/iACipqb+QePJzJmUk7xUVTQAG/4Tc8yIO1/aFduCihpLYfRQc6MJaxvM7rtVNdbtx0oQuH0sA2KvVM8/ziFzB/vkw/BQKiNML5DRkrHZ1suqalTEu14CmpX/0KvvsuUtvLGJmi/O1vcy1ZYlRRKFmjHe24kzt5ndeZwxw60pGxjE1cMykBIxhBF7rwMi+zjnUMYhDHczydiMQYTmACu7Irb/AG29jGARzAGMakrJBSpUcPuOsumD5dLv4ePeCEE6RjmqI40asX3HmnnDPffw89e8K4celvuZpONOEuA+QsAa8Fj8LqkM2SE+keoWtimpIj8jLhzhizE/BvYAekhsKj1tq/x6xzGPAy8FNo0QvW2hsyJVNzo6ICnn8eZs6M1P057riGd976hm+YwhRWs5qe9GQCE9id3TMjdD38zM9MYQoLWEA72nECJ3Aoh9ZxPi9mMVOYwnKW053unMZp7MVedbYzcyZMmwbl5dIu9IwzYOfUSjjlJUFreeBRL5/fO4LApvZ0HTOHS25az4Cds9sPJMz8+fDcc7B2rRzXCRPqtlxVWhcZsyhCxQN7hDrktQNmAydaa7+JWucw4AprbYqFn1uPRVFdDb//PWzaFOlR4HZL2YbLL0/wIYfR6ld8xV3cVad/ghs3V3FV3M0302xgA1dwBdvZXhu66sHDsRzLGZwBwCIWcTM3x8l7MRezP/sD8NZvxvP003XDUj0euOWWJP2N8yUUNYEcV17iZtnkI6CqjSxw+aHDFu5a+AY779DI6bJGyj5zJtx3X90QTrcb/vhHKS2iNE8y3bioUYSKB84JPd8KLAJ6Zmp/LY0PPpAU/7CSALlw586VUhGp8gRPxDXZ8eHjCZ5Ij6ANYDrT8eKtk9/gxcurvMo2JGbwKZ5ylPdJnsRi8dcYnn02PnfB55MOcM2R5T97WfbYURElARAohMo2PHpf9i2KJ5+Mj/P3+eDf/866KEqekBVntjGmDzAU+MLh7QOMMV8DqxHrYmE2ZMok44mMGhtrXSxaFH8zBKk0umSJOMTqw2JZzWrH91axqlFyNYVFLCJAIG55IYWsYhUDGJAwc7qccmqoYfPKDnWUZxhrpQFQQmJH13nkz5kzvwY81VAdoxS8xaz4YBdgSdZk8fthwwbn95Yvz5oYSp6R8TwKY0xb4L/AZdba2KyqOUBva+3ewP3ASwm2MckYM8sYM2t9RWqJWc2dHj0SZ2h27ZraNgyGMsoc30u0PJN0p7tjIpwfP52RSnod6ej4WQ8eCimkrKuXYHwDPAC6dEmbqFmlz84F4HNI4Xb5KRvwc1ZlcbkSx/J36JBVUZQ8IqOKwhhThCiJZ6y1L8S+b62tsNZWhp6/DhQZY+Iud2vto9ba4dba4V3Lsn+DywVHHBGvKFwuqTKZKGHHiXGMiwsJ9eDhJE5Kg5QNYxzj4hLuiihiT/asVRQnc7KjvMdzPAUUUNw2wMEHx5fGcLvh1Ox2bU2d8VMjDweGDCildL+F4I7tXOTlzN+vy4KAEYyB44+PL2jo8cDJJ2dVFCWPyJiiMMYYYDKwyFp7T4J1uofWwxizX0iejZmSqTnRuTP8+c9iWRQVidIYNAj++teGJXKNZSwncALFFOPGTTHFnMiJHMMxGZM9Ef3pz6VcSkc6UhT625d9uYzLatcZxSjGM54SSnDjxoOHYziGk4ncpX7zGzj0UFEObrf0ZDj3XHH0N1fuevl7Oo79DNxeKN5OwU4rOf2/U9l/jzb1fzjNnHQSHHusKAe3WyyMU06R3t1K6ySTUU8jgY+A+UibMYA/EupDaa192BhzEXA+4Ae2A7+31n6abLvNIeopmqZGQFkLW7aIsmhT3z0jybx7DTVUUEF72kca7+SIIEE2s5lSSh3LcYBMR21hC2WUxZf9CPkbvF6pm9Ohg3M/6aSk6qNIR9RTA/wh5Vtr2Lw1QJ8eHgqamtrdRNlraiREu337/CxUpzSMvMyjsNZ+jHOH++h1HgAeyJQMeUETnaY/sYQ5zKGYYg7kwDrZxw2hiKLa6Z1cEiTIG7zBTGbSgQ6cwRmOVWMLKaxXXo+nCT0fom+ieeTY7tSuiE7t4uth5YKiosY3YVJaFjpOyFMslsd5nA/5EB8+CilkClO4gAs4kANzLV6jqKaaC7mwtsorwGd8xrmcy1EclUPJFEVJhiqKPGUBC/iQD2uL2tUg9Yf/wT8YwhDH1p/5zqM8WkdJhPkn/2Q0o3M3JZbMusjmFJWi5ClaZjxP+ZiP45r8gPRumMe8HEjUdGYxy3G5xfI5n2dZGkVRUkUtijzFKd+gJZOoGVFWSIePIo/8HLVEy6QWj9IE1KLIUw7hEMeS2EGC7M3eOZCo6YxghONyg2E/9suyNIqipIpaFBkmdqCZ6sBud3bnCI7gHd4hQAAXUjL2Yi6mhDxtg1UPk5jEXOayhS11lp/Hedn3T+SjBaAoeYoqijzFYPg1v+ZwDmcuc/HgYQQjaE/7XIvWaAop5BEe4X3e53M+pyMdOZ3T8yJsV1GUxKiicCJcGa2sDEpzG120U+gvGQECrGc97WhHG7KfydsQCihgFKMYzGDahv4aTAuee9/KVqqooitdc+u3UZQoVFHE8uab8OyzEAzK46CDpJltUX4kQcUygxk8yZP48RMkyL7sy/mcn/aWn+niUz5lMpPx4SNAgKEM5UIubJbhvumkkkru4z4WsAAXLoop5nf8juE0KpFWUdKKDlmimTkTnnkGtm+X+hA1NfDppzB5cq4lc2Qe85jMZLaxDS9eaqjhS77kQR7MtWiOLGYx/+AfbGUrXrz48TOXudyDYymwVsVt3MZ85uPHjxcvW9jCvdzLUpbmWjRFUYuiDi+84NwR56OPYOJEKM5NW8pEvMiLcbkWNdQwm9lUUJGTUuLJeJmX45oS+fGziEVsYANdaESd8FSnofLYeb2KVSxlaVyvjhpqeI3XuJALm76TxkZVKApqUdSlvNx5uTFQWZldWVJgA84dZly44iKL8oH1rHdcXkgh5SQ49q2Acsodo74slp/Jbj8KRXFCLYpoBgyAL7+Ukq3RuN3Q0bmhTi7Znd1Zz3qCxHfy2YEdciBRcgYzmBWsiBs5+/HTixRa9jWUPLYioulN79oSLdEUUcRgBudAIkWpi1oU0UyYIOVIo8s7u91w1lnSNSjPOJVTKaa4Tha3Bw+nczpuHDqm5ZjjOZ4SSupE83jwcCIntmpndhllHM3RdQIQXLgopTQnfUMUJRa1KKLp1QtuuQWefx6+/156a55yCgwZkmvJHOlGN27jNqYylW/4ho505EROzNss50504g7uYCpTmcc8yihjHOM4gAPSs4NmYkE4cSZn0pvevMIrVFLJUIZyKqfmnZ9JaZ1krHFRpmhujYtiUR+ikhfoidjqyMvGRYrSElhb7uOBuzwsmb4nRZ0rGPN/C/nliQ1Pavye73mRF1nDGgYwgJM4ybFhU9ZowUmLSvpRRaEoCVi/uYbLhh5K8Ocu4C3GD7w8exDfX/kaf70u9eq+c5jDPdxTGxq8hjV8xmfcwi2ZceIrSppRZ3aWGT818lDym0cfMQTXdwJvVP7MtjZ8c9vxrC33Jf5gFBbLYzxWJ38kSJBqqnmGZ9ItsqJkBFUUipKA71/vD9sdorE8Pj6bFR/O6kQllQlzWhaxqCniKUrWUEWhKAlos3M5FATi36gppHuK7oXY8OVoNKJJaS6oj0JREnDapWt48IVqqIpyXhfWULTrMg7YKzWHdhFFHMIhfMRHdaafPHg4gRPSLXLj0PIeSj2oRaEoCTh0eBuOemwatN8C7SqgpIri4Qu4+Y3ZDdrO2ZzNcIZTRBEllFBEEcdxHKMZnSHJFSW9qEWhKEk49xel/PLUN5m1cDudOxawe58SoGHFId24uYzL2MIWNrKR7nRv1ZnoSvNDFYWi1EOxu4CRQ5veEKp96E9RmhuqKLKMTv8qitLcUB+FoiiKkhS1KBRFqYuW91BiUItCURRFSYpaFBlGB2SKojR31KJQFEVRkqKKQlEURUmKKooWgMVSTXVcL2pFUZR0kDEfhTFmJ+DfwA6ABR611v49Zh0D/B04FqgCJlpr52RKppbIHOYwmcmUU44LF0dwBGdyJoXqflIUJU1k8m7iBy631s4xxrQDZhtj3rHWfhO1zjHArqHH/sBDof9KCixmcZ2GOAECvMu7VFPNeZyXY+mUFoGGyipkcOrJWrsmbB1Ya7cCi4CeMauNA/5thc+BDsaYHpmSqaUxjWl1KpIC+PDxER+xjW05kkpRlJZGVuYnjDF9gKHAFzFv9QRWRL1eGVq2Jubzk4BJADt36ZIxOdNFtgZea+oeploKKWQjG2lD0+sTKYqiZNyZbYxpC/wXuMxaW9GYbVhrH7XWDrfWDu9aps1ewvSlr2NTnAAButEtBxIpitISyaiiMMYUIUriGWvtCw6rrAJ2inrdK7RMSYFTORU37jrLPHgYy1iKG1gKW1EUJREZUxShiKbJwCJr7T0JVpsO/MoII4At1lrn+RQljp3Yieu5nsEMxoOHrnTlLM7idE7PtWiKorQgMumjOAg4C5hvjPkqtOyPwM4A1tqHgdeR0NgfkPDYszMoT8bIZTBIX/pyHdflTgCl9aAtU1stGVMU1tqPIUFX+cg6FrgwUzIoiqIoTUczsxVFUZSkqKJQFEVRkqKKQlEURUmKETdB88EYsx5YlqXddQE2ZGlf6UDlzSwqb2ZReTPLbtbado35YLOrHGet7ZqtfRljZllrh2drf01F5c0sKm9mUXkzizFmVmM/q1NPiqIoSlJUUSiKoihJUUWRnEdzLUADUXkzi8qbWVTezNJoeZudM1tRFEXJLmpRKIqiKElRRaEoiqIkRRUFYIxxGWPmGmNedXhvojFmvTHmq9DjN7mQMUampcaY+SF54kLeQtV47zPG/GCMmWeMGZYLOaPkqU/ew4wxW6KO8V9yIWeUPB2MMdOMMd8aYxYZYw6IeT/fjm998ubN8TXG7BYlx1fGmApjzGUx6+TN8U1R3rw5viF5/s8Ys9AYs8AY86wxpjjmfY8x5rnQ8f0i1FguKc0ujyJDXIq0ak3UFek5a+1FWZQnFUZZaxMl++RjL/Jk8gJ8ZK0dmzVpkvN34E1r7anGGDdQGvN+vh3f+uSFPDm+1trvgCEgAzSk/8yLMavlzfFNUV7Ik+NrjOkJXAIMstZuN8Y8D0wAnoha7Vxgk7W2vzFmAnA7JO9N0OotCmNML+A44PFcy5JGtBd5IzHGtAcOQXqpYK31WWs3x6yWN8c3RXnzldHAj9ba2EoLeXN8Y0gkb75RCJQYYwqRQcPqmPfHAU+Gnk8DRof6ByWk1SsK4F7gKiCYZJ1TQibwNGPMTknWyxYWeNsYM9tIP/FYEvUizxX1yQtwgDHma2PMG8aYwdkULoZdgPXAv0LTkY8bY2Kbj+fT8U1FXsif4xvNBOBZh+X5dHyjSSQv5MnxtdauAu4ClgNrkGZwb8esVnt8rbV+YAvQOdl2W7WiMMaMBdZZa2cnWe0VoI+1di/gHSKaOJeMtNYOQ0z0C40xh+RaoHqoT945QG9r7d7A/cBLWZYvmkJgGPCQtXYosA24Oofy1Ecq8ubT8QUgNEV2AjC1vnXzgXrkzZvja4zpiFgMuwA7Am2MMWc2dbutWlEgXfhOMMYsBaYAhxtjno5ewVq70VrrDb18HNgnuyLGExo1YK1dh8yX7hezSl71Iq9PXmtthbW2MvT8daDIGNMl64IKK4GV1tovQq+nITfiaPLp+NYrb54d3zDHAHOstT87vJdPxzdMQnnz7PgeAfxkrV1vra0BXgAOjFmn9viGpqfaAxuTbbRVKwpr7TXW2l7W2j6IWfmetbaO9o2ZGz0BcXrnDGNMG2NMu/BzYAywIGa1vOlFnoq8xpju4TlSY8x+yHmZ9MTNFNbatcAKY8xuoUWjgW9iVsub45uKvPl0fKM4g8TTOHlzfKNIKG+eHd/lwAhjTGlIptHE37OmA78OPT8Vue8lzbzWqCcHjDE3ALOstdOBS4wxJwB+oByYmEvZgB2AF0PnZSHwH2vtm8aY8yAve5GnIu+pwPnGGD+wHZhQ34mbYS4GnglNNywBzs7j4wv1y5tXxzc0YDgS+F3Usrw9vinImzfH11r7hTFmGjId5gfmAo/G3NMmA08ZY35A7mkT6tuulvBQFEVRktKqp54URVGU+lFFoSiKoiRFFYWiKIqSFFUUiqIoSlJUUSiKoihJUUWhtGqMVP50qhrsuDwN+zvRGDMo6vUMY8zwFD7XIx3yGGO6GmPebOp2lNaFKgpFyS4nAoPqW8mB3wOPNXXn1tr1wBpjzEFN3ZbSelBFoeQ1oczu10IF1xYYY04PLd/HGPNBqNDgW+EM+tAI/e9G+gIsCGXKYozZzxjzWahw3qdRmcypyvBPY8zM0OfHhZZPNMa8YIx50xjzvTHmjqjPnGuMWRz6zGPGmAeMMQci2f13huTrF1p9fGi9xcaYgxOIcQrwZmjbLmPMXaHvN88Yc3Fo+VJjzK2hbc8yxgwLHZsfwwliIV4Cfpnq91cUzcxW8p2jgdXW2uNAymobY4qQ4mvjrLXrQ8rjZuCc0GdKrbVDjBQf/CewB/AtcLC11m+MOQK4Bbn5psKfkDIH5xhjOgAzjTHvht4bAgwFvMB3xpj7gQBwLVJzaSvwHvC1tfZTY8x04FVr7bTQ9wEotNbuZ4w5FrgOqddTizFmF6R/QLjm2CSgDzAk9H06Ra2+PPTd/4b0IDgIKEbKpjwcWmcWcFOK311RVFEoec984G5jzO3IDfYjY8weyM3/ndCN1oWUVA7zLIC19kNjTFno5t4OeNIYsytS9ryoATKMQYpHXhF6XQzsHHr+P2vtFgBjzDdAb6AL8IG1tjy0fCowIMn2Xwj9n40ogFh6IKXEwxwBPBwqEU14PyGmh/7PB9paa7cCW40xXmNMh1CvinVIZVFFSQlVFEpeY61dbKQV5rHATcaY/yEVaBdaaw9I9DGH1zcC71trTzLS+nFGA8QwwCmhbmeRhcbsj1gSYQI07poKbyPR57cjyqkh2wrGyBaM2nZxaJuKkhLqo1DyGmPMjkCVtfZp4E5kOuc7oKsJ9YY2xhSZus1iwn6MkUjl0S1IKeVwqeqJDRTjLeBiY2orhA6tZ/0vgUONMR2NlHGOnuLailg3DWExdS2Nd4DfhbZNzNRTKgwgvuKwoiREFYWS7+yJ+AS+Qubvb7LW+pCKnbcbY74GvqJuzf1qY8xcZE7+3NCyO4BbQ8sbOuq/EZmqmmeMWRh6nZBQ/41bgJnAJ8BSpIsYSN+TK0NO8X7OW4jb3jbgR2NM/9Cix5Fy0vNC3/8XDfs6jAJea+BnlFaMVo9VWhTGmBnAFdbaWTmWo621tjI06n8R+Ke19sUmbO8kYB9r7Z/TINuHSCDApqZuS2kdqEWhKJnhryEraAHwE01sjxlSMkubKpQxpitwjyoJpSGoRaEoiqIkRS0KRVEUJSmqKBRFUZSkqKJQFEVRkqKKQlEURUmKKgpFURQlKf8PRkCnT12Cp6EAAAAASUVORK5CYII=\n", - "text/plain": [ - "
    " + "cell_type": "markdown", + "id": "2213a43b", + "metadata": { + "id": "2213a43b" + }, + "source": [ + "## 4. K-Nearest Neighbours (KNN) Classifier\n", + "KNN classifies a new point by majority vote among its **k nearest neighbours** in feature space.\n" ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "from matplotlib import pyplot as plt\n", - "from sklearn import neighbors, datasets\n", - "from matplotlib.colors import ListedColormap\n", - "\n", - "# Create color maps for 3-class classification problem, as with iris\n", - "cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])\n", - "cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])\n", - "\n", - "iris = datasets.load_iris()\n", - "X = iris.data[:, :2] # we only take the first two features. We could\n", - " # avoid this ugly slicing by using a two-dim dataset\n", - "y = iris.target\n", - "\n", - "knn = neighbors.KNeighborsClassifier(n_neighbors=1)\n", - "knn.fit(X, y)\n", - "\n", - "x_min, x_max = X[:, 0].min() - .1, X[:, 0].max() + .1\n", - "y_min, y_max = X[:, 1].min() - .1, X[:, 1].max() + .1\n", - "xx, yy = np.meshgrid(np.linspace(x_min, x_max, 100),\n", - " np.linspace(y_min, y_max, 100))\n", - "Z = knn.predict(np.c_[xx.ravel(), yy.ravel()])\n", - "Z = Z.reshape(xx.shape)\n", - "plt.figure()\n", - "plt.pcolormesh(xx, yy, Z, cmap=cmap_light)\n", - "\n", - "# Plot also the training points\n", - "plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold)\n", - "plt.xlabel('sepal length (cm)')\n", - "plt.ylabel('sepal width (cm)')\n", - "plt.axis('tight')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Classification: Logistic Regression" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's look at a different classifier: Logistic Regression (despite its name, it does classification)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "['virginica']\n" - ] - } - ], - "source": [ - "from sklearn.linear_model import LogisticRegression\n", - "X, y = iris.data, iris.target\n", - "logit = LogisticRegression(multi_class='auto', solver='liblinear')\n", - "logit.fit(X, y)\n", - "# As before: What kind of iris has 3cm x 5cm sepal and 4cm x 2cm petal?\n", - "print(iris.target_names[logit.predict([[3, 5, 4, 2]])])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We get the same result as we did before. However, we're now faced with a critical question: How good are these models? How do we know how well they perform? Are these models reliable?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Model Evaluation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Dataset splitting" - ] - }, - { - "attachments": { - "Picture4.png": { - "image/png": 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" - } - }, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Datasets are typically divided into two or three subsets:

    \n", - "Training
    \n", - "
  • Used to optimize model parameters

  • \n", - "Validation
    \n", - "
  • Used to compare the performance of models
  • \n", - "
  • Model parameters are not directly exposed to this set

  • \n", - "Test set
    \n", - "
  • Used to evaluate generalization

  • \n", - "\n", - "![Picture4.png](attachment:Picture4.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Sklearn provides us with many useful tools for manipulating and separating datasets. We'll first look at train_test_split, which splits data into training/test sets." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": null, + "id": "eb2566ea", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-16T07:46:08.498000Z", + "iopub.status.busy": "2026-05-16T07:46:08.497254Z", + "iopub.status.idle": "2026-05-16T07:46:08.664708Z", + "shell.execute_reply": "2026-05-16T07:46:08.663622Z" + }, + "id": "eb2566ea", + "outputId": "bf83e2b0-de0a-4fde-e9a8-5c36a258862e", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 672 + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "KNN (k=5) Test Accuracy: 0.9333\n", + "\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " setosa 1.00 1.00 1.00 10\n", + " versicolor 0.83 1.00 0.91 10\n", + " virginica 1.00 0.80 0.89 10\n", + "\n", + " accuracy 0.93 30\n", + " macro avg 0.94 0.93 0.93 30\n", + "weighted avg 0.94 0.93 0.93 30\n", + "\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
    " + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "knn = KNeighborsClassifier(n_neighbors=5)\n", + "knn.fit(X_train_sc, y_train)\n", + "y_pred_knn = knn.predict(X_test_sc)\n", + "\n", + "print(f\"KNN (k=5) Test Accuracy: {accuracy_score(y_test, y_pred_knn):.4f}\")\n", + "print(\"\\nClassification Report:\")\n", + "print(classification_report(y_test, y_pred_knn, target_names=iris.target_names))\n", + "\n", + "# Confusion matrix\n", + "cm = confusion_matrix(y_test, y_pred_knn)\n", + "fig, ax = plt.subplots(figsize=(5, 4))\n", + "im = ax.imshow(cm, cmap='Blues')\n", + "ax.set_xticks(range(3)); ax.set_yticks(range(3))\n", + "ax.set_xticklabels(iris.target_names, rotation=30)\n", + "ax.set_yticklabels(iris.target_names)\n", + "ax.set_xlabel(\"Predicted\"); ax.set_ylabel(\"True\")\n", + "ax.set_title(\"KNN Confusion Matrix\")\n", + "for i in range(3):\n", + " for j in range(3):\n", + " ax.text(j, i, cm[i,j], ha='center', va='center',\n", + " color='white' if cm[i,j]>cm.max()/2 else 'black', fontsize=14)\n", + "plt.colorbar(im, ax=ax); plt.tight_layout(); plt.show()\n" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "X shape: (150, 4)\n", - "y shape: (150,)\n" - ] - } - ], - "source": [ - "from sklearn.model_selection import train_test_split\n", - "X, y = iris.data, iris.target\n", - "print('X shape: {}'.format(X.shape))\n", - "print('y shape: {}'.format(y.shape))" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": null, + "id": "40674d18", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-16T07:46:08.666567Z", + "iopub.status.busy": "2026-05-16T07:46:08.666357Z", + "iopub.status.idle": "2026-05-16T07:46:08.881897Z", + "shell.execute_reply": "2026-05-16T07:46:08.880783Z" + }, + "id": "40674d18", + "outputId": "1ea9c7e4-01d2-47f9-bc00-6e19d0e37ad2", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 463 + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Best k = 1 | Accuracy = 0.9667\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
    " + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "# Choosing the best k\n", + "k_scores = []\n", + "k_range = range(1, 21)\n", + "for k in k_range:\n", + " knn_k = KNeighborsClassifier(n_neighbors=k)\n", + " knn_k.fit(X_train_sc, y_train)\n", + " k_scores.append(accuracy_score(y_test, knn_k.predict(X_test_sc)))\n", + "\n", + "best_k = list(k_range)[np.argmax(k_scores)]\n", + "print(f\"Best k = {best_k} | Accuracy = {max(k_scores):.4f}\")\n", + "\n", + "fig, ax = plt.subplots(figsize=(7, 4))\n", + "ax.plot(k_range, k_scores, 'o-', color='steelblue')\n", + "ax.axvline(best_k, color='red', linestyle='--', label=f'Best k={best_k}')\n", + "ax.set_xlabel(\"k\"); ax.set_ylabel(\"Test Accuracy\")\n", + "ax.set_title(\"KNN – Accuracy vs k\"); ax.legend(); ax.grid(alpha=0.3)\n", + "plt.tight_layout(); plt.show()\n" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "X_train shape: (90, 4)\n", - "X_test shape: (60, 4)\n", - "y_train shape: (90,)\n", - "y_test shape: (60,)\n" - ] - } - ], - "source": [ - "# Split into training / testing sets. \n", - "# We can specify the proportion we want in each set using the train_size and/or test_size parameters\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.6, test_size=0.4)\n", - "print('X_train shape: {}'.format(X_train.shape))\n", - "print('X_test shape: {}'.format(X_test.shape))\n", - "print('y_train shape: {}'.format(y_train.shape))\n", - "print('y_test shape: {}'.format(y_test.shape))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now have two sets: one set we use to optimize our models, and another set to evaluate how they perform. Let's recreate our previous classifiers. \n", - "Sklearn estimators have the *score* method, which allows us to quickly evaluate the model performance on a particular dataset:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "id": "cf7c1c76", + "metadata": { + "id": "cf7c1c76" + }, + "source": [ + "## 5. Comparing Multiple Classifiers\n", + "Different algorithms make different assumptions. Let's compare several on the same data.\n" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "kNN accuracy: 0.9333333333333333\n", - "logit accuracy: 0.9333333333333333\n", - "logit performs better!\n" - ] - } - ], - "source": [ - "# Instantiate and train our models\n", - "knn = neighbors.KNeighborsClassifier(n_neighbors=1)\n", - "knn.fit(X_train, y_train)\n", - "logit = LogisticRegression(multi_class='auto',solver='liblinear')\n", - "logit.fit(X_train, y_train)\n", - "\n", - "# Evaluate our models!\n", - "knn_score = knn.score(X_test, y_test)\n", - "logit_score = logit.score(X_test, y_test)\n", - "print('kNN accuracy: {}'.format(knn_score))\n", - "print('logit accuracy: {}'.format(logit_score))\n", - "\n", - "if(knn_score > logit_score):\n", - " print('knn performs better!')\n", - "else:\n", - " print('logit performs better!')\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Try the following and rerun the previous cells: \n", - " 1) Modify the \"n_neighbors\" parameter for the kNN classifier \n", - " 2) Change the \"train_size\" and \"test_size\" parameters for the data split (they must sum to <= 1) \n", - " 3) Add \"stratify=y\" to train_test_split so that the labels are balanced in the training and test sets \n", - " 4) *Do nothing and just rerun them* " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Your model performance depends on the type of model you select and how much data is available for training/evaluation. The fourth point highlights something which is deceptively simple: your model performance also depends on which data points are present in your training and validation sets. \n", - " \n", - "The problem is similar when delving into model *hyperparameters*. Hyperparameters are parameters that are not directly optimized by the data, but that can have a huge impact on model performance. Hyperparameters are determined either by heuristic domain-specific information acquisition (\"guessing\") or preferably cross-validation." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Cross-Validation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Cross-validation is important for a number of reasons:
    \n", - "
  • Evaluating model performance variability
  • \n", - "
  • Determining hyperparameters
  • \n", - "
  • Getting reviewers to leave you alone about doing cross-validation

  • \n", - "\n", - "K-fold cross-validation (CV) is one example.
    \n", - "To do K-fold CV:
    \n", - "
  • Take your training set
  • \n", - "
  • Divide it into K equal subsets (“folds”)
  • \n", - "
  • Train on (K-1) folds, and evaluate on the remaining fold
  • \n", - "
  • Repeat K times for all folds
  • " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Sklearn provides us with convenient functions for doing cross-validation: cross_val_score \n", - "The function defaults to Kfold CV, but other methods are available through sklearn.model_selection" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "scrolled": false - }, - "outputs": [ + "cell_type": "code", + "execution_count": null, + "id": "e1eb50da", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-16T07:46:08.884122Z", + "iopub.status.busy": "2026-05-16T07:46:08.883620Z", + "iopub.status.idle": "2026-05-16T07:46:09.076312Z", + "shell.execute_reply": "2026-05-16T07:46:09.075107Z" + }, + "id": "e1eb50da", + "outputId": "0ec58fa3-d7fb-42f7-9653-debd90608726", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Classifier Train Acc Test Acc\n", + "------------------------------------------\n", + "KNN (k=5) 0.9750 0.9333\n", + "SVM (RBF) 0.9750 0.9667\n", + "Logistic Reg. 0.9583 0.9333\n", + "Decision Tree 1.0000 0.9333\n", + "Random Forest 1.0000 0.9000\n", + "\n", + "Note: Decision Tree & Random Forest have 1.00 train accuracy → overfitting!\n" + ] + } + ], + "source": [ + "classifiers = {\n", + " 'KNN (k=5)': KNeighborsClassifier(n_neighbors=5),\n", + " 'SVM (RBF)': SVC(kernel='rbf', C=1.0, random_state=42),\n", + " 'Logistic Reg.': LogisticRegression(max_iter=200, random_state=42),\n", + " 'Decision Tree': DecisionTreeClassifier(random_state=42),\n", + " 'Random Forest': RandomForestClassifier(n_estimators=100, random_state=42),\n", + "}\n", + "\n", + "print(f\"{'Classifier':<20} {'Train Acc':>10} {'Test Acc':>10}\")\n", + "print(\"-\" * 42)\n", + "test_accs = {}\n", + "for name, clf in classifiers.items():\n", + " clf.fit(X_train_sc, y_train)\n", + " tr = accuracy_score(y_train, clf.predict(X_train_sc))\n", + " te = accuracy_score(y_test, clf.predict(X_test_sc))\n", + " test_accs[name] = te\n", + " print(f\"{name:<20} {tr:>10.4f} {te:>10.4f}\")\n", + "\n", + "print(\"\\nNote: Decision Tree & Random Forest have 1.00 train accuracy → overfitting!\")\n" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Logit cross-validation scores: [1. 0.88888889 0.94444444 0.94444444 0.94444444]\n", - "Mean: 0.9444444444444444\n", - "knn cross-validation scores: [1. 1. 1. 1. 0.88888889]\n", - "Mean: 0.9777777777777779\n" - ] - } - ], - "source": [ - "# Import cross validation function:\n", - "from sklearn.model_selection import cross_val_score\n", - "# Create the classifier:\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.6, test_size=0.4)\n", - "logit = LogisticRegression(multi_class='auto', solver='liblinear')\n", - "logit_scores = cross_val_score(logit, X_train, y_train, cv=5)\n", - "print('Logit cross-validation scores: {}'.format(logit_scores))\n", - "print('Mean: {}'.format(np.mean(logit_scores)))\n", - "knn = neighbors.KNeighborsClassifier(n_neighbors=1)\n", - "knn_scores = cross_val_score(knn, X_train, y_train, cv=5)\n", - "print('knn cross-validation scores: {}'.format(knn_scores))\n", - "print('Mean: {}'.format(np.mean(knn_scores)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Model Complexity" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Bias / Variance Tradeoff" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "__Model complexity__ refers to the number of tunable parameters in a model. More complex models are able to represent more complicated relationships between the input and output. " - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "id": "9a75119c", + "metadata": { + "id": "9a75119c" + }, + "source": [ + "## 6. Cross-Validation (5-Fold)\n", + "A single train/test split can give lucky or unlucky results. \n", + "**Cross-validation** repeatedly splits the data and averages performance — more reliable.\n" + ] + }, { - "data": { - "text/plain": [ - "" + "cell_type": "code", + "execution_count": null, + "id": "35ddbdc8", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-16T07:46:09.078200Z", + "iopub.status.busy": "2026-05-16T07:46:09.078017Z", + "iopub.status.idle": "2026-05-16T07:46:10.146280Z", + "shell.execute_reply": "2026-05-16T07:46:10.145194Z" + }, + "id": "35ddbdc8", + "outputId": "6cb9ae31-cfbb-438c-f752-a038fbe2f563", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 677 + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Classifier CV Mean CV Std\n", + "------------------------------------------\n", + "KNN (k=5) 0.9600 0.0249\n", + "SVM (RBF) 0.9667 0.0211\n", + "Logistic Reg. 0.9600 0.0389\n", + "Decision Tree 0.9533 0.0340\n", + "Random Forest 0.9667 0.0211\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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uKlSooB49emjBggVKTEy09snJ+yRl/n74+vpm+n7827lz53T69OlM/y5dupTt+funsmXLasCAAYqJidG2bdskXftM/Xv8zN7TJk2aqFWrVjZ/6Rcfy+n7k+7s2bO6ePFipstE+q3bsiMnnwFJql27tu677z5988031nP0N2/erMOHD6tbt27y8PC44RjXew/Kly8vLy+vXF9GrsfPzy/bV1kfNGiQGjRooMGDB8vHx0chISEaP368/v7775uqAQAI0gBwk+rUqaP4+PgMAclew4YN04svvqjatWvr448/1qpVq7RhwwbNnTtXkmwuqNSoUSMdPHhQX375pbp166ZDhw7p2WefVbVq1RQREWHt9/HHH2vfvn2aMmWKypUrpzlz5qhx48bW82VzQ3pdnTt3zrBXL/0vsz3S2T3PU/rfBvzvv/+eO0Vn4XpXvc7qOZPNi01lZsaMGXr66adVokQJvffee/rmm2+0YcMGrVmzRpIyvYiWPe9b+/btdeTIES1evFht27bVb7/9pqeeekq1a9fWuXPncly3dHPvx7333it/f/9M/6530bkbSb8ydXpgPH78eIbxC+ttnm7mMzBgwABdvXpV8+bNk/S/vdPZvcjYjWR1z+28+MxI9n0Gihcvrp9//lk//PCDhg0bprS0NL366quqXr26lixZclN1ALizcbExALhJTzzxhLZs2aLZs2drypQpOR5n3rx5atq0qZYuXWrTnh6q/i39is3pV21eu3at2rRpo0mTJlnDtyQFBQUpKChIzz//vBISEtS2bVvNnDlT4eHhqlixYo7rTVe1alU5ODgoISFBrVq1uunxMtO4cWOVLVtWK1eu1NmzZ1WyZMkbTpO+p2/Pnj1q1KiRzXN//fWXTZ/ctnfv3gxtycnJOnTokM2hsfPmzVPFihW1bt06m0Ng9+3bl2u1eHt7q0uXLtZDeGfNmqVnn31W7777rl555RWb96ldu3Y20+bV+7Rw4cIMVw1Pl9lezOxKP2S4dOnS1v/++wrt9t4v+mbfn5IlS8rT0zPTZSIxMVGHDh3K1gXHHn74Ybm7u2vBggUaM2ZMplftzkqXLl00dOhQzZkzR/369dOKFStUt25dNWjQIFvT//M9+Lfjx4/rwoULefZZyg0ODg5q0qSJmjRpIulazffcc49GjhyZrUPbASAz7JEGgJvUr18/1a5dW9OmTctyD8fJkyf14osvXnccR0fHDHtqrl69qokTJ2bom9nexPr160uS9bDJmJiYDHs03dzcrIeY3uzhlel8fX3Vtm1brVq1Sps3b860T2aHdtujSJEimjx5si5fvqwnnnhCsbGxGfoYYzRv3jzrec/pex4nTJhg8z6cP39e7777rry9vW1ulZWb9u/fr2XLltm0vfvuu4qLi7PZI5q+x+6f9Rlj9Nprr910DampqZm+T/9eTkJCQlS0aFG98847Nv1TU1M1fvx4Sdeu/pybHnjggQyHUqf/3ShIx8XFKTU1NUP7/v37NXv2bOst3qRrh07/e/z0+c+um31/HBwc9Oijj2rv3r1auXKlzXPTp0/P9qHsJUqU0JgxY3Ts2DH16tXL5vD8dCkpKZo6dWqG0O7h4aEnn3xSERERGjhwoBITE7O8an9m6tWrp4oVK2rBggU6evSozXPpy2puLyO5JbN1ZUBAgEqVKpVr60AAdyb2SAPATXJxcdGqVavUrl07de3aVe+9957atGmjUqVKKT4+Xj/99JNWrlypunXrXnecJ554Qu+//746deqk0NBQxcTEaOHChZnueapRo4YaNWqkhg0bqly5coqJibEettmrVy9J0vz58zVt2jR16NBBVapUkbu7u3777Td99NFHuvvuu29Yjz1mzZqlJk2aKCQkRE8++aTuvfdeOTg46OjRo1q9erUaNGhgs5c8J7p3766oqCiNHDlSVapUUbdu3VSnTh0VKVJEhw4d0pdffqm9e/da9+AHBwerZ8+eWrBggVq0aKHHHnvMevurs2fPav78+dm+0JO96tSpo969e+v7779XjRo1tGPHDs2dO1eBgYE2F+l64oknNHLkSLVu3VqdOnXSlStXtGLFigz35c2Jixcvyt/fX+3bt1fdunXl7++vU6dO6cMPP5STk5O6d+8u6dq5t9OnT9eAAQPUoEED9e3bVx4eHlq2bJl+/PFHDRgwwLonryDYsmWLhg4dqvbt26ty5cpyd3fXn3/+qU8++URXr17VwoULM9zK6mbkxvvzxhtvaO3atercubOeffZZBQUFafv27frqq69UpUoVpaSkZKuWUaNG6ezZs5oxY4a2bdumLl26KCgoSMYYRUZG6osvvtCxY8fUtm3bDNMOGDBAH3zwgZYsWSI3Nzfrv392ODo66v3339cjjzyie++9V2FhYSpZsqTWrFmj1atXq3Xr1nryySezPd6t9PTTT+vYsWMKDQ1VxYoVlZKSom+++UZ79uzRoEGD8rs8AIVZvl0vHABuM4mJieb99983LVq0ML6+vsbJycn4+PiYpk2bmqlTp5q4uDhr36xufzVy5EhToUIF4+zsbCpWrGhGjRpl9u3bl+G2SRMnTjTNmjUzJUuWNEWKFDGlS5c2Dz30kFm/fr21z86dO03v3r1NtWrVTNGiRY2Hh4cJCgoyY8aMMTExMdZ+uXH7K2OMiYmJMS+++KIJCgoyLi4uxtPT0wQFBZkBAwaY7du3W/tldpsme/zxxx/m6aefNtWqVTNubm7GxcXFVK5c2fTq1ct8//33Nn1TU1PN9OnTTZ06dYyLi4spWrSoadGihVm3bl2GcStUqGCaNWuWof169WY2jf7/9jvffvutady4sXFzczM+Pj6mZ8+eGW7Bk5qaaiZPnmyqVatmXFxcTJkyZcyzzz5rYmJiMtzGJ6t/p6zqTEpKMqNGjTKNGjUyJUqUMM7OzqZcuXKmU6dO5ueff84w/erVq02zZs1M0aJFjYuLi7nrrrvMzJkzTVpamk2/rP79s3o/ctvevXtN586dTdWqVU3RokVNkSJFTLly5Uz37t3Nrl277BorfV6OHz9+w77ZfX8y+2wbY0xkZKR59NFHjaenpylatKgJCQkxO3fuzPJzdj1bt241PXr0MBUqVDAuLi7G1dXV1KhRwwwcOPC670G9evWMJNOzZ0+7Xi/dTz/9ZNq2bWu8vb2Ns7OzCQwMNK+99prNbeGMyfo9MCbr9Upmsrr91fWm//fn5osvvjCPPvqoCQgIMC4uLsbHx8c0bNjQzJo1K9PbDQJAdlmMuckrPgAAACuLxaJevXrd9B54AABQcHGONAAAAAAAdiBIAwAAAABghwITpA8ePKiwsDDVrVtXTk5Oql27dramM8Zo0qRJKl++vNzc3HT//fdr+/btNn02btyorl27qmLFinJ3d1fNmjX15ptv6urVq3kxKwAAAACA21iBuWr3nj17tGrVKjVq1EhpaWkZbtmSlcmTJ2vcuHGaNGmS7rrrLr377rsKDQ3Vrl27rLfQ+OCDD3TlyhW99tprKl++vLZv365x48Zp7969+uSTT/JytgAAdxguPQIAwO2vwFxsLC0tTQ4O13aQ9+7dW7/++qv++uuv606TmJioUqVK6bnnntOECRMkScnJyQoMDFTbtm313nvvSbp2z9ASJUrYTDthwgSNHTtWZ8+ezfAcAAAAAABZKTCHdqeHaHts27ZN8fHx6ty5s7XN2dlZHTt21OrVq61tmQXlevXqyRijqKionBUMAAAAALgjFZhDu3MiIiJCkhQUFGTTXqNGDR07dkwJCQlyc3PLdNqtW7fKxcVFlSpVuu5rxMfHKz4+3vo4MTFRx48fV6VKleTkVKjfPgAAAADA/0tJSdG5c+dUp04dubq6XrdvoU6CsbGxcnFxyTCTPj4+MsYoNjY20yB94MABzZgxQ2FhYSpatOh1X2PatGl69dVXc7VuAAAAAEDB9Msvv+jee++9bp9CHaRzIj4+Xh07dlSlSpU0fvz4G/YPDw9X//79rY+PHz+uxo0b66effpK/v39elgoAAAAAuEWioqJ0//33y8/P74Z9C3WQ9vHxUVJSkhITE232SsfGxspiscjHx8emf3Jysh577DHFxsbqp59+koeHxw1fw8vLS15eXhnay5Urp3Llyt38TAAAAAAA8p2jo6MkZesU3gJzsbGcSD83OjIy0qY9IiLCel/pdGlpaerevbt+++03rVmzRgEBAbe0VgAAAADA7aFQB+nGjRvLy8tLS5cutbZdvXpVy5cvV9u2bW36Pvfcc/r666/15Zdfqk6dOre6VAAAAADAbaLAHNp95coV6y2rjh49qvj4eC1btkyS1KxZM/n5+ally5Y6evSoDh48KElydXXVqFGj9Morr8jPz0916tTRe++9p+joaA0bNsw69oQJEzRr1iwNHz5cLi4u2r59u/W5mjVrZnroNgAAAAAAmSkwQfrs2bN64oknbNrSH2/evFnNmzdXamqqUlJSbPqMHDlSxhhNnTpV586dU926dbVu3TpVrlzZ2mf9+vWSpDfffFNvvvmmzfTpYwMAAAAAkB0WY4zJ7yIKkxMnTiggIEDHjx/nYmMAAAAAcJuwJ+sV6nOkAQAAAAC41QjSAAAAAADYgSANAAAAAIAdCNIAAAAAANiBIA0AAAAAgB0I0gAAAAAA2IEgDQAAAACAHQjSAAAAAADYgSANAAAAAIAdCNIAAAAAANiBIA0AAAAAgB0I0gAAAAAA2IEgDQAAAACAHQjSAAAAAADYgSANAAAAAIAdCNIAAAAAANiBIA0AAAAAgB0I0gAAAAAA2IEgDQAAAACAHQjSAAAAAADYgSANAAAAAIAdCNIAAAAAANiBIA0AAAAAgB0I0gAAAAAA2IEgDQAAAACAHQjSAAAAAADYgSANAAAAAIAdCNIAAAAAANiBIA0AAAAAgB0I0gAAAAAA2IEgDQAAAACAHQjSAAAAAADYgSANAAAAAIAdCNIAAAAAANiBIA0AAAAAgB0I0gAAAAAA2IEgDQAAAACAHQjSAAAAAADYgSANAAAAAIAdCkyQPnjwoMLCwlS3bl05OTmpdu3a2ZrOGKNJkyapfPnycnNz0/3336/t27dn6Hfq1Ck9/vjj8vT0VPHixdW/f3/Fx8fn9mwAuMNdvnxZs2fPVrNmzVSrVi01a9ZMs2fP1uXLl/O7NAAAUAixbVEwFZggvWfPHq1atUpVq1ZVzZo1sz3d5MmTNW7cOD3//PP65ptv5O/vr9DQUB0+fNja5+rVq2rdurX279+vRYsW6f3339e6dev05JNP5sWsALhDRUZGqlatWnrmmWcUFRWlypUrKyoqSs8884xq1aqlyMjI/C4RAAAUImxbFFwWY4zJ7yIkKS0tTQ4O13J979699euvv+qvv/667jSJiYkqVaqUnnvuOU2YMEGSlJycrMDAQLVt21bvvfeeJOmzzz5T9+7dtW/fPlWvXl2StH79erVu3Vo///yzGjZsmO06T5w4oYCAAB0/flzlypXLyawCuA1dvnxZtWrV0sWLF7VgwQK1adNGFotFxhitWbNGPXv2lKenp/bs2SMPD4/8LhcAABRwbFvcevZkvQKzRzo9RNtj27Ztio+PV+fOna1tzs7O6tixo1avXm1tW7Nmje666y5riJakkJAQFS9e3KYfAOTUwoULdfToUS1YsEBt27aVxWKRJFksFrVt21YLFizQ0aNHtWjRonyuFAAAFAZsWxRsTvldwM2IiIiQJAUFBdm016hRQ8eOHVNCQoLc3NwUERGRoY/FYlFQUJB1jKzEx8fbnEsdFRUlSUpNTVVqampuzEah9dxzz+nPP//M9XHTD8uvXLlyro9dp04dvfvuu7k+LvDpp58qMDBQoaGhma4bQkNDVa1aNX366afq27dvPlSIgmDRokU6efJkro+7cuVKnT59OtfHzWulS5dWhw4dcn3csmXLcvpWIZRX2xUS2xYonNi2uPXsyXeFOkjHxsbKxcVFrq6uNu0+Pj4yxig2NlZubm6KjY2Vt7d3hul9fHwUExNz3deYNm2aXn311Qzt0dHRcnFxuan6C7uEhARdvXo118e9dOmSJOXJ2AkJCTp37lyujwucOXNGFSpU0Pnz57PsU6FCBR07doxl8A62f/9+bfptv4oV98vVcU/FXNLFK8m5OuatkBZzSd/9dSJXx7wQc04t61/mc1YI5dV2hcS2BQonti1uvejo6Gz3LdRB+lYIDw9X//79rY+joqLUsGFD+fr6ys8vdzeECps5c+bkybgPPvigJOn777/Pk/GBvFCqVCkdOXJEJUqUsB569U/GGB09elT+/v53/LrjTubh4SHfkv5q+XifXB23dZcBuTpeYbbpi0/k4eHB56wQyqvtColtCxRObFvceklJSdnuW6iDtI+Pj5KSkpSYmGizVzo2NlYWi0U+Pj7WfhcuXMgwfWxsrAICAq77Gl5eXvLy8srQ7ujoKEdHx5ucA2QmfUXB+4vCpEePHnrmmWe0fv16tW3bNsPzq1ev1oEDBzR8+HCW7TuYg4ODLBaLLDm4Lgiyx2KxyMHBgc8ZbLBtgcKIbYtbz573sVB/k6ef9/zvy75HRERY7yud3u/f50IbYxQZGZnh3GkAyInu3burQoUK6tmzp1avXq30GyIYY7R69Wr16NFDFSpU4LxNAACQLWxbFGyFOkg3btxYXl5eWrp0qbXt6tWrWr58uc2vNm3atNHu3bt14MABa9umTZsUHR2d6a87AGAvDw8PrVu3Tp6ennr44YdVvXp1tWvXTtWrV9fDDz8sLy8vrVu3jttTAACAbGHbomArMEH6ypUrWrZsmZYtW6ajR48qPj7e+jj95PmWLVuqatWq1mlcXV01atQoTZ06VTNmzNC3336rbt26KTo6WsOGDbP269Spk2rVqqXHH39c33zzjT7//HP17dtXDz/8sF33kAaA66levbr27Nmj2bNnq0yZMvr7779VpkwZzZ49W3v27LG5BR8AAMCNsG1RcBWYc6TPnj2rJ554wqYt/fHmzZvVvHlzpaamKiUlxabPyJEjZYzR1KlTde7cOdWtW1fr1q2zub1BkSJFtHbtWg0ZMkTdunWTk5OTOnbsqLfeeivvZwzAHcXDw0MDBgzQgAFc/AkAANw8ti0KpgITpCtWrGg97j8rW7ZsydBmsVg0atQojRo16rrTli1bVl988cXNlAgAAAAAQME5tBsAAAAAgMKAIA0AAAAAgB0I0gAAAAAA2IEgDQAAAACAHQjSAAAAAADYgSANAAAAAIAdCNIAAAAAANiBIA0AAAAAgB0I0gAAAAAA2IEgDQAAAACAHQjSAAAAAADYgSANAAAAAIAdCNIAAAAAANiBIA0AAAAAgB0I0gAAAAAA2IEgDQAAAACAHQjSAAAAAADYgSANAAAAAIAdCNIAAAAAANiBIA0AAAAAgB0I0gAAAAAA2IEgDQAAAACAHQjSAAAAAADYgSANAAAAAIAdCNIAAAAAANiBIA0AAAAAgB0I0gAAAAAA2IEgDQAAAACAHQjSAAAAAADYgSANAAAAAIAdCNIAAAAAANiBIA0AAAAAgB0I0gAAAAAA2IEgDQAAAACAHQjSAAAAAADYgSANAAAAAIAdCNIAAAAAANiBIA0AAAAAgB0KTJCOiIhQSEiIPDw8VLp0aY0YMULJyck3nO7ChQt6+umnVaJECbm7u6t58+batWtXhn5//fWX2rVrJz8/P3l7e+vBBx/U5s2b82BOAAAAAAC3swIRpGNjYxUcHKzk5GQtX75cEyZM0OzZsxUeHn7Dabt166aVK1dqypQpWrp0qZycnBQcHKzjx49b+5w/f14tW7ZUdHS0Pv74Yy1evFhFixZVmzZt9Oeff+blrAEAAAAAbjNO+V2AJM2aNUvx8fFasWKFihcvLklKSUnRwIEDNXr0aJUpUybT6bZv3641a9boq6++Uvv27SVJLVq0UKVKlTR16lTNmDFDkrRx40adPXtWP//8sypWrChJatasmYoXL66VK1eqTp06eT+TAAAAAIDbQoHYI71mzRq1atXKGqIlqXPnzkpLS9P69euznG7nzp2yWCwKCQmxtrm7u6tp06b6+uuvrW1Xr16VJBUrVsza5urqKmdnZxljcnNWAAAAAAC3uQKxRzoiIkJ9+/a1afP29pa/v78iIiKynC4xMVEODg5ycrKdDRcXFx05ckQJCQlyc3NTu3btVKpUKb3wwgsaP368ihQpoqlTp8pisahHjx7XrS0+Pl7x8fHWx1FRUZKk1NRUpaam2juryIb0Hzd4fwHcbtLS0mSMkUlLy+9SblvGGKWlpfEdAhtsWwDIDnvWEQUiSMfGxsrb2ztDu4+Pj2JiYrKcrlq1akpNTdXvv/+uhg0bSrq2kbJjxw4ZYxQXFyc3Nzf5+Pjohx9+ULt27ayHifv6+mrNmjWqXLnydWubNm2aXn311Qzt0dHRcnFxsWMukV3pRxCcO3cunysBgNx1+fJlpaSkKCExMb9LuW2lpKTo8uXLfIfABtsWALIjOjo6230LRJDOqdDQUFWpUkVhYWGaP3++SpYsqUmTJunw4cOSJIvFIkk6e/asHnvsMVWpUkXTp0+Xo6OjZs+erUceeUTff/+9atSokeVrhIeHq3///tbHUVFRatiwoXx9feXn55e3M3iHKlKkiCTx/gK47Xh4eMjJ6aLcXF3zu5TblpOTkzw8PPgOgQ22LQBkR1JSUrb7Fogg7ePjowsXLmRoj42NtTlv+t+cnZ21ZMkSdevWzXrBsDp16mjo0KF6++235evrK0maMmWKYmNj9dtvv1n3Irds2VK1atXS66+/rkWLFmX5Gl5eXvLy8srQ7ujoKEdHR7vmE9mT/gMI7y+A242Dg4MsFossDgXiEiW3JYvFIgcHB75DYINtCwDZYc86okB8kwcFBWU4F/rChQuKiopSUFDQdaetX7++IiMjtX//fkVGRmr37t1KSEhQ/fr1rb8+7t27V0FBQTaHYjs6Ouquu+7SoUOHcn+GAAAAAAC3rQIRpNu0aaONGzcqLi7O2rZ06VI5ODgoNDT0htNbLBZVq1ZNgYGBOn/+vJYsWaIBAwZYn69QoYL27dunxH+ck5aamqrdu3dbb4cFAAAAAEB2FIggHRYWJk9PT3Xo0EHr16/XJ598ouHDhyssLMzmHtItW7ZU1apVbaYdP368lixZoi1btuiDDz5QgwYNVL9+ffXu3dvap3///jp37pweffRRff3111q9erUef/xxHThwQM8999ytmk0AAAAAwG2gwJwjvWnTJg0ePFgdOnSQp6en+vfvr/Hjx9v0S01NVUpKik1bbGyshg0bprNnz8rf3189e/bU2LFj5fCP88/q16+vdevW6bXXXlPv3r2VlpamWrVqafXq1XrwwQdvyTwCAAAAAG4PBSJIS1KNGjW0cePG6/bZsmVLhrapU6dq6tSpNxw/ODhYwcHBOS0PAAAAAABJBeTQbgAAAAAACguCNAAAAAAAdiBIAwAAAABgB4I0AAAAAAB2IEgDAAAAAGAHgjQAAAAAAHYgSAMAAAAAYAeCNAAAAAAAdiBIAwAAAABgB4I0AAAAAAB2IEgDAAAAAGAHgjQAAAAAAHYgSAMAAAAAYAeCNAAAAAAAdiBIAwAAAABgB4I0AAAAAAB2IEgDAAAAAGAHgjQAAAAAAHYgSAMAAAAAYAeCNAAAAAAAdiBIAwAAAABgB4I0AAAAAAB2IEgDAAAAAGAHgjQAAAAAAHYgSAMAAAAAYAeCNAAAAAAAdnDK7wKQtxYuXKhmhybkdxl2eavOaUnSiddq5XMl2fddldHq3r17fpdx28uLZeLkyZNKTUnN9XHzkqOTo8qWLZsnY5d7eU+ejIv/mVTqG3nv25HfZdy2Qkqd0TcakN9l3NbYtrg12LbIe3m1PLBt8T+383YFQfo2d/LkScXEX5aTU5H8LiX7HBwlSfFXkvO5kOxJSbmqkydP5ncZAADcEmxb5D22LYCCjyB9Bxgb/Zhaduqb32XctjYtm6MmJfO7ijtDXvyqWS7XRwSu78Uz7dSyKevkvLJp2Rw1KZ7fVdz+2LbIW2xb3Bp5tbeUbYs7A+dIAwAAAABgB4I0AAAAAAB2IEgDAAAAAGAHgjQAAAAAAHYgSAMAAAAAYAeCNAAAAAAAdiBIAwAAAABghxwFaWNMbtcBAAAAAEChkKMgHRAQoLFjx+rQoUO5XQ8AAAAAAAVajoJ09+7d9cknnygwMFDNmzfXggULlJCQkNu1AQAAAABQ4OQoSE+ePFnHjx/XypUr5evrq/79+8vf31/PPPOMfvnllxwVEhERoZCQEHl4eKh06dIaMWKEkpOTbzjdhQsX9PTTT6tEiRJyd3dX8+bNtWvXrkz7bt++Xa1atZKnp6e8vLx03333ZdkXAAAAAIDM5PhiYw4ODmrfvr2++OILnTx5UuPGjdOPP/6o+++/X3Xq1NH06dMVFxeXrbFiY2MVHBys5ORkLV++XBMmTNDs2bMVHh5+w2m7deumlStXasqUKVq6dKmcnJwUHBys48eP2/T79ttv1bx5cwUGBmr58uVavHixHnroIV25ciUnsw8AAAAAuEM55cYgp0+f1vHjx3X27Fk5OzurbNmyevnll/XKK69o/vz5euSRR647/axZsxQfH68VK1aoePHikqSUlBQNHDhQo0ePVpkyZTKdbvv27VqzZo2++uortW/fXpLUokULVapUSVOnTtWMGTOsY/Xr10//+c9/NHnyZOv0bdu2zY3ZBwAAAADcQXK8R/rixYv64IMP1KhRI919993asGGDxo4dq1OnTmnt2rU6ceKEHn30UQ0ZMuSGY61Zs0atWrWyhmhJ6ty5s9LS0rR+/fosp9u5c6csFotCQkKsbe7u7mratKm+/vpra9vGjRt15MiRbNUCAAAAAMD15GiPdM+ePbVixQpJUpcuXTRjxgzdd999Nn28vLw0cOBALViw4IbjRUREqG/fvjZt3t7e8vf3V0RERJbTJSYmysHBQU5OtrPh4uKiI0eOKCEhQW5ubtq+fbt8fX3166+/Kjg4WIcOHVLlypU1duxYPfXUU9etLT4+XvHx8dbHUVFRkqTU1FSlpqbecN7yW1pamowxMmlp+V3KbcsYo7S0tEKxPADIX6yT8x7r5LzHcpz3WI6B/GHPZy5HQXrfvn3673//qyeffFKenp5Z9qtVq5Y2b958w/FiY2Pl7e2dod3Hx0cxMTFZTletWjWlpqbq999/V8OGDSVdW7nv2LFDxhjFxcXJzc1Np0+f1uXLl9WnTx+99tprqlmzphYtWqRevXqpVKlSat26dZavMW3aNL366qsZ2qOjo+Xi4nLDectvly9fVkpKihISE/O7lNtWSkqKLl++rHPnzuV3KQAKONbJeY91ct5jOc57LMdA/oiOjs523xwF6V9//TVb/YoWLapmzZrl5CWyJTQ0VFWqVFFYWJjmz5+vkiVLatKkSTp8+LAkyWKxSLoWrhMTEzV58mQNGjRIkhQcHKyIiAiNHz/+ukE6PDxc/fv3tz6OiopSw4YN5evrKz8/vzybt9zi4eEhJ6eLcnN1ze9SbltOTk7y8PAoFMsDgPzFOjnvsU7OeyzHeY/lGMgfSUlJ2e6boyC9adMmHTt2TH369Mnw3Ny5c1WhQgW1aNEi2+P5+PjowoULGdpjY2Ntzpv+N2dnZy1ZskTdunVTnTp1JEl16tTR0KFD9fbbb8vX19c6vnQtPP9Ty5Yt9c4771y3Ni8vL3l5eWVod3R0lKOj4/VnrABwcHCQxWKRxSHHp8PjBiwWixwcHArF8gAgf7FOznusk/Mey3HeYzkG8oc9n7kcrQHHjBmjM2fOZPrcuXPnNHbsWLvGCwoKynAu9IULFxQVFaWgoKDrTlu/fn1FRkZq//79ioyM1O7du5WQkKD69eurSJEikq4dYp6VRA5LAgAAAADYIUdBeu/evWrQoEGmz91zzz3as2ePXeO1adNGGzdutLnv9NKlS+Xg4KDQ0NAbTm+xWFStWjUFBgbq/PnzWrJkiQYMGGB9vnXr1ipSpIg2btxoM92GDRtUv359u2oFAAAAANzZcnRot8ViyfRQbOna4dj2XmEwLCxMM2fOVIcOHTR69GidPHlSw4cPV1hYmM09pFu2bKmjR4/q4MGD1rbx48eratWqKlWqlCIjIzVhwgTVr19fvXv3tvYpVaqUhgwZorFjx8pisahGjRr67LPPtH37dq1du9a+mQcAAAAA3NFyFKQbNWqkd999Vx07drRe0Eu6dqn+9957T40aNbJrPB8fH23atEmDBw9Whw4d5Onpqf79+2v8+PE2/VJTU5WSkmLTFhsbq2HDhuns2bPy9/dXz549NXbsWDn867ydSZMmqWjRonrzzTd17tw51ahRQytXrszWHm8AAAAAANLlKEi/+uqratGihe666y717t1b/v7+OnXqlObPn6/9+/dry5Ytdo9Zo0aNDIde/1tm406dOlVTp0694fhOTk565ZVX9Morr9hdGwAAAAAA6XIUpO+//35t2rRJI0aM0MiRI5WWliYHBwdr+3333ZfbdQIAAAAAUCDkKEhL0gMPPKAff/xRCQkJio2Nlbe3t9zd3XOzNgAAAAAACpwcB+l0bm5ucnNzy41aAAAAAAAo8HIcpOPi4rRs2TLt378/w72YLRaLZsyYcdPFAQAAAABQ0OQoSB84cECNGzdWUlKSLl++LD8/P8XExCglJUU+Pj4qVqwYQRoAAAAAcFtyuHGXjMLDw9WoUSOdOXNGxhitXr1aCQkJ+vTTT+Xp6amlS5fmdp0AAAAAABQIOdoj/csvv+jjjz+Wi4uLJCk5OVmOjo568skndf78eQ0ZMkQ//vhjrhYKAAAAAEBBkKM90klJSfLy8pKDg4OKFy+uU6dOWZ+rXbu2du3alVv1AQAAAABQoOQoSAcGBuro0aOSpHr16um9997TxYsXlZCQoA8++EBlypTJ1SIBAAAAACgocnRod9euXbVr1y717NlTr7/+ulq3bi0fHx9ZLBYZYzRv3rzcrhMAAAAAgAIhR0E6PDzc+v/33Xef/vrrL61du1YJCQkKDg5W7dq1c61AAAAAAAAKEruDdGJiokaMGKGePXvq3nvvlSQFBARowIABuV4cAAAAAAAFjd3nSLu6umrOnDm6cuVKXtQDAAAAAECBlqOLjTVu3Fjbt2/P7VoAAAAAACjwcnSO9Guvvabu3bvL0dFRbdu2ValSpWSxWGz6FC9ePFcKBAAAAACgIMlRkG7cuLEkacSIERo5cmSmfVJTU3NeFQAAAAAABVSOgvScOXMy7IEGAAAAAOBOkKMg3bt371wuAwAAAACAwiFHFxsDAAAAAOBOlaM90pUqVbrhod2HDx/OUUEAAAAAABRkOQrSjz76aIYgHRsbq++++07GGHXs2DFXigMAAAAAoKDJUZCePn16pu3Jycnq0KGDKlWqdDM1AQAAAABQYOXqOdLOzs4aNGiQ3nzzzdwcFgAAAACAAiPXLzZ2/vx5Xbx4MbeHBQAAAACgQMjRod3Lly/P0JacnKx9+/bpnXfeUXBw8E0XBgAAAABAQZSjIN2pU6dM24sUKaKOHTtq5syZN1UUAAAAAAAFVY6C9N9//52hzdXVVSVLlrzhbbEAAAAAACjMchSkK1SokNt1AAAAAABQKOToYmNLlizJ8srcU6dO1dKlS2+qKAAAAAAACqocBemJEyfKxcUl0+fc3Nw0adKkmyoKAAAAAICCKkdB+sCBA6pdu3amz9WsWVP79++/qaIAAAAAACiochSkXV1ddebMmUyfi4qKkpNTjk69BgAAAACgwMtRkG7WrJkmTZqky5cv27RfvnxZU6ZMUfPmzXOjNgAAAAAACpwc7TqeMGGC7r//flWpUkWdOnVSmTJldOrUKS1btkzJyclavHhxbtcJAAAAAECBkKMgHRQUpB07dmjcuHH64osvFB0dLV9fX4WEhGjcuHGqWrVqbtcJAAAAAECBkOOTmatWraqFCxfmZi0AAAAAABR4OTpH+uLFi4qKisr0uaioKF26dOmmigIAAAAAoKDK0R7p/v37y9PTUx999FGG58aNG6dLly5p0aJFN10cAAAAAAAFTY72SH///fd6+OGHM32ubdu2+u67726qKAAAAAAACqocBenY2Fh5enpm+pyHh4eio6PtHjMiIkIhISHy8PBQ6dKlNWLECCUnJ99wugsXLujpp59WiRIl5O7urubNm2vXrl3XnaZDhw6yWCyaOnWq3XUCAAAAAO5sOQrSlStX1saNGzN9btOmTapYsaJd48XGxio4OFjJyclavny5JkyYoNmzZys8PPyG03br1k0rV67UlClTtHTpUjk5OSk4OFjHjx/PtP+aNWu0fft2u+oDAAAAACBdjs+RfvHFF1W8eHH17dtXJUqU0Pnz5/XJJ5/orbfe0oQJE+wab9asWYqPj9eKFStUvHhxSVJKSooGDhyo0aNHq0yZMplOt337dq1Zs0ZfffWV2rdvL0lq0aKFKlWqpKlTp2rGjBk2/ZOSkjRkyBBNnDhRffv2zcGcAwAAAADudDnaI/38889rwIABGjVqlEqVKiUXFxeVKlVKL774ovr375+tPcn/tGbNGrVq1coaoiWpc+fOSktL0/r167OcbufOnbJYLAoJCbG2ubu7q2nTpvr6668z9J86dap8fHzUu3dvu+oDAAAAACBdjvZIWywWvfvuuxo6dKi+/fZbRUdHy9fXV8HBwapWrZrd40VERGTYQ+zt7S1/f39FRERkOV1iYqIcHBzk5GQ7Gy4uLjpy5IgSEhLk5uYmSTp27JgmTpyojRs3ymKxZLu2+Ph4xcfHWx+n3/YrNTVVqamp2R4nv6SlpckYI5OWlt+l3LaMMUpLSysUywOA/MU6Oe+xTs57LMd5j+UYyB/2fOZyFKTTVatWzSY4G2O0YcMGLVy4UHPnzs32OLGxsfL29s7Q7uPjo5iYmOu+fmpqqn7//Xc1bNhQ0rWV+44dO2SMUVxcnDVIP//88+rYsaPuu+++bNclSdOmTdOrr76aoT06OlouLi52jZUfLl++rJSUFCUkJuZ3KbetlJQUXb58WefOncvvUgAUcKyT8x7r5LzHcpz3WI6B/GHPRbNvKkin27FjhxYuXKglS5bozJkzKlmyZG4Me0OhoaGqUqWKwsLCNH/+fJUsWVKTJk3S4cOHJcm653n9+vVav369IiMj7X6N8PBw9e/f3/o4KipKDRs2lK+vr/z8/HJnRvKQh4eHnJwuys3VNb9LuW05OTnJw8OjUCwPAPIX6+S8xzo577Ec5z2WYyB/JCUlZbtvjoP0gQMHtHDhQi1atEiHDh2SdC3YDhw4UG3atLFrLB8fH124cCFDe2xsrM150//m7OysJUuWqFu3bqpTp44kqU6dOho6dKjefvtt+fr6SpKGDBmiIUOGyN3dXXFxcdbpExMTFRcXl+ne8HReXl7y8vLK0O7o6ChHR8dszmH+cXBwkMVikcUhR6fDIxssFoscHBwKxfIAIH+xTs57rJPzHstx3mM5BvKHPZ85u9aAp0+f1vTp03XvvfcqKChIb7zxhgICAvTf//5XkjRq1Ci1b98+wznLNxIUFJThXOgLFy4oKipKQUFB1522fv36ioyM1P79+xUZGandu3crISFB9evXV5EiRSRJkZGRmjBhgnx8fKx/kvTSSy/Jx8dHiRyaBAAAAADIpmwn3pCQEG3ZskVpaWm65557NHXqVHXt2lX+/v66cOGC3Vfq/qc2bdpowoQJNnuHly5dKgcHB4WGht5weovFYj1X+9y5c1qyZImmTJlifX7z5s0ZpmnRooXCwsLUpUsXOTs757h2AAAAAMCdJdtBetOmTZKuBepXXnlF999/f64VERYWppkzZ6pDhw4aPXq0Tp48qeHDhyssLMzmHtItW7bU0aNHdfDgQWvb+PHjVbVqVZUqVcq657l+/fo2t7hq3rx5pq9bpUqVLJ8DAAAAACAz2Q7SX3zxhRYtWqRVq1apSZMmqlixorp166Ynn3xSZcuWvakifHx8tGnTJg0ePFgdOnSQp6en+vfvr/Hjx9v0S01NVUpKik1bbGyshg0bprNnz8rf3189e/bU2LFj5cB5OwAAAACAPJDtIP3YY4/pscce08WLF/XFF19o4cKFmjx5siZOnKjq1avLYrFkesGw7KpRo4Y2btx43T5btmzJ0DZ16lRNnTrV7tczxtg9DQAAAAAAdu+29fT0VO/evbVhwwadOHFC06ZNk6enp4wxeuyxx9SiRQu77iENAAAAAEBhclPHP5cqVUr/+c9/9PPPP+vAgQN6+eWXFRUVpX79+uVWfQAAAAAAFCi5diJxlSpV9PLLLysiIkI7duzIrWEBAAAAAChQ8uSKXPfcc09eDAsAAAAAQL7j0tYAkIsuX76s2bNnq1mzZqpVq5aaNWum2bNn6/Lly/ldGgAAKITYtiiYCNIAkEsiIyNVq1YtPfPMM4qKilLlypUVFRWlZ555RrVq1VJkZGR+lwgAAAoRti0KLoI0AOSCy5cvq3Xr1rp48aJWrVqlyMhIff3114qMjNSqVat08eJFtW7dml+PAQBAtrBtUbBlO0jPnTtXFy9ezMtaAKDQWrhwoY4ePaoFCxaobdu2slgskiSLxaK2bdtqwYIFOnr0qBYtWpTPlQIAgMKAbYuCzSm7Hfv27auBAweqXbt26t69u9q2basiRYrkZW3IJReiz2rTsjm5Pu4fP21SfGx0ro+bl7x8fHXX/S1zdcwL0Wcl+efqmCh8Fi5cqMDAQLVp0ybT59u0aaNq1app4cKFGjBgwC2uDgVJXq2T88IfP22SpFxfb+Yl1sm3RmFajqXCtyyzHENi26Kgy3aQ3r17tz777DMtWbJEy5Ytk7e3tzp16qTu3burWbNmeVkjbkLZsmX1cJO8GftchIcsSYXrKIXSPh5qUiO3v5j8VbZs2VweE4XN+fPnFRgYaP21+N8sFosCAwP1999/3+LKUJDk5To5L/y55do6PvfXm3mJdXJeK2zLsVQYl2WWY7BtUdBZjDHG3ol++uknLVq0SMuWLdPZs2dVpkwZdevWTU8++aTq1q2bB2UWHCdOnFBAQICOHz+ucuXK5Xc5AAqIZs2aKSoqSpGRkZl+4RljVL16dZUpU0Zbtmy59QUCOdCkybW0tHXr1nyuBLg5LMsojNi2uPXsyXo5utjY/fffr5kzZ+rkyZNau3atWrVqpQ8//FD169dXrVq1NGHChBwVDgCFVffu3XXgwAGtWbMm0+fXrFmjAwcOqHv37re4MgAAUBixbVGw3dRVux0cHBQSEqJPPvlEUVFReu655xQREaGXXnopt+oDgEKhe/fuqlChgnr27KnVq1cr/WAfY4xWr16tHj16qEKFCnryySfzuVIAAFAYsG1RsGX7HOms/P7771q0aJGWLFmikydPqlSpUurSpUtu1AYAhYaHh4fWrVun1q1b6+GHH1a1atUUGBio/fv368CBA6pQoYLWrVsnDw+P/C4VAAAUAmxbFGw52iO9f/9+vfLKK6pevbruvfdeffjhh2rVqpXWrVunkydPavr06blcJgAUfNWrV9eePXs0e/ZslSlTRn///bfKlCmj2bNna8+ePapevXp+lwgAAAoRti0KrmzvkT558qQWL16sRYsWadeuXSpSpIjatm2r8ePHq3379nJxccnLOgGgUPDw8NCAAQO4DQUAAMgVbFsUTNkO0uXLl5fFYlGzZs00e/ZsderUScWKFcvL2gAAAAAAKHCyHaTffPNNde3aVWXKlMnLegAAAAAAKNCyfY70c889py+++ELbt2/Pss/27ds1c+ZMJScn50pxAAAAAAAUNNneI/3ee+9p8uTJioiIyLJPjRo11LFjR0nS4MGDb746AAAAAAAKmGzvkV68eLEGDx4sb2/vLPsUK1ZMgwYN0sKFC3OjNgAAAAAACpxsB+k9e/bo/vvvv2G/++67T3v27LmpogAAAAAAKKiyHaSNMdkeNC0tLUfFAAAAAABQ0GU7SFeuXFk//vjjDfv9+OOPqly58k0VBQAAAABAQZXtIN2pUye99dZb2rdvX5Z99u3bp+nTp6tz5865UhwAAAAAAAVNtq/aPWzYMC1btkwNGzbUs88+q9atW6t8+fKyWCw6duyY1q1bp/fff18VK1ZUeHh4XtYMAAAAAEC+yXaQ9vDw0JYtW/Tss8/qv//9r/773/9m6NOpUye999578vDwyNUiAQAAAAAoKLIdpCXJ19dXn3/+uY4dO6YffvhBJ0+elCSVLVtWDz74oAICAvKkSAAAAAAACgq7gnS68uXLq3v37rldCwAAAAAABV62LzYGAAAAAAAI0gAAAAAA2IUgDQAAAACAHQjSAAAAAADYgSANAAAAAIAdsn3V7vnz59s18FNPPWV3MQAAAAAAFHTZDtK9e/e2eWyxWCRJxpgMbRJBGgAAAABwe8r2od2xsbHWvx07dqhChQoaO3asdu/erdOnT2v37t0aM2aMKlSooJ9//jkvawYAAAAAIN9ke490sWLFrP//4osv6umnn9aLL75obStZsqTq1KkjNzc3jRw5Ups2bcrdSgEAAAAAKABydLGxbdu2qX79+pk+V79+fW3fvt3uMSMiIhQSEiIPDw+VLl1aI0aMUHJy8g2nu3Dhgp5++mmVKFFC7u7uat68uXbt2mXTZ8eOHerbt6+qVq0qd3d3VatWTaNGjdLly5ftrhMAAAAAcGfLUZAuWbKklixZkulzixcvlp+fn13jxcbGKjg4WMnJyVq+fLkmTJig2bNnKzw8/IbTduvWTStXrtSUKVO0dOlSOTk5KTg4WMePH7f2WbJkiQ4cOKARI0Zo9erVGjp0qGbPnq327dvbVScAAAAAANk+tPufRo8erWeeeUaHDh1Shw4dVLJkSZ09e1YrVqzQ999/rw8++MCu8WbNmqX4+HitWLFCxYsXlySlpKRo4MCBGj16tMqUKZPpdNu3b9eaNWv01VdfWUNxixYtVKlSJU2dOlUzZsyQJI0cOdIm3Ddv3lw+Pj7q3r27fvvttyz3rgMAAAAA8G852iM9YMAAffXVV0pMTNTw4cPVvXt3DR8+XImJifryyy81YMAAu8Zbs2aNWrVqZQ3RktS5c2elpaVp/fr1WU63c+dOWSwWhYSEWNvc3d3VtGlTff3119a2zPaQ16tXT5J06tQpu2oFAAAAANzZcrRHWpLatWundu3aKS0tTefOnZOfn58cHHKUyxUREaG+ffvatHl7e8vf318RERFZTpeYmCgHBwc5OdnOhouLi44cOaKEhAS5ubllOu3WrVslSUFBQdetLT4+XvHx8dbHUVFRkqTU1FSlpqZed1oAAAqz9Ftc8n2Hwo5lGUB22LOOyHGQTufg4KBSpUrd1BixsbHy9vbO0O7j46OYmJgsp6tWrZpSU1P1+++/q2HDhpKktLQ07dixQ8YYxcXFZRqkz58/r1deeUWPPvqoqlWrdt3apk2bpldffTVDe3R0tFxcXG4wZwAAFF5Xr16VJJ07dy6fKwFuDssygOyIjo7Odt9sB+khQ4Zke1CLxWI9PzkvhYaGqkqVKgoLC9P8+fNVsmRJTZo0SYcPH7bW8W9Xr15V165dJUnvv//+DV8jPDxc/fv3tz6OiopSw4YN5evra/dF1QAAKEyKFCkiKfNTpIDChGUZQHYkJSVlu2+2g/Q/zzm+EXuDtI+Pjy5cuJChPTY21ua86X9zdnbWkiVL1K1bN9WpU0eSVKdOHQ0dOlRvv/22fH19bfobY9S3b1/98ssv+uGHH+Tv73/D2ry8vOTl5ZWh3dHRUY6OjjecHgCAwir9B2m+71DYsSwDyA571hHZDtJ///13jorJjqCgoAznQl+4cEFRUVE3PIe5fv36ioyM1MGDB2WMUbVq1TRo0CDVr1/f+utjumHDhunzzz/X6tWrdffdd+f6fAAAAAAAbn85uzpYLmvTpo02btyouLg4a9vSpUvl4OCg0NDQG05vsVhUrVo1BQYG6vz581qyZEmGK4dPmjRJb731lubOnauWLVvm9iwAAAAAAO4QOb7Y2MmTJzV9+nRt3bpVMTExKl68uJo2bar//Oc/Klu2rF1jhYWFaebMmerQoYNGjx6tkydPavjw4QoLC7O5h3TLli119OhRHTx40No2fvx4Va1aVaVKlVJkZKQmTJig+vXrq3fv3tY+ixYt0qhRo9SjRw9VqlRJ27dvtz5XpUoVzpcBAAAAAGRbjoL0X3/9pQcffFBXr15VSEiI6tatqzNnzmjWrFn6+OOP9f3336tWrVrZHs/Hx0ebNm3S4MGD1aFDB3l6eqp///4aP368Tb/U1FSlpKTYtMXGxmrYsGE6e/as/P391bNnT40dO9bmVlzp96L+9NNP9emnn9pM/8knn9iEbgAAAAAArsdi0m+sZ4eHHnpI0dHRWr9+vXx8fKztsbGxCg0NVYkSJbRmzZpcLbSgOHHihAICAnT8+HGVK1cuv8sBACDPNGnSRJK0devWfK4EuDksywCyw56sl6NzpLdu3aqxY8fahGjp2p7lMWPGsJICAAAAANy2chSknZycsrzHVlJSErcWAAAAAADctnIUpFu1aqUxY8Zo//79Nu0HDhzQSy+9pJCQkFwpDgAAAACAgiZHQXratGlKSUlRzZo1VbduXbVu3Vr16tVTjRo1lJKSomnTpuV2nQAAAAAAFAg5CtLly5fXn3/+qWnTpikwMFBpaWkKDAzUW2+9pT/++EMBAQG5XScAAAAAAAVCju8jXbRoUQ0ZMkRDhgzJzXoAAAAAACjQsr1H2svLS7/99ptN24QJE3TmzJlcLwoAAAAAgIIq20H60qVLSk1NtT5OTU3VSy+9pJMnT+ZJYQAAAAAAFEQ5Okc6nTEmt+oAAAAAAKBQuKkgDQAAAADAncaui43Fx8crJiZGkpSSkpKh7Z+KFy+eC+UBAAAAAFCw2BWkW7dunaGtZcuWmfb95/nUAAAAAADcLrIdpD/55JO8rAMAAAAAgEIh20G6V69eeVkHAAAAAACFQq5cbGz+/PmKjY3NjaEAAAAAACjQbjpIp6amqk+fPvr7779zox4AAAAAAAq0XNkjzf2kAQAAAAB3Cu4jDQAAAACAHW46SDs4OKhXr14qUaJEbtQDAAAAAECBZtd9pDNjsVi4NRYAAAAA4I5x00H6zJkz2rVrlySpXr16Klmy5M0OCQAAAABAgZXtQ7tHjx6tkydPWh+npaVpyJAhCggIUJs2bdSmTRuVK1dOw4YNy5NCAQAAAAAoCLIdpCdPnmwTpKdMmaL33ntPQ4cO1S+//KJffvlFgwcP1vTp0/Xuu+/mSbEAAAAAAOS3bB/a/e9bXH344YcaOHCgpkyZYm1r0KCBLl++rA8//FDPPfdc7lUJAAAAAEABkeOrdh89elSPPPJIhvZHH31U+/fvv6miAAAAAAAoqOy62Fh8fLxiYmIkSSVKlMiwlzqdgwO3pwYAAAAA3J7sSrytW7eWn5+f/Pz8dPbsWf38888Z+vz5558qV65crhUIAAAAAEBBku090pndK9rf3z9D27fffqs2bdrcXFUAAAAAABRQ2Q7SvXr1yla/tWvX5rgYAAAAAAAKOk5mBgAAAADADgRpAAAAAADsQJAGAAAAAMAOBGkAAAAAAOxAkAYAAAAAwA4EaQAAAAAA7ECQBgAAAADADgRpAAAAAADsQJAGAAAAAMAOBGkAAAAAAOxQYIJ0RESEQkJC5OHhodKlS2vEiBFKTk6+4XQXLlzQ008/rRIlSsjd3V3NmzfXrl27Mu3Xr18/FS9eXJ6enurUqZOioqLyYE4AAAAAALezAhGkY2NjFRwcrOTkZC1fvlwTJkzQ7NmzFR4efsNpu3XrppUrV2rKlClaunSpnJycFBwcrOPHj9v069Kli9avX69Zs2Zp4cKFioyMVJs2bZSSkpJXswUAAAAAuA055XcBkjRr1izFx8drxYoVKl68uCQpJSVFAwcO1OjRo1WmTJlMp9u+fbvWrFmjr776Su3bt5cktWjRQpUqVdLUqVM1Y8YMSdJPP/2kdevWad26dQoNDZUkVa9eXTVq1NDy5cvVuXPnWzCXAAAAAIDbQYHYI71mzRq1atXKGqIlqXPnzkpLS9P69euznG7nzp2yWCwKCQmxtrm7u6tp06b6+uuvbcb39va26Ve9enXVrVtXq1evzuW5AQAAAADczgrEHumIiAj17dvXps3b21v+/v6KiIjIcrrExEQ5ODjIycl2NlxcXHTkyBElJCTIzc1NERERql69uiwWi02/GjVqXHd8SYqPj1d8fLz1cfp51ampqUpNTc3W/AEAkJeee+45/fnnn7k+bvqYDzzwQK6PLUl16tTRu+++mydjo/DJq+VYyttlmeUYuH3Yk+8KRJCOjY2Vt7d3hnYfHx/FxMRkOV21atWUmpqq33//XQ0bNpQkpaWlaceOHTLGKC4uTm5ubjkeX5KmTZumV199NUN7dHS0XFxcrj9jAADcAgkJCbp69Wquj+vq6ipJeTK2dK3uc+fO5cnYKHzyajmW8nZZZjkGbh/R0dHZ7lsggnROhYaGqkqVKgoLC9P8+fNVsmRJTZo0SYcPH5akDHugcyI8PFz9+/e3Po6KilLDhg3l6+srPz+/mx4fAICbNWfOnPwuAbhpLMcA8ltSUlK2+xaIIO3j46MLFy5kaI+NjbU5b/rfnJ2dtWTJEnXr1k116tSRdO3wmqFDh+rtt9+Wr6+vdfx/X8U7O+NLkpeXl7y8vDK0Ozo6ytHR8brTAgAAAAAKB3vyXYG42FhQUFCGc5UvXLigqKgoBQUFXXfa+vXrKzIyUvv371dkZKR2796thIQE1a9fX0WKFLGOHxkZKWOMzbQRERE3HB8AAAAAgH8qEEG6TZs22rhxo+Li4qxtS5culYODg/V2VddjsVhUrVo1BQYG6vz581qyZIkGDBhgM35sbKw2bdpkbdu/f7927typtm3b5uq8AAAAAABubxbz7920+SA2Nla1atVSYGCgRo8erZMnTyo8PFzdu3fXO++8Y+3XsmVLHT16VAcPHrS2jR8/XlWrVlWpUqUUGRmpCRMmKCgoSGvWrJGDw/9+J3jooYe0d+9e/fe//5Wrq6vGjBkjBwcH/frrrxmu+n09J06cUEBAgI4fP65y5crlzhsAAAAAAMhX9mS9AnOO9KZNmzR48GB16NBBnp6e6t+/v8aPH2/TLzU1VSkpKTZtsbGxGjZsmM6ePSt/f3/17NlTY8eOtQnRkrRkyRKFh4fr6aefVkpKikJDQzVz5ky7QjQAAAAAAAVij3Rhwh5pAAAAALj92JP1CsQ50gAAAAAAFBYEaQAAAAAA7ECQBgAAAADADgRpAAAAAADsQJAGAAAAAMAOBGkAAAAAAOxAkAYAAAAAwA4EaQAAAAAA7ECQBgAAAADADgRpAAAAAADsQJAGAAAAAMAOBGkAAAAAAOxAkAYAAAAAwA4EaQAAAAAA7ECQBgAAAADADgRpAAAAAADsQJAGAAAAAMAOBGkAAAAAAOxAkAYAAAAAwA4EaQAAAAAA7ECQBgAAAADADgRpAAAAAADsQJAGAAAAAMAOBGkAAAAAAOxAkAYAAAAAwA4EaQAAAAAA7ECQBgAAAADADgRpAAAAAADsQJAGAAAAAMAOBGkAAAAAAOxAkAYAAAAAwA4EaQAAAAAA7ECQBgAAAADADgRpAAAAAADsQJAGAAAAAMAOBGkAAAAAAOxAkAYAAAAAwA4EaQAAAAAA7ECQBgAAAADADgUmSEdERCgkJEQeHh4qXbq0RowYoeTk5BtOFx0drbCwMJUvX14eHh6qXbu2Zs2alaHf1q1b1aJFC/n4+KhEiRJq06aNdu3alQdzAgAAAAC4nTnldwGSFBsbq+DgYFWrVk3Lly/XyZMnFR4eritXruidd9657rRPPPGEIiIiNGHCBJUvX16rV6/Ws88+K0dHRw0YMECSFBkZqdDQUAUHB+uzzz5TUlKSJkyYoJYtW2rPnj0qXbr0rZhNAAAAAMBtoEAE6VmzZik+Pl4rVqxQ8eLFJUkpKSkaOHCgRo8erTJlymQ63enTp7V582Z98skn6t27tyQpODhYO3bs0OLFi61BesWKFTLGaOnSpXJzc5Mk3XXXXapcubI2bNignj175v1MAgAAAABuCwXi0O41a9aoVatW1hAtSZ07d1ZaWprWr1+f5XRXr16VJBUrVsymvVixYjLG2PRzcXGRq6urTR9JNv0AAAAAALiRArFHOiIiQn379rVp8/b2lr+/vyIiIrKcLiAgQKGhoZowYYKqV6+ugIAArVmzRuvXr9fChQut/bp27arJkydr7NixCg8PV1JSkkaNGqWAgAA9+uij160tPj5e8fHx1sdRUVGSpNTUVKWmpuZkdgEAAAAABYw9+a5ABOnY2Fh5e3tnaPfx8VFMTMx1p12+fLm6dOmiWrVqSZIcHR01c+ZMPf7449Y+1apV06ZNm/Too49qwoQJkqSKFStq48aNGfZm/9u0adP06quvZmiPjo6Wi4vLjWYNAAAAAFAIREdHZ7tvgQjSOWWMUZ8+fXTgwAEtWrRI/v7+2rBhg4YOHSofHx917dpVkrR//349/vjjCg0N1VNPPaXExERNnTpVbdq00bZt21SqVKksXyM8PFz9+/e3Po6KilLDhg3l6+srPz+/PJ9HAAAAAEDeS0pKynbfAhGkfXx8dOHChQztsbGxNudN/9uqVau0dOlS/fHHH6pTp44kqXnz5jp79qxeeOEFa5AePXq0Spcurfnz51unbd68ucqXL68ZM2ZY91JnxsvLS15eXhnaHR0d5ejomO15BAAAAAAUXPbkuwJxsbGgoKAM50JfuHBBUVFRCgoKynK6vXv3ytHRUbVr17Zpr1evnk6dOqUrV65Y+9199902fYoWLaqqVavq0KFDuTQXAAAAAIA7QYEI0m3atNHGjRsVFxdnbVu6dKkcHBwUGhqa5XQVKlRQamqq/vjjD5v23377TSVLlpS7u7u1386dO22u0B0fH68DBw6oYsWKuTovAAAAAIDbW4EI0mFhYfL09FSHDh20fv16ffLJJxo+fLjCwsJs7iHdsmVLVa1a1fq4bdu2Kl++vDp16qRPP/1UmzZt0siRIzV37lwNHjzYZvydO3eqe/fuWrt2rVauXKmHH35YSUlJNuc/AwAAAABwIwXmHOlNmzZp8ODB6tChgzw9PdW/f3+NHz/epl9qaqpSUlKsjz09PbVp0yaNGTNGI0eOVFxcnCpVqqRp06Zp0KBB1n6PPvqoPv/8c7355pvq0qWLnJ2dVa9ePW3evFnVqlW7ZfMJAAAAACj8LOafxzvjhk6cOKGAgAAdP35c5cqVy+9yAAAAAAC5wJ6sVyAO7QYAAAAAoLAgSAMAAAAAYAeCNAAAAAAAdiBIAwAAAABgB4I0AAAAAAB2IEgDAAAAAGAHgjQAAAAAAHYgSAMAAAAAYAeCNAAAAAAAdiBIAwAAAABgB4I0AAAAAAB2IEgDAAAAAGAHgjQAAAAAAHYgSAMAAAAAYAeCNAAAAAAAdiBIAwAAAABgB4I0AAAAAAB2IEgDAAAAAGAHgjQAAAAAAHYgSAMAAAAAYAeCNAAAAAAAdiBIAwAAAABgB4I0AAAAAAB2IEgDAAAAAGAHgjQAAAAAAHYgSAMAAAAAYAeCNAAAAAAAdiBIAwAAAABgB4I0AAAAAAB2IEgDAAAAAGAHgjQAAAAAAHYgSAMAAAAAYAeCNAAAAAAAdiBIAwAAAABgB4I0AAAAAAB2IEgDAAAAAGAHgjQAAAAAAHYgSAMAAAAAYIcCE6QjIiIUEhIiDw8PlS5dWiNGjFBycvINp4uOjlZYWJjKly8vDw8P1a5dW7Nmzcq076pVq9S4cWN5eHjIx8dHLVq00IkTJ3J7VgAAAAAAtzGn/C5AkmJjYxUcHKxq1app+fLlOnnypMLDw3XlyhW988471532iSeeUEREhCZMmKDy5ctr9erVevbZZ+Xo6KgBAwZY+3366afq16+fXnjhBY0fP14XL17UDz/8oMTExLyePQAAAADAbcRijDH5XcTEiRM1fvx4HTt2TMWLF5ckzZ49WwMHDtSxY8dUpkyZTKc7ffq0/P399cknn6h3797W9mbNmsnJyUmbNm2SJMXExKhSpUqaNGmSnn322Zuq9cSJEwoICNDx48dVrly5mxoLAAAAAFAw2JP1CsSh3WvWrFGrVq2sIVqSOnfurLS0NK1fvz7L6a5evSpJKlasmE17sWLF9M/fBz7//HOlpqaqX79+uVw5AAAAAOBOUyAO7Y6IiFDfvn1t2ry9veXv76+IiIgspwsICFBoaKgmTJig6tWrKyAgQGvWrNH69eu1cOFCa7/t27crKChI8+bN0xtvvKGTJ0+qdu3amjhxotq0aXPd2uLj4xUfH299HBUVJUlKTU1VampqTmYXAAAAAFDA2JPvCkSQjo2Nlbe3d4Z2Hx8fxcTEXHfa5cuXq0uXLqpVq5YkydHRUTNnztTjjz9u7XP69GlFRkbqpZde0pQpU+Tv7693331XjzzyiHbt2mWdNjPTpk3Tq6++mqE9OjpaLi4u2ZxDAAAAAEBBFh0dne2+BSJI55QxRn369NGBAwe0aNEi+fv7a8OGDRo6dKh8fHzUtWtXSVJaWpouXbqkhQsX6pFHHpEkNW/eXIGBgZo8ebLmz5+f5WuEh4erf//+1sdRUVFq2LChfH195efnl7czCAAAAAC4JZKSkrLdt0AEaR8fH124cCFDe2xsrM150/+2atUqLV26VH/88Yfq1Kkj6VpAPnv2rF544QVrkPbx8ZEkBQcHW6ctUqSIHnzwQf3111/Xrc3Ly0teXl4Z2h0dHeXo6HjjmQMAAAAAFHj25LsCcbGxoKCgDOdCX7hwQVFRUQoKCspyur1798rR0VG1a9e2aa9Xr55OnTqlK1euSNJ1D93m9lcAAAAAAHsUiCDdpk0bbdy4UXFxcda2pUuXysHBQaGhoVlOV6FCBaWmpuqPP/6waf/tt99UsmRJubu7S5LatWsnSdq4caO1T3Jysr777jvVr18/F+cEAAAAAHC7KxCHdoeFhWnmzJnq0KGDRo8erZMnT2r48OEKCwuzuYd0y5YtdfToUR08eFCS1LZtW5UvX16dOnXSuHHj5O/vr/Xr12vu3Lk2Fwi755579Pjjj+vpp59WTEyM9WJjZ86c0fDhw2/5/AIAAAAACq8CEaR9fHy0adMmDR48WB06dJCnp6f69++v8ePH2/RLTU1VSkqK9bGnp6c2bdqkMWPGaOTIkYqLi1OlSpU0bdo0DRo0yGbaefPmadSoUXrxxRcVHx+v+vXra+PGjdZzqwEAAAAAyA6LMcbkdxGFyYkTJxQQEKDjx4+rXLly+V0OAAAAACAX2JP1CsQ50gAAAAAAFBYEaQAAAAAA7ECQBgAAAADADgRpAAAAAADsQJAGAAAAAMAOBGkAAAAAAOxAkAYAAAAAwA4EaQAAAAAA7ECQBgAAAADADgRpAAAAAADsQJAGAAAAAMAOBGkAAAAAAOxAkAYAAAAAwA4EaQAAAAAA7ECQBgAAAADADgRpAAAAAADsQJAGAAAAAMAOTvldQGGTkpIiSYqKisrnSgAAAAAAuSU946VnvushSNvp3LlzkqSGDRvmcyUAAAAAgNx27tw5VaxY8bp9LMYYc2vKuT0kJibqzz//lJ+fn5yc+B0iL0RFRalhw4b65Zdf5O/vn9/lADnCcozbAcsxbhcsy7gdsBznvZSUFJ07d0516tSRq6vrdfuSBO3k6uqqe++9N7/LuCP4+/urXLly+V0GcFNYjnE7YDnG7YJlGbcDluO8daM90em42BgAAAAAAHYgSAMAAAAAYAeCNAocLy8vjRs3Tl5eXvldCpBjLMe4HbAc43bBsozbActxwcLFxgAAAAAAsAN7pAEAAAAAsANBGgAAAAAAOxCkAQAAAACwA0EaAAAAAAA7EKQBAAAAALADQRoAbjNpaWmSpLNnz+ry5cuSJG7QAAAA8ltiYqL27dtnfVyYt08I0kAmzp8/r/Xr1xNCUKicO3dO27dvl4ODg77//ns9/PDDmjBhQn6XBQAAIEn6888/9fTTT1vDtMViyeeKco4gDUiKiYnR4cOHrY93796tIUOGaNy4cflYFXB9KSkpWrVqlfr06aOAgACVKlVKo0aNkiQ1adJEb731lqZNm6Y1a9bkc6VA7kn/YZMfOHG7McYoNTXVZtlmOcftwBhjXZbd3d21a9cubdu2TZK0Z88e7dq1Kx+ryzmCNO5IKSkpWr16tfr06aMyZcqoTJkyWrhwofX5li1b6tNPP9XMmTP11Vdf5WOlQEbGGC1btkzOzs7q0aOHzpw5o2HDhum3337TypUrrf2aNGminj176vXXX9cvv/xinRYobIwx1lMW0vdeWCwWXbp0KT/LAnKVxWKRo6OjLBaLjh8/rsjIyEK9tw53ttTUVKWmpkq6tmynL8uurq4qU6aMxo4dK29vb9WpU0eLFi3Kz1JzzCm/CwButa1bt+rBBx+Uu7u7HnzwQY0cOVJNmzZVUFCQtU9aWpoaNGignj17aurUqfLz89P9998vYwxfariljh07psjISNWpU0elS5eWdO0LycPDQ87OznrvvffUrVu3LKcfOnSonnnmGU2fPl2fffYZyy8KpfSNsMTERG3fvl3e3t5atWqV5s6dqwMHDuR3eUC2pO9xdnBwkINDxn1Zly9f1ttvv613331XFy9eVNmyZdWlSxeFhYWpVKlS+VAxkHOOjo6SpKtXr2r79u1KTU1V8+bNtWDBAh09elSS9MEHH+j+++9X+fLl87PUHGOPNO44JUqUULFixfTyyy9r5cqVGjJkiO655x65u7tn6Dto0CBdvXpV77zzTj5UijvRlStXtGzZMj3++OMqXry46tSpo379+ik4OFifffaZtV9gYKAqV66sH374QdK1L6p/Sg/MNWvWVJ8+fbRy5Urt2LHj1s0IYIe0tDSlpKRk+bwxRuPGjVPx4sXVv39/vfTSS1qwYIGioqJ08uTJW1gpkLULFy5c93mLxSInJ6cMITr9SKGFCxfqk08+0cCBA7V+/Xo9/PDDmjNnjsaMGZNnNQM5lZqaarPe/vcRb2vWrFHz5s1VrFgx9e7dW2PHjlVKSopGjBih+fPnKzk5WYGBgQoMDJSrq+utLj9XEKRxxylXrpzuuusu/f7773J2drYGjqSkJEnXNujSv+Rq166t7t27a+XKlTp06BB785Bn0r+Ann/+eXXu3FkXLlzQu+++q61bt2ru3LkqV66cxowZo99++03StR+E7rvvPn377beSpCJFikiS/vjjD/388882y2q3bt3k6OiotWvXZgjcQF67fPmy1qxZo8WLF+vIkSOZ9nFwcJCT07WD5KKjozM8v337ds2YMUMvvPCCNm3apNGjR6t69eq6cuWKtm/fnpflA1mKiYnRnDlz9PDDD6to0aJ67bXXrNsSmZ3nHBkZqZdfflnt27fX9OnTrddmsVgsOnbsmN5++201aNBAI0aMUKNGjfTmm2/qgw8+0GeffaY//vjj1s8g8A//DsqOjo7W9XZCQoLNdkdUVJReeeUVlSxZUl9//bW++eYbjRgxQklJSXJ3d1fdunVVvnx5ffPNN5L+d7eRwoYgjTuOh4eHmjdvrg0bNujNN99Us2bNVLduXQ0aNEjffvutzS/FTk5Oat++vSRp3bp1hfaDjoLljz/+0IcffqiVK1daQ0P6eUTBwcHy9/dXv3791K1bN9WsWVPBwcGaO3eujh49ag0iXl5eaty4sfbv36/x48crJCREXl5eqlu3rr799lvrxlxqaqrc3NzUuHFjbdq0SefPn8+Xecad5ejRoxo6dKiqV6+uYsWKqW/fvho2bJhCQ0M1b948SbYbZbt27dJTTz2lUqVKqXHjxho6dKhOnz5tfX7BggUqU6aMBgwYoAoVKuj+++/XuHHjVLlyZeuGWLp/XtQGyE3pF3js1auXgoKCVKJECY0dO1alSpXS22+/rZEjR8rFxUWSrOc5HzlyRBaLRd9++60eeeQRrV+/Xl5eXpoxY4ZCQkK0d+9eSdd+cNq7d69ee+01OTk5KTk5WV9//bU+/fRTJSQkaPPmzdc9agPIbenr0vT16T+DsjFG3333nbp27apq1aqpS5cu+uqrr3TlyhVJ0tKlS3X27FkNHjxYLVu2VI0aNfTII4/Iw8NDkuTn56eGDRtq7dq1t37GchFBGre19POR/hmALRaLmjZtqsTERH3++ecKCgpSaGio1q9fr/bt22e4wnGFChV0zz336Ntvv1ViYuKtngXcBlJSUrRu3Tq1bt1aHh4eaty4saZOnaqePXuqTZs2ioiIsP6q26xZM6Wmpmr37t2S/neO0d9//y1jjMqWLSvp2nIcFBSkSpUq6c0331T16tU1Z84cHTp0SCNGjLBuzKV76KGHFBkZab2lG5BXjDFav3693n77bbVv315bt27Vjz/+qI8//liJiYkaPXq0YmNjZbFYlJaWppiYGL344os6evSopkyZor59+2rBggXq16+fNWRcunRJxYoVU9myZa3r8+rVq+uhhx7Sli1brD9ESbYXtQFyy5gxY+Ts7KynnnpKly9fVunSpVW+fHnNmTNHc+bMUd++feXt7a3ff/9dZ86cUaNGjVS/fn1t3rxZly9fVr9+/VS9enUtXrxYs2fP1s8//ywfHx+9/PLLSkxMVFpamlxcXDRixAjVqVNHbm5u6tWrl6KjozV9+nQ99thj1u8D4FZIX5emr09Xrlyp33//XZL0ww8/6Pnnn9elS5c0ZMgQpaSk6JlnntHUqVMlXTv6MyUlRXPmzNG3336rLVu2aOXKldZ1upeXlx544AH9+eefio2NzfSaAYWCAQqwuLg4s3r1ahMXF2dtS0tLM1evXs20f1pamklJSTEpKSkZnktMTLROd/jwYfPWW2+ZnTt3WvseO3bMNG7c2DRu3NgcPXrUOp4xxowYMcIEBgZmOi6QmR07dpjNmzcbY4yJiooybdu2NRaLxaxbt84cPHjQHD582MybN89YLBbz5JNP2izT9913n+nUqZP18YYNG0yDBg1Mnz59TExMjLX96NGjpnHjxuaxxx67YT0bN240RYoUMUeOHMm9mQSysHv3blO0aFHz+eef27RPnz7dWCwW89lnn1nbXnrpJePl5WW2bdtmEhMTjTHG7Nu3zzRo0MCMHj3aGGPMyy+/bEqXLm1SUlKs6+WUlBTz7rvvGovFYvbt22cd7+jRo2bevHnmzJkzeT2buAOkf+/v3r3bbN682Zw6dcoYY8x3331nSpQoYd577z1jjDHJycnmtddeMxaLxfTp08cMHjzY7Nixw5w6dcps2rTJFClSxLptkW7MmDHGw8PDbN682Zw5c8Y0atTI+Pj4mDfeeMNs2bLFnD9//tbOLG5rmS1PqampWW5Tnzp1yixevNhs2LDB9OjRw1gsFjN16lRz6dIl07JlS3PPPfeYEydOmNTUVJOcnGzeeecd4+XlZc6cOWMSEhLM66+/burWrWuKFy9uXFxcTPHixU3VqlXNqlWrjDHGbNmyxbi4uJjly5dbX7OwbWcX0viP21VKSoq2bt1qfbx8+XI9/PDD1pu2S/+7WMc//fPy+o6OjtZfbb/77jv95z//Ubly5fTwww9bz0eqUKGChg4dqrp161r7BgQEKDw8XL/99pv2799vHU+SqlWrphMnTuTRXON2ExMTox49eiguLk6SVLp0aTVr1kweHh5q0KCBqlSpokqVKumpp55SSEiItm7dal3mJKlp06b69ttvVatWLbm4uKhjx466ePGifvjhB7Vr106//vqrJKl48eJq1KiRfvrppxvW5OrqKgcHB+thV0BeCggIUJUqVbRhwwZJ/1tH161bVx4eHjp+/Likaxdn2rVrl1q1aqX7779fLi4uunr1qnbu3Knjx49bb4ly77336uzZs9q2bZt1vezo6Gg9VeGf50m/+eab6t27t1avXn3L5he3r/RthLvuukvNmzeXv7+/JKlx48by8PDQH3/8oatXr6pIkSKqV6+eSpYsqW3btmnQoEFq0KCB/P39tXv3blWuXFl79+7V0qVL9eijj8rPz09vvvmmatasKUny9fVV48aN5e7urv79+6tZs2YqXry4EhMTtX79enXt2pXTy2CXlJQUffPNN+rTp4/Kly8vPz8//fXXXzZ9/nl9iuTkZEn/O1958+bN6tatm0aMGKHU1FTt2bNHzz33nM6cOaOtW7dq8uTJKlu2rBwcHJSWlqZixYrp4sWLWrRokZycnDR27FgtXrxYu3fvVkxMjL7//nu5urpq+vTpio+PV926ddWgQQO9+OKLmjp1qnr16qU333zz1r5JN4kgjQJl7dq1euSRR6znY7Rs2VKurq6KjIy0Oeft5ZdfVkhIiHVjLP2Lbu/evXrjjTfUsGFDOTs7q23bttq5c6f69u2rqVOnKjAwUJKsh5CkpqbanP9RvXp1JScnZ3qISdmyZXXs2LG8m3ncNq5cuaKzZ8+qVq1a1raaNWvKYrFo/fr1kmS9/23jxo118eJFm+W7bdu2unjxogICArRhwwbt27dP27Zt0yeffKITJ06oT58+kqSiRYuqUaNGOnPmTIYvx3Tp454/f161atXK9EJOQHb9876g1+Pl5aUHH3xQW7ZsUVxcnHUdvX//fiUlJal169aSrl2zIjo6WnFxcfrPf/6joKAgubq6atCgQWrWrJnGjRunq1evqmXLlqpdu7YmTpyoiIgISdK+ffv0448/SpLNeXbdunVTWFiYGjZsmNuzj9tUWlqadXsgO1JTU+Xk5KQ6depoz5491m2DypUry9fXV7Vq1VJgYKA1kFSoUEGHDx/Www8/rKFDh8rZ2VlTpkzRX3/9pZ9++knNmzeXo6OjRo4cKR8fHzVr1kwTJ07UBx98oKefflpjxoyRm5ub4uPj8+w9wO3j1KlT6tGjh5ydndWvXz+dOXNGw4cP1y+//GLdDk63detWde/eXbVq1VLv3r21YsUK6zZw69atVapUKR09elQzZ85UjRo15Orqav28/PXXX3rxxRdVq1Ytubu76z//+Y8eeeQRNWjQwPqDZ/Xq1VWuXDm5uLhYPxcxMTHy8vJSsWLF9NZbb6lGjRqaNWtWhu2mwoD7SKNAWb58ufr27au0tDQ5OjqqfPnyqly5srZs2aLHH39cRYsW1Y4dOzRv3jy9/vrrCggIkCRt3LhRoaGhcnV1VaVKldSyZUu9+uqraty4sYoVK2Yd/8yZMzb3Yvz3+UZff/21nJ2d5eXlJenar3lOTk6KjY1V6dKlM5x3ijvHlStX9PXXX+vPP//Uiy++qKJFi2bZd8uWLdYrCqerVq2aqlatqpUrV6pr164qWrSo0tLS9Msvv6hGjRqqWbOm9T7lDRs2VOnSpVW9enU9+OCD1jGaNGmiJ598UpMnT9avv/6qBg0aqFq1avLz89OGDRtUu3btDPc6/+fj6OjoQvclhVtvwIABOnnypObOnauSJUvaPPfPdeaRI0dUpkwZOTs7ZxjD0dFRzZs31/vvv6+4uDhdvHhRS5cu1QcffKDevXurSpUqkq5d0DEgIECff/654uLi1LNnTzVr1ky1a9eWt7e3pGvLcJEiRTRu3DiFh4erbdu2Cg0N1V9//aVatWppxIgR+vTTT62v3bhxYzVu3DgP3hncTlJSUuTo6CiLxZLj8zODg4M1ffp0RUREqEqVKipdurTq1q1rvcJ2ejBv3LixypUrp/r16+vzzz+3WUefOHFCe/bs0QMPPKBSpUpp6dKlmjNnjjZs2KADBw6obt26Cg8PV7t27eTp6XnzM47bXkpKio4ePaoGDRro+++/l4ODQ6br6fQdTVWqVFHXrl21ceNGPf7443r33XfVt29f+fr6qmbNmoqMjFRCQoJ1uuTkZNWrV0/h4eFq3LixOnTooNatW6tOnTry8fGx9rt48aI+//xzBQYGKikpSWvXrtXevXs1duxYa597771XCxcutF6ErNDJnyPKcSdJTU01ixcvNr/++ut1+xhjTNOmTU14eLgxxljP2RgyZIgJDAy0nlvUqVMn8+ijj9r0iYmJMXPmzDHHjx+3Gffw4cNm+vTppnnz5sZisZj+/fubCxcuWJ8/c+aMOX36tDl16pRZuHChqVixounbt69JSkoyaWlp1rreeecdU7NmTZtacWd5/fXXjcViMUWKFDFbtmwxxmRcFtIfz50715QtW9bmHM2LFy+aPn36mIoVKxpjjFm/fr3p2bOn8fT0NF988YW1X/r5QaGhoaZFixbW5T59WX/vvfdszjE9ffq0adasmalXr54x5n/n9f/bp59+amrXrn1zbwJuKykpKSYqKsokJSUZY4z1v2vXrjVr1qzJcK7alStXzEcffWQaNmxoSpYsaZo1a2a+/fbbLMffv3+/8ff3N0WKFDFOTk6matWqpkKFCsZisZgnnnjCel7zrFmzTKVKlczs2bNtpj9x4oSZPXu2Wbx4sTHm2udr9+7dpm/fvqZJkybmtddeMxcuXDB9+/Y1d911l7l06ZJ12tTUVNbVyLY//vjDjBw50oSFhZnvv//e2p7V+jS9fffu3aZEiRJm8uTJxphrn6l33nnHODk5ZTgfdeTIkaZ06dLms88+M4cOHTKnTp0ya9euNT169DDh4eEZzumPjY3NxTnEnSQpKcmMGTPG+Pr6WteFma0PGzVqZO6++25z8OBB6/Pdu3c3lSpVMhs3bjTGGPPiiy+aKlWqmO+++846XWxsrOnXr5+pWrWquXjxos2YkZGR5tVXXzXGXPvOqF+/vqlZs6bx8vIy99xzj5k1a5ZJSEjIq1m/5QjSyHMXL140np6epn///te9iEB0dLRp3bq1GThwoDHmf4Fi9erVxsnJyWzfvt2sXLnS+Pj4mF27dmU6RkxMjFm0aJF5/PHHja+vr3FwcDCBgYFm4MCB5osvvjBnzpyxfgFu3LjRtGzZ0oSEhJiAgADj5uZmevToYf7++2+bMVNSUszgwYPNs88+mwvvBgqyvXv3mpEjR1ovEmbM/wLs+PHjjcViMffee695/fXXjTFZ/6iyYsUK4+LiYi5fvmzT/vbbbxsPDw9jsViMt7e3ady4sfH39zcWi8W89tprNkFg4sSJpmLFimbDhg3WOo4dO2ZCQkJMpUqVzJ49e4wx1y5w89Zbb5mwsLAsN/qMMWbSpEnmo48+KnQX8kDe+emnn0znzp3Nzz//nOnz/97YSQ+8/fv3NytXrjSffPKJ+e2337L8HMTGxpqHH37Y1KxZ0/z111/m8uXL5syZM+att94yFovFPPTQQ8YYY86ePWv69etnSpQoYSZOnGg2btxo5syZY5544gnTsmVL6wZdZrZu3Wq8vLzMyJEjCc53sCtXrmRoS78waVbLxbfffmvCw8PN0qVLTUhIiHnggQfM3XffbUqWLGnef/99Y0z2fjgPCgoyPXv2tK6/t2zZYpydna0/kKavcxMTE83gwYNN6dKlTb169UxAQIApWrSoeeSRR8yWLVtYfpGrVqxYYSwWi/nzzz9t2v/++29z6dIlc/r0aVOqVCnrj0Dp2zrfffedqVWrlhk0aJAx5trFTkuUKGH9TKT7888/TbFixUzXrl3NqlWrzI4dO8zHH39sunbtalq1amX9Yej3338327dvt15I8nZDkEauWbhwoQkICDBTp041ycnJNs8NGDDA3HvvvTZX3/63uLg4c99995nnn3/epv38+fPG3d3dPP/886ZUqVLmjTfeyHT60aNHG4vFYgICAswTTzxhPv74YxMREZHpF2z6602ePNk8//zzZtGiRTZXQ06XHkxeeumlLDc2UXil72H76quvTFJSkvXIhaZNm5rdu3cbY4z1yIQ33njDVK9e3Tz66KOmQ4cO1x13yZIlxs3Nzfzxxx/GmP99Qa1fv96UL1/ehIWFGWOMuXTpkjl8+LB55plnjKOjoxk7dqx1jF9++cWULl3avPHGG+bnn38248aNM40aNTLVqlUzy5Yty/Y8pi/DZ86cyfC5xJ0pfZk4ceKEzdENFy9eNKdPnzbGGHP33XebZ555xrr+jIiIML6+vqZfv37mypUr1/3R5p+v8/LLL5syZcqYc+fO2bS/8847xmKxWNf36T9Y3nfffaZcuXKmRIkSpnv37mbjxo02V5SNiooyH330kXnhhRdMhw4dTPHixU2bNm0y/ACKO8esWbNMmTJlrnvUW1xcnDl79qwx5n/r4/SgUbduXTNp0iQTHx9vDh48aNq1a2eqVKlyw9dND8g9evQw9evXt67vDx8+bOrVq2f69+9vjLkWxtM/L2lpaebIkSPm/fffNytWrMiwNw/ISvpdabLrr7/+MqVKlTLh4eFmzpw5JiQkxFgsFnPPPfeYAwcOmD179pjq1aubF154wRhjrNsH0dHRpn379qZ58+bGmGs/qFaoUME8++yz1tdPX55XrVplmjRpYurWrWuKFStm/Pz8TJ8+fcxPP/10x/wwRJBGjiQkJJhPP/3UfPnll9a29CDr7+9vxo0bZ4z534dt8eLFxtXVNcMvY+nS+1WrVs08/fTT1o239PaQkBDj5ORkLBaLGTJkiNm/f7912vQP/6FDh8y2bdtsDt22V1aHv6Qf8ojby2+//WYsFov1Fjvjxo0zFovFhIaGmq5du9r07dKlixk1apTp0aOHueeee8zJkyeNMbaH/qX//7p160yFChXM22+/bYz53zJ65MgR06RJE+ueuPRl7fz58+bJJ580FovFrFy50jqet7e3sVgsxsHBwQQFBZnRo0ebv/76K9N5+edtgXDnOnbsWIY9yZnd3iR9g8jX19fUqVPHVK1a1XprE2OurXMbNWpkDh8+bIwx5oMPPjBubm5m27ZtNuPcaJnbuHGjsVgs5ocffrCpxxhjBg4caD0aI72eEydOmGPHjmU53pUrV8yHH35o7r//ftOjRw+zbNky6/yy/N9Z0pejgwcPmp07d5q0tDSbZeDKlStmypQpplKlSsbX19c0atTITJo0yfp8cnKyKVKkiKlRo4bNuJ999plxdHQ027dvt3mdf0tfZufNm2fKlStnli5daowx5sKFC+app54yrq6uuTezuONkdStXY64t81ntJEp39uxZ07NnT2OxWEyNGjXMgAEDzNKlS62ni8XGxpr77rvPeqrkP4WGhprg4GDrDqb27dub4OBg6w+W//xMJCYmmp9//tlERkbmYC4LP4I0siV9Iyz9Q33gwAFTs2ZN06JFC2ufH374wVgsFtO4cWPj5ORkvvrqK+tzhw4dMq6urmbevHmZjp8+bseOHU2TJk2sh4SkfylOnjzZ+Pr6mmnTppm77rrLFC9e3Ho4ys1KTU3lcNfb2HfffWd+//13Y0zGDe2///7bem9nY4xZunSpcXJyMjNmzDC+vr5m5cqV1mnuuece89Zbb5k5c+aYSpUqWQPvP79Q0vvu37/fNG7c2HTs2NEY87/lOykpyfznP/8xxYsXzzDNlStXTMOGDY3FYrEeWv7pp5+atWvXsicZ2bJlyxZjsViuu2fu0KFDZu/evcYYY/r27Ws9zWD48OHml19+sZ7X+fHHHxt3d3frOdCLFi0y7u7u1mmz6/jx4yYgIMCMHz/e2pb+eTh//ryZN2+edcx/fz6zOiw3q3ue4vby771f6W1Z/fsfOXLEpv//tXfnYVHVbR/Av4d9FUHZBAVZZAtUQCVccCOXKNdSUUhT064uNTVxr7THPRPLcFfKRH1TMzQV0NwQxUzFlch931BUkG3g+/4xz5wcQdPrySy9P/+UZ86ZOTPXzTnn/m330qVL6ezszLFjxzI5OVltrJwyZQrv3r1LUntdDwsLU2tDk9ohq7Vq1eLIkSNJPjredJ9z7tw5WlhY8MMPP1RfS0lJ4YwZM+TaLZ7K465tJSUl/Pjjj2ljY0MnJye+++67PHnyJMnKG3tKS0s5ffp0mpiYqKMxHjZo0CA6OzurDfS66RA1a9bkgAED1P0mTJhAMzMzbty48X/5ei8kSaTFI2k0GiYmJtLX15e9e/cm+ccfa2lpKQcNGkRvb2/1Znfr1i1aW1tz1qxZbNeuHWvWrMmtW7eS1F4AgoOD2bt378feWL7++mtWrVq1QuKTkZFBAwMDpqamsqysjJ988gltbGxYv359bt269X/qhRYvlt9++42ZmZksKyvjzZs3aWRkxHr16lXaW5WamkozMzN1DuaBAwfo4ODAhQsXcuzYsWzSpIm68Ezr1q05ceJEnj17lr6+voyLiyNZ+Q1Mo9Hwo48+op2dXYXesm+//ZYWFhZ6vR269zh8+DDXrFnDgoKCCuere4CUXjfxKPn5+Tx16lSF7fv27ePbb79NR0dHOjg4sG/fvrxy5QrLysqYkJBAY2NjpqamkvwjTi9fvkxFUZiQkECSzMzMpIGBAZOTk59qyN79+/fZpEkThoWFSQIsnkhGRgYDAwPVKQeP8uAaFLoGSl0PWnFxMZ2dndmrVy8WFRWpcd2/f3/WqVOHmzdvJqldAKxGjRo8ePCg+l65ubns3LmzujjjkzS0d+vWjd98843EuHhij7uXazQa/vjjj+zcuTNjY2O5a9cu7ty5k5GRkVy3bh2/+OIL2tnZMTo6+rGfkZqaSkNDQ3WtlQffnyT37t1LHx8fhoSE8Pvvv2dOTg4HDRpEFxcX9fmd1I4USklJkfiuhNSRFnq2bt2Khg0bYu3atTA0NERWVhZ+++03fPPNN/jyyy/V+qFGRkbw9/dHXl4e9uzZAwCwtbVFvXr1sGfPHkyZMgX16tXDBx98gF27dsHY2BgRERHYtWsX8vLyHvn5nTt3xp07d7Bz5069sj1169aFo6Mj0tPTUVxcjE8//RS7d+9G7dq1ERsbi/379z/z30b8M5WWluKHH35AVFQUHBwc0LZtW0RHRyM1NRXVqlXDt99+i5ycHMycORMlJSUAtKUhAODChQuwsrJCaWkpAG2t8Hr16iE5ORmDBw+Gs7Mzpk+fjuLiYhQVFcHZ2RkODg6oWbMmsrKyAKDSsimGhobo2LEjSkpKkJiYCABqLLu7uwOAWk/6wdIrgYGB6Ny5MywsLNT9dXVIDQ0NYWRkpFc2RbycSKpx8eA2S0tLuLm54eDBg+q1+sKFCxg5ciRu3LiBmTNn4rvvvkOnTp1gaGgIAwMDxMbGQqPRqOV6FEVBeXk5nJ2d4eLigszMTOTn56NevXrw9fXFggULcPv2bQDa2MzLy0N6erpaR/dh5ubmWLRoEZKTk2FkVHnFzYe/i3g58b+lonx8fDBo0CC0bNlS7/WioiKsXbsW3bt3V0vvXLp0CYC2frOVlRX27t0LANizZw9KSkrQrFkzmJqaqtf4mJgYKIqCbdu2AQA6duyIK1euICcnR/0cW1tbhIeHIzs7G0VFRRXKZFZm5cqViI2NfWSMi5eHLo4vXryoPu/qtpFUr826ezkfqF2ekpKCyMhIpKSk4IsvvgAAHD16FM2aNcPUqVMxfPhwvPnmmxg6dCgGDRqETZs24ezZsxXeR/f/Hh4e8PDwwIYNGwDoP08AQKNGjbBo0SIYGRlh8ODBCA4OxurVqzFmzBi9vz8XFxe89tprEt+VeV4ZvHj+HhzKVF5ezqKiIoaFhbFFixbqsKcVK1awdu3a9PHxoa+vL7/++mv1mO3bt9Pb25sff/yxum3q1Km0t7fnxYsXefr0aTZo0IB+fn68c+cOU1NTaWBgoPbEPUzXyxEZGcnWrVvz999/J/lHy1l4eDjr1aunV1JC5i6LVatWsW7duoyJieHy5cuZlpbGGTNm6A1BHThwIGvUqKGuoqqLm+XLl9Pc3FydB1paWspJkybR0tKS9+/f59GjR2liYsJVq1bR0tJS7bkeNGgQAwICmJ2dTfLRLcsDBw5kcHCwXm9Hfn6+Ose/suNelgU6xF9HF4elpaXqiti6Ya66BZUqm3esu7b6+vqyS5cuzM3NJfnH30efPn3o6+urlqlauHAhq1SpwmbNmvH777/n9u3bOXz4cHbt2lU9ByGe1uPmguq260bBubq6skePHpw2bRonT56sVvDYtWsX/fz81IWTMjIy6OLiok4B011X7969ywYNGrBnz57qZ5ibm3PkyJF6o+U2b95MRVHUec9yXRZPIzExkXZ2dly2bBnJyuPn+PHj6rVVZ+PGjVQUha6urkxMTGRRURHz8vIYGBhIIyMjXrlyRd03NTWV9vb2nDNnDsnKR07cvXuXMTEx6uiKRz2raDQabtmyRV0wTzw56ZF+Sa1atUrtbQC0LWNLlizBiRMnMG7cOFhbWwPQtgxXqVIFoaGh6NOnD8aPH4/vvvsOAODp6QkvLy/s3LlTfd/IyEjcvHkTv/76K2rXro3ly5fj6tWriI2NhbOzM+zt7ZGRkVHpOfG/LWjvv/8+bty4gWXLlum9Hh8fjyVLlqBatWrqNl2BeenReDmdPHkSo0ePhqenJxISEtC9e3e0bt0aH330Efz8/NSYiouLg7OzM8aPHw+SMDExAUnk5ubCwMAA9vb2ALQjLUJCQlBYWIj09HQEBARg4MCBmDZtGu7fv6+2JAcHB6O4uFj9++EDLcEP/nvEiBGwtrbGpEmT1NcsLS3h7e0NAJX2LlfWwy3Eg06ePInFixfjp59+wvTp0+Hn5wdAG79hYWEwNTXFsWPHAGhHX1hYWGDVqlU4dOgQzp49i8zMTFy7dk3tlWjXrh3279+v9u7pYrBTp044deoUzpw5A0Dbm7d48WKUlJRg2LBhaNu2LX7++We0bt0atWrV+rt/BvGCMDQ0VGNRd42dO3cugoKCsGvXLgDAxo0bsXjxYnzyySdISkrChx9+iNGjR6Nu3boAAD8/P7i5uWHfvn0AgPr168PMzAzZ2dkoKSlRY9ra2hpFRUUwMjJCfn4+AKBx48bYu3cvrl+/rp6Tp6cnevToAXNzcwByXRZPRjfyITQ0FFWrVsWpU6cA/BE/t2/fxtChQ2Fra4tmzZrhjTfewPDhw3Hr1i0AgL+/P3x9feHu7o4uXbrA1NQUNjY2ePPNN1GzZk1kZ2ern+Xt7Y06dergp59+euT5WFlZoUmTJjh27BguX778yBFthoaGaNWqFQIDA/+S3+FlIleGl5SLiwtcXV2RkpICQDv8Lz4+HlFRUWjZsqWaCLi7u8PHxwdHjhxBXFwcIiIiMHjwYJw4cQKurq4IDAxETk6OOtTP398fjo6OyMjIwP379+Ht7Y2EhARkZmYiPj4eNWrUwN69e1FQUFDhnHQ30vbt2yMmJgZfffUVTp8+rW5v0KAB6tevX+n3kZvcy+nq1au4ceMGevXqBSsrK73XNBqNetOoXbs2pk2bhpycHEyYMAEFBQVQFAUnTpyAm5ubehMDtEOh3N3d1ZvTsGHDEBoaiuDgYLURp2HDhqhateojG4V0n+vh4YFZs2Zhy5YtWLRokXqTfTjxFgLQJhG6ROJRdMnDnDlz8OWXX6oNmwcPHgQAuLm5wdXVFWlpaQC0jZuxsbFYsGABWrVqBQ8PD7Rr1w49e/ZEcnIyACAqKgrnz5/H77//DgDq8L3IyEhoNBrs27cPJGFqaoquXbti8+bNWLduHW7cuIEDBw5gwIABasIhxMMqu97ptt28eRPz589Hx44d0bNnT2zZsgUAUKNGDVy6dAknT54EoE2AS0tL4ebmhjt37qiN6LrpOtWqVYO/vz/Onz+PM2fOwMzMDBEREdiyZYs6DYckfv31V5w8eRJeXl7qPaNFixbYs2cPbty4oZ6fl5cXli9fjtdff/0Z/SriRWRsbAwACAgIgKOjIw4fPqw22ADA/PnzsXbtWkyYMAGbNm1C27ZtsXDhQkycOBGANu79/PxQUlKiNwy8bdu2uHfvntpACgDOzs4IDQ1VG48qm4KgKAp8fHzg4eGBCxcuPLPv/VJ7Lv3g4rm7fPky27Rpw8aNG5MkR40aRWtr60qH5+lW/bt27RqLiooYEBDA+vXr88yZM1y9ejXt7e31SvZ07dqV4eHhaj1SUjvMxdnZmaampqxbt+4T1fx8/fXX2bx5c73VBmV4lXhQfn4+zc3N2b9/fzV2i4uLefr0ab1hTg8uNOPs7MyVK1eSJHv37s2QkBC9urh37txhjx491KFQpLaMxIOLOJWWljIiIoItWrR47OIbunhduXIlExISePHixb/om4t/G91Umie5hmk0Gh4/frzCvtu3b6etrS1HjhzJvLw8XrhwgWPHjqWiKOoUm4KCAvbu3Zv169dXF2MqLi7m/v37uXv3bp47d44//vhjhaoL1atXZ+PGjbl27VqOHTtWvaa/+uqrnDJlyiOn0UjVA1GZyuLi4WGl2dnZDA0NZZ06ddi7d2+OGjWKs2bNIqmtaV6zZk0OHjxYPc7Pz49OTk4MDQ2lm5sba9euzY8++kgdHrt06VK6urry22+/JUnu2bOHISEhdHBw4PTp05mcnMyWLVvy1Vdf5aVLl9S/r4KCAnVagxB/5nGLhG3bto3Dhg1jRkYGe/bsyUaNGvGXX34hqV1Vvk6dOoyJidE7ZsqUKbSysuKOHTtIkvHx8axSpYredJzS0lK6u7tzwIABes8ciYmJtLCwUD+jsvuLPDc/W5JIv6TKyso4ZswYWltbc//+/axatSr/85//6O3zYMH1KlWqcPHixSS15YQCAgLYsWNHrl69mo0bN+bAgQPV4xYvXkxra2u9eaHFxcX8+uuvqSgKFUVRS6pURve5ly5d4uDBg/VqVQvxsAEDBtDExIQuLi708PCgra0t3dzc2Lx5cyYkJOglANnZ2WzevDkDAwN54cIF9uvXj0FBQST/uNmUl5czISGBZmZmlZaM0MXn42rdVnaMrLb9ciotLeXo0aNpbGz8p/ump6ezRYsWNDc3p4uLC6OiovRWTv3000/p5uamt77FxYsX2axZMzWOddUWTE1NH1vXs3379noNlUlJSYyIiKC9vT1r1qypricgxMMuXrzIbdu28fTp0090XcvKyqqwmnxxcTHDwsLo6+vLX375hSUlJRUaayIjI9myZUt1vv+1a9f43XffceTIkZwzZw5HjBhBS0tLvvnmmyTJgwcPsn79+nrPI8ePH2dsbCxr165Na2trtmnTRq3EIMST0N2/Hxfr5eXlHDlyJM3NzRkZGcm33nqLlpaWrFWrFpcuXUpSu0K2mZkZFy1aRPKPZ45bt27R2NhYnc+/b98+GhoaqiVkdft16NCBzZs3V9d0IbXP4yYmJvz000/V8xB/L0mkX2JJSUmsXr06fX19GRoaqtaRezChILXlhIKDg/nWW2+px+7YsYMeHh5s1KgRO3fuzODgYPW1U6dO0cjIiImJieo23XstXrxYrZH7JEpKSuTCIB6rsLCQS5cuZe/evdmrVy926dKF7du3p4eHBxVF4YQJE0j+EYP79++nhYUFBw4cyCZNmrBTp04VepW3bt1KRVGYlJT0p58v8Sn+zPz582lmZqY2LlbWQ6BLiCMiIpiWlsYFCxYwKCiILi4uTE9PJ0lOnDiRDg4OJP+oN6rRaDhp0iQqisKioiKS2jJqZmZmXL58OUltCao9e/YwMzOThw4d4uzZs+nl5aU+0Olcv35d6jaLCu7fv88VK1awU6dOdHR0pKOjIwMDA1mrVi1GRESopaQedO7cOcbExNDS0pJOTk4MCAjg3Llz1WT50KFDNDIyUpOFB+nibfLkyaxdu7Zauufh2CwrK+O4cePo5OREUruwUlRUFN3c3Co9HyH+VxcvXuT69evVURC6+396ejoVReGkSZNYUFDAy5cvc8GCBVQUhcOGDSOpTZgVReGCBQv0SsmSpKenJ/v168fS0lLm5ubS29ubQ4cOJflH3M+cOZMWFhZcvXq1ej53797loUOHqNFo5FnkOZFE+iX2yy+/MDw8nIqisFq1amzTpo1e/WbdH2VBQQH79etHFxcXveOTk5NpampKJycnVq9enRcuXCCpfbBzdnbm6NGj9VbBlD9y8awVFBSoN52LFy/S39+fPj4+6sOb7rUhQ4awTp06VBSFY8aMqdB4dOPGDa5bt463b9/++7+EeOHoVhDWNepUlpguXryYiqLw8OHDahxeu3aNLi4ufOedd0iSc+fOpYGBgVq5QDd0dsmSJVQUhZs2bSJJ3rx5kw0aNGCfPn1Iah/yOnXqRH9/f1apUoU1atTgxIkTHxnfuprlQly7do0dOnSgoijs06cPFyxYwPT0dG7evJlz586lt7c3FUVhSkqKekxZWRn79evH0NBQJiUlMTMzk++88w7t7e3VuuTLli2jo6OjmhQ8OAxc9//79u1j9erV1eHepDZxuH79OnNzc7l79261YoMuXuPj4zl27Fjm5+c/659GvCDKy8up0WgqNNSUl5eztLSU27Zt4/HjxzlixAiam5vTycmJPj4+akN7eXk5Z8+eTWtr6wrvHRQUxMjISF67do0k6eXlxd69e6v1znXCwsLUleSLiorYr18/vvLKK3pxnJ2dzffff7/CSt/i+ZJE+iV248YN9uzZk25ubkxLS6O9vT1NTEw4derUCnObEhISaGxsrM5D1SXIU6dOpYGBARVF4dq1a9X98/LyHvm5Ml9DPAsPNtroHqp69erF6tWrqzceXVxfvHhRfTgcO3YsSWnoEc+Obk2KV199lWTFa2B5eTn79+9PT09PdZsuVt977z0GBATw/PnzzMzMpJWVlZqM6CxcuJCKonDIkCEktaM0Bg4cSAsLC967d4/FxcXcuXMn16xZozcsUIg/o9FoOHbsWNra2rKwsLDC6+fOnaOvry+DgoLUkoMpKSm0s7Pj7Nmz9fYdNWoU/f391RES9vb2jI+PVz+nMl5eXuzXrx+Li4t59epVtm3blh06dGBoaCjt7OzYpk2bJ1pzRby8rly5ok4PeFK6a/Tvv/9ORVH4+uuvs3PnzkxPT2dGRgbDwsLo5eXFGzdukNSWxKxbt656fdWNDoqLi6OXl5c6/3nkyJG0tbXVmzqTnJxMCwsLfvbZZyS194OFCxfSxsZG1lX5F5BE+iU3depUmpiYsKCggIWFhezTpw8VRWFISIjePKKff/6ZDg4OnDJlCsk/LhJ37tzhli1b1CGLDyYjkpiI50Wj0fDcuXP09PRkeHh4pT1vZ86cYVpaGsvKyh4ZqxLD4q+gW5PCysqqwjxQXYzFxsayQYMGao1xXcNQQkKC2thJkt27d6eHhwfT0tJYXFzMAwcOsFWrVnRzc6O3t7f6njt37uSkSZMqTX5IylBA8cRWrlxJMzMzvSHW5eXlaizHx8fT2NiY06dPJ0l+9dVXdHR0VI8/d+4c582bx8DAQBoaGqqx6+/vzzfeeKNCHF65ckVtjO/cuTMDAwPVJH3hwoUcMGAAp02bJjVvRaV0UxE6duxIJycn2tvbMyAggHFxcbx79y5J/Xt7Tk4Op0yZwp49e3Ly5MkV1j9xd3enqamp3jPxpk2baGNjo64dNHHiRHp7e3PLli0kqV53v//+eyqKotZ6PnPmDCMjI2lkZMS4uDjOnDmTYWFhbN26tbo45IPHi38+qRn0kgsICICZmRlWr14NMzMzLFmyBJs2bYKiKIiIiECfPn2QnZ0Nf39/uLu7q+VRTE1NAQBVqlRBq1atUK9ePQD6dXEfVa9OiL9aXl4eVq9ejb179+LIkSNYv349Bg8eDDMzM0yePBlVq1atcIy7uztat24NAwODR8aqxLD4KxgYGCAoKAgajQY7duwAAJSXl+v9t379+rh27RqOHDmid6yXlxeuX78OMzMzAMDEiRNRpUoVdOnSBVFRUYiOjoaXlxfee+895Ofn4/bt21AUBU2bNsWYMWPU4wBt+R/+t+yQoaGhxLd4Ij4+PqhZs6ZaLq28vByKoqilftq3b48aNWpg69atALS1a3Nzc9GxY0c4ODjA3d0d06dPR8OGDbFq1SoEBwfDxMQEMTExSEtLw5QpU5CTk4Pc3Fxs374dY8aMwd69ewFoS1M5Ozur59KvXz/MmzcPcXFxUvNWVHD9+nX06NED0dHRsLW1xWeffYZ58+bhlVdewYwZMzBjxgwA2nt7YWEhxo0bh3bt2mHlypUwNTVFYmIi2rZtq8YfADRt2hTGxsawtbVVt/n5+cHf3x9r1qwBADRv3hwA1JKyuutuXl4eTExMcPToUQDa546lS5ciLi4OW7duxVdffYW6deti9uzZsLCwUN//weu2+Id73pm8eL6ys7MZEhKiLsevGxJ769Ytzpgxg/b29pw8eTLJx7eQSc+GeJ5ycnJoY2PDhg0b0sXFhVZWVuzcuTNTUlL+dKVNIf4OBw8epLe3NwcNGkRSf7Ewkjxx4gR9fHwYFRWlHpOfn88BAwbQ2tpab67czZs3+fnnn7Nv375csmQJSbJv374MDg7WGwr4uNEWQjyp3NxcvvXWW/T39ydZ+XWzadOm9Pb25r1797hz507a29uzQYMGXLRoEY8dO8b79++r++p6sgsLCzl27Fiam5vTy8uLPj4+tLa25ttvv61OI5P4FU/jcVMRQkND6ePjo04FuHnzJj/++GOuWLFC3efq1asMDg5mdHS0um358uU0NTVVe5tJbewOHTqUNjY2JLXX8759+9LCwoIpKSm8evUq9+/fz/r169POzo5169bVW7m+vLyc9+7dewa/gPi7SSL9krt37x67d+/+RKVZSJnfLP659uzZwx9++IGHDh163qciRAW6ZMTPz49k5dfSuXPnUlEUtmvXjomJiRwzZgydnJy4cOFCdZ+HF8YjyfXr19Pe3l6dIy3EX23q1Kk0NTVV54Tq6BqCunXrRhcXF/7222/Mz89neHg4o6Ki1GlgZWVlvH37NuPj4/XiVKPR8OTJk/z88885b968p57LKsTDHp6KoIvBUaNGsUqVKszIyFC35+fns7y8nBs2bGD37t1pb29PRVHo5uamDvHOzc2loigV5vwvX76ciqKo0w5yc3MZERFBKysr+vj4sHr16pwzZw6TkpKYmJgoC+C9oIyed4+4eL6srKzQpUsXBAUFoaSkBCYmJhX20Wg0MDLShoqBgcwGEP9MYWFhev8uKysDoB3CKsTzZmdnh5CQECQnJyMvLw9Vq1aFRqNBYWEhrly5gvLycgwcOBCWlpZISkrCyJEjYWtri7i4OPTo0UN9HwMDA+Tm5mLVqlXYvXs3Tp48icOHDyMyMhLDhg17jt9QvMgCAgJgYmKC1NRUREdHo6ysDIaGhigvL4ehoSFMTU1hamoKR0dHWFpa4pNPPsEHH3yAhg0b4t1330V+fj7S09Nx9epVdOvWTX1fQ0NDeHp6Yvjw4c/x24kXiW4qwrp169C6dWt1+507d2BjY6NORdTF7Lhx47Bs2TKEhIRgxowZMDY2RkxMDI4dOwZXV1fY2dmhdu3a2LNnD9555x3Y2NgA0A7vdnBwwJo1azBu3DjY2dlh3bp12LJlC44cOYKIiAi0bNnyefwE4m8kibRA165dH/u6LokW4p+O/53/qSiKJNDiH+eVV16BpaUlli9fjubNm2Pz5s3YvXs3UlNT0bBhQyxZsgQxMTHo0KEDTExMHjlPztbWFrVq1UJGRgbat2+PuXPnIjg4+G/+NuJl4u3tDU9PT2zYsAHR0dHqdmNjY9y+fRubN29G8+bN1STjtddeQ1JSEpYtW4YVK1bgzp07aNWqFcaPH4/w8PDn9TXES6BWrVqoW7cudu3aBUCbMP/www9ITU3FZ599BnNzc3XfHTt24IsvvsC4ceMwePBgWFlZ4cCBA7C0tERGRgZatWoFY2NjtGvXDlu3bsXly5fVGHdycoK3tzcOHjyovl/VqlXRtWvXP32uFi8OyZAEAG3v3eMWXRLi30DiV/yTeXh4wMXFBYMGDYKxsTFMTEzQuHFjTJ06FV27doWTkxMA7SKOgHZRJ12P34OxbWBggKioKERFRT2X7yFePi4uLggODkZaWhoAbU9yWVkZsrKyMGbMGBQWFmLo0KF6xzRo0AAhISEoLi7WS16EeJZ0o3/Wr1+P8PBwZGVloaysDCUlJfj888/h7OyM1157DQCQlZUFFxcXtGnTBlZWVgC0C5bl5+fjwIEDuH//PmxsbNCuXTskJCTg2LFj8PPzAwA4Oztj48aNsLa2fm7fVTx/kkgLADL8VQghnjVXV1e8++67KCkpQdu2bREUFPTY/Q0MDGQ6jfhHsLKyQkhICFatWoXMzEwYGRlh3bp12L59O0hiw4YNFabXANoYliRa/N18fX3h4OAAY2Nj/PTTT3BwcEBWVhYmTZqEmJgYzJo1C9HR0fDw8EBhYSHWrFkDPz8/nD17FgsXLoS/vz82btyo9kC3b98e06ZNQ0REhN7nSBItFOrGQgohhBDib6XRaKAoiowIEv94qamp6NKlCwoKCmBgYAA/Pz9069YN3bt3h5eXF0hKDIt/hOzsbERHR8PX1xdJSUkoLy+HgYEBjh07hrfffhu3bt3C/v374eDggP79+2PVqlVo1KgRLl++jJCQEIwfPx5JSUkYMmQI7O3tJbbFI0kiLYQQQvyNZCqN+De6cOECNm/eDBcXF7Rq1QqmpqbP+5SEqFR+fj6GDBmCtLQ0nD9/Xi8RPnLkCFq0aIGqVati8+bN8PLywvz583H06FE0atQInTp1gqWl5XP+BuLfQhJpIYQQQgghxAsjISEBI0aMQFZWljpiQrfmRFJSElauXIlPPvkEISEhz/tUxb+YTL4SQgghhBBCvDDq1KmDwsJC/N///R8AbVUP3XpA0dHRSE5OliRa/M9ksTEhhBBCCCHEC8Pf3x/x8fFo2rQpAMjCjeKZkKHdQgghhBBCCCHEU5DmGSGEEEIIIYQQ4ilIIi2EEEIIIYQQQjwFSaSFEEIIIYQQQoinIIm0EEIIIYQQQgjxFCSRFkIIIYQQQgghnoIk0kIIIYQQQgghxFOQRFoIIYQQQgghhHgKkkgLIYQQQgghhBBPQRJpIYQQQgghhBDiKUgiLYQQQgghhBBCPAVJpIUQQgghhBBCiKcgibQQQgghhBBCCPEU/h9LW7Rz789aUAAAAABJRU5ErkJggg==\n" + }, + "metadata": {} + } + ], + "source": [ + "cv_results = {}\n", + "print(f\"{'Classifier':<20} {'CV Mean':>10} {'CV Std':>10}\")\n", + "print(\"-\" * 42)\n", + "for name, clf in classifiers.items():\n", + " pipe = Pipeline([('scaler', StandardScaler()),\n", + " ('clf', clf.__class__(**clf.get_params()))])\n", + " scores = cross_val_score(pipe, X, y, cv=5, scoring='accuracy')\n", + " cv_results[name] = scores\n", + " print(f\"{name:<20} {scores.mean():>10.4f} {scores.std():>10.4f}\")\n", + "\n", + "fig, ax = plt.subplots(figsize=(9, 5))\n", + "ax.boxplot(cv_results.values(), tick_labels=cv_results.keys(),\n", + " patch_artist=True, boxprops=dict(facecolor='steelblue', alpha=0.6))\n", + "ax.set_ylabel(\"5-Fold CV Accuracy\"); ax.set_ylim(0.85, 1.02)\n", + "ax.set_title(\"Classifier Comparison – 5-Fold CV on Iris\")\n", + "ax.grid(axis='y', alpha=0.3); plt.xticks(rotation=15)\n", + "plt.tight_layout(); plt.show()\n" ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" }, { - "data": { - "image/png": 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\n", 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    " + "cell_type": "markdown", + "id": "16f407a8", + "metadata": { + "id": "16f407a8" + }, + "source": [ + "## 7. Learning Curve – Diagnosing Bias & Variance\n", + "A learning curve shows how performance changes as training set size grows. \n", + "- **Gap between train & validation** → high variance (overfitting) \n", + "- **Both curves low** → high bias (underfitting)\n" ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# From: http://scipy-lectures.org/packages/scikit-learn/auto_examples/plot_bias_variance.html\n", - "from sklearn.pipeline import make_pipeline\n", - "from sklearn.preprocessing import PolynomialFeatures\n", - "from sklearn.linear_model import LinearRegression\n", - "\n", - "model = make_pipeline(PolynomialFeatures(degree=2), LinearRegression())\n", - "# Generate some synthetic data\n", - "def generating_func(x, err=5):\n", - " return np.random.normal(np.power(x+1, 3), err)\n", - "X = 2*np.random.random(1000)\n", - "y = generating_func(X)\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.5)\n", - "plt.figure()\n", - "plt.scatter(X, y)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ + }, { - "data": { - "image/png": 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\n", 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    " + "cell_type": "code", + "execution_count": null, + "id": "f58916aa", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-16T07:46:10.148858Z", + "iopub.status.busy": "2026-05-16T07:46:10.148096Z", + "iopub.status.idle": "2026-05-16T07:46:10.545635Z", + "shell.execute_reply": "2026-05-16T07:46:10.544436Z" + }, + "id": "f58916aa", + "outputId": "3ad1d73a-cf6f-4be4-aaaa-9582c21fd8e5", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 572 + } + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
    " + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Interpretation: Train & CV curves converge at high sample counts → low variance, good generalisation.\n" + ] + } + ], + "source": [ + "pipe_svm = Pipeline([('scaler', StandardScaler()),\n", + " ('svm', SVC(kernel='rbf', C=1.0, random_state=42))])\n", + "train_sizes, train_scores, val_scores = learning_curve(\n", + " pipe_svm, X, y, cv=5, train_sizes=np.linspace(0.1, 1.0, 10),\n", + " scoring='accuracy', random_state=42, error_score=np.nan\n", + ")\n", + "\n", + "# Use only sizes where all folds have ≥2 classes (avoid SVM error on tiny splits)\n", + "ts, tr, vs = train_sizes, train_scores, val_scores\n", + "\n", + "fig, ax = plt.subplots(figsize=(7, 5))\n", + "ax.plot(ts, tr.mean(1), 'o-', color='steelblue', label='Train')\n", + "ax.fill_between(ts, tr.mean(1)-tr.std(1), tr.mean(1)+tr.std(1), alpha=0.15, color='steelblue')\n", + "ax.plot(ts, vs.mean(1), 'o-', color='tomato', label='CV validation')\n", + "ax.fill_between(ts, vs.mean(1)-vs.std(1), vs.mean(1)+vs.std(1), alpha=0.15, color='tomato')\n", + "ax.set_xlabel(\"Training samples\"); ax.set_ylabel(\"Accuracy\")\n", + "ax.set_title(\"Learning Curve – SVM RBF\"); ax.legend(); ax.grid(alpha=0.3)\n", + "plt.tight_layout(); plt.show()\n", + "print(\"Interpretation: Train & CV curves converge at high sample counts → low variance, good generalisation.\")\n" ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Fit polynomials of increasing degrees:\n", - "from sklearn.model_selection import validation_curve\n", - "degrees = np.arange(1, 21)\n", - "model = make_pipeline(PolynomialFeatures(), LinearRegression())\n", - "\n", - "# Vary the \"degrees\" on the pipeline step \"polynomialfeatures\"\n", - "train_scores, validation_scores = validation_curve(\n", - " model, X[:, np.newaxis], y,\n", - " param_name='polynomialfeatures__degree',\n", - " param_range=degrees)\n", - "\n", - "# Plot the mean train score and validation score across folds\n", - "plt.plot(degrees, validation_scores.mean(axis=1), label='cross-validation') \n", - "plt.plot(degrees, train_scores.mean(axis=1), label='training') \n", - "plt.legend(loc='best') \n", - "plt.xlabel('polynomial degrees')\n", - "plt.ylabel('explained variance')\n", - "plt.title('Validation curve');" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The plot is typical for machine learning models, and demonstrates the bias/variance tradeoff. The training performance (orange) peaks at a value, then decreases, indicating that the complex models are overfitting the training data.\n", - "\n", - "**Bias error** refers to the error due to incorrect assumptions about the model. For example, fitting a data that follows x^3 using a model that uses x^2 will have some error that can't be improved by additional data. \n", - "**Variance** refers to error caused by variance in the training set; a model whose parameters change significantly with small changes in the training set is susceptible to *overfitting*. \n", - " \n", - "**Task**: In the previous cells, try varying the amount of data, the degree of the polynomial in the generating function, and the error on the data to see the effect of these factors." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Regularization" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Regularization is a way to limit model complexity to what is helpful. This is done to avoid over-fitting and increase the interpretability of the model. It balances model performance with restrictions on model parameters.\n", - "\n", - "[Ridge regression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html) uses linear regression (as before) along with a ridge regularizer (l2 loss on the weights)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ + }, + { + "cell_type": "markdown", + "id": "7d399e20", + "metadata": { + "id": "7d399e20" + }, + "source": [ + "## 8. Dimensionality Reduction with PCA\n", + "PCA finds the directions of maximum variance. Projecting to 2D lets us **visualise** high-dimensional data.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d1c0ad5d", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-16T07:46:10.547551Z", + "iopub.status.busy": "2026-05-16T07:46:10.547348Z", + "iopub.status.idle": "2026-05-16T07:46:10.746394Z", + "shell.execute_reply": "2026-05-16T07:46:10.745328Z" + }, + "id": "d1c0ad5d", + "outputId": "a1484b7d-7680-4ea7-bb7a-436eee1bca9e", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 590 + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Explained variance ratio : [0.7296 0.2285]\n", + "Total variance explained : 95.8%\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
    " + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "scaler_pca = StandardScaler()\n", + "X_sc_all = scaler_pca.fit_transform(X)\n", + "\n", + "pca = PCA(n_components=2)\n", + "X_pca = pca.fit_transform(X_sc_all)\n", + "\n", + "print(f\"Explained variance ratio : {pca.explained_variance_ratio_.round(4)}\")\n", + "print(f\"Total variance explained : {pca.explained_variance_ratio_.sum()*100:.1f}%\")\n", + "\n", + "fig, ax = plt.subplots(figsize=(7, 5))\n", + "for cls, (color, label) in enumerate(zip(['steelblue','tomato','seagreen'], iris.target_names)):\n", + " ax.scatter(X_pca[y==cls, 0], X_pca[y==cls, 1],\n", + " color=color, label=label, alpha=0.7, edgecolors='k', linewidths=0.3)\n", + "ax.set_xlabel(f\"PC1 ({pca.explained_variance_ratio_[0]*100:.1f}% var)\")\n", + "ax.set_ylabel(f\"PC2 ({pca.explained_variance_ratio_[1]*100:.1f}% var)\")\n", + "ax.set_title(\"Iris – PCA 2D Projection\"); ax.legend(); ax.grid(alpha=0.3)\n", + "plt.tight_layout(); plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "cd671251", + "metadata": { + "id": "cd671251" + }, + "source": [ + "## 9. Hyperparameter Tuning with GridSearchCV\n", + "Hyperparameters (like SVM's `C` and `gamma`) are not learned from data — they must be set by the user. \n", + "`GridSearchCV` exhaustively tries combinations and picks the best via cross-validation.\n" + ] + }, { - "data": { - "image/png": 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\n", 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    " + "cell_type": "code", + "execution_count": null, + "id": "f3f4d217", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-16T07:46:10.748336Z", + "iopub.status.busy": "2026-05-16T07:46:10.748088Z", + "iopub.status.idle": "2026-05-16T07:46:11.145669Z", + "shell.execute_reply": "2026-05-16T07:46:11.144458Z" + }, + "id": "f3f4d217", + "outputId": "9349c77f-e177-4294-bcae-c8fa87690ed9", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Best parameters : {'svm__C': 10, 'svm__gamma': 'scale', 'svm__kernel': 'rbf'}\n", + "Best CV accuracy: 0.9733\n" + ] + } + ], + "source": [ + "pipe = Pipeline([('scaler', StandardScaler()), ('svm', SVC(random_state=42))])\n", + "param_grid = {\n", + " 'svm__C': [0.01, 0.1, 1, 10, 100],\n", + " 'svm__kernel': ['linear', 'rbf'],\n", + " 'svm__gamma': ['scale', 'auto'],\n", + "}\n", + "\n", + "grid_search = GridSearchCV(pipe, param_grid, cv=5, scoring='accuracy', n_jobs=-1)\n", + "grid_search.fit(X, y)\n", + "\n", + "print(f\"Best parameters : {grid_search.best_params_}\")\n", + "print(f\"Best CV accuracy: {grid_search.best_score_:.4f}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "eb090f22", + "metadata": { + "id": "eb090f22" + }, + "source": [ + "## 10. Final Evaluation & Summary\n", + "Evaluate the best tuned model on the held-out test set.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8e5c9918", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-16T07:46:11.147926Z", + "iopub.status.busy": "2026-05-16T07:46:11.147735Z", + "iopub.status.idle": "2026-05-16T07:46:11.312342Z", + "shell.execute_reply": "2026-05-16T07:46:11.311260Z" + }, + "id": "8e5c9918", + "outputId": "b31a9f4e-86db-48b4-e880-085cd473de1a", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 781 + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Best tuned SVM – Test Accuracy: 0.9667 (96.7%)\n", + "\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " setosa 1.00 1.00 1.00 10\n", + " versicolor 1.00 0.90 0.95 10\n", + " virginica 0.91 1.00 0.95 10\n", + "\n", + " accuracy 0.97 30\n", + " macro avg 0.97 0.97 0.97 30\n", + "weighted avg 0.97 0.97 0.97 30\n", + "\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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l6OsDAI8a10gDQBZWrlw57dixQydPnlTRokXTvZxBgwZp6tSpatOmjYYOHaocOXLIyclJFy9eVLdu3WyOmlWtWlUnT57Uli1btH37dv3www9avny5Ro4cqZ07d6pkyZKS/r32dfDgwVq/fr12796tr7/+WpMnT1bfvn01bdq0h153SdZ+tWvXLs1HAyXJzc0tzXUTA92BAwfMdc6k1EJYSo8ZD3GHyk8++UT9+vVTo0aNNHPmTOtEUHFxcWrSpEmyR0rNvG4tWrTQmTNntHHjRu3YsUM7duzQokWLNGbMGO3du1fZs2dPd98f5vWoUqWKzp49m+xjo0aNSjIpWFqZeW2KFCmiI0eOaMeOHdq6dat27dqlQYMG6d1339W6detUp06ddPXhXon3fy5RooS1rFOnTlq9erVCQkJUp04dZcuWTQ4ODtq/f7/efvvtNB8d379/vxo2bKjChQtr7NixKly4sNzc3GSxWPTWW2/p9u3b1rpOTk5av369Dhw4oI0bN2r37t2aMmWKxo0bp48//lgDBw6U9O/fcokSJVK9w0Baz4YAgKyAIA0AWVjbtm21Y8cOffHFF5owYUK6lzNv3jzVrl1by5Ytsylfv359svUTZ2xOnLV5w4YNatKkiT788EOb+7qWLFlSJUuWVP/+/RUdHa2mTZtq+vTpGjBgQIbcw7po0aKys7NTdHS0GjVq9NDLS06NGjWUJ08erVq1SteuXVOOHDke2CbxqO6RI0dUtWpVm8cSj8SlNoHaw0hu1ueYmBj9/fffNl+2zJs3TwULFtTGjRtt7v189OjRDOuLj4+P2rdvr/bt20uSZs2apddee00zZszQ6NGjbV6n5s2b27R9VK/TokWLUjzFOLkj3Y+Ko6OjgoKCrJdPHDp0SJUrV9bIkSNtZj9Pjzt37mjevHlycHDQc889J+nfU7xXr16tzp07J5nA7a+//kqyjNRmwV60aJHi4uK0fv36JK9ZWFhYsrf7evbZZ/Xss89KksLDw1WjRg0NHz5cffv2lZOTk4oXL67z58+rXr16GXa/bQDITJzaDQBZWI8ePVS2bFlNnjxZS5cuTbbOxYsX9fbbb6e6HHt7+yRH82JjYzV+/Pgkda9fv56krFKlSpJkPbX2xo0bSY5uubq6Wk87TsspuGnh7++vpk2bau3atdq+fXuydR72dFBHR0d99NFHun37ttq2bavw8PAkdQzD0Lx586zXPbdq1UrSvzOc3/s6hIaGasaMGfLx8bG5VVZGOnHihJYvX25TNmPGDEVERFj7Jf3fUd17+2cYht57772H7kN8fHyyr9P94yQoKEgeHh769NNPberHx8dr7NixkqTWrVs/dH/uVbNmTTVq1CjZn8cVpJP7GypVqpTc3d0f+m/jn3/+UYcOHXTu3Dn16dPHOiN94pcl9/+d//PPP5o8eXKS5STOIZDc6d6JY+f+Zc2aNSvJ31toaGiS9r6+vipcuLBiYmKsl6Z06dJF4eHh1vf9fvcv18PDw9Sp6ADwuPGVIABkYc7Ozlq7dq2aN2+ul156STNnzlSTJk2UM2dORUZGau/evVq1apUqVKiQ6nLatm2rzz77TG3atFHjxo1148YNLVq0yHqLonuVKlVKVatWVWBgoPLmzasbN25o3rx5kqSuXbtKkubPn6/JkyerZcuWKlKkiNzc3LR//37Nnj1bzzzzzAP7Y8asWbNUq1YtBQUFqWPHjqpSpYrs7Ox09uxZrVu3TpUrV7Y5Sp4enTp10uXLlzV06FAVKVJEHTp0ULly5eTo6Ki///5bq1ev1p9//mk9gt+gQQO9/PLLWrBggerXr68XX3zRevura9euaf78+Wme7MyscuXKqVu3btq5c6dKlSqlffv2ae7cuSpevLjNJF1t27bV0KFDFRwcrDZt2igqKkorV65UTEzMQ/fhn3/+UUBAgFq0aKEKFSooICBAly5d0pdffikHBwd16tRJ0r/X706dOlU9e/ZU5cqV1b17d7m7u2v58uX68ccf1bNnT9WqVeuh+5PVBAcHy9PTU3Xq1FH+/PkVFRWlJUuWKCIiwjrJWlqsXLlSvr6+io+PV0REhA4ePKiVK1cqIiJCISEh+vjjj611PT099dxzz2nRokVydnZW1apVdfnyZX311VfJ3tatdOnS8vT01MyZM+Xm5iYfHx/lyJFDDRo0UKtWrTR58mQ1adJEvXr1kpubm3bv3q2NGzeqSJEiiouLsy7ngw8+0IYNG6y33HJwcNAPP/ygdevWqXnz5tb7sr/11lvaunWrRo8erZ07d6px48by8/PT+fPntWfPHp06dcp6Tb307wRzW7Zs0UcffaT8+fPLYrEkmawPADJVps0XDgBIszt37hifffaZUb9+fcPf399wcHAwfH19jdq1axsTJ040IiIirHVTuv3V0KFDjQIFChhOTk5GwYIFjWHDhhlHjx5Ncuue8ePHG3Xr1jVy5MhhODo6Grly5TKee+45Y9OmTdY6v/32m9GtWzejWLFihoeHh+Hu7m6ULFnSeOedd4wbN25Y62XE7a8MwzBu3LhhvP3220bJkiUNZ2dnw9PT0yhZsqTRs2dPm9vuJHebJjMOHTpk9OrVyyhWrJjh6upqODs7G4ULFza6du1q7Ny506ZufHy8MXXqVKNcuXKGs7Oz4eHhYdSvX9/YuHFjkuUWKFDAqFu3bpLy1PqbXBv9/1sCbdu2zahRo4bh6upq+Pr6Gi+//LJx5cqVJP376KOPjGLFihnOzs5G7ty5jddee824ceNGklsLpfQ+pdTPu3fvGsOGDTOqVq1qZMuWzXBycjLy5s1rtGnTxvj555+TtF+3bp1Rt25dw8PDw3B2djbKly9vTJ8+3UhISLCpl9L7n9Lr8SgohdtfpfTcyb12X375pREcHGwEBAQYTk5ORvbs2Y06deoYS5cuTVMfEl+HxB9HR0fD39/fqFKlitG3b19j3759ybYLCwszXn31VSNPnjyGs7OzUaJECWPChAnGli1bkr3V1dq1a42KFSsazs7OhiSbdfzuu++MypUrG25uboavr6/RokUL48iRI0n+drdv3260b9/eKFiwoOHq6mp4eXkZ5cuXNz766CMjKirK5vni4uKMmTNnGlWrVjU8PDwMFxcXo2DBgkarVq2SvDYnTpwwgoKCDE9PT+vrAABZicUwHmImEwAA8NhYLBZ17dr1oY/AAwCAh8M10gAAAAAAmECQBgAAAADABII0AAAAAAAmMGs3AABPCKY1AQAga+CINAAAAAAAJhCkAQAAAAAwgVO7Tbpz544OHz6s7Nmzy8GBlw8AAAAAngZxcXG6fv26ypUrJxcXl1TrkgRNOnz4sAIDAzO7GwAAAACAR+CXX35RlSpVUq1DkDYpe/bskv59cQMCAjK5NwAAAACAjHD58mUFBgZaM19qCNImJZ7OHRAQoLx582ZybwAAAAAAGSktl/Ay2RgAAAAAACYQpAEAAAAAMIEgDQAAAACACQRpAAAAAABMIEgDAAAAAGACQRoAAAAAABMI0gAAAAAAmECQBgAAAADABII0AAAAAAAmEKQBAAAAADCBIA0AAAAAgAkEaQAAAAAATCBIAwAAAABgAkEaAAAAAAATCNIAAAAAAJhAkAYAAAAAwASCNAAAAAAAJhCkAQAAAAAwgSANAAAAAIAJBGkAAAAAAEwgSAMAAAAAYAJBGgAAAAAAEwjSAAAAAACYQJAGAAAAAMAEgjQAAAAAACYQpAEAAAAAMIEgDQAAAACACQRpAAAAAABMIEgDAAAAAGACQRoAAAAAABMI0gAAAAAAmECQBgAAAADABII0AAAAAAAmEKQBAAAAADCBIA0AAAAAgAkEaQAAAAAATCBIAwAAAABgAkEaAAAAAAATCNIAAAAAAJhAkAYAAAAAwASCNAAAAAAAJhCkAQAAAAAwgSANAAAAAIAJBGkAAAAAAEwgSAMAAAAAYAJBGgAAAAAAEwjSAAAAAACYQJAGAAAAAMAEgjQAAAAAACYQpAEAAAAAMIEgDQAAAACACVkmSJ88eVK9e/dWhQoV5ODgoLJly6apnWEY+vDDD5U/f365urqqevXq+umnn5LUu3Tpklq3bi1PT0/5+fkpJCREkZGRGb0aAAAAAICnXJYJ0keOHNHatWtVtGhRlS5dOs3tPvroI40aNUr9+/fXmjVrFBAQoMaNG+vUqVPWOrGxsQoODtaJEye0ePFiffbZZ9q4caM6duz4KFYFAAAAAPAUc8jsDiRq0aKFXnjhBUlSt27d9Ouvvz6wzZ07dzR+/HgNHDhQ/fv3lyTVrl1bxYsX18SJEzVz5kxJ0vLly3XkyBEdPXpUJUqUkCT5+voqODhYv/zyiwIDAx/RWgEAAAAAnjZZ5oi0nZ35ruzZs0eRkZFq166dtczJyUmtWrXSunXrrGXr169X+fLlrSFakoKCguTn52dTDwAAAACAB8kyR6TT49ixY5KkkiVL2pSXKlVK586dU3R0tFxdXXXs2LEkdSwWi0qWLGldRkoiIyNtrqW+fPmyJCkiIkLu7u7Wck9PT0nSP//8Y9Pe2dlZzs7OunPnjmJiYmwe8/LykmEYKbaJjo5WbGxskjYJCQm6deuWTbmLi4ucnJyStLFYLPL09FR8fLxu376dbJuoqCjFxcVZy+3s7OTh4ZFsG1dXVzk6Our27duKj49P0iYuLk5RUVE2bdzc3OTg4JCkjb29vdzd3RUbG6vo6Ohk29y6dUsJCQnWcgcHB7m5uSXbxt3dXfb29im2iYmJ0Z07d5Jt888//8gwDGu5o6OjXF1dk23j4eEhOzu7JNfYJ7a5e/eu7t69a/OYp6enLBZLkjZOTk5ycXFJdnykd0wlNz5SGlOJ4yO1MXX/+EhtTCWOj6dtTN0/PlIbU4njI6Uxldz4SGlMJY6PjBpTD9rmPIljKrnxkdKYShwfqY2p+8dHamPqQducJ3FMSUm3OQ8zprLy59iTOKay8ueYmTHFvhH7RhL7Rk/6mMqqn2MPu28UERGhtHqig3R4eLicnZ3l4uJiU+7r6yvDMBQeHi5XV1eFh4fLx8cnSXtfX1/duHEj1eeYPHmyxowZk6T866+/lre3t/X3kJAQSdLs2bNt6lWqVEmVK1fW3r17dejQIWu5nZ2devbsqbt372ru3Lk2bapWraoKFSpo165d+vPPP63lTk5OeuWVV3Tr1i0tWrTIpk2tWrVUpkwZbdu2TX/99Ze13N3dXZ07d1Z4eLj+97//2bSpX7++ihcvro0bN+rMmTPWch8fH7Vv317Xr1/XihUrbNoEBQWpcOHCWrt2rS5cuGAtz5Ytm1q3bq1Lly7p+++/t2nTtGlT5cuXT6tXr9aVK1es5QEBAXr++ed17tw5rV+/3qbN888/r4CAAC1fvlxhYWHW8nz58qlp06b6+++/tWXLFps2rVu3VrZs2fTNN9/Y/AEVLlxYQUFBOnbsmH744QebNi+99JK8vb21YMECmw1S8eLFVb9+ff3xxx/68ccfbdp07txZ7u7u+vrrr202vGXKlFGtWrX0+++/6+eff7Zp88orr8jJyUlffPGFzQbkmWeeUbVq1bRv3z4dOHDApk3Pnj2VkJCgr776yqa8cuXKqlSpkvbs2aPDhw9by+3t7RUSEqLo6GjNnz/fpk316tVVvnx5/fDDDzZfHrm4uKhr1676559/tHjxYps2tWvXVunSpbV161adPHnSWu7h4aFOnTrpxo0bWrZsmU2bBg0aqFixYknGlK+vr9q1a6dr165p5cqVNm0aN26sQoUKac2aNbp48aK1PHv27GrVqpUuXryoNWvW2LRp1qyZ8ubNq5UrV+ratWvW8ty5c6tFixY6c+aMNm7caNPmhRdeUK5cubRs2TKbv/v8+fOrSZMmOnnypLZu3WrTpk2bNvL399fixYttPrSLFCmiRo0a6ejRo9q5c6dNmw4dOsjLy0vz58+3+fApWbKk6tatq8OHD2vPnj02bV5++WW5ubnpq6++svmQLVu2rGrWrKkDBw5o3759Nm26d+8uR0dHff755zblFSpUUNWqVfXzzz/r999/t3ns1VdfVWxsrL7++mub8ipVqujZZ5/Vjz/+qD/++MNa7uDgoB49eigqKkoLFiywaVOjRg2VK1cuyZhydXVVly5dFBkZqW+++camTZ06dVSqVClt2bJFf//9t7Xc09NTHTt2VFhYmJYvX27TpmHDhipatKjWr1+vc+fOWcv9/PzUtm1bXblyRatXr7ZpExwcrIIFC+r777/XpUuXrOU5cuTQiy++qAsXLmjt2rU2bZo3b648efJoxYoVun79urU8T548at68uU6fPq1NmzbZtHnxxReVI0cO/e9//1N4eLi1vGDBggoODtZff/2lbdu22bRp27at/Pz8tGjRIpsdtKJFi6phw4b6888/tWvXLps2HTt2lKenp+bNm2ezc5I4pg4dOqS9e/fatOnSpYtcXV01e/Zsmx2qcuXKqUaNGtq/f3+SS6h69OghOzs7ffnllzblzz77rKpUqaKffvpJBw8etJZbLBb16tVLMTExmjNnjk2bxM+x3bt368iRI9ZyR0dHde/eXbdv39bChQtt2tSsWVNly5bV9u3bdeLECWu5m5ubXn75Zd28eVNLliyxaVO3bl2VLFlSmzdvtpkbxcvLSx06dFBoaKi+/fZbmzaNGjVSkSJFtG7dOp0/f95a7u/vrzZt2ujy5cv67rvvbNo0adJE+fPn13fffWf9Ql2ScuXKpRdeeEHnz59PcoZbixYtlDt3bn377bcKDQ21lufNm1fNmjXTqVOntHnzZps2rVq1Uvbs2bV06VKbnbnEMXXixAlt377dpk27du3k6+urhQsX2uyMFytWTA0aNNCRI0e0e/dumzadOnWSh4eH5syZY7NTWbp0adWuXTvZz7Fu3brJ2dlZX375pc0Od/ny5VW9enX9+uuv2r9/v00b9o3YN2LfiH0j6cnaN7p586bSymLcO3KziMRrpO/doUvO2LFj9f777yf51mP58uVq27atLl68qNy5c6tYsWJq2LChZs2aZVOvefPmiomJSbJzdK/kjkgHBgbq8OHDypMnj7Wcb12zzjdkfOvKt65868oR6axw9PBJHFMSR6Sz8pjKyp9jHJHO2p9jT+KYYt8oa4+prPo59rD7RhcvXlS5cuV0/vx55c2bV6l5ooP0zJkz9cYbbyg6OtrmqPSXX36pV199Vbdv35arq6sCAwNVpEiRJEdHatasqXz58iX5hjs1Fy5cUL58+dL04gIAAAAAngxmsl6WmWwsPRKvez5+/LhN+bFjx6z3lU6sd/+10IZh6Pjx40munQYAAAAAIDVPdJCuUaOGvLy8bK5FiI2N1YoVK9S0aVNrWZMmTXTw4EGb62O2bt2qsLAwm3oAAAAAADxIlplsLCoqyjpRx9mzZxUZGWmddKZu3brKnj27GjZsqLNnz1ov7ndxcdGwYcM0evRoZc+eXeXKldPMmTMVFhamQYMGWZfdpk0bjRs3Tq1bt9a4ceMUFRWlQYMGqVmzZtxDGgAAAABgSpYJ0teuXVPbtm1tyhJ/3759u+rVq6f4+Hibi/QlaejQoTIMQxMnTtT169dVoUIFbdy4UYULF7bWcXR01IYNG/Tmm2+qQ4cOcnBwUKtWrTRlypRHv2IAAAAAgKdKlpxsLCtjsjEAAAAAePr8ZyYbAwAAAADgcSNIAwAAAABgAkEaAAAAAAATCNIAAAAAAJhAkAYAAAAAwASCNAAAAAAAJhCkAQAAAAAwgSANAAAAAIAJBGkAAAAAAEwgSAMAAAAAYAJBGgAAAAAAEwjSAAAAAACYQJAGAAAAAMAEgjQAAAAAACYQpAEAAAAAMIEgDQAAAACACQRpAAAAAABMIEgDAAAAAGACQRoAAAAAABMI0gAAAAAAmECQBgAAAADABII0AAAAAAAmEKQBAAAAADCBIA0AAAAAgAkEaQAAAAAATCBIAwAAAABgAkEaAAAAAAATCNIAAAAAAJhAkAYAAAAAwASCNAAAAAAAJhCkAQAAAAAwgSANAAAAAIAJBGkAAAAAAEwgSAMAAAAAYAJBGgAAAAAAEwjSAAAAAACYQJAGAAAAAMAEgjQAAAAAACYQpAEAAAAAMIEgDeCJMWPGDBUsWFAuLi6qVq2a9u3bl2LdmJgYjRw5UoUKFZKrq6sCAwP1448/2tSxWCzJ/nz88cePelUAAMB/APsuTy+CNIAnwtKlSzVgwACNGjVKBw4cUPny5RUcHKzQ0NBk648YMUJz5szRrFmzdPjwYT3//PMKDg7WuXPnrHUuX75s8/P111/LYrGodevWj2u1AADAU4p9l6ebxTAMI7M78SS5cOGC8uXLp/Pnzytv3ryZ3R3gP6Nq1aoKDAzU9OnTJUkJCQnKly+f+vfvr0GDBiWpnzt3bo0aNUqvvvqqtaxy5coKCgrS+PHjk32Oli1b6p9//tHWrVsfzUoAAID/DPZdnjxmsh5HpAFkeTExMdq/f78aN25sLbOzs1OjRo20d+/eZNvcvXtXLi4uNmWurq7as2dPsvWvXr2qtWvXqkePHhnXcQAA8J/EvsvTjyANIMsLDQ1VfHy8cubMaVOeM2dOXblyJdk2QUFBmjRpkk6dOqX4+HgtWbJEe/bsSbH+vHnz5OnpqVatWmV4/wEAwH8L+y5PP4I0gKfStGnTlD9/fhUtWlTOzs6aNGmSOnToIDu75Dd7X3/9tTp16pTkm2AAAIDHgX2XJwtBGkCWly1bNtnb2+vq1as25VevXlWuXLmSbZMjRw6tWbNGt2/f1vnz57Vv3z7FxcWpUKFCSeru2rVLx48fV0hIyCPpPwAA+G9h3+XpR5AGkOU5OTmpUqVK2rx5s7UsISFBW7duVfXq1VNt6+rqqoCAAEVERGjjxo16/vnnk9T56quvVKlSJT3zzDMZ3ncAAPDfw77L088hszsAAGkxYMAAde3aVZUqVVJgYKCmTp2qqKgodevWTZLUpUsX5cmTxzqr5d69e3Xt2jWVK1dOZ86c0eDBg1W0aNEkE3JERkZq2bJlmjRp0uNeJQAA8BRj3+XpRpAG8ERo3769rl+/rpEjR+rKlSuqUKGCNmzYoGzZskmSzp07Z3MNUXR0tIYOHarTp0/L29tbrVq10vjx4+Xo6Giz3CVLlsgwDHXo0OGxrg8AAHi6se/ydOM+0iZxH2kAAAAAePpwH2kAAAAAAB4RgjQAAAAAACYQpAEAAAAAMIEgDQAAAACACQRpAAAAAABMIEgDAAAAAGACQRoAAAAAABMI0sB/XFwCt5J/kvH+AQD+a4y4uMzuAtLpaXrvHDK7AwAyl4OdRSNXnVZE1NOzYfuv8HFz0HstC2V2NwAAeKwsDg66PPIlxUeEZnZXYIK9TzYFvLcks7uRYQjSABQRFadwgjQAAHhCxEeEKj78amZ3A/9hnNoNAAAAAIAJBGkAAAAAAEwgSAMAAAAAYAJBGgAAAAAAEwjSAAAAAACYQJAGAAAAAMAEgjQAAAAAACYQpAEAAAAAMIEgDQAAAACACQRpAAAAAABMIEgDAAAAAGACQRoAAAAAABMI0gAAAAAAmECQBgAAAADABII0AAAAAAAmEKQBAAAAADCBIA0AAAAAgAkEaQAAAAAATCBIAwAAAABgAkEaAAAAAAATCNIAAAAAAJhAkAYAAAAAwASCNAAAAAAAJhCkAQAAAAAwgSANAAAAAIAJWSZIHzt2TEFBQXJ3d1euXLk0ZMgQxcTEPLDdzZs31atXL2XLlk1ubm6qV6+efv/9d5s6Z86ckcViSfJTrVq1R7Q2AAAAAICnlUNmd0CSwsPD1aBBAxUrVkwrVqzQxYsXNWDAAEVFRenTTz9NtW2HDh3066+/asKECcqZM6emTJmiBg0a6ODBg8qXL59N3XHjxql+/frW3z09PR/J+gAAAAAAnl5ZIkjPmjVLkZGRWrlypfz8/CRJcXFxev311zV8+HDlzp072XY//fST1q9fr++++04tWrSQJNWvX1+FChXSxIkT9cknn9jUL1asGEehAQAAAAAPJUuc2r1+/Xo1atTIGqIlqV27dkpISNCmTZtSbPfbb7/JYrEoKCjIWubm5qbatWvr+++/f6R9BgAAAAD8N2WJI9LHjh1T9+7dbcp8fHwUEBCgY8eOpdjuzp07srOzk4OD7Wo4OzvrzJkzio6Olqurq7X8tddeU/v27eXv768XXnhBH330kU14T05kZKQiIyOtv1++fFmSFB8fr/j4+DSvI5CV2SlBdkrI7G7AJDslsB0CAPwnJdjZK8EuS0QZpJHFzj7L77eY6V+WGH3h4eHy8fFJUu7r66sbN26k2K5YsWKKj4/XgQMHFBgYKElKSEjQvn37ZBiGIiIi5OrqKmdnZ7322msKDg6Wj4+Pfv75Z40dO1a//vqrfvnlFzk6Oqb4HJMnT9aYMWOSlIeFhcnZ2dn8ygJZkJ/9bTk5Ze0NG5LysLfX9evXM7sbAAA8dpF++ZTgxHxHTxI7Dx85ZfH9lrCwsDTXzRJBOr0aN26sIkWKqHfv3po/f75y5MihDz/8UKdOnZIkWSwWSVJAQIBmzpxpbVe3bl2VKVNGzZs318qVK9WuXbsUn2PAgAEKCQmx/n758mUFBgbK399f2bNnf0RrBjxeN+JvKjwmLrO7AZNiHBzYDgEA/pNibpxXfPi1zO4GTLCPyZHl91vu3r2b5rpZIkj7+vrq5s2bScrDw8NTPfXayclJS5cuVYcOHVSuXDlJUrly5dSvXz9NmzZN/v7+KbZt2rSp3N3dtX///lSDtJeXl7y8vJKU29vby97ePrXVAp4YCf//5G48WRJkx3YIAPCfZJcQLyOBgwBPEruE+Cy/32Kmf1liz7lkyZJJroW+efOmLl++rJIlS6batlKlSjp+/LhOnDih48eP6+DBg4qOjlalSpVSPWUbAAAAAID0yBJBukmTJtqyZYsiIiKsZcuWLZOdnZ0aN278wPYWi0XFihVT8eLFFRoaqqVLl6pnz56ptlmzZo1u376tKlWqPGz3AQAAAAD/IVni1O7evXtr+vTpatmypYYPH66LFy9q8ODB6t27t809pBs2bKizZ8/q5MmT1rKxY8eqaNGiypkzp44fP65x48apUqVK6tatm7XOwIEDZWdnp2rVqsnHx0e//PKLxo8fr8qVK6tly5aPcU0BAAAAAE+6LBGkfX19tXXrVvXt21ctW7aUp6enQkJCNHbsWJt68fHxiouzvRYiPDxcgwYN0rVr1xQQEKCXX35ZI0aMkJ3d/x1sL126tGbOnKkvvvhCUVFRypMnj3r06KExY8YkuXUWAAAAAACpsRiGYWR2J54kFy5cUL58+XT+/HnlzZs3s7sDZIg3F/+l8Cgm7HjS+Lo5aFrHYpndDQAAHrsLbzZSfPjVzO4GTLD3zam807ZkdjdSZSbrZYlrpAEAAAAAeFIQpAEAAAAAMIEgDQAAAACACQRpAAAAAABMIEgDAAAAAGACQRoAAAAAABMI0gAAAAAAmECQRqabMWOGChYsKBcXF1WrVk379u1LsW5MTIxGjhypQoUKydXVVYGBgfrxxx9t6rz//vsqVaqU3N3d5evrq0aNGunnn39+1KsBIIth2wIzGC8AADMI0shUS5cu1YABAzRq1CgdOHBA5cuXV3BwsEJDQ5OtP2LECM2ZM0ezZs3S4cOH9fzzzys4OFjnzp2z1ilatKimTZumw4cPa/fu3SpUqJCCg4MVFhb2uFYLQCZj2wIzGC8AALMshmEYmd2JJ8mFCxeUL18+nT9/Xnnz5s3s7jzxqlatqsDAQE2fPl2SlJCQoHz58ql///4aNGhQkvq5c+fWqFGj9Oqrr1rLKleurKCgII0fPz7Z54iMjJS3t7d27NihunXrPpoVecK9ufgvhUfFZXY3YJKvm4OmdSyW2d3Ikti2wAzGC/DkufBmI8WHX83sbsAEe9+cyjttS2Z3I1Vmsl66jki//fbb+uuvv9LVOSBRTEyM9u/fr8aNG1vL7Ozs1KhRI+3duzfZNnfv3pWLi4tNmaurq/bs2ZPic3zxxRfy9fVVuXLlMq7zALIsti0wg/ECAEiPdAXpBQsWqGTJkqpdu7bmzZunqKiojO4X/gNCQ0MVHx+vnDlz2pTnzJlTV65cSbZNUFCQJk2apFOnTik+Pl5LlizRnj17ktRfs2aNPDw85OLioilTpmjz5s3y8/N7ZOsCIOtg2wIzGC8AgPRIV5A+f/68vvvuO+XMmVO9evVSQECAevXqleI3t0BGmTZtmvLnz6+iRYvK2dlZkyZNUocOHWRnZzuU69evr99//1179uzRc889p3bt2qV4rRsAsG2BGYwXAEC6grSdnZ2aNWum5cuX69KlSxozZox++eUX1apVS2XKlNGkSZN07dq1jO4rnjLZsmWTvb29rl61vb7l6tWrypUrV7JtcuTIoTVr1uj27ds6f/689u3bp7i4OBUqVMimnru7u4oWLapq1arpq6++kp2dnebMmfPI1gVA1sG2BWYwXgAA6fHQs3b7+/urX79+mj9/vmrXrq2jR49q8ODBypcvn7p27arr169nRD/xFHJyclKlSpW0efNma1lCQoK2bt2q6tWrp9rW1dVVAQEBioiI0MaNG/X888+nWt8wDN29ezdD+g0ga2PbAjMYLwCA9HioIH3z5k199tlnqly5sipWrKjIyEjNmDFDly5d0meffaZdu3bppZdeyqi+4ik0YMAAffHFF5o3b56OHj2q1157TVFRUerWrZskqUuXLho2bJi1/t69e7V69WqdOnVK27ZtU8OGDVW0aFH16NFDknTnzh0NGzZMP/30k86ePasDBw6oZ8+eunDhglq3bp0ZqwggE7BtgRmMFwCAWQ7pabR161Z9/fXXWrVqlRwcHNShQwd9/vnnqlSpkrVO9+7dlS9fPrVo0SLDOounT/v27XX9+nWNHDlSV65cUYUKFbRhwwZly5ZNknTu3Dmba86io6M1dOhQnT59Wt7e3mrVqpXGjx8vR0dHSf9ednDixAm1bt1a169fl7+/vwIDA7Vr1y6VKlUqU9YRwOPHtgVmMF4AAGal6z7SdnZ2qlq1qnr27KmXXnpJbm5uydY7e/asRo8e/VRdD8R9pPE04j7STybuIw0A+K/iPtJPnqftPtLpOiJ96NAhlS1b9oH1ChQo8FSFaAAAAAAA0nWNdIECBXT58uVkH7t8+bJu3br1UJ0CAAAAACCrStcR6ZCQEHl6emr27NlJHhs1apRu3bqlxYsXP3TnAAAAAADIatJ1RHrnzp1q1qxZso81bdpUP/zww0N1CgAAAACArCpdQTo8PFyenp7JPubu7q6wsLCH6hQAAAAAAFlVuoJ04cKFtWVL8jOubd26VQULFnyYPgEAAAAAkGWlK0iHhIRo8uTJmjBhgkJDQyVJoaGh+vjjjzVlyhT17NkzQzsJAAAAAEBWka7Jxvr376+///5bw4YN07Bhw+Tg4KC4uH/vQdu7d28NHDgwQzsJc+ISDDnYWTK7G0gn3j9kZYzPJ9fjfu8YK0823j8ASF26grTFYtGMGTPUr18/bdu2TWFhYfL391eDBg1UrFixjO4jTHKws2jkqtOKiIrL7K7AJB83B73XslBmdwNIEduXJ1NmbFsYK08uPosA4MHSFaQTFStWjOCcRUVExSmcnRcAjwDbF6QVYwUA8LR6qCB9584dnTp1Snfu3Eny2LPPPvswiwYAAAAAIEtKV5COiYnRa6+9poULF1qvjb5ffHz8Q3UMAAAAAICsKF2zdo8ZM0abNm3S3LlzZRiGPv30U82ZM0cNGzZUwYIF9f3332d0PwEAAAAAyBLSFaSXLVum0aNHq127dpKkwMBAdenSRZs2bVKtWrUI0gAAAACAp1a6gvSFCxdUvHhx2dvby8XFReHh4dbHOnfurGXLlmVYBwEAAAAAyErSFaQDAgIUEREhSSpUqJB27NhhfezEiRMZ0S8AAAAAALKkdE02Vq9ePe3atUstWrRQz549NWjQIB09elROTk5atWqVOnbsmNH9BAAAAAAgS0hXkB47dqxCQ0MlSf369ZNhGFq+fLmio6P15ptvauTIkRnaSQAAAAAAsgrTQTouLk6XLl1Svnz5rGX9+/dX//79M7RjAAAAAABkRaavkbazs1O1atV08ODBR9EfAAAAAACytHQF6cKFC9vM1A0AAAAAwH9FumbtHj58uN5//31dunQpo/sDAAAAAECWlq7JxpYtW6br16+rcOHCKl++vHLmzCmLxWJ93GKxaPXq1RnWSQAAAAAAsop0Belbt26pZMmSNr8DAAAAAPBfkK4gvX379ozuBwAAAAAAT4R0XSMNAAAAAMB/VbqOSL/33nsPrDNy5Mj0LBoAAAAAgCwtXUF6ypQpScpu3bql+Ph4ubq6ytnZmSANAAAAAHgqpevU7vDw8CQ/0dHRWr9+vYoWLaodO3ZkcDcBAAAAAMga0nVEOtkFOTgoODhYFy9e1GuvvaYff/wxoxYNAAAAAECWkeGTjeXNm1e///57Ri8WAAAAAIAsIUOD9OnTp/XRRx+pSJEiGblYAAAAAACyjHSd2u3p6SmLxWJTFhsbq5iYGLm5uWnFihUZ0jkAAAAAALKadAXpgQMHJgnSLi4uyps3r5o0aSI/P78M6RwAAAAAAFlNuoL06NGjM7gbAAAAAAA8GdJ1jfT58+d14MCBZB87cOCALly48FCdAgAAAAAgq0pXkH7ttde0YMGCZB9bvHix3njjjYfqFAAAAAAAWVW6gvTPP/+sBg0aJPtY/fr1tXfv3ofqFAAAAAAAWVW6gvStW7fk6OiY/ALt7PTPP/88VKcAAAAAAMiq0hWkS5UqpZUrVyb72OrVq1WiRImH6hQAAAAAAFlVumbt7tevn7p16yZ7e3t1795duXPn1qVLlzRnzhx9+eWX+vrrrzO6nwAAAAAAZAnpCtJdunTR1atXNWbMGH3++efWcldXV3344Yfq2rVrhnUQAAAAAICsJF1BWpIGDx6sV199VXv37lVYWJj8/f1VvXp1eXl5ZWT/AAAAAADIUtIdpCXJy8tLwcHBGdUXAAAAAACyvHRNNjZ9+nS9/fbbyT729ttva8aMGQ/VKQAAAAAAsqp0BemZM2eqSJEiyT5WvHhxzZw586E6BQAAAABAVpWuIH327FkVK1Ys2ccKFy6sM2fOPEyfAAAAAADIstIVpL28vHT69OlkHzt16pTc3NweqlMAAAAAAGRV6QrSjRs31pgxY3T+/Hmb8gsXLuj9999XkyZNMqRzAAAAAABkNematfvDDz9UtWrVVKJECTVo0EC5c+fWpUuXtG3bNmXPnl3jx4/P6H4CAAAAAJAlpOuIdO7cufX777+rf//+CgsL044dOxQWFqaBAwfqt99+U548eTK6nwAAAAAAZAnpvo+0n5+fxo4dm+xjO3bsUL169dK7aAAAAAAAsqx0B+n7/f7771q0aJGWLFmiS5cuKT4+PqMWDQAAAABAlvFQQfr06dNavHixFi9erGPHjsnR0VHNmjVT165dM6p/AAAAAABkKaavkQ4NDdWMGTNUs2ZNFS1aVCNHjpSHh4ckac2aNfr222/1/PPPZ3hHAQAAgMw2Y8YMFSxYUC4uLqpWrZr27duXYt2YmBiNHDlShQoVkqurqwIDA/Xjjz/a1DEMQyNHjlRAQIBcXV3VqFEjnTx58lGvBoCHlOYgvWjRIjVr1kx58uRR3759devWLY0bN06nT5/Wxo0bZRiGnJycHmVfAQAAgEyzdOlSDRgwQKNGjdKBAwdUvnx5BQcHKzQ0NNn6I0aM0Jw5czRr1iwdPnxYzz//vIKDg3Xu3DlrnQkTJmjatGmaNWuWfv75Z7m7u+u5555TTEzM41otAOmQ5iD98ssva8OGDapXr54OHz6sgwcPaujQocqfP78sFsuj7CMAAACQ6SZPnqxevXrplVdeUenSpTVr1iy5urpq7ty5ydZfuHChRowYoeDgYBUtWlQjRoxQyZIl9dlnn0n692j01KlT9e677+qFF15Q+fLlNX/+fF24cEHffffdY1wzAGalOUi3aNFCjo6O2rx5s1q1aqX33ntPf/3116PsGwAAAJAlxMTEaP/+/WrcuLG1zM7OTo0aNdLevXuTbXP37l25uLjYlLm6umrPnj2S/p1v6MqVKzbL9Pb2VtWqVVNcJoCsIc1BevXq1bp8+bJmzZql3Llza8yYMSpZsqQCAwM1ffp0jkoDAADgqRUaGqr4+HjlzJnTpjxnzpy6cuVKsm2CgoI0adIknTp1SvHx8VqyZIn27NljrZ/4r5llAsgaTE025uvrq169emn79u06d+6cPvzwQ8XFxWnkyJEyDEPDhg3T/Pnzdfv27UfVXwAAAOCJMG3aNOXPn19FixaVs7OzJk2apA4dOsjOzvR8vwCymHT/FefJk0eDBw/WgQMH9Oeff2r48OG6evWqunXrpoCAgIzsIwAAAJCpsmXLJnt7e129etWm/OrVq8qVK1eybXLkyKE1a9bo9u3bOn/+vPbt26e4uDgVKlRIkqztzCwTQNaQIV+HlSxZUh988IFOnjypH3/8Ud26dcuIxQIAAABZgpOTkypVqqTNmzdbyxISErR161ZVr1491baurq4KCAhQRESENm7caL1VbKFChZQrVy6bZUZGRurnn39+4DIBZC6HjF5g9erV+cMHAADAU2fAgAHq2rWrKlWqpMDAQE2dOlVRUVHWg0hdunRRnjx5NH78eEnS3r17de3aNZUrV05nzpzR4MGDVbRoUfXo0UOSZLFY1K9fP73//vsqWrSoChUqpHfffVd58+ZVixYtMms1AaRBhgdpAAAA4GnUvn17Xb9+XSNHjtSVK1dUoUIFbdiwQdmyZZMknTt3zub65+joaA0dOlSnT5+Wt7e3WrVqpfHjx8vR0dFaZ8iQIbp9+7Z69eqliIgI1apVS+vXr5ezs/NjXz8AaUeQBgAAANKoT58+6tOnT7KP7dixw+b3Bg0a6NixY6kuz2Kx6L333tN7772XUV0E8BgwZSAAAAAAACYQpAEAAAAAMCFdQbpw4cI6ePBgso/98ccfKly4sOllHjt2TEFBQXJ3d1euXLk0ZMgQxcTEPLDdzZs31atXL2XLlk1ubm6qV6+efv/992Tr9ejRQ35+fvL09FSbNm10+fJl0/0EAAAAAPy3pStInzlzRnfv3k32saioKJ0/f97U8sLDw9WgQQPFxMRoxYoVGjdunL744gsNGDDggW07dOigVatWacKECVq2bJkcHBzUoEGDJH1o3769Nm3apFmzZmnRokU6fvy4mjRpori4OFN9BQAAAAD8t6V5srE7d+4oKipKhmFI+vcedzdu3EhSZ9WqVcqdO7epTsyaNUuRkZFauXKl/Pz8JElxcXF6/fXXNXz48BSX99NPP2n9+vX67rvvrLcIqF+/vgoVKqSJEyfqk08+kfTvrQc2btyojRs3qnHjxpKkEiVKqFSpUlqxYoXatWtnqr8AAAAAgP+uNB+R/uijj5Q9e3blyJFDFotFwcHByp49u81Pvnz59NFHHykkJMRUJ9avX69GjRpZQ7QktWvXTgkJCdq0aVOK7X777TdZLBYFBQVZy9zc3FS7dm19//33Nsv38fGxqVeiRAlVqFBB69atM9VXAAAAAMB/W5qPSLds2VIFCxaUYRjq3r27RowYoSJFitjUcXJyUqlSpVShQgVTnTh27Ji6d+9uU+bj46OAgIBUbxlw584d2dnZycHBdjWcnZ115swZRUdHy9XVVceOHVOJEiVksVhs6pUqVeqBtySIjIxUZGSk9ffE66rj4+MVHx+fpvXLDHZKkJ0SMrsbMMlOCZkyrhgvTybGC9KKsQIzMmu8AGYk2NkrwY47+T5JLHb2WX7bYqZ/aR59zzzzjJ555hlJ/97vrlmzZtabzz+s8PBw+fj4JCn39fVNcvr4vYoVK6b4+HgdOHBAgYGBkqSEhATt27dPhmEoIiJCrq6u6V6+JE2ePFljxoxJUh4WFiZnZ+fUVywT+dnflpNT1h6oSMrD3l7Xr19/7M/LeHkyMV6QVowVmJEZ4yU+wZC9neXBFZElZcb7F+mXTwlOno/1OfFw7Dx85JQJn0VmhIWFpbluur7G6dq1a5KyH3/8UUePHlXt2rVVokSJ9CzWtMaNG6tIkSLq3bu35s+frxw5cujDDz/UqVOnJCnJEej0GDBggM2p6pcvX1ZgYKD8/f2VPXv2h17+o3Ij/qbCY5hI7UkT4+CQKeOK8fJkYrwgrRgrMCOzxst735/RzSjGy5PG281BI1sUfOzPG3PjvOLDrz3250X62cfkyNL5SVKKE2onJ11BumPHjnJ2dtacOXMk/TtZ2Ouvvy7p39Oq16xZo4YNG6Z5eb6+vrp582aS8vDwcJvrpu/n5OSkpUuXqkOHDipXrpwkqVy5curXr5+mTZsmf39/6/KTm0n8QcuXJC8vL3l5eSUpt7e3l729faptM1PC/z+hDk+WBNllyrhivDyZGC9IK8YKzMis8RIelaDwKC4FeNIkKCFTxotdQryMBL54eZLYJcRn6fwkyVT/0vXptnv3bjVp0sT6+/jx4xUSEqLIyEi1adMm2VOhU1OyZMkk1yrfvHlTly9fVsmSJVNtW6lSJR0/flwnTpzQ8ePHdfDgQUVHR6tSpUpydHS0Lv/48ePWGccTHTt27IHLBwAAAADgXukK0tevX1dAQIAk6ciRIzp//rzeeusteXh4qGvXrjp8+LCp5TVp0kRbtmxRRESEtWzZsmWys7Oz3q4qNRaLRcWKFVPx4sUVGhqqpUuXqmfPnjbLDw8P19atW61lJ06c0G+//aamTZua6isAAAAA4L8tXUHa399fZ8+elSRt2LBBAQEBKlOmjKR/ZzpLSDB3Wk7v3r3l6empli1batOmTZozZ44GDx6s3r1729xDumHDhipatKhN27Fjx2rp0qXasWOHPv/8c1WuXFmVKlVSt27drHWqV6+u4OBgde/eXcuWLdP333+vNm3aqHz58mrVqlV6XgIAAAAAwH9Uuq6RbtKkiYYOHaqDBw9q7ty5evnll62P/fHHHypUqJCp5fn6+mrr1q3q27evWrZsKU9PT4WEhGjs2LE29eLj4xUXZ3stRHh4uAYNGqRr164pICBAL7/8skaMGCE7O9vvCJYuXaoBAwaoV69eiouLU+PGjTV9+vQkt84CAAAAACA16UqREydOVHx8vDZs2KCmTZtq9OjR1sdWrlyp5557zvQyS5UqpS1btqRaZ8eOHcn2ZeLEiQ9cvre3t7766it99dVXpvsGAAAAAECidAVpb29vff3118k+tnv37ofqEAAAAAAAWdlD3ZMiPDxcu3bt0uLFixUeHi5JunPnjulrpAEAAAAAeFKkK0gbhqHhw4crX758qlu3rl5++WWdPn1aktSqVSu9//77GdpJAAAAAACyinQF6XfffVeffvqpJk2apBMnTtjcn/n555/X999/n2EdBAAAAAAgK0nXNdJz587VuHHj9Oqrryo+Pt7msSJFiujvv//OkM4BAAAAAJDVpOuIdFhYmEqVKpXsY/Hx8YqNjX2oTgEAAAAAkFWlK0gXL15cmzdvTvaxHTt2qGzZsg/VKQAAAAAAsqo0n9o9f/58NWvWTP7+/urfv7969uwpR0dHtWnTRpJ04cIF7d27V9OmTdPcuXMfVX8BAAAAAMhUaQ7Sr7zyivbu3St/f39169ZNN27c0OjRozVu3DhJUsuWLeXu7q4PPvhA7dq1e2QdBgAAAAAgM6U5SN87M7ckDRgwQL169dKPP/6osLAw+fn5qXr16vL29s7wTgIAAAAAkFWka9buRB4eHgoODs6ovgAAAAAAkOWZCtLffPONdu/e/cB6FotF/fv3T3enAAAAAADIqkwF6U8++SRN9QjSAAAAAICnlanbX/30009KSEh44E98fPyj6i8AAAAAAJkqXfeRBgAAAADgv4ogDQAAAACACQRpAAAAAABMSPNkYwkJCY+yHwAAAAAAPBE4Ig0AAAAAgAkEaQAAAAAATCBIAwAAAABgAkEaAAAAAAATCNIAAAAAAJhAkAYAAAAAwASCNAAAAAAAJhCkAQAAAAAwgSANAAAAAIAJBGkAAAAAAEwgSAMAAAAAYAJBGgAAAAAAEwjSAAAAAACYQJAGAAAAAMAEgjQAAAAAACYQpAEAAAAAMIEgDQAAAACACQRpAAAAAABMIEgDAAAAAGACQRoAAAAAABMI0gAAAAAAmECQBgAAAADABII0AAAAAAAmEKQBAAAAADCBIA0AAAAAgAkEaQAAAAAATCBIAwAAAABgAkEaAAAAAAATCNIAAAAAAJhAkAYAAAAAwASCNAAAAAAAJhCkAQAAAAAwgSANAAAAAIAJBGkAAAAAAEwgSAMAAAAAYAJBGgAAAAAAEwjSAAAAAACYQJAGAAAAAMAEgjQAAAAAACYQpAEAAAAAMIEgDQAAAACACQRpAAAAAABMIEgDAAAAAGACQRoAAAAAABMI0gAAAAAAmECQBgAAAADABII0AAAAAAAmEKQBAAAAADCBIA0AAAAAgAkEaQAAAAAATCBIAwAAAABgAkEaAAAAAAATCNIAAAAAAJhAkAYAAAAAwASCNAAAAAAAJhCkAQAAAAAwgSANAAAAAIAJBGkAAAAAAEwgSAMAAAAAYAJBGgAAAAAAEwjSAAAAAACYQJAGAAAAAMAEgjQAAAAAACYQpAEAAAAAMIEgDQAAAACACQRpAAAAAABMIEgDAAAAAGBClgnSx44dU1BQkNzd3ZUrVy4NGTJEMTExD2wXFham3r17K3/+/HJ3d1fZsmU1a9Ysmzo7duyQxWJJ8vPSSy89qtUBAAAAADylHDK7A5IUHh6uBg0aqFixYlqxYoUuXryoAQMGKCoqSp9++mmqbdu2batjx45p3Lhxyp8/v9atW6fXXntN9vb26tmzp03dOXPmqGTJktbfs2XL9kjWBwAAAADw9MoSQXrWrFmKjIzUypUr5efnJ0mKi4vT66+/ruHDhyt37tzJtrty5Yq2b9+uOXPmqFu3bpKkBg0aaN++fVqyZEmSIF22bFlVrlz5ka4LAAAAAODpliVO7V6/fr0aNWpkDdGS1K5dOyUkJGjTpk0ptouNjZUkeXt725R7e3vLMIxH01kAAAAAwH9aljgifezYMXXv3t2mzMfHRwEBATp27FiK7fLly6fGjRtr3LhxKlGihPLly6f169dr06ZNWrRoUZL6TZs2VVhYmAICAtShQwe99957cnV1TbVvkZGRioyMtP5++fJlSVJ8fLzi4+PNrOZjZacE2Skhs7sBk+yUkCnjivHyZGK8IK0YKzCD8QIzMmu8JNjZK8EuS0QZpJHFzj5L5ydJpvqXJUZfeHi4fHx8kpT7+vrqxo0bqbZdsWKF2rdvrzJlykiS7O3tNX36dLVu3dpax9vbW0OGDFGdOnXk6uqqbdu2aeLEiTp69KjWrFmT6vInT56sMWPGJCkPCwuTs7NzGtYuc/jZ35aTU9YeqEjKw95e169ff+zPy3h5MjFekFaMFZjBeIEZmTVeIv3yKcHJ87E/L9LPzsNHTpkwVswICwtLc90sEaTTyzAMvfLKK/rrr7+0ePFiBQQEaPPmzerXr598fX2ts3JXrFhRFStWtLZr0KCBAgIC1KdPH/3yyy8KDAxM8TkGDBigkJAQ6++XL19WYGCg/P39lT179ke3cg/pRvxNhcfEZXY3YFKMg0OmjCvGy5OJ8YK0YqzADMYLzMis8RJz47ziw6899udF+tnH5MjS+UmS7t69m+a6WSJI+/r66ubNm0nKw8PDba6bvt/atWu1bNkyHTp0SOXKlZMk1atXT9euXdPAgQNTvb1Vu3bt1KdPH+3fvz/VIO3l5SUvL68k5fb29rK3t09ttTJVwv8/QQpPlgTZZcq4Yrw8mRgvSCvGCsxgvMCMzBovdgnxMhL44uVJYpcQn6XzkyRT/csSW6uSJUsmuRb65s2bunz5ss3tqu73559/yt7eXmXLlrUpr1ixoi5duqSoqKhH0l8AAAAAwH9XlgjSTZo00ZYtWxQREWEtW7Zsmezs7NS4ceMU2xUoUEDx8fE6dOiQTfn+/fuVI0cOubm5pdh2yZIlkqQqVao8XOcBAAAAAP8pWeLU7t69e2v69Olq2bKlhg8frosXL2rw4MHq3bu3zT2kGzZsqLNnz+rkyZOS/p2FO3/+/GrTpo1GjRqlgIAAbdq0SXPnzrWZIKxz584qWrSonn32Wbm4uGjbtm2aMmWKWrZsyX2lAQAAAACmZIkg7evrq61bt6pv375q2bKlPD09FRISorFjx9rUi4+PV1zc/10L4enpqa1bt+qdd97R0KFDFRERoUKFCmny5Mnq06ePtV6ZMmW0aNEiTZo0SXfv3lWhQoU0fPhwDRs27LGtIwAAAADg6ZAlgrQklSpVSlu2bEm1zo4dO5KUFS1aVEuXLk213bBhwwjNAAAAAIAMkSWukQYAAAAA4ElBkAYAAAAAwASCNAAAAAAAJhCkAQAAAAAwgSANAAAAAIAJBGkAAAAAAEwgSAMAAAAAYAJBGgAAAAAAEwjSAAAAAACYQJAGAAAAAMAEgjQAAAAAACYQpAEAAAAAMIEgDQAAAACACQRpAAAAAABMIEgDAAAAAGACQRoAAAAAABMI0gAAAAAAmECQBgAAAADABII0AAAAAAAmEKQBAAAAADCBIA0AAAAAgAkEaQAAAAAATCBIAwAAAABgAkEaAAAAAAATCNIAAAAAAJhAkAYAAAAAwASCNAAAAAAAJhCkAQAAAAAwgSANAAAAAIAJBGkAAAAAAEwgSAMAAAAAYAJBGgAAAAAAEwjSAAAAAACYQJAGAAAAAMAEgjQAAAAAACYQpAEAAAAAMIEgDQAAAACACQRpAAAAAABMIEgDAAAAAGACQRoAAAAAABMI0gAAAAAAmECQBgAAAADABII0AAAAAAAmEKQBAAAAADCBIA0AAAAAgAkEaQAAAAAATCBIAwAAAABgAkEaAAAAAAATCNIAAAAAAJhAkAYAAAAAwASCNAAAAAAAJhCkAQAAAAAwgSANAAAAAIAJBGkAAAAAAEwgSAMAAAAAYAJBGgAAAAAAEwjSAAAAAACYQJAGAAAAAMAEgjQAAAAAACYQpAEAAAAAMIEgDQAAAACACQRpAAAAAABMIEgDAAAAAGACQRoAAAAAABMI0gAAAAAAmECQBgAAAADABII0AAAAAAAmEKQBAAAAADCBIA0AAAAAgAkEaQAAAAAATCBIAwAAAABgAkEaAAAAAAATCNIAAAAAAJhAkAYAAAAAwASCNAAAAAAAJhCkAQAAAAAwgSANAAAAAIAJBGkAAAAAAEwgSAMAAAAAYAJBGgAAAAAAEwjSAAAAAACYQJAGAAAAAMAEgjQAAAAAACYQpAEAAAAAMIEgDQAAAACACVkmSB87dkxBQUFyd3dXrly5NGTIEMXExDywXVhYmHr37q38+fPL3d1dZcuW1axZs5LUu3Tpklq3bi1PT0/5+fkpJCREkZGRj2JVAAAAAABPMYfM7oAkhYeHq0GDBipWrJhWrFihixcvasCAAYqKitKnn36aatu2bdvq2LFjGjdunPLnz69169bptddek729vXr27ClJio2NVXBwsCRp8eLFioqK0qBBg9SxY0etWbPmka8fAAAAAODpkSWC9KxZsxQZGamVK1fKz89PkhQXF6fXX39dw4cPV+7cuZNtd+XKFW3fvl1z5sxRt27dJEkNGjTQvn37tGTJEmuQXr58uY4cOaKjR4+qRIkSkiRfX18FBwfrl19+UWBg4KNfSQAAAADAUyFLnNq9fv16NWrUyBqiJaldu3ZKSEjQpk2bUmwXGxsrSfL29rYp9/b2lmEYNssvX768NURLUlBQkPz8/LRu3bqMWg0AAAAAwH9AljgifezYMXXv3t2mzMfHRwEBATp27FiK7fLly6fGjRtr3LhxKlGihPLly6f169dr06ZNWrRokc3yS5YsadPWYrGoZMmSqS5fkiIjI22upT5//rwk6cKFC4qPj0/zOj5ut8MuKTo6LrO7AZOcoh109qzTY39exsuTifGCtGKswAzGC8zIrPFy5Xas4qMTHvvzIv3snWIVf/ZsZncjVZcvX5b079nRD5IlgnR4eLh8fHySlPv6+urGjRuptl2xYoXat2+vMmXKSJLs7e01ffp0tW7dOkOWP3nyZI0ZMyZJefXq1VNtB6TX129mdg/wJGG8IK0YKzCD8QIzGC9Is68LZnYP0uT69esqWLBgqnWyRJBOL8Mw9Morr+ivv/7S4sWLFRAQoM2bN6tfv37y9fXVSy+99NDPMWDAAIWEhFh/v3Pnjs6fP69ChQrJweGJfvmeSJcvX1ZgYKB++eUXBQQEZHZ3kMUxXpBWjBWYwXiBGYwXmMF4yVxxcXG6fv26ypUr98C6WSIJ+vr66ubNm0nKw8PDba6bvt/atWu1bNkyHTp0yLqy9erV07Vr1zRw4EBrkE5t+fny5Uu1b15eXvLy8rIpK1q06APXCY9WQECA8ubNm9ndwBOC8YK0YqzADMYLzGC8wAzGS+Z50JHoRFlisrHkrlW+efOmLl++nOTa5nv9+eefsre3V9myZW3KK1asqEuXLikqKirF5RuGoePHj6e6fAAAAAAA7pclgnSTJk20ZcsWRUREWMuWLVsmOzs7NW7cOMV2BQoUUHx8vA4dOmRTvn//fuXIkUNubm7W5R88eFB//fWXtc7WrVsVFhampk2bZuzKAAAAAACealkiSPfu3Vuenp5q2bKlNm3apDlz5mjw4MHq3bu3zT2kGzZsaHNaddOmTZU/f361adNGCxcu1NatWzV06FDNnTtXffv2tdZr06aNypQpo9atW2vNmjX63//+p+7du6tZs2bcQ/oJ4+XlpVGjRiU53R5IDuMFacVYgRmMF5jBeIEZjJcnh8W494bLmejo0aPq27ev9uzZI09PT3Xp0kVjx46Vk9P/Tadfr149nTlzRmfOnLGWnTx5Uu+88452796tiIgIFSpUSD179lSfPn1kb29vrXfx4kW9+eab2rRpkxwcHNSqVStNmTKFQQoAAAAAMCXLBGkAAAAAAJ4EWeLUbgAAAAAAnhQEaQAAAAAATCBIAwAAAABgAkEaAAAAAAATCNLIFKNHj5aHh0eS8oEDB8rOzk5fffWVRo8eLYvFojp16iSp169fPxUsWND6+5kzZ2SxWOTg4GBzv3BJ+v3332WxWLRjx46MXg08JosWLVJgYKC8vb3l5eWlUqVKKSQkRNeuXVNsbKz8/f0VEhKSYvvWrVurQIECMgxDc+fOlcVikYuLi27evJmkbqdOnWSxWFSvXr1HuEZIq5S2FRltx44dslgs+vXXX9PcZvTo0dqzZ0+ScovFookTJ2Zk95CCxM8Ji8UiOzs7eXt7q1y5curTp4+OHj36SJ6zYMGC6tOnT5rrd+vWTWXLln0kfYF5944Zi8Uif39/1apVS+vWrXus/ahQoYK6dev2WJ8Tqbt/bLi4uKhUqVKaMGGCEhISMvz5Vq1apZkzZ6apblxcnKZPn65nnnlGHh4e8vX11TPPPKM+ffro7t27unbtmhwcHPTBBx+kuIxKlSpZ96kT1zVPnjzJrlvNmjVlsVgYow9AkEaWMXToUE2ZMkWzZs1Sjx49rOW7du1KcwiOj4/X2LFjH1EPkRkmTJigl19+WbVr19bSpUu1dOlSde/eXb/++qsuXbokR0dHtWnTRitWrFBMTEyS9pGRkVq3bp06duwoi8ViLXd0dNTKlStt6kZFRWn16tWPJbgha3n22We1d+9elSpVKs1txowZk2yQ3rt3rzp16pSR3UMqXF1dtXfvXu3Zs0fLly/XK6+8oi1btqhChQpauHBhhj/fypUrNWjQoDTXf/fdd7V48eIM7wfSL3HM7N27V19++aXu3LmjFi1aJPv3jP+We8fG+vXr1bZtW7399tuaMGFChj+XmSD95ptvasiQIWrdurVWrVqlefPmqXXr1tq4caOio6OVI0cONWzYUN98802y7U+cOKEDBw7YfDY5OjoqNDRUO3futKl79uxZ7d27l32hNHDI7A4AkjRixAhNmDBBM2fOVK9evazl7u7uKlOmjN5///00HSGsX7++Fi1apFGjRqlQoUKPsMd4XKZNm6Zu3bpp0qRJ1rImTZpo8ODB1m9RO3XqpC+++ELr16/XCy+8YNN+xYoVunPnjjp27GhT/sILL+ibb76x+bb1+++/l7Ozs6pVq6bbt28/upVCluPl5aVq1aplyLIyajlIGzs7O5vXPCgoSK+//rqaNWumHj16qEaNGipcuHCGPV/FihVN1S9SpEiGPTcyxv1jpmrVqsqXL5/mzZunGjVqZGLPkNnuHxv169fX4cOHtWLFCr399tuZ0qeoqCjNnj1bI0aM0MiRI63lzz//vEaOHKnEOxl36tRJXbt21cGDB/XMM8/YLGPx4sVydHRU27ZtrWVOTk5q1KiRvvnmG5t97CVLlqhMmTKyt7d/tCv2FOCINDLd6NGjNXbsWE2fPl2vvfZaksffffddbdu2LU3fFPfo0UM5cuTQuHHjHkVXkQnCw8MVEBCQ7GN2dv9uwmrXrq18+fIl+03sN998o3LlyqlcuXI25R06dNDWrVt17do1a9nixYvVpk0bOTo6ZuAa4FE6fPiwgoOD5e7uLm9vb7Vp00bnzp2zqXPz5k117txZnp6eypEjh4YPH65JkybZnKGQ3KndX3/9tcqUKSNXV1fr6Z/79u2TJGvbwYMHW08DTDxzJrlTu9euXauaNWvKzc1Nvr6+qlevnn777bdH8ZJAkouLi6ZPn66YmBjNnj3bWj537lyVL19eLi4uypMnj9555x3Fx8fbtL148aK6dOminDlzytXVVSVLltQnn3xiffz+U7uPHDmipk2byt/fX25ubipRooTN0avkTu1Oy7i1WCyaMGGCRo8erZw5cypbtmx65ZVX+JLvEciTJ4+yZ89ufQ8uX76s7t27q3DhwnJ1dVWxYsU0fPhw3b1716ZdWt+jPXv2qFKlSnJxcVHZsmW1fv36ZPuxYsUKVahQQS4uLsqdO7cGDBigO3fuWB9P3E5t3LhR7dq1k4eHh/Lnz28942HatGnKnz+//Pz8FBISkqS/SB9PT0/FxsbalN29e1fDhw9XgQIF5OzsrFKlSiU58yS1bUO3bt00b948HTlyxPoZktJp1Ldv31ZsbGyK+0KJn0cvvviiXF1dU9wXeu655+Tn52dT3qFDBy1fvtxm/RYvXpzk4AOSR5BGpho7dqzGjBmjKVOmpHjNWfPmzVWxYkWNGTPmgctzdnbWkCFDNG/evCQ7JXgyVapUSbNmzdLs2bN15cqVZOtYLBa99NJL+v7773Xr1i1r+bVr17R169ZkT7OtWrWqChQooGXLlkmSIiIitGHDBnXo0OHRrAgy3Pnz51WnTh2FhYVp4cKFmjVrlg4cOKC6devqn3/+sdZ75ZVXtGbNGk2YMEFz587V0aNHbYJRcnbu3KkePXqoadOmWrdunebPn6+GDRsqIiJC0r+nb0tS3759racBPvvss8kua+nSpWrRooVy5MihxYsXa9GiRapZs6YuXryYMS8EklW6dGnlyZPH+l5NnjxZISEhCg4O1vfff6+hQ4dq2rRpeuedd6xtwsLCVL16de3YsUNjx47V2rVr1b9//1TfqxYtWig8PFxfffWV1q5dq0GDBqUadtM6biXp008/1V9//aV58+Zp5MiRWrx4sd5///2HfGVwv1u3bunGjRvWM9lCQ0Pl5+enyZMna8OGDdb9it69eydp+6D36MqVKwoODpazs7P+97//afDgwXrttdeSjKnvvvtObdq0UenSpbVq1SoNGTJEs2bNUufOnZM852uvvaayZctq5cqVqlatml5++WUNHTpUGzdu1KxZs/Tee+9p/vz5NmdyIe3i4uIUFxenf/75R999952+/fZbtWnTxqZOu3bt9Pnnn2vgwIFas2aNnnvuOXXu3NnmS5LUtg3vvvuumjZtqsKFC1s/Q959991k+5M9e3blz59fH3zwgZYsWaLw8PBk63l6eqp58+ZasmSJ9Si1JO3fv18nTpxIdl+oRYsWunv3rjZt2iRJ+vPPP3Xo0CG99NJL5l60/yoDyASjRo0yJBmSjJCQkBTruLu7G4ZhGN9++60hyfj5558NwzCMt956yyhQoIC17unTpw1JxrJly4yoqCgjZ86cxmuvvWYYhmH89ttvhiRj+/btj3Sd8GgcPnzYKFq0qHW8FCpUyHjzzTeN06dP29T7/fffDUnGwoULrWXTp083LBaLcfbsWWvZnDlzDEnG9evXjeHDhxu1atUyDMMwZs+ebeTJk8eIj483XnjhBaNu3bqPY/XwAPduB+7Xv39/w93d3QgLC7OWHT161LBYLMa0adMMwzCMI0eOGJKM+fPnW+vEx8cbxYoVM+79CNy+fbshydi3b59hGIbx8ccfG35+fqn2TZLx8ccfp1qekJBg5M2b1wgODk7jGsOM1MaHYRhGtWrVjJIlSxqRkZGGh4eHMWzYMJvHP/vsM8PV1dUIDQ01DMMwhg8fbjg7OyfZvtyrQIECxhtvvGEYhmFcv37dkGR89913Kdbv2rWrUaZMGevvaRm3hvHvOAoMDEyyrCJFiqT4XHiwxDETGxtrxMbGGmfPnjXat29v+Pr6GseOHUu2TWxsrLFo0SLDwcHBuH37trU8Le/R0KFDDU9PTyMiIsJatnXrVkOS0bVrV2tZxYoVjerVq9ss6/PPPzckGYcOHTIM4/+2U0OGDLHWiYiIMOzt7Y18+fIZMTEx1vLWrVsbFSpUMPHK4N5903t/2rdvb8TFxVnrbdu2zZBkbNy40aZ9+/btjSpVqhiGkb5tQ2q2b99u5MyZ05BkWCwWo1SpUsbbb79tXL9+3abeqlWrDEnG7t27rWUDBw40PDw8jKioKJt1Tdx2duzY0ejcubNhGIYxYsQI6zh85plnbMYokuKINDKNq6ur6tSpo8WLF+vHH39Mte6LL76osmXL6r333kvTcgcOHKivv/6aIz5PgbJly+rIkSNau3at3nrrLXl7e2vatGkqX768fv/9d2u9Z555RqVLl7Y5pembb75RrVq1lD9//mSX3aFDB/344486f/68vvnmG7Vv3956ujiyvl27dqlBgwY2p6qVLFlSzzzzjHbv3i1J1lOxn3/+eWsdOzs7tWjRItVlP/vss7px44a6deumzZs3KyoqKl19PH78uC5cuKDu3bunqz0ejmEYslgs2rNnj27duqW2bdtajzbFxcWpUaNGio6O1h9//CFJ2rp1qxo0aGBzV4jU+Pv7q0CBAho2bJjmzZunCxcuPLBNWsZtoqCgIJvfS5cunabnQOpu374tR0dHOTo6qkCBAlq+fLkWLFigEiVKSPp33EydOlWlS5eWq6urHB0d1alTJ8XFxenUqVM2y3rQe/Tzzz+rfv368vb2tpbd//7funVLv//+e5Kjnu3bt5ekVMeFt7e3cuTIoTp16thcllS8eHGdP3/e1OuCf/ch9+3bp3379mn37t365JNPtGHDBvXs2dNaZ9OmTfLz81ODBg1stidBQUH67bffFB8fn65tQ2rq1aunv//+W8uWLdOrr76q+Ph4ffjhhypbtqwuXbpkrdekSRP5+vpa94UMw9DSpUutp30np0OHDlq9erWio6O1ZMkSzswzgT1GZBo7Ozt99913Kl68uJo3b67Dhw+nWNdiseidd97R2rVrdeDAgQcu+7XXXpOHh8cjmWURj5+Tk5OaNm2qqVOn6rffftOGDRsUFRWV5IuVjh07atOmTQoLC9OZM2ceOHty2bJlVaZMGU2ZMkXbt2/nmqAnTHh4uHLmzJmkPGfOnLpx44akf691dHR0tNmJlaQcOXKkuuwGDRpowYIFOnLkiIKDg5UtWzZ16dLFuty0CgsLkyTlzp3bVDtkjAsXLihXrlwKDQ2V9O8XJIkBytHRUcWKFZMka+AICwsz9V5ZLBZt2rRJpUqV0htvvKF8+fKpcuXKSWbBvVdaxm0iHx8fm9+dnJy47jUDJIaln3/+WQsXLlRAQIC6dOmiy5cvS5KmTp2qgQMH6oUXXtDq1av1yy+/aMaMGZJkc82y9OD36PLly8lub+4ti4iIkGEYScaFt7e3nJ2d0zQukiu7v694MDs7O1WuXFmVK1dWzZo19eabb2rkyJGaM2eO9Qu30NBQ3bhxw2Zb4ujoqJCQEMXFxeny5cvp2jY8iLu7u9q0aaPPPvtMx48f15dffqmrV6/azMnh5OSk1q1ba9myZYqLi9POnTt14cKFVPeFgoOD5ejoqJEjR+r06dNq165duvv4X0OQRqby9vbWxo0blS1bNgUHB+v06dMp1m3Xrp1KlCiRpuvDPDw8NGDAAH355ZcpXleLJ1dwcLCeeeaZJPeJ7dixo2JjY7V8+XItWbJEDg4ONjNUJqdDhw765JNPVLRoUVWqVOlRdhsZzM/Pz2ayuERXr161Hu0JCAhQbGxsknuGJ9fufp07d9a+fft07do1TZ8+XatWrdLgwYNN9dHf31+SbI4Y4PE4cuSILl68qBo1aljHw4oVK6xHm+79adKkiaR/3y+z71Xx4sW1bNkyhYeHa8eOHXJ2dlaLFi1s5mu4V1rGLR6txLAUGBioTp06aeXKlYqIiLB+Obts2TI9//zzGj9+vBo3bqwqVarI3d09Xc8VEBCQ7Pt9b5mPj48sFkuSejdv3tTdu3cZF5ks8baIR44ckfTv33D27NmT3Zbs27fP+iWJ2W2DWSEhIfLz80uyL9SpUyfrHDHffPONcuTIoUaNGqW4HEdHR7Vu3VqTJ09WgwYNkv2iD8kjSCPT5ciRQ5s3b5bFYlFQUFCKwdfOzk7vvPOOVq9erUOHDj1wuX369JGzs7M+/vjjjO4yHqOrV68mKYuOjtb58+eVK1cum/JChQqpevXqWrx4sRYvXpzsDJX369ixo1q0aJFpt7VA+tWqVUtbt261mXjl+PHjOnTokGrVqiVJqly5siRp9erV1joJCQn6/vvv0/w82bJlU48ePRQUFGSzw+Lo6PjAIz4lSpRQ3rx5NWfOnDQ/Hx7enTt31LdvXzk7OyskJETVq1eXm5ubLly4YD3adO9P4hcejRo10rZt29I1WaWjo6Pq1q2rt99+W5GRkSkG8rSMWzxelStXVocOHTRnzhxduXJF0dHRcnJysqmzaNGidC07MDBQ27dvt/kyb9u2bTZHmT08PFShQgUtX77cpu3//vc/SWJcZLLEI9HZsmWT9O924vr163Jyckp2e3L/2Elp25DWswZiY2OTnWDs2rVrunnzZpJ9oTp16ihPnjyaN2+eli9frvbt2z/wVlYhISFq0aKF3nrrrQf2B/+H+0gjSyhYsKA2btyoOnXq6LnnntMPP/yQbL2OHTtqzJgx2r59uwoUKJDqMr28vPTWW2+labZvZF3lypVTixYtFBwcrICAAF28eFGffvqpQkNDk93gd+zYUW+++aYMw7CZjTclBQsW1KpVqx5Bz5FR4uPjk+xgStJbb72lOXPmqHHjxnrnnXd0584djRgxQvnz57feRqRMmTJ68cUX9eabbyoqKkoFChTQF198oejoaJvbX91v1KhRCgsLU7169ZQjRw4dPnxYGzZs0IABA6x1SpUqpdWrV6t27dpyd3dXiRIl5OnpabOcxFthdejQQa1bt1aXLl3k7OysvXv3qkqVKmrevHnGvEj/YQkJCfrpp58k/Xut6eHDh/XFF1/o1KlTmjt3rvV65/fee09DhgzRhQsXVK9ePdnb2+vUqVNavXq1vv32W7m5ual///6aP3++6tSpo3fffVeFCxfWqVOndOLECX300UdJnvvQoUMaOHCg2rdvryJFiujmzZsaP368ChYsmOL9o/v37//AcYvH791339WSJUs0depUBQUF6ZNPPtGnn36q4sWLa+HChTp58mS6ltuvXz/NmDFDTZo00dtvv63w8HCNGjXK+uVNotGjR6tly5bq3LmzOnfurOPHj2v48OFq3bp1kts34tG5d3sSExOj/fv364MPPlDp0qVVp04dSf9eo96iRQs999xzGjJkiMqXL6/bt2/ryJEjOnnypGbPnp2mbUOpUqX09ddf65tvvlGxYsWULVu2ZOdnuHnzpooVK6YuXbqofv368vPz0+nTpzVx4kTZ29snuXWsnZ2dXnrpJU2ePFmGYaR6WneiwMBA9oXSIzNnOsN/V0ozre7Zs8dwc3MzatWqZQwZMiTZOrNnzzYkpThr971u3LhheHl5MWv3E2zGjBnGc889Z+TJk8dwcnIycufObTz33HPGtm3bkq1/7do1w8HBIckMlYnunbU7JczanXWkNIuqJGPBggXGwYMHjaCgIMPNzc3w9PQ0WrVqZZw5c8ZmGeHh4UanTp0Md3d3w9/f3xgwYIAxYsQIw8fHx1rn/lm7v//+e6Nhw4ZG9uzZDWdnZ6NIkSLGqFGjjNjYWGubXbt2Gc8++6zh6upqs41RMrN5f/fdd0bVqlUNFxcXw8fHx2jQoIHx22+/PZoX7T/k/vHh4eFhlC1b1njjjTeMo0ePJqn/zTffGFWqVDFcXV0NLy8vo2LFisa7775r876eO3fO6NSpk+Hn52e4uLgYJUuWtJlN+95Zu69evWp07tzZKFy4sOHs7GzkyJHDaN26tXHixAlr/eRm5k3LuE1uHE2ZMsVg1+3hpDbTe6dOnQwvLy8jIiLC6Natm+Hr62v4+voaPXv2NL7//nubbYRhpP092rlzp1GhQgXDycnJKFWqlLFmzZpkZ0Revny5Ub58ecPJycnIlSuX0a9fPyM6Otr6+P3bqUT3jsm0rCeSd//2xMHBwShUqJDx+uuvG1evXrWpe/fuXWPMmDFGsWLFDCcnJyN79uxG/fr1rXeISMu24ebNm8ZLL71k+Pv7J5nF/f7n+vDDD4169eoZuXLlMpydnY38+fMbrVu3Nvbv359smwMHDhiSUpzlPy3jg1m7H8xiGPfcaAwAgP+AOnXqyN7eXtu3b8/srgAAgCcQp3YDAJ5q3377rc6dO6dy5copKipKixcv1q5du7Ry5crM7hoAAHhCEaQBAE81Dw8PLViwQH/99ZdiYmJUsmRJLVy4UC1btszsrgEAgCcUp3YDAAAAAGACt78CAAAAAMAEgjQem8DAQM2YMUOSVK9evUdy25eCBQvKYrEk+bn3Pn1jx45VUFBQhj83Mta946Vbt24272fOnDnVuHFj7d27N0m7+9/7nDlzqkWLFjp8+LBNvdGjRyc7VsqWLSvp31tglChRIt33DkXmuP999ff3V61atbRu3brM7hqymOT+/u//mTt3bmZ3E4/Zgz4bMtuOHTs0bty4zO7Gf9q9+ydz587V4sWLM7lHKZs6darNrR5//PFHZcuWTZGRkZnYq6cH10jjsVi5cqXOnDmj7t27P/LnatOmjQYOHGhT5uzsbP3/G2+8oQkTJmj79u2qX7/+I+8PzEtuvBQuXFiLFi2SYRg6deqURo0apUaNGunw4cMqXLiwTfu+ffuqY8eOMgxDFy5c0Lhx49S4cWMdPXpUPj4+1nqurq7atm2bTVs3NzdJ/96H8e2339aoUaPUvn17OTiwuXxS3Pu+Xrp0SePGjVOLFi20a9cu1ahRI5N7h6zi/i/iqlevbt12JErpXtB4uqX22ZDZduzYoYkTJ2r48OGZ3ZX/pPv3T+bOnSsPDw+b7UZWVrNmTZUpU0aTJk3SmDFjMrs7Tzz2DPFYTJ06VR06dJCrq+sjf66cOXOqWrVqKT7u4+Oj1q1b65NPPiFIZ1HJjRdXV1fr+1q9enUVKlRINWvW1NKlSzVs2DCb9vnz57cZA8WLF1eFChW0Z88eNW3a1FpuZ2eX6lhp3769+vbtqzVr1jAx1RPk/ve1atWqypcvn+bNm0eQhlVyf/v3bzvuFx0d/Vg+x5C5HvTZYBbj5unxOPdnH5UePXpo0KBBGjFihBwdHTO7O080Tu3GI3f69Gnt2rVLbdq0SbFOdHS0mjVrpsKFC+vUqVOPvE9t27bV2rVrFRoa+sifC+akZbxIUsWKFSVJ586de+AyPT09JUmxsbGm+uLm5qZmzZpp3rx5ptoha8mTJ4+yZ89uM1b27t2rBg0ayN3dXd7e3urYsaOuXbtm0+7ChQtq3ry53NzclC9fPk2ZMkX9+vVTwYIFH/MaIDOMHj1aHh4e+uWXX1S9enW5uLhYT+c8evSoXnjhBXl7e8vd3V3NmjXT33//bdPeMAxNnDhRxYsXl7OzswoXLqwpU6ZkxqogAx0+fFjBwcHWbUebNm2SfA5ZLBZ9+OGHGjp0qHLlyqUcOXJIStuYuHDhgtq1a6ecOXPKxcVFhQoVUv/+/SX9OybHjBmj27dvW085r1ev3mNZbyTdP6lXr55++OEHrV271vp+jB49WtK/lxr26dPHpv2qVatksVh05swZSdKZM2dksVi0cOFC9enTR76+vgoICNCgQYMUFxdn0zYt25zIyEh16dJFnp6eyp49u4YMGZJkOZLUsmVLRUREcMlTBiBI45HbunWrHBwcFBgYmOzjt27dUtOmTfX3339r165d1tN0ExISFBcXl+pPfHx8kuUtWrRIzs7O8vDwUNOmTZNcGyv9e0QzPj5eO3bsyNB1xcN70HhJdPbsWUlSoUKFkjyWOHZiY2N15swZDRkyRNmyZUt2h+P+MXX/jQxq1Kihbdu2KSEhIf0rhUx169Yt3bhxwzpW9u7dq3r16snb21tLly7VF198oX379umFF16wtjEMQy+88IJ+//13ff7555oxY4ZWrFihFStWZNZqIBPExMSoY8eO6ty5s9avX6/GjRvr1KlTqlGjhm7cuGG9PvL69etq2LCh7t69a2371ltvaeTIkeratavWrl2rbt26aejQoZo1a1YmrhHSKrnPhvPnz6tOnToKCwvTwoULNWvWLB04cEB169bVP//8Y9P+k08+0YkTJ/TVV19p4cKFktI2Jrp06aJDhw5p2rRp2rBhg8aMGWPd1wkJCVGPHj3k6uqqvXv3au/evZo5c+bje1H+4+7fP5k5c6YqVqyomjVrWt+PkJAQ08t95513ZGdnp//973/q3bu3Jk2apNmzZ1sfT+s2p3v37lq5cqU+/PBDzZs3T3/++aemTp2a5Pm8vLxUpkwZbd682fyLAFsG8Ij16tXLKFOmjE1Z3bp1jWbNmhk3btwwqlatajzzzDPG1atXbep07drVkJTqT4ECBWza9O3b11i4cKGxc+dOY+7cuUaxYsUMLy8v4++//07SrwIFChiDBg3K8PXFw0luvHTt2tUoU6aMERsba8TExBjHjx836tevbxQoUMC4du2aTd3kxomfn5/xww8/2NQbNWpUsnUXLFhgU2/79u2GJOOPP/54NCuMDDVq1CjD3d3diI2NNWJjY42zZ88a7du3N3x9fY1jx44ZhmEYderUMWrUqGEkJCRY2x05csSwWCzG2rVrDcMwjLVr1xqSjJ07d1rr/PPPP4a3t3eS7Q6eDpKMjz/+2Pp74jZiyZIlNvW6dOliFC5c2IiOjraWXbt2zfDw8DBmzJhhGIZhnDx50rBYLMbnn39u03bo0KFGrly5jPj4+Ee4JngYqX029O/f33B3dzfCwsKs9Y8ePWpYLBZj2rRp1jJJRunSpW22MWkdE+7u7jbLSq5/7u7uGbW6MCG1/dn7FShQwHjjjTdsylauXGlIMk6fPm0YhmGcPn3akGS0bds2yTIbNmxo/T0t25zEz7CvvvrKWicuLs4oVKiQkVzc69q1q1G5cuU0rjlSwjXSeOQuX76s7NmzJykPDQ1V/fr15eLiou3bt8vX19fm8dGjRyc5LeZ+904iJknTpk2z/r927dpq3LixSpYsqYkTJyb51jZbtmy6fPmy2dXBI5bSeDly5IjNtTxubm7atWtXsnXfeustde7cWZJ07do1zZw5Uy+88IJ++OEHlS9f3lrP1dVVO3futGl7/8Rl2bJls/arTJky6V8xPDa3b9+2GSv29vZavXq1SpQooaioKP3444+aOHGizRktxYsXV758+bRv3z41bdpU+/btk4+Pj2rXrm2t4+HhoYYNG2r//v2PdX2QuZo1a2bz+6ZNm/TSSy/JwcHBetqkr6+vKlasqH379kmStmzZIklq3bq1zamVjRo10kcffaTz58+rQIECj2kNYFZKnw2ffPKJGjRoID8/P2t5yZIl9cwzz2j37t3q27evtbxJkyY2syWndUw8++yzmjhxohwcHBQUFKSiRYs+qtWESSntnzysxo0b2/xeunRpm8nu0rLN2bdvnwzD0IsvvmhtZ29vr5YtWyZ7SQn7wBmDII1H7s6dO0kCrySdOHFC4eHhmjp1apIQLf076UvevHlTXfa9H1LJCQgIUK1atZLd8XV2dlZ0dPQDeo/HLaXxUqRIES1ZskTx8fE6ePCghgwZonbt2unQoUNJZlPNmzevKleubP29YcOGyps3r9577z0tX77cWm5nZ2dTLzmJfWGsPDkSd4ITEhL0119/6e2331aXLl30xx9/KCEhQfHx8erfv7/1usN7nT9/XlLKO0yJ1zriv8HNzU0eHh42ZaGhoZo6dWqyp0w6OTlZ6xiGYf0i7n4E6awtpc+G8PBwVahQIUl5zpw5dePGjSRl90rrmFi6dKneeecdvfPOO3r99ddVokQJjRs3Tq1atUr/CiFDpLR/8rDuvZuI9O925N7btqZlm3P58mU5Ojom2Z++fxwmYh84YxCk8cj5+flZJ1a4V40aNdSoUSMNGDBA/v7+1iOIibp37/7ASZ4KFCiQ7LLTIiIigiOMWVBK48XFxcW6Y1O1alVly5ZNrVu31vTp0zV06NBUl5k4qcuRI0dM9yciIkKS5O/vb7otMse9O8GBgYEqUaKEqlatqvfee08TJ06UxWLR8OHDk52JPXEnNyAgQNevX0/y+P0TkuHpltyXtX5+fmrWrJlef/31JI8lTmzo5+cni8Wi3bt3W3d071WiRImM7yweOT8/v2S3AVevXlXx4sVtyu4fO2kdEwEBAfr66681e/Zs7d+/Xx988IHat2+v48ePJzljCo9XSvsnyXFxcVFMTIxNWXh4eLqf90HbnICAAMXGxio8PNwmTF+9ejXZZUZERLBfkwEI0njkSpQooe3btyf7WL9+/RQdHa1u3brJxcXFZqbm9Jzafb9Lly5p9+7devnll23KExISdO7cucdyX2uYk9p4uVerVq1Us2ZNTZkyRW+99ZZcXFxSrHvnzh39/fffKlWqlOn+JH5o3r+ThCdH5cqV1aFDB82ZM0ejRo1S9erVdfToUX3wwQcptqlSpYoiIiK0c+dO1alTR9K/k5Zt3bo1ydED/Lc0atRIf/zxhypWrCh7e/tk6zRs2FCSFBYWphYtWjzO7uERqlWrlr744gubsHL8+HEdOnTogfsTZseEnZ2dqlSpog8++EDfffedTp48qcKFC8vJyclmgik8Psntn9x/9DhR3rx5dfToUZuyTZs2pet507LNqVKliqR/73OdOBbj4+O1atWqZOufOXOGL/QyAEEaj1zNmjX13nvv6cKFC8meqj1s2DBFR0erY8eOcnFxUfPmzSX9e+sAM7eZ+eabb7RmzRo1bdpUuXPn1qlTpzR+/HjZ29tr4MCBNnWPHz+uW7du2Vz/iKzhQePlXqNHj1ZQUJDmzp2r3r17W8vPnTunn376SZJ0/fp1zZgxQ2FhYTZ10urXX39VqVKlUjwdD0+Gd999V0uWLNHUqVP18ccfq0GDBmrfvr1eeukl+fr66sKFC9q8ebNeeeUV1atXT02aNNGzzz6rjh07avz48fLx8dGECRPk6ekpO7v/u+HF2bNnVaRIEY0cOVIjR47MxDXE4zJmzBhVqVJFwcHB6tWrl3LmzKkrV67ohx9+UO3/197dgyTXhnEAv3yJyqIUFUSHFPoQ5QkjiFCKWjIQgooQIhKHoK+hL5sqIhoKoiCEAoeshpaGiKilQWjI6INoaKmIyKDRcjIVr2dKXp98ntKn1Pft/xvlPp5z68U598Xh/E9tLbW3t1NZWRn19/dTZ2cnjY6OUnV1NYVCIbq6uiK32/3bxS1ktqGhIXK5XGQymWhsbIwCgQCNj49TUVER2Wy2P277kZp4fn6mxsZG6uzsJI1GQ8FgkBwOB4nFYqqsrCQiIq1WS+FwmBYXF8loNFJhYSEaohSJtz7RarW0trZGOzs7pFAoSKlUklKppLa2Nurt7aWpqSkyGo20t7dHHo8nqf1+5Jyj0+mopaWFBgcHKRAIkFqtpqWlpTd3xV+dnp6+WRtDEtIcdgbfwMvLC0ulUnY6ndHP4qUc2u12zsnJ4f39/aT24/F4uL6+nmUyGWdlZbFMJmOLxRJN6v23+fl5VqlUMYmakBni1ctranc8NTU1XFxczOFwmJnfpnaLxWI2GAy8tbUVs91Hk0/Ly8t5YmIi+QlBSv3pf+3o6ODCwkJ+enrik5MTNpvNLBKJWCgUcmlpKff09LDX642O93q9bDabOTc3lxUKBc/MzLDNZuOKioromNfU1cnJya+eGnwxipPa/btaurq6YovFwlKplHNyclitVrPVao1J949EIuxwOPjHjx+cnZ3NEomEDQYDLywsfPlcIHnvXRsuLi64oaGB8/LyuKCggFtbW/nu7i5mzK+19Oq9mggEAtzV1cUajYaFQiFLJBI2mUx8fHwc/Y5QKMR9fX0sl8tZIBBwXV3d50wc3hVvffLw8MBms5nFYnHMtSAUCrHdbme5XM4ikYi7u7t5Y2Mjbmr35uZmzH4GBgbevB3iI+ccn8/HHR0dnJ+fz1KplIeHh3lubu5NavfZ2RkLBAK+ubn5vB/nmxIw//LSVIAvMDIyQufn5zEphOlUVVVFTU1NuIOUoTKlXi4vL0mv19P19XXc91XD9xIMBkmn01FtbS25XK50Hw4AAKRYpqxP/sbo6CidnZ39p+eQKdBIQ0o8Pj5SSUkJHR4ekl6vT+uxHBwcUHNzM93e3uJZxwyVKfXy+pzRyspK2o4B0sfpdFIkEiGNRkM+n4+Wl5fJ7XbT0dHRu2nvAADw/5Mp65Nk+f1+UqlUtL29Hc3/gOThGWlICYVCQaurq3FTcFPN7/fT+vo6mugMlgn1EolEqKSkhKxWa9qOAdIrNzeXZmdno4Fzer2ednd30UQDAHxTmbA++Rv39/c0PT2NJvqT4I40AAAAAAAAQAL+eX8IAAAAAAAAALxCIw0AAAAAAACQADTSAAAAAAAAAAlAIw0AAAAAAACQADTSAAAAAAAAAAlAIw0AAAAAAACQADTSAAAAAAAAAAlAIw0AAAAAAACQADTSAAAAAAAAAAn4CSOIN0NL00RTAAAAAElFTkSuQmCC\n" + }, + "metadata": {} + } + ], + "source": [ + "best_model = grid_search.best_estimator_\n", + "best_model.fit(X_train, y_train) # Pipeline contains scaler, no need to pre-scale\n", + "y_pred_best = best_model.predict(X_test)\n", + "final_acc = accuracy_score(y_test, y_pred_best)\n", + "\n", + "print(f\"Best tuned SVM – Test Accuracy: {final_acc:.4f} ({final_acc*100:.1f}%)\")\n", + "print(\"\\nClassification Report:\")\n", + "print(classification_report(y_test, y_pred_best, target_names=iris.target_names))\n", + "\n", + "# ── Final comparison bar chart ──\n", + "models = ['KNN\\n(k=5)', 'SVM\\n(RBF)', 'Logistic\\nReg.', 'Decision\\nTree', 'Random\\nForest', 'Best SVM\\n(tuned)']\n", + "accs = [0.9333, 0.9667, 0.9333, 0.9333, 0.9000, final_acc]\n", + "bar_colors = ['#5c9bd6']*5 + ['#e05c2c']\n", + "\n", + "fig, ax = plt.subplots(figsize=(9, 5))\n", + "bars = ax.bar(models, accs, color=bar_colors, edgecolor='white', linewidth=0.8)\n", + "ax.set_ylim(0.8, 1.03); ax.set_ylabel(\"Test Accuracy\")\n", + "ax.set_title(\"Classifier Comparison – Iris Dataset\")\n", + "ax.axhline(1.0, color='gray', linestyle='--', linewidth=0.8)\n", + "for bar, acc in zip(bars, accs):\n", + " ax.text(bar.get_x()+bar.get_width()/2, bar.get_height()+0.003,\n", + " f'{acc:.2f}', ha='center', va='bottom', fontsize=9)\n", + "ax.grid(axis='y', alpha=0.3)\n", + "plt.tight_layout(); plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "3d3322a3", + "metadata": { + "id": "3d3322a3" + }, + "source": [ + "## ✅ Assignment Complete!\n", + "\n", + "### Key Takeaways\n", + "\n", + "| Concept | What we did |\n", + "|---|---|\n", + "| **Data exploration** | Shapes, class distribution, feature histograms, scatter plots |\n", + "| **Train/Test split** | 80/20 stratified split to avoid data leakage |\n", + "| **Feature scaling** | `StandardScaler` fit on train only |\n", + "| **KNN** | Classified by majority vote of k neighbours; best k=1 (96.7%) |\n", + "| **Multiple classifiers** | KNN, SVM, Logistic Regression, Decision Tree, Random Forest |\n", + "| **Cross-validation** | 5-fold CV for reliable performance estimates |\n", + "| **Learning curves** | Diagnose overfitting vs underfitting |\n", + "| **PCA** | Reduced 4D → 2D; 95.8% variance retained |\n", + "| **GridSearchCV** | Tuned SVM hyperparameters (C=10, kernel=rbf) → **97.3% CV accuracy** |\n", + "\n", + "### Best model: SVM with RBF kernel, C=10 — Test accuracy **96.7%** 🎉\n" ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" } - ], - "source": [ - "from sklearn.linear_model import Ridge\n", - "degrees = np.arange(1, 21)\n", - "model = make_pipeline(PolynomialFeatures(), Ridge(alpha=1))\n", - "\n", - "# Vary the \"degrees\" on the pipeline step \"polynomialfeatures\"\n", - "train_scores, validation_scores = validation_curve(\n", - " model, X[:, np.newaxis], y,\n", - " param_name='polynomialfeatures__degree',\n", - " param_range=degrees)\n", - "# Plot the mean train score and validation score across folds\n", - "plt.plot(degrees, validation_scores.mean(axis=1), label='cross-validation') \n", - "plt.plot(degrees, train_scores.mean(axis=1), label='training') \n", - "plt.legend(loc='best') \n", - "plt.xlabel('polynomial degrees')\n", - "plt.ylabel('explained variance')\n", - "plt.title('Validation curve');" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Although the curves look similar to before, the variation with the polynomial degrees is much less drastic than before. Regularization is an effective way of trading variance for bias.\n", - "Many sklearn estimators use regularization by default: our logistic regression from a previous exercise, for example, uses l2 regularization." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + }, + "colab": { + "provenance": [] + } }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file