{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# ДЗ 2 и ДЗ 3\n", "## Соколов Игорь, группа 573" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Igor\\Anaconda3\\lib\\site-packages\\h5py\\__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", " from ._conv import register_converters as _register_converters\n", "Using TensorFlow backend.\n" ] } ], "source": [ "import keras\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from skimage import transform" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Данные:\n", "Будем работать с датасетом [FashionMNIST](https://github.com/zalandoresearch/fashion-mnist). " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from keras.datasets import fashion_mnist\n", "\n", "(X_train, y_train), (X_test, y_test) = fashion_mnist.load_data()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.imshow(X_train[0].reshape([28,28]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Задание:\n", "Будем решать задачу классификации на 10 классов. Каждый класс соответствует одному из типов одежды. Исходная размерность признакового пространства: `784`, каждый пиксель является признаком. Будем снижать размерность признакового пространства с помощью метода главных компонент (`PCA`). Ваша задача оценить качество решенения задачи классификации по метрике `accuracy` в зависимости от числа главных компонент. Также оцените дисперсию функции качества в зависимости от числа главных компонент.\n", "\n", "Нарисуйте график зависимости функции качества и ее дисперсии от числа главных компонент." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.decomposition import PCA\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.metrics import accuracy_score" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.5786" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pca = PCA(n_components=3)\n", "used_indices = np.random.choice(np.arange(X_train.shape[0]), 10000, replace=False)\n", "X_train_lowdim = pca.fit_transform(X_train[used_indices].reshape([-1, 784]))\n", "lr = LogisticRegression()\n", "lr.fit(X_train_lowdim, y_train[used_indices])\n", "accuracy_score(y_test, lr.predict(pca.transform(X_test.reshape([-1, 784]))))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Используйте следующую сетку числа главных компонент: `[3, 5, 7, 12, 18, 25, 33, 40, 48, 55]`. Для ускорения сходимости можете семплировать подвыборки из `X_train`. " ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "num_components = [3, 5, 7, 12, 18, 25, 33, 40, 48, 55]\n", "score_list = []\n", "mean_score_list = []\n", "var_score_list = []\n", "num_iterations = 10" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Wall time: 44min 32s\n" ] } ], "source": [ "%%time\n", "for num in num_components: \n", " for _ in range(num_iterations):\n", " pca = PCA(n_components=num)\n", " used_indices = np.random.choice(np.arange(X_train.shape[0]), 10000, replace=False)\n", " X_train_lowdim = pca.fit_transform(X_train[used_indices].reshape([-1, 784]))\n", " lr = LogisticRegression()\n", " lr.fit(X_train_lowdim, y_train[used_indices])\n", " lr.predict(pca.transform(X_test.reshape([-1, 784])))\n", " score_list.append(accuracy_score(y_test, lr.predict(pca.transform(X_test.reshape([-1, 784])))))\n", " mean_score_list.append(np.mean(score_list))\n", " var_score_list.append(np.var(score_list))\n", " score_list = []" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from matplotlib import pylab as plt\n", "plt.rcParams['font.family'] = 'serif'\n", "plt.rcParams['font.serif'] = 'FreeSerif'\n", "plt.rcParams['lines.linewidth'] = 2\n", "plt.rcParams['lines.markersize'] = 12\n", "plt.rcParams['xtick.labelsize'] = 24\n", "plt.rcParams['ytick.labelsize'] = 24\n", "plt.rcParams['legend.fontsize'] = 24\n", "plt.rcParams['axes.titlesize'] = 36\n", "plt.rcParams['axes.labelsize'] = 24" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## График зависимости функции качества от числа главных компонент" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Igor\\Anaconda3\\lib\\site-packages\\matplotlib\\font_manager.py:1316: UserWarning: findfont: Font family ['serif'] not found. Falling back to DejaVu Sans\n", " (prop.get_family(), self.defaultFamily[fontext]))\n" ] }, { "data": { "image/png": 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pcPLkSRw/fhxBQUEIDw+HSCRCcnIyPv74YxiNRvTt2xf33nuv4NdaXFws+FiT\nyQSZTCb4eKqcWCx2dlUgz2jqz8Bmsw9C0etFKCkRQaMR4+pVMS5dkuDCBQmuXJGgoECM4mIxSkvF\nMBpFsFhEEInsSaFabYO/vxWBgTYEB1vRqpUFrVtbERRkRUCADX5+9mls5HLAZnP9ueFYWYs8h8/A\nO/A5COPn5yfouHptaZTL5YiPj8f8+fORnp6OadOmQaVSQa/Xw2azQSQSIS4uzuUyg66MGzcO2dnZ\nSEtLQ1JSEpKSkqBQKGA0Gp1T/ahUKkybNg3+/v4Vzh87diyysrKQkpKCjz76CCtWrIBYLHaOmu7Q\noQMmTJhw42+AC/W0KA8R1RGLpbJysr31sLKJrh1ksmsthXL5tZZDuRy4bgrZavFzg4jqW713h27f\nvj2WLFmCzZs3Izk5Gfn5+fDz83Ou2VqbfoNKpRJz587F7t27sX//fqSnp0Or1UIulyMkJATdu3fH\n0KFDERwcXOn5UqkUM2bMQGJiInbt2oXs7GxYrVa0b98effr0QWxsrNsnLHYkzUTkHa7vZ2g0Xisp\nm0yu/69KJPaksGw52VFerqvpWJkwEpEn1Nva001NTdaedrSI1mRuSqqIE0t7XkN9BjYbUFoqQlGR\nCDqdPUE0GkVw9enoKCdf389QLrdBKoXbp60xm82wWCwuPzM4obHn8Rl4Bz4HYbxucm9yTSaTQafT\nQa/XQy6XV1jqkIjqnsUCFBeLUFQkRnFx5aOUy5aQHa2Hcrm95dAThQGbzQaLxQK9Xg8fH5/6D4CI\nmjQmjV5AJBJBrVbDbDY7+3c6tpNwCoWixmuWU93y9meg1QIXLtgHpeTlSWC12gDYB+6o1Va0bm1B\nUJAVfn42+PpWXk62WIAyS9vXG8fnglQqhY+PDz8fiKjeMWn0IlKp1O19JxszliE8z9uegdUKpKTI\nsGOHEgkJSpw4cW20sUhkw623mjBkiB5DhugRHW2GSMQ14ImIXGGGQkSNilYrwp49CuzYoURiogJ5\nedcSQR8fKwYMMGDIED3uvtuAFi2a7vRAREQ1xaSRiBq8nBwxEhKU2LFDif37FTAYrpVuw8LM/7Qm\nGtC7twEcb0ZEVDtMGomowbFagaNH7WXnHTuUOH68fNn5lluMzrJzp05mjwxaISJqbJg0ElGDoNOJ\n8Ntvcmf/xMuXr5Wd1eprZedBgwwIDmbZmYiorjFpJCKvdeHCtbLzvn0K6PXXmgzbtDFjyBB7onjn\nnQYolR4MlIioCWDSSERew2YDjh1zlJ0VOHZMXm7/zTcbMXiwvezcuTPLzkRE9YlJIxF5lE4H7N3r\nGO2sxMWL18rOSqUV/fsbMGSZLLiPAAAgAElEQVSIAXffrUfLliw7ExF5CpNGIqp3ly5dKzv/9psc\nev21VZBatbI4WxP79DFApfJgoERE5MSkkYjczmYDjh+XOkc7//VX+bJz9+5G57Q4XbuaWHYmIvJC\nTBqJyC30emD/fsU/o50VyM299nGjVNrQt699EMvgwXq0asWyMxGRt2PSSER15tIlYONGFXbsUGLP\nHgVKS6+VnVu2tJedBw/Wo18/I1QqmwcjJSKimmLSSES1ZrMBJ05cKzsfOSKDzRbo3N+1q9E5LU63\nbiaIxVVcjIiIvBqTRiKqEYMBOHBA4ZwWJyfn2seIQmFDnz56Z9m5TRuWnYmIGgsmjURUratXxUhI\nUCAhQYnduxXQaq81GQYHO0Y7GzB8uC90unwPRkpERO7CpJGIKrDZgJMnr5Wdk5NlsNmuDWnu3Nnk\nXNu5R49rZWcfH1/odB4KmoiI3IpJIxEBAIxG4ODBa2s7nz9/7eNBLreXnR0tiqGhFg9GSkREnsCk\nkagJy88XISnJ3pq4e7cCxcXXys5BQRbcfbd9EEv//gb4+HC0MxFRU8akkagJstmAlSt9sGCBP0ym\na2XnmBiTczWWm2/maGciIrqGSSNRE1NcLMK0aQH4+Wf7+nz9+hlw7706DB5sQHg4y85ERFQ5Jo1E\nTUhamhQTJjTHuXNS+PlZ8d57Gtx/v97TYRERUQPApJGoifj+exWmT28GnU6MmBgTVq7MR2QkWxaJ\niEgYJo1EjZzBAMyd2wxr1/oAAEaNKsXChYVcxo+IiGqESSNRI5aTI8EzzwTiyBE55HIb5s8vxKOP\nlkIkqv5cIiKispg0EjVSu3YpMGVKAAoKJAgLM2PlygL06GHydFhERNRAcUINokbGagWWLvXFY481\nR0GBBIMG6fHLL1eYMBIR0Q1hSyNRI5KfL8ILLwRi504lRCIbXn65CC++WML5FomI6IYxaSRqJI4c\nkeGZZwKRkyNFYKAFH3+sQf/+Bk+HRUREjQTbH4gaOJsNWLtWjREjgpCTI8XNNxuxfXseE0YiIqpT\nbGkkasB0OhFmzGiG775TAwCeeKIEc+YUQS73cGBERNToMGkkaqDOnpXgmWeaIy1NBpXKisWLCzFi\nhM7TYRERUSPFpJGoAfr5ZyVeeikAJSVidOhgwqpVBbjpJrOnwyIiokaMSSNRA2IyAQsW+GPFCl8A\nQGysDkuXauDry9VdiIjIvZg0EjUQly6JMXFiIH7/XQGp1IbZs4vw9NNaru5CRET1gkkjUQNw4IAc\nEycG4soVCVq2tOB//yvA7bcbPR0WERE1IZxyh8iL2WzAJ5/4YMyYFrhyRYK77jJg+/YrTBiJiKje\nsaWRyEsVFYkwbVoAtm1TAQCmTClGfHwxpPxfS0REHsAfP0Re6O+/pZgwoTkyMqTw97di2bIC3HMP\nJ+smIiLPYXmayMts3KjCv/8dhIwMKTp3NmHbtitMGImIyOPY0kjkJfR6YM6cZvj6ax8AwJgxpXjr\nLQ1UKg8HRkREBCaNRF4hK0uCZ54JxNGjcigUNrz1ViHi4ko9HRYREZETk0YiD0tMVOCFFwKh0YgR\nHm7GqlUF6NbN5OmwiIiIymGfRiIPsViAxYv9MG5cC2g0Ytx9tx6//HKFCSMREXkltjQSeUB+vhiT\nJwdgzx4lxGIb4uOLMWVKCcT8NY6IiLwUk0aiepacLMOzzwYiN1eK5s0tWL68AP37c7JuIiLybmzX\nIKonNhvw+edqjBwZhNxcKW691Yjt268wYSQiogaBLY1E9aC0VITp05th82Y1AOCpp0owe3YR5HIP\nB0ZERCQQk0YiNztzRooJEwJx6pQMarUV776rwYMP6j0dFhERUY0waSRyox9/VOK//w2AVitGVJQJ\nn35agI4dzZ4Oi4iIqMaYNBK5gckEvPmmPz791BcAMGyYDosXa+Dra/NwZERERLXDpJGojl24IMbE\niYH44w8FpFIbXn+9CE88oYVI5OnIiIiIas8jSaNGo8HmzZuRnJyM/Px8qNVqdOjQAbGxsejWrVuN\nrzd58mRcuXJF0LGTJk3CwIEDy20bPXp0tedNmzYNvXv3rnFs1LTs2yfHpEmByMuToFUrC/73v3z0\n6sXJuomIqOGr96QxMzMTb7zxBoqLiwEAKpUKRUVFSE5ORkpKCuLi4jB8+PAaXdPf3x9Go+tpSwwG\nA/R6+8CDyMhIl8f5+flB7GJ2ZZlMVqOYqGmxWoGPP/bFO+/4wWoVoU8fAz7+uABBQVZPh0ZERFQn\n6jVpNBqNWLRoEYqLixEREYEpU6YgPDwcpaWl2LRpE7Zu3Yp169YhIiICPXr0EHzdBQsWVLl/0aJF\nOHz4MCIiItC2bdsqrxMSEiL4vkQAUFgowtSpAfj1VxUA4IUXivHyy8WQSDwcGBERUR2q18m9d+zY\ngStXrkCpVGLGjBkIDw8HAKjVaowbNw69evUCAKxbt67O7llUVISUlBQAwIABA+rsukQAkJoqxf33\nB+PXX1Vo1syKzz+/ihkzmDASEVHjU69J4969ewEAffv2RfPmzSvsHzZsGAAgPT0dOTk5dXZPi8UC\niUSCvn371sk1iQBgwwYVHnwwGJmZUnTtasS2bVcwZIjB02ERERG5Rb0ljTqdDufOnQMAl6Xnjh07\nQq22r5iRmppaJ/fdvXs3AOCWW26Bv79/nVyTmja9HoiPb4Zp0wKh14sQF6fF//1fHtq1s3g6NCIi\nIreptz6NOTk5sNnsc9Q5ytLXE4vFaNOmDc6cOYPs7Owbvuf58+eRnp4OQFhp+r333sPFixdhMBjg\n7++PqKgoDBo0CLfccssNx0KNQ2amBM88E4jUVDmUShveekuDsWN1ng6LiIjI7eotaSwoKHB+HxgY\n6PI4x76yx9fWrl27ANhHRQtJ/M6ePQuVSgWJRIL8/HwcOnQIhw4dQu/evfHCCy9AKuW0lk3Zjh0K\nvPhiIAoLxWjXzoyVK/PRtStXdyEioqah3rIgg+FaXy+5XO7yOIVCUeH42rBarfjtt98A2PtQVpXw\nDRgwAH369EHHjh3h4+MDwN4y+sMPP2DXrl04ePAgfHx88Oyzz7q8RkJCAhISEgAACxcuRFBQ0A3F\nTzUnlUrd8r5bLMC8eRK88459dEtsrBVr1lgREBBQ5/dq6Nz1DKhm+Bw8j8/AO/A51K16Sxodpen6\ncuTIERQWFgKovjQ9efLkCttCQ0MxadIk+Pv7Y8uWLUhKSsIDDzyA0NDQSq8xePBgDB482Pn3vLy8\nG4ieaiMoKKjO3/e8PDEmTw7E3r0SiMU2zJhRjEmTSmA2A3zEFbnjGVDN8Tl4Hp+Bd+BzEKZNmzaC\njqu3gTBKpdL5fXUTcQPXWhxry1Gabtu2bZUTeldn1KhRkMvlsNlsSE5OvqGYqGE5fFiGe+8Nxt69\nCgQFWbB+/VVMmVICF/O/ExERNWr19uOvbD/GqvorOvZV1e+xOlqtFn/++SeAG5+bUalUOgfuXLp0\n6YauRQ2DzQasXu2Dhx4KwsWLEtx2mxG//HIFffq4/mWHiIiosau3pDE0NBQikQgAkJWVVekxVqsV\nubm5AICwsLBa32vfvn0wmUwQi8Xo169fra9zPUf81LjNmNEMc+Y0g9kswtNPl2DTpjy0bs3lAImI\nqGmrt6RRpVI5y8RHjx6t9JgzZ86gtLQUANCtW7da38sxN2PPnj1veLCCXq93JrnBwcE3dC3yfr/+\nqsDXX/tAqbTik0/yMW9eEbjsOBERUT2vCONYkWXv3r2Vlqi3bNkCAIiMjBTcKfN6ubm5OH36NABh\npenqBuh89913MBqNEIlEnK+xkSsuFmHmTPsvGTNnFmPYML2HIyIiIvIe9Zo0DhkyBMHBwdDpdFi4\ncKFzAm+dToevvvoKhw4dAgDExcVVOHf06NEYPXo0vv322yrv4RgA4+Pjg9tuu63amN577z188803\nOHv2LMzma3Pu5ebm4n//+x9++OEHAPYE9EZK5uT9Fi3yw8WLEvToYcQTT2g9HQ4REZFXqdfZquVy\nOeLj4zF//nykp6dj2rRpUKlU0Ov1sNlsEIlEiIuLc7nMYHXKzs3Yp08fyATUFYuKinDw4EFs3rwZ\nYrEYarUaJpOp3DyRvXv3xoQJE2oVEzUMyckyfPaZDyQSGxYv1kAi8XRERERE3qXelzhp3749lixZ\ngs2bNyM5ORn5+fnw8/NDVFQUYmNjb6gvY2pqKq5evQpA+KjpESNGoG3btjh9+jTy8/NRUlICkUiE\nkJAQdOzYEQMHDqx1EksNg8kETJ8eAJtNhOeeK0aXLlzlhYiI6HoiW33Put1EOEaBU/2p7SSuH37o\ni4UL/dGunRmJiVegUvG/RG1xIl3vwOfgeXwG3oHPQRivm9ybyBudOyfBe+/5AQAWLixkwkhERORC\njZJGq9Va5cTcRA2JzQa88koADAYRHnqoFP3739h650RERI2ZoD6NWq0Wn376KQ4ePAipVIovv/wS\nhw8fxpkzZzB27Fh3x0jkFhs3qrBvnwKBgRa8/nqRp8MhIiLyaoJaGletWgW1Wo2PP/4YUqk9z4yO\njsb+/fvdGhyRu1y9Ksa8ec0AAHPnFqFFC674QkREVBVBLY3Hjh3DihUrnAkjAPj7+6OwsNBtgRG5\n09y5/tBoxOjXz4CHHtJ5OhwiIiKvJ6ilUa1Wo7i4uNy2vLw8BAYGuiUoInfatUuB779XQ6m0YeFC\nDbikOBERUfUEJY133303lixZgtTUVNhsNpw6dQrLly/HkCFD3B0fUZ0qLRXhlVfsZelp04rRvr3F\nwxERERE1DILK0w8++CBkMhlWr14Ni8WCTz75BIMHD8bQoUPdHR9RnVq61A9ZWVJ07mzCM8+UeDoc\nIiKiBqPapNFqtWLXrl245557EBsbWx8xEblFaqoUK1f6QCSyLxUoYJVJIiIi+ke15WmxWIy1a9cK\nWseZyFuZzUB8fAAsFhGefFKLnj1Nng6JiIioQRHUp/HWW2/F4cOH3R0LkdusWeODo0flaNPGjOnT\ni6s/gYiIiMoR1KfRZDJh6dKliI6ORosWLSAqM9x0ypQpbguOqC5kZUmwaJF9qcC33iqEry+XCiQi\nIqopQUljeHg4wsPD3R0LUZ2z2YBZs5pBpxPjgQd0uOceLhVIRERUG4KSxocfftjdcRC5xZYtSiQl\nKeHvb8X8+ZyMnoiIqLYEJY0AkJqaij179qCgoACBgYHo378/unbt6s7YiG5IQYEIc+bY52ScPbsI\nISFcKpCIiKi2BA2ESUxMxPvvv4+AgADcfvvtCAwMxLJly5CQkODu+Ihq7a23/JGXJ8EddxgQF1fq\n6XCIiIgaNEEtjVu2bMHs2bPRvn1757a77roLS5YsweDBg90VG1Gt7d8vxzff+EAut+GddwohFvTr\nEREREbki6EdpcXExwsLCym1r06YNSkq4ogZ5H70emDEjAADw/PPF6NjR7OGIiIiIGj5BSWOnTp2w\ndu1aGAz2kad6vR5ffvkloqOj3RocUW18+KEfzp2TomNHEyZP5i82REREdUFQeXrChAl4//33MX78\nePj6+qKkpATR0dF48cUX3R0fUY2cPCnF8uW+AIBFiwqhUHg4ICIiokZCUNIYGBiIefPm4erVq87R\n0y1atHB3bEQ1YrUC06cHwGQS4bHHtLj9dqOnQyIiImo0BCWNf/31F4KDg9GmTRtnspibm4u8vDx0\n797drQESCbVqlRiHD0sREmLBrFlFng6HiIioURHUp3H16tVQqVTltimVSqxevdotQRHV1IULYsye\nLQEAzJ9fiGbNuFQgERFRXRKUNBYWFiIwMLDctsDAQGg0GrcERVRTc+Y0Q1GRCEOG6BEbq/d0OERE\nRI2OoKSxZcuWSE1NLbft+PHjCAkJcUtQRDXxyy9K/PyzCr6+Nrz1lgYikacjIiIianwErz397rvv\nYtCgQWjZsiUuXbqEnTt3YtKkSe6Oj6hKxcUivPqqfanAN96wIDSUSwUSERG5g6CWxl69emH27NnQ\n6/VITk6GXq/Hq6++il69erk7PqIqLVzoj4sXJbj5ZiOee44JIxERkbsIamkEgKioKERFRbkzFqIa\nOXxYhi++UEMqtWHRIg0kkgBPh0RERNRoCWpp3Lp1KzIyMgAAp0+fxsSJEzFlyhScOnXKnbERuWQ0\n2pcKtNlEeO65EnTuzKUCiYiI3ElQ0vjTTz85B72sW7cODzzwAEaOHInPP//cnbERufS///kiLU2G\n9u3NmDq12NPhEBERNXqCksbS0lKo1WrodDpkZGTg/vvvx6BBg5Cbm+vu+IgqOHtWgvff9wMALFyo\nwXVTiBIREZEbCOrT2KJFC5w8eRJZWVmIiYmBWCxGaWkpxGJBOSdRnbHZ7GVpg0GEhx8uRb9+XCqQ\niIioPghKGh977DEsXboUUqkU//3vfwEAycnJHBhD9e7bb1U4cECB5s0tmDOHSwUSERHVF0FJ4y23\n3IIVK1aU29a7d2/07t3bLUERVSYvT4w33rDPyTh3bhGaN+cUO0RERPVF8JQ7FU6U1vpUolqZO9cf\nGo0YAwboMXKkztPhEBERNSnslEgNQlKSAps3q6FUWrFgQSGXCiQiIqpnTBrJ65WWijBzpr0s/fLL\nxWjXzuLhiIiIiJoeJo3k9d591w/Z2VJ06WLChAlaT4dDRETUJAlKGqdPn46ffvoJGo3G3fEQlXPs\nmAyrVvlALLZh8WIN2JWWiIjIMwT9CB45ciT27t2L9evXIyYmBv3798ftt98OuVzu7vioCTObgfj4\nZrBaRZgwoQQ9epg8HRIREVGTJShpdEyvU1JSgv3792P79u349NNPcfvtt6N///7o2rWru+OkJujT\nT31w7JgcoaFmxMdzqUAiIiJPqlGxz9fXFwMGDIBSqcSWLVvw+++/48SJExCLxXjqqafQvXt3d8VJ\nTcz58xK8+659qcAFCwrh42PzcERERERNm6Ck0Wq14ujRo9izZw+Sk5MRHR2N4cOHO0vUBw8exIcf\nfohVq1a5O15qAmw2YNasZtDpxBg2TIe77zZ4OiQiIqImT1DS+Oyzz8Lf3x/9+/fHY489hubNm5fb\n37t3b2zfvt0tAVLT88MPKuzcqUSzZlbMm1fo6XCIiIgIApPGV155BR06dKjymNdff71OAqKmraBA\nhDlz/AEAr71WhJAQLhVIRETkDQRNuZOdnY3MzMxy2zIyMrBnzx63BEVN15tv+uPqVQnuvNOAsWNL\nPR0OERER/UNQ0rhhwwa0aNGi3LagoCCsX7/eLUFR07Rvnxzr1/tALrdh4UINlwokIiLyIoKSRp1O\nB7VaXW6bWq2GVsvVOahu6HTA9OkBAIAXXihGVBSXCiQiIvImgpLGsLAwHDx4sNy2Q4cOISwszC1B\nUdPzwQd+yMiQIjrahMmTSzwdDhEREV1H0ECYRx99FAsWLMD+/fvRqlUrXLx4EceOHcPMmTPdHR81\nAWlpUnz8sS8AYNEiDbjQEBERkfcRlDR26tQJS5Yswd69e5GXl4eoqCiMHz8eQUFB7o6PGjmrFYiP\nD4DZLMK4cVr06sWlAomIiLyR4BVhgoKCMHz4cHfGQk3Q2rVqJCfL0bKlBTNnFnk6HCIiInJBcNJ4\n+PBh/P333ygqKv+DfcqUKXUeFDUNubliLFhgn5PxzTcL4e/PpQKJiIi8laCkcePGjdixYwfuuusu\nHDx4EIMHD8a+fftw55131uqmGo0GmzdvRnJyMvLz86FWq9GhQwfExsaiW7duNb7e5MmTceXKFUHH\nTpo0CQMHDqyw3WazITExETt37kROTg6sVitatWqFvn37YujQoZBKa7RMNwnw2mvNUFIixr336nD/\n/XpPh0NERERVEJQJ7dy5E7Nnz0bbtm2xa9cujB8/Hn379sV3331X4xtmZmbijTfeQHFxMQBApVKh\nqKgIycnJSElJQVxcXI3L4P7+/jAajS73GwwG6PX2pCQyMrLCfrPZjMWLFyMlJQUAIJVKIRaLkZGR\ngYyMDBw4cACvv/46lEpljeIi17ZtU+KXX1Tw9bXizTcLOScjERGRlxOUNGq1WrRt29Z+glQKs9mM\nqKgo/P333zW6mdFoxKJFi1BcXIyIiAhMmTIF4eHhKC0txaZNm7B161asW7cOERER6NGjh+DrLliw\noMr9ixYtwuHDhxEREeF8HWWtX78eKSkpkMlkmDBhAvr37w+RSITk5GQsX74cZ8+excqVK/HCCy/U\n6PVS5YqKRJg9uxkAYObMIrRpw6UCiYiIvJ2geRpbtWqFrKwsAEB4eDh+/fVX7NmzB76+vjW62Y4d\nO3DlyhUolUrMmDED4eHhAOwThY8bNw69evUCAKxbt65G161KUVGRswVxwIABFfZrNBps27YNgH1q\noYEDB0IsFkMkEuHWW2/FxIkTAQD79u2rsJQi1c6CBf64eFGCW24x4j//4VKBREREDYGgpHHMmDHO\ncvKjjz6Kbdu24csvv8S4ceNqdLO9e/cCAPr27YvmzZtX2D9s2DAAQHp6OnJycmp07aruabFYIJFI\n0Ldv3wr7Dx48CJPJBLVajcGDB1fY36tXL7Ru3Ro2m80ZP9XeH3/IsXatD6RSGxYt0kAi8XRERERE\nJES15Wmr1Qq5XI7o6GgAQFRUFD788MMa30in0+HcuXMA4LL03LFjR6jVapSWliI1NRWhoaE1vs/1\ndu/eDQC45ZZb4O/vX2H/8ePHAQAxMTGQu5hVukePHrhw4QJSU1NvOJ6mzGgEpk+3l6UnTixBTIzZ\nwxERERGRUNW2NIrFYixatOiGRw/n5OTAZrNPqeIoS1d2rzZt2gAAsrOzb+h+AHD+/Hmkp6cDqLw0\nXfY+rmIC4FwusexroJr7+GNfnDolQ0SEGVOnFns6HCIiIqoBQeXpmJgYnDp16oZuVFBQ4Pw+MDDQ\n5XGOfWWPr61du3YBAPz8/HDLLbdUeoxGoxEck16vd47Cppo5c0aCZcv8AADvvKMBB6ITERE1LIKa\nD4ODg7FgwQLcdtttaNGiBURl5kcZM2aMoBsZDAbn967KwACgUCgqHF8bVqsVv/32GwB7H0pXLaWO\nJFBITI7jVSrVDcXW1FitwCuvBMBoFGHMmFL06eN6eiQiIiLyToKSRqPR6BzZnJ+fX6sb1XdZ98iR\nIygsLATgujRdlugGJwpMSEhAQkICAGDhwoVcl7uMzz4T48ABKYKDbXjvPSlatHDPeyOVSvm+exif\ngXfgc/A8PgPvwOdQtwQljZMmTbrhG5WdGNtoNLpsrXO0MJZt3asNR2m6bdu2lU7oXTYurVZbZctm\n2X2uJvgePHhwudHXeXl5NYy4cbpyRYwZM0IAAK+/roHNpoO73pqgoCC+7x7GZ+Ad+Bw8j8/AO/A5\nCOMYT1IdQUnjpUuXXO5r2bKloBuV7TNYUFDgMml09GWsqo9hdbRaLf78808A1bcyBgYGQqvVVtmH\n0rFPqVRyVZgaev11fxQWivGvf+kxfLjO0+EQERFRLQlKGqtaCWXDhg2CbhQaGgqRSASbzYasrKxK\ns1qr1Yrc3FwA10Ys18a+fftgMpkgFovRr1+/Ko8NCwtDdna2c/LyyjhGWDteAwmTmKjADz+ooVJZ\n8fbbXCqQiIioIROUNF6fGGo0GmzcuBExMTGCb6RSqRAZGYmzZ8/i6NGjuOOOOyocc+bMGZSW2lcI\n6datm+BrX88xN2PPnj0REBBQ5bFdunTBwYMHkZaWBqPRWOmAmKNHj95wTE2NVivCzJn2ORlffrkY\nbdtaPBwRERER3QhBU+5cLyAgAOPHj6/xcn+OFVn27t1baTl4y5YtAIDIyEjB9fXr5ebm4vTp0wCE\nDYC54447IJPJoNVqkZSUVGH/4cOHkZubC5FIhD59+tQqpqZo8WI/5ORI0bWrEU8/rfV0OERERHSD\napU0AvbkrKbT4gwZMgTBwcHQ6XRYuHChs+yr0+nw1Vdf4dChQwCAuLi4CueOHj0ao0ePxrffflvl\nPRwDYHx8fHDbbbdVG1NAQADuv/9+AMBXX32FPXv2wGq1AgCSk5PxySefAAD69OmDdu3aCXuhTdxf\nf8mwerUPxGIbFi8uxA3OC09EREReQNCP8zlz5pTry2cwGJCVlYVRo0bV6GZyuRzx8fGYP38+0tPT\nMW3aNKhUKuj1ethsNohEIsTFxblcZrA6Zedm7NOnD2QymaDzxo4di6ysLKSkpOCjjz7CihUrIBaL\nnUlxhw4dMGHChFrF1NSYzUB8fACsVhGeeaYE3bubPB0SERER1QFBSeOgQYPK/V2pVKJdu3Zo3bp1\njW/Yvn17LFmyBJs3b0ZycjLy8/Ph5+eHqKgoxMbG3lC/wdTUVFy9ehWAsNK0g1QqxYwZM5CYmIhd\nu3YhOzsbVqsV7du3R58+fRAbG3vDyyg2FatW+eD4cRnCwsyIj+dSgURERI2FyMbFlN3CMQq8KTl1\nSor77guGwSDCl19exaBBN7aqT01xPi7P4zPwDnwOnsdn4B34HIQROo5EUJ/Gd999FydOnCi37cSJ\nE1iyZEnNI6NGyWwGXnopAAaDCGPHaus9YSQiIiL3EpQ0/v3337jpppvKbYuOjsbx48fdEhQ1PB9/\n7IsjR+Ro08aM118v8nQ4REREVMcEJY0ymQx6vb7cNr1eD4lE4pagqGH5+28pli71AwAsWVIIf3/2\neCAiImpsBCWNPXr0wMqVK50Tb5eWlmL16tXo2bOnW4Mj72c0AlOnBsJkEuE//9Gif3+WpYmIiBoj\nQUOCx40bhw8//BBPPvkkfH19UVJSgp49e+L55593d3zk5T74wA/Hj8vQtq0Zr73GsjQREVFjJShp\n9PX1xcyZM6HRaJCXl4egoKBql+ejxu/oURk++MAXALB0qQY+PixLExERNVaCksa//voLwcHBaNOm\njTNZzM3NRV5eHrp37+7WAMk7GQzA1KkBsFhEeOqpEtx5p9HTIREREZEbCerTuHr1aqhUqnLblEol\nVq9e7ZagyPstXeqHk1ocH/MAACAASURBVCdliIgwY+ZMTuJNRETU2AlKGgsLCxEYGFhuW2BgIDQa\njVuCIu/2558yfPyxL8RiG95/vwAqFcvSREREjZ2gpLFly5ZITU0tt+348eMICQlxS1DkvXQ6+yTe\nVqsIzz6rxW23cW1pIiKipkBQn8aHH34Y7777LgYNGoSWLVvi0qVL2LlzJyZNmuTu+MjLvPOOP86e\nlSE62oSXX+ZoaSIioqZCUEtjr169MHv2bOj1eiQnJ0Ov1+PVV19Fr1693B0feZHff5fj0099IJHY\n8P77GiiVno6IiIiI6ouglkYAiIqKQlRUlDtjIS+m1Yrw0ksBsNlEmDKlGD16sCxNRETUlAhOGjMy\nMnDixAkUFxfDZrs28GHMmDFuCYy8y1tv+SMzU4rOnU2YOpWjpYmIiJoaQUljQkICvvjiC3Tv3h1H\njhxBz549cfToUdx2223ujo+8wG+/yfHFFz6QyeyjpeVyT0dERERE9U1Qn8YffvgBs2bNQnx8PORy\nOeLj4zFt2jRIJBJ3x0ceVlwswn//a5/QferUYnTpYvZwREREROQJgpLGoqIixMTEAABEIhGsVitu\nvvlm/Pnnn24NjjzvjTf8kZMjRffuRkyZUuLpcIiIiMhDBJWnmzdvjsuXLyMkJAStW7fG4cOH4efn\nB6lUcJdIaoCSkhRYt84Hcrl9tDQfNxERUdMlKA148MEHkZOTg5CQEIwaNQpLly6F2WzGE0884e74\nyEM0GhHi4+1l6fj4Ytx0E8vSRERETZmgpHHgwIHO72+++WZ89tlnMJvNUHKivkZrzpxmuHhRgltv\nNeLZZ1mWJiIiaupqVXCUSqUsTTdi27cr8d13aiiVVrz3XgE43omIiIgEDYShpiM/X4zp05sBAGbO\nLEaHDhYPR0RERETegEkjlfPqq82QlyfBnXca8OSTWk+HQ0RERF6CSSM5/fijElu2qKBWW7FkiQZi\n/usgIiKifwjumFhaWorc3Fzo9fpy27t27VrnQVH9u3JFjJkz7WXp2bOL0K4dy9JERER0jaCkcdeu\nXVi9ejWUSiXkZdaQE4lE+Oijj9wWHNUPmw145ZVmKCiQoF8/A8aNK/V0SERERORlBCWN33zzDaZN\nm4abb77Z3fGQB3z/vQq//KKCn5+9LC0SeToiIiIi8jaCeq1ZrVb06NHD3bGQBxQVifDaa/ay9Ny5\nhQgNZVmaiIiIKhKUND744IP47rvvYLVa3R0P1bNjx2QoLBSja1cjxozReTocIiIi8lKCytM//fQT\nNBoNtmzZAl9f33L7PvnkE7cERvUjI8P+T6BzZzPL0kREROSSoKTx+eef///27j2u6irR//9rX9hc\nBBEEU0AFvFtqmpopGU521WpyzMnxjKcekzVpNU0djjaOpznZGU1Lj6XW5GOqaco6djFrKpvQzEjT\nMewrmRkiEBcBEREUtlz2/v3Bb28hbhvYewPb9/Px8PGA/fmsz16fz0L2m7U+a308XQ/pJJmZdT8C\nsbF6trSIiIg0z6XQOHLkSE/XQzpJVlbdMwIVGkVERKQlLq/TmJWVxZEjRygvL8dutztf/+Uvf+mR\niol3OIan4+M1AUZERESa51JoTE5O5m9/+xujR4/mm2++4fLLL+fQoUOMHz/e0/UTD7LZIDOzrqdx\n4ED1NIqIiEjzXJo9vW3bNv7whz+QlJSExWIhKSmJRx55BJPJ5On6iQcVFhqxWo307l1Lz5721guI\niIjIRcul0FhWVsaIESOAuqfA2Gw2xo4dy9dff+3RyolnOYam4+I0NC0iIiItc2l4Ojw8nKKiIvr0\n6UO/fv04cOAAISEhmM0u3xIpXZBmTouIiIirXEp9t912G3l5efTp04fZs2ezZs0aampquPvuuz1d\nP/EgzZwWERERV7kUGhMTE51fjx07lpdffpmamhoCAgI8VS/xAkdPY3y8QqOIiIi0zKV7GgHKy8vZ\nvXs327Ztw2w2U1FRwalTpzxZN/GwC8PTuqdRREREWuZSaPzuu+94+OGH+eKLL3jnnXcAKCgoYNOm\nTR6tnHiO3a7haREREXGdS6HxlVde4eGHH2bp0qXOZXYGDx5MRkaGRysnnlNUZKSy0kh4eC2hoVpu\nR0RERFrmUmg8efIko0aNavCa2WymtlbDmt2VhqZFRESkLVwKjTExMXzzzTcNXktLS2PAgAEeqZR4\nnoamRUREpC1cmj3961//mqeeeoqxY8dSVVXFiy++yNdff01SUpKn6yceopnTIiIi0hYuhcahQ4ey\nevVqvvjiCwICAoiIiODPf/4zvXv39nT9xEM0PC0iIiJt4fIjXcLDw7nttts8WRfxIscjBDU8LSIi\nIq5wKTRWVFTw0UcfkZWVhdVqbbDtj3/8o0cqJp5Tf7mduDiFRhEREWmdS6FxzZo12Gw2Jk6ciMVi\n8XSdxMNOnjRy7pyRXr1s9Oql5XZERESkdS6FxvT0dP76179iNrs8mi1dmGNoWr2MIiIi4iqXltwZ\nPnw4eXl5nq6LeElmpoamRUREpG1c6jpcuHAhK1asYPDgwfTq1avBttmzZ3ukYuI5mjktIiIibeVS\naHzjjTc4deoUkZGRVFZWOl83GAweq5h4jmZOi4iISFu5FBr37NnDunXrCAsL83R9xAv0NBgRERFp\nK5dC4yWXXILJZHLbm5aWlrJ161ZSU1MpKSkhKCiIQYMGMWPGjEbPuG6rM2fO8NFHH5GamkpRURE2\nm42wsDDi4+OZMmUKEyZMaFRmzpw5rR73kUceYdKkSR2qW1dgt18YntY9jSIiIuIql0Lj1VdfzapV\nq7jxxhsb3dN42WWXtekNs7OzeeKJJygvLwcgMDCQsrIyUlNTOXjwIHPnzuXnP/95m47pkJqaynPP\nPce5c+cAsFgsGI1GCgoKKCgooLy8vMnQ6BASEoLR2PTcID8/v3bVqas5dcrI2bNGQkNthIVpuR0R\nERFxjUuh8ZNPPgHq7m2sz2AwsH79epffrKqqilWrVlFeXk5cXBwPPPAA/fv3p6Kigrfffpt//OMf\nbN68mbi4OMaMGdOG04AjR47w9NNPU1NTw+TJk5k1axYDBgwA4OzZs3z33XcUFRW1eIwVK1bQp0+f\nNr1vd+OYOR0bW4NuSRURERFXuRQaN2zY4JY3+/TTTzl58iQBAQEsXryY8PBwAIKCgpg/fz6FhYX8\n61//YvPmzW0KjVVVVWzcuJGamhqmT5/Ovffe22B7cHAwEydOdMs5dHcamhYREZH2cGmdRndJSUkB\nICEhwRkY67v11lsByMzMbNO6kHv27KGwsJAePXowf/5891TWR12YOa3ldkRERMR1XguNlZWVHD9+\nHKDZXsQhQ4YQFBQEwLfffuvysR1hdNKkSQQEBHSwpr5NM6dFRESkPbz2XMC8vDzs9rqJF/37929y\nH6PRSFRUFMeOHSM3N9el49rtdtLT04G6J9ccP36cd999lyNHjmC1WgkPD2fMmDHcdtttREZGtnis\ntWvXUlBQwPnz5+nZsyeDBw/mZz/7GePGjWvDmXZtGp4WERGR9vBaaDx9+rTz65bWe3Rsq79/S86c\nOeNccDwnJ4cXXniB2tpa/P39MZlMFBYW8s9//pOUlBQWL17MiBEjmj1WRkYGgYGBmEwmSkpK2L9/\nP/v372fSpEk89NBD3f7Z23Z7/edOa3haREREXOe1FHT+/Hnn1xaLpdn9/P39G+3fEsfyOgAffPAB\n4eHh3H///YwaNQqDwcDRo0fZuHEjJ06cYM2aNaxdu5bg4OAGx7jmmmuYMmUKQ4YMoUePHkBdz+i2\nbdvYtWsXX331FT169OC+++5rth7JyckkJycDsHLlSiIiIlyqvzcVF0NZmZGePe0MHRruc7OnzWZz\nl7zuFxO1Qdegduh8aoOuQe3gXl4LjY6haU8e12638+CDDzJy5Ejna8OGDePRRx8lKSmJM2fOsHPn\nTueEG4dFixY1Om50dDQLFy6kZ8+evP/+++zcuZOZM2cSHR3dZD2mT5/O9OnTnd8XFxd39NTc7sAB\nPyCS2NhqTp3qevXrqIiIiC553S8maoOuQe3Q+dQGXYPawTVRUVEu7ee1iTD1J6hUVVU1u5+jh9HR\n49iW4w4cOLBBYHQYMGAAo0ePBiAtLc2l4zrMnj0bi8WC3W4nNTW1TWW7Gs2cFhERkfbyWmisfx9j\nS/crOra5+pzr0NBQ51NcWkrK/fr1A9reAxgQEOCcuFNYWNimsl3NhdCoSTAiIiLSNl4LjdHR0Rj+\n/5vocnJymtzHZrORn58PQExMjEvH9fPz45JLLgFwHr8lruzjibJdgeNpMJo5LSIiIm3ltdAYGBhI\nfHw8AIcOHWpyn2PHjlFRUQHAqFGjXD62Y9+WFgR3hNHWlt35KavV6gy5bS3b1WjmtIiIiLSXV58I\nk5CQANQtxt3UEPX7778PQHx8vMs3ZQJMnToVgOzsbA4fPtxo+48//ui8l3Hs2LENtrU2Qeedd96h\nqqoKg8HQ7ddr1PC0iIiItJdXQ+N1111HZGQklZWVrFy50rmAd2VlJa+99hr79+8HYO7cuY3Kzpkz\nhzlz5rBly5ZG24YOHep8tvT69etJS0tzhsEffviBZ555BrvdTmRkJNOmTWtQdu3atbzxxhtkZGRQ\nU3MhTOXn5/PCCy+wbds2oG5ZHleHzLuikhIDpaVGgoNtRETYOrs6IiIi0s14dbVqi8VCUlISy5cv\nJzMzk0ceeYTAwECsVit2ux2DwcDcuXObfcxgSxYuXMipU6fIyMhg+fLl+Pv7YzQanQt/h4WFkZSU\n1GhWdllZGV999RVbt27FaDQSFBREdXV1g3UiJ02axIIFCzp28p2sfi9jN781U0RERDqB1x9xEhsb\nyzPPPMPWrVtJTU2lpKSEkJAQBg8ezIwZM9p0L2N9QUFBLF++nO3bt5OSksKJEyeora0lJiaG8ePH\nM3PmTHr27Nmo3O23386AAQNIT0+npKSEs2fPYjAY6NOnD0OGDCExMbFdIbar0XI7IiIi0hEGu6dW\n3b7IOSbedBXPPBPCmjUhPPhgOUuWlHd2dTxCi7h2PrVB16B26Hxqg65B7eCaLre4t3SurCwttyMi\nIiLtp9B4kcjM1PC0iIiItJ9C40XCERrV0ygiIiLtodB4ETh9um65naAgG5GRWm5HRERE2k6h8SKQ\nnX1haFrL7YiIiEh7KDReBDQ0LSIiIh2l0HgR0MxpERER6SiFxouAZk6LiIhIRyk0XgQ0PC0iIiId\npdB4EXAMT8fGKjSKiIhI+yg0+rgzZwyUlJgIDLRxySVabkdERETaR6HRx2VlabkdERER6TiFRh+n\nmdMiIiLiDgqNPu7CzGmFRhEREWk/hUYfd2HmtJbbERERkfZTaPRxF+5pVE+jiIiItJ9Co4/Tcjsi\nIiLiDgqNPqy83EBxsYmAABt9+2q5HREREWk/hUYfVn+5HaNaWkRERDpAUcKHZWZqaFpERETcQ6HR\nh2nmtIiIiLiLQqMP08xpERERcReFRh+mmdMiIiLiLgqNPuzC8LRCo4iIiHSMQqOPOnvWwMmTJvz9\n7fTrp+V2REREpGMUGn2UY2h64MAaLbcjIiIiHaY44aM0NC0iIiLupNDoo+ov7C0iIiLSUQqNPkoz\np0VERMSdFBp9lIanRURExJ0UGn2UY3haT4MRERERd1Bo9EEVFQYKC01YLHb69VNoFBERkY5TaPRB\nmZkXltsxmTq5MiIiIuITFBp9kGZOi4iIiLspNPqgC6FRk2BERETEPRQafZBjeFozp0VERMRdFBp9\nkGZOi4iIiLspNPogxxqNGp4WERERd1Fo9DGVlQYKCkz4+dmJilJPo4iIiLiHQqOPcTw+cMCAGszm\nTq6MiIiI+AyFRh+j5XZERETEExQafYzuZxQRERFPUGj0MY7h6fh4hUYRERFxH4VGH3Ohp1HD0yIi\nIuI+Co0+RsPTIiIi4gkKjT6kshJOnDBhNtuJiVFPo4iIiLiPQqMP+fHHul7G/v1rtdyOiIiIuJVC\now9xDE3rmdMiIiLibgqNPsQxc1qhUURERNxNodGHaOa0iIiIeIpCow/RzGkRERHxFIVGH6LhaRER\nEfEUhUYfYbVCfr4Jk0nL7YiIiIj7dcrCLKWlpWzdupXU1FRKSkoICgpi0KBBzJgxg1GjRnXo2GfO\nnOGjjz4iNTWVoqIibDYbYWFhxMfHM2XKFCZMmNBkObvdzo4dO/jss8/Iy8vDZrPRt29fEhISuPnm\nmzF38TVsfvzRjN1uoH//Gvz8Ors2IiIi4mu8noSys7N54oknKC8vByAwMJCysjJSU1M5ePAgc+fO\n5ec//3m7jp2amspzzz3HuXPnALBYLBiNRgoKCigoKKC8vLzJ0FhTU8Pq1as5ePAgAGazGaPRSFZW\nFllZWezdu5fHH3+cgICAdp6152loWkRERDzJq6GxqqqKVatWUV5eTlxcHA888AD9+/enoqKCt99+\nm3/84x9s3ryZuLg4xowZ06ZjHzlyhKeffpqamhomT57MrFmzGDBgAABnz57lu+++o6ioqMmyb775\nJgcPHsTPz48FCxYwdepUDAYDqampbNiwgYyMDF588UUeeuihDl8DT9EkGBEREfEkr97T+Omnn3Ly\n5EkCAgJYvHgx/fv3ByAoKIj58+c7ewE3b97cpuNWVVWxceNGampqmD59Og8//LAzMAIEBwczceJE\nZs6c2ahsaWkpH3/8MQDz5s0jMTERo9GIwWDgiiuu4P777wfgyy+/JDs7u13n7Q1abkdEREQ8yauh\nMSUlBYCEhATCw8Mbbb/11lsByMzMJC8vz+Xj7tmzh8LCQnr06MH8+fPbVKevvvqK6upqgoKCmD59\neqPtEyZMoF+/ftjtdmf9u6KsLD0NRkRERDzHa6GxsrKS48ePAzQ79DxkyBCCgoIA+Pbbb10+tiPM\nTZo0qc33HR4+fBiAESNGYLFYmtzHUd+21MnbHPc0anhaREREPMFr9zTm5eVht9sBnMPSP2U0GomK\niuLYsWPk5ua6dFy73U56ejoAw4cP5/jx47z77rscOXIEq9VKeHg4Y8aM4bbbbiMyMrJRecf7NFcn\ngJiYmAbnYDAYXKqbt5w/D3l5JoxGO/37a3haRERE3M9rPY2nT592fh0WFtbsfo5t9fdvyZkzZ6is\nrAQgJyeHpUuXsn//fqqqqjCZTBQWFvLPf/6TpKQkjhw50qh8aWmpy3WyWq1YrVaX6uVNOTlmbDYD\n/fvX0kxnqYiIiEiHeK2n8fz5886vmxsGBvD392+0f0scy+sAfPDBB4SHh3P//fczatQoDAYDR48e\nZePGjZw4cYI1a9awdu1agoODnWUcIdCVOjn2DwwMbLRPcnIyycnJAKxcuZKIiAiX6u8O+/bV9XwO\nGWL06vt2NWaz+aI+/65AbdA1qB06n9qga1A7uJfXQqNjaNqTx7Xb7Tz44IOMHDnS+dqwYcN49NFH\nSUpK4syZM+zcudM54aa+jg45T58+vcFEmuLi4g4dry3+3//rAYQSHV1JcfEZr71vVxMREeHV6y6N\nqQ26BrVD51MbdA1qB9dERUW5tJ/XhqfrT1Cpqqpqdj9HD2P93j1Xjztw4MAGgdFhwIABjB49GoC0\ntLQmy7fUs1l/W1dc4Fszp0VERMTTvBYa698z2NL9io5tLd1jWF9oaChGY91ptJSU+/XrBzTuAXTl\nHkrHtoCAgC4aGjVzWkRERDzLa6ExOjraOQSck5PT5D42m438/Hzgwozl1vj5+XHJJZcArg0x/3Qf\nx/s0Vye4MMO6/jl0JY6FvePiNHNaREREPMNroTEwMJD4+HgADh061OQ+x44do6KiAoBRo0a5fGzH\nvi0tCO4Ioz9ddufSSy8F4Pvvv2922NxR37bUyVuqqiA317HcjnoaRURExDO8+kSYhIQEoG4x7qaG\ng99//30A4uPjXb4pE2Dq1KkAZGdnOxfrru/HH3903ss4duzYBtuuvPJK/Pz8OHfuHDt37mxU9sCB\nA+Tn52MwGJgyZYrLdfKWnBwTNpuB6OhaXLwNVERERKTNvBoar7vuOiIjI6msrGTlypXOYd/Kykpe\ne+019u/fD8DcuXMblZ0zZw5z5sxhy5YtjbYNHTqUiRMnArB+/XrS0tKcs6p/+OEHnnnmGex2O5GR\nkUybNq1B2V69enHTTTcB8Nprr7F7925sNhsAqampPP/88wBMmTKFgQMHuuMyuJWeOS0iIiLe4LUl\nd6BuLcSkpCSWL19OZmYmjzzyCIGBgVitVueTVubOndvsYwZbsnDhQk6dOkVGRgbLly/H398fo9Ho\nXPg7LCyMpKSkJmdl33nnneTk5HDw4EHWr1/PX/7yF4xGo3PW9KBBg1iwYEHHTt5DNHNaREREvMGr\noREgNjaWZ555hq1bt5KamkpJSQkhISEMHjyYGTNmtPu+waCgIJYvX8727dtJSUnhxIkT1NbWEhMT\nw/jx45k5cyY9e/ZssqzZbGbx4sXs2LGDXbt2kZubi81mIzY2lilTpjBjxgzMZq9fKpdo5rSIiIh4\ng8HuqVW3L3KOiTeeNm9eOLt2BfDyy6e4/nrXnqLjq7SIa+dTG3QNaofOpzboGtQOrulyi3uLZ1wY\nntY9jSIiIuI5Co3dWHV13expg0HL7YiIiIhnKTR2Yzk5JmprDURF1dIFH1QjIiIiPkShsRvT0LSI\niIh4i0JjN+YIjZo5LSIiIp6m0NiNZWbWLbejNRpFRETE0xQauzENT4uIiIi3KDR2YxceIaieRhER\nEfEshcZuqqambvY0wIABCo0iIiLiWQqN3VRuromaGgNRUTUEBnZ2bURERMTXKTR2UxdmTut+RhER\nEfE8hcZuSjOnRURExJsUGrspxyQYhUYRERHxBoXGbkrD0yIiIuJNCo3dlJbbEREREW9SaOyG6i+3\no55GERER8QaFxm4oP99EdbWBvn1rCQy0d3Z1RERE5CKg0NgNaRKMiIiIeJtCYzek5XZERETE2xQa\nuyHNnBYRERFvU2jshjRzWkRERLzN3NkVkLa7444Khgyp4bLLqju7KiIiInKRUGjshmbOtDJzprWz\nqyEiIiIXEQ1Pi4iIiEirFBpFREREpFUKjSIiIiLSKoVGEREREWmVQqOIiIiItEqhUURERERapdAo\nIiIiIq1SaBQRERGRVik0ioiIiEirFBpFREREpFUKjSIiIiLSKoVGEREREWmVQqOIiIiItEqhUURE\nRERapdAoIiIiIq0y2O12e2dXQkRERES6NvU0is9YsmRJZ1fhoqc26BrUDp1PbdA1qB3cS6FRRERE\nRFql0CgiIiIirVJoFJ8xffr0zq7CRU9t0DWoHTqf2qBrUDu4lybCiIiIiEir1NMoIiIiIq1SaBQR\nERGRVpk7uwIiTamsrOTw4cMcO3aM48ePk5GRQXl5OQBr164lOjq6xfJ2u50dO3bw2WefkZeXh81m\no2/fviQkJHDzzTdjNutH3xXFxcXs27ePtLQ0srOzOXPmDGazmUsuuYTLL7+cm2++mbCwsGbL19TU\n8OGHH5KSkkJBQQEmk4no6GimTZvGtddei8Fg8OLZdE8ZGRn861//IiMjg4KCAsrKyqiuriYkJIRB\ngwaRmJjIxIkTmy2vNvAMq9XK73//e06dOgXAwoULSUxMbHJftYF77Nq1i40bN7a4j7+/P3//+9+b\n3KbPhY7TFZIuKS0tjaeffrpdZWtqali9ejUHDx4EwGw2YzQaycrKIisri7179/L4448TEBDgzir7\nnOLiYhYtWkT9254DAwM5f/482dnZZGdnk5yczKOPPspll13WqHxFRQVPPPEEx48fB+p+mVdVVZGe\nnk56ejoHDhwgKSkJk8nktXPqjnbs2EFycrLz+4CAAAwGA6dPn+bAgQMcOHCAK6+8kt/97neNPvTU\nBp7z5ptvOgNjS9QG7mcymQgODm5yW3O/1/W54B4KjdJlhYaGEh8fz6BBgwgPD+fFF190qdybb77J\nwYMH8fPzY8GCBUydOhWDwUBqaiobNmwgIyODF198kYceesjDZ9C92Ww2AMaNG0diYiKXXXYZwcHB\n1NTUkJaWxl//+leKiopYvXo169ato1evXg3K/+Uvf+H48eMEBwezaNEixo0bh91uZ/fu3WzatInU\n1FS2bNnC3LlzO+P0uo2hQ4cSHR3NiBEjiIqKcn6oFRcXs337dt5//3327dvHe++9x+zZsxuUVRt4\nxvHjx9m+fTtDhgwhPT29xX3VBu43bNgw/vSnP7WpjD4X3EP3NEqXNH78eDZt2sRjjz3GnDlzGD16\ntEvlSktL+fjjjwGYN28eiYmJGI1GDAYDV1xxBffffz8AX375JdnZ2R6rvy8IDg7mqaeeYsmSJUya\nNMn5l73ZbGbs2LE89thj+Pn5UVlZyaefftqgbGZmJnv37gXqhu2uuOIKDAYDRqORxMRE5s2bB8CH\nH37ImTNnvHti3UxiYiIzZswgPj6+QS9IREQE//Zv/8bVV18NwOeff96gnNrAM2w2G5s2bQLgnnvu\naXFftUHXoM8F91FolC7JaGzfj+ZXX31FdXU1QUFBTa7PNWHCBPr164fdbiclJaWj1fRpQUFBxMbG\nNrs9OjqaoUOHAjiH3hwc1zYqKorx48c3Kjt9+nSCgoKoqqpi37597qv0RWjQoEEAlJSUNHhdbeAZ\n27dvJyMjg+uvv564uLgW91UbdA36XHAfhUbxKYcPHwZgxIgRWCyWJvcZM2YMAN9++63X6uWrHL2P\njqFsB0c7NNdDbLFYGD58OKB26KgffvgBgD59+jR4XW3gfiUlJfzf//0foaGh3Hnnna3urzboGvS5\n4D66p1F8Sm5uLgD9+/dvdp+YmBgA8vLysNvtmrnYTrW1tRw9ehRoeL3tdjt5eXmNXv+pmJgYUlNT\nnfuK66xWK4WFhXz66afs2bMHgBtvvNG5XW3gGS+99BKVlZX85je/ISgoqMV91Qaek5OTwyOPPEJh\nYSEmk4nIyEhGDv1vZQAADy9JREFUjRrFzTff3OiPJ9DngjspNIpPKS0tBWhxGRjHNqvVitVqJTAw\n0Ct18zWffPIJpaWlGAwGrrnmGufrlZWVnD9/Hmi5HcLDwwE4ffq0ZyvqI06dOuW896o+Pz8/br/9\ndm644Qbna2oD9ztw4AD79+/n0ksvZerUqa3urzbwnPLycs6ePUuPHj2orKwkJyeHnJwckpOT+e1v\nf0tCQkKD/fW54D4KjeJTrFYrQLNDEFC35EX9/fXLoe2ys7N54403gLoervp/wTvaAFpuB8e2+vtL\n84xGI6GhoQCcO3eOmpoaTCYTt99+e4NeRlAbuJvVauWll17CZDLxm9/8xuUyDmoD9wgLC2POnDlc\neeWV9OvXD7PZTHV1NWlpabz22mvk5uayfv16wsPDGTlypLOcPhfcR6FRfJKGFjzn9OnTrF69mvPn\nzxMfH++cAdoUtYP7hIWFOWft2mw2CgoK2LZtG1u2bGHnzp089thjTQ6/qQ06bsuWLRQXF3Prrbc6\nhzHbQm3gHmPGjHHee+jg5+fHuHHjGD58OEuWLKGgoIDNmzfz5JNPNiqvdug4TYQRn+JYksQxLNSU\n+tu0kGvbnD17lieffJKioiL69evHkiVLGv31Xv+attQOVVVVjfYX1xiNRqKiorj//vuZOXMmxcXF\nPPfcc84JSWoD98nKyuKjjz6id+/ejdbBbInawLuCgoK4/fbbAUhPT6esrMy5TZ8L7qPQKD7FcV9K\nS/cHObYFBATol0MbVFRU8D//8z/k5OQQERHBsmXLGi3oDXVPjXEM9bTUDo4lYlq6z0ha5xiadjzZ\nAtQG7vTyyy9js9mci2877nlz/HOorq7GarU6w4fawPuGDBkC1E1CKioqcr6uzwX30fC0+JSYmBhy\nc3PJyclpdh/HTLro6GgNV7jIarWyYsUKMjIy6NWrF8uWLSMiIqLJfQ0GAzExMWRkZLjcDtJ+jokU\nAAUFBcTHx6sN3Ki4uBiA9evXt7jfpk2b2LRpE5GRkWzYsEFt0AnqP/K0/u92fS64j3oaxadceuml\nAHz//ffOYZ+fOnToEACjRo3yWr26s6qqKp566imOHj1KSEgIy5Yto1+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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.style.use('ggplot')\n", "plt.figure(figsize=(10,7))\n", "plt.plot(num_components, mean_score_list, 'b', label='mean accuracy')\n", "plt.xlabel('number of components')\n", "plt.ylabel('mean accuracy score')\n", "plt.legend(loc='upper left')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Вывод:**\n", "Оптимальным является количество главных компонент >=40" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## График зависимости оценки дисперсии функции качества от числа главных компонент." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Igor\\Anaconda3\\lib\\site-packages\\matplotlib\\font_manager.py:1316: UserWarning: findfont: Font family ['serif'] not found. Falling back to DejaVu Sans\n", " (prop.get_family(), self.defaultFamily[fontext]))\n" ] }, { "data": { "image/png": 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32jUYDPLNgrZ/HmhOCsvN25VmsG9WCzQOblVVFSIiIgCgSVvO6iMiIhAZGQmDwYCLFy/K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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.style.use('ggplot')\n", "plt.figure(figsize=(10,7))\n", "plt.plot(num_components, var_score_list, 'b', label='varience')\n", "plt.xlabel('number of components')\n", "plt.ylabel('variance')\n", "plt.legend(loc='upper right')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Из за того что дисперсия достаточно низкая при большом количестве главных компонент, новый график точности не должен сильно отличаться от предыдущего" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# ДЗ 3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Рассмотрим зависимость значения ошибки и ее стандартного отклонения от размера обучающей выборки" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import sys" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pca_components = 40\n", "total_run = 4\n", "accuracy_metric = []\n", "variances = []\n", "set_sizes = np.arange(500, 10500, 500)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "i: 500 /10500 \n", "i: 1000 /10500 \n", "i: 1500 /10500 \n", "i: 2000 /10500 \n", "i: 2500 /10500 \n", "i: 3000 /10500 \n", "i: 3500 /10500 \n", "i: 4000 /10500 \n", "i: 4500 /10500 \n", "i: 5000 /10500 \n", "i: 5500 /10500 \n", "i: 6000 /10500 \n", "i: 6500 /10500 \n", "i: 7000 /10500 \n", "i: 7500 /10500 \n", "i: 8000 /10500 \n", "i: 8500 /10500 \n", "i: 9000 /10500 \n", "i: 9500 /10500 \n", "i: 10000 /10500 \n" ] } ], "source": [ "for set_size in set_sizes:\n", " size_var = []\n", " #print (set_size)\n", " sys.stderr.write('i: %d /10500 \\n' % (set_size))\n", " sys.stderr.flush()\n", " for i in range(total_run):\n", " pca = PCA(n_components=pca_components)\n", " used_indices = np.random.choice(np.arange(X_train.shape[0]), set_size, replace=False)\n", " X_train_lowdim = pca.fit_transform(X_train[used_indices].reshape([-1, 784]))\n", " lr = LogisticRegression()\n", " #here we train our model\n", " lr.fit(X_train_lowdim, y_train[used_indices])\n", " y_pred = lr.predict(pca.transform(X_test.reshape([-1, 784])))\n", " #here we get our variance and accuracy\n", " score = accuracy_score(y_test, y_pred)\n", " size_var.append(score)\n", "#add variance and accuracy to result array\n", " accuracy_metric.append(np.mean(size_var))\n", " variances.append(np.var(size_var))" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Igor\\Anaconda3\\lib\\site-packages\\matplotlib\\font_manager.py:1316: UserWarning: findfont: Font family ['serif'] not found. Falling back to DejaVu Sans\n", " (prop.get_family(), self.defaultFamily[fontext]))\n" ] }, { "data": { "image/png": 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5SKdKb5DnCisp2FnOnipfdP85uWl8/ep5XLP0bjJyhnT7/QkhzgwSLgcJCZdi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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = np.asarray(set_size)\n", "accuracy_y = np.asarray(accuracy_metric)\n", "variance_y = np.asarray(variances)\n", "import matplotlib.pyplot as plt\n", "plt.figure(figsize=(10,7))\n", "plt.plot(set_sizes, accuracy_metric)\n", "plt.plot(set_sizes, accuracy_y + np.sqrt(variance_y),color='#000000')\n", "plt.plot(set_sizes, accuracy_y - np.sqrt(variance_y), color='#000000')\n", "plt.fill_between(set_sizes, accuracy_y + np.sqrt(variance_y), accuracy_y - np.sqrt(variance_y), color='#f47a42')\n", "plt.grid()\n", "plt.xlabel(\"Training set size\")\n", "plt.ylabel(\"Accuracy metric\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Вывод**¶\n", "\n", "Для достижения приемлемой точности достаточно обучать классификатор на выборке размером 4000 объектов" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Теперь рассмотрим зависимость значения ошибки и ее стандартного отклонения от размера тестовой выборки" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "train_set_size = 4000\n", "accuracy_metric = []\n", "variances = []\n", "set_sizes = np.arange(500, 10500, 500)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "i: 0 \n", "i: 1 \n", "i: 2 \n", "i: 3 \n", "i: 0 \n", "i: 1 \n", "i: 2 \n", "i: 3 \n", "i: 0 \n", "i: 1 \n", "i: 2 \n", "i: 3 \n", "i: 0 \n", "i: 1 \n", "i: 2 \n", "i: 3 \n", "i: 0 \n", "i: 1 \n", "i: 2 \n", "i: 3 \n", "i: 0 \n", "i: 1 \n", "i: 2 \n", "i: 3 \n", "i: 0 \n", "i: 1 \n", "i: 2 \n", "i: 3 \n", "i: 0 \n", "i: 1 \n", "i: 2 \n", "i: 3 \n", "i: 0 \n", "i: 1 \n", "i: 2 \n", "i: 3 \n", "i: 0 \n", "i: 1 \n", "i: 2 \n", "i: 3 \n", "i: 0 \n", "i: 1 \n", "i: 2 \n", "i: 3 \n", "i: 0 \n", "i: 1 \n", "i: 2 \n", "i: 3 \n", "i: 0 \n", "i: 1 \n", "i: 2 \n", "i: 3 \n", "i: 0 \n", "i: 1 \n", "i: 2 \n", "i: 3 \n", "i: 0 \n", "i: 1 \n", "i: 2 \n", "i: 3 \n", "i: 0 \n", "i: 1 \n", "i: 2 \n", "i: 3 \n", "i: 0 \n", "i: 1 \n", "i: 2 \n", "i: 3 \n", "i: 0 \n", "i: 1 \n", "i: 2 \n", "i: 3 \n", "i: 0 \n", "i: 1 \n", "i: 2 \n", "i: 3 \n", "i: 0 \n", "i: 1 \n", "i: 2 \n", "i: 3 \n" ] } ], "source": [ "for set_size in set_sizes:\n", " size_var = []\n", " for i in range(total_run):\n", " sys.stderr.write('i: %d \\n' % (i))\n", " sys.stderr.flush()\n", " pca = PCA(n_components=pca_components)\n", " used_indices = np.random.choice(np.arange(X_train.shape[0]), train_set_size, replace=False)\n", " X_train_lowdim = pca.fit_transform(X_train[used_indices].reshape([-1, 784]))\n", " lr = LogisticRegression()\n", " #here we train our model\n", " lr.fit(X_train_lowdim, y_train[used_indices])\n", " #change size of test set\n", " used_indices = np.random.choice(np.arange(X_test.shape[0]), set_size, replace=False)\n", " y_pred = lr.predict(pca.transform(X_test[used_indices].reshape([-1, 784])))\n", " #here we get our variance and accuracy\n", " score = accuracy_score(y_test[used_indices], y_pred)\n", " size_var.append(score)\n", "#add variance and accuracy to result array\n", " accuracy_metric.append(np.mean(size_var))\n", " variances.append(np.var(size_var))\n", " " ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Igor\\Anaconda3\\lib\\site-packages\\matplotlib\\font_manager.py:1316: UserWarning: findfont: Font family ['serif'] not found. Falling back to DejaVu Sans\n", " (prop.get_family(), self.defaultFamily[fontext]))\n" ] }, { "data": { "image/png": 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WFtKuXTumTJlCSEgIxcXFfPfdd6xfv56YmBjatWtHZGSkxeNu27bNnFgOGzaMkSNH4u3t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FKpNO4Sb9CgAWPGjCk1pV4ew9rRyuK050ecD2+45+e5oS4g+LPfcXG051Ty2UpbU1pQUEDbtm1J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\nF+PP4Lp160odGKLRaIzHH3fo0KHUbIShL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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = np.asarray(set_size)\n", "accuracy_y = np.asarray(accuracy_metric)\n", "variance_y = np.asarray(variances)\n", "import matplotlib.pyplot as plt\n", "plt.figure(figsize=(10,7))\n", "plt.plot(set_sizes, accuracy_metric, color='#000000')\n", "plt.plot(set_sizes, accuracy_y + np.sqrt(variance_y),color='#000000')\n", "plt.plot(set_sizes, accuracy_y - np.sqrt(variance_y), color='#000000')\n", "plt.fill_between(set_sizes, accuracy_y + np.sqrt(variance_y), accuracy_y - np.sqrt(variance_y), color='#f47a42')\n", "plt.grid()\n", "plt.xlabel(\"Test set size\")\n", "plt.ylabel(\"Accuracy metric\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Вывод:**\n", "достаточно использовать размер тестовой выборки около 6000 объектов" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }