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July 26, 2018 11:40
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probe1.ipynb
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| { | |
| "nbformat": 4, | |
| "nbformat_minor": 0, | |
| "metadata": { | |
| "colab": { | |
| "name": "probe1.ipynb", | |
| "version": "0.3.2", | |
| "provenance": [], | |
| "collapsed_sections": [], | |
| "include_colab_link": true | |
| }, | |
| "kernelspec": { | |
| "name": "python3", | |
| "display_name": "Python 3" | |
| } | |
| }, | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "[View in Colaboratory](https://colab.research.google.com/gist/naviarh/e2582885e3c7ee877c77a799d48b281c/probe1.ipynb)" | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "id": "rPwYJ7snwVFp", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "cell_type": "code", | |
| "source": [ | |
| "#!/usr/bin/env python3\n", | |
| "# -*- coding: utf-8 -*-" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "id": "blQ3JWnZwnWB", | |
| "colab_type": "code", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 34 | |
| }, | |
| "outputId": "a457b0ac-0ff5-4df4-e34b-35ecc7f2ffe2" | |
| }, | |
| "cell_type": "code", | |
| "source": [ | |
| "# Put these at the top of every notebook, to get automatic reloading and inline plotting\n", | |
| "%reload_ext autoreload\n", | |
| "%autoreload 2\n", | |
| "%matplotlib inline\n", | |
| "!pwd" | |
| ], | |
| "execution_count": 3, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "/content\r\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "id": "1hHd-SRTwqx_", | |
| "colab_type": "code", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 59 | |
| }, | |
| "outputId": "2d372c4a-bc55-4687-a27c-6f7fbcd5ee78" | |
| }, | |
| "cell_type": "code", | |
| "source": [ | |
| "import subprocess, os\n", | |
| "os.uname()" | |
| ], | |
| "execution_count": 4, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "posix.uname_result(sysname='Linux', nodename='ae7b2cec0cf8', release='4.14.33+', version='#1 SMP Wed Jun 20 01:15:52 PDT 2018', machine='x86_64')" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 4 | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "id": "Om1YGEvcw2t5", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "cell_type": "code", | |
| "source": [ | |
| "!pip install --upgrade pip\n", | |
| "!pip3 install theano\n", | |
| "!pip3 show theano | grep Name -A 1" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "id": "d21jGjIbwwWn", | |
| "colab_type": "code", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 51 | |
| }, | |
| "outputId": "dc82477e-83eb-4210-f234-220f6338d5e7" | |
| }, | |
| "cell_type": "code", | |
| "source": [ | |
| "import matplotlib.pylab as plt\n", | |
| "import seaborn as sns\n", | |
| "sns.despine()\n", | |
| "\n", | |
| "from keras.models import Sequential\n", | |
| "from keras.layers.core import Dense, Dropout, Activation, Flatten\n", | |
| "from keras.layers.normalization import BatchNormalization\n", | |
| "from keras.layers import Merge\n", | |
| "from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, CSVLogger, EarlyStopping\n", | |
| "from keras.optimizers import RMSprop, Adam, SGD, Nadam\n", | |
| "from keras.layers.advanced_activations import *\n", | |
| "from keras.layers import Convolution1D, MaxPooling1D, AtrousConvolution1D\n", | |
| "from keras.layers.recurrent import LSTM, GRU\n", | |
| "from keras import regularizers\n", | |
| "\n", | |
| "import theano\n", | |
| "theano.config.compute_test_value = \"ignore\"\n", | |
| "\n", | |
| "import pandas as pd\n", | |
| "import numpy as np" | |
| ], | |
| "execution_count": 6, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Using TensorFlow backend.\n" | |
| ], | |
| "name": "stderr" | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7fe2190491d0>" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "id": "lK4WjRhV15lB", | |
| "colab_type": "code", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 34 | |
| }, | |
| "outputId": "25f7a443-ea2e-4f3a-f3d1-1dfe08ca8c2a" | |
| }, | |
| "cell_type": "code", | |
| "source": [ | |
| "!ls" | |
| ], | |
| "execution_count": 7, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "datalab\r\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "id": "kDDi8oulxzcb", | |
| "colab_type": "code", | |
| "colab": { | |
| "resources": { | |
| "http://localhost:8080/nbextensions/google.colab/files.js": { | |
| "data": "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", | |
| "ok": true, | |
| "headers": [ | |
| [ | |
| "content-type", | |
| "application/javascript" | |
| ] | |
| ], | |
| "status": 200, | |
| "status_text": "" | |
| } | |
| }, | |
| "base_uri": "https://localhost:8080/", | |
| "height": 89 | |
| }, | |
| "outputId": "8283ec6c-0c52-4b95-85ff-a96d2fff3cc4" | |
| }, | |
| "cell_type": "code", | |
| "source": [ | |
| "# Загрузка исторических данных по дням для BTC-USD с 25 июля 2015 по 25 июля 2018\n", | |
| "# Скачать здесь: https://drive.google.com/open?id=1VI31_Gqt2ly83v4kxsN0_FnDPSeplF0l\n", | |
| "# Загрузить:\n", | |
| "#'''\n", | |
| "from google.colab import files\n", | |
| "\n", | |
| "uploaded = files.upload()\n", | |
| "\n", | |
| "for fn in uploaded.keys():\n", | |
| " print('User uploaded file \"BTC-USD.csv\" with length {length} bytes'.format(\n", | |
| " name=fn, length=len(uploaded[fn])))\n", | |
| "#'''" | |
| ], | |
| "execution_count": 10, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/html": [ | |
| "\n", | |
| " <input type=\"file\" id=\"files-277b62ed-5429-41a4-9d4a-b4a53e60c6fa\" name=\"files[]\" multiple disabled />\n", | |
| " <output id=\"result-277b62ed-5429-41a4-9d4a-b4a53e60c6fa\">\n", | |
| " Upload widget is only available when the cell has been executed in the\n", | |
| " current browser session. Please rerun this cell to enable.\n", | |
| " </output>\n", | |
| " <script src=\"/nbextensions/google.colab/files.js\"></script> " | |
| ], | |
| "text/plain": [ | |
| "<IPython.core.display.HTML object>" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Saving BTC-USD.csv to BTC-USD.csv\n", | |
| "User uploaded file \"BTC-USD.csv\" with length 85950 bytes\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "id": "-5z_y5QSzwBx", | |
| "colab_type": "code", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 34 | |
| }, | |
| "outputId": "2329a8f2-a6cb-434c-86f6-cd7ceb0c26c0" | |
| }, | |
| "cell_type": "code", | |
| "source": [ | |
| "# Видим нужный файл BTC-USD.csv\n", | |
| "!ls" | |
| ], | |
| "execution_count": 11, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "BTC-USD.csv datalab\r\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "id": "gu6PGOEj6ARJ", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "cell_type": "code", | |
| "source": [ | |
| "#!rm BTC-USD.csv" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "id": "cpu1wHDM38Yr", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "cell_type": "code", | |
| "source": [ | |
| "# Распечатка файла данных:\n", | |
| "!cat BTC-USD.csv" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "id": "Wf9ANAvh4--u", | |
| "colab_type": "code", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 402 | |
| }, | |
| "outputId": "2ad89379-fac7-49be-ae89-839b43ee64aa" | |
| }, | |
| "cell_type": "code", | |
| "source": [ | |
| "# Здесь видим график цены по дням\n", | |
| "data = pd.read_csv('BTC-USD.csv', error_bad_lines=False)[::1]\n", | |
| "close_price = data.ix[:, 'Adj Close'].tolist()\n", | |
| "plt.plot(close_price)\n", | |
| "plt.show()" | |
| ], | |
| "execution_count": 13, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: DeprecationWarning: \n", | |
| ".ix is deprecated. Please use\n", | |
| ".loc for label based indexing or\n", | |
| ".iloc for positional indexing\n", | |
| "\n", | |
| "See the documentation here:\n", | |
| "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n", | |
| " \n" | |
| ], | |
| "name": "stderr" | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7fe22b4dcb00>" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "id": "G8viaeJp_lcU", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "cell_type": "code", | |
| "source": [ | |
| "def shuffle_in_unison(a, b):\n", | |
| " # courtsey http://stackoverflow.com/users/190280/josh-bleecher-snyder\n", | |
| " assert len(a) == len(b)\n", | |
| " shuffled_a = np.empty(a.shape, dtype=a.dtype)\n", | |
| " shuffled_b = np.empty(b.shape, dtype=b.dtype)\n", | |
| " permutation = np.random.permutation(len(a))\n", | |
| " for old_index, new_index in enumerate(permutation):\n", | |
| " shuffled_a[new_index] = a[old_index]\n", | |
| " shuffled_b[new_index] = b[old_index]\n", | |
| " return shuffled_a, shuffled_b\n", | |
| " \n", | |
| "def create_Xt_Yt(X, y, percentage=0.9):\n", | |
| " p = int(len(X) * percentage)\n", | |
| " X_train = X[0:p]\n", | |
| " Y_train = y[0:p]\n", | |
| " \n", | |
| " X_train, Y_train = shuffle_in_unison(X_train, Y_train)\n", | |
| " \n", | |
| " X_test = X[p:]\n", | |
| " Y_test = y[p:]\n", | |
| "\n", | |
| " return X_train, X_test, Y_train, Y_test" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "id": "RDexk5ZHBvbs", | |
| "colab_type": "code", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 419 | |
| }, | |
| "outputId": "3c7d1cd9-657b-4cfb-ff19-a86effe63f9a" | |
| }, | |
| "cell_type": "code", | |
| "source": [ | |
| "\n", | |
| "data = pd.read_csv('BTC-USD.csv')[::1]\n", | |
| "data = data.ix[:, 'Adj Close'].tolist()\n", | |
| "\n", | |
| "# Uncomment below to use price change time series\n", | |
| "# data = data.ix[:, 'Adj Close'].pct_change().dropna().tolist()\n", | |
| "\n", | |
| "plt.plot(data)\n", | |
| "plt.show()\n", | |
| "\n", | |
| "WINDOW = 30\n", | |
| "EMB_SIZE = 1\n", | |
| "STEP = 1\n", | |
| "FORECAST = 5\n", | |
| "\n", | |
| "# Straightforward way for creating time windows\n", | |
| "X, Y = [], []\n", | |
| "for i in range(0, len(data), STEP): \n", | |
| " try:\n", | |
| " x_i = data[i:i+WINDOW]\n", | |
| " y_i = data[i+WINDOW+FORECAST] \n", | |
| "\n", | |
| " last_close = x_i[WINDOW-1]\n", | |
| " next_close = y_i\n", | |
| "\n", | |
| " if last_close < next_close:\n", | |
| " y_i = [1, 0]\n", | |
| " else:\n", | |
| " y_i = [0, 1] \n", | |
| "\n", | |
| " except Exception as e:\n", | |
| " print(e)\n", | |
| " break\n", | |
| "\n", | |
| " X.append(x_i)\n", | |
| " Y.append(y_i)\n", | |
| "\n", | |
| "X = [(np.array(x) - np.mean(x)) / np.std(x) for x in X] # comment it to remove normalization\n", | |
| "X, Y = np.array(X), np.array(Y)\n", | |
| "\n", | |
| "X_train, X_test, Y_train, Y_test = create_Xt_Yt(X, Y)" | |
| ], | |
| "execution_count": 15, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: DeprecationWarning: \n", | |
| ".ix is deprecated. Please use\n", | |
| ".loc for label based indexing or\n", | |
| ".iloc for positional indexing\n", | |
| "\n", | |
| "See the documentation here:\n", | |
| "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n", | |
| " This is separate from the ipykernel package so we can avoid doing imports until\n" | |
| ], | |
| "name": "stderr" | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": 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taR7cpOEgvQ7Wo6Uqxeib0S7t8M9fPQufP3E8/0g1gGiHeK5WRB1K/vyllVIT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\nrqqUGVMq+cSJU5jRkP6SCv/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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7fe2085a0550>" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "list index out of range\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "id": "K5or8Ur3C1PM", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "cell_type": "code", | |
| "source": [ | |
| "# Модель нейронной сети\n", | |
| "\n", | |
| "model = Sequential()\n", | |
| "model.add(Dense(1024, input_dim=30,\n", | |
| " activity_regularizer=regularizers.l2(0.01)))\n", | |
| "model.add(BatchNormalization())\n", | |
| "model.add(LeakyReLU())\n", | |
| "\n", | |
| "model.add(Dropout(0.5))\n", | |
| "model.add(Dense(512,\n", | |
| " activity_regularizer=regularizers.l2(0.01)))\n", | |
| "model.add(BatchNormalization())\n", | |
| "model.add(LeakyReLU())\n", | |
| "model.add(Dense(2))\n", | |
| "model.add(Activation('softmax'))\n", | |
| "\n", | |
| "opt = Nadam(lr=0.001)\n", | |
| "\n", | |
| "reduce_lr = ReduceLROnPlateau(monitor='val_acc', factor=0.9, patience=25, min_lr=0.000001, verbose=1)\n", | |
| "checkpointer = ModelCheckpoint(filepath=\"test.hdf5\", verbose=1, save_best_only=True)\n", | |
| "model.compile(optimizer=opt, \n", | |
| " loss='categorical_crossentropy',\n", | |
| " metrics=['accuracy'])" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "id": "AXa8ZPzhC2Ad", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "cell_type": "code", | |
| "source": [ | |
| "# Обучим нейронку\n", | |
| "\n", | |
| "history = model.fit(X_train, Y_train, \n", | |
| " nb_epoch = 10, \n", | |
| " batch_size = 64, \n", | |
| " verbose=1, \n", | |
| " validation_data=(X_test, Y_test),\n", | |
| " callbacks=[reduce_lr, checkpointer],\n", | |
| " shuffle=True)" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "id": "c62y0VuzC5ZU", | |
| "colab_type": "code", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 571 | |
| }, | |
| "outputId": "5f919353-f08e-4e79-8470-554833cd046f" | |
| }, | |
| "cell_type": "code", | |
| "source": [ | |
| "# Посмотрим результат\n", | |
| "\n", | |
| "plt.figure()\n", | |
| "plt.plot(history.history['loss'])\n", | |
| "plt.plot(history.history['val_loss'])\n", | |
| "plt.title('model loss')\n", | |
| "plt.ylabel('loss')\n", | |
| "plt.xlabel('epoch')\n", | |
| "plt.legend(['train', 'test'], loc='best')\n", | |
| "plt.show()\n", | |
| "\n", | |
| "plt.figure()\n", | |
| "plt.plot(history.history['acc'])\n", | |
| "plt.plot(history.history['val_acc'])\n", | |
| "plt.title('model accuracy')\n", | |
| "plt.ylabel('accuracy')\n", | |
| "plt.xlabel('epoch')\n", | |
| "plt.legend(['train', 'test'], loc='best')\n", | |
| "plt.show()" | |
| ], | |
| "execution_count": 52, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": 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WiRK3y8lJU32cNNVHc1sn766vYcmaatZs2ceaLftISXJx8rR85pUXMmVstu7d\nLCOWwkBkiGSkevjoSaV89KRSqva28Pbaat5eU81bq6p4a1UVYzJTOL28kLnlhRTmpsW6XJEIOkyU\n4NQekYa6PQLBIBU7Gliypppltpb2Dj8Ak0oymVtexCnT8slI9QzZ5w0lfW9Eiof20GEikRhxOhxM\nG5/DtPE5XHfBVFZU1LF4TTXrtu1j864mHnu5ghMnh4apzpg4BrdLw1QlNhQGIsMk2ePitOmFnDa9\nkPr97SxdV82S1dUst3Ust3VkpHqYc3wBp08vpKzIm7DTYEhsKAxEYiDHm8zFc8Zz0anj2FHTzJI1\n1byzrppFyytZtLySrPQkyifmMmPiGKaX5ZKeMjIPJUn8UBiIxJDD4WB8oZfxhV6uPmcSa7fuY9mG\nWtZs2cvi1aEL3BwOmFScxYyJucyYNIZxBV6NSpIhpzAQGSHcLicnTs7jxMl5BIJBdtTsZ/Xmvaze\nso/NuxvZtKuRf7y5lcw0D+UTx/T0GkbqCWgZXRQGIiOQ0+FgQmEmEwoz+fi8MprbOlm3bV8oHLbu\nY8maapasCfUaJhZlMmPiGGZMGsP4QvUaZHCiGgbGmHLgWeDX1trfGWPGAg8DLqAKuMFa226MuQ64\nAwgA91pr749mXSKjTUaqh1OPK+DU4woIBIPsrGlm9Za9rN6yl827mti8u4ln3tqKN81DeVnvuQZv\nWlKsS5dRImphYIxJB+4BFvVZ/EPg99bavxtj/hP4nDHmIeAu4FSgA1hmjPmHtXZftGoTGc2cfc4z\nXDp3Ai0HOlm3rT7ca9jL22treHttDQ5gQlFmz7mGssJMnE71GuTQotkzaAc+Bnyrz7KzgS+Fnz8P\nfAOwwDJrbSOAMWYxMC+8XkSOID3FwynT8jllWj7BYJCdteFew+a9bNrVxNaqJp5bvI2M1D69hom5\nZKrXIH1ELQystV1AlzGm7+J0a217+HktUAQUAnV9tule3q+cnDTcx3CPWp9PUw33pfaINNrbIz8/\nk9nlxQC0tHWycmMdy9fXsHxDLUvX1bB0XQ0OB0wuzWb2tAJmT8tnyrgcXIfoNYz2thhq8dwesTyB\n3F9/9Yj92Pr61kF/aDxcUj6U1B6R4rE9phZ5mVrk5TPnTKKyrqVPr6GRjTsb+NtCS3qKm+nhXkP5\nxDFkpSfFZVsci3hoj8OF2XCHQbMxJtVa2waUALvD/wr7bFMCLB3mukTinsPhYGx+BmPzM/jYaeNp\na+8KjVDaEhq+2n3DHoDxhV5OnV7I2Lw0JhVnkZqsgYfxbrj/h18GrgQeCT++CLwD3GeMyQa6CJ0v\nuGOY6xJJOKnJbmabfGab0Lk/zEgsAAAS/klEQVSGXXt6ew0bKxvZXr0RCJ2wHleQwdSx2Uwdm82U\n0iyNUopDUZu11BgzG/glMAHoBHYB1wEPAinAduBma22nMeYq4N+BIHCPtfbRw723Zi0dOmqPSGqP\nkLb2Lmr3d7BsTRUVOxvYWtWEP9D7Y1eclx4Kh9Ispo7NJjczJYbVDo94+N443KylmsI6wak9Iqk9\nevVti/ZOP1t3N1Gxs4GKygY27WqkozPQs21eVkpPz2Hq2GwKclLjbqK9ePje0BTWInJMkj2unqm4\nIXS7z+01+9m4s5GKnQ1srGzouSoaIDM9KaLnUOrL0DUOI5zCQESOmtvlZFJxFpOKs7hozjgCwSC7\n61qw4WCwOxt4b0Mt720InZBOTXYzpTQLMzabKWOzmVDo1b0bRhiFgYgcM6fDQWl+BqX5GZw7u5Rg\nMEhdQxt2Z0Oo57CzkVWb97Jq814AktxOJhZnMnVsNmZsNhNLskj2DP7aITl2CgMRGXIOh4P8nDTy\nc9I484TQBXD1+9t7eg0bdzawYUfoH4DL6WBCoZcpfUYs6R4Ow0thICLDIseb3DPZHkBzWycbK0O9\nBruzga1V+9m8u4kX39mBAyjxZWDGZjOpNJPxBV4KctM0I2sUKQxEJCYyUj3MmuJj1hQfAAc6uti8\nu4mKHaHzDpt3N1FZ18yi90PbJye5GJ+fwbhCLxMKvYwv8FI4Jg2XU+cehoLCQERGhJQkN9Mn5DJ9\nQi4AnV0BtlU3sbVqP9urm9he08zGXY1UVDb27JPkdjI2PyM0i2tBaCbX4rx0nZweBIWBiIxIHreT\nKaXZTCnN7lnW3uFnZ10z26v3h/7V7GdbdejwUje3y0GpLzIgSn3peI5hcstEoDAQkVEjOcnF5JIs\nJpdk9Szr7PJTWdfSEw7bq/dTWdfMtureC8RcTgfFeekRATE2P0MjmPpQGIjIqOZxuygryqSsKLNn\nWZc/wO49kQGxs7aZnbXNvEUVAA4HFI9JZ1w4HMYXZDCuwJuwk/Il5lctInHN7XIyrsDLuAIvZ4aX\n+QMBqve29hxa2lG9n+21zeza08Lba0NXTjuA/Nw0xhdkMKEwMxQQhd6EGOaqMBCRhOByOinxZVDi\ny2Bueej+WYFgkNr6NrZVN7GjurmnF9F3Om8Izb00ZVwOed5kSvMzKMlLpyA3Na5GMikMRCRhOR0O\nCnPTKMxN47TjQ8uCwSB1jQdCPYdwOGyr3s/bq6si9nW7nBSPSaPEl05pOGRKfenkeJNH5SR9CgMR\nkT4cDgf52ankZ6dy8rR8IBQQ7pQkVm2oobKumV11LVTWNbN7Tws7apuBmp7905LdlPjSe8KhJC+d\n0vyMEX+oSWEgInIEDoeD3MwUppflMr0st2d5IBCkrrGNytoWdtU1U7kn9LhpVyMb+1wPAaErsEvy\nunsRoceiMWkkjZARTQoDEZFBcjodFOSkUZCTxmzj61ne2eWnam9rn15EqCexZus+1mzd17OdwwH5\nOWmUdh9qCvci8rNTh33Kb4WBiMgQ87hdPaOZ+mo90EllXZ9eRG0zlXUtLN/XynJb12d/J8Vj0kOH\nmboPN/kyyM5Iitr5CIWBiMgwSUvx9NwNrlswGKShuSPiXERlXWjI6/aayDurZaR6+OJl03um7BhK\nCgMRkRhyOBzkeJPJ8SYzY+KYnuX+QIDa+raegNhV10JdY1voTvFRoDAQERmBXE4nRWPSKRqT3jOq\nKZri54qJAQgEA7R1Hoh1GSIiI05C9Qxer1zCk68+x4TMcczKn8Es3wzGpA79sTcRkdFmWMPAGHM2\n8HdgbXjRauBnwMOAC6gCbrDWtkfl83MmU55vWFtbwbamHfxj03zGeUuZlT+Dmb4Z5KflReNjRURG\nvFj0DF631l7V/cIY8wDwe2vt340x/wl8DvifaHxwcUYhd51zB1t2VbFqz1pW1K7G1m9ix/5Knt38\nL0ozint6DAXp0T9GJyIyUoyEw0RnA18KP38e+AZRCoNu3qQM5hXPYV7xHJo7W1hdt44VdavZsG8j\nlVt28/yWlyhOLwwFQ/4JFKUXRLMcEZGYcwSDURqndAjhw0R/ADYBucAPgEettfnh9ZOAh621cw/3\nPl1d/qA7CnctauloZfnu1Szd+T4rq9fRFegCoCSzkNNKT+K0sbMYl1UyKiehEhEhNEv3oVcMcxiU\nAGcATwATgVeBDGttbnj9ZOChI4VBXd3+QRft83mpq9t/xO3aug6wZs96VtStZt3eDXSGgyE/NY+Z\n+TOYlT+DsRmjPxgG2h6JQu3RS20RKR7aw+fz9vsLa1gPE1lrdwGPh19uNsZUA6cYY1KttW1ACbB7\nOGvqT6o7hVMKZ3FK4SwOdLWzdu8GVtStZu2e9SzY/ioLtr9KXkous/JPYFb+DMZ5S0d9MIhI4hru\n0UTXAUXW2l8YYwqBAuAB4ErgkfDji8NZ00CkuJOZXXAiswtOpMPfwbq9lhV1q1m9Zx0Ld7zGwh2v\nkZOc3XOOYULmWJyOhLqEQ0RGueE+TOQF/gpkA0mEzhmsAB4CUoDtwM3W2s7Dvc9wHCYaiE5/J+v3\nVfB+bSgYDvhDF7RlJ2cx01fOrPwTmJg1fkQHQzx0fYeS2qOX2iJSPLTH4Q4TDWsYDJWREgZ9dQa6\nsPs2sqJ2NR/sWUtbVxsAWUleTvSFzjFMzi4bccEQD9/gQ0nt0UttESke2mPEnDOIZx6nm/K84yjP\nO45rAl1U1G8OB8Ma3ti1hDd2LcHryeBE33Rm5Z/AlOyJuJwj46YWIiIKgyhwO90cP8Zw/BjDZwKf\nZGPDFlbUrmJl3Rre2v0Ob+1+hySnh8L0fIrSCylKL6A4I/SYk5ytE9EiMuwUBlHmcrqYljuFablT\n+LT5JJsatrKidjWbG7eyu7maHft3RWyf4kqmKL0g9C8cEMXphWQmeRUSIhI1CoNh5HQ4mZoziak5\nkwDwB/zsadtLVUsNu1uqw481bN9fydamHRH7prlTQ72IjFA4dAeGNykjFl+KiMQZhUEMuZwuCtLz\nKUjPZyYzepZ3Bbqobd1DVUs1u1tqqGqpoaq5mi2N29jcuDXiPbyejA/1IorSC0jzpA73lyMio5jC\nYARyO90UZxRSnFHI7D7LO/yd1LTWhnoQzaGeRFVLDRUNm6lo2BzxHtnJWb2Hm3p6EvmkuFOG94sR\nkVFBYTCKJLk8jPWWMNZbErH8QFc7Na21PQHRfchp/b4K1u+riNg2NyWH4j4BMcUxlmCbh6xkL26n\nvh1EEpV++uNAijuZ8ZljGZ85NmJ5a2cb1a01fUKihqqWatbs3cCavRtCG63v3d7rySA7OZOs5Cyy\nkzPJTs6KeJ6dnEmqO1UnskXikMIgjqV5UpmYNYGJWRMiljd3tFAV7j20OpqpathDQ3sjDe1NVLfW\nsrO5/+mhPE5Pn6DoDom+zzPJSsrUNRQio4zCIAFlJKUzJWkSU3ImfeiqymAwSFtXGw3tTT0B0dje\neNDzJja2ben3/R04yEhK7w2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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7fe1fff77ba8>" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": 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RZKK6odPaUmi3JIbadrp6+k/bLyY8kJwZkScTw9SEMCJDAxway3C0bFmsAN4DkFIWCyGi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\n26cAAAAASUVORK5CYII=\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7fe2009058d0>" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "id": "Twuk_Jb3DBAu", | |
| "colab_type": "code", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 314 | |
| }, | |
| "outputId": "4d62eac7-5691-4e13-dea3-1f4b1d0e11f6" | |
| }, | |
| "cell_type": "code", | |
| "source": [ | |
| "from sklearn.metrics import classification_report\n", | |
| "from sklearn.metrics import confusion_matrix\n", | |
| "\n", | |
| "pred = model.predict(np.array(X_test))\n", | |
| "C = confusion_matrix([np.argmax(y) for y in Y_test], [np.argmax(y) for y in pred])\n", | |
| "\n", | |
| "print(C / C.astype(np.float).sum(axis=1))\n", | |
| "\n", | |
| "FROM = 0\n", | |
| "TO = FROM + 500\n", | |
| "\n", | |
| "original = Y_test[FROM:TO]\n", | |
| "predicted = pred[FROM:TO] \n", | |
| "\n", | |
| "plt.plot(original, color='black', label = 'Original data')\n", | |
| "plt.plot(predicted, color='blue', label = 'Predicted data')\n", | |
| "plt.legend(loc='best')\n", | |
| "plt.title('Actual and predicted from point %d to point %d of test set' % (FROM, TO))\n", | |
| "plt.show()" | |
| ], | |
| "execution_count": 53, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "[[0.89655172 0.12244898]\n", | |
| " [0.68965517 0.18367347]]\n" | |
| ], | |
| "name": "stdout" | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": 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Bx2c2olM7uoWfzDC2ZWVlmM1m8vPzadeuAzt2bKdBg9ygMLNQfhjb5cuXs2rV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\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7fe1fff71668>" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "id": "ot-af5h4DaI1", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "cell_type": "code", | |
| "source": [ | |
| "" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "id": "OAe5o6jqVwuv", | |
| "colab_type": "text" | |
| }, | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Проверка на рандомных курсах" | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "id": "GRaWc3a_T--t", | |
| "colab_type": "code", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 187 | |
| }, | |
| "outputId": "e4ad5b69-5f59-48ae-fae0-5351b4e67a7b" | |
| }, | |
| "cell_type": "code", | |
| "source": [ | |
| "# Посмотрим на курсы\n", | |
| "data[:10]" | |
| ], | |
| "execution_count": 54, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "[4352.299805,\n", | |
| " 427.69000199999994,\n", | |
| " 455.410004,\n", | |
| " 7283.220215,\n", | |
| " 1243.140015,\n", | |
| " 322.77999900000003,\n", | |
| " 361.799988,\n", | |
| " 2617.320068,\n", | |
| " 10594.759766,\n", | |
| " 263.440002]" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 54 | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "id": "A4X41TCMUaJ1", | |
| "colab_type": "code", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 34 | |
| }, | |
| "outputId": "7a8d8822-03a4-44db-bae3-42dd7bce13f7" | |
| }, | |
| "cell_type": "code", | |
| "source": [ | |
| "# Минимаольное значение\n", | |
| "min(data)" | |
| ], | |
| "execution_count": 55, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "211.42999300000002" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 55 | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "id": "9D7d9arBUb_Y", | |
| "colab_type": "code", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 34 | |
| }, | |
| "outputId": "cca4bd60-bf12-4e3a-f3b1-10d7b5abeba7" | |
| }, | |
| "cell_type": "code", | |
| "source": [ | |
| "# максимальное значение\n", | |
| "max(data)" | |
| ], | |
| "execution_count": 56, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "19345.490234" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 56 | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "id": "en0iiGDOUdwp", | |
| "colab_type": "code", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 34 | |
| }, | |
| "outputId": "6fb11668-86aa-490f-d5fe-116cc4a4b93a" | |
| }, | |
| "cell_type": "code", | |
| "source": [ | |
| "# Количество\n", | |
| "len(data)" | |
| ], | |
| "execution_count": 57, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "1097" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 57 | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "id": "YS0_NBjCUgjz", | |
| "colab_type": "code", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 187 | |
| }, | |
| "outputId": "a2232d9a-6f42-4653-9570-bd3bb356b2a0" | |
| }, | |
| "cell_type": "code", | |
| "source": [ | |
| "# Перемешаем рандомно список несколько раз\n", | |
| "import random\n", | |
| "random.shuffle(data)\n", | |
| "random.shuffle(data)\n", | |
| "random.shuffle(data)\n", | |
| "data[:10]" | |
| ], | |
| "execution_count": 58, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "[829.210022,\n", | |
| " 653.030029,\n", | |
| " 229.52999900000003,\n", | |
| " 613.210022,\n", | |
| " 416.0,\n", | |
| " 602.590027,\n", | |
| " 457.869995,\n", | |
| " 4618.709961,\n", | |
| " 700.070007,\n", | |
| " 415.0]" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 58 | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "id": "3bR4yk8sVoTE", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "cell_type": "code", | |
| "source": [ | |
| "# Обучим уже на рандоме\n", | |
| "history = model.fit(X_train, Y_train, \n", | |
| " nb_epoch = 10, \n", | |
| " batch_size = 64, \n", | |
| " verbose=1, \n", | |
| " validation_data=(X_test, Y_test),\n", | |
| " callbacks=[reduce_lr, checkpointer],\n", | |
| " shuffle=True)" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "id": "wJ8ECT5lXH1l", | |
| "colab_type": "code", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 571 | |
| }, | |
| "outputId": "39cfeb43-a025-4be2-ccc2-21c73e9e3d00" | |
| }, | |
| "cell_type": "code", | |
| "source": [ | |
| "# Посмотрим результат\n", | |
| "plt.figure()\n", | |
| "plt.plot(history.history['loss'])\n", | |
| "plt.plot(history.history['val_loss'])\n", | |
| "plt.title('model loss')\n", | |
| "plt.ylabel('loss')\n", | |
| "plt.xlabel('epoch')\n", | |
| "plt.legend(['train', 'test'], loc='best')\n", | |
| "plt.show()\n", | |
| "\n", | |
| "plt.figure()\n", | |
| "plt.plot(history.history['acc'])\n", | |
| "plt.plot(history.history['val_acc'])\n", | |
| "plt.title('model accuracy')\n", | |
| "plt.ylabel('accuracy')\n", | |
| "plt.xlabel('epoch')\n", | |
| "plt.legend(['train', 'test'], loc='best')\n", | |
| "plt.show()" | |
| ], | |
| "execution_count": 62, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": 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PKOn3Qw6Hg+mjM/j5LTNZMCmTAyXH+PkT63hq6U5O1Kt7p4h0Tkm/H4uL9nDD4jH84EtT\nyEiJYem6A9z5lw/YtFvdO0Xk1JT0w8DonBR+9tUZXDQnh4pj9fz275v404tbqKqpD3VoItLHqMtm\nmPC4XVy+YAQzRg/k0dd28OF2X/fOq87OY96EzFCHJyJ9hM70w8zgjHj+3/VT+dK5I2lsauHRf+zg\nvmc2UFx6LNShiUgfoKQfhpxOB+dNG8I9N89k4ohUtu8t5/ZfvcOSlQXUNTSFOjwRCaGANu8YY8YD\nLwK/sdY+aIwZAjwKeIAG4Dpr7aFAxhDJUpOi+faVE1m74wjPvpPPS+/tYdXmg1y1MI/pozP0RK9I\nBArYmb4xJg54AFjWbvE9wMPW2jOBF4DvBur44uNwOJgxZiB//OE5XDArh6qaev704lZ+8dR69h2u\nDnV4IhJkgWzeqQMuAIrbLfsG8Jz/dQmQGsDjSzux0R6uPGsE/+F/onfn/grufmwtT7xhqa5VLx+R\nSBGw5h1rbSPQaIxpv6wGwBjjAr4J/CxQx5dTG5gSy7eunMiWgjKeXraLd9cX8eG2w1w6fxgLz8jG\n5dRtHpFw5mhpaQnoAYwxdwGl1toH/e9dwF8Ba629u6vPNjY2tbjdroDGF8kam5p5ZVUhT7+5g9oT\njeQMSuCWSycwaWR6qEMTkZ7p9IZdKPrpPwrs+qyED1Be3rNRJNPTEygpUbs1dF4Wc8dmMCEnmeeW\n72bVpoP8+E+rmToqnavPziMtOSYEkQaH/jY6Unm0CYeySE9P6HRdUJO+MeZaoN5a+9NgHle6lhgX\nxY0XjGHhGdn87a2dfLSzhI27y1g8cygXzMrBG6WrLZFwEbDmHWPMVOB+IBdf98wiIAM4AZwcEnKb\ntfYbne2jpKS6R8GFQ43dW7pbFi0tLazZdpi/v5NPxbF6UhK8XLUwjxljwquLp/42OlJ5tAmHskhP\nT+j0nzXgbfo9oaTfe063LE7UN/Lq+3t548N9NDa1MGpwEtecN4qhAzu/bOxP9LfRkcqjTTiURVdJ\nX1015JSio9xcceYI7rl5JlNGprHzQKWvi+frO9TFU6Qf04Br0qWMlFhuv2IiWwrLeHrpLt7dUMyH\n24+oi6dIP6X/WOmW8cNSufumGfzzOSNpAZ5auou7HlnLtj1HQx2aiJwGJX3pNrfLyaLpQ7j31lks\nmJRFcWkN9z2zgQef30xJxfFQhyci3aDmHTltiXFR3LB4NGdNyeKpt3bx8c4SNu0u4/yZQ7lQXTxF\n+jSd6cvnljsokTuuO4NbLx5LfIybV1bv4Ud/XsOabYfoy73CRCKZkr70iMPhYNa4QfznrbO4cHYO\n1bUNPPzSNv7rbx+z91D/7vYmEo6U9KVXtHbxvMXXxXPXgUp+9thaHn99B1Xq4inSZ6hNX3pVRnIM\nt18xka17jvL00l0s31DM2u1HuGT+MBZOycbt0nmGSCjpP1ACYlzuAO66cTpf8nfxfHrpLu56dC1b\nCstCHZpIRNOZvgSM2+XkvOlDmDluIC+sKGDFhmJ+/b8bGT00mcsXjCBvcFKoQxSJOEr6EnCJsVF8\n5fzRnDU5m+dXFLC5oIz/fPIjJgxP5fIFw8kZFB7j+Yj0B0r6EjQ5gxL416smsXN/BS/4k//mgjKm\nmnQunTeM7PT4UIcoEvaU9CXoRg1J5gfXTGHb3nKeX17AR7aEj20JM8cN5JJ5wxiYEhvqEEXClpK+\nhITD4WBc7gDG5qSwMb+MF1YWsGbrYT7cdoR5EzO5eE4uqUnRoQ5TJOwo6UtIORwOJo9MY2JeKut2\nHGHJykJWbCxm9ZaDnDU5mwtn55AU7w11mCJhQ0lf+gSnw8GMMQOZatJZs/UwL64qZOlHB1ixqZhz\npg5m8cwc4mM8oQ5TpN9T0pc+xeV0MndCJjPHDmTlpoO8/F4hr63Zx7vri1g0fSiLpg8hxqs/W5HP\nS/890ie5XU4WTslm7vhBvLu+iFfX7PWd/a/bzwWzcjh76mC8Ho3mKXK69ESu9GlRHheLZgzlF1+f\nzeULhtPSAn9/dzc//NP7LF23n4bG5lCHKNKvKOlLvxAd5eaiObn88l9mc9GcXOoamnhq6S7uePh9\nVmwsprFJyV+kOwLavGOMGQ+8CPzGWvugMWYI8FfABRwErrfW1gUyBgkvsdEeLl8wnHOnDea1NXt5\n++MiHnttB/9Ys5dL5g1j5piBOJ2OUIcp0mcF7EzfGBMHPAAsa7f4Z8DvrbXzgXzgpkAdX8JbYmwU\nV589kv/62mwWnpFNWeUJ/vzyNn76yId8ZI9oEheRTgSyeacOuAAobrfsLOAl/+uXgXMDeHyJACkJ\nXq5fZLj31lnMm5BJcVkNv39hCz97fB2bdpcp+Yt8QsCad6y1jUCjMab94rh2zTlHgMyu9pGSEovb\n3bMeGunpGszrpHAui/T0BMaMzODAkWqefsOyYkMRv/37RsbkDuD6xWOYkJd2ys9IG5VHm3Aui1B2\n2fzMhtfy8toeHSA9PYGSEk3ZB5FTFl4H3HC+4ZwzslmysoD1u0r50R/fY2xuCpctGM6ILN9wzpFS\nHt2l8mgTDmXRVaUV7KR/zBgTY609DmTTselHpNcMyYjn9ismUniwiudXFLC18Cjb9nzE5Lw0Lp0/\nLKzP5ES6EuykvxS4AnjS//v1IB9fIsywzES+d/Vk7L5yXlhRwIb8UjbklzJ7QiYLJ2VpIheJOI5A\n3egyxkwF7gdygQagCLgWeAyIBvYCN1prGzrbR0lJdY+CC4fLtN6isoCWlha27jnKCysKKTxYBcDw\nrEQWTR/CVJOOyxm5j63o76NNOJRFenpCp83nAUv6vUFJv/eoLNq0tLRwpLqeZ9+ybNhVSguQmhjN\nedMGM39SVkSO7aO/jzbhUBZdJf3I++uWiOdwOBg/Io2BiV4OH63lrXX7WbX5IM+8nc+SVYUsmJTF\nudMGk5YUE+pQRXqdkr5EtIEDYrlukeHS+cNZvqGIZR8d4M21+1m67gBTTTqLZgxp7fEjEg6U9EWA\n+BgPF87O5QszhvLh9sO8+eF+1u44wtodR8jLTmLR9CGcMSpdQzxIv6ekL9KO2+VkzvhMZo8bxI59\nFbz54T427i4jv6iStKRozps+hHkTMiOy3V/Cg/5yRU7B4XAwJieFMTkpHCyr4a11B3hv80GeXrqL\nJSsLOXNyFudOHcyARM3jK/2Leu9ECJVFR5+nPKpr63l3fRHLPi6iqqYep8PB9DEZLJo+hGGZiQGK\nNDj099EmHMqiV3vvGGO8QIa1dn+PohLpZxJio7h47jDOn5nDB9sO8+bafXyw7TAfbDvMqMFJLJox\nlMl5aWr3lz6tW0nfGHMHcAz4H2AdUG2MedNae2cggxPpizxuJ/MmZjJ3wiC27S3nzQ/3s7mgjJ0H\nNpORHNPa7u+N0nSO0vd090z/YmAu8GXgZWvtD40xbwcuLJG+z+FwMC53AONyB1BUWsNba/ezessh\n/vbWTpasLODMydmcM3UwKQneUIcq0qq7z503WGtbgMXAEv8yncaI+GWnxXHD4tHc9405XDJvGE6n\ng3+s2csP/riaP7+8lb2H+ncbsYSP7p7pVxhjXgUGW2vfN8ZcBGhSUpFPSIyL4pJ5w7hg1lDe33qY\nN9fu5/2th3l/62FGD01m0fShTMxLxelQu7+ERneT/jXAecB7/vcngK8EJCKRMOBxu1gwKYv5EzPZ\nWniUN9buZ2vhUXbsq2DggFgWTRvMnAmZeD26YJbg6m7STwdKrLUlxphbgFnAfYELSyQ8OBwOxg9P\nZfzwVA4cOcab6/azZush/vrmTp5fUcD8SVksmJTFoAGxoQ5VIkR32/QfBeqNMVOAm4HngP8OWFQi\nYWhwRjw3XTCGX/3LHP5pbi4Oh4PXP9jHjx5ewy/+9jHvbz1EQ2NTqMOUMNfdpN9irV0LXAY8aK39\nB92Y7lBEPi0p3sul84dz/zfn8rV/GseYnBTs/gr+/PI2vvvgezy1dCdFJcdCHaaEqe4278QbY6YD\nVwJn+h/QSglcWCLhz+N2MnPsQGaOHcjh8lpWbCzmvc2HWLruAEvXHWBEdiILJmUxY/RA9fmXXtPd\npH8/8GfgIX+7/r3AU4ELSySyDEyJ5Ytn5XHZ/OFszC9l+cZithYcZXdRFc8s28WssYNYMCmLnEGa\n21d65rTG3jHGDABagAp/v/2A0tg7vUdl0VF/KI/SyuOs3HiQVZsPUl5dB0DOoATOnJTFzLEDe3Wk\nz/5QHsESDmXR4+kSjTFzgSeABHz3AUqB66y163oryFNR0u89KouO+lN5NDU3s7ngKCs2FLNpdxnN\nLS14PS6mj8ngzMlZDM9MxNHDfv/9qTwCLRzKojcGXLsXuMRauwXA34vnd8CCnocnIl1xOZ1Mzktj\ncl4a5dV1rNp8kJUbi1m16SCrNh1kcHocCyZlMXv8IOKiPaEOV/q47ib9ppMJH8Bau94Y0xigmESk\nEykJXi6ek8uFs3PYvqec5RuKWL+rlKeW7uLv7+5mmklnwaQsRg1J7vHZv4Sn7ib9ZmPMFcBb/vfn\nA6fdodgYE4+vmSgF8AJ3W2vfON39iEQ6p8PBuGEDGDdsAFU19by35SArNhS3DvkwaEAsCyZlMWfC\nIBJjo0IdrvQh3U36XwcewNeDpwVYA3ztcxzvBsBaa+8wxmQBbwOjP8d+RMQvMS6KxTNzOH/GUOy+\nClZsLGadLeHZd/J5bvluzhiVzoLJWYzJSdGYP9J10jfGrMSX5MH3MNZW/+tE4DFOv02/FJjof53i\nfy8ivcDhcDA6J4XROSlcc7yB97ccYsXG4tYJ3tOTo1kwKYu5EzJJjtdwz5Gqy947xpgzu/qwtXb5\n6R7QGPM6kIcv6V9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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7fe1ffe327b8>" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": 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pRXTMW0UUu24ftuhRvrbpf7AkKbBqDCPbm/qHxi6WJrwJo6Nnel+NpLgoCrOS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\nfwGRO+I4mJKrgwAAAABJRU5ErkJggg==\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7fe1ffd9f8d0>" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "id": "Zc4JcBTYY5Iu", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "cell_type": "code", | |
| "source": [ | |
| "" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "id": "Bcibu0evX43S", | |
| "colab_type": "text" | |
| }, | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Синий график - точность на обучаемых данных\n", | |
| "\n", | |
| "Зелёный график - точность прогнозов" | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "id": "2gPoXz5FXPo7", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "cell_type": "code", | |
| "source": [ | |
| "" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "id": "-ACaqdeCYf_a", | |
| "colab_type": "text" | |
| }, | |
| "cell_type": "markdown", | |
| "source": [ | |
| "**Мы видим, что на реальных курсах правильность прогнозов поднялась до 57%**\n", | |
| "\n", | |
| "**А на рандомных курсах правильность составила в среднем 50%**" | |
| ] | |
| } | |
| ] | |
| } |
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