examples/Use Grid Search to tune the hyper-parameter of base model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fireTS.models import NARX\n",
    "import pandas as pd\n",
    "import os\n",
    "from sklearn.svm import SVR\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Data\n",
    "The data is generated by the [type-1-diabetes simulator](https://github.com/jxx123/simglucose) I developed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(os.path.join('.', 'data', 'adolescent#001.csv'))\n",
    "data = data.set_index('Time')\n",
    "data.index = pd.to_datetime(data.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ytrain = data['CGM'].iloc[:1000]\n",
    "Xtrain = data.loc[:, ['insulin', 'CGM']].iloc[:1000, :]\n",
    "\n",
    "ytest = data['CGM'].iloc[1000:-1]\n",
    "Xtest = data.loc[:, ['insulin', 'CGM']].iloc[1000:-1, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Grid Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "para_grid = {\n",
    "    'gamma': [0.001, 0.003, 0.005, 0.008, 0.01, 0.015, 0.03, 0.05],\n",
    "    'C': [0.1, 0.3, 1, 3, 10, 30, 100],\n",
    "    'epsilon': [0.001, 0.003, 0.005, 0.008, 0.01, 0.015, 0.03]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 392 candidates, totalling 1176 fits\n",
      "[CV] C=0.1, epsilon=0.001, gamma=0.001 ...............................\n",
      "[CV] ................ C=0.1, epsilon=0.001, gamma=0.001, total=   0.0s\n",
      "[CV] C=0.1, epsilon=0.001, gamma=0.001 ...............................\n",
      "[CV] ................ C=0.1, epsilon=0.001, gamma=0.001, total=   0.0s\n",
      "[CV] C=0.1, epsilon=0.001, gamma=0.001 ...............................\n",
      "[CV] ................ C=0.1, epsilon=0.001, gamma=0.001, total=   0.0s\n",
      "[CV] C=0.1, epsilon=0.001, gamma=0.003 ...............................\n",
      "[CV] ................ C=0.1, epsilon=0.001, gamma=0.003, total=   0.0s\n",
      "[CV] C=0.1, epsilon=0.001, gamma=0.003 ...............................\n",
      "[CV] ................ C=0.1, epsilon=0.001, gamma=0.003, total=   0.0s\n",
      "[CV] C=0.1, epsilon=0.001, gamma=0.003 ...............................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ................ C=0.1, epsilon=0.001, gamma=0.003, total=   0.0s\n",
      "[CV] C=0.1, epsilon=0.001, gamma=0.005 ...............................\n",
      "[CV] ................ C=0.1, epsilon=0.001, gamma=0.005, total=   0.0s\n",
      "[CV] C=0.1, epsilon=0.001, gamma=0.005 ...............................\n",
      "[CV] ................ C=0.1, epsilon=0.001, gamma=0.005, total=   0.0s\n",
      "[CV] C=0.1, epsilon=0.001, gamma=0.005 ...............................\n",
      "[CV] ................ C=0.1, epsilon=0.001, gamma=0.005, total=   0.0s\n",
      "[CV] C=0.1, epsilon=0.001, gamma=0.008 ...............................\n",
      "[CV] ................ C=0.1, epsilon=0.001, gamma=0.008, total=   0.0s\n",
      "[CV] C=0.1, epsilon=0.001, gamma=0.008 ...............................\n",
      "[CV] ................ C=0.1, epsilon=0.001, gamma=0.008, total=   0.0s\n",
      "[CV] C=0.1, epsilon=0.001, gamma=0.008 ...............................\n",
      "[CV] ................ C=0.1, epsilon=0.001, gamma=0.008, total=   0.0s\n",
      "[CV] C=0.1, epsilon=0.001, gamma=0.01 ................................\n",
      "[CV] ................. C=0.1, epsilon=0.001, gamma=0.01, total=   0.0s\n",
      "[CV] C=0.1, epsilon=0.001, gamma=0.01 ................................\n",
      "[CV] ................. C=0.1, epsilon=0.001, gamma=0.01, total=   0.0s\n",
      "[CV] C=0.1, epsilon=0.001, gamma=0.01 ................................\n",
      "[CV] ................. C=0.1, epsilon=0.001, gamma=0.01, total=   0.0s\n",
      "[CV] C=0.1, epsilon=0.001, gamma=0.015 ...............................\n",
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      "[CV] C=0.1, epsilon=0.001, gamma=0.015 ...............................\n",
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      "[CV] C=0.1, epsilon=0.001, gamma=0.03 ................................\n",
      "[CV] ................. C=0.1, epsilon=0.001, gamma=0.03, total=   0.0s\n",
      "[CV] C=0.1, epsilon=0.001, gamma=0.03 ................................\n",
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      "[CV] C=0.1, epsilon=0.003, gamma=0.03 ................................\n",
      "[CV] ................. C=0.1, epsilon=0.003, gamma=0.03, total=   0.0s\n",
      "[CV] C=0.1, epsilon=0.003, gamma=0.03 ................................\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ................ C=0.1, epsilon=0.005, gamma=0.015, total=   0.0s\n",
      "[CV] C=0.1, epsilon=0.005, gamma=0.03 ................................\n",
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      "[CV] C=0.1, epsilon=0.005, gamma=0.05 ................................\n",
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      "[CV] C=0.1, epsilon=0.005, gamma=0.05 ................................\n",
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      "[CV] C=0.1, epsilon=0.005, gamma=0.05 ................................\n",
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      "[CV] C=0.1, epsilon=0.008, gamma=0.001 ...............................\n",
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      "[CV] C=0.1, epsilon=0.008, gamma=0.001 ...............................\n",
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      "[CV] C=0.1, epsilon=0.008, gamma=0.003 ...............................\n",
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      "[CV] C=0.1, epsilon=0.008, gamma=0.003 ...............................\n",
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     ]
    },
    {
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     "output_type": "stream",
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      "[CV] ................ C=0.1, epsilon=0.015, gamma=0.003, total=   0.0s\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[CV] ................ C=0.3, epsilon=0.001, gamma=0.015, total=   0.0s\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ................ C=0.3, epsilon=0.008, gamma=0.003, total=   0.0s\n",
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      "[CV] C=0.3, epsilon=0.01, gamma=0.03 .................................\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[CV] ................ C=0.3, epsilon=0.015, gamma=0.015, total=   0.0s\n",
      "[CV] C=0.3, epsilon=0.015, gamma=0.03 ................................\n",
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      "[CV] C=0.3, epsilon=0.03, gamma=0.01 .................................\n",
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      "[CV] C=0.3, epsilon=0.03, gamma=0.01 .................................\n",
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      "[CV] C=0.3, epsilon=0.03, gamma=0.01 .................................\n",
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      "[CV] C=0.3, epsilon=0.03, gamma=0.03 .................................\n",
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      "[CV] C=0.3, epsilon=0.03, gamma=0.03 .................................\n",
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      "[CV] C=0.3, epsilon=0.03, gamma=0.05 .................................\n",
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      "[CV] C=0.3, epsilon=0.03, gamma=0.05 .................................\n",
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      "[CV] C=0.3, epsilon=0.03, gamma=0.05 .................................\n",
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      "[CV] C=1, epsilon=0.001, gamma=0.001 .................................\n",
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      "[CV] C=1, epsilon=0.001, gamma=0.001 .................................\n",
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      "[CV] C=1, epsilon=0.001, gamma=0.001 .................................\n",
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      "[CV] C=1, epsilon=0.001, gamma=0.003 .................................\n",
      "[CV] .................. C=1, epsilon=0.001, gamma=0.003, total=   0.0s\n",
      "[CV] C=1, epsilon=0.001, gamma=0.003 .................................\n",
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      "[CV] C=1, epsilon=0.001, gamma=0.003 .................................\n",
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      "[CV] C=1, epsilon=0.001, gamma=0.005 .................................\n",
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      "[CV] C=1, epsilon=0.001, gamma=0.005 .................................\n",
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      "[CV] C=1, epsilon=0.001, gamma=0.005 .................................\n",
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      "[CV] C=1, epsilon=0.001, gamma=0.008 .................................\n",
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      "[CV] C=1, epsilon=0.001, gamma=0.008 .................................\n",
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      "[CV] C=1, epsilon=0.001, gamma=0.008 .................................\n",
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      "[CV] C=1, epsilon=0.001, gamma=0.01 ..................................\n",
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      "[CV] C=1, epsilon=0.001, gamma=0.015 .................................\n",
      "[CV] .................. C=1, epsilon=0.001, gamma=0.015, total=   0.0s\n",
      "[CV] C=1, epsilon=0.001, gamma=0.015 .................................\n",
      "[CV] .................. C=1, epsilon=0.001, gamma=0.015, total=   0.0s\n",
      "[CV] C=1, epsilon=0.001, gamma=0.015 .................................\n",
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      "[CV] C=1, epsilon=0.001, gamma=0.03 ..................................\n",
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      "[CV] C=1, epsilon=0.001, gamma=0.03 ..................................\n",
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      "[CV] C=1, epsilon=0.001, gamma=0.03 ..................................\n",
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      "[CV] C=1, epsilon=0.001, gamma=0.05 ..................................\n",
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      "[CV] C=1, epsilon=0.001, gamma=0.05 ..................................\n",
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      "[CV] C=1, epsilon=0.001, gamma=0.05 ..................................\n",
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      "[CV] C=1, epsilon=0.003, gamma=0.001 .................................\n",
      "[CV] .................. C=1, epsilon=0.003, gamma=0.001, total=   0.0s\n",
      "[CV] C=1, epsilon=0.003, gamma=0.001 .................................\n",
      "[CV] .................. C=1, epsilon=0.003, gamma=0.001, total=   0.0s\n",
      "[CV] C=1, epsilon=0.003, gamma=0.001 .................................\n",
      "[CV] .................. C=1, epsilon=0.003, gamma=0.001, total=   0.0s\n",
      "[CV] C=1, epsilon=0.003, gamma=0.003 .................................\n",
      "[CV] .................. C=1, epsilon=0.003, gamma=0.003, total=   0.0s\n",
      "[CV] C=1, epsilon=0.003, gamma=0.003 .................................\n",
      "[CV] .................. C=1, epsilon=0.003, gamma=0.003, total=   0.0s\n",
      "[CV] C=1, epsilon=0.003, gamma=0.003 .................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] .................. C=1, epsilon=0.003, gamma=0.003, total=   0.0s\n",
      "[CV] C=1, epsilon=0.003, gamma=0.005 .................................\n",
      "[CV] .................. C=1, epsilon=0.003, gamma=0.005, total=   0.0s\n",
      "[CV] C=1, epsilon=0.003, gamma=0.005 .................................\n",
      "[CV] .................. C=1, epsilon=0.003, gamma=0.005, total=   0.0s\n",
      "[CV] C=1, epsilon=0.003, gamma=0.005 .................................\n",
      "[CV] .................. C=1, epsilon=0.003, gamma=0.005, total=   0.0s\n",
      "[CV] C=1, epsilon=0.003, gamma=0.008 .................................\n",
      "[CV] .................. C=1, epsilon=0.003, gamma=0.008, total=   0.0s\n",
      "[CV] C=1, epsilon=0.003, gamma=0.008 .................................\n",
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      "[CV] C=1, epsilon=0.003, gamma=0.008 .................................\n",
      "[CV] .................. C=1, epsilon=0.003, gamma=0.008, total=   0.0s\n",
      "[CV] C=1, epsilon=0.003, gamma=0.01 ..................................\n",
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      "[CV] C=1, epsilon=0.003, gamma=0.015 .................................\n",
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      "[CV] C=1, epsilon=0.003, gamma=0.015 .................................\n",
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      "[CV] C=1, epsilon=0.003, gamma=0.015 .................................\n",
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      "[CV] C=1, epsilon=0.003, gamma=0.03 ..................................\n",
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      "[CV] C=1, epsilon=0.003, gamma=0.03 ..................................\n",
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      "[CV] C=1, epsilon=0.003, gamma=0.05 ..................................\n",
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      "[CV] C=1, epsilon=0.005, gamma=0.001 .................................\n",
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      "[CV] C=1, epsilon=0.005, gamma=0.001 .................................\n",
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      "[CV] C=1, epsilon=0.005, gamma=0.001 .................................\n",
      "[CV] .................. C=1, epsilon=0.005, gamma=0.001, total=   0.0s\n",
      "[CV] C=1, epsilon=0.005, gamma=0.003 .................................\n",
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      "[CV] C=1, epsilon=0.005, gamma=0.003 .................................\n",
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      "[CV] C=1, epsilon=0.005, gamma=0.003 .................................\n",
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      "[CV] C=1, epsilon=0.005, gamma=0.005 .................................\n",
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      "[CV] C=1, epsilon=0.005, gamma=0.005 .................................\n",
      "[CV] .................. C=1, epsilon=0.005, gamma=0.005, total=   0.0s\n",
      "[CV] C=1, epsilon=0.005, gamma=0.005 .................................\n",
      "[CV] .................. C=1, epsilon=0.005, gamma=0.005, total=   0.0s\n",
      "[CV] C=1, epsilon=0.005, gamma=0.008 .................................\n",
      "[CV] .................. C=1, epsilon=0.005, gamma=0.008, total=   0.0s\n",
      "[CV] C=1, epsilon=0.005, gamma=0.008 .................................\n",
      "[CV] .................. C=1, epsilon=0.005, gamma=0.008, total=   0.0s\n",
      "[CV] C=1, epsilon=0.005, gamma=0.008 .................................\n",
      "[CV] .................. C=1, epsilon=0.005, gamma=0.008, total=   0.0s\n",
      "[CV] C=1, epsilon=0.005, gamma=0.01 ..................................\n",
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      "[CV] C=1, epsilon=0.005, gamma=0.01 ..................................\n",
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      "[CV] C=1, epsilon=0.005, gamma=0.015 .................................\n",
      "[CV] .................. C=1, epsilon=0.005, gamma=0.015, total=   0.0s\n",
      "[CV] C=1, epsilon=0.005, gamma=0.015 .................................\n",
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      "[CV] C=1, epsilon=0.005, gamma=0.015 .................................\n",
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      "[CV] C=1, epsilon=0.005, gamma=0.03 ..................................\n",
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      "[CV] C=1, epsilon=0.005, gamma=0.05 ..................................\n",
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      "[CV] C=1, epsilon=0.008, gamma=0.001 .................................\n",
      "[CV] .................. C=1, epsilon=0.008, gamma=0.001, total=   0.0s\n",
      "[CV] C=1, epsilon=0.008, gamma=0.001 .................................\n",
      "[CV] .................. C=1, epsilon=0.008, gamma=0.001, total=   0.0s\n",
      "[CV] C=1, epsilon=0.008, gamma=0.001 .................................\n",
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      "[CV] C=1, epsilon=0.008, gamma=0.003 .................................\n",
      "[CV] .................. C=1, epsilon=0.008, gamma=0.003, total=   0.0s\n",
      "[CV] C=1, epsilon=0.008, gamma=0.003 .................................\n",
      "[CV] .................. C=1, epsilon=0.008, gamma=0.003, total=   0.0s\n",
      "[CV] C=1, epsilon=0.008, gamma=0.003 .................................\n",
      "[CV] .................. C=1, epsilon=0.008, gamma=0.003, total=   0.0s\n",
      "[CV] C=1, epsilon=0.008, gamma=0.005 .................................\n",
      "[CV] .................. C=1, epsilon=0.008, gamma=0.005, total=   0.0s\n",
      "[CV] C=1, epsilon=0.008, gamma=0.005 .................................\n",
      "[CV] .................. C=1, epsilon=0.008, gamma=0.005, total=   0.0s\n",
      "[CV] C=1, epsilon=0.008, gamma=0.005 .................................\n",
      "[CV] .................. C=1, epsilon=0.008, gamma=0.005, total=   0.0s\n",
      "[CV] C=1, epsilon=0.008, gamma=0.008 .................................\n",
      "[CV] .................. C=1, epsilon=0.008, gamma=0.008, total=   0.0s\n",
      "[CV] C=1, epsilon=0.008, gamma=0.008 .................................\n",
      "[CV] .................. C=1, epsilon=0.008, gamma=0.008, total=   0.0s\n",
      "[CV] C=1, epsilon=0.008, gamma=0.008 .................................\n",
      "[CV] .................. C=1, epsilon=0.008, gamma=0.008, total=   0.0s\n",
      "[CV] C=1, epsilon=0.008, gamma=0.01 ..................................\n",
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      "[CV] C=1, epsilon=0.008, gamma=0.01 ..................................\n",
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      "[CV] C=1, epsilon=0.008, gamma=0.01 ..................................\n",
      "[CV] ................... C=1, epsilon=0.008, gamma=0.01, total=   0.0s\n",
      "[CV] C=1, epsilon=0.008, gamma=0.015 .................................\n",
      "[CV] .................. C=1, epsilon=0.008, gamma=0.015, total=   0.0s\n",
      "[CV] C=1, epsilon=0.008, gamma=0.015 .................................\n",
      "[CV] .................. C=1, epsilon=0.008, gamma=0.015, total=   0.0s\n",
      "[CV] C=1, epsilon=0.008, gamma=0.015 .................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] .................. C=1, epsilon=0.008, gamma=0.015, total=   0.0s\n",
      "[CV] C=1, epsilon=0.008, gamma=0.03 ..................................\n",
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      "[CV] C=1, epsilon=0.008, gamma=0.03 ..................................\n",
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      "[CV] C=1, epsilon=0.008, gamma=0.03 ..................................\n",
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      "[CV] C=1, epsilon=0.008, gamma=0.05 ..................................\n",
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      "[CV] C=1, epsilon=0.008, gamma=0.05 ..................................\n",
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      "[CV] C=1, epsilon=0.008, gamma=0.05 ..................................\n",
      "[CV] ................... C=1, epsilon=0.008, gamma=0.05, total=   0.0s\n",
      "[CV] C=1, epsilon=0.01, gamma=0.001 ..................................\n",
      "[CV] ................... C=1, epsilon=0.01, gamma=0.001, total=   0.0s\n",
      "[CV] C=1, epsilon=0.01, gamma=0.001 ..................................\n",
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      "[CV] C=1, epsilon=0.01, gamma=0.001 ..................................\n",
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      "[CV] C=1, epsilon=0.01, gamma=0.003 ..................................\n",
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      "[CV] C=1, epsilon=0.01, gamma=0.003 ..................................\n",
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      "[CV] C=1, epsilon=0.01, gamma=0.003 ..................................\n",
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      "[CV] C=1, epsilon=0.01, gamma=0.005 ..................................\n",
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      "[CV] C=1, epsilon=0.01, gamma=0.005 ..................................\n",
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      "[CV] C=1, epsilon=0.01, gamma=0.005 ..................................\n",
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      "[CV] C=1, epsilon=0.01, gamma=0.008 ..................................\n",
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      "[CV] C=1, epsilon=0.01, gamma=0.008 ..................................\n",
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      "[CV] C=1, epsilon=0.01, gamma=0.008 ..................................\n",
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      "[CV] C=1, epsilon=0.01, gamma=0.01 ...................................\n",
      "[CV] .................... C=1, epsilon=0.01, gamma=0.01, total=   0.0s\n",
      "[CV] C=1, epsilon=0.01, gamma=0.01 ...................................\n",
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      "[CV] C=1, epsilon=0.01, gamma=0.01 ...................................\n",
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      "[CV] C=1, epsilon=0.01, gamma=0.015 ..................................\n",
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      "[CV] C=1, epsilon=0.01, gamma=0.015 ..................................\n",
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      "[CV] C=1, epsilon=0.01, gamma=0.015 ..................................\n",
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      "[CV] C=1, epsilon=0.01, gamma=0.03 ...................................\n",
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      "[CV] C=1, epsilon=0.01, gamma=0.03 ...................................\n",
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      "[CV] C=1, epsilon=0.01, gamma=0.03 ...................................\n",
      "[CV] .................... C=1, epsilon=0.01, gamma=0.03, total=   0.0s\n",
      "[CV] C=1, epsilon=0.01, gamma=0.05 ...................................\n",
      "[CV] .................... C=1, epsilon=0.01, gamma=0.05, total=   0.0s\n",
      "[CV] C=1, epsilon=0.01, gamma=0.05 ...................................\n",
      "[CV] .................... C=1, epsilon=0.01, gamma=0.05, total=   0.0s\n",
      "[CV] C=1, epsilon=0.01, gamma=0.05 ...................................\n",
      "[CV] .................... C=1, epsilon=0.01, gamma=0.05, total=   0.0s\n",
      "[CV] C=1, epsilon=0.015, gamma=0.001 .................................\n",
      "[CV] .................. C=1, epsilon=0.015, gamma=0.001, total=   0.0s\n",
      "[CV] C=1, epsilon=0.015, gamma=0.001 .................................\n",
      "[CV] .................. C=1, epsilon=0.015, gamma=0.001, total=   0.0s\n",
      "[CV] C=1, epsilon=0.015, gamma=0.001 .................................\n",
      "[CV] .................. C=1, epsilon=0.015, gamma=0.001, total=   0.0s\n",
      "[CV] C=1, epsilon=0.015, gamma=0.003 .................................\n",
      "[CV] .................. C=1, epsilon=0.015, gamma=0.003, total=   0.0s\n",
      "[CV] C=1, epsilon=0.015, gamma=0.003 .................................\n",
      "[CV] .................. C=1, epsilon=0.015, gamma=0.003, total=   0.0s\n",
      "[CV] C=1, epsilon=0.015, gamma=0.003 .................................\n",
      "[CV] .................. C=1, epsilon=0.015, gamma=0.003, total=   0.0s\n",
      "[CV] C=1, epsilon=0.015, gamma=0.005 .................................\n",
      "[CV] .................. C=1, epsilon=0.015, gamma=0.005, total=   0.0s\n",
      "[CV] C=1, epsilon=0.015, gamma=0.005 .................................\n",
      "[CV] .................. C=1, epsilon=0.015, gamma=0.005, total=   0.0s\n",
      "[CV] C=1, epsilon=0.015, gamma=0.005 .................................\n",
      "[CV] .................. C=1, epsilon=0.015, gamma=0.005, total=   0.0s\n",
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      "[CV] C=1, epsilon=0.015, gamma=0.015 .................................\n",
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      "[CV] C=1, epsilon=0.03, gamma=0.003 ..................................\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[CV] ................... C=1, epsilon=0.03, gamma=0.003, total=   0.0s\n",
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      "[CV] C=3, epsilon=0.001, gamma=0.001 .................................\n",
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      "[CV] C=3, epsilon=0.003, gamma=0.008 .................................\n",
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      "[CV] C=3, epsilon=0.003, gamma=0.015 .................................\n",
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      "[CV] C=3, epsilon=0.003, gamma=0.015 .................................\n",
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      "[CV] C=3, epsilon=0.003, gamma=0.015 .................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] .................. C=3, epsilon=0.003, gamma=0.015, total=   0.0s\n",
      "[CV] C=3, epsilon=0.003, gamma=0.03 ..................................\n",
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      "[CV] C=3, epsilon=0.005, gamma=0.001 .................................\n",
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      "[CV] C=3, epsilon=0.008, gamma=0.003 .................................\n",
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      "[CV] C=3, epsilon=0.008, gamma=0.008 .................................\n",
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      "[CV] C=3, epsilon=0.008, gamma=0.01 ..................................\n",
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     ]
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    {
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     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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      "[CV] C=10, epsilon=0.005, gamma=0.001 ................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ................. C=10, epsilon=0.005, gamma=0.001, total=   0.0s\n",
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      "[CV] C=10, epsilon=0.01, gamma=0.001 .................................\n",
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      "[CV] C=10, epsilon=0.01, gamma=0.01 ..................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ................... C=10, epsilon=0.01, gamma=0.01, total=   0.0s\n",
      "[CV] C=10, epsilon=0.01, gamma=0.015 .................................\n",
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      "[CV] C=10, epsilon=0.03, gamma=0.001 .................................\n",
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      "[CV] C=10, epsilon=0.03, gamma=0.005 .................................\n",
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      "[CV] C=10, epsilon=0.03, gamma=0.008 .................................\n",
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      "[CV] C=10, epsilon=0.03, gamma=0.05 ..................................\n",
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      "[CV] C=30, epsilon=0.001, gamma=0.001 ................................\n",
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      "[CV] C=30, epsilon=0.001, gamma=0.001 ................................\n",
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      "[CV] C=30, epsilon=0.001, gamma=0.003 ................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ................. C=30, epsilon=0.001, gamma=0.003, total=   0.1s\n",
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      "[CV] C=30, epsilon=0.001, gamma=0.03 .................................\n",
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      "[CV] C=30, epsilon=0.003, gamma=0.03 .................................\n",
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      "[CV] C=30, epsilon=0.005, gamma=0.01 .................................\n",
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      "[CV] C=30, epsilon=0.005, gamma=0.01 .................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] .................. C=30, epsilon=0.005, gamma=0.01, total=   0.1s\n",
      "[CV] C=30, epsilon=0.005, gamma=0.01 .................................\n",
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      "[CV] C=30, epsilon=0.005, gamma=0.03 .................................\n",
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      "[CV] C=30, epsilon=0.008, gamma=0.03 .................................\n",
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      "[CV] C=30, epsilon=0.008, gamma=0.03 .................................\n",
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      "[CV] C=30, epsilon=0.008, gamma=0.05 .................................\n",
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      "[CV] C=30, epsilon=0.008, gamma=0.05 .................................\n",
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      "[CV] C=30, epsilon=0.01, gamma=0.001 .................................\n",
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      "[CV] C=30, epsilon=0.01, gamma=0.001 .................................\n",
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      "[CV] C=30, epsilon=0.01, gamma=0.003 .................................\n",
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      "[CV] C=30, epsilon=0.01, gamma=0.003 .................................\n",
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      "[CV] C=30, epsilon=0.01, gamma=0.003 .................................\n",
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      "[CV] C=30, epsilon=0.01, gamma=0.005 .................................\n",
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      "[CV] C=30, epsilon=0.01, gamma=0.005 .................................\n",
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      "[CV] C=30, epsilon=0.01, gamma=0.005 .................................\n",
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      "[CV] C=30, epsilon=0.01, gamma=0.008 .................................\n",
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      "[CV] C=30, epsilon=0.01, gamma=0.008 .................................\n",
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      "[CV] C=30, epsilon=0.01, gamma=0.008 .................................\n",
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      "[CV] C=30, epsilon=0.01, gamma=0.015 .................................\n",
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      "[CV] C=30, epsilon=0.01, gamma=0.015 .................................\n",
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      "[CV] C=30, epsilon=0.01, gamma=0.015 .................................\n",
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      "[CV] C=30, epsilon=0.015, gamma=0.001 ................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ................. C=30, epsilon=0.015, gamma=0.001, total=   0.0s\n",
      "[CV] C=30, epsilon=0.015, gamma=0.001 ................................\n",
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      "[CV] C=30, epsilon=0.015, gamma=0.001 ................................\n",
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      "[CV] C=30, epsilon=0.015, gamma=0.03 .................................\n",
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      "[CV] C=30, epsilon=0.03, gamma=0.001 .................................\n",
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      "[CV] C=30, epsilon=0.03, gamma=0.003 .................................\n",
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      "[CV] C=30, epsilon=0.03, gamma=0.003 .................................\n",
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      "[CV] C=30, epsilon=0.03, gamma=0.005 .................................\n",
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      "[CV] C=30, epsilon=0.03, gamma=0.008 .................................\n",
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      "[CV] C=100, epsilon=0.001, gamma=0.001 ...............................\n",
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      "[CV] C=100, epsilon=0.001, gamma=0.008 ...............................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ................ C=100, epsilon=0.001, gamma=0.008, total=   0.4s\n",
      "[CV] C=100, epsilon=0.001, gamma=0.008 ...............................\n",
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      "[CV] C=100, epsilon=0.003, gamma=0.015 ...............................\n",
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      "[CV] C=100, epsilon=0.005, gamma=0.03 ................................\n",
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      "[CV] C=100, epsilon=0.005, gamma=0.03 ................................\n",
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      "[CV] C=100, epsilon=0.005, gamma=0.05 ................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ................. C=100, epsilon=0.005, gamma=0.05, total=   0.1s\n",
      "[CV] C=100, epsilon=0.005, gamma=0.05 ................................\n",
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      "[CV] C=100, epsilon=0.008, gamma=0.001 ...............................\n",
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      "[CV] ................ C=100, epsilon=0.008, gamma=0.005, total=   0.3s\n",
      "[CV] C=100, epsilon=0.008, gamma=0.005 ...............................\n",
      "[CV] ................ C=100, epsilon=0.008, gamma=0.005, total=   0.3s\n",
      "[CV] C=100, epsilon=0.008, gamma=0.005 ...............................\n",
      "[CV] ................ C=100, epsilon=0.008, gamma=0.005, total=   0.3s\n",
      "[CV] C=100, epsilon=0.008, gamma=0.008 ...............................\n",
      "[CV] ................ C=100, epsilon=0.008, gamma=0.008, total=   0.2s\n",
      "[CV] C=100, epsilon=0.008, gamma=0.008 ...............................\n",
      "[CV] ................ C=100, epsilon=0.008, gamma=0.008, total=   0.3s\n",
      "[CV] C=100, epsilon=0.008, gamma=0.008 ...............................\n",
      "[CV] ................ C=100, epsilon=0.008, gamma=0.008, total=   0.3s\n",
      "[CV] C=100, epsilon=0.008, gamma=0.01 ................................\n",
      "[CV] ................. C=100, epsilon=0.008, gamma=0.01, total=   0.2s\n",
      "[CV] C=100, epsilon=0.008, gamma=0.01 ................................\n",
      "[CV] ................. C=100, epsilon=0.008, gamma=0.01, total=   0.3s\n",
      "[CV] C=100, epsilon=0.008, gamma=0.01 ................................\n",
      "[CV] ................. C=100, epsilon=0.008, gamma=0.01, total=   0.3s\n",
      "[CV] C=100, epsilon=0.008, gamma=0.015 ...............................\n",
      "[CV] ................ C=100, epsilon=0.008, gamma=0.015, total=   0.2s\n",
      "[CV] C=100, epsilon=0.008, gamma=0.015 ...............................\n",
      "[CV] ................ C=100, epsilon=0.008, gamma=0.015, total=   0.3s\n",
      "[CV] C=100, epsilon=0.008, gamma=0.015 ...............................\n",
      "[CV] ................ C=100, epsilon=0.008, gamma=0.015, total=   0.3s\n",
      "[CV] C=100, epsilon=0.008, gamma=0.03 ................................\n",
      "[CV] ................. C=100, epsilon=0.008, gamma=0.03, total=   0.1s\n",
      "[CV] C=100, epsilon=0.008, gamma=0.03 ................................\n",
      "[CV] ................. C=100, epsilon=0.008, gamma=0.03, total=   0.1s\n",
      "[CV] C=100, epsilon=0.008, gamma=0.03 ................................\n",
      "[CV] ................. C=100, epsilon=0.008, gamma=0.03, total=   0.1s\n",
      "[CV] C=100, epsilon=0.008, gamma=0.05 ................................\n",
      "[CV] ................. C=100, epsilon=0.008, gamma=0.05, total=   0.1s\n",
      "[CV] C=100, epsilon=0.008, gamma=0.05 ................................\n",
      "[CV] ................. C=100, epsilon=0.008, gamma=0.05, total=   0.1s\n",
      "[CV] C=100, epsilon=0.008, gamma=0.05 ................................\n",
      "[CV] ................. C=100, epsilon=0.008, gamma=0.05, total=   0.1s\n",
      "[CV] C=100, epsilon=0.01, gamma=0.001 ................................\n",
      "[CV] ................. C=100, epsilon=0.01, gamma=0.001, total=   0.1s\n",
      "[CV] C=100, epsilon=0.01, gamma=0.001 ................................\n",
      "[CV] ................. C=100, epsilon=0.01, gamma=0.001, total=   0.1s\n",
      "[CV] C=100, epsilon=0.01, gamma=0.001 ................................\n",
      "[CV] ................. C=100, epsilon=0.01, gamma=0.001, total=   0.1s\n",
      "[CV] C=100, epsilon=0.01, gamma=0.003 ................................\n",
      "[CV] ................. C=100, epsilon=0.01, gamma=0.003, total=   0.2s\n",
      "[CV] C=100, epsilon=0.01, gamma=0.003 ................................\n",
      "[CV] ................. C=100, epsilon=0.01, gamma=0.003, total=   0.2s\n",
      "[CV] C=100, epsilon=0.01, gamma=0.003 ................................\n",
      "[CV] ................. C=100, epsilon=0.01, gamma=0.003, total=   0.2s\n",
      "[CV] C=100, epsilon=0.01, gamma=0.005 ................................\n",
      "[CV] ................. C=100, epsilon=0.01, gamma=0.005, total=   0.3s\n",
      "[CV] C=100, epsilon=0.01, gamma=0.005 ................................\n",
      "[CV] ................. C=100, epsilon=0.01, gamma=0.005, total=   0.3s\n",
      "[CV] C=100, epsilon=0.01, gamma=0.005 ................................\n",
      "[CV] ................. C=100, epsilon=0.01, gamma=0.005, total=   0.3s\n",
      "[CV] C=100, epsilon=0.01, gamma=0.008 ................................\n",
      "[CV] ................. C=100, epsilon=0.01, gamma=0.008, total=   0.2s\n",
      "[CV] C=100, epsilon=0.01, gamma=0.008 ................................\n",
      "[CV] ................. C=100, epsilon=0.01, gamma=0.008, total=   0.3s\n",
      "[CV] C=100, epsilon=0.01, gamma=0.008 ................................\n",
      "[CV] ................. C=100, epsilon=0.01, gamma=0.008, total=   0.3s\n",
      "[CV] C=100, epsilon=0.01, gamma=0.01 .................................\n",
      "[CV] .................. C=100, epsilon=0.01, gamma=0.01, total=   0.2s\n",
      "[CV] C=100, epsilon=0.01, gamma=0.01 .................................\n",
      "[CV] .................. C=100, epsilon=0.01, gamma=0.01, total=   0.3s\n",
      "[CV] C=100, epsilon=0.01, gamma=0.01 .................................\n",
      "[CV] .................. C=100, epsilon=0.01, gamma=0.01, total=   0.3s\n",
      "[CV] C=100, epsilon=0.01, gamma=0.015 ................................\n",
      "[CV] ................. C=100, epsilon=0.01, gamma=0.015, total=   0.1s\n",
      "[CV] C=100, epsilon=0.01, gamma=0.015 ................................\n",
      "[CV] ................. C=100, epsilon=0.01, gamma=0.015, total=   0.3s\n",
      "[CV] C=100, epsilon=0.01, gamma=0.015 ................................\n",
      "[CV] ................. C=100, epsilon=0.01, gamma=0.015, total=   0.3s\n",
      "[CV] C=100, epsilon=0.01, gamma=0.03 .................................\n",
      "[CV] .................. C=100, epsilon=0.01, gamma=0.03, total=   0.1s\n",
      "[CV] C=100, epsilon=0.01, gamma=0.03 .................................\n",
      "[CV] .................. C=100, epsilon=0.01, gamma=0.03, total=   0.1s\n",
      "[CV] C=100, epsilon=0.01, gamma=0.03 .................................\n",
      "[CV] .................. C=100, epsilon=0.01, gamma=0.03, total=   0.1s\n",
      "[CV] C=100, epsilon=0.01, gamma=0.05 .................................\n",
      "[CV] .................. C=100, epsilon=0.01, gamma=0.05, total=   0.1s\n",
      "[CV] C=100, epsilon=0.01, gamma=0.05 .................................\n",
      "[CV] .................. C=100, epsilon=0.01, gamma=0.05, total=   0.1s\n",
      "[CV] C=100, epsilon=0.01, gamma=0.05 .................................\n",
      "[CV] .................. C=100, epsilon=0.01, gamma=0.05, total=   0.1s\n",
      "[CV] C=100, epsilon=0.015, gamma=0.001 ...............................\n",
      "[CV] ................ C=100, epsilon=0.015, gamma=0.001, total=   0.1s\n",
      "[CV] C=100, epsilon=0.015, gamma=0.001 ...............................\n",
      "[CV] ................ C=100, epsilon=0.015, gamma=0.001, total=   0.1s\n",
      "[CV] C=100, epsilon=0.015, gamma=0.001 ...............................\n",
      "[CV] ................ C=100, epsilon=0.015, gamma=0.001, total=   0.1s\n",
      "[CV] C=100, epsilon=0.015, gamma=0.003 ...............................\n",
      "[CV] ................ C=100, epsilon=0.015, gamma=0.003, total=   0.2s\n",
      "[CV] C=100, epsilon=0.015, gamma=0.003 ...............................\n",
      "[CV] ................ C=100, epsilon=0.015, gamma=0.003, total=   0.2s\n",
      "[CV] C=100, epsilon=0.015, gamma=0.003 ...............................\n",
      "[CV] ................ C=100, epsilon=0.015, gamma=0.003, total=   0.2s\n",
      "[CV] C=100, epsilon=0.015, gamma=0.005 ...............................\n",
      "[CV] ................ C=100, epsilon=0.015, gamma=0.005, total=   0.3s\n",
      "[CV] C=100, epsilon=0.015, gamma=0.005 ...............................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ................ C=100, epsilon=0.015, gamma=0.005, total=   0.3s\n",
      "[CV] C=100, epsilon=0.015, gamma=0.005 ...............................\n",
      "[CV] ................ C=100, epsilon=0.015, gamma=0.005, total=   0.3s\n",
      "[CV] C=100, epsilon=0.015, gamma=0.008 ...............................\n",
      "[CV] ................ C=100, epsilon=0.015, gamma=0.008, total=   0.2s\n",
      "[CV] C=100, epsilon=0.015, gamma=0.008 ...............................\n",
      "[CV] ................ C=100, epsilon=0.015, gamma=0.008, total=   0.3s\n",
      "[CV] C=100, epsilon=0.015, gamma=0.008 ...............................\n",
      "[CV] ................ C=100, epsilon=0.015, gamma=0.008, total=   0.3s\n",
      "[CV] C=100, epsilon=0.015, gamma=0.01 ................................\n",
      "[CV] ................. C=100, epsilon=0.015, gamma=0.01, total=   0.2s\n",
      "[CV] C=100, epsilon=0.015, gamma=0.01 ................................\n",
      "[CV] ................. C=100, epsilon=0.015, gamma=0.01, total=   0.2s\n",
      "[CV] C=100, epsilon=0.015, gamma=0.01 ................................\n",
      "[CV] ................. C=100, epsilon=0.015, gamma=0.01, total=   0.3s\n",
      "[CV] C=100, epsilon=0.015, gamma=0.015 ...............................\n",
      "[CV] ................ C=100, epsilon=0.015, gamma=0.015, total=   0.1s\n",
      "[CV] C=100, epsilon=0.015, gamma=0.015 ...............................\n",
      "[CV] ................ C=100, epsilon=0.015, gamma=0.015, total=   0.2s\n",
      "[CV] C=100, epsilon=0.015, gamma=0.015 ...............................\n",
      "[CV] ................ C=100, epsilon=0.015, gamma=0.015, total=   0.2s\n",
      "[CV] C=100, epsilon=0.015, gamma=0.03 ................................\n",
      "[CV] ................. C=100, epsilon=0.015, gamma=0.03, total=   0.1s\n",
      "[CV] C=100, epsilon=0.015, gamma=0.03 ................................\n",
      "[CV] ................. C=100, epsilon=0.015, gamma=0.03, total=   0.1s\n",
      "[CV] C=100, epsilon=0.015, gamma=0.03 ................................\n",
      "[CV] ................. C=100, epsilon=0.015, gamma=0.03, total=   0.1s\n",
      "[CV] C=100, epsilon=0.015, gamma=0.05 ................................\n",
      "[CV] ................. C=100, epsilon=0.015, gamma=0.05, total=   0.0s\n",
      "[CV] C=100, epsilon=0.015, gamma=0.05 ................................\n",
      "[CV] ................. C=100, epsilon=0.015, gamma=0.05, total=   0.1s\n",
      "[CV] C=100, epsilon=0.015, gamma=0.05 ................................\n",
      "[CV] ................. C=100, epsilon=0.015, gamma=0.05, total=   0.1s\n",
      "[CV] C=100, epsilon=0.03, gamma=0.001 ................................\n",
      "[CV] ................. C=100, epsilon=0.03, gamma=0.001, total=   0.1s\n",
      "[CV] C=100, epsilon=0.03, gamma=0.001 ................................\n",
      "[CV] ................. C=100, epsilon=0.03, gamma=0.001, total=   0.1s\n",
      "[CV] C=100, epsilon=0.03, gamma=0.001 ................................\n",
      "[CV] ................. C=100, epsilon=0.03, gamma=0.001, total=   0.1s\n",
      "[CV] C=100, epsilon=0.03, gamma=0.003 ................................\n",
      "[CV] ................. C=100, epsilon=0.03, gamma=0.003, total=   0.2s\n",
      "[CV] C=100, epsilon=0.03, gamma=0.003 ................................\n",
      "[CV] ................. C=100, epsilon=0.03, gamma=0.003, total=   0.2s\n",
      "[CV] C=100, epsilon=0.03, gamma=0.003 ................................\n",
      "[CV] ................. C=100, epsilon=0.03, gamma=0.003, total=   0.2s\n",
      "[CV] C=100, epsilon=0.03, gamma=0.005 ................................\n",
      "[CV] ................. C=100, epsilon=0.03, gamma=0.005, total=   0.3s\n",
      "[CV] C=100, epsilon=0.03, gamma=0.005 ................................\n",
      "[CV] ................. C=100, epsilon=0.03, gamma=0.005, total=   0.3s\n",
      "[CV] C=100, epsilon=0.03, gamma=0.005 ................................\n",
      "[CV] ................. C=100, epsilon=0.03, gamma=0.005, total=   0.3s\n",
      "[CV] C=100, epsilon=0.03, gamma=0.008 ................................\n",
      "[CV] ................. C=100, epsilon=0.03, gamma=0.008, total=   0.2s\n",
      "[CV] C=100, epsilon=0.03, gamma=0.008 ................................\n",
      "[CV] ................. C=100, epsilon=0.03, gamma=0.008, total=   0.3s\n",
      "[CV] C=100, epsilon=0.03, gamma=0.008 ................................\n",
      "[CV] ................. C=100, epsilon=0.03, gamma=0.008, total=   0.3s\n",
      "[CV] C=100, epsilon=0.03, gamma=0.01 .................................\n",
      "[CV] .................. C=100, epsilon=0.03, gamma=0.01, total=   0.2s\n",
      "[CV] C=100, epsilon=0.03, gamma=0.01 .................................\n",
      "[CV] .................. C=100, epsilon=0.03, gamma=0.01, total=   0.2s\n",
      "[CV] C=100, epsilon=0.03, gamma=0.01 .................................\n",
      "[CV] .................. C=100, epsilon=0.03, gamma=0.01, total=   0.3s\n",
      "[CV] C=100, epsilon=0.03, gamma=0.015 ................................\n",
      "[CV] ................. C=100, epsilon=0.03, gamma=0.015, total=   0.2s\n",
      "[CV] C=100, epsilon=0.03, gamma=0.015 ................................\n",
      "[CV] ................. C=100, epsilon=0.03, gamma=0.015, total=   0.2s\n",
      "[CV] C=100, epsilon=0.03, gamma=0.015 ................................\n",
      "[CV] ................. C=100, epsilon=0.03, gamma=0.015, total=   0.2s\n",
      "[CV] C=100, epsilon=0.03, gamma=0.03 .................................\n",
      "[CV] .................. C=100, epsilon=0.03, gamma=0.03, total=   0.1s\n",
      "[CV] C=100, epsilon=0.03, gamma=0.03 .................................\n",
      "[CV] .................. C=100, epsilon=0.03, gamma=0.03, total=   0.1s\n",
      "[CV] C=100, epsilon=0.03, gamma=0.03 .................................\n",
      "[CV] .................. C=100, epsilon=0.03, gamma=0.03, total=   0.1s\n",
      "[CV] C=100, epsilon=0.03, gamma=0.05 .................................\n",
      "[CV] .................. C=100, epsilon=0.03, gamma=0.05, total=   0.0s\n",
      "[CV] C=100, epsilon=0.03, gamma=0.05 .................................\n",
      "[CV] .................. C=100, epsilon=0.03, gamma=0.05, total=   0.1s\n",
      "[CV] C=100, epsilon=0.03, gamma=0.05 .................................\n",
      "[CV] .................. C=100, epsilon=0.03, gamma=0.05, total=   0.1s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 1176 out of 1176 | elapsed:  1.4min finished\n"
     ]
    }
   ],
   "source": [
    "tsmdl = NARX(SVR(kernel='rbf'), auto_order=6, exog_order=[3, 3], exog_delay=[0, 0])\n",
    "tsmdl.grid_search(Xtrain, ytrain, para_grid, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NARX(auto_order=6,\n",
       "   base_estimator=SVR(C=100, cache_size=200, coef0=0.0, degree=3, epsilon=0.001, gamma=0.001,\n",
       "  kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False),\n",
       "   exog_delay=[0, 0], exog_order=[3, 3])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tsmdl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "tsmdl.fit(Xtrain, ytrain)\n",
    "ypred = tsmdl.predict(Xtest, ytest, step=6)\n",
    "ypred = pd.Series(ypred, index=ytest.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f2b6393a320>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ytest.plot(label='actual')\n",
    "ypred.plot(label='6-step-ahead prediction')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:anaconda3]",
   "language": "python",
   "name": "conda-env-anaconda3-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}