plot_basic_rnn_test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANN\n",
      "(1530, 3)\n",
      "(1530, 1, 3)\n"
     ]
    }
   ],
   "source": [
    "fi = np.load(\"results/basic_ann_exp_05_43_48.npz\")\n",
    "\n",
    "ann_res = fi['van_res']\n",
    "ann_pred = ann_res.item()['pred']\n",
    "ann_cor = ann_res.item()['cor']\n",
    "print(\"ANN\")\n",
    "print(ann_pred.shape)\n",
    "print(ann_cor.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.1, 1.1)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAD7CAYAAACL+TRnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5d534c1190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ann_pred)\n",
    "plt.ylim(-0.1, 1.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.1, 1.1)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAD7CAYAAACL+TRnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkiRJStf/AYKDoFJEOT/DAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5d53215150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ann_cor.squeeze())\n",
    "plt.ylim(-0.1, 1.1)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}