{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Genre recognition: experiment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Goal:\n",
    "\n",
    "Conclusion:\n",
    "\n",
    "Observations:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyper-parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameter under test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Pname = 'test_size'\n",
    "Pvalues = [.1, .2, .3, .4, .5, .6, .7, .8, .9, .92, .94, .96, .97, .98, .99]\n",
    "\n",
    "# Regenerate the graph or the features at each iteration.\n",
    "regen_graph = False\n",
    "regen_features = False\n",
    "regen_baseline = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "p = {}\n",
    "\n",
    "# Preprocessing.\n",
    "\n",
    "# Graph.\n",
    "p['data_scaling_graph'] = 'features'\n",
    "p['K'] = 10 + 1  # 5 to 10 + 1 for self-reference\n",
    "p['dm'] = 'euclidean'\n",
    "p['Csigma'] = 1\n",
    "p['diag'] = True\n",
    "p['laplacian'] = 'normalized'\n",
    "\n",
    "# Feature extraction.\n",
    "p['m'] = 128  # 64, 128, 512\n",
    "p['ls'] = 1\n",
    "p['ld'] = 10\n",
    "p['le'] = None\n",
    "p['lg'] = 200\n",
    "\n",
    "# Classification.\n",
    "p['scale'] = None\n",
    "p['Nvectors'] = 6\n",
    "p['svm_type'] = 'C'\n",
    "p['kernel'] = 'linear'\n",
    "p['C'] = 1\n",
    "p['nu'] = 0.5\n",
    "p['majority_voting'] = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# HDF5 data stores.\n",
    "p['folder'] = 'data'\n",
    "p['filename_gtzan'] = 'gtzan.hdf5'\n",
    "p['filename_audio'] = 'audio.hdf5'\n",
    "p['filename_graph'] = 'graph.hdf5'\n",
    "p['filename_features'] = 'features.hdf5'\n",
    "\n",
    "# Dataset (10,100,644 | 5,100,149 | 2,10,644).\n",
    "p['Ngenres'] = 5\n",
    "p['Nclips'] = 100\n",
    "p['Nframes'] = 149\n",
    "\n",
    "# Added white noise.\n",
    "p['noise_std'] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numerical parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Graph.\n",
    "p['tol'] = 1e-5\n",
    "\n",
    "# Feature extraction.\n",
    "p['rtol'] = 1e-5  # 1e-3, 1e-5, 1e-7\n",
    "p['N_inner'] = 500\n",
    "p['N_outer'] = 50\n",
    "\n",
    "# Classification.\n",
    "p['test_size'] = 0.1\n",
    "p['Ncv'] = 20\n",
    "p['dataset_classification'] = 'Z'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import time\n",
    "\n",
    "texperiment = time.time()\n",
    "\n",
    "# Result dictionary.\n",
    "res = ['accuracy', 'accuracy_std']\n",
    "res += ['sparsity', 'atoms']\n",
    "res += ['objective_g', 'objective_h', 'objective_i', 'objective_j']\n",
    "res += ['time_features', 'iterations_inner', 'iterations_outer']\n",
    "res = dict.fromkeys(res)\n",
    "for key in res.keys():\n",
    "    res[key] = []\n",
    "\n",
    "def separator(name, parameter=False):\n",
    "    if parameter:\n",
    "        name += ', {} = {}'.format(Pname, p[Pname])\n",
    "    dashes = 20 * '-'\n",
    "    print('\\n {} {} {} \\n'.format(dashes, name, dashes))\n",
    "    # Fair comparison when tuning parameters.\n",
    "    # Randomnesses: dictionary initialization, training and testing sets.\n",
    "    np.random.seed(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " -------------------- Graph -------------------- \n",
      "\n",
      "Data: (149000, 96), float32"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Elapsed time: 203.43 seconds"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "All self-referenced in the first column: True\n",
      "dist in [0.0, 1.5347918272]\n",
      "w in [0.00371022056788, 1.0]\n",
      "Ones on the diagonal: 149000 (over 149000)"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "assert: True\n",
      "W in [0.0, 1.0]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Datasets:"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  L_data    : (2341668,), float32\n",
      "  L_indices : (2341668,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2341668,), float32\n",
      "  W_indices : (2341668,), int32\n",
      "  W_indptr  : (149001,) , int32\n",
      "  W_shape   : (2,)      , int64\n",
      "Attributes:\n",
      "  K = 11\n",
      "  dm = euclidean\n",
      "  Csigma = 1\n",
      "  diag = True\n",
      "  laplacian = normalized\n",
      "Overall time: 212.49 seconds\n",
      "\n",
      " -------------------- Features -------------------- \n",
      "\n",
      "Attributes:"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  Xa: (10, 100, 644, 2, 1024) , float32\n",
      "  Xs: (10, 100, 644, 2, 96)   , float32\n",
      "Full dataset:\n",
      "  size: N=1,288,000 x n=96 -> 123,648,000 floats\n",
      "  dim: 123,648 features per clip\n",
      "  shape: (10, 100, 644, 2, 96)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 149, 2, 96)\n",
      "<type 'numpy.ndarray'>\n",
      "Data: (149000, 96), float32\n",
      "Attributes:"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  K = 11\n",
      "  dm = euclidean\n",
      "  Csigma = 1\n",
      "  diag = True\n",
      "  laplacian = normalized\n",
      "Datasets:\n",
      "  L_data    : (2341668,), float32\n",
      "  L_indices : (2341668,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2341668,), float32\n",
      "  W_indices : (2341668,), int32\n",
      "  W_indptr  : (149001,) , int32\n",
      "  W_shape   : (2,)      , int64\n",
      "Size X: 13.6 M --> 54.6 MiB\n",
      "Size Z: 18.2 M --> 72.8 MiB\n",
      "Size D: 12.0 k --> 48.0 kiB\n",
      "Size E: 12.0 k --> 48.0 kiB\n",
      "Elapsed time: 2685 seconds"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
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mb2MZPlg7/lIqc7T/HCu5t20sZ+QJVnVSDhpFfMqhA4hYOXQAESuHDiBS5dABjFp7mXAz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\nERERERERERERERGRUP4fHU5QpprWH0EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9b1ffe4810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inner loop: 1261 iterations\n",
      "g(Z) = ||X-DZ||_2^2 = 7.691408e+04\n",
      "rdiff: 0.00111579453637"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "i(Z) = ||Z||_1 = 5.535905e+04\n",
      "j(Z) = tr(Z^TLZ) = 8.916660e+03\n"
     ]
    },
    {
     "data": {
      "image/png": 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sAJpQn7yIiAyTYH3yf8fuI583o6hpRkREys0ReHT9ddi8xzcAVyePq0LuMIOy\n1+Rd7AACc7EDCMjFDiAwFzuAwFzsAAJzsQMIzMUOIKCe8l6WPvndG3agu7+JiIgUQNaE/TLgVVii\nvwC4MlhE2ehWsyJFM0oF6yKsPiY3LJdhfZ5iaVy/Hna11Arg1uSvFMM6jLI6ge5C90HgEODsZAc/\nBE7B7kYnIqFYUqzejazdY6XAr1ffs07y12N3MOs2aYMllvTjmSbryrL+6YzvH1SMpwBbJsfhLuCj\nSFGcCqzeywezJPl3A9tit5wFOAb4J0ryITlKe09yIHb5LHlOoT+Jr37dFWzE5tw7oW3UP57Gbp35\nJPBE6nmzR9bXl/W4je9yM9uwIQCXAofRTaIZLcRYGkdZ/++Nch83AxtyCbAfo2S8XXChOMp4/Ea5\nv9ePZknyUN+soyYe6d4ok4G1gOO5gU2Zz0PAl7FBnaFqnu1eX0FvCbLd68t5isXAf3v4fLP3PMVo\njv6/jfJg8uwS4O0lTRJldgDLORt4s45d4RyAtZ51LUv7/oeBg6k1178ROB34ei877BP1yefJKLOw\nptx2jzWxf6QzkwfAvViNsF811G6S5zMBvolyG2U2cBJwmJKEyMD1lPeyfmBLYHtqA++u6HZHfeaB\nz2LNMmNRIymzUSZhyTmdrNdlfAKfBtzZ4XE3ozzJKAuxKzYuAXZRshARacsljwX0OcmvCiwB5ja8\nt9qv9lC3O+ujstfkHaFPXkZZmc617+diUxp3SuCLM/e3jjKbf3E2Ly5tk6Gj3CeeDpWvyBwqX1H1\nlPfa9cn/BNgDuJzmF+Fv2O3OZACs9r0GnRP4DGyEbTVR3wHcAvyN+tr3E32O72HgKH5WygQvIpIr\nRa0Nl70m39woM8hW+15K59r3gwUZ7SwiIgH75P8M7Jxh3SCVK8lb7fs5dE7gM6mvfTd73MWo7hYo\nIlIyfW+unwGsjDX9zk2tXxVLOJLFKNOBebQetLYOsDZ27bIl6qtYwUu5FLiI+gT+QElq347y9ps5\nyls2UPmKzqHyDZV2Sf4wbLa7edi95KuWAieEDKoQbEKV1elc+14FuJvxNe6Lqa99P5bauuNs/UMV\nEZGJyVL1/wBwfOhAuhS2uX6UlajVvls95gGP0brZ/A5qte/8TGgiIiJF1Pfm+vSG51CbbWcOsD/w\nrW53Fp3VvufSufa9GnAP4xP35XXLoywfcAlEREQyy3JWcCWwWcO6Rdid6WIZPxnOKNPIVvt+gs4j\nz++LXPvgz84OAAAauklEQVR2lLtfyVHe8jnKWzZQ+YrOofIVjWMCk+FkqclX7yRVTXqTsTtjxTXK\nO4GXY9PrrgPMxqZJbUzYi6ivfT/adHsiIiL5M5Y8FvTy4SxnBV8B1sfmrK5gA/JuA47oZYd94hkF\nbOKWD1GrfWs+chERKaNgY9EmA+8BzkoehyXrYvKMcnFywwwREZGyK8Pl05n5kid4FzuAwFzsAAJy\nsQMIzMUOIDAXO4DAXOwAAnOxAwiopySfpU9+e6wvYCT1fg88r5cd9k05b24iIiIyUNdjtwZdC5t6\ntfqIaaiaLUREZOgFy3sXhdrwBCjJi4jIMOkp703K8J7zgWOB7YAtUg8Jx8UOIDAXO4CAXOwAAnOx\nAwjMxQ4gMBc7gMBc7ADyJkuf/MuxM4itGta/uv/hiIiIyLBTc72IiAyTYKPrF1C7CD+9k6N62WEf\njZKe1lZERKR8HBPohsjSJ/9o8liGTW37OuxyuthGKW+Cd7EDCMzFDiAgFzuAwFzsAAJzsQMIzMUO\nIDAXO4AAxiCZ47UHWWryX2lYPhY4t9cdioiISH7NBW6MHIP65EVEZJgE65O/OvV8ErAm8fvjRURE\nZAI2TP5ugPXBjwDrkofbzJa/Ju9iBxCYix1AQC52AIG52AEE5mIHEJiLHUBgLnYAAfV9Mpyzkr/f\nBW5JHncAT/WyIxERERmsdvemXQT8DLvN7Nca3uuTdbEEu6+uiIhIDvWU99rV5N8CPIPdO34VYFbq\nsUoPAYqIiEjOvC52AE2oT77YXOwAAnKxAwjMxQ4gMBc7gMBc7AACc7EDCCjYDWoW9rJhERERkV6U\nvSYvIiKSFqwmLyIiIgWUJcnPBD4NnJIsvwB4fbCIBMrdrwTlLp+LHUBgLnYAgbnYAQTmYgcQmIsd\nQN5kSfKnAU8Cr0iW7wKODhaRiIiIDMxlyd8rUuuujBFIivrkRURkmATrk38CmJFa3ihZJyIiIgW3\nC/AX4H7gx8CtwKujRmRnNKOUt//FxQ4gMBc7gIBc7AACc7EDCMzFDiAwFzuAwFzsAAJwWL4L2oL9\nHGyw3euBNULuKKOyN9e72AEE5mIHEJCLHUBgLnYAgbnYAQTmYgcQmIsdQEDB8t45wAHYKPu8KHuS\nFxERSQuW9xxwItZMfxawDzA91M4yUpIXEZFhEjzvTQFeC5wJLAm9sw7KnuRd7AACc7EDCMjFDiAw\nFzuAwFzsAAJzsQMIzMUOIKCe8t6UjO+bAewF7AtsAXyvl52JiIhIvpyJNdWfhI2qnxw3HKD8NXkR\nEZG0YHlvV/KR2NOU5EVEZJj0fTKcnZO/s4A3AG9OHnsnfyUcFzuAwFzsAAJysQMIzMUOIDAXO4DA\nXOwAAnOxA8ibdn3yOwB/Bvak+RnE2UEiEhERkYF5XsZ1g6TmehERGSbB8t7lTdZd1mTdICnJi4jI\nMOl7n/zGWP/7bOr74g8m/mQ4ZediBxCYix1AQC52AIG52AEE5mIHEJiLHUBgLnYAedOuT34+1h+/\nWvK3ailwSMigREREZDC2ix1AE2quFxGRYRIs730fa7KvmgN8N9TOMlKSFxGRYRIs7y3KuG6Qyp7k\nXewAAnOxAwjIxQ4gMBc7gMBc7AACc7EDCMzFDiCgvg+8q6oAc1PLc8nfDHgiIiLSg7cD1wOfAz6f\nPH971IjKX5MXERFJ6ynvVTK+78XYzWkAzgOu7WVnfeTJHruIiEjR9ZT3sjTXgzXRPwqcANwPbNjt\njqQrLnYAgbnYAQTkYgcQmIsdQGAudgCBudgBBOZiB5A3WZL8KPBR4OPJ8jTgh6ECEhERkcG5EjsZ\nuCK17qqA+5sJXALs0eY96pMXEZFhEmx0/RPAitTyzF521IWPAmcE3oeIiEjpZUnyPwNOwibEORS7\n/eypgeJ5LTao7/5A2y8KFzuAwFzsAAJysQMIzMUOIDAXO4DAXOwAAnOxA8ibdnPXVx0L7ILNWT8f\n+DTwxy728V2s6f0+YNPU+t2A/8WuuT8V+BKwI9ZSsAnwGLAQNc2LiIjk1quAzYGrU+smAzcCI8BU\nbAa9jVOvHwS8rs02lfhFRGSY9D3v/S35uwyrxacfS4BbgPdl3NYI9Ul+O+D3qeWPJY+slORFRGSY\n9JT32jXXvzL5O6vF66sDfwe+2cN+1wFuTy3fAWzb5TZOx040AB7GWgPGkmWX/C3q8ocoV3kal8tc\nvurzvMTT7+Xq87zE0+/l6vO8xNPv5erzvMTT7+Xq87zEM5Hl6vMRBmAL4IPAB5LnVfMyfn6E+pr8\n3sApqeUDgeO7iKfsNXkXO4DAXOwAAnKxAwjMxQ4gMBc7gMBc7AACc7EDCChY3vsMlqA/CxyFXTf/\n6S63MUJ9kn859c31HweO7GJ7ZU/yIiIiacHmrr8BeCnweLI8A0v087vYzwhwDrXR9VOwG93sDNwF\nXAzsD1yXcXuau15EJANvv5VTsUuhX4BduXQINrZKiuFrFRuQ3nXey3IJ3Z1YYq8m+elYH3pWP8Eu\njVsd64f/DHAa8H7gD9hI+++QPcFXjWJ9GGNdfq4IHOUsV5WjvOVzlLdsoPL1hbffvRnJY+XU88bl\ndq91895ngEljMNlZCNdj9yMplT/BlNfA07Hj6Kc/wZQLJjAJXbskX+0jfwT4F3BusvxarOad1f4t\n1v8uefRqdAKfFRF5VlLbXWlTWOVqGxjczwTb7L1TsBr18uTvYxmWq88f6vazFXja27wju2PThu9S\nsQHLZeMo4Umoh4VH2bHrWruq/8HUN4s3Pv9eLzvsEzXXi5SctybmQdV2pwNP0l2y7WW5+vzJyoDH\nFnmbtfQk4LCSJvjS8jC7AovpIe9l+cAM4Pm2H26k1mwfk5K8yIB5OBl4EXY/iw8BTxG2tgvhk+2z\nj0r9PTpE8qanvNfuA1OBo4F3Arcl69bH+tM/gf0Hj6XsSd5RwianFEd5y+coUNmSvuHVgTWBtVKP\nZsvrjkHF2Ucfw8bm9DvZppuYY/zGOAp0/HrgUPmKqqe8165P/lhsIpwNsVnuAFYFvgp8BbtuPqZR\nyjvwTqRnHqZhSTlL4l4da7q9D7g3eVSf39iwfBKwK+Xu0xXJG8cErv9vd1ZwI3aZXGMT1mRsZObz\ne91pH5S9Ji9Sx9vo2laJuvH5LOABakk7nagbl++vZByNrD5dkaj63lx/A62vhW/32iAoyUuhJaO5\nZ9M5cVeXJ1OfpBsTd/r5YvUvi5RO35vrr8Muvm8cRf824N/d7ki64ih3N4SjhOXzMHlb2OsiuInO\nNe41sUGszZL2FYxP3MsGPRq7BUcJj12KQ+UrMke5y9e1dkn+fcDZ2MC7y5J1W2KjXt8UOC6RXPCw\nErUE3axpPL0892ibYOQOxifu6xuW76vk40oVESmxTlX/CrAT8GKsFnEt8OfQQWXgsbn0x9BZm3TJ\nW591uz7t9PJM4H46923fCzyQtX9bRCQjlzwWEOg6+TxSn7w8K+nfnkP2xF0h26C0e4GH1b8tIjnQ\n94F3eVb2JO8odwuFo0P5vHUlPYdsiXsN7PrqLIPS7gUeDdi/7RjyY1dwDpWvyBzlLV/fB96J9J2H\nH/wWttnD7oB1Ojb3QrPEPQebo7tZor6uYfm+is3CJiIiKUWtDZe9Jl8ayYxq2wJ7Jo8XUju5vAU4\nk+a17QfVvy0i8qyhynt5uJRIWvCwioe9PZzu4T4PV3v4goftPCz04D1cnEyuIiIinQ1V3vPYtLYu\nbhjBuNgBdMvDBh7e7+EPHpZ6ONfDBzyMNLxv9hlwXokTvIsdQGAudgCBudgBBOZiBxCYix1AAA7L\nd0OX5MvMxQ6gEw+TPGzr4WgPV3m438P3POzjrZ+9HTeIGCNxsQMIzMUOIDAXO4DAXOwAAnOxAwio\n7HmvzlAVNi88zPTwRg/f8XCPh395OMbDK5O+dxERCWOo8t5QFTYmD+t5eE/Sl77Ew588fNDD82LH\nJiIyRIYq75W9sC7WjpNm+K09HOXhCg8PePiBh309rNan3bg+bSePXOwAAnOxAwjMxQ4gMBc7gMBc\n7AAC6inv6Tp5wdv9CF6DXeK2B3YN+6+Bw4F/6FI2EREZpLLX5IPzMM/DoR5+kzTDn+/hwx5eEDs2\nEREZZ6jy3lAVth88VDxs4WGBh0s9POThxx729za7nIiI5NdQ5T1dJ5+Bhxke9vDwbQ93eLjBw1c9\nOA9T+7GPHrmI+w7NxQ4gMBc7gMBc7AACc7EDCMzFDiAAxwSuky9yn/xo7ADyyMNzgddj/euvBq4A\nzgF2rtg9zUVEpDjGkseCuGEM1lA1W7STNMO/zMOnk6liF3v4qYe3epgbOz4REemLocp7Q1XYRh6m\ne9jdw7c83ObhJg//62FnD9NixyciIn03VHmv7IV1jSs8rOnhHR7O9vCIhws9HOlhE1+8OxO52AEE\n5GIHEJiLHUBgLnYAgbnYAQTmYgcQ0ND1yZdakrhfQu0WrRsDfwR+CRxagQcihiciIhJMKWvyHlby\nsIuH4z3c4uFmD8d5eK2a4UVEhlop814rpSmshzU8HOThLA8Pe/i7h497eEkBm+FFRCSM0uS9LApb\n2GQ0/Is9fMzD35L+9bOT/vY1k7e5mDEOgIsdQEAudgCBudgBBOZiBxCYix1AYC52AAENXZ/8KLXr\nB3MtaWp/FbX+9anYteufA8Yq8HjE8EREJL8c5T55aSr3NXkPq3s40MMZybXrF3n4lIfN1AwvIiJd\nyn3e66fcFTZphn+Rh494+GvSDP9LD+9KZqETERHpVe7yXki5KKyHqR5e7eFrHv6TzA9/oofXeZgx\ngU27fsWYUy52AAG52AEE5mIHEJiLHUBgLnYAgbnYAQQ0dH3yUSR3bNsN2AvYFbgJ61/fF1hUyckJ\niIiISFENNJF6eEFyr/Xzk3uvn+PhEA/zBhmHiIgMraGqQAYtrIcpHnbwcKyHf3u4y8PJHvb0sHLI\nfYuIiDShJD/BDa7mYT8PP/TwoIfLPXzWw1YeJvV7fx24Ae9v0FzsAAJysQMIzMUOIDAXO4DAXOwA\nAnOxAwhIffLd8rARtWvXtwYuwPrXP1aBO2LGJiIiMqx6OqPxMNnD9h6+5OFaD/d4ONXDGzzM7HeQ\nIiIifaLm+hZvXNXDPh6+5+F+D4s8fM7DNhGa4UVERHoxdEl+lBb9Lx5GPHzAw7nJaPjfe3ifhw0G\nGeQEuNgBBOZiBxCQix1AYC52AIG52AEE5mIHEJiLHUAADst3Q9cnP1p94mEysA21/vW1gN8C3wb2\nrsDSGAGKiIhM0FjyWBA3jMHyHmZ5eLOH0zzc6+FqD1/wsF2S9EVERMpiuJrrPTzl4bykWX7D2AGJ\niIgENHRJ3ns4I3YggbjYAQTmYgcQkIsdQGAudgCBudgBBOZiBxCYix1AQD0l+SKPLr8EOCx2ECIi\nItJf3sPs2EGIiIgMyHA118cOQEREZICGrrm+zFzsAAJzsQMIyMUOIDAXO4DAXOwAAnOxAwjMxQ4g\nb5TkRUREJFfUXC8iIsNEzfUiIiJSoySfTy52AIG52AEE5GIHEJiLHUBgLnYAgbnYAQTmYgeQN0ry\nIiIikivqkxcRkWGiPnkRERGpUZLPJxc7gMBc7AACcrEDCMzFDiAwFzuAwFzsAAJzsQPImyIn+VF0\nQEVEpNwclu+GivrkRURkmKhPXkRERGqU5PPJxQ4gMBc7gIBc7AACc7EDCMzFDiAwFzuAwFzsAPJG\nSV5ERERyRX3yIiIyTNQnLyIiIjVK8vnkYgcQmIsdQEAudgCBudgBBOZiBxCYix1AYC52AHmjJC8i\nIiK5oj55EREZJuqTFxERkRol+XxysQMIzMUOICAXO4DAXOwAAnOxAwjMxQ4gMBc7gLxRkhcREZFc\nUZ+8iIgME/XJi4iISI2SfD652AEE5mIHEJCLHUBgLnYAgbnYAQTmYgcQmIsdQN4oyYuIiEiuqE9e\nRESGifrkRUREpEZJPp9c7AACc7EDCMjFDiAwFzuAwFzsAAJzsQMIzMUOIG+U5EVERCRX1CcvIiLD\npBR98i8CTgTOBN4VORYREREJYBKW6Fspe03exQ4gMBc7gIBc7AACc7EDCMzFDiAwFzuAwFzsAAIq\nRU0eYE/gt8BPYwcS0ctiBxBYmctX5rKByld0Kt+QGUSS/y5wL3B1w/rdgH8D/wGOTK0/B9gdOGgA\nseXV7NgBBFbm8pW5bKDyFZ3KN2SmDGAfpwHHA99PrZsMnAC8BrgTuAT4NbAm8GZgOnD+AGITEREp\nrUEk+QuAkYZ12wA3Arckyz8F3gAcA/xlADHl3UjsAAIbiR1AQCOxAwhsJHYAgY3EDiCwkdgBBDYS\nO4C8qQxoPyNYM/ymyfI+wK7AIcnygcC2wAcybu9GYKM+xiciIpJnNwHP7/ZDg6jJNzPR0fFdF1RE\nRGTYxBpdfyewXmp5PeCOSLGIiIjIBIxQP7p+Ctb0MAJMAxYBGw88KhEREZmQnwB3AU8AtwPvSNbv\nDlyP9a9/vMVnW11ml3Zc8vqVwOb9CXkgOpXNAY8AVySPTw0ssolrddlkWlGPG3Qun6O4xw6sZe18\n4F/ANcDhLd5X1GOYpXyOYh7D6cBFWMXpWuCLLd5X1GOXpXyOYh67tMlY7Oe0eL2ox6/OZOwEYASY\nSvPa/uuAhcnzbYF/Diq4CcpSNoddVlhEr8L+4bVKgkU9blWdyuco7rEDeC61SUVmYSfjZfm/B9nK\n5yjuMVw5+TsFOy7bN7xe5GMHncvnKO6xq/ow8COal6Or45fHGe+q0pfZPUXtMru0vYDvJc8vwiZC\nWGtA8U1ElrLB4K5+6LcLgMVtXi/qcavqVD4o7rEDuAc78QRYBlwHzGt4T5GPYZbyQXGP4fLk7zSs\nQvFQw+tFPnbQuXxQ3GMHsC6WyE+leTm6On55TvLrYM37VXck6zq9Z93AcfVDlrJ54BVYc8xCYJPB\nhDYQRT1uWZXp2I1grRYXNawvyzEcoXn5inwMJ2EnMfdi3RLXNrxe9GPXqXxFPnYAXwc+Aqxo8XpX\nxy/PST7rZXaNZzpFuHlNlhgvx/oON8NmDPxl0IgGr4jHLauyHLtZwFnAB7Eab6OiH8N25SvyMVyB\ndUesC+xA85u2FPnYdSpfkY/d64H7sP74dq0RmY9fnpN8lsvsGt+zbrIu77KUbSm1ZqnfYX33c8OH\nNhBFPW5ZleHYTQV+DvyQ5j+SRT+GncpXhmP4CHazr60a1hf92FW1Kl+Rj90rsOb4m7FB6ztRPyU8\nlOf4ZbrMLj0A4eUUZwBJlrKtRe1sbRtqUwAXxQjZBt4V6biljdC6fEU/dhXsh+Xrbd5T5GOYpXxF\nPYbPoXaTlhnAX4GdG95T5GOXpXxFPXaNdqT56PoiH79xml1md1jyqDohef1KYIuBRjcxncr2Puzy\nnkXA37GDWRTVyyafxPqO3kl5jht0Ll+Rjx3YaOUVWPzVy5B2pzzHMEv5inoMN8WaqxcBV2F9u1Ce\nY5elfEU9do12pDa6vizHT0RERERERERERERERERERERERERERERERERERETCqU5xugGwf5+3/YmG\n5b/1efv9djA2NWg/fYXmU61msSOw3QT3P0L72yD3YiVsYpY8zyIqQ0j/IEXGq84DvSFwQJefndLh\n9Y83LL+yy+0P2kTnNG/8jVkFm298rMftvRqb+rMbnY5JPzyB3Z3wjQPYl4iITMDS5O8/gYexGdE+\niCWsY4GLsZmmDk3e57Af+F8B/07W/RK4FJt565Bk3THA08n2fpCsq7YaVJJtX43N5LVvattjwM+w\nW6L+sEXMY8n2L8JmUqzeY/tg6mviv8GSbHXfX05i/CM2M9hfsCmX90zec1BSlvOBG4DPpLZ1YLK/\nK4BvU0voy7Da+iLGJ+QDgS+klnfGZjC7CvgONs0z2FSk1fnGt0r2vwFwN3afhyuwE6Q1sJvMXJw8\nqvsbxb7jC7H7cqeNUKvJTwdOS/Z/ObUWhlbrD8aOc7PvY1vsOImISI5Vk3zj3NGHAp9Mnq8EXIIl\nDIcltg1S752T/J2BJZTq8lLqVZf3Bs7Fkv2awK3Ac5NtP4zd77yCTdPZrPZ/PnaSADZF6x+T5wdT\nn+TPoZbkVwC7Js/PTvY/GXgplkSrn78riX96UpYtsXst/Dp5P8C3gLeltrtPkxgBTgTenDyfDtwG\nPD9Z/h52MgV2g47GJA+wAPhwans/pvZ9rE/ttqOj2PFZqUkMI9SS/BHYfbsBXoh97yu1WX8wzb8P\nktcLeaMQKa9BNGOJFFXj7Rx3webOriawVbEE9TRWi7w19d4PUmu6XQ94QfKeVrbHEpbHbjX5F2Br\nYEnyubuS9y3CklSzvvyzk7+XJ+/p5EngD8nzq4HHgWewmn368+cCi1P72D5535ZYawXYycw9yfNn\nsDu8NVOtjYMlz5uxObjBkvz7gG90iDt9XF5D/c2dVgFmYt/jr7Fm9HZeCRyXPL8eO4bz26z3NP8+\nLkv2NQlL/o932K/IQCjJi3Tn/dRqyVUOeLRheWes+ftxrBY6vcN2Pa3vEZ1OVM/Q+v/tE03e8zT1\n/eLpOJ5KPV+BJf3q81b7qKTi+h7jBxKClbldX36rsUDpbafjbvfdVbBm8iebvLa8ybpW2+hmfeN7\nVjQsF+ne7FJyGngn0tpSrGZY9QfgvdQS4Hxg5SafWxWr6T0OvIj6u2A9RfMEegGwH/Z/cg2sSf1i\nsiWadm4BXpZsZz3s1pvdei3WPD0DeAPWz/1nrEVjjeQ9c7Hm8k6q3RBgNeQRYKNk+W1YC0Y17up9\nwvdOfb7xmJwLHJ5a3ixDDGkXAG9Nns/HyvDvNusrjP8+qq0qK2EnWJ1aD0QGRkleZLxqTexK7Ed7\nEdb8firW53s51rx9IpawPfW1t98n668Fvgj8I/XaydhgrurAu+rnfpGsvxJLoB/Bmu0bt02T5XZl\nuBBrEr8Wawa/rM12fJPnHjvZ+HkS21lY+a8DPoUl2SuTv89tsp1GF1JL3o8D78AGq12F1d6/nbz2\n2STeS5L11W2eA7yJ2sC7w5PtXQn8i/rbcbaLo/rat7DfwauAn2IDDZ9qs77V9wGwOfXHWkREZKjM\nwhJ3UR1M63kDvoCdgIjkhmryIjJIy7AxCq+OHUiPmrWsgDXVb49dbigiIiIiIiIiIiIiIiIiIiIi\nIiIiIiIiIiIiIiIiYf1//vRnYjbrZAEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9b1d587490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global objective: 1.411898e+05\n"
     ]
    },
    {
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wNeYDRwCD9N4IERER6VCfmmNsBthNYLtMPq+IiIgbXYboQh8Pmu0ENg9s2f5tU0REZBRV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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9b1d376850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outer loop: 5 iterations\n",
      "\n",
      "Z in [-0.131474122405, 0.873808443546]\n",
      "Sparsity of Z: 4,296,703 non-zero entries out of 19,072,000 entries, i.e. 22.5%.\n"
     ]
    },
    {
     "data": {
      "image/png": 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Okr/WtAS8nX02X/ScojEqx2fmgN3YcZGps6WtVtkI+ZUoea+Um0W2zytrvddZ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9b619b1b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D in [-0.0787837356329, 0.908077061176]\n",
      "d in [0.999999582767, 1.00000023842]\n",
      "Constraints on D: True\n"
     ]
    },
    {
     "data": {
      "image/png": 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UTRfl84qOvwg6c6NvL7KSrcTnw0rcpuVqudwXITlVlIvm5vydUOYHyH+w6ABJ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\n/wP4OhX3J1/eiwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9b619b1c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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OchNwZNeFkCSpJV8FXth1ISRJkiRJkiRJkiRJkjQcbwZuBh4G1o1Nfx1wDbAZ\n+CfgoLH/nQ5cD1wLnAfsMMfvHQkcvorySpI0OIcAjwduYXmwPhzYtXh+DPAvxfP1wLdZCtCfAk6Y\n4/fOBN5ar6iSJKVnPbAF+Evgm8AngJ8itHRvAp7T4G9NButxuwG3F8/XFWXZjXCjnwuBo0s+83JC\ngL8SuATYgzA//15811XAjxXTvkxoxf89sF/x+Y8CZwP/DHyLcLnKBuAGQn0AbFe871pCBuAtVWdW\nkqSmrAceBJ5OGPPgcuCc4n/HAZ8r+cyBhEA4+bgSeOyM35oVrH8T+ODY618C7gPuAs6d8pm1Y89/\nEXhX8fwM4DfG/nchcHzx/ESW5umjhBQ7hHm9l+X1cDDwbODise/aFUmSWrae0IJetAF4bfH8iYQg\n3JRpwfooQmt2t+L1AcXr3Qkt688Rzm9PeiYhkG4mZAcuKqafwfI0+PcILWSANcVrCK3n8XmdrIfj\nCAcENwN/BryYtAdxkqJyuFGpWz8Ye/4w8MDY87L7zT+Z8pb1Vczf8jwI+BAhMP5nMe0w4FLgbuAh\nYCNwRMln30cIogcBvwzsNON3pgXZ8XmdrIc1wD2EFvYm4FeAD8+aGSlnZTsDSen6JvCsmp8dD5r7\nEwLx6wmt10VbgLcTgu/9hPPVl5V812OBO4rnvzA2/T6Wp+MvBV4DfJzQQv/aHOXdnXCaYCOh5T0t\nJS9lz5a11K3J+/ONZvyvjpOB7wCPI6SsF89N/x4h9f1+Qqt8MSBfA3yMcN54czFt/Hz2ojOBTxfv\n+95YWS8EfoalDma/RjhXfQ0hWJ8y9h2z5nVUlPkrxXedC5y24txKkiRJkiRJkiRJkiRJkiRJkiRJ\nkiRJkiRJkqT4/h9VikJ1XXeF1QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9b1d530a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Qf8j6Zc4NBA4DTq2j3UrE9HRkP5APU9y/L3C0jhjqQugwB9cp/nYiA0FusoMwDc4Gbrfw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\ncRzH2cL4/yXlFnhTd5V1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9b1d4237d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Datasets:\n",
      "  D : (128, 96)             , float32\n",
      "  X : (5, 100, 149, 2, 96)  , float32\n",
      "  Z : (5, 100, 149, 2, 128) , float32\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Overall time: 2692 seconds\n",
      "\n",
      " -------------------- Classification, test_size = 0.1 -------------------- \n",
      "\n",
      "Software versions:"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  numpy: 1.8.2\n",
      "  sklearn: 0.14.1\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  D : (128, 96)               , float32\n",
      "  X : (5, 100, 149, 2, 96)    , float32\n",
      "  Z : (5, 100, 149, 2, 128)   , float32\n",
      "Full dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<type 'numpy.ndarray'>\n",
      "Flattened frames:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 298, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Feature vectors:\n",
      "  size: N=6,000 x n=128 -> 768,000 floats\n",
      "  dim: 1,536 features per clip\n",
      "  shape: (5, 100, 6, 2, 128)\n"
     ]
    },
    {
     "data": {
      "image/png": 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JMN2g79UmHVrIx48kJfsecAfG9rFDwplUGG9xE1HP7veo4f+aIcF0XeBeEksv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\nDMMwDMMwDMMwDKNE/h+vb3nB0LF4kwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9ad4d4b050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 128), float64\n",
      "Testing data: (2400, 128), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 76.1 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1536), float64\n",
      "Testing data: (200, 1536), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 64.5 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clips accuracy: 74.0 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 5400.0 training, 600.0 testing\n",
      "  77 (+/- 1.5) <- [76 76 77 76 72 77 76 78 76 77]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 1.6) <- [76 77 76 73 79 76 76 76 78 78]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  78 (+/- 1.3) <- [77 77 79 78 78 79 78 74 78 78]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 1.4) <- [76 74 77 79 77 78 77 76 78 76]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 0.9) <- [78 77 76 76 76 77 79 76 77 77]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  78 (+/- 0.7) <- [77 77 77 78 78 78 77 78 76 77]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  78 (+/- 1.4) <- [76 77 79 79 76 75 79 76 77 78]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 1.7) <- [78 75 78 77 77 78 73 75 78 78]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 1.6) <- [76 80 77 79 76 74 78 78 77 76]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  78 (+/- 1.5) <- [78 78 79 75 78 75 76 76 78 80]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 2.1) <- [79 79 77 74 79 76 78 74 80 75]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 1.7) <- [77 77 79 75 73 77 77 76 79 75]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 1.4) <- [78 78 74 78 77 80 77 76 76 77]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 1.5) <- [79 77 79 76 76 75 75 74 77 77]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  78 (+/- 1.1) <- [76 76 78 76 77 79 79 77 78 78]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  78 (+/- 2.0) <- [80 78 79 79 75 76 76 77 80 74]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 1.3) <- [75 77 75 77 77 77 77 76 79 75]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 1.4) <- [76 77 77 77 75 79 75 78 79 77]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 1.4) <- [77 76 77 79 78 76 79 77 74 76]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  78 (+/- 1.7) <- [76 77 79 75 79 77 78 77 80 74]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 77.4 (+/- 1.54)\n",
      "Mean time (20 cv): 20.96 seconds\n",
      "Overall time: 423.32 seconds\n",
      "\n",
      " -------------------- Classification, test_size = 0.2 -------------------- \n",
      "\n",
      "Software versions:"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  numpy: 1.8.2\n",
      "  sklearn: 0.14.1\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  D : (128, 96)               , float32\n",
      "  X : (5, 100, 149, 2, 96)    , float32\n",
      "  Z : (5, 100, 149, 2, 128)   , float32\n",
      "Full dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<type 'numpy.ndarray'>\n",
      "Flattened frames:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 298, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Feature vectors:\n",
      "  size: N=6,000 x n=128 -> 768,000 floats\n",
      "  dim: 1,536 features per clip\n",
      "  shape: (5, 100, 6, 2, 128)\n"
     ]
    },
    {
     "data": {
      "image/png": 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JMN2g79UmHVrIx48kJfsecAfG9rFDwplUGG9xE1HP7veo4f+aIcF0XeBeEksv\nHA7cCIV/QuFp5AzvRM9eC2nNA4HfATt4ow8GnxJhulmo8h9EJy0YySAa3L4G9So3o5NSKsIMTi0t\nJbu/tlN4OLYsKZih7A44BPX67/Xb2s73fl+Gzs0yihvlaiTss4Cf+fIGZ9usMcwsY3yhIwPZUrLx\n9Pc0cCfG7CJNMDNEmG4A3bTiWuQMStn+McBHofAAcghvR2m2+HZ30zLCBdf9lI8wD0E29oISv5eK\nMLcG1kNhHVHqczlwJcXRx4H+uALJlOxSFE29CLgD3E5oHPczpN/VKjjDR/TZnQpuX3B7gluEBLPJ\nESYvAn4LfB04CQnSixPrxSPM+eim/XsAH9Z/Cs+gTlkof9x/7EgkmNuQLcJcCXyXSDD/itrkVVD4\nBZEI/REJ5t7A99GzUD8BXASF25Dd/9lve6H/TxDIMH4Xvt/tPy9CPv0uUoMntxO4nwJfBvZCviRE\nmKUm/YDsbgGyn9fE1qkQYbrQsX81EvqQ+QsBywwkkvejOg6CeTlDHVG3GtXtb5DdxwVzPmUm+GV5\nePIycGFCiwPORFHcOHDfRj0FKJ4luygW1fSgiOWOREUMEgnmkcCBEjbuBc4FnlJDYjOKlMIU+MPB\n/Rz1qsahE/UeJJZ/QcJ5NvBOcIcgA3sv8CNwp6FB8u/HyrEeGcduKKJaj0L43+o4uAY5mJchY5gL\nbh80Pf4mlA7ZHjgHnbDlvpe+jKhndhw6sQ8BM5VTZ7Gvj7E6VvD7X45SgxPRtU+/R434N4nz8iTq\nxa7w5bscOaNf+X2/Fbja1+8uqHHejxzg0USCuRA17M/4OvgFikSfUMN3zRLMncBdi+q5C53jh5Cg\nP42OdS7FKdn54Kb49cf49/hrLqqPjWjs56vAanRLwylE42tBXMYTRZjzwG1BNvcalBZ7MbKLJ/y2\nxqNO1r2JYwmTKC703x9F7eIh4FRwN6BxqB1R2vZz4DYh27gfmEzx5LttUXTxDPB5lcf1I/HsR9fR\nXYmcx3VIMGfEtrEHii7xx7cbyn5cgNrXgyjSexNRWi+sO96njAeJBPN01Ek91dfFRcAbwf0d+DRq\nU/+NOjhBMI/321+n4+Mev61mRpizUFt5PvAW4APIv7zNn6P1yPZmAI9BYQO4Z1HH4mDgb+qIAzqe\nFd5mQhT3MPIt30P+8lDkszYiP9uPfMxm/9rbi9F84EsoguxGHaXP+s5X2Bd+W19G/uKzKCX5CuBE\n//vjyG6VWzAsAAAgAElEQVT/APzE2/1S5P9AbezB2Pdt0FDNDX75keCeQ3awEvik399a5OvOBV6P\n/MXNqk83w5f5OeRrQtt4BngXinjf4Y/9775ukxHmFpXD3Yjs7jEo/Avcd1FEHo8wd0DncK1/PR+1\nl7eh4Y7fA99GD9O4wa8/U+UqbAZ3DWWGQCqlvdYgwTGM82ncY5nWYHZniEbZ3RrM5oyIRvo7wzAM\nwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAM\nwzAMo4bYzdeNrNjN141mYDdfN5rBiOzO1b4cRpvSSFswuzMCjbIFs7mWxP0A3OuaseO0hVkeIG00\nhNQn1BuGYYxmJpPtuc0NwQSzJXBd6AGshmEYRsQAegB1S2CC2RqMA6Z64TQMwzCERZjGMHr8e8v0\npAzDMFqAybSQXzTBbA2CYLZMT8oYDbhjwL2p2aUwjDIM0EJ+sWUKMsqxCNNoBsuAac0uhGGk43qA\n8bSQX7QIszUwwTSaQQ+Zbc5NAvf6upbGMIoZ8O8tE9iZYLYG4/x7yxiGMSoYR/ZO2jbAKXUsizGE\n2xfc95tdihZgsn9vmUDCBLM1sAjTaAY5Ikx6c6xrVMc0LFUOkWC2TCBhgtka2KQfoxnkiTDzrGtU\nRzeRTxjNWErWSMUiTKMZWITZmuQ5L52MpWSNVEwwjWZgEWZrYoIpLCVrpGIpWaMZ5HHM47A0YaMw\nwRQDwAZaqC5MMFuDMEu2ZQzDGBVYSrY16cbqGhRhrqOFAgkTzNbAUrJGM7CUbGtiEaYYQILZMnVh\ngtkaWErWaAZ5I8yx4Co9dN6onh4s/Q0wiEWYRgoWYRrNIG+ECS3kvDoYS8mKrYGbaSGbM8FsDXJG\nmK7bevpGDcgbYZJjfWPkWEpW/m1b4BpaqC5MMFuDvBHmOcBedSqLMXoYSYTZMs6rgzHBhJnAs8Ba\nWijCbJmCjHLyOqMpKL9vGNWQZ5zMBLNxWEoWVgDXA0/TQnVhgtka5J3000vkwAxjpIwDnsu4rqVk\nG4dFmJFgPkML6VTLFGSUkzcl24vNojOqpwelvbJgEWbjsFmyGr8MgtkyNmdjmK1B3ghzHBZhGtWT\nZwwzRJij3ZE3gh6gAK6r2QVpInOBO1FKtmUCu5YpyChnJBGmCaZRLTaG2Zp0x96zZgA6jV7gCVos\nJWsRZmtgKVmjGYwkwjTBrD92Xbbs7Sls0o+RwjjUk7RJP0YjsTHM1sQEUz7uSSzCNFLoATZhT44w\nGoYbi9q/XYfZeoQ6Hs1tPAhmS0WYJpitQQ+wmUyG4cb49S3CNKqhB9hCvpSsy7G+MXIswpR/a2CE\n6b4IruK17SaYrUGIMLMYxrjEu2GMhHFoUkXW2ZjjgI2Mbicew/1fFgc7Qkwwi1Oyda4H1wW8A/hi\npTVNMFuDPCnZIJSjOV1jVE8P+SZV9AKPZ1x3NPB8YHmdtt2deO8w3LwMK8Un/dQ7whyPxPkocH3l\nVjTBbA3yRJhhtqJFmEY1jCOfYFqEOYQrAH3AwjrtoIMjTFcArgU3u8KKDYwwmYA6g4/7zyUxwWwN\nxpE9wrQLyI1aEMYw8wimRZhiHOrcLqzT9ntosckuNWQ+ejB0f4X14pN+6h1hTkBzSJ5A0WZJWma6\n7iinB3iYfIJZJsJ004FnofBI1SUzOpW8EWZIyVpHTdEl1FcwN9OZdb3Cv08svYorENknNE4wC1QQ\nzFEeYbrXgmsFowwNpFaTfk4G3lptoYyOxiLMkVNvwewm32Vm7cS2/r2MYDIWeA4Kz9CYSDseYVpK\ntgynAwuaXQjyTfrJkpLtJ2rUhpHGSCJMG8MUfajutqrT9vNel91OBMGcVGadMOEHGnNZSeaUbBsJ\nppsAbmqNN9pH+RPXKGo96Wci5XtwhpEjwnRjkG1mvFa44+kDbgC28unDWtPpgvkw5f1TGL8ECs8B\nrs43ou9EweSVwCdrtznXjUSnVQQzqzMKzzAsF2FOpEJqwciKe54XjE4jz2Ul45C45rnRQSfTB9yP\nIu5Zddh+N53bOVkKXEFmwQTqH2W2u2C6fweXHIMbRLOrakVIWbaCYOadJbsBizAbxU+IJip0EkEE\nswpmy92mrIn0ofHch4BaZ71AnZkOTH+7AjAZuIvy/inYW6Del5Y0SzBr1hNfgKYfx6l1+rSVBDNv\nSvYxTDAbxQCdOR6cN8Js0JMj3ORKF4+3AEEwH6c+/iP4gzpOSHQnKXvSUHrQ7RUfJV+EWYdLS1wB\n3KH+SzME040B7q3RrNM+hjup0SCYeSLMSinZieB2AbdvDco3SnEFNIGqFWyk1uSJMBv5qKX/AE6r\n8z6qJQjmRjLbhtsLXIY7A7kudHnDk9S3rg8Hdqzj9tOYhOpsE81PyU4DfuU/NyXCnITy+dNqsK1G\nCmYr9Gbz3hovS0p2AvBC4KhosesFV4vz00K4KeDm1mnjveictIKN1Jq8EWajUrJTgf3qvI9qmUQk\nmFlt4zXAkRnW6ybf5T4jJc3H1gg3Edz3SuwzCGbWWbJQn7roB3p8gNcUwQx3bphRg21NIl0wa5lm\nDOVtheghw3WY7tvgFhGlZDNEmMPuqHEc8LnqitpyvAH4bJ22XQcbcT0t0mkZSYRZYdKPq3T3lixM\nBlbJ6bYsI0nJZrm7DeS/Pnak9Gcsz0iYRVFHfYjQ0WiFCDMeMDVVMKfXYFtpvZ9JdGRK1oVnElaa\nFbc3sIx8k34GKJ4oNZX6TFJoJrOA/WszvX+Ykw42Xcue+MuAr9VweyMlj2PeHrjXr1uio+YKwJ26\n/KsqJvv33arcTj0ZQUqWfrJNWgy3xWuzCNNNAvdm/2UA6E0Znsuakk1O+qlXhBneg2BupoGCGYyh\nFhHmaBrD7EYGsYXyvajJSBzyCGayFzmZyCF1CjOAmVQ9k9UVgLvAxRtMPbIQ84A5NdzeSMk4kccV\ngPcAX6qwbj+yrSlVlmsy8Fdg9yq3U09CajGvYGaJ6OqUknXfBLfNCMqTlRVo/BkiLUh2EIJgbqTm\nEaY7CNwHyvyeLEuaYLZSStb9CNzCjNuaxPCT6QWzZhcK96EKmgTu3TVKJ420HBspO3V6aDr2LKIx\nzFI9/S6/zgSGp4EG6EzBvAc4oMrt9CFnH4/AB2K/1YpZSOCbTdYIcyVqj7+tsG6ot0FwV2q264gY\nAK6jNh3vehFPyWa1jWS2pxThvGyhtrNkD0QZKmJPWylRdvdacK/Luf0p6NI/KC+YI03JZsmC7Jr+\nk9sBWJNYGI69FQXTFYAXAFuX/rsbABeiplIRZhCDjLhucKeU+LEPuA+dxH8Htsu+3ZoyFd35opwz\nmoiOfTaVLyuZiE7+BCSOVUSYbiy4d2VfvynMAH4N7FzldkJkFBfMekSYs4GZ6qC5OqUd3YEZ7ooV\nOmqVBHMJcJW/40oWwZyKZl9WenxTKSYDt1F9pFpPRpqSbdIYpiugjloYLutF/qSU2O8KlLnkxHWl\n2NegtufGUlow45N+KglmctJPpTHMKZS2mdkMt8f4cEvTBPM50scwZ6MKKjfZ4bPACWV6P33oGp48\nzmshcEaJ2yr1AWvRSZ1RotyNIC6YpYwiiFw8JdtTItqeiBrzFhTJxI02b4S5HPivOt+WqlpmANdS\n/ezsRgnmLNQojwM+XcPtxvkwlSPuaejC+0qOeR6K4Kmwbqi3rdFlEbF6dDtns6GhTMrtNEUwXdaJ\nJSMRzKyTfsIQTS1Tsv3Ib0yPfY+/J5lN+bkOLwDOTiwL52sylVOyWWbJ5o0wp1LaZvzcjSJ/WSol\n27Cbr/cDd5KeSgm583InIYzt9KAGl0y/TgLWkVrRbgq4E/xFz+9JbHMM6WIYIsxF6eu4Wf72eVXg\nxmRI9U5BglnOKOKCOQ6d2FLrT0QGuQkJZjLCHB+L5JPl7Qa3f2zBdqhuWmFWZwoulO16MpfRTQb3\nhZQfQjop3uj6gUeobUp2Nur47Un9OmkzqJzSnI7aUyXHPB+423/OIphpbf2HlEyXFTEBdfTupzkR\nZtbOZM6UrOtCfitPSraWghlu3xfsrdIldXMo357mA4sTy+LtJ4tg5p30U02EOdX/P+4LWyIlewvp\nDXWpfy8nmDP9f/tQyvFphgrvxiJDeoj0nsmuwH8COwHv9U7xW0R3C/LhuNsn9p+4YJJS7v8Fjihd\nXNedoUd6ELq1WjmmIqdcroEMoLtjhAjzSZSySBO+uGCOBSbEylnKkAO7A9+Nfd/ev9fjfpm1YBA5\nrbVkF/UVwGtTlqdFmANodmiJ3rD7gC5Iz8Us4GYkmPUap5tOZTGeRjbBzBthpgnmLIYmOrkjy6SL\nJwPrUXtohmBm3WfeCDPunCtRD8EM6chgb/1+H6XKM4fyvnoWsCCRNQh1F7+FaYYxTPdXcEsS61WY\n9OPmgpuX+I+PMFOzblMT76Bjf5bap2RdAdzsMmOB8QLcSmnBTLnvonsZuPCInFlEgpnsvYXcd6nr\nnuaghj3fb+N5wAlEQj3LT3e/QNHo0DbvIzLKpINZQPlHf30SeH+Z30G9sIUV1smakr2RYsEsNSkg\nCOZmv83HiBrGZGR8pXrS22kfQ7c4DILZCpNU0pgBPIgcf9ZobQHKXiTtqFRKdi2le+KHockUGXG9\n6PzcgGxzSvVZjGH76NZ2Qzt07wOXdiOAkQhmueswpyLbCoLpOzBuAqq/MDP4/zEsXez2AbeYthBM\nV0DH9jDZBTNkKrJEmPWYJTsL+YR4hHkvqXbtuvz65QRzNvJV8RuGDMbeB1CHPssY5vZENhNIuzVe\nvC7eCbw38Z8pvkxp56OUYN5HHSLMfnRN1HsqrDeAIsw0x7UN8DeGRwHvAQ71Dno6auQhbE8KZrke\n3Wx00MHB74XSugej9NdsomgzRJShwkAnJFnuIMIpuAJwDJoQUY4FwNwKM3uDYFZKyd6BDGmQ7BHm\nYxQL5gBKr5USzG2R0YXztB16skCTBNOdCe7gMisEwXwUGMg4BhU6QclJAFOQHSQbVZkIk7nADuV3\n56bHOoUzgQeI7O45yjomNxbcSf5zAdyvUq5tSxLOXbDn55N+iUYJwXQTErPZ86RkbydqE+G4gu0E\nwZzP8PtEn4YudA+C+SiabVuPR2eVI4tITwM2QWEz2WfJ9qM087gMHaT4dZi1miU7C43zx8cw7yU9\nwpyB/EslwYTiYGAK8klBMO+i4mUlLlwNkbxTV3LST/KykgUMF9kpyNf7c+h2AvdV/1s4lrj+9KGO\nYBDMJ6iRYM5AFTOrwsy7fl+ArpTe+wokmMn/L0EPYZ2q/xVFmBsonsmUEEw3JnZtTWiMwTHs7d93\nQVPUZxE10jBTN0SYoDGwmGAOncgSgsnORBFtORagE1CuZ5k1wnwUNbpFRHddKRdhbkJ16Otx6L6o\nd1I+wgSY469HnA9cRPNSsntR/gJ2L5iFZ5GjHSz+2c0Hl0yrJ9L0Qwyi2ZlpgpnWEx+D7G774cvd\nh8Ed6xe8BU3CCfu8D4nm0yhrEMvIuBng/ju2scVo0tVEdA6OJOrwlSLYcdjuAoY96NiNQcebNjv7\nOMCXwXX77d3vf6skmLcg5/MYUT0G25ntI+zpFLUbV0CXrixkSDALW5CNN/oa6cHKqzCPqAORNcIc\nQHWygWG25A4F94bYgvhlJaUuM5sN7qIM+w3MAq6mOMJci0SroAyE85ecMAcNGYwr0zmbDdzEcMG8\njUgw76ZkSrawxX8PtpwUzEpPK5nPcMGc6vcZOj2riYbUpiD/kNa2+5GPrlmEOYOowZW79KIfGcUt\nFB2Mm4oq+KLiArsBdAIXohP6AOVTsskI86VE44NhIsUu6KD39NsbA1zqfw/ilyaY11CcSg4CXOoe\npS9EN+4tl7Il9vtcP676+pR1slxWEnreNyCHdDepEaYrUJySDRHmAKq3J1FUUS7CvA4d/3J0Lu+h\nKRGmKyDBWFZmpRBhgo4r1oN0fejaweRM1AWo7tIizFvIPoY5DdXnInAf0qUcgLImbwTe5L8vJWoP\ns5D4PIAyBvdTbHergNcTPa0jHPtiIrtNTLZwpyY6sqFOpnthnM8wwQxjv4VnGG53S9G5D+V9CApP\n+++JqMeN8cMqYVbsrf6HGykWzCeQTcWjzMA8VJcLkV0+5pc3Iy2bZX/ziVLUeVKy8bYY53CKbyOX\nZQzzxcBefrLj0hLrxJmNfFw8wnwM2e9E4BQgXPQ/B9n8I5SOMmehAGhhbFnocGaJMEE+KpQ9LcIs\nN+lnvvYdonUXUrHx2dWLgXk++JlKsU1CcWe4pinZIJiPMKw3XUQ/6kHdSLGT2wOJ1gOJAi9GIrcV\ncsjX+21MYXhKNn6z42Cgq4BdvVOYTSQm//DbOdevFwRzvj+GuGCuR5UUNyaQ0dxPUYTpCrEU0Srg\nx0gIy9XhAnQS56Bxm0+npJnKzJJ130P3HQ2O5HAozIbC9aSnZM9G15TGU7IhUh/wx7te2yuK0PFj\nuxNRfYXI6VpfD81IyQ6icpcTzCUoYgaNkcdTLi9hKCIvSoMtQGnmUoKZnCVbagxzLhK921AE+Ra/\n/CDgZGSb/Ugsg2NYjkTlTmTvDwJbEz1maAVqk2FGaTj2JURCGRNMtwMaS39lrFzTgX+hdjsL2VSy\nYxfSsTDkmF3Bj18t1fpuvP/fPbH/pYnrT1AHNUSYMFwwryIa4niKYsFciTqAWxF1DKF1BTMeYWZN\nyQ5QlO0pYjnFfnUAtd1ygvkilM4/BLiOopn4w7J7EE00GxMbU46XZ1vgZerUMwfZ/MOkTqRzIRN4\nCcXZjinItssJZhjDxL+v8GWoJJgx3+jC9egPEnUEB5GvW0d0DoOfX4ps8abE8YSMaM3HMINg/o7K\nEWaaYO6FbnWVPAFLgMtQL2UmivbWoZOQJcJ8nl++DTrJl/rlf/Pv56IKiKdkL0QOagrRuMJGhtIV\nQ2I2BwlvfPzx60RRw07IYNZTUkyGTuyl2g47I4eQ7O3HZ8nGZ4LNAI4H9mFI7Aou9r9EStYdgERi\nB9IFM4iuF0yOQRf8B1b5erjXlzsI5gM0JyW7NXJMS8uMZa1CNgTDIkxWA3/w24iPNc9H5y7MnI5P\nVriZ9IkBE1I6RnNRXV0IfASNxY9HAnABssODkH0OemcUynsuSn0+hG4e/21/jMtR6n0Pv4/l6Jwt\n8fWxgeII861+/8fHls1AncdB/5+bgK0SdZgimLwe+JYv79P+fTUSOxLrBlYiR/NeigXzJorHMC9H\nbWq+P/6kYP4c+YFBWl8w4xGmv56w4lhriOg2MFxElqMhkJD1WYU6dCUE0/WhIadfACeioSw/FOUm\no0csJtP2i5GAPYQ6VPEs3gxky78DjvXHtxbZR1qEOc0fy3UU344yRJjhspJKEea/0PWcwc8eHBP+\nchHmTGQb/yLK3ISgI24z4ZiXEwlm/HjCxKft9b/C09RYMH8D7OzTACelrJcQzKEJGEEw1yPjCsuX\nAOejCpiPHPOD/kDDGGYlwbwUpWFnAf9E04T/4X8/F6VO7yOKMM9HTmRf4GJfSQ8gB/MMcCq4MAX+\nFtQgpnpneaRebpAoX383pdOyc5DR3eE/r0QnJHlHmkRK1vWAOxelXUC993jPO5CMMN8HvNsvj49h\nhjRQIsLkMGCPmJEeBpyHGssc1Dn6l6+fZkSYO6Bz6UidSObGIBu43C9IzpRdjWziOtSDJta7vhqN\nqR0E3Oc7UFMoEkw3Bh33OtR4kw0pzB59CxQ+hJzc65AN3oc6I69FDu0a1Li9YBaeg8KTyN5Xqyws\nQY37+0SCuQz4PWoTi4E/MySYbhvU6TkeRYShkzrD7389srWr/fK480oTzCORA1uM2sky1HbjY2Vp\ngvk55HQWUDrCvA5lL7ZBbXa6on5XQNHSr1EdL6clBbOosxSbNVwIk3N6Y+uexvBn0IYI8zFgF3A+\n+nHj0bm/nCgY2Q116EpFmIehztilwKHIL+7j9/ky5IcPipVnK7/sBiSYc4l89QaVh1uBc9CY3/7A\nxcgnpQnmLGRfl6jMbsCPTY/19VIpJfu4//xXouzjVn7/b/S/TaT0pJ8wCe1m4CRwb0f2/AjFNrM1\nGpLZzm/v1uh4XB9RSnYu0W3zaiKYW/sd/swf2HfQRITkhcjBKG5EPZ77wL0S9UL+6m+ttT52QEvQ\nSVyLnMb9DKWohqVk5/pte8F0M/yB/RQZ0CbUWO9H6a51UHgcCn/y25+JKu4iZPAvIaqkVVC4HRnT\nx1A6LUQP9/r1d0SOcF9f1mv88dwF7ATux+A2gPuiNuneiBzVXUQnZWdkFCsT9RafJTsWOa7DgU+g\nSGUP0gVzC2p8U33adg90DeUaiscw0yLMQdSo7iZ69uChyDkHwWx2SnZ3ZOTJjEVgKTrPj/jvsQjT\njUVZgMspEkxWog7MWpTJ+JL/fBRFKSU3BtXLOmRP3hbdLE3mcXMZspGhqP9rKD16uV92DhKgm1Hv\ndjdfvptix/CQf7/G72858A3UkZnpj/s3RBHmecBin734OvAxKNyNrhkOE0emo3b0ELLVO/0rntkI\nNy0A2dF4v/8nkb1cRiSYf43972k0G/kEX0crUYS7N3AGsncYEsyhW7Ld51+7oCGKB5GN7ev3/Ud/\nXo7xn6FlBNMtA+4HF6Kp+KxhKLrUzS1B44Hn+HR5IB5hngF819fNUmRzVwPb+0k2O6KOnh8vdivA\n/UdMtF+Eosvrkf/+BvA21Mn5AgoUYoLJwcCfvL/6rn9tRxRh7obayB/QJVI7+G15wXQHMnRfWdeF\n/O19vsP3NzTUNIjO16PItnqJ7qIWJx5hXuzfryQSyNf7zuteSJAD8c5DqP9f+ro7Ftl/LMJ0k/36\nF6GAYz1D/sEdTDTMcq/f5vn+3T8Ao/RdqbJMw98buAsKT4D7FPAZ4HS9u08RXQAbeg83Imd0DRLX\nj0Fhk9/WOuAIcM8iYTgdNZRDgB+hhnQAajQPA28BdxsSsUPRyX2zlvMP4E9osPpO1MP/OkohnRgV\nv7AJ3FfQZIxbgbNQJLan/z3M2DrTl/kHKLK61G/33SjK+Znf/8lEaaq7/PbOQh2Di8HdgMa0TvXH\n4NDDYyeg1NOp4O5HJ/c5FIGE+uny2/9vlP79PPA9ZCBhMkTgKdT43uLr4Xf+WD/gz8PRfnvPIfEI\nM/UeRII8FgnGK33UPM/X6Sok7tOQcyugRngCjWV/FL08A3wC3OnAK5DT+QlKVV8WW38dsLtvEPOA\ntVB4DNx16JaLByN7+6R+Y290rn+Exn2norq5Qftje+DrEj+3ETmlNyNn9hVUl5+I7f8H6Jxfoa+F\nteAuQB2Om1H0eaV3XIEHUWM+C0WKvci2voPszaFG/ynkAP6IbiH5ef//M/3714C/gTsfObzf+W3v\nhZzoXcgZ/d0v35do5usVyF5vRR20nVEbfheyn9ti5d2CxPsLyIZW6v+FB/2xo44j9yDxfQkShQeQ\nbb0Y+Cay5xORs/sv1Ym7Q/8phIxBMwRzIbjjUB2sR+V+FTp/3wX3URSBx8d1NwKHgVuHUuRfRvV3\nDriTURZoFeqMbvDbGkDXcHcje7vK72cMcBsUNoLbgoTtz0iIjvMR6Uz/3xAJfQG1zUNQfX4WuBDc\n4aizcjzyIUDh874z+WkktBuQLZwNhXXgrkWzzp/0x7M3Gm54xnccdkTieLLf93moo3aAL+MjyD42\n+PqbBe4jqOP6HNH8FJAgPufr8j5/HK9F2YZfQyFMyET758U+UNoOuBsKv1edul5ktyHCPAD5idtQ\n2/mSP4/rkC1+1p+fbf36MCSYBQeu7CO+KuXe19D6Tz83GsP5SMQawRrM7gzRKLtbg9mcEdFIf2cY\nhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEY\nWbF7yRpZsXvJGs3A7iVrNIMR2Z2rvIoxSmikLZjdGYFG2YLZnBEn1R6yPA/TMAzDMEY9JpiGYRiG\nkQETTMMwDMPIQBsJpiuA62p2KQzDMIzRSRsJJq8APtPsQhiGYRjV4F4JbmyzSzES2kkwB4HJzS6E\nYRiGURWfA2Y1uxAjoZ0Es9u/DKOBuEPbtTdsGC3KWP9qO0wwDaM8ZwHbNLsQhtFBtK0vN8E0jPKY\n3RlGbWnbNtVugtmWYbzR1vTQpo27Mu5QcMc2uxTGqMMEswG0bSUbbU03Es1OZFdgj2YXwhhNuC50\nD/O29OUmmIZRnk62u3F07rEZrUl34r2tMME0jPJ0st2No3OjZ6M1McFsEJ3suIzWpZPtziJMo9EE\ne2vL+SgmmIZREldADbtT7c4E02g0QSjb0u7aTTDbslditC1t3bgzYClZo9FYSrZBWIRpNJq2btwZ\n6KVzj81oTdq6TZlgGkZp2rpxZ8BSskajaes21U4pThNMo9G0dePOgAmm0Wjauk1ZhGl0AG4GuHrc\n7zWM73Wq3ZlgGo0mp2C6z4F7ad1Kk5N2E8x2ioiNxvEq4JQ6bLete8MZMME0Gk3ey0qWAjPrVJbc\ntJtgWuM20piJnH+t6XTB7GUoinZHgvt4U0tjjAbytqnptNBM7naK2Dr5ejijOmZSH9vodMEcBzzr\nP88HFjWxLMboIO+lWtOoT2d4RFiEaXQCM7AIcyTEU7LjUMRpGPVkJBGmCeYIMME0SmEp2ZERv3HB\nOFrIMRkdS4425XqBSbRQSrbdBHOsv12ZYcSZQX0a1WgQTIswjUaSp01N9+8t05FrN8EE6GpqKUYd\nbnGzS1AeV8BSsiMlKZgt45iMjiVPm5rm3y3CHAGd7rxaEDcIXNbsUlRgMrIJE8z8xGbJWoRpNIQ8\nl5VYhFkFne68WpE+YGKzC1GBcI1WPQWzZXq4NcYiTKPRWEq2QZhgNp6JaNy4lS8/mgE8Sn0aVQff\n6ceNRe3fxjCNRpJXMB+lhTqs7SiYrey8O40QXbayI50J3I1N+slLuAbTZskajSTPdZjTgHtpIbts\nN0WFlG8AACAASURBVMF8is50Xq3KJP/eyoI5AwmmjWHmYxywEej2E6cqRJiuAG5JY4rmjgF3VGP2\nZTSYbsCRPcK8BxPMEdENbKYznVer0g4RZh/wEPUTzKyNu93oBZ5AUWYXlSPMPYGfNaBcALsBqxu0\nL6Ox5PHjg8ADdG5K1n2ojtdJdqMG3onOKwfuVHALG7SzIJjjG7S/kTABWE/9BLPGnTTXDW5K7bY3\nYsahjM3TyCFVGsPcmSjjUG8m0do2Z4ycPG1qAvWbnzAiaiiYrgf4MNG1M7XGIkxcP6rjQxq0w3aI\nMMdTs4kB7pWJDl89bO7FwFk13N5IiQtmuCynp0yHdyfkwBrBpAbuy2gsoU1lmYsyEXiEzhTMIec6\nr4bb9LgCShuN9gjzhcjQVjZof+0gmBOAx4Bx1WU33BjguxRfRlMPwZyCrh1tNuOAJ4EtFF/HWso5\nmWAatSBPpjBEmB2Zkg2OZm4NtxnoBp5BveHRPEv2aOBbNF4wWzk9NgF4HHiO6mwjHGNSMDdRW8Fs\nlWtb01KykCqYrgvYDpjQoFtTmmB2LiMRzI6MMIOB10swnyZKH41WFqEoaHuNhdWddogwx6MG+BTV\nNayJiXeoT4TZT00E01XbztJSspBeh9sAD6JOayN6+zaG2bmMJXubmohFmCOiToLprgQ3o3bbqzuT\ngPvQZRQrGrC/dhDMCagBPkV1DatRgtlHbaKnS6oUzV5UZ8mUbNq5Xg38E3VMGiFktaojo/WwST+e\nOo5hMhaJ5TPU1nktJrr9UjswCaUfbwUWNGB/7SCY44kEs9YRZg+tm5Ktdiw0LSX7LOl1uBtwCarn\nRgiZpWQ7lzyCGSLMjhXM52ibCNN1oYbZV5vtVVMO98mMK/ehi80fpzHlnoiuQ2zl9NgEFPlsoT1S\nsjUQTNeFzkk1NhAm/cRTshtI7xztClyKCWYdcEeAe3uzS9FAMs6SdQU6fNLPBOAO6i+YtZr005d4\nbxaDwKmVxySHnORm5Nj6616yqIfXyhFmPCU7SgRz6HrIaq6LDBFmPCXrZxvHceOA7dFTaxopmK3c\nSaslOzK6btKQtU2NQxnFzXRwhHkT9UnJxmfJ1sp5Dfj3RghPOUI5KqXXJgCboPAcjY0w19HaghlP\nyWboibpecG9I+SEIQSMm/fRUeUP7WnT20lKyaRHmdsCtUNhEQwTT9fjytEGE6d4MrlphHyDyAaOB\nrLNkvb+rOnNUU2otmPcAveAyGrtblNHg6jHpJxhpsyPMINiV7v4S0rFgghknpGSzRphLgI+lLG9k\nhAnVCUKILKuxgTDpJ56STYkwmYlugA2NmfQThnbaQDD5KJpBXA39NL/T3kiyCuZE1PZ8BqTelzNl\nu/tWrQVzE0rhDWb8zxeBIzOsFwSzlpN+gpHWUXhcAVyl9FsQ7kp1Noncgukmgqvm+CYCD9Pa6bGQ\nks3aE52s17AG2GjBrCYtW8sIcwsSzzGo/SY7R1NQm4bGpGQnoXsDt7hgui5gKtXfhGI0CmaWNhUy\nag7ZaB3HMd0Y4I4swVutxzDzCuY0ZHSVaFCE6Wod+u8PnFNhnawRZpghC9kjzHcCp2VYrxRBMFs5\nwsw7S3YQNb7kMZUSzHrMkt1IdYJZqzHMMOlnEqq/Jxleh4PkFsyq082PAq5B1xqPlClAgey+rhT9\njK6UbNbrMENHGOoumPQju6sYZdYrwsza6xokQyGpz6SftDHM88DtXKPtgyZA+UlQrgfcfinrhP1X\nangjScnOQim1kdLigukK5L9xweTEeyAIWFwQQm+4lo21Dz2BodkR5iTUXpOCmTzXg+h+npA9wrwJ\nXJaOcKlybcyxr2YRrt+uNsIcwCLMNEJKFqqf0FeJKYn3ktRaMDeTL8KcQn7BrOcY5lbU9vrGqUQR\n9C7A18uUI0uEmVcw4/sfCWEMs1VTsr3AFj8RKuuNC4KDS9pomBFcx5Ss6/bbWkd1YpBDMN37wR2Q\n8sN0lPrcQvkIM2dK1nWjO1LNrly2VIKdP1F5X00lXL9tKdl8JC4rcS8vMeclZCyhgwUzR0rWFWiu\nYPajnn7c6Uyjtjcy8ClnV0A90rRt1zMlmzXlHcPFHUCLR5hD6VjIPoYZbDMtwnyQ4TcuqOUYZh86\nd5vIFGG6XnC/SflhEhrPz5KS3Qd4XsryGeh4n/blesq/0iLMIJhZJv0EextpOwp2vjnDvppJLQVz\nfIunn2tJshN6Bnp0XJJ4hFnvlGxTBDPvGGYfegJJlnXrMelnAM3q9cLjJqBjKNHQ3WfB/VvOfUxF\nTnwCclCT/bT5ZDkeIn9KNkuvNBFhut3BvbX06m4FcLH/HMpZ6mL2ViDMkIXapGQfor6TfnIKJvOA\nF6RMHAtp3RKdJvdBcNv5LzNJT8tPJxLMEY5huh5wX05MoAqpymoEs51SsrUYw3SMnigzNkvWFZBt\nzklZr10jzGEOvhR5Z8lmLiT1S8nGBHOogZdq6DuR//6t02LvpRxJP7rhQ6weXNozReMp2Q2MLMLc\nB3hJmfUX6eUKRGNcaeNaDcQt9s8BTSM+MSDPpB/IFmF2++3iZ0VWS4pguqngji9ezU0EdzqRI0mm\nN/uAtZS2gZeiIQAoLZgzGJ6STYswp1B6DHMe8FY0Vh5IaUfu9eD80EPFywPaRTCno3s6VxFhugLy\nQw/QFoLppoDbqcqNxDuhk1HkmJa+b9sxzKwPhM47hjkFCWCzBTMYamjgpY53IWVvyuBWgzsmsXBq\n7D0IZvJm7wNIMH2duQJwC7ikkxvpGOagnzYNMN+/8Om+xYn15yKHOYic4ANIMJuZGvscUCqyj6dk\n84xhPkD6GGaaYNbS7oJgxsVgb+CD0SpuPOqYnUxkb0mHEm7CX8oG5unlxiB7KyWYyZRs3lmy3paI\nO9FEx9CNRecwCPgP9aDukrTTGOZNVH8/X4c6Li0gmK5QoUNzDHB6lTuJX4cZOlqlIsxGpWQH0X2U\nK+pWFsHM+jSPEGGuz7Jjv84dNG+WbD/FEeY0VGmhoX8YnB/7cWPQZKBydzF6EXBCYtk0NAZYTjCT\nEeYsJKJLEutVGMN0h4HbIfa9BwnK40SNep6OwxVQpPntxD7mxt4XAbch425ChDnUcLfWy/WmXK4Q\nT8nmuQ7zdrJHmLUUzH6UHYinZBcBW8nG3HLgSvRQgLHAKr9OqQhzkj/vMWfiepHdzUd210VxBIhf\nfxJqq/GUbKUxzLQIE4oFczoaOgkd0O399hf577sDnwcXj0A/Bu6o2LGFCLPBHbVcl8PMAG6mupRs\nP7pZxGO0xqUlHwfeUeZ3n4GqiiCYY6ksmI1Myd5JjSLMrGMR8THMLL2uKcAtpBqcewW4F8cW1CvC\nvJfilOytRMf7b8DB/vNsv99y98ldyvC7fkxFvdAgmPdTMcJk68R7ID6GGcYA4g38nf4V3/cjaEZm\niHTnofM0BVgGLE/sIy6YWyNhaVZKdrwXzdBIvwS8ObHOSFOyd9AQwXQHg3tpbEHaGOZCVO5ZwA7I\njsIknT1QBFIuwjwbiVAgnMN5KLJ8XO/uhcqCABLUdX52cZ5ZsslJP/OQTScjzJuIMjV7RMfpxvnf\n1wBHxP5zALBn7P8P05yUbJ7betYiwgwdqEbdGxpw/wluWYkfV5E+ASewCJ3HCrrhusCVGvoZi2zt\nOVTf62mNlOwtNFgwRzKGeQ/QlXKHheNQ+B9owKQfpgPX692NQ7384LgWAjdSvkEtQ5FQmC5dQEJ1\nI9EY5rVUHsMsJZixlGzB+c/xKHM74IiYMU9FYhkiXHz5H0bR8lKVq+iWUHNReigeYTZLMCehOpvg\ny7Ka4TepTqZkc0aYris2JlNKMLeQSTDdxJSZjq+lOOuwAAnMJjSZ52xkW/j30IF5Ecp2rEJOOS3C\nvM9vbxAJbWA+csBBMK9BNvdONN4IUToWys6SdeEOQKGO01Kyv2V4hHkdkZ3vCVxA1PG5Gz0qzP/H\nFVAKOhz79qidNEMwk0MUlLmZSegYVCOYA9RFMN1HS1yqAfB24NASvy1neCc6ziJKjznG2Qk4p0QZ\n4p3QecAVwBxl84rmy8QjzEbMkm2KYOYdw3ykxPorKXaOOXr6bh64b2fYf4gwJ/lGG2/o2yCHFQRz\nK5Qq6yb1VnOu4P+zgeg6zgkoOribKMK8ltIp2UG/7a19udIE8/HY91ha1k3y5d5I1FOfisTRC6br\nQcJ9qS/jMmSIS2PbnOt/DxFmEMxmjGH2oQZ6F6rbbRl+eUQywoynJg8G9/2U7Q4iwZyDZgRfDm6Q\n6iPMr6Fxx7D/AppktTK2Tniu5CZgLxRlLUGOdxE6J5v9sf7TH8/lDE9ZhZRsyDDEBXOe38d8JJh3\n+/3tDRweu8TpIb9+uVmyPh1bcP57Wkr2PNT5CBHFDIbakSv4/f4f6hBsjbI4VxGdy+n6Pyv8+jsC\nV9OcMcxEm3M7A/f79hVf3ovq92qqT8kGwaxRStbNR3f32jvltzlI7Fam/DYR2dnyMuOYi1DbSfqm\nJKvRUEBatBpvU/ORYM4D/gi8LLZe20aY3sG77thU9TRGEmE+4l+x9d10ZEjzYrMjKzgutxLc7/z6\nRwMngCtzU2Q3GQnKWnQyJiIxuQPd7mo18CdgsY9+F/rf7mFYWtZ1IwPaiEQ17DcuWP6SEq4nPSX7\nIHIqH/Xb+iPlU7JQPI65LYpkvwD8Gj1fM4yfPuw/z0aTXW5HHYCl/hjj6Zl5SDDnETWOJo1hDgnm\nJUgY7gOWJnqh4S4/MHwM83XAsRTdcWboGah3AYf4/54HHIicc07BdGP9LNcu4PnAC2I/buX/0x8b\nr9sN+DuRI+hFmYG/EEWY4brLP/v3y0lPyT6I0lpXoqgsMA8J0nhUfw/416N+vzsSXVICxSnZTRSP\nd8bTsZAeYd6GIuJvgptFccdzOXKcv/VlWezXvwrYyTvm5ahzMBu1nSeh8BDNuQ7Ttzk3XqlLvoBs\n6sDEenuhyP1eYGK2sU/3JnCHJBbGxzBrFWGGfeyb8tsuKMORIpgsQ+ftWVLnrbhJqG38ncrjmLsg\nW1oNbo9oLggwPMK8Dp3nqcBBsfXKdYYPB5flHuTx8g+Ae3liWThvdYkwTwYu9b2UZGHCwz5LRJhu\nVsr/4oL5FXDv9dvZGTmJq4gmPSxBAlFq0s970En8GXAsOng/BuoWg/uQ7xUGjgN+D4WNqHe3H3IU\nD/nXvqjncxPqCe+BxONehqdlvwH8DQnWzUSTdfw4EQ8jY3wUOf2YMboCUS/zfWjc9CAkmEvAHReL\naOOzZKFYMLcD/gWFL/v9v9rXQzwlOw8J/l3IoJ9Cxr9MaSe32u/jKoZHmM0QzNmxMtyOhPMO4Chw\nocGWGMN0E5GA/RU580Co64dRw/0h8DuUoopdhznUw4437k/4tNE54E7yHaULUAT3c7/NlbFO3gHA\nhciOdgY3G52vm5EzeQrdZ/gxv87WyE5+goYeLvTbuYz0lOzjyB7OBraPlXmeL9M9qP0EwbwM+AXw\nFkqnZH+j/wylqRcSXVIC6RHm3VC4DPgRSvmGCHMqerDCb1HHdJDoUWEP+P19C6UIr0Hn+WgUtaXt\nqxGElOwbgJcjcfkYsqU4hwB/9GPAsXSqG+PHCBORlVsC/BdwhsYP3R/AfZaSY5humca+3XRwL9C5\ndVkj0EOQXe8XK9OHwH0B1e93gW3AHcPQLHnXjc7NDf71IYZd6sQi1P5uo7Jgrga+hyYQ/QL4I7iQ\n+Yo/qnE+8olrgf9lSDDdYtR+bvH/iXWGXRdwJvDVYp/ungfOt3W3J5pIFo+UPwd8H5zXFLc78IDf\n1yAl59MUk2VW2FHgdkON41coN/006qmejwx8BXAVFJ4G94y26z6IorUVwGGAA3cx6k2OQymD36IG\nuQh4BXA4aihX+P9+SKk1XomE9PnAfuAuROmDm7VddkdC8TX0dPhXA2eBewtquNcDe4G7wB/T8Uhk\nQRMQPod6t6cjp3k4ErD7UQRyLfADX+Y3+e12oZlVB6DU3j3IoF4BbiFyfuv8ayUyjAdRr+s7RNHs\nM1DYovXc+9DM1YtQh+KLwH+CuwRFLDfHzssG4DRwd6Ixg19rcWEdusHCL4CvIuf8QiSS9yBn9gGd\nL27S9jnYn8/7kLPdE/X0H/OC3YyU7A9QHZ+CGunVqPf7dWAjuK+h836tX/8pfXenoVTohShq/xi4\nrf22piGBWo/s5ueokfwHEo3HUGP+sD/umaijswWdw7+jztFxaEzwdmR316GO047AReDuQU7jlcj2\nP4ii2UuV3nSPIDH/E8oO3AG8H9n+H5GzuQU5lWvRtbHfQzZ/GercBcFcA7wbOBPcOnTD/z+jc70P\nEsH7URv4POqMHu3/gz+2QdVf4XFwHwF+5vf3GuBdsXOyGc2yPo3oxuNBeD8P/AO17ZAGfqPOX+E5\ncHcjO3ybX/9S5DD39sc+gLICP4vtq9GCuS+4j/pyHAGFK8BtD6wBtwCdi02oMxrGptejNvo4ajez\ngXf4+tuE6unVqM29DnX8Po7qYTfUDjdof+40dI6PQfY8BtXnPShlfS7wL5QdWo38xEqUoRjv9/d8\n1MH/hz+XOyAfuAZNmjsadRC/AjwB7lvA6/06n0J2dRzwMi8qA0SByu2oLZ4B7ljkv59A7fI51KYK\nyPe9AXgTuia4F/gOuKuRXwvzAhajztxXkZD/E9znkBZ8CAp/83X8lC/PVshmHkJ+9ZfgQjblOOAZ\ndBvIl6O2MRfcrSgDswr59B+Bux1pzmXA95FfuBN1lr+h85eela50EfEahnoqxijnfOSMG8EazO4M\n0Si7W4PZnBHRSH9nGIZhGIbx/9s792BJqvqOf2b3zp177+69uwv37i4s+2KBDctTAiwYEFcFNPER\nBcpKViNGExIlIakyWpUUZVIxxsJSEqtiXlWkYmmVhQplAZoYkwAiikYTYoBEEBUQdlleC+xyd9nd\nX/74nXO7p2/PzOmZ6e7pmd+naqrvnenpPn3m9Pme7+882jAMwzAMwzAMwzAMwzAMwzAMwzAMwzAM\nwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzBCsbVkjVBsLVmjDGwtWaMMuip30nkXo3ekBvIb\nZaeiA0WWBSt3hqeosmBlzojTVXmwQlQIUgcRWj/pfBAwwTTKwATTKIPU8hDyAGkjf+puG/J8UsMw\nDKMETDAHg7HE1jAMwxgwTDAHg3piaxgFIDtBfrfsVBjGYCA3avdYa0wwBwNzmEYZbAQ2l50IwxgQ\n3gosa7eDCeZgYA7TKIM6VuYMA5Ax1LCYw6wA5jCNMjDBNAyl4bYmmBXARskaZWCCaRiKF8y2dbAJ\n5mDgfySrvIwi8WEowxh1JtzWHGYFyOgw5dMgZ+aWGmNUMIdpGIoJZoXI6jDPANbllBZjdDDBNAzF\n+jArRNZBP8uwis7oHRNMw1C8w7Q+zAqQdVrJMmA8p7QYo4P1YQ4ksgnkXWWnYsSwkGyFMIdplIE5\nzMHkTOBXy07EiGEh2QrRjcO0is7oFRPMwaTB0P4uckrZKWiBOcwKkdVhTmEhWaN3TDAHkwbDe3/f\nBXJ02YlIwfowK0QGhynjBCzhZBgBWB/mYDLO8ArmBJE4DRIWkq0QWRymXxzYBNPolQwOUzaA3Jpr\naiqFXOkar3kwxCFZxonEaZCwkGyFyLJwgRfMYW2BGsWRJSS7Bjgpx7RUjT8nv7nQQxqSlaWo5hR8\nbTIFckWHnUwwK0SWhQvMYRr9IotgTjCUlXjXNMgvtDisIVl/TUU7zJOBD3fYx0KyFaIbh2mCafRK\nlj7MCQYzlFYW4+QnmMMaki1LMBsB57RBPxUii8OcctthbIEaxWIOs3vMYWanCoJpDrMCmMM0ysAE\nsytkCXqv5lXxD2kfZmmCGdIAsZBshbA+TKMMsgjmJBaS9fjKN8+Q7DALZoZrk8+CvKnH85rDHDJs\nlKxRBln7MOsgtRzTUxXyFsxxcm8Qy6dALsr3HIvI6DDlEmAnvY9GDnGYE8ARrA+zEmR1mBK4r2G0\nI2tI1n9n1PEVfpUd5haKf0Rg1pDsu4Fn6T2fQxxmA3gRc5iVIKvDfA6ruIze6UYwLSxbTEh2iZu3\nmBdlrLiTVTCPAh7JsH+78y7tkJ8TwAuYYFaCrKNk92IhWaN3uhFMK3dRBZ5X48HncZ6N4jIFM7QM\nrQR203s++++3O+8E5jArgzlMowzGUCcTUg+YYEYU4TDj58nrHGWMVvXnDsELZj9Csp3O60Oy1odZ\nAcaAA4QL5rOYYBq9k6WhNum2FpItpg8T8hXMPjtM2QByZ4eduhHMXRn273TeTg7TQrIVYQx4ifBB\nP89hLX2jd7I8h9UcZkQRo2Tj2zzod0h2DtjQYZ8Mgik1inWYFpKtEHVgHgvJGsVSJ7yhZoN+IooK\nyeZ5j/c7JDtJFIVoRZaGgJ/msZdiHGYDc5iVIavDtJCs0SNSQ8tdaEPNHGaEhWQXk0UwQwRwJWoM\nDmAO00jgW/ohFZeNkjX6wVK0BX8QC8lmJe8l3ooIyfZ7Ldw8BTOxv1wMcmmGtGURTBv0UwG66cM0\nh2n0Qh142b0sJJuNIhzmPvKfVtLvkOwYSDvB6UYw51P23wG8NkPaLCQ7ZGRxmBaSNfpBVsGcBA5j\nDhOKGfTzIrnltSxBf/N+O8z4No1xNKrRa0h2GdESoSGEOkwTzIpgo2SNohkjEszQPkzrClAa9Kdv\nrd3xXyC/vM7DIYcKZuh1tXOYU2QTzNBpJdaHWRGyOkwLyRq90k1I9nksJAta8e4lk+DIH4JcEbiz\nn0Sfl2DmEV7PIpiD6DCrvHCB7AA5v+xUFIiNkh0IZG3ZKSiQOnDIvUIF0xym0iB74+EMdMHzEHxI\nNq97fCKxzYisB9mZeDNUMF+k9z7MKfcKZRx9YEWLsitjaF5X1mH+MvCGshNRIIEOU2pogbSKq+9I\nDXgIpNNIv2GhG4dp5U7pwmEyBywP3HfQQ7Lbgfcm3gsVzNCGRr8dZjtnuxwVy44jxgdVMKfda1QI\nnQ83if6o8+T/vLwayM/ne46BwveLzJSdkILopg/TQrKKr4CzCM4sQXXawijTeQY3JDvD4mspsg+z\nD4IpdZCPun+m3ecdG48mmINB6Iory9Dh5qGuoBeOBf4153MMEtOJ7bDTzSjZ5zGHCZFTysNhjqOu\nKnR+bDdMoCOeu3WYvQpmrw4zZdCP1No83DxNqGeBD7rveIfZsfFogjkY+D7MTi39uGDmXXGtBGYC\nn2QxDHhnOSrlzvowu6dBppCs1NAKOkQw/Qjcg+Qbks0aUo7Ti2D2ow8zzWFeBvxNi2OlhWRXoIt3\nTBE5zI73wqBWhqMmmFkdZp6tT88M4Ftfo4Avb6MSks3gMGUJ2dzBsJPVYa5EG8MhdVoDvb87CKbM\ngHw38PxJJlAx6iUkm7xPunSY8mcgsyn7tlnpJ3VayUZgXZvzJqMjK2JbC8lWjKwO85Du2zIE0Q9W\nJLbDzqg5zCx9mN715NmvViX8oJ9QwZlz2ywh2U5RpE3A2doXlxkfLWgh+LJNXy2ZAZYlok+TqHvM\n2of5DuDnUvbNOujnaPedNNIc5kxsO00Ukq2kYKa1YIaZUIc5BeyDmhAeSuuWURNMc5itmUDLZ55h\nwirhp5WEOsw5tOLPGpJt97usd9s0dxZyjnYh2fcD727zfX+PxK9nEp3ulrUP8yiiBkUcJ5i1Q4Ak\nltybAiYSgj1LNsGM12/LMYdZKbI4zP3u77zDsqMmmKPmMLP0YU6g7vIgFpKF7CHZOeAn9CUkK6tA\nbiR69mSAYMrfgZwQe6NTSHYb6thaMZPY+mM+Q/s8SfRhSh0Vq8Q1LDwLc697I+YypY7q1n6a52K2\nc5hpzjbpMH0fpg36qQChCxf4kCzkP1J21ATTHGZrJlHBPEDuDlN+K2VS/KDRQCvsWofFxj2zwI/p\nT0h2E3AFcHLs2J24FHhj7H+/DNxSTb9cA3JJ7PNQwYzX0ZOoYGZxmKvcNukw3bMwa/Pu//jAnyk0\n7/fRHJbNGpJN9mFWNSQrDcI7yIeF0KXxkoKZZ+U1aoI5ag4zSx9m3GHmHZI9Fyhp/q+EXpsXtXnC\nXOYcKpihDrNdSHa1277ebZPu7G6Q2Hmk5r4Tf7pHA027F6JXA+e4/Wfd/p0E8yA9Caaci4ZjYbFg\n+v5LT7wf09eBScGc1f9T+3TTBv34+73yIdlpNBY+1mWHdhXpxmEWFZIdFcfly92oCGY3DrOIkOwa\n9yqDdiIRJz4IKiQ/fEh2KmCaVqdRsl5cTgQepElsZBI4nyhcCyoGY8CrYm7YN4B8+tcS5fnJqFh1\nEsyf0VIwZRnIDSnf84I5Afw78Er3ftIlJwUzzWHuZ7HDhIV6SxogfjBRK4cpNIdkyxBM+RDI0h4O\n4BP/AqNVeXXjMPMWzD2MjsOcRiuBUWkgZBHMtcAuCgnJsta9ymDWTdf4ZIf9xolW3ApxmGuBJ1jc\n79bq2O3mYa52nwF8n2axOcZtj429Nwc8BvwQ+JgTzQl3Du/c1hDl+Tbgm4QJZvxeiTvME4ArUxz7\nOFp/+fmPp6ELKPToMKXm0vszorDsJcCX3Gdpj0tbgf4myZBskX2YsgL4GHBcDweJC+aoVF6hg37c\nKFmgmJDsI4yOYKa1moeZLIN+NgCPUkxItkyHOYsKxtUdnKB3mC8R1i+5EfgpWil3Kl/+2K3u7zng\nbvf3f9EsmF4o14F82tXHq4EngTehYdzX0RySnaC5kbINuAtYlT5tTWp0dJgch87hPibx5XhjAOBU\n4GE6C2abPkypuXQcRBt1XjA3uWtZi+alO4bUQd7uruFRt/Uh2cIXLvCr8W/u4RgzaLz5eUar8goN\nyRY5SnaUBHPUHGaWPsz1RIKZY0hWltDsdopmDq276jS7tCTeYT6CimEnNhAJZqfVs1ahdV+7Psyv\nAfeiv0maYG4GrgJOR69pD9R2AXeiodx4SHYWFTnfSNmGCvFLpN/7fj3rZ2gtmH7Ky/rmry7khIBt\nHgAAB5tJREFU2wG0++M01Pl2CsnGFy+IO8wPAZ9A3eXT7jt+IJEPS7+GKMTdQEX6H91+j1LywgV+\n6HIvgllCSLb05d+yLlwA+YdkZ6iMYEodJMvjftIYRYcZGpJdj5aFvEOyq9DyvaKk8QuzRHVXuzrM\nu8AHUQGKIZ8B2R77v46K0eNonXY9WtG3YhtwP+37MO+H2pnAUzS7s2Pd9y5E6/YTiRwmwI9QUxN3\nmJtQoYs7zAdQAUoLy3pDE4sALqwE9RzhgnkLmi8/JMxh+pBs3GG+CriYZsH0DnMjmo+vJQo/jwNb\n3fWfRuQwSxPMLWhMumKCyQ9ATk3/SI4OHDoeQEthrhP2BJKiR8n6Ftigcw3wVz0eYxqt1EbFYWYR\nzHhINs9BP2vR3+ApotGgRTKLCoi4bSt8xf8gkUnAhQffCOyI7bsO2AW1l1GH+WrggjbHPhX4H9r3\nYXoBfIrFfZj3Eg2maSWY8T7MjcD/oeHKtUT3/dOkT1lJiwD64+0nEsynaS2Yv4kKJmj/ai3R4A11\nmA3gFHedT9EsmBuAz6Eh6AOxY5zkPl9NZAj6ufi6JOPQ7dgCfIdKCaasR1tVO1rs8EXg6j6c51zg\nnhbL2aU4TKmBfBEkHhoqepSsb4ENOjuAi3o8RsEOU44BeWsx50olSx9mPCSbZyNtDbDbvcrox/QO\n8/ss1GGyBeSmxAAW7zAfotlhbkRdcnxazAa0YoZo6biz2yxreSpwH1p5z7h6I44LsYLbJkOy33Pp\ne8ylLb7/Q6jAx0OyG9G+v92omD8AtSOEOUx/r0yi9ddLRH2Y39Jzycdjg0CdYNZuRkPUoO426ZRD\nHeYh4BvA20l3mF9AG2HxUcdbiRZESIRka0f07daDVkMc5iua/227fukW4OsMnGBKDeScFh9eiP7Q\nF6Z8b5V7/5f6kIjLgbPRHyyRtoXnYcYrrlegK/BfFnuvoJCs1KiMw5SlwC8Acxkbd0mK7sO8GrjB\nzTtOIGvcoI08uwoC+zBlCeqSHiP/kOxaIsEsox/TO8x/060sR/sLL0T7wjwtHCZnoSHGs2Pv+f5L\n0DrtCdTBxtyXbAW5wM2f9HM2D6IDdb4JEm8Mxh3jHnRwju+3Oxb4D/f3rSx2mA+765uiWTB9nu9A\nw5jQWTDjDtML5jyRw7wbFbIPoEIMUb6B3muggvkjd62eNIf5QTdYJ+4w7wf+Bf1tPsuCYEoDneP5\nsMuPpMP8qjtuMiQLHerVkBvyrOhPeY8eWG5unhy7QAbBlDGQTSlDj6dpO+in7XPPWnE58B2QNOG7\nCPh74IKU416Kjhg7z908Kcj1IJ8PqNzeDNyjW5nVvJQxdIj1EbQguYpLasDbgB+wIJjih3+3CMlK\nvY8V7CQaWn+S0gQzTUhSOR0N492BCme3zLjjTHdRvlogY+mT4WUJsBMt429I+eKH0bDV5S2OuwRk\nh5t31ykNbwM5I+WDFiFZOQfkqlgerAH2Qs2vJZtnSHYN6nZ2UY7DXINW9negddjVwHeBj6Kr63h8\nxe8EaKHL5izgRuAo7coBFjvM/3TH9AsFHI/WmV8AzkMd3mF3/BpwHXATyHU65WVhLiPuN7lZ0ym/\nomnhe+5ct6FivoYFwaztRwVqC80hWZ/nb0FDuqCu72iQ14G8w6X119Aw5x6aZzHEHeYUkcOcQeuw\nd8by7YD7ezdaxzwLvBf4IxC/GtEsix3muUQDfLzDvBftEz4BarcROczjgMddPn6DZsHcioaDRfdp\nCslCB8EM6Zu7CuSV7sCb0dbW+1AB+gkqukvRlsw+4Nt6UXJL+uHAJWg7msGHQe4FjncZsRL4W7ff\nb4NcnPjuVnS+1H3oj/MkKjiTaDjkCbSgLY2l7XTgWrRFH38kzhhaSC9Chelrmo/U3WebgT/WNPJV\nkPiPCBom2IgWwjtSPo+fZznw+8A/AL+HtuCuQVvuB/XHlSMgt7u8EbQy/TLIP6M32LfREWygBeBP\nQX7HXeN5mn9yn0uXf+1355IWaUvDd+A/iw5Rv0mztFC+DnIYLeTr0Wvcg97I+9AETaOVwo1oOfgI\nyLu6PN9yNJ8OAbfqb7HAUtRlPUPUYOlEDQ3NjYHcg+a/z0T/ZIe/BD7hGqLxdJwCXAn8Bcg7Wcw6\n1Im87H7vdte0EW0E3OO+c8hdx4lod8MB4DKQX0Tv8Qaaz1eCvIy21H/sjjcPHA9ya2AeZOUk4AZ3\nng+AHCq43G1Hy8ADqEs8A22E7QP+RJ0/DTQfX1DBkt3AV0AOoIL5PlQs/glkF/pbXueO/zxaST8K\n/DXIr7tzXosKwi3AR9y+86gzvRb4FFon/jew2z18wXM9KpL3o3XoT9HRsN/SNHI+kcME7a+8AK17\n96P16S603nxE0wXoffZ+FuYoynvQRQ28qG4HXgPyFaLpbvvQ+3Ef8L9ovbkTuAvk9Wjd7hxm7TDI\n45rftYdA3gLc5srz8cAfxNK8Hy0Xc2gd+nHg88AE1Px5QeurN7vfzbv6O1HnPO+udZ/Lm+fcazV6\nDwRF7jqVxtvpvW/IGA7uIAqt5M3tWLkzlKLK3e1YmTMiiqzvDMMwDMMwDMMwDMMwDMMwDMMwDMMw\nDMMwDMMwDMMwDKNE/h+vb3nB0LF4kwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9ad47c5f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 128), float64\n",
      "Testing data: (2400, 128), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 76.1 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1536), float64\n",
      "Testing data: (200, 1536), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 64.5 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clips accuracy: 74.0 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 4800.0 training, 1200.0 testing\n",
      "  77 (+/- 1.2) <- [77 76 77 77 74 77 76 77 74 77]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 1.0) <- [77 76 76 74 78 77 77 75 76 77]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 0.9) <- [77 76 76 77 78 78 76 76 78 78]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 1.2) <- [75 75 77 78 76 78 78 75 76 76]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 0.8) <- [78 76 77 75 77 78 77 77 76 77]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  78 (+/- 0.7) <- [77 77 78 78 78 78 76 78 77 77]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  78 (+/- 1.3) <- [76 77 78 78 77 76 80 75 76 78]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 0.9) <- [75 76 75 76 75 78 77 76 76 77]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 1.3) <- [76 78 77 79 75 75 79 77 77 77]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 0.9) <- [76 78 77 76 77 75 78 76 77 78]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 1.2) <- [79 77 76 75 78 76 78 75 77 76]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 1.3) <- [76 77 78 77 74 79 76 75 78 76]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 1.1) <- [77 77 77 76 76 78 77 76 77 74]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 1.2) <- [79 77 78 75 75 76 76 77 77 76]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  78 (+/- 0.5) <- [77 78 77 77 77 78 76 78 78 78]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 1.0) <- [78 77 77 77 76 75 76 78 77 76]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 0.7) <- [76 76 76 76 78 77 76 76 77 75]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 0.9) <- [75 77 78 77 76 76 78 77 77 76]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 1.1) <- [78 76 77 76 77 77 79 77 75 76]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 0.9) <- [76 75 77 76 76 75 78 76 77 77]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 77.1 (+/- 1.10)\n",
      "Mean time (20 cv): 17.89 seconds\n",
      "Overall time: 361.93 seconds\n",
      "\n",
      " -------------------- Classification, test_size = 0.3 -------------------- \n",
      "\n",
      "Software versions:"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  numpy: 1.8.2\n",
      "  sklearn: 0.14.1\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  D : (128, 96)               , float32\n",
      "  X : (5, 100, 149, 2, 96)    , float32\n",
      "  Z : (5, 100, 149, 2, 128)   , float32\n",
      "Full dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<type 'numpy.ndarray'>\n",
      "Flattened frames:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 298, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Feature vectors:\n",
      "  size: N=6,000 x n=128 -> 768,000 floats\n",
      "  dim: 1,536 features per clip\n",
      "  shape: (5, 100, 6, 2, 128)\n"
     ]
    },
    {
     "data": {
      "image/png": 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JMN2g79UmHVrIx48kJfsecAfG9rFDwplUGG9xE1HP7veo4f+aIcF0XeBeEksv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\nDMMwDMMwDMMwDKNE/h+vb3nB0LF4kwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9ad426e190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 128), float64\n",
      "Testing data: (2400, 128), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 76.1 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1536), float64\n",
      "Testing data: (200, 1536), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 64.5 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clips accuracy: 74.0 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 4200.0 training, 1800.0 testing\n",
      "  76 (+/- 0.9) <- [76 75 76 76 74 76 76 78 76 76]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  76 (+/- 0.6) <- [77 76 76 75 77 76 76 75 76 76]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 0.9) <- [77 77 76 76 78 77 76 75 78 76]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 0.7) <- [76 75 75 77 76 77 76 76 76 76]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 0.8) <- [77 75 77 76 77 77 77 76 77 78]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 0.8) <- [77 77 76 78 78 77 75 77 76 77]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 1.0) <- [77 77 78 77 77 75 78 75 77 77]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 1.0) <- [75 77 75 76 76 78 77 75 76 76]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 0.8) <- [77 77 77 77 76 75 77 76 77 75]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 0.8) <- [76 76 77 74 76 76 77 76 78 77]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 1.5) <- [80 78 77 75 77 76 77 75 76 76]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 0.6) <- [77 77 77 76 76 76 77 75 77 76]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 0.7) <- [77 76 77 76 76 76 76 77 77 75]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 0.7) <- [77 76 76 75 76 76 76 77 76 76]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 0.5) <- [77 77 77 77 76 77 77 75 77 77]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 0.5) <- [77 76 77 77 76 76 76 77 77 76]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 0.8) <- [75 76 77 76 78 77 76 76 77 76]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 0.7) <- [75 76 75 76 77 76 78 77 76 76]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 0.5) <- [76 76 76 76 77 77 77 77 76 76]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  76 (+/- 1.0) <- [77 74 76 76 75 75 76 77 77 76]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 76.8 (+/- 0.87)\n",
      "Mean time (20 cv): 15.65 seconds\n",
      "Overall time: 317.06 seconds\n",
      "\n",
      " -------------------- Classification, test_size = 0.4 -------------------- \n",
      "\n",
      "Software versions:"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  numpy: 1.8.2\n",
      "  sklearn: 0.14.1\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  D : (128, 96)               , float32\n",
      "  X : (5, 100, 149, 2, 96)    , float32\n",
      "  Z : (5, 100, 149, 2, 128)   , float32\n",
      "Full dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<type 'numpy.ndarray'>\n",
      "Flattened frames:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 298, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Feature vectors:\n",
      "  size: N=6,000 x n=128 -> 768,000 floats\n",
      "  dim: 1,536 features per clip\n",
      "  shape: (5, 100, 6, 2, 128)\n"
     ]
    },
    {
     "data": {
      "image/png": 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JMN2g79UmHVrIx48kJfsecAfG9rFDwplUGG9xE1HP7veo4f+aIcF0XeBeEksv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\nDMMwDMMwDMMwDKNE/h+vb3nB0LF4kwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9ad48e9b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 128), float64\n",
      "Testing data: (2400, 128), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 76.1 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1536), float64\n",
      "Testing data: (200, 1536), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 64.5 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clips accuracy: 74.0 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 3600.0 training, 2400.0 testing\n",
      "  76 (+/- 0.5) <- [76 75 76 75 75 75 75 76 75 76]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  76 (+/- 0.8) <- [75 77 75 75 75 76 77 75 76 76]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 0.9) <- [77 76 77 75 77 76 76 74 77 76]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  76 (+/- 0.5) <- [75 75 75 77 76 76 75 75 76 76]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  76 (+/- 0.7) <- [76 75 77 76 77 76 76 76 75 76]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 0.7) <- [76 76 76 76 78 75 76 77 76 76]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 0.6) <- [76 77 76 77 76 74 76 76 76 76]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  76 (+/- 0.6) <- [74 76 76 76 75 76 76 75 75 76]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  76 (+/- 0.7) <- [75 76 77 77 76 75 77 75 75 75]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  76 (+/- 0.6) <- [76 76 76 74 76 76 77 76 76 77]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  76 (+/- 1.1) <- [78 77 76 74 76 75 76 75 75 75]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 0.7) <- [76 76 78 75 76 75 76 76 76 76]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 0.4) <- [76 77 75 76 76 76 76 77 76 76]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  76 (+/- 0.7) <- [77 77 75 75 76 75 76 76 77 77]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  76 (+/- 0.5) <- [76 75 76 75 76 76 76 75 76 76]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  76 (+/- 0.6) <- [76 75 75 75 75 75 76 76 76 74]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  76 (+/- 0.6) <- [75 75 75 76 77 75 76 76 76 76]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  76 (+/- 0.8) <- [75 75 75 75 77 77 77 76 76 77]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  77 (+/- 0.8) <- [76 77 77 76 76 75 78 75 75 75]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  76 (+/- 0.4) <- [76 75 75 76 75 76 76 76 76 76]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 76.3 (+/- 0.72)\n",
      "Mean time (20 cv): 13.24 seconds\n",
      "Overall time: 268.92 seconds\n",
      "\n",
      " -------------------- Classification, test_size = 0.5 -------------------- \n",
      "\n",
      "Software versions:"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  numpy: 1.8.2\n",
      "  sklearn: 0.14.1\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  D : (128, 96)               , float32\n",
      "  X : (5, 100, 149, 2, 96)    , float32\n",
      "  Z : (5, 100, 149, 2, 128)   , float32\n",
      "Full dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<type 'numpy.ndarray'>\n",
      "Flattened frames:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 298, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Feature vectors:\n",
      "  size: N=6,000 x n=128 -> 768,000 floats\n",
      "  dim: 1,536 features per clip\n",
      "  shape: (5, 100, 6, 2, 128)\n"
     ]
    },
    {
     "data": {
      "image/png": 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JMN2g79UmHVrIx48kJfsecAfG9rFDwplUGG9xE1HP7veo4f+aIcF0XeBeEksv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\nDMMwDMMwDMMwDKNE/h+vb3nB0LF4kwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9ad4195a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 128), float64\n",
      "Testing data: (2400, 128), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 76.1 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1536), float64\n",
      "Testing data: (200, 1536), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 64.5 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clips accuracy: 74.0 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 3000.0 training, 3000.0 testing\n",
      "  75 (+/- 0.7) <- [76 74 75 75 74 75 75 76 75 75]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  75 (+/- 0.7) <- [75 74 75 75 74 76 76 74 75 76]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  76 (+/- 0.8) <- [77 75 76 74 76 75 75 74 75 76]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  75 (+/- 0.7) <- [75 75 75 75 76 76 75 74 76 74]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  76 (+/- 0.5) <- [76 74 76 76 75 76 75 76 76 75]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  76 (+/- 0.7) <- [76 77 74 75 76 75 75 76 75 75]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  76 (+/- 0.9) <- [75 74 77 76 75 74 76 75 76 76]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  75 (+/- 0.4) <- [74 75 75 75 75 76 75 74 75 75]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  76 (+/- 0.5) <- [74 75 75 76 75 75 76 75 75 74]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  76 (+/- 0.6) <- [75 76 74 75 75 76 76 75 76 76]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  76 (+/- 1.0) <- [76 76 75 74 76 74 76 75 74 76]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  76 (+/- 0.5) <- [75 76 76 75 75 75 76 74 76 76]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  76 (+/- 0.4) <- [74 75 76 75 75 75 75 75 75 76]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  76 (+/- 0.6) <- [76 75 75 74 75 75 76 75 76 76]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  76 (+/- 0.7) <- [74 76 75 74 76 76 75 75 74 75]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  76 (+/- 0.4) <- [75 75 75 75 75 75 76 75 76 76]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  76 (+/- 0.6) <- [76 75 75 76 76 76 75 74 76 76]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  76 (+/- 0.6) <- [75 75 75 75 76 76 76 74 75 76]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  76 (+/- 1.0) <- [75 76 76 76 75 75 77 75 73 75]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  76 (+/- 0.5) <- [76 76 75 75 76 74 75 75 75 76]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 75.7 (+/- 0.68)\n",
      "Mean time (20 cv): 11.07 seconds\n",
      "Overall time: 225.57 seconds\n",
      "\n",
      " -------------------- Classification, test_size = 0.6 -------------------- \n",
      "\n",
      "Software versions:"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  numpy: 1.8.2\n",
      "  sklearn: 0.14.1\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  D : (128, 96)               , float32\n",
      "  X : (5, 100, 149, 2, 96)    , float32\n",
      "  Z : (5, 100, 149, 2, 128)   , float32\n",
      "Full dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<type 'numpy.ndarray'>\n",
      "Flattened frames:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 298, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Feature vectors:\n",
      "  size: N=6,000 x n=128 -> 768,000 floats\n",
      "  dim: 1,536 features per clip\n",
      "  shape: (5, 100, 6, 2, 128)\n"
     ]
    },
    {
     "data": {
      "image/png": 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JMN2g79UmHVrIx48kJfsecAfG9rFDwplUGG9xE1HP7veo4f+aIcF0XeBeEksv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\nDMMwDMMwDMMwDKNE/h+vb3nB0LF4kwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9ad4903f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 128), float64\n",
      "Testing data: (2400, 128), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 76.1 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1536), float64\n",
      "Testing data: (200, 1536), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 64.5 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clips accuracy: 74.0 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 2400.0 training, 3600.0 testing\n",
      "  75 (+/- 0.5) <- [75 74 74 73 73 74 75 75 74 74]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  75 (+/- 0.5) <- [74 73 74 74 74 74 75 74 74 75]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  75 (+/- 0.5) <- [75 74 75 73 74 74 75 74 75 74]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  75 (+/- 0.5) <- [74 74 74 73 74 75 75 73 74 74]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  75 (+/- 0.5) <- [75 74 75 75 74 75 75 75 74 74]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  75 (+/- 0.6) <- [74 75 74 74 76 74 75 74 74 75]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  75 (+/- 0.7) <- [75 74 75 75 75 74 76 75 75 74]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  75 (+/- 0.5) <- [75 74 74 74 73 75 75 74 74 75]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  75 (+/- 0.6) <- [74 73 75 76 74 74 74 74 75 74]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  75 (+/- 0.6) <- [74 75 73 73 74 75 74 75 74 75]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  75 (+/- 0.5) <- [75 75 74 73 74 74 74 74 74 75]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  75 (+/- 0.5) <- [76 75 75 74 75 74 75 75 75 75]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  75 (+/- 0.5) <- [74 74 75 74 74 75 74 75 74 74]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  75 (+/- 0.7) <- [74 75 74 74 74 73 75 74 75 75]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  75 (+/- 0.7) <- [73 74 74 73 75 76 75 75 76 75]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  75 (+/- 0.5) <- [75 74 74 74 74 74 75 74 75 74]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  75 (+/- 0.7) <- [75 75 73 76 74 75 74 75 75 75]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  75 (+/- 0.8) <- [74 74 73 73 75 75 74 73 75 75]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  75 (+/- 0.7) <- [74 75 75 75 74 75 76 75 74 74]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  75 (+/- 0.3) <- [75 74 74 74 74 74 74 74 74 74]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 74.8 (+/- 0.63)\n",
      "Mean time (20 cv): 8.79 seconds\n",
      "Overall time: 179.98 seconds\n",
      "\n",
      " -------------------- Classification, test_size = 0.7 -------------------- \n",
      "\n",
      "Software versions:"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  numpy: 1.8.2\n",
      "  sklearn: 0.14.1\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  D : (128, 96)               , float32\n",
      "  X : (5, 100, 149, 2, 96)    , float32\n",
      "  Z : (5, 100, 149, 2, 128)   , float32\n",
      "Full dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<type 'numpy.ndarray'>\n",
      "Flattened frames:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 298, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Feature vectors:\n",
      "  size: N=6,000 x n=128 -> 768,000 floats\n",
      "  dim: 1,536 features per clip\n",
      "  shape: (5, 100, 6, 2, 128)\n"
     ]
    },
    {
     "data": {
      "image/png": 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JMN2g79UmHVrIx48kJfsecAfG9rFDwplUGG9xE1HP7veo4f+aIcF0XeBeEksv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\nDMMwDMMwDMMwDKNE/h+vb3nB0LF4kwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9ad411a8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 128), float64\n",
      "Testing data: (2400, 128), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 76.1 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1536), float64\n",
      "Testing data: (200, 1536), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 64.5 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clips accuracy: 74.0 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 1800.0 training, 4200.0 testing\n",
      "  73 (+/- 0.5) <- [74 73 73 72 73 73 73 73 72 73]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  73 (+/- 0.8) <- [72 71 74 73 72 73 74 73 73 73]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  73 (+/- 0.4) <- [73 72 72 72 73 73 73 73 73 73]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  73 (+/- 0.6) <- [72 73 73 72 74 73 73 73 73 74]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  74 (+/- 0.7) <- [73 73 74 73 71 74 73 73 73 74]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  73 (+/- 0.9) <- [72 74 73 71 74 72 73 72 73 73]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  74 (+/- 0.6) <- [74 73 73 73 73 72 74 74 74 74]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  73 (+/- 0.8) <- [74 72 72 73 71 72 74 72 72 73]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  73 (+/- 0.7) <- [73 71 73 73 72 72 73 74 74 73]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  73 (+/- 0.5) <- [73 72 72 73 73 73 73 73 72 73]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  73 (+/- 0.6) <- [74 72 73 73 73 73 72 73 73 73]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  74 (+/- 0.6) <- [73 74 75 72 73 73 74 73 73 73]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  73 (+/- 0.8) <- [72 72 73 73 72 74 74 73 72 72]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  73 (+/- 0.9) <- [73 73 72 73 73 70 74 72 73 74]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  74 (+/- 0.6) <- [72 72 73 73 74 73 74 72 73 74]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  73 (+/- 0.6) <- [74 73 73 73 73 73 74 72 74 72]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  73 (+/- 1.0) <- [73 73 72 75 72 74 72 74 73 72]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  73 (+/- 0.6) <- [72 73 72 73 73 73 73 73 74 74]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  73 (+/- 0.7) <- [73 73 73 73 72 72 74 74 72 73]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  74 (+/- 0.4) <- [73 73 73 73 74 73 73 73 74 72]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 73.4 (+/- 0.71)\n",
      "Mean time (20 cv): 6.61 seconds\n",
      "Overall time: 136.28 seconds\n",
      "\n",
      " -------------------- Classification, test_size = 0.8 -------------------- \n",
      "\n",
      "Software versions:"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  numpy: 1.8.2\n",
      "  sklearn: 0.14.1\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  D : (128, 96)               , float32\n",
      "  X : (5, 100, 149, 2, 96)    , float32\n",
      "  Z : (5, 100, 149, 2, 128)   , float32\n",
      "Full dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<type 'numpy.ndarray'>\n",
      "Flattened frames:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 298, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Feature vectors:\n",
      "  size: N=6,000 x n=128 -> 768,000 floats\n",
      "  dim: 1,536 features per clip\n",
      "  shape: (5, 100, 6, 2, 128)\n"
     ]
    },
    {
     "data": {
      "image/png": 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JMN2g79UmHVrIx48kJfsecAfG9rFDwplUGG9xE1HP7veo4f+aIcF0XeBeEksv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\nDMMwDMMwDMMwDKNE/h+vb3nB0LF4kwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9b1d44b850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 128), float64\n",
      "Testing data: (2400, 128), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 76.1 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1536), float64\n",
      "Testing data: (200, 1536), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 64.5 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clips accuracy: 74.0 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 1200.0 training, 4800.0 testing\n",
      "  72 (+/- 0.6) <- [71 72 71 69 71 71 71 72 71 71]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 0.7) <- [71 70 71 71 70 72 71 70 71 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 0.6) <- [71 69 71 70 70 70 71 70 71 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 1.0) <- [70 71 71 72 72 70 70 69 69 72]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 0.7) <- [71 70 72 71 70 72 70 70 71 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 1.1) <- [70 71 71 71 70 68 72 69 71 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 0.4) <- [72 70 71 71 71 71 71 71 71 71]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 1.0) <- [69 71 71 71 69 70 73 70 70 71]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 0.8) <- [69 70 71 71 70 71 71 72 72 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 0.5) <- [70 71 70 71 72 72 70 70 71 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 0.9) <- [71 71 72 68 71 70 70 70 71 71]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  72 (+/- 0.7) <- [72 72 72 70 72 71 70 71 71 71]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 0.8) <- [69 70 71 70 70 72 71 71 69 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 0.7) <- [71 70 71 70 71 69 70 71 71 71]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 0.7) <- [70 70 71 71 71 70 71 70 71 72]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 0.7) <- [71 70 71 71 71 70 72 70 71 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 1.0) <- [71 70 70 72 69 71 69 70 71 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 0.6) <- [70 71 70 70 70 70 72 70 72 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 0.6) <- [70 70 70 72 70 70 71 72 70 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 0.4) <- [71 71 71 71 72 71 70 71 71 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 71.1 (+/- 0.80)\n",
      "Mean time (20 cv): 4.51 seconds\n",
      "Overall time: 94.35 seconds\n",
      "\n",
      " -------------------- Classification, test_size = 0.9 -------------------- \n",
      "\n",
      "Software versions:"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  numpy: 1.8.2\n",
      "  sklearn: 0.14.1\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  D : (128, 96)               , float32\n",
      "  X : (5, 100, 149, 2, 96)    , float32\n",
      "  Z : (5, 100, 149, 2, 128)   , float32\n",
      "Full dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<type 'numpy.ndarray'>\n",
      "Flattened frames:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 298, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Feature vectors:\n",
      "  size: N=6,000 x n=128 -> 768,000 floats\n",
      "  dim: 1,536 features per clip\n",
      "  shape: (5, 100, 6, 2, 128)\n"
     ]
    },
    {
     "data": {
      "image/png": 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JMN2g79UmHVrIx48kJfsecAfG9rFDwplUGG9xE1HP7veo4f+aIcF0XeBeEksv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\nDMMwDMMwDMMwDKNE/h+vb3nB0LF4kwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9ad4586610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 128), float64\n",
      "Testing data: (2400, 128), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 76.1 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1536), float64\n",
      "Testing data: (200, 1536), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 64.5 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clips accuracy: 74.0 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 600.0 training, 5400.0 testing\n",
      "  66 (+/- 0.7) <- [65 66 64 64 66 66 65 65 66 65]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  66 (+/- 1.0) <- [64 66 65 65 67 68 66 66 66 65]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  66 (+/- 0.5) <- [65 65 66 65 66 65 66 66 65 66]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  66 (+/- 1.1) <- [67 66 66 67 67 65 64 67 65 64]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  66 (+/- 1.0) <- [65 65 67 67 66 67 66 65 65 64]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  66 (+/- 1.0) <- [66 65 65 66 67 65 66 64 67 64]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  66 (+/- 0.6) <- [66 65 66 65 66 67 65 66 65 66]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  66 (+/- 0.8) <- [65 65 64 66 65 65 67 65 66 66]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  66 (+/- 0.5) <- [65 66 66 66 66 66 66 66 67 66]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  66 (+/- 1.0) <- [65 65 66 66 68 66 67 66 64 65]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  66 (+/- 1.0) <- [67 66 65 66 65 65 64 63 67 65]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  66 (+/- 0.9) <- [66 66 66 65 67 67 64 65 66 65]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  66 (+/- 0.7) <- [65 65 66 65 66 65 65 66 64 64]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  66 (+/- 0.6) <- [66 66 66 66 66 65 65 67 66 65]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  66 (+/- 0.7) <- [66 64 65 65 67 67 66 66 66 66]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  66 (+/- 1.1) <- [64 66 65 66 67 65 67 67 67 65]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  66 (+/- 0.9) <- [66 66 65 67 66 66 64 65 65 66]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  66 (+/- 0.8) <- [65 65 65 64 65 67 67 65 65 66]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  66 (+/- 1.0) <- [64 66 66 66 65 67 64 67 65 66]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  66 (+/- 0.7) <- [66 66 66 65 65 67 66 66 66 64]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 66.1 (+/- 0.87)\n",
      "Mean time (20 cv): 2.40 seconds\n",
      "Overall time: 52.03 seconds\n",
      "\n",
      " -------------------- Classification, test_size = 0.92 -------------------- \n",
      "\n",
      "Software versions:"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  numpy: 1.8.2\n",
      "  sklearn: 0.14.1\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  D : (128, 96)               , float32\n",
      "  X : (5, 100, 149, 2, 96)    , float32\n",
      "  Z : (5, 100, 149, 2, 128)   , float32\n",
      "Full dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<type 'numpy.ndarray'>\n",
      "Flattened frames:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 298, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Feature vectors:\n",
      "  size: N=6,000 x n=128 -> 768,000 floats\n",
      "  dim: 1,536 features per clip\n",
      "  shape: (5, 100, 6, 2, 128)\n"
     ]
    },
    {
     "data": {
      "image/png": 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JMN2g79UmHVrIx48kJfsecAfG9rFDwplUGG9xE1HP7veo4f+aIcF0XeBeEksv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\nDMMwDMMwDMMwDKNE/h+vb3nB0LF4kwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9ad46d5e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 128), float64\n",
      "Testing data: (2400, 128), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 76.1 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1536), float64\n",
      "Testing data: (200, 1536), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 64.5 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clips accuracy: 74.0 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 480.0 training, 5520.0 testing\n",
      "  64 (+/- 0.8) <- [63 64 63 64 63 64 64 65 65 64]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  65 (+/- 0.9) <- [63 65 64 63 64 66 64 64 65 64]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  64 (+/- 0.9) <- [64 63 63 63 64 64 65 64 62 65]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  65 (+/- 1.2) <- [65 65 65 66 64 64 64 64 63 62]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  64 (+/- 0.9) <- [62 64 64 64 65 65 64 64 64 63]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  64 (+/- 1.3) <- [64 61 65 64 66 63 65 63 65 64]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  65 (+/- 0.8) <- [64 64 65 63 64 65 64 65 62 64]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  64 (+/- 1.1) <- [63 65 63 62 62 65 65 64 65 64]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  65 (+/- 0.4) <- [64 64 65 64 63 64 64 64 64 64]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  64 (+/- 1.2) <- [63 63 63 65 65 64 65 64 61 63]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  64 (+/- 0.8) <- [65 64 64 64 63 64 62 62 64 63]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  64 (+/- 0.9) <- [63 64 64 62 65 64 63 63 64 64]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  64 (+/- 0.6) <- [63 64 64 64 64 63 63 64 62 63]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  65 (+/- 0.7) <- [65 64 65 64 65 64 64 65 64 63]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  65 (+/- 1.1) <- [65 64 63 64 63 65 62 64 65 66]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  65 (+/- 1.5) <- [61 65 64 64 66 64 65 65 66 64]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  64 (+/- 0.8) <- [64 63 64 65 65 64 63 63 64 63]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  64 (+/- 0.8) <- [64 63 63 62 64 64 65 64 62 64]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  64 (+/- 1.4) <- [63 65 66 64 64 65 61 66 63 63]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  65 (+/- 1.0) <- [63 64 65 63 63 66 65 64 64 64]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 64.4 (+/- 1.02)\n",
      "Mean time (20 cv): 1.97 seconds\n",
      "Overall time: 43.54 seconds\n",
      "\n",
      " -------------------- Classification, test_size = 0.94 -------------------- \n",
      "\n",
      "Software versions:"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  numpy: 1.8.2\n",
      "  sklearn: 0.14.1\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  D : (128, 96)               , float32\n",
      "  X : (5, 100, 149, 2, 96)    , float32\n",
      "  Z : (5, 100, 149, 2, 128)   , float32\n",
      "Full dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<type 'numpy.ndarray'>\n",
      "Flattened frames:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 298, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Feature vectors:\n",
      "  size: N=6,000 x n=128 -> 768,000 floats\n",
      "  dim: 1,536 features per clip\n",
      "  shape: (5, 100, 6, 2, 128)\n"
     ]
    },
    {
     "data": {
      "image/png": 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JMN2g79UmHVrIx48kJfsecAfG9rFDwplUGG9xE1HP7veo4f+aIcF0XeBeEksv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\nDMMwDMMwDMMwDKNE/h+vb3nB0LF4kwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9ad4496490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 128), float64\n",
      "Testing data: (2400, 128), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 76.1 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1536), float64\n",
      "Testing data: (200, 1536), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 64.5 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clips accuracy: 74.0 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 360.0 training, 5640.0 testing\n",
      "  62 (+/- 0.6) <- [61 62 61 60 60 62 61 61 62 61]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  62 (+/- 1.2) <- [61 62 63 62 62 63 59 63 61 63]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  62 (+/- 1.3) <- [62 60 60 60 61 60 61 63 60 63]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  63 (+/- 1.0) <- [62 64 64 63 62 61 62 62 63 61]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  63 (+/- 1.0) <- [61 61 63 62 61 64 64 61 62 61]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  62 (+/- 1.2) <- [62 60 63 61 62 60 61 61 64 62]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  63 (+/- 1.0) <- [62 61 62 61 63 63 62 63 60 63]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  62 (+/- 0.8) <- [62 61 60 62 61 62 63 62 63 62]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  62 (+/- 0.7) <- [63 61 63 61 61 62 62 61 63 62]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  62 (+/- 0.9) <- [61 62 60 63 62 62 62 61 59 62]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  61 (+/- 0.9) <- [61 60 61 62 59 63 61 60 62 61]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  62 (+/- 1.4) <- [60 61 64 61 64 62 60 60 63 63]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  61 (+/- 1.3) <- [62 61 61 59 63 59 60 61 61 61]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  63 (+/- 1.0) <- [63 61 62 63 62 61 62 64 62 61]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  63 (+/- 0.9) <- [62 61 61 62 62 63 61 63 61 63]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  62 (+/- 1.5) <- [59 63 60 61 62 61 64 63 63 61]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  62 (+/- 0.6) <- [61 60 63 61 62 61 61 62 61 62]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  62 (+/- 1.1) <- [60 60 61 61 63 60 63 61 62 63]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  62 (+/- 1.7) <- [60 63 62 58 63 63 59 63 62 61]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  62 (+/- 1.0) <- [61 62 63 60 62 63 62 62 61 61]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 62.1 (+/- 1.17)\n",
      "Mean time (20 cv): 1.54 seconds\n",
      "Overall time: 34.90 seconds\n",
      "\n",
      " -------------------- Classification, test_size = 0.96 -------------------- \n",
      "\n",
      "Software versions:"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  numpy: 1.8.2\n",
      "  sklearn: 0.14.1\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  D : (128, 96)               , float32\n",
      "  X : (5, 100, 149, 2, 96)    , float32\n",
      "  Z : (5, 100, 149, 2, 128)   , float32\n",
      "Full dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<type 'numpy.ndarray'>\n",
      "Flattened frames:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 298, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Feature vectors:\n",
      "  size: N=6,000 x n=128 -> 768,000 floats\n",
      "  dim: 1,536 features per clip\n",
      "  shape: (5, 100, 6, 2, 128)\n"
     ]
    },
    {
     "data": {
      "image/png": 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JMN2g79UmHVrIx48kJfsecAfG9rFDwplUGG9xE1HP7veo4f+aIcF0XeBeEksv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\nDMMwDMMwDMMwDKNE/h+vb3nB0LF4kwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9b1d2e0a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 128), float64\n",
      "Testing data: (2400, 128), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 76.1 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1536), float64\n",
      "Testing data: (200, 1536), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 64.5 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clips accuracy: 74.0 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 240.0 training, 5760.0 testing\n",
      "  59 (+/- 0.9) <- [57 58 58 57 57 59 59 59 59 59]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  59 (+/- 1.5) <- [58 58 62 59 59 57 56 58 56 58]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  58 (+/- 1.2) <- [59 57 55 58 58 58 59 57 58 60]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  59 (+/- 1.6) <- [56 60 61 58 58 58 60 56 60 56]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  59 (+/- 1.3) <- [58 58 58 59 58 61 61 58 56 58]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  59 (+/- 1.5) <- [58 54 59 59 59 58 60 59 58 60]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  59 (+/- 1.4) <- [58 57 59 59 58 59 55 60 60 60]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  59 (+/- 1.0) <- [60 58 59 57 58 57 60 58 59 58]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  59 (+/- 0.7) <- [59 58 57 60 59 58 58 58 58 58]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  59 (+/- 1.0) <- [58 57 59 59 58 58 60 58 57 59]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  58 (+/- 1.3) <- [57 57 58 56 54 60 57 57 58 58]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  59 (+/- 1.6) <- [58 62 59 58 61 58 57 58 60 58]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  58 (+/- 1.2) <- [57 58 58 57 59 55 56 57 58 57]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  59 (+/- 1.7) <- [60 59 56 62 58 58 60 59 56 57]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  59 (+/- 1.2) <- [58 59 60 59 57 57 58 58 59 61]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  59 (+/- 1.4) <- [56 59 57 59 60 57 60 59 59 60]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  59 (+/- 1.4) <- [58 58 57 60 59 56 56 59 60 58]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  58 (+/- 1.0) <- [57 57 58 58 58 58 59 58 56 59]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  58 (+/- 1.2) <- [56 58 57 56 60 59 57 58 59 59]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  59 (+/- 1.0) <- [58 58 59 57 60 60 59 58 58 58]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 58.7 (+/- 1.35)\n",
      "Mean time (20 cv): 1.09 seconds\n",
      "Overall time: 25.87 seconds\n",
      "\n",
      " -------------------- Classification, test_size = 0.97 -------------------- \n",
      "\n",
      "Software versions:"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  numpy: 1.8.2\n",
      "  sklearn: 0.14.1\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  D : (128, 96)               , float32\n",
      "  X : (5, 100, 149, 2, 96)    , float32\n",
      "  Z : (5, 100, 149, 2, 128)   , float32\n",
      "Full dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<type 'numpy.ndarray'>\n",
      "Flattened frames:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 298, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Feature vectors:\n",
      "  size: N=6,000 x n=128 -> 768,000 floats\n",
      "  dim: 1,536 features per clip\n",
      "  shape: (5, 100, 6, 2, 128)\n"
     ]
    },
    {
     "data": {
      "image/png": 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JMN2g79UmHVrIx48kJfsecAfG9rFDwplUGG9xE1HP7veo4f+aIcF0XeBeEksv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\nDMMwDMMwDMMwDKNE/h+vb3nB0LF4kwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9ad416cad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 128), float64\n",
      "Testing data: (2400, 128), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 76.1 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1536), float64\n",
      "Testing data: (200, 1536), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 64.5 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clips accuracy: 74.0 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 180.0 training, 5820.0 testing\n",
      "  56 (+/- 1.3) <- [55 56 56 55 55 57 58 58 56 54]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  57 (+/- 1.7) <- [53 56 58 58 57 56 54 57 53 57]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  56 (+/- 1.6) <- [57 53 53 57 56 56 57 55 56 57]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  56 (+/- 1.5) <- [54 58 57 57 56 56 56 55 54 53]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  56 (+/- 1.1) <- [56 56 56 55 54 57 57 55 54 54]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  56 (+/- 1.6) <- [54 53 57 56 57 55 58 57 54 58]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  57 (+/- 1.5) <- [56 54 57 56 57 59 54 57 58 58]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  57 (+/- 1.1) <- [57 55 57 56 56 54 58 56 56 56]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  57 (+/- 1.4) <- [59 55 55 58 57 56 58 55 57 56]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  56 (+/- 1.2) <- [55 55 57 56 54 58 56 55 55 56]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  55 (+/- 1.5) <- [55 55 54 53 52 55 56 55 54 57]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  57 (+/- 1.6) <- [55 60 57 54 57 57 56 55 56 56]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  56 (+/- 1.6) <- [57 57 56 54 57 51 55 54 56 57]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  57 (+/- 1.6) <- [56 56 55 59 54 56 59 58 56 56]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  56 (+/- 1.8) <- [57 56 56 56 55 53 54 56 57 60]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  56 (+/- 1.5) <- [53 57 55 57 58 55 55 55 57 56]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  56 (+/- 1.1) <- [54 54 54 55 57 56 55 56 58 55]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  56 (+/- 1.4) <- [54 56 56 55 53 57 58 56 56 57]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  56 (+/- 1.2) <- [54 56 54 55 56 57 55 57 54 54]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  57 (+/- 1.1) <- [57 58 56 55 58 58 56 55 56 56]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 56.3 (+/- 1.52)\n",
      "Mean time (20 cv): 0.86 seconds\n",
      "Overall time: 21.31 seconds\n",
      "\n",
      " -------------------- Classification, test_size = 0.98 -------------------- \n",
      "\n",
      "Software versions:"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  numpy: 1.8.2\n",
      "  sklearn: 0.14.1\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  D : (128, 96)               , float32\n",
      "  X : (5, 100, 149, 2, 96)    , float32\n",
      "  Z : (5, 100, 149, 2, 128)   , float32\n",
      "Full dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<type 'numpy.ndarray'>\n",
      "Flattened frames:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 298, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Feature vectors:\n",
      "  size: N=6,000 x n=128 -> 768,000 floats\n",
      "  dim: 1,536 features per clip\n",
      "  shape: (5, 100, 6, 2, 128)\n"
     ]
    },
    {
     "data": {
      "image/png": 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JMN2g79UmHVrIx48kJfsecAfG9rFDwplUGG9xE1HP7veo4f+aIcF0XeBeEksv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\nDMMwDMMwDMMwDKNE/h+vb3nB0LF4kwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9ad4bc5390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 128), float64\n",
      "Testing data: (2400, 128), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 76.1 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1536), float64\n",
      "Testing data: (200, 1536), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 64.5 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clips accuracy: 74.0 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 120.0 training, 5880.0 testing\n",
      "  53 (+/- 1.1) <- [52 53 53 50 53 55 53 53 53 51]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  53 (+/- 2.0) <- [52 55 54 51 55 56 51 52 50 52]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  53 (+/- 1.9) <- [53 50 48 55 52 51 52 52 53 54]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  53 (+/- 1.2) <- [52 54 53 53 53 54 53 53 53 50]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  52 (+/- 1.2) <- [51 52 52 52 52 53 54 50 50 53]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  53 (+/- 1.5) <- [50 51 53 53 54 53 52 52 51 56]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  54 (+/- 1.8) <- [52 50 56 51 52 55 54 53 54 54]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  53 (+/- 2.6) <- [53 51 53 52 51 46 55 54 54 56]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  53 (+/- 1.3) <- [54 51 55 53 54 52 54 53 53 50]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  53 (+/- 1.2) <- [51 51 53 54 53 54 51 53 54 54]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  53 (+/- 1.4) <- [51 51 51 53 51 53 53 54 55 54]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  53 (+/- 1.6) <- [52 56 53 50 55 52 52 53 51 51]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  53 (+/- 1.9) <- [55 55 52 52 53 49 52 50 53 54]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  54 (+/- 1.5) <- [55 53 52 55 51 53 56 51 54 52]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  52 (+/- 2.1) <- [53 52 53 53 52 46 50 52 54 54]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  53 (+/- 1.5) <- [51 54 52 54 54 54 51 55 51 54]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  53 (+/- 1.5) <- [52 49 52 53 51 53 52 53 55 52]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  53 (+/- 2.0) <- [51 53 54 51 52 52 55 53 48 53]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  53 (+/- 1.3) <- [53 52 53 53 53 52 52 55 50 50]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  53 (+/- 1.5) <- [53 54 52 52 54 54 54 49 54 53]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 53.1 (+/- 1.70)\n",
      "Mean time (20 cv): 0.63 seconds\n",
      "Overall time: 16.59 seconds\n",
      "\n",
      " -------------------- Classification, test_size = 0.99 -------------------- \n",
      "\n",
      "Software versions:"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  numpy: 1.8.2\n",
      "  sklearn: 0.14.1\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  D : (128, 96)               , float32\n",
      "  X : (5, 100, 149, 2, 96)    , float32\n",
      "  Z : (5, 100, 149, 2, 128)   , float32\n",
      "Full dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<type 'numpy.ndarray'>\n",
      "Flattened frames:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 298, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Feature vectors:\n",
      "  size: N=6,000 x n=128 -> 768,000 floats\n",
      "  dim: 1,536 features per clip\n",
      "  shape: (5, 100, 6, 2, 128)\n"
     ]
    },
    {
     "data": {
      "image/png": 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JMN2g79UmHVrIx48kJfsecAfG9rFDwplUGG9xE1HP7veo4f+aIcF0XeBeEksv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\nDMMwDMMwDMMwDKNE/h+vb3nB0LF4kwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9ad4492110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 128), float64\n",
      "Testing data: (2400, 128), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 76.1 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1536), float64\n",
      "Testing data: (200, 1536), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 64.5 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clips accuracy: 74.0 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 60.0 training, 5940.0 testing\n",
      "  48 (+/- 2.4) <- [48 51 44 43 46 49 47 47 49 50]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  48 (+/- 1.7) <- [46 50 49 44 49 49 48 48 47 47]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  48 (+/- 2.6) <- [45 44 49 51 49 45 50 47 44 51]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  48 (+/- 1.8) <- [46 50 48 46 50 51 49 46 47 48]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  48 (+/- 2.3) <- [43 49 46 46 47 48 48 49 44 51]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  48 (+/- 1.8) <- [44 47 49 48 50 47 46 46 46 50]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  49 (+/- 2.3) <- [46 49 53 49 45 49 48 47 45 49]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  48 (+/- 1.5) <- [47 45 48 48 47 47 49 48 48 51]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  48 (+/- 2.3) <- [50 43 48 48 47 47 52 47 46 47]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  49 (+/- 2.4) <- [48 44 47 51 50 49 47 48 51 53]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  48 (+/- 1.6) <- [48 50 47 46 45 44 49 48 47 47]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  48 (+/- 1.4) <- [46 51 48 48 49 47 47 47 49 47]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  48 (+/- 1.9) <- [50 50 51 48 47 46 49 48 45 47]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  49 (+/- 1.5) <- [51 50 47 45 48 48 48 50 48 48]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  48 (+/- 2.5) <- [47 49 51 47 46 42 49 48 48 51]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  49 (+/- 1.7) <- [50 47 47 46 50 48 47 50 45 50]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  48 (+/- 2.4) <- [51 43 46 48 44 49 46 49 48 49]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  48 (+/- 1.3) <- [47 48 48 46 46 49 48 49 47 51]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  48 (+/- 1.5) <- [51 47 46 46 48 49 46 47 47 48]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  48 (+/- 2.7) <- [47 48 52 45 44 48 46 42 50 50]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 48.2 (+/- 2.07)\n",
      "Mean time (20 cv): 0.38 seconds\n",
      "Overall time: 11.60 seconds\n",
      "\n",
      " -------------------- Baseline, test_size = 0.1 -------------------- \n",
      "\n",
      "Software versions:"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  numpy: 1.8.2\n",
      "  sklearn: 0.14.1\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  D : (128, 96)               , float32\n",
      "  X : (5, 100, 149, 2, 96)    , float32\n",
      "  Z : (5, 100, 149, 2, 128)   , float32\n",
      "Full dataset:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 149, 2, 96)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 149, 2, 96)\n",
      "<type 'numpy.ndarray'>\n",
      "Flattened frames:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 298, 96)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=96 -> 12,960,000 floats\n",
      "  dim: 25,920 features per clip\n",
      "  shape: (5, 100, 6, 45, 96)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=96 -> 12,960,000 floats\n",
      "  dim: 25,920 features per clip\n",
      "  shape: (5, 100, 6, 45, 96)\n",
      "Feature vectors:\n",
      "  size: N=6,000 x n=96 -> 576,000 floats\n",
      "  dim: 1,152 features per clip\n",
      "  shape: (5, 100, 6, 2, 96)"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ii4dkZwGTmyUxrdaCeSv6Bc4m28P8CHB1bJJgR/QORH+H6KfJ+3mOZEu7hoIZ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\nrwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9ad42e4090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 96), float64\n",
      "Testing data: (2400, 96), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 68.8 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1152), float64\n",
      "Testing data: (200, 1152), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 62.1 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clips accuracy: 68.5 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 96), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 5400.0 training, 600.0 testing\n",
      "  69 (+/- 2.2) <- [70 68 71 67 64 67 70 68 72 66]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 1.4) <- [71 68 67 67 72 69 67 70 69 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  70 (+/- 2.0) <- [71 69 73 68 68 71 71 68 71 66]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 1.4) <- [70 67 66 67 67 70 68 70 70 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  70 (+/- 2.4) <- [71 69 72 68 70 75 71 68 68 66]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 0.7) <- [71 71 70 70 71 71 70 69 70 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  70 (+/- 1.4) <- [70 68 72 69 69 67 71 69 70 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  70 (+/- 1.5) <- [69 70 68 71 65 70 70 69 70 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 1.7) <- [68 69 68 69 68 65 70 70 69 72]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 1.8) <- [72 69 66 68 69 66 69 69 70 71]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  70 (+/- 1.4) <- [71 70 69 69 69 69 73 70 71 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  70 (+/- 1.6) <- [71 70 71 68 69 69 73 68 69 71]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 1.3) <- [69 70 66 68 69 70 68 71 68 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 2.0) <- [71 66 68 66 67 69 66 69 69 72]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  70 (+/- 1.1) <- [69 70 72 68 69 70 69 71 71 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  70 (+/- 2.0) <- [72 71 69 71 67 68 71 72 69 67]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  70 (+/- 1.6) <- [67 71 69 69 68 70 71 71 72 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  70 (+/- 0.9) <- [70 68 71 70 68 70 69 69 71 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  70 (+/- 1.2) <- [72 71 69 69 71 69 72 70 68 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 1.7) <- [67 70 71 67 67 72 67 70 70 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 69.7 (+/- 1.71)\n",
      "Mean time (20 cv): 16.93 seconds\n",
      "Overall time: 342.40 seconds\n",
      "\n",
      " -------------------- Baseline, test_size = 0.2 -------------------- \n",
      "\n",
      "Software versions:"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  numpy: 1.8.2\n",
      "  sklearn: 0.14.1\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  D : (128, 96)               , float32\n",
      "  X : (5, 100, 149, 2, 96)    , float32\n",
      "  Z : (5, 100, 149, 2, 128)   , float32\n",
      "Full dataset:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 149, 2, 96)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 149, 2, 96)\n",
      "<type 'numpy.ndarray'>\n",
      "Flattened frames:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 298, 96)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=96 -> 12,960,000 floats\n",
      "  dim: 25,920 features per clip\n",
      "  shape: (5, 100, 6, 45, 96)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=96 -> 12,960,000 floats\n",
      "  dim: 25,920 features per clip\n",
      "  shape: (5, 100, 6, 45, 96)\n",
      "Feature vectors:\n",
      "  size: N=6,000 x n=96 -> 576,000 floats\n",
      "  dim: 1,152 features per clip\n",
      "  shape: (5, 100, 6, 2, 96)"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ii4dkZwGTmyUxrdaCeSv6Bc4m28P8CHB1bJJgR/QORH+H6KfJ+3mOZEu7hoIZ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\nrwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9ad494efd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 96), float64\n",
      "Testing data: (2400, 96), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 68.8 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1152), float64\n",
      "Testing data: (200, 1152), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 62.1 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clips accuracy: 68.5 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 96), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 4800.0 training, 1200.0 testing\n",
      "  69 (+/- 1.2) <- [70 69 69 69 66 67 70 68 70 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 1.3) <- [70 70 67 69 68 71 68 68 67 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  70 (+/- 1.5) <- [70 69 72 67 68 71 69 70 70 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 1.1) <- [70 68 69 66 67 69 69 68 68 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  70 (+/- 1.4) <- [71 70 71 67 70 72 70 68 69 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  70 (+/- 1.4) <- [70 70 69 72 72 68 67 69 70 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  70 (+/- 1.2) <- [68 68 71 70 69 68 71 68 70 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 1.3) <- [67 70 67 69 67 71 70 68 68 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 1.0) <- [68 67 69 70 69 68 68 68 70 71]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 1.2) <- [71 69 68 67 70 68 69 70 69 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  70 (+/- 1.0) <- [70 69 69 68 71 68 71 69 69 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 1.0) <- [70 69 70 67 68 69 71 68 68 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 0.7) <- [70 69 67 68 68 68 68 69 69 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 1.6) <- [71 69 68 65 68 69 69 70 70 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  70 (+/- 0.9) <- [69 71 71 69 69 70 69 71 71 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  70 (+/- 1.6) <- [71 69 68 70 68 68 69 73 68 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 0.9) <- [68 69 68 69 70 70 69 70 70 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 0.8) <- [70 69 70 68 68 68 69 67 69 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  70 (+/- 1.0) <- [71 70 68 69 69 68 70 70 71 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 0.8) <- [69 68 69 68 67 68 68 67 69 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 69.4 (+/- 1.26)\n",
      "Mean time (20 cv): 14.98 seconds\n",
      "Overall time: 303.28 seconds\n",
      "\n",
      " -------------------- Baseline, test_size = 0.3 -------------------- \n",
      "\n",
      "Software versions:"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  numpy: 1.8.2\n",
      "  sklearn: 0.14.1\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  D : (128, 96)               , float32\n",
      "  X : (5, 100, 149, 2, 96)    , float32\n",
      "  Z : (5, 100, 149, 2, 128)   , float32\n",
      "Full dataset:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 149, 2, 96)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 149, 2, 96)\n",
      "<type 'numpy.ndarray'>\n",
      "Flattened frames:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 298, 96)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=96 -> 12,960,000 floats\n",
      "  dim: 25,920 features per clip\n",
      "  shape: (5, 100, 6, 45, 96)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=96 -> 12,960,000 floats\n",
      "  dim: 25,920 features per clip\n",
      "  shape: (5, 100, 6, 45, 96)\n",
      "Feature vectors:\n",
      "  size: N=6,000 x n=96 -> 576,000 floats\n",
      "  dim: 1,152 features per clip\n",
      "  shape: (5, 100, 6, 2, 96)"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ii4dkZwGTmyUxrdaCeSv6Bc4m28P8CHB1bJJgR/QORH+H6KfJ+3mOZEu7hoIZ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\nrwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9ad4b6c990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 96), float64\n",
      "Testing data: (2400, 96), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 68.8 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1152), float64\n",
      "Testing data: (200, 1152), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 62.1 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clips accuracy: 68.5 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 96), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 4200.0 training, 1800.0 testing\n",
      "  68 (+/- 1.3) <- [69 68 69 66 66 68 68 70 68 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 0.7) <- [69 70 67 69 68 69 69 69 68 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 0.9) <- [70 69 69 68 69 70 69 68 70 67]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 0.9) <- [70 68 68 68 68 69 68 68 69 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  70 (+/- 0.4) <- [69 69 70 69 69 69 69 69 69 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 1.2) <- [70 71 68 71 70 68 67 67 68 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 0.9) <- [69 68 70 68 68 69 71 67 70 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 0.8) <- [68 69 67 69 68 70 69 69 69 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 0.7) <- [69 67 70 70 68 69 69 68 69 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 0.8) <- [70 68 68 68 68 68 69 69 69 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 1.3) <- [72 69 69 67 69 68 69 67 68 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 0.9) <- [69 69 70 67 67 69 70 69 69 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  68 (+/- 0.8) <- [68 69 67 68 67 68 67 70 69 67]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 1.2) <- [69 68 68 65 68 69 68 68 69 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  70 (+/- 0.6) <- [69 70 69 68 69 70 69 69 70 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 0.9) <- [69 68 70 69 67 67 69 70 68 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  70 (+/- 0.8) <- [68 69 69 69 70 70 69 70 70 67]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 0.7) <- [67 68 69 67 68 68 69 69 68 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  70 (+/- 0.7) <- [70 69 68 69 70 69 69 69 70 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 0.8) <- [68 68 69 69 66 68 68 68 69 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 69.1 (+/- 0.96)\n",
      "Mean time (20 cv): 13.23 seconds\n",
      "Overall time: 268.45 seconds\n",
      "\n",
      " -------------------- Baseline, test_size = 0.4 -------------------- \n",
      "\n",
      "Software versions:"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  numpy: 1.8.2\n",
      "  sklearn: 0.14.1\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  D : (128, 96)               , float32\n",
      "  X : (5, 100, 149, 2, 96)    , float32\n",
      "  Z : (5, 100, 149, 2, 128)   , float32\n",
      "Full dataset:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 149, 2, 96)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 149, 2, 96)\n",
      "<type 'numpy.ndarray'>\n",
      "Flattened frames:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 298, 96)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=96 -> 12,960,000 floats\n",
      "  dim: 25,920 features per clip\n",
      "  shape: (5, 100, 6, 45, 96)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=96 -> 12,960,000 floats\n",
      "  dim: 25,920 features per clip\n",
      "  shape: (5, 100, 6, 45, 96)\n",
      "Feature vectors:\n",
      "  size: N=6,000 x n=96 -> 576,000 floats\n",
      "  dim: 1,152 features per clip\n",
      "  shape: (5, 100, 6, 2, 96)"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ii4dkZwGTmyUxrdaCeSv6Bc4m28P8CHB1bJJgR/QORH+H6KfJ+3mOZEu7hoIZ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\nrwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9ad4448d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 96), float64\n",
      "Testing data: (2400, 96), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 68.8 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1152), float64\n",
      "Testing data: (200, 1152), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 62.1 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clips accuracy: 68.5 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 96), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 3600.0 training, 2400.0 testing\n",
      "  69 (+/- 0.8) <- [69 68 68 66 67 68 68 69 69 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  68 (+/- 0.6) <- [68 69 68 68 67 68 68 68 68 67]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 0.7) <- [68 69 69 67 68 69 69 67 69 67]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  68 (+/- 0.7) <- [68 69 68 68 68 68 66 67 68 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 0.6) <- [68 68 70 69 70 69 68 68 69 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 0.9) <- [68 69 69 70 70 67 69 68 67 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 0.3) <- [68 69 69 68 68 68 68 68 68 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 0.7) <- [67 69 67 68 69 69 68 68 69 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 0.8) <- [67 67 69 69 68 68 67 68 69 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 0.8) <- [68 67 68 66 69 68 68 69 69 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  68 (+/- 1.0) <- [70 68 68 66 68 67 68 67 68 67]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 0.8) <- [68 68 70 68 68 69 67 69 69 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  68 (+/- 0.8) <- [68 68 67 67 67 68 68 69 69 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 0.9) <- [68 69 68 66 67 69 68 68 70 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 0.8) <- [69 69 67 66 68 69 68 68 69 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  68 (+/- 0.6) <- [68 67 69 68 67 67 69 68 68 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 0.8) <- [67 69 68 68 69 69 68 68 69 67]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  68 (+/- 0.7) <- [68 67 68 68 68 67 69 68 68 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 0.7) <- [69 69 68 70 67 68 68 67 68 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  68 (+/- 0.6) <- [67 69 68 68 67 68 69 68 68 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 68.6 (+/- 0.77)\n",
      "Mean time (20 cv): 11.44 seconds\n",
      "Overall time: 232.49 seconds\n",
      "\n",
      " -------------------- Baseline, test_size = 0.5 -------------------- \n",
      "\n",
      "Software versions:"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  numpy: 1.8.2\n",
      "  sklearn: 0.14.1\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  D : (128, 96)               , float32\n",
      "  X : (5, 100, 149, 2, 96)    , float32\n",
      "  Z : (5, 100, 149, 2, 128)   , float32\n",
      "Full dataset:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 149, 2, 96)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 149, 2, 96)\n",
      "<type 'numpy.ndarray'>\n",
      "Flattened frames:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 298, 96)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=96 -> 12,960,000 floats\n",
      "  dim: 25,920 features per clip\n",
      "  shape: (5, 100, 6, 45, 96)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=96 -> 12,960,000 floats\n",
      "  dim: 25,920 features per clip\n",
      "  shape: (5, 100, 6, 45, 96)\n",
      "Feature vectors:\n",
      "  size: N=6,000 x n=96 -> 576,000 floats\n",
      "  dim: 1,152 features per clip\n",
      "  shape: (5, 100, 6, 2, 96)"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ii4dkZwGTmyUxrdaCeSv6Bc4m28P8CHB1bJJgR/QORH+H6KfJ+3mOZEu7hoIZ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\nrwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9ad4cdab10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 96), float64\n",
      "Testing data: (2400, 96), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 68.8 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1152), float64\n",
      "Testing data: (200, 1152), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 62.1 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clips accuracy: 68.5 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 96), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 3000.0 training, 3000.0 testing\n",
      "  68 (+/- 0.7) <- [68 67 68 66 66 67 67 68 68 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  68 (+/- 0.7) <- [67 68 69 68 66 68 68 67 67 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  68 (+/- 0.6) <- [68 68 68 67 67 68 69 67 68 67]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  68 (+/- 0.7) <- [68 68 67 67 68 68 67 66 68 67]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  68 (+/- 0.6) <- [68 67 69 68 69 69 68 68 68 67]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  68 (+/- 0.7) <- [68 68 67 68 70 68 68 67 67 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  68 (+/- 0.7) <- [69 67 68 67 67 67 67 67 66 67]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  68 (+/- 0.6) <- [67 68 67 67 69 68 67 67 67 66]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  68 (+/- 0.8) <- [66 67 67 68 69 68 67 67 68 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  68 (+/- 0.7) <- [68 66 67 66 67 67 67 67 68 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  68 (+/- 0.7) <- [68 69 67 67 68 67 68 67 67 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  68 (+/- 0.5) <- [68 67 69 67 68 68 67 68 69 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  68 (+/- 0.6) <- [68 67 66 67 67 68 67 68 67 67]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  68 (+/- 0.7) <- [68 67 68 67 67 68 68 67 69 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  68 (+/- 0.7) <- [68 67 66 66 67 68 68 67 68 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  68 (+/- 0.5) <- [67 67 68 67 67 66 68 67 67 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  68 (+/- 0.8) <- [68 68 68 67 68 69 67 68 68 67]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  68 (+/- 0.6) <- [67 67 66 67 67 68 68 66 68 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  68 (+/- 0.5) <- [68 69 68 68 67 67 68 67 68 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  68 (+/- 0.4) <- [68 68 68 68 67 68 68 67 67 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 68.1 (+/- 0.70)\n",
      "Mean time (20 cv): 9.56 seconds\n",
      "Overall time: 194.96 seconds\n",
      "\n",
      " -------------------- Baseline, test_size = 0.6 -------------------- \n",
      "\n",
      "Software versions:"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  numpy: 1.8.2\n",
      "  sklearn: 0.14.1\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  D : (128, 96)               , float32\n",
      "  X : (5, 100, 149, 2, 96)    , float32\n",
      "  Z : (5, 100, 149, 2, 128)   , float32\n",
      "Full dataset:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 149, 2, 96)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 149, 2, 96)\n",
      "<type 'numpy.ndarray'>\n",
      "Flattened frames:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 298, 96)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=96 -> 12,960,000 floats\n",
      "  dim: 25,920 features per clip\n",
      "  shape: (5, 100, 6, 45, 96)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=96 -> 12,960,000 floats\n",
      "  dim: 25,920 features per clip\n",
      "  shape: (5, 100, 6, 45, 96)\n",
      "Feature vectors:\n",
      "  size: N=6,000 x n=96 -> 576,000 floats\n",
      "  dim: 1,152 features per clip\n",
      "  shape: (5, 100, 6, 2, 96)"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ii4dkZwGTmyUxrdaCeSv6Bc4m28P8CHB1bJJgR/QORH+H6KfJ+3mOZEu7hoIZ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\nrwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9ad4711250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 96), float64\n",
      "Testing data: (2400, 96), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 68.8 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1152), float64\n",
      "Testing data: (200, 1152), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 62.1 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clips accuracy: 68.5 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 96), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 2400.0 training, 3600.0 testing\n",
      "  67 (+/- 0.7) <- [67 67 66 66 65 66 66 67 67 67]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  67 (+/- 0.5) <- [68 67 67 67 66 67 67 67 66 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  68 (+/- 0.6) <- [67 67 68 66 68 67 68 66 67 66]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  67 (+/- 0.5) <- [67 67 67 67 67 68 68 66 67 67]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  67 (+/- 0.7) <- [67 66 68 67 68 67 66 67 67 67]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  67 (+/- 0.8) <- [67 67 67 67 68 66 67 67 66 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  67 (+/- 0.5) <- [67 66 68 67 67 67 67 67 66 66]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  67 (+/- 0.6) <- [66 67 67 67 67 68 67 66 66 66]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  67 (+/- 0.5) <- [67 66 66 67 67 67 67 67 67 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  67 (+/- 0.7) <- [67 67 65 66 66 67 67 67 66 66]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  67 (+/- 0.8) <- [67 68 66 66 68 67 67 66 66 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  68 (+/- 0.6) <- [67 67 67 67 68 67 66 68 67 67]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  67 (+/- 0.9) <- [66 66 67 67 67 68 64 66 67 66]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  67 (+/- 0.6) <- [66 67 67 66 66 67 67 66 68 67]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  67 (+/- 0.5) <- [66 66 67 66 67 67 67 66 67 67]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  67 (+/- 0.5) <- [67 66 67 67 67 66 67 68 66 67]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  68 (+/- 0.7) <- [68 67 68 68 67 67 66 68 67 66]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  67 (+/- 0.6) <- [67 67 67 66 67 68 67 66 67 67]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  68 (+/- 0.5) <- [67 67 68 68 67 67 67 68 68 67]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  67 (+/- 0.5) <- [67 67 67 66 66 67 68 67 67 67]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 67.3 (+/- 0.65)\n",
      "Mean time (20 cv): 7.77 seconds\n",
      "Overall time: 159.11 seconds\n",
      "\n",
      " -------------------- Baseline, test_size = 0.7 -------------------- \n",
      "\n",
      "Software versions:"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  numpy: 1.8.2\n",
      "  sklearn: 0.14.1\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  D : (128, 96)               , float32\n",
      "  X : (5, 100, 149, 2, 96)    , float32\n",
      "  Z : (5, 100, 149, 2, 128)   , float32\n",
      "Full dataset:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 149, 2, 96)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 149, 2, 96)\n",
      "<type 'numpy.ndarray'>\n",
      "Flattened frames:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 298, 96)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=96 -> 12,960,000 floats\n",
      "  dim: 25,920 features per clip\n",
      "  shape: (5, 100, 6, 45, 96)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=96 -> 12,960,000 floats\n",
      "  dim: 25,920 features per clip\n",
      "  shape: (5, 100, 6, 45, 96)\n",
      "Feature vectors:\n",
      "  size: N=6,000 x n=96 -> 576,000 floats\n",
      "  dim: 1,152 features per clip\n",
      "  shape: (5, 100, 6, 2, 96)"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ii4dkZwGTmyUxrdaCeSv6Bc4m28P8CHB1bJJgR/QORH+H6KfJ+3mOZEu7hoIZ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\nrwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9ad432e9d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 96), float64\n",
      "Testing data: (2400, 96), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 68.8 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1152), float64\n",
      "Testing data: (200, 1152), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 62.1 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clips accuracy: 68.5 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 96), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 1800.0 training, 4200.0 testing\n",
      "  66 (+/- 0.6) <- [66 66 65 65 65 66 66 67 66 66]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  66 (+/- 0.4) <- [65 66 66 66 65 65 66 66 65 66]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  66 (+/- 0.7) <- [66 67 65 65 66 66 67 65 66 65]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  67 (+/- 0.2) <- [66 66 66 66 66 66 67 66 66 66]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  67 (+/- 0.6) <- [67 66 66 66 66 66 65 66 66 66]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  66 (+/- 0.6) <- [66 66 66 67 67 66 66 66 64 66]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  66 (+/- 0.5) <- [66 65 66 67 65 66 66 66 65 66]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  66 (+/- 0.5) <- [65 66 66 66 66 66 66 65 65 65]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  66 (+/- 0.7) <- [65 65 65 66 65 66 65 67 66 67]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  66 (+/- 0.7) <- [66 66 64 65 66 65 65 66 65 65]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  66 (+/- 0.6) <- [65 66 65 65 67 65 66 66 66 66]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  67 (+/- 0.5) <- [66 66 67 65 67 66 66 67 66 66]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  66 (+/- 0.7) <- [66 65 66 66 65 66 64 66 66 65]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  66 (+/- 0.5) <- [66 66 67 66 65 65 66 66 67 66]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  66 (+/- 0.5) <- [66 65 67 65 66 66 66 66 66 66]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  66 (+/- 0.5) <- [66 66 66 66 67 65 66 66 66 65]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  66 (+/- 0.6) <- [66 66 65 66 66 65 65 66 65 65]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  66 (+/- 0.5) <- [66 66 66 66 66 67 66 65 67 66]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  66 (+/- 0.5) <- [65 65 66 67 65 65 66 65 66 66]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  66 (+/- 0.5) <- [65 65 66 66 65 66 66 67 66 66]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 66.3 (+/- 0.59)\n",
      "Mean time (20 cv): 5.82 seconds\n",
      "Overall time: 120.19 seconds\n",
      "\n",
      " -------------------- Baseline, test_size = 0.8 -------------------- \n",
      "\n",
      "Software versions:"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  numpy: 1.8.2\n",
      "  sklearn: 0.14.1\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  D : (128, 96)               , float32\n",
      "  X : (5, 100, 149, 2, 96)    , float32\n",
      "  Z : (5, 100, 149, 2, 128)   , float32\n",
      "Full dataset:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 149, 2, 96)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 149, 2, 96)\n",
      "<type 'numpy.ndarray'>\n",
      "Flattened frames:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 298, 96)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=96 -> 12,960,000 floats\n",
      "  dim: 25,920 features per clip\n",
      "  shape: (5, 100, 6, 45, 96)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=96 -> 12,960,000 floats\n",
      "  dim: 25,920 features per clip\n",
      "  shape: (5, 100, 6, 45, 96)\n",
      "Feature vectors:\n",
      "  size: N=6,000 x n=96 -> 576,000 floats\n",
      "  dim: 1,152 features per clip\n",
      "  shape: (5, 100, 6, 2, 96)"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ii4dkZwGTmyUxrdaCeSv6Bc4m28P8CHB1bJJgR/QORH+H6KfJ+3mOZEu7hoIZ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\nrwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9ad47c51d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 96), float64\n",
      "Testing data: (2400, 96), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 68.8 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1152), float64\n",
      "Testing data: (200, 1152), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 62.1 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clips accuracy: 68.5 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 96), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 1200.0 training, 4800.0 testing\n",
      "  65 (+/- 1.0) <- [64 65 63 63 65 64 66 64 65 64]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  64 (+/- 0.4) <- [64 64 64 64 64 64 64 64 63 63]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  65 (+/- 0.8) <- [63 64 65 64 63 65 65 63 64 64]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  65 (+/- 0.4) <- [64 64 64 65 64 65 65 64 65 64]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  64 (+/- 0.5) <- [64 64 64 64 64 64 63 65 63 63]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  64 (+/- 0.7) <- [65 64 64 63 64 63 63 64 63 63]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  64 (+/- 0.9) <- [64 62 64 64 63 65 64 63 64 66]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  65 (+/- 0.9) <- [64 66 63 64 64 64 65 65 65 63]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  64 (+/- 0.7) <- [62 64 64 65 64 63 63 65 64 64]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  65 (+/- 0.6) <- [65 65 64 64 64 64 63 64 63 64]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  64 (+/- 0.5) <- [64 64 63 64 65 64 64 64 64 65]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  65 (+/- 0.8) <- [64 64 66 64 64 64 65 65 65 66]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  64 (+/- 0.7) <- [64 63 63 65 64 65 63 64 65 63]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  64 (+/- 0.7) <- [64 63 64 63 63 63 63 64 65 62]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  65 (+/- 0.7) <- [65 63 65 63 65 63 65 64 64 65]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  65 (+/- 0.7) <- [64 63 65 65 65 64 65 64 63 63]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  64 (+/- 0.3) <- [64 63 64 64 64 64 64 64 64 63]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  64 (+/- 0.7) <- [64 64 64 62 63 64 64 63 64 63]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  64 (+/- 0.3) <- [63 63 64 63 64 63 64 64 64 64]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  65 (+/- 0.7) <- [63 64 64 64 63 65 64 65 65 65]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 64.5 (+/- 0.75)\n",
      "Mean time (20 cv): 3.96 seconds\n",
      "Overall time: 82.90 seconds\n",
      "\n",
      " -------------------- Baseline, test_size = 0.9 -------------------- \n",
      "\n",
      "Software versions:"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  numpy: 1.8.2\n",
      "  sklearn: 0.14.1\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  D : (128, 96)               , float32\n",
      "  X : (5, 100, 149, 2, 96)    , float32\n",
      "  Z : (5, 100, 149, 2, 128)   , float32\n",
      "Full dataset:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 149, 2, 96)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 149, 2, 96)\n",
      "<type 'numpy.ndarray'>\n",
      "Flattened frames:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 298, 96)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=96 -> 12,960,000 floats\n",
      "  dim: 25,920 features per clip\n",
      "  shape: (5, 100, 6, 45, 96)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=96 -> 12,960,000 floats\n",
      "  dim: 25,920 features per clip\n",
      "  shape: (5, 100, 6, 45, 96)\n",
      "Feature vectors:\n",
      "  size: N=6,000 x n=96 -> 576,000 floats\n",
      "  dim: 1,152 features per clip\n",
      "  shape: (5, 100, 6, 2, 96)"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ii4dkZwGTmyUxrdaCeSv6Bc4m28P8CHB1bJJgR/QORH+H6KfJ+3mOZEu7hoIZ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\nrwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9ad4573f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 96), float64\n",
      "Testing data: (2400, 96), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 68.8 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1152), float64\n",
      "Testing data: (200, 1152), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 62.1 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clips accuracy: 68.5 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 96), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 600.0 training, 5400.0 testing\n",
      "  61 (+/- 1.2) <- [60 61 60 58 62 61 62 60 61 62]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  61 (+/- 0.8) <- [59 60 59 60 61 61 61 61 59 60]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  61 (+/- 0.6) <- [61 59 60 60 61 60 61 60 60 61]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  61 (+/- 0.8) <- [62 61 60 61 60 62 61 60 62 60]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  61 (+/- 1.0) <- [60 60 61 62 62 60 59 63 60 60]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  61 (+/- 0.7) <- [60 61 59 59 61 61 60 61 61 61]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  61 (+/- 1.0) <- [61 61 59 60 60 60 61 61 59 62]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  61 (+/- 1.0) <- [60 62 60 61 59 59 62 62 62 60]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  61 (+/- 0.9) <- [61 58 60 60 60 62 61 60 60 59]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  61 (+/- 1.0) <- [61 62 60 62 62 61 61 60 59 59]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  61 (+/- 0.7) <- [61 61 61 60 59 59 61 60 61 61]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  61 (+/- 0.7) <- [60 61 61 60 60 60 60 60 61 62]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  61 (+/- 0.6) <- [61 60 60 60 61 61 60 60 61 59]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  61 (+/- 0.8) <- [60 61 61 60 60 59 61 61 60 60]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  61 (+/- 0.9) <- [62 60 61 60 61 59 60 60 61 61]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  62 (+/- 0.6) <- [61 61 60 62 61 62 62 61 60 62]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  61 (+/- 0.6) <- [61 60 61 61 62 60 60 61 61 60]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  61 (+/- 0.6) <- [60 60 61 59 59 60 61 60 61 60]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  61 (+/- 0.7) <- [60 60 60 61 60 62 59 60 60 61]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  61 (+/- 0.6) <- [60 60 61 60 61 61 61 61 62 61]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 61.0 (+/- 0.86)\n",
      "Mean time (20 cv): 2.07 seconds\n",
      "Overall time: 45.28 seconds\n",
      "\n",
      " -------------------- Baseline, test_size = 0.92 -------------------- \n",
      "\n",
      "Software versions:"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  numpy: 1.8.2\n",
      "  sklearn: 0.14.1\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  D : (128, 96)               , float32\n",
      "  X : (5, 100, 149, 2, 96)    , float32\n",
      "  Z : (5, 100, 149, 2, 128)   , float32\n",
      "Full dataset:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 149, 2, 96)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 149, 2, 96)\n",
      "<type 'numpy.ndarray'>\n",
      "Flattened frames:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 298, 96)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=96 -> 12,960,000 floats\n",
      "  dim: 25,920 features per clip\n",
      "  shape: (5, 100, 6, 45, 96)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=96 -> 12,960,000 floats\n",
      "  dim: 25,920 features per clip\n",
      "  shape: (5, 100, 6, 45, 96)\n",
      "Feature vectors:\n",
      "  size: N=6,000 x n=96 -> 576,000 floats\n",
      "  dim: 1,152 features per clip\n",
      "  shape: (5, 100, 6, 2, 96)"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ii4dkZwGTmyUxrdaCeSv6Bc4m28P8CHB1bJJgR/QORH+H6KfJ+3mOZEu7hoIZ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\nrwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9ad4a7edd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 96), float64\n",
      "Testing data: (2400, 96), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 68.8 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1152), float64\n",
      "Testing data: (200, 1152), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 62.1 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clips accuracy: 68.5 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 96), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 480.0 training, 5520.0 testing\n",
      "  60 (+/- 1.0) <- [58 59 59 57 58 60 60 59 59 60]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  60 (+/- 0.9) <- [58 59 59 59 61 60 59 60 59 59]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  60 (+/- 0.9) <- [60 58 59 58 60 58 60 59 59 60]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  60 (+/- 0.9) <- [61 60 58 59 59 60 59 58 61 58]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  60 (+/- 1.3) <- [58 58 60 61 60 59 58 62 59 60]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  60 (+/- 0.5) <- [59 60 60 59 58 60 59 60 60 60]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  60 (+/- 1.0) <- [59 59 59 59 58 59 60 59 57 61]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  60 (+/- 0.9) <- [60 61 59 59 58 58 60 61 60 59]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  60 (+/- 0.9) <- [60 57 59 59 59 60 60 59 59 58]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  60 (+/- 0.8) <- [59 60 59 60 61 61 59 59 58 59]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  60 (+/- 1.0) <- [60 60 60 59 58 57 59 59 60 60]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  60 (+/- 0.9) <- [60 60 59 58 57 59 60 59 60 60]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  59 (+/- 0.8) <- [60 59 59 60 59 57 59 59 59 58]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  60 (+/- 0.8) <- [58 60 59 59 60 58 59 60 60 59]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  60 (+/- 0.8) <- [60 60 59 59 59 59 58 60 61 61]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  60 (+/- 0.7) <- [60 61 59 60 59 61 60 60 59 60]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  60 (+/- 0.6) <- [60 59 60 59 61 59 58 60 60 60]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  60 (+/- 0.7) <- [59 59 58 59 60 59 61 58 60 59]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  60 (+/- 0.9) <- [57 60 59 60 59 61 58 59 59 59]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  60 (+/- 0.7) <- [59 59 59 59 58 60 60 59 59 60]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 59.8 (+/- 0.90)\n",
      "Mean time (20 cv): 1.69 seconds\n",
      "Overall time: 37.66 seconds\n",
      "\n",
      " -------------------- Baseline, test_size = 0.94 -------------------- \n",
      "\n",
      "Software versions:"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  numpy: 1.8.2\n",
      "  sklearn: 0.14.1\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  D : (128, 96)               , float32\n",
      "  X : (5, 100, 149, 2, 96)    , float32\n",
      "  Z : (5, 100, 149, 2, 128)   , float32\n",
      "Full dataset:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 149, 2, 96)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 149, 2, 96)\n",
      "<type 'numpy.ndarray'>\n",
      "Flattened frames:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 298, 96)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=96 -> 12,960,000 floats\n",
      "  dim: 25,920 features per clip\n",
      "  shape: (5, 100, 6, 45, 96)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=96 -> 12,960,000 floats\n",
      "  dim: 25,920 features per clip\n",
      "  shape: (5, 100, 6, 45, 96)\n",
      "Feature vectors:\n",
      "  size: N=6,000 x n=96 -> 576,000 floats\n",
      "  dim: 1,152 features per clip\n",
      "  shape: (5, 100, 6, 2, 96)"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ii4dkZwGTmyUxrdaCeSv6Bc4m28P8CHB1bJJgR/QORH+H6KfJ+3mOZEu7hoIZ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\nrwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9ad4903410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 96), float64\n",
      "Testing data: (2400, 96), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 68.8 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1152), float64\n",
      "Testing data: (200, 1152), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 62.1 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clips accuracy: 68.5 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 96), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 360.0 training, 5640.0 testing\n",
      "  58 (+/- 1.1) <- [57 58 58 55 56 59 57 57 58 58]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  58 (+/- 1.4) <- [55 58 58 58 58 59 56 59 55 58]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  58 (+/- 1.0) <- [58 56 57 57 58 56 58 58 57 60]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  58 (+/- 0.7) <- [59 58 57 57 57 58 58 57 59 57]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  58 (+/- 1.1) <- [56 56 57 59 57 58 56 58 57 57]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  58 (+/- 1.0) <- [57 57 57 55 57 57 57 58 59 59]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  58 (+/- 1.3) <- [59 56 57 56 56 58 58 58 56 59]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  58 (+/- 0.8) <- [57 59 57 58 58 57 59 59 57 58]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  58 (+/- 0.8) <- [59 56 59 57 58 58 58 57 57 58]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  58 (+/- 1.0) <- [57 59 56 59 59 59 57 56 57 58]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  58 (+/- 0.8) <- [58 57 57 57 57 56 59 57 58 59]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  58 (+/- 0.8) <- [59 57 58 57 56 57 58 57 59 59]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  58 (+/- 1.2) <- [59 58 57 57 59 56 56 56 58 56]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  58 (+/- 1.5) <- [55 59 58 58 58 54 59 58 57 59]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  58 (+/- 0.9) <- [58 57 58 58 59 55 57 58 57 58]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  59 (+/- 1.0) <- [58 59 57 56 59 59 59 58 58 59]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  58 (+/- 1.3) <- [59 55 58 56 59 57 56 59 57 59]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  58 (+/- 1.1) <- [58 57 56 58 59 57 59 58 58 60]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  58 (+/- 1.1) <- [56 59 57 59 58 59 56 58 58 58]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  58 (+/- 0.6) <- [58 58 58 57 57 59 57 58 58 57]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 58.1 (+/- 1.08)\n",
      "Mean time (20 cv): 1.30 seconds\n",
      "Overall time: 29.85 seconds\n",
      "\n",
      " -------------------- Baseline, test_size = 0.96 -------------------- \n",
      "\n",
      "Software versions:"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  numpy: 1.8.2\n",
      "  sklearn: 0.14.1\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  D : (128, 96)               , float32\n",
      "  X : (5, 100, 149, 2, 96)    , float32\n",
      "  Z : (5, 100, 149, 2, 128)   , float32\n",
      "Full dataset:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 149, 2, 96)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 149, 2, 96)\n",
      "<type 'numpy.ndarray'>\n",
      "Flattened frames:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 298, 96)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=96 -> 12,960,000 floats\n",
      "  dim: 25,920 features per clip\n",
      "  shape: (5, 100, 6, 45, 96)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=96 -> 12,960,000 floats\n",
      "  dim: 25,920 features per clip\n",
      "  shape: (5, 100, 6, 45, 96)\n",
      "Feature vectors:\n",
      "  size: N=6,000 x n=96 -> 576,000 floats\n",
      "  dim: 1,152 features per clip\n",
      "  shape: (5, 100, 6, 2, 96)"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ii4dkZwGTmyUxrdaCeSv6Bc4m28P8CHB1bJJgR/QORH+H6KfJ+3mOZEu7hoIZ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\nrwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9ad443b350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 96), float64\n",
      "Testing data: (2400, 96), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 68.8 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1152), float64\n",
      "Testing data: (200, 1152), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 62.1 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clips accuracy: 68.5 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 96), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 240.0 training, 5760.0 testing\n",
      "  55 (+/- 1.4) <- [56 56 52 53 53 57 55 53 54 55]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  55 (+/- 1.5) <- [54 56 55 56 55 55 53 56 51 56]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  56 (+/- 1.1) <- [54 53 55 55 56 55 55 56 55 57]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  56 (+/- 1.0) <- [56 55 55 55 57 57 57 54 57 55]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  54 (+/- 1.4) <- [53 52 54 56 53 55 53 55 52 54]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  56 (+/- 1.5) <- [56 55 53 53 57 53 56 56 55 57]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  55 (+/- 1.2) <- [55 53 55 56 53 57 55 55 54 56]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  56 (+/- 1.2) <- [56 55 54 58 55 54 57 56 55 58]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  55 (+/- 1.0) <- [56 53 55 56 56 56 55 54 55 54]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  56 (+/- 1.1) <- [55 56 54 57 54 57 54 56 54 56]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  55 (+/- 1.0) <- [56 52 55 55 55 55 55 55 56 55]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  56 (+/- 1.1) <- [57 56 55 54 54 55 55 54 57 55]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  55 (+/- 1.2) <- [54 57 55 54 56 52 54 53 56 54]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  56 (+/- 1.9) <- [55 56 56 57 56 52 55 56 51 56]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  56 (+/- 1.3) <- [54 56 56 57 56 53 54 54 54 56]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  56 (+/- 1.2) <- [57 58 54 55 56 55 56 57 56 56]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  56 (+/- 1.5) <- [55 52 55 55 56 53 55 57 56 57]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  56 (+/- 1.1) <- [54 56 54 56 57 54 54 54 55 56]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  55 (+/- 1.2) <- [53 56 52 55 55 56 54 55 54 54]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  55 (+/- 0.8) <- [55 54 55 54 56 56 55 54 56 55]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 55.5 (+/- 1.37)\n",
      "Mean time (20 cv): 0.91 seconds\n",
      "Overall time: 22.00 seconds\n",
      "\n",
      " -------------------- Baseline, test_size = 0.97 -------------------- \n",
      "\n",
      "Software versions:"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  numpy: 1.8.2\n",
      "  sklearn: 0.14.1\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  D : (128, 96)               , float32\n",
      "  X : (5, 100, 149, 2, 96)    , float32\n",
      "  Z : (5, 100, 149, 2, 128)   , float32\n",
      "Full dataset:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 149, 2, 96)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 149, 2, 96)\n",
      "<type 'numpy.ndarray'>\n",
      "Flattened frames:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 298, 96)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=96 -> 12,960,000 floats\n",
      "  dim: 25,920 features per clip\n",
      "  shape: (5, 100, 6, 45, 96)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=96 -> 12,960,000 floats\n",
      "  dim: 25,920 features per clip\n",
      "  shape: (5, 100, 6, 45, 96)\n",
      "Feature vectors:\n",
      "  size: N=6,000 x n=96 -> 576,000 floats\n",
      "  dim: 1,152 features per clip\n",
      "  shape: (5, 100, 6, 2, 96)"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ii4dkZwGTmyUxrdaCeSv6Bc4m28P8CHB1bJJgR/QORH+H6KfJ+3mOZEu7hoIZ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\nrwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9ad4926350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 96), float64\n",
      "Testing data: (2400, 96), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 68.8 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1152), float64\n",
      "Testing data: (200, 1152), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 62.1 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clips accuracy: 68.5 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 96), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 180.0 training, 5820.0 testing\n",
      "  53 (+/- 2.0) <- [53 52 50 49 52 54 56 53 54 52]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  54 (+/- 2.2) <- [50 54 54 56 56 53 53 54 49 55]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  54 (+/- 1.3) <- [53 50 53 53 54 53 52 54 54 54]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  55 (+/- 0.8) <- [54 55 53 55 54 53 54 53 55 53]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  52 (+/- 1.6) <- [51 51 52 55 49 52 52 54 51 52]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  54 (+/- 1.0) <- [54 52 52 53 55 52 54 54 53 54]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  54 (+/- 1.4) <- [54 50 54 54 52 54 54 54 53 55]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  54 (+/- 1.3) <- [54 54 51 55 54 53 54 54 52 55]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  54 (+/- 1.2) <- [55 51 54 53 55 55 54 53 53 52]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  53 (+/- 1.4) <- [52 54 53 54 50 55 51 53 53 53]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  53 (+/- 1.2) <- [53 50 53 54 51 53 53 52 54 53]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  54 (+/- 1.8) <- [56 56 54 54 53 53 52 50 56 53]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  54 (+/- 1.5) <- [52 55 54 54 54 52 51 51 55 53]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  54 (+/- 1.1) <- [52 54 53 55 54 52 54 54 52 54]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  54 (+/- 1.5) <- [53 55 54 54 54 50 52 53 55 54]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  54 (+/- 1.0) <- [54 56 54 54 55 52 53 54 54 54]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  54 (+/- 1.8) <- [52 50 52 53 54 53 53 57 54 55]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  55 (+/- 1.3) <- [53 54 55 54 56 53 53 54 52 55]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  53 (+/- 1.7) <- [51 54 52 53 52 51 52 56 51 51]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  54 (+/- 1.0) <- [54 53 55 53 56 54 54 53 54 53]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 53.7 (+/- 1.55)\n",
      "Mean time (20 cv): 0.71 seconds\n",
      "Overall time: 18.06 seconds\n",
      "\n",
      " -------------------- Baseline, test_size = 0.98 -------------------- \n",
      "\n",
      "Software versions:"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  numpy: 1.8.2\n",
      "  sklearn: 0.14.1\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  D : (128, 96)               , float32\n",
      "  X : (5, 100, 149, 2, 96)    , float32\n",
      "  Z : (5, 100, 149, 2, 128)   , float32\n",
      "Full dataset:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 149, 2, 96)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 149, 2, 96)\n",
      "<type 'numpy.ndarray'>\n",
      "Flattened frames:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 298, 96)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=96 -> 12,960,000 floats\n",
      "  dim: 25,920 features per clip\n",
      "  shape: (5, 100, 6, 45, 96)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=96 -> 12,960,000 floats\n",
      "  dim: 25,920 features per clip\n",
      "  shape: (5, 100, 6, 45, 96)\n",
      "Feature vectors:\n",
      "  size: N=6,000 x n=96 -> 576,000 floats\n",
      "  dim: 1,152 features per clip\n",
      "  shape: (5, 100, 6, 2, 96)"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ii4dkZwGTmyUxrdaCeSv6Bc4m28P8CHB1bJJgR/QORH+H6KfJ+3mOZEu7hoIZ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\nrwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9ad413b050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 96), float64\n",
      "Testing data: (2400, 96), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 68.8 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1152), float64\n",
      "Testing data: (200, 1152), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 62.1 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clips accuracy: 68.5 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 96), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 120.0 training, 5880.0 testing\n",
      "  49 (+/- 4.5) <- [50 52 50 47 49 51 53 36 52 50]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  51 (+/- 2.3) <- [49 51 49 52 53 52 52 51 46 52]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  50 (+/- 2.1) <- [52 45 50 50 50 49 47 51 52 51]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  51 (+/- 1.6) <- [51 53 49 52 50 49 50 49 53 52]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  50 (+/- 1.8) <- [47 51 48 52 48 47 51 51 49 52]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  52 (+/- 1.5) <- [51 50 49 53 54 52 51 49 52 52]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  51 (+/- 1.8) <- [51 47 52 52 51 53 52 50 49 52]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  51 (+/- 1.5) <- [49 51 47 51 50 49 52 52 52 49]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  51 (+/- 1.5) <- [53 47 49 51 52 50 50 51 52 49]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  51 (+/- 1.2) <- [50 50 52 51 48 51 49 52 52 52]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  51 (+/- 1.8) <- [53 47 51 52 48 51 52 51 51 50]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  51 (+/- 2.5) <- [54 55 52 51 51 51 47 47 51 47]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  51 (+/- 1.5) <- [49 54 50 52 51 50 50 50 50 52]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  51 (+/- 2.0) <- [51 51 51 54 53 50 48 49 48 53]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  51 (+/- 2.0) <- [50 51 52 51 50 45 49 51 53 50]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  52 (+/- 1.6) <- [51 53 49 52 53 50 51 55 50 50]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  51 (+/- 2.2) <- [49 46 48 54 51 49 51 53 52 52]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  52 (+/- 1.7) <- [50 53 53 53 53 52 51 53 48 50]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  51 (+/- 1.9) <- [50 52 50 53 50 46 51 51 48 48]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  52 (+/- 1.6) <- [51 53 53 52 54 51 53 49 53 49]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 51.1 (+/- 2.15)\n",
      "Mean time (20 cv): 0.50 seconds\n",
      "Overall time: 13.91 seconds\n",
      "\n",
      " -------------------- Baseline, test_size = 0.99 -------------------- \n",
      "\n",
      "Software versions:"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  numpy: 1.8.2\n",
      "  sklearn: 0.14.1\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  D : (128, 96)               , float32\n",
      "  X : (5, 100, 149, 2, 96)    , float32\n",
      "  Z : (5, 100, 149, 2, 128)   , float32\n",
      "Full dataset:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 149, 2, 96)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 149, 2, 96)\n",
      "<type 'numpy.ndarray'>\n",
      "Flattened frames:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 298, 96)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=96 -> 12,960,000 floats\n",
      "  dim: 25,920 features per clip\n",
      "  shape: (5, 100, 6, 45, 96)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=96 -> 12,960,000 floats\n",
      "  dim: 25,920 features per clip\n",
      "  shape: (5, 100, 6, 45, 96)\n",
      "Feature vectors:\n",
      "  size: N=6,000 x n=96 -> 576,000 floats\n",
      "  dim: 1,152 features per clip\n",
      "  shape: (5, 100, 6, 2, 96)"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ii4dkZwGTmyUxrdaCeSv6Bc4m28P8CHB1bJJgR/QORH+H6KfJ+3mOZEu7hoIZ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\nrwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9ad4748690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 96), float64\n",
      "Testing data: (2400, 96), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 68.8 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1152), float64\n",
      "Testing data: (200, 1152), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 62.1 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clips accuracy: 68.5 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 96), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 60.0 training, 5940.0 testing\n",
      "  46 (+/- 2.6) <- [46 49 46 41 46 47 45 40 47 45]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  46 (+/- 2.0) <- [46 47 43 42 49 47 48 47 44 46]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  46 (+/- 2.4) <- [44 40 46 49 48 44 45 46 47 46]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  47 (+/- 2.5) <- [45 49 42 46 51 45 45 43 48 46]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  46 (+/- 2.9) <- [41 46 44 48 44 41 46 50 44 48]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  47 (+/- 1.4) <- [48 45 44 47 48 47 46 45 47 48]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  47 (+/- 2.8) <- [48 39 50 45 44 50 46 46 46 46]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  45 (+/- 3.7) <- [49 44 35 44 48 44 48 46 44 47]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  46 (+/- 1.6) <- [44 42 48 45 45 46 48 45 46 46]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  45 (+/- 4.8) <- [47 42 46 46 45 46 46 46 47 30]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  46 (+/- 2.0) <- [49 42 46 47 47 42 46 45 45 47]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  45 (+/- 4.6) <- [49 48 48 48 35 49 39 43 48 43]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  47 (+/- 2.4) <- [44 50 48 48 49 47 50 45 42 47]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  49 (+/- 1.5) <- [48 48 46 47 51 48 51 48 46 49]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  46 (+/- 3.8) <- [42 47 47 49 42 37 45 48 49 48]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  48 (+/- 2.5) <- [48 48 44 44 50 46 50 51 44 48]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  47 (+/- 2.9) <- [51 41 44 49 45 44 46 49 45 50]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  48 (+/- 1.8) <- [44 47 48 47 49 49 46 50 45 49]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  46 (+/- 2.4) <- [47 47 42 45 45 41 48 49 45 44]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  47 (+/- 2.1) <- [46 49 48 46 46 47 46 42 50 47]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 46.5 (+/- 2.95)\n",
      "Mean time (20 cv): 0.30 seconds\n",
      "Overall time: 9.78 seconds\n"
     ]
    }
   ],
   "source": [
    "#%run gtzan.ipynb\n",
    "#%run audio_preprocessing.ipynb\n",
    "if not regen_graph:\n",
    "    separator('Graph')\n",
    "    %run audio_graph.ipynb\n",
    "if not regen_features:\n",
    "    separator('Features')\n",
    "    %run audio_features.ipynb\n",
    "\n",
    "# Hyper-parameter under test.\n",
    "for p[Pname] in Pvalues:\n",
    "\n",
    "    if regen_graph:\n",
    "        separator('Graph', True)\n",
    "        %run audio_graph.ipynb\n",
    "    if regen_features:\n",
    "        separator('Features', True)\n",
    "        p['filename_features'] = 'features_{}_{}.hdf5'.format(Pname, p[Pname])\n",
    "        %run audio_features.ipynb\n",
    "    separator('Classification', True)\n",
    "    %run audio_classification.ipynb\n",
    "    \n",
    "    # Collect results.\n",
    "    for key in res:\n",
    "        res[key].append(globals()[key])\n",
    "\n",
    "# Baseline, i.e. classification with spectrograms.\n",
    "p['dataset_classification'] = 'X'\n",
    "p['scale'] = 'minmax'  # Todo: should be done in pre-processing.\n",
    "if regen_baseline:\n",
    "    res['baseline'] = []\n",
    "    res['baseline_std'] = []\n",
    "    for p[Pname] in Pvalues:\n",
    "        separator('Baseline', True)\n",
    "        %run audio_classification.ipynb\n",
    "        res['baseline'].append(accuracy)\n",
    "        res['baseline_std'].append(accuracy_std)\n",
    "else:\n",
    "    separator('Baseline')\n",
    "    %run audio_classification.ipynb\n",
    "    res['baseline'] = len(Pvalues) * [accuracy]\n",
    "    res['baseline_std'] = accuracy_std"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test_size = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.92, 0.94, 0.96, 0.97, 0.98, 0.99]\n",
      "res['accuracy_std'] = [1.5363231720210719, 1.0950963935248403, 0.86717893514288968, 0.72294293300024748, 0.67640643600328565, 0.62754813892375372, 0.71340338361624156, 0.79955032256636105, 0.868960165685013, 1.0195348949478793, 1.1721948847062518, 1.3469688402723865, 1.5173057279206219, 1.7022533880862585, 2.0739585760836468]\n",
      "res['objective_j'] = [8916.6603088378906, 8916.6603088378906, 8916.6603088378906, 8916.6603088378906, 8916.6603088378906, 8916.6603088378906, 8916.6603088378906, 8916.6603088378906, 8916.6603088378906, 8916.6603088378906, 8916.6603088378906, 8916.6603088378906, 8916.6603088378906, 8916.6603088378906, 8916.6603088378906]\n",
      "res['objective_i'] = [55359.046875, 55359.046875, 55359.046875, 55359.046875, 55359.046875, 55359.046875, 55359.046875, 55359.046875, 55359.046875, 55359.046875, 55359.046875, 55359.046875, 55359.046875, 55359.046875, 55359.046875]\n",
      "res['objective_h'] = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "res['objective_g'] = [76914.08203125, 76914.08203125, 76914.08203125, 76914.08203125, 76914.08203125, 76914.08203125, 76914.08203125, 76914.08203125, 76914.08203125, 76914.08203125, 76914.08203125, 76914.08203125, 76914.08203125, 76914.08203125, 76914.08203125]\n",
      "res['baseline'] = [69.729166666666742, 69.439166666666665, 69.101388888888863, 68.599375000000009, 68.060166666666689, 67.333888888888907, 66.250476190476164, 64.493020833333333, 60.973703703703706, 59.755344202898506, 58.062677304964552, 55.507986111111116, 53.744501718213066, 51.096088435374142, 46.473737373737357]\n",
      "res['time_features'] = [2684.9424121379852, 2684.9424121379852, 2684.9424121379852, 2684.9424121379852, 2684.9424121379852, 2684.9424121379852, 2684.9424121379852, 2684.9424121379852, 2684.9424121379852, 2684.9424121379852, 2684.9424121379852, 2684.9424121379852, 2684.9424121379852, 2684.9424121379852, 2684.9424121379852]\n",
      "res['baseline_std'] = [1.7102986662503645, 1.2586840019109906, 0.95690211162614125, 0.77039038512049951, 0.69960065990058462, 0.65298436447354025, 0.58584473963407446, 0.7472381947577772, 0.85954329596629586, 0.89706844615576686, 1.0826780027089868, 1.3656555460323629, 1.5531957132032486, 2.1520828759669808, 2.9536838364939091]\n",
      "res['sparsity'] = [22.528853817114094, 22.528853817114094, 22.528853817114094, 22.528853817114094, 22.528853817114094, 22.528853817114094, 22.528853817114094, 22.528853817114094, 22.528853817114094, 22.528853817114094, 22.528853817114094, 22.528853817114094, 22.528853817114094, 22.528853817114094, 22.528853817114094]\n",
      "res['iterations_inner'] = [1261, 1261, 1261, 1261, 1261, 1261, 1261, 1261, 1261, 1261, 1261, 1261, 1261, 1261, 1261]\n",
      "res['iterations_outer'] = [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5]\n",
      "res['accuracy'] = [77.356666666666669, 77.124999999999986, 76.824166666666628, 76.301041666666649, 75.695666666666696, 74.848888888888837, 73.402142857142877, 71.131979166666639, 66.11444444444443, 64.355072463768138, 62.116932624113488, 58.696701388888897, 56.308247422680466, 53.061904761904763, 48.231986531986529]\n"
     ]
    },
    {
     "data": {
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1ll2QzqYkGIZhGIbRoJi/zDAMo3LYO7a21F1Mgs1uMAzDMPLxyFLq9EUvZQZDOa5hGG3A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\n3Vt7PMbgMRaNfAp30OWnBb0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mOde19Y/ITfQlcrlMINnN8r67TjQT5dIoR8RwCluRW8jP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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9ad40f91d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Experiment time: 7007 seconds\n"
     ]
    }
   ],
   "source": [
    "print('{} = {}'.format(Pname, Pvalues))\n",
    "for key, value in res.items():\n",
    "    if key is not 'atoms':\n",
    "        print('res[\\'{}\\'] = {}'.format(key, value))\n",
    "\n",
    "def plot(*args, **kwargs):\n",
    "    plt.figure(figsize=(8,5))\n",
    "    x = range(len(Pvalues))\n",
    "    log = 'log' in kwargs and kwargs['log'] is True\n",
    "    pltfunc = plt.semilogy if log else plt.plot\n",
    "    params = {}\n",
    "    params['linestyle'] = '-'\n",
    "    params['marker'] = '.'\n",
    "    params['markersize'] = 10\n",
    "    for i, var in enumerate(args):\n",
    "        if 'err' in kwargs:\n",
    "            pltfunc = plt.errorbar\n",
    "            params['yerr'] = res[kwargs['err'][i]]\n",
    "            params['capsize'] = 5\n",
    "        pltfunc(x, res[var], label=var, **params)\n",
    "        for i,j in zip(x, res[var]):\n",
    "            plt.annotate('{:.2f}'.format(j), xy=(i,j), xytext=(5,5), textcoords='offset points')\n",
    "    margin = 0.25\n",
    "    params['markersize'] = 10\n",
    "    plt.xlim(-margin, len(Pvalues)-1+margin)\n",
    "    if 'ylim' in kwargs:\n",
    "        plt.ylim(kwargs['ylim'])\n",
    "    plt.title('{} vs {}'.format(', '.join(args), Pname))\n",
    "    plt.xlabel(Pname)\n",
    "    plt.ylabel(' ,'.join(args))\n",
    "    plt.xticks(x, Pvalues)\n",
    "    plt.grid(True); plt.legend(loc='best'); plt.show()\n",
    "\n",
    "def div(l):\n",
    "    div = Pvalues if Pname is l else [p[l]]\n",
    "    return np.array([1 if v is None else v for v in div])\n",
    "\n",
    "# Classification results.\n",
    "res['chance'] = len(Pvalues) * [100./p['Ngenres']]\n",
    "res['chance_std'] = 0\n",
    "err=['accuracy_std', 'baseline_std', 'chance_std']\n",
    "plot('accuracy', 'baseline', 'chance', err=err, ylim=[0,100])\n",
    "\n",
    "# Features extraction results.\n",
    "if regen_features:\n",
    "    plot('objective_g', 'objective_i', 'objective_j', log=True)\n",
    "    # Unweighted objectives.\n",
    "    print('g(Z) = ||X-DZ||_2^2, h(Z) = ||Z-EX||_2^2, i(Z) = ||Z||_1, j(Z) = tr(Z^TLZ)')\n",
    "    res['objective_g_un'] = res['objective_g'] / div('ld')\n",
    "    res['objective_i_un'] = res['objective_i'] / div('ls')\n",
    "    res['objective_j_un'] = res['objective_j'] / div('lg')\n",
    "    plot('objective_g_un', 'objective_i_un', 'objective_j_un', log=True)\n",
    "    plot('sparsity', ylim=[0,100])\n",
    "    plot('time_features')\n",
    "    plot('iterations_inner')\n",
    "    plot('iterations_outer')\n",
    "\n",
    "    for i, fig in enumerate(res['atoms']):\n",
    "        print('Dictionary atoms for {} = {}'.format(Pname, Pvalues[i]))\n",
    "        fig.show()\n",
    "\n",
    "print('Experiment time: {:.0f} seconds'.format(time.time() - texperiment))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
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