{
 "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'] = 600\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: 174.12 seconds"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "All self-referenced in the first column: True\n",
      "dist in [0.0, 1.52197754383]\n",
      "w in [0.00494863791391, 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    : (2409282,), float32\n",
      "  L_indices : (2409282,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2409282,), float32\n",
      "  W_indices : (2409282,), 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: 184.07 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    : (2409282,), float32\n",
      "  L_indices : (2409282,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2409282,), float32\n",
      "  W_indices : (2409282,), 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: 2488 seconds"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
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rqo/8OGxU46HAr7G+tcN6Fk3ilLQ8taKb+aK2WzvfDnvPj9iQvXMfxN7ydGGb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\nIiIiIiIiIiIiIiIiIiIiIiIiIqH8f/BoJ2e1+vZsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2776c23810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inner loop: 1122 iterations\n",
      "g(Z) = ||X-DZ||_2^2 = 1.038306e+05\n",
      "rdiff: 0.000175221613487"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "i(Z) = ||Z||_1 = 4.643225e+04\n",
      "j(Z) = tr(Z^TLZ) = 8.429849e+03\n"
     ]
    },
    {
     "data": {
      "image/png": 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nW/Wa7Ha6Dm2nZjbKe065VkV9t3A70FkWiWgodoCEDMUOwCATGC6koxXYsRTj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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f27741797d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global objective: 1.586927e+05\n"
     ]
    },
    {
     "data": {
      "image/png": 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nIN4IcRPvJCIisppK6r1fA1NpNgy2xSrrTgzRumHQwO7BX9xB70jg46X3LwJ2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2774084b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outer loop: 7 iterations\n",
      "\n",
      "Z in [-0.063802331686, 0.404274195433]\n",
      "Sparsity of Z: 7,789,559 non-zero entries out of 19,072,000 entries, i.e. 40.8%.\n"
     ]
    },
    {
     "data": {
      "image/png": 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rz+s7BtPRax/Da2dS7uBi4pcAeBuA68brywG8FsAtAF4D4LI96SWEEEKsgn2+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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f27b85fcb10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D in [-0.0233125798404, 0.826667249203]\n",
      "d in [0.999999701977, 1.00000035763]\n",
      "Constraints on D: True\n"
     ]
    },
    {
     "data": {
      "image/png": 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Olsfp/NPd27Lk2czTBdjeTO8l0CHAYc31OwKfAE7ume+JXpKktgmrfcdR93z3ABcBp/bN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\nJEmS0vn/1AdkQIHIkWIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2773ec1e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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YF9jBrJWaNgxlXZRd7v7OzVGjjlt1MaPe4GsBmzJ5xLMHZhrRNEzWLeROvBoma2Vlss5d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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f277415a510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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9Pcje5esqVtOXaA2K8iEQe9qUj0fKZomtL0YHGZfb/fdrZ0NIoytpOdvKLezg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\nx3Ecx3Ecx3Ecx3Ecx3GcTxj/H9SZ2sA8h+clAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f27b85fc450>"
      ]
     },
     "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: 2496 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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M23KHgssrGmoES5Bc+PKCNmOI7e5BHMsKpivMvJBsuuAH8hXm7SQh2SyFuRC5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\nHh4eHh4eHh4eHh4eHh4eHh4eNcT/B76xb8/F38RYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2750215050>"
      ]
     },
     "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: 70.8 %"
     ]
    },
    {
     "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: 63.5 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clips accuracy: 69.0 %"
     ]
    },
    {
     "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",
      "  72 (+/- 1.6) <- [72 71 74 71 68 73 71 69 73 72]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  72 (+/- 1.5) <- [72 75 73 69 73 71 71 72 71 72]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  73 (+/- 1.7) <- [72 74 75 72 72 72 71 69 74 72]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  73 (+/- 2.2) <- [75 67 72 72 73 74 76 70 72 72]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  73 (+/- 1.4) <- [74 73 72 74 71 74 74 70 73 71]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  73 (+/- 1.5) <- [73 72 72 73 74 75 72 76 71 72]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  73 (+/- 1.5) <- [74 71 73 74 72 69 73 71 73 73]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  72 (+/- 1.2) <- [72 73 75 71 72 73 72 71 71 71]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  73 (+/- 1.4) <- [72 75 70 74 72 71 73 73 72 71]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  73 (+/- 1.5) <- [73 69 72 73 73 72 72 71 72 75]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  73 (+/- 1.6) <- [74 75 74 70 72 72 71 70 73 72]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  73 (+/- 1.1) <- [72 73 73 74 71 71 70 72 73 74]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  73 (+/- 1.2) <- [73 71 74 74 72 72 71 72 72 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  72 (+/- 1.9) <- [74 73 74 69 72 71 69 71 71 74]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  73 (+/- 1.0) <- [73 72 72 72 72 72 75 74 72 73]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  73 (+/- 1.9) <- [75 73 70 75 71 72 73 71 73 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  72 (+/- 1.5) <- [72 73 72 71 72 69 72 74 74 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  73 (+/- 1.6) <- [73 74 72 72 71 74 71 70 74 75]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  72 (+/- 1.2) <- [71 72 71 70 74 72 72 71 72 74]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  72 (+/- 1.2) <- [70 72 72 71 70 71 73 74 72 71]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 72.6 (+/- 1.56)\n",
      "Mean time (20 cv): 21.64 seconds\n",
      "Overall time: 437.29 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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M23KHgssrGmoES5Bc+PKCNmOI7e5BHMsKpivMvJBsuuAH8hXm7SQh2SyFuRC5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\nHh4eHh4eHh4eHh4eHh4eHh4eNcT/B76xb8/F38RYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f274bc72c10>"
      ]
     },
     "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: 70.8 %"
     ]
    },
    {
     "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: 63.5 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clips accuracy: 69.0 %"
     ]
    },
    {
     "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",
      "  72 (+/- 1.2) <- [71 70 72 72 69 71 73 69 73 71]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  72 (+/- 1.1) <- [71 73 72 69 72 71 72 70 72 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  73 (+/- 0.5) <- [72 73 73 72 72 72 72 71 73 72]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  72 (+/- 1.1) <- [72 70 71 72 73 74 73 72 71 71]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  73 (+/- 1.1) <- [72 72 74 72 72 73 72 70 71 73]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  73 (+/- 1.3) <- [71 72 73 75 73 74 70 74 72 72]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  73 (+/- 1.1) <- [71 71 74 73 72 70 73 72 73 72]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  72 (+/- 1.3) <- [71 72 72 73 70 75 73 71 71 72]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  73 (+/- 1.1) <- [72 74 71 73 72 70 73 72 72 72]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  73 (+/- 1.0) <- [73 71 73 72 74 72 73 72 72 74]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  72 (+/- 1.0) <- [71 73 71 70 71 70 72 70 72 72]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  73 (+/- 0.8) <- [73 71 72 73 71 72 72 72 73 73]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  72 (+/- 0.8) <- [72 72 71 73 70 72 71 70 72 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  72 (+/- 1.4) <- [74 71 72 69 70 70 71 73 72 73]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  73 (+/- 0.6) <- [73 72 72 73 72 72 72 73 73 72]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  72 (+/- 1.2) <- [74 71 71 73 72 71 71 73 72 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  72 (+/- 0.7) <- [72 73 71 71 72 72 71 71 71 71]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  72 (+/- 0.9) <- [71 72 73 70 72 72 72 70 72 72]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  73 (+/- 1.2) <- [73 72 71 69 73 72 73 74 72 73]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  72 (+/- 1.3) <- [72 70 71 70 70 71 74 71 72 73]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 72.3 (+/- 1.15)\n",
      "Mean time (20 cv): 18.85 seconds\n",
      "Overall time: 381.46 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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M23KHgssrGmoES5Bc+PKCNmOI7e5BHMsKpivMvJBsuuAH8hXm7SQh2SyFuRC5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\nHh4eHh4eHh4eHh4eHh4eHh4eNcT/B76xb8/F38RYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f274b719c90>"
      ]
     },
     "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: 70.8 %"
     ]
    },
    {
     "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: 63.5 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clips accuracy: 69.0 %"
     ]
    },
    {
     "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",
      "  72 (+/- 0.8) <- [70 71 72 72 70 71 72 71 72 71]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 1.0) <- [71 73 72 69 71 71 70 71 72 71]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  72 (+/- 1.0) <- [72 72 71 70 73 71 71 70 74 71]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  72 (+/- 1.2) <- [73 70 71 72 72 74 71 71 70 72]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  72 (+/- 0.9) <- [71 71 73 72 70 72 72 70 71 72]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  72 (+/- 0.7) <- [71 72 72 73 72 72 70 72 71 72]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  72 (+/- 0.9) <- [72 72 73 72 72 70 73 72 72 71]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  72 (+/- 1.1) <- [71 72 71 72 71 74 73 71 71 71]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  72 (+/- 0.9) <- [71 73 72 72 72 72 73 72 71 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  72 (+/- 0.9) <- [72 70 73 71 73 72 72 72 72 73]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 1.2) <- [74 71 71 70 71 70 71 69 70 71]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  72 (+/- 0.7) <- [72 72 72 71 70 72 73 71 73 73]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  72 (+/- 0.6) <- [71 71 71 72 71 71 71 71 72 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 1.2) <- [73 71 71 68 71 71 71 71 70 71]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  72 (+/- 0.6) <- [72 72 71 73 71 73 72 71 72 72]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  72 (+/- 0.7) <- [73 71 71 71 72 72 71 72 72 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  72 (+/- 0.7) <- [72 73 72 70 71 72 72 72 71 71]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  72 (+/- 0.7) <- [71 71 71 70 72 72 72 70 71 71]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  72 (+/- 0.6) <- [71 72 71 71 72 71 73 71 71 73]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 1.0) <- [70 69 70 71 70 71 72 71 73 72]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 71.9 (+/- 0.97)\n",
      "Mean time (20 cv): 16.53 seconds\n",
      "Overall time: 334.93 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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M23KHgssrGmoES5Bc+PKCNmOI7e5BHMsKpivMvJBsuuAH8hXm7SQh2SyFuRC5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\nHh4eHh4eHh4eHh4eHh4eHh4eNcT/B76xb8/F38RYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f274bd85790>"
      ]
     },
     "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: 70.8 %"
     ]
    },
    {
     "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: 63.5 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clips accuracy: 69.0 %"
     ]
    },
    {
     "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",
      "  71 (+/- 0.5) <- [71 70 71 71 70 71 71 71 70 71]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 0.7) <- [70 72 71 69 70 70 71 70 71 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 0.7) <- [71 71 71 70 72 71 71 70 72 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 0.9) <- [72 69 71 72 71 72 71 70 70 71]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 0.9) <- [70 69 73 71 71 72 71 70 71 71]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  72 (+/- 0.6) <- [71 71 71 72 73 71 71 71 70 71]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  72 (+/- 0.9) <- [72 71 71 71 72 69 72 71 70 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  72 (+/- 0.9) <- [70 72 71 71 70 72 72 70 71 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 0.7) <- [69 71 71 71 71 71 72 71 71 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  72 (+/- 0.8) <- [71 70 71 70 73 71 71 71 71 71]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 0.8) <- [72 71 70 70 70 70 69 69 69 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  72 (+/- 0.7) <- [71 72 73 70 70 72 72 71 71 71]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 0.6) <- [70 70 71 71 70 70 70 72 72 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 0.8) <- [71 70 71 69 70 72 72 70 70 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 0.6) <- [70 71 71 70 70 72 71 70 71 71]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 0.4) <- [71 71 71 70 72 71 71 71 71 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 0.4) <- [71 71 71 71 71 72 71 71 70 71]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 0.6) <- [71 70 70 70 71 71 72 70 71 71]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  72 (+/- 1.0) <- [71 72 72 71 71 70 73 70 71 72]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 0.9) <- [70 70 70 71 69 70 71 71 72 72]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 71.3 (+/- 0.79)\n",
      "Mean time (20 cv): 14.23 seconds\n",
      "Overall time: 289.10 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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M23KHgssrGmoES5Bc+PKCNmOI7e5BHMsKpivMvJBsuuAH8hXm7SQh2SyFuRC5\nRjNNYWpU4xhkcItfqDzDttwCH90CuWYxCnONHMutDJ7Modf7LCQc64LtikKyq8gfXC2XP7cYXFqN\nLaGeMGOeGqX3ydLUE0VCe9pC9vmOIUwVUGUKcyGSlstQmG42uD/0H1Rh3kPx/dSWkGyswpxE5jEt\nRMJKRUnw8AQ9iJygcCR1JnC3TI6eRpjXIxN7FQFhuktFcRTidOAHyMh5wt8MC5CBgeYkdzFNtTl9\nckCoMJUwf02pwpy6cMvJjuvvof7arELOZ5bC1NH+scjk5l3I+X5tap+HI0YeLq/XLyHZ2BzmasTJ\nxTjHeQhhzkcGd0U5TFWYSLtpk86fBfxZxDEDuLngvphKKSjpLE+9ZkHV904Shxk7rSTPcWVVyd5B\neUj2ZuJDY1uJq25sNRpRmIuR36XLJ/4PcF7GNu8BXu5zhQUhWTeAnIeNJAOvJwFaSKYK6FHIOQ2R\nQ5huAvGXh+b8nhWIHb0D+JPU/w5DBkRKmCvkr3DAqffJKcA7PQmn7el2stNiMTnMMCS7gEzCnKqR\nuZVshXkE8A8SVZuyu7tpgcJsU9EPk8BNvu3hwCPFQWQiVJjb/fuQ6I4BfgVcivwmVaJKmEcHId9l\niGM7FngD8IiSfp4OXO1X49mDGGRImCtIik7CMOcQsrLPFt/+AZJFCm5AbrSioh91hCuIU5hq2Fk5\nTDW205DipZ3IfNB00chq/7qK/slhaol/bA5ztX+NWR9WFSYIKRQpTCXM7cgNnL7xjqR+4BaDc4Gn\nJMd1A8BtyKOWCgjTnetDdqq+dyJqdCXx00rSSjSvbVphbmVqkOZqvtBoEriFOIX5gF8SbQudD8vG\nHi9UmOqzDvWRp4cjRYJpPAwRBjo/fEHOgECL2HYE350BLPH+Ua93FmFuT/rjTgzu7ZWp1wBuyPdp\nOaKSz0o1OAJRaaHC1G3C/SwF9w/+w2JErT082Ec6YnEb2YQZUSVbR5iQrTDHEP/7W7IHaho1Opwu\nK8wqIdnfkOQ+rgIem9M2VJhKmKHCXIuUVS8icfKQEObjgXv8aGIZMsn1fN/m1GQ37gpwD/PvT/AG\negrwY99gC0nuMk2Y6ZCsOqsdiAHtIXFANyAjzKKiH1WuqjALin6cLs23gewqWa2COxW4xvdVV0wK\nSWC1fz2MrucwXexapOsQxxEbkl3tX2MJ8w7//gbiQrIbkAnt6bDVGuAIr95joUpFC3uORK7ZcQgh\nbyX7Bn865mamAAAgAElEQVQvcCGJ892JjJ6XIA41HZKNzSXlVckqYXpHVnsQiQQNIBWMT0GuUUwO\nc79/342w7KQn+ZeAe69/n6UWQ4WpOBQ4ETlvqYGRG0XSQmuR63Wn3/4EWRCjDuEgB8Thn+nfH+63\nv1225ZbUtl5huiHkvOuiMIci1yMrDXUIiZ9ZA5yastE0Ya5A7CBt348Aft/ft4sQv3tasI9YhVm1\n6OdBkjxuaMsavdtCtsI8MnhtqcL0Tqg0PNKIwtyA/PA7gP9jKintVoMLHYFWye707bcDR4L7Hf//\ntYhDu5FswjzM/44TkWT7jxByuxMZ+eGN5Axk1QiAjwPPRZzTz/13SphabaoLEWSEZKdChTsQA8oi\nzIyQ7NSocAXwE5IcZlZIVgl6lfyWmiN7HuagP0ebZUm0qZtxH6IyjwL3EYRM1st3UyP9buUwY0vY\nTwR+RrWQ7C3y6k4tblqnMDMI070e3JnUh2SVMNPO6Uj//7URfVSch0ROfH5sSrkci9jFj5lGmG4I\nibicTL1auQO5Nw8jTmFGOK6plaPuJCHMrUHbVb5/JzGlMAtDeV5hAl0jTB4GvA14PjKA/zrTH7qg\nvm4/omJuQUjp4cjAIF25fCKSB1TCvBtRRZcC/y/VNgyjgwzuV/tjHYE49B8iEYO8kOwZyL2gSxEe\nitwj3ibdai8e8P35FXK/LfXvTw2uU5bCDPY1hRMQslqH3Cc3kMxoOJJswlydfJzil6qEuTEoTPPt\n3DiScrofscezpTCrDkcGfWu1wnSPBn5SMFcHShWmS4fLJpGRwSYkznwVyUjq74CwkvFhwHXUK8yX\nAR/2F3YtEt69jumEud7v91N+P8sQwgQpNlKneSJywz7M54lOQEqtN0JNc49FCjMdkg0Nf5L6HNeN\niHEGCrN2wPdXq+eW+37mFf38JDhfqxADxLfLWunnLOBq/53ejFci5HghsrzWCchUhXXB9t0KyZbl\nlvH2eDwyoKmiML8DPB34IbhzCtpOIM7NIfY1kdzYroZM1XgH2QrzcK9Qaj6fuRxZFuz3hWhLf9ux\niGP+DIkDPg6xFyVMtY8QxyHO9CSmK8z1iIOIyWHGhGRXIvfA/SSL1G8L2p5OMiC8G3FQ/+v9SRZ6\nQGHySGQhgCuAt/vvz0y184RZc4jdfQ+5B09HBtppwjwF+Irfbg3imDcgIffzUooujEyBkBP+GEci\n11vv4yzCHEeI/n45rluGEOb3SUjuK8C/extejtjrbxEf8nXk/tDc4xzET29HBvmjvk9phXkCYi8n\nIBG16/33V5GtMG9jSmG6mvwm9xji1pI9FBmA7CAJx4btXoRcu18j9vkY4Axkip7iSH9OzgB2ev/b\nshzm85Cb9HHBD/Rw88G9B3H0GQrT1cC9FtgI7gnBfheRFAndhoQKj/OjuccCT/CVTLOQkOpXSHKY\n2xEntQy5QEcgxnM9CWHu94+IcVC7CnEuSpg3+WN/FlF1axHi/L5/PRMZkZ9OYrAwnTDTCjMmJLsH\nGcUNIqSwJdjmMuBZ4P4WUQnX+P1nJcIvQ4oBQAxPQ4d5K/28GHG+kDjR7yODjfOQkdhihETXBtt3\nizDHwV1IMkk7C6uBrVCLnETuZiGO7UrkRvkh8M+iytwguDemnNcEcn22ITem5qNBiHo34hzPol5h\nfg54DfI8vw+RKPdfIMUbbyNz4Qw3KZV7rga8FRk4Xk89YX4RuRcPQRRmap4xJ/vjnkiS392JOGmN\nHhRMK3FngvsT4op+LgK+TFJTkFaYp5Ms2aaD6fOBl/tjjft7/FJwz6I3FOYjEcL4DhJm/CXT83ph\n7cEuhMCWAhcA/4aERcO82XlI4eBvEOd9D2JPB5DBczhoCwfa95Pc19+kXmE6RGiE2I346ycg1e7n\nIErvD4BrkQdEnILY8CrEJ6jivRvxoW9G/OTzEVL5jR8YbEfscD3TUg6uhtwPl/lXDckCfIN8hakh\n2eOQQcVz/bktGKi5GvBUxO7yCPPJ8ttqz0DssYZcrzDldwQyOFAbhhYpzEW+A28B/gLcO4A7wZ0v\nDo0PAy9EDCaIGbuHgluHOIznIhfi7f4mWYdczJuZIszaLoRI3oEQxT1IYvssJC9yM/UKcxvwUeAV\nso/aTuoVZpg0ByHM0xCDuxc43u/zvUhY5BHAJ/z/LwL+FRlZpQnzUCRHoQpzJYmBnwjuUUjl7XKS\ncFhImJugthdxnPcGz3wD+DTyGLGX+/7cgFyjxUx3Xj8C5oN7KULwqjC1ck9V5H5/Dg8jMYydSBjp\nJn9OHg68y5/zG+XcdF1hPhq5Bl/zTvx0cMdIEYk7FNxbgfeRhMt3ITmopeAOA/cqcJ8B92qkXH4u\n4sC3I+cV4FXIjf1W4E1IVOPt4CZkPyxBbritiG1vBD4I7tmIU/oqEr57FOKsdgH3Qe17iF3dhqj3\nJyLn+3vA55F8+2uFNN2hkjd3k7Jv3oPcU2cDH0Cux9E+RXECMsg7FrG73/hjHgVuke/zI4BvIfZ5\nEokN3uP7cwz1DmkzcCa4/4c8O/BfkHv9UWQT5lwZYLpjkYUQPoPci7oaUqgwz0KI4pmIw96MDCbO\nA3c2QkZXIYrgPchgtpsKc4nv85WIwgR4J1MFie5ccL9F7v/7/P93IdfhPuDbULsDuWaP9ducgdxj\nn0QUz+8i655uQPzcZ4A/9narxTfhNbvH7/tnyPUcQAZKf+f9SIDag4it3gn8I0JEtyL+4A6E7N7g\nj+8HbqyljjBrD0Ltx8h1fD4JKW9D7E4J83Avlt7sf/8qxLYfgazyc5vf7hvIIC1NmPciDzV4LfBG\nxPc9E/GvV6auyz6kMncc4Y2diO2kCXMfYkOn+uOC+GiHhL+fAG6JF0hH+DazEJvEn+ulRZHUsrlo\nl1M/+jEcvLgCCSF1ApdjdmcQdMruLsdszpCgk/7OYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaD\nwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwthC2+boiFLb5u\n6AZs8XVDN9CQ3bnW98PQp+ikLZjdGRSdsgWzuZ6EO8E/tq7dx1nvH/Q+9UVDe2lRbwz9DyNMQzdg\nhHlQw30V3EvafIwaOJd66HemPcQ8QNpgMBzUcDVwLwT3ug4c62X+Ad0GA8hDswfbfAzd/0hZQyNM\ng8FQhpOBDwG/14FjHQWs6cBxDD0PtwxYBgy0+UAaijXCNBgMTWMU2EL7R/r4Y8wpbWU4GHCSf223\n3RlhGgyGlmEOsJP2j/T1WJ0gZkPvo1OEaSHZmQd3DDi7XoZuYAjYgSlMQ2dxDHAPnVOYw2UNzQH3\nD/6DZMQVCXcIuFVt6Y3hYIIqzE4QpilMg2IecB/tj2yYwpyBGCJiBJTCS4A/bENfDAcXhqhEmO7Y\nJo5lCtOgGAW2YTlMQwOYQ3VHMtrANoYZDzcMbqjCBlVzmFeAW1y9X1PHMps1gPivrVgO09AAGnEk\nI1h4yzAdrwcuqdC+osJkrt+mEQxWOI5hZkMJs2emlXSi6s3QGjRKmGXrBRsOPowDD1ZoXyGH6WYh\nZNko6ZnCNCgsJGvIgpsN7nZZUSUXjeR2mlCYbg64P25sW0OPYw5RCtAtAvcM33YXMCuiUludTqNO\nznKYBkWn5v9aSLbPMAKsotgwGlWYjTqfRcCbGtzW0NsYJK6A7GTg1YgN7QUeoDwqpU6nUbuzKlmD\nYozO5DBNYfYZhlOvWfCE6T4M7imR+20mhxmpQgx9iEHiru0cxCaVMPdTbk+jwTEa7ZspTANUymG6\nJeBObPA4pjD7DCWE6WokCvME4CNiIKVoAWEWhokN/QklwjIMITY0BOyjmsK0HKahCUzlwmMXzHga\n0GgKyRRmn0GdV94Fm+1fVfU9CDw0Yr/NEmYNKwybifAK0y0Fd2hBu24pTAvJGkaA3chALcYeVkS2\ny4IRZn/AjYD7CsmFyhv16wXVxQs2EzcKb5Yw9ZiGmQXNYb6I4uklam+qMGMIsxU5TFOYMxLun8HF\n+qNRpNAsxuYAltO43VhItk8wDpxHeQ5zTvA6BGyn/YQ5lHo1zByoHY2RKMK8dlo4pkU/lsM0NIoX\nAQvLm7lhxDaVMGOiXCtobpC2HyPMnscQcrHm+s95F6wbhGkKc+ZCFeYIxU4iS2F2IofZJyFZ93SZ\nfmUoh6sh13UiovEngKcghBkzSIPmCHMQKS6yxdd7HHqBddSVd8HUYBohzGZGXWCEOROhdjRKscIM\nCdOqZKfjb4HDu92JPoHaQwxhzgfWUS0k26zC3IYpzJ5HmjBjFOYwcnFNYRoaRazCVBuYR1IlaznM\nBKPY/RELPU8xhDkEHEZ0SNaN+f02qzAPdsJ0A+Be3u1eFECNqExhpkOy24i7UY0wDVnQeZgjlCtM\nEGekCrMsJHswVcnqlBtDOcLBV0zbVcQrzOWpY1SFKUyPw4FL27Nr9yL5awp6gSf9axlhjiDXbBel\nxuEGEOdmRT+GNDRSMUqcwpwgvkq2WcLsE4XpatjTgKqgqsJchaxfHBPVWAFsogOEOdPn2B1G9WdI\nxuIY5GI2g6oh2XFgDzLaLzOOVhRfQF8QplsBLIDaL7vdkz5BqDDLin4gCclWmVbSgN25Wcic434g\nIfUrfXB/9ATCwVcZhhBfV0Vh3hq57ywMYgoTkFHKgCxu3nKM0/zNUrXoZx5ClvswwgzxdOBV3e5E\nH0FzmGVFP6GTi11LVvfXCOkNpl57Gfo7++H+6AVUIUxtGzutZD6wgeYUpuUwEYUJ7THqZp75p9Dt\nY0Oy4xQSpnsduKf6DyPAAQ4OwmzF4OVggubCYxVmlZBsMwO1QWR1lzl9sCRjMwODgxGBLbmxyLax\nCnMYITwr+kngTgL3pIobtZswmw33NhqSzVOYRwNHBPtqxoj6iTDn0b7Q+0xEVYU5m2rTSrZHtMs7\n3l5koNeOqFArYQqzGkKFeSO4xRFtY+dhjtDcczNnZNHP2chk1ipQwmyHM211SHYX5QozDMlmHXuE\n+nVpmzUico7TazCFWQ2h84pRmFBtWkmjj2QaREg5JuXQbeh5M7uLg17PQ5EinUUFbTutMJUwh8qe\n99pPhKnzxqpgFeDoXYUZhmQ3F+xvECFKJcy8op8wxNYsYfZTlew4pjCrQG1iPjBSEP4Mr32VaSWN\nOq85JKHfXidMU5jVoOdJH8FVlMscQipkY3OYzUbT1L/upcSP9BNhhuopAm4WsBK4k/YYdSsV5hBC\nmEUh2R2Uh2QPZoVphBmPQSTsGbOG8V7/vkoOs1G7CxVmrxf+WA6zGuYgoXp9LGHRfMw5wHqqhWT9\n6mcN5b51LdndlIiyfiLMYao5xaXAFiLXCGwArSj6CW+2LRQ7rh3IxSyqkk0T5nakSrhRI4L+IMx5\n9Ec/W4zCR3MVQUNQIHaVl8ccCtpVWXy90ZCsKsx+CMmawqyGOcB9wecchelUTf6WwpCseyK45/gP\nw4gifZDGct+DiM3NKMIsq+hLYwlwL6LIej0kC3EKE6oR5i5ESTQy33YOYoD94BAOVoV5BbhG1jId\nRAZTD1Bc7KDl9lC4+LoL81GtyGFaSDYarmqaqlsYIoowpwZNvwLuIj8k+zDg4f79CMWRtzKECrOT\nIVn3ljau3l+gMN08cBelvlyCXKDSuHR1uBqtC8k+6N+X5TCVMKuEZHcTv3hxVt9il+DrNtpImO7q\nakrODQSj5HZj3P9VwNRTI3YgA6qiUfUQCWHmVMm6IeDWIIrRihxmP4Rkm10ztwVwI8Dt3Tt+JYQK\n83byQ7J+of/aK6D2LfL91xj1zxHWh003Ov+30wrTzQH+Avij1u2zDkU5zJOBt6S+W4xMZt1L653+\nHGTU0wrC3OLfl4Vkd/r3VapkqzyxPA19Kkq/EGa7+rkSsaVYvJX23QNplE0LycIAoiz3IPaxq2Af\nWQozbUvjSLQlXL6xFTlMU5jlGAcWd/YRY24RuLMa2FDrNA4APyNfYeqj5BQ+DTAtrRQ+y7UVCrPj\nIdkxxNj/tMJTtaugqEp2lOSZkgpVmHtovfrQUb3frzs9WDCgCkLCrBKSzauSHU36xCiJwmzUiPqF\nMNs5D7PsIctpLEGqs9uMqbVMqxKmktIe4hXmAagdIDuHOZZ6bUUO00Ky1fqQ9n3txGOAP2lguzmI\nzX0H+AnFIdm9ycfag2TnJtOE2YzC7ErRzyiw0b+PWZG+KooU5ijJDatop8JUA9X9ng08voH9DFFP\nmGVFP1CewxzxoZojaD4k2weE6WZTP1BoNaqS0jhJJWA7odelag5LnYMqzN0UF/1sJRnxZ+WT9F4Y\nDV4PlirZZqYyFMCdAe7DkX2AzhLmGNN9bQz8YKj2aCQ3WRKSrUOW3YUPDhimOYXZlaKfMWTEupPG\nTmgZiqpksy5ihMJsePmtudQTXDNGFBOSrZrDfDNC4FcyjTDdAnDHR/atDwiTuYjyaQNhOn3UVARh\nuoeBW+370wnCHE29xkKdw16SkGxZ0U9ImHkKc9TnbseQ++5gmIe5hfbcH6uAtRHt9NyPgzuxsGXr\n0KivC0OtWykv+gmRZ3d9rTDHELJskDDdqeCKQllFVbJeYbohcE/w38UozF+AW1q9r4wD9wf7nUvj\nhLnZvy8LyaZzmCnDcDWSQcUC4O+h9t9MN7YnAW+P7Fs/FP2MI5GNoTasP1pCSu7F4I71H16PrETV\nZoXpzg3CsQV9y0VWSLZMYeqIPyskq+pmDFm9ZbNvP9NzmCPIb21XBX6MatTrNh/4CbiKBWANoRlx\noHZURJhZCjMvFaC/vx+LfpolTF6NOPM8lCnMAeAU4PPg5hOXwzyU4iWa8jCXKSc9dfxGwiJhFWKz\nIdlwEvrcoH2aMBdE9rXKg6q7iXnISN/R+sfVpfNzaTwVONW/PwxxAm1UmK4GfANZGapRwtTRdKMK\nM32Ow9zlEmSQ2kwaoF+qZEeRe7YdxD5OnA/VNisRX768oG2rkEOYbhxcUOzmFqQahMpxK/kh2SoK\nM1zVrFXTSjqaw9yJOOpGCHOC4hL5EWQSfpZTVKexHHHwT6FUYboa4txyjumWFEwPUMJsdUg2puhH\nDSP9m8J4fhFhLiRuKkK/hGTHkX62o7irjJTGkdE9wGoSG15UtiZlg5iHENY49eGoKtDRdJjDLCv6\n0RF/luMKc5h6zzVKeP02D7NXFOZK/9omwnTLg5Bvnq9bB7zOt18EpJ9NG4Zkt1FNYeYN1FoZku07\nhTmfYkdetIyXnrhDkB/9PMoV5ijy+/OO+VHgd3L+N84U0U3lbVoRki3KYe5GVFReleyI/78S5nb/\nfaMKsx8Js9V9DdVT3rEnwA0Dy0gU5gAJkbYS+hi4cZIbO1JhukeCewj1RT+7iJtWoo6urEpW77kM\nwnPHRsxP7beVftqVw4xN8eh103nCK9rQF5BIyuuCY2b1LZwTvBCZ7hKmSJoJyZblMA/Kop8YhQnZ\npKLHWw58BUmaq0Hn5TDHU69pTJI8disNVXDqpJvJYcbOw9wb/GUZxgiJSg0VZrrtAmaWwpyHjFjb\nsEBFlMKcIJlGojZ8H+0Jy4aEWTUk+2Lgd5le9JNyEu4CcB/11cezEDsKFWZRlWxRSPaTJCuz5KHf\nqmS30B5ij1WYYUgW2heSXUASQvWh0GkRlHny52r+/SBSjPRN//9mQrJZA7WwSjZHYbo5kcVQ4SCy\nbxRmGWEOk+8Uw5DsBuA1wO1QU0XmC0LcXwYXuowwJ+TP/Q2481P/U1Wj/Wkmh7kFMYidyOLBWddE\nL6iG0rIIU0NETeQw3WpwPwiOuY3eX3KunSHZSIXJYcg1nI+c29/Qe4Tp7Xla0U9aYR6KDAB0kKaO\nCEqrZAtDsksoV939VCXbzqKfcWSyftk5SCvMdhHmfBJFqNc7TSzjJIu5KBmuBh7tV4MKleNu5Pdl\nDYoqhmTdAFBD7r+0XzwPiJme0/Win0bIoywkqwaa9YNChbkNal8BdOqEOtK5yITbNFHmHXMeYiTH\nB/tSzEV+pyrMCiFZNydYxk+Xi7rYk/vOnP6EoaoihamEOY/Gin5WAro2ab8ozHaGZNUh5V1bzWGu\nBm5AFvx/AHnSQqcIMzaHOYH0NV30k66Sne//NOd0DfAm/7+iHKaGZDMUpqshxXVFj3SC/qqSbXdI\nFsp9yhiShlmJ3O/tCsnOp15hhq+KecGrvlcin6BOOdYc+XnMiKIfN9t/1iVKd/t9pu1zDXGpkXYU\n/bizwP1+QQMt+mlAYTrNJeYV4OiUiSBs6eaCOyE4NkgO0z9dobbbf6chWXVgWr2lx8ojjwnkwi9g\nuiHOJymfV4WZFabIwkOA9/v3fhRf+6z/vDnoH+B+6Ke96AiojDB3Ihd+IcWEOZQzuvPO0tXon6Xx\nVgD30J6QbIHCnFowQRXmzxDHtR0hjRzCdEPgljXYnzRhHsjuWyZChbkP+Brw38j9Et53IWHuhdou\nv6Yn5Ocw95CEZLNymBPE5XWtSlYwN/Va1IeNiN+7js4qzDzCHA/ea6oiHIAptpBtDzE5TBVnu5F7\nYo//Pu0X11DnT9Nw6ttChdmKxdfdCUg5+xsKGmkO01fJunf6YogYzCMZLWRhEFkeaQfJD7oA+Dv/\nXlcXWU4yTUOhCjOPMDOO6WYjxjrh26cNcRLYRELG6dVOirBQ/qZIKTSiFGFyHEIIoSPZgziu2SmC\n1jj+Ht/voqIfyL4ZF/hjjdA/CvNI4BbaVyWb9/grPX9KmL/w73dQl8N0h4BbE2z3FOADDfYnXfRz\nf3bf3EVy3DqowvQqrnYV1K5A7Hgy1S6lCKaQl8PcQH1INm1zuhZvrMLsUki2UmXzOO2tkoVy4TGK\nPJGphjzdo1M5zPBVoX40VJhKmOpXQiLcRF2NiHsruJXEzcNUrtnl96HiKIswVQCk4CaAW/2HlivM\nI4AfU7wIdTok+8dMnTD3hyWTavVGymujVVDhD1pEModyDLlRF5I8v09RpjCzjqnfqZNJK8yFiLMK\nQ7L7mGZE7uHgPp3adtJvM8p0IwoI0+lCDZPU5zD3+vBD2jiUMLWaVo0ocF6u5ve/Med367nR0F2X\nCdOtBVe28PkaKhGmqxL90FVrighT7eNX/nNaYb4YeGOw3SFIRW0jmCR5kPgouYTJJchyjYD7W7+Y\nh6oEtSXF/dQXt6UUZh3ycphKmBqSTSvEEsJ0z5KVkrquMMsI3cMdhpyfW2hfDnM/5QpTzz3A9cAh\nbVi8A+oVpoqTmJBsqDDTA7D7qR+oPQc4IaMdTB+oKdfEEOYA2ffIUuR8jSM8+AAtJMxFwE3A3IKF\n1UPCPAQxeL1R3oaopTzoyDyPMEP1pE4xJMxREsOpojAfyDnmRPCaFZJdSKIwNSS7gekGvhY4JmNb\nfU0bR6gwJ4PXdA4T8glzD7DTL1oM9U5Op57cl9FXSEIkOiLs9sIF7wGuA5djO66GKMybiVoz2K1D\nBn6xGEXOVV4Z/RbERlYiznMfYschYaafdrKY0vymmw0uaxGPSeA2ygkzPMYJSBogVJihzaVG+swn\nCZ/GVCuqwkxPK8kizLyQ7NOBR9F1hVkUvqvD7wBfR+63doVkf0ucwvytf38PQiCT+c0bhic8p+Ig\n654IBYi+1xxmVkg2TZiL/V/VkOxCMkOybhAh7PvJvq5qkw8BNnoR0jLC1FBLOnwTIsxhrk62c7pk\nVmpU7f4C3EP9h/nIgrxlCjODMF2N5CJCvMKchxhZEWEeghDM8tTILSTMCSRcvIXs9WzTS++FRJg2\nok00R5i7kHO0I/g+dD4L/DHyBicLgtceUJgcgoRNHpPz/0ngQahtIk5h+jU6NXfhZlH8lJkihTmO\n2OwEMqC6CxmspRXmSuoJUh1DBlzNq5c1wOcy5i2mCXMj2Tf4kuAYS5CQ8QD5CjMdkgWx29hqxQ3I\n/ajTpNKEtxhxRnkKbqn/UzvfTnse4FCGWMK8APgq7XmwAwhh3kucwrzXv9+CFJu1Iyy7ABkszSO5\n3lkKcz+JwnTUE2Y6mhbYnRtgau5m1LQS5RodIGQpzFWIf7+X7IGaiq1jSc5hKwhzqsLtPuQGzQuR\nhQpztf9uMXKzwvQw1OOBC/37CeBOZN5OVkghDDeGIVkd8cQqzH3UK8y7ySaOecHv2EAyklFoDnOP\n/z6v2Gmp9NP5R9O4uUxXmDkh2TrCVFXwI+Q8QbHCDAkzDG8t9MfYQZzCvF/atWXVmhgsBb5PYkNp\naP4S4ghzGWLzR/jPp5BNTAolpTzC3Ig4hgNQ247YXpnCXILMV8tytMcBlyNObw7y+0JkEWaqb24Q\nuY4hYa5D7CBWYep2sUuUbUCiKXfnVCsuRq5TGWFqbupuZLDUacQuNnEO8C3yn0vbLMYR5XgkuJfW\n/8sNBNGH0O9tQc5biwnTzUL84d3I9QtD8Ok+qz9VMbISuT+yQrKh+NLXqgpTQ7JZRT9PAH5OfnFR\nSJh6DluiMOchP2QjQpp5a6+GCxcoORYR5krgkf79fN9pR7YBhgpzEly4Buwi6g2nSGHeTBxhTgB3\n+H7pyM2HZd1sknDcXpKK1KwlAZcg53gS3MnI00PUOGJDsjpy3w+1l0FNCSJNmKNkE2ZobAv8MbaT\nX/SzC7luB6C2x//ORhaobxJuFnL+riH/+ZJpwixzXuqE9UkQ5yHXJ885lynMHQhJrvffhQpTCStL\nYYavIVYjo3KdiH5s6v8hYeYV/eh9scSfw8XI79UIzhB1CrO2S16d7mcCuS+WELcItoZk1/jtIDuH\neTP5hKSEeSgyIGyD449ChMJ0Gl7UyuwWh2TdIKLiNyKh30tSDY4GvgTucJKiH0gIM3JqiZuIWHkJ\nxGZ2khDcLMSHZCnM9SQKc73/HXczVZk/TWHqQC28J2LmYSrXqJBJKUy3GvgzpEh1C8Uh2VYrzKnw\nzn3+7whwr8top6wfOmslzP3UOSU3hNw8D/WVtBMkzmZRRp40VE8vRqoMvVNnEfXJ7yKFeSPVCBPE\nOF9hFbIAACAASURBVEJDnI/M9TyAXNhJ8uefKtEsAY5CRvqTJOGIIsJc5Pc5SbJoQ4gihbk9+D6L\nMHcAh2csyLAACYGG6uJOktBKJ6EDkZvIV5hrEUcMcdNKliFOPyTMA+T/vjKFqaryLv+dKsxNiIrU\nfM5YoCgXkwxK0liFPChXUxUBYboaYgu3U6gwp8h5MXIOZyPn737EPhZQH5KFepU53x8jT2HmhWRH\nSQYOWQrzJjIVphvxv6dPCJPVwK31i6JUgXtEkIrKwhjJAPxY4LBU1G01UhX7cupDspupFpL9BHBR\n/VfuYpKnPSl0Ct1WxIfnRdPG/fFVYeo9cRvlRT8hYUbMw6xTmFnTSh4F/C/UbvR9z1OYDqkxaanC\nXOJ3vtH/PRF4Z0aYLsxhgozAlDCvpV5hHoKEHK4DTiNRctuBDwJvSe07rJI9DiGeRYi6OARxCpt8\n2+2pbUOFGUuY4QXfjJDIOv9Z85cwpXhzjWgJYvhLkDDgKDJCvMnvJz2a2kziuCaBX5Ocw9tzfpci\nLyQbGttSZNCzA3g28Jepfc5HVqlZSvcJcxliI3eQrzCPJalOjQ3J/hjJY44itvd/JIoujaDAwb0U\n3CS45/l5lCFhphRm7UHEIZzo/xdGZpYgFY1ZhKnn+TRkXmeoMBcjZK9KUYt+0jf4YsSJLkau407k\nPt+K3GOLme6Q/Gjfzfa/+S5kgHdfql3ewgXaLiTMdA7zZrJDsporXYpc524SZkxI9nCmpiPUDshr\njFJzJ/ppE39AfdV0GrpK1w7kvh+h3lYOR6b4vQC5Vlr0kxOSda/0UyjSOJbpBYlPBX4v9d0Cv2+d\ntpfn69IKsyphridfYWZNKymqkl2D+E7ID8ku9n07jDYrzHP9TtNOLMxhgpDhYmREdDX1hLkSOaHf\nR9aXDBXm2UjVXIiQDIYQ8lmCrK6yKjjuLqilR8+qPJQwlZCUML0qdI8HpyHiCYS8dvrXy4FH+/+F\nhKkh2SLC/CXiDHQFnXUIYZYpTCXMk+T7WnogEJvDDJ3cQ5HBy3a/36NTA59QYarR3kV3CHMp4gzu\nRZYozDLk40ieipAKybq14NKOaRkSFl8LnIwQ1/XEKcy/RGzgHcDjSBa4z1KYIKPWU/z/fE7TDSHX\n6SbyCfNBv903gBOC63O07+t2ihXmYuTeW+L/dECx1f8tJlthTpKsy7sJOCPYVlGUw4SpkOwUkcwG\ndwayUtaNyJy4JyJPs1AsRe7jRci52cjUCjqVpgC1AhUU5hQiwrJuFvAp4BXIdbygYLaBhvp3Bt+F\nEZbViG0c8N9vAL5MkvsNCNPNBv6KxHfp90N+P+vqv+do4DHgnh5En1TMqMLMWy9cCTOtMG8le5pS\nmjCVLxqZVpJWmGuRewyKc5jX+/dtU5j3kYwSj061C3OYkNy0hzGdMFcgJ/ca5HmCEyQKcwKZH3NU\n0D7MYYKEJGqI6lpFsmBCOn+J30ZX7LmJTIXpZgP/AXwG3GN9e3UwW4BvAuf4kaSGVCEJyWbkMF0N\ncQa/IlGYSrQhYYbOK4swFyMOJY00YS4jmRuaR5gPQ875DpIFCg4P2s5HDPzY4Dd2S2GuAO71ai2D\ntN0Q0vcb/RfpkOzTgHf7fIZiGfBtxIGfhhRR3QWs9BWqT02Fv8YQm9LVkx6NOJqTSRSm5rhB7PwX\n/n1ImLqQgdYC5K0EdCiiLIcQ5bsRuML36WjkngoJcytQSzlfHUjORxzc7f43bCFRmGnCVOe1lMTu\nFzCdMFMjfTeIRHf0iTvrg7b7kOvxZeSB5Vf6zx9E1JFiqd9uC3CnhDprju4U/sQQ5uGIMlGkIj2u\n5vODoR1d4Ld7GHId7wXOzNm/DsT0Hr6VpIgS//42xHYHpF3tSf6cBbUWgCitMeA0mc88NfjSYjJP\nmO694H4P8VG3Ap9GlDAkhLmN3JDs1KMSsxTm7SRFhHlFPyFhxhT96NKkuxH/r2m4PMJc7X9fiEUk\nfrWlCvMw39ntyA0M4hiyCDOtMFf4dlcASwMjUoWphHk2ohS2+85/EnhmsO+wShbgByQEroS5k+n5\nS5CTvwQhvfuoJ8yNyDk4DQkh/xOSaFfFuxVRdxsQIz2V+JDsPOQC3kaiML/t/3ez/25/MF8SpgjT\nPRdx7hpWKCFMNwuJ23+HXMJ0w8i1uDb4/81Mhf2cTvC9EwklXuHbdIswjyIJN93O9DzmOuA2qOnN\n5SuWnYZXz0TINCyaOAT4IXLdX4bYn/6+FwBfoN6RheXru5Ew9m6ECJUwLwW+JM1r/wS1y/y29yHT\nYW5BCPIVSN5og/+fV5huJMhprQKu8u9vRaoxlzIVDahTmDqNKL14+mLElreQ5Gc2UK8w0yP4TYhy\n/goJsYLcwyHSI/1jkJC53vN3p9qehgx6PuTtfBviE54ctFsqbbiXpG5A99XpsKwuGlIDdy64G3xl\ne4ggJAtMr5T9X+R8Pif47uXAO5HruR34d+ApUrzo3gfuxUFbDcnqOf0OUi2r4dPViE/R+cS7gm3T\n5+xk35ezkYHYm/3365Do3jrvk5+GPL5rPfAPyPznM/3/IghzannELeTnMLPmYYZFP7oIxDjTCXMX\n9ZXcpyMVsLt8n/7Pf6/XYg1JbcNm5L79MLhwTfDFtElhHkMysfM+JFH6eaYI042DezfTc5g6YrgW\nanf6H6dkpQpTc3mDyAXcjozQ/xn4A5LV+kOFeRdysjSnepg/5q+Bj2X0X1XpB0gISR8cvd3/PQkp\nE/8m4uQmEAPZRjJ6/hJScJQOyWYU/bhH+eOpI1juf/PlyEW5C7nQace1GbkhP+H78RskRFemMI+X\nbWt3+P1nzcM8EbjRr7GrT1r5CkmezBczTf02XT+0W4R5LAlh/hR4D7iHp/4fKqA9CCl9w5P/GcDz\ngeeBW+gV6Vzk912GEPI1yLU4AfhrJMoQqp9wCa7PI3bxBd/eLz9Y+yHU7snov6489UH//knAWSSp\nDQ3JXgJc7sOUy5HBIMA9/p77b6REPiRMfZyc9i28ycP0yXEkNqjRkkVkK8yjEYWhhLmfxOko0iP9\np/vzok57fartBcg9pdiK2NwJJA9oDwnzzqBtNwhzPrjTkfvwMuQeeX6qTZow9wJfltCzOwNx1s9j\nyo7cJDIb4P2I7V2HhGefhfi5JcC7Al+3jCRitQOx/dcD14hKrFOYD0At9CH3IkWTazzZnQx8HBkE\n3g28CllR6ShE9OxG/Mxs/931MrjhDcj1OxKJqlyHXLu8HOY4ib+cRAZVSkIakk1PoduGrL99GTKQ\n0GmLK5juF78IvNAPZOb68/lVEqGkNrYP8VV7oBY+NnEEua/eJveZexv1hKkKcz8wqygnHVNWfDJJ\nUv8uxHlfAzzXhy9fhpywj0HtAWnm9pGMPD/lt10PvBzc1ciI+RoZdbofAVeKc3DbZbvaL8D9CrgU\n3H5kRPo1ORHchJDjUcgJPhJRGhsRp5fGdsRY/wcJ4873J2gIcRTbEAN/NWJERyKDgi8wpTABeJ8/\n7iRJ2G0PYvBaHXyWhPV4H2KEtyKGc4Z/vQG5aTYhFzaLMGchTuUJ/vfdTzZhbgOe44kgdExZ8zBP\nBN5NMhLb4ff5c+BicDciK15sDn7v5f61W4R5MlPKjTcijuOLyAMAHkRu6k8E7fWxZ7uBv0cGED8G\n919ISPB7wAZvc/8F/BFTy4mxFlGK7wV+Be5mxK4XILa/CwktPh6JEpyFjHK/XtD/q4BbhEzdBkT1\nXYoMQG8D3gTuWYjd/cgfezNi3zuCnPVXgL9FiOV6ucfcXsSx7vZ9exq4uxDHeLbv1wZ/Dr9KojC3\nIA4pK4d5p++zPlf1xuR+nsJ2JHx9CeILno1EgnTBjFBh7kPWzQ3zyFuQ/NsdSNhvNeK4/oxswmzX\n0zfysArxFW9Azvta4N/A6fMb5yLn/ZZgGx00/wtyX70H+E/gH8E9GbGT/5Xr6X4C/AZqN4G7BYlU\nHI4Mit4I7lbgb4CXkqScbvP7/w6ydvYQ4hd+hNhUgNoDEl6dijTVEDs/D7kHxhB1extCQr9GljD9\nEhIu9n6m5sB9F3lM4uP8Pi725+d2xN89xNvvJpKHDmxDBIzm9g8gfn8+4g8Df1dz4DYhg8/DqJuF\nMU1hftv/ljcj/vFqqG0Ft8Of27Do52SScCwkj0+8CLG9q5GB7LBcC2CK32oOXKHKLFt38HKE/Q2G\nK5CCr07gcszuDIJO2d3lmM0ZEnTS3xkMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAw\nGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBEAtbS9YQC1tL1tAN2Fqyhm6gIbtzre+H\noXNwfwXu0lbtrEX76bVjGVoOtxNcqx7+3ClbMJszhMi0h5jnYRr6F0upf36hwRDAHQpubYv3OQt5\nNm7MowMNhh6Bu7roOZgKI8yZjaU07LjcCLiPt7Q3hl7DxcAftHif+izBRu1uAbh5LeuNwVAKVwNO\nQx5yXQgjzJmNJgiTBcAzvTEZZiaGaX0EYsy/NrrfN9B6EjcYijDbv5barBFmX8I9E9yyiIbNhGQH\nkRFX7tPHDX2PIdpHmI0O1EaQkK5hxsJdDW5+t3sRYCD1mgsjzP7Ea4GTi5u4GrCExh2Xbregwe0N\nvY8hCu3DHQvuwxX32SxhDiD9MsxcrAF6Kew+mHrNhRFmf2IZ5Rd3gW/TjMIEWNjg9obeR5nCXAGc\nUHGfzYZkNbJhmLloxi+1A9oXU5gzD65GHGEu9a+NjvR1/6YwZy7KCHOA6iH5ZhXmIKYwZzoiCdOd\nAO5r4B7T5v6orZrCnIFYgIzAyy7uEv9qhGnIQ5kdDTKVT3ST4F4fsU8LyRrKEKsw34QsJnFEe7tj\nCnMmQ4t9YhTmvoh2eVDjsZDszEWZwgwIk/OBl0Ts00KyhgK4GlKVGmMfQ8BW2m8P0UU/Nrm4/1CF\nMO/GFKYhHyVFPwyShGTPIs5xtSIkawP5mYtockLscwftz3da0c/Mg/s9mdQdTZiTwL0YYRryEUOY\nqjDPJM5xNaswBzCFOZMRTU6IHeyMbNsMTGHOQLwK2Eg8YTY7OrNpJTMfQxSvoerDo24hcDywIWKf\nrVCYtljGzEUvEqYpzBmIOYjT0kWtyy6uGluzCrMPcphuHNyS8naGFGKqZEHm/N5V0lZhVbKGIlQh\nzE6HZK3oZwZhECHMZcB9dIYwH6A/FObzgD/vdif6EDFFPyA2t6GkrcJCsoYiVFWYnSDM6GklFpLt\nH6jCvA+4gzjC3BXRLg+DSAi4HwhzFFkX1VANZUpObWcpsIk4IpvrX01hGrLQyyFZU5gzCIPAUcCR\nwLdpv8IcQFRFPxDmEKZKGkGswlwC3F/SVjEGPEhzhGnXcuaiF0OytnDBDMQcJI/0TmRuUicU5gb6\nIodphNkgYqpkQRTmZmB2xNNrxoizzzzYwgUzG6YwDR3BIHA28M/AfuKqZHfR3Eh/K/3x5Ih2PHXj\nYECswlyK2MIDJe0hIUxTmIYsRKs5rErW0ATmAPdCzSGEmeFU3GHgFgXtmy362Zl9nJ6DKczGUIUw\ntxM3UBsDtmA5TEM2otUclUKy7lBwZzXYJ3u81wzEIOKwIN9xvQG42L9vNiQ7wJRCdb1uJ0aYleCe\nCO53iZ9WsoyEMMvOs4VkDUVoV0j2POAV7e6TVcn2BNxsqB0o/j8uaJNHmCMk1aKtUJj7aW492k5h\nmN7vYy/hTP8as/g6SNHPNuIV5q1YSNaQjXYR5jCND7S6qTDde7yD7xLccnmid7/APRXYAG5NQaM5\nJOoS8h3XEInRtJIwe33Ef5AqTPd5eYpIZQwh1c+ziSv6GUAUZszgSUOyzRSb9bq9GRpHJGG6Wb5N\nbJRsmMZ9QLdymG4AeA3J5OUW79vFzLWbBJa3/vjtgBsD/g34AvDBgoaDiLNS5Dmu0GhaUSWrhNnr\nZHSQEiZnAIsb2G4o2K5MYT7o38cqzFGaK/qxhQtmNmLJSX1ebISrLxXmuH9txwjxYuDSiHZD9M8k\n9nGE1F4BPKYgV9gNhTmAVEXupfcd2MFKmI3a+hxgEXJtywhzm38fW/QzjBRqNPm0EmfpopmJWMKc\ng5BljM1BfypMJvxrOwhzvv8rQ0gavY4hYC/UHgD2kP90+7TCjCXMZqeV9JPCPBhzmI3auirMsgpE\nnVoEUUU/lcNoecfUPhpmHmLJaYjqhNmozXRtHuY8/9oOYx8mbjTdT4Q5jIzyQZxMHmHGKsxwlKXz\nMFsRku3183kwK8xmCHMXxYsRDJAQZkxIdggZ+O2j+cjGwXg9DwZUUZh76Qxhdm2ln3YqzNgT4kmj\ndEWSXoBXmADspj0KM2Z1lrxj9pPC7PU+thhuFo1PwRhCVnDaS/FiBOmQbFk+SQeAD9BcZGMHvT9I\nM1SG+yCJj+vFkOyMUpixo+mQMHodOiIHIcy8VXWq5DDDop+9wAGkGrIqLIfZ2xhKvVbBHOSZkzqC\nz3MUIWHuoNx5DSP2HLMiUAZcjf5aMMNQDb9HstRmGTl1MiTbdYXZjqKbKiHZ8LUDcJPgVjWwYagw\ni0KysQozNBodoTXovPpOYR5sOcxm7Fy3iVWYO6D2IOULF+gAsIiEizALeaD1HkxhzjC4AcQmtDC0\nHSHZvlOYvRCS7QJh8lzgT4qbuFFwF6W+TIdkW6EwQ8IsUxBFsBxmb6NVhFnkkLToZ7v/nNPWrfBv\nmg3Jqs3tpfdtzlANej2rEGYnFWbUcWZiSFZVaCdvuFHKFyk/Dnh76rvYop8qCjMMyTajMNWI+kVh\n9nofW41m7LwKYW4jCctmtHVDwE0+nNpkSLavohqA+yK4RubBHoxQ3zaP0ulM7jnAOjqrMHczAxVm\nj4ZkGSGT7Nw4uDMK2sQW/VRUmFMFIQ/Q3Gi/z3KY3Sz0cgvA5V2/dqDZHCbEEeYWEoWZVfQzhtit\nrtDTTEg2zJv3g8I8HVln11AO9d3zKK/efyHwSDo7rWR3zHH6SWFWDclOytJhHcEw2WR3FvCX/n0e\nYcYU/VStkvXtp55s0mxIth8IExorbmoV3gU8v4PHq0iY7kJwpwbbKDEVOaQB4LvA7/vPWW1Hg9dW\nKcx+GKRB7kDZkAElTF2spcg+RpBpT52qkh1gBirM2FVN9NgrgQs79KSNvBtnNPi+TGG2ch6mVpjB\nwREeG0KqgbvZz3kkA8ZOoKrCfBIygNNt7ieuSnYX1K71n7OKfnQZTF34v5m8eZgG6AeFaYQZjyoK\ncxQhzCoh2REaf7JSVxXmZro/DxOSxaUXtKEvaYRPCQkR5jZjQrJNKEw3gFxPzeftC9rOYOc1tbpM\nt6cixOSxW4mqhDlKQm5DwH3EhWTLBmqhfWvEpBVpgB62OfB2N4QRZixCwiwjpxHkCTlVFSY0Zjdd\nVZgb6I2QrBLl0jb0JY28kWb4vXcodSOgsOin2Rxm6EBDwkw5L/c4cP+Qc5wQ/ZLDVDXd7X72E2HO\nISHMsmklod1l5TBDwjyYin5GUq+GYuh5amdIFhqzm+jlHNuhMO+jN0KyvUCYaYUJ9b+hlfMwQ4NJ\nE2bY9ihgdc5x0sfsB+el5zD2xqoId27kQuBh+L0TaIQw1RbboTA1h9lsSLZPFObUte7kIKmfkVaY\nRfYxSrISVRXCdDRmN12tkm2nwoyJUeuxdUWJJW3oSxp5RT9ZhLlKytGB1s7DHCIxGJ2DqW1DQ1gK\nzM37IQH6jTDb1c+PAUdHtPPX2r0U3DPb0I80mg3JbqT1IdlQYc70QjNTmNVQteinRnWFuZ3G7EZD\nsjNKYcbGqMMcJrRNYbpTwZ3mP8QW/QAcCTzCv09XyRYpzBjC3E5pSJalxD2vtE9ymFPnsF1Odpy4\n86WDoxOBk9rQjzSq5mw8YbpZSG4/p+jHDYM7xH/Isrv0Oc7LYTZbJdvLNgfTB8KGYqi9jhBHmJDM\nI49ZD3sYmS/cLwrT1RCF2c6QLJSHZYeQh962OyT7NOCp/n1RDnMQ3CDJDbaY+ueGhiHZIoUZE5Ld\nRnlIdhlxBNBPOUxVmO1YHm+cOEWuhDmXJLrRTjQaklXb+CjwEabb0hMBzXHroEkRozBzQrLu38Gt\nKeljP4ZkjTDjEPrtAsJ0gyS2s9dPjSsZgE0VPDZa+NeVoh8lqkZZvgyxMWpVWguRE90uwpxPQnxF\nVbL6f72xlpCM9GMXLohVmHrui6pkYxVmv4THwhxmi/vpdPAxF9yLwRU9j1UJaZzOEeZOSu8HN+Yd\nyghy3f35qt0GtVuYbksLSJ47q4MmRd7CBVAekn04cFjJb+oXmwMjzKqIJMy68xn6sKLBsEY2Gh1o\ndWVayQTisNu1cLIqqBXgikJeQ8j6lwuAO2lfDnOChDB9Be+0/GoWYS4O/lelSjZQmLUDgAMXTtTX\nJ93P9vvOU5gzlTDb0U+9vnOB1wHHF7TV8HsnFWbM4PQfgaeT5DDDObow3T7GqV/vs0rRT1FIdkGw\n3zz8//bOPU6Posz3355kMpdMJplcZjIZcoUkIAGEGG4BBAHltgoKCqssLIqwHldd9bgeBHePH/Ws\nd/F4zsq66KqgchRXdwHFo8dwcVcEuQkiEISEhIQkJJnMZCb3On88VdP19vSl+r2/b+r3+cyn37en\n3u7q7qrnV7/neaq6kVyyPuknH2zblkWYB/RnV8I0A7Vi2005FabzcmPdCFFVqrEbIrwY+HhKuXZC\nwnye6ilMc24btjGxFSaIYXV1yUYNF4xvROZYu/WxY2KYKmBM4WaikWKY1SDMqYQLc0SgAgpdsjPK\nXI84uBJmr/4z9bPbHIxvR/YCDMUm/UTjogHSX7IWdjAu2bS+UC/wMcx8MG0D0tVcJxLWg8LERRfC\nLNYGlFVhur6qayoVI8yxyelDCAEmGC4gNCTTqCxh2gozyT1jj0KjhDkF96SfaAwT4gnTjLKmEO+S\n7dHnaYuo0zg0WgyzEtNKbMJMW8mnDcnqq0IMU31fL3juSpjdyHNvRwZKdgY1jL9vWQozLulHURjD\njFOtE3BXmIOk9/F6gHfJ5kM7si4xZCvMbYj9qpbCLGvSj4sagdAlWwmFaYziLoQA02JJxpC0AC8Q\nukDLjShhDjG+80QV5l6rPuVWmMZY7aGQMG3j1Qds1OfKeq7eJRs+X/Osk4y4/ZwrGMNUE4G3IYMu\nV8KcSjhotGKYY4jGuDVhmgFVsD9SNk5hbiNxpR91DKHizlKYps1tJ72P1wOM69ATphvakXYC2YQ5\nigyaiiHMmk8rcckQhMq6ZPMS5qD+vBnJUq3EC621S1YFyEPeSjxh7iOMYW4lmTBjFOZYVmF04QLI\ndsma49oJGH3Ay0iyiCfMbBjCNG+kSDL4k/X5jcKcrFVgudFjbfMQZj/yLI1LNi3j2mQFR+f+QvJK\nP6+Q7JL9EXCmdew0GJdsoyjMbdS/67jMUC1Ftu08CnOEkEsgn0vWK0wKRxAxLlnVBep8q6x5f99O\npDNXYsRvVMdExCWVpDC3ECrMKGFmJf3cC2oR8cYr6h6zCTPJJTubVMJU00F9Wn/xMczQwJsXJKcp\nTPOcuwiztMsNo9SMizUPYW4hWWFGY5hmipidIRtXFgqvPc4lOxN5F6w5dhoaSWF2Ej9IbnacB3yn\niN8Z+wfp5NRJgytMV8K0FaajolMf0i6bLNg3pBeYFklGOhG4UX82I2+QkcpWym68xuZVTiF0IYwy\n/ro7KCRMMxKHMIaZ4JJVLci19uGmMO17lOSSPQV4EMmmjXuuRwBX6c8+hin3cR8wR39PMvi2ymoF\n1lMZwjTHNApzkFjCVB3SP1QLcg39uuwB/T0rhmnOlRUGAGlH5tojLlk1CblnR+qyCfdPtej+XOMY\nZq43XUT788GCecDcIn6X1yW7g3yEOUqdKExXl2wxCvOtwIrMUoUu2UlIAoFt8HuBhbqDlkCYaun4\nZBi1GFS0o5sEpy4KCTNOYZpOZY+wRghdsklJP1P1dc4kWWHGuWT3UJglqxWmCoDzgZ+S7JKdJecb\nS7JqBJesHbt1qKeaBOpix2NPQRS5i8Ic1nUYpnJeDVthprlk/w9wNlL/APEsjOi/HgoJMy5Bx5wr\ny6sB6S5Zcw+OQlRjkkv2K8A7CF2ytVKYeUg6KQzT7JhFcXkheVyyo8BLhPay6RWmK2EuwC2L1b4h\nBnbj7kWuZSGFMUzjknVN878NODWy7/PA5ZF90/Rx9yBGYRfphGm7ZAE2EB/DtOMhs6xt3hhmVGFO\nRBZdnwg8QTphTtDXZ7tk65kw87pkjwG+5njsKcizmqm/pxGmIaRh5DlXYmpJVGEmEeZCpG/Z00NG\nkOfeQ3YMcxB3hRklTNsla+5BH7CGZIV+GLJkpO2SrUUMM88zqxOFqQayy5QVvRRPmC4K0/SlK4Cf\n6X0NFcOskMJU5p1neQjTqLE9FI5AzVSNxRTGMPO6ZOcSqgmDAcZPWDcK00xzGdV1c4lhQjJhtluu\nZpswXRRmlkv2tcAv9FJTO4l/rr3W1laY9RzDnEKo7lwI8zBghmPiwhQkqxjkpQLdoL4ESrsY1TK9\n+k8cYVbDJZtEmP2IqjTtFELCNG+BMIhmyXYTupRdk35MG49mydoEtIZkhWnqa9rcELK6UtbUp3Ij\nD2Hag+EaQfUAq6t8n2Yh/cBxEK2W6w+2wtRqzg6rqQX6OrTCDPZrWwXVmVZSttd7VUphmmWyZouL\nTK1MKWurJ4C1jFeYO4EliNEc0vsNYTp0BNWBGAkXwpxGSJi9xLpk1QTkAZhMOpswNyLGw0r6Cfbr\nY5q6zrS2eRRmdFqJMYgLgdV6X5rCBBkEtCAxrzqJYar+GNc4SExlLe4xTJN5PEfHzt6cUtYoTJBV\no6YCFwDGCHweuIxCwhwin1cjAWoA1COR5fhmIJnfNmFG4uaqDWnH/bq+a/U/klyycQoziTDTkn7i\nXLIzgOd0ubUkK8w5ur7aJRscQO5jVlZtuZFXYVbYJauWygAtEfOQe96fUqbcMDZiZmopANQssDeN\nLAAAIABJREFU4EE9S8FkFYO0P4V4swz+FTid0CVrI6tvdxHGMCdpW3Fhdv3GUNa1ZCuVJTufMOv1\nWuAnoJLWmoy6ZNdSqDBnAb8BXoWMbs0NHyE2nqTO18byAutdhyaxQxOmuhrU4frYy6zEBHOt25FO\nPYv4GKZJjzbJPHaD2ch4hQkyb3ShdU37yKcw01yy85BRPmQT5hw5fqComUt23Kj5c8C7YwrOR67L\ntZ6H6u0cxD37w5RpR10UEuY0fT7TThchg6mownwSOJ5YqCNlXdpMvA1Jlvlba990hIDSFKbx2BjC\nfAkxTrZLNoEw1SSkrWymNJfsfkLCfFyXS1CYahJifPspnG88SPXjmHm8AtWIYZ5A+IKHOMyLbKuB\nXgrnk6dhERJDP5RCl+xeCttdQOiWN33JRhZhLkDal+Gec4AbQPWBeqdDPcu60k+l5mHOBx5GOvhi\n4LfIaD0OpiOaWOEmxrtkf40YL1MOYl2yqgP4d8RY3o5kjkKoLM32/Ygf/WV9nAeBK/X/HBRmgRGd\niqg1o3xtl+wu6zfPExLmTOBZ8ilMM6iYh4z6IXTJzidUG8NyfhV1J/UiDU8TJlC7GGa0bkcAh8eU\ny0uYhyGEMAc4g7BDxyGqMOch93KBJvQFFBLmKHJv/x14Paj3y/sxC3AR8D6Hel4GXAO81xo8zEC8\nBGZaiV47uGBw0Y8Qlu2S3UGhSzYphjkFaaP2ywuIlHVI+gkOIO29T9d3lGSF2afrawjTnLMWccxi\nXLIVIEzVB+ocpK3O1dPmTogpaIhyAaiv4vai81IxC3gGN8I0tsyEyhIIkxmIPVyIs8JUV0n/AqT/\nPkdoAxbrv9cBNzjUsyEU5gLgAeAQpNPcTjhfKwqbDAYZ35l6gZ8j7yG0XbdxLtkjECN5JXJzz9L7\nBxBX1Bw9bWSJ/t9LyCh5GXCyda02YRoit5WKmU80os9vSBVCl2xUYdqEOQt4ivwKc48+3yNWuTiF\neRli2G3MQtRRlDBrEcOcrDvF5Tpr93AkccmCCiiOMO8lJMxdyLOOg02Y65D+ovQ55yD3+yipq60w\ng83AY8CXkYXP0V6Nv0Oywo9Mj6GquUj/+A4yOFys/xFVmHFxm36k3fQTDmIHrfpFY5h6QKUuRpKh\ndui/PDHM6LQS9G9nIwO3dQhhdsZM3TD17aOwnddCYUYIU7WD+qeE9bSNxyjupQul4m3AZxEiCICr\ngR/H1GMe8vxOB/4LY6EjdURCnR1Q4Ekz+2ZZMwh6gD/irjAhmzAXWVtXl+z5iJIEuU+rCfvCEqTv\nngHMlwFHHJTJ2SirwpwM6jhQ94FKiCmoeRSnMB9FOska/TnpbRC2S3Y7Y51prKH2Ar9HMkBtwhxl\nvEt2GWL0rkAIyqxCMoCoyAHCjL3lCIl+TJc/Wpd1UZjGJTtKPsJcoD/PRAyJq8I8krDRKEJX2D7E\nqPUh5A9CmMuBlRHD3UtImGakX6sYZidwHfBp5P63MJ7YepCY1yBOMUxl1kn9HWJsTgV+EHNcgykI\nYR1AVOlu5L7ORwY2jyPPeCmFMUyALwEfAV6j2+mFiFE7Hmk7enCoBkC9V3/uAPUmYCVwHwT7EOI1\nc5WnEypMO65vP8PZyGBpNmE7NYQZ55I1avI8XUejMJOmlVj3eCzxYwfhwgWGMM2r9V4BLkX61ghw\nVCRhZA7S7od1eXPOelCYJyBktTSmbAdyP3PMO3fGSqQ/H42QzGXI84zmV8wDHgLeqL+fBOpkpA8f\nW+S5/wEhaxvvQ6b+TEfa0kZSCVMFyIIrC5H2awhzCLFNhjCNoluEDKoW4u6SXQEca7lzowpzGHiT\nLvuqmDouAl4ANZkKZMlegozwvz1+NKWOlhMzhwKFWZAB9S6d0WW+twCnIQ97E2IEngKWiLpTl4F6\ng3US2yVrFOZfAndrF2sr8jDuJjQio9o1FM1YXAb8X6Qz/i/5rnqQxvgQMuJdhhjVAFgPwUPAnYgy\nmIAY2kcojGFGs2Rtl6xNmDsJF4c/EFmrM4/C/CNjcTLViXSQX+trfwaCYV1uH2LgN0JgjrHTOo5O\nYFEBQs5/oD5cskuQe/RR4AOIN6IzTIJRHUibNKrZpZ4nIMZkPTIH+DngfsJ290WdqGBgXJTDhMRz\nD5JNfRjyvB5H3Pp2DBMI/hWCz+nfHIYYwYlIW70TOE6f4wrg83oU/Bbgh8hLnP9D/98mTJNEk0aY\n/bpeo8jgK4swf4XM2TwFsQe2SzbLq7EMuf8m7GAmkIO0u9nAKxA8rNv5EHAf8N8i9d2g/+YSDtTq\nIYZ5ht6eFlM2bf51DNQyUDpPQp0GKjp9zZQLkLaymdBWrdDnicbF5yHttw+xwa8DbkbsxtnZdRp3\n7hbg7cC7xS08hjMRmzcXsdebSU/6ORl4Wtf3boTA7PtlKUy1AhmQ/AJnhan6EV5qRQYWByDYSjh4\nWayP14vY6RjC5Fx9rtORfrkLCW+kKnNXl+wFyGu1+oAvhA8ekA7/mJyMIT0qVsDHkKXW/hxx9XzB\n+s1KYDsETyIxwmchGEHiRJcANwE3Wu6B4wnJ2CjMxchUidOBTTpB5U6pA7sJRymvIG7W60Bdi7ht\nv63reB/wE+CvEML8kz6PnoLBIGOqLBjSdT0ZMXY/wz2GOd0qM6z/ZlJouEAM3WLtm5+v69NO4UIE\nBrcC79D35yR5BsFOXe5Rq9xeRDGvtfbtRFTTrcCp+hgDun7r9PGe0GVrRZjnIa7TuxBj/BQSOzGj\n/duAOwivy6qnmg3qe6Du1yNIgyuBW5BnegjwPX3MJcgo/krgp6C6QR1PaMyHCV2VTyHt4kTkeX0d\nIcQIYY7hQaRzDiCrUT2IDMbeoEnyzUibPg8h8SFdlwhhqsnI4Ok5pD2lEeZGXe+lFBLmBv1/uy09\nS7hG8y/JJky7LbxD38NRpI3eAcFuq6xRmAY7kAHs+8ToqYuQ/msIcx41V5iqDdQHQX0bcfvdTjxh\nmpCLA2GqwxFb83Ud7vkG8M8y+FadEXtq4uQ/Rp7Df+r9twIrQE2VuLiaQEiYAP8bGXA9hQwyz9ZK\n7yLtor2PcPnQJJyA3PdvAX+v696NeP6eRZTsZv2X5pK9HHmORyOhsiXIPdqFKNVBZMD/V8gg/4PI\ngLidcJqejehAbQWS8/IIwksmE3sPQrqDSH7MPuQ+Hsl4nKvLvIlwwYy4F58XwCVIfDzSGe5HHsgX\ngd+D+iVyI+YjBrbHUku7kIzGv0A63pnAN0F9F1EwZyIdDaRzm+kOTyCN6XLgvyKNYKk+//nIAzMK\n8wXg+8BnGHt/WvBrUGcgA4E/6mNuQRreAPB6hAyv1X+PAJ9EDPMo8FVEfVyIuAP/oL8bPIYYvZ9C\nMApqBzLqMrHK14H6OGEiidnfr+tjDOqw3mcn/KCvaRHiypuDjOa26H1RQ/w75AH/QB9rld6/DlFR\nBvuQEdad1r5hxIV9JzI14lxkELNGn7MVuF6XrVUM863A/4BgG6h7kfs3FVFjhgyeRu6ZqefV2qW5\nH2kbe4BfgbpL77sAUasmdnGb3n808AlksPYWpBPPAa6A4BVQtsJco895PnADBLcjqf9PIvHx6HNa\npY/9ADJonIk8k3ORAVEL8n7XD+tr+htkgPmw/v1jSP9aJfUNNoEyceptSBv7FKg/6etdiQwyNiCD\nw38hJMx/RPqAmd6EDDTVXfrc9+vf7EDa1NORa1mPJIt9Sx/zUkR5jCID0M9YZfchBj1KmDcjg+vf\n6zr1IkS9AVG5Js5Vi9V+jkLu8zbELh2L2LGfg/ok0lYUotIPIRwkfRbUesRWdiAD/+cIp/28G+nT\n1yHTJ9YiferriD2arp/f/0Oezx3IwOoEhKg26t99Acmr6EfaaR8yOwDEni5H2s8w8F2E+E7X5X8C\nfAPUI0j73aTr0EaYQHYWskrUjcBjEr9lBtJ2HwDegyjGzchA+78jA2+FPOvXIELiEqSffQ3pSy/q\na9kFgfYuqI8ipPcdRNX+CRmALkdI1cYe4EJQhyD95TKkHc1GBrkP6HK7dR0e1/ftGUQ8/BMyzWRY\n3/tj9XX9GdL2bhFvpNoHXA/qM2LCxyMrMLwKeaAeHvcgna8aWIVvdx6CarW7Vfg25xGimvbOw8PD\nw8PDw8PDw8PDw8PDw8PDw8PDw8PDw8PDw8PDw8PDw8PDw8PDw8PDw8PDw8PDw8PDw8PDw8PDw8PD\nw8PDw8PDw8PDw8PDw8PDw8PDFX4tWQ9X+LVkPWoBv5asRy1QVLtT5a+HR2NAzQG1zd5RzZNX8VwH\nMdR1oL5c61oUQn0R1A32jmqduErn8WgMxLYHl/dhehycaMXt9W8ejYt6fMatFL770MOjbuAJ0yMJ\nE/GGq9kxkfojzHqsk4cH4AnTIxnecDU/HAdFagDU1ypeG0E9ql4PD8ATZgNCnQ7qKlBdFT5RKzAB\nVFZimEfjwpWc5gLXgDqpwvUB79nwqGN4wmw8fBK4CVhZ4fNMjGw9mg+u5GTKfKiCdTHwng2PuoUn\nzLqACkB9wLHwJGBQbysJYyS98WpeuCrMVmATcGRlqzN2Lt/mPOoSnjDrA63Alxzdn63ACJU3Kl5h\nNj/yKMwRx7KlwrtkPeoWnjDrA62RbRomATsdy5aCPHXyaEy4uj+rTZh+kOaRE2o6qCMqfRZPmPWB\nPORULePlFWbzI49LthpeDXMu3+Y88uJi4PpKn8QTZn0gr8L0hOlRDniXrEezoI8qtBtPmPWBelSY\n3iXb/PAuWY9mgSfMgwjFxDB90o9HqXBdhm4iMOpYtlT4pfE8ikEflZ854AmzTmAMhMsDb8Un/XiU\nB15hejQLevEK86CBj2F61AL1GMP0ST8excC7ZA8i5I1hVlNheuPVvMiTJbsLCEBNqGyVfNKPR1Hw\nhHkQwZEwVQvViycZQ+qNV/Mij8Lcq/+q0e78IK1poZ4ENa3Mx5wETKPktqm+p4+VCE+Y9QFXhdkK\n7EMMl0/68SgVeWKY1Wp33iXb3DgMGCjzMXv1toSkH9UGXApMTivlCbM+kIcw91Cdkb53yTY/8rhk\nq6kwvVejKaEmIKQ2u8wH7qP0tjlTb73CbAC4EuYkqmu4qMJ5PGoH75L1qCba9daBMFVXjnh5L7Ce\n0trmLL31hNkA8ArToxbI45LNQZhqfgl18i7Z5kWH3roozB8CFzge1xPmQYY8CrNahOljmE0LtQTU\nYbgvEmAIc59j+d+CmpldLBbeJdu8cCRM1Qa8FknkccFkYBtVIExvDOsDeRSmMVzVSL6wtx7NgysJ\nE3gqEcOcDHQWWTfvkm1eGMLsyyh3IuK+TU3AsdAODOEV5kGDCilMtQLUaUXWySvM5kWb/qtUDLON\n4jMWvUu2eeHqkn2d3roOutoQwixlaTxPmA2EvArT1XCdA7yxxDp549V8MIRZTJZsRvmxucJtRdbN\nu2QbEmoeqHszCnUgc8izCHM58ASphKmOAnW8/tIODOMV5kGDSsUw2wkz0/LCZ8k2L2yFWW6XrDE4\npRCmH6Q5QU0CdVuta6ExE5ibUaYTeIFswpwCvES6wrwIeJv+bBSmJ8zmhuoA9RMqpzCNYSwG3iXb\nvLAJ02W5uzztri2yzQEV4AkzD2YBF+v7Vmu42JoO4EWgB1TaM54MbCadMDsJxUC5Ypi7yLgGT5i1\nxVTg9RSnMF2MSjvFE6Z3yTYvbJcsZD/jKhEmhri9V8MNMxAbXsU+qk4FdWPMP1wJcxjYSrhQQBw6\nySbMydb52pGXA5Sy1vEsZGqKV5h1DOMydX29V970/ja8S9ZjPGyFCdnPeCLVIUw/SMuHGXpbbB8v\nBnOBJTH7XcI/JoY5THoG7GRgC+4Ksw1Rh6VMt/OE2QAwRsU0nnLHMEtxyZpFErzxaj7YhLkbd4Xp\nMlAz7a2YjMWJ+DaXB0alVZMwk4jRVWGOIm9byiJMF5esrTBLIEwVIHM+N+EJs65hGt4UvS33Sj+l\nuGTNW1G88Wo+2C5ZlzffVMslOxExfBPrJC5X76iFwkwizHZgQkZssgNxnY6QTZibgE5Q54E6PKGM\nrTB3U7zCbAUOIETuCbOOkZcw864lW4pL1tWYejQeTLtoIZ/CrHSWrBkQ7ieMZx6EUMtBfd6hYD0R\npstAyUFhqgn6GK8gKvIvgdNjCpZRYY7Vaw+eMOsaNmEq6ivpxxrtezQZ2hCD5frKrhzzMEtWmPuo\nzkpW9YxDgWPj/6U6QH1Uf6knl6yVsapaQMXN/+4kJMwkd2s0ztmTUDZOYe7BE2ZTwybMEfItjVeN\naSXeJducaAO6cG9L1XTJ5mnfzYq0pQUHgA/rz0ZhtoH6fgkZonngojBnA9+KKeMSw5ys/z+C3IOe\nhLJJCrOY2LknzAZBXsIsJunHu2Q9oihFYVY6S7ZaL6quZ0wmXEYuik7CEI4hzCnIJP7pFa4XiD2J\nq5v93DsTyhhiSothTiaMc6YRpq0w2ykthmkI1xNmnaNYhemTfjxKgU2YrgrTEFmls2Rr6JJNTVip\nJrIIc5Ks8sNMxG4Y4pyV8JtywsEla9TfOMVbrMKMU9sO00pUH6hrU6+msF6eMOsc1VCYpUwr8Qqz\nOWG7ZOtJYdbaJVvNWGAaOkl2yZr9UxCiXEeoLIt9pVoeuLhkTR2jpG8TZtL12YTZhSzu4uKSjVOY\nxwHvTDhPXL1qQZjqar0As0c2SlGYLqPhUhcu8AqzOTGJUDWmkJNaAeoO8sXOS82SraVLNknVVRtp\nCtPs70YIcz0hYVZIYapJoLqs87fFTPuJKkwYT4pmWomLwtyFtKUgoWw06ce4VO32OSPlPNF6GcKs\n5tJ4qgW4CXmYHtkoVmG6jsBLXRrPZ8k2J0ybyHq36mxgDtVVmLXMks0xuFS3gXJ9wXFeZLlkQVyV\nU4CXCV2ylVKY7wA+pz/bJGUjTmHGEWZWDLMT2AmB0uVijqPMW3ayFOb0lPPE1Ws3VVaYHciIoCur\noAcQPnAHwlQfRzLkquWSNQqzAVyy6g2g3lPrWjQQTJvIikt2IYPfaib91NIlm0dhng0cUqF66CzZ\n2MUbDHnMA7YhdiNGYar+MtZnpnWO9siWmP3mPiYRZpbCNEQ5Yu2LltkbOWdclqwrYdYs6acrsvVI\nh3ngE8gmp7cjc7PyJv20FukibySX7NHACbWuRGNATSBcFCBLzXUhg7m88zAzXVsJMAqzhi5Z1Qvq\nwfRiKkDuS0+F6mGMfNw9NCS0EJncv4v4GOZv5B2VZUE3YWausVnRwYWLwnSZh2lcsujtKONJrxMZ\nLBjXcNJassW4ZD1h1jHsUVqWS3YKxSnMAxQ/2m8Ul2w3PgzgCmNc9pNNgsUqzCEaMkuWdqAPWJpR\nzqzD60iYamLOQasx8klTMwAWkE6Ys9zrl4luQpuepDDjCHMyqPdZZfJkyYLYxPUxZScjCxvsI3wW\nce1zutQzc36qJ8wGQV7C7Md5pD+mJIYpfrTfIC5ZphCOgD3SYVZF2U32tBKTWNFBPsLcQeO6ZLWa\nSjWyxr65EtKXgGty1MMQxKtBfSXyP0NGC5A3ehjCHGTMJavakGspV59wIcx2hOjspJ85wI3WvXSd\nh2kT5jrilepOpA13yzZQjE/6mW6VT4MnzAaBeY8bpC78q1qQe2re5uBquIxhLNZ4NYpL1hOmO6KE\nmaUwzW+qQZh14JIda0dpHgtTxpUw+4Ezc9TDGPhjgNMS/me7ZGcAawkV5lS9zel1UaeAenXMP6ZS\nSJj7iVeYgxQqTBNHNfVJyZJVnwO1gJAMISTMOJfsCHLt05C2DPEuWWJ+H0U7njAbAu3Adv05TWHa\nD3wvBAfkY+oo2LjedlHc1JJGimF6wnSHTZhZas7ux3kWXy+FMLMydyuJdkKSScuAzaswpwKn5XgD\ny2SElPoJycbAqCFDmLsRJbWGMOnH1D1vn3g7cLle/H25tT8aw9xOvMIcpDDpZ3akPmnzMM9DXOFR\nhfki8S5ZozCnIjYO4pN+VMzvo+jAJ/00BFwJ0274e/Q2y3iVQ2HuyjhHvcATpjuKUZgKgv24vw9z\niNLmYdbSJWvaUZSobORVmFMRMot78XIczAuUZzOeuDuRqSRdhC7ZNoQwjcIsljCnAUcA7wEutfbb\nLtkOJOEmTmGagVJUYfbowYIhurgY5jT9F82SXc/4jGFbYU4lWWFOBzbEnAtxW6tvW9fkFWYDoBjC\n3Gtt00bhZm5SsYTpFWZzIm8MEwrbXDO7ZF0V5hSHMjamAr8DVjqWtwmzO4YsXtafjUsW5IXL6AUG\nTL3yJsL1AIcj2fj2urTdUifVgpvC7ETIx1aY3VLXYA/xMUybMI3CXAU8RJjcY5CmMHX7VBP0OeOS\nhtB1u1wvh+hMmOVulJ4w88GVMLsJH2YehXmQuWRVoIP/HskwhHmAbPdnF+LSykuYpWTJ1tIl66ow\nzbKCeRTm/UiWuwsmIwTYj4iaLuSewnjCNPXdCbyEJNoUqzB7kGSifcAL1v5uZH59B6HNiptWYscw\nDeGb487Q9TV1tUhMTaJwoXVNmIFOeFKmvCFGF4U5DRm4DRFPmNOtcj6G2SDIozBf0J9dCbNUhdlI\na8lOQdpyvSxtVs/I65LdjLtXwxy/lCzZWrtkXRXmi+QjzOeAmaCWgTo8uaiaiNzj7YQuTbsuHYSE\naVyyELovB6x65VWYJoGmlUKFORUhsS7cFeaWSP1nAFv196hLdpq17UIy+21EY57GbRtVmHaWrDlf\n0hSWGda2VkvjjfnVPWG6oQ13wnxefzbGK8uoGIV5sLhkD+Ddsi7I45LtQmJAB5tLdg/ZMUxHwlST\nkGt5ETHO1wJ/m/IDo55GGJ/1av4f55K1CXMaQl7FKMwHgT8wRpiqFXmWmwivd5j0LNkOhAcMKUUU\nZrDHOjYUEuYsxtzLY4i6cE0mbZzCNApxuj5fEmFOt7Ym6afqS+N1ARvxhOmKPApzK9Ig8yb9FOuS\nbaSFC8yamp4wsxHNkk0jp8mIm6+ahFkPLtl1ZGfJuirMqUi/3YwQ4Gzg7JSMWeOSHLX22XWxCdNW\nmDspJMy15FKYKtC/uxX4F8Jrm4I8zx26/rt03bKSfoz7dUQfd7q1z+w3RGaurwe5Py9TiCjp9SD3\n1EwriYlh0ofc87wK0xNmHSMPYQ6Rzz1WrqSfOnfJji3EvBlPmC6otMKcRGNnyXYjZFgmhTlGmFsI\nCXMASa6JQ5QwBxmvMDfpz1sJ1VVUYb6Ic39QA7pe+yG4CfhHQgXWreswTEiYcYPwOJcsSCgpGsOE\nQiLrQTxEM/TfJgoRdckO6GtNSfphHpI5rM+jooMHW2HWNIa5AU+YrjCNDNwI8x6koYB70k8RhKkC\nZJWgRlCY5t4M4QnTBXkUZiku2VKWxqulS9aQYZbCfBmYACrLe2Pco4Yw+4F7gbMSytvvgwQx+tEY\n5gZgOwT7yFSY6tB4Nas6rOX6Pgt8GJkuYo7VqlcM6kaeZxZhRpN+DDma+kcJ82UKs2jXAYfKMYK9\nFCLqkjWEmZb0M0/fg53AKUjSlY0ZhCRdToXpPNkWvMLMi5yEGbwLguf0Ptekn2JcsrU2XHngCTMf\nHBXm2Ly5DboMyWXHHb8UhVlrl6yrwhxCCCZraomtMGchJHEHcFRC+ajCXCO/Uefp753AauBU/T0a\nwzyEQoX5S+JfTHArcJH+fCiwnDFvV6AQ9dqj628IcxYhYVoJdqoFeV5DjFeYa4hXmM8Di/TnaYgS\nnY/wRxRRt+oAQrBpST82YS4HlkYWejGLPZgYZtkUZq9DGYM6IUzVKIYzj0t2R2RfJRcusBfBrnOX\n7Jjx2oEnTBe4Zsm2IwZkG9VP+qlVuzNJP2vJzpIdRuK7cwv/pd4Pyl68fSrSx3ciXhsFPEzyIgZR\nwlwLXAjcrL/rN34ET+jvSUk/axE1Ox84CtQloEzcDmTZvWP050OB4wgVJvrzdAoVpk2Y9iDczpcw\nST9ZCvN5ZLUiEEJ9AZm6Eo1fQgFhqgAZFCQpTEN48wldsgv0fntazwzgWUKFWbaVfuY7lDGoA8JU\nRwFP5lTGtULeGKaNSmbJmliSV5jNh7il8eKesUnv30H+aSV6HmbuPlhrz4ZJ+slSmGZe5OPIq+U0\nVABcB/y1VVYrzEAhqmsj8AzJhDmF0CV7ACGG4xGVOZMwi9bAdsluQAROr74GI3aOBm4CrtL17EDI\n6ggd25upz2sT5lYKCXNIlxtlPGHa+RJpCnOr9Zs/AYtAfRd4NeG0uTiFuY3CZf/2QTCkzzdA6KVL\ncskaaPe0WsF4wiybwsxLmBWKYapzQP0NqMWgLkop+Fpk1LcwpUwdQAWURpiuST961KdOAnWlY+Vq\nna2YB54w8yFOYca1uy7E2AySvPRY0vFHkbVQ876LtV7eVpIVwzRt7nFClQZic9qAS3X8D0KXLAiJ\nbEBIcJpelSeK04HfIvdwELEPxogfh6gwO8ZnKcxgD3AbYv9M6GYb8GZEob1d71uCkMMRiFt0td5v\nbJH5XY/+i8Ywo1my9iIp0RjmCyQrzOOQJfjeiBDlLuIV5gOEqyQZd6y59tnAb/R33T5Vm67rBkLC\nfB5R0ich8cy5yMDFTvrRCjV5oNdICvO9SGD6I8BXUxYeX4lcfGQZqtjGWUtMRNwzZpLuCMkPK4kw\nXRVmO3LvPgFqJagPZdSt1tmK5FAnFSZM1S9GX90F6viUcvNBTU/+f91gEuNjmHGDIvPOwd8AV+t9\ned6SswexA0/KYM0JtX4f5lSEkNYCfSmhHeOSfYxCwjwJ+AXwe8SNao5pE+ZG/fKE1cBiUP8Aaguo\nz+rBxZuBHyH2YND67X8gsciRyGpWtsIEgst1/dYj9/HHyOo/3wdmgDoGIcpfIgSyFJl3uZ54hXkW\n0gZcXLK7CedEm3obhTnAeMJcoct2IGS9jXjCvAdZvL6F0B0L4UDuXr017XMAeEmvf2wI827gMOAc\npA8cwjiFGezX9Ul8qUURhKkWgfqInpAbRReSEtzhNrJUjm4bpVf7ZwS4ArkxN4D6VAwmWYEoAAAE\nF0lEQVRxngJ8Q7ZKB6bVO4FXQJ2cfa5x5w5AfRjUBSllrgd1U/q1qJWgbgVlgtfthI3vgM542wks\ns857rL7PjoSp5oMyrgs7rjATafj7gR8An5KyiZhN+BqxWinMSOxcLbCuzUY3IWGW+SXSajEyUv8u\ncv+uSyjXgRigW8I2oFpA3QzqFrc2Pu6Y3TrulEBQqhXUNaAycgzUhMj5oy7ZUeCwmDpql2ywF4In\n9T7XaSXm+H+O2I+P6QHHuRm/tV8EXIt2NwtJrtuOGNjrQX0zbHdqGainEVU2xBhhqi8jr8U6CSG2\nrwAf1Pc0hjABUTc3Ie3qDOBdyP0aguAp5Lls13/7EOV4AoXzMyEkTGt/YN4POYS0y0GEdP4n8DGE\nMB9GFNhZSBt/jkKFuRVRzGciBJ6WJWu7ZKfrupj6bAb+GbEpNmG+oLdmAfRt+vwxLtlgnb6GVxFm\nyJprfxoC8xuT9HMSMuiBcM3Ze5EBwjnAz/X/ViN2xsQwzTES3bIujfISUEcirNuKyPnVwBWg1iEj\nMtPZjGEfAe4Epaz/t1jbFn2sY4GXQD2DZI3tJFzRRlnb6cgDfwh5wP8G/B3wpHxXZt7OBMQgfBMZ\nFV0F6kWEKD4K/AjUo0jHGNTH3YLc0CDmr4VwrtVsUKuRzmwW+x5CbnAv8rDvBWU3OhsnAI8Cf9Sp\n6GsIX6ZqOtQ1wM9BPaLruIDwxbDRpJ/dwKeFzMeu+wi5X+q3SGbYDYjhuxy4T9+/twLfAe7S98YM\nbJQuO1tv/15f2zRQPwofcdXwA1DmvrQyNhpVD0TKLQB+hdyn94J6FXI/zF9L5LuJsTyvt2apsWgq\nO0gHvRlRWH+NKHTjUttgletD3HOLgf8EtUvv24Q8l/tBmVVEdhO+FzDa7uw+sgTpDx8HtSZSDsSl\npJCB46MJ93ACcCKwFdRTet9SfU179b24BXinrvco0q57kH4R1+YWCvmBPn8b4Zy3gDAGN4r00WuA\nr8o94Fsxz8+gBTF0VwCvAa4U+1LVdjeX0N33FcTI/gJ4UNuYxcCXgbcAWyAYRNY5XQ78hf7dKcDT\nyFSNnyHzLT+h/7eJsN08jdi/EyHYjLw54+sIaYIQ1Db9myeQvvtpxs9R3ANcpNVRFNsR9fhF4KfI\n81wNnIx46v6A2IPrEGK3Y4yvAO8HfiUDCLUDIasHkef7Z7ovPErhi6HNggHDUi7YC+oDCEEPhocP\nRkGtR8TN6/W1bqWwX9m4GxnsA9yut9uQ52OwC3HvngVcpvcNIYOBZxHynwCcjdjjjfqa/s2aypK6\nPF5Wa1yFxAQ9PO5B4ivVwCp8u/MQVKvdrcK3OY8Q1bR3Hh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4e\nHh4eHh4eHh4eHh4eHh4eHh4eNcT/B76xb8/F38RYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f274b6d03d0>"
      ]
     },
     "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: 70.8 %"
     ]
    },
    {
     "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: 63.5 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clips accuracy: 69.0 %"
     ]
    },
    {
     "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",
      "  70 (+/- 0.4) <- [70 69 70 70 69 70 70 70 70 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 0.3) <- [70 70 70 70 70 70 70 70 70 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  70 (+/- 0.8) <- [71 70 70 69 71 71 70 70 69 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 0.8) <- [71 69 70 71 71 71 69 69 70 71]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 0.6) <- [70 69 71 71 70 71 71 70 70 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 0.5) <- [70 71 70 71 71 71 71 71 70 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 0.9) <- [71 70 71 70 72 69 71 71 69 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 0.9) <- [71 71 70 71 69 70 72 69 69 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 0.7) <- [69 70 70 70 72 71 71 70 70 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 0.8) <- [70 70 69 70 72 71 70 70 70 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  70 (+/- 0.7) <- [71 71 70 70 71 70 69 69 69 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 0.6) <- [70 70 72 70 70 71 70 69 71 71]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  70 (+/- 0.4) <- [70 70 70 70 69 70 70 71 70 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  70 (+/- 0.6) <- [70 70 70 69 71 71 70 69 70 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 0.7) <- [70 70 71 69 70 71 70 70 70 71]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 0.6) <- [69 70 71 71 71 70 70 71 70 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 0.5) <- [71 71 70 70 71 71 70 71 70 71]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  70 (+/- 0.7) <- [70 69 69 69 71 69 71 69 71 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 0.9) <- [70 71 72 70 70 70 72 70 69 71]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  71 (+/- 0.7) <- [70 70 69 70 70 70 70 71 71 71]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 70.7 (+/- 0.70)\n",
      "Mean time (20 cv): 11.77 seconds\n",
      "Overall time: 239.85 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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M23KHgssrGmoES5Bc+PKCNmOI7e5BHMsKpivMvJBsuuAH8hXm7SQh2SyFuRC5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\nHh4eHh4eHh4eHh4eHh4eHh4eNcT/B76xb8/F38RYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2750146690>"
      ]
     },
     "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: 70.8 %"
     ]
    },
    {
     "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: 63.5 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clips accuracy: 69.0 %"
     ]
    },
    {
     "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",
      "  70 (+/- 0.6) <- [70 69 69 69 69 69 68 69 69 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  70 (+/- 0.8) <- [70 69 69 69 69 69 69 67 69 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  70 (+/- 0.7) <- [70 69 70 68 70 69 69 69 69 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  70 (+/- 0.7) <- [69 68 69 70 70 70 69 68 69 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  70 (+/- 0.6) <- [70 68 70 70 70 70 69 70 70 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  70 (+/- 0.7) <- [69 70 69 70 71 69 69 70 69 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  70 (+/- 0.8) <- [69 69 71 70 70 68 70 70 69 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  70 (+/- 0.8) <- [69 70 69 68 68 70 71 69 68 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  70 (+/- 0.5) <- [69 69 69 69 70 69 70 69 70 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  70 (+/- 0.6) <- [70 69 68 68 70 70 69 69 69 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  70 (+/- 0.7) <- [70 70 69 68 70 70 69 69 68 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  70 (+/- 0.4) <- [70 69 70 69 69 70 69 70 70 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  70 (+/- 0.5) <- [70 68 70 69 69 69 68 70 69 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  70 (+/- 0.8) <- [70 70 68 68 69 69 70 68 70 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  70 (+/- 0.7) <- [69 69 70 69 70 70 68 69 69 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  70 (+/- 0.5) <- [69 68 70 70 70 69 70 69 69 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  70 (+/- 0.6) <- [70 70 69 70 70 69 68 69 69 70]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  70 (+/- 0.7) <- [69 69 68 68 70 70 70 68 70 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  70 (+/- 0.8) <- [70 70 70 70 68 69 70 69 68 71]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  70 (+/- 0.9) <- [69 69 67 69 70 69 70 70 71 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 69.8 (+/- 0.70)\n",
      "Mean time (20 cv): 9.50 seconds\n",
      "Overall time: 194.41 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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M23KHgssrGmoES5Bc+PKCNmOI7e5BHMsKpivMvJBsuuAH8hXm7SQh2SyFuRC5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\nHh4eHh4eHh4eHh4eHh4eHh4eNcT/B76xb8/F38RYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f274b88c350>"
      ]
     },
     "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: 70.8 %"
     ]
    },
    {
     "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: 63.5 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clips accuracy: 69.0 %"
     ]
    },
    {
     "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",
      "  69 (+/- 0.6) <- [69 68 68 69 67 68 68 68 67 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  68 (+/- 0.8) <- [69 68 68 68 68 68 68 66 66 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  68 (+/- 0.6) <- [68 68 68 67 68 68 67 68 68 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  68 (+/- 0.5) <- [68 68 67 68 68 68 68 68 67 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 0.6) <- [69 67 69 68 68 69 68 68 69 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 0.7) <- [67 69 67 69 69 68 68 68 67 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 0.5) <- [68 68 69 69 69 68 69 68 68 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  68 (+/- 0.8) <- [68 68 67 68 66 69 69 67 68 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 0.6) <- [67 69 67 68 68 68 68 68 69 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  68 (+/- 0.3) <- [68 68 68 68 69 68 68 68 68 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  68 (+/- 0.6) <- [68 68 68 67 68 69 67 67 68 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 0.7) <- [69 68 70 67 68 69 69 68 69 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  68 (+/- 0.4) <- [68 67 68 68 68 68 68 68 68 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  68 (+/- 0.7) <- [68 69 68 67 68 66 68 68 68 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 0.8) <- [68 68 69 68 68 68 66 68 69 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  68 (+/- 0.9) <- [69 66 68 68 69 68 67 68 69 67]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 0.9) <- [69 69 67 69 68 69 66 69 67 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 0.7) <- [68 68 67 67 68 69 69 68 69 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 0.7) <- [69 68 68 69 67 68 69 68 67 69]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  69 (+/- 0.7) <- [68 68 67 69 68 68 69 68 69 68]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 68.6 (+/- 0.71)\n",
      "Mean time (20 cv): 7.16 seconds\n",
      "Overall time: 147.70 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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M23KHgssrGmoES5Bc+PKCNmOI7e5BHMsKpivMvJBsuuAH8hXm7SQh2SyFuRC5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\nHh4eHh4eHh4eHh4eHh4eHh4eNcT/B76xb8/F38RYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f274bf62050>"
      ]
     },
     "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: 70.8 %"
     ]
    },
    {
     "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: 63.5 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clips accuracy: 69.0 %"
     ]
    },
    {
     "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",
      "  67 (+/- 0.5) <- [67 66 66 67 67 67 67 68 66 67]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  67 (+/- 0.6) <- [67 66 66 66 66 67 66 65 66 65]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  66 (+/- 0.6) <- [65 66 67 66 66 66 65 66 67 66]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  67 (+/- 0.6) <- [66 66 66 67 66 65 66 66 65 67]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  67 (+/- 0.7) <- [67 67 68 67 66 68 66 67 67 66]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  67 (+/- 0.5) <- [66 66 66 66 68 66 66 66 66 66]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  67 (+/- 0.6) <- [67 66 66 67 66 65 67 65 66 67]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  66 (+/- 0.7) <- [66 67 66 66 65 66 68 66 66 66]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  67 (+/- 0.7) <- [65 66 66 67 67 66 67 66 67 67]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  67 (+/- 0.6) <- [67 67 67 66 66 66 66 66 66 66]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  67 (+/- 0.9) <- [67 66 68 65 67 68 66 66 66 67]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  67 (+/- 0.4) <- [66 67 67 65 67 67 66 67 67 67]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  67 (+/- 0.6) <- [66 66 67 67 67 66 65 65 66 66]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  66 (+/- 0.6) <- [66 66 65 65 66 65 66 67 66 66]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  67 (+/- 0.7) <- [66 66 67 67 67 66 65 65 67 67]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  67 (+/- 0.6) <- [67 66 67 66 67 65 67 67 66 65]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  67 (+/- 0.9) <- [67 66 65 67 65 66 64 66 67 66]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  66 (+/- 0.7) <- [65 65 66 65 66 66 67 66 66 65]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  66 (+/- 0.9) <- [67 65 66 67 66 65 67 65 64 66]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  67 (+/- 1.0) <- [67 65 67 67 65 66 66 66 68 65]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 66.7 (+/- 0.74)\n",
      "Mean time (20 cv): 4.88 seconds\n",
      "Overall time: 101.96 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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M23KHgssrGmoES5Bc+PKCNmOI7e5BHMsKpivMvJBsuuAH8hXm7SQh2SyFuRC5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\nHh4eHh4eHh4eHh4eHh4eHh4eNcT/B76xb8/F38RYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f274bb63690>"
      ]
     },
     "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: 70.8 %"
     ]
    },
    {
     "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: 63.5 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clips accuracy: 69.0 %"
     ]
    },
    {
     "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",
      "  63 (+/- 0.5) <- [62 63 62 63 63 63 63 63 64 62]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  63 (+/- 1.0) <- [63 64 63 61 63 65 63 61 63 62]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  63 (+/- 1.2) <- [62 62 60 64 63 63 63 62 63 61]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  63 (+/- 0.7) <- [63 63 62 63 62 62 62 61 62 62]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  63 (+/- 1.1) <- [61 62 64 64 63 64 61 63 64 62]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  62 (+/- 0.5) <- [62 61 62 62 62 63 62 62 62 62]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  63 (+/- 0.4) <- [63 63 63 63 62 63 63 62 63 64]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  62 (+/- 0.8) <- [63 61 61 62 62 62 64 61 62 62]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  63 (+/- 0.6) <- [62 63 63 62 62 62 64 62 62 62]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  63 (+/- 1.1) <- [62 63 63 61 64 61 64 62 61 61]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  63 (+/- 0.9) <- [64 61 62 62 62 64 62 61 63 63]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  63 (+/- 0.6) <- [62 62 63 62 62 63 62 63 62 63]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  63 (+/- 1.0) <- [63 64 62 63 64 62 61 62 62 62]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  63 (+/- 0.6) <- [62 63 62 62 63 62 62 63 64 63]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  63 (+/- 0.7) <- [63 62 63 63 63 63 61 62 63 63]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  63 (+/- 0.7) <- [61 64 63 63 63 63 63 63 63 63]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  63 (+/- 1.0) <- [63 61 62 64 62 62 60 64 62 62]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  63 (+/- 1.0) <- [61 63 64 62 63 62 64 62 63 62]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  63 (+/- 1.0) <- [63 63 64 62 62 63 62 62 60 62]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  62 (+/- 1.0) <- [61 62 62 61 60 62 62 63 64 63]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 62.9 (+/- 0.91)\n",
      "Mean time (20 cv): 2.56 seconds\n",
      "Overall time: 55.71 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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M23KHgssrGmoES5Bc+PKCNmOI7e5BHMsKpivMvJBsuuAH8hXm7SQh2SyFuRC5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\nHh4eHh4eHh4eHh4eHh4eHh4eNcT/B76xb8/F38RYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f274bb99850>"
      ]
     },
     "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: 70.8 %"
     ]
    },
    {
     "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: 63.5 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clips accuracy: 69.0 %"
     ]
    },
    {
     "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",
      "  62 (+/- 1.1) <- [60 61 61 61 61 61 62 64 63 60]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  62 (+/- 0.9) <- [61 62 63 60 61 62 62 60 61 60]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  61 (+/- 0.6) <- [61 60 60 61 63 60 61 61 61 61]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  61 (+/- 0.7) <- [61 61 61 61 60 60 62 59 61 60]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  62 (+/- 1.1) <- [61 61 61 62 63 62 60 62 62 61]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  61 (+/- 0.6) <- [60 59 61 61 61 62 61 61 61 61]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  62 (+/- 0.6) <- [63 61 63 61 61 62 62 62 61 63]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  61 (+/- 0.5) <- [61 61 60 60 61 60 62 60 61 61]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  62 (+/- 0.8) <- [62 62 62 61 60 62 63 61 60 60]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  61 (+/- 1.0) <- [60 61 62 60 62 60 62 61 59 61]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  61 (+/- 0.8) <- [62 60 61 60 61 61 61 59 62 61]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  62 (+/- 0.8) <- [61 61 61 59 61 62 61 61 61 62]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  61 (+/- 1.2) <- [61 63 60 62 63 59 61 61 60 59]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  62 (+/- 0.9) <- [60 61 61 61 61 62 61 62 63 62]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  62 (+/- 1.0) <- [62 62 60 63 61 61 60 60 62 63]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  62 (+/- 0.9) <- [60 63 60 60 62 61 61 61 61 62]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  61 (+/- 1.2) <- [61 59 61 61 62 60 59 63 60 62]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  61 (+/- 1.2) <- [59 61 61 61 61 59 64 60 62 61]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  61 (+/- 1.1) <- [62 62 62 62 60 62 59 62 59 61]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  61 (+/- 1.0) <- [60 61 60 59 59 61 62 60 62 61]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 61.5 (+/- 0.99)\n",
      "Mean time (20 cv): 2.10 seconds\n",
      "Overall time: 46.40 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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M23KHgssrGmoES5Bc+PKCNmOI7e5BHMsKpivMvJBsuuAH8hXm7SQh2SyFuRC5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\nHh4eHh4eHh4eHh4eHh4eHh4eNcT/B76xb8/F38RYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f274b6c8c90>"
      ]
     },
     "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: 70.8 %"
     ]
    },
    {
     "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: 63.5 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clips accuracy: 69.0 %"
     ]
    },
    {
     "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",
      "  60 (+/- 1.5) <- [58 58 61 58 57 59 61 62 60 60]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  60 (+/- 0.9) <- [58 61 61 60 59 60 59 60 58 59]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  60 (+/- 0.9) <- [59 57 58 59 60 58 59 60 59 61]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  60 (+/- 1.0) <- [59 59 61 60 59 59 59 57 60 59]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  60 (+/- 1.0) <- [58 59 60 61 58 61 60 59 60 59]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  59 (+/- 1.1) <- [59 58 60 57 59 59 59 57 59 61]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  60 (+/- 0.9) <- [60 58 60 60 59 61 59 60 60 62]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  59 (+/- 1.0) <- [60 58 60 59 59 58 61 59 58 58]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  59 (+/- 1.0) <- [60 59 60 59 58 59 60 58 59 57]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  59 (+/- 1.0) <- [57 60 59 59 60 59 59 60 58 58]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  59 (+/- 1.1) <- [60 58 57 59 58 61 59 58 60 60]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  60 (+/- 0.7) <- [59 60 59 58 60 59 58 59 59 60]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  59 (+/- 1.1) <- [59 60 59 59 60 57 56 59 59 59]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  60 (+/- 1.2) <- [58 60 59 60 59 57 61 61 60 60]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  60 (+/- 1.1) <- [59 61 58 61 59 59 59 59 61 60]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  60 (+/- 1.0) <- [57 60 58 58 59 59 60 60 59 60]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  59 (+/- 1.5) <- [60 57 60 59 61 57 58 60 57 60]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  60 (+/- 1.0) <- [59 59 61 60 61 58 61 59 59 60]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  60 (+/- 1.0) <- [58 61 59 59 60 61 58 59 57 59]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  60 (+/- 0.9) <- [59 60 60 58 59 58 60 58 61 58]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 59.7 (+/- 1.10)\n",
      "Mean time (20 cv): 1.61 seconds\n",
      "Overall time: 36.70 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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M23KHgssrGmoES5Bc+PKCNmOI7e5BHMsKpivMvJBsuuAH8hXm7SQh2SyFuRC5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\nHh4eHh4eHh4eHh4eHh4eHh4eNcT/B76xb8/F38RYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f274b5a5b90>"
      ]
     },
     "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: 70.8 %"
     ]
    },
    {
     "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: 63.5 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clips accuracy: 69.0 %"
     ]
    },
    {
     "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",
      "  57 (+/- 1.1) <- [55 56 55 55 55 56 58 58 56 57]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  57 (+/- 1.4) <- [57 57 59 57 59 55 55 56 56 54]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  56 (+/- 1.1) <- [55 55 54 56 56 56 56 56 58 57]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  57 (+/- 1.4) <- [56 54 57 57 57 55 57 54 58 57]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  57 (+/- 1.3) <- [54 56 56 58 57 57 58 56 54 57]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  57 (+/- 1.7) <- [56 54 54 53 57 57 57 56 57 59]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  57 (+/- 1.6) <- [56 56 57 57 57 59 53 58 58 59]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  56 (+/- 1.4) <- [58 55 57 56 56 54 57 54 54 57]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  57 (+/- 1.0) <- [58 56 55 56 56 56 58 55 56 55]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  56 (+/- 1.0) <- [54 55 55 56 56 57 57 56 55 57]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  57 (+/- 1.0) <- [55 55 55 57 56 56 57 55 58 57]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  57 (+/- 1.0) <- [58 59 57 55 58 57 56 57 57 56]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  57 (+/- 1.2) <- [56 57 58 56 57 55 55 56 58 55]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  57 (+/- 1.5) <- [55 56 57 58 56 54 57 58 54 55]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  56 (+/- 2.0) <- [56 58 56 57 56 53 52 56 57 59]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  58 (+/- 1.2) <- [54 58 56 58 58 58 58 57 56 58]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  57 (+/- 1.3) <- [55 55 57 58 55 55 56 59 54 57]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  57 (+/- 1.2) <- [55 57 57 57 56 54 58 55 56 57]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  57 (+/- 1.3) <- [56 57 55 58 57 57 58 59 54 56]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  57 (+/- 1.3) <- [57 56 54 55 58 57 58 56 58 55]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 56.8 (+/- 1.37)\n",
      "Mean time (20 cv): 1.14 seconds\n",
      "Overall time: 27.24 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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M23KHgssrGmoES5Bc+PKCNmOI7e5BHMsKpivMvJBsuuAH8hXm7SQh2SyFuRC5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\nHh4eHh4eHh4eHh4eHh4eHh4eNcT/B76xb8/F38RYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f274b7b9a10>"
      ]
     },
     "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: 70.8 %"
     ]
    },
    {
     "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: 63.5 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clips accuracy: 69.0 %"
     ]
    },
    {
     "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",
      "  54 (+/- 1.4) <- [53 53 53 53 53 54 56 57 55 53]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  55 (+/- 1.3) <- [52 55 56 55 55 53 54 55 52 55]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  54 (+/- 1.7) <- [54 50 50 55 53 54 54 53 55 55]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  55 (+/- 1.3) <- [55 54 54 56 56 53 55 52 55 56]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  54 (+/- 1.4) <- [53 53 54 55 53 55 56 56 52 52]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  54 (+/- 1.3) <- [52 53 53 53 56 55 54 54 54 55]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  55 (+/- 1.5) <- [55 52 54 54 55 55 52 57 55 56]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  55 (+/- 1.4) <- [56 54 56 56 55 52 55 52 54 54]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  55 (+/- 1.7) <- [57 55 54 55 57 55 56 51 56 52]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  55 (+/- 0.6) <- [54 54 54 54 53 55 55 54 54 55]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  54 (+/- 1.6) <- [53 54 54 54 50 55 55 53 56 52]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  55 (+/- 1.4) <- [55 57 56 53 53 54 53 54 56 55]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  55 (+/- 1.5) <- [54 55 57 54 56 53 52 53 56 56]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  54 (+/- 1.2) <- [55 55 53 55 52 52 55 55 54 54]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  54 (+/- 2.6) <- [54 57 54 55 55 50 50 52 56 58]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  55 (+/- 1.5) <- [51 56 55 55 54 55 56 56 54 56]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  55 (+/- 1.2) <- [53 53 54 55 55 54 54 57 54 53]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  55 (+/- 1.0) <- [52 54 55 54 53 54 56 56 54 55]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  54 (+/- 1.5) <- [54 56 55 55 52 52 55 56 52 54]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  55 (+/- 1.3) <- [53 55 54 52 57 55 53 55 55 53]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 54.7 (+/- 1.51)\n",
      "Mean time (20 cv): 0.88 seconds\n",
      "Overall time: 22.07 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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M23KHgssrGmoES5Bc+PKCNmOI7e5BHMsKpivMvJBsuuAH8hXm7SQh2SyFuRC5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\nHh4eHh4eHh4eHh4eHh4eHh4eNcT/B76xb8/F38RYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f274bf236d0>"
      ]
     },
     "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: 70.8 %"
     ]
    },
    {
     "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: 63.5 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clips accuracy: 69.0 %"
     ]
    },
    {
     "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",
      "  50 (+/- 4.4) <- [49 51 50 49 50 52 54 37 52 50]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  52 (+/- 1.6) <- [51 54 51 50 54 51 49 51 49 52]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  51 (+/- 2.1) <- [51 47 48 55 49 50 50 51 51 50]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  52 (+/- 1.6) <- [52 52 53 52 54 53 51 48 53 52]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  51 (+/- 1.8) <- [49 52 50 52 49 50 52 50 46 52]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  51 (+/- 1.6) <- [50 49 49 51 53 51 51 50 51 54]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  52 (+/- 1.7) <- [52 47 54 52 51 52 51 53 53 52]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  52 (+/- 1.6) <- [52 51 52 51 50 47 52 51 52 54]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  52 (+/- 1.1) <- [53 51 51 51 53 50 53 51 52 50]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  51 (+/- 1.8) <- [48 50 50 52 53 52 48 51 53 53]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  52 (+/- 2.0) <- [52 50 51 53 47 54 52 51 55 51]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  52 (+/- 1.9) <- [52 56 54 50 51 52 50 50 51 50]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  52 (+/- 1.0) <- [53 52 53 51 53 51 51 51 52 51]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  52 (+/- 1.1) <- [53 53 51 54 51 52 51 51 52 52]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  51 (+/- 2.2) <- [49 52 52 51 51 46 48 49 53 53]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  52 (+/- 1.7) <- [48 52 51 52 53 53 52 54 49 52]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  52 (+/- 2.1) <- [51 46 51 53 51 51 51 55 51 51]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  52 (+/- 1.8) <- [50 53 53 52 49 52 52 51 47 52]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  52 (+/- 1.9) <- [51 51 53 53 48 51 54 54 49 50]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  52 (+/- 1.3) <- [50 52 52 52 53 52 51 51 54 49]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 51.7 (+/- 2.04)\n",
      "Mean time (20 cv): 0.63 seconds\n",
      "Overall time: 17.12 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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M23KHgssrGmoES5Bc+PKCNmOI7e5BHMsKpivMvJBsuuAH8hXm7SQh2SyFuRC5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\nHh4eHh4eHh4eHh4eHh4eHh4eNcT/B76xb8/F38RYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f274bb7ab10>"
      ]
     },
     "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: 70.8 %"
     ]
    },
    {
     "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: 63.5 %"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clips accuracy: 69.0 %"
     ]
    },
    {
     "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",
      "  46 (+/- 2.5) <- [46 49 45 43 44 47 44 43 48 50]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  47 (+/- 2.3) <- [45 51 48 42 47 47 49 46 45 48]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  47 (+/- 2.5) <- [47 41 47 49 47 44 48 49 45 48]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  48 (+/- 2.4) <- [48 48 48 46 52 48 47 42 47 48]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  46 (+/- 2.5) <- [41 47 45 47 43 46 46 49 44 49]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  47 (+/- 1.5) <- [45 45 45 48 49 48 44 46 46 48]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  47 (+/- 1.3) <- [46 44 49 47 45 46 46 47 46 47]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  47 (+/- 1.3) <- [47 47 44 46 46 46 48 46 46 49]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  47 (+/- 2.1) <- [51 43 46 46 46 48 47 45 45 48]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  46 (+/- 4.0) <- [46 44 47 50 47 47 46 46 49 35]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  47 (+/- 1.6) <- [47 46 50 47 43 46 48 47 46 48]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  48 (+/- 1.5) <- [46 50 49 48 49 47 45 47 47 46]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  48 (+/- 1.9) <- [48 49 51 46 47 48 48 47 44 47]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  48 (+/- 1.2) <- [48 50 47 46 49 48 46 48 47 48]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  48 (+/- 2.9) <- [45 50 51 47 45 42 46 48 49 52]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  48 (+/- 2.3) <- [48 46 47 43 51 49 48 49 46 50]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  48 (+/- 2.4) <- [50 42 47 51 45 47 46 49 47 47]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  48 (+/- 1.6) <- [44 48 48 46 46 48 47 48 46 50]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  47 (+/- 1.6) <- [50 47 45 45 46 46 48 47 43 46]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  47 (+/- 1.9) <- [49 46 49 45 44 49 46 44 49 48]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 47.3 (+/- 2.25)\n",
      "Mean time (20 cv): 0.38 seconds\n",
      "Overall time: 11.94 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 0x7f275007c650>"
      ]
     },
     "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): 17.53 seconds\n",
      "Overall time: 354.40 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\nvQTRgxkHzCI5jODHkF5D4Uz+W6EZYTMo28uU3sB70bGDD6DjotIIh+I8T7pXchxq3GyWsG9rNKSy\nbXnlrBu7oWX/GoX9zM+i5U9B+qIPuG+c4iHZDgrHQSYl/nwF+Bs1FczoIzqtYyJp9R5yA9nfl7Bv\nU3Q8cUfCvmmo5/Mimesbyt4gB7nX/VOO6e/K+JcWSDDaBQ1Rvg/t/76EbA/zZOBKNLdhX7Texp+t\nHYH7c2+jZe7Yncn1I3YFc93/lHHpRUnyML1gxroRZFO0XQnGKEer0HHLJU44EkcmgFwA8pd6GGs1\nEkzpi1qwz6H9ks+hjcjowrCftAGfBC6o8Gb18DCL8QLJIVc/0/99FArm9qhHfScalokhB4AkiRfo\nw7TI9fE+4N6nESZLJYQ7ZHPXd3UqKvybUshWwFtoyKk7shO6WsJGqBESciewuZugOonPA48Gk6A/\ng4bYw9Bl0gTWwfWkF7qy/ZZo41nuJAWV8CI6E1ZSGP0IdKjBgQme4jRUMNM8zP8Bl6EGVAISAT8D\n/gwyA3gdJMlgm4IaGoNIHtvak9gZ/U42ItfmbZ/cYMsU4BPAr9Eluk5BHYvpOa9KIvSZjkXUIoHo\n/hpkYGcxB10veFXRI5NZBAzP1Uvpgxq03sMM2+qzgZ8kOEJF+jFlR5DPpuz8GCq2+5IaTasdNRBM\nORI4Hg0DrkGtqT+6DMRZdFYMOQXkv+i4y3lFPLgs0pIf6i2YST+wF8yZwNRY6HV7NJvzdnTx1TAz\nM0ITV/6tfVEFHIyG3KC4hzmNTMHkMrQiDwYuInkc4laoRbxnxn0ayU5oUtMj6FqAAdEadF7XQzXc\nLIFnKB9HoxuHB8e/ic4Fe5FrwDoo9DDvJ7/PaU+0j+n3aF2sVUg2g+gtNGwWM9RkIFo/LkTr5W7B\nvlFAb3QFn8n5DXrnxCBPoV0Q+5K81NJ2aPLJpsDF6GTeSfViM/TZvpLCcGMPQY4G2Q+tC3e6v5kQ\nLUQTdoJnTc4CuR8NrZ4L0cMQLYLoMpeLsRhtG49AjZnVGdGFrmQu2ZO4FCFahxpzz4P8A332FgG3\noe3MFiCfBzkXdRSSHKXnSWxPZWenL/8AzkoJ226LtmkP0g3qXRHLRvqBLAGZD3J1wv5vgFwI8kEV\nS9lLhzpIFVlRsg1I0M8j64HMAnkT5IzKr1tWGQ4H+XtsWy+Q60BOdu/Pcu8j9/c0iAvbyEkgC0Cc\n1S9bu8+vcrGtAAAgAElEQVSwP8hC5wH660buu9vVve8LsgpkfdcPGS/bk/odgT6MYTllJMhykA6Q\nLdFMvxsTrvGS3k+WBpW0K63cggJk7Brr6lxG+EWOBpkJ8grIIpAvgRzmvtuEyID0BrkP5Hj3O8S9\n8uNA/ubu/RTIXJDT0VClaB2sB3IDSNBPKR9yv9HF7v03QX6f+25kD/1cALLMCag/dyxI0JckV4GE\n84T67b8GOTN4fzzIlQnHnQPyLVevYl6/9M8vd1nUq94Va+sikGfd8/Oqe38gnQauXATyZfd6GMhr\nIPuATEq53lWuDXjbtZEJ7Wc9kB1AflHlNQbocyXHuGctiHLIYSA/B/kuyZEJQL4Gcn5s2yHu+7nF\nXeN5CpYSk8g90xNd3Yst+CE7FD7LpX+oLjhJDgOZATIYJCFzVTZwD/SzqGVWA2SwE4xLnSD8DeQ8\nbbSkrTb3KFqGKa4hdh6kjAC53TW67nuQvqjQHQfyXlTIgoxV+ak2MgByNsiPg9f/DBq9bUBeJM+6\nkntRA+Hx/M8s+4K8kKuwsrWWoXP/kagVGH6O2FRaMgLkdb2fPAfiQ8vdRTAPA7mpyOntrt4dpw+Z\nXIYade/JOOdQkPtB1lDYjTDBNZJfA/kzyLtA3Mw7ch51S1iQI0DmgIxDRWgeSBCel6Gunp3u3v8I\n5Ifu9cz8Bkv2Bfl38P4QOsW1c9v6qHEyLti2MUhCgohchRoqbe75DMbfyaGoYVHJsJjuIpjT9HPL\nHiAJSTCyK2oURyAfA7miyPVOAfkdaoAIyFcqL3qzIxu758sNb5Ix7nkNoyWXoBGi8Lxx5IyXQ7Td\n7NwXgTwK8p8Kn88uEcwrQIr0V8jNIHdS0w5Z2cJVyrudaKQkInQlcgbIY64RmwnyEwqGcMh094M+\nC/L+2L7tnbhF7jO4yiF9QB4AcUtFyU/QcEZ47nraIMmtIJ9z23qBPKyNVudxg0FW5yqM/Ia8vgBp\nc/uD4QiyF4gb7iPHod7UZOrXcJF9L/k1yFklXKJM40naQF5OFgNwv9Vr+QLVCORMJ4rnkhwdmIRa\n5vuDLAbpcNuvBjkxOO6z+l12vm9zv3WwXJP8BuRHsetHrk7Hx949BeJWnpAHYo3dRe67jXkRBWXf\nqPC63UYwv0KmJ9YZRXoXyEMg7y7xthHINSANDyc2FrneGRH9tf2Rs2P7T0IXVg+3vU/1BfLFE/T7\nlBdBHgQ5PuO+be5ZOhxkTzQDHyoXTLmdvP62zl07ouGJIvOqyka5h7YraFQau0RoWO56kK+nl0OO\nRcMKUcL5z4D8kULvc4pr7I5BLa204QRT0VDbQ65BmkGBNSXz0dDEWFeBNo/tf5LOyRBkgGskfxns\n/wTq/dZZMBMTKHZ2n2dU4b6a3PabIHel7LsENWwaPGxCIpDPoKG8lGxrOQL1lIMwn3wAjYpci3pL\n8yiYCUrO1N8f0HDiQhL7NeV6vUfn+z6o4eUMV/kVyKeD8s5DPdql7jdMMHAlQj38WSDhikXdRTDv\nAzmwyDFfAHkDDZ1XOO9vq9LZHfUEmvEab8c2cm1c2A9/Fsj3gvfzQfziHb9EuwR3cUJ6OInIqagn\nejPqgHnnpGLBPAu1nIL+MvkiasUekX6qURw504lmwrqMcrB78P5U5BojQHZyFSpJZL7jKtqrIFcm\nCPdVaP/TISCztZGV2HAgiai/YF6LeshD0FDove6BOrr46RXfdgid/cwF+7ZREekuJHWB5O3/Fkhs\n6IL0U+NH1pG4wLuMcaJ2u/uu90+59pdB/gUy0r2fBvJssP9UkMvc621z++QraFRmJgVTpsle7lk4\nGw19/h7t8miwYEp/kO/rZ0idrCQ8vinGE3Y/JALZHWS/5O+5sw/5ArSraWdnxIR9+leA3OR+r6Xk\nusx2cG3bT/KvLYPRPIekvtVqQrLyI6f8B4Ccj4ZfxmWfahRH2kienaWeZdjVWVfu900/sG5FUsG8\n2An9CtQL38eJVisMjO9iZCdSQ9YyXA3htGQVCIR3njNmrgUJlsCTDd2+x9Awb+gFRO63vRnt2/86\n6lHMRENykWsQP4oa6XUUTLkN9UxuQr3xNe7vNrosqmGUjoxEoxdPoN1PF5I35lcGol0N51MQ1ZQR\naGTkMZBPuvq2FuSitJtVVEL3L0JXwHjMFbJOWYFGN6IBIVnZiZ4/CL6Jka21wZEvkTd8B5wxuCua\nNRsTZ+mLepK3uQbwBDTKkZSLUE/BPBrtgjiczsQqo+cgkfuN/+6MtayIQWK9K2atz6DiuU+NHkbK\nhAtdwgys3hlKverdDKzOGTnq2d4ZhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEY\nhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYNcQmXzdKxSZfNxqBTb5uNIKK6l09l3RqAmQtyJRG\nl6JB1Hl5r1bA1vcsgQYvIG20KNWsh9kdkbOKLHhc6/v1AxGQ7et3z26FCWZNkd7OANus0SXp5phg\nGo0gsT70qncpasjWwHZ1vN9g998WNDZqwXigDxYGNIymoZkFcyAwsY73M8E0aslk998E06gTchrI\nuEaXoplpdsGcUMf7mWAatWQycDewt/VjGnXiNKCO3Vg9j2YXzBp5mNJX+5QyMcE0aslk4A7gTaBV\nE8mM+jIS2KrRhWhmmlkwB1G7kOzngWuLWPommEYtmQy8CNwF7N7gshitgQlmlTSzYA4ERoEMqMG1\nJgLvBT6UcYwJplFLvGDOA0Y1uCxGj0f6oU6GCWYVNLtgrqQ2/ZijgAuAb2QcY4Jp1BIvmKvI1S3D\n6CpGAguAPiBjih8ufwOZ1NWFajaaXTCfpTZh2VHAfcDwjGMGA6sxwTSqRvoBo4GXUcEc1NjyGC3A\nSGAJ8DhFvUyJgP2p7yiEpqBJBVMiVDCfoXaCOYfshmswGj4zwTSqZRLwMkTrMME06kMZgskItJ1r\n7+pCNRtNKpj0A9aiIlerkOxLet3UbNl2VDCtEhnV0gHMdq9NMI16MAoVzCeALYsc68cIW1dBjGYV\nzEHAG6jIVelhSoRWpsXumgNTDjQP06gVHZhgGl2GjAQ5ObbRe5jPAJsUuYAXTHMOYjSrYA5EG5oa\nCCbtwFqI3iS78TLBNGrFJDQ6AiaYRu3ZHvh1bFYfL5jPUnzcr3mYKTSzYL6BeoUjq7yW9y7BBNOo\nDyaYRgISgRxegwuNBtqATwTbvGAuBPqDZCU4bggsxzzMAppdMJeRndlaCqFgrsQE00hF2kBOqcGF\nNiBfMM2SNwAGAH+rwXyvo4CbgFODcepOMCNBvcyssOxkNDnIBDOGCWahh5nWeA0GXgXadCo9owUZ\nD/wCpNrnZhIw173OMtIAORDk7CrvZzQHvi2rdhWm0cC9wCJy4VfvYULxsOxkNDnIDLkYPUEwh1U5\neXU5IdkVwOuY5dWqDEWX5Bpd+SWkDRiLjsGE4iHZHYEdKr+f0USMcP+3rfI6vk17ldwsUqFgPkeq\nhym90QjIE1g7V0CzCuYgYBVEa4G3qK4PqBzBXIkKpoVlWxP/u1czlGk8sMjVXSgumB1U309vNAde\nMGvhYb6KtmtJgpnlYY4DlrrzzcOM0ayC6T1MqD4sa4JplMpQ97+azOww4Qc661xqlGQSNtdsqzAc\n7Tus1sMcTbaHmSWYY9FcjRWYh1lAEwimtIPcAnIliG+oGiGY7Zhgtjq18DDDhB8geht4G52MI4kO\nTDBbhRHATGBgafO9pjKKPA9TegHDUM8RMkOyjHDHrcQEs4AmEEzGAZu5/zu6beZhGo3A/+7Vephz\nY9tS6p30cvcaDNKninsazYEXq5lUF5aNh2SHASudcQbwGjAgJXlxONqmrsBCsgU0g2AOQccOPYqK\nJtRdMKUv0BtYgwlmKzMUrYvVeJjxkCykZ2ePRev3EqwfsxXwgvksOhayAqQ3OW/SC+YYNGPWEQk6\nzjKpHRuOeZipNINgtqMiNZ+aC6ZEaBJGMQ9zEGqhCSaYrcwQ4L9U52GOA16JbUurdx3oFHph8obR\nc/He3Xxg/QqvMQJ4zU3s7+tNkpH2Ork++aQyWB9mAs0imCvQjuixbpvLkgWq8zAPRYX3afc+reHy\n4VjQcIbLZpPzQSaDDAb5cYVlMJqHoahgVuNh+nBZiAmmATkPcwG5tq5cwvq12L2fRG7uYk+a4T8C\nC8mm0kyCWWMPU/oAPwTOcNYYlCaY84JyHA0chwrv6W6dQ6NHIJNB4lb+EOApYHwVkxf4hIyQtMkL\nfENngtn0yBdBjixykBfMajxMnyELuXrTQaGHmRWSXYauBtXLJmnJpxkEcwiFHmaFgim9QB4C6Q/s\noteNbg4OKEUwXwHGuYo0BjgSOAaIsAVXexJfBD4W2zYU7Qt6nconLwj7zD3mYfZ8tqL4cBEvVtV4\nmKFBVmlIdqnrfjIvM0YzCGYt+zA3QLPP1nd/s2L7S/Uwx5MbrzQa2Ad4GG3gjJ7BKAoTL4aglvnL\nVBSWlb5o3V0e25FW7yajDZ0JZvMzDG03sqixhxm9AbwDbE5hSDbNw/QhWbDEnwLaGl2AEvAh2cX6\nWvpRuWBOc//HuL+Fsf1pDdcocpXIeZiMR5cXewBtPF+jasGUg4D9ITqjuusYNWAkhWMjh6DGmw/L\nP1zBNf0E2CFp9W4j4Hm03m5Q5r2M7sUw0tfa9YRjIEdrxmtnd1GpxPvIF6OCWW7SD1jiTwHN4mGu\ngOgdcqGKUDBfozLBXI+8VGsg29L33qi3/jZAPY2vAh9FLbjJCeeWiPQCfgC8p/JrGDVkJIW/51C0\noVlCbhqzIsi2IIeCjCc5HAuJw0qkLyrK5mH2DIp4mNIbrQPLIXoLFa1Kwv7xOubFc17suGJ9mGAh\n2QKaQTB9HyaoWI2l8izZTdFZVcr1MDekUzCjtahIbwO8AtEqiJaigtlRYjmSOBgd6znZPTxGYxmJ\n9lUPCLb5kGw54yKvAs4Fvktywg8k17tJwDzXeJpgNj/DyQ7JDgNed44BqHNQSVh2fbSd9CwGXg4m\nLfAU6cMELCRbQDMIpu/DhFworJqQ7IPkPMwKBBPQsOyO5I+ne5HqBPPDaNbuEor3dRhdz0i0vnUE\n27yHuZSSPEwZ6875DFqHwgzGkKR658OxYILZExiGLtycNobbh2M9lSb+jCPfm1xMYf8lJHqY0hft\nhvD5GuZhxmgWwYx7mAmCWdISX9OAO8l5mKWGZDcEXgjez0OTh14Ots2mOsGcgM7w8TzZi7saXY4M\nQL39J+hM/JF+bttqSvcwdwHuQ3/TDckOycbr3cbk6pwJZlMjfYD+qFGdZgzHBbPSxJ8kwYz3X0KB\nhymfQdvE14I+9ibwMGVIwvCvLqPZBDPBw4zeBNaR2aEu54J8Dk1yepz0kGzCeDhpQ4eLhJXuFVeu\n0MOch050XOlYzLHoQ/I82lgajcOv7PACsCHIJnR6l5FQsofJrqhgznPHT6QyD3MJJpjNzFBy2dVp\nghn2HUJFHqZEaPsYhmQfB/6TcHDgYUoEnAfsHytDMyT9nAL8ol43azbBfBHYGw0TvBEcM5/sNP8D\n0QrxP1Qk05J+VqNhk/B7maDnRGuCbd6CCwQzWoc+EBVkM0rkyrQAXUnABLOxeMGcBRyAepqHkRsO\nUqqHuStwr+uXmgNsT7JgJk1csBE5D9PVf6lmzuQGIsNBTmp0KRrIMDTv4RXSBXMk+WJViYc5DFij\neRWe6GKIfplwbOhhDgX6AgeR7+U2Q0h2I2CfeuV9NINg+lR+gL+igjeIfMF8CG2M0lgfOBb4sTt/\nQ/Szr8w/LHoHeJN8bzXefwk5oYzPCfoilYVTR2pZojVYSLY7MIqcYB7sXh9Erh6W4GFKf2BrtM8c\nd60dKC8k6zzMSFBjbxrNyXnAjxpdiAYSCmaaYR+G4KGyPszxFGbDphH2Ya7n/h9Avmg3QUiWyah3\nPr0eN2sGwQw8zOhtVPhOiWV9PUBu6S+H9NG+qE7v7UaIriK32sSihPFwUNh4pQnmUohWx7b/G9iv\n1A8WEIZRLCTbeEaiwjYT+AfwOTRc5QWzFA9zE2AORN4om4Va8mlZsoElL73IH8oEOoft5m5/sfF8\n3QjZG43wDG7hqSOHoUKU5WFuDjwZvH+J8qNV8f7LLEIPcwxap+Nh4WWUPHyqHOSTIEfV6GKTgTuo\nrN0tm24umBKRH5JF+yyj38YODASzM/nnbOD7aKVY42a9APUO1lHYf+kpRTCfcfeMcyOVjaP0/ZfQ\nmSBS8VylRvX4CQbmQHQwagi1kwvJltKH6WeC8vg6VIqHeQg6FCCMojjBlEOAu0r6FF2KDNQxpkX5\nCvB1tPujmkWRmxnvYWb1YW5BvmA+A0wtLZlRpoIcT/mC6T3MMcA9aDsbhmTnoMObas2ewOHVX0Z6\noeX7LSaYgKY4v+PGPmYxE9gK5F3AE66v56PAFDQcuyB3aPQO+vDG+y89QeMlGwJHkF+RgegFiN6d\nUo7h7rxyCAQzWoVW3C5qXGRLkLRFsg3F92E6ooVouMx7mCvQvu6sialDIwiyBXMJnYPUZRpwEXB8\n7Jin0IXU3wtsC9KgKISMB/k+ugj2vdl1STZAw9BXkstw72HIME0qzCQUzIT5pqUf6ik9m9sWLSY3\nZrwYHwTOoiLBlAgNyc5Hu7ZCD3M2FWf+S6TtsXw7Yec4YI90Y0Ail2xZjPXRz3ETsFtyP6aMBDmx\nxEIXpbsLZth/mUG0Em3Q/uw23Iou9txBgWAC6l0W8TClL3AvcAHw99KKG72D/njlepnxxjWcaL6G\nyGDgdtQIMNKJCSagFrjzMLMyZeV4V3fKEczZwET3wJ8A/Aai+2PH+JDsAcDdwPtL/CxFkH4g+7g+\n11L4Oxq62xHtn90149gTgb+4rotKB+J3d6ZROEl/HC+YLwAbJwjFVODFWGIh6LKDm5ZQhj3RLoAd\nKFkwo7WoIA8gN2LgStTo98wGOkocshfnQ8AvgS+oUZHHeNRA7Eg593BgRglRtg70e1uGPldJM619\nADintCIXp7sLZiwcm8n96AN8AOpZfgt118eSLJhpHuZSNOljPBrK/XlKX2catwF7Je+SM0A+n7Aj\nHr4LJ5qvJZ9DxaCFJ0aQMSC3FDkoSTAvBf4ZvE+YHk+mAX9AhS1JMJ+NhVkd0WrUsh+HNnyPJ5Rp\nLmpA9ge+TU2MHhnjrns9JRl5EqEC8SWIZqFjmlPqOqCez6XudQ/1MFkfGFnEIxqOjm9cCrxFLsnG\nE++/9JQgmNIPFcrb0d+wVA8Tcok/bhKX6JcQXZPbHa1ARw5UMkXfpsAlaGh5o6C8fujLLcDuKedO\nBXZDl07MYjK5SRmeRMPacd6NGqMDEvaVTU8SzK8AR0M0Hw1d/Q71FreiUDAXJGzzzEXDJhPRjvdy\nmUV6Z/02wE4J26v0MKVPcStQRqOC+XOqWwC52ZkKHACSZZAkCGZ0B0TXBRuWUpj482n3f0MKftNo\nJURTM+75ItoAbIwOLYrRmSl7K5rk0AFyLcg2Gdcsxp6oofl/JDc2cUahRqTvy52BDvNKQIag9cx7\nLD3VwxyDLu2XJSrewwT9beNZ8PH+S08pHuYO7rjr0KEh5QimT/xJmsTFM5s8T1AikD+AnA+S1Y5M\nQNvPeBLjCFSEbwH2SDl3PHA58L0ihshk9LmBRMGUfuhKUvOpUSJlDxLMaEnuQY5edg3MbHS2lbg4\nngX8PuVCvqO7UsF8ifR1MTckeWhAXDBL9DBlFMifUO/k5CIHfw/9zDNobcH0v02adQvJHmacmIcp\no9EM7stJFMyivOjOC4aTFHAjcIULp22BeqLF+s+y8DMRpVnnccIGCnfu9JSs3a30up2rbfRkDxOy\n+xqLCeaWaMg9ztOogZfFnmhS2gz3vgoPM5HZ5Ic6NwDehbYhn8i49gS0zzYumOPRbOG7SX8Gx6Nz\nMK+kMzM8kbhgxo/dHTUyH6RGQ/W6u2CGE69XwmzUAosJZjQXotcSjgf1MDegcsGcj874k5QQsiEw\nJaFzugIPUyaiM3jMRw2Ad2UcuyMamjibitdy7DFMRCMP1Qpm4GHKbqgndT7a11mJYM5C+wNXQpTS\nbx+dDdEN7vVidO7h3UHiIb4Ysn9KBMIL5hNoo12MDvLmJY1WAY+568SZ7vZ5epiHKac6D8YLZYWC\nKb3RunhfwnlPA5uBXALixEm+7xKvIpBPo1GjG1HB+BNd7mEyHU0O+iXpHiLkC2YQkmUcKpiPAxuA\nJE0A74+5n+SIHC7Eui2ZHiYHoTklz6LddFXT3QUznHi9EmajkxCkhV+TmENVghmtc/eLeYgyGP08\niygIcRRMZ1WKh3kEcJdbO/NKdLaLtN/zKOBXriFudcHcAE1cSRHMznG7aQ2IJ0z6+QrwbRU0ZqEN\nRCUe5oEkhmPTiFai/Y/HpB8j/dHw1zYJ27dCre9n0BBvscSfuIcJOsQlqeHcGng0eN+DPExpQ7s2\ntkCNgFVU7mFuh656lCR0s9HvbBfgVBVKvoSGGTdGh+scANFtmnAYHV/CiIKQ5ahgZnmY8UUlpqO/\n63+AbZLrjERoG/MKyR7mPDeOfibq0MTxEzCkCKa0owbG06h3jXu9ccxR2Qft220pwazGw/QPdzmC\nOZfqQrKQHJb1jc1T5IdltwIW509n1TlnbhZT6Owfil5CK39a+GIXNOMXVAiGt/Ag8omoYE4heeWI\nSej6q8sS9oWEkxdsiT6YoIK5NSAuaaJUXnT3LkMwAU0yOiGjD3sy2sd2cGz7tsDTbnm6tWi5i/WX\nhUkWnphgyk5uyItvWD09ycPcCOiDCsEY1EPP+mxZgnkgatAkEL2NGkM7uvt8A1iLGj/bowbzY8nn\nlsQj6DCl/uTGGMeZTX5IdmvgMWes/ZeCCWMATXJa6+q/ywzuxIdkIVEQpQ3tD16QvB/Q+rYc+GAu\nszh6E3V23HcrQ9Fw9oMkh8EroqcL5mz3vxzBnIdWzg2prWD6CRDiU5ydTuHkwaVY41PIG7fFv1CL\nKkAiZ3FNp3OihWgdXZeF22DkbJBiU2RNRK3eB9FMvDjxhj4N52HKEPQB98bZHPe+HO+S4Py0/ss0\nbke9hKSwKGjjvgx4X2z7nuSMKNBGv1g/ZgeFHuY9wI6aeAZoqO46NPHuieC4BcCYCocodDc2c/83\nQYXSL+iQRlwww6ElB6KJXClEV7vI0FXAaegwt21Qz+yhyorfye/Qfve0Wc8gOSTrn4+70HoUx4dj\nQcVxeDBeNxwr+h9g59i5btah6C30e50MsqkOfepke+C+hDI/Sk7AdwcedIKa4WFKLx1HmzmmupMa\nCqZEbthEGQ+EfM1ZpG0gv05I/Z1IeTH5OLMBIXk6shSit9CHezO6XDBlLDqry29ixy4A1kseiNvJ\nVPIF8w5g39xb2Rq1IHcCno95Oz0oLCtjQLwFfBTZfZOgIdmXSM/wLFUwZ6O/4+bAU7nkluhNtJEo\nVzBfRmegKtPDjNahWa6fSzlgY+AKYKNcZrBE6BjJK4LjnqR4P2ZCSDby4wu31YaN9VGhfCWYFhDX\ncK2i5KnWpF8J4/AaxTS0TfGC+Ripgin9UY/L9YlHy9F5sJ8HeRn19P+dfG4el6PG2E/ICeaDmWcU\nJXoF9W6zuh9eQIfNbOK8tvXIGXX/JnlYURCdi94h100BiR5mnmYEghq9hT6LDwOXBselffYb0PYU\n9Nme4V7PR8fWf5jCCT8mo6FttzhAYp9qJ7WskJPRSZYng2wB8pPsw2UYGmL4BroSxKkUxrOrtaJm\nAecnrDZejLlo6KMMoc2jmGBuDzIJfQguduOzAqK3UA8mJVVdBqEp/nODjfeSX/k+jlpVF1KYUNCD\nBJPPA+c6D2djMtPHZRA6UHsx+jDtk3BQqYL5b7Th2oPCIQGzKC+qgaujz6P1o1wuAfZLSfPfGO3f\nuZlcY7In8A6aqegpkvgjvVBjI2ltRe9pHIsukPBRCmcqgpLCsjIG5Ab02Stl0H4j2AydY3g6+j2+\nQLqHuQNqUIXzTp8EHI3Wv4OSx+bGie4Fprphc2+gntnM7HNK4meocKXd903g1+hzti35mc93ajkK\nHJ3QwwQ1ArdyrwPBjF5G29kw5BsKKuj0poeiQ2Y6XPuWJZj7uqztvVz5cJ7oL9F6+ePYOVuhxu+Z\nIL8BLku4bsmUMWBfjgURkCNAvgCyInsMjZwG8g+QV0H+B/IsyFeC/W0gK4spftcgfwYpNzQWnv9+\nkNjsQHIDyMFaueRykDUgP0/3IuVRUufqlOkgsUZaIvddjtcEI1kG8j73m5wYO/bHIF8s90OVeXw1\nlFPv7gJ5AmSK+6w3ZBy7qdYzcB7MisL6JbPVmi7p3jeCvETBZBRyCcj5JX+E3HlV1HW5jM5Myrzt\nN7l6cDDIPW7bn0E+GzuuAyS++o7fNw3kdJCUxBA5EOQ1V+eS+rT8cdeBZCQoAcj3QH5PbimzetW7\ncurcTPeMv6PthEwHSZpsApCvgvy0NkXsvOY/QMrt667mfmPcbzsf5KOxfXfr75+37VyQbwbvPwBy\nr2v7lqKRNb/vr+r5db7/JEjCcmRyOcjJahTKItKn1bvd1e9ZFCQkyVCQ5eQtkyffBPkOOqb5CnJ5\nDYn1oZYe5o5onN4Pzh9M9pIrJ6Pez8Xo3K1nkT/N1qZoWCetM7ormUPl4VjI8zBld5BL0c82Sy3N\n6FhgMESfCqy1OFlDS+L9lzgr6kHU+joGuBOifwCfRS2vkBI8TOkD8ieQGSCfyj62UUhftL5tjCYj\nzCZ7gHIYKlqD9usGIVwZjoYMXyg4M5kb0O8x7mHeQC4JqAyqqus3oUOH4vhxnf9Ew7IfREP3cUt6\nDhq2ii1ULeujs1dtg0aQEohuQcOUp5AdJvwnmTMKyWB3jW+VkHTVIKQX2h3yL3QpQD/NZpqHuQe1\nnyx/JtX3X5ZBtBDNzj02YeGLW9B+2JC4h3kNGsq9AbjNecmeeD9o3MP0zEDDrM67TO1zvRZ9Dt7r\nvOGdbJUAACAASURBVOPwcyzX++dN/L4V2lf6foiOSh/SVRrlWF33gPzMeVKzQW4F+ULsmEHOsj8X\n5L+oFzkEnRB8HMjinOUgJ2qD3QjkIyDxilHO+d4iuwzkZRUcOSjdKkq8xsUgp8W2jUX7fb+tlnjB\nOd9y1tJ/1KtIvfbh6nmk7o9AfuUs2X2d19ANPUzZCeQRkCedVXkhyJukRjbkI8548e+/AfJH93kP\nB7kKpIzGTTZEvdpukEAlI0Fez7eqpQ2NZLiMaPkxyLp8iz7vGv8GCVZ9kMhZ7DWai1MmoVGQPu77\njhns8kmQv8VPqs29ixeuxMMmgzijSx7X8kobyFsURIukN+p5FxknW3ZRR+t32R2QnSjwruV2dCGM\ncNtpIC9QGNHZCuSZ4P1lIAmTsMhUkHlOZ76aUZ6+2d+NfMDVc1f35Dl0SsuCA9OvkXH1Eg/rA7IK\n7bt8HXW7jyEvLCkRyFOuYj2oP3rBdWaDfNw1+peCnF5JoatH2qhqzUHp5R6mc0geulDKNU4GuTZ4\nvynIi04g3iZxBXs5GA1FvJwuGqBlktco8CY69+/i7hWWvTsK5ukgv3CitxIdTD6H1NVi5CKQrwfv\nR4Lcj4Z0n3Z1r6PMopZpCHUlch/IAcH7DfX76Hy/CcgF6eWVC8gLL8uH3bNaw9Xs5UknMmvd73GS\n+90i98zsGz+hdvfOLlgJh6znxOBi9/5qkAvd61fRuXnD4/fUetWTkd6ooxMM55DlIPF5liMS53OV\nXqhz4fq25YGEOuDPfwh1GEpZySStvP2d6F7unv83Uq7XpYK5nXsQIieW/0S9oaWBkk93jXkbqdlv\n8ge0T+gK1HLPmkmihyPtriKtBzIRZK5rXHqDnKnbCs5Z331vJUyXJn9EZwpJ2ncRyJfjG8v+CJVT\nSuPVD+1DPB7ki+5z7wVyGwXWLYDsALJAH5K87YP0e5DBhec0G/J1kGtyAieHgPyrjPM/CuJCtTIU\ntegz+iQrKuMPXAM7FY1G3QSyBLX8ZyW0Dd1JMG8G+Unw/Z4D8iX3+knyjZVvuc95Qu2L2t2Qr4C4\neZblJK2DZZ1/PciRaF/5fHJDlLoIGYBGk54DeTjtoIquXMIhZzphPNO9v10rC6ChQdf/Jd9D19HL\nutbwXMMlu6ULa6sgl6Kh2ecpCG+nnnM9JXlJsj/qrTrrSgaA/EYfellGXse8HlBGwaulyL1kC9cY\n3YmGpw5wgjkGDSV/Cg3/uXX15Gj3cKSEInsKMkgFUi53BsIcFaKSz98BxGUIy8/UcKp5GUeBxCbY\nkHPQUPo3kk6ofRkSKaXOzSNvvJ70DsTzKNTL/JwaGTKf1AhOT0P6oUmbJ7v6V0adA/e8znB17gdd\nUsTCe/ZCI5nfSjugoqsW2b01arUHq3PIvnSOdZGNQBa6L3IW1a2s0ILIru4hPbILrt0b7Wd+yRkz\n16Pe2Wv6uvCE2pchvXBFdl9DfuhwFNonHqlhIQvQvrs73fa70D6zbhI67UpkoGt4Xk0RoKxzB6De\n3rloJmKdGnwZioZjk1b5qaNgygEURiAiNNpzMXnh/MRLdKCe5lJawrMMkR1AHnPtR6lrq/pze7v2\nR0CKTTZfL2opmLIXGga7g6IZlLI7mjxyV2s0WLWmq78z2doJ5i/UepaOBjdc7l7xB0fanOH1SbSP\nNmV9O9kK5DzXCJ+IDqlowXpX6WeWTdE+nmKr39SLegrmUucZOg9JJqBh2NUgr5CYd1FwmWEgn6El\no2PSi4oT4GQwyIdqW56qSKx3xR4qAfkLmo7/KjoA/i10sulr0UHIR5Y54a/RnAjF60sN7yWL0IH1\n16MzxHwMHbz8OvAHiP5Sp7IYjaVe9U7US6I38Ed0qMgW6Mw6361g8hOjuUmsd6UI5gnoGK1x6PjK\nCcB3IHqk5kU0ujP1Fsx2dLaYvYE2dDaSn2SMWzV6JnUUTH8faUfn3r25cBYuo0WoSDBnkDxXoNF6\n3Eny3KtdwQys3hlKverdDKzOGTnq2d4ZhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEY\nhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYRqnYXLJGqdhcskYjsLlkjUZQUb2r5xqIRvemGy0gbbQQ\ndVwP0zA6SawPLbjIqWEYhmGUjwmmYRiGYZSACaZh1BS5DGRoo0thGNnIto0uQU/E4vqGx/owS0KW\ngUxtdCl6ENaHWXNkIMg6kN6NLkk3xvowDaNrkQgYAgxodEkMI4OhaNs/pNEFaTZMMMtCTgJpa3Qp\njG7LIPSZMsE0ujNeKIc3tBRNiAlmeVwArN/oQhjdFt8QmWAa3RlfT4c1tBRNiAlmyUhv1IMY3OiS\nGN0Wn+wzsHaXlDaQQbW7nmF01lPzMMvEBLN0Bsf+G0Yc3xBV4WHK9iDhjDNHo5ENw6gV5mFWiPXH\nlY6vZCaYRhq1CMkeDGyPTs0FMA4YVU2hDCOGN+xMMMvEPMzSMcE0ilGBhymfADkx2NAO7AXS170f\nhWUzGrXFkn4qxASzdEwwjWJUEpL9GPBzkK3c+3a0r3xH994E06g1Q4F1mIdZNiaYpWOCaRSjzJCs\njAUmAGcAP3UbBwMvAfu79yaYRq0ZAryCCWbZmGCWjgmmUQxvuZeaJXsA8C90aamJbls7cC0mmEbX\nMQSYg4Vky6aJBFNGNXgqJxNMoxhDgFcpPSR7IHALsJxcOLcdFdHpbuYgE0yj1gxFBdM8zDJpEsGU\nCLgb2LOBhTDBNIoxFFhA6YK5H3AbhYL5MvAO2qCNBPqB9KttUY0WxjzMCmmWYSU7AVOBEQ0swxBg\nGSaYRjplCKYMRevUi25DbyeK7cAKYC7Q4a653G1fU+sCGy2JeZgV0iQeJh9GLe5GhqaGAPMwwTTS\nGQIspDQPczIwCyLRv04v0wvmS8DWbvsyLCxr1I4hqEFmglkmTSCY0hc4CriexjYa7ahgtjewDEZd\nkQEg25VxQjkh2cnkvEsoFMy5wLbAEuB1TDCN2mEh2QppAsFkEtpgPIF5mEZ9OQf4QxnHDwHmU1qW\n7IbArOD9crQBGwCsQj3MbYDFmGAatcUbdr1A+je6MM1EMwjmcGApjW80fGNogtkSyHbopAJDix0Z\nMJTEkKyM0CzvPOIe5uvAWGAVRO+ggjkdE0yjpkgvtA1bAbxGefW75WkWwVxGfiZhIzAPs7U4AZ30\nvByhSgjJypfRQeLfjx2b5GFOQBsy0JDsYLpcMGVk11zX6KYMBt6AaB0qmBaWLYNmEsxGW9kmmK3F\ncOAZYGBp43+lD9AP7XMMPcyTge+hYhiyIYV9mKFgvuT+e8Hsgr5zmQA8WvvrGt0H2QDkVJDD3IYh\naF0DbVct8acMmkEwh6GWkAmmUU+GoV0BKyit3g1B6+gbdAqmRMB4dOWRsblDpRfaN58kmCvd+5fd\n/670MEfoPTsnejd6HqcBHwH+5H7noWh9AvMwy6YZBLOGHqb0A/l7hQ2ECWZrUa6h5sdLBoJJOyCo\np+oEU84BvqHHRquC82MeZvQmsIiuzZL11xybeZTRzIwCLgVeQPvEvWEHJphl02KCyZHAIZS9vqBE\n7t6uf6qhU/QZ9cELZql95z7UtZpclux41Mh6FRjuwrbvAb5CvncJhSFZ0H7MrvQw/TXHd8G1je7B\nSNToug/YhfyQ7FJMMMuiGQRzGLUTzE+hEyCUm+gwAHgLojWoB1Hq5NpG81KGhykRuvDzq6hgeg9z\nPPCKS7B4FRiDZse+m8IkoOXu+FAwv4SGc7tKMH2/qAlmzyUumGFI1gSzTJpharzh1KQPU7YF1kcr\nTrmCOYRcQ7aCXFq20XMpx8P8MnAE8H5gLdDmohDjUA8TdEjSpmhi0J1udp+Q5ajQBvUqukP/i3mY\nRqV4wXwZOBsVSZ9Qtgz77cuiWQQz8DAlSmhsSmEH4Fa0ApUrmO3krLKVWD9mD0faUPFaSWmCuS3w\nPYhcmFW8l+k8TECFc1fgxZT668NkSYZYVwqmYI1mTyYUzHbgIMDPXrUU2FJfSgdEs+teuiajiUKy\n0dvo5NOVhkMnodNBLaHsPsy8jnITzJ6PS+CJhNLEKuwXglxYdhw5wZxPp2Am4utXPQWz3ZXHBLNH\nIhGdghkJ8F3gCIiWuQOWkQvJPgKySQMK2VQ0g2D6kCxU13BsQC6JopKQbCiYNp9s0yOfBUkTCh+O\nhdI8zLBfCHL93D7pB1QwdyF/soKQRnmYT2OC2VMZCAhEb+jb6McQPRLsXwqM0DmTGQZsX/cSNhnN\nIpjeIqqm4Qg9zHIFcxRaucA8zJ7CKcDeKfvKFczQoIJ0DzNcziuOF8yVCfu6UjD/hwlmT8WHY9Pw\nST9j3HsTzCJ0c8GUXuhDXUsPsxLBnEBuILkJZs9gDLBZyr5h5ASsmpBs3MOE4oJZ75Cs8zAl6oLr\nG42lmGAuQyevGIOOHjDBLEI3F0za0XkP33bvK2w4pA0dnP0KnX2Y0gvkQyCXgtwEcmTGBUwwexTS\nhjYm01IOqDYkuxoYhDZEXii9cKaFZFeijVaSYK4C+rtxnLXET8axFhte0BMpxcP0gnkfsI2NMc+m\nuwtm2H8JZQmmjAaZ5N6MAxZBtJach7k/8C20otwGfDHjYuPJF8yumNdzh4QVLYyuYRQQke1hlhjV\nkF6oOIZCtxqNaCxzdQ5ywjk7+TqdCUYJghm9g47jXC+9HBXhQ8mvYGHZJkVOBvkVyKEJO4sIZrQa\nzZLuQCMNi4AptS9jz6G7C6aftMCznNI9zFPRKcggF46FnGBuBtwI0a+B84FJIBulXCv0ML1VVmu+\nCRzeBdc1ChkDPIv+5v0S9pfjYbaTW5LLsxrY2d3DMx/4MkRJfZSe5aSP711ARVPYSQRyM8ighJ1+\nsepXUKPSaCqkL/BD9+YTCQcU8zBB29dp6LJ0D2Fh2Uy6u2CGCT+g1nCpS3xNcn/+9Rz32mfJboom\nPOBCvlcCx6RcKxTMheQ6yWvJCGDjLriuUcgY9PecAySl0pcjmPH+S9As2QOBu3KbonUQ/ZBsvo3O\nO5vEfHTijXIZ5sqS5E17D3M+Np9sM3Ig2oZdBIxO2F+KYC4lJ5hPkt5NYdAcgllhSJYNyAlm6GEu\nQxvAzdEwhOev6FyzDolAPu5CbuGMLQuprOEqhglm/VgP/R2fIllIygjJFvRfgnqYU8kTzFKIfhub\nkD3k/9s773CpqqsPv4delA6CSBEpAoqoFBMssXcTUzT2mmIs0Viixs+oMUWN0S8xauyJms8YS2LD\nFsXYsMWGCggWujRFsRDA9f2x1pk5c+acaffO3OHe9T4PD3NP3XNmn/3ba+21167Qwsy8A5sl7HPB\nrDnSmPEPBwP/R7q7vlwL8x102TknhXVBMOMWZqmCOQgYaIIXEcxgrV1nHLmC+RwwHCRMjLABcCUw\nAfjIVo8AbbiqZWH6xOHasAFZwUzqUZdrYSYJpgDPNKyYOVRqYQ60/2OCKQFZl6wLZk2QccDMFPd4\nuddqD+yNesaWAL0TIp1LtTDDDuS7uGAWpN4FMz6GWaJLVgK0oViDVoZNyA22WAasJms1YsEZM8g2\nLIPt/4PJumOhKi5ZCdDOwSYm8E51CQVzOsUFM5KSMZFwWa8onwOvQhDf3hAaYmHOId/C7ACstXrv\nglkbBqHP+YRGuFY/NKhsiQXv/Jd8Y6JUwYSshblxI5St2VLvjXM/tKEImUE2D2IheqIVaDpaSccC\nr0b2L9N9eTk9XwW2sM+DUSvhAKoumKyHpv37GG+4akEfNCLwbYqOYQar0OkeHVKulWRhrgSeaoRy\nRqnUwhwE3E++YEbL7YJZG/oCU4BTQRoaaR/W4ZDF5I9jluqSBW3XFgOdQKox57dZUO+CGfaOQ55E\n3azF3AYD0YCO99FoRchmXIGMYOYRFcxBwGNkA0RCLMqxkrEI6ZpSGXugPb1Z+DhmLQgtzLeBoQnW\nY9TChMJu2aSgn8vRlSEakyIWpmyUYgUPRN+b9UGi0d2hOxZcMGtFKJiz0E58Q+iDumJDksYx+6H1\nvBDLUW/bh2ZAuJVZgHVBMN/P/hmsAe5Gl1Iqdt4c+/cN4OWYNbmU4oI5GLgX7YVHBDMQKg/8+Rnw\nh4TtLpi1xcZsguWo2z7eM+9KfrBZt5RrJQT9BEshKNazL5diFuYD6NziOOE79AYa6BaSYGF6tp8q\n0xft+KR5NsqhiIUp/dFsU2mZpUKWo+9C2D564E8B6l0wQ0sxyt+Bg0FGFXjBoxbm9sDLsf2/Bq5P\nOM8EU1qhDc1s4N/kTzav1C07Ejgw1tOHRhdMaWPZbFoA0kktdwCZCJImbFFCCxPyGi9pjTY8SyPH\nPw0cpIEWsmfsWkku2WqwCOibXOclQBu5SQnnhYL5GrnDGZFyByuBtZQ+ZcupjA3ICmZD3/O4YMYt\nzO2Ap0tYCvFDcq3QOgz8kVEglzR1KaCuBVM6oIEwi2I7pqBzj6YAh6WcHDYSc9DvGBPMYDoECa6K\nYBnqXhts/94HDgFujx1YqWCOAF4HjohtDwWzMXqeABcAFzXCddYFTkWXLQK4FPUoFEACchub+DMf\nACzNrvAAaEaoE9B6cG8kkhqSg36qQPAZOi6f1CHogWYbiglm5h1aiI5jRp9N1CUL7patBaGFOYuq\nW5hsS2nTmsK2NKQeLcztgH2buhBQ14LJQGBeLIMK6pYNDgbOBnaz+ZLXxgbRw2kkoXUatzAL8Qzq\n2jLRDT6O5LINqWBqibS1cp2BrpQRJZw+kxa1SZnusrHAMY0Tvl73TCKbzmsIuW7HJLqh+YlX2d/x\nxmsYKqIRgneBm+zcN8kdf6qVhQkZKzOPQcBcYGIsF+gAsu/Qw6j3JDw/Xm4XzOrTl+zYeTUEM25h\nlhB4FrwCwWmRDRUIZtXzz44GBtTDkEETC6YMjfXWoyS5Y6M8iVaKUcCx6Kr3mDt1c/SHfwcdf5xV\nRqFuQ62JVSqWiVRiYQ5BA4+eALrrd88QWpgz9Dhpl3D+8SC3llhpRqLfOy1zUTNBWgET0WfWCW3w\niwnm5qirPSTuHhtObkq7kNOBHdEO1fjI9hpZmEC6qA1CO4ULyI2GjQTNBV+g45z727OqQDAlAPlb\nCUF3Th4SkB0KsKGXBglAkkvWLEzphrY3/6ngurMpS8wlAGaBDK7gXqUyGl3bs8kXCKiSYMoYkCtK\nOPBm4MyUfbGAnzxmoqH+J6HTP8Jgne+gDcFLtrL4wHwrtSCTKS7WlQT9DAdmWFkmA9GxMBPM4AvU\nUkiqsJPQ73Zw4dtIZyvbmcBxZZZxXWM4GrW8ESp6n1FcML8B3BP5O97bT7AwQX+34EvgBWC8RTwP\no7YW5jzgdpDbYtsHo/X1GeCrke1bosE+IX8HLkMF/peU75KdgE6z2r7cgjt0Bz7XOZPBCrSuNiRj\nWCEL82TgUQhWV3DdWehyb2mGTJzN0PpXRtSvtAe5EOS+Ek8Yjc5sGFD6PapDIwqmtAd5yjJQ7IAG\ntxToQUl7VOS+l2JRxaeUxAgEdTkcC9wBjDG354XAmdnB7qKD3vHrfgHcReqqEkBlFuYIspbLA8Be\nkX2hhQn50Ywhm6ECeJmNTRW6zyxUlHuDJFRkOQTk9+UUvk75CuppWIguBv00+p1T5rhJgCa4vzuy\n0QQz41ZKszBDTDC5DB3TrKWFeRJq5e4dc7eHncsnyI2U3d62hdwDjEF764cBN0b2lSKYP0A7dA2d\nEtESCccvQxoa4Jc0raQ3yI7owhNJydhLIFiNvhOl5pTdGTVYinVUo1yCZlrbFqRIOyo90Wjf58lm\nrWoyGtPCHIdaQWNQt1cvCi8ZtCXqgnwL2D9hfzErD7SxXAFcgYrvnugyXv8qq+T5XGzXTGMRqd9N\n1ktxT5iFCcAjwKRIL66IYEo79OW6FY3kTVrKJ2Qk8KalALwROCZ2rWHo6iyH1mDsodpsA0xFXe+7\noC/6dDL5YWV/dM3TsJ6PRaNBX89eIvgIdWVubhtSLMwMb6K//d5kF6GukYUZLIfgdeAltFMaMgjt\n4N0P7GKRw63RwI9/R87/EoKZ2igG90PwZuQaM8k+A0O2BPm1WdM90ff0LPTdTUHG25QGJ5e4YDZg\nHFNaoe7XqGAuRi2wm4CjIFiYcGKpTKN0AdwFrXfxNqsvyBb5h0uAtl+nAI+jgluI0eg7N4fmZWFm\n3DTjUNH8kIIvFtuga1FeAZyekBKumEsW1LI8BR2/GYW6K28tr9hJBNOLiO6rQA+QpDHCE8jtuYdE\nLMxgBdrohdZAD7IZN94gPyH4cDQA6Qu79tEFyjaSzCos3IhOwekY2f871B23iFRLQdYzl0kdT02R\nzdEX7wl03OVrqHDa85PWaMfgNOA+e1EPBe5M8DpMAXaIBGalLfKMBYA9h0bm3opaa7VyyYY8BOwe\n+XswWj+Woks07YaK3wfJ0eCJPApsE5miszfqpdgGzal8HdoYPwSMTU7hKHuhAn1peV8n7zq9QDYr\n4klZ10gSzE0rvFY3dEm5VZFtS9F25CEIHqrwuiHTSE7WH0PaonEkV5AvsKcDUxLGu8PvPB01HHYr\ncpPR6Ds9lzoQzGKU4c6Uyei6ezeCrAS5CuTcAsffBnK4vnjyIsiBkX3tQZaAlPGAZDbIquImfmMh\nY62MI7X3Lb+w7/I8yOfkrLMobUCWRyIUATkJ5Cb7/GrWdSpjQKI9f0AOAvm7fe5o17oWJCKc0sF6\n93fGnuWDIIfa524gH6vLUq4ASVk0W/YBEZDzoxtLfTKNQJF7ycYgi/W5AMhZVt5vgpwJcql9h+dU\nOOU1kBNBliVbP3IQyN0gw7UeFS1eJxVgGWv3rXE0smwNEkm8IctBwoCP40H+YvXrT2Ve975sJ1Du\n0volnUDeAnkhW6dlLnlrx0pXkA9BdgVZSk5Qm2xkZSqhgy7HWR19U3+PmtW7Mu8TzveVriCnlHD8\nKSCXR/7eDeSJ9OMLXmsESMKwgfyCTIenIcjXQR4o4bhJIK9YHfmcnA62zAS5DuQ/qLCG208Budo+\nDwVZQOGhuyvsnMNBbqns+1RERfWuxJOkDcgKkD3s5X0f5ABthFLPec9eCEB2tHOOAemBjrE9XGZR\n7wJ5tLxzGop83yrMAyBfgPzEGotXQLaNHLeDVpycczeyBrytNUDmn5cOdq1I71p+CfLzyN/f1L/l\nvWxlkxNB1oCsBRkTO9bccnIoiAW8yP4gk1O+1yUgV4IsArkJnaxfT4J5KchvIn8fYMI1FmQrtCMz\nnUyHQnaz/b9Kud6G9lucBvLPMooZ2L1rHO4urUDmgPwWZAuQTyP1YEOQj6wBKzNKWr4H8n/2Dq7I\nNr6yIUivyHH3gsSybcnh2WcnvwC5JrLvV/b8k4ZeotfYHWQhuWJch4IpvUA+sWf/XftuI4qccwlI\nJMBRuqKGRdv0cxKvMwJkJ5DGzlUcvccm2h4XPe4CEJvvLe9E2vPhIPOtnj4K8iN7N3+FdrxsPrAE\nqKFTYExcXkKFeUcq7mBURLUEUwJrkKahluEqe6GGaYOeeM5IezEiDY0cAfIPVGxezD7Ukou6P8ge\n5Z3TUCQAuQPkGdSiWYta2JeC/Cxy3G9Bzks4fyraI/+UnNy08iC51uM9CQ1UADLLKmKAWqm7g+wS\ne65t7VmPQjsVljRBeqA9+YSFZ+UFkO1BBqI9/oOoXcNF4XtJJ7RTMjiybZw1WmEDPwqdghOx/OQE\nCva+ZaZdd5P0Y+oJ6Yt6GWaT11GUTqjIlTnkIn1Rsb0LJJ6sI3rcBeRlXpH7QSyCW3qiHoDN0Q7g\nYpCfWr1K6VxIYO9+fIJ6PQrmudbOnW2/wQJ95zP7R5ATeCad0E7xxNh1poGUsphEePymaEfobf2N\nqoW0sjbJ8l5LYG1BLOZBngPZyT7fS6ZDJD8h492QsSAfoJ3v31s9iT6bC0F+l1KOLminoj1qjaYM\nlcj6IL8pXN9le8pz8VdDMKU1yGMg80C+b9teQC2iVtYgJ/jp5X/1QeVtD0AuQ63NOh4/iyJtrFEI\nrKHZEWQ/cixkmZn8YsiJqBvrs9yGRHZC3WCtrDIsIWOB5pxvQizjtDKlVRi5wH6jleSk5ZML7KWN\nTHjOVNJ45aqxYCaOkW0Ecjl5VqB0AXm8gbc8CmS7hl2jOSBboV6FbQscMxTtXPQD6Y2ON66INYTH\noR3Jq9AOYCuQ11EvUkfUmt0XZGcTgu1AZiT87nUmmNLJBOAkdFbAbHvfl1g7sKe1eytBfmDn/BTk\nzoRrXQsSWepL2qLWWGzWgOwDcjHIyyCnosM+V1b2NUtF7kO9DSfa912BiqLFQ0hP2xa66S+0etMJ\n7bzvE7nWueR1+DP7htr12+r3likglsFN9si+19IB7aQktQsXoR3mlGxAMhH1vt2e3kbmn1TicfGT\npECGBTkR5N+5hZDjQCxFlxxjDbWJpnQ2AViWLACg91rXl5eR7vbSfBPkWNQ9kZIDVAaQN1YrAWpl\nH46Oz6UEMsm2qFC+BHJ2gfK0RsdGY8FEEqCC+yY6LnYa2tlJEp9aC+YbIJHQdtkcbaRvIuP6cZoO\nucjq3TJ7x2+I7W+NjlteRmY8Uza1xvEtNN5hMsi/UAt0hrYn+Teq+lcp+T7SFxXJ66wBX4laTmFC\nh7tR629PE4LFJnTLSHTZylFoJ/swq9+Xo67eG+29v8feyyUmSD+3e62HRi1XEekIcj3Iw1a2tiA3\nW1txNCrs90aO74rGCswC+XMZwoRpyA/tWT1t33co6sK9IHLcPJD/QaO3bQqUjLZ24Scgj8WuG4AM\nsbbkCLvPtRQ0xjIzFyoWzLVogI5lWZD29rK8glpHRSK95AirVOfY8asoa5xoXUX2AnnWKtwOxY/P\nO39r1NWzIlc4co5pjY41HkzZYyE51zkbHXu+zhqxY5MOqvz65RcIHR9eYv9vZQ1qWu5gp+ZIFxPD\nctOoTQA5klyPyiCQq0nuKNeJYIZDTHJeVgzkfpC/2uf2+jnqypZ9TRBTxjdlGMiX1kYsQq3VpBcQ\nHQAAGKtJREFUjdDO8iNowMuXIKc29Ms1Dpnht39YuxRrJ6S/iXqZ3kHZ2b7zWyAbmBjPtr8j84pl\nNNphfgXthNyCdkoOQ63TeXb/g0B+jHrPFqLjx2aIyWQV1jDOQ1qj3o3QYxAmNalYMMehvucFqAtw\nplWC8VrRS7rMXtlCSg8avnhqC0GGkDOWWdV7FQtcaYIxTBltnY43KGhBO82YGgqm/BONPzgUDejZ\nDbUcF6Aeo7hAfFXbwZxtZQaAZQyRzmQCq6R19jqyHXU5X7ragW5yMMi7pK47LD1Ra3NcZNs29vv9\nDe3875xfTmmDRt0uQgMD56Cu7j+hcTCh4ZFY70ppJMMfbiI67+0BSls2xmleROpCs7qXU9/Uqi7Y\ntCRGoMugjULnNv4DuBNN2L+0wPnOOoW0QX9jsWQgeQeQUO+KVcQp5GYUcVouT6DJAWrBFLzeOUqt\n6t0UvM45WWrZ3jmO4ziO4ziO4ziO4ziO4ziO4ziO4ziO4ziO4ziO4ziO4ziO4ziO4ziO4ziO4ziO\n4ziO4ziO4ziO4zhOqXguWadUPJes0xR4LlmnKaio3vmKJE5IEyzv5Tj1sh5mTYrQCUSyy3w5TUhi\nfShjVWzHcRyninS2//s3aSmcVFwwHcdx6gMXzDrHBdNxHKc+cMGsc1wwHcdx6oNO9r8LZp3igplB\neoNMbupSOC0NGQ3Ss6lL4dQFoYW5UZOWwknFBTPLQGDrpi6E05KQG4BpwOFNXRKnLugMfIlbmHWL\nC2aWnmR7eI5TZSQADgP+SNYV57RsOgPv4oJZt7hgZukFdLKGzHGqTSdgNfAB0LGJy+LUB52Bmbhg\n1i0umFnCcSRvvJxa0BVYAXyO1zlH6Qy8D6wH4nWiDnHBzBIKprvHnFrQDfgIFUyvcw5oPVgJLAQ2\nbOKyOAm4YGYJBdPHMZ1aEFqYn+EWpqN0Bj4F5uNu2brEBTNLmL/RBdOpBV3JWpgumA64YNY9LphZ\n3CXr1JJuZMcwvc45oIL5GRoI1ruJy+Ik4IKZpSdaWd3CdGpBaGG6S9YJCS3MpTS5YMoUkKFNW4b6\nwwUzSy9gLt7bd2qDW5hOnFAwl5AdImoqRgL9mrgMdYcLZpaewBzcwnRqg1uYTpw6sTCljd1/vaYr\nQ33iggmAdADaAYtwwXRqQ9TCdMF0QD0N9WBh9gYCYP0mLENd0qapC1An9ASWoZXV3WNOLYhGyXqd\ncyBrYa6iaccw+9r/bmHGcMFUQsH0oB+nVvg8TCdOGCW7nKa1MF0wU3CXrNITHTdwC9OpFe6SdeKE\nFuYyoAdIFdpn6QoyqMhBoWC6SzaGC6bSC7cwndoSc8l60n8nFMxgNZoir1sV7nEw8Jsix/RFlxlz\nCzOGC6YSHcN0wXRqgVmYwRpgDRp05rRsQgsT1ONVDbfsBhRfoLovmgTeLcwYLphKH9wl69SW0MIE\nd8s6OpWjDfCFbajW1JI+FBfMDYBZuIWZhwumMg54BXfJOjVBWqP1bKVt+AzvqLV0OgGfQSD2d7Wm\nlvQB+hcZH+0LvI0LZh4tVDCljfXosIozCXgStzCd2tAF+BiCL+1vtzCdMEI2pJoWZlsKi3Ff1MJ0\nl2yMFiqY/A5YDHIhmgLqQwgW4hamUxvCCNkQn4vpRMcvoXoWZm/U7VvILRsKpluYMVqqYPYFLgSO\nAk4CnrLtHvTj1ILo+CX4XMwWgvQHuSBlZ1wwq2lhvkqqYEpHtC7OxQUzj5YqmN2AN9Dw6u+j7lhw\nl6xTG8KkBSHukm0ZbA38KGVfDSxMaYsOB7xGuoW5AZoi9BPcJZtHSxXMsMG6FngeeMy2u0vWqQUb\noNOYQtwl2zIYAPQESbIcwzyyIQ2wMKUVyOCEHb3QLEJzSBfMvqhgrsQtzDxaqmB2Az6C4AsIJkLw\nrm13C9OpBROBFyN/u0u2ZTDQ/h+VsC9uYc4H+ld4n4OB10GGxLb3BhbbtdMEsx+wELcwE2nJgrki\nYbtbmE4tmAQ8HfnbXbItgwGoEKUJZjRKdq4dXwmHAS8Af7EpTCF9UMGcR7oYh4L5OdAuO5vAgZYr\nmPGgixCzMD1NmVMtpCOwOdqghfg8zJbBAHT4J0kwQ3dpyDKgA0gJVp7sDnKWfe4HTAD2AzYENo0c\nGBXMIhZmIHgQZB4tUDClA7rW2xf5+4K1wGqgfW3L5LQgxgNvQBC1JtzCbBkMBB4kWTAHounojEAo\namVKN5D2wB+BPWzjd4F/QLDSzu8TOaEPGkw0T68r3RMuGlqY4G7ZPFqgYIYBP5mMGnHcLetUk7g7\nFjzopwUgrVExehSd+x1nIBqME2UOqYIp3dHAoOfQ9TP72Y7xwL/s8xJyA4dsDDP4BA14vDXmssWu\ns8g+e+BPjJYomBbwk4oH/jjV5CvAM7FtHvTT/OmHCtxsYP0E6y5mYQJqIQ4kmT6opXgTcCjZJbn6\no0E9oO7X3rFzFtvn01A38P6x6/Yla2GuxC3MHFqiYMbnwMVxv71TTcaRO34J7pJtCQwA5phnaxaw\nSWx/mRYmPdGxxsvRPNjtQDqj45ahYMYtzNAliy0h9gQwNHbduEu2zi1M6QbyNU0KUX1aomAWszDd\nJetUCdkQzeMZtyTcJdv8GYhajJAXpSrtUWtvYeycQhZmuCQhJsKLUOuwP7DAjllCZgxTAmA08E7s\n+pHgH2lt5fjANqwLLtkfArcAMxLcy42OC2Y+60IlcdZNxgEvJoyfu0u2+WMWJpA/x9JELlgbO2cO\nJQkmoII5AlhtAT+Qa2FuDrRGs/yEWPAPgDxlx3xoa7RCg1yy0hqkFmu8jgbOBT4k/Vk1Gi1RMIu5\nZBeibg3HaWxMMPNwl2zzZxBZCzMumEnuWCgcJZskmFuTtS4hVzC/DdwR66yZhSnd0WC0Y8gG/EDD\nXLJHA7+v8NxyGAm8CcwEhlf7Zi1RMItZmA2ZMOw4hSgkmO6Sbd4MRxt1yM+0M4iCgpk4LzxJMLci\nO34JOS7ZUDCTrp8RmkPJdQs3JOhnU9TirSLSyu7zFi6YVaOYhemC6VQBCUgXTHfJNn9GANPtc5KF\nGR/XBoJP0c79dgnXiwvmQvItTIuSlSGooRAPNluMtodj0KkoXckXzEotzI3tXzUZgE4RXIELZtVw\nC9OpEgXTiPVBx5AWJOxzl2yzRjqhCfdDUYynpktzyQIcCdwBMja2PcnCHECuhbkM6I7OzXw+smC5\nEXyJ1scd0SUOXyBXMD9GRbQShqDu3mqm1huFumOhoGBKG5BzSsuaVJhmKpgyukDElAumUwXkIOC+\nAgdsAsxKSZjxCZU3TE79Mxz97cOgHrMwJQA5GtgdnZ+ZQPAgcDVwYGxHkmBCTocsWIN603ZCp54k\nMdf2zwQuBR6J7HsLDQQqEwlQ6/JTqtuWjkTLCKmCKe2BG4BfoBZ4g2iGginrAc8Cu6QcUMwlG6aN\nagdyg/nJHacA0hZdkHzLAgcNITekP8osYKhFFg4GqbpryWkspHuBRaFDRgAzIn+vQL0NuwI/B04k\nm50nibfJjwBNE8z5seOWALuRLpjzUOt3JgS3Q/B4ZN9UYEIF0zW6A4IuVD24zHPLIWphvgdsaKlP\nDTkanSLTDfg7+XNfy6Y5isEh6EB1WuNVzML8ABXVbYCjSE5j5ThRjkLFsDNIl5RjNiHdivgYbdiG\nAKcC5zR+EZ0qsTXwkyILNmxKdvwS8zLMB44FboPgnwVSdYK6cgfFtpUjmIMpbGFCNiApQrAUHedM\nyn1biI2Bd+1fI4xjyjCQcxN2jCJjYQarUdHcKbKs2VHAwRDsB0xD369C92ljiyOk0swEUwLgOOBm\n0gWziIWZ8esfYBsmNV75nPpBApAfg3y/ES62G3A9KohpvdhCFibA68Bm6FqZExqhTE5VkbtAeqFu\nwM6olZZGTDABFbZvAJNLuFlsPqYE5AtmmGwgPka+BDUQEoKKALUwF1mnLYlnUeMhAdka5N6EHUPI\nCubglOuWw95o5yJ67zZosFK0I/AacCvwN/P6bImOzULhdzNkX/IjiXNoZoLJSNQdcAmVW5igva7v\nAP/GBbO5cglqyX2zEa41CG0czLWaSDHBnIZG0W6GBkt0a4RyOVVBBqM5WMeRnTpRqDHelFyXLKhQ\nfUF+Iv4k5gN9IwE0nYAvIfg8e0jwBXAe+dmCFgOvFLBg3yNfzKNMRfMfJ/FVYPcEqyy0MN8j0cKU\nPUB2SL+l7ATyJMilNiS2DTpMFvXejALmWYRsyEFox2UY6u5+N9IReIdUCzMTUHU0cHt6uepCMGWk\n9lLkgWLmcAlsivrN30IH1WNRURKQvnh0lHBZnIuBbRtYJqc++Rb6gjR4XINslOMs9GVNooBLFlDB\nPARtWP+DNsZVRgJ1dzllEjb2Y1ELczmpHSVph9aJJAvzUXMlFiFYjQpfmFClF7nWZXjc+ZEsPSEL\n0PqUxmR0SbA0ngW2TXE5j0FTPU6MbY+6ZAcnnHcycBdI2pqcR6OrsOyNGizboN836hoeT940mWAN\nBP9FV4Q5z8oekmJhSh/gZZAz0La+7i3MPdA1KAeiE28bQhiJuAZ4A9gitn8jNIS/FMFciFam7iB9\nixzfBMgEkO81dSnWTaQDmmT6MWBgw0LfpSPq5v+AVAtTOgE9SJ5SEvI6aqlOBZ6nNm5ZC5qQNKvY\nSeZr6IozoWA+SLpnYQIw3ZbUinIDmtKtVKLjmHF3bCEuRQOLUgjWQPBB+n5eQ9vLYxP2jUGfQ3ye\naIExTAnQZ3ILusRYDGkH7AX8Dh1aOxmNSbkf9b6EJAhmhgds/9TItiVA+wTPzXh0/PYC4E6b+5pK\nPQjmZsBDaOb8hjYSQ8n24l8m3y07AXiuyAA76A/9pI1nPo2a92UiaW6MxuIw4Igq36O5MgR4316O\nRTQsB+UA1DX0Jeku2Y2B9xJyhUaZAaxFe9a1EsyRaDTjhTW4V3NiB+By1LLqj07FSBPMndCOWYxg\nJgTTyrhndByzJ2rVlkDwaSS3bAUEa9HgmV/lWoTSGs3jeiU5gimtUO/Iq6gVHYBEp6YMQ6dRnQFs\nA9KPXHYAZkCwAHWPfhMVvml2v5C0JCCgHRjIEcxAUG2Iu2XHAXeiLvZfplwvQ70I5jS0oYib9uVi\nFiagjU58/HGibS/GTUAYDHIVcEZ500tkQ+AZkGrOQdoFGFkkOs9JZhgaqg+lBQMUIjrpPE0wi41f\nAsEq1KPxBPqiT7I5ZNVkU3SO3w46d9kpjHQDORAN8vkH6qV4Hx0CKiSYj6fsK4e4YJZqYTYCwTQ0\nmOaHkY1DUKttMip8oZdmFJp9Z56J7VXAjyPnTQSmWn2/j/z1OPdHny0QvI0aPlNRj6FZmNLe7pMS\n+RssAPYhO0czZDawBchekW3hggiTIXg3+XpZmlgwpRXaa3iDxhHMqIV5P7BHrNGZQEmCGayKDCZP\nRgfnywkOCS3SMqwEuQLkHZDzSzi2PzrG2godz3DKYziNJ5jRPKDzgW42FzjKVmTnixUg2Fdf2mAO\n8BI6plkGcqIGSpTMSLvPXejQiFOYW4CTgJ/auOI01J03GxiW33mVjmiD/BQN5310+GBTdMyxkBu1\nGlwHHBmZkzkGeA2C5ahwHWPbd0A7fSF/Ar5lEcWgbfxz9vkONMetIQHwdTKCCajhcj2qEWGnbj/g\nPxB8ll7c4P4ET+I7qBv4r9rxkQB1yaZZqmVTzHXZ0MtvDGLzgKQVyIc2CFvJtdqDrLJw4nDbMxqR\nBTYp/BPyVzov5dr7gLxSujUnt5j4XVzi8W1AloPsDLKiePCTHA5yh32/7Uu7R4Opcl2o5b3kGpDj\n7POZIL9twLXOz+3kyMPohOnw7wBkOkhKaH7qdXcCeatMz8ZzIG+UcfxLIBNBDgC5p4TjA4tcrOVq\nPrWqd0XuI91APs4NJJTr7HkEIB+B9Izs62B1o5Qo2FKKtw/IuyBLQC4EKTSNpUrI85H29Fcgv7DP\no61cA0D+BhIbKpLrQWxusbwAYp4/6WjPzWJEZJy+K4n3Duz5bwHyZrYcZZV/BLrY9FZW3gNBPkhp\n1yuqd9VuuPYFicxDkoe1YlR0rREgs2LbTge52j5vBRIP7S712q20IZK07EHxYz8A+T7IE8WPB61A\n8rJ9fgzkG0WO/wvID60i/qC0ezSY5iSYj2d/S/k2yN0NuNZNIMdE/t4eZDaZjptsZZ2nMl3nElgD\nVaJnQwaBLLUOV88Sjm8FstKEoC/aYSsiznIIiGQ7GzWhXgTzUPLmHMowEAtqkSdNIPcHmQuyBuSf\nqEXYGMUbBrJYOzhNhRxjov0nkEXk5LeV01TsZAk67SZ63mg7fheQ+eRm47kS5Nf2+QIKGhlyGGr0\nPFX++5R3rb2trPenHVDRVRtQolIufxbIJZG/T9cGqKxr9LXKeg7IQ7F9Q+0BT7feyZkNKOtRIA+W\ncNwWIG9bQ/QJJUVgyoWRSvMjkJsLHNvWGsaBVkkvL638DaY5CeZ8FRgA2RLktcLHF7zWYyCxoDB5\nHOQ3IF1ArtLft6Jr7wUyjZJSk8kpqMXzEMjXSzh+AEhkzp7M0LqbenxXkAUgfwC5s/j1G416Ecy7\nQI4ssL8/yFSQ90G2JcfT1Vg0dZpOCdAO4a/1++btPx7kxWQxkwdAPtcORc72QSDLQHqiXrwi0/hk\nLI02FUp654t7dmdFVyzzcIuikq/ZAyoy+VruIcd8l56U7JaVLtrbkqnoPE4B+WNymWRUbq+mEqQ9\n2rs6rMhxV0fEb3puIyS7mTgeDbKBid4YkJfJuFalH9rbjyTjznEz7wHyjH3eO13EZbCKfGo5O4Nc\nVriRzD2hxOMagwrvJdeBFJpThn3vz7ONj3RBLa1YAyAbk+j2kjYmsmeBnAsyh7zcr7IxyN2olfEf\nMlZI2d8nAHka5KcUnNokbUBetbpxDiW5mGVXkEgwilwDclKB408HuTVSP8vNL1opNRRMuQwklqBb\nDgJ53b5zjyKXaINOIXLykPEgF6XsuwZ1zc6uYb0qRjUFU9ZDzXQB+Q7aK34UHSOJjHdErS35LmqJ\nxXJvyrVWcbexHsCe9ncfa4h2toc/F+3NXGwNyxkg+1XyJUtHRoIstDJehPa2v00myENGoGa+vVhy\ntTWc/VEX7SLU7XAban0usGf1ekwUf4+6p9uCbGcv63n2PW8AOdmO28SucbQ13iejHYQzUSt0oZYv\n73u0A5kM8gjq5jmX4r3XOhdM+Yo9iyX5jV7mmMB+u5gLVn6GjgePtGe6tV3nJXQsqqP9fxzaoXvL\nfvsbUbFNGXOWduV/j7xrbGm/03LUxXciyNfJHS87C+Rf+hvKDlbu1pHvPMzemQH6DspI1JV8VeQa\ne6Ou43AVjZ+h400DrB7O1bKA1dnxsXL2ArkZFe7jU77LrqjgtrbvVUrjWEvBvNjeh6tBTkC9BPNQ\nq6rSdSGdokgb1NKsF7GElHpXzA8sIKcDU9BsEYKGUm+FTib9FJ2DcwQ6WfTPaKjw82g49dnA8WgU\n1XZoOPZkdM7Z4cBuEMSyUMgIdFJvWzSacT4aZbY/GhU6C42WOkaz69caCdMudQX+i2aj+AqafGE1\ncBEE5oeXjugE3MPRaMTjILCgDGmVvz5d5h5t0Aiyne0eJwCno/OXxgCba9i2tEbngC1EI8AGossA\n3Ys++57AP9F5YKvQaLD10UnIL6JRmL2BvwHt7FrhfMAedl3LrRs8R/H60liINc6vZiPdpAuZkHU2\nQJNeb4xm51iBrid5LRpuf4197owugLsIjVL9FjpvcsfcieTSCo0CnAS0R5/JEWg04nC7Twf0HTgS\ngki4urQtLVtLQ5FOwKFoMo5NrKxr0O/bDdgagvdV2HkEzbAy3creHs0UsxH6u85Fs6FcDEFk3F/O\nQKNA56DP7i40SvElYD0IdrLjLkezryxBQ/3fsjLdi77/fwXOR9/d94Cl6LzhU9FVOpaQfX+moGni\n5tmxc4EP0ee9wFK+1aLeid5HBqER8cPRJCdX5j4jp4Vg9SGXUgTzD+h6bYPR6RX/RefGfAR0QUOE\nb9WJuIAGU7wDgc07k0loI/UwurL80ajo/rl4RQx94YGAjAHmQ7BMtxdNPlBDMin3egNv55et0vJK\nN6CVhm5LJ1RAe0FwY4FzWudOkJddrVxd0MwknwL3QBAJSJK26LSC8egcxTZoQxx6Bx6C4ApqK5iz\n0GcaCsL6qACsjzaoU9EEE8+h04n2Q1cmWI2uVvBjtOH/CK1/Y9GOw00FEk2Ht7ffS7qgCbLvJZN/\nuF7qnQSo+HUHPsnP1iIjUYFcQm7Ho4jAywS75rP6nGRrYHvgcQhs3pv0R+cBf4z+JlsBayG4zfZP\nRJMhrEaFtBs69eJAtAPTT6/HlnbuRvZvMJoIoDsqVnta56SGguk4QIWCOYVs3kSnZfMEmhKsFkzB\n652j1KreTcHrnJOllu2d4ziO4ziO4ziO4ziO4ziO4ziO4ziO4ziO4ziO4ziO4zQh/w/eD3xAvlMb\nrwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f274b7cd310>"
      ]
     },
     "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): 15.31 seconds\n",
      "Overall time: 310.07 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 0x7f274bec5ed0>"
      ]
     },
     "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): 13.32 seconds\n",
      "Overall time: 270.20 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 0x7f274bb63a10>"
      ]
     },
     "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): 11.41 seconds\n",
      "Overall time: 231.89 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\nvQTRgxkHzCI5jODHkF5D4Uz+W6EZYTMo28uU3sB70bGDD6DjotIIh+I8T7pXchxq3GyWsG9rNKSy\nbXnlrBu7oWX/GoX9zM+i5U9B+qIPuG+c4iHZDgrHQSYl/nwF+Bs1FczoIzqtYyJp9R5yA9nfl7Bv\nU3Q8cUfCvmmo5/Mimesbyt4gB7nX/VOO6e/K+JcWSDDaBQ1Rvg/t/76EbA/zZOBKNLdhX7Texp+t\nHYH7c2+jZe7Yncn1I3YFc93/lHHpRUnyML1gxroRZFO0XQnGKEer0HHLJU44EkcmgFwA8pd6GGs1\nEkzpi1qwz6H9ks+hjcjowrCftAGfBC6o8Gb18DCL8QLJIVc/0/99FArm9qhHfScalokhB4AkiRfo\nw7TI9fE+4N6nESZLJYQ7ZHPXd3UqKvybUshWwFtoyKk7shO6WsJGqBESciewuZugOonPA48Gk6A/\ng4bYw9Bl0gTWwfWkF7qy/ZZo41nuJAWV8CI6E1ZSGP0IdKjBgQme4jRUMNM8zP8Bl6EGVAISAT8D\n/gwyA3gdJMlgm4IaGoNIHtvak9gZ/U42ItfmbZ/cYMsU4BPAr9Eluk5BHYvpOa9KIvSZjkXUIoHo\n/hpkYGcxB10veFXRI5NZBAzP1Uvpgxq03sMM2+qzgZ8kOEJF+jFlR5DPpuz8GCq2+5IaTasdNRBM\nORI4Hg0DrkGtqT+6DMRZdFYMOQXkv+i4y3lFPLgs0pIf6i2YST+wF8yZwNRY6HV7NJvzdnTx1TAz\nM0ITV/6tfVEFHIyG3KC4hzmNTMHkMrQiDwYuInkc4laoRbxnxn0ayU5oUtMj6FqAAdEadF7XQzXc\nLIFnKB9HoxuHB8e/ic4Fe5FrwDoo9DDvJ7/PaU+0j+n3aF2sVUg2g+gtNGwWM9RkIFo/LkTr5W7B\nvlFAb3QFn8n5DXrnxCBPoV0Q+5K81NJ2aPLJpsDF6GTeSfViM/TZvpLCcGMPQY4G2Q+tC3e6v5kQ\nLUQTdoJnTc4CuR8NrZ4L0cMQLYLoMpeLsRhtG49AjZnVGdGFrmQu2ZO4FCFahxpzz4P8A332FgG3\noe3MFiCfBzkXdRSSHKXnSWxPZWenL/8AzkoJ226LtmkP0g3qXRHLRvqBLAGZD3J1wv5vgFwI8kEV\nS9lLhzpIFVlRsg1I0M8j64HMAnkT5IzKr1tWGQ4H+XtsWy+Q60BOdu/Pcu8j9/c0iAvbyEkgC0Cc\n1S9bu8+vcrGtAAAgAElEQVSwP8hC5wH660buu9vVve8LsgpkfdcPGS/bk/odgT6MYTllJMhykA6Q\nLdFMvxsTrvGS3k+WBpW0K63cggJk7Brr6lxG+EWOBpkJ8grIIpAvgRzmvtuEyID0BrkP5Hj3O8S9\n8uNA/ubu/RTIXJDT0VClaB2sB3IDSNBPKR9yv9HF7v03QX6f+25kD/1cALLMCag/dyxI0JckV4GE\n84T67b8GOTN4fzzIlQnHnQPyLVevYl6/9M8vd1nUq94Va+sikGfd8/Oqe38gnQauXATyZfd6GMhr\nIPuATEq53lWuDXjbtZEJ7Wc9kB1AflHlNQbocyXHuGctiHLIYSA/B/kuyZEJQL4Gcn5s2yHu+7nF\nXeN5CpYSk8g90xNd3Yst+CE7FD7LpX+oLjhJDgOZATIYJCFzVTZwD/SzqGVWA2SwE4xLnSD8DeQ8\nbbSkrTb3KFqGKa4hdh6kjAC53TW67nuQvqjQHQfyXlTIgoxV+ak2MgByNsiPg9f/DBq9bUBeJM+6\nkntRA+Hx/M8s+4K8kKuwsrWWoXP/kagVGH6O2FRaMgLkdb2fPAfiQ8vdRTAPA7mpyOntrt4dpw+Z\nXIYade/JOOdQkPtB1lDYjTDBNZJfA/kzyLtA3Mw7ch51S1iQI0DmgIxDRWgeSBCel6Gunp3u3v8I\n5Ifu9cz8Bkv2Bfl38P4QOsW1c9v6qHEyLti2MUhCgohchRoqbe75DMbfyaGoYVHJsJjuIpjT9HPL\nHiAJSTCyK2oURyAfA7miyPVOAfkdaoAIyFcqL3qzIxu758sNb5Ix7nkNoyWXoBGi8Lxx5IyXQ7Td\n7NwXgTwK8p8Kn88uEcwrQIr0V8jNIHdS0w5Z2cJVyrudaKQkInQlcgbIY64RmwnyEwqGcMh094M+\nC/L+2L7tnbhF7jO4yiF9QB4AcUtFyU/QcEZ47nraIMmtIJ9z23qBPKyNVudxg0FW5yqM/Ia8vgBp\nc/uD4QiyF4gb7iPHod7UZOrXcJF9L/k1yFklXKJM40naQF5OFgNwv9Vr+QLVCORMJ4rnkhwdmIRa\n5vuDLAbpcNuvBjkxOO6z+l12vm9zv3WwXJP8BuRHsetHrk7Hx949BeJWnpAHYo3dRe67jXkRBWXf\nqPC63UYwv0KmJ9YZRXoXyEMg7y7xthHINSANDyc2FrneGRH9tf2Rs2P7T0IXVg+3vU/1BfLFE/T7\nlBdBHgQ5PuO+be5ZOhxkTzQDHyoXTLmdvP62zl07ouGJIvOqyka5h7YraFQau0RoWO56kK+nl0OO\nRcMKUcL5z4D8kULvc4pr7I5BLa204QRT0VDbQ65BmkGBNSXz0dDEWFeBNo/tf5LOyRBkgGskfxns\n/wTq/dZZMBMTKHZ2n2dU4b6a3PabIHel7LsENWwaPGxCIpDPoKG8lGxrOQL1lIMwn3wAjYpci3pL\n8yiYCUrO1N8f0HDiQhL7NeV6vUfn+z6o4eUMV/kVyKeD8s5DPdql7jdMMHAlQj38WSDhikXdRTDv\nAzmwyDFfAHkDDZ1XOO9vq9LZHfUEmvEab8c2cm1c2A9/Fsj3gvfzQfziHb9EuwR3cUJ6OInIqagn\nejPqgHnnpGLBPAu1nIL+MvkiasUekX6qURw504lmwrqMcrB78P5U5BojQHZyFSpJZL7jKtqrIFcm\nCPdVaP/TISCztZGV2HAgiai/YF6LeshD0FDove6BOrr46RXfdgid/cwF+7ZREekuJHWB5O3/Fkhs\n6IL0U+NH1pG4wLuMcaJ2u/uu90+59pdB/gUy0r2fBvJssP9UkMvc621z++QraFRmJgVTpsle7lk4\nGw19/h7t8miwYEp/kO/rZ0idrCQ8vinGE3Y/JALZHWS/5O+5sw/5ArSraWdnxIR9+leA3OR+r6Xk\nusx2cG3bT/KvLYPRPIekvtVqQrLyI6f8B4Ccj4ZfxmWfahRH2kienaWeZdjVWVfu900/sG5FUsG8\n2An9CtQL38eJVisMjO9iZCdSQ9YyXA3htGQVCIR3njNmrgUJlsCTDd2+x9Awb+gFRO63vRnt2/86\n6lHMRENykWsQP4oa6XUUTLkN9UxuQr3xNe7vNrosqmGUjoxEoxdPoN1PF5I35lcGol0N51MQ1ZQR\naGTkMZBPuvq2FuSitJtVVEL3L0JXwHjMFbJOWYFGN6IBIVnZiZ4/CL6Jka21wZEvkTd8B5wxuCua\nNRsTZ+mLepK3uQbwBDTKkZSLUE/BPBrtgjiczsQqo+cgkfuN/+6MtayIQWK9K2atz6DiuU+NHkbK\nhAtdwgys3hlKverdDKzOGTnq2d4ZhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEY\nhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYNcQmXzdKxSZfNxqBTb5uNIKK6l09l3RqAmQtyJRG\nl6JB1Hl5r1bA1vcsgQYvIG20KNWsh9kdkbOKLHhc6/v1AxGQ7et3z26FCWZNkd7OANus0SXp5phg\nGo0gsT70qncpasjWwHZ1vN9g998WNDZqwXigDxYGNIymoZkFcyAwsY73M8E0aslk998E06gTchrI\nuEaXoplpdsGcUMf7mWAatWQycDewt/VjGnXiNKCO3Vg9j2YXzBp5mNJX+5QyMcE0aslk4A7gTaBV\nE8mM+jIS2KrRhWhmmlkwB1G7kOzngWuLWPommEYtmQy8CNwF7N7gshitgQlmlTSzYA4ERoEMqMG1\nJgLvBT6UcYwJplFLvGDOA0Y1uCxGj0f6oU6GCWYVNLtgrqQ2/ZijgAuAb2QcY4Jp1BIvmKvI1S3D\n6CpGAguAPiBjih8ufwOZ1NWFajaaXTCfpTZh2VHAfcDwjGMGA6sxwTSqRvoBo4GXUcEc1NjyGC3A\nSGAJ8DhFvUyJgP2p7yiEpqBJBVMiVDCfoXaCOYfshmswGj4zwTSqZRLwMkTrMME06kMZgskItJ1r\n7+pCNRtNKpj0A9aiIlerkOxLet3UbNl2VDCtEhnV0gHMdq9NMI16MAoVzCeALYsc68cIW1dBjGYV\nzEHAG6jIVelhSoRWpsXumgNTDjQP06gVHZhgGl2GjAQ5ObbRe5jPAJsUuYAXTHMOYjSrYA5EG5oa\nCCbtwFqI3iS78TLBNGrFJDQ6AiaYRu3ZHvh1bFYfL5jPUnzcr3mYKTSzYL6BeoUjq7yW9y7BBNOo\nDyaYRgISgRxegwuNBtqATwTbvGAuBPqDZCU4bggsxzzMAppdMJeRndlaCqFgrsQE00hF2kBOqcGF\nNiBfMM2SNwAGAH+rwXyvo4CbgFODcepOMCNBvcyssOxkNDnIBDOGCWahh5nWeA0GXgXadCo9owUZ\nD/wCpNrnZhIw173OMtIAORDk7CrvZzQHvi2rdhWm0cC9wCJy4VfvYULxsOxkNDnIDLkYPUEwh1U5\neXU5IdkVwOuY5dWqDEWX5Bpd+SWkDRiLjsGE4iHZHYEdKr+f0USMcP+3rfI6vk17ldwsUqFgPkeq\nhym90QjIE1g7V0CzCuYgYBVEa4G3qK4PqBzBXIkKpoVlWxP/u1czlGk8sMjVXSgumB1U309vNAde\nMGvhYb6KtmtJgpnlYY4DlrrzzcOM0ayC6T1MqD4sa4JplMpQ97+azOww4Qc661xqlGQSNtdsqzAc\n7Tus1sMcTbaHmSWYY9FcjRWYh1lAEwimtIPcAnIliG+oGiGY7Zhgtjq18DDDhB8geht4G52MI4kO\nTDBbhRHATGBgafO9pjKKPA9TegHDUM8RMkOyjHDHrcQEs4AmEEzGAZu5/zu6beZhGo3A/+7Vephz\nY9tS6p30cvcaDNKninsazYEXq5lUF5aNh2SHASudcQbwGjAgJXlxONqmrsBCsgU0g2AOQccOPYqK\nJtRdMKUv0BtYgwlmKzMUrYvVeJjxkCykZ2ePRev3EqwfsxXwgvksOhayAqQ3OW/SC+YYNGPWEQk6\nzjKpHRuOeZipNINgtqMiNZ+aC6ZEaBJGMQ9zEGqhCSaYrcwQ4L9U52GOA16JbUurdx3oFHph8obR\nc/He3Xxg/QqvMQJ4zU3s7+tNkpH2Ork++aQyWB9mAs0imCvQjuixbpvLkgWq8zAPRYX3afc+reHy\n4VjQcIbLZpPzQSaDDAb5cYVlMJqHoahgVuNh+nBZiAmmATkPcwG5tq5cwvq12L2fRG7uYk+a4T8C\nC8mm0kyCWWMPU/oAPwTOcNYYlCaY84JyHA0chwrv6W6dQ6NHIJNB4lb+EOApYHwVkxf4hIyQtMkL\nfENngtn0yBdBjixykBfMajxMnyELuXrTQaGHmRWSXYauBtXLJmnJpxkEcwiFHmaFgim9QB4C6Q/s\noteNbg4OKEUwXwHGuYo0BjgSOAaIsAVXexJfBD4W2zYU7Qt6nconLwj7zD3mYfZ8tqL4cBEvVtV4\nmKFBVmlIdqnrfjIvM0YzCGYt+zA3QLPP1nd/s2L7S/Uwx5MbrzQa2Ad4GG3gjJ7BKAoTL4aglvnL\nVBSWlb5o3V0e25FW7yajDZ0JZvMzDG03sqixhxm9AbwDbE5hSDbNw/QhWbDEnwLaGl2AEvAh2cX6\nWvpRuWBOc//HuL+Fsf1pDdcocpXIeZiMR5cXewBtPF+jasGUg4D9ITqjuusYNWAkhWMjh6DGmw/L\nP1zBNf0E2CFp9W4j4Hm03m5Q5r2M7sUw0tfa9YRjIEdrxmtnd1GpxPvIF6OCWW7SD1jiTwHN4mGu\ngOgdcqGKUDBfozLBXI+8VGsg29L33qi3/jZAPY2vAh9FLbjJCeeWiPQCfgC8p/JrGDVkJIW/51C0\noVlCbhqzIsi2IIeCjCc5HAuJw0qkLyrK5mH2DIp4mNIbrQPLIXoLFa1Kwv7xOubFc17suGJ9mGAh\n2QKaQTB9HyaoWI2l8izZTdFZVcr1MDekUzCjtahIbwO8AtEqiJaigtlRYjmSOBgd6znZPTxGYxmJ\n9lUPCLb5kGw54yKvAs4Fvktywg8k17tJwDzXeJpgNj/DyQ7JDgNed44BqHNQSVh2fbSd9CwGXg4m\nLfAU6cMELCRbQDMIpu/DhFworJqQ7IPkPMwKBBPQsOyO5I+ne5HqBPPDaNbuEor3dRhdz0i0vnUE\n27yHuZSSPEwZ6875DFqHwgzGkKR658OxYILZExiGLtycNobbh2M9lSb+jCPfm1xMYf8lJHqY0hft\nhvD5GuZhxmgWwYx7mAmCWdISX9OAO8l5mKWGZDcEXgjez0OTh14Ots2mOsGcgM7w8TzZi7saXY4M\nQL39J+hM/JF+bttqSvcwdwHuQ3/TDckOycbr3cbk6pwJZlMjfYD+qFGdZgzHBbPSxJ8kwYz3X0KB\nhymfQdvE14I+9ibwMGVIwvCvLqPZBDPBw4zeBNaR2aEu54J8Dk1yepz0kGzCeDhpQ4eLhJXuFVeu\n0MOch050XOlYzLHoQ/I82lgajcOv7PACsCHIJnR6l5FQsofJrqhgznPHT6QyD3MJJpjNzFBy2dVp\nghn2HUJFHqZEaPsYhmQfB/6TcHDgYUoEnAfsHytDMyT9nAL8ol43azbBfBHYGw0TvBEcM5/sNP8D\n0QrxP1Qk05J+VqNhk/B7maDnRGuCbd6CCwQzWoc+EBVkM0rkyrQAXUnABLOxeMGcBRyAepqHkRsO\nUqqHuStwr+uXmgNsT7JgJk1csBE5D9PVf6lmzuQGIsNBTmp0KRrIMDTv4RXSBXMk+WJViYc5DFij\neRWe6GKIfplwbOhhDgX6AgeR7+U2Q0h2I2CfeuV9NINg+lR+gL+igjeIfMF8CG2M0lgfOBb4sTt/\nQ/Szr8w/LHoHeJN8bzXefwk5oYzPCfoilYVTR2pZojVYSLY7MIqcYB7sXh9Erh6W4GFKf2BrtM8c\nd60dKC8k6zzMSFBjbxrNyXnAjxpdiAYSCmaaYR+G4KGyPszxFGbDphH2Ya7n/h9Avmg3QUiWyah3\nPr0eN2sGwQw8zOhtVPhOiWV9PUBu6S+H9NG+qE7v7UaIriK32sSihPFwUNh4pQnmUohWx7b/G9iv\n1A8WEIZRLCTbeEaiwjYT+AfwOTRc5QWzFA9zE2AORN4om4Va8mlZsoElL73IH8oEOoft5m5/sfF8\n3QjZG43wDG7hqSOHoUKU5WFuDjwZvH+J8qNV8f7LLEIPcwxap+Nh4WWUPHyqHOSTIEfV6GKTgTuo\nrN0tm24umBKRH5JF+yyj38YODASzM/nnbOD7aKVY42a9APUO1lHYf+kpRTCfcfeMcyOVjaP0/ZfQ\nmSBS8VylRvX4CQbmQHQwagi1kwvJltKH6WeC8vg6VIqHeQg6FCCMojjBlEOAu0r6FF2KDNQxpkX5\nCvB1tPujmkWRmxnvYWb1YW5BvmA+A0wtLZlRpoIcT/mC6T3MMcA9aDsbhmTnoMObas2ewOHVX0Z6\noeX7LSaYgKY4v+PGPmYxE9gK5F3AE66v56PAFDQcuyB3aPQO+vDG+y89QeMlGwJHkF+RgegFiN6d\nUo7h7rxyCAQzWoVW3C5qXGRLkLRFsg3F92E6ooVouMx7mCvQvu6sialDIwiyBXMJnYPUZRpwEXB8\n7Jin0IXU3wtsC9KgKISMB/k+ugj2vdl1STZAw9BXkstw72HIME0qzCQUzIT5pqUf6ik9m9sWLSY3\nZrwYHwTOoiLBlAgNyc5Hu7ZCD3M2FWf+S6TtsXw7Yec4YI90Y0Ail2xZjPXRz3ETsFtyP6aMBDmx\nxEIXpbsLZth/mUG0Em3Q/uw23Iou9txBgWAC6l0W8TClL3AvcAHw99KKG72D/njlepnxxjWcaL6G\nyGDgdtQIMNKJCSagFrjzMLMyZeV4V3fKEczZwET3wJ8A/Aai+2PH+JDsAcDdwPtL/CxFkH4g+7g+\n11L4Oxq62xHtn90149gTgb+4rotKB+J3d6ZROEl/HC+YLwAbJwjFVODFWGIh6LKDm5ZQhj3RLoAd\nKFkwo7WoIA8gN2LgStTo98wGOkocshfnQ8AvgS+oUZHHeNRA7Eg593BgRglRtg70e1uGPldJM619\nADintCIXp7sLZiwcm8n96AN8AOpZfgt118eSLJhpHuZSNOljPBrK/XlKX2catwF7Je+SM0A+n7Aj\nHr4LJ5qvJZ9DxaCFJ0aQMSC3FDkoSTAvBf4ZvE+YHk+mAX9AhS1JMJ+NhVkd0WrUsh+HNnyPJ5Rp\nLmpA9ge+TU2MHhnjrns9JRl5EqEC8SWIZqFjmlPqOqCez6XudQ/1MFkfGFnEIxqOjm9cCrxFLsnG\nE++/9JQgmNIPFcrb0d+wVA8Tcok/bhKX6JcQXZPbHa1ARw5UMkXfpsAlaGh5o6C8fujLLcDuKedO\nBXZDl07MYjK5SRmeRMPacd6NGqMDEvaVTU8SzK8AR0M0Hw1d/Q71FreiUDAXJGzzzEXDJhPRjvdy\nmUV6Z/02wE4J26v0MKVPcStQRqOC+XOqWwC52ZkKHACSZZAkCGZ0B0TXBRuWUpj482n3f0MKftNo\nJURTM+75ItoAbIwOLYrRmSl7K5rk0AFyLcg2Gdcsxp6oofl/JDc2cUahRqTvy52BDvNKQIag9cx7\nLD3VwxyDLu2XJSrewwT9beNZ8PH+S08pHuYO7rjr0KEh5QimT/xJmsTFM5s8T1AikD+AnA+S1Y5M\nQNvPeBLjCFSEbwH2SDl3PHA58L0ihshk9LmBRMGUfuhKUvOpUSJlDxLMaEnuQY5edg3MbHS2lbg4\nngX8PuVCvqO7UsF8ifR1MTckeWhAXDBL9DBlFMifUO/k5CIHfw/9zDNobcH0v02adQvJHmacmIcp\no9EM7stJFMyivOjOC4aTFHAjcIULp22BeqLF+s+y8DMRpVnnccIGCnfu9JSs3a30up2rbfRkDxOy\n+xqLCeaWaMg9ztOogZfFnmhS2gz3vgoPM5HZ5Ic6NwDehbYhn8i49gS0zzYumOPRbOG7SX8Gx6Nz\nMK+kMzM8kbhgxo/dHTUyH6RGQ/W6u2CGE69XwmzUAosJZjQXotcSjgf1MDegcsGcj874k5QQsiEw\nJaFzugIPUyaiM3jMRw2Ad2UcuyMamjibitdy7DFMRCMP1Qpm4GHKbqgndT7a11mJYM5C+wNXQpTS\nbx+dDdEN7vVidO7h3UHiIb4Ysn9KBMIL5hNoo12MDvLmJY1WAY+568SZ7vZ5epiHKac6D8YLZYWC\nKb3RunhfwnlPA5uBXALixEm+7xKvIpBPo1GjG1HB+BNd7mEyHU0O+iXpHiLkC2YQkmUcKpiPAxuA\nJE0A74+5n+SIHC7Eui2ZHiYHoTklz6LddFXT3QUznHi9EmajkxCkhV+TmENVghmtc/eLeYgyGP08\niygIcRRMZ1WKh3kEcJdbO/NKdLaLtN/zKOBXriFudcHcAE1cSRHMznG7aQ2IJ0z6+QrwbRU0ZqEN\nRCUe5oEkhmPTiFai/Y/HpB8j/dHw1zYJ27dCre9n0BBvscSfuIcJOsQlqeHcGng0eN+DPExpQ7s2\ntkCNgFVU7mFuh656lCR0s9HvbBfgVBVKvoSGGTdGh+scANFtmnAYHV/CiIKQ5ahgZnmY8UUlpqO/\n63+AbZLrjERoG/MKyR7mPDeOfibq0MTxEzCkCKa0owbG06h3jXu9ccxR2Qft220pwazGw/QPdzmC\nOZfqQrKQHJb1jc1T5IdltwIW509n1TlnbhZT6Owfil5CK39a+GIXNOMXVAiGt/Ag8omoYE4heeWI\nSej6q8sS9oWEkxdsiT6YoIK5NSAuaaJUXnT3LkMwAU0yOiGjD3sy2sd2cGz7tsDTbnm6tWi5i/WX\nhUkWnphgyk5uyItvWD09ycPcCOiDCsEY1EPP+mxZgnkgatAkEL2NGkM7uvt8A1iLGj/bowbzY8nn\nlsQj6DCl/uTGGMeZTX5IdmvgMWes/ZeCCWMATXJa6+q/ywzuxIdkIVEQpQ3tD16QvB/Q+rYc+GAu\nszh6E3V23HcrQ9Fw9oMkh8EroqcL5mz3vxzBnIdWzg2prWD6CRDiU5ydTuHkwaVY41PIG7fFv1CL\nKkAiZ3FNp3OihWgdXZeF22DkbJBiU2RNRK3eB9FMvDjxhj4N52HKEPQB98bZHPe+HO+S4Py0/ss0\nbke9hKSwKGjjvgx4X2z7nuSMKNBGv1g/ZgeFHuY9wI6aeAZoqO46NPHuieC4BcCYCocodDc2c/83\nQYXSL+iQRlwww6ElB6KJXClEV7vI0FXAaegwt21Qz+yhyorfye/Qfve0Wc8gOSTrn4+70HoUx4dj\nQcVxeDBeNxwr+h9g59i5btah6C30e50MsqkOfepke+C+hDI/Sk7AdwcedIKa4WFKLx1HmzmmupMa\nCqZEbthEGQ+EfM1ZpG0gv05I/Z1IeTH5OLMBIXk6shSit9CHezO6XDBlLDqry29ixy4A1kseiNvJ\nVPIF8w5g39xb2Rq1IHcCno95Oz0oLCtjQLwFfBTZfZOgIdmXSM/wLFUwZ6O/4+bAU7nkluhNtJEo\nVzBfRmegKtPDjNahWa6fSzlgY+AKYKNcZrBE6BjJK4LjnqR4P2ZCSDby4wu31YaN9VGhfCWYFhDX\ncK2i5KnWpF8J4/AaxTS0TfGC+Ripgin9UY/L9YlHy9F5sJ8HeRn19P+dfG4el6PG2E/ICeaDmWcU\nJXoF9W6zuh9eQIfNbOK8tvXIGXX/JnlYURCdi94h100BiR5mnmYEghq9hT6LDwOXBselffYb0PYU\n9Nme4V7PR8fWf5jCCT8mo6FttzhAYp9qJ7WskJPRSZYng2wB8pPsw2UYGmL4BroSxKkUxrOrtaJm\nAecnrDZejLlo6KMMoc2jmGBuDzIJfQguduOzAqK3UA8mJVVdBqEp/nODjfeSX/k+jlpVF1KYUNCD\nBJPPA+c6D2djMtPHZRA6UHsx+jDtk3BQqYL5b7Th2oPCIQGzKC+qgaujz6P1o1wuAfZLSfPfGO3f\nuZlcY7In8A6aqegpkvgjvVBjI2ltRe9pHIsukPBRCmcqgpLCsjIG5Ab02Stl0H4j2AydY3g6+j2+\nQLqHuQNqUIXzTp8EHI3Wv4OSx+bGie4Fprphc2+gntnM7HNK4meocKXd903g1+hzti35mc93ajkK\nHJ3QwwQ1ArdyrwPBjF5G29kw5BsKKuj0poeiQ2Y6XPuWJZj7uqztvVz5cJ7oL9F6+ePYOVuhxu+Z\nIL8BLku4bsmUMWBfjgURkCNAvgCyInsMjZwG8g+QV0H+B/IsyFeC/W0gK4spftcgfwYpNzQWnv9+\nkNjsQHIDyMFaueRykDUgP0/3IuVRUufqlOkgsUZaIvddjtcEI1kG8j73m5wYO/bHIF8s90OVeXw1\nlFPv7gJ5AmSK+6w3ZBy7qdYzcB7MisL6JbPVmi7p3jeCvETBZBRyCcj5JX+E3HlV1HW5jM5Myrzt\nN7l6cDDIPW7bn0E+GzuuAyS++o7fNw3kdJCUxBA5EOQ1V+eS+rT8cdeBZCQoAcj3QH5PbimzetW7\ncurcTPeMv6PthEwHSZpsApCvgvy0NkXsvOY/QMrt667mfmPcbzsf5KOxfXfr75+37VyQbwbvPwBy\nr2v7lqKRNb/vr+r5db7/JEjCcmRyOcjJahTKItKn1bvd1e9ZFCQkyVCQ5eQtkyffBPkOOqb5CnJ5\nDYn1oZYe5o5onN4Pzh9M9pIrJ6Pez8Xo3K1nkT/N1qZoWCetM7ormUPl4VjI8zBld5BL0c82Sy3N\n6FhgMESfCqy1OFlDS+L9lzgr6kHU+joGuBOifwCfRS2vkBI8TOkD8ieQGSCfyj62UUhftL5tjCYj\nzCZ7gHIYKlqD9usGIVwZjoYMXyg4M5kb0O8x7mHeQC4JqAyqqus3oUOH4vhxnf9Ew7IfREP3cUt6\nDhq2ii1ULeujs1dtg0aQEohuQcOUp5AdJvwnmTMKyWB3jW+VkHTVIKQX2h3yL3QpQD/NZpqHuQe1\nnyx/JtX3X5ZBtBDNzj02YeGLW9B+2JC4h3kNGsq9AbjNecmeeD9o3MP0zEDDrM67TO1zvRZ9Dt7r\nvOGdbJUAACAASURBVOPwcyzX++dN/L4V2lf6foiOSh/SVRrlWF33gPzMeVKzQW4F+ULsmEHOsj8X\n5L+oFzkEnRB8HMjinOUgJ2qD3QjkIyDxilHO+d4iuwzkZRUcOSjdKkq8xsUgp8W2jUX7fb+tlnjB\nOd9y1tJ/1KtIvfbh6nmk7o9AfuUs2X2d19ANPUzZCeQRkCedVXkhyJukRjbkI8548e+/AfJH93kP\nB7kKpIzGTTZEvdpukEAlI0Fez7eqpQ2NZLiMaPkxyLp8iz7vGv8GCVZ9kMhZ7DWai1MmoVGQPu77\njhns8kmQv8VPqs29ixeuxMMmgzijSx7X8kobyFsURIukN+p5FxknW3ZRR+t32R2QnSjwruV2dCGM\ncNtpIC9QGNHZCuSZ4P1lIAmTsMhUkHlOZ76aUZ6+2d+NfMDVc1f35Dl0SsuCA9OvkXH1Eg/rA7IK\n7bt8HXW7jyEvLCkRyFOuYj2oP3rBdWaDfNw1+peCnF5JoatH2qhqzUHp5R6mc0geulDKNU4GuTZ4\nvynIi04g3iZxBXs5GA1FvJwuGqBlktco8CY69+/i7hWWvTsK5ukgv3CitxIdTD6H1NVi5CKQrwfv\nR4Lcj4Z0n3Z1r6PMopZpCHUlch/IAcH7DfX76Hy/CcgF6eWVC8gLL8uH3bNaw9Xs5UknMmvd73GS\n+90i98zsGz+hdvfOLlgJh6znxOBi9/5qkAvd61fRuXnD4/fUetWTkd6ooxMM55DlIPF5liMS53OV\nXqhz4fq25YGEOuDPfwh1GEpZySStvP2d6F7unv83Uq7XpYK5nXsQIieW/0S9oaWBkk93jXkbqdlv\n8ge0T+gK1HLPmkmihyPtriKtBzIRZK5rXHqDnKnbCs5Z331vJUyXJn9EZwpJ2ncRyJfjG8v+CJVT\nSuPVD+1DPB7ki+5z7wVyGwXWLYDsALJAH5K87YP0e5DBhec0G/J1kGtyAieHgPyrjPM/CuJCtTIU\ntegz+iQrKuMPXAM7FY1G3QSyBLX8ZyW0Dd1JMG8G+Unw/Z4D8iX3+knyjZVvuc95Qu2L2t2Qr4C4\neZblJK2DZZ1/PciRaF/5fHJDlLoIGYBGk54DeTjtoIquXMIhZzphPNO9v10rC6ChQdf/Jd9D19HL\nutbwXMMlu6ULa6sgl6Kh2ecpCG+nnnM9JXlJsj/qrTrrSgaA/EYfellGXse8HlBGwaulyL1kC9cY\n3YmGpw5wgjkGDSV/Cg3/uXX15Gj3cKSEInsKMkgFUi53BsIcFaKSz98BxGUIy8/UcKp5GUeBxCbY\nkHPQUPo3kk6ofRkSKaXOzSNvvJ70DsTzKNTL/JwaGTKf1AhOT0P6oUmbJ7v6V0adA/e8znB17gdd\nUsTCe/ZCI5nfSjugoqsW2b01arUHq3PIvnSOdZGNQBa6L3IW1a2s0ILIru4hPbILrt0b7Wd+yRkz\n16Pe2Wv6uvCE2pchvXBFdl9DfuhwFNonHqlhIQvQvrs73fa70D6zbhI67UpkoGt4Xk0RoKxzB6De\n3rloJmKdGnwZioZjk1b5qaNgygEURiAiNNpzMXnh/MRLdKCe5lJawrMMkR1AHnPtR6lrq/pze7v2\nR0CKTTZfL2opmLIXGga7g6IZlLI7mjxyV2s0WLWmq78z2doJ5i/UepaOBjdc7l7xB0fanOH1SbSP\nNmV9O9kK5DzXCJ+IDqlowXpX6WeWTdE+nmKr39SLegrmUucZOg9JJqBh2NUgr5CYd1FwmWEgn6El\no2PSi4oT4GQwyIdqW56qSKx3xR4qAfkLmo7/KjoA/i10sulr0UHIR5Y54a/RnAjF60sN7yWL0IH1\n16MzxHwMHbz8OvAHiP5Sp7IYjaVe9U7US6I38Ed0qMgW6Mw6361g8hOjuUmsd6UI5gnoGK1x6PjK\nCcB3IHqk5kU0ujP1Fsx2dLaYvYE2dDaSn2SMWzV6JnUUTH8faUfn3r25cBYuo0WoSDBnkDxXoNF6\n3Eny3KtdwQys3hlKverdDKzOGTnq2d4ZhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEY\nhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYRqnYXLJGqdhcskYjsLlkjUZQUb2r5xqIRvemGy0gbbQQ\ndVwP0zA6SawPLbjIqWEYhmGUjwmmYRiGYZSACaZh1BS5DGRoo0thGNnIto0uQU/E4vqGx/owS0KW\ngUxtdCl6ENaHWXNkIMg6kN6NLkk3xvowDaNrkQgYAgxodEkMI4OhaNs/pNEFaTZMMMtCTgJpa3Qp\njG7LIPSZMsE0ujNeKIc3tBRNiAlmeVwArN/oQhjdFt8QmWAa3RlfT4c1tBRNiAlmyUhv1IMY3OiS\nGN0Wn+wzsHaXlDaQQbW7nmF01lPzMMvEBLN0Bsf+G0Yc3xBV4WHK9iDhjDNHo5ENw6gV5mFWiPXH\nlY6vZCaYRhq1CMkeDGyPTs0FMA4YVU2hDCOGN+xMMMvEPMzSMcE0ilGBhymfADkx2NAO7AXS170f\nhWUzGrXFkn4qxASzdEwwjWJUEpL9GPBzkK3c+3a0r3xH994E06g1Q4F1mIdZNiaYpWOCaRSjzJCs\njAUmAGcAP3UbBwMvAfu79yaYRq0ZAryCCWbZmGCWjgmmUQxvuZeaJXsA8C90aamJbls7cC0mmEbX\nMQSYg4Vky6aJBFNGNXgqJxNMoxhDgFcpPSR7IHALsJxcOLcdFdHpbuYgE0yj1gxFBdM8zDJpEsGU\nCLgb2LOBhTDBNIoxFFhA6YK5H3AbhYL5MvAO2qCNBPqB9KttUY0WxjzMCmmWYSU7AVOBEQ0swxBg\nGSaYRjplCKYMRevUi25DbyeK7cAKYC7Q4a653G1fU+sCGy2JeZgV0iQeJh9GLe5GhqaGAPMwwTTS\nGQIspDQPczIwCyLRv04v0wvmS8DWbvsyLCxr1I4hqEFmglkmTSCY0hc4CriexjYa7ahgtjewDEZd\nkQEg25VxQjkh2cnkvEsoFMy5wLbAEuB1TDCN2mEh2QppAsFkEtpgPIF5mEZ9OQf4QxnHDwHmU1qW\n7IbArOD9crQBGwCsQj3MbYDFmGAatcUbdr1A+je6MM1EMwjmcGApjW80fGNogtkSyHbopAJDix0Z\nMJTEkKyM0CzvPOIe5uvAWGAVRO+ggjkdE0yjpkgvtA1bAbxGefW75WkWwVxGfiZhIzAPs7U4AZ30\nvByhSgjJypfRQeLfjx2b5GFOQBsy0JDsYLpcMGVk11zX6KYMBt6AaB0qmBaWLYNmEsxGW9kmmK3F\ncOAZYGBp43+lD9AP7XMMPcyTge+hYhiyIYV9mKFgvuT+e8Hsgr5zmQA8WvvrGt0H2QDkVJDD3IYh\naF0DbVct8acMmkEwh6GWkAmmUU+GoV0BKyit3g1B6+gbdAqmRMB4dOWRsblDpRfaN58kmCvd+5fd\n/670MEfoPTsnejd6HqcBHwH+5H7noWh9AvMwy6YZBLOGHqb0A/l7hQ2ECWZrUa6h5sdLBoJJOyCo\np+oEU84BvqHHRquC82MeZvQmsIiuzZL11xybeZTRzIwCLgVeQPvEvWEHJphl02KCyZHAIZS9vqBE\n7t6uf6qhU/QZ9cELZql95z7UtZpclux41Mh6FRjuwrbvAb5CvncJhSFZ0H7MrvQw/TXHd8G1je7B\nSNToug/YhfyQ7FJMMMuiGQRzGLUTzE+hEyCUm+gwAHgLojWoB1Hq5NpG81KGhykRuvDzq6hgeg9z\nPPCKS7B4FRiDZse+m8IkoOXu+FAwv4SGc7tKMH2/qAlmzyUumGFI1gSzTJpharzh1KQPU7YF1kcr\nTrmCOYRcQ7aCXFq20XMpx8P8MnAE8H5gLdDmohDjUA8TdEjSpmhi0J1udp+Q5ajQBvUqukP/i3mY\nRqV4wXwZOBsVSZ9Qtgz77cuiWQQz8DAlSmhsSmEH4Fa0ApUrmO3krLKVWD9mD0faUPFaSWmCuS3w\nPYhcmFW8l+k8TECFc1fgxZT668NkSYZYVwqmYI1mTyYUzHbgIMDPXrUU2FJfSgdEs+teuiajiUKy\n0dvo5NOVhkMnodNBLaHsPsy8jnITzJ6PS+CJhNLEKuwXglxYdhw5wZxPp2Am4utXPQWz3ZXHBLNH\nIhGdghkJ8F3gCIiWuQOWkQvJPgKySQMK2VQ0g2D6kCxU13BsQC6JopKQbCiYNp9s0yOfBUkTCh+O\nhdI8zLBfCHL93D7pB1QwdyF/soKQRnmYT2OC2VMZCAhEb+jb6McQPRLsXwqM0DmTGQZsX/cSNhnN\nIpjeIqqm4Qg9zHIFcxRaucA8zJ7CKcDeKfvKFczQoIJ0DzNcziuOF8yVCfu6UjD/hwlmT8WHY9Pw\nST9j3HsTzCJ0c8GUXuhDXUsPsxLBnEBuILkJZs9gDLBZyr5h5ASsmpBs3MOE4oJZ75Cs8zAl6oLr\nG42lmGAuQyevGIOOHjDBLEI3F0za0XkP33bvK2w4pA0dnP0KnX2Y0gvkQyCXgtwEcmTGBUwwexTS\nhjYm01IOqDYkuxoYhDZEXii9cKaFZFeijVaSYK4C+rtxnLXET8axFhte0BMpxcP0gnkfsI2NMc+m\nuwtm2H8JZQmmjAaZ5N6MAxZBtJach7k/8C20otwGfDHjYuPJF8yumNdzh4QVLYyuYRQQke1hlhjV\nkF6oOIZCtxqNaCxzdQ5ywjk7+TqdCUYJghm9g47jXC+9HBXhQ8mvYGHZJkVOBvkVyKEJO4sIZrQa\nzZLuQCMNi4AptS9jz6G7C6aftMCznNI9zFPRKcggF46FnGBuBtwI0a+B84FJIBulXCv0ML1VVmu+\nCRzeBdc1ChkDPIv+5v0S9pfjYbaTW5LLsxrY2d3DMx/4MkRJfZSe5aSP711ARVPYSQRyM8ighJ1+\nsepXUKPSaCqkL/BD9+YTCQcU8zBB29dp6LJ0D2Fh2Uy6u2CGCT+g1nCpS3xNcn/+9Rz32mfJboom\nPOBCvlcCx6RcKxTMheQ6yWvJCGDjLriuUcgY9PecAySl0pcjmPH+S9As2QOBu3KbonUQ/ZBsvo3O\nO5vEfHTijXIZ5sqS5E17D3M+Np9sM3Ig2oZdBIxO2F+KYC4lJ5hPkt5NYdAcgllhSJYNyAlm6GEu\nQxvAzdEwhOev6FyzDolAPu5CbuGMLQuprOEqhglm/VgP/R2fIllIygjJFvRfgnqYU8kTzFKIfhub\nkD3k/9s773CpqqsPv4delA6CSBEpAoqoFBMssXcTUzT2mmIs0Viixs+oMUWN0S8xauyJms8YS2LD\nFsXYsMWGCggWujRFsRDA9f2x1pk5c+acaffO3OHe9T4PD3NP3XNmn/3ba+21167Qwsy8A5sl7HPB\nrDnSmPEPBwP/R7q7vlwL8x102TknhXVBMOMWZqmCOQgYaIIXEcxgrV1nHLmC+RwwHCRMjLABcCUw\nAfjIVo8AbbiqZWH6xOHasAFZwUzqUZdrYSYJpgDPNKyYOVRqYQ60/2OCKQFZl6wLZk2QccDMFPd4\nuddqD+yNesaWAL0TIp1LtTDDDuS7uGAWpN4FMz6GWaJLVgK0oViDVoZNyA22WAasJms1YsEZM8g2\nLIPt/4PJumOhKi5ZCdDOwSYm8E51CQVzOsUFM5KSMZFwWa8onwOvQhDf3hAaYmHOId/C7ACstXrv\nglkbBqHP+YRGuFY/NKhsiQXv/Jd8Y6JUwYSshblxI5St2VLvjXM/tKEImUE2D2IheqIVaDpaSccC\nr0b2L9N9eTk9XwW2sM+DUSvhAKoumKyHpv37GG+4akEfNCLwbYqOYQar0OkeHVKulWRhrgSeaoRy\nRqnUwhwE3E++YEbL7YJZG/oCU4BTQRoaaR/W4ZDF5I9jluqSBW3XFgOdQKox57dZUO+CGfaOQ55E\n3azF3AYD0YCO99FoRchmXIGMYOYRFcxBwGNkA0RCLMqxkrEI6ZpSGXugPb1Z+DhmLQgtzLeBoQnW\nY9TChMJu2aSgn8vRlSEakyIWpmyUYgUPRN+b9UGi0d2hOxZcMGtFKJiz0E58Q+iDumJDksYx+6H1\nvBDLUW/bh2ZAuJVZgHVBMN/P/hmsAe5Gl1Iqdt4c+/cN4OWYNbmU4oI5GLgX7YVHBDMQKg/8+Rnw\nh4TtLpi1xcZsguWo2z7eM+9KfrBZt5RrJQT9BEshKNazL5diFuYD6NziOOE79AYa6BaSYGF6tp8q\n0xft+KR5NsqhiIUp/dFsU2mZpUKWo+9C2D564E8B6l0wQ0sxyt+Bg0FGFXjBoxbm9sDLsf2/Bq5P\nOM8EU1qhDc1s4N/kTzav1C07Ejgw1tOHRhdMaWPZbFoA0kktdwCZCJImbFFCCxPyGi9pjTY8SyPH\nPw0cpIEWsmfsWkku2WqwCOibXOclQBu5SQnnhYL5GrnDGZFyByuBtZQ+ZcupjA3ICmZD3/O4YMYt\nzO2Ap0tYCvFDcq3QOgz8kVEglzR1KaCuBVM6oIEwi2I7pqBzj6YAh6WcHDYSc9DvGBPMYDoECa6K\nYBnqXhts/94HDgFujx1YqWCOAF4HjohtDwWzMXqeABcAFzXCddYFTkWXLQK4FPUoFEACchub+DMf\nACzNrvAAaEaoE9B6cG8kkhqSg36qQPAZOi6f1CHogWYbiglm5h1aiI5jRp9N1CUL7patBaGFOYuq\nW5hsS2nTmsK2NKQeLcztgH2buhBQ14LJQGBeLIMK6pYNDgbOBnaz+ZLXxgbRw2kkoXUatzAL8Qzq\n2jLRDT6O5LINqWBqibS1cp2BrpQRJZw+kxa1SZnusrHAMY0Tvl73TCKbzmsIuW7HJLqh+YlX2d/x\nxmsYKqIRgneBm+zcN8kdf6qVhQkZKzOPQcBcYGIsF+gAsu/Qw6j3JDw/Xm4XzOrTl+zYeTUEM25h\nlhB4FrwCwWmRDRUIZtXzz44GBtTDkEETC6YMjfXWoyS5Y6M8iVaKUcCx6Kr3mDt1c/SHfwcdf5xV\nRqFuQ62JVSqWiVRiYQ5BA4+eALrrd88QWpgz9Dhpl3D+8SC3llhpRqLfOy1zUTNBWgET0WfWCW3w\niwnm5qirPSTuHhtObkq7kNOBHdEO1fjI9hpZmEC6qA1CO4ULyI2GjQTNBV+g45z727OqQDAlAPlb\nCUF3Th4SkB0KsKGXBglAkkvWLEzphrY3/6ngurMpS8wlAGaBDK7gXqUyGl3bs8kXCKiSYMoYkCtK\nOPBm4MyUfbGAnzxmoqH+J6HTP8Jgne+gDcFLtrL4wHwrtSCTKS7WlQT9DAdmWFkmA9GxMBPM4AvU\nUkiqsJPQ73Zw4dtIZyvbmcBxZZZxXWM4GrW8ESp6n1FcML8B3BP5O97bT7AwQX+34EvgBWC8RTwP\no7YW5jzgdpDbYtsHo/X1GeCrke1bosE+IX8HLkMF/peU75KdgE6z2r7cgjt0Bz7XOZPBCrSuNiRj\nWCEL82TgUQhWV3DdWehyb2mGTJzN0PpXRtSvtAe5EOS+Ek8Yjc5sGFD6PapDIwqmtAd5yjJQ7IAG\ntxToQUl7VOS+l2JRxaeUxAgEdTkcC9wBjDG354XAmdnB7qKD3vHrfgHcReqqEkBlFuYIspbLA8Be\nkX2hhQn50Ywhm6ECeJmNTRW6zyxUlHuDJFRkOQTk9+UUvk75CuppWIguBv00+p1T5rhJgCa4vzuy\n0QQz41ZKszBDTDC5DB3TrKWFeRJq5e4dc7eHncsnyI2U3d62hdwDjEF764cBN0b2lSKYP0A7dA2d\nEtESCccvQxoa4Jc0raQ3yI7owhNJydhLIFiNvhOl5pTdGTVYinVUo1yCZlrbFqRIOyo90Wjf58lm\nrWoyGtPCHIdaQWNQt1cvCi8ZtCXqgnwL2D9hfzErD7SxXAFcgYrvnugyXv8qq+T5XGzXTGMRqd9N\n1ktxT5iFCcAjwKRIL66IYEo79OW6FY3kTVrKJ2Qk8KalALwROCZ2rWHo6iyH1mDsodpsA0xFXe+7\noC/6dDL5YWV/dM3TsJ6PRaNBX89eIvgIdWVubhtSLMwMb6K//d5kF6GukYUZLIfgdeAltFMaMgjt\n4N0P7GKRw63RwI9/R87/EoKZ2igG90PwZuQaM8k+A0O2BPm1WdM90ff0LPTdTUHG25QGJ5e4YDZg\nHFNaoe7XqGAuRi2wm4CjIFiYcGKpTKN0AdwFrXfxNqsvyBb5h0uAtl+nAI+jgluI0eg7N4fmZWFm\n3DTjUNH8kIIvFtuga1FeAZyekBKumEsW1LI8BR2/GYW6K28tr9hJBNOLiO6rQA+QpDHCE8jtuYdE\nLMxgBdrohdZAD7IZN94gPyH4cDQA6Qu79tEFyjaSzCos3IhOwekY2f871B23iFRLQdYzl0kdT02R\nzdEX7wl03OVrqHDa85PWaMfgNOA+e1EPBe5M8DpMAXaIBGalLfKMBYA9h0bm3opaa7VyyYY8BOwe\n+XswWj+Woks07YaK3wfJ0eCJPApsE5miszfqpdgGzal8HdoYPwSMTU7hKHuhAn1peV8n7zq9QDYr\n4klZ10gSzE0rvFY3dEm5VZFtS9F25CEIHqrwuiHTSE7WH0PaonEkV5AvsKcDUxLGu8PvPB01HHYr\ncpPR6Ds9lzoQzGKU4c6Uyei6ezeCrAS5CuTcAsffBnK4vnjyIsiBkX3tQZaAlPGAZDbIquImfmMh\nY62MI7X3Lb+w7/I8yOfkrLMobUCWRyIUATkJ5Cb7/GrWdSpjQKI9f0AOAvm7fe5o17oWJCKc0sF6\n93fGnuWDIIfa524gH6vLUq4ASVk0W/YBEZDzoxtLfTKNQJF7ycYgi/W5AMhZVt5vgpwJcql9h+dU\nOOU1kBNBliVbP3IQyN0gw7UeFS1eJxVgGWv3rXE0smwNEkm8IctBwoCP40H+YvXrT2Ve975sJ1Du\n0volnUDeAnkhW6dlLnlrx0pXkA9BdgVZSk5Qm2xkZSqhgy7HWR19U3+PmtW7Mu8TzveVriCnlHD8\nKSCXR/7eDeSJ9OMLXmsESMKwgfyCTIenIcjXQR4o4bhJIK9YHfmcnA62zAS5DuQ/qLCG208Budo+\nDwVZQOGhuyvsnMNBbqns+1RERfWuxJOkDcgKkD3s5X0f5ABthFLPec9eCEB2tHOOAemBjrE9XGZR\n7wJ5tLxzGop83yrMAyBfgPzEGotXQLaNHLeDVpycczeyBrytNUDmn5cOdq1I71p+CfLzyN/f1L/l\nvWxlkxNB1oCsBRkTO9bccnIoiAW8yP4gk1O+1yUgV4IsArkJnaxfT4J5KchvIn8fYMI1FmQrtCMz\nnUyHQnaz/b9Kud6G9lucBvLPMooZ2L1rHO4urUDmgPwWZAuQTyP1YEOQj6wBKzNKWr4H8n/2Dq7I\nNr6yIUivyHH3gsSybcnh2WcnvwC5JrLvV/b8k4ZeotfYHWQhuWJch4IpvUA+sWf/XftuI4qccwlI\nJMBRuqKGRdv0cxKvMwJkJ5DGzlUcvccm2h4XPe4CEJvvLe9E2vPhIPOtnj4K8iN7N3+FdrxsPrAE\nqKFTYExcXkKFeUcq7mBURLUEUwJrkKahluEqe6GGaYOeeM5IezEiDY0cAfIPVGxezD7Ukou6P8ge\n5Z3TUCQAuQPkGdSiWYta2JeC/Cxy3G9Bzks4fyraI/+UnNy08iC51uM9CQ1UADLLKmKAWqm7g+wS\ne65t7VmPQjsVljRBeqA9+YSFZ+UFkO1BBqI9/oOoXcNF4XtJJ7RTMjiybZw1WmEDPwqdghOx/OQE\nCva+ZaZdd5P0Y+oJ6Yt6GWaT11GUTqjIlTnkIn1Rsb0LJJ6sI3rcBeRlXpH7QSyCW3qiHoDN0Q7g\nYpCfWr1K6VxIYO9+fIJ6PQrmudbOnW2/wQJ95zP7R5ATeCad0E7xxNh1poGUsphEePymaEfobf2N\nqoW0sjbJ8l5LYG1BLOZBngPZyT7fS6ZDJD8h492QsSAfoJ3v31s9iT6bC0F+l1KOLminoj1qjaYM\nlcj6IL8pXN9le8pz8VdDMKU1yGMg80C+b9teQC2iVtYgJ/jp5X/1QeVtD0AuQ63NOh4/iyJtrFEI\nrKHZEWQ/cixkmZn8YsiJqBvrs9yGRHZC3WCtrDIsIWOB5pxvQizjtDKlVRi5wH6jleSk5ZML7KWN\nTHjOVNJ45aqxYCaOkW0Ecjl5VqB0AXm8gbc8CmS7hl2jOSBboV6FbQscMxTtXPQD6Y2ON66INYTH\noR3Jq9AOYCuQ11EvUkfUmt0XZGcTgu1AZiT87nUmmNLJBOAkdFbAbHvfl1g7sKe1eytBfmDn/BTk\nzoRrXQsSWepL2qLWWGzWgOwDcjHIyyCnosM+V1b2NUtF7kO9DSfa912BiqLFQ0hP2xa66S+0etMJ\n7bzvE7nWueR1+DP7htr12+r3likglsFN9si+19IB7aQktQsXoR3mlGxAMhH1vt2e3kbmn1TicfGT\npECGBTkR5N+5hZDjQCxFlxxjDbWJpnQ2AViWLACg91rXl5eR7vbSfBPkWNQ9kZIDVAaQN1YrAWpl\nH46Oz6UEMsm2qFC+BHJ2gfK0RsdGY8FEEqCC+yY6LnYa2tlJEp9aC+YbIJHQdtkcbaRvIuP6cZoO\nucjq3TJ7x2+I7W+NjlteRmY8Uza1xvEtNN5hMsi/UAt0hrYn+Teq+lcp+T7SFxXJ66wBX4laTmFC\nh7tR629PE4LFJnTLSHTZylFoJ/swq9+Xo67eG+29v8feyyUmSD+3e62HRi1XEekIcj3Iw1a2tiA3\nW1txNCrs90aO74rGCswC+XMZwoRpyA/tWT1t33co6sK9IHLcPJD/QaO3bQqUjLZ24Scgj8WuG4AM\nsbbkCLvPtRQ0xjIzFyoWzLVogI5lWZD29rK8glpHRSK95AirVOfY8asoa5xoXUX2AnnWKtwOxY/P\nO39r1NWzIlc4co5pjY41HkzZYyE51zkbHXu+zhqxY5MOqvz65RcIHR9eYv9vZQ1qWu5gp+ZIFxPD\nctOoTQA5klyPyiCQq0nuKNeJYIZDTHJeVgzkfpC/2uf2+jnqypZ9TRBTxjdlGMiX1kYsQq3VpBcQ\nHQAAGKtJREFUjdDO8iNowMuXIKc29Ms1Dpnht39YuxRrJ6S/iXqZ3kHZ2b7zWyAbmBjPtr8j84pl\nNNphfgXthNyCdkoOQ63TeXb/g0B+jHrPFqLjx2aIyWQV1jDOQ1qj3o3QYxAmNalYMMehvucFqAtw\nplWC8VrRS7rMXtlCSg8avnhqC0GGkDOWWdV7FQtcaYIxTBltnY43KGhBO82YGgqm/BONPzgUDejZ\nDbUcF6Aeo7hAfFXbwZxtZQaAZQyRzmQCq6R19jqyHXU5X7ragW5yMMi7pK47LD1Ra3NcZNs29vv9\nDe3875xfTmmDRt0uQgMD56Cu7j+hcTCh4ZFY70ppJMMfbiI67+0BSls2xmleROpCs7qXU9/Uqi7Y\ntCRGoMugjULnNv4DuBNN2L+0wPnOOoW0QX9jsWQgeQeQUO+KVcQp5GYUcVouT6DJAWrBFLzeOUqt\n6t0UvM45WWrZ3jmO4ziO4ziO4ziO4ziO4ziO4ziO4ziO4ziO4ziO4ziO4ziO4ziO4ziO4ziO4ziO\n4ziO4ziO4ziO4zhOqXguWadUPJes0xR4LlmnKaio3vmKJE5IEyzv5Tj1sh5mTYrQCUSyy3w5TUhi\nfShjVWzHcRyninS2//s3aSmcVFwwHcdx6gMXzDrHBdNxHKc+cMGsc1wwHcdx6oNO9r8LZp3igplB\neoNMbupSOC0NGQ3Ss6lL4dQFoYW5UZOWwknFBTPLQGDrpi6E05KQG4BpwOFNXRKnLugMfIlbmHWL\nC2aWnmR7eI5TZSQADgP+SNYV57RsOgPv4oJZt7hgZukFdLKGzHGqTSdgNfAB0LGJy+LUB52Bmbhg\n1i0umFnCcSRvvJxa0BVYAXyO1zlH6Qy8D6wH4nWiDnHBzBIKprvHnFrQDfgIFUyvcw5oPVgJLAQ2\nbOKyOAm4YGYJBdPHMZ1aEFqYn+EWpqN0Bj4F5uNu2brEBTNLmL/RBdOpBV3JWpgumA64YNY9LphZ\n3CXr1JJuZMcwvc45oIL5GRoI1ruJy+Ik4IKZpSdaWd3CdGpBaGG6S9YJCS3MpTS5YMoUkKFNW4b6\nwwUzSy9gLt7bd2qDW5hOnFAwl5AdImoqRgL9mrgMdYcLZpaewBzcwnRqg1uYTpw6sTCljd1/vaYr\nQ33iggmAdADaAYtwwXRqQ9TCdMF0QD0N9WBh9gYCYP0mLENd0qapC1An9ASWoZXV3WNOLYhGyXqd\ncyBrYa6iaccw+9r/bmHGcMFUQsH0oB+nVvg8TCdOGCW7nKa1MF0wU3CXrNITHTdwC9OpFe6SdeKE\nFuYyoAdIFdpn6QoyqMhBoWC6SzaGC6bSC7cwndoSc8l60n8nFMxgNZoir1sV7nEw8Jsix/RFlxlz\nCzOGC6YSHcN0wXRqgVmYwRpgDRp05rRsQgsT1ONVDbfsBhRfoLovmgTeLcwYLphKH9wl69SW0MIE\nd8s6OpWjDfCFbajW1JI+FBfMDYBZuIWZhwumMg54BXfJOjVBWqP1bKVt+AzvqLV0OgGfQSD2d7Wm\nlvQB+hcZH+0LvI0LZh4tVDCljfXosIozCXgStzCd2tAF+BiCL+1vtzCdMEI2pJoWZlsKi3Ff1MJ0\nl2yMFiqY/A5YDHIhmgLqQwgW4hamUxvCCNkQn4vpRMcvoXoWZm/U7VvILRsKpluYMVqqYPYFLgSO\nAk4CnrLtHvTj1ILo+CX4XMwWgvQHuSBlZ1wwq2lhvkqqYEpHtC7OxQUzj5YqmN2AN9Dw6u+j7lhw\nl6xTG8KkBSHukm0ZbA38KGVfDSxMaYsOB7xGuoW5AZoi9BPcJZtHSxXMsMG6FngeeMy2u0vWqQUb\noNOYQtwl2zIYAPQESbIcwzyyIQ2wMKUVyOCEHb3QLEJzSBfMvqhgrsQtzDxaqmB2Az6C4AsIJkLw\nrm13C9OpBROBFyN/u0u2ZTDQ/h+VsC9uYc4H+ld4n4OB10GGxLb3BhbbtdMEsx+wELcwE2nJgrki\nYbtbmE4tmAQ8HfnbXbItgwGoEKUJZjRKdq4dXwmHAS8Af7EpTCF9UMGcR7oYh4L5OdAuO5vAgZYr\nmPGgixCzMD1NmVMtpCOwOdqghfg8zJbBAHT4J0kwQ3dpyDKgA0gJVp7sDnKWfe4HTAD2AzYENo0c\nGBXMIhZmIHgQZB4tUDClA7rW2xf5+4K1wGqgfW3L5LQgxgNvQBC1JtzCbBkMBB4kWTAHounojEAo\namVKN5D2wB+BPWzjd4F/QLDSzu8TOaEPGkw0T68r3RMuGlqY4G7ZPFqgYIYBP5mMGnHcLetUk7g7\nFjzopwUgrVExehSd+x1nIBqME2UOqYIp3dHAoOfQ9TP72Y7xwL/s8xJyA4dsDDP4BA14vDXmssWu\ns8g+e+BPjJYomBbwk4oH/jjV5CvAM7FtHvTT/OmHCtxsYP0E6y5mYQJqIQ4kmT6opXgTcCjZJbn6\no0E9oO7X3rFzFtvn01A38P6x6/Yla2GuxC3MHFqiYMbnwMVxv71TTcaRO34J7pJtCQwA5phnaxaw\nSWx/mRYmPdGxxsvRPNjtQDqj45ahYMYtzNAliy0h9gQwNHbduEu2zi1M6QbyNU0KUX1aomAWszDd\nJetUCdkQzeMZtyTcJdv8GYhajJAXpSrtUWtvYeycQhZmuCQhJsKLUOuwP7DAjllCZgxTAmA08E7s\n+pHgH2lt5fjANqwLLtkfArcAMxLcy42OC2Y+60IlcdZNxgEvJoyfu0u2+WMWJpA/x9JELlgbO2cO\nJQkmoII5AlhtAT+Qa2FuDrRGs/yEWPAPgDxlx3xoa7RCg1yy0hqkFmu8jgbOBT4k/Vk1Gi1RMIu5\nZBeibg3HaWxMMPNwl2zzZxBZCzMumEnuWCgcJZskmFuTtS4hVzC/DdwR66yZhSnd0WC0Y8gG/EDD\nXLJHA7+v8NxyGAm8CcwEhlf7Zi1RMItZmA2ZMOw4hSgkmO6Sbd4MRxt1yM+0M4iCgpk4LzxJMLci\nO34JOS7ZUDCTrp8RmkPJdQs3JOhnU9TirSLSyu7zFi6YVaOYhemC6VQBCUgXTHfJNn9GANPtc5KF\nGR/XBoJP0c79dgnXiwvmQvItTIuSlSGooRAPNluMtodj0KkoXckXzEotzI3tXzUZgE4RXIELZtVw\nC9OpEgXTiPVBx5AWJOxzl2yzRjqhCfdDUYynpktzyQIcCdwBMja2PcnCHECuhbkM6I7OzXw+smC5\nEXyJ1scd0SUOXyBXMD9GRbQShqDu3mqm1huFumOhoGBKG5BzSsuaVJhmKpgyukDElAumUwXkIOC+\nAgdsAsxKSZjxCZU3TE79Mxz97cOgHrMwJQA5GtgdnZ+ZQPAgcDVwYGxHkmBCTocsWIN603ZCp54k\nMdf2zwQuBR6J7HsLDQQqEwlQ6/JTqtuWjkTLCKmCKe2BG4BfoBZ4g2iGginrAc8Cu6QcUMwlG6aN\nagdyg/nJHacA0hZdkHzLAgcNITekP8osYKhFFg4GqbpryWkspHuBRaFDRgAzIn+vQL0NuwI/B04k\nm50nibfJjwBNE8z5seOWALuRLpjzUOt3JgS3Q/B4ZN9UYEIF0zW6A4IuVD24zHPLIWphvgdsaKlP\nDTkanSLTDfg7+XNfy6Y5isEh6EB1WuNVzML8ABXVbYCjSE5j5ThRjkLFsDNIl5RjNiHdivgYbdiG\nAKcC5zR+EZ0qsTXwkyILNmxKdvwS8zLMB44FboPgnwVSdYK6cgfFtpUjmIMpbGFCNiApQrAUHedM\nyn1biI2Bd+1fI4xjyjCQcxN2jCJjYQarUdHcKbKs2VHAwRDsB0xD369C92ljiyOk0swEUwLgOOBm\n0gWziIWZ8esfYBsmNV75nPpBApAfg3y/ES62G3A9KohpvdhCFibA68Bm6FqZExqhTE5VkbtAeqFu\nwM6olZZGTDABFbZvAJNLuFlsPqYE5AtmmGwgPka+BDUQEoKKALUwF1mnLYlnUeMhAdka5N6EHUPI\nCubglOuWw95o5yJ67zZosFK0I/AacCvwN/P6bImOzULhdzNkX/IjiXNoZoLJSNQdcAmVW5igva7v\nAP/GBbO5cglqyX2zEa41CG0czLWaSDHBnIZG0W6GBkt0a4RyOVVBBqM5WMeRnTpRqDHelFyXLKhQ\nfUF+Iv4k5gN9IwE0nYAvIfg8e0jwBXAe+dmCFgOvFLBg3yNfzKNMRfMfJ/FVYPcEqyy0MN8j0cKU\nPUB2SL+l7ATyJMilNiS2DTpMFvXejALmWYRsyEFox2UY6u5+N9IReIdUCzMTUHU0cHt6uepCMGWk\n9lLkgWLmcAlsivrN30IH1WNRURKQvnh0lHBZnIuBbRtYJqc++Rb6gjR4XINslOMs9GVNooBLFlDB\nPARtWP+DNsZVRgJ1dzllEjb2Y1ELczmpHSVph9aJJAvzUXMlFiFYjQpfmFClF7nWZXjc+ZEsPSEL\n0PqUxmR0SbA0ngW2TXE5j0FTPU6MbY+6ZAcnnHcycBdI2pqcR6OrsOyNGizboN836hoeT940mWAN\nBP9FV4Q5z8oekmJhSh/gZZAz0La+7i3MPdA1KAeiE28bQhiJuAZ4A9gitn8jNIS/FMFciFam7iB9\nixzfBMgEkO81dSnWTaQDmmT6MWBgw0LfpSPq5v+AVAtTOgE9SJ5SEvI6aqlOBZ6nNm5ZC5qQNKvY\nSeZr6IozoWA+SLpnYQIw3ZbUinIDmtKtVKLjmHF3bCEuRQOLUgjWQPBB+n5eQ9vLYxP2jUGfQ3ye\naIExTAnQZ3ILusRYDGkH7AX8Dh1aOxmNSbkf9b6EJAhmhgds/9TItiVA+wTPzXh0/PYC4E6b+5pK\nPQjmZsBDaOb8hjYSQ8n24l8m3y07AXiuyAA76A/9pI1nPo2a92UiaW6MxuIw4Igq36O5MgR4316O\nRTQsB+UA1DX0Jeku2Y2B9xJyhUaZAaxFe9a1EsyRaDTjhTW4V3NiB+By1LLqj07FSBPMndCOWYxg\nJgTTyrhndByzJ2rVlkDwaSS3bAUEa9HgmV/lWoTSGs3jeiU5gimtUO/Iq6gVHYBEp6YMQ6dRnQFs\nA9KPXHYAZkCwAHWPfhMVvml2v5C0JCCgHRjIEcxAUG2Iu2XHAXeiLvZfplwvQ70I5jS0oYib9uVi\nFiagjU58/HGibS/GTUAYDHIVcEZ500tkQ+AZkGrOQdoFGFkkOs9JZhgaqg+lBQMUIjrpPE0wi41f\nAsEq1KPxBPqiT7I5ZNVkU3SO3w46d9kpjHQDORAN8vkH6qV4Hx0CKiSYj6fsK4e4YJZqYTYCwTQ0\nmOaHkY1DUKttMip8oZdmFJp9Z56J7VXAjyPnTQSmWn2/j/z1OPdHny0QvI0aPlNRj6FZmNLe7pMS\n+RssAPYhO0czZDawBchekW3hggiTIXg3+XpZmlgwpRXaa3iDxhHMqIV5P7BHrNGZQEmCGayKDCZP\nRgfnywkOCS3SMqwEuQLkHZDzSzi2PzrG2godz3DKYziNJ5jRPKDzgW42FzjKVmTnixUg2Fdf2mAO\n8BI6plkGcqIGSpTMSLvPXejQiFOYW4CTgJ/auOI01J03GxiW33mVjmiD/BQN5310+GBTdMyxkBu1\nGlwHHBmZkzkGeA2C5ahwHWPbd0A7fSF/Ar5lEcWgbfxz9vkONMetIQHwdTKCCajhcj2qEWGnbj/g\nPxB8ll7c4P4ET+I7qBv4r9rxkQB1yaZZqmVTzHXZ0MtvDGLzgKQVyIc2CFvJtdqDrLJw4nDbMxqR\nBTYp/BPyVzov5dr7gLxSujUnt5j4XVzi8W1AloPsDLKiePCTHA5yh32/7Uu7R4Opcl2o5b3kGpDj\n7POZIL9twLXOz+3kyMPohOnw7wBkOkhKaH7qdXcCeatMz8ZzIG+UcfxLIBNBDgC5p4TjA4tcrOVq\nPrWqd0XuI91APs4NJJTr7HkEIB+B9Izs62B1o5Qo2FKKtw/IuyBLQC4EKTSNpUrI85H29Fcgv7DP\no61cA0D+BhIbKpLrQWxusbwAYp4/6WjPzWJEZJy+K4n3Duz5bwHyZrYcZZV/BLrY9FZW3gNBPkhp\n1yuqd9VuuPYFicxDkoe1YlR0rREgs2LbTge52j5vBRIP7S712q20IZK07EHxYz8A+T7IE8WPB61A\n8rJ9fgzkG0WO/wvID60i/qC0ezSY5iSYj2d/S/k2yN0NuNZNIMdE/t4eZDaZjptsZZ2nMl3nElgD\nVaJnQwaBLLUOV88Sjm8FstKEoC/aYSsiznIIiGQ7GzWhXgTzUPLmHMowEAtqkSdNIPcHmQuyBuSf\nqEXYGMUbBrJYOzhNhRxjov0nkEXk5LeV01TsZAk67SZ63mg7fheQ+eRm47kS5Nf2+QIKGhlyGGr0\nPFX++5R3rb2trPenHVDRVRtQolIufxbIJZG/T9cGqKxr9LXKeg7IQ7F9Q+0BT7feyZkNKOtRIA+W\ncNwWIG9bQ/QJJUVgyoWRSvMjkJsLHNvWGsaBVkkvL638DaY5CeZ8FRgA2RLktcLHF7zWYyCxoDB5\nHOQ3IF1ArtLft6Jr7wUyjZJSk8kpqMXzEMjXSzh+AEhkzp7M0LqbenxXkAUgfwC5s/j1G416Ecy7\nQI4ssL8/yFSQ90G2JcfT1Vg0dZpOCdAO4a/1++btPx7kxWQxkwdAPtcORc72QSDLQHqiXrwi0/hk\nLI02FUp654t7dmdFVyzzcIuikq/ZAyoy+VruIcd8l56U7JaVLtrbkqnoPE4B+WNymWRUbq+mEqQ9\n2rs6rMhxV0fEb3puIyS7mTgeDbKBid4YkJfJuFalH9rbjyTjznEz7wHyjH3eO13EZbCKfGo5O4Nc\nVriRzD2hxOMagwrvJdeBFJpThn3vz7ONj3RBLa1YAyAbk+j2kjYmsmeBnAsyh7zcr7IxyN2olfEf\nMlZI2d8nAHka5KcUnNokbUBetbpxDiW5mGVXkEgwilwDclKB408HuTVSP8vNL1opNRRMuQwklqBb\nDgJ53b5zjyKXaINOIXLykPEgF6XsuwZ1zc6uYb0qRjUFU9ZDzXQB+Q7aK34UHSOJjHdErS35LmqJ\nxXJvyrVWcbexHsCe9ncfa4h2toc/F+3NXGwNyxkg+1XyJUtHRoIstDJehPa2v00myENGoGa+vVhy\ntTWc/VEX7SLU7XAban0usGf1ekwUf4+6p9uCbGcv63n2PW8AOdmO28SucbQ13iejHYQzUSt0oZYv\n73u0A5kM8gjq5jmX4r3XOhdM+Yo9iyX5jV7mmMB+u5gLVn6GjgePtGe6tV3nJXQsqqP9fxzaoXvL\nfvsbUbFNGXOWduV/j7xrbGm/03LUxXciyNfJHS87C+Rf+hvKDlbu1pHvPMzemQH6DspI1JV8VeQa\ne6Ou43AVjZ+h400DrB7O1bKA1dnxsXL2ArkZFe7jU77LrqjgtrbvVUrjWEvBvNjeh6tBTkC9BPNQ\nq6rSdSGdokgb1NKsF7GElHpXzA8sIKcDU9BsEYKGUm+FTib9FJ2DcwQ6WfTPaKjw82g49dnA8WgU\n1XZoOPZkdM7Z4cBuEMSyUMgIdFJvWzSacT4aZbY/GhU6C42WOkaz69caCdMudQX+i2aj+AqafGE1\ncBEE5oeXjugE3MPRaMTjILCgDGmVvz5d5h5t0Aiyne0eJwCno/OXxgCba9i2tEbngC1EI8AGossA\n3Ys++57AP9F5YKvQaLD10UnIL6JRmL2BvwHt7FrhfMAedl3LrRs8R/H60liINc6vZiPdpAuZkHU2\nQJNeb4xm51iBrid5LRpuf4197owugLsIjVL9FjpvcsfcieTSCo0CnAS0R5/JEWg04nC7Twf0HTgS\ngki4urQtLVtLQ5FOwKFoMo5NrKxr0O/bDdgagvdV2HkEzbAy3creHs0UsxH6u85Fs6FcDEFk3F/O\nQKNA56DP7i40SvElYD0IdrLjLkezryxBQ/3fsjLdi77/fwXOR9/d94Cl6LzhU9FVOpaQfX+moGni\n5tmxc4EP0ee9wFK+1aLeid5HBqER8cPRJCdX5j4jp4Vg9SGXUgTzD+h6bYPR6RX/RefGfAR0QUOE\nb9WJuIAGU7wDgc07k0loI/UwurL80ajo/rl4RQx94YGAjAHmQ7BMtxdNPlBDMin3egNv55et0vJK\nN6CVhm5LJ1RAe0FwY4FzWudOkJddrVxd0MwknwL3QBAJSJK26LSC8egcxTZoQxx6Bx6C4ApqK5iz\n0GcaCsL6qACsjzaoU9EEE8+h04n2Q1cmWI2uVvBjtOH/CK1/Y9GOw00FEk2Ht7ffS7qgCbLvJZN/\nuF7qnQSo+HUHPsnP1iIjUYFcQm7Ho4jAywS75rP6nGRrYHvgcQhs3pv0R+cBf4z+JlsBayG4zfZP\nRJMhrEaFtBs69eJAtAPTT6/HlnbuRvZvMJoIoDsqVnta56SGguk4QIWCOYVs3kSnZfMEmhKsFkzB\n652j1KreTcHrnJOllu2d4ziO4ziO4ziO4ziO4ziO4ziO4ziO4ziO4ziO4ziO4zQh/w/eD3xAvlMb\nrwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f274bd03810>"
      ]
     },
     "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): 9.57 seconds\n",
      "Overall time: 195.26 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 0x7f274bea5d90>"
      ]
     },
     "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): 7.71 seconds\n",
      "Overall time: 157.97 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 0x7f274bb63b10>"
      ]
     },
     "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): 5.81 seconds\n",
      "Overall time: 119.91 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 0x7f2750207e10>"
      ]
     },
     "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): 3.95 seconds\n",
      "Overall time: 82.85 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 0x7f274b580a10>"
      ]
     },
     "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): 2.08 seconds\n",
      "Overall time: 45.42 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 0x7f274b960c50>"
      ]
     },
     "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: 37.61 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 0x7f27500e4d50>"
      ]
     },
     "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): 1.31 seconds\n",
      "Overall time: 30.05 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 0x7f274b8dda10>"
      ]
     },
     "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: 22.03 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 0x7f274be8f490>"
      ]
     },
     "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: 18.03 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\nvQTRgxkHzCI5jODHkF5D4Uz+W6EZYTMo28uU3sB70bGDD6DjotIIh+I8T7pXchxq3GyWsG9rNKSy\nbXnlrBu7oWX/GoX9zM+i5U9B+qIPuG+c4iHZDgrHQSYl/nwF+Bs1FczoIzqtYyJp9R5yA9nfl7Bv\nU3Q8cUfCvmmo5/Mimesbyt4gB7nX/VOO6e/K+JcWSDDaBQ1Rvg/t/76EbA/zZOBKNLdhX7Texp+t\nHYH7c2+jZe7Yncn1I3YFc93/lHHpRUnyML1gxroRZFO0XQnGKEer0HHLJU44EkcmgFwA8pd6GGs1\nEkzpi1qwz6H9ks+hjcjowrCftAGfBC6o8Gb18DCL8QLJIVc/0/99FArm9qhHfScalokhB4AkiRfo\nw7TI9fE+4N6nESZLJYQ7ZHPXd3UqKvybUshWwFtoyKk7shO6WsJGqBESciewuZugOonPA48Gk6A/\ng4bYw9Bl0gTWwfWkF7qy/ZZo41nuJAWV8CI6E1ZSGP0IdKjBgQme4jRUMNM8zP8Bl6EGVAISAT8D\n/gwyA3gdJMlgm4IaGoNIHtvak9gZ/U42ItfmbZ/cYMsU4BPAr9Eluk5BHYvpOa9KIvSZjkXUIoHo\n/hpkYGcxB10veFXRI5NZBAzP1Uvpgxq03sMM2+qzgZ8kOEJF+jFlR5DPpuz8GCq2+5IaTasdNRBM\nORI4Hg0DrkGtqT+6DMRZdFYMOQXkv+i4y3lFPLgs0pIf6i2YST+wF8yZwNRY6HV7NJvzdnTx1TAz\nM0ITV/6tfVEFHIyG3KC4hzmNTMHkMrQiDwYuInkc4laoRbxnxn0ayU5oUtMj6FqAAdEadF7XQzXc\nLIFnKB9HoxuHB8e/ic4Fe5FrwDoo9DDvJ7/PaU+0j+n3aF2sVUg2g+gtNGwWM9RkIFo/LkTr5W7B\nvlFAb3QFn8n5DXrnxCBPoV0Q+5K81NJ2aPLJpsDF6GTeSfViM/TZvpLCcGMPQY4G2Q+tC3e6v5kQ\nLUQTdoJnTc4CuR8NrZ4L0cMQLYLoMpeLsRhtG49AjZnVGdGFrmQu2ZO4FCFahxpzz4P8A332FgG3\noe3MFiCfBzkXdRSSHKXnSWxPZWenL/8AzkoJ226LtmkP0g3qXRHLRvqBLAGZD3J1wv5vgFwI8kEV\nS9lLhzpIFVlRsg1I0M8j64HMAnkT5IzKr1tWGQ4H+XtsWy+Q60BOdu/Pcu8j9/c0iAvbyEkgC0Cc\n1S9bu8+vcrGtAAAgAElEQVSwP8hC5wH660buu9vVve8LsgpkfdcPGS/bk/odgT6MYTllJMhykA6Q\nLdFMvxsTrvGS3k+WBpW0K63cggJk7Brr6lxG+EWOBpkJ8grIIpAvgRzmvtuEyID0BrkP5Hj3O8S9\n8uNA/ubu/RTIXJDT0VClaB2sB3IDSNBPKR9yv9HF7v03QX6f+25kD/1cALLMCag/dyxI0JckV4GE\n84T67b8GOTN4fzzIlQnHnQPyLVevYl6/9M8vd1nUq94Va+sikGfd8/Oqe38gnQauXATyZfd6GMhr\nIPuATEq53lWuDXjbtZEJ7Wc9kB1AflHlNQbocyXHuGctiHLIYSA/B/kuyZEJQL4Gcn5s2yHu+7nF\nXeN5CpYSk8g90xNd3Yst+CE7FD7LpX+oLjhJDgOZATIYJCFzVTZwD/SzqGVWA2SwE4xLnSD8DeQ8\nbbSkrTb3KFqGKa4hdh6kjAC53TW67nuQvqjQHQfyXlTIgoxV+ak2MgByNsiPg9f/DBq9bUBeJM+6\nkntRA+Hx/M8s+4K8kKuwsrWWoXP/kagVGH6O2FRaMgLkdb2fPAfiQ8vdRTAPA7mpyOntrt4dpw+Z\nXIYade/JOOdQkPtB1lDYjTDBNZJfA/kzyLtA3Mw7ch51S1iQI0DmgIxDRWgeSBCel6Gunp3u3v8I\n5Ifu9cz8Bkv2Bfl38P4QOsW1c9v6qHEyLti2MUhCgohchRoqbe75DMbfyaGoYVHJsJjuIpjT9HPL\nHiAJSTCyK2oURyAfA7miyPVOAfkdaoAIyFcqL3qzIxu758sNb5Ix7nkNoyWXoBGi8Lxx5IyXQ7Td\n7NwXgTwK8p8Kn88uEcwrQIr0V8jNIHdS0w5Z2cJVyrudaKQkInQlcgbIY64RmwnyEwqGcMh094M+\nC/L+2L7tnbhF7jO4yiF9QB4AcUtFyU/QcEZ47nraIMmtIJ9z23qBPKyNVudxg0FW5yqM/Ia8vgBp\nc/uD4QiyF4gb7iPHod7UZOrXcJF9L/k1yFklXKJM40naQF5OFgNwv9Vr+QLVCORMJ4rnkhwdmIRa\n5vuDLAbpcNuvBjkxOO6z+l12vm9zv3WwXJP8BuRHsetHrk7Hx949BeJWnpAHYo3dRe67jXkRBWXf\nqPC63UYwv0KmJ9YZRXoXyEMg7y7xthHINSANDyc2FrneGRH9tf2Rs2P7T0IXVg+3vU/1BfLFE/T7\nlBdBHgQ5PuO+be5ZOhxkTzQDHyoXTLmdvP62zl07ouGJIvOqyka5h7YraFQau0RoWO56kK+nl0OO\nRcMKUcL5z4D8kULvc4pr7I5BLa204QRT0VDbQ65BmkGBNSXz0dDEWFeBNo/tf5LOyRBkgGskfxns\n/wTq/dZZMBMTKHZ2n2dU4b6a3PabIHel7LsENWwaPGxCIpDPoKG8lGxrOQL1lIMwn3wAjYpci3pL\n8yiYCUrO1N8f0HDiQhL7NeV6vUfn+z6o4eUMV/kVyKeD8s5DPdql7jdMMHAlQj38WSDhikXdRTDv\nAzmwyDFfAHkDDZ1XOO9vq9LZHfUEmvEab8c2cm1c2A9/Fsj3gvfzQfziHb9EuwR3cUJ6OInIqagn\nejPqgHnnpGLBPAu1nIL+MvkiasUekX6qURw504lmwrqMcrB78P5U5BojQHZyFSpJZL7jKtqrIFcm\nCPdVaP/TISCztZGV2HAgiai/YF6LeshD0FDove6BOrr46RXfdgid/cwF+7ZREekuJHWB5O3/Fkhs\n6IL0U+NH1pG4wLuMcaJ2u/uu90+59pdB/gUy0r2fBvJssP9UkMvc621z++QraFRmJgVTpsle7lk4\nGw19/h7t8miwYEp/kO/rZ0idrCQ8vinGE3Y/JALZHWS/5O+5sw/5ArSraWdnxIR9+leA3OR+r6Xk\nusx2cG3bT/KvLYPRPIekvtVqQrLyI6f8B4Ccj4ZfxmWfahRH2kienaWeZdjVWVfu900/sG5FUsG8\n2An9CtQL38eJVisMjO9iZCdSQ9YyXA3htGQVCIR3njNmrgUJlsCTDd2+x9Awb+gFRO63vRnt2/86\n6lHMRENykWsQP4oa6XUUTLkN9UxuQr3xNe7vNrosqmGUjoxEoxdPoN1PF5I35lcGol0N51MQ1ZQR\naGTkMZBPuvq2FuSitJtVVEL3L0JXwHjMFbJOWYFGN6IBIVnZiZ4/CL6Jka21wZEvkTd8B5wxuCua\nNRsTZ+mLepK3uQbwBDTKkZSLUE/BPBrtgjiczsQqo+cgkfuN/+6MtayIQWK9K2atz6DiuU+NHkbK\nhAtdwgys3hlKverdDKzOGTnq2d4ZhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEY\nhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYNcQmXzdKxSZfNxqBTb5uNIKK6l09l3RqAmQtyJRG\nl6JB1Hl5r1bA1vcsgQYvIG20KNWsh9kdkbOKLHhc6/v1AxGQ7et3z26FCWZNkd7OANus0SXp5phg\nGo0gsT70qncpasjWwHZ1vN9g998WNDZqwXigDxYGNIymoZkFcyAwsY73M8E0aslk998E06gTchrI\nuEaXoplpdsGcUMf7mWAatWQycDewt/VjGnXiNKCO3Vg9j2YXzBp5mNJX+5QyMcE0aslk4A7gTaBV\nE8mM+jIS2KrRhWhmmlkwB1G7kOzngWuLWPommEYtmQy8CNwF7N7gshitgQlmlTSzYA4ERoEMqMG1\nJgLvBT6UcYwJplFLvGDOA0Y1uCxGj0f6oU6GCWYVNLtgrqQ2/ZijgAuAb2QcY4Jp1BIvmKvI1S3D\n6CpGAguAPiBjih8ufwOZ1NWFajaaXTCfpTZh2VHAfcDwjGMGA6sxwTSqRvoBo4GXUcEc1NjyGC3A\nSGAJ8DhFvUyJgP2p7yiEpqBJBVMiVDCfoXaCOYfshmswGj4zwTSqZRLwMkTrMME06kMZgskItJ1r\n7+pCNRtNKpj0A9aiIlerkOxLet3UbNl2VDCtEhnV0gHMdq9NMI16MAoVzCeALYsc68cIW1dBjGYV\nzEHAG6jIVelhSoRWpsXumgNTDjQP06gVHZhgGl2GjAQ5ObbRe5jPAJsUuYAXTHMOYjSrYA5EG5oa\nCCbtwFqI3iS78TLBNGrFJDQ6AiaYRu3ZHvh1bFYfL5jPUnzcr3mYKTSzYL6BeoUjq7yW9y7BBNOo\nDyaYRgISgRxegwuNBtqATwTbvGAuBPqDZCU4bggsxzzMAppdMJeRndlaCqFgrsQE00hF2kBOqcGF\nNiBfMM2SNwAGAH+rwXyvo4CbgFODcepOMCNBvcyssOxkNDnIBDOGCWahh5nWeA0GXgXadCo9owUZ\nD/wCpNrnZhIw173OMtIAORDk7CrvZzQHvi2rdhWm0cC9wCJy4VfvYULxsOxkNDnIDLkYPUEwh1U5\neXU5IdkVwOuY5dWqDEWX5Bpd+SWkDRiLjsGE4iHZHYEdKr+f0USMcP+3rfI6vk17ldwsUqFgPkeq\nhym90QjIE1g7V0CzCuYgYBVEa4G3qK4PqBzBXIkKpoVlWxP/u1czlGk8sMjVXSgumB1U309vNAde\nMGvhYb6KtmtJgpnlYY4DlrrzzcOM0ayC6T1MqD4sa4JplMpQ97+azOww4Qc661xqlGQSNtdsqzAc\n7Tus1sMcTbaHmSWYY9FcjRWYh1lAEwimtIPcAnIliG+oGiGY7Zhgtjq18DDDhB8geht4G52MI4kO\nTDBbhRHATGBgafO9pjKKPA9TegHDUM8RMkOyjHDHrcQEs4AmEEzGAZu5/zu6beZhGo3A/+7Vephz\nY9tS6p30cvcaDNKninsazYEXq5lUF5aNh2SHASudcQbwGjAgJXlxONqmrsBCsgU0g2AOQccOPYqK\nJtRdMKUv0BtYgwlmKzMUrYvVeJjxkCykZ2ePRev3EqwfsxXwgvksOhayAqQ3OW/SC+YYNGPWEQk6\nzjKpHRuOeZipNINgtqMiNZ+aC6ZEaBJGMQ9zEGqhCSaYrcwQ4L9U52GOA16JbUurdx3oFHph8obR\nc/He3Xxg/QqvMQJ4zU3s7+tNkpH2Ork++aQyWB9mAs0imCvQjuixbpvLkgWq8zAPRYX3afc+reHy\n4VjQcIbLZpPzQSaDDAb5cYVlMJqHoahgVuNh+nBZiAmmATkPcwG5tq5cwvq12L2fRG7uYk+a4T8C\nC8mm0kyCWWMPU/oAPwTOcNYYlCaY84JyHA0chwrv6W6dQ6NHIJNB4lb+EOApYHwVkxf4hIyQtMkL\nfENngtn0yBdBjixykBfMajxMnyELuXrTQaGHmRWSXYauBtXLJmnJpxkEcwiFHmaFgim9QB4C6Q/s\noteNbg4OKEUwXwHGuYo0BjgSOAaIsAVXexJfBD4W2zYU7Qt6nconLwj7zD3mYfZ8tqL4cBEvVtV4\nmKFBVmlIdqnrfjIvM0YzCGYt+zA3QLPP1nd/s2L7S/Uwx5MbrzQa2Ad4GG3gjJ7BKAoTL4aglvnL\nVBSWlb5o3V0e25FW7yajDZ0JZvMzDG03sqixhxm9AbwDbE5hSDbNw/QhWbDEnwLaGl2AEvAh2cX6\nWvpRuWBOc//HuL+Fsf1pDdcocpXIeZiMR5cXewBtPF+jasGUg4D9ITqjuusYNWAkhWMjh6DGmw/L\nP1zBNf0E2CFp9W4j4Hm03m5Q5r2M7sUw0tfa9YRjIEdrxmtnd1GpxPvIF6OCWW7SD1jiTwHN4mGu\ngOgdcqGKUDBfozLBXI+8VGsg29L33qi3/jZAPY2vAh9FLbjJCeeWiPQCfgC8p/JrGDVkJIW/51C0\noVlCbhqzIsi2IIeCjCc5HAuJw0qkLyrK5mH2DIp4mNIbrQPLIXoLFa1Kwv7xOubFc17suGJ9mGAh\n2QKaQTB9HyaoWI2l8izZTdFZVcr1MDekUzCjtahIbwO8AtEqiJaigtlRYjmSOBgd6znZPTxGYxmJ\n9lUPCLb5kGw54yKvAs4Fvktywg8k17tJwDzXeJpgNj/DyQ7JDgNed44BqHNQSVh2fbSd9CwGXg4m\nLfAU6cMELCRbQDMIpu/DhFworJqQ7IPkPMwKBBPQsOyO5I+ne5HqBPPDaNbuEor3dRhdz0i0vnUE\n27yHuZSSPEwZ6875DFqHwgzGkKR658OxYILZExiGLtycNobbh2M9lSb+jCPfm1xMYf8lJHqY0hft\nhvD5GuZhxmgWwYx7mAmCWdISX9OAO8l5mKWGZDcEXgjez0OTh14Ots2mOsGcgM7w8TzZi7saXY4M\nQL39J+hM/JF+bttqSvcwdwHuQ3/TDckOycbr3cbk6pwJZlMjfYD+qFGdZgzHBbPSxJ8kwYz3X0KB\nhymfQdvE14I+9ibwMGVIwvCvLqPZBDPBw4zeBNaR2aEu54J8Dk1yepz0kGzCeDhpQ4eLhJXuFVeu\n0MOch050XOlYzLHoQ/I82lgajcOv7PACsCHIJnR6l5FQsofJrqhgznPHT6QyD3MJJpjNzFBy2dVp\nghn2HUJFHqZEaPsYhmQfB/6TcHDgYUoEnAfsHytDMyT9nAL8ol43azbBfBHYGw0TvBEcM5/sNP8D\n0QrxP1Qk05J+VqNhk/B7maDnRGuCbd6CCwQzWoc+EBVkM0rkyrQAXUnABLOxeMGcBRyAepqHkRsO\nUqqHuStwr+uXmgNsT7JgJk1csBE5D9PVf6lmzuQGIsNBTmp0KRrIMDTv4RXSBXMk+WJViYc5DFij\neRWe6GKIfplwbOhhDgX6AgeR7+U2Q0h2I2CfeuV9NINg+lR+gL+igjeIfMF8CG2M0lgfOBb4sTt/\nQ/Szr8w/LHoHeJN8bzXefwk5oYzPCfoilYVTR2pZojVYSLY7MIqcYB7sXh9Erh6W4GFKf2BrtM8c\nd60dKC8k6zzMSFBjbxrNyXnAjxpdiAYSCmaaYR+G4KGyPszxFGbDphH2Ya7n/h9Avmg3QUiWyah3\nPr0eN2sGwQw8zOhtVPhOiWV9PUBu6S+H9NG+qE7v7UaIriK32sSihPFwUNh4pQnmUohWx7b/G9iv\n1A8WEIZRLCTbeEaiwjYT+AfwOTRc5QWzFA9zE2AORN4om4Va8mlZsoElL73IH8oEOoft5m5/sfF8\n3QjZG43wDG7hqSOHoUKU5WFuDjwZvH+J8qNV8f7LLEIPcwxap+Nh4WWUPHyqHOSTIEfV6GKTgTuo\nrN0tm24umBKRH5JF+yyj38YODASzM/nnbOD7aKVY42a9APUO1lHYf+kpRTCfcfeMcyOVjaP0/ZfQ\nmSBS8VylRvX4CQbmQHQwagi1kwvJltKH6WeC8vg6VIqHeQg6FCCMojjBlEOAu0r6FF2KDNQxpkX5\nCvB1tPujmkWRmxnvYWb1YW5BvmA+A0wtLZlRpoIcT/mC6T3MMcA9aDsbhmTnoMObas2ewOHVX0Z6\noeX7LSaYgKY4v+PGPmYxE9gK5F3AE66v56PAFDQcuyB3aPQO+vDG+y89QeMlGwJHkF+RgegFiN6d\nUo7h7rxyCAQzWoVW3C5qXGRLkLRFsg3F92E6ooVouMx7mCvQvu6sialDIwiyBXMJnYPUZRpwEXB8\n7Jin0IXU3wtsC9KgKISMB/k+ugj2vdl1STZAw9BXkstw72HIME0qzCQUzIT5pqUf6ik9m9sWLSY3\nZrwYHwTOoiLBlAgNyc5Hu7ZCD3M2FWf+S6TtsXw7Yec4YI90Y0Ail2xZjPXRz3ETsFtyP6aMBDmx\nxEIXpbsLZth/mUG0Em3Q/uw23Iou9txBgWAC6l0W8TClL3AvcAHw99KKG72D/njlepnxxjWcaL6G\nyGDgdtQIMNKJCSagFrjzMLMyZeV4V3fKEczZwET3wJ8A/Aai+2PH+JDsAcDdwPtL/CxFkH4g+7g+\n11L4Oxq62xHtn90149gTgb+4rotKB+J3d6ZROEl/HC+YLwAbJwjFVODFWGIh6LKDm5ZQhj3RLoAd\nKFkwo7WoIA8gN2LgStTo98wGOkocshfnQ8AvgS+oUZHHeNRA7Eg593BgRglRtg70e1uGPldJM619\nADintCIXp7sLZiwcm8n96AN8AOpZfgt118eSLJhpHuZSNOljPBrK/XlKX2catwF7Je+SM0A+n7Aj\nHr4LJ5qvJZ9DxaCFJ0aQMSC3FDkoSTAvBf4ZvE+YHk+mAX9AhS1JMJ+NhVkd0WrUsh+HNnyPJ5Rp\nLmpA9ge+TU2MHhnjrns9JRl5EqEC8SWIZqFjmlPqOqCez6XudQ/1MFkfGFnEIxqOjm9cCrxFLsnG\nE++/9JQgmNIPFcrb0d+wVA8Tcok/bhKX6JcQXZPbHa1ARw5UMkXfpsAlaGh5o6C8fujLLcDuKedO\nBXZDl07MYjK5SRmeRMPacd6NGqMDEvaVTU8SzK8AR0M0Hw1d/Q71FreiUDAXJGzzzEXDJhPRjvdy\nmUV6Z/02wE4J26v0MKVPcStQRqOC+XOqWwC52ZkKHACSZZAkCGZ0B0TXBRuWUpj482n3f0MKftNo\nJURTM+75ItoAbIwOLYrRmSl7K5rk0AFyLcg2Gdcsxp6oofl/JDc2cUahRqTvy52BDvNKQIag9cx7\nLD3VwxyDLu2XJSrewwT9beNZ8PH+S08pHuYO7rjr0KEh5QimT/xJmsTFM5s8T1AikD+AnA+S1Y5M\nQNvPeBLjCFSEbwH2SDl3PHA58L0ihshk9LmBRMGUfuhKUvOpUSJlDxLMaEnuQY5edg3MbHS2lbg4\nngX8PuVCvqO7UsF8ifR1MTckeWhAXDBL9DBlFMifUO/k5CIHfw/9zDNobcH0v02adQvJHmacmIcp\no9EM7stJFMyivOjOC4aTFHAjcIULp22BeqLF+s+y8DMRpVnnccIGCnfu9JSs3a30up2rbfRkDxOy\n+xqLCeaWaMg9ztOogZfFnmhS2gz3vgoPM5HZ5Ic6NwDehbYhn8i49gS0zzYumOPRbOG7SX8Gx6Nz\nMK+kMzM8kbhgxo/dHTUyH6RGQ/W6u2CGE69XwmzUAosJZjQXotcSjgf1MDegcsGcj874k5QQsiEw\nJaFzugIPUyaiM3jMRw2Ad2UcuyMamjibitdy7DFMRCMP1Qpm4GHKbqgndT7a11mJYM5C+wNXQpTS\nbx+dDdEN7vVidO7h3UHiIb4Ysn9KBMIL5hNoo12MDvLmJY1WAY+568SZ7vZ5epiHKac6D8YLZYWC\nKb3RunhfwnlPA5uBXALixEm+7xKvIpBPo1GjG1HB+BNd7mEyHU0O+iXpHiLkC2YQkmUcKpiPAxuA\nJE0A74+5n+SIHC7Eui2ZHiYHoTklz6LddFXT3QUznHi9EmajkxCkhV+TmENVghmtc/eLeYgyGP08\niygIcRRMZ1WKh3kEcJdbO/NKdLaLtN/zKOBXriFudcHcAE1cSRHMznG7aQ2IJ0z6+QrwbRU0ZqEN\nRCUe5oEkhmPTiFai/Y/HpB8j/dHw1zYJ27dCre9n0BBvscSfuIcJOsQlqeHcGng0eN+DPExpQ7s2\ntkCNgFVU7mFuh656lCR0s9HvbBfgVBVKvoSGGTdGh+scANFtmnAYHV/CiIKQ5ahgZnmY8UUlpqO/\n63+AbZLrjERoG/MKyR7mPDeOfibq0MTxEzCkCKa0owbG06h3jXu9ccxR2Qft220pwazGw/QPdzmC\nOZfqQrKQHJb1jc1T5IdltwIW509n1TlnbhZT6Owfil5CK39a+GIXNOMXVAiGt/Ag8omoYE4heeWI\nSej6q8sS9oWEkxdsiT6YoIK5NSAuaaJUXnT3LkMwAU0yOiGjD3sy2sd2cGz7tsDTbnm6tWi5i/WX\nhUkWnphgyk5uyItvWD09ycPcCOiDCsEY1EPP+mxZgnkgatAkEL2NGkM7uvt8A1iLGj/bowbzY8nn\nlsQj6DCl/uTGGMeZTX5IdmvgMWes/ZeCCWMATXJa6+q/ywzuxIdkIVEQpQ3tD16QvB/Q+rYc+GAu\nszh6E3V23HcrQ9Fw9oMkh8EroqcL5mz3vxzBnIdWzg2prWD6CRDiU5ydTuHkwaVY41PIG7fFv1CL\nKkAiZ3FNp3OihWgdXZeF22DkbJBiU2RNRK3eB9FMvDjxhj4N52HKEPQB98bZHPe+HO+S4Py0/ss0\nbke9hKSwKGjjvgx4X2z7nuSMKNBGv1g/ZgeFHuY9wI6aeAZoqO46NPHuieC4BcCYCocodDc2c/83\nQYXSL+iQRlwww6ElB6KJXClEV7vI0FXAaegwt21Qz+yhyorfye/Qfve0Wc8gOSTrn4+70HoUx4dj\nQcVxeDBeNxwr+h9g59i5btah6C30e50MsqkOfepke+C+hDI/Sk7AdwcedIKa4WFKLx1HmzmmupMa\nCqZEbthEGQ+EfM1ZpG0gv05I/Z1IeTH5OLMBIXk6shSit9CHezO6XDBlLDqry29ixy4A1kseiNvJ\nVPIF8w5g39xb2Rq1IHcCno95Oz0oLCtjQLwFfBTZfZOgIdmXSM/wLFUwZ6O/4+bAU7nkluhNtJEo\nVzBfRmegKtPDjNahWa6fSzlgY+AKYKNcZrBE6BjJK4LjnqR4P2ZCSDby4wu31YaN9VGhfCWYFhDX\ncK2i5KnWpF8J4/AaxTS0TfGC+Ripgin9UY/L9YlHy9F5sJ8HeRn19P+dfG4el6PG2E/ICeaDmWcU\nJXoF9W6zuh9eQIfNbOK8tvXIGXX/JnlYURCdi94h100BiR5mnmYEghq9hT6LDwOXBselffYb0PYU\n9Nme4V7PR8fWf5jCCT8mo6FttzhAYp9qJ7WskJPRSZYng2wB8pPsw2UYGmL4BroSxKkUxrOrtaJm\nAecnrDZejLlo6KMMoc2jmGBuDzIJfQguduOzAqK3UA8mJVVdBqEp/nODjfeSX/k+jlpVF1KYUNCD\nBJPPA+c6D2djMtPHZRA6UHsx+jDtk3BQqYL5b7Th2oPCIQGzKC+qgaujz6P1o1wuAfZLSfPfGO3f\nuZlcY7In8A6aqegpkvgjvVBjI2ltRe9pHIsukPBRCmcqgpLCsjIG5Ab02Stl0H4j2AydY3g6+j2+\nQLqHuQNqUIXzTp8EHI3Wv4OSx+bGie4Fprphc2+gntnM7HNK4meocKXd903g1+hzti35mc93ajkK\nHJ3QwwQ1ArdyrwPBjF5G29kw5BsKKuj0poeiQ2Y6XPuWJZj7uqztvVz5cJ7oL9F6+ePYOVuhxu+Z\nIL8BLku4bsmUMWBfjgURkCNAvgCyInsMjZwG8g+QV0H+B/IsyFeC/W0gK4spftcgfwYpNzQWnv9+\nkNjsQHIDyMFaueRykDUgP0/3IuVRUufqlOkgsUZaIvddjtcEI1kG8j73m5wYO/bHIF8s90OVeXw1\nlFPv7gJ5AmSK+6w3ZBy7qdYzcB7MisL6JbPVmi7p3jeCvETBZBRyCcj5JX+E3HlV1HW5jM5Myrzt\nN7l6cDDIPW7bn0E+GzuuAyS++o7fNw3kdJCUxBA5EOQ1V+eS+rT8cdeBZCQoAcj3QH5PbimzetW7\ncurcTPeMv6PthEwHSZpsApCvgvy0NkXsvOY/QMrt667mfmPcbzsf5KOxfXfr75+37VyQbwbvPwBy\nr2v7lqKRNb/vr+r5db7/JEjCcmRyOcjJahTKItKn1bvd1e9ZFCQkyVCQ5eQtkyffBPkOOqb5CnJ5\nDYn1oZYe5o5onN4Pzh9M9pIrJ6Pez8Xo3K1nkT/N1qZoWCetM7ormUPl4VjI8zBld5BL0c82Sy3N\n6FhgMESfCqy1OFlDS+L9lzgr6kHU+joGuBOifwCfRS2vkBI8TOkD8ieQGSCfyj62UUhftL5tjCYj\nzCZ7gHIYKlqD9usGIVwZjoYMXyg4M5kb0O8x7mHeQC4JqAyqqus3oUOH4vhxnf9Ew7IfREP3cUt6\nDhq2ii1ULeujs1dtg0aQEohuQcOUp5AdJvwnmTMKyWB3jW+VkHTVIKQX2h3yL3QpQD/NZpqHuQe1\nnyx/JtX3X5ZBtBDNzj02YeGLW9B+2JC4h3kNGsq9AbjNecmeeD9o3MP0zEDDrM67TO1zvRZ9Dt7r\nvOGdbJUAACAASURBVOPwcyzX++dN/L4V2lf6foiOSh/SVRrlWF33gPzMeVKzQW4F+ULsmEHOsj8X\n5L+oFzkEnRB8HMjinOUgJ2qD3QjkIyDxilHO+d4iuwzkZRUcOSjdKkq8xsUgp8W2jUX7fb+tlnjB\nOd9y1tJ/1KtIvfbh6nmk7o9AfuUs2X2d19ANPUzZCeQRkCedVXkhyJukRjbkI8548e+/AfJH93kP\nB7kKpIzGTTZEvdpukEAlI0Fez7eqpQ2NZLiMaPkxyLp8iz7vGv8GCVZ9kMhZ7DWai1MmoVGQPu77\njhns8kmQv8VPqs29ixeuxMMmgzijSx7X8kobyFsURIukN+p5FxknW3ZRR+t32R2QnSjwruV2dCGM\ncNtpIC9QGNHZCuSZ4P1lIAmTsMhUkHlOZ76aUZ6+2d+NfMDVc1f35Dl0SsuCA9OvkXH1Eg/rA7IK\n7bt8HXW7jyEvLCkRyFOuYj2oP3rBdWaDfNw1+peCnF5JoatH2qhqzUHp5R6mc0geulDKNU4GuTZ4\nvynIi04g3iZxBXs5GA1FvJwuGqBlktco8CY69+/i7hWWvTsK5ukgv3CitxIdTD6H1NVi5CKQrwfv\nR4Lcj4Z0n3Z1r6PMopZpCHUlch/IAcH7DfX76Hy/CcgF6eWVC8gLL8uH3bNaw9Xs5UknMmvd73GS\n+90i98zsGz+hdvfOLlgJh6znxOBi9/5qkAvd61fRuXnD4/fUetWTkd6ooxMM55DlIPF5liMS53OV\nXqhz4fq25YGEOuDPfwh1GEpZySStvP2d6F7unv83Uq7XpYK5nXsQIieW/0S9oaWBkk93jXkbqdlv\n8ge0T+gK1HLPmkmihyPtriKtBzIRZK5rXHqDnKnbCs5Z331vJUyXJn9EZwpJ2ncRyJfjG8v+CJVT\nSuPVD+1DPB7ki+5z7wVyGwXWLYDsALJAH5K87YP0e5DBhec0G/J1kGtyAieHgPyrjPM/CuJCtTIU\ntegz+iQrKuMPXAM7FY1G3QSyBLX8ZyW0Dd1JMG8G+Unw/Z4D8iX3+knyjZVvuc95Qu2L2t2Qr4C4\neZblJK2DZZ1/PciRaF/5fHJDlLoIGYBGk54DeTjtoIquXMIhZzphPNO9v10rC6ChQdf/Jd9D19HL\nutbwXMMlu6ULa6sgl6Kh2ecpCG+nnnM9JXlJsj/qrTrrSgaA/EYfellGXse8HlBGwaulyL1kC9cY\n3YmGpw5wgjkGDSV/Cg3/uXX15Gj3cKSEInsKMkgFUi53BsIcFaKSz98BxGUIy8/UcKp5GUeBxCbY\nkHPQUPo3kk6ofRkSKaXOzSNvvJ70DsTzKNTL/JwaGTKf1AhOT0P6oUmbJ7v6V0adA/e8znB17gdd\nUsTCe/ZCI5nfSjugoqsW2b01arUHq3PIvnSOdZGNQBa6L3IW1a2s0ILIru4hPbILrt0b7Wd+yRkz\n16Pe2Wv6uvCE2pchvXBFdl9DfuhwFNonHqlhIQvQvrs73fa70D6zbhI67UpkoGt4Xk0RoKxzB6De\n3rloJmKdGnwZioZjk1b5qaNgygEURiAiNNpzMXnh/MRLdKCe5lJawrMMkR1AHnPtR6lrq/pze7v2\nR0CKTTZfL2opmLIXGga7g6IZlLI7mjxyV2s0WLWmq78z2doJ5i/UepaOBjdc7l7xB0fanOH1SbSP\nNmV9O9kK5DzXCJ+IDqlowXpX6WeWTdE+nmKr39SLegrmUucZOg9JJqBh2NUgr5CYd1FwmWEgn6El\no2PSi4oT4GQwyIdqW56qSKx3xR4qAfkLmo7/KjoA/i10sulr0UHIR5Y54a/RnAjF60sN7yWL0IH1\n16MzxHwMHbz8OvAHiP5Sp7IYjaVe9U7US6I38Ed0qMgW6Mw6361g8hOjuUmsd6UI5gnoGK1x6PjK\nCcB3IHqk5kU0ujP1Fsx2dLaYvYE2dDaSn2SMWzV6JnUUTH8faUfn3r25cBYuo0WoSDBnkDxXoNF6\n3Eny3KtdwQys3hlKverdDKzOGTnq2d4ZhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEY\nhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYRqnYXLJGqdhcskYjsLlkjUZQUb2r5xqIRvemGy0gbbQQ\ndVwP0zA6SawPLbjIqWEYhmGUjwmmYRiGYZSACaZh1BS5DGRoo0thGNnIto0uQU/E4vqGx/owS0KW\ngUxtdCl6ENaHWXNkIMg6kN6NLkk3xvowDaNrkQgYAgxodEkMI4OhaNs/pNEFaTZMMMtCTgJpa3Qp\njG7LIPSZMsE0ujNeKIc3tBRNiAlmeVwArN/oQhjdFt8QmWAa3RlfT4c1tBRNiAlmyUhv1IMY3OiS\nGN0Wn+wzsHaXlDaQQbW7nmF01lPzMMvEBLN0Bsf+G0Yc3xBV4WHK9iDhjDNHo5ENw6gV5mFWiPXH\nlY6vZCaYRhq1CMkeDGyPTs0FMA4YVU2hDCOGN+xMMMvEPMzSMcE0ilGBhymfADkx2NAO7AXS170f\nhWUzGrXFkn4qxASzdEwwjWJUEpL9GPBzkK3c+3a0r3xH994E06g1Q4F1mIdZNiaYpWOCaRSjzJCs\njAUmAGcAP3UbBwMvAfu79yaYRq0ZAryCCWbZmGCWjgmmUQxvuZeaJXsA8C90aamJbls7cC0mmEbX\nMQSYg4Vky6aJBFNGNXgqJxNMoxhDgFcpPSR7IHALsJxcOLcdFdHpbuYgE0yj1gxFBdM8zDJpEsGU\nCLgb2LOBhTDBNIoxFFhA6YK5H3AbhYL5MvAO2qCNBPqB9KttUY0WxjzMCmmWYSU7AVOBEQ0swxBg\nGSaYRjplCKYMRevUi25DbyeK7cAKYC7Q4a653G1fU+sCGy2JeZgV0iQeJh9GLe5GhqaGAPMwwTTS\nGQIspDQPczIwCyLRv04v0wvmS8DWbvsyLCxr1I4hqEFmglkmTSCY0hc4CriexjYa7ahgtjewDEZd\nkQEg25VxQjkh2cnkvEsoFMy5wLbAEuB1TDCN2mEh2QppAsFkEtpgPIF5mEZ9OQf4QxnHDwHmU1qW\n7IbArOD9crQBGwCsQj3MbYDFmGAatcUbdr1A+je6MM1EMwjmcGApjW80fGNogtkSyHbopAJDix0Z\nMJTEkKyM0CzvPOIe5uvAWGAVRO+ggjkdE0yjpkgvtA1bAbxGefW75WkWwVxGfiZhIzAPs7U4AZ30\nvByhSgjJypfRQeLfjx2b5GFOQBsy0JDsYLpcMGVk11zX6KYMBt6AaB0qmBaWLYNmEsxGW9kmmK3F\ncOAZYGBp43+lD9AP7XMMPcyTge+hYhiyIYV9mKFgvuT+e8Hsgr5zmQA8WvvrGt0H2QDkVJDD3IYh\naF0DbVct8acMmkEwh6GWkAmmUU+GoV0BKyit3g1B6+gbdAqmRMB4dOWRsblDpRfaN58kmCvd+5fd\n/670MEfoPTsnejd6HqcBHwH+5H7noWh9AvMwy6YZBLOGHqb0A/l7hQ2ECWZrUa6h5sdLBoJJOyCo\np+oEU84BvqHHRquC82MeZvQmsIiuzZL11xybeZTRzIwCLgVeQPvEvWEHJphl02KCyZHAIZS9vqBE\n7t6uf6qhU/QZ9cELZql95z7UtZpclux41Mh6FRjuwrbvAb5CvncJhSFZ0H7MrvQw/TXHd8G1je7B\nSNToug/YhfyQ7FJMMMuiGQRzGLUTzE+hEyCUm+gwAHgLojWoB1Hq5NpG81KGhykRuvDzq6hgeg9z\nPPCKS7B4FRiDZse+m8IkoOXu+FAwv4SGc7tKMH2/qAlmzyUumGFI1gSzTJpharzh1KQPU7YF1kcr\nTrmCOYRcQ7aCXFq20XMpx8P8MnAE8H5gLdDmohDjUA8TdEjSpmhi0J1udp+Q5ajQBvUqukP/i3mY\nRqV4wXwZOBsVSZ9Qtgz77cuiWQQz8DAlSmhsSmEH4Fa0ApUrmO3krLKVWD9mD0faUPFaSWmCuS3w\nPYhcmFW8l+k8TECFc1fgxZT668NkSYZYVwqmYI1mTyYUzHbgIMDPXrUU2FJfSgdEs+teuiajiUKy\n0dvo5NOVhkMnodNBLaHsPsy8jnITzJ6PS+CJhNLEKuwXglxYdhw5wZxPp2Am4utXPQWz3ZXHBLNH\nIhGdghkJ8F3gCIiWuQOWkQvJPgKySQMK2VQ0g2D6kCxU13BsQC6JopKQbCiYNp9s0yOfBUkTCh+O\nhdI8zLBfCHL93D7pB1QwdyF/soKQRnmYT2OC2VMZCAhEb+jb6McQPRLsXwqM0DmTGQZsX/cSNhnN\nIpjeIqqm4Qg9zHIFcxRaucA8zJ7CKcDeKfvKFczQoIJ0DzNcziuOF8yVCfu6UjD/hwlmT8WHY9Pw\nST9j3HsTzCJ0c8GUXuhDXUsPsxLBnEBuILkJZs9gDLBZyr5h5ASsmpBs3MOE4oJZ75Cs8zAl6oLr\nG42lmGAuQyevGIOOHjDBLEI3F0za0XkP33bvK2w4pA0dnP0KnX2Y0gvkQyCXgtwEcmTGBUwwexTS\nhjYm01IOqDYkuxoYhDZEXii9cKaFZFeijVaSYK4C+rtxnLXET8axFhte0BMpxcP0gnkfsI2NMc+m\nuwtm2H8JZQmmjAaZ5N6MAxZBtJach7k/8C20otwGfDHjYuPJF8yumNdzh4QVLYyuYRQQke1hlhjV\nkF6oOIZCtxqNaCxzdQ5ywjk7+TqdCUYJghm9g47jXC+9HBXhQ8mvYGHZJkVOBvkVyKEJO4sIZrQa\nzZLuQCMNi4AptS9jz6G7C6aftMCznNI9zFPRKcggF46FnGBuBtwI0a+B84FJIBulXCv0ML1VVmu+\nCRzeBdc1ChkDPIv+5v0S9pfjYbaTW5LLsxrY2d3DMx/4MkRJfZSe5aSP711ARVPYSQRyM8ighJ1+\nsepXUKPSaCqkL/BD9+YTCQcU8zBB29dp6LJ0D2Fh2Uy6u2CGCT+g1nCpS3xNcn/+9Rz32mfJboom\nPOBCvlcCx6RcKxTMheQ6yWvJCGDjLriuUcgY9PecAySl0pcjmPH+S9As2QOBu3KbonUQ/ZBsvo3O\nO5vEfHTijXIZ5sqS5E17D3M+Np9sM3Ig2oZdBIxO2F+KYC4lJ5hPkt5NYdAcgllhSJYNyAlm6GEu\nQxvAzdEwhOev6FyzDolAPu5CbuGMLQuprOEqhglm/VgP/R2fIllIygjJFvRfgnqYU8kTzFKIfhub\nkD3k/9s773CpqqsPv4delA6CSBEpAoqoFBMssXcTUzT2mmIs0Viixs+oMUWN0S8xauyJms8YS2LD\nFsXYsMWGCggWujRFsRDA9f2x1pk5c+acaffO3OHe9T4PD3NP3XNmn/3ba+21167Qwsy8A5sl7HPB\nrDnSmPEPBwP/R7q7vlwL8x102TknhXVBMOMWZqmCOQgYaIIXEcxgrV1nHLmC+RwwHCRMjLABcCUw\nAfjIVo8AbbiqZWH6xOHasAFZwUzqUZdrYSYJpgDPNKyYOVRqYQ60/2OCKQFZl6wLZk2QccDMFPd4\nuddqD+yNesaWAL0TIp1LtTDDDuS7uGAWpN4FMz6GWaJLVgK0oViDVoZNyA22WAasJms1YsEZM8g2\nLIPt/4PJumOhKi5ZCdDOwSYm8E51CQVzOsUFM5KSMZFwWa8onwOvQhDf3hAaYmHOId/C7ACstXrv\nglkbBqHP+YRGuFY/NKhsiQXv/Jd8Y6JUwYSshblxI5St2VLvjXM/tKEImUE2D2IheqIVaDpaSccC\nr0b2L9N9eTk9XwW2sM+DUSvhAKoumKyHpv37GG+4akEfNCLwbYqOYQar0OkeHVKulWRhrgSeaoRy\nRqnUwhwE3E++YEbL7YJZG/oCU4BTQRoaaR/W4ZDF5I9jluqSBW3XFgOdQKox57dZUO+CGfaOQ55E\n3azF3AYD0YCO99FoRchmXIGMYOYRFcxBwGNkA0RCLMqxkrEI6ZpSGXugPb1Z+DhmLQgtzLeBoQnW\nY9TChMJu2aSgn8vRlSEakyIWpmyUYgUPRN+b9UGi0d2hOxZcMGtFKJiz0E58Q+iDumJDksYx+6H1\nvBDLUW/bh2ZAuJVZgHVBMN/P/hmsAe5Gl1Iqdt4c+/cN4OWYNbmU4oI5GLgX7YVHBDMQKg/8+Rnw\nh4TtLpi1xcZsguWo2z7eM+9KfrBZt5RrJQT9BEshKNazL5diFuYD6NziOOE79AYa6BaSYGF6tp8q\n0xft+KR5NsqhiIUp/dFsU2mZpUKWo+9C2D564E8B6l0wQ0sxyt+Bg0FGFXjBoxbm9sDLsf2/Bq5P\nOM8EU1qhDc1s4N/kTzav1C07Ejgw1tOHRhdMaWPZbFoA0kktdwCZCJImbFFCCxPyGi9pjTY8SyPH\nPw0cpIEWsmfsWkku2WqwCOibXOclQBu5SQnnhYL5GrnDGZFyByuBtZQ+ZcupjA3ICmZD3/O4YMYt\nzO2Ap0tYCvFDcq3QOgz8kVEglzR1KaCuBVM6oIEwi2I7pqBzj6YAh6WcHDYSc9DvGBPMYDoECa6K\nYBnqXhts/94HDgFujx1YqWCOAF4HjohtDwWzMXqeABcAFzXCddYFTkWXLQK4FPUoFEACchub+DMf\nACzNrvAAaEaoE9B6cG8kkhqSg36qQPAZOi6f1CHogWYbiglm5h1aiI5jRp9N1CUL7patBaGFOYuq\nW5hsS2nTmsK2NKQeLcztgH2buhBQ14LJQGBeLIMK6pYNDgbOBnaz+ZLXxgbRw2kkoXUatzAL8Qzq\n2jLRDT6O5LINqWBqibS1cp2BrpQRJZw+kxa1SZnusrHAMY0Tvl73TCKbzmsIuW7HJLqh+YlX2d/x\nxmsYKqIRgneBm+zcN8kdf6qVhQkZKzOPQcBcYGIsF+gAsu/Qw6j3JDw/Xm4XzOrTl+zYeTUEM25h\nlhB4FrwCwWmRDRUIZtXzz44GBtTDkEETC6YMjfXWoyS5Y6M8iVaKUcCx6Kr3mDt1c/SHfwcdf5xV\nRqFuQ62JVSqWiVRiYQ5BA4+eALrrd88QWpgz9Dhpl3D+8SC3llhpRqLfOy1zUTNBWgET0WfWCW3w\niwnm5qirPSTuHhtObkq7kNOBHdEO1fjI9hpZmEC6qA1CO4ULyI2GjQTNBV+g45z727OqQDAlAPlb\nCUF3Th4SkB0KsKGXBglAkkvWLEzphrY3/6ngurMpS8wlAGaBDK7gXqUyGl3bs8kXCKiSYMoYkCtK\nOPBm4MyUfbGAnzxmoqH+J6HTP8Jgne+gDcFLtrL4wHwrtSCTKS7WlQT9DAdmWFkmA9GxMBPM4AvU\nUkiqsJPQ73Zw4dtIZyvbmcBxZZZxXWM4GrW8ESp6n1FcML8B3BP5O97bT7AwQX+34EvgBWC8RTwP\no7YW5jzgdpDbYtsHo/X1GeCrke1bosE+IX8HLkMF/peU75KdgE6z2r7cgjt0Bz7XOZPBCrSuNiRj\nWCEL82TgUQhWV3DdWehyb2mGTJzN0PpXRtSvtAe5EOS+Ek8Yjc5sGFD6PapDIwqmtAd5yjJQ7IAG\ntxToQUl7VOS+l2JRxaeUxAgEdTkcC9wBjDG354XAmdnB7qKD3vHrfgHcReqqEkBlFuYIspbLA8Be\nkX2hhQn50Ywhm6ECeJmNTRW6zyxUlHuDJFRkOQTk9+UUvk75CuppWIguBv00+p1T5rhJgCa4vzuy\n0QQz41ZKszBDTDC5DB3TrKWFeRJq5e4dc7eHncsnyI2U3d62hdwDjEF764cBN0b2lSKYP0A7dA2d\nEtESCccvQxoa4Jc0raQ3yI7owhNJydhLIFiNvhOl5pTdGTVYinVUo1yCZlrbFqRIOyo90Wjf58lm\nrWoyGtPCHIdaQWNQt1cvCi8ZtCXqgnwL2D9hfzErD7SxXAFcgYrvnugyXv8qq+T5XGzXTGMRqd9N\n1ktxT5iFCcAjwKRIL66IYEo79OW6FY3kTVrKJ2Qk8KalALwROCZ2rWHo6iyH1mDsodpsA0xFXe+7\noC/6dDL5YWV/dM3TsJ6PRaNBX89eIvgIdWVubhtSLMwMb6K//d5kF6GukYUZLIfgdeAltFMaMgjt\n4N0P7GKRw63RwI9/R87/EoKZ2igG90PwZuQaM8k+A0O2BPm1WdM90ff0LPTdTUHG25QGJ5e4YDZg\nHFNaoe7XqGAuRi2wm4CjIFiYcGKpTKN0AdwFrXfxNqsvyBb5h0uAtl+nAI+jgluI0eg7N4fmZWFm\n3DTjUNH8kIIvFtuga1FeAZyekBKumEsW1LI8BR2/GYW6K28tr9hJBNOLiO6rQA+QpDHCE8jtuYdE\nLMxgBdrohdZAD7IZN94gPyH4cDQA6Qu79tEFyjaSzCos3IhOwekY2f871B23iFRLQdYzl0kdT02R\nzdEX7wl03OVrqHDa85PWaMfgNOA+e1EPBe5M8DpMAXaIBGalLfKMBYA9h0bm3opaa7VyyYY8BOwe\n+XswWj+Woks07YaK3wfJ0eCJPApsE5miszfqpdgGzal8HdoYPwSMTU7hKHuhAn1peV8n7zq9QDYr\n4klZ10gSzE0rvFY3dEm5VZFtS9F25CEIHqrwuiHTSE7WH0PaonEkV5AvsKcDUxLGu8PvPB01HHYr\ncpPR6Ds9lzoQzGKU4c6Uyei6ezeCrAS5CuTcAsffBnK4vnjyIsiBkX3tQZaAlPGAZDbIquImfmMh\nY62MI7X3Lb+w7/I8yOfkrLMobUCWRyIUATkJ5Cb7/GrWdSpjQKI9f0AOAvm7fe5o17oWJCKc0sF6\n93fGnuWDIIfa524gH6vLUq4ASVk0W/YBEZDzoxtLfTKNQJF7ycYgi/W5AMhZVt5vgpwJcql9h+dU\nOOU1kBNBliVbP3IQyN0gw7UeFS1eJxVgGWv3rXE0smwNEkm8IctBwoCP40H+YvXrT2Ve975sJ1Du\n0volnUDeAnkhW6dlLnlrx0pXkA9BdgVZSk5Qm2xkZSqhgy7HWR19U3+PmtW7Mu8TzveVriCnlHD8\nKSCXR/7eDeSJ9OMLXmsESMKwgfyCTIenIcjXQR4o4bhJIK9YHfmcnA62zAS5DuQ/qLCG208Budo+\nDwVZQOGhuyvsnMNBbqns+1RERfWuxJOkDcgKkD3s5X0f5ABthFLPec9eCEB2tHOOAemBjrE9XGZR\n7wJ5tLxzGop83yrMAyBfgPzEGotXQLaNHLeDVpycczeyBrytNUDmn5cOdq1I71p+CfLzyN/f1L/l\nvWxlkxNB1oCsBRkTO9bccnIoiAW8yP4gk1O+1yUgV4IsArkJnaxfT4J5KchvIn8fYMI1FmQrtCMz\nnUyHQnaz/b9Kud6G9lucBvLPMooZ2L1rHO4urUDmgPwWZAuQTyP1YEOQj6wBKzNKWr4H8n/2Dq7I\nNr6yIUivyHH3gsSybcnh2WcnvwC5JrLvV/b8k4ZeotfYHWQhuWJch4IpvUA+sWf/XftuI4qccwlI\nJMBRuqKGRdv0cxKvMwJkJ5DGzlUcvccm2h4XPe4CEJvvLe9E2vPhIPOtnj4K8iN7N3+FdrxsPrAE\nqKFTYExcXkKFeUcq7mBURLUEUwJrkKahluEqe6GGaYOeeM5IezEiDY0cAfIPVGxezD7Ukou6P8ge\n5Z3TUCQAuQPkGdSiWYta2JeC/Cxy3G9Bzks4fyraI/+UnNy08iC51uM9CQ1UADLLKmKAWqm7g+wS\ne65t7VmPQjsVljRBeqA9+YSFZ+UFkO1BBqI9/oOoXcNF4XtJJ7RTMjiybZw1WmEDPwqdghOx/OQE\nCva+ZaZdd5P0Y+oJ6Yt6GWaT11GUTqjIlTnkIn1Rsb0LJJ6sI3rcBeRlXpH7QSyCW3qiHoDN0Q7g\nYpCfWr1K6VxIYO9+fIJ6PQrmudbOnW2/wQJ95zP7R5ATeCad0E7xxNh1poGUsphEePymaEfobf2N\nqoW0sjbJ8l5LYG1BLOZBngPZyT7fS6ZDJD8h492QsSAfoJ3v31s9iT6bC0F+l1KOLminoj1qjaYM\nlcj6IL8pXN9le8pz8VdDMKU1yGMg80C+b9teQC2iVtYgJ/jp5X/1QeVtD0AuQ63NOh4/iyJtrFEI\nrKHZEWQ/cixkmZn8YsiJqBvrs9yGRHZC3WCtrDIsIWOB5pxvQizjtDKlVRi5wH6jleSk5ZML7KWN\nTHjOVNJ45aqxYCaOkW0Ecjl5VqB0AXm8gbc8CmS7hl2jOSBboV6FbQscMxTtXPQD6Y2ON66INYTH\noR3Jq9AOYCuQ11EvUkfUmt0XZGcTgu1AZiT87nUmmNLJBOAkdFbAbHvfl1g7sKe1eytBfmDn/BTk\nzoRrXQsSWepL2qLWWGzWgOwDcjHIyyCnosM+V1b2NUtF7kO9DSfa912BiqLFQ0hP2xa66S+0etMJ\n7bzvE7nWueR1+DP7htr12+r3likglsFN9si+19IB7aQktQsXoR3mlGxAMhH1vt2e3kbmn1TicfGT\npECGBTkR5N+5hZDjQCxFlxxjDbWJpnQ2AViWLACg91rXl5eR7vbSfBPkWNQ9kZIDVAaQN1YrAWpl\nH46Oz6UEMsm2qFC+BHJ2gfK0RsdGY8FEEqCC+yY6LnYa2tlJEp9aC+YbIJHQdtkcbaRvIuP6cZoO\nucjq3TJ7x2+I7W+NjlteRmY8Uza1xvEtNN5hMsi/UAt0hrYn+Teq+lcp+T7SFxXJ66wBX4laTmFC\nh7tR629PE4LFJnTLSHTZylFoJ/swq9+Xo67eG+29v8feyyUmSD+3e62HRi1XEekIcj3Iw1a2tiA3\nW1txNCrs90aO74rGCswC+XMZwoRpyA/tWT1t33co6sK9IHLcPJD/QaO3bQqUjLZ24Scgj8WuG4AM\nsbbkCLvPtRQ0xjIzFyoWzLVogI5lWZD29rK8glpHRSK95AirVOfY8asoa5xoXUX2AnnWKtwOxY/P\nO39r1NWzIlc4co5pjY41HkzZYyE51zkbHXu+zhqxY5MOqvz65RcIHR9eYv9vZQ1qWu5gp+ZIFxPD\nctOoTQA5klyPyiCQq0nuKNeJYIZDTHJeVgzkfpC/2uf2+jnqypZ9TRBTxjdlGMiX1kYsQq3VpBcQ\nHQAAGKtJREFUjdDO8iNowMuXIKc29Ms1Dpnht39YuxRrJ6S/iXqZ3kHZ2b7zWyAbmBjPtr8j84pl\nNNphfgXthNyCdkoOQ63TeXb/g0B+jHrPFqLjx2aIyWQV1jDOQ1qj3o3QYxAmNalYMMehvucFqAtw\nplWC8VrRS7rMXtlCSg8avnhqC0GGkDOWWdV7FQtcaYIxTBltnY43KGhBO82YGgqm/BONPzgUDejZ\nDbUcF6Aeo7hAfFXbwZxtZQaAZQyRzmQCq6R19jqyHXU5X7ragW5yMMi7pK47LD1Ra3NcZNs29vv9\nDe3875xfTmmDRt0uQgMD56Cu7j+hcTCh4ZFY70ppJMMfbiI67+0BSls2xmleROpCs7qXU9/Uqi7Y\ntCRGoMugjULnNv4DuBNN2L+0wPnOOoW0QX9jsWQgeQeQUO+KVcQp5GYUcVouT6DJAWrBFLzeOUqt\n6t0UvM45WWrZ3jmO4ziO4ziO4ziO4ziO4ziO4ziO4ziO4ziO4ziO4ziO4ziO4ziO4ziO4ziO4ziO\n4ziO4ziO4ziO4zhOqXguWadUPJes0xR4LlmnKaio3vmKJE5IEyzv5Tj1sh5mTYrQCUSyy3w5TUhi\nfShjVWzHcRyninS2//s3aSmcVFwwHcdx6gMXzDrHBdNxHKc+cMGsc1wwHcdx6oNO9r8LZp3igplB\neoNMbupSOC0NGQ3Ss6lL4dQFoYW5UZOWwknFBTPLQGDrpi6E05KQG4BpwOFNXRKnLugMfIlbmHWL\nC2aWnmR7eI5TZSQADgP+SNYV57RsOgPv4oJZt7hgZukFdLKGzHGqTSdgNfAB0LGJy+LUB52Bmbhg\n1i0umFnCcSRvvJxa0BVYAXyO1zlH6Qy8D6wH4nWiDnHBzBIKprvHnFrQDfgIFUyvcw5oPVgJLAQ2\nbOKyOAm4YGYJBdPHMZ1aEFqYn+EWpqN0Bj4F5uNu2brEBTNLmL/RBdOpBV3JWpgumA64YNY9LphZ\n3CXr1JJuZMcwvc45oIL5GRoI1ruJy+Ik4IKZpSdaWd3CdGpBaGG6S9YJCS3MpTS5YMoUkKFNW4b6\nwwUzSy9gLt7bd2qDW5hOnFAwl5AdImoqRgL9mrgMdYcLZpaewBzcwnRqg1uYTpw6sTCljd1/vaYr\nQ33iggmAdADaAYtwwXRqQ9TCdMF0QD0N9WBh9gYCYP0mLENd0qapC1An9ASWoZXV3WNOLYhGyXqd\ncyBrYa6iaccw+9r/bmHGcMFUQsH0oB+nVvg8TCdOGCW7nKa1MF0wU3CXrNITHTdwC9OpFe6SdeKE\nFuYyoAdIFdpn6QoyqMhBoWC6SzaGC6bSC7cwndoSc8l60n8nFMxgNZoir1sV7nEw8Jsix/RFlxlz\nCzOGC6YSHcN0wXRqgVmYwRpgDRp05rRsQgsT1ONVDbfsBhRfoLovmgTeLcwYLphKH9wl69SW0MIE\nd8s6OpWjDfCFbajW1JI+FBfMDYBZuIWZhwumMg54BXfJOjVBWqP1bKVt+AzvqLV0OgGfQSD2d7Wm\nlvQB+hcZH+0LvI0LZh4tVDCljfXosIozCXgStzCd2tAF+BiCL+1vtzCdMEI2pJoWZlsKi3Ff1MJ0\nl2yMFiqY/A5YDHIhmgLqQwgW4hamUxvCCNkQn4vpRMcvoXoWZm/U7VvILRsKpluYMVqqYPYFLgSO\nAk4CnrLtHvTj1ILo+CX4XMwWgvQHuSBlZ1wwq2lhvkqqYEpHtC7OxQUzj5YqmN2AN9Dw6u+j7lhw\nl6xTG8KkBSHukm0ZbA38KGVfDSxMaYsOB7xGuoW5AZoi9BPcJZtHSxXMsMG6FngeeMy2u0vWqQUb\noNOYQtwl2zIYAPQESbIcwzyyIQ2wMKUVyOCEHb3QLEJzSBfMvqhgrsQtzDxaqmB2Az6C4AsIJkLw\nrm13C9OpBROBFyN/u0u2ZTDQ/h+VsC9uYc4H+ld4n4OB10GGxLb3BhbbtdMEsx+wELcwE2nJgrki\nYbtbmE4tmAQ8HfnbXbItgwGoEKUJZjRKdq4dXwmHAS8Af7EpTCF9UMGcR7oYh4L5OdAuO5vAgZYr\nmPGgixCzMD1NmVMtpCOwOdqghfg8zJbBAHT4J0kwQ3dpyDKgA0gJVp7sDnKWfe4HTAD2AzYENo0c\nGBXMIhZmIHgQZB4tUDClA7rW2xf5+4K1wGqgfW3L5LQgxgNvQBC1JtzCbBkMBB4kWTAHounojEAo\namVKN5D2wB+BPWzjd4F/QLDSzu8TOaEPGkw0T68r3RMuGlqY4G7ZPFqgYIYBP5mMGnHcLetUk7g7\nFjzopwUgrVExehSd+x1nIBqME2UOqYIp3dHAoOfQ9TP72Y7xwL/s8xJyA4dsDDP4BA14vDXmssWu\ns8g+e+BPjJYomBbwk4oH/jjV5CvAM7FtHvTT/OmHCtxsYP0E6y5mYQJqIQ4kmT6opXgTcCjZJbn6\no0E9oO7X3rFzFtvn01A38P6x6/Yla2GuxC3MHFqiYMbnwMVxv71TTcaRO34J7pJtCQwA5phnaxaw\nSWx/mRYmPdGxxsvRPNjtQDqj45ahYMYtzNAliy0h9gQwNHbduEu2zi1M6QbyNU0KUX1aomAWszDd\nJetUCdkQzeMZtyTcJdv8GYhajJAXpSrtUWtvYeycQhZmuCQhJsKLUOuwP7DAjllCZgxTAmA08E7s\n+pHgH2lt5fjANqwLLtkfArcAMxLcy42OC2Y+60IlcdZNxgEvJoyfu0u2+WMWJpA/x9JELlgbO2cO\nJQkmoII5AlhtAT+Qa2FuDrRGs/yEWPAPgDxlx3xoa7RCg1yy0hqkFmu8jgbOBT4k/Vk1Gi1RMIu5\nZBeibg3HaWxMMPNwl2zzZxBZCzMumEnuWCgcJZskmFuTtS4hVzC/DdwR66yZhSnd0WC0Y8gG/EDD\nXLJHA7+v8NxyGAm8CcwEhlf7Zi1RMItZmA2ZMOw4hSgkmO6Sbd4MRxt1yM+0M4iCgpk4LzxJMLci\nO34JOS7ZUDCTrp8RmkPJdQs3JOhnU9TirSLSyu7zFi6YVaOYhemC6VQBCUgXTHfJNn9GANPtc5KF\nGR/XBoJP0c79dgnXiwvmQvItTIuSlSGooRAPNluMtodj0KkoXckXzEotzI3tXzUZgE4RXIELZtVw\nC9OpEgXTiPVBx5AWJOxzl2yzRjqhCfdDUYynpktzyQIcCdwBMja2PcnCHECuhbkM6I7OzXw+smC5\nEXyJ1scd0SUOXyBXMD9GRbQShqDu3mqm1huFumOhoGBKG5BzSsuaVJhmKpgyukDElAumUwXkIOC+\nAgdsAsxKSZjxCZU3TE79Mxz97cOgHrMwJQA5GtgdnZ+ZQPAgcDVwYGxHkmBCTocsWIN603ZCp54k\nMdf2zwQuBR6J7HsLDQQqEwlQ6/JTqtuWjkTLCKmCKe2BG4BfoBZ4g2iGginrAc8Cu6QcUMwlG6aN\nagdyg/nJHacA0hZdkHzLAgcNITekP8osYKhFFg4GqbpryWkspHuBRaFDRgAzIn+vQL0NuwI/B04k\nm50nibfJjwBNE8z5seOWALuRLpjzUOt3JgS3Q/B4ZN9UYEIF0zW6A4IuVD24zHPLIWphvgdsaKlP\nDTkanSLTDfg7+XNfy6Y5isEh6EB1WuNVzML8ABXVbYCjSE5j5ThRjkLFsDNIl5RjNiHdivgYbdiG\nAKcC5zR+EZ0qsTXwkyILNmxKdvwS8zLMB44FboPgnwVSdYK6cgfFtpUjmIMpbGFCNiApQrAUHedM\nyn1biI2Bd+1fI4xjyjCQcxN2jCJjYQarUdHcKbKs2VHAwRDsB0xD369C92ljiyOk0swEUwLgOOBm\n0gWziIWZ8esfYBsmNV75nPpBApAfg3y/ES62G3A9KohpvdhCFibA68Bm6FqZExqhTE5VkbtAeqFu\nwM6olZZGTDABFbZvAJNLuFlsPqYE5AtmmGwgPka+BDUQEoKKALUwF1mnLYlnUeMhAdka5N6EHUPI\nCubglOuWw95o5yJ67zZosFK0I/AacCvwN/P6bImOzULhdzNkX/IjiXNoZoLJSNQdcAmVW5igva7v\nAP/GBbO5cglqyX2zEa41CG0czLWaSDHBnIZG0W6GBkt0a4RyOVVBBqM5WMeRnTpRqDHelFyXLKhQ\nfUF+Iv4k5gN9IwE0nYAvIfg8e0jwBXAe+dmCFgOvFLBg3yNfzKNMRfMfJ/FVYPcEqyy0MN8j0cKU\nPUB2SL+l7ATyJMilNiS2DTpMFvXejALmWYRsyEFox2UY6u5+N9IReIdUCzMTUHU0cHt6uepCMGWk\n9lLkgWLmcAlsivrN30IH1WNRURKQvnh0lHBZnIuBbRtYJqc++Rb6gjR4XINslOMs9GVNooBLFlDB\nPARtWP+DNsZVRgJ1dzllEjb2Y1ELczmpHSVph9aJJAvzUXMlFiFYjQpfmFClF7nWZXjc+ZEsPSEL\n0PqUxmR0SbA0ngW2TXE5j0FTPU6MbY+6ZAcnnHcycBdI2pqcR6OrsOyNGizboN836hoeT940mWAN\nBP9FV4Q5z8oekmJhSh/gZZAz0La+7i3MPdA1KAeiE28bQhiJuAZ4A9gitn8jNIS/FMFciFam7iB9\nixzfBMgEkO81dSnWTaQDmmT6MWBgw0LfpSPq5v+AVAtTOgE9SJ5SEvI6aqlOBZ6nNm5ZC5qQNKvY\nSeZr6IozoWA+SLpnYQIw3ZbUinIDmtKtVKLjmHF3bCEuRQOLUgjWQPBB+n5eQ9vLYxP2jUGfQ3ye\naIExTAnQZ3ILusRYDGkH7AX8Dh1aOxmNSbkf9b6EJAhmhgds/9TItiVA+wTPzXh0/PYC4E6b+5pK\nPQjmZsBDaOb8hjYSQ8n24l8m3y07AXiuyAA76A/9pI1nPo2a92UiaW6MxuIw4Igq36O5MgR4316O\nRTQsB+UA1DX0Jeku2Y2B9xJyhUaZAaxFe9a1EsyRaDTjhTW4V3NiB+By1LLqj07FSBPMndCOWYxg\nJgTTyrhndByzJ2rVlkDwaSS3bAUEa9HgmV/lWoTSGs3jeiU5gimtUO/Iq6gVHYBEp6YMQ6dRnQFs\nA9KPXHYAZkCwAHWPfhMVvml2v5C0JCCgHRjIEcxAUG2Iu2XHAXeiLvZfplwvQ70I5jS0oYib9uVi\nFiagjU58/HGibS/GTUAYDHIVcEZ500tkQ+AZkGrOQdoFGFkkOs9JZhgaqg+lBQMUIjrpPE0wi41f\nAsEq1KPxBPqiT7I5ZNVkU3SO3w46d9kpjHQDORAN8vkH6qV4Hx0CKiSYj6fsK4e4YJZqYTYCwTQ0\nmOaHkY1DUKttMip8oZdmFJp9Z56J7VXAjyPnTQSmWn2/j/z1OPdHny0QvI0aPlNRj6FZmNLe7pMS\n+RssAPYhO0czZDawBchekW3hggiTIXg3+XpZmlgwpRXaa3iDxhHMqIV5P7BHrNGZQEmCGayKDCZP\nRgfnywkOCS3SMqwEuQLkHZDzSzi2PzrG2godz3DKYziNJ5jRPKDzgW42FzjKVmTnixUg2Fdf2mAO\n8BI6plkGcqIGSpTMSLvPXejQiFOYW4CTgJ/auOI01J03GxiW33mVjmiD/BQN5310+GBTdMyxkBu1\nGlwHHBmZkzkGeA2C5ahwHWPbd0A7fSF/Ar5lEcWgbfxz9vkONMetIQHwdTKCCajhcj2qEWGnbj/g\nPxB8ll7c4P4ET+I7qBv4r9rxkQB1yaZZqmVTzHXZ0MtvDGLzgKQVyIc2CFvJtdqDrLJw4nDbMxqR\nBTYp/BPyVzov5dr7gLxSujUnt5j4XVzi8W1AloPsDLKiePCTHA5yh32/7Uu7R4Opcl2o5b3kGpDj\n7POZIL9twLXOz+3kyMPohOnw7wBkOkhKaH7qdXcCeatMz8ZzIG+UcfxLIBNBDgC5p4TjA4tcrOVq\nPrWqd0XuI91APs4NJJTr7HkEIB+B9Izs62B1o5Qo2FKKtw/IuyBLQC4EKTSNpUrI85H29Fcgv7DP\no61cA0D+BhIbKpLrQWxusbwAYp4/6WjPzWJEZJy+K4n3Duz5bwHyZrYcZZV/BLrY9FZW3gNBPkhp\n1yuqd9VuuPYFicxDkoe1YlR0rREgs2LbTge52j5vBRIP7S712q20IZK07EHxYz8A+T7IE8WPB61A\n8rJ9fgzkG0WO/wvID60i/qC0ezSY5iSYj2d/S/k2yN0NuNZNIMdE/t4eZDaZjptsZZ2nMl3nElgD\nVaJnQwaBLLUOV88Sjm8FstKEoC/aYSsiznIIiGQ7GzWhXgTzUPLmHMowEAtqkSdNIPcHmQuyBuSf\nqEXYGMUbBrJYOzhNhRxjov0nkEXk5LeV01TsZAk67SZ63mg7fheQ+eRm47kS5Nf2+QIKGhlyGGr0\nPFX++5R3rb2trPenHVDRVRtQolIufxbIJZG/T9cGqKxr9LXKeg7IQ7F9Q+0BT7feyZkNKOtRIA+W\ncNwWIG9bQ/QJJUVgyoWRSvMjkJsLHNvWGsaBVkkvL638DaY5CeZ8FRgA2RLktcLHF7zWYyCxoDB5\nHOQ3IF1ArtLft6Jr7wUyjZJSk8kpqMXzEMjXSzh+AEhkzp7M0LqbenxXkAUgfwC5s/j1G416Ecy7\nQI4ssL8/yFSQ90G2JcfT1Vg0dZpOCdAO4a/1++btPx7kxWQxkwdAPtcORc72QSDLQHqiXrwi0/hk\nLI02FUp654t7dmdFVyzzcIuikq/ZAyoy+VruIcd8l56U7JaVLtrbkqnoPE4B+WNymWRUbq+mEqQ9\n2rs6rMhxV0fEb3puIyS7mTgeDbKBid4YkJfJuFalH9rbjyTjznEz7wHyjH3eO13EZbCKfGo5O4Nc\nVriRzD2hxOMagwrvJdeBFJpThn3vz7ONj3RBLa1YAyAbk+j2kjYmsmeBnAsyh7zcr7IxyN2olfEf\nMlZI2d8nAHka5KcUnNokbUBetbpxDiW5mGVXkEgwilwDclKB408HuTVSP8vNL1opNRRMuQwklqBb\nDgJ53b5zjyKXaINOIXLykPEgF6XsuwZ1zc6uYb0qRjUFU9ZDzXQB+Q7aK34UHSOJjHdErS35LmqJ\nxXJvyrVWcbexHsCe9ncfa4h2toc/F+3NXGwNyxkg+1XyJUtHRoIstDJehPa2v00myENGoGa+vVhy\ntTWc/VEX7SLU7XAban0usGf1ekwUf4+6p9uCbGcv63n2PW8AOdmO28SucbQ13iejHYQzUSt0oZYv\n73u0A5kM8gjq5jmX4r3XOhdM+Yo9iyX5jV7mmMB+u5gLVn6GjgePtGe6tV3nJXQsqqP9fxzaoXvL\nfvsbUbFNGXOWduV/j7xrbGm/03LUxXciyNfJHS87C+Rf+hvKDlbu1pHvPMzemQH6DspI1JV8VeQa\ne6Ou43AVjZ+h400DrB7O1bKA1dnxsXL2ArkZFe7jU77LrqjgtrbvVUrjWEvBvNjeh6tBTkC9BPNQ\nq6rSdSGdokgb1NKsF7GElHpXzA8sIKcDU9BsEYKGUm+FTib9FJ2DcwQ6WfTPaKjw82g49dnA8WgU\n1XZoOPZkdM7Z4cBuEMSyUMgIdFJvWzSacT4aZbY/GhU6C42WOkaz69caCdMudQX+i2aj+AqafGE1\ncBEE5oeXjugE3MPRaMTjILCgDGmVvz5d5h5t0Aiyne0eJwCno/OXxgCba9i2tEbngC1EI8AGossA\n3Ys++57AP9F5YKvQaLD10UnIL6JRmL2BvwHt7FrhfMAedl3LrRs8R/H60liINc6vZiPdpAuZkHU2\nQJNeb4xm51iBrid5LRpuf4197owugLsIjVL9FjpvcsfcieTSCo0CnAS0R5/JEWg04nC7Twf0HTgS\ngki4urQtLVtLQ5FOwKFoMo5NrKxr0O/bDdgagvdV2HkEzbAy3creHs0UsxH6u85Fs6FcDEFk3F/O\nQKNA56DP7i40SvElYD0IdrLjLkezryxBQ/3fsjLdi77/fwXOR9/d94Cl6LzhU9FVOpaQfX+moGni\n5tmxc4EP0ee9wFK+1aLeid5HBqER8cPRJCdX5j4jp4Vg9SGXUgTzD+h6bYPR6RX/RefGfAR0QUOE\nb9WJuIAGU7wDgc07k0loI/UwurL80ajo/rl4RQx94YGAjAHmQ7BMtxdNPlBDMin3egNv55et0vJK\nN6CVhm5LJ1RAe0FwY4FzWudOkJddrVxd0MwknwL3QBAJSJK26LSC8egcxTZoQxx6Bx6C4ApqK5iz\n0GcaCsL6qACsjzaoU9EEE8+h04n2Q1cmWI2uVvBjtOH/CK1/Y9GOw00FEk2Ht7ffS7qgCbLvJZN/\nuF7qnQSo+HUHPsnP1iIjUYFcQm7Ho4jAywS75rP6nGRrYHvgcQhs3pv0R+cBf4z+JlsBayG4zfZP\nRJMhrEaFtBs69eJAtAPTT6/HlnbuRvZvMJoIoDsqVnta56SGguk4QIWCOYVs3kSnZfMEmhKsFkzB\n652j1KreTcHrnJOllu2d4ziO4ziO4ziO4ziO4ziO4ziO4ziO4ziO4ziO4ziO4zQh/w/eD3xAvlMb\nrwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f274bc23a50>"
      ]
     },
     "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): 0.51 seconds\n",
      "Overall time: 13.90 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 0x7f2750132510>"
      ]
     },
     "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: 9.75 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'] = [1.5608471257621608, 1.1485328805819084, 0.97097535124021084, 0.79022851828512952, 0.69960711196435987, 0.69625874110699781, 0.71097445784304725, 0.74333974883956044, 0.91061356766518298, 0.992902374341881, 1.1010037236195309, 1.3692128406671615, 1.5058190643845542, 2.0381446354892905, 2.246605962044653]\n",
      "res['objective_j'] = [8429.8490524291992, 8429.8490524291992, 8429.8490524291992, 8429.8490524291992, 8429.8490524291992, 8429.8490524291992, 8429.8490524291992, 8429.8490524291992, 8429.8490524291992, 8429.8490524291992, 8429.8490524291992, 8429.8490524291992, 8429.8490524291992, 8429.8490524291992, 8429.8490524291992]\n",
      "res['objective_i'] = [46432.25, 46432.25, 46432.25, 46432.25, 46432.25, 46432.25, 46432.25, 46432.25, 46432.25, 46432.25, 46432.25, 46432.25, 46432.25, 46432.25, 46432.25]\n",
      "res['objective_h'] = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "res['objective_g'] = [103830.56640625, 103830.56640625, 103830.56640625, 103830.56640625, 103830.56640625, 103830.56640625, 103830.56640625, 103830.56640625, 103830.56640625, 103830.56640625, 103830.56640625, 103830.56640625, 103830.56640625, 103830.56640625, 103830.56640625]\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'] = [2488.0675921440125, 2488.0675921440125, 2488.0675921440125, 2488.0675921440125, 2488.0675921440125, 2488.0675921440125, 2488.0675921440125, 2488.0675921440125, 2488.0675921440125, 2488.0675921440125, 2488.0675921440125, 2488.0675921440125, 2488.0675921440125, 2488.0675921440125, 2488.0675921440125]\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'] = [40.842905830536914, 40.842905830536914, 40.842905830536914, 40.842905830536914, 40.842905830536914, 40.842905830536914, 40.842905830536914, 40.842905830536914, 40.842905830536914, 40.842905830536914, 40.842905830536914, 40.842905830536914, 40.842905830536914, 40.842905830536914, 40.842905830536914]\n",
      "res['iterations_inner'] = [1122, 1122, 1122, 1122, 1122, 1122, 1122, 1122, 1122, 1122, 1122, 1122, 1122, 1122, 1122]\n",
      "res['iterations_outer'] = [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7]\n",
      "res['accuracy'] = [72.622499999999988, 72.305000000000021, 71.944722222222197, 71.313333333333361, 70.669000000000025, 69.772222222222211, 68.565595238095241, 66.707812500000017, 62.917685185185171, 61.527173913043441, 59.656826241134773, 56.793402777777757, 54.67457044673543, 51.681717687074858, 47.269528619528636]\n"
     ]
    },
    {
     "data": {
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WnZeWpiQYhmEYhlGlmL/MMAyjfNg7NlkqLibBZjcYhmEY9cmQptjpi5kcMxhKcQ/DaABW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\nJa6z7XK6udt9jdMg4VjgfmAr8CRwNXDqgP/H8oaXyfb4tkt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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f274b87c750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Experiment time: 6933 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))"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
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   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.3"
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 },
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