{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Genre recognition: experiment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Goal: Test the influence of the graph Laplacian type.\n",
    "\n",
    "Conclusion: The normalized Laplacian provides better accuracy (~3%).\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 = 'laplacian'\n",
    "Pvalues = ['normalized', 'unnormalized', 'normalized', 'unnormalized']\n",
    "\n",
    "# Regenerate the graph or the features at each iteration.\n",
    "regen_graph = True\n",
    "regen_features = True\n",
    "regen_baseline = False"
   ]
  },
  {
   "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'] = 100\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_D']\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, laplacian = normalized -------------------- \n",
      "\n",
      "Data: (149000, 96), float32\n",
      "Elapsed time: 216.73 seconds\n",
      "All self-referenced in the first column: True\n",
      "dist in [0.0, 1.72983384132]\n",
      "w in [0.00189454166684, 1.0]\n",
      "Ones on the diagonal: 149000 (over 149000)\n",
      "assert: True\n",
      "W in [0.0, 1.0]\n",
      "Datasets:\n",
      "  L_data    : (2359600,), float32\n",
      "  L_indices : (2359600,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2359600,), float32\n",
      "  W_indices : (2359600,), 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: 225.98 seconds\n",
      "\n",
      " -------------------- Features, laplacian = normalized -------------------- \n",
      "\n",
      "Attributes:\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:\n",
      "  K = 11\n",
      "  dm = euclidean\n",
      "  Csigma = 1\n",
      "  diag = True\n",
      "  laplacian = normalized\n",
      "Datasets:\n",
      "  L_data    : (2359600,), float32\n",
      "  L_indices : (2359600,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2359600,), float32\n",
      "  W_indices : (2359600,), 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: 2734 seconds\n"
     ]
    },
    {
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       "QmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fef64017790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inner loop: 1302 iterations\n",
      "g(Z) = ||X-DZ||_2^2 = 7.104504e+04\n",
      "rdiff: 0.000143436189521\n",
      "i(Z) = ||Z||_1 = 5.698367e+04\n",
      "j(Z) = tr(Z^TLZ) = 8.738621e+03\n"
     ]
    },
    {
     "data": {
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       "LgAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fef49ebaf50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global objective: 1.367673e+05\n"
     ]
    },
    {
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       "IiIiIiIiIiIiIiIiIiLSrf4fCzvnXeJD804AAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fef49c34050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outer loop: 8 iterations\n",
      "\n",
      "Z in [-0.172690629959, 1.08530604839]\n",
      "Sparsity of Z: 3,702,669 non-zero entries out of 19,072,000 entries, i.e. 19.4%.\n"
     ]
    },
    {
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       "buN/K7oN/sxenhube0bIFsFNnJD5+SV0pt2LjeuPRLcBS/4ZwDcl27gTOpPx4f776QBeh25DexeA\n",
       "/9b/AMDLAdwA4FPozOS3QaeNn4lOG0f/+6yi/jPRaa8ni/SzoGu1/xmdJv0d6EznD0F3kPlbdJr2\n",
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       "08TlT3YDB4DPAPhydNHm90a3YQ0b+G1wNLju7P7n8l6WK9H5r4FOi39t//l1AL6n//xwdGb3IwD+\n",
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       "+J8A/g+AB1W2TchWwk2ckPkoN+xfQOePnoKL0Pmlb9f//rkg/ynoAsreh86P/FQc1WCfA+An0Gmu\n",
       "dwHw0j799ejM1TcC+N8AntWnfxHAD6HbLK8D8CoA7zdkvBZdcNub0fnlX4zuoHA1gK8G8I9Ffsu0\n",
       "vyqurdC9Wvd2HNWmPy/y/Dw6a8G16Ezxw7/Ge1L//d3oLBa/bbRHCCGEkBm4GJ2pnBDSQ02cEEII\n",
       "IYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQpbF/weDNfdr5tLW8QAAAABJRU5ErkJg\n",
       "gg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fef49dca650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D in [-0.027598535642, 0.895244240761]\n",
      "d in [0.999999582767, 1.00000035763]\n",
      "Constraints on D: True\n"
     ]
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fef49c8a450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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       "YII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fef6f589ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fef6f589d10>"
      ]
     },
     "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: 2741 seconds\n",
      "\n",
      " -------------------- Classification, laplacian = normalized -------------------- \n",
      "\n",
      "Software versions:\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)\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": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fef31489090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 128), float64\n",
      "Testing data: (2400, 128), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 76.7 %\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 %\n",
      "Clips accuracy: 68.5 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 5400.0 training, 600.0 testing\n",
      "  77 (+/- 1.8) <- [78 76 76 77 72 77 74 79 78 77]\n",
      "  78 (+/- 1.8) <- [77 78 78 75 81 77 80 76 78 80]\n",
      "  78 (+/- 1.4) <- [78 77 80 76 79 80 77 78 79 76]\n",
      "  78 (+/- 1.9) <- [77 73 78 79 78 79 79 76 80 78]\n",
      "  79 (+/- 0.7) <- [78 78 78 78 78 79 78 78 80 77]\n",
      "  79 (+/- 1.6) <- [80 79 78 80 81 81 77 80 77 78]\n",
      "  78 (+/- 1.4) <- [80 78 79 78 78 75 80 77 76 78]\n",
      "  78 (+/- 1.7) <- [74 78 81 77 77 79 79 76 76 77]\n",
      "  79 (+/- 1.0) <- [78 80 80 79 78 77 78 78 79 77]\n",
      "  79 (+/- 1.2) <- [78 79 77 78 79 77 78 78 79 81]\n",
      "  79 (+/- 2.2) <- [80 81 77 76 81 79 81 75 79 78]\n",
      "  78 (+/- 1.2) <- [80 78 79 79 76 77 77 77 79 77]\n",
      "  78 (+/- 1.9) <- [79 77 77 76 76 79 80 74 79 75]\n",
      "  78 (+/- 2.3) <- [78 77 83 74 75 77 77 76 78 79]\n",
      "  79 (+/- 1.4) <- [78 77 79 78 79 81 81 79 79 77]\n",
      "  79 (+/- 1.8) <- [80 78 78 80 76 76 77 78 81 78]\n",
      "  78 (+/- 1.3) <- [78 79 79 77 80 76 76 78 79 77]\n",
      "  78 (+/- 1.1) <- [78 79 78 78 76 79 77 77 80 78]\n",
      "  78 (+/- 0.9) <- [75 77 77 78 78 78 78 78 76 77]\n",
      "  78 (+/- 1.4) <- [77 80 78 74 76 78 78 77 77 77]\n",
      "Accuracy: 78.3 (+/- 1.67)\n",
      "Mean time (20 cv): 20.35 seconds\n",
      "Overall time: 410.97 seconds\n",
      "\n",
      " -------------------- Graph, laplacian = unnormalized -------------------- \n",
      "\n",
      "Data: (149000, 96), float32\n",
      "Elapsed time: 166.93 seconds\n",
      "All self-referenced in the first column: True\n",
      "dist in [0.0, 1.47301745415]\n",
      "w in [0.00517621124163, 1.0]\n",
      "Ones on the diagonal: 149000 (over 149000)\n",
      "assert: True\n",
      "W in [0.0, 1.0]\n",
      "Datasets:\n",
      "  L_data    : (2394468,), float32\n",
      "  L_indices : (2394468,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2394468,), float32\n",
      "  W_indices : (2394468,), 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 = unnormalized\n",
      "Overall time: 176.87 seconds\n",
      "\n",
      " -------------------- Features, laplacian = unnormalized -------------------- \n",
      "\n",
      "Attributes:\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:\n",
      "  K = 11\n",
      "  dm = euclidean\n",
      "  Csigma = 1\n",
      "  diag = True\n",
      "  laplacian = unnormalized\n",
      "Datasets:\n",
      "  L_data    : (2394468,), float32\n",
      "  L_indices : (2394468,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2394468,), float32\n",
      "  W_indices : (2394468,), 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: 3144 seconds\n"
     ]
    },
    {
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       "gkGmAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fefad6c4550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inner loop: 1441 iterations\n",
      "g(Z) = ||X-DZ||_2^2 = 9.315121e+04\n",
      "rdiff: 0.000746224531602\n",
      "i(Z) = ||Z||_1 = 5.053609e+04\n",
      "j(Z) = tr(Z^TLZ) = 8.245592e+03\n"
     ]
    },
    {
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       "AAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fef6b4b4290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global objective: 1.519329e+05\n"
     ]
    },
    {
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       "IiIdWIFYAcjVAYw+7sRXiBUZkVpRy4KI5OYvxD4cO3gH6VCrlh6IlyC2Id5mKiIiIiIiIiIiIiIi\n",
       "IiIiIiIiIiIiIiIiIiIiIiIi1fj/O6+F1nGI0e0AAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fef6ec332d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outer loop: 4 iterations\n",
      "\n",
      "Z in [-0.1504381001, 0.431620627642]\n",
      "Sparsity of Z: 6,688,349 non-zero entries out of 19,072,000 entries, i.e. 35.1%.\n"
     ]
    },
    {
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       "X96/AAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fefb0fcdf50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D in [-0.068669103086, 0.870086312294]\n",
      "d in [0.999999642372, 1.00000035763]\n",
      "Constraints on D: True\n"
     ]
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fef6ecb9490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "YII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fef6b596cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fef49e81190>"
      ]
     },
     "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: 3153 seconds\n",
      "\n",
      " -------------------- Classification, laplacian = unnormalized -------------------- \n",
      "\n",
      "Software versions:\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)\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": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fef6b6fdfd0>"
      ]
     },
     "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: 72.6 %\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: 63.0 %\n",
      "Clips accuracy: 71.5 %\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",
      "  74 (+/- 2.0) <- [73 72 75 76 69 75 75 73 74 74]\n",
      "  74 (+/- 2.3) <- [73 74 73 68 77 75 72 75 73 75]\n",
      "  75 (+/- 1.6) <- [76 75 76 76 74 76 74 71 77 74]\n",
      "  74 (+/- 2.1) <- [75 68 76 74 74 76 75 74 74 73]\n",
      "  74 (+/- 1.3) <- [75 72 72 73 73 74 75 72 75 73]\n",
      "  75 (+/- 1.6) <- [74 73 74 76 75 78 75 77 73 74]\n",
      "  74 (+/- 1.0) <- [73 73 73 74 74 73 72 72 75 75]\n",
      "  74 (+/- 1.7) <- [74 74 73 75 71 78 72 73 73 72]\n",
      "  75 (+/- 1.9) <- [73 77 74 76 74 71 75 74 73 78]\n",
      "  75 (+/- 2.0) <- [77 73 76 73 73 74 75 73 73 79]\n",
      "  75 (+/- 1.6) <- [78 77 76 72 75 74 75 72 75 76]\n",
      "  74 (+/- 1.6) <- [76 73 73 74 70 73 74 74 75 75]\n",
      "  74 (+/- 1.9) <- [77 72 75 74 73 76 74 73 75 70]\n",
      "  74 (+/- 2.1) <- [75 72 76 72 73 72 69 74 73 76]\n",
      "  74 (+/- 1.2) <- [75 72 74 74 72 73 74 71 73 75]\n",
      "  74 (+/- 2.2) <- [78 74 75 75 72 77 73 74 73 70]\n",
      "  75 (+/- 1.3) <- [74 74 74 73 76 73 75 75 77 73]\n",
      "  74 (+/- 1.4) <- [74 73 73 72 72 76 74 73 76 73]\n",
      "  74 (+/- 1.8) <- [74 75 75 72 77 72 76 73 71 73]\n",
      "  74 (+/- 1.0) <- [75 74 73 73 74 74 74 76 73 72]\n",
      "Accuracy: 74.3 (+/- 1.81)\n",
      "Mean time (20 cv): 19.83 seconds\n",
      "Overall time: 401.12 seconds\n",
      "\n",
      " -------------------- Graph, laplacian = normalized -------------------- \n",
      "\n",
      "Data: (149000, 96), float32\n",
      "Elapsed time: 172.01 seconds\n",
      "All self-referenced in the first column: True\n",
      "dist in [0.0, 1.52197754383]\n",
      "w in [0.00556439952925, 1.0]\n",
      "Ones on the diagonal: 149000 (over 149000)\n",
      "assert: True\n",
      "W in [0.0, 1.0]\n",
      "Datasets:\n",
      "  L_data    : (2413902,), float32\n",
      "  L_indices : (2413902,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2413902,), float32\n",
      "  W_indices : (2413902,), 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: 182.17 seconds\n",
      "\n",
      " -------------------- Features, laplacian = normalized -------------------- \n",
      "\n",
      "Attributes:\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:\n",
      "  K = 11\n",
      "  dm = euclidean\n",
      "  Csigma = 1\n",
      "  diag = True\n",
      "  laplacian = normalized\n",
      "Datasets:\n",
      "  L_data    : (2413902,), float32\n",
      "  L_indices : (2413902,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2413902,), float32\n",
      "  W_indices : (2413902,), 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: 3170 seconds\n"
     ]
    },
    {
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       "AAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fef304bd510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inner loop: 1458 iterations\n",
      "g(Z) = ||X-DZ||_2^2 = 7.079712e+04\n",
      "rdiff: 0.000297394692829\n",
      "i(Z) = ||Z||_1 = 5.690532e+04\n",
      "j(Z) = tr(Z^TLZ) = 7.918162e+03\n"
     ]
    },
    {
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       "bEb971pERCQJy2EJu18dQOv7/v8du/AQiU4ldxHppWexPgg7xg5knJrVpIBVyW+L3TYoIiIiIiIi\n",
       "IiIiIiIiIiIiIiIiIiIiIiIiIiIiIu39f+qi956mQErcAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fef30e1edd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global objective: 1.356206e+05\n"
     ]
    },
    {
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       "hKhJibIAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fef31424e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outer loop: 11 iterations\n",
      "\n",
      "Z in [-0.157225385308, 1.02839124203]\n",
      "Sparsity of Z: 3,731,115 non-zero entries out of 19,072,000 entries, i.e. 19.6%.\n"
     ]
    },
    {
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       "4AAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fef6b0a53d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D in [-0.0245641116053, 0.89549434185]\n",
      "d in [0.999999642372, 1.00000035763]\n",
      "Constraints on D: True\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fef30faeed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "QmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fef30e86290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "gg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fef6b5c60d0>"
      ]
     },
     "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: 3180 seconds\n",
      "\n",
      " -------------------- Classification, laplacian = normalized -------------------- \n",
      "\n",
      "Software versions:\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)\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"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fef29041490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 128), float64\n",
      "Testing data: (2400, 128), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 76.4 %\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: 62.5 %\n",
      "Clips accuracy: 71.5 %\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",
      "  76 (+/- 2.2) <- [77 75 76 77 70 76 74 77 78 77]\n",
      "  78 (+/- 1.7) <- [77 76 77 73 78 76 80 78 76 79]\n",
      "  78 (+/- 1.2) <- [79 78 78 76 78 79 77 76 78 75]\n",
      "  77 (+/- 2.0) <- [79 71 75 77 78 76 77 76 77 76]\n",
      "  77 (+/- 1.2) <- [77 76 77 77 76 78 79 75 78 75]\n",
      "  78 (+/- 1.4) <- [78 77 76 79 77 80 75 78 76 79]\n",
      "  77 (+/- 1.5) <- [79 76 76 78 77 74 79 75 78 77]\n",
      "  77 (+/- 1.6) <- [76 75 78 77 76 80 75 75 75 77]\n",
      "  78 (+/- 1.2) <- [75 79 78 78 77 76 76 78 79 78]\n",
      "  77 (+/- 2.0) <- [79 75 77 76 77 74 75 76 77 81]\n",
      "  78 (+/- 1.7) <- [79 79 79 75 80 76 78 74 78 79]\n",
      "  77 (+/- 1.4) <- [76 77 79 76 77 75 75 79 78 78]\n",
      "  77 (+/- 1.5) <- [79 76 76 77 76 78 78 75 78 74]\n",
      "  76 (+/- 1.8) <- [78 76 80 73 75 75 75 75 77 77]\n",
      "  78 (+/- 1.7) <- [75 79 79 76 78 80 81 78 76 77]\n",
      "  78 (+/- 2.2) <- [79 76 79 80 75 76 77 75 81 75]\n",
      "  78 (+/- 1.9) <- [76 77 77 78 80 75 77 79 80 74]\n",
      "  78 (+/- 1.4) <- [76 81 78 77 78 77 76 76 77 78]\n",
      "  77 (+/- 0.9) <- [79 76 76 76 78 77 77 77 76 75]\n",
      "  77 (+/- 1.5) <- [77 77 78 72 76 76 76 75 77 75]\n",
      "Accuracy: 77.3 (+/- 1.74)\n",
      "Mean time (20 cv): 21.67 seconds\n",
      "Overall time: 437.96 seconds\n",
      "\n",
      " -------------------- Graph, laplacian = unnormalized -------------------- \n",
      "\n",
      "Data: (149000, 96), float32\n",
      "Elapsed time: 175.21 seconds\n",
      "All self-referenced in the first column: True\n",
      "dist in [0.0, 1.47113442421]\n",
      "w in [0.00576181244105, 1.0]\n",
      "Ones on the diagonal: 149000 (over 149000)\n",
      "assert: True\n",
      "W in [0.0, 1.0]\n",
      "Datasets:\n",
      "  L_data    : (2410344,), float32\n",
      "  L_indices : (2410344,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2410344,), float32\n",
      "  W_indices : (2410344,), 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 = unnormalized\n",
      "Overall time: 184.53 seconds\n",
      "\n",
      " -------------------- Features, laplacian = unnormalized -------------------- \n",
      "\n",
      "Attributes:\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:\n",
      "  K = 11\n",
      "  dm = euclidean\n",
      "  Csigma = 1\n",
      "  diag = True\n",
      "  laplacian = unnormalized\n",
      "Datasets:\n",
      "  L_data    : (2410344,), float32\n",
      "  L_indices : (2410344,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2410344,), float32\n",
      "  W_indices : (2410344,), 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: 3641 seconds\n"
     ]
    },
    {
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       "SPoUcDd2UDYN+15Wyct/E+taIS/b7pUYIiIiPWc2dnJir2rnMscp2MGhKk8STk30IuLtEaxm/NLo\n",
       "IGPUTn/67sDp2HkQIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIjI2/w9ZNwPcJ0a/QAAAAABJ\n",
       "RU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fefb0f79c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inner loop: 1624 iterations\n",
      "g(Z) = ||X-DZ||_2^2 = 1.020493e+05\n",
      "rdiff: 0.000117322325577\n",
      "i(Z) = ||Z||_1 = 4.728914e+04\n",
      "j(Z) = tr(Z^TLZ) = 6.951408e+03\n"
     ]
    },
    {
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       "AwY0z/SJzGAyAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fef29054550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global objective: 1.562898e+05\n"
     ]
    },
    {
     "data": {
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       "3wp8mFxY31j+rtdiO6P9guFKwD+BQ8idAm8ityacWuZ9tOS9rkxvbvP7wKsZ7uB4ZNnejcBvGfkK\n",
       "3/FyNOedTP639CbgPHKHzn+NM32s4wGwDSPPtYiIiHRgFXIFYFAdzNjPnfgkuSIj0lfUsiAig+av\n",
       "5D4cu0QH6VCrlh7IlyB2JN9mKiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiPv4/aV/vVZ5y\n",
       "GBQAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fef30484c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outer loop: 5 iterations\n",
      "\n",
      "Z in [-0.0730217769742, 0.239454746246]\n",
      "Sparsity of Z: 7,842,042 non-zero entries out of 19,072,000 entries, i.e. 41.1%.\n"
     ]
    },
    {
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       "a98zRwAAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fef287dd890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D in [-0.0284309070557, 0.800033926964]\n",
      "d in [0.999999523163, 1.00000035763]\n",
      "Constraints on D: True\n"
     ]
    },
    {
     "data": {
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       "SUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fef283e6b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fef28535050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fef285acd10>"
      ]
     },
     "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: 3652 seconds\n",
      "\n",
      " -------------------- Classification, laplacian = unnormalized -------------------- \n",
      "\n",
      "Software versions:\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)\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"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fef20feb110>"
      ]
     },
     "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: 70.5 %\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: 63.0 %\n",
      "Clips accuracy: 67.0 %\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 (+/- 1.8) <- [73 70 71 70 68 73 72 70 73 73]\n",
      "  72 (+/- 1.7) <- [71 73 72 68 74 73 70 73 70 71]\n",
      "  72 (+/- 2.2) <- [73 75 73 70 72 75 71 68 73 70]\n",
      "  72 (+/- 2.4) <- [72 65 73 73 72 74 73 71 71 71]\n",
      "  71 (+/- 1.3) <- [73 69 72 71 70 72 70 70 72 69]\n",
      "  74 (+/- 1.1) <- [74 73 74 73 74 75 73 74 72 71]\n",
      "  72 (+/- 1.3) <- [73 72 71 73 73 69 70 71 71 72]\n",
      "  72 (+/- 1.3) <- [70 72 72 71 69 74 70 70 71 72]\n",
      "  72 (+/- 1.3) <- [70 73 71 73 72 70 74 71 73 73]\n",
      "  73 (+/- 1.3) <- [74 71 74 71 74 71 71 73 71 74]\n",
      "  72 (+/- 1.6) <- [73 74 71 69 72 71 73 69 73 70]\n",
      "  71 (+/- 1.1) <- [70 71 71 72 68 70 71 72 72 71]\n",
      "  71 (+/- 1.9) <- [72 72 73 72 70 72 67 69 70 68]\n",
      "  71 (+/- 2.0) <- [72 69 73 69 71 70 67 71 70 73]\n",
      "  72 (+/- 1.2) <- [71 71 69 72 72 71 72 73 70 73]\n",
      "  72 (+/- 2.3) <- [76 72 69 74 69 71 71 70 74 68]\n",
      "  72 (+/- 1.7) <- [71 73 72 71 72 68 71 72 73 69]\n",
      "  73 (+/- 1.5) <- [74 71 71 72 72 72 70 71 75 74]\n",
      "  72 (+/- 1.4) <- [70 73 70 70 74 72 71 72 70 70]\n",
      "  71 (+/- 1.7) <- [70 73 70 71 71 70 72 73 72 67]\n",
      "Accuracy: 71.9 (+/- 1.78)\n",
      "Mean time (20 cv): 21.83 seconds\n",
      "Overall time: 441.54 seconds\n",
      "\n",
      " -------------------- Baseline -------------------- \n",
      "\n",
      "Software versions:\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)\n"
     ]
    },
    {
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       "rwAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fef203d39d0>"
      ]
     },
     "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 %\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 %\n",
      "Clips accuracy: 68.5 %\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]\n",
      "  69 (+/- 1.4) <- [71 68 67 67 72 69 67 70 69 70]\n",
      "  70 (+/- 2.0) <- [71 69 73 68 68 71 71 68 71 66]\n",
      "  69 (+/- 1.4) <- [70 67 66 67 67 70 68 70 70 68]\n",
      "  70 (+/- 2.4) <- [71 69 72 68 70 75 71 68 68 66]\n",
      "  71 (+/- 0.7) <- [71 71 70 70 71 71 70 69 70 70]\n",
      "  70 (+/- 1.4) <- [70 68 72 69 69 67 71 69 70 69]\n",
      "  70 (+/- 1.5) <- [69 70 68 71 65 70 70 69 70 69]\n",
      "  69 (+/- 1.7) <- [68 69 68 69 68 65 70 70 69 72]\n",
      "  69 (+/- 1.8) <- [72 69 66 68 69 66 69 69 70 71]\n",
      "  70 (+/- 1.4) <- [71 70 69 69 69 69 73 70 71 68]\n",
      "  70 (+/- 1.6) <- [71 70 71 68 69 69 73 68 69 71]\n",
      "  69 (+/- 1.3) <- [69 70 66 68 69 70 68 71 68 68]\n",
      "  69 (+/- 2.0) <- [71 66 68 66 67 69 66 69 69 72]\n",
      "  70 (+/- 1.1) <- [69 70 72 68 69 70 69 71 71 69]\n",
      "  70 (+/- 2.0) <- [72 71 69 71 67 68 71 72 69 67]\n",
      "  70 (+/- 1.6) <- [67 71 69 69 68 70 71 71 72 69]\n",
      "  70 (+/- 0.9) <- [70 68 71 70 68 70 69 69 71 69]\n",
      "  70 (+/- 1.2) <- [72 71 69 69 71 69 72 70 68 69]\n",
      "  69 (+/- 1.7) <- [67 70 71 67 67 72 67 70 70 69]\n",
      "Accuracy: 69.7 (+/- 1.71)\n",
      "Mean time (20 cv): 17.10 seconds\n",
      "Overall time: 346.41 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": [
      "laplacian = ['normalized', 'unnormalized', 'normalized', 'unnormalized']\n",
      "res['accuracy_std'] = [1.6699758813960304, 1.8111826630378531, 1.7384282092996781, 1.775878303826024]\n",
      "res['objective_j'] = [8738.6207580566406, 8245.5917358398438, 7918.1617736816406, 6951.4076232910156]\n",
      "res['objective_i'] = [56983.671875, 50536.09375, 56905.32421875, 47289.140625]\n",
      "res['objective_h'] = [0, 0, 0, 0]\n",
      "res['objective_g'] = [71045.0390625, 93151.2109375, 70797.119140625, 102049.2578125]\n",
      "res['baseline'] = [69.729166666666742, 69.729166666666742, 69.729166666666742, 69.729166666666742]\n",
      "res['time_features'] = [2733.9047298431396, 3144.330353975296, 3170.067512989044, 3640.6458139419556]\n",
      "res['baseline_std'] = 1.71029866625\n",
      "res['sparsity'] = [19.414162122483223, 35.06894400167785, 19.563312709731544, 41.118089345637586]\n",
      "res['iterations_inner'] = [1302, 1441, 1458, 1624]\n",
      "res['iterations_outer'] = [8, 4, 11, 5]\n",
      "res['accuracy'] = [78.341666666666683, 74.339166666666699, 77.310833333333335, 71.877500000000055]\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fef208dcb10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python2.7/dist-packages/numpy/ma/core.py:3847: UserWarning: Warning: converting a masked element to nan.\n",
      "  warnings.warn(\"Warning: converting a masked element to nan.\")\n"
     ]
    },
    {
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       "bX8EWNvg2OeA/wAnAfOICspWolVlTnpfuWunfOxTaXshh9czgu8hqUJWUiS1Ut7asYSoTGwC5lMs\n",
       "cw9R2fgk0eVyHPDNunNsJJaV35LO83AKP0B0A91KDJxdSdHCkn/uTUQ30waiApOHN/rlUDO/RJIk\n",
       "SR0mAx6tOhKSjg62pEhqN1swJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmS\n",
       "JEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEk6Cv0f6QIduNz/V5YAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fef208a5210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "g(Z) = ||X-DZ||_2^2, h(Z) = ||Z-EX||_2^2, i(Z) = ||Z||_1, j(Z) = tr(Z^TLZ)\n"
     ]
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fef2064f850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fef207f1390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fef28632fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "SUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fef20aeb790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fef209fcd10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dictionary atoms for laplacian = normalized\n",
      "Dictionary atoms for laplacian = unnormalized\n",
      "Dictionary atoms for laplacian = normalized\n",
      "Dictionary atoms for laplacian = unnormalized\n",
      "Experiment time: 15549 seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/pymodules/python2.7/matplotlib/figure.py:371: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n",
      "  \"matplotlib is currently using a non-GUI backend, \"\n"
     ]
    }
   ],
   "source": [
    "print('{} = {}'.format(Pname, Pvalues))\n",
    "for key, value in res.items():\n",
    "    if key is not 'atoms_D':\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_h', '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_h_un'] = res['objective_h'] / div('le')\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_h_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_D']):\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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