{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Genre recognition: experiment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Goal: observe the effect of $\\lambda_g$.\n",
    "\n",
    "Conclusion: A value of $\\lambda_s$ between 1 and 10 seems reasonable (when $\\lambda_g = \\lambda_d = 100$).\n",
    "\n",
    "Observation:\n",
    "* Too sparse is very slow (17h) and gives poor accuracy.\n",
    "* Sparsity is proportional to $\\lambda_s$.\n",
    "* Increasing sparsity correlates with decreasing Dirichlet energy, which makes sense as the energy of the sparse codes diminishes. It trades with reconstruction error."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyper-parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameter under test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Pname = 'ls'\n",
    "Pvalues = [0.1, 1, 10, 100, 1e3]\n",
    "\n",
    "# Regenerate the graph or the features at each iteration.\n",
    "regen_graph = False\n",
    "regen_features = 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['K'] = 10 + 1  # 5 to 10 + 1 for self-reference\n",
    "p['dm'] = 'cosine'\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'] = 100\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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numerical parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Dataset (10,100,644 | 5,100,149 | 2,10,644).\n",
    "p['Ngenres'] = 5\n",
    "p['Nclips'] = 100\n",
    "p['Nframes'] = 149\n",
    "\n",
    "# Graph.\n",
    "p['tol'] = 1e-5\n",
    "\n",
    "# Feature extraction.\n",
    "p['rtol'] = 1e-6  # 1e-3, 1e-5, 1e-7\n",
    "p['N_inner'] = 500\n",
    "p['N_outer'] = 50\n",
    "\n",
    "# Classification.\n",
    "p['Nfolds'] = 10\n",
    "p['Ncv'] = 20\n",
    "p['dataset_classification'] = 'Z'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " -------------------- Graph -------------------- \n",
      "\n",
      "Data: (149000, 96), float32\n",
      "Elapsed time: 153.62 seconds\n",
      "All self-referenced in the first column: True\n",
      "dist in [0.0, 0.491051614285]\n",
      "w in [0.0378095172346, 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    : (2396026,), float32\n",
      "  L_indices : (2396026,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2396026,), float32\n",
      "  W_indices : (2396026,), int32\n",
      "  W_indptr  : (149001,) , int32\n",
      "  W_shape   : (2,)      , int64\n",
      "Attributes:\n",
      "  K = 11\n",
      "  dm = cosine\n",
      "  Csigma = 1\n",
      "  diag = True\n",
      "  laplacian = normalized\n",
      "Overall time: 162.83 seconds\n",
      "\n",
      " -------------------- Features, ls = 0.1 -------------------- \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 = cosine\n",
      "  Csigma = 1\n",
      "  diag = True\n",
      "  laplacian = normalized\n",
      "Datasets:\n",
      "  L_data    : (2396026,), float32\n",
      "  L_indices : (2396026,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2396026,), float32\n",
      "  W_indices : (2396026,), 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: 9701 seconds\n"
     ]
    },
    {
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       "EREREREJ5f8BCvd/BtmexT0AAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff425d78c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inner loop: 5341 iterations\n",
      "g(Z) = ||X-DZ||_2^2 = 4.705818e+04\n",
      "rdiff: 0.00038527825897\n",
      "i(Z) = ||Z||_1 = 3.500366e+04\n",
      "j(Z) = tr(Z^TLZ) = 1.133580e+05\n"
     ]
    },
    {
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       "hTWV/wLrg70Na4r+KZaUPZVHYZeF5XcD3wZuTL12JjaAKhnslrzvwrB8NpYkj8Wa2LPrpsp8vc9w\n",
       "A9Z8fTfWZH1rnfX4Ks89VqG4IMR2Pvb57wG+giXS2eFx9SrrybqBcoJ+FbtV7p+wz74UGzQH8PUQ\n",
       "78ywPFnnJcC7KQ92Oyqsbzbwb+DIOp+v2mc9HfsdvAM4Bxvct6TO8lr7A2BrKv/WIiIiPWcSlpxH\n",
       "qsOpfV79t7BKhkhH6YhcRDppHjZmYLfYgQxTtRYSsGb1nbFT9URERERERERERERERERERERERERE\n",
       "RERERERERERi+//kbihvZAqeQQAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff432640e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global objective: 1.954199e+05\n"
     ]
    },
    {
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       "PehrDFR+3/m4mx27GtZU/0rBuhebfK5xHwOpb9xmVcPyUMddiAyIBhWKdN5z2BVn5rfAYdRPlq8D\n",
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       "O6ylZP2wzWSsyb4/WVcI2JV3D7B5WP4E1jKSxb1dKH8w9/nGP5PrgCNyy29qIYa8PwIfC+XXYb9h\n",
       "XpP6Gqsfj6y1ZiyWjPXXKiHSVkoIRDonu8Kbi/0HPwfrArgQ66O+A2tiPx87uXt6XxVeG+rvBU4H\n",
       "/pxb931soFo2qDD73C9D/VzsZHss1nXQuG8Klpv9hpuwZvl7sab425vsxxeUPZaYXBli+zn2++8D\n",
       "voydkOeG9w0L9tPoJuon+mXYI7avwH77CmxwIsBXQ7y3hfpsn9cA76c+qPCIsL+5wD3A55r8vqLf\n",
       "eh72/+ldwKXYIMrlTer7Oh4A29D7z1pERET6MAE7yQ9Xh9D3vAzfwJIVkVKphUBEhqPnsTEVu8UO\n",
       "ZJCKWmzAugt2wW7RFBERERERERERERERERERERERERERERERERERERGJ4f8DKgGH5K17mu4AAAAA\n",
       "SUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff43219b750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outer loop: 50 iterations\n",
      "\n",
      "Z in [-0.336860924959, 0.360780477524]\n",
      "Sparsity of Z: 18,052,225 non-zero entries out of 19,072,000 entries, i.e. 94.7%.\n"
     ]
    },
    {
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       "vQdjWZ/dnTvk4Zg57fMx1v+JmCmFzVz+D2MUeQi7Oll3deU/DDM33ufdmMHCho2H4yTgL4H/Afxv\n",
       "4HGBdQtRJVLiQpSjr7B/DTMfnYNXYeal79v9/8WJ8w/FBJR9EjOP/Dx2LNiXAT+HsVwfCLyxO34l\n",
       "xl19C/C/gBd3x+8GfgKjLG8E3gl8yiLj9Zjgtg9g5uUvwAwUrgUeBfx973yba39/77P9mKV117Bj\n",
       "Td81OOdXMN6C6zGu+F/ujp/Vvf84xmPxZkt9QgghhCjAhRhXuRCiQ5a4EEIIIYQQQgghhBBCCCGE\n",
       "EEIIIYQQQgghhBBCCCGEEEIIIYQQQrTF/wdOXSIXqRHCPAAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff432433490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D in [-0.33844050765, 0.390165150166]\n",
      "d in [0.999999761581, 1.00000011921]\n",
      "Constraints on D: True\n"
     ]
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff43232a490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff43264e7d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff432168350>"
      ]
     },
     "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: 9708 seconds\n",
      "\n",
      " -------------------- Classification, ls = 0.1 -------------------- \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 0x7ff402556410>"
      ]
     },
     "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: 68.5 %\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: 55.5 %\n",
      "Clips accuracy: 61.5 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (500, 1536), float64\n",
      "Labels: (500,), uint8\n",
      "  69 (+/- 8.3) <- [62 76 72 60 70 82 62 54 76 72]\n",
      "  65 (+/- 4.7) <- [62 68 57 57 64 70 72 68 64 70]\n",
      "  68 (+/- 2.5) <- [64 72 72 70 68 66 66 68 70 68]\n",
      "  70 (+/- 4.0) <- [64 74 66 74 68 66 74 72 76 70]\n",
      "  68 (+/- 5.9) <- [62 68 64 74 68 78 74 57 70 64]\n",
      "  67 (+/- 7.1) <- [62 82 62 57 74 64 66 62 74 64]\n",
      "  69 (+/- 5.8) <- [56 70 64 72 68 74 68 74 78 70]\n",
      "  68 (+/- 4.7) <- [74 60 70 70 74 62 62 66 68 70]\n",
      "  67 (+/- 4.9) <- [76 60 66 68 66 70 62 66 60 72]\n",
      "  66 (+/- 4.7) <- [70 60 70 62 74 62 72 64 62 66]\n",
      "  67 (+/- 6.1) <- [66 62 68 76 78 68 70 60 57 64]\n",
      "  67 (+/- 7.0) <- [68 54 66 80 66 64 64 68 76 60]\n",
      "  69 (+/- 7.4) <- [84 62 76 76 66 68 60 66 60 68]\n",
      "  70 (+/- 6.9) <- [66 72 84 66 72 76 64 68 74 57]\n",
      "  67 (+/- 6.2) <- [70 56 74 57 66 74 62 72 66 72]\n",
      "  68 (+/- 7.1) <- [60 82 68 60 64 68 74 62 78 68]\n",
      "  68 (+/- 3.6) <- [66 70 66 62 66 76 68 70 66 70]\n",
      "  69 (+/- 4.7) <- [68 68 72 64 72 76 72 64 62 76]\n",
      "  68 (+/- 6.6) <- [60 72 68 64 78 62 76 57 74 66]\n",
      "  66 (+/- 5.4) <- [57 60 62 68 76 70 60 68 70 68]\n",
      "Accuracy: 67.8 (+/- 6.01)\n",
      "Mean time (20 cv): 35.68 seconds\n",
      "Overall time: 719.18 seconds\n",
      "\n",
      " -------------------- Features, ls = 1 -------------------- \n",
      "\n",
      "The memory_profiler extension is already loaded. To reload it, use:\n",
      "  %reload_ext memory_profiler\n",
      "The line_profiler extension is already loaded. To reload it, use:\n",
      "  %reload_ext line_profiler\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 = cosine\n",
      "  Csigma = 1\n",
      "  diag = True\n",
      "  laplacian = normalized\n",
      "Datasets:\n",
      "  L_data    : (2396026,), float32\n",
      "  L_indices : (2396026,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2396026,), float32\n",
      "  W_indices : (2396026,), 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: 12253 seconds\n"
     ]
    },
    {
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       "87sY64BXuW0tj2KJ9wrs73kx5T4RtZY/i10MfR5YgF18jcfOyxpx/e9jTSLEdUd654OIiEhhTcc6\n",
       "CXaqkdw+2IddBKqQJG2jqnURGWtLsJLua7wDGaWRtHcfCJyH9VMQERERERERERERERERERERERER\n",
       "ERERERERERER8fL/AS2ecy8EyfHhAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff402545f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inner loop: 6914 iterations\n",
      "g(Z) = ||X-DZ||_2^2 = 8.763252e+04\n",
      "rdiff: 0.0028535187222\n",
      "i(Z) = ||Z||_1 = 1.608560e+05\n",
      "j(Z) = tr(Z^TLZ) = 1.016077e+05\n"
     ]
    },
    {
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       "IiIiIiIiIiIiIiIiIiIS2/8H6MvVtA2PRqEAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff432691590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global objective: 3.500962e+05\n"
     ]
    },
    {
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       "CPC1EO+cUJ6d82rgg1STCj8Xzjcf+CtwVIPvV/Rdz8L+P70TuBhLolzdoLze9QB4K33/rEVERKSO\n",
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       "iIiIiIiIiIiIiIiIiEgM/x8k1XerPvR7HQAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff4004cda50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outer loop: 50 iterations\n",
      "\n",
      "Z in [-0.93200981617, 0.979693233967]\n",
      "Sparsity of Z: 7,669,812 non-zero entries out of 19,072,000 entries, i.e. 40.2%.\n"
     ]
    },
    {
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       "0Wg0Go1Go9FoNBqNRqPRaDQajUaj0Wg0Go1Go9FoNLaF/x8NsRvIBahk4gAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff401f5c8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D in [-0.967727899551, 0.986068367958]\n",
      "d in [0.999999642372, 1.00000035763]\n",
      "Constraints on D: True\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff432168f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff400575b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "0bYcdycAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff4020c2c90>"
      ]
     },
     "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: 12262 seconds\n",
      "\n",
      " -------------------- Classification, ls = 1 -------------------- \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 0x7ff3f1656c50>"
      ]
     },
     "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: 65.0 %\n",
      "Clips accuracy: 77.0 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (500, 1536), float64\n",
      "Labels: (500,), uint8\n",
      "  73 (+/- 5.2) <- [64 74 76 76 68 82 70 66 76 74]\n",
      "  73 (+/- 8.6) <- [68 72 74 50 82 80 74 80 74 76]\n",
      "  73 (+/- 6.4) <- [70 78 82 66 78 64 64 78 78 70]\n",
      "  72 (+/- 6.3) <- [68 78 74 64 76 76 72 72 82 60]\n",
      "  72 (+/- 5.6) <- [66 70 80 70 76 76 74 62 80 70]\n",
      "  73 (+/- 4.6) <- [76 80 74 78 74 76 64 70 68 72]\n",
      "  72 (+/- 6.2) <- [60 80 66 76 82 72 68 74 70 74]\n",
      "  74 (+/- 4.5) <- [72 72 80 76 82 74 74 70 66 70]\n",
      "  73 (+/- 6.8) <- [76 74 68 72 88 64 70 80 70 66]\n",
      "  73 (+/- 5.8) <- [78 62 82 72 70 76 74 72 64 76]\n",
      "  72 (+/- 5.7) <- [72 78 70 80 78 68 78 62 66 72]\n",
      "  72 (+/- 5.0) <- [66 66 74 80 76 70 68 74 80 70]\n",
      "  75 (+/- 5.3) <- [76 74 72 80 80 72 66 80 66 80]\n",
      "  74 (+/- 5.0) <- [70 76 76 76 86 70 70 76 72 68]\n",
      "  73 (+/- 7.5) <- [78 68 82 56 82 72 74 74 66 76]\n",
      "  74 (+/- 5.7) <- [72 82 76 76 74 74 80 72 78 60]\n",
      "  73 (+/- 4.8) <- [70 82 72 72 72 80 74 72 64 76]\n",
      "  73 (+/- 5.1) <- [66 78 82 68 70 74 80 70 70 74]\n",
      "  74 (+/- 4.6) <- [68 74 74 78 80 76 76 68 78 66]\n",
      "  73 (+/- 6.8) <- [78 80 68 82 74 80 62 64 72 68]\n",
      "Accuracy: 73.1 (+/- 5.91)\n",
      "Mean time (20 cv): 34.25 seconds\n",
      "Overall time: 690.40 seconds\n",
      "\n",
      " -------------------- Features, ls = 10 -------------------- \n",
      "\n",
      "The memory_profiler extension is already loaded. To reload it, use:\n",
      "  %reload_ext memory_profiler\n",
      "The line_profiler extension is already loaded. To reload it, use:\n",
      "  %reload_ext line_profiler\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 = cosine\n",
      "  Csigma = 1\n",
      "  diag = True\n",
      "  laplacian = normalized\n",
      "Datasets:\n",
      "  L_data    : (2396026,), float32\n",
      "  L_indices : (2396026,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2396026,), float32\n",
      "  W_indices : (2396026,), 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: 2786 seconds\n"
     ]
    },
    {
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       "NSJ9pRq5iMlrUJditdtLgBOwWw+OYU3XFawJ/DXx9cVa1++A9wBXYiddnVd47ptYM+zfsD7l/H0/\n",
       "B3aO28ywk7juwvqE62t07dTw8tf8Gbtf8pVYs/TfWqwna/A4L/B+hp009l1s/wE+gTW7T8MOUN4H\n",
       "LJ4k35+BZ9G46b1++43yFB+fjn0++ee7BDsBr/69zdyBFbxnYt/nr6idE9Fs+UPYwdAngDuxg68Z\n",
       "2OeyZnz9l7EuEeJrO73yQUREZGDNw04SHFadXD44DTsIVCVJklHTuoj021KspvtC7yBT1El/957A\n",
       "T7HzFERERERERERERERERERERERERERERERERERERES8/D+gFWhCAv6PDgAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff3f127ecd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inner loop: 1487 iterations\n",
      "g(Z) = ||X-DZ||_2^2 = 4.794027e+05\n",
      "rdiff: 0.00355280129654\n",
      "i(Z) = ||Z||_1 = 6.617271e+05\n",
      "j(Z) = tr(Z^TLZ) = 6.198570e+04\n"
     ]
    },
    {
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       "jJWxoj2oFtC8X4CvYBsfIn1Be/Ai0kvPYOck7OQdpEONWlTAmue3xy4pFBERERERERERERERERER\n",
       "EREREREREREREREREZGx+f+Ib0pKYCsNZQAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff401ee18d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global objective: 1.203116e+06\n"
     ]
    },
    {
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       "/6xFRESkQTbHGgXt6lj6vy/GN7HGjUhLUA+EiHSS57AxIft7Bxmiaj1CYJcv9sWmvIqIiIiIiIiI\n",
       "iIiIiIiIiIiIiIiIiIiIiIiIiIiISOf6/6yXqlXwAx19AAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff4005de4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outer loop: 15 iterations\n",
      "\n",
      "Z in [-0.486856818199, 1.19251167774]\n",
      "Sparsity of Z: 1,504,216 non-zero entries out of 19,072,000 entries, i.e. 7.9%.\n"
     ]
    },
    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff3f17bae50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D in [-0.72549778223, 0.919754981995]\n",
      "d in [0.999999642372, 1.00000023842]\n",
      "Constraints on D: True\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff4004a4190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff425d05950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff3f0979f90>"
      ]
     },
     "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: 2796 seconds\n",
      "\n",
      " -------------------- Classification, ls = 10 -------------------- \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 0x7ff3f8d29310>"
      ]
     },
     "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: 74.5 %\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: 61.0 %\n",
      "Clips accuracy: 68.0 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (500, 1536), float64\n",
      "Labels: (500,), uint8\n",
      "  71 (+/- 5.0) <- [78 74 68 66 62 72 74 64 74 74]\n",
      "  71 (+/- 8.6) <- [64 78 80 54 80 80 76 68 62 72]\n",
      "  72 (+/- 4.0) <- [74 74 78 74 68 68 64 76 74 72]\n",
      "  71 (+/- 6.1) <- [57 74 72 72 70 70 80 70 80 66]\n",
      "  73 (+/- 4.4) <- [62 76 68 78 74 74 76 74 72 72]\n",
      "  73 (+/- 6.1) <- [62 82 78 66 76 74 74 70 78 66]\n",
      "  70 (+/- 6.1) <- [68 80 62 72 66 66 64 70 78 78]\n",
      "  71 (+/- 5.1) <- [66 66 66 72 80 70 74 80 68 70]\n",
      "  71 (+/- 5.7) <- [78 64 66 78 72 76 64 68 66 78]\n",
      "  69 (+/- 5.7) <- [82 66 74 70 70 66 62 62 72 70]\n",
      "  67 (+/- 9.4) <- [72 66 64 78 82 76 62 48 62 64]\n",
      "  72 (+/- 6.9) <- [72 64 80 80 72 72 57 78 76 66]\n",
      "  69 (+/- 5.7) <- [68 60 74 78 74 66 62 70 62 72]\n",
      "  71 (+/- 4.3) <- [72 70 78 72 78 68 66 72 64 72]\n",
      "  70 (+/- 7.0) <- [74 57 78 57 78 74 74 70 66 72]\n",
      "  71 (+/- 5.1) <- [70 78 74 78 68 72 76 66 62 68]\n",
      "  69 (+/- 5.5) <- [66 68 68 74 68 78 76 62 60 70]\n",
      "  71 (+/- 5.4) <- [70 68 80 64 74 72 74 62 76 66]\n",
      "  70 (+/- 5.5) <- [66 74 72 76 68 66 74 64 80 62]\n",
      "  69 (+/- 9.0) <- [64 78 62 84 72 78 66 64 72 52]\n",
      "Accuracy: 70.6 (+/- 6.33)\n",
      "Mean time (20 cv): 24.71 seconds\n",
      "Overall time: 498.90 seconds\n",
      "\n",
      " -------------------- Features, ls = 100 -------------------- \n",
      "\n",
      "The memory_profiler extension is already loaded. To reload it, use:\n",
      "  %reload_ext memory_profiler\n",
      "The line_profiler extension is already loaded. To reload it, use:\n",
      "  %reload_ext line_profiler\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 = cosine\n",
      "  Csigma = 1\n",
      "  diag = True\n",
      "  laplacian = normalized\n",
      "Datasets:\n",
      "  L_data    : (2396026,), float32\n",
      "  L_indices : (2396026,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2396026,), float32\n",
      "  W_indices : (2396026,), 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: 784 seconds\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff3f8d35a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inner loop: 404 iterations\n",
      "g(Z) = ||X-DZ||_2^2 = 2.531211e+06\n",
      "rdiff: 0.00806285653041\n",
      "i(Z) = ||Z||_1 = 1.136244e+05\n",
      "j(Z) = tr(Z^TLZ) = 2.192487e+03\n"
     ]
    },
    {
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       "FBEREREREREREREREREREREREREREREREREREZH2/j8pZpmgKdsCNAAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff3f1086bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global objective: 2.647028e+06\n"
     ]
    },
    {
     "data": {
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       "XP8P7N16TVVgtvUAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff3f0aac5d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outer loop: 11 iterations\n",
      "\n",
      "Z in [0.0, 0.657184481621]\n",
      "Sparsity of Z: 18,422 non-zero entries out of 19,072,000 entries, i.e. 0.1%.\n"
     ]
    },
    {
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       "kiQpLf8PH97GlZjUh7QAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff3f018fa50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D in [-0.00530179822817, 0.607872366905]\n",
      "d in [0.999999582767, 1.00000023842]\n",
      "Constraints on D: True\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff3f0ab4850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff3f0998a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff401e90110>"
      ]
     },
     "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: 795 seconds\n",
      "\n",
      " -------------------- Classification, ls = 100 -------------------- \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 0x7ff3e8cd9990>"
      ]
     },
     "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: 25.2 %\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: 24.1 %\n",
      "Clips accuracy: 24.0 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (500, 1536), float64\n",
      "Labels: (500,), uint8\n",
      "  18 (+/- 5.1) <- [22 26 16 10 14 22 16 12 24 20]\n",
      "  18 (+/- 4.4) <- [12 14 24 22 18 14 22 24 16 14]\n",
      "  20 (+/- 3.0) <- [22 18 16 18 18 24 20 18 26 22]\n",
      "  18 (+/- 4.8) <- [18 14 24 18 24 12 16 12 20 26]\n",
      "  18 (+/- 3.9) <- [12 20 20 12 22 18 22 18 24 16]\n",
      "  19 (+/- 4.2) <- [16 18 18 24 20 12 14 26 16 22]\n",
      "  19 (+/- 4.1) <- [14 20 22 16 26 20 18 24 12 18]\n",
      "  20 (+/- 5.0) <- [22 26 20 16 26 20 14 24 18 10]\n",
      "  19 (+/- 2.9) <- [16 14 22 16 20 18 20 20 24 20]\n",
      "  20 (+/- 3.3) <- [22 26 24 22 18 18 16 18 22 16]\n",
      "  18 (+/- 3.8) <- [24 14 24 16 18 14 20 22 14 16]\n",
      "  18 (+/- 3.8) <- [24 18 22 18 14 20 20 14 12 14]\n",
      "  19 (+/- 2.5) <- [22 20 20 14 20 22 18 18 16 16]\n",
      "  19 (+/- 4.4) <- [20 22 14 22 28 18 22 14 14 16]\n",
      "  19 (+/- 4.4) <- [24 22 24 18 22 14 18 12 14 24]\n",
      "  19 (+/- 3.9) <- [18 26 20 12 20 14 18 18 20 24]\n",
      "  18 (+/- 3.9) <- [16 24 18 16 18 24 12 16 14 22]\n",
      "  20 (+/- 3.9) <- [28 24 24 18 20 16 16 16 18 18]\n",
      "  19 (+/- 5.3) <- [14 30 14 14 18 12 22 22 22 22]\n",
      "  18 (+/- 5.4) <- [20 26  6 22 22 20 18 12 18 20]\n",
      "Accuracy: 18.8 (+/- 4.23)\n",
      "Mean time (20 cv): 41.79 seconds\n",
      "Overall time: 843.73 seconds\n",
      "\n",
      " -------------------- Features, ls = 1000.0 -------------------- \n",
      "\n",
      "The memory_profiler extension is already loaded. To reload it, use:\n",
      "  %reload_ext memory_profiler\n",
      "The line_profiler extension is already loaded. To reload it, use:\n",
      "  %reload_ext line_profiler\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:"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/.virtualenvs/dlaudio/lib/python2.7/site-packages/IPython/kernel/__main__.py:86: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "/home/ubuntu/dlaudio/pyunlocbox/solvers.py:451: RuntimeWarning: invalid value encountered in multiply\n",
      "  x = self.z - self.step * self.f2.grad(self.z)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  K = 11\n",
      "  dm = cosine\n",
      "  Csigma = 1\n",
      "  diag = True\n",
      "  laplacian = normalized\n",
      "Datasets:\n",
      "  L_data    : (2396026,), float32\n",
      "  L_indices : (2396026,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2396026,), float32\n",
      "  W_indices : (2396026,), 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: 61361 seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/pymodules/python2.7/matplotlib/scale.py:90: RuntimeWarning: invalid value encountered in less_equal\n",
      "  mask = a <= 0.0\n",
      "/usr/lib/python2.7/dist-packages/numpy/ma/core.py:790: RuntimeWarning: invalid value encountered in less_equal\n",
      "  return umath.less_equal(x, self.critical_value)\n"
     ]
    },
    {
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       "d/jX8/oAHwKe29ju0xbICHE97ad05G4V8nXAE/L0v9He18cAn8/TexKX8YQokEfm6RX5s+1L/E5u\n",
       "Bx7SJUPrPXcm9vOO+XOcRBxodFveytgabnkT7X17Fe2W9zaN93pB/izSSGyoS1CqTWeX7AHE9b1b\n",
       "RWob4B7E/ZNPJ/74txwK/HWe3hX4M3p3Ke8DfIZoxV1LtIwfDPwuv+7qvN7ZRKFaaGz9y/nnmfR3\n",
       "repbgf/K0+cBNxMF8Mcdrz8BuL7xHvvk9fYmeh0gDlh+madvJ+78tpA1wDU98nwzT58BPCZPz9G+\n",
       "g9xPad/3+oD8aB10bEX8Pq4gfhe99vcUsX9ngd/kZZ8mDnLmuiz/GlHIWwcVn6K9z88lfn9fbWSF\n",
       "+L09DmlELOTShv0D7dZuSwJ+3zG/P9GtfDPRAtx8A9udY/0Dh9b9nW9pLLud7v9Xb1lgnT8xf9is\n",
       "meO2xvQ6opC2pru9x1Qj17Gsf/IexGfudW/qbsN4nXmaGW5tTDf30zuBj3RsZ5r5v49uOjN2u9d2\n",
       "8zN3W/4EotgfRPQk3Jf4DCu6vFZaEo6RS/PdSJyc1fJfxO0WWwVmD2DLBV63DdGCvRm4F/PHiW9j\n",
       "4SL5feCZxP/DnYiicDrdi0u/LgX2ytvZle7dzb08huha34LoYv4B0d38tJwV4k5Yuy346vlaQwbD\n",
       "8F/AC4mWOMAujTwbMkfs30fQ7kI/mGiJL7T8u/l1K4Cn5+lnE7+3KeKzzxK389wWWJ3XuQvze2qk\n",
       "JWWLXAqtFtQ5ROv2bOBo4raL00TX9RTRBf7kvH6z1fVt4O+A84mTrk5tPPcRohv2DGJMufW6rwAP\n",
       "z+85R5zEdS0xJtzZouunhdda5wfAJTnLT/P7dtvO3ALTrYL3JeKksU8Snx/gjUS3+wriAOVlwOUb\n",
       "yPcD4EEs3PXe+f4L5WlOn0jsn9b+vZE4Aa/ztd38kii8JxO/z2/QPiei2/LfEwdDbwTWEgdfq4j9\n",
       "sm1e//3EkAh53Y395oMkSWNrNXGS4HK1MV8fXEEcBNpI0sjYtS5pqd1EtHQfWTrIIm3MePeBwHHE\n",
       "eQqSJEmSJEmSJEmSJEmSJEmSJEmSJEml/B8cUr9dW3ZodgAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff3e8c85910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inner loop: 49601 iterations\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Data has no positive values, and therefore can not be log-scaled.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-5-d4babd0f7a1f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mae\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot_objective\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0miterations_inner\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0miterations_outer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mret\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mobjective_g\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobjective_h\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobjective_i\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobjective_j\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mret\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-5-4835eaf15bda>\u001b[0m in \u001b[0;36mplot_objective\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    254\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mld\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    255\u001b[0m             \u001b[0mname\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'g(Z) = ||X-DZ||_2^2'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 256\u001b[1;33m             \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msemilogy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobjective_g\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'.-'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    257\u001b[0m             \u001b[1;32mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m' = {:e}'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobjective_g\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    258\u001b[0m             \u001b[0mrdiff\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobjective_g\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mg_d\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0meval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mD\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/lib/pymodules/python2.7/matplotlib/pyplot.pyc\u001b[0m in \u001b[0;36msemilogy\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m   3121\u001b[0m         \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhold\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhold\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3122\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3123\u001b[1;33m         \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msemilogy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3124\u001b[0m         \u001b[0mdraw_if_interactive\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3125\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/lib/pymodules/python2.7/matplotlib/axes.pyc\u001b[0m in \u001b[0;36msemilogy\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   4375\u001b[0m         \u001b[0mb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_hold\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4376\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_hold\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTrue\u001b[0m  \u001b[1;31m# we've already processed the hold\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4377\u001b[1;33m         \u001b[0ml\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4378\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_hold\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mb\u001b[0m  \u001b[1;31m# restore the hold\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4379\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/lib/pymodules/python2.7/matplotlib/axes.pyc\u001b[0m in \u001b[0;36mplot\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   4139\u001b[0m             \u001b[0mlines\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4140\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4141\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mautoscale_view\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mscalex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mscalex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscaley\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mscaley\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4142\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mlines\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4143\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/lib/pymodules/python2.7/matplotlib/axes.pyc\u001b[0m in \u001b[0;36mautoscale_view\u001b[1;34m(self, tight, scalex, scaley)\u001b[0m\n\u001b[0;32m   1961\u001b[0m                 \u001b[0my1\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mdelta\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1962\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0m_tight\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1963\u001b[1;33m                 \u001b[0my0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mylocator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mview_limits\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1964\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_ybound\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1965\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/lib/pymodules/python2.7/matplotlib/ticker.pyc\u001b[0m in \u001b[0;36mview_limits\u001b[1;34m(self, vmin, vmax)\u001b[0m\n\u001b[0;32m   1483\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mminpos\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mminpos\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1484\u001b[0m             raise ValueError(\n\u001b[1;32m-> 1485\u001b[1;33m                 \u001b[1;34m\"Data has no positive values, and therefore can not be \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1486\u001b[0m                 \"log-scaled.\")\n\u001b[0;32m   1487\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Data has no positive values, and therefore can not be log-scaled."
     ]
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff3f049f5d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " -------------------- Classification, ls = 1000.0 -------------------- \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 0x7ff3d8611ad0>"
      ]
     },
     "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: 25.2 %\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: 24.1 %\n",
      "Clips accuracy: 24.0 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (500, 1536), float64\n",
      "Labels: (500,), uint8\n",
      "  18 (+/- 5.1) <- [22 26 16 10 14 22 16 12 24 20]\n",
      "  18 (+/- 4.4) <- [12 14 24 22 18 14 22 24 16 14]\n",
      "  20 (+/- 3.0) <- [22 18 16 18 18 24 20 18 26 22]\n",
      "  18 (+/- 4.8) <- [18 14 24 18 24 12 16 12 20 26]\n",
      "  18 (+/- 3.9) <- [12 20 20 12 22 18 22 18 24 16]\n",
      "  19 (+/- 4.2) <- [16 18 18 24 20 12 14 26 16 22]\n",
      "  19 (+/- 4.1) <- [14 20 22 16 26 20 18 24 12 18]\n",
      "  20 (+/- 5.0) <- [22 26 20 16 26 20 14 24 18 10]\n",
      "  19 (+/- 2.9) <- [16 14 22 16 20 18 20 20 24 20]\n",
      "  20 (+/- 3.3) <- [22 26 24 22 18 18 16 18 22 16]\n",
      "  18 (+/- 3.8) <- [24 14 24 16 18 14 20 22 14 16]\n",
      "  18 (+/- 3.8) <- [24 18 22 18 14 20 20 14 12 14]\n",
      "  19 (+/- 2.5) <- [22 20 20 14 20 22 18 18 16 16]\n",
      "  19 (+/- 4.4) <- [20 22 14 22 28 18 22 14 14 16]\n",
      "  19 (+/- 4.4) <- [24 22 24 18 22 14 18 12 14 24]\n",
      "  19 (+/- 3.9) <- [18 26 20 12 20 14 18 18 20 24]\n",
      "  18 (+/- 3.9) <- [16 24 18 16 18 24 12 16 14 22]\n",
      "  20 (+/- 3.9) <- [28 24 24 18 20 16 16 16 18 18]\n",
      "  19 (+/- 5.3) <- [14 30 14 14 18 12 22 22 22 22]\n",
      "  18 (+/- 5.4) <- [20 26  6 22 22 20 18 12 18 20]\n",
      "Accuracy: 18.8 (+/- 4.23)\n",
      "Mean time (20 cv): 41.86 seconds\n",
      "Overall time: 845.31 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 0x7ff3d81e29d0>"
      ]
     },
     "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: (500, 1152), float64\n",
      "Labels: (500,), uint8\n",
      "  67 (+/- 8.0) <- [72 74 60 48 62 74 72 66 66 74]\n",
      "  66 (+/- 6.5) <- [70 60 62 52 64 70 72 68 66 76]\n",
      "  68 (+/- 6.7) <- [74 68 84 62 68 70 62 62 70 62]\n",
      "  68 (+/- 5.5) <- [60 72 62 66 74 74 72 64 76 64]\n",
      "  67 (+/- 5.1) <- [57 74 66 70 68 64 72 62 74 64]\n",
      "  68 (+/- 6.1) <- [68 78 74 64 72 70 68 57 57 66]\n",
      "  66 (+/- 3.7) <- [62 70 60 68 68 66 60 68 70 68]\n",
      "  67 (+/- 5.9) <- [68 60 57 68 80 64 64 68 64 72]\n",
      "  65 (+/- 6.0) <- [68 54 68 66 76 62 68 70 62 57]\n",
      "  66 (+/- 6.3) <- [72 62 78 64 74 64 62 57 60 70]\n",
      "  65 (+/- 6.6) <- [72 64 54 66 74 64 72 56 57 66]\n",
      "  67 (+/- 6.1) <- [66 56 60 76 66 70 66 70 76 62]\n",
      "  68 (+/- 7.3) <- [78 66 57 76 70 68 57 76 57 70]\n",
      "  68 (+/- 5.1) <- [68 64 74 66 74 72 66 70 56 68]\n",
      "  68 (+/- 5.1) <- [76 57 76 68 66 68 64 66 66 70]\n",
      "  68 (+/- 7.4) <- [66 82 72 76 60 56 66 62 72 70]\n",
      "  68 (+/- 7.5) <- [70 62 60 64 60 82 80 68 64 72]\n",
      "  67 (+/- 4.6) <- [74 66 76 66 66 62 70 64 62 64]\n",
      "  68 (+/- 7.2) <- [56 70 76 70 72 60 72 70 76 56]\n",
      "  67 (+/- 5.7) <- [70 78 62 74 64 70 68 57 68 62]\n",
      "Accuracy: 67.1 (+/- 6.30)\n",
      "Mean time (20 cv): 15.11 seconds\n",
      "Overall time: 306.66 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 n_exp, p[Pname] in enumerate(Pvalues):\n",
    "\n",
    "    if regen_graph:\n",
    "        separator('Graph', True)\n",
    "        %run audio_graph.ipynb\n",
    "    if regen_features:\n",
    "        separator('Features', True)\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 not regen_graph and not regen_features:\n",
    "    # Classifier parameters are being tested.\n",
    "    for p[Pname] in Pvalues:\n",
    "        separator('Baseline', True)\n",
    "        %run audio_classification.ipynb\n",
    "else:\n",
    "    separator('Baseline')\n",
    "    %run audio_classification.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ls: [0.1, 1, 10, 100, 1000.0]\n",
      "accuracy_std: [6.0086520951041953, 5.9056244377711673, 6.3308767165377695, 4.2293734760600197, 4.2293734760600197]\n",
      "objective_j: [113358.04443359375, 101607.73315429688, 61985.699462890625, 2192.4869537353516, 2192.4869537353516]\n",
      "objective_i: [35003.662499999999, 160855.953125, 661727.109375, 113624.37744140625, 113624.37744140625]\n",
      "objective_h: [0, 0, 0, 0, 0]\n",
      "objective_g: [47058.1787109375, 87632.51953125, 479402.734375, 2531210.7421875, 2531210.7421875]\n",
      "time_features: [9700.575558900833, 12253.139140844345, 2786.0791459083557, 783.5279347896576, 61360.89183306694]\n",
      "sparsity: [94.65302537751678, 40.215037751677855, 7.887038590604027, 0.09659186241610739, 0.09659186241610739]\n",
      "iterations_inner: [5341, 6914, 1487, 404, 404]\n",
      "iterations_outer: [50, 50, 15, 11, 11]\n",
      "accuracy: [67.810000000000045, 73.060000000000016, 70.599999999999994, 18.819999999999979, 18.819999999999979]\n"
     ]
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff3e810d0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff3e810d990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff3e80b5590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff3d85e19d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff413280bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff3f05ed950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dictionary atoms for ls = 0.1\n",
      "Dictionary atoms for ls = 1\n",
      "Dictionary atoms for ls = 10\n",
      "Dictionary atoms for ls = 100\n",
      "Dictionary atoms for ls = 1000.0\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"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'function' object has no attribute 'show'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-6-a013a76f3200>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     31\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfig\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mres\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'atoms'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     32\u001b[0m         \u001b[1;32mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Dictionary atoms for {} = {}'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mPname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mPvalues\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 33\u001b[1;33m         \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     34\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     35\u001b[0m \u001b[1;32mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Experiment time: {:.0f} seconds'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mtexperiment\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'function' object has no attribute 'show'"
     ]
    }
   ],
   "source": [
    "print('{}: {}'.format(Pname, Pvalues))\n",
    "for key, value in res.items():\n",
    "    if key is not 'atoms':\n",
    "        print('{}: {}'.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",
    "    for var in args:\n",
    "        pltfunc(x, res[var], '.-', label=var)\n",
    "    plt.xlim(0, len(Pvalues)-1)\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",
    "# Classification results.\n",
    "plot('accuracy')\n",
    "\n",
    "# Features extraction results.\n",
    "if regen_features:\n",
    "    plot('objective_g', 'objective_i', 'objective_j', log=True)\n",
    "    plot('sparsity')\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))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unweighted objectives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x4305890>"
      ]
     },
     "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"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\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 / (len(Pvalues)-1)\n",
    "    params['markersize'] = 10\n",
    "    plt.xlim(-margin, len(Pvalues)-1+margin)\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",
    "Pname = 'ls'\n",
    "Pvalues = np.array([0.1,1,10,100,1e3])\n",
    "res = {}\n",
    "res['objective_j'] = [113358.04443359375, 101607.73315429688, 61985.699462890625, 2192.4869537353516, 2192.4869537353516]\n",
    "res['objective_i'] = [35003.662499999999, 160855.953125, 661727.109375, 113624.37744140625, 113624.37744140625]\n",
    "res['objective_g'] = [47058.1787109375, 87632.51953125, 479402.734375, 2531210.7421875, 2531210.7421875]\n",
    "res['objective_j'] /= np.array(100)  # lg\n",
    "res['objective_i'] /= Pvalues  # ld\n",
    "res['objective_g'] /= np.array(100)  # ls\n",
    "plot('objective_g', 'objective_i', 'objective_j', log=True)\n",
    "print('g(Z) = ||X-DZ||_2^2, h(Z) = ||Z-EX||_2^2, i(Z) = ||Z||_1, j(Z) = tr(Z^TLZ)')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}