dict_learning.ipynb

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   "source": [
    "import sklearn.decomposition\n",
    "\n",
    "import load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
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   "source": [
    "train_ids, val_ids, test_ids, train_labels, val_labels, test_labels = load.two_balanced_classes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
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   "source": [
    "model = sklearn.decomposition.DictionaryLearning()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "l = 16\n",
    "color = False\n",
    "train_data, val_data, test_data = load.load_all_data(train_ids, val_ids, test_ids, l, color)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "model.fit(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "collapsed": true
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   "outputs": [],
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  }
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