9. Clustering & Classification.ipynb

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   "source": [
    "import matplotlib.pylab as plt\n",
    "%matplotlib inline\n",
    "from matplotlib.pylab import rcParams\n",
    "rcParams['figure.figsize'] = 15, 6\n",
    "from pandas.io.data import DataReader\n",
    "from datetime import datetime\n",
    "from scipy.cluster.hierarchy import dendrogram, linkage\n",
    "from pandas.io.data import DataReader\n",
    "from datetime import datetime\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.metrics.pairwise import pairwise_distances\n",
    "from math import sqrt\n",
    "from scipy.spatial.distance import squareform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "words = pd.read_csv('data/50words_TEST.csv')"
   ]
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   "source": [
    "test = words.ix[:, 1:].as_matrix()"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "a = lambda x, y: x[0] + y[0]\n",
    "p = pairwise_distances(test, metric = DTWDistance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "source": [
    "p.shape\n",
    "Z = linkage(p, 'Ward')"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "Z"
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    "words.head()"
   ]
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   "cell_type": "code",
   "execution_count": null,
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    "words['type']  = words.ix[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "w = words[words['type'] < 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "outputs": [],
   "source": [
    "w.shape"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "w.ix[0, 1:].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "w.ix[2, 1:].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "outputs": [],
   "source": [
    "def DTWDistance(s1, s2):\n",
    "    DTW={}\n",
    "\n",
    "    for i in range(len(s1)):\n",
    "        DTW[(i, -1)] = float('inf')\n",
    "    for i in range(len(s2)):\n",
    "        DTW[(-1, i)] = float('inf')\n",
    "    DTW[(-1, -1)] = 0\n",
    "\n",
    "    for i in range(len(s1)):\n",
    "        for j in range(len(s2)):\n",
    "            dist= (s1[i]-s2[j])**2\n",
    "            DTW[(i, j)] = dist + min(DTW[(i-1, j)],DTW[(i, j-1)], DTW[(i-1, j-1)])\n",
    "\n",
    "    return sqrt(DTW[len(s1)-1, len(s2)-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# compute the distance matrix\n"
   ]
  }
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