annotate _code/Hierarchical Clustering.ipynb @ 37:d9a9a6b93026 tip

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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "from scipy.cluster.hierarchy import dendrogram, linkage, cophenet\n",
    "from scipy.spatial.distance import pdist\n",
    "import sklearn \n",
    "import numpy as np\n",
    "import csv\n",
    "\n",
    "dataFolder = '../data/'\n",
    "keyFile = 'AdobeNormalised'\n",
    "datapath = dataFolder + keyFile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = np.genfromtxt(datapath+'.csv', delimiter = ',', skip_header = 1)\n",
    "filenames = np.loadtxt(datapath+'_filenames.csv', dtype = str)\n",
    "labels = np.loadtxt(datapath+'_labels.csv', dtype = str)\n",
    "features = np.loadtxt(datapath+'_features.csv', dtype = str)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "agglo = cluster.FeatureAgglomeration()\n",
    "agglo.fit(X)\n",
    "X_reduced = agglo.transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Z = linkage(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  8.51810000e-01   4.00000000e-06   2.46000000e-04 ...,   2.10260000e-02\n",
      "    1.98220000e-02   1.04000000e-04]\n",
      " [  9.52275000e-01   7.00000000e-06   1.82600000e-03 ...,   1.79490000e-02\n",
      "    1.09020000e-02   7.20000000e-05]\n",
      " [  1.92200000e-03   1.00000000e-06   1.39000000e-04 ...,   2.35900000e-02\n",
      "    6.93800000e-03   2.61000000e-04]\n",
      " ..., \n",
      " [  9.96346000e-01   3.37000000e-04   1.23600000e-03 ...,   5.24103000e-01\n",
      "    3.36967000e-01   5.39000000e-04]\n",
      " [  9.99990000e-01   1.00000000e-06   0.00000000e+00 ...,   0.00000000e+00\n",
      "    0.00000000e+00   0.00000000e+00]\n",
      " [  9.96624000e-01   6.97000000e-04   2.59300000e-03 ...,   5.24615000e-01\n",
      "    3.34985000e-01   5.45000000e-04]]\n"
     ]
    }
   ],
   "source": [
    "print X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8977, 1536)\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'nu_0': 0, 'kappa_0': 0, 'lambda_0': 0, 'mu_0': 0}\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pyBHC as bhc\n",
    "from pyBHC import dists\n",
    "\n",
    "mu_init = []\n",
    "sigma_init = []\n",
    "S_init = []\n",
    "cd = dists.NormalFixedCovar(mu_0=mu_init,sigma_0=sigma_init, S=S_init)\n",
    "\n",
    "# temp = cd.log_marginal_likelihood(X)\n",
    "d = bhc.rbhc(X, cd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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