AnalysisEric.V1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Load Data\n",
    "\n",
    "After this executes, X and y will have all the extracted data and the labels with the given string labels in longNames and in longNamesYY that are latter used for plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1680\n",
      "84\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pickle\n",
    "\n",
    "X = pickle.load(open('train.p'))\n",
    "y = pickle.load(open('correct.p'))\n",
    "\n",
    "longNames = [\"Flick Left:1\",\n",
    "    \"Flick Right:2\",\n",
    "    \"Flick Up:3\",\n",
    "    \"Flick Down:4\",\n",
    "    \"Pan Left:5\",\n",
    "    \"Pan Right:6\",\n",
    "    \"Pan Up:7\",\n",
    "    \"Pan Down:8\",\n",
    "    \"Push:9\",\n",
    "    \"Pull:10\",\n",
    "    \"Double Tap:11\",\n",
    "    \"Finger Wave Out:12\",\n",
    "    \"Finger Wave In:13\",\n",
    "    \"Whip:14\",\n",
    "    \"Pluck:15\",\n",
    "    \"Put:16\",\n",
    "    \"Click:17\",\n",
    "    \"Double Click:18\",\n",
    "    \"Wobble:19\",\n",
    "    \"Circle:20\",\n",
    "    \"Erase:21\"]\n",
    "\n",
    "longNamesYY = [\"FL:1\",\n",
    "    \"FR:2\",\n",
    "    \"FU:3\",\n",
    "    \"FD:4\",\n",
    "    \"PL:5\",\n",
    "    \"PR:6\",\n",
    "    \"PU:7\",\n",
    "    \"PD:8\",\n",
    "    \"Psh:9\",\n",
    "    \"Pll:10\",\n",
    "    \"DT:11\",\n",
    "    \"FWO:12\",\n",
    "    \"FWI:13\",\n",
    "    \"Whp:14\",\n",
    "    \"PCK:15\",\n",
    "    \"Put:16\",\n",
    "    \"Ck:17\",\n",
    "    \"DCk:18\",\n",
    "    \"Wob:19\",\n",
    "    \"Cir:20\",\n",
    "    \"Era:21\"]\n",
    "\n",
    "print(len(X))\n",
    "print(len(X[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocess\n",
    "After this, the data will be saved in `X_filtered` and has been median filtered. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import scipy.signal as sp\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "def get_maxima(row):\n",
    "    lenfeat = 60\n",
    "    backstart = 20\n",
    "    idx = np.argmax(row[:lenfeat])\n",
    "    strt = max([idx-backstart, 0])\n",
    "    end = strt+lenfeat\n",
    "    \n",
    "    return (strt,end)\n",
    "\n",
    "X_filtered = []\n",
    "# use median filtering \n",
    "for i in range(X.shape[0]):\n",
    "    mfiltsize = 5\n",
    "    #strt,end = get_maxima(X[i,0:99])\n",
    "    strt, end = 0, 99\n",
    "    feat1 = sp.medfilt( X[i,strt:end], mfiltsize)\n",
    "    feat2 = sp.medfilt( X[i,strt+100:end+100], mfiltsize)\n",
    "    feat3 = X[i, 200:]\n",
    "    newrow = np.concatenate((feat1,feat2,feat3))\n",
    "    X_filtered.append(newrow)\n",
    "    \n",
    "    \n",
    "# scl = StandardScaler()\n",
    "# X_filtered = scl.fit_transform(X_filtered)\n",
    "X_filtered = np.array(X_filtered)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.  0.  1.  1.  2.  3.  1.  0.  3.  2.  1.  0.  3.  2.  1.  0.  3.  2.\n",
      "  1.  0.  3.  2.  1.  0.  3.  2.  1.  0.  3.  2.  1.  3.  2.  1.  3.  4.\n",
      "  2.  3.  2.  4.  4.  4.  4.  4.  4.  4.  4.  4.  5.  5.  5.  5.  5.  5.\n",
      "  0.  0.  6.  5.  5.  5.  5.  7.  7.  6.  7.  6.  7.  7.  7.  7.  7.  7.\n",
      "  7.  6.  6.  6.  6.  6.  6.  6.]\n",
      "1680\n"
     ]
    }
   ],
   "source": [
    "#Map between users and their test ids\n",
    "userMap = np.array([\n",
    "    [36, 37, 43, 47, 52, 56, 60, 64, 91, 92],\n",
    "    [38, 39, 42, 46, 51, 55, 59, 63, 67, 70],\n",
    "    [40, 45, 49, 54, 58, 62, 66, 69, 73, 75],\n",
    "    [41, 44, 48, 53, 57, 61, 65, 68, 71, 74],\n",
    "    [72, 76, 77, 78, 79, 80, 81, 82, 83, 84],\n",
    "    [85, 86, 87, 88, 89, 90, 94, 95, 96, 97],\n",
    "    [93, 100, 102, 110, 111, 112, 113, 114, 115, 116],\n",
    "    [98, 99, 101, 103, 104, 105, 106, 107, 108, 109] ])\n",
    "\n",
    "testToUser = np.zeros(80)\n",
    "for i in range(8):\n",
    "    for j in range(10):\n",
    "        if userMap[i][j] > 50:\n",
    "            testToUser[userMap[i][j] - 37] = i\n",
    "        else:\n",
    "            testToUser[userMap[i][j] - 36] = i\n",
    "\n",
    "print testToUser\n",
    "testToUser = np.tile(testToUser, 21)\n",
    "print len(testToUser)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Get reduced gesture set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class 0.0 has num instances: 160\n",
      "Class 1.0 has num instances: 160\n",
      "Class 2.0 has num instances: 160\n",
      "Class 3.0 has num instances: 160\n",
      "Class 4.0 has num instances: 160\n",
      "Class 5.0 has num instances: 160\n",
      "Class 6.0 has num instances: 160\n",
      "Class 8.0 has num instances: 160\n",
      "Class 9.0 has num instances: 80\n"
     ]
    }
   ],
   "source": [
    "# reduced gesture set\n",
    "longNamesReduced = [\"Left:1\",\n",
    "    \"Right:2\",\n",
    "    \"Up:3\",\n",
    "    \"Down:4\",\n",
    "    \"Push:5\",\n",
    "    \"Pull:6\",\n",
    "    \"Double:7\",\n",
    "    \"Single:9\",\n",
    "#    \"Finger:10\",\n",
    "#    \"Double Click:11\",\n",
    "#    \"Wobble:12\",\n",
    "#    \"Circle:13\",\n",
    "    \"Erase:14\"]\n",
    "\n",
    "longNamesYYReduced = [\"LEF:1\",\n",
    "    \"RIT:2\",\n",
    "    \"UP:3\",\n",
    "    \"DWN:4\",\n",
    "    \"PSH:5\",\n",
    "    \"PUL:6\",\n",
    "    \"DTP:7\",\n",
    "    \"WHP:9\",\n",
    "#    \"FIN:10\",\n",
    "#    \"DCL:11\",\n",
    "#    \"WOB:12\",\n",
    "#    \"CRC:13\",\n",
    "    \"ERS:14\"]\n",
    "\n",
    "#remapping\n",
    "#y = pickle.load(open('correct_80.p'))\n",
    "y_reduced = np.copy(y)\n",
    "y_reduced[y == 4] = 0.0 #l/r/u/d combined\n",
    "y_reduced[y == 5] = 1.0\n",
    "y_reduced[y == 6] = 2.0\n",
    "y_reduced[y == 7] = 3.0\n",
    "y_reduced[y == 8] = 4.0 #push\n",
    "y_reduced[y == 9] = 5.0 #pull\n",
    "y_reduced[y == 10] = 6.0 #dtap\n",
    "y_reduced[y == 11] = 7.0 #wave out\n",
    "y_reduced[y == 12] = 7.0 #wave in\n",
    "y_reduced[y == 13] = 8.0 #whip\n",
    "y_reduced[y == 14] = 5.0 #pluck\n",
    "y_reduced[y == 15] = 4.0 #put\n",
    "y_reduced[y == 16] = 8.0 #click\n",
    "y_reduced[y == 17] = 6.0 #dclick\n",
    "y_reduced[y == 18] = 7.0 #wobble\n",
    "y_reduced[y == 19] = 7.0 #circle\n",
    "y_reduced[y == 20] = 9.0 #erase\n",
    "\n",
    "toRemove = y_reduced != 7\n",
    "y_reduced = y_reduced[toRemove]\n",
    "X_filtered_reduced = X_filtered[toRemove]\n",
    "tTUC = testToUser[toRemove]\n",
    "\n",
    "for i in np.unique(y_reduced):\n",
    "    print 'Class', i, 'has num instances:', len(y_reduced[y_reduced == i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define distance function and test speed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics.pairwise import pairwise_distances\n",
    "\n",
    "# manipulated from https://github.com/pierre-rouanet/dtw/blob/master/dtw.py \n",
    "# updated to use a scikit optimized c function for distances calculation\n",
    "def dtw2(x, y, dist_metric='euclidean'):\n",
    "    \"\"\" Computes the DTW of two sequences.\n",
    "    :param array x: N1*M array\n",
    "    :param array y: N2*M array\n",
    "    :param func dist: distance used as cost measure (default L1 norm)\n",
    "    Returns the minimum distance\n",
    "    \"\"\"\n",
    "    x = np.array(x)\n",
    "    if len(x.shape) == 1:\n",
    "        x = x.reshape(-1, 1)\n",
    "    y = np.array(y)\n",
    "    if len(y.shape) == 1:\n",
    "        y = y.reshape(-1, 1)\n",
    "\n",
    "    r, c = len(x), len(y)\n",
    "\n",
    "    D = np.zeros((r + 1, c + 1))\n",
    "    D[0, 1:] = np.inf\n",
    "    D[1:, 0] = np.inf\n",
    "\n",
    "    D[1:,1:] = pairwise_distances(x,y,metric=dist_metric,n_jobs=1)\n",
    "    \n",
    "    for i in range(r):\n",
    "        for j in range(c):\n",
    "            D[i+1, j+1] += min(D[i, j], D[i, j+1], D[i+1, j])\n",
    "\n",
    "    D = D[1:, 1:]\n",
    "\n",
    "    dist = D[-1, -1] / sum(D.shape)\n",
    "\n",
    "    return dist\n",
    "\n",
    "def metric_distance(a, b, alpha=5, distin='euclidean'):\n",
    "    # possible metrics are [‘cityblock’, ‘cosine’, ‘euclidean’, ‘l1’, ‘l2’, ‘manhattan’]\n",
    "    if len(a) != 84:\n",
    "        return np.inf\n",
    "    \n",
    "    low_dist = dtw2(a[0:40], b[0:40],dist_metric=distin)\n",
    "    hi_dist = dtw2(a[40:80], b[40:80],dist_metric=distin)\n",
    "    dir_dist = alpha * (abs(a[80] - b[80]) + abs(a[81] - b[81]) + abs(a[82] - b[82]) + abs(a[83] - b[83]))\n",
    "    \n",
    "    return low_dist + hi_dist + dir_dist\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 30.6 ms, sys: 14.8 ms, total: 45.3 ms\n",
      "Wall time: 6.02 s\n"
     ]
    }
   ],
   "source": [
    "# #sequential method\n",
    "# #now test using pairwise distance\n",
    "# D = np.ones((len(X_filtered),len(X_filtered)))*-1.0\n",
    "# for i in range(0, len(X_filtered),10):\n",
    "#     for j in range(0, len(X_filtered),10):\n",
    "#         if D[i,j] == -1.0:\n",
    "#             d = metric_distance(X_filtered[i], X_filtered[j])\n",
    "#             D[i,j] = d\n",
    "#             D[j,i] = d\n",
    "\n",
    "# parallelized method for running exhustive search through all parameters\n",
    "%time D = pairwise_distances(X_filtered[0:50],metric=metric_distance, n_jobs=-1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "estimated time to run all distances 0.9408 hours\n"
     ]
    }
   ],
   "source": [
    "print \"estimated time to run all distances\", len(X_filtered)**2/2 * 6.0/50.0**2 / 60 / 60, 'hours'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Run cross validation\n",
    "K fold cross validation here is really time intensive, so we will parallelize as best we can. Also, we will use the nearest centroid classifier as a way of reducing the brute force computations of KNN. It is slightly less expressive as KNN but can have a few centroids for each class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 88.6 ms, sys: 39.9 ms, total: 128 ms\n",
      "Wall time: 1min 29s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done   1 out of   3 | elapsed:  1.5min remaining:  3.0min\n",
      "[Parallel(n_jobs=-1)]: Done   3 out of   3 | elapsed:  1.5min finished\n",
      "/Library/Python/2.7/site-packages/sklearn/neighbors/nearest_centroid.py:136: UserWarning: Averaging for metrics other than euclidean and manhattan not supported. The average is set to be the mean.\n",
      "  warnings.warn(\"Averaging for metrics other than \"\n",
      "/Library/Python/2.7/site-packages/sklearn/neighbors/nearest_centroid.py:136: UserWarning: Averaging for metrics other than euclidean and manhattan not supported. The average is set to be the mean.\n",
      "  warnings.warn(\"Averaging for metrics other than \"\n",
      "/Library/Python/2.7/site-packages/sklearn/neighbors/nearest_centroid.py:136: UserWarning: Averaging for metrics other than euclidean and manhattan not supported. The average is set to be the mean.\n",
      "  warnings.warn(\"Averaging for metrics other than \"\n"
     ]
    }
   ],
   "source": [
    "# be sure you have the latest scikit-learn installation (at least 0.16.1)\n",
    "from sklearn.cross_validation import StratifiedKFold\n",
    "from sklearn.cross_validation import StratifiedShuffleSplit\n",
    "from sklearn.cross_validation import cross_val_predict\n",
    "\n",
    "# from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.neighbors import NearestCentroid\n",
    "\n",
    "y_subset = y\n",
    "X_subset = X\n",
    "\n",
    "# clf = KNeighborsClassifier(n_neighbors=3, metric=metric_distance, algorithm='auto')\n",
    "clf = NearestCentroid(metric=metric_distance) # overwriting the KNN\n",
    "\n",
    "cv = StratifiedKFold(y_subset,n_folds=3)\n",
    "\n",
    "# parallelized cross validation to get predictions\n",
    "%time yhat = cross_val_predict(clf,X_subset,y=y_subset,cv=cv,n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.252976190476\n"
     ]
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11457be10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "conf = confusion_matrix(y_subset,yhat)\n",
    "print accuracy_score(y_subset, yhat)\n",
    "\n",
    "for i in range(len(conf)):\n",
    "    s = np.sum(conf[i])\n",
    "    for j in range(len(conf[i])):\n",
    "        conf[i][j] = 100 * conf[i][j] / float(s)\n",
    "\n",
    "fig = plt.figure(figsize=(20,20))\n",
    "ax = fig.add_subplot(111)\n",
    "cax = ax.matshow(conf)\n",
    "ax.set_xticks(range(len(longNamesYY)))\n",
    "ax.set_yticks(range(len(longNames)))\n",
    "ax.set_xticklabels(longNamesYY)\n",
    "ax.set_yticklabels(longNames)\n",
    "fig.colorbar(cax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.252976190476\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Python/2.7/site-packages/IPython/kernel/__main__.py:8: RuntimeWarning: invalid value encountered in divide\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11457bb50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# confusion matrix normalized\n",
    "\n",
    "conf = confusion_matrix(y_subset,yhat)\n",
    "print accuracy_score(y_subset,yhat)\n",
    "\n",
    "conf_normalized = conf.astype(np.float)\n",
    "for i in range(conf.shape[0]):\n",
    "    conf_normalized[:,i] = conf_normalized[:,i] / np.sum(conf[:,i] )\n",
    "\n",
    "fig = plt.figure(figsize=(20,20))\n",
    "ax = fig.add_subplot(111)\n",
    "cax = ax.matshow(conf_normalized)\n",
    "ax.set_xticks(range(len(longNamesYY)))\n",
    "ax.set_yticks(range(len(longNames)))\n",
    "ax.set_xticklabels(longNamesYY)\n",
    "ax.set_yticklabels(longNames)\n",
    "fig.colorbar(cax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#STOPPED EDITING HERE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# across user \n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.cross_validation import StratifiedKFold\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.decomposition import PCA \n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "# pca_obj = PCA(n_components=50)\n",
    "\n",
    "# clf_best = RandomForestClassifier(n_estimators=100, n_jobs = -1)\n",
    "clf_best = KNeighborsClassifier(n_neighbors=7, algorithm='brute', metric='pyfunc', func=metric_distance)\n",
    "# clf_best = SVC(kernel='linear',C=10.0, gamma=0.1, degree=3)\n",
    "# clf_best = grdobj.best_estimator_\n",
    "\n",
    "yhat = np.zeros(ycombined.shape)\n",
    "\n",
    "user = np.arange(8) # 8 users\n",
    "for u in user:\n",
    "    print(\"Testing user %d\" % (u))\n",
    "    test = np.where(tTUC==u)\n",
    "    train = np.where(tTUC!=u)\n",
    "    clf_best.fit(Xcomb[train],ycombined[train])\n",
    "    yhat[test] = clf_best.predict(Xcomb[test])\n",
    "    \n",
    "conf = confusion_matrix(ycombined,yhat)\n",
    "print accuracy_score(ycombined,yhat)\n",
    "\n",
    "for i in range(len(conf)):\n",
    "    s = np.sum(conf[i])\n",
    "    for j in range(len(conf[i])):\n",
    "        conf[i][j] = 100 * conf[i][j] / float(s)\n",
    "\n",
    "fig = plt.figure(figsize=(20,20))\n",
    "ax = fig.add_subplot(111)\n",
    "cax = ax.matshow(conf)\n",
    "ax.set_xticks(range(len(longNamesYYC)))\n",
    "ax.set_yticks(range(len(longNamesC)))\n",
    "ax.set_xticklabels(longNamesYYC)\n",
    "ax.set_yticklabels(longNamesC)\n",
    "fig.colorbar(cax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "from sklearn.cross_validation import StratifiedKFold\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.decomposition import PCA \n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "# per user calibration\n",
    "clf_best = RandomForestClassifier(n_estimators=50, n_jobs = -1)\n",
    "# clf_best = KNeighborsClassifier(n_neighbors=7)\n",
    "# clf_best = SVC(kernel='linear',C=10.0, gamma=0.1, degree=3)\n",
    "# clf_best = grdobj.best_estimator_\n",
    "\n",
    "yhat = np.zeros(y.shape)\n",
    "\n",
    "user = np.arange(8)\n",
    "for u in user:\n",
    "    user_indices = np.where(testToUser==u)\n",
    "    Xuser = X[user_indices]\n",
    "    yuser = y[user_indices]\n",
    "    cv = StratifiedKFold(y[user_indices], n_folds=10)\n",
    "    yhat_user = np.zeros(yuser.shape)\n",
    "    for trainf, test in cv:\n",
    "        clf_best.fit(Xuser[trainf],yuser[trainf])\n",
    "        yhat_user[test] = clf_best.predict(Xuser[test])\n",
    "    yhat[user_indices] = yhat_user\n",
    "    \n",
    "    \n",
    "conf = confusion_matrix(y,yhat)\n",
    "print accuracy_score(y,yhat)\n",
    "\n",
    "for i in range(len(conf)):\n",
    "    s = np.sum(conf[i])\n",
    "    for j in range(len(conf[i])):\n",
    "        conf[i][j] = 100 * conf[i][j] / float(s)\n",
    "\n",
    "fig = plt.figure(figsize=(20,20))\n",
    "ax = fig.add_subplot(111)\n",
    "cax = ax.matshow(conf)\n",
    "ax.set_xticks(range(len(longNamesYY)))\n",
    "ax.set_yticks(range(len(longNames)))\n",
    "ax.set_xticklabels(longNamesYY)\n",
    "ax.set_yticklabels(longNames)\n",
    "fig.colorbar(cax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#Define the DTW Function\n",
    "import sys\n",
    "from dtw import dtw\n",
    "\n",
    "def dtw_distance(ts_a, ts_b, max_warping_window = 10000, d = lambda x,y: abs(x-y)):\n",
    "    \"\"\"Returns the DTW similarity distance between two 2-D\n",
    "    timeseries numpy arrays.\n",
    "\n",
    "    Arguments\n",
    "    ---------\n",
    "    ts_a, ts_b : array of shape [n_samples, n_timepoints]\n",
    "        Two arrays containing n_samples of timeseries data\n",
    "        whose DTW distance between each sample of A and B\n",
    "        will be compared\n",
    "\n",
    "    d : DistanceMetric object (default = abs(x-y))\n",
    "        the distance measure used for A_i - B_j in the\n",
    "        DTW dynamic programming function\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    DTW distance between A and B\n",
    "    \"\"\"\n",
    "\n",
    "    # Create cost matrix via broadcasting with large int\n",
    "    ts_a, ts_b = np.array(ts_a), np.array(ts_b)\n",
    "    M, N = len(ts_a), len(ts_b)\n",
    "    cost = sys.maxint * np.ones((M, N))\n",
    "\n",
    "    # Initialize the first row and column\n",
    "    cost[0, 0] = d(ts_a[0], ts_b[0])\n",
    "    for i in xrange(1, M):\n",
    "        cost[i, 0] = cost[i-1, 0] + d(ts_a[i], ts_b[0])\n",
    "\n",
    "    for j in xrange(1, N):\n",
    "        cost[0, j] = cost[0, j-1] + d(ts_a[0], ts_b[j])\n",
    "\n",
    "    # Populate rest of cost matrix within window\n",
    "    for i in xrange(1, M):\n",
    "        for j in xrange(max(1, i - max_warping_window),\n",
    "                        min(N, i + max_warping_window)):\n",
    "            choices = cost[i - 1, j - 1], cost[i, j-1], cost[i-1, j]\n",
    "            cost[i, j] = min(choices) + d(ts_a[i], ts_b[j])\n",
    "\n",
    "    # Return DTW distance given window \n",
    "    return cost[-1, -1]\n",
    "\n",
    "def metric_distance(a, b):\n",
    "    \n",
    "    if len(a) != 84:\n",
    "        return 0\n",
    "    \n",
    "    low_dist, cost, path = dtw(a[0:40], b[0:40])\n",
    "    hi_dist, cost, path = dtw(a[40:80], b[40:80])\n",
    "    dir_dist = 5 * (abs(a[80] - b[80]) + abs(a[81] - b[81]) + abs(a[82] - b[82]) + abs(a[83] - b[83]))\n",
    "    \n",
    "    return low_dist + hi_dist + dir_dist\n",
    "\n",
    "#testnum = 800\n",
    "#for i in range(0, len(Xcomb), 20):\n",
    "#    print(\"dist(%d (%d), %d (%d)) = %.2f\" % (testnum, ycombined[testnum], i, ycombined[i], metric_distance(Xcomb[testnum], Xcomb[i])))\n",
    "\n",
    "# counts = np.zeros((10, 10))\n",
    "# totals = np.zeros((10, 10))\n",
    "# testnum = 0\n",
    "\n",
    "# for i in range(0, len(Xcomb)):\n",
    "    \n",
    "#     #if i % 30 == 0:\n",
    "#     print(\"%d/%d\" % (i, len(Xcomb)))\n",
    "        \n",
    "#     for j in range(i + 1, len(Xcomb)):\n",
    "#         counts[ycombined[i]][ycombined[j]] += 1\n",
    "#         counts[ycombined[j]][ycombined[i]] += 1\n",
    "#         dist = metric_distance(Xcomb[i], Xcomb[j])\n",
    "#         totals[ycombined[i]][ycombined[j]] += dist\n",
    "#         totals[ycombined[j]][ycombined[i]] += dist\n",
    "\n",
    "# print(\"This is type %d\" % (ycombined[testnum]))\n",
    "# for i in range(0, 10):\n",
    "#     if i == 7:\n",
    "#         continue\n",
    "#     print(\"Type %d = %.2f\" % (i, totals[i] / counts[i]))\n",
    "\n",
    "# print(totals / counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print_mat = totals / counts\n",
    "#for x in range(0,10):\n",
    "#    print_mat[x] = print_mat[x] / np.nanmax(print_mat[x])\n",
    "\n",
    "print(\"\\t0\\t1\\t2\\t3\\t4\\t5\\t6\\t7\\t8\\t9\")\n",
    "for x in range(0,10):\n",
    "    print(\"%d:\\t\" % (x)),\n",
    "    \n",
    "    for y in range(0, 10):\n",
    "        print(\"%.2f\\t\" % (print_mat[x][y])),\n",
    "        \n",
    "    print(\"(min = %.2f)\" % (np.nanmin(print_mat[x])))\n",
    "    \n",
    "fig = plt.figure()\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_title('colorMap')\n",
    "plt.imshow(print_mat, cmap='BrBG')\n",
    "ax.set_aspect('equal')\n",
    "\n",
    "cax = fig.add_axes([0.12, 0.1, 0.78, 0.8])\n",
    "cax.get_xaxis().set_visible(False)\n",
    "cax.get_yaxis().set_visible(False)\n",
    "cax.patch.set_alpha(0)\n",
    "cax.set_frame_on(False)\n",
    "plt.colorbar(orientation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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