distvis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from classif import *\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "# Do we still want to normalize the EFDs? (Not Z-score normalize, but rot/size normalize)\n",
    "X, Y = load_mat(\"1mm_seven_genera.csv\")\n",
    "Xn = normalize(X)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "(1071, 309)"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xn.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "def plot_dist_of(mat, index, bins=200):\n",
    "    plt.title(\"Index \" + str(index))\n",
    "    x = []\n",
    "    for freq, bin in zip(*np.histogram(mat[:, index], bins=bins)):\n",
    "        x += ([bin] * freq)\n",
    "    plt.hist(x, bins=bins)\n",
    "    plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_dist_of(X, 10)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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99a9/1aeffqrWrVv71sfFxamoqEh5eXl+Zz9yc3MVFxdX5r7cbrfcbndVygAAAHVQQGc+jDEaN26cFi9erDVr1qhdu3Z+23v27KkGDRpo9erVvnX79u3T4cOHlZiYGJyKAQBAnRbQmY+0tDRlZmZq6dKlatq0qe86Do/Ho4YNG8rj8ejRRx/V+PHjFR0drcjISD322GNKTEzkThcAACApwPAxb948SVK/fv381i9YsECpqamSpFdffVUhISEaNmyYCgsLlZycrLlz5walWAAAUPcFFD6MMT86JiIiQnPmzNGcOXOqXBQAAKi/mNsFAABYRfgAAABWET4AAIBVhA8AAGAV4QMAAFhVpW84BeC8knlaDr00qErPKxGs51+6PtB9VkdV/x4A1F6c+QAAAFYRPgAAgFWEDwAAYBXhAwAAWEX4AAAAVhE+AACAVYQPAABgFeEDAABYRfgAAABWET4AAIBVhA8AAGAVc7sAtczlc6vUl2MBQAnOfAAAAKsIHwAAwCrCBwAAsIrwAQAArCJ8AAAAqwgfAADAKsIHAACwivABAACsInwAAACrCB8AAMAqwgcAALCKuV2AaiqZH+XQS4MCfk6wjm3jWBXtO5DeAYAzHwAAwCrCBwAAsIrwAQAArCJ8AAAAqwgfAADAKsIHAACwivABAACsInwAAACrCB8AAMAqwgcAALCK8AEAAKxibhcgAFWdy6Q6c6A4OTdLVY8f7LlpmBsGuLJw5gMAAFhF+AAAAFYRPgAAgFWEDwAAYBXhAwAAWEX4AAAAVhE+AACAVQGHj08//VT33HOP4uPj5XK5tGTJEr/tqampcrlcfsuAAQOCVS8AAKjjAg4f586dU48ePTRnzpxyxwwYMEAnTpzwLW+//Xa1igQAAPVHwN9wOnDgQA0cOLDCMW63W3FxcVUuCgAA1F+OXPOxbt06xcTEqGPHjho7dqxOnTpV7tjCwkIVFBT4LQAAoP4K+twuAwYM0H333ad27drpwIED+t3vfqeBAwcqKytLoaGhpcZPmzZNU6ZMCXYZQKVVNL9IdeYwqek5WQCgtgp6+HjwwQd9f+7WrZu6d++ua665RuvWrVP//v1LjU9PT9f48eN9jwsKCpSQkBDssgAAQC3h+K227du3V4sWLZSdnV3mdrfbrcjISL8FAADUX46Hj6NHj+rUqVNq1aqV04cCAAB1QMAfu5w9e9bvLMbBgwe1bds2RUdHKzo6WlOmTNGwYcMUFxenAwcO6JlnnlGHDh2UnJwc1MIBAEDdFHD42LJli+644w7f45LrNVJSUjRv3jzt2LFDb775pvLy8hQfH6+77rpL//Vf/yW32x28qgEAQJ0VcPjo16+fjDHlbl+5cmW1CgIAAPUbc7sAAACrCB8AAMAqwgcAALCK8AEAAKwifAAAAKsIH0CQtJ2w/Iqdz+VK7h1A4AgfAADAKsIHAACwivABAACsInwAAACrCB8AAMAqwgcAALCK8AEAAKwifAAAAKsIHwAAwCrCBwAAsIrwAQAArAqr6QKA+qaiOU6quq0+KOnv0EuDargSADWNMx8AAMAqwgcAALCK8AEAAKwifAAAAKsIHwAAwCrCBwAAsIrwAQAArCJ8AAAAqwgfAADAKsIHAACwivABAACsInwAAACrCB8AAMAqwgcAALCK8AEAAKwifAAAAKsIHwAAwCrCBwAAsIrwAQAArCJ8AAAAqwgfAADAKsIHAACwivABAACsInwAAACrwmq6ACBY2k5Y7vvzoZcG1WAlV65LX4MSNfFalNQRrGPz3gKCizMfAADAKsIHAACwivABAACsInwAAACrCB8AAMAqwgcAALCK8AEAAKwKOHx8+umnuueeexQfHy+Xy6UlS5b4bTfGaNKkSWrVqpUaNmyopKQk7d+/P1j1AgCAOi7g8HHu3Dn16NFDc+bMKXP7yy+/rFmzZun111/Xpk2b1LhxYyUnJ+vChQvVLhYAANR9AX/D6cCBAzVw4MAytxljNHPmTD3//PMaPHiwJOmtt95SbGyslixZogcffLB61QIAgDovqNd8HDx4UDk5OUpKSvKt83g86t27t7Kyssp8TmFhoQoKCvwWAABQfwV1bpecnBxJUmxsrN/62NhY37bLTZs2TVOmTAlmGYDjyprDBMFTF/9+mf8FqLwav9slPT1d+fn5vuXIkSM1XRIAAHBQUMNHXFycJCk3N9dvfW5urm/b5dxutyIjI/0WAABQfwU1fLRr105xcXFavXq1b11BQYE2bdqkxMTEYB4KAADUUQFf83H27FllZ2f7Hh88eFDbtm1TdHS02rRpo8cff1wvvPCCrr32WrVr104TJ05UfHy8hgwZEsy6AQBAHRVw+NiyZYvuuOMO3+Px48dLklJSUpSRkaFnnnlG586d0+jRo5WXl6dbbrlFK1asUERERPCqBgAAdVbA4aNfv34yxpS73eVyaerUqZo6dWq1CgMAAPVTjd/tAgAAriyEDwAAYBXhAwAAWEX4AAAAVhE+AACAVUGd2wWobSqaI4T5N+yoLfO0lFcH7wPAPs58AAAAqwgfAADAKsIHAACwivABAACsInwAAACrCB8AAMAqwgcAALCK8AEAAKwifAAAAKsIHwAAwCrCBwAAsIq5XVBnlczVUdW5OWrLnCOovar7HqnuexSorzjzAQAArCJ8AAAAqwgfAADAKsIHAACwivABAACsInwAAACrCB8AAMAqwgcAALCK8AEAAKwifAAAAKsIHwAAwCrmdoEjbM5pUdb8G9WZk4M5X+yrzN/55e+pS59T3vuM1xKonTjzAQAArCJ8AAAAqwgfAADAKsIHAACwivABAACsInwAAACrCB8AAMAqwgcAALCK8AEAAKwifAAAAKsIHwAAwCrmdkGtdPmcHJfO3eHUfB3MA2JHdf+eg/06lbW/iuYkqui9CaByOPMBAACsInwAAACrCB8AAMAqwgcAALCK8AEAAKwifAAAAKsIHwAAwCrCBwAAsCro4eM///M/5XK5/JZOnToF+zAAAKCOcuQbTrt27apVq1b9/wcJ44tUAQDAvziSCsLCwhQXF+fErgEAQB3nyDUf+/fvV3x8vNq3b6+HHnpIhw8fLndsYWGhCgoK/BYAAFB/Bf3MR+/evZWRkaGOHTvqxIkTmjJlim699Vbt2rVLTZs2LTV+2rRpmjJlSrDLgGUlk21dPsnWpZNwlbfNxqRxuDLUlvdPIHWU97PzY9uAuizoZz4GDhyon//85+revbuSk5P10UcfKS8vT++++26Z49PT05Wfn+9bjhw5EuySAABALeL4laBRUVG67rrrlJ2dXeZ2t9stt9vtdBkAAKCWcPx7Ps6ePasDBw6oVatWTh8KAADUAUEPH0899ZTWr1+vQ4cO6YsvvtDQoUMVGhqqESNGBPtQAACgDgr6xy5Hjx7ViBEjdOrUKbVs2VK33HKLNm7cqJYtWwb7UAAAoA4KevhYtGhRsHcJAADqEeZ2AQAAVhE+AACAVYQPAABgFeEDAABYRfgAAABWMdc9alxl5sGoLXN2oHaq7e+P6tZXmTleyjpGeeMrmnMJsIEzHwAAwCrCBwAAsIrwAQAArCJ8AAAAqwgfAADAKsIHAACwivABAACsInwAAACrCB8AAMAqwgcAALCK8AEAAKxibhcEVW2fYwMItmC95yszf0tt3DdQFZz5AAAAVhE+AACAVYQPAABgFeEDAABYRfgAAABWET4AAIBVhA8AAGAV4QMAAFhF+AAAAFYRPgAAgFWEDwAAYBVzu1zhmPMBqF0qO1cMP7uoyzjzAQAArCJ8AAAAqwgfAADAKsIHAACwivABAACsInwAAACrCB8AAMAqwgcAALCK8AEAAKwifAAAAKsIHwAAwKorbm6X6s6HUJnnB2vOhcv3U9acD5cf49IxFW37sWOWpaI6KoO5KAC7AvmZv/zn+9Kf05r42b1SjmlbbemRMx8AAMAqwgcAALCK8AEAAKwifAAAAKsIHwAAwCrCBwAAsIrwAQAArHIsfMyZM0dt27ZVRESEevfurS+//NKpQwEAgDrEkfDxzjvvaPz48Zo8ebK++uor9ejRQ8nJyTp58qQThwMAAHWII+Hj97//vUaNGqVHHnlEXbp00euvv65GjRrpjTfecOJwAACgDgn616sXFRVp69atSk9P960LCQlRUlKSsrKySo0vLCxUYWGh73F+fr4kqaCgINilSZK8hf+s1v4r8/zqHqO8/ZQ8vtTlx7h0TEXbyttPdcdURrD2Aziltr5HL/2Zrszvh/KeX9HvkrJ+fwXrd1ogrpRj2uZkjyX7NMb8+GATZMeOHTOSzBdffOG3/umnnza9evUqNX7y5MlGEgsLCwsLC0s9WI4cOfKjWaHGJ5ZLT0/X+PHjfY+9Xq9Onz6t5s2by+VyOXbcgoICJSQk6MiRI4qMjHTsODWhPvcm1e/+6K3uqs/90VvdZbM/Y4zOnDmj+Pj4Hx0b9PDRokULhYaGKjc31299bm6u4uLiSo13u91yu91+66KiooJdVrkiIyPr5RtOqt+9SfW7P3qru+pzf/RWd9nqz+PxVGpc0C84DQ8PV8+ePbV69WrfOq/Xq9WrVysxMTHYhwMAAHWMIx+7jB8/XikpKbrxxhvVq1cvzZw5U+fOndMjjzzixOEAAEAd4kj4eOCBB/Ttt99q0qRJysnJ0Q033KAVK1YoNjbWicNVidvt1uTJk0t95FMf1OfepPrdH73VXfW5P3qru2prfy5jKnNPDAAAQHAwtwsAALCK8AEAAKwifAAAAKsIHwAAwCrCBwAAsKreho/Tp0/roYceUmRkpKKiovToo4/q7NmzFT7nwoULSktLU/PmzdWkSRMNGzas1De1bt68Wf3791dUVJSaNWum5ORkbd++3clWyuRUf5KUkZGh7t27KyIiQjExMUpLS3OqjTI52ZsknTp1Sq1bt5bL5VJeXp4DHZTPid62b9+uESNGKCEhQQ0bNlTnzp312muvOd2KJGnOnDlq27atIiIi1Lt3b3355ZcVjn/vvffUqVMnRUREqFu3bvroo4/8thtjNGnSJLVq1UoNGzZUUlKS9u/f72QL5Qpmb8XFxXr22WfVrVs3NW7cWPHx8frlL3+p48ePO91GuYL92l1qzJgxcrlcmjlzZpCrrhwnetuzZ4/uvfdeeTweNW7cWDfddJMOHz7sVAvlCnZvZ8+e1bhx49S6dWs1bNjQNxO944Iym1wtNGDAANOjRw+zceNG89lnn5kOHTqYESNGVPicMWPGmISEBLN69WqzZcsW06dPH3PzzTf7tp85c8ZER0eb1NRUs3fvXrNr1y4zbNgwExsba4qKipxuyY8T/RljzIwZM0x8fLxZuHChyc7ONtu3bzdLly51spVSnOqtxODBg83AgQONJPP999870EH5nOht/vz55re//a1Zt26dOXDggPnzn/9sGjZsaGbPnu1oL4sWLTLh4eHmjTfeMH//+9/NqFGjTFRUlMnNzS1z/IYNG0xoaKh5+eWXze7du83zzz9vGjRoYHbu3Okb89JLLxmPx2OWLFlitm/fbu69917Trl07c/78eUd7uVywe8vLyzNJSUnmnXfeMXv37jVZWVmmV69epmfPnjbb8nHitSvxwQcfmB49epj4+Hjz6quvOtxJaU70lp2dbaKjo83TTz9tvvrqK5OdnW2WLl1a7j6d4kRvo0aNMtdcc41Zu3atOXjwoPnDH/5gQkNDHf+9Xy/Dx+7du40ks3nzZt+6jz/+2LhcLnPs2LEyn5OXl2caNGhg3nvvPd+6PXv2GEkmKyvLGGPM5s2bjSRz+PBh35gdO3YYSWb//v0OdVOaU/2dPn3aNGzY0KxatcrZBirgVG8l5s6da26//XazevVq6+HD6d4u9Zvf/MbccccdwSu+DL169TJpaWm+xxcvXjTx8fFm2rRpZY4fPny4GTRokN+63r17m1//+tfGGGO8Xq+Ji4sz06dP923Py8szbrfbvP322w50UL5g91aWL7/80kgy33zzTXCKDoBT/R09etRcddVVZteuXebqq6+ukfDhRG8PPPCAGTlypDMFB8CJ3rp27WqmTp3qN+bf/u3fzHPPPRfEykurlx+7ZGVlKSoqSjfeeKNvXVJSkkJCQrRp06Yyn7N161YVFxcrKSnJt65Tp05q06aNsrKyJEkdO3ZU8+bNNX/+fBUVFen8+fOaP3++OnfurLZt2zra06Wc6u+TTz6R1+vVsWPH1LlzZ7Vu3VrDhw/XkSNHnG3oEk71Jkm7d+/W1KlT9dZbbykkxP5b38neLpefn6/o6OjgFX+ZoqIibd261a+ukJAQJSUllVtXVlaW33hJSk5O9o0/ePCgcnJy/MZ4PB717t27wl6DzYneypKfny+Xy2V1Ik3Juf68Xq8efvhhPf300+rataszxf8IJ3rzer1avny5rrvuOiUnJysmJka9e/fWkiVLHOujLE69bjfffLOWLVumY8eOyRijtWvX6h//+IfuuusuZxopqd3RvdeQnJwcxcTE+K0LCwtTdHS0cnJyyn1OeHh4qV8EsbGxvuc0bdpU69at01/+8hc1bNhQTZo00YoVK/Txxx8rLMyRb6ovt1Yn+vv666/l9Xr14osvaubMmXr//fd1+vRp3XnnnSoqKnKkl7LqdKK3wsJCjRgxQtOnT1ebNm0cqf3HONXb5b744gu98847Gj16dFDqLst3332nixcvlpoyoaK6cnJyKhxf8t9A9ukEJ3q73IULF/Tss89qxIgR1mdSdaq///mf/1FYWJh++9vfBr/oSnKit5MnT+rs2bN66aWXNGDAAP3tb3/T0KFDdd9992n9+vXONFIGp1632bNnq0uXLmrdurXCw8M1YMAAzZkzR7fddlvwm7hEnQofEyZMkMvlqnDZu3evY8c/f/68Hn30UfXt21cbN27Uhg0bdP3112vQoEE6f/58tfdf0/15vV4VFxdr1qxZSk5OVp8+ffT2229r//79Wrt2bbX2XdO9paenq3Pnzho5cmTQ913TvV1q165dGjx4sCZPnuz4/7mgaoqLizV8+HAZYzRv3ryaLicotm7dqtdee00ZGRlyuVw1XU5Qeb1eSdLgwYP1xBNP6IYbbtCECRN0991327kw02GzZ8/Wxo0btWzZMm3dulUzZsxQWlqaVq1a5ehx7f3vehA8+eSTSk1NrXBM+/btFRcXp5MnT/qt/+GHH3T69GnFxcWV+by4uDgVFRUpLy/P7/8yc3Nzfc/JzMzUoUOHlJWV5Tttn5mZqWbNmmnp0qV68MEHq96car6/Vq1aSZK6dOni296yZUu1aNGi2ld113Rva9as0c6dO/X+++9L+tddFZLUokULPffcc5oyZUoVO6v53krs3r1b/fv31+jRo/X8889XqZfKatGihUJDQ0vdUVRWXSXi4uIqHF/y39zcXN97seTxDTfcEMTqK+ZEbyVKgsc333yjNWvWWD/rITnT32effaaTJ0/6nVW8ePGinnzySc2cOVOHDh0KbhPlcKK3Fi1aKCwszO/3oiR17txZn3/+eRCrr5gTvZ0/f16/+93vtHjxYg0aNEiS1L17d23btk2vvPJKqY9sgsrRK0pqSMmFfVu2bPGtW7lyZaUu7Hv//fd96/bu3et3Yd+sWbNMXFyc8Xq9vjHFxcWmcePGZuHChQ51U5pT/e3bt89I8rvg9NSpUyYkJMSsXLnSoW78OdVbdna22blzp2954403jCTzxRdfWLti3anejDFm165dJiYmxjz99NPONXCZXr16mXHjxvkeX7x40Vx11VUVXvx29913+61LTEwsdcHpK6+84tuen59fYxecBrM3Y4wpKioyQ4YMMV27djUnT550pvBKCnZ/3333nd/P186dO018fLx59tlnzd69e51rpAxOvHaJiYmlLjgdMmTIj96pFmzB7i0/P99IMh999JHfmNGjR5s777wzyNX7q5fhw5h/3dL4k5/8xGzatMl8/vnn5tprr/V7oxw9etR07NjRbNq0ybduzJgxpk2bNmbNmjVmy5YtJjEx0SQmJvq279mzx7jdbjN27Fize/dus2vXLjNy5Ejj8XjM8ePH63x/xvzrNtSuXbuaDRs2mJ07d5q7777bdOnSxeqtxE71dqm1a9fW2K22we5t586dpmXLlmbkyJHmxIkTvsXpf+AWLVpk3G63ycjIMLt37zajR482UVFRJicnxxhjzMMPP2wmTJjgG79hwwYTFhZmXnnlFbNnzx4zefLkMm+1jYqKMkuXLjU7duwwgwcPrrFbbYPZW1FRkbn33ntN69atzbZt2/xep8LCQqu9OdFfWWrqbhcnevvggw9MgwYNzB//+Eezf/9+M3v2bBMaGmo+++yzOt/b7bffbrp27WrWrl1rvv76a7NgwQITERFh5s6d62gv9TZ8nDp1yowYMcI0adLEREZGmkceecScOXPGt/3gwYNGklm7dq1v3fnz581vfvMb06xZM9OoUSMzdOhQc+LECb/9/u1vfzN9+/Y1Ho/HNGvWzPz0pz+t8JZHpzjVX35+vvn3f/93ExUVZaKjo83QoUP9bi22waneLlVT4cOJ3iZPnmwklVquvvpqx/uZPXu2adOmjQkPDze9evUyGzdu9G27/fbbTUpKit/4d99911x33XUmPDzcdO3a1Sxfvtxvu9frNRMnTjSxsbHG7Xab/v37m3379jneR1mC2VvJ61rWculrbVOwX7vL1VT4MMaZ3ubPn286dOhgIiIiTI8ePcySJUucbqNMwe7txIkTJjU11cTHx5uIiAjTsWNHM2PGDL8z/E5wGfP/PvwGAACwoE7d7QIAAOo+wgcAALCK8AEAAKwifAAAAKsIHwAAwCrCBwAAsIrwAQAArCJ8AAAAqwgfAADAKsIHAACwivABAACs+v8AyurtPR/28LgAAAAASUVORK5CYII=\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_dist_of(Xn, 7)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}