ML-classifiers.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3D MNIST"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://medium.com/shashwats-blog/3d-mnist-b922a3d07334"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style\n",
    "from matplotlib import animation\n",
    "# import seaborn as sns\n",
    "\n",
    "import h5py\n",
    "import os, sys\n",
    "sys.path.append('data/')\n",
    "\n",
    "from voxelgrid import VoxelGrid\n",
    "from plot3D import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "# plt.rcParams['image.interpolation'] = None\n",
    "plt.rcParams['image.cmap'] = 'gray'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "with h5py.File('./3d-mnist-kaggle/train_point_clouds.h5', 'r') as f:\n",
    "  # Reading digit at zeroth index    \n",
    "  a = f[\"0\"]   \n",
    "  # Storing group contents of digit a\n",
    "  digit = (a[\"img\"][:], a[\"points\"][:], a.attrs[\"label\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "digits = []\n",
    "\n",
    "with h5py.File(\"./3d-mnist-kaggle/train_point_clouds.h5\", 'r') as h5:\n",
    "    for i in range(15):\n",
    "        d = h5[str(i)]\n",
    "        digits.append((d[\"img\"][:],d[\"points\"][:],d.attrs[\"label\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(digits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x199348a7cf8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(digit[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot some examples from original 2D-MNIST\n",
    "fig, axs = plt.subplots(3,5, figsize=(12, 12), facecolor='w', edgecolor='k')\n",
    "fig.subplots_adjust(hspace = .5, wspace=.2)\n",
    "\n",
    "for ax, d in zip(axs.ravel(), digits):\n",
    "    ax.imshow(d[0][:])\n",
    "    ax.set_title(\"Digit: \" + str(d[2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((30, 30), (25700, 3))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digit[0].shape, digit[1].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "voxel_grid = VoxelGrid(digit[1], x_y_z = [16, 16, 16])\n",
    "\n",
    "def count_plot(array):\n",
    "    cm = plt.cm.get_cmap('gist_rainbow')\n",
    "    n, bins, patches = plt.hist(array, bins=64)\n",
    "\n",
    "    bin_centers = 0.5 * (bins[:-1] + bins[1:])\n",
    "\n",
    "    # scale values to interval [0,1]\n",
    "    col = bin_centers - min(bin_centers)\n",
    "    col /= max(col)\n",
    "\n",
    "    for c, p in zip(col, patches):\n",
    "        plt.setp(p, 'facecolor', cm(c))\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3946, 3946, 3946, ...,  106,  106,  106])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voxel_grid.structure[:, -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the count of points within each voxel.\n",
    "plt.title(\"DIGIT: \" + str(digits[0][-1]))\n",
    "plt.xlabel(\"VOXEL\")\n",
    "plt.ylabel(\"POINTS INSIDE THE VOXEL\")\n",
    "count_plot(voxel_grid.structure[:,-1])    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 17 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "voxels = []\n",
    "for d in digits:\n",
    "    voxels.append(VoxelGrid(d[1], x_y_z=[16,16,16]))\n",
    "    \n",
    "# Visualizing the Voxel Grid sliced around the z-axis.\n",
    "voxels[0].plot()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Save Voxel Grid Structure as the scalar field of Point Cloud.\n",
    "cloud_vis = np.concatenate((digit[1], voxel_grid.structure), axis=1)\n",
    "np.savetxt('Cloud Visualization - ' + str(digit[2]) + '.txt', cloud_vis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hgqTnzTCUFHVsXMccdiMpDiW5H0r6/s7q63kzusb9Yp66qmOoq5rHcdvqM+s6B+qq4ZRBV82rDTZQVy2mDVnJW1d1MpQkImtE5BUR2Ski60PenyQiX/Pe3ywii73ti0XkuIi86P3d56I9cZRRlUwii6767W0HUuUzIYuuWrbjkKeuahprozuFNG2grtoYSq+rikgbgHsBXAVgKYDfFJGlgWS3ADiiqmcD+EsAn/O995qqnu/93Za1PUmUTZM0IYuuuubceanymUBdNRmbWPNqgw3UVYtpQ1ZKr6uKyPsBbFDVK73XnwAAVf0zX5rHvTQ/FJF2AAcA9AA4HcA/q+oymzoboatyjoFzDJxj4BxDs88xFKarishHAKxR1Vu91x8FsEpVb/el2eql6fdevwZgFYBpALYBeBXA2wDuVNXvJtVJXZUQQuwpckkMCdkW7G2i0uwHsEhV3xSR5QD+UUTOVdW3x1Uisg7AOgBYtGhRpga7vmI4beZkDJ4YxfETY+N+vFb0FUPc/RKS4k/733Cw3LyuGGo/MnzlwDGgCa8YTO6dEWxDXjHHnWsurxjCYs77iiHqh5F5XjFEHaekH6DGfU5MPkN50dChJA1ULiJPA7hDVWMvB2glmcWRxWaybRetpHBoJZUjn8syWsFKcnHF8DyAPhE5A8DrANYCuDGQ5lEANwP4IYCPAPh/qqoi0gNgQFVHReRMAH0AdjloUyRls2FMSGslZbGZTMhiJZWNMiyi1+hOIU0baCU1htJbSao6AuB2AI8D2A7gYVXdJiJ3i8ive8nuB9AtIjsB/BGAmtK6GsBLIvITAN8AcJuq5hpx2WwYE9JaSVlsJhNoJSVDK6kc+VyWQSuppJTNSuIcQ325nGPgHAPnGMo5x8BF9AghhNTBRfQIIYSkovGDmg3gpvs3x94fIcslXvC9qKGkpGGFWjlRl40mP4SaaENJpkM8ZR1KMrl3hs0wp/+5/34hVyyZi4F3hwsdStr4re14eMs+43tnBIdj4j4PtkNJpkOzX9m8F/c+tSN0CDjrcJbJEHLYjwwnjK7aCFpdV017v2ebvLbtoq4aTivoqkt7Z+C+Z+xkwkbrqmuW9eKTj7ycOj911QlGGVXJJIJtNo2Buqo51FXTteG53QPYOzBoXU+jddUs/w5TV52AlE2TNCHYZtMYqKuaQ101XRtWLp49bqFG03z+x7zzBcu4allvpvxZ25AV6qohuNBVOcfAOQbOMXCOodXmGKirEkIIqYO6KiGEkFQ0flCzAZhc1ge3pX0v6leYpvU341BSLeYpnW2ha9DnPZSUZnhmae8MPLxlX2Sb4+pJO5T07K430V4RVCqCK5bMxT1rL0g9lGQbs+uhJNv7SBR5H4fgMNs9ay+oy5/lvM8ylGQyBE1d1QLqqtRVTWm0AmoTa8+0Thx6ZzhznVliTqOrAvbHspG66rXnz8fAu8MNW53VBdRVHVNGVTIJ6qrpabQCahOri04ByBZz2tVVbWmkrvr0q4cwdGI0dX7qqhOQsmmSJlBXTU+jFVCbWHumdTqpM0vMaXTVNMeykbrq5ef0ZNZds7YhK9RVQ3Chq3KOgXMMnGPgHEOrzTFQVyWEEFIHdVVCCCGpaLnJZyD50jVpKCSY3mYoxvYS03Q4KGzIqsihpLDL8rihjrh2mD76fzn6p9eehy88+arTXxOHvR93UybboaSwYb+ktrqKuexDSf5htskdbTj23kjqoSSbc9bVUFLtOI2NKUbGFBed2R1Zh+13CnXVCFpJV82inBalq6ZRDgHqqmnSmUBdNb/8rsrICnVVxzSbrppFOS1KV02rHBZBs+iqLvcJddX88rsqIyvUVR3TbLpqFuW0KF01jXJIXTVdOhOoq+aX31UZWcm77rYNGzbkWkEebNq0acO6detS5b3uwoX40Z4j2DswiIoAl/XNwendU3Dw2BCmTWoDBJg3YxLaK4LuqZ2YO30SBk+Mnnzv4rO6x6U3eS+YJq5+f9qwy8VaDGFtHh1TiEikrhqW16b9YW3zl+kvI6y8Wr64dpg+zprSgYoAZ586Dfd9dAUOvHV8XF2P/+EHEusxaXNt2xndUzC5sw0dbRV0T+3A8OhY4j6KOuf8r033iauYo2K3zRvV7rjzPOzcCabb/9ZxTGqvYFJHBadM7sCYauQ5Z3q+mJyzacuIOk7tFYEIcMnZ8Z9zm/fi5g+TuOuuu/Zv2LBhU1K6lptjIISQVoW6KiGEkFS03OQzYPdLy+B7ZdVVTfPG3ZwkL1017Be+JjHYaohhymRaXTXY5uCNb0yPdVS7gueArcKbNeYkXdX2xjm2+zhJVzXVXF3qqnn++trlagpNo6uKyBoAXwDQBuBLqrox8P4kAA8CWA7gTQC/oaq7vfc+AeAWAKMA/rOqPp5UH3XVdLrqVzbvTX0D9Ljybfdp1tUtw4jSLButq5rWn0b7zCNm6qrFlJGV0uuqItIG4F4AHwLQD+B5EXlUVX/qS3YLgCOqeraIrAXwOQC/ISJLAawFcC6A+QCeFJFzVDX90ocJtLKu+tjW/U7aFCzfdp9mXd0yjKgvs0brqqb1pzkv84iZumoxZWSlGXTVlQB2quouVR0G8BCAawJprgHwgPf8GwA+KCLibX9IVd9T1X8DsNMrLzdaWVfNcgP0uPJt92nW1S3DiNIsG62rmtafRvvMI2bqqsWUkZW863bRMSwAsM/3ut/bFppGVUcAvAWg2zCvUx68ZRVW982BAKhI9ZJsdd8cdHVUMHNye91j2Hu26aPSmNYfdrlYiyGpnmDeG1ctwmevOw8LZnZh9tROLJzZZd3+sPL97fGXUXve2SaY0lHBtEltuPb8+bhn7QVGMZg+ru6bgxf/25Whxypse9xxi2qzzXlg0q7gOfDgLaus9knWmKNir+WtAGgTYGnvdJy/8JTYc8D0fPTXEzx3gp+HsM9H1Dlnc57EnbO2n0nb45XmeyPqO6j0cwwicj2AK1X1Vu/1RwGsVNX/5EuzzUvT771+DdUrg7sB/FBV/97bfj+Ab6nqN0PqWQdgHQAsWrRo+Z49ezK1mxBCWo0il8ToB3Ca7/VCAG9EpOkXkXYApwAYMMwLAFDVTQA2AdXJ57SN/dhDP8aT2w+mXtzKv4hZ0NawsXpMjQTXVlLcwmF5WUlR94gos5UUbHOUWdTsVpJ/wcjf++U+3LhqUar7I6S1krIuvtcsVlLc4osmVtL3d1bbNm9G18njlCcurhjaAbwK4IMAXgfwPIAbVXWbL83vAzhPVW/zJp//vareICLnAvgKqlcP8wF8B0Bf0uRzWivpYw/9GP/4Ymi/U1pcWkmuLYo0dkZeRkeeVlIRlk9ZrKS+nqnYcehd4/RZrKS0+WglAZ+97rxUnUNhVwyqOiIitwN4HFVd9cuquk1E7gawRVUfBXA/gL8TkZ2oXims9fJuE5GHAfwUwAiA38/TSHr61UN5FZ0bLq0k1yZDGjsjL6MjTyupCMunLFbSa4fNOwUgux2UNl+rW0mPbd2f61WDk18+q+q3VPUcVT1LVT/jbfuvXqcAVR1S1etV9WxVXamqu3x5P+Ple5+qPuaiPVFcfk5PnsXngksrybXJkMbOyMvoyNNKKsLyKYuVdNacqVbps1hJRdpMrvK7KiMrrg3DIC21iN6aZb3Yffhd9B8ZTL24lX8RswUzJ+Po8ROpFqEzXUDLdiG8uLxJC4fltYhecLE40xhs9mHY4m9ZFtHztzmqbJMFE+PypllEz1XMYQtGrr9qCT5//S8mLjKZZkHE4D52tfiey0X0bBe2tGlD3OKLJovo9R8ZRFsFmH9KF9ZftST11QIX0SOEEFIHF9EjhBCSipZbRM9GO7R97xfmTsd7I2PYd2Tw5H1qm0lXjbvXdV666g9eO4yKCKZ1dSTeQ7mMumqc4hhVf9z9uYvWVaPU02BcYcfJRlcN02LjPo9l0FX9imjt8+CiDWHnSdx3QNxnKC9aaiip2RbQA6irmkJd1b5OF21Iq51+9rrz8O2t+6mrZqg/DbzncwjNtoAeQF3VFOqq9nW6aEPa4/jY1v14PkN9rayrFlFvS80xNNsCegB1VVOoq9rX6aINabXTq5b1UlfNWH+etJSuGqfHubgX67L5MzBrSieGToyevE9tM+mqcfe6zktXff1oVR0+ZUpn4j2Uy6irximOUfVH3Z+7EbpqlHoajCvsONnoqkEt9sZVi0qvq/oV0drnwUUbbDTxuM9QGqirEkIIqYO6KiGEkFS01OQzMF4vi1NMXaqsJhpfUBEtWldNs3JoXLkmuqrJipYu1M04LbZoXTXLKqU2MYfplknlvbDnCO585GW8cuAYIOb3YLZtd9R5XFuF9MDbQwAEl5zVHVqfTZ1Jdde+A2xjTtuG4HmS9B0Q9xnKi5YaSqKuSl3VZdku85ZBVz1/4Sl4qf8tjFnkSau4pj0HqKtSV3UOdVXqqi7Ldpm3DLrq1jfetuoUgGyKa9F5XeR3VUYWqKs6hroqdVWXZbvMWwZdddn8GdZfCGkVV/9jUXld5HdVRhaoq0bgUleNU0xdqqwmGl9QES1aV7VdOdSFrmqyoqULdTNOiy1aV82ySqlNzGG6ZVx5X7vtYlza14OX+49i4J1hiC+uOH3Utt1xumdtFdLB4RFURHDZ2fnrqrXvAJuYs7TBRhOnrmoBdVVCCLGHuiohhJBUtNTkMxCtl6Vd9TCrrppUfyN11Wd3vYn2iqBSkVCVN0lXDVMPG6WrJmmlSW021VXDbtreKF3VZt/GlRcWZ166atznwp/3K5v34t6nduD4iTHjVXlN6857hdekz13S/qeuGgF1VeqqQcqqq/b1TMWOQ8n3US6DrpomXyN01TXLevHJR162yueibpdlZIG6qmOoq1JXdVm2Ca8dTu4UgHLoqmnyNUJXzfKvLHVVM1pqjoG6KnVVl2WbcNacqUbpyqCrpsnXCF31qmW91vlc1O2yjCxQV43Apa6addXDrLpqUv2N1FX3v3Uck9ormNRRCVV5k3TVMPWwUbpqklYa12YbXTV40/bPX/+LDdNVbfZtXHlF6qpxn4ta3vMWnoJTp3fhXw+8jY62ivGqvKZ1573Cq833BnVVC6irEkKIPdRVCSGEpCLTAKyIzAbwNQCLAewGcIOqHglJdzOAO72Xn1bVB7ztTwPoBXDce+/DqvqzLG1KwkZXramLYUpcK+iqJiuH2pabpKum0Vdd6qqmZZusyGmqchalq5rox82iq5qcbxNBV50+qR3HT4xibEwxMqa46Mzw4WXXZBpKEpE/BzCgqhtFZD2AWar68UCa2QC2AFgBQAG8AGC5qh7xOoY7VNVqXIi6KnXVIGXUVW1UTuqq+dXnqm6XZWShGXTVawBc7j1/AMDTAD4eSHMlgCdUdcBr2BMA1gD4asa6raGuSl3VZdlJpNkvNrSqrpoF6qpmZJ1jmKuq+wHAezw1JM0CAPt8r/u9bTX+RkReFJE/ERHJ2J5YqKtSV3VZdhI2Kid11fzqc1W3yzKyUES9iUNJIvIkgHkhb30KwAOqOtOX9oiqzgrk/2MAk1T1097rPwEwqKp/ISILVPV1EZkO4JsA/l5VH4xoxzoA6wBg0aJFy/fs2WMcpB/OMXCOISyWpDZzjoFzDBNhjsF0KCnrHMMrAC5X1f0i0gvgaVV9XyDNb3ppftd7/Vdeuq8G0v0WgBWqentSvdRVCSHEnqJ01UcB3Ow9vxnAP4WkeRzAh0VklojMAvBhAI+LSLuIzPEa2wHg1wBszdgeQgghGck6ALsRwMMicguAvQCuBwARWQHgNlW9VVUHRORPATzv5bnb2zYV1Q6iA0AbgCcB/HXG9iRiM5Rksuphsw4l1YZC9h0ZDB1WAPIdStr4re14eMs+vPPeCMa0+gvPpDpdDqsEh3zizou49+P2WdmGkkyOZ7C82o3qg8epqKGkLOdb2rrDhm/CYrZpv6vvjbjvBJe01C+fqauWQ1dd2jsD9z2zy1k7gGzqZlIa6qrRFKmrlkU1pa46waCuWg5dde/AoNN2ANnUzaQ01FWjKVJXLYtqSl11gkFdtRy66ppzwyS3bGRRN5PSUFeNpkhdtSyqaSvoqlxdNeOqh826umrtBuhDJ0ZDV041udF90uqqUW0miiZmAAARI0lEQVS6tK8HQ8Oj2HXoHYyOKUQEl54dfQP2PFYa9b8Oy5tUtsmKnKYrjxa1umrS8Qwrr3aj+uBxKmp11SznW9q6Z03pQEWA9opABLgkJGbb9rv63oj6TjCFq6sSQgipg3MMEbiykmq2xpTONkzuaMO+o8ebykoyMa6CFsv3vNfvmzcdf3rteVh++qxx5ZpaSWnuoRzczyaGztLeGYn2U9mspKwxz57aiSe3Hxxn1aSxkmx/cNYoK8nEZstSd1jMNbMu7Dsg6tHEBqSVlBJaSY23kioCfP22i7H89FnWVhKQfhG0KLIsljfRrKS00EoyLyMPs86mflpJDqGV5C7+Ma0usbD89Fm52zcmZPmCnGhWUlpoJZmXkYdZZ1N/3tBKKjlltZIqgpNDFLZWUh7HIctieRPNSkoLrSTzMvIw62zqzxtaSSltgZqt0T21E3OnT8LgidGmspJs7zN7evcU7PNeL+mdji/+hxUn5xhsraS4dsTdQzm4n00MnauWzUu0n8pmJWWN+YJFM9F/ZHCcVZPGSjK5B3MZrCQTmy1L3cGY/WZd2HdA1KOJDUgrKSW0kgghxB7e85kQQkgqWmryGXCnq6ZdYK8ZdNXafSgOvD0EQHBJhOIZVm5aXdXk/gZx5a5Z1ot7n9pRd+8MUxXXha4at26+ra5aRMxR51pSW/265RVL5uKetRcYxdjMuqr/viw3LF+I9VcvMT7nbT/vSeVQV42BumrjdVV/2WXQVbPQrLqqa9LEee358zHw7nBL6aq3rT4TP93/dsOOFXVVx1BXdRe/v6wy6KpZaFZd1TVp4nz61UMYOjFqlafZddVvbzvgXU03BuqqjqGu6i5+f1ll0FWz0Ky6qmvSxHn5OT0tp6uuOXdeQ49VEXW31FASwDkGzjFwjoFzDHZ1T6Q5hkLu+dwoqKsSQog91FUJIYSkoqUmn4FihpJqwwqLZk9BZ1sF/3rwmNXlqn9YoBFDSVEx5jmUZFKnyTGIeh43ZOViKClrrHFDSc/uehPtFUGlIidX5LSJOWpoMM1QUl5DQq6Hkn7w2mFURDCtqyPy85TlmIa1Iew42QwlhX0HUFe1gLoqdVWXUFetYhtn0dppGXTVRt/v2d+GNFBXDaEMSqAt1FXzh7pqFds4i9ZOy6CrumhDVqirOqbROmAaqKvmD3XVKrZxFq2dlkFXddGGrFBXjaDsuirnGDjHwDkGzjGUcY6BuiohhJA6CtFVRWS2iDwhIju8x1kR6b4tIkdF5J8D288Qkc1e/q+JSGeW9hBCCMlO1snn9QC+o6obRWS99/rjIek+D2AKgN8NbP8cgL9U1YdE5D4AtwD4YsY2xWJzs/M8hplMLldreZKGkkx+yZoUf7MNJYXFbDOsYjtUtHLxbMye2okntx+0Ghp0OZSUR8xR50uWoZ2w4Zu8h5JsfjGf91CSybCni++U0uuqIvIKgMtVdb+I9AJ4WlXfF5H2cgB3qOqvea8FwCEA81R1RETeD2CDql6ZVC91VeqqLkmroyZBXbUc+VyWQV3VjLmquh8AvM7hVIu83QCOqmrtTOwHsCBje2IpgxJoC3XV/MmjUwCab79QVy2mDVkpha4qIk+KyNaQv2sy1i0h2yIvX0RknYhsEZEthw4dSlVho3XANFBXzZ+0OmoS1FXLkc9lGdRVTTI32VASwDkGzjFwjoFzDHZ1T6Q5hkJ0VRH5PIA3fZPPs1X1v0SkvRy+jsHb9nUA3/RNPr+kqv87qV7qqoQQYk9Rq6tuBPAhEdkB4EPea4jIChH5kq8x3wXwdQAfFJF+EaldFXwcwB+JyE5U5xzuz9geQgghGck0uKqqbwL4YMj2LQBu9b2+LCL/LgArs7TBljRDSf6bkwR/0WhySWhyiV3moSSTy1nXQ0lhN74BGjOUZFNelqGkjz30Yzy5/WAhMUedL1mGdvK8WY5pvrhhHQ4lmdNSv3ymrkpdNYwy6Ko90zpx6J1h522wgbpqMW3ISjPoqk1FGZRAW6ir5k8ZdNVGdwoAddWi2pCVUuiqE4lG64BpoK6aP2XQVXumNX41GOqqxbQhK6XXVRtF0boq5xg4x8A5Bs4xTIQ5Bq6uSgghpA7OMUSQ5ooh6T+tpPRp/5OwvWLwX5ncsHwh1l+9JDH+sl4x2P4nmPS8Ga4Yoo6N65jD7hfCKwb3Vwzf816/b970cT+MpJWUA7SSzOK4bfWZdZ0DraRwymAl5dUGG2glFdOGrBRhJbXU5HMZjZgkslhJ3952IFU+E7JYSWU7DnlaSaaxNrpTSNMGWkmNgVaSY8pmw5iQxUpac+68VPlMoJWUjE2sebXBBlpJxbQhK7SSIijaSuIcA+cYOMfAOYaJMMdAK4kQQkgdnGMghBCSisYPbBaM66Gk02ZOxuCJURw/MTbux2t5DSW9sOcI7nzk5dg16OOGoGwvX8swlBS17n6zDCUlDSuEtaF2bh14ewiA4JKzup3EHHeuuRxKcjmcY5pvztRODA6P4p33RjCmwMWBfZbHUFLUcUr6AWrc58TkM5QnLTWUNBF01Rf2HMH1X/wBxizzAdRVo6Cumq4N1FUnrq7aUlcMZdMkTQi2+dldbyZ2CmH5oralJYuuWjbKsIheozuFNG2grtoYqKs6pmyapAnBNl90ZrfRQcuiuZpAXTUZ6qrlyOeyDOqqJaZMuirnGOrL5RwD5xg4x1DeOQbqqoQQQuqgrkoIISQVjR/YLJib7t+M7++sXuLNm9GFyR1t2Hf0uJNLvOB7UUNJppeYUZeNJr+QnWhDSaZDPP57Z1yxZC4G3h0uxVCSyb0zbIY5s8Tseihp47e24+Et+8YN35gOJcUNs9kOJZkOzdbaPKWzbdx3QNbhLJsh5LjvDeqqlrSyrpr2fs82eW3bRV01nFbQVZf2zsB9z+yyqqfRumqaNrtsQ1aoqzqmjKpkEsE2m8ZAXdUc6qrp2vDc7gHsHRi0rqfRumqaNrtsQ1aoqzqmbJqkCcE2m8ZAXdUc6qrp2rBy8exxK/ia5vM/5p0vWEaaNrtsQ1aKqLttw4YNuVfimk2bNm1Yt26ddb7rLlyIH+05gv4jg2irAPNP6cLc6ZMweGIU0ya1AVIdHz29ewoOHhs6uS3te7VtewcGURHgsr45oa/D8hw8NoSLz+oed8lYiyGpnrBLzbC8JrGFpfWX7y83aR89eMuq2HaYPgb3U+35svkzMGtKJ4ZOjOLq83px6vRJsfVcfFY3Hv/DDxjtlzT7qBZr0jGPa4PrmKPKM2lDML5L+3owNDyKXYfeweiYQkRw6dnR57n/3Kntm31euiW907Fg5mQcPX4i9pyzOX/CPk/+NndP7Rz3HWBy3pt8luKOucn3RtxnKC133XXX/g0bNmxKSpdpjkFEZgP4GoDFAHYDuEFVj4Sk+zaAiwB8T1V/zbf9bwF8AMBb3qbfUtUXk+qlrkoIIfYUpauuB/AdVe0D8B3vdRifB/DRiPf+WFXP9/4SOwVCCCH5knVg8xoAl3vPHwDwNICPBxOp6ndE5PLg9qI5+5P/ghHfQkN9PVOx49C7hdTdXhGMjNVfnfVM68Shd4YzldvVUcHQiZ8HJd6fyXpKacoPvk5bjk0debUhqRzTemzK7WwTDI/+/DyYPbUTA++OPwcaFXPWvGFp2iuo+9xFsXBmF/qPDqVqa9r2ui4jmL4iwJgj8bOro1KYrpr1imGuqu4HAO/x1BRlfEZEXhKRvxSRSRnbE0mwUwBQWKcAYFynACBzpwBg3EmrcNcphJWf5sspKV9SHXm1Iakc03psyvV3CgBCOwXbMl21zUXesDQmnQKAuk7BtL649Gn2WdYygulddQq1sp/ZcRg33b/ZXaERJF4xiMiTAMKm8T/loP5PADgAoBPAJlSvNu6OaMc6AOsAYNGiRdYVmZ6chBBSZorQVRM7BlW9Iuo9ETkoIr2qul9EegH8zKby2tUGgPdE5G8A3BGTdhOqnQdWrFhh3Q+bXs4SQkiZKUJXzTqU9CiAm73nNwP4J5vMXmcCEREA1wLYmrE9kez87K+iPRBtX8/UvKobR3tFxm3rmdaZudyujvqgBG5/nBIsP/g6bTk2deTVhqRyTOuxKbezrf48mD01/BxoVMxZ84alCX7uolg4s8u6vrj0afZZ1jKC6UM+9qnp6qhk+tWzDVknnzcCeFhEbgGwF8D1ACAiKwDcpqq3eq+/C+AXAEwTkX4At6jq4wD+QUR6UP0+exHAbRnbE8vOz/5qnsUTQsiEIFPHoKpvAvhgyPYtAG71vb4sIv+vZKmfEEKIe1pqSQxCCCHJsGMghBBSBzsGQgghdbBjIIQQUgc7BkIIIXU05R3cROQQgD0ps88BUJ7bh+ULY524tFK8jNUdp6tqT1KipuwYsiAiW0yWnZ0IMNaJSyvFy1iLh0NJhBBC6mDHQAghpI5W7BgSb2s3gWCsE5dWipexFkzLzTEQQgiJpxWvGAghhMTQUh2DiKwRkVdEZKeIRN2fuvSIyG4ReVlEXhSRLd622SLyhIjs8B5nedtFRP6nF/NLInKhr5ybvfQ7ROTmqPqKRES+LCI/E5Gtvm3OYhOR5d6+2+nldbgwsh0RsW4Qkde9Y/uiiFzte+8TXrtfEZErfdtDz2sROUNENnv74Gsikn2d95SIyGki8pSIbBeRbSLyB972CXdsY2JtnmOrqi3xB6ANwGsAzkT1jnE/AbC00e1KGctuAHMC2/4cwHrv+XoAn/OeXw3gMVSXNr8IwGZv+2wAu7zHWd7zWSWIbTWACwFszSM2AM8BeL+X5zEAV5Us1g0A7ghJu9Q7ZycBOMM7l9vizmsADwNY6z2/D8B/bGCsvQAu9J5PB/CqF9OEO7YxsTbNsW2lK4aVAHaq6i5VHQbwEIBrGtwml1wD4AHv+QOo3viotv1BrfIsgJlSvUHSlQCeUNUBVT0C4AkAa4pudBBVfQZA8N6FTmLz3puhqj/U6ifqQV9ZhRMRaxTXAHhIVd9T1X8DsBPVczr0vPb+W/4VAN/w8vv3W+Go6n5V/ZH3/BiA7QAWYAIe25hYoyjdsW2ljmEBgH2+1/2IP1hlRgH8XxF5Qar3wgaAuerdKtV7PNXbHhV3M+0PV7Et8J4Ht5eN273hky/XhlZgH2s3gKOqOhLY3nBEZDGACwBsxgQ/toFYgSY5tq3UMYSNNzarknWJql4I4CoAvy8iq2PSRsU9EfaHbWzNEPMXAZwF4HwA+wH8hbd9QsQqItMAfBPAx1T17bikIduaKt6QWJvm2LZSx9AP4DTf64UA3mhQWzKhqm94jz8D8Aiql5wH5ef30O4F8DMveVTczbQ/XMXW7z0Pbi8NqnpQVUdVdQzAX6N6bAH7WA+jOvzSHtjeMESkA9Uvyn9Q1f/jbZ6QxzYs1mY6tq3UMTwPoM+bze8EsBbAow1ukzUiMlVEpteeA/gwgK2oxlIzNG4G8E/e80cB3ORZHhcBeMu7ZH8cwIdFZJZ3Sfthb1sZcRKb994xEbnIG6e9yVdWKah9SXpch+qxBaqxrhWRSSJyBoA+VCdbQ89rb5z9KQAf8fL791vhePv7fgDbVfV/+N6acMc2KtamOrZFzNKX5Q9V0+FVVGf6P9Xo9qSM4UxU7YSfANhWiwPVccfvANjhPc72tguAe72YXwawwlfW76A60bUTwG83OjavTV9F9TL7BKr/Md3iMjYAK1D9QL4G4H/B+5FniWL9Oy+Wl1D9wuj1pf+U1+5X4DNuos5r71x5ztsHXwcwqYGxXorqcMdLAF70/q6eiMc2JtamObb85TMhhJA6WmkoiRBCiAHsGAghhNTBjoEQQkgd7BgIIYTUwY6BEEJIHewYCCGE1MGOgRBCSB3sGAghhNTx/wGPUVG2HFTneAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(cloud_vis.shape[1]):\n",
    "    plt.figure()\n",
    "    plt.plot(cloud_vis[:,i], '.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "with h5py.File(\"./3d-mnist-kaggle/full_dataset_vectors.h5\", 'r') as h5:\n",
    "    X_train, y_train = h5[\"X_train\"][:], h5[\"y_train\"][:]\n",
    "    X_test, y_test = h5[\"X_test\"][:], h5[\"y_test\"][:]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((10000, 4096), (10000,), (2000, 4096), (2000,))"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, y_train.shape, X_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(X_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR-Accuracy:  0.582\n",
      "DT-Accuracy:  0.4905\n",
      "SVM-Accuracy:  0.558\n",
      "KNN-Accuracy:  0.5905\n",
      "RF-Accuracy:  0.68\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.neighbors import KNeighborsClassifier as KNN\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "reg = LogisticRegression()\n",
    "reg.fit(X_train,y_train)\n",
    "print(\"LR-Accuracy: \", reg.score(X_test,y_test))\n",
    "\n",
    "dt = DecisionTreeClassifier()\n",
    "dt.fit(X_train,y_train)\n",
    "print(\"DT-Accuracy: \", dt.score(X_test,y_test))\n",
    "\n",
    "svm = LinearSVC()\n",
    "svm.fit(X_train,y_train)\n",
    "print(\"SVM-Accuracy: \", svm.score(X_test,y_test))\n",
    "\n",
    "knn = KNN()\n",
    "knn.fit(X_train,y_train)\n",
    "print(\"KNN-Accuracy: \", knn.score(X_test,y_test))\n",
    "\n",
    "rf = RandomForestClassifier(n_estimators=500)\n",
    "rf.fit(X_train,y_train)\n",
    "print(\"RF-Accuracy: \", rf.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}