test_distances.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from scipy import vectorize\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def draw(dist_fun):\n",
    "    x_min, x_max = 0, 100\n",
    "    y_min, y_max = 0, 100\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, 1), np.arange(y_min, y_max, 1))\n",
    "    \n",
    "    c0, c1 = np.array([20, 30]), np.array([70, 80])\n",
    "    \n",
    "    predicted = predict(np.c_[xx.ravel(), yy.ravel()], c0, c1, dist_fun).reshape(xx.shape)\n",
    "    plt.pcolormesh(xx, yy, predicted, cmap='autumn')\n",
    "    plt.scatter([c0[0], c1[0]], [c0[1], c1[1]], c=[0, 1], s=100, \n",
    "                cmap='autumn', edgecolors='black', linewidth=1.5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def predict(coords, c0, c1, dist_fun):\n",
    "    res = np.array([-1] * len(coords))\n",
    "    for i in range(len(coords)):\n",
    "        res[i] = int(dist_fun(coords[i, :], c0) > dist_fun(coords[i, :], c1))\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6Duvq3dYBl/anwt6KiEXAR4Fb6+0AVgDr612adCyOBd4PrAXIzP2ZuY+Gnhu1mcCciJgJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x253e96c8dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "euclid = lambda u, v: np.sum(np.abs(u - v) ** 2) ** (1 / 2)\n",
    "draw(euclid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5PHL1KEObso/MBN4LfDszzwd+RUOmV0ZTHytYDSwB3gUcTzVde7Sm7B8d6UWgt3WLgNJF\nxNuowvzOzLyvXv1aRCyo318A7OlXfT10EXBZRLxINf22kqpjn1v/LzY0ax/ZBezKzM318gaqgG/i\nvgFwMfBCZu7NzN8A9wHLae7+0ZFeBHrjbxFQzxGvA7Zl5tdHvPUAsKb+fQ1wf69r67XM/HxmLsrM\nxVT7wsOZeQ3wCHBlPawR2wIgM38GvBwR765XrQKeoYH7Rm0ncGFEHFf/3RzaHo3cPzrVkwuLIuIj\nVF3YoVsE/EPXv3QKiYg/Af4NeJLD88ZfoJpHvwc4nWpHviozf9GXIvsgIlYAf5+Zl0bEmVQd+zxg\nC/DJzHyrn/X1SkT8EdUB4lnADuBTVM1WI/eNiPgy8BdUZ4dtAf6aas68kftHJ7xSVJIK4ZWiklQI\nA12SCmGgS1IhDHRJKoSBLkmFMNAlqRAGuiQVwkCXpEL8HxZreGQ5ZWe6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x253e7633e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "manhattan = lambda u, v: np.sum(np.abs(u - v))\n",
    "draw(manhattan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(80, 80)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "manhattan(np.array([10, 20]), np.array([90, 20])), manhattan(np.array([80, 90]), np.array([90, 20]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6Duvq3dYBl/anwt6KiEXAR4Fb6+0AVgDr612adCyOBd4PrAXIzP2ZuY+Gnhu1mcCciJgJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x253e7529f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_ = lambda u, v: np.max(np.abs(u - v))\n",
    "draw(max_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c0, c1 = np.array([10, 20]), np.array([80, 90])\n",
    "predict(np.array([[99, 99]]), c0, c1, max_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "89"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_(np.array([10, 20]), np.array([99, 99]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6Duvq3dYBl/anwt6KiEXAR4Fb6+0AVgDr612adCyOBd4PrAXIzP2ZuY+Gnhu1mcCciJgJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x253e970def0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "min_ = lambda u, v: np.min(np.abs(u - v))\n",
    "draw(max_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6Duvq3dYBl/anwt6KiEXAR4Fb6+0AVgDr612adCyOBd4PrAXIzP2ZuY+Gnhu1mcCciJgJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x253e74fc0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "euclid3 = lambda u, v: np.sum(np.abs(u - v) ** 3) ** (1 / 3)\n",
    "draw(euclid3)"
   ]
  },
  {
   "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": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}