point.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy.integrate import quad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def f(x):\n",
    "    return np.exp(-np.power(x, 2) / 2)\n",
    "\n",
    "def prob(f, r):\n",
    "    int1, _ = quad(f, -np.inf, 2*r)\n",
    "    int2, _ = quad(f, -np.inf, 0)\n",
    "    result = int1 - int2\n",
    "    result = 1 / (np.sqrt(2 * np.pi)) * result\n",
    "    \n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.50000000000000011"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob(f, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = np.load(\"embeddings.npy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([   53.,   361.,  1288.,  2666.,  3469.,  3032.,  1720.,   535.,\n",
       "           98.,    11.]),\n",
       " array([-0.29460859, -0.23490237, -0.17519614, -0.11548992, -0.05578369,\n",
       "         0.00392254,  0.06362876,  0.12333499,  0.18304121,  0.24274744,\n",
       "         0.30245367]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5avW6QbNBsgXg7W9/+wKnJ0k6ngUdAVTVwfb8HPA54CLg2SSrANrzc234DLB2YPM1wME5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\nUqcMAEnqlAEgSZ0yACSpUwaAJHXq/wO0HA0kozwPdgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf34af560b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data[:, 127])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Распределение норм каждого вектора**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([   12.,    86.,   460.,  1777.,  3581.,  4006.,  2412.,   757.,\n",
       "          131.,    11.]),\n",
       " array([ 0.99999959,  0.99999967,  0.99999974,  0.99999982,  0.9999999 ,\n",
       "         0.99999997,  1.00000005,  1.00000013,  1.0000002 ,  1.00000028,\n",
       "         1.00000035]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CA2A3cPRKns3ATX31d7ergc4DnmqHiG4DLkxySjv5e2GrSZJG5IT5BiT5PPAW4LQk0/Su\n5rkWuDHJFcD3gcva8FuBS4Ap4MfAewCq6nCSjwL3tHEfqapjTyxLkpbRvAFQVZfPsuiCGcYWsHWW\n99kB7BiqO0nSceM3gSWpowwASeooA0CSOsoAkKSOMgAkqaMMAEnqKANAkjrKAJCkjpr3i2CSdNT4\ntltGst1Hrn3bSLb7YucegCR1lAEgSR1lAEhSRxkAktRRBoAkdZQBIEkdZQBIUkcZAJLUUYsKgCSP\nJPl2kr1JJlvt1CR7kjzcXk9p9ST5ZJKpJPcnedNSfABJ0sIsxR7Ar1fV+qqaaPPbgNurah1we5sH\nuBhY1362ANcvwbYlSQt0PA4BbQR2tumdwKV99c9Wz53AyUnOOA7blyQNYLEBUMBXk9ybZEurvbKq\nHgNor6e3+mrg0b51p1tNkjQCi70Z3Jur6kCS04E9Sb4zx9jMUKvnDeoFyRaAM888c5HtSZJms6g9\ngKo60F4PAl8GzgEeP3pop70ebMOngbV9q68BDszwnturaqKqJsbGxhbTniRpDgveA0jyc8BLqupH\nbfpC4CPAbmAzcG17vamtshu4Msku4FzgqaOHirR0RnW7XkkvPIs5BPRK4MtJjr7PX1TVXye5B7gx\nyRXA94HL2vhbgUuAKeDHwHsWsW1J0iItOACq6rvAG2ao/x/gghnqBWxd6PYkSUvLbwJLUkcZAJLU\nUQaAJHWUASBJHWUASFJHGQCS1FEGgCR1lAEgSR1lAEhSRxkAktRRBoAkdZQBIEkdZQBIUkcZAJLU\nUYt9JKQkHXejfNDRI9e+bWTbPt7cA5CkjnIP4DjwsYySXgiWfQ8gyYYkDyWZSrJtubcvSepZ1gBI\nsgr4FHAxcDZweZKzl7MHSVLPcu8BnANMVdV3q+r/AruAjcvcgySJ5T8HsBp4tG9+Gjj3eG3MY/GS\nFmtUv0eW4+qj5Q6AzFCr5wxItgBb2uzTSR5awu2fBvz9Er7fUlrJvYH9LZb9Lc5K7u+49JaPL2r1\nfzzIoOUOgGlgbd/8GuBA/4Cq2g5sPx4bTzJZVRPH470XayX3Bva3WPa3OCu5v5Xc23yW+xzAPcC6\nJGcleSmwCdi9zD1IkljmPYCqOpLkSuA2YBWwo6r2LWcPkqSeZf8iWFXdCty63NttjsuhpSWyknsD\n+1ss+1ucldzfSu5tTqmq+UdJkl50vBeQJHVUJwMgyQeSVJLTRt1LvyQfTXJ/kr1JvprkVaPuqV+S\nP0zyndbjl5OcPOqe+iW5LMm+JD9NsiKuyljptz5JsiPJwSQPjLqXYyVZm+SOJPvbf9f3jrqnfkle\nluTuJN9q/f3BqHsaVucCIMla4DeA74+6lxn8YVX9WlWtB24G/uOoGzrGHuD1VfVrwP8ArhpxP8d6\nAPgt4BujbgReMLc++QywYdRNzOII8P6qei1wHrB1hf35PQOcX1VvANYDG5KcN+KehtK5AACuA36P\nY76AthJU1Q/7Zn+OFdZjVX21qo602TvpfY9jxaiq/VW1lF8cXKwVf+uTqvoGcHjUfcykqh6rqvva\n9I+A/fTuJrAiVM/TbfbE9rOi/s7Op1MBkOTtwA+q6luj7mU2Sa5J8ijwO6y8PYB+/wb4yqibWOFm\nuvXJivkF9kKSZBx4I3DXaDt5riSrkuwFDgJ7qmpF9TefF93zAJL8DfBLMyz6MPAh4MLl7ei55uqv\nqm6qqg8DH05yFXAlcPVK6q+N+TC93fPPLWdvbdvz9reCzHvrE80vySuALwLvO2YveeSq6llgfTsf\n9uUkr6+qFXc+ZTYvugCoqrfOVE/yq8BZwLeSQO/wxX1Jzqmq/z3q/mbwF8AtLHMAzNdfks3AbwIX\n1AiuIR7iz28lmPfWJ5pbkhPp/fL/XFV9adT9zKaqnkzydXrnU14wAdCZQ0BV9e2qOr2qxqtqnN5f\nzjct5y//+SRZ1zf7duA7o+plJkk2AB8E3l5VPx51Py8A3vpkEdL7l9oNwP6q+sSo+zlWkrGjV8Il\neTnwVlbY39n5dCYAXiCuTfJAkvvpHapaUZe9AX8K/Dywp12q+p9H3VC/JO9IMg38c+CWJLeNsp92\nwvzorU/2AzeutFufJPk88HfArySZTnLFqHvq82bgXcD57f+3vUkuGXVTfc4A7mh/X++hdw7g5hH3\nNBS/CSxJHeUegCR1lAEgSR1lAEhSRxkAktRRBoAkDWEpb6CX5Nf7rnDam+QnSS4dcN3XJPm7JM8k\n+cCCtu9VQJI0uCT/Enga+GxVvX4J3/dUYApYc+z3bJI80r6/1F87nd7D3y8FnqiqPxp2m+4BSNIQ\nZrqBXpJXJ/nrJPcm+W9JXrOAt34n8JVBv2RZVQer6h7g/y1gW4ABIElLYTvw76rqnwIfAD69gPfY\nBHx+Sbuax4vuXkCStJzazer+BfBX7T5jACe1Zb8FfGSG1X5QVRf1vccZwK/S+9b40dqn6H0bGuBV\n7a6jAH9VVdcsRe8GgCQtzkuAJ9uDnJ6j3cBukJvY/Tbw5ar62eGcqtp6dLqdA3je+y+Wh4AkaRHa\nLaq/l+Qy6N3ELskbhnyby1nmwz/gVUCSNJR2A723AKcBj9O7ZfvXgOvp3SDuRGBXVc106Gem9xsH\n/juwtqp+OsuYma4C+iVgEvhHwE/pXZl09jDPTDAAJKmjPAQkSR1lAEhSRxkAktRRBoAkdZQBIEkd\nZQBIUkcZAJLUUQaAJHXU/wdUf3YeR4lJWAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf348a7a978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_norm = np.linalg.norm(data, axis=1)\n",
    "\n",
    "plt.hist(y_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9999999845398877, 9.1166199692104144e-15)"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(y_norm), np.var(y_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "r_norm = y_norm / 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.49999999226994385, 2.2791549923026036e-15)"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(r_norm), np.var(r_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([   12.,    86.,   460.,  1777.,  3581.,  4006.,  2412.,   757.,\n",
       "          131.,    11.]),\n",
       " array([ 0.4999998 ,  0.49999983,  0.49999987,  0.49999991,  0.49999995,\n",
       "         0.49999999,  0.50000002,  0.50000006,  0.5000001 ,  0.50000014,\n",
       "         0.50000018]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xsbE5lidJms1cA2ALMHkmz3rgtr72d7ezgc4EnmmHiO4AzktyXPvx97zWJkkakaNm65Dk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf34af783c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(r_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0%\n",
      "10%\n",
      "20%\n",
      "30%\n",
      "40%\n",
      "50%\n",
      "60%\n",
      "70%\n",
      "80%\n",
      "90%\n",
      "100%\n"
     ]
    }
   ],
   "source": [
    "probs = np.ndarray(len(r_norm))\n",
    "\n",
    "percent = len(r_norm) / 100\n",
    "percent = int(np.floor(percent))\n",
    "\n",
    "k = 0\n",
    "cnt = 0\n",
    "\n",
    "for i in r_norm:\n",
    "    if k % (10 * percent) == 0:\n",
    "        print(\"{}%\".format(int(k / percent)))\n",
    "    k += 1\n",
    "    \n",
    "    np.append(probs, prob(f, i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "probs = np.around(probs, decimals=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean 0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xf348525cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(probs)\n",
    "\n",
    "print(\"Mean\", np.mean(probs))"
   ]
  }
 ],
 "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}