4. Statistics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Satistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.53898357, 0.48321424, 0.48657007, 0.35605779, 0.9860772 ,\n",
       "        0.42402917, 0.23469293, 0.77512796, 0.65273786, 0.43154989],\n",
       "       [0.81875766, 0.65695979, 0.76617078, 0.71948083, 0.27799913,\n",
       "        0.09989388, 0.51315167, 0.18091894, 0.51749444, 0.62905224],\n",
       "       [0.1374541 , 0.9622578 , 0.85497674, 0.58092092, 0.61634624,\n",
       "        0.75700743, 0.47009552, 0.06667401, 0.71219479, 0.15592976],\n",
       "       [0.90037695, 0.00539437, 0.87065549, 0.52165907, 0.2324389 ,\n",
       "        0.30774764, 0.59232791, 0.65973898, 0.79159998, 0.04817417],\n",
       "       [0.17901896, 0.9111165 , 0.81534322, 0.89307325, 0.22228122,\n",
       "        0.88212989, 0.03313646, 0.47521496, 0.55507649, 0.61302788],\n",
       "       [0.6292293 , 0.58638646, 0.68864491, 0.30865223, 0.44586232,\n",
       "        0.06050179, 0.77529863, 0.09410935, 0.77237024, 0.09680208],\n",
       "       [0.24322844, 0.83163841, 0.11213468, 0.48985132, 0.49384274,\n",
       "        0.31981422, 0.05734624, 0.64686832, 0.5254565 , 0.46958156],\n",
       "       [0.92671612, 0.88635405, 0.84857968, 0.7790493 , 0.64972684,\n",
       "        0.1649992 , 0.59826828, 0.1488032 , 0.62777762, 0.59437904],\n",
       "       [0.8783295 , 0.26367598, 0.04487751, 0.90325799, 0.67530894,\n",
       "        0.83732408, 0.41818391, 0.81526403, 0.09145391, 0.54674836],\n",
       "       [0.40850781, 0.02962303, 0.41783865, 0.26513387, 0.72081394,\n",
       "        0.41577381, 0.55863028, 0.83720032, 0.23229821, 0.08979069]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = np.random.rand(10,10)\n",
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 10)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9973975789240529"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0035974071135107533"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.53281080467131514"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.53842941044090042"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.30117722286159915"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Mean and std of row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.39053118,  0.38187714,  0.45877901,  0.61380263,  0.64927112,\n",
       "        0.46921661,  0.60222378,  0.50624244,  0.27490776,  0.54313828])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(A,axis =0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.32438441,  0.29543643,  0.35045695,  0.27228791,  0.2178827 ,\n",
       "        0.26696293,  0.32834441,  0.1804856 ,  0.18123375,  0.31624931])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(A,axis =0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Mean and std of col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.24116424,  0.48699354,  0.4219599 ,  0.43972281,  0.47657735,\n",
       "        0.57537948,  0.55289209,  0.66313705,  0.46013377,  0.57202973])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(A,axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.19852809,  0.33144078,  0.28219183,  0.27736838,  0.22851456,\n",
       "        0.33649951,  0.31209182,  0.20212544,  0.27845001,  0.31922731])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(A,axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Standard scalar :  feature scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "A_ss = A - A.min()/A.max()-A.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  4.10527053e-01,   7.54472423e-01,   7.94834247e-01,\n",
       "          7.30222536e-01,   2.05492062e-01,   7.26892063e-01,\n",
       "          7.31928243e-01,   5.75438370e-01,   5.93092716e-01,\n",
       "          3.64078100e-01],\n",
       "       [  6.91227074e-01,   3.26851066e-01,   6.93898234e-01,\n",
       "          5.00701344e-02,   7.09305332e-01,   8.38725771e-02,\n",
       "          7.42069173e-03,   7.35359172e-01,   2.95379821e-01,\n",
       "          6.85238478e-01],\n",
       "       [  7.79833365e-01,   7.41786430e-01,   3.19212366e-01,\n",
       "          6.81133292e-01,   8.84713319e-01,   6.79371426e-01,\n",
       "          5.60580679e-01,   1.38029784e-01,   8.13389121e-02,\n",
       "          8.39869709e-01],\n",
       "       [  4.95468619e-01,   8.24202013e-01,  -5.68858232e-04,\n",
       "          4.31268827e-01,   3.38998416e-01,   9.81193130e-01,\n",
       "          4.35576371e-01,   6.44445825e-02,   4.62408333e-02,\n",
       "          6.89168348e-01],\n",
       "       [  4.83093638e-01,   4.35554299e-01,   8.99866132e-01,\n",
       "          9.27827109e-01,   2.75868637e-01,   2.11364337e-01,\n",
       "          5.98421053e-01,   4.63481490e-01,   5.65166049e-01,\n",
       "          4.00957652e-01],\n",
       "       [  6.48396649e-01,   8.91158478e-01,   2.62023883e-01,\n",
       "          4.97729268e-01,   4.86885273e-01,   1.39092208e-01,\n",
       "          3.41289269e-01,   3.48342574e-01,   4.56935240e-01,\n",
       "          4.67606744e-01],\n",
       "       [  2.80349932e-01,   6.49334474e-01,   6.69037668e-01,\n",
       "          5.85372776e-01,   7.04975765e-01,   8.00871254e-01,\n",
       "          5.35627470e-01,   9.70136155e-01,   1.16631608e-01,\n",
       "          2.79515900e-01],\n",
       "       [  9.08763495e-01,   3.00196542e-02,   5.99466774e-02,\n",
       "          6.81881968e-01,   7.96816096e-01,   2.63795132e-01,\n",
       "          9.82016613e-02,   8.91612556e-01,   4.33903568e-01,\n",
       "          1.27023490e-02],\n",
       "       [  2.62261736e-01,  -4.47661111e-03,   4.01181988e-01,\n",
       "          6.78168605e-01,   9.14777758e-01,   7.66458761e-01,\n",
       "          1.03201876e-01,   9.73263369e-01,   5.80404084e-01,\n",
       "          1.51452341e-01],\n",
       "       [  5.09890394e-01,   6.55040004e-01,   1.14783059e-01,\n",
       "          1.22995637e-02,   2.48635616e-01,   8.57963655e-01,\n",
       "          9.79050239e-01,   7.73205705e-01,   8.14437099e-01,\n",
       "          2.42875670e-01]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A_ss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalization :  sample Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "B = np.random.rand(5,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "Bn = (B-B.mean())/B.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.10700499, -1.58861252, -1.10418153,  1.1424935 ,  0.96929009],\n",
       "       [-0.44764127, -1.08667804,  0.37125034,  1.47084221, -0.46509629],\n",
       "       [-0.94312492,  0.75528498, -0.20116912,  0.15058261,  1.12978521],\n",
       "       [-1.74134568,  1.4336299 , -1.11531202,  0.11896235, -1.06880784],\n",
       "       [ 0.69507059,  1.17572665, -1.19415594,  0.54516859, -0.10896685]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Bn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normal Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = np.arange(-20,20,0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import math as math\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$f(x,\\mu,\\sigma^{2}) = \\frac{1}{\\sqrt{2\\pi\\sigma^{2}}} e ^{\\frac{-(x-\\mu)^{2}}{2\\sigma^{2}}}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def normal(x,m,s):\n",
    "    f = (1/np.sqrt(2*np.pi*s))*np.exp(-(x-m)**2/(2*s**2))\n",
    "    return f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MvTPwbkX3AQDUFqENAADZ34jECkplhzavS4lk7sDvcvndfi1uXKiDI0cUTyfK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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bf8d198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [15,10])\n",
    "plt.plot(x,normal(x,1.0,1.0),\\\n",
    "        x,normal(x,2.0,2.0),\\\n",
    "        x,normal(x,3.0,3.0),\\\n",
    "        x,normal(x,4.0,4.0),\\\n",
    "        x,normal(x,5.0,5.0),\\\n",
    "        x,normal(x,6.0,6.0))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scipy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reference: https://docs.scipy.org/doc/scipy/reference/stats.html"
   ]
  }
 ],
 "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": "3.7.1"
  },
  "widgets": {
   "state": {},
   "version": "1.1.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}