K-nearest neighbors ML_CoBan.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X1= [[ 0.05333732 -0.07130075  1.38360121 ...,  2.06228866 -0.73271976\n",
      "   0.65628031]\n",
      " [ 2.04394087 -2.06031368 -2.10043998 ..., -0.17908397 -0.92845698\n",
      "  -1.64399956]\n",
      " [-0.18311459 -0.1301116  -0.26928891 ...,  0.84942278  0.56037763\n",
      "  -1.06307315]\n",
      " ..., \n",
      " [ 0.97436913 -0.74090699 -0.00689424 ...,  0.20147294 -0.59224707\n",
      "  -0.00966289]\n",
      " [ 1.85875423 -1.67630722  1.84493741 ...,  0.05727444 -0.84453985\n",
      "   1.20943979]\n",
      " [-0.01156511 -0.49027094 -1.49895179 ...,  1.27844892 -0.65917331\n",
      "  -0.04166775]] \n",
      "\n",
      "\n",
      " X2=[[ 0.86099207 -0.17449397]\n",
      " [-0.74681807  1.48996032]\n",
      " [-0.48696388  0.48387458]]\n",
      "\n",
      "\n",
      "\n",
      "Z = [ -1.25108304e+00   4.24347584e-01   6.48808390e-01  -1.61543397e-01\n",
      "   1.04934660e-01   1.44478147e-01   8.79857280e-01  -3.86080537e-01\n",
      "   9.76528730e-01  -2.25826469e-01   1.20766546e+00  -1.38632732e+00\n",
      "   8.68105901e-02  -1.33196694e+00   3.34171978e-01   1.50602893e+00\n",
      "   1.67479165e+00  -1.89777361e+00  -1.03941138e+00   1.15395679e+00\n",
      "   2.32694064e-01  -8.12029054e-01   1.04709752e+00   6.13057848e-01\n",
      "  -5.97435321e-01  -2.77393934e+00  -1.10117617e-01   1.06466966e+00\n",
      "   3.18457353e-02  -1.14031192e-01   1.90227877e+00   3.70732395e-02\n",
      "  -7.19916762e-01  -6.53352623e-01  -4.94526159e-01  -1.49321471e+00\n",
      "  -2.80023445e-01  -1.12523043e+00  -1.40366709e+00   2.22551784e-01\n",
      "  -2.82927710e-01  -7.85463119e-01   4.16505128e-01  -1.58263538e+00\n",
      "   2.08976922e+00  -5.31706832e-01  -6.99101500e-01   1.11522973e+00\n",
      "  -9.90129898e-01  -2.97448516e-01  -5.36001105e-01   1.53445381e+00\n",
      "  -1.62175857e+00   1.22725726e+00   1.65050010e-02   1.07639847e+00\n",
      "   6.96900810e-02  -8.74690317e-01   1.23376373e+00  -2.61538223e-01\n",
      "  -2.93384699e-01  -1.42229642e+00  -1.18952914e+00   1.05747434e+00\n",
      "  -1.68636019e-01   2.53707357e-01  -3.61438381e-01   6.19149545e-01\n",
      "  -3.40976977e-01   1.00586277e+00  -1.63909086e+00   1.43756214e-01\n",
      "   5.12066395e-01   1.36456819e+00  -3.99852936e-01   6.93561430e-01\n",
      "   8.52477792e-01   5.92142700e-01  -3.02186309e-01  -6.57740938e-01\n",
      "  -1.48084458e+00  -1.40976052e+00   1.83207313e-01  -7.55359706e-02\n",
      "  -1.30397854e+00   3.67464032e-01   3.09056729e-01  -1.44869388e+00\n",
      "  -4.95483227e-01  -2.43374996e-01  -4.37755769e-01  -1.68975304e+00\n",
      "  -7.57150432e-01  -6.14132244e-01   1.18612399e+00  -1.68091114e+00\n",
      "   7.12921349e-01   1.45989483e+00  -2.85702575e+00  -2.46524716e+00\n",
      "   6.35443925e-01  -3.35520689e-01  -4.06505506e-01  -1.46536652e-01\n",
      "  -1.00177224e+00  -1.35234248e+00   7.84186467e-01   8.05486368e-01\n",
      "  -5.37824967e-01  -1.09843177e+00   1.01537325e+00  -1.53005133e+00\n",
      "  -1.57244961e-01   1.73604923e+00  -2.03013957e-01   2.60548414e-01\n",
      "   1.40790880e+00   3.11944245e-01  -8.25178825e-01  -5.61094575e-01\n",
      "   3.56249442e-01   1.40611089e+00   4.51130510e-01   1.56979540e+00\n",
      "  -2.02324581e-01  -4.78152278e-01   1.11895676e+00   1.44358343e+00\n",
      "  -1.42683258e-02  -7.65604009e-01   1.09046179e+00   1.54442746e+00\n",
      "  -4.65296962e-01  -4.98859026e-01  -1.06252236e+00   6.51431374e-01\n",
      "  -2.17549104e-01  -3.31521919e-01   7.17563663e-01   2.23041966e+00\n",
      "   5.29461656e-01  -1.19912663e-01   1.39781883e+00  -3.75818745e-01\n",
      "   8.94324747e-01   9.74263736e-01  -6.03638194e-02  -3.59140876e-01\n",
      "  -4.62999399e-01  -8.48860560e-01   2.22350166e-02   5.10736806e-01\n",
      "   1.10935235e-01   3.02808941e-01   9.26341384e-01  -6.34991615e-01\n",
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      "  -5.37571654e-01   9.82495109e-01   9.25649620e-01   8.79925047e-01\n",
      "   1.81031595e+00   2.46172784e-01  -3.98566235e-01  -1.46242068e-01\n",
      "  -5.29535062e-01   1.63600343e-01   5.30411235e-01   1.45164038e+00\n",
      "  -1.12365246e+00   5.43322280e-01   1.06457071e+00   1.24323178e+00\n",
      "   5.70871217e-01  -9.28478821e-01  -8.62395183e-01  -1.05075843e+00\n",
      "   8.22666479e-01  -1.19944619e+00   2.03281094e+00   1.86405991e-01\n",
      "   1.43155337e+00  -9.93003913e-01   7.06976330e-02   5.25169760e-01\n",
      "  -1.29534179e+00   4.14700071e-01  -8.92927672e-01   1.32557510e+00\n",
      "   2.30682264e-01  -1.05154664e-01  -3.11928496e-01  -8.08195364e-01\n",
      "  -5.03900101e-01   7.71754486e-01  -8.89133903e-01   1.50367892e+00\n",
      "   9.63780955e-01  -6.67937119e-01   1.76813407e+00  -1.28557504e+00\n",
      "   1.38266771e+00  -3.77395698e-01   1.49620656e+00   1.93254940e-01\n",
      "  -3.54963663e-01  -1.09020295e+00  -4.51494305e-01  -4.44044359e-01\n",
      "  -5.63253441e-01   9.15450133e-01  -3.27512070e+00   8.59904764e-01\n",
      "  -6.94958173e-01  -4.68986028e-01  -6.56638281e-01  -1.88162083e+00\n",
      "  -3.03041917e-01   2.91837152e-01   4.14755733e-01   6.89009762e-01\n",
      "   7.02302225e-01   9.06619230e-02  -1.98852873e+00  -1.00896619e+00\n",
      "   9.70665661e-01   4.76020620e-01   1.00859969e+00  -3.31010191e-01\n",
      "   8.83218730e-01   2.21046551e-01   5.12347245e-01  -4.25710340e-01\n",
      "  -7.53028687e-01  -1.49143169e+00  -1.81554299e+00   1.76409080e-01\n",
      "   5.91030106e-01  -1.67758350e-01   5.97066842e-01  -3.21469942e-04\n",
      "  -4.05247904e-01   8.26182545e-01  -8.99757890e-02  -2.04099977e-02\n",
      "   1.63050981e+00   7.03411368e-01  -5.24388631e-01  -2.04580175e-02\n",
      "  -7.49213127e-01  -3.16932371e-01   1.69513317e+00   1.33413790e+00\n",
      "  -4.54186317e-01   2.65180435e+00   7.21795768e-02  -2.40749376e+00\n",
      "  -9.17382115e-01   1.33066194e+00   9.92073591e-01   8.07104768e-01\n",
      "   4.74808363e-01   2.66615364e-01  -6.34574377e-01   4.86362522e-02\n",
      "   5.85739170e-01   1.14554115e-01  -1.15413105e+00  -1.19886552e+00\n",
      "  -4.48556907e-01  -1.55406368e+00   4.90829955e-01   7.17201571e-01\n",
      "   1.08498970e+00  -4.48474566e-01   5.51886410e-01  -1.79198405e+00\n",
      "   7.92305227e-01  -1.86822574e-02  -1.10612529e+00   1.61394758e-01\n",
      "   4.64183561e-01  -1.67552625e+00   9.26596081e-01  -2.76464299e-01\n",
      "  -1.85199387e+00   1.22470830e-01  -3.13242017e-01  -5.66325254e-02\n",
      "   1.30083812e+00  -1.23538131e+00  -4.64432955e-02  -3.93068880e-01\n",
      "  -6.77999001e-01   4.38013911e-01   3.25950019e-01  -6.61127071e-01\n",
      "  -1.41448968e-01  -1.30285054e+00   7.35578353e-01  -1.27400274e-01\n",
      "  -9.37152392e-01   7.41100219e-01  -2.30288767e-01  -1.84194569e+00\n",
      "  -7.42447922e-01   6.87553011e-01  -3.52727387e-01  -3.65708677e-01\n",
      "  -4.08879809e-01  -1.51328092e-01   1.30028231e+00   1.79183421e+00\n",
      "  -3.36052893e-01  -1.77775686e+00  -1.38718924e+00   3.51106792e-01\n",
      "   9.34932600e-01   3.76766136e-01  -1.09302401e-01  -2.03630056e+00\n",
      "   5.45362405e-02  -4.79788268e-01   3.02922392e+00   1.80326360e+00\n",
      "  -3.61027017e-01   1.09246706e+00  -7.59866292e-01   2.99489411e-01\n",
      "   9.10716641e-01   9.02663036e-01   5.98103284e-02   1.26474932e-01\n",
      "  -8.21434913e-01  -8.67305245e-01   1.02058438e+00   5.53419088e-01\n",
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      "  -6.94282158e-01  -2.40906429e-01  -3.57673146e-01  -1.13808119e-01\n",
      "  -3.87080806e-01   4.75849687e-01  -1.29294663e+00   4.98784328e-01\n",
      "  -3.16523690e-01   1.76939789e+00  -1.93495075e-01   1.12144908e+00\n",
      "   6.96190565e-02  -1.09624321e+00   8.78179204e-01   9.48742424e-01\n",
      "   1.59607931e-01   8.07985978e-02  -4.17532737e-01   8.88953902e-01\n",
      "  -2.83011223e-01  -1.13288560e+00   1.25769097e+00  -1.72641025e+00\n",
      "   6.30382398e-01  -7.89198881e-02   9.53443824e-01  -6.42510892e-01\n",
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      "  -5.18177858e-01  -1.27222927e+00  -2.23703666e+00  -1.48381198e+00\n",
      "   1.68648588e-01  -6.78188898e-01  -4.80447236e-01   8.50532242e-01\n",
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      "  -6.97511802e-01  -4.23747290e-01  -2.95850520e+00  -8.77709373e-01\n",
      "   4.60611022e-01  -3.88379881e-01   2.79715701e+00   4.33161615e-01\n",
      "   4.90892941e-01   4.06024683e-01  -1.28587510e-02   2.50478403e+00\n",
      "   4.69879827e-01  -9.17103236e-01   6.61924744e-01   1.08613431e+00\n",
      "   4.72331929e-01  -8.47399751e-01  -1.76959089e-01  -5.64239948e-01\n",
      "   8.63531895e-01  -1.36142177e+00  -1.16849875e+00   7.85240683e-01\n",
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      "   8.70558304e-02  -1.03039081e+00  -1.15408260e+00  -1.22234206e+00\n",
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      "   7.89725694e-01   7.71823400e-02   3.74026348e-01   3.74096283e-01\n",
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      "   3.47544333e-01   1.78292115e-01   4.91099845e-01   5.43135727e-01\n",
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      "  -7.32873565e-01  -5.93191468e-01   3.89789387e-01   3.82903872e-01\n",
      "  -1.23480162e-01   5.88511534e-01  -1.98133585e-01  -7.17990337e-01\n",
      "   1.52362552e-02   1.11364319e-01   6.72641176e-01   9.39782785e-01\n",
      "  -1.63000362e+00  -9.50558799e-01  -5.65997200e-01  -6.00211399e-01\n",
      "  -6.66918848e-01   1.30133421e+00  -1.35359712e+00  -6.19548842e-01\n",
      "  -6.98957672e-01   5.84284093e-01   1.22701363e+00   1.84939852e+00\n",
      "  -6.92550779e-01   5.46052127e-01   4.05056902e-01  -4.16386642e-01\n",
      "   1.81575097e+00   1.84843090e-01   1.99191167e+00  -9.97600915e-01\n",
      "  -1.46244238e+00  -6.02705713e-01  -1.63120304e-03  -1.68638705e+00\n",
      "   2.21844824e+00  -4.18196884e-01  -3.09320379e-02   1.71632699e-01\n",
      "  -1.41805551e+00  -1.32991274e+00   4.95108820e-01  -1.89407236e+00\n",
      "   1.25705420e-01  -1.03579105e+00   9.19280755e-01   7.09908105e-01\n",
      "  -3.34005867e-01  -7.20641301e-01  -3.72079604e-01   4.72228683e-01\n",
      "  -1.08915838e+00   1.26444699e+00   6.49479192e-01  -4.51939153e-01\n",
      "   1.32818290e+00  -1.70648762e-01   1.96703643e-01   9.86198281e-01\n",
      "  -8.19949051e-01   3.30862749e-01  -1.01106842e+00  -4.68278049e-01\n",
      "  -1.37307587e+00   5.64900629e-01   9.27609047e-02   7.53240509e-01\n",
      "   1.13676504e+00   8.92933495e-02   9.30447189e-01  -1.66942578e+00\n",
      "  -1.74136366e+00  -7.89932898e-02   1.91437360e-01  -1.14296901e+00\n",
      "   1.02592808e+00   1.17871452e+00  -1.75987355e+00   2.32023785e-01\n",
      "  -1.35687623e+00  -5.81383475e-01   8.08259879e-01   2.58433082e+00\n",
      "  -2.81198111e-01  -6.90615392e-01   1.58076956e+00  -1.16740071e+00\n",
      "  -1.84437794e-01   8.89962958e-01   5.55938997e-01  -3.28345202e-01\n",
      "   5.43510478e-03  -3.04750003e-01  -8.00839419e-01   8.04463003e-01\n",
      "  -6.47764419e-01   1.44388804e-01   2.00403278e-01   1.21645315e+00\n",
      "   9.83075127e-01  -3.03579300e-01   3.01064149e-01  -1.46007511e+00\n",
      "   3.61115895e-02   5.76473340e-01  -5.29044548e-03   2.06981244e+00\n",
      "   1.93331880e-02  -1.88466084e+00   4.77236927e-01   4.74329160e-01\n",
      "  -1.22927144e+00   1.39282263e+00  -9.40393599e-01  -1.26626209e+00\n",
      "  -9.70612909e-01  -6.75284909e-01  -2.06598448e+00   5.31279400e-01\n",
      "   1.60646473e+00  -2.24220987e+00   1.51867018e+00   1.64035353e-01\n",
      "   4.09463934e-01   2.83092513e-01  -7.74347398e-01  -1.32537093e-01\n",
      "   7.20013938e-01  -8.23870978e-01   4.31063213e-01   1.10737950e+00\n",
      "  -9.06560819e-01  -1.35949223e-01  -2.02814822e+00  -2.13955702e+00\n",
      "  -1.08867737e+00  -8.69095790e-01  -9.69637447e-01   2.19771409e+00\n",
      "  -1.15775482e+00   7.73046762e-02  -6.39714301e-01  -2.30011557e+00\n",
      "   1.65797227e+00   1.29751954e-01   2.20523006e+00   2.23420371e-01\n",
      "   2.00121693e+00  -5.54824446e-01  -7.89443869e-01   8.62975560e-01\n",
      "   1.23728826e+00  -8.02548707e-01  -1.92109785e+00   3.80253328e-01\n",
      "  -7.97752823e-02  -6.92398932e-01  -7.73957511e-01   1.06043909e+00\n",
      "   2.18840887e-02  -3.72487730e+00   1.83299251e+00  -6.37141253e-01\n",
      "  -1.29425498e+00  -3.14928439e-01   1.99596266e+00   3.01720853e-02\n",
      "   9.67364124e-02   4.81585160e-01  -1.22557272e-02  -1.02810718e+00\n",
      "  -5.15815848e-01   3.39911276e-01  -1.76601090e-01   1.12863844e+00\n",
      "  -2.63325343e+00  -3.47599934e-01  -8.68378367e-01   4.05208809e-02\n",
      "  -1.38948452e+00   1.95926904e-01  -3.35875250e-02  -2.31139512e+00\n",
      "  -9.83253355e-02  -3.29641200e-01  -6.39145207e-01   8.86906571e-01\n",
      "  -8.88040688e-01  -4.57946816e-01  -6.31860351e-01   6.53534003e-01\n",
      "   4.21155100e-02  -6.47800396e-01  -3.35509970e-01  -9.05806397e-02\n",
      "  -6.09618243e-01  -3.62129290e-01  -6.15487209e-01   7.59598550e-01\n",
      "   1.18056752e+00  -7.21106024e-01   3.74993226e-01  -2.48276405e-02\n",
      "  -1.02008145e+00  -2.71098032e-01   1.36149988e+00  -2.07279448e+00\n",
      "   1.68828040e-01   3.48087701e-01   4.20692298e-01   8.19849517e-01\n",
      "   1.19704615e+00  -1.94326425e-01  -1.75645468e+00   4.69617304e-01\n",
      "  -3.20746470e-01  -1.01871857e+00  -6.63469524e-01   1.05989538e-02\n",
      "   8.71607331e-01  -1.39609075e+00   7.35719746e-02  -6.59108878e-01\n",
      "  -5.09406722e-01   1.09567864e+00  -6.90968140e-01   7.33880644e-01\n",
      "   9.36402337e-01  -9.49637149e-01  -1.67527384e-01   6.46045857e-01\n",
      "  -5.46515076e-01  -2.29411758e-01  -8.57208268e-01   1.27054498e-01\n",
      "  -8.55956941e-01  -2.46475583e-01  -8.01138244e-01   1.21777552e-01\n",
      "   4.61115955e-01   5.67385421e-01   1.26925468e-01  -2.48334632e-01\n",
      "  -1.66288473e+00  -9.04488022e-01   1.02402336e-01   1.78067915e+00\n",
      "   6.00212464e-01   3.80815133e-01   4.43475016e-01  -2.56158768e-01\n",
      "  -7.04570791e-01  -1.60826554e+00  -6.86500491e-01  -1.95791080e+00\n",
      "  -1.33692628e+00   2.91988395e-01   6.34547603e-01   2.62674054e+00\n",
      "  -1.71192875e+00   8.33334170e-01  -2.09333667e+00  -9.02081033e-01\n",
      "   5.87327499e-01   4.87917721e-01   8.61557122e-01  -1.13939513e+00\n",
      "   1.29237607e+00   7.83449680e-01  -1.46140083e+00   1.51155539e-01\n",
      "   9.04543263e-01  -4.09907202e-01   1.76927413e-01   1.32296177e+00\n",
      "  -8.53483601e-01   1.59179680e+00  -2.83496441e-01   6.26959732e-01\n",
      "   1.28852709e+00  -3.75262429e-02  -1.41552629e+00   2.72739754e+00\n",
      "   1.54490680e+00  -1.05270609e+00  -3.75368852e-01  -2.33415493e+00\n",
      "  -5.05326474e-01   7.33553865e-01   1.08691494e+00   1.74328117e-01\n",
      "  -4.09013179e-02  -7.64738913e-01   1.27837965e+00  -4.06450475e-01\n",
      "   2.04004778e+00  -3.41264546e-01  -1.66378094e+00  -3.38101858e-01\n",
      "   6.35575630e-01  -2.70862753e-01  -3.75270527e-01   7.15572665e-01\n",
      "   1.71424891e+00   6.38865586e-01   1.08744409e+00   8.46801507e-01\n",
      "  -9.20943282e-01   2.14665979e+00  -9.88545027e-01  -5.39918664e-01\n",
      "   1.04482243e+00   3.96766028e-01   5.97017907e-02  -1.23237826e+00\n",
      "  -1.18396419e+00   1.61292327e-01  -3.34551922e-01   8.13762226e-01\n",
      "  -2.33682413e-01   2.35060136e+00   2.10460339e-01  -1.31565183e-02\n",
      "  -7.09855160e-01  -1.12866173e+00  -3.46338371e-01   3.32399346e-01\n",
      "   4.35853329e-01  -1.17522433e+00  -3.34894359e-01   5.99991901e-01\n",
      "  -1.39862652e-01   2.85107263e+00  -5.32351393e-01  -1.38222454e+00\n",
      "  -1.36867889e+00   1.65091032e+00  -1.99864940e+00   9.91710942e-01\n",
      "  -6.50658617e-01  -6.19039298e-01   1.70066483e+00  -6.16105449e-01\n",
      "  -7.73951417e-01   6.08550887e-01  -1.83868966e+00  -1.44444757e-01\n",
      "  -4.01339813e-01  -8.57594607e-01  -7.47150775e-01   5.09561246e-01\n",
      "  -8.22968039e-01   1.04519455e+00   4.51892288e-01   1.77258991e+00\n",
      "   2.42874486e+00   2.71662784e-01   2.57721285e-02  -9.46899168e-01\n",
      "   9.51867205e-01   8.26403428e-01   2.06445646e-01   6.02710948e-01]\n",
      "\n",
      " <class 'numpy.ndarray'> <class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "d,N=1000,10000\n",
    "X=np.random.randn(N,d) #Tức là 1000 mảng con với 10000 giá trị trong đó\n",
    "#để xem cách vận hành của random-numpy\n",
    "X1=np.random.randn(3,2)\n",
    "\n",
    "z=np.random.randn(d)\n",
    "print(\"X1= {} \\n\\n\\n X2={}\\n\\n\\n\\nZ = {}\\n\\n\".format(X,X1,z),type(X),type(z))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hàm Euclidean distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Euclidean distance from x to y:  4.69041575982343\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "# Example points in 3-dimensional space...\n",
    "x = (5, 6, 7)\n",
    "y = (8, 9, 9)\n",
    "distance = math.sqrt(sum([(a - b) ** 2 for a, b in zip(x, y)]))\n",
    "print(\"Euclidean distance from x to y: \",distance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ZIP https://docs.python.org/3.3/library/functions.html#zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Zipped:  [(1, 4), (2, 5), (3, 6), (9, 7), (10, 8), (11, 9)]\n",
      "Unzip:  (1, 2, 3, 9, 10, 11) (4, 5, 6, 7, 8, 9)\n"
     ]
    }
   ],
   "source": [
    "x = [1, 2, 3, 9, 10, 11]\n",
    "y = [4, 5, 6, 7, 8, 9]\n",
    "zipped = zip(x,y)\n",
    "print(\"Zipped: \",list(zipped))\n",
    "x2, y2 = zip(*zip(x, y)) # zip () kết hợp với toán tử * có thể được sử dụng để giải nén một danh sách(list)\n",
    "print(\"Unzip: \",x2,y2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reshape https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.reshape.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a= [[1 2 3]\n",
      " [4 5 6]] \n",
      "\n",
      "reshape(a, 6)= [1 2 3 4 5 6] \n",
      "\n",
      "np.reshape(a, (3,-1)= [[1 2]\n",
      " [3 4]\n",
      " [5 6]] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "a = np.array([[1,2,3],[4,5,6]])\n",
    "print(\"a= {} \\n\".format(a))\n",
    "print(\"reshape(a, 6)= {} \\n\".format(np.reshape(a, 6)))\n",
    "print(\"np.reshape(a, (3,-1)= {} \\n\".format(np.reshape(a, (3,-1))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "np.arange() Trả về giá trị khoảng cách đều trong một khoảng nhất định\n",
    "https://docs.scipy.org/doc/numpy/reference/generated/numpy.arange.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.  0.]\n",
      " [ 0.  0.]\n",
      " [ 0.  0.]\n",
      " [ 0.  0.]\n",
      " [ 0.  0.]\n",
      " [ 0.  0.]\n",
      " [ 0.  0.]\n",
      " [ 0.  0.]\n",
      " [ 0.  0.]\n",
      " [ 0.  0.]] \n",
      "\n",
      "\n",
      " [[ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]] \n",
      "\n",
      "\n",
      " [[ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]] \n",
      "\n",
      "a=  [[ 0.  0.]\n",
      " [ 0.  0.]\n",
      " [ 0.  0.]\n",
      " [ 0.  0.]\n",
      " [ 0.  0.]\n",
      " [ 0.  0.]\n",
      " [ 0.  0.]\n",
      " [ 0.  0.]\n",
      " [ 0.  0.]\n",
      " [ 0.  0.]]\n",
      "z= [0 1 2 3 4 5]\n",
      "z= [ 0.  1.  2.  3.  4.  5.]\n",
      "z= [ 3.   3.1  3.2  3.3  3.4  3.5  3.6  3.7  3.8  3.9  4.   4.1  4.2  4.3  4.4\n",
      "  4.5  4.6  4.7  4.8  4.9  5.   5.1  5.2  5.3  5.4  5.5  5.6  5.7  5.8  5.9]\n",
      "\n",
      "\n",
      " After reshape arange(6) is:  [[0 1]\n",
      " [2 3]\n",
      " [4 5]] \n",
      "\n",
      " WTF IS shape[1]   2\n",
      "Vay hoa ra la: shape[0] => n= rows\n",
      "\n",
      " a=  [[0 1]\n",
      " [2 3]\n",
      " [4 5]] \n",
      "\n",
      "a.shape[0]\n",
      " 3 \t Gia tri bang 3 vi co 3 hang\n"
     ]
    }
   ],
   "source": [
    "a=np.zeros((10,2)) # 10 hang 2 cot\n",
    "b=a.T  # Dao nguoc\n",
    "c=b.view()\n",
    "print(a,\"\\n\\n\\n\",b,\"\\n\\n\\n\",c,\"\\n\\na= \",a)\n",
    "c.shape\n",
    "\n",
    "# The order keyword gives the index ordering both for fetching the values from a, and then \n",
    "# placing the values into the output array. For example, let’s say you have an array:\n",
    "print(\"z=\",np.arange(6))\n",
    "print(\"z=\",np.arange(6.))\n",
    "print(\"z=\",np.arange(3,6,0.1)) #Từ 3 đến 6 với step = 0.1\n",
    "\n",
    "\n",
    "a= np.arange(6).reshape((3,2)) #tạo 1 cái arange từ 0-5 và  reshape lại thành 3 hàng 2 cột\n",
    "print(\"\\n\\n After reshape arange(6) is: \",a,\"\\n\\n WTF IS shape[1]  \",a.shape[1])\n",
    "#oh The shape attribute for numpy arrays returns the dimensions of the array. If Y has n rows and m columns, then Y.shape is (n,m). So Y.shape[0] is n.\n",
    "print(\"Vay hoa ra la: shape[0] => n= rows\\n\\n a= \",a,\"\\n\\na.shape[0]\\n\",a.shape[0],\"\\t Gia tri bang 3 vi co 3 hang\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://docs.scipy.org/doc/numpy/reference/routines.random.html Random Numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5> dist_pp: tính khoảng cách Eiclid giữa 2 vector z và x. Hàm này thực hiện cách tính trực tiếp, tức tính hiệu rồi lấy bình phương l2 norm của vector hiệu \n",
    "\n",
    "</h5>\n",
    "<h5>\n",
    "dist_ps_naive: tính bình phương khoảng cách giữa z và mỗi hàng của X. Trong hàm này, các khoảng cách được tính dựa trên việc tính từng cặp dist_pp(z,X[i])\n",
    "</h5>\n",
    "<h5>\n",
    "dist_ps_fast: tính bình phương khoảng cách giữa z và mỗi hàng của X. Với cách thức, khi có nhiều điểm dữ liệu được lưu trong X, ta cần tính tổng bình phương các phần tử của mỗi điểm dữ liệu và tính tích XTz\n",
    "</h5>\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive point2set, running time: 0.14310312271118164 s\n",
      "Fast point2set , running time: 0.07104992866516113 s\n",
      "Result difference: 2.16912579667e-11\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "import numpy as np\n",
    "from time import time # for comparing runing time\n",
    "d, N = 1000, 10000 # dimension, number of training points\n",
    "X = np.random.randn(N, d) # N d-dimensional points\n",
    "z = np.random.randn(d)\n",
    "z.shape\n",
    "# naively compute square distance between two vector\n",
    "def dist_pp(z, x):\n",
    "    d = z - x.reshape(z.shape) # force x and z to have the same dims #hiểu rồi, chỗ này x sẽ được reshape đúng với cái shape của z\n",
    "    return np.sum(d*d)\n",
    "# From one point to each point in a set, naive\n",
    "def dist_ps_naive(z, X):\n",
    "    N = X.shape[0]  # N bằng số hàng của X\n",
    "    res = np.zeros((1, N))  #tạo ra mảng =0 zeros với 1 hàng N cột(N gia tri ben trong)\n",
    "    for i in range(N):\n",
    "        res[0][i] = dist_pp(z, X[i])\n",
    "    return res\n",
    "# from one point to each point in a set, fast\n",
    "def dist_ps_fast(z, X):\n",
    "    X2 = np.sum(X*X, 1) # square of l2 norm of each ROW of X\n",
    "    z2 = np.sum(z*z) # square of l2 norm of z\n",
    "    return X2 + z2 - 2*X.dot(z) # z2 can be ignored\n",
    "t1 = time()\n",
    "D1 = dist_ps_naive(z, X)\n",
    "print('Naive point2set, running time:', time() - t1, 's')\n",
    "t1 = time()\n",
    "D2 = dist_ps_fast(z, X)\n",
    "print('Fast point2set , running time:', time() - t1, 's')\n",
    "print('Result difference:', np.linalg.norm(D1 - D2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
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    {
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      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros((1,200))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
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   "cell_type": "code",
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