MultiSVM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#-*- coding:utf-8 -*-'\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('train.csv')\n",
    "label_cnt = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = df.iloc[:,1:].as_matrix()\n",
    "data = np.insert(data, data.shape[1], 1.0, axis = 1)\n",
    "label = df.iloc[:,0].as_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter 1: loss = 10.000000\n",
      "iter 2: loss = 10.527482\n",
      "iter 3: loss = 10.519009\n",
      "iter 4: loss = 10.514134\n",
      "iter 5: loss = 10.963807\n",
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      "iter 7: loss = 11.159680\n",
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      "iter 9: loss = 10.190595\n",
      "iter 10: loss = 12.371648\n",
      "iter 11: loss = 5.677823\n",
      "iter 12: loss = 6.475963\n",
      "iter 13: loss = 5.331629\n",
      "iter 14: loss = 12.825418\n",
      "iter 15: loss = 8.160971\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter 437: loss = 15.612595\n",
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    {
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     "output_type": "stream",
     "text": [
      "iter 821: loss = 7.130583\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "iter 1189: loss = 1.886682\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter 1536: loss = 0.320395\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter 2502: loss = 0.288216\n",
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    },
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     "output_type": "stream",
     "text": [
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      "iter 3113: loss = 0.620272\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter 3429: loss = 5.477098\n",
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      "iter 3705: loss = 3.607329\n",
      "iter 3706: loss = 8.211980\n",
      "iter 3707: loss = 4.642955\n",
      "iter 3708: loss = 0.451910\n",
      "iter 3709: loss = 0.435746\n",
      "iter 3710: loss = 1.057836\n",
      "iter 3711: loss = 6.749831\n",
      "iter 3712: loss = 2.060382\n",
      "iter 3713: loss = 1.080689\n",
      "iter 3714: loss = 0.028770\n",
      "iter 3715: loss = 0.088456\n",
      "iter 3716: loss = 0.079781\n",
      "iter 3717: loss = 0.399431\n",
      "iter 3718: loss = 0.362717\n",
      "iter 3719: loss = 2.255927\n",
      "iter 3720: loss = 2.713062\n",
      "iter 3721: loss = 4.248177\n",
      "iter 3722: loss = 0.926486\n",
      "iter 3723: loss = 0.018823\n",
      "iter 3724: loss = 0.079323\n",
      "iter 3725: loss = 0.417837\n",
      "iter 3726: loss = 8.527750\n",
      "iter 3727: loss = 0.271817\n",
      "iter 3728: loss = 5.381733\n",
      "iter 3729: loss = 0.308699\n",
      "iter 3730: loss = 0.192928\n",
      "iter 3731: loss = 0.813846\n",
      "iter 3732: loss = 1.898850\n",
      "iter 3733: loss = 2.915781\n",
      "iter 3734: loss = 3.747913\n",
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      "iter 3736: loss = 0.965253\n",
      "iter 3737: loss = 6.503197\n",
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      "iter 3739: loss = 7.748478\n",
      "iter 3740: loss = 10.696250\n",
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      "iter 3743: loss = 1.001491\n",
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      "iter 3746: loss = 1.378631\n",
      "iter 3747: loss = 0.352963\n",
      "iter 3748: loss = 0.139056\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter 3749: loss = 9.830584\n",
      "iter 3750: loss = 0.350707\n",
      "iter 3751: loss = 0.542925\n",
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      "iter 3828: loss = 5.815775\n",
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      "iter 3838: loss = 0.077050\n",
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      "iter 3976: loss = 4.584666\n",
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      "iter 3980: loss = 0.399864\n",
      "iter 3981: loss = 0.131664\n",
      "iter 3982: loss = 0.605511\n",
      "iter 3983: loss = 1.060606\n",
      "iter 3984: loss = 0.102475\n",
      "iter 3985: loss = 1.391440\n",
      "iter 3986: loss = 0.198188\n",
      "iter 3987: loss = 0.921954\n",
      "iter 3988: loss = 24.419718\n",
      "iter 3989: loss = 0.171488\n",
      "iter 3990: loss = 0.030277\n",
      "iter 3991: loss = 0.413207\n",
      "iter 3992: loss = 1.310055\n",
      "iter 3993: loss = 3.992684\n",
      "iter 3994: loss = 0.774504\n",
      "iter 3995: loss = 21.354351\n",
      "iter 3996: loss = 2.153338\n",
      "iter 3997: loss = 5.189631\n",
      "iter 3998: loss = 20.021989\n",
      "iter 3999: loss = 0.524568\n",
      "iter 4000: loss = 0.806780\n"
     ]
    }
   ],
   "source": [
    "w = np.zeros((label_cnt, data.shape[1]))\n",
    "lr = 1e-8\n",
    "max_iter_cnt = 4000\n",
    "iter_cnt = 0\n",
    "while iter_cnt < max_iter_cnt:\n",
    "    idx = int(np.random.rand(1)*data.shape[0])\n",
    "    x = data[idx,:]\n",
    "    y = label[idx]\n",
    "    scores = np.dot(w, x)\n",
    "    scores_correct = scores[y]\n",
    "    L_i = max(0, np.sum(1-scores_correct+scores))\n",
    "    if L_i > 0:\n",
    "        dW = np.zeros_like(w)\n",
    "        dW += x\n",
    "        dW[y,:] = (1-label_cnt)*x\n",
    "        w -= lr*dW\n",
    "        iter_cnt+=1\n",
    "#         print('iter %d: loss = %f'%(iter_cnt, L_i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.273214285714\n"
     ]
    }
   ],
   "source": [
    "pred = np.dot(w,data.T)\n",
    "pred = np.argmax(pred, axis=0)\n",
    "print(np.sum(pred != label)/data.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.6",
   "language": "python",
   "name": "py36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}