2017-1-4(1).ipynb

{
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  {
   "cell_type": "code",
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   "source": [
    "for x in [1,2]:\n",
    "    a = x*2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "a"
   ]
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  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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    "scrolled": true
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting to training the model..., saving to weights/201612140109_e10_1k1k.p\n",
      "Training\n",
      "the length of the training data is  1998\n",
      "Before trianing, the error is:\n",
      "Training error: 0.257755994797 = 0.257755994797\n",
      "Training_correct error 0.337905704975\n",
      "Testing_correct error: 0.335038632154\n",
      "top- 1 = [] : 3\n",
      "top- 5 = [] : 21\n",
      "top- 10 = [] : 30\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean     501.754755\n",
      "std      264.648264\n",
      "min        1.000000\n",
      "25%      290.500000\n",
      "50%      541.500000\n",
      "75%      711.000000\n",
      "max      978.000000\n",
      "dtype: float64\n",
      "top- 1 = [] : 1\n",
      "top- 5 = [] : 12\n",
      "top- 10 = [] : 24\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean      551.164000\n",
      "std       260.994743\n",
      "min         1.000000\n",
      "25%       362.625000\n",
      "50%       559.000000\n",
      "75%       775.500000\n",
      "max       985.000000\n",
      "dtype: float64\n",
      "\n",
      "Epoch 0 ...\n",
      "Epoch  0 Update  40 Cost  0.112757906318\n",
      "Training error: 0.207024231553 = 0.125692605972\n",
      "Training_correct error 0.118412218988\n",
      "Testing_correct error: 0.114850081503\n",
      "top- 1 = [48] : 48\n",
      "top- 5 = [199] : 199\n",
      "top- 10 = [304] : 304\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      52.060060\n",
      "std       68.136176\n",
      "min        1.000000\n",
      "25%        7.500000\n",
      "50%       28.000000\n",
      "75%       63.000000\n",
      "max      370.500000\n",
      "dtype: float64\n",
      "top- 1 = [46] : 46\n",
      "top- 5 = [151] : 151\n",
      "top- 10 = [232] : 232\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       61.457000\n",
      "std        73.761911\n",
      "min         1.000000\n",
      "25%        11.500000\n",
      "50%        34.000000\n",
      "75%        85.000000\n",
      "max       448.000000\n",
      "dtype: float64\n",
      "epoch took: 48.0697929859\n",
      "\n",
      "Epoch 1 ...\n",
      "Epoch  1 Update  80 Cost  0.154227882624\n",
      "Epoch  1 Update  120 Cost  0.102236732841\n",
      "Training error: 0.0893101021647 = 0.117395535111\n",
      "Training_correct error 0.0951170623302\n",
      "Testing_correct error: 0.0903676599264\n",
      "top- 1 = [48, 50] : 50\n",
      "top- 5 = [199, 208] : 208\n",
      "top- 10 = [304, 320] : 320\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      52.107608\n",
      "std       67.633366\n",
      "min        1.000000\n",
      "25%        7.000000\n",
      "50%       22.000000\n",
      "75%       72.500000\n",
      "max      324.000000\n",
      "dtype: float64\n",
      "top- 1 = [46, 43] : 43\n",
      "top- 5 = [151, 169] : 169\n",
      "top- 10 = [232, 267] : 267\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       54.447000\n",
      "std        69.017665\n",
      "min         1.000000\n",
      "25%         9.500000\n",
      "50%        30.500000\n",
      "75%        61.250000\n",
      "max       425.000000\n",
      "dtype: float64\n",
      "epoch took: 48.6619460583\n",
      "\n",
      "Epoch 2 ...\n",
      "Epoch  2 Update  160 Cost  0.113972477615\n",
      "Training error: 0.124674670398 = 0.0985486656427\n",
      "Training_correct error 0.101944170892\n",
      "Testing_correct error: 0.102379269898\n",
      "top- 1 = [48, 50, 86] : 86\n",
      "top- 5 = [199, 208, 293] : 293\n",
      "top- 10 = [304, 320, 431] : 431\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      38.222723\n",
      "std       53.008296\n",
      "min        1.000000\n",
      "25%        4.000000\n",
      "50%       15.000000\n",
      "75%       50.250000\n",
      "max      398.000000\n",
      "dtype: float64\n",
      "top- 1 = [46, 43, 81] : 81\n",
      "top- 5 = [151, 169, 246] : 246\n",
      "top- 10 = [232, 267, 378] : 378\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       44.222000\n",
      "std        56.271624\n",
      "min         1.000000\n",
      "25%         5.500000\n",
      "50%        17.500000\n",
      "75%        62.000000\n",
      "max       432.000000\n",
      "dtype: float64\n",
      "epoch took: 48.1949439049\n",
      "\n",
      "Epoch 3 ...\n",
      "Epoch  3 Update  200 Cost  0.0882736444473\n",
      "Epoch  3 Update  240 Cost  0.0730952769518\n",
      "Training error: 0.0783319026232 = 0.0997497886419\n",
      "Training_correct error 0.0821037888527\n",
      "Testing_correct error: 0.083479039371\n",
      "top- 1 = [48, 50, 86, 76] : 76\n",
      "top- 5 = [199, 208, 293, 310] : 310\n",
      "top- 10 = [304, 320, 431, 411] : 411\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      37.868368\n",
      "std       57.583045\n",
      "min        1.000000\n",
      "25%        3.500000\n",
      "50%       16.000000\n",
      "75%       42.000000\n",
      "max      378.000000\n",
      "dtype: float64\n",
      "top- 1 = [46, 43, 81, 66] : 66\n",
      "top- 5 = [151, 169, 246, 242] : 242\n",
      "top- 10 = [232, 267, 378, 344] : 344\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       43.143000\n",
      "std        60.577844\n",
      "min         1.000000\n",
      "25%         6.000000\n",
      "50%        21.250000\n",
      "75%        46.000000\n",
      "max       417.000000\n",
      "dtype: float64\n",
      "epoch took: 48.4515080452\n",
      "\n",
      "Epoch 4 ...\n",
      "Epoch  4 Update  280 Cost  0.0679541006684\n",
      "Training error: 0.119521379471 = 0.086856238544\n",
      "Training_correct error 0.107121005654\n",
      "Testing_correct error: 0.120220825076\n",
      "top- 1 = [48, 50, 86, 76, 118] : 118\n",
      "top- 5 = [199, 208, 293, 310, 392] : 392\n",
      "top- 10 = [304, 320, 431, 411, 533] : 533\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      24.363864\n",
      "std       37.543915\n",
      "min        1.000000\n",
      "25%        3.000000\n",
      "50%        9.000000\n",
      "75%       26.500000\n",
      "max      279.000000\n",
      "dtype: float64\n",
      "top- 1 = [46, 43, 81, 66, 92] : 92\n",
      "top- 5 = [151, 169, 246, 242, 285] : 285\n",
      "top- 10 = [232, 267, 378, 344, 455] : 455\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       35.453000\n",
      "std        50.465682\n",
      "min         1.000000\n",
      "25%         4.000000\n",
      "50%        12.500000\n",
      "75%        41.250000\n",
      "max       324.000000\n",
      "dtype: float64\n",
      "epoch took: 48.5027868748\n",
      "\n",
      "Epoch 5 ...\n",
      "Epoch  5 Update  320 Cost  0.0801030173898\n",
      "Epoch  5 Update  360 Cost  0.0551847554743\n",
      "Training error: 0.172971352935 = 0.0897238701582\n",
      "Training_correct error 0.0772184580564\n",
      "Testing_correct error: 0.0757179409266\n",
      "top- 1 = [48, 50, 86, 76, 118, 93] : 93\n",
      "top- 5 = [199, 208, 293, 310, 392, 359] : 359\n",
      "top- 10 = [304, 320, 431, 411, 533, 495] : 495\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      28.575576\n",
      "std       44.863448\n",
      "min        1.000000\n",
      "25%        3.000000\n",
      "50%       10.500000\n",
      "75%       31.000000\n",
      "max      277.000000\n",
      "dtype: float64\n",
      "top- 1 = [46, 43, 81, 66, 92, 79] : 79\n",
      "top- 5 = [151, 169, 246, 242, 285, 279] : 279\n",
      "top- 10 = [232, 267, 378, 344, 455, 443] : 443\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       35.241500\n",
      "std        50.618402\n",
      "min         1.000000\n",
      "25%         4.500000\n",
      "50%        13.750000\n",
      "75%        41.000000\n",
      "max       273.000000\n",
      "dtype: float64\n",
      "epoch took: 48.8124659061\n",
      "\n",
      "Epoch 6 ...\n",
      "Epoch  6 Update  400 Cost  0.053138025105\n",
      "Epoch  6 Update  440 Cost  0.0752167925239\n",
      "Training error: 0.165213420987 = 0.0925588086247\n",
      "Training_correct error 0.075753249228\n",
      "Testing_correct error: 0.0882906317711\n",
      "top- 1 = [48, 50, 86, 76, 118, 93, 92] : 92\n",
      "top- 5 = [199, 208, 293, 310, 392, 359, 406] : 406\n",
      "top- 10 = [304, 320, 431, 411, 533, 495, 582] : 582\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      24.823824\n",
      "std       42.170965\n",
      "min        1.000000\n",
      "25%        3.000000\n",
      "50%        7.500000\n",
      "75%       21.000000\n",
      "max      344.000000\n",
      "dtype: float64\n",
      "top- 1 = [46, 43, 81, 66, 92, 79, 80] : 80\n",
      "top- 5 = [151, 169, 246, 242, 285, 279, 306] : 306\n",
      "top- 10 = [232, 267, 378, 344, 455, 443, 470] : 470\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       30.191000\n",
      "std        47.151795\n",
      "min         1.000000\n",
      "25%         4.000000\n",
      "50%        11.000000\n",
      "75%        25.000000\n",
      "max       333.000000\n",
      "dtype: float64\n",
      "epoch took: 48.8005611897\n",
      "\n",
      "Epoch 7 ...\n",
      "Epoch  7 Update  480 Cost  0.0967376902699\n",
      "Training error: 0.084833085537 = 0.0767989233136\n",
      "Training_correct error 0.0734879598022\n",
      "Testing_correct error: 0.0873811766505\n",
      "top- 1 = [48, 50, 86, 76, 118, 93, 92, 202] : 202\n",
      "top- 5 = [199, 208, 293, 310, 392, 359, 406, 515] : 515\n",
      "top- 10 = [304, 320, 431, 411, 533, 495, 582, 690] : 690\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      18.065065\n",
      "std       33.711923\n",
      "min        1.000000\n",
      "25%        1.500000\n",
      "50%        5.000000\n",
      "75%       14.500000\n",
      "max      263.000000\n",
      "dtype: float64\n",
      "top- 1 = [46, 43, 81, 66, 92, 79, 80, 135] : 135\n",
      "top- 5 = [151, 169, 246, 242, 285, 279, 306, 423] : 423\n",
      "top- 10 = [232, 267, 378, 344, 455, 443, 470, 557] : 557\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       27.840000\n",
      "std        46.303525\n",
      "min         1.000000\n",
      "25%         2.500000\n",
      "50%         8.000000\n",
      "75%        27.250000\n",
      "max       291.000000\n",
      "dtype: float64\n",
      "epoch took: 49.054844141\n",
      "\n",
      "Epoch 8 ...\n",
      "Epoch  8 Update  520 Cost  0.0612242631614\n",
      "Epoch  8 Update  560 Cost  0.13301704824\n",
      "Training error: 0.124444670975 = 0.0649016872048\n",
      "Training_correct error 0.0729025006294\n",
      "Testing_correct error: 0.0839079767466\n",
      "top- 1 = [48, 50, 86, 76, 118, 93, 92, 202, 205] : 205\n",
      "top- 5 = [199, 208, 293, 310, 392, 359, 406, 515, 564] : 564\n",
      "top- 10 = [304, 320, 431, 411, 533, 495, 582, 690, 738] : 738\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      12.638639\n",
      "std       22.141127\n",
      "min        1.000000\n",
      "25%        1.500000\n",
      "50%        4.000000\n",
      "75%       11.000000\n",
      "max      162.000000\n",
      "dtype: float64\n",
      "top- 1 = [46, 43, 81, 66, 92, 79, 80, 135, 156] : 156\n",
      "top- 5 = [151, 169, 246, 242, 285, 279, 306, 423, 468] : 468\n",
      "top- 10 = [232, 267, 378, 344, 455, 443, 470, 557, 596] : 596\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       19.704000\n",
      "std        34.629317\n",
      "min         1.000000\n",
      "25%         2.000000\n",
      "50%         6.000000\n",
      "75%        19.625000\n",
      "max       206.000000\n",
      "dtype: float64\n",
      "epoch took: 48.5931341648\n",
      "\n",
      "Epoch 9 ...\n",
      "Epoch  9 Update  600 Cost  0.0561785772443\n",
      "Training error: 0.0319570749998 = 0.0614430606365\n",
      "Training_correct error 0.0697435438633\n",
      "Testing_correct error: 0.0887727290392\n",
      "top- 1 = [48, 50, 86, 76, 118, 93, 92, 202, 205, 225] : 225\n",
      "top- 5 = [199, 208, 293, 310, 392, 359, 406, 515, 564, 610] : 610\n",
      "top- 10 = [304, 320, 431, 411, 533, 495, 582, 690, 738, 736] : 736\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      12.092092\n",
      "std       22.614483\n",
      "min        1.000000\n",
      "25%        1.500000\n",
      "50%        3.500000\n",
      "75%       11.250000\n",
      "max      208.000000\n",
      "dtype: float64\n",
      "top- 1 = [46, 43, 81, 66, 92, 79, 80, 135, 156, 166] : 166\n",
      "top- 5 = [151, 169, 246, 242, 285, 279, 306, 423, 468, 479] : 479\n",
      "top- 10 = [232, 267, 378, 344, 455, 443, 470, 557, 596, 614] : 614\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.00000\n",
      "mean       21.41400\n",
      "std        38.95274\n",
      "min         1.00000\n",
      "25%         2.00000\n",
      "50%         6.00000\n",
      "75%        22.50000\n",
      "max       271.00000\n",
      "dtype: float64\n",
      "epoch took: 48.7650310993\n",
      "\n",
      "Epoch 10 ...\n",
      "Epoch  10 Update  640 Cost  0.0669415146112\n",
      "Epoch  10 Update  680 Cost  0.0953700616956\n",
      "Training error: 0.0757331997156 = 0.064365811646\n",
      "Training_correct error 0.0788674727082\n",
      "Testing_correct error: 0.0984400510788\n",
      "top- 1 = [48, 50, 86, 76, 118, 93, 92, 202, 205, 225, 200] : 200\n",
      "top- 5 = [199, 208, 293, 310, 392, 359, 406, 515, 564, 610, 598] : 598\n",
      "top- 10 = [304, 320, 431, 411, 533, 495, 582, 690, 738, 736, 745] : 745\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      12.458458\n",
      "std       22.218322\n",
      "min        1.000000\n",
      "25%        1.500000\n",
      "50%        3.500000\n",
      "75%       11.000000\n",
      "max      173.000000\n",
      "dtype: float64\n",
      "top- 1 = [46, 43, 81, 66, 92, 79, 80, 135, 156, 166, 148] : 148\n",
      "top- 5 = [151, 169, 246, 242, 285, 279, 306, 423, 468, 479, 475] : 475\n",
      "top- 10 = [232, 267, 378, 344, 455, 443, 470, 557, 596, 614, 605] : 605\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       21.715000\n",
      "std        38.724454\n",
      "min         1.000000\n",
      "25%         2.000000\n",
      "50%         6.000000\n",
      "75%        19.000000\n",
      "max       289.000000\n",
      "dtype: float64\n",
      "epoch took: 49.0611460209\n",
      "\n",
      "Epoch 11 ...\n",
      "Epoch  11 Update  720 Cost  0.0681083649397\n",
      "Training error: 0.0514871068299 = 0.0676115751266\n",
      "Training_correct error 0.0683713331819\n",
      "Testing_correct error: 0.0836198031902\n",
      "top- 1 = [48, 50, 86, 76, 118, 93, 92, 202, 205, 225, 200, 161] : 161\n",
      "top- 5 = [199, 208, 293, 310, 392, 359, 406, 515, 564, 610, 598, 536] : 536\n",
      "top- 10 = [304, 320, 431, 411, 533, 495, 582, 690, 738, 736, 745, 698] : 698\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      13.920921\n",
      "std       23.795582\n",
      "min        1.000000\n",
      "25%        2.000000\n",
      "50%        4.500000\n",
      "75%       14.000000\n",
      "max      294.000000\n",
      "dtype: float64\n",
      "top- 1 = [46, 43, 81, 66, 92, 79, 80, 135, 156, 166, 148, 115] : 115\n",
      "top- 5 = [151, 169, 246, 242, 285, 279, 306, 423, 468, 479, 475, 452] : 452\n",
      "top- 10 = [232, 267, 378, 344, 455, 443, 470, 557, 596, 614, 605, 622] : 622\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.00000\n",
      "mean       20.02600\n",
      "std        33.57005\n",
      "min         1.00000\n",
      "25%         2.00000\n",
      "50%         6.25000\n",
      "75%        17.50000\n",
      "max       217.00000\n",
      "dtype: float64\n",
      "epoch took: 48.4739899635\n",
      "\n",
      "Epoch 12 ...\n",
      "Epoch  12 Update  760 Cost  0.0648547783494\n",
      "Epoch  12 Update  800 Cost  0.0944069772959\n",
      "Training error: 0.0798107907176 = 0.0687031969428\n",
      "Training_correct error 0.0725525915623\n",
      "Testing_correct error: 0.0905041620135\n",
      "top- 1 = [48, 50, 86, 76, 118, 93, 92, 202, 205, 225, 200, 161, 169] : 169\n",
      "top- 5 = [199, 208, 293, 310, 392, 359, 406, 515, 564, 610, 598, 536, 532] : 532\n",
      "top- 10 = [304, 320, 431, 411, 533, 495, 582, 690, 738, 736, 745, 698, 702] : 702\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      13.523524\n",
      "std       23.272234\n",
      "min        1.000000\n",
      "25%        2.000000\n",
      "50%        5.000000\n",
      "75%       13.000000\n",
      "max      288.000000\n",
      "dtype: float64\n",
      "top- 1 = [46, 43, 81, 66, 92, 79, 80, 135, 156, 166, 148, 115, 127] : 127\n",
      "top- 5 = [151, 169, 246, 242, 285, 279, 306, 423, 468, 479, 475, 452, 485] : 485\n",
      "top- 10 = [232, 267, 378, 344, 455, 443, 470, 557, 596, 614, 605, 622, 672] : 672\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       17.531000\n",
      "std        29.637196\n",
      "min         1.000000\n",
      "25%         2.000000\n",
      "50%         5.500000\n",
      "75%        15.000000\n",
      "max       190.000000\n",
      "dtype: float64\n",
      "epoch took: 48.6884510517\n",
      "\n",
      "Epoch 13 ...\n",
      "Epoch  13 Update  840 Cost  0.072304174304\n",
      "Epoch  13 Update  880 Cost  0.048543330282\n",
      "Training error: 0.0413294769824 = 0.0671595260501\n",
      "Training_correct error 0.0689151287079\n",
      "Testing_correct error: 0.0870760679245\n",
      "top- 1 = [48, 50, 86, 76, 118, 93, 92, 202, 205, 225, 200, 161, 169, 186] : 186\n",
      "top- 5 = [199, 208, 293, 310, 392, 359, 406, 515, 564, 610, 598, 536, 532, 595] : 595\n",
      "top- 10 = [304, 320, 431, 411, 533, 495, 582, 690, 738, 736, 745, 698, 702, 716] : 716\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      12.617618\n",
      "std       21.336281\n",
      "min        1.000000\n",
      "25%        1.500000\n",
      "50%        3.500000\n",
      "75%       12.500000\n",
      "max      245.000000\n",
      "dtype: float64\n",
      "top- 1 = [46, 43, 81, 66, 92, 79, 80, 135, 156, 166, 148, 115, 127, 143] : 143\n",
      "top- 5 = [151, 169, 246, 242, 285, 279, 306, 423, 468, 479, 475, 452, 485, 529] : 529\n",
      "top- 10 = [232, 267, 378, 344, 455, 443, 470, 557, 596, 614, 605, 622, 672, 665] : 665\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       15.855000\n",
      "std        25.879059\n",
      "min         1.000000\n",
      "25%         2.000000\n",
      "50%         5.000000\n",
      "75%        16.000000\n",
      "max       181.000000\n",
      "dtype: float64\n",
      "epoch took: 49.0571951866\n",
      "\n",
      "Epoch 14 ...\n",
      "Epoch  14 Update  920 Cost  0.0975828021765\n",
      "Training error: 0.0603713318706 = 0.055987522006\n",
      "Training_correct error 0.0552660189569\n",
      "Testing_correct error: 0.0641145408154\n",
      "top- 1 = [48, 50, 86, 76, 118, 93, 92, 202, 205, 225, 200, 161, 169, 186, 233] : 233\n",
      "top- 5 = [199, 208, 293, 310, 392, 359, 406, 515, 564, 610, 598, 536, 532, 595, 653] : 653\n",
      "top- 10 = [304, 320, 431, 411, 533, 495, 582, 690, 738, 736, 745, 698, 702, 716, 779] : 779\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       8.545546\n",
      "std       13.568803\n",
      "min        1.000000\n",
      "25%        1.500000\n",
      "50%        3.000000\n",
      "75%        8.500000\n",
      "max      157.000000\n",
      "dtype: float64\n",
      "top- 1 = [46, 43, 81, 66, 92, 79, 80, 135, 156, 166, 148, 115, 127, 143, 162] : 162\n",
      "top- 5 = [151, 169, 246, 242, 285, 279, 306, 423, 468, 479, 475, 452, 485, 529, 530] : 530\n",
      "top- 10 = [232, 267, 378, 344, 455, 443, 470, 557, 596, 614, 605, 622, 672, 665, 698] : 698\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       11.858000\n",
      "std        17.776947\n",
      "min         1.000000\n",
      "25%         2.000000\n",
      "50%         4.750000\n",
      "75%        13.125000\n",
      "max       120.000000\n",
      "dtype: float64\n",
      "epoch took: 48.892070055\n",
      "\n",
      "Epoch 15 ...\n",
      "Epoch  15 Update  960 Cost  0.0546577982605\n",
      "Epoch  15 Update  1000 Cost  0.0570617802441\n",
      "Training error: 0.039114356041 = 0.0527125261724\n",
      "Training_correct error 0.0673270151019\n",
      "Testing_correct error: 0.07998406142\n",
      "top- 1 = [48, 50, 86, 76, 118, 93, 92, 202, 205, 225, 200, 161, 169, 186, 233, 259] : 259\n",
      "top- 5 = [199, 208, 293, 310, 392, 359, 406, 515, 564, 610, 598, 536, 532, 595, 653, 714] : 714\n",
      "top- 10 = [304, 320, 431, 411, 533, 495, 582, 690, 738, 736, 745, 698, 702, 716, 779, 831] : 831\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       7.254254\n",
      "std       13.664529\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        2.500000\n",
      "75%        6.000000\n",
      "max      155.000000\n",
      "dtype: float64\n",
      "top- 1 = [46, 43, 81, 66, 92, 79, 80, 135, 156, 166, 148, 115, 127, 143, 162, 193] : 193\n",
      "top- 5 = [151, 169, 246, 242, 285, 279, 306, 423, 468, 479, 475, 452, 485, 529, 530, 630] : 630\n",
      "top- 10 = [232, 267, 378, 344, 455, 443, 470, 557, 596, 614, 605, 622, 672, 665, 698, 753] : 753\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       11.165000\n",
      "std        18.126012\n",
      "min         1.000000\n",
      "25%         1.500000\n",
      "50%         3.000000\n",
      "75%        10.000000\n",
      "max       151.000000\n",
      "dtype: float64\n",
      "epoch took: 48.6023950577\n",
      "\n",
      "Epoch 16 ...\n",
      "Epoch  16 Update  1040 Cost  0.0486805066466\n",
      "Training error: 0.0747208446264 = 0.0483071468771\n",
      "Training_correct error 0.0516675412655\n",
      "Testing_correct error: 0.0678435266018\n",
      "top- 1 = [48, 50, 86, 76, 118, 93, 92, 202, 205, 225, 200, 161, 169, 186, 233, 259, 272] : 272\n",
      "top- 5 = [199, 208, 293, 310, 392, 359, 406, 515, 564, 610, 598, 536, 532, 595, 653, 714, 735] : 735\n",
      "top- 10 = [304, 320, 431, 411, 533, 495, 582, 690, 738, 736, 745, 698, 702, 716, 779, 831, 843] : 843\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       6.183183\n",
      "std       11.160784\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        2.500000\n",
      "75%        6.000000\n",
      "max      164.000000\n",
      "dtype: float64\n",
      "top- 1 = [46, 43, 81, 66, 92, 79, 80, 135, 156, 166, 148, 115, 127, 143, 162, 193, 194] : 194\n",
      "top- 5 = [151, 169, 246, 242, 285, 279, 306, 423, 468, 479, 475, 452, 485, 529, 530, 630, 636] : 636\n",
      "top- 10 = [232, 267, 378, 344, 455, 443, 470, 557, 596, 614, 605, 622, 672, 665, 698, 753, 802] : 802\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean        9.038000\n",
      "std        14.761859\n",
      "min         1.000000\n",
      "25%         1.500000\n",
      "50%         3.500000\n",
      "75%         8.000000\n",
      "max       135.000000\n",
      "dtype: float64\n",
      "epoch took: 48.7091691494\n",
      "\n",
      "Epoch 17 ...\n",
      "Epoch  17 Update  1080 Cost  0.0539721436799\n",
      "Epoch  17 Update  1120 Cost  0.0677587613463\n",
      "Training error: 0.0419829301536 = 0.0467457361519\n",
      "Training_correct error 0.0499443151057\n",
      "Testing_correct error: 0.0650392621756\n",
      "top- 1 = [48, 50, 86, 76, 118, 93, 92, 202, 205, 225, 200, 161, 169, 186, 233, 259, 272, 263] : 263\n",
      "top- 5 = [199, 208, 293, 310, 392, 359, 406, 515, 564, 610, 598, 536, 532, 595, 653, 714, 735, 749] : 749\n",
      "top- 10 = [304, 320, 431, 411, 533, 495, 582, 690, 738, 736, 745, 698, 702, 716, 779, 831, 843, 864] : 864\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       5.903904\n",
      "std       10.514103\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        2.500000\n",
      "75%        5.250000\n",
      "max      145.000000\n",
      "dtype: float64\n",
      "top- 1 = [46, 43, 81, 66, 92, 79, 80, 135, 156, 166, 148, 115, 127, 143, 162, 193, 194, 198] : 198\n",
      "top- 5 = [151, 169, 246, 242, 285, 279, 306, 423, 468, 479, 475, 452, 485, 529, 530, 630, 636, 685] : 685\n",
      "top- 10 = [232, 267, 378, 344, 455, 443, 470, 557, 596, 614, 605, 622, 672, 665, 698, 753, 802, 807] : 807\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean        8.325000\n",
      "std        13.669537\n",
      "min         1.000000\n",
      "25%         1.500000\n",
      "50%         3.000000\n",
      "75%         7.000000\n",
      "max       123.000000\n",
      "dtype: float64\n",
      "epoch took: 48.77520895\n",
      "\n",
      "Epoch 18 ...\n",
      "Epoch  18 Update  1160 Cost  0.0441583767533\n",
      "Training error: 0.031181935221 = 0.0448585934937\n",
      "Training_correct error 0.0507593862712\n",
      "Testing_correct error: 0.0675635188818\n",
      "top- 1 = [48, 50, 86, 76, 118, 93, 92, 202, 205, 225, 200, 161, 169, 186, 233, 259, 272, 263, 297] : 297\n",
      "top- 5 = [199, 208, 293, 310, 392, 359, 406, 515, 564, 610, 598, 536, 532, 595, 653, 714, 735, 749, 776] : 776\n",
      "top- 10 = [304, 320, 431, 411, 533, 495, 582, 690, 738, 736, 745, 698, 702, 716, 779, 831, 843, 864, 887] : 887\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       5.160160\n",
      "std        9.167385\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        2.000000\n",
      "75%        5.000000\n",
      "max       96.000000\n",
      "dtype: float64\n",
      "top- 1 = [46, 43, 81, 66, 92, 79, 80, 135, 156, 166, 148, 115, 127, 143, 162, 193, 194, 198, 225] : 225\n",
      "top- 5 = [151, 169, 246, 242, 285, 279, 306, 423, 468, 479, 475, 452, 485, 529, 530, 630, 636, 685, 701] : 701\n",
      "top- 10 = [232, 267, 378, 344, 455, 443, 470, 557, 596, 614, 605, 622, 672, 665, 698, 753, 802, 807, 817] : 817\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean        8.575000\n",
      "std        15.275134\n",
      "min         1.000000\n",
      "25%         1.500000\n",
      "50%         2.500000\n",
      "75%         7.000000\n",
      "max       134.000000\n",
      "dtype: float64\n",
      "epoch took: 48.4535570145\n",
      "\n",
      "Epoch 19 ...\n",
      "Epoch  19 Update  1200 Cost  0.0592876039445\n",
      "Epoch  19 Update  1240 Cost  0.0502151399851\n",
      "Training error: 0.0314953140914 = 0.0442051738501\n",
      "Training_correct error 0.0425166003406\n",
      "Testing_correct error: 0.0482059717178\n",
      "top- 1 = [48, 50, 86, 76, 118, 93, 92, 202, 205, 225, 200, 161, 169, 186, 233, 259, 272, 263, 297, 274] : 274\n",
      "top- 5 = [199, 208, 293, 310, 392, 359, 406, 515, 564, 610, 598, 536, 532, 595, 653, 714, 735, 749, 776, 778] : 778\n",
      "top- 10 = [304, 320, 431, 411, 533, 495, 582, 690, 738, 736, 745, 698, 702, 716, 779, 831, 843, 864, 887, 881] : 881\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       4.926927\n",
      "std        7.862557\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        2.000000\n",
      "75%        5.000000\n",
      "max      100.000000\n",
      "dtype: float64\n",
      "top- 1 = [46, 43, 81, 66, 92, 79, 80, 135, 156, 166, 148, 115, 127, 143, 162, 193, 194, 198, 225, 206] : 206\n",
      "top- 5 = [151, 169, 246, 242, 285, 279, 306, 423, 468, 479, 475, 452, 485, 529, 530, 630, 636, 685, 701, 689] : 689\n",
      "top- 10 = [232, 267, 378, 344, 455, 443, 470, 557, 596, 614, 605, 622, 672, 665, 698, 753, 802, 807, 817, 814] : 814\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean        6.672000\n",
      "std         9.635105\n",
      "min         1.000000\n",
      "25%         1.500000\n",
      "50%         3.000000\n",
      "75%         7.000000\n",
      "max       109.000000\n",
      "dtype: float64\n",
      "epoch took: 48.6854188442\n",
      "saving the model...\n",
      "Evaluate the model using fast estimation...\n",
      "count    999.000000\n",
      "mean       4.926927\n",
      "std        7.862557\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        2.000000\n",
      "75%        5.000000\n",
      "max      100.000000\n",
      "dtype: float64\n",
      "count    1000.000000\n",
      "mean        6.672000\n",
      "std         9.635105\n",
      "min         1.000000\n",
      "25%         1.500000\n",
      "50%         3.000000\n",
      "75%         7.000000\n",
      "max       109.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "run main.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 更改了cost输出的模式，变为所有的minibatch都要输出最后的cost数值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting to training the model..., saving to weights/201612140109_e10_1k1k.p\n",
      "Training\n",
      "the length of the training data is  1998\n",
      "Before trianing, the error is:\n",
      "Training error: 0.254455864429 = 0.254455864429 == 0.48718854785\n",
      "Training_correct error 0.252215772867\n",
      "Testing_correct error: 0.256864190102\n",
      "top- 1 = [3] : 3\n",
      "top- 5 = [21] : 21\n",
      "top- 10 = [36] : 36\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean     471.223724\n",
      "std      297.328736\n",
      "min        1.000000\n",
      "25%      195.000000\n",
      "50%      476.500000\n",
      "75%      750.750000\n",
      "max      974.000000\n",
      "dtype: float64\n",
      "top- 1 = [2] : 2\n",
      "top- 5 = [18] : 18\n",
      "top- 10 = [30] : 30\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean      514.562500\n",
      "std       284.146446\n",
      "min         1.000000\n",
      "25%       275.000000\n",
      "50%       562.000000\n",
      "75%       759.500000\n",
      "max       983.000000\n",
      "dtype: float64\n",
      "\n",
      "Epoch 0 ...\n",
      "Epoch  0 Update  21 Cost  0.195559188724\n",
      "Epoch  0 Update  42 Cost  0.176217675209\n",
      "Epoch  0 Update  63 Cost  0.17397865653\n",
      "Training error: 0.17397865653 = 0.119634829462 == 0.299210071564\n",
      "Training_correct error 0.158254608512\n",
      "Testing_correct error: 0.150849059224\n",
      "top- 1 = [3, 66] : 66\n",
      "top- 5 = [21, 273] : 273\n",
      "top- 10 = [36, 378] : 378\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      42.668168\n",
      "std       58.981305\n",
      "min        1.000000\n",
      "25%        5.000000\n",
      "50%       19.000000\n",
      "75%       54.500000\n",
      "max      364.000000\n",
      "dtype: float64\n",
      "top- 1 = [2, 58] : 58\n",
      "top- 5 = [18, 219] : 219\n",
      "top- 10 = [30, 330] : 330\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       45.681500\n",
      "std        62.366493\n",
      "min         1.000000\n",
      "25%         6.875000\n",
      "50%        23.000000\n",
      "75%        54.500000\n",
      "max       499.000000\n",
      "dtype: float64\n",
      "epoch took: 54.0189409256\n",
      "\n",
      "Epoch 1 ...\n",
      "Epoch  1 Update  84 Cost  0.0888553485274\n",
      "Epoch  1 Update  105 Cost  0.110866591334\n",
      "Epoch  1 Update  126 Cost  0.0822771638632\n",
      "Training error: 0.0822771638632 = 0.10269010812 == 0.276221603155\n",
      "Training_correct error 0.101912304759\n",
      "Testing_correct error: 0.0996690690517\n",
      "top- 1 = [3, 66, 97] : 97\n",
      "top- 5 = [21, 273, 299] : 299\n",
      "top- 10 = [36, 378, 434] : 434\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      38.142643\n",
      "std       60.256474\n",
      "min        1.000000\n",
      "25%        4.000000\n",
      "50%       14.000000\n",
      "75%       47.000000\n",
      "max      410.000000\n",
      "dtype: float64\n",
      "top- 1 = [2, 58, 76] : 76\n",
      "top- 5 = [18, 219, 245] : 245\n",
      "top- 10 = [30, 330, 374] : 374\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       43.429000\n",
      "std        66.006051\n",
      "min         1.000000\n",
      "25%         5.500000\n",
      "50%        18.000000\n",
      "75%        53.000000\n",
      "max       482.000000\n",
      "dtype: float64\n",
      "epoch took: 54.4678580761\n",
      "\n",
      "Epoch 2 ...\n",
      "Epoch  2 Update  147 Cost  0.0703832805157\n",
      "Epoch  2 Update  168 Cost  0.0634962767363\n",
      "Epoch  2 Update  189 Cost  0.141363859177\n",
      "Training error: 0.141363859177 = 0.101892493665 == 0.273654609919\n",
      "Training_correct error 0.115335643291\n",
      "Testing_correct error: 0.115110211074\n",
      "top- 1 = [3, 66, 97, 88] : 88\n",
      "top- 5 = [21, 273, 299, 309] : 309\n",
      "top- 10 = [36, 378, 434, 458] : 458\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      34.059560\n",
      "std       51.269048\n",
      "min        1.000000\n",
      "25%        4.000000\n",
      "50%       12.500000\n",
      "75%       37.250000\n",
      "max      354.000000\n",
      "dtype: float64\n",
      "top- 1 = [2, 58, 76, 66] : 66\n",
      "top- 5 = [18, 219, 245, 254] : 254\n",
      "top- 10 = [30, 330, 374, 388] : 388\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       40.457500\n",
      "std        54.066548\n",
      "min         1.000000\n",
      "25%         5.000000\n",
      "50%        19.000000\n",
      "75%        50.000000\n",
      "max       344.000000\n",
      "dtype: float64\n",
      "epoch took: 54.0339448452\n",
      "\n",
      "Epoch 3 ...\n",
      "Epoch  3 Update  210 Cost  0.0851303935051\n",
      "Epoch  3 Update  231 Cost  0.0814256072044\n",
      "Epoch  3 Update  252 Cost  0.0704866200686\n",
      "Training error: 0.0704866200686 = 0.0963667184114 == 0.267374098301\n",
      "Training_correct error 0.0964699238539\n",
      "Testing_correct error: 0.101093195379\n",
      "top- 1 = [3, 66, 97, 88, 89] : 89\n",
      "top- 5 = [21, 273, 299, 309, 331] : 331\n",
      "top- 10 = [36, 378, 434, 458, 443] : 443\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      34.298298\n",
      "std       54.294714\n",
      "min        1.000000\n",
      "25%        3.500000\n",
      "50%       13.500000\n",
      "75%       39.500000\n",
      "max      402.000000\n",
      "dtype: float64\n",
      "top- 1 = [2, 58, 76, 66, 68] : 68\n",
      "top- 5 = [18, 219, 245, 254, 276] : 276\n",
      "top- 10 = [30, 330, 374, 388, 365] : 365\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       41.896500\n",
      "std        55.890771\n",
      "min         1.000000\n",
      "25%         4.500000\n",
      "50%        19.000000\n",
      "75%        55.250000\n",
      "max       498.000000\n",
      "dtype: float64\n",
      "epoch took: 54.3009691238\n",
      "\n",
      "Epoch 4 ...\n",
      "Epoch  4 Update  273 Cost  0.101310707629\n",
      "Epoch  4 Update  294 Cost  0.0909421890974\n",
      "Epoch  4 Update  315 Cost  0.121565461159\n",
      "Training error: 0.121565461159 = 0.0865795984864 == 0.249509364367\n",
      "Training_correct error 0.0945743173361\n",
      "Testing_correct error: 0.107270859182\n",
      "top- 1 = [3, 66, 97, 88, 89, 130] : 130\n",
      "top- 5 = [21, 273, 299, 309, 331, 448] : 448\n",
      "top- 10 = [36, 378, 434, 458, 443, 591] : 591\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      24.500501\n",
      "std       44.588193\n",
      "min        1.000000\n",
      "25%        2.500000\n",
      "50%        7.000000\n",
      "75%       21.000000\n",
      "max      374.000000\n",
      "dtype: float64\n",
      "top- 1 = [2, 58, 76, 66, 68, 108] : 108\n",
      "top- 5 = [18, 219, 245, 254, 276, 342] : 342\n",
      "top- 10 = [30, 330, 374, 388, 365, 472] : 472\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       33.453000\n",
      "std        50.045418\n",
      "min         1.000000\n",
      "25%         3.000000\n",
      "50%        11.500000\n",
      "75%        35.000000\n",
      "max       465.000000\n",
      "dtype: float64\n",
      "epoch took: 54.5202269554\n",
      "\n",
      "Epoch 5 ...\n",
      "Epoch  5 Update  336 Cost  0.0514961369336\n",
      "Epoch  5 Update  357 Cost  0.0596966482699\n",
      "Epoch  5 Update  378 Cost  0.151482820511\n",
      "Training error: 0.151482820511 = 0.0868194103241 == 0.250572383404\n",
      "Training_correct error 0.0816720798612\n",
      "Testing_correct error: 0.0806721672416\n",
      "top- 1 = [3, 66, 97, 88, 89, 130, 133] : 133\n",
      "top- 5 = [21, 273, 299, 309, 331, 448, 435] : 435\n",
      "top- 10 = [36, 378, 434, 458, 443, 591, 602] : 602\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      23.424424\n",
      "std       41.210000\n",
      "min        1.000000\n",
      "25%        2.500000\n",
      "50%        7.000000\n",
      "75%       19.000000\n",
      "max      319.000000\n",
      "dtype: float64\n",
      "top- 1 = [2, 58, 76, 66, 68, 108, 95] : 95\n",
      "top- 5 = [18, 219, 245, 254, 276, 342, 347] : 347\n",
      "top- 10 = [30, 330, 374, 388, 365, 472, 509] : 509\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       29.797500\n",
      "std        45.329069\n",
      "min         1.000000\n",
      "25%         3.000000\n",
      "50%        10.000000\n",
      "75%        32.000000\n",
      "max       305.000000\n",
      "dtype: float64\n",
      "epoch took: 54.8233160973\n",
      "\n",
      "Epoch 6 ...\n",
      "Epoch  6 Update  399 Cost  0.0814798921347\n",
      "Epoch  6 Update  420 Cost  0.0812743678689\n",
      "Epoch  6 Update  441 Cost  0.173649430275\n",
      "Training error: 0.173649430275 = 0.0900686606765 == 0.253579884768\n",
      "Training_correct error 0.112025998533\n",
      "Testing_correct error: 0.129777818918\n",
      "top- 1 = [3, 66, 97, 88, 89, 130, 133, 130] : 130\n",
      "top- 5 = [21, 273, 299, 309, 331, 448, 435, 409] : 409\n",
      "top- 10 = [36, 378, 434, 458, 443, 591, 602, 554] : 554\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      25.847848\n",
      "std       42.107665\n",
      "min        1.000000\n",
      "25%        2.500000\n",
      "50%        8.500000\n",
      "75%       26.500000\n",
      "max      355.000000\n",
      "dtype: float64\n",
      "top- 1 = [2, 58, 76, 66, 68, 108, 95, 83] : 83\n",
      "top- 5 = [18, 219, 245, 254, 276, 342, 347, 313] : 313\n",
      "top- 10 = [30, 330, 374, 388, 365, 472, 509, 442] : 442\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       36.407500\n",
      "std        53.171858\n",
      "min         1.000000\n",
      "25%         3.875000\n",
      "50%        14.000000\n",
      "75%        40.500000\n",
      "max       426.000000\n",
      "dtype: float64\n",
      "epoch took: 54.7304389477\n",
      "\n",
      "Epoch 7 ...\n",
      "Epoch  7 Update  462 Cost  0.0872214585543\n",
      "Epoch  7 Update  483 Cost  0.0912245884538\n",
      "Epoch  7 Update  504 Cost  0.0858310237527\n",
      "Training error: 0.0858310237527 = 0.0856649726629 == 0.244716614485\n",
      "Training_correct error 0.0688885301352\n",
      "Testing_correct error: 0.0832511410117\n",
      "top- 1 = [3, 66, 97, 88, 89, 130, 133, 130, 204] : 204\n",
      "top- 5 = [21, 273, 299, 309, 331, 448, 435, 409, 557] : 557\n",
      "top- 10 = [36, 378, 434, 458, 443, 591, 602, 554, 699] : 699\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      21.648148\n",
      "std       42.499276\n",
      "min        1.000000\n",
      "25%        1.500000\n",
      "50%        4.000000\n",
      "75%       15.250000\n",
      "max      352.000000\n",
      "dtype: float64\n",
      "top- 1 = [2, 58, 76, 66, 68, 108, 95, 83, 154] : 154\n",
      "top- 5 = [18, 219, 245, 254, 276, 342, 347, 313, 433] : 433\n",
      "top- 10 = [30, 330, 374, 388, 365, 472, 509, 442, 546] : 546\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       30.479500\n",
      "std        51.563375\n",
      "min         1.000000\n",
      "25%         2.500000\n",
      "50%         8.000000\n",
      "75%        25.000000\n",
      "max       445.000000\n",
      "dtype: float64\n",
      "epoch took: 55.0142090321\n",
      "\n",
      "Epoch 8 ...\n",
      "Epoch  8 Update  525 Cost  0.0911648720503\n",
      "Epoch  8 Update  546 Cost  0.103096462786\n",
      "Epoch  8 Update  567 Cost  0.134267121553\n",
      "Training error: 0.134267121553 = 0.0763626173139 == 0.23334030807\n",
      "Training_correct error 0.072225689888\n",
      "Testing_correct error: 0.0804159119725\n",
      "top- 1 = [3, 66, 97, 88, 89, 130, 133, 130, 204, 194] : 194\n",
      "top- 5 = [21, 273, 299, 309, 331, 448, 435, 409, 557, 566] : 566\n",
      "top- 10 = [36, 378, 434, 458, 443, 591, 602, 554, 699, 718] : 718\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      16.179179\n",
      "std       30.308789\n",
      "min        1.000000\n",
      "25%        1.750000\n",
      "50%        4.000000\n",
      "75%       12.500000\n",
      "max      268.000000\n",
      "dtype: float64\n",
      "top- 1 = [2, 58, 76, 66, 68, 108, 95, 83, 154, 138] : 138\n",
      "top- 5 = [18, 219, 245, 254, 276, 342, 347, 313, 433, 436] : 436\n",
      "top- 10 = [30, 330, 374, 388, 365, 472, 509, 442, 546, 598] : 598\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       23.210000\n",
      "std        39.628788\n",
      "min         1.000000\n",
      "25%         2.000000\n",
      "50%         7.000000\n",
      "75%        21.000000\n",
      "max       301.000000\n",
      "dtype: float64\n",
      "epoch took: 54.4691500664\n",
      "\n",
      "Epoch 9 ...\n",
      "Epoch  9 Update  588 Cost  0.0936339646578\n",
      "Epoch  9 Update  609 Cost  0.0604100003839\n",
      "Epoch  9 Update  630 Cost  0.031133165583\n",
      "Training error: 0.031133165583 = 0.0659403130412 == 0.209954887629\n",
      "Training_correct error 0.0809611603618\n",
      "Testing_correct error: 0.0940320417285\n",
      "top- 1 = [3, 66, 97, 88, 89, 130, 133, 130, 204, 194, 232] : 232\n",
      "top- 5 = [21, 273, 299, 309, 331, 448, 435, 409, 557, 566, 623] : 623\n",
      "top- 10 = [36, 378, 434, 458, 443, 591, 602, 554, 699, 718, 761] : 761\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      12.759760\n",
      "std       24.288159\n",
      "min        1.000000\n",
      "25%        1.500000\n",
      "50%        3.000000\n",
      "75%        9.750000\n",
      "max      218.000000\n",
      "dtype: float64\n",
      "top- 1 = [2, 58, 76, 66, 68, 108, 95, 83, 154, 138, 172] : 172\n",
      "top- 5 = [18, 219, 245, 254, 276, 342, 347, 313, 433, 436, 503] : 503\n",
      "top- 10 = [30, 330, 374, 388, 365, 472, 509, 442, 546, 598, 670] : 670\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       18.911000\n",
      "std        35.171581\n",
      "min         1.000000\n",
      "25%         2.000000\n",
      "50%         5.000000\n",
      "75%        15.000000\n",
      "max       316.000000\n",
      "dtype: float64\n",
      "epoch took: 54.6718919277\n",
      "\n",
      "Epoch 10 ...\n",
      "Epoch  10 Update  651 Cost  0.0582359358668\n",
      "Epoch  10 Update  672 Cost  0.0843362063169\n",
      "Epoch  10 Update  693 Cost  0.101022042334\n",
      "Training error: 0.101022042334 = 0.0823881775141 == 0.241874620318\n",
      "Training_correct error 0.0890559330583\n",
      "Testing_correct error: 0.108328841627\n",
      "top- 1 = [3, 66, 97, 88, 89, 130, 133, 130, 204, 194, 232, 133] : 133\n",
      "top- 5 = [21, 273, 299, 309, 331, 448, 435, 409, 557, 566, 623, 442] : 442\n",
      "top- 10 = [36, 378, 434, 458, 443, 591, 602, 554, 699, 718, 761, 626] : 626\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      22.924925\n",
      "std       40.153461\n",
      "min        1.000000\n",
      "25%        2.500000\n",
      "50%        6.500000\n",
      "75%       22.000000\n",
      "max      386.000000\n",
      "dtype: float64\n",
      "top- 1 = [2, 58, 76, 66, 68, 108, 95, 83, 154, 138, 172, 92] : 92\n",
      "top- 5 = [18, 219, 245, 254, 276, 342, 347, 313, 433, 436, 503, 355] : 355\n",
      "top- 10 = [30, 330, 374, 388, 365, 472, 509, 442, 546, 598, 670, 499] : 499\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       31.933000\n",
      "std        50.635822\n",
      "min         1.000000\n",
      "25%         3.000000\n",
      "50%        10.500000\n",
      "75%        29.500000\n",
      "max       359.000000\n",
      "dtype: float64\n",
      "epoch took: 54.9657759666\n",
      "\n",
      "Epoch 11 ...\n",
      "Epoch  11 Update  714 Cost  0.0501566529274\n",
      "Epoch  11 Update  735 Cost  0.100911095738\n",
      "Epoch  11 Update  756 Cost  0.0490036346018\n",
      "Training error: 0.0490036346018 = 0.0760749280453 == 0.231041103601\n",
      "Training_correct error 0.0724895298481\n",
      "Testing_correct error: 0.0933920443058\n",
      "top- 1 = [3, 66, 97, 88, 89, 130, 133, 130, 204, 194, 232, 133, 191] : 191\n",
      "top- 5 = [21, 273, 299, 309, 331, 448, 435, 409, 557, 566, 623, 442, 597] : 597\n",
      "top- 10 = [36, 378, 434, 458, 443, 591, 602, 554, 699, 718, 761, 626, 739] : 739\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      16.144144\n",
      "std       32.176364\n",
      "min        1.000000\n",
      "25%        1.500000\n",
      "50%        3.500000\n",
      "75%       11.000000\n",
      "max      314.000000\n",
      "dtype: float64\n",
      "top- 1 = [2, 58, 76, 66, 68, 108, 95, 83, 154, 138, 172, 92, 155] : 155\n",
      "top- 5 = [18, 219, 245, 254, 276, 342, 347, 313, 433, 436, 503, 355, 500] : 500\n",
      "top- 10 = [30, 330, 374, 388, 365, 472, 509, 442, 546, 598, 670, 499, 662] : 662\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       22.324000\n",
      "std        39.957448\n",
      "min         1.000000\n",
      "25%         2.000000\n",
      "50%         5.250000\n",
      "75%        15.500000\n",
      "max       332.000000\n",
      "dtype: float64\n",
      "epoch took: 54.3774268627\n",
      "\n",
      "Epoch 12 ...\n",
      "Epoch  12 Update  777 Cost  0.0523537844419\n",
      "Epoch  12 Update  798 Cost  0.100397005677\n",
      "Epoch  12 Update  819 Cost  0.101901717484\n",
      "Training error: 0.101901717484 = 0.0698130205274 == 0.22047175467\n",
      "Training_correct error 0.0800686553121\n",
      "Testing_correct error: 0.0992065742612\n",
      "top- 1 = [3, 66, 97, 88, 89, 130, 133, 130, 204, 194, 232, 133, 191, 250] : 250\n",
      "top- 5 = [21, 273, 299, 309, 331, 448, 435, 409, 557, 566, 623, 442, 597, 655] : 655\n",
      "top- 10 = [36, 378, 434, 458, 443, 591, 602, 554, 699, 718, 761, 626, 739, 773] : 773\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      12.997998\n",
      "std       26.137844\n",
      "min        1.000000\n",
      "25%        1.250000\n",
      "50%        3.000000\n",
      "75%        9.000000\n",
      "max      309.000000\n",
      "dtype: float64\n",
      "top- 1 = [2, 58, 76, 66, 68, 108, 95, 83, 154, 138, 172, 92, 155, 179] : 179\n",
      "top- 5 = [18, 219, 245, 254, 276, 342, 347, 313, 433, 436, 503, 355, 500, 530] : 530\n",
      "top- 10 = [30, 330, 374, 388, 365, 472, 509, 442, 546, 598, 670, 499, 662, 684] : 684\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       18.084000\n",
      "std        32.843898\n",
      "min         1.000000\n",
      "25%         2.000000\n",
      "50%         5.000000\n",
      "75%        13.000000\n",
      "max       238.000000\n",
      "dtype: float64\n",
      "epoch took: 54.5671000481\n",
      "\n",
      "Epoch 13 ...\n",
      "Epoch  13 Update  840 Cost  0.0823323354125\n",
      "Epoch  13 Update  861 Cost  0.0773301422596\n",
      "Epoch  13 Update  882 Cost  0.0514078512788\n",
      "Training error: 0.0514078512788 = 0.0601598769426 == 0.204063683748\n",
      "Training_correct error 0.0617761164904\n",
      "Testing_correct error: 0.0773594975471\n",
      "top- 1 = [3, 66, 97, 88, 89, 130, 133, 130, 204, 194, 232, 133, 191, 250, 322] : 322\n",
      "top- 5 = [21, 273, 299, 309, 331, 448, 435, 409, 557, 566, 623, 442, 597, 655, 760] : 760\n",
      "top- 10 = [36, 378, 434, 458, 443, 591, 602, 554, 699, 718, 761, 626, 739, 773, 837] : 837\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       7.845846\n",
      "std       15.857733\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        2.000000\n",
      "75%        5.000000\n",
      "max      209.000000\n",
      "dtype: float64\n",
      "top- 1 = [2, 58, 76, 66, 68, 108, 95, 83, 154, 138, 172, 92, 155, 179, 218] : 218\n",
      "top- 5 = [18, 219, 245, 254, 276, 342, 347, 313, 433, 436, 503, 355, 500, 530, 665] : 665\n",
      "top- 10 = [30, 330, 374, 388, 365, 472, 509, 442, 546, 598, 670, 499, 662, 684, 780] : 780\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       12.211000\n",
      "std        23.735203\n",
      "min         1.000000\n",
      "25%         1.500000\n",
      "50%         2.500000\n",
      "75%         8.500000\n",
      "max       195.000000\n",
      "dtype: float64\n",
      "epoch took: 54.8577709198\n",
      "\n",
      "Epoch 14 ...\n",
      "Epoch  14 Update  903 Cost  0.0257514882833\n",
      "Epoch  14 Update  924 Cost  0.0603770762682\n",
      "Epoch  14 Update  945 Cost  0.0483350642025\n",
      "Training error: 0.0483350642025 = 0.0627611353993 == 0.211395427585\n",
      "Training_correct error 0.0711337551475\n",
      "Testing_correct error: 0.0874175727367\n",
      "top- 1 = [3, 66, 97, 88, 89, 130, 133, 130, 204, 194, 232, 133, 191, 250, 322, 254] : 254\n",
      "top- 5 = [21, 273, 299, 309, 331, 448, 435, 409, 557, 566, 623, 442, 597, 655, 760, 683] : 683\n",
      "top- 10 = [36, 378, 434, 458, 443, 591, 602, 554, 699, 718, 761, 626, 739, 773, 837, 801] : 801\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       8.517518\n",
      "std       14.543279\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        2.500000\n",
      "75%        7.000000\n",
      "max      109.000000\n",
      "dtype: float64\n",
      "top- 1 = [2, 58, 76, 66, 68, 108, 95, 83, 154, 138, 172, 92, 155, 179, 218, 194] : 194\n",
      "top- 5 = [18, 219, 245, 254, 276, 342, 347, 313, 433, 436, 503, 355, 500, 530, 665, 517] : 517\n",
      "top- 10 = [30, 330, 374, 388, 365, 472, 509, 442, 546, 598, 670, 499, 662, 684, 780, 674] : 674\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       13.434000\n",
      "std        20.271176\n",
      "min         1.000000\n",
      "25%         1.500000\n",
      "50%         5.000000\n",
      "75%        16.500000\n",
      "max       155.000000\n",
      "dtype: float64\n",
      "epoch took: 54.7984859943\n",
      "\n",
      "Epoch 15 ...\n",
      "Epoch  15 Update  966 Cost  0.0682666450739\n",
      "Epoch  15 Update  987 Cost  0.0473287478089\n",
      "Epoch  15 Update  1008 Cost  0.0451587326825\n",
      "Training error: 0.0451587326825 = 0.0621490776539 == 0.20491322875\n",
      "Training_correct error 0.0785046368837\n",
      "Testing_correct error: 0.0992950201035\n",
      "top- 1 = [3, 66, 97, 88, 89, 130, 133, 130, 204, 194, 232, 133, 191, 250, 322, 254, 297] : 297\n",
      "top- 5 = [21, 273, 299, 309, 331, 448, 435, 409, 557, 566, 623, 442, 597, 655, 760, 683, 735] : 735\n",
      "top- 10 = [36, 378, 434, 458, 443, 591, 602, 554, 699, 718, 761, 626, 739, 773, 837, 801, 842] : 842\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       7.347347\n",
      "std       14.409519\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        2.000000\n",
      "75%        5.500000\n",
      "max      155.000000\n",
      "dtype: float64\n",
      "top- 1 = [2, 58, 76, 66, 68, 108, 95, 83, 154, 138, 172, 92, 155, 179, 218, 194, 203] : 203\n",
      "top- 5 = [18, 219, 245, 254, 276, 342, 347, 313, 433, 436, 503, 355, 500, 530, 665, 517, 663] : 663\n",
      "top- 10 = [30, 330, 374, 388, 365, 472, 509, 442, 546, 598, 670, 499, 662, 684, 780, 674, 773] : 773\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       11.980000\n",
      "std        22.538136\n",
      "min         1.000000\n",
      "25%         1.500000\n",
      "50%         3.000000\n",
      "75%         9.000000\n",
      "max       167.000000\n",
      "dtype: float64\n",
      "epoch took: 54.2794270515\n",
      "\n",
      "Epoch 16 ...\n",
      "Epoch  16 Update  1029 Cost  0.0415621623397\n",
      "Epoch  16 Update  1050 Cost  0.0586259439588\n",
      "Epoch  16 Update  1071 Cost  0.0932768210769\n",
      "Training error: 0.0932768210769 = 0.0516738668084 == 0.18446816504\n",
      "Training_correct error 0.0530459359288\n",
      "Testing_correct error: 0.0610174611211\n",
      "top- 1 = [3, 66, 97, 88, 89, 130, 133, 130, 204, 194, 232, 133, 191, 250, 322, 254, 297, 367] : 367\n",
      "top- 5 = [21, 273, 299, 309, 331, 448, 435, 409, 557, 566, 623, 442, 597, 655, 760, 683, 735, 824] : 824\n",
      "top- 10 = [36, 378, 434, 458, 443, 591, 602, 554, 699, 718, 761, 626, 739, 773, 837, 801, 842, 884] : 884\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       4.569570\n",
      "std        8.482058\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        1.500000\n",
      "75%        3.500000\n",
      "max      113.000000\n",
      "dtype: float64\n",
      "top- 1 = [2, 58, 76, 66, 68, 108, 95, 83, 154, 138, 172, 92, 155, 179, 218, 194, 203, 245] : 245\n",
      "top- 5 = [18, 219, 245, 254, 276, 342, 347, 313, 433, 436, 503, 355, 500, 530, 665, 517, 663, 754] : 754\n",
      "top- 10 = [30, 330, 374, 388, 365, 472, 509, 442, 546, 598, 670, 499, 662, 684, 780, 674, 773, 859] : 859\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean        6.508000\n",
      "std        12.045789\n",
      "min         1.000000\n",
      "25%         1.500000\n",
      "50%         2.000000\n",
      "75%         5.000000\n",
      "max        89.000000\n",
      "dtype: float64\n",
      "epoch took: 54.4773609638\n",
      "\n",
      "Epoch 17 ...\n",
      "Epoch  17 Update  1092 Cost  0.0450609102845\n",
      "Epoch  17 Update  1113 Cost  0.0603794828057\n",
      "Epoch  17 Update  1134 Cost  0.0437755659223\n",
      "Training error: 0.0437755659223 = 0.044334076345 == 0.16923314333\n",
      "Training_correct error 0.0520551912487\n",
      "Testing_correct error: 0.0658173114061\n",
      "top- 1 = [3, 66, 97, 88, 89, 130, 133, 130, 204, 194, 232, 133, 191, 250, 322, 254, 297, 367, 421] : 421\n",
      "top- 5 = [21, 273, 299, 309, 331, 448, 435, 409, 557, 566, 623, 442, 597, 655, 760, 683, 735, 824, 857] : 857\n",
      "top- 10 = [36, 378, 434, 458, 443, 591, 602, 554, 699, 718, 761, 626, 739, 773, 837, 801, 842, 884, 912] : 912\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       3.858859\n",
      "std        7.255127\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        1.500000\n",
      "75%        3.000000\n",
      "max       80.000000\n",
      "dtype: float64\n",
      "top- 1 = [2, 58, 76, 66, 68, 108, 95, 83, 154, 138, 172, 92, 155, 179, 218, 194, 203, 245, 277] : 277\n",
      "top- 5 = [18, 219, 245, 254, 276, 342, 347, 313, 433, 436, 503, 355, 500, 530, 665, 517, 663, 754, 791] : 791\n",
      "top- 10 = [30, 330, 374, 388, 365, 472, 509, 442, 546, 598, 670, 499, 662, 684, 780, 674, 773, 859, 867] : 867\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean        6.362000\n",
      "std        12.148108\n",
      "min         1.000000\n",
      "25%         1.000000\n",
      "50%         2.000000\n",
      "75%         4.000000\n",
      "max       109.000000\n",
      "dtype: float64\n",
      "epoch took: 54.4111101627\n",
      "\n",
      "Epoch 18 ...\n",
      "Epoch  18 Update  1155 Cost  0.0253591425717\n",
      "Epoch  18 Update  1176 Cost  0.0386092476547\n",
      "Epoch  18 Update  1197 Cost  0.0437739677727\n",
      "Training error: 0.0437739677727 = 0.0563704743981 == 0.190003275871\n",
      "Training_correct error 0.0620717555285\n",
      "Testing_correct error: 0.0811943337321\n",
      "top- 1 = [3, 66, 97, 88, 89, 130, 133, 130, 204, 194, 232, 133, 191, 250, 322, 254, 297, 367, 421, 321] : 321\n",
      "top- 5 = [21, 273, 299, 309, 331, 448, 435, 409, 557, 566, 623, 442, 597, 655, 760, 683, 735, 824, 857, 744] : 744\n",
      "top- 10 = [36, 378, 434, 458, 443, 591, 602, 554, 699, 718, 761, 626, 739, 773, 837, 801, 842, 884, 912, 832] : 832\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       7.500501\n",
      "std       15.231949\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        2.000000\n",
      "75%        5.500000\n",
      "max      159.000000\n",
      "dtype: float64\n",
      "top- 1 = [2, 58, 76, 66, 68, 108, 95, 83, 154, 138, 172, 92, 155, 179, 218, 194, 203, 245, 277, 204] : 204\n",
      "top- 5 = [18, 219, 245, 254, 276, 342, 347, 313, 433, 436, 503, 355, 500, 530, 665, 517, 663, 754, 791, 690] : 690\n",
      "top- 10 = [30, 330, 374, 388, 365, 472, 509, 442, 546, 598, 670, 499, 662, 684, 780, 674, 773, 859, 867, 803] : 803\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       11.110000\n",
      "std        21.386521\n",
      "min         1.000000\n",
      "25%         1.500000\n",
      "50%         2.500000\n",
      "75%         7.000000\n",
      "max       142.000000\n",
      "dtype: float64\n",
      "epoch took: 54.4018220901\n",
      "\n",
      "Epoch 19 ...\n",
      "Epoch  19 Update  1218 Cost  0.0434799417853\n",
      "Epoch  19 Update  1239 Cost  0.0486020892859\n",
      "Epoch  19 Update  1260 Cost  0.0301542542875\n",
      "Training error: 0.0301542542875 = 0.0483665727079 == 0.171160414815\n",
      "Training_correct error 0.0433001294732\n",
      "Testing_correct error: 0.0494225993752\n",
      "top- 1 = [3, 66, 97, 88, 89, 130, 133, 130, 204, 194, 232, 133, 191, 250, 322, 254, 297, 367, 421, 321, 381] : 381\n",
      "top- 5 = [21, 273, 299, 309, 331, 448, 435, 409, 557, 566, 623, 442, 597, 655, 760, 683, 735, 824, 857, 744, 843] : 843\n",
      "top- 10 = [36, 378, 434, 458, 443, 591, 602, 554, 699, 718, 761, 626, 739, 773, 837, 801, 842, 884, 912, 832, 912] : 912\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       3.933934\n",
      "std        6.807704\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        1.500000\n",
      "75%        3.500000\n",
      "max       82.000000\n",
      "dtype: float64\n",
      "top- 1 = [2, 58, 76, 66, 68, 108, 95, 83, 154, 138, 172, 92, 155, 179, 218, 194, 203, 245, 277, 204, 262] : 262\n",
      "top- 5 = [18, 219, 245, 254, 276, 342, 347, 313, 433, 436, 503, 355, 500, 530, 665, 517, 663, 754, 791, 690, 779] : 779\n",
      "top- 10 = [30, 330, 374, 388, 365, 472, 509, 442, 546, 598, 670, 499, 662, 684, 780, 674, 773, 859, 867, 803, 858] : 858\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean        5.697000\n",
      "std        10.087366\n",
      "min         1.000000\n",
      "25%         1.000000\n",
      "50%         2.000000\n",
      "75%         4.500000\n",
      "max        66.000000\n",
      "dtype: float64\n",
      "epoch took: 54.2822740078\n",
      "saving the model...\n",
      "Evaluate the model using fast estimation...\n",
      "count    999.000000\n",
      "mean       3.933934\n",
      "std        6.807704\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        1.500000\n",
      "75%        3.500000\n",
      "max       82.000000\n",
      "dtype: float64\n",
      "count    1000.000000\n",
      "mean        5.697000\n",
      "std        10.087366\n",
      "min         1.000000\n",
      "25%         1.000000\n",
      "50%         2.000000\n",
      "75%         4.500000\n",
      "max        66.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "run main.py\n",
    "# 【错误！没有重置ipython就运行了】运行结果是【错误】的！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/site-packages/gensim/utils.py:1015: UserWarning: Pattern library is not installed, lemmatization won't be available.\n",
      "  warnings.warn(\"Pattern library is not installed, lemmatization won't be available.\")\n",
      "Using gpu device 0: GeForce GTX TITAN X (CNMeM is enabled with initial size: 95.0% of memory, cuDNN 4007)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting to training the model..., saving to weights/201612140109_e10_1k1k.p\n",
      "Training\n",
      "the length of the training data is  1998\n",
      "Before trianing, the error is:\n",
      "Training error: 0.257755994797 = 0.257755994797 == 0.25775590539 ?? 0.190321952105\n",
      "Training_correct error 0.337905704975\n",
      "Testing_correct error: 0.335038632154\n",
      "top- 1 = [3] : 3\n",
      "top- 5 = [21] : 21\n",
      "top- 10 = [30] : 30\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean     501.754755\n",
      "std      264.648264\n",
      "min        1.000000\n",
      "25%      290.500000\n",
      "50%      541.500000\n",
      "75%      711.000000\n",
      "max      978.000000\n",
      "dtype: float64\n",
      "top- 1 = [1] : 1\n",
      "top- 5 = [12] : 12\n",
      "top- 10 = [24] : 24\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean      551.164000\n",
      "std       260.994743\n",
      "min         1.000000\n",
      "25%       362.625000\n",
      "50%       559.000000\n",
      "75%       775.500000\n",
      "max       985.000000\n",
      "dtype: float64\n",
      "\n",
      "Epoch 0 ...\n",
      "Epoch  0 Update  21 Cost  0.182155281305\n",
      "Epoch  0 Update  42 Cost  0.167487800121\n",
      "Epoch  0 Update  63 Cost  0.207024231553\n",
      "Training error: 0.207024231553 = 0.125692605972 == 0.125692546368 ?? 0.118657417595\n",
      "Training_correct error 0.118412218988\n",
      "Testing_correct error: 0.114850081503\n",
      "top- 1 = [3, 48] : 48\n",
      "top- 5 = [21, 199] : 199\n",
      "top- 10 = [30, 304] : 304\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      52.060060\n",
      "std       68.136176\n",
      "min        1.000000\n",
      "25%        7.500000\n",
      "50%       28.000000\n",
      "75%       63.000000\n",
      "max      370.500000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46] : 46\n",
      "top- 5 = [12, 151] : 151\n",
      "top- 10 = [24, 232] : 232\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       61.457000\n",
      "std        73.761911\n",
      "min         1.000000\n",
      "25%        11.500000\n",
      "50%        34.000000\n",
      "75%        85.000000\n",
      "max       448.000000\n",
      "dtype: float64\n",
      "epoch took: 57.6271688938\n",
      "\n",
      "Epoch 1 ...\n",
      "Epoch  1 Update  84 Cost  0.10254971683\n",
      "Epoch  1 Update  105 Cost  0.11504471302\n",
      "Epoch  1 Update  126 Cost  0.0893101021647\n",
      "Training error: 0.0893101021647 = 0.117395535111 == 0.117395468056 ?? 0.120890542865\n",
      "Training_correct error 0.0951170623302\n",
      "Testing_correct error: 0.0903676599264\n",
      "top- 1 = [3, 48, 50] : 50\n",
      "top- 5 = [21, 199, 208] : 208\n",
      "top- 10 = [30, 304, 320] : 320\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      52.107608\n",
      "std       67.633366\n",
      "min        1.000000\n",
      "25%        7.000000\n",
      "50%       22.000000\n",
      "75%       72.500000\n",
      "max      324.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43] : 43\n",
      "top- 5 = [12, 151, 169] : 169\n",
      "top- 10 = [24, 232, 267] : 267\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       54.447000\n",
      "std        69.017665\n",
      "min         1.000000\n",
      "25%         9.500000\n",
      "50%        30.500000\n",
      "75%        61.250000\n",
      "max       425.000000\n",
      "dtype: float64\n",
      "epoch took: 58.0809631348\n",
      "\n",
      "Epoch 2 ...\n",
      "Epoch  2 Update  147 Cost  0.0750384479761\n",
      "Epoch  2 Update  168 Cost  0.0610469020903\n",
      "Epoch  2 Update  189 Cost  0.124674670398\n",
      "Training error: 0.124674670398 = 0.0985486656427 == 0.0985486060381 ?? 0.0786473825574\n",
      "Training_correct error 0.101944170892\n",
      "Testing_correct error: 0.102379269898\n",
      "top- 1 = [3, 48, 50, 86] : 86\n",
      "top- 5 = [21, 199, 208, 293] : 293\n",
      "top- 10 = [30, 304, 320, 431] : 431\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      38.222723\n",
      "std       53.008296\n",
      "min        1.000000\n",
      "25%        4.000000\n",
      "50%       15.000000\n",
      "75%       50.250000\n",
      "max      398.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81] : 81\n",
      "top- 5 = [12, 151, 169, 246] : 246\n",
      "top- 10 = [24, 232, 267, 378] : 378\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       44.222000\n",
      "std        56.271624\n",
      "min         1.000000\n",
      "25%         5.500000\n",
      "50%        17.500000\n",
      "75%        62.000000\n",
      "max       432.000000\n",
      "dtype: float64\n",
      "epoch took: 57.9050438404\n",
      "\n",
      "Epoch 3 ...\n",
      "Epoch  3 Update  210 Cost  0.0899893343449\n",
      "Epoch  3 Update  231 Cost  0.0913775414228\n",
      "Epoch  3 Update  252 Cost  0.0783319026232\n",
      "Training error: 0.0783319026232 = 0.0997497886419 == 0.0997497290373 ?? 0.0981878265738\n",
      "Training_correct error 0.0821037888527\n",
      "Testing_correct error: 0.083479039371\n",
      "top- 1 = [3, 48, 50, 86, 76] : 76\n",
      "top- 5 = [21, 199, 208, 293, 310] : 310\n",
      "top- 10 = [30, 304, 320, 431, 411] : 411\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      37.868368\n",
      "std       57.583045\n",
      "min        1.000000\n",
      "25%        3.500000\n",
      "50%       16.000000\n",
      "75%       42.000000\n",
      "max      378.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66] : 66\n",
      "top- 5 = [12, 151, 169, 246, 242] : 242\n",
      "top- 10 = [24, 232, 267, 378, 344] : 344\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       43.143000\n",
      "std        60.577844\n",
      "min         1.000000\n",
      "25%         6.000000\n",
      "50%        21.250000\n",
      "75%        46.000000\n",
      "max       417.000000\n",
      "dtype: float64\n",
      "epoch took: 58.159487009\n",
      "\n",
      "Epoch 4 ...\n",
      "Epoch  4 Update  273 Cost  0.102640353143\n",
      "Epoch  4 Update  294 Cost  0.0877730324864\n",
      "Epoch  4 Update  315 Cost  0.119521379471\n",
      "Training error: 0.119521379471 = 0.086856238544 == 0.0868561714888 ?? 0.0563039854169\n",
      "Training_correct error 0.107121005654\n",
      "Testing_correct error: 0.120220825076\n",
      "top- 1 = [3, 48, 50, 86, 76, 118] : 118\n",
      "top- 5 = [21, 199, 208, 293, 310, 392] : 392\n",
      "top- 10 = [30, 304, 320, 431, 411, 533] : 533\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      24.363864\n",
      "std       37.543915\n",
      "min        1.000000\n",
      "25%        3.000000\n",
      "50%        9.000000\n",
      "75%       26.500000\n",
      "max      279.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66, 92] : 92\n",
      "top- 5 = [12, 151, 169, 246, 242, 285] : 285\n",
      "top- 10 = [24, 232, 267, 378, 344, 455] : 455\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       35.453000\n",
      "std        50.465682\n",
      "min         1.000000\n",
      "25%         4.000000\n",
      "50%        12.500000\n",
      "75%        41.250000\n",
      "max       324.000000\n",
      "dtype: float64\n",
      "epoch took: 58.2793269157\n",
      "\n",
      "Epoch 5 ...\n",
      "Epoch  5 Update  336 Cost  0.0586893036962\n",
      "Epoch  5 Update  357 Cost  0.0592846162617\n",
      "Epoch  5 Update  378 Cost  0.172971352935\n",
      "Training error: 0.172971352935 = 0.0897238701582 == 0.0897238180041 ?? 0.0990123078227\n",
      "Training_correct error 0.0772184580564\n",
      "Testing_correct error: 0.0757179409266\n",
      "top- 1 = [3, 48, 50, 86, 76, 118, 93] : 93\n",
      "top- 5 = [21, 199, 208, 293, 310, 392, 359] : 359\n",
      "top- 10 = [30, 304, 320, 431, 411, 533, 495] : 495\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      28.575576\n",
      "std       44.863448\n",
      "min        1.000000\n",
      "25%        3.000000\n",
      "50%       10.500000\n",
      "75%       31.000000\n",
      "max      277.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66, 92, 79] : 79\n",
      "top- 5 = [12, 151, 169, 246, 242, 285, 279] : 279\n",
      "top- 10 = [24, 232, 267, 378, 344, 455, 443] : 443\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       35.241500\n",
      "std        50.618402\n",
      "min         1.000000\n",
      "25%         4.500000\n",
      "50%        13.750000\n",
      "75%        41.000000\n",
      "max       273.000000\n",
      "dtype: float64\n",
      "epoch took: 58.5034840107\n",
      "\n",
      "Epoch 6 ...\n",
      "Epoch  6 Update  399 Cost  0.0754013508558\n",
      "Epoch  6 Update  420 Cost  0.0876249596477\n",
      "Epoch  6 Update  441 Cost  0.165213420987\n",
      "Training error: 0.165213420987 = 0.0925588086247 == 0.0925587564707 ?? 0.0867500379682\n",
      "Training_correct error 0.075753249228\n",
      "Testing_correct error: 0.0882906317711\n",
      "top- 1 = [3, 48, 50, 86, 76, 118, 93, 92] : 92\n",
      "top- 5 = [21, 199, 208, 293, 310, 392, 359, 406] : 406\n",
      "top- 10 = [30, 304, 320, 431, 411, 533, 495, 582] : 582\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      24.823824\n",
      "std       42.170965\n",
      "min        1.000000\n",
      "25%        3.000000\n",
      "50%        7.500000\n",
      "75%       21.000000\n",
      "max      344.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66, 92, 79, 80] : 80\n",
      "top- 5 = [12, 151, 169, 246, 242, 285, 279, 306] : 306\n",
      "top- 10 = [24, 232, 267, 378, 344, 455, 443, 470] : 470\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       30.191000\n",
      "std        47.151795\n",
      "min         1.000000\n",
      "25%         4.000000\n",
      "50%        11.000000\n",
      "75%        25.000000\n",
      "max       333.000000\n",
      "dtype: float64\n",
      "epoch took: 58.1983430386\n",
      "\n",
      "Epoch 7 ...\n",
      "Epoch  7 Update  462 Cost  0.0874141305685\n",
      "Epoch  7 Update  483 Cost  0.0745683759451\n",
      "Epoch  7 Update  504 Cost  0.084833085537\n",
      "Training error: 0.084833085537 = 0.0767989233136 == 0.0767988786101 ?? 0.0730526298285\n",
      "Training_correct error 0.0734879598022\n",
      "Testing_correct error: 0.0873811766505\n",
      "top- 1 = [3, 48, 50, 86, 76, 118, 93, 92, 202] : 202\n",
      "top- 5 = [21, 199, 208, 293, 310, 392, 359, 406, 515] : 515\n",
      "top- 10 = [30, 304, 320, 431, 411, 533, 495, 582, 690] : 690\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      18.065065\n",
      "std       33.711923\n",
      "min        1.000000\n",
      "25%        1.500000\n",
      "50%        5.000000\n",
      "75%       14.500000\n",
      "max      263.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66, 92, 79, 80, 135] : 135\n",
      "top- 5 = [12, 151, 169, 246, 242, 285, 279, 306, 423] : 423\n",
      "top- 10 = [24, 232, 267, 378, 344, 455, 443, 470, 557] : 557\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       27.840000\n",
      "std        46.303525\n",
      "min         1.000000\n",
      "25%         2.500000\n",
      "50%         8.000000\n",
      "75%        27.250000\n",
      "max       291.000000\n",
      "dtype: float64\n",
      "epoch took: 58.6975121498\n",
      "\n",
      "Epoch 8 ...\n",
      "Epoch  8 Update  525 Cost  0.0818914100528\n",
      "Epoch  8 Update  546 Cost  0.102885998785\n",
      "Epoch  8 Update  567 Cost  0.124444670975\n",
      "Training error: 0.124444670975 = 0.0649016872048 == 0.0649016425014 ?? 0.0534915179014\n",
      "Training_correct error 0.0729025006294\n",
      "Testing_correct error: 0.0839079767466\n",
      "top- 1 = [3, 48, 50, 86, 76, 118, 93, 92, 202, 205] : 205\n",
      "top- 5 = [21, 199, 208, 293, 310, 392, 359, 406, 515, 564] : 564\n",
      "top- 10 = [30, 304, 320, 431, 411, 533, 495, 582, 690, 738] : 738\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      12.638639\n",
      "std       22.141127\n",
      "min        1.000000\n",
      "25%        1.500000\n",
      "50%        4.000000\n",
      "75%       11.000000\n",
      "max      162.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66, 92, 79, 80, 135, 156] : 156\n",
      "top- 5 = [12, 151, 169, 246, 242, 285, 279, 306, 423, 468] : 468\n",
      "top- 10 = [24, 232, 267, 378, 344, 455, 443, 470, 557, 596] : 596\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       19.704000\n",
      "std        34.629317\n",
      "min         1.000000\n",
      "25%         2.000000\n",
      "50%         6.000000\n",
      "75%        19.625000\n",
      "max       206.000000\n",
      "dtype: float64\n",
      "epoch took: 58.1581718922\n",
      "\n",
      "Epoch 9 ...\n",
      "Epoch  9 Update  588 Cost  0.08970028162\n",
      "Epoch  9 Update  609 Cost  0.0695525407791\n",
      "Epoch  9 Update  630 Cost  0.0319570749998\n",
      "Training error: 0.0319570749998 = 0.0614430606365 == 0.0614430196583 ?? 0.0510552600026\n",
      "Training_correct error 0.0697435438633\n",
      "Testing_correct error: 0.0887727290392\n",
      "top- 1 = [3, 48, 50, 86, 76, 118, 93, 92, 202, 205, 225] : 225\n",
      "top- 5 = [21, 199, 208, 293, 310, 392, 359, 406, 515, 564, 610] : 610\n",
      "top- 10 = [30, 304, 320, 431, 411, 533, 495, 582, 690, 738, 736] : 736\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      12.092092\n",
      "std       22.614483\n",
      "min        1.000000\n",
      "25%        1.500000\n",
      "50%        3.500000\n",
      "75%       11.250000\n",
      "max      208.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66, 92, 79, 80, 135, 156, 166] : 166\n",
      "top- 5 = [12, 151, 169, 246, 242, 285, 279, 306, 423, 468, 479] : 479\n",
      "top- 10 = [24, 232, 267, 378, 344, 455, 443, 470, 557, 596, 614] : 614\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.00000\n",
      "mean       21.41400\n",
      "std        38.95274\n",
      "min         1.00000\n",
      "25%         2.00000\n",
      "50%         6.00000\n",
      "75%        22.50000\n",
      "max       271.00000\n",
      "dtype: float64\n",
      "epoch took: 58.2980179787\n",
      "\n",
      "Epoch 10 ...\n",
      "Epoch  10 Update  651 Cost  0.063362672925\n",
      "Epoch  10 Update  672 Cost  0.0694444775581\n",
      "Epoch  10 Update  693 Cost  0.0757331997156\n",
      "Training error: 0.0757331997156 = 0.064365811646 == 0.0643657743931 ?? 0.0498519651592\n",
      "Training_correct error 0.0788674727082\n",
      "Testing_correct error: 0.0984400510788\n",
      "top- 1 = [3, 48, 50, 86, 76, 118, 93, 92, 202, 205, 225, 200] : 200\n",
      "top- 5 = [21, 199, 208, 293, 310, 392, 359, 406, 515, 564, 610, 598] : 598\n",
      "top- 10 = [30, 304, 320, 431, 411, 533, 495, 582, 690, 738, 736, 745] : 745\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      12.458458\n",
      "std       22.218322\n",
      "min        1.000000\n",
      "25%        1.500000\n",
      "50%        3.500000\n",
      "75%       11.000000\n",
      "max      173.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66, 92, 79, 80, 135, 156, 166, 148] : 148\n",
      "top- 5 = [12, 151, 169, 246, 242, 285, 279, 306, 423, 468, 479, 475] : 475\n",
      "top- 10 = [24, 232, 267, 378, 344, 455, 443, 470, 557, 596, 614, 605] : 605\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       21.715000\n",
      "std        38.724454\n",
      "min         1.000000\n",
      "25%         2.000000\n",
      "50%         6.000000\n",
      "75%        19.000000\n",
      "max       289.000000\n",
      "dtype: float64\n",
      "epoch took: 58.6182560921\n",
      "\n",
      "Epoch 11 ...\n",
      "Epoch  11 Update  714 Cost  0.0437170118093\n",
      "Epoch  11 Update  735 Cost  0.114784456789\n",
      "Epoch  11 Update  756 Cost  0.0514871068299\n",
      "Training error: 0.0514871068299 = 0.0676115751266 == 0.0676115229726 ?? 0.0547507330775\n",
      "Training_correct error 0.0683713331819\n",
      "Testing_correct error: 0.0836198031902\n",
      "top- 1 = [3, 48, 50, 86, 76, 118, 93, 92, 202, 205, 225, 200, 161] : 161\n",
      "top- 5 = [21, 199, 208, 293, 310, 392, 359, 406, 515, 564, 610, 598, 536] : 536\n",
      "top- 10 = [30, 304, 320, 431, 411, 533, 495, 582, 690, 738, 736, 745, 698] : 698\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      13.920921\n",
      "std       23.795582\n",
      "min        1.000000\n",
      "25%        2.000000\n",
      "50%        4.500000\n",
      "75%       14.000000\n",
      "max      294.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66, 92, 79, 80, 135, 156, 166, 148, 115] : 115\n",
      "top- 5 = [12, 151, 169, 246, 242, 285, 279, 306, 423, 468, 479, 475, 452] : 452\n",
      "top- 10 = [24, 232, 267, 378, 344, 455, 443, 470, 557, 596, 614, 605, 622] : 622\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.00000\n",
      "mean       20.02600\n",
      "std        33.57005\n",
      "min         1.00000\n",
      "25%         2.00000\n",
      "50%         6.25000\n",
      "75%        17.50000\n",
      "max       217.00000\n",
      "dtype: float64\n",
      "epoch took: 58.0322408676\n",
      "\n",
      "Epoch 12 ...\n",
      "Epoch  12 Update  777 Cost  0.0336507894099\n",
      "Epoch  12 Update  798 Cost  0.0849446207285\n",
      "Epoch  12 Update  819 Cost  0.0798107907176\n",
      "Training error: 0.0798107907176 = 0.0687031969428 == 0.0687031447887 ?? 0.0467145889997\n",
      "Training_correct error 0.0725525915623\n",
      "Testing_correct error: 0.0905041620135\n",
      "top- 1 = [3, 48, 50, 86, 76, 118, 93, 92, 202, 205, 225, 200, 161, 169] : 169\n",
      "top- 5 = [21, 199, 208, 293, 310, 392, 359, 406, 515, 564, 610, 598, 536, 532] : 532\n",
      "top- 10 = [30, 304, 320, 431, 411, 533, 495, 582, 690, 738, 736, 745, 698, 702] : 702\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      13.523524\n",
      "std       23.272234\n",
      "min        1.000000\n",
      "25%        2.000000\n",
      "50%        5.000000\n",
      "75%       13.000000\n",
      "max      288.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66, 92, 79, 80, 135, 156, 166, 148, 115, 127] : 127\n",
      "top- 5 = [12, 151, 169, 246, 242, 285, 279, 306, 423, 468, 479, 475, 452, 485] : 485\n",
      "top- 10 = [24, 232, 267, 378, 344, 455, 443, 470, 557, 596, 614, 605, 622, 672] : 672\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       17.531000\n",
      "std        29.637196\n",
      "min         1.000000\n",
      "25%         2.000000\n",
      "50%         5.500000\n",
      "75%        15.000000\n",
      "max       190.000000\n",
      "dtype: float64\n",
      "epoch took: 58.2423369884\n",
      "\n",
      "Epoch 13 ...\n",
      "Epoch  13 Update  840 Cost  0.072304174304\n",
      "Epoch  13 Update  861 Cost  0.0690368041396\n",
      "Epoch  13 Update  882 Cost  0.0413294769824\n",
      "Training error: 0.0413294769824 = 0.0671595260501 == 0.0671594813466 ?? 0.0494841001928\n",
      "Training_correct error 0.0689151287079\n",
      "Testing_correct error: 0.0870760679245\n",
      "top- 1 = [3, 48, 50, 86, 76, 118, 93, 92, 202, 205, 225, 200, 161, 169, 186] : 186\n",
      "top- 5 = [21, 199, 208, 293, 310, 392, 359, 406, 515, 564, 610, 598, 536, 532, 595] : 595\n",
      "top- 10 = [30, 304, 320, 431, 411, 533, 495, 582, 690, 738, 736, 745, 698, 702, 716] : 716\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      12.617618\n",
      "std       21.336281\n",
      "min        1.000000\n",
      "25%        1.500000\n",
      "50%        3.500000\n",
      "75%       12.500000\n",
      "max      245.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66, 92, 79, 80, 135, 156, 166, 148, 115, 127, 143] : 143\n",
      "top- 5 = [12, 151, 169, 246, 242, 285, 279, 306, 423, 468, 479, 475, 452, 485, 529] : 529\n",
      "top- 10 = [24, 232, 267, 378, 344, 455, 443, 470, 557, 596, 614, 605, 622, 672, 665] : 665\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       15.855000\n",
      "std        25.879059\n",
      "min         1.000000\n",
      "25%         2.000000\n",
      "50%         5.000000\n",
      "75%        16.000000\n",
      "max       181.000000\n",
      "dtype: float64\n",
      "epoch took: 58.6989569664\n",
      "\n",
      "Epoch 14 ...\n",
      "Epoch  14 Update  903 Cost  0.0230688545853\n",
      "Epoch  14 Update  924 Cost  0.0697318464518\n",
      "Epoch  14 Update  945 Cost  0.0603713318706\n",
      "Training error: 0.0603713318706 = 0.055987522006 == 0.0559874773026 ?? 0.0528533793986\n",
      "Training_correct error 0.0552660189569\n",
      "Testing_correct error: 0.0641145408154\n",
      "top- 1 = [3, 48, 50, 86, 76, 118, 93, 92, 202, 205, 225, 200, 161, 169, 186, 233] : 233\n",
      "top- 5 = [21, 199, 208, 293, 310, 392, 359, 406, 515, 564, 610, 598, 536, 532, 595, 653] : 653\n",
      "top- 10 = [30, 304, 320, 431, 411, 533, 495, 582, 690, 738, 736, 745, 698, 702, 716, 779] : 779\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       8.545546\n",
      "std       13.568803\n",
      "min        1.000000\n",
      "25%        1.500000\n",
      "50%        3.000000\n",
      "75%        8.500000\n",
      "max      157.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66, 92, 79, 80, 135, 156, 166, 148, 115, 127, 143, 162] : 162\n",
      "top- 5 = [12, 151, 169, 246, 242, 285, 279, 306, 423, 468, 479, 475, 452, 485, 529, 530] : 530\n",
      "top- 10 = [24, 232, 267, 378, 344, 455, 443, 470, 557, 596, 614, 605, 622, 672, 665, 698] : 698\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       11.858000\n",
      "std        17.776947\n",
      "min         1.000000\n",
      "25%         2.000000\n",
      "50%         4.750000\n",
      "75%        13.125000\n",
      "max       120.000000\n",
      "dtype: float64\n",
      "epoch took: 58.597260952\n",
      "\n",
      "Epoch 15 ...\n",
      "Epoch  15 Update  966 Cost  0.0660632997751\n",
      "Epoch  15 Update  987 Cost  0.0347093641758\n",
      "Epoch  15 Update  1008 Cost  0.039114356041\n",
      "Training error: 0.039114356041 = 0.0527125261724 == 0.0527124814689 ?? 0.0339343771338\n",
      "Training_correct error 0.0673270151019\n",
      "Testing_correct error: 0.07998406142\n",
      "top- 1 = [3, 48, 50, 86, 76, 118, 93, 92, 202, 205, 225, 200, 161, 169, 186, 233, 259] : 259\n",
      "top- 5 = [21, 199, 208, 293, 310, 392, 359, 406, 515, 564, 610, 598, 536, 532, 595, 653, 714] : 714\n",
      "top- 10 = [30, 304, 320, 431, 411, 533, 495, 582, 690, 738, 736, 745, 698, 702, 716, 779, 831] : 831\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       7.254254\n",
      "std       13.664529\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        2.500000\n",
      "75%        6.000000\n",
      "max      155.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66, 92, 79, 80, 135, 156, 166, 148, 115, 127, 143, 162, 193] : 193\n",
      "top- 5 = [12, 151, 169, 246, 242, 285, 279, 306, 423, 468, 479, 475, 452, 485, 529, 530, 630] : 630\n",
      "top- 10 = [24, 232, 267, 378, 344, 455, 443, 470, 557, 596, 614, 605, 622, 672, 665, 698, 753] : 753\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       11.165000\n",
      "std        18.126012\n",
      "min         1.000000\n",
      "25%         1.500000\n",
      "50%         3.000000\n",
      "75%        10.000000\n",
      "max       151.000000\n",
      "dtype: float64\n",
      "epoch took: 58.198456049\n",
      "\n",
      "Epoch 16 ...\n",
      "Epoch  16 Update  1029 Cost  0.0442724451423\n",
      "Epoch  16 Update  1050 Cost  0.0652790665627\n",
      "Epoch  16 Update  1071 Cost  0.0747208446264\n",
      "Training error: 0.0747208446264 = 0.0483071468771 == 0.0483071021736 ?? 0.0346627309918\n",
      "Training_correct error 0.0516675412655\n",
      "Testing_correct error: 0.0678435266018\n",
      "top- 1 = [3, 48, 50, 86, 76, 118, 93, 92, 202, 205, 225, 200, 161, 169, 186, 233, 259, 272] : 272\n",
      "top- 5 = [21, 199, 208, 293, 310, 392, 359, 406, 515, 564, 610, 598, 536, 532, 595, 653, 714, 735] : 735\n",
      "top- 10 = [30, 304, 320, 431, 411, 533, 495, 582, 690, 738, 736, 745, 698, 702, 716, 779, 831, 843] : 843\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       6.183183\n",
      "std       11.160784\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        2.500000\n",
      "75%        6.000000\n",
      "max      164.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66, 92, 79, 80, 135, 156, 166, 148, 115, 127, 143, 162, 193, 194] : 194\n",
      "top- 5 = [12, 151, 169, 246, 242, 285, 279, 306, 423, 468, 479, 475, 452, 485, 529, 530, 630, 636] : 636\n",
      "top- 10 = [24, 232, 267, 378, 344, 455, 443, 470, 557, 596, 614, 605, 622, 672, 665, 698, 753, 802] : 802\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean        9.038000\n",
      "std        14.761859\n",
      "min         1.000000\n",
      "25%         1.500000\n",
      "50%         3.500000\n",
      "75%         8.000000\n",
      "max       135.000000\n",
      "dtype: float64\n",
      "epoch took: 58.2662038803\n",
      "\n",
      "Epoch 17 ...\n",
      "Epoch  17 Update  1092 Cost  0.0414074324071\n",
      "Epoch  17 Update  1113 Cost  0.071006372571\n",
      "Epoch  17 Update  1134 Cost  0.0419829301536\n",
      "Training error: 0.0419829301536 = 0.0467457361519 == 0.046745698899 ?? 0.0321531258523\n",
      "Training_correct error 0.0499443151057\n",
      "Testing_correct error: 0.0650392621756\n",
      "top- 1 = [3, 48, 50, 86, 76, 118, 93, 92, 202, 205, 225, 200, 161, 169, 186, 233, 259, 272, 263] : 263\n",
      "top- 5 = [21, 199, 208, 293, 310, 392, 359, 406, 515, 564, 610, 598, 536, 532, 595, 653, 714, 735, 749] : 749\n",
      "top- 10 = [30, 304, 320, 431, 411, 533, 495, 582, 690, 738, 736, 745, 698, 702, 716, 779, 831, 843, 864] : 864\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       5.903904\n",
      "std       10.514103\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        2.500000\n",
      "75%        5.250000\n",
      "max      145.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66, 92, 79, 80, 135, 156, 166, 148, 115, 127, 143, 162, 193, 194, 198] : 198\n",
      "top- 5 = [12, 151, 169, 246, 242, 285, 279, 306, 423, 468, 479, 475, 452, 485, 529, 530, 630, 636, 685] : 685\n",
      "top- 10 = [24, 232, 267, 378, 344, 455, 443, 470, 557, 596, 614, 605, 622, 672, 665, 698, 753, 802, 807] : 807\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean        8.325000\n",
      "std        13.669537\n",
      "min         1.000000\n",
      "25%         1.500000\n",
      "50%         3.000000\n",
      "75%         7.000000\n",
      "max       123.000000\n",
      "dtype: float64\n",
      "epoch took: 58.3442080021\n",
      "\n",
      "Epoch 18 ...\n",
      "Epoch  18 Update  1155 Cost  0.0281577594578\n",
      "Epoch  18 Update  1176 Cost  0.0279857199639\n",
      "Epoch  18 Update  1197 Cost  0.031181935221\n",
      "Training error: 0.031181935221 = 0.0448585934937 == 0.0448585562408 ?? 0.0304311979562\n",
      "Training_correct error 0.0507593862712\n",
      "Testing_correct error: 0.0675635188818\n",
      "top- 1 = [3, 48, 50, 86, 76, 118, 93, 92, 202, 205, 225, 200, 161, 169, 186, 233, 259, 272, 263, 297] : 297\n",
      "top- 5 = [21, 199, 208, 293, 310, 392, 359, 406, 515, 564, 610, 598, 536, 532, 595, 653, 714, 735, 749, 776] : 776\n",
      "top- 10 = [30, 304, 320, 431, 411, 533, 495, 582, 690, 738, 736, 745, 698, 702, 716, 779, 831, 843, 864, 887] : 887\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       5.160160\n",
      "std        9.167385\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        2.000000\n",
      "75%        5.000000\n",
      "max       96.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66, 92, 79, 80, 135, 156, 166, 148, 115, 127, 143, 162, 193, 194, 198, 225] : 225\n",
      "top- 5 = [12, 151, 169, 246, 242, 285, 279, 306, 423, 468, 479, 475, 452, 485, 529, 530, 630, 636, 685, 701] : 701\n",
      "top- 10 = [24, 232, 267, 378, 344, 455, 443, 470, 557, 596, 614, 605, 622, 672, 665, 698, 753, 802, 807, 817] : 817\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean        8.575000\n",
      "std        15.275134\n",
      "min         1.000000\n",
      "25%         1.500000\n",
      "50%         2.500000\n",
      "75%         7.000000\n",
      "max       134.000000\n",
      "dtype: float64\n",
      "epoch took: 58.0651648045\n",
      "\n",
      "Epoch 19 ...\n",
      "Epoch  19 Update  1218 Cost  0.0584981888533\n",
      "Epoch  19 Update  1239 Cost  0.0524895340204\n",
      "Epoch  19 Update  1260 Cost  0.0314953140914\n",
      "Training error: 0.0314953140914 = 0.0442051738501 == 0.0442051365972 ?? 0.0427306927741\n",
      "Training_correct error 0.0425166003406\n",
      "Testing_correct error: 0.0482059717178\n",
      "top- 1 = [3, 48, 50, 86, 76, 118, 93, 92, 202, 205, 225, 200, 161, 169, 186, 233, 259, 272, 263, 297, 274] : 274\n",
      "top- 5 = [21, 199, 208, 293, 310, 392, 359, 406, 515, 564, 610, 598, 536, 532, 595, 653, 714, 735, 749, 776, 778] : 778\n",
      "top- 10 = [30, 304, 320, 431, 411, 533, 495, 582, 690, 738, 736, 745, 698, 702, 716, 779, 831, 843, 864, 887, 881] : 881\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       4.926927\n",
      "std        7.862557\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        2.000000\n",
      "75%        5.000000\n",
      "max      100.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66, 92, 79, 80, 135, 156, 166, 148, 115, 127, 143, 162, 193, 194, 198, 225, 206] : 206\n",
      "top- 5 = [12, 151, 169, 246, 242, 285, 279, 306, 423, 468, 479, 475, 452, 485, 529, 530, 630, 636, 685, 701, 689] : 689\n",
      "top- 10 = [24, 232, 267, 378, 344, 455, 443, 470, 557, 596, 614, 605, 622, 672, 665, 698, 753, 802, 807, 817, 814] : 814\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean        6.672000\n",
      "std         9.635105\n",
      "min         1.000000\n",
      "25%         1.500000\n",
      "50%         3.000000\n",
      "75%         7.000000\n",
      "max       109.000000\n",
      "dtype: float64\n",
      "epoch took: 58.2002701759\n",
      "saving the model...\n",
      "Evaluate the model using fast estimation...\n",
      "count    999.000000\n",
      "mean       4.926927\n",
      "std        7.862557\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        2.000000\n",
      "75%        5.000000\n",
      "max      100.000000\n",
      "dtype: float64\n",
      "count    1000.000000\n",
      "mean        6.672000\n",
      "std         9.635105\n",
      "min         1.000000\n",
      "25%         1.500000\n",
      "50%         3.000000\n",
      "75%         7.000000\n",
      "max       109.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "run main.py\n",
    "# 这里面的 Training error: 0.257755994797 = 0.257755994797 == 0.25775590539 ?? 0.190321952105\n",
    "# 分别呆板了: cst【错误】, chkterr2(train)[0]/16【正确】, self.get_mse(train)【正确】, self.f_cost(emb1,mas1,emb2,mas2,y2)[chkterr2函数中]【错误】"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 【注意】不要直接使用cst变量作为cost，因为这里的cst是最后一个batch的cost而已，不是全部的\n",
    "#  print \"Training error:\", cst, \"=\", self.chkterr2(train)[0]/16, \"==\" #, self.get_mse(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/site-packages/gensim/utils.py:1015: UserWarning: Pattern library is not installed, lemmatization won't be available.\n",
      "  warnings.warn(\"Pattern library is not installed, lemmatization won't be available.\")\n",
      "Using gpu device 0: GeForce GTX TITAN X (CNMeM is enabled with initial size: 95.0% of memory, cuDNN 4007)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting to training the model..., saving to weights/201612140109_e10_1k1k.p\n",
      "Training\n",
      "the length of the training data is  1998\n",
      "Before trianing, the error is:\n",
      "Training error: 0.257755994797\n",
      "Training_correct error 0.337905704975\n",
      "Testing_correct error: 0.335038632154\n",
      "top- 1 = [3] : 3\n",
      "top- 5 = [21] : 21\n",
      "top- 10 = [30] : 30\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean     501.754755\n",
      "std      264.648264\n",
      "min        1.000000\n",
      "25%      290.500000\n",
      "50%      541.500000\n",
      "75%      711.000000\n",
      "max      978.000000\n",
      "dtype: float64\n",
      "top- 1 = [1] : 1\n",
      "top- 5 = [12] : 12\n",
      "top- 10 = [24] : 24\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean      551.164000\n",
      "std       260.994743\n",
      "min         1.000000\n",
      "25%       362.625000\n",
      "50%       559.000000\n",
      "75%       775.500000\n",
      "max       985.000000\n",
      "dtype: float64\n",
      "\n",
      "Epoch 0 ...\n",
      "Epoch  0 Update  21 Cost  0.182155281305\n",
      "Epoch  0 Update  42 Cost  0.167487800121\n",
      "Epoch  0 Update  63 Cost  0.207024231553\n",
      "Training error: 0.125692605972\n",
      "Training_correct error 0.118412218988\n",
      "Testing_correct error: 0.114850081503\n",
      "top- 1 = [3, 48] : 48\n",
      "top- 5 = [21, 199] : 199\n",
      "top- 10 = [30, 304] : 304\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      52.060060\n",
      "std       68.136176\n",
      "min        1.000000\n",
      "25%        7.500000\n",
      "50%       28.000000\n",
      "75%       63.000000\n",
      "max      370.500000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46] : 46\n",
      "top- 5 = [12, 151] : 151\n",
      "top- 10 = [24, 232] : 232\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       61.457000\n",
      "std        73.761911\n",
      "min         1.000000\n",
      "25%        11.500000\n",
      "50%        34.000000\n",
      "75%        85.000000\n",
      "max       448.000000\n",
      "dtype: float64\n",
      "epoch took: 48.1631519794\n",
      "\n",
      "Epoch 1 ...\n",
      "Epoch  1 Update  84 Cost  0.10254971683\n",
      "Epoch  1 Update  105 Cost  0.11504471302\n",
      "Epoch  1 Update  126 Cost  0.0893101021647\n",
      "Training error: 0.117395535111\n",
      "Training_correct error 0.0951170623302\n",
      "Testing_correct error: 0.0903676599264\n",
      "top- 1 = [3, 48, 50] : 50\n",
      "top- 5 = [21, 199, 208] : 208\n",
      "top- 10 = [30, 304, 320] : 320\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      52.107608\n",
      "std       67.633366\n",
      "min        1.000000\n",
      "25%        7.000000\n",
      "50%       22.000000\n",
      "75%       72.500000\n",
      "max      324.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43] : 43\n",
      "top- 5 = [12, 151, 169] : 169\n",
      "top- 10 = [24, 232, 267] : 267\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       54.447000\n",
      "std        69.017665\n",
      "min         1.000000\n",
      "25%         9.500000\n",
      "50%        30.500000\n",
      "75%        61.250000\n",
      "max       425.000000\n",
      "dtype: float64\n",
      "epoch took: 48.5938029289\n",
      "\n",
      "Epoch 2 ...\n",
      "Epoch  2 Update  147 Cost  0.0750384479761\n",
      "Epoch  2 Update  168 Cost  0.0610469020903\n",
      "Epoch  2 Update  189 Cost  0.124674670398\n",
      "Training error: 0.0985486656427\n",
      "Training_correct error 0.101944170892\n",
      "Testing_correct error: 0.102379269898\n",
      "top- 1 = [3, 48, 50, 86] : 86\n",
      "top- 5 = [21, 199, 208, 293] : 293\n",
      "top- 10 = [30, 304, 320, 431] : 431\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      38.222723\n",
      "std       53.008296\n",
      "min        1.000000\n",
      "25%        4.000000\n",
      "50%       15.000000\n",
      "75%       50.250000\n",
      "max      398.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81] : 81\n",
      "top- 5 = [12, 151, 169, 246] : 246\n",
      "top- 10 = [24, 232, 267, 378] : 378\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       44.222000\n",
      "std        56.271624\n",
      "min         1.000000\n",
      "25%         5.500000\n",
      "50%        17.500000\n",
      "75%        62.000000\n",
      "max       432.000000\n",
      "dtype: float64\n",
      "epoch took: 48.2974469662\n",
      "\n",
      "Epoch 3 ...\n",
      "Epoch  3 Update  210 Cost  0.0899893343449\n",
      "Epoch  3 Update  231 Cost  0.0913775414228\n",
      "Epoch  3 Update  252 Cost  0.0783319026232\n",
      "Training error: 0.0997497886419\n",
      "Training_correct error 0.0821037888527\n",
      "Testing_correct error: 0.083479039371\n",
      "top- 1 = [3, 48, 50, 86, 76] : 76\n",
      "top- 5 = [21, 199, 208, 293, 310] : 310\n",
      "top- 10 = [30, 304, 320, 431, 411] : 411\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      37.868368\n",
      "std       57.583045\n",
      "min        1.000000\n",
      "25%        3.500000\n",
      "50%       16.000000\n",
      "75%       42.000000\n",
      "max      378.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66] : 66\n",
      "top- 5 = [12, 151, 169, 246, 242] : 242\n",
      "top- 10 = [24, 232, 267, 378, 344] : 344\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       43.143000\n",
      "std        60.577844\n",
      "min         1.000000\n",
      "25%         6.000000\n",
      "50%        21.250000\n",
      "75%        46.000000\n",
      "max       417.000000\n",
      "dtype: float64\n",
      "epoch took: 48.6113131046\n",
      "\n",
      "Epoch 4 ...\n",
      "Epoch  4 Update  273 Cost  0.102640353143\n",
      "Epoch  4 Update  294 Cost  0.0877730324864\n",
      "Epoch  4 Update  315 Cost  0.119521379471\n",
      "Training error: 0.086856238544\n",
      "Training_correct error 0.107121005654\n",
      "Testing_correct error: 0.120220825076\n",
      "top- 1 = [3, 48, 50, 86, 76, 118] : 118\n",
      "top- 5 = [21, 199, 208, 293, 310, 392] : 392\n",
      "top- 10 = [30, 304, 320, 431, 411, 533] : 533\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      24.363864\n",
      "std       37.543915\n",
      "min        1.000000\n",
      "25%        3.000000\n",
      "50%        9.000000\n",
      "75%       26.500000\n",
      "max      279.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66, 92] : 92\n",
      "top- 5 = [12, 151, 169, 246, 242, 285] : 285\n",
      "top- 10 = [24, 232, 267, 378, 344, 455] : 455\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       35.453000\n",
      "std        50.465682\n",
      "min         1.000000\n",
      "25%         4.000000\n",
      "50%        12.500000\n",
      "75%        41.250000\n",
      "max       324.000000\n",
      "dtype: float64\n",
      "epoch took: 48.7130169868\n",
      "\n",
      "Epoch 5 ...\n",
      "Epoch  5 Update  336 Cost  0.0586893036962\n",
      "Epoch  5 Update  357 Cost  0.0592846162617\n",
      "Epoch  5 Update  378 Cost  0.172971352935\n",
      "Training error: 0.0897238701582\n",
      "Training_correct error 0.0772184580564\n",
      "Testing_correct error: 0.0757179409266\n",
      "top- 1 = [3, 48, 50, 86, 76, 118, 93] : 93\n",
      "top- 5 = [21, 199, 208, 293, 310, 392, 359] : 359\n",
      "top- 10 = [30, 304, 320, 431, 411, 533, 495] : 495\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      28.575576\n",
      "std       44.863448\n",
      "min        1.000000\n",
      "25%        3.000000\n",
      "50%       10.500000\n",
      "75%       31.000000\n",
      "max      277.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66, 92, 79] : 79\n",
      "top- 5 = [12, 151, 169, 246, 242, 285, 279] : 279\n",
      "top- 10 = [24, 232, 267, 378, 344, 455, 443] : 443\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       35.241500\n",
      "std        50.618402\n",
      "min         1.000000\n",
      "25%         4.500000\n",
      "50%        13.750000\n",
      "75%        41.000000\n",
      "max       273.000000\n",
      "dtype: float64\n",
      "epoch took: 48.8723609447\n",
      "\n",
      "Epoch 6 ...\n",
      "Epoch  6 Update  399 Cost  0.0754013508558\n",
      "Epoch  6 Update  420 Cost  0.0876249596477\n",
      "Epoch  6 Update  441 Cost  0.165213420987\n",
      "Training error: 0.0925588086247\n",
      "Training_correct error 0.075753249228\n",
      "Testing_correct error: 0.0882906317711\n",
      "top- 1 = [3, 48, 50, 86, 76, 118, 93, 92] : 92\n",
      "top- 5 = [21, 199, 208, 293, 310, 392, 359, 406] : 406\n",
      "top- 10 = [30, 304, 320, 431, 411, 533, 495, 582] : 582\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      24.823824\n",
      "std       42.170965\n",
      "min        1.000000\n",
      "25%        3.000000\n",
      "50%        7.500000\n",
      "75%       21.000000\n",
      "max      344.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66, 92, 79, 80] : 80\n",
      "top- 5 = [12, 151, 169, 246, 242, 285, 279, 306] : 306\n",
      "top- 10 = [24, 232, 267, 378, 344, 455, 443, 470] : 470\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       30.191000\n",
      "std        47.151795\n",
      "min         1.000000\n",
      "25%         4.000000\n",
      "50%        11.000000\n",
      "75%        25.000000\n",
      "max       333.000000\n",
      "dtype: float64\n",
      "epoch took: 48.5769851208\n",
      "\n",
      "Epoch 7 ...\n",
      "Epoch  7 Update  462 Cost  0.0874141305685\n",
      "Epoch  7 Update  483 Cost  0.0745683759451\n",
      "Epoch  7 Update  504 Cost  0.084833085537\n",
      "Training error: 0.0767989233136\n",
      "Training_correct error 0.0734879598022\n",
      "Testing_correct error: 0.0873811766505\n",
      "top- 1 = [3, 48, 50, 86, 76, 118, 93, 92, 202] : 202\n",
      "top- 5 = [21, 199, 208, 293, 310, 392, 359, 406, 515] : 515\n",
      "top- 10 = [30, 304, 320, 431, 411, 533, 495, 582, 690] : 690\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      18.065065\n",
      "std       33.711923\n",
      "min        1.000000\n",
      "25%        1.500000\n",
      "50%        5.000000\n",
      "75%       14.500000\n",
      "max      263.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66, 92, 79, 80, 135] : 135\n",
      "top- 5 = [12, 151, 169, 246, 242, 285, 279, 306, 423] : 423\n",
      "top- 10 = [24, 232, 267, 378, 344, 455, 443, 470, 557] : 557\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       27.840000\n",
      "std        46.303525\n",
      "min         1.000000\n",
      "25%         2.500000\n",
      "50%         8.000000\n",
      "75%        27.250000\n",
      "max       291.000000\n",
      "dtype: float64\n",
      "epoch took: 48.8722219467\n",
      "\n",
      "Epoch 8 ...\n",
      "Epoch  8 Update  525 Cost  0.0818914100528\n",
      "Epoch  8 Update  546 Cost  0.102885998785\n",
      "Epoch  8 Update  567 Cost  0.124444670975\n",
      "Training error: 0.0649016872048\n",
      "Training_correct error 0.0729025006294\n",
      "Testing_correct error: 0.0839079767466\n",
      "top- 1 = [3, 48, 50, 86, 76, 118, 93, 92, 202, 205] : 205\n",
      "top- 5 = [21, 199, 208, 293, 310, 392, 359, 406, 515, 564] : 564\n",
      "top- 10 = [30, 304, 320, 431, 411, 533, 495, 582, 690, 738] : 738\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      12.638639\n",
      "std       22.141127\n",
      "min        1.000000\n",
      "25%        1.500000\n",
      "50%        4.000000\n",
      "75%       11.000000\n",
      "max      162.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66, 92, 79, 80, 135, 156] : 156\n",
      "top- 5 = [12, 151, 169, 246, 242, 285, 279, 306, 423, 468] : 468\n",
      "top- 10 = [24, 232, 267, 378, 344, 455, 443, 470, 557, 596] : 596\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       19.704000\n",
      "std        34.629317\n",
      "min         1.000000\n",
      "25%         2.000000\n",
      "50%         6.000000\n",
      "75%        19.625000\n",
      "max       206.000000\n",
      "dtype: float64\n",
      "epoch took: 48.4689497948\n",
      "\n",
      "Epoch 9 ...\n",
      "Epoch  9 Update  588 Cost  0.08970028162\n",
      "Epoch  9 Update  609 Cost  0.0695525407791\n",
      "Epoch  9 Update  630 Cost  0.0319570749998\n",
      "Training error: 0.0614430606365\n",
      "Training_correct error 0.0697435438633\n",
      "Testing_correct error: 0.0887727290392\n",
      "top- 1 = [3, 48, 50, 86, 76, 118, 93, 92, 202, 205, 225] : 225\n",
      "top- 5 = [21, 199, 208, 293, 310, 392, 359, 406, 515, 564, 610] : 610\n",
      "top- 10 = [30, 304, 320, 431, 411, 533, 495, 582, 690, 738, 736] : 736\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      12.092092\n",
      "std       22.614483\n",
      "min        1.000000\n",
      "25%        1.500000\n",
      "50%        3.500000\n",
      "75%       11.250000\n",
      "max      208.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66, 92, 79, 80, 135, 156, 166] : 166\n",
      "top- 5 = [12, 151, 169, 246, 242, 285, 279, 306, 423, 468, 479] : 479\n",
      "top- 10 = [24, 232, 267, 378, 344, 455, 443, 470, 557, 596, 614] : 614\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.00000\n",
      "mean       21.41400\n",
      "std        38.95274\n",
      "min         1.00000\n",
      "25%         2.00000\n",
      "50%         6.00000\n",
      "75%        22.50000\n",
      "max       271.00000\n",
      "dtype: float64\n",
      "epoch took: 48.6656470299\n",
      "\n",
      "Epoch 10 ...\n",
      "Epoch  10 Update  651 Cost  0.063362672925\n",
      "Epoch  10 Update  672 Cost  0.0694444775581\n",
      "Epoch  10 Update  693 Cost  0.0757331997156\n",
      "Training error: 0.064365811646\n",
      "Training_correct error 0.0788674727082\n",
      "Testing_correct error: 0.0984400510788\n",
      "top- 1 = [3, 48, 50, 86, 76, 118, 93, 92, 202, 205, 225, 200] : 200\n",
      "top- 5 = [21, 199, 208, 293, 310, 392, 359, 406, 515, 564, 610, 598] : 598\n",
      "top- 10 = [30, 304, 320, 431, 411, 533, 495, 582, 690, 738, 736, 745] : 745\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      12.458458\n",
      "std       22.218322\n",
      "min        1.000000\n",
      "25%        1.500000\n",
      "50%        3.500000\n",
      "75%       11.000000\n",
      "max      173.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66, 92, 79, 80, 135, 156, 166, 148] : 148\n",
      "top- 5 = [12, 151, 169, 246, 242, 285, 279, 306, 423, 468, 479, 475] : 475\n",
      "top- 10 = [24, 232, 267, 378, 344, 455, 443, 470, 557, 596, 614, 605] : 605\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       21.715000\n",
      "std        38.724454\n",
      "min         1.000000\n",
      "25%         2.000000\n",
      "50%         6.000000\n",
      "75%        19.000000\n",
      "max       289.000000\n",
      "dtype: float64\n",
      "epoch took: 48.9857690334\n",
      "\n",
      "Epoch 11 ...\n",
      "Epoch  11 Update  714 Cost  0.0437170118093\n",
      "Epoch  11 Update  735 Cost  0.114784456789\n",
      "Epoch  11 Update  756 Cost  0.0514871068299\n",
      "Training error: 0.0676115751266\n",
      "Training_correct error 0.0683713331819\n",
      "Testing_correct error: 0.0836198031902\n",
      "top- 1 = [3, 48, 50, 86, 76, 118, 93, 92, 202, 205, 225, 200, 161] : 161\n",
      "top- 5 = [21, 199, 208, 293, 310, 392, 359, 406, 515, 564, 610, 598, 536] : 536\n",
      "top- 10 = [30, 304, 320, 431, 411, 533, 495, 582, 690, 738, 736, 745, 698] : 698\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      13.920921\n",
      "std       23.795582\n",
      "min        1.000000\n",
      "25%        2.000000\n",
      "50%        4.500000\n",
      "75%       14.000000\n",
      "max      294.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66, 92, 79, 80, 135, 156, 166, 148, 115] : 115\n",
      "top- 5 = [12, 151, 169, 246, 242, 285, 279, 306, 423, 468, 479, 475, 452] : 452\n",
      "top- 10 = [24, 232, 267, 378, 344, 455, 443, 470, 557, 596, 614, 605, 622] : 622\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.00000\n",
      "mean       20.02600\n",
      "std        33.57005\n",
      "min         1.00000\n",
      "25%         2.00000\n",
      "50%         6.25000\n",
      "75%        17.50000\n",
      "max       217.00000\n",
      "dtype: float64\n",
      "epoch took: 48.3144078255\n",
      "\n",
      "Epoch 12 ...\n",
      "Epoch  12 Update  777 Cost  0.0336507894099\n",
      "Epoch  12 Update  798 Cost  0.0849446207285\n",
      "Epoch  12 Update  819 Cost  0.0798107907176\n",
      "Training error: 0.0687031969428\n",
      "Training_correct error 0.0725525915623\n",
      "Testing_correct error: 0.0905041620135\n",
      "top- 1 = [3, 48, 50, 86, 76, 118, 93, 92, 202, 205, 225, 200, 161, 169] : 169\n",
      "top- 5 = [21, 199, 208, 293, 310, 392, 359, 406, 515, 564, 610, 598, 536, 532] : 532\n",
      "top- 10 = [30, 304, 320, 431, 411, 533, 495, 582, 690, 738, 736, 745, 698, 702] : 702\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      13.523524\n",
      "std       23.272234\n",
      "min        1.000000\n",
      "25%        2.000000\n",
      "50%        5.000000\n",
      "75%       13.000000\n",
      "max      288.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66, 92, 79, 80, 135, 156, 166, 148, 115, 127] : 127\n",
      "top- 5 = [12, 151, 169, 246, 242, 285, 279, 306, 423, 468, 479, 475, 452, 485] : 485\n",
      "top- 10 = [24, 232, 267, 378, 344, 455, 443, 470, 557, 596, 614, 605, 622, 672] : 672\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       17.531000\n",
      "std        29.637196\n",
      "min         1.000000\n",
      "25%         2.000000\n",
      "50%         5.500000\n",
      "75%        15.000000\n",
      "max       190.000000\n",
      "dtype: float64\n",
      "epoch took: 48.5714669228\n",
      "\n",
      "Epoch 13 ...\n",
      "Epoch  13 Update  840 Cost  0.072304174304\n",
      "Epoch  13 Update  861 Cost  0.0690368041396\n",
      "Epoch  13 Update  882 Cost  0.0413294769824\n",
      "Training error: 0.0671595260501\n",
      "Training_correct error 0.0689151287079\n",
      "Testing_correct error: 0.0870760679245\n",
      "top- 1 = [3, 48, 50, 86, 76, 118, 93, 92, 202, 205, 225, 200, 161, 169, 186] : 186\n",
      "top- 5 = [21, 199, 208, 293, 310, 392, 359, 406, 515, 564, 610, 598, 536, 532, 595] : 595\n",
      "top- 10 = [30, 304, 320, 431, 411, 533, 495, 582, 690, 738, 736, 745, 698, 702, 716] : 716\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean      12.617618\n",
      "std       21.336281\n",
      "min        1.000000\n",
      "25%        1.500000\n",
      "50%        3.500000\n",
      "75%       12.500000\n",
      "max      245.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66, 92, 79, 80, 135, 156, 166, 148, 115, 127, 143] : 143\n",
      "top- 5 = [12, 151, 169, 246, 242, 285, 279, 306, 423, 468, 479, 475, 452, 485, 529] : 529\n",
      "top- 10 = [24, 232, 267, 378, 344, 455, 443, 470, 557, 596, 614, 605, 622, 672, 665] : 665\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       15.855000\n",
      "std        25.879059\n",
      "min         1.000000\n",
      "25%         2.000000\n",
      "50%         5.000000\n",
      "75%        16.000000\n",
      "max       181.000000\n",
      "dtype: float64\n",
      "epoch took: 48.9028871059\n",
      "\n",
      "Epoch 14 ...\n",
      "Epoch  14 Update  903 Cost  0.0230688545853\n",
      "Epoch  14 Update  924 Cost  0.0697318464518\n",
      "Epoch  14 Update  945 Cost  0.0603713318706\n",
      "Training error: 0.055987522006\n",
      "Training_correct error 0.0552660189569\n",
      "Testing_correct error: 0.0641145408154\n",
      "top- 1 = [3, 48, 50, 86, 76, 118, 93, 92, 202, 205, 225, 200, 161, 169, 186, 233] : 233\n",
      "top- 5 = [21, 199, 208, 293, 310, 392, 359, 406, 515, 564, 610, 598, 536, 532, 595, 653] : 653\n",
      "top- 10 = [30, 304, 320, 431, 411, 533, 495, 582, 690, 738, 736, 745, 698, 702, 716, 779] : 779\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       8.545546\n",
      "std       13.568803\n",
      "min        1.000000\n",
      "25%        1.500000\n",
      "50%        3.000000\n",
      "75%        8.500000\n",
      "max      157.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66, 92, 79, 80, 135, 156, 166, 148, 115, 127, 143, 162] : 162\n",
      "top- 5 = [12, 151, 169, 246, 242, 285, 279, 306, 423, 468, 479, 475, 452, 485, 529, 530] : 530\n",
      "top- 10 = [24, 232, 267, 378, 344, 455, 443, 470, 557, 596, 614, 605, 622, 672, 665, 698] : 698\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       11.858000\n",
      "std        17.776947\n",
      "min         1.000000\n",
      "25%         2.000000\n",
      "50%         4.750000\n",
      "75%        13.125000\n",
      "max       120.000000\n",
      "dtype: float64\n",
      "epoch took: 48.7711780071\n",
      "\n",
      "Epoch 15 ...\n",
      "Epoch  15 Update  966 Cost  0.0660632997751\n",
      "Epoch  15 Update  987 Cost  0.0347093641758\n",
      "Epoch  15 Update  1008 Cost  0.039114356041\n",
      "Training error: 0.0527125261724\n",
      "Training_correct error 0.0673270151019\n",
      "Testing_correct error: 0.07998406142\n",
      "top- 1 = [3, 48, 50, 86, 76, 118, 93, 92, 202, 205, 225, 200, 161, 169, 186, 233, 259] : 259\n",
      "top- 5 = [21, 199, 208, 293, 310, 392, 359, 406, 515, 564, 610, 598, 536, 532, 595, 653, 714] : 714\n",
      "top- 10 = [30, 304, 320, 431, 411, 533, 495, 582, 690, 738, 736, 745, 698, 702, 716, 779, 831] : 831\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       7.254254\n",
      "std       13.664529\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        2.500000\n",
      "75%        6.000000\n",
      "max      155.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66, 92, 79, 80, 135, 156, 166, 148, 115, 127, 143, 162, 193] : 193\n",
      "top- 5 = [12, 151, 169, 246, 242, 285, 279, 306, 423, 468, 479, 475, 452, 485, 529, 530, 630] : 630\n",
      "top- 10 = [24, 232, 267, 378, 344, 455, 443, 470, 557, 596, 614, 605, 622, 672, 665, 698, 753] : 753\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean       11.165000\n",
      "std        18.126012\n",
      "min         1.000000\n",
      "25%         1.500000\n",
      "50%         3.000000\n",
      "75%        10.000000\n",
      "max       151.000000\n",
      "dtype: float64\n",
      "epoch took: 48.4336400032\n",
      "\n",
      "Epoch 16 ...\n",
      "Epoch  16 Update  1029 Cost  0.0442724451423\n",
      "Epoch  16 Update  1050 Cost  0.0652790665627\n",
      "Epoch  16 Update  1071 Cost  0.0747208446264\n",
      "Training error: 0.0483071468771\n",
      "Training_correct error 0.0516675412655\n",
      "Testing_correct error: 0.0678435266018\n",
      "top- 1 = [3, 48, 50, 86, 76, 118, 93, 92, 202, 205, 225, 200, 161, 169, 186, 233, 259, 272] : 272\n",
      "top- 5 = [21, 199, 208, 293, 310, 392, 359, 406, 515, 564, 610, 598, 536, 532, 595, 653, 714, 735] : 735\n",
      "top- 10 = [30, 304, 320, 431, 411, 533, 495, 582, 690, 738, 736, 745, 698, 702, 716, 779, 831, 843] : 843\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       6.183183\n",
      "std       11.160784\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        2.500000\n",
      "75%        6.000000\n",
      "max      164.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66, 92, 79, 80, 135, 156, 166, 148, 115, 127, 143, 162, 193, 194] : 194\n",
      "top- 5 = [12, 151, 169, 246, 242, 285, 279, 306, 423, 468, 479, 475, 452, 485, 529, 530, 630, 636] : 636\n",
      "top- 10 = [24, 232, 267, 378, 344, 455, 443, 470, 557, 596, 614, 605, 622, 672, 665, 698, 753, 802] : 802\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean        9.038000\n",
      "std        14.761859\n",
      "min         1.000000\n",
      "25%         1.500000\n",
      "50%         3.500000\n",
      "75%         8.000000\n",
      "max       135.000000\n",
      "dtype: float64\n",
      "epoch took: 48.6501510143\n",
      "\n",
      "Epoch 17 ...\n",
      "Epoch  17 Update  1092 Cost  0.0414074324071\n",
      "Epoch  17 Update  1113 Cost  0.071006372571\n",
      "Epoch  17 Update  1134 Cost  0.0419829301536\n",
      "Training error: 0.0467457361519\n",
      "Training_correct error 0.0499443151057\n",
      "Testing_correct error: 0.0650392621756\n",
      "top- 1 = [3, 48, 50, 86, 76, 118, 93, 92, 202, 205, 225, 200, 161, 169, 186, 233, 259, 272, 263] : 263\n",
      "top- 5 = [21, 199, 208, 293, 310, 392, 359, 406, 515, 564, 610, 598, 536, 532, 595, 653, 714, 735, 749] : 749\n",
      "top- 10 = [30, 304, 320, 431, 411, 533, 495, 582, 690, 738, 736, 745, 698, 702, 716, 779, 831, 843, 864] : 864\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       5.903904\n",
      "std       10.514103\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        2.500000\n",
      "75%        5.250000\n",
      "max      145.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66, 92, 79, 80, 135, 156, 166, 148, 115, 127, 143, 162, 193, 194, 198] : 198\n",
      "top- 5 = [12, 151, 169, 246, 242, 285, 279, 306, 423, 468, 479, 475, 452, 485, 529, 530, 630, 636, 685] : 685\n",
      "top- 10 = [24, 232, 267, 378, 344, 455, 443, 470, 557, 596, 614, 605, 622, 672, 665, 698, 753, 802, 807] : 807\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean        8.325000\n",
      "std        13.669537\n",
      "min         1.000000\n",
      "25%         1.500000\n",
      "50%         3.000000\n",
      "75%         7.000000\n",
      "max       123.000000\n",
      "dtype: float64\n",
      "epoch took: 48.7393450737\n",
      "\n",
      "Epoch 18 ...\n",
      "Epoch  18 Update  1155 Cost  0.0281577594578\n",
      "Epoch  18 Update  1176 Cost  0.0279857199639\n",
      "Epoch  18 Update  1197 Cost  0.031181935221\n",
      "Training error: 0.0448585934937\n",
      "Training_correct error 0.0507593862712\n",
      "Testing_correct error: 0.0675635188818\n",
      "top- 1 = [3, 48, 50, 86, 76, 118, 93, 92, 202, 205, 225, 200, 161, 169, 186, 233, 259, 272, 263, 297] : 297\n",
      "top- 5 = [21, 199, 208, 293, 310, 392, 359, 406, 515, 564, 610, 598, 536, 532, 595, 653, 714, 735, 749, 776] : 776\n",
      "top- 10 = [30, 304, 320, 431, 411, 533, 495, 582, 690, 738, 736, 745, 698, 702, 716, 779, 831, 843, 864, 887] : 887\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       5.160160\n",
      "std        9.167385\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        2.000000\n",
      "75%        5.000000\n",
      "max       96.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66, 92, 79, 80, 135, 156, 166, 148, 115, 127, 143, 162, 193, 194, 198, 225] : 225\n",
      "top- 5 = [12, 151, 169, 246, 242, 285, 279, 306, 423, 468, 479, 475, 452, 485, 529, 530, 630, 636, 685, 701] : 701\n",
      "top- 10 = [24, 232, 267, 378, 344, 455, 443, 470, 557, 596, 614, 605, 622, 672, 665, 698, 753, 802, 807, 817] : 817\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean        8.575000\n",
      "std        15.275134\n",
      "min         1.000000\n",
      "25%         1.500000\n",
      "50%         2.500000\n",
      "75%         7.000000\n",
      "max       134.000000\n",
      "dtype: float64\n",
      "epoch took: 48.4379248619\n",
      "\n",
      "Epoch 19 ...\n",
      "Epoch  19 Update  1218 Cost  0.0584981888533\n",
      "Epoch  19 Update  1239 Cost  0.0524895340204\n",
      "Epoch  19 Update  1260 Cost  0.0314953140914\n",
      "Training error: 0.0442051738501\n",
      "Training_correct error 0.0425166003406\n",
      "Testing_correct error: 0.0482059717178\n",
      "top- 1 = [3, 48, 50, 86, 76, 118, 93, 92, 202, 205, 225, 200, 161, 169, 186, 233, 259, 272, 263, 297, 274] : 274\n",
      "top- 5 = [21, 199, 208, 293, 310, 392, 359, 406, 515, 564, 610, 598, 536, 532, 595, 653, 714, 735, 749, 776, 778] : 778\n",
      "top- 10 = [30, 304, 320, 431, 411, 533, 495, 582, 690, 738, 736, 745, 698, 702, 716, 779, 831, 843, 864, 887, 881] : 881\n",
      "Discription of evaluation (ranking) for training data:\n",
      "count    999.000000\n",
      "mean       4.926927\n",
      "std        7.862557\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        2.000000\n",
      "75%        5.000000\n",
      "max      100.000000\n",
      "dtype: float64\n",
      "top- 1 = [1, 46, 43, 81, 66, 92, 79, 80, 135, 156, 166, 148, 115, 127, 143, 162, 193, 194, 198, 225, 206] : 206\n",
      "top- 5 = [12, 151, 169, 246, 242, 285, 279, 306, 423, 468, 479, 475, 452, 485, 529, 530, 630, 636, 685, 701, 689] : 689\n",
      "top- 10 = [24, 232, 267, 378, 344, 455, 443, 470, 557, 596, 614, 605, 622, 672, 665, 698, 753, 802, 807, 817, 814] : 814\n",
      "Discription of evaluation (ranking) for testing data:\n",
      "count    1000.000000\n",
      "mean        6.672000\n",
      "std         9.635105\n",
      "min         1.000000\n",
      "25%         1.500000\n",
      "50%         3.000000\n",
      "75%         7.000000\n",
      "max       109.000000\n",
      "dtype: float64\n",
      "epoch took: 48.505466938\n",
      "saving the model...\n",
      "Evaluate the model using fast estimation...\n",
      "count    999.000000\n",
      "mean       4.926927\n",
      "std        7.862557\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        2.000000\n",
      "75%        5.000000\n",
      "max      100.000000\n",
      "dtype: float64\n",
      "count    1000.000000\n",
      "mean        6.672000\n",
      "std         9.635105\n",
      "min         1.000000\n",
      "25%         1.500000\n",
      "50%         3.000000\n",
      "75%         7.000000\n",
      "max       109.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "run main.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.25775599479675293, 0.12569260597229004, 0.11739553511142731, 0.098548665642738342, 0.099749788641929626, 0.086856238543987274, 0.089723870158195496, 0.092558808624744415, 0.076798923313617706, 0.064901687204837799, 0.061443060636520386, 0.06436581164598465, 0.067611575126647949, 0.068703196942806244, 0.06715952605009079, 0.055987522006034851, 0.052712526172399521, 0.0483071468770504, 0.04674573615193367, 0.044858593493700027, 0.044205173850059509]\n",
      "[0.33790570497512817, 0.11841221898794174, 0.095117062330245972, 0.10194417089223862, 0.08210378885269165, 0.10712100565433502, 0.07721845805644989, 0.075753249228000641, 0.073487959802150726, 0.072902500629425049, 0.069743543863296509, 0.07886747270822525, 0.068371333181858063, 0.072552591562271118, 0.068915128707885742, 0.055266018956899643, 0.067327015101909637, 0.051667541265487671, 0.049944315105676651, 0.050759386271238327, 0.042516600340604782]\n",
      "[0.33503863215446472, 0.11485008150339127, 0.09036765992641449, 0.10237926989793777, 0.083479039371013641, 0.12022082507610321, 0.075717940926551819, 0.088290631771087646, 0.087381176650524139, 0.083907976746559143, 0.0887727290391922, 0.098440051078796387, 0.083619803190231323, 0.090504162013530731, 0.087076067924499512, 0.064114540815353394, 0.079984061419963837, 0.067843526601791382, 0.065039262175559998, 0.067563518881797791, 0.048205971717834473]\n"
     ]
    }
   ],
   "source": [
    "print sls.mse\n",
    "print sls.mse_train\n",
    "print sls.mse_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ppekppY8Dx5DbaqJ8de4M7353bgP6evoe0JcDOh3A/KWrShRMkiRJkvaPQorB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\nN0uwq1M19bRanazHq9VJP5Z9OZTcns9LIi5h2WfLPLp8Sm40TFRERMq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HoPjT9u0sTYrh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\niIiIiIiUQ/8PvuIHKEPwwigAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd31f301890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#【X不用】\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "matplotlib.rcParams['figure.figsize'] = (15.0, 7.5)\n",
    "plt.plot(sls.mse,'ro-')\n",
    "plt.plot(sls.mse_train,'bo-')\n",
    "plt.plot(sls.mse_test,'go-')\n",
    "# plt.plot(sls.tops_test[1],'ro--')\n",
    "# plt.plot(sls.tops_test[5],'bo--')\n",
    "# plt.plot(sls.tops_test[10],'go--')\n",
    "plt.ylabel('Amount of documents belongs to @top*')\n",
    "plt.xlabel('Epoches Times')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TvXt3Onbs2Gh7nTp1YuLEiWRkZACwZcsWDh06xFVXXVVv/Zdffpnjx49TUFBQ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vz4SqZNazy28cILO7f75c7sbMzg5s3w4oswZw7MnZttffpsC4YjR8Khh0JZHge8rVm/hlEXjWLp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\nkkqQYVCSJEmSStD/BwA2Ou7wFIBcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd31ee36b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#【正确，使用！】\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "matplotlib.rcParams['figure.figsize'] = (15.0, 7.5)\n",
    "plt.title('MSE distribution')\n",
    "plt.plot(sls.mse,'ro-', label=\"Training data (similarity = 1 + similarity = 2)\")\n",
    "plt.plot(sls.mse_train,'bo-', label=\"Training data (similarity = 1)\")\n",
    "plt.plot(sls.mse_test,'go-', label=\"Testing data (similarity = 1)\")\n",
    "plt.legend()\n",
    "\n",
    "plt.ylabel('MSE')\n",
    "plt.xlabel('Epoches Times')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>en_article</th>\n",
       "      <th>transformation_en</th>\n",
       "      <th>jp_article</th>\n",
       "      <th>transformation_jp</th>\n",
       "      <th>similarity</th>\n",
       "      <th>dis_similarity</th>\n",
       "      <th>en2jp_projection</th>\n",
       "      <th>xa</th>\n",
       "      <th>xb</th>\n",
       "      <th>xb_wrong</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>u.s. commerce department seasonal adjust annua...</td>\n",
       "      <td>[5, 2, 9, 6, 6, 6, 6, 2, 6, 6]</td>\n",
       "      <td>米 商務省 発表 する 建設 支出 以下 とおり 季節調整 年率 総 建設 支出 民間 部門...</td>\n",
       "      <td>[2, 3, 10, 7, 3, 10, 8, 8, 9, 8, 9, 3, 10, 7, ...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[1, 8, 2, 3, 9, 7, 2, 9, 7, 2, 9, 7, 2, 9, 7, ...</td>\n",
       "      <td>5 2 9 6 6 6 6 2 6 6</td>\n",
       "      <td>1 8 2 3 9 7 2 9 7 2 9 7 2 9 7 2 8 2 9 7 2 9 7 2</td>\n",
       "      <td>10 3 10 3 1 10 3 9 7 2 9 7 2 1 1 10 3 6 6 6 8 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chinese budget smartphone maker xiaomi plan se...</td>\n",
       "      <td>[2, 6, 2, 2, 2, 1, 2, 2, 5, 6, 2, 2, 2, 2, 10,...</td>\n",
       "      <td>中国 低価格 スマホ メーカー 小米科技 () 雷軍 董事長 以上 販売 する 計画 明らか...</td>\n",
       "      <td>[7, 5, 8, 2, 8, 3, 8, 7, 3, 8, 7, 10, 7, 7, 9,...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[8, 2, 9, 7, 2, 8, 2, 8, 2, 8, 2, 6, 8, 2, 8, ...</td>\n",
       "      <td>2 6 2 2 2 1 2 2 5 6 2 2 2 2 10 8 5 6 1 8 2 5 2...</td>\n",
       "      <td>8 2 9 7 2 8 2 8 2 8 2 6 8 2 8 2 1 9 7 2 8 2 8 ...</td>\n",
       "      <td>10 3 10 3 6 10 3 10 8 2 6 6 10 9 7 2 1 10 10 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>samsung electronics co ltd fell more percent t...</td>\n",
       "      <td>[2, 2, 2, 2, 5, 5, 5, 5, 2, 5, 6, 5, 5, 5, 6, ...</td>\n",
       "      <td>ソウル 株式市場 サムスン電子 株価 超 下落 する 安値 付ける 一部 アナリスト ウォン...</td>\n",
       "      <td>[3, 3, 3, 9, 3, 2, 7, 3, 3, 9, 9, 3, 8, 6, 8, ...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[8, 2, 8, 2, 8, 2, 8, 2, 1, 1, 1, 1, 8, 2, 1, ...</td>\n",
       "      <td>2 2 2 2 5 5 5 5 2 5 6 5 5 5 6 5 6 1 6 5 5 5 5 ...</td>\n",
       "      <td>8 2 8 2 8 2 8 2 1 1 1 1 8 2 1 9 7 2 1 1 1 9 7 ...</td>\n",
       "      <td>1 1 1 1 1 4 6 6 8 2 4 10 10 10 8 2 10 1 1 4 4 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fiat deal gain full control chrysler group llc...</td>\n",
       "      <td>[2, 1, 5, 1, 8, 2, 8, 10, 5, 2, 1, 5, 5, 5, 5,...</td>\n",
       "      <td>欧州 株式市場 イタリア 自動車 大手 FIAT 株価 一時 急伸 する 8月 以来 高値 ...</td>\n",
       "      <td>[10, 3, 10, 9, 4, 6, 9, 2, 8, 7, 4, 3, 3, 3, 7...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[8, 2, 6, 1, 6, 4, 8, 2, 4, 4, 1, 8, 2, 6, 1, ...</td>\n",
       "      <td>2 1 5 1 8 2 8 10 5 2 1 5 5 5 5 7 7 2 7 2 1 1 5...</td>\n",
       "      <td>8 2 6 1 6 4 8 2 4 4 1 8 2 6 1 1 1 1 10 3 10 3 ...</td>\n",
       "      <td>10 3 10 3 1 10 3 9 7 2 9 7 2 1 1 10 3 8 2 3 10...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>fiat share jump thursday it strike deal gain f...</td>\n",
       "      <td>[2, 1, 5, 5, 2, 8, 1, 5, 1, 8, 2, 8, 10, 7, 4,...</td>\n",
       "      <td>欧州 株式市場 イタリア 自動車 大手 FIAT 株価 一時 急伸 する 8月 以来 高値 ...</td>\n",
       "      <td>[10, 3, 10, 9, 4, 6, 9, 2, 8, 7, 4, 3, 3, 3, 7...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[8, 2, 6, 1, 1, 8, 2, 4, 6, 1, 6, 4, 8, 2, 4, ...</td>\n",
       "      <td>2 1 5 5 2 8 1 5 1 8 2 8 10 7 4 7 2 7 2 1 1 5 8...</td>\n",
       "      <td>8 2 6 1 1 8 2 4 6 1 6 4 8 2 4 4 10 3 10 10 3 8...</td>\n",
       "      <td>10 3 10 3 1 6 9 7 2 9 7 2 1 1 1 6 5 9 7 2 8 2 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>u.s. treasuries price rise thursday benchmark ...</td>\n",
       "      <td>[5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 5, 5, 5, 6, 10,...</td>\n",
       "      <td>カッコ 前 営業日 米 東部 時間 時分 前 営業日 終盤 米 東部 時間 時分 前 営業日...</td>\n",
       "      <td>[10, 4, 3, 2, 5, 3, 4, 4, 3, 3, 2, 5, 3, 4, 4,...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 9, 7, 2, 1, 1, 1, ...</td>\n",
       "      <td>5 5 5 5 5 5 5 5 5 6 5 5 5 6 10 5 5 5 2 7 2 6 5...</td>\n",
       "      <td>1 1 1 1 1 1 1 1 1 9 7 2 1 1 1 9 7 2 4 1 1 1 8 ...</td>\n",
       "      <td>10 3 1 1 1 1 1 1 1 10 3 10 10 4 4 8 2 6 4 4 1 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>auction detail see 3-year note</td>\n",
       "      <td>[1, 1, 5, 1, 5]</td>\n",
       "      <td>米財務省 銘柄 統合 入札 実施 する 発表 する 発行 いずれ 償還 1月 11月 11月</td>\n",
       "      <td>[6, 9, 7, 6, 4, 7, 10, 7, 6, 10, 6, 9, 8, 8]</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[6, 6, 1, 6, 1]</td>\n",
       "      <td>1 1 5 1 5</td>\n",
       "      <td>6 6 1 6 1</td>\n",
       "      <td>9 7 2 10 3 6 1 1 1 1 1 5 5 9 7 2 10 3 8 2 8 2 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>brent crude sink barrel thursday libya prepare...</td>\n",
       "      <td>[5, 5, 5, 5, 5, 8, 1, 1, 2, 8, 4, 4, 4, 2, 4, ...</td>\n",
       "      <td>原油 先物 相場 リビア 大 規模 油田 生産 停止 要因 なる いる 抗議活動 休止 する...</td>\n",
       "      <td>[9, 3, 1, 9, 2, 9, 7, 4, 4, 9, 7, 4, 4, 4, 7, ...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[1, 1, 1, 1, 1, 4, 6, 6, 8, 2, 4, 10, 10, 10, ...</td>\n",
       "      <td>5 5 5 5 5 8 1 1 2 8 4 4 4 2 4 5 5 8 8 5 2 2 1 ...</td>\n",
       "      <td>1 1 1 1 1 4 6 6 8 2 4 10 10 10 8 2 10 1 1 4 4 ...</td>\n",
       "      <td>1 8 2 1 1 1 8 2 1 1 1 1 1 1 9 7 2 1 1 1 1 1 10...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>gauge u.s. factory activity hold 2-1/2-year hi...</td>\n",
       "      <td>[5, 5, 4, 4, 7, 5, 5, 5, 2, 8, 2, 2, 9, 5, 3, ...</td>\n",
       "      <td>米 労働省 発表 する 12月 週 新規 失業保険 申請 件数 季節調整 前週 り 連続 減...</td>\n",
       "      <td>[2, 8, 10, 7, 4, 9, 8, 8, 8, 8, 9, 3, 3, 8, 8,...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[1, 1, 10, 10, 10, 3, 1, 1, 1, 8, 2, 4, 8, 2, ...</td>\n",
       "      <td>5 5 4 4 7 5 5 5 2 8 2 2 9 5 3 5 4 5 5 5 5 10 4...</td>\n",
       "      <td>1 1 10 10 10 3 1 1 1 8 2 4 8 2 8 2 3 1 5 1 10 ...</td>\n",
       "      <td>8 2 8 2 8 2 1 9 7 2 8 2 9 7 2 6 1 9 7 2 9 7 2 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>work massive panama canal extension project ma...</td>\n",
       "      <td>[8, 1, 8, 2, 1, 1, 7, 2, 1, 9, 5, 2, 9, 6, 9, ...</td>\n",
       "      <td>パナマ運河 拡張 工事 めぐる 建設 会社 成る 企業 連合 パナマ運河 PC コスト 超過...</td>\n",
       "      <td>[9, 3, 9, 9, 3, 9, 4, 6, 7, 9, 2, 9, 9, 3, 5, ...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[4, 6, 4, 8, 2, 6, 6, 10, 3, 8, 2, 6, 3, 1, 8,...</td>\n",
       "      <td>8 1 8 2 1 1 7 2 1 9 5 2 9 6 9 6 7 2 8 5 6 9 2 ...</td>\n",
       "      <td>4 6 4 8 2 6 6 10 3 8 2 6 3 1 8 2 3 9 7 2 3 9 7...</td>\n",
       "      <td>8 2 6 8 2 3 1 6 10 3 9 7 2 6 10 3 8 2 6 1 4 1 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>increase december car sale france spain prompt...</td>\n",
       "      <td>[6, 5, 2, 2, 8, 8, 7, 7, 4, 7, 2, 5, 2, 7, 5, ...</td>\n",
       "      <td>12月 フランス イタリア スペイン 自動車販売 台数 前年 同月 増加 する 欧州 自動車...</td>\n",
       "      <td>[4, 10, 10, 7, 8, 8, 9, 7, 8, 7, 10, 9, 9, 5, ...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[9, 7, 2, 1, 8, 2, 8, 2, 4, 4, 10, 3, 10, 3, 1...</td>\n",
       "      <td>6 5 2 2 8 8 7 7 4 7 2 5 2 7 5 5 6 5 6 5 5 5 5 ...</td>\n",
       "      <td>9 7 2 1 8 2 8 2 4 4 10 3 10 3 10 10 3 8 2 1 8 ...</td>\n",
       "      <td>1 1 1 1 1 4 6 6 8 2 4 10 3 1 1 1 8 2 1 8 2 1 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>federal reserve provide liquidity foreign cent...</td>\n",
       "      <td>[7, 7, 1, 1, 1, 7, 5, 5, 5, 2, 10, 7, 8, 5, 7,...</td>\n",
       "      <td>米 連邦準備理事会 FRB 傘下 ニューヨーク連銀 発表 する 最新 週間 データ よる 通...</td>\n",
       "      <td>[2, 2, 2, 6, 8, 10, 7, 7, 3, 7, 7, 8, 3, 2, 3,...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[10, 3, 10, 3, 6, 6, 6, 10, 3, 1, 1, 1, 8, 2, ...</td>\n",
       "      <td>7 7 1 1 1 7 5 5 5 2 10 7 8 5 7 7 7 8 1 1 5 2 5...</td>\n",
       "      <td>10 3 10 3 6 6 6 10 3 1 1 1 8 2 4 10 3 4 1 10 3...</td>\n",
       "      <td>6 6 1 1 1 1 1 1 1 6 6 10 3 10 8 2 4 1 9 7 2 1 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>new car sale spain rise percent year early car...</td>\n",
       "      <td>[2, 2, 2, 8, 5, 5, 6, 5, 2, 2, 2, 2, 8, 5, 6, ...</td>\n",
       "      <td>スペイン 自動車 業界団体 よる 新車 販売 政府 補助 支援 する 前年 なる 減少 する...</td>\n",
       "      <td>[7, 9, 9, 7, 8, 8, 10, 10, 4, 7, 9, 7, 8, 7, 4...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[8, 2, 8, 2, 8, 2, 4, 1, 1, 9, 7, 2, 1, 8, 2, ...</td>\n",
       "      <td>2 2 2 8 5 5 6 5 2 2 2 2 8 5 6 7 1 2 2 5 5 5 6 ...</td>\n",
       "      <td>8 2 8 2 8 2 4 1 1 9 7 2 1 8 2 8 2 8 2 8 2 4 1 ...</td>\n",
       "      <td>1 10 3 8 2 8 2 9 7 2 1 8 2 1 1 10 3 1 1 1 8 2 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>south korean auto maker hyundai motor co kia m...</td>\n",
       "      <td>[8, 8, 2, 2, 2, 2, 2, 2, 2, 2, 2, 7, 6, 5, 4, ...</td>\n",
       "      <td>韓国 自動車メーカー 現代自動車 系列 起亜自動車 世界 販売台数 両社 合計 前年 目標 ...</td>\n",
       "      <td>[7, 3, 6, 7, 3, 5, 3, 6, 9, 9, 2, 3, 7, 8, 7, ...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[4, 4, 8, 2, 8, 2, 8, 2, 8, 2, 8, 2, 8, 2, 8, ...</td>\n",
       "      <td>8 8 2 2 2 2 2 2 2 2 2 7 6 5 4 4 6 2 5 5 6 6 5 ...</td>\n",
       "      <td>4 4 8 2 8 2 8 2 8 2 8 2 8 2 8 2 8 2 8 2 10 3 9...</td>\n",
       "      <td>8 2 8 2 10 6 8 2 6 9 7 2 9 7 2 9 7 2 6 1 1 8 2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>u.s. manufacturing end year high note grow dec...</td>\n",
       "      <td>[5, 4, 5, 6, 5, 5, 6, 5, 2, 4, 4, 6, 6, 6, 4, ...</td>\n",
       "      <td>Markit 発表 する 12月 米 製造業 購買担当者景気指数 PM 改定 1月 以来 高...</td>\n",
       "      <td>[1, 10, 7, 4, 2, 10, 10, 10, 10, 9, 3, 4, 10, ...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[1, 10, 1, 9, 7, 2, 1, 1, 9, 7, 2, 1, 8, 2, 10...</td>\n",
       "      <td>5 4 5 6 5 5 6 5 2 4 4 6 6 6 4 2 9 6 2 6 5 5 7 ...</td>\n",
       "      <td>1 10 1 9 7 2 1 1 9 7 2 1 8 2 10 10 9 7 2 9 7 2...</td>\n",
       "      <td>1 8 2 1 1 1 1 1 1 1 4 1 9 7 2 9 7 2 1 9 7 2 1 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>oil price fell more thursday libya prepare res...</td>\n",
       "      <td>[5, 5, 5, 5, 5, 8, 1, 1, 2, 8, 7, 5, 5, 5, 2, ...</td>\n",
       "      <td>原油 先物 相場 リビア 大 規模 油田 生産 停止 要因 なる いる 抗議活動 休止 する...</td>\n",
       "      <td>[9, 3, 1, 9, 2, 9, 7, 4, 4, 9, 7, 4, 4, 4, 7, ...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[1, 1, 1, 1, 1, 4, 6, 6, 8, 2, 4, 10, 3, 1, 1,...</td>\n",
       "      <td>5 5 5 5 5 8 1 1 2 8 7 5 5 5 2 5 2 5 5 6 6 5 7 ...</td>\n",
       "      <td>1 1 1 1 1 4 6 6 8 2 4 10 3 1 1 1 8 2 1 8 2 1 1...</td>\n",
       "      <td>6 10 3 10 3 9 7 2 10 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>freddie mac average u.s. mortgage rate percent...</td>\n",
       "      <td>[1, 2, 6, 5, 1, 6, 5, 10, 5, 5]</td>\n",
       "      <td>米 連邦 住宅 貸付 抵当 公社 フレディマック 調査 住宅ローン 金利 手数料 以下 通り...</td>\n",
       "      <td>[2, 10, 10, 4, 9, 8, 2, 7, 3, 9, 9, 8, 8, 9, 3...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[6, 8, 2, 9, 7, 2, 1, 6, 9, 7, 2, 1, 4, 1, 1]</td>\n",
       "      <td>1 2 6 5 1 6 5 10 5 5</td>\n",
       "      <td>6 8 2 9 7 2 1 6 9 7 2 1 4 1 1</td>\n",
       "      <td>10 3 4 4 1 10 3 1 10 3 10 3 8 2 10 3 8 2 9 7 2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>u.s. construction spending rise it high level ...</td>\n",
       "      <td>[5, 2, 6, 5, 2, 5, 5, 5, 6, 6, 5, 10, 2, 1, 6,...</td>\n",
       "      <td>米 商務省 発表 する 11月 建設 支出 前 月 年率 3月 以来 高水準 公共 部門 減...</td>\n",
       "      <td>[2, 3, 10, 7, 8, 3, 10, 4, 8, 8, 4, 3, 4, 3, 5...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[1, 8, 2, 9, 7, 2, 1, 8, 2, 1, 1, 1, 9, 7, 2, ...</td>\n",
       "      <td>5 2 6 5 2 5 5 5 6 6 5 10 2 1 6 5 1 6 2 6 6 5 6...</td>\n",
       "      <td>1 8 2 9 7 2 1 8 2 1 1 1 9 7 2 9 7 2 1 4 8 2 6 ...</td>\n",
       "      <td>1 6 1 1 1 1 9 7 2 3 10 3 10 3 10 3 1 10 3 1 10...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>russia world big oil producer raise output pos...</td>\n",
       "      <td>[7, 4, 5, 5, 5, 1, 4, 9, 2, 5, 5, 5, 6, 5, 5, ...</td>\n",
       "      <td>ロシア エネルギー省 発表 する データ よる 石油 生産量 日量 ソビエト連邦 崩壊 最 ...</td>\n",
       "      <td>[5, 4, 10, 7, 7, 7, 5, 9, 5, 7, 7, 7, 4, 3, 5,...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[10, 3, 10, 1, 1, 1, 6, 10, 3, 8, 2, 1, 1, 1, ...</td>\n",
       "      <td>7 4 5 5 5 1 4 9 2 5 5 5 6 5 5 5 6 2 2 5 6 5 1 ...</td>\n",
       "      <td>10 3 10 1 1 1 6 10 3 8 2 1 1 1 9 7 2 1 1 1 9 7...</td>\n",
       "      <td>1 10 1 9 7 2 1 1 9 7 2 1 8 2 10 10 9 7 2 9 7 2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>morning fix</td>\n",
       "      <td>[5, 5]</td>\n",
       "      <td>ロンドン 市場 午後 値 決め 以下 通り ドル オンス 午後 ドル 午前 ドル</td>\n",
       "      <td>[4, 9, 4, 3, 6, 8, 8, 2, 4, 4, 2, 9, 2]</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[1, 1]</td>\n",
       "      <td>5 5</td>\n",
       "      <td>1 1</td>\n",
       "      <td>4 10 3 3 8 2 10 1 4 1 6 4 6 5 10 3 10 3 6 6 3 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>premium italian spanish bond offer benchmark g...</td>\n",
       "      <td>[2, 7, 7, 5, 1, 5, 7, 5, 5, 6, 5, 5, 2, 4, 6, ...</td>\n",
       "      <td>カッコ 先物 欧州 市場 前 営業日 終値 現物 前 営業日 終盤 時分 先物 清算 ユーロ...</td>\n",
       "      <td>[10, 3, 10, 9, 4, 3, 3, 4, 4, 3, 3, 4, 3, 4, 1...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[8, 2, 10, 3, 10, 3, 1, 6, 1, 10, 3, 1, 1, 9, ...</td>\n",
       "      <td>2 7 7 5 1 5 7 5 5 6 5 5 2 4 6 5 8 5 5 8 4 5 4 ...</td>\n",
       "      <td>8 2 10 3 10 3 1 6 1 10 3 1 1 9 7 2 1 1 8 2 10 ...</td>\n",
       "      <td>6 6 8 2 8 2 1 3 4 4 4 6 8 2 10 3 9 7 2 1 8 2 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>china yuan pull back slightly record high doll...</td>\n",
       "      <td>[2, 2, 5, 5, 5, 5, 5, 5, 5, 7, 5, 5, 5, 5, 3, ...</td>\n",
       "      <td>年明け 上海 外国為替市場 人民元 相場 引け 強含み 昨年 12月 付ける 最 高値 水準...</td>\n",
       "      <td>[9, 3, 8, 9, 1, 3, 2, 3, 4, 3, 7, 3, 1, 3, 9, ...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[8, 2, 8, 2, 1, 1, 1, 1, 1, 1, 1, 10, 3, 1, 1,...</td>\n",
       "      <td>2 2 5 5 5 5 5 5 5 7 5 5 5 5 3 5 2 5 9 5 5 2 5 ...</td>\n",
       "      <td>8 2 8 2 1 1 1 1 1 1 1 10 3 1 1 1 1 5 1 8 2 1 3...</td>\n",
       "      <td>4 10 1 1 9 7 2 9 7 2 9 7 2 10 10 1 1 1 1 10 3 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>italian carmaker fiat spa say wednesday it hav...</td>\n",
       "      <td>[7, 2, 2, 2, 8, 5, 2, 7, 4, 1, 7, 1, 5, 2, 2, ...</td>\n",
       "      <td>イタリア 自動車 大手 FIAT 傘下 米 クライスラー グループ 完全子会社 合意 する ...</td>\n",
       "      <td>[10, 9, 4, 6, 6, 2, 3, 1, 7, 7, 7, 10, 7, 4, 6...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[10, 3, 8, 2, 8, 2, 8, 2, 4, 1, 8, 2, 10, 3, 1...</td>\n",
       "      <td>7 2 2 2 8 5 2 7 4 1 7 1 5 2 2 8 10 2 7 7 7 1 5...</td>\n",
       "      <td>10 3 8 2 8 2 8 2 4 1 8 2 10 3 10 6 10 3 6 1 8 ...</td>\n",
       "      <td>4 6 4 4 8 2 8 2 4 8 2 10 1 8 2 8 2 8 2 4 1 8 2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>brent crude rise barrel thursday decline oil s...</td>\n",
       "      <td>[5, 5, 5, 5, 5, 5, 5, 2, 7, 5, 8, 8, 6, 1, 8, ...</td>\n",
       "      <td>原油 先物 相場 リビア 大 規模 油田 生産 停止 要因 なる いる 抗議活動 休止 する...</td>\n",
       "      <td>[9, 3, 1, 9, 2, 9, 7, 4, 4, 9, 7, 4, 4, 4, 7, ...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 8, 2, 10, 3, 1, 4, 4, 9,...</td>\n",
       "      <td>5 5 5 5 5 5 5 2 7 5 8 8 6 1 8 8 8 4 4 4 2 1 5 ...</td>\n",
       "      <td>1 1 1 1 1 1 1 8 2 10 3 1 4 4 9 7 2 6 4 4 4 10 ...</td>\n",
       "      <td>6 6 6 8 2 10 3 6 6 8 2 10 3 3 1 10 3 3 6 6 9 7...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>china have approve more company list mainland ...</td>\n",
       "      <td>[2, 7, 2, 5, 1, 1, 1, 5, 10, 4, 2, 7, 2, 2, 5,...</td>\n",
       "      <td>中国 再開 する 新規株式公開 承認 加速 する いる 国営 メディア 新た 上場 承認 す...</td>\n",
       "      <td>[7, 6, 7, 3, 9, 7, 7, 4, 8, 7, 10, 3, 9, 7, 7,...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[8, 2, 10, 3, 8, 2, 1, 6, 6, 6, 1, 4, 10, 8, 2...</td>\n",
       "      <td>2 7 2 5 1 1 1 5 10 4 2 7 2 2 5 1 2 2 1 2 8 10 ...</td>\n",
       "      <td>8 2 10 3 8 2 1 6 6 6 1 4 10 8 2 10 3 8 2 8 2 1...</td>\n",
       "      <td>4 10 3 1 10 3 10 3 10 3 6 8 2 4 9 7 2 1 10 8 2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>janet yellen come step close final approval fe...</td>\n",
       "      <td>[10, 7, 2, 7, 5, 4, 2, 7, 7, 6, 3, 5, 8, 5, 5,...</td>\n",
       "      <td>米 上院 連邦準備理事会 FRB 次期 議長 指名 する イエレン 副議長 指名 承認 採決...</td>\n",
       "      <td>[2, 9, 2, 2, 4, 8, 10, 7, 2, 2, 10, 9, 10, 4, ...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[4, 10, 3, 8, 2, 10, 3, 1, 10, 8, 2, 10, 3, 10...</td>\n",
       "      <td>10 7 2 7 5 4 2 7 7 6 3 5 8 5 5 7 10 8 8 8 1 10...</td>\n",
       "      <td>4 10 3 8 2 10 3 1 10 8 2 10 3 10 3 9 7 2 5 1 4...</td>\n",
       "      <td>6 8 2 1 10 3 1 1 1 1 1 10 3 8 2 8 2 4 6 1 6 4 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>libya hop resume oil production it large oilfi...</td>\n",
       "      <td>[8, 2, 1, 5, 6, 2, 1, 8, 6, 2, 5, 9, 1, 1, 6, ...</td>\n",
       "      <td>リビア 大 規模 油田 エル シャララ 生産 停止 要因 なる いる 抗議活動 デモ隊 活動...</td>\n",
       "      <td>[9, 2, 9, 7, 7, 9, 4, 4, 9, 7, 4, 4, 10, 9, 7,...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[4, 8, 2, 6, 1, 9, 7, 2, 8, 2, 6, 4, 9, 7, 2, ...</td>\n",
       "      <td>8 2 1 5 6 2 1 8 6 2 5 9 1 1 6 5 8 8 8 5 9 8 7 ...</td>\n",
       "      <td>4 8 2 6 1 9 7 2 8 2 6 4 9 7 2 8 2 1 3 6 6 9 7 ...</td>\n",
       "      <td>8 2 8 2 6 10 3 8 2 8 2 8 2 8 2 8 2 8 2 6 8 2 8...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>italian carmaker fiat spa strike deal gain ful...</td>\n",
       "      <td>[7, 2, 2, 2, 8, 1, 5, 1, 8, 2, 8, 10, 5, 5, 6,...</td>\n",
       "      <td>イタリア 自動車 大手 FIAT 傘下 米 クライスラー グループ 完全子会社 合意 する ...</td>\n",
       "      <td>[10, 9, 4, 6, 6, 2, 3, 1, 7, 7, 7, 10, 7, 4, 6...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[10, 3, 8, 2, 8, 2, 8, 2, 4, 6, 1, 6, 4, 8, 2,...</td>\n",
       "      <td>7 2 2 2 8 1 5 1 8 2 8 10 5 5 6 9 7 2 7 9 10 8 ...</td>\n",
       "      <td>10 3 8 2 8 2 8 2 4 6 1 6 4 8 2 4 4 1 1 9 7 2 3...</td>\n",
       "      <td>8 2 8 2 1 9 7 2 8 2 10 3 5 1 8 2 3 9 7 2 6 1 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>number american file new claim unemployment be...</td>\n",
       "      <td>[2, 8, 2, 2, 9, 5, 3, 5, 4, 5, 5, 5, 5, 3, 5, ...</td>\n",
       "      <td>米 労働省 発表 する 12月 週 新規 失業保険 申請 件数 季節調整 前週 り 連続 減...</td>\n",
       "      <td>[2, 8, 10, 7, 4, 9, 8, 8, 8, 8, 9, 3, 3, 8, 8,...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[8, 2, 4, 8, 2, 8, 2, 3, 1, 5, 1, 10, 1, 1, 1,...</td>\n",
       "      <td>2 8 2 2 9 5 3 5 4 5 5 5 5 3 5 4 5 5 6 2 9 8 5 ...</td>\n",
       "      <td>8 2 4 8 2 8 2 3 1 5 1 10 1 1 1 1 5 1 10 1 1 9 ...</td>\n",
       "      <td>8 2 10 10 3 1 10 1 9 7 2 1 1 10 3 1 8 2 10 10 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>britain top share index fell first trading day...</td>\n",
       "      <td>[6, 7, 1, 5, 5, 6, 5, 5, 2, 6, 6, 2, 1, 6, 5, ...</td>\n",
       "      <td>カッコ 前 営業日 総合 株価指数 ロンドン 終値 前 営業日 終値 ロンドン株式市場 反落...</td>\n",
       "      <td>[10, 4, 3, 1, 3, 4, 3, 4, 3, 3, 9, 9, 7, 3, 7,...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[9, 7, 2, 10, 3, 6, 1, 1, 9, 7, 2, 1, 1, 8, 2,...</td>\n",
       "      <td>6 7 1 5 5 6 5 5 2 6 6 2 1 6 5 6 5 2 5 7 2 4 5 ...</td>\n",
       "      <td>9 7 2 10 3 6 1 1 9 7 2 1 1 8 2 9 7 2 9 7 2 8 2...</td>\n",
       "      <td>1 1 1 1 8 2 1 1 1 5 1 1 1 1 1 10 3 1 1 8 2 1 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>970</th>\n",
       "      <td>sometimes news be bad news j.c. penney co inc ...</td>\n",
       "      <td>[9, 8, 2, 7, 8, 2, 2, 2, 2, 1, 5, 5, 9, 2, 2, ...</td>\n",
       "      <td>米 百貨店 大手 JC ペニー 株価 ニューヨーク市場 急落 する 終値 なる 同社 年末 ...</td>\n",
       "      <td>[2, 8, 4, 6, 6, 9, 9, 4, 7, 3, 7, 1, 9, 8, 8, ...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[3, 4, 8, 2, 10, 3, 4, 8, 2, 8, 2, 8, 2, 8, 2,...</td>\n",
       "      <td>9 8 2 7 8 2 2 2 2 1 5 5 9 2 2 8 5 2 2 3 2 2 2 ...</td>\n",
       "      <td>3 4 8 2 10 3 4 8 2 8 2 8 2 8 2 6 1 1 3 8 2 8 2...</td>\n",
       "      <td>10 3 10 3 1 10 3 9 7 2 9 7 2 1 1 10 3 8 2 3 10...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>971</th>\n",
       "      <td>european central bank unexpected cut interest ...</td>\n",
       "      <td>[7, 7, 5, 5, 1, 6, 6, 2, 5, 5, 5, 7, 5, 3, 5, ...</td>\n",
       "      <td>欧州中央銀行 EC 主要政策金利 据え置く 上限 金利 限界 貸出金利 下限 金利 中銀 預...</td>\n",
       "      <td>[7, 10, 9, 10, 10, 9, 8, 8, 8, 9, 10, 8, 9, 9,...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[10, 3, 10, 3, 1, 1, 6, 9, 7, 2, 9, 7, 2, 8, 2...</td>\n",
       "      <td>7 7 5 5 1 6 6 2 5 5 5 7 5 3 5 5 5 5 5 4 2 7 7 ...</td>\n",
       "      <td>10 3 10 3 1 1 6 9 7 2 9 7 2 8 2 1 1 1 10 3 1 5...</td>\n",
       "      <td>8 2 10 3 6 3 4 6 1 4 1 1 9 7 2 10 3 10 3 4 1 3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>972</th>\n",
       "      <td>doji jpy sideline eur volatility ahead tonight...</td>\n",
       "      <td>[5, 5, 8, 5, 7, 7, 7, 4, 5, 4, 2, 4, 7, 7, 5, ...</td>\n",
       "      <td>ドル 円 テクニカル分析 米 雇用統計 前 調整 続く 見通し 今晩 米 雇用統計 発表 控...</td>\n",
       "      <td>[2, 8, 2, 2, 8, 4, 10, 4, 9, 8, 2, 8, 10, 8, 8...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[1, 1, 4, 1, 10, 3, 10, 3, 10, 3, 10, 1, 10, 8...</td>\n",
       "      <td>5 5 8 5 7 7 7 4 5 4 2 4 7 7 5 5 5 5 5 7 5 5 5 ...</td>\n",
       "      <td>1 1 4 1 10 3 10 3 10 3 10 1 10 8 2 10 10 3 10 ...</td>\n",
       "      <td>10 3 10 3 1 1 9 7 2 9 7 2 1 1 1 5 8 2 1 1 9 7 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>973</th>\n",
       "      <td>u.s. labor department report initial state job...</td>\n",
       "      <td>[5, 3, 9, 6, 2, 8, 5, 3, 9, 6, 6]</td>\n",
       "      <td>米 労働省 発表 する 新規 失業保険 週間 申請 件数 以下 通り 失業保険 修正 日付 ...</td>\n",
       "      <td>[2, 8, 10, 7, 8, 8, 3, 8, 8, 8, 8, 8, 8, 8, 9,...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[1, 5, 3, 9, 7, 2, 8, 2, 4, 1, 5, 3, 9, 7, 2, ...</td>\n",
       "      <td>5 3 9 6 2 8 5 3 9 6 6</td>\n",
       "      <td>1 5 3 9 7 2 8 2 4 1 5 3 9 7 2 9 7 2</td>\n",
       "      <td>10 3 10 3 1 10 3 9 7 2 9 7 2 1 1 10 3 6 4 8 2 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>974</th>\n",
       "      <td>president barack obama invited german chancell...</td>\n",
       "      <td>[10, 8, 8, 7, 7, 8, 8, 8, 8, 8, 6, 8, 2, 8, 5,...</td>\n",
       "      <td>米 ホワイトハウス オバマ大統領 ドイツ メルケル 首相 電話 会談 する メルケル 首相 ...</td>\n",
       "      <td>[2, 4, 9, 7, 10, 10, 9, 7, 7, 10, 10, 4, 8, 7,...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[4, 4, 4, 10, 3, 10, 3, 4, 4, 4, 4, 4, 9, 7, 2...</td>\n",
       "      <td>10 8 8 7 7 8 8 8 8 8 6 8 2 8 5 7 7 2 9 8 8 7 1...</td>\n",
       "      <td>4 4 4 10 3 10 3 4 4 4 4 4 9 7 2 4 8 2 4 1 10 3...</td>\n",
       "      <td>1 1 6 6 6 4 1 8 2 9 7 2 1 6 9 7 2 6 8 2 1 9 7 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>european central bank leave interest rate unch...</td>\n",
       "      <td>[7, 7, 5, 7, 6, 6, 5, 5, 7, 1, 8, 2, 7, 2, 4, ...</td>\n",
       "      <td>欧州中央銀行 EC 主要政策金利 据え置く 上限 金利 限界 貸出金利 下限 金利 中銀 預...</td>\n",
       "      <td>[7, 10, 9, 10, 10, 9, 8, 8, 8, 9, 10, 8, 9, 9,...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[10, 3, 10, 3, 1, 10, 3, 9, 7, 2, 9, 7, 2, 1, ...</td>\n",
       "      <td>7 7 5 7 6 6 5 5 7 1 8 2 7 2 4 6 6 6 7 7 7 7 5 ...</td>\n",
       "      <td>10 3 10 3 1 10 3 9 7 2 9 7 2 1 1 10 3 6 4 8 2 ...</td>\n",
       "      <td>10 3 10 3 1 10 3 9 7 2 9 7 2 1 1 1 1 8 2 1 4 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>china will spend more km mile new railway line...</td>\n",
       "      <td>[2, 1, 6, 5, 8, 8, 2, 2, 2, 6, 8, 8, 8, 8, 7, ...</td>\n",
       "      <td>新華社 よる 中国 今年 新規 鉄道路線 建設 投じる 建設 する 路線 以上 なる 予定 ...</td>\n",
       "      <td>[7, 7, 7, 4, 8, 3, 3, 9, 3, 7, 10, 3, 7, 4, 7,...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[8, 2, 6, 9, 7, 2, 1, 4, 4, 8, 2, 8, 2, 8, 2, ...</td>\n",
       "      <td>2 1 6 5 8 8 2 2 2 6 8 8 8 8 7 10 2 8 2 10 1 2 ...</td>\n",
       "      <td>8 2 6 9 7 2 1 4 4 8 2 8 2 8 2 9 7 2 4 4 4 4 10...</td>\n",
       "      <td>10 3 10 3 1 10 3 9 7 2 9 7 2 1 1 1 1 10 3 3 8 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>european central bank leave interest rate unch...</td>\n",
       "      <td>[7, 7, 5, 7, 6, 6, 5, 5, 7, 2, 9, 7, 7, 5, 3, ...</td>\n",
       "      <td>欧州中央銀行 EC 主要政策金利 据え置く 上限 金利 限界 貸出金利 下限 金利 中銀 預...</td>\n",
       "      <td>[7, 10, 9, 10, 10, 9, 8, 8, 8, 9, 10, 8, 9, 9,...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[10, 3, 10, 3, 1, 10, 3, 9, 7, 2, 9, 7, 2, 1, ...</td>\n",
       "      <td>7 7 5 7 6 6 5 5 7 2 9 7 7 5 3 2 7 5 4 2 5 2 4 ...</td>\n",
       "      <td>10 3 10 3 1 10 3 9 7 2 9 7 2 1 1 10 3 8 2 3 10...</td>\n",
       "      <td>10 3 10 3 1 1 6 9 7 2 9 7 2 8 2 1 1 1 10 3 1 5...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>european central bank keep interest rate stead...</td>\n",
       "      <td>[7, 7, 5, 5, 6, 6, 5, 5, 5, 3, 2, 5, 5, 6, 4, ...</td>\n",
       "      <td>欧州中央銀行 EC 主要政策金利 据え置く 上限 金利 限界 貸出金利 下限 金利 中銀 預...</td>\n",
       "      <td>[7, 10, 9, 10, 10, 9, 8, 8, 8, 9, 10, 8, 9, 9,...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[10, 3, 10, 3, 1, 1, 9, 7, 2, 9, 7, 2, 1, 1, 1...</td>\n",
       "      <td>7 7 5 5 6 6 5 5 5 3 2 5 5 6 4 5 5 4 5 5 4 3 2 ...</td>\n",
       "      <td>10 3 10 3 1 1 9 7 2 9 7 2 1 1 1 5 8 2 1 1 9 7 ...</td>\n",
       "      <td>8 2 10 3 1 6 6 9 7 2 9 7 2 6 8 2 4 10 3 4 8 2 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>european central bank keep interest rate stead...</td>\n",
       "      <td>[7, 7, 5, 5, 6, 6, 5, 5, 5, 3, 2, 5, 5, 6, 4, ...</td>\n",
       "      <td>欧州中央銀行 EC 主要政策金利 据え置く 上限 金利 限界 貸出金利 下限 金利 中銀 預...</td>\n",
       "      <td>[7, 10, 9, 10, 10, 9, 8, 8, 8, 9, 10, 8, 9, 9,...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[10, 3, 10, 3, 1, 1, 9, 7, 2, 9, 7, 2, 1, 1, 1...</td>\n",
       "      <td>7 7 5 5 6 6 5 5 5 3 2 5 5 6 4 5 5 4 5 5 4 3 2 ...</td>\n",
       "      <td>10 3 10 3 1 1 9 7 2 9 7 2 1 1 1 5 8 2 1 1 9 7 ...</td>\n",
       "      <td>1 1 9 7 2 8 2 10 3 8 2 6 1 9 7 2 1 1 9 7 2 9 7...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>980</th>\n",
       "      <td>european central bank keep interest rate recor...</td>\n",
       "      <td>[7, 7, 5, 5, 6, 6, 5, 5, 2, 1, 7, 5, 7, 5, 7, ...</td>\n",
       "      <td>欧州中央銀行 EC 主要政策金利 据え置く 上限 金利 限界 貸出金利 下限 金利 中銀 預...</td>\n",
       "      <td>[7, 10, 9, 10, 10, 9, 8, 8, 8, 9, 10, 8, 9, 9,...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[10, 3, 10, 3, 1, 1, 9, 7, 2, 9, 7, 2, 1, 1, 8...</td>\n",
       "      <td>7 7 5 5 6 6 5 5 2 1 7 5 7 5 7 4 5 9 7 9 2 8 5 ...</td>\n",
       "      <td>10 3 10 3 1 1 9 7 2 9 7 2 1 1 8 2 6 10 3 1 10 ...</td>\n",
       "      <td>10 3 1 1 1 1 9 7 2 1 1 10 1 1 1 10 8 2 10 3 1 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>european central bank leave interest rate unch...</td>\n",
       "      <td>[7, 7, 5, 7, 6, 6, 5, 5, 7, 1, 1, 1, 2, 2, 2, ...</td>\n",
       "      <td>欧州中央銀行 EC 主要政策金利 据え置く 上限 金利 限界 貸出金利 下限 金利 中銀 預...</td>\n",
       "      <td>[7, 10, 9, 10, 10, 9, 8, 8, 8, 9, 10, 8, 9, 9,...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[10, 3, 10, 3, 1, 10, 3, 9, 7, 2, 9, 7, 2, 1, ...</td>\n",
       "      <td>7 7 5 7 6 6 5 5 7 1 1 1 2 2 2 4 2 5 4 5 7 7 2 ...</td>\n",
       "      <td>10 3 10 3 1 10 3 9 7 2 9 7 2 1 1 10 3 6 6 6 8 ...</td>\n",
       "      <td>1 9 7 2 9 7 2 1 10 3 8 2 1 10 4 1 10 3 8 2 4 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>european central bank unexpected cut interest ...</td>\n",
       "      <td>[7, 7, 5, 5, 1, 6, 6, 2, 5, 5, 5, 7, 5, 3, 5, ...</td>\n",
       "      <td>欧州中央銀行 EC 主要政策金利 据え置く 上限 金利 限界 貸出金利 下限 金利 中銀 預...</td>\n",
       "      <td>[7, 10, 9, 10, 10, 9, 8, 8, 8, 9, 10, 8, 9, 9,...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[10, 3, 10, 3, 1, 1, 6, 9, 7, 2, 9, 7, 2, 8, 2...</td>\n",
       "      <td>7 7 5 5 1 6 6 2 5 5 5 7 5 3 5 5 5 5 5 4 2 7 7 ...</td>\n",
       "      <td>10 3 10 3 1 1 6 9 7 2 9 7 2 8 2 1 1 1 10 3 1 5...</td>\n",
       "      <td>9 7 2 6 4 4 1 8 2 9 7 2 9 7 2 8 2 8 2 10 3 6 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>be result federal reserve purchase treasury th...</td>\n",
       "      <td>[2, 4, 7, 7, 1, 5, 5, 6, 2, 10, 7, 7, 2, 2, 1,...</td>\n",
       "      <td>米 連邦準備理事会 FRB 傘下 ニューヨーク連銀 国債 買い切る オペ 実施 する 国債 ...</td>\n",
       "      <td>[2, 2, 2, 6, 8, 10, 8, 8, 4, 7, 10, 7, 7, 9, 8...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[8, 2, 10, 10, 3, 10, 3, 6, 1, 1, 9, 7, 2, 8, ...</td>\n",
       "      <td>2 4 7 7 1 5 5 6 2 10 7 7 2 2 1 1 1 2 7 1 1 2 7...</td>\n",
       "      <td>8 2 10 10 3 10 3 6 1 1 9 7 2 8 2 4 10 3 10 3 8...</td>\n",
       "      <td>6 4 4 8 2 1 10 3 1 1 1 8 2 10 8 2 1 1 1 8 2 9 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>european central bank leave interest rate unch...</td>\n",
       "      <td>[7, 7, 5, 7, 6, 6, 5, 5, 7, 1, 1, 1, 2, 2, 2, ...</td>\n",
       "      <td>欧州中央銀行 EC 主要政策金利 据え置く 上限 金利 限界 貸出金利 下限 金利 中銀 預...</td>\n",
       "      <td>[7, 10, 9, 10, 10, 9, 8, 8, 8, 9, 10, 8, 9, 9,...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[10, 3, 10, 3, 1, 10, 3, 9, 7, 2, 9, 7, 2, 1, ...</td>\n",
       "      <td>7 7 5 7 6 6 5 5 7 1 1 1 2 2 2 4 2 5 4 5 7 7 2 ...</td>\n",
       "      <td>10 3 10 3 1 10 3 9 7 2 9 7 2 1 1 10 3 6 6 6 8 ...</td>\n",
       "      <td>6 1 1 1 1 1 8 2 1 1 4 6 4 4 1 6 9 7 2 3 8 2 1 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>985</th>\n",
       "      <td>european central bank leave interest rate unch...</td>\n",
       "      <td>[7, 7, 5, 7, 6, 6, 5, 5, 5, 5, 7, 9, 2, 7, 7, ...</td>\n",
       "      <td>欧州中央銀行 EC 主要政策金利 据え置く 上限 金利 限界 貸出金利 下限 金利 中銀 預...</td>\n",
       "      <td>[7, 10, 9, 10, 10, 9, 8, 8, 8, 9, 10, 8, 9, 9,...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[10, 3, 10, 3, 1, 10, 3, 9, 7, 2, 9, 7, 2, 1, ...</td>\n",
       "      <td>7 7 5 7 6 6 5 5 5 5 7 9 2 7 7 9 5 2 5 6 2 2 5 ...</td>\n",
       "      <td>10 3 10 3 1 10 3 9 7 2 9 7 2 1 1 1 1 10 3 3 8 ...</td>\n",
       "      <td>4 10 3 1 6 4 10 3 10 3 9 7 2 9 7 2 8 2 10 3 1 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986</th>\n",
       "      <td>european central bank leave interest rate unch...</td>\n",
       "      <td>[7, 7, 5, 7, 6, 6, 5, 5, 5, 5, 7, 9, 2, 7, 7, ...</td>\n",
       "      <td>欧州中央銀行 EC 主要政策金利 据え置く 上限 金利 限界 貸出金利 下限 金利 中銀 預...</td>\n",
       "      <td>[7, 10, 9, 10, 10, 9, 8, 8, 8, 9, 10, 8, 9, 9,...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[10, 3, 10, 3, 1, 10, 3, 9, 7, 2, 9, 7, 2, 1, ...</td>\n",
       "      <td>7 7 5 7 6 6 5 5 5 5 7 9 2 7 7 9 5 2 5 6 2 2 5 ...</td>\n",
       "      <td>10 3 10 3 1 10 3 9 7 2 9 7 2 1 1 1 1 10 3 3 8 ...</td>\n",
       "      <td>8 2 10 3 10 3 1 6 1 10 3 1 1 9 7 2 1 1 8 2 10 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>987</th>\n",
       "      <td>european central bank leave interest rate unch...</td>\n",
       "      <td>[7, 7, 5, 7, 6, 6, 5, 5, 7, 2, 9, 7, 7, 5, 3, ...</td>\n",
       "      <td>欧州中央銀行 EC 主要政策金利 据え置く 上限 金利 限界 貸出金利 下限 金利 中銀 預...</td>\n",
       "      <td>[7, 10, 9, 10, 10, 9, 8, 8, 8, 9, 10, 8, 9, 9,...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[10, 3, 10, 3, 1, 10, 3, 9, 7, 2, 9, 7, 2, 1, ...</td>\n",
       "      <td>7 7 5 7 6 6 5 5 7 2 9 7 7 5 3 2 7 5 4 2 5 2 4 ...</td>\n",
       "      <td>10 3 10 3 1 10 3 9 7 2 9 7 2 1 1 10 3 8 2 3 10...</td>\n",
       "      <td>8 2 4 9 7 2 4 1 8 2 8 2 8 2 1 8 2 4 4 3 3 9 7 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988</th>\n",
       "      <td>daimler ag mercedes-benz unit saw vehicle sale...</td>\n",
       "      <td>[2, 2, 2, 2, 5, 2, 2, 2, 5, 5, 6, 6, 8, 5, 2, ...</td>\n",
       "      <td>ドイツ 自動車 大手 ダイムラー メルセデス・ベンツ 部門 中国 自動車販売 香港 除く 前...</td>\n",
       "      <td>[7, 9, 4, 6, 10, 5, 7, 8, 6, 8, 9, 8, 7, 7, 7,...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[8, 2, 8, 2, 8, 2, 8, 2, 1, 8, 2, 8, 2, 8, 2, ...</td>\n",
       "      <td>2 2 2 2 5 2 2 2 5 5 6 6 8 5 2 1 2 5 2 6 2 2 6 ...</td>\n",
       "      <td>8 2 8 2 8 2 8 2 1 8 2 8 2 8 2 1 1 9 7 2 9 7 2 ...</td>\n",
       "      <td>1 6 6 8 2 1 8 2 4 6 1 8 2 8 2 4 1 6 4 8 2 1 1 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>989</th>\n",
       "      <td>european central bank leave interest rate unch...</td>\n",
       "      <td>[7, 7, 5, 7, 6, 6, 5, 5, 7, 2, 9, 7, 7, 5, 3, ...</td>\n",
       "      <td>欧州中央銀行 EC 主要政策金利 据え置く 上限 金利 限界 貸出金利 下限 金利 中銀 預...</td>\n",
       "      <td>[7, 10, 9, 10, 10, 9, 8, 8, 8, 9, 10, 8, 9, 9,...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[10, 3, 10, 3, 1, 10, 3, 9, 7, 2, 9, 7, 2, 1, ...</td>\n",
       "      <td>7 7 5 7 6 6 5 5 7 2 9 7 7 5 3 2 7 5 4 2 5 2 4 ...</td>\n",
       "      <td>10 3 10 3 1 10 3 9 7 2 9 7 2 1 1 10 3 8 2 3 10...</td>\n",
       "      <td>10 3 10 3 1 10 3 9 7 2 9 7 2 1 1 10 3 6 6 6 8 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>990</th>\n",
       "      <td>european central bank keep interest rate stead...</td>\n",
       "      <td>[7, 7, 5, 5, 6, 6, 5, 5, 5, 3, 2, 5, 5, 6, 4, ...</td>\n",
       "      <td>欧州中央銀行 EC 主要政策金利 据え置く 上限 金利 限界 貸出金利 下限 金利 中銀 預...</td>\n",
       "      <td>[7, 10, 9, 10, 10, 9, 8, 8, 8, 9, 10, 8, 9, 9,...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[10, 3, 10, 3, 1, 1, 9, 7, 2, 9, 7, 2, 1, 1, 1...</td>\n",
       "      <td>7 7 5 5 6 6 5 5 5 3 2 5 5 6 4 5 5 4 5 5 4 3 2 ...</td>\n",
       "      <td>10 3 10 3 1 1 9 7 2 9 7 2 1 1 1 5 8 2 1 1 9 7 ...</td>\n",
       "      <td>10 3 10 3 1 10 3 8 2 10 3 9 7 2 9 7 2 1 1 1 1 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991</th>\n",
       "      <td>european central bank cut interest rate record...</td>\n",
       "      <td>[7, 7, 5, 1, 6, 6, 5, 5, 5, 1, 3, 6, 2, 5, 1, ...</td>\n",
       "      <td>欧州中央銀行 EC 主要政策金利 据え置く 上限 金利 限界 貸出金利 下限 金利 中銀 預...</td>\n",
       "      <td>[7, 10, 9, 10, 10, 9, 8, 8, 8, 9, 10, 8, 9, 9,...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[10, 3, 10, 3, 1, 6, 9, 7, 2, 9, 7, 2, 1, 1, 1...</td>\n",
       "      <td>7 7 5 1 6 6 5 5 5 1 3 6 2 5 1 1 5 5 5 7 7 7 3 ...</td>\n",
       "      <td>10 3 10 3 1 6 9 7 2 9 7 2 1 1 1 6 5 9 7 2 8 2 ...</td>\n",
       "      <td>8 2 10 3 8 2 4 1 8 2 10 3 10 3 8 2 8 2 8 2 8 2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>992</th>\n",
       "      <td>european central bank hold borrow rate thursda...</td>\n",
       "      <td>[7, 7, 5, 7, 6, 6, 5, 2, 1, 2, 1, 3, 5, 4, 2, ...</td>\n",
       "      <td>欧州中央銀行 EC 主要政策金利 据え置く 上限 金利 限界 貸出金利 下限 金利 中銀 預...</td>\n",
       "      <td>[7, 10, 9, 10, 10, 9, 8, 8, 8, 9, 10, 8, 9, 9,...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[10, 3, 10, 3, 1, 10, 3, 9, 7, 2, 9, 7, 2, 1, ...</td>\n",
       "      <td>7 7 5 7 6 6 5 2 1 2 1 3 5 4 2 5 5 4 7 5 9 7 7 ...</td>\n",
       "      <td>10 3 10 3 1 10 3 9 7 2 9 7 2 1 8 2 6 8 2 6 5 1...</td>\n",
       "      <td>10 3 10 3 1 10 3 9 7 2 9 7 2 1 8 2 6 8 2 6 5 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>993</th>\n",
       "      <td>european central bank hold borrow rate thursda...</td>\n",
       "      <td>[7, 7, 5, 7, 6, 6, 5, 2, 1, 2, 1, 3, 5, 4, 2, ...</td>\n",
       "      <td>欧州中央銀行 EC 主要政策金利 据え置く 上限 金利 限界 貸出金利 下限 金利 中銀 預...</td>\n",
       "      <td>[7, 10, 9, 10, 10, 9, 8, 8, 8, 9, 10, 8, 9, 9,...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[10, 3, 10, 3, 1, 10, 3, 9, 7, 2, 9, 7, 2, 1, ...</td>\n",
       "      <td>7 7 5 7 6 6 5 2 1 2 1 3 5 4 2 5 5 4 7 5 9 7 7 ...</td>\n",
       "      <td>10 3 10 3 1 10 3 9 7 2 9 7 2 1 8 2 6 8 2 6 5 1...</td>\n",
       "      <td>9 7 2 4 3 8 2 4 4 3 3 9 7 2 1 8 2 4 8 2 4 5 8 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>alcoa inc report massive quarterly loss thursd...</td>\n",
       "      <td>[2, 2, 6, 1, 6, 5, 5, 7, 5, 2, 5, 5, 1, 1, 1, ...</td>\n",
       "      <td>米 アルミ 大手 アルコア 発表 する 四半期 決算 大幅 赤字 なる 過去 精錬 取得 絡...</td>\n",
       "      <td>[2, 3, 4, 8, 10, 7, 10, 8, 7, 3, 7, 7, 4, 4, 9...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[8, 2, 8, 2, 9, 7, 2, 6, 9, 7, 2, 1, 1, 10, 3,...</td>\n",
       "      <td>2 2 6 1 6 5 5 7 5 2 5 5 1 1 1 2 1 1 1 5 2 5 5 ...</td>\n",
       "      <td>8 2 8 2 9 7 2 6 9 7 2 1 1 10 3 1 8 2 1 1 6 6 6...</td>\n",
       "      <td>1 1 1 10 3 1 1 1 1 10 3 1 9 7 2 9 7 2 8 2 10 3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>european central bank cut interest rate record...</td>\n",
       "      <td>[7, 7, 5, 1, 6, 6, 5, 5, 5, 1, 3, 6, 2, 5, 1, ...</td>\n",
       "      <td>欧州中央銀行 EC 主要政策金利 据え置く 上限 金利 限界 貸出金利 下限 金利 中銀 預...</td>\n",
       "      <td>[7, 10, 9, 10, 10, 9, 8, 8, 8, 9, 10, 8, 9, 9,...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[10, 3, 10, 3, 1, 6, 9, 7, 2, 9, 7, 2, 1, 1, 1...</td>\n",
       "      <td>7 7 5 1 6 6 5 5 5 1 3 6 2 5 1 1 5 5 5 7 7 7 3 ...</td>\n",
       "      <td>10 3 10 3 1 6 9 7 2 9 7 2 1 1 1 6 5 9 7 2 8 2 ...</td>\n",
       "      <td>8 2 1 6 1 8 2 1 1 1 8 2 8 2 8 2 9 7 2 1 3 1 8 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>european central bank unexpected cut interest ...</td>\n",
       "      <td>[7, 7, 5, 5, 1, 6, 6, 2, 5, 5, 5, 7, 5, 3, 5, ...</td>\n",
       "      <td>欧州中央銀行 EC 主要政策金利 据え置く 上限 金利 限界 貸出金利 下限 金利 中銀 預...</td>\n",
       "      <td>[7, 10, 9, 10, 10, 9, 8, 8, 8, 9, 10, 8, 9, 9,...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[10, 3, 10, 3, 1, 1, 6, 9, 7, 2, 9, 7, 2, 8, 2...</td>\n",
       "      <td>7 7 5 5 1 6 6 2 5 5 5 7 5 3 5 5 5 5 5 4 2 7 7 ...</td>\n",
       "      <td>10 3 10 3 1 1 6 9 7 2 9 7 2 8 2 1 1 1 10 3 1 5...</td>\n",
       "      <td>10 3 8 2 1 1 1 1 8 2 1 6 1 4 8 2 6 9 7 2 8 2 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>thai stock rise thursday investor build positi...</td>\n",
       "      <td>[5, 5, 5, 5, 5, 2, 7, 5, 5, 5, 5, 5, 5, 6, 5, ...</td>\n",
       "      <td>午前 バンコク 株式市場 株価 上昇 する 配当 目当て 買う ほか タイ 通貨 バーツ 売...</td>\n",
       "      <td>[9, 10, 3, 9, 2, 7, 10, 2, 1, 9, 10, 5, 3, 2, ...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[1, 1, 1, 1, 1, 8, 2, 10, 3, 1, 1, 1, 1, 1, 1,...</td>\n",
       "      <td>5 5 5 5 5 2 7 5 5 5 5 5 5 6 5 1 5 5 7 6 7 7 5 ...</td>\n",
       "      <td>1 1 1 1 1 8 2 10 3 1 1 1 1 1 1 9 7 2 1 6 1 1 1...</td>\n",
       "      <td>10 3 10 3 1 1 9 7 2 9 7 2 1 1 1 5 8 2 1 1 9 7 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>european central bank leave interest rate unch...</td>\n",
       "      <td>[7, 7, 5, 7, 6, 6, 5, 5, 5, 5, 2, 5, 8, 8, 10,...</td>\n",
       "      <td>欧州中央銀行 EC 主要政策金利 据え置く 上限 金利 限界 貸出金利 下限 金利 中銀 預...</td>\n",
       "      <td>[7, 10, 9, 10, 10, 9, 8, 8, 8, 9, 10, 8, 9, 9,...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[10, 3, 10, 3, 1, 10, 3, 9, 7, 2, 9, 7, 2, 1, ...</td>\n",
       "      <td>7 7 5 7 6 6 5 5 5 5 2 5 8 8 10 7 7 7 7 7 7 7 5...</td>\n",
       "      <td>10 3 10 3 1 10 3 9 7 2 9 7 2 1 1 1 1 8 2 1 4 4...</td>\n",
       "      <td>1 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>european central bank leave interest rate unch...</td>\n",
       "      <td>[7, 7, 5, 7, 6, 6, 5, 5, 7, 1, 1, 1, 2, 2, 2, ...</td>\n",
       "      <td>欧州中央銀行 EC 主要政策金利 据え置く 上限 金利 限界 貸出金利 下限 金利 中銀 預...</td>\n",
       "      <td>[7, 10, 9, 10, 10, 9, 8, 8, 8, 9, 10, 8, 9, 9,...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[10, 3, 10, 3, 1, 10, 3, 9, 7, 2, 9, 7, 2, 1, ...</td>\n",
       "      <td>7 7 5 7 6 6 5 5 7 1 1 1 2 2 2 4 2 5 4 5 7 7 2 ...</td>\n",
       "      <td>10 3 10 3 1 10 3 9 7 2 9 7 2 1 1 10 3 6 6 6 8 ...</td>\n",
       "      <td>8 2 6 6 10 8 2 8 2 1 10 3 1 1 10 3 10 3 10 3 4...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>999 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            en_article  \\\n",
       "0    u.s. commerce department seasonal adjust annua...   \n",
       "1    chinese budget smartphone maker xiaomi plan se...   \n",
       "2    samsung electronics co ltd fell more percent t...   \n",
       "3    fiat deal gain full control chrysler group llc...   \n",
       "4    fiat share jump thursday it strike deal gain f...   \n",
       "5    u.s. treasuries price rise thursday benchmark ...   \n",
       "6                       auction detail see 3-year note   \n",
       "7    brent crude sink barrel thursday libya prepare...   \n",
       "8    gauge u.s. factory activity hold 2-1/2-year hi...   \n",
       "9    work massive panama canal extension project ma...   \n",
       "10   increase december car sale france spain prompt...   \n",
       "11   federal reserve provide liquidity foreign cent...   \n",
       "12   new car sale spain rise percent year early car...   \n",
       "13   south korean auto maker hyundai motor co kia m...   \n",
       "14   u.s. manufacturing end year high note grow dec...   \n",
       "15   oil price fell more thursday libya prepare res...   \n",
       "16   freddie mac average u.s. mortgage rate percent...   \n",
       "17   u.s. construction spending rise it high level ...   \n",
       "18   russia world big oil producer raise output pos...   \n",
       "19                                         morning fix   \n",
       "20   premium italian spanish bond offer benchmark g...   \n",
       "21   china yuan pull back slightly record high doll...   \n",
       "22   italian carmaker fiat spa say wednesday it hav...   \n",
       "23   brent crude rise barrel thursday decline oil s...   \n",
       "24   china have approve more company list mainland ...   \n",
       "25   janet yellen come step close final approval fe...   \n",
       "26   libya hop resume oil production it large oilfi...   \n",
       "27   italian carmaker fiat spa strike deal gain ful...   \n",
       "28   number american file new claim unemployment be...   \n",
       "29   britain top share index fell first trading day...   \n",
       "..                                                 ...   \n",
       "970  sometimes news be bad news j.c. penney co inc ...   \n",
       "971  european central bank unexpected cut interest ...   \n",
       "972  doji jpy sideline eur volatility ahead tonight...   \n",
       "973  u.s. labor department report initial state job...   \n",
       "974  president barack obama invited german chancell...   \n",
       "975  european central bank leave interest rate unch...   \n",
       "976  china will spend more km mile new railway line...   \n",
       "977  european central bank leave interest rate unch...   \n",
       "978  european central bank keep interest rate stead...   \n",
       "979  european central bank keep interest rate stead...   \n",
       "980  european central bank keep interest rate recor...   \n",
       "981  european central bank leave interest rate unch...   \n",
       "982  european central bank unexpected cut interest ...   \n",
       "983  be result federal reserve purchase treasury th...   \n",
       "984  european central bank leave interest rate unch...   \n",
       "985  european central bank leave interest rate unch...   \n",
       "986  european central bank leave interest rate unch...   \n",
       "987  european central bank leave interest rate unch...   \n",
       "988  daimler ag mercedes-benz unit saw vehicle sale...   \n",
       "989  european central bank leave interest rate unch...   \n",
       "990  european central bank keep interest rate stead...   \n",
       "991  european central bank cut interest rate record...   \n",
       "992  european central bank hold borrow rate thursda...   \n",
       "993  european central bank hold borrow rate thursda...   \n",
       "994  alcoa inc report massive quarterly loss thursd...   \n",
       "995  european central bank cut interest rate record...   \n",
       "996  european central bank unexpected cut interest ...   \n",
       "997  thai stock rise thursday investor build positi...   \n",
       "998  european central bank leave interest rate unch...   \n",
       "999  european central bank leave interest rate unch...   \n",
       "\n",
       "                                     transformation_en  \\\n",
       "0                       [5, 2, 9, 6, 6, 6, 6, 2, 6, 6]   \n",
       "1    [2, 6, 2, 2, 2, 1, 2, 2, 5, 6, 2, 2, 2, 2, 10,...   \n",
       "2    [2, 2, 2, 2, 5, 5, 5, 5, 2, 5, 6, 5, 5, 5, 6, ...   \n",
       "3    [2, 1, 5, 1, 8, 2, 8, 10, 5, 2, 1, 5, 5, 5, 5,...   \n",
       "4    [2, 1, 5, 5, 2, 8, 1, 5, 1, 8, 2, 8, 10, 7, 4,...   \n",
       "5    [5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 5, 5, 5, 6, 10,...   \n",
       "6                                      [1, 1, 5, 1, 5]   \n",
       "7    [5, 5, 5, 5, 5, 8, 1, 1, 2, 8, 4, 4, 4, 2, 4, ...   \n",
       "8    [5, 5, 4, 4, 7, 5, 5, 5, 2, 8, 2, 2, 9, 5, 3, ...   \n",
       "9    [8, 1, 8, 2, 1, 1, 7, 2, 1, 9, 5, 2, 9, 6, 9, ...   \n",
       "10   [6, 5, 2, 2, 8, 8, 7, 7, 4, 7, 2, 5, 2, 7, 5, ...   \n",
       "11   [7, 7, 1, 1, 1, 7, 5, 5, 5, 2, 10, 7, 8, 5, 7,...   \n",
       "12   [2, 2, 2, 8, 5, 5, 6, 5, 2, 2, 2, 2, 8, 5, 6, ...   \n",
       "13   [8, 8, 2, 2, 2, 2, 2, 2, 2, 2, 2, 7, 6, 5, 4, ...   \n",
       "14   [5, 4, 5, 6, 5, 5, 6, 5, 2, 4, 4, 6, 6, 6, 4, ...   \n",
       "15   [5, 5, 5, 5, 5, 8, 1, 1, 2, 8, 7, 5, 5, 5, 2, ...   \n",
       "16                     [1, 2, 6, 5, 1, 6, 5, 10, 5, 5]   \n",
       "17   [5, 2, 6, 5, 2, 5, 5, 5, 6, 6, 5, 10, 2, 1, 6,...   \n",
       "18   [7, 4, 5, 5, 5, 1, 4, 9, 2, 5, 5, 5, 6, 5, 5, ...   \n",
       "19                                              [5, 5]   \n",
       "20   [2, 7, 7, 5, 1, 5, 7, 5, 5, 6, 5, 5, 2, 4, 6, ...   \n",
       "21   [2, 2, 5, 5, 5, 5, 5, 5, 5, 7, 5, 5, 5, 5, 3, ...   \n",
       "22   [7, 2, 2, 2, 8, 5, 2, 7, 4, 1, 7, 1, 5, 2, 2, ...   \n",
       "23   [5, 5, 5, 5, 5, 5, 5, 2, 7, 5, 8, 8, 6, 1, 8, ...   \n",
       "24   [2, 7, 2, 5, 1, 1, 1, 5, 10, 4, 2, 7, 2, 2, 5,...   \n",
       "25   [10, 7, 2, 7, 5, 4, 2, 7, 7, 6, 3, 5, 8, 5, 5,...   \n",
       "26   [8, 2, 1, 5, 6, 2, 1, 8, 6, 2, 5, 9, 1, 1, 6, ...   \n",
       "27   [7, 2, 2, 2, 8, 1, 5, 1, 8, 2, 8, 10, 5, 5, 6,...   \n",
       "28   [2, 8, 2, 2, 9, 5, 3, 5, 4, 5, 5, 5, 5, 3, 5, ...   \n",
       "29   [6, 7, 1, 5, 5, 6, 5, 5, 2, 6, 6, 2, 1, 6, 5, ...   \n",
       "..                                                 ...   \n",
       "970  [9, 8, 2, 7, 8, 2, 2, 2, 2, 1, 5, 5, 9, 2, 2, ...   \n",
       "971  [7, 7, 5, 5, 1, 6, 6, 2, 5, 5, 5, 7, 5, 3, 5, ...   \n",
       "972  [5, 5, 8, 5, 7, 7, 7, 4, 5, 4, 2, 4, 7, 7, 5, ...   \n",
       "973                  [5, 3, 9, 6, 2, 8, 5, 3, 9, 6, 6]   \n",
       "974  [10, 8, 8, 7, 7, 8, 8, 8, 8, 8, 6, 8, 2, 8, 5,...   \n",
       "975  [7, 7, 5, 7, 6, 6, 5, 5, 7, 1, 8, 2, 7, 2, 4, ...   \n",
       "976  [2, 1, 6, 5, 8, 8, 2, 2, 2, 6, 8, 8, 8, 8, 7, ...   \n",
       "977  [7, 7, 5, 7, 6, 6, 5, 5, 7, 2, 9, 7, 7, 5, 3, ...   \n",
       "978  [7, 7, 5, 5, 6, 6, 5, 5, 5, 3, 2, 5, 5, 6, 4, ...   \n",
       "979  [7, 7, 5, 5, 6, 6, 5, 5, 5, 3, 2, 5, 5, 6, 4, ...   \n",
       "980  [7, 7, 5, 5, 6, 6, 5, 5, 2, 1, 7, 5, 7, 5, 7, ...   \n",
       "981  [7, 7, 5, 7, 6, 6, 5, 5, 7, 1, 1, 1, 2, 2, 2, ...   \n",
       "982  [7, 7, 5, 5, 1, 6, 6, 2, 5, 5, 5, 7, 5, 3, 5, ...   \n",
       "983  [2, 4, 7, 7, 1, 5, 5, 6, 2, 10, 7, 7, 2, 2, 1,...   \n",
       "984  [7, 7, 5, 7, 6, 6, 5, 5, 7, 1, 1, 1, 2, 2, 2, ...   \n",
       "985  [7, 7, 5, 7, 6, 6, 5, 5, 5, 5, 7, 9, 2, 7, 7, ...   \n",
       "986  [7, 7, 5, 7, 6, 6, 5, 5, 5, 5, 7, 9, 2, 7, 7, ...   \n",
       "987  [7, 7, 5, 7, 6, 6, 5, 5, 7, 2, 9, 7, 7, 5, 3, ...   \n",
       "988  [2, 2, 2, 2, 5, 2, 2, 2, 5, 5, 6, 6, 8, 5, 2, ...   \n",
       "989  [7, 7, 5, 7, 6, 6, 5, 5, 7, 2, 9, 7, 7, 5, 3, ...   \n",
       "990  [7, 7, 5, 5, 6, 6, 5, 5, 5, 3, 2, 5, 5, 6, 4, ...   \n",
       "991  [7, 7, 5, 1, 6, 6, 5, 5, 5, 1, 3, 6, 2, 5, 1, ...   \n",
       "992  [7, 7, 5, 7, 6, 6, 5, 2, 1, 2, 1, 3, 5, 4, 2, ...   \n",
       "993  [7, 7, 5, 7, 6, 6, 5, 2, 1, 2, 1, 3, 5, 4, 2, ...   \n",
       "994  [2, 2, 6, 1, 6, 5, 5, 7, 5, 2, 5, 5, 1, 1, 1, ...   \n",
       "995  [7, 7, 5, 1, 6, 6, 5, 5, 5, 1, 3, 6, 2, 5, 1, ...   \n",
       "996  [7, 7, 5, 5, 1, 6, 6, 2, 5, 5, 5, 7, 5, 3, 5, ...   \n",
       "997  [5, 5, 5, 5, 5, 2, 7, 5, 5, 5, 5, 5, 5, 6, 5, ...   \n",
       "998  [7, 7, 5, 7, 6, 6, 5, 5, 5, 5, 2, 5, 8, 8, 10,...   \n",
       "999  [7, 7, 5, 7, 6, 6, 5, 5, 7, 1, 1, 1, 2, 2, 2, ...   \n",
       "\n",
       "                                            jp_article  \\\n",
       "0    米 商務省 発表 する 建設 支出 以下 とおり 季節調整 年率 総 建設 支出 民間 部門...   \n",
       "1    中国 低価格 スマホ メーカー 小米科技 () 雷軍 董事長 以上 販売 する 計画 明らか...   \n",
       "2    ソウル 株式市場 サムスン電子 株価 超 下落 する 安値 付ける 一部 アナリスト ウォン...   \n",
       "3    欧州 株式市場 イタリア 自動車 大手 FIAT 株価 一時 急伸 する 8月 以来 高値 ...   \n",
       "4    欧州 株式市場 イタリア 自動車 大手 FIAT 株価 一時 急伸 する 8月 以来 高値 ...   \n",
       "5    カッコ 前 営業日 米 東部 時間 時分 前 営業日 終盤 米 東部 時間 時分 前 営業日...   \n",
       "6       米財務省 銘柄 統合 入札 実施 する 発表 する 発行 いずれ 償還 1月 11月 11月   \n",
       "7    原油 先物 相場 リビア 大 規模 油田 生産 停止 要因 なる いる 抗議活動 休止 する...   \n",
       "8    米 労働省 発表 する 12月 週 新規 失業保険 申請 件数 季節調整 前週 り 連続 減...   \n",
       "9    パナマ運河 拡張 工事 めぐる 建設 会社 成る 企業 連合 パナマ運河 PC コスト 超過...   \n",
       "10   12月 フランス イタリア スペイン 自動車販売 台数 前年 同月 増加 する 欧州 自動車...   \n",
       "11   米 連邦準備理事会 FRB 傘下 ニューヨーク連銀 発表 する 最新 週間 データ よる 通...   \n",
       "12   スペイン 自動車 業界団体 よる 新車 販売 政府 補助 支援 する 前年 なる 減少 する...   \n",
       "13   韓国 自動車メーカー 現代自動車 系列 起亜自動車 世界 販売台数 両社 合計 前年 目標 ...   \n",
       "14   Markit 発表 する 12月 米 製造業 購買担当者景気指数 PM 改定 1月 以来 高...   \n",
       "15   原油 先物 相場 リビア 大 規模 油田 生産 停止 要因 なる いる 抗議活動 休止 する...   \n",
       "16   米 連邦 住宅 貸付 抵当 公社 フレディマック 調査 住宅ローン 金利 手数料 以下 通り...   \n",
       "17   米 商務省 発表 する 11月 建設 支出 前 月 年率 3月 以来 高水準 公共 部門 減...   \n",
       "18   ロシア エネルギー省 発表 する データ よる 石油 生産量 日量 ソビエト連邦 崩壊 最 ...   \n",
       "19            ロンドン 市場 午後 値 決め 以下 通り ドル オンス 午後 ドル 午前 ドル   \n",
       "20   カッコ 先物 欧州 市場 前 営業日 終値 現物 前 営業日 終盤 時分 先物 清算 ユーロ...   \n",
       "21   年明け 上海 外国為替市場 人民元 相場 引け 強含み 昨年 12月 付ける 最 高値 水準...   \n",
       "22   イタリア 自動車 大手 FIAT 傘下 米 クライスラー グループ 完全子会社 合意 する ...   \n",
       "23   原油 先物 相場 リビア 大 規模 油田 生産 停止 要因 なる いる 抗議活動 休止 する...   \n",
       "24   中国 再開 する 新規株式公開 承認 加速 する いる 国営 メディア 新た 上場 承認 す...   \n",
       "25   米 上院 連邦準備理事会 FRB 次期 議長 指名 する イエレン 副議長 指名 承認 採決...   \n",
       "26   リビア 大 規模 油田 エル シャララ 生産 停止 要因 なる いる 抗議活動 デモ隊 活動...   \n",
       "27   イタリア 自動車 大手 FIAT 傘下 米 クライスラー グループ 完全子会社 合意 する ...   \n",
       "28   米 労働省 発表 する 12月 週 新規 失業保険 申請 件数 季節調整 前週 り 連続 減...   \n",
       "29   カッコ 前 営業日 総合 株価指数 ロンドン 終値 前 営業日 終値 ロンドン株式市場 反落...   \n",
       "..                                                 ...   \n",
       "970  米 百貨店 大手 JC ペニー 株価 ニューヨーク市場 急落 する 終値 なる 同社 年末 ...   \n",
       "971  欧州中央銀行 EC 主要政策金利 据え置く 上限 金利 限界 貸出金利 下限 金利 中銀 預...   \n",
       "972  ドル 円 テクニカル分析 米 雇用統計 前 調整 続く 見通し 今晩 米 雇用統計 発表 控...   \n",
       "973  米 労働省 発表 する 新規 失業保険 週間 申請 件数 以下 通り 失業保険 修正 日付 ...   \n",
       "974  米 ホワイトハウス オバマ大統領 ドイツ メルケル 首相 電話 会談 する メルケル 首相 ...   \n",
       "975  欧州中央銀行 EC 主要政策金利 据え置く 上限 金利 限界 貸出金利 下限 金利 中銀 預...   \n",
       "976  新華社 よる 中国 今年 新規 鉄道路線 建設 投じる 建設 する 路線 以上 なる 予定 ...   \n",
       "977  欧州中央銀行 EC 主要政策金利 据え置く 上限 金利 限界 貸出金利 下限 金利 中銀 預...   \n",
       "978  欧州中央銀行 EC 主要政策金利 据え置く 上限 金利 限界 貸出金利 下限 金利 中銀 預...   \n",
       "979  欧州中央銀行 EC 主要政策金利 据え置く 上限 金利 限界 貸出金利 下限 金利 中銀 預...   \n",
       "980  欧州中央銀行 EC 主要政策金利 据え置く 上限 金利 限界 貸出金利 下限 金利 中銀 預...   \n",
       "981  欧州中央銀行 EC 主要政策金利 据え置く 上限 金利 限界 貸出金利 下限 金利 中銀 預...   \n",
       "982  欧州中央銀行 EC 主要政策金利 据え置く 上限 金利 限界 貸出金利 下限 金利 中銀 預...   \n",
       "983  米 連邦準備理事会 FRB 傘下 ニューヨーク連銀 国債 買い切る オペ 実施 する 国債 ...   \n",
       "984  欧州中央銀行 EC 主要政策金利 据え置く 上限 金利 限界 貸出金利 下限 金利 中銀 預...   \n",
       "985  欧州中央銀行 EC 主要政策金利 据え置く 上限 金利 限界 貸出金利 下限 金利 中銀 預...   \n",
       "986  欧州中央銀行 EC 主要政策金利 据え置く 上限 金利 限界 貸出金利 下限 金利 中銀 預...   \n",
       "987  欧州中央銀行 EC 主要政策金利 据え置く 上限 金利 限界 貸出金利 下限 金利 中銀 預...   \n",
       "988  ドイツ 自動車 大手 ダイムラー メルセデス・ベンツ 部門 中国 自動車販売 香港 除く 前...   \n",
       "989  欧州中央銀行 EC 主要政策金利 据え置く 上限 金利 限界 貸出金利 下限 金利 中銀 預...   \n",
       "990  欧州中央銀行 EC 主要政策金利 据え置く 上限 金利 限界 貸出金利 下限 金利 中銀 預...   \n",
       "991  欧州中央銀行 EC 主要政策金利 据え置く 上限 金利 限界 貸出金利 下限 金利 中銀 預...   \n",
       "992  欧州中央銀行 EC 主要政策金利 据え置く 上限 金利 限界 貸出金利 下限 金利 中銀 預...   \n",
       "993  欧州中央銀行 EC 主要政策金利 据え置く 上限 金利 限界 貸出金利 下限 金利 中銀 預...   \n",
       "994  米 アルミ 大手 アルコア 発表 する 四半期 決算 大幅 赤字 なる 過去 精錬 取得 絡...   \n",
       "995  欧州中央銀行 EC 主要政策金利 据え置く 上限 金利 限界 貸出金利 下限 金利 中銀 預...   \n",
       "996  欧州中央銀行 EC 主要政策金利 据え置く 上限 金利 限界 貸出金利 下限 金利 中銀 預...   \n",
       "997  午前 バンコク 株式市場 株価 上昇 する 配当 目当て 買う ほか タイ 通貨 バーツ 売...   \n",
       "998  欧州中央銀行 EC 主要政策金利 据え置く 上限 金利 限界 貸出金利 下限 金利 中銀 預...   \n",
       "999  欧州中央銀行 EC 主要政策金利 据え置く 上限 金利 限界 貸出金利 下限 金利 中銀 預...   \n",
       "\n",
       "                                     transformation_jp  similarity  \\\n",
       "0    [2, 3, 10, 7, 3, 10, 8, 8, 9, 8, 9, 3, 10, 7, ...         5.0   \n",
       "1    [7, 5, 8, 2, 8, 3, 8, 7, 3, 8, 7, 10, 7, 7, 9,...         5.0   \n",
       "2    [3, 3, 3, 9, 3, 2, 7, 3, 3, 9, 9, 3, 8, 6, 8, ...         5.0   \n",
       "3    [10, 3, 10, 9, 4, 6, 9, 2, 8, 7, 4, 3, 3, 3, 7...         5.0   \n",
       "4    [10, 3, 10, 9, 4, 6, 9, 2, 8, 7, 4, 3, 3, 3, 7...         5.0   \n",
       "5    [10, 4, 3, 2, 5, 3, 4, 4, 3, 3, 2, 5, 3, 4, 4,...         5.0   \n",
       "6         [6, 9, 7, 6, 4, 7, 10, 7, 6, 10, 6, 9, 8, 8]         5.0   \n",
       "7    [9, 3, 1, 9, 2, 9, 7, 4, 4, 9, 7, 4, 4, 4, 7, ...         5.0   \n",
       "8    [2, 8, 10, 7, 4, 9, 8, 8, 8, 8, 9, 3, 3, 8, 8,...         5.0   \n",
       "9    [9, 3, 9, 9, 3, 9, 4, 6, 7, 9, 2, 9, 9, 3, 5, ...         5.0   \n",
       "10   [4, 10, 10, 7, 8, 8, 9, 7, 8, 7, 10, 9, 9, 5, ...         5.0   \n",
       "11   [2, 2, 2, 6, 8, 10, 7, 7, 3, 7, 7, 8, 3, 2, 3,...         5.0   \n",
       "12   [7, 9, 9, 7, 8, 8, 10, 10, 4, 7, 9, 7, 8, 7, 4...         5.0   \n",
       "13   [7, 3, 6, 7, 3, 5, 3, 6, 9, 9, 2, 3, 7, 8, 7, ...         5.0   \n",
       "14   [1, 10, 7, 4, 2, 10, 10, 10, 10, 9, 3, 4, 10, ...         5.0   \n",
       "15   [9, 3, 1, 9, 2, 9, 7, 4, 4, 9, 7, 4, 4, 4, 7, ...         5.0   \n",
       "16   [2, 10, 10, 4, 9, 8, 2, 7, 3, 9, 9, 8, 8, 9, 3...         5.0   \n",
       "17   [2, 3, 10, 7, 8, 3, 10, 4, 8, 8, 4, 3, 4, 3, 5...         5.0   \n",
       "18   [5, 4, 10, 7, 7, 7, 5, 9, 5, 7, 7, 7, 4, 3, 5,...         5.0   \n",
       "19             [4, 9, 4, 3, 6, 8, 8, 2, 4, 4, 2, 9, 2]         5.0   \n",
       "20   [10, 3, 10, 9, 4, 3, 3, 4, 4, 3, 3, 4, 3, 4, 1...         5.0   \n",
       "21   [9, 3, 8, 9, 1, 3, 2, 3, 4, 3, 7, 3, 1, 3, 9, ...         5.0   \n",
       "22   [10, 9, 4, 6, 6, 2, 3, 1, 7, 7, 7, 10, 7, 4, 6...         5.0   \n",
       "23   [9, 3, 1, 9, 2, 9, 7, 4, 4, 9, 7, 4, 4, 4, 7, ...         5.0   \n",
       "24   [7, 6, 7, 3, 9, 7, 7, 4, 8, 7, 10, 3, 9, 7, 7,...         5.0   \n",
       "25   [2, 9, 2, 2, 4, 8, 10, 7, 2, 2, 10, 9, 10, 4, ...         5.0   \n",
       "26   [9, 2, 9, 7, 7, 9, 4, 4, 9, 7, 4, 4, 10, 9, 7,...         5.0   \n",
       "27   [10, 9, 4, 6, 6, 2, 3, 1, 7, 7, 7, 10, 7, 4, 6...         5.0   \n",
       "28   [2, 8, 10, 7, 4, 9, 8, 8, 8, 8, 9, 3, 3, 8, 8,...         5.0   \n",
       "29   [10, 4, 3, 1, 3, 4, 3, 4, 3, 3, 9, 9, 7, 3, 7,...         5.0   \n",
       "..                                                 ...         ...   \n",
       "970  [2, 8, 4, 6, 6, 9, 9, 4, 7, 3, 7, 1, 9, 8, 8, ...         5.0   \n",
       "971  [7, 10, 9, 10, 10, 9, 8, 8, 8, 9, 10, 8, 9, 9,...         5.0   \n",
       "972  [2, 8, 2, 2, 8, 4, 10, 4, 9, 8, 2, 8, 10, 8, 8...         5.0   \n",
       "973  [2, 8, 10, 7, 8, 8, 3, 8, 8, 8, 8, 8, 8, 8, 9,...         5.0   \n",
       "974  [2, 4, 9, 7, 10, 10, 9, 7, 7, 10, 10, 4, 8, 7,...         5.0   \n",
       "975  [7, 10, 9, 10, 10, 9, 8, 8, 8, 9, 10, 8, 9, 9,...         5.0   \n",
       "976  [7, 7, 7, 4, 8, 3, 3, 9, 3, 7, 10, 3, 7, 4, 7,...         5.0   \n",
       "977  [7, 10, 9, 10, 10, 9, 8, 8, 8, 9, 10, 8, 9, 9,...         5.0   \n",
       "978  [7, 10, 9, 10, 10, 9, 8, 8, 8, 9, 10, 8, 9, 9,...         5.0   \n",
       "979  [7, 10, 9, 10, 10, 9, 8, 8, 8, 9, 10, 8, 9, 9,...         5.0   \n",
       "980  [7, 10, 9, 10, 10, 9, 8, 8, 8, 9, 10, 8, 9, 9,...         5.0   \n",
       "981  [7, 10, 9, 10, 10, 9, 8, 8, 8, 9, 10, 8, 9, 9,...         5.0   \n",
       "982  [7, 10, 9, 10, 10, 9, 8, 8, 8, 9, 10, 8, 9, 9,...         5.0   \n",
       "983  [2, 2, 2, 6, 8, 10, 8, 8, 4, 7, 10, 7, 7, 9, 8...         5.0   \n",
       "984  [7, 10, 9, 10, 10, 9, 8, 8, 8, 9, 10, 8, 9, 9,...         5.0   \n",
       "985  [7, 10, 9, 10, 10, 9, 8, 8, 8, 9, 10, 8, 9, 9,...         5.0   \n",
       "986  [7, 10, 9, 10, 10, 9, 8, 8, 8, 9, 10, 8, 9, 9,...         5.0   \n",
       "987  [7, 10, 9, 10, 10, 9, 8, 8, 8, 9, 10, 8, 9, 9,...         5.0   \n",
       "988  [7, 9, 4, 6, 10, 5, 7, 8, 6, 8, 9, 8, 7, 7, 7,...         5.0   \n",
       "989  [7, 10, 9, 10, 10, 9, 8, 8, 8, 9, 10, 8, 9, 9,...         5.0   \n",
       "990  [7, 10, 9, 10, 10, 9, 8, 8, 8, 9, 10, 8, 9, 9,...         5.0   \n",
       "991  [7, 10, 9, 10, 10, 9, 8, 8, 8, 9, 10, 8, 9, 9,...         5.0   \n",
       "992  [7, 10, 9, 10, 10, 9, 8, 8, 8, 9, 10, 8, 9, 9,...         5.0   \n",
       "993  [7, 10, 9, 10, 10, 9, 8, 8, 8, 9, 10, 8, 9, 9,...         5.0   \n",
       "994  [2, 3, 4, 8, 10, 7, 10, 8, 7, 3, 7, 7, 4, 4, 9...         5.0   \n",
       "995  [7, 10, 9, 10, 10, 9, 8, 8, 8, 9, 10, 8, 9, 9,...         5.0   \n",
       "996  [7, 10, 9, 10, 10, 9, 8, 8, 8, 9, 10, 8, 9, 9,...         5.0   \n",
       "997  [9, 10, 3, 9, 2, 7, 10, 2, 1, 9, 10, 5, 3, 2, ...         5.0   \n",
       "998  [7, 10, 9, 10, 10, 9, 8, 8, 8, 9, 10, 8, 9, 9,...         5.0   \n",
       "999  [7, 10, 9, 10, 10, 9, 8, 8, 8, 9, 10, 8, 9, 9,...         5.0   \n",
       "\n",
       "     dis_similarity                                   en2jp_projection  \\\n",
       "0               1.0  [1, 8, 2, 3, 9, 7, 2, 9, 7, 2, 9, 7, 2, 9, 7, ...   \n",
       "1               1.0  [8, 2, 9, 7, 2, 8, 2, 8, 2, 8, 2, 6, 8, 2, 8, ...   \n",
       "2               1.0  [8, 2, 8, 2, 8, 2, 8, 2, 1, 1, 1, 1, 8, 2, 1, ...   \n",
       "3               1.0  [8, 2, 6, 1, 6, 4, 8, 2, 4, 4, 1, 8, 2, 6, 1, ...   \n",
       "4               1.0  [8, 2, 6, 1, 1, 8, 2, 4, 6, 1, 6, 4, 8, 2, 4, ...   \n",
       "5               1.0  [1, 1, 1, 1, 1, 1, 1, 1, 1, 9, 7, 2, 1, 1, 1, ...   \n",
       "6               1.0                                    [6, 6, 1, 6, 1]   \n",
       "7               1.0  [1, 1, 1, 1, 1, 4, 6, 6, 8, 2, 4, 10, 10, 10, ...   \n",
       "8               1.0  [1, 1, 10, 10, 10, 3, 1, 1, 1, 8, 2, 4, 8, 2, ...   \n",
       "9               1.0  [4, 6, 4, 8, 2, 6, 6, 10, 3, 8, 2, 6, 3, 1, 8,...   \n",
       "10              1.0  [9, 7, 2, 1, 8, 2, 8, 2, 4, 4, 10, 3, 10, 3, 1...   \n",
       "11              1.0  [10, 3, 10, 3, 6, 6, 6, 10, 3, 1, 1, 1, 8, 2, ...   \n",
       "12              1.0  [8, 2, 8, 2, 8, 2, 4, 1, 1, 9, 7, 2, 1, 8, 2, ...   \n",
       "13              1.0  [4, 4, 8, 2, 8, 2, 8, 2, 8, 2, 8, 2, 8, 2, 8, ...   \n",
       "14              1.0  [1, 10, 1, 9, 7, 2, 1, 1, 9, 7, 2, 1, 8, 2, 10...   \n",
       "15              1.0  [1, 1, 1, 1, 1, 4, 6, 6, 8, 2, 4, 10, 3, 1, 1,...   \n",
       "16              1.0      [6, 8, 2, 9, 7, 2, 1, 6, 9, 7, 2, 1, 4, 1, 1]   \n",
       "17              1.0  [1, 8, 2, 9, 7, 2, 1, 8, 2, 1, 1, 1, 9, 7, 2, ...   \n",
       "18              1.0  [10, 3, 10, 1, 1, 1, 6, 10, 3, 8, 2, 1, 1, 1, ...   \n",
       "19              1.0                                             [1, 1]   \n",
       "20              1.0  [8, 2, 10, 3, 10, 3, 1, 6, 1, 10, 3, 1, 1, 9, ...   \n",
       "21              1.0  [8, 2, 8, 2, 1, 1, 1, 1, 1, 1, 1, 10, 3, 1, 1,...   \n",
       "22              1.0  [10, 3, 8, 2, 8, 2, 8, 2, 4, 1, 8, 2, 10, 3, 1...   \n",
       "23              1.0  [1, 1, 1, 1, 1, 1, 1, 8, 2, 10, 3, 1, 4, 4, 9,...   \n",
       "24              1.0  [8, 2, 10, 3, 8, 2, 1, 6, 6, 6, 1, 4, 10, 8, 2...   \n",
       "25              1.0  [4, 10, 3, 8, 2, 10, 3, 1, 10, 8, 2, 10, 3, 10...   \n",
       "26              1.0  [4, 8, 2, 6, 1, 9, 7, 2, 8, 2, 6, 4, 9, 7, 2, ...   \n",
       "27              1.0  [10, 3, 8, 2, 8, 2, 8, 2, 4, 6, 1, 6, 4, 8, 2,...   \n",
       "28              1.0  [8, 2, 4, 8, 2, 8, 2, 3, 1, 5, 1, 10, 1, 1, 1,...   \n",
       "29              1.0  [9, 7, 2, 10, 3, 6, 1, 1, 9, 7, 2, 1, 1, 8, 2,...   \n",
       "..              ...                                                ...   \n",
       "970             1.0  [3, 4, 8, 2, 10, 3, 4, 8, 2, 8, 2, 8, 2, 8, 2,...   \n",
       "971             1.0  [10, 3, 10, 3, 1, 1, 6, 9, 7, 2, 9, 7, 2, 8, 2...   \n",
       "972             1.0  [1, 1, 4, 1, 10, 3, 10, 3, 10, 3, 10, 1, 10, 8...   \n",
       "973             1.0  [1, 5, 3, 9, 7, 2, 8, 2, 4, 1, 5, 3, 9, 7, 2, ...   \n",
       "974             1.0  [4, 4, 4, 10, 3, 10, 3, 4, 4, 4, 4, 4, 9, 7, 2...   \n",
       "975             1.0  [10, 3, 10, 3, 1, 10, 3, 9, 7, 2, 9, 7, 2, 1, ...   \n",
       "976             1.0  [8, 2, 6, 9, 7, 2, 1, 4, 4, 8, 2, 8, 2, 8, 2, ...   \n",
       "977             1.0  [10, 3, 10, 3, 1, 10, 3, 9, 7, 2, 9, 7, 2, 1, ...   \n",
       "978             1.0  [10, 3, 10, 3, 1, 1, 9, 7, 2, 9, 7, 2, 1, 1, 1...   \n",
       "979             1.0  [10, 3, 10, 3, 1, 1, 9, 7, 2, 9, 7, 2, 1, 1, 1...   \n",
       "980             1.0  [10, 3, 10, 3, 1, 1, 9, 7, 2, 9, 7, 2, 1, 1, 8...   \n",
       "981             1.0  [10, 3, 10, 3, 1, 10, 3, 9, 7, 2, 9, 7, 2, 1, ...   \n",
       "982             1.0  [10, 3, 10, 3, 1, 1, 6, 9, 7, 2, 9, 7, 2, 8, 2...   \n",
       "983             1.0  [8, 2, 10, 10, 3, 10, 3, 6, 1, 1, 9, 7, 2, 8, ...   \n",
       "984             1.0  [10, 3, 10, 3, 1, 10, 3, 9, 7, 2, 9, 7, 2, 1, ...   \n",
       "985             1.0  [10, 3, 10, 3, 1, 10, 3, 9, 7, 2, 9, 7, 2, 1, ...   \n",
       "986             1.0  [10, 3, 10, 3, 1, 10, 3, 9, 7, 2, 9, 7, 2, 1, ...   \n",
       "987             1.0  [10, 3, 10, 3, 1, 10, 3, 9, 7, 2, 9, 7, 2, 1, ...   \n",
       "988             1.0  [8, 2, 8, 2, 8, 2, 8, 2, 1, 8, 2, 8, 2, 8, 2, ...   \n",
       "989             1.0  [10, 3, 10, 3, 1, 10, 3, 9, 7, 2, 9, 7, 2, 1, ...   \n",
       "990             1.0  [10, 3, 10, 3, 1, 1, 9, 7, 2, 9, 7, 2, 1, 1, 1...   \n",
       "991             1.0  [10, 3, 10, 3, 1, 6, 9, 7, 2, 9, 7, 2, 1, 1, 1...   \n",
       "992             1.0  [10, 3, 10, 3, 1, 10, 3, 9, 7, 2, 9, 7, 2, 1, ...   \n",
       "993             1.0  [10, 3, 10, 3, 1, 10, 3, 9, 7, 2, 9, 7, 2, 1, ...   \n",
       "994             1.0  [8, 2, 8, 2, 9, 7, 2, 6, 9, 7, 2, 1, 1, 10, 3,...   \n",
       "995             1.0  [10, 3, 10, 3, 1, 6, 9, 7, 2, 9, 7, 2, 1, 1, 1...   \n",
       "996             1.0  [10, 3, 10, 3, 1, 1, 6, 9, 7, 2, 9, 7, 2, 8, 2...   \n",
       "997             1.0  [1, 1, 1, 1, 1, 8, 2, 10, 3, 1, 1, 1, 1, 1, 1,...   \n",
       "998             1.0  [10, 3, 10, 3, 1, 10, 3, 9, 7, 2, 9, 7, 2, 1, ...   \n",
       "999             1.0  [10, 3, 10, 3, 1, 10, 3, 9, 7, 2, 9, 7, 2, 1, ...   \n",
       "\n",
       "                                                    xa  \\\n",
       "0                                  5 2 9 6 6 6 6 2 6 6   \n",
       "1    2 6 2 2 2 1 2 2 5 6 2 2 2 2 10 8 5 6 1 8 2 5 2...   \n",
       "2    2 2 2 2 5 5 5 5 2 5 6 5 5 5 6 5 6 1 6 5 5 5 5 ...   \n",
       "3    2 1 5 1 8 2 8 10 5 2 1 5 5 5 5 7 7 2 7 2 1 1 5...   \n",
       "4    2 1 5 5 2 8 1 5 1 8 2 8 10 7 4 7 2 7 2 1 1 5 8...   \n",
       "5    5 5 5 5 5 5 5 5 5 6 5 5 5 6 10 5 5 5 2 7 2 6 5...   \n",
       "6                                            1 1 5 1 5   \n",
       "7    5 5 5 5 5 8 1 1 2 8 4 4 4 2 4 5 5 8 8 5 2 2 1 ...   \n",
       "8    5 5 4 4 7 5 5 5 2 8 2 2 9 5 3 5 4 5 5 5 5 10 4...   \n",
       "9    8 1 8 2 1 1 7 2 1 9 5 2 9 6 9 6 7 2 8 5 6 9 2 ...   \n",
       "10   6 5 2 2 8 8 7 7 4 7 2 5 2 7 5 5 6 5 6 5 5 5 5 ...   \n",
       "11   7 7 1 1 1 7 5 5 5 2 10 7 8 5 7 7 7 8 1 1 5 2 5...   \n",
       "12   2 2 2 8 5 5 6 5 2 2 2 2 8 5 6 7 1 2 2 5 5 5 6 ...   \n",
       "13   8 8 2 2 2 2 2 2 2 2 2 7 6 5 4 4 6 2 5 5 6 6 5 ...   \n",
       "14   5 4 5 6 5 5 6 5 2 4 4 6 6 6 4 2 9 6 2 6 5 5 7 ...   \n",
       "15   5 5 5 5 5 8 1 1 2 8 7 5 5 5 2 5 2 5 5 6 6 5 7 ...   \n",
       "16                                1 2 6 5 1 6 5 10 5 5   \n",
       "17   5 2 6 5 2 5 5 5 6 6 5 10 2 1 6 5 1 6 2 6 6 5 6...   \n",
       "18   7 4 5 5 5 1 4 9 2 5 5 5 6 5 5 5 6 2 2 5 6 5 1 ...   \n",
       "19                                                 5 5   \n",
       "20   2 7 7 5 1 5 7 5 5 6 5 5 2 4 6 5 8 5 5 8 4 5 4 ...   \n",
       "21   2 2 5 5 5 5 5 5 5 7 5 5 5 5 3 5 2 5 9 5 5 2 5 ...   \n",
       "22   7 2 2 2 8 5 2 7 4 1 7 1 5 2 2 8 10 2 7 7 7 1 5...   \n",
       "23   5 5 5 5 5 5 5 2 7 5 8 8 6 1 8 8 8 4 4 4 2 1 5 ...   \n",
       "24   2 7 2 5 1 1 1 5 10 4 2 7 2 2 5 1 2 2 1 2 8 10 ...   \n",
       "25   10 7 2 7 5 4 2 7 7 6 3 5 8 5 5 7 10 8 8 8 1 10...   \n",
       "26   8 2 1 5 6 2 1 8 6 2 5 9 1 1 6 5 8 8 8 5 9 8 7 ...   \n",
       "27   7 2 2 2 8 1 5 1 8 2 8 10 5 5 6 9 7 2 7 9 10 8 ...   \n",
       "28   2 8 2 2 9 5 3 5 4 5 5 5 5 3 5 4 5 5 6 2 9 8 5 ...   \n",
       "29   6 7 1 5 5 6 5 5 2 6 6 2 1 6 5 6 5 2 5 7 2 4 5 ...   \n",
       "..                                                 ...   \n",
       "970  9 8 2 7 8 2 2 2 2 1 5 5 9 2 2 8 5 2 2 3 2 2 2 ...   \n",
       "971  7 7 5 5 1 6 6 2 5 5 5 7 5 3 5 5 5 5 5 4 2 7 7 ...   \n",
       "972  5 5 8 5 7 7 7 4 5 4 2 4 7 7 5 5 5 5 5 7 5 5 5 ...   \n",
       "973                              5 3 9 6 2 8 5 3 9 6 6   \n",
       "974  10 8 8 7 7 8 8 8 8 8 6 8 2 8 5 7 7 2 9 8 8 7 1...   \n",
       "975  7 7 5 7 6 6 5 5 7 1 8 2 7 2 4 6 6 6 7 7 7 7 5 ...   \n",
       "976  2 1 6 5 8 8 2 2 2 6 8 8 8 8 7 10 2 8 2 10 1 2 ...   \n",
       "977  7 7 5 7 6 6 5 5 7 2 9 7 7 5 3 2 7 5 4 2 5 2 4 ...   \n",
       "978  7 7 5 5 6 6 5 5 5 3 2 5 5 6 4 5 5 4 5 5 4 3 2 ...   \n",
       "979  7 7 5 5 6 6 5 5 5 3 2 5 5 6 4 5 5 4 5 5 4 3 2 ...   \n",
       "980  7 7 5 5 6 6 5 5 2 1 7 5 7 5 7 4 5 9 7 9 2 8 5 ...   \n",
       "981  7 7 5 7 6 6 5 5 7 1 1 1 2 2 2 4 2 5 4 5 7 7 2 ...   \n",
       "982  7 7 5 5 1 6 6 2 5 5 5 7 5 3 5 5 5 5 5 4 2 7 7 ...   \n",
       "983  2 4 7 7 1 5 5 6 2 10 7 7 2 2 1 1 1 2 7 1 1 2 7...   \n",
       "984  7 7 5 7 6 6 5 5 7 1 1 1 2 2 2 4 2 5 4 5 7 7 2 ...   \n",
       "985  7 7 5 7 6 6 5 5 5 5 7 9 2 7 7 9 5 2 5 6 2 2 5 ...   \n",
       "986  7 7 5 7 6 6 5 5 5 5 7 9 2 7 7 9 5 2 5 6 2 2 5 ...   \n",
       "987  7 7 5 7 6 6 5 5 7 2 9 7 7 5 3 2 7 5 4 2 5 2 4 ...   \n",
       "988  2 2 2 2 5 2 2 2 5 5 6 6 8 5 2 1 2 5 2 6 2 2 6 ...   \n",
       "989  7 7 5 7 6 6 5 5 7 2 9 7 7 5 3 2 7 5 4 2 5 2 4 ...   \n",
       "990  7 7 5 5 6 6 5 5 5 3 2 5 5 6 4 5 5 4 5 5 4 3 2 ...   \n",
       "991  7 7 5 1 6 6 5 5 5 1 3 6 2 5 1 1 5 5 5 7 7 7 3 ...   \n",
       "992  7 7 5 7 6 6 5 2 1 2 1 3 5 4 2 5 5 4 7 5 9 7 7 ...   \n",
       "993  7 7 5 7 6 6 5 2 1 2 1 3 5 4 2 5 5 4 7 5 9 7 7 ...   \n",
       "994  2 2 6 1 6 5 5 7 5 2 5 5 1 1 1 2 1 1 1 5 2 5 5 ...   \n",
       "995  7 7 5 1 6 6 5 5 5 1 3 6 2 5 1 1 5 5 5 7 7 7 3 ...   \n",
       "996  7 7 5 5 1 6 6 2 5 5 5 7 5 3 5 5 5 5 5 4 2 7 7 ...   \n",
       "997  5 5 5 5 5 2 7 5 5 5 5 5 5 6 5 1 5 5 7 6 7 7 5 ...   \n",
       "998  7 7 5 7 6 6 5 5 5 5 2 5 8 8 10 7 7 7 7 7 7 7 5...   \n",
       "999  7 7 5 7 6 6 5 5 7 1 1 1 2 2 2 4 2 5 4 5 7 7 2 ...   \n",
       "\n",
       "                                                    xb  \\\n",
       "0      1 8 2 3 9 7 2 9 7 2 9 7 2 9 7 2 8 2 9 7 2 9 7 2   \n",
       "1    8 2 9 7 2 8 2 8 2 8 2 6 8 2 8 2 1 9 7 2 8 2 8 ...   \n",
       "2    8 2 8 2 8 2 8 2 1 1 1 1 8 2 1 9 7 2 1 1 1 9 7 ...   \n",
       "3    8 2 6 1 6 4 8 2 4 4 1 8 2 6 1 1 1 1 10 3 10 3 ...   \n",
       "4    8 2 6 1 1 8 2 4 6 1 6 4 8 2 4 4 10 3 10 10 3 8...   \n",
       "5    1 1 1 1 1 1 1 1 1 9 7 2 1 1 1 9 7 2 4 1 1 1 8 ...   \n",
       "6                                            6 6 1 6 1   \n",
       "7    1 1 1 1 1 4 6 6 8 2 4 10 10 10 8 2 10 1 1 4 4 ...   \n",
       "8    1 1 10 10 10 3 1 1 1 8 2 4 8 2 8 2 3 1 5 1 10 ...   \n",
       "9    4 6 4 8 2 6 6 10 3 8 2 6 3 1 8 2 3 9 7 2 3 9 7...   \n",
       "10   9 7 2 1 8 2 8 2 4 4 10 3 10 3 10 10 3 8 2 1 8 ...   \n",
       "11   10 3 10 3 6 6 6 10 3 1 1 1 8 2 4 10 3 4 1 10 3...   \n",
       "12   8 2 8 2 8 2 4 1 1 9 7 2 1 8 2 8 2 8 2 8 2 4 1 ...   \n",
       "13   4 4 8 2 8 2 8 2 8 2 8 2 8 2 8 2 8 2 8 2 10 3 9...   \n",
       "14   1 10 1 9 7 2 1 1 9 7 2 1 8 2 10 10 9 7 2 9 7 2...   \n",
       "15   1 1 1 1 1 4 6 6 8 2 4 10 3 1 1 1 8 2 1 8 2 1 1...   \n",
       "16                       6 8 2 9 7 2 1 6 9 7 2 1 4 1 1   \n",
       "17   1 8 2 9 7 2 1 8 2 1 1 1 9 7 2 9 7 2 1 4 8 2 6 ...   \n",
       "18   10 3 10 1 1 1 6 10 3 8 2 1 1 1 9 7 2 1 1 1 9 7...   \n",
       "19                                                 1 1   \n",
       "20   8 2 10 3 10 3 1 6 1 10 3 1 1 9 7 2 1 1 8 2 10 ...   \n",
       "21   8 2 8 2 1 1 1 1 1 1 1 10 3 1 1 1 1 5 1 8 2 1 3...   \n",
       "22   10 3 8 2 8 2 8 2 4 1 8 2 10 3 10 6 10 3 6 1 8 ...   \n",
       "23   1 1 1 1 1 1 1 8 2 10 3 1 4 4 9 7 2 6 4 4 4 10 ...   \n",
       "24   8 2 10 3 8 2 1 6 6 6 1 4 10 8 2 10 3 8 2 8 2 1...   \n",
       "25   4 10 3 8 2 10 3 1 10 8 2 10 3 10 3 9 7 2 5 1 4...   \n",
       "26   4 8 2 6 1 9 7 2 8 2 6 4 9 7 2 8 2 1 3 6 6 9 7 ...   \n",
       "27   10 3 8 2 8 2 8 2 4 6 1 6 4 8 2 4 4 1 1 9 7 2 3...   \n",
       "28   8 2 4 8 2 8 2 3 1 5 1 10 1 1 1 1 5 1 10 1 1 9 ...   \n",
       "29   9 7 2 10 3 6 1 1 9 7 2 1 1 8 2 9 7 2 9 7 2 8 2...   \n",
       "..                                                 ...   \n",
       "970  3 4 8 2 10 3 4 8 2 8 2 8 2 8 2 6 1 1 3 8 2 8 2...   \n",
       "971  10 3 10 3 1 1 6 9 7 2 9 7 2 8 2 1 1 1 10 3 1 5...   \n",
       "972  1 1 4 1 10 3 10 3 10 3 10 1 10 8 2 10 10 3 10 ...   \n",
       "973                1 5 3 9 7 2 8 2 4 1 5 3 9 7 2 9 7 2   \n",
       "974  4 4 4 10 3 10 3 4 4 4 4 4 9 7 2 4 8 2 4 1 10 3...   \n",
       "975  10 3 10 3 1 10 3 9 7 2 9 7 2 1 1 10 3 6 4 8 2 ...   \n",
       "976  8 2 6 9 7 2 1 4 4 8 2 8 2 8 2 9 7 2 4 4 4 4 10...   \n",
       "977  10 3 10 3 1 10 3 9 7 2 9 7 2 1 1 10 3 8 2 3 10...   \n",
       "978  10 3 10 3 1 1 9 7 2 9 7 2 1 1 1 5 8 2 1 1 9 7 ...   \n",
       "979  10 3 10 3 1 1 9 7 2 9 7 2 1 1 1 5 8 2 1 1 9 7 ...   \n",
       "980  10 3 10 3 1 1 9 7 2 9 7 2 1 1 8 2 6 10 3 1 10 ...   \n",
       "981  10 3 10 3 1 10 3 9 7 2 9 7 2 1 1 10 3 6 6 6 8 ...   \n",
       "982  10 3 10 3 1 1 6 9 7 2 9 7 2 8 2 1 1 1 10 3 1 5...   \n",
       "983  8 2 10 10 3 10 3 6 1 1 9 7 2 8 2 4 10 3 10 3 8...   \n",
       "984  10 3 10 3 1 10 3 9 7 2 9 7 2 1 1 10 3 6 6 6 8 ...   \n",
       "985  10 3 10 3 1 10 3 9 7 2 9 7 2 1 1 1 1 10 3 3 8 ...   \n",
       "986  10 3 10 3 1 10 3 9 7 2 9 7 2 1 1 1 1 10 3 3 8 ...   \n",
       "987  10 3 10 3 1 10 3 9 7 2 9 7 2 1 1 10 3 8 2 3 10...   \n",
       "988  8 2 8 2 8 2 8 2 1 8 2 8 2 8 2 1 1 9 7 2 9 7 2 ...   \n",
       "989  10 3 10 3 1 10 3 9 7 2 9 7 2 1 1 10 3 8 2 3 10...   \n",
       "990  10 3 10 3 1 1 9 7 2 9 7 2 1 1 1 5 8 2 1 1 9 7 ...   \n",
       "991  10 3 10 3 1 6 9 7 2 9 7 2 1 1 1 6 5 9 7 2 8 2 ...   \n",
       "992  10 3 10 3 1 10 3 9 7 2 9 7 2 1 8 2 6 8 2 6 5 1...   \n",
       "993  10 3 10 3 1 10 3 9 7 2 9 7 2 1 8 2 6 8 2 6 5 1...   \n",
       "994  8 2 8 2 9 7 2 6 9 7 2 1 1 10 3 1 8 2 1 1 6 6 6...   \n",
       "995  10 3 10 3 1 6 9 7 2 9 7 2 1 1 1 6 5 9 7 2 8 2 ...   \n",
       "996  10 3 10 3 1 1 6 9 7 2 9 7 2 8 2 1 1 1 10 3 1 5...   \n",
       "997  1 1 1 1 1 8 2 10 3 1 1 1 1 1 1 9 7 2 1 6 1 1 1...   \n",
       "998  10 3 10 3 1 10 3 9 7 2 9 7 2 1 1 1 1 8 2 1 4 4...   \n",
       "999  10 3 10 3 1 10 3 9 7 2 9 7 2 1 1 10 3 6 6 6 8 ...   \n",
       "\n",
       "                                              xb_wrong  \n",
       "0    10 3 10 3 1 10 3 9 7 2 9 7 2 1 1 10 3 6 6 6 8 ...  \n",
       "1    10 3 10 3 6 10 3 10 8 2 6 6 10 9 7 2 1 10 10 1...  \n",
       "2    1 1 1 1 1 4 6 6 8 2 4 10 10 10 8 2 10 1 1 4 4 ...  \n",
       "3    10 3 10 3 1 10 3 9 7 2 9 7 2 1 1 10 3 8 2 3 10...  \n",
       "4    10 3 10 3 1 6 9 7 2 9 7 2 1 1 1 6 5 9 7 2 8 2 ...  \n",
       "5    10 3 1 1 1 1 1 1 1 10 3 10 10 4 4 8 2 6 4 4 1 ...  \n",
       "6    9 7 2 10 3 6 1 1 1 1 1 5 5 9 7 2 10 3 8 2 8 2 ...  \n",
       "7    1 8 2 1 1 1 8 2 1 1 1 1 1 1 9 7 2 1 1 1 1 1 10...  \n",
       "8    8 2 8 2 8 2 1 9 7 2 8 2 9 7 2 6 1 9 7 2 9 7 2 ...  \n",
       "9    8 2 6 8 2 3 1 6 10 3 9 7 2 6 10 3 8 2 6 1 4 1 ...  \n",
       "10   1 1 1 1 1 4 6 6 8 2 4 10 3 1 1 1 8 2 1 8 2 1 1...  \n",
       "11   6 6 1 1 1 1 1 1 1 6 6 10 3 10 8 2 4 1 9 7 2 1 ...  \n",
       "12   1 10 3 8 2 8 2 9 7 2 1 8 2 1 1 10 3 1 1 1 8 2 ...  \n",
       "13   8 2 8 2 10 6 8 2 6 9 7 2 9 7 2 9 7 2 6 1 1 8 2...  \n",
       "14   1 8 2 1 1 1 1 1 1 1 4 1 9 7 2 9 7 2 1 9 7 2 1 ...  \n",
       "15                              6 10 3 10 3 9 7 2 10 1  \n",
       "16   10 3 4 4 1 10 3 1 10 3 10 3 8 2 10 3 8 2 9 7 2...  \n",
       "17   1 6 1 1 1 1 9 7 2 3 10 3 10 3 10 3 1 10 3 1 10...  \n",
       "18   1 10 1 9 7 2 1 1 9 7 2 1 8 2 10 10 9 7 2 9 7 2...  \n",
       "19   4 10 3 3 8 2 10 1 4 1 6 4 6 5 10 3 10 3 6 6 3 ...  \n",
       "20   6 6 8 2 8 2 1 3 4 4 4 6 8 2 10 3 9 7 2 1 8 2 1...  \n",
       "21   4 10 1 1 9 7 2 9 7 2 9 7 2 10 10 1 1 1 1 10 3 ...  \n",
       "22   4 6 4 4 8 2 8 2 4 8 2 10 1 8 2 8 2 8 2 4 1 8 2...  \n",
       "23   6 6 6 8 2 10 3 6 6 8 2 10 3 3 1 10 3 3 6 6 9 7...  \n",
       "24   4 10 3 1 10 3 10 3 10 3 6 8 2 4 9 7 2 1 10 8 2...  \n",
       "25   6 8 2 1 10 3 1 1 1 1 1 10 3 8 2 8 2 4 6 1 6 4 ...  \n",
       "26   8 2 8 2 6 10 3 8 2 8 2 8 2 8 2 8 2 8 2 6 8 2 8...  \n",
       "27   8 2 8 2 1 9 7 2 8 2 10 3 5 1 8 2 3 9 7 2 6 1 1...  \n",
       "28   8 2 10 10 3 1 10 1 9 7 2 1 1 10 3 1 8 2 10 10 ...  \n",
       "29   1 1 1 1 8 2 1 1 1 5 1 1 1 1 1 10 3 1 1 8 2 1 4...  \n",
       "..                                                 ...  \n",
       "970  10 3 10 3 1 10 3 9 7 2 9 7 2 1 1 10 3 8 2 3 10...  \n",
       "971  8 2 10 3 6 3 4 6 1 4 1 1 9 7 2 10 3 10 3 4 1 3...  \n",
       "972  10 3 10 3 1 1 9 7 2 9 7 2 1 1 1 5 8 2 1 1 9 7 ...  \n",
       "973  10 3 10 3 1 10 3 9 7 2 9 7 2 1 1 10 3 6 4 8 2 ...  \n",
       "974  1 1 6 6 6 4 1 8 2 9 7 2 1 6 9 7 2 6 8 2 1 9 7 ...  \n",
       "975  10 3 10 3 1 10 3 9 7 2 9 7 2 1 1 1 1 8 2 1 4 4...  \n",
       "976  10 3 10 3 1 10 3 9 7 2 9 7 2 1 1 1 1 10 3 3 8 ...  \n",
       "977  10 3 10 3 1 1 6 9 7 2 9 7 2 8 2 1 1 1 10 3 1 5...  \n",
       "978  8 2 10 3 1 6 6 9 7 2 9 7 2 6 8 2 4 10 3 4 8 2 ...  \n",
       "979  1 1 9 7 2 8 2 10 3 8 2 6 1 9 7 2 1 1 9 7 2 9 7...  \n",
       "980  10 3 1 1 1 1 9 7 2 1 1 10 1 1 1 10 8 2 10 3 1 ...  \n",
       "981  1 9 7 2 9 7 2 1 10 3 8 2 1 10 4 1 10 3 8 2 4 1...  \n",
       "982  9 7 2 6 4 4 1 8 2 9 7 2 9 7 2 8 2 8 2 10 3 6 1...  \n",
       "983  6 4 4 8 2 1 10 3 1 1 1 8 2 10 8 2 1 1 1 8 2 9 ...  \n",
       "984  6 1 1 1 1 1 8 2 1 1 4 6 4 4 1 6 9 7 2 3 8 2 1 ...  \n",
       "985  4 10 3 1 6 4 10 3 10 3 9 7 2 9 7 2 8 2 10 3 1 ...  \n",
       "986  8 2 10 3 10 3 1 6 1 10 3 1 1 9 7 2 1 1 8 2 10 ...  \n",
       "987  8 2 4 9 7 2 4 1 8 2 8 2 8 2 1 8 2 4 4 3 3 9 7 ...  \n",
       "988  1 6 6 8 2 1 8 2 4 6 1 8 2 8 2 4 1 6 4 8 2 1 1 ...  \n",
       "989  10 3 10 3 1 10 3 9 7 2 9 7 2 1 1 10 3 6 6 6 8 ...  \n",
       "990  10 3 10 3 1 10 3 8 2 10 3 9 7 2 9 7 2 1 1 1 1 ...  \n",
       "991  8 2 10 3 8 2 4 1 8 2 10 3 10 3 8 2 8 2 8 2 8 2...  \n",
       "992  10 3 10 3 1 10 3 9 7 2 9 7 2 1 8 2 6 8 2 6 5 1...  \n",
       "993  9 7 2 4 3 8 2 4 4 3 3 9 7 2 1 8 2 4 8 2 4 5 8 ...  \n",
       "994  1 1 1 10 3 1 1 1 1 10 3 1 9 7 2 9 7 2 8 2 10 3...  \n",
       "995  8 2 1 6 1 8 2 1 1 1 8 2 8 2 8 2 9 7 2 1 3 1 8 ...  \n",
       "996  10 3 8 2 1 1 1 1 8 2 1 6 1 4 8 2 6 9 7 2 8 2 1...  \n",
       "997  10 3 10 3 1 1 9 7 2 9 7 2 1 1 1 5 8 2 1 1 9 7 ...  \n",
       "998                                                1 1  \n",
       "999  8 2 6 6 10 8 2 8 2 1 10 3 1 1 10 3 10 3 10 3 4...  \n",
       "\n",
       "[999 rows x 10 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['5 2 9 6 6 6 6 2 6 6',\n",
       " '10 3 10 3 1 10 3 9 7 2 9 7 2 1 1 10 3 6 6 6 8 2 8 2 8 2 10 8 2 1 10 1 10 3 10 3 8 2 10 3 10 3 10 3 4 10 3 10 3 4 10 4 4 10 3 4 9 7 2 10 6 9 7 2 10 6 5 8 2 5 10 3 8 2 9 7 2 4 10 3 10 3 10 3 8 2 10 3 10 3 8 2 8 2 9 7 2 5 1 1 8 2 8 2 1 1 8 2 10 3 1 8 2 10 3 6 1 4 10 3 1 8 2 1 10 3 10 3 6 1 4 9 7 2 4 9 7 2 8 2 10 3 6 6 8 2 8 2 1 10 3 1 4 8 2 10 3 10 4 6 10 3 10 3 8 2 10 3 4 10 3 4 10 3 10 3 10 3 8 2 4 8 2 10 3 10 3 10 3 3 6 10 3 5 5 9 7 2 8 2 8 2 4 1 8 2 6 10 3 10 3 6 8 2 8 2 10 3 10 3 6 10 3 10 3 8 2 4 6 10 3 10 3 10 3 8 2 10 10 3 10 3 4 5 4 8 2 8 2 5 4 5 10 3 10 4 4 8 2 1 10 4 6 4 10 4 10 3 10 3 1 10 8 2 4 6 8 2 5 1 10 3 4 4 9 7 2 8 2 5 4 9 7 2 6 4 9 7 2 6 1 4 4 10 3 10 3 5 5 10 3 8 2 10 3 4 8 2 6 4 10 4 10 3 8 2 10 3 6 10 6 10 3 6 10 3 10 4 10 8 2 10 4 10 1 4 9 7 2 5 10 10 3 6 1 5 5 9 7 2 1 1 10 4 10 3 8 2 4 10 3 1 10 3 10 3 10 3 10 3 10 3 1 5 4 4 8 2 10 3 8 2 6 8 2 6 10 4 8 2 10 3 8 2 4 1 10 3 9 7 2 5 5 8 2 9 7 2 10 1 1 9 7 2 9 7 2 9 7 2 5 4 4 6 1 1 4 10 3 10 1 4 4 1 10 10 10 10 8 2 10 3 1 8 2 10 1 1 9 7 2 5 9 7 2 1 5 1 9 7 2 10 4 10 10 3 9 7 2 10 5 6 4 10 4 10 10 3 10 3 10 10 10 1 4 10 4 10 3 10 3 1 1 4 10 10 10 10 3 10 3 10 3 10 3 10 10 10 3 4 4 10 10 10 10 10 1 4 10 10 3 10 3 10 3 6 10 3 10 10 3 8 2 4 1 10 3 10 10 6 6 10 3 1 10 6 10 1 1 10 3 10 3 10 3 1 10 6 6 1 10 3 9 7 2 9 7 2 9 7 2 10 3 8 2 9 7 2 9 7 2 9 7 2 10 6 9 7 2 8 2 8 2 10 10 1 10 3 10 9 7 2 10 4 4 6 6 6 6 3 4 6 8 2 4 1 4 6 1 6 6 10 3 9 7 2 10 3 10 9 7 2 10',\n",
       " 1.0]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_2[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['5 2 9 6 6 6 6 2 6 6', '1 8 2 3 9 7 2 9 7 2 9 7 2 9 7 2 8 2 9 7 2 9 7 2', 5.0]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_1[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['5 2 9 6 6 6 6 2 6 6', '1 8 2 3 9 7 2 9 7 2 9 7 2 9 7 2 8 2 9 7 2 9 7 2', 5.0]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['5 2 9 6 6 6 6 2 6 6',\n",
       " '10 3 10 3 1 10 3 9 7 2 9 7 2 1 1 10 3 6 6 6 8 2 8 2 8 2 10 8 2 1 10 1 10 3 10 3 8 2 10 3 10 3 10 3 4 10 3 10 3 4 10 4 4 10 3 4 9 7 2 10 6 9 7 2 10 6 5 8 2 5 10 3 8 2 9 7 2 4 10 3 10 3 10 3 8 2 10 3 10 3 8 2 8 2 9 7 2 5 1 1 8 2 8 2 1 1 8 2 10 3 1 8 2 10 3 6 1 4 10 3 1 8 2 1 10 3 10 3 6 1 4 9 7 2 4 9 7 2 8 2 10 3 6 6 8 2 8 2 1 10 3 1 4 8 2 10 3 10 4 6 10 3 10 3 8 2 10 3 4 10 3 4 10 3 10 3 10 3 8 2 4 8 2 10 3 10 3 10 3 3 6 10 3 5 5 9 7 2 8 2 8 2 4 1 8 2 6 10 3 10 3 6 8 2 8 2 10 3 10 3 6 10 3 10 3 8 2 4 6 10 3 10 3 10 3 8 2 10 10 3 10 3 4 5 4 8 2 8 2 5 4 5 10 3 10 4 4 8 2 1 10 4 6 4 10 4 10 3 10 3 1 10 8 2 4 6 8 2 5 1 10 3 4 4 9 7 2 8 2 5 4 9 7 2 6 4 9 7 2 6 1 4 4 10 3 10 3 5 5 10 3 8 2 10 3 4 8 2 6 4 10 4 10 3 8 2 10 3 6 10 6 10 3 6 10 3 10 4 10 8 2 10 4 10 1 4 9 7 2 5 10 10 3 6 1 5 5 9 7 2 1 1 10 4 10 3 8 2 4 10 3 1 10 3 10 3 10 3 10 3 10 3 1 5 4 4 8 2 10 3 8 2 6 8 2 6 10 4 8 2 10 3 8 2 4 1 10 3 9 7 2 5 5 8 2 9 7 2 10 1 1 9 7 2 9 7 2 9 7 2 5 4 4 6 1 1 4 10 3 10 1 4 4 1 10 10 10 10 8 2 10 3 1 8 2 10 1 1 9 7 2 5 9 7 2 1 5 1 9 7 2 10 4 10 10 3 9 7 2 10 5 6 4 10 4 10 10 3 10 3 10 10 10 1 4 10 4 10 3 10 3 1 1 4 10 10 10 10 3 10 3 10 3 10 3 10 10 10 3 4 4 10 10 10 10 10 1 4 10 10 3 10 3 10 3 6 10 3 10 10 3 8 2 4 1 10 3 10 10 6 6 10 3 1 10 6 10 1 1 10 3 10 3 10 3 1 10 6 6 1 10 3 9 7 2 9 7 2 9 7 2 10 3 8 2 9 7 2 9 7 2 9 7 2 10 6 9 7 2 8 2 8 2 10 10 1 10 3 10 9 7 2 10 4 4 6 6 6 6 3 4 6 8 2 4 1 4 6 1 6 6 10 3 9 7 2 10 3 10 9 7 2 10',\n",
       " 1.0]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[999]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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fbmdk5sgK7a96/u4RW1tbw9ji7Oxs6tati5mZGUIIVqxYwfDhwzl58iRz5swhOjoaKSWP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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd31eff27d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "matplotlib.rcParams['figure.figsize'] = (15.0, 7.5)\n",
    "plt.plot(sls.tops[1],'ro-', label = \"TOP-1 @ Trianing data (similarity = 1)\")\n",
    "plt.plot(sls.tops[5],'bo-', label = \"TOP-5 @ Trianing data (similarity = 1)\" )\n",
    "plt.plot(sls.tops[10],'go-', label = \"TOP-10 @ Trianing data (similarity = 1)\")\n",
    "plt.plot(sls.tops_test[1],'ro--', label = \"TOP-1 @ Test data (similarity = 1)\")\n",
    "plt.plot(sls.tops_test[5],'bo--', label = \"TOP-5 @ Test data (similarity = 1)\")\n",
    "plt.plot(sls.tops_test[10],'go--', label = \"TOP-10 @ Test data (similarity = 1)\")\n",
    "plt.ylabel('Amount of documents belongs to top[1,5,10]')\n",
    "plt.xlabel('Epoches')\n",
    "plt.title('Tendancy of accuracy during training')\n",
    "plt.legend(loc = 2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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