├── README.md ├── baseline_and_fill_missing_data_by_history.ipynb ├── data ├── Benchmark.csv ├── Load_history.csv ├── expected_result.csv ├── full_load.csv ├── full_load_history.csv ├── full_temp.csv ├── full_temp_time.csv ├── load.csv ├── predict.csv ├── readme.txt ├── submission_template.csv ├── temp.csv ├── temperature_history.csv ├── test.csv ├── time_full_load.csv ├── time_load.csv ├── time_temp.csv └── weights.csv ├── load_data.py ├── load_prediction_model_stateful_lstm.ipynb ├── load_prediction_model_xgboost.ipynb ├── plot.py ├── report └── IRDM_GROUP30_Yijing_YANG_Xinyi_HE_Ying_WEN.pdf ├── requirement.txt ├── results ├── 2_weeks_result_2_layers_lstm_with_history_load_as_input.png ├── 2_weeks_result_2_layers_lstm_without_history_load_as_input.png ├── 2_weeks_result_3_layers_lstm_with_history_load_as_input.png ├── 4_months_result_2_layers_lstm_with_history_load_as_input.png ├── 4_months_result_2_layers_lstm_without_history_load_as_input.png └── 4_months_result_3_layers_lstm_with_history_load_as_input.png └── stateful_lstm.jpg /README.md: -------------------------------------------------------------------------------- 1 | # Time Series Analysis: Load Forecasting Track of Global Energy 2 | Time series prediction project for IRDM(COMPGI15) 2016 @ UCL 3 | 4 | Group 30 ([Yijing YANG](mailto:yijing.yang.15@ucl.ac.uk), [Xinyi HE](xinyi.he.15@ucl.ac.uk), [Ying WEN](mailto:ying.wen@cs.ucl.ac.uk)) 5 | 6 | 7 | ## Models 8 | * Stateful Recurrent Neural Network: [Link](./load_prediction_model_stateful_lstm.ipynb) (The details can be found in the ipython notebook) 9 | 10 | To more intuitively and vividly illustrate the stateful LSTM model, the following figure shows the structure and processes of the stateful LSTM model: 11 | ![figure](./stateful_lstm.jpg) 12 | 13 | * XGBoost: [Link](./load_prediction_model_xgboost.ipynb) 14 | 15 | ## Results 16 | The following figure is fitting and forecasting resluts of stateful LSTM model (3 layers LSTM with history load as input). 17 | 18 | Note, this figure shows model’s fitting and forecasting results from 17/06/2008 to 30/06/2008. In the first half, 17/06/2008 to 23/06/2008 is training fitting data; The second half from 24/06/2008 to 30/06/2008 is testing forecasting data. Besides, the blue line represents expected data and the green line represents predicted data and zone 21 is load sum of the other 20 zones. 19 | 20 | To find more result figures, please check the [results folder](./results). 21 | 22 | ![result_figure](./results/2_weeks_result_3_layers_lstm_with_history_load_as_input.png) 23 | 24 | 25 | 26 | ## Requirements 27 | * Python 2.7 28 | * Keras 29 | * XGBoost 30 | 31 | To install all the requirements: 32 | 33 | ``` 34 | pip install requirement.txt 35 | ``` 36 | 37 | ## Dataset 38 | * [Load Forecasting](https://www.kaggle.com/c/global-energy-forecasting-competition-2012-load-forecasting/data) 39 | -------------------------------------------------------------------------------- /baseline_and_fill_missing_data_by_history.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": false 8 | }, 9 | "outputs": [ 10 | { 11 | "name": "stderr", 12 | "output_type": "stream", 13 | "text": [ 14 | "Using Theano backend.\n" 15 | ] 16 | } 17 | ], 18 | "source": [ 19 | "from __future__ import print_function\n", 20 | "import numpy as np\n", 21 | "import matplotlib.pyplot as plt\n", 22 | "from load_data import read_load_history, read_temperature_history, normalization, de_normalization" 23 | ] 24 | }, 25 | { 26 | "cell_type": "code", 27 | "execution_count": 2, 28 | "metadata": { 29 | "collapsed": false 30 | }, 31 | "outputs": [ 32 | { 33 | "name": "stdout", 34 | "output_type": "stream", 35 | "text": [ 36 | "Loading Data\n" 37 | ] 38 | } 39 | ], 40 | "source": [ 41 | "print('Loading Data')\n", 42 | "length_temperature, time_temperature, temperature = read_temperature_history()\n", 43 | "length_load, time_load, load = read_load_history(if_full=True)" 44 | ] 45 | }, 46 | { 47 | "cell_type": "code", 48 | "execution_count": 3, 49 | "metadata": { 50 | "collapsed": false 51 | }, 52 | "outputs": [], 53 | "source": [ 54 | "temperature = temperature.reshape((len(temperature),11))\n", 55 | "time_temperature = time_temperature.reshape((len(time_temperature),4))\n", 56 | "load = load.reshape((len(load),20))" 57 | ] 58 | }, 59 | { 60 | "cell_type": "code", 61 | "execution_count": 4, 62 | "metadata": { 63 | "collapsed": false 64 | }, 65 | "outputs": [], 66 | "source": [ 67 | "t = temperature[0:39414]\n", 68 | "tt = time_temperature[0:39414]\n", 69 | "l = load[0:39414]" 70 | ] 71 | }, 72 | { 73 | "cell_type": "code", 74 | "execution_count": 5, 75 | "metadata": { 76 | "collapsed": false 77 | }, 78 | "outputs": [], 79 | "source": [ 80 | "# uses history average to fill the missing data\n", 81 | "step_size = 365 * 24\n", 82 | "for i in range(168+18):\n", 83 | " index = len(t)\n", 84 | " temp = np.zeros((1,11))\n", 85 | " time = np.zeros((1,4))\n", 86 | " time = np.add(time,tt[index - step_size - 24])\n", 87 | " time[0][0] += 1\n", 88 | " load_temp = np.zeros((1,20))\n", 89 | " load_temp = np.add(load_temp,l[index - 7*24])\n", 90 | " l = np.append(l,load_temp,axis=0)\n", 91 | " tt = np.append(tt,time,axis=0)\n", 92 | " for j in range(1,5):\n", 93 | " previous_index = index - j*step_size - 24\n", 94 | " previous_temp = t[previous_index]\n", 95 | " temp = np.add(temp,previous_temp)\n", 96 | " temp = temp/4\n", 97 | " t = np.append(t,temp,axis=0)" 98 | ] 99 | }, 100 | { 101 | "cell_type": "code", 102 | "execution_count": 6, 103 | "metadata": { 104 | "collapsed": false 105 | }, 106 | "outputs": [], 107 | "source": [ 108 | "import pandas as pd\n", 109 | "df_t = pd.DataFrame(t)\n", 110 | "df_t.to_csv('./data/full_temp.csv', header=False, index=False)\n", 111 | "df_tt = pd.DataFrame(tt)\n", 112 | "df_tt.to_csv('./data/full_temp_time.csv', header=False, index=False)\n", 113 | "df_l = pd.DataFrame(l)\n", 114 | "df_l.to_csv('./data/full_load.csv', header=False, index=False)" 115 | ] 116 | }, 117 | { 118 | "cell_type": "code", 119 | "execution_count": 22, 120 | "metadata": { 121 | "collapsed": true 122 | }, 123 | "outputs": [], 124 | "source": [ 125 | "# baseline uses previous years' load history average\n", 126 | "t = temperature[0:39414 - 168]\n", 127 | "tt = time_temperature[0:39414 - 168]\n", 128 | "l = load[0:39414-168]\n", 129 | "step_size = 365 * 24\n", 130 | "for i in range(168):\n", 131 | " index = len(t)\n", 132 | " load_temp = np.zeros((1,20))\n", 133 | " for j in range(1,5):\n", 134 | " previous_index = index - j*step_size - 24\n", 135 | " previous_temp = l[previous_index]\n", 136 | " load_temp = np.add(load_temp,previous_temp)\n", 137 | " load_temp = load_temp/4\n", 138 | " l = np.append(l,load_temp,axis=0)" 139 | ] 140 | }, 141 | { 142 | "cell_type": "code", 143 | "execution_count": 23, 144 | "metadata": { 145 | "collapsed": false 146 | }, 147 | "outputs": [ 148 | { 149 | "name": "stdout", 150 | "output_type": "stream", 151 | "text": [ 152 | "39414\n", 153 | "rsme: 167462.797639\n" 154 | ] 155 | } 156 | ], 157 | "source": [ 158 | "# baseline uses previous years' load history average\n", 159 | "print(len(l))\n", 160 | "from load_data import read_benchmark\n", 161 | "import pandas as pd\n", 162 | "predicted_output = l\n", 163 | "benchmark = load[0:39414][-1-168:-1]\n", 164 | "from sklearn.metrics import mean_squared_error\n", 165 | "pred_future = predicted_output[-1-168:-1]\n", 166 | "temp = np.zeros((len(pred_future),1))\n", 167 | "benchmark_temp = np.zeros((len(benchmark),1))\n", 168 | "for i in range(len(pred_future)):\n", 169 | " temp[i][0] = np.sum(pred_future[i])\n", 170 | " benchmark_temp[i][0] = np.sum(benchmark[i])\n", 171 | "pred_future = np.concatenate((pred_future,temp),axis=1)\n", 172 | "benchmark = np.concatenate((benchmark,benchmark_temp),axis=1)\n", 173 | "rsme = np.sqrt(mean_squared_error(benchmark, pred_future))\n", 174 | "print('rsme:',rsme)" 175 | ] 176 | }, 177 | { 178 | "cell_type": "code", 179 | "execution_count": 24, 180 | "metadata": { 181 | "collapsed": true 182 | }, 183 | "outputs": [], 184 | "source": [ 185 | "# baseline uses previous week's load as prediction \n", 186 | "t = temperature[0:39414 - 168]\n", 187 | "tt = time_temperature[0:39414 - 168]\n", 188 | "l = load[0:39414 - 168]\n", 189 | "step_size = 7 * 24\n", 190 | "for i in range(168):\n", 191 | " index = len(t)\n", 192 | " load_temp = np.zeros((1,20))\n", 193 | " previous_index = index - j*step_size\n", 194 | " previous_temp = l[previous_index]\n", 195 | " load_temp = np.add(load_temp,previous_temp)\n", 196 | " l = np.append(l,load_temp,axis=0)" 197 | ] 198 | }, 199 | { 200 | "cell_type": "code", 201 | "execution_count": 25, 202 | "metadata": { 203 | "collapsed": false 204 | }, 205 | "outputs": [ 206 | { 207 | "name": "stdout", 208 | "output_type": "stream", 209 | "text": [ 210 | "39414\n", 211 | "rsme: 227129.421051\n" 212 | ] 213 | } 214 | ], 215 | "source": [ 216 | "# baseline uses previous week's load as prediction \n", 217 | "print(len(l))\n", 218 | "from load_data import read_benchmark\n", 219 | "import pandas as pd\n", 220 | "predicted_output = l\n", 221 | "benchmark = load[0:39414][-1-168:-1]\n", 222 | "from sklearn.metrics import mean_squared_error\n", 223 | "pred_future = predicted_output[-1-168:-1]\n", 224 | "temp = np.zeros((len(pred_future),1))\n", 225 | "benchmark_temp = np.zeros((len(benchmark),1))\n", 226 | "for i in range(len(pred_future)):\n", 227 | " temp[i][0] = np.sum(pred_future[i])\n", 228 | " benchmark_temp[i][0] = np.sum(benchmark[i])\n", 229 | "pred_future = np.concatenate((pred_future,temp),axis=1)\n", 230 | "benchmark = np.concatenate((benchmark,benchmark_temp),axis=1)\n", 231 | "rsme = np.sqrt(mean_squared_error(benchmark, pred_future))\n", 232 | "print('rsme:',rsme)" 233 | ] 234 | }, 235 | { 236 | "cell_type": "code", 237 | "execution_count": null, 238 | "metadata": { 239 | "collapsed": true 240 | }, 241 | "outputs": [], 242 | "source": [] 243 | } 244 | ], 245 | "metadata": { 246 | "kernelspec": { 247 | "display_name": "Python 2", 248 | "language": "python", 249 | "name": "python2" 250 | }, 251 | "language_info": { 252 | "codemirror_mode": { 253 | "name": "ipython", 254 | "version": 2 255 | }, 256 | "file_extension": ".py", 257 | "mimetype": "text/x-python", 258 | "name": "python", 259 | "nbconvert_exporter": "python", 260 | "pygments_lexer": "ipython2", 261 | "version": "2.7.6" 262 | } 263 | }, 264 | "nbformat": 4, 265 | "nbformat_minor": 0 266 | } 267 | -------------------------------------------------------------------------------- /data/expected_result.csv: -------------------------------------------------------------------------------- 1 | 16165.0,151110.0,163047.0,345.0,5484.0,156594.0,163047.0,2712.0,81380.0,41052.0,93710.0,112572.0,14818.0,18287.0,54974.0,24078.0,31226.0,177469.0,66554.0,76152.0,1450775.0 2 | 14756.0,142943.0,154235.0,322.0,4846.0,147789.0,154235.0,2534.0,81471.0,38983.0,91541.0,108726.0,13676.0,16331.0,50637.0,21928.0,29062.0,161349.0,60072.0,71726.0,1367162.0 3 | 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4,2008,7,6,408,381,361,354,353,347,377,464,571,602,577,569,568,574,565,562,569,581,586,587,582,569,482,413 28 | 4,2008,7,7,354,330,316,314,320,338,415,457,482,510,515,513,514,509,506,490,510,554,573,573,570,551,471,389 29 | 5,2008,7,1,5484,4846,4649,4505,4765,5249,6456,6759,6843,7438,8169,9107,9659,10142,10668,10992,11747,12303,12740,12429,11739,10475,8865,7160 30 | 5,2008,7,2,5977,5529,5035,4965,5025,5339,6649,6969,6954,7533,8327,8777,9304,9770,10291,11021,11786,12312,13093,12431,11085,10195,8575,6900 31 | 5,2008,7,3,5534,4910,4507,4372,4643,5266,6491,6901,7041,7486,7985,8613,9683,9820,10311,10675,11416,12225,12531,12324,12033,10805,9573,7533 32 | 5,2008,7,4,6285,5543,5283,5004,5211,5916,6784,7146,7312,7837,8364,9020,9804,10577,10792,11524,11739,12456,12194,12079,11712,10736,9559,8347 33 | 5,2008,7,5,7015,6425,5788,5564,5469,5285,5441,6492,7870,9066,9741,10080,10431,10837,11619,10488,9494,9916,10728,10671,10682,10461,9245,7778 34 | 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7,2008,7,7,153209,145491,140362,140213,143880,151520,170850,185881,197689,208359,217636,226021,230649,231814,228173,233969,237479,238473,232573,232935,229200,214230,193719,173903 50 | 8,2008,7,1,2712,2534,2423,2452,2546,2812,3174,3316,3442,3721,4013,4342,4612,4846,4650,4986,5003,5234,5187,5100,4941,4522,3819,3231 51 | 8,2008,7,2,2842,2719,2593,2533,2573,2947,3283,3407,3521,3684,3969,4295,4461,4698,4965,5194,5342,5485,5437,5310,5145,4620,4037,3319 52 | 8,2008,7,3,3014,2707,2646,2587,2699,2913,3233,3394,3543,3777,4085,4246,4614,4725,4877,5100,5104,5325,5279,5295,5123,4881,4197,3585 53 | 8,2008,7,4,3237,2905,2835,2828,2864,3085,3382,3614,3853,4227,4513,4806,4999,5121,5276,5369,5452,5399,5281,5011,5083,4917,4500,3942 54 | 8,2008,7,5,3662,3356,3141,2908,2941,3087,3242,3559,4130,4582,4798,4938,5163,5160,5049,5087,5336,5562,5385,5203,5031,5106,4553,3974 55 | 8,2008,7,6,3366,3078,2971,2824,2761,2807,2895,3313,4088,4456,4603,4824,5196,5186,5246,5261,5396,5522,5518,5383,4882,4490,3727,3080 56 | 8,2008,7,7,2579,2449,2357,2352,2466,2784,3105,3297,3455,3752,4057,4282,4400,4492,4607,4685,4813,5031,5110,5019,4859,4320,3536,3002 57 | 9,2008,7,1,81380,81471,80596,80295,79951,79139,76952,75066,72975,69163,66855,65150,62574,56096,49463,44314,44069,45523,52936,62928,71301,75201,76251,77010 58 | 9,2008,7,2,77634,78275,78120,77752,75464,71321,66634,63667,62908,61561,59339,57339,54048,47708,43981,43752,41056,42225,52704,61452,69231,74347,77378,79279 59 | 9,2008,7,3,80605,81459,81596,81810,80978,78556,74771,72003,70901,69376,68644,67212,62682,55596,49877,48510,49295,54469,57945,66025,72776,76739,78886,79315 60 | 9,2008,7,4,80332,80583,80869,81287,80587,78594,76102,75057,73189,71057,69128,66814,61405,52682,44854,41675,40124,45955,55905,65893,72082,75739,77181,77670 61 | 9,2008,7,5,78534,79504,79666,79948,79488,79501,80454,79884,77023,74035,72280,70182,64465,56793,50915,50926,49797,51834,64687,71186,74523,76063,76671,76903 62 | 9,2008,7,6,77751,77769,77799,77868,77564,78663,79937,78915,76785,73949,71725,69851,65712,58532,52354,48576,47083,50307,58110,67227,73908,76270,77180,77937 63 | 9,2008,7,7,78689,78762,79176,78940,78726,76857,73343,72413,71289,70644,69703,67261,63492,57630,54630,55121,54427,58429,62405,68298,74278,78084,79746,80608 64 | 10,2008,7,1,41052,38983,37520,36488,37250,39991,44112,48304,49564,51859,53046,54478,55011,55444,55819,56425,57199,56920,57606,58065,55144,52688,48596,44414 65 | 10,2008,7,2,41464,39288,38629,37498,38150,40744,43962,47465,49468,51789,53038,53512,53334,54555,55715,55772,55983,55151,55088,55006,54120,51623,47605,43522 66 | 10,2008,7,3,40542,39284,36883,36820,37627,39471,42750,48000,49287,51719,52762,55020,55408,55973,56932,55258,55399,56062,55737,56645,56021,53400,49596,45414 67 | 10,2008,7,4,42132,40838,39293,39186,39876,41416,45469,49105,51444,52982,54978,55102,55439,55776,56989,55996,55999,56287,57174,58597,58098,57363,52602,48673 68 | 10,2008,7,5,45099,42455,41489,40234,39288,40622,40337,42493,46309,48963,51963,53707,54721,54202,54155,54662,54127,53944,53220,53627,53192,52718,49787,46358 69 | 10,2008,7,6,43103,41678,39927,39630,38740,38830,38370,41657,45151,48043,48211,48790,48505,49783,49724,49713,50048,50819,52232,52128,51946,49971,45584,43300 70 | 10,2008,7,7,39391,37641,36356,35509,35587,39275,42336,47577,50230,52870,55238,55679,56344,56826,56007,52869,52254,52086,53027,53595,55083,52469,47348,44464 71 | 11,2008,7,1,93710,91541,86390,83255,86205,90612,105661,119435,125138,137399,142796,151869,152773,164244,161609,169087,168315,171134,173838,165999,157104,145405,125953,113487 72 | 11,2008,7,2,100402,95363,92561,88620,89684,95518,110230,122152,128051,134362,142555,143846,156746,159254,165649,168189,172581,170326,172870,165013,161503,145416,129880,109415 73 | 11,2008,7,3,96904,86683,84222,83381,84265,88925,105285,119039,124997,134265,142865,148246,157257,160063,166054,168350,167795,165351,162680,160334,152748,146092,131072,114251 74 | 11,2008,7,4,101279,95806,90063,86673,87607,93014,106413,118391,125175,135088,147223,156246,161726,167421,169320,166808,171178,167561,163942,150423,151110,146631,138729,131880 75 | 11,2008,7,5,121056,109827,105759,103569,102716,102865,101718,112872,129280,142599,152631,161749,167367,167808,178633,175970,170746,165275,165008,165242,160908,156522,148687,132420 76 | 11,2008,7,6,121456,113729,105431,99287,97746,90801,92244,101537,116645,130682,136382,144242,149695,154849,159918,166594,170125,175386,176566,173433,154817,143038,127707,107602 77 | 11,2008,7,7,101464,92411,83736,83025,83604,87164,103893,119844,126287,134517,147779,149785,154483,156530,162400,165004,168534,166386,164490,157136,152864,141075,129747,112821 78 | 12,2008,7,1,112572,108726,101109,96610,100281,106508,125057,138041,144625,163344,171685,186444,189690,206301,202311,215496,218106,224478,227976,215128,201898,185738,158395,140268 79 | 12,2008,7,2,120869,112998,108680,103107,104214,112437,130834,141817,148629,158620,171242,173808,195451,197698,208278,214036,225378,223375,226340,213456,208309,185710,164183,134696 80 | 12,2008,7,3,116340,101599,97772,96407,97252,103910,124679,137959,144334,158408,171703,180442,196249,198952,208997,214560,217785,215615,210491,206203,194871,186208,165684,141435 81 | 12,2008,7,4,122005,113673,105305,100519,101576,109138,126246,138193,146557,161767,180712,195416,205500,214901,218728,216597,225940,220804,213950,192612,193908,188587,178066,168174 82 | 12,2008,7,5,151554,134768,128182,124761,123222,123124,121992,138492,162673,183570,200311,215517,224462,225648,243926,240489,231988,223435,222328,222280,216036,210660,199675,175244 83 | 12,2008,7,6,158482,146454,133675,124512,122119,112299,113897,126211,148429,169641,179170,191475,199666,207644,215961,227592,234067,242628,242888,236757,208420,192020,170453,140929 84 | 12,2008,7,7,131059,117401,104687,102930,103319,107794,127243,142478,149575,162095,182119,185446,194266,195790,205098,210513,219790,217870,214074,202720,196872,180645,165153,140349 85 | 13,2008,7,1,14818,13676,13083,13077,13282,14520,17743,19183,19037,19864,20842,22234,22585,23493,22642,23058,23787,24469,25784,25368,23950,22765,20661,17955 86 | 13,2008,7,2,15693,14457,13986,13374,13592,15056,18133,19360,19559,20073,21449,21658,22434,23440,22573,22583,22749,23839,25757,25276,24602,22835,20754,17984 87 | 13,2008,7,3,15756,14455,13752,13648,13756,15057,18067,19595,19412,20216,21625,22292,23138,23025,21424,21717,22679,23258,24480,25212,24548,24652,22423,19156 88 | 13,2008,7,4,16709,15332,14439,14001,14305,15332,18143,19580,20041,21254,22006,22664,23425,24357,23557,23093,22832,23219,24158,24035,24320,24582,22315,19972 89 | 13,2008,7,5,17613,16410,15323,15045,14761,15456,16232,18170,20365,21812,23147,23902,24306,23984,22911,23286,23324,24870,25446,24677,24868,24970,22515,19295 90 | 13,2008,7,6,16798,15548,14586,14295,14213,13817,14584,17113,20688,22102,22439,23036,23610,24079,23639,23731,24138,24823,26297,25937,24041,23281,20164,17583 91 | 13,2008,7,7,15001,13759,13148,13106,13286,14409,17362,19011,19357,20553,21381,21534,22045,22364,22022,20939,21126,22166,24334,23737,22994,22490,19861,16538 92 | 14,2008,7,1,18287,16331,15010,14231,14647,16044,18848,20230,20862,23333,25458,27998,29539,30719,30340,31661,33279,34297,36328,35385,31446,29139,25778,21420 93 | 14,2008,7,2,18281,16414,15704,14838,15230,16596,18971,19823,20796,23308,25481,27012,27373,29478,30155,30869,31401,31245,32926,31863,30146,27876,24648,20476 94 | 14,2008,7,3,17530,16254,14472,14207,14641,15607,17976,19784,20123,22617,24606,27889,29455,30784,31422,29990,30500,32172,32814,32690,31242,28956,25843,21553 95 | 14,2008,7,4,18344,17009,15633,15325,15627,16343,19126,20406,21776,23672,26338,27972,29625,30771,31622,31000,31387,32682,34850,34654,33096,32147,28336,24618 96 | 14,2008,7,5,21297,18830,17790,16591,15735,16132,16522,19227,22781,25891,29597,32901,35570,35678,35185,36222,36074,36722,36508,35322,33754,32742,29712,25871 97 | 14,2008,7,6,22189,20335,18411,17806,16713,16231,16321,20091,24252,27530,28412,30158,30867,33245,32917,33000,33658,35337,37354,35113,33685,31000,26482,23328 98 | 14,2008,7,7,19086,16915,15449,14543,14470,16144,18109,20487,22295,25185,28477,30524,32216,33490,31903,28469,28082,28938,31405,31147,31783,29349,24867,21534 99 | 15,2008,7,1,54974,50637,47926,47384,47910,49518,57463,61153,64008,68335,74284,81245,83350,86610,87155,89695,89914,91212,96950,94607,83754,77049,72772,63355 100 | 15,2008,7,2,58014,53474,50338,48209,47920,50619,58750,63077,65175,69807,74596,79729,81682,85285,83561,82396,86996,90522,91428,88688,86197,79208,72658,63296 101 | 15,2008,7,3,56382,53625,50533,49657,49102,51619,58716,62900,65118,69934,76165,81406,85721,87032,85608,85671,85382,81741,88672,88612,83855,85316,77962,66222 102 | 15,2008,7,4,60829,55952,52480,51506,50554,52817,60075,63228,65879,70471,74703,81286,85571,90875,91738,91018,93311,92707,95216,92708,90679,86564,76048,72165 103 | 15,2008,7,5,63068,59269,57054,54014,53679,54686,55681,60640,68553,74451,83072,88493,93917,94236,92556,90580,92271,95472,91797,88869,89081,84885,78724,70384 104 | 15,2008,7,6,62480,58459,54504,54811,52582,49944,51543,59039,69917,74843,77812,83117,86533,89291,89429,90908,92185,93515,97979,96261,85965,83850,73889,64646 105 | 15,2008,7,7,57384,52733,49576,48861,48356,49580,57582,61778,66556,73511,79065,82580,82376,83338,81643,79458,81315,79958,84604,83616,81139,77427,70647,59917 106 | 16,2008,7,1,24078,21928,21352,21154,20982,21713,24975,27534,29627,32328,36388,38979,40344,43391,42819,44982,46753,48711,49871,49139,44091,39790,35508,30527 107 | 16,2008,7,2,27713,25217,23604,22428,22818,23994,27060,29201,31024,33984,36376,38825,40325,41828,42816,43745,47029,47114,49527,47435,42404,38286,34294,29371 108 | 16,2008,7,3,25759,22766,21365,20568,20929,22209,25303,28623,31032,34070,36476,40190,41912,43653,44652,44122,44314,45875,48126,47578,44295,41576,37260,32173 109 | 16,2008,7,4,28204,26650,24833,24511,24809,24974,27312,30456,31911,34906,37082,39522,41030,46886,45416,46135,47720,49293,49670,45566,45306,44431,40644,35706 110 | 16,2008,7,5,31319,28298,27134,25556,25439,24495,25849,28946,34228,38776,41486,45137,46889,46916,47779,48423,44379,41124,40464,40826,40017,39173,36960,33466 111 | 16,2008,7,6,29168,27276,26102,24486,24127,22912,24017,28322,33944,37970,38981,39440,41981,44619,45267,46750,47892,47102,48512,46130,42853,38607,34538,29653 112 | 16,2008,7,7,25838,24399,22118,21803,21563,22052,25344,28402,31810,34251,37620,40181,40277,39048,37252,37775,40812,42291,44502,42155,39956,37367,32206,27516 113 | 17,2008,7,1,31226,29062,27701,27310,27775,29875,33242,34410,35374,37727,40435,42261,44701,47229,47155,48784,50014,51082,51059,50668,44657,42507,38192,35392 114 | 17,2008,7,2,32762,29872,29014,27894,28428,30165,33991,35006,35883,37673,40438,41972,44424,45937,45933,46610,47923,49745,48364,47952,45227,43191,38938,33924 115 | 17,2008,7,3,31507,29375,27965,27008,27925,30168,33983,35173,36020,37893,40681,43091,45636,46176,47165,46819,45906,45699,47338,46836,46333,44186,40823,36329 116 | 17,2008,7,4,32915,31225,29564,28448,28703,30069,33375,35373,36134,38522,40454,42854,46224,46773,46874,47232,49246,48534,46584,46671,45908,45157,42333,38281 117 | 17,2008,7,5,33759,31898,29909,29191,28938,28897,29107,31452,34940,38713,42608,45169,46683,48963,48648,48845,45255,44250,44859,44079,43353,42576,40754,36599 118 | 17,2008,7,6,32818,29318,28126,27558,27207,27034,27509,30465,34854,36946,39380,41157,41785,43346,44426,44821,46453,47837,48136,44646,44179,42120,38835,34015 119 | 17,2008,7,7,30944,28246,28037,27780,27297,29325,33221,34719,36309,38534,40833,43468,44970,45345,43393,42021,43189,45038,46810,45232,44513,42431,38860,34609 120 | 18,2008,7,1,177469,161349,155954,154191,155666,168068,197963,210120,218572,234786,260078,277854,287602,307602,304783,315348,327071,337947,352525,349452,323300,294848,260241,223147 121 | 18,2008,7,2,200769,182212,170297,162736,167605,182544,210427,219292,226445,243398,260712,277609,287664,298371,304813,308512,329109,328620,349630,338582,312511,285535,252829,215893 122 | 18,2008,7,3,188627,166928,156428,151165,155699,171551,200459,215686,226280,243615,260221,284047,295489,307551,313554,308507,311970,319156,338673,337453,320757,302338,268751,231187 123 | 18,2008,7,4,201495,188315,175417,172541,176191,184606,207125,221588,227598,244834,259977,275959,286539,320671,311271,311582,323121,332164,339997,315507,315446,309267,282174,248370 124 | 18,2008,7,5,217384,196067,187023,176632,175940,171592,180379,201103,230955,259686,279431,303720,315451,315394,320380,322568,299535,281134,278810,283519,280573,275240,258565,233228 125 | 18,2008,7,6,203224,188807,179737,168815,166041,159429,165873,191943,225230,251179,262255,268926,286352,302781,307842,316948,325330,324044,338880,326963,309763,281333,249524,213292 126 | 18,2008,7,7,185011,173388,158009,155819,156865,167505,196149,211458,226245,242401,264847,282767,285586,281132,271433,273698,292638,299690,318386,307954,297351,278515,238395,203476 127 | 19,2008,7,1,66554,60072,57820,55265,56526,61655,73134,76755,78393,82066,92615,100053,104474,112608,110989,116090,121175,125894,131060,129238,113765,106543,94667,82450 128 | 19,2008,7,2,70987,64133,59649,56538,56475,63128,76193,78293,80279,86328,92974,100084,104236,112535,107168,109451,117868,125537,130465,124378,117247,109810,94547,79474 129 | 19,2008,7,3,68731,61971,57736,56641,57803,64052,75434,78243,78222,86292,95484,103272,107824,112605,111178,109437,110249,113783,121603,123333,116728,117595,104977,85906 130 | 19,2008,7,4,77252,69463,64244,60755,59625,65122,76078,79796,81537,87538,93501,100393,111092,116026,121327,121728,125462,125567,125335,122184,119294,116910,103240,93264 131 | 19,2008,7,5,81885,73152,69690,65225,62284,64435,65980,74289,86484,97199,107384,118125,126642,127481,125277,122826,121927,123088,119804,116130,116832,114443,104514,91425 132 | 19,2008,7,6,79416,70255,66810,62461,61292,58028,60541,70528,88296,97800,102864,108368,113597,118340,119488,122874,125118,130793,133649,127739,116979,112908,96703,79101 133 | 19,2008,7,7,68060,61068,58680,55826,55533,61450,72927,77961,80739,87935,95153,99722,106826,104442,105985,100756,105238,109228,117191,113689,106420,100171,91445,74506 134 | 20,2008,7,1,76152,71726,68679,68638,69751,75177,87423,93082,94415,98783,103061,108136,112445,116496,110208,114049,112846,118660,121332,119228,117226,112006,100350,87981 135 | 20,2008,7,2,79654,76204,72255,70526,70921,78056,89632,95018,96626,98854,103163,108144,110060,113866,116366,118500,119330,123145,126270,123594,121960,114206,104648,89869 136 | 20,2008,7,3,82967,76118,73349,71520,73449,77654,88844,94701,96485,100226,104846,106285,112217,113667,114083,115855,113990,119500,122411,122624,120983,118700,106653,93870 137 | 20,2008,7,4,86427,79031,75889,75013,75388,79649,89884,96860,101038,107277,110923,114755,116933,118025,118432,117003,116825,117373,118412,112005,114349,112955,106325,95116 138 | 20,2008,7,5,89651,82871,77439,72852,73264,74435,77959,84809,95513,103071,105558,106858,110454,109101,105478,105267,109615,115559,115409,112167,109423,113692,104077,93084 139 | 20,2008,7,6,81262,75157,72317,69720,68278,67757,69981,79239,92560,98079,99903,102644,110036,109527,109577,109224,111774,116598,120330,118391,108882,103527,91187,78441 140 | 20,2008,7,7,68701,65929,63832,63483,65513,72115,83189,89770,92017,97212,101982,105324,106537,108151,108401,107718,109024,114642,119606,116721,114536,107361,94342,82934 141 | 21,2008,7,1,1450775,1367162,1320255,1295413,1320742,1397212,1580666,1696736,1759300,1869980,1985167,2104197,2154340,2244967,2241377,2298007,2340562,2385589,2422280,2381285,2257806,2110045,1899107,1688765 142 | 21,2008,7,2,1535200,1453675,1389384,1354427,1361383,1432363,1617001,1720708,1778688,1878127,1988926,2055116,2129734,2185658,2223490,2271021,2344312,2363462,2418548,2345062,2230505,2086718,1887428,1653601 143 | 21,2008,7,3,1487837,1381281,1321774,1297051,1318950,1403782,1584299,1710376,1775349,1878186,1981889,2078743,2182147,2211296,2246030,2256299,2281576,2317186,2344661,2332352,2267019,2168095,1984498,1733777 144 | 21,2008,7,4,1571996,1477443,1414808,1379736,1394004,1476409,1625613,1735514,1802209,1908764,2012271,2107448,2187145,2284206,2283258,2302160,2330363,2354091,2326072,2250475,2234744,2158191,1996590,1831916 145 | 21,2008,7,5,1659911,1547267,1473509,1428399,1415563,1412632,1432283,1552368,1750488,1922011,2046682,2144228,2201953,2210556,2262579,2198797,2107022,2100521,2126972,2120764,2104966,2073789,1936091,1741864 146 | 21,2008,7,6,1585727,1479130,1412651,1355301,1322363,1282509,1313578,1463779,1685408,1836122,1902741,1976312,2057744,2113729,2159834,2203117,2229774,2264820,2312641,2270472,2135851,2022448,1822366,1588054 147 | 21,2008,7,7,1442307,1351579,1280047,1267810,1284920,1355974,1542517,1677710,1772193,1884185,2007709,2082829,2127422,2127273,2109270,2126963,2185141,2210245,2232494,2199402,2155061,2021884,1820705,1603514 -------------------------------------------------------------------------------- /data/readme.txt: -------------------------------------------------------------------------------- 1 | In each of the 5 data files, there is a header row. Three columns of calendar variables: year, month of the year and day of the month. The last 24 columns are the 24 hours of the day. 2 | 3 | In "Load_history.csv", Column A is zone_id ranging from 1 to 20. 4 | 5 | In "Temperature_history.csv", Column A is station_id ranging from 1 to 11. 6 | 7 | In "submission_template.csv", "weights.csv", and "Benchmark.csv", Column A is id, the identifier for each row; Column B is zone_id ranging from 1 to 21, where the 21st "zone" represents system level, which is the sum of the other 20 zones. 8 | 9 | "Benchmark.csv" shows the results from a benchmark model. 10 | 11 | Please make sure the submission strictly follow the format as indicated in "submission_template.csv", where the year was sorted in smallest to largest order first, then month, then day, and then zone_id. -------------------------------------------------------------------------------- /data/submission_template.csv: -------------------------------------------------------------------------------- 1 | id,zone_id,year,month,day,h1,h2,h3,h4,h5,h6,h7,h8,h9,h10,h11,h12,h13,h14,h15,h16,h17,h18,h19,h20,h21,h22,h23,h24 2 | 1,1,2005,3,6,,,,,,,,,,,,,,,,,,,,,,,, 3 | 2,2,2005,3,6,,,,,,,,,,,,,,,,,,,,,,,, 4 | 3,3,2005,3,6,,,,,,,,,,,,,,,,,,,,,,,, 5 | 4,4,2005,3,6,,,,,,,,,,,,,,,,,,,,,,,, 6 | 5,5,2005,3,6,,,,,,,,,,,,,,,,,,,,,,,, 7 | 6,6,2005,3,6,,,,,,,,,,,,,,,,,,,,,,,, 8 | 7,7,2005,3,6,,,,,,,,,,,,,,,,,,,,,,,, 9 | 8,8,2005,3,6,,,,,,,,,,,,,,,,,,,,,,,, 10 | 9,9,2005,3,6,,,,,,,,,,,,,,,,,,,,,,,, 11 | 10,10,2005,3,6,,,,,,,,,,,,,,,,,,,,,,,, 12 | 11,11,2005,3,6,,,,,,,,,,,,,,,,,,,,,,,, 13 | 12,12,2005,3,6,,,,,,,,,,,,,,,,,,,,,,,, 14 | 13,13,2005,3,6,,,,,,,,,,,,,,,,,,,,,,,, 15 | 14,14,2005,3,6,,,,,,,,,,,,,,,,,,,,,,,, 16 | 15,15,2005,3,6,,,,,,,,,,,,,,,,,,,,,,,, 17 | 16,16,2005,3,6,,,,,,,,,,,,,,,,,,,,,,,, 18 | 17,17,2005,3,6,,,,,,,,,,,,,,,,,,,,,,,, 19 | 18,18,2005,3,6,,,,,,,,,,,,,,,,,,,,,,,, 20 | 19,19,2005,3,6,,,,,,,,,,,,,,,,,,,,,,,, 21 | 20,20,2005,3,6,,,,,,,,,,,,,,,,,,,,,,,, 22 | 21,21,2005,3,6,,,,,,,,,,,,,,,,,,,,,,,, 23 | 22,1,2005,3,7,,,,,,,,,,,,,,,,,,,,,,,, 24 | 23,2,2005,3,7,,,,,,,,,,,,,,,,,,,,,,,, 25 | 24,3,2005,3,7,,,,,,,,,,,,,,,,,,,,,,,, 26 | 25,4,2005,3,7,,,,,,,,,,,,,,,,,,,,,,,, 27 | 26,5,2005,3,7,,,,,,,,,,,,,,,,,,,,,,,, 28 | 27,6,2005,3,7,,,,,,,,,,,,,,,,,,,,,,,, 29 | 28,7,2005,3,7,,,,,,,,,,,,,,,,,,,,,,,, 30 | 29,8,2005,3,7,,,,,,,,,,,,,,,,,,,,,,,, 31 | 30,9,2005,3,7,,,,,,,,,,,,,,,,,,,,,,,, 32 | 31,10,2005,3,7,,,,,,,,,,,,,,,,,,,,,,,, 33 | 32,11,2005,3,7,,,,,,,,,,,,,,,,,,,,,,,, 34 | 33,12,2005,3,7,,,,,,,,,,,,,,,,,,,,,,,, 35 | 34,13,2005,3,7,,,,,,,,,,,,,,,,,,,,,,,, 36 | 35,14,2005,3,7,,,,,,,,,,,,,,,,,,,,,,,, 37 | 36,15,2005,3,7,,,,,,,,,,,,,,,,,,,,,,,, 38 | 37,16,2005,3,7,,,,,,,,,,,,,,,,,,,,,,,, 39 | 38,17,2005,3,7,,,,,,,,,,,,,,,,,,,,,,,, 40 | 39,18,2005,3,7,,,,,,,,,,,,,,,,,,,,,,,, 41 | 40,19,2005,3,7,,,,,,,,,,,,,,,,,,,,,,,, 42 | 41,20,2005,3,7,,,,,,,,,,,,,,,,,,,,,,,, 43 | 42,21,2005,3,7,,,,,,,,,,,,,,,,,,,,,,,, 44 | 43,1,2005,3,8,,,,,,,,,,,,,,,,,,,,,,,, 45 | 44,2,2005,3,8,,,,,,,,,,,,,,,,,,,,,,,, 46 | 45,3,2005,3,8,,,,,,,,,,,,,,,,,,,,,,,, 47 | 46,4,2005,3,8,,,,,,,,,,,,,,,,,,,,,,,, 48 | 47,5,2005,3,8,,,,,,,,,,,,,,,,,,,,,,,, 49 | 48,6,2005,3,8,,,,,,,,,,,,,,,,,,,,,,,, 50 | 49,7,2005,3,8,,,,,,,,,,,,,,,,,,,,,,,, 51 | 50,8,2005,3,8,,,,,,,,,,,,,,,,,,,,,,,, 52 | 51,9,2005,3,8,,,,,,,,,,,,,,,,,,,,,,,, 53 | 52,10,2005,3,8,,,,,,,,,,,,,,,,,,,,,,,, 54 | 53,11,2005,3,8,,,,,,,,,,,,,,,,,,,,,,,, 55 | 54,12,2005,3,8,,,,,,,,,,,,,,,,,,,,,,,, 56 | 55,13,2005,3,8,,,,,,,,,,,,,,,,,,,,,,,, 57 | 56,14,2005,3,8,,,,,,,,,,,,,,,,,,,,,,,, 58 | 57,15,2005,3,8,,,,,,,,,,,,,,,,,,,,,,,, 59 | 58,16,2005,3,8,,,,,,,,,,,,,,,,,,,,,,,, 60 | 59,17,2005,3,8,,,,,,,,,,,,,,,,,,,,,,,, 61 | 60,18,2005,3,8,,,,,,,,,,,,,,,,,,,,,,,, 62 | 61,19,2005,3,8,,,,,,,,,,,,,,,,,,,,,,,, 63 | 62,20,2005,3,8,,,,,,,,,,,,,,,,,,,,,,,, 64 | 63,21,2005,3,8,,,,,,,,,,,,,,,,,,,,,,,, 65 | 64,1,2005,3,9,,,,,,,,,,,,,,,,,,,,,,,, 66 | 65,2,2005,3,9,,,,,,,,,,,,,,,,,,,,,,,, 67 | 66,3,2005,3,9,,,,,,,,,,,,,,,,,,,,,,,, 68 | 67,4,2005,3,9,,,,,,,,,,,,,,,,,,,,,,,, 69 | 68,5,2005,3,9,,,,,,,,,,,,,,,,,,,,,,,, 70 | 69,6,2005,3,9,,,,,,,,,,,,,,,,,,,,,,,, 71 | 70,7,2005,3,9,,,,,,,,,,,,,,,,,,,,,,,, 72 | 71,8,2005,3,9,,,,,,,,,,,,,,,,,,,,,,,, 73 | 72,9,2005,3,9,,,,,,,,,,,,,,,,,,,,,,,, 74 | 73,10,2005,3,9,,,,,,,,,,,,,,,,,,,,,,,, 75 | 74,11,2005,3,9,,,,,,,,,,,,,,,,,,,,,,,, 76 | 75,12,2005,3,9,,,,,,,,,,,,,,,,,,,,,,,, 77 | 76,13,2005,3,9,,,,,,,,,,,,,,,,,,,,,,,, 78 | 77,14,2005,3,9,,,,,,,,,,,,,,,,,,,,,,,, 79 | 78,15,2005,3,9,,,,,,,,,,,,,,,,,,,,,,,, 80 | 79,16,2005,3,9,,,,,,,,,,,,,,,,,,,,,,,, 81 | 80,17,2005,3,9,,,,,,,,,,,,,,,,,,,,,,,, 82 | 81,18,2005,3,9,,,,,,,,,,,,,,,,,,,,,,,, 83 | 82,19,2005,3,9,,,,,,,,,,,,,,,,,,,,,,,, 84 | 83,20,2005,3,9,,,,,,,,,,,,,,,,,,,,,,,, 85 | 84,21,2005,3,9,,,,,,,,,,,,,,,,,,,,,,,, 86 | 85,1,2005,3,10,,,,,,,,,,,,,,,,,,,,,,,, 87 | 86,2,2005,3,10,,,,,,,,,,,,,,,,,,,,,,,, 88 | 87,3,2005,3,10,,,,,,,,,,,,,,,,,,,,,,,, 89 | 88,4,2005,3,10,,,,,,,,,,,,,,,,,,,,,,,, 90 | 89,5,2005,3,10,,,,,,,,,,,,,,,,,,,,,,,, 91 | 90,6,2005,3,10,,,,,,,,,,,,,,,,,,,,,,,, 92 | 91,7,2005,3,10,,,,,,,,,,,,,,,,,,,,,,,, 93 | 92,8,2005,3,10,,,,,,,,,,,,,,,,,,,,,,,, 94 | 93,9,2005,3,10,,,,,,,,,,,,,,,,,,,,,,,, 95 | 94,10,2005,3,10,,,,,,,,,,,,,,,,,,,,,,,, 96 | 95,11,2005,3,10,,,,,,,,,,,,,,,,,,,,,,,, 97 | 96,12,2005,3,10,,,,,,,,,,,,,,,,,,,,,,,, 98 | 97,13,2005,3,10,,,,,,,,,,,,,,,,,,,,,,,, 99 | 98,14,2005,3,10,,,,,,,,,,,,,,,,,,,,,,,, 100 | 99,15,2005,3,10,,,,,,,,,,,,,,,,,,,,,,,, 101 | 100,16,2005,3,10,,,,,,,,,,,,,,,,,,,,,,,, 102 | 101,17,2005,3,10,,,,,,,,,,,,,,,,,,,,,,,, 103 | 102,18,2005,3,10,,,,,,,,,,,,,,,,,,,,,,,, 104 | 103,19,2005,3,10,,,,,,,,,,,,,,,,,,,,,,,, 105 | 104,20,2005,3,10,,,,,,,,,,,,,,,,,,,,,,,, 106 | 105,21,2005,3,10,,,,,,,,,,,,,,,,,,,,,,,, 107 | 106,1,2005,3,11,,,,,,,,,,,,,,,,,,,,,,,, 108 | 107,2,2005,3,11,,,,,,,,,,,,,,,,,,,,,,,, 109 | 108,3,2005,3,11,,,,,,,,,,,,,,,,,,,,,,,, 110 | 109,4,2005,3,11,,,,,,,,,,,,,,,,,,,,,,,, 111 | 110,5,2005,3,11,,,,,,,,,,,,,,,,,,,,,,,, 112 | 111,6,2005,3,11,,,,,,,,,,,,,,,,,,,,,,,, 113 | 112,7,2005,3,11,,,,,,,,,,,,,,,,,,,,,,,, 114 | 113,8,2005,3,11,,,,,,,,,,,,,,,,,,,,,,,, 115 | 114,9,2005,3,11,,,,,,,,,,,,,,,,,,,,,,,, 116 | 115,10,2005,3,11,,,,,,,,,,,,,,,,,,,,,,,, 117 | 116,11,2005,3,11,,,,,,,,,,,,,,,,,,,,,,,, 118 | 117,12,2005,3,11,,,,,,,,,,,,,,,,,,,,,,,, 119 | 118,13,2005,3,11,,,,,,,,,,,,,,,,,,,,,,,, 120 | 119,14,2005,3,11,,,,,,,,,,,,,,,,,,,,,,,, 121 | 120,15,2005,3,11,,,,,,,,,,,,,,,,,,,,,,,, 122 | 121,16,2005,3,11,,,,,,,,,,,,,,,,,,,,,,,, 123 | 122,17,2005,3,11,,,,,,,,,,,,,,,,,,,,,,,, 124 | 123,18,2005,3,11,,,,,,,,,,,,,,,,,,,,,,,, 125 | 124,19,2005,3,11,,,,,,,,,,,,,,,,,,,,,,,, 126 | 125,20,2005,3,11,,,,,,,,,,,,,,,,,,,,,,,, 127 | 126,21,2005,3,11,,,,,,,,,,,,,,,,,,,,,,,, 128 | 127,1,2005,3,12,,,,,,,,,,,,,,,,,,,,,,,, 129 | 128,2,2005,3,12,,,,,,,,,,,,,,,,,,,,,,,, 130 | 129,3,2005,3,12,,,,,,,,,,,,,,,,,,,,,,,, 131 | 130,4,2005,3,12,,,,,,,,,,,,,,,,,,,,,,,, 132 | 131,5,2005,3,12,,,,,,,,,,,,,,,,,,,,,,,, 133 | 132,6,2005,3,12,,,,,,,,,,,,,,,,,,,,,,,, 134 | 133,7,2005,3,12,,,,,,,,,,,,,,,,,,,,,,,, 135 | 134,8,2005,3,12,,,,,,,,,,,,,,,,,,,,,,,, 136 | 135,9,2005,3,12,,,,,,,,,,,,,,,,,,,,,,,, 137 | 136,10,2005,3,12,,,,,,,,,,,,,,,,,,,,,,,, 138 | 137,11,2005,3,12,,,,,,,,,,,,,,,,,,,,,,,, 139 | 138,12,2005,3,12,,,,,,,,,,,,,,,,,,,,,,,, 140 | 139,13,2005,3,12,,,,,,,,,,,,,,,,,,,,,,,, 141 | 140,14,2005,3,12,,,,,,,,,,,,,,,,,,,,,,,, 142 | 141,15,2005,3,12,,,,,,,,,,,,,,,,,,,,,,,, 143 | 142,16,2005,3,12,,,,,,,,,,,,,,,,,,,,,,,, 144 | 143,17,2005,3,12,,,,,,,,,,,,,,,,,,,,,,,, 145 | 144,18,2005,3,12,,,,,,,,,,,,,,,,,,,,,,,, 146 | 145,19,2005,3,12,,,,,,,,,,,,,,,,,,,,,,,, 147 | 146,20,2005,3,12,,,,,,,,,,,,,,,,,,,,,,,, 148 | 147,21,2005,3,12,,,,,,,,,,,,,,,,,,,,,,,, 149 | 148,1,2005,6,20,,,,,,,,,,,,,,,,,,,,,,,, 150 | 149,2,2005,6,20,,,,,,,,,,,,,,,,,,,,,,,, 151 | 150,3,2005,6,20,,,,,,,,,,,,,,,,,,,,,,,, 152 | 151,4,2005,6,20,,,,,,,,,,,,,,,,,,,,,,,, 153 | 152,5,2005,6,20,,,,,,,,,,,,,,,,,,,,,,,, 154 | 153,6,2005,6,20,,,,,,,,,,,,,,,,,,,,,,,, 155 | 154,7,2005,6,20,,,,,,,,,,,,,,,,,,,,,,,, 156 | 155,8,2005,6,20,,,,,,,,,,,,,,,,,,,,,,,, 157 | 156,9,2005,6,20,,,,,,,,,,,,,,,,,,,,,,,, 158 | 157,10,2005,6,20,,,,,,,,,,,,,,,,,,,,,,,, 159 | 158,11,2005,6,20,,,,,,,,,,,,,,,,,,,,,,,, 160 | 159,12,2005,6,20,,,,,,,,,,,,,,,,,,,,,,,, 161 | 160,13,2005,6,20,,,,,,,,,,,,,,,,,,,,,,,, 162 | 161,14,2005,6,20,,,,,,,,,,,,,,,,,,,,,,,, 163 | 162,15,2005,6,20,,,,,,,,,,,,,,,,,,,,,,,, 164 | 163,16,2005,6,20,,,,,,,,,,,,,,,,,,,,,,,, 165 | 164,17,2005,6,20,,,,,,,,,,,,,,,,,,,,,,,, 166 | 165,18,2005,6,20,,,,,,,,,,,,,,,,,,,,,,,, 167 | 166,19,2005,6,20,,,,,,,,,,,,,,,,,,,,,,,, 168 | 167,20,2005,6,20,,,,,,,,,,,,,,,,,,,,,,,, 169 | 168,21,2005,6,20,,,,,,,,,,,,,,,,,,,,,,,, 170 | 169,1,2005,6,21,,,,,,,,,,,,,,,,,,,,,,,, 171 | 170,2,2005,6,21,,,,,,,,,,,,,,,,,,,,,,,, 172 | 171,3,2005,6,21,,,,,,,,,,,,,,,,,,,,,,,, 173 | 172,4,2005,6,21,,,,,,,,,,,,,,,,,,,,,,,, 174 | 173,5,2005,6,21,,,,,,,,,,,,,,,,,,,,,,,, 175 | 174,6,2005,6,21,,,,,,,,,,,,,,,,,,,,,,,, 176 | 175,7,2005,6,21,,,,,,,,,,,,,,,,,,,,,,,, 177 | 176,8,2005,6,21,,,,,,,,,,,,,,,,,,,,,,,, 178 | 177,9,2005,6,21,,,,,,,,,,,,,,,,,,,,,,,, 179 | 178,10,2005,6,21,,,,,,,,,,,,,,,,,,,,,,,, 180 | 179,11,2005,6,21,,,,,,,,,,,,,,,,,,,,,,,, 181 | 180,12,2005,6,21,,,,,,,,,,,,,,,,,,,,,,,, 182 | 181,13,2005,6,21,,,,,,,,,,,,,,,,,,,,,,,, 183 | 182,14,2005,6,21,,,,,,,,,,,,,,,,,,,,,,,, 184 | 183,15,2005,6,21,,,,,,,,,,,,,,,,,,,,,,,, 185 | 184,16,2005,6,21,,,,,,,,,,,,,,,,,,,,,,,, 186 | 185,17,2005,6,21,,,,,,,,,,,,,,,,,,,,,,,, 187 | 186,18,2005,6,21,,,,,,,,,,,,,,,,,,,,,,,, 188 | 187,19,2005,6,21,,,,,,,,,,,,,,,,,,,,,,,, 189 | 188,20,2005,6,21,,,,,,,,,,,,,,,,,,,,,,,, 190 | 189,21,2005,6,21,,,,,,,,,,,,,,,,,,,,,,,, 191 | 190,1,2005,6,22,,,,,,,,,,,,,,,,,,,,,,,, 192 | 191,2,2005,6,22,,,,,,,,,,,,,,,,,,,,,,,, 193 | 192,3,2005,6,22,,,,,,,,,,,,,,,,,,,,,,,, 194 | 193,4,2005,6,22,,,,,,,,,,,,,,,,,,,,,,,, 195 | 194,5,2005,6,22,,,,,,,,,,,,,,,,,,,,,,,, 196 | 195,6,2005,6,22,,,,,,,,,,,,,,,,,,,,,,,, 197 | 196,7,2005,6,22,,,,,,,,,,,,,,,,,,,,,,,, 198 | 197,8,2005,6,22,,,,,,,,,,,,,,,,,,,,,,,, 199 | 198,9,2005,6,22,,,,,,,,,,,,,,,,,,,,,,,, 200 | 199,10,2005,6,22,,,,,,,,,,,,,,,,,,,,,,,, 201 | 200,11,2005,6,22,,,,,,,,,,,,,,,,,,,,,,,, 202 | 201,12,2005,6,22,,,,,,,,,,,,,,,,,,,,,,,, 203 | 202,13,2005,6,22,,,,,,,,,,,,,,,,,,,,,,,, 204 | 203,14,2005,6,22,,,,,,,,,,,,,,,,,,,,,,,, 205 | 204,15,2005,6,22,,,,,,,,,,,,,,,,,,,,,,,, 206 | 205,16,2005,6,22,,,,,,,,,,,,,,,,,,,,,,,, 207 | 206,17,2005,6,22,,,,,,,,,,,,,,,,,,,,,,,, 208 | 207,18,2005,6,22,,,,,,,,,,,,,,,,,,,,,,,, 209 | 208,19,2005,6,22,,,,,,,,,,,,,,,,,,,,,,,, 210 | 209,20,2005,6,22,,,,,,,,,,,,,,,,,,,,,,,, 211 | 210,21,2005,6,22,,,,,,,,,,,,,,,,,,,,,,,, 212 | 211,1,2005,6,23,,,,,,,,,,,,,,,,,,,,,,,, 213 | 212,2,2005,6,23,,,,,,,,,,,,,,,,,,,,,,,, 214 | 213,3,2005,6,23,,,,,,,,,,,,,,,,,,,,,,,, 215 | 214,4,2005,6,23,,,,,,,,,,,,,,,,,,,,,,,, 216 | 215,5,2005,6,23,,,,,,,,,,,,,,,,,,,,,,,, 217 | 216,6,2005,6,23,,,,,,,,,,,,,,,,,,,,,,,, 218 | 217,7,2005,6,23,,,,,,,,,,,,,,,,,,,,,,,, 219 | 218,8,2005,6,23,,,,,,,,,,,,,,,,,,,,,,,, 220 | 219,9,2005,6,23,,,,,,,,,,,,,,,,,,,,,,,, 221 | 220,10,2005,6,23,,,,,,,,,,,,,,,,,,,,,,,, 222 | 221,11,2005,6,23,,,,,,,,,,,,,,,,,,,,,,,, 223 | 222,12,2005,6,23,,,,,,,,,,,,,,,,,,,,,,,, 224 | 223,13,2005,6,23,,,,,,,,,,,,,,,,,,,,,,,, 225 | 224,14,2005,6,23,,,,,,,,,,,,,,,,,,,,,,,, 226 | 225,15,2005,6,23,,,,,,,,,,,,,,,,,,,,,,,, 227 | 226,16,2005,6,23,,,,,,,,,,,,,,,,,,,,,,,, 228 | 227,17,2005,6,23,,,,,,,,,,,,,,,,,,,,,,,, 229 | 228,18,2005,6,23,,,,,,,,,,,,,,,,,,,,,,,, 230 | 229,19,2005,6,23,,,,,,,,,,,,,,,,,,,,,,,, 231 | 230,20,2005,6,23,,,,,,,,,,,,,,,,,,,,,,,, 232 | 231,21,2005,6,23,,,,,,,,,,,,,,,,,,,,,,,, 233 | 232,1,2005,6,24,,,,,,,,,,,,,,,,,,,,,,,, 234 | 233,2,2005,6,24,,,,,,,,,,,,,,,,,,,,,,,, 235 | 234,3,2005,6,24,,,,,,,,,,,,,,,,,,,,,,,, 236 | 235,4,2005,6,24,,,,,,,,,,,,,,,,,,,,,,,, 237 | 236,5,2005,6,24,,,,,,,,,,,,,,,,,,,,,,,, 238 | 237,6,2005,6,24,,,,,,,,,,,,,,,,,,,,,,,, 239 | 238,7,2005,6,24,,,,,,,,,,,,,,,,,,,,,,,, 240 | 239,8,2005,6,24,,,,,,,,,,,,,,,,,,,,,,,, 241 | 240,9,2005,6,24,,,,,,,,,,,,,,,,,,,,,,,, 242 | 241,10,2005,6,24,,,,,,,,,,,,,,,,,,,,,,,, 243 | 242,11,2005,6,24,,,,,,,,,,,,,,,,,,,,,,,, 244 | 243,12,2005,6,24,,,,,,,,,,,,,,,,,,,,,,,, 245 | 244,13,2005,6,24,,,,,,,,,,,,,,,,,,,,,,,, 246 | 245,14,2005,6,24,,,,,,,,,,,,,,,,,,,,,,,, 247 | 246,15,2005,6,24,,,,,,,,,,,,,,,,,,,,,,,, 248 | 247,16,2005,6,24,,,,,,,,,,,,,,,,,,,,,,,, 249 | 248,17,2005,6,24,,,,,,,,,,,,,,,,,,,,,,,, 250 | 249,18,2005,6,24,,,,,,,,,,,,,,,,,,,,,,,, 251 | 250,19,2005,6,24,,,,,,,,,,,,,,,,,,,,,,,, 252 | 251,20,2005,6,24,,,,,,,,,,,,,,,,,,,,,,,, 253 | 252,21,2005,6,24,,,,,,,,,,,,,,,,,,,,,,,, 254 | 253,1,2005,6,25,,,,,,,,,,,,,,,,,,,,,,,, 255 | 254,2,2005,6,25,,,,,,,,,,,,,,,,,,,,,,,, 256 | 255,3,2005,6,25,,,,,,,,,,,,,,,,,,,,,,,, 257 | 256,4,2005,6,25,,,,,,,,,,,,,,,,,,,,,,,, 258 | 257,5,2005,6,25,,,,,,,,,,,,,,,,,,,,,,,, 259 | 258,6,2005,6,25,,,,,,,,,,,,,,,,,,,,,,,, 260 | 259,7,2005,6,25,,,,,,,,,,,,,,,,,,,,,,,, 261 | 260,8,2005,6,25,,,,,,,,,,,,,,,,,,,,,,,, 262 | 261,9,2005,6,25,,,,,,,,,,,,,,,,,,,,,,,, 263 | 262,10,2005,6,25,,,,,,,,,,,,,,,,,,,,,,,, 264 | 263,11,2005,6,25,,,,,,,,,,,,,,,,,,,,,,,, 265 | 264,12,2005,6,25,,,,,,,,,,,,,,,,,,,,,,,, 266 | 265,13,2005,6,25,,,,,,,,,,,,,,,,,,,,,,,, 267 | 266,14,2005,6,25,,,,,,,,,,,,,,,,,,,,,,,, 268 | 267,15,2005,6,25,,,,,,,,,,,,,,,,,,,,,,,, 269 | 268,16,2005,6,25,,,,,,,,,,,,,,,,,,,,,,,, 270 | 269,17,2005,6,25,,,,,,,,,,,,,,,,,,,,,,,, 271 | 270,18,2005,6,25,,,,,,,,,,,,,,,,,,,,,,,, 272 | 271,19,2005,6,25,,,,,,,,,,,,,,,,,,,,,,,, 273 | 272,20,2005,6,25,,,,,,,,,,,,,,,,,,,,,,,, 274 | 273,21,2005,6,25,,,,,,,,,,,,,,,,,,,,,,,, 275 | 274,1,2005,6,26,,,,,,,,,,,,,,,,,,,,,,,, 276 | 275,2,2005,6,26,,,,,,,,,,,,,,,,,,,,,,,, 277 | 276,3,2005,6,26,,,,,,,,,,,,,,,,,,,,,,,, 278 | 277,4,2005,6,26,,,,,,,,,,,,,,,,,,,,,,,, 279 | 278,5,2005,6,26,,,,,,,,,,,,,,,,,,,,,,,, 280 | 279,6,2005,6,26,,,,,,,,,,,,,,,,,,,,,,,, 281 | 280,7,2005,6,26,,,,,,,,,,,,,,,,,,,,,,,, 282 | 281,8,2005,6,26,,,,,,,,,,,,,,,,,,,,,,,, 283 | 282,9,2005,6,26,,,,,,,,,,,,,,,,,,,,,,,, 284 | 283,10,2005,6,26,,,,,,,,,,,,,,,,,,,,,,,, 285 | 284,11,2005,6,26,,,,,,,,,,,,,,,,,,,,,,,, 286 | 285,12,2005,6,26,,,,,,,,,,,,,,,,,,,,,,,, 287 | 286,13,2005,6,26,,,,,,,,,,,,,,,,,,,,,,,, 288 | 287,14,2005,6,26,,,,,,,,,,,,,,,,,,,,,,,, 289 | 288,15,2005,6,26,,,,,,,,,,,,,,,,,,,,,,,, 290 | 289,16,2005,6,26,,,,,,,,,,,,,,,,,,,,,,,, 291 | 290,17,2005,6,26,,,,,,,,,,,,,,,,,,,,,,,, 292 | 291,18,2005,6,26,,,,,,,,,,,,,,,,,,,,,,,, 293 | 292,19,2005,6,26,,,,,,,,,,,,,,,,,,,,,,,, 294 | 293,20,2005,6,26,,,,,,,,,,,,,,,,,,,,,,,, 295 | 294,21,2005,6,26,,,,,,,,,,,,,,,,,,,,,,,, 296 | 295,1,2005,9,10,,,,,,,,,,,,,,,,,,,,,,,, 297 | 296,2,2005,9,10,,,,,,,,,,,,,,,,,,,,,,,, 298 | 297,3,2005,9,10,,,,,,,,,,,,,,,,,,,,,,,, 299 | 298,4,2005,9,10,,,,,,,,,,,,,,,,,,,,,,,, 300 | 299,5,2005,9,10,,,,,,,,,,,,,,,,,,,,,,,, 301 | 300,6,2005,9,10,,,,,,,,,,,,,,,,,,,,,,,, 302 | 301,7,2005,9,10,,,,,,,,,,,,,,,,,,,,,,,, 303 | 302,8,2005,9,10,,,,,,,,,,,,,,,,,,,,,,,, 304 | 303,9,2005,9,10,,,,,,,,,,,,,,,,,,,,,,,, 305 | 304,10,2005,9,10,,,,,,,,,,,,,,,,,,,,,,,, 306 | 305,11,2005,9,10,,,,,,,,,,,,,,,,,,,,,,,, 307 | 306,12,2005,9,10,,,,,,,,,,,,,,,,,,,,,,,, 308 | 307,13,2005,9,10,,,,,,,,,,,,,,,,,,,,,,,, 309 | 308,14,2005,9,10,,,,,,,,,,,,,,,,,,,,,,,, 310 | 309,15,2005,9,10,,,,,,,,,,,,,,,,,,,,,,,, 311 | 310,16,2005,9,10,,,,,,,,,,,,,,,,,,,,,,,, 312 | 311,17,2005,9,10,,,,,,,,,,,,,,,,,,,,,,,, 313 | 312,18,2005,9,10,,,,,,,,,,,,,,,,,,,,,,,, 314 | 313,19,2005,9,10,,,,,,,,,,,,,,,,,,,,,,,, 315 | 314,20,2005,9,10,,,,,,,,,,,,,,,,,,,,,,,, 316 | 315,21,2005,9,10,,,,,,,,,,,,,,,,,,,,,,,, 317 | 316,1,2005,9,11,,,,,,,,,,,,,,,,,,,,,,,, 318 | 317,2,2005,9,11,,,,,,,,,,,,,,,,,,,,,,,, 319 | 318,3,2005,9,11,,,,,,,,,,,,,,,,,,,,,,,, 320 | 319,4,2005,9,11,,,,,,,,,,,,,,,,,,,,,,,, 321 | 320,5,2005,9,11,,,,,,,,,,,,,,,,,,,,,,,, 322 | 321,6,2005,9,11,,,,,,,,,,,,,,,,,,,,,,,, 323 | 322,7,2005,9,11,,,,,,,,,,,,,,,,,,,,,,,, 324 | 323,8,2005,9,11,,,,,,,,,,,,,,,,,,,,,,,, 325 | 324,9,2005,9,11,,,,,,,,,,,,,,,,,,,,,,,, 326 | 325,10,2005,9,11,,,,,,,,,,,,,,,,,,,,,,,, 327 | 326,11,2005,9,11,,,,,,,,,,,,,,,,,,,,,,,, 328 | 327,12,2005,9,11,,,,,,,,,,,,,,,,,,,,,,,, 329 | 328,13,2005,9,11,,,,,,,,,,,,,,,,,,,,,,,, 330 | 329,14,2005,9,11,,,,,,,,,,,,,,,,,,,,,,,, 331 | 330,15,2005,9,11,,,,,,,,,,,,,,,,,,,,,,,, 332 | 331,16,2005,9,11,,,,,,,,,,,,,,,,,,,,,,,, 333 | 332,17,2005,9,11,,,,,,,,,,,,,,,,,,,,,,,, 334 | 333,18,2005,9,11,,,,,,,,,,,,,,,,,,,,,,,, 335 | 334,19,2005,9,11,,,,,,,,,,,,,,,,,,,,,,,, 336 | 335,20,2005,9,11,,,,,,,,,,,,,,,,,,,,,,,, 337 | 336,21,2005,9,11,,,,,,,,,,,,,,,,,,,,,,,, 338 | 337,1,2005,9,12,,,,,,,,,,,,,,,,,,,,,,,, 339 | 338,2,2005,9,12,,,,,,,,,,,,,,,,,,,,,,,, 340 | 339,3,2005,9,12,,,,,,,,,,,,,,,,,,,,,,,, 341 | 340,4,2005,9,12,,,,,,,,,,,,,,,,,,,,,,,, 342 | 341,5,2005,9,12,,,,,,,,,,,,,,,,,,,,,,,, 343 | 342,6,2005,9,12,,,,,,,,,,,,,,,,,,,,,,,, 344 | 343,7,2005,9,12,,,,,,,,,,,,,,,,,,,,,,,, 345 | 344,8,2005,9,12,,,,,,,,,,,,,,,,,,,,,,,, 346 | 345,9,2005,9,12,,,,,,,,,,,,,,,,,,,,,,,, 347 | 346,10,2005,9,12,,,,,,,,,,,,,,,,,,,,,,,, 348 | 347,11,2005,9,12,,,,,,,,,,,,,,,,,,,,,,,, 349 | 348,12,2005,9,12,,,,,,,,,,,,,,,,,,,,,,,, 350 | 349,13,2005,9,12,,,,,,,,,,,,,,,,,,,,,,,, 351 | 350,14,2005,9,12,,,,,,,,,,,,,,,,,,,,,,,, 352 | 351,15,2005,9,12,,,,,,,,,,,,,,,,,,,,,,,, 353 | 352,16,2005,9,12,,,,,,,,,,,,,,,,,,,,,,,, 354 | 353,17,2005,9,12,,,,,,,,,,,,,,,,,,,,,,,, 355 | 354,18,2005,9,12,,,,,,,,,,,,,,,,,,,,,,,, 356 | 355,19,2005,9,12,,,,,,,,,,,,,,,,,,,,,,,, 357 | 356,20,2005,9,12,,,,,,,,,,,,,,,,,,,,,,,, 358 | 357,21,2005,9,12,,,,,,,,,,,,,,,,,,,,,,,, 359 | 358,1,2005,9,13,,,,,,,,,,,,,,,,,,,,,,,, 360 | 359,2,2005,9,13,,,,,,,,,,,,,,,,,,,,,,,, 361 | 360,3,2005,9,13,,,,,,,,,,,,,,,,,,,,,,,, 362 | 361,4,2005,9,13,,,,,,,,,,,,,,,,,,,,,,,, 363 | 362,5,2005,9,13,,,,,,,,,,,,,,,,,,,,,,,, 364 | 363,6,2005,9,13,,,,,,,,,,,,,,,,,,,,,,,, 365 | 364,7,2005,9,13,,,,,,,,,,,,,,,,,,,,,,,, 366 | 365,8,2005,9,13,,,,,,,,,,,,,,,,,,,,,,,, 367 | 366,9,2005,9,13,,,,,,,,,,,,,,,,,,,,,,,, 368 | 367,10,2005,9,13,,,,,,,,,,,,,,,,,,,,,,,, 369 | 368,11,2005,9,13,,,,,,,,,,,,,,,,,,,,,,,, 370 | 369,12,2005,9,13,,,,,,,,,,,,,,,,,,,,,,,, 371 | 370,13,2005,9,13,,,,,,,,,,,,,,,,,,,,,,,, 372 | 371,14,2005,9,13,,,,,,,,,,,,,,,,,,,,,,,, 373 | 372,15,2005,9,13,,,,,,,,,,,,,,,,,,,,,,,, 374 | 373,16,2005,9,13,,,,,,,,,,,,,,,,,,,,,,,, 375 | 374,17,2005,9,13,,,,,,,,,,,,,,,,,,,,,,,, 376 | 375,18,2005,9,13,,,,,,,,,,,,,,,,,,,,,,,, 377 | 376,19,2005,9,13,,,,,,,,,,,,,,,,,,,,,,,, 378 | 377,20,2005,9,13,,,,,,,,,,,,,,,,,,,,,,,, 379 | 378,21,2005,9,13,,,,,,,,,,,,,,,,,,,,,,,, 380 | 379,1,2005,9,14,,,,,,,,,,,,,,,,,,,,,,,, 381 | 380,2,2005,9,14,,,,,,,,,,,,,,,,,,,,,,,, 382 | 381,3,2005,9,14,,,,,,,,,,,,,,,,,,,,,,,, 383 | 382,4,2005,9,14,,,,,,,,,,,,,,,,,,,,,,,, 384 | 383,5,2005,9,14,,,,,,,,,,,,,,,,,,,,,,,, 385 | 384,6,2005,9,14,,,,,,,,,,,,,,,,,,,,,,,, 386 | 385,7,2005,9,14,,,,,,,,,,,,,,,,,,,,,,,, 387 | 386,8,2005,9,14,,,,,,,,,,,,,,,,,,,,,,,, 388 | 387,9,2005,9,14,,,,,,,,,,,,,,,,,,,,,,,, 389 | 388,10,2005,9,14,,,,,,,,,,,,,,,,,,,,,,,, 390 | 389,11,2005,9,14,,,,,,,,,,,,,,,,,,,,,,,, 391 | 390,12,2005,9,14,,,,,,,,,,,,,,,,,,,,,,,, 392 | 391,13,2005,9,14,,,,,,,,,,,,,,,,,,,,,,,, 393 | 392,14,2005,9,14,,,,,,,,,,,,,,,,,,,,,,,, 394 | 393,15,2005,9,14,,,,,,,,,,,,,,,,,,,,,,,, 395 | 394,16,2005,9,14,,,,,,,,,,,,,,,,,,,,,,,, 396 | 395,17,2005,9,14,,,,,,,,,,,,,,,,,,,,,,,, 397 | 396,18,2005,9,14,,,,,,,,,,,,,,,,,,,,,,,, 398 | 397,19,2005,9,14,,,,,,,,,,,,,,,,,,,,,,,, 399 | 398,20,2005,9,14,,,,,,,,,,,,,,,,,,,,,,,, 400 | 399,21,2005,9,14,,,,,,,,,,,,,,,,,,,,,,,, 401 | 400,1,2005,9,15,,,,,,,,,,,,,,,,,,,,,,,, 402 | 401,2,2005,9,15,,,,,,,,,,,,,,,,,,,,,,,, 403 | 402,3,2005,9,15,,,,,,,,,,,,,,,,,,,,,,,, 404 | 403,4,2005,9,15,,,,,,,,,,,,,,,,,,,,,,,, 405 | 404,5,2005,9,15,,,,,,,,,,,,,,,,,,,,,,,, 406 | 405,6,2005,9,15,,,,,,,,,,,,,,,,,,,,,,,, 407 | 406,7,2005,9,15,,,,,,,,,,,,,,,,,,,,,,,, 408 | 407,8,2005,9,15,,,,,,,,,,,,,,,,,,,,,,,, 409 | 408,9,2005,9,15,,,,,,,,,,,,,,,,,,,,,,,, 410 | 409,10,2005,9,15,,,,,,,,,,,,,,,,,,,,,,,, 411 | 410,11,2005,9,15,,,,,,,,,,,,,,,,,,,,,,,, 412 | 411,12,2005,9,15,,,,,,,,,,,,,,,,,,,,,,,, 413 | 412,13,2005,9,15,,,,,,,,,,,,,,,,,,,,,,,, 414 | 413,14,2005,9,15,,,,,,,,,,,,,,,,,,,,,,,, 415 | 414,15,2005,9,15,,,,,,,,,,,,,,,,,,,,,,,, 416 | 415,16,2005,9,15,,,,,,,,,,,,,,,,,,,,,,,, 417 | 416,17,2005,9,15,,,,,,,,,,,,,,,,,,,,,,,, 418 | 417,18,2005,9,15,,,,,,,,,,,,,,,,,,,,,,,, 419 | 418,19,2005,9,15,,,,,,,,,,,,,,,,,,,,,,,, 420 | 419,20,2005,9,15,,,,,,,,,,,,,,,,,,,,,,,, 421 | 420,21,2005,9,15,,,,,,,,,,,,,,,,,,,,,,,, 422 | 421,1,2005,9,16,,,,,,,,,,,,,,,,,,,,,,,, 423 | 422,2,2005,9,16,,,,,,,,,,,,,,,,,,,,,,,, 424 | 423,3,2005,9,16,,,,,,,,,,,,,,,,,,,,,,,, 425 | 424,4,2005,9,16,,,,,,,,,,,,,,,,,,,,,,,, 426 | 425,5,2005,9,16,,,,,,,,,,,,,,,,,,,,,,,, 427 | 426,6,2005,9,16,,,,,,,,,,,,,,,,,,,,,,,, 428 | 427,7,2005,9,16,,,,,,,,,,,,,,,,,,,,,,,, 429 | 428,8,2005,9,16,,,,,,,,,,,,,,,,,,,,,,,, 430 | 429,9,2005,9,16,,,,,,,,,,,,,,,,,,,,,,,, 431 | 430,10,2005,9,16,,,,,,,,,,,,,,,,,,,,,,,, 432 | 431,11,2005,9,16,,,,,,,,,,,,,,,,,,,,,,,, 433 | 432,12,2005,9,16,,,,,,,,,,,,,,,,,,,,,,,, 434 | 433,13,2005,9,16,,,,,,,,,,,,,,,,,,,,,,,, 435 | 434,14,2005,9,16,,,,,,,,,,,,,,,,,,,,,,,, 436 | 435,15,2005,9,16,,,,,,,,,,,,,,,,,,,,,,,, 437 | 436,16,2005,9,16,,,,,,,,,,,,,,,,,,,,,,,, 438 | 437,17,2005,9,16,,,,,,,,,,,,,,,,,,,,,,,, 439 | 438,18,2005,9,16,,,,,,,,,,,,,,,,,,,,,,,, 440 | 439,19,2005,9,16,,,,,,,,,,,,,,,,,,,,,,,, 441 | 440,20,2005,9,16,,,,,,,,,,,,,,,,,,,,,,,, 442 | 441,21,2005,9,16,,,,,,,,,,,,,,,,,,,,,,,, 443 | 442,1,2005,12,25,,,,,,,,,,,,,,,,,,,,,,,, 444 | 443,2,2005,12,25,,,,,,,,,,,,,,,,,,,,,,,, 445 | 444,3,2005,12,25,,,,,,,,,,,,,,,,,,,,,,,, 446 | 445,4,2005,12,25,,,,,,,,,,,,,,,,,,,,,,,, 447 | 446,5,2005,12,25,,,,,,,,,,,,,,,,,,,,,,,, 448 | 447,6,2005,12,25,,,,,,,,,,,,,,,,,,,,,,,, 449 | 448,7,2005,12,25,,,,,,,,,,,,,,,,,,,,,,,, 450 | 449,8,2005,12,25,,,,,,,,,,,,,,,,,,,,,,,, 451 | 450,9,2005,12,25,,,,,,,,,,,,,,,,,,,,,,,, 452 | 451,10,2005,12,25,,,,,,,,,,,,,,,,,,,,,,,, 453 | 452,11,2005,12,25,,,,,,,,,,,,,,,,,,,,,,,, 454 | 453,12,2005,12,25,,,,,,,,,,,,,,,,,,,,,,,, 455 | 454,13,2005,12,25,,,,,,,,,,,,,,,,,,,,,,,, 456 | 455,14,2005,12,25,,,,,,,,,,,,,,,,,,,,,,,, 457 | 456,15,2005,12,25,,,,,,,,,,,,,,,,,,,,,,,, 458 | 457,16,2005,12,25,,,,,,,,,,,,,,,,,,,,,,,, 459 | 458,17,2005,12,25,,,,,,,,,,,,,,,,,,,,,,,, 460 | 459,18,2005,12,25,,,,,,,,,,,,,,,,,,,,,,,, 461 | 460,19,2005,12,25,,,,,,,,,,,,,,,,,,,,,,,, 462 | 461,20,2005,12,25,,,,,,,,,,,,,,,,,,,,,,,, 463 | 462,21,2005,12,25,,,,,,,,,,,,,,,,,,,,,,,, 464 | 463,1,2005,12,26,,,,,,,,,,,,,,,,,,,,,,,, 465 | 464,2,2005,12,26,,,,,,,,,,,,,,,,,,,,,,,, 466 | 465,3,2005,12,26,,,,,,,,,,,,,,,,,,,,,,,, 467 | 466,4,2005,12,26,,,,,,,,,,,,,,,,,,,,,,,, 468 | 467,5,2005,12,26,,,,,,,,,,,,,,,,,,,,,,,, 469 | 468,6,2005,12,26,,,,,,,,,,,,,,,,,,,,,,,, 470 | 469,7,2005,12,26,,,,,,,,,,,,,,,,,,,,,,,, 471 | 470,8,2005,12,26,,,,,,,,,,,,,,,,,,,,,,,, 472 | 471,9,2005,12,26,,,,,,,,,,,,,,,,,,,,,,,, 473 | 472,10,2005,12,26,,,,,,,,,,,,,,,,,,,,,,,, 474 | 473,11,2005,12,26,,,,,,,,,,,,,,,,,,,,,,,, 475 | 474,12,2005,12,26,,,,,,,,,,,,,,,,,,,,,,,, 476 | 475,13,2005,12,26,,,,,,,,,,,,,,,,,,,,,,,, 477 | 476,14,2005,12,26,,,,,,,,,,,,,,,,,,,,,,,, 478 | 477,15,2005,12,26,,,,,,,,,,,,,,,,,,,,,,,, 479 | 478,16,2005,12,26,,,,,,,,,,,,,,,,,,,,,,,, 480 | 479,17,2005,12,26,,,,,,,,,,,,,,,,,,,,,,,, 481 | 480,18,2005,12,26,,,,,,,,,,,,,,,,,,,,,,,, 482 | 481,19,2005,12,26,,,,,,,,,,,,,,,,,,,,,,,, 483 | 482,20,2005,12,26,,,,,,,,,,,,,,,,,,,,,,,, 484 | 483,21,2005,12,26,,,,,,,,,,,,,,,,,,,,,,,, 485 | 484,1,2005,12,27,,,,,,,,,,,,,,,,,,,,,,,, 486 | 485,2,2005,12,27,,,,,,,,,,,,,,,,,,,,,,,, 487 | 486,3,2005,12,27,,,,,,,,,,,,,,,,,,,,,,,, 488 | 487,4,2005,12,27,,,,,,,,,,,,,,,,,,,,,,,, 489 | 488,5,2005,12,27,,,,,,,,,,,,,,,,,,,,,,,, 490 | 489,6,2005,12,27,,,,,,,,,,,,,,,,,,,,,,,, 491 | 490,7,2005,12,27,,,,,,,,,,,,,,,,,,,,,,,, 492 | 491,8,2005,12,27,,,,,,,,,,,,,,,,,,,,,,,, 493 | 492,9,2005,12,27,,,,,,,,,,,,,,,,,,,,,,,, 494 | 493,10,2005,12,27,,,,,,,,,,,,,,,,,,,,,,,, 495 | 494,11,2005,12,27,,,,,,,,,,,,,,,,,,,,,,,, 496 | 495,12,2005,12,27,,,,,,,,,,,,,,,,,,,,,,,, 497 | 496,13,2005,12,27,,,,,,,,,,,,,,,,,,,,,,,, 498 | 497,14,2005,12,27,,,,,,,,,,,,,,,,,,,,,,,, 499 | 498,15,2005,12,27,,,,,,,,,,,,,,,,,,,,,,,, 500 | 499,16,2005,12,27,,,,,,,,,,,,,,,,,,,,,,,, 501 | 500,17,2005,12,27,,,,,,,,,,,,,,,,,,,,,,,, 502 | 501,18,2005,12,27,,,,,,,,,,,,,,,,,,,,,,,, 503 | 502,19,2005,12,27,,,,,,,,,,,,,,,,,,,,,,,, 504 | 503,20,2005,12,27,,,,,,,,,,,,,,,,,,,,,,,, 505 | 504,21,2005,12,27,,,,,,,,,,,,,,,,,,,,,,,, 506 | 505,1,2005,12,28,,,,,,,,,,,,,,,,,,,,,,,, 507 | 506,2,2005,12,28,,,,,,,,,,,,,,,,,,,,,,,, 508 | 507,3,2005,12,28,,,,,,,,,,,,,,,,,,,,,,,, 509 | 508,4,2005,12,28,,,,,,,,,,,,,,,,,,,,,,,, 510 | 509,5,2005,12,28,,,,,,,,,,,,,,,,,,,,,,,, 511 | 510,6,2005,12,28,,,,,,,,,,,,,,,,,,,,,,,, 512 | 511,7,2005,12,28,,,,,,,,,,,,,,,,,,,,,,,, 513 | 512,8,2005,12,28,,,,,,,,,,,,,,,,,,,,,,,, 514 | 513,9,2005,12,28,,,,,,,,,,,,,,,,,,,,,,,, 515 | 514,10,2005,12,28,,,,,,,,,,,,,,,,,,,,,,,, 516 | 515,11,2005,12,28,,,,,,,,,,,,,,,,,,,,,,,, 517 | 516,12,2005,12,28,,,,,,,,,,,,,,,,,,,,,,,, 518 | 517,13,2005,12,28,,,,,,,,,,,,,,,,,,,,,,,, 519 | 518,14,2005,12,28,,,,,,,,,,,,,,,,,,,,,,,, 520 | 519,15,2005,12,28,,,,,,,,,,,,,,,,,,,,,,,, 521 | 520,16,2005,12,28,,,,,,,,,,,,,,,,,,,,,,,, 522 | 521,17,2005,12,28,,,,,,,,,,,,,,,,,,,,,,,, 523 | 522,18,2005,12,28,,,,,,,,,,,,,,,,,,,,,,,, 524 | 523,19,2005,12,28,,,,,,,,,,,,,,,,,,,,,,,, 525 | 524,20,2005,12,28,,,,,,,,,,,,,,,,,,,,,,,, 526 | 525,21,2005,12,28,,,,,,,,,,,,,,,,,,,,,,,, 527 | 526,1,2005,12,29,,,,,,,,,,,,,,,,,,,,,,,, 528 | 527,2,2005,12,29,,,,,,,,,,,,,,,,,,,,,,,, 529 | 528,3,2005,12,29,,,,,,,,,,,,,,,,,,,,,,,, 530 | 529,4,2005,12,29,,,,,,,,,,,,,,,,,,,,,,,, 531 | 530,5,2005,12,29,,,,,,,,,,,,,,,,,,,,,,,, 532 | 531,6,2005,12,29,,,,,,,,,,,,,,,,,,,,,,,, 533 | 532,7,2005,12,29,,,,,,,,,,,,,,,,,,,,,,,, 534 | 533,8,2005,12,29,,,,,,,,,,,,,,,,,,,,,,,, 535 | 534,9,2005,12,29,,,,,,,,,,,,,,,,,,,,,,,, 536 | 535,10,2005,12,29,,,,,,,,,,,,,,,,,,,,,,,, 537 | 536,11,2005,12,29,,,,,,,,,,,,,,,,,,,,,,,, 538 | 537,12,2005,12,29,,,,,,,,,,,,,,,,,,,,,,,, 539 | 538,13,2005,12,29,,,,,,,,,,,,,,,,,,,,,,,, 540 | 539,14,2005,12,29,,,,,,,,,,,,,,,,,,,,,,,, 541 | 540,15,2005,12,29,,,,,,,,,,,,,,,,,,,,,,,, 542 | 541,16,2005,12,29,,,,,,,,,,,,,,,,,,,,,,,, 543 | 542,17,2005,12,29,,,,,,,,,,,,,,,,,,,,,,,, 544 | 543,18,2005,12,29,,,,,,,,,,,,,,,,,,,,,,,, 545 | 544,19,2005,12,29,,,,,,,,,,,,,,,,,,,,,,,, 546 | 545,20,2005,12,29,,,,,,,,,,,,,,,,,,,,,,,, 547 | 546,21,2005,12,29,,,,,,,,,,,,,,,,,,,,,,,, 548 | 547,1,2005,12,30,,,,,,,,,,,,,,,,,,,,,,,, 549 | 548,2,2005,12,30,,,,,,,,,,,,,,,,,,,,,,,, 550 | 549,3,2005,12,30,,,,,,,,,,,,,,,,,,,,,,,, 551 | 550,4,2005,12,30,,,,,,,,,,,,,,,,,,,,,,,, 552 | 551,5,2005,12,30,,,,,,,,,,,,,,,,,,,,,,,, 553 | 552,6,2005,12,30,,,,,,,,,,,,,,,,,,,,,,,, 554 | 553,7,2005,12,30,,,,,,,,,,,,,,,,,,,,,,,, 555 | 554,8,2005,12,30,,,,,,,,,,,,,,,,,,,,,,,, 556 | 555,9,2005,12,30,,,,,,,,,,,,,,,,,,,,,,,, 557 | 556,10,2005,12,30,,,,,,,,,,,,,,,,,,,,,,,, 558 | 557,11,2005,12,30,,,,,,,,,,,,,,,,,,,,,,,, 559 | 558,12,2005,12,30,,,,,,,,,,,,,,,,,,,,,,,, 560 | 559,13,2005,12,30,,,,,,,,,,,,,,,,,,,,,,,, 561 | 560,14,2005,12,30,,,,,,,,,,,,,,,,,,,,,,,, 562 | 561,15,2005,12,30,,,,,,,,,,,,,,,,,,,,,,,, 563 | 562,16,2005,12,30,,,,,,,,,,,,,,,,,,,,,,,, 564 | 563,17,2005,12,30,,,,,,,,,,,,,,,,,,,,,,,, 565 | 564,18,2005,12,30,,,,,,,,,,,,,,,,,,,,,,,, 566 | 565,19,2005,12,30,,,,,,,,,,,,,,,,,,,,,,,, 567 | 566,20,2005,12,30,,,,,,,,,,,,,,,,,,,,,,,, 568 | 567,21,2005,12,30,,,,,,,,,,,,,,,,,,,,,,,, 569 | 568,1,2005,12,31,,,,,,,,,,,,,,,,,,,,,,,, 570 | 569,2,2005,12,31,,,,,,,,,,,,,,,,,,,,,,,, 571 | 570,3,2005,12,31,,,,,,,,,,,,,,,,,,,,,,,, 572 | 571,4,2005,12,31,,,,,,,,,,,,,,,,,,,,,,,, 573 | 572,5,2005,12,31,,,,,,,,,,,,,,,,,,,,,,,, 574 | 573,6,2005,12,31,,,,,,,,,,,,,,,,,,,,,,,, 575 | 574,7,2005,12,31,,,,,,,,,,,,,,,,,,,,,,,, 576 | 575,8,2005,12,31,,,,,,,,,,,,,,,,,,,,,,,, 577 | 576,9,2005,12,31,,,,,,,,,,,,,,,,,,,,,,,, 578 | 577,10,2005,12,31,,,,,,,,,,,,,,,,,,,,,,,, 579 | 578,11,2005,12,31,,,,,,,,,,,,,,,,,,,,,,,, 580 | 579,12,2005,12,31,,,,,,,,,,,,,,,,,,,,,,,, 581 | 580,13,2005,12,31,,,,,,,,,,,,,,,,,,,,,,,, 582 | 581,14,2005,12,31,,,,,,,,,,,,,,,,,,,,,,,, 583 | 582,15,2005,12,31,,,,,,,,,,,,,,,,,,,,,,,, 584 | 583,16,2005,12,31,,,,,,,,,,,,,,,,,,,,,,,, 585 | 584,17,2005,12,31,,,,,,,,,,,,,,,,,,,,,,,, 586 | 585,18,2005,12,31,,,,,,,,,,,,,,,,,,,,,,,, 587 | 586,19,2005,12,31,,,,,,,,,,,,,,,,,,,,,,,, 588 | 587,20,2005,12,31,,,,,,,,,,,,,,,,,,,,,,,, 589 | 588,21,2005,12,31,,,,,,,,,,,,,,,,,,,,,,,, 590 | 589,1,2006,2,13,,,,,,,,,,,,,,,,,,,,,,,, 591 | 590,2,2006,2,13,,,,,,,,,,,,,,,,,,,,,,,, 592 | 591,3,2006,2,13,,,,,,,,,,,,,,,,,,,,,,,, 593 | 592,4,2006,2,13,,,,,,,,,,,,,,,,,,,,,,,, 594 | 593,5,2006,2,13,,,,,,,,,,,,,,,,,,,,,,,, 595 | 594,6,2006,2,13,,,,,,,,,,,,,,,,,,,,,,,, 596 | 595,7,2006,2,13,,,,,,,,,,,,,,,,,,,,,,,, 597 | 596,8,2006,2,13,,,,,,,,,,,,,,,,,,,,,,,, 598 | 597,9,2006,2,13,,,,,,,,,,,,,,,,,,,,,,,, 599 | 598,10,2006,2,13,,,,,,,,,,,,,,,,,,,,,,,, 600 | 599,11,2006,2,13,,,,,,,,,,,,,,,,,,,,,,,, 601 | 600,12,2006,2,13,,,,,,,,,,,,,,,,,,,,,,,, 602 | 601,13,2006,2,13,,,,,,,,,,,,,,,,,,,,,,,, 603 | 602,14,2006,2,13,,,,,,,,,,,,,,,,,,,,,,,, 604 | 603,15,2006,2,13,,,,,,,,,,,,,,,,,,,,,,,, 605 | 604,16,2006,2,13,,,,,,,,,,,,,,,,,,,,,,,, 606 | 605,17,2006,2,13,,,,,,,,,,,,,,,,,,,,,,,, 607 | 606,18,2006,2,13,,,,,,,,,,,,,,,,,,,,,,,, 608 | 607,19,2006,2,13,,,,,,,,,,,,,,,,,,,,,,,, 609 | 608,20,2006,2,13,,,,,,,,,,,,,,,,,,,,,,,, 610 | 609,21,2006,2,13,,,,,,,,,,,,,,,,,,,,,,,, 611 | 610,1,2006,2,14,,,,,,,,,,,,,,,,,,,,,,,, 612 | 611,2,2006,2,14,,,,,,,,,,,,,,,,,,,,,,,, 613 | 612,3,2006,2,14,,,,,,,,,,,,,,,,,,,,,,,, 614 | 613,4,2006,2,14,,,,,,,,,,,,,,,,,,,,,,,, 615 | 614,5,2006,2,14,,,,,,,,,,,,,,,,,,,,,,,, 616 | 615,6,2006,2,14,,,,,,,,,,,,,,,,,,,,,,,, 617 | 616,7,2006,2,14,,,,,,,,,,,,,,,,,,,,,,,, 618 | 617,8,2006,2,14,,,,,,,,,,,,,,,,,,,,,,,, 619 | 618,9,2006,2,14,,,,,,,,,,,,,,,,,,,,,,,, 620 | 619,10,2006,2,14,,,,,,,,,,,,,,,,,,,,,,,, 621 | 620,11,2006,2,14,,,,,,,,,,,,,,,,,,,,,,,, 622 | 621,12,2006,2,14,,,,,,,,,,,,,,,,,,,,,,,, 623 | 622,13,2006,2,14,,,,,,,,,,,,,,,,,,,,,,,, 624 | 623,14,2006,2,14,,,,,,,,,,,,,,,,,,,,,,,, 625 | 624,15,2006,2,14,,,,,,,,,,,,,,,,,,,,,,,, 626 | 625,16,2006,2,14,,,,,,,,,,,,,,,,,,,,,,,, 627 | 626,17,2006,2,14,,,,,,,,,,,,,,,,,,,,,,,, 628 | 627,18,2006,2,14,,,,,,,,,,,,,,,,,,,,,,,, 629 | 628,19,2006,2,14,,,,,,,,,,,,,,,,,,,,,,,, 630 | 629,20,2006,2,14,,,,,,,,,,,,,,,,,,,,,,,, 631 | 630,21,2006,2,14,,,,,,,,,,,,,,,,,,,,,,,, 632 | 631,1,2006,2,15,,,,,,,,,,,,,,,,,,,,,,,, 633 | 632,2,2006,2,15,,,,,,,,,,,,,,,,,,,,,,,, 634 | 633,3,2006,2,15,,,,,,,,,,,,,,,,,,,,,,,, 635 | 634,4,2006,2,15,,,,,,,,,,,,,,,,,,,,,,,, 636 | 635,5,2006,2,15,,,,,,,,,,,,,,,,,,,,,,,, 637 | 636,6,2006,2,15,,,,,,,,,,,,,,,,,,,,,,,, 638 | 637,7,2006,2,15,,,,,,,,,,,,,,,,,,,,,,,, 639 | 638,8,2006,2,15,,,,,,,,,,,,,,,,,,,,,,,, 640 | 639,9,2006,2,15,,,,,,,,,,,,,,,,,,,,,,,, 641 | 640,10,2006,2,15,,,,,,,,,,,,,,,,,,,,,,,, 642 | 641,11,2006,2,15,,,,,,,,,,,,,,,,,,,,,,,, 643 | 642,12,2006,2,15,,,,,,,,,,,,,,,,,,,,,,,, 644 | 643,13,2006,2,15,,,,,,,,,,,,,,,,,,,,,,,, 645 | 644,14,2006,2,15,,,,,,,,,,,,,,,,,,,,,,,, 646 | 645,15,2006,2,15,,,,,,,,,,,,,,,,,,,,,,,, 647 | 646,16,2006,2,15,,,,,,,,,,,,,,,,,,,,,,,, 648 | 647,17,2006,2,15,,,,,,,,,,,,,,,,,,,,,,,, 649 | 648,18,2006,2,15,,,,,,,,,,,,,,,,,,,,,,,, 650 | 649,19,2006,2,15,,,,,,,,,,,,,,,,,,,,,,,, 651 | 650,20,2006,2,15,,,,,,,,,,,,,,,,,,,,,,,, 652 | 651,21,2006,2,15,,,,,,,,,,,,,,,,,,,,,,,, 653 | 652,1,2006,2,16,,,,,,,,,,,,,,,,,,,,,,,, 654 | 653,2,2006,2,16,,,,,,,,,,,,,,,,,,,,,,,, 655 | 654,3,2006,2,16,,,,,,,,,,,,,,,,,,,,,,,, 656 | 655,4,2006,2,16,,,,,,,,,,,,,,,,,,,,,,,, 657 | 656,5,2006,2,16,,,,,,,,,,,,,,,,,,,,,,,, 658 | 657,6,2006,2,16,,,,,,,,,,,,,,,,,,,,,,,, 659 | 658,7,2006,2,16,,,,,,,,,,,,,,,,,,,,,,,, 660 | 659,8,2006,2,16,,,,,,,,,,,,,,,,,,,,,,,, 661 | 660,9,2006,2,16,,,,,,,,,,,,,,,,,,,,,,,, 662 | 661,10,2006,2,16,,,,,,,,,,,,,,,,,,,,,,,, 663 | 662,11,2006,2,16,,,,,,,,,,,,,,,,,,,,,,,, 664 | 663,12,2006,2,16,,,,,,,,,,,,,,,,,,,,,,,, 665 | 664,13,2006,2,16,,,,,,,,,,,,,,,,,,,,,,,, 666 | 665,14,2006,2,16,,,,,,,,,,,,,,,,,,,,,,,, 667 | 666,15,2006,2,16,,,,,,,,,,,,,,,,,,,,,,,, 668 | 667,16,2006,2,16,,,,,,,,,,,,,,,,,,,,,,,, 669 | 668,17,2006,2,16,,,,,,,,,,,,,,,,,,,,,,,, 670 | 669,18,2006,2,16,,,,,,,,,,,,,,,,,,,,,,,, 671 | 670,19,2006,2,16,,,,,,,,,,,,,,,,,,,,,,,, 672 | 671,20,2006,2,16,,,,,,,,,,,,,,,,,,,,,,,, 673 | 672,21,2006,2,16,,,,,,,,,,,,,,,,,,,,,,,, 674 | 673,1,2006,2,17,,,,,,,,,,,,,,,,,,,,,,,, 675 | 674,2,2006,2,17,,,,,,,,,,,,,,,,,,,,,,,, 676 | 675,3,2006,2,17,,,,,,,,,,,,,,,,,,,,,,,, 677 | 676,4,2006,2,17,,,,,,,,,,,,,,,,,,,,,,,, 678 | 677,5,2006,2,17,,,,,,,,,,,,,,,,,,,,,,,, 679 | 678,6,2006,2,17,,,,,,,,,,,,,,,,,,,,,,,, 680 | 679,7,2006,2,17,,,,,,,,,,,,,,,,,,,,,,,, 681 | 680,8,2006,2,17,,,,,,,,,,,,,,,,,,,,,,,, 682 | 681,9,2006,2,17,,,,,,,,,,,,,,,,,,,,,,,, 683 | 682,10,2006,2,17,,,,,,,,,,,,,,,,,,,,,,,, 684 | 683,11,2006,2,17,,,,,,,,,,,,,,,,,,,,,,,, 685 | 684,12,2006,2,17,,,,,,,,,,,,,,,,,,,,,,,, 686 | 685,13,2006,2,17,,,,,,,,,,,,,,,,,,,,,,,, 687 | 686,14,2006,2,17,,,,,,,,,,,,,,,,,,,,,,,, 688 | 687,15,2006,2,17,,,,,,,,,,,,,,,,,,,,,,,, 689 | 688,16,2006,2,17,,,,,,,,,,,,,,,,,,,,,,,, 690 | 689,17,2006,2,17,,,,,,,,,,,,,,,,,,,,,,,, 691 | 690,18,2006,2,17,,,,,,,,,,,,,,,,,,,,,,,, 692 | 691,19,2006,2,17,,,,,,,,,,,,,,,,,,,,,,,, 693 | 692,20,2006,2,17,,,,,,,,,,,,,,,,,,,,,,,, 694 | 693,21,2006,2,17,,,,,,,,,,,,,,,,,,,,,,,, 695 | 694,1,2006,2,18,,,,,,,,,,,,,,,,,,,,,,,, 696 | 695,2,2006,2,18,,,,,,,,,,,,,,,,,,,,,,,, 697 | 696,3,2006,2,18,,,,,,,,,,,,,,,,,,,,,,,, 698 | 697,4,2006,2,18,,,,,,,,,,,,,,,,,,,,,,,, 699 | 698,5,2006,2,18,,,,,,,,,,,,,,,,,,,,,,,, 700 | 699,6,2006,2,18,,,,,,,,,,,,,,,,,,,,,,,, 701 | 700,7,2006,2,18,,,,,,,,,,,,,,,,,,,,,,,, 702 | 701,8,2006,2,18,,,,,,,,,,,,,,,,,,,,,,,, 703 | 702,9,2006,2,18,,,,,,,,,,,,,,,,,,,,,,,, 704 | 703,10,2006,2,18,,,,,,,,,,,,,,,,,,,,,,,, 705 | 704,11,2006,2,18,,,,,,,,,,,,,,,,,,,,,,,, 706 | 705,12,2006,2,18,,,,,,,,,,,,,,,,,,,,,,,, 707 | 706,13,2006,2,18,,,,,,,,,,,,,,,,,,,,,,,, 708 | 707,14,2006,2,18,,,,,,,,,,,,,,,,,,,,,,,, 709 | 708,15,2006,2,18,,,,,,,,,,,,,,,,,,,,,,,, 710 | 709,16,2006,2,18,,,,,,,,,,,,,,,,,,,,,,,, 711 | 710,17,2006,2,18,,,,,,,,,,,,,,,,,,,,,,,, 712 | 711,18,2006,2,18,,,,,,,,,,,,,,,,,,,,,,,, 713 | 712,19,2006,2,18,,,,,,,,,,,,,,,,,,,,,,,, 714 | 713,20,2006,2,18,,,,,,,,,,,,,,,,,,,,,,,, 715 | 714,21,2006,2,18,,,,,,,,,,,,,,,,,,,,,,,, 716 | 715,1,2006,2,19,,,,,,,,,,,,,,,,,,,,,,,, 717 | 716,2,2006,2,19,,,,,,,,,,,,,,,,,,,,,,,, 718 | 717,3,2006,2,19,,,,,,,,,,,,,,,,,,,,,,,, 719 | 718,4,2006,2,19,,,,,,,,,,,,,,,,,,,,,,,, 720 | 719,5,2006,2,19,,,,,,,,,,,,,,,,,,,,,,,, 721 | 720,6,2006,2,19,,,,,,,,,,,,,,,,,,,,,,,, 722 | 721,7,2006,2,19,,,,,,,,,,,,,,,,,,,,,,,, 723 | 722,8,2006,2,19,,,,,,,,,,,,,,,,,,,,,,,, 724 | 723,9,2006,2,19,,,,,,,,,,,,,,,,,,,,,,,, 725 | 724,10,2006,2,19,,,,,,,,,,,,,,,,,,,,,,,, 726 | 725,11,2006,2,19,,,,,,,,,,,,,,,,,,,,,,,, 727 | 726,12,2006,2,19,,,,,,,,,,,,,,,,,,,,,,,, 728 | 727,13,2006,2,19,,,,,,,,,,,,,,,,,,,,,,,, 729 | 728,14,2006,2,19,,,,,,,,,,,,,,,,,,,,,,,, 730 | 729,15,2006,2,19,,,,,,,,,,,,,,,,,,,,,,,, 731 | 730,16,2006,2,19,,,,,,,,,,,,,,,,,,,,,,,, 732 | 731,17,2006,2,19,,,,,,,,,,,,,,,,,,,,,,,, 733 | 732,18,2006,2,19,,,,,,,,,,,,,,,,,,,,,,,, 734 | 733,19,2006,2,19,,,,,,,,,,,,,,,,,,,,,,,, 735 | 734,20,2006,2,19,,,,,,,,,,,,,,,,,,,,,,,, 736 | 735,21,2006,2,19,,,,,,,,,,,,,,,,,,,,,,,, 737 | 736,1,2006,5,25,,,,,,,,,,,,,,,,,,,,,,,, 738 | 737,2,2006,5,25,,,,,,,,,,,,,,,,,,,,,,,, 739 | 738,3,2006,5,25,,,,,,,,,,,,,,,,,,,,,,,, 740 | 739,4,2006,5,25,,,,,,,,,,,,,,,,,,,,,,,, 741 | 740,5,2006,5,25,,,,,,,,,,,,,,,,,,,,,,,, 742 | 741,6,2006,5,25,,,,,,,,,,,,,,,,,,,,,,,, 743 | 742,7,2006,5,25,,,,,,,,,,,,,,,,,,,,,,,, 744 | 743,8,2006,5,25,,,,,,,,,,,,,,,,,,,,,,,, 745 | 744,9,2006,5,25,,,,,,,,,,,,,,,,,,,,,,,, 746 | 745,10,2006,5,25,,,,,,,,,,,,,,,,,,,,,,,, 747 | 746,11,2006,5,25,,,,,,,,,,,,,,,,,,,,,,,, 748 | 747,12,2006,5,25,,,,,,,,,,,,,,,,,,,,,,,, 749 | 748,13,2006,5,25,,,,,,,,,,,,,,,,,,,,,,,, 750 | 749,14,2006,5,25,,,,,,,,,,,,,,,,,,,,,,,, 751 | 750,15,2006,5,25,,,,,,,,,,,,,,,,,,,,,,,, 752 | 751,16,2006,5,25,,,,,,,,,,,,,,,,,,,,,,,, 753 | 752,17,2006,5,25,,,,,,,,,,,,,,,,,,,,,,,, 754 | 753,18,2006,5,25,,,,,,,,,,,,,,,,,,,,,,,, 755 | 754,19,2006,5,25,,,,,,,,,,,,,,,,,,,,,,,, 756 | 755,20,2006,5,25,,,,,,,,,,,,,,,,,,,,,,,, 757 | 756,21,2006,5,25,,,,,,,,,,,,,,,,,,,,,,,, 758 | 757,1,2006,5,26,,,,,,,,,,,,,,,,,,,,,,,, 759 | 758,2,2006,5,26,,,,,,,,,,,,,,,,,,,,,,,, 760 | 759,3,2006,5,26,,,,,,,,,,,,,,,,,,,,,,,, 761 | 760,4,2006,5,26,,,,,,,,,,,,,,,,,,,,,,,, 762 | 761,5,2006,5,26,,,,,,,,,,,,,,,,,,,,,,,, 763 | 762,6,2006,5,26,,,,,,,,,,,,,,,,,,,,,,,, 764 | 763,7,2006,5,26,,,,,,,,,,,,,,,,,,,,,,,, 765 | 764,8,2006,5,26,,,,,,,,,,,,,,,,,,,,,,,, 766 | 765,9,2006,5,26,,,,,,,,,,,,,,,,,,,,,,,, 767 | 766,10,2006,5,26,,,,,,,,,,,,,,,,,,,,,,,, 768 | 767,11,2006,5,26,,,,,,,,,,,,,,,,,,,,,,,, 769 | 768,12,2006,5,26,,,,,,,,,,,,,,,,,,,,,,,, 770 | 769,13,2006,5,26,,,,,,,,,,,,,,,,,,,,,,,, 771 | 770,14,2006,5,26,,,,,,,,,,,,,,,,,,,,,,,, 772 | 771,15,2006,5,26,,,,,,,,,,,,,,,,,,,,,,,, 773 | 772,16,2006,5,26,,,,,,,,,,,,,,,,,,,,,,,, 774 | 773,17,2006,5,26,,,,,,,,,,,,,,,,,,,,,,,, 775 | 774,18,2006,5,26,,,,,,,,,,,,,,,,,,,,,,,, 776 | 775,19,2006,5,26,,,,,,,,,,,,,,,,,,,,,,,, 777 | 776,20,2006,5,26,,,,,,,,,,,,,,,,,,,,,,,, 778 | 777,21,2006,5,26,,,,,,,,,,,,,,,,,,,,,,,, 779 | 778,1,2006,5,27,,,,,,,,,,,,,,,,,,,,,,,, 780 | 779,2,2006,5,27,,,,,,,,,,,,,,,,,,,,,,,, 781 | 780,3,2006,5,27,,,,,,,,,,,,,,,,,,,,,,,, 782 | 781,4,2006,5,27,,,,,,,,,,,,,,,,,,,,,,,, 783 | 782,5,2006,5,27,,,,,,,,,,,,,,,,,,,,,,,, 784 | 783,6,2006,5,27,,,,,,,,,,,,,,,,,,,,,,,, 785 | 784,7,2006,5,27,,,,,,,,,,,,,,,,,,,,,,,, 786 | 785,8,2006,5,27,,,,,,,,,,,,,,,,,,,,,,,, 787 | 786,9,2006,5,27,,,,,,,,,,,,,,,,,,,,,,,, 788 | 787,10,2006,5,27,,,,,,,,,,,,,,,,,,,,,,,, 789 | 788,11,2006,5,27,,,,,,,,,,,,,,,,,,,,,,,, 790 | 789,12,2006,5,27,,,,,,,,,,,,,,,,,,,,,,,, 791 | 790,13,2006,5,27,,,,,,,,,,,,,,,,,,,,,,,, 792 | 791,14,2006,5,27,,,,,,,,,,,,,,,,,,,,,,,, 793 | 792,15,2006,5,27,,,,,,,,,,,,,,,,,,,,,,,, 794 | 793,16,2006,5,27,,,,,,,,,,,,,,,,,,,,,,,, 795 | 794,17,2006,5,27,,,,,,,,,,,,,,,,,,,,,,,, 796 | 795,18,2006,5,27,,,,,,,,,,,,,,,,,,,,,,,, 797 | 796,19,2006,5,27,,,,,,,,,,,,,,,,,,,,,,,, 798 | 797,20,2006,5,27,,,,,,,,,,,,,,,,,,,,,,,, 799 | 798,21,2006,5,27,,,,,,,,,,,,,,,,,,,,,,,, 800 | 799,1,2006,5,28,,,,,,,,,,,,,,,,,,,,,,,, 801 | 800,2,2006,5,28,,,,,,,,,,,,,,,,,,,,,,,, 802 | 801,3,2006,5,28,,,,,,,,,,,,,,,,,,,,,,,, 803 | 802,4,2006,5,28,,,,,,,,,,,,,,,,,,,,,,,, 804 | 803,5,2006,5,28,,,,,,,,,,,,,,,,,,,,,,,, 805 | 804,6,2006,5,28,,,,,,,,,,,,,,,,,,,,,,,, 806 | 805,7,2006,5,28,,,,,,,,,,,,,,,,,,,,,,,, 807 | 806,8,2006,5,28,,,,,,,,,,,,,,,,,,,,,,,, 808 | 807,9,2006,5,28,,,,,,,,,,,,,,,,,,,,,,,, 809 | 808,10,2006,5,28,,,,,,,,,,,,,,,,,,,,,,,, 810 | 809,11,2006,5,28,,,,,,,,,,,,,,,,,,,,,,,, 811 | 810,12,2006,5,28,,,,,,,,,,,,,,,,,,,,,,,, 812 | 811,13,2006,5,28,,,,,,,,,,,,,,,,,,,,,,,, 813 | 812,14,2006,5,28,,,,,,,,,,,,,,,,,,,,,,,, 814 | 813,15,2006,5,28,,,,,,,,,,,,,,,,,,,,,,,, 815 | 814,16,2006,5,28,,,,,,,,,,,,,,,,,,,,,,,, 816 | 815,17,2006,5,28,,,,,,,,,,,,,,,,,,,,,,,, 817 | 816,18,2006,5,28,,,,,,,,,,,,,,,,,,,,,,,, 818 | 817,19,2006,5,28,,,,,,,,,,,,,,,,,,,,,,,, 819 | 818,20,2006,5,28,,,,,,,,,,,,,,,,,,,,,,,, 820 | 819,21,2006,5,28,,,,,,,,,,,,,,,,,,,,,,,, 821 | 820,1,2006,5,29,,,,,,,,,,,,,,,,,,,,,,,, 822 | 821,2,2006,5,29,,,,,,,,,,,,,,,,,,,,,,,, 823 | 822,3,2006,5,29,,,,,,,,,,,,,,,,,,,,,,,, 824 | 823,4,2006,5,29,,,,,,,,,,,,,,,,,,,,,,,, 825 | 824,5,2006,5,29,,,,,,,,,,,,,,,,,,,,,,,, 826 | 825,6,2006,5,29,,,,,,,,,,,,,,,,,,,,,,,, 827 | 826,7,2006,5,29,,,,,,,,,,,,,,,,,,,,,,,, 828 | 827,8,2006,5,29,,,,,,,,,,,,,,,,,,,,,,,, 829 | 828,9,2006,5,29,,,,,,,,,,,,,,,,,,,,,,,, 830 | 829,10,2006,5,29,,,,,,,,,,,,,,,,,,,,,,,, 831 | 830,11,2006,5,29,,,,,,,,,,,,,,,,,,,,,,,, 832 | 831,12,2006,5,29,,,,,,,,,,,,,,,,,,,,,,,, 833 | 832,13,2006,5,29,,,,,,,,,,,,,,,,,,,,,,,, 834 | 833,14,2006,5,29,,,,,,,,,,,,,,,,,,,,,,,, 835 | 834,15,2006,5,29,,,,,,,,,,,,,,,,,,,,,,,, 836 | 835,16,2006,5,29,,,,,,,,,,,,,,,,,,,,,,,, 837 | 836,17,2006,5,29,,,,,,,,,,,,,,,,,,,,,,,, 838 | 837,18,2006,5,29,,,,,,,,,,,,,,,,,,,,,,,, 839 | 838,19,2006,5,29,,,,,,,,,,,,,,,,,,,,,,,, 840 | 839,20,2006,5,29,,,,,,,,,,,,,,,,,,,,,,,, 841 | 840,21,2006,5,29,,,,,,,,,,,,,,,,,,,,,,,, 842 | 841,1,2006,5,30,,,,,,,,,,,,,,,,,,,,,,,, 843 | 842,2,2006,5,30,,,,,,,,,,,,,,,,,,,,,,,, 844 | 843,3,2006,5,30,,,,,,,,,,,,,,,,,,,,,,,, 845 | 844,4,2006,5,30,,,,,,,,,,,,,,,,,,,,,,,, 846 | 845,5,2006,5,30,,,,,,,,,,,,,,,,,,,,,,,, 847 | 846,6,2006,5,30,,,,,,,,,,,,,,,,,,,,,,,, 848 | 847,7,2006,5,30,,,,,,,,,,,,,,,,,,,,,,,, 849 | 848,8,2006,5,30,,,,,,,,,,,,,,,,,,,,,,,, 850 | 849,9,2006,5,30,,,,,,,,,,,,,,,,,,,,,,,, 851 | 850,10,2006,5,30,,,,,,,,,,,,,,,,,,,,,,,, 852 | 851,11,2006,5,30,,,,,,,,,,,,,,,,,,,,,,,, 853 | 852,12,2006,5,30,,,,,,,,,,,,,,,,,,,,,,,, 854 | 853,13,2006,5,30,,,,,,,,,,,,,,,,,,,,,,,, 855 | 854,14,2006,5,30,,,,,,,,,,,,,,,,,,,,,,,, 856 | 855,15,2006,5,30,,,,,,,,,,,,,,,,,,,,,,,, 857 | 856,16,2006,5,30,,,,,,,,,,,,,,,,,,,,,,,, 858 | 857,17,2006,5,30,,,,,,,,,,,,,,,,,,,,,,,, 859 | 858,18,2006,5,30,,,,,,,,,,,,,,,,,,,,,,,, 860 | 859,19,2006,5,30,,,,,,,,,,,,,,,,,,,,,,,, 861 | 860,20,2006,5,30,,,,,,,,,,,,,,,,,,,,,,,, 862 | 861,21,2006,5,30,,,,,,,,,,,,,,,,,,,,,,,, 863 | 862,1,2006,5,31,,,,,,,,,,,,,,,,,,,,,,,, 864 | 863,2,2006,5,31,,,,,,,,,,,,,,,,,,,,,,,, 865 | 864,3,2006,5,31,,,,,,,,,,,,,,,,,,,,,,,, 866 | 865,4,2006,5,31,,,,,,,,,,,,,,,,,,,,,,,, 867 | 866,5,2006,5,31,,,,,,,,,,,,,,,,,,,,,,,, 868 | 867,6,2006,5,31,,,,,,,,,,,,,,,,,,,,,,,, 869 | 868,7,2006,5,31,,,,,,,,,,,,,,,,,,,,,,,, 870 | 869,8,2006,5,31,,,,,,,,,,,,,,,,,,,,,,,, 871 | 870,9,2006,5,31,,,,,,,,,,,,,,,,,,,,,,,, 872 | 871,10,2006,5,31,,,,,,,,,,,,,,,,,,,,,,,, 873 | 872,11,2006,5,31,,,,,,,,,,,,,,,,,,,,,,,, 874 | 873,12,2006,5,31,,,,,,,,,,,,,,,,,,,,,,,, 875 | 874,13,2006,5,31,,,,,,,,,,,,,,,,,,,,,,,, 876 | 875,14,2006,5,31,,,,,,,,,,,,,,,,,,,,,,,, 877 | 876,15,2006,5,31,,,,,,,,,,,,,,,,,,,,,,,, 878 | 877,16,2006,5,31,,,,,,,,,,,,,,,,,,,,,,,, 879 | 878,17,2006,5,31,,,,,,,,,,,,,,,,,,,,,,,, 880 | 879,18,2006,5,31,,,,,,,,,,,,,,,,,,,,,,,, 881 | 880,19,2006,5,31,,,,,,,,,,,,,,,,,,,,,,,, 882 | 881,20,2006,5,31,,,,,,,,,,,,,,,,,,,,,,,, 883 | 882,21,2006,5,31,,,,,,,,,,,,,,,,,,,,,,,, 884 | 883,1,2006,8,2,,,,,,,,,,,,,,,,,,,,,,,, 885 | 884,2,2006,8,2,,,,,,,,,,,,,,,,,,,,,,,, 886 | 885,3,2006,8,2,,,,,,,,,,,,,,,,,,,,,,,, 887 | 886,4,2006,8,2,,,,,,,,,,,,,,,,,,,,,,,, 888 | 887,5,2006,8,2,,,,,,,,,,,,,,,,,,,,,,,, 889 | 888,6,2006,8,2,,,,,,,,,,,,,,,,,,,,,,,, 890 | 889,7,2006,8,2,,,,,,,,,,,,,,,,,,,,,,,, 891 | 890,8,2006,8,2,,,,,,,,,,,,,,,,,,,,,,,, 892 | 891,9,2006,8,2,,,,,,,,,,,,,,,,,,,,,,,, 893 | 892,10,2006,8,2,,,,,,,,,,,,,,,,,,,,,,,, 894 | 893,11,2006,8,2,,,,,,,,,,,,,,,,,,,,,,,, 895 | 894,12,2006,8,2,,,,,,,,,,,,,,,,,,,,,,,, 896 | 895,13,2006,8,2,,,,,,,,,,,,,,,,,,,,,,,, 897 | 896,14,2006,8,2,,,,,,,,,,,,,,,,,,,,,,,, 898 | 897,15,2006,8,2,,,,,,,,,,,,,,,,,,,,,,,, 899 | 898,16,2006,8,2,,,,,,,,,,,,,,,,,,,,,,,, 900 | 899,17,2006,8,2,,,,,,,,,,,,,,,,,,,,,,,, 901 | 900,18,2006,8,2,,,,,,,,,,,,,,,,,,,,,,,, 902 | 901,19,2006,8,2,,,,,,,,,,,,,,,,,,,,,,,, 903 | 902,20,2006,8,2,,,,,,,,,,,,,,,,,,,,,,,, 904 | 903,21,2006,8,2,,,,,,,,,,,,,,,,,,,,,,,, 905 | 904,1,2006,8,3,,,,,,,,,,,,,,,,,,,,,,,, 906 | 905,2,2006,8,3,,,,,,,,,,,,,,,,,,,,,,,, 907 | 906,3,2006,8,3,,,,,,,,,,,,,,,,,,,,,,,, 908 | 907,4,2006,8,3,,,,,,,,,,,,,,,,,,,,,,,, 909 | 908,5,2006,8,3,,,,,,,,,,,,,,,,,,,,,,,, 910 | 909,6,2006,8,3,,,,,,,,,,,,,,,,,,,,,,,, 911 | 910,7,2006,8,3,,,,,,,,,,,,,,,,,,,,,,,, 912 | 911,8,2006,8,3,,,,,,,,,,,,,,,,,,,,,,,, 913 | 912,9,2006,8,3,,,,,,,,,,,,,,,,,,,,,,,, 914 | 913,10,2006,8,3,,,,,,,,,,,,,,,,,,,,,,,, 915 | 914,11,2006,8,3,,,,,,,,,,,,,,,,,,,,,,,, 916 | 915,12,2006,8,3,,,,,,,,,,,,,,,,,,,,,,,, 917 | 916,13,2006,8,3,,,,,,,,,,,,,,,,,,,,,,,, 918 | 917,14,2006,8,3,,,,,,,,,,,,,,,,,,,,,,,, 919 | 918,15,2006,8,3,,,,,,,,,,,,,,,,,,,,,,,, 920 | 919,16,2006,8,3,,,,,,,,,,,,,,,,,,,,,,,, 921 | 920,17,2006,8,3,,,,,,,,,,,,,,,,,,,,,,,, 922 | 921,18,2006,8,3,,,,,,,,,,,,,,,,,,,,,,,, 923 | 922,19,2006,8,3,,,,,,,,,,,,,,,,,,,,,,,, 924 | 923,20,2006,8,3,,,,,,,,,,,,,,,,,,,,,,,, 925 | 924,21,2006,8,3,,,,,,,,,,,,,,,,,,,,,,,, 926 | 925,1,2006,8,4,,,,,,,,,,,,,,,,,,,,,,,, 927 | 926,2,2006,8,4,,,,,,,,,,,,,,,,,,,,,,,, 928 | 927,3,2006,8,4,,,,,,,,,,,,,,,,,,,,,,,, 929 | 928,4,2006,8,4,,,,,,,,,,,,,,,,,,,,,,,, 930 | 929,5,2006,8,4,,,,,,,,,,,,,,,,,,,,,,,, 931 | 930,6,2006,8,4,,,,,,,,,,,,,,,,,,,,,,,, 932 | 931,7,2006,8,4,,,,,,,,,,,,,,,,,,,,,,,, 933 | 932,8,2006,8,4,,,,,,,,,,,,,,,,,,,,,,,, 934 | 933,9,2006,8,4,,,,,,,,,,,,,,,,,,,,,,,, 935 | 934,10,2006,8,4,,,,,,,,,,,,,,,,,,,,,,,, 936 | 935,11,2006,8,4,,,,,,,,,,,,,,,,,,,,,,,, 937 | 936,12,2006,8,4,,,,,,,,,,,,,,,,,,,,,,,, 938 | 937,13,2006,8,4,,,,,,,,,,,,,,,,,,,,,,,, 939 | 938,14,2006,8,4,,,,,,,,,,,,,,,,,,,,,,,, 940 | 939,15,2006,8,4,,,,,,,,,,,,,,,,,,,,,,,, 941 | 940,16,2006,8,4,,,,,,,,,,,,,,,,,,,,,,,, 942 | 941,17,2006,8,4,,,,,,,,,,,,,,,,,,,,,,,, 943 | 942,18,2006,8,4,,,,,,,,,,,,,,,,,,,,,,,, 944 | 943,19,2006,8,4,,,,,,,,,,,,,,,,,,,,,,,, 945 | 944,20,2006,8,4,,,,,,,,,,,,,,,,,,,,,,,, 946 | 945,21,2006,8,4,,,,,,,,,,,,,,,,,,,,,,,, 947 | 946,1,2006,8,5,,,,,,,,,,,,,,,,,,,,,,,, 948 | 947,2,2006,8,5,,,,,,,,,,,,,,,,,,,,,,,, 949 | 948,3,2006,8,5,,,,,,,,,,,,,,,,,,,,,,,, 950 | 949,4,2006,8,5,,,,,,,,,,,,,,,,,,,,,,,, 951 | 950,5,2006,8,5,,,,,,,,,,,,,,,,,,,,,,,, 952 | 951,6,2006,8,5,,,,,,,,,,,,,,,,,,,,,,,, 953 | 952,7,2006,8,5,,,,,,,,,,,,,,,,,,,,,,,, 954 | 953,8,2006,8,5,,,,,,,,,,,,,,,,,,,,,,,, 955 | 954,9,2006,8,5,,,,,,,,,,,,,,,,,,,,,,,, 956 | 955,10,2006,8,5,,,,,,,,,,,,,,,,,,,,,,,, 957 | 956,11,2006,8,5,,,,,,,,,,,,,,,,,,,,,,,, 958 | 957,12,2006,8,5,,,,,,,,,,,,,,,,,,,,,,,, 959 | 958,13,2006,8,5,,,,,,,,,,,,,,,,,,,,,,,, 960 | 959,14,2006,8,5,,,,,,,,,,,,,,,,,,,,,,,, 961 | 960,15,2006,8,5,,,,,,,,,,,,,,,,,,,,,,,, 962 | 961,16,2006,8,5,,,,,,,,,,,,,,,,,,,,,,,, 963 | 962,17,2006,8,5,,,,,,,,,,,,,,,,,,,,,,,, 964 | 963,18,2006,8,5,,,,,,,,,,,,,,,,,,,,,,,, 965 | 964,19,2006,8,5,,,,,,,,,,,,,,,,,,,,,,,, 966 | 965,20,2006,8,5,,,,,,,,,,,,,,,,,,,,,,,, 967 | 966,21,2006,8,5,,,,,,,,,,,,,,,,,,,,,,,, 968 | 967,1,2006,8,6,,,,,,,,,,,,,,,,,,,,,,,, 969 | 968,2,2006,8,6,,,,,,,,,,,,,,,,,,,,,,,, 970 | 969,3,2006,8,6,,,,,,,,,,,,,,,,,,,,,,,, 971 | 970,4,2006,8,6,,,,,,,,,,,,,,,,,,,,,,,, 972 | 971,5,2006,8,6,,,,,,,,,,,,,,,,,,,,,,,, 973 | 972,6,2006,8,6,,,,,,,,,,,,,,,,,,,,,,,, 974 | 973,7,2006,8,6,,,,,,,,,,,,,,,,,,,,,,,, 975 | 974,8,2006,8,6,,,,,,,,,,,,,,,,,,,,,,,, 976 | 975,9,2006,8,6,,,,,,,,,,,,,,,,,,,,,,,, 977 | 976,10,2006,8,6,,,,,,,,,,,,,,,,,,,,,,,, 978 | 977,11,2006,8,6,,,,,,,,,,,,,,,,,,,,,,,, 979 | 978,12,2006,8,6,,,,,,,,,,,,,,,,,,,,,,,, 980 | 979,13,2006,8,6,,,,,,,,,,,,,,,,,,,,,,,, 981 | 980,14,2006,8,6,,,,,,,,,,,,,,,,,,,,,,,, 982 | 981,15,2006,8,6,,,,,,,,,,,,,,,,,,,,,,,, 983 | 982,16,2006,8,6,,,,,,,,,,,,,,,,,,,,,,,, 984 | 983,17,2006,8,6,,,,,,,,,,,,,,,,,,,,,,,, 985 | 984,18,2006,8,6,,,,,,,,,,,,,,,,,,,,,,,, 986 | 985,19,2006,8,6,,,,,,,,,,,,,,,,,,,,,,,, 987 | 986,20,2006,8,6,,,,,,,,,,,,,,,,,,,,,,,, 988 | 987,21,2006,8,6,,,,,,,,,,,,,,,,,,,,,,,, 989 | 988,1,2006,8,7,,,,,,,,,,,,,,,,,,,,,,,, 990 | 989,2,2006,8,7,,,,,,,,,,,,,,,,,,,,,,,, 991 | 990,3,2006,8,7,,,,,,,,,,,,,,,,,,,,,,,, 992 | 991,4,2006,8,7,,,,,,,,,,,,,,,,,,,,,,,, 993 | 992,5,2006,8,7,,,,,,,,,,,,,,,,,,,,,,,, 994 | 993,6,2006,8,7,,,,,,,,,,,,,,,,,,,,,,,, 995 | 994,7,2006,8,7,,,,,,,,,,,,,,,,,,,,,,,, 996 | 995,8,2006,8,7,,,,,,,,,,,,,,,,,,,,,,,, 997 | 996,9,2006,8,7,,,,,,,,,,,,,,,,,,,,,,,, 998 | 997,10,2006,8,7,,,,,,,,,,,,,,,,,,,,,,,, 999 | 998,11,2006,8,7,,,,,,,,,,,,,,,,,,,,,,,, 1000 | 999,12,2006,8,7,,,,,,,,,,,,,,,,,,,,,,,, 1001 | 1000,13,2006,8,7,,,,,,,,,,,,,,,,,,,,,,,, 1002 | 1001,14,2006,8,7,,,,,,,,,,,,,,,,,,,,,,,, 1003 | 1002,15,2006,8,7,,,,,,,,,,,,,,,,,,,,,,,, 1004 | 1003,16,2006,8,7,,,,,,,,,,,,,,,,,,,,,,,, 1005 | 1004,17,2006,8,7,,,,,,,,,,,,,,,,,,,,,,,, 1006 | 1005,18,2006,8,7,,,,,,,,,,,,,,,,,,,,,,,, 1007 | 1006,19,2006,8,7,,,,,,,,,,,,,,,,,,,,,,,, 1008 | 1007,20,2006,8,7,,,,,,,,,,,,,,,,,,,,,,,, 1009 | 1008,21,2006,8,7,,,,,,,,,,,,,,,,,,,,,,,, 1010 | 1009,1,2006,8,8,,,,,,,,,,,,,,,,,,,,,,,, 1011 | 1010,2,2006,8,8,,,,,,,,,,,,,,,,,,,,,,,, 1012 | 1011,3,2006,8,8,,,,,,,,,,,,,,,,,,,,,,,, 1013 | 1012,4,2006,8,8,,,,,,,,,,,,,,,,,,,,,,,, 1014 | 1013,5,2006,8,8,,,,,,,,,,,,,,,,,,,,,,,, 1015 | 1014,6,2006,8,8,,,,,,,,,,,,,,,,,,,,,,,, 1016 | 1015,7,2006,8,8,,,,,,,,,,,,,,,,,,,,,,,, 1017 | 1016,8,2006,8,8,,,,,,,,,,,,,,,,,,,,,,,, 1018 | 1017,9,2006,8,8,,,,,,,,,,,,,,,,,,,,,,,, 1019 | 1018,10,2006,8,8,,,,,,,,,,,,,,,,,,,,,,,, 1020 | 1019,11,2006,8,8,,,,,,,,,,,,,,,,,,,,,,,, 1021 | 1020,12,2006,8,8,,,,,,,,,,,,,,,,,,,,,,,, 1022 | 1021,13,2006,8,8,,,,,,,,,,,,,,,,,,,,,,,, 1023 | 1022,14,2006,8,8,,,,,,,,,,,,,,,,,,,,,,,, 1024 | 1023,15,2006,8,8,,,,,,,,,,,,,,,,,,,,,,,, 1025 | 1024,16,2006,8,8,,,,,,,,,,,,,,,,,,,,,,,, 1026 | 1025,17,2006,8,8,,,,,,,,,,,,,,,,,,,,,,,, 1027 | 1026,18,2006,8,8,,,,,,,,,,,,,,,,,,,,,,,, 1028 | 1027,19,2006,8,8,,,,,,,,,,,,,,,,,,,,,,,, 1029 | 1028,20,2006,8,8,,,,,,,,,,,,,,,,,,,,,,,, 1030 | 1029,21,2006,8,8,,,,,,,,,,,,,,,,,,,,,,,, 1031 | 1030,1,2006,11,22,,,,,,,,,,,,,,,,,,,,,,,, 1032 | 1031,2,2006,11,22,,,,,,,,,,,,,,,,,,,,,,,, 1033 | 1032,3,2006,11,22,,,,,,,,,,,,,,,,,,,,,,,, 1034 | 1033,4,2006,11,22,,,,,,,,,,,,,,,,,,,,,,,, 1035 | 1034,5,2006,11,22,,,,,,,,,,,,,,,,,,,,,,,, 1036 | 1035,6,2006,11,22,,,,,,,,,,,,,,,,,,,,,,,, 1037 | 1036,7,2006,11,22,,,,,,,,,,,,,,,,,,,,,,,, 1038 | 1037,8,2006,11,22,,,,,,,,,,,,,,,,,,,,,,,, 1039 | 1038,9,2006,11,22,,,,,,,,,,,,,,,,,,,,,,,, 1040 | 1039,10,2006,11,22,,,,,,,,,,,,,,,,,,,,,,,, 1041 | 1040,11,2006,11,22,,,,,,,,,,,,,,,,,,,,,,,, 1042 | 1041,12,2006,11,22,,,,,,,,,,,,,,,,,,,,,,,, 1043 | 1042,13,2006,11,22,,,,,,,,,,,,,,,,,,,,,,,, 1044 | 1043,14,2006,11,22,,,,,,,,,,,,,,,,,,,,,,,, 1045 | 1044,15,2006,11,22,,,,,,,,,,,,,,,,,,,,,,,, 1046 | 1045,16,2006,11,22,,,,,,,,,,,,,,,,,,,,,,,, 1047 | 1046,17,2006,11,22,,,,,,,,,,,,,,,,,,,,,,,, 1048 | 1047,18,2006,11,22,,,,,,,,,,,,,,,,,,,,,,,, 1049 | 1048,19,2006,11,22,,,,,,,,,,,,,,,,,,,,,,,, 1050 | 1049,20,2006,11,22,,,,,,,,,,,,,,,,,,,,,,,, 1051 | 1050,21,2006,11,22,,,,,,,,,,,,,,,,,,,,,,,, 1052 | 1051,1,2006,11,23,,,,,,,,,,,,,,,,,,,,,,,, 1053 | 1052,2,2006,11,23,,,,,,,,,,,,,,,,,,,,,,,, 1054 | 1053,3,2006,11,23,,,,,,,,,,,,,,,,,,,,,,,, 1055 | 1054,4,2006,11,23,,,,,,,,,,,,,,,,,,,,,,,, 1056 | 1055,5,2006,11,23,,,,,,,,,,,,,,,,,,,,,,,, 1057 | 1056,6,2006,11,23,,,,,,,,,,,,,,,,,,,,,,,, 1058 | 1057,7,2006,11,23,,,,,,,,,,,,,,,,,,,,,,,, 1059 | 1058,8,2006,11,23,,,,,,,,,,,,,,,,,,,,,,,, 1060 | 1059,9,2006,11,23,,,,,,,,,,,,,,,,,,,,,,,, 1061 | 1060,10,2006,11,23,,,,,,,,,,,,,,,,,,,,,,,, 1062 | 1061,11,2006,11,23,,,,,,,,,,,,,,,,,,,,,,,, 1063 | 1062,12,2006,11,23,,,,,,,,,,,,,,,,,,,,,,,, 1064 | 1063,13,2006,11,23,,,,,,,,,,,,,,,,,,,,,,,, 1065 | 1064,14,2006,11,23,,,,,,,,,,,,,,,,,,,,,,,, 1066 | 1065,15,2006,11,23,,,,,,,,,,,,,,,,,,,,,,,, 1067 | 1066,16,2006,11,23,,,,,,,,,,,,,,,,,,,,,,,, 1068 | 1067,17,2006,11,23,,,,,,,,,,,,,,,,,,,,,,,, 1069 | 1068,18,2006,11,23,,,,,,,,,,,,,,,,,,,,,,,, 1070 | 1069,19,2006,11,23,,,,,,,,,,,,,,,,,,,,,,,, 1071 | 1070,20,2006,11,23,,,,,,,,,,,,,,,,,,,,,,,, 1072 | 1071,21,2006,11,23,,,,,,,,,,,,,,,,,,,,,,,, 1073 | 1072,1,2006,11,24,,,,,,,,,,,,,,,,,,,,,,,, 1074 | 1073,2,2006,11,24,,,,,,,,,,,,,,,,,,,,,,,, 1075 | 1074,3,2006,11,24,,,,,,,,,,,,,,,,,,,,,,,, 1076 | 1075,4,2006,11,24,,,,,,,,,,,,,,,,,,,,,,,, 1077 | 1076,5,2006,11,24,,,,,,,,,,,,,,,,,,,,,,,, 1078 | 1077,6,2006,11,24,,,,,,,,,,,,,,,,,,,,,,,, 1079 | 1078,7,2006,11,24,,,,,,,,,,,,,,,,,,,,,,,, 1080 | 1079,8,2006,11,24,,,,,,,,,,,,,,,,,,,,,,,, 1081 | 1080,9,2006,11,24,,,,,,,,,,,,,,,,,,,,,,,, 1082 | 1081,10,2006,11,24,,,,,,,,,,,,,,,,,,,,,,,, 1083 | 1082,11,2006,11,24,,,,,,,,,,,,,,,,,,,,,,,, 1084 | 1083,12,2006,11,24,,,,,,,,,,,,,,,,,,,,,,,, 1085 | 1084,13,2006,11,24,,,,,,,,,,,,,,,,,,,,,,,, 1086 | 1085,14,2006,11,24,,,,,,,,,,,,,,,,,,,,,,,, 1087 | 1086,15,2006,11,24,,,,,,,,,,,,,,,,,,,,,,,, 1088 | 1087,16,2006,11,24,,,,,,,,,,,,,,,,,,,,,,,, 1089 | 1088,17,2006,11,24,,,,,,,,,,,,,,,,,,,,,,,, 1090 | 1089,18,2006,11,24,,,,,,,,,,,,,,,,,,,,,,,, 1091 | 1090,19,2006,11,24,,,,,,,,,,,,,,,,,,,,,,,, 1092 | 1091,20,2006,11,24,,,,,,,,,,,,,,,,,,,,,,,, 1093 | 1092,21,2006,11,24,,,,,,,,,,,,,,,,,,,,,,,, 1094 | 1093,1,2006,11,25,,,,,,,,,,,,,,,,,,,,,,,, 1095 | 1094,2,2006,11,25,,,,,,,,,,,,,,,,,,,,,,,, 1096 | 1095,3,2006,11,25,,,,,,,,,,,,,,,,,,,,,,,, 1097 | 1096,4,2006,11,25,,,,,,,,,,,,,,,,,,,,,,,, 1098 | 1097,5,2006,11,25,,,,,,,,,,,,,,,,,,,,,,,, 1099 | 1098,6,2006,11,25,,,,,,,,,,,,,,,,,,,,,,,, 1100 | 1099,7,2006,11,25,,,,,,,,,,,,,,,,,,,,,,,, 1101 | 1100,8,2006,11,25,,,,,,,,,,,,,,,,,,,,,,,, 1102 | 1101,9,2006,11,25,,,,,,,,,,,,,,,,,,,,,,,, 1103 | 1102,10,2006,11,25,,,,,,,,,,,,,,,,,,,,,,,, 1104 | 1103,11,2006,11,25,,,,,,,,,,,,,,,,,,,,,,,, 1105 | 1104,12,2006,11,25,,,,,,,,,,,,,,,,,,,,,,,, 1106 | 1105,13,2006,11,25,,,,,,,,,,,,,,,,,,,,,,,, 1107 | 1106,14,2006,11,25,,,,,,,,,,,,,,,,,,,,,,,, 1108 | 1107,15,2006,11,25,,,,,,,,,,,,,,,,,,,,,,,, 1109 | 1108,16,2006,11,25,,,,,,,,,,,,,,,,,,,,,,,, 1110 | 1109,17,2006,11,25,,,,,,,,,,,,,,,,,,,,,,,, 1111 | 1110,18,2006,11,25,,,,,,,,,,,,,,,,,,,,,,,, 1112 | 1111,19,2006,11,25,,,,,,,,,,,,,,,,,,,,,,,, 1113 | 1112,20,2006,11,25,,,,,,,,,,,,,,,,,,,,,,,, 1114 | 1113,21,2006,11,25,,,,,,,,,,,,,,,,,,,,,,,, 1115 | 1114,1,2006,11,26,,,,,,,,,,,,,,,,,,,,,,,, 1116 | 1115,2,2006,11,26,,,,,,,,,,,,,,,,,,,,,,,, 1117 | 1116,3,2006,11,26,,,,,,,,,,,,,,,,,,,,,,,, 1118 | 1117,4,2006,11,26,,,,,,,,,,,,,,,,,,,,,,,, 1119 | 1118,5,2006,11,26,,,,,,,,,,,,,,,,,,,,,,,, 1120 | 1119,6,2006,11,26,,,,,,,,,,,,,,,,,,,,,,,, 1121 | 1120,7,2006,11,26,,,,,,,,,,,,,,,,,,,,,,,, 1122 | 1121,8,2006,11,26,,,,,,,,,,,,,,,,,,,,,,,, 1123 | 1122,9,2006,11,26,,,,,,,,,,,,,,,,,,,,,,,, 1124 | 1123,10,2006,11,26,,,,,,,,,,,,,,,,,,,,,,,, 1125 | 1124,11,2006,11,26,,,,,,,,,,,,,,,,,,,,,,,, 1126 | 1125,12,2006,11,26,,,,,,,,,,,,,,,,,,,,,,,, 1127 | 1126,13,2006,11,26,,,,,,,,,,,,,,,,,,,,,,,, 1128 | 1127,14,2006,11,26,,,,,,,,,,,,,,,,,,,,,,,, 1129 | 1128,15,2006,11,26,,,,,,,,,,,,,,,,,,,,,,,, 1130 | 1129,16,2006,11,26,,,,,,,,,,,,,,,,,,,,,,,, 1131 | 1130,17,2006,11,26,,,,,,,,,,,,,,,,,,,,,,,, 1132 | 1131,18,2006,11,26,,,,,,,,,,,,,,,,,,,,,,,, 1133 | 1132,19,2006,11,26,,,,,,,,,,,,,,,,,,,,,,,, 1134 | 1133,20,2006,11,26,,,,,,,,,,,,,,,,,,,,,,,, 1135 | 1134,21,2006,11,26,,,,,,,,,,,,,,,,,,,,,,,, 1136 | 1135,1,2006,11,27,,,,,,,,,,,,,,,,,,,,,,,, 1137 | 1136,2,2006,11,27,,,,,,,,,,,,,,,,,,,,,,,, 1138 | 1137,3,2006,11,27,,,,,,,,,,,,,,,,,,,,,,,, 1139 | 1138,4,2006,11,27,,,,,,,,,,,,,,,,,,,,,,,, 1140 | 1139,5,2006,11,27,,,,,,,,,,,,,,,,,,,,,,,, 1141 | 1140,6,2006,11,27,,,,,,,,,,,,,,,,,,,,,,,, 1142 | 1141,7,2006,11,27,,,,,,,,,,,,,,,,,,,,,,,, 1143 | 1142,8,2006,11,27,,,,,,,,,,,,,,,,,,,,,,,, 1144 | 1143,9,2006,11,27,,,,,,,,,,,,,,,,,,,,,,,, 1145 | 1144,10,2006,11,27,,,,,,,,,,,,,,,,,,,,,,,, 1146 | 1145,11,2006,11,27,,,,,,,,,,,,,,,,,,,,,,,, 1147 | 1146,12,2006,11,27,,,,,,,,,,,,,,,,,,,,,,,, 1148 | 1147,13,2006,11,27,,,,,,,,,,,,,,,,,,,,,,,, 1149 | 1148,14,2006,11,27,,,,,,,,,,,,,,,,,,,,,,,, 1150 | 1149,15,2006,11,27,,,,,,,,,,,,,,,,,,,,,,,, 1151 | 1150,16,2006,11,27,,,,,,,,,,,,,,,,,,,,,,,, 1152 | 1151,17,2006,11,27,,,,,,,,,,,,,,,,,,,,,,,, 1153 | 1152,18,2006,11,27,,,,,,,,,,,,,,,,,,,,,,,, 1154 | 1153,19,2006,11,27,,,,,,,,,,,,,,,,,,,,,,,, 1155 | 1154,20,2006,11,27,,,,,,,,,,,,,,,,,,,,,,,, 1156 | 1155,21,2006,11,27,,,,,,,,,,,,,,,,,,,,,,,, 1157 | 1156,1,2006,11,28,,,,,,,,,,,,,,,,,,,,,,,, 1158 | 1157,2,2006,11,28,,,,,,,,,,,,,,,,,,,,,,,, 1159 | 1158,3,2006,11,28,,,,,,,,,,,,,,,,,,,,,,,, 1160 | 1159,4,2006,11,28,,,,,,,,,,,,,,,,,,,,,,,, 1161 | 1160,5,2006,11,28,,,,,,,,,,,,,,,,,,,,,,,, 1162 | 1161,6,2006,11,28,,,,,,,,,,,,,,,,,,,,,,,, 1163 | 1162,7,2006,11,28,,,,,,,,,,,,,,,,,,,,,,,, 1164 | 1163,8,2006,11,28,,,,,,,,,,,,,,,,,,,,,,,, 1165 | 1164,9,2006,11,28,,,,,,,,,,,,,,,,,,,,,,,, 1166 | 1165,10,2006,11,28,,,,,,,,,,,,,,,,,,,,,,,, 1167 | 1166,11,2006,11,28,,,,,,,,,,,,,,,,,,,,,,,, 1168 | 1167,12,2006,11,28,,,,,,,,,,,,,,,,,,,,,,,, 1169 | 1168,13,2006,11,28,,,,,,,,,,,,,,,,,,,,,,,, 1170 | 1169,14,2006,11,28,,,,,,,,,,,,,,,,,,,,,,,, 1171 | 1170,15,2006,11,28,,,,,,,,,,,,,,,,,,,,,,,, 1172 | 1171,16,2006,11,28,,,,,,,,,,,,,,,,,,,,,,,, 1173 | 1172,17,2006,11,28,,,,,,,,,,,,,,,,,,,,,,,, 1174 | 1173,18,2006,11,28,,,,,,,,,,,,,,,,,,,,,,,, 1175 | 1174,19,2006,11,28,,,,,,,,,,,,,,,,,,,,,,,, 1176 | 1175,20,2006,11,28,,,,,,,,,,,,,,,,,,,,,,,, 1177 | 1176,21,2006,11,28,,,,,,,,,,,,,,,,,,,,,,,, 1178 | 1177,1,2008,7,1,,,,,,,,,,,,,,,,,,,,,,,, 1179 | 1178,2,2008,7,1,,,,,,,,,,,,,,,,,,,,,,,, 1180 | 1179,3,2008,7,1,,,,,,,,,,,,,,,,,,,,,,,, 1181 | 1180,4,2008,7,1,,,,,,,,,,,,,,,,,,,,,,,, 1182 | 1181,5,2008,7,1,,,,,,,,,,,,,,,,,,,,,,,, 1183 | 1182,6,2008,7,1,,,,,,,,,,,,,,,,,,,,,,,, 1184 | 1183,7,2008,7,1,,,,,,,,,,,,,,,,,,,,,,,, 1185 | 1184,8,2008,7,1,,,,,,,,,,,,,,,,,,,,,,,, 1186 | 1185,9,2008,7,1,,,,,,,,,,,,,,,,,,,,,,,, 1187 | 1186,10,2008,7,1,,,,,,,,,,,,,,,,,,,,,,,, 1188 | 1187,11,2008,7,1,,,,,,,,,,,,,,,,,,,,,,,, 1189 | 1188,12,2008,7,1,,,,,,,,,,,,,,,,,,,,,,,, 1190 | 1189,13,2008,7,1,,,,,,,,,,,,,,,,,,,,,,,, 1191 | 1190,14,2008,7,1,,,,,,,,,,,,,,,,,,,,,,,, 1192 | 1191,15,2008,7,1,,,,,,,,,,,,,,,,,,,,,,,, 1193 | 1192,16,2008,7,1,,,,,,,,,,,,,,,,,,,,,,,, 1194 | 1193,17,2008,7,1,,,,,,,,,,,,,,,,,,,,,,,, 1195 | 1194,18,2008,7,1,,,,,,,,,,,,,,,,,,,,,,,, 1196 | 1195,19,2008,7,1,,,,,,,,,,,,,,,,,,,,,,,, 1197 | 1196,20,2008,7,1,,,,,,,,,,,,,,,,,,,,,,,, 1198 | 1197,21,2008,7,1,,,,,,,,,,,,,,,,,,,,,,,, 1199 | 1198,1,2008,7,2,,,,,,,,,,,,,,,,,,,,,,,, 1200 | 1199,2,2008,7,2,,,,,,,,,,,,,,,,,,,,,,,, 1201 | 1200,3,2008,7,2,,,,,,,,,,,,,,,,,,,,,,,, 1202 | 1201,4,2008,7,2,,,,,,,,,,,,,,,,,,,,,,,, 1203 | 1202,5,2008,7,2,,,,,,,,,,,,,,,,,,,,,,,, 1204 | 1203,6,2008,7,2,,,,,,,,,,,,,,,,,,,,,,,, 1205 | 1204,7,2008,7,2,,,,,,,,,,,,,,,,,,,,,,,, 1206 | 1205,8,2008,7,2,,,,,,,,,,,,,,,,,,,,,,,, 1207 | 1206,9,2008,7,2,,,,,,,,,,,,,,,,,,,,,,,, 1208 | 1207,10,2008,7,2,,,,,,,,,,,,,,,,,,,,,,,, 1209 | 1208,11,2008,7,2,,,,,,,,,,,,,,,,,,,,,,,, 1210 | 1209,12,2008,7,2,,,,,,,,,,,,,,,,,,,,,,,, 1211 | 1210,13,2008,7,2,,,,,,,,,,,,,,,,,,,,,,,, 1212 | 1211,14,2008,7,2,,,,,,,,,,,,,,,,,,,,,,,, 1213 | 1212,15,2008,7,2,,,,,,,,,,,,,,,,,,,,,,,, 1214 | 1213,16,2008,7,2,,,,,,,,,,,,,,,,,,,,,,,, 1215 | 1214,17,2008,7,2,,,,,,,,,,,,,,,,,,,,,,,, 1216 | 1215,18,2008,7,2,,,,,,,,,,,,,,,,,,,,,,,, 1217 | 1216,19,2008,7,2,,,,,,,,,,,,,,,,,,,,,,,, 1218 | 1217,20,2008,7,2,,,,,,,,,,,,,,,,,,,,,,,, 1219 | 1218,21,2008,7,2,,,,,,,,,,,,,,,,,,,,,,,, 1220 | 1219,1,2008,7,3,,,,,,,,,,,,,,,,,,,,,,,, 1221 | 1220,2,2008,7,3,,,,,,,,,,,,,,,,,,,,,,,, 1222 | 1221,3,2008,7,3,,,,,,,,,,,,,,,,,,,,,,,, 1223 | 1222,4,2008,7,3,,,,,,,,,,,,,,,,,,,,,,,, 1224 | 1223,5,2008,7,3,,,,,,,,,,,,,,,,,,,,,,,, 1225 | 1224,6,2008,7,3,,,,,,,,,,,,,,,,,,,,,,,, 1226 | 1225,7,2008,7,3,,,,,,,,,,,,,,,,,,,,,,,, 1227 | 1226,8,2008,7,3,,,,,,,,,,,,,,,,,,,,,,,, 1228 | 1227,9,2008,7,3,,,,,,,,,,,,,,,,,,,,,,,, 1229 | 1228,10,2008,7,3,,,,,,,,,,,,,,,,,,,,,,,, 1230 | 1229,11,2008,7,3,,,,,,,,,,,,,,,,,,,,,,,, 1231 | 1230,12,2008,7,3,,,,,,,,,,,,,,,,,,,,,,,, 1232 | 1231,13,2008,7,3,,,,,,,,,,,,,,,,,,,,,,,, 1233 | 1232,14,2008,7,3,,,,,,,,,,,,,,,,,,,,,,,, 1234 | 1233,15,2008,7,3,,,,,,,,,,,,,,,,,,,,,,,, 1235 | 1234,16,2008,7,3,,,,,,,,,,,,,,,,,,,,,,,, 1236 | 1235,17,2008,7,3,,,,,,,,,,,,,,,,,,,,,,,, 1237 | 1236,18,2008,7,3,,,,,,,,,,,,,,,,,,,,,,,, 1238 | 1237,19,2008,7,3,,,,,,,,,,,,,,,,,,,,,,,, 1239 | 1238,20,2008,7,3,,,,,,,,,,,,,,,,,,,,,,,, 1240 | 1239,21,2008,7,3,,,,,,,,,,,,,,,,,,,,,,,, 1241 | 1240,1,2008,7,4,,,,,,,,,,,,,,,,,,,,,,,, 1242 | 1241,2,2008,7,4,,,,,,,,,,,,,,,,,,,,,,,, 1243 | 1242,3,2008,7,4,,,,,,,,,,,,,,,,,,,,,,,, 1244 | 1243,4,2008,7,4,,,,,,,,,,,,,,,,,,,,,,,, 1245 | 1244,5,2008,7,4,,,,,,,,,,,,,,,,,,,,,,,, 1246 | 1245,6,2008,7,4,,,,,,,,,,,,,,,,,,,,,,,, 1247 | 1246,7,2008,7,4,,,,,,,,,,,,,,,,,,,,,,,, 1248 | 1247,8,2008,7,4,,,,,,,,,,,,,,,,,,,,,,,, 1249 | 1248,9,2008,7,4,,,,,,,,,,,,,,,,,,,,,,,, 1250 | 1249,10,2008,7,4,,,,,,,,,,,,,,,,,,,,,,,, 1251 | 1250,11,2008,7,4,,,,,,,,,,,,,,,,,,,,,,,, 1252 | 1251,12,2008,7,4,,,,,,,,,,,,,,,,,,,,,,,, 1253 | 1252,13,2008,7,4,,,,,,,,,,,,,,,,,,,,,,,, 1254 | 1253,14,2008,7,4,,,,,,,,,,,,,,,,,,,,,,,, 1255 | 1254,15,2008,7,4,,,,,,,,,,,,,,,,,,,,,,,, 1256 | 1255,16,2008,7,4,,,,,,,,,,,,,,,,,,,,,,,, 1257 | 1256,17,2008,7,4,,,,,,,,,,,,,,,,,,,,,,,, 1258 | 1257,18,2008,7,4,,,,,,,,,,,,,,,,,,,,,,,, 1259 | 1258,19,2008,7,4,,,,,,,,,,,,,,,,,,,,,,,, 1260 | 1259,20,2008,7,4,,,,,,,,,,,,,,,,,,,,,,,, 1261 | 1260,21,2008,7,4,,,,,,,,,,,,,,,,,,,,,,,, 1262 | 1261,1,2008,7,5,,,,,,,,,,,,,,,,,,,,,,,, 1263 | 1262,2,2008,7,5,,,,,,,,,,,,,,,,,,,,,,,, 1264 | 1263,3,2008,7,5,,,,,,,,,,,,,,,,,,,,,,,, 1265 | 1264,4,2008,7,5,,,,,,,,,,,,,,,,,,,,,,,, 1266 | 1265,5,2008,7,5,,,,,,,,,,,,,,,,,,,,,,,, 1267 | 1266,6,2008,7,5,,,,,,,,,,,,,,,,,,,,,,,, 1268 | 1267,7,2008,7,5,,,,,,,,,,,,,,,,,,,,,,,, 1269 | 1268,8,2008,7,5,,,,,,,,,,,,,,,,,,,,,,,, 1270 | 1269,9,2008,7,5,,,,,,,,,,,,,,,,,,,,,,,, 1271 | 1270,10,2008,7,5,,,,,,,,,,,,,,,,,,,,,,,, 1272 | 1271,11,2008,7,5,,,,,,,,,,,,,,,,,,,,,,,, 1273 | 1272,12,2008,7,5,,,,,,,,,,,,,,,,,,,,,,,, 1274 | 1273,13,2008,7,5,,,,,,,,,,,,,,,,,,,,,,,, 1275 | 1274,14,2008,7,5,,,,,,,,,,,,,,,,,,,,,,,, 1276 | 1275,15,2008,7,5,,,,,,,,,,,,,,,,,,,,,,,, 1277 | 1276,16,2008,7,5,,,,,,,,,,,,,,,,,,,,,,,, 1278 | 1277,17,2008,7,5,,,,,,,,,,,,,,,,,,,,,,,, 1279 | 1278,18,2008,7,5,,,,,,,,,,,,,,,,,,,,,,,, 1280 | 1279,19,2008,7,5,,,,,,,,,,,,,,,,,,,,,,,, 1281 | 1280,20,2008,7,5,,,,,,,,,,,,,,,,,,,,,,,, 1282 | 1281,21,2008,7,5,,,,,,,,,,,,,,,,,,,,,,,, 1283 | 1282,1,2008,7,6,,,,,,,,,,,,,,,,,,,,,,,, 1284 | 1283,2,2008,7,6,,,,,,,,,,,,,,,,,,,,,,,, 1285 | 1284,3,2008,7,6,,,,,,,,,,,,,,,,,,,,,,,, 1286 | 1285,4,2008,7,6,,,,,,,,,,,,,,,,,,,,,,,, 1287 | 1286,5,2008,7,6,,,,,,,,,,,,,,,,,,,,,,,, 1288 | 1287,6,2008,7,6,,,,,,,,,,,,,,,,,,,,,,,, 1289 | 1288,7,2008,7,6,,,,,,,,,,,,,,,,,,,,,,,, 1290 | 1289,8,2008,7,6,,,,,,,,,,,,,,,,,,,,,,,, 1291 | 1290,9,2008,7,6,,,,,,,,,,,,,,,,,,,,,,,, 1292 | 1291,10,2008,7,6,,,,,,,,,,,,,,,,,,,,,,,, 1293 | 1292,11,2008,7,6,,,,,,,,,,,,,,,,,,,,,,,, 1294 | 1293,12,2008,7,6,,,,,,,,,,,,,,,,,,,,,,,, 1295 | 1294,13,2008,7,6,,,,,,,,,,,,,,,,,,,,,,,, 1296 | 1295,14,2008,7,6,,,,,,,,,,,,,,,,,,,,,,,, 1297 | 1296,15,2008,7,6,,,,,,,,,,,,,,,,,,,,,,,, 1298 | 1297,16,2008,7,6,,,,,,,,,,,,,,,,,,,,,,,, 1299 | 1298,17,2008,7,6,,,,,,,,,,,,,,,,,,,,,,,, 1300 | 1299,18,2008,7,6,,,,,,,,,,,,,,,,,,,,,,,, 1301 | 1300,19,2008,7,6,,,,,,,,,,,,,,,,,,,,,,,, 1302 | 1301,20,2008,7,6,,,,,,,,,,,,,,,,,,,,,,,, 1303 | 1302,21,2008,7,6,,,,,,,,,,,,,,,,,,,,,,,, 1304 | 1303,1,2008,7,7,,,,,,,,,,,,,,,,,,,,,,,, 1305 | 1304,2,2008,7,7,,,,,,,,,,,,,,,,,,,,,,,, 1306 | 1305,3,2008,7,7,,,,,,,,,,,,,,,,,,,,,,,, 1307 | 1306,4,2008,7,7,,,,,,,,,,,,,,,,,,,,,,,, 1308 | 1307,5,2008,7,7,,,,,,,,,,,,,,,,,,,,,,,, 1309 | 1308,6,2008,7,7,,,,,,,,,,,,,,,,,,,,,,,, 1310 | 1309,7,2008,7,7,,,,,,,,,,,,,,,,,,,,,,,, 1311 | 1310,8,2008,7,7,,,,,,,,,,,,,,,,,,,,,,,, 1312 | 1311,9,2008,7,7,,,,,,,,,,,,,,,,,,,,,,,, 1313 | 1312,10,2008,7,7,,,,,,,,,,,,,,,,,,,,,,,, 1314 | 1313,11,2008,7,7,,,,,,,,,,,,,,,,,,,,,,,, 1315 | 1314,12,2008,7,7,,,,,,,,,,,,,,,,,,,,,,,, 1316 | 1315,13,2008,7,7,,,,,,,,,,,,,,,,,,,,,,,, 1317 | 1316,14,2008,7,7,,,,,,,,,,,,,,,,,,,,,,,, 1318 | 1317,15,2008,7,7,,,,,,,,,,,,,,,,,,,,,,,, 1319 | 1318,16,2008,7,7,,,,,,,,,,,,,,,,,,,,,,,, 1320 | 1319,17,2008,7,7,,,,,,,,,,,,,,,,,,,,,,,, 1321 | 1320,18,2008,7,7,,,,,,,,,,,,,,,,,,,,,,,, 1322 | 1321,19,2008,7,7,,,,,,,,,,,,,,,,,,,,,,,, 1323 | 1322,20,2008,7,7,,,,,,,,,,,,,,,,,,,,,,,, 1324 | 1323,21,2008,7,7,,,,,,,,,,,,,,,,,,,,,,,, 1325 | 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103,19,2005,3,10,1 119 | 976,10,2006,8,6,1 120 | 135,9,2005,3,12,1 121 | 567,21,2005,12,30,20 122 | 1303,1,2008,7,7,8 123 | 936,12,2006,8,4,1 124 | 687,15,2006,2,17,1 125 | 1316,14,2008,7,7,8 126 | 892,10,2006,8,2,1 127 | 398,20,2005,9,14,1 128 | 230,20,2005,6,23,1 129 | 354,18,2005,9,12,1 130 | 873,12,2006,5,31,1 131 | 264,12,2005,6,25,1 132 | 274,1,2005,6,26,1 133 | 356,20,2005,9,12,1 134 | 388,10,2005,9,14,1 135 | 686,14,2006,2,17,1 136 | 1267,7,2008,7,5,8 137 | 421,1,2005,9,16,1 138 | 1250,11,2008,7,4,8 139 | 679,7,2006,2,17,1 140 | 716,2,2006,2,19,1 141 | 1090,19,2006,11,24,1 142 | 734,20,2006,2,19,1 143 | 11,11,2005,3,6,1 144 | 1152,18,2006,11,27,1 145 | 78,15,2005,3,9,1 146 | 754,19,2006,5,25,1 147 | 442,1,2005,12,25,1 148 | 963,18,2006,8,5,1 149 | 740,5,2006,5,25,1 150 | 642,12,2006,2,15,1 151 | 498,15,2005,12,27,1 152 | 1027,19,2006,8,8,1 153 | 366,9,2005,9,13,1 154 | 1121,8,2006,11,26,1 155 | 1007,20,2006,8,7,1 156 | 944,20,2006,8,4,1 157 | 509,5,2005,12,28,1 158 | 1293,12,2008,7,6,8 159 | 836,17,2006,5,29,1 160 | 531,6,2005,12,29,1 161 | 259,7,2005,6,25,1 162 | 281,8,2005,6,26,1 163 | 883,1,2006,8,2,1 164 | 44,2,2005,3,8,1 165 | 661,10,2006,2,16,1 166 | 899,17,2006,8,2,1 167 | 728,14,2006,2,19,1 168 | 671,20,2006,2,16,1 169 | 380,2,2005,9,14,1 170 | 731,17,2006,2,19,1 171 | 890,8,2006,8,2,1 172 | 816,18,2006,5,28,1 173 | 302,8,2005,9,10,1 174 | 431,11,2005,9,16,1 175 | 416,17,2005,9,15,1 176 | 87,3,2005,3,10,1 177 | 738,3,2006,5,25,1 178 | 153,6,2005,6,20,1 179 | 126,21,2005,3,11,20 180 | 142,16,2005,3,12,1 181 | 624,15,2006,2,14,1 182 | 791,14,2006,5,27,1 183 | 847,7,2006,5,30,1 184 | 1060,10,2006,11,23,1 185 | 555,9,2005,12,30,1 186 | 601,13,2006,2,13,1 187 | 1252,13,2008,7,4,8 188 | 597,9,2006,2,13,1 189 | 176,8,2005,6,21,1 190 | 1084,13,2006,11,24,1 191 | 927,3,2006,8,4,1 192 | 493,10,2005,12,27,1 193 | 594,6,2006,2,13,1 194 | 347,11,2005,9,12,1 195 | 647,17,2006,2,15,1 196 | 984,18,2006,8,6,1 197 | 1189,13,2008,7,1,8 198 | 48,6,2005,3,8,1 199 | 677,5,2006,2,17,1 200 | 923,20,2006,8,3,1 201 | 234,3,2005,6,24,1 202 | 1116,3,2006,11,26,1 203 | 774,18,2006,5,26,1 204 | 222,12,2005,6,23,1 205 | 1095,3,2006,11,25,1 206 | 1030,1,2006,11,22,1 207 | 29,8,2005,3,7,1 208 | 1277,17,2008,7,5,8 209 | 896,14,2006,8,2,1 210 | 1167,12,2006,11,28,1 211 | 174,6,2005,6,21,1 212 | 744,9,2006,5,25,1 213 | 1081,10,2006,11,24,1 214 | 51,9,2005,3,8,1 215 | 1248,9,2008,7,4,8 216 | 827,8,2006,5,29,1 217 | 792,15,2006,5,27,1 218 | 1159,4,2006,11,28,1 219 | 702,9,2006,2,18,1 220 | 288,15,2005,6,26,1 221 | 989,2,2006,8,7,1 222 | 358,1,2005,9,13,1 223 | 1270,10,2008,7,5,8 224 | 657,6,2006,2,16,1 225 | 1143,9,2006,11,27,1 226 | 617,8,2006,2,14,1 227 | 449,8,2005,12,25,1 228 | 1298,17,2008,7,6,8 229 | 1130,17,2006,11,26,1 230 | 789,12,2006,5,27,1 231 | 1312,10,2008,7,7,8 232 | 1062,12,2006,11,23,1 233 | 73,10,2005,3,9,1 234 | 831,12,2006,5,29,1 235 | 82,19,2005,3,9,1 236 | 24,3,2005,3,7,1 237 | 261,9,2005,6,25,1 238 | 851,11,2006,5,30,1 239 | 1295,14,2008,7,6,8 240 | 877,16,2006,5,31,1 241 | 55,13,2005,3,8,1 242 | 1301,20,2008,7,6,8 243 | 1047,18,2006,11,22,1 244 | 918,15,2006,8,3,1 245 | 208,19,2005,6,22,1 246 | 429,9,2005,9,16,1 247 | 769,13,2006,5,26,1 248 | 516,12,2005,12,28,1 249 | 468,6,2005,12,26,1 250 | 22,1,2005,3,7,1 251 | 441,21,2005,9,16,20 252 | 282,9,2005,6,26,1 253 | 564,18,2005,12,30,1 254 | 641,11,2006,2,15,1 255 | 119,14,2005,3,11,1 256 | 1223,5,2008,7,3,8 257 | 1178,2,2008,7,1,8 258 | 168,21,2005,6,20,20 259 | 451,10,2005,12,25,1 260 | 726,12,2006,2,19,1 261 | 215,5,2005,6,23,1 262 | 960,15,2006,8,5,1 263 | 405,6,2005,9,15,1 264 | 374,17,2005,9,13,1 265 | 1187,11,2008,7,1,8 266 | 714,21,2006,2,18,20 267 | 352,16,2005,9,12,1 268 | 513,9,2005,12,28,1 269 | 964,19,2006,8,5,1 270 | 194,5,2005,6,22,1 271 | 826,7,2006,5,29,1 272 | 1276,16,2008,7,5,8 273 | 97,13,2005,3,10,1 274 | 1165,10,2006,11,28,1 275 | 622,13,2006,2,14,1 276 | 151,4,2005,6,20,1 277 | 674,2,2006,2,17,1 278 | 618,9,2006,2,14,1 279 | 1105,13,2006,11,25,1 280 | 973,7,2006,8,6,1 281 | 196,7,2005,6,22,1 282 | 104,20,2005,3,10,1 283 | 1086,15,2006,11,24,1 284 | 1296,15,2008,7,6,8 285 | 630,21,2006,2,14,20 286 | 57,15,2005,3,8,1 287 | 773,17,2006,5,26,1 288 | 323,8,2005,9,11,1 289 | 910,7,2006,8,3,1 290 | 182,14,2005,6,21,1 291 | 1022,14,2006,8,8,1 292 | 1229,11,2008,7,3,8 293 | 688,16,2006,2,17,1 294 | 843,3,2006,5,30,1 295 | 350,14,2005,9,12,1 296 | 370,13,2005,9,13,1 297 | 1163,8,2006,11,28,1 298 | 954,9,2006,8,5,1 299 | 76,13,2005,3,9,1 300 | 483,21,2005,12,26,20 301 | 391,13,2005,9,14,1 302 | 559,13,2005,12,30,1 303 | 906,3,2006,8,3,1 304 | 1166,11,2006,11,28,1 305 | 631,1,2006,2,15,1 306 | 532,7,2005,12,29,1 307 | 459,18,2005,12,25,1 308 | 756,21,2006,5,25,20 309 | 339,3,2005,9,12,1 310 | 541,16,2005,12,29,1 311 | 293,20,2005,6,26,1 312 | 855,15,2006,5,30,1 313 | 640,10,2006,2,15,1 314 | 834,15,2006,5,29,1 315 | 887,5,2006,8,2,1 316 | 900,18,2006,8,2,1 317 | 1208,11,2008,7,2,8 318 | 4,4,2005,3,6,1 319 | 1094,2,2006,11,25,1 320 | 1234,16,2008,7,3,8 321 | 1278,18,2008,7,5,8 322 | 367,10,2005,9,13,1 323 | 955,10,2006,8,5,1 324 | 146,20,2005,3,12,1 325 | 974,8,2006,8,6,1 326 | 23,2,2005,3,7,1 327 | 895,13,2006,8,2,1 328 | 669,18,2006,2,16,1 329 | 1119,6,2006,11,26,1 330 | 586,19,2005,12,31,1 331 | 1198,1,2008,7,2,8 332 | 1066,16,2006,11,23,1 333 | 165,18,2005,6,20,1 334 | 938,14,2006,8,4,1 335 | 903,21,2006,8,2,20 336 | 946,1,2006,8,5,1 337 | 693,21,2006,2,17,20 338 | 1251,12,2008,7,4,8 339 | 981,15,2006,8,6,1 340 | 447,6,2005,12,25,1 341 | 1136,2,2006,11,27,1 342 | 368,11,2005,9,13,1 343 | 345,9,2005,9,12,1 344 | 707,14,2006,2,18,1 345 | 357,21,2005,9,12,20 346 | 92,8,2005,3,10,1 347 | 917,14,2006,8,3,1 348 | 272,20,2005,6,25,1 349 | 1291,10,2008,7,6,8 350 | 811,13,2006,5,28,1 351 | 305,11,2005,9,10,1 352 | 1015,7,2006,8,8,1 353 | 1196,20,2008,7,1,8 354 | 1171,16,2006,11,28,1 355 | 572,5,2005,12,31,1 356 | 396,18,2005,9,14,1 357 | 956,11,2006,8,5,1 358 | 813,15,2006,5,28,1 359 | 546,21,2005,12,29,20 360 | 1036,7,2006,11,22,1 361 | 988,1,2006,8,7,1 362 | 1138,4,2006,11,27,1 363 | 195,6,2005,6,22,1 364 | 278,5,2005,6,26,1 365 | 705,12,2006,2,18,1 366 | 1118,5,2006,11,26,1 367 | 1290,9,2008,7,6,8 368 | 163,16,2005,6,20,1 369 | 254,2,2005,6,25,1 370 | 161,14,2005,6,20,1 371 | 578,11,2005,12,31,1 372 | 1285,4,2008,7,6,8 373 | 856,16,2006,5,30,1 374 | 1001,14,2006,8,7,1 375 | 470,8,2005,12,26,1 376 | 2,2,2005,3,6,1 377 | 210,21,2005,6,22,20 378 | 623,14,2006,2,14,1 379 | 477,15,2005,12,26,1 380 | 233,2,2005,6,24,1 381 | 413,14,2005,9,15,1 382 | 1043,14,2006,11,22,1 383 | 737,2,2006,5,25,1 384 | 684,12,2006,2,17,1 385 | 450,9,2005,12,25,1 386 | 563,17,2005,12,30,1 387 | 91,7,2005,3,10,1 388 | 1070,20,2006,11,23,1 389 | 748,13,2006,5,25,1 390 | 886,4,2006,8,2,1 391 | 1322,20,2008,7,7,8 392 | 1221,3,2008,7,3,8 393 | 53,11,2005,3,8,1 394 | 207,18,2005,6,22,1 395 | 820,1,2006,5,29,1 396 | 355,19,2005,9,12,1 397 | 749,14,2006,5,25,1 398 | 611,2,2006,2,14,1 399 | 412,13,2005,9,15,1 400 | 610,1,2006,2,14,1 401 | 590,2,2006,2,13,1 402 | 1204,7,2008,7,2,8 403 | 1256,17,2008,7,4,8 404 | 418,19,2005,9,15,1 405 | 638,8,2006,2,15,1 406 | 614,5,2006,2,14,1 407 | 42,21,2005,3,7,20 408 | 596,8,2006,2,13,1 409 | 1087,16,2006,11,24,1 410 | 682,10,2006,2,17,1 411 | 410,11,2005,9,15,1 412 | 739,4,2006,5,25,1 413 | 746,11,2006,5,25,1 414 | 1065,15,2006,11,23,1 415 | 1020,12,2006,8,8,1 416 | 393,15,2005,9,14,1 417 | 806,8,2006,5,28,1 418 | 713,20,2006,2,18,1 419 | 1053,3,2006,11,23,1 420 | 569,2,2005,12,31,1 421 | 1246,7,2008,7,4,8 422 | 634,4,2006,2,15,1 423 | 116,11,2005,3,11,1 424 | 181,13,2005,6,21,1 425 | 1279,19,2008,7,5,8 426 | 200,11,2005,6,22,1 427 | 30,9,2005,3,7,1 428 | 928,4,2006,8,4,1 429 | 609,21,2006,2,13,20 430 | 518,14,2005,12,28,1 431 | 243,12,2005,6,24,1 432 | 1155,21,2006,11,27,20 433 | 650,20,2006,2,15,1 434 | 1214,17,2008,7,2,8 435 | 676,4,2006,2,17,1 436 | 581,14,2005,12,31,1 437 | 1125,12,2006,11,26,1 438 | 307,13,2005,9,10,1 439 | 495,12,2005,12,27,1 440 | 1184,8,2008,7,1,8 441 | 454,13,2005,12,25,1 442 | 275,2,2005,6,26,1 443 | 1313,11,2008,7,7,8 444 | 519,15,2005,12,28,1 445 | 912,9,2006,8,3,1 446 | 1006,19,2006,8,7,1 447 | 172,4,2005,6,21,1 448 | 306,12,2005,9,10,1 449 | 696,3,2006,2,18,1 450 | 894,12,2006,8,2,1 451 | 949,4,2006,8,5,1 452 | 150,3,2005,6,20,1 453 | 32,11,2005,3,7,1 454 | 1192,16,2008,7,1,8 455 | 727,13,2006,2,19,1 456 | 227,17,2005,6,23,1 457 | 190,1,2005,6,22,1 458 | 446,5,2005,12,25,1 459 | 515,11,2005,12,28,1 460 | 1260,21,2008,7,4,160 461 | 1323,21,2008,7,7,160 462 | 992,5,2006,8,7,1 463 | 465,3,2005,12,26,1 464 | 351,15,2005,9,12,1 465 | 1111,19,2006,11,25,1 466 | 627,18,2006,2,14,1 467 | 406,7,2005,9,15,1 468 | 389,11,2005,9,14,1 469 | 848,8,2006,5,30,1 470 | 77,14,2005,3,9,1 471 | 225,15,2005,6,23,1 472 | 59,17,2005,3,8,1 473 | 694,1,2006,2,18,1 474 | 401,2,2005,9,15,1 475 | 185,17,2005,6,21,1 476 | 134,8,2005,3,12,1 477 | 475,13,2005,12,26,1 478 | 680,8,2006,2,17,1 479 | 28,7,2005,3,7,1 480 | 743,8,2006,5,25,1 481 | 1158,3,2006,11,28,1 482 | 662,11,2006,2,16,1 483 | 417,18,2005,9,15,1 484 | 331,16,2005,9,11,1 485 | 456,15,2005,12,25,1 486 | 17,17,2005,3,6,1 487 | 721,7,2006,2,19,1 488 | 817,19,2006,5,28,1 489 | 958,13,2006,8,5,1 490 | 741,6,2006,5,25,1 491 | 1008,21,2006,8,7,20 492 | 364,7,2005,9,13,1 493 | 736,1,2006,5,25,1 494 | 607,19,2006,2,13,1 495 | 1193,17,2008,7,1,8 496 | 260,8,2005,6,25,1 497 | 381,3,2005,9,14,1 498 | 783,6,2006,5,27,1 499 | 202,13,2005,6,22,1 500 | 781,4,2006,5,27,1 501 | 930,6,2006,8,4,1 502 | 529,4,2005,12,29,1 503 | 970,4,2006,8,6,1 504 | 1145,11,2006,11,27,1 505 | 779,2,2006,5,27,1 506 | 916,13,2006,8,3,1 507 | 1154,20,2006,11,27,1 508 | 1054,4,2006,11,23,1 509 | 1078,7,2006,11,24,1 510 | 562,16,2005,12,30,1 511 | 1097,5,2006,11,25,1 512 | 437,17,2005,9,16,1 513 | 240,9,2005,6,24,1 514 | 1124,11,2006,11,26,1 515 | 613,4,2006,2,14,1 516 | 1149,15,2006,11,27,1 517 | 423,3,2005,9,16,1 518 | 72,9,2005,3,9,1 519 | 428,8,2005,9,16,1 520 | 166,19,2005,6,20,1 521 | 1249,10,2008,7,4,8 522 | 1099,7,2006,11,25,1 523 | 1265,5,2008,7,5,8 524 | 636,6,2006,2,15,1 525 | 1227,9,2008,7,3,8 526 | 402,3,2005,9,15,1 527 | 503,20,2005,12,27,1 528 | 37,16,2005,3,7,1 529 | 19,19,2005,3,6,1 530 | 8,8,2005,3,6,1 531 | 997,10,2006,8,7,1 532 | 18,18,2005,3,6,1 533 | 520,16,2005,12,28,1 534 | 322,7,2005,9,11,1 535 | 1157,2,2006,11,28,1 536 | 1142,8,2006,11,27,1 537 | 1044,15,2006,11,22,1 538 | 199,10,2005,6,22,1 539 | 819,21,2006,5,28,20 540 | 301,7,2005,9,10,1 541 | 408,9,2005,9,15,1 542 | 1019,11,2006,8,8,1 543 | 1085,14,2006,11,24,1 544 | 81,18,2005,3,9,1 545 | 996,9,2006,8,7,1 546 | 794,17,2006,5,27,1 547 | 110,5,2005,3,11,1 548 | 966,21,2006,8,5,20 549 | 539,14,2005,12,29,1 550 | 192,3,2005,6,22,1 551 | 407,8,2005,9,15,1 552 | 822,3,2006,5,29,1 553 | 169,1,2005,6,21,1 554 | 245,14,2005,6,24,1 555 | 889,7,2006,8,2,1 556 | 1172,17,2006,11,28,1 557 | 404,5,2005,9,15,1 558 | 884,2,2006,8,2,1 559 | 137,11,2005,3,12,1 560 | 880,19,2006,5,31,1 561 | 1114,1,2006,11,26,1 562 | 1072,1,2006,11,24,1 563 | 1059,9,2006,11,23,1 564 | 1286,5,2008,7,6,8 565 | 65,2,2005,3,9,1 566 | 130,4,2005,3,12,1 567 | 865,4,2006,5,31,1 568 | 648,18,2006,2,15,1 569 | 1297,16,2008,7,6,8 570 | 835,16,2006,5,29,1 571 | 675,3,2006,2,17,1 572 | 1210,13,2008,7,2,8 573 | 1287,6,2008,7,6,8 574 | 639,9,2006,2,15,1 575 | 995,8,2006,8,7,1 576 | 796,19,2006,5,27,1 577 | 549,3,2005,12,30,1 578 | 898,16,2006,8,2,1 579 | 144,18,2005,3,12,1 580 | 574,7,2005,12,31,1 581 | 802,4,2006,5,28,1 582 | 757,1,2006,5,26,1 583 | 164,17,2005,6,20,1 584 | 157,10,2005,6,20,1 585 | 318,3,2005,9,11,1 586 | 760,4,2006,5,26,1 587 | 1079,8,2006,11,24,1 588 | 7,7,2005,3,6,1 589 | 660,9,2006,2,16,1 590 | 85,1,2005,3,10,1 591 | 1075,4,2006,11,24,1 592 | 70,7,2005,3,9,1 593 | 290,17,2005,6,26,1 594 | 320,5,2005,9,11,1 595 | 812,14,2006,5,28,1 596 | 615,6,2006,2,14,1 597 | 583,16,2005,12,31,1 598 | 361,4,2005,9,13,1 599 | 61,19,2005,3,8,1 600 | 458,17,2005,12,25,1 601 | 1269,9,2008,7,5,8 602 | 968,2,2006,8,6,1 603 | 1017,9,2006,8,8,1 604 | 1080,9,2006,11,24,1 605 | 522,18,2005,12,28,1 606 | 755,20,2006,5,25,1 607 | 125,20,2005,3,11,1 608 | 63,21,2005,3,8,20 609 | 324,9,2005,9,11,1 610 | 708,15,2006,2,18,1 611 | 273,21,2005,6,25,20 612 | 584,17,2005,12,31,1 613 | 524,20,2005,12,28,1 614 | 235,4,2005,6,24,1 615 | 699,6,2006,2,18,1 616 | 961,16,2006,8,5,1 617 | 1014,6,2006,8,8,1 618 | 1107,15,2006,11,25,1 619 | 801,3,2006,5,28,1 620 | 787,10,2006,5,27,1 621 | 160,13,2005,6,20,1 622 | 1016,8,2006,8,8,1 623 | 628,19,2006,2,14,1 624 | 1308,6,2008,7,7,8 625 | 1108,16,2006,11,25,1 626 | 83,20,2005,3,9,1 627 | 808,10,2006,5,28,1 628 | 523,19,2005,12,28,1 629 | 1176,21,2006,11,28,20 630 | 999,12,2006,8,7,1 631 | 864,3,2006,5,31,1 632 | 824,5,2006,5,29,1 633 | 206,17,2005,6,22,1 634 | 466,4,2005,12,26,1 635 | 561,15,2005,12,30,1 636 | 1180,4,2008,7,1,8 637 | 1091,20,2006,11,24,1 638 | 942,18,2006,8,4,1 639 | 665,14,2006,2,16,1 640 | 967,1,2006,8,6,1 641 | 1213,16,2008,7,2,8 642 | 629,20,2006,2,14,1 643 | 860,20,2006,5,30,1 644 | 283,10,2005,6,26,1 645 | 786,9,2006,5,27,1 646 | 127,1,2005,3,12,1 647 | 885,3,2006,8,2,1 648 | 349,13,2005,9,12,1 649 | 587,20,2005,12,31,1 650 | 266,14,2005,6,25,1 651 | 147,21,2005,3,12,20 652 | 829,10,2006,5,29,1 653 | 978,12,2006,8,6,1 654 | 131,5,2005,3,12,1 655 | 1061,11,2006,11,23,1 656 | 244,13,2005,6,24,1 657 | 220,10,2005,6,23,1 658 | 1055,5,2006,11,23,1 659 | 121,16,2005,3,11,1 660 | 1321,19,2008,7,7,8 661 | 1289,8,2008,7,6,8 662 | 505,1,2005,12,28,1 663 | 485,2,2005,12,27,1 664 | 1310,8,2008,7,7,8 665 | 494,11,2005,12,27,1 666 | 993,6,2006,8,7,1 667 | 26,5,2005,3,7,1 668 | 343,7,2005,9,12,1 669 | 1133,20,2006,11,26,1 670 | 557,11,2005,12,30,1 671 | 571,4,2005,12,31,1 672 | 133,7,2005,3,12,1 673 | 1098,6,2006,11,25,1 674 | 635,5,2006,2,15,1 675 | 934,10,2006,8,4,1 676 | 212,2,2005,6,23,1 677 | 833,14,2006,5,29,1 678 | 45,3,2005,3,8,1 679 | 175,7,2005,6,21,1 680 | 340,4,2005,9,12,1 681 | 1046,17,2006,11,22,1 682 | 336,21,2005,9,11,20 683 | 626,17,2006,2,14,1 684 | 1309,7,2008,7,7,8 685 | 440,20,2005,9,16,1 686 | 552,6,2005,12,30,1 687 | 453,12,2005,12,25,1 688 | 1088,17,2006,11,24,1 689 | 269,17,2005,6,25,1 690 | 862,1,2006,5,31,1 691 | 875,14,2006,5,31,1 692 | 685,13,2006,2,17,1 693 | 461,20,2005,12,25,1 694 | 425,5,2005,9,16,1 695 | 1029,21,2006,8,8,20 696 | 980,14,2006,8,6,1 697 | 277,4,2005,6,26,1 698 | 911,8,2006,8,3,1 699 | 575,8,2005,12,31,1 700 | 224,14,2005,6,23,1 701 | 1146,12,2006,11,27,1 702 | 882,21,2006,5,31,20 703 | 58,16,2005,3,8,1 704 | 1005,18,2006,8,7,1 705 | 589,1,2006,2,13,1 706 | 919,16,2006,8,3,1 707 | 1083,12,2006,11,24,1 708 | 535,10,2005,12,29,1 709 | 941,17,2006,8,4,1 710 | 114,9,2005,3,11,1 711 | 782,5,2006,5,27,1 712 | 985,19,2006,8,6,1 713 | 478,16,2005,12,26,1 714 | 872,11,2006,5,31,1 715 | 975,9,2006,8,6,1 716 | 1280,20,2008,7,5,8 717 | 830,11,2006,5,29,1 718 | 947,2,2006,8,5,1 719 | 625,16,2006,2,14,1 720 | 46,4,2005,3,8,1 721 | 526,1,2005,12,29,1 722 | 363,6,2005,9,13,1 723 | 1243,4,2008,7,4,8 724 | 362,5,2005,9,13,1 725 | 178,10,2005,6,21,1 726 | 1181,5,2008,7,1,8 727 | 39,18,2005,3,7,1 728 | 462,21,2005,12,25,20 729 | 690,18,2006,2,17,1 730 | 1292,11,2008,7,6,8 731 | 1002,15,2006,8,7,1 732 | 604,16,2006,2,13,1 733 | 1261,1,2008,7,5,8 734 | 517,13,2005,12,28,1 735 | 1320,18,2008,7,7,8 736 | 108,3,2005,3,11,1 737 | 1021,13,2006,8,8,1 738 | 1153,19,2006,11,27,1 739 | 1319,17,2008,7,7,8 740 | 719,5,2006,2,19,1 741 | 1009,1,2006,8,8,1 742 | 216,6,2005,6,23,1 743 | 888,6,2006,8,2,1 744 | 866,5,2006,5,31,1 745 | 313,19,2005,9,10,1 746 | 1233,15,2008,7,3,8 747 | 701,8,2006,2,18,1 748 | 870,9,2006,5,31,1 749 | 1102,10,2006,11,25,1 750 | 1096,4,2006,11,25,1 751 | 112,7,2005,3,11,1 752 | 386,8,2005,9,14,1 753 | 998,11,2006,8,7,1 754 | 544,19,2005,12,29,1 755 | 435,15,2005,9,16,1 756 | 799,1,2006,5,28,1 757 | 60,18,2005,3,8,1 758 | 576,9,2005,12,31,1 759 | 1013,5,2006,8,8,1 760 | 327,12,2005,9,11,1 761 | 534,9,2005,12,29,1 762 | 761,5,2006,5,26,1 763 | 528,3,2005,12,29,1 764 | 1004,17,2006,8,7,1 765 | 1057,7,2006,11,23,1 766 | 467,5,2005,12,26,1 767 | 400,1,2005,9,15,1 768 | 649,19,2006,2,15,1 769 | 582,15,2005,12,31,1 770 | 852,12,2006,5,30,1 771 | 616,7,2006,2,14,1 772 | 359,2,2005,9,13,1 773 | 711,18,2006,2,18,1 774 | 5,5,2005,3,6,1 775 | 921,18,2006,8,3,1 776 | 1113,21,2006,11,25,20 777 | 987,21,2006,8,6,20 778 | 1134,21,2006,11,26,20 779 | 543,18,2005,12,29,1 780 | 162,15,2005,6,20,1 781 | 299,5,2005,9,10,1 782 | 758,2,2006,5,26,1 783 | 659,8,2006,2,16,1 784 | 228,18,2005,6,23,1 785 | 143,17,2005,3,12,1 786 | 681,9,2006,2,17,1 787 | 1039,10,2006,11,22,1 788 | 1037,8,2006,11,22,1 789 | 815,17,2006,5,28,1 790 | 593,5,2006,2,13,1 791 | 1245,6,2008,7,4,8 792 | 752,17,2006,5,25,1 793 | 111,6,2005,3,11,1 794 | 1201,4,2008,7,2,8 795 | 580,13,2005,12,31,1 796 | 644,14,2006,2,15,1 797 | 1129,16,2006,11,26,1 798 | 291,18,2005,6,26,1 799 | 13,13,2005,3,6,1 800 | 348,12,2005,9,12,1 801 | 952,7,2006,8,5,1 802 | 496,13,2005,12,27,1 803 | 102,18,2005,3,10,1 804 | 1045,16,2006,11,22,1 805 | 742,7,2006,5,25,1 806 | 1212,15,2008,7,2,8 807 | 770,14,2006,5,26,1 808 | 778,1,2006,5,27,1 809 | 170,2,2005,6,21,1 810 | 148,1,2005,6,20,1 811 | 399,21,2005,9,14,20 812 | 1206,9,2008,7,2,8 813 | 1230,12,2008,7,3,8 814 | 90,6,2005,3,10,1 815 | 1169,14,2006,11,28,1 816 | 132,6,2005,3,12,1 817 | 818,20,2006,5,28,1 818 | 145,19,2005,3,12,1 819 | 733,19,2006,2,19,1 820 | 558,12,2005,12,30,1 821 | 171,3,2005,6,21,1 822 | 284,11,2005,6,26,1 823 | 1183,7,2008,7,1,8 824 | 253,1,2005,6,25,1 825 | 972,6,2006,8,6,1 826 | 237,6,2005,6,24,1 827 | 122,17,2005,3,11,1 828 | 668,17,2006,2,16,1 829 | 452,11,2005,12,25,1 830 | 1228,10,2008,7,3,8 831 | 124,19,2005,3,11,1 832 | 1109,17,2006,11,25,1 833 | 62,20,2005,3,8,1 834 | 373,16,2005,9,13,1 835 | 271,19,2005,6,25,1 836 | 566,20,2005,12,30,1 837 | 387,9,2005,9,14,1 838 | 204,15,2005,6,22,1 839 | 298,4,2005,9,10,1 840 | 1274,14,2008,7,5,8 841 | 474,12,2005,12,26,1 842 | 951,6,2006,8,5,1 843 | 990,3,2006,8,7,1 844 | 1161,6,2006,11,28,1 845 | 950,5,2006,8,5,1 846 | 646,16,2006,2,15,1 847 | 328,13,2005,9,11,1 848 | 289,16,2005,6,26,1 849 | 504,21,2005,12,27,20 850 | 120,15,2005,3,11,1 851 | 780,3,2006,5,27,1 852 | 1237,19,2008,7,3,8 853 | 765,9,2006,5,26,1 854 | 790,13,2006,5,27,1 855 | 943,19,2006,8,4,1 856 | 844,4,2006,5,30,1 857 | 473,11,2005,12,26,1 858 | 777,21,2006,5,26,20 859 | 270,18,2005,6,25,1 860 | 909,6,2006,8,3,1 861 | 177,9,2005,6,21,1 862 | 1225,7,2008,7,3,8 863 | 893,11,2006,8,2,1 864 | 221,11,2005,6,23,1 865 | 1050,21,2006,11,22,20 866 | 537,12,2005,12,29,1 867 | 319,4,2005,9,11,1 868 | 706,13,2006,2,18,1 869 | 205,16,2005,6,22,1 870 | 585,18,2005,12,31,1 871 | 1231,13,2008,7,3,8 872 | 542,17,2005,12,29,1 873 | 12,12,2005,3,6,1 874 | 525,21,2005,12,28,20 875 | 1139,5,2006,11,27,1 876 | 263,11,2005,6,25,1 877 | 861,21,2006,5,30,20 878 | 902,20,2006,8,2,1 879 | 935,11,2006,8,4,1 880 | 279,6,2005,6,26,1 881 | 107,2,2005,3,11,1 882 | 20,20,2005,3,6,1 883 | 149,2,2005,6,20,1 884 | 392,14,2005,9,14,1 885 | 619,10,2006,2,14,1 886 | 959,14,2006,8,5,1 887 | 1101,9,2006,11,25,1 888 | 344,8,2005,9,12,1 889 | 297,3,2005,9,10,1 890 | 664,13,2006,2,16,1 891 | 250,19,2005,6,24,1 892 | 839,20,2006,5,29,1 893 | 1048,19,2006,11,22,1 894 | 403,4,2005,9,15,1 895 | 1038,9,2006,11,22,1 896 | 1100,8,2006,11,25,1 897 | 268,16,2005,6,25,1 898 | 1304,2,2008,7,7,8 899 | 154,7,2005,6,20,1 900 | 939,15,2006,8,4,1 901 | 197,8,2005,6,22,1 902 | 637,7,2006,2,15,1 903 | 1173,18,2006,11,28,1 904 | 123,18,2005,3,11,1 905 | 1300,19,2008,7,6,8 906 | 139,13,2005,3,12,1 907 | 443,2,2005,12,25,1 908 | 1000,13,2006,8,7,1 909 | 809,11,2006,5,28,1 910 | 35,14,2005,3,7,1 911 | 871,10,2006,5,31,1 912 | 1194,18,2008,7,1,8 913 | 1103,11,2006,11,25,1 914 | 1305,3,2008,7,7,8 915 | 1170,15,2006,11,28,1 916 | 507,3,2005,12,28,1 917 | 463,1,2005,12,26,1 918 | 50,8,2005,3,8,1 919 | 430,10,2005,9,16,1 920 | 376,19,2005,9,13,1 921 | 653,2,2006,2,16,1 922 | 1191,15,2008,7,1,8 923 | 1051,1,2006,11,23,1 924 | 1186,10,2008,7,1,8 925 | 821,2,2006,5,29,1 926 | 54,12,2005,3,8,1 927 | 502,19,2005,12,27,1 928 | 377,20,2005,9,13,1 929 | 1131,18,2006,11,26,1 930 | 426,6,2005,9,16,1 931 | 158,11,2005,6,20,1 932 | 375,18,2005,9,13,1 933 | 853,13,2006,5,30,1 934 | 335,20,2005,9,11,1 935 | 455,14,2005,12,25,1 936 | 189,21,2005,6,21,20 937 | 798,21,2006,5,27,20 938 | 14,14,2005,3,6,1 939 | 43,1,2005,3,8,1 940 | 603,15,2006,2,13,1 941 | 751,16,2006,5,25,1 942 | 536,11,2005,12,29,1 943 | 1281,21,2008,7,5,160 944 | 436,16,2005,9,16,1 945 | 606,18,2006,2,13,1 946 | 439,19,2005,9,16,1 947 | 96,12,2005,3,10,1 948 | 326,11,2005,9,11,1 949 | 651,21,2006,2,15,20 950 | 191,2,2005,6,22,1 951 | 759,3,2006,5,26,1 952 | 700,7,2006,2,18,1 953 | 1306,4,2008,7,7,8 954 | 236,5,2005,6,24,1 955 | 501,18,2005,12,27,1 956 | 256,4,2005,6,25,1 957 | 1241,2,2008,7,4,8 958 | 188,20,2005,6,21,1 959 | 1003,16,2006,8,7,1 960 | 252,21,2005,6,24,20 961 | 211,1,2005,6,23,1 962 | 66,3,2005,3,9,1 963 | 1218,21,2008,7,2,160 964 | 1011,3,2006,8,8,1 965 | 84,21,2005,3,9,20 966 | 673,1,2006,2,17,1 967 | 1283,2,2008,7,6,8 968 | 1120,7,2006,11,26,1 969 | 265,13,2005,6,25,1 970 | 632,2,2006,2,15,1 971 | 80,17,2005,3,9,1 972 | 492,9,2005,12,27,1 973 | 1023,15,2006,8,8,1 974 | 193,4,2005,6,22,1 975 | 303,9,2005,9,10,1 976 | 167,20,2005,6,20,1 977 | 214,4,2005,6,23,1 978 | 718,4,2006,2,19,1 979 | 620,11,2006,2,14,1 980 | 1215,18,2008,7,2,8 981 | 1148,14,2006,11,27,1 982 | 255,3,2005,6,25,1 983 | 99,15,2005,3,10,1 984 | 514,10,2005,12,28,1 985 | 591,3,2006,2,13,1 986 | 341,5,2005,9,12,1 987 | 715,1,2006,2,19,1 988 | 538,13,2005,12,29,1 989 | 965,20,2006,8,5,1 990 | 672,21,2006,2,16,20 991 | 15,15,2005,3,6,1 992 | 633,3,2006,2,15,1 993 | 100,16,2005,3,10,1 994 | 1144,10,2006,11,27,1 995 | 480,18,2005,12,26,1 996 | 321,6,2005,9,11,1 997 | 360,3,2005,9,13,1 998 | 722,8,2006,2,19,1 999 | 922,19,2006,8,3,1 1000 | 962,17,2006,8,5,1 1001 | 788,11,2006,5,27,1 1002 | 141,15,2005,3,12,1 1003 | 854,14,2006,5,30,1 1004 | 1164,9,2006,11,28,1 1005 | 655,4,2006,2,16,1 1006 | 1034,5,2006,11,22,1 1007 | 1126,13,2006,11,26,1 1008 | 457,16,2005,12,25,1 1009 | 36,15,2005,3,7,1 1010 | 795,18,2006,5,27,1 1011 | 1268,8,2008,7,5,8 1012 | 658,7,2006,2,16,1 1013 | 849,9,2006,5,30,1 1014 | 599,11,2006,2,13,1 1015 | 725,11,2006,2,19,1 1016 | 257,5,2005,6,25,1 1017 | 652,1,2006,2,16,1 1018 | 40,19,2005,3,7,1 1019 | 510,6,2005,12,28,1 1020 | 608,20,2006,2,13,1 1021 | 16,16,2005,3,6,1 1022 | 1052,2,2006,11,23,1 1023 | 723,9,2006,2,19,1 1024 | 747,12,2006,5,25,1 1025 | 433,13,2005,9,16,1 1026 | 1076,5,2006,11,24,1 1027 | 602,14,2006,2,13,1 1028 | 489,6,2005,12,27,1 1029 | 34,13,2005,3,7,1 1030 | 667,16,2006,2,16,1 1031 | 1266,6,2008,7,5,8 1032 | 908,5,2006,8,3,1 1033 | 797,20,2006,5,27,1 1034 | 1238,20,2008,7,3,8 1035 | 800,2,2006,5,28,1 1036 | 1179,3,2008,7,1,8 1037 | 98,14,2005,3,10,1 1038 | 52,10,2005,3,8,1 1039 | 179,11,2005,6,21,1 1040 | 1262,2,2008,7,5,8 1041 | 1168,13,2006,11,28,1 1042 | 1197,21,2008,7,1,160 1043 | 68,5,2005,3,9,1 1044 | 920,17,2006,8,3,1 1045 | 1185,9,2008,7,1,8 1046 | 745,10,2006,5,25,1 1047 | 957,12,2006,8,5,1 1048 | 394,16,2005,9,14,1 1049 | 904,1,2006,8,3,1 1050 | 1140,6,2006,11,27,1 1051 | 371,14,2005,9,13,1 1052 | 27,6,2005,3,7,1 1053 | 991,4,2006,8,7,1 1054 | 825,6,2006,5,29,1 1055 | 683,11,2006,2,17,1 1056 | 598,10,2006,2,13,1 1057 | 383,5,2005,9,14,1 1058 | 311,17,2005,9,10,1 1059 | 588,21,2005,12,31,20 1060 | 248,17,2005,6,24,1 1061 | 828,9,2006,5,29,1 1062 | 1242,3,2008,7,4,8 1063 | 10,10,2005,3,6,1 1064 | 219,9,2005,6,23,1 1065 | 579,12,2005,12,31,1 1066 | 203,14,2005,6,22,1 1067 | 1132,19,2006,11,26,1 1068 | 276,3,2005,6,26,1 1069 | 879,18,2006,5,31,1 1070 | 329,14,2005,9,11,1 1071 | 3,3,2005,3,6,1 1072 | 69,6,2005,3,9,1 1073 | 568,1,2005,12,31,1 1074 | 476,14,2005,12,26,1 1075 | 570,3,2005,12,31,1 1076 | 365,8,2005,9,13,1 1077 | 1273,13,2008,7,5,8 1078 | 353,17,2005,9,12,1 1079 | 804,6,2006,5,28,1 1080 | 704,11,2006,2,18,1 1081 | 730,16,2006,2,19,1 1082 | 1232,14,2008,7,3,8 1083 | 1010,2,2006,8,8,1 1084 | 953,8,2006,8,5,1 1085 | 670,19,2006,2,16,1 1086 | 971,5,2006,8,6,1 1087 | 117,12,2005,3,11,1 1088 | 390,12,2005,9,14,1 1089 | 868,7,2006,5,31,1 1090 | 217,7,2005,6,23,1 1091 | 448,7,2005,12,25,1 1092 | 49,7,2005,3,8,1 1093 | 89,5,2005,3,10,1 1094 | 1147,13,2006,11,27,1 1095 | 152,5,2005,6,20,1 1096 | 977,11,2006,8,6,1 1097 | 511,7,2005,12,28,1 1098 | 695,2,2006,2,18,1 1099 | 874,13,2006,5,31,1 1100 | 1259,20,2008,7,4,8 1101 | 232,1,2005,6,24,1 1102 | 21,21,2005,3,6,20 1103 | 464,2,2005,12,26,1 1104 | 545,20,2005,12,29,1 1105 | 251,20,2005,6,24,1 1106 | 249,18,2005,6,24,1 1107 | 878,17,2006,5,31,1 1108 | 840,21,2006,5,29,20 1109 | 937,13,2006,8,4,1 1110 | 891,9,2006,8,2,1 1111 | 397,19,2005,9,14,1 1112 | 1,1,2005,3,6,1 1113 | 823,4,2006,5,29,1 1114 | 183,15,2005,6,21,1 1115 | 94,10,2005,3,10,1 1116 | 533,8,2005,12,29,1 1117 | 832,13,2006,5,29,1 1118 | 1028,20,2006,8,8,1 1119 | 1026,18,2006,8,8,1 1120 | 560,14,2005,12,30,1 1121 | 286,13,2005,6,26,1 1122 | 678,6,2006,2,17,1 1123 | 74,11,2005,3,9,1 1124 | 933,9,2006,8,4,1 1125 | 1258,19,2008,7,4,8 1126 | 209,20,2005,6,22,1 1127 | 369,12,2005,9,13,1 1128 | 129,3,2005,3,12,1 1129 | 294,21,2005,6,26,20 1130 | 491,8,2005,12,27,1 1131 | 385,7,2005,9,14,1 1132 | 764,8,2006,5,26,1 1133 | 914,11,2006,8,3,1 1134 | 1224,6,2008,7,3,8 1135 | 1056,6,2006,11,23,1 1136 | 506,2,2005,12,28,1 1137 | 932,8,2006,8,4,1 1138 | 481,19,2005,12,26,1 1139 | 1117,4,2006,11,26,1 1140 | 859,19,2006,5,30,1 1141 | 1069,19,2006,11,23,1 1142 | 317,2,2005,9,11,1 1143 | 372,15,2005,9,13,1 1144 | 384,6,2005,9,14,1 1145 | 1226,8,2008,7,3,8 1146 | 333,18,2005,9,11,1 1147 | 1040,11,2006,11,22,1 1148 | 1311,9,2008,7,7,8 1149 | 312,18,2005,9,10,1 1150 | 1244,5,2008,7,4,8 1151 | 846,6,2006,5,30,1 1152 | 540,15,2005,12,29,1 1153 | 334,19,2005,9,11,1 1154 | 1207,10,2008,7,2,8 1155 | 105,21,2005,3,10,20 1156 | 1042,13,2006,11,22,1 1157 | 1064,14,2006,11,23,1 1158 | 136,10,2005,3,12,1 1159 | 258,6,2005,6,25,1 1160 | 1205,8,2008,7,2,8 1161 | 9,9,2005,3,6,1 1162 | 1294,13,2008,7,6,8 1163 | 556,10,2005,12,30,1 1164 | 876,15,2006,5,31,1 1165 | 1318,16,2008,7,7,8 1166 | 316,1,2005,9,11,1 1167 | 186,18,2005,6,21,1 1168 | 850,10,2006,5,30,1 1169 | 296,2,2005,9,10,1 1170 | 1092,21,2006,11,24,20 1171 | 482,20,2005,12,26,1 1172 | 226,16,2005,6,23,1 1173 | 1182,6,2008,7,1,8 1174 | 1217,20,2008,7,2,8 1175 | 663,12,2006,2,16,1 1176 | 497,14,2005,12,27,1 1177 | 198,9,2005,6,22,1 1178 | 1199,2,2008,7,2,8 1179 | 346,10,2005,9,12,1 1180 | 553,7,2005,12,30,1 1181 | 994,7,2006,8,7,1 1182 | 1067,17,2006,11,23,1 1183 | 338,2,2005,9,12,1 1184 | 422,2,2005,9,16,1 1185 | 983,17,2006,8,6,1 1186 | 869,8,2006,5,31,1 1187 | 763,7,2006,5,26,1 1188 | 484,1,2005,12,27,1 1189 | 395,17,2005,9,14,1 1190 | 427,7,2005,9,16,1 1191 | 1211,14,2008,7,2,8 1192 | 1058,8,2006,11,23,1 1193 | 500,17,2005,12,27,1 1194 | 1041,12,2006,11,22,1 1195 | 729,15,2006,2,19,1 1196 | 1190,14,2008,7,1,8 1197 | 1106,14,2006,11,25,1 1198 | 488,5,2005,12,27,1 1199 | 1012,4,2006,8,8,1 1200 | 838,19,2006,5,29,1 1201 | 1209,12,2008,7,2,8 1202 | 315,21,2005,9,10,20 1203 | 735,21,2006,2,19,20 1204 | 867,6,2006,5,31,1 1205 | 1302,21,2008,7,6,160 1206 | 86,2,2005,3,10,1 1207 | 1315,13,2008,7,7,8 1208 | 776,20,2006,5,26,1 1209 | 1255,16,2008,7,4,8 1210 | 656,5,2006,2,16,1 1211 | 1236,18,2008,7,3,8 1212 | 314,20,2005,9,10,1 1213 | 184,16,2005,6,21,1 1214 | 1135,1,2006,11,27,1 1215 | 1235,17,2008,7,3,8 1216 | 213,3,2005,6,23,1 1217 | 863,2,2006,5,31,1 1218 | 106,1,2005,3,11,1 1219 | 378,21,2005,9,13,20 1220 | 1024,16,2006,8,8,1 1221 | 33,12,2005,3,7,1 1222 | 881,20,2006,5,31,1 1223 | 460,19,2005,12,25,1 1224 | 262,10,2005,6,25,1 1225 | 1284,3,2008,7,6,8 1226 | 1151,17,2006,11,27,1 1227 | 1220,2,2008,7,3,8 1228 | 793,16,2006,5,27,1 1229 | 424,4,2005,9,16,1 1230 | 41,20,2005,3,7,1 1231 | 1177,1,2008,7,1,8 1232 | 434,14,2005,9,16,1 1233 | 499,16,2005,12,27,1 1234 | 156,9,2005,6,20,1 1235 | 1104,12,2006,11,25,1 1236 | 841,1,2006,5,30,1 1237 | 1123,10,2006,11,26,1 1238 | 1122,9,2006,11,26,1 1239 | 521,17,2005,12,28,1 1240 | 621,12,2006,2,14,1 1241 | 490,7,2005,12,27,1 1242 | 438,18,2005,9,16,1 1243 | 332,17,2005,9,11,1 1244 | 837,18,2006,5,29,1 1245 | 785,8,2006,5,27,1 1246 | 79,16,2005,3,9,1 1247 | 238,7,2005,6,24,1 1248 | 925,1,2006,8,4,1 1249 | 548,2,2005,12,30,1 1250 | 807,9,2006,5,28,1 1251 | 310,16,2005,9,10,1 1252 | 1063,13,2006,11,23,1 1253 | 1222,4,2008,7,3,8 1254 | 845,5,2006,5,30,1 1255 | 419,20,2005,9,15,1 1256 | 31,10,2005,3,7,1 1257 | 1263,3,2008,7,5,8 1258 | 857,17,2006,5,30,1 1259 | 762,6,2006,5,26,1 1260 | 287,14,2005,6,26,1 1261 | 605,17,2006,2,13,1 1262 | 1317,15,2008,7,7,8 1263 | 1253,14,2008,7,4,8 1264 | 241,10,2005,6,24,1 1265 | 1082,11,2006,11,24,1 1266 | 986,20,2006,8,6,1 1267 | 342,6,2005,9,12,1 1268 | 1035,6,2006,11,22,1 1269 | 229,19,2005,6,23,1 1270 | 654,3,2006,2,16,1 1271 | 697,4,2006,2,18,1 1272 | 292,19,2005,6,26,1 1273 | 445,4,2005,12,25,1 1274 | 308,14,2005,9,10,1 1275 | 814,16,2006,5,28,1 1276 | 692,20,2006,2,17,1 1277 | 280,7,2005,6,26,1 1278 | 246,15,2005,6,24,1 1279 | 1271,11,2008,7,5,8 1280 | 982,16,2006,8,6,1 1281 | 67,4,2005,3,9,1 1282 | 1264,4,2008,7,5,8 1283 | 1239,21,2008,7,3,160 1284 | 766,10,2006,5,26,1 1285 | 1216,19,2008,7,2,8 1286 | 551,5,2005,12,30,1 1287 | 712,19,2006,2,18,1 1288 | 1257,18,2008,7,4,8 1289 | 1031,2,2006,11,22,1 1290 | 415,16,2005,9,15,1 1291 | 1112,20,2006,11,25,1 1292 | 218,8,2005,6,23,1 1293 | 940,16,2006,8,4,1 1294 | 775,19,2006,5,26,1 1295 | 772,16,2006,5,26,1 1296 | 242,11,2005,6,24,1 1297 | 945,21,2006,8,4,20 1298 | 409,10,2005,9,15,1 1299 | 1137,3,2006,11,27,1 1300 | 1141,7,2006,11,27,1 1301 | 732,18,2006,2,19,1 1302 | 64,1,2005,3,9,1 1303 | 969,3,2006,8,6,1 1304 | 138,12,2005,3,12,1 1305 | 187,19,2005,6,21,1 1306 | 285,12,2005,6,26,1 1307 | 924,21,2006,8,3,20 1308 | 173,5,2005,6,21,1 1309 | 201,12,2005,6,22,1 1310 | 691,19,2006,2,17,1 1311 | 1202,5,2008,7,2,8 1312 | 1272,12,2008,7,5,8 1313 | 101,17,2005,3,10,1 1314 | 140,14,2005,3,12,1 1315 | 703,10,2006,2,18,1 1316 | 771,15,2006,5,26,1 1317 | 810,12,2006,5,28,1 1318 | 550,4,2005,12,30,1 1319 | 247,16,2005,6,24,1 1320 | 1247,8,2008,7,4,8 1321 | 527,2,2005,12,29,1 1322 | 1115,2,2006,11,26,1 1323 | 1089,18,2006,11,24,1 1324 | 56,14,2005,3,8,1 1325 | -------------------------------------------------------------------------------- /load_data.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pandas as pd 3 | import os 4 | 5 | TEMPERATURE_FILE_PATH = './data/temperature_history.csv' 6 | LOAD_FILE_PATH = './data/Load_history.csv' 7 | 8 | 9 | 10 | def normalization(data, if_mean=True, if_std=True, if_log = False): 11 | if if_log: 12 | data = np.log(data + 1) 13 | df = pd.DataFrame(data=data) 14 | rst = df 15 | mean = df.mean(axis=0) 16 | std = df.std(axis=0) 17 | if if_mean: 18 | rst = rst.sub(mean, axis=1) 19 | if if_std: 20 | rst = rst.div(std, axis=1) 21 | return np.array(mean), np.array(std), np.array(rst) 22 | 23 | def de_normalization(mean,std,data,if_mean=True, if_std=True, if_log = False): 24 | df = pd.DataFrame(data=data,copy=True) 25 | rst = df 26 | if if_mean: 27 | rst = rst.mul(std, axis=1) 28 | if if_std: 29 | rst = rst.add(mean, axis=1) 30 | rst = np.array(rst) 31 | if if_log: 32 | scale_array = np.empty_like(rst) 33 | scale_array.fill(np.e) 34 | rst = np.power(scale_array, rst) - 1 35 | return rst 36 | 37 | 38 | def _read_load_from_raw(if_full=False): 39 | print 'Making load data from raw...' 40 | raw_load_file = LOAD_FILE_PATH 41 | if if_full: 42 | raw_load_file = './data/full_load_history.csv' 43 | raw_data = pd.read_csv(raw_load_file, sep=',') 44 | T = [] 45 | L = [] 46 | for name, group in raw_data.groupby('zone_id'): 47 | if name == 1: 48 | for row in group.iterrows(): 49 | tstep = np.zeros((4, 1), dtype=float) 50 | for i in range(1, 4): 51 | if i == 1: 52 | tstep[i - 1][0] = row[1][i] 53 | else: 54 | tstep[i - 1][0] = row[1][i] 55 | for i in range(4, len(row[1])): 56 | temp = np.zeros((20, 1), dtype=float) 57 | if row[1][i] != np.nan: 58 | tstep_temp = np.copy(tstep) 59 | tstep_temp[3][0] = i - 3 60 | if isinstance(row[1][i], str): 61 | temp[0][0] = float(row[1][i].replace(',', '')) 62 | else: 63 | temp[0][0] = float(row[1][i]) 64 | T.append(tstep_temp) 65 | L.append(temp) 66 | else: 67 | index = int(name) 68 | count = 0 69 | for row in group.iterrows(): 70 | for i in range(4, len(row[1])): 71 | if row[1][i] != np.nan: 72 | if isinstance(row[1][i], str): 73 | L[count][ 74 | index - 1][0] = \ 75 | float(row[1][i].replace(',', '')) 76 | else: 77 | L[count][index - 1][0] = float(row[1][i]) 78 | count += 1 79 | if len(L) != len(T): 80 | raise ValueError("TypeError") 81 | length = len(T) 82 | Time = np.array(T, dtype=np.float64) 83 | Load = np.array(L, dtype=np.float64) 84 | _load_to_csv(length, Time, Load, if_full) 85 | Time = Time.reshape(length, 1, 4) 86 | Load = Load.reshape(length, 1, 20) 87 | print 'Done' 88 | return length, Time, Load 89 | 90 | 91 | def _load_to_csv(length, Time, Load, if_full=False): 92 | load_file = './data/load.csv' 93 | time_load_file = './data/time_load.csv' 94 | if if_full: 95 | load_file = './data/full_load.csv' 96 | time_load_file = './data/time_full_load.csv' 97 | time_df = pd.DataFrame(Time.reshape(length, 4)) 98 | time_df.to_csv(time_load_file, header=False, index=False) 99 | load_df = pd.DataFrame(Load.reshape(length, 20)) 100 | load_df.to_csv(load_file, header=False, index=False) 101 | 102 | 103 | def _temp_to_csv(length, Time, Temp): 104 | time_df = pd.DataFrame(Time.reshape(length, 4)) 105 | time_df.to_csv('./data/time_temp.csv', header=False, index=False) 106 | temp_df = pd.DataFrame(Temp.reshape(length, 11)) 107 | temp_df.to_csv('./data/temp.csv', header=False, index=False) 108 | 109 | 110 | def _read_temp_from_raw(): 111 | print 'Making temperature data from raw...' 112 | raw_data = pd.read_csv(TEMPERATURE_FILE_PATH, sep=',') 113 | T = [] 114 | TIME = [] 115 | for name, group in raw_data.groupby('station_id'): 116 | if name == 1: 117 | for row in group.iterrows(): 118 | tstep = np.zeros((4, 1), dtype=float) 119 | for i in range(1, 4): 120 | if i == 1: 121 | tstep[i - 1][0] = row[1][i] 122 | else: 123 | tstep[i - 1][0] = row[1][i] 124 | for i in range(4, len(row[1])): 125 | temp = np.zeros((11, 1), dtype=float) 126 | if row[1][i] != np.nan: 127 | temp[0][0] = row[1][i] 128 | tstep_temp = np.copy(tstep) 129 | tstep_temp[3][0] = i - 3 130 | TIME.append(tstep_temp) 131 | T.append(temp) 132 | else: 133 | index = int(name) 134 | count = 0 135 | for row in group.iterrows(): 136 | for i in range(4, len(row[1])): 137 | if row[1][i] != np.nan: 138 | T[count][index - 1][0] = row[1][i] 139 | count += 1 140 | if len(TIME) != len(T): 141 | raise ValueError("TypeError") 142 | length = len(TIME) 143 | Time = np.array(TIME, dtype=np.float64) 144 | Temp = np.array(T, dtype=np.float64) 145 | _temp_to_csv(length, Time, Temp) 146 | Time = Time.reshape(length, 1, 4) 147 | Temp = Temp.reshape(length, 1, 11) 148 | print 'Done' 149 | return length, Time, Temp 150 | 151 | 152 | def read_temperature_history(if_full=False): 153 | temp_file = './data/temp.csv' 154 | time_temp_file = './data/time_temp.csv' 155 | if if_full: 156 | temp_file = './data/full_temp.csv' 157 | time_temp_file = './data/full_temp_time.csv' 158 | if os.path.exists(temp_file) and \ 159 | os.path.exists(time_temp_file): 160 | Time = np.array(pd.read_csv(time_temp_file, header=None)) 161 | Temp = np.array(pd.read_csv(temp_file, header=None)) 162 | if len(Time) != len(Temp): 163 | raise ValueError("TypeError") 164 | length = len(Time) 165 | Time = Time.reshape((length, 1, 4)) 166 | Temp = Temp.reshape((length, 1, 11)) 167 | return length, Time, Temp 168 | else: 169 | return _read_temp_from_raw() 170 | 171 | 172 | def read_load_history(if_full=False): 173 | load_file = './data/load.csv' 174 | time_load_file = './data/time_load.csv' 175 | if if_full: 176 | load_file = './data/full_load.csv' 177 | time_load_file = './data/full_temp_time.csv' 178 | if os.path.exists(load_file) and \ 179 | os.path.exists(time_load_file): 180 | Load = np.array(pd.read_csv(load_file, header=None)) 181 | Time = np.array(pd.read_csv(time_load_file, header=None)) 182 | if len(Time) != len(Load): 183 | raise ValueError("TypeError") 184 | length = len(Load) 185 | Time = Time.reshape(length, 1, 4) 186 | Load = Load.reshape(length, 1, 20) 187 | return length, Time, Load 188 | else: 189 | return _read_load_from_raw(if_full) 190 | 191 | def _read_benchmark_from_raw(): 192 | if os.path.exists('./data/predict.csv'): 193 | raw_data = pd.read_csv('./data/predict.csv', header=None) 194 | L = [] 195 | for name, group in raw_data.groupby(0): 196 | if name == 1: 197 | for row in group.iterrows(): 198 | for i in range(1, len(row[1])): 199 | temp = np.zeros((21, 1), dtype=float) 200 | if row[1][i] != np.nan: 201 | temp[0][0] = row[1][i] 202 | L.append(temp) 203 | else: 204 | index = int(name) 205 | count = 0 206 | for row in group.iterrows(): 207 | for i in range(1, len(row[1])): 208 | if row[1][i] != np.nan: 209 | L[count][index - 1][0] = row[1][i] 210 | count += 1 211 | L = np.array(L) 212 | L_tf = pd.DataFrame(L.reshape((len(L),21))) 213 | L_tf.to_csv('./data/expected_result.csv', header=False, index=False) 214 | return L.reshape((len(L),1,21)) 215 | 216 | def read_benchmark(): 217 | if os.path.exists('./data/expected_result.csv'): 218 | bechmark = np.array(pd.read_csv('./data/expected_result.csv', header=None)) 219 | return bechmark.reshape(len(bechmark), 1, 21) 220 | else: 221 | return _read_benchmark_from_raw() 222 | 223 | def setup_training_data(use_load_for_training = True, july_data = False): 224 | print('Loading Data') 225 | length_temperature, time_temperature, temperature = read_temperature_history(if_full=True) 226 | length_load, time_load, load = read_load_history(if_full=True) 227 | mean_time, std_time, time_temperature = normalization(time_temperature.reshape(len(time_temperature),4)) 228 | mean, std, temperature = normalization(temperature.reshape(len(temperature),11)) 229 | mean_load, std_load, load = normalization(load.reshape(len(load), 20),if_log=True) 230 | temperature = temperature.reshape((len(temperature), 1, 11)) 231 | time_temperature = time_temperature.reshape((len(time_temperature), 1, 4)) 232 | load = load.reshape((len(load), 1, 20)) 233 | 234 | # uses the load of previous week as inputs for forcasting 235 | if use_load_for_training: 236 | load_for_inputs = np.copy(load) 237 | for i in range(len(load_for_inputs)-1,-1,-1): 238 | index = i - 24 * 7 239 | if index < 0: 240 | index = i 241 | load_for_inputs[i] = np.copy(load_for_inputs[index]) 242 | 243 | inputs_full = np.concatenate((temperature,time_temperature,load_for_inputs),axis=2) 244 | inputs_full = inputs_full.reshape((len(inputs_full), 1, 35)) 245 | else: 246 | inputs_full = np.concatenate((temperature,time_temperature),axis=2) 247 | inputs_full = inputs_full.reshape((len(inputs_full), 1, 15)) 248 | load = load.reshape(len(load),20) 249 | inputs_full_temp = np.copy(inputs_full) 250 | inputs_full = inputs_full[0:39414] 251 | expected_output_full = load[0:39414] 252 | inputs = inputs_full[0:39414 - 168] 253 | expected_output = load[0:39414 - 168] 254 | if july_data: 255 | inputs_full = inputs_full_temp 256 | expected_output_full = load 257 | inputs = inputs_full[0:39414 - 168] 258 | expected_output = load[0:39414 - 168] 259 | 260 | print('Full inputs shape',inputs_full.shape) 261 | print('Full output shape',expected_output_full.shape) 262 | print('Input shape:', inputs.shape) 263 | print('Output shape',expected_output.shape) 264 | return mean_load, std_load, inputs_full, inputs, expected_output, expected_output_full 265 | 266 | def main(): 267 | length, Time, Temp = read_temperature_history() 268 | mean, std ,Temp= normalization(Temp.reshape(len(Temp),11)) 269 | print length, len(Time), len(Temp), len(Time[0][0]), len(Temp[0][0]) 270 | # length, Time, Load = read_load_history(if_full=True) 271 | # print length, len(Time), len(Load), len(Time[0]), len(Load[0]) 272 | # print len(read_benchmark()) 273 | 274 | if __name__ == "__main__": 275 | main() 276 | -------------------------------------------------------------------------------- /load_prediction_model_xgboost.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## XGBoost Model for Time Series Prediction\n", 8 | "#### Group Project of Information Retrieval and Data Mining 2016 @ UCL\n", 9 | "\n", 10 | "Authors: [Yijing Yang](yijing.yang.15@ucl.ac.uk), Xinyi He, Ying Wen\n", 11 | "\n", 12 | "This notebook trains a XGBoost model for load forcasting on [Global Energy Forecasting Competition 2012](https://www.kaggle.com/c/global-energy-forecasting-competition-2012-load-forecasting) dataset with [XGBoost](https://github.com/dmlc/xgboost).\n", 13 | "\n", 14 | "Except where otherwise noted, this work is subject to a Creative Common Attribution-NonCommercial 4.0 License. [Details.](https://creativecommons.org/licenses/by-nc/4.0/)\n", 15 | "\n" 16 | ] 17 | }, 18 | { 19 | "cell_type": "code", 20 | "execution_count": 1, 21 | "metadata": { 22 | "collapsed": true 23 | }, 24 | "outputs": [], 25 | "source": [ 26 | "import numpy as np\n", 27 | "from scipy import stats\n", 28 | "import pandas as pd\n", 29 | "import matplotlib.pyplot as plt\n", 30 | "import datetime\n", 31 | "import operator\n", 32 | "import xgboost as xgb\n", 33 | "from sklearn.metrics import mean_squared_error" 34 | ] 35 | }, 36 | { 37 | "cell_type": "markdown", 38 | "metadata": {}, 39 | "source": [ 40 | "## Data Processing" 41 | ] 42 | }, 43 | { 44 | "cell_type": "code", 45 | "execution_count": 2, 46 | "metadata": { 47 | "collapsed": false 48 | }, 49 | "outputs": [], 50 | "source": [ 51 | "data_load = pd.read_csv('/Users/yangyijing/Desktop/IR_group/data/full_load.csv',header = None)\n", 52 | "data_load.columns = ['zone1','zone2','zone3','zone4','zone5','zone6','zone7','zone8','zone9','zone10','zone11','zone12','zone13','zone14','zone15','zone16','zone17','zone18','zone19','zone20']\n", 53 | "data_load = np.log(data_load + 1)\n", 54 | "time = pd.read_csv('/Users/yangyijing/Desktop/IR_group/data/time_load.csv',header = None)\n", 55 | "time.columns = ['year','month','day','hour'] " 56 | ] 57 | }, 58 | { 59 | "cell_type": "code", 60 | "execution_count": 6, 61 | "metadata": { 62 | "collapsed": false 63 | }, 64 | "outputs": [], 65 | "source": [ 66 | "for i in range(0,len(time)):\n", 67 | " if time.ix[i,0] == 0:\n", 68 | " time.ix[i,0] = '2004'\n", 69 | " if time.ix[i,0] == 1:\n", 70 | " time.ix[i,0] = '2005'\n", 71 | " if time.ix[i,0] == 2:\n", 72 | " time.ix[i,0] = '2006'\n", 73 | " if time.ix[i,0] == 3:\n", 74 | " time.ix[i,0] = '2007'\n", 75 | " if time.ix[i,0] == 4:\n", 76 | " time.ix[i,0] = '2008'\n", 77 | "time1 = time\n", 78 | "time1['date'] = time1.apply(lambda x:pd.datetime.strptime(\"{0} {1} {2}\".format(x['year'],x['month'], x['day']), '%Y %m %d'),axis=1)\n", 79 | "time1['date_time'] = pd.to_datetime(time1['date']) + pd.TimedeltaIndex(time1['hour'], unit='H')\n", 80 | "load_withtime = pd.concat([time1,data_load],axis = 1)" 81 | ] 82 | }, 83 | { 84 | "cell_type": "code", 85 | "execution_count": 10, 86 | "metadata": { 87 | "collapsed": false 88 | }, 89 | "outputs": [], 90 | "source": [ 91 | "temp = pd.read_csv('/Users/yangyijing/Desktop/IR_group/data/full_temp.csv',header = None)\n", 92 | "temp.columns =['station1','station2','station3','station4','station5','station6','station7','station8','station9','station10','station11']\n", 93 | "load_withtime = pd.concat([load_withtime,temp],axis = 1)" 94 | ] 95 | }, 96 | { 97 | "cell_type": "markdown", 98 | "metadata": {}, 99 | "source": [ 100 | "#### Split the records based on zone_id" 101 | ] 102 | }, 103 | { 104 | "cell_type": "markdown", 105 | "metadata": {}, 106 | "source": [ 107 | "This step is the preparation for building time lag features. We split records based on zone_id and generate a new column called 'zone_id'." 108 | ] 109 | }, 110 | { 111 | "cell_type": "code", 112 | "execution_count": 13, 113 | "metadata": { 114 | "collapsed": false 115 | }, 116 | "outputs": [], 117 | "source": [ 118 | "def data_zone(i):\n", 119 | " t = load_withtime.ix[:,[0,1,2,3,4,5,5+i,-11,-10,-9,-8,-7,-6,-5,-4,-3,-2,-1]]\n", 120 | " t.columns = ['year','month','day','hour','date','date_time','load','station1','station2','station3','station4','station5','station6','station7','station8','station9','station10','station11'] \n", 121 | " s = [i] * len(t)\n", 122 | " t['zone_id'] = s\n", 123 | " load = pd.Series(t['load'])\n", 124 | " lagload = pd.concat([load.shift(j) for j in range(1,25)],axis=1)\n", 125 | " lagload.columns = ['lag_t-%d' %j for j in range(1,25)]\n", 126 | " load_lag = pd.concat([t, lagload], axis=1)\n", 127 | " l = len(load_lag)\n", 128 | " load_lag = load_lag.tail(l - 24)\n", 129 | " return load_lag" 130 | ] 131 | }, 132 | { 133 | "cell_type": "code", 134 | "execution_count": 14, 135 | "metadata": { 136 | "collapsed": false, 137 | "scrolled": true 138 | }, 139 | "outputs": [ 140 | { 141 | "name": "stderr", 142 | "output_type": "stream", 143 | "text": [ 144 | "/Users/yangyijing/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:5: SettingWithCopyWarning: \n", 145 | "A value is trying to be set on a copy of a slice from a DataFrame.\n", 146 | "Try using .loc[row_indexer,col_indexer] = value instead\n", 147 | "\n", 148 | "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" 149 | ] 150 | } 151 | ], 152 | "source": [ 153 | "t1 = data_zone(1)\n", 154 | "t2 = data_zone(2)\n", 155 | "t3 = data_zone(3)\n", 156 | "t4 = data_zone(4)\n", 157 | "t5 = data_zone(5)\n", 158 | "t6 = data_zone(6)\n", 159 | "t7 = data_zone(7)\n", 160 | "t8 = data_zone(8)\n", 161 | "t9 = data_zone(9)\n", 162 | "t10 = data_zone(10)\n", 163 | "t11 = data_zone(11)\n", 164 | "t12 = data_zone(12)\n", 165 | "t13 = data_zone(13)\n", 166 | "t14 = data_zone(14)\n", 167 | "t15 = data_zone(15)\n", 168 | "t16 = data_zone(16)\n", 169 | "t17 = data_zone(17)\n", 170 | "t18 = data_zone(18)\n", 171 | "t19 = data_zone(19)\n", 172 | "t20 = data_zone(20)\n", 173 | "combinezones = pd.concat([t1,t2,t3,t4,t5,t6,t7,t8,t9,t10,t11,t12,t13,t14,t15,t16,t17,t18,t19,t20],axis = 0)" 174 | ] 175 | }, 176 | { 177 | "cell_type": "code", 178 | "execution_count": 17, 179 | "metadata": { 180 | "collapsed": false 181 | }, 182 | "outputs": [], 183 | "source": [ 184 | "df = combinezones[combinezones['zone_id'] == 1]\n", 185 | "df = df.head(39222)\n", 186 | "for i in range(2,21):\n", 187 | " df1 = combinezones[combinezones['zone_id'] == i]\n", 188 | " df1 = df1.head(39222)\n", 189 | " df = pd.concat([df,df1],axis = 0)\n", 190 | " " 191 | ] 192 | }, 193 | { 194 | "cell_type": "markdown", 195 | "metadata": { 196 | "collapsed": true 197 | }, 198 | "source": [ 199 | "#### Feature importance" 200 | ] 201 | }, 202 | { 203 | "cell_type": "markdown", 204 | "metadata": {}, 205 | "source": [ 206 | "Now, we have lagged loads based on 24 hours as new features. We use XGBoost to test the feature importance of the 24 features. As a result, we can find several most important lag features." 207 | ] 208 | }, 209 | { 210 | "cell_type": "code", 211 | "execution_count": 18, 212 | "metadata": { 213 | "collapsed": true 214 | }, 215 | "outputs": [], 216 | "source": [ 217 | "def ceate_feature_map(features):\n", 218 | " outfile = open('xgb.fmap', 'w')\n", 219 | " i = 0\n", 220 | " for feat in features:\n", 221 | " outfile.write('{0}\\t{1}\\tq\\n'.format(i, feat))\n", 222 | " i = i + 1\n", 223 | "\n", 224 | " outfile.close()" 225 | ] 226 | }, 227 | { 228 | "cell_type": "code", 229 | "execution_count": 19, 230 | "metadata": { 231 | "collapsed": true 232 | }, 233 | "outputs": [], 234 | "source": [ 235 | "def get_data(df):\n", 236 | " train = df\n", 237 | " features = list(df.columns[19:])\n", 238 | " y_train = train.load\n", 239 | " x_train = train[features]\n", 240 | "\n", 241 | " return features, x_train, y_train" 242 | ] 243 | }, 244 | { 245 | "cell_type": "code", 246 | "execution_count": 20, 247 | "metadata": { 248 | "collapsed": false, 249 | "scrolled": false 250 | }, 251 | "outputs": [], 252 | "source": [ 253 | "features, x_train, y_train = get_data(df)\n", 254 | "ceate_feature_map(features)\n", 255 | "\n", 256 | "xgb_params = {\"objective\": \"reg:linear\", \"eta\": 0.01, \"max_depth\": 8, \"seed\": 42, \"silent\": 1}\n", 257 | "num_rounds = 500\n", 258 | "\n", 259 | "dtrain = xgb.DMatrix(x_train, label=y_train)\n", 260 | "gbdt = xgb.train(xgb_params, dtrain, num_rounds)\n", 261 | "\n", 262 | "importance = gbdt.get_fscore(fmap='xgb.fmap')\n", 263 | "importance = sorted(importance.items(), key=operator.itemgetter(1))\n", 264 | "\n", 265 | "df = pd.DataFrame(importance, columns=['feature', 'fscore'])\n", 266 | "df['fscore'] = df['fscore'] / df['fscore'].sum()\n", 267 | "\n", 268 | "plt.figure()\n", 269 | "df.plot()\n", 270 | "df.plot(kind='barh', x='feature', y='fscore', legend=False, figsize=(6, 10))\n", 271 | "plt.title('XGBoost Feature Importance')\n", 272 | "plt.xlabel('relative importance')\n", 273 | "plt.gcf().savefig('feature_importance_xgb.png')" 274 | ] 275 | }, 276 | { 277 | "cell_type": "markdown", 278 | "metadata": {}, 279 | "source": [ 280 | "#### Transforming categorical variables" 281 | ] 282 | }, 283 | { 284 | "cell_type": "markdown", 285 | "metadata": {}, 286 | "source": [ 287 | "After feature importance, we select lag1,2,3,24 as the important lag features. Then we need to transform categorical variables,year,month,day,hour,zone_id, to dummies." 288 | ] 289 | }, 290 | { 291 | "cell_type": "code", 292 | "execution_count": 21, 293 | "metadata": { 294 | "collapsed": false 295 | }, 296 | "outputs": [], 297 | "source": [ 298 | "data = combinezones.ix[:,[0,1,2,3,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,-1]].reset_index()\n", 299 | "data = data.drop('index',axis = 1)" 300 | ] 301 | }, 302 | { 303 | "cell_type": "code", 304 | "execution_count": 23, 305 | "metadata": { 306 | "collapsed": false 307 | }, 308 | "outputs": [], 309 | "source": [ 310 | "# Create and merge binary year features\n", 311 | "predictBy=1\n", 312 | "predictBy = -(predictBy-1)\n", 313 | "year = pd.get_dummies(pd.DatetimeIndex(data.date_time).year, prefix='Year')\n", 314 | "year = year.set_index(pd.DatetimeIndex(data.date_time))\n", 315 | "if predictBy != 0:\n", 316 | " if predictBy > 0 or type(predictBy) != int:\n", 317 | " raise ValueError(\"predictBy must be greater than or equal to 1 and an int. Found predictBy={} and type={}\"\n", 318 | " .format(-(predictBy+1), type(predictBy)))\n", 319 | " else:\n", 320 | " year = year.shift(predictBy)\n", 321 | "data = data.set_index('date_time')\n", 322 | "data = pd.concat([data, year], axis=1)" 323 | ] 324 | }, 325 | { 326 | "cell_type": "code", 327 | "execution_count": 25, 328 | "metadata": { 329 | "collapsed": true 330 | }, 331 | "outputs": [], 332 | "source": [ 333 | "# Create and merge binary month features\n", 334 | "months = pd.get_dummies(pd.DatetimeIndex(data.index).month, prefix='Month')\n", 335 | "months = months.set_index(pd.DatetimeIndex(data.index))\n", 336 | "if predictBy != 0:\n", 337 | " if predictBy > 0 or type(predictBy) != int:\n", 338 | " raise ValueError(\"predictBy must be greater than or equal to 1 and an int. Found predictBy={} and type={}\"\n", 339 | " .format(-(predictBy+1), type(predictBy)))\n", 340 | " else:\n", 341 | " months = months.shift(predictBy)\n", 342 | "data = pd.concat([data, months], axis=1)" 343 | ] 344 | }, 345 | { 346 | "cell_type": "code", 347 | "execution_count": 27, 348 | "metadata": { 349 | "collapsed": true 350 | }, 351 | "outputs": [], 352 | "source": [ 353 | "# Create and merge binary day features\n", 354 | "days = pd.get_dummies(pd.DatetimeIndex(data.index).day, prefix='Day')\n", 355 | "days = days.set_index(pd.DatetimeIndex(data.index))\n", 356 | "if predictBy != 0:\n", 357 | " if predictBy > 0 or type(predictBy) != int:\n", 358 | " raise ValueError(\"predictBy must be greater than or equal to 1 and an int. Found predictBy={} and type={}\"\n", 359 | " .format(-(predictBy+1), type(predictBy)))\n", 360 | " else:\n", 361 | " days = days.shift(predictBy)\n", 362 | "data = pd.concat([data, days], axis=1)" 363 | ] 364 | }, 365 | { 366 | "cell_type": "code", 367 | "execution_count": 29, 368 | "metadata": { 369 | "collapsed": true 370 | }, 371 | "outputs": [], 372 | "source": [ 373 | "# Create and merge binary hour features\n", 374 | "hours = pd.get_dummies(pd.DatetimeIndex(data.index).hour, prefix='Hour')\n", 375 | "hours = hours.set_index(pd.DatetimeIndex(data.index))\n", 376 | "if predictBy != 0:\n", 377 | " if predictBy > 0 or type(predictBy) != int:\n", 378 | " raise ValueError(\"predictBy must be greater than or equal to 1 and an int. Found predictBy={} and type={}\"\n", 379 | " .format(-(predictBy+1), type(predictBy)))\n", 380 | " else:\n", 381 | " hours = hours.shift(predictBy)\n", 382 | "data = pd.concat([data, hours], axis=1)" 383 | ] 384 | }, 385 | { 386 | "cell_type": "code", 387 | "execution_count": 31, 388 | "metadata": { 389 | "collapsed": true 390 | }, 391 | "outputs": [], 392 | "source": [ 393 | "# Create and merge binary zone features\n", 394 | "zones = pd.get_dummies(data.zone_id,prefix='Zone')\n", 395 | "if predictBy != 0:\n", 396 | " if predictBy > 0 or type(predictBy) != int:\n", 397 | " raise ValueError(\"predictBy must be greater than or equal to 1 and an int. Found predictBy={} and type={}\"\n", 398 | " .format(-(predictBy+1), type(predictBy)))\n", 399 | " else:\n", 400 | " zones = zones.shift(predictBy)\n", 401 | "data = pd.concat([data, zones], axis=1)" 402 | ] 403 | }, 404 | { 405 | "cell_type": "markdown", 406 | "metadata": {}, 407 | "source": [ 408 | "generate the actual data(records from 23/06/2008 to 30/06/2008) for comparison with our predictions" 409 | ] 410 | }, 411 | { 412 | "cell_type": "code", 413 | "execution_count": 323, 414 | "metadata": { 415 | "collapsed": false 416 | }, 417 | "outputs": [], 418 | "source": [ 419 | "data_test = data_load.tail(354).head(168)\n", 420 | "data_test = np.exp(data_test) - 1\n", 421 | "data_test = data_test.reset_index()\n", 422 | "data_test = data_test.drop('index',axis = 1)" 423 | ] 424 | }, 425 | { 426 | "cell_type": "markdown", 427 | "metadata": {}, 428 | "source": [ 429 | "## Training Model" 430 | ] 431 | }, 432 | { 433 | "cell_type": "markdown", 434 | "metadata": {}, 435 | "source": [ 436 | "#### Matching temperatures of stations with zones" 437 | ] 438 | }, 439 | { 440 | "cell_type": "markdown", 441 | "metadata": {}, 442 | "source": [ 443 | "This part is used to match the stations with zones. We train XGBoost model for each zones with all 11 stations. For each time with one station, we get MSE when comparing predictions with acutal data. We consider the stataion with the least MSE as the correct station for the zone and use the temperature of this station to predict the load of this zone." 444 | ] 445 | }, 446 | { 447 | "cell_type": "code", 448 | "execution_count": 175, 449 | "metadata": { 450 | "collapsed": false 451 | }, 452 | "outputs": [], 453 | "source": [ 454 | "def data_xgboost(station_id,zone_id):\n", 455 | " train = data[data['zone_id'] == zone_id]\n", 456 | " features = list(data.columns[17:])\n", 457 | " features.append(data.columns[station_id+4])\n", 458 | " Y = train.load\n", 459 | " X = train[features]\n", 460 | " X_train = X.head(39222)\n", 461 | " Y_train = Y.head(39222)\n", 462 | " X_test = X.tail(354+168).head(168+168)\n", 463 | " X_test = X_test.tail(len(X_test)-168)\n", 464 | " X_testlag = data.tail(354+168).head(168+168)\n", 465 | " return X_train,Y_train,X_testlag,X_test" 466 | ] 467 | }, 468 | { 469 | "cell_type": "code", 470 | "execution_count": 176, 471 | "metadata": { 472 | "collapsed": true 473 | }, 474 | "outputs": [], 475 | "source": [ 476 | "def prediction(index,X_train,Y_train,X_testlag,X_test,gbdt):\n", 477 | " dtest = xgb.DMatrix(X_test)\n", 478 | " ypred = gbdt.predict(dtest)\n", 479 | " ypred = ypred[index]\n", 480 | " X_testlag.ix[168 + index,4] = ypred\n", 481 | " lag = X_testlag['load']\n", 482 | " load = pd.Series(lag)\n", 483 | " lagload = pd.concat([load.shift(j) for j in [1,2,3,24]],axis=1)\n", 484 | " lagload.columns = ['lag_t-%d' %j for j in [1,2,3,24]]\n", 485 | " load_lag = pd.concat([load,lagload], axis=1)\n", 486 | " test = X_test\n", 487 | " load_lag= load_lag.tail(len(load_lag)-168)\n", 488 | " test['lag_t-1'] = load_lag['lag_t-1']\n", 489 | " test['lag_t-2'] = load_lag['lag_t-2']\n", 490 | " test['lag_t-3'] = load_lag['lag_t-3']\n", 491 | " test['lag_t-24'] = load_lag['lag_t-24']\n", 492 | " return ypred,test,X_testlag\n" 493 | ] 494 | }, 495 | { 496 | "cell_type": "code", 497 | "execution_count": 177, 498 | "metadata": { 499 | "collapsed": false 500 | }, 501 | "outputs": [], 502 | "source": [ 503 | "def zone_station(station_id,zone_id):\n", 504 | " X_train,Y_train,X_testlag,X_test = data_xgboost(station_id,zone_id)\n", 505 | " xgb_params = {\"objective\": \"reg:linear\", \"eta\": 0.01, \"max_depth\": 8, \"seed\": 42, \"silent\": 1}\n", 506 | " num_rounds = 1000\n", 507 | " dtrain = xgb.DMatrix(X_train, label=Y_train)\n", 508 | " gbdt = xgb.train(xgb_params, dtrain, num_rounds)\n", 509 | " pre = []\n", 510 | " for i in range(0,168): \n", 511 | " ypred,test,testlag = prediction(i,X_train,Y_train,X_testlag,X_test,gbdt)\n", 512 | " ypred = np.exp(ypred) - 1\n", 513 | " pre.append(ypred)\n", 514 | " X_testlag = testlag\n", 515 | " X_test = test\n", 516 | " return pre" 517 | ] 518 | }, 519 | { 520 | "cell_type": "code", 521 | "execution_count": 178, 522 | "metadata": { 523 | "collapsed": false 524 | }, 525 | "outputs": [], 526 | "source": [ 527 | "def fit_zone(zone_id):\n", 528 | " mse = []\n", 529 | " for i in range(1,12):\n", 530 | " pre = zone_station(i,zone_id)\n", 531 | " m = ((pre - data_test.ix[:,zone_id-1]) ** 2).mean()\n", 532 | " mse.append(m)\n", 533 | " return mse" 534 | ] 535 | }, 536 | { 537 | "cell_type": "code", 538 | "execution_count": 179, 539 | "metadata": { 540 | "collapsed": false 541 | }, 542 | "outputs": [], 543 | "source": [ 544 | "mse1 = fit_zone(1)" 545 | ] 546 | }, 547 | { 548 | "cell_type": "code", 549 | "execution_count": 180, 550 | "metadata": { 551 | "collapsed": false 552 | }, 553 | "outputs": [ 554 | { 555 | "data": { 556 | "text/plain": [ 557 | "[21270952.80542777,\n", 558 | " 3927653.8992050355,\n", 559 | " 18121670.56752389,\n", 560 | " 22002925.579662774,\n", 561 | " 19475431.258014724,\n", 562 | " 8419631.395374523,\n", 563 | " 12542541.833644276,\n", 564 | " 18832205.3362693,\n", 565 | " 15809877.502839724,\n", 566 | " 9878435.277929336,\n", 567 | " 11106709.427944656]" 568 | ] 569 | }, 570 | "execution_count": 180, 571 | "metadata": {}, 572 | "output_type": "execute_result" 573 | } 574 | ], 575 | "source": [ 576 | "mse1" 577 | ] 578 | }, 579 | { 580 | "cell_type": "code", 581 | "execution_count": 181, 582 | "metadata": { 583 | "collapsed": false 584 | }, 585 | "outputs": [ 586 | { 587 | "data": { 588 | "text/plain": [ 589 | "[392999315.4694524,\n", 590 | " 249274255.8481766,\n", 591 | " 280670680.9364505,\n", 592 | " 420464942.50753546,\n", 593 | " 362733863.32131356,\n", 594 | " 221252753.96372646,\n", 595 | " 284793706.56537443,\n", 596 | " 305123177.8136957,\n", 597 | " 287098076.3789874,\n", 598 | " 237228090.66902184,\n", 599 | " 206671079.1043431]" 600 | ] 601 | }, 602 | "execution_count": 181, 603 | "metadata": {}, 604 | "output_type": "execute_result" 605 | } 606 | ], 607 | "source": [ 608 | "mse2 = fit_zone(2)\n", 609 | "mse2" 610 | ] 611 | }, 612 | { 613 | "cell_type": "code", 614 | "execution_count": 182, 615 | "metadata": { 616 | "collapsed": false, 617 | "scrolled": true 618 | }, 619 | "outputs": [ 620 | { 621 | "data": { 622 | "text/plain": [ 623 | "[433609966.2443199,\n", 624 | " 274159245.70301276,\n", 625 | " 318407590.9105416,\n", 626 | " 485972255.21537143,\n", 627 | " 497630909.7235389,\n", 628 | " 255603428.42413068,\n", 629 | " 351064111.7350747,\n", 630 | " 383132785.5359805,\n", 631 | " 357854719.89796513,\n", 632 | " 273755473.738232,\n", 633 | " 246250869.90055284]" 634 | ] 635 | }, 636 | "execution_count": 182, 637 | "metadata": {}, 638 | "output_type": "execute_result" 639 | } 640 | ], 641 | "source": [ 642 | "mse3 = fit_zone(3)\n", 643 | "mse3" 644 | ] 645 | }, 646 | { 647 | "cell_type": "code", 648 | "execution_count": 243, 649 | "metadata": { 650 | "collapsed": false 651 | }, 652 | "outputs": [ 653 | { 654 | "data": { 655 | "text/plain": [ 656 | "[155327.34163537849,\n", 657 | " 168043.3274320794,\n", 658 | " 156496.7434227795,\n", 659 | " 163957.2337537399,\n", 660 | " 151713.83484553505,\n", 661 | " 168533.45339572334,\n", 662 | " 168609.24686965984,\n", 663 | " 165972.18801073532,\n", 664 | " 147877.59308559677,\n", 665 | " 169944.55046247618,\n", 666 | " 165121.26244331436]" 667 | ] 668 | }, 669 | "execution_count": 243, 670 | "metadata": {}, 671 | "output_type": "execute_result" 672 | } 673 | ], 674 | "source": [ 675 | "mse4 = fit_zone(4)\n", 676 | "mse4" 677 | ] 678 | }, 679 | { 680 | "cell_type": "code", 681 | "execution_count": 244, 682 | "metadata": { 683 | "collapsed": false 684 | }, 685 | "outputs": [ 686 | { 687 | "data": { 688 | "text/plain": [ 689 | "[3107361.7154635256,\n", 690 | " 1711032.8410049293,\n", 691 | " 2186285.1662513707,\n", 692 | " 3543525.8156238594,\n", 693 | " 2413399.3343559727,\n", 694 | " 2562802.997875697,\n", 695 | " 1590116.060498284,\n", 696 | " 3155075.558573044,\n", 697 | " 2162558.3221690455,\n", 698 | " 2516188.5055949907,\n", 699 | " 1342579.7630209592]" 700 | ] 701 | }, 702 | "execution_count": 244, 703 | "metadata": {}, 704 | "output_type": "execute_result" 705 | } 706 | ], 707 | "source": [ 708 | "mse5 = fit_zone(5)\n", 709 | "mse5" 710 | ] 711 | }, 712 | { 713 | "cell_type": "code", 714 | "execution_count": 245, 715 | "metadata": { 716 | "collapsed": false 717 | }, 718 | "outputs": [ 719 | { 720 | "data": { 721 | "text/plain": [ 722 | "[473633123.9924499,\n", 723 | " 263592376.41835228,\n", 724 | " 269190145.73303205,\n", 725 | " 501647250.1424596,\n", 726 | " 455931423.42795354,\n", 727 | " 260082638.10179323,\n", 728 | " 390149162.7630609,\n", 729 | " 331033477.9336039,\n", 730 | " 350951883.85667086,\n", 731 | " 248267388.66798392,\n", 732 | " 159214491.42981917]" 733 | ] 734 | }, 735 | "execution_count": 245, 736 | "metadata": {}, 737 | "output_type": "execute_result" 738 | } 739 | ], 740 | "source": [ 741 | "mse6 = fit_zone(6)\n", 742 | "mse6" 743 | ] 744 | }, 745 | { 746 | "cell_type": "code", 747 | "execution_count": 246, 748 | "metadata": { 749 | "collapsed": false 750 | }, 751 | "outputs": [ 752 | { 753 | "data": { 754 | "text/plain": [ 755 | "[433609966.2443199,\n", 756 | " 274159245.70301276,\n", 757 | " 318407590.9105416,\n", 758 | " 485972255.21537143,\n", 759 | " 497630909.7235389,\n", 760 | " 255603428.42413068,\n", 761 | " 351064111.7350747,\n", 762 | " 383132785.5359805,\n", 763 | " 357854719.89796513,\n", 764 | " 273755473.738232,\n", 765 | " 246250869.90055284]" 766 | ] 767 | }, 768 | "execution_count": 246, 769 | "metadata": {}, 770 | "output_type": "execute_result" 771 | } 772 | ], 773 | "source": [ 774 | "mse7 = fit_zone(7)\n", 775 | "mse7" 776 | ] 777 | }, 778 | { 779 | "cell_type": "code", 780 | "execution_count": 247, 781 | "metadata": { 782 | "collapsed": false 783 | }, 784 | "outputs": [ 785 | { 786 | "data": { 787 | "text/plain": [ 788 | "[326985.0045926078,\n", 789 | " 157647.8759511372,\n", 790 | " 268429.33310787333,\n", 791 | " 328184.81575939927,\n", 792 | " 294851.3494319672,\n", 793 | " 253689.18290295775,\n", 794 | " 205091.79513037528,\n", 795 | " 252298.05897135075,\n", 796 | " 214043.941442838,\n", 797 | " 237374.21393530504,\n", 798 | " 175156.47503426313]" 799 | ] 800 | }, 801 | "execution_count": 247, 802 | "metadata": {}, 803 | "output_type": "execute_result" 804 | } 805 | ], 806 | "source": [ 807 | "mse8 = fit_zone(8)\n", 808 | "mse8" 809 | ] 810 | }, 811 | { 812 | "cell_type": "code", 813 | "execution_count": 248, 814 | "metadata": { 815 | "collapsed": false 816 | }, 817 | "outputs": [ 818 | { 819 | "data": { 820 | "text/plain": [ 821 | "[318402756.371569,\n", 822 | " 310295108.65602887,\n", 823 | " 377846523.96056557,\n", 824 | " 291659701.4955377,\n", 825 | " 311736911.91899586,\n", 826 | " 289928971.03480136,\n", 827 | " 326421974.8833237,\n", 828 | " 329569987.6404961,\n", 829 | " 468226531.81805956,\n", 830 | " 292716362.3081562,\n", 831 | " 382525882.9385512]" 832 | ] 833 | }, 834 | "execution_count": 248, 835 | "metadata": {}, 836 | "output_type": "execute_result" 837 | } 838 | ], 839 | "source": [ 840 | "mse9 = fit_zone(9)\n", 841 | "mse9" 842 | ] 843 | }, 844 | { 845 | "cell_type": "code", 846 | "execution_count": 249, 847 | "metadata": { 848 | "collapsed": false 849 | }, 850 | "outputs": [ 851 | { 852 | "data": { 853 | "text/plain": [ 854 | "[2138115430.3176327,\n", 855 | " 2647096352.0681014,\n", 856 | " 2617999483.9701447,\n", 857 | " 2555747620.3655305,\n", 858 | " 2658368073.8178506,\n", 859 | " 2665798574.7475104,\n", 860 | " 2579333664.600109,\n", 861 | " 2601950167.214591,\n", 862 | " 2662031569.4605217,\n", 863 | " 2638980411.2825646,\n", 864 | " 2671652631.333048]" 865 | ] 866 | }, 867 | "execution_count": 249, 868 | "metadata": {}, 869 | "output_type": "execute_result" 870 | } 871 | ], 872 | "source": [ 873 | "mse10 = fit_zone(10)\n", 874 | "mse10" 875 | ] 876 | }, 877 | { 878 | "cell_type": "code", 879 | "execution_count": 250, 880 | "metadata": { 881 | "collapsed": false 882 | }, 883 | "outputs": [ 884 | { 885 | "data": { 886 | "text/plain": [ 887 | "[182652317.50432193,\n", 888 | " 1559040211.8469188,\n", 889 | " 190481363.29324314,\n", 890 | " 339817228.66322994,\n", 891 | " 291802216.56015,\n", 892 | " 605460607.5666136,\n", 893 | " 642436518.166125,\n", 894 | " 374915100.22330266,\n", 895 | " 454979694.24576515,\n", 896 | " 586971237.9855677,\n", 897 | " 1352435109.2712781]" 898 | ] 899 | }, 900 | "execution_count": 250, 901 | "metadata": {}, 902 | "output_type": "execute_result" 903 | } 904 | ], 905 | "source": [ 906 | "mse11 = fit_zone(11)\n", 907 | "mse11" 908 | ] 909 | }, 910 | { 911 | "cell_type": "code", 912 | "execution_count": 251, 913 | "metadata": { 914 | "collapsed": false 915 | }, 916 | "outputs": [ 917 | { 918 | "data": { 919 | "text/plain": [ 920 | "[816121302.622349,\n", 921 | " 4602955426.223239,\n", 922 | " 373901565.0030951,\n", 923 | " 1719146934.311291,\n", 924 | " 636070932.5243165,\n", 925 | " 3405121294.1647573,\n", 926 | " 2251249556.0268316,\n", 927 | " 1551764031.9099965,\n", 928 | " 1923317299.3634615,\n", 929 | " 2753304487.227988,\n", 930 | " 3803719432.358491]" 931 | ] 932 | }, 933 | "execution_count": 251, 934 | "metadata": {}, 935 | "output_type": "execute_result" 936 | } 937 | ], 938 | "source": [ 939 | "mse12 = fit_zone(12)\n", 940 | "mse12" 941 | ] 942 | }, 943 | { 944 | "cell_type": "code", 945 | "execution_count": 252, 946 | "metadata": { 947 | "collapsed": false 948 | }, 949 | "outputs": [ 950 | { 951 | "data": { 952 | "text/plain": [ 953 | "[21025961.783017185,\n", 954 | " 6465778.65945715,\n", 955 | " 17461109.56695033,\n", 956 | " 22646926.402513135,\n", 957 | " 17628211.841774818,\n", 958 | " 11653729.483474161,\n", 959 | " 10061744.709833344,\n", 960 | " 19228820.489822112,\n", 961 | " 13342301.722694837,\n", 962 | " 10441142.012077795,\n", 963 | " 7005695.559240286]" 964 | ] 965 | }, 966 | "execution_count": 252, 967 | "metadata": {}, 968 | "output_type": "execute_result" 969 | } 970 | ], 971 | "source": [ 972 | "mse13 = fit_zone(13)\n", 973 | "mse13" 974 | ] 975 | }, 976 | { 977 | "cell_type": "code", 978 | "execution_count": 253, 979 | "metadata": { 980 | "collapsed": false 981 | }, 982 | "outputs": [ 983 | { 984 | "data": { 985 | "text/plain": [ 986 | "[7254727.993312182,\n", 987 | " 21780740.72786538,\n", 988 | " 8050108.445405632,\n", 989 | " 6293393.204260754,\n", 990 | " 8420109.938435014,\n", 991 | " 7776772.407155903,\n", 992 | " 8339379.877752161,\n", 993 | " 6794406.074812086,\n", 994 | " 7309943.16821764,\n", 995 | " 8491224.298802694,\n", 996 | " 15355356.231732098]" 997 | ] 998 | }, 999 | "execution_count": 253, 1000 | "metadata": {}, 1001 | "output_type": "execute_result" 1002 | } 1003 | ], 1004 | "source": [ 1005 | "mse14 = fit_zone(14)\n", 1006 | "mse14" 1007 | ] 1008 | }, 1009 | { 1010 | "cell_type": "code", 1011 | "execution_count": 254, 1012 | "metadata": { 1013 | "collapsed": false 1014 | }, 1015 | "outputs": [ 1016 | { 1017 | "data": { 1018 | "text/plain": [ 1019 | "[239581180.54342836,\n", 1020 | " 281307666.60536397,\n", 1021 | " 242281771.8825172,\n", 1022 | " 163857704.08976027,\n", 1023 | " 281456200.6455739,\n", 1024 | " 164195332.0316177,\n", 1025 | " 224540483.3030723,\n", 1026 | " 142981255.08335012,\n", 1027 | " 240972218.8831353,\n", 1028 | " 149923900.44419545,\n", 1029 | " 344537369.0185592]" 1030 | ] 1031 | }, 1032 | "execution_count": 254, 1033 | "metadata": {}, 1034 | "output_type": "execute_result" 1035 | } 1036 | ], 1037 | "source": [ 1038 | "mse15 = fit_zone(15)\n", 1039 | "mse15" 1040 | ] 1041 | }, 1042 | { 1043 | "cell_type": "code", 1044 | "execution_count": 255, 1045 | "metadata": { 1046 | "collapsed": false 1047 | }, 1048 | "outputs": [ 1049 | { 1050 | "data": { 1051 | "text/plain": [ 1052 | "[9030183.883793607,\n", 1053 | " 57717641.66828687,\n", 1054 | " 7087420.662493124,\n", 1055 | " 11395228.724334737,\n", 1056 | " 7772363.263351134,\n", 1057 | " 21783161.113339756,\n", 1058 | " 15589746.281293914,\n", 1059 | " 9428809.056999117,\n", 1060 | " 11032435.793432236,\n", 1061 | " 18358360.337781712,\n", 1062 | " 33793895.04063553]" 1063 | ] 1064 | }, 1065 | "execution_count": 255, 1066 | "metadata": {}, 1067 | "output_type": "execute_result" 1068 | } 1069 | ], 1070 | "source": [ 1071 | "mse16 = fit_zone(16)\n", 1072 | "mse16" 1073 | ] 1074 | }, 1075 | { 1076 | "cell_type": "code", 1077 | "execution_count": 256, 1078 | "metadata": { 1079 | "collapsed": false 1080 | }, 1081 | "outputs": [ 1082 | { 1083 | "data": { 1084 | "text/plain": [ 1085 | "[11079495.08232228,\n", 1086 | " 15828984.768230217,\n", 1087 | " 9785031.754987868,\n", 1088 | " 8758832.698415779,\n", 1089 | " 11899343.973843459,\n", 1090 | " 5424060.033943743,\n", 1091 | " 8119351.585552146,\n", 1092 | " 6859097.070451848,\n", 1093 | " 8065537.535505201,\n", 1094 | " 5703442.744417916,\n", 1095 | " 14214847.068800347]" 1096 | ] 1097 | }, 1098 | "execution_count": 256, 1099 | "metadata": {}, 1100 | "output_type": "execute_result" 1101 | } 1102 | ], 1103 | "source": [ 1104 | "mse17 = fit_zone(17)\n", 1105 | "mse17" 1106 | ] 1107 | }, 1108 | { 1109 | "cell_type": "code", 1110 | "execution_count": 257, 1111 | "metadata": { 1112 | "collapsed": false 1113 | }, 1114 | "outputs": [ 1115 | { 1116 | "data": { 1117 | "text/plain": [ 1118 | "[1438385165.849941,\n", 1119 | " 3077555521.2414136,\n", 1120 | " 1302939299.8252237,\n", 1121 | " 2140505312.558862,\n", 1122 | " 2038107924.5151875,\n", 1123 | " 1872511059.3027067,\n", 1124 | " 1999377143.3373427,\n", 1125 | " 1691966895.3661199,\n", 1126 | " 1955689498.2139997,\n", 1127 | " 1993838400.5300016,\n", 1128 | " 2913478856.522496]" 1129 | ] 1130 | }, 1131 | "execution_count": 257, 1132 | "metadata": {}, 1133 | "output_type": "execute_result" 1134 | } 1135 | ], 1136 | "source": [ 1137 | "mse18 = fit_zone(18)\n", 1138 | "mse18" 1139 | ] 1140 | }, 1141 | { 1142 | "cell_type": "code", 1143 | "execution_count": 258, 1144 | "metadata": { 1145 | "collapsed": false 1146 | }, 1147 | "outputs": [ 1148 | { 1149 | "data": { 1150 | "text/plain": [ 1151 | "[583721115.6792753,\n", 1152 | " 647232114.556185,\n", 1153 | " 568582995.7399657,\n", 1154 | " 525984584.73740286,\n", 1155 | " 640658283.5762403,\n", 1156 | " 366102864.1813942,\n", 1157 | " 455674934.63970554,\n", 1158 | " 413035240.37604994,\n", 1159 | " 497307390.45445824,\n", 1160 | " 388109017.3767198,\n", 1161 | " 726446323.200489]" 1162 | ] 1163 | }, 1164 | "execution_count": 258, 1165 | "metadata": {}, 1166 | "output_type": "execute_result" 1167 | } 1168 | ], 1169 | "source": [ 1170 | "mse19 = fit_zone(19)\n", 1171 | "mse19" 1172 | ] 1173 | }, 1174 | { 1175 | "cell_type": "code", 1176 | "execution_count": 259, 1177 | "metadata": { 1178 | "collapsed": false 1179 | }, 1180 | "outputs": [ 1181 | { 1182 | "data": { 1183 | "text/plain": [ 1184 | "[3280938785.6571865,\n", 1185 | " 3023055272.304011,\n", 1186 | " 3259010216.7908034,\n", 1187 | " 3070343253.6242156,\n", 1188 | " 3300624823.0070605,\n", 1189 | " 3182229323.254192,\n", 1190 | " 3143042210.8285456,\n", 1191 | " 3216596835.239583,\n", 1192 | " 3259491106.3356524,\n", 1193 | " 3172313365.5962057,\n", 1194 | " 2757424096.696284]" 1195 | ] 1196 | }, 1197 | "execution_count": 259, 1198 | "metadata": {}, 1199 | "output_type": "execute_result" 1200 | } 1201 | ], 1202 | "source": [ 1203 | "mse20 = fit_zone(20)\n", 1204 | "mse20" 1205 | ] 1206 | }, 1207 | { 1208 | "cell_type": "markdown", 1209 | "metadata": {}, 1210 | "source": [ 1211 | "## Testing" 1212 | ] 1213 | }, 1214 | { 1215 | "cell_type": "markdown", 1216 | "metadata": {}, 1217 | "source": [ 1218 | "#### After matching the stations and zones, we use the corresponding temperatures as one of the features for predictions of testing data." 1219 | ] 1220 | }, 1221 | { 1222 | "cell_type": "code", 1223 | "execution_count": 260, 1224 | "metadata": { 1225 | "collapsed": true 1226 | }, 1227 | "outputs": [], 1228 | "source": [ 1229 | "pre1 = zone_station(2,1)\n", 1230 | "pre2 = zone_station(11,2)\n", 1231 | "pre3 = zone_station(11,3)\n", 1232 | "pre4 = zone_station(9,4)\n", 1233 | "pre5 = zone_station(11,5)\n", 1234 | "pre6 = zone_station(11,6)\n", 1235 | "pre7 = zone_station(11,7)\n", 1236 | "pre8 = zone_station(2,8)\n", 1237 | "pre9 = zone_station(6,9)\n", 1238 | "pre10 = zone_station(1,10)\n", 1239 | "pre11 = zone_station(1,11)\n", 1240 | "pre12 = zone_station(3,12)\n", 1241 | "pre13 = zone_station(2,13)\n", 1242 | "pre14 = zone_station(4,14)\n", 1243 | "pre15 = zone_station(8,15)\n", 1244 | "pre16 = zone_station(3,16)\n", 1245 | "pre17 = zone_station(6,17)\n", 1246 | "pre18 = zone_station(3,18)\n", 1247 | "pre19 = zone_station(6,19)\n", 1248 | "pre20 = zone_station(11,20)" 1249 | ] 1250 | }, 1251 | { 1252 | "cell_type": "code", 1253 | "execution_count": 326, 1254 | "metadata": { 1255 | "collapsed": false 1256 | }, 1257 | "outputs": [], 1258 | "source": [ 1259 | "pred_future = pd.DataFrame({'pre1' : pre1,\n", 1260 | " 'pre2':pre2,\n", 1261 | " 'pre3':pre3,\n", 1262 | " 'pre4':pre4, \n", 1263 | " 'pre5':pre5,\n", 1264 | " 'pre6':pre6,\n", 1265 | " 'pre7':pre7,\n", 1266 | " 'pre8':pre8,\n", 1267 | " 'pre9':pre9,\n", 1268 | " 'pre10':pre10,\n", 1269 | " 'pre11':pre11,\n", 1270 | " 'pre12':pre12,\n", 1271 | " 'pre13':pre13,\n", 1272 | " 'pre14':pre14,\n", 1273 | " 'pre15':pre15,\n", 1274 | " 'pre16':pre16,\n", 1275 | " 'pre17':pre17,\n", 1276 | " 'pre18':pre18,\n", 1277 | " 'pre19':pre19,\n", 1278 | " 'pre20':pre20})\n", 1279 | "pred_future = pred_future.ix[:,[0,11,13,14,15,16,17,18,19,1,2,3,4,5,6,7,8,9,10,12]]\n", 1280 | "pred_future.columns=['zone1','zone2','zone3','zone4','zone5','zone6','zone7','zone8','zone9','zone10','zone11','zone12','zone13','zone14','zone15','zone16','zone17','zone18','zone19','zone20']" 1281 | ] 1282 | }, 1283 | { 1284 | "cell_type": "code", 1285 | "execution_count": 324, 1286 | "metadata": { 1287 | "collapsed": false 1288 | }, 1289 | "outputs": [], 1290 | "source": [ 1291 | "b = data_test" 1292 | ] 1293 | }, 1294 | { 1295 | "cell_type": "code", 1296 | "execution_count": 332, 1297 | "metadata": { 1298 | "collapsed": false 1299 | }, 1300 | "outputs": [ 1301 | { 1302 | "name": "stdout", 1303 | "output_type": "stream", 1304 | "text": [ 1305 | "rsme: 50456.8166504\n" 1306 | ] 1307 | } 1308 | ], 1309 | "source": [ 1310 | "from sklearn.metrics import mean_squared_error\n", 1311 | "temp = np.zeros((len(pred_future),1))\n", 1312 | "temp1 = np.zeros((len(pred_future),1))\n", 1313 | "for i in range(len(pred_future)):\n", 1314 | " temp[i][0] = np.sum(pred_future.ix[i,:])\n", 1315 | " temp1[i][0] = np.sum(b.ix[i,:])\n", 1316 | "pred_future = np.concatenate((pred_future,temp),axis=1)\n", 1317 | "b = np.concatenate((b,temp1),axis=1)\n", 1318 | "rsme = np.sqrt(mean_squared_error(b, pred_future))\n", 1319 | "print('rsme:',rsme)" 1320 | ] 1321 | } 1322 | ], 1323 | "metadata": { 1324 | "kernelspec": { 1325 | "display_name": "Python 2", 1326 | "language": "python", 1327 | "name": "python2" 1328 | }, 1329 | "language_info": { 1330 | "codemirror_mode": { 1331 | "name": "ipython", 1332 | "version": 2 1333 | }, 1334 | "file_extension": ".py", 1335 | "mimetype": "text/x-python", 1336 | "name": "python", 1337 | "nbconvert_exporter": "python", 1338 | "pygments_lexer": "ipython2", 1339 | "version": "2.7.10" 1340 | } 1341 | }, 1342 | "nbformat": 4, 1343 | "nbformat_minor": 0 1344 | } 1345 | -------------------------------------------------------------------------------- /plot.py: -------------------------------------------------------------------------------- 1 | import matplotlib.pyplot as plt 2 | 3 | 4 | def plot_by_column(expected_output, predicted_output, sub_num=11, begin=0, 5 | time_steps=168, caption='Load by time for zone: '): 6 | if sub_num > len(predicted_output[0]): 7 | sub_num = len(predicted_output[0]) 8 | row = -(-sub_num // 3) 9 | plt.figure(figsize=(16, row * 4)) 10 | for i in range(len(predicted_output[0])): 11 | index = i + 1 12 | plt.subplot(row, 3, index) 13 | plt.plot(expected_output[begin:begin+time_steps, i]) 14 | plt.plot(predicted_output[begin:begin+time_steps, i]) 15 | plt.title(caption + str(index)) 16 | plt.show() 17 | 18 | 19 | def plot_by_row(expected_output, predicted_output, sub_num=20, 20 | caption='Load by zone for hour: '): 21 | if sub_num > len(expected_output): 22 | sub_num = len(expected_output) 23 | row = -(-sub_num // 3) 24 | plt.figure(figsize=(16, row * 4)) 25 | for i in range(sub_num): 26 | index = i + 1 27 | plt.subplot(row, 3, index) 28 | plt.plot(expected_output[i]) 29 | plt.plot(predicted_output[i]) 30 | plt.title(caption + str(index)) 31 | plt.show() 32 | -------------------------------------------------------------------------------- /report/IRDM_GROUP30_Yijing_YANG_Xinyi_HE_Ying_WEN.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ying-wen/time_series_prediction/9486d3d2ffa1d830f04568ad170491fa8e3e1841/report/IRDM_GROUP30_Yijing_YANG_Xinyi_HE_Ying_WEN.pdf -------------------------------------------------------------------------------- /requirement.txt: 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