├── .gitignore ├── README.md └── passenger_predict ├── add_holiday.py ├── compute_error.py ├── data ├── .DS_Store ├── bus_count │ ├── final_线路11_bus_count.txt │ ├── final_线路6_bus_count.txt │ ├── gd_线路11_bus_aver_final.txt │ ├── gd_线路6_bus_aver_final.txt │ ├── 线路11_bus_count.txt │ └── 线路6_bus_count.txt ├── cardtype_线路11_count ├── cardtype_线路11_count_weight.txt ├── cardtype_线路6_count ├── cardtype_线路6_count_weight.txt ├── count │ ├── final_线路11_count.txt │ ├── final_线路11_count_cardtype.txt │ ├── final_线路6_count.txt │ ├── final_线路6_count_cardtype.txt │ ├── 线路11_count.txt │ ├── 线路11_count_cardtype.txt │ ├── 线路6_count.txt │ └── 线路6_count_cardtype.txt ├── date_holiday_result.txt ├── final_data_test.txt ├── final_data_test_线路11_28.txt ├── final_data_test_线路6_28.txt ├── gd_line_desc.txt ├── gd_weather_report.txt ├── gd_线路11_aver_weight_final.txt ├── gd_线路6_aver_weight_final.txt ├── result │ ├── .DS_Store │ ├── 2015-11-26 │ │ ├── .DS_Store │ │ ├── final_result.txt │ │ ├── traffic_线路11GBDT_result.txt │ │ └── traffic_线路6GBDT_result.txt │ ├── 2015-11-28 │ │ ├── final_result.txt │ │ ├── traffic_线路11GBDT_result.txt │ │ └── traffic_线路6GBDT_result.txt │ └── 2015-11-29 │ │ ├── final_result.txt │ │ ├── traffic_线路11GBDT_result.txt │ │ └── traffic_线路6GBDT_result.txt ├── test_data.csv ├── test_data_final.csv └── train_data_sample.txt ├── gen_aver_bus_cnt.py ├── gen_bus_count.py ├── gen_cardtype_count_aver_weight.py ├── gen_line_count.py ├── gen_line_count_cardtype.py ├── gen_test_data.py ├── model ├── .DS_Store ├── 2015-11-26 │ ├── traffic_GBDT_线路11.model │ ├── traffic_GBDT_线路11.model_01.npy │ ├── traffic_GBDT_线路11.model_02.npy │ ├── traffic_GBDT_线路6.model │ ├── traffic_GBDT_线路6.model_01.npy │ └── traffic_GBDT_线路6.model_02.npy ├── 2015-11-28 │ ├── traffic_GBDT_线路11.model │ ├── traffic_GBDT_线路11.model_01.npy │ ├── traffic_GBDT_线路11.model_02.npy │ ├── traffic_GBDT_线路6.model │ ├── traffic_GBDT_线路6.model_01.npy │ └── traffic_GBDT_线路6.model_02.npy └── 2015-11-29 │ ├── traffic_GBDT_线路11.model │ ├── traffic_GBDT_线路11.model_01.npy │ ├── traffic_GBDT_线路11.model_02.npy │ ├── traffic_GBDT_线路6.model │ ├── traffic_GBDT_线路6.model_01.npy │ └── traffic_GBDT_线路6.model_02.npy ├── part2.iml ├── predict_GBDT_2015-11-26.py ├── predict_GBDT_2015-11-28.py ├── process_cardtype_count_deal_time.py ├── process_cardtype_count_weight.py ├── process_deal_time.py ├── summit_result.py ├── traffic_GBDT_2015-11-26.py └── traffic_GBDT_2015-11-28.py /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | 5 | # C extensions 6 | *.so 7 | 8 | # Distribution / packaging 9 | .Python 10 | env/ 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | *.egg-info/ 23 | .installed.cfg 24 | *.egg 25 | 26 | # PyInstaller 27 | # Usually these files are written by a python script from a template 28 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 29 | *.manifest 30 | *.spec 31 | 32 | # Installer logs 33 | pip-log.txt 34 | pip-delete-this-directory.txt 35 | 36 | # Unit test / coverage reports 37 | htmlcov/ 38 | .tox/ 39 | .coverage 40 | .coverage.* 41 | .cache 42 | nosetests.xml 43 | coverage.xml 44 | *,cover 45 | 46 | # Translations 47 | *.mo 48 | *.pot 49 | 50 | # Django stuff: 51 | *.log 52 | 53 | # Sphinx documentation 54 | docs/_build/ 55 | 56 | # PyBuilder 57 | target/ 58 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # tianchiCode 2 | 3 | 本项目是[阿里巴巴天池比赛广东客流预测](https://tianchi.aliyun.com/competition/introduction.htm?spm=5176.100223.111.2.lUkfuu&raceId=231514)的一些代码和数据，data当中由于github大小的限制，有一些数据没有上传，放到了我的百度云盘当中[客流预测数据](http://pan.baidu.com/s/1jHxMDsa) 4 | 5 | 解压data.zip到对应的data目录即可重现代码 6 | 7 | 我的blog地址：[小石头的码疯窝](http://hacker.duanshishi.com) 8 | 9 | 10 | ------- 11 | Burness Duan 12 | -------------------------------------------------------------------------------- /passenger_predict/add_holiday.py: -------------------------------------------------------------------------------- 1 | #-*-coding:utf-8-* 2 | __author__ = 'burness' 3 | import pandas as pd 4 | def add_holiday(gd_train_line_pd_weather): 5 | gd_train_date_holiday = pd.read_csv('./data/date_holiday_result.txt') 6 | gd_train_date_holiday.columns=['date','holiday'] 7 | gd_train_date_holiday['date']=pd.to_datetime(gd_train_date_holiday['date']) 8 | gd_train_line_pd_weather['date_val']=pd.to_datetime(gd_train_line_pd_weather['date']) 9 | gd_train_line_pd_weather['dayofweek']=gd_train_line_pd_weather['date_val'].apply(lambda x: x.dayofweek) 10 | 11 | gd_train_line_pd_weather_holiday = pd.merge(gd_train_line_pd_weather,gd_train_date_holiday,left_on='date_val',right_on='date') 12 | return gd_train_line_pd_weather_holiday -------------------------------------------------------------------------------- /passenger_predict/compute_error.py: -------------------------------------------------------------------------------- 1 | __author__ = 'burness' 2 | def compute_error(predict_labels, test_labels): 3 | error = abs(test_labels-predict_labels)/test_labels 4 | return error.mean() -------------------------------------------------------------------------------- /passenger_predict/data/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/burness/tianchiCode/6a15371eba7dc2af2f828c25daac016531c40ea1/passenger_predict/data/.DS_Store -------------------------------------------------------------------------------- /passenger_predict/data/bus_count/gd_线路11_bus_aver_final.txt: -------------------------------------------------------------------------------- 1 | 0,0,7 2 | 0,1,2 3 | 0,2,2 4 | 0,3,2 5 | 0,4,2 6 | 0,5,10 7 | 0,6,52 8 | 0,7,81 9 | 0,8,83 10 | 0,9,82 11 | 0,10,78 12 | 0,11,70 13 | 0,12,64 14 | 0,13,64 15 | 0,14,65 16 | 0,15,77 17 | 0,16,83 18 | 0,17,83 19 | 0,18,83 20 | 0,19,71 21 | 0,20,57 22 | 0,21,48 23 | 0,22,32 24 | 0,23,9 25 | 1,0,7 26 | 1,1,2 27 | 1,2,2 28 | 1,3,2 29 | 1,4,2 30 | 1,5,10 31 | 1,6,40 32 | 1,7,58 33 | 1,8,68 34 | 1,9,75 35 | 1,10,76 36 | 1,11,74 37 | 1,12,75 38 | 1,13,77 39 | 1,14,75 40 | 1,15,76 41 | 1,16,73 42 | 1,17,67 43 | 1,18,63 44 | 1,19,60 45 | 1,20,55 46 | 1,21,49 47 | 1,22,32 48 | 1,23,8 49 | 2,0,12 50 | 2,1,4 51 | 2,2,3 52 | 2,3,7 53 | 2,4,2 54 | 2,5,12 55 | 2,6,38 56 | 2,7,61 57 | 2,8,72 58 | 2,9,77 59 | 2,10,77 60 | 2,11,76 61 | 2,12,76 62 | 2,13,80 63 | 2,14,80 64 | 2,15,81 65 | 2,16,76 66 | 2,17,70 67 | 2,18,67 68 | 2,19,63 69 | 2,20,57 70 | 2,21,55 71 | 2,22,47 72 | 2,23,22 73 | -------------------------------------------------------------------------------- /passenger_predict/data/bus_count/gd_线路6_bus_aver_final.txt: -------------------------------------------------------------------------------- 1 | 0,0,4 2 | 0,1,2 3 | 0,2,2 4 | 0,3,2 5 | 0,4,2 6 | 0,5,15 7 | 0,6,45 8 | 0,7,65 9 | 0,8,68 10 | 0,9,67 11 | 0,10,64 12 | 0,11,56 13 | 0,12,53 14 | 0,13,53 15 | 0,14,54 16 | 0,15,63 17 | 0,16,69 18 | 0,17,69 19 | 0,18,69 20 | 0,19,59 21 | 0,20,48 22 | 0,21,39 23 | 0,22,24 24 | 0,23,6 25 | 1,0,4 26 | 1,1,2 27 | 1,2,1 28 | 1,3,1 29 | 1,4,1 30 | 1,5,14 31 | 1,6,37 32 | 1,7,47 33 | 1,8,52 34 | 1,9,58 35 | 1,10,60 36 | 1,11,59 37 | 1,12,59 38 | 1,13,60 39 | 1,14,60 40 | 1,15,62 41 | 1,16,58 42 | 1,17,53 43 | 1,18,51 44 | 1,19,48 45 | 1,20,44 46 | 1,21,38 47 | 1,22,24 48 | 1,23,5 49 | 2,0,4 50 | 2,1,2 51 | 2,4,1 52 | 2,5,14 53 | 2,6,35 54 | 2,7,51 55 | 2,8,60 56 | 2,9,63 57 | 2,10,61 58 | 2,11,61 59 | 2,12,60 60 | 2,13,64 61 | 2,14,64 62 | 2,15,67 63 | 2,16,62 64 | 2,17,59 65 | 2,18,57 66 | 2,19,50 67 | 2,20,45 68 | 2,21,37 69 | 2,22,27 70 | 2,23,10 71 | 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3157 | 2014122716,71 3158 | 2014122717,75 3159 | 2014122718,68 3160 | 2014122719,63 3161 | 2014122720,59 3162 | 2014122721,49 3163 | 2014122722,32 3164 | 2014122723,8 3165 | 2014122800,6 3166 | 2014122801,1 3167 | 2014122805,11 3168 | 2014122806,43 3169 | 2014122807,61 3170 | 2014122808,73 3171 | 2014122809,81 3172 | 2014122810,80 3173 | 2014122811,83 3174 | 2014122812,82 3175 | 2014122813,83 3176 | 2014122814,82 3177 | 2014122815,81 3178 | 2014122816,81 3179 | 2014122817,74 3180 | 2014122818,70 3181 | 2014122819,65 3182 | 2014122820,60 3183 | 2014122821,52 3184 | 2014122822,35 3185 | 2014122823,11 3186 | 2014122900,8 3187 | 2014122901,3 3188 | 2014122905,10 3189 | 2014122906,59 3190 | 2014122907,90 3191 | 2014122908,90 3192 | 2014122909,89 3193 | 2014122910,87 3194 | 2014122911,83 3195 | 2014122912,76 3196 | 2014122913,70 3197 | 2014122914,72 3198 | 2014122915,84 3199 | 2014122916,92 3200 | 2014122917,92 3201 | 2014122918,90 3202 | 2014122919,76 3203 | 2014122920,62 3204 | 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-------------------------------------------------------------------------------- /passenger_predict/data/date_holiday_result.txt: -------------------------------------------------------------------------------- 1 | 2014-10-24,0 2 | 2014-10-25,1 3 | 2014-10-26,1 4 | 2014-10-27,0 5 | 2014-10-20,0 6 | 2014-10-21,0 7 | 2014-10-22,0 8 | 2014-10-23,0 9 | 2014-12-26,0 10 | 2014-12-27,1 11 | 2014-12-24,0 12 | 2014-12-25,0 13 | 2014-10-28,0 14 | 2014-10-29,0 15 | 2014-12-20,1 16 | 2014-12-21,1 17 | 2014-12-28,1 18 | 2014-08-27,0 19 | 2014-08-26,0 20 | 2014-08-25,0 21 | 2014-08-24,1 22 | 2014-08-23,1 23 | 2014-08-22,0 24 | 2014-08-21,0 25 | 2014-08-20,0 26 | 2014-12-29,0 27 | 2014-09-08,2 28 | 2014-09-09,0 29 | 2014-08-29,0 30 | 2014-08-28,0 31 | 2014-11-10,0 32 | 2014-11-11,0 33 | 2014-11-12,0 34 | 2014-11-13,0 35 | 2014-10-19,1 36 | 2014-10-18,1 37 | 2014-11-16,1 38 | 2014-11-17,0 39 | 2014-10-15,0 40 | 2014-10-14,0 41 | 2014-10-17,0 42 | 2014-10-16,0 43 | 2014-10-11,0 44 | 2014-10-10,0 45 | 2014-10-13,0 46 | 2014-10-12,1 47 | 2014-12-31,0 48 | 2014-12-30,0 49 | 2014-10-31,0 50 | 2014-10-30,0 51 | 2014-12-22,0 52 | 2014-12-23,0 53 | 2014-11-18,0 54 | 2014-08-18,0 55 | 2014-08-19,0 56 | 2014-11-19,0 57 | 2014-08-13,0 58 | 2014-08-10,1 59 | 2014-08-11,0 60 | 2014-08-16,1 61 | 2014-08-17,1 62 | 2014-08-14,0 63 | 2014-08-15,0 64 | 2014-09-17,0 65 | 2014-09-16,0 66 | 2014-09-15,0 67 | 2014-09-14,1 68 | 2014-09-13,1 69 | 2014-09-12,0 70 | 2014-09-11,0 71 | 2014-09-10,0 72 | 2014-09-19,0 73 | 2014-09-18,0 74 | 2014-11-03,0 75 | 2014-11-02,1 76 | 2014-11-01,1 77 | 2014-09-02,0 78 | 2014-11-07,0 79 | 2014-11-06,0 80 | 2014-11-05,0 81 | 2014-11-04,0 82 | 2014-11-09,1 83 | 2014-11-08,1 84 | 2014-12-08,0 85 | 2014-12-09,0 86 | 2014-11-22,1 87 | 2014-12-07,1 88 | 2014-12-01,0 89 | 2014-12-02,0 90 | 2014-12-03,0 91 | 2014-12-04,0 92 | 2014-12-05,0 93 | 2014-12-06,1 94 | 2014-11-14,0 95 | 2014-09-22,0 96 | 2014-09-28,0 97 | 2014-09-29,0 98 | 2014-08-09,1 99 | 2014-08-08,0 100 | 2014-09-23,0 101 | 2014-08-05,0 102 | 2014-08-04,0 103 | 2014-08-07,0 104 | 2014-08-06,0 105 | 2014-08-01,0 106 | 2014-09-20,1 107 | 2014-08-03,1 108 | 2014-08-02,1 109 | 2014-09-21,1 110 | 2014-09-26,0 111 | 2014-11-15,1 112 | 2014-09-27,1 113 | 2014-09-24,0 114 | 2014-11-30,1 115 | 2014-09-25,0 116 | 2014-09-04,0 117 | 2014-12-19,0 118 | 2014-12-18,0 119 | 2014-09-05,0 120 | 2014-12-13,1 121 | 2014-12-12,0 122 | 2014-12-11,0 123 | 2014-12-10,0 124 | 2014-12-17,0 125 | 2014-09-06,1 126 | 2014-12-15,0 127 | 2014-12-14,1 128 | 2014-09-07,1 129 | 2014-12-16,0 130 | 2014-09-30,0 131 | 2014-09-01,0 132 | 2014-11-29,1 133 | 2014-11-28,0 134 | 2014-08-30,1 135 | 2014-08-31,1 136 | 2014-11-21,0 137 | 2014-11-20,0 138 | 2014-11-23,1 139 | 2014-09-03,0 140 | 2014-11-25,0 141 | 2014-11-24,0 142 | 2014-11-27,0 143 | 2014-11-26,0 144 | 2014-10-08,0 145 | 2014-10-09,0 146 | 2014-10-02,2 147 | 2014-10-03,2 148 | 2014-10-01,2 149 | 2014-10-06,1 150 | 2014-10-07,1 151 | 2014-10-04,1 152 | 2014-10-05,1 153 | 2015-01-01,2 154 | 2015-01-02,1 155 | 2015-01-03,1 156 | 2015-01-04,0 157 | 2015-01-05,0 158 | 2015-01-06,0 159 | 2015-01-07,0 160 | -------------------------------------------------------------------------------- /passenger_predict/data/final_data_test.txt: -------------------------------------------------------------------------------- 1 | date,time,dayofweek,weatherA_val,weatherB_val,weatherPeriod,weatherE,temperatureA,temperatureB,temperaturePeriod,temperatureE,holiday,bus_cnt 2 | 2015-01-01,6,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2,38 3 | 2015-01-01,7,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2,61 4 | 2015-01-01,8,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2,72 5 | 2015-01-01,9,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2,77 6 | 2015-01-01,10,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2,77 7 | 2015-01-01,11,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2,76 8 | 2015-01-01,12,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2,76 9 | 2015-01-01,13,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2,80 10 | 2015-01-01,14,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2,80 11 | 2015-01-01,15,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2,81 12 | 2015-01-01,16,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2,76 13 | 2015-01-01,17,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2,70 14 | 2015-01-01,18,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2,67 15 | 2015-01-01,19,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2,63 16 | 2015-01-01,20,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2,57 17 | 2015-01-01,21,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2,55 18 | 2015-01-02,6,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1,40 19 | 2015-01-03,6,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1,40 20 | 2015-01-02,7,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1,58 21 | 2015-01-03,7,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1,58 22 | 2015-01-02,8,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1,68 23 | 2015-01-03,8,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1,68 24 | 2015-01-02,9,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1,75 25 | 2015-01-03,9,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1,75 26 | 2015-01-02,10,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1,76 27 | 2015-01-03,10,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1,76 28 | 2015-01-02,11,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1,74 29 | 2015-01-03,11,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1,74 30 | 2015-01-02,12,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1,75 31 | 2015-01-03,12,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1,75 32 | 2015-01-02,13,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1,77 33 | 2015-01-03,13,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1,77 34 | 2015-01-02,14,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1,75 35 | 2015-01-03,14,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1,75 36 | 2015-01-02,15,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1,76 37 | 2015-01-03,15,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1,76 38 | 2015-01-02,16,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1,73 39 | 2015-01-03,16,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1,73 40 | 2015-01-02,17,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1,67 41 | 2015-01-03,17,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1,67 42 | 2015-01-02,18,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1,63 43 | 2015-01-03,18,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1,63 44 | 2015-01-02,19,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1,60 45 | 2015-01-03,19,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1,60 46 | 2015-01-02,20,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1,55 47 | 2015-01-03,20,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1,55 48 | 2015-01-02,21,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1,49 49 | 2015-01-03,21,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1,49 50 | 2015-01-04,6,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0,52 51 | 2015-01-05,6,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0,52 52 | 2015-01-06,6,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0,52 53 | 2015-01-07,6,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0,52 54 | 2015-01-04,7,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0,81 55 | 2015-01-05,7,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0,81 56 | 2015-01-06,7,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0,81 57 | 2015-01-07,7,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0,81 58 | 2015-01-04,8,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0,83 59 | 2015-01-05,8,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0,83 60 | 2015-01-06,8,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0,83 61 | 2015-01-07,8,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0,83 62 | 2015-01-04,9,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0,82 63 | 2015-01-05,9,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0,82 64 | 2015-01-06,9,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0,82 65 | 2015-01-07,9,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0,82 66 | 2015-01-04,10,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0,78 67 | 2015-01-05,10,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0,78 68 | 2015-01-06,10,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0,78 69 | 2015-01-07,10,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0,78 70 | 2015-01-04,11,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0,70 71 | 2015-01-05,11,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0,70 72 | 2015-01-06,11,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0,70 73 | 2015-01-07,11,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0,70 74 | 2015-01-04,12,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0,64 75 | 2015-01-05,12,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0,64 76 | 2015-01-06,12,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0,64 77 | 2015-01-07,12,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0,64 78 | 2015-01-04,13,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0,64 79 | 2015-01-05,13,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0,64 80 | 2015-01-06,13,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0,64 81 | 2015-01-07,13,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0,64 82 | 2015-01-04,14,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0,65 83 | 2015-01-05,14,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0,65 84 | 2015-01-06,14,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0,65 85 | 2015-01-07,14,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0,65 86 | 2015-01-04,15,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0,77 87 | 2015-01-05,15,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0,77 88 | 2015-01-06,15,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0,77 89 | 2015-01-07,15,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0,77 90 | 2015-01-04,16,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0,83 91 | 2015-01-05,16,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0,83 92 | 2015-01-06,16,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0,83 93 | 2015-01-07,16,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0,83 94 | 2015-01-04,17,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0,83 95 | 2015-01-05,17,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0,83 96 | 2015-01-06,17,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0,83 97 | 2015-01-07,17,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0,83 98 | 2015-01-04,18,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0,83 99 | 2015-01-05,18,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0,83 100 | 2015-01-06,18,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0,83 101 | 2015-01-07,18,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0,83 102 | 2015-01-04,19,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0,71 103 | 2015-01-05,19,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0,71 104 | 2015-01-06,19,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0,71 105 | 2015-01-07,19,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0,71 106 | 2015-01-04,20,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0,57 107 | 2015-01-05,20,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0,57 108 | 2015-01-06,20,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0,57 109 | 2015-01-07,20,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0,57 110 | 2015-01-04,21,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0,48 111 | 2015-01-05,21,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0,48 112 | 2015-01-06,21,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0,48 113 | 2015-01-07,21,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0,48 114 | -------------------------------------------------------------------------------- /passenger_predict/data/final_data_test_线路11_28.txt: -------------------------------------------------------------------------------- 1 | date,time,dayofweek,weatherA_val,weatherB_val,weatherPeriod,weatherE,temperatureA,temperatureB,temperaturePeriod,temperatureE,holiday,bus_cnt,cardtype0_weight,cardtype1_weight,cardtype2_weight,cardtype3_weight,cardtype4_weight,cardtype5_weight,cardtype6_weight 2 | 2015-01-01,6,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2,38,0.0,0.00728267836037,0.0235001001919,0.569081355967,0.0103911887427,0.0,0.389744676738 3 | 2015-01-01,7,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2,61,0.0,0.0066634424719,0.0376035059815,0.57857772771,0.00996983922039,0.0,0.367185484616 4 | 2015-01-01,8,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2,72,0.0,0.00724920184301,0.0494060878696,0.598731509717,0.00901662145281,0.000172805648553,0.335423773469 5 | 2015-01-01,9,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2,77,0.0,0.00657624652813,0.0538058491059,0.58106167064,0.00955176883247,7.0363073459e-05,0.34893410182 6 | 2015-01-01,10,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2,77,0.0,0.00618217618927,0.0596400219576,0.608533310946,0.0109094527987,0.000402868869292,0.31433216924 7 | 2015-01-01,11,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2,76,0.0,0.00777635758383,0.0645846632751,0.656507486264,0.00771025751154,0.000510265867635,0.262910969497 8 | 2015-01-01,12,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2,76,0.0,0.00970820540313,0.0829378903229,0.697569245684,0.00680157854504,0.000426136363637,0.202556943681 9 | 2015-01-01,13,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2,80,0.0,0.0101933744864,0.0729992162452,0.707514182317,0.00801394474225,0.0,0.201279282208 10 | 2015-01-01,14,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2,80,0.0,0.00846882267565,0.0758711519027,0.727920827667,0.00942139604069,0.0,0.178317801713 11 | 2015-01-01,15,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2,81,0.0,0.00901702924199,0.0784510717095,0.739985850616,0.00674243422386,0.0,0.165803614209 12 | 2015-01-01,16,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2,76,0.0,0.00828117735795,0.086004650012,0.735997072228,0.00857628981067,0.00016846361186,0.16097234698 13 | 2015-01-01,17,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2,70,0.0,0.0101764497526,0.0905205535336,0.754253279033,0.00706369882035,0.0,0.137986018861 14 | 2015-01-01,18,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2,67,0.0,0.00752504229334,0.0818568705545,0.806669057606,0.00519801927882,0.000273148149529,0.0984778621179 15 | 2015-01-01,19,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2,63,0.0,0.00896851902672,0.0820348903882,0.76511989593,0.00628734477625,0.0,0.137589349879 16 | 2015-01-01,20,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2,57,0.0,0.00973544319799,0.0833352669835,0.773747593766,0.010194596803,0.000100806451613,0.122886292798 17 | 2015-01-01,21,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2,55,0.0,0.0107718859112,0.0758054073306,0.812580003684,0.0061844747644,0.0,0.0946582283095 18 | 2015-01-02,6,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1,40,0.0,0.0107564931568,0.0470731347195,0.564131444577,0.00997333454004,7.87206290749e-05,0.367986872377 19 | 2015-01-03,6,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1,40,0.0,0.0107564931568,0.0470731347195,0.564131444577,0.00997333454004,7.87206290749e-05,0.367986872377 20 | 2015-01-02,7,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1,58,0.0,0.00779956659147,0.0510966826543,0.628475935333,0.00793837031775,0.000116309954075,0.304573135149 21 | 2015-01-03,7,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1,58,0.0,0.00779956659147,0.0510966826543,0.628475935333,0.00793837031775,0.000116309954075,0.304573135149 22 | 2015-01-02,8,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1,68,0.0,0.00599914195983,0.0514155728847,0.639221787659,0.00734572690954,7.07942837917e-05,0.295946976303 23 | 2015-01-03,8,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1,68,0.0,0.00599914195983,0.0514155728847,0.639221787659,0.00734572690954,7.07942837917e-05,0.295946976303 24 | 2015-01-02,9,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1,75,0.0,0.00566369364619,0.0484384342784,0.609051779548,0.00845020549213,0.000113295548615,0.328282591486 25 | 2015-01-03,9,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1,75,0.0,0.00566369364619,0.0484384342784,0.609051779548,0.00845020549213,0.000113295548615,0.328282591486 26 | 2015-01-02,10,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1,76,0.0,0.00583744245581,0.0550232691055,0.615415652728,0.00895747769565,0.000175576808764,0.314590581206 27 | 2015-01-03,10,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1,76,0.0,0.00583744245581,0.0550232691055,0.615415652728,0.00895747769565,0.000175576808764,0.314590581206 28 | 2015-01-02,11,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1,74,0.0,0.00685916372007,0.0688353550699,0.652531986361,0.00782295322183,0.000440611732971,0.263509929894 29 | 2015-01-03,11,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1,74,0.0,0.00685916372007,0.0688353550699,0.652531986361,0.00782295322183,0.000440611732971,0.263509929894 30 | 2015-01-02,12,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1,75,0.0,0.00884663337081,0.0870658166014,0.704561764996,0.00703337961232,0.000115380823477,0.192377024596 31 | 2015-01-03,12,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1,75,0.0,0.00884663337081,0.0870658166014,0.704561764996,0.00703337961232,0.000115380823477,0.192377024596 32 | 2015-01-02,13,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1,77,0.0,0.00893402219229,0.0835398143432,0.707452038438,0.00830739994741,0.000108503309441,0.19165822177 33 | 2015-01-03,13,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1,77,0.0,0.00893402219229,0.0835398143432,0.707452038438,0.00830739994741,0.000108503309441,0.19165822177 34 | 2015-01-02,14,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1,75,0.0,0.00866302789205,0.0781411859423,0.725058605439,0.00817284102803,0.000124603607923,0.17983973609 35 | 2015-01-03,14,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1,75,0.0,0.00866302789205,0.0781411859423,0.725058605439,0.00817284102803,0.000124603607923,0.17983973609 36 | 2015-01-02,15,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1,76,0.0,0.00824835009202,0.0833951325965,0.731117362657,0.00838349335008,0.000151452840799,0.168704208464 37 | 2015-01-03,15,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1,76,0.0,0.00824835009202,0.0833951325965,0.731117362657,0.00838349335008,0.000151452840799,0.168704208464 38 | 2015-01-02,16,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1,73,0.0,0.00862593928537,0.100391079605,0.732849170836,0.00803625431775,0.000129518341587,0.149968037615 39 | 2015-01-03,16,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1,73,0.0,0.00862593928537,0.100391079605,0.732849170836,0.00803625431775,0.000129518341587,0.149968037615 40 | 2015-01-02,17,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1,67,0.0,0.00837556541032,0.0985416627156,0.765813526648,0.00651423762871,0.000145900658424,0.120609106939 41 | 2015-01-03,17,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1,67,0.0,0.00837556541032,0.0985416627156,0.765813526648,0.00651423762871,0.000145900658424,0.120609106939 42 | 2015-01-02,18,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1,63,0.0,0.00679816818561,0.0943297176813,0.82174319264,0.00507187279855,0.000112020041206,0.0719450286538 43 | 2015-01-03,18,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1,63,0.0,0.00679816818561,0.0943297176813,0.82174319264,0.00507187279855,0.000112020041206,0.0719450286538 44 | 2015-01-02,19,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1,60,0.0,0.0073529965067,0.0759516105055,0.815555443696,0.00590738040649,0.000157375991688,0.0950751928939 45 | 2015-01-03,19,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1,60,0.0,0.0073529965067,0.0759516105055,0.815555443696,0.00590738040649,0.000157375991688,0.0950751928939 46 | 2015-01-02,20,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1,55,0.0,0.00923583163277,0.0757453509063,0.80067561187,0.00631362094809,0.000245591917242,0.107783992726 47 | 2015-01-03,20,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1,55,0.0,0.00923583163277,0.0757453509063,0.80067561187,0.00631362094809,0.000245591917242,0.107783992726 48 | 2015-01-02,21,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1,49,0.0,0.00966516068262,0.0761853403707,0.837883113502,0.0051688882446,0.000104701647449,0.070992795553 49 | 2015-01-03,21,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1,49,0.0,0.00966516068262,0.0761853403707,0.837883113502,0.0051688882446,0.000104701647449,0.070992795553 50 | 2015-01-04,6,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0,52,0.0,0.00828035972861,0.212422944413,0.53198099705,0.00963912876989,9.16094294201e-05,0.237584960609 51 | 2015-01-05,6,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0,52,0.0,0.00828035972861,0.212422944413,0.53198099705,0.00963912876989,9.16094294201e-05,0.237584960609 52 | 2015-01-06,6,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0,52,0.0,0.00828035972861,0.212422944413,0.53198099705,0.00963912876989,9.16094294201e-05,0.237584960609 53 | 2015-01-07,6,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0,52,0.0,0.00828035972861,0.212422944413,0.53198099705,0.00963912876989,9.16094294201e-05,0.237584960609 54 | 2015-01-04,7,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0,81,0.0,0.00801122047959,0.0941005149409,0.70874583852,0.00661684041151,0.000170651741944,0.182354933906 55 | 2015-01-05,7,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0,81,0.0,0.00801122047959,0.0941005149409,0.70874583852,0.00661684041151,0.000170651741944,0.182354933906 56 | 2015-01-06,7,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0,81,0.0,0.00801122047959,0.0941005149409,0.70874583852,0.00661684041151,0.000170651741944,0.182354933906 57 | 2015-01-07,7,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0,81,0.0,0.00801122047959,0.0941005149409,0.70874583852,0.00661684041151,0.000170651741944,0.182354933906 58 | 2015-01-04,8,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0,83,0.0,0.0063509559882,0.0297608459325,0.726484255772,0.00653140323523,0.000188472319686,0.230684066752 59 | 2015-01-05,8,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0,83,0.0,0.0063509559882,0.0297608459325,0.726484255772,0.00653140323523,0.000188472319686,0.230684066752 60 | 2015-01-06,8,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0,83,0.0,0.0063509559882,0.0297608459325,0.726484255772,0.00653140323523,0.000188472319686,0.230684066752 61 | 2015-01-07,8,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0,83,0.0,0.0063509559882,0.0297608459325,0.726484255772,0.00653140323523,0.000188472319686,0.230684066752 62 | 2015-01-04,9,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0,82,0.0,0.00641306506317,0.021794971559,0.605515060358,0.00875141595567,0.000162993658746,0.357362493405 63 | 2015-01-05,9,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0,82,0.0,0.00641306506317,0.021794971559,0.605515060358,0.00875141595567,0.000162993658746,0.357362493405 64 | 2015-01-06,9,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0,82,0.0,0.00641306506317,0.021794971559,0.605515060358,0.00875141595567,0.000162993658746,0.357362493405 65 | 2015-01-07,9,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0,82,0.0,0.00641306506317,0.021794971559,0.605515060358,0.00875141595567,0.000162993658746,0.357362493405 66 | 2015-01-04,10,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0,78,0.0,0.00715588268058,0.0202808882404,0.565775764274,0.00942655514204,0.000147676334657,0.397213233328 67 | 2015-01-05,10,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0,78,0.0,0.00715588268058,0.0202808882404,0.565775764274,0.00942655514204,0.000147676334657,0.397213233328 68 | 2015-01-06,10,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0,78,0.0,0.00715588268058,0.0202808882404,0.565775764274,0.00942655514204,0.000147676334657,0.397213233328 69 | 2015-01-07,10,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0,78,0.0,0.00715588268058,0.0202808882404,0.565775764274,0.00942655514204,0.000147676334657,0.397213233328 70 | 2015-01-04,11,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0,70,5.14138817481e-06,0.00830272086739,0.0326643443455,0.600321863624,0.0103109334623,0.00031272098725,0.348082275325 71 | 2015-01-05,11,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0,70,5.14138817481e-06,0.00830272086739,0.0326643443455,0.600321863624,0.0103109334623,0.00031272098725,0.348082275325 72 | 2015-01-06,11,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0,70,5.14138817481e-06,0.00830272086739,0.0326643443455,0.600321863624,0.0103109334623,0.00031272098725,0.348082275325 73 | 2015-01-07,11,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0,70,5.14138817481e-06,0.00830272086739,0.0326643443455,0.600321863624,0.0103109334623,0.00031272098725,0.348082275325 74 | 2015-01-04,12,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0,64,0.0,0.00984076966675,0.0567528061177,0.670794768269,0.00877359246444,0.000209099237412,0.253628964244 75 | 2015-01-05,12,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0,64,0.0,0.00984076966675,0.0567528061177,0.670794768269,0.00877359246444,0.000209099237412,0.253628964244 76 | 2015-01-06,12,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0,64,0.0,0.00984076966675,0.0567528061177,0.670794768269,0.00877359246444,0.000209099237412,0.253628964244 77 | 2015-01-07,12,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0,64,0.0,0.00984076966675,0.0567528061177,0.670794768269,0.00877359246444,0.000209099237412,0.253628964244 78 | 2015-01-04,13,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0,64,0.0,0.0103457137516,0.0453596934837,0.678270855261,0.00919082090717,0.000171134336983,0.25666178226 79 | 2015-01-05,13,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0,64,0.0,0.0103457137516,0.0453596934837,0.678270855261,0.00919082090717,0.000171134336983,0.25666178226 80 | 2015-01-06,13,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0,64,0.0,0.0103457137516,0.0453596934837,0.678270855261,0.00919082090717,0.000171134336983,0.25666178226 81 | 2015-01-07,13,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0,64,0.0,0.0103457137516,0.0453596934837,0.678270855261,0.00919082090717,0.000171134336983,0.25666178226 82 | 2015-01-04,14,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0,65,0.0,0.00986053127909,0.0344439998299,0.701547993119,0.00955587735774,0.00013220767647,0.244459390738 83 | 2015-01-05,14,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0,65,0.0,0.00986053127909,0.0344439998299,0.701547993119,0.00955587735774,0.00013220767647,0.244459390738 84 | 2015-01-06,14,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0,65,0.0,0.00986053127909,0.0344439998299,0.701547993119,0.00955587735774,0.00013220767647,0.244459390738 85 | 2015-01-07,14,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0,65,0.0,0.00986053127909,0.0344439998299,0.701547993119,0.00955587735774,0.00013220767647,0.244459390738 86 | 2015-01-04,15,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0,77,0.0,0.00889338572616,0.0598569303272,0.69533925195,0.00919959699141,0.000135471290072,0.226575363715 87 | 2015-01-05,15,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0,77,0.0,0.00889338572616,0.0598569303272,0.69533925195,0.00919959699141,0.000135471290072,0.226575363715 88 | 2015-01-06,15,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0,77,0.0,0.00889338572616,0.0598569303272,0.69533925195,0.00919959699141,0.000135471290072,0.226575363715 89 | 2015-01-07,15,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0,77,0.0,0.00889338572616,0.0598569303272,0.69533925195,0.00919959699141,0.000135471290072,0.226575363715 90 | 2015-01-04,16,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0,83,0.0,0.0103406511528,0.0966623021631,0.699864077745,0.00916959099325,0.000197692452758,0.183765685493 91 | 2015-01-05,16,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0,83,0.0,0.0103406511528,0.0966623021631,0.699864077745,0.00916959099325,0.000197692452758,0.183765685493 92 | 2015-01-06,16,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0,83,0.0,0.0103406511528,0.0966623021631,0.699864077745,0.00916959099325,0.000197692452758,0.183765685493 93 | 2015-01-07,16,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0,83,0.0,0.0103406511528,0.0966623021631,0.699864077745,0.00916959099325,0.000197692452758,0.183765685493 94 | 2015-01-04,17,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0,83,0.0,0.00969240599546,0.106450751698,0.778064725931,0.0072205042772,0.000274527397141,0.0982970847015 95 | 2015-01-05,17,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0,83,0.0,0.00969240599546,0.106450751698,0.778064725931,0.0072205042772,0.000274527397141,0.0982970847015 96 | 2015-01-06,17,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0,83,0.0,0.00969240599546,0.106450751698,0.778064725931,0.0072205042772,0.000274527397141,0.0982970847015 97 | 2015-01-07,17,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0,83,0.0,0.00969240599546,0.106450751698,0.778064725931,0.0072205042772,0.000274527397141,0.0982970847015 98 | 2015-01-04,18,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0,83,0.0,0.0068360076526,0.0878495660617,0.85173201207,0.0042432742373,0.000123194790761,0.0492159451877 99 | 2015-01-05,18,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0,83,0.0,0.0068360076526,0.0878495660617,0.85173201207,0.0042432742373,0.000123194790761,0.0492159451877 100 | 2015-01-06,18,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0,83,0.0,0.0068360076526,0.0878495660617,0.85173201207,0.0042432742373,0.000123194790761,0.0492159451877 101 | 2015-01-07,18,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0,83,0.0,0.0068360076526,0.0878495660617,0.85173201207,0.0042432742373,0.000123194790761,0.0492159451877 102 | 2015-01-04,19,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0,71,0.0,0.0175631895464,0.0584678199237,0.842776598754,0.00495912384013,0.00013332205424,0.0760999458812 103 | 2015-01-05,19,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0,71,0.0,0.0175631895464,0.0584678199237,0.842776598754,0.00495912384013,0.00013332205424,0.0760999458812 104 | 2015-01-06,19,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0,71,0.0,0.0175631895464,0.0584678199237,0.842776598754,0.00495912384013,0.00013332205424,0.0760999458812 105 | 2015-01-07,19,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0,71,0.0,0.0175631895464,0.0584678199237,0.842776598754,0.00495912384013,0.00013332205424,0.0760999458812 106 | 2015-01-04,20,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0,57,0.0,0.0104045087179,0.0598334891783,0.828622796189,0.0056291144243,0.000105856515184,0.0954042349754 107 | 2015-01-05,20,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0,57,0.0,0.0104045087179,0.0598334891783,0.828622796189,0.0056291144243,0.000105856515184,0.0954042349754 108 | 2015-01-06,20,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0,57,0.0,0.0104045087179,0.0598334891783,0.828622796189,0.0056291144243,0.000105856515184,0.0954042349754 109 | 2015-01-07,20,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0,57,0.0,0.0104045087179,0.0598334891783,0.828622796189,0.0056291144243,0.000105856515184,0.0954042349754 110 | 2015-01-04,21,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0,48,0.0,0.0102885454079,0.0916618710493,0.834519746778,0.00509531405117,0.000189393474582,0.0582451292387 111 | 2015-01-05,21,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0,48,0.0,0.0102885454079,0.0916618710493,0.834519746778,0.00509531405117,0.000189393474582,0.0582451292387 112 | 2015-01-06,21,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0,48,0.0,0.0102885454079,0.0916618710493,0.834519746778,0.00509531405117,0.000189393474582,0.0582451292387 113 | 2015-01-07,21,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0,48,0.0,0.0102885454079,0.0916618710493,0.834519746778,0.00509531405117,0.000189393474582,0.0582451292387 114 | -------------------------------------------------------------------------------- /passenger_predict/data/final_data_test_线路6_28.txt: -------------------------------------------------------------------------------- 1 | date,time,dayofweek,weatherA_val,weatherB_val,weatherPeriod,weatherE,temperatureA,temperatureB,temperaturePeriod,temperatureE,holiday,bus_cnt,cardtype0_weight,cardtype1_weight,cardtype2_weight,cardtype3_weight,cardtype4_weight,cardtype5_weight,cardtype6_weight 2 | 2015-01-01,6,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2,35,0.0,0.00975393290828,0.0218814083278,0.684482014598,0.00773953701519,0.0,0.276143107151 3 | 2015-01-01,7,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2,51,0.0,0.0109617370482,0.0415489846949,0.734778429814,0.0065790420018,0.0,0.206131806442 4 | 2015-01-01,8,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2,60,0.0,0.00707948047841,0.0440203887451,0.715488431686,0.00680437820875,0.000289345238391,0.226317975644 5 | 2015-01-01,9,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2,63,0.0,0.00773759235722,0.0541576650715,0.693254663612,0.00546716495546,0.000138657792568,0.239244256212 6 | 2015-01-01,10,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2,61,0.0,0.00829490237968,0.0599467651678,0.705214506077,0.00846042869974,0.000481952804298,0.217601444872 7 | 2015-01-01,11,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2,61,0.0,0.00764612208688,0.0683054231123,0.732780343112,0.00622189366157,0.000627852395579,0.184418365631 8 | 2015-01-01,12,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2,60,0.0,0.010037942424,0.0681964060692,0.767668515648,0.00595646702444,0.000175438596491,0.147965230238 9 | 2015-01-01,13,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2,64,0.0,0.00952636005759,0.0757218525114,0.771750242463,0.00535863579494,0.000314593705701,0.137328315468 10 | 2015-01-01,14,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2,64,0.0,0.0101987973897,0.0718770045349,0.786723048612,0.00632749055076,0.0,0.124873658912 11 | 2015-01-01,15,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2,67,0.0,0.00807101219231,0.0703024222703,0.790658153086,0.00669281955332,0.000139899272524,0.124135693626 12 | 2015-01-01,16,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2,62,0.0,0.00912386642709,0.0729188207272,0.80253014746,0.00672741850521,0.000411123979911,0.108288622901 13 | 2015-01-01,17,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2,59,0.0,0.0101394189235,0.0788825084685,0.820904498149,0.0044152426009,0.000153846153846,0.0855044857042 14 | 2015-01-01,18,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2,57,0.0,0.00835495111372,0.0734183077924,0.845964628254,0.00449038293187,0.0,0.0677717299079 15 | 2015-01-01,19,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2,50,0.0,0.0129840463535,0.0737367180375,0.811470174426,0.00592586009459,0.000255102040817,0.0956280990472 16 | 2015-01-01,20,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2,45,0.0,0.0104247156102,0.0774453753294,0.817658331831,0.00609865317939,0.000142775556825,0.0882301484935 17 | 2015-01-01,21,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2,37,0.0,0.0107233073526,0.0712066145335,0.835955683918,0.00545438379811,0.0,0.0766600103975 18 | 2015-01-02,6,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1,37,0.0,0.0132754700188,0.0459541555359,0.687575612961,0.00791271735729,0.000226991514356,0.245055052612 19 | 2015-01-03,6,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1,37,0.0,0.0132754700188,0.0459541555359,0.687575612961,0.00791271735729,0.000226991514356,0.245055052612 20 | 2015-01-02,7,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1,47,0.0,0.00975618181297,0.0560374271331,0.756521334128,0.00592117367506,0.000221864989563,0.171542018261 21 | 2015-01-03,7,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1,47,0.0,0.00975618181297,0.0560374271331,0.756521334128,0.00592117367506,0.000221864989563,0.171542018261 22 | 2015-01-02,8,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1,52,0.0,0.00623776087655,0.055872603063,0.753548559398,0.00538996730469,0.000206080157008,0.178745029201 23 | 2015-01-03,8,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1,52,0.0,0.00623776087655,0.055872603063,0.753548559398,0.00538996730469,0.000206080157008,0.178745029201 24 | 2015-01-02,9,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1,58,0.0,0.0060687539186,0.052984048396,0.713927037254,0.00547337188405,0.000191710727469,0.22135507782 25 | 2015-01-03,9,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1,58,0.0,0.0060687539186,0.052984048396,0.713927037254,0.00547337188405,0.000191710727469,0.22135507782 26 | 2015-01-02,10,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1,60,0.0,0.00639807496496,0.0607482487414,0.699929622354,0.00599931718157,0.000300065892265,0.226624670866 27 | 2015-01-03,10,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1,60,0.0,0.00639807496496,0.0607482487414,0.699929622354,0.00599931718157,0.000300065892265,0.226624670866 28 | 2015-01-02,11,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1,59,0.0,0.007226560316,0.0693733531466,0.724990556549,0.00601165449182,0.000248273389772,0.192149602107 29 | 2015-01-03,11,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1,59,0.0,0.007226560316,0.0693733531466,0.724990556549,0.00601165449182,0.000248273389772,0.192149602107 30 | 2015-01-02,12,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1,59,0.0,0.00913062692974,0.0854557679485,0.760228899613,0.00595749338197,0.000211314251492,0.139015897875 31 | 2015-01-03,12,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1,59,0.0,0.00913062692974,0.0854557679485,0.760228899613,0.00595749338197,0.000211314251492,0.139015897875 32 | 2015-01-02,13,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1,60,0.0,0.00932424538381,0.082443407659,0.763546059603,0.00624220577175,0.000169158936949,0.138274922646 33 | 2015-01-03,13,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1,60,0.0,0.00932424538381,0.082443407659,0.763546059603,0.00624220577175,0.000169158936949,0.138274922646 34 | 2015-01-02,14,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1,60,0.0,0.00839908917901,0.0750953009245,0.776607751395,0.00566975434057,0.000203026326401,0.134025077834 35 | 2015-01-03,14,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1,60,0.0,0.00839908917901,0.0750953009245,0.776607751395,0.00566975434057,0.000203026326401,0.134025077834 36 | 2015-01-02,15,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1,62,0.0,0.00797654429119,0.081572228481,0.775501884672,0.00556182687603,0.000225982114286,0.129161533565 37 | 2015-01-03,15,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1,62,0.0,0.00797654429119,0.081572228481,0.775501884672,0.00556182687603,0.000225982114286,0.129161533565 38 | 2015-01-02,16,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1,58,0.0,0.00891282576494,0.0884931452453,0.782102747983,0.00591972335735,0.000175926641058,0.114395631009 39 | 2015-01-03,16,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1,58,0.0,0.00891282576494,0.0884931452453,0.782102747983,0.00591972335735,0.000175926641058,0.114395631009 40 | 2015-01-02,17,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1,53,1.19806392869e-05,0.00813621888135,0.0855705716564,0.816198165357,0.00434457467493,0.000163645742979,0.0855748430481 41 | 2015-01-03,17,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1,53,1.19806392869e-05,0.00813621888135,0.0855705716564,0.816198165357,0.00434457467493,0.000163645742979,0.0855748430481 42 | 2015-01-02,18,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1,51,0.0,0.00586893808963,0.0758210266651,0.864672537189,0.00379700918703,0.000132890895766,0.0497075979733 43 | 2015-01-03,18,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1,51,0.0,0.00586893808963,0.0758210266651,0.864672537189,0.00379700918703,0.000132890895766,0.0497075979733 44 | 2015-01-02,19,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1,48,0.0,0.00758988701196,0.0752094785386,0.84374697674,0.00436043211662,0.000129629066995,0.0689635965262 45 | 2015-01-03,19,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1,48,0.0,0.00758988701196,0.0752094785386,0.84374697674,0.00436043211662,0.000129629066995,0.0689635965262 46 | 2015-01-02,20,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1,44,0.0,0.00836272370989,0.0745269082419,0.831085063913,0.00495417285921,0.000236670369632,0.0808344609069 47 | 2015-01-03,20,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1,44,0.0,0.00836272370989,0.0745269082419,0.831085063913,0.00495417285921,0.000236670369632,0.0808344609069 48 | 2015-01-02,21,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1,38,0.0,0.0100187879525,0.0735068530745,0.851601798718,0.00524054139845,0.000169389528076,0.059462629328 49 | 2015-01-03,21,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1,38,0.0,0.0100187879525,0.0735068530745,0.851601798718,0.00524054139845,0.000169389528076,0.059462629328 50 | 2015-01-04,6,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0,45,0.0,0.0117344558926,0.216674977612,0.596657014348,0.00787487957161,0.000332934658785,0.166725737917 51 | 2015-01-05,6,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0,45,0.0,0.0117344558926,0.216674977612,0.596657014348,0.00787487957161,0.000332934658785,0.166725737917 52 | 2015-01-06,6,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0,45,0.0,0.0117344558926,0.216674977612,0.596657014348,0.00787487957161,0.000332934658785,0.166725737917 53 | 2015-01-07,6,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0,45,0.0,0.0117344558926,0.216674977612,0.596657014348,0.00787487957161,0.000332934658785,0.166725737917 54 | 2015-01-04,7,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0,65,0.0,0.00876198520872,0.0909389560825,0.775993160887,0.00586682347989,0.000306521851746,0.11813255249 55 | 2015-01-05,7,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0,65,0.0,0.00876198520872,0.0909389560825,0.775993160887,0.00586682347989,0.000306521851746,0.11813255249 56 | 2015-01-06,7,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0,65,0.0,0.00876198520872,0.0909389560825,0.775993160887,0.00586682347989,0.000306521851746,0.11813255249 57 | 2015-01-07,7,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0,65,0.0,0.00876198520872,0.0909389560825,0.775993160887,0.00586682347989,0.000306521851746,0.11813255249 58 | 2015-01-04,8,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0,68,0.0,0.00632813413542,0.0305501231007,0.815164149173,0.00529181773628,0.000396374617688,0.142269401237 59 | 2015-01-05,8,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0,68,0.0,0.00632813413542,0.0305501231007,0.815164149173,0.00529181773628,0.000396374617688,0.142269401237 60 | 2015-01-06,8,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0,68,0.0,0.00632813413542,0.0305501231007,0.815164149173,0.00529181773628,0.000396374617688,0.142269401237 61 | 2015-01-07,8,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0,68,0.0,0.00632813413542,0.0305501231007,0.815164149173,0.00529181773628,0.000396374617688,0.142269401237 62 | 2015-01-04,9,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0,67,0.0,0.00719310063262,0.0229124235855,0.711488430031,0.00623801277564,0.000289871432255,0.251878161543 63 | 2015-01-05,9,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0,67,0.0,0.00719310063262,0.0229124235855,0.711488430031,0.00623801277564,0.000289871432255,0.251878161543 64 | 2015-01-06,9,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0,67,0.0,0.00719310063262,0.0229124235855,0.711488430031,0.00623801277564,0.000289871432255,0.251878161543 65 | 2015-01-07,9,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0,67,0.0,0.00719310063262,0.0229124235855,0.711488430031,0.00623801277564,0.000289871432255,0.251878161543 66 | 2015-01-04,10,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0,64,0.0,0.00733667544526,0.0237933258464,0.668632554344,0.00732532778284,0.000238656614015,0.292673459967 67 | 2015-01-05,10,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0,64,0.0,0.00733667544526,0.0237933258464,0.668632554344,0.00732532778284,0.000238656614015,0.292673459967 68 | 2015-01-06,10,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0,64,0.0,0.00733667544526,0.0237933258464,0.668632554344,0.00732532778284,0.000238656614015,0.292673459967 69 | 2015-01-07,10,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0,64,0.0,0.00733667544526,0.0237933258464,0.668632554344,0.00732532778284,0.000238656614015,0.292673459967 70 | 2015-01-04,11,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0,56,6.47668393782e-06,0.00876970953249,0.031427323009,0.686080721478,0.00752234031619,0.000290050648136,0.265903378332 71 | 2015-01-05,11,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0,56,6.47668393782e-06,0.00876970953249,0.031427323009,0.686080721478,0.00752234031619,0.000290050648136,0.265903378332 72 | 2015-01-06,11,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0,56,6.47668393782e-06,0.00876970953249,0.031427323009,0.686080721478,0.00752234031619,0.000290050648136,0.265903378332 73 | 2015-01-07,11,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0,56,6.47668393782e-06,0.00876970953249,0.031427323009,0.686080721478,0.00752234031619,0.000290050648136,0.265903378332 74 | 2015-01-04,12,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0,53,0.0,0.0100875308213,0.0614316454399,0.727748982244,0.00668193876159,0.000271904897908,0.193777997835 75 | 2015-01-05,12,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0,53,0.0,0.0100875308213,0.0614316454399,0.727748982244,0.00668193876159,0.000271904897908,0.193777997835 76 | 2015-01-06,12,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0,53,0.0,0.0100875308213,0.0614316454399,0.727748982244,0.00668193876159,0.000271904897908,0.193777997835 77 | 2015-01-07,12,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0,53,0.0,0.0100875308213,0.0614316454399,0.727748982244,0.00668193876159,0.000271904897908,0.193777997835 78 | 2015-01-04,13,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0,53,0.0,0.0106397609817,0.0478547844948,0.745928857992,0.00679469640033,0.000258937778309,0.188522962353 79 | 2015-01-05,13,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0,53,0.0,0.0106397609817,0.0478547844948,0.745928857992,0.00679469640033,0.000258937778309,0.188522962353 80 | 2015-01-06,13,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0,53,0.0,0.0106397609817,0.0478547844948,0.745928857992,0.00679469640033,0.000258937778309,0.188522962353 81 | 2015-01-07,13,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0,53,0.0,0.0106397609817,0.0478547844948,0.745928857992,0.00679469640033,0.000258937778309,0.188522962353 82 | 2015-01-04,14,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0,54,0.0,0.00986974403839,0.0409391712914,0.753062318011,0.00871237861462,0.000359147457153,0.187057240587 83 | 2015-01-05,14,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0,54,0.0,0.00986974403839,0.0409391712914,0.753062318011,0.00871237861462,0.000359147457153,0.187057240587 84 | 2015-01-06,14,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0,54,0.0,0.00986974403839,0.0409391712914,0.753062318011,0.00871237861462,0.000359147457153,0.187057240587 85 | 2015-01-07,14,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0,54,0.0,0.00986974403839,0.0409391712914,0.753062318011,0.00871237861462,0.000359147457153,0.187057240587 86 | 2015-01-04,15,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0,63,0.0,0.00965327074128,0.0479149403766,0.747012601013,0.00797372596371,0.000287908542303,0.187157553363 87 | 2015-01-05,15,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0,63,0.0,0.00965327074128,0.0479149403766,0.747012601013,0.00797372596371,0.000287908542303,0.187157553363 88 | 2015-01-06,15,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0,63,0.0,0.00965327074128,0.0479149403766,0.747012601013,0.00797372596371,0.000287908542303,0.187157553363 89 | 2015-01-07,15,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0,63,0.0,0.00965327074128,0.0479149403766,0.747012601013,0.00797372596371,0.000287908542303,0.187157553363 90 | 2015-01-04,16,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0,69,0.0,0.0103990604501,0.0908468307273,0.740962537883,0.00785076543208,0.00032907190642,0.149611733601 91 | 2015-01-05,16,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0,69,0.0,0.0103990604501,0.0908468307273,0.740962537883,0.00785076543208,0.00032907190642,0.149611733601 92 | 2015-01-06,16,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0,69,0.0,0.0103990604501,0.0908468307273,0.740962537883,0.00785076543208,0.00032907190642,0.149611733601 93 | 2015-01-07,16,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0,69,0.0,0.0103990604501,0.0908468307273,0.740962537883,0.00785076543208,0.00032907190642,0.149611733601 94 | 2015-01-04,17,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0,69,0.0,0.00938836347214,0.102553594938,0.809717781027,0.00501766372545,0.000413503034753,0.0729090938033 95 | 2015-01-05,17,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0,69,0.0,0.00938836347214,0.102553594938,0.809717781027,0.00501766372545,0.000413503034753,0.0729090938033 96 | 2015-01-06,17,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0,69,0.0,0.00938836347214,0.102553594938,0.809717781027,0.00501766372545,0.000413503034753,0.0729090938033 97 | 2015-01-07,17,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0,69,0.0,0.00938836347214,0.102553594938,0.809717781027,0.00501766372545,0.000413503034753,0.0729090938033 98 | 2015-01-04,18,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0,69,0.0,0.00649633310857,0.0772794756116,0.87394084114,0.00342227519928,0.00021757491277,0.0386435000274 99 | 2015-01-05,18,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0,69,0.0,0.00649633310857,0.0772794756116,0.87394084114,0.00342227519928,0.00021757491277,0.0386435000274 100 | 2015-01-06,18,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0,69,0.0,0.00649633310857,0.0772794756116,0.87394084114,0.00342227519928,0.00021757491277,0.0386435000274 101 | 2015-01-07,18,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0,69,0.0,0.00649633310857,0.0772794756116,0.87394084114,0.00342227519928,0.00021757491277,0.0386435000274 102 | 2015-01-04,19,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0,59,0.0,0.00743023299895,0.0519907821342,0.873218048709,0.004573758035,0.000272125617427,0.0625150525058 103 | 2015-01-05,19,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0,59,0.0,0.00743023299895,0.0519907821342,0.873218048709,0.004573758035,0.000272125617427,0.0625150525058 104 | 2015-01-06,19,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0,59,0.0,0.00743023299895,0.0519907821342,0.873218048709,0.004573758035,0.000272125617427,0.0625150525058 105 | 2015-01-07,19,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0,59,0.0,0.00743023299895,0.0519907821342,0.873218048709,0.004573758035,0.000272125617427,0.0625150525058 106 | 2015-01-04,20,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0,48,0.0,0.00890538088973,0.0616511696561,0.849777125804,0.00476516155521,0.00038571107224,0.0745154510225 107 | 2015-01-05,20,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0,48,0.0,0.00890538088973,0.0616511696561,0.849777125804,0.00476516155521,0.00038571107224,0.0745154510225 108 | 2015-01-06,20,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0,48,0.0,0.00890538088973,0.0616511696561,0.849777125804,0.00476516155521,0.00038571107224,0.0745154510225 109 | 2015-01-07,20,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0,48,0.0,0.00890538088973,0.0616511696561,0.849777125804,0.00476516155521,0.00038571107224,0.0745154510225 110 | 2015-01-04,21,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0,39,0.0,0.0107290720633,0.0754104911401,0.859580741548,0.00474437318518,0.000330697312879,0.0492046247502 111 | 2015-01-05,21,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0,39,0.0,0.0107290720633,0.0754104911401,0.859580741548,0.00474437318518,0.000330697312879,0.0492046247502 112 | 2015-01-06,21,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0,39,0.0,0.0107290720633,0.0754104911401,0.859580741548,0.00474437318518,0.000330697312879,0.0492046247502 113 | 2015-01-07,21,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0,39,0.0,0.0107290720633,0.0754104911401,0.859580741548,0.00474437318518,0.000330697312879,0.0492046247502 114 | -------------------------------------------------------------------------------- /passenger_predict/data/gd_line_desc.txt: -------------------------------------------------------------------------------- 1 | 线路1,18,广州市内 2 | 线路2,26,广佛跨区域 3 | 线路3,22,广州市内 4 | 线路4,31,广佛跨区域 5 | 线路5,21,广州市内 6 | 线路6,14,广州市内 7 | 线路7,27,广佛跨区域 8 | 线路8,34,广佛跨区域 9 | 线路9,30,广州市内 10 | 线路10,30,广佛跨区域 11 | 线路11,31,广州市内 12 | 线路12,17,广佛跨区域 13 | 线路13,12,广州市内 14 | 线路14,23,广州市内 15 | 线路15,37,广佛跨区域 16 | 线路16,24,广州市内 17 | 线路17,13,广州市内 18 | 线路18,10,广州市内 19 | 线路19,34,广州市内 20 | 线路20,50,广州市内 21 | 线路21,22,广州市内 22 | -------------------------------------------------------------------------------- /passenger_predict/data/gd_weather_report.txt: -------------------------------------------------------------------------------- 1 | 2014/8/1,晴/雷阵雨,36℃/26℃,无持续风向≤3级/无持续风向≤3级 2 | 2014/8/2,雷阵雨/雷阵雨,35℃/26℃,无持续风向≤3级/无持续风向≤3级 3 | 2014/8/3,雷阵雨/雷阵雨,35℃/25℃,无持续风向≤3级/无持续风向≤3级 4 | 2014/8/4,多云/多云,34℃/26℃,无持续风向≤3级/无持续风向≤3级 5 | 2014/8/5,雷阵雨/多云,34℃/26℃,无持续风向≤3级/无持续风向≤3级 6 | 2014/8/6,雷阵雨/雷阵雨,34℃/26℃,无持续风向≤3级/无持续风向≤3级 7 | 2014/8/7,雷阵雨/雷阵雨,32℃/26℃,无持续风向≤3级/无持续风向≤3级 8 | 2014/8/8,雷阵雨/雷阵雨,34℃/25℃,无持续风向≤3级/无持续风向≤3级 9 | 2014/8/9,雷阵雨/雷阵雨,34℃/25℃,无持续风向≤3级/无持续风向≤3级 10 | 2014/8/10,雷阵雨/雷阵雨,33℃/26℃,无持续风向≤3级/无持续风向≤3级 11 | 2014/8/11,雷阵雨/雷阵雨,33℃/26℃,无持续风向≤3级/无持续风向≤3级 12 | 2014/8/12,雷阵雨/雷阵雨,33℃/25℃,无持续风向≤3级/无持续风向≤3级 13 | 2014/8/13,大雨/中到大雨,30℃/24℃,无持续风向≤3级/无持续风向≤3级 14 | 2014/8/14,中到大雨/雷阵雨,31℃/25℃,无持续风向≤3级/无持续风向≤3级 15 | 2014/8/15,多云/多云,33℃/25℃,无持续风向≤3级/无持续风向≤3级 16 | 2014/8/16,多云/多云,34℃/25℃,无持续风向≤3级/无持续风向≤3级 17 | 2014/8/17,多云/多云,34℃/25℃,无持续风向≤3级/无持续风向≤3级 18 | 2014/8/18,多云/雷阵雨,34℃/25℃,无持续风向≤3级/无持续风向≤3级 19 | 2014/8/19,大雨/大到暴雨,32℃/24℃,无持续风向≤3级/无持续风向≤3级 20 | 2014/8/20,中到大雨/雷阵雨,30℃/25℃,无持续风向≤3级/无持续风向≤3级 21 | 2014/8/21,雷阵雨/雷阵雨,31℃/25℃,无持续风向≤3级/无持续风向≤3级 22 | 2014/8/22,多云/多云,33℃/24℃,无持续风向≤3级/无持续风向≤3级 23 | 2014/8/23,多云/多云,33℃/25℃,无持续风向≤3级/无持续风向≤3级 24 | 2014/8/24,多云/晴,35℃/25℃,无持续风向≤3级/无持续风向≤3级 25 | 2014/8/25,晴/晴,35℃/25℃,无持续风向≤3级/无持续风向≤3级 26 | 2014/8/26,晴/多云,35℃/25℃,无持续风向≤3级/无持续风向≤3级 27 | 2014/8/27,雷阵雨/多云,33℃/25℃,无持续风向≤3级/无持续风向≤3级 28 | 2014/8/28,多云/多云,33℃/25℃,无持续风向≤3级/无持续风向≤3级 29 | 2014/8/29,多云/晴,34℃/25℃,无持续风向≤3级/无持续风向≤3级 30 | 2014/8/30,晴/晴,35℃/25℃,无持续风向≤3级/无持续风向≤3级 31 | 2014/8/31,多云/多云,34℃/25℃,无持续风向≤3级/无持续风向≤3级 32 | 2014/9/1,多云/晴,34℃/24℃,无持续风向≤3级/无持续风向≤3级 33 | 2014/9/2,晴/晴,34℃/24℃,无持续风向≤3级/无持续风向≤3级 34 | 2014/9/3,晴/多云,34℃/25℃,无持续风向≤3级/无持续风向≤3级 35 | 2014/9/4,雷阵雨/雷阵雨,34℃/24℃,无持续风向≤3级/无持续风向≤3级 36 | 2014/9/5,雷阵雨/雷阵雨,32℃/24℃,无持续风向≤3级/无持续风向≤3级 37 | 2014/9/6,多云/多云,34℃/25℃,无持续风向≤3级/无持续风向≤3级 38 | 2014/9/7,晴/多云,34℃/25℃,无持续风向≤3级/无持续风向≤3级 39 | 2014/9/8,雷阵雨/多云,33℃/25℃,无持续风向≤3级/无持续风向≤3级 40 | 2014/9/9,多云/多云,34℃/25℃,无持续风向≤3级/无持续风向≤3级 41 | 2014/9/10,多云/多云,34℃/25℃,无持续风向≤3级/无持续风向≤3级 42 | 2014/9/11,多云/多云,34℃/25℃,无持续风向≤3级/无持续风向≤3级 43 | 2014/9/12,雷阵雨/雷阵雨,34℃/25℃,无持续风向≤3级/无持续风向≤3级 44 | 2014/9/13,雷阵雨/阵雨,33℃/25℃,无持续风向≤3级/无持续风向≤3级 45 | 2014/9/14,晴/多云,35℃/25℃,无持续风向≤3级/无持续风向≤3级 46 | 2014/9/15,多云/大雨,35℃/25℃,东北风3-4级/东风4-5级 47 | 2014/9/16,大雨/中雨,30℃/25℃,东风4-5级/东南风3-4级 48 | 2014/9/17,雷阵雨/雷阵雨,31℃/25℃,无持续风向≤3级/无持续风向≤3级 49 | 2014/9/18,多云/多云,33℃/25℃,无持续风向≤3级/无持续风向≤3级 50 | 2014/9/19,多云/多云,35℃/24℃,无持续风向≤3级/无持续风向≤3级 51 | 2014/9/20,多云/晴,32℃/23℃,东北风3-4级/东北风3-4级 52 | 2014/9/21,阴/多云,29℃/24℃,东北风3-4级/无持续风向≤3级 53 | 2014/9/22,多云/多云,30℃/23℃,无持续风向≤3级/无持续风向≤3级 54 | 2014/9/23,晴/晴,31℃/23℃,无持续风向≤3级/无持续风向≤3级 55 | 2014/9/24,晴/晴,32℃/23℃,无持续风向≤3级/无持续风向≤3级 56 | 2014/9/25,晴/晴,33℃/23℃,无持续风向≤3级/无持续风向≤3级 57 | 2014/9/26,晴/晴,33℃/24℃,无持续风向≤3级/无持续风向≤3级 58 | 2014/9/27,多云/多云,33℃/24℃,无持续风向≤3级/无持续风向≤3级 59 | 2014/9/28,晴/晴,33℃/24℃,无持续风向≤3级/无持续风向≤3级 60 | 2014/9/29,晴/多云,34℃/24℃,无持续风向≤3级/无持续风向≤3级 61 | 2014/9/30,多云/多云,33℃/24℃,无持续风向≤3级/无持续风向≤3级 62 | 2014/10/1,多云/多云,32℃/24℃,无持续风向≤3级/无持续风向≤3级 63 | 2014/10/2,多云/多云,33℃/23℃,无持续风向≤3级/无持续风向≤3级 64 | 2014/10/3,多云/多云,33℃/24℃,无持续风向≤3级/无持续风向≤3级 65 | 2014/10/4,多云/多云,33℃/22℃,无持续风向≤3级/无持续风向≤3级 66 | 2014/10/5,晴/晴,30℃/20℃,无持续风向≤3级/无持续风向≤3级 67 | 2014/10/6,晴/晴,30℃/18℃,无持续风向≤3级/无持续风向≤3级 68 | 2014/10/7,晴/晴,31℃/17℃,无持续风向≤3级/无持续风向≤3级 69 | 2014/10/8,晴/晴,30℃/18℃,无持续风向≤3级/无持续风向≤3级 70 | 2014/10/9,晴/晴,30℃/19℃,无持续风向≤3级/无持续风向≤3级 71 | 2014/10/10,晴/晴,30℃/21℃,无持续风向≤3级/无持续风向≤3级 72 | 2014/10/11,晴/晴,31℃/19℃,无持续风向≤3级/无持续风向≤3级 73 | 2014/10/12,晴/晴,30℃/19℃,无持续风向≤3级/北风3-4级 74 | 2014/10/13,晴/晴,29℃/18℃,北风3-4级/无持续风向≤3级 75 | 2014/10/14,晴/晴,29℃/17℃,无持续风向≤3级/无持续风向≤3级 76 | 2014/10/15,晴/晴,30℃/16℃,无持续风向≤3级/无持续风向≤3级 77 | 2014/10/16,晴/晴,30℃/18℃,无持续风向≤3级/无持续风向≤3级 78 | 2014/10/17,晴/晴,30℃/18℃,无持续风向≤3级/无持续风向≤3级 79 | 2014/10/18,晴/晴,30℃/19℃,无持续风向≤3级/无持续风向≤3级 80 | 2014/10/19,晴/晴,30℃/20℃,无持续风向≤3级/无持续风向≤3级 81 | 2014/10/20,多云/多云,30℃/20℃,无持续风向≤3级/无持续风向≤3级 82 | 2014/10/21,多云/多云,31℃/21℃,无持续风向≤3级/无持续风向≤3级 83 | 2014/10/22,多云/多云,30℃/21℃,无持续风向≤3级/无持续风向≤3级 84 | 2014/10/23,多云/多云,29℃/20℃,无持续风向≤3级/无持续风向≤3级 85 | 2014/10/24,阴/阴,27℃/21℃,无持续风向≤3级/无持续风向≤3级 86 | 2014/10/25,多云/阴,29℃/21℃,无持续风向≤3级/无持续风向≤3级 87 | 2014/10/26,多云/多云,29℃/22℃,无持续风向≤3级/无持续风向≤3级 88 | 2014/10/27,晴/多云,30℃/21℃,无持续风向≤3级/无持续风向≤3级 89 | 2014/10/28,多云/多云,30℃/20℃,无持续风向≤3级/无持续风向≤3级 90 | 2014/10/29,多云/晴,30℃/21℃,无持续风向≤3级/无持续风向≤3级 91 | 2014/10/30,晴/晴,31℃/20℃,无持续风向≤3级/无持续风向≤3级 92 | 2014/10/31,晴/晴,31℃/20℃,无持续风向≤3级/无持续风向≤3级 93 | 2014/11/1,晴/多云,31℃/20℃,无持续风向≤3级/无持续风向≤3级 94 | 2014/11/2,阴/阴,26℃/18℃,北风3-4级/北风3-4级 95 | 2014/11/3,多云/多云,25℃/17℃,无持续风向≤3级/无持续风向≤3级 96 | 2014/11/4,多云/多云,26℃/17℃,无持续风向≤3级/无持续风向≤3级 97 | 2014/11/5,多云/小雨,27℃/20℃,无持续风向≤3级/无持续风向≤3级 98 | 2014/11/6,多云/阴,25℃/19℃,无持续风向≤3级/无持续风向≤3级 99 | 2014/11/7,小雨/小到中雨,22℃/16℃,无持续风向≤3级/无持续风向≤3级 100 | 2014/11/8,小到中雨/阴,19℃/17℃,无持续风向≤3级/无持续风向≤3级 101 | 2014/11/9,阴/阴,22℃/18℃,无持续风向≤3级/无持续风向≤3级 102 | 2014/11/10,阴/小雨,22℃/17℃,无持续风向≤3级/无持续风向≤3级 103 | 2014/11/11,阴/小雨,23℃/17℃,无持续风向≤3级/无持续风向≤3级 104 | 2014/11/12,小雨/阴,20℃/16℃,无持续风向≤3级/无持续风向≤3级 105 | 2014/11/13,阴/多云,20℃/16℃,无持续风向≤3级/无持续风向≤3级 106 | 2014/11/14,多云/多云,23℃/16℃,无持续风向≤3级/无持续风向≤3级 107 | 2014/11/15,多云/多云,24℃/17℃,无持续风向≤3级/无持续风向≤3级 108 | 2014/11/16,多云/晴,25℃/15℃,无持续风向≤3级/无持续风向≤3级 109 | 2014/11/17,晴/晴,23℃/14℃,无持续风向≤3级/无持续风向≤3级 110 | 2014/11/18,晴/晴,22℃/13℃,无持续风向≤3级/无持续风向≤3级 111 | 2014/11/19,晴/晴,23℃/14℃,无持续风向≤3级/无持续风向≤3级 112 | 2014/11/20,晴/晴,24℃/15℃,无持续风向≤3级/无持续风向≤3级 113 | 2014/11/21,晴/晴,26℃/15℃,无持续风向≤3级/无持续风向≤3级 114 | 2014/11/22,晴/晴,26℃/17℃,无持续风向≤3级/无持续风向≤3级 115 | 2014/11/23,晴/晴,28℃/17℃,无持续风向≤3级/无持续风向≤3级 116 | 2014/11/24,晴/多云,28℃/18℃,无持续风向≤3级/无持续风向≤3级 117 | 2014/11/25,多云/多云,27℃/16℃,无持续风向≤3级/无持续风向≤3级 118 | 2014/11/26,多云/阴,25℃/18℃,无持续风向≤3级/无持续风向≤3级 119 | 2014/11/27,阴/多云,25℃/20℃,无持续风向≤3级/无持续风向≤3级 120 | 2014/11/28,多云/阴,26℃/21℃,无持续风向≤3级/无持续风向≤3级 121 | 2014/11/29,阴/阴,26℃/20℃,无持续风向≤3级/无持续风向≤3级 122 | 2014/11/30,小雨/小雨,28℃/15℃,无持续风向≤3级/北风4-5级 123 | 2014/12/1,小雨/阴,16℃/11℃,北风4-5级/北风3-4级 124 | 2014/12/2,小雨/小雨,14℃/11℃,无持续风向≤3级/无持续风向≤3级 125 | 2014/12/3,中雨/中到大雨,15℃/9℃,无持续风向≤3级/北风3-4级 126 | 2014/12/4,小雨/多云,13℃/9℃,北风4-5级/无持续风向≤3级 127 | 2014/12/5,多云/多云,17℃/8℃,无持续风向≤3级/无持续风向≤3级 128 | 2014/12/6,多云/多云,19℃/12℃,无持续风向≤3级/无持续风向≤3级 129 | 2014/12/7,小雨/小雨,17℃/11℃,无持续风向≤3级/无持续风向≤3级 130 | 2014/12/8,小雨/多云,17℃/11℃,无持续风向≤3级/无持续风向≤3级 131 | 2014/12/9,多云/多云,20℃/14℃,无持续风向≤3级/无持续风向≤3级 132 | 2014/12/10,阴/阴,20℃/12℃,无持续风向≤3级/无持续风向≤3级 133 | 2014/12/11,小雨/多云,15℃/11℃,北风3-4级/北风3-4级 134 | 2014/12/12,多云/多云,15℃/8℃,北风3-4级/无持续风向≤3级 135 | 2014/12/13,多云/多云,16℃/8℃,无持续风向≤3级/无持续风向≤3级 136 | 2014/12/14,多云/多云,18℃/13℃,无持续风向≤3级/无持续风向≤3级 137 | 2014/12/15,多云/多云,21℃/11℃,无持续风向微风转3-4级/北风微风转3-4级 138 | 2014/12/16,多云/多云,16℃/6℃,北风4-5级/北风4-5级 139 | 2014/12/17,晴/晴,15℃/5℃,北风3-4级/无持续风向≤3级 140 | 2014/12/18,晴/小雨,17℃/9℃,无持续风向≤3级/无持续风向≤3级 141 | 2014/12/19,小雨/多云,14℃/7℃,无持续风向≤3级/无持续风向≤3级 142 | 2014/12/20,晴/晴,18℃/5℃,无持续风向≤3级/无持续风向≤3级 143 | 2014/12/21,晴/晴,18℃/5℃,北风3-4级/北风3-4级 144 | 2014/12/22,晴/多云,16℃/8℃,无持续风向≤3级/无持续风向≤3级 145 | 2014/12/23,多云/阴,17℃/11℃,无持续风向≤3级/无持续风向≤3级 146 | 2014/12/24,阴/多云,20℃/13℃,无持续风向≤3级/无持续风向≤3级 147 | 2014/12/25,小雨/小雨,18℃/13℃,无持续风向≤3级/无持续风向≤3级 148 | 2014/12/26,小雨/小雨,17℃/12℃,无持续风向≤3级/无持续风向≤3级 149 | 2014/12/27,小雨/小雨,18℃/9℃,无持续风向≤3级/无持续风向≤3级 150 | 2014/12/28,多云/晴,17℃/7℃,北风3-4级/无持续风向≤3级 151 | 2014/12/29,晴/晴,18℃/4℃,无持续风向≤3级/无持续风向≤3级 152 | 2014/12/30,晴/晴,19℃/6℃,无持续风向≤3级/无持续风向≤3级 153 | 2014/12/31,晴/晴,22℃/8℃,无持续风向≤3级/北风3-4级 154 | 2015/1/1,晴/晴,19℃/6℃,无持续风向≤3级/无持续风向≤3级 155 | 2015/1/2,晴/晴,20℃/7℃,无持续风向≤3级/无持续风向≤3级 156 | 2015/1/3,晴/晴,21℃/8℃,无持续风向≤3级/无持续风向≤3级 157 | 2015/1/4,多云/多云,23℃/14℃,无持续风向≤3级/无持续风向≤3级 158 | 2015/1/5,阴/阴,23℃/17℃,无持续风向≤3级/无持续风向≤3级 159 | 2015/1/6,小雨/小雨,24℃/14℃,无持续风向≤3级/北风3-4级 160 | 2015/1/7,多云/多云,17℃/10℃,北风3-4级/北风3-4级 161 | 2015/1/8,多云/晴,18℃/6℃,无持续风向≤3级/无持续风向≤3级 162 | 2015/1/9,多云/多云,19℃/10℃,无持续风向≤3级/无持续风向≤3级 163 | 2015/1/10,多云/多云,19℃/11℃,无持续风向≤3级/无持续风向≤3级 164 | 2015/1/11,小雨/小雨,17℃/9℃,无持续风向≤3级/无持续风向≤3级 165 | 2015/1/12,中到大雨/中雨,12℃/9℃,无持续风向≤3级/无持续风向≤3级 166 | 2015/1/13,小雨/多云,15℃/7℃,无持续风向≤3级/无持续风向≤3级 167 | 2015/1/14,晴/晴,19℃/6℃,无持续风向≤3级/无持续风向≤3级 168 | 2015/1/15,晴/晴,19℃/5℃,无持续风向≤3级/无持续风向≤3级 169 | 2015/1/16,晴/晴,20℃/8℃,无持续风向≤3级/无持续风向≤3级 170 | 2015/1/17,多云/晴,19℃/8℃,无持续风向≤3级/无持续风向≤3级 171 | 2015/1/18,晴/晴,20℃/8℃,无持续风向≤3级/无持续风向≤3级 172 | 2015/1/19,晴/晴,20℃/7℃,无持续风向≤3级/无持续风向≤3级 173 | 2015/1/20,晴/多云,21℃/10℃,无持续风向≤3级/无持续风向≤3级 174 | 2015/1/21,霾/霾,22℃/9℃,无持续风向≤3级/无持续风向≤3级 175 | 2015/1/22,晴/晴,22℃/7℃,无持续风向≤3级/无持续风向≤3级 176 | 2015/1/23,晴/多云,23℃/10℃,无持续风向≤3级/无持续风向≤3级 177 | 2015/1/24,小雨/多云,21℃/14℃,无持续风向≤3级/无持续风向≤3级 178 | 2015/1/25,多云/多云,23℃/15℃,无持续风向≤3级/无持续风向≤3级 179 | 2015/1/26,多云/多云,25℃/14℃,无持续风向≤3级/无持续风向≤3级 180 | 2015/1/27,多云/多云,24℃/14℃,无持续风向≤3级/无持续风向≤3级 181 | 2015/1/28,多云/多云,21℃/13℃,无持续风向≤3级/无持续风向≤3级 182 | 2015/1/29,多云/阴,21℃/12℃,无持续风向≤3级/无持续风向≤3级 183 | 2015/1/30,阴/多云,17℃/8℃,北风3-4级/无持续风向≤3级 184 | 2015/1/31,多云/多云,17℃/9℃,无持续风向≤3级/无持续风向≤3级 185 | -------------------------------------------------------------------------------- /passenger_predict/data/gd_线路11_aver_weight_final.txt: -------------------------------------------------------------------------------- 1 | holiday_type,time,cardtype0_weight,cardtype1_weight,cardtype2_weight,cardtype3_weight,cardtype4_weight,cardtype5_weight,cardtype6_weight 2 | 0,0,0.0,0.0117439100349,0.0487686677326,0.902002791568,0.00641144927589,0.0,0.0310731813881 3 | 0,1,0.0,0.0263281612964,0.0457337993115,0.88197398209,0.00169199503577,5.4860653939e-05,0.0442172016128 4 | 0,2,0.0,0.00931306448599,0.0354179313197,0.818711246259,0.00486425159857,0.0,0.131693506336 5 | 0,3,0.0,0.00752661807367,0.0546345482014,0.76457616382,0.00336119327405,0.000293427230047,0.169608049401 6 | 0,4,0.0,0.00607481337338,0.0563914765204,0.823498115592,0.00302128139933,0.0,0.111014313115 7 | 0,5,0.0,0.0139011679046,0.031754445968,0.644064645831,0.00784842073084,0.000105218855219,0.30232610071 8 | 0,6,0.0,0.00828035972861,0.212422944413,0.53198099705,0.00963912876989,9.16094294201e-05,0.237584960609 9 | 0,7,0.0,0.00801122047959,0.0941005149409,0.70874583852,0.00661684041151,0.000170651741944,0.182354933906 10 | 0,8,0.0,0.0063509559882,0.0297608459325,0.726484255772,0.00653140323523,0.000188472319686,0.230684066752 11 | 0,9,0.0,0.00641306506317,0.021794971559,0.605515060358,0.00875141595567,0.000162993658746,0.357362493405 12 | 0,10,0.0,0.00715588268058,0.0202808882404,0.565775764274,0.00942655514204,0.000147676334657,0.397213233328 13 | 0,11,5.14138817481e-06,0.00830272086739,0.0326643443455,0.600321863624,0.0103109334623,0.00031272098725,0.348082275325 14 | 0,12,0.0,0.00984076966675,0.0567528061177,0.670794768269,0.00877359246444,0.000209099237412,0.253628964244 15 | 0,13,0.0,0.0103457137516,0.0453596934837,0.678270855261,0.00919082090717,0.000171134336983,0.25666178226 16 | 0,14,0.0,0.00986053127909,0.0344439998299,0.701547993119,0.00955587735774,0.00013220767647,0.244459390738 17 | 0,15,0.0,0.00889338572616,0.0598569303272,0.69533925195,0.00919959699141,0.000135471290072,0.226575363715 18 | 0,16,0.0,0.0103406511528,0.0966623021631,0.699864077745,0.00916959099325,0.000197692452758,0.183765685493 19 | 0,17,0.0,0.00969240599546,0.106450751698,0.778064725931,0.0072205042772,0.000274527397141,0.0982970847015 20 | 0,18,0.0,0.0068360076526,0.0878495660617,0.85173201207,0.0042432742373,0.000123194790761,0.0492159451877 21 | 0,19,0.0,0.0175631895464,0.0584678199237,0.842776598754,0.00495912384013,0.00013332205424,0.0760999458812 22 | 0,20,0.0,0.0104045087179,0.0598334891783,0.828622796189,0.0056291144243,0.000105856515184,0.0954042349754 23 | 0,21,0.0,0.0102885454079,0.0916618710493,0.834519746778,0.00509531405117,0.000189393474582,0.0582451292387 24 | 0,22,0.0,0.0108086537386,0.0562521370003,0.89788260227,0.00467712049796,0.000105426414965,0.0302740600782 25 | 0,23,0.0,0.0135890490797,0.0509242437557,0.904702962,0.00681973765863,0.000102124183007,0.0238618833229 26 | 1,0,0.0,0.00993217947931,0.0609952137205,0.899268410551,0.00607986662354,0.0,0.0237243296259 27 | 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0,13,0.0,0.0106397609817,0.0478547844948,0.745928857992,0.00679469640033,0.000258937778309,0.188522962353 16 | 0,14,0.0,0.00986974403839,0.0409391712914,0.753062318011,0.00871237861462,0.000359147457153,0.187057240587 17 | 0,15,0.0,0.00965327074128,0.0479149403766,0.747012601013,0.00797372596371,0.000287908542303,0.187157553363 18 | 0,16,0.0,0.0103990604501,0.0908468307273,0.740962537883,0.00785076543208,0.00032907190642,0.149611733601 19 | 0,17,0.0,0.00938836347214,0.102553594938,0.809717781027,0.00501766372545,0.000413503034753,0.0729090938033 20 | 0,18,0.0,0.00649633310857,0.0772794756116,0.87394084114,0.00342227519928,0.00021757491277,0.0386435000274 21 | 0,19,0.0,0.00743023299895,0.0519907821342,0.873218048709,0.004573758035,0.000272125617427,0.0625150525058 22 | 0,20,0.0,0.00890538088973,0.0616511696561,0.849777125804,0.00476516155521,0.00038571107224,0.0745154510225 23 | 0,21,0.0,0.0107290720633,0.0754104911401,0.859580741548,0.00474437318518,0.000330697312879,0.0492046247502 24 | 0,22,0.0,0.0114491328257,0.0553224177891,0.897232678738,0.00422114252244,0.000199648177199,0.0315749799479 25 | 0,23,0.0,0.00997107055459,0.0489772086236,0.911854084623,0.0049382966603,0.000208407833648,0.024050931705 26 | 1,0,0.0,0.00801191940474,0.0645447380004,0.908539639376,0.00238212739739,0.0,0.0165215758211 27 | 1,1,0.0,0.0105426243659,0.0529556860735,0.889387570158,0.00279237072386,0.0,0.0443217486789 28 | 1,2,0.0,0.0126016238767,0.0576690009773,0.738455924954,0.0122073039316,0.000620347394541,0.178445798865 29 | 1,3,0.0,0.0215145163374,0.090965439433,0.649598695736,0.0112726869404,0.0,0.226648661553 30 | 1,4,0.0,0.0164979351262,0.0443209753463,0.748214742301,0.00807153104846,0.0,0.182894816178 31 | 1,5,0.0,0.0182226784009,0.0221375532212,0.588483473861,0.00735552310826,5.66764905916e-05,0.363744094918 32 | 1,6,0.0,0.0132754700188,0.0459541555359,0.687575612961,0.00791271735729,0.000226991514356,0.245055052612 33 | 1,7,0.0,0.00975618181297,0.0560374271331,0.756521334128,0.00592117367506,0.000221864989563,0.171542018261 34 | 1,8,0.0,0.00623776087655,0.055872603063,0.753548559398,0.00538996730469,0.000206080157008,0.178745029201 35 | 1,9,0.0,0.0060687539186,0.052984048396,0.713927037254,0.00547337188405,0.000191710727469,0.22135507782 36 | 1,10,0.0,0.00639807496496,0.0607482487414,0.699929622354,0.00599931718157,0.000300065892265,0.226624670866 37 | 1,11,0.0,0.007226560316,0.0693733531466,0.724990556549,0.00601165449182,0.000248273389772,0.192149602107 38 | 1,12,0.0,0.00913062692974,0.0854557679485,0.760228899613,0.00595749338197,0.000211314251492,0.139015897875 39 | 1,13,0.0,0.00932424538381,0.082443407659,0.763546059603,0.00624220577175,0.000169158936949,0.138274922646 40 | 1,14,0.0,0.00839908917901,0.0750953009245,0.776607751395,0.00566975434057,0.000203026326401,0.134025077834 41 | 1,15,0.0,0.00797654429119,0.081572228481,0.775501884672,0.00556182687603,0.000225982114286,0.129161533565 42 | 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-------------------------------------------------------------------------------- 1 | 6,2015-01-01,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2 2 | 7,2015-01-01,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2 3 | 8,2015-01-01,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2 4 | 9,2015-01-01,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2 5 | 10,2015-01-01,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2 6 | 11,2015-01-01,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2 7 | 12,2015-01-01,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2 8 | 13,2015-01-01,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2 9 | 14,2015-01-01,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2 10 | 15,2015-01-01,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2 11 | 16,2015-01-01,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2 12 | 17,2015-01-01,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2 13 | 18,2015-01-01,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2 14 | 19,2015-01-01,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2 15 | 20,2015-01-01,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2 16 | 21,2015-01-01,3,10,10,0,10.0,19.0,6.0,13.0,12.5,2 17 | 6,2015-01-02,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1 18 | 7,2015-01-02,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1 19 | 8,2015-01-02,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1 20 | 9,2015-01-02,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1 21 | 10,2015-01-02,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1 22 | 11,2015-01-02,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1 23 | 12,2015-01-02,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1 24 | 13,2015-01-02,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1 25 | 14,2015-01-02,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1 26 | 15,2015-01-02,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1 27 | 16,2015-01-02,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1 28 | 17,2015-01-02,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1 29 | 18,2015-01-02,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1 30 | 19,2015-01-02,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1 31 | 20,2015-01-02,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1 32 | 21,2015-01-02,4,10,10,0,10.0,20.0,7.0,13.0,13.5,1 33 | 6,2015-01-03,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1 34 | 7,2015-01-03,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1 35 | 8,2015-01-03,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1 36 | 9,2015-01-03,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1 37 | 10,2015-01-03,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1 38 | 11,2015-01-03,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1 39 | 12,2015-01-03,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1 40 | 13,2015-01-03,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1 41 | 14,2015-01-03,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1 42 | 15,2015-01-03,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1 43 | 16,2015-01-03,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1 44 | 17,2015-01-03,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1 45 | 18,2015-01-03,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1 46 | 19,2015-01-03,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1 47 | 20,2015-01-03,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1 48 | 21,2015-01-03,5,10,10,0,10.0,21.0,8.0,13.0,14.5,1 49 | 6,2015-01-04,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0 50 | 7,2015-01-04,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0 51 | 8,2015-01-04,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0 52 | 9,2015-01-04,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0 53 | 10,2015-01-04,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0 54 | 11,2015-01-04,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0 55 | 12,2015-01-04,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0 56 | 13,2015-01-04,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0 57 | 14,2015-01-04,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0 58 | 15,2015-01-04,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0 59 | 16,2015-01-04,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0 60 | 17,2015-01-04,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0 61 | 18,2015-01-04,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0 62 | 19,2015-01-04,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0 63 | 20,2015-01-04,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0 64 | 21,2015-01-04,6,9,9,0,9.0,23.0,14.0,9.0,18.5,0 65 | 6,2015-01-05,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0 66 | 7,2015-01-05,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0 67 | 8,2015-01-05,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0 68 | 9,2015-01-05,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0 69 | 10,2015-01-05,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0 70 | 11,2015-01-05,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0 71 | 12,2015-01-05,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0 72 | 13,2015-01-05,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0 73 | 14,2015-01-05,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0 74 | 15,2015-01-05,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0 75 | 16,2015-01-05,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0 76 | 17,2015-01-05,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0 77 | 18,2015-01-05,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0 78 | 19,2015-01-05,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0 79 | 20,2015-01-05,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0 80 | 21,2015-01-05,0,8,8,0,8.0,23.0,17.0,6.0,20.0,0 81 | 6,2015-01-06,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0 82 | 7,2015-01-06,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0 83 | 8,2015-01-06,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0 84 | 9,2015-01-06,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0 85 | 10,2015-01-06,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0 86 | 11,2015-01-06,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0 87 | 12,2015-01-06,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0 88 | 13,2015-01-06,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0 89 | 14,2015-01-06,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0 90 | 15,2015-01-06,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0 91 | 16,2015-01-06,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0 92 | 17,2015-01-06,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0 93 | 18,2015-01-06,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0 94 | 19,2015-01-06,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0 95 | 20,2015-01-06,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0 96 | 21,2015-01-06,1,7,7,0,7.0,24.0,14.0,10.0,19.0,0 97 | 6,2015-01-07,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0 98 | 7,2015-01-07,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0 99 | 8,2015-01-07,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0 100 | 9,2015-01-07,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0 101 | 10,2015-01-07,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0 102 | 11,2015-01-07,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0 103 | 12,2015-01-07,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0 104 | 13,2015-01-07,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0 105 | 14,2015-01-07,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0 106 | 15,2015-01-07,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0 107 | 16,2015-01-07,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0 108 | 17,2015-01-07,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0 109 | 18,2015-01-07,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0 110 | 19,2015-01-07,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0 111 | 20,2015-01-07,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0 112 | 21,2015-01-07,2,9,9,0,9.0,17.0,10.0,7.0,13.5,0 113 | -------------------------------------------------------------------------------- /passenger_predict/data/train_data_sample.txt: -------------------------------------------------------------------------------- 1 | 广州,线路6,d1b5feda077818e286aefe5b1cbc75c6,7e017a9d22141cc1b536d417a69c4b43,广州,2014122908,普通卡 2 | 广州,线路6,4fe4beb30f21df1b16b0769a0ceac473,f983f75ad1146366dea7b4bd12b36294,广州,2014122909,普通卡 3 | 广州,线路6,4fe4beb30f21df1b16b0769a0ceac473,7615005a78528a56514375a2eb2486a2,广州,2014122909,普通卡 4 | 广州,线路11,78da304535276e90f941f644028848c2,e7921ba6f9fba313c2326d54bd26ffa5,广州,2014122813,普通卡 5 | 广州,线路11,78da304535276e90f941f644028848c2,040e0386b1ee6f8b7f03f1f0d4b355c0,广州,2014122813,普通卡 6 | 广州,线路6,d6d5806e8ea3db1b7ce2d61d995bb8da,393591054157b0a725e285f011293a85,广州,2014122906,老人卡 7 | 广州,线路11,562689c04e6feda9314312569ec1ba05,e3c4ec853a3d968e7398462d270c326f,广州,2014122920,普通卡 -------------------------------------------------------------------------------- /passenger_predict/gen_aver_bus_cnt.py: -------------------------------------------------------------------------------- 1 | #-*-coding:utf-8-*- 2 | import pandas as pd 3 | holiday_pd = pd.read_csv('./data/date_holiday_result.txt') 4 | holiday_pd.columns= ['date','holiday_type'] 5 | for line in ['线路6','线路11']: 6 | bus_line_name = './data/bus_count/final_'+line+'_bus_count.txt' 7 | bus_line_count = pd.read_csv(bus_line_name) 8 | bus_line_count.columns=['date','time','bus_cnt'] 9 | bus_line_count['date_val'] = pd.to_datetime(bus_line_count['date']) 10 | holiday_pd['date_val'] = pd.to_datetime(holiday_pd['date']) 11 | print bus_line_count.head() 12 | print holiday_pd.head() 13 | bus_line_count_holiday = pd.merge(bus_line_count,holiday_pd, on='date_val') 14 | print bus_line_count_holiday.head() 15 | bus_line_grouped = bus_line_count_holiday.groupby(['holiday_type','time'])['bus_cnt'].mean().astype(int) 16 | print bus_line_grouped 17 | bus_line_grouped_name = './data/bus_count/gd_'+line+'_bus_aver_final.txt' 18 | bus_line_grouped.to_csv(bus_line_grouped_name) -------------------------------------------------------------------------------- /passenger_predict/gen_bus_count.py: -------------------------------------------------------------------------------- 1 | #-*-coding:utf-8-*- 2 | __author__ = 'burness' 3 | import pandas as pd 4 | gd_train_data = pd.read_csv('./data/gd_train_data.txt',header=False) 5 | gd_train_data.columns = ['use_city','line_name','terminal_id','card_id','create_city','deal_time','card_type'] 6 | 7 | # 每天线路的客流count 8 | # gd_train_count = gd_train_data.groupby(['line_name','deal_time']).count() 9 | for line in ['线路6','线路11']: 10 | gd_bus_line_count = gd_train_data[gd_train_data.line_name==line].groupby('deal_time')['terminal_id'].nunique() 11 | print gd_bus_line_count 12 | count_name = './data/bus_count/'+line+'_bus_count.txt' 13 | gd_bus_line_count.to_csv(count_name) 14 | # print gd_train_count 15 | # print gd_train_data.head(10) -------------------------------------------------------------------------------- /passenger_predict/gen_cardtype_count_aver_weight.py: -------------------------------------------------------------------------------- 1 | #-*-coding:utf-8-*- 2 | import pandas as pd 3 | holiday_pd = pd.read_csv('./data/date_holiday_result.txt') 4 | holiday_pd.columns= ['date','holiday_type'] 5 | for line in ['线路6','线路11']: 6 | cardtype_weight = './data/cardtype_%s_count_weight.txt'%line 7 | cardtype_weight_data = pd.read_csv(cardtype_weight) 8 | cardtype_weight_data['date_val'] = pd.to_datetime(cardtype_weight_data['date']) 9 | holiday_pd['date_val'] = pd.to_datetime(holiday_pd['date']) 10 | print cardtype_weight_data.head() 11 | print holiday_pd.head() 12 | cardtype_weight_holiday = pd.merge(cardtype_weight_data,holiday_pd, on='date_val') 13 | print cardtype_weight_holiday.head() 14 | cardtype_weight_grouped = cardtype_weight_holiday.groupby(['holiday_type','time'])['cardtype0_weight', 15 | 'cardtype1_weight', 16 | 'cardtype2_weight', 17 | 'cardtype3_weight', 18 | 'cardtype4_weight', 19 | 'cardtype5_weight', 20 | 'cardtype6_weight'].mean().astype(float) 21 | print cardtype_weight_grouped 22 | bus_line_grouped_name = './data/gd_'+line+'_aver_weight_final.txt' 23 | cardtype_weight_grouped.to_csv(bus_line_grouped_name) -------------------------------------------------------------------------------- /passenger_predict/gen_line_count.py: -------------------------------------------------------------------------------- 1 | #-*-coding:utf-8-*- 2 | __author__ = 'burness' 3 | import pandas as pd 4 | gd_train_data = pd.read_csv('./data/gd_train_data.txt',header=False) 5 | gd_train_data.columns = ['use_city','line_name','terminal_id','card_id','create_city','deal_time','card_type'] 6 | 7 | # 每天线路的客流count 8 | # gd_train_count = gd_train_data.groupby(['line_name','deal_time']).count() 9 | for line in ['线路6','线路11']: 10 | gd_train_line_count = gd_train_data[gd_train_data['line_name']==line]['deal_time'].value_counts() 11 | count_name = './data/count/'+line+'_count.txt' 12 | gd_train_line_count.to_csv(count_name) 13 | # print gd_train_count 14 | # print gd_train_data.head(10) -------------------------------------------------------------------------------- /passenger_predict/gen_line_count_cardtype.py: -------------------------------------------------------------------------------- 1 | #-*-coding:utf-8-*- 2 | __author__ = 'burness' 3 | import pandas as pd 4 | gd_train_data = pd.read_csv('./data/gd_train_data.txt',header=False) 5 | gd_train_data.columns = ['use_city','line_name','terminal_id','card_id','create_city','deal_time','card_type'] 6 | 7 | # 每天线路的客流count 8 | # gd_train_count = gd_train_data.groupby(['line_name','deal_time']).count() 9 | for line in ['线路6','线路11']: 10 | gd_train_line_count = gd_train_data[gd_train_data['line_name']==line].groupby('card_type')['deal_time'].value_counts() 11 | count_name = './data/count/'+line+'_count_cardtype.txt' 12 | gd_train_line_count.to_csv(count_name) 13 | # print gd_train_count 14 | # print gd_train_data.head(10)__author__ = 'burness' 15 | -------------------------------------------------------------------------------- /passenger_predict/gen_test_data.py: -------------------------------------------------------------------------------- 1 | #-*-coding:utf-8-*- 2 | import copy 3 | __author__ = 'burness' 4 | with open('./data/test_data.csv') as f: 5 | with open('./data/test_data_final.csv','w+') as fwrite: 6 | for line in f.readlines(): 7 | for i in range(6,22,1): 8 | tmpline =copy.deepcopy(line) 9 | tmpline = str(i)+','+tmpline 10 | fwrite.write(tmpline) 11 | 12 | -------------------------------------------------------------------------------- /passenger_predict/model/.DS_Store: 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#-*-coding:utf-8-*- 2 | ''' 3 | 这个脚本用来预测20150101-20150107的数据,不经过dummies 4 | ''' 5 | import pandas as pd 6 | import copy 7 | from sklearn.externals import joblib 8 | __author__ = 'burness' 9 | test_data = pd.read_csv('./data/test_data_final.csv',header=None) 10 | print test_data.count() 11 | test_data.columns = ['time','date','dayofweek','weatherA_val','weatherB_val','weatherPeriod','weatherE', 12 | 'temperatureA','temperatureB','temperaturePeriod','temperatureE','holiday'] 13 | # print test_data 14 | 15 | final_data = test_data 16 | # print test_data 17 | 18 | for line in ['线路6','线路11']: 19 | model_name = './model/2015-11-26/traffic_GBDT_'+line+'.model' 20 | gd_model = joblib.load(model_name) 21 | # 增加time字段 22 | 23 | final_data = final_data[['date','time','dayofweek','weatherA_val', 24 | 'weatherB_val','weatherPeriod','weatherE', 25 | 'temperatureA','temperatureB','temperaturePeriod','temperatureE','holiday']] 26 | # print final_data 27 | bus_cnt_name= './data/bus_count/gd_'+line+'_bus_aver_final.txt' 28 | bus_cnt = pd.read_csv(bus_cnt_name) 29 | bus_cnt.columns = ['holiday','time','bus_cnt'] 30 | final_data = pd.merge(final_data,bus_cnt,on=['holiday','time']) 31 | print final_data 32 | final_data.to_csv('./data/final_data_test.txt',index=False) 33 | # 34 | # # print test_data_final 35 | # # test_data_final.to_csv('test.dat') 36 | final_data_train = final_data[['time','dayofweek','weatherA_val', 37 | 'weatherB_val','weatherPeriod','weatherE', 38 | 'temperatureA','temperatureB','temperaturePeriod','temperatureE','holiday','bus_cnt']] 39 | predict_label = gd_model.predict(final_data_train) 40 | # print predict_label 41 | result_data = pd.DataFrame() 42 | result_data['date'] = final_data['date'].str.split('-').str[0]+final_data['date'].str.split('-').str[1]+final_data['date'].str.split('-').str[2]; 43 | result_data['time'] = final_data['time'] 44 | result_data['cnt'] = pd.DataFrame(predict_label).astype(int) 45 | result_data['line'] = line 46 | result_name = './data/result/2015-11-26/traffic_'+line+'GBDT_result.txt' 47 | result_data.to_csv(result_name,index=False,header=False) 48 | # 49 | -------------------------------------------------------------------------------- /passenger_predict/predict_GBDT_2015-11-28.py: -------------------------------------------------------------------------------- 1 | #-*-coding:utf-8-*- 2 | ''' 3 | 这个脚本用来预测20150101-20150107的数据,不经过dummies 4 | ''' 5 | import pandas as pd 6 | import copy 7 | from sklearn.externals import joblib 8 | __author__ = 'burness' 9 | test_data = pd.read_csv('./data/test_data_final.csv',header=None) 10 | print test_data.count() 11 | test_data.columns = ['time','date','dayofweek','weatherA_val','weatherB_val','weatherPeriod','weatherE', 12 | 'temperatureA','temperatureB','temperaturePeriod','temperatureE','holiday'] 13 | # print test_data 14 | 15 | # print test_data 16 | 17 | for line in ['线路6','线路11']: 18 | final_data = test_data 19 | model_name = './model/2015-11-28/traffic_GBDT_'+line+'.model' 20 | gd_model = joblib.load(model_name) 21 | 22 | final_data = final_data[['date','time','dayofweek','weatherA_val', 23 | 'weatherB_val','weatherPeriod','weatherE', 24 | 'temperatureA','temperatureB','temperaturePeriod','temperatureE','holiday']] 25 | # print final_data 26 | bus_cnt_name= './data/bus_count/gd_'+line+'_bus_aver_final.txt' 27 | bus_cnt = pd.read_csv(bus_cnt_name) 28 | bus_cnt.columns = ['holiday','time','bus_cnt'] 29 | final_data = pd.merge(final_data,bus_cnt,on=['holiday','time']) 30 | # add the averg weight 31 | aver_weight = pd.read_csv('./data/gd_%s_aver_weight_final.txt'%line) 32 | final_data = pd.merge(final_data,aver_weight,left_on=['holiday','time'],right_on=['holiday_type','time']) 33 | print final_data.columns 34 | final_data = final_data[['date','time','dayofweek','weatherA_val','weatherB_val','weatherPeriod','weatherE', 35 | 'temperatureA','temperatureB','temperaturePeriod','temperatureE','holiday','bus_cnt', 36 | 'cardtype0_weight','cardtype1_weight','cardtype2_weight','cardtype3_weight', 37 | 'cardtype4_weight','cardtype5_weight','cardtype6_weight']] 38 | final_data.to_csv('./data/final_data_test_%s_28.txt'%line,index=False) 39 | # 40 | # # print test_data_final 41 | # # test_data_final.to_csv('test.dat') 42 | final_data_train = final_data[['time','dayofweek','weatherA_val', 43 | 'weatherB_val','weatherPeriod','weatherE', 44 | 'temperatureA','temperatureB','temperaturePeriod','temperatureE','holiday','bus_cnt', 45 | 'cardtype0_weight','cardtype1_weight','cardtype2_weight','cardtype3_weight', 46 | 'cardtype4_weight','cardtype5_weight','cardtype6_weight']] 47 | predict_label = gd_model.predict(final_data_train) 48 | # print predict_label 49 | result_data = pd.DataFrame() 50 | result_data['date'] = final_data['date'].str.split('-').str[0]+final_data['date'].str.split('-').str[1]+final_data['date'].str.split('-').str[2]; 51 | result_data['time'] = final_data['time'] 52 | result_data['cnt'] = pd.DataFrame(predict_label).astype(int) 53 | result_data['line'] = line 54 | result_name = './data/result/2015-11-28/traffic_'+line+'GBDT_result.txt' 55 | result_data.to_csv(result_name,index=False,header=False) 56 | # -------------------------------------------------------------------------------- /passenger_predict/process_cardtype_count_deal_time.py: -------------------------------------------------------------------------------- 1 | #-*-coding:utf-8-*- 2 | __author__ = 'burness' 3 | ''' 4 | 这个脚本用来处理cardtype count当中的时间问题 5 | ''' 6 | def process_deal_time(path,file_name): 7 | file_name_path = path+file_name 8 | file_name_write = path +'final_'+file_name 9 | with open(file_name_path) as f: 10 | with open(file_name_write,'w+') as f2: 11 | for line in f.readlines(): 12 | line_list = line.split(',') 13 | deal_time_str = line_list[1] 14 | if deal_time_str[4] == '0' and deal_time_str[6] == '0': 15 | deal_time_str=deal_time_str[:4]+'/'+deal_time_str[5:6]+'/'+deal_time_str[7:8]+','+deal_time_str[8:] 16 | elif deal_time_str[4] == '0' and deal_time_str[6]!= '0': 17 | deal_time_str=deal_time_str[:4]+'/'+deal_time_str[5:6]+'/'+deal_time_str[6:8]+','+deal_time_str[8:] 18 | elif deal_time_str[4] != '0' and deal_time_str[6] == '0': 19 | deal_time_str = deal_time_str[:4]+'/'+deal_time_str[4:6]+'/'+deal_time_str[7:8]+','+deal_time_str[8:] 20 | else: 21 | deal_time_str = deal_time_str[:4]+'/'+deal_time_str[4:6]+'/'+deal_time_str[6:8]+','+deal_time_str[8:] 22 | f2.write(line_list[0]+','+deal_time_str+','+line_list[2]) 23 | 24 | process_deal_time('./data/count/','线路6_count_cardtype.txt') 25 | process_deal_time('./data/count/','线路11_count_cardtype.txt') 26 | # process_deal_time('./data/bus_count/','线路6_bus_count.txt') 27 | # process_deal_time('./data/bus_count/','线路11_bus_count.txt') 28 | 29 | -------------------------------------------------------------------------------- /passenger_predict/process_cardtype_count_weight.py: -------------------------------------------------------------------------------- 1 | #-*-coding:utf-8-* 2 | import pandas as pd 3 | # def compute_weight(pd): 4 | 5 | for line in ['线路6','线路11']: 6 | cardtype_count_name = './data/count/final_'+line+'_count_cardtype.txt' 7 | gd_cardtype_cnt = pd.read_csv(cardtype_count_name,header=None) 8 | gd_cardtype_cnt.columns = ['card_type','date','time','cardtype_cnt'] 9 | cardtype_list = sorted(gd_cardtype_cnt['card_type'].unique()) 10 | print len(cardtype_list) 11 | cardtype_dict = dict(zip(cardtype_list,range(len(cardtype_list)+1))) 12 | gd_cardtype_cnt['card_type_val'] = gd_cardtype_cnt['card_type'].map(cardtype_dict) 13 | gd_cardtype_cnt = gd_cardtype_cnt[['date','time','cardtype_cnt','card_type_val']] 14 | gd_cardtype_cnt_group = gd_cardtype_cnt.groupby(['date','time','card_type_val'])['cardtype_cnt'].sum() 15 | line_count_file = './data/count/final_%s_count.txt'%line 16 | line_count = pd.read_csv(line_count_file,header=None) 17 | line_count.columns=['date','time','count'] 18 | print gd_cardtype_cnt.columns 19 | gd_cardtype0_cnt = gd_cardtype_cnt[gd_cardtype_cnt['card_type_val']==0] 20 | gd_cardtype0_cnt.columns = ['date','time','cardtype0_cnt','0'] 21 | gd_cardtype1_cnt = gd_cardtype_cnt[gd_cardtype_cnt['card_type_val']==1] 22 | gd_cardtype1_cnt.columns = ['date','time','cardtype1_cnt','1'] 23 | gd_cardtype2_cnt = gd_cardtype_cnt[gd_cardtype_cnt['card_type_val']==2] 24 | gd_cardtype2_cnt.columns = ['date','time','cardtype2_cnt','2'] 25 | gd_cardtype3_cnt = gd_cardtype_cnt[gd_cardtype_cnt['card_type_val']==3] 26 | gd_cardtype3_cnt.columns = ['date','time','cardtype3_cnt','3'] 27 | 28 | gd_cardtype4_cnt = gd_cardtype_cnt[gd_cardtype_cnt['card_type_val']==4] 29 | gd_cardtype4_cnt.columns = ['date','time','cardtype4_cnt','4'] 30 | 31 | gd_cardtype5_cnt = gd_cardtype_cnt[gd_cardtype_cnt['card_type_val']==5] 32 | gd_cardtype5_cnt.columns = ['date','time','cardtype5_cnt','5'] 33 | 34 | gd_cardtype6_cnt = gd_cardtype_cnt[gd_cardtype_cnt['card_type_val']==6] 35 | gd_cardtype6_cnt.columns = ['date','time','cardtype6_cnt','6'] 36 | line_count_weight = pd.merge(line_count,gd_cardtype0_cnt,how='left',on=['date','time']) 37 | line_count_weight = pd.merge(line_count_weight,gd_cardtype1_cnt,how='left',on=['date','time']) 38 | line_count_weight = pd.merge(line_count_weight,gd_cardtype2_cnt,how='left',on=['date','time']) 39 | line_count_weight = pd.merge(line_count_weight,gd_cardtype3_cnt,how='left',on=['date','time']) 40 | line_count_weight = pd.merge(line_count_weight,gd_cardtype4_cnt,how='left',on=['date','time']) 41 | line_count_weight = pd.merge(line_count_weight,gd_cardtype5_cnt,how='left',on=['date','time']) 42 | line_count_weight = pd.merge(line_count_weight,gd_cardtype6_cnt,how='left',on=['date','time']) 43 | line_count_weight = line_count_weight.fillna(value=0) 44 | line_count_weight['cardtype0_weight'] = line_count_weight['cardtype0_cnt']/(1.0*line_count_weight['count']) 45 | line_count_weight['cardtype1_weight'] = line_count_weight['cardtype1_cnt']/(1.0*line_count_weight['count']) 46 | line_count_weight['cardtype2_weight'] = line_count_weight['cardtype2_cnt']/(1.0*line_count_weight['count']) 47 | line_count_weight['cardtype3_weight'] = line_count_weight['cardtype3_cnt']/(1.0*line_count_weight['count']) 48 | line_count_weight['cardtype4_weight'] = line_count_weight['cardtype4_cnt']/(1.0*line_count_weight['count']) 49 | line_count_weight['cardtype5_weight'] = line_count_weight['cardtype5_cnt']/(1.0*line_count_weight['count']) 50 | line_count_weight['cardtype6_weight'] = line_count_weight['cardtype6_cnt']/(1.0*line_count_weight['count']) 51 | line_count_weight = line_count_weight[['date','time','cardtype0_weight','cardtype1_weight','cardtype2_weight','cardtype3_weight', 52 | 'cardtype4_weight','cardtype5_weight','cardtype6_weight']] 53 | print line_count_weight.head() 54 | # join and compute the weight 55 | cardtype_name = './data/cardtype_%s_count_weight.txt'%line 56 | line_count_weight.to_csv(cardtype_name,header=True,index=False) 57 | -------------------------------------------------------------------------------- /passenger_predict/process_deal_time.py: -------------------------------------------------------------------------------- 1 | #-*-coding:utf-8-*- 2 | __author__ = 'burness' 3 | ''' 4 | 这个脚本用来处理count和bus_count文件当中的时间问题 5 | ''' 6 | def process_deal_time(path,file_name): 7 | file_name_path = path+file_name 8 | file_name_write = path +'final_'+file_name 9 | with open(file_name_path) as f: 10 | with open(file_name_write,'w+') as f2: 11 | for line in f.readlines(): 12 | if line[4] == '0' and line[6] == '0': 13 | line=line[:4]+'/'+line[5:6]+'/'+line[7:8]+','+line[8:] 14 | elif line[4] == '0' and line[6]!= '0': 15 | line=line[:4]+'/'+line[5:6]+'/'+line[6:8]+','+line[8:] 16 | elif line[4] != '0' and line[6] == '0': 17 | line = line[:4]+'/'+line[4:6]+'/'+line[7:8]+','+line[8:] 18 | else: 19 | line = line[:4]+'/'+line[4:6]+'/'+line[6:8]+','+line[8:] 20 | f2.write(line) 21 | 22 | process_deal_time('./data/count/','线路6_count.txt') 23 | process_deal_time('./data/count/','线路11_count.txt') 24 | process_deal_time('./data/bus_count/','线路6_bus_count.txt') 25 | process_deal_time('./data/bus_count/','线路11_bus_count.txt') 26 | 27 | -------------------------------------------------------------------------------- /passenger_predict/summit_result.py: -------------------------------------------------------------------------------- 1 | #-*-coding:utf-8-*- 2 | 3 | with open('./data/result/2015-11-28/traffic_线路6GBDT_result.txt') as f: 4 | with open('./data/result/2015-11-28/final_result.txt','a') as fwrite: 5 | for line in f.readlines(): 6 | tmp_list = line.strip().split(',') 7 | if len(tmp_list[1])==1: 8 | tmp_list[1] = '0'+tmp_list[1] 9 | line_write = tmp_list[3]+','+tmp_list[0]+','+tmp_list[1]+','+tmp_list[2]+'\n' 10 | fwrite.write(line_write) 11 | 12 | with open('./data/result/2015-11-28/traffic_线路11GBDT_result.txt') as f: 13 | with open('./data/result/2015-11-28/final_result.txt','a') as fwrite: 14 | for line in f.readlines(): 15 | tmp_list = line.strip().split(',') 16 | if len(tmp_list[1])==1: 17 | tmp_list[1] = '0'+tmp_list[1] 18 | line_write = tmp_list[3]+','+tmp_list[0]+','+tmp_list[1]+','+tmp_list[2]+'\n' 19 | fwrite.write(line_write) 20 | -------------------------------------------------------------------------------- /passenger_predict/traffic_GBDT_2015-11-26.py: -------------------------------------------------------------------------------- 1 | #-*-coding:utf-8-*- 2 | ''' 3 | 这个脚本用来训练为经过dummies的模型，并且保存 4 | ''' 5 | __author__ = 'burness' 6 | import pandas as pd 7 | from add_holiday import add_holiday 8 | from compute_error import compute_error 9 | gd_lines_info = pd.read_csv('./data/gd_line_desc.txt') 10 | gd_lines_info.columns=['line_name','stop_cnt','line_type'] 11 | 12 | gd_weather_info = pd.read_csv('./data/gd_weather_report.txt') 13 | gd_weather_info.columns=['date','weather','temperature','wind_direction_force'] 14 | 15 | gd_lines_info['line_type_val']=gd_lines_info['line_type'].map({'广州市内':0,'广佛跨区域':1}) 16 | 17 | gd_lines_info=gd_lines_info.drop(['line_type'],axis=1) 18 | for line in ['线路6','线路11']: 19 | # for line in ['线路11']: 20 | train_count_file = './data/count/final_%s_count.txt'%line 21 | gd_train_line_pd = pd.read_csv(train_count_file) 22 | gd_train_line_pd.columns = ['date','time','cnt'] 23 | # print gd_train_line_pd.count() 24 | gd_train_line_pd.head() 25 | # join the weather 26 | print gd_weather_info.columns 27 | gd_train_line_pd_weather = pd.merge(gd_train_line_pd,gd_weather_info,on='date') 28 | # print gd_train_line_pd_weather.count() 29 | gd_train_line_pd_weather['date_val']=pd.to_datetime(gd_train_line_pd_weather['date']) 30 | gd_train_line_pd_weather.head() 31 | gd_train_line_pd_weather['dayofweek']=gd_train_line_pd_weather['date_val'].apply(lambda x: x.dayofweek) 32 | gd_train_line_pd_weather['weatherA']=gd_train_line_pd_weather['weather'].str.split('/').str[0] 33 | gd_train_line_pd_weather['weatherB']=gd_train_line_pd_weather['weather'].str.split('/').str[1] 34 | gd_train_line_pd_weather['weatherA_val']=gd_train_line_pd_weather['weatherA'].map({'大到暴雨':0,'大雨':1,'中到大雨':2,'中雨':3, 35 | '小到中雨':4,'雷阵雨':5,'阵雨':6,'小雨':7,'阴':8 36 | ,'多云':9,'晴':10}) 37 | gd_train_line_pd_weather['weatherB_val']=gd_train_line_pd_weather['weatherB'].map({'大到暴雨':0,'大雨':1,'中到大雨':2,'中雨':3, 38 | '小到中雨':4,'雷阵雨':5,'阵雨':6,'小雨':7,'阴':8 39 | ,'多云':9,'晴':10}) 40 | gd_train_line_pd_weather[['weatherA_val','weatherB_val']]=gd_train_line_pd_weather[['weatherA_val','weatherB_val']].astype(int) 41 | gd_train_line_pd_weather['weatherPeriod']=abs(gd_train_line_pd_weather['weatherA_val']-gd_train_line_pd_weather['weatherB_val']) 42 | gd_train_line_pd_weather['weatherE']=(gd_train_line_pd_weather['weatherA_val']+gd_train_line_pd_weather['weatherB_val'])/2.0 43 | # gd_train_line_pd_weather.dtypes 44 | gd_train_line_pd_weather=gd_train_line_pd_weather.drop(['weather','weatherA','weatherB'],axis=1) 45 | gd_train_line_pd_weather['temperatureA']=gd_train_line_pd_weather['temperature'].str.split('/').str[0].str.extract('(\d+)') 46 | gd_train_line_pd_weather['temperatureB']=gd_train_line_pd_weather['temperature'].str.split('/').str[1].str.extract('(\d+)') 47 | gd_train_line_pd_weather[['temperatureA','temperatureB']]=gd_train_line_pd_weather[['temperatureA','temperatureB']].astype(float) 48 | gd_train_line_pd_weather['temperaturePeriod']=abs(gd_train_line_pd_weather['temperatureA']-gd_train_line_pd_weather['temperatureB']) 49 | gd_train_line_pd_weather['temperatureE']=(gd_train_line_pd_weather['temperatureA']+gd_train_line_pd_weather['temperatureB'])/2.0 50 | 51 | # 增加bus数量 52 | bus_line_name = './data/bus_count/final_'+line+'_bus_count.txt' 53 | bus_line = pd.read_csv(bus_line_name) 54 | bus_line.columns=['date','time','bus_cnt'] 55 | bus_line['date_val'] = pd.to_datetime(bus_line['date']) 56 | bus_line = bus_line.drop('date',axis=1) 57 | # print bus_line.dtypes 58 | # print gd_train_line_pd_weather.dtypes 59 | gd_train_line_pd_weather = pd.merge(gd_train_line_pd_weather,bus_line,on=['date_val','time']) 60 | # print gd_train_line_pd_weather.columns 61 | 62 | # 滤除非6点到21点得数据 63 | # print gd_train_line_pd_weather[gd_train_line_pd_weather['time']==23] 64 | gd_train_line_pd_weather = gd_train_line_pd_weather[gd_train_line_pd_weather['time']>=6] 65 | gd_train_line_pd_weather = gd_train_line_pd_weather[gd_train_line_pd_weather['time']<=21] 66 | 67 | # 加上holiday信息 68 | gd_train_line_pd_weather = add_holiday(gd_train_line_pd_weather) 69 | print gd_train_line_pd_weather.head() 70 | print gd_train_line_pd_weather.count() 71 | 72 | gd_train_line_pd_final = gd_train_line_pd_weather[['cnt','time','dayofweek','weatherA_val', 73 | 'weatherB_val','weatherPeriod','weatherE', 74 | 'temperatureA','temperatureB','temperaturePeriod','temperatureE','holiday','bus_cnt']] 75 | 76 | from sklearn.ensemble import GradientBoostingRegressor 77 | from sklearn import grid_search 78 | data=gd_train_line_pd_final[['time','dayofweek','weatherA_val','weatherB_val','weatherPeriod','weatherE', 79 | 'temperatureA','temperatureB','temperaturePeriod','temperatureE','holiday','bus_cnt']] 80 | labels = gd_train_line_pd_final['cnt'] 81 | # # # print data.head(40) 82 | from sklearn.cross_validation import train_test_split 83 | train_data,test_data,train_labels,test_labels=train_test_split(data,labels,test_size=7*15) 84 | est = GradientBoostingRegressor() 85 | parameters={'loss':('ls', 'lad', 'huber', 'quantile'),'learning_rate':[0.04*(i+1) for i in range(25)], 86 | 'n_estimators':[75,100,125,150],'max_depth':[2,3,4]} 87 | clf=grid_search.GridSearchCV(est,parameters) 88 | print 'performing grid_searching...' 89 | print 'parameters:' 90 | from time import time 91 | t0=time() 92 | clf.fit(train_data,train_labels) 93 | print 'grid_searching takes %0.3fs'%(time()-t0) 94 | best_parameters=clf.best_params_ 95 | 96 | for para_name in sorted(parameters.keys()): 97 | print para_name 98 | print best_parameters[para_name] 99 | # 100 | # 101 | # 102 | est.set_params(learning_rate=best_parameters['learning_rate'], 103 | loss=best_parameters['loss'],max_depth=best_parameters['max_depth'],n_estimators=best_parameters['n_estimators']) 104 | est.fit(train_data,train_labels) 105 | print '保存model....' 106 | from sklearn.externals import joblib 107 | model_name = './model/2015-11-26/traffic_GBDT_'+line+'.model' 108 | joblib.dump(est,model_name) 109 | 110 | 111 | # validation procee 112 | est = joblib.load('./model/2015-11-26/traffic_GBDT_'+line+'.model') 113 | sum = 0.0 114 | for i in range(200): 115 | val_train_data,val_test_data,val_train_labels,val_test_labels=train_test_split(data,labels,test_size=7*15) 116 | predict_labels = est.predict(val_test_data) 117 | print predict_labels 118 | error = compute_error(predict_labels,val_test_labels) 119 | print 'val error: %f '% error 120 | sum+=error 121 | print 'averge error: %f'%(sum/200) 122 | -------------------------------------------------------------------------------- /passenger_predict/traffic_GBDT_2015-11-28.py: -------------------------------------------------------------------------------- 1 | #-*-coding:utf-8-*- 2 | ''' 3 | 相对于26那个版本多一个month字段 4 | ''' 5 | __author__ = 'burness' 6 | import pandas as pd 7 | from add_holiday import add_holiday 8 | from compute_error import compute_error 9 | gd_lines_info = pd.read_csv('./data/gd_line_desc.txt') 10 | gd_lines_info.columns=['line_name','stop_cnt','line_type'] 11 | 12 | gd_weather_info = pd.read_csv('./data/gd_weather_report.txt') 13 | gd_weather_info.columns=['date','weather','temperature','wind_direction_force'] 14 | 15 | gd_lines_info['line_type_val']=gd_lines_info['line_type'].map({'广州市内':0,'广佛跨区域':1}) 16 | 17 | gd_lines_info=gd_lines_info.drop(['line_type'],axis=1) 18 | for line in ['线路6','线路11']: 19 | # for line in ['线路11']: 20 | train_count_file = './data/count/final_%s_count.txt'%line 21 | gd_train_line_pd = pd.read_csv(train_count_file) 22 | gd_train_line_pd.columns = ['date','time','cnt'] 23 | # print gd_train_line_pd.count() 24 | gd_train_line_pd.head() 25 | # join the weather 26 | print gd_weather_info.columns 27 | gd_train_line_pd_weather = pd.merge(gd_train_line_pd,gd_weather_info,on='date') 28 | # print gd_train_line_pd_weather.count() 29 | gd_train_line_pd_weather['date_val']=pd.to_datetime(gd_train_line_pd_weather['date']) 30 | gd_train_line_pd_weather.head() 31 | gd_train_line_pd_weather['dayofweek']=gd_train_line_pd_weather['date_val'].apply(lambda x: x.dayofweek) 32 | gd_train_line_pd_weather['weatherA']=gd_train_line_pd_weather['weather'].str.split('/').str[0] 33 | gd_train_line_pd_weather['weatherB']=gd_train_line_pd_weather['weather'].str.split('/').str[1] 34 | gd_train_line_pd_weather['weatherA_val']=gd_train_line_pd_weather['weatherA'].map({'大到暴雨':0,'大雨':1,'中到大雨':2,'中雨':3, 35 | '小到中雨':4,'雷阵雨':5,'阵雨':6,'小雨':7,'阴':8 36 | ,'多云':9,'晴':10}) 37 | gd_train_line_pd_weather['weatherB_val']=gd_train_line_pd_weather['weatherB'].map({'大到暴雨':0,'大雨':1,'中到大雨':2,'中雨':3, 38 | '小到中雨':4,'雷阵雨':5,'阵雨':6,'小雨':7,'阴':8 39 | ,'多云':9,'晴':10}) 40 | gd_train_line_pd_weather[['weatherA_val','weatherB_val']]=gd_train_line_pd_weather[['weatherA_val','weatherB_val']].astype(int) 41 | gd_train_line_pd_weather['weatherPeriod']=abs(gd_train_line_pd_weather['weatherA_val']-gd_train_line_pd_weather['weatherB_val']) 42 | gd_train_line_pd_weather['weatherE']=(gd_train_line_pd_weather['weatherA_val']+gd_train_line_pd_weather['weatherB_val'])/2.0 43 | # gd_train_line_pd_weather.dtypes 44 | gd_train_line_pd_weather=gd_train_line_pd_weather.drop(['weather','weatherA','weatherB'],axis=1) 45 | gd_train_line_pd_weather['temperatureA']=gd_train_line_pd_weather['temperature'].str.split('/').str[0].str.extract('(\d+)') 46 | gd_train_line_pd_weather['temperatureB']=gd_train_line_pd_weather['temperature'].str.split('/').str[1].str.extract('(\d+)') 47 | gd_train_line_pd_weather[['temperatureA','temperatureB']]=gd_train_line_pd_weather[['temperatureA','temperatureB']].astype(float) 48 | gd_train_line_pd_weather['temperaturePeriod']=abs(gd_train_line_pd_weather['temperatureA']-gd_train_line_pd_weather['temperatureB']) 49 | gd_train_line_pd_weather['temperatureE']=(gd_train_line_pd_weather['temperatureA']+gd_train_line_pd_weather['temperatureB'])/2.0 50 | 51 | # 增加bus数量 52 | bus_line_name = './data/bus_count/final_'+line+'_bus_count.txt' 53 | bus_line = pd.read_csv(bus_line_name) 54 | bus_line.columns=['date','time','bus_cnt'] 55 | bus_line['date_val'] = pd.to_datetime(bus_line['date']) 56 | bus_line = bus_line.drop('date',axis=1) 57 | # print bus_line.dtypes 58 | # print gd_train_line_pd_weather.dtypes 59 | gd_train_line_pd_weather = pd.merge(gd_train_line_pd_weather,bus_line,on=['date_val','time']) 60 | 61 | # 增加各种不同卡对应的占比 62 | cardtype_name = './data/cardtype_%s_count_weight.txt'%line 63 | cardtype_ratio = pd.read_csv(cardtype_name) 64 | # print gd_train_line_pd_weather.columns 65 | gd_train_line_pd_weather = pd.merge(gd_train_line_pd_weather,cardtype_ratio, on=['date','time']) 66 | # print gd_train_line_pd_weather.head() 67 | 68 | # 滤除非6点到21点得数据 69 | gd_train_line_pd_weather = gd_train_line_pd_weather[gd_train_line_pd_weather['time']>=6] 70 | gd_train_line_pd_weather = gd_train_line_pd_weather[gd_train_line_pd_weather['time']<=21] 71 | # 72 | # # 加上holiday信息 73 | gd_train_line_pd_weather = add_holiday(gd_train_line_pd_weather) 74 | # print gd_train_line_pd_weather.head() 75 | # print gd_train_line_pd_weather.count() 76 | # 77 | gd_train_line_pd_final = gd_train_line_pd_weather[['cnt','time','dayofweek','weatherA_val', 78 | 'weatherB_val','weatherPeriod','weatherE', 79 | 'temperatureA','temperatureB','temperaturePeriod','temperatureE','holiday','bus_cnt' 80 | ,'cardtype0_weight','cardtype1_weight','cardtype2_weight','cardtype3_weight', 81 | 'cardtype4_weight','cardtype5_weight','cardtype6_weight']] 82 | # 83 | from sklearn.ensemble import GradientBoostingRegressor 84 | from sklearn import grid_search 85 | data=gd_train_line_pd_final[['time','dayofweek','weatherA_val','weatherB_val','weatherPeriod','weatherE', 86 | 'temperatureA','temperatureB','temperaturePeriod','temperatureE','holiday','bus_cnt', 87 | 'cardtype0_weight','cardtype1_weight','cardtype2_weight','cardtype3_weight', 88 | 'cardtype4_weight','cardtype5_weight','cardtype6_weight']] 89 | labels = gd_train_line_pd_final['cnt'] 90 | # # # print data.head(40) 91 | from sklearn.cross_validation import train_test_split 92 | train_data,test_data,train_labels,test_labels=train_test_split(data,labels,test_size=7*15) 93 | est = GradientBoostingRegressor() 94 | parameters={'loss':('ls', 'lad', 'huber', 'quantile'),'learning_rate':[0.04*(i+1) for i in range(25)], 95 | 'n_estimators':[125,150,200,250],'max_depth':[3,4,5,6]} 96 | clf=grid_search.GridSearchCV(est,parameters) 97 | print 'performing grid_searching...' 98 | print 'parameters:' 99 | from time import time 100 | t0=time() 101 | clf.fit(train_data,train_labels) 102 | print 'grid_searching takes %0.3fs'%(time()-t0) 103 | best_parameters=clf.best_params_ 104 | 105 | for para_name in sorted(parameters.keys()): 106 | print para_name 107 | print best_parameters[para_name] 108 | # 109 | # 110 | # 111 | est.set_params(learning_rate=best_parameters['learning_rate'], 112 | loss=best_parameters['loss'],max_depth=best_parameters['max_depth'],n_estimators=best_parameters['n_estimators']) 113 | est.fit(train_data,train_labels) 114 | print '保存model....' 115 | from sklearn.externals import joblib 116 | model_name = './model/2015-11-29/traffic_GBDT_'+line+'.model' 117 | joblib.dump(est,model_name) 118 | 119 | 120 | # validation procee 121 | est = joblib.load('./model/2015-11-29/traffic_GBDT_'+line+'.model') 122 | sum = 0.0 123 | for i in range(200): 124 | val_train_data,val_test_data,val_train_labels,val_test_labels=train_test_split(data,labels,test_size=7*15) 125 | predict_labels = est.predict(val_test_data) 126 | # print predict_labels 127 | error = compute_error(predict_labels,val_test_labels) 128 | print 'val error: %f '% error 129 | sum+=error 130 | print 'averge error: %f'%(sum/200) 131 | --------------------------------------------------------------------------------