├── res ├── cof.jpg └── elecnew.csv ├── README.md ├── .gitignore └── elecnew.ipynb /res/cof.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pisunk/GPR-RF-Electricity-forecast/HEAD/res/cof.jpg -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 2 | # Monthly-Electricity-forecast use GPR-RFr 3 | 某区域月电量预测，采用高斯过程回归、随机森林回归预测日电量，通过日电量累加的方式来获得月电量的预测 4 | # Data Set 5 | Date DailyElectricity MaxTemperature MinTemperature Season GDP holiday MonthElectricity 6 | 其中MonthElectricity是该月电量，DailyElectricity是当日电量，其余为输入特征 7 | # Correlation of Features 8 | ![Correlation of Features](res/cof.jpg) 9 | # 步骤： 10 | ``` 11 | 一、下载数据 12 | 在res目录中下载 13 | 二、特征变量相关性 14 | 观察特征变量之间的相关性，并给出正态分布图 15 | 三、正态分布图 16 | 四、Scale+PCA预处理 17 | 数据归一化后用PCA降维消除特征间的相关性 18 | 五、算法模型 19 | 1.RFr 20 | RFr+k折交叉验证 21 | 2.GPR 22 | 六、月电量预测 23 | 简单的采用日累加的方法 24 | 25 | ``` 26 | # 输入说明： 27 | 要求用户在存入本月后五天以及次月所有天数的模型所需特征数据。举例：本月为9月，则用户需要给定9月剩余5天的特征数据一加10月31天的特征数据作为输入。即在数据库中，必须完整录入整个月的特征数据才能完成预测。 28 | 具体如下： 29 | 本月后五天数据，每条数据必须包括： 30 | 最高最低温度（可用接口填入）、是否节假日、年、月、季节。 31 | 次月所有天数据，每条数据必须包括： 32 | 是否节假日、年、月、季节。 33 | 自动预测模块根据数据格式，自动检测该月的月长度，从而进行预测值切割，为最终月电量预测做准备。 34 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | MANIFEST 27 | 28 | # PyInstaller 29 | # Usually these files are written by a python script from a template 30 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 31 | *.manifest 32 | *.spec 33 | 34 | # Installer logs 35 | pip-log.txt 36 | pip-delete-this-directory.txt 37 | 38 | # Unit test / coverage reports 39 | htmlcov/ 40 | .tox/ 41 | .coverage 42 | .coverage.* 43 | .cache 44 | nosetests.xml 45 | coverage.xml 46 | *.cover 47 | .hypothesis/ 48 | .pytest_cache/ 49 | 50 | # Translations 51 | *.mo 52 | *.pot 53 | 54 | # Django stuff: 55 | *.log 56 | local_settings.py 57 | db.sqlite3 58 | 59 | # Flask stuff: 60 | instance/ 61 | .webassets-cache 62 | 63 | # Scrapy stuff: 64 | .scrapy 65 | 66 | # Sphinx documentation 67 | docs/_build/ 68 | 69 | # PyBuilder 70 | target/ 71 | 72 | # Jupyter Notebook 73 | .ipynb_checkpoints 74 | 75 | # pyenv 76 | .python-version 77 | 78 | # celery beat schedule file 79 | celerybeat-schedule 80 | 81 | # SageMath parsed files 82 | *.sage.py 83 | 84 | # Environments 85 | .env 86 | .venv 87 | env/ 88 | venv/ 89 | ENV/ 90 | env.bak/ 91 | venv.bak/ 92 | 93 | # Spyder project settings 94 | .spyderproject 95 | .spyproject 96 | 97 | # Rope project settings 98 | .ropeproject 99 | 100 | # mkdocs documentation 101 | /site 102 | 103 | # mypy 104 | .mypy_cache/ 105 | -------------------------------------------------------------------------------- /res/elecnew.csv: -------------------------------------------------------------------------------- 1 | Date,DailyElectricity,MaxTemperature,MinTemperature,Season,GDP,holiday,MonthElectricity 2 | 2014/1/1,0.93,21,12,3,1182.31,2,38.91 3 | 2014/1/2,1.29,21,14,3,1182.31,0,38.91 4 | 2014/1/3,1.61,21,14,3,1182.31,0,38.91 5 | 2014/1/4,1.49,20,12,3,1182.31,1,38.91 6 | 2014/1/5,1.39,21,13,3,1182.31,1,38.91 7 | 2014/1/6,1.57,22,16,3,1182.31,0,38.91 8 | 2014/1/7,1.65,20,15,3,1182.31,0,38.91 9 | 2014/1/8,1.66,16,11,3,1182.31,0,38.91 10 | 2014/1/9,1.64,18,11,3,1182.31,0,38.91 11 | 2014/1/10,1.61,21,13,3,1182.31,0,38.91 12 | 2014/1/11,1.61,21,13,3,1182.31,1,38.91 13 | 2014/1/12,1.42,20,10,3,1182.31,1,38.91 14 | 2014/1/13,1.52,16,8,3,1182.31,0,38.91 15 | 2014/1/14,1.61,18,7,3,1182.31,0,38.91 16 | 2014/1/15,1.59,18,8,3,1182.31,0,38.91 17 | 2014/1/16,1.57,20,10,3,1182.31,0,38.91 18 | 2014/1/17,1.54,19,10,3,1182.31,0,38.91 19 | 2014/1/18,1.47,19,9,3,1182.31,1,38.91 20 | 2014/1/19,1.3,20,10,3,1182.31,1,38.91 21 | 2014/1/20,1.32,20,9,3,1182.31,0,38.91 22 | 2014/1/21,1.31,18,8,3,1182.31,0,38.91 23 | 2014/1/22,1.24,19,9,3,1182.31,0,38.91 24 | 2014/1/23,1.16,20,9,3,1182.31,0,38.91 25 | 2014/1/24,1.04,22,13,3,1182.31,0,38.91 26 | 2014/1/25,0.89,23,15,3,1182.31,1,38.91 27 | 2014/1/26,0.76,25,15,3,1182.31,0,38.91 28 | 2014/1/27,0.68,22,14,3,1182.31,0,38.91 29 | 2014/1/28,0.63,26,15,3,1182.31,0,38.91 30 | 2014/1/29,0.55,26,16,3,1182.31,0,38.91 31 | 2014/1/30,0.46,25,16,3,1182.31,0,38.91 32 | 2014/1/31,0.4,27,16,3,1182.31,2,38.91 33 | 2014/2/1,0.4,27,17,3,1182.31,2,33.38 34 | 2014/2/2,0.41,27,17,3,1182.31,2,33.38 35 | 2014/2/3,0.45,27,17,3,1182.31,1,33.38 36 | 2014/2/4,0.5,23,17,3,1182.31,1,33.38 37 | 2014/2/5,0.57,24,17,3,1182.31,1,33.38 38 | 2014/2/6,0.65,23,18,3,1182.31,1,33.38 39 | 2014/2/7,0.79,18,12,3,1182.31,0,33.38 40 | 2014/2/8,0.89,11,8,3,1182.31,0,33.38 41 | 2014/2/9,0.93,12,7,3,1182.31,1,33.38 42 | 2014/2/10,1.11,9,5,3,1182.31,0,33.38 43 | 2014/2/11,1.26,10,5,3,1182.31,0,33.38 44 | 2014/2/12,1.34,7,4,3,1182.31,0,33.38 45 | 2014/2/13,1.39,12,6,3,1182.31,0,33.38 46 | 2014/2/14,1.35,15,7,3,1182.31,0,33.38 47 | 2014/2/15,1.36,16,9,3,1182.31,1,33.38 48 | 2014/2/16,1.24,15,11,3,1182.31,1,33.38 49 | 2014/2/17,1.37,21,15,3,1182.31,0,33.38 50 | 2014/2/18,1.5,9,6,3,1182.31,0,33.38 51 | 2014/2/19,1.55,14,6,3,1182.31,0,33.38 52 | 2014/2/20,1.56,18,6,3,1182.31,0,33.38 53 | 2014/2/21,1.57,20,11,3,1182.31,0,33.38 54 | 2014/2/22,1.53,19,12,3,1182.31,1,33.38 55 | 2014/2/23,1.32,23,15,3,1182.31,1,33.38 56 | 2014/2/24,1.48,24,15,3,1182.31,0,33.38 57 | 2014/2/25,1.61,23,17,3,1182.31,0,33.38 58 | 2014/2/26,1.62,26,17,3,1182.31,0,33.38 59 | 2014/2/27,1.64,24,18,3,1182.31,0,33.38 60 | 2014/2/28,1.99,25,18,3,1182.31,0,33.38 61 | 2014/3/1,1.5,21,17,0,1182.31,1,50.21 62 | 2014/3/2,1.3,21,16,0,1182.31,1,50.21 63 | 2014/3/3,1.49,20,16,0,1182.31,0,50.21 64 | 2014/3/4,1.65,19,14,0,1182.31,0,50.21 65 | 2014/3/5,1.66,17,14,0,1182.31,0,50.21 66 | 2014/3/6,1.67,17,14,0,1182.31,0,50.21 67 | 2014/3/7,1.68,14,12,0,1182.31,0,50.21 68 | 2014/3/8,1.63,14,12,0,1182.31,1,50.21 69 | 2014/3/9,1.41,16,13,0,1182.31,1,50.21 70 | 2014/3/10,1.57,17,14,0,1182.31,0,50.21 71 | 2014/3/11,1.7,21,14,0,1182.31,0,50.21 72 | 2014/3/12,1.71,21,15,0,1182.31,0,50.21 73 | 2014/3/13,1.71,18,13,0,1182.31,0,50.21 74 | 2014/3/14,1.7,19,17,0,1182.31,0,50.21 75 | 2014/3/15,1.66,20,16,0,1182.31,1,50.21 76 | 2014/3/16,1.38,24,18,0,1182.31,1,50.21 77 | 2014/3/17,1.55,26,18,0,1182.31,0,50.21 78 | 2014/3/18,1.72,27,20,0,1182.31,0,50.21 79 | 2014/3/19,1.74,26,15,0,1182.31,0,50.21 80 | 2014/3/20,1.76,19,14,0,1182.31,0,50.21 81 | 2014/3/21,1.72,20,14,0,1182.31,0,50.21 82 | 2014/3/22,1.66,22,16,0,1182.31,1,50.21 83 | 2014/3/23,1.41,26,17,0,1182.31,1,50.21 84 | 2014/3/24,1.57,27,18,0,1182.31,0,50.21 85 | 2014/3/25,1.73,26,20,0,1182.31,0,50.21 86 | 2014/3/26,1.74,28,20,0,1182.31,0,50.21 87 | 2014/3/27,1.77,27,21,0,1182.31,0,50.21 88 | 2014/3/28,1.79,28,22,0,1182.31,0,50.21 89 | 2014/3/29,1.72,24,20,0,1182.31,1,50.21 90 | 2014/3/30,1.43,23,19,0,1182.31,1,50.21 91 | 2014/3/31,1.48,24,20,0,1182.31,0,50.21 92 | 2014/4/1,1.64,22,17,0,1414.61,0,50.77 93 | 2014/4/2,1.69,20,17,0,1414.61,0,50.77 94 | 2014/4/3,1.7,26,19,0,1414.61,0,50.77 95 | 2014/4/4,1.67,27,18,0,1414.61,0,50.77 96 | 2014/4/5,1.06,24,19,0,1414.61,2,50.77 97 | 2014/4/6,1.03,25,19,0,1414.61,1,50.77 98 | 2014/4/7,1.43,25,20,0,1414.61,1,50.77 99 | 2014/4/8,1.68,27,21,0,1414.61,0,50.77 100 | 2014/4/9,1.75,29,21,0,1414.61,0,50.77 101 | 2014/4/10,1.78,29,21,0,1414.61,0,50.77 102 | 2014/4/11,1.7,30,22,0,1414.61,0,50.77 103 | 2014/4/12,1.75,30,22,0,1414.61,1,50.77 104 | 2014/4/13,1.58,29,23,0,1414.61,1,50.77 105 | 2014/4/14,1.73,27,21,0,1414.61,0,50.77 106 | 2014/4/15,1.79,29,22,0,1414.61,0,50.77 107 | 2014/4/16,1.79,29,22,0,1414.61,0,50.77 108 | 2014/4/17,1.83,30,22,0,1414.61,0,50.77 109 | 2014/4/18,1.87,30,23,0,1414.61,0,50.77 110 | 2014/4/19,1.83,30,22,0,1414.61,1,50.77 111 | 2014/4/20,1.59,29,23,0,1414.61,1,50.77 112 | 2014/4/21,1.76,28,23,0,1414.61,0,50.77 113 | 2014/4/22,1.88,27,22,0,1414.61,0,50.77 114 | 2014/4/23,1.85,29,22,0,1414.61,0,50.77 115 | 2014/4/24,1.83,27,23,0,1414.61,0,50.77 116 | 2014/4/25,1.86,29,23,0,1414.61,0,50.77 117 | 2014/4/26,1.82,28,22,0,1414.61,1,50.77 118 | 2014/4/27,1.61,28,23,0,1414.61,1,50.77 119 | 2014/4/28,1.73,28,21,0,1414.61,0,50.77 120 | 2014/4/29,1.84,23,20,0,1414.61,0,50.77 121 | 2014/4/30,1.7,24,19,0,1414.61,0,50.77 122 | 2014/5/1,0.97,26,20,0,1414.61,2,56.54 123 | 2014/5/2,1.06,29,22,0,1414.61,1,56.54 124 | 2014/5/3,1.47,26,20,0,1414.61,1,56.54 125 | 2014/5/4,1.62,23,19,0,1414.61,0,56.54 126 | 2014/5/5,1.71,22,19,0,1414.61,0,56.54 127 | 2014/5/6,1.74,21,18,0,1414.61,0,56.54 128 | 2014/5/7,1.75,25,20,0,1414.61,0,56.54 129 | 2014/5/8,1.78,23,21,0,1414.61,0,56.54 130 | 2014/5/9,1.77,28,22,0,1414.61,0,56.54 131 | 2014/5/10,1.74,26,23,0,1414.61,1,56.54 132 | 2014/5/11,1.51,27,23,0,1414.61,1,56.54 133 | 2014/5/12,1.63,29,24,0,1414.61,0,56.54 134 | 2014/5/13,1.88,32,26,0,1414.61,0,56.54 135 | 2014/5/14,1.98,31,26,0,1414.61,0,56.54 136 | 2014/5/15,2.04,30,25,0,1414.61,0,56.54 137 | 2014/5/16,2.05,30,25,0,1414.61,0,56.54 138 | 2014/5/17,1.96,30,24,0,1414.61,1,56.54 139 | 2014/5/18,1.61,30,24,0,1414.61,1,56.54 140 | 2014/5/19,1.82,30,24,0,1414.61,0,56.54 141 | 2014/5/20,1.96,28,24,0,1414.61,0,56.54 142 | 2014/5/21,1.95,31,26,0,1414.61,0,56.54 143 | 2014/5/22,2.01,29,24,0,1414.61,0,56.54 144 | 2014/5/23,1.92,31,24,0,1414.61,0,56.54 145 | 2014/5/24,1.9,33,25,0,1414.61,1,56.54 146 | 2014/5/25,1.74,32,26,0,1414.61,1,56.54 147 | 2014/5/26,1.99,33,26,0,1414.61,0,56.54 148 | 2014/5/27,2.19,34,27,0,1414.61,0,56.54 149 | 2014/5/28,2.22,33,26,0,1414.61,0,56.54 150 | 2014/5/29,2.24,33,26,0,1414.61,0,56.54 151 | 2014/5/30,2.23,33,26,0,1414.61,0,56.54 152 | 2014/5/31,2.1,34,27,0,1414.61,1,56.54 153 | 2014/6/1,1.69,33,26,1,1414.61,1,62.13 154 | 2014/6/2,1.28,33,26,1,1414.61,2,62.13 155 | 2014/6/3,1.84,33,26,1,1414.61,0,62.13 156 | 2014/6/4,2.14,32,26,1,1414.61,0,62.13 157 | 2014/6/5,2.26,31,25,1,1414.61,0,62.13 158 | 2014/6/6,2.14,31,25,1,1414.61,0,62.13 159 | 2014/6/7,2.03,31,25,1,1414.61,1,62.13 160 | 2014/6/8,1.83,33,25,1,1414.61,1,62.13 161 | 2014/6/9,2.06,33,25,1,1414.61,0,62.13 162 | 2014/6/10,2.18,33,25,1,1414.61,0,62.13 163 | 2014/6/11,2.16,35,26,1,1414.61,0,62.13 164 | 2014/6/12,2.1,35,26,1,1414.61,0,62.13 165 | 2014/6/13,2.14,35,27,1,1414.61,0,62.13 166 | 2014/6/14,2.12,33,26,1,1414.61,1,62.13 167 | 2014/6/15,1.83,32,26,1,1414.61,1,62.13 168 | 2014/6/16,2.11,32,26,1,1414.61,0,62.13 169 | 2014/6/17,2.32,32,26,1,1414.61,0,62.13 170 | 2014/6/18,2.28,32,26,1,1414.61,0,62.13 171 | 2014/6/19,2.32,31,27,1,1414.61,0,62.13 172 | 2014/6/20,2.3,32,26,1,1414.61,0,62.13 173 | 2014/6/21,2.06,31,26,1,1414.61,1,62.13 174 | 2014/6/22,1.83,31,26,1,1414.61,1,62.13 175 | 2014/6/23,2.04,31,26,1,1414.61,0,62.13 176 | 2014/6/24,2.14,31,27,1,1414.61,0,62.13 177 | 2014/6/25,2.17,33,27,1,1414.61,0,62.13 178 | 2014/6/26,2.24,33,27,1,1414.61,0,62.13 179 | 2014/6/27,2.3,35,27,1,1414.61,0,62.13 180 | 2014/6/28,2.25,33,27,1,1414.61,1,62.13 181 | 2014/6/29,1.92,33,27,1,1414.61,1,62.13 182 | 2014/6/30,2.05,33,27,1,1414.61,0,62.13 183 | 2014/7/1,2.17,33,27,1,1546.49,0,69.75 184 | 2014/7/2,2.24,34,27,1,1546.49,0,69.75 185 | 2014/7/3,2.33,34,27,1,1546.49,0,69.75 186 | 2014/7/4,2.36,34,27,1,1546.49,0,69.75 187 | 2014/7/5,2.32,35,28,1,1546.49,1,69.75 188 | 2014/7/6,2.0,35,28,1,1546.49,1,69.75 189 | 2014/7/7,2.23,34,27,1,1546.49,0,69.75 190 | 2014/7/8,2.39,35,27,1,1546.49,0,69.75 191 | 2014/7/9,2.34,35,27,1,1546.49,0,69.75 192 | 2014/7/10,2.41,34,27,1,1546.49,0,69.75 193 | 2014/7/11,2.34,33,27,1,1546.49,0,69.75 194 | 2014/7/12,2.23,34,27,1,1546.49,1,69.75 195 | 2014/7/13,1.89,34,27,1,1546.49,1,69.75 196 | 2014/7/14,2.18,34,27,1,1546.49,0,69.75 197 | 2014/7/15,2.37,34,27,1,1546.49,0,69.75 198 | 2014/7/16,2.36,32,27,1,1546.49,0,69.75 199 | 2014/7/17,2.37,31,26,1,1546.49,0,69.75 200 | 2014/7/18,2.31,30,25,1,1546.49,0,69.75 201 | 2014/7/19,2.14,33,26,1,1546.49,1,69.75 202 | 2014/7/20,1.88,34,26,1,1546.49,1,69.75 203 | 2014/7/21,2.12,35,27,1,1546.49,0,69.75 204 | 2014/7/22,2.33,35,27,1,1546.49,0,69.75 205 | 2014/7/23,2.44,34,28,1,1546.49,0,69.75 206 | 2014/7/24,2.48,33,27,1,1546.49,0,69.75 207 | 2014/7/25,2.29,32,26,1,1546.49,0,69.75 208 | 2014/7/26,2.2,33,26,1,1546.49,1,69.75 209 | 2014/7/27,1.87,35,27,1,1546.49,1,69.75 210 | 2014/7/28,2.11,35,27,1,1546.49,0,69.75 211 | 2014/7/29,2.32,36,27,1,1546.49,0,69.75 212 | 2014/7/30,2.36,36,27,1,1546.49,0,69.75 213 | 2014/7/31,2.37,35,27,1,1546.49,0,69.75 214 | 2014/8/1,2.36,34,26,1,1546.49,0,67.68 215 | 2014/8/2,2.2,33,26,1,1546.49,1,67.68 216 | 2014/8/3,1.88,34,26,1,1546.49,1,67.68 217 | 2014/8/4,2.18,34,27,1,1546.49,0,67.68 218 | 2014/8/5,2.35,34,27,1,1546.49,0,67.68 219 | 2014/8/6,2.34,31,26,1,1546.49,0,67.68 220 | 2014/8/7,2.32,32,26,1,1546.49,0,67.68 221 | 2014/8/8,2.33,34,27,1,1546.49,0,67.68 222 | 2014/8/9,2.27,33,27,1,1546.49,1,67.68 223 | 2014/8/10,1.95,33,27,1,1546.49,1,67.68 224 | 2014/8/11,2.21,33,27,1,1546.49,0,67.68 225 | 2014/8/12,2.35,30,25,1,1546.49,0,67.68 226 | 2014/8/13,2.19,31,25,1,1546.49,0,67.68 227 | 2014/8/14,2.16,33,26,1,1546.49,0,67.68 228 | 2014/8/15,2.21,33,26,1,1546.49,0,67.68 229 | 2014/8/16,2.17,33,26,1,1546.49,1,67.68 230 | 2014/8/17,1.87,33,27,1,1546.49,1,67.68 231 | 2014/8/18,2.18,31,25,1,1546.49,0,67.68 232 | 2014/8/19,2.33,31,25,1,1546.49,0,67.68 233 | 2014/8/20,2.09,32,26,1,1546.49,0,67.68 234 | 2014/8/21,2.09,33,26,1,1546.49,0,67.68 235 | 2014/8/22,2.15,33,26,1,1546.49,0,67.68 236 | 2014/8/23,2.12,34,26,1,1546.49,1,67.68 237 | 2014/8/24,1.85,34,26,1,1546.49,1,67.68 238 | 2014/8/25,2.12,34,26,1,1546.49,0,67.68 239 | 2014/8/26,2.31,32,26,1,1546.49,0,67.68 240 | 2014/8/27,2.32,31,26,1,1546.49,0,67.68 241 | 2014/8/28,2.33,34,27,1,1546.49,0,67.68 242 | 2014/8/29,2.33,33,26,1,1546.49,0,67.68 243 | 2014/8/30,2.25,32,26,1,1546.49,1,67.68 244 | 2014/8/31,1.87,33,26,1,1546.49,1,67.68 245 | 2014/9/1,2.02,33,26,2,1546.49,0,63.09 246 | 2014/9/2,2.23,34,26,2,1546.49,0,63.09 247 | 2014/9/3,2.32,33,26,2,1546.49,0,63.09 248 | 2014/9/4,2.36,33,26,2,1546.49,0,63.09 249 | 2014/9/5,2.3,33,25,2,1546.49,0,63.09 250 | 2014/9/6,2.12,33,26,2,1546.49,1,63.09 251 | 2014/9/7,1.77,32,26,2,1546.49,1,63.09 252 | 2014/9/8,1.25,33,26,2,1546.49,2,63.09 253 | 2014/9/9,1.77,34,26,2,1546.49,0,63.09 254 | 2014/9/10,2.22,34,26,2,1546.49,0,63.09 255 | 2014/9/11,2.28,32,26,2,1546.49,0,63.09 256 | 2014/9/12,2.26,33,26,2,1546.49,0,63.09 257 | 2014/9/13,2.16,34,26,2,1546.49,1,63.09 258 | 2014/9/14,1.93,33,24,2,1546.49,1,63.09 259 | 2014/9/15,2.15,29,25,2,1546.49,0,63.09 260 | 2014/9/16,2.13,31,25,2,1546.49,0,63.09 261 | 2014/9/17,2.14,33,25,2,1546.49,0,63.09 262 | 2014/9/18,2.2,32,26,2,1546.49,0,63.09 263 | 2014/9/19,2.3,31,25,2,1546.49,0,63.09 264 | 2014/9/20,2.21,31,25,2,1546.49,1,63.09 265 | 2014/9/21,1.75,31,24,2,1546.49,1,63.09 266 | 2014/9/22,1.94,31,24,2,1546.49,0,63.09 267 | 2014/9/23,2.09,33,25,2,1546.49,0,63.09 268 | 2014/9/24,2.14,33,25,2,1546.49,0,63.09 269 | 2014/9/25,2.2,34,26,2,1546.49,0,63.09 270 | 2014/9/26,2.23,34,26,2,1546.49,0,63.09 271 | 2014/9/27,2.22,34,26,2,1546.49,1,63.09 272 | 2014/9/28,2.09,34,26,2,1546.49,0,63.09 273 | 2014/9/29,2.17,32,25,2,1546.49,0,63.09 274 | 2014/9/30,2.14,32,25,2,1546.49,0,63.09 275 | 2014/10/1,1.27,33,25,2,1727.77,2,55.81 276 | 2014/10/2,1.09,33,25,2,1727.77,2,55.81 277 | 2014/10/3,1.25,31,24,2,1727.77,2,55.81 278 | 2014/10/4,1.55,31,23,2,1727.77,1,55.81 279 | 2014/10/5,1.7,29,21,2,1727.77,1,55.81 280 | 2014/10/6,1.85,30,23,2,1727.77,1,55.81 281 | 2014/10/7,1.93,30,22,2,1727.77,1,55.81 282 | 2014/10/8,1.93,29,21,2,1727.77,0,55.81 283 | 2014/10/9,1.95,30,23,2,1727.77,0,55.81 284 | 2014/10/10,1.96,30,23,2,1727.77,0,55.81 285 | 2014/10/11,1.95,29,22,2,1727.77,0,55.81 286 | 2014/10/12,1.71,29,22,2,1727.77,1,55.81 287 | 2014/10/13,1.82,29,21,2,1727.77,0,55.81 288 | 2014/10/14,1.9,29,21,2,1727.77,0,55.81 289 | 2014/10/15,1.89,29,21,2,1727.77,0,55.81 290 | 2014/10/16,1.9,29,21,2,1727.77,0,55.81 291 | 2014/10/17,1.89,30,21,2,1727.77,0,55.81 292 | 2014/10/18,1.85,30,21,2,1727.77,1,55.81 293 | 2014/10/19,1.59,30,23,2,1727.77,1,55.81 294 | 2014/10/20,1.82,30,23,2,1727.77,0,55.81 295 | 2014/10/21,2.03,28,22,2,1727.77,0,55.81 296 | 2014/10/22,2.03,28,22,2,1727.77,0,55.81 297 | 2014/10/23,1.94,28,22,2,1727.77,0,55.81 298 | 2014/10/24,1.92,29,22,2,1727.77,0,55.81 299 | 2014/10/25,1.86,29,22,2,1727.77,1,55.81 300 | 2014/10/26,1.61,30,23,2,1727.77,1,55.81 301 | 2014/10/27,1.82,30,22,2,1727.77,0,55.81 302 | 2014/10/28,1.94,29,23,2,1727.77,0,55.81 303 | 2014/10/29,1.96,31,23,2,1727.77,0,55.81 304 | 2014/10/30,1.97,31,23,2,1727.77,0,55.81 305 | 2014/10/31,1.93,31,23,2,1727.77,0,55.81 306 | 2014/11/1,1.32,26,19,2,1727.77,1,52.76 307 | 2014/11/2,1.54,25,19,2,1727.77,1,52.76 308 | 2014/11/3,1.77,25,19,2,1727.77,0,52.76 309 | 2014/11/4,1.82,24,20,2,1727.77,0,52.76 310 | 2014/11/5,1.89,24,20,2,1727.77,0,52.76 311 | 2014/11/6,1.95,21,18,2,1727.77,0,52.76 312 | 2014/11/7,1.9,21,18,2,1727.77,0,52.76 313 | 2014/11/8,1.62,21,18,2,1727.77,1,52.76 314 | 2014/11/9,1.86,23,19,2,1727.77,1,52.76 315 | 2014/11/10,1.88,20,17,2,1727.77,0,52.76 316 | 2014/11/11,1.88,20,17,2,1727.77,0,52.76 317 | 2014/11/12,1.89,22,17,2,1727.77,0,52.76 318 | 2014/11/13,1.86,22,17,2,1727.77,0,52.76 319 | 2014/11/14,1.75,26,19,2,1727.77,0,52.76 320 | 2014/11/15,1.45,26,19,2,1727.77,1,52.76 321 | 2014/11/16,1.68,22,15,2,1727.77,1,52.76 322 | 2014/11/17,1.92,22,15,2,1727.77,0,52.76 323 | 2014/11/18,1.97,24,17,2,1727.77,0,52.76 324 | 2014/11/19,1.97,24,17,2,1727.77,0,52.76 325 | 2014/11/20,1.97,26,18,2,1727.77,0,52.76 326 | 2014/11/21,1.84,26,18,2,1727.77,0,52.76 327 | 2014/11/22,1.54,29,21,2,1727.77,1,52.76 328 | 2014/11/23,1.74,29,21,2,1727.77,1,52.76 329 | 2014/11/24,1.86,26,18,2,1727.77,0,52.76 330 | 2014/11/25,1.83,26,18,2,1727.77,0,52.76 331 | 2014/11/26,1.75,27,20,2,1727.77,0,52.76 332 | 2014/11/27,1.74,27,20,2,1727.77,0,52.76 333 | 2014/11/28,1.67,27,23,2,1727.77,0,52.76 334 | 2014/11/29,1.37,28,22,2,1727.77,1,52.76 335 | 2014/11/30,1.53,19,12,2,1727.77,1,52.76 336 | 2014/12/1,1.52,15,12,3,1727.77,0,51.44 337 | 2014/12/2,1.7,16,10,3,1727.77,0,51.44 338 | 2014/12/3,1.73,15,10,3,1727.77,0,51.44 339 | 2014/12/4,1.75,16,12,3,1727.77,0,51.44 340 | 2014/12/5,1.75,19,11,3,1727.77,0,51.44 341 | 2014/12/6,1.7,17,14,3,1727.77,1,51.44 342 | 2014/12/7,1.44,18,12,3,1727.77,1,51.44 343 | 2014/12/8,1.6,22,16,3,1727.77,0,51.44 344 | 2014/12/9,1.74,21,16,3,1727.77,0,51.44 345 | 2014/12/10,1.75,19,12,3,1727.77,0,51.44 346 | 2014/12/11,1.75,16,12,3,1727.77,0,51.44 347 | 2014/12/12,1.75,17,9,3,1727.77,0,51.44 348 | 2014/12/13,1.7,19,11,3,1727.77,1,51.44 349 | 2014/12/14,1.41,21,13,3,1727.77,1,51.44 350 | 2014/12/15,1.58,17,12,3,1727.77,0,51.44 351 | 2014/12/16,1.72,15,7,3,1727.77,0,51.44 352 | 2014/12/17,1.73,17,7,3,1727.77,0,51.44 353 | 2014/12/18,1.75,14,9,3,1727.77,0,51.44 354 | 2014/12/19,1.76,18,9,3,1727.77,0,51.44 355 | 2014/12/20,1.68,18,9,3,1727.77,1,51.44 356 | 2014/12/21,1.43,16,8,3,1727.77,1,51.44 357 | 2014/12/22,1.59,18,10,3,1727.77,0,51.44 358 | 2014/12/23,1.75,17,11,3,1727.77,0,51.44 359 | 2014/12/24,1.74,17,14,3,1727.77,0,51.44 360 | 2014/12/25,1.74,17,13,3,1727.77,0,51.44 361 | 2014/12/26,1.73,17,13,3,1727.77,0,51.44 362 | 2014/12/27,1.71,16,11,3,1727.77,1,51.44 363 | 2014/12/28,1.52,17,9,3,1727.77,1,51.44 364 | 2014/12/29,1.6,20,9,3,1727.77,0,51.44 365 | 2014/12/30,1.63,21,10,3,1727.77,0,51.44 366 | 2014/12/31,1.49,19,11,3,1727.77,0,51.44 367 | 2015/1/1,0.89,19,9,3,1337.9,2,49.52 368 | 2015/1/2,1.09,21,11,3,1337.9,1,49.52 369 | 2015/1/3,1.42,22,13,3,1337.9,1,49.52 370 | 2015/1/4,1.48,23,16,3,1337.9,0,49.52 371 | 2015/1/5,1.61,24,18,3,1337.9,0,49.52 372 | 2015/1/6,1.7,22,15,3,1337.9,0,49.52 373 | 2015/1/7,1.73,19,11,3,1337.9,0,49.52 374 | 2015/1/8,1.72,19,9,3,1337.9,0,49.52 375 | 2015/1/9,1.73,19,11,3,1337.9,0,49.52 376 | 2015/1/10,1.69,20,12,3,1337.9,1,49.52 377 | 2015/1/11,1.48,14,10,3,1337.9,1,49.52 378 | 2015/1/12,1.63,15,10,3,1337.9,0,49.52 379 | 2015/1/13,1.75,18,8,3,1337.9,0,49.52 380 | 2015/1/14,1.75,18,7,3,1337.9,0,49.52 381 | 2015/1/15,1.75,20,9,3,1337.9,0,49.52 382 | 2015/1/16,1.72,20,11,3,1337.9,0,49.52 383 | 2015/1/17,1.68,21,11,3,1337.9,1,49.52 384 | 2015/1/18,1.43,20,11,3,1337.9,1,49.52 385 | 2015/1/19,1.55,20,12,3,1337.9,0,49.52 386 | 2015/1/20,1.71,21,13,3,1337.9,0,49.52 387 | 2015/1/21,1.71,20,12,3,1337.9,0,49.52 388 | 2015/1/22,1.7,22,12,3,1337.9,0,49.52 389 | 2015/1/23,1.7,20,13,3,1337.9,0,49.52 390 | 2015/1/24,1.66,23,15,3,1337.9,1,49.52 391 | 2015/1/25,1.45,24,16,3,1337.9,1,49.52 392 | 2015/1/26,1.57,24,17,3,1337.9,0,49.52 393 | 2015/1/27,1.68,22,16,3,1337.9,0,49.52 394 | 2015/1/28,1.68,21,14,3,1337.9,0,49.52 395 | 2015/1/29,1.66,20,12,3,1337.9,0,49.52 396 | 2015/1/30,1.64,18,11,3,1337.9,0,49.52 397 | 2015/1/31,1.56,17,12,3,1337.9,1,49.52 398 | 2015/2/1,1.32,19,12,3,1337.9,1,25.8 399 | 2015/2/2,1.46,21,14,3,1337.9,0,25.8 400 | 2015/2/3,1.56,20,14,3,1337.9,0,25.8 401 | 2015/2/4,1.54,15,9,3,1337.9,0,25.8 402 | 2015/2/5,1.53,17,9,3,1337.9,0,25.8 403 | 2015/2/6,1.5,17,11,3,1337.9,0,25.8 404 | 2015/2/7,1.42,19,11,3,1337.9,1,25.8 405 | 2015/2/8,1.25,18,10,3,1337.9,1,25.8 406 | 2015/2/9,1.25,18,10,3,1337.9,0,25.8 407 | 2015/2/10,1.2,20,12,3,1337.9,0,25.8 408 | 2015/2/11,1.11,20,12,3,1337.9,0,25.8 409 | 2015/2/12,1.0,20,12,3,1337.9,0,25.8 410 | 2015/2/13,0.87,24,14,3,1337.9,0,25.8 411 | 2015/2/14,0.73,22,17,3,1337.9,1,25.8 412 | 2015/2/15,0.64,24,17,3,1337.9,0,25.8 413 | 2015/2/16,0.58,22,16,3,1337.9,0,25.8 414 | 2015/2/17,0.51,24,16,3,1337.9,0,25.8 415 | 2015/2/18,0.42,23,17,3,1337.9,2,25.8 416 | 2015/2/19,0.37,22,16,3,1337.9,2,25.8 417 | 2015/2/20,0.37,23,17,3,1337.9,2,25.8 418 | 2015/2/21,0.39,23,18,3,1337.9,1,25.8 419 | 2015/2/22,0.43,24,18,3,1337.9,1,25.8 420 | 2015/2/23,0.48,25,18,3,1337.9,1,25.8 421 | 2015/2/24,0.56,26,19,3,1337.9,1,25.8 422 | 2015/2/25,0.64,26,19,3,1337.9,0,25.8 423 | 2015/2/26,0.77,26,19,3,1337.9,0,25.8 424 | 2015/2/27,0.92,25,19,3,1337.9,0,25.8 425 | 2015/2/28,0.98,19,15,3,1337.9,0,25.8 426 | 2015/3/1,0.96,19,15,0,1337.9,1,47.68 427 | 2015/3/2,1.14,22,14,0,1337.9,0,47.68 428 | 2015/3/3,1.28,18,14,0,1337.9,0,47.68 429 | 2015/3/4,1.35,18,15,0,1337.9,0,47.68 430 | 2015/3/5,1.35,19,16,0,1337.9,0,47.68 431 | 2015/3/6,1.41,17,13,0,1337.9,0,47.68 432 | 2015/3/7,1.43,22,15,0,1337.9,1,47.68 433 | 2015/3/8,1.26,21,17,0,1337.9,1,47.68 434 | 2015/3/9,1.43,21,16,0,1337.9,0,47.68 435 | 2015/3/10,1.55,17,14,0,1337.9,0,47.68 436 | 2015/3/11,1.59,16,13,0,1337.9,0,47.68 437 | 2015/3/12,1.62,21,14,0,1337.9,0,47.68 438 | 2015/3/13,1.63,24,17,0,1337.9,0,47.68 439 | 2015/3/14,1.61,25,18,0,1337.9,1,47.68 440 | 2015/3/15,1.37,26,21,0,1337.9,1,47.68 441 | 2015/3/16,1.51,27,21,0,1337.9,0,47.68 442 | 2015/3/17,1.68,27,21,0,1337.9,0,47.68 443 | 2015/3/18,1.7,28,21,0,1337.9,0,47.68 444 | 2015/3/19,1.76,28,22,0,1337.9,0,47.68 445 | 2015/3/20,1.79,28,21,0,1337.9,0,47.68 446 | 2015/3/21,1.75,25,20,0,1337.9,1,47.68 447 | 2015/3/22,1.47,24,19,0,1337.9,1,47.68 448 | 2015/3/23,1.6,20,17,0,1337.9,0,47.68 449 | 2015/3/24,1.73,22,17,0,1337.9,0,47.68 450 | 2015/3/25,1.74,24,18,0,1337.9,0,47.68 451 | 2015/3/26,1.73,24,17,0,1337.9,0,47.68 452 | 2015/3/27,1.74,26,19,0,1337.9,0,47.68 453 | 2015/3/28,1.69,28,20,0,1337.9,1,47.68 454 | 2015/3/29,1.46,28,20,0,1337.9,1,47.68 455 | 2015/3/30,1.61,28,21,0,1337.9,0,47.68 456 | 2015/3/31,1.74,29,23,0,1337.9,0,47.68 457 | 2015/4/1,1.77,28,24,0,1597.33,0,52.34 458 | 2015/4/2,1.85,29,24,0,1597.33,0,52.34 459 | 2015/4/3,1.86,29,24,0,1597.33,0,52.34 460 | 2015/4/4,1.65,30,25,0,1597.33,1,52.34 461 | 2015/4/5,1.08,31,24,0,1597.33,2,52.34 462 | 2015/4/6,1.35,29,23,0,1597.33,1,52.34 463 | 2015/4/7,1.85,23,17,0,1597.33,0,52.34 464 | 2015/4/8,1.77,19,14,0,1597.33,0,52.34 465 | 2015/4/9,1.75,17,14,0,1597.33,0,52.34 466 | 2015/4/10,1.76,16,14,0,1597.33,0,52.34 467 | 2015/4/11,1.72,20,15,0,1597.33,1,52.34 468 | 2015/4/12,1.48,23,16,0,1597.33,1,52.34 469 | 2015/4/13,1.63,25,14,0,1597.33,0,52.34 470 | 2015/4/14,1.77,27,16,0,1597.33,0,52.34 471 | 2015/4/15,1.78,28,17,0,1597.33,0,52.34 472 | 2015/4/16,1.8,30,19,0,1597.33,0,52.34 473 | 2015/4/17,1.84,28,22,0,1597.33,0,52.34 474 | 2015/4/18,1.82,30,24,0,1597.33,1,52.34 475 | 2015/4/19,1.55,27,21,0,1597.33,1,52.34 476 | 2015/4/20,1.8,24,19,0,1597.33,0,52.34 477 | 2015/4/21,1.84,27,19,0,1597.33,0,52.34 478 | 2015/4/22,1.86,26,21,0,1597.33,0,52.34 479 | 2015/4/23,1.84,27,22,0,1597.33,0,52.34 480 | 2015/4/24,1.87,28,22,0,1597.33,0,52.34 481 | 2015/4/25,1.85,29,22,0,1597.33,1,52.34 482 | 2015/4/26,1.58,29,22,0,1597.33,1,52.34 483 | 2015/4/27,1.76,30,21,0,1597.33,0,52.34 484 | 2015/4/28,1.94,29,23,0,1597.33,0,52.34 485 | 2015/4/29,1.99,30,24,0,1597.33,0,52.34 486 | 2015/4/30,1.93,31,24,0,1597.33,0,52.34 487 | 2015/5/1,1.14,32,25,0,1597.33,2,58.75 488 | 2015/5/2,1.09,31,25,0,1597.33,1,58.75 489 | 2015/5/3,1.49,31,26,0,1597.33,1,58.75 490 | 2015/5/4,1.94,29,25,0,1597.33,0,58.75 491 | 2015/5/5,2.08,29,24,0,1597.33,0,58.75 492 | 2015/5/6,2.05,29,25,0,1597.33,0,58.75 493 | 2015/5/7,2.0,28,23,0,1597.33,0,58.75 494 | 2015/5/8,2.08,28,23,0,1597.33,0,58.75 495 | 2015/5/9,2.03,29,24,0,1597.33,1,58.75 496 | 2015/5/10,1.65,27,23,0,1597.33,1,58.75 497 | 2015/5/11,1.88,30,21,0,1597.33,0,58.75 498 | 2015/5/12,1.91,29,23,0,1597.33,0,58.75 499 | 2015/5/13,1.99,30,25,0,1597.33,0,58.75 500 | 2015/5/14,2.07,31,26,0,1597.33,0,58.75 501 | 2015/5/15,2.14,32,26,0,1597.33,0,58.75 502 | 2015/5/16,2.0,28,22,0,1597.33,1,58.75 503 | 2015/5/17,1.62,27,23,0,1597.33,1,58.75 504 | 2015/5/18,1.86,28,25,0,1597.33,0,58.75 505 | 2015/5/19,2.1,31,26,0,1597.33,0,58.75 506 | 2015/5/20,2.09,29,23,0,1597.33,0,58.75 507 | 2015/5/21,1.96,27,23,0,1597.33,0,58.75 508 | 2015/5/22,1.88,31,26,0,1597.33,0,58.75 509 | 2015/5/23,1.84,26,23,0,1597.33,1,58.75 510 | 2015/5/24,1.58,27,24,0,1597.33,1,58.75 511 | 2015/5/25,1.88,30,25,0,1597.33,0,58.75 512 | 2015/5/26,2.03,31,26,0,1597.33,0,58.75 513 | 2015/5/27,2.11,31,26,0,1597.33,0,58.75 514 | 2015/5/28,2.18,33,27,0,1597.33,0,58.75 515 | 2015/5/29,2.24,33,27,0,1597.33,0,58.75 516 | 2015/5/30,2.18,33,27,0,1597.33,1,58.75 517 | 2015/5/31,1.66,32,26,0,1597.33,1,58.75 518 | 2015/6/1,1.81,31,26,1,1597.33,0,64.49 519 | 2015/6/2,2.12,32,27,1,1597.33,0,64.49 520 | 2015/6/3,2.23,33,27,1,1597.33,0,64.49 521 | 2015/6/4,2.28,33,26,1,1597.33,0,64.49 522 | 2015/6/5,2.25,31,25,1,1597.33,0,64.49 523 | 2015/6/6,2.09,31,26,1,1597.33,1,64.49 524 | 2015/6/7,1.84,32,26,1,1597.33,1,64.49 525 | 2015/6/8,2.12,32,26,1,1597.33,0,64.49 526 | 2015/6/9,2.31,31,26,1,1597.33,0,64.49 527 | 2015/6/10,2.34,32,26,1,1597.33,0,64.49 528 | 2015/6/11,2.35,33,26,1,1597.33,0,64.49 529 | 2015/6/12,2.29,32,26,1,1597.33,0,64.49 530 | 2015/6/13,2.19,32,26,1,1597.33,1,64.49 531 | 2015/6/14,1.93,32,26,1,1597.33,1,64.49 532 | 2015/6/15,2.13,33,26,1,1597.33,0,64.49 533 | 2015/6/16,2.3,33,26,1,1597.33,0,64.49 534 | 2015/6/17,2.35,32,26,1,1597.33,0,64.49 535 | 2015/6/18,2.39,34,27,1,1597.33,0,64.49 536 | 2015/6/19,2.34,35,27,1,1597.33,0,64.49 537 | 2015/6/20,1.58,35,27,1,1597.33,2,64.49 538 | 2015/6/21,1.58,34,26,1,1597.33,1,64.49 539 | 2015/6/22,2.01,32,26,1,1597.33,1,64.49 540 | 2015/6/23,2.19,30,25,1,1597.33,0,64.49 541 | 2015/6/24,2.16,30,25,1,1597.33,0,64.49 542 | 2015/6/25,2.21,32,27,1,1597.33,0,64.49 543 | 2015/6/26,2.27,33,27,1,1597.33,0,64.49 544 | 2015/6/27,2.26,33,27,1,1597.33,1,64.49 545 | 2015/6/28,2.04,33,27,1,1597.33,1,64.49 546 | 2015/6/29,2.21,33,27,1,1597.33,0,64.49 547 | 2015/6/30,2.32,34,27,1,1597.33,0,64.49 548 | 2015/7/1,2.34,33,28,1,1628.56,0,68.18 549 | 2015/7/2,2.43,34,28,1,1628.56,0,68.18 550 | 2015/7/3,2.45,33,28,1,1628.56,0,68.18 551 | 2015/7/4,2.37,33,27,1,1628.56,1,68.18 552 | 2015/7/5,2.02,32,26,1,1628.56,1,68.18 553 | 2015/7/6,2.12,32,27,1,1628.56,0,68.18 554 | 2015/7/7,2.22,32,26,1,1628.56,0,68.18 555 | 2015/7/8,2.18,30,25,1,1628.56,0,68.18 556 | 2015/7/9,2.13,30,25,1,1628.56,0,68.18 557 | 2015/7/10,2.11,31,25,1,1628.56,0,68.18 558 | 2015/7/11,2.17,34,26,1,1628.56,1,68.18 559 | 2015/7/12,1.99,34,27,1,1628.56,1,68.18 560 | 2015/7/13,2.33,35,28,1,1628.56,0,68.18 561 | 2015/7/14,2.49,35,28,1,1628.56,0,68.18 562 | 2015/7/15,2.48,34,27,1,1628.56,0,68.18 563 | 2015/7/16,2.43,33,26,1,1628.56,0,68.18 564 | 2015/7/17,2.36,32,26,1,1628.56,0,68.18 565 | 2015/7/18,2.28,32,26,1,1628.56,1,68.18 566 | 2015/7/19,1.93,33,26,1,1628.56,1,68.18 567 | 2015/7/20,2.17,32,26,1,1628.56,0,68.18 568 | 2015/7/21,2.15,28,25,1,1628.56,0,68.18 569 | 2015/7/22,2.17,32,26,1,1628.56,0,68.18 570 | 2015/7/23,2.19,31,26,1,1628.56,0,68.18 571 | 2015/7/24,2.11,30,24,1,1628.56,0,68.18 572 | 2015/7/25,2.03,31,25,1,1628.56,1,68.18 573 | 2015/7/26,1.75,31,25,1,1628.56,1,68.18 574 | 2015/7/27,2.03,32,25,1,1628.56,0,68.18 575 | 2015/7/28,2.23,32,25,1,1628.56,0,68.18 576 | 2015/7/29,2.22,32,25,1,1628.56,0,68.18 577 | 2015/7/30,2.17,32,25,1,1628.56,0,68.18 578 | 2015/7/31,2.13,32,25,1,1628.56,0,68.18 579 | 2015/8/1,2.0,33,25,1,1628.56,1,67.76 580 | 2015/8/2,1.68,33,25,1,1628.56,1,67.76 581 | 2015/8/3,2.06,33,25,1,1628.56,0,67.76 582 | 2015/8/4,2.29,34,25,1,1628.56,0,67.76 583 | 2015/8/5,2.34,35,26,1,1628.56,0,67.76 584 | 2015/8/6,2.39,35,27,1,1628.56,0,67.76 585 | 2015/8/7,2.42,36,27,1,1628.56,0,67.76 586 | 2015/8/8,2.38,36,28,1,1628.56,1,67.76 587 | 2015/8/9,2.07,35,27,1,1628.56,1,67.76 588 | 2015/8/10,2.23,32,26,1,1628.56,0,67.76 589 | 2015/8/11,2.26,31,25,1,1628.56,0,67.76 590 | 2015/8/12,2.28,32,26,1,1628.56,0,67.76 591 | 2015/8/13,2.34,32,26,1,1628.56,0,67.76 592 | 2015/8/14,2.24,30,25,1,1628.56,0,67.76 593 | 2015/8/15,2.0,30,25,1,1628.56,1,67.76 594 | 2015/8/16,1.66,32,26,1,1628.56,1,67.76 595 | 2015/8/17,2.02,33,27,1,1628.56,0,67.76 596 | 2015/8/18,2.27,34,27,1,1628.56,0,67.76 597 | 2015/8/19,2.36,34,27,1,1628.56,0,67.76 598 | 2015/8/20,2.37,33,27,1,1628.56,0,67.76 599 | 2015/8/21,2.32,33,26,1,1628.56,0,67.76 600 | 2015/8/22,2.26,34,27,1,1628.56,1,67.76 601 | 2015/8/23,1.97,34,26,1,1628.56,1,67.76 602 | 2015/8/24,2.24,34,27,1,1628.56,0,67.76 603 | 2015/8/25,2.39,34,26,1,1628.56,0,67.76 604 | 2015/8/26,2.4,34,26,1,1628.56,0,67.76 605 | 2015/8/27,2.38,33,26,1,1628.56,0,67.76 606 | 2015/8/28,2.31,32,25,1,1628.56,0,67.76 607 | 2015/8/29,2.14,31,25,1,1628.56,1,67.76 608 | 2015/8/30,1.78,29,24,1,1628.56,1,67.76 609 | 2015/8/31,1.91,30,25,1,1628.56,0,67.76 610 | 2015/9/1,2.03,30,25,2,1628.56,0,61.04 611 | 2015/9/2,1.99,29,25,2,1628.56,0,61.04 612 | 2015/9/3,1.38,31,25,2,1628.56,2,61.04 613 | 2015/9/4,1.65,33,26,2,1628.56,1,61.04 614 | 2015/9/5,2.02,34,26,2,1628.56,1,61.04 615 | 2015/9/6,2.04,33,26,2,1628.56,0,61.04 616 | 2015/9/7,2.18,30,25,2,1628.56,0,61.04 617 | 2015/9/8,2.17,31,25,2,1628.56,0,61.04 618 | 2015/9/9,2.19,32,25,2,1628.56,0,61.04 619 | 2015/9/10,2.21,32,25,2,1628.56,0,61.04 620 | 2015/9/11,2.22,32,25,2,1628.56,0,61.04 621 | 2015/9/12,2.16,33,24,2,1628.56,1,61.04 622 | 2015/9/13,1.8,30,24,2,1628.56,1,61.04 623 | 2015/9/14,1.98,31,24,2,1628.56,0,61.04 624 | 2015/9/15,2.17,31,24,2,1628.56,0,61.04 625 | 2015/9/16,2.18,31,25,2,1628.56,0,61.04 626 | 2015/9/17,2.2,31,25,2,1628.56,0,61.04 627 | 2015/9/18,2.23,32,25,2,1628.56,0,61.04 628 | 2015/9/19,2.21,32,25,2,1628.56,1,61.04 629 | 2015/9/20,1.94,30,24,2,1628.56,1,61.04 630 | 2015/9/21,1.97,28,24,2,1628.56,0,61.04 631 | 2015/9/22,2.11,30,25,2,1628.56,0,61.04 632 | 2015/9/23,2.19,33,25,2,1628.56,0,61.04 633 | 2015/9/24,2.27,33,26,2,1628.56,0,61.04 634 | 2015/9/25,2.3,33,26,2,1628.56,0,61.04 635 | 2015/9/26,2.16,30,25,2,1628.56,1,61.04 636 | 2015/9/27,1.42,32,25,2,1628.56,2,61.04 637 | 2015/9/28,1.68,32,24,2,1628.56,0,61.04 638 | 2015/9/29,2.0,32,25,2,1628.56,0,61.04 639 | 2015/9/30,1.99,32,25,2,1628.56,0,61.04 640 | 2015/10/1,1.24,32,24,2,1171.27,2,55.02 641 | 2015/10/2,0.99,30,24,2,1171.27,2,55.02 642 | 2015/10/3,1.08,29,24,2,1171.27,2,55.02 643 | 2015/10/4,1.29,26,24,2,1171.27,1,55.02 644 | 2015/10/5,1.61,28,24,2,1171.27,1,55.02 645 | 2015/10/6,1.85,29,24,2,1171.27,1,55.02 646 | 2015/10/7,1.94,28,24,2,1171.27,1,55.02 647 | 2015/10/8,2.0,30,23,2,1171.27,0,55.02 648 | 2015/10/9,1.99,29,22,2,1171.27,0,55.02 649 | 2015/10/10,1.85,26,19,2,1171.27,0,55.02 650 | 2015/10/11,1.51,21,17,2,1171.27,1,55.02 651 | 2015/10/12,1.66,25,18,2,1171.27,0,55.02 652 | 2015/10/13,1.83,27,21,2,1171.27,0,55.02 653 | 2015/10/14,1.88,29,20,2,1171.27,0,55.02 654 | 2015/10/15,1.92,30,21,2,1171.27,0,55.02 655 | 2015/10/16,1.93,30,21,2,1171.27,0,55.02 656 | 2015/10/17,1.87,30,21,2,1171.27,1,55.02 657 | 2015/10/18,1.55,30,21,2,1171.27,1,55.02 658 | 2015/10/19,1.74,30,20,2,1171.27,0,55.02 659 | 2015/10/20,1.9,28,21,2,1171.27,0,55.02 660 | 2015/10/21,1.93,30,21,2,1171.27,0,55.02 661 | 2015/10/22,1.99,30,23,2,1171.27,0,55.02 662 | 2015/10/23,2.02,30,23,2,1171.27,0,55.02 663 | 2015/10/24,1.95,30,24,2,1171.27,1,55.02 664 | 2015/10/25,1.69,30,24,2,1171.27,1,55.02 665 | 2015/10/26,1.87,30,23,2,1171.27,0,55.02 666 | 2015/10/27,2.03,29,23,2,1171.27,0,55.02 667 | 2015/10/28,2.05,30,24,2,1171.27,0,55.02 668 | 2015/10/29,2.02,29,23,2,1171.27,0,55.02 669 | 2015/10/30,2.01,30,21,2,1171.27,0,55.02 670 | 2015/10/31,1.83,26,20,2,1171.27,1,55.02 671 | 2015/11/1,1.32,23,17,2,1171.27,1,52.76 672 | 2015/11/2,1.54,22,18,2,1171.27,0,52.76 673 | 2015/11/3,1.77,25,21,2,1171.27,0,52.76 674 | 2015/11/4,1.82,27,22,2,1171.27,0,52.76 675 | 2015/11/5,1.89,29,24,2,1171.27,0,52.76 676 | 2015/11/6,1.95,29,24,2,1171.27,0,52.76 677 | 2015/11/7,1.9,28,24,2,1171.27,1,52.76 678 | 2015/11/8,1.62,30,23,2,1171.27,1,52.76 679 | 2015/11/9,1.86,30,22,2,1171.27,0,52.76 680 | 2015/11/10,1.88,26,21,2,1171.27,0,52.76 681 | 2015/11/11,1.88,25,21,2,1171.27,0,52.76 682 | 2015/11/12,1.89,27,22,2,1171.27,0,52.76 683 | 2015/11/13,1.86,20,18,2,1171.27,0,52.76 684 | 2015/11/14,1.75,24,19,2,1171.27,1,52.76 685 | 2015/11/15,1.45,25,22,2,1171.27,1,52.76 686 | 2015/11/16,1.68,29,23,2,1171.27,0,52.76 687 | 2015/11/17,1.92,29,23,2,1171.27,0,52.76 688 | 2015/11/18,1.97,31,22,2,1171.27,0,52.76 689 | 2015/11/19,1.97,30,23,2,1171.27,0,52.76 690 | 2015/11/20,1.97,29,23,2,1171.27,0,52.76 691 | 2015/11/21,1.84,27,22,2,1171.27,1,52.76 692 | 2015/11/22,1.54,29,22,2,1171.27,1,52.76 693 | 2015/11/23,1.74,28,21,2,1171.27,0,52.76 694 | 2015/11/24,1.86,27,18,2,1171.27,0,52.76 695 | 2015/11/25,1.83,23,13,2,1171.27,0,52.76 696 | 2015/11/26,1.75,20,11,2,1171.27,0,52.76 697 | 2015/11/27,1.74,20,13,2,1171.27,0,52.76 698 | 2015/11/28,1.67,21,15,2,1171.27,1,52.76 699 | 2015/11/29,1.37,24,17,2,1171.27,1,52.76 700 | 2015/11/30,1.53,24,18,2,1171.27,0,52.76 701 | 2015/12/1,1.69,24,20,3,1171.27,0,51.95 702 | 2015/12/2,1.76,27,16,3,1171.27,0,51.95 703 | 2015/12/3,1.77,16,15,3,1171.27,0,51.95 704 | 2015/12/4,1.76,18,15,3,1171.27,0,51.95 705 | 2015/12/5,1.7,18,12,3,1171.27,1,51.95 706 | 2015/12/6,1.41,16,11,3,1171.27,1,51.95 707 | 2015/12/7,1.6,17,12,3,1171.27,0,51.95 708 | 2015/12/8,1.75,19,14,3,1171.27,0,51.95 709 | 2015/12/9,1.76,15,13,3,1171.27,0,51.95 710 | 2015/12/10,1.75,20,14,3,1171.27,0,51.95 711 | 2015/12/11,1.76,20,16,3,1171.27,0,51.95 712 | 2015/12/12,1.69,19,16,3,1171.27,1,51.95 713 | 2015/12/13,1.42,22,17,3,1171.27,1,51.95 714 | 2015/12/14,1.59,18,13,3,1171.27,0,51.95 715 | 2015/12/15,1.74,19,11,3,1171.27,0,51.95 716 | 2015/12/16,1.73,16,8,3,1171.27,0,51.95 717 | 2015/12/17,1.75,15,8,3,1171.27,0,51.95 718 | 2015/12/18,1.76,15,9,3,1171.27,0,51.95 719 | 2015/12/19,1.7,17,12,3,1171.27,1,51.95 720 | 2015/12/20,1.46,19,15,3,1171.27,1,51.95 721 | 2015/12/21,1.62,18,14,3,1171.27,0,51.95 722 | 2015/12/22,1.76,21,18,3,1171.27,0,51.95 723 | 2015/12/23,1.77,23,19,3,1171.27,0,51.95 724 | 2015/12/24,1.78,25,15,3,1171.27,0,51.95 725 | 2015/12/25,1.76,18,13,3,1171.27,0,51.95 726 | 2015/12/26,1.7,16,12,3,1171.27,1,51.95 727 | 2015/12/27,1.5,14,12,3,1171.27,1,51.95 728 | 2015/12/28,1.61,18,12,3,1171.27,0,51.95 729 | 2015/12/29,1.71,17,12,3,1171.27,0,51.95 730 | 2015/12/30,1.66,16,13,3,1171.27,0,51.95 731 | 2015/12/31,1.53,19,13,3,1171.27,1,51.95 732 | 2016/1/1,0.91,20,15,3,1497.96,2,48.45 733 | 2016/1/2,1.03,22,15,3,1497.96,1,48.45 734 | 2016/1/3,1.36,23,19,3,1497.96,1,48.45 735 | 2016/1/4,1.61,23,18,3,1497.96,0,48.45 736 | 2016/1/5,1.74,21,16,3,1497.96,0,48.45 737 | 2016/1/6,1.75,23,13,3,1497.96,0,48.45 738 | 2016/1/7,1.74,20,14,3,1497.96,0,48.45 739 | 2016/1/8,1.75,21,14,3,1497.96,0,48.45 740 | 2016/1/9,1.71,20,14,3,1497.96,1,48.45 741 | 2016/1/10,1.52,19,15,3,1497.96,1,48.45 742 | 2016/1/11,1.63,19,14,3,1497.96,0,48.45 743 | 2016/1/12,1.74,17,12,3,1497.96,0,48.45 744 | 2016/1/13,1.73,18,11,3,1497.96,0,48.45 745 | 2016/1/14,1.75,17,12,3,1497.96,0,48.45 746 | 2016/1/15,1.75,13,12,3,1497.96,0,48.45 747 | 2016/1/16,1.69,15,13,3,1497.96,1,48.45 748 | 2016/1/17,1.48,18,10,3,1497.96,1,48.45 749 | 2016/1/18,1.59,17,11,3,1497.96,0,48.45 750 | 2016/1/19,1.7,16,13,3,1497.96,0,48.45 751 | 2016/1/20,1.69,14,13,3,1497.96,0,48.45 752 | 2016/1/21,1.7,14,11,3,1497.96,0,48.45 753 | 2016/1/22,1.7,10,6,3,1497.96,0,48.45 754 | 2016/1/23,1.66,6,3,3,1497.96,1,48.45 755 | 2016/1/24,1.53,4,0,3,1497.96,1,48.45 756 | 2016/1/25,1.59,9,2,3,1497.96,0,48.45 757 | 2016/1/26,1.65,11,6,3,1497.96,0,48.45 758 | 2016/1/27,1.6,10,7,3,1497.96,0,48.45 759 | 2016/1/28,1.52,15,12,3,1497.96,0,48.45 760 | 2016/1/29,1.39,18,15,3,1497.96,0,48.45 761 | 2016/1/30,1.22,19,12,3,1497.96,1,48.45 762 | 2016/1/31,1.02,16,10,3,1497.96,1,48.45 763 | 2016/2/1,0.97,9,6,3,1497.96,0,27.65 764 | 2016/2/2,0.92,10,6,3,1497.96,0,27.65 765 | 2016/2/3,0.82,12,8,3,1497.96,0,27.65 766 | 2016/2/4,0.7,17,9,3,1497.96,0,27.65 767 | 2016/2/5,0.6,17,7,3,1497.96,0,27.65 768 | 2016/2/6,0.52,18,6,3,1497.96,0,27.65 769 | 2016/2/7,0.45,17,9,3,1497.96,1,27.65 770 | 2016/2/8,0.4,20,8,3,1497.96,2,27.65 771 | 2016/2/9,0.39,21,11,3,1497.96,2,27.65 772 | 2016/2/10,0.4,21,15,3,1497.96,2,27.65 773 | 2016/2/11,0.43,23,17,3,1497.96,1,27.65 774 | 2016/2/12,0.48,25,18,3,1497.96,1,27.65 775 | 2016/2/13,0.53,26,19,3,1497.96,1,27.65 776 | 2016/2/14,0.58,20,10,3,1497.96,0,27.65 777 | 2016/2/15,0.77,13,9,3,1497.96,0,27.65 778 | 2016/2/16,0.97,14,8,3,1497.96,0,27.65 779 | 2016/2/17,1.08,15,11,3,1497.96,0,27.65 780 | 2016/2/18,1.19,15,11,3,1497.96,0,27.65 781 | 2016/2/19,1.28,16,12,3,1497.96,0,27.65 782 | 2016/2/20,1.28,18,11,3,1497.96,1,27.65 783 | 2016/2/21,1.18,17,14,3,1497.96,1,27.65 784 | 2016/2/22,1.29,17,12,3,1497.96,0,27.65 785 | 2016/2/23,1.42,13,10,3,1497.96,0,27.65 786 | 2016/2/24,1.51,13,10,3,1497.96,0,27.65 787 | 2016/2/25,1.56,15,12,3,1497.96,0,27.65 788 | 2016/2/26,1.59,14,11,3,1497.96,0,27.65 789 | 2016/2/27,1.55,18,11,3,1497.96,1,27.65 790 | 2016/2/28,1.34,21,10,3,1497.96,1,27.65 791 | 2016/2/29,1.45,23,12,3,1497.96,0,27.65 792 | 2016/3/1,1.58,23,13,0,1497.96,0,52.26 793 | 2016/3/2,1.63,24,12,0,1497.96,0,52.26 794 | 2016/3/3,1.66,24,14,0,1497.96,0,52.26 795 | 2016/3/4,1.69,25,16,0,1497.96,0,52.26 796 | 2016/3/5,1.65,25,18,0,1497.96,1,52.26 797 | 2016/3/6,1.4,25,19,0,1497.96,1,52.26 798 | 2016/3/7,1.58,26,19,0,1497.96,0,52.26 799 | 2016/3/8,1.72,25,19,0,1497.96,0,52.26 800 | 2016/3/9,1.74,21,10,0,1497.96,0,52.26 801 | 2016/3/10,1.74,11,8,0,1497.96,0,52.26 802 | 2016/3/11,1.76,10,8,0,1497.96,0,52.26 803 | 2016/3/12,1.72,12,10,0,1497.96,1,52.26 804 | 2016/3/13,1.45,14,12,0,1497.96,1,52.26 805 | 2016/3/14,1.61,17,12,0,1497.96,0,52.26 806 | 2016/3/15,1.77,16,13,0,1497.96,0,52.26 807 | 2016/3/16,1.77,16,13,0,1497.96,0,52.26 808 | 2016/3/17,1.78,18,15,0,1497.96,0,52.26 809 | 2016/3/18,1.8,22,19,0,1497.96,0,52.26 810 | 2016/3/19,1.78,25,20,0,1497.96,1,52.26 811 | 2016/3/20,1.48,23,20,0,1497.96,1,52.26 812 | 2016/3/21,1.64,20,17,0,1497.96,0,52.26 813 | 2016/3/22,1.8,19,17,0,1497.96,0,52.26 814 | 2016/3/23,1.82,19,14,0,1497.96,0,52.26 815 | 2016/3/24,1.81,15,11,0,1497.96,0,52.26 816 | 2016/3/25,1.79,16,10,0,1497.96,0,52.26 817 | 2016/3/26,1.72,20,11,0,1497.96,1,52.26 818 | 2016/3/27,1.45,22,12,0,1497.96,1,52.26 819 | 2016/3/28,1.62,22,14,0,1497.96,0,52.26 820 | 2016/3/29,1.77,23,16,0,1497.96,0,52.26 821 | 2016/3/30,1.78,24,18,0,1497.96,0,52.26 822 | 2016/3/31,1.75,25,19,0,1497.96,0,52.26 823 | 2016/4/1,1.75,28,20,0,1655.96,0,52.68 824 | 2016/4/2,1.65,26,20,0,1655.96,1,52.68 825 | 2016/4/3,1.32,27,21,0,1655.96,1,52.68 826 | 2016/4/4,0.94,27,21,0,1655.96,2,52.68 827 | 2016/4/5,1.42,28,21,0,1655.96,0,52.68 828 | 2016/4/6,1.82,28,21,0,1655.96,0,52.68 829 | 2016/4/7,1.88,29,22,0,1655.96,0,52.68 830 | 2016/4/8,1.92,28,22,0,1655.96,0,52.68 831 | 2016/4/9,1.88,27,22,0,1655.96,1,52.68 832 | 2016/4/10,1.57,24,20,0,1655.96,1,52.68 833 | 2016/4/11,1.71,26,22,0,1655.96,0,52.68 834 | 2016/4/12,1.84,25,21,0,1655.96,0,52.68 835 | 2016/4/13,1.81,25,21,0,1655.96,0,52.68 836 | 2016/4/14,1.87,25,22,0,1655.96,0,52.68 837 | 2016/4/15,1.89,27,23,0,1655.96,0,52.68 838 | 2016/4/16,1.82,27,22,0,1655.96,1,52.68 839 | 2016/4/17,1.54,28,21,0,1655.96,1,52.68 840 | 2016/4/18,1.69,23,18,0,1655.96,0,52.68 841 | 2016/4/19,1.85,25,21,0,1655.96,0,52.68 842 | 2016/4/20,1.87,25,21,0,1655.96,0,52.68 843 | 2016/4/21,1.89,25,21,0,1655.96,0,52.68 844 | 2016/4/22,1.89,25,20,0,1655.96,0,52.68 845 | 2016/4/23,1.85,25,21,0,1655.96,1,52.68 846 | 2016/4/24,1.59,26,21,0,1655.96,1,52.68 847 | 2016/4/25,1.79,27,23,0,1655.96,0,52.68 848 | 2016/4/26,2.0,28,23,0,1655.96,0,52.68 849 | 2016/4/27,2.01,28,22,0,1655.96,0,52.68 850 | 2016/4/28,2.01,30,23,0,1655.96,0,52.68 851 | 2016/4/29,1.97,29,22,0,1655.96,0,52.68 852 | 2016/4/30,1.64,25,22,0,1655.96,1,52.68 853 | 2016/5/1,0.96,27,22,0,1655.96,2,60.3 854 | 2016/5/2,1.07,29,23,0,1655.96,1,60.3 855 | 2016/5/3,1.71,29,25,0,1655.96,0,60.3 856 | 2016/5/4,1.93,29,23,0,1655.96,0,60.3 857 | 2016/5/5,2.06,31,25,0,1655.96,0,60.3 858 | 2016/5/6,2.14,32,26,0,1655.96,0,60.3 859 | 2016/5/7,2.03,31,25,0,1655.96,1,60.3 860 | 2016/5/8,1.82,32,25,0,1655.96,1,60.3 861 | 2016/5/9,2.05,32,26,0,1655.96,0,60.3 862 | 2016/5/10,2.12,26,22,0,1655.96,0,60.3 863 | 2016/5/11,2.02,30,21,0,1655.96,0,60.3 864 | 2016/5/12,2.07,30,23,0,1655.96,0,60.3 865 | 2016/5/13,2.11,31,24,0,1655.96,0,60.3 866 | 2016/5/14,2.04,30,24,0,1655.96,1,60.3 867 | 2016/5/15,1.7,32,23,0,1655.96,1,60.3 868 | 2016/5/16,1.78,28,20,0,1655.96,0,60.3 869 | 2016/5/17,1.97,27,21,0,1655.96,0,60.3 870 | 2016/5/18,2.0,29,23,0,1655.96,0,60.3 871 | 2016/5/19,2.05,29,23,0,1655.96,0,60.3 872 | 2016/5/20,2.06,28,22,0,1655.96,0,60.3 873 | 2016/5/21,1.96,27,22,0,1655.96,1,60.3 874 | 2016/5/22,1.67,29,23,0,1655.96,1,60.3 875 | 2016/5/23,1.94,31,23,0,1655.96,0,60.3 876 | 2016/5/24,2.18,32,25,0,1655.96,0,60.3 877 | 2016/5/25,2.23,33,26,0,1655.96,0,60.3 878 | 2016/5/26,2.24,32,26,0,1655.96,0,60.3 879 | 2016/5/27,2.25,30,25,0,1655.96,0,60.3 880 | 2016/5/28,2.07,30,24,0,1655.96,1,60.3 881 | 2016/5/29,1.75,31,26,0,1655.96,1,60.3 882 | 2016/5/30,2.05,32,26,0,1655.96,0,60.3 883 | 2016/5/31,2.27,34,27,0,1655.96,0,60.3 884 | 2016/6/1,2.3,33,27,1,1655.96,0,66.36 885 | 2016/6/2,2.41,34,27,1,1655.96,0,66.36 886 | 2016/6/3,2.48,34,27,1,1655.96,0,66.36 887 | 2016/6/4,2.28,33,24,1,1655.96,1,66.36 888 | 2016/6/5,1.79,29,24,1,1655.96,1,66.36 889 | 2016/6/6,1.95,29,23,1,1655.96,0,66.36 890 | 2016/6/7,2.21,32,25,1,1655.96,0,66.36 891 | 2016/6/8,2.17,30,25,1,1655.96,0,66.36 892 | 2016/6/9,1.38,31,25,1,1655.96,2,66.36 893 | 2016/6/10,1.6,32,25,1,1655.96,1,66.36 894 | 2016/6/11,1.92,30,25,1,1655.96,1,66.36 895 | 2016/6/12,1.93,29,25,1,1655.96,0,66.36 896 | 2016/6/13,2.14,31,25,1,1655.96,0,66.36 897 | 2016/6/14,2.3,32,26,1,1655.96,0,66.36 898 | 2016/6/15,2.34,32,26,1,1655.96,0,66.36 899 | 2016/6/16,2.3,30,26,1,1655.96,0,66.36 900 | 2016/6/17,2.28,32,26,1,1655.96,0,66.36 901 | 2016/6/18,2.3,33,27,1,1655.96,1,66.36 902 | 2016/6/19,2.0,33,27,1,1655.96,1,66.36 903 | 2016/6/20,2.28,34,26,1,1655.96,0,66.36 904 | 2016/6/21,2.47,35,27,1,1655.96,0,66.36 905 | 2016/6/22,2.49,34,27,1,1655.96,0,66.36 906 | 2016/6/23,2.5,34,27,1,1655.96,0,66.36 907 | 2016/6/24,2.52,35,27,1,1655.96,0,66.36 908 | 2016/6/25,2.45,35,27,1,1655.96,1,66.36 909 | 2016/6/26,2.11,35,27,1,1655.96,1,66.36 910 | 2016/6/27,2.34,33,25,1,1655.96,0,66.36 911 | 2016/6/28,2.43,33,26,1,1655.96,0,66.36 912 | 2016/6/29,2.37,32,26,1,1655.96,0,66.36 913 | 2016/6/30,2.32,32,26,1,1655.96,0,66.36 914 | 2016/7/1,2.31,32,26,1,1762.06,0,73.06 915 | 2016/7/2,2.28,32,26,1,1762.06,1,73.06 916 | 2016/7/3,1.95,32,26,1,1762.06,1,73.06 917 | 2016/7/4,2.21,32,26,1,1762.06,0,73.06 918 | 2016/7/5,2.34,32,26,1,1762.06,0,73.06 919 | 2016/7/6,2.36,34,26,1,1762.06,0,73.06 920 | 2016/7/7,2.41,36,27,1,1762.06,0,73.06 921 | 2016/7/8,2.52,36,28,1,1762.06,0,73.06 922 | 2016/7/9,2.54,31,24,1,1762.06,1,73.06 923 | 2016/7/10,2.06,33,25,1,1762.06,1,73.06 924 | 2016/7/11,2.19,32,25,1,1762.06,0,73.06 925 | 2016/7/12,2.27,31,25,1,1762.06,0,73.06 926 | 2016/7/13,2.25,33,26,1,1762.06,0,73.06 927 | 2016/7/14,2.33,33,26,1,1762.06,0,73.06 928 | 2016/7/15,2.36,34,27,1,1762.06,0,73.06 929 | 2016/7/16,2.35,34,27,1,1762.06,1,73.06 930 | 2016/7/17,2.06,34,28,1,1762.06,1,73.06 931 | 2016/7/18,2.34,32,27,1,1762.06,0,73.06 932 | 2016/7/19,2.48,33,26,1,1762.06,0,73.06 933 | 2016/7/20,2.46,34,27,1,1762.06,0,73.06 934 | 2016/7/21,2.48,35,27,1,1762.06,0,73.06 935 | 2016/7/22,2.51,36,27,1,1762.06,0,73.06 936 | 2016/7/23,2.47,36,27,1,1762.06,1,73.06 937 | 2016/7/24,2.16,36,28,1,1762.06,1,73.06 938 | 2016/7/25,2.46,35,27,1,1762.06,0,73.06 939 | 2016/7/26,2.62,33,27,1,1762.06,0,73.06 940 | 2016/7/27,2.6,35,27,1,1762.06,0,73.06 941 | 2016/7/28,2.6,36,27,1,1762.06,0,73.06 942 | 2016/7/29,2.61,36,27,1,1762.06,0,73.06 943 | 2016/7/30,2.51,36,26,1,1762.06,1,73.06 944 | 2016/7/31,1.97,35,26,1,1762.06,1,73.06 945 | 2016/8/1,2.28,28,24,1,1762.06,0,70.03 946 | 2016/8/2,1.96,30,24,1,1762.06,0,70.03 947 | 2016/8/3,2.15,32,25,1,1762.06,0,70.03 948 | 2016/8/4,2.26,33,26,1,1762.06,0,70.03 949 | 2016/8/5,2.29,35,27,1,1762.06,0,70.03 950 | 2016/8/6,2.35,35,27,1,1762.06,1,70.03 951 | 2016/8/7,2.16,34,27,1,1762.06,1,70.03 952 | 2016/8/8,2.41,31,26,1,1762.06,0,70.03 953 | 2016/8/9,2.5,31,25,1,1762.06,0,70.03 954 | 2016/8/10,2.32,31,25,1,1762.06,0,70.03 955 | 2016/8/11,2.33,29,25,1,1762.06,0,70.03 956 | 2016/8/12,2.35,31,26,1,1762.06,0,70.03 957 | 2016/8/13,2.25,31,26,1,1762.06,1,70.03 958 | 2016/8/14,1.9,31,25,1,1762.06,1,70.03 959 | 2016/8/15,2.09,30,25,1,1762.06,0,70.03 960 | 2016/8/16,2.23,29,25,1,1762.06,0,70.03 961 | 2016/8/17,2.2,28,25,1,1762.06,0,70.03 962 | 2016/8/18,2.2,32,26,1,1762.06,0,70.03 963 | 2016/8/19,2.27,34,26,1,1762.06,0,70.03 964 | 2016/8/20,2.31,32,26,1,1762.06,1,70.03 965 | 2016/8/21,2.03,35,26,1,1762.06,1,70.03 966 | 2016/8/22,2.28,35,26,1,1762.06,0,70.03 967 | 2016/8/23,2.49,35,27,1,1762.06,0,70.03 968 | 2016/8/24,2.52,35,27,1,1762.06,0,70.03 969 | 2016/8/25,2.5,34,26,1,1762.06,0,70.03 970 | 2016/8/26,2.57,31,24,1,1762.06,0,70.03 971 | 2016/8/27,2.35,33,26,1,1762.06,1,70.03 972 | 2016/8/28,1.97,31,26,1,1762.06,1,70.03 973 | 2016/8/29,2.07,30,25,1,1762.06,0,70.03 974 | 2016/8/30,2.19,33,25,1,1762.06,0,70.03 975 | 2016/8/31,2.25,32,25,2,1762.06,0,70.03 976 | 2016/9/1,2.3,30,25,2,1762.06,0,65.51 977 | 2016/9/2,2.3,30,25,2,1762.06,0,65.51 978 | 2016/9/3,2.2,31,25,2,1762.06,1,65.51 979 | 2016/9/4,1.88,33,25,2,1762.06,1,65.51 980 | 2016/9/5,2.11,32,26,2,1762.06,0,65.51 981 | 2016/9/6,2.29,30,25,2,1762.06,0,65.51 982 | 2016/9/7,2.3,31,25,2,1762.06,0,65.51 983 | 2016/9/8,2.29,31,26,2,1762.06,0,65.51 984 | 2016/9/9,2.27,30,25,2,1762.06,0,65.51 985 | 2016/9/10,2.2,31,25,2,1762.06,1,65.51 986 | 2016/9/11,1.93,32,25,2,1762.06,1,65.51 987 | 2016/9/12,2.14,34,25,2,1762.06,0,65.51 988 | 2016/9/13,2.33,33,25,2,1762.06,0,65.51 989 | 2016/9/14,2.29,34,26,2,1762.06,0,65.51 990 | 2016/9/15,1.47,33,26,2,1762.06,2,65.51 991 | 2016/9/16,1.66,33,26,2,1762.06,1,65.51 992 | 2016/9/17,2.11,32,26,2,1762.06,1,65.51 993 | 2016/9/18,2.14,32,25,2,1762.06,0,65.51 994 | 2016/9/19,2.27,31,23,2,1762.06,0,65.51 995 | 2016/9/20,2.29,31,24,2,1762.06,0,65.51 996 | 2016/9/21,2.28,32,25,2,1762.06,0,65.51 997 | 2016/9/22,2.32,32,25,2,1762.06,0,65.51 998 | 2016/9/23,2.33,32,25,2,1762.06,0,65.51 999 | 2016/9/24,2.3,33,26,2,1762.06,1,65.51 1000 | 2016/9/25,2.1,35,26,2,1762.06,1,65.51 1001 | 2016/9/26,2.32,34,26,2,1762.06,0,65.51 1002 | 2016/9/27,2.51,32,24,2,1762.06,0,65.51 1003 | 2016/9/28,2.49,28,24,2,1762.06,0,65.51 1004 | 2016/9/29,2.22,24,20,2,1762.06,0,65.51 1005 | 2016/9/30,1.87,28,21,2,1762.06,0,65.51 1006 | 2016/10/1,1.07,31,23,2,1191.69,2,60.32 1007 | 2016/10/2,0.94,32,24,2,1191.69,2,60.32 1008 | 2016/10/3,1.19,32,24,2,1191.69,2,60.32 1009 | 2016/10/4,1.62,33,25,2,1191.69,1,60.32 1010 | 2016/10/5,1.95,32,25,2,1191.69,1,60.32 1011 | 2016/10/6,2.12,32,25,2,1191.69,1,60.32 1012 | 2016/10/7,2.23,31,25,2,1191.69,1,60.32 1013 | 2016/10/8,2.23,29,23,2,1191.69,0,60.32 1014 | 2016/10/9,1.93,28,22,2,1191.69,0,60.32 1015 | 2016/10/10,1.97,26,20,2,1191.69,0,60.32 1016 | 2016/10/11,2.02,25,20,2,1191.69,0,60.32 1017 | 2016/10/12,1.99,29,22,2,1191.69,0,60.32 1018 | 2016/10/13,2.02,30,22,2,1191.69,0,60.32 1019 | 2016/10/14,2.11,31,22,2,1191.69,0,60.32 1020 | 2016/10/15,2.08,31,23,2,1191.69,1,60.32 1021 | 2016/10/16,1.81,30,22,2,1191.69,1,60.32 1022 | 2016/10/17,2.05,26,22,2,1191.69,0,60.32 1023 | 2016/10/18,2.15,25,22,2,1191.69,0,60.32 1024 | 2016/10/19,2.08,30,23,2,1191.69,0,60.32 1025 | 2016/10/20,2.13,26,23,2,1191.69,0,60.32 1026 | 2016/10/21,2.08,31,24,2,1191.69,0,60.32 1027 | 2016/10/22,2.04,31,25,2,1191.69,1,60.32 1028 | 2016/10/23,1.87,30,25,2,1191.69,1,60.32 1029 | 2016/10/24,2.07,31,25,2,1191.69,0,60.32 1030 | 2016/10/25,2.23,32,25,2,1191.69,0,60.32 1031 | 2016/10/26,2.24,32,25,2,1191.69,0,60.32 1032 | 2016/10/27,2.25,32,24,2,1191.69,0,60.32 1033 | 2016/10/28,2.28,28,20,2,1191.69,0,60.32 1034 | 2016/10/29,2.17,25,19,2,1191.69,1,60.32 1035 | 2016/10/30,1.63,26,19,2,1191.69,1,60.32 1036 | 2016/10/31,1.77,26,19,2,1191.69,0,60.32 1037 | 2016/11/1,1.88,25,17,2,1191.69,0,56.62 1038 | 2016/11/2,1.91,26,17,2,1191.69,0,56.62 1039 | 2016/11/3,1.91,25,17,2,1191.69,0,56.62 1040 | 2016/11/4,1.92,27,18,2,1191.69,0,56.62 1041 | 2016/11/5,1.89,28,20,2,1191.69,1,56.62 1042 | 2016/11/6,1.63,28,21,2,1191.69,1,56.62 1043 | 2016/11/7,1.85,26,17,2,1191.69,0,56.62 1044 | 2016/11/8,2.03,20,17,2,1191.69,0,56.62 1045 | 2016/11/9,1.93,16,13,2,1191.69,0,56.62 1046 | 2016/11/10,1.91,17,12,2,1191.69,0,56.62 1047 | 2016/11/11,1.9,20,15,2,1191.69,0,56.62 1048 | 2016/11/12,1.86,28,21,2,1191.69,1,56.62 1049 | 2016/11/13,1.61,30,21,2,1191.69,1,56.62 1050 | 2016/11/14,1.84,30,21,2,1191.69,0,56.62 1051 | 2016/11/15,2.04,29,22,2,1191.69,0,56.62 1052 | 2016/11/16,2.05,28,22,2,1191.69,0,56.62 1053 | 2016/11/17,2.04,29,21,2,1191.69,0,56.62 1054 | 2016/11/18,2.04,28,23,2,1191.69,0,56.62 1055 | 2016/11/19,2.01,28,22,2,1191.69,1,56.62 1056 | 2016/11/20,1.74,28,23,2,1191.69,1,56.62 1057 | 2016/11/21,1.93,24,21,2,1191.69,0,56.62 1058 | 2016/11/22,2.03,21,11,2,1191.69,0,56.62 1059 | 2016/11/23,1.95,15,11,2,1191.69,0,56.62 1060 | 2016/11/24,1.91,15,12,2,1191.69,0,56.62 1061 | 2016/11/25,1.9,14,12,2,1191.69,0,56.62 1062 | 2016/11/26,1.86,17,12,2,1191.69,1,56.62 1063 | 2016/11/27,1.58,19,11,2,1191.69,1,56.62 1064 | 2016/11/28,1.74,20,14,2,1191.69,0,56.62 1065 | 2016/11/29,1.89,20,13,2,1191.69,0,56.62 1066 | 2016/11/30,1.84,21,13,3,1191.69,0,56.62 1067 | 2016/12/1,1.81,20,15,3,1191.69,0,56.2 1068 | 2016/12/2,1.86,21,17,3,1191.69,0,56.2 1069 | 2016/12/3,1.84,23,18,3,1191.69,1,56.2 1070 | 2016/12/4,1.56,25,18,3,1191.69,1,56.2 1071 | 2016/12/5,1.76,19,13,3,1191.69,0,56.2 1072 | 2016/12/6,1.9,22,14,3,1191.69,0,56.2 1073 | 2016/12/7,1.88,23,13,3,1191.69,0,56.2 1074 | 2016/12/8,1.89,23,13,3,1191.69,0,56.2 1075 | 2016/12/9,1.89,24,14,3,1191.69,0,56.2 1076 | 2016/12/10,1.84,23,17,3,1191.69,1,56.2 1077 | 2016/12/11,1.6,24,17,3,1191.69,1,56.2 1078 | 2016/12/12,1.74,25,17,3,1191.69,0,56.2 1079 | 2016/12/13,1.91,21,15,3,1191.69,0,56.2 1080 | 2016/12/14,1.91,19,11,3,1191.69,0,56.2 1081 | 2016/12/15,1.89,17,10,3,1191.69,0,56.2 1082 | 2016/12/16,1.87,18,10,3,1191.69,0,56.2 1083 | 2016/12/17,1.84,22,15,3,1191.69,1,56.2 1084 | 2016/12/18,1.61,24,16,3,1191.69,1,56.2 1085 | 2016/12/19,1.76,25,17,3,1191.69,0,56.2 1086 | 2016/12/20,1.88,26,17,3,1191.69,0,56.2 1087 | 2016/12/21,1.9,20,17,3,1191.69,0,56.2 1088 | 2016/12/22,1.91,21,13,3,1191.69,0,56.2 1089 | 2016/12/23,1.9,19,15,3,1191.69,0,56.2 1090 | 2016/12/24,1.87,23,17,3,1191.69,1,56.2 1091 | 2016/12/25,1.66,25,12,3,1191.69,1,56.2 1092 | 2016/12/26,1.78,17,8,3,1191.69,0,56.2 1093 | 2016/12/27,1.88,15,9,3,1191.69,0,56.2 1094 | 2016/12/28,1.88,16,10,3,1191.69,0,56.2 1095 | 2016/12/29,1.86,17,11,3,1191.69,0,56.2 1096 | 2016/12/30,1.79,20,12,3,1191.69,0,56.2 1097 | 2016/12/31,1.53,23,15,3,1191.69,1,56.2 1098 | 2017/1/1,0.93,25,16,3,1638.41,2,38.97 1099 | 2017/1/2,1.43,26,16,3,1638.41,1,38.97 1100 | 2017/1/3,1.8,26,17,3,1638.41,0,38.97 1101 | 2017/1/4,1.87,26,18,3,1638.41,0,38.97 1102 | 2017/1/5,1.88,26,18,3,1638.41,0,38.97 1103 | 2017/1/6,1.87,26,18,3,1638.41,0,38.97 1104 | 2017/1/7,1.84,24,15,3,1638.41,1,38.97 1105 | 2017/1/8,1.64,23,15,3,1638.41,1,38.97 1106 | 2017/1/9,1.79,22,16,3,1638.41,0,38.97 1107 | 2017/1/10,1.83,21,14,3,1638.41,0,38.97 1108 | 2017/1/11,1.81,16,13,3,1638.41,0,38.97 1109 | 2017/1/12,1.8,13,11,3,1638.41,0,38.97 1110 | 2017/1/13,1.77,13,11,3,1638.41,0,38.97 1111 | 2017/1/14,1.68,13,11,3,1638.41,1,38.97 1112 | 2017/1/15,1.5,15,12,3,1638.41,1,38.97 1113 | 2017/1/16,1.58,16,13,3,1638.41,0,38.97 1114 | 2017/1/17,1.54,20,16,3,1638.41,0,38.97 1115 | 2017/1/18,1.45,19,15,3,1638.41,0,38.97 1116 | 2017/1/19,1.33,19,13,3,1638.41,0,38.97 1117 | 2017/1/20,1.15,20,9,3,1638.41,0,38.97 1118 | 2017/1/21,0.98,20,10,3,1638.41,1,38.97 1119 | 2017/1/22,0.84,20,12,3,1638.41,0,38.97 1120 | 2017/1/23,0.77,18,14,3,1638.41,0,38.97 1121 | 2017/1/24,0.68,21,14,3,1638.41,0,38.97 1122 | 2017/1/25,0.59,21,12,3,1638.41,0,38.97 1123 | 2017/1/26,0.51,21,12,3,1638.41,0,38.97 1124 | 2017/1/27,0.43,23,16,3,1638.41,1,38.97 1125 | 2017/1/28,0.39,23,16,3,1638.41,2,38.97 1126 | 2017/1/29,0.41,22,16,3,1638.41,2,38.97 1127 | 2017/1/30,0.42,18,14,3,1638.41,2,38.97 1128 | 2017/1/31,0.46,23,13,3,1638.41,1,38.97 1129 | 2017/2/1,0.51,22,15,3,1638.41,1,40.35 1130 | 2017/2/2,0.58,19,15,3,1638.41,1,40.35 1131 | 2017/2/3,0.64,21,15,3,1638.41,0,40.35 1132 | 2017/2/4,0.77,24,15,3,1638.41,0,40.35 1133 | 2017/2/5,0.85,24,17,3,1638.41,1,40.35 1134 | 2017/2/6,1.1,19,16,3,1638.41,0,40.35 1135 | 2017/2/7,1.26,20,9,3,1638.41,0,40.35 1136 | 2017/2/8,1.37,17,8,3,1638.41,0,40.35 1137 | 2017/2/9,1.44,16,8,3,1638.41,0,40.35 1138 | 2017/2/10,1.5,15,8,3,1638.41,0,40.35 1139 | 2017/2/11,1.38,18,8,3,1638.41,1,40.35 1140 | 2017/2/12,1.29,20,10,3,1638.41,1,40.35 1141 | 2017/2/13,1.56,22,11,3,1638.41,0,40.35 1142 | 2017/2/14,1.65,23,13,3,1638.41,0,40.35 1143 | 2017/2/15,1.69,25,14,3,1638.41,0,40.35 1144 | 2017/2/16,1.72,25,15,3,1638.41,0,40.35 1145 | 2017/2/17,1.75,27,15,3,1638.41,0,40.35 1146 | 2017/2/18,1.71,25,17,3,1638.41,1,40.35 1147 | 2017/2/19,1.46,24,17,3,1638.41,1,40.35 1148 | 2017/2/20,1.73,24,18,3,1638.41,0,40.35 1149 | 2017/2/21,1.83,24,18,3,1638.41,0,40.35 1150 | 2017/2/22,1.84,14,10,3,1638.41,0,40.35 1151 | 2017/2/23,1.85,10,8,3,1638.41,0,40.35 1152 | 2017/2/24,1.88,10,7,3,1638.41,0,40.35 1153 | 2017/2/25,1.84,13,9,3,1638.41,1,40.35 1154 | 2017/2/26,1.55,20,14,3,1638.41,1,40.35 1155 | 2017/2/27,1.78,22,15,3,1638.41,0,40.35 1156 | 2017/2/28,1.82,21,15,0,1638.41,0,40.35 1157 | 2017/3/1,1.81,21,13,0,1638.41,0,57.54 1158 | 2017/3/2,1.86,23,14,0,1638.41,0,57.54 1159 | 2017/3/3,1.88,20,15,0,1638.41,0,57.54 1160 | 2017/3/4,1.81,17,14,0,1638.41,1,57.54 1161 | 2017/3/5,1.47,17,13,0,1638.41,1,57.54 1162 | 2017/3/6,1.8,18,14,0,1638.41,0,57.54 1163 | 2017/3/7,1.91,19,15,0,1638.41,0,57.54 1164 | 2017/3/8,1.9,21,16,0,1638.41,0,57.54 1165 | 2017/3/9,1.93,23,17,0,1638.41,0,57.54 1166 | 2017/3/10,1.93,25,18,0,1638.41,0,57.54 1167 | 2017/3/11,1.87,17,13,0,1638.41,1,57.54 1168 | 2017/3/12,1.55,17,14,0,1638.41,1,57.54 1169 | 2017/3/13,1.86,21,16,0,1638.41,0,57.54 1170 | 2017/3/14,1.94,19,16,0,1638.41,0,57.54 1171 | 2017/3/15,1.92,23,18,0,1638.41,0,57.54 1172 | 2017/3/16,1.92,21,20,0,1638.41,0,57.54 1173 | 2017/3/17,1.94,25,19,0,1638.41,0,57.54 1174 | 2017/3/18,1.87,25,20,0,1638.41,1,57.54 1175 | 2017/3/19,1.54,22,19,0,1638.41,1,57.54 1176 | 2017/3/20,1.86,25,19,0,1638.41,0,57.54 1177 | 2017/3/21,1.99,25,19,0,1638.41,0,57.54 1178 | 2017/3/22,1.96,18,14,0,1638.41,0,57.54 1179 | 2017/3/23,1.97,13,11,0,1638.41,0,57.54 1180 | 2017/3/24,1.99,24,15,0,1638.41,0,57.54 1181 | 2017/3/25,1.88,25,17,0,1638.41,1,57.54 1182 | 2017/3/26,1.55,26,17,0,1638.41,1,57.54 1183 | 2017/3/27,1.84,27,21,0,1638.41,0,57.54 1184 | 2017/3/28,1.93,20,14,0,1638.41,0,57.54 1185 | 2017/3/29,1.97,23,13,0,1638.41,0,57.54 1186 | 2017/3/30,1.98,25,14,0,1638.41,0,57.54 1187 | 2017/3/31,1.91,25,15,0,1638.41,0,57.54 1188 | 2017/4/1,1.8,26,19,0,1886.98,0,56.29 1189 | 2017/4/2,1.6,27,19,0,1886.98,1,56.29 1190 | 2017/4/3,1.37,25,20,0,1886.98,1,56.29 1191 | 2017/4/4,0.93,27,21,0,1886.98,2,56.29 1192 | 2017/4/5,1.65,28,22,0,1886.98,0,56.29 1193 | 2017/4/6,1.95,28,23,0,1886.98,0,56.29 1194 | 2017/4/7,2.0,29,23,0,1886.98,0,56.29 1195 | 2017/4/8,1.98,26,19,0,1886.98,1,56.29 1196 | 2017/4/9,1.73,21,17,0,1886.98,1,56.29 1197 | 2017/4/10,2.04,20,17,0,1886.98,0,56.29 1198 | 2017/4/11,2.13,23,17,0,1886.98,0,56.29 1199 | 2017/4/12,1.98,27,21,0,1886.98,0,56.29 1200 | 2017/4/13,1.96,29,22,0,1886.98,0,56.29 1201 | 2017/4/14,1.98,31,22,0,1886.98,0,56.29 1202 | 2017/4/15,1.94,31,22,0,1886.98,1,56.29 1203 | 2017/4/16,1.6,32,24,0,1886.98,1,56.29 1204 | 2017/4/17,2.02,28,23,0,1886.98,0,56.29 1205 | 2017/4/18,2.21,28,19,0,1886.98,0,56.29 1206 | 2017/4/19,2.25,23,17,0,1886.98,0,56.29 1207 | 2017/4/20,2.24,24,17,0,1886.98,0,56.29 1208 | 2017/4/21,2.21,25,20,0,1886.98,0,56.29 1209 | 2017/4/22,1.94,24,20,0,1886.98,1,56.29 1210 | 2017/4/23,1.64,26,22,0,1886.98,1,56.29 1211 | 2017/4/24,1.91,24,20,0,1886.98,0,56.29 1212 | 2017/4/25,2.01,27,17,0,1886.98,0,56.29 1213 | 2017/4/26,2.07,28,18,0,1886.98,0,56.29 1214 | 2017/4/27,2.01,29,20,0,1886.98,0,56.29 1215 | 2017/4/28,1.96,29,20,0,1886.98,0,56.29 1216 | 2017/4/29,1.82,29,24,0,1886.98,1,56.29 1217 | 2017/4/30,1.36,30,24,0,1886.98,1,56.29 1218 | 2017/5/1,0.94,26,21,0,1886.98,2,63.08 1219 | 2017/5/2,1.77,29,22,0,1886.98,0,63.08 1220 | 2017/5/3,2.17,30,24,0,1886.98,0,63.08 1221 | 2017/5/4,2.14,31,24,0,1886.98,0,63.08 1222 | 2017/5/5,2.18,29,23,0,1886.98,0,63.08 1223 | 2017/5/6,2.19,28,22,0,1886.98,1,63.08 1224 | 2017/5/7,1.86,32,24,0,1886.98,1,63.08 1225 | 2017/5/8,2.13,32,24,0,1886.98,0,63.08 1226 | 2017/5/9,2.18,30,24,0,1886.98,0,63.08 1227 | 2017/5/10,2.27,28,23,0,1886.98,0,63.08 1228 | 2017/5/11,2.31,29,23,0,1886.98,0,63.08 1229 | 2017/5/12,2.3,27,22,0,1886.98,0,63.08 1230 | 2017/5/13,2.15,29,21,0,1886.98,1,63.08 1231 | 2017/5/14,1.77,30,22,0,1886.98,1,63.08 1232 | 2017/5/15,2.13,28,23,0,1886.98,0,63.08 1233 | 2017/5/16,2.14,28,23,0,1886.98,0,63.08 1234 | 2017/5/17,2.2,28,23,0,1886.98,0,63.08 1235 | 2017/5/18,2.22,28,23,0,1886.98,0,63.08 1236 | 2017/5/19,2.16,28,24,0,1886.98,0,63.08 1237 | 2017/5/20,2.03,30,24,0,1886.98,1,63.08 1238 | 2017/5/21,1.75,27,23,0,1886.98,1,63.08 1239 | 2017/5/22,2.08,29,22,0,1886.98,0,63.08 1240 | 2017/5/23,2.27,29,21,0,1886.98,0,63.08 1241 | 2017/5/24,2.27,30,22,0,1886.98,0,63.08 1242 | 2017/5/25,2.22,32,22,0,1886.98,0,63.08 1243 | 2017/5/26,2.17,32,23,0,1886.98,0,63.08 1244 | 2017/5/27,2.14,31,24,0,1886.98,0,63.08 1245 | 2017/5/28,1.96,32,25,0,1886.98,1,63.08 1246 | 2017/5/29,1.77,32,26,1,1886.98,1,63.08 1247 | 2017/5/30,1.2,32,26,1,1886.98,2,63.08 1248 | 2017/5/31,2.01,34,27,1,1886.98,0,63.08 1249 | 2017/6/1,2.41,33,26,1,1886.98,0,73.44 1250 | 2017/6/2,2.54,34,28,1,1886.98,0,73.44 1251 | 2017/6/3,2.53,34,27,1,1886.98,1,73.44 1252 | 2017/6/4,2.24,32,26,1,1886.98,1,73.44 1253 | 2017/6/5,2.59,34,27,1,1886.98,0,73.44 1254 | 2017/6/6,2.7,34,27,1,1886.98,0,73.44 1255 | 2017/6/7,2.65,33,26,1,1886.98,0,73.44 1256 | 2017/6/8,2.66,33,27,1,1886.98,0,73.44 1257 | 2017/6/9,2.6,33,27,1,1886.98,0,73.44 1258 | 2017/6/10,2.51,30,26,1,1886.98,1,73.44 1259 | 2017/6/11,2.15,30,25,1,1886.98,1,73.44 1260 | 2017/6/12,2.45,31,26,1,1886.98,0,73.44 1261 | 2017/6/13,2.42,30,25,1,1886.98,0,73.44 1262 | 2017/6/14,2.41,28,24,1,1886.98,0,73.44 1263 | 2017/6/15,2.49,29,24,1,1886.98,0,73.44 1264 | 2017/6/16,2.45,29,26,1,1886.98,0,73.44 1265 | 2017/6/17,2.21,30,25,1,1886.98,1,73.44 1266 | 2017/6/18,1.81,31,25,1,1886.98,1,73.44 1267 | 2017/6/19,2.21,30,26,1,1886.98,0,73.44 1268 | 2017/6/20,2.32,33,26,1,1886.98,0,73.44 1269 | 2017/6/21,2.4,33,26,1,1886.98,0,73.44 1270 | 2017/6/22,2.47,33,27,1,1886.98,0,73.44 1271 | 2017/6/23,2.54,33,27,1,1886.98,0,73.44 1272 | 2017/6/24,2.47,33,27,1,1886.98,1,73.44 1273 | 2017/6/25,2.12,33,27,1,1886.98,1,73.44 1274 | 2017/6/26,2.55,33,27,1,1886.98,0,73.44 1275 | 2017/6/27,2.67,33,27,1,1886.98,0,73.44 1276 | 2017/6/28,2.67,32,26,1,1886.98,0,73.44 1277 | 2017/6/29,2.65,31,26,1,1886.98,0,73.44 1278 | 2017/6/30,2.55,28,24,1,1886.98,0,73.44 1279 | 2017/7/1,2.38,30,25,1,2323.33,1,75.52 1280 | 2017/7/2,1.97,32,25,1,2323.33,1,75.52 1281 | 2017/7/3,2.28,30,26,1,2323.33,0,75.52 1282 | 2017/7/4,2.36,31,25,1,2323.33,0,75.52 1283 | 2017/7/5,2.45,30,25,1,2323.33,0,75.52 1284 | 2017/7/6,2.43,32,26,1,2323.33,0,75.52 1285 | 2017/7/7,2.43,32,26,1,2323.33,0,75.52 1286 | 2017/7/8,2.33,32,26,1,2323.33,1,75.52 1287 | 2017/7/9,2.01,33,26,1,2323.33,1,75.52 1288 | 2017/7/10,2.47,33,26,1,2323.33,0,75.52 1289 | 2017/7/11,2.62,34,26,1,2323.33,0,75.52 1290 | 2017/7/12,2.62,33,26,1,2323.33,0,75.52 1291 | 2017/7/13,2.65,30,25,1,2323.33,0,75.52 1292 | 2017/7/14,2.68,30,25,1,2323.33,0,75.52 1293 | 2017/7/15,2.53,30,25,1,2323.33,1,75.52 1294 | 2017/7/16,2.01,31,24,1,2323.33,1,75.52 1295 | 2017/7/17,2.33,32,25,1,2323.33,0,75.52 1296 | 2017/7/18,2.31,33,25,1,2323.33,0,75.52 1297 | 2017/7/19,2.37,34,26,1,2323.33,0,75.52 1298 | 2017/7/20,2.45,30,26,1,2323.33,0,75.52 1299 | 2017/7/21,2.55,29,25,1,2323.33,0,75.52 1300 | 2017/7/22,2.52,34,26,1,2323.33,1,75.52 1301 | 2017/7/23,2.03,34,26,1,2323.33,1,75.52 1302 | 2017/7/24,2.39,35,26,1,2323.33,0,75.52 1303 | 2017/7/25,2.59,35,26,1,2323.33,0,75.52 1304 | 2017/7/26,2.64,36,27,1,2323.33,0,75.52 1305 | 2017/7/27,2.63,37,29,1,2323.33,0,75.52 1306 | 2017/7/28,2.7,34,27,1,2323.33,0,75.52 1307 | 2017/7/29,2.7,33,27,1,2323.33,1,75.52 1308 | 2017/7/30,2.36,33,27,1,2323.33,1,75.52 1309 | 2017/7/31,2.73,30,26,1,2323.33,0,75.52 1310 | 2017/8/1,2.84,31,25,1,2323.33,0,81.11 1311 | 2017/8/2,2.73,33,26,1,2323.33,0,81.11 1312 | 2017/8/3,2.61,35,26,1,2323.33,0,81.11 1313 | 2017/8/4,2.56,35,27,1,2323.33,0,81.11 1314 | 2017/8/5,2.54,34,28,1,2323.33,1,81.11 1315 | 2017/8/6,2.24,34,28,1,2323.33,1,81.11 1316 | 2017/8/7,2.73,33,27,1,2323.33,0,81.11 1317 | 2017/8/8,2.86,33,27,1,2323.33,0,81.11 1318 | 2017/8/9,2.81,34,28,1,2323.33,0,81.11 1319 | 2017/8/10,2.74,34,28,1,2323.33,0,81.11 1320 | 2017/8/11,2.75,34,28,1,2323.33,0,81.11 1321 | 2017/8/12,2.67,34,28,1,2323.33,1,81.11 1322 | 2017/8/13,2.27,33,26,1,2323.33,1,81.11 1323 | 2017/8/14,2.7,34,27,1,2323.33,0,81.11 1324 | 2017/8/15,2.78,35,27,1,2323.33,0,81.11 1325 | 2017/8/16,2.77,36,27,1,2323.33,0,81.11 1326 | 2017/8/17,2.8,36,27,1,2323.33,0,81.11 1327 | 2017/8/18,2.82,36,27,1,2323.33,0,81.11 1328 | 2017/8/19,2.73,37,28,1,2323.33,1,81.11 1329 | 2017/8/20,2.36,28,26,1,2323.33,1,81.11 1330 | 2017/8/21,2.83,32,26,1,2323.33,0,81.11 1331 | 2017/8/22,2.94,34,26,1,2323.33,0,81.11 1332 | 2017/8/23,2.59,33,26,1,2323.33,0,81.11 1333 | 2017/8/24,2.62,29,26,1,2323.33,0,81.11 1334 | 2017/8/25,2.66,29,25,1,2323.33,0,81.11 1335 | 2017/8/26,2.52,32,26,1,2323.33,1,81.11 1336 | 2017/8/27,1.91,35,26,1,2323.33,1,81.11 1337 | 2017/8/28,2.18,34,26,1,2323.33,0,81.11 1338 | 2017/8/29,2.4,35,26,2,2323.33,0,81.11 1339 | 2017/8/30,2.58,35,26,2,2323.33,0,81.11 1340 | 2017/8/31,2.57,34,25,2,2323.33,0,81.11 1341 | 2017/9/1,2.53,28,25,2,2323.33,0,76.97 1342 | 2017/9/2,2.48,32,25,2,2323.33,1,76.97 1343 | 2017/9/3,2.12,32,26,2,2323.33,1,76.97 1344 | 2017/9/4,2.35,32,26,2,2323.33,0,76.97 1345 | 2017/9/5,2.48,32,26,2,2323.33,0,76.97 1346 | 2017/9/6,2.53,33,26,2,2323.33,0,76.97 1347 | 2017/9/7,2.53,33,26,2,2323.33,0,76.97 1348 | 2017/9/8,2.5,34,26,2,2323.33,0,76.97 1349 | 2017/9/9,2.47,35,26,2,2323.33,1,76.97 1350 | 2017/9/10,2.15,35,26,2,2323.33,1,76.97 1351 | 2017/9/11,2.58,34,26,2,2323.33,0,76.97 1352 | 2017/9/12,2.75,34,26,2,2323.33,0,76.97 1353 | 2017/9/13,2.74,35,26,2,2323.33,0,76.97 1354 | 2017/9/14,2.7,35,26,2,2323.33,0,76.97 1355 | 2017/9/15,2.65,35,26,2,2323.33,0,76.97 1356 | 2017/9/16,2.59,34,26,2,2323.33,1,76.97 1357 | 2017/9/17,2.28,33,25,2,2323.33,1,76.97 1358 | 2017/9/18,2.64,34,26,2,2323.33,0,76.97 1359 | 2017/9/19,2.72,33,26,2,2323.33,0,76.97 1360 | 2017/9/20,2.72,32,26,2,2323.33,0,76.97 1361 | 2017/9/21,2.72,33,26,2,2323.33,0,76.97 1362 | 2017/9/22,2.7,33,26,2,2323.33,0,76.97 1363 | 2017/9/23,2.65,34,26,2,2323.33,1,76.97 1364 | 2017/9/24,2.35,35,26,2,2323.33,1,76.97 1365 | 2017/9/25,2.64,35,26,2,2323.33,0,76.97 1366 | 2017/9/26,2.73,34,26,2,2323.33,0,76.97 1367 | 2017/9/27,2.79,32,26,2,2323.33,0,76.97 1368 | 2017/9/28,2.81,32,27,2,2323.33,0,76.97 1369 | 2017/9/29,2.7,32,27,2,2323.33,0,76.97 1370 | 2017/9/30,2.37,33,27,2,2323.33,0,76.97 1371 | 2017/10/1,1.4,31,26,2,2097.4,2,63.74 1372 | 2017/10/2,1.28,32,25,2,2097.4,2,63.74 1373 | 2017/10/3,1.34,33,26,2,2097.4,2,63.74 1374 | 2017/10/4,1.25,33,26,2,2097.4,2,63.74 1375 | 2017/10/5,1.77,34,27,2,2097.4,1,63.74 1376 | 2017/10/6,2.22,33,26,2,2097.4,1,63.74 1377 | 2017/10/7,2.39,33,26,2,2097.4,1,63.74 1378 | 2017/10/8,2.35,33,26,2,2097.4,1,63.74 1379 | 2017/10/9,2.59,32,26,2,2097.4,0,63.74 1380 | 2017/10/10,2.7,30,22,2,2097.4,0,63.74 1381 | 2017/10/11,2.76,27,22,2,2097.4,0,63.74 1382 | 2017/10/12,2.69,23,20,2,2097.4,0,63.74 1383 | 2017/10/13,2.4,24,20,2,2097.4,0,63.74 1384 | 2017/10/14,2.15,27,21,2,2097.4,1,63.74 1385 | 2017/10/15,1.72,29,22,2,2097.4,1,63.74 1386 | 2017/10/16,2.0,28,22,2,2097.4,0,63.74 1387 | 2017/10/17,2.13,29,21,2,2097.4,0,63.74 1388 | 2017/10/18,2.19,27,20,2,2097.4,0,63.74 1389 | 2017/10/19,2.17,26,17,2,2097.4,0,63.74 1390 | 2017/10/20,2.14,26,17,2,2097.4,0,63.74 1391 | 2017/10/21,2.05,28,20,2,2097.4,1,63.74 1392 | 2017/10/22,1.71,28,20,2,2097.4,1,63.74 1393 | 2017/10/23,2.01,20,0,2,2097.4,0,63.74 1394 | 2017/10/24,2.12,20,0,2,2097.4,0,63.74 1395 | 2017/10/25,2.14,19,0,2,2097.4,0,63.74 1396 | 2017/10/26,2.16,18,0,2,2097.4,0,63.74 1397 | 2017/10/27,2.16,26,18,2,2097.4,0,63.74 1398 | 2017/10/28,2.07,25,16,2,2097.4,1,63.74 1399 | 2017/10/29,1.72,26,17,2,2097.4,1,63.74 1400 | 2017/10/30,1.97,28,17,2,2097.4,0,63.74 1401 | 2017/10/31,1.99,27,18,2,2097.4,0,63.74 1402 | 2017/11/1,1.97,25,19,2,2097.4,0,59.89 1403 | 2017/11/2,2.06,26,19,2,2097.4,0,59.89 1404 | 2017/11/3,2.08,26,20,2,2097.4,0,59.89 1405 | 2017/11/4,2.0,24,20,2,2097.4,1,59.89 1406 | 2017/11/5,1.62,25,19,2,2097.4,1,59.89 1407 | 2017/11/6,1.97,28,20,2,2097.4,0,59.89 1408 | 2017/11/7,2.09,30,22,2,2097.4,0,59.89 1409 | 2017/11/8,2.11,29,22,2,2097.4,0,59.89 1410 | 2017/11/9,2.14,27,22,2,2097.4,0,59.89 1411 | 2017/11/10,2.19,23,20,2,2097.4,0,59.89 1412 | 2017/11/11,2.11,24,20,2,2097.4,1,59.89 1413 | 2017/11/12,1.72,24,20,2,2097.4,1,59.89 1414 | 2017/11/13,2.02,26,19,2,2097.4,0,59.89 1415 | 2017/11/14,2.12,28,21,2,2097.4,0,59.89 1416 | 2017/11/15,2.11,24,16,2,2097.4,0,59.89 1417 | 2017/11/16,2.1,18,16,2,2097.4,0,59.89 1418 | 2017/11/17,2.14,16,13,2,2097.4,0,59.89 1419 | 2017/11/18,2.05,16,13,2,2097.4,1,59.89 1420 | 2017/11/19,1.62,19,14,2,2097.4,1,59.89 1421 | 2017/11/20,1.95,18,13,2,2097.4,0,59.89 1422 | 2017/11/21,2.04,16,12,2,2097.4,0,59.89 1423 | 2017/11/22,2.03,16,14,2,2097.4,0,59.89 1424 | 2017/11/23,2.03,19,14,2,2097.4,0,59.89 1425 | 2017/11/24,2.04,22,16,2,2097.4,0,59.89 1426 | 2017/11/25,1.95,23,18,2,2097.4,1,59.89 1427 | 2017/11/26,1.61,28,21,2,2097.4,1,59.89 1428 | 2017/11/27,1.92,26,19,2,2097.4,0,59.89 1429 | 2017/11/28,2.03,22,16,3,2097.4,0,59.89 1430 | 2017/11/29,2.06,23,15,3,2097.4,0,59.89 1431 | 2017/11/30,2.01,23,16,3,2097.4,0,59.89 1432 | 2017/12/1,1.94,22,16,3,2097.4,0,58.59 1433 | 2017/12/2,1.88,20,15,3,2097.4,1,58.59 1434 | 2017/12/3,1.53,21,14,3,2097.4,1,58.59 1435 | 2017/12/4,1.89,21,14,3,2097.4,0,58.59 1436 | 2017/12/5,2.0,20,10,3,2097.4,0,58.59 1437 | 2017/12/6,1.99,18,10,3,2097.4,0,58.59 1438 | 2017/12/7,2.01,21,11,3,2097.4,0,58.59 1439 | 2017/12/8,2.0,22,15,3,2097.4,0,58.59 1440 | 2017/12/9,1.93,20,14,3,2097.4,1,58.59 1441 | 2017/12/10,1.59,20,16,3,2097.4,1,58.59 1442 | 2017/12/11,1.89,20,16,3,2097.4,0,58.59 1443 | 2017/12/12,1.99,19,13,3,2097.4,0,58.59 1444 | 2017/12/13,2.01,16,12,3,2097.4,0,58.59 1445 | 2017/12/14,2.01,12,9,3,2097.4,0,58.59 1446 | 2017/12/15,2.0,16,6,3,2097.4,0,58.59 1447 | 2017/12/16,1.93,17,7,3,2097.4,1,58.59 1448 | 2017/12/17,1.61,18,7,3,2097.4,1,58.59 1449 | 2017/12/18,1.93,20,10,3,2097.4,0,58.59 1450 | 2017/12/19,2.03,20,12,3,2097.4,0,58.59 1451 | 2017/12/20,2.03,21,14,3,2097.4,0,58.59 1452 | 2017/12/21,2.03,22,15,3,2097.4,0,58.59 1453 | 2017/12/22,2.0,21,11,3,2097.4,0,58.59 1454 | 2017/12/23,1.94,22,13,3,2097.4,1,58.59 1455 | 2017/12/24,1.65,22,15,3,2097.4,1,58.59 1456 | 2017/12/25,1.91,21,16,3,2097.4,0,58.59 1457 | 2017/12/26,1.98,23,15,3,2097.4,0,58.59 1458 | 2017/12/27,1.99,22,13,3,2097.4,0,58.59 1459 | 2017/12/28,1.97,21,13,3,2097.4,0,58.59 1460 | 2017/12/29,1.91,22,12,3,2097.4,0,58.59 1461 | 2017/12/30,1.73,22,15,3,2097.4,1,58.59 1462 | 2017/12/31,1.29,24,18,3,2097.4,1,58.59 1463 | 2018/1/1,0.87,25,16,3,1797.58,2,59.34 1464 | 2018/1/2,1.64,21,18,3,1797.58,0,59.34 1465 | 2018/1/3,1.94,18,15,3,1797.58,0,59.34 1466 | 2018/1/4,1.98,17,13,3,1797.58,0,59.34 1467 | 2018/1/5,2.0,15,5,3,1797.58,0,59.34 1468 | 2018/1/6,1.97,8,5,3,1797.58,1,59.34 1469 | 2018/1/7,1.7,13,8,3,1797.58,1,59.34 1470 | 2018/1/8,1.97,16,7,3,1797.58,0,59.34 1471 | 2018/1/9,2.09,16,7,3,1797.58,0,59.34 1472 | 2018/1/10,2.08,17,7,3,1797.58,0,59.34 1473 | 2018/1/11,2.06,19,10,3,1797.58,0,59.34 1474 | 2018/1/12,2.06,20,11,3,1797.58,0,59.34 1475 | 2018/1/13,1.99,22,12,3,1797.58,1,59.34 1476 | 2018/1/14,1.68,24,14,3,1797.58,1,59.34 1477 | 2018/1/15,1.92,25,17,3,1797.58,0,59.34 1478 | 2018/1/16,2.0,24,16,3,1797.58,0,59.34 1479 | 2018/1/17,2.01,23,17,3,1797.58,0,59.34 1480 | 2018/1/18,2.05,24,17,3,1797.58,0,59.34 1481 | 2018/1/19,2.07,24,17,3,1797.58,0,59.34 1482 | 2018/1/20,1.95,22,15,3,1797.58,1,59.34 1483 | 2018/1/21,1.71,22,16,3,1797.58,1,59.34 1484 | 2018/1/22,1.96,22,14,3,1797.58,0,59.34 1485 | 2018/1/23,2.02,18,13,3,1797.58,0,59.34 1486 | 2018/1/24,2.02,16,8,3,1797.58,0,59.34 1487 | 2018/1/25,2.01,13,7,3,1797.58,0,59.34 1488 | 2018/1/26,1.99,8,6,3,1797.58,0,59.34 1489 | 2018/1/27,1.92,7,4,3,1797.58,1,59.34 1490 | 2018/1/28,1.69,7,5,3,1797.58,1,59.34 1491 | 2018/1/29,1.95,13,5,3,1797.58,0,59.34 1492 | 2018/1/30,2.04,11,5,3,1797.58,0,59.34 1493 | 2018/1/31,2.0,12,5,3,1797.58,0,59.34 1494 | 2018/2/1,1.92,14,7,3,1797.58,0,28.75 1495 | 2018/2/2,1.9,10,5,3,1797.58,0,28.75 1496 | 2018/2/3,1.78,15,6,3,1797.58,1,28.75 1497 | 2018/2/4,1.6,14,8,3,1797.58,1,28.75 1498 | 2018/2/5,1.63,17,9,3,1797.58,0,28.75 1499 | 2018/2/6,1.52,17,14,3,1797.58,0,28.75 1500 | 2018/2/7,1.4,18,14,3,1797.58,0,28.75 1501 | 2018/2/8,1.22,17,10,3,1797.58,0,28.75 1502 | 2018/2/9,1.06,18,9,3,1797.58,0,28.75 1503 | 2018/2/10,0.88,20,12,3,1797.58,1,28.75 1504 | 2018/2/11,0.75,20,12,3,1797.58,0,28.75 1505 | 2018/2/12,0.68,25,15,3,1797.58,0,28.75 1506 | 2018/2/13,0.6,25,15,3,1797.58,0,28.75 1507 | 2018/2/14,0.53,26,16,3,1797.58,0,28.75 1508 | 2018/2/15,0.45,23,17,3,1797.58,1,28.75 1509 | 2018/2/16,0.41,24,19,3,1797.58,2,28.75 1510 | 2018/2/17,0.42,28,17,3,1797.58,2,28.75 1511 | 2018/2/18,0.43,21,14,3,1797.58,2,28.75 1512 | 2018/2/19,0.48,14,12,3,1797.58,1,28.75 1513 | 2018/2/20,0.53,16,10,3,1797.58,1,28.75 1514 | 2018/2/21,0.6,20,14,3,1797.58,1,28.75 1515 | 2018/2/22,0.7,20,13,3,1797.58,0,28.75 1516 | 2018/2/23,0.88,23,15,3,1797.58,0,28.75 1517 | 2018/2/24,1.01,24,18,3,1797.58,0,28.75 1518 | 2018/2/25,1.08,25,17,3,1797.58,1,28.75 1519 | 2018/2/26,1.31,28,18,0,1797.58,0,28.75 1520 | 2018/2/27,1.45,28,20,0,1797.58,0,28.75 1521 | 2018/2/28,1.53,28,21,0,1797.58,0,28.75 1522 | 2018/3/1,1.61,27,22,0,1797.58,0,59.33 1523 | 2018/3/2,1.61,27,21,0,1797.58,0,59.33 1524 | 2018/3/3,1.67,22,20,0,1797.58,1,59.33 1525 | 2018/3/4,1.51,25,14,0,1797.58,1,59.33 1526 | 2018/3/5,1.82,13,10,0,1797.58,0,59.33 1527 | 2018/3/6,1.87,17,8,0,1797.58,0,59.33 1528 | 2018/3/7,1.9,22,11,0,1797.58,0,59.33 1529 | 2018/3/8,1.91,24,15,0,1797.58,0,59.33 1530 | 2018/3/9,1.92,25,14,0,1797.58,0,59.33 1531 | 2018/3/10,1.88,27,16,0,1797.58,1,59.33 1532 | 2018/3/11,1.59,26,17,0,1797.58,1,59.33 1533 | 2018/3/12,1.89,25,20,0,1797.58,0,59.33 1534 | 2018/3/13,2.0,27,19,0,1797.58,0,59.33 1535 | 2018/3/14,2.04,25,19,0,1797.58,0,59.33 1536 | 2018/3/15,2.05,26,19,0,1797.58,0,59.33 1537 | 2018/3/16,2.06,26,14,0,1797.58,0,59.33 1538 | 2018/3/17,1.99,20,13,0,1797.58,1,59.33 1539 | 2018/3/18,1.66,21,12,0,1797.58,1,59.33 1540 | 2018/3/19,2.0,24,14,0,1797.58,0,59.33 1541 | 2018/3/20,2.05,25,15,0,1797.58,0,59.33 1542 | 2018/3/21,2.04,26,16,0,1797.58,0,59.33 1543 | 2018/3/22,2.05,25,18,0,1797.58,0,59.33 1544 | 2018/3/23,2.06,25,18,0,1797.58,0,59.33 1545 | 2018/3/24,2.02,27,18,0,1797.58,1,59.33 1546 | 2018/3/25,1.68,28,19,0,1797.58,1,59.33 1547 | 2018/3/26,1.99,26,18,0,1797.58,0,59.33 1548 | 2018/3/27,2.1,28,20,0,1797.58,0,59.33 1549 | 2018/3/28,2.12,28,20,0,1797.58,0,59.33 1550 | 2018/3/29,2.12,29,20,0,1797.58,0,59.33 1551 | 2018/3/30,2.12,29,20,0,1797.58,0,59.33 1552 | 2018/3/31,2.0,29,20,0,1797.58,1,59.33 1553 | 2018/4/1,1.72,30,21,0,2071.02,1,60.53 1554 | 2018/4/2,2.06,29,15,0,2071.02,0,60.53 1555 | 2018/4/3,2.15,24,15,0,2071.02,0,60.53 1556 | 2018/4/4,2.07,19,11,0,2071.02,0,60.53 1557 | 2018/4/5,1.18,23,12,0,2071.02,2,60.53 1558 | 2018/4/6,1.54,27,16,0,2071.02,1,60.53 1559 | 2018/4/7,1.81,28,21,0,2071.02,1,60.53 1560 | 2018/4/8,1.84,28,21,0,2071.02,0,60.53 1561 | 2018/4/9,2.04,29,22,0,2071.02,0,60.53 1562 | 2018/4/10,2.13,29,24,0,2071.02,0,60.53 1563 | 2018/4/11,2.19,30,24,0,2071.02,0,60.53 1564 | 2018/4/12,2.24,18,16,0,2071.02,0,60.53 1565 | 2018/4/13,2.29,18,15,0,2071.02,0,60.53 1566 | 2018/4/14,2.24,24,16,0,2071.02,1,60.53 1567 | 2018/4/15,1.73,27,19,0,2071.02,1,60.53 1568 | 2018/4/16,1.99,27,20,0,2071.02,0,60.53 1569 | 2018/4/17,2.09,27,21,0,2071.02,0,60.53 1570 | 2018/4/18,2.14,29,22,0,2071.02,0,60.53 1571 | 2018/4/19,2.16,29,22,0,2071.02,0,60.53 1572 | 2018/4/20,2.18,29,22,0,2071.02,0,60.53 1573 | 2018/4/21,2.15,24,20,0,2071.02,1,60.53 1574 | 2018/4/22,1.82,26,21,0,2071.02,1,60.53 1575 | 2018/4/23,2.18,25,22,0,2071.02,0,60.53 1576 | 2018/4/24,2.26,24,22,0,2071.02,0,60.53 1577 | 2018/4/25,2.2,28,22,0,2071.02,0,60.53 1578 | 2018/4/26,2.19,28,22,0,2071.02,0,60.53 1579 | 2018/4/27,2.21,29,23,0,2071.02,0,60.53 1580 | 2018/4/28,2.18,31,24,0,2071.02,0,60.53 1581 | 2018/4/29,1.95,31,24,0,2071.02,1,60.53 1582 | 2018/4/30,1.6,30,24,0,2071.02,1,60.53 1583 | 2018/5/1,1.11,26,21,0,2071.02,2,77.33 1584 | 2018/5/2,2.0,29,24,0,2071.02,0,77.33 1585 | 2018/5/3,2.39,30,24,0,2071.02,0,77.33 1586 | 2018/5/4,2.27,30,24,0,2071.02,0,77.33 1587 | 2018/5/5,2.29,28,23,0,2071.02,1,77.33 1588 | 2018/5/6,2.04,26,21,0,2071.02,1,77.33 1589 | 2018/5/7,2.39,26,21,0,2071.02,0,77.33 1590 | 2018/5/8,2.38,28,22,0,2071.02,0,77.33 1591 | 2018/5/9,2.32,29,23,0,2071.02,0,77.33 1592 | 2018/5/10,2.25,31,24,0,2071.02,0,77.33 1593 | 2018/5/11,2.29,33,25,0,2071.02,0,77.33 1594 | 2018/5/12,2.29,33,25,0,2071.02,1,77.33 1595 | 2018/5/13,1.96,33,26,0,2071.02,1,77.33 1596 | 2018/5/14,2.48,32,26,0,2071.02,0,77.33 1597 | 2018/5/15,2.63,33,26,0,2071.02,0,77.33 1598 | 2018/5/16,2.62,34,26,0,2071.02,0,77.33 1599 | 2018/5/17,2.69,35,26,0,2071.02,0,77.33 1600 | 2018/5/18,2.76,34,26,0,2071.02,0,77.33 1601 | 2018/5/19,2.72,35,27,0,2071.02,1,77.33 1602 | 2018/5/20,2.34,35,27,0,2071.02,1,77.33 1603 | 2018/5/21,2.75,35,26,0,2071.02,0,77.33 1604 | 2018/5/22,2.88,33,26,0,2071.02,0,77.33 1605 | 2018/5/23,2.92,34,27,0,2071.02,0,77.33 1606 | 2018/5/24,2.93,34,27,0,2071.02,0,77.33 1607 | 2018/5/25,2.89,35,26,0,2071.02,0,77.33 1608 | 2018/5/26,2.84,36,26,0,2071.02,1,77.33 1609 | 2018/5/27,2.4,35,27,0,2071.02,1,77.33 1610 | 2018/5/28,2.74,36,28,0,2071.02,0,77.33 1611 | 2018/5/29,2.9,34,27,1,2071.02,0,77.33 1612 | 2018/5/30,2.95,32,26,1,2071.02,0,77.33 1613 | 2018/5/31,2.91,34,26,1,2071.02,0,77.33 1614 | 2018/6/1,2.79,33,26,1,2071.02,0,74.68 1615 | 2018/6/2,2.62,31,26,1,2071.02,1,74.68 1616 | 2018/6/3,2.23,28,25,1,2071.02,1,74.68 1617 | 2018/6/4,2.66,27,24,1,2071.02,0,74.68 1618 | 2018/6/5,2.7,26,24,1,2071.02,0,74.68 1619 | 2018/6/6,2.54,30,25,1,2071.02,0,74.68 1620 | 2018/6/7,2.44,33,25,1,2071.02,0,74.68 1621 | 2018/6/8,2.38,34,25,1,2071.02,0,74.68 1622 | 2018/6/9,2.35,30,25,1,2071.02,1,74.68 1623 | 2018/6/10,2.08,27,23,1,2071.02,1,74.68 1624 | 2018/6/11,2.53,30,25,1,2071.02,0,74.68 1625 | 2018/6/12,2.6,31,24,1,2071.02,0,74.68 1626 | 2018/6/13,2.4,31,25,1,2071.02,0,74.68 1627 | 2018/6/14,2.39,33,25,1,2071.02,0,74.68 1628 | 2018/6/15,2.41,33,26,1,2071.02,0,74.68 1629 | 2018/6/16,2.36,32,27,1,2071.02,1,74.68 1630 | 2018/6/17,1.96,33,27,1,2071.02,1,74.68 1631 | 2018/6/18,1.52,32,27,1,2071.02,2,74.68 1632 | 2018/6/19,2.47,30,27,1,2071.02,0,74.68 1633 | 2018/6/20,2.83,29,24,1,2071.02,0,74.68 1634 | 2018/6/21,2.85,31,25,1,2071.02,0,74.68 1635 | 2018/6/22,2.72,31,25,1,2071.02,0,74.68 1636 | 2018/6/23,2.47,33,25,1,2071.02,1,74.68 1637 | 2018/6/24,2.18,33,26,1,2071.02,1,74.68 1638 | 2018/6/25,2.55,34,26,1,2071.02,0,74.68 1639 | 2018/6/26,2.64,35,27,1,2071.02,0,74.68 1640 | 2018/6/27,2.71,35,27,1,2071.02,0,74.68 1641 | 2018/6/28,2.78,34,27,1,2071.02,0,74.68 1642 | 2018/6/29,2.84,34,27,1,2071.02,0,74.68 1643 | 2018/6/30,2.68,33,27,1,2071.02,1,74.68 1644 | 2018/7/1,2.17,33,27,1,2204.74,1,83.91 1645 | 2018/7/2,2.65,33,27,1,2204.74,0,83.91 1646 | 2018/7/3,2.77,33,28,1,2204.74,0,83.91 1647 | 2018/7/4,2.86,31,27,1,2204.74,0,83.91 1648 | 2018/7/5,2.86,31,26,1,2204.74,0,83.91 1649 | 2018/7/6,2.86,32,26,1,2204.74,0,83.91 1650 | 2018/7/7,2.65,34,26,1,2204.74,1,83.91 1651 | 2018/7/8,2.16,35,26,1,2204.74,1,83.91 1652 | 2018/7/9,2.62,34,26,1,2204.74,0,83.91 1653 | 2018/7/10,2.81,30,25,1,2204.74,0,83.91 1654 | 2018/7/11,2.9,30,24,1,2204.74,0,83.91 1655 | 2018/7/12,2.97,30,25,1,2204.74,0,83.91 1656 | 2018/7/13,2.84,31,26,1,2204.74,0,83.91 1657 | 2018/7/14,2.59,34,26,1,2204.74,1,83.91 1658 | 2018/7/15,2.07,31,27,1,2204.74,1,83.91 1659 | 2018/7/16,2.58,33,27,1,2204.74,0,83.91 1660 | 2018/7/17,2.87,34,27,1,2204.74,0,83.91 1661 | 2018/7/18,2.89,34,26,1,2204.74,0,83.91 1662 | 2018/7/19,2.9,34,26,1,2204.74,0,83.91 1663 | 2018/7/20,2.9,31,25,1,2204.74,0,83.91 1664 | 2018/7/21,2.82,31,25,1,2204.74,1,83.91 1665 | 2018/7/22,2.4,33,25,1,2204.74,1,83.91 1666 | 2018/7/23,2.82,32,26,1,2204.74,0,83.91 1667 | 2018/7/24,2.85,33,26,1,2204.74,0,83.91 1668 | 2018/7/25,2.84,34,26,1,2204.74,0,83.91 1669 | 2018/7/26,2.8,34,26,1,2204.74,0,83.91 1670 | 2018/7/27,2.81,34,26,1,2204.74,0,83.91 1671 | 2018/7/28,2.74,34,26,1,2204.74,1,83.91 1672 | 2018/7/29,2.3,33,26,1,2204.74,1,83.91 1673 | 2018/7/30,2.76,33,26,1,2204.74,0,83.91 1674 | 2018/7/31,2.85,32,26,1,2204.74,0,83.91 1675 | 2018/8/1,2.86,33,26,1,2204.74,0,82.96 1676 | 2018/8/2,2.94,34,26,1,2204.74,0,82.96 1677 | 2018/8/3,2.84,34,26,1,2204.74,0,82.96 1678 | 2018/8/4,2.74,34,26,1,2204.74,1,82.96 1679 | 2018/8/5,2.34,34,26,1,2204.74,1,82.96 1680 | 2018/8/6,2.79,35,26,1,2204.74,0,82.96 1681 | 2018/8/7,2.91,32,26,1,2204.74,0,82.96 1682 | 2018/8/8,2.93,28,25,1,2204.74,0,82.96 1683 | 2018/8/9,3.0,29,25,1,2204.74,0,82.96 1684 | 2018/8/10,2.87,32,26,1,2204.74,0,82.96 1685 | 2018/8/11,2.59,31,26,1,2204.74,1,82.96 1686 | 2018/8/12,2.12,32,26,1,2204.74,1,82.96 1687 | 2018/8/13,2.68,32,26,1,2204.74,0,82.96 1688 | 2018/8/14,2.82,31,26,1,2204.74,0,82.96 1689 | 2018/8/15,2.71,31,26,1,2204.74,0,82.96 1690 | 2018/8/16,2.67,32,26,1,2204.74,0,82.96 1691 | 2018/8/17,2.67,32,26,1,2204.74,0,82.96 1692 | 2018/8/18,2.57,33,26,1,2204.74,1,82.96 1693 | 2018/8/19,2.18,33,27,1,2204.74,1,82.96 1694 | 2018/8/20,2.61,33,26,1,2204.74,0,82.96 1695 | 2018/8/21,2.8,34,26,1,2204.74,0,82.96 1696 | 2018/8/22,2.78,35,27,1,2204.74,0,82.96 1697 | 2018/8/23,2.77,35,27,1,2204.74,0,82.96 1698 | 2018/8/24,2.81,31,25,1,2204.74,0,82.96 1699 | 2018/8/25,2.77,29,24,1,2204.74,1,82.96 1700 | 2018/8/26,2.3,29,24,1,2204.74,1,82.96 1701 | 2018/8/27,2.62,29,24,1,2204.74,0,82.96 1702 | 2018/8/28,2.6,30,25,1,2204.74,0,82.96 1703 | 2018/8/29,2.52,30,23,2,2204.74,0,82.96 1704 | 2018/8/30,2.4,31,25,2,2204.74,0,82.96 1705 | 2018/8/31,2.75,32,25,2,2204.74,0,82.96 1706 | -------------------------------------------------------------------------------- /elecnew.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import re\n", 10 | "import os \n", 11 | "import pandas as pd\n", 12 | "import numpy as np" 13 | ] 14 | }, 15 | { 16 | "cell_type": "code", 17 | "execution_count": 2, 18 | "metadata": {}, 19 | "outputs": [], 20 | "source": [ 21 | "path = 'C:\\\\jupyter-notebook-files\\\\py3\\\\供电量数据'\n", 22 | "files = os.listdir(path)" 23 | ] 24 | }, 25 | { 26 | "cell_type": "code", 27 | "execution_count": 3, 28 | "metadata": { 29 | "scrolled": false 30 | }, 31 | "outputs": [ 32 | { 33 | "name": "stdout", 34 | "output_type": "stream", 35 | "text": [ 36 | "['dataPreprocessing_drop_duplicates.csv', 'weather.csv', '月报表_月电量导出报表分析1401.xls', '月报表_月电量导出报表分析1402.xls', '月报表_月电量导出报表分析1403.xls', '月报表_月电量导出报表分析1404.xls', '月报表_月电量导出报表分析1405.xls', '月报表_月电量导出报表分析1406.xls', '月报表_月电量导出报表分析1407.xls', '月报表_月电量导出报表分析1408.xls', '月报表_月电量导出报表分析1409.xls', '月报表_月电量导出报表分析1410.xls', '月报表_月电量导出报表分析1411.xls', '月报表_月电量导出报表分析1412.xls', '月报表_月电量导出报表分析1501.xls', '月报表_月电量导出报表分析1502.xls', '月报表_月电量导出报表分析1503.xls', '月报表_月电量导出报表分析1504.xls', '月报表_月电量导出报表分析1505.xls', '月报表_月电量导出报表分析1506.xls', '月报表_月电量导出报表分析1507.xls', '月报表_月电量导出报表分析1508.xls', '月报表_月电量导出报表分析1509.xls', '月报表_月电量导出报表分析1510.xls', '月报表_月电量导出报表分析1511.xls', '月报表_月电量导出报表分析1512.xls', '月报表_月电量导出报表分析1601.xls', '月报表_月电量导出报表分析1602.xls', '月报表_月电量导出报表分析1603.xls', '月报表_月电量导出报表分析1604.xls', '月报表_月电量导出报表分析1605.xls', '月报表_月电量导出报表分析1606.xls', '月报表_月电量导出报表分析1607.xls', '月报表_月电量导出报表分析1608.xls', '月报表_月电量导出报表分析1609.xls', '月报表_月电量导出报表分析1610.xls', '月报表_月电量导出报表分析1611.xls', '月报表_月电量导出报表分析1612.xls', '月报表_月电量导出报表分析1701.xls', '月报表_月电量导出报表分析1702.xls', '月报表_月电量导出报表分析1703.xls', '月报表_月电量导出报表分析1704.xls', '月报表_月电量导出报表分析1705.xls', '月报表_月电量导出报表分析1706.xls', '月报表_月电量导出报表分析1707.xls', '月报表_月电量导出报表分析1708.xls', '月报表_月电量导出报表分析1709.xls', '月报表_月电量导出报表分析1710.xls']\n" 37 | ] 38 | } 39 | ], 40 | "source": [ 41 | "print (files)" 42 | ] 43 | }, 44 | { 45 | "cell_type": "code", 46 | "execution_count": 4, 47 | "metadata": {}, 48 | "outputs": [], 49 | "source": [ 50 | "os.chdir(path)" 51 | ] 52 | }, 53 | { 54 | "cell_type": "code", 55 | "execution_count": 5, 56 | "metadata": {}, 57 | "outputs": [ 58 | { 59 | "data": { 60 | "text/plain": [ 61 | "'C:\\\\jupyter-notebook-files\\\\py3\\\\供电量数据'" 62 | ] 63 | }, 64 | "execution_count": 5, 65 | "metadata": {}, 66 | "output_type": "execute_result" 67 | } 68 | ], 69 | "source": [ 70 | "os.getcwd()" 71 | ] 72 | }, 73 | { 74 | "cell_type": "code", 75 | "execution_count": 6, 76 | "metadata": {}, 77 | "outputs": [], 78 | "source": [ 79 | "data_csv = pd.read_csv('dataPreprocessing_drop_duplicates.csv')" 80 | ] 81 | }, 82 | { 83 | "cell_type": "code", 84 | "execution_count": 7, 85 | "metadata": { 86 | "scrolled": true 87 | }, 88 | "outputs": [ 89 | { 90 | "data": { 91 | "text/html": [ 92 | "
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DateDailyElectricity
02014/01/0192750.080000
12014/01/02129481.000000
22014/01/03161360.000000
32014/01/04148608.000000
42014/01/05138985.570786
52014/01/06156729.000000
62014/01/07165086.000000
72014/01/08165642.000000
82014/01/09164249.000000
92014/01/10160739.000000
102014/01/11160739.000000
112014/01/12141705.000000
122014/01/13151591.000000
132014/01/14160998.000000
142014/01/15159013.000000
152014/01/16156966.000000
162014/01/17154241.000000
172014/01/18146531.000000
182014/01/19129968.000000
192014/01/20131560.000000
202014/01/21131031.000000
212014/01/22124284.000000
222014/01/23115689.000000
232014/01/24103834.000000
242014/01/2589054.080000
252014/01/2676452.400000
262014/01/2767931.356070
272014/01/2862932.000000
282014/01/2954918.400000
292014/01/3046260.480000
.........
13702017/10/02128278.000000
13712017/10/03134228.000000
13722017/10/04124562.000000
13732017/10/05176747.000000
13742017/10/06222162.000000
13752017/10/07239224.000000
13762017/10/08234974.000000
13772017/10/09258824.000000
13782017/10/10270325.000000
13792017/10/11276078.000000
13802017/10/12269369.000000
13812017/10/13240332.000000
13822017/10/14215387.000000
13832017/10/15172456.000000
13842017/10/16200448.000000
13852017/10/17213012.000000
13862017/10/18218822.000000
13872017/10/19216934.000000
13882017/10/20214429.000000
13892017/10/21204973.000000
13902017/10/22171328.000000
13912017/10/23200922.000000
13922017/10/24212075.000000
13932017/10/25214232.000000
13942017/10/26215863.000000
13952017/10/27215716.000000
13962017/10/28207028.000000
13972017/10/29171648.000000
13982017/10/30197594.000000
13992017/10/31198831.000000
\n", 422 | "

1400 rows × 2 columns

\n", 423 | "
" 424 | ], 425 | "text/plain": [ 426 | " Date DailyElectricity\n", 427 | "0 2014/01/01 92750.080000\n", 428 | "1 2014/01/02 129481.000000\n", 429 | "2 2014/01/03 161360.000000\n", 430 | "3 2014/01/04 148608.000000\n", 431 | "4 2014/01/05 138985.570786\n", 432 | "5 2014/01/06 156729.000000\n", 433 | "6 2014/01/07 165086.000000\n", 434 | "7 2014/01/08 165642.000000\n", 435 | "8 2014/01/09 164249.000000\n", 436 | "9 2014/01/10 160739.000000\n", 437 | "10 2014/01/11 160739.000000\n", 438 | "11 2014/01/12 141705.000000\n", 439 | "12 2014/01/13 151591.000000\n", 440 | "13 2014/01/14 160998.000000\n", 441 | "14 2014/01/15 159013.000000\n", 442 | "15 2014/01/16 156966.000000\n", 443 | "16 2014/01/17 154241.000000\n", 444 | "17 2014/01/18 146531.000000\n", 445 | "18 2014/01/19 129968.000000\n", 446 | "19 2014/01/20 131560.000000\n", 447 | "20 2014/01/21 131031.000000\n", 448 | "21 2014/01/22 124284.000000\n", 449 | "22 2014/01/23 115689.000000\n", 450 | "23 2014/01/24 103834.000000\n", 451 | "24 2014/01/25 89054.080000\n", 452 | "25 2014/01/26 76452.400000\n", 453 | "26 2014/01/27 67931.356070\n", 454 | "27 2014/01/28 62932.000000\n", 455 | "28 2014/01/29 54918.400000\n", 456 | "29 2014/01/30 46260.480000\n", 457 | "... ... ...\n", 458 | "1370 2017/10/02 128278.000000\n", 459 | "1371 2017/10/03 134228.000000\n", 460 | "1372 2017/10/04 124562.000000\n", 461 | "1373 2017/10/05 176747.000000\n", 462 | "1374 2017/10/06 222162.000000\n", 463 | "1375 2017/10/07 239224.000000\n", 464 | "1376 2017/10/08 234974.000000\n", 465 | "1377 2017/10/09 258824.000000\n", 466 | "1378 2017/10/10 270325.000000\n", 467 | "1379 2017/10/11 276078.000000\n", 468 | "1380 2017/10/12 269369.000000\n", 469 | "1381 2017/10/13 240332.000000\n", 470 | "1382 2017/10/14 215387.000000\n", 471 | "1383 2017/10/15 172456.000000\n", 472 | "1384 2017/10/16 200448.000000\n", 473 | "1385 2017/10/17 213012.000000\n", 474 | "1386 2017/10/18 218822.000000\n", 475 | "1387 2017/10/19 216934.000000\n", 476 | "1388 2017/10/20 214429.000000\n", 477 | "1389 2017/10/21 204973.000000\n", 478 | "1390 2017/10/22 171328.000000\n", 479 | "1391 2017/10/23 200922.000000\n", 480 | "1392 2017/10/24 212075.000000\n", 481 | "1393 2017/10/25 214232.000000\n", 482 | "1394 2017/10/26 215863.000000\n", 483 | "1395 2017/10/27 215716.000000\n", 484 | "1396 2017/10/28 207028.000000\n", 485 | "1397 2017/10/29 171648.000000\n", 486 | "1398 2017/10/30 197594.000000\n", 487 | "1399 2017/10/31 198831.000000\n", 488 | "\n", 489 | "[1400 rows x 2 columns]" 490 | ] 491 | }, 492 | "execution_count": 7, 493 | "metadata": {}, 494 | "output_type": "execute_result" 495 | } 496 | ], 497 | "source": [ 498 | "data_csv" 499 | ] 500 | }, 501 | { 502 | "cell_type": "code", 503 | "execution_count": 8, 504 | "metadata": { 505 | "scrolled": true 506 | }, 507 | "outputs": [], 508 | "source": [ 509 | "data_clip = data_csv['Date'].str.split('/',expand=True)" 510 | ] 511 | }, 512 | { 513 | "cell_type": "code", 514 | "execution_count": 10, 515 | "metadata": {}, 516 | "outputs": [], 517 | "source": [ 518 | "df_year = data_clip[0]" 519 | ] 520 | }, 521 | { 522 | "cell_type": "code", 523 | "execution_count": 11, 524 | "metadata": {}, 525 | "outputs": [], 526 | "source": [ 527 | "df_month = data_clip[1]" 528 | ] 529 | }, 530 | { 531 | "cell_type": "code", 532 | "execution_count": 12, 533 | "metadata": {}, 534 | "outputs": [], 535 | "source": [ 536 | "df_day = data_clip[2]" 537 | ] 538 | }, 539 | { 540 | "cell_type": "code", 541 | "execution_count": 13, 542 | "metadata": {}, 543 | "outputs": [], 544 | "source": [ 545 | "data_csv['year'] = df_year\n", 546 | "data_csv['month'] = df_month\n", 547 | "data_csv['day'] = df_day" 548 | ] 549 | }, 550 | { 551 | "cell_type": "code", 552 | "execution_count": 19, 553 | "metadata": {}, 554 | "outputs": [], 555 | "source": [ 556 | "import matplotlib.pyplot as plt\n", 557 | "from sklearn.model_selection import cross_val_score\n", 558 | "from sklearn.model_selection import train_test_split" 559 | ] 560 | }, 561 | { 562 | "cell_type": "code", 563 | "execution_count": 76, 564 | "metadata": {}, 565 | "outputs": [], 566 | "source": [ 567 | "# x_train,x_test,y_train,y_test = train_test_split(X,Y,test_size=0.3,random_state=0)" 568 | ] 569 | }, 570 | { 571 | "cell_type": "code", 572 | "execution_count": 32, 573 | "metadata": { 574 | "scrolled": false 575 | }, 576 | "outputs": [], 577 | "source": [ 578 | "# %matplotlib inline" 579 | ] 580 | }, 581 | { 582 | "cell_type": "code", 583 | "execution_count": 77, 584 | "metadata": {}, 585 | "outputs": [], 586 | "source": [ 587 | "# #随机森林\n", 588 | "# #交叉验证\n", 589 | "# from sklearn.ensemble import RandomForestRegressor\n", 590 | "# max_features = [.1,.6,.7,.8,.9,1]\n", 591 | "# test_loss = []\n", 592 | "# test_accuarcy = []\n", 593 | "# for max_feat in max_features:\n", 594 | "# clf = RandomForestRegressor(n_estimators=100,max_features=max_feat)\n", 595 | "# loss = -cross_val_score(clf,X,Y,cv=5,scoring='neg_mean_squared_error')#loss 损失函数\n", 596 | "# test_loss.append(loss.mean())" 597 | ] 598 | }, 599 | { 600 | "cell_type": "code", 601 | "execution_count": 78, 602 | "metadata": {}, 603 | "outputs": [], 604 | "source": [ 605 | "# plt.subplot(1,2,1)\n", 606 | "# plt.plot(max_features,test_loss)\n", 607 | "# plt.title(\"RF\")" 608 | ] 609 | }, 610 | { 611 | "cell_type": "code", 612 | "execution_count": 79, 613 | "metadata": {}, 614 | "outputs": [], 615 | "source": [ 616 | "# clf_rf = RandomForestRegressor(n_estimators=100,max_features=.8)\n", 617 | "# clf_rf.fit(x_train,y_train)" 618 | ] 619 | }, 620 | { 621 | "cell_type": "code", 622 | "execution_count": 80, 623 | "metadata": {}, 624 | "outputs": [], 625 | "source": [ 626 | "# result = clf_rf.predict(x_test)" 627 | ] 628 | }, 629 | { 630 | "cell_type": "code", 631 | "execution_count": 81, 632 | "metadata": {}, 633 | "outputs": [], 634 | "source": [ 635 | "# clf_rf.score(x_test,y_test)" 636 | ] 637 | }, 638 | { 639 | "cell_type": "code", 640 | "execution_count": 82, 641 | "metadata": { 642 | "scrolled": true 643 | }, 644 | "outputs": [], 645 | "source": [ 646 | "# #1400 rows x 5 columns\n", 647 | "# data_csv.describe" 648 | ] 649 | }, 650 | { 651 | "cell_type": "code", 652 | "execution_count": 20, 653 | "metadata": { 654 | "scrolled": true 655 | }, 656 | "outputs": [], 657 | "source": [ 658 | "weather_csv = pd.read_csv('weather.csv',encoding = 'gb18030')" 659 | ] 660 | }, 661 | { 662 | "cell_type": "code", 663 | "execution_count": 22, 664 | "metadata": {}, 665 | "outputs": [], 666 | "source": [ 667 | "data_csv['weather'] = weather_csv['temperature'][:1400]" 668 | ] 669 | }, 670 | { 671 | "cell_type": "code", 672 | "execution_count": 24, 673 | "metadata": {}, 674 | "outputs": [], 675 | "source": [ 676 | "weather_clip = data_csv['weather'].str.split('/',expand=True)" 677 | ] 678 | }, 679 | { 680 | "cell_type": "code", 681 | "execution_count": 25, 682 | "metadata": {}, 683 | "outputs": [], 684 | "source": [ 685 | "weather_high = weather_clip[0].apply(lambda x:x.split('℃')[0]) \n", 686 | "weather_low = weather_clip[1].apply(lambda x:x.split('℃')[0])" 687 | ] 688 | }, 689 | { 690 | "cell_type": "code", 691 | "execution_count": 26, 692 | "metadata": {}, 693 | "outputs": [], 694 | "source": [ 695 | "data_csv['Maximum temperature'] = weather_high\n", 696 | "data_csv['Lowest temperature'] = weather_low" 697 | ] 698 | }, 699 | { 700 | "cell_type": "code", 701 | "execution_count": 27, 702 | "metadata": {}, 703 | "outputs": [ 704 | { 705 | "data": { 706 | "text/html": [ 707 | "
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DateDailyElectricityyearmonthdayweatherMaximum temperatureLowest temperature
02014/01/0192750.0800002014010120℃ / 12℃2012
12014/01/02129481.0000002014010222℃ / 14℃2214
22014/01/03161360.0000002014010322℃ / 14℃2214
32014/01/04148608.0000002014010421℃ / 12℃2112
42014/01/05138985.5707862014010520℃ / 13℃2013
52014/01/06156729.0000002014010622℃ / 14℃2214
62014/01/07165086.0000002014010722℃ / 15℃2215
72014/01/08165642.0000002014010820℃ / 11℃2011
82014/01/09164249.0000002014010918℃ / 11℃1811
92014/01/10160739.0000002014011017℃ / 13℃1713
102014/01/11160739.0000002014011120℃ / 13℃2013
112014/01/12141705.0000002014011220℃ / 9℃209
122014/01/13151591.0000002014011316℃ / 8℃168
132014/01/14160998.0000002014011416℃ / 7℃167
142014/01/15159013.0000002014011517℃ / 8℃178
152014/01/16156966.0000002014011618℃ / 10℃1810
162014/01/17154241.0000002014011720℃ / 10℃2010
172014/01/18146531.0000002014011820℃ / 9℃209
182014/01/19129968.0000002014011920℃ / 10℃2010
192014/01/20131560.0000002014012020℃ / 9℃209
202014/01/21131031.0000002014012119℃ / 8℃198
212014/01/22124284.0000002014012218℃ / 9℃189
222014/01/23115689.0000002014012318℃ / 9℃189
232014/01/24103834.0000002014012421℃ / 13℃2113
242014/01/2589054.0800002014012522℃ / 15℃2215
252014/01/2676452.4000002014012625℃ / 15℃2515
262014/01/2767931.3560702014012722℃ / 15℃2215
272014/01/2862932.0000002014012823℃ / 15℃2315
282014/01/2954918.4000002014012924℃ / 16℃2416
292014/01/3046260.4800002014013026℃ / 16℃2616
...........................
13702017/10/02128278.0000002017100232℃ / 27℃3227
13712017/10/03134228.0000002017100334℃ / 27℃3427
13722017/10/04124562.0000002017100431℃ / 25℃3125
13732017/10/05176747.0000002017100532℃ / 25℃3225
13742017/10/06222162.0000002017100633℃ / 26℃3326
13752017/10/07239224.0000002017100733℃ / 26℃3326
13762017/10/08234974.0000002017100834℃ / 27℃3427
13772017/10/09258824.0000002017100933℃ / 26℃3326
13782017/10/10270325.0000002017101033℃ / 26℃3326
13792017/10/11276078.0000002017101133℃ / 26℃3326
13802017/10/12269369.0000002017101232℃ / 26℃3226
13812017/10/13240332.0000002017101330℃ / 22℃3022
13822017/10/14215387.0000002017101427℃ / 22℃2722
13832017/10/15172456.0000002017101523℃ / 20℃2320
13842017/10/16200448.0000002017101624℃ / 20℃2420
13852017/10/17213012.0000002017101728℃ / 21℃2821
13862017/10/18218822.0000002017101829℃ / 22℃2922
13872017/10/19216934.0000002017101928℃ / 22℃2822
13882017/10/20214429.0000002017102029℃ / 21℃2921
13892017/10/21204973.0000002017102128℃ / 20℃2820
13902017/10/22171328.0000002017102226℃ / 17℃2617
13912017/10/23200922.0000002017102326℃ / 17℃2617
13922017/10/24212075.0000002017102428℃ / 20℃2820
13932017/10/25214232.0000002017102528℃ / 20℃2820
13942017/10/26215863.0000002017102628℃ / 20℃2820
13952017/10/27215716.0000002017102728℃ / 20℃2820
13962017/10/28207028.0000002017102829℃ / 19℃2919
13972017/10/29171648.0000002017102927℃ / 19℃2719
13982017/10/30197594.0000002017103026℃ / 16℃2616
13992017/10/31198831.0000002017103126℃ / 16℃2616
\n", 1409 | "

1400 rows × 8 columns

\n", 1410 | "
" 1411 | ], 1412 | "text/plain": [ 1413 | " Date DailyElectricity year month day weather \\\n", 1414 | "0 2014/01/01 92750.080000 2014 01 01 20℃ / 12℃ \n", 1415 | "1 2014/01/02 129481.000000 2014 01 02 22℃ / 14℃ \n", 1416 | "2 2014/01/03 161360.000000 2014 01 03 22℃ / 14℃ \n", 1417 | "3 2014/01/04 148608.000000 2014 01 04 21℃ / 12℃ \n", 1418 | "4 2014/01/05 138985.570786 2014 01 05 20℃ / 13℃ \n", 1419 | "5 2014/01/06 156729.000000 2014 01 06 22℃ / 14℃ \n", 1420 | "6 2014/01/07 165086.000000 2014 01 07 22℃ / 15℃ \n", 1421 | "7 2014/01/08 165642.000000 2014 01 08 20℃ / 11℃ \n", 1422 | "8 2014/01/09 164249.000000 2014 01 09 18℃ / 11℃ \n", 1423 | "9 2014/01/10 160739.000000 2014 01 10 17℃ / 13℃ \n", 1424 | "10 2014/01/11 160739.000000 2014 01 11 20℃ / 13℃ \n", 1425 | "11 2014/01/12 141705.000000 2014 01 12 20℃ / 9℃ \n", 1426 | "12 2014/01/13 151591.000000 2014 01 13 16℃ / 8℃ \n", 1427 | "13 2014/01/14 160998.000000 2014 01 14 16℃ / 7℃ \n", 1428 | "14 2014/01/15 159013.000000 2014 01 15 17℃ / 8℃ \n", 1429 | "15 2014/01/16 156966.000000 2014 01 16 18℃ / 10℃ \n", 1430 | "16 2014/01/17 154241.000000 2014 01 17 20℃ / 10℃ \n", 1431 | "17 2014/01/18 146531.000000 2014 01 18 20℃ / 9℃ \n", 1432 | "18 2014/01/19 129968.000000 2014 01 19 20℃ / 10℃ \n", 1433 | "19 2014/01/20 131560.000000 2014 01 20 20℃ / 9℃ \n", 1434 | "20 2014/01/21 131031.000000 2014 01 21 19℃ / 8℃ \n", 1435 | "21 2014/01/22 124284.000000 2014 01 22 18℃ / 9℃ \n", 1436 | "22 2014/01/23 115689.000000 2014 01 23 18℃ / 9℃ \n", 1437 | "23 2014/01/24 103834.000000 2014 01 24 21℃ / 13℃ \n", 1438 | "24 2014/01/25 89054.080000 2014 01 25 22℃ / 15℃ \n", 1439 | "25 2014/01/26 76452.400000 2014 01 26 25℃ / 15℃ \n", 1440 | "26 2014/01/27 67931.356070 2014 01 27 22℃ / 15℃ \n", 1441 | "27 2014/01/28 62932.000000 2014 01 28 23℃ / 15℃ \n", 1442 | "28 2014/01/29 54918.400000 2014 01 29 24℃ / 16℃ \n", 1443 | "29 2014/01/30 46260.480000 2014 01 30 26℃ / 16℃ \n", 1444 | "... ... ... ... ... .. ... \n", 1445 | "1370 2017/10/02 128278.000000 2017 10 02 32℃ / 27℃ \n", 1446 | "1371 2017/10/03 134228.000000 2017 10 03 34℃ / 27℃ \n", 1447 | "1372 2017/10/04 124562.000000 2017 10 04 31℃ / 25℃ \n", 1448 | "1373 2017/10/05 176747.000000 2017 10 05 32℃ / 25℃ \n", 1449 | "1374 2017/10/06 222162.000000 2017 10 06 33℃ / 26℃ \n", 1450 | "1375 2017/10/07 239224.000000 2017 10 07 33℃ / 26℃ \n", 1451 | "1376 2017/10/08 234974.000000 2017 10 08 34℃ / 27℃ \n", 1452 | "1377 2017/10/09 258824.000000 2017 10 09 33℃ / 26℃ \n", 1453 | "1378 2017/10/10 270325.000000 2017 10 10 33℃ / 26℃ \n", 1454 | "1379 2017/10/11 276078.000000 2017 10 11 33℃ / 26℃ \n", 1455 | "1380 2017/10/12 269369.000000 2017 10 12 32℃ / 26℃ \n", 1456 | "1381 2017/10/13 240332.000000 2017 10 13 30℃ / 22℃ \n", 1457 | "1382 2017/10/14 215387.000000 2017 10 14 27℃ / 22℃ \n", 1458 | "1383 2017/10/15 172456.000000 2017 10 15 23℃ / 20℃ \n", 1459 | "1384 2017/10/16 200448.000000 2017 10 16 24℃ / 20℃ \n", 1460 | "1385 2017/10/17 213012.000000 2017 10 17 28℃ / 21℃ \n", 1461 | "1386 2017/10/18 218822.000000 2017 10 18 29℃ / 22℃ \n", 1462 | "1387 2017/10/19 216934.000000 2017 10 19 28℃ / 22℃ \n", 1463 | "1388 2017/10/20 214429.000000 2017 10 20 29℃ / 21℃ \n", 1464 | "1389 2017/10/21 204973.000000 2017 10 21 28℃ / 20℃ \n", 1465 | "1390 2017/10/22 171328.000000 2017 10 22 26℃ / 17℃ \n", 1466 | "1391 2017/10/23 200922.000000 2017 10 23 26℃ / 17℃ \n", 1467 | "1392 2017/10/24 212075.000000 2017 10 24 28℃ / 20℃ \n", 1468 | "1393 2017/10/25 214232.000000 2017 10 25 28℃ / 20℃ \n", 1469 | "1394 2017/10/26 215863.000000 2017 10 26 28℃ / 20℃ \n", 1470 | "1395 2017/10/27 215716.000000 2017 10 27 28℃ / 20℃ \n", 1471 | "1396 2017/10/28 207028.000000 2017 10 28 29℃ / 19℃ \n", 1472 | "1397 2017/10/29 171648.000000 2017 10 29 27℃ / 19℃ \n", 1473 | "1398 2017/10/30 197594.000000 2017 10 30 26℃ / 16℃ \n", 1474 | "1399 2017/10/31 198831.000000 2017 10 31 26℃ / 16℃ \n", 1475 | "\n", 1476 | " Maximum temperature Lowest temperature \n", 1477 | "0 20 12 \n", 1478 | "1 22 14 \n", 1479 | "2 22 14 \n", 1480 | "3 21 12 \n", 1481 | "4 20 13 \n", 1482 | "5 22 14 \n", 1483 | "6 22 15 \n", 1484 | "7 20 11 \n", 1485 | "8 18 11 \n", 1486 | "9 17 13 \n", 1487 | "10 20 13 \n", 1488 | "11 20 9 \n", 1489 | "12 16 8 \n", 1490 | "13 16 7 \n", 1491 | "14 17 8 \n", 1492 | "15 18 10 \n", 1493 | "16 20 10 \n", 1494 | "17 20 9 \n", 1495 | "18 20 10 \n", 1496 | "19 20 9 \n", 1497 | "20 19 8 \n", 1498 | "21 18 9 \n", 1499 | "22 18 9 \n", 1500 | "23 21 13 \n", 1501 | "24 22 15 \n", 1502 | "25 25 15 \n", 1503 | "26 22 15 \n", 1504 | "27 23 15 \n", 1505 | "28 24 16 \n", 1506 | "29 26 16 \n", 1507 | "... ... ... \n", 1508 | "1370 32 27 \n", 1509 | "1371 34 27 \n", 1510 | "1372 31 25 \n", 1511 | "1373 32 25 \n", 1512 | "1374 33 26 \n", 1513 | "1375 33 26 \n", 1514 | "1376 34 27 \n", 1515 | "1377 33 26 \n", 1516 | "1378 33 26 \n", 1517 | "1379 33 26 \n", 1518 | "1380 32 26 \n", 1519 | "1381 30 22 \n", 1520 | "1382 27 22 \n", 1521 | "1383 23 20 \n", 1522 | "1384 24 20 \n", 1523 | "1385 28 21 \n", 1524 | "1386 29 22 \n", 1525 | "1387 28 22 \n", 1526 | "1388 29 21 \n", 1527 | "1389 28 20 \n", 1528 | "1390 26 17 \n", 1529 | "1391 26 17 \n", 1530 | "1392 28 20 \n", 1531 | "1393 28 20 \n", 1532 | "1394 28 20 \n", 1533 | "1395 28 20 \n", 1534 | "1396 29 19 \n", 1535 | "1397 27 19 \n", 1536 | "1398 26 16 \n", 1537 | "1399 26 16 \n", 1538 | "\n", 1539 | "[1400 rows x 8 columns]" 1540 | ] 1541 | }, 1542 | "execution_count": 27, 1543 | "metadata": {}, 1544 | "output_type": "execute_result" 1545 | } 1546 | ], 1547 | "source": [ 1548 | "data_csv" 1549 | ] 1550 | }, 1551 | { 1552 | "cell_type": "code", 1553 | "execution_count": 28, 1554 | "metadata": {}, 1555 | "outputs": [], 1556 | "source": [ 1557 | "X_GPR = data_csv[['year','month','day','Maximum temperature','Lowest temperature']].values\n", 1558 | "Y_GPR = data_csv[['DailyElectricity']].values" 1559 | ] 1560 | }, 1561 | { 1562 | "cell_type": "code", 1563 | "execution_count": 36, 1564 | "metadata": {}, 1565 | "outputs": [], 1566 | "source": [ 1567 | "# x_train_gpr,x_test_gpr,y_train_gpr,y_test_gpr = train_test_split(X_GPR,Y_GPR,test_size=0.3,random_state=0)\n", 1568 | "x_train_gpr = X_GPR[:1369]\n", 1569 | "y_train_gpr = Y_GPR[:1369]\n", 1570 | "x_test_gpr = X_GPR[1369:]\n", 1571 | "y_test_gpr = Y_GPR[1369:]" 1572 | ] 1573 | }, 1574 | { 1575 | "cell_type": "code", 1576 | "execution_count": 30, 1577 | "metadata": {}, 1578 | "outputs": [], 1579 | "source": [ 1580 | "from sklearn.gaussian_process import GaussianProcessRegressor \n", 1581 | "from sklearn.gaussian_process.kernels import RBF, ConstantKernel as C# REF就是高斯核函数 " 1582 | ] 1583 | }, 1584 | { 1585 | "cell_type": "code", 1586 | "execution_count": 47, 1587 | "metadata": {}, 1588 | "outputs": [], 1589 | "source": [ 1590 | "#核函数\n", 1591 | "kernel = C(0.1, (0.001,0.1))*RBF(0.5,(1e-4,1000))" 1592 | ] 1593 | }, 1594 | { 1595 | "cell_type": "code", 1596 | "execution_count": 48, 1597 | "metadata": {}, 1598 | "outputs": [], 1599 | "source": [ 1600 | "#创建高斯过程回归,并训练\n", 1601 | "# alpha就是添加到协方差矩阵对角线上的值，n_restarts_optimizer规定了优化过程的次数\n", 1602 | "reg = GaussianProcessRegressor(kernel=kernel,n_restarts_optimizer=10,alpha=0.1)" 1603 | ] 1604 | }, 1605 | { 1606 | "cell_type": "code", 1607 | "execution_count": 49, 1608 | "metadata": { 1609 | "scrolled": true 1610 | }, 1611 | "outputs": [ 1612 | { 1613 | "data": { 1614 | "text/plain": [ 1615 | "GaussianProcessRegressor(alpha=0.1, copy_X_train=True,\n", 1616 | " kernel=0.316**2 * RBF(length_scale=0.5),\n", 1617 | " n_restarts_optimizer=10, normalize_y=False,\n", 1618 | " optimizer='fmin_l_bfgs_b', random_state=None)" 1619 | ] 1620 | }, 1621 | "execution_count": 49, 1622 | "metadata": {}, 1623 | "output_type": "execute_result" 1624 | } 1625 | ], 1626 | "source": [ 1627 | "reg.fit(x_train_gpr,y_train_gpr)" 1628 | ] 1629 | }, 1630 | { 1631 | "cell_type": "code", 1632 | "execution_count": 34, 1633 | "metadata": {}, 1634 | "outputs": [], 1635 | "source": [ 1636 | "result_gpr = reg.predict(x_test_gpr)" 1637 | ] 1638 | }, 1639 | { 1640 | "cell_type": "code", 1641 | "execution_count": 50, 1642 | "metadata": {}, 1643 | "outputs": [ 1644 | { 1645 | "data": { 1646 | "text/plain": [ 1647 | "0.16670391864222822" 1648 | ] 1649 | }, 1650 | "execution_count": 50, 1651 | "metadata": {}, 1652 | "output_type": "execute_result" 1653 | } 1654 | ], 1655 | "source": [ 1656 | "reg.score(x_test_gpr,y_test_gpr)" 1657 | ] 1658 | }, 1659 | { 1660 | "cell_type": "code", 1661 | "execution_count": 193, 1662 | "metadata": {}, 1663 | "outputs": [], 1664 | "source": [ 1665 | "from mpl_toolkits.mplot3d import Axes3D#实现数据可视化3D " 1666 | ] 1667 | }, 1668 | { 1669 | "cell_type": "code", 1670 | "execution_count": 51, 1671 | "metadata": {}, 1672 | "outputs": [], 1673 | "source": [ 1674 | "from sklearn import preprocessing" 1675 | ] 1676 | }, 1677 | { 1678 | "cell_type": "code", 1679 | "execution_count": 52, 1680 | "metadata": {}, 1681 | "outputs": [], 1682 | "source": [ 1683 | "X_GPR_n = data_csv[['year','month','day','Maximum temperature','Lowest temperature']].values\n", 1684 | "Y_GPR_n = data_csv[['DailyElectricity']].values" 1685 | ] 1686 | }, 1687 | { 1688 | "cell_type": "code", 1689 | "execution_count": 53, 1690 | "metadata": {}, 1691 | "outputs": [], 1692 | "source": [ 1693 | "x_train_gpr_n = X_GPR_n[:1369]\n", 1694 | "y_train_gpr_n = Y_GPR_n[:1369]\n", 1695 | "x_test_gpr_n = X_GPR_n[1369:]\n", 1696 | "y_test_gpr_n = Y_GPR_n[1369:]" 1697 | ] 1698 | }, 1699 | { 1700 | "cell_type": "code", 1701 | "execution_count": 54, 1702 | "metadata": {}, 1703 | "outputs": [], 1704 | "source": [ 1705 | "scaler=preprocessing.StandardScaler()" 1706 | ] 1707 | }, 1708 | { 1709 | "cell_type": "code", 1710 | "execution_count": 55, 1711 | "metadata": {}, 1712 | "outputs": [ 1713 | { 1714 | "name": "stderr", 1715 | "output_type": "stream", 1716 | "text": [ 1717 | "C:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:475: DataConversionWarning: Data with input dtype object was converted to float64 by StandardScaler.\n", 1718 | " warnings.warn(msg, DataConversionWarning)\n" 1719 | ] 1720 | } 1721 | ], 1722 | "source": [ 1723 | "x_train_gpr_n_scaled = scaler.fit_transform(x_train_gpr_n)" 1724 | ] 1725 | }, 1726 | { 1727 | "cell_type": "code", 1728 | "execution_count": 56, 1729 | "metadata": { 1730 | "scrolled": true 1731 | }, 1732 | "outputs": [ 1733 | { 1734 | "name": "stdout", 1735 | "output_type": "stream", 1736 | "text": [ 1737 | "[-5.58053725e-14 -7.78534773e-17 1.29755795e-17 4.15218545e-17\n", 1738 | " 1.03804636e-17]\n", 1739 | "[1. 1. 1. 1. 1.]\n" 1740 | ] 1741 | } 1742 | ], 1743 | "source": [ 1744 | "print(x_train_gpr_n_scaled.mean(axis=0))\n", 1745 | "print(x_train_gpr_n_scaled.std(axis=0))" 1746 | ] 1747 | }, 1748 | { 1749 | "cell_type": "code", 1750 | "execution_count": 57, 1751 | "metadata": {}, 1752 | "outputs": [ 1753 | { 1754 | "name": "stderr", 1755 | "output_type": "stream", 1756 | "text": [ 1757 | "C:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:475: DataConversionWarning: Data with input dtype object was converted to float64 by StandardScaler.\n", 1758 | " warnings.warn(msg, DataConversionWarning)\n" 1759 | ] 1760 | } 1761 | ], 1762 | "source": [ 1763 | "x_test_gpr_n_scaled = scaler.transform(x_test_gpr_n)" 1764 | ] 1765 | }, 1766 | { 1767 | "cell_type": "code", 1768 | "execution_count": 58, 1769 | "metadata": {}, 1770 | "outputs": [ 1771 | { 1772 | "name": "stdout", 1773 | "output_type": "stream", 1774 | "text": [ 1775 | "[1.47835573 1.12837176 0.03155553 0.38522452 0.28486979]\n", 1776 | "[4.44089210e-16 2.22044605e-16 1.01681106e+00 4.92025061e-01\n", 1777 | " 5.94324574e-01]\n" 1778 | ] 1779 | } 1780 | ], 1781 | "source": [ 1782 | "print(x_test_gpr_n_scaled.mean(axis=0))\n", 1783 | "print(x_test_gpr_n_scaled.std(axis=0))" 1784 | ] 1785 | }, 1786 | { 1787 | "cell_type": "code", 1788 | "execution_count": 59, 1789 | "metadata": {}, 1790 | "outputs": [], 1791 | "source": [ 1792 | "from sklearn.gaussian_process import GaussianProcessRegressor \n", 1793 | "from sklearn.gaussian_process.kernels import RBF, ConstantKernel as C# REF就是高斯核函数 " 1794 | ] 1795 | }, 1796 | { 1797 | "cell_type": "code", 1798 | "execution_count": 72, 1799 | "metadata": {}, 1800 | "outputs": [], 1801 | "source": [ 1802 | "#核函数\n", 1803 | "kernel = C(0.01, (0.001,0.1))*RBF(0.5,(1e-4,10))\n", 1804 | "# kernel = RBF(1.0,(1e-4,10))\n", 1805 | "#创建高斯过程回归,并训练\n", 1806 | "# alpha就是添加到协方差矩阵对角线上的值，n_restarts_optimizer规定了优化过程的次数\n", 1807 | "reg = GaussianProcessRegressor(kernel=kernel,n_restarts_optimizer=10,alpha=0.1)" 1808 | ] 1809 | }, 1810 | { 1811 | "cell_type": "code", 1812 | "execution_count": 73, 1813 | "metadata": {}, 1814 | "outputs": [ 1815 | { 1816 | "data": { 1817 | "text/plain": [ 1818 | "GaussianProcessRegressor(alpha=0.1, copy_X_train=True,\n", 1819 | " kernel=0.1**2 * RBF(length_scale=0.5),\n", 1820 | " n_restarts_optimizer=10, normalize_y=False,\n", 1821 | " optimizer='fmin_l_bfgs_b', random_state=None)" 1822 | ] 1823 | }, 1824 | "execution_count": 73, 1825 | "metadata": {}, 1826 | "output_type": "execute_result" 1827 | } 1828 | ], 1829 | "source": [ 1830 | "reg.fit(x_train_gpr_n_scaled,y_train_gpr_n)" 1831 | ] 1832 | }, 1833 | { 1834 | "cell_type": "code", 1835 | "execution_count": 253, 1836 | "metadata": {}, 1837 | "outputs": [], 1838 | "source": [ 1839 | "result_gpr = reg.predict(x_test_gpr_n_scaled)" 1840 | ] 1841 | }, 1842 | { 1843 | "cell_type": "code", 1844 | "execution_count": 75, 1845 | "metadata": { 1846 | "scrolled": true 1847 | }, 1848 | "outputs": [ 1849 | { 1850 | "data": { 1851 | "text/plain": [ 1852 | "0.07158780091941086" 1853 | ] 1854 | }, 1855 | "execution_count": 75, 1856 | "metadata": {}, 1857 | "output_type": "execute_result" 1858 | } 1859 | ], 1860 | "source": [ 1861 | "reg.score(x_test_gpr_n_scaled,y_test_gpr_n)" 1862 | ] 1863 | }, 1864 | { 1865 | "cell_type": "code", 1866 | "execution_count": 67, 1867 | "metadata": {}, 1868 | "outputs": [ 1869 | { 1870 | "name": "stderr", 1871 | "output_type": "stream", 1872 | "text": [ 1873 | "C:\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", 1874 | " \n" 1875 | ] 1876 | }, 1877 | { 1878 | "data": { 1879 | "text/plain": [ 1880 | "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n", 1881 | " max_features=0.6, max_leaf_nodes=None,\n", 1882 | " min_impurity_decrease=0.0, min_impurity_split=None,\n", 1883 | " min_samples_leaf=1, min_samples_split=2,\n", 1884 | " min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1,\n", 1885 | " oob_score=False, random_state=None, verbose=0, warm_start=False)" 1886 | ] 1887 | }, 1888 | "execution_count": 67, 1889 | "metadata": {}, 1890 | "output_type": "execute_result" 1891 | } 1892 | ], 1893 | "source": [ 1894 | "clf_rf = RandomForestRegressor(n_estimators=100,max_features=.6)\n", 1895 | "clf_rf.fit(x_train_gpr_n_scaled,y_train_gpr_n)" 1896 | ] 1897 | }, 1898 | { 1899 | "cell_type": "code", 1900 | "execution_count": 68, 1901 | "metadata": {}, 1902 | "outputs": [ 1903 | { 1904 | "data": { 1905 | "text/plain": [ 1906 | "array([ 92750.08, 129481. , 161360. , ..., 281262. , 270311. ,\n", 1907 | " 236674. ])" 1908 | ] 1909 | }, 1910 | "execution_count": 68, 1911 | "metadata": {}, 1912 | "output_type": "execute_result" 1913 | } 1914 | ], 1915 | "source": [ 1916 | "y_train_gpr_n.ravel()" 1917 | ] 1918 | }, 1919 | { 1920 | "cell_type": "code", 1921 | "execution_count": 69, 1922 | "metadata": {}, 1923 | "outputs": [ 1924 | { 1925 | "data": { 1926 | "text/plain": [ 1927 | "-0.4370027408599726" 1928 | ] 1929 | }, 1930 | "execution_count": 69, 1931 | "metadata": {}, 1932 | "output_type": "execute_result" 1933 | } 1934 | ], 1935 | "source": [ 1936 | "clf_rf.score(x_test_gpr_n_scaled,y_test_gpr_n)" 1937 | ] 1938 | }, 1939 | { 1940 | "cell_type": "code", 1941 | "execution_count": 70, 1942 | "metadata": { 1943 | "scrolled": true 1944 | }, 1945 | "outputs": [ 1946 | { 1947 | "name": "stderr", 1948 | "output_type": "stream", 1949 | "text": [ 1950 | "C:\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", 1951 | " estimator.fit(X_train, y_train, **fit_params)\n", 1952 | "C:\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", 1953 | " estimator.fit(X_train, y_train, **fit_params)\n", 1954 | "C:\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", 1955 | " estimator.fit(X_train, y_train, **fit_params)\n", 1956 | "C:\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", 1957 | " estimator.fit(X_train, y_train, **fit_params)\n", 1958 | "C:\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", 1959 | " estimator.fit(X_train, y_train, **fit_params)\n", 1960 | "C:\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", 1961 | " estimator.fit(X_train, y_train, **fit_params)\n", 1962 | "C:\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", 1963 | " estimator.fit(X_train, y_train, **fit_params)\n", 1964 | "C:\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", 1965 | " estimator.fit(X_train, y_train, **fit_params)\n", 1966 | "C:\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", 1967 | " estimator.fit(X_train, y_train, **fit_params)\n", 1968 | "C:\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", 1969 | " estimator.fit(X_train, y_train, **fit_params)\n", 1970 | "C:\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", 1971 | " estimator.fit(X_train, y_train, **fit_params)\n", 1972 | "C:\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", 1973 | " estimator.fit(X_train, y_train, **fit_params)\n", 1974 | "C:\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", 1975 | " estimator.fit(X_train, y_train, **fit_params)\n", 1976 | "C:\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", 1977 | " estimator.fit(X_train, y_train, **fit_params)\n", 1978 | "C:\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", 1979 | " estimator.fit(X_train, y_train, **fit_params)\n", 1980 | "C:\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", 1981 | " estimator.fit(X_train, y_train, **fit_params)\n", 1982 | "C:\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", 1983 | " estimator.fit(X_train, y_train, **fit_params)\n", 1984 | "C:\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", 1985 | " estimator.fit(X_train, y_train, **fit_params)\n", 1986 | "C:\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", 1987 | " estimator.fit(X_train, y_train, **fit_params)\n", 1988 | "C:\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", 1989 | " estimator.fit(X_train, y_train, **fit_params)\n", 1990 | "C:\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", 1991 | " estimator.fit(X_train, y_train, **fit_params)\n", 1992 | "C:\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", 1993 | " estimator.fit(X_train, y_train, **fit_params)\n", 1994 | "C:\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", 1995 | " estimator.fit(X_train, y_train, **fit_params)\n", 1996 | "C:\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", 1997 | " estimator.fit(X_train, y_train, **fit_params)\n", 1998 | "C:\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", 1999 | " estimator.fit(X_train, y_train, **fit_params)\n", 2000 | "C:\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", 2001 | " estimator.fit(X_train, y_train, **fit_params)\n", 2002 | "C:\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", 2003 | " estimator.fit(X_train, y_train, **fit_params)\n", 2004 | "C:\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", 2005 | " estimator.fit(X_train, y_train, **fit_params)\n", 2006 | "C:\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", 2007 | " estimator.fit(X_train, y_train, **fit_params)\n", 2008 | "C:\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", 2009 | " estimator.fit(X_train, y_train, **fit_params)\n" 2010 | ] 2011 | } 2012 | ], 2013 | "source": [ 2014 | "from sklearn.ensemble import RandomForestRegressor\n", 2015 | "max_features = [.1,.6,.7,.8,.9,1]\n", 2016 | "test_loss = []\n", 2017 | "test_accuarcy = []\n", 2018 | "for max_feat in max_features:\n", 2019 | " clf = RandomForestRegressor(n_estimators=100,max_features=max_feat)\n", 2020 | " loss = -cross_val_score(clf,x_train_gpr_n_scaled,y_train_gpr_n,cv=5,scoring='neg_mean_squared_error')#loss 损失函数\n", 2021 | " test_loss.append(loss.mean())" 2022 | ] 2023 | }, 2024 | { 2025 | "cell_type": "code", 2026 | "execution_count": 83, 2027 | "metadata": {}, 2028 | "outputs": [], 2029 | "source": [ 2030 | "# plt.subplot(1,2,1)\n", 2031 | "# plt.plot(max_features,test_loss)\n", 2032 | "# plt.title(\"RF\")" 2033 | ] 2034 | } 2035 | ], 2036 | "metadata": { 2037 | "kernelspec": { 2038 | "display_name": "Python 3", 2039 | "language": "python", 2040 | "name": "python3" 2041 | }, 2042 | "language_info": { 2043 | "codemirror_mode": { 2044 | "name": "ipython", 2045 | "version": 3 2046 | }, 2047 | "file_extension": ".py", 2048 | "mimetype": "text/x-python", 2049 | "name": "python", 2050 | "nbconvert_exporter": "python", 2051 | "pygments_lexer": "ipython3", 2052 | "version": "3.6.3" 2053 | } 2054 | }, 2055 | "nbformat": 4, 2056 | "nbformat_minor": 2 2057 | } 2058 | --------------------------------------------------------------------------------