├── Figure_1.png ├── README.md ├── main.py └── As_train.csv /Figure_1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/w1ida/LSTM-example/HEAD/Figure_1.png -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # LSTM-example 2 | 3 | Train Score: 0.027 RMSE 4 | 5 | Test Score: 0.003 RMSE 6 | 7 | 蓝色是原始数据，绿色是对训练数据进行预测， 红色是对测试数据进行预测 8 | ![image](https://github.com/w1ida/LSTM-example/blob/master/Figure_1.png) 9 | -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | import numpy 2 | import matplotlib.pyplot as plt 3 | import matplotlib.dates as mdate 4 | from pandas import read_csv 5 | import pandas as pd 6 | import math 7 | from keras.models import Sequential 8 | from keras.layers import Dense 9 | from keras.layers import LSTM 10 | from sklearn.preprocessing import MinMaxScaler 11 | from sklearn.metrics import mean_squared_error 12 | 13 | 14 | # load the dataset 导入数据 15 | dataframe = read_csv('As_train.csv', engine='python', skipfooter=0) 16 | 17 | dataframe['date'] = pd.to_datetime(dataframe['date']) 18 | # dataframe.set_index('date', inplace=True) 19 | dataset = dataframe['As'].values 20 | dataset = numpy.array(dataset) 21 | dataset.resize(len(dataset), 1) 22 | datetimeIndex = pd.DatetimeIndex(dataframe['date']) 23 | 24 | ''' 25 | 数据转化: 26 | 27 | 将一列变成两列，第一列是 t 月的乘客数，第二列是 t+1 列的乘客数 28 | look_back 就是预测下一步所需要的 time steps： 29 | 30 | timesteps 就是 LSTM 认为每个输入数据与前多少个陆续输入的数据 31 | 有联系。例如具有这样用段序列数据 “…ABCDBCEDF…”，当 timesteps 32 | 为 3 时，在模型预测中如果输入数据为“D”，那么之前接收的数据如果 33 | 为“B”和“C”则此时的预测输出为 B 的概率更大，之前接收的数据如果 34 | 为“C”和“E”，则此时的预测输出为 F 的概率更大。 35 | ''' 36 | 37 | 38 | def create_dataset(dataset, look_back=1): 39 | dataX, dataY = [], [] 40 | for i in range(len(dataset) - look_back - 1): 41 | a = dataset[i:(i + look_back), 0] 42 | dataX.append(a) 43 | dataY.append(dataset[i + look_back, 0]) 44 | return numpy.array(dataX), numpy.array(dataY) 45 | 46 | 47 | # fix random seed for reproducibility 48 | numpy.random.seed(7) 49 | # normalize the dataset 50 | scaler = MinMaxScaler(feature_range=(0, 1)) 51 | dataset = scaler.fit_transform(dataset) 52 | 53 | ''' 54 | 当激活函数为 sigmoid 或者 tanh 时， 55 | 要把数据正则话，此时 LSTM 比较敏感 56 | 设定 70% 是训练数据，余下的是测试数据 57 | ''' 58 | # split into train and test sets 59 | train_size = int(len(dataset) * 0.7) 60 | test_size = len(dataset) - train_size 61 | train, test = dataset[0:train_size, :], dataset[train_size:len(dataset), :] 62 | # X=t and Y=t+1 时的数据，并且此时的维度为 [samples, features] 63 | # use this function to prepare the train and test datasets for modeling 64 | look_back = 1 65 | trainX, trainY = create_dataset(train, look_back) 66 | testX, testY = create_dataset(test, look_back) 67 | 68 | # 投入到 LSTM 的 X 需要有这样的结构： [samples, time steps, features]，所以做一下变换 69 | # reshape input to be [samples, time steps, features] 70 | trainX = numpy.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1])) 71 | testX = numpy.reshape(testX, (testX.shape[0], 1, testX.shape[1])) 72 | 73 | ''' 74 | 建立 LSTM 模型： 75 | 输入层有 1 个input，隐藏层有 4 个神经元，输出层就是预测一个值，激活函数用 sigmoid，迭代 100 次，batch size 为 1 76 | ''' 77 | # create and fit the LSTM network 78 | model = Sequential() 79 | model.add(LSTM(4, input_shape=(1, look_back))) 80 | model.add(Dense(1)) 81 | model.compile(loss='mean_squared_error', optimizer='adam') 82 | model.fit(trainX, trainY, epochs=100, batch_size=1, verbose=2) 83 | 84 | # 预测： 85 | # make predictions 86 | trainPredict = model.predict(trainX) 87 | testPredict = model.predict(testX) 88 | 89 | # invert predictions 计算误差之前要先把预测数据转换成同一单位 90 | trainPredict = scaler.inverse_transform(trainPredict) 91 | trainY = scaler.inverse_transform([trainY]) 92 | testPredict = scaler.inverse_transform(testPredict) 93 | testY = scaler.inverse_transform([testY]) 94 | # 计算 mean squared error 95 | trainScore = math.sqrt(mean_squared_error(trainY[0], trainPredict[:, 0])) 96 | print('Train Score: %.3f RMSE' % (trainScore)) 97 | testScore = math.sqrt(mean_squared_error(testY[0], testPredict[:, 0])) 98 | print('Test Score: %.3f RMSE' % (testScore)) 99 | 100 | # shift train predictions for plotting 101 | trainPredictPlot = numpy.empty_like(dataset) 102 | trainPredictPlot[:, :] = numpy.nan 103 | trainPredictPlot[look_back:len(trainPredict) + look_back, :] = trainPredict 104 | 105 | # shift test predictions for plotting 106 | testPredictPlot = numpy.empty_like(dataset) 107 | testPredictPlot[:, :] = numpy.nan 108 | testPredictPlot[len(trainPredict) + (look_back * 2) + 109 | 1:len(dataset) - 1, :] = testPredict 110 | 111 | # plot baseline and predictions 112 | fig1 = plt.figure(figsize=(15, 8)) 113 | ax1 = fig1.add_subplot(111) 114 | ax1.xaxis.set_major_formatter(mdate.DateFormatter('%Y-%m-%d')) # 设置时间标签显示格式 115 | # plt.xticks(rotation=45)#X轴文字旋转 116 | # 画出结果 117 | plt.plot(datetimeIndex, scaler.inverse_transform(dataset), color='blue') 118 | plt.plot(datetimeIndex, trainPredictPlot, color='green') 119 | plt.plot(datetimeIndex, testPredictPlot, color='red') 120 | plt.show() 121 | # print(create_dataset(dataset)) 122 | # plt.plot(dataset) 123 | # plt.show() 124 | # 参考 https://blog.csdn.net/aliceyangxi1987/article/details/73420583 125 | -------------------------------------------------------------------------------- /As_train.csv: -------------------------------------------------------------------------------- 1 | date,As 2 | 2012-9-14,0.001925 3 | 2012-9-15,0.00185833 4 | 2012-9-16,0.00205 5 | 2012-9-17,0.0013 6 | 2012-9-18,0.00175833 7 | 2012-9-19,0.00139167 8 | 2012-9-20,0.00083333 9 | 2012-9-21,0 10 | 2012-9-22,0.00051667 11 | 2012-9-23,0.000275 12 | 2012-9-24,0.00030833 13 | 2012-9-25,0 14 | 2012-9-26,0.00268462 15 | 2012-9-27,0.00433333 16 | 2012-9-28,0.00435 17 | 2012-9-29,0.00424167 18 | 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425 | 2014-1-15,0.2794 426 | 2014-1-16,0 427 | 2014-1-17,0 428 | 2014-1-18,0 429 | 2014-1-20,0.0036 430 | 2014-1-21,0.00323333 431 | 2014-1-22,0.00353333 432 | 2014-1-23,0.00321667 433 | 2014-1-24,0.00315 434 | 2014-1-25,0.00338333 435 | 2014-1-26,0.0032 436 | 2014-1-27,0.00286667 437 | 2014-1-28,0.00326 438 | 2014-1-29,0.00343333 439 | 2014-1-30,0.00331667 440 | 2014-1-31,0.00333333 441 | 2014-2-1,0.00315 442 | 2014-2-2,0.00313333 443 | 2014-2-3,0.00293333 444 | 2014-2-4,0.00276667 445 | 2014-2-5,0.00253333 446 | 2014-2-6,0.00253333 447 | 2014-2-7,0.0012 448 | 2014-2-8,0 449 | 2014-2-9,0 450 | 2014-2-10,0.00327143 451 | 2014-2-11,0.00451667 452 | 2014-2-12,0.00418333 453 | 2014-2-13,0.00421667 454 | 2014-2-14,0.00431667 455 | 2014-2-15,0.00426667 456 | 2014-2-16,0.00406667 457 | 2014-2-17,0.00415 458 | 2014-2-18,0.00376667 459 | 2014-2-19,0.00405 460 | 2014-2-20,0.00388333 461 | 2014-2-21,0.00388333 462 | 2014-2-22,0.0036 463 | 2014-2-23,0.00326667 464 | 2014-2-24,0.00363333 465 | 2014-2-25,0.00596667 466 | 2014-2-26,0.0061 467 | 2014-2-27,0.00573333 468 | 2014-2-28,0.0058 469 | 2014-3-1,0.00501667 470 | 2014-3-2,0.00523333 471 | 2014-3-3,0.00458333 472 | 2014-3-4,0.00526667 473 | 2014-3-5,0.0046 474 | 2014-3-6,0.0057 475 | 2014-3-7,0.00646667 476 | 2014-3-8,0.00611667 477 | 2014-3-9,0.00526667 478 | 2014-3-10,0.00593333 479 | 2014-3-11,0.00513333 480 | 2014-3-12,0.00565 481 | 2014-3-13,0.00608333 482 | 2014-3-14,0.00535 483 | 2014-3-15,0.00486667 484 | 2014-3-16,0.00465 485 | 2014-3-17,0.00475 486 | 2014-3-18,0.00456667 487 | 2014-3-19,0.00386667 488 | 2014-3-20,0.00481667 489 | 2014-3-21,0.00483333 490 | 2014-3-22,0.0045 491 | 2014-3-23,0.0048 492 | 2014-3-24,0.00415 493 | 2014-4-5,0.00575 494 | 2014-4-6,0.00613333 495 | 2014-4-7,0.00591667 496 | 2014-4-8,0.00131667 497 | 2014-4-9,0.00025 498 | 2014-4-10,0.00026667 499 | 2014-4-11,0.00028333 500 | 2014-4-12,0.00023333 501 | 2014-4-13,0.00018333 502 | 2014-4-14,0.0002 503 | 2014-4-15,0.00018333 504 | 2014-4-16,0.00016667 505 | 2014-4-17,0.00018333 506 | 2014-4-18,0.0002 507 | 2014-4-19,0.00018333 508 | 2014-4-20,0.00015 509 | 2014-4-21,0.0002 510 | 2014-4-22,0.00016667 511 | 2014-4-23,0 512 | 2014-4-24,0.0062 513 | 2014-4-25,0.00023333 514 | 2014-4-26,0.00045 515 | 2014-4-27,0.00036667 516 | 2014-4-28,0.00035 517 | 2014-4-29,0.00031667 518 | 2014-4-30,0.00036667 519 | 2014-5-1,0.00041667 520 | 2014-5-2,0.00036667 521 | 2014-5-3,0.00033333 522 | 2014-5-4,0.00036667 523 | 2014-5-5,0.00038333 524 | 2014-5-6,0.0004 525 | 2014-5-7,0.00222 526 | 2014-5-8,0.00245 527 | 2014-5-9,0.00243333 528 | 2014-5-10,0.00265 529 | 2014-5-11,0.00306667 530 | 2014-5-12,0.00335 531 | 2014-5-13,0.00288333 532 | 2014-5-14,0.00296667 533 | 2014-5-15,0.00261667 534 | 2014-5-16,0.00281667 535 | 2014-5-17,0.00503333 536 | 2014-5-18,0.00293333 537 | 2014-5-19,0.00245 538 | 2014-5-20,0.00214286 539 | 2014-5-21,0.00056667 540 | 2014-5-22,0.000025 541 | 2014-5-23,0.00138571 542 | 2014-5-24,0.00218333 543 | 2014-5-25,0.00430833 544 | 2014-5-26,0.00495 545 | 2014-5-27,0.004875 546 | 2014-5-28,0.006075 547 | 2014-5-29,0.00539167 548 | 2014-5-30,0.00475 549 | 2014-5-31,0.00475 550 | 2014-6-1,0.00383636 551 | 2014-6-2,0.00210833 552 | 2014-6-3,0.002525 553 | 2014-6-4,0.00209167 554 | 2014-6-5,0.00179167 555 | 2014-6-6,0.00175833 556 | 2014-6-7,0.00119167 557 | 2014-6-8,0.00223333 558 | 2014-6-9,0.00195 559 | 2014-6-10,0.00188333 560 | 2014-6-11,0.003125 561 | 2014-6-12,0.00184167 562 | 2014-6-13,0.00179167 563 | 2014-6-14,0.0016 564 | 2014-6-15,0.00150833 565 | 2014-6-16,0.00166667 566 | 2014-6-17,0.0016125 567 | 2014-6-18,0.00128333 568 | 2014-6-19,0.00292 569 | 2014-6-20,0.00316667 570 | 2014-6-21,0.00368333 571 | 2014-6-22,0.00421667 572 | 2014-6-23,0.00277143 573 | 2014-6-24,0.00266667 574 | 2014-6-25,0.00276667 575 | 2014-6-26,0.0029 576 | 2014-6-27,0.00255 577 | 2014-6-28,0.002925 578 | 2014-6-29,0.00308333 579 | 2014-6-30,0.00265 580 | 2014-7-1,0.00145 581 | 2014-7-2,0.00333333 582 | 2014-7-3,0.00498333 583 | 2014-7-4,0.00445 584 | 2014-7-5,0.00433333 585 | 2014-7-6,0.00123333 586 | 2014-7-7,0.0008 587 | 2014-7-8,0.00256667 588 | 2014-7-9,0.00291667 589 | 2014-7-10,0.00285 590 | 2014-7-11,0.00231667 591 | 2014-7-12,0.00242 592 | 2014-7-14,0.0026 593 | 2014-7-15,0.00238333 594 | 2014-7-16,0.00218333 595 | 2014-7-17,0.00205 596 | 2014-7-18,0.00221667 597 | 2014-7-19,0.00235 598 | 2014-7-20,0.00216667 599 | 2014-7-21,0.00203333 600 | 2014-7-22,0.00196667 601 | 2014-7-23,0.00185 602 | 2014-7-24,0.00176667 603 | 2014-7-25,0.00176667 604 | 2014-7-26,0.00161667 605 | 2014-7-27,0.00203333 606 | 2014-7-28,0.00208333 607 | 2014-7-29,0.00206667 608 | 2014-7-30,0.00176667 609 | 2014-7-31,0.00165 610 | 2014-8-1,0.00163333 611 | 2014-8-2,0.00168333 612 | 2014-8-3,0.00161667 613 | 2014-8-4,0.00151667 614 | 2014-8-5,0.00135 615 | 2014-8-6,0.00143333 616 | 2014-8-7,0.00146667 617 | 2014-8-8,0.0012 618 | 2014-8-11,0.00022 619 | 2014-8-12,0.00081429 620 | 2014-8-13,0.00103333 621 | 2014-8-14,0.00111667 622 | 2014-8-15,0.00091667 623 | 2014-8-16,0.00103333 624 | 2014-8-17,0.00078333 625 | 2014-8-18,0.00058333 626 | 2014-8-19,0.0004 627 | 2014-8-20,0.0130875 628 | 2014-8-21,0.01341667 629 | 2014-8-22,0.01345 630 | 2014-8-23,0.01285 631 | 2014-8-24,0.0142 632 | 2014-8-25,0.01303 633 | 2014-8-26,0.01263333 634 | 2014-8-27,0.01413333 635 | 2014-8-28,0.01533333 636 | 2014-8-29,0.01513333 637 | 2014-8-30,0.01461667 638 | 2014-8-31,0.01465 639 | 2014-9-1,0.01406667 640 | 2014-9-2,0.0143 641 | 2014-9-3,0.01807778 642 | 2014-9-4,0.025625 643 | 2014-9-5,0.019025 644 | 2014-9-10,0.01199 645 | 2014-9-11,0.01794286 646 | 2014-9-12,0.01763333 647 | 2014-9-13,0.006375 648 | 2014-9-14,0.01146667 649 | 2014-9-15,0.0073875 650 | 2014-9-16,0.00963333 651 | 2014-9-17,0.00906667 652 | 2014-9-18,0.00320625 653 | 2014-9-19,0.01902857 654 | 2014-9-20,0.0043 655 | 2014-9-21,0.0046 656 | 2014-9-22,0.00176 657 | 2014-9-23,0.00055 658 | 2014-9-24,0.00198333 659 | 2014-9-25,0.002 660 | 2014-9-26,0.0028 661 | 2014-9-27,0.00366667 662 | 2014-9-28,0.00183333 663 | 2014-9-29,0.00128333 664 | 2014-9-30,0.00116667 665 | 2014-10-1,0.00088333 666 | 2014-10-2,0.00056667 667 | 2014-10-3,0.00188333 668 | 2014-10-4,0.00091667 669 | 2014-10-5,0.00161667 670 | 2014-10-6,0.00146667 671 | 2014-10-7,0.00046667 672 | 2014-10-8,0 673 | 2014-10-9,0.00547143 674 | 2014-10-10,0.0076 675 | 2014-10-11,0.00676667 676 | 2014-10-12,0.0071 677 | 2014-10-13,0.0057 678 | 2014-10-14,0.0068 679 | 2014-10-15,0.00671667 680 | 2014-10-16,0.00675 681 | 2014-10-17,0.00726667 682 | 2014-10-18,0.00718333 683 | 2014-10-19,0.00753333 684 | 2014-10-20,0.0068 685 | 2014-10-21,0.00738333 686 | 2014-10-22,0.00771667 687 | 2014-10-23,0.00716667 688 | 2014-10-24,0.0071 689 | 2014-10-25,0.00688333 690 | 2014-10-26,0.0066 691 | 2014-10-27,0.00693333 692 | 2014-10-28,0.00678333 693 | 2014-10-29,0.0069 694 | 2014-10-30,0.00666667 695 | 2014-10-31,0.00603333 696 | 2014-11-1,0.00616667 697 | 2014-11-2,0.00548333 698 | 2014-11-3,0.00513333 699 | 2014-11-4,0.00506667 700 | 2014-11-5,0.00406667 701 | 2014-11-6,0.00411667 702 | 2014-11-7,0.00466667 703 | 2014-11-8,0.00513333 704 | 2014-11-9,0.00503333 705 | 2014-11-10,0.00465 706 | 2014-11-11,0.00433333 707 | 2014-11-12,0.00515 708 | 2014-11-19,0.00426 709 | 2014-11-20,0.00366667 710 | 2014-11-22,0.00463333 711 | 2014-11-23,0.00445 712 | 2014-11-24,0.00366667 713 | 2014-11-25,0.0033 714 | 2014-11-26,0.00431667 715 | 2014-11-27,0.00403333 716 | 2014-11-28,0.00263333 717 | 2014-11-29,0.00256667 718 | 2014-11-30,0.00201667 719 | 2014-12-1,0.00273333 720 | 2014-12-2,0.00336667 721 | 2014-12-3,0.0114875 722 | 2014-12-4,0.0075 723 | 2014-12-5,0.00561429 724 | 2014-12-6,0.00648333 725 | 2014-12-7,0.0058 726 | 2014-12-8,0.00565 727 | 2014-12-9,0.00568333 728 | 2014-12-10,0.00603333 729 | 2014-12-11,0.00573333 730 | 2014-12-12,0.0056 731 | 2014-12-13,0.00595 732 | 2014-12-14,0.00595 733 | 2014-12-18,0.00426 734 | 2014-12-19,0.00458333 735 | 2014-12-20,0.00421667 736 | 2014-12-21,0.00421667 737 | 2014-12-22,0.0043 738 | 2014-12-23,0.00436667 739 | 2014-12-24,0.00455 740 | 2014-12-25,0.00438333 741 | 2014-12-26,0.00411667 742 | 2014-12-27,0.00471667 743 | 2014-12-28,0.00446667 744 | 2014-12-29,0.001 745 | 2014-12-30,0.00341667 746 | 2014-12-31,0.00333333 747 | 2015-1-1,0.0023 748 | 2015-1-2,0.00263333 749 | 2015-1-3,0.00311667 750 | 2015-1-4,0.00283333 751 | 2015-1-5,0.00225 752 | 2015-1-6,0.00216667 753 | 2015-1-7,0.00208333 754 | 2015-1-8,0.00223333 755 | 2015-1-9,0.00185 756 | 2015-1-10,0.00185 757 | 2015-1-11,0.00198333 758 | 2015-1-12,0.00185 759 | 2015-1-13,0.00202 760 | 2015-1-21,0.001875 761 | 2015-1-22,0.00178333 762 | 2015-1-23,0.00181667 763 | 2015-1-24,0.00181667 764 | 2015-1-25,0.0019 765 | 2015-1-26,0.00173333 766 | 2015-1-27,0.00078333 767 | 2015-1-28,0.00475556 768 | 2015-1-29,0.00521667 769 | 2015-1-30,0.00471667 770 | 2015-1-31,0.00483333 771 | 2015-2-1,0.00508333 772 | 2015-2-2,0.00954444 773 | 2015-2-3,0.00528333 774 | 2015-2-4,0.00535 775 | 2015-2-5,0.00496667 776 | 2015-2-6,0.00511667 777 | 2015-2-7,0.00425 778 | 2015-2-8,0.00428333 779 | 2015-2-9,0.00373333 780 | 2015-2-10,0.0082 781 | 2015-2-11,0.00431667 782 | 2015-2-12,0.00438333 783 | 2015-2-13,0.0046 784 | 2015-2-14,0.00411667 785 | 2015-2-15,0.00511667 786 | 2015-2-16,0.00501667 787 | 2015-2-17,0.00235 788 | 2015-2-18,0.00112143 789 | 2015-2-19,0.00366667 790 | 2015-2-20,0.00393333 791 | 2015-2-21,0.00333333 792 | 2015-2-22,0.00185 793 | 2015-2-23,0.00115 794 | 2015-2-24,0.00144375 795 | 2015-2-25,0.00375556 796 | 2015-2-26,0.00405 797 | 2015-2-27,0.0045 798 | 2015-2-28,0.00336667 799 | 2015-3-1,0.003 800 | 2015-3-2,0.01734167 801 | 2015-3-3,0.0036 802 | 2015-3-4,0.00336667 803 | 2015-3-5,0.00295 804 | 2015-3-6,0.01261111 805 | 2015-3-7,0.0018 806 | 2015-3-8,0.00208333 807 | 2015-3-9,0.00213333 808 | 2015-3-10,0.00208333 809 | 2015-4-16,0.00115 810 | 2015-4-17,0.00095 811 | 2015-4-18,0.00063333 812 | 2015-4-19,0.0005625 813 | 2015-4-20,0.000675 814 | 2015-4-21,0.00364545 815 | 2015-4-22,0.00178333 816 | 2015-4-23,0.00156667 817 | 2015-4-24,0.00156667 818 | 2015-4-25,0.00353333 819 | 2015-4-26,0.00393333 820 | 2015-4-27,0.00406667 821 | 2015-4-28,0.01286667 822 | 2015-4-29,0.00153333 823 | 2015-4-30,0.00148333 824 | 2015-5-1,0.00143333 825 | 2015-5-2,0.00131667 826 | 2015-5-3,0.00141667 827 | 2015-5-4,0.00287 828 | 2015-5-5,0.00146667 829 | 2015-5-6,0.00161667 830 | 2015-5-7,0.00158333 831 | 2015-5-8,0.00158333 832 | 2015-5-9,0.00161667 833 | 2015-5-10,0.00166667 834 | 2015-5-11,0.00606667 835 | 2015-5-12,0.00203333 836 | 2015-5-13,0.00223333 837 | 2015-5-14,0.002 838 | 2015-5-15,0.00286667 839 | 2015-5-16,0.003 840 | 2015-5-19,0.00595 841 | 2015-5-20,0.00575 842 | 2015-5-21,0.00998333 843 | 2015-5-22,0.00446667 844 | 2015-5-23,0.00358333 845 | 2015-5-24,0.00318333 846 | 2015-5-25,0.00584444 847 | 2015-5-26,0.00286667 848 | 2015-5-27,0.00263333 849 | 2015-5-28,0.00188333 850 | 2015-5-29,0.01168333 851 | 2015-5-30,0.00221667 852 | 2015-5-31,0.00201667 853 | 2015-6-1,0.00195 854 | 2015-6-2,0.0019375 855 | 2015-6-3,0.00167143 856 | 2015-6-4,0.0017 857 | 2015-6-5,0.00106667 858 | 2015-6-6,0.00131667 859 | 2015-6-7,0.00203333 860 | 2015-6-8,0.0053 861 | 2015-6-9,0.00436364 862 | 2015-6-10,0.00153333 863 | 2015-6-11,0.0085 864 | 2015-6-12,0.00955556 865 | 2015-6-13,0.00183333 866 | 2015-6-14,0.00261667 867 | 2015-6-15,0.0101 868 | 2015-6-16,0.00333333 869 | 2015-6-17,0.00293333 870 | 2015-6-18,0.00148333 871 | 2015-6-19,0.0014 872 | 2015-6-20,0.00082857 873 | 2015-6-21,0.0012125 874 | 2015-6-22,0.00161667 875 | 2015-6-23,0.00573333 876 | 2015-6-24,0.0012 877 | 2015-6-25,0.00141429 878 | 2015-6-26,0.0010875 879 | 2015-6-27,0.00091667 880 | 2015-6-28,0.0007875 881 | 2015-6-29,0.00066667 882 | 2015-6-30,0.0016125 883 | 2015-7-1,0.00988182 884 | 2015-7-2,0.00122857 885 | 2015-7-3,0.0020125 886 | 2015-7-4,0.00806667 887 | 2015-7-5,0.003075 888 | 2015-7-6,0.00471111 889 | 2015-7-7,0.0017 890 | 2015-7-8,0.00165714 891 | 2015-7-9,0.00185 892 | 2015-7-10,0.00248333 893 | 2015-7-11,0.00263333 894 | 2015-7-12,0.0012 895 | 2015-7-13,0.00559091 896 | 2015-7-14,0.0018 897 | 2015-7-15,0.00188333 898 | 2015-7-16,0.00142857 899 | 2015-7-17,0.00125714 900 | 2015-7-18,0.00105 901 | 2015-7-19,0.00081667 902 | 2015-7-20,0.01327778 903 | --------------------------------------------------------------------------------