Question

我认为这是一个简单的问题，但对我而言不是（df中有一个表：

Date        X1  X2  Y1
07.02.2019  5   1   1
08.02.2019  6   2   1
09.02.2019  1   3   0
10.02.2019  4   4   1
11.02.2019  1   1   0
12.02.2019  4   2   1
13.02.2019  5   5   1
14.02.2019  6   5   1
15.02.2019  1   1   0
16.02.2019  4   5   1
17.02.2019  1   2   0
18.02.2019  1   1   
19.02.2019  2   1   
20.02.2019  3   2   
21.02.2019  4   14

我需要根据参数X1和X2为Y1建立一个神经网络，然后将其应用于日期大于17.02.2019的行，并将网络预测结果保存在单独的df2中

 import pandas as pd
    import numpy as np
    import re
    from sklearn.neural_network import MLPClassifier 

    df = pd.read_csv("ob.csv", encoding = 'cp1251', sep = ';')
    df['Date'] = pd.to_datetime(df['Date'], format='%d.%m.%Y')
    startdate = pd.to_datetime('2019-02-17') 


    X = ['X1', 'X2'] ????
    y = ['Y1'] ????
    clf = MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1) 
    clf.fit(x, y) 
    clf.predict(???????)  ????? df2 = ????

在哪里？ -我不知道如何正确设置条件

Answer 1

 #args is parsed from the command line
 #file is an exogenous variable
 with open(args.inPath + file, "r") as fpIn:
   with open(args.outPath + file, "w") as fpOut:
     for line in fpIn:
       if re.match(some match): canWrite = True
       if re.match(some match 2): break
       if canWrite: fpOut.write(line)

学习神经网络并保存结果

1 个答案: