我正在创建一个神经网络,目前正在处理train, test split
部分,但是我遇到了IndexError: too many indices for array
的错误。我的代码是:
import csv
import math
import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense
import datetime
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
X1 = Values[1:16801] #16,800个值
Y1 = P1[1:16801]#16,800个值
train_size = int(len(X1) * 0.67)
test_size = len(X1) - train_size
train, test = X1[0:train_size,], X1[train_size:len(X1),]
def Data(X1, look_back=1):
dataX, dataY = [], []
for i in range(len(X1)-look_back-1):
a = X1[i:(i+look_back), 0]
dataX.append(a)
dataY.append(Y1[i + look_back, 0])
return numpy.array(dataX), numpy.array(dataY)
look_back = 1
trainX, testX = Data(train, look_back)
testX, testY = Data(test, look_back)
look_back = 1
trainX, testX = Data(train, look_back)
testX, testY = Data(test, look_back)
我的X1有16,800个值,看起来像这样:
[0.03454225 0.02062136 0.00186715 ... 0.92857565 0.64930691 0.20325924]
我的Y1数据看起来像这样: [ 2.25226244 1.44078451 0.99174488 ... 12.8397099 9.75722427 7.98525797]
我的错误跟踪信息是:
IndexError Traceback (most recent call last)
<ipython-input-11-afedcaa56e0b> in <module>()
86
87 look_back = 1
---> 88 trainX, testX = Data_split(train, look_back)
89
90 testX, testY = Data_split(test, look_back)
<ipython-input-11-afedcaa56e0b> in Data(X1, look_back)
78 dataX, dataY = [], []
79 for i in range(len(X1)-look_back-1):
---> 80 a = X1[i:(i+look_back), 0]
81 dataX.append(a)
82 dataY.append(Y1[i + look_back, 0])
IndexError: too many indices for array
我之前问了一个非常相似的问题,得到了一个答案,但遗憾的是我无法将那个解决方案应用到这个错误上
回答:
问题出在一个数组的维度上。你试图使用多个维度的索引来访问不存在的元素。请看第80行。
a = X1[i:(i+look_back), 0] 在你的情况下,度量只是单维的。
样本二维度量表示(,)
“,” 是对具有行和列的二维数组的引用,但不幸的是,你的X1是一个ndarray。
[0.03454225 0.02062136 0.00186715 ... 0.92857565 0.64930691 0.20325924]
类似的问题示例:-
>>> np.ndarray(4)
array([2.0e-323, 1.5e-323, 2.0e-323, 1.5e-323])
>>> a[1:2,0]
Traceback (most recent call last):
File "<pyshell#38>", line 1, in <module>
a[1:2,0]
IndexError: too many indices for array
>>> a[1:2]
array([-2.68156159e+154])
>>>