Я запускаю следующий скрипт Python для загрузки моего CSV-файла

Question

Однако я получил такие ошибки, как:

Traceback (most recent call last)
<ipython-input-1-56b4f2337f02> in <module>

         47 testPredict = model.predict(testX)
         48 # invert predictions
    ---> 49 trainPredict = scaler.inverse_transform(trainPredict)
         50 trainY = scaler.inverse_transform([trainY])
         51 testPredict = scaler.inverse_transform(testPredict)

~\Anaconda3\lib\site-packages\sklearn\preprocessing\data.py in inverse_transform(self, X)
    404                         force_all_finite="allow-nan")
    405 
--> 406         X -= self.min_
    407         X /= self.scale_
    408         return X

ValueError: non-broadcastable output operand with shape (1464,1) doesn't match the broadcast shape (1464,4)

Мой набор данных имеет вид: ECDataset_Mainall.csv Может кто-нибудь объяснить немногооб этом? Пожалуйста ..

LSTM для прогнозирования энергии

import numpy
import matplotlib.pyplot as plt
from pandas import read_csv
import math
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error

преобразовать массив значений в матрицу набора данных

def create_dataset(dataset, look_back=1):
    dataX, dataY = [], []
    for i in range(len(dataset)-look_back-1):
        a = dataset[i:(i+look_back), 0]
        dataX.append(a)
        dataY.append(dataset[i + look_back, 0])
    return numpy.array(dataX), numpy.array(dataY)
# fix random seed for reproducibility
numpy.random.seed(7)

# загрузить набор данных

dataframe = read_csv('ECDataset_Mainall.csv', engine='python')
dataset = dataframe.values
dataset = dataset.astype('float32')

нормализовать набор данных

scaler = MinMaxScaler(feature_range=(0, 4))
dataset = scaler.fit_transform(dataset)

# разделить на наборы поездов и тестов

train_size = int(len(dataset) * 0.67)
test_size = len(dataset) - train_size
train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]
# reshape into X=t and Y=t+1
look_back = 1
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)
# reshape input to be [samples, time steps, features]
trainX = numpy.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))  
testX = numpy.reshape(testX, (testX.shape[0], 1, testX.shape[1]))  

# создать и приспособить сеть LSTM

model = Sequential()
model.add(LSTM(4, input_shape=(1, look_back)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=100, batch_size=1, verbose=2)
# make predictions
trainPredict = model.predict(trainX)

testPredict = model.predict(testX)

обратные прогнозы

trainPredict = scaler.inverse_transform(trainPredict)
trainY = scaler.inverse_transform([trainY])
testPredict = scaler.inverse_transform(testPredict)
testY = scaler.inverse_transform([testY])
# calculate root mean squared error
trainScore = math.sqrt(mean_squared_error(trainY[0], trainPredict[:,0]))
print('Train Score: %.2f RMSE' % (trainScore))
testScore = math.sqrt(mean_squared_error(testY[0], testPredict[:,0]))
print('Test Score: %.2f RMSE' % (testScore))
# shift train predictions for plotting
trainPredictPlot = numpy.empty_like(dataset)
trainPredictPlot[:, :] = numpy.nan
trainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict
# shift test predictions for plotting
testPredictPlot = numpy.empty_like(dataset)
testPredictPlot[:, :] = numpy.nan
testPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1, :] = testPredict
# plot baseline and predictions
plt.plot(scaler.inverse_transform(dataset))
plt.plot(trainPredictPlot,label="train")
plt.plot(testPredictPlot,label="test")
plt.legend()
plt.show()