Я пытаюсь предсказать столбец энергии ветра [0], используя столбец скорости ветра [2]. Мой код возвращает ошибку: «Неразмещаемый выходной операнд с формой (719,1) не соответствует форме трансляции (719,6)!»
Любая помощь приветствуется. Спасибо!
Ниже приведен код, который я получил, и ошибка вернулась. Вот также набор данных .
from math import sqrt
from numpy import concatenate
import numpy as np
from matplotlib import pyplot
from pandas import read_csv
from pandas import DataFrame
from pandas import concat
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_absolute_error
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTMhere
np.random.seed(7)
dataset = read_csv("dataset.csv")
dataset.head()
# setting index to 'Date' column
dataset.set_index(['Date'], inplace=True)
def series_to_supervised(data, n_in=1, n_out=1, dropnan=True): #Use 1 past observation (n_in) to predict 1 future (n_out)
"""
Frame a time series as a supervised learning dataset.
Arguments:
data: Sequence of observations as a list or NumPy array.
n_in: Number of lag observations as input (X).
n_out: Number of observations as output (y).
dropnan: Boolean whether or not to drop rows with NaN values.
Returns:
Pandas DataFrame of series framed for supervised learning.
"""
n_vars = 1 if type(data) is list else data.shape[1]
df = DataFrame(data)
cols, names = list(), list()
# input sequence (t-n, ... t-1)
for i in range(n_in, 0, -1):
cols.append(df.shift(i))
names += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]
# forecast sequence (t, t+1, ... t+n)
for i in range(0, n_out):
cols.append(df.shift(-i))
if i == 0:
names += [('var%d(t)' % (j+1)) for j in range(n_vars)]
else:
names += [('var%d(t+%d)' % (j+1, i)) for j in range(n_vars)]
# put it all together
agg = concat(cols, axis=1)
agg.columns = names
# drop rows with NaN values
if dropnan:
agg.dropna(inplace=True)
return agg
values = dataset.values
encoder = LabelEncoder()
values[:,4] = encoder.fit_transform(values[:,4])
values = values.astype('float32')
values
scaler = MinMaxScaler(feature_range=(0,1))
scaled = scaler.fit_transform(values)
reframed = series_to_supervised(scaled, 1, 1)
reframed.head()
reframed.drop(reframed.columns[[0,1,3,4,5,7,8,9,10,11]], axis=1, inplace=True) #drop only 7,8,9,10,11 for wind power// drop also 0,1,3,4,5,6 to keep only one input variable (here wind speed)
reframed.head()
values = reframed.values
n_train_hours = 34320
train = values[:n_train_hours, :]
test = values[n_train_hours:, :]
train_X, train_y = train[:, :-1], train[:, -1]
test_X, test_y = test[:, :-1], test[:,-1]
train_X = train_X.reshape((train_X.shape[0], 1, train_X.shape[1]))
test_X = test_X.reshape((test_X.shape[0], 1, test_X.shape[1]))
model = Sequential()
model.add(LSTM(50, input_shape=(train_X.shape[1], train_X.shape[2])))
model.add(Dense(1))
model.compile(loss='mse', optimizer='adam', metrics=['mape', 'mae'])
history = model.fit(train_X, train_y, epochs = 50, batch_size=72, validation_data=(test_X, test_y), verbose=2, shuffle=False)
# make a prediction
yhat = model.predict(test_X)
test_X = test_X.reshape((test_X.shape[0], test_X.shape[2]))
# invert scaling for forecast
inv_yhat = concatenate((yhat, test_X[:, 1:]), axis=1)
inv_yhat = scaler.inverse_transform(inv_yhat) ###### The problem occurs here
inv_yhat = inv_yhat[:,0]
# invert scaling for actual
test_y = test_y.reshape((len(test_y), 1))
inv_y = concatenate((test_y, test_X[:, 1:]), axis=1)
inv_y = scaler.inverse_transform(inv_y)
inv_y = inv_y[:,0]
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-84-ac263e37d0c7> in <module>()
1 inv_yhat = concatenate((yhat, test_X[:, 1:]), axis=1)
----> 2 inv_yhat = scaler.inverse_transform(inv_yhat)
3 inv_yhat = inv_yhat[:,0]
4
5 # invert scaling for actual
/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_data.py in inverse_transform(self, X)
434 force_all_finite="allow-nan")
435
--> 436 X -= self.min_
437 X /= self.scale_
438 return X
ValueError: non-broadcastable output operand with shape (719,1) doesn't match the broadcast shape (719,6)