'''
part 1:多個x時間變數用於**y的時間
但是y不作為x的乙份子
'''# 創造資料
from numpy import array
from numpy import hstack
from keras.models import sequential
from keras.layers import lstm
from keras.layers import dense
# define input sequence
in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90])
in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95])
out_seq = array([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))])
# convert to [rows, columns] structure
in_seq1 = in_seq1.reshape((len(in_seq1), 1))
in_seq2 = in_seq2.reshape((len(in_seq2), 1))
out_seq = out_seq.reshape((len(out_seq), 1))
# horizontally stack columns
dataset = hstack((in_seq1, in_seq2, out_seq))
print(dataset)
'''[[ 10 15 25]
[ 20 25 45]
[ 30 35 65]
[ 40 45 85]
[ 50 55 105]
[ 60 65 125]
[ 70 75 145]
[ 80 85 165]
[ 90 95 185]]
現在定為時間步長為3
舉個例子，就是用
10 15
20 25
30 35
**65
'''# split a multivariate sequence into samples
def split_sequences(sequences, n_steps):
x, y = list(), list()
for i in range(len(sequences)):
end_ix = i + n_steps
if end_ix > len(sequences):
break
seq_x, seq_y = sequences[i:end_ix, :-1], sequences[end_ix-1, -1] # 和單變數最大的不同在這裡
return array(x), array(y)
n_steps = 3
x,y = split_sequences(dataset,n_steps)
print(x.shape,y.shape)
for i in range(len(x)):
print(x[i],y[i])
n_features = x.shape[2]
'''[[10 15]
[20 25]
[30 35]] 65
[[20 25]
[30 35]
[40 45]] 85
[[30 35]
[40 45]
[50 55]] 105
[[40 45]
[50 55]
[60 65]] 125
[[50 55]
[60 65]
[70 75]] 145
[[60 65]
[70 75]
[80 85]] 165
[[70 75]
[80 85]
[90 95]] 185
'''model = sequential()
model.add(lstm(50,activation='relu',input_shape=(n_steps,n_features)))
model.add(dense(1))
model.compile(optimizer='adam',loss='mse')
model.fit(x,y,epochs=200,verbose=0)
#**模型
x_input = array([[80, 85], [90, 95], [100, 105]])
x_input = x_input.reshape((1, n_steps, n_features)) # 轉換成樣本量+步長+特徵的格式
yhat = model.predict(x_input, verbose=0)
print(yhat) # 209.00851
'''part 2:多個x時間變數**多個x時間變數的下一步
即平行**
'''from numpy import array
from numpy import hstack
# split a multivariate sequence into samples
def split_sequences(sequences, n_steps):
x, y = list(), list()
for i in range(len(sequences)):
end_ix = i + n_steps
if end_ix > len(sequences)-1:
break
# 最關鍵的不一樣在這一步
seq_x, seq_y = sequences[i:end_ix, :], sequences[end_ix, :]
return array(x), array(y)
in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90])
in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95])
out_seq = array([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))])
in_seq1 = in_seq1.reshape((len(in_seq1), 1))
in_seq2 = in_seq2.reshape((len(in_seq2), 1))
out_seq = out_seq.reshape((len(out_seq), 1))
dataset = hstack((in_seq1, in_seq2, out_seq))
# choose a number of time steps
n_steps = 3
# convert into input/output
x, y = split_sequences(dataset, n_steps)
print(x.shape, y.shape)
# summarize the data
for i in range(len(x)):
print(x[i], y[i])
'''[[10 15 25]
[20 25 45]
[30 35 65]] [40 45 85]
[[20 25 45]
[30 35 65]
[40 45 85]] [ 50 55 105]
[[ 30 35 65]
[ 40 45 85]
[ 50 55 105]] [ 60 65 125]
[[ 40 45 85]
[ 50 55 105]
[ 60 65 125]] [ 70 75 145]
[[ 50 55 105]
[ 60 65 125]
[ 70 75 145]] [ 80 85 165]
[[ 60 65 125]
[ 70 75 145]
[ 80 85 165]] [ 90 95 185]
'''n_features = x.shape[2]
# define stack model
model = sequential()
model.add(lstm(100, activation='relu', return_sequences=true, input_shape=(n_steps, n_features)))
model.add(lstm(100, activation='relu'))
# 和多對一不同點在於，這裡多對多的dense的神經元=features數目
model.add(dense(n_features))
model.compile(optimizer='adam', loss='mse')
# fit model
model.fit(x, y, epochs=400, verbose=0)
# demonstrate prediction
x_input = array([[70,75,145], [80,85,165], [90,95,185]])
x_input = x_input.reshape((1, n_steps, n_features))
yhat = model.predict(x_input, verbose=0)
print(yhat) # [[100.58976 107.512665 207.87833 ]]
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