cost_data = np.empty((len(matrix) - 6, 7))
count = 0
for i in range(len(cost_data)):
time_step = 0
while (time_step < 7):
cost_data[i][time_step] = matrix[count][2]
time_step = time_step + 1
count = count + 1
count = count - 6
training_epoch = 1000
batch_size = 1
time_steps = 7
RNN = RNN(input_size=1,
state_size=10,
hidden_sum=1,
output_size=1,
time_steps=7,
batch_size=batch_size,
learning_rate=0.001)
i = 0
for i in range(len(input_data) - 1):
batch_x = np.reshape(input_data[i], [batch_size, time_steps, 1])
batch_y = np.reshape(input_data[i + 1], [batch_size, time_steps])
batch_c = np.reshape(cost_data[i], [batch_size, time_steps])
cost = RNN.Persistent_opt(batch_x, batch_y, batch_c)
print(cost)
