28 lines
823 B
Python
28 lines
823 B
Python
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from numpy import shape
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import numpy as np
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def reorganize(X_train, Y_train, seq_length):
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# Organize the input and output to feed into RNN model
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x_data = []
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for i in range(len(X_train) - seq_length):
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x_new = X_train[i:i + seq_length]
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x_data.append(x_new)
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# Y_train
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y_data = Y_train[seq_length:]
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y_data = y_data.reshape((-1, 1))
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return x_data, y_data
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def check_control_constraint(X, dim, uppper_bound, lower_bound):
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for i in range(0, shape(X)[0]):
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for j in range(0, shape(X)[0]):
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for k in range(0, dim):
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if X[i, j, k] >= uppper_bound[i, j, k]:
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X[i, j, k] = uppper_bound[i, j, k] - 0.01
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if X[i, j, k] <= lower_bound[i, j, k]:
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X[i, j, k] = lower_bound[i, j, k] + 0.01
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return X
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