utils.py

from keras import backend as K


class MinMaxNormalization(object):
    '''MinMax Normalization --> [-1, 1]
       x = (x - min) / (max - min).
       x = x * 2 - 1
    '''

    def __init__(self):
        pass

    def fit(self, X):
        self._min = X.min()
        self._max = X.max()
        print("min:", self._min, "max:", self._max)

    def transform(self, X):
        X = 1. * (X - self._min) / (self._max - self._min)
        X = X * 2. - 1.
        return X

    def fit_transform(self, X):
        self.fit(X)
        return self.transform(X)

    def inverse_transform(self, X):
        X = (X + 1.) / 2.
        X = 1. * X * (self._max - self._min) + self._min
        return X

def mean_squared_error(y_true, y_pred):
    return K.mean(K.square(y_pred - y_true))

def rmse(y_true, y_pred):
    return mean_squared_error(y_true, y_pred) ** 0.5