class TimeSeriesMinMaxScaler(BaseEstimator, TransformerMixin):
def __init__(self, *args):
self.min_max_scaler = StandardScaler(*args)
def fit(self, X, y=None):
self.min_max_scaler._reset()
return self.partial_fit(X, y)
def partial_fit(self, X, y=None):
self.min_max_scaler.partial_fit(X.reshape((-1, X.shape[2])))
def transform(self, X):
X = [self.min_max_scaler.transform(x) for x in X]
return np.array(X)
def inverse_transform(self, X):
X = [self.min_max_scaler.inverse_transform(x) for x in X]
return np.array(X)
