'Online computation of min and max on X for later scaling.
All of X is processed as a single batch. This is intended for cases
when `fit` is not feasible due to very large number of `n_samples`
or because X is read from a continuous stream.
Parameters
X : array-like, shape [n_samples, n_features]
The data used to compute the mean and standard deviation
used for later scaling along the features axis.
y : Passthrough for ``Pipeline`` compatibility.'

def partial_fit(self, X, y=None):

    feature_range = self.feature_range
    if (feature_range[0] >= feature_range[1]):
        raise ValueError(('Minimum    of    desired    feature    range    must    be    smaller    than    maximum.    Got    %s.' % str(feature_range)))
    if sparse.issparse(X):
        raise TypeError('MinMaxScaler    does    no    support    sparse    input.    You    may    consider    to    use    MaxAbsScaler    instead.')
    X = check_array(X, copy=self.copy, warn_on_dtype=True, estimator=self, dtype=FLOAT_DTYPES)
    data_min = np.min(X, axis=0)
    data_max = np.max(X, axis=0)
    if (not hasattr(self, 'n_samples_seen_')):
        self.n_samples_seen_ = X.shape[0]
    else:
        data_min = np.minimum(self.data_min_, data_min)
        data_max = np.maximum(self.data_max_, data_max)
        self.n_samples_seen_ += X.shape[0]
    data_range = (data_max - data_min)
    self.scale_ = ((feature_range[1] - feature_range[0]) / _handle_zeros_in_scale(data_range))
    self.min_ = (feature_range[0] - (data_min * self.scale_))
    self.data_min_ = data_min
    self.data_max_ = data_max
    self.data_range_ = data_range
    return self
