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, ensure_2d=False, warn_on_dtype=True,
                        estimator=self, dtype=FLOAT_DTYPES)

        if X.ndim == 1:
            warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)

        data_min = np.min(X, axis=0)
        data_max = np.max(X, axis=0)

        # First pass
        if not hasattr(self, 'n_samples_seen_'):
            self.n_samples_seen_ = X.shape[0]
        # Next steps
        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
Get a task from the task queue.
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/preprocessing/data.py/MinMaxScaler.partial_fit--crossover with--dataset/ETHPy150Open ODM2/ODMToolsPython/odmtools/common/taskServer.py/Dispatcher.getTask