CIMtools/applicability_domain/bounding_box.py
def fit(self, X, y=None):
        """Find min and max values of every feature.

        Parameters
        ----------
        X : {array-like, sparse matrix}, shape (n_samples, n_features)
            The training input samples.
        y : Ignored
            not used, present for API consistency by convention.

        Returns
        -------
        self : object
        """
        # Check that X have correct shape
        X = check_array(X)

        self._x_min = X.min(axis=0) # axis=0 will find the minimum values ​​by columns (for each feature)
        self._x_max = X.max(axis=0) # axis=0 will find the minimum values ​​by columns (for each feature)
        return self
def fit(self, X, y=None):
        """Find min and max values of every feature.

        Parameters
        ----------
        X : {array-like, sparse matrix}, shape (n_samples, n_features)
            The training input samples.
        y : Ignored
            not used, present for API consistency by convention.

        Returns
        -------
        self : object
        """
        # Check that X have correct shape
        X = check_array(X)

        self._x_min = X.min(axis=0) # axis=0 will find the minimum values ​​by columns (for each feature)
        self._x_max = X.max(axis=0) # axis=0 will find the minimum values ​​by columns (for each feature)
        return self
Find min and max values of every feature.

        Parameters
        ----------
        X : {array-like, sparse matrix}, shape (n_samples, n_features)
            The training input samples.
        y : Ignored
            not used, present for API consistency by convention.

        Returns
        -------
        self : object