a7ff52f2e7c84b8e5c3f110dbee792f1962f526b,scipy/sparse/data.py,_minmax_mixin,_min_or_max_axis,#_minmax_mixin#Any#Any#,106

Before Change

        out = np.zeros(len(mat.indptr) - 1, dtype=self.dtype)
        out[major_index] = value
        out = np.asmatrix(out)
        if axis == 1:
            out = out.T
        return self.__class__(out)

    def _min_or_max(self, axis, min_or_max):
        if axis is None:

After Change

        not_full = np.diff(mat.indptr)[major_index] < N
        value[not_full] = min_or_max(value[not_full], 0)

        mask = value != 0
        major_index = np.compress(mask, major_index)
        value = np.compress(mask, value)

        from . import coo_matrix
        if axis == 0:

Instances