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

Before Change

        major_index, value = mat._minor_reduce(min_or_max)
        min_or_max(value, 0, out=value)

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

After Change

            return coo_matrix((value, (np.zeros(len(value)), major_index)),
                              dtype=self.dtype, shape=(1, M))
        else:
            return coo_matrix((value, (major_index, np.zeros(len(value)))),
                              dtype=self.dtype, shape=(M, 1))

    def _min_or_max(self, axis, min_or_max):
        if axis is None:
            if 0 in self.shape:

Instances