da9516b689891a227f67420c38849ba2f40367e5,scipy/sparse/data.py,_minmax_mixin,_min_or_max_axis,#_minmax_mixin#Any#Any#,106

Before Change

        // can"t use indices > data length with reduceat`
        trunc = np.searchsorted(indptr, indptr[-1])
        min_or_max.reduceat(mat.data, indptr[:trunc], out=out[:trunc])
        nnz = np.diff(indptr)
        // compare to 0 in non-full rows
        mask = nnz < N
        out[mask] = min_or_max(out[mask], zero)
        // reduceat will have filled empty rows with another data entry
        out[nnz == 0] = zero

        out = lil_matrix(out, dtype=self.dtype)

After Change

        mat.sum_duplicates()

        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)

Instances