basic.py 文件源码

def perform(self, node, inputs, outputs):
        (a_indices, a_indptr, b, g_ab) = inputs
        (out,) = outputs
        g_a_data = numpy.zeros(a_indices.shape, dtype=g_ab.dtype)
        for i in xrange(len(a_indptr) - 1):  # loop over rows
            ind0 = a_indptr[i]
            ind1 = a_indptr[i + 1]
            # loop over values in that row (columns)
            for j_idx in xrange(ind0, ind1):
                j = a_indices[j_idx]
                # grad is dot product of i-th row of gradient with j-th row of b
                # Depending on the type of g_ab and b (sparse or dense),
                # the following dot product can result in a scalar or
                # a (1, 1) sparse matrix.
                dot_val = numpy.dot(g_ab[i], b[j].T)
                if isinstance(dot_val, scipy.sparse.spmatrix):
                    dot_val = dot_val[0, 0]
                g_a_data[j_idx] = dot_val
        out[0] = g_a_data