test_autograd.py 文件源码

def random_square_matrix_of_rank(l, rank):
    assert rank <= l
    A = torch.randn(l, l)
    u, s, v = A.svd()
    for i in range(l):
        if i >= rank:
            s[i] = 0
        elif s[i] == 0:
            s[i] = 1
    return u.mm(torch.diag(s)).mm(v.transpose(0, 1))