def test_circulant_transpose():
a = torch.randn(5)
C = circulant.circulant(a)
C_T_actual = C.t()
C_T_result = circulant.circulant(circulant.circulant_transpose(a))
assert(utils.approx_equal(C_T_actual, C_T_result))
def test_frobenius_circulant_approximation():
A = torch.randn(5, 5)
C1 = circulant.frobenius_circulant_approximation(A)
C2 = circulant.frobenius_circulant_approximation(circulant.circulant(C1))
assert(utils.approx_equal(C1, C2))
def test_circulant_matmul():
a = torch.randn(5)
M = torch.randn(5, 5)
aM_result = circulant.circulant_matmul(a, M)
C = circulant.circulant(a)
aM_actual = C.mm(M)
assert(utils.approx_equal(aM_result, aM_actual))
def test_circulant_inv_matmul(self):
a = torch.randn(5)
M = torch.randn(5, 5)
aM_result = circulant.circulant_inv_matmul(a, M)
C = circulant.circulant(a)
aM_actual = C.inverse().mm(M)
self.assertTrue(utils.approx_equal(aM_result, aM_actual))