def test_multiply_float64(self):
x = np.asarray(np.random.rand(4, 4), np.float64)
y = np.asarray(np.random.rand(4, 4), np.float64)
x_gpu = gpuarray.to_gpu(x)
y_gpu = gpuarray.to_gpu(y)
z_gpu = linalg.multiply(x_gpu, y_gpu)
assert np.allclose(x * y, z_gpu.get())
def test_multiply_complex128(self):
x = np.asarray(np.random.rand(4, 4) + 1j*np.random.rand(4, 4), np.complex128)
y = np.asarray(np.random.rand(4, 4) + 1j*np.random.rand(4, 4), np.complex128)
x_gpu = gpuarray.to_gpu(x)
y_gpu = gpuarray.to_gpu(y)
z_gpu = linalg.multiply(x_gpu, y_gpu)
assert np.allclose(x*y, z_gpu.get())
def test_multiply_float64(self):
x = np.asarray(np.random.rand(4, 4), np.float64)
y = np.asarray(np.random.rand(4, 4), np.float64)
x_gpu = gpuarray.to_gpu(x)
y_gpu = gpuarray.to_gpu(y)
z_gpu = linalg.multiply(x_gpu, y_gpu)
assert np.allclose(x*y, z_gpu.get())
def test_multiply_complex128(self):
x = np.asarray(
np.random.rand(4, 4) + 1j * np.random.rand(4, 4), np.complex128)
y = np.asarray(
np.random.rand(4, 4) + 1j * np.random.rand(4, 4), np.complex128)
x_gpu = gpuarray.to_gpu(x)
y_gpu = gpuarray.to_gpu(y)
z_gpu = linalg.multiply(x_gpu, y_gpu)
assert np.allclose(x * y, z_gpu.get())
def _impl_test_multiply(self, N, dtype):
mk_matrix = lambda N, dtype: np.asarray(np.random.rand(N, N), dtype)
x = mk_matrix(N, dtype)
y = mk_matrix(N, dtype)
if np.iscomplexobj(x):
x += 1j * mk_matrix(N, dtype)
y += 1j * mk_matrix(N, dtype)
x_gpu = gpuarray.to_gpu(x)
y_gpu = gpuarray.to_gpu(y)
z_gpu = linalg.multiply(x_gpu, y_gpu)
assert np.allclose(x * y, z_gpu.get())
def impl_test_multiply(self, N, dtype):
mk_matrix = lambda N, dtype: np.asarray(np.random.rand(N, N), dtype)
x = mk_matrix(N, dtype)
y = mk_matrix(N, dtype)
if np.iscomplexobj(x):
x += 1j * mk_matrix(N, dtype)
y += 1j * mk_matrix(N, dtype)
x_gpu = gpuarray.to_gpu(x)
y_gpu = gpuarray.to_gpu(y)
z_gpu = linalg.multiply(x_gpu, y_gpu)
assert np.allclose(x * y, z_gpu.get())