def test_rff_feature_map_comp_grad_theano_execute():
if not theano_available:
raise SkipTest("Theano not available.")
D = 2
x = np.random.randn(D)
m = 10
sigma = 1.
omega, u = rff_sample_basis(D, m, sigma)
for i in range(m):
rff_feature_map_comp_grad_theano(x, omega[:, i], u[i])
def test_rff_feature_map_comp_grad_theano_execute():
if not theano_available:
raise SkipTest("Theano not available.")
D = 2
x = np.random.randn(D)
m = 10
sigma = 1.
omega, u = rff_sample_basis(D, m, sigma)
for i in range(m):
rff_feature_map_comp_grad_theano(x, omega[:, i], u[i])
def test_rff_feature_map_grad_theano_result_equals_manual():
if not theano_available:
raise SkipTest("Theano not available.")
D = 2
x = np.random.randn(D)
X = x[np.newaxis, :]
m = 10
sigma = 1.
omega, u = rff_sample_basis(D, m, sigma)
grad_manual = rff_feature_map_grad(X, omega, u)[:, 0, :]
for i in range(m):
# phi_manual is a monte carlo average, so have to normalise by np.sqrt(m) here
grad = rff_feature_map_comp_grad_theano(x, omega[:, i], u[i]) / np.sqrt(m)
assert_close(grad, grad_manual[:, i])
def test_rff_feature_map_grad_theano_result_equals_manual():
if not theano_available:
raise SkipTest("Theano not available.")
D = 2
x = np.random.randn(D)
X = x[np.newaxis, :]
m = 10
sigma = 1.
omega, u = rff_sample_basis(D, m, sigma)
grad_manual = rff_feature_map_grad(X, omega, u)[:, 0, :]
for i in range(m):
# phi_manual is a monte carlo average, so have to normalise by np.sqrt(m) here
grad = rff_feature_map_comp_grad_theano(x, omega[:, i],
u[i]) / np.sqrt(m)
assert_close(grad, grad_manual[:, i])
