def test_rbf():
np.random.seed(0)
d = 3 # Input dimension
k = 2 # Number of outputs
b = 100 # basis functions
# Training Dataset
X0 = np.random.rand(100, d)
A = np.random.rand(d, k)
Y0 = np.sin(X0).dot(A) + 1e-3 * (np.random.rand(100, k) - 0.5
) # Just something smooth
rbf = RbfController(3, 2, b)
rbf.set_XY(X0, Y0)
# Generate input
m = np.random.rand(1, d) # But MATLAB defines it as m'
s = np.random.rand(d, d)
s = s.dot(s.T) # Make s positive semidefinite
M, S, V = rbf.compute_action(m, s, squash=False)
print("M\n", M)
print("S\n", S)
print("V\n", V)
# convert data to the struct expected by the MATLAB implementation
lengthscales = rbf.model.covar_module.lengthscale.cpu().detach().numpy(
).squeeze()
variance = 1 * np.ones(k)
noise = rbf.model.likelihood.noise.cpu().detach().numpy().squeeze()
hyp = np.log(
np.hstack((lengthscales, np.sqrt(variance[:, None]),
np.sqrt(noise[:, None])))).T
gpmodel = oct2py.io.Struct()
gpmodel.hyp = hyp
gpmodel.inputs = X0
gpmodel.targets = Y0
# Call gp0 in octave
M_mat, S_mat, V_mat = octave.gp2(gpmodel, m.T, s, nout=3)
assert M.shape == M_mat.T.shape
assert S.shape == S_mat.shape
assert V.shape == V_mat.shape
np.testing.assert_allclose(M, M_mat.T, rtol=1e-4)
np.testing.assert_allclose(S, S_mat, rtol=1e-4)
np.testing.assert_allclose(V, V_mat, rtol=1e-4)
def test_rbf():
np.random.seed(0)
d = 3 # Input dimension
k = 2 # Number of outputs
b = 100 # basis functions
# Training Dataset
X0 = np.random.rand(100, d)
A = np.random.rand(d, k)
Y0 = np.sin(X0).dot(A) + 1e-3 * (np.random.rand(100, k) - 0.5
) # Just something smooth
rbf = RbfController(3, 2, b)
rbf.set_XY(X0, Y0)
# Generate input
m = np.random.rand(1, d) # But MATLAB defines it as m'
s = np.random.rand(d, d)
s = s.dot(s.T) # Make s positive semidefinite
M, S, V = compute_action_wrapper(rbf, m, s)
# convert data to the struct expected by the MATLAB implementation
lengthscales = np.stack(
[model.kern.lengthscales.value for model in rbf.models])
variance = np.stack([model.kern.variance.value for model in rbf.models])
noise = np.stack([model.likelihood.variance.value for model in rbf.models])
hyp = np.log(
np.hstack((lengthscales, np.sqrt(variance[:, None]),
np.sqrt(noise[:, None])))).T
gpmodel = oct2py.io.Struct()
gpmodel.hyp = hyp
gpmodel.inputs = X0
gpmodel.targets = Y0
# Call gp0 in octave
M_mat, S_mat, V_mat = octave.gp2(gpmodel, m.T, s, nout=3)
assert M.shape == M_mat.T.shape
assert S.shape == S_mat.shape
assert V.shape == V_mat.shape
np.testing.assert_allclose(M, M_mat.T, rtol=1e-4)
np.testing.assert_allclose(S, S_mat, rtol=1e-4)
np.testing.assert_allclose(V, V_mat, rtol=1e-4)