def test_invalid_dim(self, dim):
"""Tests that a warning is issued if the feature vectors do not have the correct
dimension"""
with pytest.raises(ValueError, match="Dimension of parameter vector"):
features = np.ones((dim, dim + 1))
expf = embed.ExpFeatures(features)
expf(np.zeros(dim))
def test_jacobian_predefined(self, dim):
"""Tests that the jacobian is computed correctly for pre-defined features and parameters"""
k = dim - 2
features = feats[k]
expf = embed.ExpFeatures(features)
g = expf.jacobian(ps[k])
assert np.allclose(g, jacobian_f[k])
def test_jacobian_identity(self, dim):
"""Tests that the jacobian is computed correctly compared to a general calculation using an
identity matrix of feature vectors"""
k = dim - 2
features = np.eye(dim)
expf = embed.ExpFeatures(features)
exp = embed.Exp(dim)
assert np.allclose(expf.jacobian(ps[k]), exp.jacobian(ps[k]))
def test_jacobian_zero_params(self, dim):
"""Tests that the jacobian is equal to a matrix with -1 in all entries when parameters are
zero"""
features = np.ones((dim, dim))
params = np.zeros(dim)
expf = embed.ExpFeatures(features)
g = expf.jacobian(params)
assert np.allclose(-np.ones((dim, dim)), g)
def test_exp_features_shape(self, dim, m):
"""Tests that the weights and jacobian have the correct shape for a range of different
sizes for the input features matrix"""
features = np.ones((m, dim))
expf = embed.ExpFeatures(features)
params = np.ones(dim)
assert expf(params).shape == (m,)
assert expf.jacobian(params).shape == (m, dim)
def test_predefined(self, dim):
"""Tests that weights are computed correctly for pre-defined features and parameters"""
k = dim - 2
features = feats[k]
expf = embed.ExpFeatures(features)
assert np.allclose(expf(ps[k]), weights_f[k])
def test_zero_params(self, dim):
"""Tests that weights are equal to one when parameters are zero"""
features = np.ones((dim, dim))
expf = embed.ExpFeatures(features)
params = np.zeros(dim)
assert np.allclose(expf(params), np.ones(dim))
