def test_is_full_row_rank(self):
N, rank = 30, 10
X = rndm.randn(rank, N)
self.assertTrue(np.allclose(utils.is_full_row_rank(X), X))
Y = np.vstack([X, rndm.randn(rank).dot(X)])
with self.assertRaises(ValueError) as context:
utils.is_full_row_rank(Y)
self.assertTrue('not full row rank' in str(context.exception))
def __init__(self, kernel_type, projection=False, **params):
self.kernel_type = kernel_type
self.projection = projection
self.params_keys = set(params.keys())
self.__check_args_coherence()
# Sampling
self.sampling_mode = 'GS' # Gram-Schmidt
self.list_of_samples = []
# when using .sample_k_dpp_*
self.size_k_dpp = 0
self.E_poly = None # evaluation of the
# Attributes relative to K correlation kernel:
# K, K_eig_vals, K_eig_vecs, A_zono
self.K = is_symmetric(params.get('K', None))
if self.projection:
self.K = is_projection(self.K)
e_vals, e_vecs = params.get('K_eig_dec', [None, None])
if self.projection:
self.K_eig_vals = is_equal_to_O_or_1(e_vals)
else:
self.K_eig_vals = is_in_01(e_vals)
self.eig_vecs = is_orthonormal_columns(e_vecs)
self.A_zono = is_full_row_rank(params.get('A_zono', None))
# Attributes relative to L likelihood kernel:
# L, L_eig_vals, L_eig_vecs, L_gram_factor, L_dual, L_dual_eig_vals, L_dual_eig_vecs
self.L = is_symmetric(params.get('L', None))
if self.projection:
self.L = is_projection(self.L)
e_vals, e_vecs = params.get('L_eig_dec', [None, None])
if self.projection:
self.L_eig_vals = is_equal_to_O_or_1(e_vals)
else:
self.L_eig_vals = is_geq_0(e_vals)
if self.eig_vecs is None: # K_eig_vecs = L_eig_vecs
self.eig_vecs = is_orthonormal_columns(e_vecs)
# L' "dual" likelihood kernel, L' = Phi Phi.T, Phi = L_gram_factor
self.L_gram_factor = params.get('L_gram_factor', None)
self.L_dual = None
self.L_dual_eig_vals = None
self.L_dual_eig_vecs = None
if self.L_gram_factor is not None:
Phi = self.L_gram_factor
d, N = Phi.shape
if d < N:
self.L_dual = Phi.dot(Phi.T)
print('L_dual = Phi Phi.T was computed: Phi (dxN) with d<N')
else:
if self.L is None:
self.L = Phi.T.dot(Phi)
print('L = Phi.T Phi was computed: Phi (dxN) with d>=N')
def test_is_full_row_rank(self):
N, rank = 30, 10
list_of_inputs = [(True, None), (True, rndm.randn(rank, N)),
(False, rndm.randn(N + 1, N)),
(False, np.zeros((rank, N))),
(False, np.ones((rank, N)))]
for idx, (flag, _input) in enumerate(list_of_inputs):
with self.subTest(index=idx, is_full_row_rank=flag):
if flag:
self.assertTrue(utils.is_full_row_rank(_input) is _input)
else:
with self.assertRaises(ValueError) as context:
utils.is_full_row_rank(_input)
self.assertIn('not full row rank', str(context.exception))
def __init__(self, kernel_type, projection=False, **params):
self.kernel_type = kernel_type
self.projection = projection
self.params_keys = set(params.keys())
self.__check_args_coherence()
# Sampling
self.sampling_mode = 'GS' # Gram-Schmidt
self.list_of_samples = []
# when using .sample_k_dpp_*
self.size_k_dpp = 0
self.E_poly = None # evaluation of the
# Attributes relative to K correlation kernel:
# K, K_eig_vals, K_eig_vecs, A_zono
self.K = is_symmetric(params.get('K', None))
if self.projection:
self.K = is_projection(self.K)
e_vals, e_vecs = params.get('K_eig_dec', [None, None])
if self.projection:
self.K_eig_vals = is_equal_to_O_or_1(e_vals)
else:
self.K_eig_vals = is_in_01(e_vals)
self.eig_vecs = is_orthonormal_columns(e_vecs)
self.A_zono = is_full_row_rank(params.get('A_zono', None))
# Attributes relative to L likelihood kernel:
# L, L_eig_vals, L_eig_vecs, L_gram_factor, L_dual
self.L = is_symmetric(params.get('L', None))
if self.projection:
self.L = is_projection(self.L)
e_vals, e_vecs = params.get('L_eig_dec', [None, None])
if self.projection:
self.L_eig_vals = is_equal_to_O_or_1(e_vals)
else:
self.L_eig_vals = is_geq_0(e_vals)
if self.eig_vecs is None: # K_eig_vecs = L_eig_vecs
self.eig_vecs = is_orthonormal_columns(e_vecs)
# L' "dual" likelihood kernel, L' = Phi Phi.T, Phi = L_gram_factor
self.L_gram_factor = params.get('L_gram_factor', None)
self.L_dual = None
if self.L_gram_factor is not None:
Phi = self.L_gram_factor
d, N = Phi.shape
if d < N:
self.L_dual = Phi.dot(Phi.T)
print('L_dual = Phi Phi.T was computed: Phi (dxN) with d<N')
else:
if self.L is None:
self.L = Phi.T.dot(Phi)
print('L = Phi.T Phi was computed: Phi (dxN) with d>=N')
# L likelihood function representation
# eval_L(X, Y) = L(X, Y)
# eval_L(X) = L(X, X)
self.eval_L, self.X_data = params.get('L_eval_X_data', [None, None])
self.intermediate_sample_info = None
if self.eval_L is not None:
if not callable(self.eval_L):
raise ValueError(
'eval_L should be a positive semi-definite kernel function'
)
if self.X_data is not None:
if not (self.X_data.size and self.X_data.ndim == 2):
err_print = [
'Wrong shape = {}'.format(self.X_data.shape),
'X_data should be a non empty (N x d) ndarray'
]
raise ValueError('\n'.join(err_print))
