def test_proj_dpp_sampler_as_kDPP_with_likelihood_kernel(self):
""" Test whether projection DPP sampled as a k-DPP with k=rank(K) generates samples with the right 1 and 2 points inclusion probabilities when DPP defined by orthogonal projection likelihood kernel L from its eigendecomposition
"""
eig_vals = np.ones(self.rank)
eig_vecs, _ = qr(rndm.randn(self.N, self.rank), mode='economic')
dpp = FiniteDPP(kernel_type='likelihood',
projection=True,
**{'L': (eig_vecs * eig_vals).dot(eig_vecs.T)})
dpp.flush_samples()
for _ in range(self.nb_samples):
dpp.sample_exact_k_dpp(self.rank)
dpp.compute_L()
dpp.K = dpp.L
self.assertTrue(self.singleton_adequation(dpp, dpp.list_of_samples))
self.assertTrue(self.doubleton_adequation(dpp, dpp.list_of_samples))
def test_proj_dpp_sampler_as_kDPP_with_likelihood_kernel_eig_proj_false(
self):
""" Test whether projection DPP sampled as a k-DPP with k=rank(K) generates samples with the right 1 and 2 points inclusion probabilities when DPP defined by orthogonal projection likelihood kernel L from its eigendecomposition and projection is set to False in order to go through the computation of elementary symmetric polynomials etc
"""
eig_vals = np.zeros(self.N)
eig_vals[:self.rank] = 1.0
eig_vecs, _ = qr(rndm.randn(self.N, self.N), mode='economic')
dpp = FiniteDPP(kernel_type='likelihood',
projection=False,
**{'L_eig_dec': (eig_vals, eig_vecs)})
dpp.flush_samples()
for _ in range(self.nb_samples):
dpp.sample_exact_k_dpp(self.rank)
dpp.compute_L()
dpp.K = dpp.L
self.assertTrue(self.singleton_adequation(dpp, dpp.list_of_samples))
self.assertTrue(self.doubleton_adequation(dpp, dpp.list_of_samples))
