def test_isotropic_zero_mean_equals_log_gaussian_pdf():
D = 2
x = np.random.randn(D)
g = IsotropicZeroMeanGaussian(sigma=np.sqrt(2))
log_pdf = log_gaussian_pdf(x,
mu=np.zeros(D),
Sigma=np.eye(D) * 2,
is_cholesky=False,
compute_grad=False)
assert_close(log_pdf, g.log_pdf(x))
def proposal(self, current, current_log_pdf):
"""
Returns a sample from the proposal centred at current, acceptance probability,
and its log-pdf under the target.
"""
if current_log_pdf is None:
current_log_pdf = self.target.log_pdf(current)
L_R = self.construct_proposal_covariance_(current)
proposal = sample_gaussian(N=1, mu=current, Sigma=L_R, is_cholesky=True)[0]
proposal_log_prob = log_gaussian_pdf(proposal, current, L_R, is_cholesky=True)
proposal_log_pdf = self.target.log_pdf(proposal)
# probability of proposing y when would be sitting at proposal
L_R_inv = self.construct_proposal_covariance_(proposal)
proopsal_log_prob_inv = log_gaussian_pdf(current, proposal, L_R_inv, is_cholesky=True)
log_acc_prob = proposal_log_pdf - current_log_pdf + proopsal_log_prob_inv - proposal_log_prob
return proposal, np.exp(log_acc_prob), proposal_log_pdf
def log_prior_log_pdf(x):
D = len(x)
return log_gaussian_pdf(x, mu=0. * np.ones(D), Sigma=np.eye(D) * 5)
def test_isotropic_zero_mean_equals_log_gaussian_pdf():
D = 2
x = np.random.randn(D)
g = IsotropicZeroMeanGaussian(sigma=np.sqrt(2))
log_pdf = log_gaussian_pdf(x, mu=np.zeros(D), Sigma=np.eye(D) * 2, is_cholesky=False, compute_grad=False)
assert_close(log_pdf, g.log_pdf(x))
def log_prior_log_pdf(x):
D = len(x)
return log_gaussian_pdf(x, mu=0.*np.ones(D), Sigma=np.eye(D) * 5)
