def test_log_gaussian_pdf_multiple_equals_log_gaussian_pdf_looped_isotropic_cov(
):
N = 100
D = 3
X = np.random.randn(N, D)
mu = np.random.randn(D)
cov_scaling = 2.
log_pdfs = log_gaussian_pdf_multiple(X, mu, cov_scaling=cov_scaling)
grads = log_gaussian_pdf_multiple(X,
mu,
compute_grad=True,
cov_scaling=cov_scaling)
print log_pdfs
for i, x in enumerate(X):
log_pdf = log_gaussian_pdf(x, mu, cov_scaling=cov_scaling)
grad = log_gaussian_pdf(x,
mu,
compute_grad=True,
cov_scaling=cov_scaling)
assert_allclose(log_pdf, log_pdfs[i])
assert_allclose(grad, grads[i])
def proposal_log_pdf(self, current, proposals):
log_probs = log_gaussian_pdf_multiple(proposals,
mu=current,
Sigma=self.L_C,
is_cholesky=True,
cov_scaling=self.step_size)
return log_probs
def test_log_gaussian_pdf_multiple_equals_log_gaussian_pdf_looped_full_cov():
N = 100
D = 3
X = np.random.randn(N, D)
mu = np.random.randn(D)
L_C = np.linalg.cholesky(np.dot(X.T, X) + np.eye(D))
cov_scaling = 2.
log_pdfs = log_gaussian_pdf_multiple(X, mu, L_C, is_cholesky=True, cov_scaling=cov_scaling)
grads = log_gaussian_pdf_multiple(X, mu, L_C, is_cholesky=True, compute_grad=True, cov_scaling=cov_scaling)
print log_pdfs
for i, x in enumerate(X):
log_pdf = log_gaussian_pdf(x, mu, L_C, is_cholesky=True, cov_scaling=cov_scaling)
grad = log_gaussian_pdf(x, mu, L_C, is_cholesky=True, compute_grad=True, cov_scaling=cov_scaling)
assert_allclose(log_pdf, log_pdfs[i])
assert_allclose(grad, grads[i])
def test_log_gaussian_pdf_multiple_equals_log_gaussian_pdf_looped_isotropic_cov():
N = 100
D = 3
X = np.random.randn(N, D)
mu = np.random.randn(D)
cov_scaling = 2.
log_pdfs = log_gaussian_pdf_multiple(X, mu, cov_scaling=cov_scaling)
grads = log_gaussian_pdf_multiple(X, mu, compute_grad=True, cov_scaling=cov_scaling)
print log_pdfs
for i, x in enumerate(X):
log_pdf = log_gaussian_pdf(x, mu, cov_scaling=cov_scaling)
grad = log_gaussian_pdf(x, mu, compute_grad=True, cov_scaling=cov_scaling)
assert_allclose(log_pdf, log_pdfs[i])
assert_allclose(grad, grads[i])
def proposal_log_pdf(self, current, proposals, drift=None):
if drift is None:
drift = self._compute_drift(current)
mu = current + drift
log_probs = log_gaussian_pdf_multiple(proposals, mu=mu, Sigma=self.L_C,
is_cholesky=True,
cov_scaling=self.step_size)
return log_probs
def proposal_log_pdf(self, current, proposals, drift=None):
if drift is None:
drift = self._compute_drift(current)
mu = current + drift
log_probs = log_gaussian_pdf_multiple(proposals,
mu=mu,
Sigma=self.L_C,
is_cholesky=True,
cov_scaling=self.step_size)
return log_probs
def test_log_gaussian_pdf_multiple_equals_log_gaussian_pdf_looped_full_cov():
N = 100
D = 3
X = np.random.randn(N, D)
mu = np.random.randn(D)
L_C = np.linalg.cholesky(np.dot(X.T, X) + np.eye(D))
cov_scaling = 2.
log_pdfs = log_gaussian_pdf_multiple(X,
mu,
L_C,
is_cholesky=True,
cov_scaling=cov_scaling)
grads = log_gaussian_pdf_multiple(X,
mu,
L_C,
is_cholesky=True,
compute_grad=True,
cov_scaling=cov_scaling)
print log_pdfs
for i, x in enumerate(X):
log_pdf = log_gaussian_pdf(x,
mu,
L_C,
is_cholesky=True,
cov_scaling=cov_scaling)
grad = log_gaussian_pdf(x,
mu,
L_C,
is_cholesky=True,
compute_grad=True,
cov_scaling=cov_scaling)
assert_allclose(log_pdf, log_pdfs[i])
assert_allclose(grad, grads[i])
def proposal_log_pdf(self, current, proposals):
log_probs = log_gaussian_pdf_multiple(proposals, mu=current,
Sigma=self.L_C, is_cholesky=True,
cov_scaling=self.step_size)
return log_probs
