How to use the lab.B.iqf function in lab
To help you get started, we’ve selected a few lab examples, based on popular ways it is used in public projects.
Secure your code as it's written. Use Snyk Code to scan source code in minutes - no build needed - and fix issues immediately.
wesselb / stheno / stheno / graph.py View on Github 
x_n = MultiInput(*(p(xi.get())
for p, xi in zip(self.e.kernel.ps, x.get())))
else:
x_n = x
# Construct the noise kernel matrix.
K_n = self.e.kernel(x_n)
# The approximation can only handle diagonal noise matrices.
if not isinstance(K_n, Diagonal):
raise RuntimeError('Kernel matrix of noise must be diagonal.')
# And construct the components for the inducing point approximation.
L_z = B.cholesky(self._K_z)
self._A = B.add(B.eye(self._K_z),
B.iqf(K_n, B.transpose(B.solve(L_z, K_zx))))
y_bar = uprank(self.y) - self.e.mean(x_n) - self.graph.means[p_x](x)
prod_y_bar = B.solve(L_z, B.iqf(K_n, B.transpose(K_zx), y_bar))
# Compute the optimal mean.
self._mu = B.add(self.graph.means[p_z](z),
B.iqf(self._A, B.solve(L_z, self._K_z), prod_y_bar))
# Compute the ELBO.
# NOTE: The calculation of `trace_part` asserts that `K_n` is diagonal.
# The rest, however, is completely generic.
trace_part = B.ratio(Diagonal(self.graph.kernels[p_x].elwise(x)[:, 0]) -
Diagonal(B.iqf_diag(self._K_z, K_zx)), K_n)
det_part = B.logdet(2 * B.pi * K_n) + B.logdet(self._A)
iqf_part = B.iqf(K_n, y_bar)[0, 0] - B.iqf(self._A, prod_y_bar)[0, 0]
self._elbo = -0.5 * (trace_part + det_part + iqf_part)else:
x_n = x
# Construct the noise kernel matrix.
K_n = self.e.kernel(x_n)
# The approximation can only handle diagonal noise matrices.
if not isinstance(K_n, Diagonal):
raise RuntimeError('Kernel matrix of noise must be diagonal.')
# And construct the components for the inducing point approximation.
L_z = B.cholesky(self._K_z)
self._A = B.add(B.eye(self._K_z),
B.iqf(K_n, B.transpose(B.solve(L_z, K_zx))))
y_bar = uprank(self.y) - self.e.mean(x_n) - self.graph.means[p_x](x)
prod_y_bar = B.solve(L_z, B.iqf(K_n, B.transpose(K_zx), y_bar))
# Compute the optimal mean.
self._mu = B.add(self.graph.means[p_z](z),
B.iqf(self._A, B.solve(L_z, self._K_z), prod_y_bar))
# Compute the ELBO.
# NOTE: The calculation of `trace_part` asserts that `K_n` is diagonal.
# The rest, however, is completely generic.
trace_part = B.ratio(Diagonal(self.graph.kernels[p_x].elwise(x)[:, 0]) -
Diagonal(B.iqf_diag(self._K_z, K_zx)), K_n)
det_part = B.logdet(2 * B.pi * K_n) + B.logdet(self._A)
iqf_part = B.iqf(K_n, y_bar)[0, 0] - B.iqf(self._A, prod_y_bar)[0, 0]
self._elbo = -0.5 * (trace_part + det_part + iqf_part)self._A = B.add(B.eye(self._K_z),
B.iqf(K_n, B.transpose(B.solve(L_z, K_zx))))
y_bar = uprank(self.y) - self.e.mean(x_n) - self.graph.means[p_x](x)
prod_y_bar = B.solve(L_z, B.iqf(K_n, B.transpose(K_zx), y_bar))
# Compute the optimal mean.
self._mu = B.add(self.graph.means[p_z](z),
B.iqf(self._A, B.solve(L_z, self._K_z), prod_y_bar))
# Compute the ELBO.
# NOTE: The calculation of `trace_part` asserts that `K_n` is diagonal.
# The rest, however, is completely generic.
trace_part = B.ratio(Diagonal(self.graph.kernels[p_x].elwise(x)[:, 0]) -
Diagonal(B.iqf_diag(self._K_z, K_zx)), K_n)
det_part = B.logdet(2 * B.pi * K_n) + B.logdet(self._A)
iqf_part = B.iqf(K_n, y_bar)[0, 0] - B.iqf(self._A, prod_y_bar)[0, 0]
self._elbo = -0.5 * (trace_part + det_part + iqf_part)K_n = self.e.kernel(x_n)
# The approximation can only handle diagonal noise matrices.
if not isinstance(K_n, Diagonal):
raise RuntimeError('Kernel matrix of noise must be diagonal.')
# And construct the components for the inducing point approximation.
L_z = B.cholesky(self._K_z)
self._A = B.add(B.eye(self._K_z),
B.iqf(K_n, B.transpose(B.solve(L_z, K_zx))))
y_bar = uprank(self.y) - self.e.mean(x_n) - self.graph.means[p_x](x)
prod_y_bar = B.solve(L_z, B.iqf(K_n, B.transpose(K_zx), y_bar))
# Compute the optimal mean.
self._mu = B.add(self.graph.means[p_z](z),
B.iqf(self._A, B.solve(L_z, self._K_z), prod_y_bar))
# Compute the ELBO.
# NOTE: The calculation of `trace_part` asserts that `K_n` is diagonal.
# The rest, however, is completely generic.
trace_part = B.ratio(Diagonal(self.graph.kernels[p_x].elwise(x)[:, 0]) -
Diagonal(B.iqf_diag(self._K_z, K_zx)), K_n)
det_part = B.logdet(2 * B.pi * K_n) + B.logdet(self._A)
iqf_part = B.iqf(K_n, y_bar)[0, 0] - B.iqf(self._A, prod_y_bar)[0, 0]
self._elbo = -0.5 * (trace_part + det_part + iqf_part)
