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Fixing issue #70 #71

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May 2, 2022
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Fixing issue #70 #71

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14 changes: 7 additions & 7 deletions gpflux/sampling/sample.py
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Original file line number Diff line number Diff line change
Expand Up @@ -156,12 +156,12 @@ def _efficient_sample_matheron_rule(
:param whiten: Determines the parameterisation of the inducing variables.
"""
L = tf.shape(kernel.feature_coefficients)[0] # num eigenfunctions # noqa: F841
M, P = tf.shape(q_mu)[0], tf.shape(q_mu)[1] # num inducing, num output heads

prior_weights = tf.sqrt(kernel.feature_coefficients) * tf.random.normal(
tf.shape(kernel.feature_coefficients), dtype=default_float()
) # [L, 1]
(L, P), dtype=default_float()
) # [L, P]

M, P = tf.shape(q_mu)[0], tf.shape(q_mu)[1] # num inducing, num output heads
u_sample_noise = tf.matmul(
q_sqrt,
tf.random.normal((P, M, 1), dtype=default_float()), # [P, M, M] # [P, M, 1]
Expand All @@ -175,8 +175,8 @@ def _efficient_sample_matheron_rule(
u_sample = tf.matmul(Luu, u_sample) # [M, P]

phi_Z = kernel.feature_functions(inducing_variable.Z) # [M, L]
weight_space_prior_Z = phi_Z @ prior_weights # [M, 1]
diff = u_sample - weight_space_prior_Z # [M, P] -- using implicit broadcasting
weight_space_prior_Z = phi_Z @ prior_weights # [M, P]
diff = u_sample - weight_space_prior_Z # [M, P]
v = compute_A_inv_b(Kmm, diff) # [M, P]
tf.debugging.assert_equal(tf.shape(v), [M, P])

Expand All @@ -188,11 +188,11 @@ def __call__(self, X: TensorType) -> tf.Tensor:
"""
N = tf.shape(X)[0]
phi_X = kernel.feature_functions(X) # [N, L]
weight_space_prior_X = phi_X @ prior_weights # [N, 1]
weight_space_prior_X = phi_X @ prior_weights # [N, P]
Knm = tf.linalg.matrix_transpose(Kuf(inducing_variable, kernel, X)) # [N, M]
function_space_update_X = Knm @ v # [N, P]

tf.debugging.assert_equal(tf.shape(weight_space_prior_X), [N, 1])
tf.debugging.assert_equal(tf.shape(weight_space_prior_X), [N, P])
tf.debugging.assert_equal(tf.shape(function_space_update_X), [N, P])

return weight_space_prior_X + function_space_update_X # [N, P]
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