https://github.com/GPflow/GPflow
Tip revision: 37db81f85896ff812533c6d4885feaaed7fff47d authored by ST John on 29 January 2020, 19:09:29 UTC
Merge branch 'develop' of github.com:GPflow/GPflow into st/reorder_covariances
Merge branch 'develop' of github.com:GPflow/GPflow into st/reorder_covariances
Tip revision: 37db81f
test_multioutput_features.py
import numpy as np
import pytest
import tensorflow as tf
import gpflow
import gpflow.inducing_variables.multioutputs as mf
import gpflow.kernels.mo_kernels as mk
from gpflow.covariances import Kuf, Kuu
rng = np.random.RandomState(9911)
# ------------------------------------------
# Helpers
# ------------------------------------------
def make_kernel():
return gpflow.kernels.SquaredExponential()
def make_kernels(num):
return [make_kernel() for _ in range(num)]
def make_ip():
x = rng.permutation(Datum.X)
return gpflow.inducing_variables.InducingPoints(x[:Datum.M, ...])
def make_ips(num):
return [make_ip() for _ in range(num)]
# ------------------------------------------
# Data classes: storing constants
# ------------------------------------------
class Datum:
D = 1
L = 2
P = 3
M = 10
N = 100
W = rng.randn(P, L)
X = rng.randn(N)[:, None]
Xnew = rng.randn(N)[:, None]
multioutput_inducing_variable_list = [
mf.SharedIndependentInducingVariables(make_ip()),
mf.SeparateIndependentInducingVariables(make_ips(Datum.P))
]
multioutput_kernel_list = [
mk.SharedIndependent(make_kernel(), Datum.P),
mk.SeparateIndependent(make_kernels(Datum.L)),
mk.LinearCoregionalization(make_kernels(Datum.L), Datum.W)
]
@pytest.mark.parametrize('inducing_variable', multioutput_inducing_variable_list)
@pytest.mark.parametrize('kernel', multioutput_kernel_list)
def test_kuu(inducing_variable, kernel):
K = Kuu(inducing_variable, kernel, jitter=1e-9)
_ = tf.linalg.cholesky(K)
@pytest.mark.parametrize('inducing_variable', multioutput_inducing_variable_list)
@pytest.mark.parametrize('kernel', multioutput_kernel_list)
def test_kuf(inducing_variable, kernel):
_ = Kuf(inducing_variable, kernel, Datum.Xnew)
@pytest.mark.parametrize('func', [Kuu, Kuf])
def test_mixed_shared(func):
inducing_variable = mf.SharedIndependentInducingVariables(make_ip())
kernel = mk.LinearCoregionalization(make_kernels(Datum.L), Datum.W)
if func is Kuu:
t = tf.linalg.cholesky(func(inducing_variable, kernel, jitter=1e-9))
else:
t = func(inducing_variable, kernel, Datum.Xnew)
print(t.shape)