def test_gp_nonfinite_phenotype():
random = RandomState(94584)
N = 50
X = random.randn(N, 100)
offset = 0.5
mean = OffsetMean(N)
mean.offset = offset
mean.fix_offset()
cov = LinearCov(X)
cov.scale = 1.0
y = zeros(N)
y[0] = nan
with pytest.raises(ValueError):
GP(y, mean, cov)
y[0] = -inf
with pytest.raises(ValueError):
GP(y, mean, cov)
y[0] = +inf
with pytest.raises(ValueError):
GP(y, mean, cov)
def test_gp_value_1():
random = RandomState(94584)
N = 50
X = random.randn(N, 100)
offset = 0.5
mean = OffsetMean(N)
mean.offset = offset
mean.fix_offset()
cov = LinearCov(X)
cov.scale = 1.0
y = random.randn(N)
gp = GP(y, mean, cov)
assert_allclose(gp.value(), -153.623791551399108)
def test_gp_gradient():
random = RandomState(94584)
N = 50
X = random.randn(N, 100)
offset = 0.5
mean = OffsetMean(N)
mean.offset = offset
mean.fix_offset()
cov = LinearCov(X)
cov.scale = 1.0
y = random.randn(N)
gp = GP(y, mean, cov)
assert_allclose(gp._check_grad(), 0, atol=1e-5)
def test_gp_maximize():
random = RandomState(94584)
N = 50
X = random.randn(N, 100)
offset = 0.5
mean = OffsetMean(N)
mean.offset = offset
mean.fix_offset()
cov = LinearCov(X)
cov.scale = 1.0
y = random.randn(N)
gp = GP(y, mean, cov)
assert_allclose(gp.value(), -153.623791551)
gp.fit(verbose=False)
assert_allclose(gp.value(), -79.8992122415)