def test_vardec_poisson_2_matrices():
random = RandomState(0)
nsamples = 20
X = random.randn(nsamples, 2)
X = (X - X.mean(0)) / X.std(0)
X = concatenate((ones((nsamples, 1)), X), axis=1)
lik = "poisson"
K = random.randn(nsamples, 10)
K = K @ K.T
K /= K.diagonal().mean()
K += eye(nsamples) * 1e-4
y = X @ random.randn(3) + mvn(random, zeros(nsamples), K)
y = exp((y - y.mean()) / y.std())
vardec = VarDec(y, lik, X)
vardec.append(K)
vardec.append_iid()
vardec.fit(verbose=False)
assert_allclose(vardec.covariance[0].scale, 0.10726852397002325, atol=1e-5)
assert_allclose(vardec.covariance[1].scale,
4.168569936272955e-11,
atol=1e-5)
assert_allclose(vardec.lml(), -26.36419072811823)
def test_vardec_2_matrices():
random = RandomState(0)
nsamples = 20
X = random.randn(nsamples, 2)
X = (X - X.mean(0)) / X.std(0)
X = concatenate((ones((nsamples, 1)), X), axis=1)
lik = "normal"
K = random.randn(nsamples, 10)
K = K @ K.T
K /= K.diagonal().mean()
K += eye(nsamples) * 1e-4
y = X @ random.randn(3) + mvn(random, zeros(nsamples),
K) + random.randn(nsamples)
vardec = VarDec(y, lik, X)
vardec.append(K)
vardec.append_iid()
vardec.fit(verbose=False)
assert_allclose(vardec.covariance[0].scale, 0.32199728815536727, rtol=1e-5)
assert_allclose(vardec.covariance[1].scale, 1.4182987383374532, rtol=1e-5)
assert_allclose(vardec.lml(), -33.63946372828994, rtol=1e-5)
def test_vardec_multitrait():
random = RandomState(0)
nsamples = 20
X = random.randn(nsamples, 2)
X = (X - X.mean(0)) / X.std(0)
X = concatenate((ones((nsamples, 1)), X), axis=1)
lik = "normal"
K = random.randn(nsamples, 10)
K = K @ K.T
K /= K.diagonal().mean()
K += eye(nsamples) * 1e-4
y0 = X @ random.randn(3) + mvn(random, zeros(nsamples), K)
y1 = X @ random.randn(3) + mvn(random, zeros(nsamples), K)
Y = stack((y0, y1), axis=1)
vardec = VarDec(Y, lik, X)
vardec.append(K)
vardec.append_iid()
vardec.fit(verbose=False)
assert_allclose(
vardec.covariance[0].scale,
[
[0.7051133960584494, 0.18484625482083472],
[0.18484625482083472, 0.048457649666404354],
],
rtol=1e-4,
)
assert_allclose(
vardec.covariance[1].scale,
[
[1.4901383238452581e-05, 1.78191074575448e-05],
[1.78191074575448e-05, 1.4592715995489043],
],
atol=1e-6,
rtol=1e-4,
)
assert_allclose(vardec.lml(), -17.39609607331073)
assert_allclose(
vardec.effsizes,
[
[-0.6356567160957514, 0.06692298344869516],
[-0.3888579153976617, -1.5350512471600464],
[-0.13499034585739983, 0.08961009904531193],
],
rtol=1e-4,
)
def test_vardec():
random = RandomState(0)
nsamples = 20
X = random.randn(nsamples, 2)
X = (X - X.mean(0)) / X.std(0)
X = concatenate((ones((nsamples, 1)), X), axis=1)
lik = "normal"
K0 = random.randn(nsamples, 10)
K0 = K0 @ K0.T
K0 /= K0.diagonal().mean()
K0 += eye(nsamples) * 1e-4
K1 = random.randn(nsamples, 10)
K1 = K1 @ K1.T
K1 /= K1.diagonal().mean()
K1 += eye(nsamples) * 1e-4
y = X @ random.randn(3) + mvn(random, zeros(nsamples), K0)
y += mvn(random, zeros(nsamples), K1)
vardec = VarDec(y, lik, X)
vardec.append(K0)
vardec.append(K1)
vardec.append_iid()
vardec.fit(verbose=False)
assert_allclose(vardec.covariance[0].scale, 0.38473522809891697)
assert_allclose(vardec.covariance[1].scale, 1.1839796169221422)
assert_allclose(vardec.covariance[2].scale,
2.061153622438558e-09,
atol=1e-5)
assert_allclose(vardec.lml(), -21.91827344966165)
assert_allclose(
vardec.effsizes,
[-0.5008873352111712, -1.193536406235688, -0.28254298530554534])
def test_vardec_poisson():
random = RandomState(0)
nsamples = 20
X = random.randn(nsamples, 2)
X = (X - X.mean(0)) / X.std(0)
X = concatenate((ones((nsamples, 1)), X), axis=1)
lik = "poisson"
K0 = random.randn(nsamples, 10)
K0 = K0 @ K0.T
K0 /= K0.diagonal().mean()
K0 += eye(nsamples) * 1e-4
K1 = random.randn(nsamples, 10)
K1 = K1 @ K1.T
K1 /= K1.diagonal().mean()
K1 += eye(nsamples) * 1e-4
y = X @ random.randn(3) + mvn(random, zeros(nsamples), K0)
y += mvn(random, zeros(nsamples), K1)
y = exp((y - y.mean()) / y.std())
vardec = VarDec(y, lik, X)
vardec.append(K0)
vardec.append(K1)
vardec.append_iid()
vardec.fit(verbose=False)
assert_allclose(vardec.covariance[0].scale,
2.808478303826397e-09,
atol=1e-5)
assert_allclose(vardec.covariance[1].scale, 0.3503800007985209)
assert_allclose(vardec.covariance[2].scale,
2.061153622438558e-09,
atol=1e-5)
assert_allclose(vardec.lml(), -28.887285796984564)