# Source code for GPy.testing.model_tests

# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
from __future__ import division

import unittest
import numpy as np
import GPy

[docs]class MiscTests(unittest.TestCase):
[docs]    def setUp(self):
self.N = 20
self.N_new = 50
self.D = 1
self.X = np.random.uniform(-3., 3., (self.N, 1))
self.Y = np.sin(self.X) + np.random.randn(self.N, self.D) * 0.05
self.X_new = np.random.uniform(-3., 3., (self.N_new, 1))

[docs]    def test_setXY(self):
m = GPy.models.GPRegression(self.X, self.Y)
m.set_XY(np.vstack([self.X, np.random.rand(1,self.X.shape[1])]), np.vstack([self.Y, np.random.rand(1,self.Y.shape[1])]))
m._trigger_params_changed()
m.predict(m.X)

[docs]    def test_raw_predict_numerical_stability(self):
"""
Test whether the predicted variance of normal GP goes negative under numerical unstable situation.
Thanks simbartonels@github for reporting the bug and providing the following example.
"""

# set seed for reproducability
np.random.seed(3)
# Definition of the Branin test function
def branin(X):
y = (X[:,1]-5.1/(4*np.pi**2)*X[:,0]**2+5*X[:,0]/np.pi-6)**2
y += 10*(1-1/(8*np.pi))*np.cos(X[:,0])+10
return(y)
# Training set defined as a 5*5 grid:
xg1 = np.linspace(-5,10,5)
xg2 = np.linspace(0,15,5)
X = np.zeros((xg1.size * xg2.size,2))
for i,x1 in enumerate(xg1):
for j,x2 in enumerate(xg2):
X[i+xg1.size*j,:] = [x1,x2]
Y = branin(X)[:,None]
# Fit a GP
# Create an exponentiated quadratic plus bias covariance function
k = GPy.kern.RBF(input_dim=2, ARD = True)
# Build a GP model
m = GPy.models.GPRegression(X,Y,k)
# fix the noise variance
m.likelihood.variance.fix(1e-5)
# Randomize the model and optimize
m.randomize()
m.optimize()
# Compute the mean of model prediction on 1e5 Monte Carlo samples
Xp = np.random.uniform(size=(int(1e5),2))
Xp[:,0] = Xp[:,0]*15-5
Xp[:,1] = Xp[:,1]*15
_, var = m.predict(Xp)
self.assertTrue(np.all(var>=0.))

[docs]    def test_raw_predict(self):
k = GPy.kern.RBF(1)
m = GPy.models.GPRegression(self.X, self.Y, kernel=k)
m.randomize()
m.likelihood.variance = .5
Kinv = np.linalg.pinv(k.K(self.X) + np.eye(self.N) * m.likelihood.variance)
K_hat = k.K(self.X_new) - k.K(self.X_new, self.X).dot(Kinv).dot(k.K(self.X, self.X_new))
mu_hat = k.K(self.X_new, self.X).dot(Kinv).dot(m.Y_normalized)

mu, covar = m.predict_noiseless(self.X_new, full_cov=True)
self.assertEquals(mu.shape, (self.N_new, self.D))
self.assertEquals(covar.shape, (self.N_new, self.N_new))
np.testing.assert_almost_equal(K_hat, covar)
np.testing.assert_almost_equal(mu_hat, mu)

mu, var = m.predict_noiseless(self.X_new)
self.assertEquals(mu.shape, (self.N_new, self.D))
self.assertEquals(var.shape, (self.N_new, 1))
np.testing.assert_almost_equal(np.diag(K_hat)[:, None], var)
np.testing.assert_almost_equal(mu_hat, mu)

[docs]    def test_normalizer(self):
k = GPy.kern.RBF(1)
Y = self.Y
mu, std = Y.mean(0), Y.std(0)
m = GPy.models.GPRegression(self.X, Y, kernel=k, normalizer=True)
m.optimize(messages=True)
k = GPy.kern.RBF(1)
m2 = GPy.models.GPRegression(self.X, (Y-mu)/std, kernel=k, normalizer=False)
m2[:] = m[:]

mu1, var1 = m.predict(m.X, full_cov=True)
mu2, var2 = m2.predict(m2.X, full_cov=True)
np.testing.assert_allclose(mu1, (mu2*std)+mu)
np.testing.assert_allclose(var1, var2*std**2)

mu1, var1 = m.predict(m.X, full_cov=False)
mu2, var2 = m2.predict(m2.X, full_cov=False)

np.testing.assert_allclose(mu1, (mu2*std)+mu)
np.testing.assert_allclose(var1, var2*std**2)

q50n = m.predict_quantiles(m.X, (50,))
q50 = m2.predict_quantiles(m2.X, (50,))

np.testing.assert_allclose(q50n[0], (q50[0]*std)+mu)

# Test variance component:
qs = np.array([2.5, 97.5])
# The quantiles get computed before unormalization
# And transformed using the mean transformation:
c = np.random.choice(self.X.shape[0])
q95 = m2.predict_quantiles(self.X[[c]], qs)
mu, var = m2.predict(self.X[[c]])
from scipy.stats import norm
np.testing.assert_allclose((mu+(norm.ppf(qs/100.)*np.sqrt(var))).flatten(), np.array(q95).flatten())

[docs]    def test_multioutput_regression_with_normalizer(self):
"""
Test that normalizing works in multi-output case
"""

# Create test inputs
X = self.X
Y1 = np.sin(X) + np.random.randn(*X.shape) * 0.2
Y2 = -np.sin(X) + np.random.randn(*X.shape) * 0.05
Y = np.hstack((Y1, Y2))

mu, std = Y.mean(0), Y.std(0)
m = GPy.models.GPRegression(X, Y, normalizer=True)
m.optimize(messages=True)
k = GPy.kern.RBF(1)
m2 = GPy.models.GPRegression(X, (Y-mu)/std, normalizer=False)
m2[:] = m[:]

mu1, var1 = m.predict(m.X, full_cov=True)
mu2, var2 = m2.predict(m2.X, full_cov=True)
np.testing.assert_allclose(mu1, (mu2*std)+mu)
np.testing.assert_allclose(var1, var2[:, :, None]*std[None, None, :]**2)

mu1, var1 = m.predict(m.X, full_cov=False)
mu2, var2 = m2.predict(m2.X, full_cov=False)

np.testing.assert_allclose(mu1, (mu2*std)+mu)
np.testing.assert_allclose(var1, var2*std[None, :]**2)

q50n = m.predict_quantiles(m.X, (50,))
q50 = m2.predict_quantiles(m2.X, (50,))

np.testing.assert_allclose(q50n[0], (q50[0]*std)+mu)

# Test variance component:
qs = np.array([2.5, 97.5])
# The quantiles get computed before unormalization
# And transformed using the mean transformation:
c = np.random.choice(X.shape[0])
q95 = m2.predict_quantiles(X[[c]], qs)
mu, var = m2.predict(X[[c]])
from scipy.stats import norm
np.testing.assert_allclose((mu.T+(norm.ppf(qs/100.)*np.sqrt(var))).T.flatten(), np.array(q95).flatten())

[docs]    def check_jacobian(self):
try:
except:
def k(X, X2, alpha=1., lengthscale=None):
if lengthscale is None:
lengthscale = np.ones(X.shape[1])
exp = 0.
for q in range(X.shape[1]):
exp += ((X[:, [q]] - X2[:, [q]].T)/lengthscale[q])**2
#exp = np.sqrt(exp)
return alpha * np.exp(-.5*exp)
dk = ag.elementwise_grad(lambda x, x2: k(x, x2, alpha=ke.variance.values, lengthscale=ke.lengthscale.values))

ke = GPy.kern.RBF(1, ARD=True)
#ke.randomize()
ke.variance = .2#.randomize()
ke.lengthscale[:] = .5
ke.randomize()
X = np.linspace(-1, 1, 1000)[:,None]
X2 = np.array([[0.]]).T

m = GPRegression(self.X, self.Y)
def f(x):
m.X[:] = x
return m.log_likelihood()
def df(x):
m.X[:] = x
def ddf(x):
m.X[:] = x

[docs]    def test_predict_uncertain_inputs(self):
""" Projection of Gaussian through a linear function is still gaussian, and moments are analytical to compute, so we can check this case for predictions easily """
X = np.linspace(-5,5, 10)[:, None]
Y = 2*X + np.random.randn(*X.shape)*1e-3
m = GPy.models.BayesianGPLVM(Y, 1, X=X, kernel=GPy.kern.Linear(1), num_inducing=1)
m.Gaussian_noise[:] = 1e-4
m.X.mean[:] = X[:]
m.X.variance[:] = 1e-5
m.X.fix()
m.optimize()
X_pred_mu = np.random.randn(5, 1)
X_pred_var = np.random.rand(5, 1) + 1e-5
from GPy.core.parameterization.variational import NormalPosterior
X_pred = NormalPosterior(X_pred_mu, X_pred_var)
# mu = \int f(x)q(x|mu,S) dx = \int 2x.q(x|mu,S) dx = 2.mu
# S = \int (f(x) - m)^2q(x|mu,S) dx = \int f(x)^2 q(x) dx - mu**2 = 4(mu^2 + S) - (2.mu)^2 = 4S
Y_mu_true = 2*X_pred_mu
Y_var_true = 4*X_pred_var
Y_mu_pred, Y_var_pred = m.predict_noiseless(X_pred)
np.testing.assert_allclose(Y_mu_true, Y_mu_pred, rtol=1e-3)
np.testing.assert_allclose(Y_var_true, Y_var_pred, rtol=1e-3)

[docs]    def test_sparse_raw_predict(self):
k = GPy.kern.RBF(1)
m = GPy.models.SparseGPRegression(self.X, self.Y, kernel=k)
m.randomize()
Z = m.Z[:]

# Not easy to check if woodbury_inv is correct in itself as it requires a large derivation and expression
Kinv = m.posterior.woodbury_inv
K_hat = k.K(self.X_new) - k.K(self.X_new, Z).dot(Kinv).dot(k.K(Z, self.X_new))
# K_hat = np.clip(K_hat, 1e-15, np.inf)

mu, covar = m.predict_noiseless(self.X_new, full_cov=True)
self.assertEquals(mu.shape, (self.N_new, self.D))
self.assertEquals(covar.shape, (self.N_new, self.N_new))
np.testing.assert_almost_equal(K_hat, covar)
# np.testing.assert_almost_equal(mu_hat, mu)

mu, var = m.predict_noiseless(self.X_new)
self.assertEquals(mu.shape, (self.N_new, self.D))
self.assertEquals(var.shape, (self.N_new, 1))
np.testing.assert_almost_equal(np.diag(K_hat)[:, None], var)
# np.testing.assert_almost_equal(mu_hat, mu)

[docs]    def test_likelihood_replicate(self):
m = GPy.models.GPRegression(self.X, self.Y)
m2 = GPy.models.GPRegression(self.X, self.Y)
np.testing.assert_equal(m.log_likelihood(), m2.log_likelihood())
m.randomize()
m2[:] = m[''].values()
np.testing.assert_almost_equal(m.log_likelihood(), m2.log_likelihood())
m.randomize()
m2[''] = m[:]
np.testing.assert_almost_equal(m.log_likelihood(), m2.log_likelihood())
m.randomize()
m2[:] = m[:]
np.testing.assert_almost_equal(m.log_likelihood(), m2.log_likelihood())
m.randomize()
m2[''] = m['']
np.testing.assert_almost_equal(m.log_likelihood(), m2.log_likelihood())

m.kern.lengthscale.randomize()
m2[:] = m[:]
np.testing.assert_almost_equal(m.log_likelihood(), m2.log_likelihood())

m.Gaussian_noise.randomize()
m2[:] = m[:]
np.testing.assert_almost_equal(m.log_likelihood(), m2.log_likelihood())

m['.*var'] = 2
m2['.*var'] = m['.*var']
np.testing.assert_almost_equal(m.log_likelihood(), m2.log_likelihood())

[docs]    def test_likelihood_set(self):
m = GPy.models.GPRegression(self.X, self.Y)
m2 = GPy.models.GPRegression(self.X, self.Y)
np.testing.assert_equal(m.log_likelihood(), m2.log_likelihood())

m.kern.lengthscale.randomize()
m2.kern.lengthscale = m.kern.lengthscale
np.testing.assert_equal(m.log_likelihood(), m2.log_likelihood())

m.kern.lengthscale.randomize()
m2['.*lengthscale'] = m.kern.lengthscale
np.testing.assert_equal(m.log_likelihood(), m2.log_likelihood())

m.kern.lengthscale.randomize()
m2['.*lengthscale'] = m.kern['.*lengthscale']
np.testing.assert_equal(m.log_likelihood(), m2.log_likelihood())

m.kern.lengthscale.randomize()
m2.kern.lengthscale = m.kern['.*lengthscale']
np.testing.assert_equal(m.log_likelihood(), m2.log_likelihood())

[docs]    def test_missing_data(self):
Q = 4

k = GPy.kern.Linear(Q, ARD=True) + GPy.kern.White(Q, np.exp(-2)) # + kern.bias(Q)
m = _create_missing_data_model(k, Q)
mul, varl = m.predict(m.X)

k = GPy.kern.RBF(Q, ARD=True) + GPy.kern.White(Q, np.exp(-2)) # + kern.bias(Q)
m2 = _create_missing_data_model(k, Q)
m2.kern.rbf.lengthscale[:] = 1e6

m2.X[:] = m.X.param_array
m2.likelihood[:] = m.likelihood[:]
m2.kern.white[:] = m.kern.white[:]
mu, var = m.predict(m.X)
np.testing.assert_allclose(mul, mu)
np.testing.assert_allclose(varl, var)

q50 = m.predict_quantiles(m.X, (50,))
np.testing.assert_allclose(mul, q50[0])

[docs]    def test_likelihood_replicate_kern(self):
m = GPy.models.GPRegression(self.X, self.Y)
m2 = GPy.models.GPRegression(self.X, self.Y)
np.testing.assert_equal(m.log_likelihood(), m2.log_likelihood())
m.kern.randomize()
m2.kern[''] = m.kern[:]
np.testing.assert_almost_equal(m.log_likelihood(), m2.log_likelihood())
m.kern.randomize()
m2.kern[:] = m.kern[:]
np.testing.assert_almost_equal(m.log_likelihood(), m2.log_likelihood())
m.kern.randomize()
m2.kern[''] = m.kern['']
np.testing.assert_almost_equal(m.log_likelihood(), m2.log_likelihood())
m.kern.randomize()
m2.kern[:] = m.kern[''].values()
np.testing.assert_almost_equal(m.log_likelihood(), m2.log_likelihood())

[docs]    def test_big_model(self):
m = GPy.examples.dimensionality_reduction.mrd_simulation(optimize=0, plot=0, plot_sim=0)
m.X.fix()
print(m)
m.unfix()
print(m)
m.fix()
print(m)
m.inducing_inputs.unfix()
print(m)
m.unfix()
print(m)

[docs]    def test_mrd(self):
from GPy.inference.latent_function_inference import InferenceMethodList, VarDTC
from GPy.likelihoods import Gaussian
Y1 = np.random.normal(0, 1, (40, 13))
Y2 = np.random.normal(0, 1, (40, 6))
Y3 = np.random.normal(0, 1, (40, 8))
Q = 5
m = GPy.models.MRD(dict(data1=Y1, data2=Y2, data3=Y3), Q,
)
m.randomize()

m = GPy.models.MRD(dict(data1=Y1, data2=Y2, data3=Y3), Q, initx='PCA_single',
initz='random',
kernel=[GPy.kern.RBF(Q, ARD=1) for _ in range(3)],
inference_method=InferenceMethodList([VarDTC() for _ in range(3)]),
likelihoods = [Gaussian(name='Gaussian_noise'.format(i)) for i in range(3)])
m.randomize()

m = GPy.models.MRD(dict(data1=Y1, data2=Y2, data3=Y3), Q, initx='random',
initz='random',
kernel=GPy.kern.RBF(Q, ARD=1),
)
m.randomize()

m = GPy.models.MRD(dict(data1=Y1, data2=Y2, data3=Y3), Q, X=np.random.normal(0,1,size=(40,Q)),
X_variance=False,
kernel=GPy.kern.RBF(Q, ARD=1),
likelihoods = [Gaussian(name='Gaussian_noise'.format(i)) for i in range(3)])
m.randomize()

[docs]    def test_model_set_params(self):
m = GPy.models.GPRegression(self.X, self.Y)
lengthscale = np.random.uniform()
m.kern.lengthscale = lengthscale
np.testing.assert_equal(m.kern.lengthscale, lengthscale)
m.kern.lengthscale *= 1
m['.*var'] -= .1
np.testing.assert_equal(m.kern.lengthscale, lengthscale)
m.optimize()
print(m)

Y1 = np.random.normal(0, 1, (40, 13))
Y2 = np.random.normal(0, 1, (40, 6))
m = GPy.models.MRD([Y1, Y2], 5)
self.count = 0
m.update_model(False)
m['.*Gaussian'] = .001
self.assertEquals(self.count, 0)
m['.*Gaussian'].constrain_bounded(0,.01)
self.assertEquals(self.count, 0)
m.Z.fix()
self.assertEquals(self.count, 0)
m.update_model(True)
self.assertEquals(self.count, 1)
self.count+=1

[docs]    def test_model_optimize(self):
X = np.random.uniform(-3., 3., (20, 1))
Y = np.sin(X) + np.random.randn(20, 1) * 0.05
m = GPy.models.GPRegression(X, Y)
m.optimize()
print(m)

[docs]    def test_input_warped_gp_identity(self):
"""
A InputWarpedGP with the identity warping function should be
equal to a standard GP.
"""
k = GPy.kern.RBF(1)
m = GPy.models.GPRegression(self.X, self.Y, kernel=k)
m.optimize()
preds = m.predict(self.X)

warp_k = GPy.kern.RBF(1)
warp_f = GPy.util.input_warping_functions.IdentifyWarping()
warp_m = GPy.models.InputWarpedGP(self.X, self.Y, kernel=warp_k, warping_function=warp_f)
warp_m.optimize()
warp_preds = warp_m.predict(self.X)

np.testing.assert_almost_equal(preds, warp_preds, decimal=4)

n_X = 100
np.random.seed(0)
X = np.random.randn(n_X, 2)
Y = np.sum(np.sin(X), 1).reshape(n_X, 1)

k1 = GPy.kern.Linear(2)
m1 = GPy.models.InputWarpedGP(X, Y, kernel=k1)
m1.randomize()

k2 = GPy.kern.RBF(2)
m2 = GPy.models.InputWarpedGP(X, Y, kernel=k2)
m2.randomize()

k3 = GPy.kern.Matern52(2)
m3 = GPy.models.InputWarpedGP(X, Y, kernel=k3)
m3.randomize()

[docs]    def test_kumar_warping_parameters(self):
np.random.seed(1)
X = np.random.rand(5, 2)
epsilon = 1e-6

# testing warping indices
warping_ind_1 = [0, 1, 2]
warping_ind_2 = [-1, 1, 2]
warping_ind_3 = [0, 1.5, 2]
self.failUnlessRaises(ValueError, GPy.util.input_warping_functions.KumarWarping, X, warping_ind_1)
self.failUnlessRaises(ValueError, GPy.util.input_warping_functions.KumarWarping, X, warping_ind_2)
self.failUnlessRaises(ValueError, GPy.util.input_warping_functions.KumarWarping, X, warping_ind_3)

# testing Xmin and Xmax
Xmin_1, Xmax_1 = None, [1, 1]
Xmin_2, Xmax_2 = [0, 0], None
Xmin_3, Xmax_3 = [0, 0, 0], [1, 1]
self.failUnlessRaises(ValueError, GPy.util.input_warping_functions.KumarWarping, X, [0, 1], epsilon, Xmin_1, Xmax_1)
self.failUnlessRaises(ValueError, GPy.util.input_warping_functions.KumarWarping, X, [0, 1], epsilon, Xmin_2, Xmax_2)
self.failUnlessRaises(ValueError, GPy.util.input_warping_functions.KumarWarping, X, [0, 1], epsilon, Xmin_3, Xmax_3)

[docs]    def test_warped_gp_identity(self):
"""
A WarpedGP with the identity warping function should be
equal to a standard GP.
"""
k = GPy.kern.RBF(1)
m = GPy.models.GPRegression(self.X, self.Y, kernel=k)
m.optimize()
preds = m.predict(self.X)

warp_k = GPy.kern.RBF(1)
warp_f = GPy.util.warping_functions.IdentityFunction(closed_inverse=False)
warp_m = GPy.models.WarpedGP(self.X, self.Y, kernel=warp_k,
warping_function=warp_f)
warp_m.optimize()
warp_preds = warp_m.predict(self.X)

warp_k_exact = GPy.kern.RBF(1)
warp_f_exact = GPy.util.warping_functions.IdentityFunction()
warp_m_exact = GPy.models.WarpedGP(self.X, self.Y, kernel=warp_k_exact,
warping_function=warp_f_exact)
warp_m_exact.optimize()
warp_preds_exact = warp_m_exact.predict(self.X)

np.testing.assert_almost_equal(preds, warp_preds, decimal=4)
np.testing.assert_almost_equal(preds, warp_preds_exact, decimal=4)

[docs]    def test_warped_gp_log(self):
"""
A WarpedGP with the log warping function should be
equal to a standard GP with log labels.
Note that we predict the median here.
"""
k = GPy.kern.RBF(1)
Y = np.abs(self.Y)
logY = np.log(Y)
m = GPy.models.GPRegression(self.X, logY, kernel=k)
m.optimize()
preds = m.predict(self.X)[0]

warp_k = GPy.kern.RBF(1)
warp_f = GPy.util.warping_functions.LogFunction(closed_inverse=False)
warp_m = GPy.models.WarpedGP(self.X, Y, kernel=warp_k,
warping_function=warp_f)
warp_m.optimize()
warp_preds = warp_m.predict(self.X, median=True)[0]

warp_k_exact = GPy.kern.RBF(1)
warp_f_exact = GPy.util.warping_functions.LogFunction()
warp_m_exact = GPy.models.WarpedGP(self.X, Y, kernel=warp_k_exact,
warping_function=warp_f_exact)
warp_m_exact.optimize(messages=True)
warp_preds_exact = warp_m_exact.predict(self.X, median=True)[0]

np.testing.assert_almost_equal(np.exp(preds), warp_preds, decimal=4)
np.testing.assert_almost_equal(np.exp(preds), warp_preds_exact, decimal=4)

[docs]    def test_warped_gp_cubic_sine(self, max_iters=100):
"""
A test replicating the cubic sine regression problem from
Snelson's paper. This test doesn't have any assertions, it's
just to ensure coverage of the tanh warping function code.
"""
X = (2 * np.pi) * np.random.random(151) - np.pi
Y = np.sin(X) + np.random.normal(0,0.2,151)
Y = np.array([np.power(abs(y),float(1)/3) * (1,-1)[y<0] for y in Y])
X = X[:, None]
Y = Y[:, None]

warp_m = GPy.models.WarpedGP(X, Y)#, kernel=warp_k)#, warping_function=warp_f)
warp_m['.*\.d'].constrain_fixed(1.0)
warp_m.optimize_restarts(parallel=False, robust=False, num_restarts=5,
max_iters=max_iters)
warp_m.predict(X)
warp_m.predict_quantiles(X)
warp_m.log_predictive_density(X, Y)
warp_m.predict_in_warped_space = False
warp_m.plot()
warp_m.predict_in_warped_space = True
warp_m.plot()

[docs]    def test_offset_regression(self):
#Tests GPy.models.GPOffsetRegression. Using two small time series
#from a sine wave, we confirm the algorithm determines that the
#likelihood is maximised when the offset hyperparameter is approximately
#equal to the actual offset in X between the two time series.
offset = 3
X1 = np.arange(0,50,5.0)[:,None]
X2 = np.arange(0+offset,50+offset,5.0)[:,None]
X = np.vstack([X1,X2])
ind = np.vstack([np.zeros([10,1]),np.ones([10,1])])
X = np.hstack([X,ind])
Y = np.sin((X[0:10,0])/30.0)[:,None]
Y = np.vstack([Y,Y])

m = GPy.models.GPOffsetRegression(X,Y)
m.rbf.lengthscale=5.0 #make it something other than one to check our gradients properly!
m.optimize()
assert np.abs(m.offset[0]-offset)<0.1, ("GPOffsetRegression model failing to estimate correct offset (value estimated = %0.2f instead of %0.2f)" % (m.offset[0], offset))

X = np.random.uniform(-4, 4, (20, 5))
points = np.random.uniform(X.min(0), X.max(0), X.shape[1])
ks = []
for i in range(points.shape[0]):
if (i%2==0) and (i%3!=0):
self.assertRaises(AssertionError, GPy.kern.LogisticBasisFuncKernel, 1, points, ARD=i%2==0, ARD_slope=i%3==0, active_dims=[i])
else:
ks.append(GPy.kern.LogisticBasisFuncKernel(1, points, ARD=i%2==0, ARD_slope=i%3==0, active_dims=[i]))
k.randomize()

Y = np.random.normal(0, 1, (X.shape[0], 1))
m = GPy.models.GPRegression(X, Y, kernel=k.copy())

[docs]    def test_posterior_inf_basis_funcs(self):
X = np.random.uniform(-4, 1, (50, 1))

# Logistic:
k = GPy.kern.LogisticBasisFuncKernel(1, [0, -2])

true_w = [1, 2]
true_slope = [5, -2]

Y = 0
for w, s, c in zip(true_w, true_slope, k.centers[0]):
Y += w/(1+np.exp(-s*(X-c)))
Y += np.random.normal(0, .000001)

m = GPy.models.GPRegression(X,Y,kernel=k.copy())
#m.likelihood.fix(1e-6)
m.optimize()

wu, wv = m.kern.posterior_inf()
#_sort = np.argsort(wu.flat)

#from scipy.stats import norm
#confidence_intervals = np.array(norm.interval(.95, loc=wu.flat[_sort], scale=np.sqrt(np.diag(wv))[_sort])).T
#for i in range(wu.size):
#    s,t = confidence_intervals[i]
#    v = true_w[i]
#    assert ((s<v)&(v<t)), "didnt find true w within the 95% confidence interval of the predicted values"

np.testing.assert_allclose(np.sort(wu.flat), np.sort(true_w), rtol=1e-4)
np.testing.assert_allclose(np.diag(wv), 0, atol=1e-4)
np.testing.assert_allclose(np.sort(m.kern.slope.flat), np.sort(true_slope), rtol=1e-4)

[docs]    def setUp(self):
######################################
# # 1 dimensional example

# sample inputs and outputs
self.X1D = np.random.uniform(-3., 3., (20, 1))
self.Y1D = np.sin(self.X1D) + np.random.randn(20, 1) * 0.05

######################################
# # 2 dimensional example

# sample inputs and outputs
self.X2D = np.random.uniform(-3., 3., (40, 2))
self.Y2D = np.sin(self.X2D[:, 0:1]) * np.sin(self.X2D[:, 1:2]) + np.random.randn(40, 1) * 0.05

[docs]    def check_model(self, kern, model_type='GPRegression', dimension=1, uncertain_inputs=False):
if dimension == 1:
X = self.X1D
Y = self.Y1D
else:
X = self.X2D
Y = self.Y2D
# Get model type (GPRegression, SparseGPRegression, etc)
model_fit = getattr(GPy.models, model_type)

# noise = GPy.kern.White(dimension)
kern = kern  #  + noise
if uncertain_inputs:
m = model_fit(X, Y, kernel=kern, X_variance=np.random.rand(X.shape[0], X.shape[1]))
else:
m = model_fit(X, Y, kernel=kern)
m.randomize()
# contrain all parameters to be positive

[docs]    def test_GPRegression_rbf_1d(self):
''' Testing the GP regression with rbf kernel with white kernel on 1d data '''
rbf = GPy.kern.RBF(1)
self.check_model(rbf, model_type='GPRegression', dimension=1)

[docs]    def test_GPRegression_rbf_2D(self):
''' Testing the GP regression with rbf kernel on 2d data '''
rbf = GPy.kern.RBF(2)
self.check_model(rbf, model_type='GPRegression', dimension=2)

[docs]    def test_GPRegression_rbf_ARD_2D(self):
''' Testing the GP regression with rbf kernel on 2d data '''
k = GPy.kern.RBF(2, ARD=True)
self.check_model(k, model_type='GPRegression', dimension=2)

[docs]    def test_GPRegression_mlp_1d(self):
''' Testing the GP regression with mlp kernel with white kernel on 1d data '''
mlp = GPy.kern.MLP(1)
self.check_model(mlp, model_type='GPRegression', dimension=1)

# TODO:
# def test_GPRegression_poly_1d(self):
#    ''' Testing the GP regression with polynomial kernel with white kernel on 1d data '''
#    mlp = GPy.kern.Poly(1, degree=5)
#    self.check_model(mlp, model_type='GPRegression', dimension=1)

[docs]    def test_GPRegression_matern52_1D(self):
''' Testing the GP regression with matern52 kernel on 1d data '''
matern52 = GPy.kern.Matern52(1)
self.check_model(matern52, model_type='GPRegression', dimension=1)

[docs]    def test_GPRegression_matern52_2D(self):
''' Testing the GP regression with matern52 kernel on 2d data '''
matern52 = GPy.kern.Matern52(2)
self.check_model(matern52, model_type='GPRegression', dimension=2)

[docs]    def test_GPRegression_matern52_ARD_2D(self):
''' Testing the GP regression with matern52 kernel on 2d data '''
matern52 = GPy.kern.Matern52(2, ARD=True)
self.check_model(matern52, model_type='GPRegression', dimension=2)

[docs]    def test_GPRegression_matern32_1D(self):
''' Testing the GP regression with matern32 kernel on 1d data '''
matern32 = GPy.kern.Matern32(1)
self.check_model(matern32, model_type='GPRegression', dimension=1)

[docs]    def test_GPRegression_matern32_2D(self):
''' Testing the GP regression with matern32 kernel on 2d data '''
matern32 = GPy.kern.Matern32(2)
self.check_model(matern32, model_type='GPRegression', dimension=2)

[docs]    def test_GPRegression_matern32_ARD_2D(self):
''' Testing the GP regression with matern32 kernel on 2d data '''
matern32 = GPy.kern.Matern32(2, ARD=True)
self.check_model(matern32, model_type='GPRegression', dimension=2)

[docs]    def test_GPRegression_exponential_1D(self):
''' Testing the GP regression with exponential kernel on 1d data '''
exponential = GPy.kern.Exponential(1)
self.check_model(exponential, model_type='GPRegression', dimension=1)

[docs]    def test_GPRegression_exponential_2D(self):
''' Testing the GP regression with exponential kernel on 2d data '''
exponential = GPy.kern.Exponential(2)
self.check_model(exponential, model_type='GPRegression', dimension=2)

[docs]    def test_GPRegression_exponential_ARD_2D(self):
''' Testing the GP regression with exponential kernel on 2d data '''
exponential = GPy.kern.Exponential(2, ARD=True)
self.check_model(exponential, model_type='GPRegression', dimension=2)

[docs]    def test_GPRegression_bias_kern_1D(self):
''' Testing the GP regression with bias kernel on 1d data '''
bias = GPy.kern.Bias(1)
self.check_model(bias, model_type='GPRegression', dimension=1)

[docs]    def test_GPRegression_bias_kern_2D(self):
''' Testing the GP regression with bias kernel on 2d data '''
bias = GPy.kern.Bias(2)
self.check_model(bias, model_type='GPRegression', dimension=2)

[docs]    def test_GPRegression_linear_kern_1D_ARD(self):
''' Testing the GP regression with linear kernel on 1d data '''
linear = GPy.kern.Linear(1, ARD=True)
self.check_model(linear, model_type='GPRegression', dimension=1)

[docs]    def test_GPRegression_linear_kern_2D_ARD(self):
''' Testing the GP regression with linear kernel on 2d data '''
linear = GPy.kern.Linear(2, ARD=True)
self.check_model(linear, model_type='GPRegression', dimension=2)

[docs]    def test_GPRegression_linear_kern_1D(self):
''' Testing the GP regression with linear kernel on 1d data '''
linear = GPy.kern.Linear(1)
self.check_model(linear, model_type='GPRegression', dimension=1)

[docs]    def test_GPRegression_linear_kern_2D(self):
''' Testing the GP regression with linear kernel on 2d data '''
linear = GPy.kern.Linear(2)
self.check_model(linear, model_type='GPRegression', dimension=2)

[docs]    def test_SparseGPRegression_rbf_white_kern_1d(self):
''' Testing the sparse GP regression with rbf kernel with white kernel on 1d data '''
rbf = GPy.kern.RBF(1)
self.check_model(rbf, model_type='SparseGPRegression', dimension=1)

[docs]    def test_SparseGPRegression_rbf_white_kern_2D(self):
''' Testing the sparse GP regression with rbf kernel on 2d data '''
rbf = GPy.kern.RBF(2)
self.check_model(rbf, model_type='SparseGPRegression', dimension=2)

[docs]    def test_SparseGPRegression_rbf_linear_white_kern_1D(self):
''' Testing the sparse GP regression with rbf kernel on 1d data '''
rbflin = GPy.kern.RBF(1) + GPy.kern.Linear(1) + GPy.kern.White(1, 1e-5)
self.check_model(rbflin, model_type='SparseGPRegression', dimension=1)

[docs]    def test_SparseGPRegression_rbf_linear_white_kern_2D(self):
''' Testing the sparse GP regression with rbf kernel on 2d data '''
rbflin = GPy.kern.RBF(2) + GPy.kern.Linear(2)
self.check_model(rbflin, model_type='SparseGPRegression', dimension=2)

[docs]    def test_SparseGPRegression_rbf_white_kern_2D_uncertain_inputs(self):
''' Testing the sparse GP regression with rbf, linear kernel on 2d data with uncertain inputs'''
rbflin = GPy.kern.RBF(2) + GPy.kern.White(2)
self.check_model(rbflin, model_type='SparseGPRegression', dimension=2, uncertain_inputs=1)

[docs]    def test_SparseGPRegression_rbf_white_kern_1D_uncertain_inputs(self):
''' Testing the sparse GP regression with rbf, linear kernel on 1d data with uncertain inputs'''
rbflin = GPy.kern.RBF(1) + GPy.kern.White(1)
self.check_model(rbflin, model_type='SparseGPRegression', dimension=1, uncertain_inputs=1)

[docs]    def test_TPRegression_matern52_1D(self):
''' Testing the TP regression with matern52 kernel on 1d data '''
matern52 = GPy.kern.Matern52(1) + GPy.kern.White(1)
self.check_model(matern52, model_type='TPRegression', dimension=1)

[docs]    def test_TPRegression_rbf_2D(self):
''' Testing the TP regression with rbf kernel on 2d data '''
rbf = GPy.kern.RBF(2)
self.check_model(rbf, model_type='TPRegression', dimension=2)

[docs]    def test_TPRegression_rbf_ARD_2D(self):
''' Testing the GP regression with rbf kernel on 2d data '''
k = GPy.kern.RBF(2, ARD=True)
self.check_model(k, model_type='TPRegression', dimension=2)

[docs]    def test_TPRegression_matern52_2D(self):
''' Testing the TP regression with matern52 kernel on 2d data '''
matern52 = GPy.kern.Matern52(2)
self.check_model(matern52, model_type='TPRegression', dimension=2)

[docs]    def test_TPRegression_matern52_ARD_2D(self):
''' Testing the TP regression with matern52 kernel on 2d data '''
matern52 = GPy.kern.Matern52(2, ARD=True)
self.check_model(matern52, model_type='TPRegression', dimension=2)

[docs]    def test_TPRegression_matern32_1D(self):
''' Testing the TP regression with matern32 kernel on 1d data '''
matern32 = GPy.kern.Matern32(1)
self.check_model(matern32, model_type='TPRegression', dimension=1)

[docs]    def test_TPRegression_matern32_2D(self):
''' Testing the TP regression with matern32 kernel on 2d data '''
matern32 = GPy.kern.Matern32(2)
self.check_model(matern32, model_type='TPRegression', dimension=2)

[docs]    def test_TPRegression_matern32_ARD_2D(self):
''' Testing the TP regression with matern32 kernel on 2d data '''
matern32 = GPy.kern.Matern32(2, ARD=True)
self.check_model(matern32, model_type='TPRegression', dimension=2)

[docs]    def test_GPLVM_rbf_bias_white_kern_2D(self):
""" Testing GPLVM with rbf + bias kernel """
N, input_dim, D = 50, 1, 2
X = np.random.rand(N, input_dim)
k = GPy.kern.RBF(input_dim, 0.5, 0.9 * np.ones((1,))) + GPy.kern.Bias(input_dim, 0.1) + GPy.kern.White(input_dim, 0.05) + GPy.kern.Matern32(input_dim) + GPy.kern.Matern52(input_dim)
K = k.K(X)
Y = np.random.multivariate_normal(np.zeros(N), K, input_dim).T
m = GPy.models.GPLVM(Y, input_dim, kernel=k)

[docs]    def test_SparseGPLVM_rbf_bias_white_kern_2D(self):
""" Testing GPLVM with rbf + bias kernel """
N, input_dim, D = 50, 1, 2
X = np.random.rand(N, input_dim)
k = GPy.kern.RBF(input_dim, 0.5, 0.9 * np.ones((1,))) + GPy.kern.Bias(input_dim, 0.1) + GPy.kern.White(input_dim, 0.05) + GPy.kern.Matern32(input_dim) + GPy.kern.Matern52(input_dim)
K = k.K(X)
Y = np.random.multivariate_normal(np.zeros(N), K, input_dim).T
m = GPy.models.SparseGPLVM(Y, input_dim, kernel=k)

[docs]    def test_BCGPLVM_rbf_bias_white_kern_2D(self):
""" Testing GPLVM with rbf + bias kernel """
N, input_dim, D = 50, 1, 2
X = np.random.rand(N, input_dim)
k = GPy.kern.RBF(input_dim, 0.5, 0.9 * np.ones((1,))) + GPy.kern.Bias(input_dim, 0.1) + GPy.kern.White(input_dim, 0.05)
K = k.K(X)
Y = np.random.multivariate_normal(np.zeros(N), K, input_dim).T
m = GPy.models.BCGPLVM(Y, input_dim, kernel=k)

[docs]    def test_GPLVM_rbf_linear_white_kern_2D(self):
""" Testing GPLVM with rbf + bias kernel """
N, input_dim, D = 50, 1, 2
X = np.random.rand(N, input_dim)
k = GPy.kern.Linear(input_dim) + GPy.kern.Bias(input_dim, 0.1) + GPy.kern.White(input_dim, 0.05)
K = k.K(X)
Y = np.random.multivariate_normal(np.zeros(N), K, input_dim).T
m = GPy.models.GPLVM(Y, input_dim, init='PCA', kernel=k)

[docs]    def test_GP_EP_probit(self):
N = 20
Nhalf = int(N/2)
X = np.hstack([np.random.normal(5, 2, Nhalf), np.random.normal(10, 2, Nhalf)])[:, None]
Y = np.hstack([np.ones(Nhalf), np.zeros(Nhalf)])[:, None]
kernel = GPy.kern.RBF(1)
m = GPy.models.GPClassification(X, Y, kernel=kernel)

[docs]    def test_sparse_EP_DTC_probit(self):
N = 20
Nhalf = int(N/2)
X = np.hstack([np.random.normal(5, 2, Nhalf), np.random.normal(10, 2, Nhalf)])[:, None]
Y = np.hstack([np.ones(Nhalf), np.zeros(Nhalf)])[:, None]
Z = np.linspace(0, 15, 4)[:, None]
kernel = GPy.kern.RBF(1)
m = GPy.models.SparseGPClassification(X, Y, kernel=kernel, Z=Z)

[docs]    def test_sparse_EP_DTC_probit_uncertain_inputs(self):
N = 20
Nhalf = int(N/2)
X = np.hstack([np.random.normal(5, 2, Nhalf), np.random.normal(10, 2, Nhalf)])[:, None]
Y = np.hstack([np.ones(Nhalf), np.zeros(Nhalf)])[:, None]
Z = np.linspace(0, 15, 4)[:, None]
X_var = np.random.uniform(0.1, 0.2, X.shape)
kernel = GPy.kern.RBF(1)
m = GPy.models.SparseGPClassificationUncertainInput(X, X_var, Y, kernel=kernel, Z=Z)

[docs]    def test_multioutput_regression_1D(self):
X1 = np.random.rand(50, 1) * 8
X2 = np.random.rand(30, 1) * 5
X = np.vstack((X1, X2))
Y1 = np.sin(X1) + np.random.randn(*X1.shape) * 0.05
Y2 = -np.sin(X2) + np.random.randn(*X2.shape) * 0.05
Y = np.vstack((Y1, Y2))

k1 = GPy.kern.RBF(1)
m = GPy.models.GPCoregionalizedRegression(X_list=[X1, X2], Y_list=[Y1, Y2], kernel=k1)
#import ipdb;ipdb.set_trace()
#m.constrain_fixed('.*rbf_var', 1.)

[docs]    def test_simple_MultivariateGaussian_prior(self):
X = np.random.multivariate_normal(
[1, 5], np.diag([0.5, 0.3]), (100, 1)).reshape(100, 2)
Y = X + np.random.randn(100, 2) * 0.05
kernel = GPy.kern.RBF(input_dim=2, variance=1,lengthscale=1, ARD=True)
kernel.unconstrain()
kernel.variance.set_prior(GPy.priors.Gaussian(150, 5))
kernel.lengthscale.set_prior(GPy.priors.MultivariateGaussian(
np.array([20, 20]), np.diag([5, 5])))
m = GPy.models.GPRegression(X, Y, kernel=kernel)
m.optimize()
print(m.kern.variance)
print(m.kern.lengthscale)

[docs]    def test_simple_MultivariateGaussian_prior_matrixmean(self):
X = np.random.multivariate_normal(
[1, 5], np.diag([0.5, 0.3]), (100, 1)).reshape(100, 2)
Y = X + np.random.randn(100, 2) * 0.05
kernel = GPy.kern.RBF(input_dim=2, variance=1,lengthscale=1, ARD=True)
kernel.unconstrain()
kernel.variance.set_prior(GPy.priors.Gaussian(150, 5))
kernel.lengthscale.set_prior(GPy.priors.MultivariateGaussian(
np.array([[20, 20]]), np.diag([5, 5])))
m = GPy.models.GPRegression(X, Y, kernel=kernel)
m.optimize()
print(m.kern.variance)
print(m.kern.lengthscale)

[docs]    def test_multioutput_sparse_regression_1D(self):
X1 = np.random.rand(500, 1) * 8
X2 = np.random.rand(300, 1) * 5
X = np.vstack((X1, X2))
Y1 = np.sin(X1) + np.random.randn(*X1.shape) * 0.05
Y2 = -np.sin(X2) + np.random.randn(*X2.shape) * 0.05
Y = np.vstack((Y1, Y2))

k1 = GPy.kern.RBF(1)
m = GPy.models.SparseGPCoregionalizedRegression(X_list=[X1, X2], Y_list=[Y1, Y2], kernel=k1)

[docs]    def test_gp_heteroscedastic_regression(self):
num_obs = 25
X = np.random.randint(0, 140, num_obs)
X = X[:, None]
Y = 25. + np.sin(X / 20.) * 2. + np.random.rand(num_obs)[:, None]
kern = GPy.kern.Bias(1) + GPy.kern.RBF(1)
m = GPy.models.GPHeteroscedasticRegression(X, Y, kern)

[docs]    def test_sparse_gp_heteroscedastic_regression(self):
num_obs = 25
X = np.random.randint(0, 140, num_obs)
X = X[:, None]
Y = 25. + np.sin(X / 20.) * 2. + np.random.rand(num_obs)[:, None]
kern = GPy.kern.Bias(1) + GPy.kern.RBF(1)
likelihoods_list = [GPy.likelihoods.Gaussian(name="Gaussian_noise_%s" %j) for j in noise_terms]
likelihood = GPy.likelihoods.MixedNoise(likelihoods_list=likelihoods_list)
m = GPy.core.SparseGP(X, Y, X[np.random.choice(num_obs, 10)],
kern, likelihood,
inference_method=GPy.inference.latent_function_inference.VarDTC(),

[docs]    def test_gp_kronecker_gaussian(self):
np.random.seed(0)
N1, N2 = 30, 20
X1 = np.random.randn(N1, 1)
X2 = np.random.randn(N2, 1)
X1.sort(0); X2.sort(0)
k1 = GPy.kern.RBF(1)  # + GPy.kern.White(1)
k2 = GPy.kern.RBF(1)  # + GPy.kern.White(1)
Y = np.random.randn(N1, N2)
Y = Y - Y.mean(0)
Y = Y / Y.std(0)
m = GPy.models.GPKroneckerGaussianRegression(X1, X2, Y, k1, k2)

# build the model the dumb way
assert (N1 * N2 < 1000), "too much data for standard GPs!"
yy, xx = np.meshgrid(X2, X1)
Xgrid = np.vstack((xx.flatten(order='F'), yy.flatten(order='F'))).T
kg = GPy.kern.RBF(1, active_dims=[0]) * GPy.kern.RBF(1, active_dims=[1])
mm = GPy.models.GPRegression(Xgrid, Y.reshape(-1, 1, order='F'), kernel=kg)

m.randomize()
mm[:] = m[:]
self.assertTrue(np.allclose(m.log_likelihood(), mm.log_likelihood()))
X1test = np.random.randn(100, 1)
X2test = np.random.randn(100, 1)
mean1, var1 = m.predict(X1test, X2test)
yy, xx = np.meshgrid(X2test, X1test)
Xgrid = np.vstack((xx.flatten(order='F'), yy.flatten(order='F'))).T
mean2, var2 = mm.predict(Xgrid)
self.assertTrue( np.allclose(mean1, mean2) )
self.assertTrue( np.allclose(var1, var2) )

[docs]    def test_gp_VGPC(self):
np.random.seed(10)
num_obs = 25
X = np.random.randint(0, 140, num_obs)
X = X[:, None]
Y = 25. + np.sin(X / 20.) * 2. + np.random.rand(num_obs)[:, None]
kern = GPy.kern.Bias(1) + GPy.kern.RBF(1)
lik = GPy.likelihoods.Gaussian()
m = GPy.models.GPVariationalGaussianApproximation(X, Y, kernel=kern, likelihood=lik)
m.randomize()

[docs]    def test_ssgplvm(self):
from GPy import kern
from GPy.models import SSGPLVM
from GPy.examples.dimensionality_reduction import _simulate_matern

np.random.seed(10)
D1, D2, D3, N, num_inducing, Q = 13, 5, 8, 45, 3, 9
_, _, Ylist = _simulate_matern(D1, D2, D3, N, num_inducing, False)
Y = Ylist[0]
k = kern.Linear(Q, ARD=True)  # + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
# k = kern.RBF(Q, ARD=True, lengthscale=10.)
m = SSGPLVM(Y, Q, init="rand", num_inducing=num_inducing, kernel=k, group_spike=True)
m.randomize()

[docs]    def test_multiout_regression(self):
np.random.seed(0)
import GPy

N = 10
N_train = 5
D = 4
noise_var = .3

k = GPy.kern.RBF(1,lengthscale=0.1)
x = np.random.rand(N,1)
cov = k.K(x)

k_r = GPy.kern.RBF(2,lengthscale=.4)
x_r = np.random.rand(D,2)
cov_r = k_r.K(x_r)

cov_all = np.kron(cov_r,cov)
L = GPy.util.linalg.jitchol(cov_all)

y_latent = L.dot(np.random.randn(N*D)).reshape(D,N).T

x_test = x[N_train:]
y_test = y_latent[N_train:]
x = x[:N_train]
y = y_latent[:N_train]+np.random.randn(N_train,D)*np.sqrt(noise_var)

Mr = D
Mc = x.shape[0]
Qr = 5
Qc = x.shape[1]

m_mr = GPy.models.GPMultioutRegression(x,y,Xr_dim=Qr, kernel_row=GPy.kern.RBF(Qr,ARD=True), num_inducing=(Mc,Mr),init='GP')
m_mr.optimize_auto(max_iters=1)
m_mr.randomize()

m_mr = GPy.models.GPMultioutRegression(x,y,Xr_dim=Qr, kernel_row=GPy.kern.RBF(Qr,ARD=True), num_inducing=(Mc,Mr),init='rand')
m_mr.optimize_auto(max_iters=1)
m_mr.randomize()

[docs]    def test_multiout_regression_md(self):
import GPy
np.random.seed(0)

N = 20
N_train = 5
D = 8
noise_var = 0.3

k = GPy.kern.RBF(1,lengthscale=0.1)
x_raw = np.random.rand(N*D,1)

# dimension assignment
D_list = []
for i in range(2):
while True:
D_sub_list = []
ratios = []
r_p = 0.
for j in range(3):
ratios.append(np.random.rand()*(1-r_p)+r_p)
D_sub_list.append(int((ratios[-1]-r_p)*4*N_train))
r_p = ratios[-1]
D_sub_list.append(4*N_train - np.sum(D_sub_list))
if (np.array(D_sub_list)!=0).all():
D_list.extend([a+N-N_train for a in D_sub_list])
break

cov = k.K(x_raw)

k_r = GPy.kern.RBF(2,lengthscale=.4)
x_r = np.random.rand(D,2)
cov_r = k_r.K(x_r)

cov_all = np.repeat(np.repeat(cov_r,D_list,axis=0),D_list,axis=1)*cov
L = GPy.util.linalg.jitchol(cov_all)

y_latent = L.dot(np.random.randn(N*D))

x = np.zeros((D*N_train,))
y = np.zeros((D*N_train,))
x_test = np.zeros((D*(N-N_train),))
y_test = np.zeros((D*(N-N_train),))
indexD = np.zeros((D*N_train),dtype=np.int)
indexD_test = np.zeros((D*(N-N_train)),dtype=np.int)

offset_all = 0
offset_train = 0
offset_test = 0
for i in range(D):
D_test = N-N_train
D_train = D_list[i] - N+N_train
y[offset_train:offset_train+D_train] = y_latent[offset_all:offset_all+D_train]
x[offset_train:offset_train+D_train] = x_raw[offset_all:offset_all+D_train,0]
y_test[offset_test:offset_test+D_test] = y_latent[offset_all+D_train:offset_all+D_train+D_test]
x_test[offset_test:offset_test+D_test] = x_raw[offset_all+D_train:offset_all+D_train+D_test,0]
indexD[offset_train:offset_train+D_train] = i
indexD_test[offset_test:offset_test+D_test] = i
offset_train += D_train
offset_test += D_test
offset_all += D_train+D_test

y_noisefree = y.copy()
y += np.random.randn(*y.shape)*np.sqrt(noise_var)
x_flat = x.flatten()[:,None]
y_flat = y.flatten()[:,None]

Mr, Mc, Qr, Qc = 4,3,2,1

m = GPy.models.GPMultioutRegressionMD(x_flat,y_flat,indexD,Xr_dim=Qr, kernel_row=GPy.kern.RBF(Qr,ARD=False), num_inducing=(Mc,Mr))
m.optimize_auto(max_iters=1)
m.randomize()

m = GPy.models.GPMultioutRegressionMD(x_flat,y_flat,indexD,Xr_dim=Qr, kernel_row=GPy.kern.RBF(Qr,ARD=False), num_inducing=(Mc,Mr),init='rand')
m.optimize_auto(max_iters=1)
m.randomize()

[docs]    def test_posterior_covariance(self):
k = GPy.kern.Poly(2, order=1)
X1 = np.array([
[-2, 2],
[-1, 1]
])
X2 = np.array([
[2, 3],
[-1, 3]
])
Y = np.array([[1], [2]])
m = GPy.models.GPRegression(X1, Y, kernel=k)

result = m._raw_posterior_covariance_between_points(X1, X2)
expected = np.array([[0.4, 2.2], [1.0, 1.0]]) / 3.0

self.assertTrue(np.allclose(result, expected))

[docs]    def test_posterior_covariance_missing_data(self):
Q = 4
k = GPy.kern.Linear(Q, ARD=True)
m = _create_missing_data_model(k, Q)

with self.assertRaises(RuntimeError):
m._raw_posterior_covariance_between_points(np.array([[1], [2]]), np.array([[3], [4]]))

[docs]    def test_multioutput_model_with_derivative_observations(self):
f = lambda x: np.sin(x)+0.1*(x-2.)**2-0.005*x**3
fd = lambda x: np.cos(x)+0.2*(x-2.)-0.015*x**2
N=10
M=10
sigma=0.05
x = np.array([np.linspace(1,10,N)]).T
y = f(x) + np.array(sigma*np.random.normal(0,1,(N,1)))

xd = np.array([np.linspace(2,8,M)]).T

# squared exponential kernel:
se = GPy.kern.RBF(input_dim = 1, lengthscale=1.5, variance=0.2)
# We need to generate separate kernel for the derivative observations and give the created kernel as an input:
se_der = GPy.kern.DiffKern(se, 0)

#Then
gauss = GPy.likelihoods.Gaussian(variance=sigma**2)
gauss = GPy.likelihoods.Gaussian(variance=0.1)
gauss_der = GPy.likelihoods.Gaussian(variance=sigma**2)

# Then create the model, we give everything in lists, the order of the inputs indicates the order of the outputs
# Now we have the regular observations first and derivative observations second, meaning that the kernels and
# the likelihoods must follow the same order
m = GPy.models.MultioutputGP(X_list=[x, xd], Y_list=[y, yd], kernel_list=[se, se_der], likelihood_list = [gauss, gauss])
m.randomize()

m.optimize(messages=0, ipython_notebook=False)

[docs]    def test_multioutput_model_with_ep(self):
f = lambda x: np.sin(x)+0.1*(x-2.)**2-0.005*x**3
fd = lambda x: np.cos(x)+0.2*(x-2.)-0.015*x**2
N=10
sigma=0.05
x = np.array([np.linspace(1,10,N)]).T
y = f(x) + np.array(sigma*np.random.normal(0,1,(N,1)))

M=7
xd = np.array([np.linspace(2,8,M)]).T
yd = 2*(fd(xd)>0) -1

# squared exponential kernel:
se = GPy.kern.RBF(input_dim = 1, lengthscale=1.5, variance=0.2)
# We need to generate separate kernel for the derivative observations and give the created kernel as an input:
se_der = GPy.kern.DiffKern(se, 0)

#Then
gauss = GPy.likelihoods.Gaussian(variance=sigma**2)

# Then create the model, we give everything in lists
m = GPy.models.MultioutputGP(X_list=[x, xd], Y_list=[y, yd], kernel_list=[se, se_der], likelihood_list = [gauss, probit], inference_method=GPy.inference.latent_function_inference.EP(ep_mode="nested"))

"""
model.predict when normalizer=True
"""
N, M, Q = 10, 15, 3
X = np.random.rand(M,Q)
Y = np.random.rand(M,1)
x = np.random.rand(N, Q)
model = GPy.models.GPRegression(X=X, Y=Y, normalizer=True)
x, 'x')
x, 'x')

[docs]    def test_posterior_covariance_between_points_with_normalizer(self):
"""
Check that model.posterior_covariance_between_points returns
the covariance from model.predict when normalizer=True
"""
np.random.seed(3)
N, M, Q = 10, 15, 3
X = np.random.rand(M,Q)
Y = np.random.rand(M,1)
x = np.random.rand(2, Q)
model = GPy.models.GPRegression(X=X, Y=Y, normalizer=True)

c1 = model.posterior_covariance_between_points(x,x)
c2 = model.predict(x, full_cov=True)[1]
np.testing.assert_allclose(c1,c2)

def _create_missing_data_model(kernel, Q):
D1, D2, D3, N, num_inducing = 13, 5, 8, 400, 3
_, _, Ylist = GPy.examples.dimensionality_reduction._simulate_matern(D1, D2, D3, N, num_inducing, False)
Y = Ylist[0]

inan = np.random.binomial(1, .9, size=Y.shape).astype(bool) # 80% missing data
Ymissing = Y.copy()
Ymissing[inan] = np.nan

m = GPy.models.bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch(Ymissing, Q, init="random", num_inducing=num_inducing,
kernel=kernel, missing_data=True)

return m

if __name__ == "__main__":
print("Running unit tests, please be (very) patient...")
unittest.main()