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This commit is contained in:
Martin Bubel
parent
779f31da9c
commit
0b92d3a57c
1 changed files with 169 additions and 68 deletions
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@ -8,18 +8,21 @@ The test cases for various inference algorithms
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import unittest
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import numpy as np
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import GPy
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#np.seterr(invalid='raise')
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# np.seterr(invalid='raise')
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class InferenceXTestCase(unittest.TestCase):
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def genData(self):
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np.random.seed(1111)
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Ylist = GPy.examples.dimensionality_reduction._simulate_matern(5, 1, 1, 10, 3, False)[0]
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Ylist = GPy.examples.dimensionality_reduction._simulate_matern(
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5, 1, 1, 10, 3, False
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)[0]
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return Ylist[0]
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def test_inferenceX_BGPLVM_Linear(self):
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Ys = self.genData()
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m = GPy.models.BayesianGPLVM(Ys,3,kernel=GPy.kern.Linear(3,ARD=True))
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m = GPy.models.BayesianGPLVM(Ys, 3, kernel=GPy.kern.Linear(3, ARD=True))
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m.optimize()
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x, mi = m.infer_newX(m.Y, optimize=True)
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np.testing.assert_array_almost_equal(m.X.mean, mi.X.mean, decimal=2)
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@ -27,8 +30,9 @@ class InferenceXTestCase(unittest.TestCase):
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def test_inferenceX_BGPLVM_RBF(self):
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Ys = self.genData()
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m = GPy.models.BayesianGPLVM(Ys,3,kernel=GPy.kern.RBF(3,ARD=True))
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m = GPy.models.BayesianGPLVM(Ys, 3, kernel=GPy.kern.RBF(3, ARD=True))
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import warnings
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with warnings.catch_warnings():
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warnings.simplefilter("ignore")
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m.optimize()
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@ -38,67 +42,110 @@ class InferenceXTestCase(unittest.TestCase):
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def test_inferenceX_GPLVM_Linear(self):
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Ys = self.genData()
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m = GPy.models.GPLVM(Ys,3,kernel=GPy.kern.Linear(3,ARD=True))
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m = GPy.models.GPLVM(Ys, 3, kernel=GPy.kern.Linear(3, ARD=True))
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m.optimize()
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x, mi = m.infer_newX(m.Y, optimize=True)
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np.testing.assert_array_almost_equal(m.X, mi.X, decimal=2)
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def test_inferenceX_GPLVM_RBF(self):
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Ys = self.genData()
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m = GPy.models.GPLVM(Ys,3,kernel=GPy.kern.RBF(3,ARD=True))
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m = GPy.models.GPLVM(Ys, 3, kernel=GPy.kern.RBF(3, ARD=True))
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m.optimize()
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x, mi = m.infer_newX(m.Y, optimize=True)
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np.testing.assert_array_almost_equal(m.X, mi.X, decimal=2)
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class InferenceGPEP(unittest.TestCase):
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class InferenceGPEP(unittest.TestCase):
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def genData(self):
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np.random.seed(1)
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k = GPy.kern.RBF(1, variance=7., lengthscale=0.2)
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X = np.random.rand(200,1)
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f = np.random.multivariate_normal(np.zeros(200), k.K(X) + 1e-5 * np.eye(X.shape[0]))
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k = GPy.kern.RBF(1, variance=7.0, lengthscale=0.2)
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X = np.random.rand(200, 1)
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f = np.random.multivariate_normal(
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np.zeros(200), k.K(X) + 1e-5 * np.eye(X.shape[0])
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)
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lik = GPy.likelihoods.Bernoulli()
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p = lik.gp_link.transf(f) # squash the latent function
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Y = lik.samples(f).reshape(-1,1)
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p = lik.gp_link.transf(f) # squash the latent function
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Y = lik.samples(f).reshape(-1, 1)
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return X, Y
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def genNoisyData(self):
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np.random.seed(1)
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X = np.random.rand(100,1)
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X = np.random.rand(100, 1)
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self.real_std = 0.1
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noise = np.random.randn(*X[:, 0].shape)*self.real_std
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Y = (np.sin(X[:, 0]*2*np.pi) + noise)[:, None]
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self.f = np.random.rand(X.shape[0],1)
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noise = np.random.randn(*X[:, 0].shape) * self.real_std
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Y = (np.sin(X[:, 0] * 2 * np.pi) + noise)[:, None]
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self.f = np.random.rand(X.shape[0], 1)
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Y_extra_noisy = Y.copy()
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Y_extra_noisy[50] += 4.
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Y_extra_noisy[50] += 4.0
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# Y_extra_noisy[80:83] -= 2.
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return X, Y, Y_extra_noisy
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def test_inference_EP(self):
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from paramz import ObsAr
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X, Y = self.genData()
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lik = GPy.likelihoods.Bernoulli()
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k = GPy.kern.RBF(1, variance=7., lengthscale=0.2)
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inf = GPy.inference.latent_function_inference.expectation_propagation.EP(max_iters=30, delta=0.5)
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self.model = GPy.core.GP(X=X,
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Y=Y,
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kernel=k,
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inference_method=inf,
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likelihood=lik)
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k = GPy.kern.RBF(1, variance=7.0, lengthscale=0.2)
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inf = GPy.inference.latent_function_inference.expectation_propagation.EP(
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max_iters=30, delta=0.5
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)
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self.model = GPy.core.GP(
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X=X, Y=Y, kernel=k, inference_method=inf, likelihood=lik
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)
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K = self.model.kern.K(X)
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mean_prior = np.zeros(K.shape[0])
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post_params, ga_approx, cav_params, log_Z_tilde = self.model.inference_method.expectation_propagation(mean_prior, K, ObsAr(Y), lik, None)
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(
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post_params,
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ga_approx,
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cav_params,
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log_Z_tilde,
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) = self.model.inference_method.expectation_propagation(
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mean_prior, K, ObsAr(Y), lik, None
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)
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mu_tilde = ga_approx.v / ga_approx.tau.astype(float)
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p, m, d = self.model.inference_method._inference(Y, mean_prior, K, ga_approx, cav_params, lik, Y_metadata=None, Z_tilde=log_Z_tilde)
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p0, m0, d0 = super(GPy.inference.latent_function_inference.expectation_propagation.EP, inf).inference(k, X,lik ,mu_tilde[:,None], mean_function=None, variance=1./ga_approx.tau, K=K, Z_tilde=log_Z_tilde + np.sum(- 0.5*np.log(ga_approx.tau) + 0.5*(ga_approx.v*ga_approx.v*1./ga_approx.tau)))
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p, m, d = self.model.inference_method._inference(
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Y,
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mean_prior,
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K,
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ga_approx,
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cav_params,
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lik,
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Y_metadata=None,
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Z_tilde=log_Z_tilde,
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)
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p0, m0, d0 = super(
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GPy.inference.latent_function_inference.expectation_propagation.EP, inf
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).inference(
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k,
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X,
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lik,
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mu_tilde[:, None],
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mean_function=None,
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variance=1.0 / ga_approx.tau,
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K=K,
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Z_tilde=log_Z_tilde
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+ np.sum(
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-0.5 * np.log(ga_approx.tau)
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+ 0.5 * (ga_approx.v * ga_approx.v * 1.0 / ga_approx.tau)
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),
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)
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assert (np.sum(np.array([m - m0,
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np.sum(d['dL_dK'] - d0['dL_dK']),
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np.sum(d['dL_dthetaL'] - d0['dL_dthetaL']),
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np.sum(d['dL_dm'] - d0['dL_dm']),
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np.sum(p._woodbury_vector - p0._woodbury_vector),
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np.sum(p.woodbury_inv - p0.woodbury_inv)])) < 1e6)
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assert (
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np.sum(
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np.array(
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[
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m - m0,
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np.sum(d["dL_dK"] - d0["dL_dK"]),
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np.sum(d["dL_dthetaL"] - d0["dL_dthetaL"]),
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np.sum(d["dL_dm"] - d0["dL_dm"]),
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np.sum(p._woodbury_vector - p0._woodbury_vector),
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np.sum(p.woodbury_inv - p0.woodbury_inv),
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]
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)
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)
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< 1e6
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)
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# NOTE: adding a test like above for parameterized likelihood- the above test is
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# only for probit likelihood which does not have any tunable hyperparameter which is why
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@ -110,70 +157,124 @@ class InferenceGPEP(unittest.TestCase):
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# and it is possible that any error might creep up because of quadrature implementation.
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def test_inference_EP_non_classification(self):
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from paramz import ObsAr
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X, Y, Y_extra_noisy = self.genNoisyData()
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deg_freedom = 5.
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deg_freedom = 5.0
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init_noise_var = 0.08
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lik_studentT = GPy.likelihoods.StudentT(deg_free=deg_freedom, sigma2=init_noise_var)
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lik_studentT = GPy.likelihoods.StudentT(
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deg_free=deg_freedom, sigma2=init_noise_var
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)
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# like_gaussian_noise = GPy.likelihoods.MixedNoise()
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k = GPy.kern.RBF(1, variance=2., lengthscale=1.1)
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ep_inf_alt = GPy.inference.latent_function_inference.expectation_propagation.EP(max_iters=4, delta=0.5)
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k = GPy.kern.RBF(1, variance=2.0, lengthscale=1.1)
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ep_inf_alt = GPy.inference.latent_function_inference.expectation_propagation.EP(
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max_iters=4, delta=0.5
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)
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# ep_inf_nested = GPy.inference.latent_function_inference.expectation_propagation.EP(ep_mode='nested', max_iters=100, delta=0.5)
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m = GPy.core.GP(X=X,Y=Y_extra_noisy,kernel=k,likelihood=lik_studentT,inference_method=ep_inf_alt)
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m = GPy.core.GP(
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X=X,
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Y=Y_extra_noisy,
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kernel=k,
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likelihood=lik_studentT,
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inference_method=ep_inf_alt,
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)
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K = m.kern.K(X)
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mean_prior = np.zeros(K.shape[0])
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post_params, ga_approx, cav_params, log_Z_tilde = m.inference_method.expectation_propagation(mean_prior, K, ObsAr(Y_extra_noisy), lik_studentT, None)
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(
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post_params,
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ga_approx,
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cav_params,
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log_Z_tilde,
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) = m.inference_method.expectation_propagation(
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mean_prior, K, ObsAr(Y_extra_noisy), lik_studentT, None
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)
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mu_tilde = ga_approx.v / ga_approx.tau.astype(float)
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p, m, d = m.inference_method._inference(Y_extra_noisy, mean_prior, K, ga_approx, cav_params, lik_studentT, Y_metadata=None, Z_tilde=log_Z_tilde)
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p0, m0, d0 = super(GPy.inference.latent_function_inference.expectation_propagation.EP, ep_inf_alt).inference(k, X,lik_studentT ,mu_tilde[:,None], mean_function=None, variance=1./ga_approx.tau, K=K, Z_tilde=log_Z_tilde + np.sum(- 0.5*np.log(ga_approx.tau) + 0.5*(ga_approx.v*ga_approx.v*1./ga_approx.tau)))
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p, m, d = m.inference_method._inference(
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Y_extra_noisy,
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mean_prior,
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K,
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ga_approx,
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cav_params,
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lik_studentT,
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Y_metadata=None,
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Z_tilde=log_Z_tilde,
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)
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p0, m0, d0 = super(
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GPy.inference.latent_function_inference.expectation_propagation.EP,
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ep_inf_alt,
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).inference(
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k,
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X,
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lik_studentT,
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mu_tilde[:, None],
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mean_function=None,
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variance=1.0 / ga_approx.tau,
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K=K,
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Z_tilde=log_Z_tilde
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+ np.sum(
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-0.5 * np.log(ga_approx.tau)
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+ 0.5 * (ga_approx.v * ga_approx.v * 1.0 / ga_approx.tau)
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),
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)
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assert (
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np.sum(
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np.array(
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[
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m - m0,
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np.sum(d["dL_dK"] - d0["dL_dK"]),
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np.sum(d["dL_dthetaL"] - d0["dL_dthetaL"]),
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np.sum(d["dL_dm"] - d0["dL_dm"]),
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np.sum(p._woodbury_vector - p0._woodbury_vector),
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np.sum(p.woodbury_inv - p0.woodbury_inv),
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]
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)
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)
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< 1e6
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)
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assert (np.sum(np.array([m - m0,
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np.sum(d['dL_dK'] - d0['dL_dK']),
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np.sum(d['dL_dthetaL'] - d0['dL_dthetaL']),
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np.sum(d['dL_dm'] - d0['dL_dm']),
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np.sum(p._woodbury_vector - p0._woodbury_vector),
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np.sum(p.woodbury_inv - p0.woodbury_inv)])) < 1e6)
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class VarDtcTest(unittest.TestCase):
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def test_var_dtc_inference_with_mean(self):
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""" Check dL_dm in var_dtc is calculated correctly"""
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"""Check dL_dm in var_dtc is calculated correctly"""
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np.random.seed(1)
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x = np.linspace(0.,2*np.pi,100)[:,None]
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y = -np.cos(x)+np.random.randn(*x.shape)*0.3+1
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m = GPy.models.SparseGPRegression(x,y, mean_function=GPy.mappings.Linear(input_dim=1, output_dim=1))
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x = np.linspace(0.0, 2 * np.pi, 100)[:, None]
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y = -np.cos(x) + np.random.randn(*x.shape) * 0.3 + 1
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m = GPy.models.SparseGPRegression(
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x, y, mean_function=GPy.mappings.Linear(input_dim=1, output_dim=1)
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)
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self.assertTrue(m.checkgrad())
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class HMCSamplerTest(unittest.TestCase):
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def test_sampling(self):
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np.random.seed(1)
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x = np.linspace(0.,2*np.pi,100)[:,None]
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y = -np.cos(x)+np.random.randn(*x.shape)*0.3+1
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x = np.linspace(0.0, 2 * np.pi, 100)[:, None]
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y = -np.cos(x) + np.random.randn(*x.shape) * 0.3 + 1
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m = GPy.models.GPRegression(x,y)
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m.kern.lengthscale.set_prior(GPy.priors.Gamma.from_EV(1.,10.))
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m.kern.variance.set_prior(GPy.priors.Gamma.from_EV(1.,10.))
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m.likelihood.variance.set_prior(GPy.priors.Gamma.from_EV(1.,10.))
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m = GPy.models.GPRegression(x, y)
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m.kern.lengthscale.set_prior(GPy.priors.Gamma.from_EV(1.0, 10.0))
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m.kern.variance.set_prior(GPy.priors.Gamma.from_EV(1.0, 10.0))
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m.likelihood.variance.set_prior(GPy.priors.Gamma.from_EV(1.0, 10.0))
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hmc = GPy.inference.mcmc.HMC(m,stepsize=1e-2)
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hmc = GPy.inference.mcmc.HMC(m, stepsize=1e-2)
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s = hmc.sample(num_samples=3)
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class MCMCSamplerTest(unittest.TestCase):
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class MCMCSamplerTest(unittest.TestCase):
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def test_sampling(self):
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np.random.seed(1)
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x = np.linspace(0.,2*np.pi,100)[:,None]
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y = -np.cos(x)+np.random.randn(*x.shape)*0.3+1
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x = np.linspace(0.0, 2 * np.pi, 100)[:, None]
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y = -np.cos(x) + np.random.randn(*x.shape) * 0.3 + 1
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m = GPy.models.GPRegression(x,y)
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m.kern.lengthscale.set_prior(GPy.priors.Gamma.from_EV(1.,10.))
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m.kern.variance.set_prior(GPy.priors.Gamma.from_EV(1.,10.))
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m.likelihood.variance.set_prior(GPy.priors.Gamma.from_EV(1.,10.))
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m = GPy.models.GPRegression(x, y)
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m.kern.lengthscale.set_prior(GPy.priors.Gamma.from_EV(1.0, 10.0))
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m.kern.variance.set_prior(GPy.priors.Gamma.from_EV(1.0, 10.0))
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m.likelihood.variance.set_prior(GPy.priors.Gamma.from_EV(1.0, 10.0))
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mcmc = GPy.inference.mcmc.Metropolis_Hastings(m)
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mcmc.sample(Ntotal=100, Nburn=10)
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if __name__ == "__main__":
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unittest.main()
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