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63 lines
1.8 KiB
Python
63 lines
1.8 KiB
Python
import numpy as np
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import GPy
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class TestSVGP_nonconvex:
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"""
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Inference in the SVGP with a student-T likelihood
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"""
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def setup(self):
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X = np.linspace(0, 10, 100).reshape(-1, 1)
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Z = np.linspace(0, 10, 10).reshape(-1, 1)
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Y = np.sin(X) + np.random.randn(*X.shape) * 0.1
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Y[50] += 3
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lik = GPy.likelihoods.StudentT(deg_free=2)
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k = GPy.kern.RBF(1, lengthscale=5.0) + GPy.kern.White(1, 1e-6)
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self.m = GPy.core.SVGP(X, Y, Z=Z, likelihood=lik, kernel=k)
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def test_grad(self):
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self.setup()
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assert self.m.checkgrad(step=1e-4)
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class TestSVGP_classification:
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"""
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Inference in the SVGP with a Bernoulli likelihood
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"""
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def setup(self):
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X = np.linspace(0, 10, 100).reshape(-1, 1)
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Z = np.linspace(0, 10, 10).reshape(-1, 1)
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Y = np.where((np.sin(X) + np.random.randn(*X.shape) * 0.1) > 0, 1, 0)
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lik = GPy.likelihoods.Bernoulli()
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k = GPy.kern.RBF(1, lengthscale=5.0) + GPy.kern.White(1, 1e-6)
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self.m = GPy.core.SVGP(X, Y, Z=Z, likelihood=lik, kernel=k)
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def test_grad(self):
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self.setup()
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assert self.m.checkgrad(step=1e-4)
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class TestSVGP_Poisson_with_meanfunction:
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"""
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Inference in the SVGP with a Bernoulli likelihood
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"""
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def setup(self):
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X = np.linspace(0, 10, 100).reshape(-1, 1)
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Z = np.linspace(0, 10, 10).reshape(-1, 1)
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latent_f = np.exp(0.1 * X * 0.05 * X**2)
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Y = np.array([np.random.poisson(f) for f in latent_f.flatten()]).reshape(-1, 1)
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mf = GPy.mappings.Linear(1, 1)
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lik = GPy.likelihoods.Poisson()
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k = GPy.kern.RBF(1, lengthscale=5.0) + GPy.kern.White(1, 1e-6)
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self.m = GPy.core.SVGP(X, Y, Z=Z, likelihood=lik, kernel=k, mean_function=mf)
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def test_grad(self):
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self.setup()
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assert self.m.checkgrad(step=1e-4)
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