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84 lines
3 KiB
Python
84 lines
3 KiB
Python
# Copyright (c) 2014, Max Zwiessele
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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"""
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The test cases for various inference algorithms
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"""
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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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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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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.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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np.testing.assert_array_almost_equal(m.X.variance, mi.X.variance, decimal=2)
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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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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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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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np.testing.assert_array_almost_equal(m.X.variance, mi.X.variance, decimal=2)
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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.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.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 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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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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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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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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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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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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