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Martin Bubel
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1 changed files with 51 additions and 50 deletions
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@ -5,77 +5,78 @@ import unittest
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import numpy as np
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import GPy
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class PriorTests(unittest.TestCase):
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def test_studentT(self):
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xmin, xmax = 1, 2.5*np.pi
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xmin, xmax = 1, 2.5 * np.pi
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b, C, SNR = 1, 0, 0.1
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X = np.linspace(xmin, xmax, 500)
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y = b*X + C + 1*np.sin(X)
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y += 0.05*np.random.randn(len(X))
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y = b * X + C + 1 * np.sin(X)
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y += 0.05 * np.random.randn(len(X))
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X, y = X[:, None], y[:, None]
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studentT = GPy.priors.StudentT(1, 2, 4)
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m = GPy.models.SparseGPRegression(X, y)
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m.Z.set_prior(studentT)
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# setting a StudentT prior on non-negative parameters
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# should raise an assertionerror.
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self.assertRaises(AssertionError, m.rbf.set_prior, studentT)
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# The gradients need to be checked
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self.assertTrue(m.checkgrad())
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assert m.checkgrad()
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# Check the singleton pattern:
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self.assertIs(studentT, GPy.priors.StudentT(1,2,4))
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self.assertIsNot(studentT, GPy.priors.StudentT(2,2,4))
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self.assertIs(studentT, GPy.priors.StudentT(1, 2, 4))
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self.assertIsNot(studentT, GPy.priors.StudentT(2, 2, 4))
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def test_lognormal(self):
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xmin, xmax = 1, 2.5*np.pi
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xmin, xmax = 1, 2.5 * np.pi
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b, C, SNR = 1, 0, 0.1
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X = np.linspace(xmin, xmax, 500)
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y = b*X + C + 1*np.sin(X)
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y += 0.05*np.random.randn(len(X))
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y = b * X + C + 1 * np.sin(X)
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y += 0.05 * np.random.randn(len(X))
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X, y = X[:, None], y[:, None]
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m = GPy.models.GPRegression(X, y)
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lognormal = GPy.priors.LogGaussian(1, 2)
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m.rbf.set_prior(lognormal)
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m.randomize()
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self.assertTrue(m.checkgrad())
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assert m.checkgrad()
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def test_Gamma(self):
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xmin, xmax = 1, 2.5*np.pi
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xmin, xmax = 1, 2.5 * np.pi
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b, C, SNR = 1, 0, 0.1
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X = np.linspace(xmin, xmax, 500)
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y = b*X + C + 1*np.sin(X)
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y += 0.05*np.random.randn(len(X))
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y = b * X + C + 1 * np.sin(X)
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y += 0.05 * np.random.randn(len(X))
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X, y = X[:, None], y[:, None]
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m = GPy.models.GPRegression(X, y)
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Gamma = GPy.priors.Gamma(1, 1)
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m.rbf.set_prior(Gamma)
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m.randomize()
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self.assertTrue(m.checkgrad())
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assert m.checkgrad()
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def test_InverseGamma(self):
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# Test that this prior object can be instantiated and performs its basic functions
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# in integration.
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xmin, xmax = 1, 2.5*np.pi
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xmin, xmax = 1, 2.5 * np.pi
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b, C, SNR = 1, 0, 0.1
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X = np.linspace(xmin, xmax, 500)
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y = b*X + C + 1*np.sin(X)
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y += 0.05*np.random.randn(len(X))
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y = b * X + C + 1 * np.sin(X)
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y += 0.05 * np.random.randn(len(X))
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X, y = X[:, None], y[:, None]
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m = GPy.models.GPRegression(X, y)
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InverseGamma = GPy.priors.InverseGamma(1, 1)
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m.rbf.set_prior(InverseGamma)
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m.randomize()
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self.assertTrue(m.checkgrad())
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assert m.checkgrad()
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def test_incompatibility(self):
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xmin, xmax = 1, 2.5*np.pi
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xmin, xmax = 1, 2.5 * np.pi
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b, C, SNR = 1, 0, 0.1
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X = np.linspace(xmin, xmax, 500)
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y = b*X + C + 1*np.sin(X)
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y += 0.05*np.random.randn(len(X))
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y = b * X + C + 1 * np.sin(X)
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y += 0.05 * np.random.randn(len(X))
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X, y = X[:, None], y[:, None]
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m = GPy.models.GPRegression(X, y)
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gaussian = GPy.priors.Gaussian(1, 1)
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@ -84,55 +85,55 @@ class PriorTests(unittest.TestCase):
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self.assertRaises(AssertionError, m.rbf.set_prior, gaussian)
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def test_set_prior(self):
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xmin, xmax = 1, 2.5*np.pi
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xmin, xmax = 1, 2.5 * np.pi
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b, C, SNR = 1, 0, 0.1
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X = np.linspace(xmin, xmax, 500)
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y = b*X + C + 1*np.sin(X)
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y += 0.05*np.random.randn(len(X))
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y = b * X + C + 1 * np.sin(X)
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y += 0.05 * np.random.randn(len(X))
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X, y = X[:, None], y[:, None]
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m = GPy.models.GPRegression(X, y)
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gaussian = GPy.priors.Gaussian(1, 1)
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#m.rbf.set_prior(gaussian)
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# m.rbf.set_prior(gaussian)
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# setting a Gaussian prior on non-negative parameters
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# should raise an assertionerror.
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self.assertRaises(AssertionError, m.rbf.set_prior, gaussian)
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def test_uniform(self):
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xmin, xmax = 1, 2.5*np.pi
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xmin, xmax = 1, 2.5 * np.pi
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b, C, SNR = 1, 0, 0.1
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X = np.linspace(xmin, xmax, 500)
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y = b*X + C + 1*np.sin(X)
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y += 0.05*np.random.randn(len(X))
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y = b * X + C + 1 * np.sin(X)
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y += 0.05 * np.random.randn(len(X))
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X, y = X[:, None], y[:, None]
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m = GPy.models.SparseGPRegression(X, y)
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uniform = GPy.priors.Uniform(0, 2)
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m.rbf.set_prior(uniform)
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m.randomize()
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self.assertTrue(m.checkgrad())
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assert m.checkgrad()
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m.Z.set_prior(uniform)
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m.randomize()
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self.assertTrue(m.checkgrad())
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assert m.checkgrad()
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m.Z.unconstrain()
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uniform = GPy.priors.Uniform(-1, 10)
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m.Z.set_prior(uniform)
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m.randomize()
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self.assertTrue(m.checkgrad())
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assert m.checkgrad()
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m.Z.constrain_negative()
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uniform = GPy.priors.Uniform(-1, 0)
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m.Z.set_prior(uniform)
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m.randomize()
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self.assertTrue(m.checkgrad())
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assert m.checkgrad()
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def test_set_gaussian_for_reals(self):
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xmin, xmax = 1, 2.5*np.pi
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xmin, xmax = 1, 2.5 * np.pi
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b, C, SNR = 1, 0, 0.1
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X = np.linspace(xmin, xmax, 500)
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y = b*X + C + 1*np.sin(X)
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y += 0.05*np.random.randn(len(X))
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y = b * X + C + 1 * np.sin(X)
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y += 0.05 * np.random.randn(len(X))
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X, y = X[:, None], y[:, None]
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m = GPy.models.SparseGPRegression(X, y)
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@ -140,16 +141,15 @@ class PriorTests(unittest.TestCase):
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m.Z.set_prior(gaussian)
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# setting a Gaussian prior on non-negative parameters
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# should raise an assertionerror.
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#self.assertRaises(AssertionError, m.Z.set_prior, gaussian)
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self.assertTrue(m.checkgrad())
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# self.assertRaises(AssertionError, m.Z.set_prior, gaussian)
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assert m.checkgrad()
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def test_fixed_domain_check(self):
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xmin, xmax = 1, 2.5*np.pi
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xmin, xmax = 1, 2.5 * np.pi
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b, C, SNR = 1, 0, 0.1
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X = np.linspace(xmin, xmax, 500)
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y = b*X + C + 1*np.sin(X)
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y += 0.05*np.random.randn(len(X))
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y = b * X + C + 1 * np.sin(X)
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y += 0.05 * np.random.randn(len(X))
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X, y = X[:, None], y[:, None]
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m = GPy.models.GPRegression(X, y)
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@ -160,11 +160,11 @@ class PriorTests(unittest.TestCase):
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self.assertRaises(AssertionError, m.rbf.set_prior, gaussian)
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def test_fixed_domain_check1(self):
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xmin, xmax = 1, 2.5*np.pi
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xmin, xmax = 1, 2.5 * np.pi
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b, C, SNR = 1, 0, 0.1
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X = np.linspace(xmin, xmax, 500)
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y = b*X + C + 1*np.sin(X)
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y += 0.05*np.random.randn(len(X))
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y = b * X + C + 1 * np.sin(X)
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y += 0.05 * np.random.randn(len(X))
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X, y = X[:, None], y[:, None]
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m = GPy.models.GPRegression(X, y)
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@ -174,6 +174,7 @@ class PriorTests(unittest.TestCase):
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# should raise an assertionerror.
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self.assertRaises(AssertionError, m.rbf.set_prior, gaussian)
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if __name__ == "__main__":
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print("Running unit tests, please be (very) patient...")
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unittest.main()
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