# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import unittest
import numpy as np
import GPy
[docs]class PriorTests(unittest.TestCase):
[docs] def test_studentT(self):
xmin, xmax = 1, 2.5*np.pi
b, C, SNR = 1, 0, 0.1
X = np.linspace(xmin, xmax, 500)
y = b*X + C + 1*np.sin(X)
y += 0.05*np.random.randn(len(X))
X, y = X[:, None], y[:, None]
studentT = GPy.priors.StudentT(1, 2, 4)
m = GPy.models.SparseGPRegression(X, y)
m.Z.set_prior(studentT)
# setting a StudentT prior on non-negative parameters
# should raise an assertionerror.
self.assertRaises(AssertionError, m.rbf.set_prior, studentT)
# The gradients need to be checked
self.assertTrue(m.checkgrad())
# Check the singleton pattern:
self.assertIs(studentT, GPy.priors.StudentT(1,2,4))
self.assertIsNot(studentT, GPy.priors.StudentT(2,2,4))
[docs] def test_lognormal(self):
xmin, xmax = 1, 2.5*np.pi
b, C, SNR = 1, 0, 0.1
X = np.linspace(xmin, xmax, 500)
y = b*X + C + 1*np.sin(X)
y += 0.05*np.random.randn(len(X))
X, y = X[:, None], y[:, None]
m = GPy.models.GPRegression(X, y)
lognormal = GPy.priors.LogGaussian(1, 2)
m.rbf.set_prior(lognormal)
m.randomize()
self.assertTrue(m.checkgrad())
[docs] def test_Gamma(self):
xmin, xmax = 1, 2.5*np.pi
b, C, SNR = 1, 0, 0.1
X = np.linspace(xmin, xmax, 500)
y = b*X + C + 1*np.sin(X)
y += 0.05*np.random.randn(len(X))
X, y = X[:, None], y[:, None]
m = GPy.models.GPRegression(X, y)
Gamma = GPy.priors.Gamma(1, 1)
m.rbf.set_prior(Gamma)
m.randomize()
self.assertTrue(m.checkgrad())
[docs] def test_InverseGamma(self):
# Test that this prior object can be instantiated and performs its basic functions
# in integration.
xmin, xmax = 1, 2.5*np.pi
b, C, SNR = 1, 0, 0.1
X = np.linspace(xmin, xmax, 500)
y = b*X + C + 1*np.sin(X)
y += 0.05*np.random.randn(len(X))
X, y = X[:, None], y[:, None]
m = GPy.models.GPRegression(X, y)
InverseGamma = GPy.priors.InverseGamma(1, 1)
m.rbf.set_prior(InverseGamma)
m.randomize()
self.assertTrue(m.checkgrad())
[docs] def test_incompatibility(self):
xmin, xmax = 1, 2.5*np.pi
b, C, SNR = 1, 0, 0.1
X = np.linspace(xmin, xmax, 500)
y = b*X + C + 1*np.sin(X)
y += 0.05*np.random.randn(len(X))
X, y = X[:, None], y[:, None]
m = GPy.models.GPRegression(X, y)
gaussian = GPy.priors.Gaussian(1, 1)
# setting a Gaussian prior on non-negative parameters
# should raise an assertionerror.
self.assertRaises(AssertionError, m.rbf.set_prior, gaussian)
[docs] def test_set_prior(self):
xmin, xmax = 1, 2.5*np.pi
b, C, SNR = 1, 0, 0.1
X = np.linspace(xmin, xmax, 500)
y = b*X + C + 1*np.sin(X)
y += 0.05*np.random.randn(len(X))
X, y = X[:, None], y[:, None]
m = GPy.models.GPRegression(X, y)
gaussian = GPy.priors.Gaussian(1, 1)
#m.rbf.set_prior(gaussian)
# setting a Gaussian prior on non-negative parameters
# should raise an assertionerror.
self.assertRaises(AssertionError, m.rbf.set_prior, gaussian)
[docs] def test_set_gaussian_for_reals(self):
xmin, xmax = 1, 2.5*np.pi
b, C, SNR = 1, 0, 0.1
X = np.linspace(xmin, xmax, 500)
y = b*X + C + 1*np.sin(X)
y += 0.05*np.random.randn(len(X))
X, y = X[:, None], y[:, None]
m = GPy.models.SparseGPRegression(X, y)
gaussian = GPy.priors.Gaussian(1, 1)
m.Z.set_prior(gaussian)
# setting a Gaussian prior on non-negative parameters
# should raise an assertionerror.
#self.assertRaises(AssertionError, m.Z.set_prior, gaussian)
self.assertTrue(m.checkgrad())
[docs] def test_fixed_domain_check(self):
xmin, xmax = 1, 2.5*np.pi
b, C, SNR = 1, 0, 0.1
X = np.linspace(xmin, xmax, 500)
y = b*X + C + 1*np.sin(X)
y += 0.05*np.random.randn(len(X))
X, y = X[:, None], y[:, None]
m = GPy.models.GPRegression(X, y)
m.rbf.fix()
gaussian = GPy.priors.Gaussian(1, 1)
# setting a Gaussian prior on non-negative parameters
# should raise an assertionerror.
self.assertRaises(AssertionError, m.rbf.set_prior, gaussian)
[docs] def test_fixed_domain_check1(self):
xmin, xmax = 1, 2.5*np.pi
b, C, SNR = 1, 0, 0.1
X = np.linspace(xmin, xmax, 500)
y = b*X + C + 1*np.sin(X)
y += 0.05*np.random.randn(len(X))
X, y = X[:, None], y[:, None]
m = GPy.models.GPRegression(X, y)
m.kern.lengthscale.fix()
gaussian = GPy.priors.Gaussian(1, 1)
# setting a Gaussian prior on non-negative parameters
# should raise an assertionerror.
self.assertRaises(AssertionError, m.rbf.set_prior, gaussian)
if __name__ == "__main__":
print("Running unit tests, please be (very) patient...")
unittest.main()