mirror of
https://github.com/SheffieldML/GPy.git
synced 2026-05-03 00:32:39 +02:00
134 lines
4.7 KiB
Python
134 lines
4.7 KiB
Python
# Copyright (c) 2012, 2013 GPy authors (see AUTHORS.txt).
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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import unittest
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import numpy as np
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import GPy
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verbose = False
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try:
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import sympy
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SYMPY_AVAILABLE=True
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except ImportError:
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SYMPY_AVAILABLE=False
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class KernelTests(unittest.TestCase):
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def test_kerneltie(self):
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K = GPy.kern.rbf(5, ARD=True)
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K.rbf.lengthscale[0].tie_to(K.rbf.lengthscale[2])
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K.rbf.lengthscale[1].tie_to(K.rbf.lengthscale[3])
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K.rbf.lengthscale[2].constrain_fixed()
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X = np.random.rand(5,5)
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Y = np.ones((5,1))
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m = GPy.models.GPRegression(X,Y,K)
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#self.assertRaises(RuntimeError, lambda: m.kern.rbf.lengthscale[3].tie_to(m.kern.rbf.lengthscale[1]))
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#self.assertRaises(RuntimeError, lambda: m.kern.rbf.lengthscale[3].tie_to(m.kern.rbf.lengthscale[0]))
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#self.assertRaises(RuntimeError, lambda: m.kern.rbf.lengthscale.tie_to(m.kern.rbf.lengthscale))
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import ipdb;ipdb.set_trace()
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self.assertTrue(m.checkgrad())
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def test_rbfkernel(self):
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kern = GPy.kern.rbf(5)
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self.assertTrue(GPy.kern.kern_test(kern, verbose=verbose))
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def test_rbf_sympykernel(self):
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if SYMPY_AVAILABLE:
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kern = GPy.kern.rbf_sympy(5)
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self.assertTrue(GPy.kern.kern_test(kern, verbose=verbose))
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def test_eq_sympykernel(self):
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kern = GPy.kern.eq_sympy(5, 3, output_ind=4)
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self.assertTrue(GPy.kern.kern_test(kern, verbose=verbose))
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def test_sinckernel(self):
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kern = GPy.kern.sinc(5)
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self.assertTrue(GPy.kern.kern_test(kern, verbose=verbose))
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def test_rbf_invkernel(self):
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kern = GPy.kern.rbf_inv(5)
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self.assertTrue(GPy.kern.kern_test(kern, verbose=verbose))
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def test_Matern32kernel(self):
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kern = GPy.kern.Matern32(5)
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self.assertTrue(GPy.kern.kern_test(kern, verbose=verbose))
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def test_Matern52kernel(self):
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kern = GPy.kern.Matern52(5)
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self.assertTrue(GPy.kern.kern_test(kern, verbose=verbose))
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def test_linearkernel(self):
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kern = GPy.kern.linear(5)
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self.assertTrue(GPy.kern.kern_test(kern, verbose=verbose))
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def test_periodic_exponentialkernel(self):
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kern = GPy.kern.periodic_exponential(1)
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self.assertTrue(GPy.kern.kern_test(kern, verbose=verbose))
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def test_periodic_Matern32kernel(self):
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kern = GPy.kern.periodic_Matern32(1)
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self.assertTrue(GPy.kern.kern_test(kern, verbose=verbose))
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def test_periodic_Matern52kernel(self):
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kern = GPy.kern.periodic_Matern52(1)
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self.assertTrue(GPy.kern.kern_test(kern, verbose=verbose))
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def test_rational_quadratickernel(self):
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kern = GPy.kern.rational_quadratic(1)
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self.assertTrue(GPy.kern.kern_test(kern, verbose=verbose))
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def test_gibbskernel(self):
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kern = GPy.kern.gibbs(5, mapping=GPy.mappings.Linear(5, 1))
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self.assertTrue(GPy.kern.kern_test(kern, verbose=verbose))
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def test_heterokernel(self):
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kern = GPy.kern.hetero(5, mapping=GPy.mappings.Linear(5, 1), transform=GPy.core.transformations.Logexp())
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self.assertTrue(GPy.kern.kern_test(kern, verbose=verbose))
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def test_mlpkernel(self):
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kern = GPy.kern.mlp(5)
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self.assertTrue(GPy.kern.kern_test(kern, verbose=verbose))
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def test_polykernel(self):
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kern = GPy.kern.poly(5, degree=4)
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self.assertTrue(GPy.kern.kern_test(kern, verbose=verbose))
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def test_fixedkernel(self):
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"""
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Fixed effect kernel test
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"""
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X = np.random.rand(30, 4)
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K = np.dot(X, X.T)
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kernel = GPy.kern.fixed(4, K)
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kern = GPy.kern.poly(5, degree=4)
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self.assertTrue(GPy.kern.kern_test(kern, verbose=verbose))
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# def test_coregionalization(self):
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# X1 = np.random.rand(50,1)*8
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# X2 = np.random.rand(30,1)*5
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# index = np.vstack((np.zeros_like(X1),np.ones_like(X2)))
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# X = np.hstack((np.vstack((X1,X2)),index))
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# Y1 = np.sin(X1) + np.random.randn(*X1.shape)*0.05
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# Y2 = np.sin(X2) + np.random.randn(*X2.shape)*0.05 + 2.
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# Y = np.vstack((Y1,Y2))
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# k1 = GPy.kern.rbf(1) + GPy.kern.bias(1)
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# k2 = GPy.kern.coregionalize(2,1)
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# kern = k1**k2
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# self.assertTrue(GPy.kern.kern_test(kern, verbose=verbose))
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if __name__ == "__main__":
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# K = GPy.kern.rbf(5, ARD=True)
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# K.rbf.lengthscale[0].tie_to(K.rbf.lengthscale[2])
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# K.rbf.lengthscale[1].tie_to(K.rbf.lengthscale[3])
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# K.rbf.lengthscale[2].constrain_fixed()
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#
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# K.rbf.lengthscale[2:].tie_to(K.rbf.variance)
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# X = np.random.rand(5,5)
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# Y = np.ones((5,1))
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# m = GPy.models.GPRegression(X,Y,K)
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print "Running unit tests, please be (very) patient..."
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unittest.main()
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