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Merge branch 'master' of github.com:SheffieldML/GPy

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Nicolo Fusi 2013-03-11 12:33:14 +00:00
commit 1eba520b69
33 changed files with 782 additions and 358 deletions
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47
GPy/testing/gplvm_tests.py Normal file
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@ -0,0 +1,47 @@
# Copyright (c) 2012, Nicolo Fusi
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import unittest
import numpy as np
import GPy
class GPLVMTests(unittest.TestCase):
def test_bias_kern(self):
N, M, Q, D = 10, 3, 2, 4
X = np.random.rand(N, Q)
k = GPy.kern.rbf(Q) + GPy.kern.white(Q, 0.00001)
K = k.K(X)
Y = np.random.multivariate_normal(np.zeros(N),K,D).T
k = GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001)
m = GPy.models.GPLVM(Y, Q, kernel = k)
m.ensure_default_constraints()
m.randomize()
self.assertTrue(m.checkgrad())
def test_linear_kern(self):
N, M, Q, D = 10, 3, 2, 4
X = np.random.rand(N, Q)
k = GPy.kern.rbf(Q) + GPy.kern.white(Q, 0.00001)
K = k.K(X)
Y = np.random.multivariate_normal(np.zeros(N),K,D).T
k = GPy.kern.linear(Q) + GPy.kern.white(Q, 0.00001)
m = GPy.models.GPLVM(Y, Q, kernel = k)
m.ensure_default_constraints()
m.randomize()
self.assertTrue(m.checkgrad())
def test_rbf_kern(self):
N, M, Q, D = 10, 3, 2, 4
X = np.random.rand(N, Q)
k = GPy.kern.rbf(Q) + GPy.kern.white(Q, 0.00001)
K = k.K(X)
Y = np.random.multivariate_normal(np.zeros(N),K,D).T
k = GPy.kern.rbf(Q) + GPy.kern.white(Q, 0.00001)
m = GPy.models.GPLVM(Y, Q, kernel = k)
m.ensure_default_constraints()
m.randomize()
self.assertTrue(m.checkgrad())
if __name__ == "__main__":
print "Running unit tests, please be (very) patient..."
unittest.main()

48
GPy/testing/sparse_gplvm_tests.py Normal file
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# Copyright (c) 2012, Nicolo Fusi, James Hensman
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import unittest
import numpy as np
import GPy
class sparse_GPLVMTests(unittest.TestCase):
def test_bias_kern(self):
N, M, Q, D = 10, 3, 2, 4
X = np.random.rand(N, Q)
k = GPy.kern.rbf(Q) + GPy.kern.white(Q, 0.00001)
K = k.K(X)
Y = np.random.multivariate_normal(np.zeros(N),K,D).T
k = GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001)
m = GPy.models.sparse_GPLVM(Y, Q, kernel = k, M=M)
m.ensure_default_constraints()
m.randomize()
self.assertTrue(m.checkgrad())
@unittest.skip('linear kernels do not have dKdiag_dX')
def test_linear_kern(self):
N, M, Q, D = 10, 3, 2, 4
X = np.random.rand(N, Q)
k = GPy.kern.rbf(Q) + GPy.kern.white(Q, 0.00001)
K = k.K(X)
Y = np.random.multivariate_normal(np.zeros(N),K,D).T
k = GPy.kern.linear(Q) + GPy.kern.white(Q, 0.00001)
m = GPy.models.sparse_GPLVM(Y, Q, kernel = k, M=M)
m.ensure_default_constraints()
m.randomize()
self.assertTrue(m.checkgrad())
def test_rbf_kern(self):
N, M, Q, D = 10, 3, 2, 4
X = np.random.rand(N, Q)
k = GPy.kern.rbf(Q) + GPy.kern.white(Q, 0.00001)
K = k.K(X)
Y = np.random.multivariate_normal(np.zeros(N),K,D).T
k = GPy.kern.rbf(Q) + GPy.kern.white(Q, 0.00001)
m = GPy.models.sparse_GPLVM(Y, Q, kernel = k, M=M)
m.ensure_default_constraints()
m.randomize()
self.assertTrue(m.checkgrad())
if __name__ == "__main__":
print "Running unit tests, please be (very) patient..."
unittest.main()

19
GPy/testing/unit_tests.py
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@ -157,13 +157,28 @@ class GradientTests(unittest.TestCase):
def test_GP_EP_probit(self):
N = 20
X = np.hstack([np.random.normal(5,2,N/2),np.random.normal(10,2,N/2)])[:,None]
Y = np.hstack([np.ones(N/2),np.repeat(-1,N/2)])[:,None]
Y = np.hstack([np.ones(N/2),np.zeros(N/2)])[:,None]
kernel = GPy.kern.rbf(1)
distribution = GPy.likelihoods.likelihood_functions.probit()
likelihood = GPy.likelihoods.EP(Y, distribution)
m = GPy.models.GP(X, likelihood, kernel)
m.ensure_default_constraints()
self.assertTrue(m.EPEM)
m.update_likelihood_approximation()
self.assertTrue(m.checkgrad())
#self.assertTrue(m.EPEM)
def test_sparse_EP_DTC_probit(self):
N = 20
X = np.hstack([np.random.normal(5,2,N/2),np.random.normal(10,2,N/2)])[:,None]
Y = np.hstack([np.ones(N/2),np.zeros(N/2)])[:,None]
Z = np.linspace(0,15,4)[:,None]
kernel = GPy.kern.rbf(1)
distribution = GPy.likelihoods.likelihood_functions.probit()
likelihood = GPy.likelihoods.EP(Y, distribution)
m = GPy.models.sparse_GP(X, likelihood, kernel,Z)
m.ensure_default_constraints()
m.update_likelihood_approximation()
self.assertTrue(m.checkgrad())
@unittest.skip("FITC will be broken for a while")
def test_generalized_FITC(self):