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78 lines
2.9 KiB
Python
78 lines
2.9 KiB
Python
# Copyright (c) 2012, Nicolo Fusi
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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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class BGPLVMTests(unittest.TestCase):
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def test_bias_kern(self):
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N, M, Q, D = 10, 3, 2, 4
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X = np.random.rand(N, Q)
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k = GPy.kern.rbf(Q) + GPy.kern.white(Q, 0.00001)
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K = k.K(X)
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Y = np.random.multivariate_normal(np.zeros(N),K,D).T
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Y -= Y.mean(axis=0)
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k = GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001)
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m = GPy.models.Bayesian_GPLVM(Y, Q, kernel = k, M=M)
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m.constrain_positive('(rbf|bias|noise|white|S)')
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m.randomize()
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self.assertTrue(m.checkgrad())
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def test_linear_kern(self):
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N, M, Q, D = 10, 3, 2, 4
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X = np.random.rand(N, Q)
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k = GPy.kern.rbf(Q) + GPy.kern.white(Q, 0.00001)
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K = k.K(X)
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Y = np.random.multivariate_normal(np.zeros(N),K,D).T
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Y -= Y.mean(axis=0)
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k = GPy.kern.linear(Q) + GPy.kern.white(Q, 0.00001)
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m = GPy.models.Bayesian_GPLVM(Y, Q, kernel = k, M=M)
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m.constrain_positive('(linear|bias|noise|white|S)')
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m.randomize()
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self.assertTrue(m.checkgrad())
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def test_rbf_kern(self):
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N, M, Q, D = 10, 3, 2, 4
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X = np.random.rand(N, Q)
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k = GPy.kern.rbf(Q) + GPy.kern.white(Q, 0.00001)
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K = k.K(X)
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Y = np.random.multivariate_normal(np.zeros(N),K,D).T
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Y -= Y.mean(axis=0)
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k = GPy.kern.rbf(Q) + GPy.kern.white(Q, 0.00001)
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m = GPy.models.Bayesian_GPLVM(Y, Q, kernel = k, M=M)
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m.constrain_positive('(rbf|bias|noise|white|S)')
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m.randomize()
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self.assertTrue(m.checkgrad())
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def test_rbf_bias_kern(self):
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N, M, Q, D = 10, 3, 2, 4
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X = np.random.rand(N, Q)
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k = GPy.kern.rbf(Q) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001)
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K = k.K(X)
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Y = np.random.multivariate_normal(np.zeros(N),K,D).T
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Y -= Y.mean(axis=0)
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k = GPy.kern.rbf(Q) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001)
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m = GPy.models.Bayesian_GPLVM(Y, Q, kernel = k, M=M)
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m.constrain_positive('(rbf|bias|noise|white|S)')
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m.randomize()
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self.assertTrue(m.checkgrad())
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#@unittest.skip('psi2 cross terms are NotImplemented for this combination')
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def test_linear_bias_kern(self):
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N, M, Q, D = 10, 3, 2, 4
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X = np.random.rand(N, Q)
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k = GPy.kern.linear(Q) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001)
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K = k.K(X)
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Y = np.random.multivariate_normal(np.zeros(N),K,D).T
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Y -= Y.mean(axis=0)
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k = GPy.kern.linear(Q) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001)
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m = GPy.models.Bayesian_GPLVM(Y, Q, kernel = k, M=M)
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m.constrain_positive('(linear|bias|noise|white|S)')
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m.randomize()
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self.assertTrue(m.checkgrad())
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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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