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45 lines
1.8 KiB
Python
45 lines
1.8 KiB
Python
# Copyright (c) 2012, Nicolo Fusi, James Hensman
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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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from ..models import SparseGPLVM
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class sparse_GPLVMTests(unittest.TestCase):
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def test_bias_kern(self):
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N, num_inducing, input_dim, D = 10, 3, 2, 4
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X = np.random.rand(N, input_dim)
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k = GPy.kern.RBF(input_dim) + GPy.kern.White(input_dim, 0.00001)
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K = k.K(X)
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Y = np.random.multivariate_normal(np.zeros(N),K,input_dim).T
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k = GPy.kern.Bias(input_dim) + GPy.kern.White(input_dim, 0.00001)
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m = SparseGPLVM(Y, input_dim, kernel=k, num_inducing=num_inducing)
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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, num_inducing, input_dim, D = 10, 3, 2, 4
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X = np.random.rand(N, input_dim)
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k = GPy.kern.RBF(input_dim) + GPy.kern.White(input_dim, 0.00001)
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K = k.K(X)
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Y = np.random.multivariate_normal(np.zeros(N),K,input_dim).T
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k = GPy.kern.Linear(input_dim) + GPy.kern.White(input_dim, 0.00001)
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m = SparseGPLVM(Y, input_dim, kernel=k, num_inducing=num_inducing)
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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, num_inducing, input_dim, D = 10, 3, 2, 4
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X = np.random.rand(N, input_dim)
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k = GPy.kern.RBF(input_dim) + GPy.kern.White(input_dim, 0.00001)
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K = k.K(X)
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Y = np.random.multivariate_normal(np.zeros(N),K,input_dim).T
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k = GPy.kern.RBF(input_dim) + GPy.kern.White(input_dim, 0.00001)
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m = SparseGPLVM(Y, input_dim, kernel=k, num_inducing=num_inducing)
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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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