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50 lines
2.2 KiB
Python
50 lines
2.2 KiB
Python
# Copyright (c) 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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class BCGPLVMTests(unittest.TestCase):
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def test_kernel_backconstraint(self):
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num_data, num_inducing, input_dim, output_dim = 10, 3, 2, 4
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X = np.random.rand(num_data, 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(num_data),K,output_dim).T
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k = GPy.kern.mlp(input_dim) + GPy.kern.bias(input_dim)
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bk = GPy.kern.rbf(output_dim)
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mapping = GPy.mappings.Kernel(output_dim=input_dim, X=Y, kernel=bk)
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m = GPy.models.BCGPLVM(Y, input_dim, kernel = k, mapping=mapping)
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m.randomize()
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self.assertTrue(m.checkgrad())
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def test_linear_backconstraint(self):
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num_data, num_inducing, input_dim, output_dim = 10, 3, 2, 4
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X = np.random.rand(num_data, 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(num_data),K,output_dim).T
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k = GPy.kern.mlp(input_dim) + GPy.kern.bias(input_dim)
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bk = GPy.kern.rbf(output_dim)
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mapping = GPy.mappings.Linear(output_dim=input_dim, input_dim=output_dim)
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m = GPy.models.BCGPLVM(Y, input_dim, kernel = k, mapping=mapping)
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m.randomize()
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self.assertTrue(m.checkgrad())
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def test_mlp_backconstraint(self):
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num_data, num_inducing, input_dim, output_dim = 10, 3, 2, 4
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X = np.random.rand(num_data, 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(num_data),K,output_dim).T
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k = GPy.kern.mlp(input_dim) + GPy.kern.bias(input_dim)
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bk = GPy.kern.rbf(output_dim)
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mapping = GPy.mappings.MLP(output_dim=input_dim, input_dim=output_dim, hidden_dim=[5, 4, 7])
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m = GPy.models.BCGPLVM(Y, input_dim, kernel = k, mapping=mapping)
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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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