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67 lines
2.5 KiB
Python
67 lines
2.5 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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class MappingGradChecker(GPy.core.Model):
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"""
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This class has everything we need to check the gradient of a mapping. It
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implement a simple likelihood which is a weighted sum of the outputs of the
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mapping. the gradients are checked against the parameters of the mapping
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and the input.
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"""
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def __init__(self, mapping, X, name='map_grad_check'):
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super(MappingGradChecker, self).__init__(name)
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self.mapping = mapping
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self.link_parameter(self.mapping)
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self.X = GPy.core.Param('X',X)
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self.link_parameter(self.X)
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self.dL_dY = np.random.randn(self.X.shape[0], self.mapping.output_dim)
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def log_likelihood(self):
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return np.sum(self.mapping.f(self.X) * self.dL_dY)
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def parameters_changed(self):
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self.X.gradient = self.mapping.gradients_X(self.dL_dY, self.X)
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self.mapping.update_gradients(self.dL_dY, self.X)
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class MappingTests(unittest.TestCase):
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def test_kernelmapping(self):
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X = np.random.randn(100,3)
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Z = np.random.randn(10,3)
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mapping = GPy.mappings.Kernel(3, 2, Z, GPy.kern.RBF(3))
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self.assertTrue(MappingGradChecker(mapping, X).checkgrad())
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def test_linearmapping(self):
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mapping = GPy.mappings.Linear(3, 2)
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X = np.random.randn(100,3)
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self.assertTrue(MappingGradChecker(mapping, X).checkgrad())
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def test_mlpmapping(self):
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mapping = GPy.mappings.MLP(input_dim=3, hidden_dim=5, output_dim=2)
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X = np.random.randn(100,3)
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self.assertTrue(MappingGradChecker(mapping, X).checkgrad())
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def test_addmapping(self):
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m1 = GPy.mappings.MLP(input_dim=3, hidden_dim=5, output_dim=2)
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m2 = GPy.mappings.Linear(input_dim=3, output_dim=2)
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mapping = GPy.mappings.Additive(m1, m2)
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X = np.random.randn(100,3)
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self.assertTrue(MappingGradChecker(mapping, X).checkgrad())
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def test_compoundmapping(self):
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m1 = GPy.mappings.MLP(input_dim=3, hidden_dim=5, output_dim=2)
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Z = np.random.randn(10,2)
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m2 = GPy.mappings.Kernel(2, 4, Z, GPy.kern.RBF(2))
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mapping = GPy.mappings.Compound(m1, m2)
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X = np.random.randn(100,3)
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self.assertTrue(MappingGradChecker(mapping, X).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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