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63 lines
No EOL
2.5 KiB
Python
63 lines
No EOL
2.5 KiB
Python
'''
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Copyright (c) 2015, Max Zwiessele
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All rights reserved.
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Redistribution and use in source and binary forms, with or without
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modification, are permitted provided that the following conditions are met:
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* Redistributions of source code must retain the above copyright notice, this
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list of conditions and the following disclaimer.
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* Redistributions in binary form must reproduce the above copyright notice,
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this list of conditions and the following disclaimer in the documentation
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and/or other materials provided with the distribution.
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* Neither the name of paramax nor the names of its
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contributors may be used to endorse or promote products derived from
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this software without specific prior written permission.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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'''
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import unittest
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import GPy, numpy as np
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class KLGrad(GPy.core.Model):
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def __init__(self, Xvar, kl):
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super(KLGrad, self).__init__(name="klgrad")
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self.kl = kl
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self.link_parameter(Xvar)
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self.Xvar = Xvar
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self._obj = 0
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def parameters_changed(self):
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self.Xvar.gradient[:] = 0
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self.kl.update_gradients_KL(self.Xvar)
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self._obj = self.kl.KL_divergence(self.Xvar)
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def objective_function(self):
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return self._obj
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class Test(unittest.TestCase):
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def setUp(self):
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np.random.seed(12345)
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self.Xvar = GPy.core.parameterization.variational.NormalPosterior(
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np.random.uniform(0,1,(10,3)),
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np.random.uniform(1e-5,.01, (10,3))
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)
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def testNormal(self):
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klgrad = KLGrad(self.Xvar, GPy.core.parameterization.variational.NormalPrior())
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np.testing.assert_(klgrad.checkgrad())
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if __name__ == "__main__":
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#import sys;sys.argv = ['', 'Test.testNormal']
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unittest.main() |