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98 lines
4.8 KiB
Python
98 lines
4.8 KiB
Python
#===============================================================================
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# Copyright (c) 2016, Max Zwiessele, Alan Saul
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# All rights reserved.
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#
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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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#
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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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#
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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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#
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# * Neither the name of GPy.testing.util_tests 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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#
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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, numpy as np
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class TestDebug(unittest.TestCase):
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def test_checkFinite(self):
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from GPy.util.debug import checkFinite
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array = np.random.normal(0, 1, 100).reshape(25,4)
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self.assertTrue(checkFinite(array, name='test'))
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array[np.random.binomial(1, .3, array.shape).astype(bool)] = np.nan
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self.assertFalse(checkFinite(array))
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def test_checkFullRank(self):
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from GPy.util.debug import checkFullRank
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from GPy.util.linalg import tdot
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array = np.random.normal(0, 1, 100).reshape(25,4)
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self.assertFalse(checkFullRank(tdot(array), name='test'))
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array = np.random.normal(0, 1, (25,25))
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self.assertTrue(checkFullRank(tdot(array)))
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def test_fixed_inputs_median(self):
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""" test fixed_inputs convenience function """
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from GPy.plotting.matplot_dep.util import fixed_inputs
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import GPy
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X = np.random.randn(10, 3)
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Y = np.sin(X) + np.random.randn(10, 3)*1e-3
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m = GPy.models.GPRegression(X, Y)
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fixed = fixed_inputs(m, [1], fix_routine='median', as_list=True, X_all=False)
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self.assertTrue((0, np.median(X[:,0])) in fixed)
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self.assertTrue((2, np.median(X[:,2])) in fixed)
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self.assertTrue(len([t for t in fixed if t[0] == 1]) == 0) # Unfixed input should not be in fixed
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def test_fixed_inputs_mean(self):
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from GPy.plotting.matplot_dep.util import fixed_inputs
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import GPy
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X = np.random.randn(10, 3)
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Y = np.sin(X) + np.random.randn(10, 3)*1e-3
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m = GPy.models.GPRegression(X, Y)
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fixed = fixed_inputs(m, [1], fix_routine='mean', as_list=True, X_all=False)
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self.assertTrue((0, np.mean(X[:,0])) in fixed)
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self.assertTrue((2, np.mean(X[:,2])) in fixed)
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self.assertTrue(len([t for t in fixed if t[0] == 1]) == 0) # Unfixed input should not be in fixed
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def test_fixed_inputs_zero(self):
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from GPy.plotting.matplot_dep.util import fixed_inputs
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import GPy
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X = np.random.randn(10, 3)
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Y = np.sin(X) + np.random.randn(10, 3)*1e-3
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m = GPy.models.GPRegression(X, Y)
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fixed = fixed_inputs(m, [1], fix_routine='zero', as_list=True, X_all=False)
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self.assertTrue((0, 0.0) in fixed)
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self.assertTrue((2, 0.0) in fixed)
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self.assertTrue(len([t for t in fixed if t[0] == 1]) == 0) # Unfixed input should not be in fixed
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def test_fixed_inputs_uncertain(self):
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from GPy.plotting.matplot_dep.util import fixed_inputs
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import GPy
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from GPy.core.parameterization.variational import NormalPosterior
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X_mu = np.random.randn(10, 3)
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X_var = np.random.randn(10, 3)
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X = NormalPosterior(X_mu, X_var)
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Y = np.sin(X_mu) + np.random.randn(10, 3)*1e-3
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m = GPy.models.BayesianGPLVM(Y, X=X_mu, X_variance=X_var, input_dim=3)
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fixed = fixed_inputs(m, [1], fix_routine='median', as_list=True, X_all=False)
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self.assertTrue((0, np.median(X.mean.values[:,0])) in fixed)
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self.assertTrue((2, np.median(X.mean.values[:,2])) in fixed)
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self.assertTrue(len([t for t in fixed if t[0] == 1]) == 0) # Unfixed input should not be in fixed
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