#=============================================================================== # Copyright (c) 2016, Max Zwiessele, Alan Saul # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright notice, this # list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # * Neither the name of GPy.testing.util_tests nor the names of its # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE # DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE # FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL # DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR # SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. #=============================================================================== import unittest, numpy as np import GPy class TestDebug(unittest.TestCase): def test_checkFinite(self): from GPy.util.debug import checkFinite array = np.random.normal(0, 1, 100).reshape(25,4) self.assertTrue(checkFinite(array, name='test')) array[np.random.binomial(1, .3, array.shape).astype(bool)] = np.nan self.assertFalse(checkFinite(array)) def test_checkFullRank(self): from GPy.util.debug import checkFullRank from GPy.util.linalg import tdot array = np.random.normal(0, 1, 100).reshape(25,4) self.assertFalse(checkFullRank(tdot(array), name='test')) array = np.random.normal(0, 1, (25,25)) self.assertTrue(checkFullRank(tdot(array))) def test_fixed_inputs_median(self): """ test fixed_inputs convenience function """ from GPy.plotting.matplot_dep.util import fixed_inputs import GPy X = np.random.randn(10, 3) Y = np.sin(X) + np.random.randn(10, 3)*1e-3 m = GPy.models.GPRegression(X, Y) fixed = fixed_inputs(m, [1], fix_routine='median', as_list=True, X_all=False) self.assertTrue((0, np.median(X[:,0])) in fixed) self.assertTrue((2, np.median(X[:,2])) in fixed) self.assertTrue(len([t for t in fixed if t[0] == 1]) == 0) # Unfixed input should not be in fixed def test_fixed_inputs_mean(self): from GPy.plotting.matplot_dep.util import fixed_inputs import GPy X = np.random.randn(10, 3) Y = np.sin(X) + np.random.randn(10, 3)*1e-3 m = GPy.models.GPRegression(X, Y) fixed = fixed_inputs(m, [1], fix_routine='mean', as_list=True, X_all=False) self.assertTrue((0, np.mean(X[:,0])) in fixed) self.assertTrue((2, np.mean(X[:,2])) in fixed) self.assertTrue(len([t for t in fixed if t[0] == 1]) == 0) # Unfixed input should not be in fixed def test_fixed_inputs_zero(self): from GPy.plotting.matplot_dep.util import fixed_inputs import GPy X = np.random.randn(10, 3) Y = np.sin(X) + np.random.randn(10, 3)*1e-3 m = GPy.models.GPRegression(X, Y) fixed = fixed_inputs(m, [1], fix_routine='zero', as_list=True, X_all=False) self.assertTrue((0, 0.0) in fixed) self.assertTrue((2, 0.0) in fixed) self.assertTrue(len([t for t in fixed if t[0] == 1]) == 0) # Unfixed input should not be in fixed def test_fixed_inputs_uncertain(self): from GPy.plotting.matplot_dep.util import fixed_inputs import GPy from GPy.core.parameterization.variational import NormalPosterior X_mu = np.random.randn(10, 3) X_var = np.random.randn(10, 3) X = NormalPosterior(X_mu, X_var) Y = np.sin(X_mu) + np.random.randn(10, 3)*1e-3 m = GPy.models.BayesianGPLVM(Y, X=X_mu, X_variance=X_var, input_dim=3) fixed = fixed_inputs(m, [1], fix_routine='median', as_list=True, X_all=False) self.assertTrue((0, np.median(X.mean.values[:,0])) in fixed) self.assertTrue((2, np.median(X.mean.values[:,2])) in fixed) self.assertTrue(len([t for t in fixed if t[0] == 1]) == 0) # Unfixed input should not be in fixed def test_DSYR(self): from GPy.util.linalg import DSYR, DSYR_numpy A = np.arange(9.0).reshape(3,3) A = np.dot(A.T, A) b = np.ones(3, dtype=float) alpha = 1.0 DSYR(A, b, alpha) R = np.array([ [46, 55, 64], [55, 67, 79], [64, 79, 94]] ) self.assertTrue(abs(np.sum(A - R)) < 1e-12) def test_subarray(self): import GPy X = np.zeros((3,6), dtype=bool) X[[1,1,1],[0,4,5]] = 1 X[1:,[2,3]] = 1 d = GPy.util.subarray_and_sorting.common_subarrays(X,axis=1) self.assertTrue(len(d) == 3) X[:, d[tuple(X[:,0])]] self.assertTrue(d[tuple(X[:,4])] == d[tuple(X[:,0])] == [0, 4, 5]) self.assertTrue(d[tuple(X[:,1])] == [1]) def test_offset_cluster(self): #Tests the GPy.util.cluster_with_offset.cluster utility with a small #test data set. Not using random noise just in case it occasionally #causes it not to cluster correctly. #groundtruth cluster identifiers are: [0,1,1,0] #data contains a list of the four sets of time series (3 per data point) data = [np.array([[ 2.18094245, 1.96529789, 2.00265523, 2.18218742, 2.06795428], [ 1.62254829, 1.75748448, 1.83879347, 1.87531326, 1.52503496], [ 1.54589609, 1.61607914, 2.00463192, 1.48771394, 1.63339218]]), np.array([[ 2.86766106, 2.97953437, 2.91958876, 2.92510506, 3.03239241], [ 2.57368423, 2.59954886, 3.10000395, 2.75806125, 2.89865704], [ 2.58916318, 2.53698259, 2.63858411, 2.63102504, 2.51853901]]), np.array([[ 2.77834168, 2.9618564 , 2.88482141, 3.24259745, 2.9716821 ], [ 2.60675576, 2.67095624, 2.94824436, 2.80520631, 2.87247516], [ 2.49543562, 2.5492281 , 2.6505866 , 2.65015308, 2.59738616]]), np.array([[ 1.76783086, 2.21666738, 2.07939706, 1.9268263 , 2.23360121], [ 1.94305547, 1.94648592, 2.1278921 , 2.09481457, 2.08575238], [ 1.69336013, 1.72285186, 1.6339506 , 1.61212022, 1.39198698]])] #inputs contains their associated X values inputs = [np.array([[ 0. ], [ 0.68040097], [ 1.20316795], [ 1.798749 ], [ 2.14891733]]), np.array([[ 0. ], [ 0.51910637], [ 0.98259352], [ 1.57442965], [ 1.82515098]]), np.array([[ 0. ], [ 0.66645478], [ 1.59464591], [ 1.69769551], [ 1.80932752]]), np.array([[ 0. ], [ 0.87512108], [ 1.71881079], [ 2.67162871], [ 3.23761907]])] #try doing the clustering active = GPy.util.cluster_with_offset.cluster(data,inputs) #check to see that the clustering has correctly clustered the time series. clusters = set([frozenset(cluster) for cluster in active]) assert set([1,2]) in clusters, "Offset Clustering algorithm failed" assert set([0,3]) in clusters, "Offset Clustering algoirthm failed" class TestUnivariateGaussian(unittest.TestCase): def setUp(self): self.zz = [-5.0, -0.8, 0.0, 0.5, 2.0, 10.0] def test_logPdfNormal(self): from GPy.util.univariate_Gaussian import logPdfNormal pySols = [-13.4189385332, -1.2389385332, -0.918938533205, -1.0439385332, -2.9189385332, -50.9189385332] diff = 0.0 for i in range(len(pySols)): diff += abs(logPdfNormal(self.zz[i]) - pySols[i]) self.assertTrue(diff < 1e-10) def test_cdfNormal(self): from GPy.util.univariate_Gaussian import cdfNormal pySols = [2.86651571879e-07, 0.211855398583, 0.5, 0.691462461274, 0.977249868052, 1.0] diff = 0.0 for i in range(len(pySols)): diff += abs(cdfNormal(self.zz[i]) - pySols[i]) self.assertTrue(diff < 1e-10) def test_logCdfNormal(self): from GPy.util.univariate_Gaussian import logCdfNormal pySols = [-15.064998394, -1.55185131919, -0.69314718056, -0.368946415289, -0.023012909329, 0.0] diff = 0.0 for i in range(len(pySols)): diff += abs(logCdfNormal(self.zz[i]) - pySols[i]) self.assertTrue(diff < 1e-10) def test_derivLogCdfNormal(self): from GPy.util.univariate_Gaussian import derivLogCdfNormal pySols = [5.18650396941, 1.3674022693, 0.79788456081, 0.50916043387, 0.0552478626962, 0.0] diff = 0.0 for i in range(len(pySols)): diff += abs(derivLogCdfNormal(self.zz[i]) - pySols[i]) self.assertTrue(diff < 1e-8)