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Martin Bubel
parent
323d29bc7d
commit
975fb7e383
1 changed files with 143 additions and 110 deletions
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@ -28,24 +28,26 @@
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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 numpy as np
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import GPy
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class TestDebug(unittest.TestCase):
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class UtilTest:
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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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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, 0.3, 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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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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@ -54,66 +56,76 @@ class TestDebug(unittest.TestCase):
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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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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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self.assertTrue(
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len([t for t in fixed if t[0] == 1]) == 0
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) # 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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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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self.assertTrue(
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len([t for t in fixed if t[0] == 1]) == 0
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) # 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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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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self.assertTrue(
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len([t for t in fixed if t[0] == 1]) == 0
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) # 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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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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self.assertTrue(
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len([t for t in fixed if t[0] == 1]) == 0
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) # Unfixed input should not be in fixed
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def test_DSYR(self):
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from GPy.util.linalg import DSYR, DSYR_numpy
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A = np.arange(9.0).reshape(3, 3)
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A = np.dot(A.T, A)
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b = np.ones(3, dtype=float)
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alpha = 1.0
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DSYR(A, b, alpha)
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R = np.array([
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[46, 55, 64],
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[55, 67, 79],
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[64, 79, 94]]
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)
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R = np.array([[46, 55, 64], [55, 67, 79], [64, 79, 94]])
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self.assertTrue(abs(np.sum(A - R)) < 1e-12)
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def test_subarray(self):
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import GPy
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X = np.zeros((3, 6), dtype=bool)
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X[[1, 1, 1], [0, 4, 5]] = 1
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X[1:, [2, 3]] = 1
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@ -131,38 +143,45 @@ class TestDebug(unittest.TestCase):
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# data contains a list of the four sets of time series (3 per data point)
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data = [np.array([[ 2.18094245, 1.96529789, 2.00265523, 2.18218742, 2.06795428],
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data = [
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np.array(
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[
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[2.18094245, 1.96529789, 2.00265523, 2.18218742, 2.06795428],
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[1.62254829, 1.75748448, 1.83879347, 1.87531326, 1.52503496],
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[ 1.54589609, 1.61607914, 2.00463192, 1.48771394, 1.63339218]]),
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np.array([[ 2.86766106, 2.97953437, 2.91958876, 2.92510506, 3.03239241],
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[1.54589609, 1.61607914, 2.00463192, 1.48771394, 1.63339218],
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]
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),
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np.array(
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[
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[2.86766106, 2.97953437, 2.91958876, 2.92510506, 3.03239241],
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[2.57368423, 2.59954886, 3.10000395, 2.75806125, 2.89865704],
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[ 2.58916318, 2.53698259, 2.63858411, 2.63102504, 2.51853901]]),
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np.array([[ 2.77834168, 2.9618564 , 2.88482141, 3.24259745, 2.9716821 ],
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[2.58916318, 2.53698259, 2.63858411, 2.63102504, 2.51853901],
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]
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),
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np.array(
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[
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[2.77834168, 2.9618564, 2.88482141, 3.24259745, 2.9716821],
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[2.60675576, 2.67095624, 2.94824436, 2.80520631, 2.87247516],
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[ 2.49543562, 2.5492281 , 2.6505866 , 2.65015308, 2.59738616]]),
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np.array([[ 1.76783086, 2.21666738, 2.07939706, 1.9268263 , 2.23360121],
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[2.49543562, 2.5492281, 2.6505866, 2.65015308, 2.59738616],
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]
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),
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np.array(
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[
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[1.76783086, 2.21666738, 2.07939706, 1.9268263, 2.23360121],
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[1.94305547, 1.94648592, 2.1278921, 2.09481457, 2.08575238],
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[ 1.69336013, 1.72285186, 1.6339506 , 1.61212022, 1.39198698]])]
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[1.69336013, 1.72285186, 1.6339506, 1.61212022, 1.39198698],
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]
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),
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]
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# inputs contains their associated X values
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inputs = [np.array([[ 0. ],
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[ 0.68040097],
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[ 1.20316795],
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[ 1.798749 ],
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[ 2.14891733]]), np.array([[ 0. ],
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[ 0.51910637],
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[ 0.98259352],
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[ 1.57442965],
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[ 1.82515098]]), np.array([[ 0. ],
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[ 0.66645478],
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[ 1.59464591],
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[ 1.69769551],
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[ 1.80932752]]), np.array([[ 0. ],
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[ 0.87512108],
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[ 1.71881079],
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[ 2.67162871],
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[ 3.23761907]])]
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inputs = [
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np.array([[0.0], [0.68040097], [1.20316795], [1.798749], [2.14891733]]),
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np.array([[0.0], [0.51910637], [0.98259352], [1.57442965], [1.82515098]]),
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np.array([[0.0], [0.66645478], [1.59464591], [1.69769551], [1.80932752]]),
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np.array([[0.0], [0.87512108], [1.71881079], [2.67162871], [3.23761907]]),
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]
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# try doing the clustering
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active = GPy.util.cluster_with_offset.cluster(data, inputs)
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@ -178,12 +197,15 @@ class TestUnivariateGaussian(unittest.TestCase):
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def test_logPdfNormal(self):
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from GPy.util.univariate_Gaussian import logPdfNormal
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pySols = [-13.4189385332,
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pySols = [
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-13.4189385332,
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-1.2389385332,
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-0.918938533205,
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-1.0439385332,
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-2.9189385332,
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-50.9189385332]
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-50.9189385332,
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]
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diff = 0.0
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for i in range(len(pySols)):
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diff += abs(logPdfNormal(self.zz[i]) - pySols[i])
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@ -191,12 +213,15 @@ class TestUnivariateGaussian(unittest.TestCase):
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def test_cdfNormal(self):
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from GPy.util.univariate_Gaussian import cdfNormal
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pySols = [2.86651571879e-07,
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pySols = [
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2.86651571879e-07,
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0.211855398583,
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0.5,
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0.691462461274,
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0.977249868052,
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1.0]
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1.0,
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]
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diff = 0.0
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for i in range(len(pySols)):
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diff += abs(cdfNormal(self.zz[i]) - pySols[i])
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@ -204,29 +229,37 @@ class TestUnivariateGaussian(unittest.TestCase):
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def test_logCdfNormal(self):
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from GPy.util.univariate_Gaussian import logCdfNormal
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pySols = [-15.064998394,
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pySols = [
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-15.064998394,
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-1.55185131919,
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-0.69314718056,
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-0.368946415289,
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-0.023012909329,
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0.0]
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0.0,
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]
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diff = 0.0
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for i in range(len(pySols)):
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diff += abs(logCdfNormal(self.zz[i]) - pySols[i])
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self.assertTrue(diff < 1e-10)
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def test_derivLogCdfNormal(self):
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from GPy.util.univariate_Gaussian import derivLogCdfNormal
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pySols = [5.18650396941,
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pySols = [
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5.18650396941,
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1.3674022693,
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0.79788456081,
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0.50916043387,
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0.0552478626962,
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0.0]
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0.0,
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]
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diff = 0.0
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for i in range(len(pySols)):
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diff += abs(derivLogCdfNormal(self.zz[i]) - pySols[i])
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self.assertTrue(diff < 1e-8)
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class TestStandardize(unittest.TestCase):
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def setUp(self):
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self.normalizer = GPy.util.normalizer.Standardize()
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