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85 lines
2.2 KiB
Python
85 lines
2.2 KiB
Python
# Copyright (c) 2014, Max Zwiessele
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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"""
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The test cases for various inference algorithms
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"""
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import unittest, itertools
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import numpy as np
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import GPy
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#np.seterr(invalid='raise')
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class InferenceXTestCase(unittest.TestCase):
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def genData(self):
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np.random.seed(1)
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D1,D2,N = 12,12,50
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x = np.linspace(0, 4 * np.pi, N)[:, None]
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s1 = np.vectorize(lambda x: np.sin(x))
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s2 = np.vectorize(lambda x: np.cos(x)**2)
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s3 = np.vectorize(lambda x:-np.exp(-np.cos(2 * x)))
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sS = np.vectorize(lambda x: np.cos(x))
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s1 = s1(x)
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s2 = s2(x)
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s3 = s3(x)
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sS = sS(x)
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s1 -= s1.mean(); s1 /= s1.std(0)
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s2 -= s2.mean(); s2 /= s2.std(0)
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s3 -= s3.mean(); s3 /= s3.std(0)
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sS -= sS.mean(); sS /= sS.std(0)
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S1 = np.hstack([s1, sS])
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S2 = np.hstack([s3, sS])
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P1 = np.random.randn(S1.shape[1], D1)
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P2 = np.random.randn(S2.shape[1], D2)
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Y1 = S1.dot(P1)
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Y2 = S2.dot(P2)
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Y1 += .01 * np.random.randn(*Y1.shape)
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Y2 += .01 * np.random.randn(*Y2.shape)
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Y1 -= Y1.mean(0)
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Y2 -= Y2.mean(0)
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Y1 /= Y1.std(0)
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Y2 /= Y2.std(0)
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slist = [s1, s2, s3, sS]
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slist_names = ["s1", "s2", "s3", "sS"]
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Ylist = [Y1, Y2]
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return Ylist
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def test_inferenceX_BGPLVM(self):
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Ys = self.genData()
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m = GPy.models.BayesianGPLVM(Ys[0],5,kernel=GPy.kern.Linear(5,ARD=True))
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x,mi = m.infer_newX(m.Y, optimize=False)
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self.assertTrue(mi.checkgrad())
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m.optimize(max_iters=10000)
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x, mi = m.infer_newX(m.Y)
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print(m.X.mean - mi.X.mean)
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self.assertTrue(np.allclose(m.X.mean, mi.X.mean, rtol=1e-4, atol=1e-4))
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self.assertTrue(np.allclose(m.X.variance, mi.X.variance, rtol=1e-4, atol=1e-4))
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def test_inferenceX_GPLVM(self):
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Ys = self.genData()
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m = GPy.models.GPLVM(Ys[0],3,kernel=GPy.kern.RBF(3,ARD=True))
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x,mi = m.infer_newX(m.Y, optimize=False)
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self.assertTrue(mi.checkgrad())
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# m.optimize(max_iters=10000)
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# x,mi = m.infer_newX(m.Y)
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# self.assertTrue(np.allclose(m.X, x))
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if __name__ == "__main__":
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unittest.main()
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