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61 lines
1.9 KiB
Python
61 lines
1.9 KiB
Python
# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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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 PriorTests(unittest.TestCase):
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def test_lognormal(self):
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xmin, xmax = 1, 2.5*np.pi
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b, C, SNR = 1, 0, 0.1
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X = np.linspace(xmin, xmax, 500)
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y = b*X + C + 1*np.sin(X)
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y += 0.05*np.random.randn(len(X))
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X, y = X[:, None], y[:, None]
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m = GPy.models.GP_regression(X, y)
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m.ensure_default_constraints()
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lognormal = GPy.priors.log_Gaussian(1, 2)
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m.set_prior('rbf', lognormal)
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m.randomize()
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self.assertTrue(m.checkgrad())
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def test_gamma(self):
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xmin, xmax = 1, 2.5*np.pi
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b, C, SNR = 1, 0, 0.1
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X = np.linspace(xmin, xmax, 500)
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y = b*X + C + 1*np.sin(X)
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y += 0.05*np.random.randn(len(X))
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X, y = X[:, None], y[:, None]
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m = GPy.models.GP_regression(X, y)
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m.ensure_default_constraints()
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gamma = GPy.priors.gamma(1, 1)
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m.set_prior('rbf', gamma)
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m.randomize()
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self.assertTrue(m.checkgrad())
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def test_incompatibility(self):
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xmin, xmax = 1, 2.5*np.pi
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b, C, SNR = 1, 0, 0.1
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X = np.linspace(xmin, xmax, 500)
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y = b*X + C + 1*np.sin(X)
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y += 0.05*np.random.randn(len(X))
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X, y = X[:, None], y[:, None]
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m = GPy.models.GP_regression(X, y)
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m.ensure_default_constraints()
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gaussian = GPy.priors.Gaussian(1, 1)
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success = False
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# setting a Gaussian prior on non-negative parameters
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# should raise an assertionerror.
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try:
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m.set_prior('rbf', gaussian)
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except AssertionError:
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success = True
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self.assertTrue(success)
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if __name__ == "__main__":
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print "Running unit tests, please be (very) patient..."
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unittest.main()
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