61 lines
1.5 KiB
Python
61 lines
1.5 KiB
Python
"""
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GTSAM Copyright 2010-2019, Georgia Tech Research Corporation,
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Atlanta, Georgia 30332-0415
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All Rights Reserved
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See LICENSE for the license information
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Unit tests for Gaussian Bayes Nets.
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Author: Frank Dellaert
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"""
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# pylint: disable=invalid-name, no-name-in-module, no-member
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from __future__ import print_function
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import unittest
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import gtsam
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import numpy as np
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from gtsam import GaussianBayesNet, GaussianConditional
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from gtsam.utils.test_case import GtsamTestCase
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# some keys
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_x_ = 11
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_y_ = 22
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_z_ = 33
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def smallBayesNet():
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"""Create a small Bayes Net for testing"""
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bayesNet = GaussianBayesNet()
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I_1x1 = np.eye(1, dtype=float)
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bayesNet.push_back(GaussianConditional(_x_, [9.0], I_1x1, _y_, I_1x1))
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bayesNet.push_back(GaussianConditional(_y_, [5.0], I_1x1))
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return bayesNet
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class TestGaussianBayesNet(GtsamTestCase):
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"""Tests for Gaussian Bayes nets."""
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def test_matrix(self):
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"""Test matrix method"""
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R, d = smallBayesNet().matrix() # get matrix and RHS
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R1 = np.array([[1.0, 1.0], [0.0, 1.0]])
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d1 = np.array([9.0, 5.0])
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np.testing.assert_equal(R, R1)
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np.testing.assert_equal(d, d1)
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def test_sample(self):
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"""Test sample method"""
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bayesNet = smallBayesNet()
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sample = bayesNet.sample()
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self.assertIsInstance(sample, gtsam.VectorValues)
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# standard deviation is 1.0 for both, so we set tolerance to 3*sigma
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mean = bayesNet.optimize()
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self.gtsamAssertEquals(sample, mean, tol=3.0)
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if __name__ == "__main__":
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unittest.main()
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