fix argument name
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				|  | @ -98,11 +98,11 @@ class NonlinearFactorGraph { | |||
|   string dot( | ||||
|       const gtsam::Values& values, | ||||
|       const gtsam::KeyFormatter& keyFormatter = gtsam::DefaultKeyFormatter, | ||||
|       const GraphvizFormatting& formatting = GraphvizFormatting()); | ||||
|       const GraphvizFormatting& writer = GraphvizFormatting()); | ||||
|   void saveGraph( | ||||
|       const string& s, const gtsam::Values& values, | ||||
|       const gtsam::KeyFormatter& keyFormatter = gtsam::DefaultKeyFormatter, | ||||
|       const GraphvizFormatting& formatting = GraphvizFormatting()) const; | ||||
|       const GraphvizFormatting& writer = GraphvizFormatting()) const; | ||||
| 
 | ||||
|   // enabling serialization functionality | ||||
|   void serialize() const; | ||||
|  |  | |||
|  | @ -0,0 +1,53 @@ | |||
| """ | ||||
| GTSAM Copyright 2010-2019, Georgia Tech Research Corporation, | ||||
| Atlanta, Georgia 30332-0415 | ||||
| All Rights Reserved | ||||
| 
 | ||||
| See LICENSE for the license information | ||||
| 
 | ||||
| Unit tests for Linear Factor Graphs. | ||||
| Author: Frank Dellaert & Gerry Chen | ||||
| """ | ||||
| # pylint: disable=invalid-name, no-name-in-module, no-member | ||||
| 
 | ||||
| from __future__ import print_function | ||||
| 
 | ||||
| import unittest | ||||
| 
 | ||||
| import gtsam | ||||
| import numpy as np | ||||
| from gtsam import GaussianBayesNet, GaussianConditional | ||||
| from gtsam.utils.test_case import GtsamTestCase | ||||
| 
 | ||||
| # some keys | ||||
| _x_ = 11 | ||||
| _y_ = 22 | ||||
| _z_ = 33 | ||||
| 
 | ||||
| 
 | ||||
| def smallBayesNet(): | ||||
|     """Create a small Bayes Net for testing""" | ||||
|     bayesNet = GaussianBayesNet() | ||||
|     I_1x1 = np.eye(1, dtype=float) | ||||
|     bayesNet.push_back(GaussianConditional( | ||||
|         _x_, [9.0], I_1x1, _y_, I_1x1)) | ||||
|     bayesNet.push_back(GaussianConditional(_y_, [5.0], I_1x1)) | ||||
|     return bayesNet | ||||
| 
 | ||||
| 
 | ||||
| class TestGaussianBayesNet(GtsamTestCase): | ||||
|     """Tests for Gaussian Bayes nets.""" | ||||
| 
 | ||||
|     def test_matrix(self): | ||||
|         """Test matrix method""" | ||||
|         R, d = smallBayesNet().matrix()  # get matrix and RHS | ||||
|         R1 = np.array([ | ||||
|             [1.0, 1.0], | ||||
|             [0.0, 1.0]]) | ||||
|         d1 = np.array([9.0, 5.0]) | ||||
|         np.testing.assert_equal(R, R1) | ||||
|         np.testing.assert_equal(d, d1) | ||||
| 
 | ||||
| 
 | ||||
| if __name__ == '__main__': | ||||
|     unittest.main() | ||||
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