testHybridGaussianConditional.cpp
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1 /* ----------------------------------------------------------------------------
2 
3  * GTSAM Copyright 2010, Georgia Tech Research Corporation,
4  * Atlanta, Georgia 30332-0415
5  * All Rights Reserved
6  * Authors: Frank Dellaert, et al. (see THANKS for the full author list)
7 
8  * See LICENSE for the license information
9 
10  * -------------------------------------------------------------------------- */
11 
27 #include <gtsam/inference/Symbol.h>
29 
30 #include <memory>
31 #include <vector>
32 
33 // Include for test suite
35 
36 using namespace gtsam;
40 
41 // Common constants
42 static const Key modeKey = M(0);
43 static const DiscreteKey mode(modeKey, 2);
44 static const VectorValues vv{{Z(0), Vector1(4.9)}, {X(0), Vector1(5.0)}};
45 static const DiscreteValues assignment0{{M(0), 0}}, assignment1{{M(0), 1}};
46 static const HybridValues hv0{vv, assignment0};
47 static const HybridValues hv1{vv, assignment1};
48 
49 /* ************************************************************************* */
50 namespace equal_constants {
51 // Create a simple HybridGaussianConditional
52 const double commonSigma = 2.0;
53 const std::vector<GaussianConditional::shared_ptr> conditionals{
55  commonSigma),
57  commonSigma)};
59 } // namespace equal_constants
60 
61 /* ************************************************************************* */
64  using namespace equal_constants;
65 
66  // Check that the conditional (negative log) normalization constant is the min
67  // of all constants which are all equal, in this case, hence:
68  const double K = hybrid_conditional.negLogConstant();
69  EXPECT_DOUBLES_EQUAL(K, conditionals[0]->negLogConstant(), 1e-8);
70  EXPECT_DOUBLES_EQUAL(K, conditionals[1]->negLogConstant(), 1e-8);
71 
74 }
75 
76 /* ************************************************************************* */
78 TEST(HybridGaussianConditional, LogProbability) {
79  using namespace equal_constants;
80  for (size_t mode : {0, 1}) {
81  const HybridValues hv{vv, {{M(0), mode}}};
82  EXPECT_DOUBLES_EQUAL(conditionals[mode]->logProbability(vv),
84  }
85 }
86 
87 /* ************************************************************************* */
90  using namespace equal_constants;
91  auto actual = hybrid_conditional.errorTree(vv);
92 
93  // Check result.
94  DiscreteKeys discrete_keys{mode};
95  std::vector<double> leaves = {conditionals[0]->error(vv),
96  conditionals[1]->error(vv)};
97  AlgebraicDecisionTree<Key> expected(discrete_keys, leaves);
98 
99  EXPECT(assert_equal(expected, actual, 1e-6));
100 
101  // Check for non-tree version.
102  for (size_t mode : {0, 1}) {
103  const HybridValues hv{vv, {{M(0), mode}}};
105  hybrid_conditional.error(hv), 1e-8);
106  }
107 }
108 
109 /* ************************************************************************* */
113  using namespace equal_constants;
114 
115  // Compute likelihood
116  auto likelihood = hybrid_conditional.likelihood(vv);
117 
118  // Check that the hybrid conditional error and the likelihood error are the
119  // same.
120  EXPECT_DOUBLES_EQUAL(hybrid_conditional.error(hv0), likelihood->error(hv0),
121  1e-8);
122  EXPECT_DOUBLES_EQUAL(hybrid_conditional.error(hv1), likelihood->error(hv1),
123  1e-8);
124 
125  // Check that likelihood error is as expected, i.e., just the errors of the
126  // individual likelihoods, in the `equal_constants` case.
127  std::vector<DiscreteKey> discrete_keys = {mode};
128  std::vector<double> leaves = {conditionals[0]->likelihood(vv)->error(vv),
129  conditionals[1]->likelihood(vv)->error(vv)};
130  AlgebraicDecisionTree<Key> expected(discrete_keys, leaves);
131  EXPECT(assert_equal(expected, likelihood->errorTree(vv), 1e-6));
132 
133  // Check that the ratio of probPrime to evaluate is the same for all modes.
134  std::vector<double> ratio(2);
135  for (size_t mode : {0, 1}) {
136  const HybridValues hv{vv, {{M(0), mode}}};
137  ratio[mode] =
138  std::exp(-likelihood->error(hv)) / hybrid_conditional.evaluate(hv);
139  }
140  EXPECT_DOUBLES_EQUAL(ratio[0], ratio[1], 1e-8);
141 }
142 
143 /* ************************************************************************* */
145 // Create a HybridGaussianConditional with mode-dependent noise models.
146 // 0 is low-noise, 1 is high-noise.
147 const std::vector<GaussianConditional::shared_ptr> conditionals{
149  0.5),
151  3.0)};
153 } // namespace mode_dependent_constants
154 
155 /* ************************************************************************* */
156 // Create a test for continuousParents.
157 TEST(HybridGaussianConditional, ContinuousParents) {
158  using namespace mode_dependent_constants;
159  const KeyVector continuousParentKeys = hybrid_conditional.continuousParents();
160  // Check that the continuous parent keys are correct:
161  EXPECT(continuousParentKeys.size() == 1);
162  EXPECT(continuousParentKeys[0] == X(0));
163 
166 }
167 
168 /* ************************************************************************* */
171  using namespace mode_dependent_constants;
172  auto actual = hybrid_conditional.errorTree(vv);
173 
174  // Check result.
175  DiscreteKeys discrete_keys{mode};
176  double negLogConstant0 = conditionals[0]->negLogConstant();
177  double negLogConstant1 = conditionals[1]->negLogConstant();
178  double minErrorConstant = std::min(negLogConstant0, negLogConstant1);
179 
180  // Expected error is e(X) + log(sqrt(|2πΣ|)).
181  // We normalize log(sqrt(|2πΣ|)) with min(negLogConstant)
182  // so it is non-negative.
183  std::vector<double> leaves = {
184  conditionals[0]->error(vv) + negLogConstant0 - minErrorConstant,
185  conditionals[1]->error(vv) + negLogConstant1 - minErrorConstant};
186  AlgebraicDecisionTree<Key> expected(discrete_keys, leaves);
187 
188  EXPECT(assert_equal(expected, actual, 1e-6));
189 
190  // Check for non-tree version.
191  for (size_t mode : {0, 1}) {
192  const HybridValues hv{vv, {{M(0), mode}}};
194  conditionals[mode]->negLogConstant() -
195  minErrorConstant,
196  hybrid_conditional.error(hv), 1e-8);
197  }
198 }
199 
200 /* ************************************************************************* */
204  using namespace mode_dependent_constants;
205 
206  // Compute likelihood
207  auto likelihood = hybrid_conditional.likelihood(vv);
208 
209  // Check that the hybrid conditional error and the likelihood error are as
210  // expected, this invariant is the same as the equal noise case:
211  EXPECT_DOUBLES_EQUAL(hybrid_conditional.error(hv0), likelihood->error(hv0),
212  1e-8);
213  EXPECT_DOUBLES_EQUAL(hybrid_conditional.error(hv1), likelihood->error(hv1),
214  1e-8);
215 
216  // Check the detailed JacobianFactor calculation for mode==1.
217  {
218  // We have a JacobianFactor
219  const auto [gf1, _] = (*likelihood)(assignment1);
220  const auto jf1 = std::dynamic_pointer_cast<JacobianFactor>(gf1);
221  CHECK(jf1);
222 
223  // Check that the JacobianFactor error with constants is equal to the
224  // conditional error:
226  jf1->error(hv1) + conditionals[1]->negLogConstant() -
228  1e-8);
229  }
230 
231  // Check that the ratio of probPrime to evaluate is the same for all modes.
232  std::vector<double> ratio(2);
233  for (size_t mode : {0, 1}) {
234  const HybridValues hv{vv, {{M(0), mode}}};
235  ratio[mode] =
236  std::exp(-likelihood->error(hv)) / hybrid_conditional.evaluate(hv);
237  }
238  EXPECT_DOUBLES_EQUAL(ratio[0], ratio[1], 1e-8);
239 }
240 
241 /* ************************************************************************* */
242 // Test pruning a HybridGaussianConditional with two discrete keys, based on a
243 // DecisionTreeFactor with 3 keys:
245  // Create a two key conditional:
246  DiscreteKeys modes{{M(1), 2}, {M(2), 2}};
247  std::vector<GaussianConditional::shared_ptr> gcs;
248  for (size_t i = 0; i < 4; i++) {
249  gcs.push_back(
251  }
252  auto empty = std::make_shared<GaussianConditional>();
255 
256  DiscreteKeys keys = modes;
257  keys.push_back({M(3), 2});
258  {
259  for (size_t i = 0; i < 8; i++) {
260  std::vector<double> potentials{0, 0, 0, 0, 0, 0, 0, 0};
261  potentials[i] = 1;
262  const DecisionTreeFactor decisionTreeFactor(keys, potentials);
263  // Prune the HybridGaussianConditional
264  const auto pruned = hgc.prune(decisionTreeFactor);
265  // Check that the pruned HybridGaussianConditional has 1 conditional
266  EXPECT_LONGS_EQUAL(1, pruned->nrComponents());
267  }
268  }
269  {
270  const std::vector<double> potentials{0, 0, 0.5, 0, //
271  0, 0, 0.5, 0};
272  const DecisionTreeFactor decisionTreeFactor(keys, potentials);
273 
274  const auto pruned = hgc.prune(decisionTreeFactor);
275 
276  // Check that the pruned HybridGaussianConditional has 2 conditionals
277  EXPECT_LONGS_EQUAL(2, pruned->nrComponents());
278  }
279  {
280  const std::vector<double> potentials{0.2, 0, 0.3, 0, //
281  0, 0, 0.5, 0};
282  const DecisionTreeFactor decisionTreeFactor(keys, potentials);
283 
284  const auto pruned = hgc.prune(decisionTreeFactor);
285 
286  // Check that the pruned HybridGaussianConditional has 3 conditionals
287  EXPECT_LONGS_EQUAL(3, pruned->nrComponents());
288  }
289 }
290 
291 /* *************************************************************************
292  */
293 int main() {
294  TestResult tr;
295  return TestRegistry::runAllTests(tr);
296 }
297 /* *************************************************************************
298  */
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