refactor into smaller tests
Frank Dellaert
parent
3fd5c2501e
commit
9857e62a56
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@ -16,10 +16,10 @@
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*/
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#include <gtsam/base/Vector.h>
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#include <gtsam/inference/BayesNet.h>
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#include <gtsam/discrete/DiscreteBayesNet.h>
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#include <gtsam/discrete/DiscreteBayesTree.h>
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#include <gtsam/discrete/DiscreteFactorGraph.h>
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#include <gtsam/inference/BayesNet.h>
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#include <CppUnitLite/TestHarness.h>
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@ -32,7 +32,8 @@ static constexpr bool debug = false;
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/* ************************************************************************* */
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struct TestFixture {
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vector<DiscreteKey> keys;
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DiscreteKeys keys;
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std::vector<DiscreteValues> assignments;
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DiscreteBayesNet bayesNet;
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boost::shared_ptr<DiscreteBayesTree> bayesTree;
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@ -47,6 +48,9 @@ struct TestFixture {
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keys.push_back(key_i);
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}
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// Enumerate all assignments.
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assignments = DiscreteValues::CartesianProduct(keys);
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// Create thin-tree Bayesnet.
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bayesNet.add(keys[14] % "1/3");
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@ -74,9 +78,9 @@ struct TestFixture {
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};
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/* ************************************************************************* */
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// Check that BN and BT give the same answer on all configurations
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TEST(DiscreteBayesTree, ThinTree) {
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const TestFixture self;
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const auto& keys = self.keys;
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TestFixture self;
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if (debug) {
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GTSAM_PRINT(self.bayesNet);
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@ -95,47 +99,56 @@ TEST(DiscreteBayesTree, ThinTree) {
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EXPECT_LONGS_EQUAL(i, *(clique_i->conditional_->beginFrontals()));
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}
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auto R = self.bayesTree->roots().front();
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// Check whether BN and BT give the same answer on all configurations
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auto allPosbValues = DiscreteValues::CartesianProduct(
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keys[0] & keys[1] & keys[2] & keys[3] & keys[4] & keys[5] & keys[6] &
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keys[7] & keys[8] & keys[9] & keys[10] & keys[11] & keys[12] & keys[13] &
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keys[14]);
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for (size_t i = 0; i < allPosbValues.size(); ++i) {
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DiscreteValues x = allPosbValues[i];
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for (const auto& x : self.assignments) {
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double expected = self.bayesNet.evaluate(x);
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double actual = self.bayesTree->evaluate(x);
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DOUBLES_EQUAL(expected, actual, 1e-9);
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}
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}
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// Calculate all some marginals for DiscreteValues==all1
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Vector marginals = Vector::Zero(15);
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double joint_12_14 = 0, joint_9_12_14 = 0, joint_8_12_14 = 0, joint_8_12 = 0,
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joint82 = 0, joint12 = 0, joint24 = 0, joint45 = 0, joint46 = 0,
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joint_4_11 = 0, joint_11_13 = 0, joint_11_13_14 = 0,
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joint_11_12_13_14 = 0, joint_9_11_12_13 = 0, joint_8_11_12_13 = 0;
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for (size_t i = 0; i < allPosbValues.size(); ++i) {
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DiscreteValues x = allPosbValues[i];
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/* ************************************************************************* */
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// Check calculation of separator marginals
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TEST(DiscreteBayesTree, separatorMarginal) {
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TestFixture self;
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// Calculate some marginals for DiscreteValues==all1
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double marginal_14 = 0, joint_8_12 = 0;
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for (auto& x : self.assignments) {
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double px = self.bayesTree->evaluate(x);
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for (size_t i = 0; i < 15; i++)
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if (x[i]) marginals[i] += px;
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if (x[12] && x[14]) {
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joint_12_14 += px;
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if (x[9]) joint_9_12_14 += px;
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if (x[8]) joint_8_12_14 += px;
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}
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if (x[8] && x[12]) joint_8_12 += px;
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if (x[2]) {
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if (x[8]) joint82 += px;
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if (x[1]) joint12 += px;
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}
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if (x[4]) {
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if (x[2]) joint24 += px;
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if (x[5]) joint45 += px;
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if (x[6]) joint46 += px;
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if (x[11]) joint_4_11 += px;
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}
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if (x[14]) marginal_14 += px;
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}
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DiscreteValues all1 = self.assignments.back();
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// check separator marginal P(S0)
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auto clique = (*self.bayesTree)[0];
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DiscreteFactorGraph separatorMarginal0 =
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clique->separatorMarginal(EliminateDiscrete);
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DOUBLES_EQUAL(joint_8_12, separatorMarginal0(all1), 1e-9);
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// check separator marginal P(S9), should be P(14)
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clique = (*self.bayesTree)[9];
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DiscreteFactorGraph separatorMarginal9 =
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clique->separatorMarginal(EliminateDiscrete);
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DOUBLES_EQUAL(marginal_14, separatorMarginal9(all1), 1e-9);
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// check separator marginal of root, should be empty
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clique = (*self.bayesTree)[11];
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DiscreteFactorGraph separatorMarginal11 =
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clique->separatorMarginal(EliminateDiscrete);
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LONGS_EQUAL(0, separatorMarginal11.size());
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}
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/* ************************************************************************* */
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// Check shortcuts in the tree
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TEST(DiscreteBayesTree, shortcut) {
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TestFixture self;
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// Calculate some marginals for DiscreteValues==all1
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double joint_11_13 = 0, joint_11_13_14 = 0, joint_11_12_13_14 = 0,
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joint_9_11_12_13 = 0, joint_8_11_12_13 = 0;
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for (auto& x : self.assignments) {
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double px = self.bayesTree->evaluate(x);
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if (x[11] && x[13]) {
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joint_11_13 += px;
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if (x[8] && x[12]) joint_8_11_12_13 += px;
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@ -148,32 +161,12 @@ TEST(DiscreteBayesTree, ThinTree) {
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}
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}
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}
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DiscreteValues all1 = allPosbValues.back();
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DiscreteValues all1 = self.assignments.back();
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// check separator marginal P(S0)
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auto clique = (*self.bayesTree)[0];
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DiscreteFactorGraph separatorMarginal0 =
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clique->separatorMarginal(EliminateDiscrete);
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DOUBLES_EQUAL(joint_8_12, separatorMarginal0(all1), 1e-9);
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DOUBLES_EQUAL(joint_12_14, 0.1875, 1e-9);
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DOUBLES_EQUAL(joint_8_12_14, 0.0375, 1e-9);
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DOUBLES_EQUAL(joint_9_12_14, 0.15, 1e-9);
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// check separator marginal P(S9), should be P(14)
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clique = (*self.bayesTree)[9];
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DiscreteFactorGraph separatorMarginal9 =
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clique->separatorMarginal(EliminateDiscrete);
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DOUBLES_EQUAL(marginals[14], separatorMarginal9(all1), 1e-9);
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// check separator marginal of root, should be empty
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clique = (*self.bayesTree)[11];
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DiscreteFactorGraph separatorMarginal11 =
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clique->separatorMarginal(EliminateDiscrete);
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LONGS_EQUAL(0, separatorMarginal11.size());
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auto R = self.bayesTree->roots().front();
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// check shortcut P(S9||R) to root
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clique = (*self.bayesTree)[9];
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auto clique = (*self.bayesTree)[9];
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DiscreteBayesNet shortcut = clique->shortcut(R, EliminateDiscrete);
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LONGS_EQUAL(1, shortcut.size());
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DOUBLES_EQUAL(joint_11_13_14 / joint_11_13, shortcut.evaluate(all1), 1e-9);
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@ -202,15 +195,67 @@ TEST(DiscreteBayesTree, ThinTree) {
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shortcut.print("shortcut:");
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}
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}
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}
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/* ************************************************************************* */
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// Check all marginals
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TEST(DiscreteBayesTree, marginalFactor) {
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TestFixture self;
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Vector marginals = Vector::Zero(15);
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for (size_t i = 0; i < self.assignments.size(); ++i) {
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DiscreteValues& x = self.assignments[i];
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double px = self.bayesTree->evaluate(x);
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for (size_t i = 0; i < 15; i++)
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if (x[i]) marginals[i] += px;
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}
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// Check all marginals
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DiscreteFactor::shared_ptr marginalFactor;
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DiscreteValues all1 = self.assignments.back();
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for (size_t i = 0; i < 15; i++) {
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marginalFactor = self.bayesTree->marginalFactor(i, EliminateDiscrete);
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auto marginalFactor = self.bayesTree->marginalFactor(i, EliminateDiscrete);
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double actual = (*marginalFactor)(all1);
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DOUBLES_EQUAL(marginals[i], actual, 1e-9);
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}
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}
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/* ************************************************************************* */
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// Check a number of joint marginals.
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TEST(DiscreteBayesTree, Joints) {
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TestFixture self;
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// Calculate some marginals for DiscreteValues==all1
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Vector marginals = Vector::Zero(15);
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double joint_12_14 = 0, joint_9_12_14 = 0, joint_8_12_14 = 0, joint82 = 0,
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joint12 = 0, joint24 = 0, joint45 = 0, joint46 = 0, joint_4_11 = 0;
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for (size_t i = 0; i < self.assignments.size(); ++i) {
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DiscreteValues& x = self.assignments[i];
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double px = self.bayesTree->evaluate(x);
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for (size_t i = 0; i < 15; i++)
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if (x[i]) marginals[i] += px;
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if (x[12] && x[14]) {
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joint_12_14 += px;
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if (x[9]) joint_9_12_14 += px;
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if (x[8]) joint_8_12_14 += px;
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}
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if (x[2]) {
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if (x[8]) joint82 += px;
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if (x[1]) joint12 += px;
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}
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if (x[4]) {
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if (x[2]) joint24 += px;
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if (x[5]) joint45 += px;
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if (x[6]) joint46 += px;
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if (x[11]) joint_4_11 += px;
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}
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}
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// regression tests:
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DOUBLES_EQUAL(joint_12_14, 0.1875, 1e-9);
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DOUBLES_EQUAL(joint_8_12_14, 0.0375, 1e-9);
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DOUBLES_EQUAL(joint_9_12_14, 0.15, 1e-9);
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DiscreteValues all1 = self.assignments.back();
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DiscreteBayesNet::shared_ptr actualJoint;
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// Check joint P(8, 2)
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@ -240,7 +285,7 @@ TEST(DiscreteBayesTree, ThinTree) {
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/* ************************************************************************* */
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TEST(DiscreteBayesTree, Dot) {
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const TestFixture self;
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TestFixture self;
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string actual = self.bayesTree->dot();
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EXPECT(actual ==
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"digraph G{\n"
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