Much more comprehensive tests
Frank Dellaert
parent
12349b9201
commit
44fb786b7a
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@ -62,32 +62,117 @@ TEST(HybridBayesNet, Add) {
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}
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/* ****************************************************************************/
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// Test evaluate for a pure discrete Bayes net P(Asia).
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// Test API for a pure discrete Bayes net P(Asia).
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TEST(HybridBayesNet, EvaluatePureDiscrete) {
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HybridBayesNet bayesNet;
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bayesNet.emplace_shared<DiscreteConditional>(Asia, "4/6");
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HybridValues values;
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values.insert(asiaKey, 0);
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EXPECT_DOUBLES_EQUAL(0.4, bayesNet.evaluate(values), 1e-9);
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const auto pAsia = std::make_shared<DiscreteConditional>(Asia, "4/6");
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bayesNet.push_back(pAsia);
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HybridValues zero{{}, {{asiaKey, 0}}}, one{{}, {{asiaKey, 1}}};
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// choose
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GaussianBayesNet empty;
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EXPECT(assert_equal(empty, bayesNet.choose(zero.discrete()), 1e-9));
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// evaluate
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EXPECT_DOUBLES_EQUAL(0.4, bayesNet.evaluate(zero), 1e-9);
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EXPECT_DOUBLES_EQUAL(0.4, bayesNet(zero), 1e-9);
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// optimize
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EXPECT(assert_equal(one, bayesNet.optimize()));
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EXPECT(assert_equal(VectorValues{}, bayesNet.optimize(one.discrete())));
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// sample
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std::mt19937_64 rng(42);
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EXPECT(assert_equal(zero, bayesNet.sample(&rng)));
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EXPECT(assert_equal(one, bayesNet.sample(one, &rng)));
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EXPECT(assert_equal(zero, bayesNet.sample(zero, &rng)));
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// error
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EXPECT_DOUBLES_EQUAL(-log(0.4), bayesNet.error(zero), 1e-9);
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EXPECT_DOUBLES_EQUAL(-log(0.6), bayesNet.error(one), 1e-9);
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// logProbability
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EXPECT_DOUBLES_EQUAL(log(0.4), bayesNet.logProbability(zero), 1e-9);
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EXPECT_DOUBLES_EQUAL(log(0.6), bayesNet.logProbability(one), 1e-9);
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// toFactorGraph
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HybridGaussianFactorGraph expectedFG{pAsia}, fg = bayesNet.toFactorGraph({});
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EXPECT(assert_equal(expectedFG, fg));
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// prune, imperative :-(
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EXPECT(assert_equal(bayesNet, bayesNet.prune(2)));
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EXPECT_LONGS_EQUAL(1, bayesNet.prune(1).at(0)->size());
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}
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/* ****************************************************************************/
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// Test creation of a tiny hybrid Bayes net.
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TEST(HybridBayesNet, Tiny) {
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auto bn = tiny::createHybridBayesNet();
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EXPECT_LONGS_EQUAL(3, bn.size());
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auto bayesNet = tiny::createHybridBayesNet(); // P(z|x,mode)P(x)P(mode)
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EXPECT_LONGS_EQUAL(3, bayesNet.size());
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const VectorValues vv{{Z(0), Vector1(5.0)}, {X(0), Vector1(5.0)}};
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auto fg = bn.toFactorGraph(vv);
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HybridValues zero{vv, {{M(0), 0}}}, one{vv, {{M(0), 1}}};
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// Check Invariants for components
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HybridGaussianConditional::shared_ptr hgc = bayesNet.at(0)->asHybrid();
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GaussianConditional::shared_ptr gc0 = hgc->choose(zero.discrete()),
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gc1 = hgc->choose(one.discrete());
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GaussianConditional::shared_ptr px = bayesNet.at(1)->asGaussian();
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GaussianConditional::CheckInvariants(*gc0, vv);
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GaussianConditional::CheckInvariants(*gc1, vv);
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GaussianConditional::CheckInvariants(*px, vv);
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HybridGaussianConditional::CheckInvariants(*hgc, zero);
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HybridGaussianConditional::CheckInvariants(*hgc, one);
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// choose
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GaussianBayesNet expectedChosen;
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expectedChosen.push_back(gc0);
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expectedChosen.push_back(px);
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auto chosen0 = bayesNet.choose(zero.discrete());
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auto chosen1 = bayesNet.choose(one.discrete());
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EXPECT(assert_equal(expectedChosen, chosen0, 1e-9));
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// logProbability
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const double logP0 = chosen0.logProbability(vv) + log(0.4); // 0.4 is prior
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const double logP1 = chosen1.logProbability(vv) + log(0.6); // 0.6 is prior
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EXPECT_DOUBLES_EQUAL(logP0, bayesNet.logProbability(zero), 1e-9);
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EXPECT_DOUBLES_EQUAL(logP1, bayesNet.logProbability(one), 1e-9);
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// evaluate
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EXPECT_DOUBLES_EQUAL(exp(logP0), bayesNet.evaluate(zero), 1e-9);
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// optimize
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EXPECT(assert_equal(one, bayesNet.optimize()));
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EXPECT(assert_equal(chosen0.optimize(), bayesNet.optimize(zero.discrete())));
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// sample
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std::mt19937_64 rng(42);
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EXPECT(assert_equal({{M(0), 1}}, bayesNet.sample(&rng).discrete()));
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// error
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const double error0 = chosen0.error(vv) + gc0->negLogConstant() -
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px->negLogConstant() - log(0.4);
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const double error1 = chosen1.error(vv) + gc1->negLogConstant() -
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px->negLogConstant() - log(0.6);
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EXPECT_DOUBLES_EQUAL(error0, bayesNet.error(zero), 1e-9);
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EXPECT_DOUBLES_EQUAL(error1, bayesNet.error(one), 1e-9);
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EXPECT_DOUBLES_EQUAL(error0 + logP0, error1 + logP1, 1e-9);
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// toFactorGraph
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auto fg = bayesNet.toFactorGraph({{Z(0), Vector1(5.0)}});
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EXPECT_LONGS_EQUAL(3, fg.size());
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// Check that the ratio of probPrime to evaluate is the same for all modes.
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std::vector<double> ratio(2);
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for (size_t mode : {0, 1}) {
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const HybridValues hv{vv, {{M(0), mode}}};
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ratio[mode] = std::exp(-fg.error(hv)) / bn.evaluate(hv);
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}
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ratio[0] = std::exp(-fg.error(zero)) / bayesNet.evaluate(zero);
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ratio[1] = std::exp(-fg.error(one)) / bayesNet.evaluate(one);
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EXPECT_DOUBLES_EQUAL(ratio[0], ratio[1], 1e-8);
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// prune, imperative :-(
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auto pruned = bayesNet.prune(1);
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EXPECT_LONGS_EQUAL(1, pruned.at(0)->asHybrid()->nrComponents());
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EXPECT(!pruned.equals(bayesNet));
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}
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/* ****************************************************************************/
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@ -223,12 +308,15 @@ TEST(HybridBayesNet, Optimize) {
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/* ****************************************************************************/
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// Test Bayes net error
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TEST(HybridBayesNet, Pruning) {
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// Create switching network with three continuous variables and two discrete:
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// ϕ(x0) ϕ(x0,x1,m0) ϕ(x1,x2,m1) ϕ(x0;z0) ϕ(x1;z1) ϕ(x2;z2) ϕ(m0) ϕ(m0,m1)
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Switching s(3);
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HybridBayesNet::shared_ptr posterior =
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s.linearizedFactorGraph.eliminateSequential();
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EXPECT_LONGS_EQUAL(5, posterior->size());
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// Optimize
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HybridValues delta = posterior->optimize();
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auto actualTree = posterior->evaluate(delta.continuous());
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@ -254,7 +342,6 @@ TEST(HybridBayesNet, Pruning) {
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logProbability += posterior->at(0)->asHybrid()->logProbability(hybridValues);
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logProbability += posterior->at(1)->asHybrid()->logProbability(hybridValues);
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logProbability += posterior->at(2)->asHybrid()->logProbability(hybridValues);
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// NOTE(dellaert): the discrete errors were not added in logProbability tree!
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logProbability +=
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posterior->at(3)->asDiscrete()->logProbability(hybridValues);
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logProbability +=
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@ -316,10 +403,9 @@ TEST(HybridBayesNet, UpdateDiscreteConditionals) {
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#endif
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// regression
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DiscreteKeys dkeys{{M(0), 2}, {M(1), 2}, {M(2), 2}};
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DecisionTreeFactor::ADT potentials(
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dkeys, std::vector<double>{0, 0, 0, 0.505145423, 0, 1, 0, 0.494854577});
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DiscreteConditional expected_discrete_conditionals(1, dkeys, potentials);
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s.modes, std::vector<double>{0, 0, 0, 0.505145423, 0, 1, 0, 0.494854577});
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DiscreteConditional expected_discrete_conditionals(1, s.modes, potentials);
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// Prune!
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posterior->prune(maxNrLeaves);
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