Fix tests to 97%
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@@ -186,11 +186,11 @@ TEST_CASE("Test Bayesian Network", "[Network]")
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auto str = net.graph("Test Graph");
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REQUIRE(str.size() == 7);
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REQUIRE(str[0] == "digraph BayesNet {\nlabel=<BayesNet Test Graph>\nfontsize=30\nfontcolor=blue\nlabelloc=t\nlayout=circo\n");
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REQUIRE(str[1] == "A [shape=circle] \n");
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REQUIRE(str[2] == "A -> B");
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REQUIRE(str[3] == "A -> C");
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REQUIRE(str[4] == "B [shape=circle] \n");
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REQUIRE(str[5] == "C [shape=circle] \n");
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REQUIRE(str[1] == "\"A\" [shape=circle] \n");
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REQUIRE(str[2] == "\"A\" -> \"B\"");
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REQUIRE(str[3] == "\"A\" -> \"C\"");
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REQUIRE(str[4] == "\"B\" [shape=circle] \n");
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REQUIRE(str[5] == "\"C\" [shape=circle] \n");
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REQUIRE(str[6] == "}\n");
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}
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SECTION("Test predict")
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@@ -373,7 +373,7 @@ TEST_CASE("Dump CPT", "[Network]")
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0.3333
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0.3333
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0.3333
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[ CPUFloatType{3} ]
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[ CPUDoubleType{3} ]
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* petallength: (4) : [4, 3, 3]
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(1,.,.) =
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0.9388 0.1000 0.2000
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@@ -394,7 +394,7 @@ TEST_CASE("Dump CPT", "[Network]")
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0.0204 0.1000 0.2000
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0.1250 0.0526 0.1667
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0.2000 0.0606 0.8235
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[ CPUFloatType{4,3,3} ]
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[ CPUDoubleType{4,3,3} ]
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* petalwidth: (3) : [3, 6, 3]
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(1,.,.) =
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0.5000 0.0417 0.0714
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@@ -419,12 +419,12 @@ TEST_CASE("Dump CPT", "[Network]")
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0.1111 0.0909 0.8000
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0.0667 0.2000 0.8667
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0.0303 0.2500 0.7500
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[ CPUFloatType{3,6,3} ]
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[ CPUDoubleType{3,6,3} ]
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* sepallength: (3) : [3, 3]
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0.8679 0.1321 0.0377
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0.0943 0.3019 0.0566
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0.0377 0.5660 0.9057
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[ CPUFloatType{3,3} ]
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[ CPUDoubleType{3,3} ]
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* sepalwidth: (6) : [6, 3, 3]
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(1,.,.) =
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0.0392 0.5000 0.2857
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@@ -455,7 +455,7 @@ TEST_CASE("Dump CPT", "[Network]")
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0.5098 0.0833 0.1429
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0.5000 0.0476 0.1250
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0.2857 0.0571 0.1132
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[ CPUFloatType{6,3,3} ]
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[ CPUDoubleType{6,3,3} ]
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)";
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REQUIRE(res == expected);
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}
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@@ -549,19 +549,32 @@ TEST_CASE("Test Smoothing B", "[Network]")
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{ "C", {0, 1} }
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};
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auto weights = std::vector<double>(C.size(), 1);
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// Simple
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std::cout << "LAPLACE\n";
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// See https://www.overleaf.com/read/tfnhpfysfkfx#2d576c example for calculations
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INFO("Test Smoothing B - Laplace");
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net.fit(Data, C, weights, { "X", "Y", "Z" }, "C", states, bayesnet::Smoothing_t::LAPLACE);
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std::cout << net.dump_cpt();
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std::cout << "Predict proba of {0, 1, 2} y {1, 2, 3} = " << net.predict_proba({ {0, 1}, {1, 2}, {2, 3} }) << std::endl;
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std::cout << "ORIGINAL\n";
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auto laplace_values = std::vector<std::vector<float>>({ {0.377418, 0.622582}, {0.217821, 0.782179} });
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auto laplace_score = net.predict_proba({ {0, 1}, {1, 2}, {2, 3} });
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for (auto i = 0; i < 2; ++i) {
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for (auto j = 0; j < 2; ++j) {
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REQUIRE(laplace_score.at(i).at(j) == Catch::Approx(laplace_values.at(i).at(j)).margin(threshold));
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}
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}
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INFO("Test Smoothing B - Original");
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net.fit(Data, C, weights, { "X", "Y", "Z" }, "C", states, bayesnet::Smoothing_t::ORIGINAL);
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std::cout << net.dump_cpt();
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std::cout << "Predict proba of {0, 1, 2} y {1, 2, 3} = " << net.predict_proba({ {0, 1}, {1, 2}, {2, 3} }) << std::endl;
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std::cout << "CESTNIK\n";
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auto original_values = std::vector<std::vector<float>>({ {0.344769, 0.655231}, {0.0421263, 0.957874} });
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auto original_score = net.predict_proba({ {0, 1}, {1, 2}, {2, 3} });
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for (auto i = 0; i < 2; ++i) {
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for (auto j = 0; j < 2; ++j) {
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REQUIRE(original_score.at(i).at(j) == Catch::Approx(original_values.at(i).at(j)).margin(threshold));
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}
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}
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INFO("Test Smoothing B - Cestnik");
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net.fit(Data, C, weights, { "X", "Y", "Z" }, "C", states, bayesnet::Smoothing_t::CESTNIK);
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std::cout << net.dump_cpt();
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std::cout << "Predict proba of {0, 1, 2} y {1, 2, 3} = " << net.predict_proba({ {0, 1}, {1, 2}, {2, 3} }) << std::endl;
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}
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auto cestnik_values = std::vector<std::vector<float>>({ {0.353422, 0.646578}, {0.12364, 0.87636} });
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auto cestnik_score = net.predict_proba({ {0, 1}, {1, 2}, {2, 3} });
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for (auto i = 0; i < 2; ++i) {
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for (auto j = 0; j < 2; ++j) {
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REQUIRE(cestnik_score.at(i).at(j) == Catch::Approx(cestnik_values.at(i).at(j)).margin(threshold));
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}
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}
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}
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