Refactor Tests and add BayesMetrics test

tests/TestBayesMetrics.cc

@@ -0,0 +1,55 @@
+#include <catch2/catch_test_macros.hpp>
+#include <catch2/catch_approx.hpp>
+#include <catch2/generators/catch_generators.hpp>
+#include "BayesMetrics.h"
+
+using namespace std;
+
+TEST_CASE("Metrics Test", "[Metrics]")
+{
+    SECTION("Test Constructor")
+    {
+        torch::Tensor samples = torch::rand({ 10, 5 });
+        vector<string> features = { "feature1", "feature2", "feature3", "feature4", "feature5" };
+        string className = "class1";
+        int classNumStates = 2;
+
+        bayesnet::Metrics obj(samples, features, className, classNumStates);
+
+        REQUIRE(obj.getScoresKBest().size() == 0);
+    }
+
+    SECTION("Test SelectKBestWeighted")
+    {
+        torch::Tensor samples = torch::rand({ 10, 5 });
+        vector<string> features = { "feature1", "feature2", "feature3", "feature4", "feature5" };
+        string className = "class1";
+        int classNumStates = 2;
+
+        bayesnet::Metrics obj(samples, features, className, classNumStates);
+
+        torch::Tensor weights = torch::ones({ 5 });
+
+        vector<int> kBest = obj.SelectKBestWeighted(weights, true, 3);
+
+        REQUIRE(kBest.size() == 3);
+    }
+
+    SECTION("Test mutualInformation")
+    {
+        torch::Tensor samples = torch::rand({ 10, 5 });
+        vector<string> features = { "feature1", "feature2", "feature3", "feature4", "feature5" };
+        string className = "class1";
+        int classNumStates = 2;
+
+        bayesnet::Metrics obj(samples, features, className, classNumStates);
+
+        torch::Tensor firstFeature = samples.select(1, 0);
+        torch::Tensor secondFeature = samples.select(1, 1);
+        torch::Tensor weights = torch::ones({ 10 });
+
+        double mi = obj.mutualInformation(firstFeature, secondFeature, weights);
+
+        REQUIRE(mi >= 0);
+    }
+}