Refactor ExactInference

src/Network.cc

@@ -1,5 +1,4 @@
 #include "Network.h"
-#include "ExactInference.h"
 namespace bayesnet {
     Network::Network() : laplaceSmoothing(1), root(nullptr), features(vector<string>()), className(""), classNumStates(0) {}
     Network::Network(int smoothing) : laplaceSmoothing(smoothing), root(nullptr), features(vector<string>()), className(""), classNumStates(0) {}
@@ -157,12 +156,11 @@ namespace bayesnet {
             throw invalid_argument("Sample size (" + to_string(sample.size()) +
                 ") does not match the number of features (" + to_string(features.size()) + ")");
         }
-        auto inference = ExactInference(*this);
         map<string, int> evidence;
         for (int i = 0; i < sample.size(); ++i) {
             evidence[features[i]] = sample[i];
         }
-        vector<double> classProbabilities = inference.variableElimination(evidence);
+        vector<double> classProbabilities = exactInference(evidence);
 
         // Find the class with the maximum posterior probability
         auto maxElem = max_element(classProbabilities.begin(), classProbabilities.end());
@@ -171,4 +169,29 @@ namespace bayesnet {
 
         return make_pair(predictedClass, maxProbability);
     }
+    double Network::computeFactor(map<string, int>& completeEvidence)
+    {
+        double result = 1.0;
+        for (auto node : getNodes()) {
+            result *= node.second->getFactorValue(completeEvidence);
+        }
+        return result;
+    }
+    vector<double> Network::exactInference(map<string, int>& evidence)
+    {
+        vector<double> result;
+        int classNumStates = getClassNumStates();
+        for (int i = 0; i < classNumStates; ++i) {
+            result.push_back(1.0);
+            auto complete_evidence = map<string, int>(evidence);
+            complete_evidence[getClassName()] = i;
+            result[i] = computeFactor(complete_evidence);
+        }
+        // Normalize result
+        auto sum = accumulate(result.begin(), result.end(), 0.0);
+        for (int i = 0; i < result.size(); ++i) {
+            result[i] /= sum;
+        }
+        return result;
+    }
 }