Refactor ExactInference

src/CMakeLists.txt

@@ -1,2 +1,2 @@
-add_library(BayesNet Network.cc Node.cc ExactInference.cc)
+add_library(BayesNet Network.cc Node.cc)
 target_link_libraries(BayesNet "${TORCH_LIBRARIES}")

src/ExactInference.cc

@@ -1,31 +0,0 @@
-#include "ExactInference.h"
-
-namespace bayesnet {
-    ExactInference::ExactInference(Network& net) : network(net) {}
-    double ExactInference::computeFactor(map<string, int>& completeEvidence)
-    {
-        double result = 1.0;
-        for (auto node : network.getNodes()) {
-            result *= node.second->getFactorValue(completeEvidence);
-        }
-        return result;
-    }
-    vector<double> ExactInference::variableElimination(map<string, int>& evidence)
-    {
-        vector<double> result;
-        string candidate;
-        int classNumStates = network.getClassNumStates();
-        for (int i = 0; i < classNumStates; ++i) {
-            result.push_back(1.0);
-            auto complete_evidence = map<string, int>(evidence);
-            complete_evidence[network.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;
-    }
-}

src/ExactInference.h

@@ -1,20 +0,0 @@
-#ifndef EXACTINFERENCE_H
-#define EXACTINFERENCE_H
-#include "Network.h"
-#include "Node.h"
-#include <map>
-#include <vector>
-#include <string>
-using namespace std;
-
-namespace bayesnet {
-    class ExactInference {
-    private:
-        Network network;
-        double computeFactor(map<string, int>&);
-    public:
-        ExactInference(Network&);
-        vector<double> variableElimination(map<string, int>&);
-    };
-}
-#endif

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;
+    }
 }

src/Network.h

@@ -17,6 +17,8 @@ namespace bayesnet {
         int laplaceSmoothing;
         bool isCyclic(const std::string&, std::unordered_set<std::string>&, std::unordered_set<std::string>&);
         pair<int, double> predict_sample(const vector<int>&);
+        vector<double> exactInference(map<string, int>&);
+        double computeFactor(map<string, int>&);
     public:
         Network();
         Network(int);