Added ExactInference and Factor classes

src/CMakeLists.txt

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

src/ExactInference.cc

@@ -0,0 +1,48 @@
+#include "ExactInference.h"
+
+namespace bayesnet {
+    ExactInference::ExactInference(Network& net) : network(net), evidence(map<string, int>()), candidates(net.getFeatures()) {}
+    void ExactInference::setEvidence(const map<string, int>& evidence)
+    {
+        this->evidence = evidence;
+    }
+    ExactInference::~ExactInference()
+    {
+        for (auto& factor : factors) {
+            delete factor;
+        }
+    }
+    void ExactInference::buildFactors()
+    {
+        for (auto node : network.getNodes()) {
+            factors.push_back(node.second->toFactor());
+        }
+    }
+    string ExactInference::nextCandidate()
+    {
+        string result = "";
+        map<string, Node*> nodes = network.getNodes();
+        int minFill = INT_MAX;
+        for (auto candidate : candidates) {
+            unsigned fill = nodes[candidate]->minFill();
+            if (fill < minFill) {
+                minFill = fill;
+                result = candidate;
+            }
+        }
+        return result;
+    }
+    vector<double> ExactInference::variableElimination()
+    {
+        vector<double> result;
+        string candidate;
+        buildFactors();
+        // Eliminate evidence
+        while ((candidate = nextCandidate()) != "") {
+            // Erase candidate from candidates (Erase–remove idiom)
+            candidates.erase(remove(candidates.begin(), candidates.end(), candidate), candidates.end());
+
+        }
+        return result;
+    }
+}

src/ExactInference.h

@@ -0,0 +1,27 @@
+#ifndef EXACTINFERENCE_H
+#define EXACTINFERENCE_H
+#include "Network.h"
+#include "Factor.h"
+#include "Node.h"
+#include <map>
+#include <vector>
+#include <string>
+using namespace std;
+
+namespace bayesnet {
+    class ExactInference {
+    private:
+        Network network;
+        map<string, int> evidence;
+        vector<Factor*> factors;
+        vector<string> candidates; // variables to be removed
+        void buildFactors();
+        string nextCandidate(); // Return the next variable to eliminate using MinFill criterion
+    public:
+        ExactInference(Network&);
+        ~ExactInference();
+        void setEvidence(const map<string, int>&);
+        vector<double> variableElimination();
+    };
+}
+#endif

src/Factor.cc

@@ -0,0 +1,10 @@
+#include "Factor.h"
+#include <vector>
+#include <string>
+
+using namespace std;
+
+namespace bayesnet {
+    Factor::Factor(vector<string>& variables, vector<int>& cardinalities, torch::Tensor& values) : variables(variables), cardinalities(cardinalities), values(values) {}
+    Factor::~Factor() = default;
+}

src/Factor.h

@@ -0,0 +1,27 @@
+#ifndef FACTOR_H
+#define FACTOR_H
+#include <torch/torch.h>
+#include <vector>
+#include <string>
+using namespace std;
+
+namespace bayesnet {
+    class Factor {
+    private:
+        vector<string> variables;
+        vector<int> cardinalities;
+        torch::Tensor values;
+    public:
+        Factor(vector<string>&, vector<int>&, torch::Tensor&);
+        ~Factor();
+        Factor(const Factor&);
+        Factor& operator=(const Factor&);
+        void setVariables(vector<string>&);
+        void setCardinalities(vector<int>&);
+        void setValues(torch::Tensor&);
+        vector<string>& getVariables();
+        vector<int>& getCardinalities();
+        torch::Tensor& getValues();
+    };
+}
+#endif

src/Network.cc

@@ -1,4 +1,5 @@
 #include "Network.h"
+#include "ExactInference.h"
 namespace bayesnet {
     Network::Network() : laplaceSmoothing(1), root(nullptr), features(vector<string>()), className("") {}
     Network::Network(int smoothing) : laplaceSmoothing(smoothing), root(nullptr), features(vector<string>()), className("") {}
@@ -26,6 +27,10 @@ namespace bayesnet {
             root = nodes[name];
         }
     }
+    vector<string> Network::getFeatures()
+    {
+        return features;
+    }
     void Network::setRoot(string name)
     {
         if (nodes.find(name) == nodes.end()) {
@@ -120,37 +125,7 @@ namespace bayesnet {
             node->setCPT(cpt);
         }
     }
-    // pair<int, double> Network::predict_sample(const vector<int>& sample)
-    // {
 
-
-    //     // For each possible class, calculate the posterior probability
-    //     Node* classNode = nodes[className];
-    //     int numClassStates = classNode->getNumStates();
-    //     vector<double> classProbabilities(numClassStates, 0.0);
-    //     for (int classState = 0; classState < numClassStates; ++classState) {
-    //         // Start with the prior probability of the class
-    //         classProbabilities[classState] = classNode->getCPT()[classState].item<double>();
-
-    //         // Multiply by the likelihood of each feature given the class
-    //         for (auto& pair : nodes) {
-    //             if (pair.first != className) {
-    //                 Node* node = pair.second;
-    //                 int featureValue = featureValues[pair.first];
-
-    //                 // We use the class as the parent state to index into the CPT
-    //                 classProbabilities[classState] *= node->getCPT()[classState][featureValue].item<double>();
-    //             }
-    //         }
-    //     }
-
-    //     // Find the class with the maximum posterior probability
-    //     auto maxElem = max_element(classProbabilities.begin(), classProbabilities.end());
-    //     int predictedClass = distance(classProbabilities.begin(), maxElem);
-    //     double maxProbability = *maxElem;
-
-    //     return make_pair(predictedClass, maxProbability);
-    // }
     vector<int> Network::predict(const vector<vector<int>>& samples)
     {
         vector<int> predictions;
@@ -195,15 +170,13 @@ namespace bayesnet {
             throw invalid_argument("Sample size (" + to_string(sample.size()) +
                 ") does not match the number of features (" + to_string(features.size()) + ")");
         }
-        // Map the feature values to their corresponding nodes
+        auto inference = ExactInference(*this);
-        map<string, int> featureValues;
+        map<string, int> evidence;
-        for (int i = 0; i < features.size(); ++i) {
+        for (int i = 0; i < sample.size(); ++i) {
-            featureValues[features[i]] = sample[i];
+            evidence[features[i]] = sample[i];
         }
-
+        inference.setEvidence(evidence);
-        // For each possible class, calculate the posterior probability
+        vector<double> classProbabilities = inference.variableElimination();
-        Network network = *this;
-        vector<double> classProbabilities = eliminateVariables(network, featureValues);
 
         // Normalize the probabilities to sum to 1
         double sum = accumulate(classProbabilities.begin(), classProbabilities.end(), 0.0);
@@ -217,8 +190,4 @@ namespace bayesnet {
 
         return make_pair(predictedClass, maxProbability);
     }
-    vector<double> eliminateVariables(network, featureValues)
-    {
-        
-    }
 }

src/Network.h

@@ -16,7 +16,6 @@ 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> eliminateVariables(Network&, const map<string, int>&);
     public:
         Network();
         Network(int);
@@ -25,6 +24,7 @@ namespace bayesnet {
         void addNode(string, int);
         void addEdge(const string, const string);
         map<string, Node*>& getNodes();
+        vector<string> getFeatures();
         void fit(const vector<vector<int>>&, const vector<int>&, const vector<string>&, const string&);
         void estimateParameters();
         void setRoot(string);

src/Node.cc

@@ -53,4 +53,47 @@ namespace bayesnet {
     {
         this->cpt = cpt;
     }
+    /*
+     The MinFill criterion is a heuristic for variable elimination.
+     The variable that minimizes the number of edges that need to be added to the graph to make it triangulated.
+     This is done by counting the number of edges that need to be added to the graph if the variable is eliminated.
+     The variable with the minimum number of edges is chosen.
+     Here this is done computing the length of the combinations of the node neighbors taken 2 by 2.
+    */
+    unsigned Node::minFill()
+    {
+        set<string> neighbors;
+        for (auto child : children) {
+            neighbors.emplace(child->getName());
+        }
+        for (auto parent : parents) {
+            neighbors.emplace(parent->getName());
+        }
+        return combinations(neighbors).size();
+    }
+    vector<string> Node::combinations(const set<string>& neighbors)
+    {
+        vector<string> source(neighbors.begin(), neighbors.end());
+        vector<string> result;
+        for (int i = 0; i < source.size(); ++i) {
+            string temp = source[i];
+            for (int j = i + 1; j < source.size(); ++j) {
+                result.push_back(temp + source[j]);
+            }
+        }
+        return result;
+    }
+    Factor* Node::toFactor()
+    {
+        vector<string> variables;
+        vector<int> cardinalities;
+        variables.push_back(name);
+        cardinalities.push_back(numStates);
+        for (auto parent : parents) {
+            variables.push_back(parent->getName());
+            cardinalities.push_back(parent->getNumStates());
+        }
+        return new Factor(variables, cardinalities, cpt);
+
+    }
 }

src/Node.h

@@ -1,6 +1,7 @@
 #ifndef NODE_H
 #define NODE_H
 #include <torch/torch.h>
+#include "Factor.h"
 #include <vector>
 #include <string>
 namespace bayesnet {
@@ -15,6 +16,7 @@ namespace bayesnet {
         torch::Tensor cpTable;
         int numStates;
         torch::Tensor cpt;
+        vector<string> combinations(const set<string>&);
     public:
         Node(const std::string&, int);
         void addParent(Node*);
@@ -28,7 +30,9 @@ namespace bayesnet {
         void setCPT(const torch::Tensor&);
         int getNumStates() const;
         void setNumStates(int);
+        unsigned minFill();
         int getId() const { return id; }
+        Factor* toFactor();
     };
 }
 #endif