Inference working

.vscode/settings.json

@@ -86,7 +86,9 @@
         "*.tcc": "cpp",
         "functional": "cpp",
         "iterator": "cpp",
-        "memory_resource": "cpp"
+        "memory_resource": "cpp",
+        "format": "cpp",
+        "valarray": "cpp"
     },
     "cmake.configureOnOpen": false,
     "C_Cpp.default.configurationProvider": "ms-vscode.cmake-tools"

data/iris.net

@@ -2,4 +2,4 @@ class sepallength
 class sepalwidth
 class petallength
 class petalwidth
-# petalwidth petallength
+petalwidth petallength

sample/test.cc

@@ -21,20 +21,47 @@
 //     std::cout << t << std::endl;
 // }
 #include <torch/torch.h>
-
+#include <iostream>
+#include <vector>
+#include <string>
+using namespace std;
 int main()
 {
-    //torch::Tensor t = torch::rand({ 5, 4, 3 }); // 3D tensor for this example
     //int i = 3, j = 1, k = 2; // Indices for the cell you want to update
     // Print original tensor
-    torch::Tensor t = torch::tensor({ {1, 2, 3}, {4, 5, 6} }); // 3D tensor for this example
+    // torch::Tensor t = torch::tensor({ {1, 2, 3}, {4, 5, 6} }); // 3D tensor for this example
-    std::cout << t << std::endl;
+    auto variables = vector<string>{ "A", "B" };
-    std::cout << "sum(0)" << std::endl;
+    auto cardinalities = vector<int>{ 5, 4 };
-    std::cout << t.sum(0) << std::endl;
+    torch::Tensor values = torch::rand({ 5, 4 });
-    std::cout << "sum(1)" << std::endl;
+    auto candidate = "B";
-    std::cout << t.sum(1) << std::endl;
+    vector<string> newVariables;
-    std::cout << "Normalized" << std::endl;
+    vector<int> newCardinalities;
-    std::cout << t / t.sum(0) << std::endl;
+    for (int i = 0; i < variables.size(); i++) {
+        if (variables[i] != candidate) {
+            newVariables.push_back(variables[i]);
+            newCardinalities.push_back(cardinalities[i]);
+        }
+    }
+    torch::Tensor newValues = values.sum(1);
+    cout << "original values" << endl;
+    cout << values << endl;
+    cout << "newValues" << endl;
+    cout << newValues << endl;
+    cout << "newVariables" << endl;
+    for (auto& variable : newVariables) {
+        cout << variable << endl;
+    }
+    cout << "newCardinalities" << endl;
+    for (auto& cardinality : newCardinalities) {
+        cout << cardinality << endl;
+    }
+    // std::cout << t << std::endl;
+    // std::cout << "sum(0)" << std::endl;
+    // std::cout << t.sum(0) << std::endl;
+    // std::cout << "sum(1)" << std::endl;
+    // std::cout << t.sum(1) << std::endl;
+    // std::cout << "Normalized" << std::endl;
+    // std::cout << t / t.sum(0) << std::endl;
 
     // New value
     // torch::Tensor new_val = torch::tensor(10.0f);

src/CMakeLists.txt

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

src/ExactInference.cc

@@ -1,65 +1,30 @@
 #include "ExactInference.h"
 
 namespace bayesnet {
-    ExactInference::ExactInference(Network& net) : network(net), evidence(map<string, int>()), candidates(net.getFeatures()) {}
+    ExactInference::ExactInference(Network& net) : network(net) {}
-    void ExactInference::setEvidence(const map<string, int>& evidence)
+    double ExactInference::computeFactor(map<string, int>& completeEvidence)
-    {
-        this->evidence = evidence;
-    }
-    ExactInference::~ExactInference()
-    {
-        for (auto& factor : factors) {
-            delete factor;
-        }
-    }
-    void ExactInference::buildFactors()
     {
+        double result = 1.0;
         for (auto node : network.getNodes()) {
-            factors.push_back(node.second->toFactor());
+            result *= node.second->getFactorValue(completeEvidence);
-        }
-    }
-    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> ExactInference::variableElimination(map<string, int>& evidence)
     {
         vector<double> result;
         string candidate;
-        buildFactors();
+        int classNumStates = network.getClassNumStates();
-        // Eliminate evidence
+        for (int i = 0; i < classNumStates; ++i) {
-        while ((candidate = nextCandidate()) != "") {
+            result.push_back(1.0);
-            // Erase candidate from candidates (Erase–remove idiom)
+            auto complete_evidence = map<string, int>(evidence);
-            candidates.erase(remove(candidates.begin(), candidates.end(), candidate), candidates.end());
+            complete_evidence[network.getClassName()] = i;
-            // sum-product variable elimination algorithm as explained in the book probabilistic graphical models
+            result[i] = computeFactor(complete_evidence);
-            // 1. Multiply all factors containing the variable
+        }
-            vector<Factor*> factorsToMultiply;
+        // Normalize result
-            for (auto factor : factors) {
+        auto sum = accumulate(result.begin(), result.end(), 0.0);
-                if (factor->contains(candidate)) {
+        for (int i = 0; i < result.size(); ++i) {
-                    factorsToMultiply.push_back(factor);
+            result[i] /= sum;
-                }
-            }
-            Factor* product = Factor::product(factorsToMultiply);
-            // 2. Sum out the variable
-            Factor* sum = product->sumOut(candidate);
-            // 3. Remove factors containing the variable
-            for (auto factor : factorsToMultiply) {
-                factors.erase(remove(factors.begin(), factors.end(), factor), factors.end());
-                delete factor;
-            }
-            // 4. Add the resulting factor to the list of factors
-            factors.push_back(sum);
-
         }
         return result;
     }

src/ExactInference.h

@@ -1,7 +1,6 @@
 #ifndef EXACTINFERENCE_H
 #define EXACTINFERENCE_H
 #include "Network.h"
-#include "Factor.h"
 #include "Node.h"
 #include <map>
 #include <vector>
@@ -12,16 +11,10 @@ namespace bayesnet {
     class ExactInference {
     private:
         Network network;
-        map<string, int> evidence;
+        double computeFactor(map<string, int>&);
-        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();
+        vector<double> variableElimination(map<string, int>&);
-        void setEvidence(const map<string, int>&);
-        vector<double> variableElimination();
     };
 }
 #endif

src/Factor.cc

@@ -1,87 +0,0 @@
-#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;
-    Factor::Factor(const Factor& other) : variables(other.variables), cardinalities(other.cardinalities), values(other.values) {}
-    Factor& Factor::operator=(const Factor& other)
-    {
-        if (this != &other) {
-            variables = other.variables;
-            cardinalities = other.cardinalities;
-            values = other.values;
-        }
-        return *this;
-    }
-    void Factor::setVariables(vector<string>& variables)
-    {
-        this->variables = variables;
-    }
-    void Factor::setCardinalities(vector<int>& cardinalities)
-    {
-        this->cardinalities = cardinalities;
-    }
-    void Factor::setValues(torch::Tensor& values)
-    {
-        this->values = values;
-    }
-    vector<string>& Factor::getVariables()
-    {
-        return variables;
-    }
-    vector<int>& Factor::getCardinalities()
-    {
-        return cardinalities;
-    }
-    torch::Tensor& Factor::getValues()
-    {
-        return values;
-    }
-    bool Factor::contains(string& variable)
-    {
-        for (int i = 0; i < variables.size(); i++) {
-            if (variables[i] == variable) {
-                return true;
-            }
-        }
-        return false;
-    }
-    Factor* Factor::sumOut(string& candidate)
-    {
-        vector<string> newVariables;
-        vector<int> newCardinalities;
-        for (int i = 0; i < variables.size(); i++) {
-            if (variables[i] != candidate) {
-                newVariables.push_back(variables[i]);
-                newCardinalities.push_back(cardinalities[i]);
-            }
-        }
-        torch::Tensor newValues = values.sum(0);
-        return new Factor(newVariables, newCardinalities, newValues);
-    }
-    Factor* Factor::product(vector<Factor*>& factors)
-    {
-        vector<string> newVariables;
-        vector<int> newCardinalities;
-        for (auto factor : factors) {
-            vector<string> variables = factor->getVariables();
-            for (auto idx = 0; idx < variables.size(); ++idx) {
-                string variable = variables[idx];
-                if (find(newVariables.begin(), newVariables.end(), variable) == newVariables.end()) {
-                    newVariables.push_back(variable);
-                    newCardinalities.push_back(factor->getCardinalities()[idx]);
-                }
-            }
-        }
-        torch::Tensor newValues = factors[0]->getValues();
-        for (int i = 1; i < factors.size(); i++) {
-            newValues = newValues.matmul(factors[i]->getValues());
-        }
-        return new Factor(newVariables, newCardinalities, newValues);
-    }
-
-}

src/Factor.h

@@ -1,31 +0,0 @@
-#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();
-        bool contains(string&);
-        torch::Tensor& getValues();
-        static Factor* product(vector<Factor*>&);
-        Factor* sumOut(string&);
-
-    };
-}
-#endif

src/Network.cc

@@ -1,9 +1,9 @@
 #include "Network.h"
 #include "ExactInference.h"
 namespace bayesnet {
-    Network::Network() : laplaceSmoothing(1), root(nullptr), features(vector<string>()), className("") {}
+    Network::Network() : laplaceSmoothing(1), root(nullptr), features(vector<string>()), className(""), classNumStates(0) {}
-    Network::Network(int smoothing) : laplaceSmoothing(smoothing), root(nullptr), features(vector<string>()), className("") {}
+    Network::Network(int smoothing) : laplaceSmoothing(smoothing), root(nullptr), features(vector<string>()), className(""), classNumStates(0) {}
-    Network::Network(Network& other) : laplaceSmoothing(other.laplaceSmoothing), root(other.root), features(other.features), className(other.className)
+    Network::Network(Network& other) : laplaceSmoothing(other.laplaceSmoothing), root(other.root), features(other.features), className(other.className), classNumStates(other.getClassNumStates())
     {
         for (auto& pair : other.nodes) {
             nodes[pair.first] = new Node(*pair.second);
@@ -31,6 +31,14 @@ namespace bayesnet {
     {
         return features;
     }
+    int Network::getClassNumStates()
+    {
+        return classNumStates;
+    }
+    string Network::getClassName()
+    {
+        return className;
+    }
     void Network::setRoot(string name)
     {
         if (nodes.find(name) == nodes.end()) {
@@ -93,6 +101,7 @@ namespace bayesnet {
             this->dataset[featureNames[i]] = dataset[i];
         }
         this->dataset[className] = labels;
+        this->classNumStates = *max_element(labels.begin(), labels.end()) + 1;
         estimateParameters();
     }
 
@@ -100,29 +109,7 @@ namespace bayesnet {
     {
         auto dimensions = vector<int64_t>();
         for (auto [name, node] : nodes) {
-            // Get dimensions of the CPT
+            node->computeCPT(dataset, laplaceSmoothing);
-            dimensions.clear();
-            dimensions.push_back(node->getNumStates());
-            for (auto father : node->getParents()) {
-                dimensions.push_back(father->getNumStates());
-            }
-            auto length = dimensions.size();
-            // Create a tensor of zeros with the dimensions of the CPT
-            torch::Tensor cpt = torch::zeros(dimensions, torch::kFloat) + laplaceSmoothing;
-            // Fill table with counts
-            for (int n_sample = 0; n_sample < dataset[name].size(); ++n_sample) {
-                torch::List<c10::optional<torch::Tensor>> coordinates;
-                coordinates.push_back(torch::tensor(dataset[name][n_sample]));
-                for (auto father : node->getParents()) {
-                    coordinates.push_back(torch::tensor(dataset[father->getName()][n_sample]));
-                }
-                // Increment the count of the corresponding coordinate
-                cpt.index_put_({ coordinates }, cpt.index({ coordinates }) + 1);
-            }
-            // Normalize the counts
-            cpt = cpt / cpt.sum(0);
-            // store thre resulting cpt in the node
-            node->setCPT(cpt);
         }
     }
 
@@ -175,14 +162,8 @@ namespace bayesnet {
         for (int i = 0; i < sample.size(); ++i) {
             evidence[features[i]] = sample[i];
         }
-        inference.setEvidence(evidence);
+        vector<double> classProbabilities = inference.variableElimination(evidence);
-        vector<double> classProbabilities = inference.variableElimination();
 
-        // Normalize the probabilities to sum to 1
-        double sum = accumulate(classProbabilities.begin(), classProbabilities.end(), 0.0);
-        for (double& prob : classProbabilities) {
-            prob /= sum;
-        }
         // Find the class with the maximum posterior probability
         auto maxElem = max_element(classProbabilities.begin(), classProbabilities.end());
         int predictedClass = distance(classProbabilities.begin(), maxElem);

src/Network.h

@@ -11,6 +11,7 @@ namespace bayesnet {
         map<string, Node*> nodes;
         map<string, vector<int>> dataset;
         Node* root;
+        int classNumStates;
         vector<string> features;
         string className;
         int laplaceSmoothing;
@@ -25,6 +26,8 @@ namespace bayesnet {
         void addEdge(const string, const string);
         map<string, Node*>& getNodes();
         vector<string> getFeatures();
+        int getClassNumStates();
+        string getClassName();
         void fit(const vector<vector<int>>&, const vector<int>&, const vector<string>&, const string&);
         void estimateParameters();
         void setRoot(string);

src/Node.cc

@@ -1,10 +1,9 @@
 #include "Node.h"
 
 namespace bayesnet {
-    int Node::next_id = 0;
 
     Node::Node(const std::string& name, int numStates)
-        : id(next_id++), name(name), numStates(numStates), cpt(torch::Tensor()), parents(vector<Node*>()), children(vector<Node*>())
+        : name(name), numStates(numStates), cpTable(torch::Tensor()), parents(vector<Node*>()), children(vector<Node*>())
     {
     }
 
@@ -47,11 +46,7 @@ namespace bayesnet {
     }
     torch::Tensor& Node::getCPT()
     {
-        return cpt;
+        return cpTable;
-    }
-    void Node::setCPT(const torch::Tensor& cpt)
-    {
-        this->cpt = cpt;
     }
     /*
      The MinFill criterion is a heuristic for variable elimination.
@@ -83,17 +78,37 @@ namespace bayesnet {
         }
         return result;
     }
-    Factor* Node::toFactor()
+    void Node::computeCPT(map<string, vector<int>>& dataset, const int laplaceSmoothing)
     {
-        vector<string> variables;
+        // Get dimensions of the CPT
-        vector<int> cardinalities;
+        dimensions.push_back(numStates);
-        variables.push_back(name);
+        for (auto father : getParents()) {
-        cardinalities.push_back(numStates);
+            dimensions.push_back(father->getNumStates());
-        for (auto parent : parents) {
-            variables.push_back(parent->getName());
-            cardinalities.push_back(parent->getNumStates());
         }
-        return new Factor(variables, cardinalities, cpt);
+        auto length = dimensions.size();
-
+        // Create a tensor of zeros with the dimensions of the CPT
+        cpTable = torch::zeros(dimensions, torch::kFloat) + laplaceSmoothing;
+        // Fill table with counts
+        for (int n_sample = 0; n_sample < dataset[name].size(); ++n_sample) {
+            torch::List<c10::optional<torch::Tensor>> coordinates;
+            coordinates.push_back(torch::tensor(dataset[name][n_sample]));
+            for (auto father : getParents()) {
+                coordinates.push_back(torch::tensor(dataset[father->getName()][n_sample]));
+            }
+            // Increment the count of the corresponding coordinate
+            cpTable.index_put_({ coordinates }, cpTable.index({ coordinates }) + 1);
+        }
+        // Normalize the counts
+        cpTable = cpTable / cpTable.sum(0);
+    }
+    float Node::getFactorValue(map<string, int>& evidence)
+    {
+        torch::List<c10::optional<torch::Tensor>> coordinates;
+        // following predetermined order of indices in the cpTable (see Node.h)
+        coordinates.push_back(torch::tensor(evidence[name]));
+        for (auto parent : getParents()) {
+            coordinates.push_back(torch::tensor(evidence[parent->getName()]));
+        }
+        return cpTable.index({ coordinates }).item<float>();
     }
 }

src/Node.h

@@ -1,21 +1,18 @@
 #ifndef NODE_H
 #define NODE_H
 #include <torch/torch.h>
-#include "Factor.h"
 #include <vector>
 #include <string>
 namespace bayesnet {
     using namespace std;
     class Node {
     private:
-        static int next_id;
-        const int id;
         string name;
         vector<Node*> parents;
         vector<Node*> children;
-        torch::Tensor cpTable;
+        int numStates; // number of states of the variable
-        int numStates;
+        torch::Tensor cpTable; // Order of indices is 0-> node variable, 1-> 1st parent, 2-> 2nd parent, ...
-        torch::Tensor cpt;
+        vector<int64_t> dimensions; // dimensions of the cpTable
         vector<string> combinations(const set<string>&);
     public:
         Node(const std::string&, int);
@@ -27,12 +24,11 @@ namespace bayesnet {
         vector<Node*>& getParents();
         vector<Node*>& getChildren();
         torch::Tensor& getCPT();
-        void setCPT(const torch::Tensor&);
+        void computeCPT(map<string, vector<int>>&, const int);
         int getNumStates() const;
         void setNumStates(int);
         unsigned minFill();
-        int getId() const { return id; }
+        float getFactorValue(map<string, int>&);
-        Factor* toFactor();
     };
 }
 #endif