Inference working

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(int smoothing) : laplaceSmoothing(smoothing), root(nullptr), features(vector<string>()), className("") {}
-    Network::Network(Network& other) : laplaceSmoothing(other.laplaceSmoothing), root(other.root), features(other.features), className(other.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(""), classNumStates(0) {}
+    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
-            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);
+            node->computeCPT(dataset, laplaceSmoothing);
         }
     }
 
@@ -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();
+        vector<double> classProbabilities = inference.variableElimination(evidence);
 
-        // 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);