Add SPODE

.vscode/settings.json

@@ -90,7 +90,13 @@
         "format": "cpp",
         "valarray": "cpp",
         "regex": "cpp",
-        "span": "cpp"
+        "span": "cpp",
+        "cfenv": "cpp",
+        "cinttypes": "cpp",
+        "csetjmp": "cpp",
+        "future": "cpp",
+        "queue": "cpp",
+        "typeindex": "cpp"
     },
     "cmake.configureOnOpen": false,
     "C_Cpp.default.configurationProvider": "ms-vscode.cmake-tools"

src/CMakeLists.txt

@@ -1,2 +1,2 @@
-add_library(BayesNet Network.cc Node.cc Metrics.cc BaseClassifier.cc KDB.cc TAN.cc)
+add_library(BayesNet Network.cc Node.cc Metrics.cc BaseClassifier.cc KDB.cc TAN.cc SPODE.cc)
 target_link_libraries(BayesNet "${TORCH_LIBRARIES}")

src/Metrics.cc

@@ -121,7 +121,7 @@ namespace bayesnet {
     and the indices of the weights as nodes of this square matrix using
     Kruskal algorithm
     */
-    vector<pair<int, int>> Metrics::maximumSpanningTree(Tensor& weights)
+    vector<pair<int, int>> Metrics::maximumSpanningTree(int root, Tensor& weights)
     {
         auto result = vector<pair<int, int>>();
 

src/Metrics.hpp

@@ -22,7 +22,7 @@ namespace bayesnet {
         vector<float> conditionalEdgeWeights();
         Tensor conditionalEdge();
         vector<pair<string, string>> doCombinations(const vector<string>&);
-        vector<pair<int, int>> maximumSpanningTree(Tensor& weights);
+        vector<pair<int, int>> maximumSpanningTree(int root, Tensor& weights);
     };
 }
 #endif

src/SPODE.cc

@@ -0,0 +1,20 @@
+#include "SPODE.h"
+
+namespace bayesnet {
+
+    SPODE::SPODE(int root) : BaseClassifier(Network()), root(root) {}
+
+    void SPODE::train()
+    {
+        // 0. Add all nodes to the model
+        addNodes();
+        // 1. Add edges from the class node to all other nodes
+        // 2. Add edges from the root node to all other nodes
+        for (int i = 0; i < static_cast<int>(features.size()); ++i) {
+            model.addEdge(className, features[i]);
+            if (i != root) {
+                model.addEdge(features[root], features[i]);
+            }
+        }
+    }
+}

src/SPODE.h

@@ -0,0 +1,14 @@
+#ifndef SPODE_H
+#define SPODE_H
+#include "BaseClassifier.h"
+namespace bayesnet {
+    class SPODE : public BaseClassifier {
+    private:
+        int root;
+    protected:
+        void train() override;
+    public:
+        SPODE(int root);
+    };
+}
+#endif

src/TAN.cc

@@ -10,16 +10,25 @@ namespace bayesnet {
     {
         // 0. Add all nodes to the model
         addNodes();
-        // 1. Compute mutual information between each feature and the class
+        // 1. Compute mutual information between each feature and the class and set the root node
+        // as the highest mutual information with the class
+        auto mi = vector <pair<int, float >>();
+        Tensor class_dataset = dataset.index({ "...", -1 });
+        for (int i = 0; i < static_cast<int>(features.size()); ++i) {
+            Tensor feature_dataset = dataset.index({ "...", i });
+            auto mi_value = metrics.mutualInformation(class_dataset, feature_dataset);
+            mi.push_back({ i, mi_value });
+        }
+        sort(mi.begin(), mi.end());
+        auto root = mi[mi.size() - 1].first;
+        // 2. Compute mutual information between each feature and the class
         auto weights = metrics.conditionalEdge();
-        // 2. Compute the maximum spanning tree
-        auto mst = metrics.maximumSpanningTree(weights);
-        // 3. Add edges from the maximum spanning tree to the model
+        // 3. Compute the maximum spanning tree
+        auto mst = metrics.maximumSpanningTree(root, weights);
+        // 4. Add edges from the maximum spanning tree to the model
         for (auto i = 0; i < mst.size(); ++i) {
             auto [from, to] = mst[i];
             model.addEdge(features[from], features[to]);
         }
-
     }
-
 }