bayesnet/network/Node.cc

@@ -93,36 +93,42 @@ namespace bayesnet {
     void Node::computeCPT(const torch::Tensor& dataset, const std::vector<std::string>& features, const double smoothing, const torch::Tensor& weights)
     {
         dimensions.clear();
+        dimensions.reserve(parents.size() + 1);
         // Get dimensions of the CPT
         dimensions.push_back(numStates);
-        transform(parents.begin(), parents.end(), back_inserter(dimensions), [](const auto& parent) { return parent->getNumStates(); });
+        for (const auto& parent : parents) {
-        // Create a tensor of zeros with the dimensions of the CPT
+            dimensions.push_back(parent->getNumStates());
-        cpTable = torch::zeros(dimensions, torch::kDouble) + smoothing;
+        }
-        // Fill table with counts
+        //transform(parents.begin(), parents.end(), back_inserter(dimensions), [](const auto& parent) { return parent->getNumStates(); });
-        auto pos = find(features.begin(), features.end(), name);
+        // Create a tensor initialized with smoothing
-        if (pos == features.end()) {
+        cpTable = torch::full(dimensions, smoothing, torch::kDouble);
-            throw std::logic_error("Feature " + name + " not found in dataset");
+        // Create a map for quick feature index lookup
+        std::unordered_map<std::string, int> featureIndexMap;
+        for (size_t i = 0; i < features.size(); ++i) {
+            featureIndexMap[features[i]] = i;
+        }
+        // Fill table with counts
+        // Get the index of this node's feature
+        int name_index = featureIndexMap[name];
+        // Get parent indices in dataset
+        std::vector<int> parent_indices;
+        parent_indices.reserve(parents.size());
+        for (const auto& parent : parents) {
+            parent_indices.push_back(featureIndexMap[parent->getName()]);
         }
-        int name_index = pos - features.begin();
         c10::List<c10::optional<at::Tensor>> coordinates;
         for (int n_sample = 0; n_sample < dataset.size(1); ++n_sample) {
             coordinates.clear();
             auto sample = dataset.index({ "...", n_sample });
             coordinates.push_back(sample[name_index]);
-            for (auto parent : parents) {
+            for (size_t i = 0; i < parent_indices.size(); ++i) {
-                pos = find(features.begin(), features.end(), parent->getName());
+                coordinates.push_back(sample[parent_indices[i]]);
-                if (pos == features.end()) {
-                    throw std::logic_error("Feature parent " + parent->getName() + " not found in dataset");
-                }
-                int parent_index = pos - features.begin();
-                coordinates.push_back(sample[parent_index]);
             }
             // Increment the count of the corresponding coordinate
             cpTable.index_put_({ coordinates }, weights.index({ n_sample }), true);
         }
-        // Normalize the counts
+        // Normalize the counts (dividing each row by the sum of the row)
-        // Divide each row by the sum of the row
+        cpTable /= cpTable.sum(0, true);
-        cpTable = cpTable / cpTable.sum(0);
     }
     double Node::getFactorValue(std::map<std::string, int>& evidence)
     {

sample/CMakeLists.txt

@@ -18,7 +18,7 @@ include_directories(
     ../tests/lib/Files
     lib/json/include
     /usr/local/include
-    ${FImdlp_INCLUDE_DIRS}
+    /usr/local/include/fimdlp/
 )
 
 add_executable(bayesnet_sample sample.cc) 

sample/sample.cc

@@ -60,7 +60,21 @@ int main(int argc, char* argv[])
     auto clf = bayesnet::BoostAODE(false); // false for not using voting in predict
     std::cout << "Library version: " << clf.getVersion() << std::endl;
     tie(X, y, features, className, states) = loadDataset(file_name, true);
-    clf.fit(X, y, features, className, states, bayesnet::Smoothing_t::LAPLACE);
+    torch::Tensor weights = torch::full({ X.size(1) }, 15, torch::kDouble);
+    torch::Tensor dataset;
+    try {
+        auto yresized = torch::transpose(y.view({ y.size(0), 1 }), 0, 1);
+        dataset = torch::cat({ X, yresized }, 0);
+    }
+    catch (const std::exception& e) {
+        std::stringstream oss;
+        oss << "* Error in X and y dimensions *\n";
+        oss << "X dimensions: " << dataset.sizes() << "\n";
+        oss << "y dimensions: " << y.sizes();
+        throw std::runtime_error(oss.str());
+    }
+    //Classifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const Smoothing_t smoothing) override;
+    clf.fit(dataset, features, className, states, weights, bayesnet::Smoothing_t::LAPLACE);
     auto score = clf.score(X, y);
     std::cout << "File: " << file_name << " Model: BoostAODE score: " << score << std::endl;
     return 0;