Complete Adding weights to Models

src/BayesNet/BaseClassifier.h

@@ -13,6 +13,7 @@ namespace bayesnet {
         // X is nxm tensor, y is nx1 tensor
         virtual BaseClassifier& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) = 0;
         virtual BaseClassifier& fit(torch::Tensor& dataset, vector<string>& features, string className, map<string, vector<int>>& states) = 0;
+        virtual BaseClassifier& fit(torch::Tensor& dataset, vector<string>& features, string className, map<string, vector<int>>& states, const torch::Tensor& weights) = 0;
         virtual ~BaseClassifier() = default;
         torch::Tensor virtual predict(torch::Tensor& X) = 0;
         vector<int> virtual predict(vector<vector<int>>& X) = 0;

src/BayesNet/Classifier.cc

@@ -5,7 +5,7 @@ namespace bayesnet {
     using namespace torch;
 
     Classifier::Classifier(Network model) : model(model), m(0), n(0), metrics(Metrics()), fitted(false) {}
-    Classifier& Classifier::build(vector<string>& features, string className, map<string, vector<int>>& states)
+    Classifier& Classifier::build(vector<string>& features, string className, map<string, vector<int>>& states, const torch::Tensor& weights)
     {
         this->features = features;
         this->className = className;
@@ -16,14 +16,11 @@ namespace bayesnet {
         auto n_classes = states[className].size();
         metrics = Metrics(dataset, features, className, n_classes);
         model.initialize();
-        // TODO weights can't be ones
-        const torch::Tensor weights = torch::ones({ m }, torch::kFloat);
         buildModel(weights);
         trainModel(weights);
         fitted = true;
         return *this;
     }
-
     void Classifier::buildDataset(Tensor& ytmp)
     {
         try {
@@ -46,7 +43,8 @@ namespace bayesnet {
     {
         dataset = X;
         buildDataset(y);
-        return build(features, className, states);
+        const torch::Tensor weights = torch::ones({ dataset.size(1) }, torch::kFloat);
+        return build(features, className, states, weights);
     }
     // X is nxm where n is the number of features and m the number of samples
     Classifier& Classifier::fit(vector<vector<int>>& X, vector<int>& y, vector<string>& features, string className, map<string, vector<int>>& states)
@@ -57,12 +55,19 @@ namespace bayesnet {
         }
         auto ytmp = torch::tensor(y, kInt32);
         buildDataset(ytmp);
-        return build(features, className, states);
+        const torch::Tensor weights = torch::ones({ dataset.size(1) }, torch::kFloat);
+        return build(features, className, states, weights);
     }
     Classifier& Classifier::fit(torch::Tensor& dataset, vector<string>& features, string className, map<string, vector<int>>& states)
     {
         this->dataset = dataset;
-        return build(features, className, states);
+        const torch::Tensor weights = torch::ones({ dataset.size(1) }, torch::kFloat);
+        return build(features, className, states, weights);
+    }
+    Classifier& Classifier::fit(torch::Tensor& dataset, vector<string>& features, string className, map<string, vector<int>>& states, const torch::Tensor& weights)
+    {
+        this->dataset = dataset;
+        return build(features, className, states, weights);
     }
     void Classifier::checkFitParameters()
     {

src/BayesNet/Classifier.h

@@ -11,7 +11,7 @@ namespace bayesnet {
     class Classifier : public BaseClassifier {
     private:
         void buildDataset(torch::Tensor& y);
-        Classifier& build(vector<string>& features, string className, map<string, vector<int>>& states);
+        Classifier& build(vector<string>& features, string className, map<string, vector<int>>& states, const torch::Tensor& weights);
     protected:
         bool fitted;
         int m, n; // m: number of samples, n: number of features
@@ -30,6 +30,7 @@ namespace bayesnet {
         Classifier& fit(vector<vector<int>>& X, vector<int>& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
         Classifier& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
         Classifier& fit(torch::Tensor& dataset, vector<string>& features, string className, map<string, vector<int>>& states) override;
+        Classifier& fit(torch::Tensor& dataset, vector<string>& features, string className, map<string, vector<int>>& states, const torch::Tensor& weights) override;
         void addNodes();
         int getNumberOfNodes() const override;
         int getNumberOfEdges() const override;