Add conversion methods

src/experimental_clfs/XA1DE.cpp

@@ -61,6 +61,7 @@ namespace platform {
             std::cout << "* Time to build the model: " << timert.getDuration() << " seconds" << std::endl;
             // exit(1);
         }
+        fitted = true;
         return *this;
     }
     std::vector<std::vector<double>> XA1DE::predict_proba(std::vector<std::vector<int>>& test_data)
@@ -115,6 +116,9 @@ namespace platform {
     }
     std::vector<int> XA1DE::predict(std::vector<std::vector<int>>& test_data)
     {
+        if (!fitted) {
+            throw std::logic_error(CLASSIFIER_NOT_FITTED);
+        }
         auto probabilities = predict_proba(test_data);
         std::vector<int> predictions(probabilities.size(), 0);
 
@@ -147,4 +151,47 @@ namespace platform {
         }
         return static_cast<float>(correct) / predictions.size();
     }
+    std::vector<std::vector<int>> to_matrix(const torch::Tensor& X)
+    {
+        // Ensure tensor is contiguous in memory
+        auto X_contig = X.contiguous();
+
+        // Access tensor data pointer directly
+        auto data_ptr = X_contig.data_ptr<int>();
+
+        // IF you are using int64_t as the data type, use the following line
+        //auto data_ptr = X_contig.data_ptr<int64_t>();
+        //std::vector<std::vector<int64_t>> data(X.size(0), std::vector<int64_t>(X.size(1)));
+
+        // Prepare output container
+        std::vector<std::vector<int>> data(X.size(0), std::vector<int>(X.size(1)));
+
+        // Fill the 2D vector in a single loop using pointer arithmetic
+        int rows = X.size(0);
+        int cols = X.size(1);
+        for (int i = 0; i < rows; ++i) {
+            std::copy(data_ptr + i * cols, data_ptr + (i + 1) * cols, data[i].begin());
+        }
+        return data;
+    }
+    std::vector<int> to_vector(const torch::Tensor& y)
+    {
+        // Ensure the tensor is contiguous in memory
+        auto y_contig = y.contiguous();
+
+        // Access data pointer
+        auto data_ptr = y_contig.data_ptr<int>();
+
+        // Prepare output container
+        std::vector<int> data(y.size(0));
+
+        // Copy data efficiently
+        std::copy(data_ptr, data_ptr + y.size(0), data.begin());
+
+        return data;
+    }
+    XA1DE& XA1DE::fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const bayesnet::Smoothing_t smoothing)
+    {
+        return fit(to_matrix(X), to_vector(y), features, className, states, smoothing);
+    }
 }