Implement the proba branch and begin with the voting one

src/bayesnetUtils.cc

@@ -0,0 +1,39 @@
+
+#include "bayesnetUtils.h"
+namespace bayesnet {
+    // Return the indices in descending order
+    std::vector<int> argsort(std::vector<double>& nums)
+    {
+        int n = nums.size();
+        std::vector<int> indices(n);
+        iota(indices.begin(), indices.end(), 0);
+        sort(indices.begin(), indices.end(), [&nums](int i, int j) {return nums[i] > nums[j];});
+        return indices;
+    }
+    template<typename T>
+    std::vector<std::vector<T>> tensorToVector(torch::Tensor& dtensor)
+    {
+        // convert mxn tensor to nxm std::vector
+        std::vector<std::vector<T>> result;
+        // Iterate over cols
+        for (int i = 0; i < dtensor.size(1); ++i) {
+            auto col_tensor = dtensor.index({ "...", i });
+            auto col = std::vector<T>(col_tensor.data_ptr<T>(), col_tensor.data_ptr<T>() + dtensor.size(0));
+            result.push_back(col);
+        }
+        return result;
+    }
+    torch::Tensor vectorToTensor(std::vector<std::vector<int>>& vector)
+    {
+        // convert nxm std::vector to mxn tensor
+        long int m = vector[0].size();
+        long int n = vector.size();
+        auto tensor = torch::zeros({ m, n }, torch::kInt32);
+        for (int i = 0; i < m; ++i) {
+            for (int j = 0; j < n; ++j) {
+                tensor[i][j] = vector[j][i];
+            }
+        }
+        return tensor;
+    }
+}