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tests/TestUtils.h

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+#ifndef TEST_UTILS_H
+#define TEST_UTILS_H
+#include <torch/torch.h>
+#include <string>
+#include <vector>
+#include <map>
+#include <tuple>
+#include "ArffFiles.h"
+#include "CPPFImdlp.h"
+
+bool file_exists(const std::string& name);
+std::pair<vector<mdlp::labels_t>, map<std::string, int>> discretize(std::vector<mdlp::samples_t>& X, mdlp::labels_t& y, std::vector<string> features);
+std::vector<mdlp::labels_t> discretizeDataset(std::vector<mdlp::samples_t>& X, mdlp::labels_t& y);
+std::tuple<vector<vector<int>>, std::vector<int>, std::vector<string>, std::string, map<std::string, std::vector<int>>> loadFile(const std::string& name);
+std::tuple<torch::Tensor, torch::Tensor, std::vector<string>, std::string, map<std::string, std::vector<int>>> loadDataset(const std::string& name, bool class_last, bool discretize_dataset);
+
+class RawDatasets {
+public:
+    RawDatasets(const std::string& file_name, bool discretize)
+    {
+        // Xt can be either discretized or not
+        tie(Xt, yt, featurest, classNamet, statest) = loadDataset(file_name, true, discretize);
+        // Xv is always discretized
+        tie(Xv, yv, featuresv, classNamev, statesv) = loadFile(file_name);
+        auto yresized = torch::transpose(yt.view({ yt.size(0), 1 }), 0, 1);
+        dataset = torch::cat({ Xt, yresized }, 0);
+        nSamples = dataset.size(1);
+        weights = torch::full({ nSamples }, 1.0 / nSamples, torch::kDouble);
+        weightsv = std::vector<double>(nSamples, 1.0 / nSamples);
+        classNumStates = discretize ? statest.at(classNamet).size() : 0;
+    }
+    torch::Tensor Xt, yt, dataset, weights;
+    std::vector<vector<int>> Xv;
+    std::vector<double> weightsv;
+    std::vector<int> yv;
+    std::vector<string> featurest, featuresv;
+    map<std::string, std::vector<int>> statest, statesv;
+    std::string classNamet, classNamev;
+    int nSamples, classNumStates;
+    double epsilon = 1e-5;
+};
+
+#endif //TEST_UTILS_H