Begin experiment

src/Platform/platformUtils.cc

@@ -1,5 +1,7 @@
 #include "platformUtils.h"
 
+using namespace torch;
+
 pair<vector<mdlp::labels_t>, map<string, int>> discretize(vector<mdlp::samples_t>& X, mdlp::labels_t& y, vector<string> features)
 {
     vector<mdlp::labels_t> Xd;
@@ -14,6 +16,18 @@ pair<vector<mdlp::labels_t>, map<string, int>> discretize(vector<mdlp::samples_t
     return { Xd, maxes };
 }
 
+vector<mdlp::labels_t> discretizeDataset(vector<mdlp::samples_t>& X, mdlp::labels_t& y)
+{
+    vector<mdlp::labels_t> Xd;
+    auto fimdlp = mdlp::CPPFImdlp();
+    for (int i = 0; i < X.size(); i++) {
+        fimdlp.fit(X[i], y);
+        mdlp::labels_t& xd = fimdlp.transform(X[i]);
+        Xd.push_back(xd);
+    }
+    return Xd;
+}
+
 bool file_exists(const std::string& name)
 {
     if (FILE* file = fopen(name.c_str(), "r")) {
@@ -24,6 +38,48 @@ bool file_exists(const std::string& name)
     }
 }
 
+tuple < Tensor, Tensor, vector<string>> loadDataset(string name, bool discretize)
+{
+    auto handler = ArffFiles();
+    handler.load(PATH + static_cast<string>(name) + ".arff");
+    // Get Dataset X, y
+    vector<mdlp::samples_t>& X = handler.getX();
+    mdlp::labels_t& y = handler.getY();
+    // Get className & Features
+    auto className = handler.getClassName();
+    vector<string> features;
+    for (auto feature : handler.getAttributes()) {
+        features.push_back(feature.first);
+    }
+    Tensor Xd;
+    if (discretize) {
+        auto Xr = discretizeDataset(X, y);
+        Xd = torch::zeros({ static_cast<int64_t>(Xr[0].size()), static_cast<int64_t>(Xr.size()) }, torch::kInt64);
+        for (int i = 0; i < features.size(); ++i) {
+            Xd.index_put_({ "...", i }, torch::tensor(Xr[i], torch::kInt64));
+        }
+    } else {
+        Xd = torch::zeros({ static_cast<int64_t>(X[0].size()), static_cast<int64_t>(X.size()) }, torch::kFloat64);
+        for (int i = 0; i < features.size(); ++i) {
+            Xd.index_put_({ "...", i }, torch::tensor(X[i], torch::kFloat64));
+        }
+    }
+    return { Xd, torch::tensor(y, torch::kInt64), features };
+}
+
+pair <map<string, int>, map<string, vector<int>>> discretize_info(Tensor& X, Tensor& y, vector<string> features, string className)
+{
+    map<string, int> maxes;
+    map<string, vector<int>> states;
+    for (int i = 0; i < X.size(1); i++) {
+        maxes[features[i]] = X.select(1, i).max().item<int>() + 1;
+        states[features[i]] = vector<int>(maxes[features[i]]);
+    }
+    maxes[className] = y.max().item<int>() + 1;
+    states[className] = vector<int>(maxes[className]);
+    return { maxes, states };
+}
+
 tuple<vector<vector<int>>, vector<int>, vector<string>, string, map<string, vector<int>>> loadFile(string name)
 {
     auto handler = ArffFiles();