Refactor library structure and add sample

sample/sample.cc

@@ -0,0 +1,62 @@
+#include "ArffFiles.h"
+#include "CPPFImdlp.h"
+#include "BoostAODE.h"
+
+std::vector<mdlp::labels_t> discretizeDataset(std::vector<mdlp::samples_t>& X, mdlp::labels_t& y)
+{
+    std::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;
+}
+tuple<torch::Tensor, torch::Tensor, std::vector<std::string>, std::string, map<std::string, std::vector<int>>> loadDataset(const std::string& name, bool class_last)
+{
+    auto handler = ArffFiles();
+    handler.load(name, class_last);
+    // Get Dataset X, y
+    std::vector<mdlp::samples_t>& X = handler.getX();
+    mdlp::labels_t& y = handler.getY();
+    // Get className & Features
+    auto className = handler.getClassName();
+    std::vector<std::string> features;
+    auto attributes = handler.getAttributes();
+    transform(attributes.begin(), attributes.end(), back_inserter(features), [](const auto& pair) { return pair.first; });
+    torch::Tensor Xd;
+    auto states = map<std::string, std::vector<int>>();
+    auto Xr = discretizeDataset(X, y);
+    Xd = torch::zeros({ static_cast<int>(Xr.size()), static_cast<int>(Xr[0].size()) }, torch::kInt32);
+    for (int i = 0; i < features.size(); ++i) {
+        states[features[i]] = std::vector<int>(*max_element(Xr[i].begin(), Xr[i].end()) + 1);
+        auto item = states.at(features[i]);
+        iota(begin(item), end(item), 0);
+        Xd.index_put_({ i, "..." }, torch::tensor(Xr[i], torch::kInt32));
+    }
+    states[className] = std::vector<int>(*max_element(y.begin(), y.end()) + 1);
+    iota(begin(states.at(className)), end(states.at(className)), 0);
+    return { Xd, torch::tensor(y, torch::kInt32), features, className, states };
+}
+
+int main(int argc, char* argv[])
+{
+    if (argc < 2) {
+        std::cerr << "Usage: " << argv[0] << " <file_name>" << std::endl;
+        return 1;
+    }
+    std::string file_name = argv[1];
+    torch::Tensor X, y;
+    std::vector<std::string> features;
+    std::string className;
+    map<std::string, std::vector<int>> states;
+    auto clf = bayesnet::BoostAODE(false); // false for not using voting in predict
+    std::cout << "Library version: " << clf.getVersion() << std::endl;
+    tie(X, y, features, className, states) = loadDataset(file_name, true);
+    clf.fit(X, y, features, className, states);
+    auto score = clf.score(X, y);
+    std::cout << "File: " << file_name << " score: " << score << std::endl;
+    return 0;
+}
+