Fix BinDisc quantile mistakes (#9)

* Fix BinDisc quantile mistakes

* Fix FImdlp tests

* Fix tests, samples and remove uneeded support files

* Add coypright header to sources
Fix coverage report
Add coverage badge to README

* Update sonar github action

* Move sources to a folder and change ArffFiles files to library

* Add recursive submodules to github action

src/Discretizer.cpp

@@ -0,0 +1,54 @@
+// ****************************************************************
+// SPDX - FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX - FileType: SOURCE
+// SPDX - License - Identifier: MIT
+// ****************************************************************
+
+#include "Discretizer.h"
+
+namespace mdlp {
+
+    labels_t& Discretizer::transform(const samples_t& data)
+    {
+        discretizedData.clear();
+        discretizedData.reserve(data.size());
+        // CutPoints always have at least two items
+        // Have to ignore first and last cut points provided
+        auto first = cutPoints.begin() + 1;
+        auto last = cutPoints.end() - 1;
+        auto bound = direction == bound_dir_t::LEFT ? std::lower_bound<std::vector<precision_t>::iterator, precision_t> : std::upper_bound<std::vector<precision_t>::iterator, precision_t>;
+        for (const precision_t& item : data) {
+            auto pos = bound(first, last, item);
+            auto number = pos - first;
+            discretizedData.push_back(static_cast<label_t>(number));
+        }
+        return discretizedData;
+    }
+    labels_t& Discretizer::fit_transform(samples_t& X_, labels_t& y_)
+    {
+        fit(X_, y_);
+        return transform(X_);
+    }
+    void Discretizer::fit_t(const torch::Tensor& X_, const torch::Tensor& y_)
+    {
+        auto num_elements = X_.numel();
+        samples_t X(X_.data_ptr<precision_t>(), X_.data_ptr<precision_t>() + num_elements);
+        labels_t y(y_.data_ptr<int>(), y_.data_ptr<int>() + num_elements);
+        fit(X, y);
+    }
+    torch::Tensor Discretizer::transform_t(const torch::Tensor& X_)
+    {
+        auto num_elements = X_.numel();
+        samples_t X(X_.data_ptr<precision_t>(), X_.data_ptr<precision_t>() + num_elements);
+        auto result = transform(X);
+        return torch::tensor(result, torch_label_t);
+    }
+    torch::Tensor Discretizer::fit_transform_t(const torch::Tensor& X_, const torch::Tensor& y_)
+    {
+        auto num_elements = X_.numel();
+        samples_t X(X_.data_ptr<precision_t>(), X_.data_ptr<precision_t>() + num_elements);
+        labels_t y(y_.data_ptr<int>(), y_.data_ptr<int>() + num_elements);
+        auto result = fit_transform(X, y);
+        return torch::tensor(result, torch_label_t);
+    }
+}