added current c++ code

workspace/pca/aKNN.h

@@ -0,0 +1,73 @@
+#ifndef AKNN_H
+#define AKNN_H
+
+#include "nanoflann.hpp"
+
+using namespace nanoflann;
+
+template <typename T, int dim> class aKNN {
+
+	struct DataSet {
+
+		std::vector<T> elems;
+
+		inline size_t kdtree_get_point_count() const {return elems.size();}
+		inline float kdtree_distance(const float* p1, const size_t idxP2, size_t) const {
+			float dist = 0;
+			for (int i = 0; i < dim; ++i) {
+				float delta = (p1[i] - kdtree_get_pt(idxP2, i));
+				dist += delta*delta;
+			}
+			return dist;
+		}
+
+		inline float kdtree_get_pt(const size_t idx, int pos) const {
+			return elems[idx].feature[pos];
+		}
+
+		template <class BBOX> bool kdtree_get_bbox(BBOX&) const {return false;}
+
+	} data;
+
+	typedef KDTreeSingleIndexAdaptor<L2_Simple_Adaptor<float, DataSet>, DataSet, dim> MyTree;
+
+	MyTree* tree = nullptr;
+
+
+
+public:
+
+	/** add a new element */
+	void add(const T& elem) {
+		data.elems.push_back(elem);
+	}
+
+	/** build the KD-Tree */
+	void build() {
+		tree = new MyTree(dim, data, KDTreeSingleIndexAdaptorParams(10) );
+		tree->buildIndex();
+	}
+
+	/** get the nearest n elements */
+	template <typename T2> std::vector<T> get(const T2* query, const int numResults) {
+
+		float distances[numResults];
+		size_t indices[numResults];
+
+		KNNResultSet<float> res(numResults);
+		res.init(indices, distances);
+
+		tree->knnSearch(query, numResults, indices, distances);
+		std::vector<T> vec;
+		for (int i = 0; i < numResults; ++i) {
+			vec.push_back(data.elems[indices[i]]);
+		}
+		return vec;
+
+	}
+
+
+};
+
+#endif // AKNN_H
+