#ifndef KNNARRAY_H
#define KNNARRAY_H

/**
 * this wrapper class provides all methods needed for nanoflanns KNN-search.
 * in order for this wrapper class to work, your data-structure must provide
 * the following methods:
 *
 * PointList:
 *		size() - return the number of contained points
 *		operator [] - access points via their index
 * Point
 *		operator [] - access each dimension via its index
 *
 * example:
 *		std::vector<Point3> points;
 *		KNNArray<std::vector<Point3>> arr(points);
 *		KNN<KNNArray<std::vector<Point3>>, 3> knn(arr);
 */
template <typename T> class KNNArray {

private:

	/** the underlying data structure */
	const T& elem;

public:

	/** ctor with the underlying data structure */
	KNNArray(const T& elem) : elem(elem) {
		;
	}

	/** get the number of elements to search throrugh */
	inline int kdtree_get_point_count() const {
		return elem.size();
	}

	/** use nanoflanns default bbox */
	template <class BBOX> inline bool kdtree_get_bbox(BBOX& bb) const {
		(void) bb; return false;
	}

	/** get the idx-th element's dim-th coordinate */
	inline float kdtree_get_pt(const size_t idx, const int dim) const {
		return elem[idx][dim];
	}

	/** get the SQUARED distance between the given coordinates and the provided element */
	inline float kdtree_distance(const float* p1, const size_t idx_p2, size_t) const {
		const float d0 = p1[0] - elem[idx_p2][0];
		const float d1 = p1[1] - elem[idx_p2][1];
		const float d2 = p1[2] - elem[idx_p2][2];
		return (d0*d0) + (d1*d1) + (d2*d2);
	}

};

#endif // KNNARRAY_H