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Fast high-dimensional exact KNN search.
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README.md

ofxCoverTree

Introduction

A cover tree is a data-structure for efficiently finding nearest neighbors between points in a high dimensional space.

ofxCoverTree makes it easy to use CoverTree in openFrameworks. ####All credit for the actual core implementation of the cover tree goes to the original authors, my part was to clean up and document the API, and restructuring the code to make it easier to use.

This project uses no openframeworks specific code, but the folder structure is so as to be easily used as an openframeworks addon.

This implementation supports parallel tree construction and multiple concurrent readers (but only a single writer) using the Multi suffixed methods.

Performance when compiled in release mode is often 10-100x greater than in debug mode.

Installation

Copy into your addons folder (e.g git clone https://github.com/aman-tiwari/ofxCoverTree).

Optional: Run make in the downloaded folder to create the benchmark.

Examples

  • example-emoji
    • This example consists of image-space k-nearest-neighbour search over 6480 emoji. Each emoji is unrolled into a feature vector of 4096 dimensions (32 * 32 * 4).
    • Each row is one row of search results, with the leftmost emoji being the search query.
    • Press up and down to change the number of neighbours searched for, and b to switch between brute-force and cover-tree search.

Usage

####Initialisation

#include "ofxCoverTree.h"

size_t dims = 128;
	
ofxCoverTree:item item_1(dims);
ofxCoverTree:item item_2(dims);
	
/* fill in item_1 and item_2 */
for(size_t i = 0; i < dims; i++) {
	item_1[i] = random();
	item_2[i] = random();
}
	
item_1.id = 1;
item_2.id = 2;
	
/* Need atleast 1 point to construct the tree */
ofxCoverTree::Default tree(item_1);
	
/* insert more items into the tree */
tree.insert(item_2);

/* need to call to rebuild tree after modifying */
tree.update();
	
/* alternatively, can construct from a vector of points */
std::vector<ofxCoverTree:item> items;
items.push_back(item_1); items.push_back(item_2);

ofxCoverTree::Default plant(items);

/* vector constructor automatically calls plant.update() for us */
// plant.update() <- superfluous

/* if the cover tree isn't performing well enough, 
 * or taking too long to build, try 
 * tuning the base parameter */
ofxCoverTree::Default plant_2(items, 2.0);

Efficient tree building sometimes requires tuning the base parameter in the constructor. I am not entirely sure what determines the optimal value to use, but it depends on the distribution and fractal dimension of the data used. Generally values in the range 1.3 to 2.5 should be used.

K-Nearest Neighbors

ofxCoverTree::item search_item(dims);
for(size_t i = 0; i < dims; i++) {
	search_item[i] = i / 0.5;
}

/* finds the 2 nearest neighbors */
auto results = tree.nearNeighbors(search_item, 2);

/* can search for more items than in tree without crashing */
std::vector<ofxCoverTree::item> two_results = tree.nearNeighbors(search_item, 200);

Neighbors within a radius

/* finds the neighbors within a radius of a point */
auto findings = tree.rangeNeighbors(search_item, 1.5);

Extending the point class

If you'd like to stuff extra data into the points stored by the cover tree, you can extend ofxCoverTree::point like so:

struct named_point : ofxCoverTree::point {
	using ofxCoverTree::point::point; // required!
	std::string name;
};

ofxCoverTree::CoverTree<named_point> named_point_tree(...);

named_point_tree will then store and return named_points.

NB: ofxCoverTree::point is just Eigen::VectorXd

Parallel construction

This uses a work-stealing fork-join algorithm to construct the cover tree in parallel.

std::vector<ofxCoverTree::item> points = ...
ofxCoverTree::ParallelMake<> maker(0, points.size(), points);
maker.compute();

ofxCoverTree::Default* cover_tree = maker.get_result();

Full API

In the following api, point and numeric refer to the template parameters of ofxCoverTree::CoverTree, representing the points store and the type used for numeric calculations (by default, ofxCoverTree::item and float respectively), i.e:

template <typename point = ofxCoverTree::item, typename numeric = float>
class CoverTree {
...
}

#####ofxCoverTree::CoverTree(const point& p, numeric base = 1.3) Constructs a cover tree containing p with a base of base, with default value of 1.3.

#####ofxCoverTree::CoverTree(const point& p, numeric base = 1.3) Constructs a cover tree containing p with a base of base, with default value of 1.3.

#####ofxCoverTree::CoverTree(std::vector<point>& pList, numeric base = 1.3) Constructs a cover tree containing the points in pList with a base of base, with default value of 1.3.

#####ofxCoverTree::CoverTree(std::vector<point>& pList, int begin, int end, numeric _base = 1.3) Constructs a cover tree containing the points in pList[begin:end] with a base of base, with a default value of 1.3.


#####ofxCoverTree::Default An alias for ofxCoverTree::CoverTree<ofxCoverTree::item, float>


The following are methods of the ofxCoverTree::CoverTree class.

#####update() Updates the tree.

#####point& nearestNeighbour(const point queryPt) const Returns the nearest neighbor to queryPt.

#####std::vector<point> nearNeighbors(const point queryPt, int N) const Returns the N nearest neighbors to queryPt.

#####std::vector<point> rangeNeighbors(const point queryPt, numeric range) const Returns the points within range of queryPt.

#####void merge(CoverTree* ct) Merges this with ct, resulting in a cover tree containing both sets of points. Using ct after merging is undefined behaviour.


#####ofxCoverTree::ParallelMake<point, numeric>(std::vector<point>& points) Construct the parallel cover tree builder. The following are methods of the parallel builder:

#####void compute() Builds the cover tree using a work-stealing fork-join. Blocks till the cover tree is built.

#####ofxCoverTree::CoverTree<point, numeric> get_result() Returns the built cover tree.

License

Apache 2.0

Dependencies

Eigen is used but included under the libs folder.

Compatibility

At least 0.9.0^, although since no oF specific code is used, it should work with any version of openframeworks that compiles with C++11.

#####C++11 or newer#####

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