Displaying 1 to 7 from 7 results

leaflet-knn - k-next-nearest-neighbor searches for Leaflet

  •    Javascript

Next-nearest neighbor searches with sphere-knn, with a Leaflet-friendly API.Generates a lookup function from an L.geoJson layer object.

mddf - multi-dimensional data format with attachments for proximity search using a kd-b tree

  •    Javascript

Multidimensional data is important for maps, because you are always interested in things within a range defined in two or three dimensions (though mddf can do N dimensions). Many popular methods of storing map data are not memory efficient, and you must load the entire dataset into RAM or a specialized heavy database engine before it can be used. mddf arranges data more sensibly, so it's actually possible to seek into the file, reading only a small segment, and pull out a collection of nearby points. This means map programs could load fast, work with massive maps and run on tiny devices.There is nothing in mddf that is specifically about maps, but that is the use case that motivates this work.

annoy-node - Node bindings for Annoy, an efficient Approximate Nearest Neighbors implementation written in C++

  •    C++

Node bindings for Annoy, an efficient Approximate Nearest Neighbors implementation written in C++. Status: Tests pass, including one that loads 3 million vectors, but API coverage is not complete. Run on OS X and Linux with Node 6.3 and 4.6. Not tried on Windows yet.

static-kdtree - A static kdtree data structure

  •    Javascript

kd-trees are a compact data structure for answering orthogonal range and nearest neighbor queries on higher dimensional point data in linear time. While they are not as efficient at answering orthogonal range queries as range trees - especially in low dimensions - kdtrees consume exponentially less space, support k-nearest neighbor queries and are relatively cheap to construct. This makes them useful in small to medium dimensions for achieving a modest speed up over a linear scan. It is also worth mentioning that for approximate nearest neighbor queries or queries with a fixed size radius, grids and locality sensitive hashing are strictly better options. In these charts the transition between "Medium" and "Big" depends on how many points there are in the data structure. As the number of points grows larger, the dimension at which kdtrees become practical goes up.




knn - A k-nearest neighboor classifier algorithm.

  •    Javascript

Instantiates the KNN algorithm. Predict the values of the dataset.