nearest-neighbor

I am trying to use scikit's Nearest Neighbor implementation to find the closest column vectors to a given column vector, out of a matrix of random values. This code is supposed to find the nearest neighbors of column 21 then check the actual cosine similarity of those neighbors against column 21. from sklearn.neighbors import NearestNeighbors import sklearn.metrics.pairwise as smp import numpy as np test=np.random.randint(0,5,(50,50)) nbrs = NearestNeighbors(n_neighbors=5, algorithm='auto', metric=smp.cosine_similarity).fit(test) distances, indices = nbrs.kneighbors(test) x=21 for idx,d in

I have two large sparse matrices: In [3]: trainX Out[3]: <6034195x755258 sparse matrix of type '<type 'numpy.float64'>' with 286674296 stored elements in Compressed Sparse Row format> In [4]: testX Out[4]: <2013337x755258 sparse matrix of type '<type 'numpy.float64'>' with 95423596 stored elements in Compressed Sparse Row format> About 5 GB RAM in total to load. Note these matrices are HIGHLY sparse (0.0062% occupied). For each row in testX , I want to find the Nearest Neighbor in trainX and return its corresponding label, found in trainY . trainY is a list with the same length as trainX and

While working on the simulation of particle interactions, I stumbled across grid indexing in Morton-order (Z-order)( Wikipedia link ) which is regarded to provide an efficient nearest neighbor cell search. The main reason that I've read is the almost sequential ordering of spatially close cells in memory. Being in the middle of a first implementation, I can not wrap my head around how to efficiently implement the algorithm for the nearest neighbors, especially in comparison to a basic uniform grid. Given a cell (x,y) it is trivial to obtain the 8 neighbor cell indices and compute the

I am working on a dataset in order to compare the effect of different distance metrics. I am using the KNN algorithm. The KNN algorithm in R uses the Euclidian distance by default. So I wrote my own one. I would like to find the number of correct class label matches between the nearest neighbor and target. I have prepared the data at first. Then I called the data ( wdbc_n ), I chose K=1. I have used Euclidian distance as a test. library(philentropy) knn <- function(xmat, k,method){ n <- nrow(xmat) if (n <= k) stop("k can not be more than n-1") neigh <- matrix(0, nrow = n, ncol = k) for(i in 1

I am trying to implement an efficient algorithm for nearest-neighbour search problem. I have read tutorials about some data structures, which support operations for this kind of problems (for example, R-tree , cover tree , etc.), but all of them are difficult to implement. Also I cannot find sample source code for these data structures. I know C++ and I am trying to solve this problem in this language. Ideally, I need links that describe how to implement these data structures using source code. You could try a linesweep algorithm to find the closest pair of points: http://community.topcoder