nearest-neighbor

I have 2 sets of points, set1 and set2 . Both sets of points have a data associated with the point. Points in set1 are "ephemeral", and only exist on the given date. Points in set2 are "permanent", are constructed at a given date, and then exist forever after that date. set.seed(1) dates <- seq(as.Date('2011-01-01'),as.Date('2011-12-31'),by='days') set1 <- data.frame(lat=40+runif(10000), lon=-70+runif(10000),date=sample(dates,10000,replace=TRUE)) set2 <- data.frame(lat=40+runif(100), lon=-70+runif(100),date=sample(dates,100,replace=TRUE)) Here's my problem: For each point in set1 (ephemeral)

I'm reading a paper where they mention that they were able to find the single nearest neighbor in O(1) using a prefix tree. I will describe the general problem and then the classical solution and finally the proposed solution in the paper: Problem : given a list of bit vectors L (all vectors have the same length) and query bit vector q, we would like to find the nearest neighbor of q. The distance metric is the hamming distance (how many bits are different). The naive approach would be to go through the list and calculate the hamming distance between each vector in the list and q, which will

I am modeling (in Postgres 9.6.1 / postGIS 2.3.1) a booking system for local services provided by suppliers: create table supplier ( id serial primary key, name text not null check (char_length(title) < 280), type service_type, duration interval, ... geo_position geography(POINT,4326) ... ); Each supplier keeps a calendar with time slots when he/she is available to be booked: create table timeslot ( id serial primary key, supplier_id integer not null references supplier(id), slot tstzrange not null, constraint supplier_overlapping_timeslot_not_allowed exclude using gist (supplier_id with =,

I have two dataframes. Each one contains locations (X,Y) and a value for that point. For each point in the first dataframe I want to find the closest point in the second dataframe and then find the difference. I have code that is working, but it uses a for loop, which is slow. Any suggestions for how to speed this up? I know that it is generally a good idea to get rid of for loops in pandas, for performance, but I don't see how to do that in this case. Here is some sample code: import pandas as pd import numpy as np df1=pd.DataFrame(np.random.rand(10,3), columns=['val', 'X', 'Y']) df2=pd

Given a collection of thousands of points in 3D, I need to get the list of neighbours for each particle that fall inside some cutoff value (in terms of euclidean distance), and if possible, sorted from nearest fo farthest. Which is the fastest GPU algorithm for this purpose in the CUDA or OpenCL languages? One of the fastest GPU MD codes I'm aware of, HALMD , uses a (highly tuned) version of the same sort of approach that is used in the CUDA SDK examples , "Particles". Both the HALMD paper and the Particles whitepaper are very clearly written. The underling algorithm is to assign particles

I have a fairly large set of 2D points (~20000) in a set, and for each point in the x-y plane want to determine which point from the set is closest. (Actually, the points are of different types, and I just want to know which type is closest. And the x-y plane is a bitmap, say 640x480.) From this answer to the question " All k nearest neighbors in 2D, C++ " I got the idea to make a grid. I created n*m C++ vectors and put the points in the vector, depending on which bin it falls into. The idea is that you only have to check the distance of the points in the bin, instead of all points. If there