I am implementing K nearest neighbour algorithm for a very sparse data. I want to calculate the distance between a test instance and each sample in the training set, but I am confused.
And another algorithm I\'m looking for: A free C/C++ implementation of the average distance to nearest neighbour problem.
I am designing an automated trading software for the foreign exchange market. In a MYSQL database I have years of market data at five-minute intervals. I have 5 different metrics for this data alongs
I am designing an automated trading software for the foreign exchange market. In a MYSQL database I have years of market data at five-minute intervals. I have 4 different metrics for this data alongs
I want do do some flocking simulation, as described here. For this I need to search for the nearest neighbours of each of my 2D points. However, I cannot use a static data structure like a k-d tree 开
In a cubic box I have a large collection points in R^3. I\'d like to find the k nearest neighbors for eac开发者_如何学Pythonh point. Normally I\'d think to use something like a k-d tree, but in this c
From a sorted map, I want to retrieve a subset of n entries, starting m entries before a specified value v. For example, for the key set k = {0.2, 0.3, 0.4, 0.6, 0.8, 0.9, 1.0}, a query with n=5, m=2
I need开发者_Python百科 to discover all network neighbors in Linux(they are running Linux too) and I need to get theirs IP addresses(3rd layer). Any ideas how to do that?
Given a collection of thousands of points in 3D, I need to get the list of neighbours for each particl开发者_StackOverflowe that fall inside some cutoff value (in terms of euclidean distance), and if
I\'ve been trying to use开发者_运维知识库 the Ibk nearest neighbor algorithm that goes together with the weka machine learning library.
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