Kellogs Special K

This script yields each subset of size k from a super set of size n. There are two methods. The first operates on sets of integers of the form range(n). The seconds operates on arbitrary sets or lists. Platforms: Windows, Mac, *nix, Python, BSD Solaris

Kommuni-K is a complete communication system. It gathers many funtions into one single modularized client/server system : - Instant messaging (ICQ - like) - Chatrooms - Global file sharing (napster-like) - Bookmark sharing - GUI to netsoul Platforms: Windows, Mac, *nix, C/C++, BSD Solaris

K PHP Develop is an integrated Web development tool designed for team work. It supports HTML, javascript, PHP, and SQL syntax highlighting and file locking. It also has a query analyzer and database access (depends on KSQL plugins). Platforms: *nix, C/C++, BSD

K-Pregs is a PHP library. It comes with many functions related to Regular Expressions. All these are Perl compatible, fast and very efficient functions. Some example validation functions: Visa, MasterCard, Email, Url, etc... Platforms: Windows, Mac, *nix, PHP, BSD Solaris

The k-means algorithm is widely used in a number applications like speech processing and image compression.This script implements the algorithm in a simple but general way. It performs four basic steps.1. Define k arbitrary prototypes from the data samples.2. Assign each sample to the nearest... Platforms: Matlab

This function is based on Yen's k-Shortest Path algorithm (1971)It retuns:1) [shortestPaths]: the list of K shortest paths (in cell array 1xK)2) [totalCosts] : costs of the K shortest paths (in array 1xK)Yen's algorithm prevents loops.This function calls a slightly modified/simplified function... Platforms: Matlab

Fuzzy k-nearest neighbors classifier that can work with training samples whose label info is fuzzified. The prototype is as follows.[y,predict_class] = f_knn(tr,tr_memberships,te,k)tr: training samplestr_memberships: membership values of traning sampleste: testing samplesk: k value vector (more... Platforms: Matlab

An implementation of "k-Means Projective Clustering" by P. K. Agarwal and N. H. Mustafa.This method of clustering is based on finding few subspaces such that each point is close to a subspace. Platforms: Matlab

Our basic idea is based on (n,k)-gray code which was introduced in one paper named :"Generalized Gray Codes with Applications". Our extention is allowing each digit ranged from different digit which is widely useful in some situations.For example, (3,2)-Gray code is (0,0), (0,1),... Platforms: Matlab

isParetoSetMember returns the logical Pareto membership of a set of N points in K dimensions. It takes as input a N-by-N matrix and return a N-binary-array, denoting for each of the N points, whether it is Pareto (1) o not (0).The concept of Pareto dominance considered herein is the standard... Platforms: Matlab

Efficient implementation of K-medoids clustering methods. This method is similar to K-means but more robust.For more detail, please seehttp://en.wikipedia.org/wiki/K-medoidsInput data are assumed column vectors.tryload data;label=kmedoids(X,3);scatterd(X,3); Platforms: Matlab

When the dataset is small, when you have to run only a few number of search, or when the dimensions of points is large, the brute search method is still faster than kd-trees data structure. Computing the distances one by one take a minor time than building the tree.Some of this problems have... Platforms: Matlab

The code implemented a special case of the ellipsoid fitting technique proposed in the paper Least Squares Ellipsoid Specific Fitting, corresponding to case k= 4. Just put the two files in the same directory and run "testFit4". You can change the noise level to observe the fitting accuracy and... Platforms: Matlab

K-means image segmentation based on histogram to reduce memory usage which is constant for any image size. Platforms: Matlab

Codes for fuzzy k means clustering, including k means with extragrades, Gustafson Kessel algorithm, fuzzy linear discriminant analysis. Performance measure is also calculated. Platforms: Matlab

This is just a brute force implementation of k nearest neighbor search without using any fancy data structure, such as kd-tree. However it is the fastest knn matlab implementation I can find.A partial sort mex function is implemented which is a simple wrapper of c++ partial_sort.Provided the sort... Platforms: Matlab

[L, C, D] = FKMEANS(X, k) partitions the vectors in the n-by-p matrix Xinto k (or, rarely, fewer) clusters by applying the well known batchK-means algorithm. Rows of X correspond to points, columns correspond tovariables. The output k-by-p matrix C contains the cluster centroids. Then-element... Platforms: Matlab

http://simulations.narod.ru/Using:ind=all_combinations(n,k)Number of all posible variants is nv=C(n,k)=n!/(k!*(n-k)!) (binomial coefficient)ind is nv-by-k matrix. Example, 2 indexes from 1 2 3:all_combinations(3,2)ans = 1 2 1 3 2 3Example 3 from 5:all_combinations(5,3)ans = 1 2 3 1 2 4 1 2 5 1 3... Platforms: Matlab

This programme is an application of the Chinese Remainder Theorem for Integers - for obtaining a solution to the "k-threshold system for sharing a secret". The concept is explained in the book "A course in Number Theory and Cryptography by Neal Koblitz" in Ch 1, Prob 24 / P27 and it's solution in... Platforms: Matlab

This is a small but efficient tool to perform K-nearest neighbor search, which has wide Science and Engineering applications, such as pattern recognition, data mining and signal processing.The code was initially implemented through vectorization. After discussions with John D'Errico, I realized... Platforms: Matlab