K 14

14. GUIDE MATLAB d-deOCt SERIAL COMMUNICATION BETWEEN PIC16F628A AND MATLAB.For more information, visit: http://www.matpic.com 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

This programs explains the formulation of Ybus by singular transformation method of an IEEE 14 bus system 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

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

These are demo and presentation files used in the Webinar delivered on Oct 14, 2008 "Solving Data Management and Analysis Challenges using MATLABdlT« and Statistics Toolboxd-OC?D?".Recorded Webinar link:http://www.mathworks.com/company/events/we.../wbnr31021.html Platforms: Matlab

This is a tool for K-means clustering. After trying several different ways to program, I got the conclusion that using simple loops to perform distance calculation and comparison is most efficient and accurate because of the JIT acceleration in MATLAB.The code is very simple and well documented,... Platforms: Matlab

1 we express a number as a linear combination of powers of 102 for example1980=1*10^3+9*10^2+8*10^1+0*10^0we get [1 9 8 1]apply divisibility rule by 7remaining digits-2(last digit)k=11 9 8 -2(1)=196k=219 - 2(6)=7k=30-2(7)=-14 which is divisible by 7stop we get a negative number Platforms: Matlab

Using the Double Group Formulation of k.p Theory, one may return full zone bulk band diagrams for SiGe alloys. Second order degenerate perturbation theory returns effective mass Hamiltonians and canonical transformations may return arbitrary oriented growth directions.This all inclusive code will... Platforms: Matlab

k-clique algorithm as defined in the paper "Uncovering the overlapping community structure of complex networks in nature and society" - G. Palla, I. DerdoTenyi, I. Farkas, and T. Vicsek - Nature 435, 814d-deOCt818 (2005)[X,Y,Z] = k_clique(k,A)Inputs:k - clique sizeA - adjacency matrixOutputs:X -... Platforms: Matlab

A Fixed-Point Iterative Schema for Error Minimization in k-Sparse DecompositionA a new algorithm to solve the sparse approximation problem over redundant dictionaries based on LiMapS, where the input signal is restricted to be a linear combination of k atoms from a fixed dictionary. Platforms: Matlab