[synclust.git] / src / sources / kmeansClustering.h

#ifndef SYNCLUST_KMEANSCLUSTERING_H
#define SYNCLUST_KMEANSCLUSTERING_H

#include <cgds/Vector.h>

// auxiliary function to obtain a random sample of 1..n with K elements
void sample(
	int* centers, 
	int n, 
	int K
);

// auxiliary function to compare two sets of centers
int unequalCenters(
	int* ctrs1, 
	int* ctrs2, 
	int n, 
	int K
);

// assign a vector (represented by its distances to others, as distances[index,])
// to a cluster, represented by its center index ==> output is integer in 0..K-1
int assignCluster(
	int index, 
	double* distances, 
	int* centers, 
	int n, 
	int K
);

// k-means based on a distance matrix (nstart=10, maxiter=100)
int* kmeansWithDistances_core(
	double* pDistances, 
	int n, 
	int K, 
	int nstart, 
	int maxiter
);

#endif
Commit	Line	Data
	1	#ifndef SYNCLUST_KMEANSCLUSTERING_H
	2	#define SYNCLUST_KMEANSCLUSTERING_H
	3
	4	#include <cgds/Vector.h>
	5
	6	// auxiliary function to obtain a random sample of 1..n with K elements
	7	void sample(
	8	int* centers,
	9	int n,
	10	int K
	11	);
	12
	13	// auxiliary function to compare two sets of centers
	14	int unequalCenters(
	15	int* ctrs1,
	16	int* ctrs2,
	17	int n,
	18	int K
	19	);
	20
	21	// assign a vector (represented by its distances to others, as distances[index,])
	22	// to a cluster, represented by its center index ==> output is integer in 0..K-1
	23	int assignCluster(
	24	int index,
	25	double* distances,
	26	int* centers,
	27	int n,
	28	int K
	29	);
	30
	31	// k-means based on a distance matrix (nstart=10, maxiter=100)
	32	int* kmeansWithDistances_core(
	33	double* pDistances,
	34	int n,
	35	int K,
	36	int nstart,
	37	int maxiter
	38	);
	39
	40	#endif