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/***************************************************************
*
* I_cluster_exec (C, maxclass, iterations,
* convergence, separation, min_class_size,
* checkpoint, interrupted)
*
* maxclass maximum number of classes
* iterations maximum number of iterations
* convergence percentage of points stable
* separation minimum distance between class centroids
* checkpoint routine to be called at various steps
* interrupted boolean to check for interrupt
*
* returns:
* 0 ok
* -1 out of memory
* -2 interrupted
* 1 not enough data points
*************************************************************/
#include <grass/cluster.h>
int I_cluster_exec(struct Cluster *C, int maxclass, int iterations,
double convergence,
double separation, int min_class_size,
int (*checkpoint) (), int *interrupted)
{
int changes;
/* set interrupted to false */
*interrupted = 0;
/* check for valid inputs */
if (C->npoints < 2) {
fprintf(stderr, "cluster: not enough data points (%d)\n", C->npoints);
return 1;
}
/* check other parms */
if (maxclass < 0)
maxclass = 1;
C->nclasses = maxclass;
if (min_class_size <= 0)
min_class_size = 17;
if (min_class_size < 2)
min_class_size = 2;
if (iterations <= 0)
iterations = 20;
if (convergence <= 0.0)
convergence = 98.0;
if (separation < 0.0)
separation = 0.5;
/* allocate memory */
if (!I_cluster_exec_allocate(C))
return -1;
/* generate class means */
I_cluster_means(C);
if (checkpoint)
(*checkpoint) (C, 1);
/* now assign points to nearest class */
I_cluster_assign(C, interrupted);
if (*interrupted)
return -2;
I_cluster_sum2(C);
if (checkpoint)
(*checkpoint) (C, 2);
/* get rid of empty classes now */
I_cluster_reclass(C, 1);
for (C->iteration = 1;; C->iteration++) {
if (*interrupted)
return -2;
changes = 0;
/* re-assign points to nearest class */
changes = I_cluster_reassign(C, interrupted);
if (*interrupted)
return -2;
/* if too many points have changed class, re-assign points */
C->percent_stable = (C->npoints - changes) * 100.0;
C->percent_stable /= (double)C->npoints;
if (checkpoint)
(*checkpoint) (C, 3);
if (C->iteration >= iterations)
break;
if (C->percent_stable < convergence)
continue;
/* otherwise merge non-distinct classes */
if (I_cluster_distinct(C, separation))
break;
if (checkpoint)
(*checkpoint) (C, 4);
I_cluster_merge(C);
}
/* get rid of small classes */
I_cluster_reclass(C, min_class_size);
I_cluster_sum2(C);
/* compute the resulting signatures */
I_cluster_signatures(C);
return 0;
}
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