Classification subroutine find the closest cluster of the principal components for each given day in EOF space. More...
#include <classif.h>
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Functions | |
void | class_days_pc_clusters (int *days_class_cluster, double *pc_eof_days, double *eof_days_cluster, char *type, int neof, int ncluster, int ndays) |
Output a vector (dimension days) containing the closer (Euclidian distance) cluster number. |
Classification subroutine find the closest cluster of the principal components for each given day in EOF space.
Definition in file class_days_pc_clusters.c.
void class_days_pc_clusters | ( | int * | days_class_cluster, | |
double * | pc_eof_days, | |||
double * | eof_days_cluster, | |||
char * | type, | |||
int | neof, | |||
int | ncluster, | |||
int | ndays | |||
) |
Output a vector (dimension days) containing the closer (Euclidian distance) cluster number.
The distance is computed as the distance between a day's principal components for each EOF and the cluster principal components for each EOF. To evaluate the closest one, all the square of the distances for all EOFs are summed for each cluster, before the square root is applied.
[out] | days_class_cluster | Cluster number associated for each day. |
[in] | pc_eof_days | Principal Components of EOF (daily data). |
[in] | eof_days_cluster | Clusters' centroid positions for each eof. |
[in] | type | Type of distance used. Possible values: euclidian. |
[in] | neof | Number of EOFs. |
[in] | ncluster | Number of clusters. |
[in] | ndays | Number of days in the pc_eof_days vector. |
Definition at line 60 of file class_days_pc_clusters.c.
Referenced by generate_clusters(), wt_downscaling(), and wt_learning().
00061 { 00072 double dist_min; /* Minimum distance found between a given day PC (summed over all EOF) and each cluster centroid. */ 00073 int clust_dist_min; /* Cluster number which has the minimum distance dist_min */ 00074 double dist_sum; /* Sum of distances (partial computation) over all EOFs */ 00075 double val; /* Distance between a given day PC (for a particular EOF) and one cluster centroid. */ 00076 double dist_clust; /* Distance (full computation of dist_sum). */ 00077 00078 int day; /* Loop counter for days */ 00079 int clust; /* Loop counter for cluster */ 00080 int eof; /* Loop counter for eofs */ 00081 00082 if ( !strcmp(type, "euclidian") ) { 00083 /* Euclidian distance type */ 00084 00085 /* Parse each day */ 00086 for (day=0; day<ndays; day++) { 00087 00088 /* Initialize */ 00089 dist_min = 9999999999.0; 00090 clust_dist_min = 999; 00091 00092 #if DEBUG >= 7 00093 (void) fprintf(stderr, "day=%d\n", day); 00094 #endif 00095 00096 /* Parse each cluster */ 00097 for (clust=0; clust<ncluster; clust++) { 00098 00099 #if DEBUG >= 7 00100 (void) fprintf(stderr, "clust=%d\n", clust); 00101 #endif 00102 00103 dist_sum = 0.0; 00104 /* Sum all distances (over EOF) between the PC of the day and the PC of the cluster centroid for each EOF respectively */ 00105 for (eof=0; eof<neof; eof++) { 00106 val = pc_eof_days[day+eof*ndays] - eof_days_cluster[eof+clust*neof]; 00107 #if DEBUG >= 9 00108 printf("%d %d %lf %lf\n",clust,eof,pc_eof_days[day+eof*ndays],eof_days_cluster[eof+clust*neof]); 00109 #endif 00110 /* Euclidian distance: square */ 00111 dist_sum += (val * val); 00112 } 00113 /* Euclidian distance: square root of squares */ 00114 dist_clust = sqrt(dist_sum); 00115 00116 #if DEBUG >= 7 00117 (void) fprintf(stderr, "dist_clust=%lf\n", dist_clust); 00118 #endif 00119 00120 /* Is it a cluster which has less distance as the minimum found yet ? */ 00121 if (dist_clust < dist_min) { 00122 /* Save cluster number */ 00123 clust_dist_min = clust; 00124 dist_min = dist_clust; 00125 } 00126 } 00127 if (clust_dist_min == 999) { 00128 /* Failing algorithm */ 00129 (void) fprintf(stderr, "%s: ABORT: Impossible: no cluster was selected!! Problem in algorithm...\n", __FILE__); 00130 (void) abort(); 00131 } 00132 /* Assign cluster with minimum distance to all EOFs for this day */ 00133 days_class_cluster[day] = clust_dist_min; 00134 #if DEBUG >= 9 00135 (void) fprintf(stderr, "%s: day %d cluster %d\n", __FILE__, day, clust_dist_min); 00136 #endif 00137 } 00138 } 00139 else { 00140 /* Unknown distance type */ 00141 (void) fprintf(stderr, "%s: ABORT: Unknown distance type=%s!!\n", __FILE__, type); 00142 (void) abort(); 00143 } 00144 }