| 1 | #include <stdlib.h> |
| 2 | #include <time.h> |
| 3 | #include <math.h> |
| 4 | #include "sources/utils/boolean.h" |
| 5 | #include "sources/kmeansClustering.h" |
| 6 | |
| 7 | // auxiliary function to obtain a random sample of 1..n with K elements |
| 8 | void sample(int* centers, int n, int K) |
| 9 | { |
| 10 | // refVect = (0,1,...,n-1,n) |
| 11 | int* refVect = (int*)malloc(n*sizeof(int)); |
| 12 | for (int i=0; i<n; i++) |
| 13 | refVect[i] = i; |
| 14 | |
| 15 | int curSize = n; // current size of the sampling set |
| 16 | for (int j=0; j<K; j++) |
| 17 | { |
| 18 | // pick an index in sampling set: |
| 19 | int index = rand()%curSize; |
| 20 | centers[j] = refVect[index]; |
| 21 | // move this index outside of sampling set: |
| 22 | refVect[index] = refVect[--curSize]; |
| 23 | } |
| 24 | |
| 25 | free(refVect); |
| 26 | } |
| 27 | |
| 28 | // auxiliary function to compare two sets of centers |
| 29 | int unequalCenters(int* ctrs1, int* ctrs2, int n, int K) |
| 30 | { |
| 31 | // HACK: special case at initialization, ctrs2 = 0 |
| 32 | if (K > 1 && ctrs2[0]==0 && ctrs2[1]==0) |
| 33 | return S_TRUE; |
| 34 | |
| 35 | // compVect[i] == 1 iff index i is found in ctrs1 or ctrs2 |
| 36 | int* compVect = (int*)calloc(n,sizeof(int)); |
| 37 | |
| 38 | int kountNonZero = 0; // count non-zero elements in compVect |
| 39 | for (int j=0; j<K; j++) |
| 40 | { |
| 41 | if (compVect[ctrs1[j]] == 0) |
| 42 | kountNonZero++; |
| 43 | compVect[ctrs1[j]] = 1; |
| 44 | if (compVect[ctrs2[j]] == 0) |
| 45 | kountNonZero++; |
| 46 | compVect[ctrs2[j]] = 1; |
| 47 | } |
| 48 | |
| 49 | free(compVect); |
| 50 | |
| 51 | // if we found more than K non-zero elements, ctrs1 and ctrs2 differ |
| 52 | return (kountNonZero > K); |
| 53 | } |
| 54 | |
| 55 | // assign a vector (represented by its distances to others, as distances[index,]) |
| 56 | // to a cluster, represented by its center ==> output is integer in 0..K-1 |
| 57 | int assignCluster(int index, double* distances, int* centers, int n, int K) |
| 58 | { |
| 59 | int minIndex = 0; |
| 60 | double minDist = distances[index*n+centers[0]]; |
| 61 | |
| 62 | for (int j=1; j<K; j++) |
| 63 | { |
| 64 | if (distances[index*n+centers[j]] < minDist) |
| 65 | { |
| 66 | minDist = distances[index*n+centers[j]]; |
| 67 | minIndex = j; |
| 68 | } |
| 69 | } |
| 70 | |
| 71 | return minIndex; |
| 72 | } |
| 73 | |
| 74 | // k-means based on a distance matrix (nstart=10, maxiter=100) |
| 75 | int* kmeansWithDistances_core( |
| 76 | double* distances, int n, int K, int nstart, int maxiter) |
| 77 | { |
| 78 | int* bestClusts = (int*)malloc(n*sizeof(int)); |
| 79 | double bestDistor = INFINITY; |
| 80 | double avgClustSize = (double)n/K; |
| 81 | int* ctrs = (int*)malloc(K*sizeof(int)); |
| 82 | int* oldCtrs = (int*)malloc(K*sizeof(int)); |
| 83 | Vector** clusters = (Vector**)malloc(K*sizeof(Vector*)); |
| 84 | for (int j=0; j<K; j++) |
| 85 | clusters[j] = vector_new(int); |
| 86 | |
| 87 | // set random number generator seed |
| 88 | srand(time(NULL)); |
| 89 | |
| 90 | for (int startKount=0; startKount < nstart; startKount++) |
| 91 | { |
| 92 | // centers (random) [re]initialization |
| 93 | sample(ctrs,n,K); |
| 94 | for (int j=0; j<K; j++) |
| 95 | oldCtrs[j] = 0; |
| 96 | int kounter = 0; |
| 97 | |
| 98 | /************* |
| 99 | * main loop |
| 100 | *************/ |
| 101 | |
| 102 | // while old and "new" centers differ.. |
| 103 | while (unequalCenters(ctrs,oldCtrs,n,K) && kounter++ < maxiter) |
| 104 | { |
| 105 | // (re)initialize clusters to empty sets |
| 106 | for (int j=0; j<K; j++) |
| 107 | vector_clear(clusters[j]); |
| 108 | |
| 109 | // estimate clusters belongings |
| 110 | for (int i=0; i<n; i++) |
| 111 | { |
| 112 | int affectation = assignCluster(i, distances, ctrs, n, K); |
| 113 | vector_push(clusters[affectation], i); |
| 114 | } |
| 115 | |
| 116 | // copy current centers to old centers |
| 117 | for (int j=0; j<K; j++) |
| 118 | oldCtrs[j] = ctrs[j]; |
| 119 | |
| 120 | // recompute centers |
| 121 | for (int j=0; j<K; j++) |
| 122 | { |
| 123 | int minIndex = -1; |
| 124 | double minSumDist = INFINITY; |
| 125 | VectorIterator* iter1 = vector_get_iterator(clusters[j]); |
| 126 | vectorI_reset_begin(iter1); |
| 127 | while (vectorI_has_data(iter1)) |
| 128 | { |
| 129 | int index1; vectorI_get(iter1, index1); |
| 130 | // attempt to use current index as center |
| 131 | double sumDist = 0.0; |
| 132 | VectorIterator* iter2 = vector_get_iterator(clusters[j]); |
| 133 | vectorI_reset_begin(iter2); |
| 134 | while (vectorI_has_data(iter2)) |
| 135 | { |
| 136 | int index2; vectorI_get(iter2, index2); |
| 137 | sumDist += distances[index1*n+index2]; |
| 138 | vectorI_move_next(iter2); |
| 139 | } |
| 140 | if (sumDist < minSumDist) |
| 141 | { |
| 142 | minSumDist = sumDist; |
| 143 | minIndex = index1; |
| 144 | } |
| 145 | vectorI_destroy(iter2); |
| 146 | vectorI_move_next(iter1); |
| 147 | } |
| 148 | if (minIndex >= 0) |
| 149 | ctrs[j] = minIndex; |
| 150 | // HACK: some 'random' index (a cluster should never be empty) |
| 151 | // this case should never happen anyway |
| 152 | // (pathological dataset with replicates) |
| 153 | else |
| 154 | ctrs[j] = 0; |
| 155 | vectorI_destroy(iter1); |
| 156 | } |
| 157 | } /***** end main loop *****/ |
| 158 | |
| 159 | // finally compute distorsions : |
| 160 | double distor = 0.0; |
| 161 | for (int j=0; j<K; j++) |
| 162 | { |
| 163 | VectorIterator* iter = vector_get_iterator(clusters[j]); |
| 164 | vectorI_reset_begin(iter); |
| 165 | while (vectorI_has_data(iter)) |
| 166 | { |
| 167 | int index; vectorI_get(iter, index); |
| 168 | distor += distances[index*n+ctrs[j]]; |
| 169 | vectorI_move_next(iter); |
| 170 | } |
| 171 | vectorI_destroy(iter); |
| 172 | } |
| 173 | if (distor < bestDistor) |
| 174 | { |
| 175 | // copy current clusters into bestClusts |
| 176 | for (int j=0; j<K; j++) |
| 177 | { |
| 178 | VectorIterator* iter = vector_get_iterator(clusters[j]); |
| 179 | vectorI_reset_begin(iter); |
| 180 | while (vectorI_has_data(iter)) |
| 181 | { |
| 182 | int index; vectorI_get(iter, index); |
| 183 | bestClusts[index] = j; |
| 184 | vectorI_move_next(iter); |
| 185 | } |
| 186 | vectorI_destroy(iter); |
| 187 | } |
| 188 | bestDistor = distor; |
| 189 | } |
| 190 | } |
| 191 | |
| 192 | free(ctrs); |
| 193 | free(oldCtrs); |
| 194 | for (int j=0; j<K; j++) |
| 195 | vector_destroy(clusters[j]); |
| 196 | free(clusters); |
| 197 | |
| 198 | return bestClusts; |
| 199 | } |