comparison dsp/segmentation/cluster_melt.c @ 480:175e51ae78eb

Untabify, indent, tidy
author Chris Cannam <cannam@all-day-breakfast.com>
date Fri, 31 May 2019 10:53:39 +0100
parents 7e8d1f26b098
children de5f557a270f
comparison
equal deleted inserted replaced
479:7e52c034cf62 480:175e51ae78eb
19 19
20 #define DEFAULT_LAMBDA 0.02; 20 #define DEFAULT_LAMBDA 0.02;
21 #define DEFAULT_LIMIT 20; 21 #define DEFAULT_LIMIT 20;
22 22
23 double kldist(double* a, double* b, int n) { 23 double kldist(double* a, double* b, int n) {
24 /* NB assume that all a[i], b[i] are non-negative 24 /* NB assume that all a[i], b[i] are non-negative
25 because a, b represent probability distributions */ 25 because a, b represent probability distributions */
26 double q, d; 26 double q, d;
27 int i; 27 int i;
28 28
29 d = 0; 29 d = 0;
30 for (i = 0; i < n; i++) 30 for (i = 0; i < n; i++) {
31 { 31 q = (a[i] + b[i]) / 2.0;
32 q = (a[i] + b[i]) / 2.0; 32 if (q > 0) {
33 if (q > 0) 33 if (a[i] > 0) {
34 { 34 d += a[i] * log(a[i] / q);
35 if (a[i] > 0) 35 }
36 d += a[i] * log(a[i] / q); 36 if (b[i] > 0) {
37 if (b[i] > 0) 37 d += b[i] * log(b[i] / q);
38 d += b[i] * log(b[i] / q); 38 }
39 } 39 }
40 } 40 }
41 return d; 41 return d;
42 } 42 }
43 43
44 void cluster_melt(double *h, int m, int n, double *Bsched, int t, int k, int l, int *c) { 44 void cluster_melt(double *h, int m, int n, double *Bsched, int t, int k, int l, int *c) {
45 double lambda, sum, beta, logsumexp, maxlp; 45 double lambda, sum, beta, logsumexp, maxlp;
46 int i, j, a, b, b0, b1, limit, /* B, */ it, maxiter, maxiter0, maxiter1; 46 int i, j, a, b, b0, b1, limit, /* B, */ it, maxiter, maxiter0, maxiter1;
47 double** cl; /* reference histograms for each cluster */ 47 double** cl; /* reference histograms for each cluster */
48 int** nc; /* neighbour counts for each histogram */ 48 int** nc; /* neighbour counts for each histogram */
49 double** lp; /* soft assignment probs for each histogram */ 49 double** lp; /* soft assignment probs for each histogram */
50 int* oldc; /* previous hard assignments (to check convergence) */ 50 int* oldc; /* previous hard assignments (to check convergence) */
51 51
52 /* NB h is passed as a 1d row major array */ 52 /* NB h is passed as a 1d row major array */
53 53
54 /* parameter values */ 54 /* parameter values */
55 lambda = DEFAULT_LAMBDA; 55 lambda = DEFAULT_LAMBDA;
56 if (l > 0) 56 if (l > 0) {
57 limit = l; 57 limit = l;
58 else 58 } else {
59 limit = DEFAULT_LIMIT; /* use default if no valid neighbourhood limit supplied */ 59 limit = DEFAULT_LIMIT; /* use default if no valid neighbourhood limit supplied */
60 // B = 2 * limit + 1; 60 }
61 maxiter0 = 20; /* number of iterations at initial temperature */ 61
62 maxiter1 = 5; /* number of iterations at subsequent temperatures */ 62 maxiter0 = 20; /* number of iterations at initial temperature */
63 63 maxiter1 = 5; /* number of iterations at subsequent temperatures */
64 /* allocate memory */ 64
65 cl = (double**) malloc(k*sizeof(double*)); 65 /* allocate memory */
66 for (i= 0; i < k; i++) 66 cl = (double**) malloc(k*sizeof(double*));
67 cl[i] = (double*) malloc(m*sizeof(double)); 67 for (i= 0; i < k; i++) {
68 68 cl[i] = (double*) malloc(m*sizeof(double));
69 nc = (int**) malloc(n*sizeof(int*)); 69 }
70 for (i= 0; i < n; i++) 70
71 nc[i] = (int*) malloc(k*sizeof(int)); 71 nc = (int**) malloc(n*sizeof(int*));
72 72 for (i= 0; i < n; i++) {
73 lp = (double**) malloc(n*sizeof(double*)); 73 nc[i] = (int*) malloc(k*sizeof(int));
74 for (i= 0; i < n; i++) 74 }
75 lp[i] = (double*) malloc(k*sizeof(double)); 75
76 76 lp = (double**) malloc(n*sizeof(double*));
77 oldc = (int*) malloc(n * sizeof(int)); 77 for (i= 0; i < n; i++) {
78 78 lp[i] = (double*) malloc(k*sizeof(double));
79 /* initialise */ 79 }
80 for (i = 0; i < k; i++) 80
81 { 81 oldc = (int*) malloc(n * sizeof(int));
82 sum = 0; 82
83 for (j = 0; j < m; j++) 83 /* initialise */
84 { 84 for (i = 0; i < k; i++) {
85 cl[i][j] = rand(); /* random initial reference histograms */ 85 sum = 0;
86 sum += cl[i][j] * cl[i][j]; 86 for (j = 0; j < m; j++) {
87 } 87 cl[i][j] = rand(); /* random initial reference histograms */
88 sum = sqrt(sum); 88 sum += cl[i][j] * cl[i][j];
89 for (j = 0; j < m; j++) 89 }
90 { 90 sum = sqrt(sum);
91 cl[i][j] /= sum; /* normalise */ 91 for (j = 0; j < m; j++) {
92 } 92 cl[i][j] /= sum; /* normalise */
93 } 93 }
94 //print_array(cl, k, m); 94 }
95 95
96 for (i = 0; i < n; i++) 96 for (i = 0; i < n; i++) {
97 c[i] = 1; /* initially assign all histograms to cluster 1 */ 97 c[i] = 1; /* initially assign all histograms to cluster 1 */
98 98 }
99 for (a = 0; a < t; a++) 99
100 { 100 for (a = 0; a < t; a++) {
101 beta = Bsched[a]; 101
102 102 beta = Bsched[a];
103 if (a == 0) 103
104 maxiter = maxiter0; 104 if (a == 0) {
105 else 105 maxiter = maxiter0;
106 maxiter = maxiter1; 106 } else {
107 107 maxiter = maxiter1;
108 for (it = 0; it < maxiter; it++) 108 }
109 { 109
110 //if (it == maxiter - 1) 110 for (it = 0; it < maxiter; it++) {
111 // mexPrintf("hasn't converged after %d iterations\n", maxiter); 111
112 112 //if (it == maxiter - 1)
113 for (i = 0; i < n; i++) 113 // mexPrintf("hasn't converged after %d iterations\n", maxiter);
114 { 114
115 /* save current hard assignments */ 115 for (i = 0; i < n; i++) {
116 oldc[i] = c[i]; 116
117 117 /* save current hard assignments */
118 /* calculate soft assignment logprobs for each cluster */ 118 oldc[i] = c[i];
119 sum = 0; 119
120 for (j = 0; j < k; j++) 120 /* calculate soft assignment logprobs for each cluster */
121 { 121 sum = 0;
122 lp[i][ j] = -beta * kldist(cl[j], &h[i*m], m); 122
123 123 for (j = 0; j < k; j++) {
124 /* update matching neighbour counts for this histogram, based on current hard assignments */ 124
125 /* old version: 125 lp[i][ j] = -beta * kldist(cl[j], &h[i*m], m);
126 nc[i][j] = 0; 126
127 if (i >= limit && i <= n - 1 - limit) 127 /* update matching neighbour counts for this histogram, based on current hard assignments */
128 { 128
129 for (b = i - limit; b <= i + limit; b++) 129 b0 = i - limit;
130 { 130 if (b0 < 0) {
131 if (c[b] == j+1) 131 b0 = 0;
132 nc[i][j]++; 132 }
133 } 133 b1 = i + limit;
134 nc[i][j] = B - nc[i][j]; 134 if (b1 >= n) {
135 } 135 b1 = n - 1;
136 */ 136 }
137 b0 = i - limit; 137 nc[i][j] = b1 - b0 + 1; /* = B except at edges */
138 if (b0 < 0) 138 for (b = b0; b <= b1; b++) {
139 b0 = 0; 139 if (c[b] == j+1) {
140 b1 = i + limit; 140 nc[i][j]--;
141 if (b1 >= n) 141 }
142 b1 = n - 1; 142 }
143 nc[i][j] = b1 - b0 + 1; /* = B except at edges */ 143
144 for (b = b0; b <= b1; b++) 144 sum += exp(lp[i][j]);
145 if (c[b] == j+1) 145 }
146 nc[i][j]--; 146
147 147 /* normalise responsibilities and add duration logprior */
148 sum += exp(lp[i][j]); 148 logsumexp = log(sum);
149 } 149 for (j = 0; j < k; j++) {a
150 150 lp[i][j] -= logsumexp + lambda * nc[i][j];
151 /* normalise responsibilities and add duration logprior */ 151 }
152 logsumexp = log(sum); 152 }
153 for (j = 0; j < k; j++) 153
154 lp[i][j] -= logsumexp + lambda * nc[i][j]; 154 /* update the assignments now that we know the duration priors
155 } 155 based on the current assignments */
156 //print_array(lp, n, k); 156 for (i = 0; i < n; i++) {
157 /* 157 maxlp = lp[i][0];
158 for (i = 0; i < n; i++) 158 c[i] = 1;
159 { 159 for (j = 1; j < k; j++) {
160 for (j = 0; j < k; j++) 160 if (lp[i][j] > maxlp) {
161 mexPrintf("%d ", nc[i][j]); 161 maxlp = lp[i][j];
162 mexPrintf("\n"); 162 c[i] = j+1;
163 } 163 }
164 */ 164 }
165 165 }
166 166
167 /* update the assignments now that we know the duration priors 167 /* break if assignments haven't changed */
168 based on the current assignments */ 168 i = 0;
169 for (i = 0; i < n; i++) 169 while (i < n && oldc[i] == c[i]) {
170 { 170 i++;
171 maxlp = lp[i][0]; 171 }
172 c[i] = 1; 172 if (i == n) {
173 for (j = 1; j < k; j++) 173 break;
174 if (lp[i][j] > maxlp) 174 }
175 { 175
176 maxlp = lp[i][j]; 176 /* update reference histograms now we know new responsibilities */
177 c[i] = j+1; 177 for (j = 0; j < k; j++) {
178 } 178 for (b = 0; b < m; b++) {
179 } 179 cl[j][b] = 0;
180 180 for (i = 0; i < n; i++) {
181 /* break if assignments haven't changed */ 181 cl[j][b] += exp(lp[i][j]) * h[i*m+b];
182 i = 0; 182 }
183 while (i < n && oldc[i] == c[i]) 183 }
184 i++; 184
185 if (i == n) 185 sum = 0;
186 break; 186 for (i = 0; i < n; i++) {
187 187 sum += exp(lp[i][j]);
188 /* update reference histograms now we know new responsibilities */ 188 }
189 for (j = 0; j < k; j++) 189 for (b = 0; b < m; b++) {
190 { 190 cl[j][b] /= sum; /* normalise */
191 for (b = 0; b < m; b++) 191 }
192 { 192 }
193 cl[j][b] = 0; 193 }
194 for (i = 0; i < n; i++) 194 }
195 { 195
196 cl[j][b] += exp(lp[i][j]) * h[i*m+b]; 196 /* free memory */
197 } 197 for (i = 0; i < k; i++) {
198 } 198 free(cl[i]);
199 199 }
200 sum = 0; 200 free(cl);
201 for (i = 0; i < n; i++) 201 for (i = 0; i < n; i++) {
202 sum += exp(lp[i][j]); 202 free(nc[i]);
203 for (b = 0; b < m; b++) 203 }
204 cl[j][b] /= sum; /* normalise */ 204 free(nc);
205 } 205 for (i = 0; i < n; i++) {
206 206 free(lp[i]);
207 //print_array(cl, k, m); 207 }
208 //mexPrintf("\n\n"); 208 free(lp);
209 } 209 free(oldc);
210 }
211
212 /* free memory */
213 for (i = 0; i < k; i++)
214 free(cl[i]);
215 free(cl);
216 for (i = 0; i < n; i++)
217 free(nc[i]);
218 free(nc);
219 for (i = 0; i < n; i++)
220 free(lp[i]);
221 free(lp);
222 free(oldc);
223 } 210 }
224 211
225 212