File: svm_fisher.c

package info (click to toggle)
bow 19991122-4
  • links: PTS
  • area: main
  • in suites: woody
  • size: 2,544 kB
  • ctags: 2,987
  • sloc: ansic: 38,660; lisp: 1,072; makefile: 594; perl: 492; yacc: 149; sh: 91
file content (241 lines) | stat: -rw-r--r-- 6,544 bytes parent folder | download | duplicates (3)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
/* Copyright (C) 1999 Greg Schohn - gcs@jprc.com */

/* ******************** svm_fisher.c ******************* 
 * An implementation of the naive bayes fisher kernel.
 * This is still a work very much in progress, ridden with
 * numerical precision problems.
 */

#include <bow/svm.h>

static bow_barrel *rainbow_nb_barrel;
static double fisher_norm0;

static int total_num_words_occurences;
static double *dIi;  /* approximate diagonal inverse information matrix for classes */
static double *dIij; /* approximate diagonal inverse information matrix for class-words */

typedef struct _NPair {
  int N;
  int index;
} NPair;

void svm_set_fisher_barrel_weights(bow_wv **docs, int ndocs) {
  int i,j;
  total_num_words_occurences = 0;
  for (i=0; i<ndocs; i++) {
    docs[i]->normalizer = 1.0;
    for (j=0; j<docs[i]->num_entries; j++) {
      docs[i]->entry[j].weight = (float) docs[i]->entry[j].count;
      total_num_words_occurences += docs[i]->entry[j].count;
    }
  }
}

double svm_kernel_fisher(bow_wv *wv1, bow_wv *wv2) {
  bow_cdoc  *cd;
  int        max_entries;   /* max number of elements that can be in both */
  int        nclasses;
  int        nwords;
  NPair     *Nvector;
  double     rval;
  double     tmp;
  bow_we    *v1, *v2;

  double t2, pci;
  int i, j, k;

  nwords = total_num_words_occurences;
  nclasses = bow_barrel_num_classes(rainbow_nb_barrel);
  max_entries = MIN(wv1->num_entries, wv2->num_entries);

  Nvector = (NPair *) alloca(max_entries*sizeof(NPair));

  v1 = wv1->entry;
  v2 = wv2->entry;

  /* compute the N(wi,X1)*N(wi,X2) vector */
  for (i=j=k=0; (i<max_entries) && (j<max_entries); ) {
    if(v1[i].wi > v2[j].wi) {
      j++;
    }
    else if (v1[i].wi < v2[j].wi) {
      i++;
    }
    else {
      Nvector[k].index = v1[i].wi;
      Nvector[k].N = (v1[i].count)*(v2[j].count);
      k++;
      i++;
      j++;
    }
  }

  max_entries = k;
  rval = 0.0;

  /* now we have all of the P(X*|C*) terms - in ascending order with
   * regards to class index */
  for (i=0; i<nclasses; i++) {
    for (j=0, tmp=0; j<max_entries; j++) {
      t2 = bow_naivebayes_pr_wi_ci(rainbow_nb_barrel, Nvector[j].index, i, -1, 0, 0, NULL, NULL);
      t2 = t2*t2;
      tmp += Nvector[j].N / (dIij[i*nclasses + Nvector[j].index] * t2);
      assert(finite(tmp));
    }

    /* compute P(x[12]|ci)/P(x[12]) */
    {
      double p_w;
      bow_we *v;
      bow_wv *w;
      int k,h,n;
      
      t2 = fisher_norm0;

      for (w=wv1, n=0; n<2; n++, w=wv2) {
	v = w->entry;
	for (h=0; h<w->num_entries; h++) {
	  double sum, t;
	  bow_dv *dv = bow_wi2dvf_dv(rainbow_nb_barrel->wi2dvf, v[h].wi);
	  assert(dv);
	  /* sum up the number of words that appeared in all of the classes */
	  for (k=0, sum=0.0; k<dv->length; k++) {
	    sum += dv->entry[k].weight;
	  }

	  p_w = log(sum/nwords)*v[h].weight;
	  t = (double) bow_naivebayes_pr_wi_ci (rainbow_nb_barrel, v[h].wi, i, -1,
						       0.0, 0.0, NULL, NULL);
	  t = log(t) * v[h].weight;
	  t2 += t - p_w;
	  assert(finite(t2));
	  //printf("P(w%d|c%d)^%f, p_w^%f\n", v[h].wi, k, t, p_w);
	}
      }
    }

    cd = GET_CDOC_ARRAY_EL(rainbow_nb_barrel,i);
    pci = cd->prior;
    rval += exp(t2 + log(dIi[i] + (tmp*pci*pci)));
    assert(finite(rval));
  }

  //rval = exp(rval);

  printf("kernel=%f\n",rval);
  return rval;
}

void svm_setup_fisher(bow_barrel *old_barrel, bow_wv **docs, int nclasses, int ndocs) {
  double *PXk, PX;
  int     i,j,k;

  rainbow_method *tmp = old_barrel->method;

  old_barrel->method = &bow_method_naivebayes;
  rainbow_nb_barrel = bow_barrel_new_vpc_merge_then_weight (old_barrel);
  old_barrel->method = tmp;

    /* set some global variables that naivebayes.c uses */
  naivebayes_score_returns_doc_pr = 1;
  naivebayes_score_unsorted = 1;

  fprintf(stderr, "Finding maximum kernel value for normalizing\n");

  i = bow_num_words()*nclasses;
  dIi = (double *) malloc(sizeof(double)*nclasses);
  PXk = (double *) malloc(sizeof(double)*nclasses);
  dIij = (double *) malloc(sizeof(double)*i);

  for (j=0; j<i; j++) {
    dIij[j] = 0.0;
  }

  for (j=0; j<nclasses; j++) {
    dIi[j] = 0.0;
  }

  for (i=0; i<ndocs; i++) {
    double max_lpr;
    bow_score *scores = malloc(sizeof(bow_score)*nclasses);

    /* compute the P(X|class) * P(class) terms (since they're used so often) */
    PX = 0.0;
    /* NOTE: with the ...returns_doc_pr variable set, the scores are not probabilities,
     * but instead log probabilities */
    bow_naivebayes_score(rainbow_nb_barrel, docs[i], scores, nclasses, -1);

    max_lpr = scores[0].weight;
    for (k=1; k<nclasses; k++) {
      if (scores[k].weight > max_lpr) 
	max_lpr = scores[k].weight;
    }

    for (k=0; k<nclasses; k++) {
      bow_cdoc *cd = GET_CDOC_ARRAY_EL(rainbow_nb_barrel,k);
      /* the max lpr over everything is the same as multiplying both the
       * denominator & the numerator by some large constant */
      PXk[k] = cd->prior * exp(scores[k].weight - max_lpr);
      /* hacky-hacky-hacky-smoothing */
#define THRESH 1e-1
      if (PXk[k] < THRESH) {
	PXk[k] = THRESH;
	printf("underflow on P(X%d|C%d) - setting to small val\n",i,k);
	fflush(stdout);
      }
      PX += PXk[k];
      assert(finite(PXk[k]) && PXk[k] != 0.0);
    }
    free(scores);


    /* compute term for Iij - d/d-theta_ij * log(P(X|theta)) */
    for (j=0; j<docs[i]->num_entries; j++) {
      for (k=0; k<nclasses; k++) {
	double tmp;

	tmp = bow_naivebayes_pr_wi_ci (rainbow_nb_barrel, docs[i]->entry[j].wi,
				       k, -1, 0.0, 0.0, NULL, NULL);

	dIij[k*nclasses + docs[i]->entry[j].wi] += 
	  ((((docs[i]->entry[j].count * PXk[k]) /tmp) /PX) * 
	   (((docs[i]->entry[j].count * PXk[k]) /tmp) /PX));
      }
    }

    /* compute term for Ii - d/d-theta_i * log(P(X|theta)) */
    for (k=0; k<nclasses; k++) {
      /* M is in both of these terms, so we don't need to worry about it... */
      dIi[k] += (PXk[k]/ PX)*(PXk[k]/ PX);
      assert(finite(dIi[k]));
    }

    if (!(i % 100)) {
      fprintf(stderr, "\r%f%%", (float) ((double)i)/((double)ndocs)*100.0);
    }
  }
  free(PXk);

    /* now invert the values class values */
  for (i=0; i<nclasses; i++) {
    dIi[i] = 1/dIi[i];
  }

  fprintf(stderr,"%f%%\n",(float)100.0);

    /* set "fisher normalizer" */
  {
    double max = -1;     
    int from=0;
    fisher_norm0 = 0;  /* keep in mind, this is log(scalar) */
    for (i=0; i<ndocs; i++) {
      double tmp = svm_kernel_fisher(docs[i],docs[i]);
      if (max < tmp) {
	max = tmp;
	from = i;
      }
    }
    fisher_norm0 = log(1/max);
  }
}