static int tagger_lognorm(crfsuite_tagger_t* tagger, floatval_t *ptr_norm)
{
crf1dt_t* crf1dt = (crf1dt_t*)tagger->internal;
crf1dt_set_level(crf1dt, LEVEL_ALPHABETA);
*ptr_norm = crf1dc_lognorm(crf1dt->ctx);
return 0;
}
void crf1dc_debug_context(FILE *fp)
{
int y1, y2, y3;
floatval_t norm = 0;
const int L = 3;
const int T = 3;
crf1d_context_t *ctx = crf1dc_new(CTXF_MARGINALS, L, T);
floatval_t *trans = NULL, *state = NULL;
floatval_t scores[3][3][3];
int labels[3];
/* Initialize the state scores. */
state = EXP_STATE_SCORE(ctx, 0);
state[0] = .4; state[1] = .5; state[2] = .1;
state = EXP_STATE_SCORE(ctx, 1);
state[0] = .4; state[1] = .1; state[2] = .5;
state = EXP_STATE_SCORE(ctx, 2);
state[0] = .4; state[1] = .1; state[2] = .5;
/* Initialize the transition scores. */
trans = EXP_TRANS_SCORE(ctx, 0);
trans[0] = .3; trans[1] = .1; trans[2] = .4;
trans = EXP_TRANS_SCORE(ctx, 1);
trans[0] = .6; trans[1] = .2; trans[2] = .1;
trans = EXP_TRANS_SCORE(ctx, 2);
trans[0] = .5; trans[1] = .2; trans[2] = .1;
ctx->num_items = ctx->cap_items;
crf1dc_alpha_score(ctx);
crf1dc_beta_score(ctx);
/* Compute the score of every label sequence. */
for (y1 = 0;y1 < L;++y1) {
floatval_t s1 = EXP_STATE_SCORE(ctx, 0)[y1];
for (y2 = 0;y2 < L;++y2) {
floatval_t s2 = s1;
s2 *= EXP_TRANS_SCORE(ctx, y1)[y2];
s2 *= EXP_STATE_SCORE(ctx, 1)[y2];
for (y3 = 0;y3 < L;++y3) {
floatval_t s3 = s2;
s3 *= EXP_TRANS_SCORE(ctx, y2)[y3];
s3 *= EXP_STATE_SCORE(ctx, 2)[y3];
scores[y1][y2][y3] = s3;
}
}
}
/* Compute the partition factor. */
norm = 0.;
for (y1 = 0;y1 < L;++y1) {
for (y2 = 0;y2 < L;++y2) {
for (y3 = 0;y3 < L;++y3) {
norm += scores[y1][y2][y3];
}
}
}
/* Check the partition factor. */
fprintf(fp, "Check for the partition factor... ");
check_values(fp, exp(ctx->log_norm), norm);
/* Compute the sequence probabilities. */
for (y1 = 0;y1 < L;++y1) {
for (y2 = 0;y2 < L;++y2) {
for (y3 = 0;y3 < L;++y3) {
floatval_t logp;
labels[0] = y1;
labels[1] = y2;
labels[2] = y3;
logp = crf1dc_score(ctx, labels) - crf1dc_lognorm(ctx);
fprintf(fp, "Check for the sequence %d-%d-%d... ", y1, y2, y3);
check_values(fp, exp(logp), scores[y1][y2][y3] / norm);
}
}
}
/* Compute the marginal probability at t=0 */
for (y1 = 0;y1 < L;++y1) {
floatval_t a, b, c, s = 0.;
for (y2 = 0;y2 < L;++y2) {
for (y3 = 0;y3 < L;++y3) {
s += scores[y1][y2][y3];
}
}
a = ALPHA_SCORE(ctx, 0)[y1];
b = BETA_SCORE(ctx, 0)[y1];
c = 1. / ctx->scale_factor[0];
fprintf(fp, "Check for the marginal probability (0,%d)... ", y1);
check_values(fp, a * b * c, s / norm);
}
/* Compute the marginal probability at t=1 */
for (y2 = 0;y2 < L;++y2) {
floatval_t a, b, c, s = 0.;
for (y1 = 0;y1 < L;++y1) {
for (y3 = 0;y3 < L;++y3) {
s += scores[y1][y2][y3];
}
}
a = ALPHA_SCORE(ctx, 1)[y2];
b = BETA_SCORE(ctx, 1)[y2];
c = 1. / ctx->scale_factor[1];
fprintf(fp, "Check for the marginal probability (1,%d)... ", y2);
check_values(fp, a * b * c, s / norm);
}
/* Compute the marginal probability at t=2 */
for (y3 = 0;y3 < L;++y3) {
floatval_t a, b, c, s = 0.;
for (y1 = 0;y1 < L;++y1) {
for (y2 = 0;y2 < L;++y2) {
s += scores[y1][y2][y3];
}
}
a = ALPHA_SCORE(ctx, 2)[y3];
b = BETA_SCORE(ctx, 2)[y3];
c = 1. / ctx->scale_factor[2];
fprintf(fp, "Check for the marginal probability (2,%d)... ", y3);
check_values(fp, a * b * c, s / norm);
}
/* Compute the marginal probabilities of transitions. */
for (y1 = 0;y1 < L;++y1) {
for (y2 = 0;y2 < L;++y2) {
floatval_t a, b, s, t, p = 0.;
for (y3 = 0;y3 < L;++y3) {
p += scores[y1][y2][y3];
}
a = ALPHA_SCORE(ctx, 0)[y1];
b = BETA_SCORE(ctx, 1)[y2];
s = EXP_STATE_SCORE(ctx, 1)[y2];
t = EXP_TRANS_SCORE(ctx, y1)[y2];
fprintf(fp, "Check for the marginal probability (0,%d)-(1,%d)... ", y1, y2);
check_values(fp, a * t * s * b, p / norm);
}
}
for (y2 = 0;y2 < L;++y2) {
for (y3 = 0;y3 < L;++y3) {
floatval_t a, b, s, t, p = 0.;
for (y1 = 0;y1 < L;++y1) {
p += scores[y1][y2][y3];
}
a = ALPHA_SCORE(ctx, 1)[y2];
b = BETA_SCORE(ctx, 2)[y3];
s = EXP_STATE_SCORE(ctx, 2)[y3];
t = EXP_TRANS_SCORE(ctx, y2)[y3];
fprintf(fp, "Check for the marginal probability (1,%d)-(2,%d)... ", y2, y3);
check_values(fp, a * t * s * b, p / norm);
}
}
}
