discrime.c

General Hidden Markov Model Library 一个通用的隐马尔科夫模型的C代码库

      sum = 0.0;
      /* iterate over all classes and training sequences */
      for (m = 0; m < noC; m++) {
        sq = sqs[m];
        if (m == class)
          for (l = 0; l < sq->seq_number; l++)
            sum -=
              omega[class][l] * expect_a[class][l][+class][i * mo[class]->N +
                                                           j_id];
        else
          for (l = 0; l < sq->seq_number; l++)
            sum +=
              lfactor * omegati[m][l][class] * expect_a[m][l][class][i *
                                                                     mo
                                                                     [class]->
                                                                     N +
                                                                     j_id];
      }
      /* check for valid new parameter */
      a_old[j] = mo[class]->s[i].out_a[j];
      if (fabs (lagrangian) == 0.0)
        a_new[j] = a_old[j];
      else
        a_new[j] = sum / lagrangian;
    }

    /* update parameter */
    for (j = 0; j < mo[class]->s[i].out_states; j++) {
      mo[class]->s[i].out_a[j] = TRIM (a_old[j], a_new[j]);
      /*printf("update parameter A[%d] in state %d of model %d with: %5.3g",
         j, i, class, mo[class]->s[i].out_a[j]);
         printf(" (%5.3g) old value, %5.3g estimted\n", a_old[j], a_new[j]); */

      /* mirror out_a to corresponding in_a */
      j_id = mo[class]->s[i].out_id[j];
      for (g = 0; g < mo[class]->s[j_id].in_states; g++)
        if (i == mo[class]->s[j_id].in_id[g]) {
          mo[class]->s[j_id].in_a[g] = mo[class]->s[i].out_a[j];
          break;
        }
    }
  }

STOP:     /* Label STOP from ARRAY_[CM]ALLOC */
  m_free (a_old);
  m_free (a_new);
  return;
#undef CUR_PROC
}


/*----------------------------------------------------------------------------*/
static void discrime_update_b_closed (ghmm_dmodel ** mo, ghmm_dseq ** sqs, int noC,
                               int class, double lfactor,
                               double *****expect_b, long double **omega,
                               long double ***omegati)
{
#define CUR_PROC "discrime_update_b_closed"

  int i, h, l, m;

  int hist, size;

  double *b_old = NULL;
  double *b_new = NULL;

  double sum, lagrangian;

  ARRAY_CALLOC (b_old, mo[class]->M);
  ARRAY_CALLOC (b_new, mo[class]->M);

  /* itarate over all states of the k-th model */
  for (i = 0; i < mo[class]->N; i++) {
    if (mo[class]->s[i].fix)
      continue;

    size = ghmm_ipow (mo[class], mo[class]->M, mo[class]->order[i] + 1);
    for (hist = 0; hist < size; hist += mo[class]->M) {

      /* compute lagrangian multiplier */
      lagrangian = 0.0;
      for (h = hist; h < hist + mo[class]->M; h++) {

        /* iterate over all classes and training sequences */
        for (m = 0; m < noC; m++) {
          if (m == class)
            for (l = 0; l < sqs[m]->seq_number; l++)
              lagrangian -= omega[class][l] * expect_b[class][l][class][i][h];
          else
            for (l = 0; l < sqs[m]->seq_number; l++)
              lagrangian +=
                lfactor * omegati[m][l][class] * expect_b[m][l][class][i][h];
        }
      }

      for (h = hist; h < hist + mo[class]->M; h++) {
        /* iterate over all classes and training sequences */
        sum = 0.0;
        for (m = 0; m < noC; m++) {
          if (m == class)
            for (l = 0; l < sqs[m]->seq_number; l++)
              sum -= omega[class][l] * expect_b[class][l][class][i][h];
          else
            for (l = 0; l < sqs[m]->seq_number; l++)
              sum +=
                lfactor * omegati[m][l][class] * expect_b[m][l][class][i][h];
        }
        /* check for valid new parameters */
        b_old[h] = mo[class]->s[i].b[h];
        if (fabs (lagrangian) == 0.0)
          b_new[h] = b_old[h];
        else
          b_new[h] = sum / lagrangian;
      }

      /* update parameters */
      for (h = hist; h < hist + mo[class]->M; h++) {
        mo[class]->s[i].b[h] = TRIM (b_old[h], b_new[h]);
        /*printf("update parameter B[%d] in state %d of model %d with: %5.3g",
           h, i, class, mo[class]->s[i].b[h]);
           printf(" (%5.3g) old value, estimate %5.3g\n", b_old[h], b_new[h]); */
      }
    }
  }

STOP:     /* Label STOP from ARRAY_[CM]ALLOC */
  m_free (b_old);
  m_free (b_new);
  return;
#undef CUR_PROC
}


/*----------------------------------------------------------------------------*/
static void discrime_find_factor (ghmm_dmodel * mo, ghmm_dseq ** sqs, int noC, int k,
                           double lfactor, double ****expect_pi,
                           double ****expect_a, double *****expect_b,
                           long double **omega, long double ***omegati)
{
#define CUR_PROC "discrime_find_factor"

  int h, i, j, l, m;
  int size, hist, j_id;
  double self, other;
  ghmm_dseq *sq;

  /* itarate over all states of the k-th model */
  for (i = 0; i < mo->N; i++) {
    /* PI */
    /* iterate over all classes */
    self = other = 0.0;
    for (m = 0; m < noC; m++) {
      sq = sqs[m];
      /* if current class and class of training sequence match, add the first term */
      if (m == k)
        for (l = 0; l < sq->seq_number; l++)
          self += omega[k][l] * expect_pi[k][l][k][i];
      /* otherwise the second */
      else
        for (l = 0; l < sq->seq_number; l++)
          other += lfactor * omegati[m][l][k] * expect_pi[m][l][k][i];
    }
    if (self < other)
      lfactor *= self / other;
    /*printf("PI[%d]: %g - %g = %g \t %g\n", k, self, other, self-other, lfactor);*/

    /* A */
    for (j = 0; j < mo->s[i].out_states; j++) {
      j_id = mo->s[i].out_id[j];
      /* iterate over all classes and training sequences */
      self = other = 0.0;
      for (m = 0; m < noC; m++) {
        sq = sqs[m];
        if (m == k)
          for (l = 0; l < sq->seq_number; l++)
            self += omega[k][l] * expect_a[k][l][k][i * mo->N + j_id];
        else
          for (l = 0; l < sq->seq_number; l++)
            other +=
              lfactor * omegati[m][l][k] * expect_a[m][l][k][i * mo->N +
                                                             j_id];
      }
      if (self < other)
        lfactor *= self / other;
      /*printf(" A[%d]: %g - %g = %g \t %g\n", k, self, other, self-other, lfactor);*/
    }

    /* B */
    if (mo->s[i].fix)
      continue;

    size = ghmm_ipow(mo, mo->M, mo->order[i] + 1);
    for (hist = 0; hist < size; hist += mo->M) {
      for (h = hist; h < hist + mo->M; h++) {
        self = other = 0.0;
        /* iterate over all classes and training sequences */
        for (m = 0; m < noC; m++) {
          if (m == k)
            for (l = 0; l < sqs[m]->seq_number; l++)
              self += omega[k][l] * expect_b[k][l][k][i][h];
          else
            for (l = 0; l < sqs[m]->seq_number; l++)
              other += lfactor * omegati[m][l][k] * expect_b[m][l][k][i][h];
        }
        if (self < other)
          lfactor *= self / other;
        /*printf(" B[%d]: %g - %g = %g \t %g\n", k, self, other, self-other, lfactor);*/
      }
    }
  }

  return;

#undef CUR_PROC
}



/*----------------------------------------------------------------------------*/
static int discrime_onestep (ghmm_dmodel ** mo, ghmm_dseq ** sqs, int noC, int grad,
                      int class)
{
#define CUR_PROC "discrime_onestep"

  int j, l, retval = -1;

  /* expected use of parameter (for partial derivatives) */
  double *****expect_b, ****expect_a, ****expect_pi;

  /* matrix of log probabilities */
  double ***log_p;

  /* matrix of outer derivatives */
  long double **omega, ***omegati;

  long double psum = 0, nsum = 0;
  double lfactor;

  if (-1 == discrime_galloc (mo, sqs, noC, &expect_b, &expect_a,
                             &expect_pi, &omega, &omegati, &log_p)) {
    printf ("Allocation Error.\n");
    return -1;
  }

  if (-1 ==
      discrime_precompute (mo, sqs, noC, expect_b, expect_a, expect_pi,
                           log_p)) {
    printf ("precompute Error.\n");
    goto FREE;
  }

  if (-1 == discrime_calculate_omega (mo, sqs, noC, omega, omegati, log_p)) {
    printf ("omega Error.\n");
    goto FREE;
  }

  if (grad) {
    /* updateing parameters */
    discrime_update_pi_gradient (mo, sqs, noC, class, expect_pi, omega,
                                 omegati);
    discrime_update_a_gradient (mo, sqs, noC, class, expect_a, omega,
                                omegati);
    discrime_update_b_gradient (mo, sqs, noC, class, expect_b, omega,
                                omegati);
  }
  else {
    /* calculate a pleaseant lfactor */
    for (j = 0; j < noC; j++) {
      for (l = 0; l < sqs[j]->seq_number; l++) {
        if (j == class)
          psum += omega[class][l];
        else
          nsum += omegati[j][l][class];
      }
    }
    if (fabs (nsum) > 1e-200 && psum < nsum)
      lfactor = (psum / nsum);
    else
      lfactor = 1.0;

    /* determing maximum lfactor */
    discrime_find_factor (mo[class], sqs, noC, class, lfactor, expect_pi,
                          expect_a, expect_b, omega, omegati);
    lfactor *= .99;
    printf ("Calculated L: %g\n", lfactor);

    /* updating parameters */
    discrime_update_pi_closed (mo, sqs, noC, class, lfactor, expect_pi, omega,
                               omegati);
    discrime_update_a_closed (mo, sqs, noC, class, lfactor, expect_a, omega,
                              omegati);
    discrime_update_b_closed (mo, sqs, noC, class, lfactor, expect_b, omega,
                              omegati);
  }

  retval = 0;
FREE:
  discrime_gfree (mo, sqs, noC, expect_b, expect_a, expect_pi, omega,
                  omegati, log_p);
  return retval;
#undef CUR_PROC
}


/*----------------------------------------------------------------------------*/
int ghmm_dmodel_label_discriminative(ghmm_dmodel** mo, ghmm_dseq** sqs, int noC, int max_steps, 
		    int gradient)
{
#define CUR_PROC "ghmm_dmodel_label_discriminative"

  double last_perf, cur_perf;
  int retval=-1, last_cer, cur_cer;

  double lambda=0.0;

  double noiselevel = 0.0667;

  int fp, fn, totalseqs = 0, totalsyms = 0;

  int *falseP=NULL, *falseN=NULL;

  double * prior_backup=NULL;

  int i, k, step;

  ghmm_dmodel * last;

  ARRAY_CALLOC (falseP, noC);
  ARRAY_CALLOC (falseN, noC);
  ARRAY_CALLOC (prior_backup, noC);

  for (i = 0; i < noC; i++) {
    totalseqs += sqs[i]->seq_number;
    for (k = 0; k < sqs[i]->seq_number; k++)
      totalsyms += sqs[i]->seq_len[k];
  }
  
  /* setting priors from number of training sequences and
     remember the old values */
  for (i=0; i<noC; i++) {
    prior_backup[i] = mo[i]->prior;
    mo[i]->prior = (double)sqs[i]->seq_number / totalseqs;
    printf("original prior: %g \t new prior %g\n", prior_backup[i], mo[i]->prior);
  }

  last_perf = ghmm_dmodel_label_discrim_perf (mo, sqs, noC);
  cur_perf = last_perf;

  discrime_print_statistics (mo, sqs, noC, falseP, falseN);
  fp = fn = 0;
  for (i = 0; i < noC; i++) {
    printf ("Model %d likelihood: %g, \t false positives: %d\n", i,
            ghmm_dmodel_likelihood (mo[i], sqs[i]), falseP[i]);
    fn += falseN[i];
    fp += falseP[i];
  }
  printf
    ("\n%d false positives and %d false negatives after ML-initialisation; Performance: %g.\n",
     fp, fn, last_perf);

  last_cer = cur_cer = fp;

  for (k = 0; k < noC; k++) {
    last = NULL;
    step = 0;
    if (gradient)
      lambda = .3;

    do {
      if (last)
        ghmm_dmodel_free (&last);

      /* save a copy of the currently trained model */
      last = ghmm_dmodel_copy (mo[k]);
      last_cer = cur_cer;
      last_perf = cur_perf;

      noiselevel /= 1.8;
      discrime_lambda = lambda / totalsyms;

      printf
        ("==============================================================\n");
      printf
        ("Optimising class %d, current step %d, lambda: %g  noise: %g, gradient: %d\n",
         k, step, discrime_lambda, noiselevel, gradient);

/*       if (gradient)   */
/* 	ghmm_dmodel_add_noise(mo[k], noiselevel, 0); */

      discrime_onestep (mo, sqs, noC, gradient, k);

      cur_perf = ghmm_dmodel_label_discrim_perf (mo, sqs, noC);
      discrime_print_statistics (mo, sqs, noC, falseP, falseN);

      fp = fn = 0;
      for (i = 0; i < noC; i++) {
        printf ("Model %d likelihood: %g, \t false positives: %d\n", i,
                ghmm_dmodel_likelihood (mo[i], sqs[i]), falseP[i]);
        fn += falseN[i];
        fp += falseP[i];
      }
      cur_cer = fp;
      printf ("MAP=%12g -> training -> MAP=%12g", last_perf, cur_perf);
      printf ("  %d false positives, %d false negatives\n", fp, fn);
      printf
        ("==============================================================\n");

    } while ((last_perf < cur_perf || cur_cer < last_cer) && (step++ < max_steps));

    mo[k] = ghmm_dmodel_copy (last);
    ghmm_dmodel_free (&last);
    cur_perf = last_perf;
    cur_cer = last_cer;
  }

  /* resetting priors to old values */
  for (i = 0; i < noC; i++)
    mo[i]->prior = prior_backup[i];
  retval = 0;
STOP:     /* Label STOP from ARRAY_[CM]ALLOC */
  m_free (prior_backup);
  m_free (falseP);
  m_free (falseN);

  return retval;

#undef CUR_PROC
}