diff --git a/test/source.c b/test/source.c
index f0094b3..644923c 100644
--- a/test/source.c
+++ b/test/source.c
@@ -58,1516 +58,3 @@ static inline int _ccv_run_bbf_feature(ccv_bbf_feature_t *feature, int *step, un
 #undef nf_at
   return 1;
 }
-
-static int _ccv_read_bbf_stage_classifier(const char *file, ccv_bbf_stage_classifier_t *classifier)
-{
-  FILE *r = fopen(file, "r");
-  if (r == 0)
-    return -1;
-  int stat = 0;
-  stat |= fscanf(r, "%d", &classifier->count);
-  union {
-    float fl;
-    int i;
-  } fli;
-  stat |= fscanf(r, "%d", &fli.i);
-  classifier->threshold = fli.fl;
-  classifier->feature = (ccv_bbf_feature_t *)ccmalloc(classifier->count * sizeof(ccv_bbf_feature_t));
-  classifier->alpha = (float *)ccmalloc(classifier->count * 2 * sizeof(float));
-  int i, j;
-  for (i = 0; i < classifier->count; i++)
-  {
-    stat |= fscanf(r, "%d", &classifier->feature[i].size);
-    for (j = 0; j < classifier->feature[i].size; j++)
-    {
-      stat |= fscanf(r, "%d %d %d", &classifier->feature[i].px[j], &classifier->feature[i].py[j], &classifier->feature[i].pz[j]);
-      stat |= fscanf(r, "%d %d %d", &classifier->feature[i].nx[j], &classifier->feature[i].ny[j], &classifier->feature[i].nz[j]);
-    }
-    union {
-      float fl;
-      int i;
-    } flia, flib;
-    stat |= fscanf(r, "%d %d", &flia.i, &flib.i);
-    classifier->alpha[i * 2] = flia.fl;
-    classifier->alpha[i * 2 + 1] = flib.fl;
-  }
-  fclose(r);
-  return 0;
-}
-
-#ifdef HAVE_GSL
-
-static unsigned int _ccv_bbf_time_measure()
-{
-  struct timeval tv;
-  gettimeofday(&tv, 0);
-  return tv.tv_sec * 1000000 + tv.tv_usec;
-}
-
-#define less_than(a, b, aux) ((a) < (b))
-CCV_IMPLEMENT_QSORT(_ccv_sort_32f, float, less_than)
-#undef less_than
-
-static void _ccv_bbf_eval_data(ccv_bbf_stage_classifier_t *classifier, unsigned char **posdata, int posnum, unsigned char **negdata, int negnum, ccv_size_t size, float *peval, float *neval)
-{
-  int i, j;
-  int steps[] = {_ccv_width_padding(size.width),
-                 _ccv_width_padding(size.width >> 1),
-                 _ccv_width_padding(size.width >> 2)};
-  int isizs0 = steps[0] * size.height;
-  int isizs01 = isizs0 + steps[1] * (size.height >> 1);
-  for (i = 0; i < posnum; i++)
-  {
-    unsigned char *u8[] = {posdata[i], posdata[i] + isizs0, posdata[i] + isizs01};
-    float sum = 0;
-    float *alpha = classifier->alpha;
-    ccv_bbf_feature_t *feature = classifier->feature;
-    for (j = 0; j < classifier->count; ++j, alpha += 2, ++feature)
-      sum += alpha[_ccv_run_bbf_feature(feature, steps, u8)];
-    peval[i] = sum;
-  }
-  for (i = 0; i < negnum; i++)
-  {
-    unsigned char *u8[] = {negdata[i], negdata[i] + isizs0, negdata[i] + isizs01};
-    float sum = 0;
-    float *alpha = classifier->alpha;
-    ccv_bbf_feature_t *feature = classifier->feature;
-    for (j = 0; j < classifier->count; ++j, alpha += 2, ++feature)
-      sum += alpha[_ccv_run_bbf_feature(feature, steps, u8)];
-    neval[i] = sum;
-  }
-}
-
-static int _ccv_prune_positive_data(ccv_bbf_classifier_cascade_t *cascade, unsigned char **posdata, int posnum, ccv_size_t size)
-{
-  float *peval = (float *)ccmalloc(posnum * sizeof(float));
-  int i, j, k, rpos = posnum;
-  for (i = 0; i < cascade->count; i++)
-  {
-    _ccv_bbf_eval_data(cascade->stage_classifier + i, posdata, rpos, 0, 0, size, peval, 0);
-    k = 0;
-    for (j = 0; j < rpos; j++)
-      if (peval[j] >= cascade->stage_classifier[i].threshold)
-      {
-        posdata[k] = posdata[j];
-        ++k;
-      }
-      else
-      {
-        ccfree(posdata[j]);
-      }
-    rpos = k;
-  }
-  ccfree(peval);
-  return rpos;
-}
-
-static int _ccv_prepare_background_data(ccv_bbf_classifier_cascade_t *cascade, char **bgfiles, int bgnum, unsigned char **negdata, int negnum)
-{
-  int t, i, j, k, q;
-  int negperbg;
-  int negtotal = 0;
-  int steps[] = {_ccv_width_padding(cascade->size.width),
-                 _ccv_width_padding(cascade->size.width >> 1),
-                 _ccv_width_padding(cascade->size.width >> 2)};
-  int isizs0 = steps[0] * cascade->size.height;
-  int isizs1 = steps[1] * (cascade->size.height >> 1);
-  int isizs2 = steps[2] * (cascade->size.height >> 2);
-  int *idcheck = (int *)ccmalloc(negnum * sizeof(int));
-
-  gsl_rng_env_setup();
-
-  gsl_rng *rng = gsl_rng_alloc(gsl_rng_default);
-  gsl_rng_set(rng, (unsigned long int)idcheck);
-
-  ccv_size_t imgsz = cascade->size;
-  int rneg = negtotal;
-  for (t = 0; negtotal < negnum; t++)
-  {
-    PRINT(CCV_CLI_INFO, "preparing negative data ...  0%%");
-    for (i = 0; i < bgnum; i++)
-    {
-      negperbg = (t < 2) ? (negnum - negtotal) / (bgnum - i) + 1 : negnum - negtotal;
-      ccv_dense_matrix_t *image = 0;
-      ccv_read(bgfiles[i], &image, CCV_IO_GRAY | CCV_IO_ANY_FILE);
-      assert((image->type & CCV_C1) && (image->type & CCV_8U));
-      if (image == 0)
-      {
-        PRINT(CCV_CLI_ERROR, "\n%s file corrupted\n", bgfiles[i]);
-        continue;
-      }
-      if (t % 2 != 0)
-        ccv_flip(image, 0, 0, CCV_FLIP_X);
-      if (t % 4 >= 2)
-        ccv_flip(image, 0, 0, CCV_FLIP_Y);
-      ccv_bbf_param_t params = {.interval = 3, .min_neighbors = 0, .accurate = 1, .flags = 0, .size = cascade->size};
-      ccv_array_t *detected = ccv_bbf_detect_objects(image, &cascade, 1, params);
-      memset(idcheck, 0, ccv_min(detected->rnum, negperbg) * sizeof(int));
-      for (j = 0; j < ccv_min(detected->rnum, negperbg); j++)
-      {
-        int r = gsl_rng_uniform_int(rng, detected->rnum);
-        int flag = 1;
-        ccv_rect_t *rect = (ccv_rect_t *)ccv_array_get(detected, r);
-        while (flag)
-        {
-          flag = 0;
-          for (k = 0; k < j; k++)
-            if (r == idcheck[k])
-            {
-              flag = 1;
-              r = gsl_rng_uniform_int(rng, detected->rnum);
-              break;
-            }
-          rect = (ccv_rect_t *)ccv_array_get(detected, r);
-          if ((rect->x < 0) || (rect->y < 0) || (rect->width + rect->x > image->cols) || (rect->height + rect->y > image->rows))
-          {
-            flag = 1;
-            r = gsl_rng_uniform_int(rng, detected->rnum);
-          }
-        }
-        idcheck[j] = r;
-        ccv_dense_matrix_t *temp = 0;
-        ccv_dense_matrix_t *imgs0 = 0;
-        ccv_dense_matrix_t *imgs1 = 0;
-        ccv_dense_matrix_t *imgs2 = 0;
-        ccv_slice(image, (ccv_matrix_t **)&temp, 0, rect->y, rect->x, rect->height, rect->width);
-        ccv_resample(temp, &imgs0, 0, imgsz.height, imgsz.width, CCV_INTER_AREA);
-        assert(imgs0->step == steps[0]);
-        ccv_matrix_free(temp);
-        ccv_sample_down(imgs0, &imgs1, 0, 0, 0);
-        assert(imgs1->step == steps[1]);
-        ccv_sample_down(imgs1, &imgs2, 0, 0, 0);
-        assert(imgs2->step == steps[2]);
-
-        negdata[negtotal] = (unsigned char *)ccmalloc(isizs0 + isizs1 + isizs2);
-        unsigned char *u8s0 = negdata[negtotal];
-        unsigned char *u8s1 = negdata[negtotal] + isizs0;
-        unsigned char *u8s2 = negdata[negtotal] + isizs0 + isizs1;
-        unsigned char *u8[] = {u8s0, u8s1, u8s2};
-        memcpy(u8s0, imgs0->data.u8, imgs0->rows * imgs0->step);
-        ccv_matrix_free(imgs0);
-        memcpy(u8s1, imgs1->data.u8, imgs1->rows * imgs1->step);
-        ccv_matrix_free(imgs1);
-        memcpy(u8s2, imgs2->data.u8, imgs2->rows * imgs2->step);
-        ccv_matrix_free(imgs2);
-
-        flag = 1;
-        ccv_bbf_stage_classifier_t *classifier = cascade->stage_classifier;
-        for (k = 0; k < cascade->count; ++k, ++classifier)
-        {
-          float sum = 0;
-          float *alpha = classifier->alpha;
-          ccv_bbf_feature_t *feature = classifier->feature;
-          for (q = 0; q < classifier->count; ++q, alpha += 2, ++feature)
-            sum += alpha[_ccv_run_bbf_feature(feature, steps, u8)];
-          if (sum < classifier->threshold)
-          {
-            flag = 0;
-            break;
-          }
-        }
-        if (!flag)
-          ccfree(negdata[negtotal]);
-        else
-        {
-          ++negtotal;
-          if (negtotal >= negnum)
-            break;
-        }
-      }
-      ccv_array_free(detected);
-      ccv_matrix_free(image);
-      ccv_drain_cache();
-      PRINT(CCV_CLI_INFO, "\rpreparing negative data ... %2d%%", 100 * negtotal / negnum);
-      fflush(0);
-      if (negtotal >= negnum)
-        break;
-    }
-    if (rneg == negtotal)
-      break;
-    rneg = negtotal;
-    PRINT(CCV_CLI_INFO, "\nentering additional round %d\n", t + 1);
-  }
-  gsl_rng_free(rng);
-  ccfree(idcheck);
-  ccv_drain_cache();
-  PRINT(CCV_CLI_INFO, "\n");
-  return negtotal;
-}
-
-static void _ccv_prepare_positive_data(ccv_dense_matrix_t **posimg, unsigned char **posdata, ccv_size_t size, int posnum)
-{
-  PRINT(CCV_CLI_INFO, "preparing positive data ...  0%%");
-  int i;
-  for (i = 0; i < posnum; i++)
-  {
-    ccv_dense_matrix_t *imgs0 = posimg[i];
-    ccv_dense_matrix_t *imgs1 = 0;
-    ccv_dense_matrix_t *imgs2 = 0;
-    assert((imgs0->type & CCV_C1) && (imgs0->type & CCV_8U) && imgs0->rows == size.height && imgs0->cols == size.width);
-    ccv_sample_down(imgs0, &imgs1, 0, 0, 0);
-    ccv_sample_down(imgs1, &imgs2, 0, 0, 0);
-    int isizs0 = imgs0->rows * imgs0->step;
-    int isizs1 = imgs1->rows * imgs1->step;
-    int isizs2 = imgs2->rows * imgs2->step;
-
-    posdata[i] = (unsigned char *)ccmalloc(isizs0 + isizs1 + isizs2);
-    memcpy(posdata[i], imgs0->data.u8, isizs0);
-    memcpy(posdata[i] + isizs0, imgs1->data.u8, isizs1);
-    memcpy(posdata[i] + isizs0 + isizs1, imgs2->data.u8, isizs2);
-
-    PRINT(CCV_CLI_INFO, "\rpreparing positive data ... %2d%%", 100 * (i + 1) / posnum);
-    fflush(0);
-
-    ccv_matrix_free(imgs1);
-    ccv_matrix_free(imgs2);
-  }
-  ccv_drain_cache();
-  PRINT(CCV_CLI_INFO, "\n");
-}
-
-typedef struct
-{
-  double fitness;
-  int pk, nk;
-  int age;
-  double error;
-  ccv_bbf_feature_t feature;
-} ccv_bbf_gene_t;
-
-static inline void _ccv_bbf_genetic_fitness(ccv_bbf_gene_t *gene)
-{
-  gene->fitness = (1 - gene->error) * exp(-0.01 * gene->age) * exp((gene->pk + gene->nk) * log(1.015));
-}
-
-static inline int _ccv_bbf_exist_gene_feature(ccv_bbf_gene_t *gene, int x, int y, int z)
-{
-  int i;
-  for (i = 0; i < gene->pk; i++)
-    if (z == gene->feature.pz[i] && x == gene->feature.px[i] && y == gene->feature.py[i])
-      return 1;
-  for (i = 0; i < gene->nk; i++)
-    if (z == gene->feature.nz[i] && x == gene->feature.nx[i] && y == gene->feature.ny[i])
-      return 1;
-  return 0;
-}
-
-static inline void _ccv_bbf_randomize_gene(gsl_rng *rng, ccv_bbf_gene_t *gene, int *rows, int *cols)
-{
-  int i;
-  do
-  {
-    gene->pk = gsl_rng_uniform_int(rng, CCV_BBF_POINT_MAX - 1) + 1;
-    gene->nk = gsl_rng_uniform_int(rng, CCV_BBF_POINT_MAX - 1) + 1;
-  } while (gene->pk + gene->nk < CCV_BBF_POINT_MIN); /* a hard restriction of at least 3 points have to be examed */
-  gene->feature.size = ccv_max(gene->pk, gene->nk);
-  gene->age = 0;
-  for (i = 0; i < CCV_BBF_POINT_MAX; i++)
-  {
-    gene->feature.pz[i] = -1;
-    gene->feature.nz[i] = -1;
-  }
-  int x, y, z;
-  for (i = 0; i < gene->pk; i++)
-  {
-    do
-    {
-      z = gsl_rng_uniform_int(rng, 3);
-      x = gsl_rng_uniform_int(rng, cols[z]);
-      y = gsl_rng_uniform_int(rng, rows[z]);
-    } while (_ccv_bbf_exist_gene_feature(gene, x, y, z));
-    gene->feature.pz[i] = z;
-    gene->feature.px[i] = x;
-    gene->feature.py[i] = y;
-  }
-  for (i = 0; i < gene->nk; i++)
-  {
-    do
-    {
-      z = gsl_rng_uniform_int(rng, 3);
-      x = gsl_rng_uniform_int(rng, cols[z]);
-      y = gsl_rng_uniform_int(rng, rows[z]);
-    } while (_ccv_bbf_exist_gene_feature(gene, x, y, z));
-    gene->feature.nz[i] = z;
-    gene->feature.nx[i] = x;
-    gene->feature.ny[i] = y;
-  }
-}
-
-static inline double _ccv_bbf_error_rate(ccv_bbf_feature_t *feature, unsigned char **posdata, int posnum, unsigned char **negdata, int negnum, ccv_size_t size, double *pw, double *nw)
-{
-  int i;
-  int steps[] = {_ccv_width_padding(size.width),
-                 _ccv_width_padding(size.width >> 1),
-                 _ccv_width_padding(size.width >> 2)};
-  int isizs0 = steps[0] * size.height;
-  int isizs01 = isizs0 + steps[1] * (size.height >> 1);
-  double error = 0;
-  for (i = 0; i < posnum; i++)
-  {
-    unsigned char *u8[] = {posdata[i], posdata[i] + isizs0, posdata[i] + isizs01};
-    if (!_ccv_run_bbf_feature(feature, steps, u8))
-      error += pw[i];
-  }
-  for (i = 0; i < negnum; i++)
-  {
-    unsigned char *u8[] = {negdata[i], negdata[i] + isizs0, negdata[i] + isizs01};
-    if (_ccv_run_bbf_feature(feature, steps, u8))
-      error += nw[i];
-  }
-  return error;
-}
-
-#define less_than(fit1, fit2, aux) ((fit1).fitness >= (fit2).fitness)
-static CCV_IMPLEMENT_QSORT(_ccv_bbf_genetic_qsort, ccv_bbf_gene_t, less_than)
-#undef less_than
-
-    static ccv_bbf_feature_t _ccv_bbf_genetic_optimize(unsigned char **posdata, int posnum, unsigned char **negdata, int negnum, int ftnum, ccv_size_t size, double *pw, double *nw)
-{
-  ccv_bbf_feature_t best;
-  /* seed (random method) */
-  gsl_rng_env_setup();
-  gsl_rng *rng = gsl_rng_alloc(gsl_rng_default);
-  union {
-    unsigned long int li;
-    double db;
-  } dbli;
-  dbli.db = pw[0] + nw[0];
-  gsl_rng_set(rng, dbli.li);
-  int i, j;
-  int pnum = ftnum * 100;
-  assert(pnum > 0);
-  ccv_bbf_gene_t *gene = (ccv_bbf_gene_t *)ccmalloc(pnum * sizeof(ccv_bbf_gene_t));
-  int rows[] = {size.height, size.height >> 1, size.height >> 2};
-  int cols[] = {size.width, size.width >> 1, size.width >> 2};
-  for (i = 0; i < pnum; i++)
-    _ccv_bbf_randomize_gene(rng, &gene[i], rows, cols);
-  unsigned int timer = _ccv_bbf_time_measure();
-#ifdef USE_OPENMP
-#pragma omp parallel for private(i) schedule(dynamic)
-#endif
-  for (i = 0; i < pnum; i++)
-    gene[i].error = _ccv_bbf_error_rate(&gene[i].feature, posdata, posnum, negdata, negnum, size, pw, nw);
-  timer = _ccv_bbf_time_measure() - timer;
-  for (i = 0; i < pnum; i++)
-    _ccv_bbf_genetic_fitness(&gene[i]);
-  double best_err = 1;
-  int rnum = ftnum * 39; /* number of randomize */
-  int mnum = ftnum * 40; /* number of mutation */
-  int hnum = ftnum * 20; /* number of hybrid */
-  /* iteration stop crit : best no change in 40 iterations */
-  int it = 0, t;
-  for (t = 0; it < 40; ++it, ++t)
-  {
-    int min_id = 0;
-    double min_err = gene[0].error;
-    for (i = 1; i < pnum; i++)
-      if (gene[i].error < min_err)
-      {
-        min_id = i;
-        min_err = gene[i].error;
-      }
-    min_err = gene[min_id].error = _ccv_bbf_error_rate(&gene[min_id].feature, posdata, posnum, negdata, negnum, size, pw, nw);
-    if (min_err < best_err)
-    {
-      best_err = min_err;
-      memcpy(&best, &gene[min_id].feature, sizeof(best));
-      PRINT(CCV_CLI_INFO, "best bbf feature with error %f\n|-size: %d\n|-positive point: ", best_err, best.size);
-      for (i = 0; i < best.size; i++)
-        PRINT(CCV_CLI_INFO, "(%d %d %d), ", best.px[i], best.py[i], best.pz[i]);
-      PRINT(CCV_CLI_INFO, "\n|-negative point: ");
-      for (i = 0; i < best.size; i++)
-        PRINT(CCV_CLI_INFO, "(%d %d %d), ", best.nx[i], best.ny[i], best.nz[i]);
-      PRINT(CCV_CLI_INFO, "\n");
-      it = 0;
-    }
-    PRINT(CCV_CLI_INFO, "minimum error achieved in round %d(%d) : %f with %d ms\n", t, it, min_err, timer / 1000);
-    _ccv_bbf_genetic_qsort(gene, pnum, 0);
-    for (i = 0; i < ftnum; i++)
-      ++gene[i].age;
-    for (i = ftnum; i < ftnum + mnum; i++)
-    {
-      int parent = gsl_rng_uniform_int(rng, ftnum);
-      memcpy(gene + i, gene + parent, sizeof(ccv_bbf_gene_t));
-      /* three mutation strategy : 1. add, 2. remove, 3. refine */
-      int pnm, pn = gsl_rng_uniform_int(rng, 2);
-      int *pnk[] = {&gene[i].pk, &gene[i].nk};
-      int *pnx[] = {gene[i].feature.px, gene[i].feature.nx};
-      int *pny[] = {gene[i].feature.py, gene[i].feature.ny};
-      int *pnz[] = {gene[i].feature.pz, gene[i].feature.nz};
-      int x, y, z;
-      int victim, decay = 1;
-      do
-      {
-        switch (gsl_rng_uniform_int(rng, 3))
-        {
-        case 0: /* add */
-          if (gene[i].pk == CCV_BBF_POINT_MAX && gene[i].nk == CCV_BBF_POINT_MAX)
-            break;
-          while (*pnk[pn] + 1 > CCV_BBF_POINT_MAX)
-            pn = gsl_rng_uniform_int(rng, 2);
-          do
-          {
-            z = gsl_rng_uniform_int(rng, 3);
-            x = gsl_rng_uniform_int(rng, cols[z]);
-            y = gsl_rng_uniform_int(rng, rows[z]);
-          } while (_ccv_bbf_exist_gene_feature(&gene[i], x, y, z));
-          pnz[pn][*pnk[pn]] = z;
-          pnx[pn][*pnk[pn]] = x;
-          pny[pn][*pnk[pn]] = y;
-          ++(*pnk[pn]);
-          gene[i].feature.size = ccv_max(gene[i].pk, gene[i].nk);
-          decay = gene[i].age = 0;
-          break;
-        case 1:                                             /* remove */
-          if (gene[i].pk + gene[i].nk <= CCV_BBF_POINT_MIN) /* at least 3 points have to be examed */
-            break;
-          while (*pnk[pn] - 1 <= 0) // || *pnk[pn] + *pnk[!pn] - 1 < CCV_BBF_POINT_MIN)
-            pn = gsl_rng_uniform_int(rng, 2);
-          victim = gsl_rng_uniform_int(rng, *pnk[pn]);
-          for (j = victim; j < *pnk[pn] - 1; j++)
-          {
-            pnz[pn][j] = pnz[pn][j + 1];
-            pnx[pn][j] = pnx[pn][j + 1];
-            pny[pn][j] = pny[pn][j + 1];
-          }
-          pnz[pn][*pnk[pn] - 1] = -1;
-          --(*pnk[pn]);
-          gene[i].feature.size = ccv_max(gene[i].pk, gene[i].nk);
-          decay = gene[i].age = 0;
-          break;
-        case 2: /* refine */
-          pnm = gsl_rng_uniform_int(rng, *pnk[pn]);
-          do
-          {
-            z = gsl_rng_uniform_int(rng, 3);
-            x = gsl_rng_uniform_int(rng, cols[z]);
-            y = gsl_rng_uniform_int(rng, rows[z]);
-          } while (_ccv_bbf_exist_gene_feature(&gene[i], x, y, z));
-          pnz[pn][pnm] = z;
-          pnx[pn][pnm] = x;
-          pny[pn][pnm] = y;
-          decay = gene[i].age = 0;
-          break;
-        }
-      } while (decay);
-    }
-    for (i = ftnum + mnum; i < ftnum + mnum + hnum; i++)
-    {
-      /* hybrid strategy: taking positive points from dad, negative points from mum */
-      int dad, mum;
-      do
-      {
-        dad = gsl_rng_uniform_int(rng, ftnum);
-        mum = gsl_rng_uniform_int(rng, ftnum);
-      } while (dad == mum || gene[dad].pk + gene[mum].nk < CCV_BBF_POINT_MIN); /* at least 3 points have to be examed */
-      for (j = 0; j < CCV_BBF_POINT_MAX; j++)
-      {
-        gene[i].feature.pz[j] = -1;
-        gene[i].feature.nz[j] = -1;
-      }
-      gene[i].pk = gene[dad].pk;
-      for (j = 0; j < gene[i].pk; j++)
-      {
-        gene[i].feature.pz[j] = gene[dad].feature.pz[j];
-        gene[i].feature.px[j] = gene[dad].feature.px[j];
-        gene[i].feature.py[j] = gene[dad].feature.py[j];
-      }
-      gene[i].nk = gene[mum].nk;
-      for (j = 0; j < gene[i].nk; j++)
-      {
-        gene[i].feature.nz[j] = gene[mum].feature.nz[j];
-        gene[i].feature.nx[j] = gene[mum].feature.nx[j];
-        gene[i].feature.ny[j] = gene[mum].feature.ny[j];
-      }
-      gene[i].feature.size = ccv_max(gene[i].pk, gene[i].nk);
-      gene[i].age = 0;
-    }
-    for (i = ftnum + mnum + hnum; i < ftnum + mnum + hnum + rnum; i++)
-      _ccv_bbf_randomize_gene(rng, &gene[i], rows, cols);
-    timer = _ccv_bbf_time_measure();
-#ifdef USE_OPENMP
-#pragma omp parallel for private(i) schedule(dynamic)
-#endif
-    for (i = 0; i < pnum; i++)
-      gene[i].error = _ccv_bbf_error_rate(&gene[i].feature, posdata, posnum, negdata, negnum, size, pw, nw);
-    timer = _ccv_bbf_time_measure() - timer;
-    for (i = 0; i < pnum; i++)
-      _ccv_bbf_genetic_fitness(&gene[i]);
-  }
-  ccfree(gene);
-  gsl_rng_free(rng);
-  return best;
-}
-
-#define less_than(fit1, fit2, aux) ((fit1).error < (fit2).error)
-static CCV_IMPLEMENT_QSORT(_ccv_bbf_best_qsort, ccv_bbf_gene_t, less_than)
-#undef less_than
-
-    static ccv_bbf_gene_t _ccv_bbf_best_gene(ccv_bbf_gene_t *gene, int pnum, int point_min, unsigned char **posdata, int posnum, unsigned char **negdata, int negnum, ccv_size_t size, double *pw, double *nw)
-{
-  int i;
-  unsigned int timer = _ccv_bbf_time_measure();
-#ifdef USE_OPENMP
-#pragma omp parallel for private(i) schedule(dynamic)
-#endif
-  for (i = 0; i < pnum; i++)
-    gene[i].error = _ccv_bbf_error_rate(&gene[i].feature, posdata, posnum, negdata, negnum, size, pw, nw);
-  timer = _ccv_bbf_time_measure() - timer;
-  _ccv_bbf_best_qsort(gene, pnum, 0);
-  int min_id = 0;
-  double min_err = gene[0].error;
-  for (i = 0; i < pnum; i++)
-    if (gene[i].nk + gene[i].pk >= point_min)
-    {
-      min_id = i;
-      min_err = gene[i].error;
-      break;
-    }
-  PRINT(CCV_CLI_INFO, "local best bbf feature with error %f\n|-size: %d\n|-positive point: ", min_err, gene[min_id].feature.size);
-  for (i = 0; i < gene[min_id].feature.size; i++)
-    PRINT(CCV_CLI_INFO, "(%d %d %d), ", gene[min_id].feature.px[i], gene[min_id].feature.py[i], gene[min_id].feature.pz[i]);
-  PRINT(CCV_CLI_INFO, "\n|-negative point: ");
-  for (i = 0; i < gene[min_id].feature.size; i++)
-    PRINT(CCV_CLI_INFO, "(%d %d %d), ", gene[min_id].feature.nx[i], gene[min_id].feature.ny[i], gene[min_id].feature.nz[i]);
-  PRINT(CCV_CLI_INFO, "\nthe computation takes %d ms\n", timer / 1000);
-  return gene[min_id];
-}
-
-static ccv_bbf_feature_t _ccv_bbf_convex_optimize(unsigned char **posdata, int posnum, unsigned char **negdata, int negnum, ccv_bbf_feature_t *best_feature, ccv_size_t size, double *pw, double *nw)
-{
-  ccv_bbf_gene_t best_gene;
-  /* seed (random method) */
-  gsl_rng_env_setup();
-  gsl_rng *rng = gsl_rng_alloc(gsl_rng_default);
-  union {
-    unsigned long int li;
-    double db;
-  } dbli;
-  dbli.db = pw[0] + nw[0];
-  gsl_rng_set(rng, dbli.li);
-  int i, j, k, q, p, g, t;
-  int rows[] = {size.height, size.height >> 1, size.height >> 2};
-  int cols[] = {size.width, size.width >> 1, size.width >> 2};
-  int pnum = rows[0] * cols[0] + rows[1] * cols[1] + rows[2] * cols[2];
-  ccv_bbf_gene_t *gene = (ccv_bbf_gene_t *)ccmalloc((pnum * (CCV_BBF_POINT_MAX * 2 + 1) * 2 + CCV_BBF_POINT_MAX * 2 + 1) * sizeof(ccv_bbf_gene_t));
-  if (best_feature == 0)
-  {
-    /* bootstrapping the best feature, start from two pixels, one for positive, one for negative
-		 * the bootstrapping process go like this: first, it will assign a random pixel as positive
-		 * and enumerate every possible pixel as negative, and pick the best one. Then, enumerate every
-		 * possible pixel as positive, and pick the best one, until it converges */
-    memset(&best_gene, 0, sizeof(ccv_bbf_gene_t));
-    for (i = 0; i < CCV_BBF_POINT_MAX; i++)
-      best_gene.feature.pz[i] = best_gene.feature.nz[i] = -1;
-    best_gene.pk = 1;
-    best_gene.nk = 0;
-    best_gene.feature.size = 1;
-    best_gene.feature.pz[0] = gsl_rng_uniform_int(rng, 3);
-    best_gene.feature.px[0] = gsl_rng_uniform_int(rng, cols[best_gene.feature.pz[0]]);
-    best_gene.feature.py[0] = gsl_rng_uniform_int(rng, rows[best_gene.feature.pz[0]]);
-    for (t = 0;; ++t)
-    {
-      g = 0;
-      if (t % 2 == 0)
-      {
-        for (i = 0; i < 3; i++)
-          for (j = 0; j < cols[i]; j++)
-            for (k = 0; k < rows[i]; k++)
-              if (i != best_gene.feature.pz[0] || j != best_gene.feature.px[0] || k != best_gene.feature.py[0])
-              {
-                gene[g] = best_gene;
-                gene[g].pk = gene[g].nk = 1;
-                gene[g].feature.nz[0] = i;
-                gene[g].feature.nx[0] = j;
-                gene[g].feature.ny[0] = k;
-                g++;
-              }
-      }
-      else
-      {
-        for (i = 0; i < 3; i++)
-          for (j = 0; j < cols[i]; j++)
-            for (k = 0; k < rows[i]; k++)
-              if (i != best_gene.feature.nz[0] || j != best_gene.feature.nx[0] || k != best_gene.feature.ny[0])
-              {
-                gene[g] = best_gene;
-                gene[g].pk = gene[g].nk = 1;
-                gene[g].feature.pz[0] = i;
-                gene[g].feature.px[0] = j;
-                gene[g].feature.py[0] = k;
-                g++;
-              }
-      }
-      PRINT(CCV_CLI_INFO, "bootstrapping round : %d\n", t);
-      ccv_bbf_gene_t local_gene = _ccv_bbf_best_gene(gene, g, 2, posdata, posnum, negdata, negnum, size, pw, nw);
-      if (local_gene.error >= best_gene.error - 1e-10)
-        break;
-      best_gene = local_gene;
-    }
-  }
-  else
-  {
-    best_gene.feature = *best_feature;
-    best_gene.pk = best_gene.nk = best_gene.feature.size;
-    for (i = 0; i < CCV_BBF_POINT_MAX; i++)
-      if (best_feature->pz[i] == -1)
-      {
-        best_gene.pk = i;
-        break;
-      }
-    for (i = 0; i < CCV_BBF_POINT_MAX; i++)
-      if (best_feature->nz[i] == -1)
-      {
-        best_gene.nk = i;
-        break;
-      }
-  }
-  /* after bootstrapping, the float search technique will do the following permutations:
-	 * a). add a new point to positive or negative
-	 * b). remove a point from positive or negative
-	 * c). move an existing point in positive or negative to another position
-	 * the three rules applied exhaustively, no heuristic used. */
-  for (t = 0;; ++t)
-  {
-    g = 0;
-    for (i = 0; i < 3; i++)
-      for (j = 0; j < cols[i]; j++)
-        for (k = 0; k < rows[i]; k++)
-          if (!_ccv_bbf_exist_gene_feature(&best_gene, j, k, i))
-          {
-            /* add positive point */
-            if (best_gene.pk < CCV_BBF_POINT_MAX - 1)
-            {
-              gene[g] = best_gene;
-              gene[g].feature.pz[gene[g].pk] = i;
-              gene[g].feature.px[gene[g].pk] = j;
-              gene[g].feature.py[gene[g].pk] = k;
-              gene[g].pk++;
-              gene[g].feature.size = ccv_max(gene[g].pk, gene[g].nk);
-              g++;
-            }
-            /* add negative point */
-            if (best_gene.nk < CCV_BBF_POINT_MAX - 1)
-            {
-              gene[g] = best_gene;
-              gene[g].feature.nz[gene[g].nk] = i;
-              gene[g].feature.nx[gene[g].nk] = j;
-              gene[g].feature.ny[gene[g].nk] = k;
-              gene[g].nk++;
-              gene[g].feature.size = ccv_max(gene[g].pk, gene[g].nk);
-              g++;
-            }
-            /* refine positive point */
-            for (q = 0; q < best_gene.pk; q++)
-            {
-              gene[g] = best_gene;
-              gene[g].feature.pz[q] = i;
-              gene[g].feature.px[q] = j;
-              gene[g].feature.py[q] = k;
-              g++;
-            }
-            /* add positive point, remove negative point */
-            if (best_gene.pk < CCV_BBF_POINT_MAX - 1 && best_gene.nk > 1)
-            {
-              for (q = 0; q < best_gene.nk; q++)
-              {
-                gene[g] = best_gene;
-                gene[g].feature.pz[gene[g].pk] = i;
-                gene[g].feature.px[gene[g].pk] = j;
-                gene[g].feature.py[gene[g].pk] = k;
-                gene[g].pk++;
-                for (p = q; p < best_gene.nk - 1; p++)
-                {
-                  gene[g].feature.nz[p] = gene[g].feature.nz[p + 1];
-                  gene[g].feature.nx[p] = gene[g].feature.nx[p + 1];
-                  gene[g].feature.ny[p] = gene[g].feature.ny[p + 1];
-                }
-                gene[g].feature.nz[gene[g].nk - 1] = -1;
-                gene[g].nk--;
-                gene[g].feature.size = ccv_max(gene[g].pk, gene[g].nk);
-                g++;
-              }
-            }
-            /* refine negative point */
-            for (q = 0; q < best_gene.nk; q++)
-            {
-              gene[g] = best_gene;
-              gene[g].feature.nz[q] = i;
-              gene[g].feature.nx[q] = j;
-              gene[g].feature.ny[q] = k;
-              g++;
-            }
-            /* add negative point, remove positive point */
-            if (best_gene.pk > 1 && best_gene.nk < CCV_BBF_POINT_MAX - 1)
-            {
-              for (q = 0; q < best_gene.pk; q++)
-              {
-                gene[g] = best_gene;
-                gene[g].feature.nz[gene[g].nk] = i;
-                gene[g].feature.nx[gene[g].nk] = j;
-                gene[g].feature.ny[gene[g].nk] = k;
-                gene[g].nk++;
-                for (p = q; p < best_gene.pk - 1; p++)
-                {
-                  gene[g].feature.pz[p] = gene[g].feature.pz[p + 1];
-                  gene[g].feature.px[p] = gene[g].feature.px[p + 1];
-                  gene[g].feature.py[p] = gene[g].feature.py[p + 1];
-                }
-                gene[g].feature.pz[gene[g].pk - 1] = -1;
-                gene[g].pk--;
-                gene[g].feature.size = ccv_max(gene[g].pk, gene[g].nk);
-                g++;
-              }
-            }
-          }
-    if (best_gene.pk > 1)
-      for (q = 0; q < best_gene.pk; q++)
-      {
-        gene[g] = best_gene;
-        for (i = q; i < best_gene.pk - 1; i++)
-        {
-          gene[g].feature.pz[i] = gene[g].feature.pz[i + 1];
-          gene[g].feature.px[i] = gene[g].feature.px[i + 1];
-          gene[g].feature.py[i] = gene[g].feature.py[i + 1];
-        }
-        gene[g].feature.pz[gene[g].pk - 1] = -1;
-        gene[g].pk--;
-        gene[g].feature.size = ccv_max(gene[g].pk, gene[g].nk);
-        g++;
-      }
-    if (best_gene.nk > 1)
-      for (q = 0; q < best_gene.nk; q++)
-      {
-        gene[g] = best_gene;
-        for (i = q; i < best_gene.nk - 1; i++)
-        {
-          gene[g].feature.nz[i] = gene[g].feature.nz[i + 1];
-          gene[g].feature.nx[i] = gene[g].feature.nx[i + 1];
-          gene[g].feature.ny[i] = gene[g].feature.ny[i + 1];
-        }
-        gene[g].feature.nz[gene[g].nk - 1] = -1;
-        gene[g].nk--;
-        gene[g].feature.size = ccv_max(gene[g].pk, gene[g].nk);
-        g++;
-      }
-    gene[g] = best_gene;
-    g++;
-    PRINT(CCV_CLI_INFO, "float search round : %d\n", t);
-    ccv_bbf_gene_t local_gene = _ccv_bbf_best_gene(gene, g, CCV_BBF_POINT_MIN, posdata, posnum, negdata, negnum, size, pw, nw);
-    if (local_gene.error >= best_gene.error - 1e-10)
-      break;
-    best_gene = local_gene;
-  }
-  ccfree(gene);
-  gsl_rng_free(rng);
-  return best_gene.feature;
-}
-
-static int _ccv_write_bbf_stage_classifier(const char *file, ccv_bbf_stage_classifier_t *classifier)
-{
-  FILE *w = fopen(file, "wb");
-  if (w == 0)
-    return -1;
-  fprintf(w, "%d\n", classifier->count);
-  union {
-    float fl;
-    int i;
-  } fli;
-  fli.fl = classifier->threshold;
-  fprintf(w, "%d\n", fli.i);
-  int i, j;
-  for (i = 0; i < classifier->count; i++)
-  {
-    fprintf(w, "%d\n", classifier->feature[i].size);
-    for (j = 0; j < classifier->feature[i].size; j++)
-    {
-      fprintf(w, "%d %d %d\n", classifier->feature[i].px[j], classifier->feature[i].py[j], classifier->feature[i].pz[j]);
-      fprintf(w, "%d %d %d\n", classifier->feature[i].nx[j], classifier->feature[i].ny[j], classifier->feature[i].nz[j]);
-    }
-    union {
-      float fl;
-      int i;
-    } flia, flib;
-    flia.fl = classifier->alpha[i * 2];
-    flib.fl = classifier->alpha[i * 2 + 1];
-    fprintf(w, "%d %d\n", flia.i, flib.i);
-  }
-  fclose(w);
-  return 0;
-}
-
-static int _ccv_read_background_data(const char *file, unsigned char **negdata, int *negnum, ccv_size_t size)
-{
-  int stat = 0;
-  FILE *r = fopen(file, "rb");
-  if (r == 0)
-    return -1;
-  stat |= fread(negnum, sizeof(int), 1, r);
-  int i;
-  int isizs012 = _ccv_width_padding(size.width) * size.height +
-                 _ccv_width_padding(size.width >> 1) * (size.height >> 1) +
-                 _ccv_width_padding(size.width >> 2) * (size.height >> 2);
-  for (i = 0; i < *negnum; i++)
-  {
-    negdata[i] = (unsigned char *)ccmalloc(isizs012);
-    stat |= fread(negdata[i], 1, isizs012, r);
-  }
-  fclose(r);
-  return 0;
-}
-
-static int _ccv_write_background_data(const char *file, unsigned char **negdata, int negnum, ccv_size_t size)
-{
-  FILE *w = fopen(file, "w");
-  if (w == 0)
-    return -1;
-  fwrite(&negnum, sizeof(int), 1, w);
-  int i;
-  int isizs012 = _ccv_width_padding(size.width) * size.height +
-                 _ccv_width_padding(size.width >> 1) * (size.height >> 1) +
-                 _ccv_width_padding(size.width >> 2) * (size.height >> 2);
-  for (i = 0; i < negnum; i++)
-    fwrite(negdata[i], 1, isizs012, w);
-  fclose(w);
-  return 0;
-}
-
-static int _ccv_resume_bbf_cascade_training_state(const char *file, int *i, int *k, int *bg, double *pw, double *nw, int posnum, int negnum)
-{
-  int stat = 0;
-  FILE *r = fopen(file, "r");
-  if (r == 0)
-    return -1;
-  stat |= fscanf(r, "%d %d %d", i, k, bg);
-  int j;
-  union {
-    double db;
-    int i[2];
-  } dbi;
-  for (j = 0; j < posnum; j++)
-  {
-    stat |= fscanf(r, "%d %d", &dbi.i[0], &dbi.i[1]);
-    pw[j] = dbi.db;
-  }
-  for (j = 0; j < negnum; j++)
-  {
-    stat |= fscanf(r, "%d %d", &dbi.i[0], &dbi.i[1]);
-    nw[j] = dbi.db;
-  }
-  fclose(r);
-  return 0;
-}
-
-static int _ccv_save_bbf_cacade_training_state(const char *file, int i, int k, int bg, double *pw, double *nw, int posnum, int negnum)
-{
-  FILE *w = fopen(file, "w");
-  if (w == 0)
-    return -1;
-  fprintf(w, "%d %d %d\n", i, k, bg);
-  int j;
-  union {
-    double db;
-    int i[2];
-  } dbi;
-  for (j = 0; j < posnum; ++j)
-  {
-    dbi.db = pw[j];
-    fprintf(w, "%d %d ", dbi.i[0], dbi.i[1]);
-  }
-  fprintf(w, "\n");
-  for (j = 0; j < negnum; ++j)
-  {
-    dbi.db = nw[j];
-    fprintf(w, "%d %d ", dbi.i[0], dbi.i[1]);
-  }
-  fprintf(w, "\n");
-  fclose(w);
-  return 0;
-}
-
-void ccv_bbf_classifier_cascade_new(ccv_dense_matrix_t **posimg, int posnum, char **bgfiles, int bgnum, int negnum, ccv_size_t size, const char *dir, ccv_bbf_new_param_t params)
-{
-  int i, j, k;
-  /* allocate memory for usage */
-  ccv_bbf_classifier_cascade_t *cascade = (ccv_bbf_classifier_cascade_t *)ccmalloc(sizeof(ccv_bbf_classifier_cascade_t));
-  cascade->count = 0;
-  cascade->size = size;
-  cascade->stage_classifier = (ccv_bbf_stage_classifier_t *)ccmalloc(sizeof(ccv_bbf_stage_classifier_t));
-  unsigned char **posdata = (unsigned char **)ccmalloc(posnum * sizeof(unsigned char *));
-  unsigned char **negdata = (unsigned char **)ccmalloc(negnum * sizeof(unsigned char *));
-  double *pw = (double *)ccmalloc(posnum * sizeof(double));
-  double *nw = (double *)ccmalloc(negnum * sizeof(double));
-  float *peval = (float *)ccmalloc(posnum * sizeof(float));
-  float *neval = (float *)ccmalloc(negnum * sizeof(float));
-  double inv_balance_k = 1. / params.balance_k;
-  /* balance factor k, and weighted with 0.01 */
-  params.balance_k *= 0.01;
-  inv_balance_k *= 0.01;
-
-  int steps[] = {_ccv_width_padding(cascade->size.width),
-                 _ccv_width_padding(cascade->size.width >> 1),
-                 _ccv_width_padding(cascade->size.width >> 2)};
-  int isizs0 = steps[0] * cascade->size.height;
-  int isizs01 = isizs0 + steps[1] * (cascade->size.height >> 1);
-
-  i = 0;
-  k = 0;
-  int bg = 0;
-  int cacheK = 10;
-  /* state resume code */
-  char buf[1024];
-  sprintf(buf, "%s/stat.txt", dir);
-  _ccv_resume_bbf_cascade_training_state(buf, &i, &k, &bg, pw, nw, posnum, negnum);
-  if (i > 0)
-  {
-    cascade->count = i;
-    ccfree(cascade->stage_classifier);
-    cascade->stage_classifier = (ccv_bbf_stage_classifier_t *)ccmalloc(i * sizeof(ccv_bbf_stage_classifier_t));
-    for (j = 0; j < i; j++)
-    {
-      sprintf(buf, "%s/stage-%d.txt", dir, j);
-      _ccv_read_bbf_stage_classifier(buf, &cascade->stage_classifier[j]);
-    }
-  }
-  if (k > 0)
-    cacheK = k;
-  int rpos, rneg = 0;
-  if (bg)
-  {
-    sprintf(buf, "%s/negs.txt", dir);
-    _ccv_read_background_data(buf, negdata, &rneg, cascade->size);
-  }
-
-  for (; i < params.layer; i++)
-  {
-    if (!bg)
-    {
-      rneg = _ccv_prepare_background_data(cascade, bgfiles, bgnum, negdata, negnum);
-      /* save state of background data */
-      sprintf(buf, "%s/negs.txt", dir);
-      _ccv_write_background_data(buf, negdata, rneg, cascade->size);
-      bg = 1;
-    }
-    double totalw;
-    /* save state of cascade : level, weight etc. */
-    sprintf(buf, "%s/stat.txt", dir);
-    _ccv_save_bbf_cacade_training_state(buf, i, k, bg, pw, nw, posnum, negnum);
-    ccv_bbf_stage_classifier_t classifier;
-    if (k > 0)
-    {
-      /* resume state of classifier */
-      sprintf(buf, "%s/stage-%d.txt", dir, i);
-      _ccv_read_bbf_stage_classifier(buf, &classifier);
-    }
-    else
-    {
-      /* initialize classifier */
-      for (j = 0; j < posnum; j++)
-        pw[j] = params.balance_k;
-      for (j = 0; j < rneg; j++)
-        nw[j] = inv_balance_k;
-      classifier.count = k;
-      classifier.threshold = 0;
-      classifier.feature = (ccv_bbf_feature_t *)ccmalloc(cacheK * sizeof(ccv_bbf_feature_t));
-      classifier.alpha = (float *)ccmalloc(cacheK * 2 * sizeof(float));
-    }
-    _ccv_prepare_positive_data(posimg, posdata, cascade->size, posnum);
-    rpos = _ccv_prune_positive_data(cascade, posdata, posnum, cascade->size);
-    PRINT(CCV_CLI_INFO, "%d postivie data and %d negative data in training\n", rpos, rneg);
-    /* reweight to 1.00 */
-    totalw = 0;
-    for (j = 0; j < rpos; j++)
-      totalw += pw[j];
-    for (j = 0; j < rneg; j++)
-      totalw += nw[j];
-    for (j = 0; j < rpos; j++)
-      pw[j] = pw[j] / totalw;
-    for (j = 0; j < rneg; j++)
-      nw[j] = nw[j] / totalw;
-    for (;; k++)
-    {
-      /* get overall true-positive, false-positive rate and threshold */
-      double tp = 0, fp = 0, etp = 0, efp = 0;
-      _ccv_bbf_eval_data(&classifier, posdata, rpos, negdata, rneg, cascade->size, peval, neval);
-      _ccv_sort_32f(peval, rpos, 0);
-      classifier.threshold = peval[(int)((1. - params.pos_crit) * rpos)] - 1e-6;
-      for (j = 0; j < rpos; j++)
-      {
-        if (peval[j] >= 0)
-          ++tp;
-        if (peval[j] >= classifier.threshold)
-          ++etp;
-      }
-      tp /= rpos;
-      etp /= rpos;
-      for (j = 0; j < rneg; j++)
-      {
-        if (neval[j] >= 0)
-          ++fp;
-        if (neval[j] >= classifier.threshold)
-          ++efp;
-      }
-      fp /= rneg;
-      efp /= rneg;
-      PRINT(CCV_CLI_INFO, "stage classifier real TP rate : %f, FP rate : %f\n", tp, fp);
-      PRINT(CCV_CLI_INFO, "stage classifier TP rate : %f, FP rate : %f at threshold : %f\n", etp, efp, classifier.threshold);
-      if (k > 0)
-      {
-        /* save classifier state */
-        sprintf(buf, "%s/stage-%d.txt", dir, i);
-        _ccv_write_bbf_stage_classifier(buf, &classifier);
-        sprintf(buf, "%s/stat.txt", dir);
-        _ccv_save_bbf_cacade_training_state(buf, i, k, bg, pw, nw, posnum, negnum);
-      }
-      if (etp > params.pos_crit && efp < params.neg_crit)
-        break;
-      /* TODO: more post-process is needed in here */
-
-      /* select the best feature in current distribution through genetic algorithm optimization */
-      ccv_bbf_feature_t best;
-      if (params.optimizer == CCV_BBF_GENETIC_OPT)
-      {
-        best = _ccv_bbf_genetic_optimize(posdata, rpos, negdata, rneg, params.feature_number, cascade->size, pw, nw);
-      }
-      else if (params.optimizer == CCV_BBF_FLOAT_OPT)
-      {
-        best = _ccv_bbf_convex_optimize(posdata, rpos, negdata, rneg, 0, cascade->size, pw, nw);
-      }
-      else
-      {
-        best = _ccv_bbf_genetic_optimize(posdata, rpos, negdata, rneg, params.feature_number, cascade->size, pw, nw);
-        best = _ccv_bbf_convex_optimize(posdata, rpos, negdata, rneg, &best, cascade->size, pw, nw);
-      }
-      double err = _ccv_bbf_error_rate(&best, posdata, rpos, negdata, rneg, cascade->size, pw, nw);
-      double rw = (1 - err) / err;
-      totalw = 0;
-      /* reweight */
-      for (j = 0; j < rpos; j++)
-      {
-        unsigned char *u8[] = {posdata[j], posdata[j] + isizs0, posdata[j] + isizs01};
-        if (!_ccv_run_bbf_feature(&best, steps, u8))
-          pw[j] *= rw;
-        pw[j] *= params.balance_k;
-        totalw += pw[j];
-      }
-      for (j = 0; j < rneg; j++)
-      {
-        unsigned char *u8[] = {negdata[j], negdata[j] + isizs0, negdata[j] + isizs01};
-        if (_ccv_run_bbf_feature(&best, steps, u8))
-          nw[j] *= rw;
-        nw[j] *= inv_balance_k;
-        totalw += nw[j];
-      }
-      for (j = 0; j < rpos; j++)
-        pw[j] = pw[j] / totalw;
-      for (j = 0; j < rneg; j++)
-        nw[j] = nw[j] / totalw;
-      double c = log(rw);
-      PRINT(CCV_CLI_INFO, "coefficient of feature %d: %f\n", k + 1, c);
-      classifier.count = k + 1;
-      /* resizing classifier */
-      if (k >= cacheK)
-      {
-        ccv_bbf_feature_t *feature = (ccv_bbf_feature_t *)ccmalloc(cacheK * 2 * sizeof(ccv_bbf_feature_t));
-        memcpy(feature, classifier.feature, cacheK * sizeof(ccv_bbf_feature_t));
-        ccfree(classifier.feature);
-        float *alpha = (float *)ccmalloc(cacheK * 4 * sizeof(float));
-        memcpy(alpha, classifier.alpha, cacheK * 2 * sizeof(float));
-        ccfree(classifier.alpha);
-        classifier.feature = feature;
-        classifier.alpha = alpha;
-        cacheK *= 2;
-      }
-      /* setup new feature */
-      classifier.feature[k] = best;
-      classifier.alpha[k * 2] = -c;
-      classifier.alpha[k * 2 + 1] = c;
-    }
-    cascade->count = i + 1;
-    ccv_bbf_stage_classifier_t *stage_classifier = (ccv_bbf_stage_classifier_t *)ccmalloc(cascade->count * sizeof(ccv_bbf_stage_classifier_t));
-    memcpy(stage_classifier, cascade->stage_classifier, i * sizeof(ccv_bbf_stage_classifier_t));
-    ccfree(cascade->stage_classifier);
-    stage_classifier[i] = classifier;
-    cascade->stage_classifier = stage_classifier;
-    k = 0;
-    bg = 0;
-    for (j = 0; j < rpos; j++)
-      ccfree(posdata[j]);
-    for (j = 0; j < rneg; j++)
-      ccfree(negdata[j]);
-  }
-
-  ccfree(neval);
-  ccfree(peval);
-  ccfree(nw);
-  ccfree(pw);
-  ccfree(negdata);
-  ccfree(posdata);
-  ccfree(cascade);
-}
-#else
-void ccv_bbf_classifier_cascade_new(ccv_dense_matrix_t **posimg, int posnum, char **bgfiles, int bgnum, int negnum, ccv_size_t size, const char *dir, ccv_bbf_new_param_t params)
-{
-  fprintf(stderr, " ccv_bbf_classifier_cascade_new requires libgsl support, please compile ccv with libgsl.\n");
-}
-#endif
-
-static int _ccv_is_equal(const void *_r1, const void *_r2, void *data)
-{
-  const ccv_comp_t *r1 = (const ccv_comp_t *)_r1;
-  const ccv_comp_t *r2 = (const ccv_comp_t *)_r2;
-  int distance = (int)(r1->rect.width * 0.25 + 0.5);
-
-  return r2->rect.x <= r1->rect.x + distance &&
-         r2->rect.x >= r1->rect.x - distance &&
-         r2->rect.y <= r1->rect.y + distance &&
-         r2->rect.y >= r1->rect.y - distance &&
-         r2->rect.width <= (int)(r1->rect.width * 1.5 + 0.5) &&
-         (int)(r2->rect.width * 1.5 + 0.5) >= r1->rect.width;
-}
-
-static int _ccv_is_equal_same_class(const void *_r1, const void *_r2, void *data)
-{
-  const ccv_comp_t *r1 = (const ccv_comp_t *)_r1;
-  const ccv_comp_t *r2 = (const ccv_comp_t *)_r2;
-  int distance = (int)(r1->rect.width * 0.25 + 0.5);
-
-  return r2->classification.id == r1->classification.id &&
-         r2->rect.x <= r1->rect.x + distance &&
-         r2->rect.x >= r1->rect.x - distance &&
-         r2->rect.y <= r1->rect.y + distance &&
-         r2->rect.y >= r1->rect.y - distance &&
-         r2->rect.width <= (int)(r1->rect.width * 1.5 + 0.5) &&
-         (int)(r2->rect.width * 1.5 + 0.5) >= r1->rect.width;
-}
-
-ccv_array_t *ccv_bbf_detect_objects(ccv_dense_matrix_t *a, ccv_bbf_classifier_cascade_t **_cascade, int count, ccv_bbf_param_t params)
-{
-  int hr = a->rows / params.size.height;
-  int wr = a->cols / params.size.width;
-  double scale = pow(2., 1. / (params.interval + 1.));
-  int next = params.interval + 1;
-  int scale_upto = (int)(log((double)ccv_min(hr, wr)) / log(scale));
-  ccv_dense_matrix_t **pyr = (ccv_dense_matrix_t **)alloca((scale_upto + next * 2) * 4 * sizeof(ccv_dense_matrix_t *));
-  memset(pyr, 0, (scale_upto + next * 2) * 4 * sizeof(ccv_dense_matrix_t *));
-  if (params.size.height != _cascade[0]->size.height || params.size.width != _cascade[0]->size.width)
-    ccv_resample(a, &pyr[0], 0, a->rows * _cascade[0]->size.height / params.size.height, a->cols * _cascade[0]->size.width / params.size.width, CCV_INTER_AREA);
-  else
-    pyr[0] = a;
-  int i, j, k, t, x, y, q;
-  for (i = 1; i < ccv_min(params.interval + 1, scale_upto + next * 2); i++)
-    ccv_resample(pyr[0], &pyr[i * 4], 0, (int)(pyr[0]->rows / pow(scale, i)), (int)(pyr[0]->cols / pow(scale, i)), CCV_INTER_AREA);
-  for (i = next; i < scale_upto + next * 2; i++)
-    ccv_sample_down(pyr[i * 4 - next * 4], &pyr[i * 4], 0, 0, 0);
-  if (params.accurate)
-    for (i = next * 2; i < scale_upto + next * 2; i++)
-    {
-      ccv_sample_down(pyr[i * 4 - next * 4], &pyr[i * 4 + 1], 0, 1, 0);
-      ccv_sample_down(pyr[i * 4 - next * 4], &pyr[i * 4 + 2], 0, 0, 1);
-      ccv_sample_down(pyr[i * 4 - next * 4], &pyr[i * 4 + 3], 0, 1, 1);
-    }
-  ccv_array_t *idx_seq;
-  ccv_array_t *seq = ccv_array_new(sizeof(ccv_comp_t), 64, 0);
-  ccv_array_t *seq2 = ccv_array_new(sizeof(ccv_comp_t), 64, 0);
-  ccv_array_t *result_seq = ccv_array_new(sizeof(ccv_comp_t), 64, 0);
-  /* detect in multi scale */
-  for (t = 0; t < count; t++)
-  {
-    ccv_bbf_classifier_cascade_t *cascade = _cascade[t];
-    float scale_x = (float)params.size.width / (float)cascade->size.width;
-    float scale_y = (float)params.size.height / (float)cascade->size.height;
-    ccv_array_clear(seq);
-    for (i = 0; i < scale_upto; i++)
-    {
-      int dx[] = {0, 1, 0, 1};
-      int dy[] = {0, 0, 1, 1};
-      int i_rows = pyr[i * 4 + next * 8]->rows - (cascade->size.height >> 2);
-      int steps[] = {pyr[i * 4]->step, pyr[i * 4 + next * 4]->step, pyr[i * 4 + next * 8]->step};
-      int i_cols = pyr[i * 4 + next * 8]->cols - (cascade->size.width >> 2);
-      int paddings[] = {pyr[i * 4]->step * 4 - i_cols * 4,
-                        pyr[i * 4 + next * 4]->step * 2 - i_cols * 2,
-                        pyr[i * 4 + next * 8]->step - i_cols};
-      for (q = 0; q < (params.accurate ? 4 : 1); q++)
-      {
-        unsigned char *u8[] = {pyr[i * 4]->data.u8 + dx[q] * 2 + dy[q] * pyr[i * 4]->step * 2, pyr[i * 4 + next * 4]->data.u8 + dx[q] + dy[q] * pyr[i * 4 + next * 4]->step, pyr[i * 4 + next * 8 + q]->data.u8};
-        for (y = 0; y < i_rows; y++)
-        {
-          for (x = 0; x < i_cols; x++)
-          {
-            float sum;
-            int flag = 1;
-            ccv_bbf_stage_classifier_t *classifier = cascade->stage_classifier;
-            for (j = 0; j < cascade->count; ++j, ++classifier)
-            {
-              sum = 0;
-              float *alpha = classifier->alpha;
-              ccv_bbf_feature_t *feature = classifier->feature;
-              for (k = 0; k < classifier->count; ++k, alpha += 2, ++feature)
-                sum += alpha[_ccv_run_bbf_feature(feature, steps, u8)];
-              if (sum < classifier->threshold)
-              {
-                flag = 0;
-                break;
-              }
-            }
-            if (flag)
-            {
-              ccv_comp_t comp;
-              comp.rect = ccv_rect((int)((x * 4 + dx[q] * 2) * scale_x + 0.5), (int)((y * 4 + dy[q] * 2) * scale_y + 0.5), (int)(cascade->size.width * scale_x + 0.5), (int)(cascade->size.height * scale_y + 0.5));
-              comp.neighbors = 1;
-              comp.classification.id = t;
-              comp.classification.confidence = sum;
-              ccv_array_push(seq, &comp);
-            }
-            u8[0] += 4;
-            u8[1] += 2;
-            u8[2] += 1;
-          }
-          u8[0] += paddings[0];
-          u8[1] += paddings[1];
-          u8[2] += paddings[2];
-        }
-      }
-      scale_x *= scale;
-      scale_y *= scale;
-    }
-
-    /* the following code from OpenCV's haar feature implementation */
-    if (params.min_neighbors == 0)
-    {
-      for (i = 0; i < seq->rnum; i++)
-      {
-        ccv_comp_t *comp = (ccv_comp_t *)ccv_array_get(seq, i);
-        ccv_array_push(result_seq, comp);
-      }
-    }
-    else
-    {
-      idx_seq = 0;
-      ccv_array_clear(seq2);
-      // group retrieved rectangles in order to filter out noise
-      int ncomp = ccv_array_group(seq, &idx_seq, _ccv_is_equal_same_class, 0);
-      ccv_comp_t *comps = (ccv_comp_t *)ccmalloc((ncomp + 1) * sizeof(ccv_comp_t));
-      memset(comps, 0, (ncomp + 1) * sizeof(ccv_comp_t));
-
-      // count number of neighbors
-      for (i = 0; i < seq->rnum; i++)
-      {
-        ccv_comp_t r1 = *(ccv_comp_t *)ccv_array_get(seq, i);
-        int idx = *(int *)ccv_array_get(idx_seq, i);
-
-        if (comps[idx].neighbors == 0)
-          comps[idx].classification.confidence = r1.classification.confidence;
-
-        ++comps[idx].neighbors;
-
-        comps[idx].rect.x += r1.rect.x;
-        comps[idx].rect.y += r1.rect.y;
-        comps[idx].rect.width += r1.rect.width;
-        comps[idx].rect.height += r1.rect.height;
-        comps[idx].classification.id = r1.classification.id;
-        comps[idx].classification.confidence = ccv_max(comps[idx].classification.confidence, r1.classification.confidence);
-      }
-
-      // calculate average bounding box
-      for (i = 0; i < ncomp; i++)
-      {
-        int n = comps[i].neighbors;
-        if (n >= params.min_neighbors)
-        {
-          ccv_comp_t comp;
-          comp.rect.x = (comps[i].rect.x * 2 + n) / (2 * n);
-          comp.rect.y = (comps[i].rect.y * 2 + n) / (2 * n);
-          comp.rect.width = (comps[i].rect.width * 2 + n) / (2 * n);
-          comp.rect.height = (comps[i].rect.height * 2 + n) / (2 * n);
-          comp.neighbors = comps[i].neighbors;
-          comp.classification.id = comps[i].classification.id;
-          comp.classification.confidence = comps[i].classification.confidence;
-          ccv_array_push(seq2, &comp);
-        }
-      }
-
-      // filter out small face rectangles inside large face rectangles
-      for (i = 0; i < seq2->rnum; i++)
-      {
-        ccv_comp_t r1 = *(ccv_comp_t *)ccv_array_get(seq2, i);
-        int flag = 1;
-
-        for (j = 0; j < seq2->rnum; j++)
-        {
-          ccv_comp_t r2 = *(ccv_comp_t *)ccv_array_get(seq2, j);
-          int distance = (int)(r2.rect.width * 0.25 + 0.5);
-
-          if (i != j &&
-              r1.classification.id == r2.classification.id &&
-              r1.rect.x >= r2.rect.x - distance &&
-              r1.rect.y >= r2.rect.y - distance &&
-              r1.rect.x + r1.rect.width <= r2.rect.x + r2.rect.width + distance &&
-              r1.rect.y + r1.rect.height <= r2.rect.y + r2.rect.height + distance &&
-              (r2.neighbors > ccv_max(3, r1.neighbors) || r1.neighbors < 3))
-          {
-            flag = 0;
-            break;
-          }
-        }
-
-        if (flag)
-          ccv_array_push(result_seq, &r1);
-      }
-      ccv_array_free(idx_seq);
-      ccfree(comps);
-    }
-  }
-
-  ccv_array_free(seq);
-  ccv_array_free(seq2);
-
-  ccv_array_t *result_seq2;
-  /* the following code from OpenCV's haar feature implementation */
-  if (params.flags & CCV_BBF_NO_NESTED)
-  {
-    result_seq2 = ccv_array_new(sizeof(ccv_comp_t), 64, 0);
-    idx_seq = 0;
-    // group retrieved rectangles in order to filter out noise
-    int ncomp = ccv_array_group(result_seq, &idx_seq, _ccv_is_equal, 0);
-    ccv_comp_t *comps = (ccv_comp_t *)ccmalloc((ncomp + 1) * sizeof(ccv_comp_t));
-    memset(comps, 0, (ncomp + 1) * sizeof(ccv_comp_t));
-
-    // count number of neighbors
-    for (i = 0; i < result_seq->rnum; i++)
-    {
-      ccv_comp_t r1 = *(ccv_comp_t *)ccv_array_get(result_seq, i);
-      int idx = *(int *)ccv_array_get(idx_seq, i);
-
-      if (comps[idx].neighbors == 0 || comps[idx].classification.confidence < r1.classification.confidence)
-      {
-        comps[idx].classification.confidence = r1.classification.confidence;
-        comps[idx].neighbors = 1;
-        comps[idx].rect = r1.rect;
-        comps[idx].classification.id = r1.classification.id;
-      }
-    }
-
-    // calculate average bounding box
-    for (i = 0; i < ncomp; i++)
-      if (comps[i].neighbors)
-        ccv_array_push(result_seq2, &comps[i]);
-
-    ccv_array_free(result_seq);
-    ccfree(comps);
-  }
-  else
-  {
-    result_seq2 = result_seq;
-  }
-
-  for (i = 1; i < scale_upto + next * 2; i++)
-    ccv_matrix_free(pyr[i * 4]);
-  if (params.accurate)
-    for (i = next * 2; i < scale_upto + next * 2; i++)
-    {
-      ccv_matrix_free(pyr[i * 4 + 1]);
-      ccv_matrix_free(pyr[i * 4 + 2]);
-      ccv_matrix_free(pyr[i * 4 + 3]);
-    }
-  if (params.size.height != _cascade[0]->size.height || params.size.width != _cascade[0]->size.width)
-    ccv_matrix_free(pyr[0]);
-
-  return result_seq2;
-}
-
-ccv_bbf_classifier_cascade_t *ccv_bbf_read_classifier_cascade(const char *directory)
-{
-  char buf[1024];
-  sprintf(buf, "%s/cascade.txt", directory);
-  int s, i;
-  FILE *r = fopen(buf, "r");
-  if (r == 0)
-    return 0;
-  ccv_bbf_classifier_cascade_t *cascade = (ccv_bbf_classifier_cascade_t *)ccmalloc(sizeof(ccv_bbf_classifier_cascade_t));
-  s = fscanf(r, "%d %d %d", &cascade->count, &cascade->size.width, &cascade->size.height);
-  assert(s > 0);
-  cascade->stage_classifier = (ccv_bbf_stage_classifier_t *)ccmalloc(cascade->count * sizeof(ccv_bbf_stage_classifier_t));
-  for (i = 0; i < cascade->count; i++)
-  {
-    sprintf(buf, "%s/stage-%d.txt", directory, i);
-    if (_ccv_read_bbf_stage_classifier(buf, &cascade->stage_classifier[i]) < 0)
-    {
-      cascade->count = i;
-      break;
-    }
-  }
-  fclose(r);
-  return cascade;
-}
-
-ccv_bbf_classifier_cascade_t *ccv_bbf_classifier_cascade_read_binary(char *s)
-{
-  int i;
-  ccv_bbf_classifier_cascade_t *cascade = (ccv_bbf_classifier_cascade_t *)ccmalloc(sizeof(ccv_bbf_classifier_cascade_t));
-  memcpy(&cascade->count, s, sizeof(cascade->count));
-  s += sizeof(cascade->count);
-  memcpy(&cascade->size.width, s, sizeof(cascade->size.width));
-  s += sizeof(cascade->size.width);
-  memcpy(&cascade->size.height, s, sizeof(cascade->size.height));
-  s += sizeof(cascade->size.height);
-  ccv_bbf_stage_classifier_t *classifier = cascade->stage_classifier = (ccv_bbf_stage_classifier_t *)ccmalloc(cascade->count * sizeof(ccv_bbf_stage_classifier_t));
-  for (i = 0; i < cascade->count; i++, classifier++)
-  {
-    memcpy(&classifier->count, s, sizeof(classifier->count));
-    s += sizeof(classifier->count);
-    memcpy(&classifier->threshold, s, sizeof(classifier->threshold));
-    s += sizeof(classifier->threshold);
-    classifier->feature = (ccv_bbf_feature_t *)ccmalloc(classifier->count * sizeof(ccv_bbf_feature_t));
-    classifier->alpha = (float *)ccmalloc(classifier->count * 2 * sizeof(float));
-    memcpy(classifier->feature, s, classifier->count * sizeof(ccv_bbf_feature_t));
-    s += classifier->count * sizeof(ccv_bbf_feature_t);
-    memcpy(classifier->alpha, s, classifier->count * 2 * sizeof(float));
-    s += classifier->count * 2 * sizeof(float);
-  }
-  return cascade;
-}
-
-int ccv_bbf_classifier_cascade_write_binary(ccv_bbf_classifier_cascade_t *cascade, char *s, int slen)
-{
-  int i;
-  int len = sizeof(cascade->count) + sizeof(cascade->size.width) + sizeof(cascade->size.height);
-  ccv_bbf_stage_classifier_t *classifier = cascade->stage_classifier;
-  for (i = 0; i < cascade->count; i++, classifier++)
-    len += sizeof(classifier->count) + sizeof(classifier->threshold) + classifier->count * sizeof(ccv_bbf_feature_t) + classifier->count * 2 * sizeof(float);
-  if (slen >= len)
-  {
-    memcpy(s, &cascade->count, sizeof(cascade->count));
-    s += sizeof(cascade->count);
-    memcpy(s, &cascade->size.width, sizeof(cascade->size.width));
-    s += sizeof(cascade->size.width);
-    memcpy(s, &cascade->size.height, sizeof(cascade->size.height));
-    s += sizeof(cascade->size.height);
-    classifier = cascade->stage_classifier;
-    for (i = 0; i < cascade->count; i++, classifier++)
-    {
-      memcpy(s, &classifier->count, sizeof(classifier->count));
-      s += sizeof(classifier->count);
-      memcpy(s, &classifier->threshold, sizeof(classifier->threshold));
-      s += sizeof(classifier->threshold);
-      memcpy(s, classifier->feature, classifier->count * sizeof(ccv_bbf_feature_t));
-      s += classifier->count * sizeof(ccv_bbf_feature_t);
-      memcpy(s, classifier->alpha, classifier->count * 2 * sizeof(float));
-      s += classifier->count * 2 * sizeof(float);
-    }
-  }
-  return len;
-}
-
-void ccv_bbf_classifier_cascade_free(ccv_bbf_classifier_cascade_t *cascade)
-{
-  int i;
-  for (i = 0; i < cascade->count; ++i)
-  {
-    ccfree(cascade->stage_classifier[i].feature);
-    ccfree(cascade->stage_classifier[i].alpha);
-  }
-  ccfree(cascade->stage_classifier);
-  ccfree(cascade);
-}
\ No newline at end of file
diff --git a/test/source.c++ b/test/source.c++
index 6a6b390..97e29e8 100644
--- a/test/source.c++
+++ b/test/source.c++
@@ -88,1415 +88,4 @@ static int _ccv_read_bbf_stage_classifier(const char* file, ccv_bbf_stage_classi
   return 0;
 }
 
-#ifdef HAVE_GSL
-
-static unsigned int _ccv_bbf_time_measure()
-{
-  struct timeval tv;
-  gettimeofday(&tv, 0);
-  return tv.tv_sec * 1000000 + tv.tv_usec;
-}
-
-#define less_than(a, b, aux) ((a) < (b))
-CCV_IMPLEMENT_QSORT(_ccv_sort_32f, float, less_than)
-#undef less_than
-
-static void _ccv_bbf_eval_data(ccv_bbf_stage_classifier_t* classifier, unsigned char** posdata, int posnum, unsigned char** negdata, int negnum, ccv_size_t size, float* peval, float* neval)
-{
-  int i, j;
-  int steps[] = { _ccv_width_padding(size.width),
-          _ccv_width_padding(size.width >> 1),
-          _ccv_width_padding(size.width >> 2) };
-  int isizs0 = steps[0] * size.height;
-  int isizs01 = isizs0 + steps[1] * (size.height >> 1);
-  for (i = 0; i < posnum; i++)
-  {
-    unsigned char* u8[] = { posdata[i], posdata[i] + isizs0, posdata[i] + isizs01 };
-    float sum = 0;
-    float* alpha = classifier->alpha;
-    ccv_bbf_feature_t* feature = classifier->feature;
-    for (j = 0; j < classifier->count; ++j, alpha += 2, ++feature)
-      sum += alpha[_ccv_run_bbf_feature(feature, steps, u8)];
-    peval[i] = sum;
-  }
-  for (i = 0; i < negnum; i++)
-  {
-    unsigned char* u8[] = { negdata[i], negdata[i] + isizs0, negdata[i] + isizs01 };
-    float sum = 0;
-    float* alpha = classifier->alpha;
-    ccv_bbf_feature_t* feature = classifier->feature;
-    for (j = 0; j < classifier->count; ++j, alpha += 2, ++feature)
-      sum += alpha[_ccv_run_bbf_feature(feature, steps, u8)];
-    neval[i] = sum;
-  }
-}
-
-static int _ccv_prune_positive_data(ccv_bbf_classifier_cascade_t* cascade, unsigned char** posdata, int posnum, ccv_size_t size)
-{
-  float* peval = (float*)ccmalloc(posnum * sizeof(float));
-  int i, j, k, rpos = posnum;
-  for (i = 0; i < cascade->count; i++)
-  {
-    _ccv_bbf_eval_data(cascade->stage_classifier + i, posdata, rpos, 0, 0, size, peval, 0);
-    k = 0;
-    for (j = 0; j < rpos; j++)
-      if (peval[j] >= cascade->stage_classifier[i].threshold)
-      {
-        posdata[k] = posdata[j];
-        ++k;
-      } else {
-        ccfree(posdata[j]);
-      }
-    rpos = k;
-  }
-  ccfree(peval);
-  return rpos;
-}
-
-static int _ccv_prepare_background_data(ccv_bbf_classifier_cascade_t* cascade, char** bgfiles, int bgnum, unsigned char** negdata, int negnum)
-{
-  int t, i, j, k, q;
-  int negperbg;
-  int negtotal = 0;
-  int steps[] = { _ccv_width_padding(cascade->size.width),
-          _ccv_width_padding(cascade->size.width >> 1),
-          _ccv_width_padding(cascade->size.width >> 2) };
-  int isizs0 = steps[0] * cascade->size.height;
-  int isizs1 = steps[1] * (cascade->size.height >> 1);
-  int isizs2 = steps[2] * (cascade->size.height >> 2);
-  int* idcheck = (int*)ccmalloc(negnum * sizeof(int));
-
-  gsl_rng_env_setup();
-
-  gsl_rng* rng = gsl_rng_alloc(gsl_rng_default);
-  gsl_rng_set(rng, (unsigned long int)idcheck);
-
-  ccv_size_t imgsz = cascade->size;
-  int rneg = negtotal;
-  for (t = 0; negtotal < negnum; t++)
-  {
-    PRINT(CCV_CLI_INFO, "preparing negative data ...  0%%");
-    for (i = 0; i < bgnum; i++)
-    {
-      negperbg = (t < 2) ? (negnum - negtotal) / (bgnum - i) + 1 : negnum - negtotal;
-      ccv_dense_matrix_t* image = 0;
-      ccv_read(bgfiles[i], &image, CCV_IO_GRAY | CCV_IO_ANY_FILE);
-      assert((image->type & CCV_C1) && (image->type & CCV_8U));
-      if (image == 0)
-      {
-        PRINT(CCV_CLI_ERROR, "\n%s file corrupted\n", bgfiles[i]);
-        continue;
-      }
-      if (t % 2 != 0)
-        ccv_flip(image, 0, 0, CCV_FLIP_X);
-      if (t % 4 >= 2)
-        ccv_flip(image, 0, 0, CCV_FLIP_Y);
-      ccv_bbf_param_t params = { .interval = 3, .min_neighbors = 0, .accurate = 1, .flags = 0, .size = cascade->size };
-      ccv_array_t* detected = ccv_bbf_detect_objects(image, &cascade, 1, params);
-      memset(idcheck, 0, ccv_min(detected->rnum, negperbg) * sizeof(int));
-      for (j = 0; j < ccv_min(detected->rnum, negperbg); j++)
-      {
-        int r = gsl_rng_uniform_int(rng, detected->rnum);
-        int flag = 1;
-        ccv_rect_t* rect = (ccv_rect_t*)ccv_array_get(detected, r);
-        while (flag) {
-          flag = 0;
-          for (k = 0; k < j; k++)
-            if (r == idcheck[k])
-            {
-              flag = 1;
-              r = gsl_rng_uniform_int(rng, detected->rnum);
-              break;
-            }
-          rect = (ccv_rect_t*)ccv_array_get(detected, r);
-          if ((rect->x < 0) || (rect->y < 0) || (rect->width + rect->x > image->cols) || (rect->height + rect->y > image->rows))
-          {
-            flag = 1;
-            r = gsl_rng_uniform_int(rng, detected->rnum);
-          }
-        }
-        idcheck[j] = r;
-        ccv_dense_matrix_t* temp = 0;
-        ccv_dense_matrix_t* imgs0 = 0;
-        ccv_dense_matrix_t* imgs1 = 0;
-        ccv_dense_matrix_t* imgs2 = 0;
-        ccv_slice(image, (ccv_matrix_t**)&temp, 0, rect->y, rect->x, rect->height, rect->width);
-        ccv_resample(temp, &imgs0, 0, imgsz.height, imgsz.width, CCV_INTER_AREA);
-        assert(imgs0->step == steps[0]);
-        ccv_matrix_free(temp);
-        ccv_sample_down(imgs0, &imgs1, 0, 0, 0);
-        assert(imgs1->step == steps[1]);
-        ccv_sample_down(imgs1, &imgs2, 0, 0, 0);
-        assert(imgs2->step == steps[2]);
-
-        negdata[negtotal] = (unsigned char*)ccmalloc(isizs0 + isizs1 + isizs2);
-        unsigned char* u8s0 = negdata[negtotal];
-        unsigned char* u8s1 = negdata[negtotal] + isizs0;
-        unsigned char* u8s2 = negdata[negtotal] + isizs0 + isizs1;
-        unsigned char* u8[] = { u8s0, u8s1, u8s2 };
-        memcpy(u8s0, imgs0->data.u8, imgs0->rows * imgs0->step);
-        ccv_matrix_free(imgs0);
-        memcpy(u8s1, imgs1->data.u8, imgs1->rows * imgs1->step);
-        ccv_matrix_free(imgs1);
-        memcpy(u8s2, imgs2->data.u8, imgs2->rows * imgs2->step);
-        ccv_matrix_free(imgs2);
-
-        flag = 1;
-        ccv_bbf_stage_classifier_t* classifier = cascade->stage_classifier;
-        for (k = 0; k < cascade->count; ++k, ++classifier)
-        {
-          float sum = 0;
-          float* alpha = classifier->alpha;
-          ccv_bbf_feature_t* feature = classifier->feature;
-          for (q = 0; q < classifier->count; ++q, alpha += 2, ++feature)
-            sum += alpha[_ccv_run_bbf_feature(feature, steps, u8)];
-          if (sum < classifier->threshold)
-          {
-            flag = 0;
-            break;
-          }
-        }
-        if (!flag)
-          ccfree(negdata[negtotal]);
-        else {
-          ++negtotal;
-          if (negtotal >= negnum)
-            break;
-        }
-      }
-      ccv_array_free(detected);
-      ccv_matrix_free(image);
-      ccv_drain_cache();
-      PRINT(CCV_CLI_INFO, "\rpreparing negative data ... %2d%%", 100 * negtotal / negnum);
-      fflush(0);
-      if (negtotal >= negnum)
-        break;
-    }
-    if (rneg == negtotal)
-      break;
-    rneg = negtotal;
-    PRINT(CCV_CLI_INFO, "\nentering additional round %d\n", t + 1);
-  }
-  gsl_rng_free(rng);
-  ccfree(idcheck);
-  ccv_drain_cache();
-  PRINT(CCV_CLI_INFO, "\n");
-  return negtotal;
-}
-
-static void _ccv_prepare_positive_data(ccv_dense_matrix_t** posimg, unsigned char** posdata, ccv_size_t size, int posnum)
-{
-  PRINT(CCV_CLI_INFO, "preparing positive data ...  0%%");
-  int i;
-  for (i = 0; i < posnum; i++)
-  {
-    ccv_dense_matrix_t* imgs0 = posimg[i];
-    ccv_dense_matrix_t* imgs1 = 0;
-    ccv_dense_matrix_t* imgs2 = 0;
-    assert((imgs0->type & CCV_C1) && (imgs0->type & CCV_8U) && imgs0->rows == size.height && imgs0->cols == size.width);
-    ccv_sample_down(imgs0, &imgs1, 0, 0, 0);
-    ccv_sample_down(imgs1, &imgs2, 0, 0, 0);
-    int isizs0 = imgs0->rows * imgs0->step;
-    int isizs1 = imgs1->rows * imgs1->step;
-    int isizs2 = imgs2->rows * imgs2->step;
-
-    posdata[i] = (unsigned char*)ccmalloc(isizs0 + isizs1 + isizs2);
-    memcpy(posdata[i], imgs0->data.u8, isizs0);
-    memcpy(posdata[i] + isizs0, imgs1->data.u8, isizs1);
-    memcpy(posdata[i] + isizs0 + isizs1, imgs2->data.u8, isizs2);
-
-    PRINT(CCV_CLI_INFO, "\rpreparing positive data ... %2d%%", 100 * (i + 1) / posnum);
-    fflush(0);
-
-    ccv_matrix_free(imgs1);
-    ccv_matrix_free(imgs2);
-  }
-  ccv_drain_cache();
-  PRINT(CCV_CLI_INFO, "\n");
-}
-
-typedef struct {
-  double fitness;
-  int pk, nk;
-  int age;
-  double error;
-  ccv_bbf_feature_t feature;
-} ccv_bbf_gene_t;
-
-static inline void _ccv_bbf_genetic_fitness(ccv_bbf_gene_t* gene)
-{
-  gene->fitness = (1 - gene->error) * exp(-0.01 * gene->age) * exp((gene->pk + gene->nk) * log(1.015));
-}
-
-static inline int _ccv_bbf_exist_gene_feature(ccv_bbf_gene_t* gene, int x, int y, int z)
-{
-  int i;
-  for (i = 0; i < gene->pk; i++)
-    if (z == gene->feature.pz[i] && x == gene->feature.px[i] && y == gene->feature.py[i])
-      return 1;
-  for (i = 0; i < gene->nk; i++)
-    if (z == gene->feature.nz[i] && x == gene->feature.nx[i] && y == gene->feature.ny[i])
-      return 1;
-  return 0;
-}
-
-static inline void _ccv_bbf_randomize_gene(gsl_rng* rng, ccv_bbf_gene_t* gene, int* rows, int* cols)
-{
-  int i;
-  do {
-    gene->pk = gsl_rng_uniform_int(rng, CCV_BBF_POINT_MAX - 1) + 1;
-    gene->nk = gsl_rng_uniform_int(rng, CCV_BBF_POINT_MAX - 1) + 1;
-  } while (gene->pk + gene->nk < CCV_BBF_POINT_MIN); /* a hard restriction of at least 3 points have to be examed */
-  gene->feature.size = ccv_max(gene->pk, gene->nk);
-  gene->age = 0;
-  for (i = 0; i < CCV_BBF_POINT_MAX; i++)
-  {
-    gene->feature.pz[i] = -1;
-    gene->feature.nz[i] = -1;
-  }
-  int x, y, z;
-  for (i = 0; i < gene->pk; i++)
-  {
-    do {
-      z = gsl_rng_uniform_int(rng, 3);
-      x = gsl_rng_uniform_int(rng, cols[z]);
-      y = gsl_rng_uniform_int(rng, rows[z]);
-    } while (_ccv_bbf_exist_gene_feature(gene, x, y, z));
-    gene->feature.pz[i] = z;
-    gene->feature.px[i] = x;
-    gene->feature.py[i] = y;
-  }
-  for (i = 0; i < gene->nk; i++)
-  {
-    do {
-      z = gsl_rng_uniform_int(rng, 3);
-      x = gsl_rng_uniform_int(rng, cols[z]);
-      y = gsl_rng_uniform_int(rng, rows[z]);
-    } while ( _ccv_bbf_exist_gene_feature(gene, x, y, z));
-    gene->feature.nz[i] = z;
-    gene->feature.nx[i] = x;
-    gene->feature.ny[i] = y;
-  }
-}
-
-static inline double _ccv_bbf_error_rate(ccv_bbf_feature_t* feature, unsigned char** posdata, int posnum, unsigned char** negdata, int negnum, ccv_size_t size, double* pw, double* nw)
-{
-  int i;
-  int steps[] = { _ccv_width_padding(size.width),
-          _ccv_width_padding(size.width >> 1),
-          _ccv_width_padding(size.width >> 2) };
-  int isizs0 = steps[0] * size.height;
-  int isizs01 = isizs0 + steps[1] * (size.height >> 1);
-  double error = 0;
-  for (i = 0; i < posnum; i++)
-  {
-    unsigned char* u8[] = { posdata[i], posdata[i] + isizs0, posdata[i] + isizs01 };
-    if (!_ccv_run_bbf_feature(feature, steps, u8))
-      error += pw[i];
-  }
-  for (i = 0; i < negnum; i++)
-  {
-    unsigned char* u8[] = { negdata[i], negdata[i] + isizs0, negdata[i] + isizs01 };
-    if ( _ccv_run_bbf_feature(feature, steps, u8))
-      error += nw[i];
-  }
-  return error;
-}
-
-#define less_than(fit1, fit2, aux) ((fit1).fitness >= (fit2).fitness)
-static CCV_IMPLEMENT_QSORT(_ccv_bbf_genetic_qsort, ccv_bbf_gene_t, less_than)
-#undef less_than
-
-static ccv_bbf_feature_t _ccv_bbf_genetic_optimize(unsigned char** posdata, int posnum, unsigned char** negdata, int negnum, int ftnum, ccv_size_t size, double* pw, double* nw)
-{
-  ccv_bbf_feature_t best;
-  /* seed (random method) */
-  gsl_rng_env_setup();
-  gsl_rng* rng = gsl_rng_alloc(gsl_rng_default);
-  union { unsigned long int li; double db; } dbli;
-  dbli.db = pw[0] + nw[0];
-  gsl_rng_set(rng, dbli.li);
-  int i, j;
-  int pnum = ftnum * 100;
-  assert(pnum > 0);
-  ccv_bbf_gene_t* gene = (ccv_bbf_gene_t*)ccmalloc(pnum * sizeof(ccv_bbf_gene_t));
-  int rows[] = { size.height, size.height >> 1, size.height >> 2 };
-  int cols[] = { size.width, size.width >> 1, size.width >> 2 };
-  for (i = 0; i < pnum; i++)
-    _ccv_bbf_randomize_gene(rng, &gene[i], rows, cols);
-  unsigned int timer = _ccv_bbf_time_measure();
-#ifdef USE_OPENMP
-#pragma omp parallel for private(i) schedule(dynamic)
-#endif
-  for (i = 0; i < pnum; i++)
-    gene[i].error = _ccv_bbf_error_rate(&gene[i].feature, posdata, posnum, negdata, negnum, size, pw, nw);
-  timer = _ccv_bbf_time_measure() - timer;
-  for (i = 0; i < pnum; i++)
-    _ccv_bbf_genetic_fitness(&gene[i]);
-  double best_err = 1;
-  int rnum = ftnum * 39; /* number of randomize */
-  int mnum = ftnum * 40; /* number of mutation */
-  int hnum = ftnum * 20; /* number of hybrid */
-  /* iteration stop crit : best no change in 40 iterations */
-  int it = 0, t;
-  for (t = 0 ; it < 40; ++it, ++t)
-  {
-    int min_id = 0;
-    double min_err = gene[0].error;
-    for (i = 1; i < pnum; i++)
-      if (gene[i].error < min_err)
-      {
-        min_id = i;
-        min_err = gene[i].error;
-      }
-    min_err = gene[min_id].error = _ccv_bbf_error_rate(&gene[min_id].feature, posdata, posnum, negdata, negnum, size, pw, nw);
-    if (min_err < best_err)
-    {
-      best_err = min_err;
-      memcpy(&best, &gene[min_id].feature, sizeof(best));
-      PRINT(CCV_CLI_INFO, "best bbf feature with error %f\n|-size: %d\n|-positive point: ", best_err, best.size);
-      for (i = 0; i < best.size; i++)
-        PRINT(CCV_CLI_INFO, "(%d %d %d), ", best.px[i], best.py[i], best.pz[i]);
-      PRINT(CCV_CLI_INFO, "\n|-negative point: ");
-      for (i = 0; i < best.size; i++)
-        PRINT(CCV_CLI_INFO, "(%d %d %d), ", best.nx[i], best.ny[i], best.nz[i]);
-      PRINT(CCV_CLI_INFO, "\n");
-      it = 0;
-    }
-    PRINT(CCV_CLI_INFO, "minimum error achieved in round %d(%d) : %f with %d ms\n", t, it, min_err, timer / 1000);
-    _ccv_bbf_genetic_qsort(gene, pnum, 0);
-    for (i = 0; i < ftnum; i++)
-      ++gene[i].age;
-    for (i = ftnum; i < ftnum + mnum; i++)
-    {
-      int parent = gsl_rng_uniform_int(rng, ftnum);
-      memcpy(gene + i, gene + parent, sizeof(ccv_bbf_gene_t));
-      /* three mutation strategy : 1. add, 2. remove, 3. refine */
-      int pnm, pn = gsl_rng_uniform_int(rng, 2);
-      int* pnk[] = { &gene[i].pk, &gene[i].nk };
-      int* pnx[] = { gene[i].feature.px, gene[i].feature.nx };
-      int* pny[] = { gene[i].feature.py, gene[i].feature.ny };
-      int* pnz[] = { gene[i].feature.pz, gene[i].feature.nz };
-      int x, y, z;
-      int victim, decay = 1;
-      do {
-        switch (gsl_rng_uniform_int(rng, 3))
-        {
-          case 0: /* add */
-            if (gene[i].pk == CCV_BBF_POINT_MAX && gene[i].nk == CCV_BBF_POINT_MAX)
-              break;
-            while (*pnk[pn] + 1 > CCV_BBF_POINT_MAX)
-              pn = gsl_rng_uniform_int(rng, 2);
-            do {
-              z = gsl_rng_uniform_int(rng, 3);
-              x = gsl_rng_uniform_int(rng, cols[z]);
-              y = gsl_rng_uniform_int(rng, rows[z]);
-            } while (_ccv_bbf_exist_gene_feature(&gene[i], x, y, z));
-            pnz[pn][*pnk[pn]] = z;
-            pnx[pn][*pnk[pn]] = x;
-            pny[pn][*pnk[pn]] = y;
-            ++(*pnk[pn]);
-            gene[i].feature.size = ccv_max(gene[i].pk, gene[i].nk);
-            decay = gene[i].age = 0;
-            break;
-          case 1: /* remove */
-            if (gene[i].pk + gene[i].nk <= CCV_BBF_POINT_MIN) /* at least 3 points have to be examed */
-              break;
-            while (*pnk[pn] - 1 <= 0) // || *pnk[pn] + *pnk[!pn] - 1 < CCV_BBF_POINT_MIN)
-              pn = gsl_rng_uniform_int(rng, 2);
-            victim = gsl_rng_uniform_int(rng, *pnk[pn]);
-            for (j = victim; j < *pnk[pn] - 1; j++)
-            {
-              pnz[pn][j] = pnz[pn][j + 1];
-              pnx[pn][j] = pnx[pn][j + 1];
-              pny[pn][j] = pny[pn][j + 1];
-            }
-            pnz[pn][*pnk[pn] - 1] = -1;
-            --(*pnk[pn]);
-            gene[i].feature.size = ccv_max(gene[i].pk, gene[i].nk);
-            decay = gene[i].age = 0;
-            break;
-          case 2: /* refine */
-            pnm = gsl_rng_uniform_int(rng, *pnk[pn]);
-            do {
-              z = gsl_rng_uniform_int(rng, 3);
-              x = gsl_rng_uniform_int(rng, cols[z]);
-              y = gsl_rng_uniform_int(rng, rows[z]);
-            } while (_ccv_bbf_exist_gene_feature(&gene[i], x, y, z));
-            pnz[pn][pnm] = z;
-            pnx[pn][pnm] = x;
-            pny[pn][pnm] = y;
-            decay = gene[i].age = 0;
-            break;
-        }
-      } while (decay);
-    }
-    for (i = ftnum + mnum; i < ftnum + mnum + hnum; i++)
-    {
-      /* hybrid strategy: taking positive points from dad, negative points from mum */
-      int dad, mum;
-      do {
-        dad = gsl_rng_uniform_int(rng, ftnum);
-        mum = gsl_rng_uniform_int(rng, ftnum);
-      } while (dad == mum || gene[dad].pk + gene[mum].nk < CCV_BBF_POINT_MIN); /* at least 3 points have to be examed */
-      for (j = 0; j < CCV_BBF_POINT_MAX; j++)
-      {
-        gene[i].feature.pz[j] = -1;
-        gene[i].feature.nz[j] = -1;
-      }
-      gene[i].pk = gene[dad].pk;
-      for (j = 0; j < gene[i].pk; j++)
-      {
-        gene[i].feature.pz[j] = gene[dad].feature.pz[j];
-        gene[i].feature.px[j] = gene[dad].feature.px[j];
-        gene[i].feature.py[j] = gene[dad].feature.py[j];
-      }
-      gene[i].nk = gene[mum].nk;
-      for (j = 0; j < gene[i].nk; j++)
-      {
-        gene[i].feature.nz[j] = gene[mum].feature.nz[j];
-        gene[i].feature.nx[j] = gene[mum].feature.nx[j];
-        gene[i].feature.ny[j] = gene[mum].feature.ny[j];
-      }
-      gene[i].feature.size = ccv_max(gene[i].pk, gene[i].nk);
-      gene[i].age = 0;
-    }
-    for (i = ftnum + mnum + hnum; i < ftnum + mnum + hnum + rnum; i++)
-      _ccv_bbf_randomize_gene(rng, &gene[i], rows, cols);
-    timer = _ccv_bbf_time_measure();
-#ifdef USE_OPENMP
-#pragma omp parallel for private(i) schedule(dynamic)
-#endif
-    for (i = 0; i < pnum; i++)
-      gene[i].error = _ccv_bbf_error_rate(&gene[i].feature, posdata, posnum, negdata, negnum, size, pw, nw);
-    timer = _ccv_bbf_time_measure() - timer;
-    for (i = 0; i < pnum; i++)
-      _ccv_bbf_genetic_fitness(&gene[i]);
-  }
-  ccfree(gene);
-  gsl_rng_free(rng);
-  return best;
-}
-
-#define less_than(fit1, fit2, aux) ((fit1).error < (fit2).error)
-static CCV_IMPLEMENT_QSORT(_ccv_bbf_best_qsort, ccv_bbf_gene_t, less_than)
-#undef less_than
-
-static ccv_bbf_gene_t _ccv_bbf_best_gene(ccv_bbf_gene_t* gene, int pnum, int point_min, unsigned char** posdata, int posnum, unsigned char** negdata, int negnum, ccv_size_t size, double* pw, double* nw)
-{
-  int i;
-  unsigned int timer = _ccv_bbf_time_measure();
-#ifdef USE_OPENMP
-#pragma omp parallel for private(i) schedule(dynamic)
-#endif
-  for (i = 0; i < pnum; i++)
-    gene[i].error = _ccv_bbf_error_rate(&gene[i].feature, posdata, posnum, negdata, negnum, size, pw, nw);
-  timer = _ccv_bbf_time_measure() - timer;
-  _ccv_bbf_best_qsort(gene, pnum, 0);
-  int min_id = 0;
-  double min_err = gene[0].error;
-  for (i = 0; i < pnum; i++)
-    if (gene[i].nk + gene[i].pk >= point_min)
-    {
-      min_id = i;
-      min_err = gene[i].error;
-      break;
-    }
-  PRINT(CCV_CLI_INFO, "local best bbf feature with error %f\n|-size: %d\n|-positive point: ", min_err, gene[min_id].feature.size);
-  for (i = 0; i < gene[min_id].feature.size; i++)
-    PRINT(CCV_CLI_INFO, "(%d %d %d), ", gene[min_id].feature.px[i], gene[min_id].feature.py[i], gene[min_id].feature.pz[i]);
-  PRINT(CCV_CLI_INFO, "\n|-negative point: ");
-  for (i = 0; i < gene[min_id].feature.size; i++)
-    PRINT(CCV_CLI_INFO, "(%d %d %d), ", gene[min_id].feature.nx[i], gene[min_id].feature.ny[i], gene[min_id].feature.nz[i]);
-  PRINT(CCV_CLI_INFO, "\nthe computation takes %d ms\n", timer / 1000);
-  return gene[min_id];
-}
-
-static ccv_bbf_feature_t _ccv_bbf_convex_optimize(unsigned char** posdata, int posnum, unsigned char** negdata, int negnum, ccv_bbf_feature_t* best_feature, ccv_size_t size, double* pw, double* nw)
-{
-  ccv_bbf_gene_t best_gene;
-  /* seed (random method) */
-  gsl_rng_env_setup();
-  gsl_rng* rng = gsl_rng_alloc(gsl_rng_default);
-  union { unsigned long int li; double db; } dbli;
-  dbli.db = pw[0] + nw[0];
-  gsl_rng_set(rng, dbli.li);
-  int i, j, k, q, p, g, t;
-  int rows[] = { size.height, size.height >> 1, size.height >> 2 };
-  int cols[] = { size.width, size.width >> 1, size.width >> 2 };
-  int pnum = rows[0] * cols[0] + rows[1] * cols[1] + rows[2] * cols[2];
-  ccv_bbf_gene_t* gene = (ccv_bbf_gene_t*)ccmalloc((pnum * (CCV_BBF_POINT_MAX * 2 + 1) * 2 + CCV_BBF_POINT_MAX * 2 + 1) * sizeof(ccv_bbf_gene_t));
-  if (best_feature == 0)
-  {
-    /* bootstrapping the best feature, start from two pixels, one for positive, one for negative
-     * the bootstrapping process go like this: first, it will assign a random pixel as positive
-     * and enumerate every possible pixel as negative, and pick the best one. Then, enumerate every
-     * possible pixel as positive, and pick the best one, until it converges */
-    memset(&best_gene, 0, sizeof(ccv_bbf_gene_t));
-    for (i = 0; i < CCV_BBF_POINT_MAX; i++)
-      best_gene.feature.pz[i] = best_gene.feature.nz[i] = -1;
-    best_gene.pk = 1;
-    best_gene.nk = 0;
-    best_gene.feature.size = 1;
-    best_gene.feature.pz[0] = gsl_rng_uniform_int(rng, 3);
-    best_gene.feature.px[0] = gsl_rng_uniform_int(rng, cols[best_gene.feature.pz[0]]);
-    best_gene.feature.py[0] = gsl_rng_uniform_int(rng, rows[best_gene.feature.pz[0]]);
-    for (t = 0; ; ++t)
-    {
-      g = 0;
-      if (t % 2 == 0)
-      {
-        for (i = 0; i < 3; i++)
-          for (j = 0; j < cols[i]; j++)
-            for (k = 0; k < rows[i]; k++)
-              if (i != best_gene.feature.pz[0] || j != best_gene.feature.px[0] || k != best_gene.feature.py[0])
-              {
-                gene[g] = best_gene;
-                gene[g].pk = gene[g].nk = 1;
-                gene[g].feature.nz[0] = i;
-                gene[g].feature.nx[0] = j;
-                gene[g].feature.ny[0] = k;
-                g++;
-              }
-      } else {
-        for (i = 0; i < 3; i++)
-          for (j = 0; j < cols[i]; j++)
-            for (k = 0; k < rows[i]; k++)
-              if (i != best_gene.feature.nz[0] || j != best_gene.feature.nx[0] || k != best_gene.feature.ny[0])
-              {
-                gene[g] = best_gene;
-                gene[g].pk = gene[g].nk = 1;
-                gene[g].feature.pz[0] = i;
-                gene[g].feature.px[0] = j;
-                gene[g].feature.py[0] = k;
-                g++;
-              }
-      }
-      PRINT(CCV_CLI_INFO, "bootstrapping round : %d\n", t);
-      ccv_bbf_gene_t local_gene = _ccv_bbf_best_gene(gene, g, 2, posdata, posnum, negdata, negnum, size, pw, nw);
-      if (local_gene.error >= best_gene.error - 1e-10)
-        break;
-      best_gene = local_gene;
-    }
-  } else {
-    best_gene.feature = *best_feature;
-    best_gene.pk = best_gene.nk = best_gene.feature.size;
-    for (i = 0; i < CCV_BBF_POINT_MAX; i++)
-      if (best_feature->pz[i] == -1)
-      {
-        best_gene.pk = i;
-        break;
-      }
-    for (i = 0; i < CCV_BBF_POINT_MAX; i++)
-      if (best_feature->nz[i] == -1)
-      {
-        best_gene.nk = i;
-        break;
-      }
-  }
-  /* after bootstrapping, the float search technique will do the following permutations:
-   * a). add a new point to positive or negative
-   * b). remove a point from positive or negative
-   * c). move an existing point in positive or negative to another position
-   * the three rules applied exhaustively, no heuristic used. */
-  for (t = 0; ; ++t)
-  {
-    g = 0;
-    for (i = 0; i < 3; i++)
-      for (j = 0; j < cols[i]; j++)
-        for (k = 0; k < rows[i]; k++)
-          if (!_ccv_bbf_exist_gene_feature(&best_gene, j, k, i))
-          {
-            /* add positive point */
-            if (best_gene.pk < CCV_BBF_POINT_MAX - 1)
-            {
-              gene[g] = best_gene;
-              gene[g].feature.pz[gene[g].pk] = i;
-              gene[g].feature.px[gene[g].pk] = j;
-              gene[g].feature.py[gene[g].pk] = k;
-              gene[g].pk++;
-              gene[g].feature.size = ccv_max(gene[g].pk, gene[g].nk);
-              g++;
-            }
-            /* add negative point */
-            if (best_gene.nk < CCV_BBF_POINT_MAX - 1)
-            {
-              gene[g] = best_gene;
-              gene[g].feature.nz[gene[g].nk] = i;
-              gene[g].feature.nx[gene[g].nk] = j;
-              gene[g].feature.ny[gene[g].nk] = k;
-              gene[g].nk++;
-              gene[g].feature.size = ccv_max(gene[g].pk, gene[g].nk);
-              g++;
-            }
-            /* refine positive point */
-            for (q = 0; q < best_gene.pk; q++)
-            {
-              gene[g] = best_gene;
-              gene[g].feature.pz[q] = i;
-              gene[g].feature.px[q] = j;
-              gene[g].feature.py[q] = k;
-              g++;
-            }
-            /* add positive point, remove negative point */
-            if (best_gene.pk < CCV_BBF_POINT_MAX - 1 && best_gene.nk > 1)
-            {
-              for (q = 0; q < best_gene.nk; q++)
-              {
-                gene[g] = best_gene;
-                gene[g].feature.pz[gene[g].pk] = i;
-                gene[g].feature.px[gene[g].pk] = j;
-                gene[g].feature.py[gene[g].pk] = k;
-                gene[g].pk++;
-                for (p = q; p < best_gene.nk - 1; p++)
-                {
-                  gene[g].feature.nz[p] = gene[g].feature.nz[p + 1];
-                  gene[g].feature.nx[p] = gene[g].feature.nx[p + 1];
-                  gene[g].feature.ny[p] = gene[g].feature.ny[p + 1];
-                }
-                gene[g].feature.nz[gene[g].nk - 1] = -1;
-                gene[g].nk--;
-                gene[g].feature.size = ccv_max(gene[g].pk, gene[g].nk);
-                g++;
-              }
-            }
-            /* refine negative point */
-            for (q = 0; q < best_gene.nk; q++)
-            {
-              gene[g] = best_gene;
-              gene[g].feature.nz[q] = i;
-              gene[g].feature.nx[q] = j;
-              gene[g].feature.ny[q] = k;
-              g++;
-            }
-            /* add negative point, remove positive point */
-            if (best_gene.pk > 1 && best_gene.nk < CCV_BBF_POINT_MAX - 1)
-            {
-              for (q = 0; q < best_gene.pk; q++)
-              {
-                gene[g] = best_gene;
-                gene[g].feature.nz[gene[g].nk] = i;
-                gene[g].feature.nx[gene[g].nk] = j;
-                gene[g].feature.ny[gene[g].nk] = k;
-                gene[g].nk++;
-                for (p = q; p < best_gene.pk - 1; p++)
-                {
-                  gene[g].feature.pz[p] = gene[g].feature.pz[p + 1];
-                  gene[g].feature.px[p] = gene[g].feature.px[p + 1];
-                  gene[g].feature.py[p] = gene[g].feature.py[p + 1];
-                }
-                gene[g].feature.pz[gene[g].pk - 1] = -1;
-                gene[g].pk--;
-                gene[g].feature.size = ccv_max(gene[g].pk, gene[g].nk);
-                g++;
-              }
-            }
-          }
-    if (best_gene.pk > 1)
-      for (q = 0; q < best_gene.pk; q++)
-      {
-        gene[g] = best_gene;
-        for (i = q; i < best_gene.pk - 1; i++)
-        {
-          gene[g].feature.pz[i] = gene[g].feature.pz[i + 1];
-          gene[g].feature.px[i] = gene[g].feature.px[i + 1];
-          gene[g].feature.py[i] = gene[g].feature.py[i + 1];
-        }
-        gene[g].feature.pz[gene[g].pk - 1] = -1;
-        gene[g].pk--;
-        gene[g].feature.size = ccv_max(gene[g].pk, gene[g].nk);
-        g++;
-      }
-    if (best_gene.nk > 1)
-      for (q = 0; q < best_gene.nk; q++)
-      {
-        gene[g] = best_gene;
-        for (i = q; i < best_gene.nk - 1; i++)
-        {
-          gene[g].feature.nz[i] = gene[g].feature.nz[i + 1];
-          gene[g].feature.nx[i] = gene[g].feature.nx[i + 1];
-          gene[g].feature.ny[i] = gene[g].feature.ny[i + 1];
-        }
-        gene[g].feature.nz[gene[g].nk - 1] = -1;
-        gene[g].nk--;
-        gene[g].feature.size = ccv_max(gene[g].pk, gene[g].nk);
-        g++;
-      }
-    gene[g] = best_gene;
-    g++;
-    PRINT(CCV_CLI_INFO, "float search round : %d\n", t);
-    ccv_bbf_gene_t local_gene = _ccv_bbf_best_gene(gene, g, CCV_BBF_POINT_MIN, posdata, posnum, negdata, negnum, size, pw, nw);
-    if (local_gene.error >= best_gene.error - 1e-10)
-      break;
-    best_gene = local_gene;
-  }
-  ccfree(gene);
-  gsl_rng_free(rng);
-  return best_gene.feature;
-}
-
-static int _ccv_write_bbf_stage_classifier(const char* file, ccv_bbf_stage_classifier_t* classifier)
-{
-  FILE* w = fopen(file, "wb");
-  if (w == 0) return -1;
-  fprintf(w, "%d\n", classifier->count);
-  union { float fl; int i; } fli;
-  fli.fl = classifier->threshold;
-  fprintf(w, "%d\n", fli.i);
-  int i, j;
-  for (i = 0; i < classifier->count; i++)
-  {
-    fprintf(w, "%d\n", classifier->feature[i].size);
-    for (j = 0; j < classifier->feature[i].size; j++)
-    {
-      fprintf(w, "%d %d %d\n", classifier->feature[i].px[j], classifier->feature[i].py[j], classifier->feature[i].pz[j]);
-      fprintf(w, "%d %d %d\n", classifier->feature[i].nx[j], classifier->feature[i].ny[j], classifier->feature[i].nz[j]);
-    }
-    union { float fl; int i; } flia, flib;
-    flia.fl = classifier->alpha[i * 2];
-    flib.fl = classifier->alpha[i * 2 + 1];
-    fprintf(w, "%d %d\n", flia.i, flib.i);
-  }
-  fclose(w);
-  return 0;
-}
-
-static int _ccv_read_background_data(const char* file, unsigned char** negdata, int* negnum, ccv_size_t size)
-{
-  int stat = 0;
-  FILE* r = fopen(file, "rb");
-  if (r == 0) return -1;
-  stat |= fread(negnum, sizeof(int), 1, r);
-  int i;
-  int isizs012 = _ccv_width_padding(size.width) * size.height +
-           _ccv_width_padding(size.width >> 1) * (size.height >> 1) +
-           _ccv_width_padding(size.width >> 2) * (size.height >> 2);
-  for (i = 0; i < *negnum; i++)
-  {
-    negdata[i] = (unsigned char*)ccmalloc(isizs012);
-    stat |= fread(negdata[i], 1, isizs012, r);
-  }
-  fclose(r);
-  return 0;
-}
-
-static int _ccv_write_background_data(const char* file, unsigned char** negdata, int negnum, ccv_size_t size)
-{
-  FILE* w = fopen(file, "w");
-  if (w == 0) return -1;
-  fwrite(&negnum, sizeof(int), 1, w);
-  int i;
-  int isizs012 = _ccv_width_padding(size.width) * size.height +
-           _ccv_width_padding(size.width >> 1) * (size.height >> 1) +
-           _ccv_width_padding(size.width >> 2) * (size.height >> 2);
-  for (i = 0; i < negnum; i++)
-    fwrite(negdata[i], 1, isizs012, w);
-  fclose(w);
-  return 0;
-}
-
-static int _ccv_resume_bbf_cascade_training_state(const char* file, int* i, int* k, int* bg, double* pw, double* nw, int posnum, int negnum)
-{
-  int stat = 0;
-  FILE* r = fopen(file, "r");
-  if (r == 0) return -1;
-  stat |= fscanf(r, "%d %d %d", i, k, bg);
-  int j;
-  union { double db; int i[2]; } dbi;
-  for (j = 0; j < posnum; j++)
-  {
-    stat |= fscanf(r, "%d %d", &dbi.i[0], &dbi.i[1]);
-    pw[j] = dbi.db;
-  }
-  for (j = 0; j < negnum; j++)
-  {
-    stat |= fscanf(r, "%d %d", &dbi.i[0], &dbi.i[1]);
-    nw[j] = dbi.db;
-  }
-  fclose(r);
-  return 0;
-}
-
-static int _ccv_save_bbf_cacade_training_state(const char* file, int i, int k, int bg, double* pw, double* nw, int posnum, int negnum)
-{
-  FILE* w = fopen(file, "w");
-  if (w == 0) return -1;
-  fprintf(w, "%d %d %d\n", i, k, bg);
-  int j;
-  union { double db; int i[2]; } dbi;
-  for (j = 0; j < posnum; ++j)
-  {
-    dbi.db = pw[j];
-    fprintf(w, "%d %d ", dbi.i[0], dbi.i[1]);
-  }
-  fprintf(w, "\n");
-  for (j = 0; j < negnum; ++j)
-  {
-    dbi.db = nw[j];
-    fprintf(w, "%d %d ", dbi.i[0], dbi.i[1]);
-  }
-  fprintf(w, "\n");
-  fclose(w);
-  return 0;
-}
-
-void ccv_bbf_classifier_cascade_new(ccv_dense_matrix_t** posimg, int posnum, char** bgfiles, int bgnum, int negnum, ccv_size_t size, const char* dir, ccv_bbf_new_param_t params)
-{
-  int i, j, k;
-  /* allocate memory for usage */
-  ccv_bbf_classifier_cascade_t* cascade = (ccv_bbf_classifier_cascade_t*)ccmalloc(sizeof(ccv_bbf_classifier_cascade_t));
-  cascade->count = 0;
-  cascade->size = size;
-  cascade->stage_classifier = (ccv_bbf_stage_classifier_t*)ccmalloc(sizeof(ccv_bbf_stage_classifier_t));
-  unsigned char** posdata = (unsigned char**)ccmalloc(posnum * sizeof(unsigned char*));
-  unsigned char** negdata = (unsigned char**)ccmalloc(negnum * sizeof(unsigned char*));
-  double* pw = (double*)ccmalloc(posnum * sizeof(double));
-  double* nw = (double*)ccmalloc(negnum * sizeof(double));
-  float* peval = (float*)ccmalloc(posnum * sizeof(float));
-  float* neval = (float*)ccmalloc(negnum * sizeof(float));
-  double inv_balance_k = 1. / params.balance_k;
-  /* balance factor k, and weighted with 0.01 */
-  params.balance_k *= 0.01;
-  inv_balance_k *= 0.01;
-
-  int steps[] = { _ccv_width_padding(cascade->size.width),
-          _ccv_width_padding(cascade->size.width >> 1),
-          _ccv_width_padding(cascade->size.width >> 2) };
-  int isizs0 = steps[0] * cascade->size.height;
-  int isizs01 = isizs0 + steps[1] * (cascade->size.height >> 1);
-
-  i = 0;
-  k = 0;
-  int bg = 0;
-  int cacheK = 10;
-  /* state resume code */
-  char buf[1024];
-  sprintf(buf, "%s/stat.txt", dir);
-  _ccv_resume_bbf_cascade_training_state(buf, &i, &k, &bg, pw, nw, posnum, negnum);
-  if (i > 0)
-  {
-    cascade->count = i;
-    ccfree(cascade->stage_classifier);
-    cascade->stage_classifier = (ccv_bbf_stage_classifier_t*)ccmalloc(i * sizeof(ccv_bbf_stage_classifier_t));
-    for (j = 0; j < i; j++)
-    {
-      sprintf(buf, "%s/stage-%d.txt", dir, j);
-      _ccv_read_bbf_stage_classifier(buf, &cascade->stage_classifier[j]);
-    }
-  }
-  if (k > 0)
-    cacheK = k;
-  int rpos, rneg = 0;
-  if (bg)
-  {
-    sprintf(buf, "%s/negs.txt", dir);
-    _ccv_read_background_data(buf, negdata, &rneg, cascade->size);
-  }
-
-  for (; i < params.layer; i++)
-  {
-    if (!bg)
-    {
-      rneg = _ccv_prepare_background_data(cascade, bgfiles, bgnum, negdata, negnum);
-      /* save state of background data */
-      sprintf(buf, "%s/negs.txt", dir);
-      _ccv_write_background_data(buf, negdata, rneg, cascade->size);
-      bg = 1;
-    }
-    double totalw;
-    /* save state of cascade : level, weight etc. */
-    sprintf(buf, "%s/stat.txt", dir);
-    _ccv_save_bbf_cacade_training_state(buf, i, k, bg, pw, nw, posnum, negnum);
-    ccv_bbf_stage_classifier_t classifier;
-    if (k > 0)
-    {
-      /* resume state of classifier */
-      sprintf( buf, "%s/stage-%d.txt", dir, i );
-      _ccv_read_bbf_stage_classifier(buf, &classifier);
-    } else {
-      /* initialize classifier */
-      for (j = 0; j < posnum; j++)
-        pw[j] = params.balance_k;
-      for (j = 0; j < rneg; j++)
-        nw[j] = inv_balance_k;
-      classifier.count = k;
-      classifier.threshold = 0;
-      classifier.feature = (ccv_bbf_feature_t*)ccmalloc(cacheK * sizeof(ccv_bbf_feature_t));
-      classifier.alpha = (float*)ccmalloc(cacheK * 2 * sizeof(float));
-    }
-    _ccv_prepare_positive_data(posimg, posdata, cascade->size, posnum);
-    rpos = _ccv_prune_positive_data(cascade, posdata, posnum, cascade->size);
-    PRINT(CCV_CLI_INFO, "%d postivie data and %d negative data in training\n", rpos, rneg);
-    /* reweight to 1.00 */
-    totalw = 0;
-    for (j = 0; j < rpos; j++)
-      totalw += pw[j];
-    for (j = 0; j < rneg; j++)
-      totalw += nw[j];
-    for (j = 0; j < rpos; j++)
-      pw[j] = pw[j] / totalw;
-    for (j = 0; j < rneg; j++)
-      nw[j] = nw[j] / totalw;
-    for (; ; k++)
-    {
-      /* get overall true-positive, false-positive rate and threshold */
-      double tp = 0, fp = 0, etp = 0, efp = 0;
-      _ccv_bbf_eval_data(&classifier, posdata, rpos, negdata, rneg, cascade->size, peval, neval);
-      _ccv_sort_32f(peval, rpos, 0);
-      classifier.threshold = peval[(int)((1. - params.pos_crit) * rpos)] - 1e-6;
-      for (j = 0; j < rpos; j++)
-      {
-        if (peval[j] >= 0)
-          ++tp;
-        if (peval[j] >= classifier.threshold)
-          ++etp;
-      }
-      tp /= rpos; etp /= rpos;
-      for (j = 0; j < rneg; j++)
-      {
-        if (neval[j] >= 0)
-          ++fp;
-        if (neval[j] >= classifier.threshold)
-          ++efp;
-      }
-      fp /= rneg; efp /= rneg;
-      PRINT(CCV_CLI_INFO, "stage classifier real TP rate : %f, FP rate : %f\n", tp, fp);
-      PRINT(CCV_CLI_INFO, "stage classifier TP rate : %f, FP rate : %f at threshold : %f\n", etp, efp, classifier.threshold);
-      if (k > 0)
-      {
-        /* save classifier state */
-        sprintf(buf, "%s/stage-%d.txt", dir, i);
-        _ccv_write_bbf_stage_classifier(buf, &classifier);
-        sprintf(buf, "%s/stat.txt", dir);
-        _ccv_save_bbf_cacade_training_state(buf, i, k, bg, pw, nw, posnum, negnum);
-      }
-      if (etp > params.pos_crit && efp < params.neg_crit)
-        break;
-      /* TODO: more post-process is needed in here */
-
-      /* select the best feature in current distribution through genetic algorithm optimization */
-      ccv_bbf_feature_t best;
-      if (params.optimizer == CCV_BBF_GENETIC_OPT)
-      {
-        best = _ccv_bbf_genetic_optimize(posdata, rpos, negdata, rneg, params.feature_number, cascade->size, pw, nw);
-      } else if (params.optimizer == CCV_BBF_FLOAT_OPT) {
-        best = _ccv_bbf_convex_optimize(posdata, rpos, negdata, rneg, 0, cascade->size, pw, nw);
-      } else {
-        best = _ccv_bbf_genetic_optimize(posdata, rpos, negdata, rneg, params.feature_number, cascade->size, pw, nw);
-        best = _ccv_bbf_convex_optimize(posdata, rpos, negdata, rneg, &best, cascade->size, pw, nw);
-      }
-      double err = _ccv_bbf_error_rate(&best, posdata, rpos, negdata, rneg, cascade->size, pw, nw);
-      double rw = (1 - err) / err;
-      totalw = 0;
-      /* reweight */
-      for (j = 0; j < rpos; j++)
-      {
-        unsigned char* u8[] = { posdata[j], posdata[j] + isizs0, posdata[j] + isizs01 };
-        if (!_ccv_run_bbf_feature(&best, steps, u8))
-          pw[j] *= rw;
-        pw[j] *= params.balance_k;
-        totalw += pw[j];
-      }
-      for (j = 0; j < rneg; j++)
-      {
-        unsigned char* u8[] = { negdata[j], negdata[j] + isizs0, negdata[j] + isizs01 };
-        if (_ccv_run_bbf_feature(&best, steps, u8))
-          nw[j] *= rw;
-        nw[j] *= inv_balance_k;
-        totalw += nw[j];
-      }
-      for (j = 0; j < rpos; j++)
-        pw[j] = pw[j] / totalw;
-      for (j = 0; j < rneg; j++)
-        nw[j] = nw[j] / totalw;
-      double c = log(rw);
-      PRINT(CCV_CLI_INFO, "coefficient of feature %d: %f\n", k + 1, c);
-      classifier.count = k + 1;
-      /* resizing classifier */
-      if (k >= cacheK)
-      {
-        ccv_bbf_feature_t* feature = (ccv_bbf_feature_t*)ccmalloc(cacheK * 2 * sizeof(ccv_bbf_feature_t));
-        memcpy(feature, classifier.feature, cacheK * sizeof(ccv_bbf_feature_t));
-        ccfree(classifier.feature);
-        float* alpha = (float*)ccmalloc(cacheK * 4 * sizeof(float));
-        memcpy(alpha, classifier.alpha, cacheK * 2 * sizeof(float));
-        ccfree(classifier.alpha);
-        classifier.feature = feature;
-        classifier.alpha = alpha;
-        cacheK *= 2;
-      }
-      /* setup new feature */
-      classifier.feature[k] = best;
-      classifier.alpha[k * 2] = -c;
-      classifier.alpha[k * 2 + 1] = c;
-    }
-    cascade->count = i + 1;
-    ccv_bbf_stage_classifier_t* stage_classifier = (ccv_bbf_stage_classifier_t*)ccmalloc(cascade->count * sizeof(ccv_bbf_stage_classifier_t));
-    memcpy(stage_classifier, cascade->stage_classifier, i * sizeof(ccv_bbf_stage_classifier_t));
-    ccfree(cascade->stage_classifier);
-    stage_classifier[i] = classifier;
-    cascade->stage_classifier = stage_classifier;
-    k = 0;
-    bg = 0;
-    for (j = 0; j < rpos; j++)
-      ccfree(posdata[j]);
-    for (j = 0; j < rneg; j++)
-      ccfree(negdata[j]);
-  }
-
-  ccfree(neval);
-  ccfree(peval);
-  ccfree(nw);
-  ccfree(pw);
-  ccfree(negdata);
-  ccfree(posdata);
-  ccfree(cascade);
-}
-#else
-void ccv_bbf_classifier_cascade_new(ccv_dense_matrix_t** posimg, int posnum, char** bgfiles, int bgnum, int negnum, ccv_size_t size, const char* dir, ccv_bbf_new_param_t params)
-{
-  fprintf(stderr, " ccv_bbf_classifier_cascade_new requires libgsl support, please compile ccv with libgsl.\n");
-}
-#endif
-
-static int _ccv_is_equal(const void* _r1, const void* _r2, void* data)
-{
-  const ccv_comp_t* r1 = (const ccv_comp_t*)_r1;
-  const ccv_comp_t* r2 = (const ccv_comp_t*)_r2;
-  int distance = (int)(r1->rect.width * 0.25 + 0.5);
-
-  return r2->rect.x <= r1->rect.x + distance &&
-       r2->rect.x >= r1->rect.x - distance &&
-       r2->rect.y <= r1->rect.y + distance &&
-       r2->rect.y >= r1->rect.y - distance &&
-       r2->rect.width <= (int)(r1->rect.width * 1.5 + 0.5) &&
-       (int)(r2->rect.width * 1.5 + 0.5) >= r1->rect.width;
-}
-
-static int _ccv_is_equal_same_class(const void* _r1, const void* _r2, void* data)
-{
-  const ccv_comp_t* r1 = (const ccv_comp_t*)_r1;
-  const ccv_comp_t* r2 = (const ccv_comp_t*)_r2;
-  int distance = (int)(r1->rect.width * 0.25 + 0.5);
-
-  return r2->classification.id == r1->classification.id &&
-       r2->rect.x <= r1->rect.x + distance &&
-       r2->rect.x >= r1->rect.x - distance &&
-       r2->rect.y <= r1->rect.y + distance &&
-       r2->rect.y >= r1->rect.y - distance &&
-       r2->rect.width <= (int)(r1->rect.width * 1.5 + 0.5) &&
-       (int)(r2->rect.width * 1.5 + 0.5) >= r1->rect.width;
-}
-
-ccv_array_t* ccv_bbf_detect_objects(ccv_dense_matrix_t* a, ccv_bbf_classifier_cascade_t** _cascade, int count, ccv_bbf_param_t params)
-{
-  int hr = a->rows / params.size.height;
-  int wr = a->cols / params.size.width;
-  double scale = pow(2., 1. / (params.interval + 1.));
-  int next = params.interval + 1;
-  int scale_upto = (int)(log((double)ccv_min(hr, wr)) / log(scale));
-  ccv_dense_matrix_t** pyr = (ccv_dense_matrix_t**)alloca((scale_upto + next * 2) * 4 * sizeof(ccv_dense_matrix_t*));
-  memset(pyr, 0, (scale_upto + next * 2) * 4 * sizeof(ccv_dense_matrix_t*));
-  if (params.size.height != _cascade[0]->size.height || params.size.width != _cascade[0]->size.width)
-    ccv_resample(a, &pyr[0], 0, a->rows * _cascade[0]->size.height / params.size.height, a->cols * _cascade[0]->size.width / params.size.width, CCV_INTER_AREA);
-  else
-    pyr[0] = a;
-  int i, j, k, t, x, y, q;
-  for (i = 1; i < ccv_min(params.interval + 1, scale_upto + next * 2); i++)
-    ccv_resample(pyr[0], &pyr[i * 4], 0, (int)(pyr[0]->rows / pow(scale, i)), (int)(pyr[0]->cols / pow(scale, i)), CCV_INTER_AREA);
-  for (i = next; i < scale_upto + next * 2; i++)
-    ccv_sample_down(pyr[i * 4 - next * 4], &pyr[i * 4], 0, 0, 0);
-  if (params.accurate)
-    for (i = next * 2; i < scale_upto + next * 2; i++)
-    {
-      ccv_sample_down(pyr[i * 4 - next * 4], &pyr[i * 4 + 1], 0, 1, 0);
-      ccv_sample_down(pyr[i * 4 - next * 4], &pyr[i * 4 + 2], 0, 0, 1);
-      ccv_sample_down(pyr[i * 4 - next * 4], &pyr[i * 4 + 3], 0, 1, 1);
-    }
-  ccv_array_t* idx_seq;
-  ccv_array_t* seq = ccv_array_new(sizeof(ccv_comp_t), 64, 0);
-  ccv_array_t* seq2 = ccv_array_new(sizeof(ccv_comp_t), 64, 0);
-  ccv_array_t* result_seq = ccv_array_new(sizeof(ccv_comp_t), 64, 0);
-  /* detect in multi scale */
-  for (t = 0; t < count; t++)
-  {
-    ccv_bbf_classifier_cascade_t* cascade = _cascade[t];
-    float scale_x = (float) params.size.width / (float) cascade->size.width;
-    float scale_y = (float) params.size.height / (float) cascade->size.height;
-    ccv_array_clear(seq);
-    for (i = 0; i < scale_upto; i++)
-    {
-      int dx[] = {0, 1, 0, 1};
-      int dy[] = {0, 0, 1, 1};
-      int i_rows = pyr[i * 4 + next * 8]->rows - (cascade->size.height >> 2);
-      int steps[] = { pyr[i * 4]->step, pyr[i * 4 + next * 4]->step, pyr[i * 4 + next * 8]->step };
-      int i_cols = pyr[i * 4 + next * 8]->cols - (cascade->size.width >> 2);
-      int paddings[] = { pyr[i * 4]->step * 4 - i_cols * 4,
-                 pyr[i * 4 + next * 4]->step * 2 - i_cols * 2,
-                 pyr[i * 4 + next * 8]->step - i_cols };
-      for (q = 0; q < (params.accurate ? 4 : 1); q++)
-      {
-        unsigned char* u8[] = { pyr[i * 4]->data.u8 + dx[q] * 2 + dy[q] * pyr[i * 4]->step * 2, pyr[i * 4 + next * 4]->data.u8 + dx[q] + dy[q] * pyr[i * 4 + next * 4]->step, pyr[i * 4 + next * 8 + q]->data.u8 };
-        for (y = 0; y < i_rows; y++)
-        {
-          for (x = 0; x < i_cols; x++)
-          {
-            float sum;
-            int flag = 1;
-            ccv_bbf_stage_classifier_t* classifier = cascade->stage_classifier;
-            for (j = 0; j < cascade->count; ++j, ++classifier)
-            {
-              sum = 0;
-              float* alpha = classifier->alpha;
-              ccv_bbf_feature_t* feature = classifier->feature;
-              for (k = 0; k < classifier->count; ++k, alpha += 2, ++feature)
-                sum += alpha[_ccv_run_bbf_feature(feature, steps, u8)];
-              if (sum < classifier->threshold)
-              {
-                flag = 0;
-                break;
-              }
-            }
-            if (flag)
-            {
-              ccv_comp_t comp;
-              comp.rect = ccv_rect((int)((x * 4 + dx[q] * 2) * scale_x + 0.5), (int)((y * 4 + dy[q] * 2) * scale_y + 0.5), (int)(cascade->size.width * scale_x + 0.5), (int)(cascade->size.height * scale_y + 0.5));
-              comp.neighbors = 1;
-              comp.classification.id = t;
-              comp.classification.confidence = sum;
-              ccv_array_push(seq, &comp);
-            }
-            u8[0] += 4;
-            u8[1] += 2;
-            u8[2] += 1;
-          }
-          u8[0] += paddings[0];
-          u8[1] += paddings[1];
-          u8[2] += paddings[2];
-        }
-      }
-      scale_x *= scale;
-      scale_y *= scale;
-    }
-
-    /* the following code from OpenCV's haar feature implementation */
-    if(params.min_neighbors == 0)
-    {
-      for (i = 0; i < seq->rnum; i++)
-      {
-        ccv_comp_t* comp = (ccv_comp_t*)ccv_array_get(seq, i);
-        ccv_array_push(result_seq, comp);
-      }
-    } else {
-      idx_seq = 0;
-      ccv_array_clear(seq2);
-      // group retrieved rectangles in order to filter out noise
-      int ncomp = ccv_array_group(seq, &idx_seq, _ccv_is_equal_same_class, 0);
-      ccv_comp_t* comps = (ccv_comp_t*)ccmalloc((ncomp + 1) * sizeof(ccv_comp_t));
-      memset(comps, 0, (ncomp + 1) * sizeof(ccv_comp_t));
-
-      // count number of neighbors
-      for(i = 0; i < seq->rnum; i++)
-      {
-        ccv_comp_t r1 = *(ccv_comp_t*)ccv_array_get(seq, i);
-        int idx = *(int*)ccv_array_get(idx_seq, i);
-
-        if (comps[idx].neighbors == 0)
-          comps[idx].classification.confidence = r1.classification.confidence;
-
-        ++comps[idx].neighbors;
-
-        comps[idx].rect.x += r1.rect.x;
-        comps[idx].rect.y += r1.rect.y;
-        comps[idx].rect.width += r1.rect.width;
-        comps[idx].rect.height += r1.rect.height;
-        comps[idx].classification.id = r1.classification.id;
-        comps[idx].classification.confidence = ccv_max(comps[idx].classification.confidence, r1.classification.confidence);
-      }
-
-      // calculate average bounding box
-      for(i = 0; i < ncomp; i++)
-      {
-        int n = comps[i].neighbors;
-        if(n >= params.min_neighbors)
-        {
-          ccv_comp_t comp;
-          comp.rect.x = (comps[i].rect.x * 2 + n) / (2 * n);
-          comp.rect.y = (comps[i].rect.y * 2 + n) / (2 * n);
-          comp.rect.width = (comps[i].rect.width * 2 + n) / (2 * n);
-          comp.rect.height = (comps[i].rect.height * 2 + n) / (2 * n);
-          comp.neighbors = comps[i].neighbors;
-          comp.classification.id = comps[i].classification.id;
-          comp.classification.confidence = comps[i].classification.confidence;
-          ccv_array_push(seq2, &comp);
-        }
-      }
-
-      // filter out small face rectangles inside large face rectangles
-      for(i = 0; i < seq2->rnum; i++)
-      {
-        ccv_comp_t r1 = *(ccv_comp_t*)ccv_array_get(seq2, i);
-        int flag = 1;
-
-        for(j = 0; j < seq2->rnum; j++)
-        {
-          ccv_comp_t r2 = *(ccv_comp_t*)ccv_array_get(seq2, j);
-          int distance = (int)(r2.rect.width * 0.25 + 0.5);
-
-          if(i != j &&
-             r1.classification.id == r2.classification.id &&
-             r1.rect.x >= r2.rect.x - distance &&
-             r1.rect.y >= r2.rect.y - distance &&
-             r1.rect.x + r1.rect.width <= r2.rect.x + r2.rect.width + distance &&
-             r1.rect.y + r1.rect.height <= r2.rect.y + r2.rect.height + distance &&
-             (r2.neighbors > ccv_max(3, r1.neighbors) || r1.neighbors < 3))
-          {
-            flag = 0;
-            break;
-          }
-        }
-
-        if(flag)
-          ccv_array_push(result_seq, &r1);
-      }
-      ccv_array_free(idx_seq);
-      ccfree(comps);
-    }
-  }
-
-  ccv_array_free(seq);
-  ccv_array_free(seq2);
-
-  ccv_array_t* result_seq2;
-  /* the following code from OpenCV's haar feature implementation */
-  if (params.flags & CCV_BBF_NO_NESTED)
-  {
-    result_seq2 = ccv_array_new(sizeof(ccv_comp_t), 64, 0);
-    idx_seq = 0;
-    // group retrieved rectangles in order to filter out noise
-    int ncomp = ccv_array_group(result_seq, &idx_seq, _ccv_is_equal, 0);
-    ccv_comp_t* comps = (ccv_comp_t*)ccmalloc((ncomp + 1) * sizeof(ccv_comp_t));
-    memset(comps, 0, (ncomp + 1) * sizeof(ccv_comp_t));
-
-    // count number of neighbors
-    for(i = 0; i < result_seq->rnum; i++)
-    {
-      ccv_comp_t r1 = *(ccv_comp_t*)ccv_array_get(result_seq, i);
-      int idx = *(int*)ccv_array_get(idx_seq, i);
-
-      if (comps[idx].neighbors == 0 || comps[idx].classification.confidence < r1.classification.confidence)
-      {
-        comps[idx].classification.confidence = r1.classification.confidence;
-        comps[idx].neighbors = 1;
-        comps[idx].rect = r1.rect;
-        comps[idx].classification.id = r1.classification.id;
-      }
-    }
-
-    // calculate average bounding box
-    for(i = 0; i < ncomp; i++)
-      if(comps[i].neighbors)
-        ccv_array_push(result_seq2, &comps[i]);
-
-    ccv_array_free(result_seq);
-    ccfree(comps);
-  } else {
-    result_seq2 = result_seq;
-  }
-
-  for (i = 1; i < scale_upto + next * 2; i++)
-    ccv_matrix_free(pyr[i * 4]);
-  if (params.accurate)
-    for (i = next * 2; i < scale_upto + next * 2; i++)
-    {
-      ccv_matrix_free(pyr[i * 4 + 1]);
-      ccv_matrix_free(pyr[i * 4 + 2]);
-      ccv_matrix_free(pyr[i * 4 + 3]);
-    }
-  if (params.size.height != _cascade[0]->size.height || params.size.width != _cascade[0]->size.width)
-    ccv_matrix_free(pyr[0]);
-
-  return result_seq2;
-}
-
-ccv_bbf_classifier_cascade_t* ccv_bbf_read_classifier_cascade(const char* directory)
-{
-  char buf[1024];
-  sprintf(buf, "%s/cascade.txt", directory);
-  int s, i;
-  FILE* r = fopen(buf, "r");
-  if (r == 0)
-    return 0;
-  ccv_bbf_classifier_cascade_t* cascade = (ccv_bbf_classifier_cascade_t*)ccmalloc(sizeof(ccv_bbf_classifier_cascade_t));
-  s = fscanf(r, "%d %d %d", &cascade->count, &cascade->size.width, &cascade->size.height);
-  assert(s > 0);
-  cascade->stage_classifier = (ccv_bbf_stage_classifier_t*)ccmalloc(cascade->count * sizeof(ccv_bbf_stage_classifier_t));
-  for (i = 0; i < cascade->count; i++)
-  {
-    sprintf(buf, "%s/stage-%d.txt", directory, i);
-    if (_ccv_read_bbf_stage_classifier(buf, &cascade->stage_classifier[i]) < 0)
-    {
-      cascade->count = i;
-      break;
-    }
-  }
-  fclose(r);
-  return cascade;
-}
-
-ccv_bbf_classifier_cascade_t* ccv_bbf_classifier_cascade_read_binary(char* s)
-{
-  int i;
-  ccv_bbf_classifier_cascade_t* cascade = (ccv_bbf_classifier_cascade_t*)ccmalloc(sizeof(ccv_bbf_classifier_cascade_t));
-  memcpy(&cascade->count, s, sizeof(cascade->count)); s += sizeof(cascade->count);
-  memcpy(&cascade->size.width, s, sizeof(cascade->size.width)); s += sizeof(cascade->size.width);
-  memcpy(&cascade->size.height, s, sizeof(cascade->size.height)); s += sizeof(cascade->size.height);
-  ccv_bbf_stage_classifier_t* classifier = cascade->stage_classifier = (ccv_bbf_stage_classifier_t*)ccmalloc(cascade->count * sizeof(ccv_bbf_stage_classifier_t));
-  for (i = 0; i < cascade->count; i++, classifier++)
-  {
-    memcpy(&classifier->count, s, sizeof(classifier->count)); s += sizeof(classifier->count);
-    memcpy(&classifier->threshold, s, sizeof(classifier->threshold)); s += sizeof(classifier->threshold);
-    classifier->feature = (ccv_bbf_feature_t*)ccmalloc(classifier->count * sizeof(ccv_bbf_feature_t));
-    classifier->alpha = (float*)ccmalloc(classifier->count * 2 * sizeof(float));
-    memcpy(classifier->feature, s, classifier->count * sizeof(ccv_bbf_feature_t)); s += classifier->count * sizeof(ccv_bbf_feature_t);
-    memcpy(classifier->alpha, s, classifier->count * 2 * sizeof(float)); s += classifier->count * 2 * sizeof(float);
-  }
-  return cascade;
-
-}
-
-int ccv_bbf_classifier_cascade_write_binary(ccv_bbf_classifier_cascade_t* cascade, char* s, int slen)
-{
-  int i;
-  int len = sizeof(cascade->count) + sizeof(cascade->size.width) + sizeof(cascade->size.height);
-  ccv_bbf_stage_classifier_t* classifier = cascade->stage_classifier;
-  for (i = 0; i < cascade->count; i++, classifier++)
-    len += sizeof(classifier->count) + sizeof(classifier->threshold) + classifier->count * sizeof(ccv_bbf_feature_t) + classifier->count * 2 * sizeof(float);
-  if (slen >= len)
-  {
-    memcpy(s, &cascade->count, sizeof(cascade->count)); s += sizeof(cascade->count);
-    memcpy(s, &cascade->size.width, sizeof(cascade->size.width)); s += sizeof(cascade->size.width);
-    memcpy(s, &cascade->size.height, sizeof(cascade->size.height)); s += sizeof(cascade->size.height);
-    classifier = cascade->stage_classifier;
-    for (i = 0; i < cascade->count; i++, classifier++)
-    {
-      memcpy(s, &classifier->count, sizeof(classifier->count)); s += sizeof(classifier->count);
-      memcpy(s, &classifier->threshold, sizeof(classifier->threshold)); s += sizeof(classifier->threshold);
-      memcpy(s, classifier->feature, classifier->count * sizeof(ccv_bbf_feature_t)); s += classifier->count * sizeof(ccv_bbf_feature_t);
-      memcpy(s, classifier->alpha, classifier->count * 2 * sizeof(float)); s += classifier->count * 2 * sizeof(float);
-    }
-  }
-  return len;
-}
-
-void ccv_bbf_classifier_cascade_free(ccv_bbf_classifier_cascade_t* cascade)
-{
-  int i;
-  for (i = 0; i < cascade->count; ++i)
-  {
-    ccfree(cascade->stage_classifier[i].feature);
-    ccfree(cascade->stage_classifier[i].alpha);
-  }
-  ccfree(cascade->stage_classifier);
-  ccfree(cascade);
-}
\ No newline at end of file
+#ifdef HAVE_GSL
\ No newline at end of file
diff --git a/test/source.cc b/test/source.cc
index f0094b3..644923c 100644
--- a/test/source.cc
+++ b/test/source.cc
@@ -58,1516 +58,3 @@ static inline int _ccv_run_bbf_feature(ccv_bbf_feature_t *feature, int *step, un
 #undef nf_at
   return 1;
 }
-
-static int _ccv_read_bbf_stage_classifier(const char *file, ccv_bbf_stage_classifier_t *classifier)
-{
-  FILE *r = fopen(file, "r");
-  if (r == 0)
-    return -1;
-  int stat = 0;
-  stat |= fscanf(r, "%d", &classifier->count);
-  union {
-    float fl;
-    int i;
-  } fli;
-  stat |= fscanf(r, "%d", &fli.i);
-  classifier->threshold = fli.fl;
-  classifier->feature = (ccv_bbf_feature_t *)ccmalloc(classifier->count * sizeof(ccv_bbf_feature_t));
-  classifier->alpha = (float *)ccmalloc(classifier->count * 2 * sizeof(float));
-  int i, j;
-  for (i = 0; i < classifier->count; i++)
-  {
-    stat |= fscanf(r, "%d", &classifier->feature[i].size);
-    for (j = 0; j < classifier->feature[i].size; j++)
-    {
-      stat |= fscanf(r, "%d %d %d", &classifier->feature[i].px[j], &classifier->feature[i].py[j], &classifier->feature[i].pz[j]);
-      stat |= fscanf(r, "%d %d %d", &classifier->feature[i].nx[j], &classifier->feature[i].ny[j], &classifier->feature[i].nz[j]);
-    }
-    union {
-      float fl;
-      int i;
-    } flia, flib;
-    stat |= fscanf(r, "%d %d", &flia.i, &flib.i);
-    classifier->alpha[i * 2] = flia.fl;
-    classifier->alpha[i * 2 + 1] = flib.fl;
-  }
-  fclose(r);
-  return 0;
-}
-
-#ifdef HAVE_GSL
-
-static unsigned int _ccv_bbf_time_measure()
-{
-  struct timeval tv;
-  gettimeofday(&tv, 0);
-  return tv.tv_sec * 1000000 + tv.tv_usec;
-}
-
-#define less_than(a, b, aux) ((a) < (b))
-CCV_IMPLEMENT_QSORT(_ccv_sort_32f, float, less_than)
-#undef less_than
-
-static void _ccv_bbf_eval_data(ccv_bbf_stage_classifier_t *classifier, unsigned char **posdata, int posnum, unsigned char **negdata, int negnum, ccv_size_t size, float *peval, float *neval)
-{
-  int i, j;
-  int steps[] = {_ccv_width_padding(size.width),
-                 _ccv_width_padding(size.width >> 1),
-                 _ccv_width_padding(size.width >> 2)};
-  int isizs0 = steps[0] * size.height;
-  int isizs01 = isizs0 + steps[1] * (size.height >> 1);
-  for (i = 0; i < posnum; i++)
-  {
-    unsigned char *u8[] = {posdata[i], posdata[i] + isizs0, posdata[i] + isizs01};
-    float sum = 0;
-    float *alpha = classifier->alpha;
-    ccv_bbf_feature_t *feature = classifier->feature;
-    for (j = 0; j < classifier->count; ++j, alpha += 2, ++feature)
-      sum += alpha[_ccv_run_bbf_feature(feature, steps, u8)];
-    peval[i] = sum;
-  }
-  for (i = 0; i < negnum; i++)
-  {
-    unsigned char *u8[] = {negdata[i], negdata[i] + isizs0, negdata[i] + isizs01};
-    float sum = 0;
-    float *alpha = classifier->alpha;
-    ccv_bbf_feature_t *feature = classifier->feature;
-    for (j = 0; j < classifier->count; ++j, alpha += 2, ++feature)
-      sum += alpha[_ccv_run_bbf_feature(feature, steps, u8)];
-    neval[i] = sum;
-  }
-}
-
-static int _ccv_prune_positive_data(ccv_bbf_classifier_cascade_t *cascade, unsigned char **posdata, int posnum, ccv_size_t size)
-{
-  float *peval = (float *)ccmalloc(posnum * sizeof(float));
-  int i, j, k, rpos = posnum;
-  for (i = 0; i < cascade->count; i++)
-  {
-    _ccv_bbf_eval_data(cascade->stage_classifier + i, posdata, rpos, 0, 0, size, peval, 0);
-    k = 0;
-    for (j = 0; j < rpos; j++)
-      if (peval[j] >= cascade->stage_classifier[i].threshold)
-      {
-        posdata[k] = posdata[j];
-        ++k;
-      }
-      else
-      {
-        ccfree(posdata[j]);
-      }
-    rpos = k;
-  }
-  ccfree(peval);
-  return rpos;
-}
-
-static int _ccv_prepare_background_data(ccv_bbf_classifier_cascade_t *cascade, char **bgfiles, int bgnum, unsigned char **negdata, int negnum)
-{
-  int t, i, j, k, q;
-  int negperbg;
-  int negtotal = 0;
-  int steps[] = {_ccv_width_padding(cascade->size.width),
-                 _ccv_width_padding(cascade->size.width >> 1),
-                 _ccv_width_padding(cascade->size.width >> 2)};
-  int isizs0 = steps[0] * cascade->size.height;
-  int isizs1 = steps[1] * (cascade->size.height >> 1);
-  int isizs2 = steps[2] * (cascade->size.height >> 2);
-  int *idcheck = (int *)ccmalloc(negnum * sizeof(int));
-
-  gsl_rng_env_setup();
-
-  gsl_rng *rng = gsl_rng_alloc(gsl_rng_default);
-  gsl_rng_set(rng, (unsigned long int)idcheck);
-
-  ccv_size_t imgsz = cascade->size;
-  int rneg = negtotal;
-  for (t = 0; negtotal < negnum; t++)
-  {
-    PRINT(CCV_CLI_INFO, "preparing negative data ...  0%%");
-    for (i = 0; i < bgnum; i++)
-    {
-      negperbg = (t < 2) ? (negnum - negtotal) / (bgnum - i) + 1 : negnum - negtotal;
-      ccv_dense_matrix_t *image = 0;
-      ccv_read(bgfiles[i], &image, CCV_IO_GRAY | CCV_IO_ANY_FILE);
-      assert((image->type & CCV_C1) && (image->type & CCV_8U));
-      if (image == 0)
-      {
-        PRINT(CCV_CLI_ERROR, "\n%s file corrupted\n", bgfiles[i]);
-        continue;
-      }
-      if (t % 2 != 0)
-        ccv_flip(image, 0, 0, CCV_FLIP_X);
-      if (t % 4 >= 2)
-        ccv_flip(image, 0, 0, CCV_FLIP_Y);
-      ccv_bbf_param_t params = {.interval = 3, .min_neighbors = 0, .accurate = 1, .flags = 0, .size = cascade->size};
-      ccv_array_t *detected = ccv_bbf_detect_objects(image, &cascade, 1, params);
-      memset(idcheck, 0, ccv_min(detected->rnum, negperbg) * sizeof(int));
-      for (j = 0; j < ccv_min(detected->rnum, negperbg); j++)
-      {
-        int r = gsl_rng_uniform_int(rng, detected->rnum);
-        int flag = 1;
-        ccv_rect_t *rect = (ccv_rect_t *)ccv_array_get(detected, r);
-        while (flag)
-        {
-          flag = 0;
-          for (k = 0; k < j; k++)
-            if (r == idcheck[k])
-            {
-              flag = 1;
-              r = gsl_rng_uniform_int(rng, detected->rnum);
-              break;
-            }
-          rect = (ccv_rect_t *)ccv_array_get(detected, r);
-          if ((rect->x < 0) || (rect->y < 0) || (rect->width + rect->x > image->cols) || (rect->height + rect->y > image->rows))
-          {
-            flag = 1;
-            r = gsl_rng_uniform_int(rng, detected->rnum);
-          }
-        }
-        idcheck[j] = r;
-        ccv_dense_matrix_t *temp = 0;
-        ccv_dense_matrix_t *imgs0 = 0;
-        ccv_dense_matrix_t *imgs1 = 0;
-        ccv_dense_matrix_t *imgs2 = 0;
-        ccv_slice(image, (ccv_matrix_t **)&temp, 0, rect->y, rect->x, rect->height, rect->width);
-        ccv_resample(temp, &imgs0, 0, imgsz.height, imgsz.width, CCV_INTER_AREA);
-        assert(imgs0->step == steps[0]);
-        ccv_matrix_free(temp);
-        ccv_sample_down(imgs0, &imgs1, 0, 0, 0);
-        assert(imgs1->step == steps[1]);
-        ccv_sample_down(imgs1, &imgs2, 0, 0, 0);
-        assert(imgs2->step == steps[2]);
-
-        negdata[negtotal] = (unsigned char *)ccmalloc(isizs0 + isizs1 + isizs2);
-        unsigned char *u8s0 = negdata[negtotal];
-        unsigned char *u8s1 = negdata[negtotal] + isizs0;
-        unsigned char *u8s2 = negdata[negtotal] + isizs0 + isizs1;
-        unsigned char *u8[] = {u8s0, u8s1, u8s2};
-        memcpy(u8s0, imgs0->data.u8, imgs0->rows * imgs0->step);
-        ccv_matrix_free(imgs0);
-        memcpy(u8s1, imgs1->data.u8, imgs1->rows * imgs1->step);
-        ccv_matrix_free(imgs1);
-        memcpy(u8s2, imgs2->data.u8, imgs2->rows * imgs2->step);
-        ccv_matrix_free(imgs2);
-
-        flag = 1;
-        ccv_bbf_stage_classifier_t *classifier = cascade->stage_classifier;
-        for (k = 0; k < cascade->count; ++k, ++classifier)
-        {
-          float sum = 0;
-          float *alpha = classifier->alpha;
-          ccv_bbf_feature_t *feature = classifier->feature;
-          for (q = 0; q < classifier->count; ++q, alpha += 2, ++feature)
-            sum += alpha[_ccv_run_bbf_feature(feature, steps, u8)];
-          if (sum < classifier->threshold)
-          {
-            flag = 0;
-            break;
-          }
-        }
-        if (!flag)
-          ccfree(negdata[negtotal]);
-        else
-        {
-          ++negtotal;
-          if (negtotal >= negnum)
-            break;
-        }
-      }
-      ccv_array_free(detected);
-      ccv_matrix_free(image);
-      ccv_drain_cache();
-      PRINT(CCV_CLI_INFO, "\rpreparing negative data ... %2d%%", 100 * negtotal / negnum);
-      fflush(0);
-      if (negtotal >= negnum)
-        break;
-    }
-    if (rneg == negtotal)
-      break;
-    rneg = negtotal;
-    PRINT(CCV_CLI_INFO, "\nentering additional round %d\n", t + 1);
-  }
-  gsl_rng_free(rng);
-  ccfree(idcheck);
-  ccv_drain_cache();
-  PRINT(CCV_CLI_INFO, "\n");
-  return negtotal;
-}
-
-static void _ccv_prepare_positive_data(ccv_dense_matrix_t **posimg, unsigned char **posdata, ccv_size_t size, int posnum)
-{
-  PRINT(CCV_CLI_INFO, "preparing positive data ...  0%%");
-  int i;
-  for (i = 0; i < posnum; i++)
-  {
-    ccv_dense_matrix_t *imgs0 = posimg[i];
-    ccv_dense_matrix_t *imgs1 = 0;
-    ccv_dense_matrix_t *imgs2 = 0;
-    assert((imgs0->type & CCV_C1) && (imgs0->type & CCV_8U) && imgs0->rows == size.height && imgs0->cols == size.width);
-    ccv_sample_down(imgs0, &imgs1, 0, 0, 0);
-    ccv_sample_down(imgs1, &imgs2, 0, 0, 0);
-    int isizs0 = imgs0->rows * imgs0->step;
-    int isizs1 = imgs1->rows * imgs1->step;
-    int isizs2 = imgs2->rows * imgs2->step;
-
-    posdata[i] = (unsigned char *)ccmalloc(isizs0 + isizs1 + isizs2);
-    memcpy(posdata[i], imgs0->data.u8, isizs0);
-    memcpy(posdata[i] + isizs0, imgs1->data.u8, isizs1);
-    memcpy(posdata[i] + isizs0 + isizs1, imgs2->data.u8, isizs2);
-
-    PRINT(CCV_CLI_INFO, "\rpreparing positive data ... %2d%%", 100 * (i + 1) / posnum);
-    fflush(0);
-
-    ccv_matrix_free(imgs1);
-    ccv_matrix_free(imgs2);
-  }
-  ccv_drain_cache();
-  PRINT(CCV_CLI_INFO, "\n");
-}
-
-typedef struct
-{
-  double fitness;
-  int pk, nk;
-  int age;
-  double error;
-  ccv_bbf_feature_t feature;
-} ccv_bbf_gene_t;
-
-static inline void _ccv_bbf_genetic_fitness(ccv_bbf_gene_t *gene)
-{
-  gene->fitness = (1 - gene->error) * exp(-0.01 * gene->age) * exp((gene->pk + gene->nk) * log(1.015));
-}
-
-static inline int _ccv_bbf_exist_gene_feature(ccv_bbf_gene_t *gene, int x, int y, int z)
-{
-  int i;
-  for (i = 0; i < gene->pk; i++)
-    if (z == gene->feature.pz[i] && x == gene->feature.px[i] && y == gene->feature.py[i])
-      return 1;
-  for (i = 0; i < gene->nk; i++)
-    if (z == gene->feature.nz[i] && x == gene->feature.nx[i] && y == gene->feature.ny[i])
-      return 1;
-  return 0;
-}
-
-static inline void _ccv_bbf_randomize_gene(gsl_rng *rng, ccv_bbf_gene_t *gene, int *rows, int *cols)
-{
-  int i;
-  do
-  {
-    gene->pk = gsl_rng_uniform_int(rng, CCV_BBF_POINT_MAX - 1) + 1;
-    gene->nk = gsl_rng_uniform_int(rng, CCV_BBF_POINT_MAX - 1) + 1;
-  } while (gene->pk + gene->nk < CCV_BBF_POINT_MIN); /* a hard restriction of at least 3 points have to be examed */
-  gene->feature.size = ccv_max(gene->pk, gene->nk);
-  gene->age = 0;
-  for (i = 0; i < CCV_BBF_POINT_MAX; i++)
-  {
-    gene->feature.pz[i] = -1;
-    gene->feature.nz[i] = -1;
-  }
-  int x, y, z;
-  for (i = 0; i < gene->pk; i++)
-  {
-    do
-    {
-      z = gsl_rng_uniform_int(rng, 3);
-      x = gsl_rng_uniform_int(rng, cols[z]);
-      y = gsl_rng_uniform_int(rng, rows[z]);
-    } while (_ccv_bbf_exist_gene_feature(gene, x, y, z));
-    gene->feature.pz[i] = z;
-    gene->feature.px[i] = x;
-    gene->feature.py[i] = y;
-  }
-  for (i = 0; i < gene->nk; i++)
-  {
-    do
-    {
-      z = gsl_rng_uniform_int(rng, 3);
-      x = gsl_rng_uniform_int(rng, cols[z]);
-      y = gsl_rng_uniform_int(rng, rows[z]);
-    } while (_ccv_bbf_exist_gene_feature(gene, x, y, z));
-    gene->feature.nz[i] = z;
-    gene->feature.nx[i] = x;
-    gene->feature.ny[i] = y;
-  }
-}
-
-static inline double _ccv_bbf_error_rate(ccv_bbf_feature_t *feature, unsigned char **posdata, int posnum, unsigned char **negdata, int negnum, ccv_size_t size, double *pw, double *nw)
-{
-  int i;
-  int steps[] = {_ccv_width_padding(size.width),
-                 _ccv_width_padding(size.width >> 1),
-                 _ccv_width_padding(size.width >> 2)};
-  int isizs0 = steps[0] * size.height;
-  int isizs01 = isizs0 + steps[1] * (size.height >> 1);
-  double error = 0;
-  for (i = 0; i < posnum; i++)
-  {
-    unsigned char *u8[] = {posdata[i], posdata[i] + isizs0, posdata[i] + isizs01};
-    if (!_ccv_run_bbf_feature(feature, steps, u8))
-      error += pw[i];
-  }
-  for (i = 0; i < negnum; i++)
-  {
-    unsigned char *u8[] = {negdata[i], negdata[i] + isizs0, negdata[i] + isizs01};
-    if (_ccv_run_bbf_feature(feature, steps, u8))
-      error += nw[i];
-  }
-  return error;
-}
-
-#define less_than(fit1, fit2, aux) ((fit1).fitness >= (fit2).fitness)
-static CCV_IMPLEMENT_QSORT(_ccv_bbf_genetic_qsort, ccv_bbf_gene_t, less_than)
-#undef less_than
-
-    static ccv_bbf_feature_t _ccv_bbf_genetic_optimize(unsigned char **posdata, int posnum, unsigned char **negdata, int negnum, int ftnum, ccv_size_t size, double *pw, double *nw)
-{
-  ccv_bbf_feature_t best;
-  /* seed (random method) */
-  gsl_rng_env_setup();
-  gsl_rng *rng = gsl_rng_alloc(gsl_rng_default);
-  union {
-    unsigned long int li;
-    double db;
-  } dbli;
-  dbli.db = pw[0] + nw[0];
-  gsl_rng_set(rng, dbli.li);
-  int i, j;
-  int pnum = ftnum * 100;
-  assert(pnum > 0);
-  ccv_bbf_gene_t *gene = (ccv_bbf_gene_t *)ccmalloc(pnum * sizeof(ccv_bbf_gene_t));
-  int rows[] = {size.height, size.height >> 1, size.height >> 2};
-  int cols[] = {size.width, size.width >> 1, size.width >> 2};
-  for (i = 0; i < pnum; i++)
-    _ccv_bbf_randomize_gene(rng, &gene[i], rows, cols);
-  unsigned int timer = _ccv_bbf_time_measure();
-#ifdef USE_OPENMP
-#pragma omp parallel for private(i) schedule(dynamic)
-#endif
-  for (i = 0; i < pnum; i++)
-    gene[i].error = _ccv_bbf_error_rate(&gene[i].feature, posdata, posnum, negdata, negnum, size, pw, nw);
-  timer = _ccv_bbf_time_measure() - timer;
-  for (i = 0; i < pnum; i++)
-    _ccv_bbf_genetic_fitness(&gene[i]);
-  double best_err = 1;
-  int rnum = ftnum * 39; /* number of randomize */
-  int mnum = ftnum * 40; /* number of mutation */
-  int hnum = ftnum * 20; /* number of hybrid */
-  /* iteration stop crit : best no change in 40 iterations */
-  int it = 0, t;
-  for (t = 0; it < 40; ++it, ++t)
-  {
-    int min_id = 0;
-    double min_err = gene[0].error;
-    for (i = 1; i < pnum; i++)
-      if (gene[i].error < min_err)
-      {
-        min_id = i;
-        min_err = gene[i].error;
-      }
-    min_err = gene[min_id].error = _ccv_bbf_error_rate(&gene[min_id].feature, posdata, posnum, negdata, negnum, size, pw, nw);
-    if (min_err < best_err)
-    {
-      best_err = min_err;
-      memcpy(&best, &gene[min_id].feature, sizeof(best));
-      PRINT(CCV_CLI_INFO, "best bbf feature with error %f\n|-size: %d\n|-positive point: ", best_err, best.size);
-      for (i = 0; i < best.size; i++)
-        PRINT(CCV_CLI_INFO, "(%d %d %d), ", best.px[i], best.py[i], best.pz[i]);
-      PRINT(CCV_CLI_INFO, "\n|-negative point: ");
-      for (i = 0; i < best.size; i++)
-        PRINT(CCV_CLI_INFO, "(%d %d %d), ", best.nx[i], best.ny[i], best.nz[i]);
-      PRINT(CCV_CLI_INFO, "\n");
-      it = 0;
-    }
-    PRINT(CCV_CLI_INFO, "minimum error achieved in round %d(%d) : %f with %d ms\n", t, it, min_err, timer / 1000);
-    _ccv_bbf_genetic_qsort(gene, pnum, 0);
-    for (i = 0; i < ftnum; i++)
-      ++gene[i].age;
-    for (i = ftnum; i < ftnum + mnum; i++)
-    {
-      int parent = gsl_rng_uniform_int(rng, ftnum);
-      memcpy(gene + i, gene + parent, sizeof(ccv_bbf_gene_t));
-      /* three mutation strategy : 1. add, 2. remove, 3. refine */
-      int pnm, pn = gsl_rng_uniform_int(rng, 2);
-      int *pnk[] = {&gene[i].pk, &gene[i].nk};
-      int *pnx[] = {gene[i].feature.px, gene[i].feature.nx};
-      int *pny[] = {gene[i].feature.py, gene[i].feature.ny};
-      int *pnz[] = {gene[i].feature.pz, gene[i].feature.nz};
-      int x, y, z;
-      int victim, decay = 1;
-      do
-      {
-        switch (gsl_rng_uniform_int(rng, 3))
-        {
-        case 0: /* add */
-          if (gene[i].pk == CCV_BBF_POINT_MAX && gene[i].nk == CCV_BBF_POINT_MAX)
-            break;
-          while (*pnk[pn] + 1 > CCV_BBF_POINT_MAX)
-            pn = gsl_rng_uniform_int(rng, 2);
-          do
-          {
-            z = gsl_rng_uniform_int(rng, 3);
-            x = gsl_rng_uniform_int(rng, cols[z]);
-            y = gsl_rng_uniform_int(rng, rows[z]);
-          } while (_ccv_bbf_exist_gene_feature(&gene[i], x, y, z));
-          pnz[pn][*pnk[pn]] = z;
-          pnx[pn][*pnk[pn]] = x;
-          pny[pn][*pnk[pn]] = y;
-          ++(*pnk[pn]);
-          gene[i].feature.size = ccv_max(gene[i].pk, gene[i].nk);
-          decay = gene[i].age = 0;
-          break;
-        case 1:                                             /* remove */
-          if (gene[i].pk + gene[i].nk <= CCV_BBF_POINT_MIN) /* at least 3 points have to be examed */
-            break;
-          while (*pnk[pn] - 1 <= 0) // || *pnk[pn] + *pnk[!pn] - 1 < CCV_BBF_POINT_MIN)
-            pn = gsl_rng_uniform_int(rng, 2);
-          victim = gsl_rng_uniform_int(rng, *pnk[pn]);
-          for (j = victim; j < *pnk[pn] - 1; j++)
-          {
-            pnz[pn][j] = pnz[pn][j + 1];
-            pnx[pn][j] = pnx[pn][j + 1];
-            pny[pn][j] = pny[pn][j + 1];
-          }
-          pnz[pn][*pnk[pn] - 1] = -1;
-          --(*pnk[pn]);
-          gene[i].feature.size = ccv_max(gene[i].pk, gene[i].nk);
-          decay = gene[i].age = 0;
-          break;
-        case 2: /* refine */
-          pnm = gsl_rng_uniform_int(rng, *pnk[pn]);
-          do
-          {
-            z = gsl_rng_uniform_int(rng, 3);
-            x = gsl_rng_uniform_int(rng, cols[z]);
-            y = gsl_rng_uniform_int(rng, rows[z]);
-          } while (_ccv_bbf_exist_gene_feature(&gene[i], x, y, z));
-          pnz[pn][pnm] = z;
-          pnx[pn][pnm] = x;
-          pny[pn][pnm] = y;
-          decay = gene[i].age = 0;
-          break;
-        }
-      } while (decay);
-    }
-    for (i = ftnum + mnum; i < ftnum + mnum + hnum; i++)
-    {
-      /* hybrid strategy: taking positive points from dad, negative points from mum */
-      int dad, mum;
-      do
-      {
-        dad = gsl_rng_uniform_int(rng, ftnum);
-        mum = gsl_rng_uniform_int(rng, ftnum);
-      } while (dad == mum || gene[dad].pk + gene[mum].nk < CCV_BBF_POINT_MIN); /* at least 3 points have to be examed */
-      for (j = 0; j < CCV_BBF_POINT_MAX; j++)
-      {
-        gene[i].feature.pz[j] = -1;
-        gene[i].feature.nz[j] = -1;
-      }
-      gene[i].pk = gene[dad].pk;
-      for (j = 0; j < gene[i].pk; j++)
-      {
-        gene[i].feature.pz[j] = gene[dad].feature.pz[j];
-        gene[i].feature.px[j] = gene[dad].feature.px[j];
-        gene[i].feature.py[j] = gene[dad].feature.py[j];
-      }
-      gene[i].nk = gene[mum].nk;
-      for (j = 0; j < gene[i].nk; j++)
-      {
-        gene[i].feature.nz[j] = gene[mum].feature.nz[j];
-        gene[i].feature.nx[j] = gene[mum].feature.nx[j];
-        gene[i].feature.ny[j] = gene[mum].feature.ny[j];
-      }
-      gene[i].feature.size = ccv_max(gene[i].pk, gene[i].nk);
-      gene[i].age = 0;
-    }
-    for (i = ftnum + mnum + hnum; i < ftnum + mnum + hnum + rnum; i++)
-      _ccv_bbf_randomize_gene(rng, &gene[i], rows, cols);
-    timer = _ccv_bbf_time_measure();
-#ifdef USE_OPENMP
-#pragma omp parallel for private(i) schedule(dynamic)
-#endif
-    for (i = 0; i < pnum; i++)
-      gene[i].error = _ccv_bbf_error_rate(&gene[i].feature, posdata, posnum, negdata, negnum, size, pw, nw);
-    timer = _ccv_bbf_time_measure() - timer;
-    for (i = 0; i < pnum; i++)
-      _ccv_bbf_genetic_fitness(&gene[i]);
-  }
-  ccfree(gene);
-  gsl_rng_free(rng);
-  return best;
-}
-
-#define less_than(fit1, fit2, aux) ((fit1).error < (fit2).error)
-static CCV_IMPLEMENT_QSORT(_ccv_bbf_best_qsort, ccv_bbf_gene_t, less_than)
-#undef less_than
-
-    static ccv_bbf_gene_t _ccv_bbf_best_gene(ccv_bbf_gene_t *gene, int pnum, int point_min, unsigned char **posdata, int posnum, unsigned char **negdata, int negnum, ccv_size_t size, double *pw, double *nw)
-{
-  int i;
-  unsigned int timer = _ccv_bbf_time_measure();
-#ifdef USE_OPENMP
-#pragma omp parallel for private(i) schedule(dynamic)
-#endif
-  for (i = 0; i < pnum; i++)
-    gene[i].error = _ccv_bbf_error_rate(&gene[i].feature, posdata, posnum, negdata, negnum, size, pw, nw);
-  timer = _ccv_bbf_time_measure() - timer;
-  _ccv_bbf_best_qsort(gene, pnum, 0);
-  int min_id = 0;
-  double min_err = gene[0].error;
-  for (i = 0; i < pnum; i++)
-    if (gene[i].nk + gene[i].pk >= point_min)
-    {
-      min_id = i;
-      min_err = gene[i].error;
-      break;
-    }
-  PRINT(CCV_CLI_INFO, "local best bbf feature with error %f\n|-size: %d\n|-positive point: ", min_err, gene[min_id].feature.size);
-  for (i = 0; i < gene[min_id].feature.size; i++)
-    PRINT(CCV_CLI_INFO, "(%d %d %d), ", gene[min_id].feature.px[i], gene[min_id].feature.py[i], gene[min_id].feature.pz[i]);
-  PRINT(CCV_CLI_INFO, "\n|-negative point: ");
-  for (i = 0; i < gene[min_id].feature.size; i++)
-    PRINT(CCV_CLI_INFO, "(%d %d %d), ", gene[min_id].feature.nx[i], gene[min_id].feature.ny[i], gene[min_id].feature.nz[i]);
-  PRINT(CCV_CLI_INFO, "\nthe computation takes %d ms\n", timer / 1000);
-  return gene[min_id];
-}
-
-static ccv_bbf_feature_t _ccv_bbf_convex_optimize(unsigned char **posdata, int posnum, unsigned char **negdata, int negnum, ccv_bbf_feature_t *best_feature, ccv_size_t size, double *pw, double *nw)
-{
-  ccv_bbf_gene_t best_gene;
-  /* seed (random method) */
-  gsl_rng_env_setup();
-  gsl_rng *rng = gsl_rng_alloc(gsl_rng_default);
-  union {
-    unsigned long int li;
-    double db;
-  } dbli;
-  dbli.db = pw[0] + nw[0];
-  gsl_rng_set(rng, dbli.li);
-  int i, j, k, q, p, g, t;
-  int rows[] = {size.height, size.height >> 1, size.height >> 2};
-  int cols[] = {size.width, size.width >> 1, size.width >> 2};
-  int pnum = rows[0] * cols[0] + rows[1] * cols[1] + rows[2] * cols[2];
-  ccv_bbf_gene_t *gene = (ccv_bbf_gene_t *)ccmalloc((pnum * (CCV_BBF_POINT_MAX * 2 + 1) * 2 + CCV_BBF_POINT_MAX * 2 + 1) * sizeof(ccv_bbf_gene_t));
-  if (best_feature == 0)
-  {
-    /* bootstrapping the best feature, start from two pixels, one for positive, one for negative
-		 * the bootstrapping process go like this: first, it will assign a random pixel as positive
-		 * and enumerate every possible pixel as negative, and pick the best one. Then, enumerate every
-		 * possible pixel as positive, and pick the best one, until it converges */
-    memset(&best_gene, 0, sizeof(ccv_bbf_gene_t));
-    for (i = 0; i < CCV_BBF_POINT_MAX; i++)
-      best_gene.feature.pz[i] = best_gene.feature.nz[i] = -1;
-    best_gene.pk = 1;
-    best_gene.nk = 0;
-    best_gene.feature.size = 1;
-    best_gene.feature.pz[0] = gsl_rng_uniform_int(rng, 3);
-    best_gene.feature.px[0] = gsl_rng_uniform_int(rng, cols[best_gene.feature.pz[0]]);
-    best_gene.feature.py[0] = gsl_rng_uniform_int(rng, rows[best_gene.feature.pz[0]]);
-    for (t = 0;; ++t)
-    {
-      g = 0;
-      if (t % 2 == 0)
-      {
-        for (i = 0; i < 3; i++)
-          for (j = 0; j < cols[i]; j++)
-            for (k = 0; k < rows[i]; k++)
-              if (i != best_gene.feature.pz[0] || j != best_gene.feature.px[0] || k != best_gene.feature.py[0])
-              {
-                gene[g] = best_gene;
-                gene[g].pk = gene[g].nk = 1;
-                gene[g].feature.nz[0] = i;
-                gene[g].feature.nx[0] = j;
-                gene[g].feature.ny[0] = k;
-                g++;
-              }
-      }
-      else
-      {
-        for (i = 0; i < 3; i++)
-          for (j = 0; j < cols[i]; j++)
-            for (k = 0; k < rows[i]; k++)
-              if (i != best_gene.feature.nz[0] || j != best_gene.feature.nx[0] || k != best_gene.feature.ny[0])
-              {
-                gene[g] = best_gene;
-                gene[g].pk = gene[g].nk = 1;
-                gene[g].feature.pz[0] = i;
-                gene[g].feature.px[0] = j;
-                gene[g].feature.py[0] = k;
-                g++;
-              }
-      }
-      PRINT(CCV_CLI_INFO, "bootstrapping round : %d\n", t);
-      ccv_bbf_gene_t local_gene = _ccv_bbf_best_gene(gene, g, 2, posdata, posnum, negdata, negnum, size, pw, nw);
-      if (local_gene.error >= best_gene.error - 1e-10)
-        break;
-      best_gene = local_gene;
-    }
-  }
-  else
-  {
-    best_gene.feature = *best_feature;
-    best_gene.pk = best_gene.nk = best_gene.feature.size;
-    for (i = 0; i < CCV_BBF_POINT_MAX; i++)
-      if (best_feature->pz[i] == -1)
-      {
-        best_gene.pk = i;
-        break;
-      }
-    for (i = 0; i < CCV_BBF_POINT_MAX; i++)
-      if (best_feature->nz[i] == -1)
-      {
-        best_gene.nk = i;
-        break;
-      }
-  }
-  /* after bootstrapping, the float search technique will do the following permutations:
-	 * a). add a new point to positive or negative
-	 * b). remove a point from positive or negative
-	 * c). move an existing point in positive or negative to another position
-	 * the three rules applied exhaustively, no heuristic used. */
-  for (t = 0;; ++t)
-  {
-    g = 0;
-    for (i = 0; i < 3; i++)
-      for (j = 0; j < cols[i]; j++)
-        for (k = 0; k < rows[i]; k++)
-          if (!_ccv_bbf_exist_gene_feature(&best_gene, j, k, i))
-          {
-            /* add positive point */
-            if (best_gene.pk < CCV_BBF_POINT_MAX - 1)
-            {
-              gene[g] = best_gene;
-              gene[g].feature.pz[gene[g].pk] = i;
-              gene[g].feature.px[gene[g].pk] = j;
-              gene[g].feature.py[gene[g].pk] = k;
-              gene[g].pk++;
-              gene[g].feature.size = ccv_max(gene[g].pk, gene[g].nk);
-              g++;
-            }
-            /* add negative point */
-            if (best_gene.nk < CCV_BBF_POINT_MAX - 1)
-            {
-              gene[g] = best_gene;
-              gene[g].feature.nz[gene[g].nk] = i;
-              gene[g].feature.nx[gene[g].nk] = j;
-              gene[g].feature.ny[gene[g].nk] = k;
-              gene[g].nk++;
-              gene[g].feature.size = ccv_max(gene[g].pk, gene[g].nk);
-              g++;
-            }
-            /* refine positive point */
-            for (q = 0; q < best_gene.pk; q++)
-            {
-              gene[g] = best_gene;
-              gene[g].feature.pz[q] = i;
-              gene[g].feature.px[q] = j;
-              gene[g].feature.py[q] = k;
-              g++;
-            }
-            /* add positive point, remove negative point */
-            if (best_gene.pk < CCV_BBF_POINT_MAX - 1 && best_gene.nk > 1)
-            {
-              for (q = 0; q < best_gene.nk; q++)
-              {
-                gene[g] = best_gene;
-                gene[g].feature.pz[gene[g].pk] = i;
-                gene[g].feature.px[gene[g].pk] = j;
-                gene[g].feature.py[gene[g].pk] = k;
-                gene[g].pk++;
-                for (p = q; p < best_gene.nk - 1; p++)
-                {
-                  gene[g].feature.nz[p] = gene[g].feature.nz[p + 1];
-                  gene[g].feature.nx[p] = gene[g].feature.nx[p + 1];
-                  gene[g].feature.ny[p] = gene[g].feature.ny[p + 1];
-                }
-                gene[g].feature.nz[gene[g].nk - 1] = -1;
-                gene[g].nk--;
-                gene[g].feature.size = ccv_max(gene[g].pk, gene[g].nk);
-                g++;
-              }
-            }
-            /* refine negative point */
-            for (q = 0; q < best_gene.nk; q++)
-            {
-              gene[g] = best_gene;
-              gene[g].feature.nz[q] = i;
-              gene[g].feature.nx[q] = j;
-              gene[g].feature.ny[q] = k;
-              g++;
-            }
-            /* add negative point, remove positive point */
-            if (best_gene.pk > 1 && best_gene.nk < CCV_BBF_POINT_MAX - 1)
-            {
-              for (q = 0; q < best_gene.pk; q++)
-              {
-                gene[g] = best_gene;
-                gene[g].feature.nz[gene[g].nk] = i;
-                gene[g].feature.nx[gene[g].nk] = j;
-                gene[g].feature.ny[gene[g].nk] = k;
-                gene[g].nk++;
-                for (p = q; p < best_gene.pk - 1; p++)
-                {
-                  gene[g].feature.pz[p] = gene[g].feature.pz[p + 1];
-                  gene[g].feature.px[p] = gene[g].feature.px[p + 1];
-                  gene[g].feature.py[p] = gene[g].feature.py[p + 1];
-                }
-                gene[g].feature.pz[gene[g].pk - 1] = -1;
-                gene[g].pk--;
-                gene[g].feature.size = ccv_max(gene[g].pk, gene[g].nk);
-                g++;
-              }
-            }
-          }
-    if (best_gene.pk > 1)
-      for (q = 0; q < best_gene.pk; q++)
-      {
-        gene[g] = best_gene;
-        for (i = q; i < best_gene.pk - 1; i++)
-        {
-          gene[g].feature.pz[i] = gene[g].feature.pz[i + 1];
-          gene[g].feature.px[i] = gene[g].feature.px[i + 1];
-          gene[g].feature.py[i] = gene[g].feature.py[i + 1];
-        }
-        gene[g].feature.pz[gene[g].pk - 1] = -1;
-        gene[g].pk--;
-        gene[g].feature.size = ccv_max(gene[g].pk, gene[g].nk);
-        g++;
-      }
-    if (best_gene.nk > 1)
-      for (q = 0; q < best_gene.nk; q++)
-      {
-        gene[g] = best_gene;
-        for (i = q; i < best_gene.nk - 1; i++)
-        {
-          gene[g].feature.nz[i] = gene[g].feature.nz[i + 1];
-          gene[g].feature.nx[i] = gene[g].feature.nx[i + 1];
-          gene[g].feature.ny[i] = gene[g].feature.ny[i + 1];
-        }
-        gene[g].feature.nz[gene[g].nk - 1] = -1;
-        gene[g].nk--;
-        gene[g].feature.size = ccv_max(gene[g].pk, gene[g].nk);
-        g++;
-      }
-    gene[g] = best_gene;
-    g++;
-    PRINT(CCV_CLI_INFO, "float search round : %d\n", t);
-    ccv_bbf_gene_t local_gene = _ccv_bbf_best_gene(gene, g, CCV_BBF_POINT_MIN, posdata, posnum, negdata, negnum, size, pw, nw);
-    if (local_gene.error >= best_gene.error - 1e-10)
-      break;
-    best_gene = local_gene;
-  }
-  ccfree(gene);
-  gsl_rng_free(rng);
-  return best_gene.feature;
-}
-
-static int _ccv_write_bbf_stage_classifier(const char *file, ccv_bbf_stage_classifier_t *classifier)
-{
-  FILE *w = fopen(file, "wb");
-  if (w == 0)
-    return -1;
-  fprintf(w, "%d\n", classifier->count);
-  union {
-    float fl;
-    int i;
-  } fli;
-  fli.fl = classifier->threshold;
-  fprintf(w, "%d\n", fli.i);
-  int i, j;
-  for (i = 0; i < classifier->count; i++)
-  {
-    fprintf(w, "%d\n", classifier->feature[i].size);
-    for (j = 0; j < classifier->feature[i].size; j++)
-    {
-      fprintf(w, "%d %d %d\n", classifier->feature[i].px[j], classifier->feature[i].py[j], classifier->feature[i].pz[j]);
-      fprintf(w, "%d %d %d\n", classifier->feature[i].nx[j], classifier->feature[i].ny[j], classifier->feature[i].nz[j]);
-    }
-    union {
-      float fl;
-      int i;
-    } flia, flib;
-    flia.fl = classifier->alpha[i * 2];
-    flib.fl = classifier->alpha[i * 2 + 1];
-    fprintf(w, "%d %d\n", flia.i, flib.i);
-  }
-  fclose(w);
-  return 0;
-}
-
-static int _ccv_read_background_data(const char *file, unsigned char **negdata, int *negnum, ccv_size_t size)
-{
-  int stat = 0;
-  FILE *r = fopen(file, "rb");
-  if (r == 0)
-    return -1;
-  stat |= fread(negnum, sizeof(int), 1, r);
-  int i;
-  int isizs012 = _ccv_width_padding(size.width) * size.height +
-                 _ccv_width_padding(size.width >> 1) * (size.height >> 1) +
-                 _ccv_width_padding(size.width >> 2) * (size.height >> 2);
-  for (i = 0; i < *negnum; i++)
-  {
-    negdata[i] = (unsigned char *)ccmalloc(isizs012);
-    stat |= fread(negdata[i], 1, isizs012, r);
-  }
-  fclose(r);
-  return 0;
-}
-
-static int _ccv_write_background_data(const char *file, unsigned char **negdata, int negnum, ccv_size_t size)
-{
-  FILE *w = fopen(file, "w");
-  if (w == 0)
-    return -1;
-  fwrite(&negnum, sizeof(int), 1, w);
-  int i;
-  int isizs012 = _ccv_width_padding(size.width) * size.height +
-                 _ccv_width_padding(size.width >> 1) * (size.height >> 1) +
-                 _ccv_width_padding(size.width >> 2) * (size.height >> 2);
-  for (i = 0; i < negnum; i++)
-    fwrite(negdata[i], 1, isizs012, w);
-  fclose(w);
-  return 0;
-}
-
-static int _ccv_resume_bbf_cascade_training_state(const char *file, int *i, int *k, int *bg, double *pw, double *nw, int posnum, int negnum)
-{
-  int stat = 0;
-  FILE *r = fopen(file, "r");
-  if (r == 0)
-    return -1;
-  stat |= fscanf(r, "%d %d %d", i, k, bg);
-  int j;
-  union {
-    double db;
-    int i[2];
-  } dbi;
-  for (j = 0; j < posnum; j++)
-  {
-    stat |= fscanf(r, "%d %d", &dbi.i[0], &dbi.i[1]);
-    pw[j] = dbi.db;
-  }
-  for (j = 0; j < negnum; j++)
-  {
-    stat |= fscanf(r, "%d %d", &dbi.i[0], &dbi.i[1]);
-    nw[j] = dbi.db;
-  }
-  fclose(r);
-  return 0;
-}
-
-static int _ccv_save_bbf_cacade_training_state(const char *file, int i, int k, int bg, double *pw, double *nw, int posnum, int negnum)
-{
-  FILE *w = fopen(file, "w");
-  if (w == 0)
-    return -1;
-  fprintf(w, "%d %d %d\n", i, k, bg);
-  int j;
-  union {
-    double db;
-    int i[2];
-  } dbi;
-  for (j = 0; j < posnum; ++j)
-  {
-    dbi.db = pw[j];
-    fprintf(w, "%d %d ", dbi.i[0], dbi.i[1]);
-  }
-  fprintf(w, "\n");
-  for (j = 0; j < negnum; ++j)
-  {
-    dbi.db = nw[j];
-    fprintf(w, "%d %d ", dbi.i[0], dbi.i[1]);
-  }
-  fprintf(w, "\n");
-  fclose(w);
-  return 0;
-}
-
-void ccv_bbf_classifier_cascade_new(ccv_dense_matrix_t **posimg, int posnum, char **bgfiles, int bgnum, int negnum, ccv_size_t size, const char *dir, ccv_bbf_new_param_t params)
-{
-  int i, j, k;
-  /* allocate memory for usage */
-  ccv_bbf_classifier_cascade_t *cascade = (ccv_bbf_classifier_cascade_t *)ccmalloc(sizeof(ccv_bbf_classifier_cascade_t));
-  cascade->count = 0;
-  cascade->size = size;
-  cascade->stage_classifier = (ccv_bbf_stage_classifier_t *)ccmalloc(sizeof(ccv_bbf_stage_classifier_t));
-  unsigned char **posdata = (unsigned char **)ccmalloc(posnum * sizeof(unsigned char *));
-  unsigned char **negdata = (unsigned char **)ccmalloc(negnum * sizeof(unsigned char *));
-  double *pw = (double *)ccmalloc(posnum * sizeof(double));
-  double *nw = (double *)ccmalloc(negnum * sizeof(double));
-  float *peval = (float *)ccmalloc(posnum * sizeof(float));
-  float *neval = (float *)ccmalloc(negnum * sizeof(float));
-  double inv_balance_k = 1. / params.balance_k;
-  /* balance factor k, and weighted with 0.01 */
-  params.balance_k *= 0.01;
-  inv_balance_k *= 0.01;
-
-  int steps[] = {_ccv_width_padding(cascade->size.width),
-                 _ccv_width_padding(cascade->size.width >> 1),
-                 _ccv_width_padding(cascade->size.width >> 2)};
-  int isizs0 = steps[0] * cascade->size.height;
-  int isizs01 = isizs0 + steps[1] * (cascade->size.height >> 1);
-
-  i = 0;
-  k = 0;
-  int bg = 0;
-  int cacheK = 10;
-  /* state resume code */
-  char buf[1024];
-  sprintf(buf, "%s/stat.txt", dir);
-  _ccv_resume_bbf_cascade_training_state(buf, &i, &k, &bg, pw, nw, posnum, negnum);
-  if (i > 0)
-  {
-    cascade->count = i;
-    ccfree(cascade->stage_classifier);
-    cascade->stage_classifier = (ccv_bbf_stage_classifier_t *)ccmalloc(i * sizeof(ccv_bbf_stage_classifier_t));
-    for (j = 0; j < i; j++)
-    {
-      sprintf(buf, "%s/stage-%d.txt", dir, j);
-      _ccv_read_bbf_stage_classifier(buf, &cascade->stage_classifier[j]);
-    }
-  }
-  if (k > 0)
-    cacheK = k;
-  int rpos, rneg = 0;
-  if (bg)
-  {
-    sprintf(buf, "%s/negs.txt", dir);
-    _ccv_read_background_data(buf, negdata, &rneg, cascade->size);
-  }
-
-  for (; i < params.layer; i++)
-  {
-    if (!bg)
-    {
-      rneg = _ccv_prepare_background_data(cascade, bgfiles, bgnum, negdata, negnum);
-      /* save state of background data */
-      sprintf(buf, "%s/negs.txt", dir);
-      _ccv_write_background_data(buf, negdata, rneg, cascade->size);
-      bg = 1;
-    }
-    double totalw;
-    /* save state of cascade : level, weight etc. */
-    sprintf(buf, "%s/stat.txt", dir);
-    _ccv_save_bbf_cacade_training_state(buf, i, k, bg, pw, nw, posnum, negnum);
-    ccv_bbf_stage_classifier_t classifier;
-    if (k > 0)
-    {
-      /* resume state of classifier */
-      sprintf(buf, "%s/stage-%d.txt", dir, i);
-      _ccv_read_bbf_stage_classifier(buf, &classifier);
-    }
-    else
-    {
-      /* initialize classifier */
-      for (j = 0; j < posnum; j++)
-        pw[j] = params.balance_k;
-      for (j = 0; j < rneg; j++)
-        nw[j] = inv_balance_k;
-      classifier.count = k;
-      classifier.threshold = 0;
-      classifier.feature = (ccv_bbf_feature_t *)ccmalloc(cacheK * sizeof(ccv_bbf_feature_t));
-      classifier.alpha = (float *)ccmalloc(cacheK * 2 * sizeof(float));
-    }
-    _ccv_prepare_positive_data(posimg, posdata, cascade->size, posnum);
-    rpos = _ccv_prune_positive_data(cascade, posdata, posnum, cascade->size);
-    PRINT(CCV_CLI_INFO, "%d postivie data and %d negative data in training\n", rpos, rneg);
-    /* reweight to 1.00 */
-    totalw = 0;
-    for (j = 0; j < rpos; j++)
-      totalw += pw[j];
-    for (j = 0; j < rneg; j++)
-      totalw += nw[j];
-    for (j = 0; j < rpos; j++)
-      pw[j] = pw[j] / totalw;
-    for (j = 0; j < rneg; j++)
-      nw[j] = nw[j] / totalw;
-    for (;; k++)
-    {
-      /* get overall true-positive, false-positive rate and threshold */
-      double tp = 0, fp = 0, etp = 0, efp = 0;
-      _ccv_bbf_eval_data(&classifier, posdata, rpos, negdata, rneg, cascade->size, peval, neval);
-      _ccv_sort_32f(peval, rpos, 0);
-      classifier.threshold = peval[(int)((1. - params.pos_crit) * rpos)] - 1e-6;
-      for (j = 0; j < rpos; j++)
-      {
-        if (peval[j] >= 0)
-          ++tp;
-        if (peval[j] >= classifier.threshold)
-          ++etp;
-      }
-      tp /= rpos;
-      etp /= rpos;
-      for (j = 0; j < rneg; j++)
-      {
-        if (neval[j] >= 0)
-          ++fp;
-        if (neval[j] >= classifier.threshold)
-          ++efp;
-      }
-      fp /= rneg;
-      efp /= rneg;
-      PRINT(CCV_CLI_INFO, "stage classifier real TP rate : %f, FP rate : %f\n", tp, fp);
-      PRINT(CCV_CLI_INFO, "stage classifier TP rate : %f, FP rate : %f at threshold : %f\n", etp, efp, classifier.threshold);
-      if (k > 0)
-      {
-        /* save classifier state */
-        sprintf(buf, "%s/stage-%d.txt", dir, i);
-        _ccv_write_bbf_stage_classifier(buf, &classifier);
-        sprintf(buf, "%s/stat.txt", dir);
-        _ccv_save_bbf_cacade_training_state(buf, i, k, bg, pw, nw, posnum, negnum);
-      }
-      if (etp > params.pos_crit && efp < params.neg_crit)
-        break;
-      /* TODO: more post-process is needed in here */
-
-      /* select the best feature in current distribution through genetic algorithm optimization */
-      ccv_bbf_feature_t best;
-      if (params.optimizer == CCV_BBF_GENETIC_OPT)
-      {
-        best = _ccv_bbf_genetic_optimize(posdata, rpos, negdata, rneg, params.feature_number, cascade->size, pw, nw);
-      }
-      else if (params.optimizer == CCV_BBF_FLOAT_OPT)
-      {
-        best = _ccv_bbf_convex_optimize(posdata, rpos, negdata, rneg, 0, cascade->size, pw, nw);
-      }
-      else
-      {
-        best = _ccv_bbf_genetic_optimize(posdata, rpos, negdata, rneg, params.feature_number, cascade->size, pw, nw);
-        best = _ccv_bbf_convex_optimize(posdata, rpos, negdata, rneg, &best, cascade->size, pw, nw);
-      }
-      double err = _ccv_bbf_error_rate(&best, posdata, rpos, negdata, rneg, cascade->size, pw, nw);
-      double rw = (1 - err) / err;
-      totalw = 0;
-      /* reweight */
-      for (j = 0; j < rpos; j++)
-      {
-        unsigned char *u8[] = {posdata[j], posdata[j] + isizs0, posdata[j] + isizs01};
-        if (!_ccv_run_bbf_feature(&best, steps, u8))
-          pw[j] *= rw;
-        pw[j] *= params.balance_k;
-        totalw += pw[j];
-      }
-      for (j = 0; j < rneg; j++)
-      {
-        unsigned char *u8[] = {negdata[j], negdata[j] + isizs0, negdata[j] + isizs01};
-        if (_ccv_run_bbf_feature(&best, steps, u8))
-          nw[j] *= rw;
-        nw[j] *= inv_balance_k;
-        totalw += nw[j];
-      }
-      for (j = 0; j < rpos; j++)
-        pw[j] = pw[j] / totalw;
-      for (j = 0; j < rneg; j++)
-        nw[j] = nw[j] / totalw;
-      double c = log(rw);
-      PRINT(CCV_CLI_INFO, "coefficient of feature %d: %f\n", k + 1, c);
-      classifier.count = k + 1;
-      /* resizing classifier */
-      if (k >= cacheK)
-      {
-        ccv_bbf_feature_t *feature = (ccv_bbf_feature_t *)ccmalloc(cacheK * 2 * sizeof(ccv_bbf_feature_t));
-        memcpy(feature, classifier.feature, cacheK * sizeof(ccv_bbf_feature_t));
-        ccfree(classifier.feature);
-        float *alpha = (float *)ccmalloc(cacheK * 4 * sizeof(float));
-        memcpy(alpha, classifier.alpha, cacheK * 2 * sizeof(float));
-        ccfree(classifier.alpha);
-        classifier.feature = feature;
-        classifier.alpha = alpha;
-        cacheK *= 2;
-      }
-      /* setup new feature */
-      classifier.feature[k] = best;
-      classifier.alpha[k * 2] = -c;
-      classifier.alpha[k * 2 + 1] = c;
-    }
-    cascade->count = i + 1;
-    ccv_bbf_stage_classifier_t *stage_classifier = (ccv_bbf_stage_classifier_t *)ccmalloc(cascade->count * sizeof(ccv_bbf_stage_classifier_t));
-    memcpy(stage_classifier, cascade->stage_classifier, i * sizeof(ccv_bbf_stage_classifier_t));
-    ccfree(cascade->stage_classifier);
-    stage_classifier[i] = classifier;
-    cascade->stage_classifier = stage_classifier;
-    k = 0;
-    bg = 0;
-    for (j = 0; j < rpos; j++)
-      ccfree(posdata[j]);
-    for (j = 0; j < rneg; j++)
-      ccfree(negdata[j]);
-  }
-
-  ccfree(neval);
-  ccfree(peval);
-  ccfree(nw);
-  ccfree(pw);
-  ccfree(negdata);
-  ccfree(posdata);
-  ccfree(cascade);
-}
-#else
-void ccv_bbf_classifier_cascade_new(ccv_dense_matrix_t **posimg, int posnum, char **bgfiles, int bgnum, int negnum, ccv_size_t size, const char *dir, ccv_bbf_new_param_t params)
-{
-  fprintf(stderr, " ccv_bbf_classifier_cascade_new requires libgsl support, please compile ccv with libgsl.\n");
-}
-#endif
-
-static int _ccv_is_equal(const void *_r1, const void *_r2, void *data)
-{
-  const ccv_comp_t *r1 = (const ccv_comp_t *)_r1;
-  const ccv_comp_t *r2 = (const ccv_comp_t *)_r2;
-  int distance = (int)(r1->rect.width * 0.25 + 0.5);
-
-  return r2->rect.x <= r1->rect.x + distance &&
-         r2->rect.x >= r1->rect.x - distance &&
-         r2->rect.y <= r1->rect.y + distance &&
-         r2->rect.y >= r1->rect.y - distance &&
-         r2->rect.width <= (int)(r1->rect.width * 1.5 + 0.5) &&
-         (int)(r2->rect.width * 1.5 + 0.5) >= r1->rect.width;
-}
-
-static int _ccv_is_equal_same_class(const void *_r1, const void *_r2, void *data)
-{
-  const ccv_comp_t *r1 = (const ccv_comp_t *)_r1;
-  const ccv_comp_t *r2 = (const ccv_comp_t *)_r2;
-  int distance = (int)(r1->rect.width * 0.25 + 0.5);
-
-  return r2->classification.id == r1->classification.id &&
-         r2->rect.x <= r1->rect.x + distance &&
-         r2->rect.x >= r1->rect.x - distance &&
-         r2->rect.y <= r1->rect.y + distance &&
-         r2->rect.y >= r1->rect.y - distance &&
-         r2->rect.width <= (int)(r1->rect.width * 1.5 + 0.5) &&
-         (int)(r2->rect.width * 1.5 + 0.5) >= r1->rect.width;
-}
-
-ccv_array_t *ccv_bbf_detect_objects(ccv_dense_matrix_t *a, ccv_bbf_classifier_cascade_t **_cascade, int count, ccv_bbf_param_t params)
-{
-  int hr = a->rows / params.size.height;
-  int wr = a->cols / params.size.width;
-  double scale = pow(2., 1. / (params.interval + 1.));
-  int next = params.interval + 1;
-  int scale_upto = (int)(log((double)ccv_min(hr, wr)) / log(scale));
-  ccv_dense_matrix_t **pyr = (ccv_dense_matrix_t **)alloca((scale_upto + next * 2) * 4 * sizeof(ccv_dense_matrix_t *));
-  memset(pyr, 0, (scale_upto + next * 2) * 4 * sizeof(ccv_dense_matrix_t *));
-  if (params.size.height != _cascade[0]->size.height || params.size.width != _cascade[0]->size.width)
-    ccv_resample(a, &pyr[0], 0, a->rows * _cascade[0]->size.height / params.size.height, a->cols * _cascade[0]->size.width / params.size.width, CCV_INTER_AREA);
-  else
-    pyr[0] = a;
-  int i, j, k, t, x, y, q;
-  for (i = 1; i < ccv_min(params.interval + 1, scale_upto + next * 2); i++)
-    ccv_resample(pyr[0], &pyr[i * 4], 0, (int)(pyr[0]->rows / pow(scale, i)), (int)(pyr[0]->cols / pow(scale, i)), CCV_INTER_AREA);
-  for (i = next; i < scale_upto + next * 2; i++)
-    ccv_sample_down(pyr[i * 4 - next * 4], &pyr[i * 4], 0, 0, 0);
-  if (params.accurate)
-    for (i = next * 2; i < scale_upto + next * 2; i++)
-    {
-      ccv_sample_down(pyr[i * 4 - next * 4], &pyr[i * 4 + 1], 0, 1, 0);
-      ccv_sample_down(pyr[i * 4 - next * 4], &pyr[i * 4 + 2], 0, 0, 1);
-      ccv_sample_down(pyr[i * 4 - next * 4], &pyr[i * 4 + 3], 0, 1, 1);
-    }
-  ccv_array_t *idx_seq;
-  ccv_array_t *seq = ccv_array_new(sizeof(ccv_comp_t), 64, 0);
-  ccv_array_t *seq2 = ccv_array_new(sizeof(ccv_comp_t), 64, 0);
-  ccv_array_t *result_seq = ccv_array_new(sizeof(ccv_comp_t), 64, 0);
-  /* detect in multi scale */
-  for (t = 0; t < count; t++)
-  {
-    ccv_bbf_classifier_cascade_t *cascade = _cascade[t];
-    float scale_x = (float)params.size.width / (float)cascade->size.width;
-    float scale_y = (float)params.size.height / (float)cascade->size.height;
-    ccv_array_clear(seq);
-    for (i = 0; i < scale_upto; i++)
-    {
-      int dx[] = {0, 1, 0, 1};
-      int dy[] = {0, 0, 1, 1};
-      int i_rows = pyr[i * 4 + next * 8]->rows - (cascade->size.height >> 2);
-      int steps[] = {pyr[i * 4]->step, pyr[i * 4 + next * 4]->step, pyr[i * 4 + next * 8]->step};
-      int i_cols = pyr[i * 4 + next * 8]->cols - (cascade->size.width >> 2);
-      int paddings[] = {pyr[i * 4]->step * 4 - i_cols * 4,
-                        pyr[i * 4 + next * 4]->step * 2 - i_cols * 2,
-                        pyr[i * 4 + next * 8]->step - i_cols};
-      for (q = 0; q < (params.accurate ? 4 : 1); q++)
-      {
-        unsigned char *u8[] = {pyr[i * 4]->data.u8 + dx[q] * 2 + dy[q] * pyr[i * 4]->step * 2, pyr[i * 4 + next * 4]->data.u8 + dx[q] + dy[q] * pyr[i * 4 + next * 4]->step, pyr[i * 4 + next * 8 + q]->data.u8};
-        for (y = 0; y < i_rows; y++)
-        {
-          for (x = 0; x < i_cols; x++)
-          {
-            float sum;
-            int flag = 1;
-            ccv_bbf_stage_classifier_t *classifier = cascade->stage_classifier;
-            for (j = 0; j < cascade->count; ++j, ++classifier)
-            {
-              sum = 0;
-              float *alpha = classifier->alpha;
-              ccv_bbf_feature_t *feature = classifier->feature;
-              for (k = 0; k < classifier->count; ++k, alpha += 2, ++feature)
-                sum += alpha[_ccv_run_bbf_feature(feature, steps, u8)];
-              if (sum < classifier->threshold)
-              {
-                flag = 0;
-                break;
-              }
-            }
-            if (flag)
-            {
-              ccv_comp_t comp;
-              comp.rect = ccv_rect((int)((x * 4 + dx[q] * 2) * scale_x + 0.5), (int)((y * 4 + dy[q] * 2) * scale_y + 0.5), (int)(cascade->size.width * scale_x + 0.5), (int)(cascade->size.height * scale_y + 0.5));
-              comp.neighbors = 1;
-              comp.classification.id = t;
-              comp.classification.confidence = sum;
-              ccv_array_push(seq, &comp);
-            }
-            u8[0] += 4;
-            u8[1] += 2;
-            u8[2] += 1;
-          }
-          u8[0] += paddings[0];
-          u8[1] += paddings[1];
-          u8[2] += paddings[2];
-        }
-      }
-      scale_x *= scale;
-      scale_y *= scale;
-    }
-
-    /* the following code from OpenCV's haar feature implementation */
-    if (params.min_neighbors == 0)
-    {
-      for (i = 0; i < seq->rnum; i++)
-      {
-        ccv_comp_t *comp = (ccv_comp_t *)ccv_array_get(seq, i);
-        ccv_array_push(result_seq, comp);
-      }
-    }
-    else
-    {
-      idx_seq = 0;
-      ccv_array_clear(seq2);
-      // group retrieved rectangles in order to filter out noise
-      int ncomp = ccv_array_group(seq, &idx_seq, _ccv_is_equal_same_class, 0);
-      ccv_comp_t *comps = (ccv_comp_t *)ccmalloc((ncomp + 1) * sizeof(ccv_comp_t));
-      memset(comps, 0, (ncomp + 1) * sizeof(ccv_comp_t));
-
-      // count number of neighbors
-      for (i = 0; i < seq->rnum; i++)
-      {
-        ccv_comp_t r1 = *(ccv_comp_t *)ccv_array_get(seq, i);
-        int idx = *(int *)ccv_array_get(idx_seq, i);
-
-        if (comps[idx].neighbors == 0)
-          comps[idx].classification.confidence = r1.classification.confidence;
-
-        ++comps[idx].neighbors;
-
-        comps[idx].rect.x += r1.rect.x;
-        comps[idx].rect.y += r1.rect.y;
-        comps[idx].rect.width += r1.rect.width;
-        comps[idx].rect.height += r1.rect.height;
-        comps[idx].classification.id = r1.classification.id;
-        comps[idx].classification.confidence = ccv_max(comps[idx].classification.confidence, r1.classification.confidence);
-      }
-
-      // calculate average bounding box
-      for (i = 0; i < ncomp; i++)
-      {
-        int n = comps[i].neighbors;
-        if (n >= params.min_neighbors)
-        {
-          ccv_comp_t comp;
-          comp.rect.x = (comps[i].rect.x * 2 + n) / (2 * n);
-          comp.rect.y = (comps[i].rect.y * 2 + n) / (2 * n);
-          comp.rect.width = (comps[i].rect.width * 2 + n) / (2 * n);
-          comp.rect.height = (comps[i].rect.height * 2 + n) / (2 * n);
-          comp.neighbors = comps[i].neighbors;
-          comp.classification.id = comps[i].classification.id;
-          comp.classification.confidence = comps[i].classification.confidence;
-          ccv_array_push(seq2, &comp);
-        }
-      }
-
-      // filter out small face rectangles inside large face rectangles
-      for (i = 0; i < seq2->rnum; i++)
-      {
-        ccv_comp_t r1 = *(ccv_comp_t *)ccv_array_get(seq2, i);
-        int flag = 1;
-
-        for (j = 0; j < seq2->rnum; j++)
-        {
-          ccv_comp_t r2 = *(ccv_comp_t *)ccv_array_get(seq2, j);
-          int distance = (int)(r2.rect.width * 0.25 + 0.5);
-
-          if (i != j &&
-              r1.classification.id == r2.classification.id &&
-              r1.rect.x >= r2.rect.x - distance &&
-              r1.rect.y >= r2.rect.y - distance &&
-              r1.rect.x + r1.rect.width <= r2.rect.x + r2.rect.width + distance &&
-              r1.rect.y + r1.rect.height <= r2.rect.y + r2.rect.height + distance &&
-              (r2.neighbors > ccv_max(3, r1.neighbors) || r1.neighbors < 3))
-          {
-            flag = 0;
-            break;
-          }
-        }
-
-        if (flag)
-          ccv_array_push(result_seq, &r1);
-      }
-      ccv_array_free(idx_seq);
-      ccfree(comps);
-    }
-  }
-
-  ccv_array_free(seq);
-  ccv_array_free(seq2);
-
-  ccv_array_t *result_seq2;
-  /* the following code from OpenCV's haar feature implementation */
-  if (params.flags & CCV_BBF_NO_NESTED)
-  {
-    result_seq2 = ccv_array_new(sizeof(ccv_comp_t), 64, 0);
-    idx_seq = 0;
-    // group retrieved rectangles in order to filter out noise
-    int ncomp = ccv_array_group(result_seq, &idx_seq, _ccv_is_equal, 0);
-    ccv_comp_t *comps = (ccv_comp_t *)ccmalloc((ncomp + 1) * sizeof(ccv_comp_t));
-    memset(comps, 0, (ncomp + 1) * sizeof(ccv_comp_t));
-
-    // count number of neighbors
-    for (i = 0; i < result_seq->rnum; i++)
-    {
-      ccv_comp_t r1 = *(ccv_comp_t *)ccv_array_get(result_seq, i);
-      int idx = *(int *)ccv_array_get(idx_seq, i);
-
-      if (comps[idx].neighbors == 0 || comps[idx].classification.confidence < r1.classification.confidence)
-      {
-        comps[idx].classification.confidence = r1.classification.confidence;
-        comps[idx].neighbors = 1;
-        comps[idx].rect = r1.rect;
-        comps[idx].classification.id = r1.classification.id;
-      }
-    }
-
-    // calculate average bounding box
-    for (i = 0; i < ncomp; i++)
-      if (comps[i].neighbors)
-        ccv_array_push(result_seq2, &comps[i]);
-
-    ccv_array_free(result_seq);
-    ccfree(comps);
-  }
-  else
-  {
-    result_seq2 = result_seq;
-  }
-
-  for (i = 1; i < scale_upto + next * 2; i++)
-    ccv_matrix_free(pyr[i * 4]);
-  if (params.accurate)
-    for (i = next * 2; i < scale_upto + next * 2; i++)
-    {
-      ccv_matrix_free(pyr[i * 4 + 1]);
-      ccv_matrix_free(pyr[i * 4 + 2]);
-      ccv_matrix_free(pyr[i * 4 + 3]);
-    }
-  if (params.size.height != _cascade[0]->size.height || params.size.width != _cascade[0]->size.width)
-    ccv_matrix_free(pyr[0]);
-
-  return result_seq2;
-}
-
-ccv_bbf_classifier_cascade_t *ccv_bbf_read_classifier_cascade(const char *directory)
-{
-  char buf[1024];
-  sprintf(buf, "%s/cascade.txt", directory);
-  int s, i;
-  FILE *r = fopen(buf, "r");
-  if (r == 0)
-    return 0;
-  ccv_bbf_classifier_cascade_t *cascade = (ccv_bbf_classifier_cascade_t *)ccmalloc(sizeof(ccv_bbf_classifier_cascade_t));
-  s = fscanf(r, "%d %d %d", &cascade->count, &cascade->size.width, &cascade->size.height);
-  assert(s > 0);
-  cascade->stage_classifier = (ccv_bbf_stage_classifier_t *)ccmalloc(cascade->count * sizeof(ccv_bbf_stage_classifier_t));
-  for (i = 0; i < cascade->count; i++)
-  {
-    sprintf(buf, "%s/stage-%d.txt", directory, i);
-    if (_ccv_read_bbf_stage_classifier(buf, &cascade->stage_classifier[i]) < 0)
-    {
-      cascade->count = i;
-      break;
-    }
-  }
-  fclose(r);
-  return cascade;
-}
-
-ccv_bbf_classifier_cascade_t *ccv_bbf_classifier_cascade_read_binary(char *s)
-{
-  int i;
-  ccv_bbf_classifier_cascade_t *cascade = (ccv_bbf_classifier_cascade_t *)ccmalloc(sizeof(ccv_bbf_classifier_cascade_t));
-  memcpy(&cascade->count, s, sizeof(cascade->count));
-  s += sizeof(cascade->count);
-  memcpy(&cascade->size.width, s, sizeof(cascade->size.width));
-  s += sizeof(cascade->size.width);
-  memcpy(&cascade->size.height, s, sizeof(cascade->size.height));
-  s += sizeof(cascade->size.height);
-  ccv_bbf_stage_classifier_t *classifier = cascade->stage_classifier = (ccv_bbf_stage_classifier_t *)ccmalloc(cascade->count * sizeof(ccv_bbf_stage_classifier_t));
-  for (i = 0; i < cascade->count; i++, classifier++)
-  {
-    memcpy(&classifier->count, s, sizeof(classifier->count));
-    s += sizeof(classifier->count);
-    memcpy(&classifier->threshold, s, sizeof(classifier->threshold));
-    s += sizeof(classifier->threshold);
-    classifier->feature = (ccv_bbf_feature_t *)ccmalloc(classifier->count * sizeof(ccv_bbf_feature_t));
-    classifier->alpha = (float *)ccmalloc(classifier->count * 2 * sizeof(float));
-    memcpy(classifier->feature, s, classifier->count * sizeof(ccv_bbf_feature_t));
-    s += classifier->count * sizeof(ccv_bbf_feature_t);
-    memcpy(classifier->alpha, s, classifier->count * 2 * sizeof(float));
-    s += classifier->count * 2 * sizeof(float);
-  }
-  return cascade;
-}
-
-int ccv_bbf_classifier_cascade_write_binary(ccv_bbf_classifier_cascade_t *cascade, char *s, int slen)
-{
-  int i;
-  int len = sizeof(cascade->count) + sizeof(cascade->size.width) + sizeof(cascade->size.height);
-  ccv_bbf_stage_classifier_t *classifier = cascade->stage_classifier;
-  for (i = 0; i < cascade->count; i++, classifier++)
-    len += sizeof(classifier->count) + sizeof(classifier->threshold) + classifier->count * sizeof(ccv_bbf_feature_t) + classifier->count * 2 * sizeof(float);
-  if (slen >= len)
-  {
-    memcpy(s, &cascade->count, sizeof(cascade->count));
-    s += sizeof(cascade->count);
-    memcpy(s, &cascade->size.width, sizeof(cascade->size.width));
-    s += sizeof(cascade->size.width);
-    memcpy(s, &cascade->size.height, sizeof(cascade->size.height));
-    s += sizeof(cascade->size.height);
-    classifier = cascade->stage_classifier;
-    for (i = 0; i < cascade->count; i++, classifier++)
-    {
-      memcpy(s, &classifier->count, sizeof(classifier->count));
-      s += sizeof(classifier->count);
-      memcpy(s, &classifier->threshold, sizeof(classifier->threshold));
-      s += sizeof(classifier->threshold);
-      memcpy(s, classifier->feature, classifier->count * sizeof(ccv_bbf_feature_t));
-      s += classifier->count * sizeof(ccv_bbf_feature_t);
-      memcpy(s, classifier->alpha, classifier->count * 2 * sizeof(float));
-      s += classifier->count * 2 * sizeof(float);
-    }
-  }
-  return len;
-}
-
-void ccv_bbf_classifier_cascade_free(ccv_bbf_classifier_cascade_t *cascade)
-{
-  int i;
-  for (i = 0; i < cascade->count; ++i)
-  {
-    ccfree(cascade->stage_classifier[i].feature);
-    ccfree(cascade->stage_classifier[i].alpha);
-  }
-  ccfree(cascade->stage_classifier);
-  ccfree(cascade);
-}
\ No newline at end of file