text: #include "data.h"
  #include "utils.h"
  #include "image.h"
  #include "cuda.h"
  
  #include <stdio.h>
  #include <stdlib.h>
  #include <string.h>
  
  pthread_mutex_t mutex = PTHREAD_MUTEX_INITIALIZER;
  
  list *get_paths(char *filename)
  {
      char *path;
      FILE *file = fopen(filename, "r");
      if(!file) file_error(filename);
      list *lines = make_list();
      while((path=fgetl(file))){
          list_insert(lines, path);
      }
      fclose(file);
      return lines;
  }
  
  /*
  char **get_random_paths_indexes(char **paths, int n, int m, int *indexes)
  {
      char **random_paths = calloc(n, sizeof(char*));
      int i;
      pthread_mutex_lock(&mutex);
      for(i = 0; i < n; ++i){
          int index = rand()%m;
          indexes[i] = index;
          random_paths[i] = paths[index];
          if(i == 0) printf("%s\n", paths[index]);
      }
      pthread_mutex_unlock(&mutex);
      return random_paths;
  }
  */
  
  char **get_random_paths(char **paths, int n, int m)
  {
      char **random_paths = calloc(n, sizeof(char*));
      int i;
      pthread_mutex_lock(&mutex);
      for(i = 0; i < n; ++i){
          int index = rand()%m;
          random_paths[i] = paths[index];
          //if(i == 0) printf("%s\n", paths[index]);
      }
      pthread_mutex_unlock(&mutex);
      return random_paths;
  }
  
  char **find_replace_paths(char **paths, int n, char *find, char *replace)
  {
      char **replace_paths = calloc(n, sizeof(char*));
      int i;
      for(i = 0; i < n; ++i){
          char replaced[4096];
          find_replace(paths[i], find, replace, replaced);
          replace_paths[i] = copy_string(replaced);
      }
      return replace_paths;
  }
  
  matrix load_image_paths_gray(char **paths, int n, int w, int h)
  {
      int i;
      matrix X;
      X.rows = n;
      X.vals = calloc(X.rows, sizeof(float*));
      X.cols = 0;
  
      for(i = 0; i < n; ++i){
          image im = load_image(paths[i], w, h, 3);
  
          image gray = grayscale_image(im);
          free_image(im);
          im = gray;
  
          X.vals[i] = im.data;
          X.cols = im.h*im.w*im.c;
      }
      return X;
  }
  
  matrix load_image_paths(char **paths, int n, int w, int h)
  {
      int i;
      matrix X;
      X.rows = n;
      X.vals = calloc(X.rows, sizeof(float*));
      X.cols = 0;
  
      for(i = 0; i < n; ++i){
          image im = load_image_color(paths[i], w, h);
          X.vals[i] = im.data;
          X.cols = im.h*im.w*im.c;
      }
      return X;
  }
  
  matrix load_image_augment_paths(char **paths, int n, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure, int center)
  {
      int i;
      matrix X;
      X.rows = n;
      X.vals = calloc(X.rows, sizeof(float*));
      X.cols = 0;
  
      for(i = 0; i < n; ++i){
          image im = load_image_color(paths[i], 0, 0);
          image crop;
          if(center){
              crop = center_crop_image(im, size, size);
          } else {
              crop = random_augment_image(im, angle, aspect, min, max, size, size);
          }
          int flip = rand()%2;
          if (flip) flip_image(crop);
          random_distort_image(crop, hue, saturation, exposure);
  
          /*
          show_image(im, "orig");
          show_image(crop, "crop");
          cvWaitKey(0);
          */
          free_image(im);
          X.vals[i] = crop.data;
          X.cols = crop.h*crop.w*crop.c;
      }
      return X;
  }
  
  
  box_label *read_boxes(char *filename, int *n)
  {
      FILE *file = fopen(filename, "r");
      if(!file) file_error(filename);
      float x, y, h, w;
      int id;
      int count = 0;
      int size = 64;
      box_label *boxes = calloc(size, sizeof(box_label));
      while(fscanf(file, "%d %f %f %f %f", &id, &x, &y, &w, &h) == 5){
          if(count == size) {
              size = size * 2;
              boxes = realloc(boxes, size*sizeof(box_label));
          }
          boxes[count].id = id;
          boxes[count].x = x;
          boxes[count].y = y;
          boxes[count].h = h;
          boxes[count].w = w;
          boxes[count].left   = x - w/2;
          boxes[count].right  = x + w/2;
          boxes[count].top    = y - h/2;
          boxes[count].bottom = y + h/2;
          ++count;
      }
      fclose(file);
      *n = count;
      return boxes;
  }
  
  void randomize_boxes(box_label *b, int n)
  {
      int i;
      for(i = 0; i < n; ++i){
          box_label swap = b[i];
          int index = rand()%n;
          b[i] = b[index];
          b[index] = swap;
      }
  }
  
  void correct_boxes(box_label *boxes, int n, float dx, float dy, float sx, float sy, int flip)
  {
      int i;
      for(i = 0; i < n; ++i){
          if(boxes[i].x == 0 && boxes[i].y == 0) {
              boxes[i].x = 999999;
              boxes[i].y = 999999;
              boxes[i].w = 999999;
              boxes[i].h = 999999;
              continue;
          }
          boxes[i].left   = boxes[i].left  * sx - dx;
          boxes[i].right  = boxes[i].right * sx - dx;
          boxes[i].top    = boxes[i].top   * sy - dy;
          boxes[i].bottom = boxes[i].bottom* sy - dy;
  
          if(flip){
              float swap = boxes[i].left;
              boxes[i].left = 1. - boxes[i].right;
              boxes[i].right = 1. - swap;
          }
  
          boxes[i].left =  constrain(0, 1, boxes[i].left);
          boxes[i].right = constrain(0, 1, boxes[i].right);
          boxes[i].top =   constrain(0, 1, boxes[i].top);
          boxes[i].bottom =   constrain(0, 1, boxes[i].bottom);
  
          boxes[i].x = (boxes[i].left+boxes[i].right)/2;
          boxes[i].y = (boxes[i].top+boxes[i].bottom)/2;
          boxes[i].w = (boxes[i].right - boxes[i].left);
          boxes[i].h = (boxes[i].bottom - boxes[i].top);
  
          boxes[i].w = constrain(0, 1, boxes[i].w);
          boxes[i].h = constrain(0, 1, boxes[i].h);
      }
  }
  
  void fill_truth_swag(char *path, float *truth, int classes, int flip, float dx, float dy, float sx, float sy)
  {
      char labelpath[4096];
      find_replace(path, "images", "labels", labelpath);
      find_replace(labelpath, "JPEGImages", "labels", labelpath);
      find_replace(labelpath, ".jpg", ".txt", labelpath);
      find_replace(labelpath, ".JPG", ".txt", labelpath);
      find_replace(labelpath, ".JPEG", ".txt", labelpath);
  
      int count = 0;
      box_label *boxes = read_boxes(labelpath, &count);
      randomize_boxes(boxes, count);
      correct_boxes(boxes, count, dx, dy, sx, sy, flip);
      float x,y,w,h;
      int id;
      int i;
  
      for (i = 0; i < count && i < 30; ++i) {
          x =  boxes[i].x;
          y =  boxes[i].y;
          w =  boxes[i].w;
          h =  boxes[i].h;
          id = boxes[i].id;
  
          if (w < .0 || h < .0) continue;
  
          int index = (4+classes) * i;
  
          truth[index++] = x;
          truth[index++] = y;
          truth[index++] = w;
          truth[index++] = h;
  
          if (id < classes) truth[index+id] = 1;
      }
      free(boxes);
  }
  
  void fill_truth_region(char *path, float *truth, int classes, int num_boxes, int flip, float dx, float dy, float sx, float sy)
  {
      char labelpath[4096];
      find_replace(path, "images", "labels", labelpath);
      find_replace(labelpath, "JPEGImages", "labels", labelpath);
  
      find_replace(labelpath, ".jpg", ".txt", labelpath);
      find_replace(labelpath, ".png", ".txt", labelpath);
      find_replace(labelpath, ".JPG", ".txt", labelpath);
      find_replace(labelpath, ".JPEG", ".txt", labelpath);
      int count = 0;
      box_label *boxes = read_boxes(labelpath, &count);
      randomize_boxes(boxes, count);
      correct_boxes(boxes, count, dx, dy, sx, sy, flip);
      float x,y,w,h;
      int id;
      int i;
  
      for (i = 0; i < count; ++i) {
          x =  boxes[i].x;
          y =  boxes[i].y;
          w =  boxes[i].w;
          h =  boxes[i].h;
          id = boxes[i].id;
  
          if (w < .005 || h < .005) continue;
  
          int col = (int)(x*num_boxes);
          int row = (int)(y*num_boxes);
  
          x = x*num_boxes - col;
          y = y*num_boxes - row;
  
          int index = (col+row*num_boxes)*(5+classes);
          if (truth[index]) continue;
          truth[index++] = 1;
  
          if (id < classes) truth[index+id] = 1;
          index += classes;
  
          truth[index++] = x;
          truth[index++] = y;
          truth[index++] = w;
          truth[index++] = h;
      }
      free(boxes);
  }
  
  void load_rle(image im, int *rle, int n)
  {
      int count = 0;
      int curr = 0;
      int i,j;
      for(i = 0; i < n; ++i){
          for(j = 0; j < rle[i]; ++j){
              im.data[count++] = curr;
          }
          curr = 1 - curr;
      }
      for(; count < im.h*im.w*im.c; ++count){
          im.data[count] = curr;
      }
  }
  
  void or_image(image src, image dest, int c)
  {
      int i;
      for(i = 0; i < src.w*src.h; ++i){
          if(src.data[i]) dest.data[dest.w*dest.h*c + i] = 1;
      }
  }
  
  void exclusive_image(image src)
  {
      int k, j, i;
      int s = src.w*src.h;
      for(k = 0; k < src.c-1; ++k){
          for(i = 0; i < s; ++i){
              if (src.data[k*s + i]){
                  for(j = k+1; j < src.c; ++j){
                      src.data[j*s + i] = 0;
                  }
              }
          }
      }
  }
  
  box bound_image(image im)
  {
      int x,y;
      int minx = im.w;
      int miny = im.h;
      int maxx = 0;
      int maxy = 0;
      for(y = 0; y < im.h; ++y){
          for(x = 0; x < im.w; ++x){
              if(im.data[y*im.w + x]){
                  minx = (x < minx) ? x : minx;
                  miny = (y < miny) ? y : miny;
                  maxx = (x > maxx) ? x : maxx;
                  maxy = (y > maxy) ? y : maxy;
              }
          }
      }
      box b = {minx, miny, maxx-minx + 1, maxy-miny + 1};
      //printf("%f %f %f %f\n", b.x, b.y, b.w, b.h);
      return b;
  }
  
  void fill_truth_iseg(char *path, int num_boxes, float *truth, int classes, int w, int h, augment_args aug, int flip, int mw, int mh)
  {
      char labelpath[4096];
      find_replace(path, "images", "mask", labelpath);
      find_replace(labelpath, "JPEGImages", "mask", labelpath);
      find_replace(labelpath, ".jpg", ".txt", labelpath);
      find_replace(labelpath, ".JPG", ".txt", labelpath);
      find_replace(labelpath, ".JPEG", ".txt", labelpath);
      FILE *file = fopen(labelpath, "r");
      if(!file) file_error(labelpath);
      char buff[32788];
      int id;
      int i = 0;
      image part = make_image(w, h, 1);
      while((fscanf(file, "%d %s", &id, buff) == 2) && i < num_boxes){
          int n = 0;
          int *rle = read_intlist(buff, &n, 0);
          load_rle(part, rle, n);
          image sized = rotate_crop_image(part, aug.rad, aug.scale, aug.w, aug.h, aug.dx, aug.dy, aug.aspect);
          if(flip) flip_image(sized);
          box b = bound_image(sized);
          if(b.w > 0){
              image crop = crop_image(sized, b.x, b.y, b.w, b.h);
              image mask = resize_image(crop, mw, mh);
              truth[i*(4 + mw*mh + 1) + 0] = (b.x + b.w/2.)/sized.w;
              truth[i*(4 + mw*mh + 1) + 1] = (b.y + b.h/2.)/sized.h;
              truth[i*(4 + mw*mh + 1) + 2] = b.w/sized.w;
              truth[i*(4 + mw*mh + 1) + 3] = b.h/sized.h;
              int j;
              for(j = 0; j < mw*mh; ++j){
                  truth[i*(4 + mw*mh + 1) + 4 + j] = mask.data[j];
              }
              truth[i*(4 + mw*mh + 1) + 4 + mw*mh] = id;
              free_image(crop);
              free_image(mask);
              ++i;
          }
          free_image(sized);
          free(rle);
      }
      fclose(file);
      free_image(part);
  }
  
  
  void fill_truth_detection(char *path, int num_boxes, float *truth, int classes, int flip, float dx, float dy, float sx, float sy)
  {
      char labelpath[4096];
      find_replace(path, "images", "labels", labelpath);
      find_replace(labelpath, "JPEGImages", "labels", labelpath);
  
      find_replace(labelpath, "raw", "labels", labelpath);
      find_replace(labelpath, ".jpg", ".txt", labelpath);
      find_replace(labelpath, ".png", ".txt", labelpath);
      find_replace(labelpath, ".JPG", ".txt", labelpath);
      find_replace(labelpath, ".JPEG", ".txt", labelpath);
      int count = 0;
      box_label *boxes = read_boxes(labelpath, &count);
      randomize_boxes(boxes, count);
      correct_boxes(boxes, count, dx, dy, sx, sy, flip);
      if(count > num_boxes) count = num_boxes;
      float x,y,w,h;
      int id;
      int i;
  
      for (i = 0; i < count; ++i) {
          x =  boxes[i].x;
          y =  boxes[i].y;
          w =  boxes[i].w;
          h =  boxes[i].h;
          id = boxes[i].id;
  
          if ((w < .001 || h < .001)) continue;
  
          truth[i*5+0] = x;
          truth[i*5+1] = y;
          truth[i*5+2] = w;
          truth[i*5+3] = h;
          truth[i*5+4] = id;
      }
      free(boxes);
  }
  
  #define NUMCHARS 37
  
  void print_letters(float *pred, int n)
  {
      int i;
      for(i = 0; i < n; ++i){
          int index = max_index(pred+i*NUMCHARS, NUMCHARS);
          printf("%c", int_to_alphanum(index));
      }
      printf("\n");
  }
  
  void fill_truth_captcha(char *path, int n, float *truth)
  {
      char *begin = strrchr(path, '/');
      ++begin;
      int i;
      for(i = 0; i < strlen(begin) && i < n && begin[i] != '.'; ++i){
          int index = alphanum_to_int(begin[i]);
          if(index > 35) printf("Bad %c\n", begin[i]);
          truth[i*NUMCHARS+index] = 1;
      }
      for(;i < n; ++i){
          truth[i*NUMCHARS + NUMCHARS-1] = 1;
      }
  }
  
  data load_data_captcha(char **paths, int n, int m, int k, int w, int h)
  {
      if(m) paths = get_random_paths(paths, n, m);
      data d = {0};
      d.shallow = 0;
      d.X = load_image_paths(paths, n, w, h);
      d.y = make_matrix(n, k*NUMCHARS);
      int i;
      for(i = 0; i < n; ++i){
          fill_truth_captcha(paths[i], k, d.y.vals[i]);
      }
      if(m) free(paths);
      return d;
  }
  
  data load_data_captcha_encode(char **paths, int n, int m, int w, int h)
  {
      if(m) paths = get_random_paths(paths, n, m);
      data d = {0};
      d.shallow = 0;
      d.X = load_image_paths(paths, n, w, h);
      d.X.cols = 17100;
      d.y = d.X;
      if(m) free(paths);
      return d;
  }
  
  void fill_truth(char *path, char **labels, int k, float *truth)
  {
      int i;
      memset(truth, 0, k*sizeof(float));
      int count = 0;
      for(i = 0; i < k; ++i){
          if(strstr(path, labels[i])){
              truth[i] = 1;
              ++count;
          }
      }
      if(count != 1 && (k != 1 || count != 0)) printf("Too many or too few labels: %d, %s\n", count, path);
  }
  
  void fill_hierarchy(float *truth, int k, tree *hierarchy)
  {
      int j;
      for(j = 0; j < k; ++j){
          if(truth[j]){
              int parent = hierarchy->parent[j];
              while(parent >= 0){
                  truth[parent] = 1;
                  parent = hierarchy->parent[parent];
              }
          }
      }
      int i;
      int count = 0;
      for(j = 0; j < hierarchy->groups; ++j){
          //printf("%d\n", count);
          int mask = 1;
          for(i = 0; i < hierarchy->group_size[j]; ++i){
              if(truth[count + i]){
                  mask = 0;
                  break;
              }
          }
          if (mask) {
              for(i = 0; i < hierarchy->group_size[j]; ++i){
                  truth[count + i] = SECRET_NUM;
              }
          }
          count += hierarchy->group_size[j];
      }
  }
  
  matrix load_regression_labels_paths(char **paths, int n)
  {
      matrix y = make_matrix(n, 1);
      int i;
      for(i = 0; i < n; ++i){
          char labelpath[4096];
          find_replace(paths[i], "images", "targets", labelpath);
          find_replace(labelpath, "JPEGImages", "targets", labelpath);
          find_replace(labelpath, ".jpg", ".txt", labelpath);
          find_replace(labelpath, ".png", ".txt", labelpath);
  
          FILE *file = fopen(labelpath, "r");
          fscanf(file, "%f", &(y.vals[i][0]));
          fclose(file);
      }
      return y;
  }
  
  matrix load_labels_paths(char **paths, int n, char **labels, int k, tree *hierarchy)
  {
      matrix y = make_matrix(n, k);
      int i;
      for(i = 0; i < n && labels; ++i){
          fill_truth(paths[i], labels, k, y.vals[i]);
          if(hierarchy){
              fill_hierarchy(y.vals[i], k, hierarchy);
          }
      }
      return y;
  }
  
  matrix load_tags_paths(char **paths, int n, int k)
  {
      matrix y = make_matrix(n, k);
      int i;
      int count = 0;
      for(i = 0; i < n; ++i){
          char label[4096];
          find_replace(paths[i], "imgs", "labels", label);
          find_replace(label, "_iconl.jpeg", ".txt", label);
          FILE *file = fopen(label, "r");
          if(!file){
              find_replace(label, "labels", "labels2", label);
              file = fopen(label, "r");
              if(!file) continue;
          }
          ++count;
          int tag;
          while(fscanf(file, "%d", &tag) == 1){
              if(tag < k){
                  y.vals[i][tag] = 1;
              }
          }
          fclose(file);
      }
      printf("%d/%d\n", count, n);
      return y;
  }
  
  char **get_labels(char *filename)
  {
      list *plist = get_paths(filename);
      char **labels = (char **)list_to_array(plist);
      free_list(plist);
      return labels;
  }
  
  void free_data(data d)
  {
      if(!d.shallow){
          free_matrix(d.X);
          free_matrix(d.y);
      }else{
          free(d.X.vals);
          free(d.y.vals);
      }
  }
  
  image get_segmentation_image(char *path, int w, int h, int classes)
  {
      char labelpath[4096];
      find_replace(path, "images", "mask", labelpath);
      find_replace(labelpath, "JPEGImages", "mask", labelpath);
      find_replace(labelpath, ".jpg", ".txt", labelpath);
      find_replace(labelpath, ".JPG", ".txt", labelpath);
      find_replace(labelpath, ".JPEG", ".txt", labelpath);
      image mask = make_image(w, h, classes);
      FILE *file = fopen(labelpath, "r");
      if(!file) file_error(labelpath);
      char buff[32788];
      int id;
      image part = make_image(w, h, 1);
      while(fscanf(file, "%d %s", &id, buff) == 2){
          int n = 0;
          int *rle = read_intlist(buff, &n, 0);
          load_rle(part, rle, n);
          or_image(part, mask, id);
          free(rle);
      }
      //exclusive_image(mask);
      fclose(file);
      free_image(part);
      return mask;
  }
  
  image get_segmentation_image2(char *path, int w, int h, int classes)
  {
      char labelpath[4096];
      find_replace(path, "images", "mask", labelpath);
      find_replace(labelpath, "JPEGImages", "mask", labelpath);
      find_replace(labelpath, ".jpg", ".txt", labelpath);
      find_replace(labelpath, ".JPG", ".txt", labelpath);
      find_replace(labelpath, ".JPEG", ".txt", labelpath);
      image mask = make_image(w, h, classes+1);
      int i;
      for(i = 0; i < w*h; ++i){
          mask.data[w*h*classes + i] = 1;
      }
      FILE *file = fopen(labelpath, "r");
      if(!file) file_error(labelpath);
      char buff[32788];
      int id;
      image part = make_image(w, h, 1);
      while(fscanf(file, "%d %s", &id, buff) == 2){
          int n = 0;
          int *rle = read_intlist(buff, &n, 0);
          load_rle(part, rle, n);
          or_image(part, mask, id);
          for(i = 0; i < w*h; ++i){
              if(part.data[i]) mask.data[w*h*classes + i] = 0;
          }
          free(rle);
      }
      //exclusive_image(mask);
      fclose(file);
      free_image(part);
      return mask;
  }
  
  data load_data_seg(int n, char **paths, int m, int w, int h, int classes, int min, int max, float angle, float aspect, float hue, float saturation, float exposure, int div)
  {
      char **random_paths = get_random_paths(paths, n, m);
      int i;
      data d = {0};
      d.shallow = 0;
  
      d.X.rows = n;
      d.X.vals = calloc(d.X.rows, sizeof(float*));
      d.X.cols = h*w*3;
  
  
      d.y.rows = n;
      d.y.cols = h*w*classes/div/div;
      d.y.vals = calloc(d.X.rows, sizeof(float*));
  
      for(i = 0; i < n; ++i){
          image orig = load_image_color(random_paths[i], 0, 0);
          augment_args a = random_augment_args(orig, angle, aspect, min, max, w, h);
          image sized = rotate_crop_image(orig, a.rad, a.scale, a.w, a.h, a.dx, a.dy, a.aspect);
  
          int flip = rand()%2;
          if(flip) flip_image(sized);
          random_distort_image(sized, hue, saturation, exposure);
          d.X.vals[i] = sized.data;
  
          image mask = get_segmentation_image(random_paths[i], orig.w, orig.h, classes);
          //image mask = make_image(orig.w, orig.h, classes+1);
          image sized_m = rotate_crop_image(mask, a.rad, a.scale/div, a.w/div, a.h/div, a.dx/div, a.dy/div, a.aspect);
  
          if(flip) flip_image(sized_m);
          d.y.vals[i] = sized_m.data;
  
          free_image(orig);
          free_image(mask);
  
          /*
             image rgb = mask_to_rgb(sized_m, classes);
             show_image(rgb, "part");
             show_image(sized, "orig");
             cvWaitKey(0);
             free_image(rgb);
           */
      }
      free(random_paths);
      return d;
  }
  
  data load_data_iseg(int n, char **paths, int m, int w, int h, int classes, int boxes, int coords, int min, int max, float angle, float aspect, float hue, float saturation, float exposure)
  {
      char **random_paths = get_random_paths(paths, n, m);
      int i;
      data d = {0};
      d.shallow = 0;
  
      d.X.rows = n;
      d.X.vals = calloc(d.X.rows, sizeof(float*));
      d.X.cols = h*w*3;
  
      d.y = make_matrix(n, (coords+1)*boxes);
  
      for(i = 0; i < n; ++i){
          image orig = load_image_color(random_paths[i], 0, 0);
          augment_args a = random_augment_args(orig, angle, aspect, min, max, w, h);
          image sized = rotate_crop_image(orig, a.rad, a.scale, a.w, a.h, a.dx, a.dy, a.aspect);
  
          int flip = rand()%2;
          if(flip) flip_image(sized);
          random_distort_image(sized, hue, saturation, exposure);
          d.X.vals[i] = sized.data;
          //show_image(sized, "image");
  
          fill_truth_iseg(random_paths[i], boxes, d.y.vals[i], classes, orig.w, orig.h, a, flip, 14, 14);
  
          free_image(orig);
  
          /*
             image rgb = mask_to_rgb(sized_m, classes);
             show_image(rgb, "part");
             show_image(sized, "orig");
             cvWaitKey(0);
             free_image(rgb);
           */
      }
      free(random_paths);
      return d;
  }
  
  data load_data_region(int n, char **paths, int m, int w, int h, int size, int classes, float jitter, float hue, float saturation, float exposure)
  {
      char **random_paths = get_random_paths(paths, n, m);
      int i;
      data d = {0};
      d.shallow = 0;
  
      d.X.rows = n;
      d.X.vals = calloc(d.X.rows, sizeof(float*));
      d.X.cols = h*w*3;
  
  
      int k = size*size*(5+classes);
      d.y = make_matrix(n, k);
      for(i = 0; i < n; ++i){
          image orig = load_image_color(random_paths[i], 0, 0);
  
          int oh = orig.h;
          int ow = orig.w;
  
          int dw = (ow*jitter);
          int dh = (oh*jitter);
  
          int pleft  = rand_uniform(-dw, dw);
          int pright = rand_uniform(-dw, dw);
          int ptop   = rand_uniform(-dh, dh);
          int pbot   = rand_uniform(-dh, dh);
  
          int swidth =  ow - pleft - pright;
          int sheight = oh - ptop - pbot;
  
          float sx = (float)swidth  / ow;
          float sy = (float)sheight / oh;
  
          int flip = rand()%2;
          image cropped = crop_image(orig, pleft, ptop, swidth, sheight);
  
          float dx = ((float)pleft/ow)/sx;
          float dy = ((float)ptop /oh)/sy;
  
          image sized = resize_image(cropped, w, h);
          if(flip) flip_image(sized);
          random_distort_image(sized, hue, saturation, exposure);
          d.X.vals[i] = sized.data;
  
          fill_truth_region(random_paths[i], d.y.vals[i], classes, size, flip, dx, dy, 1./sx, 1./sy);
  
          free_image(orig);
          free_image(cropped);
      }
      free(random_paths);
      return d;
  }
  
  data load_data_compare(int n, char **paths, int m, int classes, int w, int h)
  {
      if(m) paths = get_random_paths(paths, 2*n, m);
      int i,j;
      data d = {0};
      d.shallow = 0;
  
      d.X.rows = n;
      d.X.vals = calloc(d.X.rows, sizeof(float*));
      d.X.cols = h*w*6;
  
      int k = 2*(classes);
      d.y = make_matrix(n, k);
      for(i = 0; i < n; ++i){
          image im1 = load_image_color(paths[i*2],   w, h);
          image im2 = load_image_color(paths[i*2+1], w, h);
  
          d.X.vals[i] = calloc(d.X.cols, sizeof(float));
          memcpy(d.X.vals[i],         im1.data, h*w*3*sizeof(float));
          memcpy(d.X.vals[i] + h*w*3, im2.data, h*w*3*sizeof(float));
  
          int id;
          float iou;
  
          char imlabel1[4096];
          char imlabel2[4096];
          find_replace(paths[i*2],   "imgs", "labels", imlabel1);
          find_replace(imlabel1, "jpg", "txt", imlabel1);
          FILE *fp1 = fopen(imlabel1, "r");
  
          while(fscanf(fp1, "%d %f", &id, &iou) == 2){
              if (d.y.vals[i][2*id] < iou) d.y.vals[i][2*id] = iou;
          }
  
          find_replace(paths[i*2+1], "imgs", "labels", imlabel2);
          find_replace(imlabel2, "jpg", "txt", imlabel2);
          FILE *fp2 = fopen(imlabel2, "r");
  
          while(fscanf(fp2, "%d %f", &id, &iou) == 2){
              if (d.y.vals[i][2*id + 1] < iou) d.y.vals[i][2*id + 1] = iou;
          }
  
          for (j = 0; j < classes; ++j){
              if (d.y.vals[i][2*j] > .5 &&  d.y.vals[i][2*j+1] < .5){
                  d.y.vals[i][2*j] = 1;
                  d.y.vals[i][2*j+1] = 0;
              } else if (d.y.vals[i][2*j] < .5 &&  d.y.vals[i][2*j+1] > .5){
                  d.y.vals[i][2*j] = 0;
                  d.y.vals[i][2*j+1] = 1;
              } else {
                  d.y.vals[i][2*j]   = SECRET_NUM;
                  d.y.vals[i][2*j+1] = SECRET_NUM;
              }
          }
          fclose(fp1);
          fclose(fp2);
  
          free_image(im1);
          free_image(im2);
      }
      if(m) free(paths);
      return d;
  }
  
  data load_data_swag(char **paths, int n, int classes, float jitter)
  {
      int index = rand()%n;
      char *random_path = paths[index];
  
      image orig = load_image_color(random_path, 0, 0);
      int h = orig.h;
      int w = orig.w;
  
      data d = {0};
      d.shallow = 0;
      d.w = w;
      d.h = h;
  
      d.X.rows = 1;
      d.X.vals = calloc(d.X.rows, sizeof(float*));
      d.X.cols = h*w*3;
  
      int k = (4+classes)*30;
      d.y = make_matrix(1, k);
  
      int dw = w*jitter;
      int dh = h*jitter;
  
      int pleft  = rand_uniform(-dw, dw);
      int pright = rand_uniform(-dw, dw);
      int ptop   = rand_uniform(-dh, dh);
      int pbot   = rand_uniform(-dh, dh);
  
      int swidth =  w - pleft - pright;
      int sheight = h - ptop - pbot;
  
      float sx = (float)swidth  / w;
      float sy = (float)sheight / h;
  
      int flip = rand()%2;
      image cropped = crop_image(orig, pleft, ptop, swidth, sheight);
  
      float dx = ((float)pleft/w)/sx;
      float dy = ((float)ptop /h)/sy;
  
      image sized = resize_image(cropped, w, h);
      if(flip) flip_image(sized);
      d.X.vals[0] = sized.data;
  
      fill_truth_swag(random_path, d.y.vals[0], classes, flip, dx, dy, 1./sx, 1./sy);
  
      free_image(orig);
      free_image(cropped);
  
      return d;
  }
  
  data load_data_detection(int n, char **paths, int m, int w, int h, int boxes, int classes, float jitter, float hue, float saturation, float exposure)
  {
      char **random_paths = get_random_paths(paths, n, m);
      int i;
      data d = {0};
      d.shallow = 0;
  
      d.X.rows = n;
      d.X.vals = calloc(d.X.rows, sizeof(float*));
      d.X.cols = h*w*3;
  
      d.y = make_matrix(n, 5*boxes);
      for(i = 0; i < n; ++i){
          image orig = load_image_color(random_paths[i], 0, 0);
          image sized = make_image(w, h, orig.c);
          fill_image(sized, .5);
  
          float dw = jitter * orig.w;
          float dh = jitter * orig.h;
  
          float new_ar = (orig.w + rand_uniform(-dw, dw)) / (orig.h + rand_uniform(-dh, dh));
          float scale = rand_uniform(.25, 2);
  
          float nw, nh;
  
          if(new_ar < 1){
              nh = scale * h;
              nw = nh * new_ar;
          } else {
              nw = scale * w;
              nh = nw / new_ar;
          }
  
          float dx = rand_uniform(0, w - nw);
          float dy = rand_uniform(0, h - nh);
  
          place_image(orig, nw, nh, dx, dy, sized);
  
          random_distort_image(sized, hue, saturation, exposure);
  
          int flip = rand()%2;
          if(flip) flip_image(sized);
          d.X.vals[i] = sized.data;
  
  
          fill_truth_detection(random_paths[i], boxes, d.y.vals[i], classes, flip, -dx/w, -dy/h, nw/w, nh/h);
  
          free_image(orig);
      }
      free(random_paths);
      return d;
  }
  
  void *load_thread(void *ptr)
  {
      //printf("Loading data: %d\n", rand());
      load_args a = *(struct load_args*)ptr;
      if(a.exposure == 0) a.exposure = 1;
      if(a.saturation == 0) a.saturation = 1;
      if(a.aspect == 0) a.aspect = 1;
  
      if (a.type == OLD_CLASSIFICATION_DATA){
          *a.d = load_data_old(a.paths, a.n, a.m, a.labels, a.classes, a.w, a.h);
      } else if (a.type == REGRESSION_DATA){
          *a.d = load_data_regression(a.paths, a.n, a.m, a.min, a.max, a.size, a.angle, a.aspect, a.hue, a.saturation, a.exposure);
      } else if (a.type == CLASSIFICATION_DATA){
          *a.d = load_data_augment(a.paths, a.n, a.m, a.labels, a.classes, a.hierarchy, a.min, a.max, a.size, a.angle, a.aspect, a.hue, a.saturation, a.exposure, a.center);
      } else if (a.type == SUPER_DATA){
          *a.d = load_data_super(a.paths, a.n, a.m, a.w, a.h, a.scale);
      } else if (a.type == WRITING_DATA){
          *a.d = load_data_writing(a.paths, a.n, a.m, a.w, a.h, a.out_w, a.out_h);
      } else if (a.type == INSTANCE_DATA){
          *a.d = load_data_iseg(a.n, a.paths, a.m, a.w, a.h, a.classes, a.num_boxes, a.coords, a.min, a.max, a.angle, a.aspect, a.hue, a.saturation, a.exposure);
      } else if (a.type == SEGMENTATION_DATA){
          *a.d = load_data_seg(a.n, a.paths, a.m, a.w, a.h, a.classes, a.min, a.max, a.angle, a.aspect, a.hue, a.saturation, a.exposure, a.scale);
      } else if (a.type == REGION_DATA){
          *a.d = load_data_region(a.n, a.paths, a.m, a.w, a.h, a.num_boxes, a.classes, a.jitter, a.hue, a.saturation, a.exposure);
      } else if (a.type == DETECTION_DATA){
          *a.d = load_data_detection(a.n, a.paths, a.m, a.w, a.h, a.num_boxes, a.classes, a.jitter, a.hue, a.saturation, a.exposure);
      } else if (a.type == SWAG_DATA){
          *a.d = load_data_swag(a.paths, a.n, a.classes, a.jitter);
      } else if (a.type == COMPARE_DATA){
          *a.d = load_data_compare(a.n, a.paths, a.m, a.classes, a.w, a.h);
      } else if (a.type == IMAGE_DATA){
          *(a.im) = load_image_color(a.path, 0, 0);
          *(a.resized) = resize_image(*(a.im), a.w, a.h);
      } else if (a.type == LETTERBOX_DATA){
          *(a.im) = load_image_color(a.path, 0, 0);
          *(a.resized) = letterbox_image(*(a.im), a.w, a.h);
      } else if (a.type == TAG_DATA){
          *a.d = load_data_tag(a.paths, a.n, a.m, a.classes, a.min, a.max, a.size, a.angle, a.aspect, a.hue, a.saturation, a.exposure);
      }
      free(ptr);
      return 0;
  }
  
  pthread_t load_data_in_thread(load_args args)
  {
      pthread_t thread;
      struct load_args *ptr = calloc(1, sizeof(struct load_args));
      *ptr = args;
      if(pthread_create(&thread, 0, load_thread, ptr)) error("Thread creation failed");
      return thread;
  }
  
  void *load_threads(void *ptr)
  {
      int i;
      load_args args = *(load_args *)ptr;
      if (args.threads == 0) args.threads = 1;
      data *out = args.d;
      int total = args.n;
      free(ptr);
      data *buffers = calloc(args.threads, sizeof(data));
      pthread_t *threads = calloc(args.threads, sizeof(pthread_t));
      for(i = 0; i < args.threads; ++i){
          args.d = buffers + i;
          args.n = (i+1) * total/args.threads - i * total/args.threads;
          threads[i] = load_data_in_thread(args);
      }
      for(i = 0; i < args.threads; ++i){
          pthread_join(threads[i], 0);
      }
      *out = concat_datas(buffers, args.threads);
      out->shallow = 0;
      for(i = 0; i < args.threads; ++i){
          buffers[i].shallow = 1;
          free_data(buffers[i]);
      }
      free(buffers);
      free(threads);
      return 0;
  }
  
  void load_data_blocking(load_args args)
  {
      struct load_args *ptr = calloc(1, sizeof(struct load_args));
      *ptr = args;
      load_thread(ptr);
  }
  
  pthread_t load_data(load_args args)
  {
      pthread_t thread;
      struct load_args *ptr = calloc(1, sizeof(struct load_args));
      *ptr = args;
      if(pthread_create(&thread, 0, load_threads, ptr)) error("Thread creation failed");
      return thread;
  }
  
  data load_data_writing(char **paths, int n, int m, int w, int h, int out_w, int out_h)
  {
      if(m) paths = get_random_paths(paths, n, m);
      char **replace_paths = find_replace_paths(paths, n, ".png", "-label.png");
      data d = {0};
      d.shallow = 0;
      d.X = load_image_paths(paths, n, w, h);
      d.y = load_image_paths_gray(replace_paths, n, out_w, out_h);
      if(m) free(paths);
      int i;
      for(i = 0; i < n; ++i) free(replace_paths[i]);
      free(replace_paths);
      return d;
  }
  
  data load_data_old(char **paths, int n, int m, char **labels, int k, int w, int h)
  {
      if(m) paths = get_random_paths(paths, n, m);
      data d = {0};
      d.shallow = 0;
      d.X = load_image_paths(paths, n, w, h);
      d.y = load_labels_paths(paths, n, labels, k, 0);
      if(m) free(paths);
      return d;
  }
  
  /*
     data load_data_study(char **paths, int n, int m, char **labels, int k, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure)
     {
     data d = {0};
     d.indexes = calloc(n, sizeof(int));
     if(m) paths = get_random_paths_indexes(paths, n, m, d.indexes);
     d.shallow = 0;
     d.X = load_image_augment_paths(paths, n, min, max, size, angle, aspect, hue, saturation, exposure);
     d.y = load_labels_paths(paths, n, labels, k);
     if(m) free(paths);
     return d;
     }
   */
  
  data load_data_super(char **paths, int n, int m, int w, int h, int scale)
  {
      if(m) paths = get_random_paths(paths, n, m);
      data d = {0};
      d.shallow = 0;
  
      int i;
      d.X.rows = n;
      d.X.vals = calloc(n, sizeof(float*));
      d.X.cols = w*h*3;
  
      d.y.rows = n;
      d.y.vals = calloc(n, sizeof(float*));
      d.y.cols = w*scale * h*scale * 3;
  
      for(i = 0; i < n; ++i){
          image im = load_image_color(paths[i], 0, 0);
          image crop = random_crop_image(im, w*scale, h*scale);
          int flip = rand()%2;
          if (flip) flip_image(crop);
          image resize = resize_image(crop, w, h);
          d.X.vals[i] = resize.data;
          d.y.vals[i] = crop.data;
          free_image(im);
      }
  
      if(m) free(paths);
      return d;
  }
  
  data load_data_regression(char **paths, int n, int m, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure)
  {
      if(m) paths = get_random_paths(paths, n, m);
      data d = {0};
      d.shallow = 0;
      d.X = load_image_augment_paths(paths, n, min, max, size, angle, aspect, hue, saturation, exposure, 0);
      d.y = load_regression_labels_paths(paths, n);
      if(m) free(paths);
      return d;
  }
  
  data resize_data(data orig, int w, int h)
  {
      data d = {0};
      d.shallow = 0;
      d.w = w;
      d.h = h;
      int i;
      d.X.rows = orig.X.rows;
      d.X.cols = w*h*3;
      d.X.vals = calloc(d.X.rows, sizeof(float));
  
      d.y = copy_matrix(orig.y);
      for(i = 0; i < orig.X.rows; ++i){
          image im = float_to_image(orig.w, orig.h, 3, orig.X.vals[i]);
          d.X.vals[i] = resize_image(im, w, h).data;
      }
      return d;
  }
  
  data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure, int center)
  {
      if(m) paths = get_random_paths(paths, n, m);
      data d = {0};
      d.shallow = 0;
      d.w=size;
      d.h=size;
      d.X = load_image_augment_paths(paths, n, min, max, size, angle, aspect, hue, saturation, exposure, center);
      d.y = load_labels_paths(paths, n, labels, k, hierarchy);
      if(m) free(paths);
      return d;
  }
  
  data load_data_tag(char **paths, int n, int m, int k, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure)
  {
      if(m) paths = get_random_paths(paths, n, m);
      data d = {0};
      d.w = size;
      d.h = size;
      d.shallow = 0;
      d.X = load_image_augment_paths(paths, n, min, max, size, angle, aspect, hue, saturation, exposure, 0);
      d.y = load_tags_paths(paths, n, k);
      if(m) free(paths);
      return d;
  }
  
  matrix concat_matrix(matrix m1, matrix m2)
  {
      int i, count = 0;
      matrix m;
      m.cols = m1.cols;
      m.rows = m1.rows+m2.rows;
      m.vals = calloc(m1.rows + m2.rows, sizeof(float*));
      for(i = 0; i < m1.rows; ++i){
          m.vals[count++] = m1.vals[i];
      }
      for(i = 0; i < m2.rows; ++i){
          m.vals[count++] = m2.vals[i];
      }
      return m;
  }
  
  data concat_data(data d1, data d2)
  {
      data d = {0};
      d.shallow = 1;
      d.X = concat_matrix(d1.X, d2.X);
      d.y = concat_matrix(d1.y, d2.y);
      return d;
  }
  
  data concat_datas(data *d, int n)
  {
      int i;
      data out = {0};
      for(i = 0; i < n; ++i){
          data new = concat_data(d[i], out);
          free_data(out);
          out = new;
      }
      return out;
  }
  
  data load_categorical_data_csv(char *filename, int target, int k)
  {
      data d = {0};
      d.shallow = 0;
      matrix X = csv_to_matrix(filename);
      float *truth_1d = pop_column(&X, target);
      float **truth = one_hot_encode(truth_1d, X.rows, k);
      matrix y;
      y.rows = X.rows;
      y.cols = k;
      y.vals = truth;
      d.X = X;
      d.y = y;
      free(truth_1d);
      return d;
  }
  
  data load_cifar10_data(char *filename)
  {
      data d = {0};
      d.shallow = 0;
      long i,j;
      matrix X = make_matrix(10000, 3072);
      matrix y = make_matrix(10000, 10);
      d.X = X;
      d.y = y;
  
      FILE *fp = fopen(filename, "rb");
      if(!fp) file_error(filename);
      for(i = 0; i < 10000; ++i){
          unsigned char bytes[3073];
          fread(bytes, 1, 3073, fp);
          int class = bytes[0];
          y.vals[i][class] = 1;
          for(j = 0; j < X.cols; ++j){
              X.vals[i][j] = (double)bytes[j+1];
          }
      }
      scale_data_rows(d, 1./255);
      //normalize_data_rows(d);
      fclose(fp);
      return d;
  }
  
  void get_random_batch(data d, int n, float *X, float *y)
  {
      int j;
      for(j = 0; j < n; ++j){
          int index = rand()%d.X.rows;
          memcpy(X+j*d.X.cols, d.X.vals[index], d.X.cols*sizeof(float));
          memcpy(y+j*d.y.cols, d.y.vals[index], d.y.cols*sizeof(float));
      }
  }
  
  void get_next_batch(data d, int n, int offset, float *X, float *y)
  {
      int j;
      for(j = 0; j < n; ++j){
          int index = offset + j;
          memcpy(X+j*d.X.cols, d.X.vals[index], d.X.cols*sizeof(float));
          if(y) memcpy(y+j*d.y.cols, d.y.vals[index], d.y.cols*sizeof(float));
      }
  }
  
  void smooth_data(data d)
  {
      int i, j;
      float scale = 1. / d.y.cols;
      float eps = .1;
      for(i = 0; i < d.y.rows; ++i){
          for(j = 0; j < d.y.cols; ++j){
              d.y.vals[i][j] = eps * scale + (1-eps) * d.y.vals[i][j];
          }
      }
  }
  
  data load_all_cifar10()
  {
      data d = {0};
      d.shallow = 0;
      int i,j,b;
      matrix X = make_matrix(50000, 3072);
      matrix y = make_matrix(50000, 10);
      d.X = X;
      d.y = y;
  
  
      for(b = 0; b < 5; ++b){
          char buff[256];
          sprintf(buff, "data/cifar/cifar-10-batches-bin/data_batch_%d.bin", b+1);
          FILE *fp = fopen(buff, "rb");
          if(!fp) file_error(buff);
          for(i = 0; i < 10000; ++i){
              unsigned char bytes[3073];
              fread(bytes, 1, 3073, fp);
              int class = bytes[0];
              y.vals[i+b*10000][class] = 1;
              for(j = 0; j < X.cols; ++j){
                  X.vals[i+b*10000][j] = (double)bytes[j+1];
              }
          }
          fclose(fp);
      }
      //normalize_data_rows(d);
      scale_data_rows(d, 1./255);
      smooth_data(d);
      return d;
  }
  
  data load_go(char *filename)
  {
      FILE *fp = fopen(filename, "rb");
      matrix X = make_matrix(3363059, 361);
      matrix y = make_matrix(3363059, 361);
      int row, col;
  
      if(!fp) file_error(filename);
      char *label;
      int count = 0;
      while((label = fgetl(fp))){
          int i;
          if(count == X.rows){
              X = resize_matrix(X, count*2);
              y = resize_matrix(y, count*2);
          }
          sscanf(label, "%d %d", &row, &col);
          char *board = fgetl(fp);
  
          int index = row*19 + col;
          y.vals[count][index] = 1;
  
          for(i = 0; i < 19*19; ++i){
              float val = 0;
              if(board[i] == '1') val = 1;
              else if(board[i] == '2') val = -1;
              X.vals[count][i] = val;
          }
          ++count;
          free(label);
          free(board);
      }
      X = resize_matrix(X, count);
      y = resize_matrix(y, count);
  
      data d = {0};
      d.shallow = 0;
      d.X = X;
      d.y = y;
  
  
      fclose(fp);
  
      return d;
  }
  
  
  void randomize_data(data d)
  {
      int i;
      for(i = d.X.rows-1; i > 0; --i){
          int index = rand()%i;
          float *swap = d.X.vals[index];
          d.X.vals[index] = d.X.vals[i];
          d.X.vals[i] = swap;
  
          swap = d.y.vals[index];
          d.y.vals[index] = d.y.vals[i];
          d.y.vals[i] = swap;
      }
  }
  
  void scale_data_rows(data d, float s)
  {
      int i;
      for(i = 0; i < d.X.rows; ++i){
          scale_array(d.X.vals[i], d.X.cols, s);
      }
  }
  
  void translate_data_rows(data d, float s)
  {
      int i;
      for(i = 0; i < d.X.rows; ++i){
          translate_array(d.X.vals[i], d.X.cols, s);
      }
  }
  
  data copy_data(data d)
  {
      data c = {0};
      c.w = d.w;
      c.h = d.h;
      c.shallow = 0;
      c.num_boxes = d.num_boxes;
      c.boxes = d.boxes;
      c.X = copy_matrix(d.X);
      c.y = copy_matrix(d.y);
      return c;
  }
  
  void normalize_data_rows(data d)
  {
      int i;
      for(i = 0; i < d.X.rows; ++i){
          normalize_array(d.X.vals[i], d.X.cols);
      }
  }
  
  data get_data_part(data d, int part, int total)
  {
      data p = {0};
      p.shallow = 1;
      p.X.rows = d.X.rows * (part + 1) / total - d.X.rows * part / total;
      p.y.rows = d.y.rows * (part + 1) / total - d.y.rows * part / total;
      p.X.cols = d.X.cols;
      p.y.cols = d.y.cols;
      p.X.vals = d.X.vals + d.X.rows * part / total;
      p.y.vals = d.y.vals + d.y.rows * part / total;
      return p;
  }
  
  data get_random_data(data d, int num)
  {
      data r = {0};
      r.shallow = 1;
  
      r.X.rows = num;
      r.y.rows = num;
  
      r.X.cols = d.X.cols;
      r.y.cols = d.y.cols;
  
      r.X.vals = calloc(num, sizeof(float *));
      r.y.vals = calloc(num, sizeof(float *));
  
      int i;
      for(i = 0; i < num; ++i){
          int index = rand()%d.X.rows;
          r.X.vals[i] = d.X.vals[index];
          r.y.vals[i] = d.y.vals[index];
      }
      return r;
  }
  
  data *split_data(data d, int part, int total)
  {
      data *split = calloc(2, sizeof(data));
      int i;
      int start = part*d.X.rows/total;
      int end = (part+1)*d.X.rows/total;
      data train;
      data test;
      train.shallow = test.shallow = 1;
  
      test.X.rows = test.y.rows = end-start;
      train.X.rows = train.y.rows = d.X.rows - (end-start);
      train.X.cols = test.X.cols = d.X.cols;
      train.y.cols = test.y.cols = d.y.cols;
  
      train.X.vals = calloc(train.X.rows, sizeof(float*));
      test.X.vals = calloc(test.X.rows, sizeof(float*));
      train.y.vals = calloc(train.y.rows, sizeof(float*));
      test.y.vals = calloc(test.y.rows, sizeof(float*));
  
      for(i = 0; i < start; ++i){
          train.X.vals[i] = d.X.vals[i];
          train.y.vals[i] = d.y.vals[i];
      }
      for(i = start; i < end; ++i){
          test.X.vals[i-start] = d.X.vals[i];
          test.y.vals[i-start] = d.y.vals[i];
      }
      for(i = end; i < d.X.rows; ++i){
          train.X.vals[i-(end-start)] = d.X.vals[i];
          train.y.vals[i-(end-start)] = d.y.vals[i];
      }
      split[0] = train;
      split[1] = test;
      return split;
  }
  
  