int main(int argc, char *argv[])
{
int i;
StudentInfo_P pMaxScore = NULL;
StudentInfo_T info_list[4] = {
{"Tom", 8},
{"Jerry", 72},
{"Moby", 90},
{"Kirby", 89}
};
/*
* 指针数组
* 结构体指针数组
*/
StudentInfo_P pInfos[4] = {
&info_list[0],
&info_list[1],
&info_list[2],
&info_list[3]
};
printf("Student's Scores Info:\n");
for (i = 0; i < 4; i++)
printf("name %5s : score %5d\n", info_list[i].name, info_list[i].score);
pMaxScore = get_max_score((void **)pInfos, 4, cmp_student);
printf("Top score is %s:%d\n", pMaxScore->name, pMaxScore->score);
return 0;
}
*/bool AStarPath::path_search(MapCoord &start, MapCoord &goal)
{//DEBUG(0,LEVEL_DEBUGGING,"SEARCH: %d: %d,%d -> %d,%d\n",actor->get_actor_num(),start.x,start.y,goal.x,goal.y);
astar_node *start_node = new astar_node;
start_node->loc = start;
start_node->to_start = 0;
start_node->to_goal = path_cost_est(start, goal);
start_node->score = start_node->to_start + start_node->to_goal;
start_node->len = 0;
push_open_node(start_node);
const uint32 max_score = get_max_score(start_node->to_goal);
const uint32 max_steps = 8*2*4; // walk up to four screen lengths before searching again
while(!open_nodes.empty())
{
astar_node *nnode = pop_open_node(); // next closest
if(nnode->loc == goal || nnode->len >= max_steps)
{
if(nnode->loc != goal)
DEBUG(0,LEVEL_DEBUGGING,"out of steps, making partial path (nnode->len=%d)\n",nnode->len);
//DEBUG(0,LEVEL_DEBUGGING,"GOAL\n");
final_node = nnode;
create_path();
delete_nodes();
return(true); // reached goal - success
}
// check cardinal neighbors (starting at top going clockwise)
search_node_neighbors(nnode, goal, max_score);
// node and neighbors checked, put into closed
closed_nodes.push_back(nnode);
}
//DEBUG(0,LEVEL_DEBUGGING,"FAIL\n");
delete_nodes();
return(false); // out of open nodes - failure
}/* Return the cost of moving one step from `c1' to `c2', which is always 1. This
static void do_classify(args_t *args)
{
annots_reader_reset(args);
double max_score = sqrt(args->som[0]->kdim);
while ( annots_reader_next(args) )
{
double score = 0;
switch (args->merge)
{
case MERGE_MIN: score = get_min_score(args, -1); break;
case MERGE_MAX: score = get_max_score(args, -1); break;
case MERGE_AVG: score = get_avg_score(args, -1); break;
}
printf("%e\n", 1.0 - score/max_score);
}
annots_reader_close(args);
}
void init_motif (struct MOTIF **motif, char *fileName, float cutoff,
char *id)
{
char chr,
*cur_id = NULL,
*na = NULL,
*buf = NULL;
int i,
pos,
found_id = 0,
fA, fC, fG, fT;
FILE *fp = ckopen (fileName, "r");
buf = (char *) ckalloc (BUFSIZE);
cur_id = (char *) ckalloc (STRSIZE);
na = (char *) ckalloc (STRSIZE);
while (fgets( buf, BUFSIZE, fp ))
{
if (sscanf (buf, "ID %s", cur_id) == 1) {
if (strcmp (cur_id, id) == 0)
{
found_id = 1;
*motif = ckalloc (sizeof (struct MOTIF));
(*motif)->id = (char *) ckalloc ((strlen (id) + 1) * sizeof (char));
strcpy ((*motif)->id, id);
}
} else if ((found_id) && sscanf (buf, "NA %s", na) == 1) {
(*motif)->name = (char *) ckalloc ((strlen (na) + 1) * sizeof (char));
strcpy ((*motif)->name, na);
} else if ((found_id) && strncmp (buf, "P0", 2) == 0) {
allocate_submat (motif);
pos = 0;
while ((fgets (buf, BUFSIZE, fp)) && (buf[0] != 'X'))
{
if ((sscanf (buf,"%d %d %d %d %d %c", &i, &fA, &fC, &fG, &fT, &chr)) !=6)
fatal ("Invalid transfac matrix file format.");
(*motif)->submat[rowA][pos] = fA;
(*motif)->submat[rowC][pos] = fC;
(*motif)->submat[rowG][pos] = fG;
(*motif)->submat[rowT][pos] = fT;
pos++;
}
(*motif)->min = get_min_score ((*motif)->submat, pos);
(*motif)->max = get_max_score ((*motif)->submat, pos);
/* equation taken from the MOTIF program (http://motif.genome.ad.jp) */
(*motif)->threshold = (cutoff) * (*motif)->max + (1 - cutoff) * (*motif)->min;
(*motif)->len = pos;
(*motif)->next = NULL;
break;
}
}
if (!found_id)
fatalf ("*** ERROR: did not find id number '%s' in transfac file. ***\n\n", id);
free (cur_id);
free (na);
free (buf);
}
static void do_train(args_t *args)
{
// read training sites
int i, igood = 0, ibad = 0, ngood = 0, nbad = 0, ntrain = 0;
annots_reader_reset(args);
while ( annots_reader_next(args) )
{
// determine which of the nfold's SOMs to train
int isom = 0;
if ( args->dclass == args->good_class )
{
if ( ++igood >= args->nfold ) igood = 0;
isom = igood;
ngood++;
}
else if ( args->dclass == args->bad_class )
{
if ( ++ibad >= args->nfold ) ibad = 0;
isom = ibad;
nbad++;
}
else
error("Could not determine the class: %d (vs %d and %d)\n", args->dclass,args->good_class,args->bad_class);
// save the values for evaluation
ntrain++;
hts_expand(double, ntrain*args->mvals, args->mtrain_dat, args->train_dat);
hts_expand(int, ntrain, args->mtrain_class, args->train_class);
memcpy(args->train_dat+(ntrain-1)*args->mvals, args->vals, args->mvals*sizeof(double));
args->train_class[ntrain-1] = (args->dclass==args->good_class ? 1 : 0) | isom<<1; // store class + chunk used for training
}
annots_reader_close(args);
// init maps
if ( !args->ntrain ) args->ntrain = ngood/args->nfold;
srandom(args->rand_seed);
args->som = (som_t**) malloc(sizeof(som_t*)*args->nfold);
for (i=0; i<args->nfold; i++) args->som[i] = som_init(args);
// train
for (i=0; i<ntrain; i++)
{
int is_good = args->train_class[i] & 1;
int isom = args->train_class[i] >> 1;
if ( is_good || args->train_bad )
som_train_site(args->som[isom], args->train_dat+i*args->mvals, is_good);
}
// norm and create plots
for (i=0; i<args->nfold; i++)
{
som_norm_counts(args->som[i]);
if ( args->prefix )
{
char *bname = msprintf("%s.som.%d", args->prefix,i);
som_create_plot(args->som[i], bname);
free(bname);
}
}
// evaluate
float *good = (float*) malloc(sizeof(float)*ngood); assert(good);
float *bad = (float*) malloc(sizeof(float)*nbad); assert(bad);
igood = ibad = 0;
double max_score = sqrt(args->som[0]->kdim);
for (i=0; i<ntrain; i++)
{
double score = 0;
int is_good = args->train_class[i] & 1;
int isom = args->train_class[i] >> 1; // this vector was used for training isom-th SOM, skip
if ( args->nfold==1 ) isom = -1;
memcpy(args->vals, args->train_dat+i*args->mvals, args->mvals*sizeof(double));
switch (args->merge)
{
case MERGE_MIN: score = get_min_score(args, isom); break;
case MERGE_MAX: score = get_max_score(args, isom); break;
case MERGE_AVG: score = get_avg_score(args, isom); break;
}
score = 1.0 - score/max_score;
if ( is_good )
good[igood++] = score;
else
bad[ibad++] = score;
}
qsort(good, ngood, sizeof(float), cmpfloat_desc);
qsort(bad, nbad, sizeof(float), cmpfloat_desc);
FILE *fp = NULL;
if ( args->prefix ) fp = open_file(NULL,"w","%s.eval", args->prefix);
igood = 0;
ibad = 0;
float prev_score = good[0]>bad[0] ? good[0] : bad[0];
int printed = 0;
while ( igood<ngood || ibad<nbad )
{
if ( igood<ngood && good[igood]==prev_score ) { igood++; continue; }
if ( ibad<nbad && bad[ibad]==prev_score ) { ibad++; continue; }
if ( fp )
fprintf(fp,"%e\t%f\t%f\n", prev_score, (float)igood/ngood, (float)ibad/nbad);
if ( !printed && (float)igood/ngood > 0.9 )
{
printf("%.2f\t%.2f\t%e\t# %% of bad [1] and good [2] sites at a cutoff [3]\n", 100.*ibad/nbad,100.*igood/ngood,prev_score);
printed = 1;
}
if ( igood<ngood && ibad<nbad ) prev_score = good[igood]>bad[ibad] ? good[igood] : bad[ibad];
else if ( igood<ngood ) prev_score = good[igood];
else prev_score = bad[ibad];
}
if ( !printed ) printf("%.2f\t%.2f\t%e\t# %% of bad [1] and good [2] sites at a cutoff [3]\n", 100.*ibad/nbad,100.*igood/ngood,prev_score);
if ( fp )
{
if ( fclose(fp) ) error("%s.eval: fclose failed: %s\n",args->prefix,strerror(errno));
create_eval_plot(args);
som_write_map(args->prefix, args->som, args->nfold);
}
free(good);
free(bad);
}
