void
cvector_free(struct cvector *vector)
{
cvector_clear(vector);
free(vector->strings);
free(vector);
}
static struct cvector *
cvector_reuse(struct cvector *vector)
{
if (vector == NULL)
return cvector_new();
else {
cvector_clear(vector);
return vector;
}
}
void test_cvector_clear()
{
cvector vec;
int tester = 1;
/* start the test */
/* run the functions */
cvector_init(&vec, int_function_registry());
cvector_push_back(vec, &tester);
cvector_push_back(vec, &tester);
cvector_push_back(vec, &tester);
cvector_clear(vec);
/* test the results */
FO_ASSERT_EQUAL(vec->size, 0);
FO_ASSERT_PTR_EQUAL(vec->data[0], NULL);
cvector_destroy(vec);
}
void test_cvector_clear()
{
cvector vec;
int tester = 1;
/* start the test */
printf("Test cvector_clear:");
/* run the functions */
cvector_init(&vec, int_function_registry());
cvector_push_back(vec, &tester);
cvector_push_back(vec, &tester);
cvector_push_back(vec, &tester);
cvector_clear(vec);
/* test the results */
CU_ASSERT_EQUAL(vec->size, 0);
CU_ASSERT_EQUAL(vec->data[0], NULL);
cvector_destroy(vec);
test_failure();
printf("\n");
}
int
main(int argc, char *argv[]) {
MRI_SURFACE *mris ;
char **av, *curv_name, *surf_name, *hemi, fname[STRLEN],
*cp, *subject_name, subjects_dir[STRLEN],
**c1_subjects, **c2_subjects ;
int ac, nargs, n, num_class1, num_class2, i, nvertices,
avgs, max_snr_avgs, nlabels = 0, done ;
float **c1_thickness, **c2_thickness, *curvs, *total_mean,
*c1_mean, *c2_mean,
*class_mean, *c1_var, *c2_var, *class_var,*pvals,
**c1_avg_thickness,
*vbest_snr, *vbest_avgs, *vtotal_var, *vsnr, **c2_avg_thickness,
*vbest_pvalues, current_min_label_area, current_fthresh ;
MRI_SP *mrisp ;
LABEL *area, **labels = NULL ;
FILE *fp = NULL ;
double snr, max_snr ;
struct timeb start ;
int msec, minutes, seconds ;
double **c1_label_thickness, **c2_label_thickness ;
int *sorted_indices = NULL, vno ;
float *test_thickness, *test_avg_thickness ;
double label_avg ;
/* rkt: check for and handle version tag */
nargs = handle_version_option (argc, argv, "$Id: mris_classify_thickness.c,v 1.8 2011/03/02 00:04:29 nicks Exp $", "$Name: stable5 $");
if (nargs && argc - nargs == 1)
exit (0);
argc -= nargs;
if (write_flag && DIAG_VERBOSE_ON)
fp = fopen("scalespace.dat", "w") ;
Progname = argv[0] ;
ErrorInit(NULL, NULL, NULL) ;
DiagInit(NULL, NULL, NULL) ;
ac = argc ;
av = argv ;
for ( ; argc > 1 && ISOPTION(*argv[1]) ; argc--, argv++) {
nargs = get_option(argc, argv) ;
argc -= nargs ;
argv += nargs ;
}
TimerStart(&start) ;
/* subject_name hemi surface curvature */
if (argc < 7)
usage_exit() ;
if (output_subject == NULL)
ErrorExit(ERROR_BADPARM,
"output subject must be specified with -o <subject name>");
cp = getenv("SUBJECTS_DIR") ;
if (!cp)
ErrorExit(ERROR_BADPARM, "%s: SUBJECTS_DIR not defined in environment",
Progname) ;
strcpy(subjects_dir, cp) ;
hemi = argv[1] ;
surf_name = argv[2] ;
curv_name = argv[3] ;
#define ARGV_OFFSET 4
/* first determine the number of subjects in each class */
num_class1 = 0 ;
n = ARGV_OFFSET ;
do {
num_class1++ ;
n++ ;
if (argv[n] == NULL || n >= argc)
ErrorExit(ERROR_BADPARM, "%s: must spectify ':' between class lists",
Progname) ;
} while (argv[n][0] != ':') ;
/* find # of vertices in output subject surface */
sprintf(fname, "%s/%s/surf/%s.%s",
subjects_dir,output_subject,hemi,surf_name);
mris = MRISread(fname) ;
if (!mris)
ErrorExit(ERROR_NOFILE, "%s: could not read surface file %s",
Progname, fname) ;
nvertices = mris->nvertices ;
MRISfree(&mris) ;
total_mean = (float *)calloc(nvertices, sizeof(float)) ;
if (!total_mean)
ErrorExit(ERROR_NOMEMORY,
"%s: could not allocate mean list of %d curvatures",
Progname, n, nvertices) ;
c1_mean = (float *)calloc(nvertices, sizeof(float)) ;
if (!c1_mean)
ErrorExit(ERROR_NOMEMORY,
"%s: could not allocate c1 mean list of %d curvatures",
Progname, n, nvertices) ;
pvals = (float *)calloc(nvertices, sizeof(float)) ;
if (!pvals)
ErrorExit(ERROR_NOMEMORY,
"%s: could not allocate pvals",
Progname, n, nvertices) ;
c2_mean = (float *)calloc(nvertices, sizeof(float)) ;
if (!c2_mean)
ErrorExit(ERROR_NOMEMORY,
"%s: could not allocate c2 mean list of %d curvatures",
Progname, n, nvertices) ;
c1_var = (float *)calloc(nvertices, sizeof(float)) ;
if (!c1_var)
ErrorExit(ERROR_NOMEMORY,
"%s: could not allocate c1 var list of %d curvatures",
Progname, n, nvertices) ;
c2_var = (float *)calloc(nvertices, sizeof(float)) ;
if (!c2_var)
ErrorExit(ERROR_NOMEMORY,
"%s: could not allocate c2 var list of %d curvatures",
Progname, n, nvertices) ;
num_class2 = 0 ;
n++ ; /* skip ':' */
if (n >= argc)
ErrorExit(ERROR_BADPARM, "%s: class2 list empty", Progname) ;
do {
num_class2++ ;
n++ ;
if (n >= argc)
break ;
} while (argv[n] != NULL) ;
fprintf(stderr, "%d subjects in class 1, %d subjects in class 2\n",
num_class1, num_class2) ;
c1_subjects = (char **)calloc(num_class1, sizeof(char *)) ;
c1_thickness = (float **)calloc(num_class1, sizeof(char *)) ;
c1_avg_thickness = (float **)calloc(num_class1, sizeof(char *)) ;
c2_subjects = (char **)calloc(num_class2, sizeof(char *)) ;
c2_thickness = (float **)calloc(num_class2, sizeof(char *)) ;
c2_avg_thickness = (float **)calloc(num_class2, sizeof(char *)) ;
for (n = 0 ; n < num_class1 ; n++) {
c1_subjects[n] = argv[ARGV_OFFSET+n] ;
c1_thickness[n] = (float *)calloc(nvertices, sizeof(float)) ;
c1_avg_thickness[n] = (float *)calloc(nvertices, sizeof(float)) ;
if (!c1_thickness[n] || !c1_avg_thickness[n])
ErrorExit(ERROR_NOMEMORY,
"%s: could not allocate %dth list of %d curvatures",
Progname, n, nvertices) ;
strcpy(c1_subjects[n], argv[ARGV_OFFSET+n]) ;
/* fprintf(stderr, "class1[%d] - %s\n", n, c1_subjects[n]) ;*/
}
i = n+1+ARGV_OFFSET ; /* starting index */
for (n = 0 ; n < num_class2 ; n++) {
c2_subjects[n] = argv[i+n] ;
c2_thickness[n] = (float *)calloc(nvertices, sizeof(float)) ;
c2_avg_thickness[n] = (float *)calloc(nvertices, sizeof(float)) ;
if (!c2_thickness[n] || !c2_avg_thickness[n])
ErrorExit(ERROR_NOMEMORY,
"%s: could not allocate %dth list of %d curvatures",
Progname, n, nvertices) ;
strcpy(c2_subjects[n], argv[i+n]) ;
/* fprintf(stderr, "class2[%d] - %s\n", n, c2_subjects[n]) ;*/
}
if (label_name) {
area = LabelRead(output_subject, label_name) ;
if (!area)
ErrorExit(ERROR_NOFILE, "%s: could not read label %s", Progname,
label_name) ;
} else
area = NULL ;
if (read_dir) {
sprintf(fname, "%s/%s/surf/%s.%s",
subjects_dir,output_subject,hemi,surf_name);
mris = MRISread(fname) ;
if (!mris)
ErrorExit(ERROR_NOFILE, "%s: could not read surface file %s",
Progname, fname) ;
MRISsaveVertexPositions(mris, CANONICAL_VERTICES) ;
/* real all the curvatures in for group1 */
for (n = 0 ; n < num_class1+num_class2 ; n++) {
/* transform each subject's curvature into the output subject's space */
subject_name = n < num_class1 ? c1_subjects[n]:c2_subjects[n-num_class1];
fprintf(stderr, "reading subject %d of %d: %s\n",
n+1, num_class1+num_class2, subject_name) ;
sprintf(fname, "%s/%s.%s", read_dir,hemi,subject_name);
if (MRISreadValues(mris, fname) != NO_ERROR)
ErrorExit(Gerror,
"%s: could not read curvature file %s",Progname,fname);
if (area)
MRISmaskNotLabel(mris, area) ;
curvs = (n < num_class1) ? c1_thickness[n] : c2_thickness[n-num_class1] ;
class_mean = (n < num_class1) ? c1_mean : c2_mean ;
class_var = (n < num_class1) ? c1_var : c2_var ;
MRISexportValVector(mris, curvs) ;
cvector_accumulate(curvs, total_mean, nvertices) ;
cvector_accumulate(curvs, class_mean, nvertices) ;
cvector_accumulate_square(curvs, class_var, nvertices) ;
}
} else {
/* real all the curvatures in for group1 */
for (n = 0 ; n < num_class1+num_class2 ; n++) {
/* transform each subject's curvature into the output subject's space */
subject_name = n < num_class1 ? c1_subjects[n]:c2_subjects[n-num_class1];
fprintf(stderr, "reading subject %d of %d: %s\n",
n+1, num_class1+num_class2, subject_name) ;
sprintf(fname, "%s/%s/surf/%s.%s",
subjects_dir,subject_name,hemi,surf_name);
mris = MRISread(fname) ;
if (!mris)
ErrorExit(ERROR_NOFILE, "%s: could not read surface file %s",
Progname, fname) ;
MRISsaveVertexPositions(mris, CANONICAL_VERTICES) ;
if (strchr(curv_name, '/') != NULL)
strcpy(fname, curv_name) ; /* full path specified */
else
sprintf(fname,"%s/%s/surf/%s.%s",
subjects_dir,subject_name,hemi,curv_name);
if (MRISreadCurvatureFile(mris, fname) != NO_ERROR)
ErrorExit(Gerror,"%s: could no read curvature file %s",Progname,fname);
mrisp = MRIStoParameterization(mris, NULL, 1, 0) ;
MRISfree(&mris) ;
sprintf(fname, "%s/%s/surf/%s.%s",
subjects_dir,output_subject,hemi,surf_name);
mris = MRISread(fname) ;
if (!mris)
ErrorExit(ERROR_NOFILE, "%s: could not read surface file %s",
Progname, fname) ;
MRISfromParameterization(mrisp, mris, 0) ;
if (area)
MRISmaskNotLabel(mris, area) ;
curvs = (n < num_class1) ? c1_thickness[n] : c2_thickness[n-num_class1] ;
class_mean = (n < num_class1) ? c1_mean : c2_mean ;
class_var = (n < num_class1) ? c1_var : c2_var ;
MRISextractCurvatureVector(mris, curvs) ;
cvector_accumulate(curvs, total_mean, nvertices) ;
cvector_accumulate(curvs, class_mean, nvertices) ;
cvector_accumulate_square(curvs, class_var, nvertices) ;
MRISPfree(&mrisp) ;
MRISfree(&mris) ;
}
}
/* compute within-group means, and total mean */
cvector_normalize(total_mean, num_class1+num_class2, nvertices) ;
cvector_normalize(c1_mean, num_class1, nvertices) ;
cvector_normalize(c2_mean, num_class2, nvertices) ;
cvector_compute_variance(c1_var, c1_mean, num_class1, nvertices) ;
cvector_compute_variance(c2_var, c2_mean, num_class2, nvertices) ;
cvector_compute_t_test(c1_mean, c1_var, c2_mean, c2_var,
num_class1, num_class2, pvals, nvertices) ;
sprintf(fname, "%s/%s/surf/%s.%s",
subjects_dir,output_subject,hemi,surf_name);
fprintf(stderr, "reading output surface %s...\n", fname) ;
mris = MRISread(fname) ;
if (!mris)
ErrorExit(ERROR_NOFILE, "%s: could not read surface file %s",
Progname, fname) ;
if (area)
MRISripNotLabel(mris, area) ;
vbest_snr = cvector_alloc(nvertices) ;
vbest_pvalues = cvector_alloc(nvertices) ;
vbest_avgs = cvector_alloc(nvertices) ;
vtotal_var = cvector_alloc(nvertices) ;
vsnr = cvector_alloc(nvertices) ;
if (read_dir == NULL) /* recompute everything */
{
if (use_buggy_snr)
cvector_multiply_variances(c1_var, c2_var, num_class1, num_class2,
vtotal_var, nvertices) ;
else
cvector_add_variances(c1_var, c2_var, num_class1, num_class2,
vtotal_var, nvertices) ;
if (use_no_distribution)
snr = cvector_compute_dist_free_snr(c1_thickness, num_class1,
c2_thickness, num_class2,
c1_mean, c2_mean,
vsnr, nvertices, &i);
else
snr = cvector_compute_snr(c1_mean, c2_mean, vtotal_var, vsnr, nvertices,
&i, 0.0f);
fprintf(stderr,
"raw SNR %2.2f, n=%2.4f, d=%2.4f, vno=%d\n",
sqrt(snr), c1_mean[i]-c2_mean[i], sqrt(vtotal_var[i]), i) ;
max_snr = snr ;
max_snr_avgs = 0 ;
cvector_track_best_snr(vsnr, vbest_snr, vbest_avgs, 0, nvertices) ;
for (n = 0 ; n < num_class1 ; n++)
cvector_copy(c1_thickness[n], c1_avg_thickness[n], nvertices) ;
for (n = 0 ; n < num_class2 ; n++)
cvector_copy(c2_thickness[n], c2_avg_thickness[n], nvertices) ;
/* now incrementally average the data, keeping track of the best
snr at each location, and at what scale it occurred. vbest_avgs
and vbest_snr will contain the scale and the snr at that scale.
*/
for (avgs = 1 ; avgs <= max_avgs ; avgs++) {
/* c?_avg_thickness is the thickness at the current scale */
if (!(avgs % 50))
fprintf(stderr, "testing %d averages...\n", avgs) ;
cvector_clear(c1_mean, nvertices) ;
cvector_clear(c2_mean, nvertices) ;
cvector_clear(c1_var, nvertices) ;
cvector_clear(c2_var, nvertices) ;
cvector_clear(total_mean, nvertices) ;
for (n = 0 ; n < num_class1 ; n++) {
MRISimportCurvatureVector(mris, c1_avg_thickness[n]) ;
MRISaverageCurvatures(mris, 1) ;
MRISextractCurvatureVector(mris, c1_avg_thickness[n]) ;
cvector_accumulate(c1_avg_thickness[n], total_mean, nvertices) ;
cvector_accumulate(c1_avg_thickness[n], c1_mean, nvertices) ;
cvector_accumulate_square(c1_avg_thickness[n], c1_var, nvertices) ;
}
for (n = 0 ; n < num_class2 ; n++) {
MRISimportCurvatureVector(mris, c2_avg_thickness[n]) ;
MRISaverageCurvatures(mris, 1) ;
MRISextractCurvatureVector(mris, c2_avg_thickness[n]) ;
cvector_accumulate(c2_avg_thickness[n], total_mean, nvertices) ;
cvector_accumulate(c2_avg_thickness[n], c2_mean, nvertices) ;
cvector_accumulate_square(c2_avg_thickness[n], c2_var, nvertices) ;
}
cvector_normalize(total_mean, num_class1+num_class2, nvertices) ;
cvector_normalize(c1_mean, num_class1, nvertices) ;
cvector_normalize(c2_mean, num_class2, nvertices) ;
cvector_compute_variance(c1_var, c1_mean, num_class1, nvertices) ;
cvector_compute_variance(c2_var, c2_mean, num_class2, nvertices) ;
if (use_buggy_snr)
cvector_multiply_variances(c1_var, c2_var, num_class1, num_class2,
vtotal_var, nvertices) ;
else
cvector_add_variances(c1_var, c2_var, num_class1, num_class2,
vtotal_var, nvertices) ;
if (use_no_distribution)
snr =
cvector_compute_dist_free_snr(c1_avg_thickness,num_class1,
c2_avg_thickness, num_class2, c1_mean,
c2_mean, vsnr, nvertices, &i);
else
snr =
cvector_compute_snr(c1_mean, c2_mean, vtotal_var, vsnr, nvertices,&i,
bonferroni ? log((double)avgs) : 0.0f);
if (write_flag && DIAG_VERBOSE_ON) {
fprintf(fp, "%d %2.1f %2.2f %2.2f %2.2f ",
avgs, sqrt((float)avgs), sqrt(snr), c1_mean[i]-c2_mean[i],
sqrt(vtotal_var[i])) ;
fflush(fp) ;
for (n = 0 ; n < num_class1 ; n++)
fprintf(fp, "%2.2f ", c1_avg_thickness[n][i]) ;
for (n = 0 ; n < num_class2 ; n++)
fprintf(fp, "%2.2f ", c2_avg_thickness[n][i]) ;
fprintf(fp, "\n") ;
fclose(fp) ;
}
if (snr > max_snr) {
fprintf(stderr,
"new max SNR found at avgs=%d (%2.1f mm)=%2.1f, n=%2.4f, "
"d=%2.4f, vno=%d\n",
avgs, sqrt((float)avgs), sqrt(snr), c1_mean[i]-c2_mean[i],
sqrt(vtotal_var[i]), i) ;
max_snr = snr ;
max_snr_avgs = avgs ;
}
cvector_track_best_snr(vsnr, vbest_snr, vbest_avgs, avgs, nvertices) ;
}
if (compute_stats)
cvector_compute_t(vbest_snr, vbest_pvalues,num_class1+num_class2,
nvertices) ;
printf("max snr=%2.2f at %d averages\n", max_snr, max_snr_avgs) ;
if (write_flag) {
MRISimportValVector(mris, vbest_snr) ;
sprintf(fname, "./%s.%s_best_snr", hemi,prefix) ;
MRISwriteValues(mris, fname) ;
MRISimportValVector(mris, vbest_avgs) ;
sprintf(fname, "./%s.%s_best_avgs", hemi, prefix) ;
MRISwriteValues(mris, fname) ;
if (compute_stats) {
MRISimportValVector(mris, vbest_pvalues) ;
sprintf(fname, "./%s.%s_best_pval", hemi,prefix) ;
MRISwriteValues(mris, fname) ;
}
}
}
else /* read from directory containing precomputed optimal values */
{
sprintf(fname, "%s/%s.%s_best_snr", read_dir, hemi, prefix) ;
if (MRISreadValues(mris, fname) != NO_ERROR)
ErrorExit(Gerror, "%s: MRISreadValues(%s) failed",Progname,fname) ;
MRISexportValVector(mris, vbest_snr) ;
sprintf(fname, "%s/%s.%s_best_avgs", read_dir, hemi, prefix) ;
if (MRISreadValues(mris, fname) != NO_ERROR)
ErrorExit(Gerror, "%s: MRISreadValues(%s) failed",Progname,fname) ;
MRISexportValVector(mris, vbest_avgs) ;
}
if (write_dir) {
sprintf(fname, "%s/%s.%s_best_snr", write_dir, hemi,prefix) ;
MRISimportValVector(mris, vbest_snr) ;
if (MRISwriteValues(mris, fname) != NO_ERROR)
ErrorExit(Gerror, "%s: MRISwriteValues(%s) failed",Progname,fname) ;
sprintf(fname, "%s/%s.%s_best_avgs", write_dir, hemi, prefix) ;
MRISimportValVector(mris, vbest_avgs) ;
if (MRISwriteValues(mris, fname) != NO_ERROR)
ErrorExit(Gerror, "%s: MRISwriteValues(%s) failed",Progname,fname) ;
}
if (nsort < -1)
nsort = mris->nvertices ;
if (nsort <= 0) {
nlabels = 0 ;
current_min_label_area = min_label_area ;
for (done = 0, current_fthresh = fthresh ;
!FZERO(current_fthresh) && !done ;
current_fthresh *= 0.95) {
int npos_labels, nneg_labels ;
LABEL **pos_labels, **neg_labels ;
for (current_min_label_area = min_label_area ;
current_min_label_area > 0.5 ;
current_min_label_area *= 0.75) {
MRISclearMarks(mris) ;
sprintf(fname, "%s-%s_thickness", hemi, prefix ? prefix : "") ;
mark_thresholded_vertices(mris, vbest_snr, vbest_avgs,current_fthresh);
segment_and_write_labels(output_subject, fname, mris,
&pos_labels, &npos_labels, 0,
current_min_label_area) ;
MRISclearMarks(mris) ;
mark_thresholded_vertices(mris, vbest_snr,vbest_avgs,-current_fthresh);
segment_and_write_labels(output_subject, fname, mris, &neg_labels,
&nneg_labels, npos_labels,
current_min_label_area) ;
nlabels = nneg_labels + npos_labels ;
if (nlabels) {
labels = (LABEL **)calloc(nlabels, sizeof(LABEL *)) ;
for (i = 0 ; i < npos_labels ; i++)
labels[i] = pos_labels[i] ;
for (i = 0 ; i < nneg_labels ; i++)
labels[i+npos_labels] = neg_labels[i] ;
free(pos_labels) ;
free(neg_labels) ;
}
done = (nlabels >= min_labels) ;
if (done) /* found enough points */
break ;
/* couldn't find enough points - free stuff and try again */
for (i = 0 ; i < nlabels ; i++)
LabelFree(&labels[i]) ;
if (nlabels)
free(labels) ;
#if 0
fprintf(stderr,"%d labels found (min %d), reducing constraints...\n",
nlabels, min_labels) ;
#endif
}
}
printf("%d labels found with F > %2.1f and area > %2.0f\n",
nlabels, current_fthresh, current_min_label_area) ;
for (i = 0 ; i < nlabels ; i++)
fprintf(stderr, "label %d: %d points, %2.1f mm\n",
i, labels[i]->n_points, LabelArea(labels[i], mris)) ;
}
/* read or compute thickness at optimal scale and put it into
c?_avg_thickness.
*/
if (!read_dir) {
fprintf(stderr, "extracting thickness at optimal scale...\n") ;
/* now build feature vectors for each subject */
extract_thickness_at_best_scale(mris, c1_avg_thickness, vbest_avgs,
c1_thickness, nvertices, num_class1);
fprintf(stderr, "extracting thickness for class 2...\n") ;
extract_thickness_at_best_scale(mris, c2_avg_thickness, vbest_avgs,
c2_thickness, nvertices, num_class2);
} else /* read in precomputed optimal thicknesses */
{
char fname[STRLEN] ;
fprintf(stderr, "reading precomputed thickness vectors\n") ;
for (n = 0 ; n < num_class1 ; n++) {
sprintf(fname, "%s/%s.%s", read_dir, hemi, argv[ARGV_OFFSET+n]) ;
fprintf(stderr, "reading thickness vector from %s...\n", fname) ;
if (MRISreadValues(mris, fname) != NO_ERROR)
ErrorExit(Gerror, "%s: could not read thickness file %s",
Progname,fname) ;
MRISexportValVector(mris, c1_avg_thickness[n]) ;
}
for (n = 0 ; n < num_class2 ; n++) {
sprintf(fname, "%s/%s.%s", read_dir, hemi,
argv[n+num_class1+1+ARGV_OFFSET]) ;
fprintf(stderr, "reading curvature vector from %s...\n", fname) ;
if (MRISreadValues(mris, fname) != NO_ERROR)
ErrorExit(Gerror, "%s: could not read thickness file %s",
Progname,fname) ;
MRISexportValVector(mris, c2_avg_thickness[n]) ;
}
}
if (write_dir) /* write out optimal thicknesses */
{
char fname[STRLEN] ;
for (n = 0 ; n < num_class1 ; n++) {
sprintf(fname, "%s/%s.%s", write_dir, hemi, argv[ARGV_OFFSET+n]) ;
fprintf(stderr, "writing curvature vector to %s...\n", fname) ;
MRISimportValVector(mris, c1_avg_thickness[n]) ;
MRISwriteValues(mris, fname) ;
}
for (n = 0 ; n < num_class2 ; n++) {
sprintf(fname, "%s/%s.%s", write_dir, hemi,
argv[n+num_class1+1+ARGV_OFFSET]) ;
fprintf(stderr, "writing curvature vector to %s...\n", fname) ;
MRISimportValVector(mris, c2_avg_thickness[n]) ;
MRISwriteValues(mris, fname) ;
}
}
/* should free c?_thickness here */
if (nsort <= 0) {
/* We have the thickness values at the most powerful scale stored for
each subject in the c1_avg_thickness and c2_avg_thickness vectors.
Now collapse them across each label and build feature vector for
classification.
*/
c1_label_thickness = (double **)calloc(num_class1, sizeof(double *)) ;
c2_label_thickness = (double **)calloc(num_class2, sizeof(double *)) ;
for (n = 0 ; n < num_class1 ; n++)
c1_label_thickness[n] = (double *)calloc(nlabels, sizeof(double)) ;
for (n = 0 ; n < num_class2 ; n++)
c2_label_thickness[n] = (double *)calloc(nlabels, sizeof(double)) ;
fprintf(stderr, "collapsing thicknesses within labels for class 1\n") ;
for (n = 0 ; n < num_class1 ; n++)
for (i = 0 ; i < nlabels ; i++)
c1_label_thickness[n][i] =
cvector_average_in_label(c1_avg_thickness[n], labels[i], nvertices) ;
fprintf(stderr, "collapsing thicknesses within labels for class 2\n") ;
for (n = 0 ; n < num_class2 ; n++)
for (i = 0 ; i < nlabels ; i++)
c2_label_thickness[n][i] =
cvector_average_in_label(c2_avg_thickness[n], labels[i], nvertices) ;
sprintf(fname, "%s_%s_class1.dat", hemi,prefix) ;
fprintf(stderr, "writing class 1 info to %s...\n", fname) ;
fp = fopen(fname, "w") ;
for (i = 0 ; i < nlabels ; i++) /* for each row */
{
for (n = 0 ; n < num_class1 ; n++) /* for each column */
fprintf(fp, "%2.2f ", c1_label_thickness[n][i]) ;
fprintf(fp, "\n") ;
}
fclose(fp) ;
sprintf(fname, "%s_%s_class2.dat", hemi,prefix) ;
fprintf(stderr, "writing class 2 info to %s...\n", fname) ;
fp = fopen(fname, "w") ;
for (i = 0 ; i < nlabels ; i++) {
for (n = 0 ; n < num_class2 ; n++)
fprintf(fp, "%2.2f ", c2_label_thickness[n][i]) ;
fprintf(fp, "\n") ;
}
fclose(fp) ;
} else {
sorted_indices = cvector_sort(vbest_snr, nvertices) ;
vno = sorted_indices[0] ;
write_vertex_data("c1.dat", vno, c1_avg_thickness,num_class1);
write_vertex_data("c2.dat", vno, c2_avg_thickness,num_class2);
printf("sorting complete\n") ;
/* re-write class means at these locations */
sprintf(fname, "%s_%s_class1.dat", hemi,prefix) ;
fprintf(stderr, "writing class 1 info to %s...\n", fname) ;
fp = fopen(fname, "w") ;
for (i = 0 ; i < nsort ; i++) {
for (n = 0 ; n < num_class1 ; n++)
fprintf(fp, "%2.2f ", c1_avg_thickness[n][sorted_indices[i]]) ;
fprintf(fp, "\n") ;
}
fclose(fp) ;
sprintf(fname, "%s_%s_class2.dat", hemi,prefix) ;
fprintf(stderr, "writing class 2 info to %s...\n", fname) ;
fp = fopen(fname, "w") ;
for (i = 0 ; i < nsort ; i++) {
for (n = 0 ; n < num_class2 ; n++)
fprintf(fp, "%2.2f ", c2_avg_thickness[n][sorted_indices[i]]) ;
fprintf(fp, "\n") ;
}
fclose(fp) ;
}
if (test_subject) {
test_thickness = cvector_alloc(nvertices) ;
test_avg_thickness = cvector_alloc(nvertices) ;
MRISfree(&mris) ;
fprintf(stderr, "reading subject %s\n", test_subject) ;
sprintf(fname, "%s/%s/surf/%s.%s",
subjects_dir,test_subject,hemi,surf_name);
mris = MRISread(fname) ;
if (!mris)
ErrorExit(ERROR_NOFILE, "%s: could not read surface file %s",
Progname, fname) ;
MRISsaveVertexPositions(mris, CANONICAL_VERTICES) ;
if (strchr(curv_name, '/') != NULL)
strcpy(fname, curv_name) ; /* full path specified */
else
sprintf(fname,"%s/%s/surf/%s.%s",
subjects_dir,test_subject,hemi,curv_name);
if (MRISreadCurvatureFile(mris, fname) != NO_ERROR)
ErrorExit(Gerror,"%s: could no read curvature file %s",Progname,fname);
mrisp = MRIStoParameterization(mris, NULL, 1, 0) ;
MRISfree(&mris) ;
sprintf(fname, "%s/%s/surf/%s.%s",
subjects_dir,output_subject,hemi,surf_name);
mris = MRISread(fname) ;
if (!mris)
ErrorExit(ERROR_NOFILE, "%s: could not read surface file %s",
Progname, fname) ;
MRISfromParameterization(mrisp, mris, 0) ;
if (area)
MRISmaskNotLabel(mris, area) ;
MRISextractCurvatureVector(mris, test_thickness) ;
for (avgs = 0 ; avgs <= max_avgs ; avgs++) {
cvector_extract_best_avg(vbest_avgs, test_thickness,test_avg_thickness,
avgs-1, nvertices) ;
MRISimportCurvatureVector(mris, test_thickness) ;
MRISaverageCurvatures(mris, 1) ;
MRISextractCurvatureVector(mris, test_thickness) ;
}
if (nsort <= 0) {
sprintf(fname, "%s_%s.dat", hemi,test_subject) ;
fprintf(stderr, "writing test subject feature vector to %s...\n",
fname) ;
fp = fopen(fname, "w") ;
for (i = 0 ; i < nlabels ; i++) /* for each row */
{
label_avg =
cvector_average_in_label(test_avg_thickness, labels[i], nvertices) ;
fprintf(fp, "%2.2f\n", label_avg) ;
}
fclose(fp) ;
} else /* use sorting instead of connected areas */
{
double classification, offset, w ;
int total_correct, total_wrong, first_wrong, vno ;
sprintf(fname, "%s_%s.dat", hemi,test_subject) ;
fprintf(stderr, "writing test subject feature vector to %s...\n",
fname) ;
fp = fopen(fname, "w") ;
first_wrong = -1 ;
total_wrong = total_correct = 0 ;
for (i = 0 ; i < nsort ; i++) {
vno = sorted_indices[i] ;
fprintf(fp, "%2.2f\n ", test_avg_thickness[sorted_indices[i]]) ;
offset = (c1_mean[vno]+c2_mean[vno])/2.0 ;
w = (c1_mean[vno]-c2_mean[vno]) ;
classification = (test_avg_thickness[vno] - offset) * w ;
if (((classification < 0) && (true_class == 1)) ||
((classification > 0) && (true_class == 2))) {
total_wrong++ ;
if (first_wrong < 0)
first_wrong = i ;
} else
total_correct++ ;
}
fclose(fp) ;
fprintf(stderr, "%d of %d correct = %2.1f%% (first wrong %d (%d)),"
"min snr=%2.1f\n",
total_correct, total_correct+total_wrong,
100.0*total_correct / (total_correct+total_wrong),
first_wrong, first_wrong >= 0 ? sorted_indices[first_wrong]:-1,
vbest_snr[sorted_indices[nsort-1]]) ;
if (first_wrong >= 0) {
write_vertex_data("c1w.dat", sorted_indices[first_wrong],
c1_avg_thickness,num_class1);
write_vertex_data("c2w.dat", sorted_indices[first_wrong],
c2_avg_thickness,num_class2);
}
}
}
msec = TimerStop(&start) ;
free(total_mean);
free(c1_mean) ;
free(c2_mean) ;
free(c1_var);
free(c2_var);
seconds = nint((float)msec/1000.0f) ;
minutes = seconds / 60 ;
seconds = seconds % 60 ;
fprintf(stderr, "classification took %d minutes and %d seconds.\n",
minutes, seconds) ;
exit(0) ;
return(0) ; /* for ansi */
}
/**
* @brief runs the labeled test files to determine accuracy
*
* This function will open each pair of files in the testdata directory to
* analyze how accurate the copyright agent is. This function will respond with
* the number of false negatives, false positives, and correct answers for each
* file and total tally of these numbers. This will also produce 3 files, one
* containing all matches that the copyright agent found, all the things that it
* didn't find, and all of the false positives.
*/
void run_test_files(copyright copy)
{
/* locals */
cvector compare;
copyright_iterator iter;
cvector_iterator curr;
FILE* istr, * m_out, * n_out, * p_out;
char buffer[READMAX + 1];
char file_name[FILENAME_MAX];
char copy_buf[FILENAME_MAX];
char name_buf[FILENAME_MAX];
char* first, * last, * loc, tmp;
int i, matches, correct = 0, falsep = 0, falsen = 0;
/* grab the copyright files */
memset(copy_buf, '\0', sizeof(copy_buf));
memset(name_buf, '\0', sizeof(copy_buf));
snprintf(copy_buf, sizeof(copy_buf),
"%s/mods-enabled/copyright/agent/copyright.dic",
sysconfigdir);
snprintf(name_buf, sizeof(name_buf),
"%s/mods-enabled/copyright/agent/names.dic",
sysconfigdir);
/* create data structures */
copyright_init(©, copy_buf, name_buf);
cvector_init(&compare, string_function_registry());
/* open the logging files */
m_out = fopen("Matches", "w");
n_out = fopen("False_Negatives", "w");
p_out = fopen("False_Positives", "w");
/* big problem if any of the log files didn't open correctly */
if(!m_out || !n_out || !p_out)
{
fprintf(cerr, "ERROR did not successfully open one of the log files\n");
fprintf(cerr, "ERROR the files that needed to be opened were:\n");
fprintf(cerr, "ERROR Matches, False_Positives, False_Negatives\n");
exit(-1);
}
/* loop over every file in the test directory */
for(i = 0; i < TESTFILE_NUMBER; i++)
{
sprintf(file_name, "%s%d_raw", test_dir, i);
/* attempt to open the labeled test file */
istr = fopen(file_name, "r");
if(!istr)
{
fprintf(cerr, "ERROR Must run testing from correct directory. The\n");
fprintf(cerr, "ERROR correct directory is installation dependent but\n");
fprintf(cerr, "ERROR the working directory should include the folder:\n");
fprintf(cerr, "ERROR %s\n", test_dir);
exit(-1);
}
/* initialize the buffer and read in any information */
memset(buffer, '\0', sizeof(buffer));
buffer[fread(buffer, sizeof(char), READMAX, istr)] = '\0';
matches = 0;
/* set everything in the buffer to lower case */
for(first = buffer; *first; first++)
{
*first = tolower(*first);
}
/* loop through and find all <s>...</s> tags */
loc = buffer;
while((first = strstr(loc, "<s>")) != NULL)
{
last = strstr(loc, "</s>");
if(last == NULL)
{
fprintf(cerr, "ERROR unmatched \"<s>\"\n");
fprintf(cerr, "ERROR in file: \"%s\"\n", file_name);
exit(-1);
}
if(last <= first)
{
fprintf(cerr, "ERROR unmatched \"</s>\"\n");
fprintf(cerr, "ERROR in file: \"%s\"\n", file_name);
exit(-1);
}
tmp = *last;
*last = 0;
cvector_push_back(compare, first + 3);
*last = tmp;
loc = last + 4;
}
/* close the previous file and open the corresponding raw data */
fclose(istr);
file_name[strlen(file_name) - 4] = '\0';
istr = fopen(file_name, "r");
if(!istr)
{
fprintf(cerr, "ERROR Unmatched file in the test directory");
fprintf(cerr, "ERROR File with no match: \"%s\"_raw\n", file_name);
fprintf(cerr, "ERROR File that caused error: \"%s\"\n", file_name);
}
/* perform the analysis on the current file */
copyright_analyze(copy, istr, REPORTALL);
fclose(istr);
/* loop over every match that the copyright object found */
for(iter = copyright_begin(copy); iter != copyright_end(copy); iter++)
{
cvector_iterator best = cvector_begin(compare);
char score[2048];
char dst[2048];
memset(dst, '\0', sizeof(dst));
memset(score, '\0', sizeof(score));
/* log the coyright entry */
fprintf(m_out, "====%s================================\n", file_name);
fprintf(m_out, "DICT: %s\tNAME: %s\n",copy_entry_dict(*iter), copy_entry_name(*iter));
fprintf(m_out, "TEXT[%s]\n",copy_entry_text(*iter));
/* loop over the vector looking for matches */
for(curr = cvector_begin(compare); curr != cvector_end(compare); curr++)
{
if(longest_common(dst, copy_entry_text(*iter), (char*)*curr) > strlen(score))
{
strcpy(score, dst);
best = curr;
}
}
/* log the entry as found if it matched something in compare */
if(cvector_size(compare) != 0 &&
(strcmp(copy_entry_dict(*iter), "by") || strlen(score) > THRESHOLD))
{
cvector_remove(compare, best);
matches++;
}
else if(!strcmp(copy_entry_dict(*iter), "email") || !strcmp(copy_entry_dict(*iter), "url"))
{
matches++;
}
else
{
fprintf(p_out, "====%s================================\n", file_name);
fprintf(p_out, "DICT: %s\tNAME: %s\n",copy_entry_dict(*iter), copy_entry_name(*iter));
fprintf(p_out, "TEXT[%s]\n",copy_entry_text(*iter));
}
}
/* log all the false negatives */
for(curr = cvector_begin(compare); curr != cvector_end(compare); curr++)
{
fprintf(n_out, "====%s================================\n", file_name);
fprintf(n_out, "%s\n", (char*)*curr);
}
fprintf(cout, "====%s================================\n", file_name);
fprintf(cout, "Correct: %d\n", matches);
fprintf(cout, "False Positives: %d\n", copyright_size(copy) - matches);
fprintf(cout, "False Negatives: %d\n", cvector_size(compare));
/* clean up for the next file */
correct += matches;
falsep += copyright_size(copy) - matches;
falsen += cvector_size(compare);
cvector_clear(compare);
}
fprintf(cout, "==== Totals ================================\n");
fprintf(cout, "Total Found: %d\n", correct + falsep);
fprintf(cout, "Correct: %d\n", correct);
fprintf(cout, "False Positives: %d\n", falsep);
fprintf(cout, "False Negatives: %d\n", falsen);
fclose(m_out);
fclose(n_out);
fclose(p_out);
copyright_destroy(copy);
cvector_destroy(compare);
}