void
procstat_sandbox_methods(struct procstat *procstat, struct kinfo_proc *kipp)
{
struct sandbox_method_stat *smsp, *smsp_free;
size_t len;
u_int i;
if (!hflag) {
printf("%5s %-10s %-20s %-10s %6s %5s %8s %8s", "PID", "COMM",
"CLASS", "METHOD", "INVOKE", "FAULT", "LMIN", "LMAX");
if (Xflag)
printf(" %8s %8s %8s %8s", "SMIN", "SMAX", "SMEAN",
"SMEDIAN");
printf("\n");
}
smsp_free = smsp = procstat_getsbmethods(procstat, kipp, &len);
if (smsp == NULL)
return;
for (i = 0; i < (len / sizeof(*smsp)); i++, smsp++) {
if (smsp->sms_methodid == SANDBOX_METHODID_FREE)
continue;
printf("%5d ", kipp->ki_pid);
printf("%-10s ", kipp->ki_comm);
printf("%-20s ", smsp->sms_class_name);
printf("%-10s ", smsp->sms_method_name);
printf("%6jd ", (uintmax_t)smsp->sms_stat_invoke);
printf("%5jd ", (uintmax_t)smsp->sms_stat_fault);
printf("%8jd ", (uintmax_t)smsp->sms_stat_minrun);
printf("%8jd", (uintmax_t)smsp->sms_stat_maxrun);
if (Xflag) {
printf(" %8jd", sample_min(SANDBOX_METHOD_MAXSAMPVEC,
smsp->sms_stat_sampvec));
printf(" %8jd", sample_max(SANDBOX_METHOD_MAXSAMPVEC,
smsp->sms_stat_sampvec));
printf(" %8jd", sample_mean(SANDBOX_METHOD_MAXSAMPVEC,
smsp->sms_stat_sampvec));
printf(" %8jd", sample_median(
SANDBOX_METHOD_MAXSAMPVEC,
smsp->sms_stat_sampvec));
}
printf("\n");
}
procstat_freesbmethods(procstat, smsp_free);
}
int main(int argv, char** argc) {
printf("\n\nRunning code for question 1.11:\n\n");
int train_area1[4] = {150-1,330-1,264-1,328-1};
/*read images from file*/
pnm_img * kande1 = pnm_read(IMG_IN_DIR "kande1.pnm"),
* kande2 = pnm_read(IMG_IN_DIR "kande2.pnm");
pnm_img * train_img =pnm_subimage(kande1, train_area1);
mtrx * train_set = img2train_set(train_img);
vect * s_mean = sample_mean(train_set);
mtrx * s_cov = sample_cov(train_set, s_mean);
double* map = (double*)malloc(kande2->width*kande2->height*sizeof(double));
pdf_map(kande2, s_mean,s_cov,&map);
vect* w_mean = weighted_avg_pos(kande2, map);
pnm_pixmap p = {0,255,0};
for (int x = -2; x<4; x++)
for (int y=-2; y<4; y++) {
pnm_set_pixel(kande2, (*w_mean->data)+x, *(w_mean->data+1)+y, &p);
}
// pnm_write(kande1, IMG_OUT_DIR"cas10center.pnm");
mtrx* w_cov = weighted_2dcov(map, w_mean, kande2);
FILE* fp = fopen(TEX_OUT_DIR"c11.tex", "w");
vect2tex(w_mean, "celevenwmean", fp);
mtrx2tex(w_cov, "celevenwcov", fp);
fclose(fp);
gplot_img2splot(kande2, 0 ,TEX_OUT_DIR"case11.kande2.gnuplot.dat");
gplot_pdf2splot(w_mean, w_cov, kande2, 5, TEX_OUT_DIR"case11.pdf.gnuplot.dat");
gsl_matrix_free(train_set);
gsl_matrix_free(s_cov);
gsl_vector_free(s_mean);
pnm_destroy(kande1);
pnm_destroy(train_img);
}
calc_data do_cucl(int k, int n, double m, double g, QVector<double> trend_m, QVector<double> trend_s,
double level)
{
QVector<QVector<double>> matrix = createMatrix(k,n,m,g,trend_m,trend_s);
QVector<double> y;
for(auto sample : matrix)
{
y.push_back(sample_mean(sample));
}
QVector<double> x;
for(int i = 0; i<k;i++)
{
x.push_back(i);
}
QVector<bool> u = U(y);
QVector<bool> l = L(y);
int a = Asum(u,l);
int b = Bsum(u,l);
//Тренд средних
double F = f(k);
double t_stat_mean = a/F;
double L = l_const(k);
double t_stat_s = (b-F*F)/L;
double t_cr = t_test_cr(k,level);
qDebug() << "a" << a;
qDebug() << "b" << b;
qDebug() << "t mean" << t_stat_mean;
qDebug() << "t s" << t_stat_s;
qDebug() << t_cr;
calc_data data;
data.x=x;
data.y=y;
data.t_A=t_stat_mean;
data.t_B=t_stat_s;
data.t_cr=t_cr;
data.A=a;
data.B=b;
return data;
}
static int rx_cal_dc_off(struct rx_cal *cal, struct gain_mode *gains,
int16_t *dc_i, int16_t *dc_q)
{
int status = BLADERF_ERR_UNEXPECTED;
float mean_i, mean_q;
status = load_gains(cal, gains);
if (status != 0) {
return status;
}
status = rx_samples(cal->dev, cal->samples, cal->num_samples,
&cal->ts, RX_CAL_TS_INC);
if (status != 0) {
return status;
}
sample_mean(cal->samples, cal->num_samples, &mean_i, &mean_q);
*dc_i = float_to_int16(mean_i);
*dc_q = float_to_int16(mean_q);
return 0;
}
void report_xvalid(double *xdata, double *xpred, double *xdiff, double *xstd,
double *xzscore, int ndata, int var) {
/*
* DATE: Tue Oct 6 11:55:44 MET 1992
* BY : Edzer J. Pebesma
* PURPOSE: report summary statistics of these five lists
* SIDE EFFECTS: none
*/
int i, nXdata = 0, nXpred = 0, nXdiff = 0, n_std = 0, nZscore = 0,
compare(const double *a, const double *b);
double min[5], max[5], p25[5], p75[5], p50[5], mean[5], std[5];
double corr = 0.0;
set_mv_double(&corr);
calc_r(xdata, xpred, ndata, &corr);
for (i = 0; i < 5; i ++) {
set_mv_double(&(min[i]));
set_mv_double(&(p25[i]));
set_mv_double(&(p50[i]));
set_mv_double(&(p75[i]));
set_mv_double(&(max[i]));
set_mv_double(&(mean[i]));
set_mv_double(&(std[i]));
}
/* select not missing values, put mv's at the end: */
/* sorting arrays: */
qsort(xdata, (size_t) ndata, sizeof(double),
(int (*)(const void *,const void *)) compare);
while (!is_mv_double(&(xdata[nXdata])) && nXdata < ndata)
nXdata++;
qsort(xpred, (size_t) ndata, sizeof(double),
(int (*)(const void *,const void *)) compare);
while (!is_mv_double(&(xpred[nXpred])) && nXpred < ndata)
nXpred++;
qsort(xdiff, (size_t) ndata, sizeof(double),
(int (*)(const void *,const void *)) compare);
while (!is_mv_double(&(xdiff[nXdiff])) && nXdiff < ndata)
nXdiff++;
if (var) { /* do everything for xstd and xzscore */
qsort(xstd, (size_t) ndata, sizeof(double),
(int (*)(const void *,const void *)) compare);
while ((! is_mv_double(&(xstd[n_std]))) && (n_std < ndata))
n_std++;
qsort(xzscore, (size_t) ndata, sizeof(double),
(int (*)(const void *,const void *)) compare);
while ((! is_mv_double(&(xzscore[nZscore]))) && (nZscore < ndata))
nZscore++;
}
/* calculate statistics: */
if (nXdata) {
min[0]=xdata[0];
max[0]=xdata[nXdata-1];
mean[0] = sample_mean(xdata, nXdata);
if (nXdata > 1) {
p25[0]=est_quant(xdata, 0.25, nXdata);
p50[0]=est_quant(xdata, 0.5, nXdata);
p75[0]=est_quant(xdata, 0.75, nXdata);
std[0] = sample_std(xdata, mean[0], nXdata);
}
}
if (nXpred) {
min[1]=xpred[0];
max[1]=xpred[nXpred-1];
mean[1] = sample_mean(xpred, nXpred);
if (nXpred > 1) {
p25[1]=est_quant(xpred, 0.25, nXpred);
p50[1]=est_quant(xpred, 0.5, nXpred);
p75[1]=est_quant(xpred, 0.75, nXpred);
std[1] = sample_std(xpred, mean[1], nXpred);
}
}
if (nXdiff) {
min[2]=xdiff[0];
max[2]=xdiff[nXdiff-1];
mean[2] = sample_mean(xdiff, nXdiff);
if (nXdiff > 1) {
p25[2]=est_quant(xdiff, 0.25, nXdiff);
p50[2]=est_quant(xdiff, 0.5, nXdiff);
p75[2]=est_quant(xdiff, 0.75, nXdiff);
std[2] = sample_std(xdiff, mean[2], nXdiff);
}
}
if (var) {
if (n_std) {
min[3]=xstd[0];
max[3]=xstd[n_std-1];
mean[3] = sample_mean(xstd, n_std);
if (n_std > 1) {
p25[3]=est_quant(xstd, 0.25, n_std);
p50[3]=est_quant(xstd, 0.5, n_std);
p75[3]=est_quant(xstd, 0.75, n_std);
std[3] = sample_std(xstd, mean[3], n_std);
}
}
if (nZscore) {
min[4]=xzscore[0];
max[4]=xzscore[nZscore-1];
mean[4] = sample_mean(xzscore, nZscore);
if (nZscore > 1) {
p25[4]=est_quant(xzscore, 0.25, nZscore);
p50[4]=est_quant(xzscore, 0.5, nZscore);
p75[4]=est_quant(xzscore, 0.75, nZscore);
std[4] = sample_std(xzscore, mean[4], nZscore);
}
}
}
/* output: */
printlog("corr(Obs, Pred): %s [%s]\n\n",
my_dtoa("%6.4g", &corr),
method_string(get_method()));
printlog(" observed predicted pred.-obs. pred.std. zscore\n");
printlog("======================================================================\n");
printlog("%-10s%12s", "minimum", my_dtoa("%6.4g", &(min[0])));
printlog("%12s", my_dtoa("%6.4g", &(min[1])));
printlog("%12s", my_dtoa("%6.4g", &(min[2])));
printlog("%12s", my_dtoa("%6.4g", &(min[3])));
printlog("%12s\n", my_dtoa("%6.4g", &(min[4])));
printlog("%-10s%12s", "1st q.", my_dtoa("%6.4g", &(p25[0])));
printlog("%12s", my_dtoa("%6.4g", &(p25[1])));
printlog("%12s", my_dtoa("%6.4g", &(p25[2])));
printlog("%12s", my_dtoa("%6.4g", &(p25[3])));
printlog("%12s\n", my_dtoa("%6.4g", &(p25[4])));
printlog("%-10s%12s", "median", my_dtoa("%6.4g", &(p50[0])));
printlog("%12s", my_dtoa("%6.4g", &(p50[1])));
printlog("%12s", my_dtoa("%6.4g", &(p50[2])));
printlog("%12s", my_dtoa("%6.4g", &(p50[3])));
printlog("%12s\n", my_dtoa("%6.4g", &(p50[4])));
printlog("%-10s%12s", "3rd q.", my_dtoa("%6.4g", &(p75[0])));
printlog("%12s", my_dtoa("%6.4g", &(p75[1])));
printlog("%12s", my_dtoa("%6.4g", &(p75[2])));
printlog("%12s", my_dtoa("%6.4g", &(p75[3])));
printlog("%12s\n", my_dtoa("%6.4g", &(p75[4])));
printlog("%-10s%12s", "maximum", my_dtoa("%6.4g", &(max[0])));
printlog("%12s", my_dtoa("%6.4g", &(max[1])));
printlog("%12s", my_dtoa("%6.4g", &(max[2])));
printlog("%12s", my_dtoa("%6.4g", &(max[3])));
printlog("%12s\n\n", my_dtoa("%6.4g", &(max[4])));
printlog("%-10s%12d%12d%12d%12d%12d\n", "n",
nXdata, nXpred, nXdiff, n_std, nZscore);
printlog("%-10s%12s", "mean", my_dtoa("%6.4g", &(mean[0])));
printlog("%12s", my_dtoa("%6.4g", &(mean[1])));
printlog("%12s", my_dtoa("%6.4g", &(mean[2])));
printlog("%12s", my_dtoa("%6.4g", &(mean[3])));
printlog("%12s\n", my_dtoa("%6.4g", &(mean[4])));
printlog("%-10s%12s", "std.dev.", my_dtoa("%6.4g", &(std[0])));
printlog("%12s", my_dtoa("%6.4g", &(std[1])));
printlog("%12s", my_dtoa("%6.4g", &(std[2])));
printlog("%12s", my_dtoa("%6.4g", &(std[3])));
printlog("%12s\n", my_dtoa("%6.4g", &(std[4])));
return;
}
Dtype sample_mean(const int* const seqs) {
return sample_mean(seqs, sample_size_);
}
