/*******************************************************************
Subroutine to compute the Mean of Matrix
matrix *X: the pointer to the matrix
char direction: 'c' - compute the mean of each column
'r' - compute the mean of each row
vector *mean: the pointer to the mean vector
*******************************************************************/
int mmean(matrix *X, char direction, vector *mean)
{
int row_l, row_n;
int i;
int result;
row_l = X->n;
row_n = X->m;
if (direction == 'c') { // compute the mean of each column
mean->l = row_l;
msum(X, 'c', mean);
for (i=0; i<row_l; i++) {
*(mean->pr + i) /= row_n;
}
result = 1;
} else if (direction == 'r') { // compute the mean of each row
mean->l = row_n;
msum(X, 'r', mean);
for (i=0; i<row_n; i++) {
*(mean->pr + i) /= row_l;
}
result = 1;
} else {
result = 0;
printf("the direction parameter should be 'c' or 'r'");
}
return result;
}
int main()
{
freopen("sdmin.in","r",stdin);
freopen("sdmin.out","w",stdout);
scanf("%d %d",&n,&sus);
for(int i=1; i<=n; ++i) scanf("%d %d %d",&seg[i].xs,&seg[i].xd,&seg[i].y);
//cautare ternara
double left=-2000001,right=2000001;
for(;right-left>EPS;) {
double lt=(2.0*left + right)/3.0,rt=(left + 2.0*right)/3.0;
double flt=msum(lt),drt=msum(rt);
if(flt<drt) right=rt;
else left=lt;
}
printf("%.6lf %.6lf",msum(left),left);
return 0;
}
double *_FR(double *d, double *dat, double *prob, double T, int N) {
double t=T; int i=0; int j=1;
do {
double q = sum(prob,i, j); double s = msum(prob, dat, i, j);
double sc = abs(abs(s)/sqrt(abs(q))); d[j]=sc;
if(sc>t) {
double scc = sc;
while(sc>t & j<N & scc/q >= sc) {
scc=sc; j++;
q = sum(prob,i, j); s = msum(prob, dat, i, j); sc = abs(abs(s)/sqrt(abs(q)));
}
for(int k=i;k<j;k++) { d[k]=sc; } i=j;
}
else { i++; } j++;
} while(j<N);
return(d);
}
SEXP exactmean(SEXP x){
SEXP ans;
listnode expansion;
size_t n;
expansion.next = NULL;
n = dplRlength(x);
ans = PROTECT(allocVector(REALSXP, 1));
/* Note: x must be a numeric vector */
REAL(ans)[0] = msum(REAL(x), n, &expansion) / n;
UNPROTECT(1);
return ans;
}
/*******************************************************************
Part of Sub-level Kurtosis Calculate:
sum(Zjk*ones(1,p).*(data_proj))./sum(Zjk)
*******************************************************************/
void kurtmodel(matrix *mZjk, double sumZjk, matrix *data, vector *meanZjk)
{
int i;
matrix Mt;
mnew(&Mt, data->m, data->n);
mmDotMul(mZjk, data, &Mt);
msum(&Mt, 'c', meanZjk);
for (i=0; i<(meanZjk->l); i++) {
*(meanZjk->pr + i) /= sumZjk;
};
mdelete(&Mt);
}
inline RealType log_pdf(const dirichlet_distribution<RealType, Policy>& dist, const std::vector<T>& x, bool extended_precision)
{
if (!extended_precision) {
return log_pdf(dist, x);
}
std::vector<RealType> alpha = dist.alpha();
std::vector<RealType> tmp(alpha.size(), 0.0);
for (size_t i = 0; i < alpha.size(); i++) {
tmp[i] = (alpha[i]-1.0)*std::log(x[i]);
}
return msum(tmp) - mbeta_log(alpha);
}
void ckurtmodel(matrix *mZjk, double sumZjk,
matrix *data_re, matrix *data_im,
vector *meanZjk_re, vector *meanZjk_im)
{
int i;
matrix Mt_re;
matrix Mt_im;
matrix mZjk_im;
mnew(&Mt_re, data_re->m, data_re->n);
mnew(&Mt_im, data_im->m, data_im->n);
mnew(&mZjk_im, mZjk->m, mZjk->n);
cmmDotMul(mZjk, &mZjk_im, data_re, data_im, &Mt_re, &Mt_im);
msum(&Mt_re, 'c', meanZjk_re);
msum(&Mt_im, 'c', meanZjk_im);
for (i=0; i<(meanZjk_re->l); i++) {
*(meanZjk_re->pr + i) /= sumZjk;
*(meanZjk_im->pr + i) /= sumZjk;
};
mdelete(&Mt_re);
mdelete(&Mt_im);
mdelete(&mZjk_im);
}
/* Written by Mikko Korpela */
SEXP sens2(SEXP x){
SEXP ans;
size_t i, n;
double previous, this, next;
double *x_const;
dplr_double sum1, sum2;
listnode tmp, *tmp_p;
n = dplRlength(x);
ans = PROTECT(allocVector(REALSXP, 1));
if(n < 2){
REAL(ans)[0] = R_NaN;
UNPROTECT(1);
return ans;
}
/* Note: x must be a numeric vector */
x_const = REAL(x);
/* Setup for grow_exp and msum */
tmp.next = NULL;
tmp.valid = FALSE;
tmp_p = &tmp;
/* In the sum of absolute differences between consecutive elements
of an array, each number will appear multiplied by -2, -1, 0,
1, or 2 (first and last number by -1, 0, or 1) */
this = x_const[0];
next = x_const[1];
if(this > next){
grow_exp(tmp_p, this);
} else if(this < next){
grow_exp(tmp_p, -this);
}
for(i = 1; i < n-1; i++){
previous = x_const[i-1];
this = x_const[i];
next = x_const[i+1];
if(this > previous){
if(this > next){
grow_exp(tmp_p, this);
grow_exp(tmp_p, this);
} else if(this == next){
grow_exp(tmp_p, this);
}
} else if(this < previous){
if(this < next){
grow_exp(tmp_p, -this);
grow_exp(tmp_p, -this);
} else if(this == next){
grow_exp(tmp_p, -this);
}
} else if(this > next){
grow_exp(tmp_p, this);
} else if(this < next){
grow_exp(tmp_p, -this);
}
}
this = x_const[n-1];
previous = x_const[n-2];
if(this > previous){
grow_exp(tmp_p, this);
} else if(this < previous){
grow_exp(tmp_p, -this);
}
/* Sum of absolute differences */
sum1 = 0.0f;
while(tmp_p != NULL && tmp_p->valid == TRUE){
sum1 += tmp_p->data;
tmp_p = tmp_p->next;
}
sum2 = msum(x_const, n, &tmp);
REAL(ans)[0] = sum1/(sum2-sum2/n);
UNPROTECT(1);
return ans;
}
