Vec MVTR::simulate_fake_x()const{
uint p = xdim();
Vec x(p);
x[0] = 1.0;
for(uint i=0; i<p; ++i) x[i] = rnorm();
return x;
}
double MvReg::loglike(const Vector &beta_siginv) const {
Matrix Beta(xdim(), ydim());
Vector::const_iterator it = beta_siginv.cbegin();
std::copy(it, it + Beta.size(), Beta.begin());
it += Beta.size();
SpdMatrix siginv(ydim());
siginv.unvectorize(it, true);
return log_likelihood_ivar(Beta, siginv);
}
double GridMapping::dx() const{
if (xdim() != 0) {
double x1,x0,y,z;
indexToWorld(0, 0, 0, x0, y, z);
indexToWorld(1, 0, 0, x1, y, z);
return fabs(x1-x0);
}
return 0;
}
bool GridMapping::operator== (const GridMapping & other){
return (other.xdim() == xdim() &&
other.ydim() == ydim() &&
other.zdim() == zdim() &&
other._trans == _trans &&
other._itrans == _itrans &&
other.size() == size());
}
void RM::make_X_y(Matrix &X, Vector &Y)const{
uint p = xdim();
uint n = dat().size();
X = Matrix(n,p);
Y = Vector(n);
for(uint i=0; i<n; ++i){
Ptr<RegressionData> rdp = dat()[i];
const Vector &x(rdp->x());
assert(x.size()==p);
X.set_row(i,x);
Y[i] = rdp->y();
}
}
Matrix MvRegSuf::conditional_beta_hat(const SelectorMatrix &included) const {
Matrix ans(xdim(), ydim());
std::map<Selector, Cholesky> chol_map;
for (int i = 0; i < ydim(); ++i) {
const Selector &inc(included.col(i));
auto it = chol_map.find(inc);
if (it == chol_map.end()) {
chol_map[it->first] = Cholesky(inc.select(xtx()));
it = chol_map.find(inc);
}
ans.col(i) = inc.expand(it->second.solve(inc.select(xty_.col(i))));
}
return ans;
}
double LRM::log_likelihood(const Vector & beta, Vector *g, Matrix *h,
bool initialize_derivs)const{
const LRM::DatasetType &data(dat());
if(initialize_derivs){
if(g){
g->resize(beta.size());
*g=0;
if(h){
h->resize(beta.size(), beta.size());
*h=0;}}}
double ans = 0;
int n = data.size();
bool all_coefficients_included = coef().nvars() == xdim();
const Selector &inc(coef().inc());
for(int i = 0; i < n; ++i){
bool y = data[i]->y();
const Vector & x(data[i]->x());
double eta = predict(x) + log_alpha_;
double loglike = plogis(eta, 0, 1, y, true);
ans += loglike;
if(g){
double logp = y ? loglike : plogis(eta, 0, 1, true, true);
double p = exp(logp);
if (all_coefficients_included) {
*g += (y-p) * x;
if(h){
h->add_outer(x,x, -p*(1-p));
}
} else {
Vector reduced_x = inc.select(x);
*g += (y - p) * reduced_x;
if (h) {
h->add_outer(reduced_x, reduced_x, -p * (1 - p));
}
}
}
}
return ans;
}
void GlmModel::drop_all() {
for (int i = 0; i < xdim(); ++i) drop(i);
}
void GridMapping::localToUpperLeftIndex(const double l_x,const double l_y,const double l_z, int &i,int &j,int &k) const{
i = (int)floor(l_x*double(xdim()-1));
j = (int)floor(l_y*double(ydim()-1));
k = (int)floor(l_z*double(zdim()-1));
}
bool GridField<T>::isUndefined(int i,int j,int k) const{
return i < 0 || i >= xdim() || j < 0 || j >= ydim() || k < 0 || k >= zdim();
}
void GlmModel::add_all() {
for (int i = 0; i < xdim(); ++i) add(i);
}
Vector MvReg::simulate_fake_x(RNG &rng) const {
uint p = xdim();
Vector x(p, 1.0);
for (uint i = 1; i < p; ++i) x[i] = rnorm_mt(rng);
return x;
}
//Local dim
double GridMapping::local_dx() const{
return 1.0/double(xdim()-1);
}
//Funktioner
//Index to index
int GridMapping::indexAt(int i, int j, int k) const{
return i+(xdim())*j+(xdim()*ydim()*k);
}
void GridMapping::indexAt(int index, int &i, int &j, int &k) const{
k = index /(xdim()*ydim());
j = (index-(k*xdim()*ydim()))/(xdim());
i = (index-j*xdim()-k*xdim()*ydim());
}
bool GridMapping::isValid(int i, int j, int k) const{
return ( i >= 0 && i < xdim()
&& j >= 0 && j < ydim()
&& k >= 0 && k < zdim());
}
//Local to...
void GridMapping::localToIndex(double l_x, double l_y,double l_z,int &i,int &j,int &k) const{
i = (int)round(l_x*double(xdim()-1));
j = (int)round(l_y*double(ydim()-1));
k = (int)round(l_z*double(zdim()-1));
}
void GridMapping::indexToLocal(int i,int j,int k, double &l_x, double &l_y,double &l_z) const{
l_x = double(i)/double(xdim()-1);
l_y = double(j)/double(ydim()-1);
l_z = double(k)/double(zdim()-1);//TODO om zdim = 1 så funkar inte detta, en ifsats kan lösa detta
}