/
etsTargetFunction.cpp
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/
etsTargetFunction.cpp
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#include <cmath>
#include <R.h>
#include <Rcpp.h>
using namespace Rcpp;
//for isnan, math.h is needed
//#include <math.h>
#include "etsTargetFunction.h"
#include <R_ext/Print.h>
void EtsTargetFunction::init(std::vector<double> & p_y, int p_nstate, int p_errortype,
int p_trendtype, int p_seasontype, bool p_damped,
std::vector<double> & p_lower, std::vector<double> & p_upper, std::string p_opt_crit,
int p_nmse, std::string p_bounds, int p_m,
bool p_optAlpha, bool p_optBeta, bool p_optGamma, bool p_optPhi,
bool p_givenAlpha, bool p_givenBeta, bool p_givenGamma, bool p_givenPhi,
double alpha, double beta, double gamma, double phi) {
this->y = p_y;
this->n = this->y.size();
this->nstate = p_nstate;
this->errortype = p_errortype;
this->trendtype = p_trendtype;
this->seasontype = p_seasontype;
this->damped = p_damped;
this->lower = p_lower;
this->upper = p_upper;
this->opt_crit = p_opt_crit;
this->nmse = p_nmse;
this->bounds = p_bounds;
this->m = p_m;
this->optAlpha = p_optAlpha;
this->optBeta = p_optBeta;
this->optGamma = p_optGamma;
this->optPhi = p_optPhi;
this->givenAlpha = p_givenAlpha;
this->givenBeta = p_givenBeta;
this->givenGamma = p_givenGamma;
this->givenPhi = p_givenPhi;
/* Rprintf("optAlpha: %d\n", optAlpha);
Rprintf("optBeta: %d\n", optBeta);
Rprintf("optGamma: %d\n", optGamma);
Rprintf("optPhi: %d\n", optPhi);
Rprintf("givenAlpha: %d\n", givenAlpha);
Rprintf("givenBeta: %d\n", givenBeta);
Rprintf("givenGamma: %d\n", givenGamma);
Rprintf("givenPhi: %d\n", givenPhi);
*/
this->alpha = alpha;
this->beta = beta;
this->gamma = gamma;
this->phi = phi;
this->lik = 0;
this->objval = 0;
// for(int i=0; i < 10; i++) this->amse.push_back(0);
// for(int i=0; i < n; i++) this->e.push_back(0);
this->amse.resize(30, 0);
this->e.resize(n, 0);
}
void EtsTargetFunction::eval(const double* p_par, int p_par_length) {
bool equal=true;
// ---------show params----------
// Rprintf("par: ");
// for(int j=0;j < p_par_length;j++) {
// Rprintf("%f ", p_par[j]);
// }
// Rprintf(" objval: %f\n", this->objval);
//Rprintf("\n");
// ---------show params----------
// Check if the parameter configuration has changed, if not, just return.
if(p_par_length != this->par.size()) {
equal=false;
} else {
for(int j=0;j < p_par_length;j++) {
if(p_par[j] != this->par[j]) {
equal=false;
break;
}
}
}
if(equal) return;
this->par.clear();
for(int j=0;j < p_par_length;j++) {
this->par.push_back(p_par[j]);
}
int j=0;
if(optAlpha) this->alpha = par[j++];
if(optBeta) this->beta = par[j++];
if(optGamma) this->gamma = par[j++];
if(optPhi) this->phi = par[j++];
if(!this->check_params()) {
this->objval = R_PosInf;
return;
}
this->state.clear();
for(int i=par.size()-nstate; i < par.size(); i++) {
this->state.push_back(par[i]);
}
// Add extra state
if(seasontype!=0) {//"N"=0, "M"=2
//init.state <- c(init.state, m*(seasontype==2) - sum(init.state[(2+(trendtype!=0)):nstate]))
double sum=0;
for(int i=(1+((trendtype!=0) ? 1 : 0));i<nstate;i++) {
sum += state[i];
}
double new_state = m*((seasontype==2) ? 1 : 0) - sum;
state.push_back(new_state);
}
// Check states
if(seasontype==2)
{
double min = R_PosInf;
int start = 1;
if(trendtype!=0) start=2;
for(int i=start; i<state.size(); i++) {
if(state[i] < min) min = state[i];
}
if(min < 0) {
this->objval = R_PosInf;
return;
}
// seas.states <- init.state[-(1:(1+(trendtype!=0)))]
//if(min(seas.states) < 0)
// return(1e8)
};
int p = state.size();
for(int i=0; i <= p*this->y.size(); i++) state.push_back(0);
etscalc(&this->y[0], &this->n, &this->state[0], &this->m, &this->errortype, &this->trendtype, &this->seasontype,
&this->alpha, &this->beta, &this->gamma, &this->phi, &this->e[0], &this->lik, &this->amse[0], &this->nmse);
// Avoid perfect fits
if (this->lik < -1e10) this->lik = -1e10;
// isnan() is a C99 function
//if (isnan(this->lik)) this->lik = 1e8;
if (ISNAN(this->lik)) this->lik = R_PosInf;
if(fabs(this->lik+99999) < 1e-7) this->lik = R_PosInf;
if(this->opt_crit=="lik") this->objval = this->lik;
else if(this->opt_crit=="mse") this->objval = this->amse[0];
else if(this->opt_crit=="amse") {
//return(mean(e$amse[1:nmse]))
double mean=0;
for(int i=0;i < this->nmse;i++) {
mean+=amse[i]/this->nmse;
}
this->objval=mean;
}
else if(this->opt_crit=="sigma") {
//return(mean(e$e^2))
double mean=0;
int ne=e.size();
for(int i=0;i<ne;i++) {
mean+=e[i]*e[i]/ne;
}
this->objval=mean;
}
else if(this->opt_crit=="mae") {
//return(mean(abs(e$e)))
double mean=0;
int ne=e.size();
for(int i=0;i<ne;i++) {
mean+=fabs(e[i])/ne;
}
this->objval=mean;
}
}
bool EtsTargetFunction::check_params() {
if(bounds != "admissible")
{
if(optAlpha)
{
if(alpha < lower[0] || alpha > upper[0])
return(false);
}
if(optBeta)
{
if(beta < lower[1] || beta > alpha || beta > upper[1])
return(false);
}
if(optPhi)
{
if(phi < lower[3] || phi > upper[3])
return(false);
}
if(optGamma)
{
if(gamma < lower[2] || gamma > 1-alpha || gamma > upper[2])
return(false);
}
}
if(bounds != "usual")
{
if(!admissible()) return(false);
}
return(TRUE);
}
bool EtsTargetFunction::admissible() {
if(phi < 0 || phi > 1+1e-8) return(false);
//If gamma was set by the user or it is optimized, the bounds need to be enforced
if(!optGamma && !givenGamma) {
if(alpha < 1-1/phi || alpha > 1+1/phi) return(false);
if(optBeta || givenBeta)
{
if(beta < alpha * (phi-1) || beta > (1+phi)*(2-alpha)) return(false);
}
}
else if(m > 1) //Seasonal model
{
if(!optBeta && !givenBeta) beta = 0;
//max(1-1/phi-alpha,0)
double d = 1-1/phi-alpha;
if(gamma < ((d > 0) ? d : 0) || gamma > 1+1/phi-alpha) return(false);
if(alpha < 1-1/phi-gamma*(1-m+phi+phi*m)/(2*phi*m)) return(false);
if(beta < -(1-phi)*(gamma/m+alpha)) return(false);
// End of easy tests. Now use characteristic equation
std::vector<double> op_new(m+2, alpha+beta-alpha*phi);
// P <- c(phi*(1-alpha-gamma),alpha+beta-alpha*phi+gamma-1,rep(alpha+beta-alpha*phi,m-2),(alpha+beta-phi),1)
op_new[0] = phi*(1-alpha-gamma);
op_new[1] = phi*(alpha+beta-alpha*phi+gamma-1);
op_new[m] = alpha+beta-phi;
op_new[m+1] = 1;
Environment base("package:base");
Function polyroot = base["polyroot"];
Function abs = base["abs"];
NumericVector res = abs(polyroot(op_new));
// Rprintf("alpha = %f, beta = %f, gamma = %f, phi = %f, m = %i\n",
// alpha, beta, gamma, phi, m);
// Rprintf("C: c(");
// for(int i=0;i<opr.size();i++) {
// Rprintf("%f, ", opr[i]);
// }
// Rprintf(")\n");
// Rprintf("C_new: c(");
//Rprintf("maxpolyroot: %f\n", max);
double max_root = max(res);
if(max_root > 1+1e-10) return(false);
// P <- c(phi*(1-alpha-gamma),alpha+beta-alpha*phi+gamma-1,rep(alpha+beta-alpha*phi,m-2),(alpha+beta-phi),1)
// roots <- polyroot(P)
// if(max(abs(roots)) > 1+1e-10) return(false);
}
//Passed all tests
return(true);
}