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MCMC.cpp
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MCMC.cpp
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#include <stdlib.h>
#include <gsl/gsl_rng.h>
#include <gsl/gsl_randist.h>
#include <random>
#include <chrono>
#include <ctime>
#include <cstdio>
#include <stdio.h>
#include <math.h>
#include <vector>
#include <list>
#include <iostream>
#include <string>
// when armadillo is loaded, remove this below
//#include <Rcpp.h>
#include <RcppArmadillo.h>
#include <RcppArmadilloExtensions/sample.h>
#include <RcppEigen.h>
using namespace Rcpp;
using namespace std;
#define ARMA_64BIT_WORD 1
#include <bigmemory/MatrixAccessor.hpp>
#include <bigmemory/isna.hpp>
// [[Rcpp::depends(BH)]]
// [[Rcpp::depends(bigmemory)]]
// [[Rcpp::depends(Rcpp)]]
// [[Rcpp::depends(RcppArmadillo)]]
// [[Rcpp::depends(RcppEigen)]]
// Enable C++11 via this plugin (Rcpp 0.10.3 or later)
// [[Rcpp::plugins(cpp11)]]
////////////////////////////////////////////////////////////////////////////////
/// Profiling utilities
////////////////////////////////////////////////////////////////////////////////
// RcppExport SEXP start_profiler(SEXP str) {
// ProfilerStart(as<const char*>(str));
// return R_NilValue;
// }
// RcppExport SEXP stop_profiler() {
// ProfilerStop();
// return R_NilValue;
// }
////////////////////////////////////////////////////////////////////////////////
/// A few declarations
////////////////////////////////////////////////////////////////////////////////
////////////////////////////////////////////////////////////////////////////////
/// Utilities
////////////////////////////////////////////////////////////////////////////////
#define PBSTR "||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||"
#define PBWIDTH 60
void printProgress (double percentage)
{
int val = (int) (percentage * 100);
int lpad = (int) (percentage * PBWIDTH);
int rpad = PBWIDTH - lpad;
printf ("\r%3d%% [%.*s%*s]", val, lpad, PBSTR, rpad, "");
fflush (stdout);
}
template <typename T>
Rcpp::NumericVector arma2vec(const T& x) {
return Rcpp::NumericVector(x.begin(), x.end());
}
template <typename T>
arma::vec vec2arma(const T& x) {
return Rcpp::as<arma::vec>(x);
}
// [[Rcpp::export]]
NumericVector sample_num( NumericVector x,
int size,
bool replace,
NumericVector prob = NumericVector::create()
)
{
NumericVector ret = RcppArmadillo::sample(x, size, replace, prob) ;
return ret ;
}
// #define MIN_NUM = std::numeric_limits<float>::min(); // problem is that it does not know the type
const double MIN_NUM = std::numeric_limits<float>::min();
// #define PIraw = 3.14159265358979323846;
// const double PI= 3.14159265358979323846;
// # define PI 3.14159265358979323846 /* pi */
//const double MAX_NUM = std::numeric_limits<float>::max();
// arma::Mat<double> BMsubset(SEXP & A, const arma::uvec & mycols){
// Rcpp::XPtr<BigMatrix> bigMat(A);
// arma::Mat<double> X((double*) bigMat->matrix(), bigMat->nrow(), bigMat->ncol(), false, false);
// // consider saying true, perhaps is faster
// return(X.cols(mycols));
// }
// // [[Rcpp::export]]
// arma::Mat<double> BMsubset(SEXP & A, const arma::uvec & mycols,const arma::uvec & myrows ){
// Rcpp::XPtr<BigMatrix> bigMat(A);
// arma::Mat<double> X((double*) bigMat->matrix(), bigMat->nrow(), bigMat->ncol(), false, false);
// // consider saying true, perhaps is faster
// return(X.submat(myrows,mycols));
// }
// [[Rcpp::export]]
arma::Mat<double> BMsubset(SEXP A, const arma::uvec & myrows, const arma::uvec & mycols ){
Rcpp::XPtr<BigMatrix> bigMat(A);
arma::Mat<double> X0((double*) bigMat->matrix(), bigMat->nrow(), bigMat->ncol(), false, false);
// consider saying true, perhaps is faster
// Subset matrix
if(myrows.n_elem == X0.n_rows){
X0=X0.cols(mycols);
}else if(mycols.n_elem == X0.n_rows){
X0=X0.rows(myrows);
}else{
X0=X0.submat(myrows,mycols);
}
return(X0);
}
// [[Rcpp::export]]
arma::vec upperTmat(const arma::mat mat){
arma::vec output((mat.n_cols*(mat.n_cols-1))/2);
arma::mat::const_iterator it = mat.begin() + mat.n_rows; //iterator at rows to skip in every step, starts at second column
long toSkipInVec = 0;
for(int i = 1; i < mat.n_cols; i++) //Starts with 1 to skip the diagonal
{
std::copy(it, it + i, output.begin() + toSkipInVec);
toSkipInVec += i;
it += mat.n_rows;
}
return output;
}
////////////////////////////////////////////////////////////////////////////////
/// Fitness functions
////////////////////////////////////////////////////////////////////////////////
/// Fitness class
class FITNESS{
private:
int mode;
public:
FITNESS(int m=1){
mode=m;
};
// fitness functions
double w(const double &s , const int &x, const double e=2){
switch(mode){
case 1: // multiplicative
return 1 + (s * x) ;
break;
case 2: // additive
return (s * x) ;
break;
case 3: // inverse multiplicative
// return 1 / (1 + s * x);
return pow((1 + s),x);
break;
default: // multiplicative
return 1 - (s * x) ;
break;
}
}
// void operator +*=(double w, double s,int x,int mode) // operator not needed unless class
void wupdate(double &prevw, const double &s,const int &x) {
switch(mode){
case 2: // additive
prevw= prevw + w(s,x);
break;
default: // additive
prevw= prevw * w(s,x);
break;
}
}
};
/// Expectation functions
// [[Rcpp::export]]
arma::colvec Ey_go(const arma::Mat<double> & X, // careful arma::mat default is double
const arma::colvec & s,
const int & mode,
double epi=1,
double mu=1
){
/*
* Gamma expectation per haplotype - MULTIPLICATIVE MODEL
*/
// Initialize class and set fitness model
FITNESS fit(mode);
// Initialize vector of distribution means
arma::colvec myprod(X.n_rows);
// myprod.fill(1);
myprod.fill(mu); // IMPORTANT
int i,j;
for (i = 0; i < X.n_cols; i ++) {
for( j=0; j < X.n_rows ; j++){
fit.wupdate(myprod(j),s(i),X(j,i)); // works because these expressions generate a reference
}
}
for( j=0; j<X.n_rows; j++) if(myprod(j)<0) myprod(j)=MIN_NUM; // **WARNING** this is necessary for non-NaN likelihood
if(epi!=1) myprod=pow(myprod,epi); // probably not very efficient
return(myprod);
}
// [[Rcpp::export]]
double test_wfn (double s=0.1,double x=1, int m=1){
FITNESS fitfunction(m);
double myw=1;
fitfunction.wupdate( myw, s, x);
return(myw);
}
// [[Rcpp::export]]
void test_Ey_go(){
cout << "Simulating 10 ind and 5 SNPs" << endl;
arma::Mat<double> X(10, 5);
X.fill(0);
for(int j=0; j< X.n_cols; j++ ){
for(int i=0; i< X.n_rows; i++ ){
if(Rcpp::runif(1)(0) > 0.5){
X(i,j) =1;
}
}
}
cout << "selection coefficients uniform" << endl;
arma::vec s = Rcpp::runif(X.n_cols,0,1);
cout << "Multiplicative " << endl << Ey_go(X,s,1) << endl;
cout << "Additive " <<endl << Ey_go(X,s,2) << endl;
cout << "Inverse " << endl << Ey_go(X,s,5) << endl;
}
//
// // [[Rcpp::export]]
// arma::colvec Vy(const arma::colvec & y, const arma::colvec & h){
// /*
// * Variance of trait per genotype
// */
//
// // Declarations
// arma::colvec hunique = unique(h);
// arma::colvec v(hunique.n_elem);
// // cout << hunique << endl;
//
//
// for(int i=0; i< hunique.n_elem; i++ ){
// // Create temporal vector
// arma::colvec ytmp;
// // Fill with all values corresponding to the same genotype
// for(int j=0; j<y.n_elem;j++){
// if(h(j) == hunique(i)) {
// ytmp.resize(ytmp.size()+1);
// ytmp(ytmp.size()-1) = y(j);
// }
// }
// // Compute variance
// if(ytmp.n_elem ==1){
// // v(i)=0;
// v(i)=-9;
// }else{
// v(i)=arma::var( ytmp );
// }
// }
// return(v);
// }
//
//
// // [[Rcpp::export]]
// arma::colvec My(const arma::colvec & y, const arma::colvec & h){
// /*
// * Mean trait per genotype
// */
//
// // Declarations
// arma::colvec hunique = unique(h);
// arma::colvec v(hunique.n_elem);
// // cout << hunique << endl;
//
//
// for(int i=0; i< hunique.n_elem; i++ ){
// // Create temporal vector
// arma::colvec ytmp;
// // Fill with all values corresponding to the same genotype
// for(int j=0; j<y.n_elem;j++){
// if(h(j) == hunique(i)) {
// ytmp.resize(ytmp.size()+1);
// ytmp(ytmp.size()-1) = y(j);
// }
// }
// // Compute variance
// if(ytmp.n_elem ==1){
// // v(i)=0;
// v(i)=ytmp(0);
// }else{
// v(i)=arma::mean( ytmp );
// }
// }
// return(v);
// }
////////////////////////////////////////////////////////////////////////////////
/// Likelihood, Probabilities, Proposals
////////////////////////////////////////////////////////////////////////////////
////////////////////////////////////////////////////////////////////////////////
// [[Rcpp::export]]
double runif_reflect(double minhere,double maxhere,double min,double max){
// int counter=1;
double newval;
if( min == max){
newval= min; // if values want to be kept constant
}else{
newval =Rcpp::runif(1,minhere,maxhere)(0);
if(newval<min){newval = (min-newval) + min;}
else if(newval>max){newval = max- (newval-max);}
// newval= runif_reflect(minhere,maxhere,min,max); // bad idea, segfault
}
// Check if it is out of bounds
if(newval < min || newval>max){
newval=(max-min)/2;
}
return(newval);
}
// // Zero point mass proposal
// // [[Rcpp::export]]
// double pProposal(double & p, double min=0, double max=1, double bw=0.1){
// if(p<min || p> max){
// cout << "Parameter p needs to be in between minimum and maximum!" << endl;
// }
// double minhere=p-bw;
// double maxhere=p+bw;
// double newval = runif_reflect(minhere,maxhere,min,max);
// return(newval);
// }
//
// // [[Rcpp::export]]
// double bProposal(double & b, double min=-0.01, double max=10, double bw=0.1){
// if(b<min || b> max){
// cout <<"Parameter b needs to be in between minimum and maximum"<< endl;
// }
// double minhere=b-bw;
// double maxhere=b+bw;
// double newval = runif_reflect(minhere,maxhere,min,max);
// return(newval);
// }
//
// // [[Rcpp::export]]
// double aProposal(double & a, double min=-10, double max=10, double bw=0.1){
// if(a<min || a> max){
// cout <<"Parameter a needs to be in between minimum and maximum"<< endl;
// }
// double minhere=a-bw;
// double maxhere=a+bw;
// double newval = runif_reflect(minhere,maxhere,min,max);
// return(newval);
// }
//
// // [[Rcpp::export]]
// double epiProposal(double & a, double min=-0.1, double max=5, double bw=0.1){
// if(a<min || a> max){
// cout <<"Parameter epi needs to be in between minimum and maximum"<< endl;
// }
//
// double minhere=a-bw;
// double maxhere=a+bw;
// double newval = runif_reflect(minhere,maxhere,min,max);
// return(newval);
// }
//
// double muProposal(double & a, double min=-0.1, double max=10, double bw=0.1){
// if(a<min || a> max){
// cout <<"Parameter mu needs to be in between minimum and maximum"<< endl;
// }
//
// double minhere=a-bw;
// double maxhere=a+bw;
// double newval = runif_reflect(minhere,maxhere,min,max);
// return(newval);
// }
//
//
// // [[Rcpp::export]]
// void test_pProposal(double p=0.5){
// cout << pProposal(p) << endl;
// }
// // [[Rcpp::export]]
// void test_bProposal(double b=0.2){
// cout << bProposal (b) << endl;
// }
// // [[Rcpp::export]]
// void test_aProposal(double a=0.1){
// cout << bProposal (a) << endl;
// }
//
// // [[Rcpp::export]]
// void test_muProposal(double a=0.2){
// cout << muProposal (a) << endl;
// }
// // [[Rcpp::export]]
// void test_epiProposal(double a=0.1){
// cout << epiProposal(a) << endl;
// }
class GPROPOSAL{
private:
double b; double bmin; double bmax;
double a; double amin; double amax;
double p; double pmin=0; double pmax=1;
double mu; double mumin; double mumax;
double epi; double epimin; double epimax;
double svar; double svarmin; double svarmax;
double ss; double ssmin; double ssmax;
int nupdates=1;
bool verbose;
double bw;
public:
GPROPOSAL(
double b_=0.5,double bmin_=0,double bmax_=1,
double a_=0.1,double amin_=0,double amax_=1,
double p_=0.5,
double mu_=1,double mumin_=0, double mumax_=10,
double epi_=1,double epimin_=0, double epimax_=5,
double svar_=0.5,double svarmin_=0, double svarmax_=5,
double ss_=0.1,double ssmin_=0, double ssmax_=1,
double bw_=0.1,
bool verbose_=false
){
b=b_;bmin=bmin_;bmax=bmax_;
a=a_;amin=amin_;amax=amax_;
p=p_;
mu=mu_;mumin=mumin_;mumax=mumax_;
epi=epi_;epimin=epimin_;epimax=epimax_;
svar=svar_;svarmin=svarmin_;svarmax=svarmax_;
ss=ss_;ssmin=ssmin_;ssmax=ssmax_;
bw=bw_;
verbose=verbose_;
}
void setupdatesnum(int ups){nupdates=ups;}
void setverbose(bool verbose_){verbose=verbose_;}
void printatributes(){
cout <<"bw = " << bw << endl;
cout <<"b = " << b << " " << bmin << " " << bmax << endl;
cout <<"a = " << a << " " << amin << " " << amax << endl;
cout <<"p = " << p << " " << pmin << " " << pmax << endl;
cout <<"mu = " << mu << " " << mumin << " " << mumax << endl;
cout <<"epi = " << epi << " " << epimin << " " << epimax << endl;
cout <<"svar = " << svar << " " << svarmin << " " << svarmin << endl;
cout <<"ss = " << ss << " " << ssmin << " " << ssmin << endl;
}
arma::vec fn(arma::vec g){
// New proposal
arma::vec news=g;
// Update one position
double minhere,maxhere;
double newval;
if(verbose) cout << "Loop to substitute position" << endl;
for(int j=0; j< nupdates; j++){
int randomIndex = rand() % g.size();
minhere=g(randomIndex)-bw;
maxhere=g(randomIndex)+bw;
switch(randomIndex){
if(verbose) cout << g(randomIndex) << endl;
case 0: newval= runif_reflect(minhere,maxhere,bmin,bmax); break;
case 1: newval= runif_reflect(minhere,maxhere,amin,amax); break;
case 2: newval= runif_reflect(minhere,maxhere,pmin,pmax); break;
case 3: newval= runif_reflect(minhere,maxhere,mumin,mumax); break;
case 4: newval= runif_reflect(minhere,maxhere,epimin,epimax); break;
case 5: newval= runif_reflect(minhere,maxhere,svarmin,svarmax); break;
case 6: newval= runif_reflect(minhere,maxhere,ssmin,ssmax); break;
}
if(verbose) cout << newval << endl;
news(randomIndex) = newval;
}
if(verbose) cout << "End loop" << endl;
return(news);
}
};
// [[Rcpp::export]]
void test_GProposal(double b=1,double a=1,
double p=1, double mu=1,
double epi=1, double svar=1,double ss=0.1,
int iter=3,bool verbose = true){
arma::vec g(6);
g(0)= b;
g(1)= a;
g(2)= p;
g(3)= mu;
g(4)= epi;
g(5)= svar;
g(6)= ss;
GPROPOSAL GProp; // mode 1 = uniform ; mode 2 = LD
GProp.setverbose(verbose);
GProp.printatributes();
cout << "Original " << endl;
cout << g << endl;
cout << "Testing proposals "<< endl;
for(int i=0; i<iter; i++){
g=GProp.fn(g);
cout << g<< endl;
}
}
// gproposal=GProp.fn(par_chain.col(i));
///////////////////////////////////////////////////////////////////////////////
// Selection proposal with LD
class PROPOSAL {
private:
double bw;
int nupdates;
double min;
double max;
int mode;
bool verbose;
arma::mat R; // for LD implementatioon
public:
PROPOSAL(
int nupdates_,
int mode_=1
){
nupdates=nupdates_;
mode=mode_;
verbose=false;
}
PROPOSAL(
double bw_,
int nupdates_,
double min_,
double max_,
int mode_=1,
bool verbose_= false
){
bw=bw_;nupdates=nupdates_;min=min_;max=max_;mode=mode_;verbose=verbose_;
// initialize R2 for default cases
arma::mat onemat(1,1);
onemat.fill(1);
R=onemat;
}
PROPOSAL(arma::mat R2,
double bw_,
int nupdates_,
double min_,
double max_,
int mode_=1,
bool verbose_= false
){
bw=bw_;nupdates=nupdates_;min=min_;max=max_;mode=mode_;verbose=verbose_;
// initialize R2 for default cases
R=R2;
}
void printatributes(){
cout <<"bw = " << bw << endl;
cout <<"nupdates = " <<nupdates << endl;
cout <<"min = " << min << endl;
cout <<"max = " <<max << endl;
cout <<"mode = " <<mode << endl;
cout <<"verbose = " <<verbose << endl;
}
arma::vec fn(arma::vec s){
switch(mode){
case 1:
return update(s);
break;
case 2:
return updateLD(s);
break;
default:
return update(s);
break;
}
}
arma::vec fn(arma::vec s, double svar){
switch(mode){
case 1:
return update(s);
break;
case 2:
return updateLD(s);
case 3:
return updatelog(s,svar);
break;
default:
return update(s);
break;
}
}
arma::vec update(arma::vec s){
/*
* Make proposal change of one or more selection coefficients
* from a previous vector.
* Do not allow to go further thana bandwidth of 0.1
*/
// New proposal
arma::colvec news=s;
// Update one position
double minhere,maxhere,newval;
if(verbose) cout << "Loop to substitute position" << endl;
for(int j=0; j< nupdates; j++){
int randomIndex = rand() % s.size();
minhere=s(randomIndex)-bw;
maxhere=s(randomIndex)+bw;
newval = runif_reflect(minhere,maxhere,min,max);
news(randomIndex) = newval;
}
if(verbose) cout << "End loop" << endl;
return(news);
}
arma::vec updatelog(arma::vec s,double svar){
// New proposal
arma::colvec news= s;
// Update one position
double meanhere,newval;
if(verbose) cout << "Loop to substitute position" << endl;
for(int j=0; j< nupdates; j++){
int randomIndex = rand() % s.size();
meanhere=log(1+s(randomIndex));
if(std::isinf(meanhere)){
meanhere= (max-min)/2;
}
newval = Rcpp::rnorm(1,0,svar)(0);
news(randomIndex) = exp(newval)-1;
if(verbose) cout << newval << endl;
if(verbose) cout << news(randomIndex) << endl;
}
if(verbose) cout << "End loop" << endl;
return(news);
}
arma::vec updateLD(arma::vec s){
// New proposal
arma::colvec news=s;
// Update one position
double minhere,maxhere,newval;
if(verbose) cout << "Loop to substitute position" << endl;
int randomIndex = rand() % s.size();
minhere=s(randomIndex)-bw;
maxhere=s(randomIndex)+bw;
newval = runif_reflect(minhere,maxhere,min,max);
news(randomIndex) = newval;
double diff = news(randomIndex) - s(randomIndex);
if(verbose) cout << "Difference with original value = " << diff << endl;
for(int i=0; i<s.n_elem & i!= randomIndex; i++){
news(i) -= diff * R(randomIndex,i) ;
if(verbose) cout << news(i) << endl;
if(news(i) < min) news(i) =min+abs(news(i) -min);
if(news(i) > max) news(i) =max-abs(news(i) -max);
}
return(news);
}
};
// [[Rcpp::export]]
arma::vec PropoS(int nupdates,double svar = 0.5){
arma::vec s(nupdates);
s.fill(0);
PROPOSAL ps(nupdates,3);
return(ps.fn(s,svar));
}
// // [[Rcpp::export]]
// void test_ProposalsLD(
// arma::mat X,
// double min=0,
// double max=1,
// double bw=0.1,
// int nupdates=1,
// int mode=2,
// int iterations=1,
// bool verbose=true
// ){
//
// arma::vec s = Rcpp::runif(X.n_cols,0,1);
// PROPOSAL Prop(LDrelative(X),bw,nupdates,min,max,2,verbose); // mode 1 = uniform ; mode 2 = LD
//
// cout << "Original " << endl;
// // cout << s << endl;
// cout << "Testing proposals under mode = "<< mode << endl;
// for(int i=0; i<3; i++){
// s=Prop.fn(s);
// // cout << s << endl;
// }
// }
// [[Rcpp::export]]
arma::vec call_Proopsals(
arma::vec s,
int m=10,
double min=0,
double max=1,
double bw=0.1,
int nupdates=1,
int mode=1,
bool verbose=true){
PROPOSAL Prop(bw,nupdates,min,max,mode,verbose); // mode 1 = uniform ; mode 2 = LD
Prop.printatributes();
return Prop.fn(s);
}
// [[Rcpp::export]]
void test_Proposals(int m=10,
double min=0,
double max=1,
double bw=0.1,
int nupdates=1,
int mode=1,
int iterations=3,
bool verbose=true
){
arma::vec s = Rcpp::runif(m,0,1);
PROPOSAL Prop(bw,nupdates,min,max,mode,verbose); // mode 1 = uniform ; mode 2 = LD
Prop.printatributes();
cout << "Testing proposals under mode = "<< mode << endl;
for(int i=0; i<3; i++){
s=Prop.fn(s);
cout << s << endl;
}
}
////////////////////////////////////////////////////////////////////////////////
class PRIOR{
public:
double min;
double max;
double mean;
double svar;
double ss;
int mode;
// Constructors
// PRIOR(double min_=0,double max_=1,
// double mean_=0,double variance_=1,
// int mode_=1){
// min=min_; max=max_;mean=mean_; variance=variance_; mode=mode_; };
PRIOR(double par1=0,double par2=1, int mode_=1){
mode=mode_;
switch(mode){
case 1: // moc mode, return 1
break;
case 2: // true uniform
min=par1;
max=par2;
break;
case 3: // log +1 normal
mean=par1;
svar=par2;
break;
case 4: // log +1 mixture normal with sparcity
svar=par1;
ss=par2;
break;
default:
min=0;max=1;mean=0,svar=0.5;
break;
}
}
void printatributes(){
cout <<"min = " << min << endl;
cout <<"max = " <<max << endl;
cout <<"s variance = " <<svar << endl;
cout <<"mode = " <<mode << endl;
}
// Prior functions
double uniform(const arma::colvec & s){
double L= 0;
int N=s.n_elem;
for(int i=0;i<N ;i++){
L+= R::dunif(s(i),min,max,true);
}
return L;
}
double loggaussian(const arma::colvec & s, double svar){
// int n=s.n_elem;
arma::vec x = log(1+s);
// double L = -.5*n*log(2*PI) -.5*n*log(svar) -(1/(2*svar))*sum(arma::pow((x-mean),2));
double L=0;
for(int i = 0; i<s.n_elem; i++){
L+= R::dnorm(x(i),0,svar,true);
}
return L;
}
double logmixgaussian(const arma::colvec & s, double svar, double ss){
arma::vec x = log(1+s);
double L=0;
for(int i = 0; i<s.n_elem; i++){
// L+= R::dnorm(x(i),0,svar,true);
if(x(i)==0){
L += log(ss + (1-ss) * R::dnorm(x(i),0,svar,false)) ;
}else{
L += (1-ss) * R::dnorm(x(i),0,svar,true);
}
}
return L;
}
// Prior distributor
double fn(const arma::colvec & s){
switch(mode){
case 1: // moc mode, return 1
return 1.0; break;
case 2: // true uniform
return uniform(s); break;
default:
return 1.0; break;
}
}
double fn(const arma::colvec & s,const double & svar,const double & ss){
switch(mode){
case 1: // moc mode, return 1
return 1.0; break;
case 2: // true uniform
return uniform(s); break;
case 3:
return loggaussian(s,svar); break;
case 4:
return logmixgaussian(s,svar,ss); break;
default:
return 1.0; break;
}
}
};
// [[Rcpp::export]]
void test_Prior(int m=10,
double min=0,
double max=1,
double mean=0,
double variance=1,
double sparsity=0.1,
int mode=1
){
arma::vec s;
if(mode==1){
s = exp( Rcpp::rnorm(m,0,variance) ) - 1;
}else{
s = Rcpp::runif(m,0,1);
}
cout << s << endl;
cout << "Prior mode = 1" << endl;
PRIOR Pri; // mode 1 = uniform moc
cout << Pri.fn(s) << endl;
cout << "Prior mode = 2" << endl;
PRIOR Pri2(min,max,2); // mode 1 = uniform moc
cout << Pri2.fn(s) << endl;
cout << "Prior mode = 3" << endl;
PRIOR Pri3(mean,variance,3); // mode 1 = uniform moc
cout << Pri3.fn(s,variance,sparsity) << endl;
cout << "Prior mode = 4" << endl;
PRIOR Pri4(mean,variance,4); // mode 1 = uniform moc
cout << Pri3.fn(s,variance,sparsity) << endl;
}
////////////////////////////////////////////////////////////////////////////////
// [[Rcpp::export]]
arma::vec hsub(const arma::vec & h){
arma::vec hunique = unique(h);
arma::vec hpos(h.n_elem);
for(int i=0; i<h.n_elem;i++){
for(int j=0; j< hunique.n_elem;j++){
if(h(i) == hunique(j)) hpos(i) = j;
}
}
return(hpos);
}
// [[Rcpp::export]]
double trialLL(double hs=10){
arma::vec e(20);
e.fill(2);
return e(hs);
}
// [[Rcpp::export]]
double LLGaussMix(double y,double e,double v,double p){
double LL;
if(y==0){
LL = p + (1-p) * R::pnorm(0,e,v,true,false) ;
}else{
LL = (1-p) * R::dnorm(y,e,v,false);
}
return log(LL);
}
////////////////////////////////////////////////////////////////////////////////
/// Likelihood class
class LIKELIHOOD{
private:
int mode;
bool TEST;
bool verbose;
arma::vec y;
arma::vec h;
arma::Mat<double> X;
public:
//Constructor
LIKELIHOOD(
const arma::vec y_,