DESIGN_mrf::DESIGN_mrf(datamatrix & dm,datamatrix & iv,
GENERAL_OPTIONS * o,DISTR * dp,FC_linear * fcl,
const MAP::map & m,vector<ST::string> & op,
vector<ST::string> & vn)
: DESIGN(o,dp,fcl)
{
if (errors==false)
{
read_options(op,vn);
discrete = true;
ma = m;
type = Mrf;
nrpar = ma.get_nrregions();
consecutive=true;
Zout = datamatrix(nrpar,1,1);
index_Zout = statmatrix<int>(Zout.rows(),1);
index_Zout.indexinit();
}
if (errors==false)
init_data(dm,iv);
if (errors==false)
{
compute_penalty();
XWX = envmatdouble(0,nrpar);
XWres = datamatrix(nrpar,1);
Wsum = datamatrix(nrpar,1,1);
compute_precision(1.0);
compute_basisNull();
identity=true;
}
/*
ofstream out("c:\\bayesx\\trunk\\testh\\results\\dm.raw");
dm.prettyPrint(out);
ofstream out2("c:\\bayesx\\trunk\\testh\\results\\iv.raw");
iv.prettyPrint(out2);
*/
}
void DESIGN_mrf::compute_basisNull(void)
{
int i,j;
basisNull = datamatrix(1,nrpar,1);
if (centermethod==meanf || centermethod==cmeanintegral)
{
unsigned k;
for (k=0;k<nrpar;k++)
{
if (posbeg[k] != -1)
basisNull(0,k) = posend[k]-posbeg[k]+1;
else
basisNull(0,k) = 0;
}
}
position_lin = -1;
for(i=0;i<basisNull.rows();i++)
{
basisNullt.push_back(datamatrix(basisNull.cols(),1));
for(j=0;j<basisNull.cols();j++)
basisNullt[i](j,0) = basisNull(i,j);
}
// TEST
/*
ofstream out("c:\\bayesx\\test\\results\\data.res");
data.prettyPrint(out);
ofstream out2("c:\\bayesx\\test\\results\\designlin.res");
designlinear.prettyPrint(out2);
*/
// TEST
}
FC_cv::FC_cv(GENERAL_OPTIONS * o,DISTR * lp,
const ST::string & t, const ST::string & fp,
vector<FC_hrandom> * FChs)
: FC(o,t,lp->nrobs,1,fp)
{
likep = lp;
hrandoms = FChs;
sampled_etas = datamatrix(likep->nrobs,o->compute_samplesize(),0);
sampled_responses = datamatrix(likep->nrobs,o->compute_samplesize(),0);
sampled_likelihood = datamatrix(likep->nrobs,o->compute_samplesize(),0);
FC_sampled_l = FC(o,"",likep->nrobs,1,fp+"_like");
effect = datamatrix(likep->nrobs,1,0);
linpred = datamatrix(likep->nrobs,1,0);
size = hrandoms->size();
get_ind();
}
double FC_cv::compute_logscore(void)
{
unsigned s,i;
// ofstream out("c:\\bayesx\\testh\\results\\sampled_likelihood.res");
// sampled_likelihood.prettyPrint(out);
unsigned S = sampled_likelihood.cols(); // S = number of samples
unsigned I = sampled_likelihood.rows(); // I = number of observations
// i-th row, s-th column contains
// log-likelihood of i-th
// observation in sample s
if (log_score.rows() != nrcat)
log_score = datamatrix(nrcat,1,0);
datamatrix lhelp(nrcat,S,0);
unsigned in;
for (i=0;i<I;i++)
{
in = ind(i,0);
for (s=0;s<S;s++)
{
lhelp(in,s) += sampled_likelihood(i,s);
}
// log_score(in,0) = log(S) - log(log_score(in,0));
}
for (i=0;i<nrcat;i++)
{
log_score(i,0) = 0;
for (s=0;s<S;s++)
log_score(i,0) += exp(lhelp(i,s));
log_score(i,0) = log(static_cast<double>(S)) - log(log_score(i,0));
}
return log_score.mean(0);
}
FULLCOND_mult::FULLCOND_mult(MCMCoptions * o,DISTRIBUTION * dp,
FULLCOND_nonp_basis * ba1,
FULLCOND_nonp_basis * ba2,
bool fi,
const ST::string & ti,
const ST::string & fp, const ST::string & pres,
const unsigned & c)
: FULLCOND(o,datamatrix(dp->get_nrobs(),1),ti,1,1,fp)
{
basis1p = ba1;
basis2p = ba2;
first = fi;
ttype = mrf_rw;
}
FULLCOND_mult::FULLCOND_mult(MCMCoptions * o,DISTRIBUTION * dp,
FULLCOND_random * rp,
FULLCOND_nonp_basis * ba,
bool fi,
const ST::string & ti,
const ST::string & fp, const ST::string & pres,
const unsigned & c)
: FULLCOND(o,datamatrix(dp->get_nrobs(),1),ti,1,1,fp)
{
reffectp = rp;
basis1p = ba;
first = fi;
ttype = re_rw;
}
baseline_reml::baseline_reml(MCMCoptions * o,
const datamatrix & d, const datamatrix & leftint,
const datamatrix & lefttrunc, const unsigned & nrk,
const unsigned & degr, const unsigned & tgr, const unsigned & nrq,
const unsigned & nrb, const knotpos & kp, const fieldtype & ft,
const ST::string & ti, const ST::string & fp,
const ST::string & pres, const double & l, const double & sl,
const knotpos & gp, const int & gs, const bool & catsp, const double & rv)
: spline_basis(o,d,nrk,degr,kp,ft,ti,fp,pres,l,sl,catsp,0.0,0.0,0.0,0.0,gs,rv)
{
unsigned i,j,k;
baseline=true;
varcoeff=false;
// refcheck=false;
gridpos = gp;
double tmax=d.max(0);
if(gridpos == MCMC::equidistant)
{
tgrid = tgr;
tvalues = datamatrix(tgrid+1,1);
tstep = tmax / tgrid;
for(i=0;i<tvalues.rows();i++)
tvalues(i,0) = i*tstep;
}
else if(gridpos == MCMC::quantiles)
{
tgrid = nrq*nrb;
tvalues = datamatrix(tgrid+1,1);
nrquant = nrq;
nrbetween = nrb;
datamatrix tquantiles = datamatrix(nrquant+1,1,0);
for(i=1; i<nrquant; i++)
{
tquantiles(i,0) = d.quantile(((double)i/nrquant)*100,0);
}
tquantiles(nrquant,0) = tmax;
double intmax, intmin, intstep;
for(i=0; i<nrquant; i++)
{
intmin=tquantiles(i,0);
intmax=tquantiles(i+1,0);
intstep=(intmax-intmin)/nrbetween;
for(j=0; j<nrbetween; j++)
{
tvalues(i*nrbetween+j,0) = intmin + j*intstep;
}
}
tvalues(tgrid,0) = tmax;
}
else
{
make_index(d);
vector<int>::iterator freqwork = freqoutput.begin();
int * workindex = index.getV();
tvalues = datamatrix(nrdiffobs,1,0);
for(j=0;j<d.rows();j++,freqwork++,workindex++)
if(freqwork==freqoutput.begin() || *freqwork!=*(freqwork-1))
tvalues(*freqwork,0) = d(*workindex,0);
}
tsteps = datamatrix(tvalues.rows()-1,1,0);
for(i=0; i<tsteps.rows(); i++)
tsteps(i,0) = tvalues(i+1,0)-tvalues(i,0);
interact_var = datamatrix(d.rows(),1,1);
datamatrix betahelp(nrpar,1,0);
DG = datamatrix(tvalues.rows(),degree+1,0);
DGfirst = vector<int>(tvalues.rows());
for(i=0;i<tvalues.rows();i++)
{
betahelp.assign( bspline(tvalues(i,0)) );
j=degree+1;
while(knot[j] <= tvalues(i,0) && j<nrknots+degree)
j++;
for(k=0;k<degree+1;k++)
DG(i,k) = betahelp(k+j-(degree+1),0);
DGfirst[i] = j-(degree+1);
}
tleft = vector<unsigned>(d.rows(),0);
tright = vector<unsigned>(d.rows(),1);
ttrunc = vector<unsigned>(d.rows(),0);
// indices for truncation times
if(lefttrunc.rows()>1)
{
for(i=0; i<lefttrunc.rows(); i++)
{
j=1;
while(j<tvalues.rows() && tvalues(j,0) < lefttrunc(i,0))
{
ttrunc[i]++;
j++;
}
}
}
for(i=0; i<d.rows(); i++)
{
j=0;
while(j<tvalues.rows()-2 && tvalues(j,0)<d(i,0))
{
tright[i]++;
j++;
}
}
if(leftint.rows()>1)
{
for(i=0; i<d.rows(); i++)
{
if( leftint(i,0) < d(i,0))
{
j=0;
while(j<tvalues.rows()-1 && tvalues(j,0)<leftint(i,0))
{
tleft[i]++;
j++;
}
}
else if(leftint(i,0)> d(i,0))
{
tleft[i] = tright[i]+1;
}
else
{
tleft[i] = tright[i];
}
}
}
else
{
for(i=0; i<d.rows(); i++)
{
tleft[i] = tright[i];
}
}
}
void baseline_reml::createreml(datamatrix & X,datamatrix & Z,
const unsigned & Xpos, const unsigned & Zpos)
{
unsigned i,j;
double * workdata;
double * workZ;
double * workX;
unsigned Xcols = X.cols();
datamatrix refhelp;
if(refcheck)
{
refhelp = bspline(reference);
if(!varcoeff)
X_ref = datamatrix(1,1);
else
X_ref = datamatrix(1,2);
}
// X für Daten berechen
datamatrix knoten = datamatrix(nrpar,1,0.0);
for(i=0;i<nrpar;i++)
knoten(i,0) = knot[i];
multBS_index(spline,knoten);
workdata = spline.getV();
workX = X.getV()+Xpos;
if(varcoeff)
{
double * workintact = data_forfixed.getV();
double * workX_VCM = X_VCM.getV()+1;
for (i=0;i<spline.rows();i++,workdata++,workintact++,workX+=Xcols,workX_VCM+=2)
{
*workX = *workintact;
*(workX+1) = *workdata**workintact;
*workX_VCM = *workdata;
}
}
else
{
for (i=0;i<spline.rows();i++,workdata++,workX+=Xcols)
{
*workX = *workdata;
}
}
if(refcheck)
{
if(!varcoeff)
{
for(i=0; i<knoten.rows(); i++)
X_ref(0,0) += knoten(i,0)*refhelp(i,0);
// X_ref(0,0) -= splinemean;
}
else
{
X_ref(0,0) = 1.0;
for(i=0; i<knoten.rows(); i++)
X_ref(0,1) += knoten(i,0)*refhelp(i,0);
}
}
// Z für Daten berechnen
compute_Kweights();
datamatrix diffmatrix = weighteddiffmat(2,weight);
diffmatrix = diffmatrix.transposed()*diffmatrix.transposed().sscp().inverse();
if(refcheck)
Z_ref = datamatrix(1,dimZ);
unsigned Zcols = Z.cols();
for(j=0;j<dimZ;j++)
{
multBS_index(spline,diffmatrix.getCol(j));
workdata = spline.getV();
workZ = Z.getV()+Zpos+j;
if(refcheck)
{
Z_ref(0,j) = (refhelp.transposed()*diffmatrix.getCol(j))(0,0);
}
if(varcoeff)
{
double * workintact = data_forfixed.getV();
double * workZ_VCM = Z_VCM.getV()+j;
for (i=0;i<spline.rows();i++,workdata++,workintact++,workZ+=Zcols,workZ_VCM+=dimZ)
{
*workZ = *workdata**workintact;
*workZ_VCM = *workdata;
}
}
else
{
for (i=0;i<spline.rows();i++,workdata++,workZ+=Zcols)
{
*workZ = *workdata;
}
}
}
if(!varcoeff)
{
// X für tvalues berechen
t_X = datamatrix(tvalues.rows(),2,1);
spline = datamatrix(tvalues.rows(),1,0);
multDG(spline,knoten);
workdata = spline.getV();
for (i=0;i<spline.rows();i++,workdata++)
{
t_X(i,1) = *workdata;
}
// Z für tvalues berechnen
t_Z = datamatrix(tvalues.rows(),dimZ,0);
for(j=0;j<dimZ;j++)
{
multDG(spline,diffmatrix.getCol(j));
workdata = spline.getV();
for (i=0;i<spline.rows();i++,workdata++)
{
t_Z(i,j) = *workdata;
}
}
}
if(gridsize>0)
{
X_grid=t_X.getCol(1);
Z_grid=t_Z;
}
}
double FC_cv::compute_energyscore(void)
{
unsigned s,i,j;
unsigned S = sampled_responses.cols();
unsigned I = sampled_responses.rows();
//double * srp = sampled_responses.getV();
// double * esp1;
// double * esp2;
datamatrix es1 = datamatrix(nrcat,S,0);
datamatrix es2 = datamatrix(nrcat,S,0);
/*
for (i=0;i<I;i++)
{
esp1 = es1.getV()+ind(i,0)*S;
esp2 = es2.getV()+ind(i,0)*S;
for (s=0;s<S-1;s++,srp++,esp1++,esp2++)
{
*esp1 += pow(sampled_responses(i,s)-likep->response_untransformed(i,0),2);
*esp2 += pow(sampled_responses(i,s+1)-sampled_responses(i,s),2);
}
esp1++;
*esp1 += pow(sampled_responses(i,s)-likep->response_untransformed(i,0),2);
}
*/
unsigned in;
for (i=0;i<I;i++)
{
in = ind(i,0);
for (s=0;s<S-1;s++)
{
es1(in,s) += pow(sampled_responses(i,s)-likep->response(i,0),2);
es2(in,s) += pow(sampled_responses(i,s+1)-sampled_responses(i,s),2);
}
es1(in,S-1) += pow(sampled_responses(i,s)-likep->response(i,0),2);
}
// ofstream out("c:\\bayesx\\testh\\results\\es1.res");
// es1.prettyPrint(out);
if (e_score.rows() != nrcat)
e_score = datamatrix(nrcat,1,0);
double h;
for (j=0;j<nrcat;j++)
{
for (s=0;s<S;s++)
e_score(j,0) += sqrt(es1(j,s));
e_score(j,0) /= S;
h=0;
for(s=0;s<S-1;s++)
h += sqrt(es2(j,s));
e_score(j,0) -= h/(2*(S-1));
}
return e_score.mean(0);
}
