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/* glmrand.cc
Mark Woolrich, FMRIB Image Analysis Group
Copyright (C) 1999-2000 University of Oxford */
#include "miscmaths/miscmaths.h"
#include "utils/log.h"
#include <vector.h>
#include <algo.h>
using namespace FILM;
using namespace Utilities;
namespace FILM {
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#endif
void GlmRand::addData(ColumnVector& p_y, Matrix& p_x)
{
Tracer ts("GlmRand::addData");
yorig = p_y;
x = &p_x;
sizeTS = p_y.Nrows();
randomise();
}
void GlmRand::randomise()
{
Tracer ts("GlmRand::randomise");
y.ReSize(sizeTS, numrand+1);
vector<float> yorigvec;
// put in origy:
y.getSeries(1) = yorig.AsColumn();
//// void columnVector2Vector(const ColumnVector& cvec, vector<float>& vec)
ColumnVector cvec = yorig;
vector<float>& vec = yorigvec;
for(int j=1; j<=sizeTS; j++)
{
vec.push_back(cvec(j));
}
////////
// put in num randomised versions of yorig:
for(int i=1; i<=numrand; i++)
{
random_shuffle(yorigvec.begin(), yorigvec.end());
//// void vector2ColumnVector(const vector<float>& vec, ColumnVector& cvec)
vec = yorigvec;
for(int j=1; j<=sizeTS; j++)
{
cvec(j) = vec[j-1];
}
////////
y.getSeries(i+1) = cvec.AsColumn();
}
ComputeResids();
Computecb();
ComputeVar();
ComputeTStats();
}
void GlmRand::ComputeTStats()
{
Tracer ts("GlmRand::ComputeTStats");
datats(datatscount) = cb(1)/sqrt(var(1));
for(int i=1; i<=numrand; i++)
{
randts(((datatscount-1)*numrand)+i) = cb(i+1)/sqrt(var(i+1));
}
datatscount++;
}
const Volume& GlmRand::ComputeZStats()
{
Tracer ts("GlmRand::ComputeZStats");
Log::getInstance().out("randts",randts);
Log::getInstance().out("datats",datats);
Histogram hist(randts, randts.getVolumeSize());
hist.generate();
Volume logprob(numTS);
float logtotal = log((float)hist.integrateAll());
cerr << logtotal << endl;
T2z& t2z = T2z::getInstance();
for(int i=1; i<=numTS; i++)
{
float numtoinf = (float)hist.integrateToInf(datats(i));
if(!(numtoinf>0))
numtoinf = 1;
logprob(i) = log(numtoinf)-logtotal;
datazs(i) = t2z.convertlogp2z(logprob(i));
}
Log::getInstance().out("logprob",logprob);
Log::getInstance().out("datazs",datazs);
return datazs;
}
void GlmRand::ComputeResids()
{
Tracer ts("GlmRand::ComputeResids");
int batch_pos = 1;
Matrix& d = *x;
pinv_x = (d.t()*d).i()*d.t();
// R = I - x*pinv(x)
Matrix I(sizeTS, sizeTS);
Identity(I);
RMat = I-d*pinv_x;
r.ReSize(sizeTS, numrand+1);
while(batch_pos <= numrand+1)
{
if(batch_pos+batch_size - 1 > numrand+1)
r.Columns(batch_pos, numrand+1) = RMat*y.Columns(batch_pos, numrand+1);
else
r.Columns(batch_pos, batch_pos+batch_size-1) = RMat*y.Columns(batch_pos, batch_pos+batch_size-1);
batch_pos += batch_size;
}
}
void GlmRand::Computecb()
{
Tracer ts("Computecb");
int batch_pos = 1;
cb.ReSize(numrand+1);
while(batch_pos <= numrand+1)
{
if(batch_pos+batch_size - 1 > numrand+1)
cb.Rows(batch_pos, numrand+1) = (c.t()*pinv_x*y.Columns(batch_pos, numrand+1)).t();
else
cb.Rows(batch_pos, batch_pos+batch_size-1) = (c.t()*pinv_x*y.Columns(batch_pos, batch_pos+batch_size-1)).t();
batch_pos += batch_size;
}
}
void GlmRand::ComputeVar()
{
Tracer ts("ComputeVar");
int batch_pos = 1;
var.ReSize(numrand+1);
Matrix varmatfull(batch_size, batch_size);
ColumnVector vartempfull(batch_size);
Matrix& d = *x;
// inv_xx = inv(x'x)
float var_on_e = (c.t()*((d.t()*d).i())*c).AsScalar();
while(batch_pos <= numrand+1)
{
if(batch_pos+batch_size - 1 > numrand+1)
{
// var = e*var_on_e
// e is the estimate of the variance of the timeseries, sigma^2
Matrix varmat = (r.Columns(batch_pos, numrand+1).t()*r.Columns(batch_pos, numrand+1))*var_on_e/sizeTS;
ColumnVector vartemp;
//getdiag(vartemp, varmat); // obsolete fn
vartemp = diag(varmat); // MJ NOTE: new fn
var.Rows(batch_pos, numrand+1) = vartemp;
}
else
{
varmatfull = (r.Columns(batch_pos, batch_pos+batch_size-1).t()*r.Columns(batch_pos, batch_pos+batch_size-1))*var_on_e/sizeTS;
//getdiag(vartempfull, varmatfull); // obsolete fn
vartempfull = diag(varmatfull); // MJ NOTE: new fn