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Chi2Function: lot of fixes and improvements

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This commit is contained in:
Antonin Portelli 2014-03-12 19:18:16 +00:00
parent 8712bed33b
commit 450bad006f
1 changed files with 34 additions and 15 deletions
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49
latan/Chi2Function.cpp
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@ -137,32 +137,37 @@ void Chi2Function::resizeBuffer(void) const
Index size;
size = (data_.getYDim() + data_.getStatXDim())*data_.getNFitPoint();
buffer_->v.conservativeResize(size);
buffer_->x.resize(data_.getXDim());
buffer_->invVar.resize(size, size);
buffer_->xInd.resize(data_.getStatXDim());
buffer_->dInd.resize(data_.getNFitPoint());
buffer_->v.setConstant(size, 0.0);
buffer_->x.setConstant(data_.getXDim(), 0.0);
buffer_->invVar.setConstant(size, size, 0.0);
buffer_->xInd.setConstant(data_.getStatXDim(), 0);
buffer_->dInd.setConstant(data_.getNFitPoint(), 0);
}
// compute variance matrix inverse /////////////////////////////////////////////
void Chi2Function::setVarianceBlock(const Index l1, const Index l2,
ConstBlock<DMatBase> m) const
{
Index nPoint = data_.getNFitPoint();
const Index nPoint = data_.getNFitPoint();
FOR_VEC(buffer_->dInd, k2)
FOR_VEC(buffer_->dInd, k1)
{
buffer_->invVar(l1*nPoint + k1, l2*nPoint + k2) = m(buffer_->dInd(k1),
buffer_->dInd(k2));
if (data_.isDataCorrelated(buffer_->dInd(k1), buffer_->dInd(k2)))
{
buffer_->invVar(l1*nPoint + k1, l2*nPoint + k2) =
m(buffer_->dInd(k1), buffer_->dInd(k2));
}
}
}
void Chi2Function::initBuffer(void) const
{
const Index xDim = data_.getXDim();
const Index yDim = data_.getYDim();
const Index nData = data_.getNData();
const Index xDim = data_.getXDim();
const Index statXDim = data_.getStatXDim();
const Index yDim = data_.getYDim();
const Index nData = data_.getNData();
const Index nPoint = data_.getNFitPoint();
Index is, kf;
// resize buffer
@ -192,23 +197,37 @@ void Chi2Function::initBuffer(void) const
for (Index j2 = 0; j2 < yDim; ++j2)
for (Index j1 = 0; j1 < yDim; ++j1)
{
setVarianceBlock(j1, j2, data_.yyVar(j1, j2));
if (data_.isYYCorrelated(j1, j2))
{
setVarianceBlock(j1, j2, data_.yyVar(j1, j2));
}
}
// set x/x variance matrix
FOR_VEC(buffer_->xInd, i2)
FOR_VEC(buffer_->xInd, i1)
{
setVarianceBlock(i1, i2, data_.xxVar(buffer_->xInd(i1),
buffer_->xInd(i2)));
if (data_.isXXCorrelated(buffer_->xInd(i1), buffer_->xInd(i2)))
{
setVarianceBlock(i1 + yDim, i2 + yDim,
data_.xxVar(buffer_->xInd(i1), buffer_->xInd(i2)));
}
}
// set y/x variance matrix
FOR_VEC(buffer_->xInd, i)
for (Index j = 0; j < yDim; ++j)
{
setVarianceBlock(j, i, data_.yxVar(j, buffer_->xInd(i)));
if (data_.isYXCorrelated(j, buffer_->xInd(i)))
{
setVarianceBlock(j, i + yDim, data_.yxVar(j, buffer_->xInd(i)));
}
}
auto lowerYX = buffer_->invVar.block(yDim*nPoint, 0, yDim*statXDim,
yDim*nPoint);
auto upperYX = buffer_->invVar.block(0, yDim*nPoint, yDim*nPoint,
yDim*statXDim);
lowerYX = upperYX.transpose();
// inversion
buffer_->invVar = buffer_->invVar.inverse().eval();