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/***********************************************/
/**
* @file gnssDesignMatrix.cpp
*
* @brief Management of sparse design matrix.
*
* @author Torsten Mayer-Guerr
* @date 2018-04-01
*
*/
/***********************************************/
#include "base/import.h"
#include "parallel/matrixDistributed.h"
#include "gnss/gnssNormalEquationInfo.h"
#include "gnssDesignMatrix.h"
/***********************************************/
GnssDesignMatrix::GnssDesignMatrix(const GnssNormalEquationInfo &normalEquationInfo_, const_MatrixSliceRef l_) :
normalEquationInfo(normalEquationInfo_),
blockIndices(normalEquationInfo.blockIndices()),
indexUsedParameter(normalEquationInfo.blockCount()),
countUsedParameter(normalEquationInfo.blockCount()),
row(0),
rows(l_.rows()),
A(l_.rows(), normalEquationInfo.parameterCount()),
l(l_)
{
}
/***********************************************/
void GnssDesignMatrix::init(const_MatrixSliceRef l)
{
try
{
this->l = l;
if(A.rows() < l.rows())
A = Matrix(l.rows(), blockIndices.back());
row = 0;
rows = l.rows();
for(UInt i : indexUsedBlock)
{
for(UInt k=0; k<indexUsedParameter[i].size(); k++)
A.column(blockIndices[i]+indexUsedParameter[i][k], countUsedParameter[i][k]).setNull();
indexUsedParameter[i].clear();
countUsedParameter[i].clear();
}
indexUsedBlock.clear();
}
catch(std::exception &e)
{
GROOPS_RETHROW(e)
}
}
/***********************************************/
GnssDesignMatrix &GnssDesignMatrix::selectRows(UInt row_, UInt rows_)
{
try
{
row = row_;
rows = rows_;
if(!rows)
rows = l.rows()-row_;
return *this;
}
catch(std::exception &e)
{
GROOPS_RETHROW(e)
}
}
/***********************************************/
MatrixSlice GnssDesignMatrix::column(const GnssParameterIndex &index)
{
try
{
const UInt block = normalEquationInfo.block(index);
const UInt col = normalEquationInfo.index(index) - normalEquationInfo.blockIndex(block);
const UInt cols = normalEquationInfo.count(index);
const auto iter = std::lower_bound(indexUsedBlock.begin(), indexUsedBlock.end(), block);
if(iter == indexUsedBlock.end() || block < *iter)
indexUsedBlock.insert(iter, block);
constexpr UInt gap = 32;
UInt idx = 0;
while((idx < indexUsedParameter[block].size()) && (indexUsedParameter[block][idx]+countUsedParameter[block][idx]+gap < col))
idx++;
if((idx >= indexUsedParameter[block].size()) || (col+cols+gap < indexUsedParameter[block][idx]))
{
indexUsedParameter[block].insert(indexUsedParameter[block].begin()+idx, col);
countUsedParameter[block].insert(countUsedParameter[block].begin()+idx, cols);
}
else // merge
{
const UInt end = std::max(col+cols, indexUsedParameter[block][idx]+countUsedParameter[block][idx]);
indexUsedParameter[block][idx] = std::min(col, indexUsedParameter[block][idx]);
countUsedParameter[block][idx] = end - indexUsedParameter[block][idx];
// merge with following parameter group?
while((idx+1 < indexUsedParameter[block].size()) && (indexUsedParameter[block][idx]+countUsedParameter[block][idx]+gap >= indexUsedParameter[block][idx+1]))
{
countUsedParameter[block][idx] = std::max(countUsedParameter[block][idx], indexUsedParameter[block][idx+1]+countUsedParameter[block][idx+1]-indexUsedParameter[block][idx]);
indexUsedParameter[block].erase(indexUsedParameter[block].begin()+idx+1);
countUsedParameter[block].erase(countUsedParameter[block].begin()+idx+1);
}
}
return A.slice(row, blockIndices[block]+col, rows, cols);
}
catch(std::exception &e)
{
GROOPS_RETHROW(e)
}
}
/***********************************************/
Matrix GnssDesignMatrix::mult(const_MatrixSliceRef x)
{
try
{
Matrix y(rows, x.columns());
for(UInt block : indexUsedBlock)
for(UInt k=0; k<indexUsedParameter[block].size(); k++)
{
const UInt index = blockIndices[block]+indexUsedParameter[block][k];
const UInt count = countUsedParameter[block][k];
matMult(1., A.slice(row, index, rows, count), x.row(index, count), y);
}
return y;
}
catch(std::exception &e)
{
GROOPS_RETHROW(e)
}
}
/***********************************************/
Matrix GnssDesignMatrix::mult(const std::vector<Matrix> &x, UInt startBlock, UInt countBlock)
{
try
{
Matrix y(rows, x.at(startBlock).columns());
for(UInt block : indexUsedBlock)
if((startBlock <= block) && (block < startBlock+countBlock))
for(UInt k=0; k<indexUsedParameter[block].size(); k++)
{
const UInt index = blockIndices[block]+indexUsedParameter[block][k];
const UInt count = countUsedParameter[block][k];
matMult(1., A.slice(row, index, rows, count), x.at(block).row(indexUsedParameter[block][k], count), y);
}
return y;
}
catch(std::exception &e)
{
GROOPS_RETHROW(e)
}
}
/***********************************************/
void GnssDesignMatrix::transMult(const_MatrixSliceRef l, std::vector<Matrix> &x, UInt startBlock, UInt countBlock)
{
try
{
for(UInt block : indexUsedBlock)
if((startBlock <= block) && (block < startBlock+countBlock))
for(UInt k=0; k<indexUsedParameter[block].size(); k++)
{
const UInt index = blockIndices[block]+indexUsedParameter[block][k];
const UInt count = countUsedParameter[block][k];
matMult(1., A.slice(row, index, rows, count).trans(), l, x.at(block).row(indexUsedParameter[block][k], count));
}
}
catch(std::exception &e)
{
GROOPS_RETHROW(e)
}
}
/***********************************************/
void GnssDesignMatrix::accumulateNormals(MatrixDistributed &normals, std::vector<Matrix> &n, Double &lPl, UInt &obsCount)
{
try
{
for(UInt ii=0; ii<indexUsedBlock.size(); ii++)
{
const UInt blocki = indexUsedBlock[ii];
normals.setBlock(blocki, blocki);
if(!normals.N(blocki, blocki).size())
normals.N(blocki, blocki) = Matrix(normals.blockSize(blocki), Matrix::SYMMETRIC);
for(UInt kk=ii+1; kk<indexUsedBlock.size(); kk++)
{
const UInt blockk = indexUsedBlock[kk];
normals.setBlock(blocki, blockk);
if(!normals.N(blocki, blockk).size())
normals.N(blocki, blockk) = Matrix(normals.blockSize(blocki), normals.blockSize(blockk));
}
for(UInt i=0; i<indexUsedParameter[blocki].size(); i++)
{
const UInt index = indexUsedParameter[blocki][i];
const UInt count = countUsedParameter[blocki][i];
const const_MatrixSlice Ai(A.slice(row, blockIndices[blocki]+index, rows, count).trans());
// right hand side
matMult(1., Ai, l.row(row, rows), n.at(blocki).row(index, count));
// diagonal block
rankKUpdate(1., Ai.trans(), normals.N(blocki, blocki).slice(index, index, count, count));
for(UInt k=i+1; k<indexUsedParameter[blocki].size(); k++)
matMult(1., Ai, A.slice(row, blockIndices[blocki]+indexUsedParameter[blocki][k], rows, countUsedParameter[blocki][k]),
normals.N(blocki, blocki).slice(index, indexUsedParameter[blocki][k], count, countUsedParameter[blocki][k]));
// other blocks
for(UInt kk=ii+1; kk<indexUsedBlock.size(); kk++)
{
const UInt blockk = indexUsedBlock[kk];
for(UInt k=0; k<indexUsedParameter[blockk].size(); k++)
matMult(1., Ai, A.slice(row, blockIndices[blockk]+indexUsedParameter[blockk][k], rows, countUsedParameter[blockk][k]),
normals.N(blocki, blockk).slice(index, indexUsedParameter[blockk][k], count, countUsedParameter[blockk][k]));
}
}
}
// accumulate right hand side
obsCount += rows;
lPl += quadsum(l);
}
catch(std::exception &e)
{
GROOPS_RETHROW(e)
}
}
/***********************************************/
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