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/*
* vim: ts=4 sw=4 et tw=0 wm=0
*
* libcola - A library providing force-directed network layout using the
* stress-majorization method subject to separation constraints.
*
* Copyright (C) 2006-2008 Monash University
*
* This library is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* License as published by the Free Software Foundation; either
* version 2.1 of the License, or (at your option) any later version.
* See the file LICENSE.LGPL distributed with the library.
*
* This library is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
*
* Author(s): Tim Dwyer
*/
/*
* Yale Sparse Matrix implementation (from Wikipedia definition).
* It stores an initial sparse n×n matrix M in row form using three arrays, A,
* IA, JA. NZ denotes the number of nonzero entries in matrix M. The array A
* then is of length NZ and holds all nonzero entries of M. The array IA stores
* at IA(i) the position of the first element of row i in the sparse array A.
* The length of row i is determined by IA(i+1) - IA(i). Therefore IA needs to
* be of length N + 1. In array JA, the column index of the element A(j) is
* stored. JA is of length NZ.
*/
#ifndef _SPARSE_MATRIX_H
#define _SPARSE_MATRIX_H
#include <valarray>
#include <map>
#include <cstdio>
#include "libvpsc/assertions.h"
namespace cola {
struct SparseMap {
SparseMap(unsigned n = 0) : n(n) {};
unsigned n;
typedef std::pair<unsigned, unsigned> SparseIndex;
typedef std::map<SparseIndex,double> SparseLookup;
typedef SparseLookup::const_iterator ConstIt;
SparseLookup lookup;
double& operator[](const SparseIndex& k) {
return lookup[k];
}
double& operator()(const unsigned i, const unsigned j) {
return lookup[std::make_pair(i,j)];
}
double getIJ(const unsigned i, const unsigned j) const {
COLA_ASSERT(i<n);
COLA_ASSERT(j<n);
ConstIt v=lookup.find(std::make_pair(i,j));
if(v!=lookup.end()) {
return v->second;
}
return 0;
}
size_t nonZeroCount() const {
return lookup.size();
}
void resize(unsigned n) {
this->n = n;
}
void clear() {
lookup.clear();
}
};
/*
* Yale Sparse Matrix implementation (from Wikipedia definition).
* It stores an initial sparse n×n matrix M in row form using three arrays, A,
* IA, JA. NZ denotes the number of nonzero entries in matrix M. The array A
* then is of length NZ and holds all nonzero entries of M. The array IA stores
* at IA(i) the position of the first element of row i in the sparse array A.
* The length of row i is determined by IA(i+1) - IA(i). Therefore IA needs to
* be of length N + 1. In array JA, the column index of the element A(j) is
* stored. JA is of length NZ.
*/
class SparseMatrix {
public:
SparseMatrix(SparseMap const & m)
: n(m.n), NZ((unsigned)m.nonZeroCount()), sparseMap(m),
A(std::valarray<double>(NZ)), IA(std::valarray<unsigned>(n+1)), JA(std::valarray<unsigned>(NZ)) {
unsigned cnt=0;
int lastrow=-1;
for(SparseMap::ConstIt i=m.lookup.begin(); i!=m.lookup.end(); i++) {
SparseMap::SparseIndex p = i->first;
COLA_ASSERT(p.first<n);
COLA_ASSERT(p.second<n);
A[cnt]=i->second;
if((int)p.first!=lastrow) {
for(unsigned r=lastrow+1;r<=p.first;r++) {
IA[r]=cnt;
}
lastrow=p.first;
}
JA[cnt]=p.second;
cnt++;
}
for(unsigned r=lastrow+1;r<=n;r++) {
IA[r]=NZ;
}
}
void rightMultiply(std::valarray<double> const & v, std::valarray<double> & r) const {
COLA_ASSERT(v.size()>=n);
COLA_ASSERT(r.size()>=n);
for(unsigned i=0;i<n;i++) {
r[i]=0;
for(unsigned j=IA[i];j<IA[i+1];j++) {
r[i]+=A[j]*v[JA[j]];
}
}
}
double getIJ(const unsigned i, const unsigned j) const {
return sparseMap.getIJ(i,j);
}
void print() const {
for(unsigned i=0;i<n;i++) {
for(unsigned j=0;j<n;j++) {
printf("%f ",getIJ(i,j));
}
printf("\n");
}
}
unsigned rowSize() const {
return n;
}
private:
const unsigned n,NZ;
SparseMap const & sparseMap;
std::valarray<double> A;
std::valarray<unsigned> IA, JA;
};
} //namespace cola
#endif /* _SPARSE_MATRIX_H */
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