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Clementine-audio-player-Mac.../3rdparty/libprojectm/HungarianMethod.hpp
2010-06-06 21:43:45 +00:00

193 lines
7.9 KiB
C++

#ifndef HUNGARIAN_METHOD_HPP
#define HUNGARIAN_METHOD_HPP
//#include "Common.hpp"
#include <cstdlib>
#include <cstdio>
#include <cstring>
#include <limits>
/// A function object which calculates the maximum-weighted bipartite matching between
/// two sets via the hungarian method.
template <int N=20>
class HungarianMethod {
public :
static const int MAX_SIZE = N;
private:
int n, max_match; //n workers and n jobs
double lx[N], ly[N]; //labels of X and Y parts
int xy[N]; //xy[x] - vertex that is matched with x,
int yx[N]; //yx[y] - vertex that is matched with y
bool S[N], T[N]; //sets S and T in algorithm
double slack[N]; //as in the algorithm description
double slackx[N]; //slackx[y] such a vertex, that
// l(slackx[y]) + l(y) - w(slackx[y],y) = slack[y]
int prev[N]; //array for memorizing alternating paths
void init_labels(const double cost[N][N])
{
memset(lx, 0, sizeof(lx));
memset(ly, 0, sizeof(ly));
for (int x = 0; x < n; x++)
for (int y = 0; y < n; y++)
lx[x] = std::max(lx[x], cost[x][y]);
}
void augment(const double cost[N][N]) //main function of the algorithm
{
if (max_match == n) return; //check wether matching is already perfect
int x, y, root; //just counters and root vertex
int q[N], wr = 0, rd = 0; //q - queue for bfs, wr,rd - write and read
//pos in queue
memset(S, false, sizeof(S)); //init set S
memset(T, false, sizeof(T)); //init set T
memset(prev, -1, sizeof(prev)); //init set prev - for the alternating tree
for (x = 0; x < n; x++) //finding root of the tree
if (xy[x] == -1)
{
q[wr++] = root = x;
prev[x] = -2;
S[x] = true;
break;
}
for (y = 0; y < n; y++) //initializing slack array
{
slack[y] = lx[root] + ly[y] - cost[root][y];
slackx[y] = root;
}
while (true) //main cycle
{
while (rd < wr) //building tree with bfs cycle
{
x = q[rd++]; //current vertex from X part
for (y = 0; y < n; y++) //iterate through all edges in equality graph
if (cost[x][y] == lx[x] + ly[y] && !T[y])
{
if (yx[y] == -1) break; //an exposed vertex in Y found, so
//augmenting path exists!
T[y] = true; //else just add y to T,
q[wr++] = yx[y]; //add vertex yx[y], which is matched
//with y, to the queue
add_to_tree(yx[y], x, cost); //add edges (x,y) and (y,yx[y]) to the tree
}
if (y < n) break; //augmenting path found!
}
if (y < n) break; //augmenting path found!
update_labels(); //augmenting path not found, so improve labeling
wr = rd = 0;
for (y = 0; y < n; y++)
//in this cycle we add edges that were added to the equality graph as a
//result of improving the labeling, we add edge (slackx[y], y) to the tree if
//and only if !T[y] && slack[y] == 0, also with this edge we add another one
//(y, yx[y]) or augment the matching, if y was exposed
if (!T[y] && slack[y] == 0)
{
if (yx[y] == -1) //exposed vertex in Y found - augmenting path exists!
{
x = slackx[y];
break;
}
else
{
T[y] = true; //else just add y to T,
if (!S[yx[y]])
{
q[wr++] = yx[y]; //add vertex yx[y], which is matched with
//y, to the queue
add_to_tree(yx[y], slackx[y],cost); //and add edges (x,y) and (y,
//yx[y]) to the tree
}
}
}
if (y < n) break; //augmenting path found!
}
if (y < n) //we found augmenting path!
{
max_match++; //increment matching
//in this cycle we inverse edges along augmenting path
for (int cx = x, cy = y, ty; cx != -2; cx = prev[cx], cy = ty)
{
ty = xy[cx];
yx[cy] = cx;
xy[cx] = cy;
}
augment(cost); //recall function, go to step 1 of the algorithm
}
}//end of augment() function
void update_labels()
{
int x, y;
double delta = std::numeric_limits<double>::max();
for (y = 0; y < n; y++) //calculate delta using slack
if (!T[y])
delta = std::min(delta, slack[y]);
for (x = 0; x < n; x++) //update X labels
if (S[x]) lx[x] -= delta;
for (y = 0; y < n; y++) //update Y labels
if (T[y]) ly[y] += delta;
for (y = 0; y < n; y++) //update slack array
if (!T[y])
slack[y] -= delta;
}
void add_to_tree(int x, int prevx, const double cost[N][N])
//x - current vertex,prevx - vertex from X before x in the alternating path,
//so we add edges (prevx, xy[x]), (xy[x], x)
{
S[x] = true; //add x to S
prev[x] = prevx; //we need this when augmenting
for (int y = 0; y < n; y++) //update slacks, because we add new vertex to S
if (lx[x] + ly[y] - cost[x][y] < slack[y])
{
slack[y] = lx[x] + ly[y] - cost[x][y];
slackx[y] = x;
}
}
public:
/// Computes the best matching of two sets given its cost matrix.
/// See the matching() method to get the computed match result.
/// \param cost a matrix of two sets I,J where cost[i][j] is the weight of edge i->j
/// \param logicalSize the number of elements in both I and J
/// \returns the total cost of the best matching
inline double operator()(const double cost[N][N], int logicalSize)
{
n = logicalSize;
assert(n <= N);
double ret = 0; //weight of the optimal matching
max_match = 0; //number of vertices in current matching
memset(xy, -1, sizeof(xy));
memset(yx, -1, sizeof(yx));
init_labels(cost); //step 0
augment(cost); //steps 1-3
for (int x = 0; x < n; x++) //forming answer there
ret += cost[x][xy[x]];
return ret;
}
/// Gets the matching element in 2nd set of the ith element in the first set
/// \param i the index of the ith element in the first set (passed in operator())
/// \returns an index j, denoting the matched jth element of the 2nd set
inline int matching(int i) const {
return xy[i];
}
/// Gets the matching element in 1st set of the jth element in the 2nd set
/// \param j the index of the jth element in the 2nd set (passed in operator())
/// \returns an index i, denoting the matched ith element of the 1st set
/// \note inverseMatching(matching(i)) == i
inline int inverseMatching(int j) const {
return yx[j];
}
};
#endif