int totalNQueens(int n) {
if (n < 1)
return 0;
vector<int> usedRows(n, -1);
vector<vector<bool> > used(n, vector<bool>(n, false));
int num = 0;
findNQueens(0, n, usedRows, used, num);
return num;
}
void withoutReplacementImpl(withoutReplacementArgs& args)
{
boost::numeric::ublas::matrix<double>& matrix = args.matrix;
int n = args.n;
int seed = args.seed;
double alpha = args.alpha;
if(matrix.size1() != matrix.size2())
{
throw std::runtime_error("Matrix must be square");
}
if(n < 1)
{
throw std::runtime_error("Input n must be at least 1");
}
int dimension = matrix.size1();
boost::mt19937 randomSource;
randomSource.seed(seed);
std::vector<double> columnSums(dimension,0);
for(int row = 0; row < dimension; row++)
{
for(int column = 0; column < dimension; column++)
{
columnSums[column] += std::fabs(matrix(row, column));
}
}
std::vector<double> currentColumnSums(dimension), newCurrentColumnSums;
std::vector<int> availableColumns(dimension), newAvailableColumns;
std::vector<int> availableRows(dimension), newAvailableRows;
std::vector<int> previousEntry(1), newPreviousEntry;
std::vector<bool> usedRows(dimension), newUsedRows;
std::vector<mpfr_class> weights(1, 1), newWeights;
//Get out the choices at the start.
std::vector<possibility> possibilities;
for(int i = 0; i < dimension; i++)
{
for(int j = 0; j < dimension; j++)
{
possibility nextPos;
nextPos.parent = 0;
nextPos.previousEntry = i;
nextPos.newEntry = j;
possibilities.push_back(nextPos);
}
}
//These choices are all derived from a single particle at the start
std::copy(columnSums.begin(), columnSums.end(), currentColumnSums.begin());
for(int i = 0; i < dimension; i++)
{
availableColumns[i] = i;
availableRows[i] = i;
}
std::fill(usedRows.begin(), usedRows.end(), false);
sampling::sampfordFromParetoNaiveArgs samplingArgs;
samplingArgs.n = n;
samplingArgs.weights.resize(dimension*dimension);
for(int i = 0; i < (int)possibilities.size(); i++)
{
possibility& pos = possibilities[i];
if(pos.previousEntry == pos.newEntry)
{
samplingArgs.weights[i] = alpha * std::fabs(matrix(pos.previousEntry, pos.newEntry)) / (dimension*(dimension - 1));
}
else
{
samplingArgs.weights[i] = std::fabs(matrix(pos.previousEntry, pos.newEntry)) / dimension;
}
}
int currentSamples = 1;
for(int permutationCounter = 0; permutationCounter < dimension; permutationCounter++)
{
//Draw sample
if((int)possibilities.size() <= n)
{
newCurrentColumnSums.resize(possibilities.size()*dimension);
newAvailableColumns.resize(possibilities.size()*(dimension - permutationCounter-1));
newAvailableRows.resize(possibilities.size()*(dimension - permutationCounter-1));
newUsedRows.resize(possibilities.size()*dimension);
newPreviousEntry.resize(possibilities.size());
int newAvailableColumnsIndex = 0;
int newAvailableRowsIndex = 0;
for(int i = 0; i < (int)possibilities.size(); i++)
{
possibility& pos = possibilities[i];
std::copy(currentColumnSums.begin() + pos.parent * dimension, currentColumnSums.begin() + (pos.parent + 1) * dimension, newCurrentColumnSums.begin() + dimension * i);
newCurrentColumnSums[dimension*i + pos.newEntry] -= std::fabs(matrix(pos.previousEntry, pos.newEntry));
std::copy(usedRows.begin() + pos.parent*dimension, usedRows.begin() + (pos.parent + 1) * dimension, newUsedRows.begin() + dimension*i);
newUsedRows[dimension*i + pos.previousEntry] = true;
for(int j = 0; j < dimension - permutationCounter; j++)
{
if(availableColumns[j + pos.parent*(dimension - permutationCounter)] != pos.newEntry)
{
newAvailableColumns.at(newAvailableColumnsIndex) = availableColumns.at(j + pos.parent*(dimension - permutationCounter));
newAvailableColumnsIndex++;
}
if(availableRows[j + pos.parent*(dimension - permutationCounter)] != pos.previousEntry)
{
newAvailableRows.at(newAvailableRowsIndex) = availableRows.at(j + pos.parent*(dimension - permutationCounter));
newAvailableRowsIndex++;
}
}
if(newUsedRows[i*dimension + pos.newEntry])
{
newPreviousEntry[i] = -1;
}
else
{
newPreviousEntry[i] = pos.newEntry;
}
}
newWeights.swap(samplingArgs.weights);
currentSamples = possibilities.size();
}
else
{
sampfordFromParetoNaive(samplingArgs, randomSource);
newCurrentColumnSums.resize(n*dimension);
newAvailableColumns.resize(n*(dimension - permutationCounter-1));
newAvailableRows.resize(n*(dimension - permutationCounter-1));
newUsedRows.resize(n*dimension);
newWeights.resize(n);
newPreviousEntry.resize(n);
int newAvailableColumnsIndex = 0;
int newAvailableRowsIndex = 0;
for(int i = 0; i < n; i++)
{
possibility& pos = possibilities[samplingArgs.indices[i]];
std::copy(currentColumnSums.begin() + pos.parent * dimension, currentColumnSums.begin() + (pos.parent + 1) * dimension, newCurrentColumnSums.begin() + dimension * i);
newCurrentColumnSums[dimension*i + pos.newEntry] -= std::fabs(matrix(pos.previousEntry, pos.newEntry));
std::copy(usedRows.begin() + pos.parent*dimension, usedRows.begin() + (pos.parent + 1) * dimension, newUsedRows.begin() + dimension*i);
newUsedRows[dimension*i + pos.previousEntry] = true;
for(int j = 0; j < dimension - permutationCounter; j++)
{
if(availableColumns[j + pos.parent*(dimension - permutationCounter)] != pos.newEntry)
{
newAvailableColumns.at(newAvailableColumnsIndex) = availableColumns.at(j + pos.parent*(dimension - permutationCounter));
newAvailableColumnsIndex++;
}
if(availableRows[j + pos.parent*(dimension - permutationCounter)] != pos.previousEntry)
{
newAvailableRows.at(newAvailableRowsIndex) = availableRows.at(j + pos.parent*(dimension - permutationCounter));
newAvailableRowsIndex++;
}
}
if(newUsedRows[i*dimension + pos.newEntry])
{
newPreviousEntry[i] = -1;
}
else
{
newPreviousEntry[i] = pos.newEntry;
}
newWeights[i] = samplingArgs.weights[samplingArgs.indices[i]] / samplingArgs.rescaledWeights[samplingArgs.indices[i]];
}
currentSamples = n;
}
previousEntry.swap(newPreviousEntry);
currentColumnSums.swap(newCurrentColumnSums);
availableColumns.swap(newAvailableColumns);
availableRows.swap(newAvailableRows);
usedRows.swap(newUsedRows);
weights.swap(newWeights);
//If we're not at the end, get out the list of possibilities and also weights
if(permutationCounter != dimension - 1)
{
possibilities.clear();
samplingArgs.weights.clear();
for(int i = 0; i < currentSamples; i++)
{
if(previousEntry[i] == -1)
{
for(int j = 0; j < dimension - permutationCounter - 1; j++)
{
for(int k = 0; k < dimension - permutationCounter - 1; k++)
{
possibility pos;
pos.parent = i;
pos.previousEntry = availableRows[(dimension - permutationCounter - 1) * i + j];
pos.newEntry = availableColumns[(dimension - permutationCounter - 1) * i + k];
possibilities.push_back(pos);
if(j == k || usedRows[pos.newEntry + dimension * i])
{
if(dimension - 2 != permutationCounter)
{
samplingArgs.weights.push_back(weights[i] * alpha * std::fabs(matrix(pos.previousEntry, pos.newEntry))/ (dimension - permutationCounter - 2));
}
else
{
samplingArgs.weights.push_back(weights[i] * alpha * std::fabs(matrix(pos.previousEntry, pos.newEntry)));
}
}
else
{
samplingArgs.weights.push_back(weights[i] * std::fabs(matrix(pos.previousEntry, pos.newEntry)));
}
}
}
}
else
{
for(int j = 0; j < dimension - permutationCounter - 1; j++)
{
possibility pos;
pos.parent = i;
pos.previousEntry = previousEntry[i];
pos.newEntry = availableColumns[(dimension - permutationCounter - 1) * i + j];
possibilities.push_back(pos);
if(j == previousEntry[i] || usedRows[pos.newEntry + dimension * i])
{
if(dimension - 2 != permutationCounter)
{
samplingArgs.weights.push_back(weights[i] * alpha * std::fabs(matrix(pos.previousEntry, pos.newEntry)) / (dimension - permutationCounter - 2));
}
else
{
samplingArgs.weights.push_back(weights[i] * alpha * std::fabs(matrix(pos.previousEntry, pos.newEntry)));
}
}
else
{
samplingArgs.weights.push_back(weights[i] * std::fabs(matrix(pos.previousEntry, pos.newEntry)));
}
}
}
}
}
}
args.estimate = 0;
for(int i = 0; i < (int)weights.size(); i++)
{
args.estimate += weights[i];
}
}
