void CProblem::set_d_function(){
max_d = dist_linear(0);
d.resize(n);
for (unsigned int u=0; u<n; u++) {
d[u].resize(n);
for (unsigned int v=0; v<n; v++) {
d[u][v] = dist_linear(distances[u][v]);
}
}
}
miScalar distance(dist_measure distance_measure, miVector2d *v1, miVector2d *v2) {
switch(distance_measure) {
case DIST_LINEAR: return dist_linear(v1,v2);
case DIST_LINEAR_SQUARED: return dist_linear_squared(v1,v2);
case DIST_MANHATTAN: return dist_manhattan(v1,v2);
// case DIST_MINKOWSKI: return 30; // actually, this should be based on the parameter p. This is for 2, since that is the same as linear/euclidean distance
default: return -1;
}
}
void CProblem::set_d_function()
{
d.resize(n);
for (unsigned int u = 0; u < n; u++)
{
d[u].resize(n);
for (unsigned int v = 0; v < u; v++)
{
d[u][v] = dist_linear(distances[u][v]);
d[v][u] = d[u][v];
if (d[u][v] <= max_distance && u != v)
{ /* save vertices for which additional constraints to be created*/
Info.numAddVar++;
Info.ConstrIndex.push_back(u);
Info.ConstrIndex.push_back(v);
}
}
}
Info.numVar += Info.numAddVar;
std::cout << " numAddVar " << Info.numAddVar << "\n";
}
