public static LinearLinkage Apply(IRandom random, LinearLinkage p1, LinearLinkage p2) {
var length = p1.Length;
var child = new LinearLinkage(length);
var endNodes = new HashSet<int>();
for (var i = 0; i < length; i++) {
if ((p1[i] == i && p2[i] == i)
|| ((p1[i] == i || p2[i] == i) && random.NextDouble() < 0.5)) {
child[i] = i;
endNodes.Add(i);
}
}
for (var i = 0; i < length; i++) {
if (endNodes.Contains(i)) continue;
var p1End = endNodes.Contains(p1[i]);
var p2End = endNodes.Contains(p2[i]);
if ((p1End && p2End) || (!p1End && !p2End)) {
child[i] = random.NextDouble() < 0.5 ? p1[i] : p2[i];
} else if (p1End) {
child[i] = p1[i];
} else {
child[i] = p2[i];
}
}
child.LinearizeTreeStructures();
return child;
}
public static void Apply(IRandom random, LinearLinkage lle, int index)
{
var groups = lle.Select((val, idx) => Tuple.Create(idx, val))
.Where(x => x.Item1 == x.Item2)
.Select(x => x.Item2).ToList();
var z = groups.Count;
if (random.NextDouble() < 0.5)
{
lle[index] = index; // divide the cluster into two
}
else
{
var c = random.Next(z);
if (groups[c] > index)
{
lle[index] = groups[c]; // combine the portion with another class
}
else
{
// combine the other class here
lle[groups[c]] = lle[index];
lle[index] = index;
}
lle.LinearizeTreeStructures();
}
}
public static LinearLinkage Apply(IRandom random, LinearLinkage p1, LinearLinkage p2) {
var length = p1.Length;
var child = new LinearLinkage(length);
var bp = random.Next(length - 1);
for (var i = 0; i <= bp; i++) child[i] = p1[i];
for (var i = bp + 1; i < length; i++) child[i] = p2[i];
child.LinearizeTreeStructures();
return child;
}
public static LinearLinkage Apply(IRandom random, LinearLinkage p1, LinearLinkage p2)
{
var length = p1.Length;
var child = new LinearLinkage(length);
var bp = random.Next(length - 1);
for (var i = 0; i <= bp; i++)
{
child[i] = p1[i];
}
for (var i = bp + 1; i < length; i++)
{
child[i] = p2[i];
}
child.LinearizeTreeStructures();
return(child);
}
public static void Apply(IRandom random, LinearLinkage lle, int index) {
var groups = lle.Select((val, idx) => Tuple.Create(idx, val))
.Where(x => x.Item1 == x.Item2)
.Select(x => x.Item2).ToList();
var z = groups.Count;
if (random.NextDouble() < 0.5)
lle[index] = index; // divide the cluster into two
else {
var c = random.Next(z);
if (groups[c] > index)
lle[index] = groups[c]; // combine the portion with another class
else {
// combine the other class here
lle[groups[c]] = lle[index];
lle[index] = index;
}
lle.LinearizeTreeStructures();
}
}
public static LinearLinkage Apply(IRandom random, LinearLinkage p1, LinearLinkage p2)
{
var length = p1.Length;
var child = new LinearLinkage(length);
var endNodes = new HashSet <int>();
for (var i = 0; i < length; i++)
{
if ((p1[i] == i && p2[i] == i) ||
((p1[i] == i || p2[i] == i) && random.NextDouble() < 0.5))
{
child[i] = i;
endNodes.Add(i);
}
}
for (var i = 0; i < length; i++)
{
if (endNodes.Contains(i))
{
continue;
}
var p1End = endNodes.Contains(p1[i]);
var p2End = endNodes.Contains(p2[i]);
if ((p1End && p2End) || (!p1End && !p2End))
{
child[i] = random.NextDouble() < 0.5 ? p1[i] : p2[i];
}
else if (p1End)
{
child[i] = p1[i];
}
else
{
child[i] = p2[i];
}
}
child.LinearizeTreeStructures();
return(child);
}
