public override void Perform()
{
if (Value < 0.9)
{
return;
}
if (_creature.Health / _creature.MaxHealth < 0.9)
{
return;
}
var x = _creature.X;
var y = _creature.Y;
int dx, dy = 0;
bool empty = false;
for (dx = -1; dx <= 1 && !empty; dx++)
{
for (dy = -1; dy <= 1 && !empty; dy++)
{
if (GAME.World[x + dx, y + dy].Content.Count == 0)
{
empty = true;
}
}
}
if (!empty)
{
return;
}
var network = _creature.GetNetwork();
var creature1 = new SimpleCreature(x, y, _creature.MySpawner, network, 2)
{
Health = _creature.Health / 2,
Lifetime = _creature.Lifetime
};
var creature2 = new SimpleCreature(x + dx, y + dy, _creature.MySpawner, network, 5)
{
Health = _creature.Health / 2,
Lifetime = _creature.Lifetime
};
GAME.World[x, y].Enter(creature1);
GAME.World[x + dx, y + dy].Enter(creature2);
_creature.MySpawner.AddNewCreatures(creature1, creature2);
_creature.Health = 0;
}
private void AddCreature(NeuralNetwork network = null, int changeCount = 0)
{
int x = (int)SpawnPos.X;
int y = (int)SpawnPos.Y;
int dx = 0, dy = 0;
while (dx * dx + dy * dy >= _spawnRadius * _spawnRadius ||
GAME.World[x + dx, y + dy].MyType != Cell.CellType.Ground ||
GAME.World[x + dx, y + dy].Content.Find(e => e is Creature) != null)
{
dx = _rand.Next(-_spawnRadius, _spawnRadius + 1);
dy = _rand.Next(-_spawnRadius, _spawnRadius + 1);
}
var creature = new SimpleCreature(x + dx, y + dy, this, network, changeCount);
_creatures.Add(creature);
}
