public EvolutionStrategy()
{
this._random = new Random();
this._tpdict = null;
this._chromosomes = null;
this._tp_size = 2;
this._iteration = 0;
this._warp = null;
this._borders = null;
}
public void InitializePatternDictionary(List <int[, ]> input_samples, int tp_size, WarpOptions warp, BorderOptions borders)
{
List <int> sizes = new List <int>();
for (int i = 1; i <= tp_size; i++)
{
sizes.Add(i);
}
this._tpdict = new TPDict(input_samples, sizes, warp, borders);
this._tp_size = tp_size;
this._warp = warp;
this._borders = borders;
}
public Chromosome Mutate(List <int> tp_sizes, int mut_times, BorderOptions borders)
{
Chromosome clone = this.Clone();
int times = Math.Max(0, this._random.Next(mut_times)) + 1;
for (int i = 0; i < times; i++)
{
int size = tp_sizes[this._random.Next(tp_sizes.Count)];
int x = clone._random.Next(clone._width - size + 1);
int y = clone._random.Next(clone._height - size + 1);
PatternList patterns = clone._tpdict.GetTPArray(size);
PatternList border_patterns = new PatternList();
if (borders != null)
{
if (borders["top"] && y == 0)
{
border_patterns.AddRange(clone._tpdict.GetTPBorderArray(size, "top"));
}
if (borders["bot"] && y == clone._height - size)
{
border_patterns.AddRange(clone._tpdict.GetTPBorderArray(size, "bot"));
}
if (borders["left"] && x == 0)
{
border_patterns.AddRange(clone._tpdict.GetTPBorderArray(size, "left"));
}
if (borders["right"] && x == clone._width - size)
{
border_patterns.AddRange(clone._tpdict.GetTPBorderArray(size, "right"));
}
}
if (border_patterns.Length > 0)
{
patterns = border_patterns;
}
if (patterns.Length > 0)
{
clone.ApplyTP(patterns.Sample(clone._random), x, y);
clone._calculated = false;
}
}
return(clone);
}
public void RandomInitialize(List <int> tp_sizes, BorderOptions borders)
{
for (int y = 0; y < this._height; y++)
{
for (int x = 0; x < this._width; x++)
{
int size = tp_sizes[this._random.Next(tp_sizes.Count)];
size = Math.Min(size, this._height - y);
size = Math.Min(size, this._width - x);
PatternList patterns = this._tpdict.GetTPArray(size);
PatternList border_patterns = new PatternList();
if (borders != null)
{
if (borders["top"] && y == 0)
{
border_patterns.AddRange(this._tpdict.GetTPBorderArray(size, "top"));
}
if (borders["bot"] && y == this._height - size)
{
border_patterns.AddRange(this._tpdict.GetTPBorderArray(size, "bot"));
}
if (borders["left"] && x == 0)
{
border_patterns.AddRange(this._tpdict.GetTPBorderArray(size, "left"));
}
if (borders["right"] && x == this._width - size)
{
border_patterns.AddRange(this._tpdict.GetTPBorderArray(size, "rigth"));
}
}
if (border_patterns.Length > 0)
{
patterns = border_patterns;
}
if (patterns.Length > 0)
{
this.ApplyTP(patterns.Sample(this._random), x, y);
}
}
}
}
public TPDict(List <int[, ]> input_samples, List <int> sizes, WarpOptions warp = null, BorderOptions borders = null)
{
Tuple <Dictionary <int, PatternCounts>, Dictionary <int, PatternList>, Dictionary <int, BorderPatternList> > temp =
Helper.CalculateTilePatternProbabilities(input_samples, sizes, warp, borders);
this._q_counts = temp.Item1;
this._patterns = temp.Item2;
this._border_patterns = temp.Item3;
}
public static Tuple <Dictionary <int, PatternCounts>, Dictionary <int, PatternList>, Dictionary <int, BorderPatternList> > CalculateTilePatternProbabilities(List <int[, ]> maps, List <int> tp_sizes, WarpOptions warp = null, BorderOptions borders = null)
{
Dictionary <int, PatternCounts> p = new Dictionary <int, PatternCounts>();
Dictionary <int, PatternList> patterns = new Dictionary <int, PatternList>();
Dictionary <int, BorderPatternList> border_patterns = new Dictionary <int, BorderPatternList>();
foreach (int size in tp_sizes)
{
p[size] = new PatternCounts();
patterns[size] = new PatternList();
border_patterns[size] = new BorderPatternList();
for (int i = 0; i < maps.Count; i++)
{
int[,] map = maps[i];
int ySize = size;
if (warp != null && warp["y"])
{
ySize = 1;
}
for (int y = 0; y < map.GetLength(0) - ySize + 1; y++)
{
int xSize = size;
if (warp != null && warp["x"])
{
xSize = 1;
}
for (int x = 0; x < map.GetLength(1) - xSize + 1; x++)
{
Pattern pattern = CalculateTilePattern(map, x, y, size);
p[size].Add(pattern);
if (borders != null)
{
bool temp_border = false;
if (borders["top"] && y == 0)
{
temp_border = true;
border_patterns[size].Add("top", pattern);
}
if (borders["bot"] && y == map.GetLength(0) - size)
{
temp_border = true;
border_patterns[size].Add("bot", pattern);
}
if (borders["left"] && x == 0)
{
temp_border = true;
border_patterns[size].Add("left", pattern);
}
if (borders["right"] && x == map.GetLength(1) - size)
{
temp_border = true;
border_patterns[size].Add("right", pattern);
}
if (!temp_border)
{
patterns[size].Add(pattern);
}
}
else
{
patterns[size].Add(pattern);
}
}
}
}
}
return(Tuple.Create(p, patterns, border_patterns));
}
/// <summary>
/// Initialize the pattern dictionary that is used during generation
/// only call that function when you want to change any aspect of the patterns
/// </summary>
/// <param name="input_samples">the input integer matrix that the algorithm sample from</param>
/// <param name="tp_size">the size of the tile patterns used in generation larger than 1</param>
/// <param name="warp">an object that allow the patterns to sample by wrapping across the edges</param>
/// <param name="borders">an object that allow the edges to be similar to the edges from the input_samples</param>
public void InitializePatternDictionary(List <int[, ]> input_samples, int tp_size, WarpOptions warp = null, BorderOptions borders = null)
{
if (tp_size <= 1)
{
tp_size = 2;
}
this._es.InitializePatternDictionary(input_samples, tp_size, warp, borders);
}
/// <summary>
/// Run the algorithm for a fixed amount of iterations.
/// This function doesn't need anything to be called before hand.
/// You can call step to enhance the generation after that function is done
/// </summary>
/// <param name="input_samples">the input integer matrix that the algorithm sample from</param>
/// <param name="tp_size">the size of the tile patterns used in generation larger than 1</param>
/// <param name="width">the width of the generated map</param>
/// <param name="height">the height of the generated map</param>
/// <param name="iterations">the number of iterations that the algorithm should do before finishing</param>
/// <param name="warp">an object that allow the patterns to sample by wrapping across the edges</param>
/// <param name="borders">an object that allow the edges to be similar to the edges from the input_samples</param>
/// <param name="pop_size">the number of generated map at once (having more maps in parallel) helps find good solution faster</param>
/// <param name="inter_weight">the Asymmetric weight defined from Lucas and Volz work. It balances between having the input_sample have at least one of each pattern in the generated image or vice versa.</param>
/// <param name="mut_times">the maximum number of modifications the algorithm is allowed to do in one step</param>
/// <param name="noise">noise value to push the algorithm away from certain arrays and embrace some new noise</param>
/// <returns>The best generated map so far</returns>
public int[,] Generate(List <int[, ]> input_samples, int tp_size, int width, int height, int iterations = 10000, WarpOptions warp = null, BorderOptions borders = null, int pop_size = 1, double inter_weight = 0.5, int mut_times = 1, double noise = 0)
{
this.InitializePatternDictionary(input_samples, tp_size, warp, borders);
this.InitializeGeneration(width, height, pop_size);
while (this.GetIteration() < iterations)
{
this.Step(inter_weight, mut_times, noise);
}
return(this.GetMap());
}