//levy flight - refer to url: https://www.mathworks.com/matlabcentral/fileexchange/45112-flower-pollination-algorithm/content/fpa_demo.m
public double[] LevyFlight(int subsetSize)
{
//levy exponent and coefficient. For more details see Chapter 11 of the following book:
// Xin-She Yang, Nature-Inspired Optimization Algorithms, Elsevier, (2014).
double beta = 3 / 2;
double A = Gamma(1 + beta);
double B = Math.Sin(Math.PI * beta / 2);
double C = Gamma((1 + beta) / 2);
double D = (beta - 1) / 2;
double E = beta * Math.Pow(2, D);
double F = 1 / beta;
double sigma = Math.Pow((A * B / (C * E)), F);
double G, H, I, step;
double[] levyF = new double[subsetSize];
for (int i = 0; i < subsetSize; i++)
{
double randNum = SimpleRNG.GetNormal();//generate random number with normal distribution
G = sigma * randNum;
H = SimpleRNG.GetNormal();
I = Math.Abs(H);
step = G / Math.Pow(I, F);
levyF[i] = 0.01 * step;
}
return(levyF);
}
void DoShoot(Weapon _weapon, GameObject _projectile)
{
var _deltaPos = Vector2.zero;
if (!Mathf.Approximately(positionDeviation.x, 0))
{
_deltaPos.x = (float)SimpleRNG.GetNormal(0, positionDeviation.x);
}
if (!Mathf.Approximately(positionDeviation.y, 0))
{
_deltaPos.y = (float)SimpleRNG.GetNormal(0, positionDeviation.y);
}
_projectile.transform.Translate(_deltaPos);
if (!Mathf.Approximately(directionDeviation, 0))
{
var _delta = (float)SimpleRNG.GetNormal(0, directionDeviation);
var _rotation = Quaternion.AngleAxis(_delta, Vector3.forward);
var _velocity = _projectile.rigidbody2D.velocity;
_velocity = _rotation * _velocity;
_projectile.rigidbody2D.velocity = _velocity;
}
}
public void RealCrossover(ref ScheduleGenome p2, ref ScheduleGenome o1)
{
switch (realCrossover)
{
case RealCrossoverOp.MeanWithNoise:
// Mean-with-noise Crossover:
for (int i = 0; i < _modesLength; i++)
{
double mean = TimeGenes[i] + p2.TimeGenes[i];
mean = mean / 2.0;
o1.TimeGenes[i] = SimpleRNG.GetNormal(mean, 0.5);
if (o1.TimeGenes[i] < 0.0)
{
o1.TimeGenes[i] = 0.0;
}
}
break;
case RealCrossoverOp.Uniform:
// Uniform Crossover:
int cutpoint = SimpleRNG.Next(0, _modesLength + 1);
for (int i = 0; i < cutpoint; i++)
{
o1.TimeGenes[i] = TimeGenes[i];
}
for (int i = cutpoint; i < _modesLength; i++)
{
o1.TimeGenes[i] = p2.TimeGenes[i];
}
break;
}
}
public void Mutate()
{
for (int i = 0; i < _jobsLength; i++)
{
if (SimpleRNG.GetUniform() < _mutationRate)
{
int r = SimpleRNG.Next(0, _jobsLength);
int temp = JobGenes[i];
JobGenes[i] = JobGenes[r];
JobGenes[r] = temp;
// Mutate the delay time
r = SimpleRNG.Next(0, _modesLength);
double mutatedDelay = 0;
//mutatedDelay = SimpleRNG.GetExponential(_delayMean);
mutatedDelay = SimpleRNG.GetNormal(TimeGenes[r], 1.0);
//mutatedDelay = _rand.NextDouble() * _delayMean;
if (mutatedDelay < 0.0)
{
mutatedDelay = 0.0;
}
TimeGenes[r] = mutatedDelay;
}
}
//Mutate the Mode vector:
for (int i = 0; i < _modesLength; i++)
{
if (SimpleRNG.GetUniform() < _mutationRate)
{
ModeGenes[i] = SimpleRNG.Next(0, _numberOfModes);
}
}
}
public static void RandomizeResult(ref Game game)
{
//SimpleRNG.SetSeedFromSystemTime();
var blackFavor = (game.BlackPlayer.Rating - game.WhitePlayer.Rating) / 40;
var simulatedResult = SimpleRNG.GetNormal(32, 7) + blackFavor;
game.BlackResult = MakeProperResult(simulatedResult);
game.WhiteResult = 64 - game.BlackResult;
}
private List <int> ShortenOperator(List <int> mutatedValues, int segmentPosition)
{
int prolongLength = (int)Math.Ceiling(Math.Abs(SimpleRNG.GetNormal())) + 1;
int sign = mutatedValues[segmentPosition] / Math.Abs(mutatedValues[segmentPosition]);
// Operator can be applied with length less or equal to difference between
// maximum length of segment and longest segment
if (sign < 0)
{
int minimum = mutatedValues.Min();
if (prolongLength > Math.Abs(-39 - minimum))
{
prolongLength = Math.Abs(-39 - minimum);
}
}
else
{
int maximum = mutatedValues.Max();
if (prolongLength > 29 - maximum)
{
prolongLength = 29 - maximum;
}
}
if (prolongLength == 0)
{
return(mutatedValues);
}
// Maximum allowed value for shortening operator is the length of the segment
if (prolongLength > Math.Abs(mutatedValues[segmentPosition]))
{
prolongLength = Math.Abs(mutatedValues[segmentPosition]);
}
mutatedValues[segmentPosition] = sign * (Math.Abs(mutatedValues[segmentPosition]) - prolongLength);
if (mutatedValues[segmentPosition] == 0)
{
if (segmentPosition == 0 || segmentPosition == mutatedValues.Count - 1)
{
mutatedValues.RemoveAt(segmentPosition);
segmentPosition = -1;
}
else
{
mutatedValues[segmentPosition - 1] += mutatedValues[segmentPosition + 1];
mutatedValues.RemoveRange(segmentPosition, 2);
segmentPosition = -1;
}
}
int randomParallelSegmentIndex = FindRandomParalellSegment(mutatedValues, segmentPosition, sign > 0);
mutatedValues[randomParallelSegmentIndex] = sign * (Math.Abs(mutatedValues[randomParallelSegmentIndex]) + prolongLength);
return(mutatedValues);
}
public override Vector3 ReadSensor()
{
float distanceMod = System.Convert.ToSingle(SimpleRNG.GetNormal(DEFAULT_MEAN, standardDeviation));
float angleMod = Random.Range(MIN_GEN_ANGLE, MAX_GEN_ANGLE);
Vector3 modificator = CoordinatesConverter.PolarToCartesian(distanceMod, angleMod);
Vector3 result = transform.position + modificator;
UpdateMarker(result);
recorder.SetData(transform.position.x.ToString(), REC_X_TAG);
recorder.SetData(transform.position.z.ToString(), REC_Z_TAG);
return(result);
}
public void TestStandardDeviationForGame()
{
const int numSamples = 100000;
var rs = new RunningStat();
rs.Clear();
var mean = 32;
var stdev = 7;
for (int i = 0; i < numSamples; ++i)
{
rs.Push(SimpleRNG.GetNormal(mean, stdev));
}
PrintStatisticResults("normal", mean, stdev * stdev, rs.Mean(), rs.Variance(), rs.resultlist.Min(), rs.resultlist.Max());
PrintGameResultStats(rs);
}
void Reset()
{
m_StepCount = 0;
int CellsCount = (int)Math.Sqrt(PARTICLES_COUNT);
for (int i = 0; i < PARTICLES_COUNT; i++)
{
int CellY = (i / CellsCount) - CellsCount / 2;
int CellX = (i % CellsCount) - CellsCount / 2;
// Point2D Pos = new Point2D( SIMULATION_SPACE_SIZE * (float) SimpleRNG.GetNormal(), SIMULATION_SPACE_SIZE * (float) SimpleRNG.GetNormal() );
Point2D Pos = new Point2D(40.0f * (CellX + (float)SimpleRNG.GetNormal()), 40.0f * (CellY + (float)SimpleRNG.GetNormal()));
m_Particles[0][i].P = m_Particles[1][i].P = Pos;
m_Particles[0][i].Size = m_Particles[1][i].Size = 0;
}
}
public void InitializeWeightsAndBiases()
{
SimpleRNG.SetSeedFromSystemTime();
for (int layer = 0; layer < neuralNetAccessor.NumberOfLayers; layer++)
{
for (int node = 0; node < neuralNetAccessor.NodesInLayer(layer); node++)
{
var sigmoid = neuralNetAccessor.GetSigmoid(layer, node);
sigmoid.Bias = (float)SimpleRNG.GetNormal(0, 1);
float standardDeviation = 1.0f / Mathf.Sqrt(sigmoid.Weights.Length);
for (int i = 0; i < sigmoid.Weights.Length; i++)
{
sigmoid.Weights[i] = (float)SimpleRNG.GetNormal(0, standardDeviation);
}
}
}
}
private Individual MutateIndividual(Individual individual)
{
int mutationEffect = random.Next(0, 2);
if (mutationEffect == 0)
{
double value = random.NextDouble();
for (int i = 0; i < individual.References.Count; i++)
{
double chance = random.NextDouble();
if (chance < value)
{
int xChange = (int)(SimpleRNG.GetNormal());
int yChange = (int)(SimpleRNG.GetNormal());
int xSign = Math.Sign(SimpleRNG.GetNormal());
int ySign = Math.Sign(SimpleRNG.GetNormal());
individual.References[i].X += xSign * xChange;
individual.References[i].Y += ySign * yChange;
}
}
}
else
{
double value = random.NextDouble();
for (int i = 0; i < individual.References.Count; i++)
{
double chance = random.NextDouble();
if (chance < value)
{
int randX = random.Next(1, 33);
int randY = random.Next(6, 28);
individual.References[i].X = randX;
individual.References[i].Y = randY;
}
}
}
return(individual);
}
public Action StartSparkline(IEnumerable <AddTimeValue> addTimeValues)
{
Func <int> tickTime = () => {
// Convert.ToInt32(Math.Floor(SimpleRNG.GetUniform()*2000));
return(1000 / 2);
};
Func <AddTimeValue, Action> tickGenerator = addTimeValue => {
var x = 100.0;
return(() => {
while (true)
{
var newx = x + SimpleRNG.GetNormal() * 2;
if (newx < 0.0)
{
newx = 0.0;
}
_dispatcher.BeginInvoke((Action)(() => {
var guid = addTimeValue(newx);
if (SimpleRNG.GetUniform() > 0.75)
{
ShowFlag(guid, newx);
}
}));
x = newx;
Thread.Sleep(tickTime());
}
});
};
Func <Action, WaitCallback> toWaitCallback = gen => wc => gen();
Action start = () => {
var tickGenerators = addTimeValues.Select(tickGenerator).ToArray();
foreach (var generateTicks in tickGenerators)
{
ThreadPool.QueueUserWorkItem(toWaitCallback(generateTicks));
}
};
return(start);
}
public Form1()
{
InitializeComponent();
for (int x = 0; x <= 100; x++)
{
float sigma = Math.Max(1e-4f, GRAPH_WIDTH * x / 100);
double avgNormal = 0.0f;
for (int i = 0; i < 10000; i++)
{
double rnd = SimpleRNG.GetNormal(0, sigma);
rnd = rnd % Math.PI; // Simple wrapping (https://en.wikipedia.org/wiki/Wrapped_distribution)
// rnd = rnd % (2.0*Math.PI); // Simple wrapping (https://en.wikipedia.org/wiki/Wrapped_distribution)
avgNormal += Math.Cos(rnd);
}
avgNormal /= 10000;
m_distribution.Add(new float2(sigma, (float)avgNormal));
}
panelOutput.UpdateBitmap();
}
private void InitialiseGraph()
{
float[] position = new float[this.input_dim];
float[] position2 = new float[this.input_dim];
SimpleRNG r = new SimpleRNG();
double r1 = SimpleRNG.GetNormal();
double r2 = SimpleRNG.GetNormal();
double r3 = SimpleRNG.GetNormal();
double r4 = SimpleRNG.GetNormal();
position[0] = (float)r1;
position[1] = (float)r2;
position2[0] = (float)r3;
position2[1] = (float)r4;
this.graph.AddNode(position, tlen);
this.graph.AddNode(position2, tlen);
}
private void panelOutputNormalDistribution_BitmapUpdating(int W, int H, Graphics G)
{
G.FillRectangle(Brushes.White, 0, 0, W, H);
int[] Buckets = new int[128];
int Peak = 0;
for (int i = 0; i < 10000; i++)
{
float Random = (float)SimpleRNG.GetNormal(0.0, 4.0);
int BucketIndex = (int)(64 * (1.0f + 0.05f * Random));
BucketIndex = Math.Max(0, Math.Min(127, BucketIndex));
Buckets[BucketIndex]++;
Peak = Math.Max(Peak, Buckets[BucketIndex]);
}
for (int i = 0; i < 128; i++)
{
float h = H * Buckets[i] / (1.1f * Peak);
G.FillRectangle(Brushes.Black, W * (i + 0) / 128.0f, H - h - 1, W / 128.0f, h);
}
}
public Problem Bat(Problem prob)
{
//default parameters
int populationSize = 5; //number of bats in the population
int maxGeneration = 100;
int subsetSize = 200;
double loudness = 0.5;
double pulseRate = 0.5;
int totalInstances = prob.X.Count(); //problem size
double frequencyMin = 0; //minimum frequency. Frequency range determine the scalings
double frequencyMax = 2; //maximum frequency.
int lowerBound = -2; //set lower bound - lower boundary
int upperBound = 2; //set upper bound - upper boundary
double[] batFitnessVal = new double[populationSize];
double[] newbatFitnessVal = new double[populationSize];
double globalBest = double.MinValue;
ObjectInstanceSelection globalBestBat = null;
Random r = new Random();
//initialize population
List <ObjectInstanceSelection> bats = InitializeBat(populationSize, subsetSize, totalInstances, prob);
List <ObjectInstanceSelection> newBats = new List <ObjectInstanceSelection>(bats.Count); //create a clone of bats
bats.ForEach((item) =>
{
newBats.Add(new ObjectInstanceSelection(item.__Attribute_Values, item.__Attribute_Values_Continuous, item.__Frequency, item.__Velocity, item.__Pointers, item.__Fitness)); //create a clone of flowers
});
batFitnessVal = fi.EvaluateObjectiveFunction(bats, prob); //evaluate fitness value for all the bats
newbatFitnessVal = fi.EvaluateObjectiveFunction(newBats, prob); //evaluate fitness value for new bats. Note: this will be the same for this function call, since pollination has not occur
BatFitness(batFitnessVal, bats); //fitness value for each bats
BatFitness(newbatFitnessVal, newBats); //fitness value for new bats
globalBestBat = EvaluateSolution(batFitnessVal, newbatFitnessVal, globalBest, bats, newBats, globalBestBat, loudness); //get the global best flower
globalBest = globalBestBat.__Fitness;
//start bat algorithm
double rand = r.NextDouble(); //generate random number
for (int i = 0; i < maxGeneration; i++)
{
//loop over all bats or solutions
for (int j = 0; j < populationSize; j++)
{
bats[j].__Frequency = frequencyMin + (frequencyMin - frequencyMax) * rand; //adjust frequency
for (int k = 0; k < subsetSize; k++)
{
double randNum = SimpleRNG.GetNormal(); //generate random number with normal distribution
newBats[j].__Velocity[k] = bats[j].__Velocity[k] + (bats[j].__Attribute_Values_Continuous[k] - globalBestBat.Attribute_Values_Continuous[k]) * bats[j].__Frequency; //update velocity
newBats[j].__Attribute_Values_Continuous[k] = bats[j].__Attribute_Values_Continuous[k] + bats[j].__Velocity[k]; //update bat position in continuous space
newBats[j].__Attribute_Values_Continuous[k] = SimpleBounds(newBats[j].__Attribute_Values_Continuous[k], lowerBound, upperBound); //ensure that value does not go beyond defined boundary
if (rand > pulseRate) //The factor 0.001 limits the step sizes of random walks
{
newBats[j].__Attribute_Values_Continuous[k] = globalBestBat.Attribute_Values_Continuous[k] + 0.001 * randNum;
}
newBats[j].__Attribute_Values[k] = fi.Binarize(newBats[j].__Attribute_Values_Continuous[k], r.NextDouble()); //convert to binary
}
}
//evaluate new solution
newbatFitnessVal = fi.EvaluateObjectiveFunction(newBats, prob); //evaluate fitness value for all the bats
BatFitness(newbatFitnessVal, newBats); //fitness value for new bats
globalBestBat = EvaluateSolution(batFitnessVal, newbatFitnessVal, globalBest, bats, newBats, globalBestBat, loudness); //get the global best flower
globalBest = globalBestBat.__Fitness;
}
//ensure that at least, 40 instances is selected for classification
int countSelected = globalBestBat.__Attribute_Values.Count(q => q == 1); //count the total number of selected instances
int diff, c = 0, d = 0;
int Min = 40; //minimum number of selected instances
if (countSelected < Min)
{
//if there are less than N, add N instances, where N = the number of selected instances
diff = Min - countSelected;
while (c < diff)
{
if (globalBestBat.__Attribute_Values[d++] == 1)
{
continue;
}
else
{
globalBestBat.__Attribute_Values[d++] = 1;
c++;
}
}
}
Problem subBest = fi.buildModel(globalBestBat, prob); //build model for the best Instance Mast
return(subBest);
}
//Binary Bat
public Problem BinaryBat(Problem prob, out double storagePercentage)
{
//default parameters
int populationSize = 3; //number of bats in the population
int subsetSize = 100;
int maxGeneration = 3;
double loudness = 0.5;
double pulseRate = 0.5;
int totalInstances = prob.X.Count(); //problem size
double frequencyMin = 0; //minimum frequency. Frequency range determine the scalings
double frequencyMax = 2; //maximum frequency.
int lowerBound = -2; //set lower bound - lower boundary
int upperBound = 2; //set upper bound - upper boundary
double[] batFitnessVal = new double[populationSize];
double[] newbatFitnessVal = new double[populationSize];
double globalBest = double.MinValue;
ObjectInstanceSelection globalBestBat = null;
Random r = new Random();
FlowerPollinationAlgorithm fpa = new FlowerPollinationAlgorithm();
//initialize population
List <ObjectInstanceSelection> bats = InitializeBinaryBat(populationSize, subsetSize, totalInstances, prob);
List <ObjectInstanceSelection> newBats = new List <ObjectInstanceSelection>(bats.Count); //create a clone of bats
bats.ForEach((item) =>
{
newBats.Add(new ObjectInstanceSelection(item.Attribute_Values, item.Attribute_Values_Continuous, item.Frequency, item.Velocity, item.Pointers, item.Fitness)); //create a clone of flowers
});
batFitnessVal = EvaluateObjectiveFunction(bats, prob); //evaluate fitness value for all the bats
newbatFitnessVal = EvaluateObjectiveFunction(newBats, prob); //evaluate fitness value for new bats. Note: this will be the same for this function call, since pollination has not occur
BatFitness(batFitnessVal, bats); //fitness value for each bats
BatFitness(newbatFitnessVal, newBats); //fitness value for new bats
globalBestBat = EvaluateSolution(batFitnessVal, newbatFitnessVal, globalBest, bats, newBats, globalBestBat, loudness); //get the global best flower
globalBest = globalBestBat.Fitness;
//start bat algorithm
double rand = r.NextDouble(); //generate random number
for (int i = 0; i < maxGeneration; i++)
{
//loop over all bats or solutions
for (int j = 0; j < populationSize; j++)
{
for (int k = 0; k < subsetSize; k++)
{
bats[j].Frequency = frequencyMin + (frequencyMin - frequencyMax) * r.NextDouble(); //Adjust frequency
double randNum = SimpleRNG.GetNormal(); //generate random number with normal distribution
newBats[j].Velocity[k] = newBats[j].Velocity[k] + (bats[j].Attribute_Values[k] - globalBestBat.Attribute_Values[k]) * bats[j].Frequency; //update velocity
//newBats[j].Attribute_Values[k] = fpa.ConvertToBinary(newBats[j].Velocity[k], newBats[j].Attribute_Values[k]); //update bat position in the binary space
newBats[j].Attribute_Values[k] = TransferFunction(newBats[j].Velocity[k], newBats[j].Attribute_Values[k]); //update bat position in the binary space
if (rand > pulseRate)
{
newBats[j].Attribute_Values[k] = globalBestBat.Attribute_Values[k]; //change some of the dimensions of the position vector with some dimension of global best. Refer to reference for more explaination
}
}
}
//Select best solutions from the original population and matured population for the next generation;
fpa.SelectBestSolution(bats, newBats);
//evaluate new solution
newbatFitnessVal = EvaluateObjectiveFunction(newBats, prob); //evaluate fitness value for all the bats
BatFitness(newbatFitnessVal, newBats); //fitness value for new bats
globalBestBat = EvaluateSolution(batFitnessVal, newbatFitnessVal, globalBest, bats, newBats, globalBestBat, loudness); //get the global best flower
globalBest = globalBestBat.Fitness;
//if solution has converged to a optimal user-defined point, stop search
int Max = 60; // maximum percentage reduction
if (globalBest >= Max) //if the percentage reduction has approached 60%, stop search!
{
break;
}
}
//ensure that at least, N instances are selected for classification
int min = 15; //minimum number of selected instances
globalBestBat = fpa.AddInstances(globalBestBat, min);
Problem subBest = fi.buildModelMultiClass(globalBestBat, prob); //build model for the best Instance Mast
storagePercentage = Training.StoragePercentage(subBest, prob); //calculate the percent of the original training set was retained by the reduction algorithm
return(subBest);
}
private void Button1_Click(object sender, EventArgs e)
{
Random rand = new Random();
if (type < 2)
{
double Q1 = 0, Q2 = 0, Q3 = 0, N = 0;
try
{
Q1 = Convert.ToDouble(Q1textBox.Text);
Q2 = Convert.ToDouble(Q2textBox.Text);
Q3 = Convert.ToDouble(Q3textBox.Text);
N = Convert.ToDouble(NtextBox.Text);
}
catch (Exception ex)
{
MessageBox.Show("Невірні дані\n" + ex.Message, "Помилка", MessageBoxButtons.OK, MessageBoxIcon.Error);
return;
}
if (type == 0)
{
if (comboBox1.Text == Distributions.Normal)
{
for (int i = 0; i < N; i++)
{
RezDat.Add(SimpleRNG.GetNormal(Q1, Q2));
}
}
else if (comboBox1.Text == Distributions.Exp)
{
for (int i = 0; i < N; i++)
{
RezDat.Add(Math.Log(1.0 / (1 - rand.NextDouble())) / Q1);
}
}
else if (comboBox1.Text == Distributions.Line)
{
for (int i = 0; i < N; i++)
{
RezDat.Add((rand.NextDouble() * (Q2 - Q1) + Q1));
}
}
}
else if (type == 1)
{
for (int i = 0; i < FirstData.Length; i++)
{
double dat = RegresType.Model(FirstData[i], new double[] { Q1, Q2, Q3 }, comboBox1.Text);
double eps = SimpleRNG.GetNormal(0, N);
if (double.IsInfinity(dat) || double.IsNaN(dat))
{
MessageBox.Show("Неможливо створити даний тип регресії\n", "Помилка", MessageBoxButtons.OK, MessageBoxIcon.Error);
RezDat.Clear();
return;
}
dat = dat + eps;
RezDat.Add(dat);
}
}
}
else if (type == 2)
{
double A0 = 0, N = 0;
double[] A = new double[ISA.Count];
try
{
N = Convert.ToDouble(NtextBox.Text);
A0 = Convert.ToDouble(Q2textBox.Text);
string[] As = Regex.Split(Q1textBox.Text, ";");
if (As.Length != A.Length)
{
throw new System.ArgumentException("Невірний вектор A", "Помилка");
}
for (int i = 0; i < As.Length && i < A.Length; i++)
{
A[i] = Convert.ToDouble(As[i]);
}
}
catch { goto error; }
for (int i = 0; i < ISA[0].unsortl.Length; i++)
{
double dat = 0;
for (int j = 0; j < A.Length; j++)
{
dat += A[j] * ISA[j].unsortl[i];
}
dat += A0;
double eps = SimpleRNG.GetNormal(0, N);
dat = dat + eps;
RezDat.Add(dat);
}
}
this.DialogResult = DialogResult.OK;
this.Close();
return;
error:
this.DialogResult = DialogResult.Cancel;
this.Close();
}
public void UpdateVegetationAtPatch(IntVector2 patchCoord, Transform patchHolder)
{
// reset SimpleRNG using tree seed
SimpleRNG.SetSeed((uint)(treeNoiseSeed + patchCoord.x + patchCoord.y * terrainManager.worldPatchesX));
// set RNG seed depending on tree seed, world seed, and patch coordinates
treeRNG = new System.Random(treeNoiseSeed + patchCoord.x + patchCoord.y * terrainManager.worldPatchesX);
if (treePrefabs.Length > 0)
{
// get the number of trees in the patch (vegetation density)
int numberOftrees = 0;
if (true)
{
numberOftrees = Mathf.RoundToInt(Mathf.Max(0f, (float)SimpleRNG.GetNormal(treeDensity, treeDensityStdev)));
}
else
{
numberOftrees = Mathf.RoundToInt(vegetationNoiseOutsideModel.FractalNoise2D(patchCoord.x, patchCoord.y, 4, treeNoiseFrequency, treeNoiseScale) * treeDensity);
}
// place trees
for (int i = 0; i < numberOftrees; i++)
{
// scale x and z coordinates for placing prefab prototypes in terrain. Note that this is not needed when placing GameObjects
//Vector3 point = terrainManager.GetRandomPointInPatchSurface(patchCoord, treeRNG, scaleToTerrain: true);
Vector3 point = loadSceneryAndLore.GetValidRandomPositionInPatch(patchCoord, 1f, treeRNG);
float slope = terrainManager.GetSlopeAtPoint(point.x, point.z);
if (slope < 0.7f)
{ //make sure tree are not on cliffs
GameObject temp = Instantiate(treePrefabs[Random.Range(0, treePrefabs.Length)], point, Quaternion.identity);
//temp.transform.Translate(Vector3.down * 1f);
temp.transform.localScale = Vector3.one * (treeScale.min + (float)treeRNG.NextDouble() * (treeScale.max - treeScale.min));
temp.transform.Rotate(Vector3.up * Random.Range(0f, 360f));
temp.transform.parent = patchHolder.transform;
// TreeInstance temp = new TreeInstance ();
// temp.position = point;
// temp.rotation = Random.Range (0f, 360f) * Mathf.Deg2Rad;
// temp.prototypeIndex = Random.Range (0, treePrefabs.Length);
// temp.color = Color.white;
// temp.lightmapColor = Color.white;
//
// actTerrain.AddTreeInstance (temp);
}
}
}
//Debug.Log (terrainData.treeInstanceCount);
// reset SimpleRNG using cactus seed
SimpleRNG.SetSeed((uint)(cactusNoiseSeed + patchCoord.x + patchCoord.y * terrainManager.worldPatchesX));
// set RNG seed depending on cactus seed, world seed, and patch coordinates
cactusRNG = new System.Random(cactusNoiseSeed + patchCoord.x + patchCoord.y * terrainManager.worldPatchesX);
if (cactusPrefabs.Length > 0)
{
// get the number of cactus in the patch (vegetation density)
int numberOfCactus = 0;
if (true)
{
numberOfCactus = Mathf.RoundToInt(Mathf.Max((float)SimpleRNG.GetNormal(cactusDensity, cactusDensityStdev)));
}
else
{
numberOfCactus = Mathf.RoundToInt(vegetationNoiseOutsideModel.FractalNoise2D(patchCoord.x, patchCoord.y, 4, cactusNoiseFrequency, cactusNoiseScale) * cactusDensity);
}
// place trees
for (int i = 0; i < numberOfCactus; i++)
{
//Debug.Log(patchCoord.ToString());
// scale x and z coordinates for placing prefab prototypes in terrain. Note that this is not needed when placing GameObjects
//Vector3 point = terrainManager.GetRandomPointInPatchSurface(patchCoord, cactusRNG, scaleToTerrain: true);
Vector3 point = loadSceneryAndLore.GetValidRandomPositionInPatch(patchCoord, 1f, cactusRNG);
// normal diffusion
//point = new Vector3(point.x + (float)SimpleRNG.GetNormal(0, .01f), 0f, point.z + (float)SimpleRNG.GetNormal(0, 0.01f));
float slope = terrainManager.GetSlopeAtPoint(point.x, point.z);
if (slope < 0.7f)
{ //make sure cactus are not on cliffs
GameObject temp = Instantiate(cactusPrefabs[cactusRNG.Next(0, cactusPrefabs.Length)], point, Quaternion.identity);
//temp.transform.Translate(Vector3.down * 0.2f);
temp.transform.localScale = Vector3.one * (cactusScale.min + (float)cactusRNG.NextDouble() * (cactusScale.max - cactusScale.min));
temp.transform.Rotate(Vector3.up * Random.Range(0f, 360f));
temp.transform.parent = patchHolder.transform;
//TreeInstance temp = new TreeInstance();
//temp.position = point;
//temp.rotation = Random.Range(0f, 360f) * Mathf.Deg2Rad;
//temp.prototypeIndex = Random.Range(treePrefabs.Length, treePrefabs.Length + cactusPrefabs.Length);
//float randomScale = Random.Range(1f, cactusMaxScale);
//temp.widthScale = randomScale;
//temp.heightScale = randomScale;
//temp.color = Color.white;
//temp.lightmapColor = Color.white;
//actTerrain.AddTreeInstance(temp);
}
}
}
}
protected override void OnDoWork(System.ComponentModel.DoWorkEventArgs e)
{
int matrixHorizontalSize = 0;
double currentHorizontalPos = 0;
int counter = 0;
// Get Circle/Square fractions needed for round laser beam
double[,] fractions = CalcCircleSquareAreaFractions();
while (currentHorizontalPos < SampleSizeHor)
{
currentHorizontalPos = LaserBeamSize + (counter * ScanSpeed) / LaserFrequency;
counter++;
}
int[,] SampleOriginal = new int[SampleMatrix.GetLength(0), SampleMatrix.GetLength(1)];
SampleOriginal = SampleMatrix;
matrixHorizontalSize = counter;
double[,] ablatedSampleMatrix = new double
[(int)Math.Ceiling((double)SampleSizeVer / LineSpacing), // the vertical sample size divided by the LaserBeamSize
matrixHorizontalSize // the horizontal sample size divided by ScanSpeed and multiplied by LaserFrequency, to get the number of pulses in a line
]; // ** removed, i can calculate time if i need it; 1 for the resulting sum, and 1 for time
double horLocation = 0; // the horizontal, x, location of the laser beam (within a line)
int verLocation = 0; // the vertical, y, location of the laser beam (lines)
int laserPulseNumber = 0; // will be used to show which the current pulse is, within a single line scen
int lineNumber = 0; // will be used to show which the current line is
double localSum = 0; // initiation and declaration
// GasFlow * 0.01 is there because of 10 ms time precision
double ratio = (double)GasFlow * 0.01 / (double)ChamberVolume;
// now to sum up these exponential fall-out values in time, so we get a representation
// of how the concentration of ablated material/element changes in the gas with time
int maxTime = 0;
//Determine the approxiate washout time of the model, so we don't waste calculations
int washoutTime = (int)Math.Round(6.746 * Math.Pow(ratio * 100, -1.0661) * 100);
// If ratio is for example 1, (meaning 1 ml chamber, 100 ml/s gas flow), to keep the AUC for
// integrate A*e^((-t)*1) from 0 to (6.746*((1*100)^(-1.0661))*100)
// A, ergo peak, should be the ablated matrix value.
// If the ratio is 0.5, the peak should be twice the ablated matrix value.
double peakHeightFactor = ratio * 100;
double exp = Math.Exp(1);
int timeInCentiseconds = (int)Math.Floor((double)SampleSizeHor * 100 / ScanSpeed); // how much time it takes for one line to complete
double[,] finalBackmixedMatrix = new double[ablatedSampleMatrix.GetLength(0),
timeInCentiseconds];
// Calculate the number of required calculations, so we can display the percent done in the
// progress bar.
for (int i = 0; i < (ablatedSampleMatrix.GetLength(0)); i++)
{
for (int j = 0; j < (ablatedSampleMatrix.GetLength(1)); j++)
{
int offsetTime = (int)Math.Floor(((double)j / (double)LaserFrequency) * 100);
if ((offsetTime + washoutTime) < timeInCentiseconds)
{
maxTime = offsetTime + washoutTime;
}
else
{
maxTime = timeInCentiseconds;
}
HowManyCalculations += maxTime - offsetTime;
}
}
HowManyCalculations += ablatedSampleMatrix.GetLength(1) * ablatedSampleMatrix.GetLength(0);
// Simulate ablation (without backmixing)
//// Noise
Random random = new Random();
SimpleRNG.SetSeedFromSystemTime();
RNGCryptoServiceProvider secureRandom = new RNGCryptoServiceProvider();
byte[] randBytes = new byte[4];
double RSD = 3.009 * Math.Exp(-0.2293 * LaserBeamSize) + 0.0568;
/* Derived from MATLAB function fitting
* a = 3.009 (0.9428, 5.076)
* b = 0.2293 (0.1363, 0.3223)
* c = 0.0568 (0.00159, 0.112)
*/
while ((verLocation + LaserBeamSize) <= SampleSizeVer) // whole sample
{
if (CancellationPending)
{
e.Cancel = true;
return;
}
else
{
while ((horLocation + LaserBeamSize) <= SampleSizeHor) // one line
{
localSum = 0;
// this double for loop sums up the values within LaserBeamSize (down and right) of horLocation and verLocation
int LBS = LaserBeamSize;
for (int i = 0; i < LBS; i++)
{
for (int j = 0; j < LBS; j++)
{
// Square beam
if (BeamShape == false)
{
// Summation of values in the area of the matrix, that the laser ablates
localSum = localSum + SampleMatrix[i + verLocation, j + (int)Math.Floor(horLocation)];
// Ablate the thin sample, so that nothing remains in the impact site
if (ThinSample)
{
SampleMatrix[i + verLocation, j + (int)Math.Floor(horLocation)] = 0;
}
// Ablate a certain fraction
else if (!ThinSample && !ThickSample)
{
SampleMatrix[i + verLocation, j + (int)Math.Floor(horLocation)] -= (int)Math.Round(SampleOriginal[i + verLocation, j + (int)Math.Floor(horLocation)] * (1 / (double)NumberOfShots));
}
}
// Round beam. Multiply border areas with calculated fractions.
else if (BeamShape == true)
{
// Summation of values in the area of the matrix, that the laser ablates
localSum = localSum + SampleMatrix[i + verLocation, j + (int)Math.Floor(horLocation)] * fractions[i, j];
// Ablate the thin sample, so that nothing remains in the impact site
if (ThinSample)
{
SampleMatrix[i + verLocation, j + (int)Math.Floor(horLocation)] -= (int)Math.Round(SampleMatrix[i + verLocation, j + (int)Math.Floor(horLocation)] * fractions[i, j]);
}
else if (!ThinSample && !ThickSample)
{
SampleMatrix[i + verLocation, j + (int)Math.Floor(horLocation)] -= (int)Math.Round(SampleOriginal[i + verLocation, j + (int)Math.Floor(horLocation)] * fractions[i, j] * (1 / (double)NumberOfShots));
}
}
}
}
// Introduce ablation noise
secureRandom.GetBytes(randBytes);
double currentRandom = Math.Abs((double)BitConverter.ToInt32(randBytes, 0));
double noise2 = 0;
double noise = 0;
// noise = AblationNoise * localSum * ((random.NextDouble() - 0.5) * 2);
if (AblationNoise != 0)
{
noise = localSum * SimpleRNG.GetNormal(0, AblationNoise);
noise2 = SimpleRNG.GetNormal(0, AblationNoise);
}
ablatedSampleMatrix[lineNumber, laserPulseNumber] = Math.Abs(noise2 + localSum);
// resultingMatrix[lineNumber, laserPulseNumber, 1] = (int)Math.Floor((double)laserPulseNumber * 100/ LaserFrequency); // time at which the laser beam pulse had happened (in 1/100 seconds)
laserPulseNumber++;
CurrentCalc++;
horLocation = ScanSpeed * laserPulseNumber * (1 / (double)LaserFrequency);
// the next horizontal location equals the current one + how much the laser moves in one pulse
}
}
// the next vertical location is the current one + the width of the laser (LaserBeamSize); moves down one width to the next line
ReportProgress((int)(CurrentCalc * 100 / HowManyCalculations), "Simulating ... (1/3 - Sample ablation)");
lineNumber++;
laserPulseNumber = 0;
horLocation = 0;
verLocation = verLocation + LineSpacing;
// progressBar.Value = (int)(100 * ((double)verLocation / (double)SampleSizeVer));
// progressBar.Update();
}
// commented because we are putting everything in a single function
// return ablatedSampleMatrix;
// }
// this is an inherent physical property of the LA (and ICP-MS?) machine, not its technical function
// public double[,] SampleBackmix(ref double[,] ablatedSampleMatrix, ref System.Windows.Forms.ProgressBar progressBar, ref System.Windows.Forms.Label timeRemaining)
// {
//Report second step - sample backmixing
// progressReport.Text = "Simulating ... (2/3 - Sample backmixing)";
// form.Refresh();
for (int i = 0; i < (ablatedSampleMatrix.GetLength(0)); i++)
{
if (CancellationPending)
{
e.Cancel = true;
return;
}
else
{
for (int j = 0; j < (ablatedSampleMatrix.GetLength(1)); j++)
{
int offsetTime = (int)Math.Ceiling(((double)j / (double)LaserFrequency) * 100);
if ((offsetTime + washoutTime) < timeInCentiseconds)
{
maxTime = offsetTime + washoutTime;
}
else
{
maxTime = timeInCentiseconds;
}
for (int k = offsetTime; k < maxTime; k++)
{
// so it starts the curve at the right point,*100 time precision
double noise = 0;
noise = TransferNoise * ((random.NextDouble() - 0.5) * 2);
finalBackmixedMatrix[i, k] += Math.Abs(noise + (ablatedSampleMatrix[i, j] * peakHeightFactor * Math.Pow(exp, (-(k - offsetTime)) * ratio)));
}
CurrentCalc += maxTime - offsetTime;
}
}
ReportProgress((int)(CurrentCalc * 100 / HowManyCalculations), "Simulating ... (2/3 - Sample backmixing)");
}
double[,] ICPMSMatrix = new double[finalBackmixedMatrix.GetLength(0),
(int)Math.Ceiling(finalBackmixedMatrix.GetLength(1) / (AcqTime * 100))]; // time precision ( the * 100 part)
// Detector Noise generation
// Random detectorNoise = new Random();
// double strength = AmountNoise * 1 / (AdvancedNoiseSettings[10] * AdvancedNoiseSettings[11]);
// Complete noise generation
Random holisticNoise = new Random();
// ICP-MS Acquisition
for (int i = 0; i < ICPMSMatrix.GetLength(0); i++)
{
int j = 0; // raw data counter
int k = 0; // final data counter
double localSum1 = 0;
while (j < finalBackmixedMatrix.GetLength(1))
{
// adds up all the raw data within an acquisition time
// localSum1 += finalBackmixedMatrix[i, j] + strength*detectorNoise.NextDouble();
// Trapezoidal rule of approximating integration
if ((j + 1 < finalBackmixedMatrix.GetLength(1)) && ((j + 1) % (AcqTime * 100) != 0))
{
localSum1 += ((finalBackmixedMatrix[i, j] + finalBackmixedMatrix[i, j + 1]) / 2) * 0.01;
}
else
{
localSum1 += finalBackmixedMatrix[i, j] * 0.01;
}
j++;
// writes the data to the final array
if ((j % (AcqTime * 100) == 0) || j == finalBackmixedMatrix.GetLength(1)) // time precision (the * 100 part)
{
double noise = DetectorNoise * ((random.NextDouble() - 0.5) * 2);
//ICPMSMatrix[i, k] = localSum1 + holisticNoise.NextDouble()*AmountNoise ;
//ICPMSMatrix[i, k] = localSum1;
ICPMSMatrix[i, k] = Math.Abs(noise + localSum1);
localSum1 = 0;
k++;
}
}
}
ReportProgress((int)(CurrentCalc * 100 / HowManyCalculations), "Simulating ... (3/3 - ICPMS acquisition)");
e.Result = ICPMSMatrix;
}
static void doTileZeroSimulation()
{
string adaptID = "Game difficulty - Player skill";
string gameID = "TileZeroSimul";
string playerID = "EvolvingAI";
// [SC] do not update xml files
bool updateDatafiles = false;
// [SC] to randomly decide which player moves first
Random rnd = new Random();
// [SC] creating a game object
Game tzGame = new Game();
// [SC] disable logging to speed-up simulation
Cfg.enableLog(false);
double numberOfSimulations = 10;
double numberOfMatches = 400; // [SC] the number of games to be played with each AI evolution
int playableTileCount = 54;
double minRT = 60000;
double distMean = 0.75;
double distSD = 0.1;
double lowerLimit = 0.5;
double upperLimit = 1.0;
// [SC] a player is represented by an AI evolving from Very Easy AI to Very Hard AI
string[] aiOneEvolution = { TileZero.Cfg.VERY_EASY_AI
, TileZero.Cfg.EASY_AI
, TileZero.Cfg.MEDIUM_COLOR_AI
, TileZero.Cfg.MEDIUM_SHAPE_AI
, TileZero.Cfg.HARD_AI
, TileZero.Cfg.VERY_HARD_AI };
// [SC] start simulations
for (int simIndex = 0; simIndex < numberOfSimulations; simIndex++)
{
TwoA twoA = new TwoA(new MyBridge());
twoA.SetTargetDistribution(distMean, distSD, lowerLimit, upperLimit);
double avgAccuracy = 0;
// [SC] start AI player evolution from easiest to hardest
foreach (string aiOneID in aiOneEvolution)
{
// [SC] play a number of games before eacg evolution
for (int matchIndex = 0; matchIndex < numberOfMatches; matchIndex++)
{
// [SC] get a recommended opponent AI from TwoA
string aiTwoID = null;
aiTwoID = twoA.TargetScenarioID(adaptID, gameID, playerID);
Cfg.clearLog();
// [SC] start one match simualtion
int startPlayerIndex = rnd.Next(2); // [SC] decide whether the player or the opponent moves first
tzGame.initNewGame(aiOneID, aiTwoID, playableTileCount, startPlayerIndex);
tzGame.startNewGame();
// [SC] simulate player's response time
SimpleRNG.SetSeedFromRandom();
double rt = SimpleRNG.GetNormal(120000, 10000); // [SC] max is 900000
if (rt < minRT)
{
rt = minRT;
}
// [SC] obtain player's accuracy
double correctAnswer = 0;
if (tzGame.getPlayerByIndex(0).WinFlag)
{
correctAnswer = 1;
}
avgAccuracy += correctAnswer;
printMsg("Counter: " + (matchIndex + 1) + "; Start index: " + startPlayerIndex);
//printMsg(TileZero.Cfg.getLog());
// [SC] update ratings
twoA.UpdateRatings(adaptID, gameID, playerID, aiTwoID, rt, correctAnswer, updateDatafiles);
}
}
printMsg(String.Format("Accurracy for simulation {0}: {1}", simIndex, avgAccuracy / (numberOfMatches * aiOneEvolution.Length)));
// [SC] creating a file with final ratings
using (StreamWriter ratingfile = new StreamWriter("S4/ratingfile" + simIndex + ".txt")) {
string line = ""
+ "\"ScenarioID\""
+ "\t" + "\"Rating\""
+ "\t" + "\"PlayCount\""
+ "\t" + "\"Uncertainty\"";
ratingfile.WriteLine(line);
foreach (string scenarioID in aiOneEvolution)
{
line = ""
+ "\"" + scenarioID + "\""
+ "\t" + "\"" + twoA.ScenarioRating(adaptID, gameID, scenarioID) + "\""
+ "\t" + "\"" + twoA.ScenarioPlayCount(adaptID, gameID, scenarioID) + "\""
+ "\t" + "\"" + twoA.ScenarioUncertainty(adaptID, gameID, scenarioID) + "\"";
ratingfile.WriteLine(line);
}
}
// [SC] creating a file with history of gameplays including player and scenario ratings
using (StreamWriter datafile = new StreamWriter("S4/datafile" + simIndex + ".txt")) {
GameplaysData gameplays = twoA.GameplaysData;
TwoAAdaptation adaptNode = gameplays.Adaptation.First(p => p.AdaptationID.Equals(adaptID));
TwoAGame gameNode = adaptNode.Game.First(p => p.GameID.Equals(gameID));
string line = ""
+ "\"ID\""
+ "\t" + "\"PlayerID\""
+ "\t" + "\"ScenarioID\""
+ "\t" + "\"Timestamp\""
+ "\t" + "\"RT\""
+ "\t" + "\"Accuracy\""
+ "\t" + "\"PlayerRating\""
+ "\t" + "\"ScenarioRating\"";
datafile.WriteLine(line);
int id = 1;
foreach (TwoAGameplay gp in gameNode.Gameplay)
{
line = ""
+ "\"" + id++ + "\""
+ "\t" + "\"" + gp.PlayerID + "\""
+ "\t" + "\"" + gp.ScenarioID + "\""
+ "\t" + "\"" + gp.Timestamp + "\""
+ "\t" + "\"" + gp.RT + "\""
+ "\t" + "\"" + gp.Accuracy + "\""
+ "\t" + "\"" + gp.PlayerRating + "\""
+ "\t" + "\"" + gp.ScenarioRating + "\"";
datafile.WriteLine(line);
}
}
}
}
private List <int> ProlongOperator(List <int> mutatedValues, int segmentPosition)
{
int prolongLength = (int)Math.Ceiling(Math.Abs(SimpleRNG.GetNormal())) + 1;
int sign = mutatedValues[segmentPosition] / Math.Abs(mutatedValues[segmentPosition]);
// Prolonging operator may not be applicable in full length
// If segment extends throughout the whole field, operator is not applicable at all
if (sign < 0)
{
if (mutatedValues[segmentPosition] + sign * prolongLength < -39)
{
prolongLength = Math.Abs(-39 - mutatedValues[segmentPosition]);
}
}
else
{
if (mutatedValues[segmentPosition] + sign * prolongLength > 29)
{
prolongLength = 29 - mutatedValues[segmentPosition];
}
}
if (prolongLength == 0)
{
return(mutatedValues);
}
mutatedValues[segmentPosition] = sign * (Math.Abs(mutatedValues[segmentPosition]) + prolongLength);
int toShorten = prolongLength;
while (toShorten > 0)
{
int randomParallelSegmentIndex = FindRandomParalellSegment(mutatedValues, segmentPosition);
if (Math.Abs(mutatedValues[randomParallelSegmentIndex]) > toShorten)
{
mutatedValues[randomParallelSegmentIndex] =
sign * (Math.Abs(mutatedValues[randomParallelSegmentIndex]) - toShorten);
toShorten = 0;
}
else
{
toShorten -= Math.Abs(mutatedValues[randomParallelSegmentIndex]);
if (randomParallelSegmentIndex > 0 & randomParallelSegmentIndex < mutatedValues.Count - 1)
{
mutatedValues[randomParallelSegmentIndex - 1] += mutatedValues[randomParallelSegmentIndex + 1];
mutatedValues.RemoveRange(randomParallelSegmentIndex, 2);
if (segmentPosition > randomParallelSegmentIndex)
{
segmentPosition -= 2;
}
}
else
{
mutatedValues.RemoveAt(randomParallelSegmentIndex);
if (randomParallelSegmentIndex == 0)
{
segmentPosition -= 1;
}
}
}
}
return(mutatedValues);
}
public Problem CuckooSearch(Problem prob, out double storagePercentage)
{
int nNests = 5; //number of nests, or number of solutions
int subsetSize = 100;
int maxGen = 5; //maximum generation
double discoveryRate = 0.25; //discovery rate of alien eggs
double tolerance = Math.Exp(-5);
int lowerBound = -5;
int upperBound = 5;
int totalInstances = prob.X.Count(); //problem size
double[] cuckooFitnessVal = new double[nNests];
double[] newCuckooFitnessVal = new double[nNests];
ObjectInstanceSelection globalBestCuckoo = null;
double globalBest = double.MinValue;
Random rand = new Random();
FlowerPollinationAlgorithm fpa = new FlowerPollinationAlgorithm();
//initialize population
List <ObjectInstanceSelection> cuckoos = InitializeBinaryCuckoo(nNests, subsetSize, totalInstances, prob);
List <ObjectInstanceSelection> newCuckoos = new List <ObjectInstanceSelection>(cuckoos.Count); //create a clone of bats
cuckoos.ForEach((item) =>
{
newCuckoos.Add(new ObjectInstanceSelection(item.Attribute_Values, item.Attribute_Values_Continuous, item.Pointers, item.Fitness)); //create a clone of flowers
});
cuckooFitnessVal = EvaluateObjectiveFunction(cuckoos, prob); //evaluate fitness value for all the bats
newCuckooFitnessVal = EvaluateObjectiveFunction(newCuckoos, prob); //evaluate fitness value for new bats. Note: this will be the same for this function call, since pollination has not occur
CuckooFitness(cuckooFitnessVal, cuckoos); //fitness value for each bats
CuckooFitness(newCuckooFitnessVal, newCuckoos); //fitness value for new bats
globalBestCuckoo = EvaluateSolution(cuckooFitnessVal, newCuckooFitnessVal, globalBest, cuckoos, newCuckoos, globalBestCuckoo); //get the global best flower
globalBest = globalBestCuckoo.__Fitness;
//generate new solutions
double beta = 3 / 2;
double A = fp.Gamma(1 + beta) * Math.Sin(Math.PI * (beta / 2));
double B = fp.Gamma((1 + beta) / 2) * beta;
double C = (beta - 1) / 2;
double D = Math.Pow(2, C);
double E = A / (B * D);
double sigma = Math.Pow(E, (1 / beta));
double F;
double G;
double step;
double stepSize;
int x = 0;
for (int i = 0; i <= maxGen; i++)
{
for (int j = 0; j < nNests; j++)
{
for (int k = 0; k < subsetSize; k++)
{
F = SimpleRNG.GetNormal() * sigma;
G = SimpleRNG.GetNormal();
step = F / Math.Pow(Math.Abs(G), (1 / beta));
//In the next equation, the difference factor (s-best) means that when the solution is the best solution, it remains unchanged.
//Here the factor 0.01 comes from the fact that L/100 should the typical step size of walks/flights where L is the typical lenghtscale;
//otherwise, Levy flights may become too aggresive/efficient, which makes new solutions (even) jump out side of the design domain (and thus wasting evaluations).
stepSize = 0.01 * step * (cuckoos[j].Attribute_Values[k] - globalBestCuckoo.Attribute_Values[k]);
//Now the actual random walks or levyy flights
newCuckoos[j].Attribute_Values[k] = fi.Binarize((newCuckoos[j].Attribute_Values[k] + stepSize) * SimpleRNG.GetNormal(), rand.NextDouble());
if (cuckoos[j].Attribute_Values[k] == 1 && newCuckoos[j].Attribute_Values[k] == 0)
{
x++;
}
}
}
//discovery and randomization - replace some nest by constructing new solutions
newCuckoos = EmptyNest(cuckoos, newCuckoos, discoveryRate, subsetSize, nNests);
//Select best solutions from the original population and matured population for the next generation;
fpa.SelectBestSolution(cuckoos, newCuckoos);
//evaluate new solution
newCuckooFitnessVal = EvaluateObjectiveFunction(newCuckoos, prob); //evaluate fitness value for all the bats
CuckooFitness(newCuckooFitnessVal, newCuckoos); //fitness value for new bats
globalBestCuckoo = EvaluateSolution(cuckooFitnessVal, newCuckooFitnessVal, globalBest, cuckoos, newCuckoos, globalBestCuckoo); //get the global best flower
globalBest = globalBestCuckoo.Fitness;
//if solution has converged to a optimal user-defined point, stop search
int Max = 60; // maximum percentage reduction
if (globalBest >= Max) //if the percentage reduction has approached 60%, stop search!
{
break;
}
}
//ensure that at least, N instances are selected for classification
int min = 40; //minimum number of selected instances
globalBestCuckoo = fpa.AddInstances(globalBestCuckoo, min);
Problem subBest = fi.buildModelMultiClass(globalBestCuckoo, prob); //build model for the best Instance Mast
storagePercentage = Training.StoragePercentage(subBest, prob); //calculate the percent of the original training set was retained by the reduction algorithm
return(subBest);
}
private List <int> BreakOperator(List <int> mutatedValues, int segmentPosition)
{
// 1.) Choose random position in the selected segment
int selectedPosition = random.Next(1, Math.Abs(mutatedValues[segmentPosition]));
// 2.) Check if break operator can be applied
int upMovements = 0;
int rightMovements = 0;
for (int i = 0; i < segmentPosition; i++)
{
if (mutatedValues[i] < 0)
{
rightMovements -= mutatedValues[i];
}
else
{
upMovements += mutatedValues[i];
}
}
// Operator can only be applied if segments don't have either X or Y coordinate at maximum possible value
if (upMovements == 29 || rightMovements == 39)
{
return(mutatedValues);
}
// Operator cannot be applied to segments of length 1
if (Math.Abs(mutatedValues[segmentPosition]) == 1)
{
return(mutatedValues);
}
// 3.) Generate length of breaking segment using normal distribution
int breakLength = (int)Math.Abs(Math.Ceiling(SimpleRNG.GetNormal()));
if (breakLength == 0)
{
return(mutatedValues);
}
// Shorten break length to maximum allowed value if needed
if (mutatedValues[segmentPosition] < 0)
{
if (upMovements + breakLength > 30)
{
breakLength = 30 - upMovements;
}
}
else
{
if (rightMovements + breakLength > 40)
{
breakLength = 40 - rightMovements;
}
}
// 4.) Mutate parent
int currentChangedSegmentPosition = segmentPosition + 1;
int sign = mutatedValues[segmentPosition] / (Math.Abs(mutatedValues[segmentPosition]));
// a) Shorten segment that is going to be broken
int otherHalfOfBrokenSegment = sign * (Math.Abs(mutatedValues[segmentPosition]) - selectedPosition);
mutatedValues[segmentPosition] = sign * selectedPosition;
// b) Add perpendicular breaking segment
mutatedValues.Insert(segmentPosition + 1, -1 * sign * breakLength);
// c) Add remaining part of shortened segment to the path
mutatedValues.Insert(segmentPosition + 2, otherHalfOfBrokenSegment);
// d) Shorten next following segment parallel with the break segment
int toShorten = breakLength;
while (toShorten > 0)
{
int randomParallelSegmentIndex = FindRandomParalellSegment(mutatedValues, currentChangedSegmentPosition);
if (Math.Abs(mutatedValues[randomParallelSegmentIndex]) > toShorten)
{
mutatedValues[randomParallelSegmentIndex] =
sign * -1 * (Math.Abs(mutatedValues[randomParallelSegmentIndex]) - toShorten);
toShorten = 0;
}
else
{
toShorten -= Math.Abs(mutatedValues[randomParallelSegmentIndex]);
if (randomParallelSegmentIndex > 0 & randomParallelSegmentIndex < mutatedValues.Count - 1)
{
mutatedValues[randomParallelSegmentIndex - 1] += mutatedValues[randomParallelSegmentIndex + 1];
mutatedValues.RemoveRange(randomParallelSegmentIndex, 2);
if (currentChangedSegmentPosition > randomParallelSegmentIndex)
{
currentChangedSegmentPosition -= 2;
}
}
else
{
mutatedValues.RemoveAt(randomParallelSegmentIndex);
if (randomParallelSegmentIndex == 0)
{
currentChangedSegmentPosition -= 1;
}
}
}
}
return(mutatedValues);
}
