private void Gs_IterationCompleted(object sender, IterationCompletedEventArgs e)
{
AddPointsToChart(e.IterationCount, e.BestChromosome.Fitness, e.AverageFitness);
Decode(e.BestChromosome);
PopulationChart.Refresh();
Application.DoEvents();
}
private static void G_IterationCompleted(object sender, IterationCompletedEventArgs e)
{
var chromosome = e.BestChromosome;
var v1 = (int)chromosome.Data[0];
var v2 = (int)chromosome.Data[1];
var v3 = (int)chromosome.Data[2];
var v4 = (int)chromosome.Data[3];
Console.WriteLine($"{e.BestChromosome.Fitness} {e.AverageFitness} Iteration completed | {v1} {v2} {v3} {v4} | {v1 + v2 + v3 + v4}");
}
private void Gs_IterationCompleted(object sender, IterationCompletedEventArgs e)
{
BestSolution = e.BestChromosome;
AddPointsToChart(e.IterationCount, BestSolution.Fitness, e.AverageFitness);
label1.Text = $"Total Length : {(int)CalculateFitness_TSPProblem(BestSolution)}";
map.Refresh();
PopulationChart.Refresh();
Application.DoEvents();
}
private void reactionDiffusionControl_IterationCompleted(object sender, IterationCompletedEventArgs e)
{
TimeSpan frameTime = DateTime.Now - this._startFrameTime;
this.Dispatcher.BeginInvoke(
DispatcherPriority.Background,
new DispatcherOperationCallback(
delegate(object param)
{
this.textBlock6.Text = param as string;
return(null);
}), frameTime.TotalMilliseconds.ToString());
this._startFrameTime = DateTime.Now;
}
private void Gs_IterationCompleted(object sender, IterationCompletedEventArgs e)
{
var best_chromosome = e.BestChromosome;
queen_positions = new List <Point>();
foreach (var pt in best_chromosome.Data)
{
queen_positions.Add((Point)pt);
}
AddPointsToChart(e.IterationCount, best_chromosome.Fitness, e.AverageFitness);
Application.DoEvents();
Chessboard.Refresh();
}
private void reactionDiffusionControl_IterationCompleted( object sender, IterationCompletedEventArgs e )
{
TimeSpan frameTime = DateTime.Now - this._startFrameTime;
this.Dispatcher.BeginInvoke(
DispatcherPriority.Background,
new DispatcherOperationCallback(
delegate( object param )
{
this.textBlock6.Text = param as string;
return null;
} ), frameTime.TotalMilliseconds.ToString() );
this._startFrameTime = DateTime.Now;
}
private void Gs_IterationCompleted(object sender, IterationCompletedEventArgs e)
{
resultlist.Items.Add("= Iteration Completed =========");
int val1 = (int)e.BestChromosome.Data[0];
int val2 = (int)e.BestChromosome.Data[1];
int val3 = (int)e.BestChromosome.Data[2];
int val4 = (int)e.BestChromosome.Data[3];
int val5 = (int)e.BestChromosome.Data[4];
int val6 = (int)e.BestChromosome.Data[5];
int val7 = (int)e.BestChromosome.Data[6];
int val8 = (int)e.BestChromosome.Data[7];
int val9 = (int)e.BestChromosome.Data[8];
int val10 = (int)e.BestChromosome.Data[9];
resultlist.Items.Add($"{val1} + {val2} + {val3} + {val4} + {val5} = {val1 + val2 + val3 + val4 + val5}");
resultlist.Items.Add($"{val6} * {val7} * {val8} * {val9} * {val10} = {val6 * val7 * val8 * val9 * val10}");
AddPointsToChart(e.IterationCount, e.BestChromosome.Fitness, e.AverageFitness);
PopulationChart.Refresh();
Application.DoEvents();
}
/// <summary>
/// Raises the InterationCompleted event.
/// </summary>
/// <param name="e"></param>
protected virtual void OnInterationCompleted(IterationCompletedEventArgs e)
{
Trace.Assert(e != null);
if (IterationCompleted != null)
{
IterationCompleted(this, e);
}
}
/// <summary>
/// Computes one frame of the reaction.
/// </summary>
/// <returns>An array containing the reaction products for the next frame. </returns>
public unsafe double[, ,] ComputeReaction()
{
Debug.Assert(CheckInvariant(), "ComputeReaction: CheckInvariant failed");
double Xa;
double Xb;
double Xc;
double c;
double d;
double f;
double g0;
double feedbackFromC;
double feedbackFromB;
iteration++;
// If boundary conditions have changed since the previous frame, update them.
if (this._boundaryConditionsPrev != this._boundaryConditions)
{
this.SetBoundaryConditions(this._boundaryConditions);
this._boundaryConditionsPrev = this._boundaryConditions;
}
// Copy boundary values to the out buffer.
for (int i = 0; i < vesselWidth; i++)
{
reactionOut[indexA, i, 0] = reaction[indexA, i, 0];
reactionOut[indexB, i, 0] = reaction[indexB, i, 0];
reactionOut[indexC, i, 0] = reaction[indexC, i, 0];
reactionOut[indexA, i, vesselHeight - 1] = reaction[indexA, i, vesselHeight - 1];
reactionOut[indexB, i, vesselHeight - 1] = reaction[indexB, i, vesselHeight - 1];
reactionOut[indexC, i, vesselHeight - 1] = reaction[indexC, i, vesselHeight - 1];
}
for (int j = 0; j < vesselHeight; j++)
{
reactionOut[indexA, 0, j] = reaction[indexA, 0, j];
reactionOut[indexB, 0, j] = reaction[indexB, 0, j];
reactionOut[indexC, 0, j] = reaction[indexC, 0, j];
reactionOut[indexA, vesselWidth - 1, j] = reaction[indexA, vesselWidth - 1, j];
reactionOut[indexB, vesselWidth - 1, j] = reaction[indexB, vesselWidth - 1, j];
reactionOut[indexC, vesselWidth - 1, j] = reaction[indexC, vesselWidth - 1, j];
}
// Traverse the grid and compute concentrations for
// all three reactants. This implements the Gontar model.
for (int i = 1; i < vesselWidth - 1; i++)
{
for (int j = 1; j < vesselHeight - 1; j++)
{
try
{
// Compute concentrations at cell (i,j).
feedbackFromC = Math.Exp( -W1 / weight( indexC, i, j ) );
feedbackFromB = Math.Exp( -W2 * weight( indexB, i, j ) );
g0 = ( K1 * K2 * feedbackFromC * feedbackFromB ) / ( 1.0 + K1 * feedbackFromC );
// Compute concentrations at cell (i,j).
//c = -W1 / weight(2, i, j);
//f = -W2 * weight(1, i, j);
//e_to_c = Math.Exp(c);
//e_to_f = Math.Exp(f);
//d = 1.0 + K1 * e_to_c;
//g0 = (K1 * K2 * e_to_c * e_to_f) / d;
Xc = b * (g0 / (g0 + 1));
if (Xc <= minConcentration) Xc = minConcentration;
Xb = (K1 * feedbackFromC / (1 + K1 * feedbackFromC)) * (b - Xc);
if (Xb <= minConcentration) Xb = minConcentration;
Xa = b - Xb - Xc;
if (Xa <= minConcentration) Xa = minConcentration;
if (Xa > b) Xa = b;
if (Xb > b) Xb = b;
if (Xc > b) Xc = b;
reactionOut[indexA, i, j] = Xa;
reactionOut[indexB, i, j] = Xb;
reactionOut[indexC, i, j] = Xc;
}
catch (Exception ex)
{
Console.WriteLine("caught exception: ", ex.ToString());
}
}
}
int ubi = reaction.GetUpperBound(1) + 1;
int ubj = reaction.GetUpperBound(2) + 1;
// Copy the out buffer to the in buffer.
fixed (double* pOutBuff = reactionOut, pInBuff = reaction)
{
uint buffSize = (uint)(3 * ubi * ubj * sizeof(double));
uint count = buffSize;
double* ps = pOutBuff;
double* pd = pInBuff;
lock (reactionOut.SyncRoot)
{
try
{
CopyMemory((void*)pd, (void*)ps, count);
}
catch (Exception ex)
{
Trace.WriteLine(ex.Message);
}
}
}
// Raise the IterationCompleted event.
IterationCompletedEventArgs e = new IterationCompletedEventArgs(this.iteration);
OnInterationCompleted(e);
return reactionOut;
}
private static void G_IterationCompleted(object sender, IterationCompletedEventArgs e)
{
Decode(e.BestChromosome);
}
/// <summary>
/// Run the k-means clustering algorithm for a specified amount of iterations.
/// </summary>
/// <param name="iterations">Iterations to run.</param>
/// <param name="allowEmptyClusters">True to allow the optimization to leave clusters empty.</param>
public void Run(int iterations, bool allowEmptyClusters)
{
var rnd = new Random();
bool hasChanged;
var iteration = 0;
do
{
hasChanged = false;
// Compute cluster averages
var averages = new List <VectorNd>(this.Clusters.Count);
this.Clusters.ForEach(cluster => averages.Add(cluster.Average()));
// Create placeholder clusters for next iteration
var newClusters = new List <KMeansCluster>(this.Clusters.Count);
for (var i = 0; i < this.Clusters.Count; i++)
{
newClusters.Add(new KMeansCluster());
}
// Find the closest average for each vector in each cluster
this.Clusters.ForEach(
cluster =>
{
var ind = this.Clusters.IndexOf(cluster);
for (var i = 0; i < cluster.Count; i++)
{
var vector = cluster[i];
var simIndex = this.FindIndexOfSimilar(averages, vector);
newClusters[simIndex].Add(vector);
if (simIndex != ind)
{
hasChanged = true;
}
}
});
// Check for empty clusters
if (!allowEmptyClusters)
{
newClusters.ForEach(
cluster =>
{
if (cluster.Count == 0)
{
Console.WriteLine("Cluster has no mass");
var biggest = newClusters.OrderByDescending(x => x.Count)
.First();
var randomVector = biggest[rnd.Next(biggest.Count)];
biggest.Remove(randomVector);
cluster.Add(randomVector);
}
});
}
// Update clusters and increase iteration
this.Clusters = newClusters;
var iterArgs = new IterationCompletedEventArgs
{
Iteration = iteration, Clusters = newClusters
};
this.OnIterationCompleted(iterArgs);
iteration++;
this.currentIterations++;
} while (hasChanged &&
iteration < iterations &&
this.currentIterations < this.maxIterations);
}
/// <summary> /// Method to call when an iteration is completed. /// </summary> /// <param name="iterArgs">Data for the current iteration.</param> protected virtual void OnIterationCompleted(IterationCompletedEventArgs iterArgs) => this.IterationCompleted?.Invoke(this, iterArgs);
