private void Solve_Click(object sender, RoutedEventArgs e)
{
FEM fem = new FEM(obj);
fem.RunSimulation();
DataContext = new ExObjectView(fem.DefformatedObject, obj.Width, obj.Length, obj.Height);
}
public static List <string> Function(Population pop, Dictionary <int, Matrix <double> > outputs, Matrix <double> coords, Matrix <double> outputTargets, int subdivisions, Metrics metrics)
{
//can manually specify a target occupancy using this function
//occupancyTarget = CreateTargetOccupancy(outputs[pop.Genomes[0].ID].RowCount, outputs[pop.Genomes[0].ID].ColumnCount, coords);
//convert to binary
//var occupancy = Fitness.OccupancyFromOutputs(outputs);
var occupancy = outputs;
var fitnesses = new List <double>();
var fitnessStrings = new List <string>();
Accord.Math.Random.Generator.Seed = 0;
//loop over each member of the population and calculate fitness components
Parallel.ForEach(occupancy.Keys, (key) =>
//foreach (KeyValuePair<int, Matrix<double> > output in occupancy)
{
//All Weights are Max 10, Min 0
var outputID = key;
var outputValues = occupancy[key];
var totalFitness = 0.0;
var fitnessString = "";
//flatten occupancy matrix
var vals = outputValues.ToRowMajorArray();
if ((metrics & Metrics.Luminance) == Metrics.Luminance)
{
var outputLum = ImageProcessing.Image.GetMeanLuminance(outputValues, subdivisions);
var targetLum = ImageProcessing.Image.GetMeanLuminance(outputTargets, subdivisions);
var absdiff = Math.Abs(outputLum - targetLum);
absdiff = Utils.Map(absdiff, 0, 1.0, 0, 10);
totalFitness += Math.Pow(absdiff, 2);
fitnessString += $" | Luminance : {Math.Round(Math.Pow(absdiff, 2), 2)}";
}
if ((metrics & Metrics.Contrast) == Metrics.Contrast)
{
var outputContrast = ImageProcessing.Image.GetContrast(outputValues, subdivisions);
var targetContrast = ImageProcessing.Image.GetContrast(outputTargets, subdivisions);
var absdiff = Math.Abs(outputContrast - targetContrast);
absdiff = Utils.Map(absdiff, 0, 1.0, 0, 10);
totalFitness += Math.Pow(absdiff, 2);
fitnessString += $" | Contrast : {Math.Round(Math.Pow(absdiff, 2), 2)}";
}
if ((metrics & Metrics.Hue) == Metrics.Hue)
{
//var outputHue = ImageAnalysis.GetHue(outputValues, subdivisions);
//var targetHue = ImageAnalysis.GetHue(outputTargets, subdivisions);
var temp = ImageProcessing.Image.GetHueDist(outputValues, outputTargets, subdivisions);
//var outputHue = outputValues.Column(0).Average();
//var targetHue = outputTargets.Column(0).Average();
//var absdiff = Math.Abs(outputHue - targetHue);
//var outputa = MathNet.Numerics.Statistics.Statistics.StandardDeviation(outputValues.Column(0));
//var targeta = MathNet.Numerics.Statistics.Statistics.StandardDeviation(outputTargets.Column(0));
//absdiff += Math.Abs(outputa - targeta);
//absdiff = Utils.Map(absdiff, 0, 255, 0, 10);
var absdiff = Utils.Map(temp, 0, 1.0, 0, 10);
totalFitness += absdiff;
fitnessString += $" | Hue : {Math.Round(absdiff, 2)}";
}
if ((metrics & Metrics.HueVar) == Metrics.HueVar)
{
var outputHue = ImageProcessing.Image.GetHueVar(outputValues, subdivisions);
var targetHue = ImageProcessing.Image.GetHueVar(outputTargets, subdivisions);
var absdiff = Math.Abs(outputHue - targetHue);
absdiff = Utils.Map(absdiff, 0, 100, 0, 10);
totalFitness += Math.Pow(absdiff, 2);
fitnessString += $" | HueVar : {Math.Round(Math.Pow(absdiff, 2), 2)}";
}
if ((metrics & Metrics.Sat) == Metrics.Sat)
{
var outputHue = ImageProcessing.Image.GetSat(outputValues, subdivisions);
var targetHue = ImageProcessing.Image.GetSat(outputTargets, subdivisions);
var absdiff = Math.Abs(outputHue - targetHue);
absdiff = Utils.Map(absdiff, 0, 1.0, 0, 10);
totalFitness += Math.Pow(absdiff, 2);
fitnessString += $" | Sat : {Math.Round(Math.Pow(absdiff, 2), 2)}";
}
if ((metrics & Metrics.SatVar) == Metrics.SatVar)
{
var outputHue = ImageProcessing.Image.GetSatVar(outputValues, subdivisions);
var targetHue = ImageProcessing.Image.GetSatVar(outputTargets, subdivisions);
var absdiff = Math.Abs(outputHue - targetHue);
absdiff = Utils.Map(absdiff, 0, 1.0, 0, 10);
totalFitness += Math.Pow(absdiff, 2);
fitnessString += $" | SatVar : {Math.Round(Math.Pow(absdiff, 2), 2)}";
}
if ((metrics & Metrics.Height) == Metrics.Height)
{
//Height
var fitnessHeight = 0.0;
for (int i = 0; i < subdivisions; i++)
{
var cellsInHoriz = vals.Skip(i * subdivisions * subdivisions).Take(subdivisions * subdivisions).Sum();
if (cellsInHoriz > 0)
{
fitnessHeight++;
}
}
fitnessHeight = Utils.Map(fitnessHeight, 0, subdivisions, 0, 10);
totalFitness += fitnessHeight;
fitnessString += $" | Height : {Math.Round(fitnessHeight, 2)}";
}
if ((metrics & Metrics.Depth) == Metrics.Depth)
{
var fitnessDepth = 0.0;
for (int i = 0; i < subdivisions; i++)
{
var cellsInVert = 0.0;
for (int j = 0; j < subdivisions; j++)
{
cellsInVert += vals.Skip(j * subdivisions * subdivisions + i * subdivisions).Take(subdivisions).Sum();
}
if (cellsInVert > 0)
{
fitnessDepth++;
}
}
fitnessDepth = Utils.Map(fitnessDepth, 0, subdivisions, 0, 10);
totalFitness += fitnessDepth;
fitnessString += $" | Depth : {Math.Round(fitnessDepth, 2)}";
}
if ((metrics & Metrics.Size) == Metrics.Size)
{
//Cell Count
var occupancyCount = vals.Sum();
var fitnessSize = (10.0 - (10.0 * occupancyCount / (Math.Pow(subdivisions, coords.ColumnCount)))); //punish high cell count
totalFitness += fitnessSize;
fitnessString += $" | Size : {Math.Round(fitnessSize, 2)}";
}
if ((metrics & Metrics.Displacement) == Metrics.Displacement)
{
//FEM
var fitnessDisplacement = 0.0;
try
{
var FEMModel = FEM.CreateModel(coords, outputValues, subdivisions, subdivisions, subdivisions);
pop.GetGenomeByID(outputID).FEMModel = FEMModel;
var displacements = FEM.GetDisplacements(FEMModel);
var scaled = displacements.Max(i => Math.Abs(i)) * 10e7;
var standardised = 10 - Utils.Map(scaled, 0.01, 1.00, 0, 10);
if (standardised > 10)
{
standardised = 10.0;
}
if (standardised < 0)
{
standardised = 0.0;
}
fitnessDisplacement = standardised;
}
catch
{
fitnessDisplacement = -10.0;
}
totalFitness += fitnessDisplacement;
fitnessString += $" | Displacement : {Math.Round(fitnessDisplacement, 2)}";
}
if ((metrics & Metrics.BottomLayer) == Metrics.BottomLayer)
{
//Bottom Layer
var bottomLayerCount = vals.Take(subdivisions * subdivisions).Sum();
var fitnessBottomLayer = 0.0;
if (bottomLayerCount > 50.0)
{
fitnessBottomLayer = 10.0;
}
else
{
fitnessBottomLayer = bottomLayerCount / 5.0;
}
totalFitness += fitnessBottomLayer;
fitnessString += $" | BottomLayer : {Math.Round(fitnessBottomLayer, 2)}";
}
if ((metrics & Metrics.TopLayer) == Metrics.TopLayer)
{
//Top Layer
var topLayerCount = vals.Skip(subdivisions * subdivisions * (subdivisions - 1)).Take(subdivisions * subdivisions).Sum();
var fitnessTopLayer = 0.0;
if (topLayerCount > 60.0)
{
fitnessTopLayer = 10.0;
}
else
{
fitnessTopLayer = topLayerCount / 6.0;
}
totalFitness += fitnessTopLayer;
fitnessString += $" | TopLayer : {Math.Round(fitnessTopLayer, 2)}";
}
//could tidy this error checking
if ((metrics & Metrics.L1) == Metrics.L1 & outputTargets != null)
{
var fitnessL1Norm = (outputValues - outputTargets).PointwiseAbs().ToRowMajorArray().Sum();
fitnessL1Norm = Utils.Map(fitnessL1Norm, 0.00, subdivisions * subdivisions * outputTargets.ColumnCount, 0, 10.0);
totalFitness += fitnessL1Norm;
fitnessString += $" | L1Norm : {Math.Round(fitnessL1Norm, 2)}";
}
if ((metrics & Metrics.L2) == Metrics.L2 & outputTargets != null)
{
var fitnessL2Norm = Math.Sqrt((outputValues - outputTargets).PointwisePower(2).ToRowMajorArray().Sum());
fitnessL2Norm = Utils.Map(fitnessL2Norm, 0.00, Math.Sqrt(subdivisions * subdivisions * outputTargets.ColumnCount), 0, 10.0);
totalFitness += fitnessL2Norm;
fitnessString += $" | L2Norm : {Math.Round(fitnessL2Norm, 2)}";
}
if ((metrics & Metrics.Gabor) == Metrics.Gabor & outputTargets != null)
{
var gaborFitness = Superimpose(outputTargets, outputValues, 4 * subdivisions, subdivisions);
gaborFitness = Utils.Map(gaborFitness, 0, 40.0, 0.0, 10.0);
totalFitness += gaborFitness;
fitnessString += $" | Gabor : {Math.Round(gaborFitness, 2)}";
}
// fitnessString += $" | Total : {Math.Round(totalFitness, 2)}";
pop.GetGenomeByID(outputID).Fitness = totalFitness;
pop.GetGenomeByID(outputID).FitnessAsString = fitnessString;
}
);
if ((metrics & Metrics.Pattern) == Metrics.Pattern)
{
if (Fitness.bowModel == null)
{
Fitness.bowModel = ImageProcessing.Image.CreateBowModel();
}
var featDistance = ImageProcessing.Image.GetFeatureDistances(occupancy, subdivisions, Fitness.bowModel);
foreach (var key in featDistance.Keys)
{
var featCorrect = featDistance[key];
featCorrect = Utils.Map(featCorrect, 0, 40.0, 0.0, 10.0);
pop.GetGenomeByID(key).Fitness += featCorrect;
var fitnessString = $" | Pattern : {Math.Round(featCorrect, 2)}";
pop.GetGenomeByID(key).FitnessAsString += fitnessString;
}
}
Parallel.ForEach(occupancy.Keys, (key) =>
{
pop.GetGenomeByID(key).FitnessAsString += $" | Total : {Math.Round(pop.GetGenomeByID(key).Fitness, 2)}";
});
//need to figure out total fitness
SortByFitness(pop);
fitnesses = pop.Genomes.Select(i => i.Fitness).ToList();
fitnessStrings = pop.Genomes.Select(i => i.FitnessAsString).ToList();
return(fitnessStrings);
//have a config setting for min max fitnesses
}
public CommandWindowTests()
{
cmd = main.Cmd;
fem = main.Fem;
}
