public void RefreshView(object genome)
{
var box = decoder.Decode(genome as NeatGenome);
var samples = dataset.GetSamplesMatrix();
var n = samples.GetLength(1);
var m = samples.GetLength(2);
int c = 0, i = 0, j = 0;
evaluator.Test(box, evaluator.GetAllInputs().ToArray(), (idx, output) =>
{
outputs[c, i, j] = output;
j++;
if (j == m)
{
i++;
}
if (i == n)
{
c++;
}
c %= nbClusters;
i %= n;
j %= m;
});
refreshPlot();
}
public void Initialize(IGenomeDecoder<NeatGenome, IBlackBox> genomeDecoder, IMapClusteringDataset dataset, MapClusteringEvaluator evaluator, int nbClusters)
{
this.dataset = dataset;
this.nbClusters = nbClusters;
this.decoder = genomeDecoder;
this.evaluator = evaluator;
samples = dataset.GetSamplesMatrix();
outputs = new double[nbClusters, samples.GetLength(1), samples.GetLength(2)];
this.nbInputs = samples.GetLength(0);
maxValue = 0.0;
for (var i = 0; i < samples.GetLength(0); i++)
{
for (var j = 0; j < samples.GetLength(1); j++)
{
for (var k = 0; k < samples.GetLength(2); k++)
{
var value = samples[i, j, k];
if (value > maxValue) maxValue = value;
}
}
}
maxValue *= 2; // margin
}
public void Initialize(IGenomeDecoder <NeatGenome, IBlackBox> genomeDecoder, IMapClusteringDataset dataset, MapClusteringEvaluator evaluator, int nbClusters)
{
this.dataset = dataset;
this.nbClusters = nbClusters;
this.decoder = genomeDecoder;
this.evaluator = evaluator;
samples = dataset.GetSamplesMatrix();
outputs = new double[nbClusters, samples.GetLength(1), samples.GetLength(2)];
this.nbInputs = samples.GetLength(0);
maxValue = 0.0;
for (var i = 0; i < samples.GetLength(0); i++)
{
for (var j = 0; j < samples.GetLength(1); j++)
{
for (var k = 0; k < samples.GetLength(2); k++)
{
var value = samples[i, j, k];
if (value > maxValue)
{
maxValue = value;
}
}
}
}
maxValue *= 2; // margin
}
public MapClusteringView(IGenomeDecoder <NeatGenome, IBlackBox> genomeDecoder,
IMapClusteringDataset dataset,
MapClusteringEvaluator evaluator,
int nbClusters)
: this()
{
this.dataset = dataset;
this.nbClusters = nbClusters;
this.decoder = genomeDecoder;
this.evaluator = evaluator;
samples = dataset.GetSamplesMatrix();
outputs = new double[nbClusters, samples.GetLength(1), samples.GetLength(2)];
inputView.LabelName = "Input";
outputView.LabelName = "Output";
inputView.SetDimensions(samples.GetLength(0), samples.GetLength(1), samples.GetLength(2));
outputView.SetDimensions(nbClusters, samples.GetLength(1), samples.GetLength(2));
maxValue = 0.0;
minValue = double.PositiveInfinity;
for (var i = 0; i < samples.GetLength(0); i++)
{
for (var j = 0; j < samples.GetLength(1); j++)
{
for (var k = 0; k < samples.GetLength(2); k++)
{
var value = samples[i, j, k];
if (value > maxValue)
{
maxValue = value;
}
if (value < minValue)
{
minValue = value;
}
}
}
}
// Add some margin
maxValue *= 2;
minValue *= 2;
inputView.OnClusterChanged += (id) =>
{
currentInputIdx = id;
RefreshInput();
};
outputView.OnClusterChanged += (id) =>
{
currentClusterIdx = id;
RefreshOutput();
};
RefreshInput();
}
public override void Initialize(string name, XmlElement xmlConfig)
{
base.Initialize(name, xmlConfig);
_dataset = CreateDataset();
_dataset.LoadFromFile(DatasetFileName);
samples = _dataset.GetSamplesMatrix();
nbInputs = _dataset.InputCount;
n = samples.GetLength(1);
m = samples.GetLength(2);
}
public override void Initialize(string name, XmlElement xmlConfig)
{
base.Initialize(name, xmlConfig);
_dataset = CreateDataset();
_dataset.LoadFromFile(DatasetFileName);
samples = _dataset.GetSamplesMatrix();
nbInputs = _dataset.InputCount;
n = samples.GetLength(1);
m = samples.GetLength(2);
}
public MapClusteringView(IGenomeDecoder<NeatGenome, IBlackBox> genomeDecoder,
IMapClusteringDataset dataset,
MapClusteringEvaluator evaluator,
int nbClusters)
: this()
{
this.dataset = dataset;
this.nbClusters = nbClusters;
this.decoder = genomeDecoder;
this.evaluator = evaluator;
samples = dataset.GetSamplesMatrix();
outputs = new double[nbClusters, samples.GetLength(1), samples.GetLength(2)];
inputView.LabelName = "Input";
outputView.LabelName = "Output";
inputView.SetDimensions(samples.GetLength(0), samples.GetLength(1), samples.GetLength(2));
outputView.SetDimensions(nbClusters, samples.GetLength(1), samples.GetLength(2));
maxValue = 0.0;
minValue = double.PositiveInfinity;
for (var i = 0; i < samples.GetLength(0); i++)
{
for (var j = 0; j < samples.GetLength(1); j++)
{
for (var k = 0; k < samples.GetLength(2); k++)
{
var value = samples[i, j, k];
if (value > maxValue) maxValue = value;
if (value < minValue) minValue = value;
}
}
}
// Add some margin
maxValue *= 2;
minValue *= 2;
inputView.OnClusterChanged += (id) =>
{
currentInputIdx = id;
RefreshInput();
};
outputView.OnClusterChanged += (id) =>
{
currentClusterIdx = id;
RefreshOutput();
};
RefreshInput();
}
/// <summary>
/// Construct evaluator with the provided task arguments/variables.
/// </summary>
public MapClusteringEvaluator(IMapClusteringDataset dataset, int nbClusters, Phenotype phenotype)
{
this.nbClusters = nbClusters;
this.phenotype = phenotype;
// Build input layers (samples matrices)
nbInputs = dataset.InputCount;
samples = dataset.GetSamplesMatrix();
// Extract useful values
n = samples.GetLength(1); // layers width
m = samples.GetLength(2); // layers height
nbInputsNN = samples.Length;
nbOutputsNN = nbClusters * n * m;
}
/// <summary>
/// Construct evaluator with the provided task arguments/variables.
/// </summary>
public MapClusteringEvaluator(IMapClusteringDataset dataset, int nbClusters, Phenotype phenotype)
{
this.nbClusters = nbClusters;
this.phenotype = phenotype;
// Build input layers (samples matrices)
nbInputs = dataset.InputCount;
samples = dataset.GetSamplesMatrix();
// Extract useful values
n = samples.GetLength(1); // layers width
m = samples.GetLength(2); // layers height
nbInputsNN = samples.Length;
nbOutputsNN = nbClusters * n * m;
}
/// <summary>
/// Construct evaluator with the provided task arguments/variables.
/// </summary>
public WindowMapClusteringEvaluator(IMapClusteringDataset dataset, int nbClusters, Phenotype phenotype, bool[,] filter)
{
this.nbClusters = nbClusters;
this.phenotype = phenotype;
this.filter = filter;
Debug.Assert(filter.GetLength(0) % 2 != 0 && filter.GetLength(1) % 2 != 0);
// Build input layers (samples matrices)
nbInputs = dataset.InputCount;
samples = dataset.GetSamplesMatrix();
// Extract useful values
f = filter.GetLength(0); // filter width
f2 = filter.Length; // f^2
t = (f - 1) / 2; // filter thickness
nbInputsNN = nbInputs * f2;
nbOutputsNN = nbClusters * f2;
n = samples.GetLength(1); // layers width
m = samples.GetLength(2); // layers height
}
/// <summary>
/// Construct evaluator with the provided task arguments/variables.
/// </summary>
public WindowMapClusteringEvaluator(IMapClusteringDataset dataset, int nbClusters, Phenotype phenotype, bool[,] filter)
{
this.nbClusters = nbClusters;
this.phenotype = phenotype;
this.filter = filter;
Debug.Assert(filter.GetLength(0) % 2 != 0 && filter.GetLength(1) % 2 != 0);
// Build input layers (samples matrices)
nbInputs = dataset.InputCount;
samples = dataset.GetSamplesMatrix();
// Extract useful values
f = filter.GetLength(0); // filter width
f2 = filter.Length; // f^2
t = (f - 1) / 2; // filter thickness
nbInputsNN = nbInputs * f2;
nbOutputsNN = nbClusters * f2;
n = samples.GetLength(1); // layers width
m = samples.GetLength(2); // layers height
}