/// <summary>
/// Samples a two-domain function randomly with the provided distribution.
/// </summary>
/// <param name="function">The real-domain function to sample.</param>
/// <param name="distribution">Random distribution of the real domain sample points.</param>
/// <param name="sampleCount">The number of samples to generate.</param>
/// <typeparam name="T">The value type of the function to sample.</typeparam>
/// <returns>The generated sample vector.</returns>
/// <exception cref="ArgumentNullException" />
/// <exception cref="ArgumentOutOfRangeException" />
public static T[] Random <T>(
Func <double, double, T> function,
IContinuousDistribution distribution,
int sampleCount)
{
if (ReferenceEquals(function, null))
{
throw new ArgumentNullException("function");
}
if (ReferenceEquals(distribution, null))
{
throw new ArgumentNullException("distribution");
}
if (sampleCount < 0)
{
throw new ArgumentOutOfRangeException("sampleCount");
}
var samples = new T[sampleCount];
for (int i = 0; i < samples.Length; i++)
{
samples[i] = function(distribution.Sample(), distribution.Sample());
}
return(samples);
}
/// <summary>
/// Generate samples by sampling a function at sample pairs from a probability distribution.
/// </summary>
public static T[] RandomMap2 <T>(int length, IContinuousDistribution distribution, Func <double, double, T> map)
{
var data = new T[length];
for (int i = 0; i < data.Length; i++)
{
data[i] = map(distribution.Sample(), distribution.Sample());
}
return(data);
}
/// <summary>
/// Samples a two-domain function randomly with the provided distribution.
/// </summary>
/// <param name="function">The real-domain function to sample.</param>
/// <param name="distribution">Random distribution of the real domain sample points.</param>
/// <param name="sampleCount">The number of samples to generate.</param>
/// <typeparam name="T">The value type of the function to sample.</typeparam>
/// <returns>The generated sample vector.</returns>
/// <exception cref="ArgumentNullException" />
/// <exception cref="ArgumentOutOfRangeException" />
public static T[] Random <T>(Func <double, double, T> function, IContinuousDistribution distribution, int sampleCount)
{
function.ShouldNotBeNull("function");
distribution.ShouldNotBeNull("distribution");
sampleCount.ShouldBePositiveOrZero("sampleCount");
return
(Enumerable
.Range(0, sampleCount)
.AsParallel()
.AsOrdered()
.Select(i => function(distribution.Sample(), distribution.Sample()))
.ToArray());
}
/// <summary>
/// Generates a vector with random elements
/// </summary>
/// <param name="length">Number of elements in the vector.</param>
/// <param name="randomDistribution">Continuous Random Distribution or Source</param>
/// <returns>
/// A vector with n-random elements distributed according
/// to the specified random distribution.
/// </returns>
/// <exception cref="ArgumentException">If the n vector is non-positive.</exception>
public override Vector <Complex> Random(int length, IContinuousDistribution randomDistribution)
{
if (length < 1)
{
throw new ArgumentException(Resources.ArgumentMustBePositive, "length");
}
var vector = CreateVector(length);
for (var index = 0; index < length; index++)
{
vector.At(index, new Complex(randomDistribution.Sample(), randomDistribution.Sample()));
}
return(vector);
}
public static IEnumerable <double> LatinHyperCubeSample(this IContinuousDistribution ContinuousDistribution, int divisions)
{
for (int i = 0; i < divisions; i++)
{
yield return(ContinuousDistribution.Sample());
}
}
public static List<PatternClass> Create2DimTeachingVectors(double p1, double p2, IContinuousDistribution generatorFirstFeature1, IContinuousDistribution generatorFirstFeature2, IContinuousDistribution generatorSecondFeature1, IContinuousDistribution generatorSecondFeature2, int nrOfTeachingVectors)
{
List<PatternClass> createdTeachingVectors = new List<PatternClass>();
if (probabilityGenerator == null)
{
probabilityGenerator = new ContinuousUniform(0, 1);
probabilityGenerator.RandomSource = new Random(DateTime.Now.Millisecond + 10);
}
for (int i = 0; i < nrOfTeachingVectors; i++)
{
double value = 0;
double value1 = 0;
int classNumber = CreateClass(p1, p2);
if (classNumber == 1)
{
value = generatorFirstFeature1.Sample();
value1 = generatorSecondFeature1.Sample();
}
else if (classNumber == 2)
{
value = generatorFirstFeature2.Sample();
value1 = generatorSecondFeature2.Sample();
}
createdTeachingVectors.Add(new PatternClass(new FeatureVector(value,value1), classNumber));
}
return createdTeachingVectors;
}
/// <summary>
/// Create a new child genome from a given parent genome.
/// </summary>
/// <param name="parent">The parent genome.</param>
/// <returns>A new child genome.</returns>
public NeatGenome <T> CreateChildGenome(NeatGenome <T> parent)
{
Debug.Assert(_metaNeatGenome == parent.MetaNeatGenome, "Parent genome has unexpected MetaNeatGenome.");
// Attempt to find a new connection that we can add to the genome.
DirectedConnection directedConn;
if (!TryGetConnection(parent, out directedConn, out int insertIdx))
{ // Failed to find a new connection.
return(null);
}
// Determine the connection weight.
// 50% of the time use weights very close to zero.
// Note. this recreates the strategy used in SharpNEAT 2.x.
// ENHANCEMENT: Reconsider the distribution of new weights and if there are better approaches (distributions) we could use.
T weight = _rng.NextBool() ? _weightDistB.Sample() : _weightDistA.Sample();
// Create a new connection gene array that consists of the parent connection genes plus the new gene
// inserted at the correct (sorted) position.
var parentConnArr = parent.ConnectionGenes._connArr;
var parentWeightArr = parent.ConnectionGenes._weightArr;
int parentLen = parentConnArr.Length;
// Create the child genome's ConnectionGenes object.
int childLen = parentLen + 1;
var connGenes = new ConnectionGenes <T>(childLen);
var connArr = connGenes._connArr;
var weightArr = connGenes._weightArr;
// Copy genes up to insertIdx.
Array.Copy(parentConnArr, connArr, insertIdx);
Array.Copy(parentWeightArr, weightArr, insertIdx);
// Copy the new genome into its insertion point.
connArr[insertIdx] = new DirectedConnection(
directedConn.SourceId,
directedConn.TargetId);
weightArr[insertIdx] = weight;
// Copy remaining genes (if any).
Array.Copy(parentConnArr, insertIdx, connArr, insertIdx + 1, parentLen - insertIdx);
Array.Copy(parentWeightArr, insertIdx, weightArr, insertIdx + 1, parentLen - insertIdx);
// Create and return a new genome.
// Notes.
// The set of hidden node IDs remains unchanged from the parent, therefore we are able to re-use parent.HiddenNodeIdArray.
// However, the presence of a new connection invalidates parent.NodeIndexByIdMap for use in the new genome, because the allocated
// node indexes are dependent on node depth in the acyclic graph, which in turn can be modified by the presence of a new connection.
return(_genomeBuilder.Create(
_genomeIdSeq.Next(),
_generationSeq.Peek,
connGenes,
parent.HiddenNodeIdArray));
}
/// <summary>
/// Populates a matrix with random elements.
/// </summary>
/// <param name="matrix">The matrix to populate.</param>
/// <param name="distribution">Continuous Random Distribution to generate elements from.</param>
protected override void DoRandom(Matrix <Complex32> matrix, IContinuousDistribution distribution)
{
for (var i = 0; i < matrix.RowCount; i++)
{
for (var j = 0; j < matrix.ColumnCount; j++)
{
matrix.At(i, j, Convert.ToSingle(distribution.Sample()));
}
}
}
/// <summary>
/// Populates a matrix with random elements.
/// </summary>
/// <param name="matrix">The matrix to populate.</param>
/// <param name="distribution">Continuous Random Distribution to generate elements from.</param>
protected override void DoRandom(Matrix <double> matrix, IContinuousDistribution distribution)
{
for (var i = 0; i < matrix.RowCount; i++)
{
for (var j = 0; j < matrix.ColumnCount; j++)
{
matrix.At(i, j, distribution.Sample());
}
}
}
public void Invoke(double[] weightArr)
{
// Select a subset of connection genes to mutate.
int[] selectedIdxArr = _selectionStrategy.SelectSubset(weightArr.Length);
// Loop over the connection genes to be mutated, and mutate them.
for (int i = 0; i < selectedIdxArr.Length; i++)
{
weightArr[selectedIdxArr[i]] += _dist.Sample();
}
}
public static List<PatternClass> Create2dimSampleObject(IContinuousDistribution generator, IContinuousDistribution generator1, int count, int classNumber)
{
List<PatternClass> sampleObjects = new List<PatternClass>();
probabilityGenerator = new ContinuousUniform(0, 1);
probabilityGenerator.RandomSource = new Random(DateTime.Now.Millisecond + 10);
for (int i = 0; i < count; i++)
{
sampleObjects.Add(new PatternClass(new FeatureVector(generator.Sample(), generator1.Sample()), classNumber));
}
return sampleObjects;
}
/// <summary>
/// Populates a matrix with random elements.
/// </summary>
/// <param name="matrix">The matrix to populate.</param>
/// <param name="distribution">Continuous Random Distribution to generate elements from.</param>
protected override void DoRandom(Matrix <Complex32> matrix, IContinuousDistribution distribution)
{
CommonParallel.For(
0,
matrix.RowCount,
i =>
{
for (var j = 0; j < matrix.ColumnCount; j++)
{
matrix.At(i, j, Convert.ToSingle(distribution.Sample()));
}
});
}
/// <summary>
/// Create a new child genome from a given parent genome.
/// </summary>
/// <param name="parent">The parent genome.</param>
/// <returns>A new child genome.</returns>
public NeatGenome <T> CreateChildGenome(NeatGenome <T> parent)
{
// Attempt to find a new connection that we can add to the genome.
if (!TryGetConnection(parent, out DirectedConnection directedConn, out int insertIdx))
{ // Failed to find a new connection.
return(null);
}
// Determine the connection weight.
// 50% of the time use weights very close to zero.
// Note. this recreates the strategy used in SharpNEAT 2.x.
// TODO: Reconsider the distribution of new weights and if there are better approaches (distributions) we could use.
T weight = _rng.NextBool() ? _weightDistB.Sample() : _weightDistA.Sample();
// Create a new connection gene array that consists of the parent connection genes plus the new gene
// inserted at the correct (sorted) position.
var parentConnArr = parent.ConnectionGenes._connArr;
var parentWeightArr = parent.ConnectionGenes._weightArr;
int parentLen = parentConnArr.Length;
// Create the child genome's ConnectionGenes object.
int childLen = parentLen + 1;
var connGenes = new ConnectionGenes <T>(childLen);
var connArr = connGenes._connArr;
var weightArr = connGenes._weightArr;
// Copy genes up to insertIdx.
Array.Copy(parentConnArr, connArr, insertIdx);
Array.Copy(parentWeightArr, weightArr, insertIdx);
// Copy the new genome into its insertion point.
connArr[insertIdx] = new DirectedConnection(
directedConn.SourceId,
directedConn.TargetId);
weightArr[insertIdx] = weight;
// Copy remaining genes (if any).
Array.Copy(parentConnArr, insertIdx, connArr, insertIdx + 1, parentLen - insertIdx);
Array.Copy(parentWeightArr, insertIdx, weightArr, insertIdx + 1, parentLen - insertIdx);
// Create and return a new genome.
// Note. The set of hidden node IDs remains unchanged from the parent, therefore we are able to re-use
// both parent.HiddenNodeIdArray and NodeIndexByIdMap.
return(_genomeBuilder.Create(
_genomeIdSeq.Next(),
_generationSeq.Peek,
connGenes,
parent.HiddenNodeIdArray,
parent.NodeIndexByIdMap));
}
/// <summary>
/// Generates a vector with random elements
/// </summary>
/// <param name="length">Number of elements in the vector.</param>
/// <param name="randomDistribution">Continuous Random Distribution or Source</param>
/// <returns>
/// A vector with n-random elements distributed according
/// to the specified random distribution.
/// </returns>
/// <exception cref="ArgumentNullException">If the n vector is non positive<see langword="null" />.</exception>
public override Vector <float> Random(int length, IContinuousDistribution randomDistribution)
{
if (length < 1)
{
throw new ArgumentException(Resources.ArgumentMustBePositive, "length");
}
var v = CreateVector(length);
for (var index = 0; index < Count; index++)
{
v[index] = Convert.ToSingle(randomDistribution.Sample());
}
return(v);
}
/// <summary>
/// Generates a vector with random elements
/// </summary>
/// <param name="length">Number of elements in the vector.</param>
/// <param name="randomDistribution">Continuous Random Distribution or Source</param>
/// <returns>
/// A vector with n-random elements distributed according
/// to the specified random distribution.
/// </returns>
/// <exception cref="ArgumentNullException">If the n vector is non positive<see langword="null" />.</exception>
public override Vector <Complex32> Random(int length, IContinuousDistribution randomDistribution)
{
if (length < 1)
{
throw new ArgumentException(Resources.ArgumentMustBePositive, "length");
}
var v = (DenseVector)CreateVector(length);
for (var index = 0; index < v.Data.Length; index++)
{
v.Data[index] = new Complex32((float)randomDistribution.Sample(), (float)randomDistribution.Sample());
}
return(v);
}
//static VectorBuilder<double> vBuilder;
//public static Particle[] Generate2D(int numberOfParticles, IDistribution distributionx, IDistribution distributiony, IDistribution distributionv, IDistribution distributiono)
//{
// return Enumerable.Range(0, numberOfParticles).Select(_ =>
// new Particle
// (
// distributionx.Sample(), distributiony.Sample(), distributionv.Sample(), distributiono.Sample() % 2.0 * System.Math.PI, weight: 1d
// )
// ).ToArray();
//}
public static List <Particle> BuildSwarm(int numberOfParticles,
IContinuousDistribution xdistribution, IContinuousDistribution ydistribution,
IContinuousDistribution vdistribution = null, IContinuousDistribution odistribution = null)
{
return(Enumerable.Range(0, numberOfParticles).Select(_ =>
{
var x = xdistribution.Sample();
var y = ydistribution.Sample();
var v = vdistribution?.Sample();
var o = odistribution?.Sample();
return new Particle(x, y, 1d / numberOfParticles)
{
Velocity = v ?? 0, Orientation = o ?? 0
};
}).ToList());
}
/// <summary>
/// Create a new dense vector with values sampled from the provided random distribution.
/// </summary>
public static DenseVector CreateRandom(int length, IContinuousDistribution distribution)
{
return new DenseVector(DenseVectorStorage<double>.OfInit(length,
i => distribution.Sample()));
}
/// <summary>
/// Create a new dense matrix with values sampled from the provided random distribution.
/// </summary>
public static DenseMatrix CreateRandom(int rows, int columns, IContinuousDistribution distribution)
{
return(new DenseMatrix(DenseColumnMajorMatrixStorage <Complex> .OfInit(rows, columns,
(i, j) => new Complex(distribution.Sample(), distribution.Sample()))));
}
/// <summary>
/// Create a new dense vector with values sampled from the provided random distribution.
/// </summary>
public static DenseVector CreateRandom(int length, IContinuousDistribution distribution)
{
return new DenseVector(DenseVectorStorage<Complex32>.OfInit(length,
i => new Complex32((float)distribution.Sample(), (float)distribution.Sample())));
}
/// <summary>
/// Generates a vector with random elements
/// </summary>
/// <param name="length">Number of elements in the vector.</param>
/// <param name="randomDistribution">Continuous Random Distribution or Source</param>
/// <returns>
/// A vector with n-random elements distributed according
/// to the specified random distribution.
/// </returns>
/// <exception cref="ArgumentNullException">If the length vector is non poisitive<see langword="null" />.</exception>
public override Vector Random(int length, IContinuousDistribution randomDistribution)
{
if (length < 0)
{
throw new ArgumentException(Resources.ArgumentMustBePositive, "length");
}
var v = (SparseVector)this.CreateVector(length);
for (var index = 0; index < v.Count; index++)
{
v[index] = randomDistribution.Sample();
}
return v;
}
/// <summary>
/// Create a new dense vector with values sampled from the provided random distribution.
/// </summary>
public static DenseVector CreateRandom(int size, IContinuousDistribution distribution)
{
var storage = new DenseVectorStorage<Complex>(size);
for (var i = 0; i < storage.Data.Length; i++)
{
storage.Data[i] = new Complex(distribution.Sample(), distribution.Sample());
}
return new DenseVector(storage);
}
/// <summary>
/// Create a new dense matrix with values sampled from the provided random distribution.
/// </summary>
public static DenseMatrix CreateRandom(int rows, int columns, IContinuousDistribution distribution)
{
return new DenseMatrix(DenseColumnMajorMatrixStorage<float>.OfInit(rows, columns, (i, j) => (float) distribution.Sample()));
}
/// <summary>
/// Computes and returns the next sample.
/// </summary>
public double ReadNextSample()
{
return(_distribution.Sample());
}
/// <summary>
/// Create a new dense matrix with values sampled from the provided random distribution.
/// </summary>
public static DenseMatrix CreateRandom(int rows, int columns, IContinuousDistribution distribution)
{
var storage = new DenseColumnMajorMatrixStorage<Complex>(rows, columns);
for (var i = 0; i < storage.Data.Length; i++)
{
storage.Data[i] = new Complex(distribution.Sample(), distribution.Sample());
}
return new DenseMatrix(storage);
}
float _GetBias()
{
return(_zeroBias ? 0f : Convert.ToSingle(_distribution.Sample()));
}
/// <summary>
/// Generates a vector with random elements
/// </summary>
/// <param name="length">Number of elements in the vector.</param>
/// <param name="randomDistribution">Continuous Random Distribution or Source</param>
/// <returns>
/// A vector with n-random elements distributed according
/// to the specified random distribution.
/// </returns>
/// <exception cref="ArgumentNullException">If the n vector is non positive<see langword="null" />.</exception>
public override Vector<Complex32> Random(int length, IContinuousDistribution randomDistribution)
{
if (length < 1)
{
throw new ArgumentException(Resources.ArgumentMustBePositive, "length");
}
var v = (DenseVector)CreateVector(length);
for (var index = 0; index < v.Data.Length; index++)
{
v.Data[index] = new Complex32((float)randomDistribution.Sample(), (float)randomDistribution.Sample());
}
return v;
}
/// <summary>
/// Generates matrix with random elements.
/// </summary>
/// <param name="numberOfRows">Number of rows.</param>
/// <param name="numberOfColumns">Number of columns.</param>
/// <param name="distribution">Continuous Random Distribution or Source</param>
/// <returns>
/// An <c>numberOfRows</c>-by-<c>numberOfColumns</c> matrix with elements distributed according to the provided distribution.
/// </returns>
/// <exception cref="ArgumentException">If the parameter <paramref name="numberOfRows"/> is not positive.</exception>
/// <exception cref="ArgumentException">If the parameter <paramref name="numberOfColumns"/> is not positive.</exception>
public override Matrix Random(int numberOfRows, int numberOfColumns, IContinuousDistribution distribution)
{
if (numberOfRows < 1)
{
throw new ArgumentException(Resources.ArgumentMustBePositive, "numberOfRows");
}
if (numberOfColumns < 1)
{
throw new ArgumentException(Resources.ArgumentMustBePositive, "numberOfColumns");
}
var matrix = (SparseMatrix)CreateMatrix(numberOfRows, numberOfColumns);
for (var i = 0; i < matrix.RowCount; i++)
{
for (var j = 0; j < matrix.ColumnCount; j++)
{
var value = distribution.Sample();
if (value != 0.0)
{
matrix.SetValueAt(i, j, value);
}
}
}
return matrix;
}
/// <summary>
/// Create a new dense vector with values sampled from the provided random distribution.
/// </summary>
public static DenseVector CreateRandom(int length, IContinuousDistribution distribution)
{
return(new DenseVector(DenseVectorStorage <double> .OfInit(length,
i => distribution.Sample())));
}
/// <summary>
/// Create a new diagonal matrix with diagonal values sampled from the provided random distribution.
/// </summary>
public static DiagonalMatrix CreateRandom(int rows, int columns, IContinuousDistribution distribution)
{
return(new DiagonalMatrix(DiagonalMatrixStorage <Complex32> .OfInit(rows, columns,
i => new Complex32((float)distribution.Sample(), (float)distribution.Sample()))));
}
private bool ShouldMutate()
{
return(_mutationRateDistribution.Sample() <= _parameters.MutationRate);
}
public double Sample()
{
return(originalDistribution.Sample());
}
public float GetBias()
{
return(Convert.ToSingle(_distribution.Sample()));
}
public double Sample()
{
return(estimate - originalDistribution.Sample());
}
/// <summary>
/// Create a new dense vector with values sampled from the provided random distribution.
/// </summary>
public static DenseVector CreateRandom(int length, IContinuousDistribution distribution)
{
return(new DenseVector(DenseVectorStorage <Complex32> .OfInit(length,
i => new Complex32((float)distribution.Sample(), (float)distribution.Sample()))));
}
/// <summary>
/// Create a new diagonal matrix with diagonal values sampled from the provided random distribution.
/// </summary>
public static DiagonalMatrix CreateRandom(int rows, int columns, IContinuousDistribution distribution)
{
return new DiagonalMatrix(DiagonalMatrixStorage<Complex>.OfInit(rows, columns,
i => new Complex(distribution.Sample(), distribution.Sample())));
}
/// <summary>
/// Create a new diagonal matrix with diagonal values sampled from the provided random distribution.
/// </summary>
public static DiagonalMatrix CreateRandom(int rows, int columns, IContinuousDistribution distribution)
{
return(new DiagonalMatrix(DiagonalMatrixStorage <double> .OfInit(rows, columns,
i => distribution.Sample())));
}
private bool ShouldCrossover()
{
return(_crossoverRateDistribution.Sample() < _crossoverRate);
}
/// <summary>
/// Generates matrix with random elements.
/// </summary>
/// <param name="numberOfRows">Number of rows.</param>
/// <param name="numberOfColumns">Number of columns.</param>
/// <param name="distribution">Continuous Random Distribution or Source</param>
/// <returns>
/// An <c>numberOfRows</c>-by-<c>numberOfColumns</c> matrix with elements distributed according to the provided distribution.
/// </returns>
/// <exception cref="ArgumentException">If the parameter <paramref name="numberOfRows"/> is not positive.</exception>
/// <exception cref="ArgumentException">If the parameter <paramref name="numberOfColumns"/> is not positive.</exception>
public virtual Matrix Random(int numberOfRows, int numberOfColumns, IContinuousDistribution distribution)
{
if (numberOfRows < 1)
{
throw new ArgumentException(Resources.ArgumentMustBePositive, "numberOfRows");
}
if (numberOfColumns < 1)
{
throw new ArgumentException(Resources.ArgumentMustBePositive, "numberOfColumns");
}
var matrix = CreateMatrix(numberOfRows, numberOfColumns);
CommonParallel.For(
0,
ColumnCount,
j =>
{
for (var i = 0; i < matrix.RowCount; i++)
{
matrix[i, j] = distribution.Sample();
}
});
return matrix;
}
