/// <summary>
/// Run example
/// </summary>
public void Run()
{
// 1. Get 10 random samples of f(x) = (x * x) / 2 using continuous uniform distribution on [-10, 10]
var uniform = new ContinuousUniform(-10, 10);
var result = Generate.RandomMap(10, uniform, Function);
Console.WriteLine(@" 1. Get 10 random samples of f(x) = (x * x) / 2 using continuous uniform distribution on [-10, 10]");
for (var i = 0; i < result.Length; i++)
{
Console.Write(result[i].ToString("N") + @" ");
}
Console.WriteLine();
Console.WriteLine();
// 2. Get 10 random samples of f(x) = (x * x) / 2 using Exponential(1) distribution and retrieve sample points
var exponential = new Exponential(1);
double[] samplePoints = Generate.Random(10, exponential);
result = Generate.Map(samplePoints, Function);
Console.WriteLine(@"2. Get 10 random samples of f(x) = (x * x) / 2 using Exponential(1) distribution and retrieve sample points");
Console.Write(@"Points: ");
for (var i = 0; i < samplePoints.Length; i++)
{
Console.Write(samplePoints[i].ToString("N") + @" ");
}
Console.WriteLine();
Console.Write(@"Values: ");
for (var i = 0; i < result.Length; i++)
{
Console.Write(result[i].ToString("N") + @" ");
}
Console.WriteLine();
Console.WriteLine();
// 3. Get 10 random samples of f(x, y) = (x * y) / 2 using ChiSquare(10) distribution
var chiSquare = new ChiSquared(10);
result = Generate.RandomMap2(10, chiSquare, TwoDomainFunction);
Console.WriteLine(@" 3. Get 10 random samples of f(x, y) = (x * y) / 2 using ChiSquare(10) distribution");
for (var i = 0; i < result.Length; i++)
{
Console.Write(result[i].ToString("N") + @" ");
}
Console.WriteLine();
}
static ZergCharacter()
{
GOAL_PRIORITIES = new Dictionary<State, byte>(){
{State.AttackGoal, 16},
{State.FeedGoal, 8},
{State.RetreatGoal, 1},
{State.ServeGoal, 2}
};
var goals = new Stack<State>();
var goalEnums = (State[])Enum.GetValues(typeof(State));
foreach (var enumValue in goalEnums)
{
for (int i = 0; i < GOAL_PRIORITIES[enumValue]; i++)
{
goals.Push(enumValue);
}
}
int count = goals.Count;
RND = new DiscreteUniform(0, count - 1);
GOALS_LOOKUP_TABLE = new State[count];
var rnd = new ContinuousUniform(0.0, 1.0);
while (goals.Any())
{
int idx = (int)Math.Round(rnd.Sample() * (--count), 0);
GOALS_LOOKUP_TABLE[idx] = goals.Pop();
}
}
/// <summary>
/// Build linear samples.
/// </summary>
/// <param name="x">X sample values.</param>
/// <param name="y">Y samples values.</param>
/// <param name="xtest">X test values.</param>
/// <param name="ytest">Y test values.</param>
/// <param name="samples">Sample values.</param>
/// <param name="sampleOffset">Sample offset.</param>
/// <param name="slope">Slope number.</param>
/// <param name="intercept">Intercept criteria.</param>
public static void Build(out double[] x, out double[] y, out double[] xtest, out double[] ytest, int samples = 3, double sampleOffset = -0.5, double slope = 2.0, double intercept = -1.0)
{
// Fixed-seed "random" distribution to ensure we always test with the same data
var uniform = new ContinuousUniform(0.0, 1.0, new SystemRandomSource(42));
// build linear samples
x = new double[samples];
y = new double[samples];
for (int i = 0; i < x.Length; i++)
{
x[i] = i + sampleOffset;
y[i] = (x[i]*slope) + intercept;
}
// build linear test vectors randomly between the sample points
xtest = new double[samples + 1];
ytest = new double[samples + 1];
if (samples == 1)
{
// y = const
xtest[0] = sampleOffset - uniform.Sample();
xtest[1] = sampleOffset + uniform.Sample();
ytest[0] = ytest[1] = (sampleOffset*slope) + intercept;
}
else
{
for (int i = 0; i < xtest.Length; i++)
{
xtest[i] = (i - 1) + sampleOffset + uniform.Sample();
ytest[i] = (xtest[i]*slope) + intercept;
}
}
}
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;
}
public DropOutLayer(
int in_size, int out_size, double drop_prob = 0.5, string layer_name = "",
Vector <double> weights = null, Vector <double> biases = null)
: base(in_size, out_size, layer_name, weights, biases)
{
// 擬似乱数器生成
rand = new ContinuousUniform();
// DropOut する確率
if (drop_prob > 1.0 || drop_prob < 0)
{
Console.WriteLine("0 < drop_prob < 1.0. drop_prob is set 0.5");
_drop_prob = 0.5;
}
else
{
_drop_prob = drop_prob;
}
// マスク初期化
_drop_mask = new int[out_size];
CreateDropMask();
LayerType = "DropOutLayer";
GenericsType = _activation.Type();
}
public void RejectTest()
{
var uniform = new ContinuousUniform(0.0, 1.0, new SystemRandomSource(1));
var rs = new RejectionSampler<double>(x => Math.Pow(x, 1.7)*Math.Pow(1.0 - x, 5.3), x => 0.021, uniform.Sample);
Assert.IsNotNull(rs.RandomSource);
rs.RandomSource = uniform.RandomSource;
Assert.IsNotNull(rs.RandomSource);
}
public void NullRandomNumberGenerator()
{
var uniform = new ContinuousUniform(0.0, 1.0);
uniform.RandomSource = new MersenneTwister();
var rs = new RejectionSampler<double>(x => System.Math.Pow(x, 1.7) * System.Math.Pow(1.0 - x, 5.3),
x => System.Double.NegativeInfinity,
uniform.Sample);
rs.RandomSource = null;
}
public void NoUpperBound()
{
var uniform = new ContinuousUniform(0.0, 1.0);
uniform.RandomSource = new MersenneTwister();
var rs = new RejectionSampler<double>(x => System.Math.Pow(x, 1.7) * System.Math.Pow(1.0 - x, 5.3),
x => System.Double.NegativeInfinity,
uniform.Sample);
double s = rs.Sample();
}
public void SampleArrayTest()
{
var uniform = new ContinuousUniform(0.0, 1.0, new SystemRandomSource(1));
var rs = new RejectionSampler<double>(x => Math.Pow(x, 1.7)*Math.Pow(1.0 - x, 5.3), x => 0.021, uniform.Sample)
{
RandomSource = uniform.RandomSource
};
rs.Sample(5);
}
public void SetupDistributions()
{
dists = new IDistribution[8];
dists[0] = new Beta(1.0, 1.0);
dists[1] = new ContinuousUniform(0.0, 1.0);
dists[2] = new Gamma(1.0, 1.0);
dists[3] = new Normal(0.0, 1.0);
dists[4] = new Bernoulli(0.6);
dists[5] = new Weibull(1.0, 1.0);
dists[6] = new DiscreteUniform(1, 10);
dists[7] = new LogNormal(1.0, 1.0);
}
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;
}
public void RejectTest()
{
var uniform = new ContinuousUniform(0.0, 1.0);
uniform.RandomSource = new MersenneTwister();
var rs = new RejectionSampler<double>(x => System.Math.Pow(x, 1.7) * System.Math.Pow(1.0 - x, 5.3),
x => 0.021,
uniform.Sample);
Assert.IsNotNull(rs.RandomSource);
rs.RandomSource = uniform.RandomSource;
Assert.IsNotNull(rs.RandomSource);
}
/// <summary>
/// Uses Infer3DExactLMS to infer a random world point from a number of different projections of the point.
/// It writes out the squared errors corresponding to different number of projections into a file.
/// </summary>
/// <param name="numProjections">the max number of projections to use.</param>
/// <param name="gaussianNoiseSigma">the standard deviation of the gaussian noise to be added to the projected points.</param>
public static void ShowErrorInfer3DExactLMS(int numProjections, double gaussianNoiseSigma, string fileName)
{
ContinuousUniform dist = new ContinuousUniform(0, 1);
Normal gaussianNoise = new Normal(0, gaussianNoiseSigma);
DenseMatrix worldPoint = new DenseMatrix(4,1);
worldPoint = (DenseMatrix) worldPoint.Random(4,1, dist);
ProjectedPoint[] projections = new ProjectedPoint[numProjections];
for (int i = 0; i < projections.Length; i++)
{
projections[i] = new ProjectedPoint();
projections[i].worldToImage = new DenseMatrix(3, 4);
projections[i].worldToImage = (DenseMatrix)projections[i].worldToImage.Random(3, 4, dist);
projections[i].projectedPoint = (projections[i].worldToImage * worldPoint);
projections[i].projectedPoint += (DenseMatrix)projections[i].projectedPoint.Random(3, 1, gaussianNoise);
}
File.WriteAllLines(fileName,
Enumerable.Range(2, numProjections)
.Select(i => String.Format("{0}\t{1}", i, (worldPoint - Infer3DExactLMS(projections.Take(i))).L2Norm())));
}
static void SamplesUnchecked(System.Random rnd, double[] values, double alpha, double beta, double scale, double location)
{
var randThetas = new double[values.Length];
var randWs = new double[values.Length];
ContinuousUniform.SamplesUnchecked(rnd, randThetas, -Constants.PiOver2, Constants.PiOver2);
Exponential.SamplesUnchecked(rnd, randWs, 1.0);
if (!1.0.AlmostEqual(alpha))
{
for (int i = 0; i < values.Length; i++)
{
var randTheta = randThetas[i];
var theta = (1.0 / alpha) * Math.Atan(beta * Math.Tan(Constants.PiOver2 * alpha));
var angle = alpha * (randTheta + theta);
var part1 = beta * Math.Tan(Constants.PiOver2 * alpha);
var factor = Math.Pow(1.0 + (part1 * part1), 1.0 / (2.0 * alpha));
var factor1 = Math.Sin(angle) / Math.Pow(Math.Cos(randTheta), 1.0 / alpha);
var factor2 = Math.Pow(Math.Cos(randTheta - angle) / randWs[i], (1 - alpha) / alpha);
values[i] = location + scale * (factor * factor1 * factor2);
}
}
else
{
for (int i = 0; i < values.Length; i++)
{
var randTheta = randThetas[i];
var part1 = Constants.PiOver2 + (beta * randTheta);
var summand = part1 * Math.Tan(randTheta);
var subtrahend = beta * Math.Log(Constants.PiOver2 * randWs[i] * Math.Cos(randTheta) / part1);
values[i] = location + scale * Constants.TwoInvPi * (summand - subtrahend);
}
}
}
public void ValidateInverseCumulativeDistribution(double lower, double upper)
{
var n = new ContinuousUniform(lower, upper);
for (var i = 0; i < 11; i++)
{
var x = i - 5.0;
if (x <= lower)
{
Assert.AreEqual(lower, n.InverseCumulativeDistribution(0.0), 1e-12);
Assert.AreEqual(lower, ContinuousUniform.InvCDF(lower, upper, 0.0), 1e-12);
}
else if (x >= upper)
{
Assert.AreEqual(upper, n.InverseCumulativeDistribution(1.0), 1e-12);
Assert.AreEqual(upper, ContinuousUniform.InvCDF(lower, upper, 1.0), 1e-12);
}
else
{
Assert.AreEqual(x, n.InverseCumulativeDistribution((x - lower)/(upper - lower)), 1e-12);
Assert.AreEqual(x, ContinuousUniform.InvCDF(lower, upper, (x - lower)/(upper - lower)), 1e-12);
}
}
}
public void NoUpperBound()
{
var uniform = new ContinuousUniform(0.0, 1.0, new SystemRandomSource(1));
var rs = new RejectionSampler<double>(x => Math.Pow(x, 1.7)*Math.Pow(1.0 - x, 5.3), x => Double.NegativeInfinity, uniform.Sample);
Assert.That(() => rs.Sample(), Throws.ArgumentException);
}
public void ValidateMinimum(double lower, double upper)
{
var n = new ContinuousUniform(lower, upper);
Assert.AreEqual(lower, n.Minimum);
}
public void ValidateMode(double lower, double upper)
{
var n = new ContinuousUniform(lower, upper);
Assert.AreEqual((lower + upper) / 2.0, n.Mode);
}
public void CanCreateContinuousUniform()
{
var n = new ContinuousUniform();
Assert.AreEqual(0.0, n.LowerBound);
Assert.AreEqual(1.0, n.UpperBound);
}
public void DiagonalDenseMatrixTransposeThisAndMultiply()
{
var dist = new ContinuousUniform(-1.0, 1.0, new SystemRandomSource(1));
Assert.IsInstanceOf<DiagonalMatrix>(Matrix<Complex>.Build.DiagonalIdentity(3, 3));
var wide = Matrix<Complex>.Build.Random(3, 8, dist);
Assert.IsTrue((Matrix<Complex>.Build.DiagonalIdentity(3).Multiply(2d).TransposeThisAndMultiply(wide)).Equals(wide.Multiply(2d)));
Assert.IsTrue((Matrix<Complex>.Build.Diagonal(3, 5, 2d).TransposeThisAndMultiply(wide)).Equals(wide.Multiply(2d).Stack(Matrix<Complex>.Build.Dense(2, 8))));
Assert.IsTrue((Matrix<Complex>.Build.Diagonal(3, 2, 2d).TransposeThisAndMultiply(wide)).Equals(wide.Multiply(2d).SubMatrix(0, 2, 0, 8)));
var tall = Matrix<Complex>.Build.Random(8, 3, dist);
Assert.IsTrue((Matrix<Complex>.Build.DiagonalIdentity(8).Multiply(2d).TransposeThisAndMultiply(tall)).Equals(tall.Multiply(2d)));
Assert.IsTrue((Matrix<Complex>.Build.Diagonal(8, 10, 2d).TransposeThisAndMultiply(tall)).Equals(tall.Multiply(2d).Stack(Matrix<Complex>.Build.Dense(2, 3))));
Assert.IsTrue((Matrix<Complex>.Build.Diagonal(8, 2, 2d).TransposeThisAndMultiply(tall)).Equals(tall.Multiply(2d).SubMatrix(0, 2, 0, 3)));
}
/// <summary>
/// Creates the list of objects with specified class
/// </summary>
/// <param name="generator"></param>
/// <param name="count"></param>
/// <param name="classNumber"></param>
/// <returns></returns>
public static List<PatternClass> CreateSampleObject(int p1,int p2 ,IContinuousDistribution generator1,IContinuousDistribution generator2,int count)
{
List<PatternClass> sampleObjects = new List<PatternClass>();
probabilityGenerator = new ContinuousUniform(0, 1);
probabilityGenerator.RandomSource = new Random(DateTime.Now.Millisecond + 10);
int counter = 0; ;
for (; counter < count; )
{
//sampleObjects.Add(new PatternClass(new FeatureVector(generator.Sample()), classNumber));
}
return sampleObjects;
}
public void ValidateSkewness(double lower, double upper)
{
var n = new ContinuousUniform(lower, upper);
Assert.AreEqual(0.0, n.Skewness);
}
public void DiagonalDenseMatrixMultiply()
{
var dist = new ContinuousUniform(-1.0, 1.0, new SystemRandomSource(1));
Assert.IsInstanceOf<DiagonalMatrix>(Matrix<Complex32>.Build.DiagonalIdentity(3, 3));
var wide = Matrix<Complex32>.Build.Random(3, 8, dist);
Assert.IsTrue((Matrix<Complex32>.Build.DiagonalIdentity(3).Multiply(2f)*wide).Equals(wide.Multiply(2f)));
Assert.IsTrue((Matrix<Complex32>.Build.Diagonal(5, 3, 2f)*wide).Equals(wide.Multiply(2f).Stack(Matrix<Complex32>.Build.Dense(2, 8))));
Assert.IsTrue((Matrix<Complex32>.Build.Diagonal(2, 3, 2f)*wide).Equals(wide.Multiply(2f).SubMatrix(0, 2, 0, 8)));
var tall = Matrix<Complex32>.Build.Random(8, 3, dist);
Assert.IsTrue((Matrix<Complex32>.Build.DiagonalIdentity(8).Multiply(2f)*tall).Equals(tall.Multiply(2f)));
Assert.IsTrue((Matrix<Complex32>.Build.Diagonal(10, 8, 2f)*tall).Equals(tall.Multiply(2f).Stack(Matrix<Complex32>.Build.Dense(2, 3))));
Assert.IsTrue((Matrix<Complex32>.Build.Diagonal(2, 8, 2f)*tall).Equals(tall.Multiply(2f).SubMatrix(0, 2, 0, 3)));
}
public void DenseDiagonalMatrixMultiply()
{
var dist = new ContinuousUniform(-1.0, 1.0, new SystemRandomSource(1));
Assert.IsInstanceOf<DiagonalMatrix>(Matrix<Complex32>.Build.DiagonalIdentity(3, 3));
var tall = Matrix<Complex32>.Build.Random(8, 3, dist);
Assert.IsTrue((tall*Matrix<Complex32>.Build.DiagonalIdentity(3).Multiply(2f)).Equals(tall.Multiply(2f)));
Assert.IsTrue((tall*Matrix<Complex32>.Build.Diagonal(3, 5, 2f)).Equals(tall.Multiply(2f).Append(Matrix<Complex32>.Build.Dense(8, 2))));
Assert.IsTrue((tall*Matrix<Complex32>.Build.Diagonal(3, 2, 2f)).Equals(tall.Multiply(2f).SubMatrix(0, 8, 0, 2)));
var wide = Matrix<Complex32>.Build.Random(3, 8, dist);
Assert.IsTrue((wide*Matrix<Complex32>.Build.DiagonalIdentity(8).Multiply(2f)).Equals(wide.Multiply(2f)));
Assert.IsTrue((wide*Matrix<Complex32>.Build.Diagonal(8, 10, 2f)).Equals(wide.Multiply(2f).Append(Matrix<Complex32>.Build.Dense(3, 2))));
Assert.IsTrue((wide*Matrix<Complex32>.Build.Diagonal(8, 2, 2f)).Equals(wide.Multiply(2f).SubMatrix(0, 3, 0, 2)));
}
static double SampleUnchecked(System.Random rnd, double location, double scale, double skew, double p)
{
var u = ContinuousUniform.Sample(rnd, 0, 1);
return(InvCDF(location, scale, skew, p, u));
}
public void CanSample()
{
var n = new ContinuousUniform();
n.Sample();
}
public void CanCreateContinuousUniform(double lower, double upper)
{
var n = new ContinuousUniform(lower, upper);
Assert.AreEqual(lower, n.LowerBound);
Assert.AreEqual(upper, n.UpperBound);
}
public void SampleTest()
{
var uniform = new ContinuousUniform(0.0, 1.0, new MersenneTwister());
var rs = new RejectionSampler<double>(x => Math.Pow(x, 1.7)*Math.Pow(1.0 - x, 5.3), x => 0.021, uniform.Sample)
{
RandomSource = uniform.RandomSource
};
rs.Sample();
}
public ContGen()
{
_uniform = new MathNet.Numerics.Distributions.ContinuousUniform();
}
public void NoUpperBound()
{
var uniform = new ContinuousUniform(0.0, 1.0, new MersenneTwister());
var rs = new RejectionSampler<double>(x => Math.Pow(x, 1.7)*Math.Pow(1.0 - x, 5.3), x => Double.NegativeInfinity, uniform.Sample);
Assert.Throws<ArgumentOutOfRangeException>(() => rs.Sample());
}
public void DenseDiagonalMatrixTransposeAndMultiply()
{
var dist = new ContinuousUniform(-1.0, 1.0, new SystemRandomSource(1));
Assert.IsInstanceOf<DiagonalMatrix>(Matrix<Complex>.Build.DiagonalIdentity(3, 3));
var tall = Matrix<Complex>.Build.Random(8, 3, dist);
Assert.IsTrue(tall.TransposeAndMultiply(Matrix<Complex>.Build.DiagonalIdentity(3).Multiply(2d)).Equals(tall.Multiply(2d)));
Assert.IsTrue(tall.TransposeAndMultiply(Matrix<Complex>.Build.Diagonal(5, 3, 2d)).Equals(tall.Multiply(2d).Append(Matrix<Complex>.Build.Dense(8, 2))));
Assert.IsTrue(tall.TransposeAndMultiply(Matrix<Complex>.Build.Diagonal(2, 3, 2d)).Equals(tall.Multiply(2d).SubMatrix(0, 8, 0, 2)));
var wide = Matrix<Complex>.Build.Random(3, 8, dist);
Assert.IsTrue(wide.TransposeAndMultiply(Matrix<Complex>.Build.DiagonalIdentity(8).Multiply(2d)).Equals(wide.Multiply(2d)));
Assert.IsTrue(wide.TransposeAndMultiply(Matrix<Complex>.Build.Diagonal(10, 8, 2d)).Equals(wide.Multiply(2d).Append(Matrix<Complex>.Build.Dense(3, 2))));
Assert.IsTrue(wide.TransposeAndMultiply(Matrix<Complex>.Build.Diagonal(2, 8, 2d)).Equals(wide.Multiply(2d).SubMatrix(0, 3, 0, 2)));
}
public void NullRandomNumberGenerator()
{
var uniform = new ContinuousUniform(0.0, 1.0, new SystemRandomSource(1));
var rs = new RejectionSampler<double>(x => Math.Pow(x, 1.7)*Math.Pow(1.0 - x, 5.3), x => Double.NegativeInfinity, uniform.Sample);
Assert.That(() => rs.RandomSource = null, Throws.TypeOf<ArgumentNullException>());
}
public void CanSampleSequence()
{
var n = new ContinuousUniform();
var ied = n.Samples();
ied.Take(5).ToArray();
}
public void ValidateToString()
{
var n = new ContinuousUniform(1.0, 2.0);
Assert.AreEqual("ContinuousUniform(Lower = 1, Upper = 2)", n.ToString());
}
