private void SetNextOrderAmount()
{
switch (OptionA)
{
case EnumTypes.BuyerAOptions.Static:
NextOrderAmount = this.Amount;
break;
case EnumTypes.BuyerAOptions.Random:
Random rnd = new Random();
int rand = rnd.Next(Convert.ToInt32(this.MinAmount), Convert.ToInt32(this.MaxAmount) + 1);
NextOrderAmount = rand;
break;
case EnumTypes.BuyerAOptions.Poisson:
MathNet.Numerics.Distributions.Poisson poisson = new MathNet.Numerics.Distributions.Poisson(this.Lambda);
NextOrderAmount = poisson.Sample();
break;
case EnumTypes.BuyerAOptions.Gauss:
MathNet.Numerics.Distributions.Normal normal = new MathNet.Numerics.Distributions.Normal(this.MeanOptionA, this.DeviationOptionA);
NextOrderAmount = normal.Sample();
break;
}
}
public List <double> GetRandomAccumulatedOfNormal(ref List <double> normalAccumulated)
{
var normalD = new MathNet.Numerics.Distributions.Normal();
int testCnt = 0;
double pValue = 0.0;
double[] temp = new double[ScenCnt];
List <double> normal = null;
while (pValue < 0.95)
{
normalAccumulated.CopyTo(temp);
normal = GetRandomOfNormal();
for (int scenSeq = 0; scenSeq < normalAccumulated.Count(); scenSeq++)
{
temp[scenSeq] += normal[scenSeq];
}
var sw = new Accord.Statistics.Testing.ShapiroWilkTest(temp.ToArray());
pValue = sw.PValue;
testCnt++;
}
for (int scenSeq = 0; scenSeq < normalAccumulated.Count(); scenSeq++)
{
normalAccumulated[scenSeq] += normal[scenSeq];
}
return(normal);
}
// 1000개 랜덤
public List <double> GetRandomOfNormal()
{
List <double> rdSet = new List <double>();
var normalD = new MathNet.Numerics.Distributions.Normal();
double pValue = 0.0;
int testCnt = 0;
while (pValue < 0.9)
{
rdSet.Clear();
for (int i = 0; i < ScenCnt; i++)
{
rdSet.Add(normalD.InverseCumulativeDistribution(RD.NextDouble()));
}
// Create a new Shapiro-Wilk test:
var sw = new Accord.Statistics.Testing.ShapiroWilkTest(rdSet.ToArray());
pValue = sw.PValue;
testCnt++;
}
return(rdSet);
}
public static double GetLambda(double[] sample)
{
List <double> arr = new List <double>(sample);
var normal2 = new MathNet.Numerics.Distributions.Normal();
arr.Sort();
double dMax = 0.0;
double dp, dm;
double MX = StatisticData.CalculateMathExpectation(sample);
double D = StatisticData.CalculateDispersoin(sample, MX);
for (int i = 0; i < arr.Count; i++)
{
double v = (arr[i] - MX) / Math.Sqrt(D);
dp = Math.Abs((double)(i + 1) / arr.Count - normal2.CumulativeDistribution(v));
dm = Math.Abs(normal2.CumulativeDistribution(v) - (double)i / arr.Count);
if (dp > dMax)
{
dMax = dp;
}
if (dm > dMax)
{
dMax = dm;
}
}
return(dMax * Math.Sqrt(arr.Count));
}
private Matrix <double> RandomizeGaussianMatrixMaker(double damagedSD)
{
MathNet.Numerics.Distributions.Normal normalDist = new MathNet.Numerics.Distributions.Normal(0, damagedSD);
Matrix <double> newMatrix = DenseMatrix.CreateRandom(SendLayer.UnitCount, ReceiveLayer.UnitCount, normalDist);
return(newMatrix);
}
// sensor reading
public void get_corrupted_accelerations(ref Vector <float> xdotdot_bf_corrupted, Vector <float> xdotdot_ideal, Vector <float> attitude_ideal)
{
// The accelerations in the earth frame are perfectly known from the differential equation.
// But the accelerometer returns the accelerations in the body frame, therefore a transformation is needed.
// R is a transformation matrix from the body to the earth frame, so R.inverse() transforms from earth to body frame.
Matrix <float> R = Matrix <float> .Build.Dense(3, 3, 0);
rotation(ref R, attitude_ideal);
xdotdot_bf_corrupted = R.Inverse() * xdotdot_ideal;
// If there is no acceleration in the earth frame (xdotdot_ideal = 0), the accelerometer would still measure the gravity acceleration.
// Therefore the gravity vector has to be added.
float[] gravity = { 0, 0, GRAVITY };
xdotdot_bf_corrupted = xdotdot_bf_corrupted + R.Inverse() * Vector <float> .Build.DenseOfArray(gravity);
// add chip tilt
rotation(ref R, chip_tilt);
xdotdot_bf_corrupted = R.Inverse() * xdotdot_bf_corrupted;
MathNet.Numerics.Distributions.Normal distribution = new MathNet.Numerics.Distributions.Normal(0.0, 1.0); //using standard normal distribution
// add noise and an offset
xdotdot_bf_corrupted[0] += (float)distribution.Sample() * ACCELEROMETER_STANDEV_X + ACCELEROMETER_OFFSET_X;
xdotdot_bf_corrupted[1] += (float)distribution.Sample() * ACCELEROMETER_STANDEV_Y + ACCELEROMETER_OFFSET_Y;
xdotdot_bf_corrupted[2] += (float)distribution.Sample() * ACCELEROMETER_STANDEV_Z + ACCELEROMETER_OFFSET_Z;
// remove the offset from the calibration
xdotdot_bf_corrupted -= calibrated_offsets;
}
/// <summary>
/// Produce a vector of curves where the element at index i is a realization of a simulation at
/// simulationDates i. If you require the rates directly use <see cref="GetSimulatedRates(Date[])"/>
/// </summary>
/// <param name="simulationDates">Dates on which the simulation is run. Must all be greater than the
/// anchor date.</param>
/// <returns></returns>
public ICurve[] GetSimulatedCurves(Date[] simulationDates, Currency curveCcy = null)
{
if (curveCcy == null)
{
curveCcy = Currency.ANY;
}
var results = new ICurve[simulationDates.Length];
var dist = new Normal();
var previousDate = anchorDate;
var previousRates = initialRates.Clone() as double[];
var currentRates = new double[initialRates.Length];
// Iterate through the simulation dates
for (var simCounter = 0; simCounter < simulationDates.Length; simCounter++)
{
var currentDate = simulationDates[simCounter];
var dt = (currentDate - previousDate) / 365.0;
var sdt = Math.Sqrt(dt);
var curveDates = new Date[initialRates.Length];
// Random realizations to be used in simulation.
var eps1 = dist.Sample();
var eps2 = dist.Sample();
var eps3 = dist.Sample();
// Iterate thrrough the dates on the curve
for (var i = 0; i < initialRates.Length; i++)
{
curveDates[i] = simulationDates[simCounter].AddTenor(tenors[i]);
if (useRelative)
{
//TODO: add mean correction.
var exponent = components[0, i] * vols[0] * sdt * eps1 +
components[1, i] * vols[1] * sdt * eps2 +
components[2, i] * vols[2] * sdt * eps3;
currentRates[i] = previousRates[i] * Math.Exp(exponent);
}
else
{
var change = components[0, i] * vols[0] * sdt * eps1 + components[1, i] * vols[1] * sdt * eps2 +
components[2, i] * vols[2] * sdt * eps3;
currentRates[i] = previousRates[i] + change;
if (floorAtZero)
{
currentRates[i] = Math.Max(0.0, currentRates[i]);
}
}
}
currentRates = currentRates.Multiply(multiplier);
results[simCounter] = new DatesAndRates(curveCcy, simulationDates[simCounter], curveDates, currentRates,
simulationDates[simCounter].AddMonths(360));
previousRates = currentRates.Clone() as double[];
previousDate = new Date(currentDate);
}
return(results);
}
public String get_z()
{
double z;
MathNet.Numerics.Distributions.Normal result = new MathNet.Numerics.Distributions.Normal();
z = result.InverseCumulativeDistribution(probabilidad_cumplir);
return(Convert.ToString(z));
}
public Random(int mean, int min, int max)
{
_mean = mean;
_min = min;
_max = max;
var stddev = Math.Min(Math.Abs(mean - min), Math.Abs(max - mean)) / 3.0;
_dist = new MathNet.Numerics.Distributions.Normal(mean, stddev);
}
public void get_corrupted_height(ref float height_corrupted, float height_ideal)
{
MathNet.Numerics.Distributions.Normal distribution = new MathNet.Numerics.Distributions.Normal(0.0, 1.0);
// add noise and an offset
height_corrupted = height_ideal + (float)distribution.Sample() * BAROMETER_STANDEV + BAROMETER_OFFSET;
// remove the offset from the calibration
height_corrupted -= calibrated_offset;
}
private static double CumulativeProbabilityNormalDistributionFunction(double x)
{
/*
* Related links
* https://stackoverflow.com/questions/1662943/standard-normal-distribution-z-value-function-in-c-sharp
* https://numerics.mathdotnet.com/
* https://en.wikipedia.org/wiki/Normal_distribution
* https://en.wikipedia.org/wiki/Error_function
*/
MathNet.Numerics.Distributions.Normal result = new MathNet.Numerics.Distributions.Normal();
return(result.CumulativeDistribution(x));
}
public void GetRandom(out double[] rdIR, out double[] rdEQ)
{
var normalD = new MathNet.Numerics.Distributions.Normal();
rdIR = new double[EndMth];
rdEQ = new double[EndMth];
for (int i = 0; i < EndMth; i++)
{
rdIR[i] = normalD.InverseCumulativeDistribution(RD.NextDouble());
rdEQ[i] = normalD.InverseCumulativeDistribution(RD.NextDouble());
}
}
public static IEnumerable<Z2<float>> TestData()
{
var g = new MathNet.Numerics.Distributions.Normal(0.0, 1.0);
var randy = new MathNet.Numerics.Random.MersenneTwister();
g.RandomSource = randy;
var dblsX = new double[100000];
var dblsY = new double[100000];
g.Samples(dblsX);
g.Samples(dblsY);
return dblsX.Select((d,i) =>
new Z2<float>((float)d, (float)dblsY[i]));
}
public Carnivore(Random rng, Position pos = null, IAnimalParams customParameters = null) : base(rng, pos)
{
if (customParameters is null)
{
Params = new CarnivoreParams();
}
else
{
Params = customParameters;
}
var norm = new MathNet.Numerics.Distributions.Normal(Params.BirthWeight, Params.BirthSigma);
Weight = norm.Sample();
}
public void CurtainsLeadTime()
{
var mean = 17.54F;
var std = 15.16F;
var dist = new MathNet.Numerics.Distributions.Normal(mean, std);
List <double> results = new List <double>();
for (double d = 0; d <= 1.0; d += 0.05)
{
results.Add(dist.InverseCumulativeDistribution(d));
}
}
public static void TestRandom()
{
var rd = new MathNet.Numerics.Random.MersenneTwister();
var normalD = new MathNet.Numerics.Distributions.Normal();
List <double> vals = new List <double>();
for (int i = 0; i < 100000; i++)
{
vals.Add(normalD.InverseCumulativeDistribution(rd.NextDouble()));
}
// Create a new Shapiro-Wilk test:
var sw = new Accord.Statistics.Testing.ShapiroWilkTest(vals.ToArray());
}
public void GenerateWeights(int weightCount, Random randomWeight)
{
Weights = new double[weightCount];
WeightErrorDistribution = new double[weightCount];
for (int i = 0; i < weightCount; i++)
{
double mean = 0;
double stdDev = 0.3;
MathNet.Numerics.Distributions.Normal normalDist = new MathNet.Numerics.Distributions.Normal(mean, stdDev);
double randomGaussianValue = normalDist.Sample();
//randomGaussianValue = randomWeight.NextDouble() - 0.5;
//randomGaussianValue = 1;
Weights[i] = randomGaussianValue;
bias = randomGaussianValue;
}
}
/// <summary>
/// Removes any values in the list that are outside of a two-tailed p value range.
/// Does not remove any outliers if number of value is less or equal to 3.
/// </summary>
/// <param name="values"></param>
/// <param name="pVal"></param>
/// <returns>list with no outliers</returns>
public static IEnumerable <double> removeOutliers_NormalDist(this IEnumerable <double> values, double pVal = 0.05)
{
List <double> outliers = new List <double>();
if (values.Count() > 3)
{
MathNet.Numerics.Distributions.Normal distribution = MathNet.Numerics.Distributions.Normal.Estimate(values);
foreach (double val in values)
{
double cur_p = distribution.CumulativeDistribution(val);
if (cur_p < (pVal / 2) || cur_p > (1 - (pVal / 2)))
{
outliers.Add(val);
}
}
}
return(values.Except(outliers));
}
public void SetNextOrderIfNeeded(DateTime currentTime)
{
if (NextOrderTime == new DateTime() || NextOrderTime <= currentTime)
{
switch (OptionB)
{
case EnumTypes.BuyerBOptions.Static:
NextOrderTime = currentTime.AddMinutes(this.Minutes);
break;
case EnumTypes.BuyerBOptions.Gauss:
MathNet.Numerics.Distributions.Normal normal = new MathNet.Numerics.Distributions.Normal(this.MeanOptionB, this.DeviationOptionB);
NextOrderTime = currentTime.AddMinutes(normal.Sample());
break;
}
SetNextOrderAmount();
}
}
public void get_corrupted_MagneticVectorBodyFrame(ref Vector <float> magField_bf_corrupted, Vector <float> attitude_ideal)
{
Matrix <float> R = Matrix <float> .Build.Dense(3, 3, 0);
rotation(ref R, attitude_ideal);
// from earth to body frame
magField_bf_corrupted = R.Inverse() * magneticEarthFieldVector;
// add chip tilt
rotation(ref R, chip_tilt);
magField_bf_corrupted = R.Inverse() * (magField_bf_corrupted);
MathNet.Numerics.Distributions.Normal distribution = new MathNet.Numerics.Distributions.Normal(0.0, 1.0);
magField_bf_corrupted[0] += (float)distribution.Sample() * MAGNETOMETER_OFFSET_X + MAGNETOMETER_STANDEV_X;
magField_bf_corrupted[1] += (float)distribution.Sample() * MAGNETOMETER_OFFSET_Y + MAGNETOMETER_STANDEV_Y;
magField_bf_corrupted[2] += (float)distribution.Sample() * MAGNETOMETER_OFFSET_Z + MAGNETOMETER_STANDEV_Z;
}
public Func <double> CreateRandomFunc()
{
var random = this.Seed == 0 ? DefaultRandom.Value : new Random(this.Seed);
switch (this.Distribution)
{
case Distribution.Triangular:
return(() =>
{
double p = random.NextDouble();
return p < (this.Average - this.Minimum) / (this.Maximum - this.Minimum)
? this.Minimum
+ Math.Sqrt(
p * (this.Maximum - this.Minimum) * (this.Average - this.Minimum))
: this.Maximum
- Math.Sqrt(
(1 - p) * (this.Maximum - this.Minimum) * (this.Maximum - this.Average));
});
case Distribution.Normal:
{
var normal = new MathNet.Numerics.Distributions.Normal(
this.Average,
this.StandardDeviation,
random);
return(() => normal.Sample());
}
case Distribution.LogNormal:
{
var logNormal = new MathNet.Numerics.Distributions.LogNormal(
this.Average,
this.StandardDeviation,
random);
return(() => logNormal.Sample());
}
case Distribution.Uniform:
return(() => random.NextDouble() * (this.Maximum - this.Minimum) + this.Minimum);
}
return(() => double.NaN);
}
public void get_corrupted_angveloc(ref Vector <float> thetadot_bf_corrupted, Vector <float> thetadot_ideal, Vector <float> attitude_ideal)
{
// The angular velocities in the earth frame are perfectly known from the differential equation.
// But the gyroscope returns angular accelerations in the body frame, therefore a transformation is needed.
// The easiest way is to use the functions for the differential equations.
thetadot2omega(ref thetadot_bf_corrupted, thetadot_ideal, attitude_ideal);
// add chip tilt
thetadot2omega(ref thetadot_bf_corrupted, thetadot_bf_corrupted, chip_tilt);
MathNet.Numerics.Distributions.Normal distribution = new MathNet.Numerics.Distributions.Normal(0.0, 1.0);
// add noise and an offset
thetadot_bf_corrupted[0] += (float)distribution.Sample() * DEG2RAD(GYROSCOPE_STANDEV_R) + DEG2RAD(GYROSCOPE_OFFSET_R);
thetadot_bf_corrupted[1] += (float)distribution.Sample() * DEG2RAD(GYROSCOPE_STANDEV_P) + DEG2RAD(GYROSCOPE_OFFSET_P);
thetadot_bf_corrupted[2] += (float)distribution.Sample() * DEG2RAD(GYROSCOPE_STANDEV_Y) + DEG2RAD(GYROSCOPE_OFFSET_Y);
// remove the offset from the calibration
thetadot_bf_corrupted -= calibrated_offsets;
}
//generate ransdom number based off if gaussian or not
static double generateRandom(int numMems, Random rnd)
{
double random = rnd.Next(0, numMems);
if (USING_GUASSIAN)
{
double stdDev;
if (numMems > 2)
{
stdDev = numMems / (numMems * 3);
}
else
{
stdDev = 0;
}
var normalDist = new MathNet.Numerics.Distributions.Normal(random, stdDev);
random = Math.Abs(normalDist.Sample());
}
return(random);
}
private Tuple <Genome, Genome> Crossover(Genome genome1, Genome genome2)
{
MathNet.Numerics.Distributions.Normal normal = new MathNet.Numerics.Distributions.Normal(0, GASettings.MutStdDev);
double[] one = new double[genome1.ann.Weights.Length], two = new double[genome2.ann.Weights.Length];
for (int i = 0; i < one.Length; i++)
{
if (Rand.NextDouble() < GASettings.CrossProb)
{
one[i] = genome1.ann.Weights[i];
two[i] = genome2.ann.Weights[i];
}
else
{
one[i] = genome2.ann.Weights[i];
two[i] = genome1.ann.Weights[i];
}
if (Rand.NextDouble() < GASettings.MutProb)
{
one[i] += normal.Sample();
one[i] = one[i] > ANNSettings.MaxBoundary ? ANNSettings.MaxBoundary : one[i];
one[i] = one[i] < ANNSettings.MinBoundary ? ANNSettings.MinBoundary : one[i];
}
if (Rand.NextDouble() < GASettings.MutProb)
{
two[i] += normal.Sample();
two[i] = two[i] > ANNSettings.MaxBoundary ? ANNSettings.MaxBoundary : two[i];
two[i] = two[i] < ANNSettings.MinBoundary ? ANNSettings.MinBoundary : two[i];
}
}
Genome genome3 = null, genome4 = null;
Parallel.Invoke(() => {
genome3 = new Genome(one);
}, () => {
genome4 = new Genome(two);
});
return(Tuple.Create(genome3, genome4));
}
public IΦHatResultElement Calculate(
INormalFactory normalFactory,
IΦHatResultElementFactory ΦHatResultElementFactory,
IlIndexElement lIndexElement,
IsIndexElement sIndexElement,
IΛIndexElement ΛIndexElement,
IExpectedValueΦ expectedValueΦ,
IVarianceΦ varianceΦ,
IVHat VHat,
Iυ2 υ2)
{
// https://stackoverflow.com/questions/1662943/standard-normal-distribution-z-value-function-in-c-sharp
MathNet.Numerics.Distributions.Normal normal = (MathNet.Numerics.Distributions.Normal)normalFactory.Create();
return(ΦHatResultElementFactory.Create(
sIndexElement,
lIndexElement,
ΛIndexElement,
(decimal)expectedValueΦ.GetElementAtAsdecimal(
sIndexElement,
lIndexElement,
ΛIndexElement)
+
(decimal)normal.CumulativeDistribution(
(double)(1 - υ2.GetElementAtAsdecimal(
ΛIndexElement)))
*
(decimal)Math.Pow(
Math.Sqrt(
(double)VHat.GetElementAtAsdecimal(
ΛIndexElement)),
-1)
*
(decimal)varianceΦ.GetElementAtAsdecimal(
sIndexElement,
lIndexElement,
ΛIndexElement)));
}
public decimal Calculate(
INormalFactory normalFactory,
ItIndexElement tIndexElement,
IΛIndexElement ΛIndexElement,
IExpectedValueI expectedValueI,
IVarianceI varianceI,
decimal υ2)
{
// https://stackoverflow.com/questions/1662943/standard-normal-distribution-z-value-function-in-c-sharp
MathNet.Numerics.Distributions.Normal normal = (MathNet.Numerics.Distributions.Normal)normalFactory.Create();
return
((decimal)expectedValueI.GetElementAtAsdecimal(
tIndexElement,
ΛIndexElement)
+
(decimal)normal.CumulativeDistribution((double)(1 - υ2))
*
(decimal)Math.Sqrt(
(double)varianceI.GetElementAtAsdecimal(
tIndexElement,
ΛIndexElement)));
}
public double TakeSamples()
{
var dateTimeElapsed = 0.0;
var dateTime = DateTime.Now;
if (this.DistributionName == "Binomial")
{
fullName = $"{DistributionName}-Samples:{SamplesNumber}-Trials:{TrialsNumber}";
var binomaial = new MathNet.Numerics.Distributions.Binomial(0.5, this.TrialsNumber);
var generatedsamples = binomaial.Samples().Take(SamplesNumber).ToArray();
}
else if (this.DistributionName == "Geometric")
{
fullName = $"{DistributionName}-Samples:{SamplesNumber}";
var geometric = new MathNet.Numerics.Distributions.Geometric(0.5);
var generatedsamples = geometric.Samples().Take(SamplesNumber).ToArray();
}
else if (this.DistributionName == "Poisson")
{
fullName = $"{DistributionName}-Samples:{SamplesNumber}";
var poisson = new MathNet.Numerics.Distributions.Poisson(0.5);
var generatedsamples = poisson.Samples().Take(SamplesNumber).ToArray();
}
else if (this.DistributionName == "Normal")
{
fullName = $"{DistributionName}-Samples:{SamplesNumber}";
var normal = new MathNet.Numerics.Distributions.Normal(0.5, 2);
var generatedsamples = normal.Samples().Take(SamplesNumber).ToArray();
}
dateTimeElapsed = (DateTime.Now - dateTime).TotalMilliseconds;
return(dateTimeElapsed);
}
// 50년 누적값이 몇개의 난수셋을 거치면서 안정되는지 확인
public static void TestRandom2()
{
var rd = new MathNet.Numerics.Random.MersenneTwister();
var normalD = new MathNet.Numerics.Distributions.Normal();
double val = 0.0;
double avg = 0.0;
List <double> avgVal = new List <double>();
for (int i = 1; i <= 1000000; i++)
{
double cumVal = 1.0;
for (int j = 0; j < 50; j++)
{
val = normalD.InverseCumulativeDistribution(rd.NextDouble());
cumVal *= 1.0 + (val / 10.0);
}
if (i == 1)
{
avg = cumVal;
}
else
{
avg = avg * (Convert.ToDouble(i - 1) / Convert.ToDouble(i))
+ cumVal * (1 / Convert.ToDouble(i));
}
// 1000개마다 평균값을 저장
if (i % 1000 == 0)
{
avgVal.Add(avg);
}
}
}
/// <summary>
/// Gets a new Decision Vector, based on the PCX logic.
/// </summary>
/// <param name="parents">A list of parent <see cref="DecisionVector"/>s.</param>
/// <returns>A new <see cref="DecisionVector"/>.</returns>
/// <exception cref="ArgumentOutOfRangeException">
/// Thrown if:
/// - there are less than two parents; or
/// - the parents have different length or zero length decision vectors; or
/// - any of the parents have non-continuous Decision Vector elements.
/// </exception>
public DecisionVector Operate(params DecisionVector[] parents)
{
if (parents.Length < 2)
{
throw new ArgumentOutOfRangeException(nameof(parents),
"There must be at least two parents.");
}
// TODO: These calls to .Any() are slow - can we remove the error checking?
if (parents.Any(p => p.GetContinuousElements().Count == 0))
{
throw new ArgumentOutOfRangeException(nameof(parents),
"Parents must have non-zero length decision vectors.");
}
if (parents.Any(p => p.GetContinuousElements().Count != parents.First().Count))
{
throw new ArgumentOutOfRangeException(nameof(parents),
"Parents must have the same length and fully continuous decision vectors.");
}
// 1: Pre-process
var parentDVs = Matrix <double> .Build.DenseOfColumns(parents.Select(dv => dv.Select(d => (double)d)));
var motherDV = Vector <double> .Build.DenseOfArray(parents.ElementAt(0).Select(d => (double)d).ToArray());
// 1a: centroid of all parents
var centroid = parentDVs.RowSums().Divide(parents.Count());
// 1b: vector distance from centroid to mother (following Deb's C code, not paper)
var motherCentroidVectorDistance = centroid - motherDV;
var motherCentroidAbsoluteDistance = motherCentroidVectorDistance.L2Norm();
if (motherCentroidAbsoluteDistance < 1e-20)
{
return(DecisionVector.CreateForEmpty());
}
// 1c: vector distance from other parents to mother
var otherParentDVs = parentDVs.RemoveColumn(0);
var parentMotherVectorDistances = otherParentDVs.EnumerateColumns()
.Select(v => v - motherDV).ToArray();
var parentMotherAbsoluteDistances = parentMotherVectorDistances.Select(v => v.L2Norm()).ToArray();
if (parentMotherAbsoluteDistances.Any(d => d < 1e-20))
{
return(DecisionVector.CreateForEmpty());
}
// 1d: perpendicular distances from other parents to centroid-mother vector
var orthogonalDistances = parentMotherVectorDistances
.Select((v, i) => parentMotherAbsoluteDistances.ElementAt(i) *
Math.Sqrt(1.0 - Math.Pow(
v.DotProduct(motherCentroidVectorDistance) /
(parentMotherAbsoluteDistances.ElementAt(i) * motherCentroidAbsoluteDistance),
2.0)));
var meanOrthogonalDistance = orthogonalDistances.Mean();
// 2: Now create a new individual
var normRnd = new MathNet.Numerics.Distributions.Normal(rngManager.Rng);
var samplesEta = new double[motherDV.Count];
normRnd.Samples(samplesEta);
var newRandomDv = Vector <double> .Build.DenseOfArray(samplesEta)
.Multiply(sigmaEta * meanOrthogonalDistance);
//Remove component of randomness in direction of ?
var offset1 = motherCentroidVectorDistance
.Multiply(newRandomDv.DotProduct(motherCentroidVectorDistance))
.Divide(Math.Pow(motherCentroidAbsoluteDistance, 2.0));
newRandomDv -= offset1;
var offset2 = motherCentroidVectorDistance
.Multiply(sigmaZeta * normRnd.Sample());
newRandomDv += offset2;
// Modification of Deb2002 which should maintain stability.
var finalDv = motherDV +
newRandomDv.Divide(Math.Sqrt(motherDV.Count));
return(DecisionVector.CreateFromArray(parents.First().GetDecisionSpace(), finalDv.ToArray()));
}
public override void UpdateDistribution()
{
MathNet.Numerics.Random.RandomSource rand;
if(RandomSeed)
{
rand = new MathNet.Numerics.Random.MersenneTwister();
}
else
{
rand = new MathNet.Numerics.Random.MersenneTwister(Seed);
}
_gauss = new MathNet.Numerics.Distributions.Normal(_mean, _deviation, rand);
}
private static AggregationResult RunAggregation(ClusterNetwork net, double bias)
{
Dictionary<Vertex, double> _attributes = new Dictionary<Vertex, double>();
Dictionary<Vertex, double> _aggregates = new Dictionary<Vertex, double>();
MathNet.Numerics.Distributions.Normal normal = new MathNet.Numerics.Distributions.Normal(0d, 5d);
AggregationResult result = new AggregationResult();
result.Modularity = net.NewmanModularity;
double average = 0d;
foreach (Vertex v in net.Vertices)
{
_attributes[v] = normal.Sample();
_aggregates[v] = _attributes[v];
average += _attributes[v];
}
average /= (double)net.VertexCount;
double avgEstimate = double.MaxValue;
result.FinalVariance = double.MaxValue;
result.FinalOffset = 0d;
for (int k = 0; k < Properties.Settings.Default.ConsensusRounds; k++)
{
foreach (Vertex v in net.Vertices.ToArray())
{
Vertex w = v.RandomNeighbor;
List<Vertex> intraNeighbors = new List<Vertex>();
List<Vertex> interNeighbors = new List<Vertex>();
ClassifyNeighbors(net, v, intraNeighbors, interNeighbors);
double r = net.NextRandomDouble();
if (r <= bias && interNeighbors.Count > 0)
w = interNeighbors.ElementAt(net.NextRandom(interNeighbors.Count));
_aggregates[v] = aggregate(_aggregates[v], _aggregates[w]);
_aggregates[w] = aggregate(_aggregates[v], _aggregates[w]);
}
avgEstimate = 0d;
foreach (Vertex v in net.Vertices.ToArray())
avgEstimate += _aggregates[v];
avgEstimate /= (double)net.VertexCount;
result.FinalVariance = 0d;
foreach (Vertex v in net.Vertices.ToArray())
result.FinalVariance += Math.Pow(_aggregates[v] - avgEstimate, 2d);
result.FinalVariance /= (double)net.VertexCount;
double intraVar = 0d;
foreach (int c in net.ClusterIDs)
{
double localavg = 0d;
double localvar = 0d;
foreach (Vertex v in net.GetNodesInCluster(c))
localavg += _aggregates[v];
localavg /= net.GetClusterSize(c);
foreach (Vertex v in net.GetNodesInCluster(c))
localvar += Math.Pow(_aggregates[v] - localavg, 2d);
localvar /= net.GetClusterSize(c);
intraVar += localvar;
}
intraVar /= 50d;
//Console.WriteLine("i = {0:0000}, Avg = {1:0.000}, Estimate = {2:0.000}, Intra-Var = {3:0.000}, Total Var = {4:0.000}", result.iterations, average, avgEstimate, intraVar, totalVar);
}
result.FinalOffset = average - avgEstimate;
return result;
}
public double TakeSamples()
{
var dateTimeElapsed = 0.0;
var dateTime = DateTime.Now;
//IEnumerable<int> generatedSamplesEnumerable = Enumerable.Empty<int>();
//IEnumerable<double> generatedSamplesDoubleEnumerable = Enumerable.Empty<double>();
if (this.DistributionName == "Binomial")
{
fullName = $"{DistributionName}-Samples:{SamplesNumber}-Trials:{TrialsNumber}";
var binomaial = new MathNet.Numerics.Distributions.Binomial(0.5, this.TrialsNumber);
var generatedsamples = binomaial.Samples().Take(SamplesNumber).ToArray();
//generatedSamplesEnumerable = binomaial.Samples().Take(SamplesNumber);
//foreach (var item in generatedSamplesEnumerable)
//{
// var test = item;
//}
}
else if (this.DistributionName == "Geometric")
{
fullName = $"{DistributionName}-Samples:{SamplesNumber}";
var geometric = new MathNet.Numerics.Distributions.Geometric(0.5);
var generatedsamples = geometric.Samples().Take(SamplesNumber).ToArray();
//generatedSamplesEnumerable = geometric.Samples().Take(SamplesNumber);
//foreach (var item in generatedSamplesEnumerable)
//{
// var test = item;
//}
}
else if (this.DistributionName == "Poisson")
{
fullName = $"{DistributionName}-Samples:{SamplesNumber}";
var poisson = new MathNet.Numerics.Distributions.Poisson(0.5);
var generatedsamples = poisson.Samples().Take(SamplesNumber).ToArray();
//generatedSamplesEnumerable = poisson.Samples().Take(SamplesNumber);
//foreach (var item in generatedSamplesEnumerable)
//{
// var test = item;
//}
}
else if (this.DistributionName == "Normal")
{
fullName = $"{DistributionName}-Samples:{SamplesNumber}";
var normal = new MathNet.Numerics.Distributions.Normal(0.5, 2);
var generatedsamples = normal.Samples().Take(SamplesNumber).ToArray();
//generatedSamplesDoubleEnumerable = normal.Samples().Take(SamplesNumber);
//foreach(var item in generatedSamplesDoubleEnumerable)
//{
// var test = item;
//}
}
dateTimeElapsed = (DateTime.Now - dateTime).TotalMilliseconds;
return(dateTimeElapsed);
}
private Matrix<double> RandomizeGaussianMatrixMaker(double damagedSD, int rowCount)
{
MathNet.Numerics.Distributions.Normal normalDist = new MathNet.Numerics.Distributions.Normal(0, damagedSD);
Matrix<double> newMatrix = DenseMatrix.CreateRandom(rowCount, UnitCount, normalDist);
return newMatrix;
}
/// <summary>
/// Se estiman las propiedades estáticas en función de la profundidad del core
/// </summary>
public void Estimar()
{
// se toma la informacion de segmentacion vs areas de interes y se estiman las propiedades petrofisicas estaticas
// se preparan los generadores de CT aleatorios para cada phantom
var phantom1High = new MathNet.Numerics.Distributions.Normal(padre.actual.phantom1.mediaHigh, padre.actual.phantom1.desvHigh);
var phantom1Low = new MathNet.Numerics.Distributions.Normal(padre.actual.phantom1.mediaLow, padre.actual.phantom1.desvLow);
var phantom2High = new MathNet.Numerics.Distributions.Normal(padre.actual.phantom2.mediaHigh, padre.actual.phantom2.desvHigh);
var phantom2Low = new MathNet.Numerics.Distributions.Normal(padre.actual.phantom2.mediaLow, padre.actual.phantom2.desvLow);
var phantom3High = new MathNet.Numerics.Distributions.Normal(padre.actual.phantom3.mediaHigh, padre.actual.phantom3.desvHigh);
var phantom3Low = new MathNet.Numerics.Distributions.Normal(padre.actual.phantom3.mediaLow, padre.actual.phantom3.desvLow);
// se prepara un vector de valores CT high y low para cada phantom
// este vector representa un slide y solo se guarda un valor promedio del slide
double[] temp = new double[padre.actual.datacuboHigh.dataCube[0].segCore.Count];
MathNet.Numerics.Statistics.DescriptiveStatistics stats;
// se preparan los vectores para densidad y zeff
this.Dfm = new double[padre.actual.datacuboHigh.dataCube.Count];
this.Zfme = new double[padre.actual.datacuboHigh.dataCube.Count];
this.Pefm = new double[padre.actual.datacuboHigh.dataCube.Count];
double ctP1High, ctP2High, ctP3High, ctP1Low, ctP2Low, ctP3Low;
double A, B, C, D, E, F;
List<double> Df, Zf, Zeff, Pef;
int iarea;
// se empieza a recorrer cada slide que se encuentre dentro de las areas de interes
// para cada slide se genera una colección de datos aleatorios simulando cada phantom, y se estima la media para cada phantom en cada slide
bool slide = false;
for (int i = 0; i < padre.actual.datacuboHigh.dataCube.Count; i++)
{
slide = false;
iarea = -1;
for (int j = 0; j < padre.actual.areasCore.Count; j++)
{
// se busca si este slide esta dentro de al menos un area de interes
if ((i >= padre.actual.areasCore[j].ini) & (i <= padre.actual.areasCore[j].fin))
{
slide = true;
iarea = j;
}
}
if (slide)
{
// el slide pertenece al menos a un area de interes, por tanto se procede a calcular la densidad y zeff para este slide
// se generan los valores CT para cada phantom y se toma su media
phantom1High.Samples(temp);
stats = new MathNet.Numerics.Statistics.DescriptiveStatistics(temp);
ctP1High = stats.Mean;
phantom2High.Samples(temp);
stats = new MathNet.Numerics.Statistics.DescriptiveStatistics(temp);
ctP2High = stats.Mean;
phantom3High.Samples(temp);
stats = new MathNet.Numerics.Statistics.DescriptiveStatistics(temp);
ctP3High = stats.Mean;
phantom1Low.Samples(temp);
stats = new MathNet.Numerics.Statistics.DescriptiveStatistics(temp);
ctP1Low = stats.Mean;
phantom2Low.Samples(temp);
stats = new MathNet.Numerics.Statistics.DescriptiveStatistics(temp);
ctP2Low = stats.Mean;
phantom3Low.Samples(temp);
stats = new MathNet.Numerics.Statistics.DescriptiveStatistics(temp);
ctP3Low = stats.Mean;
// se resuelve el sistema lineal para obtener las constantes A,B,C,D,E,F
var matriz = MathNet.Numerics.LinearAlgebra.Matrix<double>.Build.DenseOfArray(new double[,] { { ctP1Low, ctP1High, 1 }, { ctP2Low, ctP2High, 1 }, { ctP3Low, ctP3High, 1 } });
var sol = MathNet.Numerics.LinearAlgebra.Vector<double>.Build.Dense(new double[] { padre.actual.phantom1.densidad, padre.actual.phantom2.densidad, padre.actual.phantom3.densidad });
var x = matriz.Solve(sol);
A = x[0];
B = x[1];
C = x[2];
sol = MathNet.Numerics.LinearAlgebra.Vector<double>.Build.Dense(new double[] { padre.actual.phantom1.zeff, padre.actual.phantom2.zeff, padre.actual.phantom3.zeff });
x = matriz.Solve(sol);
D = x[0];
E = x[1];
F = x[2];
// se empieza a recorrer cada voxel, en la segmentacion del actual i-slide, se revisa que este dentro del area de interes
Df = new List<double>();
Zf = new List<double>();
Zeff = new List<double>();
Pef = new List<double>();
int jkindex = 0;
double dx;
double dy;
double tDf, tZf, tZeff, tPef;
// dado que se recorre fila a fila, entonces el indice j corresponde al eje Y y el indice k al eje X
for (int j = 0; j < padre.actual.datacuboHigh.widthSeg; j++)
{
for (int k = 0; k < padre.actual.datacuboHigh.widthSeg; k++)
{
// se calcula la distancia de la posicion (j,k) al centro del area de interes
// si la distancia es menor que el radio entonces se agrega al calculo, sino no
dx = k - padre.actual.areasCore[iarea].x;
dx = dx * dx;
dy = j - padre.actual.areasCore[iarea].y;
dy = dy * dy;
if (Math.Sqrt(dx + dy) <= padre.actual.datacuboHigh.widthSeg)
{
// la coordenada (j,k) esta dentro del area de interes
// se calculan las propiedades estaticas
tDf = A * padre.actual.datacuboLow.dataCube[i].segCore[jkindex] + B * padre.actual.datacuboHigh.dataCube[i].segCore[jkindex] + C;
Df.Add(tDf);
tZf = D * padre.actual.datacuboLow.dataCube[i].segCore[jkindex] + E * padre.actual.datacuboHigh.dataCube[i].segCore[jkindex] + F;
Zf.Add(tZf);
//tZeff = Math.Pow(Math.Pow((tZf / (0.9342 * tDf + 0.1759)), 10), 1 / 36);
tZeff = Math.Pow((tZf / (0.9342 * tDf + 0.1759)), 1/3.6);
Zeff.Add(tZeff);
tPef = Math.Pow(Math.Pow((tZeff / 10), 36), 0.1);
Pef.Add(tPef);
}
jkindex++;
}
}
stats = new MathNet.Numerics.Statistics.DescriptiveStatistics(Df);
Dfm[i] = stats.Mean;
stats = new MathNet.Numerics.Statistics.DescriptiveStatistics(Zeff);
Zfme[i] = stats.Mean;
stats = new MathNet.Numerics.Statistics.DescriptiveStatistics(Pef);
Pefm[i] = stats.Mean;
}
else
{
// se llenan los vectores de densidad y zeff con valores -1... si el valor es -1 entonces no se grafica
this.Dfm[i] = -1;
this.Zfme[i] = -1;
this.Pefm[i] = -1;
}
}
DateTime fin = DateTime.Now;
}
internal static double probability(double x, double mean, double stdev, bool cumulative)
{
var normalDistribution = new MathNet.Numerics.Distributions.Normal(mean, stdev);
return(cumulative ? normalDistribution.CumulativeDistribution(x) : normalDistribution.Density(x));
}
static void Main(string[] args)
{
double bias;
try{
// The neighbor selection bias is given as command line argument
bias1 = double.Parse(args[0]);
bias2 = double.Parse(args[1]);
}
catch(Exception)
{
Console.WriteLine("Usage: mono ./DemoSimulation.exe [initial_bias] [secondary_bias]");
return;
}
// The number of clusters (c) and the nodes within a cluster (Nc)
int c = 20;
int Nc = 20;
// The number of desired edges
int m = 6 * c * Nc;
// In order to yield a connected network, at least ...
double inter_thresh = 3d * ((c * Math.Log(c)) / 2d);
// ... edges between communities are required
// So the maximum number of edges within communities we s create is ...
double intra_edges = m - inter_thresh;
Console.WriteLine("Number of intra_edge pairs = " + c * Combinatorics.Combinations(Nc, 2));
Console.WriteLine("Number of inter_edge pairs = " + (Combinatorics.Combinations(c * Nc, 2) - (c * Combinatorics.Combinations(Nc, 2))));
// Calculate the p_i necessary to yield the desired number of intra_edges
double pi = intra_edges / (c * Combinatorics.Combinations(Nc, 2));
// From this we can compute p_e ...
double p_e = (m - c * MathNet.Numerics.Combinatorics.Combinations(Nc, 2) * pi) / (Combinatorics.Combinations(c * Nc, 2) - c * MathNet.Numerics.Combinatorics.Combinations(Nc, 2));
Console.WriteLine("Generating cluster network with p_i = {0:0.0000}, p_e = {1:0.0000}", pi, p_e);
// Create the network ...
network = new NETGen.NetworkModels.Cluster.ClusterNetwork(c, Nc, pi, p_e);
// ... and reduce it to the GCC
network.ReduceToLargestConnectedComponent();
Console.WriteLine("Created network has {0} vertices and {1} edges. Modularity = {2:0.00}", network.VertexCount, network.EdgeCount, network.NewmanModularity);
// Run the OopenGL visualization
NetworkColorizer colorizer = new NetworkColorizer();
NetworkVisualizer.Start(network, new FruchtermanReingoldLayout(15), colorizer);
currentBias = bias1;
// Setup the synchronization simulation, passing the bias strategy as a lambda expression
sync = new EpidemicSynchronization(
network,
colorizer,
v => {
Vertex neighbor = v.RandomNeighbor;
double r = network.NextRandomDouble();
// classify neighbors
List<Vertex> intraNeighbors = new List<Vertex>();
List<Vertex> interNeighbors = new List<Vertex>();
ClassifyNeighbors(network, v, intraNeighbors, interNeighbors);
neighbor = intraNeighbors.ElementAt(network.NextRandom(intraNeighbors.Count));
// biasing strategy ...
if (r <= currentBias && interNeighbors.Count > 0)
neighbor = interNeighbors.ElementAt(network.NextRandom(interNeighbors.Count));
return neighbor;
},
0.9d);
Dictionary<int, double> _groupMus = new Dictionary<int, double>();
Dictionary<int, double> _groupSigmas = new Dictionary<int, double>();
MathNet.Numerics.Distributions.Normal avgs_normal = new MathNet.Numerics.Distributions.Normal(300d, 50d);
MathNet.Numerics.Distributions.Normal devs_normal = new MathNet.Numerics.Distributions.Normal(20d, 5d);
for(int i=0; i<c; i++)
{
double groupAvg = avgs_normal.Sample();
double groupStdDev = devs_normal.Sample();
foreach(Vertex v in network.GetNodesInCluster(i))
{
sync._MuPeriods[v] = groupAvg;
sync._SigmaPeriods[v] = groupStdDev;
}
}
sync.OnStep+=new EpidemicSynchronization.StepHandler(collectLocalOrder);
// Run the simulation synchronously
sync.Run();
Console.ReadKey();
// Collect and print the results
SyncResults res = sync.Collect();
Console.WriteLine("Order {0:0.00} reached after {1} rounds", res.order, res.time);
}