private void startAsyncButton_Click(object sender, EventArgs e)
{
int numOfCashier = 0;
int numOfSelfCheckout = 0;
int numOfMachines = 0;
double arrPro = 0;
if (int.TryParse(this.numOfCashierTextBox.Text, out numOfCashier) &&
int.TryParse(this.numOfSelfCheckoutTextBox.Text, out numOfSelfCheckout) &&
int.TryParse(this.numOfSelfCheckMachineTextBox.Text, out numOfMachines) &&
double.TryParse(this.arrivalRateTextBox.Text, out arrPro))
{
int maxIteration;
if (int.TryParse(this.maxIterationTextBox.Text, out maxIteration) && maxIteration > 0)
{
_sim = new SimulationDemo.Simulation(numCashier: numOfCashier, numSelfChechout: numOfSelfCheckout, numMachine: numOfMachines, maxIteration: maxIteration);
}
else
{
_sim = new SimulationDemo.Simulation(numCashier: numOfCashier, numSelfChechout: numOfSelfCheckout, numMachine: numOfMachines);
}
IDistribution dist = new Poison(arrPro);
DistributionHelper.UpdateDistribution(EventEnum.Arrival, dist);
// Start the asynchronous operation.
backgroundWorker1.RunWorkerAsync();
this.startbutton.Enabled = false;
this.numOfCashierTextBox.Enabled = false;
this.numOfSelfCheckoutTextBox.Enabled = false;
this.numOfSelfCheckMachineTextBox.Enabled = false;
this.startbutton.Text = "Started";
}
}
public static Result TestSpearmanMethod(List <double> excerpt)
{
double[,] h = new double[excerpt.Count - 1, excerpt.Count - 1];
for (int i = 0; i < excerpt.Count - 1; i++)
{
for (int j = i + 1; j < excerpt.Count; j++)
{
if (excerpt[i] == excerpt[j])
{
h[i, j - 1] = 0.5;
}
else if (excerpt[i] < excerpt[j])
{
h[i, j - 1] = 1;
}
}
}
double v = 0;
for (int i = 0; i < excerpt.Count - 1; i++)
{
for (int j = i + 1; j < excerpt.Count; j++)
{
v += (j - 1) * h[i, j - 1];
}
}
double rc = 1 - 12 * v / excerpt.Count / (excerpt.Count * excerpt.Count - 1);
double d = 1.0 / (excerpt.Count - 1);
double s = rc / Math.Sqrt(d);
double q = DistributionHelper.GetNormalDistributionQuantile(TimeSeriesEnvironment.Current.Alpha);
return(new Result(q, s));
}
/// <summary>
/// Transforms the generic <paramref name="preconsolidationStress"/> into
/// a <see cref="MacroStabilityInwardsPreconsolidationStress"/>.
/// </summary>
/// <param name="preconsolidationStress">The preconsolidation stress to use
/// in the transformation.</param>
/// <returns>A <see cref="MacroStabilityInwardsPreconsolidationStress"/>
/// based on the given data.</returns>
/// <exception cref="ArgumentNullException">Thrown when <paramref name="preconsolidationStress"/>
/// is <c>null</c>.</exception>
/// <exception cref="ImportedDataTransformException">Thrown when the
/// <paramref name="preconsolidationStress"/> could not be transformed into
/// a <see cref="MacroStabilityInwardsPreconsolidationStress"/>.</exception>
public static MacroStabilityInwardsPreconsolidationStress Transform(PreconsolidationStress preconsolidationStress)
{
if (preconsolidationStress == null)
{
throw new ArgumentNullException(nameof(preconsolidationStress));
}
var location = new Point2D(preconsolidationStress.XCoordinate,
preconsolidationStress.ZCoordinate);
try
{
DistributionHelper.ValidateLogNormalDistribution(preconsolidationStress.StressDistributionType,
preconsolidationStress.StressShift);
var distribution = new VariationCoefficientLogNormalDistribution
{
Mean = (RoundedDouble)preconsolidationStress.StressMean,
CoefficientOfVariation = (RoundedDouble)preconsolidationStress.StressCoefficientOfVariation
};
return(new MacroStabilityInwardsPreconsolidationStress(location, distribution));
}
catch (Exception e) when(e is DistributionValidationException || e is ArgumentOutOfRangeException)
{
string errorMessage = CreateErrorMessage(location, e.Message);
throw new ImportedDataTransformException(errorMessage, e);
}
catch (ArgumentException e)
{
throw new ImportedDataTransformException(e.Message, e);
}
}
public void getOutcomeMinMax_Test()
{
double[][] samples = new double[10][];
for (int i = 0; i < samples.Length; i++)
{
samples[i] = new double[] { i * 0.1, -0.2, 0.2, 0.3, 10 };
}
samples[3] = null;
samples[4][1] = -0.4; //MinMIn
samples[5][2] = 0.41; //MaxMax
samples[6][3] = 0.42; //MaxActual
samples[5][3] = -0.2; //MinActual
double maxMax, minMin, maxActual, minActual;
double maxDistance, maxDistanceCount;
double maxMaxCount, minMinCount, maxActualCount, minActualCount;
DistributionHelper.getSampleOutcomesMinMax(samples, 0, out maxMax, out maxMaxCount, out minMin, out minMinCount, out maxDistance, out maxDistanceCount, out maxActual, out maxActualCount, out minActual, out minActualCount);
//Todo: Write another for minValuesPercent != 0
Assert.AreEqual(0.41, maxMax);
Assert.AreEqual(-0.4, minMin);
Assert.AreEqual(0.42, maxActual);
Assert.AreEqual(-0.2, minActual);
Assert.AreEqual(10, maxActualCount);
Assert.AreEqual(10, maxMaxCount);
Assert.AreEqual(10, minMinCount);
Assert.AreEqual(10, maxDistanceCount);
Assert.AreEqual(10, minActualCount);
}
public void getMinMax_NAN_Test()
{
double[] inputs = new double[200];
int v = 0;
for (int i = 0; i < inputs.Length; i += 2)
{
inputs[i] = v++;
}
for (int i = 1; i < inputs.Length; i += 2)
{
inputs[i] = double.NaN;
}
Assert.AreEqual(inputs[0], 0);
Assert.AreEqual(inputs[2], 1);
Assert.AreEqual(inputs[4], 2);
Assert.AreEqual(inputs[6], 3);
Assert.AreEqual(inputs[1], double.NaN);
Assert.AreEqual(inputs[3], double.NaN);
Assert.AreEqual(inputs[5], double.NaN);
Assert.AreEqual(inputs[7], double.NaN);
double min, max;
DistributionHelper.getMinMax(inputs, 10, out min, out max);
Assert.AreEqual(5, min, 1);
Assert.AreEqual(95, max, 1);
}
public static PieceConstIntensityResult PieceConstIntensity(Queue q, int m)
{
int n = q.Count;
double min = q.Min();
double dt = (q.Max() - min) / m;
int Ns = 0;
List <PieceConstIntensityClassResult> results = new List <PieceConstIntensityClassResult>();
for (int i = 0; i < m - 1; i++)
{
int ns = q.Count(x => x >= min + dt * i && x < min + dt * (i + 1));
Ns += ns;
double val = ns / dt / (n - Ns);
ConfidenceInterval cnf = new ConfidenceInterval(val,
x => x * DistributionHelper.ChiSquareDistributionQuantile(Globals.Alpha / 2, 2 * ns) / ns / 2,
x => x * DistributionHelper.ChiSquareDistributionQuantile(1 - Globals.Alpha / 2, 2 * ns) / ns / 2,
min + dt * i,
min + dt * (i + 1));
PieceConstIntensityClassResult res = new PieceConstIntensityClassResult(i + 1, cnf, ns);
results.Add(res);
}
return(new PieceConstIntensityResult(results));
}
public static Result TestCorrelation(double r, int n, int k)
{
double q = DistributionHelper.GetStudentDistributionQuantile(
TimeSeriesEnvironment.Current.Alpha,
n - k - 2);
double t = r * Math.Sqrt(n - k - 2) / Math.Sqrt(1 - r * r);
return(new Result(q, t));
}
private void UpdateArrivalRate_Click(object sender, EventArgs e)
{
double arrPro = 0;
if (double.TryParse(this.arrivalRateTextBox.Text, out arrPro))
{
IDistribution dist = new Poison(arrPro);
DistributionHelper.UpdateDistribution(EventEnum.Arrival, dist);
}
;
}
public void ValidateShiftedLogNormalDistribution_ValidDistribution_DoesNotThrowException()
{
// Setup
const long distributionType = SoilLayerConstants.LogNormalDistributionValue;
// Call
TestDelegate call = () => DistributionHelper.ValidateShiftedLogNormalDistribution(distributionType);
// Assert
Assert.DoesNotThrow(call);
}
public void ValidateLogNormalDistribution_DistributionTypeNull_DoesNotThrowException()
{
// Setup
var random = new Random(21);
double shift = random.NextDouble();
// Call
TestDelegate call = () => DistributionHelper.ValidateLogNormalDistribution(null, shift);
// Assert
Assert.DoesNotThrow(call);
}
public void ValidateShiftedLogNormalDistribution_InvalidDistributionType_ThrowsDistributionValidationException()
{
// Setup
const long invalidDistributionType = -1;
// Call
TestDelegate call = () => DistributionHelper.ValidateShiftedLogNormalDistribution(invalidDistributionType);
// Assert
var exception = Assert.Throws <DistributionValidationException>(call);
Assert.AreEqual("Parameter moet verschoven lognormaal verdeeld zijn.", exception.Message);
}
public static Image visualizeArray(double[] input, int width, int height, int lineSize = 3)
{
Bitmap bmp = new Bitmap(width, height);
Graphics g = Graphics.FromImage(bmp);
g.Clear(Color.LightGray);
double stepSize = Convert.ToDouble(input.Length) / Convert.ToDouble(width);
double min, max;
DistributionHelper.getMinMax(input, out min, out max);
int oldX = -1, oldY = -1;
Color cUp = Color.Green;
Color cDown = Color.Blue;
for (int x = 0; x < width; x++)
{
int index = Convert.ToInt32(stepSize * x);
if (index < input.Length && double.IsNaN(input[index]) == false)
{
int y = height - Convert.ToInt32((input[index] - min) / (max - min) * height);
if (oldX != -1)
{
for (int yOffset = -(lineSize / 2); yOffset < (lineSize / 2); yOffset++)
{
if (y + yOffset > 0 && y + yOffset < height)
{
bmp.SetPixel(x, y + yOffset, y > oldY ? cDown : cUp);
}
}
}
oldX = x;
oldY = y;
}
else
{
for (int y = 0; y < height; y++)
{
bmp.SetPixel(x, y, Color.Yellow);
}
}
}
return(bmp);
}
public void getMinMax_Test_SmallValues()
{
double[] inputs = new double[100];
for (int i = 0; i < inputs.Length; i++)
{
inputs[i] = i * 0.001;
}
double min, max;
DistributionHelper.getMinMax(inputs, 10, out min, out max);
Assert.AreEqual(5 * 0.001, min, 1 * 0.001);
Assert.AreEqual(95 * 0.001, max, 1 * 0.001);
}
/// <summary>
/// Validates the distribution properties of a parameter which is defined as a
/// log normal distribution.
/// </summary>
/// <param name="soilLayerName">The name of the soil layer.</param>
/// <param name="distributionType">The distribution type of the parameter.</param>
/// <param name="parameterName">The name of the parameter.</param>
/// <exception cref="ImportedDataTransformException">Thrown when the distribution properties are invalid.</exception>
private static void ValidateStochasticShiftedLogNormalDistributionParameter(string soilLayerName,
long?distributionType,
string parameterName)
{
try
{
DistributionHelper.ValidateShiftedLogNormalDistribution(
distributionType);
}
catch (DistributionValidationException e)
{
string errorMessage = CreateErrorMessageForParameter(soilLayerName, parameterName, e.Message);
throw new ImportedDataTransformException(errorMessage, e);
}
}
public void ValidateLogNormalDistribution_ShiftNonZero_ThrowsDistributionValidationException()
{
// Setup
const long distributionType = SoilLayerConstants.LogNormalDistributionValue;
var random = new Random(21);
double shift = random.NextDouble();
// Call
TestDelegate call = () => DistributionHelper.ValidateLogNormalDistribution(distributionType,
shift);
// Assert
var exception = Assert.Throws <DistributionValidationException>(call);
Assert.AreEqual("Parameter moet lognormaal verdeeld zijn met een verschuiving gelijk aan 0.", exception.Message);
}
public static double optimizeOutcomeCodePercentage(int rounds, out double outcomeCodePercent, double[][] priceData, double[][] outcomes, out double buyDist, out double sellDist)
{
outcomeCodePercent = 0.5;
buyDist = double.NaN;
sellDist = double.NaN;
double lastScore = double.MinValue;
double direction = -0.01;
int round;
for (round = 1; round < rounds; round++)
{
double successRatioCode;
bool[][] outcomeCodes = OutcomeGenerator.getOutcomeCode(priceData, outcomes, outcomeCodePercent, out successRatioCode);
if (successRatioCode < 0.9)
{
throw new TooLittleValidDataException("Too low success ratio: " + successRatioCode);
}
DistributionHelper.getOutcomeCodeDistribution(outcomeCodes, out buyDist, out sellDist);
double score = ((buyDist + sellDist) / 2) * outcomeCodePercent;
if (score < lastScore) //Wrong direction
{
direction = direction * (-1);
Logger.log("New opt. direction: " + direction);
}
if (outcomeCodePercent <= 0 && direction <= 0)
{
direction = Math.Abs(direction);
}
outcomeCodePercent += (direction / (1 + (round / 20)));
Logger.log("PercDist OPT. Round " + round + " -> " + outcomeCodePercent + "% = |s" + Math.Round(score, 4) + "| b" + Math.Round(buyDist, 4) + " s" + Math.Round(sellDist, 4));
lastScore = score;
round++;
}
return(outcomeCodePercent);
}
public void ValidateLogNormalDistribution_InvalidDistributionType_ThrowsDistributionValidationException()
{
// Setup
const long distributionType = -1;
var random = new Random(21);
double shift = random.NextDouble();
// Call
TestDelegate call = () => DistributionHelper.ValidateLogNormalDistribution(distributionType,
shift);
// Assert
var exception = Assert.Throws <DistributionValidationException>(call);
Assert.AreEqual("Parameter moet lognormaal verdeeld zijn.", exception.Message);
}
public void getMinMax_Test_MoreComplex()
{
double[] inputs = new double[200];
for (int i = 0; i < 100; i++)
{
inputs[i] = i;
}
for (int i = 100; i < 200; i++)
{
inputs[i] = 100;
}
double min, max;
DistributionHelper.getMinMax(inputs, 10, out min, out max);
Assert.AreEqual(10, min, 1);
Assert.AreEqual(100, max, 1);
}
public static StatEstimationResult StatEstimation(Queue q, int m)
{
int n = q.Count;
List <double> times = q.ToTime;
double dt = times.Last() / m;
List <StatEstimationClassResult> intervals = new List <StatEstimationClassResult>();
double ua = DistributionHelper.GetNormalDistributionQuantile(Globals.Alpha / 2);
double constantPart = ua * ua / n / dt / 2;
for (int i = 0; i < m; i++)
{
int ns = times.Count(x => x >= dt * i && (i == m - 1 || x < dt * (i + 1)));
double us = ns / dt / n;
double difPart = ua * Math.Sqrt(ns + ua * ua / 4) / n / dt;
ConfidenceInterval cnf = new ConfidenceInterval(us, x => x + constantPart - difPart, x => x + constantPart + difPart, dt * i, dt * (i + 1));
intervals.Add(new StatEstimationClassResult(i + 1, cnf));
}
return(new StatEstimationResult(intervals));
}
public static Result TestSignsMethod(List <double> excerpt)
{
double l = 0,
m = (excerpt.Count - 1) / 2,
d = (excerpt.Count + 1) / 12;
for (int i = 0; i < excerpt.Count - 1; i++)
{
if (excerpt[i] == excerpt[i + 1])
{
l += 0.5;
}
else if (excerpt[i] > excerpt[i + 1])
{
l += 1;
}
}
double u = (l - m) / Math.Sqrt(d);
double q = DistributionHelper.GetNormalDistributionQuantile(TimeSeriesEnvironment.Current.Alpha);
return(new Result(q, u));
}
public static double findOutcomeCodeForDesiredDistribution(double desiredDistribution, double tolerance, double[][] priceData, double[][] outcomeMatrix, out double buyDist, out double sellDist)
{
double outcomeCodePercent = 0.5;
int round = 0;
while (true)
{
double successRatioCode;
bool[][] outcomeCodes = OutcomeGenerator.getOutcomeCode(priceData, outcomeMatrix, outcomeCodePercent, out successRatioCode);
if (successRatioCode < 0.9)
{
throw new TooLittleValidDataException("Too few outcome codes: " + successRatioCode);
}
DistributionHelper.getOutcomeCodeDistribution(outcomeCodes, out buyDist, out sellDist);
double score = (buyDist + sellDist) / 2;
if (score > desiredDistribution - tolerance && score < desiredDistribution + tolerance)
{
break;
}
else if (score > desiredDistribution + tolerance)
{
outcomeCodePercent += (outcomeCodePercent / (10 + round));
}
else if (score < desiredDistribution - tolerance)
{
outcomeCodePercent -= (outcomeCodePercent / (10 + round));
}
Logger.log("SetDist OPT. Round " + round + " -> " + outcomeCodePercent + "% = b" + Math.Round(buyDist, 4) + " s" + Math.Round(sellDist, 4) + " =" + Math.Round(score, 4));
round++;
}
return(outcomeCodePercent);
}
public void getOutcomeCodeDistribution_Test()
{
bool[][] inputs = new bool[100][];
for (int i = 0; i < 50; i++)
{
inputs[i] = new bool[] { true, true };
}
for (int i = 50; i < 100; i++)
{
inputs[i] = new bool[] { false, true };
}
for (int i = 50; i < 75; i++)
{
inputs[i][(int)OutcomeCodeMatrixIndices.Sell] = false;
}
double buyR, sellR;
DistributionHelper.getOutcomeCodeDistribution(inputs, out buyR, out sellR);
Assert.AreEqual(0.5, buyR);
Assert.AreEqual(0.75, sellR);
}
public void getSampleCodesBuyMaxSellMax_Test()
{
double[][] samples = new double[10][];
for (int i = 0; i < samples.Length; i++)
{
samples[i] = new double[] { i * 0.1, 0.2, 0.2, 10 };
}
samples[3] = null;
samples[4][1] = 0.4; //Buy
samples[5][2] = 0.41; //Sell
double buy, sell;
double buyCount, sellCount;
DistributionHelper.getSampleOutcomeCodesBuyMaxSellMax(samples, 0, out buy, out buyCount, out sell, out sellCount);
//Todo: Write another for minValuesPercent != 0
Assert.AreEqual(0.4, buy);
Assert.AreEqual(0.41, sell);
Assert.AreEqual(10, buyCount);
Assert.AreEqual(10, sellCount);
}
public LearningIndicator(WalkerIndicator indicator, double[][] prices, bool[][] outcomeCodes, double[][] outcomes, long timeframe, double targetPercent, double minPercentThreshold, int steps, bool createStatistics)
{
this.targetPercent = targetPercent;
this.timeframe = timeframe;
double validRatio;
double[] values = IndicatorRunner.getIndicatorValues(prices, indicator.Clone(), out validRatio);
if (validRatio < 0.5)
{
throw new TooLittleValidDataException("Not enough valid values: " + validRatio);
}
//May be does not work properly... todo:
double min, max, usedValuesRatio;
//DistributionHelper.getMinMax(values, 4, out min, out max);
DistributionHelper.getMinMax(values, out min, out max);
outcomeCodeSamplingTable = IndicatorSampler.sampleValuesOutcomeCode(values, outcomeCodes, min, max, steps, out usedValuesRatio);
if (usedValuesRatio < 0.5)
{
throw new TooLittleValidDataException("Not enough sampling for outcomeCode: " + usedValuesRatio);
}
outcomeSamplingTable = IndicatorSampler.sampleValuesOutcome(values, prices, outcomes, min, max, out usedValuesRatio, 40);
if (usedValuesRatio < 0.5)
{
throw new TooLittleValidDataException("Not enough sampling for outcome: " + usedValuesRatio);
}
this.usedValues = usedValuesRatio;
if (createStatistics)
{
//Predictive power calculation
predictivePower = new double[33];
IndicatorSampler.getStatisticsOutcomeCodes(values, outcomeCodes, out predictivePower[0], out predictivePower[1], out predictivePower[2], out predictivePower[3]);
IndicatorSampler.getStatisticsOutcomes(values, prices, outcomes, out predictivePower[4], out predictivePower[5], out predictivePower[6], out predictivePower[7], out predictivePower[8], out predictivePower[9]);
DistributionHelper.getSampleOutcomeCodesBuyMaxSellMax(outcomeCodeSamplingTable, minPercentThreshold, out predictivePower[10], out predictivePower[11], out predictivePower[12], out predictivePower[13]);
DistributionHelper.getSampleOutcomesMinMax(outcomeSamplingTable, minPercentThreshold, out predictivePower[14], out predictivePower[15], out predictivePower[16], out predictivePower[17], out predictivePower[18], out predictivePower[19], out predictivePower[20], out predictivePower[21], out predictivePower[22], out predictivePower[23]);
//Outcome Code
List <double> buyCodesDist = new List <double>(),
sellCodesDist = new List <double>(),
buySellDistanceDist = new List <double>(),
minMaxDistanceDist = new List <double>(),
minDist = new List <double>(),
maxDist = new List <double>(),
actualDist = new List <double>();
double totalCodeSamples = 0;
foreach (double[] row in outcomeCodeSamplingTable)
{
totalCodeSamples += row[(int)SampleValuesOutcomeCodesIndices.SamplesCount];
}
int regardedStates = 0;
foreach (double[] row in outcomeCodeSamplingTable)
{
if ((row[(int)SampleValuesOutcomeCodesIndices.SamplesCount] / totalCodeSamples) * 100 >= minPercentThreshold) //minPercentThreshold
{
buyCodesDist.Add(row[(int)SampleValuesOutcomeCodesIndices.BuyRatio]);
sellCodesDist.Add(row[(int)SampleValuesOutcomeCodesIndices.SellRatio]);
buySellDistanceDist.Add(Math.Abs(row[(int)SampleValuesOutcomeCodesIndices.BuyRatio] - row[(int)SampleValuesOutcomeCodesIndices.SellRatio]));
regardedStates++;
}
}
predictivePower[(int)LearningIndicatorPredictivePowerIndecies.valuesOverMinPercentRatioCode] = Convert.ToDouble(regardedStates) / outcomeCodeSamplingTable.Length;
if (regardedStates <= 2)
{
throw new TooLittleStatesException("Too little sates: " + regardedStates);
}
predictivePower[(int)LearningIndicatorPredictivePowerIndecies.buyCodeStD] = buyCodesDist.StandardDeviation();
predictivePower[(int)LearningIndicatorPredictivePowerIndecies.sellCodeStD] = sellCodesDist.StandardDeviation();
predictivePower[(int)LearningIndicatorPredictivePowerIndecies.buySellCodeDistanceStD] = buySellDistanceDist.StandardDeviation();
//Outcome
double totalSamples = 0;
foreach (double[] row in outcomeSamplingTable)
{
totalSamples += row[(int)SampleValuesOutcomeIndices.SamplesCount];
}
//Avgs
regardedStates = 0;
foreach (double[] row in outcomeSamplingTable)
{
if ((row[(int)SampleValuesOutcomeIndices.SamplesCount] / totalSamples) * 100 > minPercentThreshold) //minPercentThreshold
{
maxDist.Add(row[(int)SampleValuesOutcomeIndices.MaxAvg]);
minDist.Add(row[(int)SampleValuesOutcomeIndices.MinAvg]);
minMaxDistanceDist.Add(Math.Abs(row[(int)SampleValuesOutcomeIndices.MaxAvg]) + row[(int)SampleValuesOutcomeIndices.MinAvg]);
actualDist.Add(row[(int)SampleValuesOutcomeIndices.ActualAvg]);
regardedStates++;
}
}
predictivePower[(int)LearningIndicatorPredictivePowerIndecies.valuesOverMinPercentRatioOutcome] += Convert.ToDouble(regardedStates) / outcomeSamplingTable.Length;
//avg distances
predictivePower[(int)LearningIndicatorPredictivePowerIndecies.maxStD] = maxDist.StandardDeviation();
predictivePower[(int)LearningIndicatorPredictivePowerIndecies.minStD] = minDist.StandardDeviation();
predictivePower[(int)LearningIndicatorPredictivePowerIndecies.minMaxDistanceStd] = minMaxDistanceDist.StandardDeviation();
predictivePower[(int)LearningIndicatorPredictivePowerIndecies.actualStD] = actualDist.StandardDeviation();
if (double.IsNaN(predictivePower[(int)LearningIndicatorPredictivePowerIndecies.buyCodeStD]) ||
double.IsNaN(predictivePower[(int)LearningIndicatorPredictivePowerIndecies.sellCodeStD]) ||
double.IsNaN(predictivePower[(int)LearningIndicatorPredictivePowerIndecies.buySellCodeDistanceStD]) ||
double.IsNaN(predictivePower[(int)LearningIndicatorPredictivePowerIndecies.maxStD]) ||
double.IsNaN(predictivePower[(int)LearningIndicatorPredictivePowerIndecies.minStD]) ||
double.IsNaN(predictivePower[(int)LearningIndicatorPredictivePowerIndecies.minMaxDistanceStd]) ||
double.IsNaN(predictivePower[(int)LearningIndicatorPredictivePowerIndecies.actualStD]))
{
throw new Exception("Not a valid predictive power!");
}
//End predictive power calculation
}
this.indicator = indicator;
}