public void SetText(String text, Fit fit)
{
this.name = text;
switch (fit)
{
case Fit.AlignLeft:
AlignLeft();
break;
case Fit.AlignCenter:
CenterLabel();
break;
case Fit.AlignRight:
AlignRight();
break;
case Fit.Stretch:
StretchLabel();
break;
}
}
public FitData CalcFit(List <DataPoint> points)
{
FitData result = new FitData("Power function");
var xdata = points.Select(x => x.X).ToArray();
var ydata = points.Select(x => x.Y).ToArray();
Tuple <double, double> p = Fit.Power(xdata, ydata);
result.A = p.Item1;
result.B = p.Item2;
result.Points = DataPoints(points).ToList();
return(result);
//local function for return list of regresion Points[need to C# 8]
IEnumerable <DataPoint> DataPoints(List <DataPoint> points)
{
foreach (var point in points)
{
yield return(new DataPoint(point.X, result.A * Math.Exp(result.B * point.X)));
}
}
}
public void FitsToBestLineThroughOrigin()
{
// Mathematica: Fit[{{1,4.986},{2,2.347},{3,2.061},{4,-2.995},{5,-2.352},{6,-5.782}}, {x}, x]
// -> -0.467791 x
var x = Enumerable.Range(1, 6).Select(Convert.ToDouble).ToArray();
var y = new[] { 4.986, 2.347, 2.061, -2.995, -2.352, -5.782 };
var resp = Fit.LineThroughOrigin(x, y);
Assert.AreEqual(-0.467791, resp, 1e-4);
var resf = Fit.LineThroughOriginFunc(x, y);
foreach (var z in Enumerable.Range(-3, 10))
{
Assert.AreEqual(-0.467791 * z, resf(z), 1e-4);
}
var respSeq = SimpleRegression.FitThroughOrigin(Generate.Map2(x, y, Tuple.Create));
Assert.AreEqual(-0.467791, respSeq, 1e-4);
}
public void SetText(String text, Fit fit)
{
this.name = text;
switch (fit)
{
case Fit.AlignLeft:
AlignLeft();
break;
case Fit.AlignCenter:
CenterLabel();
break;
case Fit.AlignRight:
AlignRight();
break;
case Fit.Stretch:
StretchLabel();
break;
}
}
protected void doFit(Measurements hi, Measurements med, Measurements low)
{
// Voltages need to be shifted for calibration constants to be correct.
double[] voltageX = { Vhi.setting + 4.096, Vmed.setting + 4.096, Vlow.setting + 4.096 };
double[] voltageY = { Vhi.average + 4.096, Vmed.average + 4.096, Vlow.average + 4.096 };
Tuple <double, double> fitParams = Fit.Line(voltageX, voltageY);
gain = fitParams.Item2;
offset = fitParams.Item1;
gainInt = (UInt16)(fitParams.Item2 * 32768);
// offsetInt is a 12-bit 2's compliment integer.
// The sign needs to be reversed for it to be applied correctly.
if (offset < 0)
{
offsetInt = (UInt16)(-fitParams.Item1 * 500);
}
else
{
offsetInt = (UInt16)(4096 - fitParams.Item1 * 500);
}
string slope = gainInt.ToString("X");
string intercept = offsetInt.ToString("X");
}
/// https://numerics.mathdotnet.com/Regression.html#Linearizing-non-linear-models-by-transformation
/// https://github.com/mathnet/mathnet-numerics/blob/master/src/Numerics/Fit.cs
/// https://discuss.mathdotnet.com/t/exponential-fit/131/2
/// https://discuss.mathdotnet.com/t/curve-fitting-power/605
/// Power function: y = a * (x ^ b)
private void ApplyPowerFunctionFitting()
{
double[] x = Points.Select(p => p.X).ToArray();
double[] y = Points.Select(p => p.Y).ToArray();
Tuple <double, double> p = Fit.Power(x, y); // a=1.017, r=0.687
A = p.Item1;
B = p.Item2;
Func <double, double> f = Fit.LinearCombinationFunc(
Points.Select(p => p.X).ToArray(),
Points.Select(p => p.Y).ToArray(),
x => p.Item1 * Math.Pow(x, p.Item2));
List <DataPoint> fittedPoints = new List <DataPoint>();
foreach (var item in Points)
{
fittedPoints.Add(new DataPoint(item.X, f(item.X)));
}
FittedPoints = fittedPoints;
}
public Tuple <double[], double[]> CalculateMovingLinearFit(double[] data, int period)
{
double[] a = new double[data.Length];
double[] b = new double[data.Length];
for (int i = data.Length - 1; i >= 0; i--)
{
var numTake = i - period >= 0 ? period : i;
var subset = data.Skip(i + 1 - numTake).Take(numTake).ToArray();
Tuple <double, double> fit = Tuple.Create(0d, 0d);
if (subset.Length >= 2)
{
fit = Fit.Line(Enumerable.Range(0, subset.Length).Select(_ => (double)_).ToArray(), subset);
}
a[i] = fit.Item1;
b[i] = fit.Item2;
}
return(Tuple.Create(a, b));
}
Vector3 getDirectionVector(List <Vector3> points)
{
int len = points.Count;
double[] xdata = new double[len];
double[] zdata = new double[len];
for (int i = 0; i < len; i++)
{
xdata[i] = points[i].x;
zdata[i] = points[i].z;
}
Tuple <double, double> p = Fit.Line(xdata, zdata);
float b = (float)p.Item1;
float a = (float)p.Item2;
Vector2 dirVector;
if (float.IsNaN(b))
{
if (zdata[0] < zdata[1])
{
dirVector = new Vector2(0, 1);
}
else
{
dirVector = new Vector2(0, -1);
}
}
else
{
Vector2 A = new Vector2(points[0].x, a * points[0].x + b);
Vector2 B = new Vector2(points[len - 1].x, a * points[len - 1].x + b);
dirVector = (B - A).normalized;
}
return(new Vector3(dirVector.x, 0, dirVector.y));
}
static void Main(string[] args)
{
if (args.Length < 1)
{
Console.WriteLine("MathNetNumericsSample <data file>");
return;
}
var order = 1;
var xs = new List <double>();
var ys = new List <double>();
using (var reader = new StreamReader(args[0], Encoding.UTF8)) {
order = reader.ReadLine()
.TryParse <int>();
if (order < 1)
{
Console.WriteLine("Invalid order");
return;
}
while (!reader.EndOfStream)
{
var pair = reader.ReadLine()
.Split(new[] { "\t" }, StringSplitOptions.RemoveEmptyEntries);
if (pair.Length < 2)
{
Console.WriteLine("Invalid pair");
return;
}
xs.Add(pair[0].TryParse <double>());
ys.Add(pair[1].TryParse <double>());
}
}
// [Curve Fitting: Linear Regression](http://numerics.mathdotnet.com/Regression.html)
var coefficients = Fit.Polynomial(xs.ToArray(), ys.ToArray(), order);
Console.WriteLine(String.Join("\n", coefficients.Select((p, i) => i + "\t" + p.ToString("0.0000"))));
}
/// <summary>
/// Enlarge each element in the group to match the largest one.
/// </summary>
public static void MakeSameSize(this List <LayoutElement> elements, Fit axis)
{
if (axis == Fit.Width)
{
float newWidth = 0;
foreach (var le in elements)
{
if (le.SizeF.Width > newWidth)
{
newWidth = le.SizeF.Width;
}
}
foreach (var le in elements)
{
le.SizeF = new SizeF(newWidth, le.SizeF.Height);
}
}
else
{
float newHeight = 0;
foreach (var le in elements)
{
if (le.SizeF.Height > newHeight)
{
newHeight = le.SizeF.Height;
}
}
foreach (var le in elements)
{
le.SizeF = new SizeF(le.SizeF.Width, newHeight);
}
}
}
void OnDrawGizmos()
{
List <Vector3> points = new List <Vector3>(parentTransform.childCount);
foreach (Transform child in parentTransform)
{
if (child.gameObject.activeInHierarchy)
{
points.Add(child.position);
}
}
if (fitType == FitType.Line)
{
Fit.Line(points, out origin, ref direction, 1, true);
}
else if (fitType == FitType.LineFast)
{
Fit.LineFast(points, out origin, ref direction, 1, true);
}
else if (fitType == FitType.Plane)
{
Fit.Plane(points, out origin, out direction, 100, true);
}
else if (fitType == FitType.OrdinaryLine)
{
Fit.Polynomial(points, 1, true);
}
else if (fitType == FitType.OrdinaryParabola)
{
Fit.Polynomial(points, 2, true);
}
else if (fitType == FitType.OrdinaryCubic)
{
Fit.Polynomial(points, 3, true);
}
}
private double[][] CalcP(List <DataRaw> data)
{
var x = new List <double>();
var yc = new List <double>();
var ys = new List <double>();
var yh = new List <double>();
var yd = new List <double>();
data.ForEach(i =>
{
var da = (i.日期 - DateTime.Parse("2020-1-12")).TotalDays;
x.Add(da);
yc.Add(i.确诊人数);
ys.Add(i.疑似人数);
yh.Add(i.治愈人数);
yd.Add(i.死亡人数);
});
var Pc = Fit.Polynomial(x.ToArray(), yc.ToArray(), 3);
var Ps = Fit.Polynomial(x.ToArray(), ys.ToArray(), 3);
var Ph = Fit.Polynomial(x.ToArray(), yh.ToArray(), 3);
var Pd = Fit.Polynomial(x.ToArray(), yd.ToArray(), 3);
return(new double[][] { Pc, Ps, Ph, Pd });
}
public static double CalcPolynomialRegression(string buysField, int seconds, int degrees = 1)
{
/// get frames in the last n seconds
var ratioFrames = lstRatioFrames.DistinctBy(frame => frame.dateTime).Where(frame => frame.seconds == seconds && TDAChart.svcDateTime.Subtract(frame.dateTime).TotalSeconds <= seconds).ToList();
var n = ratioFrames.Count;
var lstBuysD = ratioFrames.Select(item => (double)Convert.ToDouble(item[buysField])).ToList();
var xData = ratioFrames.Select(item => (double)ratioFrames.IndexOf(item)).ToArray();
/// Fitting a fifth degree polynomial to the data can have up to 4 curves
///
var cb = Fit.Polynomial(xData, lstBuysD.ToArray(), degrees).Select(t => (float?)t).ToList();
var x = (double)xData.Last();
var buysPoly = 0d;
for (int i = 0; i < degrees; i++)
{
var xx = Math.Pow(x, i);
buysPoly += (double)(cb[i] * xx);
}
return((double)buysPoly);
}
public double[] CreateWavelenghtToPixelLocationsUsingReferenceSpectra(int bluePeakLocation, int redPeakLocation, int RefLengthanyChannel)
{
double[] wavelengthArray = new double[RefLengthanyChannel];
//r = a x + b;
double[] xdata = { redPeakLocation, bluePeakLocation };
double[] ydata = { 610.65, 449.1 };
Tuple <double, double> p = Fit.Line(xdata, ydata);
double b = p.Item1; // intercept
double a = p.Item2; // slope
Console.WriteLine("wavelengthslope: " + a + " , intercept: " + b);
for (int i = 0; i < RefLengthanyChannel; i++)
{
wavelengthArray[i] = i * a + b;
//Console.WriteLine("i "+i+" wavearray: " + wavelengthArray[i]);
}
return(wavelengthArray);
}
public void Analyze(List <ReceivedMeasurement> measurements, AnalysisVM viewmodel)
{
var measurementDates = measurements.Select(x => x.RecordCreateTime.ToOADate()).ToArray();
DirectRegressionMethod regressionMethod = (DirectRegressionMethod)Enum.Parse(typeof(DirectRegressionMethod), viewmodel.regressionMethod);
var polynomialCoefficients = Fit.Polynomial(measurementDates,
measurements.Select(y => y.Value).ToArray(), viewmodel.polynomialDegree, regressionMethod).ToList();
var functionFormula = GetPolynomialFormula(polynomialCoefficients);
var newDates = GenerateNewDates(measurementDates);
var approximationResults = GetApproximationResults(newDates.ToArray(), polynomialCoefficients);
_result = new ComputationResultVM()
{
X_values = measurementDates.Select(x => DateTime.FromOADate(x).ToString("yyyy-MM-dd HH:mm:ss")).ToList(),
X2_values = newDates.Select(x => DateTime.FromOADate(x).ToString("yyyy-MM-dd HH:mm:ss")).ToList(),
Y_values = measurements.Select(x => x.Value).ToList(),
Y2_values = approximationResults,
Formula = functionFormula
};
}
public void Analyze(List <ReceivedMeasurement> measurements, AnalysisVM viewmodel)
{
var dates = measurements.Select(x => x.RecordCreateTime.ToOADate()).ToArray();
DirectRegressionMethod regressionMethod = (DirectRegressionMethod)Enum.Parse(typeof(DirectRegressionMethod), viewmodel.regressionMethod);
var polynomialCoefficients = Fit.Polynomial(dates,
measurements.Select(y => y.Value).ToArray(), viewmodel.polynomialDegree, regressionMethod).ToList();
var predictionDate = DateTime.Parse(viewmodel.predictionDate);
var predictionResults = GetPredictionResults(predictionDate.ToOADate(), polynomialCoefficients);
List <string> predictionDates = new List <string>()
{
viewmodel.predictionDate
};
_result = new ComputationResultVM()
{
X_values = predictionDates,
Y_values = predictionResults,
Formula = ""
};
}
public void FitsToLineSameAsExcelTrendLine()
{
// X Y
// 1 0.2
// 2 0.3
// 4 1.3
// 6 4.2
// -> y = -1.078 + 0.7932*x
var x = new[] { 1.0, 2.0, 4.0, 6.0 };
var y = new[] { 0.2, 0.3, 1.3, 4.2 };
var resp = Fit.Line(x, y);
Assert.AreEqual(-1.078, resp.Item1, 1e-3);
Assert.AreEqual(0.7932, resp.Item2, 1e-3);
var resf = Fit.LineFunc(x, y);
foreach (var z in Enumerable.Range(-3, 10))
{
Assert.AreEqual(-1.078 + 0.7932 * z, resf(z), 1e-2);
}
}
public void FitsToExponentialSameAsExcelTrendLine()
{
// X Y
// 1 0.2
// 2 0.3
// 4 1.3
// 6 4.2
// -> y = 0.0981*exp(0.6284*x)
var x = new[] { 1.0, 2.0, 4.0, 6.0 };
var y = new[] { 0.2, 0.3, 1.3, 4.2 };
var resp = Fit.Exponential(x, y);
Assert.AreEqual(0.0981, resp.Item1, 1e-3);
Assert.AreEqual(0.6284, resp.Item2, 1e-3);
var resf = Fit.ExponentialFunc(x, y);
foreach (var z in Enumerable.Range(-3, 10))
{
Assert.AreEqual(0.0981 * Math.Exp(0.6284 * z), resf(z), 1e-2);
}
}
public static double[] PolynomialRegression(double[] xVals,
double[] yVals,int year)
{
double x1, x2;
double[] p = Fit.Polynomial(xVals, yVals, 2);
if (xVals.Length != yVals.Length)
{
throw new Exception("Input values should be with the same length.");
}
// p2X^2 + p1X + p0 = year
// X => score
double p0 = p[0] - year;
double p1 = p[1];
double p2 = p[2];
double denta = p1 * p1 - 4 * p2 * p0;
if (denta < 0)
{
return new double[]{ };
}
if(denta == 0)
{
x1 = -p1 / (2*p2);
return new double[] { x1 };
}
double sqrtDenta = System.Math.Sqrt(denta);
double denominator = 2 * p2;
x1 = (-p1 + sqrtDenta) / denominator;
x2 = (-p1 - sqrtDenta) / denominator;
return new double[]{ x1, x2 };
}
/**
* Method to Calculate points from file and R02
* @param IList<double> - data of Y values
* @param IList<double> - data of X values
* @return IList<ObservablePoint> - list of observable potins
*/
private IList <ObservablePoint> GetNewPoints(IList <double> yPoints, IList <double> xPoints)
{
int maxNumber = (int)(0.2 * xPoints.Count);
var item = Fit.Line(xPoints.Take(maxNumber).ToArray(), yPoints.Take(maxNumber).ToArray());
double a = item.Item2;
double b = item.Item1;
IList <ObservablePoint> points = new List <ObservablePoint>();
IList <ObservablePoint> pointsData = (IList <ObservablePoint>)mainLineSeries.Values;
double moveRight = xPoints[0] * 1.002;
double moveLeft = xPoints[0] * 0.998;
bool rightOverLeft = moveRight > moveLeft;
f02 = null;
for (int i = 0; i < xPoints.Count; i++)
{
double x = xPoints[i] * 1.002;
if (!rightOverLeft)
{
x = xPoints[i] * 0.998;
}
var y = (xPoints[i] * a) + (b);
var data = pointsData[i];
var yLow = data.Y * 0.95;
var yHigh = data.Y * 1.05;
if (yLow > y && y < yHigh)
{
f02 = data;
}
points.Add(new ObservablePoint(x, y));
}
return(points);
}
public static double[] CalculateCenterOfCircle(TouchPoint[][] touchPoints)
{
List <double[]> X = new List <double[]>();
List <double> Y = new List <double>();
foreach (var pointsInSameRow in touchPoints)
{
var point0 = pointsInSameRow[0];
var point1 = pointsInSameRow[1];
var point2 = pointsInSameRow[2];
var x0 = point0.X;
var y0 = point0.Y;
var x1 = point1.X;
var y1 = point1.Y;
var x2 = point2.X;
var y2 = point2.Y;
var Y0 = x1 * x1 + y1 * y1 - x0 * x0 - y0 * y0;
var Y1 = x2 * x2 + y2 * y2 - x1 * x1 - y1 * y1;
Y.Add(Y0);
Y.Add(Y1);
var X11 = (x1 - x0) * 2;
var X12 = (y1 - y0) * 2;
X.Add(new[] { X11, X12 });
var X21 = (x2 - x1) * 2;
var X22 = (y2 - y1) * 2;
X.Add(new[] { X21, X22 });
}
double[] result = Fit.MultiDim(X.ToArray(), Y.ToArray(), false, DirectRegressionMethod.NormalEquations);
return(result);
}
/// https://numerics.mathdotnet.com/Regression.html#Evaluating-the-model-at-specific-data-points
/// https://numerics.mathdotnet.com/Regression.html#Linearizing-non-linear-models-by-transformation
/// https://discuss.mathdotnet.com/t/exponential-fit/131/2
/// Exponential: y = a * exp(b * x)
private void ApplyExponentialFitting()
{
double[] x = Points.Select(p => p.X).ToArray();
double[] y = Points.Select(p => p.Y).ToArray();
double[] p = Exponential(x, y); // a=1.017, r=0.687
A = p[0];
B = p[1];
Func <double, double> f = Fit.LinearCombinationFunc(
Points.Select(p => p.X).ToArray(),
Points.Select(p => p.Y).ToArray(),
x => p[0] * Math.Exp(p[1] * x));
List <DataPoint> fittedPoints = new List <DataPoint>();
foreach (var item in Points)
{
fittedPoints.Add(new DataPoint(item.X, f(item.X)));
}
FittedPoints = fittedPoints;
}
public void UpdateFromTarget()
{
Guid = TargetDesktop.Guid;
var background = TargetDesktop.Background;
var updatedBackgroundPath = background.Path;
if (!Equals(_backgroundPath, updatedBackgroundPath))
{
_backgroundPath = updatedBackgroundPath;
OnPropertyChanged(nameof(BackgroundPath));
}
var updatedFit = background.Fit;
if (!Equals(_fit, updatedFit))
{
_fit = updatedFit;
OnPropertyChanged(nameof(Fit));
}
}
public void FitsToLogarithmSameAsExcelTrendLine()
{
// X Y
// 1 0.2
// 2 0.3
// 4 1.3
// 6 4.2
// -> y = -0.4338 + 1.9981*ln(x)
var x = new[] { 1.0, 2.0, 4.0, 6.0 };
var y = new[] { 0.2, 0.3, 1.3, 4.2 };
var resp = Fit.Logarithm(x, y);
Assert.AreEqual(-0.4338, resp.Item1, 1e-3);
Assert.AreEqual(1.9981, resp.Item2, 1e-3);
var resf = Fit.LogarithmFunc(x, y);
foreach (var z in Enumerable.Range(-3, 10))
{
Assert.AreEqual(-0.4338 + 1.9981 * Math.Log(z), resf(z), 1e-2);
}
}
public void FitsToPowerSameAsExcelTrendLine()
{
// X Y
// 1 0.2
// 2 0.3
// 4 1.3
// 6 4.2
// -> y = 0.1454*x^1.7044
var x = new[] { 1.0, 2.0, 4.0, 6.0 };
var y = new[] { 0.2, 0.3, 1.3, 4.2 };
var resp = Fit.Power(x, y);
Assert.AreEqual(0.1454, resp.Item1, 1e-3);
Assert.AreEqual(1.7044, resp.Item2, 1e-3);
var resf = Fit.PowerFunc(x, y);
foreach (var z in Enumerable.Range(-3, 10))
{
Assert.AreEqual(0.1454 * Math.Pow(z, 1.7044), resf(z), 1e-2);
}
}
public static AnalysisResults AnalyzeSurroundingHouses(List <House> houses, BovetaSearch search)
{
var results = new AnalysisResults();
// Should always be the case. Extra check just to make sure.
houses = houses.Where(house => house.livingArea > 0).ToList();
// Should we use listing price or sold price? Better if possible.
bool useSoldPrice = false;
if (houses.Where(house => house.soldPrice > 0).Count() >=
houses.Where(house => house.listPrice > 0).Count())
{
useSoldPrice = true;
houses = houses.Where(house => house.soldPrice > 0).ToList();
}
else
{
houses = houses.Where(house => house.listPrice > 0).ToList();
}
// For convenience
int numHouses = houses.Count();
// At least MIN_RESULTS houses in big initial radius
if (numHouses < MIN_RESULTS)
{
return(results);
}
// Optimize the number of houses/search radius
houses = houses.OrderBy(w => w.distanceToReferencePoint).ToList();
double bestRadius = 0;
double bestScore = Double.MinValue;
for (double radius = 100; radius < 10000; radius = radius * 2)
{
int s = houses.Where(w => w.distanceToReferencePoint <= radius).Count();
double order = Math.Log(s, LOG_BASE);
double score = order - radius / RADIUS_W;
Console.WriteLine(" Radius: " + radius + "Score: " + score);
if (score > bestScore & s > MIN_RESULTS)
{
bestRadius = radius;
bestScore = score;
}
}
results.SearchRadius = bestRadius;
houses = houses.Where(w => w.distanceToReferencePoint < bestRadius).ToList();
if (houses.Count < MIN_RESULTS)
{
return(results);
}
List <double> xdata = new List <double>();
List <double> ydata = new List <double>();
List <double> weights = new List <double>();
foreach (var house in houses)
{
int housePrice = house.listPrice;
if (useSoldPrice)
{
housePrice = house.soldPrice;
}
weights.Add(1 + POL_WEIGHT_NUM / (POL_WEIGHT_DENOM + house.distanceToReferencePoint));
xdata.Add(house.livingArea);
ydata.Add(housePrice);
}
var p = Fit.PolynomialWeighted(xdata.ToArray(), ydata.ToArray(), weights.ToArray(), 1);
double estimation = p[0] + p[1] * search.Size;
if (search.Condition != HouseCondition.Average)
{
// Adjust weights to accomodate for condition
List <double> modelerr = new List <double>();
// 1. Find error in current model. Use this as measure for over/under valuation
for (int i = 0; i < ydata.Count; ++i)
{
double modelError = ydata[i] - (p[0] + p[1] * xdata[i]);
modelerr.Add(modelError);
}
// 2. Adjust weights so that overvalued houses are worth more.
for (int i = 0; i < modelerr.Count; i++)
{
if (modelerr[i] < 0) // Model price > real price. Probably bad condition
{
if (search.Condition == HouseCondition.AboveAverage)
{
weights[i] = weights[i] * 0.5; // Less representive of true house price
}
else if (search.Condition == HouseCondition.BelowAverage)
{
weights[i] = weights[i] * 2; // More representive of true house price
}
}
else // Model price < real price. Probably good condition
{
if (search.Condition == HouseCondition.AboveAverage)
{
weights[i] = weights[i] * 2; // More representive of true house price
}
else if (search.Condition == HouseCondition.BelowAverage)
{
weights[i] = weights[i] * 0.5; // Less representive of true house price
}
}
}
// Recalculate polynomial
p = Fit.PolynomialWeighted(xdata.ToArray(), ydata.ToArray(), weights.ToArray(), 1);
}
results.Intercept = p[0];
results.BetaSize = p[1];
results.NumDatapointsUsed = houses.Count();
results.EstimatedValue = (int)(results.Intercept + results.BetaSize * search.Size);
if (search.Condition == HouseCondition.BelowAverage & results.EstimatedValue > estimation)
{
// Oops. Our advanced searched failed. Reduce cost by 10% for now.....
results.EstimatedValue = results.EstimatedValue * 0.9;
}
if (search.Condition == HouseCondition.AboveAverage & results.EstimatedValue < estimation)
{
// Oops. Our advanced searched failed. Increase cost by 10% for now.....
results.EstimatedValue = results.EstimatedValue * 1.1;
}
results.ConfidenceScore = 0.6;
return(results);
}
public void CreateControllerElements(IMyTerminalBlock block)
{
try
{
if (DsControl)
{
return;
}
var comp = block?.GameLogic?.GetAs <DefenseShields>();
TerminalHelpers.Separator(comp?.Shield, "DS-C_sep0");
ToggleShield = TerminalHelpers.AddOnOff(comp?.Shield, "DS-C_ToggleShield", Localization.GetText("TerminalToggleShieldTitle"), Localization.GetText("TerminalToggleShieldTooltip"), Localization.GetText("TerminalSwitchUp"), Localization.GetText("TerminalSwitchDown"), DsUi.GetRaiseShield, DsUi.SetRaiseShield);
TerminalHelpers.Separator(comp?.Shield, "DS-C_sep1");
//ChargeSlider = TerminalHelpers.AddSlider(comp?.Shield, "DS-C_ChargeRate", "Shield Charge Rate", "Percentage Of Power The Shield May Consume", DsUi.GetRate, DsUi.SetRate);
//ChargeSlider.SetLimits(20, 95);
PowerScaleSelect = TerminalHelpers.AddCombobox(comp?.Shield, "DS-C_PowerScale", Localization.GetText("TerminalPowerScaleSelectTitle"), Localization.GetText("TerminalPowerScaleSelectTooltip"), DsUi.GetPowerScale, DsUi.SetPowerScale, DsUi.ListPowerScale);
PowerWatts = TerminalHelpers.AddSlider(comp?.Shield, "DS-C_PowerWatts", Localization.GetText("TerminalPowerWattsTitle"), Localization.GetText("TerminalPowerWattsTooltip"), DsUi.GetPowerWatts, DsUi.SetPowerWatts, DsUi.EnablePowerWatts);
PowerWatts.SetLimits(1, 999);
if (comp != null && comp.GridIsMobile)
{
TerminalHelpers.Separator(comp.Shield, "DS-C_sep2");
}
Fit = TerminalHelpers.AddSlider(comp?.Shield, "DS-CFit", Localization.GetText("TerminalFitTitle"), Localization.GetText("TerminalFitTooltip"), DsUi.GetFit, DsUi.SetFit);
Fit.SetLimits(0, 22);
SphereFit = TerminalHelpers.AddCheckbox(comp?.Shield, "DS-C_SphereFit", Localization.GetText("TerminalSphereFitTitle"), Localization.GetText("TerminalSphereFitTooltip"), DsUi.GetSphereFit, DsUi.SetSphereFit);
FortifyShield = TerminalHelpers.AddCheckbox(comp?.Shield, "DS-C_ShieldFortify", Localization.GetText("TerminalFortifyShieldTitle"), Localization.GetText("TerminalFortifyShieldTooltip"), DsUi.GetFortify, DsUi.SetFortify);
TerminalHelpers.Separator(comp?.Shield, "DS-C_sep3");
WidthSlider = TerminalHelpers.AddSlider(comp?.Shield, "DS-C_WidthSlider", Localization.GetText("TerminalWidthSliderTitle"), Localization.GetText("TerminalWidthSliderTooltip"), DsUi.GetWidth, DsUi.SetWidth);
WidthSlider.SetLimits(30, 1000);
HeightSlider = TerminalHelpers.AddSlider(comp?.Shield, "DS-C_HeightSlider", Localization.GetText("TerminalHeightSliderTitle"), Localization.GetText("TerminalHeightSliderTooltip"), DsUi.GetHeight, DsUi.SetHeight);
HeightSlider.SetLimits(30, 1000);
DepthSlider = TerminalHelpers.AddSlider(comp?.Shield, "DS-C_DepthSlider", Localization.GetText("TerminalDepthSliderTitle"), Localization.GetText("TerminalDepthSliderTooltip"), DsUi.GetDepth, DsUi.SetDepth);
DepthSlider.SetLimits(30, 1000);
OffsetWidthSlider = TerminalHelpers.AddSlider(comp?.Shield, "DS-C_OffsetWidthSlider", Localization.GetText("TerminalOffsetWidthSliderTitle"), Localization.GetText("TerminalOffsetWidthSliderTooltip"), DsUi.GetOffsetWidth, DsUi.SetOffsetWidth);
OffsetWidthSlider.SetLimits(-69, 69);
OffsetHeightSlider = TerminalHelpers.AddSlider(comp?.Shield, "DS-C_OffsetHeightSlider", Localization.GetText("TerminalOffsetHeightSliderTitle"), Localization.GetText("TerminalOffsetHeightSliderTooltip"), DsUi.GetOffsetHeight, DsUi.SetOffsetHeight);
OffsetHeightSlider.SetLimits(-69, 69);
OffsetDepthSlider = TerminalHelpers.AddSlider(comp?.Shield, "DS-C_OffsetDepthSlider", Localization.GetText("TerminalOffsetDepthSliderTitle"), Localization.GetText("TerminalOffsetDepthSliderTooltip"), DsUi.GetOffsetDepth, DsUi.SetOffsetDepth);
OffsetDepthSlider.SetLimits(-69, 69);
TerminalHelpers.Separator(comp?.Shield, "DS-C_sep4");
BatteryBoostCheckBox = TerminalHelpers.AddCheckbox(comp?.Shield, "DS-C_UseBatteries", Localization.GetText("TerminalBatteryBoostCheckBoxTitle"), Localization.GetText("TerminalBatteryBoostCheckBoxTooltip"), DsUi.GetBatteries, DsUi.SetBatteries);
SendToHudCheckBox = TerminalHelpers.AddCheckbox(comp?.Shield, "DS-C_HideIcon", Localization.GetText("TerminalSendToHudCheckBoxTitle"), Localization.GetText("TerminalSendToHudCheckBoxTooltip"), DsUi.GetSendToHud, DsUi.SetSendToHud);
TerminalHelpers.Separator(comp?.Shield, "DS-C_sep5");
ShellSelect = TerminalHelpers.AddCombobox(comp?.Shield, "DS-C_ShellSelect", Localization.GetText("TerminalShellSelectTitle"), Localization.GetText("TerminalShellSelectTooltip"), DsUi.GetShell, DsUi.SetShell, DsUi.ListShell);
ShellVisibility = TerminalHelpers.AddCombobox(comp?.Shield, "DS-C_ShellSelect", Localization.GetText("TerminalShellVisibilityTitle"), Localization.GetText("TerminalShellVisibilityTooltip"), DsUi.GetVisible, DsUi.SetVisible, DsUi.ListVisible);
HideActiveCheckBox = TerminalHelpers.AddCheckbox(comp?.Shield, "DS-C_HideActive", Localization.GetText("TerminalHideActiveCheckBoxTitle"), Localization.GetText("TerminalHideActiveCheckBoxTooltip"), DsUi.GetHideActive, DsUi.SetHideActive);
//RefreshAnimationCheckBox = TerminalHelpers.AddCheckbox(comp?.Shield, "DS-C_RefreshAnimation", "Show Refresh Animation ", "Show Random Refresh Animation", DsUi.GetRefreshAnimation, DsUi.SetRefreshAnimation);
//HitWaveAnimationCheckBox = TerminalHelpers.AddCheckbox(comp?.Shield, "DS-C_HitWaveAnimation", "Show Hit Wave Animation", "Show Wave Effect On Shield Damage", DsUi.GetHitWaveAnimation, DsUi.SetHitWaveAnimation);
NoWarningSoundsCheckBox = TerminalHelpers.AddCheckbox(comp?.Shield, "DS-C_NoWarningSounds", Localization.GetText("TerminalNoWarningSoundsCheckBoxTitle"), Localization.GetText("TerminalNoWarningSoundsCheckBoxTooltip"), DsUi.GetNoWarningSounds, DsUi.SetNoWarningSounds);
DimShieldHitsCheckBox = TerminalHelpers.AddCheckbox(comp?.Shield, "DS-C_DimShieldHits", Localization.GetText("TerminalDimShieldHitsCheckBoxTitle"), Localization.GetText("TerminalDimShieldHitsCheckBoxTooltip"), DsUi.GetDimShieldHits, DsUi.SetDimShieldHits);
TerminalHelpers.Separator(comp?.Shield, "DS-C_sep6");
SideShunting = TerminalHelpers.AddCheckbox(comp?.Shield, "DS-C_SideRedirect", Localization.GetText("TerminalSideShuntingTitle"), Localization.GetText("TerminalSideShuntingTooltip"), DsUi.GetSideShunting, DsUi.SetSideShunting);
ShowShunting = TerminalHelpers.AddCheckbox(comp?.Shield, "DS-C_ShowRedirect", Localization.GetText("TerminalShowShuntingTitle"), Localization.GetText("TerminalShowShuntingTooltip"), DsUi.GetShowShunting, DsUi.SetShowShunting);
TopShield = TerminalHelpers.AddOnOff(comp?.Shield, "DS-C_TopShield", Localization.GetText("TerminalTopShieldTitle"), Localization.GetText("TerminalTopShieldTooltip"), Localization.GetText("TerminalSwitchPush"), Localization.GetText("TerminalSwitchPull"), DsUi.GeTopShield, DsUi.SetTopShield, DsUi.RedirectEnabled);
BottomShield = TerminalHelpers.AddOnOff(comp?.Shield, "DS-C_BottomShield", Localization.GetText("TerminalBottomShieldTitle"), Localization.GetText("TerminalBottomShieldTooltip"), Localization.GetText("TerminalSwitchPush"), Localization.GetText("TerminalSwitchPull"), DsUi.GetBottomShield, DsUi.SetBottomShield, DsUi.RedirectEnabled);
LeftShield = TerminalHelpers.AddOnOff(comp?.Shield, "DS-C_LeftShield", Localization.GetText("TerminalLeftShieldTitle"), Localization.GetText("TerminalLeftShieldTooltip"), Localization.GetText("TerminalSwitchPush"), Localization.GetText("TerminalSwitchPull"), DsUi.GetLeftShield, DsUi.SetLeftShield, DsUi.RedirectEnabled);
RightShield = TerminalHelpers.AddOnOff(comp?.Shield, "DS-C_RightShield", Localization.GetText("TerminalRightShieldTitle"), Localization.GetText("TerminalRightShieldTooltip"), Localization.GetText("TerminalSwitchPush"), Localization.GetText("TerminalSwitchPull"), DsUi.GetRightShield, DsUi.SetRightShield, DsUi.RedirectEnabled);
FrontShield = TerminalHelpers.AddOnOff(comp?.Shield, "DS-C_FrontShield", Localization.GetText("TerminalFrontShieldTitle"), Localization.GetText("TerminalFrontShieldTooltip"), Localization.GetText("TerminalSwitchPush"), Localization.GetText("TerminalSwitchPull"), DsUi.GetFrontShield, DsUi.SetFrontShield, DsUi.RedirectEnabled);
BackShield = TerminalHelpers.AddOnOff(comp?.Shield, "DS-C_BackShield", Localization.GetText("TerminalBackShieldTitle"), Localization.GetText("TerminalBackShieldTooltip"), Localization.GetText("TerminalSwitchPush"), Localization.GetText("TerminalSwitchPull"), DsUi.GetBackShield, DsUi.SetBackShield, DsUi.RedirectEnabled);
CreateAction <IMyUpgradeModule>(ToggleShield);
CreateAction <IMyUpgradeModule>(SphereFit);
CreateFitAction <IMyUpgradeModule>(Fit);
CreateAction <IMyUpgradeModule>(FortifyShield);
CreateAction <IMyUpgradeModule>(HideActiveCheckBox);
//CreateAction<IMyUpgradeModule>(RefreshAnimationCheckBox);
//CreateAction<IMyUpgradeModule>(HitWaveAnimationCheckBox);
CreateAction <IMyUpgradeModule>(SendToHudCheckBox);
CreateAction <IMyUpgradeModule>(BatteryBoostCheckBox);
CreateDepthAction <IMyUpgradeModule>(DepthSlider);
CreateWidthAction <IMyUpgradeModule>(WidthSlider);
CreateHeightAction <IMyUpgradeModule>(HeightSlider);
CreateShuntAction <IMyUpgradeModule>(TopShield);
CreateShuntAction <IMyUpgradeModule>(BottomShield);
CreateShuntAction <IMyUpgradeModule>(LeftShield);
CreateShuntAction <IMyUpgradeModule>(RightShield);
CreateShuntAction <IMyUpgradeModule>(FrontShield);
CreateShuntAction <IMyUpgradeModule>(BackShield);
CreateAction <IMyUpgradeModule>(SideShunting);
DsControl = true;
}
catch (Exception ex) { Log.Line($"Exception in CreateControlerUi: {ex}"); }
}
//Private method to perform some operation that will NOT be exposed in Decisions
private double[] PerformPolynomialRegression(double[] xData, double[] yData)
{
//Use External Math.Net library to perform operations
return(Fit.Polynomial(xData, yData, 3));
}
/// <summary>
/// Makes all of the input layout elements have the same width or height
/// </summary>
/// <param name="elements">A list of elements to resize to the max size of all elements or the margins</param>
/// <param name="axis">Fit the width or the height</param>
/// <param name="margin">True if use margin size false to use arges element in input list</param>
public void MatchElementsSize(List<LayoutElement> elements, Fit axis, bool margin)
{
if (axis == Fit.Width)
{
if (margin)
{
float newWidth = PaperWidth - _printerSettings.DefaultPageSettings.Margins.Left - _printerSettings.DefaultPageSettings.Margins.Right;
foreach (LayoutElement le in elements)
le.Size = new SizeF(newWidth, le.Size.Height);
}
else
{
float newWidth = 0;
foreach (LayoutElement le in elements)
if (le.Size.Width > newWidth) newWidth = le.Size.Width;
foreach (LayoutElement le in elements)
le.Size = new SizeF(newWidth, le.Size.Height);
}
}
else
{
if (margin)
{
float newHeight = PaperHeight - _printerSettings.DefaultPageSettings.Margins.Top - _printerSettings.DefaultPageSettings.Margins.Bottom;
foreach (LayoutElement le in elements)
le.Size = new SizeF(le.Size.Width, newHeight);
}
else
{
float newHeight = 0;
foreach (LayoutElement le in elements)
if (le.Size.Height > newHeight) newHeight = le.Size.Height;
foreach (LayoutElement le in elements)
le.Size = new SizeF(le.Size.Width, newHeight);
}
}
}
static void Main(string[] args)
{
//首先生成 x 点的向量，在区间 [0,2.5] 内等间距分布；然后计算这些点处的 erf(x)。
//double[] x = Generate.LinearRange(start: 0, step: 0.1, stop: 2.5);
double[] x = Generate.LinearRange(start: 1, step: 1, stop: 10);
List <double> y = new List <double>();
foreach (double x0 in x)
{
y.Add(SpecialFunctions.Erf(x0));
}
//确定 6 阶逼近多项式的系数。
double[] p = Fit.Polynomial(x, y.ToArray(), order: 6);
Write("p = ");
foreach (double coefficient in p)
{
Write($"{coefficient:F2} ");
}
WriteLine();
//为了查看拟合情况如何，在各数据点处计算多项式，并生成说明数据、拟合和误差的一个表。
WriteLine();
var funcP = Fit.PolynomialFunc(x, y.ToArray(), 6);
WriteLine($"{"x",10}|{"y",10}|{"Fit",10}|{"FitError",10}");
WriteLine($"{new string('-', 10)}|{new string('-', 10)}|{new string('-', 10)}|{new string('-', 10)}");
foreach (double x0 in Generate.LinearRange(1, 1, 10))
{
double y0 = SpecialFunctions.Erf(x0);
double fit = funcP(x0);
double fitError = fit - y0;
WriteLine($"{x0,10:F2}|{y0,10:F2}|{fit,10:F2}|{fitError,10:F2}");
}
ReadKey();
/*
*
* using MathNet.Numerics;
* using System.Collections.Generic;
* using static System.Console;
*
* namespace ConsoleApp1
* {
* class Program
* {
* static void Main()
* {
* //首先生成 x 点的向量，在区间 [0,2.5] 内等间距分布；然后计算这些点处的 erf(x)。
* double[] x = Generate.LinearRange(start: 0, step: 0.1, stop: 2.5);
* List<double> y = new List<double>();
* foreach (double x0 in x)
* {
* y.Add(SpecialFunctions.Erf(x0));
* }
* //确定 6 阶逼近多项式的系数。
* double[] p = Fit.Polynomial(x, y.ToArray(), order: 6);
* Write("p = ");
* foreach (double coefficient in p)
* {
* Write($"{coefficient:F5} ");
* }
* WriteLine();
* //为了查看拟合情况如何，在各数据点处计算多项式，并生成说明数据、拟合和误差的一个表。
* WriteLine();
* var funcP = Fit.PolynomialFunc(x, y.ToArray(), 6);
* WriteLine($"{"x",10}|{"y",10}|{"Fit",10}|{"FitError",10}");
* WriteLine($"{new string('-', 10)}|{new string('-', 10)}|{new string('-', 10)}|{new string('-', 10)}");
* foreach (double x0 in Generate.LinearRange(0, 0.1, 1.5))
* {
* double y0 = SpecialFunctions.Erf(x0);
* double fit = funcP(x0);
* double fitError = fit - y0;
* WriteLine($"{x0,10:F4}|{y0,10:F4}|{fit,10:F4}|{fitError,10:F4}");
* }
* }
* }
* }
*/
}
private void button4_Click_2(object sender, EventArgs e)
{
chart2.Series.Clear();
List <double> xdataList = new List <double>();
List <double> ydataList = new List <double>();
/*xdataList.Add(0);
* ydataList.Add(0);*/
DataGridViewRowCollection drc = dataGridViewInterpolation.Rows;
settings.CollectionH.Clear();
settings.CollectionV.Clear();
foreach (DataGridViewRow item in drc)
{
try
{
//save to settings
/*
* settings.CollectionH.Add(item.Cells[0].Value.ToString());
* settings.CollectionV.Add(item.Cells[1].Value.ToString());
* settings.Save();*/
//add to ArrayList
xdataList.Add(Convert.ToDouble(item.Cells[1].Value));
ydataList.Add(Convert.ToDouble(item.Cells[0].Value));
}
catch (Exception ex)
{
MessageBox.Show("Please introduce only numbers. " + ex.Message);
}
}
xdataList.Add(0);
ydataList.Add(0);
double[] xdata = xdataList.ToArray();
double[] ydata = ydataList.ToArray();
double[] p = Fit.Polynomial(xdata, ydata, 2);
chart2.Series.Add("Data");
chart2.Series.Add("Interpolation");
//set chart type
chart2.Series["Data"].ChartType = System.Windows.Forms.DataVisualization.Charting.SeriesChartType.Point;
chart2.Series["Data"].BorderWidth = myBorderWidth;
//chart2.Series["Data"].LineDashStyle = System.Windows.Forms.DataVisualization.Charting.ChartDashStyle.Dot;
chart2.Series["Data"].MarkerStyle = MarkerStyle.Circle;
chart2.Series["Data"].MarkerSize = 10;
chart2.Series["Data"].Color = System.Drawing.Color.Green;
//set chart type
chart2.Series["Interpolation"].ChartType = System.Windows.Forms.DataVisualization.Charting.SeriesChartType.Line;
chart2.Series["Interpolation"].BorderWidth = myBorderWidth;
chart2.Series["Interpolation"].BorderDashStyle = ChartDashStyle.Dot;
chart2.Series["Interpolation"].Color = System.Drawing.Color.Blue;
for (int i = 0; i < xdata.Length; i++)
{
chart2.Series["Data"].Points.AddXY(Convert.ToDouble(xdata[i]), Convert.ToDouble(ydata[i]));
}
for (int i = 0; i < 50; i++)
{
double newx = ((xdata[xdata.Length - 1] - xdata[0]) / 50) * i + xdata[0];
double newy = p[0] + p[1] * newx + p[2] * Math.Pow(newx, 2);
chart2.Series["Interpolation"].Points.AddXY(newx, newy);
}
//calculate v at head of 50mm (solve quadratic equation at 50mm):
double sqrtpart = p[1] * p[1] - 4 * p[2] * (p[0] - 50);
textBoxAnswer.Text = ((-p[1] + Math.Sqrt(sqrtpart)) / (2 * p[2])).ToString("#0.000");
textBox3.Text = ((-p[1] - Math.Sqrt(sqrtpart)) / (2 * p[2])).ToString("#0.000");
double answer = (-p[1] + Math.Sqrt(sqrtpart)) / (2 * (p[2]));
textBox4.Text = (p[0] + p[1] * answer + p[2] * Math.Pow(answer, 2)).ToString("#0.000");
}
private void poAutoruToolStripMenuItem_Click(object sender, EventArgs e)
{
try
{
OdbcConnection connection = new OdbcConnection();
connection.ConnectionString = "DSN=PostgreSQL35W;UID=masterwordcounter;PWD=masterwordcounter";
connection.Open();
string q = "SELECT * FROM (SELECT sum(s.num_of_repeats) prosjek, avg(s.pct_of_occurence), s.word_text, t.author FROM stats s, texts t WHERE s.text_id = t.id AND t.author = 'Agatha Christie' AND type_of_stats = 2 GROUP BY s.word_text,t.author ORDER BY prosjek DESC) top_sve OFFSET 0 LIMIT 200";
OdbcCommand getAllTexts = new OdbcCommand(q, connection);
OdbcDataReader odr = getAllTexts.ExecuteReader();
int a = 0;
lista_stats_text.Clear();
double ukupno = 0;
while (odr.Read())
{
a++;
Statistika s = new Statistika();
s.Broj_ponavljanja = odr.GetInt32(0);
s.Procenat_pojave_unutar_teksta = odr.GetDecimal(1) * 100;
s.Id = a;
s.Rijec_tekst = odr.GetString(2);
lista_stats_text.Add(s);
ukupno += s.Broj_ponavljanja;
}
odr.Close();
List <koordinate> pravi_rez = new List <koordinate>();
List <koordinate> ideal_rez = new List <koordinate>();
double suma_razlike = 0;
for (int i = 0; i < lista_stats_text.Count; i++)
{
if (double.IsNegativeInfinity(Math.Log10(lista_stats_text[i].Broj_ponavljanja)))
{
break;
}
if (double.IsNegativeInfinity(Math.Log10(lista_stats_text[0].Broj_ponavljanja / (i + 1))))
{
break;
}
koordinate k = new koordinate();
k.X = Math.Log10(i + 1);
k.Y = Math.Log10(lista_stats_text[i].Broj_ponavljanja);
pravi_rez.Add(k);
koordinate k1 = new koordinate();
k1.X = Math.Log10(i + 1);
k1.Y = Math.Log10(lista_stats_text[0].Broj_ponavljanja / (i + 1));
ideal_rez.Add(k1);
}
try
{
for (int i = 0; i < pravi_rez.Count; i++)
{
double a1 = Math.Round(Math.Pow((pravi_rez[i].X - ideal_rez[i].X), 2) + Math.Pow((pravi_rez[i].Y - ideal_rez[i].Y), 2), 4);
suma_razlike += a1;
suma_razlike = Math.Round(suma_razlike, 4);
}
}
catch (ArithmeticException ae)
{
MessageBox.Show(ae.Message);
}
double MSE = suma_razlike / pravi_rez.Count;
double[] lista_x = new double[ideal_rez.Count];
double[] lista_y = new double[ideal_rez.Count];
int j = 0;
foreach (koordinate k in pravi_rez)
{
lista_x[j] = k.X;
lista_y[j] = k.Y;
j++;
}
double[] f = Fit.Polynomial(lista_x, lista_y, 2, MathNet.Numerics.LinearRegression.DirectRegressionMethod.NormalEquations);
var bestfit = Tuple.Create(0.0, 0.0);
bestfit = Fit.Line(lista_x, lista_y);
l_text_stats.Text = "Date riječi u tabeli predstavljaju " + Math.Round((double)(podsuma / ukupno) * 100, 2) + "% od cijelog teksta. Ukupno posebnih riječi ima " + lista_stats_text.Count + " a cijeli tekst ima " + ukupno + " riječi.";
l_nastavak.Text = "Suma odstupanja od idealnog Zipfovog zakona je " + suma_razlike + ", MSE je " + Math.Round(MSE, 5) + ", RMSE je " + Math.Round(Math.Sqrt(MSE), 5) + ". Best-fit polinom za rezultate je y(x) = " + Math.Round(f[1], 2) + "X + " + Math.Round(f[2], 2) + "X ^ 2 + " + Math.Round(f[0], 2);
if (c_analiza.SelectedIndex == 0)
{
MSENDPF = Math.Round(MSE, 5);
}
else
{
MSEGPDF = Math.Round(MSE, 5);
}
if (MSENDPF != 0.0 && MSEGPDF != 0.0)
{
StatistikaTekst st = new StatistikaTekst();
st.Ime_teksta = c_tekst.SelectedItem.ToString();
st.RMSE_G1 = MSEGPDF;
st.RMSE_N1 = MSENDPF;
double h = Math.Round(st.RMSE_N1 - st.RMSE_G1, 5);
st.Pct_diff = Math.Round(h / st.RMSE_N1, 5);
if (st.Pct_diff > 1)
{
st.Pct_diff = st.Pct_diff - 1;
}
lista_PDF.Add(st);
MSEGPDF = 0.0;
MSENDPF = 0.0;
}
}
catch (Exception es)
{
MessageBox.Show(es.Message);
}
}
/// <summary>
/// Creates a text label to fit within a specified area
/// </summary>
public Label(String name, Rectangle area, Fit fit)
: base(name, area)
{
SetText(name, fit);
bgTransparency = bgColor.A / 255.0f;
}
