public void ChiSquareDistribution()
{
int B = 8;
Random rng = new Random(0);
//BinomialDistribution d = new BinomialDistribution(0.25, 6);
//DiscreteUniformDistribution d = new DiscreteUniformDistribution(0, 4);
//BernoulliDistribution d = new BernoulliDistribution(0.25);
DiscreteDistribution d = new PoissonDistribution(2.0);
Sample s = new Sample();
ChiSquaredDistribution nullDistribution = null;
for (int i = 0; i < 512; i++) {
Histogram h = new Histogram(B);
for (int j = 0; j < 1024; j++) {
int k = d.GetRandomValue(rng);
h.Add(k);
//if (k < h.Count) h[k].Increment();
//h[d.GetRandomValue(rng)].Increment();
}
TestResult cr = h.ChiSquaredTest(d);
nullDistribution = (ChiSquaredDistribution) cr.Distribution;
//Console.WriteLine(((ChiSquaredDistribution) cr.Distribution).DegreesOfFreedom);
s.Add(cr.Statistic);
}
Console.WriteLine(nullDistribution.DegreesOfFreedom);
TestResult kr = s.KuiperTest(nullDistribution);
Console.WriteLine(kr.LeftProbability);
}
public void TwoSampleKS2()
{
int n = 2 * 3 * 3; int m = 2 * 2 * 3;
Random rng = new Random(0);
NormalDistribution d = new NormalDistribution();
Histogram h = new Histogram((int) AdvancedIntegerMath.LCM(n, m) + 1);
//int[] h = new int[AdvancedIntegerMath.LCM(n, m) + 1];
int count = 1000;
for (int i = 0; i < count; i++) {
Sample A = new Sample();
for (int j = 0; j < n; j++) A.Add(d.GetRandomValue(rng));
Sample B = new Sample();
for (int j = 0; j < m; j++) B.Add(d.GetRandomValue(rng));
TestResult r = Sample.KolmogorovSmirnovTest(A, B);
int k = (int) Math.Round(r.Statistic * AdvancedIntegerMath.LCM(n, m));
//Console.WriteLine("{0} {1}", r.Statistic, k);
h[k].Increment();
//h[k] = h[k] + 1;
}
KolmogorovTwoSampleExactDistribution ks = new KolmogorovTwoSampleExactDistribution(n, m);
double chi2 = 0.0; int dof = 0;
for (int i = 0; i < h.Count; i++) {
double ne = ks.ProbabilityMass(i) * count;
Console.WriteLine("{0} {1} {2}", i, h[i].Counts, ne);
if (ne > 4) {
chi2 += MoreMath.Sqr(h[i].Counts - ne) / ne;
dof++;
}
}
Console.WriteLine("chi^2={0} dof={1}", chi2, dof);
TestResult r2 = h.ChiSquaredTest(ks);
ChiSquaredDistribution rd = (ChiSquaredDistribution) r2.Distribution;
Console.WriteLine("chi^2={0} dof={1} P={2}", r2.Statistic, rd.DegreesOfFreedom, r2.RightProbability);
}
//Method that calculates the calibration parameters
void calibrazione(double[] X_kinect, double[] Y_kinect, double[] Z_kinect)
{
//Grid coordinates
double[] X_Ref_B = new double[12] { -517.5, -172.5, 172.5, 517.5, -517.5, -172.5, 172.5, 517.5, -517.5, -172.5, 172.5, 517.5 };// coordinate X dei punti della griglia di riferimento (origine posta al centro della griglia stessa)
double[] Y_Ref_B = new double[12] { 345.0, 345.0, 345.0, 345.0, 0.0, 0.0, 0.0, 0.0, -345.0, -345.0, -345.0, -345.0 };// coordinate Y dei punti della griglia di riferimento (origine posta al centro della griglia stessa)
double[] Z_Ref_B = new double[12];
Sample XKINECT = new Sample(X_kinect);
Sample YKINECT = new Sample(Y_kinect);
Sample ZKINECT = new Sample(Z_kinect);
// barycenter of Kinect coordinates
double X_Bar = XKINECT.Mean;
double Y_Bar = YKINECT.Mean;
double Z_Bar = ZKINECT.Mean;
//Kinect barycentric coordinates contaneirs
double[] X_Kinect_Bar = new double[12];
double[] Y_Kinect_Bar = new double[12];
double[] Z_Kinect_Bar = new double[12];
//Kinect barycentric coordinates
for (int i = 0; i < 12; i++)
{
X_Kinect_Bar[i] = X_kinect[i] - X_Bar;
Y_Kinect_Bar[i] = Y_kinect[i] - Y_Bar;
Z_Kinect_Bar[i] = Z_kinect[i] - Z_Bar;
}
//Modello rotazione + 2 parametri di scala Yoss=AX+b oppure B=Yoss-b=AX X=B*A^-1
RectangularMatrix A = new RectangularMatrix(36, 5);
ColumnVector B = new ColumnVector(36);
ColumnVector bb = new ColumnVector(36);
ColumnVector Yoss = new ColumnVector(36);
ColumnVector X = new ColumnVector(5);
SquareMatrix X1 = new SquareMatrix(5);
ColumnVector X2 = new ColumnVector(5);
for (int i = 0; i < 12; i++)
{
Yoss[3 * i] = X_Ref_B[i];
Yoss[3 * i + 1] = Y_Ref_B[i];
Yoss[3 * i + 2] = Z_Ref_B[i];
A[3 * i, 0] = Y_Kinect_Bar[i];
A[3 * i, 1] = -Z_Kinect_Bar[i];
A[3 * i, 2] = 0.0;
A[3 * i, 3] = X_Kinect_Bar[i];
A[3 * i, 4] = 0.0;
A[3 * i + 1, 0] = -X_Kinect_Bar[i];
A[3 * i + 1, 1] = 0.0;
A[3 * i + 1, 2] = Z_Kinect_Bar[i];
A[3 * i + 1, 3] = 0.0;
A[3 * i + 1, 4] = Y_Kinect_Bar[i];
A[3 * i + 2, 0] = 0.0;
A[3 * i + 2, 1] = X_Kinect_Bar[i];
A[3 * i + 2, 2] = -Y_Kinect_Bar[i];
A[3 * i + 2, 3] = 0.0;
A[3 * i + 2, 4] = 0.0;
bb[3 * i] = X_Kinect_Bar[i];
bb[3 * i + 1] = Y_Kinect_Bar[i];
bb[3 * i + 2] = Z_Kinect_Bar[i];
B[3 * i] = X_Ref_B[i] - X_Kinect_Bar[i];
B[3 * i + 1] = Y_Ref_B[i] - Y_Kinect_Bar[i];
B[3 * i + 2] = Z_Ref_B[i] - Z_Kinect_Bar[i];
}
X1 = (SquareMatrix)(A.Transpose() * A);
X2 = (A.Transpose() * B);
X = (X1.Inverse()) * X2;
a = X[0];
b = X[1];
c = X[2];
lambdaX = X[3];
lambdaY = X[4];
this.sw9.Write("{0:#####.0000} {1:#####.0000} {2:#####.0000} {3:#####.0000} {4:#####.0000} ", a, b, c, lambdaX, lambdaY);
this.sw9.WriteLine();
}
/// <summary>
/// Meetod kõigi sisendite (ehk eelduste) välja arvutamiseks
/// </summary>
/// <param name="finDataAdapter">Adapter kõigi finantsandmete kättesaamise jaoks</param>
/// <param name="dcfInput">DCF arvutuste eelduste hoidja</param>
public static void CalculateInput(FinDataAdapter finDataAdapter, DcfInput dcfInput)
{
CalculateCorrelation(finDataAdapter, dcfInput);
int requiredPeriodsForMean = 1;
List<FinData> finDatas = finDataAdapter.FinDataDao.FinDatas;
int k = 0;
Sample sampleRevenue = new Sample();
Sample sampleTax = new Sample();
Sample sampleInterest = new Sample();
Sample sampleTotalAssets = new Sample();
Sample sampleTotalLiabilities = new Sample();
Sample sampleTotalCurrentAssets = new Sample();
Sample sampleTotalCurrentLiabilities = new Sample();
Sample sampleAllCosts = new Sample();
Sample sampleEbitda = new Sample();
Sample sampleDepreciation = new Sample();
Sample sampleEbit = new Sample();
for (int i = finDatas.Count - 1; i >= 4; i--)
{
if (k < 12)
{
if (k == 0)
{
try
{
dcfInput.SharesOutstanding = (double)finDatas[i].BsCommonSharesOutstanding;
}
catch (InvalidOperationException) { }
}
try
{
double RevenueGrowth = (double)(finDatas[i].IsRevenue / finDatas[i - 4].IsRevenue) - 1;
sampleRevenue.Add(RevenueGrowth);
}
catch (InvalidOperationException) { }
try
{
double taxRate = (double)((finDatas[i].IsPretaxIncome - finDatas[i].IsIncomeAfterTax) / finDatas[i].IsPretaxIncome);
sampleTax.Add(taxRate);
}
catch (InvalidOperationException) { }
double debt = 0;
if (finDatas[i].BsCurrentPortionOfLongTermDebt != null)
{
debt += (double)finDatas[i].BsCurrentPortionOfLongTermDebt;
}
if (finDatas[i].BsTotalLongTermDebt != null)
{
debt += (double)finDatas[i].BsTotalLongTermDebt;
}
if (finDatas[i].FrInterestExpense != null && debt != 0.0)
{
double interest = (double)(finDatas[i].FrInterestExpense) / debt;
if (interest < 0.2)
{
sampleInterest.Add(interest);
}
}
if (finDatas[i].IsRevenue != null && finDatas[i].IsRevenue != 0.0 && finDatas[i].BsTotalAssets != null)
{
double temp = (double)(finDatas[i].BsTotalAssets / finDatas[i].IsRevenue);
if (temp > 0.5 && temp < 50)
{
sampleTotalAssets.Add(temp);
}
}
if (finDatas[i].IsRevenue != null && finDatas[i].IsRevenue != 0.0 && finDatas[i].BsTotalLiabilities != null)
{
double temp = (double)(finDatas[i].BsTotalLiabilities / finDatas[i].IsRevenue);
if (temp > 0.5 && temp < 50)
{
sampleTotalLiabilities.Add(temp);
}
}
if (finDatas[i].IsRevenue != null && finDatas[i].IsRevenue != 0.0 && finDatas[i].BsTotalCurrentAssets != null)
{
double temp = (double)(finDatas[i].BsTotalCurrentAssets / finDatas[i].IsRevenue);
if (temp > 0.5 && temp < 50)
{
sampleTotalCurrentAssets.Add(temp);
}
}
if (finDatas[i].IsRevenue != null && finDatas[i].IsRevenue != 0.0 && finDatas[i].BsTotalCurrentLiabilities != null)
{
double temp = (double)(finDatas[i].BsTotalCurrentLiabilities / finDatas[i].IsRevenue);
if (temp > 0.5 && temp < 50)
{
sampleTotalCurrentLiabilities.Add(temp);
}
}
double allCosts = 0.0;
if (finDatas[i].IsTotalOperatingExpenses != null)
{
allCosts += (double)finDatas[i].IsTotalOperatingExpenses;
}
if (finDatas[i].IsDepreciationAmortization != null)
{
allCosts -= (double)finDatas[i].IsDepreciationAmortization;
}
if (finDatas[i].IsRevenue != null && finDatas[i].IsRevenue != 0.0 && allCosts != 0.0)
{
double temp = (double)(allCosts / finDatas[i].IsRevenue);
if (temp > 0.1 && temp < 1)
{
sampleAllCosts.Add(temp);
}
}
if (finDatas[i].IsRevenue != null && finDatas[i].IsRevenue != 0.0 && finDatas[i].FrEbitda != null)
{
double temp = (double)(finDatas[i].FrEbitda / finDatas[i].IsRevenue);
if (temp > -1.0 && temp < 1.0)
{
sampleEbitda.Add(temp);
}
}
if (finDatas[i].IsRevenue != null && finDatas[i].IsRevenue != 0.0 && finDatas[i].IsDepreciationAmortization != null)
{
double temp = (double)(finDatas[i].IsDepreciationAmortization / finDatas[i].IsRevenue);
if (temp > -1.0 && temp < 1.0)
{
sampleDepreciation.Add(temp);
}
}
if (finDatas[i].IsRevenue != null && finDatas[i].IsRevenue != 0.0 && finDatas[i].FrEbit != null)
{
double temp = (double)(finDatas[i].FrEbit / finDatas[i].IsRevenue);
if (temp > -1.0 && temp < 1.0)
{
sampleEbit.Add(temp);
}
}
}
k++;
}
if (sampleRevenue.Count >= requiredPeriodsForMean)
{
dcfInput.GrowthRatePrognosis = sampleRevenue.Mean;
}
if (sampleTax.Count >= requiredPeriodsForMean)
{
dcfInput.TaxRate = sampleTax.Mean;
}
if (sampleInterest.Count >= requiredPeriodsForMean)
{
dcfInput.CostOfDebt = sampleInterest.Mean;
}
if (sampleTotalAssets.Count >= requiredPeriodsForMean)
{
dcfInput.TotalAssetsPrcRevenue = sampleTotalAssets.Mean;
}
if (sampleTotalLiabilities.Count >= requiredPeriodsForMean)
{
dcfInput.TotalLiabilitiesPrcRevenue = sampleTotalLiabilities.Mean;
}
if (sampleTotalCurrentAssets.Count >= requiredPeriodsForMean)
{
dcfInput.TotalCurrentAssetsPrcRevenue = sampleTotalCurrentAssets.Mean;
}
if (sampleTotalCurrentLiabilities.Count >= requiredPeriodsForMean)
{
dcfInput.TotalCurrentLiabilitiesPrcRevenue = sampleTotalCurrentLiabilities.Mean;
}
if (sampleAllCosts.Count >= requiredPeriodsForMean)
{
dcfInput.AllCostsPrcRevenue = sampleAllCosts.Mean;
}
if (sampleEbitda.Count >= requiredPeriodsForMean)
{
dcfInput.EbitdaPrcRevenue = sampleEbitda.Mean;
}
if (sampleDepreciation.Count >= requiredPeriodsForMean)
{
dcfInput.DepreciationPrcRevenue = sampleDepreciation.Mean;
}
if (sampleEbit.Count >= requiredPeriodsForMean)
{
dcfInput.EbitPrcRevenue = sampleEbit.Mean;
}
}
// Method that calculates the calibration parameters
public void Calibration(double[] X_Kinect, double[] Y_Kinect, double[] Z_Kinect)
{
double[] X_Ref_B = new double[dim];
double[] Y_Ref_B = new double[dim];
double[] Z_Ref_B = new double[dim];
if (colonne_griglia_calibrazione % 2 == 0 && righe_griglia_calibrazione % 2 == 0)
{
for (int i = 0; i < righe_griglia_calibrazione; i++)
{
for (int j = 0; j < colonne_griglia_calibrazione; j++)
{
X_Ref_B[colonne_griglia_calibrazione*i + j] = (-colonne_griglia_calibrazione/2 + j)*step + step/2;
Y_Ref_B[colonne_griglia_calibrazione*i + j] = (righe_griglia_calibrazione/2 - i)*step - step/2;
}
}
}
if (colonne_griglia_calibrazione % 2 == 0 && righe_griglia_calibrazione % 2 != 0)
{
for (int i = 0; i < righe_griglia_calibrazione; i++)
{
for (int j = 0; j < colonne_griglia_calibrazione; j++)
{
X_Ref_B[colonne_griglia_calibrazione * i + j] = (-colonne_griglia_calibrazione / 2 + j) * step + step / 2;
Y_Ref_B[colonne_griglia_calibrazione * i + j] = ((righe_griglia_calibrazione-1)/ 2 - i) * step;
}
}
}
if (colonne_griglia_calibrazione % 2 != 0 && righe_griglia_calibrazione % 2 == 0)
{
for (int i = 0; i < righe_griglia_calibrazione; i++)
{
for (int j = 0; j < colonne_griglia_calibrazione; j++)
{
X_Ref_B[colonne_griglia_calibrazione*i + j] = (-(colonne_griglia_calibrazione-1)/2 + j)*step;
Y_Ref_B[colonne_griglia_calibrazione*i + j] = (righe_griglia_calibrazione/2 - i)*step - step/2;
}
}
}
if (colonne_griglia_calibrazione % 2 != 0 && righe_griglia_calibrazione % 2 != 0)
{
for (int i = 0; i < righe_griglia_calibrazione; i++)
{
for (int j = 0; j < colonne_griglia_calibrazione; j++)
{
X_Ref_B[colonne_griglia_calibrazione * i + j] = (-(colonne_griglia_calibrazione - 1) / 2 + j) * step;
Y_Ref_B[colonne_griglia_calibrazione * i + j] = ((righe_griglia_calibrazione - 1) / 2 - i) * step;
}
}
}
// Grid coordinates
for (int k = 0; k < dim; k++)
{
this.Win.sw5.WriteLine("{0:#####.0000} {1:#####.0000} {2:#####.0000} ", X_Ref_B[k], Y_Ref_B[k], Z_Ref_B[k]);
}
this.Win.sw5.WriteLine();
Sample XKINECT = new Sample(X_Kinect);
Sample YKINECT = new Sample(Y_Kinect);
Sample ZKINECT = new Sample(Z_Kinect);
// barycenter of Kinect coordinates
double X_Bar = XKINECT.Mean;
double Y_Bar = YKINECT.Mean;
double Z_Bar = ZKINECT.Mean;
//Kinect barycentric coordinates contaneirs
double[] X_Kinect_Bar = new double[dim];
double[] Y_Kinect_Bar = new double[dim];
double[] Z_Kinect_Bar = new double[dim];
//Kinect barycentric coordinates
for (int i = 0; i < dim; i++)
{
X_Kinect_Bar[i] = X_Kinect[i] - X_Bar;
Y_Kinect_Bar[i] = Y_Kinect[i] - Y_Bar;
Z_Kinect_Bar[i] = Z_Kinect[i] - Z_Bar;
this.Win.sw3.WriteLine("{0:#####.0000} {1:#####.0000} {2:#####.0000} ", X_Kinect_Bar[i], Y_Kinect_Bar[i], Z_Kinect_Bar[i]);
}
this.Win.sw3.WriteLine();
//Modello rotazione + 2 parametri di scala Yoss = AX+b oppure B = Yoss-bb = AX X = B*A^-1
RectangularMatrix A = new RectangularMatrix(dim * 3, 5);
ColumnVector B = new ColumnVector(dim * 3);
ColumnVector bb = new ColumnVector(dim * 3);
ColumnVector Yoss = new ColumnVector(dim * 3);
ColumnVector X = new ColumnVector(5);
SquareMatrix X1 = new SquareMatrix(5);
ColumnVector X2 = new ColumnVector(5);
for (int i = 0; i < dim; i++)
{
Yoss[3 * i ] = X_Ref_B[i];
Yoss[3 * i + 1] = Y_Ref_B[i];
Yoss[3 * i + 2] = Z_Ref_B[i];
A[3 * i, 0] = Y_Kinect_Bar[i];
A[3 * i, 1] = -Z_Kinect_Bar[i];
A[3 * i, 2] = 0.0;
A[3 * i, 3] = X_Kinect_Bar[i];
A[3 * i, 4] = 0.0;
A[3 * i + 1, 0] = -X_Kinect_Bar[i];
A[3 * i + 1, 1] = 0.0;
A[3 * i + 1, 2] = Z_Kinect_Bar[i];
A[3 * i + 1, 3] = 0.0;
A[3 * i + 1, 4] = Y_Kinect_Bar[i];
A[3 * i + 2, 0] = 0.0;
A[3 * i + 2, 1] = X_Kinect_Bar[i];
A[3 * i + 2, 2] = -Y_Kinect_Bar[i];
A[3 * i + 2, 3] = 0.0;
A[3 * i + 2, 4] = 0.0;
bb[3 * i] = X_Kinect_Bar[i];
bb[3 * i + 1] = Y_Kinect_Bar[i];
bb[3 * i + 2] = Z_Kinect_Bar[i];
B[3 * i] = X_Ref_B[i] - X_Kinect_Bar[i];
B[3 * i + 1] = Y_Ref_B[i] - Y_Kinect_Bar[i];
B[3 * i + 2] = Z_Ref_B[i] - Z_Kinect_Bar[i];
}
X1 = (SquareMatrix)(A.Transpose() * A);
X2 = (A.Transpose() * B);
X = (X1.Inverse()) * X2;
a = X[0];
b = X[1];
c = X[2];
lambdaX = X[3];
lambdaY = X[4];
this.Win.sw12.Write("{0:#####.0000} {1:#####.0000} {2:#####.0000} {3:#####.0000} {4:#####.0000} ", a, b, c, lambdaX, lambdaY);
this.Win.sw12.WriteLine();
this.Win.textBlock8.Text = string.Format("Calibration parameters:\na = {0} \nb = {1} \nc = {2} \nlambdaX = {3} \nlambdaY = {4}", a, b, c, lambdaX, lambdaY);
SquareMatrix ROT = new SquareMatrix(3);
ROT[0, 0] = 1 + lambdaX;
ROT[0, 1] = a;
ROT[0, 2] = -b;
ROT[1, 0] = -a;
ROT[1, 1] = 1 + lambdaY;
ROT[1, 2] = c;
ROT[2, 0] = b;
ROT[2, 1] = -c;
ROT[2, 2] = 1;
}
