private (double M1, double M2) M(Vector <double> pricesVector, Vector <double> weightsVector, NumericMatrix <double> correlationMatrix, Vector <double> volatilityVector)
{
Vector <double> pricesWeightsVector = pricesVector * weightsVector;
double m1 = pricesWeightsVector.Sum();
NumericMatrix <double> volatilityMatrix = new NumericMatrix <double>(volatilityVector.Length, volatilityVector.Length, 0, 0);
for (int i = 0; i < volatilityVector.Length; i++)
{
for (int j = 0; j < volatilityVector.Length; j++)
{
volatilityMatrix[i, j] = volatilityVector[i] * volatilityVector[j] * correlationMatrix[i, j] * _t;
}
}
NumericMatrix <double> expMatrix = volatilityMatrix.Exp();
double m2 = 0;
for (int row = 0; row < expMatrix.Rows; row++)
{
m2 += (pricesWeightsVector * expMatrix.getRow(row)).Sum() * weightsVector[row] * pricesVector[row];
}
return(m1, m2);
}
// 13.12.4 Bilinear Interpolation
public static void BilinearInterpolation1()
{
Console.WriteLine("Vectors initialised: ");
// Create mesh arrays
int startIndex = 0;
int N = 1;
Vector <double> x1arr = new Vector <double>(N + 1, startIndex, 0.0);
x1arr[0] = 20.0; x1arr[1] = 21.0;
Vector <double> x2arr = new Vector <double>(x1arr);
x2arr[0] = 14.0; x2arr[1] = 15.0;
Console.WriteLine(x1arr);
Console.WriteLine(x2arr);
// Control values;
NumericMatrix <double> Control
= new NumericMatrix <double>(N + 1, N + 1, startIndex, startIndex);
Control[0, 0] = 91.0; Control[1, 1] = 95.0;
Control[0, 1] = 210.0; Control[1, 0] = 162.0;
BilinearInterpolator myInterpolator
= new BilinearInterpolator(x1arr, x2arr, Control);
double x = 20.2; double y = 14.5; // 146.1
double value = myInterpolator.Solve(x, y);
Console.WriteLine("Interpolated value: {0}", value);
}
static void Main(string[] args)
{
const string directoryPath = @"C:\Users\Káťa\Desktop\Diplomka\mesh\meshes";
var matrix = loadFacesR(directoryPath);
REngine.SetEnvironmentVariables();
REngine engine = REngine.GetInstance();
NumericMatrix group1 = engine.CreateNumericMatrix(matrix);
//List<float>[] faceMatrix = new List<float>[] { };
//double[,] squere = new double[,] { { 13, -4, 2 },
// {-4 , 11 , -2}, {2 , -2 , 8 } };
//runR(squere);
//int[] i = new int[] {};
// pokus();
// var matrix = getDataMatrix(directoryPath);
// runR();
//REngine.SetEnvironmentVariables();
//REngine engine = REngine.GetInstance();
//// REngine requires explicit initialization.
//// You can set some parameters.
//engine.Initialize();
//NumericMatrix group1 = engine.CreateNumericMatrix(new double[,] {
// { 0, 0, 0, 0, 1 },
// { 0, 0, 0, 1, 1 },
// { 0, 0, 1, 1, 1 },
// { 0, 0, 0, 1, 1 },
// { 0, 0, 0, 0, 1 }
//});
//engine.SetSymbol("group1", group1);
//GenericVector testResult = engine.Evaluate("eigen(group1)").AsList();
//Console.WriteLine("P-value = {0}", group1);
// //.NET Framework array to R vector.
//NumericVector group1 = engine.CreateNumericVector(new double[] { 30.02, 29.99, 30.11, 29.97, 30.01, 29.99 });
//engine.SetSymbol("group1", group1);
//// Direct parsing from R script.
//NumericVector group2 = engine.Evaluate("group2 <- c(29.89, 29.93, 29.72, 29.98, 30.02, 29.98)").AsNumeric();
//// Test difference of mean and get the P-value.
//GenericVector testResult = engine.Evaluate("t.test(group1, group2)").AsList();
//double p = testResult["p.value"].AsNumeric().First();
//Console.WriteLine("Group1: [{0}]", string.Join(", ", group1));
//Console.WriteLine("Group2: [{0}]", string.Join(", ", group2));
//Console.WriteLine("P-value = {0:0.000}", p);
//you should always dispose of the REngine properly.
// After disposing of the engine, you cannot reinitialize nor reuse it
// engine.Dispose();
Console.ReadLine();
}
public void initMesh(int JSteps, int NSteps)
{
N = NSteps;
J = JSteps;
k = taxis.spread / N;
h = xaxis.spread / J;
h2 = h * h;
// Other numbers
DN = N;
DJ = J;
DJJ = J * J;
// Allow range[ A, B ] in x direction and [ t1, T ] in t direction; create mesh arrays
m = new Mesher(xaxis.low, xaxis.high, taxis.low, taxis.high);
xarr = m.xarr(J);
tarr = m.yarr(N);
// The 3 data structures should be 'compatible' with each other, indices
vecOld = new Vector <double>(xarr.Size, xarr.MinIndex);
vecNew = new Vector <double>(xarr.Size, xarr.MinIndex);
res = new NumericMatrix <double>(tarr.Size, xarr.Size, tarr.MinIndex, xarr.MinIndex);
}
private void frmSaveESFFull_Load(object sender, EventArgs e)
{
try
{
frmProgress pfrmProgress = new frmProgress();
pfrmProgress.lblStatus.Text = "Creating EVs:";
pfrmProgress.pgbProgress.Style = ProgressBarStyle.Marquee;
pfrmProgress.Show();
m_pEngine.Evaluate("sample.n <- length(sample.nb)");
m_pEngine.Evaluate("sample.listb <- nb2listw(sample.nb, style='B')");
m_pEngine.Evaluate("B <- listw2mat(sample.listb); M <- diag(sample.n) - matrix(1/sample.n, sample.n, sample.n); MBM <- M%*%B%*%M");
m_pEngine.Evaluate("eig <- eigen(MBM)");
nmEVs = m_pEngine.Evaluate("eig$vectors").AsNumericMatrix();
cvEVName = m_pEngine.Evaluate("paste('EV', 1:sample.n, sep='')").AsCharacter();
nvEValue = m_pEngine.Evaluate("eig$values").AsNumeric();
intNEVs = nmEVs.RowCount;
for (int i = 0; i < intNEVs; i++)
{
string strItemName = cvEVName[i] + " (" + Math.Round(nvEValue[i], 3).ToString() + ")";
clistFields.Items.Add(strItemName);
}
//m_pEngine.Evaluate("rm(list = ls(all = TRUE))"); //Remove all items from memory.
pfrmProgress.Close();
}
catch (Exception ex)
{
frmErrorLog pfrmErrorLog = new frmErrorLog(); pfrmErrorLog.ex = ex; pfrmErrorLog.ShowDialog();
return;
}
}
public NumericMatrix <V> extract(SpreadSheetRange <AI1, AI2> range)
{
// Slice a matrix
AI1 rmin = range.upperLeft.first;
AI2 cmin = range.upperLeft.second;
AI1 rmax = range.lowerRight.first;
AI2 cmax = range.lowerRight.second;
int Rmin = r[rmin]; int Rmax = r[rmax];
int Cmin = c[cmin]; int Cmax = c[cmax];
// Now must find the integer indices corresponding to these
NumericMatrix <V> result = new NumericMatrix <V>(Rmax - Rmin + 1, Cmax - Cmin + 1, Rmin, Cmin);
for (int ri = Rmin; ri <= Rmax; ri++)
{
for (int ci = Cmin; ci <= Cmax; ci++)
{
result[ri, ci] = mat[ri, ci];
}
}
return(result);
}
private void ProblemViewModelChanged(object sender, System.ComponentModel.PropertyChangedEventArgs e)
{
if (e.PropertyName != nameof(ProblemViewModel.ProblemCalculationViewModel))
{
return;
}
_correspondenceMatrix = null;
_globalStiffnessMatrix = null;
_compactedMatrix = null;
_compactedForcesVector = null;
_solvedDisplacementsVector = null;
_fullSolvedDisplacementsVector = null;
_solvedReactionForces = null;
_canBeSolved = null;
OnPropertyChanged(nameof(CorrespondenceMatrix));
OnPropertyChanged(nameof(GlobalStiffnessMatrix));
OnPropertyChanged(nameof(CompactedMatrix));
OnPropertyChanged(nameof(CompactedForcesVector));
OnPropertyChanged(nameof(SolvedDisplacementsVector));
OnPropertyChanged(nameof(FullSolvedDisplacementsVector));
OnPropertyChanged(nameof(SolvedReactionForces));
OnPropertyChanged(nameof(CanBeSolved));
}
public static void Main()
{
// InstrumentFactory myFactory = new ConsoleInstrumentFactory(); //GetInstrumentFactory();
// TwoFactorOption myOption = myFactory.CreateOption();
int type = 1; // Call/put
double w1 = 1.0;
double w2 = -1.0;
double K = 5.0;
BasketStrategy myPayoff = new BasketStrategy(K, type, w1, w2);
Range <double> r1 = new Range <double>(0.0, 200.0);
int N1 = 20;
Range <double> r2 = new Range <double>(0.0, 200.0);
int N2 = 20;
// Calling extension methods
NumericMatrix <double> matrix = myPayoff.PayoffMatrix(r1, r2, N1, N2);
Console.WriteLine("Starting Excel, please wait..");
myPayoff.DisplayInExcel(r1, r2, N1, N2);
}
static void Main(string[] args)
{
//Mediator med = new Mediator();
//med.calculate();
//Console.ReadLine();
//convertible bond with option on equity basket chapter 7
Vector <double> pricesVector = new Vector <double>(new double[] { 1, 2 }, 0);
Vector <double> weightsVector = new Vector <double>(new double[] { 1, 2 }, 0);
Vector <double> volatilityVector = new Vector <double>(new double[] { 1, 2 }, 0);
NumericMatrix <double> correlationMatrix = new NumericMatrix <double>(2, 2, 0, 0);
correlationMatrix.setRow(new Array <double>(new double[] { 1, 2 }, 0), 0);
correlationMatrix.setRow(new Array <double>(new double[] { 1, 2 }, 0), 1);
Option o = new Option(OptionType.Put, 2, 2, 3, pricesVector, weightsVector, correlationMatrix, volatilityVector);
Console.WriteLine(o.Price());
//dual currency linked bonds with exchange option chapter 7
Option exchangeOption = new Option(OptionType.Call, 2, 2, 2, 3, 0.5, 0.4, 1);
Console.WriteLine(exchangeOption.Price());
Console.ReadLine();
}
public static void Main()
{
// Create and start the stopwatch
Stopwatch stopWatch = new Stopwatch();
stopWatch.Start();
int N = 10000;
int StartIndex = 1;
// Create a matrix
NumericMatrix <double> A = new NumericMatrix <double>(N, N, StartIndex, StartIndex);
for (int i = A.MinRowIndex; i <= A.MaxRowIndex; ++i)
{
for (int j = A.MinColumnIndex; j <= A.MaxColumnIndex; ++j)
{
A[i, j] = Math.Sqrt((double)(i + j));
}
A[i, i] = 4.0 * Math.Sqrt((double)(i + i));
}
stopWatch.Stop();
// Get the elapsed time as a TimeSpan value.
TimeSpan ts = stopWatch.Elapsed;
// Format and display the TimeSpan value.
string elapsedTime = String.Format("Elapsed time {0:00}:{1:00}:{2:00}.{3:00}",
ts.Hours, ts.Minutes, ts.Seconds,
ts.Milliseconds / 10);
Console.WriteLine(elapsedTime, "RunTime");
}
public void initMesh(int JSteps, int NSteps)
{
N = NSteps;
J = JSteps;
k = taxis.spread / N;
h = xaxis.spread / J;
h2 = h * h;
// Other numbers
DN = N;
DJ = J;
DJJ = J * J;
// Allow range[ A, B ] in x direction and [ t1, T ] in t direction; create mesh arrays
m = new Mesher(xaxis.low, xaxis.high, taxis.low, taxis.high);
xarr = m.xarr(J);
tarr = m.yarr(N);
tarr[tarr.MinIndex] = 0.0;
tarr[tarr.MaxIndex] = taxis.high;
for (int n = tarr.MinIndex + 1; n < tarr.MaxIndex - 1; n++)
{
tarr[n] = tarr[n - 1] + k;
}
// The 3 data structures should be 'compatible' with each other, indices
vecOld = new Vector <double>(J + 1, 1);
vecNew = new Vector <double>(J + 1, 1);
res = new NumericMatrix <double>(N + 1, J + 1, 1, 1);
}
//get the dataset/datatable of the selected ID
private DataTable DataFrameToDataTable()
{
string[] colnames = df.ColumnNames;
int ncol = colnames.Length;
int nrow = df.RowCount;
NumericMatrix dfm = Engine.GetSymbol("df").AsNumericMatrix();
//DataTable navdata = DataFrameToDataTable();
DataSet ds = new DataSet("DB");
DataTable dt = new DataTable("data");
for (int i = 0; i < ncol; i++)
{
dt.Columns.Add(colnames[i]);
}
for (int i = 0; i < nrow; i++)
{
for (int j = 0; j < ncol; j++)
{
dt.Rows.Add(dfm[i, j]);
}
}
ds.Tables.Add(dt);
return(dt);
//return ds;
}
/// <summary>
/// Run PCA in R engine
/// </summary>
static void PCAR()
{
const string directoryPath = @"C:\Users\Káťa\Desktop\Diplomka\mesh\meshes";
var matrix = loadFacesR(directoryPath);
REngine.SetEnvironmentVariables();
REngine engine = REngine.GetInstance();
NumericMatrix group1 = engine.CreateNumericMatrix(matrix);
engine.SetSymbol("group1", group1);
GenericVector testResult = engine.Evaluate("pr.out=prcomp(group1, scale=TRUE)").AsList();
var eigenVec = testResult["x"].AsNumericMatrix();
engine.Evaluate("pr.out$sdev");
engine.Evaluate("pr.var = pr.out$sdev ^ 2");
engine.Evaluate("pr.var");
engine.Evaluate("pve = pr.var / sum(pr.var)");
engine.Evaluate("pve");
engine.Evaluate("plot(cumsum(pve), xlab='Principal Component', ylab='Cumulative Proportion of Variance Explained', ylim=c(0,1),type='b')");
saveVector(eigenVec);
// var eigenVec = testResult["pr.out&x"].AsNumericMatrix();
Console.ReadLine();
}
public AssocMatrix()
{
mat = new NumericMatrix <V>(10, 10);
r = new AssocArray <AI1, int> ();
c = new AssocArray <AI2, int> ();
keyRows = new Set <AI1>();
keyColumns = new Set <AI2>();
}
// initialise data member
private void Ini(Vector <double> x1arr, Vector <double> x2arr, NumericMatrix <double> gridValues)
{
// Arrays must have the same size
x1Arr = x1arr;
x2Arr = x2arr;
gridVals = gridValues;
}
// Add Matrix to the spreadsheet with row and column labels.
public void AddMatrix <T>(string name, NumericMatrix <T> matrix, List <string> rowLabels, List <string> columnLabels)
{
try
{
// Check label count vs. matrix.
if (matrix.Columns != columnLabels.Count)
{
throw (new IndexOutOfRangeException("Count mismatch between # matrix columns and # column labels"));
}
if (matrix.Rows != rowLabels.Count)
{
throw (new IndexOutOfRangeException("Count mismatch between # matrix rows and # row labels"));
}
// Add sheet.
Excel.Workbook pWorkbook;
Excel.Worksheet pSheet;
if (pExcel.ActiveWorkbook == null)
{
pWorkbook = (Excel.Workbook)InvokeMethodInternational(pExcel.Workbooks, "Add", Excel.XlWBATemplate.xlWBATWorksheet);
pSheet = (Excel.Worksheet)pWorkbook.ActiveSheet;
}
else
{
pWorkbook = pExcel.ActiveWorkbook;
pSheet = (Excel.Worksheet)InvokeMethodInternational(pWorkbook.Worksheets, "Add", Type.Missing, Type.Missing, 1, Type.Missing);
}
pSheet.Name = name;
// Current indeces in spreadsheet.
long sheetRow = 1;
long sheetColumn = 1;
// Add column labels starting at column 2.
sheetColumn = 2;
Excel.Range pRange = pSheet.Cells;
for (int i = 0; i != columnLabels.Count; i++)
{
Excel.Range item = (Excel.Range)pSheet.Cells[sheetRow, sheetColumn];
SetPropertyInternational(item, "Value2", columnLabels[i]);
sheetColumn++;
}
// Add row labels + values.
sheetColumn = 1;
sheetRow = 2;
for (int i = matrix.MinRowIndex; i <= matrix.MaxRowIndex; i++)
{
Vector <T> row = createVector(matrix, i);
ToSheetHorizontal(pSheet, sheetRow, sheetColumn, rowLabels[i - matrix.MinRowIndex], row);
sheetRow++;
}
}
catch (IndexOutOfRangeException e)
{
Console.WriteLine("Exception: " + e);
}
}
public AssocMatrix(AssocMatrix <AI1, AI2, V> mat2)
{
mat = new NumericMatrix <V>(mat2.mat);
r = new AssocArray <AI1, int>(mat2.r);
c = new AssocArray <AI2, int>(mat2.c);
keyRows = new Set <AI1>(mat2.keyRows);
keyColumns = new Set <AI2>(mat2.keyColumns);
}
//Default constructor everything starts at 1 and has length 1
public Tensor()
{
m_tensor = new Array <NumericMatrix <T> >(1, 1);
m_tensor[1] = new NumericMatrix <T>(1, 1, 1, 1);
m_rows = 1;
m_columns = 1;
m_depth = 1;
}
public Worker(NumericMatrix <double> A, int lower, int upper, double d,
int nsimulations)
{
m_l = lower;
m_u = upper;
m_A = A;
m_d = d;
NSIM = nsimulations;
}
public RecursiveDataStructure(NumericMatrix <double> mat1, NumericMatrix <double> mat2, NumericMatrix <double> mat3)
{
m1 = mat1;
m2 = mat2;
m3 = mat3;
lock1 = new object();
lock2 = new object();
}
static double CreateMatrix(object val)
{
const int N = 3000;
const int StartIndex = 0;
NumericMatrix <double> mat1 = new NumericMatrix <double>(N, N, StartIndex, StartIndex);
mat1[mat1.MinRowIndex, mat1.MinColumnIndex] = Convert.ToDouble(val);
return(mat1[mat1.MinRowIndex, mat1.MinColumnIndex]);
}
// take only 2° and 3° column fromDate and toDate of matrix
public NumericMatrix <Date> GetShortScheduleDate()
{
Array <Date> myFromDateArray = this.FromDateArray();
Array <Date> myToDateArray = this.ToDateArray();
NumericMatrix <Date> myOutMatrix = new NumericMatrix <Date>(myFromDateArray.Length, 2, 0, 0);
myOutMatrix.Column(0, myFromDateArray);
myOutMatrix.Column(1, myToDateArray);
return(myOutMatrix);
}
//constructor for option on the equity basket
public Option(OptionType optionType, double expiry, double strike, double interest, Vector <double> pricesVector,
Vector <double> weightsVector, NumericMatrix <double> correlationMatrix, Vector <double> volatilityVector)
{
_type = optionType;
_t = expiry;
_k = strike;
_b = 0;
_r = interest;
_sig = VolatilityConvertibleBond(pricesVector, weightsVector, correlationMatrix, volatilityVector);
_s = expiry;
_underlyingPrice = M(pricesVector, weightsVector, correlationMatrix, volatilityVector).M1;
}
public static void Main()
{
// 1. Create the option (Factory Method pattern)
Option myOption = CreateOption();
// 2. Define the pde of concern (Bridge pattern)
IIBVPImp pde = new BSIBVPImp(myOption);
// 3. Discrete mesh sizes.
int J = 325;
int N = J;
Console.Write("NS: ");
J = Convert.ToInt32(Console.ReadLine());
Console.Write("NT (NT ~ O(NS^2 for explicit, NT any value for implicit): ");
N = Convert.ToInt32(Console.ReadLine());
// 4. The domain in which the PDE is defined.
Range <double> rangeX = new Range <double>(0.0, myOption.FarFieldCondition);
Range <double> rangeT = new Range <double>(0.0, myOption.ExpiryDate);
// 5. Create FDM Solver.
Console.Write("Implicit [1] or Explicit Euler [2]: ");
int choice = Convert.ToInt32(Console.ReadLine());
IBVPFDM fdm = new ImplicitEulerIBVP(pde, rangeX, rangeT, J, N);
if (choice != 1)
{
fdm = new ExplicitEulerIBVP(pde, rangeX, rangeT, J, N);
}
// 6. Calculate the matrix result.
NumericMatrix <double> sol = fdm.result();
// 7. Display the results in Excel.
ExcelMechanisms exl = new ExcelMechanisms();
try
{
exl.printOneExcel(fdm.XValues, fdm.vecNew, "Euler method", "Stock", "Value", "V");
}
catch (Exception e)
{
Console.WriteLine(e);
}
}
//Constructor with the ammount of rows, columns and depth, all startindexes are 1.
public Tensor(int rows, int columns, int nthird)
{
m_tensor = new Array <NumericMatrix <T> >(nthird, 1);
for (int i = m_tensor.MinIndex; i <= m_tensor.MaxIndex; i++)
{
m_tensor[i] = new NumericMatrix <T>(rows, columns, 1, 1);
}
m_rows = rows;
m_columns = columns;
m_depth = nthird;
}
public List <Tuple <int, int> > GetPairings(int noScripts, int noPairings, string rScript)
{
REngineClass.GetREngine().Evaluate(@"source('REngine\\RScripts\\Generate\\" + rScript + "')");
NumericMatrix matrix = REngineClass.GetREngine().Evaluate(string.Format("matrix <- generatePairings(noOfScripts = {0}, noOfPairings = {1})", noScripts, noPairings)).AsNumericMatrix();
List <Tuple <int, int> > pairings = new List <Tuple <int, int> >();
for (int i = 0; i < matrix.RowCount; i++)
{
pairings.Add(new Tuple <int, int>((int)matrix[i, 0], (int)matrix[i, 1]));
}
return(pairings);
}
//Constructor with all sizes and startindexes
public Tensor(int rows, int columns, int nthird, int rowStart, int columnStart, int thirdStart)
{
m_tensor = new Array <NumericMatrix <T> >(nthird, thirdStart); // Size nthird, start index
for (int i = m_tensor.MinIndex; i <= m_tensor.MaxIndex; i++)
{
m_tensor[i] = new NumericMatrix <T>(rows, columns, rowStart, columnStart);
}
m_rows = rows;
m_columns = columns;
m_depth = nthird;
}
public static NumericMatrix <double> Exp <T>(this NumericMatrix <T> m) where T : IConvertible
{
NumericMatrix <double> expMatrix = new NumericMatrix <double>(m.Rows, m.Columns, 0, 0);
for (int i = 0; i < m.Rows; i++)
{
for (int j = 0; j < m.Columns; j++)
{
expMatrix[i, j] = Math.Exp(Convert.ToDouble(m[i, j]));
}
}
return(expMatrix);
}
public static int Main(string[] args)
{
var a = new NumericMatrix<int>(3, 3);
a[0][0] = 11;
a[0][1] = 12;
a[0][2] = 13;
a[1][0] = 21;
a[1][1] = 22;
a[1][2] = 23;
a[2][0] = 31;
a[2][1] = 32;
a[2][2] = 33;
var b = new NumericMatrix<int>(5, 3);
b[0][0] = 11;
b[0][1] = 12;
b[0][2] = 13;
b[1][0] = 21;
b[1][1] = 22;
b[1][2] = 23;
b[2][0] = 31;
b[2][1] = 32;
b[2][2] = 33;
b[3][0] = 41;
b[3][1] = 42;
b[3][2] = 43;
b[4][0] = 51;
b[4][1] = 52;
b[4][2] = 53;
Console.WriteLine("a: {0}\nb: {1}", a, b);
Console.WriteLine("a.b is okay as of a: {0}", a.IsCompatibleBeingLeft(b));
Console.WriteLine("b.a is okay as of a: {0}", a.IsCompatibleBeingRight(b));
Console.WriteLine("a.b is okay as of b: {0}", b.IsCompatibleBeingRight(a));
Console.WriteLine("b.a is okay as of b: {0}", b.IsCompatibleBeingLeft(a));
var ba = b.MultiplyBy(a);
Console.WriteLine("b.a: {0}", ba);
Console.WriteLine("b.a^t: {0}", ba.Transposed() );
return 0;
}
public static void Example10()
{
// I will pay 5% quarterly 30/360 for 5y starting from 5 Oct 09 on a nominal of 1.000.000 USD. I want to see
// interest rates cash flows
// input data
Date startDate = new Date(2009, 10, 5);
Date endDate = startDate.AddYears(5);
int paymentPerYear = 4; // Quarterly
bool firstShortPeriod = true;
double rate = 0.05;
// I use standard constructor
DateSchedule myDS = new DateSchedule(startDate, endDate, paymentPerYear, firstShortPeriod);
// I get start and end date schedule
NumericMatrix <Date> myShortSchedule = myDS.GetShortScheduleDate();
// my nominals
Array <double> nominals = new Array <double>(myShortSchedule.Rows, 0, 1000000);
// year fraction from/to according 30/360
Array <double> yearFractions = new Array <double>(myShortSchedule.Rows, 0);
// days from/to according 30/360
Array <double> days = new Array <double>(myShortSchedule.Rows, 0);
// interest to pay
Array <double> interest = new Array <double>(myShortSchedule.Rows, 0);
Console.WriteLine("{0},\t{1},\t{2},\t{3},\t{4}", "days", "yearFrac", "rate",
"nominals", "interest");
// running sum
double totalInterest = 0.0;
// iterate interest calculation using schedule
for (int i = 0; i < myShortSchedule.Rows; i++)
{
yearFractions[i] = myShortSchedule[i, 0].YF_30_360(myShortSchedule[i, 1]);
days[i] = myShortSchedule[i, 0].D_30_360(myShortSchedule[i, 1]);
interest[i] = yearFractions[i] * rate * nominals[i];
Console.WriteLine("{0},\t{1:F5},\t{2:P},\t{3:C},\t{4:C}", days[i], yearFractions[i], rate,
nominals[i], interest[i]);
totalInterest += interest[i];
}
Console.WriteLine("{0},\t{1:F2}", "Total interests: ", totalInterest);
}
public static void Main()
{
AssocArray <int, double> assArr = new AssocArray <int, double> ();
assArr[0] = 2.0;
assArr[100] = 3.0;
assArr[10] = 43.0;
assArr[1000] = 34.0;
Console.WriteLine(assArr[100]);
foreach (KeyValuePair <int, double> kvp in assArr)
{
Console.WriteLine("{0}, {1}", kvp.Key, kvp.Value);
}
Set <string> RowNames = new Set <string>();
RowNames.Insert("A1");
RowNames.Insert("A2");
RowNames.Insert("A3");
RowNames.Insert("A4");
RowNames.Insert("B1");
Set <string> ColNames = new Set <string>();
ColNames.Insert("C1");
ColNames.Insert("C2");
ColNames.Insert("C3");
ColNames.Insert("C4");
ColNames.Insert("C5");
//double defaultValue = 10.0;
// Contents of associative matrix (numeric values)
NumericMatrix <double> mat1 = new NumericMatrix <double>(RowNames.Size(), ColNames.Size());
mat1.initCells(3.0);
AssocMatrix <string, string, double> myMat = new AssocMatrix <string, string, double>(RowNames, ColNames, mat1);
Factory fs = new DoubleFactory();
object b;
b = fs.create();
Set <double> s1 = (Set <double>)b;
Console.WriteLine(s1.Size());
}
public static int Main(string[] args)
{
var a = new NumericMatrix<int>(3, 3);
a[0][0] = 101;
a[0][1] = -102;
a[0][2] = 103;
a[1][0] = 201;
a[1][1] = 202;
a[1][2] = 203;
a[2][0] = 301;
a[2][1] = -302;
a[2][2] = 303;
Console.WriteLine("a: {0}; |a|: {1}", a, a.Det());
var b = a.Transposed().MultiplyBy(2);
Console.WriteLine("b: {0}; |b|: {1}", b, b.Det());
return 0;
}
public static int Main(string[] args)
{
var a = new NumericMatrix<double>(2, 3);
a[0][0] = 1.5;
a[0][1] = 2.5;
a[0][2] = 3.5;
a[1][0] = 3.5;
a[1][1] = 2.5;
a[1][2] = 1.5;
var b = new NumericMatrix<double>(3, 4);
b[0][0] = 1.5;
b[0][1] = 2.5;
b[0][2] = 3.5;
b[0][3] = 4.5;
b[1][0] = 4.5;
b[1][1] = 3.5;
b[1][2] = 2.5;
b[1][3] = 1.5;
b[2][0] = 5.5;
b[2][1] = 6.5;
b[2][2] = 7.5;
b[2][3] = 8.5;
Console.WriteLine("a: {0}\nb: {1}", a, b);
Console.WriteLine("a.b is okay as of a: {0}", a.IsCompatibleBeingLeft(b));
Console.WriteLine("b.a is okay as of a: {0}", a.IsCompatibleBeingRight(b));
Console.WriteLine("a.b is okay as of b: {0}", b.IsCompatibleBeingRight(a));
Console.WriteLine("b.a is okay as of b: {0}", b.IsCompatibleBeingLeft(a));
var ab = a.MultiplyBy(b);
Console.WriteLine("a.b: {0}", ab );
var abt = a.Transposed();
Console.WriteLine("a.b^t: {0}", abt );
return 0;
}
public cacheSolve(NumericMatrix matrix)
{
data = matrix;
}
public makeCacheMatrix(NumericMatrix Martix)
{
_matrix = Matrix;
}
/// <summary>
/// Converts the specified expression to a NumericMatrix.
/// </summary>
/// <param name="expression">The expression.</param>
/// <returns>The NumericMatrix. Returns <c>null</c> if the specified expression is not vector.</returns>
public static NumericMatrix AsNumericMatrix(this SymbolicExpression expression)
{
if (!expression.IsVector())
{
return null;
}
int rowCount = 0;
int columnCount = 0;
if (expression.IsMatrix())
{
if (expression.Type == SymbolicExpressionType.NumericVector)
{
return new NumericMatrix(expression.Engine, expression.DangerousGetHandle());
}
else
{
rowCount = expression.GetFunction<Rf_nrows>()(expression.DangerousGetHandle());
columnCount = expression.GetFunction<Rf_ncols>()(expression.DangerousGetHandle());
}
}
if (columnCount == 0)
{
rowCount = expression.GetFunction<Rf_length>()(expression.DangerousGetHandle());
columnCount = 1;
}
IntPtr coerced = expression.GetFunction<Rf_coerceVector>()(expression.DangerousGetHandle(), SymbolicExpressionType.NumericVector);
var dim = new IntegerVector(expression.Engine, new[] { rowCount, columnCount });
SymbolicExpression dimSymbol = expression.Engine.GetPredefinedSymbol("R_DimSymbol");
var matrix = new NumericMatrix(expression.Engine, coerced);
matrix.SetAttribute(dimSymbol, dim);
return matrix;
}
private Tuple<NumericVector, NumericVector> cluster(NumericMatrix matrix)
{
RConnector.Engine.SetSymbol("dataMatrix", matrix);
if (0 == this.clusters.Value)
{
RConnector.Engine.Evaluate("dataCluster<-Mclust(dataMatrix)");
var clusterAssignment = RConnector.Engine.Evaluate("dataCluster$classification").AsNumeric();
return Tuple.Create<NumericVector, NumericVector>(clusterAssignment, null);
}
else
{
RConnector.Engine.Evaluate("dataMeans<-kmeans(dataMatrix," + this.clusters.Value + ")");
var clusterAssignment = RConnector.Engine.Evaluate("dataMeans$cluster").AsNumeric();
var clusterInfo = RConnector.Engine.Evaluate("dataMeans$withinss").AsNumeric();
return Tuple.Create(clusterAssignment, clusterInfo);
}
}