public static VectorR Transform(VectorR v, MatrixR m)
{
VectorR result = new VectorR(v.GetSize());
if (!m.IsSquared())
{
throw new ArgumentOutOfRangeException(
"Dimension", m.GetRows(), "The matrix must be squared!");
}
if (m.GetRows() != v.GetSize())
{
throw new ArgumentOutOfRangeException(
"Size", v.GetSize(), "The size of the vector must be equal"
+ "to the number of rows of the matrix!");
}
for (int i = 0; i < m.GetRows(); i++)
{
result[i] = 0.0;
for (int j = 0; j < m.GetCols(); j++)
{
result[i] += v[j] * m[j, i];
}
}
return(result);
}
public VectorR GetUnitVector()
{
VectorR result = new VectorR(vector);
result.Normalize();
return(result);
}
public VectorR Clone()
{
// returns a deep copy of the vector
VectorR v = new VectorR(vector);
v.vector = (double[])vector.Clone();
return(v);
}
public static double DotProduct(VectorR v1, VectorR v2)
{
double result = 0.0;
for (int i = 0; i < v1.size; i++)
{
result += v1[i] * v2[i];
}
return(result);
}
public static VectorR operator /(double d, VectorR v)
{
VectorR result = new VectorR(v.size);
for (int i = 0; i < v.size; i++)
{
result[i] = d / v[i];
}
return(result);
}
public static VectorR operator /(VectorR v, double d)
{
VectorR result = new VectorR(v.size);
for (int i = 0; i < v.size; i++)
{
result[i] = v[i] / d;
}
return(result);
}
public static VectorR operator -(VectorR v1, VectorR v2)
{
VectorR result = new VectorR(v1.size);
for (int i = 0; i < v1.size; i++)
{
result[i] = v1[i] - v2[i];
}
return(result);
}
public VectorR GetColVector(int n)
{
if (n < 0 || n > Cols)
{
throw new ArgumentOutOfRangeException(
"n", n, "n is out of range!");
}
VectorR v = new VectorR(Rows);
for (int i = 0; i < Rows; i++)
{
v[i] = matrix[i, n];
}
return(v);
}
public static VectorR LinearRegression(double[] xarray, double[] yarray, ModelFunction[] f, out double sigma)
{
int m = f.Length;
MatrixR A = new MatrixR(m, m);
VectorR b = new VectorR(m);
int n = xarray.Length;
for (int k = 0; k < m; k++)
{
b[k] = 0.0;
for (int i = 0; i < n; i++)
{
b[k] += f[k](xarray[i]) * yarray[i];
}
}
for (int j = 0; j < m; j++)
{
for (int k = 0; k < m; k++)
{
A[j, k] = 0.0;
for (int i = 0; i < n; i++)
{
A[j, k] += f[j](xarray[i]) * f[k](xarray[i]);
}
}
}
//LinearSystem ls = new LinearSystem();
//VectorR coef = ls.GaussJordan(A, b);
VectorR coef = GaussJordan(A, b);
// Calculate the standard deviation:
double s = 0.0;
for (int i = 0; i < n; i++)
{
double s1 = 0.0;
for (int j = 0; j < m; j++)
{
s1 += coef[j] * f[j](xarray[i]);
}
s += (yarray[i] - s1) * (yarray[i] - s1);
}
sigma = Math.Sqrt(s / (n - m));
return(coef);
}
public static VectorR PolynomialFit(double[] xarray, double[] yarray, int m, out double sigma)
{
m++;
MatrixR A = new MatrixR(m, m);
VectorR b = new VectorR(m);
int n = xarray.Length;
for (int k = 0; k < m; k++)
{
b[k] = 0.0;
for (int i = 0; i < n; i++)
{
b[k] += Math.Pow(xarray[i], k) * yarray[i];
}
}
for (int j = 0; j < m; j++)
{
for (int k = 0; k < m; k++)
{
A[j, k] = 0.0;
for (int i = 0; i < n; i++)
{
A[j, k] += Math.Pow(xarray[i], j + k);
}
}
}
VectorR coef = GaussJordan(A, b);
// Calculate the standard deviation:
double s = 0.0;
for (int i = 0; i < n; i++)
{
double s1 = 0.0;
for (int j = 0; j < m; j++)
{
s1 += coef[j] * Math.Pow(xarray[i], j);
}
s += (yarray[i] - s1) * (yarray[i] - s1);
}
sigma = Math.Sqrt(s / (n - m));
return(coef);
}
public static VectorR GaussJordan(MatrixR A, VectorR b)
{
Triangulate(A, b);
int n = b.GetSize();
VectorR x = new VectorR(n);
for (int i = n - 1; i >= 0; i--)
{
double d = A[i, i];
if (Math.Abs(d) < 1.0e-500)
{
throw new ArgumentException("Diagonal element is too small!");
}
x[i] = (b[i] - VectorR.DotProduct(A.GetRowVector(i), x)) / d;
}
return(x);
}
public MatrixR ReplaceCol(VectorR v, int n)
{
if (n < 0 || n > Cols)
{
throw new ArgumentOutOfRangeException(
"n", n, "n is out of range!");
}
if (v.GetSize() != Rows)
{
throw new ArgumentOutOfRangeException(
"Vector size", v.GetSize(), "vector size is out of range!");
}
for (int i = 0; i < Rows; i++)
{
matrix[i, n] = v[i];
}
return(new MatrixR(matrix));
}
public static MatrixR Transform(VectorR v1, VectorR v2)
{
if (v1.GetSize() != v2.GetSize())
{
throw new ArgumentOutOfRangeException(
"v1", v1.GetSize(), "The vectors must have the same size!");
}
MatrixR result = new MatrixR(v1.GetSize(), v1.GetSize());
for (int i = 0; i < v1.GetSize(); i++)
{
for (int j = 0; j < v1.GetSize(); j++)
{
result[j, i] = v1[i] * v2[j];
}
}
return(result);
}
public static VectorR TriVectorProduct(VectorR v1, VectorR v2, VectorR v3)
{
if (v1.size != 3)
{
throw new ArgumentOutOfRangeException(
"v1", v1, "Vector v1 must be 3 dimensional!");
}
if (v1.size != 3)
{
throw new ArgumentOutOfRangeException(
"v2", v2, "Vector v2 must be 3 dimensional!");
}
if (v1.size != 3)
{
throw new ArgumentOutOfRangeException(
"v3", v3, "Vector v3 must be 3 dimensional!");
}
return(v2 * VectorR.DotProduct(v1, v3) - v3 * VectorR.DotProduct(v1, v2));
}
public static VectorR CrossProduct(VectorR v1, VectorR v2)
{
if (v1.size != 3)
{
throw new ArgumentOutOfRangeException(
"v1", v1, "Vector v1 must be 3 dimensional!");
}
if (v2.size != 3)
{
throw new ArgumentOutOfRangeException(
"v2", v2, "Vector v2 must be 3 dimensional!");
}
VectorR result = new VectorR(3);
result[0] = v1[1] * v2[2] - v1[2] * v2[1];
result[1] = v1[2] * v2[0] - v1[0] * v2[2];
result[2] = v1[0] * v2[1] - v1[1] * v2[0];
return(result);
}
private static double Pivot(MatrixR A, VectorR b, int q)
{
int n = b.GetSize();
int i = q;
double d = 0.0;
for (int j = q; j < n; j++)
{
double dd = Math.Abs(A[j, q]);
if (dd > d)
{
d = dd;
i = j;
}
}
if (i > q)
{
A.GetRowSwap(q, i);
b.GetSwap(q, i);
}
return(A[q, q]);
}
public static MatrixR operator *(MatrixR m1, MatrixR m2)
{
if (m1.GetCols() != m2.GetRows())
{
throw new ArgumentOutOfRangeException(
"Columns", m1, "The numbers of columns of the first matrix must be equal to" +
" the number of rows of the second matrix!");
}
MatrixR result = new MatrixR(m1.GetRows(), m2.GetCols());
VectorR v1 = new VectorR(m1.GetCols());
VectorR v2 = new VectorR(m2.GetRows());
for (int i = 0; i < m1.GetRows(); i++)
{
v1 = m1.GetRowVector(i);
for (int j = 0; j < m2.GetCols(); j++)
{
v2 = m2.GetColVector(j);
result[i, j] = VectorR.DotProduct(v1, v2);
}
}
return(result);
}
private static void Triangulate(MatrixR A, VectorR b)
{
int n = A.GetRows();
VectorR v = new VectorR(n);
for (int i = 0; i < n - 1; i++)
{
double d = Pivot(A, b, i);
if (Math.Abs(d) < 1.0e-500)
{
throw new ArgumentException("Diagonal element is too small!");
}
for (int j = i + 1; j < n; j++)
{
double dd = A[j, i] / d;
for (int k = i + 1; k < n; k++)
{
A[j, k] -= dd * A[i, k];
}
b[j] -= dd * b[i];
}
}
}
public static double TriScalarProduct(VectorR v1, VectorR v2, VectorR v3)
{
if (v1.size != 3)
{
throw new ArgumentOutOfRangeException(
"v1", v1, "Vector v1 must be 3 dimensional!");
}
if (v1.size != 3)
{
throw new ArgumentOutOfRangeException(
"v2", v2, "Vector v2 must be 3 dimensional!");
}
if (v1.size != 3)
{
throw new ArgumentOutOfRangeException(
"v3", v3, "Vector v3 must be 3 dimensional!");
}
double result = v1[0] * (v2[1] * v3[2] - v2[2] * v3[1]) +
v1[1] * (v2[2] * v3[0] - v2[0] * v3[2]) +
v1[2] * (v2[0] * v3[1] - v2[1] * v3[0]);
return(result);
}
private void AddData()
{
double[] x0 = new double[] { 1, 2, 3, 4, 5 };
double[] y0 = new double[] { 5.5, 43.1, 128, 290.7, 498.4 };
VectorR[] results = new VectorR[3];
for (int i = 0; i < results.Length; i++)
{
double sigma = 0;
results[i] = CurveFittingAlgorithms.PolynomialFit(x0, y0, i + 1, out sigma);
}
// Plot results:
myChart.DataCollection.DataList.Clear();
LineCharts.DataSeries ds;
// Plot original data:
ds = new LineCharts.DataSeries();
ds.LineColor = Brushes.Transparent;
ds.SeriesName = "Original";
ds.Symbols.SymbolType = LineCharts.Symbols.SymbolTypeEnum.Triangle;
ds.Symbols.BorderColor = Brushes.Black;
for (int i = 0; i < x0.Length; i++)
{
ds.LineSeries.Points.Add(new Point(x0[i], y0[i]));
}
myChart.DataCollection.DataList.Add(ds);
// 1st order fitting data:
ds = new LineCharts.DataSeries();
ds.LineColor = Brushes.DarkGreen;
ds.LineThickness = 2;
ds.SeriesName = "1st Order Fitting";
for (int i = 0; i < 141; i++)
{
double x = -1.0 + i / 20.0;
double y = results[0][0] + results[0][1] * x;
ds.LineSeries.Points.Add(new Point(x, y));
}
myChart.DataCollection.DataList.Add(ds);
// 2nd order fitting data:
ds = new LineCharts.DataSeries();
ds.LineColor = Brushes.Red;
ds.LineThickness = 2;
ds.LinePattern = LineCharts.DataSeries.LinePatternEnum.Dash;
ds.SeriesName = "2nd Order Fitting";
for (int i = 0; i < 141; i++)
{
double x = -1.0 + i / 20.0;
double y = results[1][0] + results[1][1] * x + results[1][2] * x * x;
ds.LineSeries.Points.Add(new Point(x, y));
}
myChart.DataCollection.DataList.Add(ds);
// 3rd order fitting data:
ds = new LineCharts.DataSeries();
ds.LineColor = Brushes.DarkBlue;
ds.LineThickness = 2;
ds.LinePattern = LineCharts.DataSeries.LinePatternEnum.DashDot;
ds.SeriesName = "3rd Order Fitting";
for (int i = 0; i < 141; i++)
{
double x = -1.0 + i / 20.0;
double y = results[2][0] + results[2][1] * x + results[2][2] * x * x + results[2][3] * x * x * x;
ds.LineSeries.Points.Add(new Point(x, y));
}
myChart.DataCollection.DataList.Add(ds);
myChart.IsLegend = true;
myChart.LegendPosition = LineCharts.Legend.LegendPositionEnum.NorthWest;
}
private void AddData()
{
double[] x0 = new double[] { 0, 1, 2, 3, 4, 5 };
double[] y0 = new double[] { 2, 1, 4, 4, 3, 2 };
// First order polynormial (m = 1):
CurveFittingAlgorithms.ModelFunction[] f = new CurveFittingAlgorithms.ModelFunction[] { f0, f1 };
double sigma = 0.0;
VectorR results1 = CurveFittingAlgorithms.LinearRegression(x0, y0, f, out sigma);
// Second order polynormial (m = 2):
f = new CurveFittingAlgorithms.ModelFunction[] { f0, f1, f2 };
VectorR results2 = CurveFittingAlgorithms.LinearRegression(x0, y0, f, out sigma);
// Third order polynormial (m = 3):
f = new CurveFittingAlgorithms.ModelFunction[] { f0, f1, f2, f3 };
VectorR results3 = CurveFittingAlgorithms.LinearRegression(x0, y0, f, out sigma);
// Plot results:
myChart.DataCollection.DataList.Clear();
LineCharts.DataSeries ds;
// Plot original data:
ds = new LineCharts.DataSeries();
ds.LineColor = Brushes.Transparent;
ds.SeriesName = "Original";
ds.Symbols.SymbolType = LineCharts.Symbols.SymbolTypeEnum.Triangle;
ds.Symbols.BorderColor = Brushes.Black;
for (int i = 0; i < x0.Length; i++)
{
ds.LineSeries.Points.Add(new Point(x0[i], y0[i]));
}
myChart.DataCollection.DataList.Add(ds);
// 1st order fitting data:
ds = new LineCharts.DataSeries();
ds.LineColor = Brushes.DarkGreen;
ds.LineThickness = 2;
ds.SeriesName = "1st Order Fitting";
for (int i = 0; i < 141; i++)
{
double x = -1.0 + i / 20.0;
double y = results1[0] + results1[1] * x;
ds.LineSeries.Points.Add(new Point(x, y));
}
myChart.DataCollection.DataList.Add(ds);
// 2nd order fitting data:
ds = new LineCharts.DataSeries();
ds.LineColor = Brushes.Red;
ds.LineThickness = 2;
ds.LinePattern = LineCharts.DataSeries.LinePatternEnum.Dash;
ds.SeriesName = "2nd Order Fitting";
for (int i = 0; i < 141; i++)
{
double x = -1.0 + i / 20.0;
double y = results2[0] + results2[1] * x + results2[2] * x * x;
ds.LineSeries.Points.Add(new Point(x, y));
}
myChart.DataCollection.DataList.Add(ds);
// 3rd order fitting data:
ds = new LineCharts.DataSeries();
ds.LineColor = Brushes.DarkBlue;
ds.LineThickness = 2;
ds.LinePattern = LineCharts.DataSeries.LinePatternEnum.DashDot;
ds.SeriesName = "3rd Order Fitting";
for (int i = 0; i < 141; i++)
{
double x = -1.0 + i / 20.0;
double y = results3[0] + results3[1] * x + results3[2] * x * x + results3[3] * x * x * x;
ds.LineSeries.Points.Add(new Point(x, y));
}
myChart.DataCollection.DataList.Add(ds);
myChart.IsLegend = true;
myChart.LegendPosition = LineCharts.Legend.LegendPositionEnum.NorthWest;
}
public bool Equals(VectorR v)
{
return(vector == v.vector);
}