public static void TestHarness_LOOP()
{
RandomAugmented random = RandomAugmented.getSeededRandomAugmented();
ArrayList pool1 = new ArrayList();
ArrayList pool2 = new ArrayList();
for (int i = 0; i < 10; ++i)
{
pool1.Add(new Point2D(random.NextDouble(), random.NextDouble()));
pool2.Add(new Point2D(random.NextDouble(), random.NextDouble()));
}
double m, c;
findHull(pool1, pool2, out m, out c);
ArrayList hull = new ArrayList();
hull.Add(new Point2D(0, c)); hull.Add(new Point2D(1, m + c));
GenericChartForm charts = new GenericChartForm();
charts.setChartCounts(1, 1);
charts[0].addSeries(new Series("Pool1", ChartType.Point, pool1));
charts[0].addSeries(new Series("Pool2", ChartType.Point, pool2));
charts[0].addSeries(new Series("Hull", ChartType.Line, hull));
charts[0].Refresh();
charts.ShowDialog();
}
public static Color getRandomColour(RandomAugmented random)
{
double r = random.NextDouble();
double g = random.NextDouble();
double b = random.NextDouble();
return(Color.FromArgb((int)(255 * r), (int)(255 * g), (int)(255 * b)));
}
public static void getRandomBrilliantColour(RandomAugmented random, out double r, out double g, out double b)
{
r = g = b = 0.5;
while
(
(isValueNearBottom(r) && isValueNearBottom(g) && isValueNearBottom(b)) ||
(isValueNearMiddle(r) && isValueNearMiddle(g)) || (isValueNearMiddle(r) && isValueNearMiddle(b)) || (isValueNearMiddle(g) && isValueNearMiddle(b))
)
{
r = random.NextDouble();
g = random.NextDouble();
b = random.NextDouble();
}
}
public static void TestHarness()
{
RandomAugmented ra = RandomAugmented.getSeededRandomAugmented();
int N = 4;
// Create a matrix
Matrix m = new Matrix(N, N);
for (int i = 0; i < N; ++i)
{
for (int j = i; j < N; ++j)
{
m[i, j] = ra.NextDouble(10);
m[j, i] = m[i, j];
}
}
// Space for results
Matrix eigenvectors = new Matrix(N, N);
Vector eigenvalues = new Vector(N);
// Get values
calculateEigensystem(m, eigenvectors, eigenvalues);
sortEigensystem(eigenvectors, eigenvalues);
Console.WriteLine("Eigenvalues");
Console.WriteLine(eigenvalues);
Console.WriteLine("Eigenvectors");
Console.WriteLine(eigenvectors);
// Test each eigenpair
Vector eigenvector = new Vector(N);
for (int i = 0; i < N; ++i)
{
eigenvectors.getColumn(i, eigenvector);
Console.WriteLine("Eigenpair {0}", i);
Console.WriteLine(m.multiply(eigenvector));
Console.WriteLine(eigenvector.multiply(eigenvalues[i]));
}
}
public double getProb(Vector a, Vector b, double alpha, double epsilon, int N_max)
{
// A neat reference to match the document
int m = correlation.rows;
Matrix c = correlation_root;
// Check that we are not in univariate land....
if (1 == m)
{
return(Distributions.CDistStandardNormal(b[0]) - Distributions.CDistStandardNormal(a[0]));
}
// Our variables
double[] d = new double[m];
double[] e = new double[m];
double[] f = new double[m];
double[] y = new double[m];
double[] w = new double[m];
// Initialisation code
double intsum = 0.0;
int N = 0;
double varsum = 0.0;
d[0] = Distributions.CDistStandardNormal(a[0] / c[0, 0]);
e[0] = Distributions.CDistStandardNormal(b[0] / c[0, 0]);
f[0] = e[0] - d[0];
double error = 0.0;
do
{
for (int i = 0; i < m - 1; ++i)
{
w[i] = random.NextDouble();
}
for (int i = 1; i < m; ++i)
{
y[i - 1] = Distributions.InvDist_SN(d[i - 1] + w[i - 1] * (e[i - 1] - d[i - 1]));
double sum_cij_yj = 0.0;
for (int j = 0; j <= i - 1; ++j)
{
sum_cij_yj += c[i, j] * y[j];
}
d[i] = Distributions.CDistStandardNormal((a[i] - sum_cij_yj) / c[i, i]);
e[i] = Distributions.CDistStandardNormal((b[i] - sum_cij_yj) / c[i, i]);
f[i] = (e[i] - d[i]) * f[i - 1];
++N;
double delta = (f[m - 1] - intsum) / N;
intsum += delta;
varsum = (N - 2) * varsum / N + delta * delta;
error = alpha * Math.Sqrt(varsum);
}
} while (error > epsilon && N < N_max);
if (N >= N_max)
{
//System.Console.WriteLine("Exceeded");
//throw new GenericException("Reached maximum number of iterations");
}
return(intsum);
}