private static int[] getPattern(int index)
{
int n = (int)Math.Pow(index, (1.0 / 3.0));
index -= (n * n * n);
int[] p = new int[3];
foreach (int i in LinearAlgebra.range(p.Length))
{
p[i] = n;
}
if (index == 0)
{
return(p);
}
index--;
int v = index % 3;
index = index / 3;
if (index < n)
{
p[v] = index % n;
return(p);
}
index -= n;
p[v] = index / n;
p[++v % 3] = index % n;
return(p);
}
public void calcEigen()
{
if (!isEigenCalculated)
{
Matrix eigenV;
Lambda = LinearAlgebra.EigenPowerMethod(this, out eigenV);
EigenV = eigenV.getList();
isEigenCalculated = true;
}
}
public static Dictionary <int, int> binDataOne(List <double> data, int numBins)
{
Dictionary <int, int> dataBins = new Dictionary <int, int>();
foreach (int i in LinearAlgebra.range(numBins))
{
dataBins.Add(i, 0);
}
return(binDataOne(data, dataBins));
}
protected void PlotLines(Graphics gfx, int extendX, int extendY)
// PlotLines – performs all the drawing operations needed to visualize the density vectors.
{ // widths and heights are mixed up
drawBackground(gfx, extendX, extendY);
float lineWidth = 2f;
//if (data.Count > drawAreaWidth / stripHeight)
stripHeight = (float)drawAreaWidth / (data.Count) - stripSpacing;
SolidBrush brush = new SolidBrush(Color.Black);
int maxLabels = (int)numLabels.Value;
int skipLabel = (labels.Count + maxLabels - 1) / maxLabels;
foreach (int i in LinearAlgebra.range(data.Count))
{
int maxLines = (int)(data[i].Count * (float)numYmax.Value);
int minLines = (int)(data[i].Count * (float)numYmin.Value);
lineWidth = (float)drawAreaHight / (maxLines - minLines);
float x = drawAreaX + (i * (stripHeight + stripSpacing));
for (int j = minLines; j < maxLines; j++)
{
setBrush(ref brush, data[i][j]);
float y = lineWidth + drawAreaY + lineWidth * (j - minLines);
gfx.FillRectangle(brush, x, (drawAreaHight + 2 * drawAreaY) - y, stripHeight, lineWidth);
}
//if (!labelsAdded) VLabel(labels[i], x , labelY);
if (i % skipLabel == 0) // skip excessive labels
{
VLabel(gfx, labels[i], x, labelY, true);
}
}
//drawBrush.Dispose();
//drawFont.Dispose();
//this.Update();
labelsAdded = true;
}
protected virtual void PlotLines(float R, float G, float B)
// PlotLines – performs all the drawing operations needed to visualize the density vectors.
{
drawBackground();
int lineWidth = 2;
if (data.Count > drawAreaHight / stripHeight)
{
stripHeight = drawAreaHight / (data.Count + 2);
}
foreach (int i in LinearAlgebra.range(data.Count))
{
lineWidth = drawAreaWidth / data[i].Count;
int y = drawAreaHight + drawAreaY - stripHeight - (i * (stripHeight + stripSpacing));
foreach (int j in LinearAlgebra.range(data[i].Count))
{
int color = (int)(255 * ((double)data[i][j] * (double)numGain.Value));
if (color > 255)
{
color = 255;
}
SolidBrush brush = new SolidBrush(Color.FromArgb((int)(R * color), (int)(G * color), (int)(B * color)));
//Console.WriteLine("i: " + i + "j: " + j);
int x = 2 + drawAreaX + lineWidth * j;
graphics.FillRectangle(brush, x, y, lineWidth, stripHeight);
}
if (!labelsAdded)
{
addLabel(y, labels[i]);
}
}
this.Update();
labelsAdded = true;
}
public void BarbasiAlbert(int links, int clusters)
{
if (clusters <= 1)
{
BarbasiAlbert(links);
}
else
{
int clusterSize = size / clusters;
if (clusterSize * clusters != size)
{
size = clusterSize * clusters;
}
adjMatrix = createMatrix(size);
foreach (int i in range(clusters))
{
var subNet = barbasiAlbert(clusterSize, links);
LinearAlgebra.insertMat <double>(adjMatrix, subNet, i);
}
// connect the clusters together.
foreach (int i in range(clusters, 1))
{
foreach (int j in range(clusters, 1))
{
if (i == j)
{
continue;
}
foreach (int k in range(links))
{
connectClustersRand(i, j, clusterSize);
}
}
}
}
}
public void copy(Matrix B)
{
LinearAlgebra.mCopy(B, this);
}
public void scalarMult(double scalar)
{
LinearAlgebra.scalarMult(this, scalar);
}
public List <double[]> matMultiply(List <double[]> B)
{
return((LinearAlgebra.MatMultiply(this, new Matrix(B))).getList());
}
public Matrix matMultiply(Matrix B)
{
return(LinearAlgebra.MatMultiply(this, B));
}
