/**
* Computes the Moore–Penrose pseudoinverse using the SVD method.
*
* Modified version of the original implementation by Kim van der Linde.
*/
public static GeneralMatrix pinv(GeneralMatrix x)
{
if (x.Rank() < 1)
return null;
if (x.ColumnDimension > x.RowDimension)
return pinv(x.Transpose()).Transpose();
SingularValueDecomposition svdX = new SingularValueDecomposition(x);
double[] singularValues = svdX.SingularValues;
double tol = Math.Max(x.ColumnDimension, x.RowDimension)
* singularValues[0] * 2E-16;
double[] singularValueReciprocals = new double[singularValues.Count()];
for (int i = 0; i < singularValues.Count(); i++)
singularValueReciprocals[i] = Math.Abs(singularValues[i]) < tol ? 0
: (1.0 / singularValues[i]);
double[][] u = svdX.GetU().Array;
double[][] v = svdX.GetV().Array;
int min = Math.Min(x.ColumnDimension, u[0].Count());
double[][] inverse = new double[x.ColumnDimension][];
for (int i = 0; i < x.ColumnDimension; i++) {
inverse[i] = new double[x.RowDimension];
for (int j = 0; j < u.Count(); j++)
for (int k = 0; k < min; k++)
inverse[i][j] += v[i][k] * singularValueReciprocals[k] * u[j][k];
}
return new GeneralMatrix(inverse);
}
private void computeaccCalButton_Click(object sender, EventArgs e)
{
int i,j;
calStatusText.Text = "Computing Calibration...";
// Construct D matrix
// D = [x.^2, y.^2, z.^2, x.*y, x.*z, y.*z, x, y, z, ones(N,1)];
for (i = 0; i < SAMPLES; i++ )
{
// x^2 term
D.SetElement(i,0, loggedData[i,0]*loggedData[i,0]);
// y^2 term
D.SetElement(i,1,loggedData[i,1]*loggedData[i,1]);
// z^2 term
D.SetElement(i, 2, loggedData[i, 2] * loggedData[i, 2]);
// x*y term
D.SetElement(i,3,loggedData[i,0]*loggedData[i,1]);
// x*z term
D.SetElement(i,4,loggedData[i,0]*loggedData[i,2]);
// y*z term
D.SetElement(i,5,loggedData[i,1]*loggedData[i,2]);
// x term
D.SetElement(i,6,loggedData[i,0]);
// y term
D.SetElement(i,7,loggedData[i,1]);
// z term
D.SetElement(i,8,loggedData[i,2]);
// Constant term
D.SetElement(i,9,1);
}
// QR=triu(qr(D))
QRDecomposition QR = new QRDecomposition(D);
// [U,S,V] = svd(D)
SingularValueDecomposition SVD = new SingularValueDecomposition(QR.R);
GeneralMatrix V = SVD.GetV();
GeneralMatrix A = new GeneralMatrix(3, 3);
double[] p = new double[V.RowDimension];
for (i = 0; i < V.RowDimension; i++ )
{
p[i] = V.GetElement(i,V.ColumnDimension-1);
}
/*
A = [p(1) p(4)/2 p(5)/2;
p(4)/2 p(2) p(6)/2;
p(5)/2 p(6)/2 p(3)];
*/
if (p[0] < 0)
{
for (i = 0; i < V.RowDimension; i++)
{
p[i] = -p[i];
}
}
A.SetElement(0,0,p[0]);
A.SetElement(0,1,p[3]/2);
A.SetElement(1,2,p[4]/2);
A.SetElement(1,0,p[3]/2);
A.SetElement(1,1,p[1]);
A.SetElement(1,2,p[5]/2);
A.SetElement(2,0,p[4]/2);
A.SetElement(2,1,p[5]/2);
A.SetElement(2,2,p[2]);
CholeskyDecomposition Chol = new CholeskyDecomposition(A);
GeneralMatrix Ut = Chol.GetL();
GeneralMatrix U = Ut.Transpose();
double[] bvect = {p[6]/2,p[7]/2,p[8]/2};
double d = p[9];
GeneralMatrix b = new GeneralMatrix(bvect,3);
GeneralMatrix v = Ut.Solve(b);
double vnorm_sqrd = v.GetElement(0,0)*v.GetElement(0,0) + v.GetElement(1,0)*v.GetElement(1,0) + v.GetElement(2,0)*v.GetElement(2,0);
double s = 1/Math.Sqrt(vnorm_sqrd - d);
GeneralMatrix c = U.Solve(v);
for (i = 0; i < 3; i++)
{
c.SetElement(i, 0, -c.GetElement(i, 0));
}
U = U.Multiply(s);
for (i = 0; i < 3; i++)
{
for (j = 0; j < 3; j++)
{
calMat[i, j] = U.GetElement(i, j);
}
}
for (i = 0; i < 3; i++)
{
bias[i] = c.GetElement(i, 0);
}
accAlignment00.Text = calMat[0, 0].ToString();
accAlignment01.Text = calMat[0, 1].ToString();
accAlignment02.Text = calMat[0, 2].ToString();
accAlignment10.Text = calMat[1, 0].ToString();
accAlignment11.Text = calMat[1, 1].ToString();
accAlignment12.Text = calMat[1, 2].ToString();
accAlignment20.Text = calMat[2, 0].ToString();
accAlignment21.Text = calMat[2, 1].ToString();
accAlignment22.Text = calMat[2, 2].ToString();
biasX.Text = bias[0].ToString();
biasY.Text = bias[1].ToString();
biasZ.Text = bias[2].ToString();
calStatusText.Text = "Done";
flashCommitButton.Enabled = true;
accAlignmentCommitButton.Enabled = true;
}
public GeneralMatrix[] decompose(int svCount, GeneralMatrix m)
{
SingularValueDecomposition s = new SingularValueDecomposition(m);
try
{
sv = svCount;
frameScale = m.RowDimension;
if (m.RowDimension > m.ColumnDimension)
{
frameScale = m.ColumnDimension;
}
else if (m.RowDimension < m.ColumnDimension)
{
frameScale = m.RowDimension;
}
// Make square matrix of data
if (m.RowDimension != m.ColumnDimension)
{
squareM = m.GetMatrix(0, frameScale - 1, 0, frameScale - 1);
}
else
{
squareM = m;
}
//perform the SVD here:
SingularValueDecomposition svd = new SingularValueDecomposition(squareM);
GeneralMatrix U = svd.GetU().GetMatrix(0, frameScale - 1, 0, sv - 1);
GeneralMatrix V = svd.GetV();
double[] D = svd.SingularValues;
GeneralMatrix dMat = makeDiagonalSquare(D, sv);
GeneralMatrix vMat = V.Transpose().GetMatrix(0, sv - 1, 0, frameScale - 1);
/*Console.WriteLine(U.RowDimension + "x" + U.ColumnDimension
+ " * " + dMat.RowDimension + "x" + dMat.ColumnDimension
+ " * " + vMat.RowDimension + "x" + vMat.ColumnDimension);
recomposition = U.Multiply(dMat).Multiply(vMat);
int[,] recompositionData = new int[recomposition.ColumnDimension, recomposition.RowDimension];
for (int i = 0; i < recomposition.ColumnDimension; i++)
{
for (int j = 0; j < recomposition.RowDimension; j++)
{
recompositionData[j, i] = (int)(recomposition.GetElement(j, i));
}
}*/
GeneralMatrix[] result = new GeneralMatrix[2];
result[0] = U;
result[1] = vMat;
return result;
}
catch (Exception ex)
{
Console.WriteLine(ex.ToString());
}
return null;
}
private void GetFiles_Decompose_WriteToArff()
{
try {
// sampleFactor is the amount to divide by the total size of the
// data set
// when determining the subsample that will be used in svd
Console.WriteLine("Creating arff file...");
DisplayMessage("Creating arff file...");
// Create folder
System.IO.Directory.CreateDirectory(OutputDir + "Results/");
StreamWriter output = new StreamWriter(OutputDir + "Results/" + "resultsGalaxy.arff");
output.Write("@relation 'galaxy'\n");
output.Write("@attribute class real\n");
output.Write("@attribute colorF real\n");
output.Write("@attribute bulgeF real\n");
output.Write("@attribute constF real\n");
for (int i = 0; i < sv * 3; i++) {
output.Write("@attribute " + i + " real\n");
}
output.Write("@data\n");
Console.WriteLine("Begin galaxy sampling");
DisplayMessage("Begin galaxy sampling");
// Initialize a three matrices that will hold all of the images
// (r,g,b of each image where each row is an image)
dataRed = new GeneralMatrix(galaxyData.Count() / sampleFactor,
imageScaleSize * imageScaleSize);
dataGreen = new GeneralMatrix(galaxyData.Count() / sampleFactor,
imageScaleSize * imageScaleSize);
dataBlue = new GeneralMatrix(galaxyData.Count() / sampleFactor,
imageScaleSize * imageScaleSize);
// subsample from galaxydata
System.Threading.Tasks.Parallel.For(0, galaxyData.Count() / sampleFactor, (int index) => {
Bitmap tempImage = getImage(OutputDir + "galaxies/"
+ galaxyData[sampleFactor * index][0] + ".jpg", imageScaleSize);
for (int i = 0; i < imageScaleSize; i++) {
for (int j = 0; j < imageScaleSize; j++) {
int pixelColor = tempImage.GetPixel(i, j).ToArgb();
int[] rgb = new int[3];
rgb[0] += ((pixelColor & 0x00ff0000) >> 16);
rgb[1] += ((pixelColor & 0x0000ff00) >> 8);
rgb[2] += (pixelColor & 0x000000ff);
dataRed.SetElement(index, i * imageScaleSize + j, rgb[0]);
dataGreen.SetElement(index, i * imageScaleSize + j, rgb[1]);
dataBlue.SetElement(index, i * imageScaleSize + j, rgb[2]);
}
}
//if (index % 10 == 0)
//DisplayImage(index);
//Console.WriteLine("Galaxy " + (sampleFactor * index) + " finished");
});
Console.WriteLine("Galaxy sampling finished\nBegin R, G and B channel SVD");
DisplayMessage("Galaxy sampling finished, Begin R, G and B channel SVD");
// Perform svd on subsample:
var redWorker = System.Threading.Tasks.Task.Factory.StartNew(() => svdR = new SingularValueDecomposition(dataRed));
var greenWorker = System.Threading.Tasks.Task.Factory.StartNew(() => svdG = new SingularValueDecomposition(dataGreen));
var blueWorker = System.Threading.Tasks.Task.Factory.StartNew(() => svdB = new SingularValueDecomposition(dataBlue));
System.Threading.Tasks.Task.WaitAll(redWorker, greenWorker, blueWorker);
// Create the basis for each component
GeneralMatrix rV = svdR.GetV();
Console.Write("dim rU: " + rV.RowDimension + ", "
+ rV.ColumnDimension);
GeneralMatrix gV = svdG.GetV();
GeneralMatrix bV = svdB.GetV();
rV = GetSVs(rV, sv);
Console.Write("Svs: " + sv);
Console.Write("Dim SsV: " + rV.RowDimension + ", "
+ rV.ColumnDimension);
gV = GetSVs(gV, sv);
bV = GetSVs(bV, sv);
// Perform the pseudoinverses
frV = pinv(rV.Transpose());
fgV = pinv(gV.Transpose());
fbV = pinv(bV.Transpose());
// Stores frV, fgV and fbV to file
WriteToFile();
Console.WriteLine("SVD finished");
DisplayMessage("SVD finished, load full dataset for testing");
Console.WriteLine("Begin filling dataset for testing");
// fill the 'full' datasets
dataRed = new GeneralMatrix(galaxyData.Count(), imageScaleSize
* imageScaleSize);
dataGreen = new GeneralMatrix(galaxyData.Count(), imageScaleSize
* imageScaleSize);
dataBlue = new GeneralMatrix(galaxyData.Count(), imageScaleSize
* imageScaleSize);
System.Threading.Tasks.Parallel.For(0, galaxyData.Count(), (int index) => {
Bitmap tempImage = getImage(OutputDir + "galaxies/"
+ galaxyData[index][0] + ".jpg", imageScaleSize);
for (int i = 0; i < imageScaleSize; i++) {
for (int j = 0; j < imageScaleSize; j++) {
int pixelColor = tempImage.GetPixel(i, j).ToArgb();
int[] rgb = new int[3];
rgb[0] += ((pixelColor & 0x00ff0000) >> 16);
rgb[1] += ((pixelColor & 0x0000ff00) >> 8);
rgb[2] += (pixelColor & 0x000000ff);
dataRed.SetElement(index, i * imageScaleSize + j, rgb[0]);
dataGreen.SetElement(index, i * imageScaleSize + j,
rgb[1]);
dataBlue.SetElement(index, i * imageScaleSize + j, rgb[2]);
}
}
});
Console.WriteLine("Finished filling dataset for testing");
DisplayMessage("Finished filling dataset for testing, begin projecting galaxies to U coordinate system");
Console.WriteLine("Begin projecting galaxies to U coordinate system, writing to ARFF file");
// Do the coordinate conversion
rV = dataRed.Multiply(frV);
gV = dataGreen.Multiply(fgV);
bV = dataBlue.Multiply(fbV);
Console.Write("Dim Final rU: " + rV.ColumnDimension
+ ", " + rV.RowDimension);
Console.WriteLine("galaxyData.Count(): " + galaxyData.Count());
// write to the output file here:
for (int imageIndex = 0; imageIndex < galaxyData.Count(); imageIndex++) {
Bitmap tempImage = getImage(OutputDir + "galaxies/"
+ galaxyData[imageIndex][0] + ".jpg", imageScaleSize);
float colorFactor = (GetColor(tempImage)[0] / GetColor(tempImage)[2]);
float centralBulgeFactor = getCentralBulge(tempImage);
float consistencyFactor = GetConsistency(tempImage);
output.Write(galaxyData[imageIndex][1] + ", ");
output.Write(colorFactor + ", ");
output.Write(centralBulgeFactor + ", ");
output.Write(consistencyFactor + ", ");
// output data (r,g,b)
for (int i = 0; i < rV.ColumnDimension; i++) {
output.Write(rV.GetElement(imageIndex, i) + ", ");
output.Write(gV.GetElement(imageIndex, i) + ", ");
if (i == rV.ColumnDimension - 1) {
output.Write(bV.GetElement(imageIndex, i) + "\n");
}
else {
output.Write(bV.GetElement(imageIndex, i) + ", ");
}
}
//if (imageIndex % (galaxyData.Count() / 100) == 0)
// DisplayImage(imageIndex);
DisplayMessage("Finished galaxy " + imageIndex.ToString() + " - " + (100 * imageIndex / galaxyData.Count()).ToString() + "%");
}
output.Flush();
output.Close();
output.Dispose();
Console.Write("Finished creating arff file...");
DisplayMessage("Finished creating arff file...");
}
catch (Exception ex) {
Console.Write(ex.ToString());
}
}
public int classify(Bitmap currentImage)
{
try {
currentImage.Save(OutputDir + "Results/temp.png", System.Drawing.Imaging.ImageFormat.Png);
// Create folder
System.IO.Directory.CreateDirectory(OutputDir + "Results/");
StreamWriter output = new StreamWriter(OutputDir + "Results/" + "temp.arff");
output.Write("@relation 'galaxy'\n");
output.Write("@attribute class real\n");
output.Write("@attribute colorF real\n");
output.Write("@attribute bulgeF real\n");
output.Write("@attribute constF real\n");
for (int i = 0; i < sv * 3; i++) {
output.Write("@attribute " + i + " real\n");
}
output.Write("@data\n");
Bitmap tempImage = getImage(OutputDir + "Results/temp.png",
imageScaleSize);
for (int i = 0; i < imageScaleSize; i++) {
for (int j = 0; j < imageScaleSize; j++) {
int pixelColor = tempImage.GetPixel(i, j).ToArgb();
int[] rgb = new int[3];
rgb[0] += ((pixelColor & 0x00ff0000) >> 16);
rgb[1] += ((pixelColor & 0x0000ff00) >> 8);
rgb[2] += (pixelColor & 0x000000ff);
dataRed.SetElement(galaxyData.Count() - 1, i * imageScaleSize + j,
rgb[0]);
dataGreen.SetElement(galaxyData.Count() - 1,
i * imageScaleSize + j, rgb[1]);
dataBlue.SetElement(galaxyData.Count() - 1, i * imageScaleSize + j,
rgb[2]);
}
}
var redWorker = System.Threading.Tasks.Task.Factory.StartNew(() => svdR = new SingularValueDecomposition(dataRed));
var greenWorker = System.Threading.Tasks.Task.Factory.StartNew(() => svdG = new SingularValueDecomposition(dataGreen));
var blueWorker = System.Threading.Tasks.Task.Factory.StartNew(() => svdB = new SingularValueDecomposition(dataBlue));
System.Threading.Tasks.Task.WaitAll(redWorker, greenWorker, blueWorker);
GeneralMatrix rU = svdR.GetU();
GeneralMatrix gU = svdG.GetU();
GeneralMatrix bU = svdB.GetU();
rU = GetSVs(rU, sv);
gU = GetSVs(gU, sv);
bU = GetSVs(bU, sv);
float colorFactor = (GetColor(tempImage)[0] / GetColor(tempImage)[2]);
float centralBulgeFactor = getCentralBulge(tempImage);
float consistencyFactor = GetConsistency(tempImage);
output.Write(galaxyData[galaxyData.Count() - 1][1] + ", ");
output.Write(colorFactor + ", ");
output.Write(centralBulgeFactor + ", ");
output.Write(consistencyFactor + ", ");
// output data (r,g,b)
for (int i = 0; i < rU.ColumnDimension; i++) {
output.Write(rU.GetElement(galaxyData.Count() - 1, i) + ", ");
output.Write(gU.GetElement(galaxyData.Count() - 1, i) + ", ");
if (i == rU.ColumnDimension - 1) {
output.Write(bU.GetElement(galaxyData.Count() - 1, i) + "\n");
}
else {
output.Write(bU.GetElement(galaxyData.Count() - 1, i) + ", ");
}
}
output.Flush();
output.Close();
output.Dispose();
weka.core.converters.ConverterUtils.DataSource source = new weka.core.converters.ConverterUtils.DataSource(OutputDir + "Results/temp.arff");
weka.core.Instances test = source.getDataSet();
test.setClassIndex(0);
int classPrediction = (int)Math.Round(fc.classifyInstance(test.instance(0)));
if (classPrediction < -6) {
classPrediction = -6;
}
else if (classPrediction > 11) {
classPrediction = 11;
}
return classPrediction;
}
catch (Exception ex) {
Console.Write(ex.ToString());
}
return -99;
}