public List <Data.Point> getSolution()
{
List <Data.Point> result = new List <Data.Point>();
/* double maxGradient = -255;
* double minGradient = 255;
*
* for (int i = 0; i < swingSharpness.GetLength(0); i++)
* for (int j = 0; j < swingSharpness.GetLength(1); j++)
* {
* if (maxGradient < swingSharpness[i, j]) maxGradient = swingSharpness[i, j];
* if (minGradient > swingSharpness[i, j]) minGradient = swingSharpness[i, j];
* }
*/
double maxGradient = swingSharpness.Cast <double>().Max();
double minGradient = swingSharpness.Cast <double>().Min();
double delta = matematical.getDeltaThreshold();
double threshold = minGradient + ((maxGradient - minGradient) * delta);
for (int x = 0; x < swingSharpness.GetLength(0); x++)
{
for (int y = 0; y < swingSharpness.GetLength(1); y++)
{
if (swingSharpness[x, y] >= threshold)
{
result.Add(new Data.Point(x, y));
}
}
}
return(result);
}
public override void SetMatrixForPlotting(double[,] newMat, string[] WontBeUsed = null, string[] WontBeUsed2 = null)
{
double min = newMat.Cast <double>().Min();
double max = newMat.Cast <double>().Max();
CreateNewMat(min, max);
}
public unsafe static Bitmap DensityGraph(double[,] data, int maxCores, ref Bitmap image, bool greyscale = false, double rangeHigh = 0, double rangeLow = 0)
{
int num = Environment.ProcessorCount;
if (maxCores < 1 || maxCores >= num)
{
num = Environment.ProcessorCount;
}
else
{
num = maxCores;
}
Bitmap imgBuffer = (Bitmap)image.Clone();
BitmapData bitmapData = imgBuffer.LockBits(new Rectangle(0, 0, imgBuffer.Width, imgBuffer.Height), ImageLockMode.ReadWrite, imgBuffer.PixelFormat);
int bytesPerPixel = Bitmap.GetPixelFormatSize(imgBuffer.PixelFormat) / 8;
int heightInPixels = bitmapData.Height;
int widthInBytes = bitmapData.Width * bytesPerPixel;
byte * PtrFirstPixel = (byte *)bitmapData.Scan0;
bool isAutoscale = rangeHigh - rangeLow == 0;
double maxValue = isAutoscale ? data.Cast <double>().Max() : rangeHigh;
double minValue = isAutoscale ? data.Cast <double>().Min() : rangeLow;
Parallel.For(0, heightInPixels, new ParallelOptions()
{
MaxDegreeOfParallelism = num
}, y =>
{
byte *currentLine = PtrFirstPixel + (y * bitmapData.Stride);
int red;
int green;
int blue;
double h, s, v;
for (int x = 0; x < bitmapData.Width; x++)
{
double value = Math.Min(maxValue, data[y, x]);
value = Math.Max(minValue, data[y, x]);
value = maxValue - minValue == 0 ? 0 : (value - minValue) / (maxValue - minValue);
h = greyscale ? 0 : 360.0 * value;
s = greyscale ? 0 : 1;
v = greyscale ? value : 1;
HsvToRgb(h, s, v, out red, out green, out blue);
currentLine[x * bytesPerPixel] = (byte)red;
currentLine[x * bytesPerPixel + 1] = (byte)green;
currentLine[x * bytesPerPixel + 2] = (byte)blue;
}
});
imgBuffer.UnlockBits(bitmapData);
return(imgBuffer);
}
private static bool isParametersChanged(
double sigma,
double T,
double kappa,
double B,
double risk_free,
double credit_spread,
double stock_growth,
double[,] couponInfoMatrix,
double[,] callInfoMatrix,
double[,] putInfoMatrix)
{
if (_sigma != sigma ||
_T != T ||
_kappa != kappa ||
_B != B ||
_risk_free != risk_free ||
_credit_spread != credit_spread ||
_stock_growth != stock_growth ||
_couponInfoMatrix.Length != couponInfoMatrix.Length ||
_callInfoMatrix.Length != callInfoMatrix.Length ||
_putInfoMatrix.Length != putInfoMatrix.Length)
{
return(true);
}
if (!couponInfoMatrix.Cast <double>().SequenceEqual(_couponInfoMatrix.Cast <double>()) ||
!callInfoMatrix.Cast <double>().SequenceEqual(_callInfoMatrix.Cast <double>()) ||
!putInfoMatrix.Cast <double>().SequenceEqual(_putInfoMatrix.Cast <double>()))
{
return(true);
}
return(false);
}
private void ShowResultText()
{
//string precision = "";
//string recall = "";
//double acc = ((double)RealNum / (double)TotalNum) * 100;
int sum = (int)confusionMatrix.Cast <double>().Sum();
int correctSum = 0;
for (int j = 0; j < confusionMatrix.GetLength(1); j++)
{
for (int i = 0; i < confusionMatrix.GetLength(0); i++)
{
if (i == j)
{
correctSum += (int)confusionMatrix[i, j];
}
}
}
double acc = (double)correctSum / (double)sum * 100;
ResultText_TB.Text = "Total : " + sum.ToString() + " / Acc : " + string.Format("{0:0.00}", acc);
}
public static KMeansClusteringSolution CreateKMeansSolution(IClusteringProblemData problemData, int k, int restarts)
{
var dataset = problemData.Dataset;
IEnumerable <string> allowedInputVariables = problemData.AllowedInputVariables;
IEnumerable <int> rows = problemData.TrainingIndices;
int info;
double[,] centers;
int[] xyc;
double[,] inputMatrix = AlglibUtil.PrepareInputMatrix(dataset, allowedInputVariables, rows);
if (inputMatrix.Cast <double>().Any(x => double.IsNaN(x) || double.IsInfinity(x)))
{
throw new NotSupportedException("k-Means clustering does not support NaN or infinity values in the input dataset.");
}
alglib.kmeansgenerate(inputMatrix, inputMatrix.GetLength(0), inputMatrix.GetLength(1), k, restarts + 1, out info, out centers, out xyc);
if (info != 1)
{
throw new ArgumentException("Error in calculation of k-Means clustering solution");
}
KMeansClusteringSolution solution = new KMeansClusteringSolution(new KMeansClusteringModel(centers, allowedInputVariables), (IClusteringProblemData)problemData.Clone());
return(solution);
}
public Matrix(double[,] matrix, bool rowMajor = true)
{
// NOTE copies elements
if (rowMajor)
{
rows = matrix.GetLength(0);
cols = matrix.GetLength(1);
mat = matrix.Cast <double>().ToArray();
}
// column-major
else
{
rows = matrix.GetLength(1);
cols = matrix.GetLength(0);
mat = new double[rows * cols];
for (int row = 0; row < rows; row++)
{
for (int col = 0; col < cols; col++)
{
this[row, col] = matrix[matrix.GetLowerBound(0) + col, matrix.GetLowerBound(1) + row];
}
}
}
}
public static double[,] SwapMatrixRows(double[,] source, int row, int size, out int swaps)
{
var list = new List <List <double> >();
var flatMatrix = source.Cast <double>();
for (var i = 0; i < size; i++)
{
var matrixRow = flatMatrix.Skip(i * size).Take(size).ToList();
list.Add(matrixRow);
}
for (var i = 1; i < size - row; i++)
{
if (list[row + i][row + i] != 0)
{
swaps = row + i;
var listRow = list[row];
list.RemoveAt(row);
list.Insert(row + i, listRow);
return(list.To2DArray());
}
}
throw new InfiniteSystemSolutionsException();
}
public static double[,] ThresholdFilter(double[,] original, double threshold)
{
var width = original.GetLength(0);
var height = original.GetLength(1);
if (threshold < 0 || threshold > 1)
{
return(new double[width, height]);
}
var res = new double[width, height];
var whitePixels = (int)(threshold * original.Length);
var tmp = original.Cast <double>().OrderByDescending(x => x).ToList();
var bright = tmp.FirstOrDefault(y => (whitePixels <= tmp.Count(z => z > y)));
for (var i = 0; i < width; i++)
{
for (var j = 0; j < height; j++)
{
res[i, j] = original[i, j] > bright ? 1 : 0;
}
}
return(res);
}
public double[] QueryBack(double[,] xTargets)
{
//BACK QUERY NEURAL NETWORK
double[,] finalOutputs = Transpose(xTargets);
double[,] finalInputs = LogitFunction(finalOutputs);
//CALCULATE THE SIGNAL OUT OF THE HIDDEN LAYER
double[,] hiddenOutputs = Multiply(Transpose(weightOutput), finalInputs);
hiddenOutputs = Subtract(hiddenOutputs, MatrixMinimum(hiddenOutputs));
hiddenOutputs = Division(hiddenOutputs, MatrixMaximum(hiddenOutputs));
hiddenOutputs = Multiply(0.98, hiddenOutputs);
hiddenOutputs = Addition(0.01, hiddenOutputs);
//CALCULATE THE SIGNAL INTO THE HIDEEN LAYER
double[,] hiddenInputs = LogitFunction(hiddenOutputs);
//CALCULATE THE SIGNAL OUT OF THE INPUT LAYER
double[,] inputs = Multiply(Transpose(weightInput), hiddenInputs);
inputs = Subtract(inputs, MatrixMinimum(inputs));
inputs = Division(inputs, MatrixMaximum(inputs));
inputs = Multiply(0.98, inputs);
inputs = Addition(0.01, inputs);
return(inputs.Cast <double>().ToArray());
}
static void LinqLoop()
{
if (!data.Cast <double>().Any(d => double.IsNaN(d) || double.IsInfinity(d)))
{
Console.WriteLine("FOUND!");
}
}
public static double[,] ThresholdFilter(double[,] original, double whitePixelsFraction)
{
var n = original.GetLength(0) * original.GetLength(1);
var amountOfWhitePixels = (int)(whitePixelsFraction * n);
var pixels = original.Cast <double>().ToList();
pixels.Sort();
var newMass = pixels.ToArray();
var middleCell = 1.0;
if (amountOfWhitePixels > 0)
{
middleCell = newMass[n - amountOfWhitePixels];
}
for (var i = n - 1 - amountOfWhitePixels; i >= 0; i--)
{
if (newMass[i] == middleCell && amountOfWhitePixels > 0 && amountOfWhitePixels < n)
{
amountOfWhitePixels++;
}
}
for (var i = n - 1 - amountOfWhitePixels; i >= 0; i--)
{
WhiteCicle(original, newMass[i], false);
}
for (var i = n - 1; i >= n - amountOfWhitePixels; i--)
{
WhiteCicle(original, newMass[i], true);
}
return(original);
}
private static void AssertInputMatrix(double[,] inputMatrix)
{
if (inputMatrix.Cast <double>().Any(x => Double.IsNaN(x) || Double.IsInfinity(x)))
{
throw new NotSupportedException("Random forest modeling does not support NaN or infinity values in the input dataset.");
}
}
// Determinant of matrixA
private void determinantA_Click(object sender, EventArgs e)
{
res = new Result();
if (combo1[0].Text == combo1[1].Text && combo1[0].Text != "" && combo1[1].Text != "")
{
matrixA = matrix.FillMatrix(textBoxes1, int.Parse(combo1[0].Text), int.Parse(combo1[1].Text));
double determinant = matrix.Determinant(matrixA, int.Parse(combo1[0].Text), int.Parse(combo1[1].Text));
double[] temp = matrixA.Cast <double>().ToArray();
WriteLabel("Deter:", res);
int x = 20, y = 55;
int count1 = 0;
for (int i = 0; i < int.Parse(combo1[0].Text) * int.Parse(combo1[1].Text); i++)
{
if (count1 == int.Parse(combo1[1].Text))
{
count1 = 0;
x = 20;
y += 25;
}
textBoxes1.Add(CreateTextBoxes(x, y, res, temp[i].ToString()));
x += 40;
count1++;
}
Label lab = new Label();
lab.Text = "Determinant of matrixA: ";
lab.Location = new Point(20, 270);
lab.Size = new Size(200, 50);
lab.Font = new Font("Tobota", 10, FontStyle.Bold);
res.Controls.Add(lab);
TextBox tex = new TextBox();
tex.Top = 270;
tex.Left = 230;
tex.Width = 60;
tex.TextAlign = HorizontalAlignment.Center;
tex.Text = determinant.ToString();
res.Controls.Add(tex);
res.Show();
}
else if (combo1[0].Text == "" && combo1[1].Text == "" || combo1[0].Text == "" || combo1[1].Text == "")
{
MessageBox.Show("The number of rows and columns in matrixA is not specified!");
}
else if (combo1[0].Text != combo1[1].Text)
{
MessageBox.Show("The matrix must be square!");
}
}
public Graph(double[,] adjacencyTable, bool isDirected)
{
adjacencyTable.GetLength(0).Should().Be(adjacencyTable.GetLength(1));
_adjacencyTable = adjacencyTable;
IsDirected = isDirected;
NumberOfVertices = adjacencyTable.GetLength(0);
NumberOfEdges = adjacencyTable.Cast <double>().Count(edgeDistance => !double.IsPositiveInfinity(edgeDistance));
}
public static IClassificationSolution CreateLinearDiscriminantAnalysisSolution(IClassificationProblemData problemData)
{
var dataset = problemData.Dataset;
string targetVariable = problemData.TargetVariable;
IEnumerable <string> allowedInputVariables = problemData.AllowedInputVariables;
IEnumerable <int> rows = problemData.TrainingIndices;
int nClasses = problemData.ClassNames.Count();
double[,] inputMatrix = AlglibUtil.PrepareInputMatrix(dataset, allowedInputVariables.Concat(new string[] { targetVariable }), rows);
if (inputMatrix.Cast <double>().Any(x => double.IsNaN(x) || double.IsInfinity(x)))
{
throw new NotSupportedException("Linear discriminant analysis does not support NaN or infinity values in the input dataset.");
}
// change class values into class index
int targetVariableColumn = inputMatrix.GetLength(1) - 1;
List <double> classValues = problemData.ClassValues.OrderBy(x => x).ToList();
for (int row = 0; row < inputMatrix.GetLength(0); row++)
{
inputMatrix[row, targetVariableColumn] = classValues.IndexOf(inputMatrix[row, targetVariableColumn]);
}
int info;
double[] w;
alglib.fisherlda(inputMatrix, inputMatrix.GetLength(0), allowedInputVariables.Count(), nClasses, out info, out w);
if (info < 1)
{
throw new ArgumentException("Error in calculation of linear discriminant analysis solution");
}
ISymbolicExpressionTree tree = new SymbolicExpressionTree(new ProgramRootSymbol().CreateTreeNode());
ISymbolicExpressionTreeNode startNode = new StartSymbol().CreateTreeNode();
tree.Root.AddSubtree(startNode);
ISymbolicExpressionTreeNode addition = new Addition().CreateTreeNode();
startNode.AddSubtree(addition);
int col = 0;
foreach (string column in allowedInputVariables)
{
VariableTreeNode vNode = (VariableTreeNode) new HeuristicLab.Problems.DataAnalysis.Symbolic.Variable().CreateTreeNode();
vNode.VariableName = column;
vNode.Weight = w[col];
addition.AddSubtree(vNode);
col++;
}
var model = LinearDiscriminantAnalysis.CreateDiscriminantFunctionModel(tree, new SymbolicDataAnalysisExpressionTreeInterpreter(), problemData, rows);
SymbolicDiscriminantFunctionClassificationSolution solution = new SymbolicDiscriminantFunctionClassificationSolution(model, (IClassificationProblemData)problemData.Clone());
return(solution);
}
private void calcularValores()
{
int rows = matrix.GetLength(0);
int columns = matrix.GetLength(1);
for (int a = 0; a < 100; a++)
{
for (int i = 0; i < rows; i++)
{
for (int j = 0; j < columns; j++)
{
matrix[i, j] = ((((i == 0) ? fN : matrix[i - 1, j]) +
((j == 0) ? fO : matrix[i, j - 1]) +
((i == rows - 1) ? fS : matrix[i + 1, j]) +
((j == columns - 1) ? fE : matrix[i, j + 1])) / 4)
+ cMatrix[i, j];
}
}
}
mostrarGrid();
min = matrix.Cast <double>().Min();
max = matrix.Cast <double>().Max();
double valor = max / 9;
foreach (DataGridViewRow r in grid.Rows)
{
for (int i = 0; i < r.Cells.Count; i++)
{
double val = Convert.ToDouble(r.Cells[i].Value);
r.Cells[i].Style.BackColor =
(val > valor) ? ((val > valor * 2) ? ((val > valor * 3)
? ((val > valor * 4) ? ((val > valor * 5) ?
((val > valor * 6) ? ((val > valor * 7) ?
((val > valor * 8) ? c9 : c8) : c7) : c6) : c5) : c4)
: c3) : c2) : c1;
r.Cells[i].Style.ForeColor =
(r.Cells[i].Style.BackColor == c4) ? Color.Black :
Color.White;
}
}
grid.Invalidate();
}
// Get Fractal Data
public int[,] GetData()
{
int[,] map = new int[size, size];
double[,] data = DiamondSquareAlgorithm();
double max = data.Cast <double>().Max();
double min = data.Cast <double>().Min();
for (int x = 0; x < size - 1; x++)
{
for (int y = 0; y < size - 1; y++)
{
double n = data[x, y];
n += roughness / 100 - 1 + Math.Abs(min) / 100;
map[x, y] = (int)n;
}
}
return(map);
}
public void ExponentiateMatrixTest_ReturnMultiplicateMatrix()
{
double[,] matrix = { { 1, 1, 1 }, { 0, 0, 1 }, { 0, 0, 1 } };
double[,] expectedMatrix = { { 1, 1, 3 }, { 0, 0, 1 }, { 0, 0, 1 } };
var actualMatrix = FoxMath.ExponentiateMatrix(matrix);
bool equal = actualMatrix.Rank == expectedMatrix.Rank && Enumerable.Range(0, actualMatrix.Rank).All(dimension => actualMatrix.GetLength(dimension) == expectedMatrix.GetLength(dimension)) &&
actualMatrix.Cast <double>().SequenceEqual(expectedMatrix.Cast <double>());
Assert.AreEqual(equal, true);
}
private static double CalcThresholdValue(double[,] original, double whitePixelsFraction)
{
var picWidth = original.GetLength(0);
var picHeight = original.GetLength(1);
var pixelsCount = picWidth * picHeight;
var whitePixelsCount = (int)(pixelsCount * whitePixelsFraction);
return(whitePixelsCount > 0
? original.Cast <double>().OrderByDescending(x => x).ElementAt(whitePixelsCount - 1)
: Double.MaxValue);
}
/// <summary>
/// Определение границ сингулярности изображения
/// </summary>
/// <param name="image">Анализируемое изображение</param>
/// <param name="converterType">Тип конвертера</param>
/// <returns>Интервал сингулярностей изображения</returns>
internal Interval GetSingularityBounds(DirectBitmap image, ConverterType converterType)
{
Intensivities = new double[image.Width, image.Height];
CalculateIntensivities(image, converterType);
CalculateDensity();
var densityValues = Densities.Cast <double>();
return(new Interval(densityValues.Min(), densityValues.Max()));
}
private static double FindThresholdValue(double[,] original, double whitePixelsFraction)
{
var pixelsList = original.Cast <double>().ToList();
pixelsList.Sort();
var whitePixelsCount = (int)(whitePixelsFraction * pixelsList.Count);
return(whitePixelsCount == 0 ?
pixelsList.Last() + 1:
pixelsList[pixelsList.Count - whitePixelsCount]);
}
private static void DodecFace(int a, int b, int c, int d, int e, PrimitiveType shadeType)
{
double[] n0 = new double[3], d1 = new double[3], d2 = new double[3];
Diff3(_dodec.Cast <double>().Skip(19 * a).Take(3).ToArray(),
_dodec.Cast <double>().Skip(19 * b).Take(3).ToArray(), ref d1);
Diff3(_dodec.Cast <double>().Skip(19 * b).Take(3).ToArray(),
_dodec.Cast <double>().Skip(19 * c).Take(3).ToArray(), ref d2);
Crossprod(d1, d2, ref n0);
Normalize(n0);
GL.Begin(shadeType);
GL.Normal3(n0[0], n0[1], n0[2]);
GL.Vertex3(_dodec[a, 0], _dodec[a, 1], _dodec[a, 2]);
GL.Vertex3(_dodec[b, 0], _dodec[b, 1], _dodec[b, 2]);
GL.Vertex3(_dodec[c, 0], _dodec[c, 1], _dodec[c, 2]);
GL.Vertex3(_dodec[d, 0], _dodec[d, 1], _dodec[d, 2]);
GL.Vertex3(_dodec[e, 0], _dodec[e, 1], _dodec[e, 2]);
GL.End();
}
public void EstimateGmm()
{
IDataset dataset = new BasicTextDataset(mz, data, coordinates: null);
var result = _gmm.EstimateGmm(dataset);
Console.WriteLine(result);
// Assert
Assert.Multiple(testDelegate: () =>
{
Assert.IsNotNull(result);
Assert.True(condition: data.Cast <double>()
.Take(count: data.GetLength(dimension: 1))
.SequenceEqual(result.OriginalMeanSpectrum));
Assert.True(condition: mz.SequenceEqual(result.OriginalMz));
Assert.False(result.IsMerged);
Assert.False(result.IsNoiseReduced);
Assert.Null(result.MzMergingThreshold);
});
}
public static IClassificationSolution CreateLogitClassificationSolution(IClassificationProblemData problemData, out double rmsError, out double relClassError)
{
var dataset = problemData.Dataset;
string targetVariable = problemData.TargetVariable;
var doubleVariableNames = problemData.AllowedInputVariables.Where(dataset.VariableHasType <double>);
var factorVariableNames = problemData.AllowedInputVariables.Where(dataset.VariableHasType <string>);
IEnumerable <int> rows = problemData.TrainingIndices;
double[,] inputMatrix = dataset.ToArray(doubleVariableNames.Concat(new string[] { targetVariable }), rows);
var factorVariableValues = dataset.GetFactorVariableValues(factorVariableNames, rows);
var factorMatrix = dataset.ToArray(factorVariableValues, rows);
inputMatrix = factorMatrix.HorzCat(inputMatrix);
if (inputMatrix.Cast <double>().Any(x => double.IsNaN(x) || double.IsInfinity(x)))
{
throw new NotSupportedException("Multinomial logit classification does not support NaN or infinity values in the input dataset.");
}
alglib.logitmodel lm = new alglib.logitmodel();
alglib.mnlreport rep = new alglib.mnlreport();
int nRows = inputMatrix.GetLength(0);
int nFeatures = inputMatrix.GetLength(1) - 1;
double[] classValues = dataset.GetDoubleValues(targetVariable).Distinct().OrderBy(x => x).ToArray();
int nClasses = classValues.Count();
// map original class values to values [0..nClasses-1]
Dictionary <double, double> classIndices = new Dictionary <double, double>();
for (int i = 0; i < nClasses; i++)
{
classIndices[classValues[i]] = i;
}
for (int row = 0; row < nRows; row++)
{
inputMatrix[row, nFeatures] = classIndices[inputMatrix[row, nFeatures]];
}
int info;
alglib.mnltrainh(inputMatrix, nRows, nFeatures, nClasses, out info, out lm, out rep);
if (info != 1)
{
throw new ArgumentException("Error in calculation of logit classification solution");
}
rmsError = alglib.mnlrmserror(lm, inputMatrix, nRows);
relClassError = alglib.mnlrelclserror(lm, inputMatrix, nRows);
MultinomialLogitClassificationSolution solution = new MultinomialLogitClassificationSolution(new MultinomialLogitModel(lm, targetVariable, doubleVariableNames, factorVariableValues, classValues), (IClassificationProblemData)problemData.Clone());
return(solution);
}
private static int[,] SaltandPepper(int[,] arr, double[,] noise, double densitySalt, double densityPepper, int white, SaltandPapperNoise noiseType)
{
int[,] result = new int[arr.GetLength(0), arr.GetLength(1)];
ArrGen <int> d = new ArrGen <int>();
var vectorArr = arr.Cast <int>().ToArray();
var vectorNoise = noise.Cast <double>().ToArray();
switch (noiseType)
{
case SaltandPapperNoise.salt:
for (int i = 0; i < noise.Length; i++)
{
if (vectorNoise[i] >= (1 - densitySalt / 2))
{
vectorArr[i] = white;
}
}
result = d.VecorToArrayRowByRow(arr.GetLength(0), arr.GetLength(1), vectorArr);
break;
case SaltandPapperNoise.pepper:
for (int i = 0; i < noise.Length; i++)
{
if (vectorNoise[i] <= densityPepper / 2)
{
vectorArr[i] = 0;
}
}
result = d.VecorToArrayRowByRow(arr.GetLength(0), arr.GetLength(1), vectorArr);
break;
case SaltandPapperNoise.saltandpepper:
for (int i = 0; i < noise.Length; i++)
{
if (vectorNoise[i] >= (1 - densitySalt / 2))
{
vectorArr[i] = white;
}
}
for (int i = 0; i < noise.Length; i++)
{
if (vectorNoise[i] <= densityPepper / 2)
{
vectorArr[i] = 0;
}
}
result = d.VecorToArrayRowByRow(arr.GetLength(0), arr.GetLength(1), vectorArr);
break;
}
return(result);
}
/// <summary>
/// Converts double[,] array into 8 bit grayscale image. If <paramref name="image"/> is <see cref="WriteableBitmap"/> values are updated directly in the
/// image else values are set in the new image which is then returned (since original cannot be modified)
/// </summary>
/// <param name="image"></param>
/// <param name="data"></param>
/// <returns>Either new image (if source is not writable) or source image with updated vales</returns>
public static BitmapSource FromDouble2D(this BitmapSource image, double[,] data)
{
int rows = data.GetLength(0);
int cols = data.GetLength(1);
WriteableBitmap result = null;
if (image is WriteableBitmap)
{
result = image as WriteableBitmap;
}
else
{
result = new WriteableBitmap(cols, rows, 96, 96, PixelFormats.Gray8, BitmapPalettes.Gray256);
}
// Find min max of source data, used later for scaling
double srcMin = data.Cast <double>().Min();
double srcMax = data.Cast <double>().Max();
// Init temp array of pixel values, later to be written into destination image
byte[] pixels = new byte[rows * cols];
// Compute pixel values
for (int row = 0; row < rows; row++)
{
for (int col = 0; col < cols; col++)
{
pixels[row * cols + col] = (byte)Scale(data[row, col], srcMin, srcMax, 0, 255);
}
}
// Copy pixels to image
Int32Rect roi = new Int32Rect(0, 0, cols, rows);
result.WritePixels(roi, pixels, cols, 0);
return(result);
}
public void IndexesOfMin(out int i, out int j)
{
double min = arr.Cast <double>().Min();
int resultI = 0;
int resultJ = 0;
for (int _i = 0; _i < rows; _i++)
{
for (int _j = 0; _j < colls; _j++)
{
if (arr[_i, _j] == min)
{
resultI = _i;
resultJ = _j;
}
}
}
i = resultI;
j = resultJ;
}
public static bool HasInvalidData(this double[,] mat)
{
//for (int i = 0; i < mat.Count; i++)
//{
// for (int j = 0; j < lists[i].Count; j++)
// {
// exData[j, i] = lists[i][j];
// }
//}
var flattened = mat.Cast <double>().ToArray();
return(flattened.HasInvalidData());
}
public void DoWork()
{
_topo = LoadTopoFromFile(_fileName);
var a = _topo.SelectMany(x => x).ToList();
_topoMin = a.Min();
_topoMax = a.Max();
_newTopo = NewTopo(_topo);
var b = _newTopo.Cast <double>().ToList();
MinValue = b.Min();
MaxValue = b.Max();
}