public void Peaks_Azimuth0_Azimuth90()
{
// Arrange
const int DistanceMax = 3;
const double DistanceStep = 0.1;
var radiuses = new List <double> {
4, 10, 20, 40
};
List <double> distances = getDistances(DistanceStep, DistanceMax);
string dirAzimuth0 = "RadiusDistanceOutput_Azimuth0";
string dirAzimuth90 = "RadiusDistanceOutput_Azimuth90";
var gp = new GnuPlot();
gp.Set("style data lines");
gp.Set("xtics 0.5");
gp.Set("grid xtics ytics");
gp.Set("size square");
gp.HoldOn();
radiuses.Reverse();
foreach (double radius in radiuses)
{
Dictionary <double, double> peaks0 = this.getPeaks0(distances, radius, dirAzimuth0);
Dictionary <double, double> peaks90 = this.getPeaks90(distances, radius, dirAzimuth90);
//gp.HoldOn();
// gp.Set(string.Format("terminal win {0}", radius));
gp.Plot(peaks0);
gp.Plot(peaks90);
// gp.HoldOff();
}
gp.Wait();
}
private void button2_Click(object sender, EventArgs e)
{
GnuPlot gp = new GnuPlot();
gp.HoldOn();
gp.Set("title 'Phase-Locked Signals'");
gp.Set("samples 2000");
gp.Unset("key");
gp.Plot("sin(x)");
gp.Plot("cos(x)");
}
public void CalculateOpticalConstants_DrudeLorentz_Gnuplot()
{
// Arrange
var optConst = ParameterHelper.ReadOpticalConstants("opt_const.txt");
var drudeLorentz = new DrudeLorentz();
var dict = new Dictionary <double, Complex>();
foreach (var waveLength in optConst.WaveLengthList)
{
var freq = new SpectrumUnit(waveLength / OpticalConstants.WaveLengthMultiplier,
SpectrumUnitType.WaveLength);
dict.Add(waveLength, drudeLorentz.GetPermittivity(freq));
}
ParameterHelper.WriteOpticalConstants("opt_const_drudeLorentz.txt", dict);
using (var gp = new GnuPlot())
{
gp.HoldOn();
gp.Set("style data lines");
gp.Plot(getPermitivittyFunc(optConst));
gp.Plot(dict);
gp.Wait();
}
// Act
// Assert
}
private static void PlotFit(double min_x, double max_x, Vector <double> mu, Vector <double> sigma, Vector <double> theta, int p, double lambda)
{
// Plot training data and fit
var x = Matrix <double> .Build.DenseOfColumnArrays(Generate.LinearRange(min_x - 15, .05, max_x + 25));
Matrix <double> X_poly = MapPolyFeatures(x, p);
for (int i = 0; i < X_poly.ColumnCount; i++)
{
Vector <double> v = X_poly.Column(i);
v = v - mu[i];
v = v / sigma[i];
X_poly.SetColumn(i, v);
}
X_poly = X_poly.InsertColumn(0, Vector <double> .Build.Dense(X_poly.RowCount, 1));
var hypothesis = X_poly * theta;
GnuPlot.Set(string.Format("title \"Polynomial Regression Fit (lambda = {0:f6})\"", lambda));
GnuPlot.Set("xlabel \"Change in water level (x)\"");
GnuPlot.Set("ylabel \"Water flowing out of the dam (y)\"");
GnuPlot.Set("autoscale xy");
//GnuPlot.Set("xr[-100:100]");
//GnuPlot.Set("yr[-200:400]");
GnuPlot.Plot(x.Column(0).ToArray(), hypothesis.ToArray(), "with lines dashtype 2 lw 1 lc rgb \"blue\" notitle");
}
public void RadiusChangeOutput_InterparticleDistance_Spectrum()
{
// Arrange
GnuPlot gp = null;
var radius1 = 20;
var radiuses = new List <double> {
4, 10, 15, 20
};
double distance = 70;
string dirAzimuth45 = "RadiusChangeOutput_InterparticleDistance_Spectrum";
//foreach (var radius1 in radiuses)
//{
gp = new GnuPlot();
setLineStyle(gp);
gp.HoldOn();
setColorPalette(gp);
gp.Set(String.Format("title \"{0}\"", radius1));
foreach (double radius in radiuses)
{
Dictionary <double, double> azim45 = SimpleFormatter.Read(
this.getFileFormatDiffRadiuses(dirAzimuth45, distance, radius1, radius))
.ToDictionary(x => x.Key * 1e9, x => x.Value);
gp.Plot(azim45, string.Format(@"title ""{0}""", radius));
//gp.Clear();
}
//}
gp.Wait();
}
public void RadiusChangeOutput_Azimuth_Spectrum()
{
// Arrange
const int DistanceMax = 3;
const double DistanceStep = 0.1;
var radius1 = 4;
var radiuses = new List <double> {
4, 10, 20, 40, 70, 100, 200
};
List <double> distances = getDistances(DistanceStep, DistanceMax);
string dirAzimuth45 = "RadiusChangeOutput_Azimuth45";
var gp = new GnuPlot();
gp.Set("style data lines");
gp.HoldOn();
foreach (double radius in radiuses)
{
Dictionary <decimal, List <double> > spectrum = this.zipToDictionaryDiffRadiuses(distances, dirAzimuth45, radius1, radius);
string filename = Path.Combine(BasePath, this.TestContext.TestName, "Azim45.txt");
SimpleFormatter.WriteDictionary(filename, spectrum, distances);
foreach (double distance in distances.Take(5))
{
Dictionary <double, double> azim45 = SimpleFormatter.Read(
this.getFileFormatDiffRadiuses(dirAzimuth45, distance, radius1, radius));
gp.Plot(azim45, string.Format(@"title ""{0}""", radius));
}
gp.Clear();
}
gp.Wait();
}
public void AreClose_Azimuth45_AzimuthSum()
{
// Arrange
const int DistanceMax = 3;
const double DistanceStep = 0.1;
var radiuses = new List <double> {
4, 10, 20, 40, 70, 100, 200
};
List <double> distances = getDistances(DistanceStep, DistanceMax);
string dirAzimuthSum = "RadiusDistanceOutput_AzimuthSum";
string dirAzimuth45 = "RadiusDistanceOutput_Azimuth45";
var gp = new GnuPlot();
gp.Set("style data lines");
foreach (double radius in radiuses)
{
foreach (double distance in distances)
{
Dictionary <double, double> azim45 = SimpleFormatter.Read(
this.getFileFormat(dirAzimuth45, distance, radius));
Dictionary <double, double> azimSum = SimpleFormatter.Read(
this.getFileFormat(dirAzimuthSum, distance, radius));
gp.HoldOn();
gp.Plot(azim45);
gp.Plot(azimSum);
gp.HoldOff();
AssertHelper.DictionaryAreClose(azim45, azimSum, 0.5);
}
}
}
public void AreCloseDistance3_OpticalConst_DrudeLorentz()
{
// Arrange
var distance = 2.9;
var radiuses = new List <double> {
4, 10, 20, 40, 70, 100, 200
};
string dirOpticalConst = "RadiusDistanceOutput_Azimuth45_EffectiveCrossExt";
string dirDrudeLorentz = "RadiusDistanceDrudeLorentz_Azimuth45_EffectiveCrossExt";
var gp = new GnuPlot();
gp.Set("style data lines");
gp.HoldOn();
foreach (double radius in radiuses)
{
Dictionary <double, double> optical = SimpleFormatter.Read(
this.getFileFormat(dirOpticalConst, distance, radius));
Dictionary <double, double> drudeLorentz = SimpleFormatter.Read(
this.getFileFormat(dirDrudeLorentz, distance, radius));
gp.Plot(optical, @"title ""optical""");
gp.Plot(drudeLorentz, @"title ""drude-lorentz""");
//AssertHelper.DictionaryAreClose(optical, drudeLorentz, 0.1);
}
gp.Wait();
}
public void AreClose_Radius40_OpticalConst_DrudeLorentz()
{
// Arrange
const double DistanceMax = 0.5;
const double DistanceStep = 0.1;
List <double> distances = getDistances(DistanceStep, DistanceMax);
var radius = 40;
string dirOpticalConst = "RadiusDistanceOutput_Azimuth45_EffectiveCrossExt";
string dirDrudeLorentz = "RadiusDistanceDrudeLorentz_Azimuth45_EffectiveCrossExt";
var gp = new GnuPlot();
gp.Set("style data lines");
gp.HoldOn();
foreach (double distance in distances)
{
Dictionary <double, double> optical = SimpleFormatter.Read(
this.getFileFormat(dirOpticalConst, distance, radius));
Dictionary <double, double> drudeLorentz = SimpleFormatter.Read(
this.getFileFormat(dirDrudeLorentz, distance, radius));
gp.Plot(optical, @"title ""optical""");
gp.Plot(drudeLorentz, @"title ""drude-lorentz""");
//AssertHelper.DictionaryAreClose(optical, drudeLorentz, 0.1);
}
gp.Wait();
}
public void Calculate_PerformanceMetrics()
{
var serialTicks = 192769194.79518071;
List <double> all = new List <double>();
var gp = new GnuPlot();
gp.Set("style data linespoints");
gp.HoldOn();
for (int i = 256; i <= 1024; i = i * 2)
{
List <long> list = new List <long>();
for (int j = 0; j < 20; j++)
{
Stopwatch watch = Stopwatch.StartNew();
//ArrayExtensions.MaxDegreeOfParallelism = i;
FDTDProgram.Calculate();
watch.Stop();
list.Add(watch.ElapsedTicks);
}
all.Add(list.Average());
}
gp.Plot(all.Select(x => serialTicks / x));
gp.Wait();
}
public static void PlotData(Matrix <double> X, Vector <double> y)
{
List <(double x1, double x2)> pos = new List <(double x1, double x2)>();
List <(double x1, double x2)> neg = new List <(double x1, double x2)>();
for (int i = 0; i < y.Count; i++)
{
if (y[i] == 1)
{
pos.Add((X[i, 0], X[i, 1]));
}
else
{
neg.Add((X[i, 0], X[i, 1]));
}
}
GnuPlot.HoldOn();
GnuPlot.Set("title \"\"");
GnuPlot.Set("xlabel \"Microchip test 1\"");
GnuPlot.Set("ylabel \"Microchip test 2\"");
GnuPlot.Plot(pos.Select(p => p.x1).ToArray(), pos.Select(p => p.x2).ToArray(), "pt 1 ps 1 lc rgb \"black\" title \"y=1\"");
GnuPlot.Plot(neg.Select(p => p.x1).ToArray(), neg.Select(p => p.x2).ToArray(), "pt 7 ps 1 lc rgb \"yellow\" title \"y=0\"");
}
public static void PlotData(Matrix <double> X, Vector <double> y)
{
List <(double x1, double x2)> pos = new List <(double x1, double x2)>();
List <(double x1, double x2)> neg = new List <(double x1, double x2)>();
for (int i = 0; i < y.Count; i++)
{
if (y[i] == 1)
{
pos.Add((X[i, 0], X[i, 1]));
}
else
{
neg.Add((X[i, 0], X[i, 1]));
}
}
GnuPlot.HoldOn();
GnuPlot.Set("title \"Data\"");
//GnuPlot.Set("key bottom right");
GnuPlot.Set("key outside noautotitle");
//GnuPlot.Set("key title \"Legend\"");
GnuPlot.Set("xlabel \"Exam 1 score\"");
GnuPlot.Set("ylabel \"Exam 2 score\"");
GnuPlot.Plot(pos.Select(p => p.x1).ToArray(), pos.Select(p => p.x2).ToArray(), "pt 1 ps 1 lc rgb \"black\" title \"Admitted\"");
GnuPlot.Plot(neg.Select(p => p.x1).ToArray(), neg.Select(p => p.x2).ToArray(), "pt 7 ps 1 lc rgb \"yellow\" title \"Not admitted\"");
}
static void Main(string[] args)
{
System.Globalization.CultureInfo customCulture = (System.Globalization.CultureInfo)System.Threading.Thread.CurrentThread.CurrentCulture.Clone();
customCulture.NumberFormat.NumberDecimalSeparator = ".";
System.Threading.Thread.CurrentThread.CurrentCulture = customCulture;
ParseArgs(args);
foreach (var shape in shapes)
{
Helper.SetShape(shape.shape, shape.args);
}
if (Helper.Plot)
{
GnuPlot.Set("", "xrange[-10:10]", "yrange[-10:10]", "size square" /*, "terminal gif animate delay 5"*/,
$"output '{Helper.outputPath}{Helper.outputFilename.Replace(".txt", ".gif")}'");
Helper.PlotPoints(Helper.ShapePoints, "with points pt '+' lc rgb 'black'", Helper.outputPath + "shape.txt");
}
Network network = new Network(Helper.ShapePoints, epochs, neurons, learningRate, sigma0, method);
network.Learn();
}
public static void PlotData(double[] x, double[] y)
{
GnuPlot.Set("title \"Water flowing\"");
GnuPlot.Set("key bottom right");
GnuPlot.Set("xlabel \"Change in water level (x)\"");
GnuPlot.Set("ylabel \"Water flowing out of the dam (y)\"");
GnuPlot.Plot(x, y, "pt 2 ps 1 lc rgb \"red\" notitle");
}
public void MakePlot(double[] xBound, double[] yBound)
{
string xr = "xr[" + xBound[0] + ":" + xBound[1] + "]";
string yr = "yr[" + yBound[0] + ":" + yBound[1] + "]";
GnuPlot.Set("cntrparam levels 30", "isosamples 40", xr, yr, "decimalsign locale");
GnuPlot.SPlot(filename, "with pm3d");
}
// plot the data
public static void PlotData(double[] x, double[] y)
{
GnuPlot.Set("title \"Linear Regression\"");
GnuPlot.Set("key bottom right");
//GnuPlot.Set("key title \"Legend\"");
GnuPlot.Set("xlabel \"Population of City in 10,000s\"");
GnuPlot.Set("ylabel \"Profit in $10,000s\"");
GnuPlot.Plot(x, y, "pt 2 ps 1 lc rgb \"red\" title \"Training data\"");
}
private static void PlotJ(Matrix <double> j_history, string title, string color)
{
double[] x = MathNet.Numerics.Generate.LinearRange(1, 1, j_history.RowCount);
double[] y = j_history.Column(0).ToArray();
GnuPlot.Set("xlabel \"Number of iteration\"");
GnuPlot.Set("ylabel \"Cost J\"");
GnuPlot.Plot(x, y, "with lines linestyle 1 lc rgb \"" + color + "\" linewidth 2 title \"" + title + "\" ");
}
public void RadiusChangeOutputPeaks_Azimuth0_Azimuth90()
{
// Arrange
const int DistanceMax = 3;
const double DistanceStep = 0.1;
var radiuses = new List <double> {
4, 10, 20, 40, 70, 100, 200
};
var radius1 = 4;
List <double> distances = getDistances(DistanceStep, DistanceMax);
string dirAzimuth0 = "RadiusChangeOutput_Azimuth0";
string dirAzimuth90 = "RadiusChangeOutput_Azimuth90";
var gp = new GnuPlot();
gp.Set("style data lines");
gp.HoldOn();
radiuses.Reverse();
foreach (double radius in radiuses)
{
var peaks0 = new Dictionary <double, double>();
var peaks90 = new Dictionary <double, double>();
foreach (double distance in distances)
{
Dictionary <double, double> azim0 = SimpleFormatter.Read(
this.getFileFormatDiffRadiuses(dirAzimuth0, distance, radius1, radius));
Dictionary <double, double> azim90 = SimpleFormatter.Read(
this.getFileFormatDiffRadiuses(dirAzimuth90, distance, radius1, radius));
peaks0.Add(distance, azim0.MaxPair().Key);
peaks90.Add(distance, azim0.MaxPair().Key);
}
// gp.HoldOn();
// gp.Set(string.Format("terminal win {0}", radius));
gp.Plot(peaks0);
gp.Plot(peaks90);
// gp.HoldOff();
string basepath = Path.Combine(BasePath, this.TestContext.TestName);
string filename0 = Path.Combine(
basepath,
string.Format("peaks_0deg_{0}.txt", radius));
SimpleFormatter.Write(filename0, peaks0);
string filename90 = Path.Combine(
basepath,
string.Format("peaks_90deg_{0}.txt", radius));
SimpleFormatter.Write(filename90, peaks90);
}
gp.Wait();
}
// Plot J cost function
private static void PlotJ(Matrix <double> X, Matrix <double> y, Matrix <double> theta)
{
// Grid over which we will calculate J
double[] theta0_vals = MathNet.Numerics.Generate.LinearSpaced(100, -10, 10);
double[] theta1_vals = MathNet.Numerics.Generate.LinearSpaced(100, -1, 4);
// initialize J_vals to a matrix of 0's
int size = theta0_vals.Length * theta1_vals.Length;
double[] sx = new double[size];
double[] sy = new double[size];
double[] sz = new double[size];
// Fill out J_vals
int idx = 0;
for (int i = 0; i < theta0_vals.Length; i++)
{
for (int k = 0; k < theta1_vals.Length; k++)
{
Matrix <double> t = Matrix <double> .Build.Dense(2, 1);
t[0, 0] = theta0_vals[i];
t[1, 0] = theta1_vals[k];
sx[idx] = theta0_vals[i];
sy[idx] = theta1_vals[k];
sz[idx] = ComputeCost(X, y, t);
idx++;
}
}
GnuPlot.HoldOn();
GnuPlot.Set("terminal wxt 1");
GnuPlot.Set("title \"Cost function J\"");
GnuPlot.Set("key bottom right");
GnuPlot.Set("xlabel \"{/Symbol q}_0\"");
GnuPlot.Set("ylabel \"{/Symbol q}_1\"");
// surface plot
GnuPlot.SPlot(sx, sy, sz, "palette title \"J({/Symbol q}_0,{/Symbol q}_1)\"");
// Contour plot
GnuPlot.Set("terminal wxt 2");
GnuPlot.Set("cntrparam levels auto 10", "logscale z", "xr[-10:10]", "yr[-1:4]");
GnuPlot.Unset("key", "label");
GnuPlot.Contour(sx, sy, sz);
GnuPlot.Plot(new double[] { theta[0, 0] }, new double[] { theta[1, 0] }, "pt 2 ps 1 lc rgb \"red\"");
}
private static void DisplayData(Vector <double>[] X)
{
int w = 20;
int h = 20;
int row = 0;
int col = 0;
int offsetRow = 0;
int offsetCol = 0;
int dim = (int)Math.Sqrt(X.Length);
double[,] d = new double[h * dim, w *dim];
for (int i = 0; i < X.Length; i++)
{
for (int k = 0; k < (w * h); k++)
{
d[row + offsetRow, col + offsetCol] = X[i][k];
offsetCol++;
if (offsetCol % w == 0)
{
offsetRow++;
offsetCol = 0;
}
}
col += w;
if (col >= (w * dim))
{
row += h;
col = 0;
}
offsetRow = 0;
offsetCol = 0;
}
//d = RotateRight(d);
//GnuPlot.Unset("key");
GnuPlot.Unset("colorbox");
//GnuPlot.Unset("tics");
GnuPlot.Set("grid");
GnuPlot.Set("palette grey");
//GnuPlot.Set("xrange [0:200]");
//GnuPlot.Set("yrange [0:200]");
GnuPlot.Set("pm3d map");
GnuPlot.SPlot(d, "with image");
}
private static void PlotValidationCurve(double[] x, double[] jtrain, double[] jvc)
{
GnuPlot.HoldOn();
GnuPlot.Set("title \"Validation curve\"");
GnuPlot.Set("xlabel \"Lamda\"");
GnuPlot.Set("key top right box");
GnuPlot.Set("ylabel \"Error\"");
GnuPlot.Set("autoscale xy");
GnuPlot.Plot(x, jtrain, "with lines ls 1 lw 2 lc rgb \"cyan\" title \"Train\" ");
GnuPlot.Plot(x, jvc, "with lines ls 1 lw 2 lc rgb \"orange\" title \"Cross Validation\" ");
GnuPlot.HoldOff();
}
private static void PlotLinearLearningCurve(double[] x, double[] jtrain, double[] jvc)
{
GnuPlot.HoldOn();
GnuPlot.Set("title \"Learning curve for linear regression\"");
GnuPlot.Set("xlabel \"Number of training examples\"");
GnuPlot.Set("key top right box");
GnuPlot.Set("ylabel \"Error\"");
GnuPlot.Set("xr[0:13]");
GnuPlot.Set("yr[0:150]");
GnuPlot.Plot(x, jtrain, "with lines ls 1 lw 2 lc rgb \"cyan\" title \"Train\" ");
GnuPlot.Plot(x, jvc, "with lines ls 1 lw 2 lc rgb \"orange\" title \"Cross Validation\" ");
GnuPlot.HoldOff();
}
private static void Main(string[] args)
{
string fileTrain1 = "approximation_train_1.txt";
string fileTrain2 = "approximation_train_2.txt";
string fileTest = "approximation_test.txt";
string filePathTrain1 = Path.GetFullPath(fileTrain1);
string filePathTrain2 = Path.GetFullPath(fileTrain2);
string filePathTest = Path.GetFullPath(fileTest);
List <double> outputValues = new List <double>();
DataGetter dataGetterTrain1 = new DataGetter(filePathTrain1);
DataGetter dataGetterTrain2 = new DataGetter(filePathTrain2);
DataGetter dataGetterTest = new DataGetter(filePathTest);
double learnRate = 0.1;
double momentum = 0.1;
int numberOfRadialNeurons = 20;
int numberOfEpochs = 300;
//Console.WriteLine("SIEC NEURONOWA RBF WYKORZYSTYWANA DO APROKSYMACJI FUNKJI");
//Console.WriteLine("Prosze wprowadzic wartosc wspolczynnika nauki:");
//learnRate = Convert.ToDouble(Console.ReadLine(), System.Globalization.CultureInfo.InvariantCulture);
//Console.WriteLine("Prosze wprowadzic wartosc wspolczynnika momentum:");
//momentum = Convert.ToDouble(Console.ReadLine(), System.Globalization.CultureInfo.InvariantCulture);
NeuralNetwork neuralNetwork = new NeuralNetwork(dataGetterTrain1.getInputData(), learnRate, momentum, numberOfRadialNeurons);
Console.WriteLine("ZBIOR TRENINGOWY 1: ");
neuralNetwork.Train(numberOfEpochs, dataGetterTrain2);
Console.WriteLine("ZBIOR TRENINGOWY 2: ");
neuralNetwork.Train(numberOfEpochs, dataGetterTrain1);
Console.WriteLine("ZBIOR TESTOWY: ");
outputValues = neuralNetwork.Test(dataGetterTest);
GnuPlot.Set("term wxt 0");
GnuPlot.HoldOn();
GnuPlot.Set("xlabel 'X Values'");
GnuPlot.Set("ylabel 'Y Values'");
GnuPlot.Plot(dataGetterTest.getInputData().ToArray(), dataGetterTest.getExpectedData().ToArray(), "title 'funkcja oryginalna'");
GnuPlot.Plot(dataGetterTest.getInputData().ToArray(), outputValues.ToArray(), "title 'funkcja po aproksymacji'");
GnuPlot.HoldOff();
GnuPlot.Set("term wxt 1");
GnuPlot.Set("xlabel 'Numer epoki'");
GnuPlot.Set("ylabel 'Wartosc bledu'");
GnuPlot.Plot(neuralNetwork.ErrorX.ToArray(), neuralNetwork.ErrorY.ToArray(), "with lines title 'Blad aproksymacji'");
Console.ReadKey();
}
private static void PlotLinearBoundary(Matrix <double> X, Vector <double> y, Vector <double> theta)
{
GnuPlot.Set("terminal wxt 1");
PlotData(X.RemoveColumn(0), y);
// Only need 2 points to define a line, so choose two endpoints
double x1 = X.Column(2).Minimum() - 2;
double x2 = X.Column(2).Maximum() + 2;
Vector <double> X_plot = Vector <double> .Build.DenseOfArray(new double [] { x1, x2 });
var y_plot = (-1 / theta[2]) * (theta[1] * X_plot + theta[0]);
GnuPlot.Plot(X_plot.ToArray(), y_plot.ToArray(), "with lines linestyle 1 linewidth 1 title \"Decision Boundary\" ");
// GnuPlot.Set("xrange [30:100]");
// GnuPlot.Set("yrange [30:100]");
}
private static void PlotProgresskMeans(Matrix <double> X, Matrix <double> centroids, Matrix <double> previous_centroids, Vector <double> idx, int K, int i)
{
GnuPlot.Set($"title \"Iteration number {i+1}");
PlotDataPoints(X, idx, K);
// Plot the centroids as black x's
double[] x1 = centroids.Column(0).ToArray();
double[] x2 = centroids.Column(1).ToArray();
GnuPlot.Plot(x1, x2, "pt 2 ps 1 lw 3 lc rgb \"black\" notitle");
// Plot the history of the centroids with lines
for (int j = 0; j < centroids.RowCount; j++)
{
double[] x = new double[] { centroids[j, 0], previous_centroids[j, 0] };
double[] y = new double[] { centroids[j, 1], previous_centroids[j, 1] };
GnuPlot.Plot(x, y, "with lines linestyle 1 linewidth 2 notitle");
}
}
private static void PlotBoundary(Matrix <double> x, C_SVC svc)
{
double min = x.Column(0).Min();
double max = x.Column(0).Max();
double[] x0 = MathNet.Numerics.Generate.LinearSpaced(100, min, max);
min = x.Column(1).Min();
max = x.Column(1).Max();
double[] x1 = MathNet.Numerics.Generate.LinearSpaced(100, min, max);
int size = x0.Length * x1.Length;
double[] sx = new double[size];
double[] sy = new double[size];
double[] sz = new double[size];
int idx = 0;
for (int i = 0; i < x0.Length; i++)
{
for (int j = 0; j < x1.Length; j++)
{
sx[idx] = x0[i];
sy[idx] = x1[j];
svm_node n1 = new svm_node();
n1.index = 1;
n1.value = x0[i];
svm_node n2 = new svm_node();
n2.index = 2;
n2.value = x1[j];
double z = svc.Predict(new [] { n1, n2 });
sz[idx] = z;
idx++;
}
}
GnuPlot.Set("cntrparam levels discrete 0.5");
GnuPlot.Contour(sx, sy, sz, "title \"Decision Boundary\"");
}
void PlotAverageSpeedAndSize()
{
GnuPlot.Set("xlabel 'Time [Seconds]'");
GnuPlot.Set("ylabel 'Avg. speed'");
GnuPlot.Set("y2label 'Avg. size'");
GnuPlot.Set("ytics nomirror");
GnuPlot.Set("y2tics");
GnuPlot.Set("tics out");
GnuPlot.Set(String.Format("yrange [{0}:{1}]", JumpmanAttributes.MIN_SPEED, JumpmanAttributes.MAX_SPEED));
GnuPlot.Set(String.Format("y2range [{0}:{1}]", JumpmanAttributes.MIN_SIZE, JumpmanAttributes.MAX_SIZE));
//GnuPlot.Set("autoscale y");
//GnuPlot.Set("autoscale y2");
GnuPlot.Set("key left top Left box 3");
GnuPlot.HoldOn();
GnuPlot.Plot(tracker.averageJumpmanAttrTmpPath, "using 1:5 title 'Speed' w lp axes x1y1");
GnuPlot.Plot(tracker.averageJumpmanAttrTmpPath, "using 1:6 title 'Size' w lp axes x1y2");
GnuPlot.HoldOff();
}
void PlotNutritionAndJumpmen()
{
GnuPlot.Set("xlabel 'Time [Seconds]'");
GnuPlot.Set("ylabel 'Total nutrition'");
GnuPlot.Set("y2label 'Amount of jumpmen'");
GnuPlot.Set("ytics nomirror");
GnuPlot.Set("y2tics");
GnuPlot.Set("tics out");
GnuPlot.Unset("yrange");
GnuPlot.Unset("y2range");
//GnuPlot.Set("autoscale y");
//GnuPlot.Set("autoscale y2");
GnuPlot.Set("key left top Left box 3");
GnuPlot.HoldOn();
GnuPlot.Plot(tracker.nutritionTmpPath, "using 1:2 title 'Nutrition' w l axes x1y1");
GnuPlot.Plot(tracker.jumpmanCountTmpPath, "using 1:2 title 'Jumpmen' w lp axes x1y2");
GnuPlot.HoldOff();
}
public void MakeContour(double[] xBound, double[] yBound)
{
int id = x.Count() - 1;
double[] x1plot = new double[id + 1];
double[] x2plot = new double[id + 1];
for (int i = 0; i <= id; i++)
{
x1plot[i] = x[i][0];
x2plot[i] = x[i][1];
}
string xr = "xr[" + xBound[0] + ":" + xBound[1] + "]";
string yr = "yr[" + yBound[0] + ":" + yBound[1] + "]";
GnuPlot.Unset("key"); //hide the key or legend
GnuPlot.HoldOn();
GnuPlot.Set(xr, yr, "cntrparam levels 20", "isosamples 50", xr, yr, "decimalsign locale"); //notice cntrparam levels (# height levels)
GnuPlot.Plot(x1plot, x2plot, "with linespoints linestyle 1");
GnuPlot.Contour(filename, "lc rgb 'blue'");
}
public void Radius10_Azimuth_Spectrum()
{
// Arrange
const int DistanceMax = 3;
const double DistanceStep = 0.1;
int radius = 10;
List <double> distances = getDistances(DistanceStep, DistanceMax);
string dirAzimuth45 = "RadiusDistanceOutput_Azimuth45_EffectiveCrossExt";
string dirAzimuth0 = "RadiusDistanceOutput_Azimuth0_EffectiveCrossExt";
string dirAzimuth90 = "RadiusDistanceOutput_Azimuth90_EffectiveCrossExt";
var gp = new GnuPlot();
gp.Set("style data lines");
gp.HoldOn();
Dictionary <decimal, List <double> > spectrum = this.zipToDictionary(distances, dirAzimuth45, radius);
string filename = Path.Combine(BasePath, this.TestContext.TestName, "Azim45.txt");
SimpleFormatter.WriteDictionary(filename, spectrum, distances);
foreach (double distance in distances)
{
Dictionary <double, double> azim45 = SimpleFormatter.Read(
this.getFileFormat(dirAzimuth45, distance, radius));
gp.Plot(azim45, string.Format(@"title ""{0}""", distance));
}
spectrum = this.zipToDictionary(distances, dirAzimuth0, radius);
filename = Path.Combine(BasePath, this.TestContext.TestName, "Azim0.txt");
SimpleFormatter.WriteDictionary(filename, spectrum, distances);
spectrum = this.zipToDictionary(distances, dirAzimuth90, radius);
filename = Path.Combine(BasePath, this.TestContext.TestName, "Azim90.txt");
SimpleFormatter.WriteDictionary(filename, spectrum, distances);
gp.Wait();
}
