private static void exportdata(List <double> states, string name, string unit, bool Exportimage = false, List <int> second = null)
{
System.Text.StringBuilder sb = new System.Text.StringBuilder();
sb.AppendLine($"x");
foreach (var item in states)
{
sb.AppendLine(item.ToString());
}
Directory.CreateDirectory(System.IO.Path.Combine("/D/", "acc"));
Directory.CreateDirectory(System.IO.Path.Combine("/D/", "acc_export"));
System.IO.File.WriteAllText(
System.IO.Path.Combine("/D/", "acc", $"linacc.csv"),
sb.ToString());
if (Exportimage)
{
var test = states.Min(x => x);
var pl = new PLStream();
pl.sdev("pngcairo"); // png rendering
pl.sfnam($"{name}.png"); // output filename
pl.spal0("cmap0_alternate.pal"); // alternate color palette
pl.init();
pl.env(
0, states.Count, // x-axis range
states.Min(x => x), states.Max(x => x), // y-axis range
AxesScale.Independent, // scale x and y independently
AxisBox.BoxTicksLabelsAxes); // draw box, ticks, and num ticks
pl.lab(
"Sample", // x-axis label
unit, // y-axis label
name); // plot title
pl.line(
(from x in Enumerable.Range(0, states.Count()) select(double) x).ToArray(),
(from p in states select(double) p).ToArray()
);
if (second != null)
{
pl.col0(4);
var Roots = GetListFromIndices(second, states);
pl.poin(
(from x in second select(double) x).ToArray(),
(from p in Roots select(double) p).ToArray(),
'!'
);
}
string csv = String.Join(",", states.Select(x => x.ToString()).ToArray());
pl.eop();
}
}
private static void PlotRegressionChart(MLContext mlContext,
string testDataSetPath,
int numberOfRecordsToRead,
string[] args)
{
ITransformer trainedModel;
using (var stream = new FileStream(ModelPath, FileMode.Open, FileAccess.Read, FileShare.Read))
{
trainedModel = mlContext.Model.Load(stream, out var modelInputSchema);
}
// Create prediction engine related to the loaded trained model
var predFunction = mlContext.Model.CreatePredictionEngine <TaxiTrip, TaxiTripFarePredictionWithContribution>(trainedModel);
string chartFileName = "";
using (var pl = new PLStream())
{
if (args.Length == 1 && args[0] == "svg")
{
pl.sdev("svg");
chartFileName = "TaxiRegressionDistribution.svg";
pl.sfnam(chartFileName);
}
else
{
pl.sdev("pngcairo");
chartFileName = "TaxiRegressionDistribution.png";
pl.sfnam(chartFileName);
}
// use white background with black foreground
pl.spal0("cmap0_alternate.pal");
// Initialize plplot
pl.init();
// set axis limits
const int xMinLimit = 0;
const int xMaxLimit = 35; //Rides larger than $35 are not shown in the chart
const int yMinLimit = 0;
const int yMaxLimit = 35; //Rides larger than $35 are not shown in the chart
pl.env(xMinLimit, xMaxLimit, yMinLimit, yMaxLimit, AxesScale.Independent, AxisBox.BoxTicksLabelsAxes);
// Set scaling for mail title text 125% size of default
pl.schr(0, 1.25);
// The main title
pl.lab("Actual", "Predicted", "Distribution of Taxi Fare Prediction");
// plot using different colors
// see http://plplot.sourceforge.net/examples.php?demo=02 for palette indices
pl.col0(1);
int totalNumber = numberOfRecordsToRead;
var testData = new TaxiTripCsvReader().GetDataFromCsv(testDataSetPath, totalNumber).ToList();
//This code is the symbol to paint
char code = (char)9;
// plot using other color
//pl.col0(9); //Light Green
//pl.col0(4); //Red
pl.col0(2); //Blue
double yTotal = 0;
double xTotal = 0;
double xyMultiTotal = 0;
double xSquareTotal = 0;
for (int i = 0; i < testData.Count; i++)
{
var x = new double[1];
var y = new double[1];
//Make Prediction
var FarePrediction = predFunction.Predict(testData[i]);
x[0] = testData[i].FareAmount;
y[0] = FarePrediction.FareAmount;
//Paint a dot
pl.poin(x, y, code);
xTotal += x[0];
yTotal += y[0];
double multi = x[0] * y[0];
xyMultiTotal += multi;
double xSquare = x[0] * x[0];
xSquareTotal += xSquare;
double ySquare = y[0] * y[0];
Console.WriteLine($"-------------------------------------------------");
Console.WriteLine($"Predicted : {FarePrediction.FareAmount}");
Console.WriteLine($"Actual: {testData[i].FareAmount}");
Console.WriteLine($"-------------------------------------------------");
}
// Regression Line calculation explanation:
// https://www.khanacademy.org/math/statistics-probability/describing-relationships-quantitative-data/more-on-regression/v/regression-line-example
double minY = yTotal / totalNumber;
double minX = xTotal / totalNumber;
double minXY = xyMultiTotal / totalNumber;
double minXsquare = xSquareTotal / totalNumber;
double m = ((minX * minY) - minXY) / ((minX * minX) - minXsquare);
double b = minY - (m * minX);
//Generic function for Y for the regression line
// y = (m * x) + b;
double x1 = 1;
//Function for Y1 in the line
double y1 = (m * x1) + b;
double x2 = 39;
//Function for Y2 in the line
double y2 = (m * x2) + b;
var xArray = new double[2];
var yArray = new double[2];
xArray[0] = x1;
yArray[0] = y1;
xArray[1] = x2;
yArray[1] = y2;
pl.col0(4);
pl.line(xArray, yArray);
// end page (writes output to disk)
pl.eop();
// output version of PLplot
pl.gver(out var verText);
Console.WriteLine("PLplot version " + verText);
} // the pl object is disposed here
// Open Chart File In Microsoft Photos App (Or default app, like browser for .svg)
Console.WriteLine("Showing chart...");
var p = new Process();
string chartFileNamePath = @".\" + chartFileName;
p.StartInfo = new ProcessStartInfo(chartFileNamePath)
{
UseShellExecute = true
};
p.Start();
}
public static void PlotRegressionChart(PlotChartGeneratorModel generationModel)
{
using (var pl = new PLStream())
{
pl.sdev("pngcairo");
pl.sfnam(generationModel.ImageName);
// use white background with black foreground
pl.spal0("cmap0_alternate.pal");
// Initialize plplot
pl.init();
// set axis limits
pl.env(generationModel.MinLimitX, generationModel.MaxLimitX, generationModel.MinLimitY, generationModel.MaxLimitY,
AxesScale.Independent, AxisBox.CustomXYBoxTicksLabels);
// Set scaling for mail title text 125% size of default
//pl.schr(0, 1.25);
// The main title
pl.lab(generationModel.LabelX, generationModel.LabelY, generationModel.Title);
// plot using different colors
// see http://plplot.sourceforge.net/examples.php?demo=02 for palette indices
pl.col0(1);
// This code is the symbol to paint
char code = (char)9;
double yTotal = 0;
double xTotal = 0;
double xyMultiTotal = 0;
double xSquareTotal = 0;
var totalNumber = 0;
generationModel.PointsList.ForEach(pointsList =>
{
double y0 = 0;
double x0 = 0;
totalNumber += pointsList.Points.Count();
// plot using other color
pl.col0(pointsList.Color);
pointsList.Points.ForEach(point =>
{
var x = new double[1];
var y = new double[1];
x[0] = point.X;
y[0] = point.Y;
if (pointsList.PaintDots)
{
// Paint a dot
pl.poin(x, y, code);
}
else
{
if (!generationModel.DrawRegressionLine)
{
// Draw lines between points
pl.join(x0, y0, point.X, point.Y);
x0 = point.X;
y0 = point.Y;
}
}
xTotal += point.X;
yTotal += point.Y;
xyMultiTotal += point.X * point.Y;
xSquareTotal += point.X * point.X;
});
});
// Regression Line calculation explanation:
// https://www.khanacademy.org/math/statistics-probability/describing-relationships-quantitative-data/more-on-regression/v/regression-line-example
if (generationModel.DrawRegressionLine)
{
if (generationModel.RegressionPointsList != null)
{
var xArray = generationModel.RegressionPointsList.Points.Select(dp => dp.X).ToArray();
var yArray = generationModel.RegressionPointsList.Points.Select(dp => dp.Y).ToArray();
pl.col0(generationModel.RegressionPointsList.Color);
pl.line(xArray, yArray);
}
else
{
double minY = yTotal / totalNumber;
double minX = xTotal / totalNumber;
double minXY = xyMultiTotal / totalNumber;
double minXsquare = xSquareTotal / totalNumber;
double m = ((minX * minY) - minXY) / ((minX * minX) - minXsquare);
double b = minY - (m * minX);
double x1 = generationModel.MinLimitX;
//Function for Y1 in the line
double y1 = (m * x1) + b;
double x2 = generationModel.MaxLimitX;
//Function for Y2 in the line
double y2 = (m * x2) + b;
var xArray = new double[2];
var yArray = new double[2];
xArray[0] = x1;
yArray[0] = y1;
xArray[1] = x2;
yArray[1] = y2;
pl.col0(4);
pl.line(xArray, yArray);
}
}
if (generationModel.DashedPoint != null)
{
pl.col0(CommonConstants.PPLplotColorGreen);
pl.width(2);
// Horizontal Line should go from (0,DashedPoint.Y) to (DashedPoint.X, DashedPoint.Y)
DrawDashedLined(pl, 0, generationModel.DashedPoint.Y, generationModel.DashedPoint.X, generationModel.DashedPoint.Y);
// Vertical Line should go from (DashedPoint.X,0) to (DashedPoint.X, DashedPoint.Y)
DrawDashedLined(pl, generationModel.DashedPoint.X, 0, generationModel.DashedPoint.X, generationModel.DashedPoint.Y);
}
// end page (writes output to disk)
pl.eop();
// output version of PLplot
pl.gver(out var verText);
} // the pl object is disposed here
// Open Chart File In Microsoft Photos App (Or default app, like browser for .svg)
Console.WriteLine("Showing chart...");
var p = new Process();
string chartFileNamePath = @".\" + generationModel.ImageName;
p.StartInfo = new ProcessStartInfo(chartFileNamePath)
{
UseShellExecute = true
};
p.Start();
}
public static void PlotRegressionChart(MLContext mlContext,
string testDataSetPath,
string simulationPath)
{
ITransformer trainedModel;
using (var stream = new FileStream(simulationPath, FileMode.Open, FileAccess.Read, FileShare.Read))
{
trainedModel = mlContext.Model.Load(stream, out var modelInputSchema);
}
// Create prediction engine related to the loaded trained model
var predFunction = mlContext.Model.CreatePredictionEngine <ExecutionModel, ExecutionModelPrediction>(trainedModel);
string chartFileName;
using (var pl = new PLStream())
{
pl.sdev("pngcairo");
chartFileName = "RegressionDistribution.png";
pl.sfnam(chartFileName);
// use white background with black foreground
pl.spal0("cmap0_alternate.pal");
// Initialize plplot
pl.init();
//var totalNumber = numberOfRecordsToRead;
var testData = new CsvReader().GetDataFromCsv(testDataSetPath).ToList();
var totalNumber = testData.Count;
var(xMinLimit, xMaxLimit) = GetMinMax(testData);
// set axis limits
pl.env(xMinLimit, xMaxLimit, xMinLimit, xMaxLimit, AxesScale.Independent, AxisBox.BoxTicksLabelsAxes);
// Set scaling for mail title text 125% size of default
pl.schr(0, 1.25);
// The main title
pl.lab("Measured", "Predicted", "Distribution of TotalTime Prediction");
// plot using different colors
// see http://plplot.sourceforge.net/examples.php?demo=02 for palette indices
pl.col0(1);
//This code is the symbol to paint
const char code = (char)9;
// plot using other color
//pl.col0(9); //Light Green
//pl.col0(4); //Red
pl.col0(2); //Blue
double yTotal = 0;
double xTotal = 0;
double xyMultiTotal = 0;
double xSquareTotal = 0;
foreach (var t in testData)
{
var x = new double[1];
var y = new double[1];
//Make Prediction
var prediction = predFunction.Predict(t);
x[0] = t.TotalTime;
y[0] = prediction.TotalTime;
//Paint a dot
pl.poin(x, y, code);
xTotal += x[0];
yTotal += y[0];
var multi = x[0] * y[0];
xyMultiTotal += multi;
var xSquare = x[0] * x[0];
xSquareTotal += xSquare;
//Console.WriteLine($"-------------------------------------------------");
//Console.WriteLine($"Predicted : {prediction.TotalTime}");
//Console.WriteLine($"Actual: {testData[i].TotalTime}");
//Console.WriteLine($"-------------------------------------------------");
}
var minY = yTotal / totalNumber;
var minX = xTotal / totalNumber;
var minXy = xyMultiTotal / totalNumber;
var minXsquare = xSquareTotal / totalNumber;
var m = ((minX * minY) - minXy) / ((minX * minX) - minXsquare);
var b = minY - (m * minX);
//Generic function for Y for the regression line
// y = (m * x) + b;
const int x1 = 1;
var y1 = (m * x1) + b;
var x2 = xMaxLimit;
//Function for Y2 in the line
var y2 = (m * x2) + b;
var xArray = new double[2];
var yArray = new double[2];
xArray[0] = x1;
yArray[0] = y1;
xArray[1] = x2;
yArray[1] = y2;
pl.col0(4);
pl.line(xArray, yArray);
// end page (writes output to disk)
pl.eop();
// output version of PLplot
pl.gver(out var verText);
//Console.WriteLine("PLplot version " + verText);
} // the pl object is disposed here
// Open Chart File
Console.WriteLine("Showing chart...");
var p = new Process();
var chartFileNamePath = @".\" + chartFileName;
p.StartInfo = new ProcessStartInfo(chartFileNamePath)
{
UseShellExecute = true
};
p.Start();
}