static void ProcessData(MLContext context, IDataView data)
{
var houses = context.Data.CreateEnumerable <HouseBlockData>(data, reuseRowObject: false).ToArray();
// plot median house value by longitude
var pl = new PLStream();
pl.sdev("pngcairo"); // png rendering
pl.sfnam("data.png"); // output filename
pl.spal0("cmap0_alternate.pal"); // alternate color palette
pl.init();
pl.env(
0, 10, // x-axis range
0, 600000, // y-axis range
AxesScale.Independent, // scale x and y independently
AxisBox.BoxTicksLabelsAxes); // draw box, ticks, and num ticks
pl.lab(
"Median Income", // x-axis label
"Median House Value", // y-axis label
"House value by longitude"); // plot title
pl.sym(
houses.Select(h => (double)h.MedianIncome).ToArray(),
houses.Select(h => (double)h.MedianHouseValue).ToArray(),
(char)218
);
pl.eop();
}
static void PlotData(MLContext context, IDataView data)
{
var transactions = context.Data.CreateEnumerable <CreditCardData>(data, reuseRowObject: false).ToArray();
// plot median house value by longitude
var pl = new PLStream();
pl.sdev("pngcairo"); // png rendering
pl.sfnam("data.png"); // output filename
pl.spal0("cmap0_alternate.pal"); // alternate color palette
pl.init();
pl.env(
0, 5000, // x-axis range
0, 2500, // y-axis range
AxesScale.Independent, // scale x and y independently
AxisBox.BoxTicksLabelsAxes); // draw box, ticks, and num ticks
pl.lab(
"Time", // x-axis label
"Amount", // y-axis label
"Transactions by Time"); // plot title
pl.sym(
transactions.Select(txn => (double)txn.Time).ToArray(),
transactions.Select(txn => (double)txn.Amount).ToArray(),
(char)218
);
pl.eop();
}
private static void demo2(PLStream pl)
{
pl.bop();
pl.ssub(10, 10);
fillColor(pl, out int[] r, out int[] g, out int[] b);
pl.scmap0(r, g, b);
draw_gridmap(pl, 100, 16);
pl.eop();
}
private static void PlotToFile(string fileName, Action <PLStream> action)
{
using (var pl = new PLStream())
{
pl.sdev("pngcairo");
pl.sfnam(fileName);
pl.spal0("cmap0_alternate.pal");
pl.init();
action(pl);
pl.eop();
}
}
private static void demo1(PLStream pl)
{
pl.bop();
int nx = 4;
int ny = 4;
{
pl.ssub(nx, ny);
draw_gridmap(pl, nx * ny, 0);
}
pl.eop();
}
private void Graph(string[] args,
double[] x1, double[] y1,
double[] x2, double[] y2,
double[] x3, double[] y3)
{
string chartFileName = string.Empty;
var pl = new PLStream();
if (args.Length == 1 && args[0] == "svg")
{
chartFileName = @"Logistic.svg";
pl.sdev("svg");
pl.sfnam("Logistic.svg");
}
else
{
chartFileName = @"Logistic.png";
pl.sdev("pngcairo");
pl.sfnam("Logistic.png");
}
pl.spal0("cmap0_alternate.pal");
pl.init();
const int xMin = -4;
const int xMax = 4;
const int yMin = -5;
const int yMax = 20;
pl.env(xMin, xMax, yMin, yMax, AxesScale.Independent, AxisBox.BoxTicksLabels);
// Set scaling for mail title text 125% size of default
pl.lab("X1", "X2", "Title");
pl.col0(3);
pl.sym(x1, y1, (char)228);
pl.col0(9);
pl.sym(x2, y2, (char)228);
pl.col0(2);
pl.line(x3, y3);
pl.eop();
pl.gver(out var verText);
var p = new Process();
string chartFileNamePath = @".\" + chartFileName;
p.StartInfo = new ProcessStartInfo(chartFileNamePath)
{
UseShellExecute = true
};
p.Start();
}
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 Graph(string[] args, double[] x, double[] y)
{
string chartFileName = string.Empty;
var pl = new PLStream();
if (args.Length == 1 && args[0] == "svg")
{
chartFileName = @"KNN.svg";
pl.sdev("svg");
pl.sfnam("KNN.svg");
}
else
{
chartFileName = @"KNN.png";
pl.sdev("pngcairo");
pl.sfnam("KNN.png");
}
pl.spal0("cmap0_alternate.pal");
pl.init();
const int xMin = 0;
const int xMax = 1;
const int yMin = 0;
const int yMax = 1;
pl.env(xMin, xMax, yMin, yMax, AxesScale.Independent, AxisBox.BoxTicksLabelsAxes);
pl.schr(0, 0.75);
pl.lab("X-axis", "Y-axis", "Title");
pl.col0(3);
pl.sym(x, y, (char)210);
pl.eop();
pl.gver(out var verText);
var p = new Process();
string chartFileNamePath = @".\" + chartFileName;
p.StartInfo = new ProcessStartInfo(chartFileNamePath)
{
UseShellExecute = true
};
p.Start();
}
private static void DrawPlot(IList <DayInfo> days, IList <DayInfo> anomalies)
{
days = days.Where(x => x.Date >= new DateTime(2017, 9, 1) && x.Date <= new DateTime(2017, 9, 30)).ToList();
anomalies = anomalies.Where(x => x.Date >= new DateTime(2017, 9, 1) && x.Date <= new DateTime(2017, 9, 30)).ToList();
using (var plot = new PLStream())
{
plot.sdev("pngcairo"); // png rendering
plot.sfnam("data.png"); // output filename
plot.spal0("cmap0_alternate.pal"); // alternate color palette
plot.init();
plot.env(
1, // x-axis range
days.Count,
0, // y-axis range
150,
AxesScale.Independent, // scale x and y independently
AxisBox.BoxTicksLabelsAxes); // draw box, ticks, and num ticks
plot.lab(
"Date", // x-axis label
"Count", // y-axis label
"Press releases September 2017"); // plot title
plot.line(
(from x in Enumerable.Range(1, days.Count) select(double) x).ToArray(),
(from p in days select(double) p.Count).ToArray());
// plot the spikes
plot.col0(2); // blue color
plot.schr(3, 3); // scale characters
plot.string2(
(from s in anomalies select(double) days.ToList().FindIndex(x => x.Date == s.Date) + 1).ToArray(),
(from s in anomalies select(double) s.Count + 15).ToArray(),
"↓");
plot.eop();
}
}
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();
}
/// <summary>
/// The main program entry point.
/// </summary>
/// <param name="args">The command line parameters.</param>
static void Main()
{
// create the machine learning context
var context = new MLContext();
// load the data file
Console.WriteLine("Loading data...");
var dataView = context.Data.LoadFromTextFile <SalesRecord>(path: dataPath, hasHeader: true, separatorChar: ',');
// get an array of data points
var sales = context.Data.CreateEnumerable <SalesRecord>(dataView, reuseRowObject: false).ToArray();
// plot the data
var pl = new PLStream();
pl.sdev("pngcairo"); // png rendering
pl.sfnam("data.png"); // output filename
pl.spal0("cmap0_alternate.pal"); // alternate color palette
pl.init();
pl.env(
0, 36, // x-axis range
0, 800, // y-axis range
AxesScale.Independent, // scale x and y independently
AxisBox.BoxTicksLabelsAxes); // draw box, ticks, and num ticks
pl.lab(
"Date", // x-axis label
"Sales", // y-axis label
"Shampoo sales over time"); // plot title
pl.line(
(from x in Enumerable.Range(0, sales.Count()) select(double) x).ToArray(),
(from p in sales select(double) p.Sales).ToArray()
);
// build a training pipeline for detecting spikes
var pipeline = context.Transforms.DetectIidSpike(
outputColumnName: nameof(SalesPrediction.Prediction),
inputColumnName: nameof(SalesRecord.Sales),
confidence: 95,
pvalueHistoryLength: sales.Count() / 4); // 25% of x-range
// train the model
Console.WriteLine("Detecting spikes...");
var model = pipeline.Fit(dataView);
// predict spikes in the data
var transformed = model.Transform(dataView);
var predictions = context.Data.CreateEnumerable <SalesPrediction>(transformed, reuseRowObject: false).ToArray();
// find the spikes in the data
var spikes = (from i in Enumerable.Range(0, predictions.Count())
where predictions[i].Prediction[0] == 1
select(Day: i, Sales: sales[i].Sales));
// plot the spikes
pl.col0(2); // blue color
pl.schr(3, 3); // scale characters
pl.string2(
(from s in spikes select(double) s.Day).ToArray(),
(from s in spikes select(double) s.Sales + 40).ToArray(),
"!");
// build a training pipeline for detecting change points
var pipeline2 = context.Transforms.DetectIidChangePoint(
outputColumnName: nameof(SalesPrediction.Prediction),
inputColumnName: nameof(SalesRecord.Sales),
confidence: 95,
changeHistoryLength: sales.Count() / 4); // 25% of x-range
// train the model
Console.WriteLine("Detecting change points...");
var model2 = pipeline2.Fit(dataView);
// get predictions
transformed = model2.Transform(dataView);
predictions = context.Data.CreateEnumerable <SalesPrediction>(transformed, reuseRowObject: false).ToArray();
// find the change points in the data
var changes = (from i in Enumerable.Range(0, predictions.Count())
where predictions[i].Prediction[0] == 1
select(Day: i, Sales: sales[i].Sales));
// plot the change points as vertical red lines
pl.col0(3);
foreach (var c in changes)
{
pl.line(new double[] { c.Day, c.Day }, new double[] { 0, 800 });
}
pl.eop();
Console.WriteLine("Saved output file: data.png");
}
private static void Main(string[] args)
{
// generate data for plotting
const double sineFactor = 0.012585;
const int exampleCount = 1000;
var phaseOffset = 125;
var x0 = new double[exampleCount];
var y0 = new double[exampleCount];
for (var j = 0; j < exampleCount; j++)
{
x0[j] = j;
y0[j] = (double)(System.Math.Sin(sineFactor * (j + phaseOffset)) * 1);
}
var x1 = new double[exampleCount];
var y1 = new double[exampleCount];
phaseOffset = 250;
for (var j = 0; j < exampleCount; j++)
{
x1[j] = j;
y1[j] = (double)(System.Math.Sin(sineFactor * (j + phaseOffset)) * 1);
}
var x2 = new double[exampleCount];
var y2 = new double[exampleCount];
phaseOffset = 375;
for (var j = 0; j < exampleCount; j++)
{
x2[j] = j;
y2[j] = (double)(System.Math.Sin(sineFactor * (j + phaseOffset)) * 1);
}
var x3 = new double[exampleCount];
var y3 = new double[exampleCount];
phaseOffset = 500;
for (var j = 0; j < exampleCount; j++)
{
x3[j] = j;
y3[j] = (double)(System.Math.Sin(sineFactor * (j + phaseOffset)) * 1);
}
// create PLplot object
var pl = new PLStream();
// use SVG backend and write to SineWaves.svg in current directory
if (args.Length == 1 && args[0] == "svg")
{
pl.sdev("svg");
pl.sfnam("SineWaves.svg");
}
else
{
pl.sdev("pngcairo");
pl.sfnam("SineWaves.png");
}
// use white background with black foreground
pl.spal0("cmap0_alternate.pal");
// Initialize plplot
pl.init();
// set axis limits
const int xMin = 0;
const int xMax = 1000;
const int yMin = -1;
const int yMax = 1;
pl.env(xMin, xMax, yMin, yMax, AxesScale.Independent, AxisBox.BoxTicksLabelsAxes);
// Set scaling for mail title text 125% size of default
pl.schr(0, 1.25);
// The main title
pl.lab("X", "Y", "PLplot demo of four sine waves");
// plot using different colors
// see http://plplot.sourceforge.net/examples.php?demo=02 for palette indices
pl.col0(9);
pl.line(x0, y0);
pl.col0(1);
pl.line(x1, y1);
pl.col0(2);
pl.line(x2, y2);
pl.col0(4);
pl.line(x3, y3);
// end page (writes output to disk)
pl.eop();
// output version
pl.gver(out var verText);
Console.WriteLine("PLplot version " + verText);
}
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 Main(string[] args)
{
Storage();
// generate data for plotting
const double sineFactor = 0.012585;
const int exampleCount = 1000;
var phaseOffset = 125;
var x0 = new double[exampleCount];
var y0 = new double[exampleCount];
for (var j = 0; j < exampleCount; j++)
{
x0[j] = j;
y0[j] = (double)(System.Math.Sin(sineFactor * (j + phaseOffset)) * 1);
}
var x1 = new double[exampleCount];
var y1 = new double[exampleCount];
phaseOffset = 250;
for (var j = 0; j < exampleCount; j++)
{
x1[j] = j;
y1[j] = (double)(System.Math.Sin(sineFactor * (j + phaseOffset)) * 1);
}
var x2 = new double[exampleCount];
var y2 = new double[exampleCount];
phaseOffset = 375;
for (var j = 0; j < exampleCount; j++)
{
x2[j] = j;
y2[j] = (double)(System.Math.Sin(sineFactor * (j + phaseOffset)) * 1);
}
var x3 = new double[exampleCount];
var y3 = new double[exampleCount];
phaseOffset = 500;
for (var j = 0; j < exampleCount; j++)
{
x3[j] = j;
y3[j] = (double)(System.Math.Sin(sineFactor * (j + phaseOffset)) * 1);
}
// create PLplot object
var pl = new PLStream();
// use SVG backend and write to SineWaves.svg in current directory
if (args.Length == 1 && args[0] == "svg")
{
pl.sdev("svg");
pl.sfnam("SineWaves.svg");
}
else
{
pl.sdev("pngcairo");
pl.sfnam("SineWaves.png");
}
// use white background with black foreground
pl.spal0("cmap0_alternate.pal");
// Initialize plplot
pl.init();
// set axis limits
const int xMin = 0;
const int xMax = 1000;
const int yMin = -1;
const int yMax = 1;
pl.env(xMin, xMax, yMin, yMax, AxesScale.Independent, AxisBox.BoxTicksLabelsAxes);
// Set scaling for mail title text 125% size of default
pl.schr(0, 1.25);
// The main title
pl.lab("X", "Y", "PLplot demo of four sine waves");
// plot using different colors
// see http://plplot.sourceforge.net/examples.php?demo=02 for palette indices
pl.col0(9);
pl.line(x0, y0);
pl.col0(1);
pl.line(x1, y1);
pl.col0(2);
pl.line(x2, y2);
pl.col0(4);
pl.line(x3, y3);
// end page (writes output to disk)
pl.eop();
// output version
pl.gver(out var verText);
Console.WriteLine("PLplot version " + verText);
////////histogram/////////////////////
double[] y00 = { 5.0, 15.0, 12.0, 24.0, 28.0, 30.0, 20.0, 8.0, 12.0, 3.0 };
double[] pos = { 0.0, 0.25, 0.5, 0.75, 1.0 };
double[] red = { 0.0, 0.25, 0.5, 1.0, 1.0 };
double[] green = { 1.0, 0.5, 0.5, 0.5, 1.0 };
double[] blue = { 1.0, 1.0, 0.5, 0.25, 0.0 };
PLStream pls = new PLStream();
pls.sdev("pngcairo");
pls.sfnam("Histogram.png");
pls.spal0("cmap0_alternate.pal");
pls.init();
pls.adv(0);
pls.vsta();
pls.wind(1980.0, 1990.0, 0.0, 35.0);
pls.box("bc", 1.0, 0, "bcnv", 10.0, 0);
pls.col0(2);
pls.lab("Year", "Widget Sales (millions)", "#frPLplot Example 12");
for (int i = 0; i < 10; i++)
{
// pls.col0(i + 1);
pls.col1(i / 9.0);
pls.psty(0);
double[] x = new double[4];
double[] y = new double[4];
x[0] = 1980.0 + i;
y[0] = 0.0;
x[1] = 1980.0 + i;
y[1] = y00[i];
x[2] = 1980.0 + i + 1.0;
y[2] = y00[i];
x[3] = 1980.0 + i + 1.0;
y[3] = 0.0;
pls.fill(x, y);
pls.col0(1);
pls.lsty(LineStyle.Continuous);
pls.line(x, y);
// sprintf(string, "%.0f", y0[i]);
String text = ((int)(y00[i] + 0.5)).ToString();
pls.ptex((1980.0 + i + .5), (y00[i] + 1.0), 1.0, 0.0, .5, text);
// sprintf(string, "%d", 1980 + i);
String text1 = (1980 + i).ToString();
pls.mtex("b", 1.0, ((i + 1) * .1 - .05), 0.5, text1);
}
pls.eop();
Console.ReadLine();
}
/// <summary>
/// The main program entry point.
/// </summary>
/// <param name="args">The command line parameters.</param>
static void Main()
{
// create the machine learning context
var context = new MLContext();
// load the data file
Console.WriteLine("Loading data...");
var dataView = context.Data.LoadFromTextFile <MeterData>(path: dataPath, hasHeader: true, separatorChar: ',');
// get an array of data points
var values = context.Data.CreateEnumerable <MeterData>(dataView, reuseRowObject: false).ToArray();
// plot the data
var pl = new PLStream();
pl.sdev("pngcairo"); // png rendering
pl.sfnam("data.png"); // output filename
pl.spal0("cmap0_alternate.pal"); // alternate color palette
pl.init();
pl.env(
0, 90, // x-axis range
0, 5000, // y-axis range
AxesScale.Independent, // scale x and y independently
AxisBox.BoxTicksLabelsAxes); // draw box, ticks, and num ticks
pl.lab(
"Day", // x-axis label
"Power consumption", // y-axis label
"Power consumption over time"); // plot title
pl.line(
(from x in Enumerable.Range(0, values.Count()) select(double) x).ToArray(),
(from p in values select(double) p.Consumption).ToArray()
);
// build a training pipeline for detecting spikes
var pipeline = context.Transforms.SsaSpikeEstimator(
nameof(SpikePrediction.Prediction),
nameof(MeterData.Consumption),
confidence: 98,
pvalueHistoryLength: 30,
trainingWindowSize: 90,
seasonalityWindowSize: 30);
// train the model
Console.WriteLine("Detecting spikes...");
var model = pipeline.Fit(dataView);
// predict spikes in the data
var transformed = model.Transform(dataView);
var predictions = context.Data.CreateEnumerable <SpikePrediction>(transformed, reuseRowObject: false).ToArray();
// find the spikes in the data
var spikes = (from i in Enumerable.Range(0, predictions.Count())
where predictions[i].Prediction[0] == 1
select(Day: i, Consumption: values[i].Consumption));
// plot the spikes
pl.col0(2); // blue color
pl.schr(3, 3); // scale characters
pl.string2(
(from s in spikes select(double) s.Day).ToArray(),
(from s in spikes select(double) s.Consumption + 200).ToArray(),
"↓");
pl.eop();
}
/// <summary>
/// The main program entry point.
/// </summary>
/// <param name="args">The command line arguments</param>
static void Main(string[] args)
{
// create the machine learning context
var context = new MLContext();
// load the dataset
Console.WriteLine("Loading data...");
var data = context.Data.LoadFromTextFile <HouseBlockData>(
path: dataPath,
hasHeader: true,
separatorChar: ',');
// keep only records with a median house value < 500,000
data = context.Data.FilterRowsByColumn(
data,
"MedianHouseValue",
upperBound: 499_999
);
// get an array of housing data
var houses = context.Data.CreateEnumerable <HouseBlockData>(data, reuseRowObject: false).ToArray();
// plot median house value by longitude
var pl = new PLStream();
pl.sdev("pngcairo"); // png rendering
pl.sfnam("data.png"); // output filename
pl.spal0("cmap0_alternate.pal"); // alternate color palette
pl.init();
pl.env(
0, 10, // x-axis range
0, 600000, // y-axis range
AxesScale.Independent, // scale x and y independently
AxisBox.BoxTicksLabelsAxes); // draw box, ticks, and num ticks
pl.lab(
"Median Income", // x-axis label
"Median House Value", // y-axis label
"House value by longitude"); // plot title
pl.sym(
houses.Select(h => (double)h.MedianIncome).ToArray(),
houses.Select(h => (double)h.MedianHouseValue).ToArray(),
(char)218
);
pl.eop();
// build a data loading pipeline
// step 1: divide the median house value by 1000
var pipeline = context.Transforms.CustomMapping <HouseBlockData, ToMedianHouseValue>(
(input, output) => { output.NormalizedMedianHouseValue = input.MedianHouseValue / 1000; },
contractName: "MedianHouseValue"
);
// get a 10-record preview of the transformed data
// var model = pipeline.Fit(data);
// var transformedData = model.Transform(data);
// var preview = transformedData.Preview(maxRows: 10);
// show the preview
// WritePreview(preview);
// step 2: bin the longitude
var pipeline2 = pipeline.Append(context.Transforms.NormalizeBinning(
inputColumnName: "Longitude",
outputColumnName: "BinnedLongitude",
maximumBinCount: 10
))
// step 3: bin the latitude
.Append(context.Transforms.NormalizeBinning(
inputColumnName: "Latitude",
outputColumnName: "BinnedLatitude",
maximumBinCount: 10
))
// step 4: one-hot encode the longitude
.Append(context.Transforms.Categorical.OneHotEncoding(
inputColumnName: "BinnedLongitude",
outputColumnName: "EncodedLongitude"
))
// step 5: one-hot encode the latitude
.Append(context.Transforms.Categorical.OneHotEncoding(
inputColumnName: "BinnedLatitude",
outputColumnName: "EncodedLatitude"
));
// step 6: cross the two one-hot encoded columns
var pipeline3 = pipeline2.Append(context.Transforms.CustomMapping <FromLocation, ToLocation>(
(input, output) =>
{
output.Location = new float[input.EncodedLongitude.Length * input.EncodedLatitude.Length];
var index = 0;
for (var i = 0; i < input.EncodedLongitude.Length; i++)
{
for (var j = 0; j < input.EncodedLatitude.Length; j++)
{
output.Location[index++] = input.EncodedLongitude[i] * input.EncodedLatitude[j];
}
}
},
contractName: "Location"
))
// step 7: remove all the columns we don't need anymore
.Append(context.Transforms.DropColumns(
"MedianHouseValue",
"Longitude",
"Latitude",
"BinnedLongitude",
"BinnedLatitude",
"EncodedLongitude",
"EncodedLatitude"
));
// get a 10-record preview of the transformed data
var model = pipeline3.Fit(data);
var transformedData = model.Transform(data);
var preview = transformedData.Preview(maxRows: 10);
// show the location vector
//WritePreview(preview);
WritePreviewColumn(preview, "Location");
}
public static void CreateCovidChart(CovidParser.Country[] countries, CovidChartTypes type, int startDay = 0, int endDay = 2147483647)
{
if (startDay < 0)
{
Debug.LogWarning("Start day is lower than 0, fixing");
startDay = 0;
}
int xMin = startDay;
int xMax = countries[0].data.Count;
int yMin = 0;
CovidParser.Country.Data c = FindMax(countries, type, startDay, endDay);
long yMax = 0;
string title = "COVID-19 in ";
for (int i = 0; i < countries.Length; i++)
{
title += countries[i].name + " ";
}
switch (type)
{
case CovidChartTypes.deaths:
yMax = c.deaths;
title += " deaths";
break;
case CovidChartTypes.confirmed:
yMax = c.confirmed;
title += " confirmed";
break;
case CovidChartTypes.recovered:
yMax = c.recovered;
title += " recovered";
break;
case CovidChartTypes.all:
yMax = c.confirmed;
break;
default:
break;
}
List <double>[] values = null; //new List<double>();
List <double> keys = new List <double>();
for (int j = 0; j < countries.Length; j++)
{
for (int i = startDay; i < countries[j].data.Count && i < endDay; i++)
{
if (j == 0)
{
keys.Add(i);
}
switch (type)
{
case CovidChartTypes.deaths:
if (values == null || values[j] == null)
{
values = new List <double> [countries.Length];
values[j] = new List <double>();
}
values[j].Add(countries[j].data[i].deaths);
break;
case CovidChartTypes.confirmed:
if (values == null || values[j] == null)
{
values = new List <double> [countries.Length];
values[j] = new List <double>();
}
values[j].Add(countries[j].data[i].confirmed);
break;
case CovidChartTypes.recovered:
if (values == null || values[j] == null)
{
values = new List <double> [countries.Length];
values[j] = new List <double>();
}
values[j].Add(countries[j].data[i].recovered);
break;
case CovidChartTypes.all:
//Debug.LogError("All is not supported yet");
if (values == null)
{
values = new List <double> [countries.Length * 3];
}
if (values[j * 3] == null)
{
values[j * 3] = new List <double>();
values[j * 3 + 1] = new List <double>();
values[j * 3 + 2] = new List <double>();
}
values[j * 3].Add(countries[j].data[i].confirmed);
values[j * 3 + 1].Add(countries[j].data[i].deaths);
values[j * 3 + 2].Add(countries[j].data[i].recovered);
break;
default:
break;
}
}
}
var pl = new PLStream();
pl.sdev("pngcairo");
pl.sfnam("covid.png");
pl.init();
pl.col0(15);
// Set to use 10000 instead of 1 * 10^5
pl.syax(10, 10);
if (endDay < xMax)
{
xMax = endDay - 1;
}
pl.env(xMin, xMax, yMin, yMax, AxesScale.Independent, AxisBox.BoxTicksLabels);
//pl.setcontlabelformat(10, 10);
pl.col0(15);
pl.lab("Days", "Cases", title);
Pattern[] lg_patterns = new Pattern[values.Length];
double[] lg_scales = new double[values.Length];
string[] lg_texts = new string[values.Length];
int[] lg_lcolors = new int[values.Length];
double[] lg_lwidths = new double[values.Length];
int[] lg_scolors = new int[values.Length];
int[] lg_snumbers = new int[values.Length];
string[] lg_symbols = new string[values.Length];
LineStyle[] lg_lstyles = new LineStyle[values.Length];
LegendEntry[] lg_entries = new LegendEntry[values.Length];
double lg_spacing = 2.8 / (countries.Length + 0.5);
double lg_tscale = 1.4 / (countries.Length + 0.5);
double lg_toffset = 0.5;
switch (type)
{
case CovidChartTypes.deaths:
pl.scmap0(new int[] { 255, 255 }, new int[] { 255, 0 }, new int[] { 0, 0 });
break;
case CovidChartTypes.confirmed:
break;
case CovidChartTypes.recovered:
break;
case CovidChartTypes.all:
pl.scmap0(new int[] { 255, 255, 0, 204, 128, 0 }, new int[] { 255, 0, 255, 153, 0, 128 }, new int[] { 0, 0, 0, 0, 0, 0 });
break;
default:
break;
}
for (int i = 0; i < values.Length; i++)
{
pl.col0(i);
pl.line(keys.ToArray(), values[i].ToArray());
lg_entries[i] = LegendEntry.Line;
lg_lstyles[i] = LineStyle.Continuous;
lg_lcolors[i] = i;
lg_scolors[i] = i;
lg_snumbers[i] = i;
lg_lwidths[i] = 2;
lg_texts[i] = countries[i / 3].name;
if (i % 3 == 0)
{
lg_texts[i] += " confirmed";
}
else if (i % 3 == 1)
{
lg_texts[i] += " deaths";
}
else
{
lg_texts[i] += " recovered";
}
lg_scales[i] = 1;
}
pl.legend(out double width, out double height, Legend.BoundingBox, Position.Left | Position.Top, 0, 0, 0.1, 0, 1, LineStyle.Continuous, 0, 0, lg_entries, lg_toffset, lg_tscale, lg_spacing, 1.0,
lg_lcolors, lg_texts, lg_lcolors, lg_patterns, lg_scales, lg_lwidths, lg_lcolors, lg_lstyles, lg_lwidths, lg_scolors, lg_scales, lg_snumbers, lg_symbols);
pl.eop();
pl.ResetOpts();
IDisposable disp = (IDisposable)pl;
disp.Dispose();
//pl.gver(out var varText);
//Debug.Log("Plplot version: " + varText);
}
public string generateNNGraph(string matrixRef, int NumberInputsNodes, int NumberHiddenNodes, int NumberOutputNodes,
int NumberOfEpochs, double LearningRate_eta, string title, int oneCol, string oneLab, int twoCol, string twoLab, string fileName)
{
var matrixComp = Value(matrixRef);
if (matrixComp.GetType() == typeof(string))
{
return("Could not find variable " + matrixRef);
}
MatrixData _graphData = (MatrixData)matrixComp;
double[] x = _graphData.GetColumnCopy <double>(0);
double[] y = _graphData.GetColumnCopy <double>(1);
double[] y1 = _graphData.GetColumnCopy <double>(2);
double xMax = _graphData.Max(0);
var plot = new PLStream();
plot.width(1);
plot.sdev("svg");
plot.sfnam(fileName + ".svg");
plot.scolbg(255, 255, 255);
plot.init();
plot.env(0, xMax, 0, 105, AxesScale.Independent, AxisBox.BoxTicksLabelsAxes);
Dictionary <int, string> cols = new Dictionary <int, string>();
cols.Add(0, "black");
cols.Add(1, "red");
cols.Add(2, "yellow");
cols.Add(3, "green");
cols.Add(4, "aquamarine");
cols.Add(5, "pink");
cols.Add(6, "wheat");
cols.Add(7, "grey");
cols.Add(8, "brown");
cols.Add(9, "blue");
cols.Add(10, "BlueViolet");
cols.Add(11, "cyan");
cols.Add(12, "turquoise");
cols.Add(13, "magenta");
cols.Add(14, "salmon");
cols.Add(15, "white");
plot.col0(1);
plot.lab("Epoch", "Accuracy %", title);
plot.ptex(xMax - 10, 25, 1.0, 0, 1, "Input Nodes: " + NumberInputsNodes);
plot.ptex(xMax - 10, 20, 1.0, 0, 1, "Hidden Nodes: " + NumberHiddenNodes);
plot.ptex(xMax - 10, 15, 1.0, 0, 1, "Output Nodes: " + NumberOutputNodes);
plot.ptex(xMax - 10, 10, 1.0, 0, 1, "Epochs: " + NumberOfEpochs);
plot.ptex(xMax - 10, 5, 1.0, 0, 1, "Learning Rate: " + LearningRate_eta);
plot.col0(1);
plot.col0(oneCol);
plot.line(x, y);
plot.ptex(xMax - 10, 35, 1.0, 0, 1, cols[oneCol] + ": " + oneLab);
plot.col0(oneCol);
plot.col0(twoCol);
plot.line(x, y1);
plot.ptex(xMax - 10, 30, 1.0, 0, 1, cols[twoCol] + ": " + twoLab);
plot.col0(twoCol);
plot.eop();
return(Directory.GetCurrentDirectory() + "\\" + fileName + ".svg");
}
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 string GenerateGraph()
{
double[] x = _graphData.GetColumnCopy <double>(0);
double[] y = _graphData.GetColumnCopy <double>(1);
double[] y1 = _graphData.GetColumnCopy <double>(2);
double xMax = _graphData.Max(0);
//double yMax = _graphData.Max(1);
//yMax = (_graphData.Max(2) > yMax)? _graphData.Max(2) : yMax;
//double yMin = _graphData.Min(1);
//yMin = (_graphData.Min(2) < yMin)? _graphData.Min(2): yMin;
var plot = new PLStream();
plot.width(1);
plot.sdev("svg");
plot.sfnam("Test.svg");
plot.scolbg(255, 255, 255);
plot.init();
plot.env(0, xMax, 0, 105, AxesScale.Independent, AxisBox.BoxTicksLabelsAxes);
//####### PLPlot colour guide:
//0 black (default background)
//1 red (default foreground)
//2 yellow
//3 green
//4 aquamarine
//5 pink
//6 wheat
//7 grey
//8 brown
//9 blue
//10 BlueViolet
//11 cyan
//12 turquoise
//13 magenta
//14 salmon
//15 white
plot.col0(1);
if (!(_testingData == null))
{
plot.lab("Epoch", "Accuracy %", "Test vs Train Accuracy");
}
else
{
plot.lab("Epoch", "Accuracy %", "Test vs Val Accuracy");
}
plot.ptex(xMax - 10, 25, 1.0, 0, 1, "Input Nodes: " + NumberInputsNodes);
plot.ptex(xMax - 10, 20, 1.0, 0, 1, "Hidden Nodes: " + NumberHiddenNodes);
plot.ptex(xMax - 10, 15, 1.0, 0, 1, "Output Nodes: " + NumberOutputNodes);
plot.ptex(xMax - 10, 10, 1.0, 0, 1, "Epochs: " + NumberOfEpochs);
plot.ptex(xMax - 10, 5, 1.0, 0, 1, "Learning Rate: " + LearningRate_eta);
plot.col0(1);
plot.col0(9);
plot.line(x, y);
plot.ptex(xMax - 10, 35, 1.0, 0, 1, "Blue: Train");
plot.col0(9);
plot.col0(3);
plot.line(x, y1);
if (!(_testingData == null))
{
plot.ptex(xMax - 10, 30, 1.0, 0, 1, "Green: Test");
}
else
{
plot.ptex(xMax - 10, 30, 1.0, 0, 1, "Green: Val");
}
plot.col0(3);
plot.eop();
return("Saved \"Test.svg\"");
}