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();
}
}
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();
}
/// <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 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();
}
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();
}