// ************************************************************************
private void DrawPoints(params DrawInfo[] drawInfos)
{
Model.PlotModel.Series.Clear();
foreach (var drawInfo in drawInfos)
{
if (drawInfo.DrawStyle == DrawStyle.Line)
{
var s = new OxyPlot.Series.LineSeries {
Title = drawInfo.Name, MarkerType = MarkerType.Square, Color = drawInfo.Color
};
foreach (var pt in drawInfo.Points)
{
s.Points.Add(new DataPoint(pt.X, pt.Y));
}
Model.PlotModel.Series.Insert(0, s);
}
else if (drawInfo.DrawStyle == DrawStyle.Point)
{
var s = new OxyPlot.Series.ScatterSeries {
Title = drawInfo.Name, MarkerType = MarkerType.Circle, MarkerSize = 2, MarkerFill = drawInfo.Color
};
foreach (var pt in drawInfo.Points)
{
s.Points.Add(new ScatterPoint(pt.X, pt.Y));
}
Model.PlotModel.Series.Insert(0, s);
}
}
Model.PlotModel.PlotView?.InvalidatePlot();
}
public void PlotMirror(double w, double h)
{
var model = new PlotModel();
model.PlotType = PlotType.Cartesian;
OxyPlot.Series.ScatterSeries sser = new OxyPlot.Series.ScatterSeries()
{
MarkerType = MarkerType.Square,
BinSize = 2,
MarkerSize = 0.75
};
sser.ItemsSource = m_Points;
model.Series.Add(sser);
model.Axes.Add(new OxyPlot.Axes.LinearAxis {
Position = AxisPosition.Bottom, Maximum = w / 2.0, Minimum = -w / 2.0, Title = "x, [mm]"
});
model.Axes.Add(new OxyPlot.Axes.LinearAxis {
Position = AxisPosition.Left, Maximum = h / 2.0, Minimum = -h / 2.0, Title = "y, [mm]"
});
model.Axes.Add(new OxyPlot.Axes.LinearColorAxis {
Position = AxisPosition.Right,
Palette = OxyPalettes.Rainbow(200),
});
m_Canvas.Model = model;
m_Canvas.InvalidatePlot(true);
}
public PlotModel ScatterPlot(IReadOnlyCollection <IdentData> data, Func <object, ScatterPoint> mapping, string title, string xTitle, OxyColor markerColor)
{
//series1.Mapping = item => new DataPoint(((MyType)item).Time,((MyType)item).Value)
var model = ModelBaseConfig();
model.Title = title;
var s1 = new ScatterSeries
{
MarkerType = MarkerType.Circle,
MarkerStrokeThickness = 0,
MarkerSize = 1.0,
MarkerFill = markerColor,
ItemsSource = data,
Mapping = mapping
};
var yAxis = new LinearAxis
{
Position = AxisPosition.Left,
Title = "Mass Error (PPM)",
MajorGridlineStyle = LineStyle.Solid,
MajorStep = 10.0,
MinorStep = 5.0,
Minimum = -20.0,
Maximum = 20.0,
MinimumRange = 40.0,
FilterMinValue = -20.0,
FilterMaxValue = 20.0
};
var xAxis = new LinearAxis
{
Position = AxisPosition.Bottom,
Title = xTitle
//Minimum = 1000,
//Maximum = 4600,
};
var xAxisCenter = new LinearAxis
{
Position = AxisPosition.Top, // To let the other axis be locked to the bottom, and force this one to be horizontal
PositionAtZeroCrossing = true,
TickStyle = TickStyle.None, // To change to show a value, need to do axis synchronization
AxislineStyle = LineStyle.Solid,
AxislineThickness = 1.0,
TextColor = OxyColors.Undefined // Force invisible labels
};
model.Axes.Add(yAxis);
model.Axes.Add(xAxis);
model.Axes.Add(xAxisCenter);
model.Series.Add(s1);
return(model);
}
public PlotModel ScatterPlot(IReadOnlyCollection <IdentData> data, string xDataField, string yDataField, string title, string xTitle, OxyColor markerColor)
{
var model = ModelBaseConfig();
model.Title = title;
var s1 = new ScatterSeries
{
MarkerType = MarkerType.Circle,
MarkerStrokeThickness = 0,
MarkerSize = 1.0,
MarkerFill = markerColor,
ItemsSource = data,
//DataFieldX = xDataField,
//DataFieldY = yDataField,
Mapping = item => new ScatterPoint(Convert.ToDouble(typeof(IdentData).GetProperty(xDataField)?.GetValue(item)), Convert.ToDouble(typeof(IdentData).GetProperty(yDataField)?.GetValue(item)))
};
var yAxis = new LinearAxis
{
Position = AxisPosition.Left,
Title = "Mass Error (PPM)",
MajorGridlineStyle = LineStyle.Solid,
MajorStep = 10.0,
MinorStep = 5.0,
Minimum = -20.0,
Maximum = 20.0,
MinimumRange = 40.0,
FilterMinValue = -20.0,
FilterMaxValue = 20.0
};
var xAxis = new LinearAxis
{
Position = AxisPosition.Bottom,
Title = xTitle
};
var xAxisCenter = new LinearAxis
{
Position = AxisPosition.Top, // To let the other axis be locked to the bottom, and force this one to be horizontal
PositionAtZeroCrossing = true,
TickStyle = TickStyle.None, // To change to show a value, need to do axis synchronization
AxislineStyle = LineStyle.Solid,
AxislineThickness = 1.0,
TextColor = OxyColors.Undefined // Force invisible labels
};
model.Axes.Add(yAxis);
model.Axes.Add(xAxis);
model.Axes.Add(xAxisCenter);
model.Series.Add(s1);
return(model);
}
private OxyPlot.Series.ScatterSeries CreateSeries(List <double> CouchAng, List <double> GantryAng, int size)
{
var scatterSeries = new OxyPlot.Series.ScatterSeries();
int CoordLength = CouchAng.Count;
for (int iii = 0; iii < CoordLength; iii++)
{
var point = new ScatterPoint(CouchAng[iii], GantryAng[iii], size);
scatterSeries.Points.Add(point);
}
return(scatterSeries);
}
private void CreateMdsChartModel()
{
var model = new PlotModel();
var xAxis = new OxyPlot.Axes.LinearAxis
{
Position = OxyPlot.Axes.AxisPosition.Bottom,
MajorGridlineStyle = LineStyle.None
};
model.Axes.Add(xAxis);
var yAxis = new OxyPlot.Axes.LinearAxis
{
Position = OxyPlot.Axes.AxisPosition.Left,
MajorGridlineStyle = LineStyle.None
};
model.Axes.Add(yAxis);
var series = new OxyPlot.Series.ScatterSeries
{
ItemsSource = (Data.MdsCoords.Rows.Cast <filterReportDS.MdsCoordsRow>()
.Select(dr => new DataPoint(dr.X, dr.Y))),
DataFieldX = "X",
DataFieldY = "Y",
MarkerType = MarkerType.Circle,
MarkerSize = 2,
MarkerFill = OxyColor.FromRgb(79, 129, 189)
};
model.Series.Add(series);
foreach (filterReportDS.MdsCoordsRow dr in Data.MdsCoords.Rows)
{
var annotation = new OxyPlot.Annotations.TextAnnotation
{
Text = dr.StrategyName,
TextPosition = new DataPoint(dr.X, dr.Y),
TextHorizontalAlignment = OxyPlot.HorizontalAlignment.Center,
TextVerticalAlignment = OxyPlot.VerticalAlignment.Top,
Font = "Segoe UI",
TextColor = OxyColor.FromRgb(0, 0, 0),
StrokeThickness = 0
};
model.Annotations.Add(annotation);
}
MdsChartModel = model;
}
private void PlotGraph()
{
PlotData = new PlotModel();
var yAxis = new OxyPlot.Axes.LinearAxis {
Position = AxisPosition.Left,
Title = "Fiber Attenuation",
MajorGridlineStyle = LineStyle.Solid,
MinorGridlineStyle = LineStyle.None
};
var xAxis = new OxyPlot.Axes.LinearAxis {
Position = AxisPosition.Bottom,
Title = "Wavelengths (nm)",
MajorGridlineStyle = LineStyle.Solid,
MinorGridlineStyle = LineStyle.None
};
PlotData.Axes.Add(xAxis);
PlotData.Axes.Add(yAxis);
DataPoint[] linePoints = new DataPoint[loss.Count];
ScatterPoint[] markerPoints = new ScatterPoint[loss.Count];
for (int i = 0; i < loss.Count; i++)
{
if (markers.Contains(wavelengths[i]))
{
markerPoints[i] = new ScatterPoint((double)wavelengths[i], (double)loss[i]);
}
else
{
markerPoints[i] = new ScatterPoint((double)wavelengths[i], 0);
}
linePoints[i] = new DataPoint((double)wavelengths[i], (double)loss[i]);
}
var lineSeries = new OxyPlot.Series.LineSeries {
StrokeThickness = 2,
ItemsSource = linePoints
};
var circleSeries = new OxyPlot.Series.ScatterSeries {
MarkerSize = 3,
MarkerType = MarkerType.Circle
}; // W850 TO W1550 S10;
circleSeries.Points.AddRange(markerPoints.Where(i => i.Y > 0));
PlotData.Series.Add(lineSeries);
PlotData.Series.Add(circleSeries);
PlotData.InvalidatePlot(true);
}
private PlotModel CreatePlotModel()
{
var model = new PlotModel
{
Title = "Recent ECGs",
};
var xAxis = new LinearAxis
{
Title = "Transit time",
Unit = "s",
Position = AxisPosition.Bottom
};
model.Axes.Add(xAxis);
var yAxis = new LinearAxis
{
Title = "Lost energy",
Unit = "kWh"
};
model.Axes.Add(yAxis);
var scatterSeries = new ScatterSeries
{
MarkerFill = OxyColors.Gray
};
foreach (var datum in _graphData)
{
scatterSeries.Points.Add(new ScatterPoint(datum.TransitTime, datum.LostEnergy));
}
model.Series.Add(scatterSeries);
var recentSeries = new ScatterSeries
{
MarkerFill = OxyColors.Blue,
MarkerType = MarkerType.Circle
};
foreach (var datum in _recentGraphData)
{
recentSeries.Points.Add(new ScatterPoint(datum.TransitTime, datum.LostEnergy));
}
model.Series.Add(recentSeries);
return(model);
}
private void DrawFunctions()
{
model.Series.Clear();
var xMin = model.DefaultXAxis.ActualMinimum;
var xMax = model.DefaultXAxis.ActualMaximum;
for (int i = 0; i < parametersFunctions.Count; i++)
{
operatingFunction.SetParametersValues(parametersFunctions[i]);
var series = new FunctionSeries(operatingFunction.FunctionValue, xMin, xMax, (xMax - xMin) / 1000);
if (i == 0)
{
series.Color = OxyColors.Blue;
}
else if (i == parametersFunctions.Count - 1)
{
series.Color = OxyColors.Red;
series.Title = "результат";
}
else
{
series.LineStyle = LineStyle.Dash;
}
model.Series.Add(series);
}
if (operatingDataSet.Count != 0)
{
var scatterSeries = new OxyPlot.Series.ScatterSeries {
MarkerType = MarkerType.Circle,
MarkerFill = OxyColors.DarkCyan
};
foreach (var point in operatingDataSet)
{
scatterSeries.Points.Add(new ScatterPoint(point.X, point.Y, 3));
}
model.Series.Add(scatterSeries);
}
Plot.InvalidatePlot();
}
public void MakeScatterChart([ItemNotNull][JetBrains.Annotations.NotNull] List <CalculationOutcome> outcomes, [JetBrains.Annotations.NotNull] string pngfullName, [ItemNotNull][JetBrains.Annotations.NotNull] List <SeriesEntry> series)
{
_calculationProfiler.StartPart(Utili.GetCurrentMethodAndClass());
var plotModel1 = new PlotModel
{
LegendBorderThickness = 0,
LegendOrientation = LegendOrientation.Horizontal,
LegendPlacement = LegendPlacement.Outside,
LegendPosition = LegendPosition.BottomCenter,
DefaultFontSize = FontSize,
LegendFontSize = FontSize,
DefaultFont = "Arial",
LegendFont = "Arial"
};
var ca = new CategoryAxis
{
Minimum = 0.4,
Title = "Number of Persons",
Position = AxisPosition.Bottom
};
ca.Title = Xaxislabel;
ca.Labels.Add("0");
ca.Labels.Add("1");
ca.Labels.Add("2");
ca.Labels.Add("3");
ca.Labels.Add("4");
ca.Labels.Add("5");
ca.Labels.Add("6");
ca.MaximumPadding = 0.02;
plotModel1.Axes.Add(ca);
var la = new LinearAxis
{
Minimum = 0,
Position = AxisPosition.Left,
Title = Yaxislabel,
MaximumPadding = 0.02
};
plotModel1.Axes.Add(la);
var sc = new LineSeries
{
LineStyle = LineStyle.Dash,
MarkerFill = OxyColors.SkyBlue,
MarkerSize = 5,
MarkerType = MarkerType.Circle,
StrokeThickness = 3
};
AddNRWPoints(sc, 1);
sc.Title = Averagelabel;
plotModel1.Series.Add(sc);
var energyIntensities = outcomes.Select(x => x.EnergyIntensity).Distinct().ToList();
energyIntensities.Sort((x, y) => string.Compare(x, y, StringComparison.Ordinal));
series.Sort((x, y) => string.Compare(x.Version, y.Version, StringComparison.Ordinal));
OxyPalette p = OxyPalettes.Hue64;
if (series.Count > 1)
{
p = OxyPalettes.HueDistinct(series.Count);
}
var i = 0;
for (var index = 0; index < series.Count; index++)
{
var seriesEntry = series[index];
var entrylist = outcomes
.Where(x => x.EnergyIntensity == seriesEntry.EnergyIntensity &&
x.LPGVersion == seriesEntry.Version)
.ToList();
var sc1 = new ScatterSeries();
foreach (var entry in entrylist)
{
sc1.Points.Add(
new ScatterPoint(entry.NumberOfPersons + seriesEntry.Offset, entry.ElectricityDouble));
}
sc1.Title = seriesEntry.DisplayName;
var oc = p.Colors[i++];
sc1.MarkerStroke = OxyColor.FromAColor(128, oc);
sc1.MarkerFill = oc;
if (index == 0)
{
sc1.MarkerType = MarkerType.Diamond;
}
else
{
sc1.MarkerType = MarkerType.Star;
}
sc1.MarkerStrokeThickness = 1;
plotModel1.Series.Add(sc1);
}
PngExporter.Export(plotModel1, pngfullName, _width, _height, OxyColor.FromRgb(255, 255, 255), _dpi);
_calculationProfiler.StopPart(Utili.GetCurrentMethodAndClass());
//ChartPDFCreator.OxyPDFCreator.Run(plotModel1, pdfFullName);
}
public NetworkStatsWindow()
{
InitializeComponent();
if (ChosenTask == TaskType.Approximation) // number of plots is dependent on type of task
{
PlotModels = new PlotModel[2];
}
else
{
PlotModels = new PlotModel[1];
}
#region error of epochs plot (two series - learning and test error) (set on 0 index of PlotModels)
var epochErrorPlotModel = new PlotModel
{
Title = "Error of epoch",
IsLegendVisible = true,
};
//create learniing erro series
var learningEpochErrorDataPoints = new List <DataPoint>();
for (int i = 0; i < Creator.BestNetworkEpochHistory.Count; i++) // adding data
{
learningEpochErrorDataPoints.Add(new DataPoint(i, Creator.BestNetworkEpochHistory[i]));
}
var learningSeries = new OxyPlot.Series.LineSeries()
{
Title = "Learning set error",
Smooth = false,
LineJoin = LineJoin.Bevel,
};
learningSeries.ItemsSource = learningEpochErrorDataPoints;
epochErrorPlotModel.Series.Add(learningSeries);
//create test error series
if (Creator.BestNetworkTestHistory != null)
{
var testEpochErrorDataPoints = new List <DataPoint>();
for (int i = 0; i < Creator.BestNetworkTestHistory.Count; i++) // adding data
{
testEpochErrorDataPoints.Add(new DataPoint(i, Creator.BestNetworkTestHistory[i]));
}
var testSeries = new OxyPlot.Series.LineSeries()
{
Title = "Test set error",
Smooth = false,
LineJoin = LineJoin.Bevel,
};
testSeries.ItemsSource = testEpochErrorDataPoints;
epochErrorPlotModel.Series.Add(testSeries);
}
//set plot
PlotModels[0] = epochErrorPlotModel;
#endregion
if (ChosenTask == TaskType.Approximation)
{
var approximationFunctionPlotModel = new PlotModel
{
Title = "Network approximation function",
IsLegendVisible = true,
};
var approximationFunctionDataPoints = new List <DataPoint>(); //network outputs approximation
for (int i = 0; i < Creator.ApproximationFunctionPoints.GetLength(0); i++) // adding data
{
approximationFunctionDataPoints.Add(new DataPoint(Creator.ApproximationFunctionPoints[i, 0], Creator.ApproximationFunctionPoints[i, 1]));
}
var approximationSeries = new OxyPlot.Series.LineSeries()
{
Title = "Network approximation",
Selectable = false
};
approximationSeries.ItemsSource = approximationFunctionDataPoints;
approximationFunctionPlotModel.Series.Add(approximationSeries);
//set of 'real' points
var testSet = Creator.DataProvider.DataSet;
var testPoints = new List <ScatterPoint>(); //network outputs approximation
for (int i = 0; i < testSet.GetLength(0); i++) // adding data
{
if (CurrentNetwork.IsBiasExisting)
{
testPoints.Add(new ScatterPoint(testSet[i].X[1], testSet[i].D[0], 1.5));
}
else
{
testPoints.Add(new ScatterPoint(testSet[i].X[0], testSet[i].D[0], 1.5));
}
}
var testSeries = new OxyPlot.Series.ScatterSeries()
{
Title = "Test points",
};
testSeries.ItemsSource = testPoints;
approximationFunctionPlotModel.Series.Add(testSeries);
//set of learning points
var learningSet = Creator.DataProvider.LearnSet;
var learningPoints = new List <ScatterPoint>(); //network outputs approximation
int pointSize = 2;
for (int i = 0; i < learningSet.GetLength(0); i++) // adding data
{
if (CurrentNetwork.IsBiasExisting)
{
learningPoints.Add(new ScatterPoint(learningSet[i].X[1], learningSet[i].D[0], pointSize));
}
else
{
learningPoints.Add(new ScatterPoint(learningSet[i].X[0], learningSet[i].D[0], pointSize));
}
}
var learningPointSeries = new OxyPlot.Series.ScatterSeries()
{
Title = "Learning points",
};
learningPointSeries.ItemsSource = learningPoints;
approximationFunctionPlotModel.Series.Add(learningPointSeries);
//save created plot to plot models
PlotModels[1] = approximationFunctionPlotModel;
var colorBox = new TextBox
{
Background = new SolidColorBrush(Colors.SteelBlue),
BorderThickness = new Thickness(0)
};
NetworkStatsMainGrid.Children.Add(colorBox);
Grid.SetColumn(colorBox, 4);
Grid.SetColumnSpan(colorBox, 4);
Grid.SetRow(colorBox, 1);
Grid.SetRowSpan(colorBox, 3);
}
else if (ChosenTask == TaskType.Classification)
{
var classGrid = new UniformGrid
{
Rows = Creator.ClassificationFullResults.RowCount + 1,
Columns = Creator.ClassificationFullResults.ColumnCount + 1,
};
//No way to set Border on textblock defined in code behind; changing for TextBox is a way to make border but still it doesn't look like a table
classGrid.Children.Add(new TextBox()
{
BorderThickness = new Thickness(1), Text = "Class number", FontWeight = System.Windows.FontWeights.Bold, VerticalAlignment = System.Windows.VerticalAlignment.Center, TextAlignment = System.Windows.TextAlignment.Center, MinHeight = 50
});
for (int columnIndex = 0; columnIndex < Creator.ClassificationFullResults.ColumnCount; columnIndex++) //ceate column names as class number bolded
{
classGrid.Children.Add(new TextBox()
{
BorderThickness = new Thickness(1), Text = columnIndex.ToString(), FontWeight = System.Windows.FontWeights.Bold, VerticalAlignment = System.Windows.VerticalAlignment.Center, TextAlignment = System.Windows.TextAlignment.Center, MinHeight = 50
});
}
for (int rowIndex = 0; rowIndex < Creator.ClassificationFullResults.RowCount; rowIndex++)
{
classGrid.Children.Add(new TextBox()
{
BorderThickness = new Thickness(1), Text = rowIndex.ToString(), FontWeight = System.Windows.FontWeights.Bold, VerticalAlignment = System.Windows.VerticalAlignment.Center, TextAlignment = System.Windows.TextAlignment.Center, MinHeight = 50
}); //add row name = class number bolded
for (int columnIndex = 0; columnIndex < Creator.ClassificationFullResults.ColumnCount; columnIndex++)
{
classGrid.Children.Add(new TextBox()
{
BorderThickness = new Thickness(1), Text = Creator.ClassificationFullResults[rowIndex, columnIndex].ToString(), VerticalAlignment = System.Windows.VerticalAlignment.Center, TextAlignment = System.Windows.TextAlignment.Center, MinHeight = 50
});
}
}
NetworkStatsMainGrid.Children.Add(classGrid);
Grid.SetColumn(classGrid, 4);
Grid.SetColumnSpan(classGrid, 4);
Grid.SetRow(classGrid, 1);
Grid.SetRowSpan(classGrid, 3);
}
}
/// <summary>
/// The mobility fit FitLine plot.
/// </summary>
/// <param name="fitline">
/// The fitline.
/// </param>
/// <returns>
/// The <see cref="PlotModel"/>.
/// </returns>
private PlotModel MobilityFitLinePlot(FitLine fitline)
{
IEnumerable<ContinuousXYPoint> fitPointList = fitline.FitPointCollection.Select(x => x.Point);
IEnumerable<ContinuousXYPoint> outlierList = fitline.OutlierCollection.Select(x => x.Point);
Func<object, ScatterPoint> fitPointMap = obj =>
{
ContinuousXYPoint point = (ContinuousXYPoint)obj;
double size = 5;
double color = 0;
ScatterPoint sp = new ScatterPoint(point.X, point.Y, size, color);
return sp;
};
Func<object, ScatterPoint> OutlierPointMap = obj =>
{
ContinuousXYPoint point = (ContinuousXYPoint)obj;
double size = 5;
double color = 1;
ScatterPoint sp = new ScatterPoint(point.X, point.Y, size, color);
return sp;
};
PlotModel model = new PlotModel();
model.TitlePadding = 0;
model.Title = "Mobility Fit FitLine";
ScatterSeries fitPointSeries = new ScatterSeries
{
Mapping = fitPointMap,
ItemsSource = fitPointList,
};
ScatterSeries outlierSeries = new ScatterSeries
{
Mapping = OutlierPointMap,
ItemsSource = outlierList,
};
Func<object, DataPoint> lineMap = obj =>
{
ContinuousXYPoint point = (ContinuousXYPoint)obj;
double x = point.X;
double y = fitline.ModelPredictX2Y(x);
DataPoint sp = new DataPoint(x, y);
return sp;
};
LineSeries fitlineSeries = new LineSeries()
{
Mapping = lineMap,
ItemsSource = fitPointList,
Color = OxyColors.Purple
};
var yAxis = new LinearAxis()
{
Title = "IMS scan time (milliseconds)",
MajorGridlineStyle = LineStyle.Solid,
Position = AxisPosition.Left,
//MajorStep = 100.0,
//MinorStep = 50.0,
//Minimum = 0,
//Maximum = 360,
//FilterMinValue = 0,
//FilterMaxValue = 360,
};
var xAxis = new LinearAxis()
{
Title = "Pressure / (Temperature * Voltage) (1 / V))",
Position = AxisPosition.Bottom,
MajorGridlineStyle = LineStyle.Solid,
//MajorStep = 100.0,
//MinorStep = 50.0,
//Minimum = 1000,
//Maximum = 2000,
//MinimumRange = 100.0,
//FilterMinValue = 1000,
//FilterMaxValue = 2000,
};
model.Axes.Add(yAxis);
model.Axes.Add(xAxis);
model.Series.Add(fitPointSeries);
model.Series.Add(outlierSeries);
model.Series.Add(fitlineSeries);
return model;
}
/// <summary>
/// The association hypothesis plot.
/// </summary>
/// <param name="hypothesis">
/// The hypothesis.
/// </param>
/// <param name="datasetName">
/// The dataset name.
/// </param>
/// <param name="targetDescriptor">
/// The target descriptor.
/// </param>
/// <returns>
/// The <see cref="PlotModel"/>.
/// </returns>
private PlotModel AssociationHypothesisPlot(AssociationHypothesis hypothesis, string datasetName, IImsTarget target, bool plotXAxisFromZero = false)
{
PlotModel model = new PlotModel();
model.LegendBorderThickness = 0;
model.LegendOrientation = LegendOrientation.Vertical;
model.LegendPlacement = LegendPlacement.Inside;
model.LegendPosition = LegendPosition.LeftTop;
model.TitlePadding = 0;
model.Title = "Optimal Association Hypothesis Plot";
model.Subtitle = string.Format("Target: {0}, dataset: {1}", target.TargetDescriptor, datasetName) ;
model.Axes.Add(
new LinearAxis
{
Title = "IMS arrival time (milliseconds)",
MajorGridlineStyle = LineStyle.Solid,
Position = AxisPosition.Left,
});
model.Axes.Add(
new LinearAxis
{
Title = "P/(T*V) with P and T nondimensionalized (1/V)",
Position = AxisPosition.Bottom,
MajorGridlineStyle = LineStyle.Solid,
});
// Add all the points
IEnumerable<ObservedPeak> onTrackPeaks = hypothesis.OnTrackObservations;
IEnumerable<ObservedPeak> offTrackPeaks = hypothesis.AllObservations.Where(x => !hypothesis.IsOnTrack(x));
Func<ObservedPeak, ScatterPoint> fitPointMap = obj =>
{
ObservedPeak observation = obj;
ContinuousXYPoint xyPoint = observation.ToContinuousXyPoint(false, 0);
double size = MapToPointSize(observation);
ScatterPoint sp = new ScatterPoint(xyPoint.X, xyPoint.Y, size);
return sp;
};
var ontrackSeries= new ScatterSeries
{
Title = "[Peaks On Tracks]",
MarkerFill = OxyColors.BlueViolet,
MarkerType = MarkerType.Circle
};
var offtrackSeries= new ScatterSeries
{
Title = "[Peaks Off Tracks]",
MarkerFill = OxyColors.Red,
MarkerType = MarkerType.Circle
};
ontrackSeries.Points.AddRange(onTrackPeaks.Select(x => fitPointMap(x)));
offtrackSeries.Points.AddRange(offTrackPeaks.Select(x => fitPointMap(x)));
model.Series.Add(ontrackSeries);
model.Series.Add(offtrackSeries);
var allTracks = hypothesis.Tracks;
// Add the tracks as linear axes
int count = 1;
foreach (var track in allTracks)
{
FitLine fitline = track.FitLine;
LineAnnotation annotation = new LineAnnotation();
annotation.Slope = fitline.Slope;
annotation.Intercept = fitline.Intercept;
annotation.TextPadding = 3;
annotation.TextMargin = 2;
annotation.Text = string.Format("Conformer {0} - mz: {1:F2}; ccs: {2:F2}, Isotopic Score: {3:F3}; Track Probability: {4:F2}; R2: {5:F5};",
count, track.AverageMzInDalton, track.GetMobilityInfoForTarget(target).CollisionCrossSectionArea, track.TrackStatistics.IsotopicScore, track.TrackProbability, track.FitLine.RSquared);
count++;
model.Annotations.Add(annotation);
//Func<object, DataPoint> lineMap = obj =>
//{
// ObservedPeak observation = (ObservedPeak)obj;
// ContinuousXYPoint xyPoint = observation.ToContinuousXyPoint();
// double x = xyPoint.X;
// double y = fitline.ModelPredictX2Y(x);
// DataPoint sp = new DataPoint(x, y);
// return sp;
//};
//model.Series.Add(new LineSeries()
//{
// Mapping = lineMap,
// ItemsSource = track.ObservedPeaks,
// Color = OxyColors.Purple
//});
}
return model;
}
//Solutions
private void LinearRegression()
{
var data = Matrix.Build.DenseOfRows(
File.ReadAllLines(@"C:\SK\Programming\VSProjects\Stuff\Files\stanford_dl_ex-master\ex1\housing.data")
.Select(l => l.Split(' ').Where(x => x.Any()).Select(double.Parse)));
PermuteRows(data);
InsertOnesColumn(ref data);
int trainNum = (int)(data.RowCount * 0.80);
int featNum = data.Row(0).Count - 1;
var train = data.SubMatrix(0, trainNum, 0, data.ColumnCount);
var test = data.SubMatrix(trainNum, data.RowCount - trainNum, 0, data.ColumnCount);
train = DenseMatrix.OfRows(train.EnumerateRows().OrderBy(row => row.Last()));
test = DenseMatrix.OfRows(test.EnumerateRows().OrderBy(row => row.Last()));
var X = train.SubMatrix(0, train.RowCount, 0, featNum);
var y = train.Column(featNum);
var theta = Vector.Build.Dense(featNum);
theta = (X.Transpose() * X).Inverse() * X.Transpose() * y;
{
var s1 = new ScatterSeries() { Title = "Test Solution", MarkerSize = 1, };
s1.Points.AddRange(Enumerable.Range(0, test.RowCount).Select(i => new ScatterPoint(i, test.Row(i).Last())));
var s2 = new ScatterSeries() { Title = "Test Guess", MarkerSize = 1, };
s2.Points.AddRange(Enumerable.Range(0, test.RowCount).Select(i => new ScatterPoint(i, test.Row(i).SubVector(0, featNum) * theta)));
var s3 = new ScatterSeries() { Title = "Test Error", MarkerSize = 1, };
s3.Points.AddRange(Enumerable.Range(0, test.RowCount).Select(i => new ScatterPoint(i, Math.Abs(s1.Points[i].Y - s2.Points[i].Y))));
Console.WriteLine("Test error: " + s3.Points.Select(p => p.Y).Sum() / s3.Points.Count());
new Plotter("Test", s1, s2, s3);
}
{
var s1 = new ScatterSeries() { Title = "Train Solution", MarkerSize = 1, };
s1.Points.AddRange(Enumerable.Range(0, train.RowCount).Select(i => new ScatterPoint(i, train.Row(i).Last())));
var s2 = new ScatterSeries() { Title = "Train Guess", MarkerSize = 1, };
s2.Points.AddRange(Enumerable.Range(0, train.RowCount).Select(i => new ScatterPoint(i, train.Row(i).SubVector(0, featNum) * theta)));
var s3 = new ScatterSeries() { Title = "Train Error", MarkerSize = 1, };
s3.Points.AddRange(Enumerable.Range(0, train.RowCount).Select(i => new ScatterPoint(i, Math.Abs(s1.Points[i].Y - s2.Points[i].Y))));
Console.WriteLine("Train error: " + s3.Points.Select(p => p.Y).Sum() / s3.Points.Count());
new Plotter("Train", s1, s2, s3);
}
Func<Vector<double>, double> error = row => theta * row.SubVector(0, featNum) - row[featNum];
Func<double> J = () => train.EnumerateRows().Select(error).Sum(err => 0.5 * err * err);
Func<Vector<double>> Grad = () => Vector.Build.DenseOfEnumerable(Enumerable.Range(0, featNum)
.Select(_ => train.EnumerateRows().Sum(row => row[_] * error(row))));
var j = J();
var g = Grad();
}
private void LogicRegression()
{
Console.WriteLine("Loading files");
var trainX = Matrix.Build.DenseOfRows(MNIST.LoadImages(@"C:\SK\Programming\VSProjects\Stuff\Files\data\common\train-images-idx3-ubyte"));
var trainY = Vector.Build.Dense(MNIST.LoadLabels(@"C:\SK\Programming\VSProjects\Stuff\Files\data\common\train-labels-idx1-ubyte"));
var testX = Matrix.Build.DenseOfRows(MNIST.LoadImages(@"C:\SK\Programming\VSProjects\Stuff\Files\data\common\t10k-images-idx3-ubyte"));
var testY = Vector.Build.Dense(MNIST.LoadLabels(@"C:\SK\Programming\VSProjects\Stuff\Files\data\common\t10k-labels-idx1-ubyte"));
Console.WriteLine("Learning");
var EPSILON = 0.00000000001;
var train = trainX.InsertColumn(trainX.ColumnCount, trainY);
train = DenseMatrix.OfRows(train.EnumerateRows().Where(row => row.Last() <= 1.0).ToList());
InsertOnesColumn(ref train);
var test = testX.InsertColumn(testX.ColumnCount, testY);
test = DenseMatrix.OfRows(test.EnumerateRows().Where(row => row.Last() <= 1.0).ToList());
InsertOnesColumn(ref test);
int featNum = train.Row(0).Count - 1;
var theta = Vector.Build.Dense(featNum);
Func<Vector<double>, Vector<double>, double> h = (th, x) => 1 / (1 + Math.Exp(-th * x));
Func<Vector<double>, double> J = th => -train.EnumerateRows().Sum(row =>
{
var hval = h(th, row.SubVector(0, featNum));
var res = 0.0;
if (row[featNum] == 1.0)
{
if (hval == 0.0)
{
res += 1.0 / EPSILON;
}
else
{
res += -Math.Log(hval);
}
}
if (row[featNum] == 0.0)
{
if (1 - hval == 0.0)
{
res += 1.0 / EPSILON;
}
else
{
res += -Math.Log(1 - hval);
}
}
return res;
}
);
Func<Vector<double>, Vector<double>> Grad = th =>
{
var sum = Vector.Build.Dense(featNum);
foreach (var row in train.EnumerateRows())
{
var xi = row.SubVector(0, featNum);
var yi = row[featNum];
sum += xi * (h(th, xi) - yi);
}
return sum;
};
theta = FindMin(J, theta, Grad);
{
var s1 = new ScatterSeries() { Title = "Test Solution", MarkerSize = 1, };
s1.Points.AddRange(Enumerable.Range(0, test.RowCount).Select(i => new ScatterPoint(i, test.Row(i).Last())));
var s2 = new ScatterSeries() { Title = "Test Guess", MarkerSize = 1, };
s2.Points.AddRange(Enumerable.Range(0, test.RowCount).Select(i => new ScatterPoint(i, h(theta, test.Row(i).SubVector(0, featNum)) > 0.5 ? 1.0 : 0.0)));
var s3 = new ScatterSeries() { Title = "Test Error", MarkerSize = 1, };
s3.Points.AddRange(Enumerable.Range(0, test.RowCount).Select(i => new ScatterPoint(i, Math.Abs(s1.Points[i].Y - s2.Points[i].Y))));
Console.WriteLine("Test error: " + s3.Points.Select(p => p.Y).Sum() / s3.Points.Count());
new Plotter("Test", s1, s2, s3);
}
{
var s1 = new ScatterSeries() { Title = "Train Solution", MarkerSize = 1, };
s1.Points.AddRange(Enumerable.Range(0, train.RowCount).Select(i => new ScatterPoint(i, train.Row(i).Last())));
var s2 = new ScatterSeries() { Title = "Train Guess", MarkerSize = 1, };
s2.Points.AddRange(Enumerable.Range(0, train.RowCount).Select(i => new ScatterPoint(i, h(theta, train.Row(i).SubVector(0, featNum)) > 0.5 ? 1.0 : 0.0)));
var s3 = new ScatterSeries() { Title = "Train Error", MarkerSize = 1, };
s3.Points.AddRange(Enumerable.Range(0, train.RowCount).Select(i => new ScatterPoint(i, Math.Abs(s1.Points[i].Y - s2.Points[i].Y))));
Console.WriteLine("Train error: " + s3.Points.Select(p => p.Y).Sum() / s3.Points.Count());
new Plotter("Train", s1, s2, s3);
}
}
private void CreateMdsChartModel()
{
var model = new PlotModel();
var xAxis = new OxyPlot.Axes.LinearAxis
{
Position = OxyPlot.Axes.AxisPosition.Bottom,
MajorGridlineStyle = LineStyle.None
};
model.Axes.Add(xAxis);
var yAxis = new OxyPlot.Axes.LinearAxis
{
Position = OxyPlot.Axes.AxisPosition.Left,
MajorGridlineStyle = LineStyle.None
};
model.Axes.Add(yAxis);
var series = new OxyPlot.Series.ScatterSeries
{
ItemsSource = (Data.MdsCoords.Rows.Cast<filterReportDS.MdsCoordsRow>()
.Select(dr => new DataPoint(dr.X, dr.Y))),
DataFieldX = "X",
DataFieldY = "Y",
MarkerType = MarkerType.Circle,
MarkerSize = 2,
MarkerFill = OxyColor.FromRgb(79, 129, 189)
};
model.Series.Add(series);
foreach(filterReportDS.MdsCoordsRow dr in Data.MdsCoords.Rows)
{
var annotation = new OxyPlot.Annotations.TextAnnotation
{
Text = dr.StrategyName,
TextPosition = new DataPoint(dr.X, dr.Y),
TextHorizontalAlignment = OxyPlot.HorizontalAlignment.Center,
TextVerticalAlignment = OxyPlot.VerticalAlignment.Top,
Font = "Segoe UI",
TextColor = OxyColor.FromRgb(0, 0, 0),
StrokeThickness = 0
};
model.Annotations.Add(annotation);
}
MdsChartModel = model;
}
private void Form1_Load(object sender, EventArgs e)
{
Inclination_Calc sine_fit = new Inclination_Calc();
LineFitting line_fit = new LineFitting();
Tuple <double, double> points;
//System.Drawing.Bitmap bmp = new System.Drawing.Bitmap(@"C:\Users\Test3\source\repos\PrintedImageBooth\python\precession measurement new baumer location\1.bmp");
//points = line_fit.fit_Baumer_Image(bmp);
for (int i = 0; i < 16; i++)
{
//System.Drawing.Bitmap bmp = new System.Drawing.Bitmap(@"C:/Users/Test3/source/repos/PrintedImageBooth/python/precession_image_" + (i + 1) + ".bmp");
//points = line_fit.fit_metrology_booth(bmp);
System.Drawing.Bitmap bmp = new System.Drawing.Bitmap(@"C:\Users\Test3\source\repos\PrintedImageBooth\python\precession measurement new baumer location\" + (i + 1) + ".bmp");
points = line_fit.fit_Baumer_Image(bmp);
Console.WriteLine(points.Item1);
Console.WriteLine(points.Item2);
Ypoint1_list.Add(points.Item1);
Ypoint2_list.Add(points.Item2);
}
//double[] Angle = { 0, 22.5, 45, 67.5, 90, 112.5, 135, 157.5, 180, 202.5, 225, 247.5, 270, 292.5, 315, 337.5 };
//double[] Angle_Rad = { 0, 0.3926991, 0.785398, 1.178097, 1.5708, 1.9634954, 2.35619, 2.7488936, 3.14159, 3.5342917, 3.92699, 4.3196899, 4.71239, 5.1050881, 5.49779, 5.8904862 };
//double[] Ypoint1 = { 1853.81529, 1847.660185, 1844.26392, 1844.565391, 1850.022335, 1853.779641, 1861.10121, 1869.905686, 1876.988192, 1882.889382, 1886.051181, 1886.038851, 1885.149105, 1876.565114, 1868.736134, 1860.421681 };
//double[] Ypoint2 = { 1853.248, 1846.372287, 1843.176348, 1843.989568, 1850.517779, 1857.074984, 1867.463348, 1878.007069, 1887.979784, 1895.239886, 1898.598238, 1897.837502, 1894.264412, 1883.825911, 1873.735975, 1862.378465 };
double inclination_angle;
Func <double, double> sinewave;
Func <double, double> sinewave_2;
Ypoint1 = Ypoint1_list.ToArray();
Ypoint2 = Ypoint2_list.ToArray();
Tuple <double, Func <double, double>, Func <double, double>, Func <double, double> > tuple_return = sine_fit.fit_sinewave(Angle_Rad, Ypoint1, Ypoint2);
//Inclination_angle = tuple_return.Item1;
var line1 = new OxyPlot.Series.ScatterSeries();
var line2 = new OxyPlot.Series.ScatterSeries();
for (int i = 0; i < Angle_Rad.Length; i++)
{
line1.Points.Add(new ScatterPoint(Angle_Rad[i], Ypoint1[i]));
line2.Points.Add(new ScatterPoint(Angle_Rad[i], Ypoint2[i]));
}
var myModel = new PlotModel {
Title = "Inclination"
};
myModel.Series.Add(line1);
myModel.Series.Add(line2);
//myModel.Series.Add(new FunctionSeries(Math.Cos, 0, 10, 0.1, "cos(x)"));
myModel.Series.Add(new FunctionSeries(tuple_return.Item2, 0, 2 * Math.PI, 0.1, "lower"));
myModel.Series.Add(new FunctionSeries(tuple_return.Item3, 0, 2 * Math.PI, 0.1, "upper"));
// myModel.Series.Add(new FunctionSeries(batFn2, 0, 360, 0.1, "linear"));
var textAnnotation = new TextAnnotation
{
Text = "Angle: " + Math.Round(tuple_return.Item1, 6).ToString(),
TextPosition = new DataPoint(1, 1890),
FontSize = 20
};
myModel.Annotations.Add(textAnnotation);
this.plotView1.Model = myModel;
}
private void InitializeGraph()
{
PlotModel plot = new PlotModel();
OxyPlot.Axes.LinearAxis costAxis = new OxyPlot.Axes.LinearAxis(AxisPosition.Left);
costAxis.Key = "cost";
OxyPlot.Axes.LinearAxis progressionAxis = new OxyPlot.Axes.LinearAxis(AxisPosition.Right);
progressionAxis.Key = "progression";
plot.Axes.Add(costAxis);
plot.Axes.Add(progressionAxis);
plot.Axes.Add(new OxyPlot.Axes.LinearAxis(AxisPosition.Bottom));
OxyPlot.Series.ScatterSeries costSeries = new OxyPlot.Series.ScatterSeries();
costSeries.YAxisKey = "cost";
OxyPlot.Series.StairStepSeries progressionSeries = new OxyPlot.Series.StairStepSeries(OxyColor.FromRgb(0, 255, 0), LINES_THICKNESS, "Poprawa");
progressionSeries.YAxisKey = "progression";
OxyPlot.Series.StairStepSeries regressionSeries = new OxyPlot.Series.StairStepSeries(OxyColor.FromRgb(255, 0, 0), LINES_THICKNESS, "Pogorszenie");
regressionSeries.YAxisKey = "progression";
plot.Series.Add(costSeries);
plot.Series.Add(progressionSeries);
plot.Series.Add(regressionSeries);
VM.GraphPlotModel = plot;
}
public void UpdateChart()
{
if (Thread.CurrentThread != Dispatcher.Thread)
{
Dispatcher.BeginInvoke(new Action(UpdateChart));
return;
}
//--- remove all series
PlotViewModel.Series.Clear();
while (Drawer.Candles.MoveNext())
{
this.Candles.Items.Add(new ChartBar(Drawer.Candles.Current));
this.Volumes.Items.Add(new ChartBarVol(Drawer.Candles.Current)
{
});
}
//create exes
CreateAxes();
//---
if (Candles.Items.Count > 0)
{
PlotViewModel.Series.Add(Candles);
PlotViewModel.Series.Add(Volumes);
//draw volumes
//PlotViewModel.Series.Add(Volumes);
}
//----------------- HORIZONTAL LINES --------------
var levels = new List <double>(Drawer.HorizontalLines);
levels.Sort();
for (int l = 1; l < levels.Count; l++)
{
if (levels[l] - levels[l - 1] < 0.00001)
{
levels[l] = levels[l - 1] + 0.00001;
}
}
foreach (var level in levels)
{
//if (level < range.High + range.Length && level > range.Low - range.Length)
{
LineSeriesEx line = new LineSeriesEx(false)
{
MarkerStrokeThickness = 1,
LineStyle = LineStyle.Solid,
Color = OxyColor.FromArgb(200, 1, 1, 200),
StrokeThickness = 0.5f,
};
line.Points.Add(new DataPoint(0, level));
line.Points.Add(new DataPoint(PlotViewModel.Axes[0].Maximum - 1, level));
PlotViewModel.Series.Add(line);
}
}
//----------------- lines ----------------------
this.Lines.Clear();
foreach (var line in Drawer.Lines.Concat(Drawer.VerticalLines))
{
var ingnoreZoom = Drawer.VerticalLines.Contains(line);
LineSeriesEx lineserie = new LineSeriesEx(ingnoreZoom)
{
MarkerStrokeThickness = 2,
LineStyle = LineStyle.Solid,
Color = OxyColor.FromArgb(line.Color.A, line.Color.R, line.Color.G, line.Color.B),
StrokeThickness = 3f,
YAxisKey = line.AxisId ?? this.Candles_Yaxis.Key
};
lineserie.Points.AddRange(
line.Points.Select(dot => new DataPoint(dot.X.ToAxisDouble(), dot.Y)));
PlotViewModel.Series.Add(lineserie);
this.Lines.Add(lineserie);
}
//-------plot points ---------------
var pointsSerie = new OxyPlot.Series.ScatterSeries()
{
MarkerSize = 15, MarkerType = MarkerType.Circle
};
for (int p = 0; p < Drawer.Points.Count; p++)
{
pointsSerie.Points.Add(
new ScatterPoint(Drawer.Points[p].Time.ToAxisDouble(), Drawer.Points[p].Value, 5));
}
PlotViewModel.Series.Add(pointsSerie);
//---------- ADJUST X to show 100 candles
PlotViewModel.Axes[0].Minimum = Drawer.InitialView.start.ToAxisDouble();
PlotViewModel.Axes[0].Maximum = Drawer.InitialView.end.ToAxisDouble();
PlotViewModel.Axes[0].Reset();
//--------- ADJUST Y
AdjustYAxisZoom();
PlotViewModel.InvalidatePlot(true);
return;
}
private void SoftMax()
{
Console.WriteLine("Loading files");
var trainX = Matrix.Build.DenseOfRows(MNIST.LoadImages(@"C:\SK\Programming\VSProjects\Stuff\Files\data\common\train-images-idx3-ubyte"));
var trainY = Vector.Build.Dense(MNIST.LoadLabels(@"C:\SK\Programming\VSProjects\Stuff\Files\data\common\train-labels-idx1-ubyte"));
var testX = Matrix.Build.DenseOfRows(MNIST.LoadImages(@"C:\SK\Programming\VSProjects\Stuff\Files\data\common\t10k-images-idx3-ubyte"));
var testY = Vector.Build.Dense(MNIST.LoadLabels(@"C:\SK\Programming\VSProjects\Stuff\Files\data\common\t10k-labels-idx1-ubyte"));
Console.WriteLine("Learning");
var EPSILON = 0.00001;
var train = trainX.InsertColumn(trainX.ColumnCount, trainY);
train = train.SubMatrix(0, train.RowCount, 0, train.ColumnCount);
//train = DenseMatrix.OfRows(train.EnumerateRows().Where(row => row.Last() <= 1.0).ToList());
//InsertOnesColumn(ref train);
var test = testX.InsertColumn(testX.ColumnCount, testY);
//test = DenseMatrix.OfRows(test.EnumerateRows().Where(row => row.Last() <= 1.0).ToList());
//InsertOnesColumn(ref test);
int featNum = train.Row(0).Count - 1;
int choiceNum = 10;
var theta = Matrix.Build.Dense(featNum, choiceNum);
var r = new Random(1234);
theta = theta.Map(x => r.NextDouble() * EPSILON);
Func<Matrix<double>, Vector<double>, Vector<double>> h = (th, x) => (th.Transpose() * x).Map(Math.Exp).Normalize(1.0);
Func<Matrix<double>, double> J = th =>
{
double sum = 0;
object l = new { };
Parallel.ForEach(train.EnumerateRows(), row =>
{
var hmul = h(th, row.SubVector(0, featNum));
int yi = (int)row[featNum]; //Assumes the y values are exactly from 0 to choiceNum-1
double hval = hmul[yi];
lock (l)
{
sum += -(hval == 0.0 ? -1 / EPSILON : Math.Log(hval));
}
});
return sum;
};
Func<Matrix<double>, Matrix<double>> Grad = th =>
{
var mat = new Vector<double>[choiceNum];
for (int i = 0; i < mat.Length; ++i)
{
mat[i] = Vector.Build.Dense(featNum);
}
Parallel.ForEach(train.EnumerateRows(), row =>
{
var xi = row.SubVector(0, featNum);
var hmul = h(th, xi);
int yi = (int)row[featNum];
for (int k = 0; k < choiceNum; ++k)
{
lock (mat)
{
mat[k] += xi * (hmul[k] - (yi == k ? 1.0 : 0.0));
}
}
});
return Matrix.Build.DenseOfColumns(mat);
};
theta = FindMin(J, theta, Grad, 100);
Func<Vector<double>, int, double> guess = (row, i) =>
{
var xi = row.SubVector(0, featNum);
var hmul = h(theta, xi);
return (double)hmul.MaximumIndex();
};
{
var s1 = new ScatterSeries() { Title = "Test Solution", MarkerSize = 10, };
s1.Points.AddRange(Enumerable.Range(0, test.RowCount).Select(i => new ScatterPoint(i, test.Row(i).Last())));
var s2 = new ScatterSeries() { Title = "Test Guess", MarkerSize = 10, };
s2.Points.AddRange(Enumerable.Range(0, test.RowCount).Select(i => new ScatterPoint(i, guess(test.Row(i), i))));
var s3 = new ScatterSeries() { Title = "Test Error", MarkerSize = 10, };
s3.Points.AddRange(Enumerable.Range(0, test.RowCount).Select(i => new ScatterPoint(i, s1.Points[i].Y == s2.Points[i].Y ? 0.0 : 1.0)));
Console.WriteLine("Test error: " + s3.Points.Select(p => p.Y).Sum() / s3.Points.Count());
new Plotter("Test", s1, s2, s3);
}
{
var s1 = new ScatterSeries() { Title = "Train Solution", MarkerSize = 10, };
s1.Points.AddRange(Enumerable.Range(0, train.RowCount).Select(i => new ScatterPoint(i, train.Row(i).Last())));
var s2 = new ScatterSeries() { Title = "Train Guess", MarkerSize = 10, };
s2.Points.AddRange(Enumerable.Range(0, train.RowCount).Select(i => new ScatterPoint(i, guess(train.Row(i), i))));
var s3 = new ScatterSeries() { Title = "Train Error", MarkerSize = 10, };
s3.Points.AddRange(Enumerable.Range(0, train.RowCount).Select(i => new ScatterPoint(i, s1.Points[i].Y == s2.Points[i].Y ? 0.0 : 1.0)));
Console.WriteLine("Train error: " + s3.Points.Select(p => p.Y).Sum() / s3.Points.Count());
new Plotter("Train", s1, s2, s3);
}
}
private PlotModel AnnotateRemovedPeaks(PlotModel associationHypothsisPlot, IDictionary<string, IList<ObservedPeak>> preFilteredPeaks)
{
Func<ObservedPeak, ScatterPoint> fitPointMap = obj =>
{
ObservedPeak observation = obj;
ContinuousXYPoint xyPoint = observation.ToContinuousXyPoint(false, 0);
double size = MapToPointSize(observation);
ScatterPoint sp = new ScatterPoint(xyPoint.X, xyPoint.Y, size);
return sp;
};
foreach (KeyValuePair<string, IList<ObservedPeak>> pair in preFilteredPeaks)
{
string rejectionReason = pair.Key;
IEnumerable<ObservedPeak> peaks = pair.Value;
ScatterSeries series = new ScatterSeries
{
Title = rejectionReason,
MarkerType = MarkerType.Circle
};
associationHypothsisPlot.Series.Add(series);
series.Points.AddRange(peaks.Select(x => fitPointMap(x)));
}
return associationHypothsisPlot;
}
/// <summary>
/// Initializes a new instance of the <see cref = "ScatterSeries{T}" /> class.
/// </summary>
protected ScatterSeries()
{
InternalSeries = new OxyPlot.Series.ScatterSeries();
}
