source: trunk/sources/HeuristicLab.Problems.DataAnalysis.Views/3.4/Controls/GradientChart.cs @ 14099

Last change on this file since 14099 was 14099, checked in by pfleck, 6 years ago

#2597:

  • Renamed IConfidenceBoundRegressionModel/Solution+Views to IConfidenceRegressionModel/Solution+Views.
  • Moved IConfidenceRegressionModel and IConfidenceRegressionSolution into own files.
  • Moved some (helper) controls into separate sub folder.
  • Fixed typo.
File size: 25.3 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
4 *
5 * This file is part of HeuristicLab.
6 *
7 * HeuristicLab is free software: you can redistribute it and/or modify
8 * it under the terms of the GNU General Public License as published by
9 * the Free Software Foundation, either version 3 of the License, or
10 * (at your option) any later version.
11 *
12 * HeuristicLab is distributed in the hope that it will be useful,
13 * but WITHOUT ANY WARRANTY; without even the implied warranty of
14 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
15 * GNU General Public License for more details.
16 *
17 * You should have received a copy of the GNU General Public License
18 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
19 */
20#endregion
21
22using System;
23using System.Collections.Generic;
24using System.Drawing;
25using System.Globalization;
26using System.Linq;
27using System.Threading;
28using System.Threading.Tasks;
29using System.Windows.Forms;
30using System.Windows.Forms.DataVisualization.Charting;
31using HeuristicLab.Common;
32using HeuristicLab.MainForm.WindowsForms;
33using HeuristicLab.Visualization.ChartControlsExtensions;
34
35namespace HeuristicLab.Problems.DataAnalysis.Views {
36  public partial class GradientChart : UserControl {
37    private ModifiableDataset sharedFixedVariables; // used for syncronising variable values between charts
38    private ModifiableDataset internalDataset; // holds the x values for each point drawn
39
40    private CancellationTokenSource cancelCurrentRecalculateSource;
41
42    private readonly List<IRegressionSolution> solutions;
43    private readonly Dictionary<IRegressionSolution, Series> seriesCache;
44    private readonly Dictionary<IRegressionSolution, Series> ciSeriesCache;
45
46    private readonly ToolStripMenuItem configToolStripMenuItem;
47    private readonly GradientChartConfigurationDialog configurationDialog;
48
49    #region Properties
50    public string XAxisTitle {
51      get { return chart.ChartAreas[0].AxisX.Title; }
52      set { chart.ChartAreas[0].AxisX.Title = value; }
53    }
54
55    public string YAxisTitle {
56      get { return chart.ChartAreas[0].AxisY.Title; }
57      set { chart.ChartAreas[0].AxisY.Title = value; }
58    }
59
60    public bool ShowLegend {
61      get { return chart.Legends[0].Enabled; }
62      set { chart.Legends[0].Enabled = value; }
63    }
64    public bool ShowCursor {
65      get { return chart.Annotations[0].Visible; }
66      set {
67        chart.Annotations[0].Visible = value;
68        if (!value) chart.ChartAreas[0].AxisX.Title = string.Empty;
69      }
70    }
71
72    public bool ShowConfigButton {
73      get { return configurationButton.Visible; }
74      set { configurationButton.Visible = value; }
75    }
76
77    private int xAxisTicks = 5;
78    public int XAxisTicks {
79      get { return xAxisTicks; }
80      set {
81        if (value != xAxisTicks) {
82          xAxisTicks = value;
83          SetupAxis(chart.ChartAreas[0].AxisX, trainingMin, trainingMax, XAxisTicks, FixedXAxisMin, FixedXAxisMax);
84          RecalculateInternalDataset();
85        }
86      }
87    }
88    private double? fixedXAxisMin;
89    public double? FixedXAxisMin {
90      get { return fixedXAxisMin; }
91      set {
92        if ((value.HasValue && fixedXAxisMin.HasValue && !value.Value.IsAlmost(fixedXAxisMin.Value)) || (value.HasValue != fixedXAxisMin.HasValue)) {
93          fixedXAxisMin = value;
94          if (trainingMin < trainingMax) {
95            SetupAxis(chart.ChartAreas[0].AxisX, trainingMin, trainingMax, XAxisTicks, FixedXAxisMin, FixedXAxisMax);
96            RecalculateInternalDataset();
97            // set the vertical line position
98            if (VerticalLineAnnotation.X <= fixedXAxisMin) {
99              var axisX = chart.ChartAreas[0].AxisX;
100              var step = (axisX.Maximum - axisX.Minimum) / drawingSteps;
101              VerticalLineAnnotation.X = axisX.Minimum + step;
102            }
103          }
104        }
105      }
106    }
107    private double? fixedXAxisMax;
108    public double? FixedXAxisMax {
109      get { return fixedXAxisMax; }
110      set {
111        if ((value.HasValue && fixedXAxisMax.HasValue && !value.Value.IsAlmost(fixedXAxisMax.Value)) || (value.HasValue != fixedXAxisMax.HasValue)) {
112          fixedXAxisMax = value;
113          if (trainingMin < trainingMax) {
114            SetupAxis(chart.ChartAreas[0].AxisX, trainingMin, trainingMax, XAxisTicks, FixedXAxisMin, FixedXAxisMax);
115            RecalculateInternalDataset();
116            // set the vertical line position
117            if (VerticalLineAnnotation.X >= fixedXAxisMax) {
118              var axisX = chart.ChartAreas[0].AxisX;
119              var step = (axisX.Maximum - axisX.Minimum) / drawingSteps;
120              VerticalLineAnnotation.X = axisX.Maximum - step;
121            }
122          }
123        }
124      }
125    }
126
127    private int yAxisTicks = 5;
128    public int YAxisTicks {
129      get { return yAxisTicks; }
130      set {
131        if (value != yAxisTicks) {
132          yAxisTicks = value;
133          SetupAxis(chart.ChartAreas[0].AxisY, yMin, yMax, YAxisTicks, FixedYAxisMin, FixedYAxisMax);
134          RecalculateInternalDataset();
135        }
136      }
137    }
138    private double? fixedYAxisMin;
139    public double? FixedYAxisMin {
140      get { return fixedYAxisMin; }
141      set {
142        if ((value.HasValue && fixedYAxisMin.HasValue && !value.Value.IsAlmost(fixedYAxisMin.Value)) || (value.HasValue != fixedYAxisMin.HasValue)) {
143          fixedYAxisMin = value;
144          SetupAxis(chart.ChartAreas[0].AxisY, yMin, yMax, YAxisTicks, FixedYAxisMin, FixedYAxisMax);
145        }
146      }
147    }
148    private double? fixedYAxisMax;
149    public double? FixedYAxisMax {
150      get { return fixedYAxisMax; }
151      set {
152        if ((value.HasValue && fixedYAxisMax.HasValue && !value.Value.IsAlmost(fixedYAxisMax.Value)) || (value.HasValue != fixedYAxisMax.HasValue)) {
153          fixedYAxisMax = value;
154          SetupAxis(chart.ChartAreas[0].AxisY, yMin, yMax, YAxisTicks, FixedYAxisMin, FixedYAxisMax);
155        }
156      }
157    }
158
159    private double trainingMin = 1;
160    private double trainingMax = -1;
161
162    private int drawingSteps = 1000;
163    public int DrawingSteps {
164      get { return drawingSteps; }
165      set {
166        if (value != drawingSteps) {
167          drawingSteps = value;
168          RecalculateInternalDataset();
169          ResizeAllSeriesData();
170        }
171      }
172    }
173
174    private string freeVariable;
175    public string FreeVariable {
176      get { return freeVariable; }
177      set {
178        if (value == freeVariable) return;
179        if (solutions.Any(s => !s.ProblemData.Dataset.DoubleVariables.Contains(value))) {
180          throw new ArgumentException("Variable does not exist in the ProblemData of the Solutions.");
181        }
182        freeVariable = value;
183        RecalculateInternalDataset();
184      }
185    }
186
187    private double yMin;
188    public double YMin {
189      get { return yMin; }
190    }
191    private double yMax;
192    public double YMax {
193      get { return yMax; }
194    }
195
196    public bool IsZoomed {
197      get { return chart.ChartAreas[0].AxisX.ScaleView.IsZoomed; }
198    }
199
200    private VerticalLineAnnotation VerticalLineAnnotation {
201      get { return (VerticalLineAnnotation)chart.Annotations.SingleOrDefault(x => x is VerticalLineAnnotation); }
202    }
203
204    internal ElementPosition InnerPlotPosition {
205      get { return chart.ChartAreas[0].InnerPlotPosition; }
206    }
207    #endregion
208
209    public GradientChart() {
210      InitializeComponent();
211
212      solutions = new List<IRegressionSolution>();
213      seriesCache = new Dictionary<IRegressionSolution, Series>();
214      ciSeriesCache = new Dictionary<IRegressionSolution, Series>();
215
216      // Configure axis
217      chart.CustomizeAllChartAreas();
218      chart.ChartAreas[0].CursorX.IsUserSelectionEnabled = true;
219      chart.ChartAreas[0].AxisX.ScaleView.Zoomable = true;
220      chart.ChartAreas[0].CursorX.Interval = 0;
221
222      chart.ChartAreas[0].CursorY.IsUserSelectionEnabled = true;
223      chart.ChartAreas[0].AxisY.ScaleView.Zoomable = true;
224      chart.ChartAreas[0].CursorY.Interval = 0;
225
226      configToolStripMenuItem = new ToolStripMenuItem("Configuration");
227      configToolStripMenuItem.Click += config_Click;
228      chart.ContextMenuStrip.Items.Add(new ToolStripSeparator());
229      chart.ContextMenuStrip.Items.Add(configToolStripMenuItem);
230      configurationDialog = new GradientChartConfigurationDialog(this);
231
232      Disposed += GradientChart_Disposed;
233    }
234
235    private void GradientChart_Disposed(object sender, EventArgs e) {
236      if (cancelCurrentRecalculateSource != null)
237        cancelCurrentRecalculateSource.Cancel();
238    }
239
240    public void Configure(IEnumerable<IRegressionSolution> solutions, ModifiableDataset sharedFixedVariables, string freeVariable, int drawingSteps, bool initializeAxisRanges = true) {
241      if (!SolutionsCompatible(solutions))
242        throw new ArgumentException("Solutions are not compatible with the problem data.");
243      this.freeVariable = freeVariable;
244      this.drawingSteps = drawingSteps;
245
246      this.solutions.Clear();
247      this.solutions.AddRange(solutions);
248
249      // add an event such that whenever a value is changed in the shared dataset,
250      // this change is reflected in the internal dataset (where the value becomes a whole column)
251      if (this.sharedFixedVariables != null)
252        this.sharedFixedVariables.ItemChanged -= sharedFixedVariables_ItemChanged;
253      this.sharedFixedVariables = sharedFixedVariables;
254      this.sharedFixedVariables.ItemChanged += sharedFixedVariables_ItemChanged;
255
256      RecalculateTrainingLimits(initializeAxisRanges);
257      RecalculateInternalDataset();
258
259      chart.Series.Clear();
260      seriesCache.Clear();
261      ciSeriesCache.Clear();
262      foreach (var solution in this.solutions) {
263        var series = CreateSeries(solution);
264        seriesCache.Add(solution, series.Item1);
265        if (series.Item2 != null)
266          ciSeriesCache.Add(solution, series.Item2);
267      }
268
269      ResizeAllSeriesData();
270      OrderAndColorSeries();
271    }
272
273    public async Task RecalculateAsync(bool updateOnFinish = true, bool resetYAxis = true) {
274      if (IsDisposed
275        || sharedFixedVariables == null || !solutions.Any() || string.IsNullOrEmpty(freeVariable)
276        || trainingMin.IsAlmost(trainingMax) || trainingMin > trainingMax || drawingSteps == 0)
277        return;
278
279      calculationPendingTimer.Start();
280
281      Update(); // immediately show label
282
283      // cancel previous recalculate call
284      if (cancelCurrentRecalculateSource != null)
285        cancelCurrentRecalculateSource.Cancel();
286      cancelCurrentRecalculateSource = new CancellationTokenSource();
287
288      // Set cursor and x-axis
289      // Make sure to allow a small offset to be able to distinguish the vertical line annotation from the axis
290      var defaultValue = sharedFixedVariables.GetDoubleValue(freeVariable, 0);
291      var step = (trainingMax - trainingMin) / drawingSteps;
292      var minimum = chart.ChartAreas[0].AxisX.Minimum;
293      var maximum = chart.ChartAreas[0].AxisX.Maximum;
294      if (defaultValue <= minimum)
295        VerticalLineAnnotation.X = minimum + step;
296      else if (defaultValue >= maximum)
297        VerticalLineAnnotation.X = maximum - step;
298      else
299        VerticalLineAnnotation.X = defaultValue;
300
301      if (ShowCursor)
302        chart.ChartAreas[0].AxisX.Title = FreeVariable + " : " + defaultValue.ToString("N3", CultureInfo.CurrentCulture);
303
304      // Update series
305      var cancellationToken = cancelCurrentRecalculateSource.Token;
306      try {
307        var limits = await UpdateAllSeriesDataAsync(cancellationToken);
308        //cancellationToken.ThrowIfCancellationRequested();
309
310        yMin = limits.Lower;
311        yMax = limits.Upper;
312        // Set y-axis
313        if (resetYAxis)
314          SetupAxis(chart.ChartAreas[0].AxisY, yMin, yMax, YAxisTicks, FixedYAxisMin, FixedYAxisMax);
315
316        UpdateOutOfTrainingRangeStripLines();
317
318        calculationPendingTimer.Stop();
319        calculationPendingLabel.Visible = false;
320        if (updateOnFinish)
321          Update();
322      }
323      catch (OperationCanceledException) { }
324      catch (AggregateException ae) {
325        if (!ae.InnerExceptions.Any(e => e is OperationCanceledException))
326          throw;
327      }
328    }
329
330    private void SetupAxis(Axis axis, double minValue, double maxValue, int ticks, double? fixedAxisMin, double? fixedAxisMax) {
331      double axisMin, axisMax, axisInterval;
332      ChartUtil.CalculateAxisInterval(minValue, maxValue, ticks, out axisMin, out axisMax, out axisInterval);
333      axis.Minimum = fixedAxisMin ?? axisMin;
334      axis.Maximum = fixedAxisMax ?? axisMax;
335      axis.Interval = (axis.Maximum - axis.Minimum) / ticks;
336
337      try {
338        chart.ChartAreas[0].RecalculateAxesScale();
339      }
340      catch (InvalidOperationException) {
341        // Can occur if eg. axis min == axis max
342      }
343    }
344
345    private void RecalculateTrainingLimits(bool initializeAxisRanges) {
346      trainingMin = solutions.Select(s => s.ProblemData.Dataset.GetDoubleValues(freeVariable, s.ProblemData.TrainingIndices).Min()).Max();
347      trainingMax = solutions.Select(s => s.ProblemData.Dataset.GetDoubleValues(freeVariable, s.ProblemData.TrainingIndices).Max()).Min();
348
349      if (initializeAxisRanges) {
350        double xmin, xmax, xinterval;
351        ChartUtil.CalculateAxisInterval(trainingMin, trainingMax, XAxisTicks, out xmin, out xmax, out xinterval);
352        FixedXAxisMin = xmin;
353        FixedXAxisMax = xmax;
354      }
355    }
356
357    private void RecalculateInternalDataset() {
358      if (sharedFixedVariables == null)
359        return;
360
361      // we expand the range in order to get nice tick intervals on the x axis
362      double xmin, xmax, xinterval;
363      ChartUtil.CalculateAxisInterval(trainingMin, trainingMax, XAxisTicks, out xmin, out xmax, out xinterval);
364
365      if (FixedXAxisMin.HasValue) xmin = FixedXAxisMin.Value;
366      if (FixedXAxisMax.HasValue) xmax = FixedXAxisMax.Value;
367      double step = (xmax - xmin) / drawingSteps;
368
369      var xvalues = new List<double>();
370      for (int i = 0; i < drawingSteps; i++)
371        xvalues.Add(xmin + i * step);
372
373      var variables = sharedFixedVariables.DoubleVariables.ToList();
374      internalDataset = new ModifiableDataset(variables,
375        variables.Select(x => x == FreeVariable
376          ? xvalues
377          : Enumerable.Repeat(sharedFixedVariables.GetDoubleValue(x, 0), xvalues.Count).ToList()
378        )
379      );
380    }
381
382    private Tuple<Series, Series> CreateSeries(IRegressionSolution solution) {
383      var series = new Series {
384        ChartType = SeriesChartType.Line,
385        Name = solution.ProblemData.TargetVariable + " " + solutions.IndexOf(solution)
386      };
387      series.LegendText = series.Name;
388
389      var confidenceBoundSolution = solution as IConfidenceRegressionSolution;
390      Series confidenceIntervalSeries = null;
391      if (confidenceBoundSolution != null) {
392        confidenceIntervalSeries = new Series {
393          ChartType = SeriesChartType.Range,
394          YValuesPerPoint = 2,
395          Name = "95% Conf. Interval " + series.Name,
396          IsVisibleInLegend = false
397        };
398      }
399      return Tuple.Create(series, confidenceIntervalSeries);
400    }
401
402    private void OrderAndColorSeries() {
403      chart.SuspendRepaint();
404
405      chart.Series.Clear();
406      // Add mean series for applying palette colors
407      foreach (var solution in solutions) {
408        chart.Series.Add(seriesCache[solution]);
409      }
410
411      chart.Palette = ChartColorPalette.BrightPastel;
412      chart.ApplyPaletteColors();
413      chart.Palette = ChartColorPalette.None;
414
415      // Add confidence interval series before its coresponding series for correct z index
416      foreach (var solution in solutions) {
417        Series ciSeries;
418        if (ciSeriesCache.TryGetValue(solution, out ciSeries)) {
419          var series = seriesCache[solution];
420          ciSeries.Color = Color.FromArgb(40, series.Color);
421          int idx = chart.Series.IndexOf(seriesCache[solution]);
422          chart.Series.Insert(idx, ciSeries);
423        }
424      }
425
426      chart.ResumeRepaint(true);
427    }
428
429    private async Task<DoubleLimit> UpdateAllSeriesDataAsync(CancellationToken cancellationToken) {
430      var updateTasks = solutions.Select(solution => UpdateSeriesDataAsync(solution, cancellationToken));
431
432      double min = double.MaxValue, max = double.MinValue;
433      foreach (var update in updateTasks) {
434        var limit = await update;
435        if (limit.Lower < min) min = limit.Lower;
436        if (limit.Upper > max) max = limit.Upper;
437      }
438
439      return new DoubleLimit(min, max);
440    }
441
442    private Task<DoubleLimit> UpdateSeriesDataAsync(IRegressionSolution solution, CancellationToken cancellationToken) {
443      return Task.Run(() => {
444        var xvalues = internalDataset.GetDoubleValues(FreeVariable).ToList();
445        var yvalues = solution.Model.GetEstimatedValues(internalDataset, Enumerable.Range(0, internalDataset.Rows)).ToList();
446
447        double min = double.MaxValue, max = double.MinValue;
448
449        var series = seriesCache[solution];
450        for (int i = 0; i < xvalues.Count; i++) {
451          series.Points[i].SetValueXY(xvalues[i], yvalues[i]);
452          if (yvalues[i] < min) min = yvalues[i];
453          if (yvalues[i] > max) max = yvalues[i];
454        }
455
456        var confidenceBoundSolution = solution as IConfidenceRegressionSolution;
457        if (confidenceBoundSolution != null) {
458          var confidenceIntervalSeries = ciSeriesCache[solution];
459
460          cancellationToken.ThrowIfCancellationRequested();
461          var variances =
462            confidenceBoundSolution.Model.GetEstimatedVariances(internalDataset,
463              Enumerable.Range(0, internalDataset.Rows)).ToList();
464          for (int i = 0; i < xvalues.Count; i++) {
465            var lower = yvalues[i] - 1.96 * Math.Sqrt(variances[i]);
466            var upper = yvalues[i] + 1.96 * Math.Sqrt(variances[i]);
467            confidenceIntervalSeries.Points[i].SetValueXY(xvalues[i], lower, upper);
468            if (lower < min) min = lower;
469            if (upper > max) max = upper;
470          }
471        }
472
473        cancellationToken.ThrowIfCancellationRequested();
474        return new DoubleLimit(min, max);
475      }, cancellationToken);
476    }
477
478    private void ResizeAllSeriesData() {
479      if (internalDataset == null)
480        return;
481
482      var xvalues = internalDataset.GetDoubleValues(FreeVariable).ToList();
483      foreach (var solution in solutions)
484        ResizeSeriesData(solution, xvalues);
485    }
486    private void ResizeSeriesData(IRegressionSolution solution, IList<double> xvalues = null) {
487      if (xvalues == null)
488        xvalues = internalDataset.GetDoubleValues(FreeVariable).ToList();
489
490      var series = seriesCache[solution];
491      series.Points.SuspendUpdates();
492      series.Points.Clear();
493      for (int i = 0; i < xvalues.Count; i++)
494        series.Points.Add(new DataPoint(xvalues[i], 0.0));
495      series.Points.ResumeUpdates();
496
497      Series confidenceIntervalSeries;
498      if (ciSeriesCache.TryGetValue(solution, out confidenceIntervalSeries)) {
499        confidenceIntervalSeries.Points.SuspendUpdates();
500        confidenceIntervalSeries.Points.Clear();
501        for (int i = 0; i < xvalues.Count; i++)
502          confidenceIntervalSeries.Points.Add(new DataPoint(xvalues[i], new[] { -1.0, 1.0 }));
503        confidenceIntervalSeries.Points.ResumeUpdates();
504      }
505    }
506
507    public async Task AddSolutionAsync(IRegressionSolution solution) {
508      if (!SolutionsCompatible(solutions.Concat(new[] { solution })))
509        throw new ArgumentException("The solution is not compatible with the problem data.");
510      if (solutions.Contains(solution))
511        return;
512
513      solutions.Add(solution);
514      RecalculateTrainingLimits(true);
515
516      var series = CreateSeries(solution);
517      seriesCache.Add(solution, series.Item1);
518      if (series.Item2 != null)
519        ciSeriesCache.Add(solution, series.Item2);
520
521      ResizeSeriesData(solution);
522      OrderAndColorSeries();
523
524      await RecalculateAsync();
525      var args = new EventArgs<IRegressionSolution>(solution);
526      OnSolutionAdded(this, args);
527    }
528
529    public async Task RemoveSolutionAsync(IRegressionSolution solution) {
530      if (!solutions.Remove(solution))
531        return;
532
533      RecalculateTrainingLimits(true);
534
535      seriesCache.Remove(solution);
536      ciSeriesCache.Remove(solution);
537
538      await RecalculateAsync();
539      var args = new EventArgs<IRegressionSolution>(solution);
540      OnSolutionRemoved(this, args);
541    }
542
543    private static bool SolutionsCompatible(IEnumerable<IRegressionSolution> solutions) {
544      foreach (var solution1 in solutions) {
545        var variables1 = solution1.ProblemData.Dataset.DoubleVariables;
546        foreach (var solution2 in solutions) {
547          if (solution1 == solution2)
548            continue;
549          var variables2 = solution2.ProblemData.Dataset.DoubleVariables;
550          if (!variables1.All(variables2.Contains))
551            return false;
552        }
553      }
554      return true;
555    }
556
557    private void UpdateOutOfTrainingRangeStripLines() {
558      var axisX = chart.ChartAreas[0].AxisX;
559      var lowerStripLine = axisX.StripLines[0];
560      var upperStripLine = axisX.StripLines[1];
561
562      lowerStripLine.IntervalOffset = axisX.Minimum;
563      lowerStripLine.StripWidth = Math.Abs(trainingMin - axisX.Minimum);
564
565      upperStripLine.IntervalOffset = trainingMax;
566      upperStripLine.StripWidth = Math.Abs(axisX.Maximum - trainingMax);
567    }
568
569    #region Events
570    public event EventHandler<EventArgs<IRegressionSolution>> SolutionAdded;
571    public void OnSolutionAdded(object sender, EventArgs<IRegressionSolution> args) {
572      var added = SolutionAdded;
573      if (added == null) return;
574      added(sender, args);
575    }
576
577    public event EventHandler<EventArgs<IRegressionSolution>> SolutionRemoved;
578    public void OnSolutionRemoved(object sender, EventArgs<IRegressionSolution> args) {
579      var removed = SolutionRemoved;
580      if (removed == null) return;
581      removed(sender, args);
582    }
583
584    public event EventHandler VariableValueChanged;
585    public void OnVariableValueChanged(object sender, EventArgs args) {
586      var changed = VariableValueChanged;
587      if (changed == null) return;
588      changed(sender, args);
589    }
590
591    public event EventHandler ZoomChanged;
592    public void OnZoomChanged(object sender, EventArgs args) {
593      var changed = ZoomChanged;
594      if (changed == null) return;
595      changed(sender, args);
596    }
597
598    private void sharedFixedVariables_ItemChanged(object o, EventArgs<int, int> e) {
599      if (o != sharedFixedVariables) return;
600      var variables = sharedFixedVariables.DoubleVariables.ToList();
601      var rowIndex = e.Value;
602      var columnIndex = e.Value2;
603
604      var variableName = variables[columnIndex];
605      if (variableName == FreeVariable) return;
606      var v = sharedFixedVariables.GetDoubleValue(variableName, rowIndex);
607      var values = new List<double>(Enumerable.Repeat(v, DrawingSteps));
608      internalDataset.ReplaceVariable(variableName, values);
609    }
610
611    private void chart_AnnotationPositionChanging(object sender, AnnotationPositionChangingEventArgs e) {
612      var step = (trainingMax - trainingMin) / drawingSteps;
613      double newLocation = step * (long)Math.Round(e.NewLocationX / step);
614      var axisX = chart.ChartAreas[0].AxisX;
615      if (newLocation >= axisX.Maximum)
616        newLocation = axisX.Maximum - step;
617      if (newLocation <= axisX.Minimum)
618        newLocation = axisX.Minimum + step;
619
620      e.NewLocationX = newLocation;
621      var annotation = VerticalLineAnnotation;
622      var x = annotation.X;
623      sharedFixedVariables.SetVariableValue(x, FreeVariable, 0);
624
625      if (ShowCursor) {
626        chart.ChartAreas[0].AxisX.Title = FreeVariable + " : " + x.ToString("N3", CultureInfo.CurrentCulture);
627        chart.Update();
628      }
629
630      OnVariableValueChanged(this, EventArgs.Empty);
631    }
632
633    private void chart_MouseMove(object sender, MouseEventArgs e) {
634      bool hitCursor = chart.HitTest(e.X, e.Y).ChartElementType == ChartElementType.Annotation;
635      chart.Cursor = hitCursor ? Cursors.VSplit : Cursors.Default;
636    }
637
638    private void chart_DragDrop(object sender, DragEventArgs e) {
639      var data = e.Data.GetData(HeuristicLab.Common.Constants.DragDropDataFormat);
640      if (data != null) {
641        var solution = data as IRegressionSolution;
642        if (!solutions.Contains(solution))
643          AddSolutionAsync(solution);
644      }
645    }
646    private void chart_DragEnter(object sender, DragEventArgs e) {
647      if (!e.Data.GetDataPresent(HeuristicLab.Common.Constants.DragDropDataFormat)) return;
648      e.Effect = DragDropEffects.None;
649
650      var data = e.Data.GetData(HeuristicLab.Common.Constants.DragDropDataFormat);
651      var regressionSolution = data as IRegressionSolution;
652      if (regressionSolution != null) {
653        e.Effect = DragDropEffects.Copy;
654      }
655    }
656
657    private void calculationPendingTimer_Tick(object sender, EventArgs e) {
658      calculationPendingLabel.Visible = true;
659      Update();
660    }
661
662    private void config_Click(object sender, EventArgs e) {
663      configurationDialog.ShowDialog(this);
664    }
665
666    private void chart_SelectionRangeChanged(object sender, CursorEventArgs e) {
667      OnZoomChanged(this, EventArgs.Empty);
668    }
669    #endregion
670  }
671}
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