private void UpdateStatistics() { DoubleMatrix matrix = new DoubleMatrix(9, seriesCache.Count); matrix.SortableView = false; List<string> columnNames = new List<string>(); foreach (Series series in seriesCache.Values) { DataPoint datapoint = series.Points.FirstOrDefault(); if (datapoint != null) { IRun run = (IRun)datapoint.Tag; string selectedAxis = xAxisValue; IItem value = null; if (Enum.IsDefined(typeof(AxisDimension), selectedAxis)) { AxisDimension axisDimension = (AxisDimension)Enum.Parse(typeof(AxisDimension), selectedAxis); switch (axisDimension) { case AxisDimension.Color: value = new StringValue(run.Color.ToString()); break; } } else value = Content.GetValue(run, selectedAxis); string columnName = string.Empty; if (value is DoubleValue || value is IntValue) columnName = selectedAxis + ": "; columnName += value.ToString(); columnNames.Add(columnName); } } matrix.ColumnNames = columnNames; matrix.RowNames = new string[] { "Count", "Minimum", "Maximum", "Median", "Average", "Standard Deviation", "Variance", "25th Percentile", "75th Percentile" }; for (int i = 0; i < seriesCache.Count; i++) { Series series = seriesCache.ElementAt(i).Value; double[] seriesValues = series.Points.Select(p => p.YValues[0]).OrderBy(d => d).ToArray(); matrix[0, i] = seriesValues.Length; matrix[1, i] = seriesValues.Min(); matrix[2, i] = seriesValues.Max(); matrix[3, i] = seriesValues.Median(); matrix[4, i] = seriesValues.Average(); matrix[5, i] = seriesValues.StandardDeviation(); matrix[6, i] = seriesValues.Variance(); matrix[7, i] = seriesValues.Quantile(0.25); matrix[8, i] = seriesValues.Quantile(0.75); } statisticsMatrixView.Content = matrix; }
protected PathValue(PathValue original, Cloner cloner) : base(original, cloner) { stringValue = cloner.Clone(original.stringValue); }
public new bool[] Check() { bool[] result = new bool[ConstrainedValue.Rows]; if (!Active) return result; for (int row = 0; row < ConstrainedValue.Rows; ++row) { object item = null; if (ConstrainedValue.VariableHasType<double>(constraintColumn)) { item = new HeuristicLab.Data.DoubleValue(ConstrainedValue.GetCell<double>(ConstraintColumn, row)); } else if (ConstrainedValue.VariableHasType<DateTime>(constraintColumn)) { item = new HeuristicLab.Data.DateTimeValue(ConstrainedValue.GetCell<DateTime>(ConstraintColumn, row)); } else { item = new StringValue(ConstrainedValue.GetCell<string>(ConstraintColumn, row)); } result[row] = !base.Check(item); } return result; }
private List<IFilter> CreateFilters(string match, ComparisonOperation comparisonOperation, int columnIndex) { IPreprocessingData preprocessingData = Content.FilterLogic.PreprocessingData; IStringConvertibleValue value; if (preprocessingData.VariableHasType<double>(columnIndex)) { value = new DoubleValue(); } else if (preprocessingData.VariableHasType<String>(columnIndex)) { value = new StringValue(); } else if (preprocessingData.VariableHasType<DateTime>(columnIndex)) { value = new DateTimeValue(); } else { throw new ArgumentException("unsupported type"); } value.SetValue(match); var comparisonFilter = new ComparisonFilter(preprocessingData, GetConstraintOperation(comparisonOperation), value, true); comparisonFilter.ConstraintColumn = columnIndex; return new List<Filter.IFilter>() { comparisonFilter }; }
private CopyColumnTransformation CreateCopyTransformation(string oldColumn, string newColumn) { var newColumName = new StringValue(newColumn); var copyTransformation = new CopyColumnTransformation(); copyTransformation.ColumnParameter.ValidValues.Add(newColumName); copyTransformation.ColumnParameter.Value = newColumName; copyTransformation.CopiedColumnNameParameter.Value.Value = oldColumn; return copyTransformation; }
public override void CollectParameterValues(IDictionary<string, IItem> values) { base.CollectParameterValues(values); values["Type"] = new StringValue(GetType().GetPrettyName(false)); }
protected StringValue(StringValue original, Cloner cloner) : base(original, cloner) { this.value = original.value != null ? original.value : string.Empty; this.readOnly = original.readOnly; }
private void RenameTransformationColumnParameter(List<Transformation<double>> transformations) { foreach (var transformation in transformations) { var newColumnName = new StringValue(renamedColumns[transformation.Column]); transformation.ColumnParameter.ValidValues.Add(newColumnName); transformation.ColumnParameter.Value = newColumnName; } }
private IStringConvertibleValue CreateStringConvertibleValue(int columnIndex) { IStringConvertibleValue value; if (Content.ConstrainedValue.VariableHasType<double>(columnIndex)) { value = new DoubleValue(); } else if (Content.ConstrainedValue.VariableHasType<String>(columnIndex)) { value = new StringValue(); } else if (Content.ConstrainedValue.VariableHasType<DateTime>(columnIndex)) { value = new DateTimeValue(); } else { throw new ArgumentException("unsupported type"); } return value; }
protected PathValue(PathValue original, Cloner cloner) : base(original, cloner) { stringValue = cloner.Clone(original.stringValue); }
protected StringValue(StringValue original, Cloner cloner) : base(original, cloner) { this.value = original.value != null ? original.value : string.Empty; this.readOnly = original.readOnly; }
public ICheckedItemList<StringValue> CreateVariableItemList(IEnumerable<string> checkedItems = null) { ICheckedItemList<StringValue> itemList = new CheckedItemList<StringValue>(); foreach (string name in PreprocessingData.GetDoubleVariableNames()) { var n = new StringValue(name); itemList.Add(n, (checkedItems == null) ? true : checkedItems.Contains(name)); } return new ReadOnlyCheckedItemList<StringValue>(itemList); }
private void AddTypeSelectedForSimilarityTableEntry(StringValue[] typeSelectedForSimilarity) { if (!ResultCollection.ContainsKey(TypeSelectedForSimilarityParameterName)) { var newTable = new DataTable(TypeSelectedForSimilarityParameterName, ""); newTable.VisualProperties.YAxisTitle = "Percentage"; newTable.VisualProperties.YAxisMaximumAuto = false; ResultCollection.Add(new Result(TypeSelectedForSimilarityParameterName, newTable)); } var table = ((DataTable)ResultCollection[TypeSelectedForSimilarityParameterName].Value); // all rows must have the same number of values so we can just take the first int numberOfValues = table.Rows.Select(r => r.Values.Count).DefaultIfEmpty().First(); double count = typeSelectedForSimilarity.Count(); var groupedValues = typeSelectedForSimilarity.Select(x => x.Value).GroupBy(x => x); foreach (var type in groupedValues) { if (!table.Rows.ContainsKey(type.Key)) { // initialize a new row for the symbol and pad with zeros DataRow row = new DataRow(type.Key, "", Enumerable.Repeat(0.0, numberOfValues)); row.VisualProperties.StartIndexZero = true; table.Rows.Add(row); } table.Rows[type.Key].Values.Add(type.Count() / count * 100); } // add a zero for each data row that was not modified in the previous loop foreach (var row in table.Rows.Where(r => r.Values.Count != numberOfValues + 1)) row.Values.Add(0.0); }