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);
}
