static void UpdateAsset(string[] value, ClassValues asset)
{
asset.className = value[1];
asset.health = float.Parse(value[2]);
asset.scoreOnKill = int.Parse(value[3]);
asset.typeClass = StringToBehaviour(value[4]);
}
private void ResetTargetVariableDependentMembers()
{
DeregisterParameterEvents();
((IStringConvertibleMatrix)ClassNamesParameter.Value).Columns = 1;
((IStringConvertibleMatrix)ClassNamesParameter.Value).Rows = ClassValuesCache.Count;
for (int i = 0; i < Classes; i++)
{
ClassNamesParameter.Value[i, 0] = "Class " + ClassValuesCache[i];
}
ClassNamesParameter.Value.ColumnNames = new List <string>()
{
"ClassNames"
};
ClassNamesParameter.Value.RowNames = ClassValues.Select(s => "ClassValue: " + s);
PositiveClassParameter.ValidValues.Clear();
foreach (var className in ClassNames)
{
PositiveClassParameter.ValidValues.Add(new StringValue(className).AsReadOnly());
}
((IStringConvertibleMatrix)ClassificationPenaltiesParameter.Value).Rows = Classes;
((IStringConvertibleMatrix)ClassificationPenaltiesParameter.Value).Columns = Classes;
ClassificationPenaltiesParameter.Value.RowNames = ClassNames.Select(name => "Actual " + name);
ClassificationPenaltiesParameter.Value.ColumnNames = ClassNames.Select(name => "Estimated " + name);
for (int i = 0; i < Classes; i++)
{
for (int j = 0; j < Classes; j++)
{
if (i != j)
{
ClassificationPenaltiesParameter.Value[i, j] = 1;
}
else
{
ClassificationPenaltiesParameter.Value[i, j] = 0;
}
}
}
RegisterParameterEvents();
}
public AnovaResult Anova(double probability)
{
if (probability > 1 || probability < 0)
{
throw new Exception("Probability must be between 1 and 0");
}
//Verify if all groups have the same number of datapoints
List <int> noValues = ClassValues.Select(a => a.Value.Count).Distinct().ToList();
if (noValues.Count != 1)
{
Console.WriteLine(Locus + " does not present quantitation values for all replicates");
throw new Exception("Number of values in all classes must be the same for anova test");
}
string allGroups = string.Join(",", MyChart.Series.Select(a => a.Name).ToList());
AnovaResult result = MyChart.DataManipulator.Statistics.Anova(probability, allGroups);
return(result);
}
public IEnumerable <double> GetEstimatedClassValues(IEnumerable <double> estimatedValues)
{
if (!Thresholds.Any() && !ClassValues.Any())
{
throw new ArgumentException("No thresholds and class values were set for the current symbolic classification model.");
}
foreach (var x in estimatedValues)
{
int classIndex = 0;
// find first threshold value which is larger than x => class index = threshold index + 1
for (int i = 0; i < thresholds.Length; i++)
{
if (x > thresholds[i])
{
classIndex++;
}
else
{
break;
}
}
yield return(classValues.ElementAt(classIndex - 1));
}
}