private static double CalculateHeuristicValue(Dictionary <CharacterFrequencyAnalyzer.Category, double> a, List <double> n)
{
return(a.Zip(CharacterFrequencyAnalyzer.CalculateRelativeAggregateFrequency(n), (b, c) =>
{
return (Math.Pow((b.Value - c.Value), 2));
}).Sum());
}
public List <HeuristicTuple> Classify(IEnumerable <string> input)
{
// Check if there are available training values to use for classifcation, otherwise throw InvalidOperationException.
if (new List <List <List <double> > >()
{
qtyClassifierValues, designatorClassifierValues, descriptionClassifierValues, mfgClassifierValues, mpnClassifierValues
}.All(list => list.Count == 0))
{
throw new InvalidOperationException("No training data has been loaded to allow for classification to occur");
}
Dictionary <CharacterFrequencyAnalyzer.Category, double> classifyingInput = CharacterFrequencyAnalyzer.CalculateRelativeAggregateFrequency(input);
List <HeuristicTuple> calculatedDeltas = new List <HeuristicTuple>();
try
{
foreach (List <double> trainingData in qtyClassifierValues)
{
calculatedDeltas.Add(new HeuristicTuple(ColumnType.Quantity, CalculateHeuristicValue(classifyingInput, trainingData)));
}
foreach (List <double> trainingData in designatorClassifierValues)
{
calculatedDeltas.Add(new HeuristicTuple(ColumnType.Designator, CalculateHeuristicValue(classifyingInput, trainingData)));
}
foreach (List <double> trainingData in descriptionClassifierValues)
{
calculatedDeltas.Add(new HeuristicTuple(ColumnType.Description, CalculateHeuristicValue(classifyingInput, trainingData)));
}
foreach (List <double> trainingData in mfgClassifierValues)
{
calculatedDeltas.Add(new HeuristicTuple(ColumnType.Manufacturer, CalculateHeuristicValue(classifyingInput, trainingData)));
}
foreach (List <double> trainingData in mpnClassifierValues)
{
calculatedDeltas.Add(new HeuristicTuple(ColumnType.PartNumber, CalculateHeuristicValue(classifyingInput, trainingData)));
}
calculatedDeltas.Sort();
}
catch
{
throw;
}
return(calculatedDeltas);
}