public ClassifierModel TrainModel()
{
var attributes = Attributes.Where(x => !x.IsClass);
//[ClassValue][Attribute][AttributeValue]
int[][][] countMatrix = new int[ClassInfo.NominalOptionCount][][];
decimal[][][] modelMatrix = new decimal[countMatrix.Length][][];
for (int i = 0; i < countMatrix.Length; i++)
{
countMatrix[i] = new int[attributes.Count()][];
modelMatrix[i] = new decimal[countMatrix[i].Length][];
int j = 0;
foreach (var attr in attributes)
{
countMatrix[i][j] = new int[attr.NominalOptionCount];
modelMatrix[i][j] = new decimal[attr.NominalOptionCount];
j++;
}
}
foreach (var row in Data)
{
var data = GetRowData(row, Attributes);
var classValue = data.ElementAt(ClassIndex);
int i = 0, j = 0;
foreach (var o in data)
{
if (i != ClassIndex)
{
countMatrix[classValue][j][o]++;
j++;
}
i++;
}
}
for (int i = 0; i < countMatrix.Length; i++)
{
var countBase = (decimal)countMatrix[i][0].Sum();
if (countBase != 0)
{
for (int j = 0; j < countMatrix[i].Length; j++)
{
for (int k = 0; k < countMatrix[i][j].Length; k++)
{
modelMatrix[i][j][k] = countMatrix[i][j][k] / countBase;
}
}
}
}
_TrainedModel = new ClassifierModel(ClassInfo, attributes, modelMatrix);
return(_TrainedModel);
}
public DamerauLevensteinClassifier(ClassifierModel model)
{
if (model == null)
{
throw new ArgumentNullException(nameof(model));
}
MAX = model.MaxChanges;
SHORTSYNNONYMACCEPTANCETHRESHOLD = model.ShortSynnonymAcceptanceThreshold;
STRINGLENGTHTHRESHOLD = model.StringLengthThreshold;
BREAKONCOMPLETEMATCH = model.BreakOnCompleteMatch;
FULLSTRINGMATCHPROPORTION = model.FullStringMatchProportion;
EACHWORDMATCHPROPORTION = model.EachWordMatchProportion;
MAXITERATIVEGROUPVALUE = model.MaxIterativeWordGroupValue;
dLmetric = new DamerauLevensteinMetric(new DamerauLevensteinMetricModel {
MaxLength = MAX
});
}
private void InitialiseClassifiers()
{
if (!IsSupported)
{
return;
}
var modelDir = MLUtils.ModelFolder;
if (modelDir != null)
{
var haarcascades = modelDir.GetFiles("haarcascade*.xml", SearchOption.AllDirectories).ToList();
foreach (var modelFile in haarcascades)
{
try
{
var tag = modelFile.Directory?.Name.Transform(To.SentenceCase);
if (string.IsNullOrEmpty(tag))
{
tag = "Object";
}
var classifier = new CascadeClassifier(modelFile.FullName);
var model = new ClassifierModel(modelFile.Name, tag, classifier);
Logging.Log($"Initialised EMGU classifier with {model.ClassifierFile} for tag '{tag}'");
classifiers.Add(model);
}
catch (Exception ex)
{
Logging.LogError($"Unable to initialize Emgu face detection: {ex}");
}
}
}
}
