public void AddAttribute(Attribute attribute)
{
_attributes.Add(attribute);
}
private static double CalculateNormalizedValue(TestData current, Attribute pairAttribute)
{
return Math.Round(Double.Parse(current.Value.ToString()) / pairAttribute.Precision, 0) * pairAttribute.Precision;
}
private double GetProbabilityForContinuousValues(int i, TestData current, Attribute pairAttribute)
{
var normalizedValue = CalculateNormalizedValue(current, pairAttribute);
//continuous
var searchedClasses = FindContinuousSearchedClasses(i, current);
// formula!!!
var values = searchedClasses.GetValues();
var probabilityValue = NormalDistribution(normalizedValue, values[0], values[1], false);
return probabilityValue;
}
private void CreateDiscreteModel(int targetAttribIndex, Attribute attribute, string currentAttributteName, int i)
{
foreach (var classes in _naiveBayesModel.TargetAttributeClasses)
{
var ocurrenceModelList = new List<IOcurrenceModel>();
// total ocurrences by class
var selectedData = _naiveBayesModel.DataModel.Data.Cast<List<object>>().Where(item =>item[targetAttribIndex].ToString().Trim().Equals(classes.ClassName, StringComparison.InvariantCultureIgnoreCase)).ToList();
for (var j = 0; j < attribute.Definition.Count; j++)
{
var ocurrenceModel = new DiscreteAttributeOcurrenceModel();
ocurrenceModel.CurrentAttributeName = currentAttributteName;
ocurrenceModel.CurrentAttributeValue = attribute.Definition[j];
ocurrenceModel.TargetAttributeClassName = classes.ClassName;
// ocurrences of the current definition by class
var value = ((double) selectedData.Cast<List<object>>().Where(item =>item[i].ToString().Equals(attribute.Definition[j], StringComparison.InvariantCultureIgnoreCase)) .Count() + 1.0)/((double) selectedData.Count + (double) attribute.Definition.Count);
ocurrenceModel.Value = value;
ocurrenceModelList.Add(ocurrenceModel);
}
//total of the current attribute in range of the class
var totalOcurrenceModel = new DiscreteAttributeOcurrenceModel();
totalOcurrenceModel.CurrentAttributeName = currentAttributteName + " total";
totalOcurrenceModel.TargetAttributeClassName = classes.ClassName;
totalOcurrenceModel.Value = ocurrenceModelList.Cast<DiscreteAttributeOcurrenceModel>().Sum(m => m.Value);
ocurrenceModelList.Add(totalOcurrenceModel);
_naiveBayesModel.AddOcurrenceMatrixRange(ocurrenceModelList);
}
}
private void CreateContinuousModel(int targetAttribIndex, Attribute attribute, int i)
{
foreach (var classes in _naiveBayesModel.TargetAttributeClasses)
{
var ocurrenceModelList = new List<IOcurrenceModel>();
var selectedData = _naiveBayesModel.DataModel.Data.Cast<List<object>>().Where(item =>item[targetAttribIndex].ToString().Trim().Equals(classes.ClassName, StringComparison.InvariantCultureIgnoreCase)).ToList();
for (var j = 0; j < attribute.Definition.Count; j++)
{
var ocurrenceModel = new ContinousAttributeOcurrenceModel();
ocurrenceModel.CurrentAttributeName = attribute.Name;
ocurrenceModel.TargetAttributeClassName = classes.ClassName;
var selectedItemsValues = selectedData.Select(s => (double) s[i]).ToList();
var currentDistribution = new StandardDeviation(selectedItemsValues);
ocurrenceModel.Mean = currentDistribution.GetMean();
ocurrenceModel.StdDev = currentDistribution.GetStandardDeviation();
ocurrenceModel.WeightSum = currentDistribution.GetWeightSum();
ocurrenceModel.Precision = _naiveBayesModel.DataModel.Attributes[i].Precision;
ocurrenceModelList.Add(ocurrenceModel);
}
_naiveBayesModel.AddOcurrenceMatrixRange(ocurrenceModelList);
}
}
private void CalculateProbabilityForAttribute(int i, Attribute pairAttribute, TestData current, ref List<double> probabilityList)
{
if (pairAttribute.Definition.Count == 1 &&
pairAttribute.Definition[0].Equals("Real", StringComparison.InvariantCultureIgnoreCase))
{
var probabilityValue = GetProbabilityForContinuousValues(i, current, pairAttribute);
probabilityList.Add(probabilityValue);
}
else
{
//discrete
var searchedClasses = FindDiscreteSearchedClasses(i, current);
var value = searchedClasses.GetValues().First();
probabilityList.Add(value);
}
}
