public TestDataModel()
{
var Age = new TestData();
Age.DataName = "Age";
Age.Value = 47;
var Sex = new TestData();
Sex.DataName = "Sex";
Sex.Value = "M";
var BP = new TestData();
BP.DataName = "BP";
BP.Value = "LOW";
var Cholesterol = new TestData();
Cholesterol.DataName = "Cholesterol";
Cholesterol.Value = "HIGH";
var Na = new TestData();
Na.DataName = "Na";
Na.Value = 0.739309;
var K = new TestData();
K.DataName = "K";
K.Value = 0.056468;
_testData = new List<TestData>()
{
Age, Sex, BP, Cholesterol, Na, K
};
}
public TestDataModel(IList<object> row)
{
var Age = new TestData();
Age.DataName = "Age";
Age.Value = (double) row[0];
var Sex = new TestData();
Sex.DataName = "Sex";
Sex.Value = (string) row [1];
var BP = new TestData();
BP.DataName = "BP";
BP.Value = (string) row[2];
var Cholesterol = new TestData();
Cholesterol.DataName = "Cholesterol";
Cholesterol.Value = (string) row[3];
var Na = new TestData();
Na.DataName = "Na";
Na.Value = (double) row[4];
var K = new TestData();
K.DataName = "K";
K.Value = (double) row[5];
var Droug = new TestData();
K.DataName = "Droug";
K.Value = (string) row[6];
_testData = new List<TestData>()
{
Age, Sex, BP, Cholesterol, Na, K, Droug
};
}
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 Attribute FindPairAttribute(TestData current)
{
return _naiveBayesModel.DataModel.Attributes.FirstOrDefault(item => item.Name.Equals(current.DataName, StringComparison.InvariantCultureIgnoreCase));
}
private IOcurrenceModel FindDiscreteSearchedClasses(int i, TestData current)
{
return _naiveBayesModel.OcurrenceMatrix.First(
m =>
m.GetAttributeName().Equals(current.DataName, StringComparison.InvariantCultureIgnoreCase) &&
m.GetAttributeValue()
.Equals(current.Value.ToString(), StringComparison.InvariantCultureIgnoreCase) &&
m.GetTargetClass()
.Equals(_naiveBayesModel.TargetAttributeClasses[i].ClassName,
StringComparison.InvariantCultureIgnoreCase));
}
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);
}
}
