/**
* <summary> Training algorithm for the quadratic discriminant analysis classifier (Introduction to Machine Learning, Alpaydin, 2015).</summary>
*
* <param name="trainSet"> Training data given to the algorithm.</param>
* <param name="parameters">-</param>
*/
public override void Train(InstanceList.InstanceList trainSet, Parameter.Parameter parameters)
{
var w0 = new Dictionary <string, double>();
var w = new Dictionary <string, Vector>();
var W = new Dictionary <string, Matrix>();
var classLists = trainSet.DivideIntoClasses();
var priorDistribution = trainSet.ClassDistribution();
for (var i = 0; i < classLists.Size(); i++)
{
var ci = ((InstanceListOfSameClass)classLists.Get(i)).GetClassLabel();
var averageVector = new Vector(classLists.Get(i).ContinuousAttributeAverage());
var classCovariance = classLists.Get(i).Covariance(averageVector);
var determinant = classCovariance.Determinant();
classCovariance.Inverse();
var Wi = (Matrix)classCovariance.Clone();
Wi.MultiplyWithConstant(-0.5);
W[ci] = Wi;
var wi = classCovariance.MultiplyWithVectorFromLeft(averageVector);
w[ci] = wi;
var w0i = -0.5 * (wi.DotProduct(averageVector) + System.Math.Log(determinant)) +
System.Math.Log(priorDistribution.GetProbability(ci));
w0[ci] = w0i;
}
model = new QdaModel(priorDistribution, W, w, w0);
}
/**
* <summary> Training algorithm for the linear discriminant analysis classifier (Introduction to Machine Learning, Alpaydin, 2015).</summary>
*
* <param name="trainSet"> Training data given to the algorithm.</param>
* <param name="parameters">-</param>
*/
public override void Train(InstanceList.InstanceList trainSet, Parameter.Parameter parameters)
{
Vector averageVector;
var w0 = new Dictionary <string, double>();
var w = new Dictionary <string, Vector>();
var priorDistribution = trainSet.ClassDistribution();
var classLists = trainSet.DivideIntoClasses();
var covariance = new Matrix(trainSet.Get(0).ContinuousAttributeSize(),
trainSet.Get(0).ContinuousAttributeSize());
for (var i = 0; i < classLists.Size(); i++)
{
averageVector = new Vector(classLists.Get(i).ContinuousAttributeAverage());
var classCovariance = classLists.Get(i).Covariance(averageVector);
classCovariance.MultiplyWithConstant(classLists.Get(i).Size() - 1);
covariance.Add(classCovariance);
}
covariance.DivideByConstant(trainSet.Size() - classLists.Size());
covariance.Inverse();
for (var i = 0; i < classLists.Size(); i++)
{
var ci = ((InstanceListOfSameClass)classLists.Get(i)).GetClassLabel();
averageVector = new Vector(classLists.Get(i).ContinuousAttributeAverage());
var wi = covariance.MultiplyWithVectorFromRight(averageVector);
w[ci] = wi;
var w0i = -0.5 * wi.DotProduct(averageVector) + System.Math.Log(priorDistribution.GetProbability(ci));
w0[ci] = w0i;
}
model = new LdaModel(priorDistribution, w, w0);
}
/**
* <summary> Training algorithm for K-Means classifier. K-Means finds the mean of each class for training.</summary>
*
* <param name="trainSet"> Training data given to the algorithm.</param>
* <param name="parameters">distanceMetric: distance metric used to calculate the distance between two instances.</param>
*/
public override void Train(InstanceList.InstanceList trainSet, Parameter.Parameter parameters)
{
var priorDistribution = trainSet.ClassDistribution();
var classMeans = new InstanceList.InstanceList();
var classLists = trainSet.DivideIntoClasses();
for (var i = 0; i < classLists.Size(); i++)
{
classMeans.Add(classLists.Get(i).Average());
}
model = new KMeansModel(priorDistribution, classMeans, ((KMeansParameter)parameters).GetDistanceMetric());
}
/**
* <summary> Training algorithm for Naive Bayes algorithm. It basically calls trainContinuousVersion for continuous data sets,
* trainDiscreteVersion for discrete data sets.</summary>
* <param name="trainSet">Training data given to the algorithm</param>
* <param name="parameters">-</param>
*/
public override void Train(InstanceList.InstanceList trainSet, Parameter.Parameter parameters)
{
var priorDistribution = trainSet.ClassDistribution();
var classLists = trainSet.DivideIntoClasses();
if (classLists.Get(0).Get(0).GetAttribute(0) is DiscreteAttribute)
{
TrainDiscreteVersion(priorDistribution, classLists);
}
else
{
TrainContinuousVersion(priorDistribution, classLists);
}
}
/**
* <summary> Returns instances of the items at the list of instance lists from the partitions.</summary>
*
* <returns>Instances of the items at the list of instance lists from the partitions.</returns>
*/
public List <Instance.Instance>[] GetClassInstances()
{
return(_instances.DivideIntoClasses().GetLists());
}
