/**
* <summary> Training algorithm for auto encoders. An auto encoder is a neural network which attempts to replicate its input at its output.</summary>
*
* <param name="trainSet"> Training data given to the algorithm.</param>
* <param name="parameters">Parameters of the auto encoder.</param>
*/
public override void Train(InstanceList.InstanceList trainSet, Parameter.Parameter parameters)
{
var partition = trainSet.StratifiedPartition(0.2, new Random(parameters.GetSeed()));
model = new AutoEncoderModel(partition.Get(1), partition.Get(0),
(MultiLayerPerceptronParameter)parameters);
}
/**
* <summary> Training algorithm for the multilayer perceptron algorithm. 20 percent of the data is separated as cross-validation
* data used for selecting the best weights. 80 percent of the data is used for training the multilayer perceptron with
* gradient descent.</summary>
*
* <param name="trainSet"> Training data given to the algorithm</param>
* <param name="parameters">Parameters of the multilayer perceptron.</param>
*/
public override void Train(InstanceList.InstanceList trainSet, Parameter.Parameter parameters)
{
var partition = trainSet.StratifiedPartition(
((MultiLayerPerceptronParameter) parameters).GetCrossValidationRatio(),
new Random(parameters.GetSeed()));
model = new MultiLayerPerceptronModel(partition.Get(1), partition.Get(0), (MultiLayerPerceptronParameter)
parameters);
}
/**
* <summary> Training algorithm for C4.5 univariate decision tree classifier. 20 percent of the data are left aside for pruning
* 80 percent of the data is used for constructing the tree.</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)
{
DecisionTree tree;
if (((C45Parameter)parameters).IsPrune())
{
var partition = trainSet.StratifiedPartition(
((C45Parameter)parameters).GetCrossValidationRatio(), new Random(parameters.GetSeed()));
tree = new DecisionTree(new DecisionNode(partition.Get(1), null, null, false));
tree.Prune(partition.Get(0));
}
else
{
tree = new DecisionTree(new DecisionNode(trainSet, null, null, false));
}
model = tree;
}
/**
* <summary> Training algorithm for random classifier.</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)
{
model = new RandomModel(new List <String>(trainSet.ClassDistribution().Keys), parameters.GetSeed());
}
/**
* <summary> Training algorithm for deep network classifier.</summary>
*
* <param name="trainSet"> Training data given to the algorithm.</param>
* <param name="parameters">Parameters of the deep network algorithm. crossValidationRatio and seed are used as parameters.</param>
*/
public override void Train(InstanceList.InstanceList trainSet, Parameter.Parameter parameters)
{
var partition = trainSet.StratifiedPartition(
((DeepNetworkParameter)parameters).GetCrossValidationRatio(), new Random(parameters.GetSeed()));
model = new DeepNetworkModel(partition.Get(1), partition.Get(0), (DeepNetworkParameter)parameters);
}