org.apache.mahout.classifier.discriminative
Class LinearTrainer

java.lang.Object
  org.apache.mahout.classifier.discriminative.LinearTrainer

Direct Known Subclasses:

PerceptronTrainer, WinnowTrainer

public abstract class LinearTrainer
extends Object

Implementors of this class need to provide a way to train linear discriminative classifiers. As this is just the reference implementation we assume that the dataset fits into main memory - this should be the first thing to change when switching to Hadoop.

Constructor Summary

protected

LinearTrainer(int dimension, double threshold, double init, double initBias) Initialize the trainer.

Method Summary

LinearModel	getModel() Retrieves the trained model if called after train, otherwise the raw model.
void	train(Vector labelset, Matrix dataset) Initializes training.
protected abstract void	update(double label, Vector dataPoint, LinearModel model) Implement this method to match your training strategy.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

LinearTrainer

protected LinearTrainer(int dimension,
                        double threshold,
                        double init,
                        double initBias)

Initialize the trainer. Distance is initialized to cosine distance, all weights are represented through a dense vector.

Parameters:

dimension - number of expected features.

threshold - threshold to use for classification.

init - initial value of weight vector.

initBias - initial classification bias.

Method Detail

train

public void train(Vector labelset,
                  Matrix dataset)
           throws TrainingException

Initializes training. Runs through all data points in the training set and updates the weight vector whenever a classification error occurs. Can be called multiple times.

Parameters:

dataset - the dataset to train on. Each column is treated as point.

labelset - the set of labels, one for each data point. If the cardinalities of data- and labelset do not match, a CardinalityException is thrown

Throws:

TrainingException

getModel

public LinearModel getModel()

Retrieves the trained model if called after train, otherwise the raw model.

update

protected abstract void update(double label,
                               Vector dataPoint,
                               LinearModel model)

Implement this method to match your training strategy.

Parameters:

model - the model to update.

label - the target label of the wrongly classified data point.

dataPoint - the data point that was classified incorrectly.