org.apache.mahout.classifier

Interface OnlineLearner

All Superinterfaces:

AutoCloseable, Closeable

All Known Implementing Classes:

AbstractOnlineLogisticRegression, AdaptiveLogisticRegression, ClusterClassifier, CrossFoldLearner, GradientMachine, OnlineLogisticRegression, PassiveAggressive

public interface OnlineLearner
extends Closeable

The simplest interface for online learning algorithms.

Method Summary

Modifier and Type	Method and Description
void	close() Prepares the classifier for classification and deallocates any temporary data structures.
void	train(int actual, Vector instance) Updates the model using a particular target variable value and a feature vector.
void	train(long trackingKey, int actual, Vector instance) Updates the model using a particular target variable value and a feature vector.
void	train(long trackingKey, String groupKey, int actual, Vector instance) Updates the model using a particular target variable value and a feature vector.

Method Detail

train

void train(int actual,
           Vector instance)

Updates the model using a particular target variable value and a feature vector.

There may an assumption that if multiple passes through the training data are necessary, then the training examples will be presented in the same order. This is because the order of training examples may be used to assign records to different data splits for evaluation by cross-validation. Without the order invariance, records might be assigned to training and test splits and error estimates could be seriously affected.

If re-ordering is necessary, then using the alternative API which allows a tracking key to be added to the training example can be used.

Parameters:

actual - The value of the target variable. This value should be in the half-open interval [0..n) where n is the number of target categories.

instance - The feature vector for this example.

train

void train(long trackingKey,
           String groupKey,
           int actual,
           Vector instance)

Updates the model using a particular target variable value and a feature vector.

There may an assumption that if multiple passes through the training data are necessary that the tracking key for a record will be the same for each pass and that there will be a relatively large number of distinct tracking keys and that the low-order bits of the tracking keys will not correlate with any of the input variables. This tracking key is used to assign training examples to different test/training splits.

Examples of useful tracking keys include id-numbers for the training records derived from a database id for the base table from the which the record is derived, or the offset of the original data record in a data file.

Parameters:

trackingKey - The tracking key for this training example.

groupKey - An optional value that allows examples to be grouped in the computation of the update to the model.

actual - The value of the target variable. This value should be in the half-open interval [0..n) where n is the number of target categories.

instance - The feature vector for this example.

train

void train(long trackingKey,
           int actual,
           Vector instance)

Updates the model using a particular target variable value and a feature vector.

There may an assumption that if multiple passes through the training data are necessary that the tracking key for a record will be the same for each pass and that there will be a relatively large number of distinct tracking keys and that the low-order bits of the tracking keys will not correlate with any of the input variables. This tracking key is used to assign training examples to different test/training splits.

Examples of useful tracking keys include id-numbers for the training records derived from a database id for the base table from the which the record is derived, or the offset of the original data record in a data file.

Parameters:

trackingKey - The tracking key for this training example.

actual - The value of the target variable. This value should be in the half-open interval [0..n) where n is the number of target categories.

instance - The feature vector for this example.

close

void close()

Prepares the classifier for classification and deallocates any temporary data structures. An online classifier should be able to accept more training after being closed, but closing the classifier may make classification more efficient.

Specified by:

close in interface AutoCloseable

Specified by:

close in interface Closeable