smile.classification

Interface Classifier<T>

Type Parameters:

T - the type of input object

All Known Subinterfaces:

OnlineClassifier<T>

All Known Implementing Classes:

AdaBoost, DecisionTree, FLD, GradientTreeBoost, KNN, LDA, LogisticRegression, Maxent, NaiveBayes, NeuralNetwork, QDA, RandomForest, RBFNetwork, RDA, SVM

public interface Classifier<T>

A classifier assigns an input object into one of a given number of categories. The input object is formally termed an instance, and the categories are termed classes. The instance is usually described by a vector of features, which together constitute a description of all known characteristics of the instance.

Classification normally refers to a supervised procedure, i.e. a procedure that produces an inferred function to predict the output value of new instances based on a training set of pairs consisting of an input object and a desired output value. The inferred function is called a classifier if the output is discrete or a regression function if the output is continuous.

Author:

Haifeng Li

Method Summary

Methods

Modifier and Type	Method and Description
int	predict(T x) Predicts the class label of an instance.
int	predict(T x, double[] posteriori) Predicts the class label of an instance and also calculate a posteriori probabilities.

Method Detail

predict

int predict(T x)

Predicts the class label of an instance.

Parameters:

x - the instance to be classified.

Returns:

the predicted class label

predict

int predict(T x,
          double[] posteriori)

Predicts the class label of an instance and also calculate a posteriori probabilities. Classifiers may NOT support this method since not all classification algorithms are able to calculate such a posteriori probabilities.

Parameters:

x - the instance to be classified.

posteriori - the array to store a posteriori probabilities on output.

Returns:

the predicted class label