/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.mahout.classifier.sgd;
import org.apache.hadoop.io.Writable;
/**
* A prior is used to regularize the learning algorithm. This allows a trade-off to
* be made between complexity of the model being learned and the accuracy with which
* the model fits the training data. There are different definitions of complexity
* which can be approximated using different priors. For large sparse systems, such
* as text classification, the L1 prior is often used which favors sparse models.
*/
public interface PriorFunction extends Writable {
/**
* Applies the regularization to a coefficient.
* @param oldValue The previous value.
* @param generations The number of generations.
* @param learningRate The learning rate with lambda baked in.
* @return The new coefficient value after regularization.
*/
double age(double oldValue, double generations, double learningRate);
/**
* Returns the log of the probability of a particular coefficient value according to the prior.
* @param betaIJ The coefficient.
* @return The log probability.
*/
double logP(double betaIJ);
}