// Copyright 2012 Georg-August-Universität Göttingen, Germany // // Licensed 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 de.ugoe.cs.autoquest.usageprofiles; import java.util.Collection; import java.util.LinkedList; import java.util.List; import java.util.Random; import de.ugoe.cs.autoquest.eventcore.Event; /** *

* Implements a Deterministic Finite Automata (DFA) capable of random session generation. It is a * special case of a first-order Markov model, where the transition probability is equally high for * all following states. *

* * @author Steffen Herbold * @version 1.0 */ public class DeterministicFiniteAutomaton extends FirstOrderMarkovModel { /** *

* Id for object serialization. *

*/ private static final long serialVersionUID = 1L; /** *

* Constructor. Creates a new DeterministicFiniteAutomaton. *

* * @param r * random number generator used by probabilistic methods of the class */ public DeterministicFiniteAutomaton(Random r) { super(r); } /** *

* Calculates the proability of the next state. Each of the following states in the automaton is * equally probable. *

* * @see de.ugoe.cs.autoquest.usageprofiles.IStochasticProcess#getProbability(java.util.List, * de.ugoe.cs.autoquest.eventcore.Event) */ @Override public double getProbability(List context, Event symbol) { if (context == null) { throw new IllegalArgumentException("context must not be null"); } if (symbol == null) { throw new IllegalArgumentException("symbol must not be null"); } double result = 0.0d; List contextCopy; if (context.size() >= trieOrder) { contextCopy = new LinkedList(context.subList(context.size() - trieOrder + 1, context.size())); } else { contextCopy = new LinkedList(context); } Collection followers = trie.getFollowingSymbols(contextCopy); if (followers.size() != 0 && followers.contains(symbol)) { result = 1.0d / followers.size(); } return result; } }