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Hidden Markov Model (HMM) library for time-inhomogeneous Markov processes
/**
* Copyright (C) 2015-2016, BMW Car IT GmbH and BMW AG
* Author: Stefan Holder ([email protected])
*
* 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 com.bmw.hmm;
import java.util.Objects;
/**
* State of the most likely sequence with additional information.
*
* @param the state type
* @param the observation type
* @param the transition descriptor type
*/
public class SequenceState {
public final S state;
/**
* Null if HMM was started with initial state probabilities and state is the initial state.
*/
public final O observation;
/**
* Null if transition descriptor was not provided.
*/
public final D transitionDescriptor;
public SequenceState(S state, O observation, D transitionDescriptor) {
this.state = state;
this.observation = observation;
this.transitionDescriptor = transitionDescriptor;
}
@Override
public String toString() {
return "SequenceState [state=" + state + ", observation=" + observation
+ ", transitionDescriptor=" + transitionDescriptor
+ "]";
}
@Override
public int hashCode() {
return Objects.hash(state, observation, transitionDescriptor);
}
@Override
public boolean equals(Object obj) {
if (this == obj)
return true;
if (obj == null)
return false;
if (getClass() != obj.getClass())
return false;
SequenceState other = (SequenceState) obj;
return Objects.equals(state, other.state) &&
Objects.equals(observation, other.observation) &&
Objects.equals(transitionDescriptor, other.transitionDescriptor);
}
}