code: /**
   * Copyright (C) 2015-2016, BMW Car IT GmbH and BMW AG
   * Author: Stefan Holder (stefan.holder@bmw.de)
   *
   * 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.graphhopper.matching;
  
  /**
   * Based on Newson, Paul, and John Krumm. "Hidden Markov map matching through
   * noise and sparseness." Proceedings of the 17th ACM SIGSPATIAL International
   * Conference on Advances in Geographic Information Systems. ACM, 2009.
   */
  public class HmmProbabilities {
  
      private final double sigma;
      private final double beta;
  
      /**
       * @param sigma standard deviation of the normal distribution [m] used for
       *              modeling the GPS error
       * @param beta  beta parameter of the exponential distribution used for modeling
       *              transition probabilities
       */
      public HmmProbabilities(double sigma, double beta) {
          this.sigma = sigma;
          this.beta = beta;
      }
  
      /**
       * Returns the logarithmic emission probability density.
       *
       * @param distance Absolute distance [m] between GPS measurement and map
       *                 matching candidate.
       */
      public double emissionLogProbability(double distance) {
          return Distributions.logNormalDistribution(sigma, distance);
      }
  
      /**
       * Returns the logarithmic transition probability density for the given
       * transition parameters.
       *
       * @param routeLength    Length of the shortest route [m] between two
       *                       consecutive map matching candidates.
       * @param linearDistance Linear distance [m] between two consecutive GPS
       *                       measurements.
       */
      public double transitionLogProbability(double routeLength, double linearDistance) {
          // Transition metric taken from Newson & Krumm.
          double transitionMetric = Math.abs(linearDistance - routeLength);
          return Distributions.logExponentialDistribution(beta, transitionMetric);
      }
  
  }
  