com.graphhopper.matching.util.Distributions Maven / Gradle / Ivy
/**
* 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.graphhopper.matching.util;
import static java.lang.Math.PI;
import static java.lang.Math.exp;
import static java.lang.Math.log;
import static java.lang.Math.pow;
import static java.lang.Math.sqrt;
/**
* Implements various probability distributions.
*/
public class Distributions {
static double normalDistribution(double sigma, double x) {
return 1.0 / (sqrt(2.0 * PI) * sigma) * exp(-0.5 * pow(x / sigma, 2));
}
/**
* Use this function instead of Math.log(normalDistribution(sigma, x)) to avoid an
* arithmetic underflow for very small probabilities.
*/
public static double logNormalDistribution(double sigma, double x) {
return Math.log(1.0 / (sqrt(2.0 * PI) * sigma)) + (-0.5 * pow(x / sigma, 2));
}
/**
* @param beta =1/lambda with lambda being the standard exponential distribution rate parameter
*/
static double exponentialDistribution(double beta, double x) {
return 1.0 / beta * exp(-x / beta);
}
/**
* Use this function instead of Math.log(exponentialDistribution(beta, x)) to avoid an
* arithmetic underflow for very small probabilities.
*
* @param beta =1/lambda with lambda being the standard exponential distribution rate parameter
*/
static double logExponentialDistribution(double beta, double x) {
return log(1.0 / beta) - (x / beta);
}
}