org.wallerlab.yoink.adaptive.smooth.smoothfunction.PermutedSmoothFunction.java Source code

Introduction

Here is the source code for org.wallerlab.yoink.adaptive.smooth.smoothfunction.PermutedSmoothFunction.java

Source

/*
 * Copyright 2014-2015 the original author or authors.
 *
 * 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 org.wallerlab.yoink.adaptive.smooth.smoothfunction;

import org.springframework.stereotype.Service;
import org.wallerlab.yoink.api.service.adaptive.SmoothFunction;

/**
 * this class is for smooth function used in PAP and SAP.
 * 
 * @author Min Zheng
 *
 */
@Service("permutedSmoothFunction")
public class PermutedSmoothFunction implements SmoothFunction {

    /**
     * this smooth function is used in PAP and SAP methods. for details please
     * see: Heyden, Andreas, Hai Lin, and Donald G. Truhlar. "Adaptive
     * partitioning in combined quantum mechanical and molecular mechanical
     * calculations of potential energy functions for multiscale simulations."
     * The Journal of Physical Chemistry B 111.9 (2007): 2231-2241.
     * 
     * @param currentValue
     *            , currentValue(variable) in smooth function
     * @param min
     *            , minimum value in smooth function
     * @param max
     *            , maximum value in smooth function
     * @return smooth factor
     */
    public double evaluate(double currentValue, double min, double max) {
        double smoothFactor;
        if (currentValue > max) {
            smoothFactor = 0;
        } else if (currentValue < min) {
            smoothFactor = 1;
        } else {
            double alpha = (currentValue - min) / (max - min);
            smoothFactor = -6 * (Math.pow((alpha), 5)) + 15 * (Math.pow((alpha), 4)) - 10 * (Math.pow((alpha), 3))
                    + 1;
        }
        return smoothFactor;
    }
}