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OptaPlanner solves planning problems. This lightweight, embeddable planning engine implements powerful and scalable algorithms to optimize business resource scheduling and planning. This module contains the examples which demonstrate how to use it in a normal Java application.

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/*
 * Copyright 2018 Red Hat, Inc. and/or its affiliates.
 *
 * 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.optaplanner.examples.common.solver.drools.functions;

import java.io.ObjectInput;
import java.io.ObjectOutput;
import java.io.Serializable;

import org.kie.api.runtime.rule.AccumulateFunction;

public class LoadBalanceAccumulateFunction implements AccumulateFunction {

    protected static class LoadBalanceData implements Serializable {

        private long n;
        private long sum;
        // the sum of squared deviation from zero
        private long squaredSum;

    }

    @Override
    public LoadBalanceData createContext() {
        return new LoadBalanceData();
    }

    @Override
    public void init(LoadBalanceData data) {
        data.n = 0L;
        data.sum = 0L;
        data.squaredSum = 0L;
    }

    @Override
    public void accumulate(LoadBalanceData data, Object o) {
        long value = (long) o;
        data.n++;
        data.sum += value;
        data.squaredSum += value * value;
    }

    @Override
    public boolean supportsReverse() {
        return true;
    }

    @Override
    public void reverse(LoadBalanceData data, Object o) {
        long value = (long) o;
        data.n--;
        data.sum -= value;
        data.squaredSum -= value * value;
    }

    @Override
    public Class getResultType() {
        return LoadBalanceResult.class;
    }

    @Override
    public LoadBalanceResult getResult(LoadBalanceData data) {
        return new LoadBalanceResult(data.n, data.sum, data.squaredSum);
    }

    @Override
    public void writeExternal(ObjectOutput out) {
    }

    @Override
    public void readExternal(ObjectInput in) {
    }

    public static class LoadBalanceResult implements Serializable {

        private final long n;
        private final long sum;
        private final long squaredSum;

        public LoadBalanceResult(long n, long sum, long squaredSum) {
            this.n = n;
            this.sum = sum;
            this.squaredSum = squaredSum;
        }

        public long getMeanDeviationSquaredSumRootMillis() {
            return getMeanDeviationSquaredSumRoot(1_000.0);
        }

        public long getMeanDeviationSquaredSumRootMicros() {
            return getMeanDeviationSquaredSumRoot(1_000_000.0);
        }

        /**
         * Like standard deviation, but doesn't divide by n.
         *
         * @param scaleMultiplier {@code > 0}
         * @return {@code >= 0}, {@code latexmath:[f(n) = \sqrt{\sum_{i=1}^{n} (x_i - \overline{x})^2}]} multiplied by
         *         scaleMultiplier
         */
        public long getMeanDeviationSquaredSumRoot(double scaleMultiplier) {
            // quicklatex.com: f(n) = \sqrt{\sum_{i=1}^{n} (x_i - \overline{x})^2} = \sqrt{\sum_{i=1}^{n} x_i^2 - \frac{(\sum_{i=1}^{n} x_i)^2}{n}}
            double meanDeviationSquaredSum = (double) squaredSum - ((double) (sum * sum) / n);
            return (long) (Math.sqrt(meanDeviationSquaredSum) * scaleMultiplier);
        }
    }

}