package com.ag.algroithm.instance;

import java.util.Arrays;

/* Copyright (c) 2012 Kevin L. Stern
 * 
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to deal
 * in the Software without restriction, including without limitation the rights
 * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 * copies of the Software, and to permit persons to whom the Software is
 * furnished to do so, subject to the following conditions:
 * 
 * The above copyright notice and this permission notice shall be included in
 * all copies or substantial portions of the Software.
 * 
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 * SOFTWARE.
 */

/**
 * An implementation of the Hungarian algorithm for solving the assignment
 * problem. An instance of the assignment problem consists of a number of
 * workers along with a number of jobs and a cost matrix which gives the cost of
 * assigning the i'th worker to the j'th job at position (i, j). The goal is to
 * find an assignment of workers to jobs so that no job is assigned more than
 * one worker and so that no worker is assigned to more than one job in such a
 * manner so as to minimize the total cost of completing the jobs.
 * <p>
 * 
 * An assignment for a cost matrix that has more workers than jobs will
 * necessarily include unassigned workers, indicated by an assignment value of
 * -1; in no other circumstance will there be unassigned workers. Similarly, an
 * assignment for a cost matrix that has more jobs than workers will necessarily
 * include unassigned jobs; in no other circumstance will there be unassigned
 * jobs. For completeness, an assignment for a square cost matrix will give
 * exactly one unique worker to each job.
 * <p>
 * 
 * This version of the Hungarian algorithm runs in time O(n^3), where n is the
 * maximum among the number of workers and the number of jobs.
 * 
 * @author Kevin L. Stern
 */
public class HungarianAlgorithm {
  private final double[][] costMatrix;
  private final int rows, cols, dim;
  private final double[] labelByWorker, labelByJob;
  private final int[] minSlackWorkerByJob;
  private final double[] minSlackValueByJob;
  private final int[] matchJobByWorker, matchWorkerByJob;
  private final int[] parentWorkerByCommittedJob;
  private final boolean[] committedWorkers;

  /**
   * Construct an instance of the algorithm.
   * 
   * @param costMatrix
   *          the cost matrix, where matrix[i][j] holds the cost of assigning
   *          worker i to job j, for all i, j. The cost matrix must not be
   *          irregular in the sense that all rows must be the same length; in
   *          addition, all entries must be non-infinite numbers.
   */
  public HungarianAlgorithm(double[][] costMatrix) {
    this.dim = Math.max(costMatrix.length, costMatrix[0].length);
    this.rows = costMatrix.length;
    this.cols = costMatrix[0].length;
    this.costMatrix = new double[this.dim][this.dim];
    for (int w = 0; w < this.dim; w++) {
      if (w < costMatrix.length) {
        if (costMatrix[w].length != this.cols) {
          throw new IllegalArgumentException("Irregular cost matrix");
        }
        for (int j = 0; j < this.cols; j++) {
          if (Double.isInfinite(costMatrix[w][j])) {
            throw new IllegalArgumentException("Infinite cost");
          }
          if (Double.isNaN(costMatrix[w][j])) {
            throw new IllegalArgumentException("NaN cost");
          }
        }
        this.costMatrix[w] = Arrays.copyOf(costMatrix[w], this.dim);
      } else {
        this.costMatrix[w] = new double[this.dim];
      }
    }
    labelByWorker = new double[this.dim];
    labelByJob = new double[this.dim];
    minSlackWorkerByJob = new int[this.dim];
    minSlackValueByJob = new double[this.dim];
    committedWorkers = new boolean[this.dim];
    parentWorkerByCommittedJob = new int[this.dim];
    matchJobByWorker = new int[this.dim];
    Arrays.fill(matchJobByWorker, -1);
    matchWorkerByJob = new int[this.dim];
    Arrays.fill(matchWorkerByJob, -1);
  }

  /**
   * Compute an initial feasible solution by assigning zero labels to the
   * workers and by assigning to each job a label equal to the minimum cost
   * among its incident edges.
   */
  protected void computeInitialFeasibleSolution() {
    for (int j = 0; j < dim; j++) {
      labelByJob[j] = Double.POSITIVE_INFINITY;
    }
    for (int w = 0; w < dim; w++) {
      for (int j = 0; j < dim; j++) {
        if (costMatrix[w][j] < labelByJob[j]) {
          labelByJob[j] = costMatrix[w][j];
        }
      }
    }
  }

  /**
   * Execute the algorithm.
   * 
   * @return the minimum cost matching of workers to jobs based upon the
   *         provided cost matrix. A matching value of -1 indicates that the
   *         corresponding worker is unassigned.
   */
  public int[] execute() {
    /*
     * Heuristics to improve performance: Reduce rows and columns by their
     * smallest element, compute an initial non-zero dual feasible solution and
     * create a greedy matching from workers to jobs of the cost matrix.
     */
    reduce();
    computeInitialFeasibleSolution();
    greedyMatch();

    int w = fetchUnmatchedWorker();
    while (w < dim) {
      initializePhase(w);
      executePhase();
      w = fetchUnmatchedWorker();
    }
    int[] result = Arrays.copyOf(matchJobByWorker, rows);
    for (w = 0; w < result.length; w++) {
      if (result[w] >= cols) {
        result[w] = -1;
      }
    }
    return result;
  }

  /**
   * Execute a single phase of the algorithm. A phase of the Hungarian algorithm
   * consists of building a set of committed workers and a set of committed jobs
   * from a root unmatched worker by following alternating unmatched/matched
   * zero-slack edges. If an unmatched job is encountered, then an augmenting
   * path has been found and the matching is grown. If the connected zero-slack
   * edges have been exhausted, the labels of committed workers are increased by
   * the minimum slack among committed workers and non-committed jobs to create
   * more zero-slack edges (the labels of committed jobs are simultaneously
   * decreased by the same amount in order to maintain a feasible labeling).
   * <p>
   * 
   * The runtime of a single phase of the algorithm is O(n^2), where n is the
   * dimension of the internal square cost matrix, since each edge is visited at
   * most once and since increasing the labeling is accomplished in time O(n) by
   * maintaining the minimum slack values among non-committed jobs. When a phase
   * completes, the matching will have increased in size.
   */
  protected void executePhase() {
    while (true) {
      int minSlackWorker = -1, minSlackJob = -1;
      double minSlackValue = Double.POSITIVE_INFINITY;
      for (int j = 0; j < dim; j++) {
        if (parentWorkerByCommittedJob[j] == -1) {
          if (minSlackValueByJob[j] < minSlackValue) {
            minSlackValue = minSlackValueByJob[j];
            minSlackWorker = minSlackWorkerByJob[j];
            minSlackJob = j;
          }
        }
      }
      if (minSlackValue > 0) {
        updateLabeling(minSlackValue);
      }
      parentWorkerByCommittedJob[minSlackJob] = minSlackWorker;
      if (matchWorkerByJob[minSlackJob] == -1) {
        /*
         * An augmenting path has been found.
         */
        int committedJob = minSlackJob;
        int parentWorker = parentWorkerByCommittedJob[committedJob];
        while (true) {
          int temp = matchJobByWorker[parentWorker];
          match(parentWorker, committedJob);
          committedJob = temp;
          if (committedJob == -1) {
            break;
          }
          parentWorker = parentWorkerByCommittedJob[committedJob];
        }
        return;
      } else {
        /*
         * Update slack values since we increased the size of the committed
         * workers set.
         */
        int worker = matchWorkerByJob[minSlackJob];
        committedWorkers[worker] = true;
        for (int j = 0; j < dim; j++) {
          if (parentWorkerByCommittedJob[j] == -1) {
            double slack = costMatrix[worker][j] - labelByWorker[worker]
                - labelByJob[j];
            if (minSlackValueByJob[j] > slack) {
              minSlackValueByJob[j] = slack;
              minSlackWorkerByJob[j] = worker;
            }
          }
        }
      }
    }
  }

  /**
   * 
   * @return the first unmatched worker or {@link #dim} if none.
   */
  protected int fetchUnmatchedWorker() {
    int w;
    for (w = 0; w < dim; w++) {
      if (matchJobByWorker[w] == -1) {
        break;
      }
    }
    return w;
  }

  /**
   * Find a valid matching by greedily selecting among zero-cost matchings. This
   * is a heuristic to jump-start the augmentation algorithm.
   */
  protected void greedyMatch() {
    for (int w = 0; w < dim; w++) {
      for (int j = 0; j < dim; j++) {
        if (matchJobByWorker[w] == -1 && matchWorkerByJob[j] == -1
            && costMatrix[w][j] - labelByWorker[w] - labelByJob[j] == 0) {
          match(w, j);
        }
      }
    }
  }

  /**
   * Initialize the next phase of the algorithm by clearing the committed
   * workers and jobs sets and by initializing the slack arrays to the values
   * corresponding to the specified root worker.
   * 
   * @param w
   *          the worker at which to root the next phase.
   */
  protected void initializePhase(int w) {
    Arrays.fill(committedWorkers, false);
    Arrays.fill(parentWorkerByCommittedJob, -1);
    committedWorkers[w] = true;
    for (int j = 0; j < dim; j++) {
      minSlackValueByJob[j] = costMatrix[w][j] - labelByWorker[w]
          - labelByJob[j];
      minSlackWorkerByJob[j] = w;
    }
  }

  /**
   * Helper method to record a matching between worker w and job j.
   */
  protected void match(int w, int j) {
    matchJobByWorker[w] = j;
    matchWorkerByJob[j] = w;
  }

  /**
   * Reduce the cost matrix by subtracting the smallest element of each row from
   * all elements of the row as well as the smallest element of each column from
   * all elements of the column. Note that an optimal assignment for a reduced
   * cost matrix is optimal for the original cost matrix.
   */
  protected void reduce() {
    for (int w = 0; w < dim; w++) {
      double min = Double.POSITIVE_INFINITY;
      for (int j = 0; j < dim; j++) {
        if (costMatrix[w][j] < min) {
          min = costMatrix[w][j];
        }
      }
      for (int j = 0; j < dim; j++) {
        costMatrix[w][j] -= min;
      }
    }
    double[] min = new double[dim];
    for (int j = 0; j < dim; j++) {
      min[j] = Double.POSITIVE_INFINITY;
    }
    for (int w = 0; w < dim; w++) {
      for (int j = 0; j < dim; j++) {
        if (costMatrix[w][j] < min[j]) {
          min[j] = costMatrix[w][j];
        }
      }
    }
    for (int w = 0; w < dim; w++) {
      for (int j = 0; j < dim; j++) {
        costMatrix[w][j] -= min[j];
      }
    }
  }

  /**
   * Update labels with the specified slack by adding the slack value for
   * committed workers and by subtracting the slack value for committed jobs. In
   * addition, update the minimum slack values appropriately.
   */
  protected void updateLabeling(double slack) {
    for (int w = 0; w < dim; w++) {
      if (committedWorkers[w]) {
        labelByWorker[w] += slack;
      }
    }
    for (int j = 0; j < dim; j++) {
      if (parentWorkerByCommittedJob[j] != -1) {
        labelByJob[j] -= slack;
      } else {
        minSlackValueByJob[j] -= slack;
      }
    }
  }
}
