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    *
    * Licensed to the Apache Software Foundation (ASF) under one
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    * to you 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.
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  package org.apache.hadoop.hbase.util;
  
  import java.util.Arrays;
  import java.util.Deque;
  import java.util.LinkedList;
  
  import org.apache.hadoop.hbase.classification.InterfaceAudience;
  
  /**
   * Computes the optimal (minimal cost) assignment of jobs to workers (or other
   * analogous) concepts given a cost matrix of each pair of job and worker, using
   * the algorithm by James Munkres in "Algorithms for the Assignment and
   * Transportation Problems", with additional optimizations as described by Jin
   * Kue Wong in "A New Implementation of an Algorithm for the Optimal Assignment
   * Problem: An Improved Version of Munkres' Algorithm". The algorithm runs in
   * O(n^3) time and need O(n^2) auxiliary space where n is the number of jobs or
   * workers, whichever is greater.
   */
  @InterfaceAudience.Private
  public class MunkresAssignment {
  
    // The original algorithm by Munkres uses the terms STAR and PRIME to denote
    // different states of zero values in the cost matrix. These values are
    // represented as byte constants instead of enums to save space in the mask
    // matrix by a factor of 4n^2 where n is the size of the problem.
    private static final byte NONE = 0;
    private static final byte STAR = 1;
    private static final byte PRIME = 2;
  
    // The algorithm requires that the number of column is at least as great as
    // the number of rows. If that is not the case, then the cost matrix should
    // be transposed before computation, and the solution matrix transposed before
    // returning to the caller.
    private final boolean transposed;
  
    // The number of rows of internal matrices.
    private final int rows;
  
    // The number of columns of internal matrices.
    private final int cols;
  
    // The cost matrix, the cost of assigning each row index to column index.
    private float[][] cost;
  
    // Mask of zero cost assignment states.
    private byte[][] mask;
  
    // Covering some rows of the cost matrix.
    private boolean[] rowsCovered;
  
    // Covering some columns of the cost matrix.
    private boolean[] colsCovered;
  
    // The alternating path between starred zeroes and primed zeroes
    private Deque<Pair<Integer, Integer>> path;
  
    // The solution, marking which rows should be assigned to which columns. The
    // positions of elements in this array correspond to the rows of the cost
    // matrix, and the value of each element correspond to the columns of the cost
    // matrix, i.e. assignments[i] = j indicates that row i should be assigned to
    // column j.
    private int[] assignments;
  
    // Improvements described by Jin Kue Wong cache the least value in each row,
    // as well as the column index of the least value in each row, and the pending
    // adjustments to each row and each column.
    private float[] leastInRow;
    private int[] leastInRowIndex;
    private float[] rowAdjust;
    private float[] colAdjust;
  
    /**
     * Construct a new problem instance with the specified cost matrix. The cost
     * matrix must be rectangular, though not necessarily square. If one dimension
     * is greater than the other, some elements in the greater dimension will not
     * be assigned. The input cost matrix will not be modified.
     * @param costMatrix
     */
    public MunkresAssignment(float[][] costMatrix) {
      // The algorithm assumes that the number of columns is at least as great as
      // the number of rows. If this is not the case of the input matrix, then
     // all internal structures must be transposed relative to the input.
     this.transposed = costMatrix.length > costMatrix[0].length;
     if (this.transposed) {
       this.rows = costMatrix[0].length;
       this.cols = costMatrix.length;
     } else {
       this.rows = costMatrix.length;
       this.cols = costMatrix[0].length;
     }
 
     cost = new float[rows][cols];
     mask = new byte[rows][cols];
     rowsCovered = new boolean[rows];
     colsCovered = new boolean[cols];
     path = new LinkedList<Pair<Integer, Integer>>();
 
     leastInRow = new float[rows];
     leastInRowIndex = new int[rows];
     rowAdjust = new float[rows];
     colAdjust = new float[cols];
 
     assignments = null;
 
     // Copy cost matrix.
     if (transposed) {
       for (int r = 0; r < rows; r++) {
         for (int c = 0; c < cols; c++) {
           cost[r][c] = costMatrix[c][r];
         }
       }
     } else {
       for (int r = 0; r < rows; r++) {
         System.arraycopy(costMatrix[r], 0, cost[r], 0, cols);
       }
     }
 
     // Costs must be finite otherwise the matrix can get into a bad state where
     // no progress can be made. If your use case depends on a distinction
     // between costs of MAX_VALUE and POSITIVE_INFINITY, you're doing it wrong.
     for (int r = 0; r < rows; r++) {
       for (int c = 0; c < cols; c++) {
         if (cost[r][c] == Float.POSITIVE_INFINITY) {
           cost[r][c] = Float.MAX_VALUE;
         }
       }
     }
   }
 
   /**
    * Get the optimal assignments. The returned array will have the same number
    * of elements as the number of elements as the number of rows in the input
    * cost matrix. Each element will indicate which column should be assigned to
    * that row or -1 if no column should be assigned, i.e. if result[i] = j then
    * row i should be assigned to column j. Subsequent invocations of this method
    * will simply return the same object without additional computation.
    * @return an array with the optimal assignments
    */
   public int[] solve() {
     // If this assignment problem has already been solved, return the known
     // solution
     if (assignments != null) {
       return assignments;
     }
 
     preliminaries();
 
     // Find the optimal assignments.
     while (!testIsDone()) {
       while (!stepOne()) {
         stepThree();
       }
       stepTwo();
     }
 
     // Extract the assignments from the mask matrix.
     if (transposed) {
       assignments = new int[cols];
       outer:
       for (int c = 0; c < cols; c++) {
         for (int r = 0; r < rows; r++) {
           if (mask[r][c] == STAR) {
             assignments[c] = r;
             continue outer;
           }
         }
         // There is no assignment for this row of the input/output.
         assignments[c] = -1;
       }
     } else {
       assignments = new int[rows];
       outer:
       for (int r = 0; r < rows; r++) {
         for (int c = 0; c < cols; c++) {
           if (mask[r][c] == STAR) {
             assignments[r] = c;
             continue outer;
           }
         }
       }
     }
 
     // Once the solution has been computed, there is no need to keep any of the
     // other internal structures. Clear all unnecessary internal references so
     // the garbage collector may reclaim that memory.
     cost = null;
     mask = null;
     rowsCovered = null;
     colsCovered = null;
     path = null;
     leastInRow = null;
     leastInRowIndex = null;
     rowAdjust = null;
     colAdjust = null;
 
     return assignments;
   }
 
   /**
    * Corresponds to the "preliminaries" step of the original algorithm.
    * Guarantees that the matrix is an equivalent non-negative matrix with at
    * least one zero in each row.
    */
   private void preliminaries() {
     for (int r = 0; r < rows; r++) {
       // Find the minimum cost of each row.
       float min = Float.POSITIVE_INFINITY;
       for (int c = 0; c < cols; c++) {
         min = Math.min(min, cost[r][c]);
       }
 
       // Subtract that minimum cost from each element in the row.
       for (int c = 0; c < cols; c++) {
         cost[r][c] -= min;
 
         // If the element is now zero and there are no zeroes in the same row
         // or column which are already starred, then star this one. There
         // must be at least one zero because of subtracting the min cost.
         if (cost[r][c] == 0 && !rowsCovered[r] && !colsCovered[c]) {
           mask[r][c] = STAR;
           // Cover this row and column so that no other zeroes in them can be
           // starred.
           rowsCovered[r] = true;
           colsCovered[c] = true;
         }
       }
     }
 
     // Clear the covered rows and columns.
     Arrays.fill(rowsCovered, false);
     Arrays.fill(colsCovered, false);
   }
 
   /**
    * Test whether the algorithm is done, i.e. we have the optimal assignment.
    * This occurs when there is exactly one starred zero in each row.
    * @return true if the algorithm is done
    */
   private boolean testIsDone() {
     // Cover all columns containing a starred zero. There can be at most one
     // starred zero per column. Therefore, a covered column has an optimal
     // assignment.
     for (int r = 0; r < rows; r++) {
       for (int c = 0; c < cols; c++) {
         if (mask[r][c] == STAR) {
           colsCovered[c] = true;
         }
       }
     }
 
     // Count the total number of covered columns.
     int coveredCols = 0;
     for (int c = 0; c < cols; c++) {
       coveredCols += colsCovered[c] ? 1 : 0;
     }
 
     // Apply an row and column adjustments that are pending.
     for (int r = 0; r < rows; r++) {
       for (int c = 0; c < cols; c++) {
         cost[r][c] += rowAdjust[r];
         cost[r][c] += colAdjust[c];
       }
     }
 
     // Clear the pending row and column adjustments.
     Arrays.fill(rowAdjust, 0);
     Arrays.fill(colAdjust, 0);
 
     // The covers on columns and rows may have been reset, recompute the least
     // value for each row.
     for (int r = 0; r < rows; r++) {
       leastInRow[r] = Float.POSITIVE_INFINITY;
       for (int c = 0; c < cols; c++) {
         if (!rowsCovered[r] && !colsCovered[c] && cost[r][c] < leastInRow[r]) {
           leastInRow[r] = cost[r][c];
           leastInRowIndex[r] = c;
         }
       }
     }
 
     // If all columns are covered, then we are done. Since there may be more
     // columns than rows, we are also done if the number of covered columns is
     // at least as great as the number of rows.
     return (coveredCols == cols || coveredCols >= rows);
   }
 
   /**
    * Corresponds to step 1 of the original algorithm.
    * @return false if all zeroes are covered
    */
   private boolean stepOne() {
     while (true) {
       Pair<Integer, Integer> zero = findUncoveredZero();
       if (zero == null) {
         // No uncovered zeroes, need to manipulate the cost matrix in step
         // three.
         return false;
       } else {
         // Prime the uncovered zero and find a starred zero in the same row.
         mask[zero.getFirst()][zero.getSecond()] = PRIME;
         Pair<Integer, Integer> star = starInRow(zero.getFirst());
         if (star != null) {
           // Cover the row with both the newly primed zero and the starred zero.
           // Since this is the only place where zeroes are primed, and we cover
           // it here, and rows are only uncovered when primes are erased, then
           // there can be at most one primed uncovered zero.
           rowsCovered[star.getFirst()] = true;
           colsCovered[star.getSecond()] = false;
           updateMin(star.getFirst(), star.getSecond());
         } else {
           // Will go to step two after, where a path will be constructed,
           // starting from the uncovered primed zero (there is only one). Since
           // we have already found it, save it as the first node in the path.
           path.clear();
           path.offerLast(new Pair<Integer, Integer>(zero.getFirst(),
               zero.getSecond()));
           return true;
         }
       }
     }
   }
 
   /**
    * Corresponds to step 2 of the original algorithm.
    */
   private void stepTwo() {
     // Construct a path of alternating starred zeroes and primed zeroes, where
     // each starred zero is in the same column as the previous primed zero, and
     // each primed zero is in the same row as the previous starred zero. The
     // path will always end in a primed zero.
     while (true) {
       Pair<Integer, Integer> star = starInCol(path.getLast().getSecond());
       if (star != null) {
         path.offerLast(star);
       } else {
         break;
       }
       Pair<Integer, Integer> prime = primeInRow(path.getLast().getFirst());
       path.offerLast(prime);
     }
 
     // Augment path - unmask all starred zeroes and star all primed zeroes. All
     // nodes in the path will be either starred or primed zeroes. The set of
     // starred zeroes is independent and now one larger than before.
     for (Pair<Integer, Integer> p : path) {
       if (mask[p.getFirst()][p.getSecond()] == STAR) {
         mask[p.getFirst()][p.getSecond()] = NONE;
       } else {
         mask[p.getFirst()][p.getSecond()] = STAR;
       }
     }
 
     // Clear all covers from rows and columns.
     Arrays.fill(rowsCovered, false);
     Arrays.fill(colsCovered, false);
 
     // Remove the prime mask from all primed zeroes.
     for (int r = 0; r < rows; r++) {
       for (int c = 0; c < cols; c++) {
         if (mask[r][c] == PRIME) {
           mask[r][c] = NONE;
         }
       }
     }
   }
 
   /**
    * Corresponds to step 3 of the original algorithm.
    */
   private void stepThree() {
     // Find the minimum uncovered cost.
     float min = leastInRow[0];
     for (int r = 1; r < rows; r++) {
       if (leastInRow[r] < min) {
         min = leastInRow[r];
       }
     }
 
     // Add the minimum cost to each of the costs in a covered row, or subtract
     // the minimum cost from each of the costs in an uncovered column. As an
     // optimization, do not actually modify the cost matrix yet, but track the
     // adjustments that need to be made to each row and column.
     for (int r = 0; r < rows; r++) {
       if (rowsCovered[r]) {
         rowAdjust[r] += min;
       }
     }
     for (int c = 0; c < cols; c++) {
       if (!colsCovered[c]) {
         colAdjust[c] -= min;
       }
     }
 
     // Since the cost matrix is not being updated yet, the minimum uncovered
     // cost per row must be updated.
     for (int r = 0; r < rows; r++) {
       if (!colsCovered[leastInRowIndex[r]]) {
         // The least value in this row was in an uncovered column, meaning that
         // it would have had the minimum value subtracted from it, and therefore
         // will still be the minimum value in that row.
         leastInRow[r] -= min;
       } else {
         // The least value in this row was in a covered column and would not
         // have had the minimum value subtracted from it, so the minimum value
         // could be some in another column.
         for (int c = 0; c < cols; c++) {
           if (cost[r][c] + colAdjust[c] + rowAdjust[r] < leastInRow[r]) {
             leastInRow[r] = cost[r][c] + colAdjust[c] + rowAdjust[r];
             leastInRowIndex[r] = c;
           }
         }
       }
     }
   }
 
   /**
    * Find a zero cost assignment which is not covered. If there are no zero cost
    * assignments which are uncovered, then null will be returned.
    * @return pair of row and column indices of an uncovered zero or null
    */
   private Pair<Integer, Integer> findUncoveredZero() {
     for (int r = 0; r < rows; r++) {
       if (leastInRow[r] == 0) {
         return new Pair<Integer, Integer>(r, leastInRowIndex[r]);
       }
     }
     return null;
   }
 
   /**
    * A specified row has become covered, and a specified column has become
    * uncovered. The least value per row may need to be updated.
    * @param row the index of the row which was just covered
    * @param col the index of the column which was just uncovered
    */
   private void updateMin(int row, int col) {
     // If the row is covered we want to ignore it as far as least values go.
     leastInRow[row] = Float.POSITIVE_INFINITY;
 
     for (int r = 0; r < rows; r++) {
       // Since the column has only just been uncovered, it could not have any
       // pending adjustments. Only covered rows can have pending adjustments
       // and covered costs do not count toward row minimums. Therefore, we do
       // not need to consider rowAdjust[r] or colAdjust[col].
       if (!rowsCovered[r] && cost[r][col] < leastInRow[r]) {
         leastInRow[r] = cost[r][col];
         leastInRowIndex[r] = col;
       }
     }
   }
 
   /**
    * Find a starred zero in a specified row. If there are no starred zeroes in
    * the specified row, then null will be returned.
    * @param r the index of the row to be searched
    * @return pair of row and column indices of starred zero or null
    */
   private Pair<Integer, Integer> starInRow(int r) {
     for (int c = 0; c < cols; c++) {
       if (mask[r][c] == STAR) {
         return new Pair<Integer, Integer>(r, c);
       }
     }
     return null;
   }
 
   /**
    * Find a starred zero in the specified column. If there are no starred zeroes
    * in the specified row, then null will be returned.
    * @param c the index of the column to be searched
    * @return pair of row and column indices of starred zero or null
    */
   private Pair<Integer, Integer> starInCol(int c) {
     for (int r = 0; r < rows; r++) {
       if (mask[r][c] == STAR) {
         return new Pair<Integer, Integer>(r, c);
       }
     }
     return null;
   }
 
   /**
    * Find a primed zero in the specified row. If there are no primed zeroes in
    * the specified row, then null will be returned.
    * @param r the index of the row to be searched
    * @return pair of row and column indices of primed zero or null
    */
   private Pair<Integer, Integer> primeInRow(int r) {
     for (int c = 0; c < cols; c++) {
       if (mask[r][c] == PRIME) {
         return new Pair<Integer, Integer>(r, c);
       }
     }
     return null;
   }
 }