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find--k-best-1.00

  • Category : matlab
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  • Update : 2012-11-26
  • Size : 27kb
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  • Author :hansen
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Introduction - If you have any usage issues, please Google them yourself
Implementation of the Murty algorithm to obtain the best K assignments. Includes the implementation of the Jonker-Volgenant algorithm. Usual applications are multiple target tracking algorithms, Joint Probabilistic Data Association (JPDA), Multiple Hypothesis Tracking (MHT), Global Nearest Neighbours (GNN), etc. Based on the original code by Jonker-Volgenant, and the Murty Matlab implementation by Eric Trautmann Can be used both from Java or Matlab. Time for a 100x100 assignment problem, with k=20: • Matlab implementation: 54.5 sec • Java implementation: 1 sec
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java-k-best-1.00\src\com\google\code\javakbest\Murty.java
................\...\...\......\....\.........\JVC.java
................\...\...\......\....\.........\PartitionNode.java
................\...\...\......\....\.........\Test.java
................\...\...\......\....\.........\Node.java
................\README.TXT
................\java-k-best.jar
................\findkbest.m
................\LICENSE
................\src\com\google\code\javakbest
................\...\...\......\code
................\...\...\google
................\...\com
................\src
java-k-best-1.00
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