Difference between revisions of "Parallel Enclosing Circle Algorithm"

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|Has owner=Oliver Chang,
 
|Has owner=Oliver Chang,
 
|Has start date=July 31, 2017
 
|Has start date=July 31, 2017
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|Has deadline=August 4, 2017
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|Has deadline=October 4, 2017
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|Has project status=Active
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|Has project status=Complete
 
|Is dependent on=Enclosing Circle Algorithm,
 
|Is dependent on=Enclosing Circle Algorithm,
 
}}
 
}}
  
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A thin-wrapper around [[Enclosing_Circle_Algorithm_(Rework)|the enclosing circle algorithm]] which allows for instance-level parallelization.
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This project consists of the python files in <code>E:\McNair\Projects\OliverLovesCircles\src\python</code>.
  
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Project directory: <code>E:\McNair\Projects\OliverLovesCircles</code>
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Parallelization is implemented via Python2's [https://docs.python.org/2/library/subprocess.html#subprocess.Popen <code>subprocess.open()</code>] which is non-blocking and available in the standard library.
  
  
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== Parameters ==
 
== Parameters ==
  
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* <code>iterations_per_k</code>: the number of iterations to attempt for each <code>k</code> to find minimum for that <code>k</code>
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* in <code>circles.py</code>:
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* <code>n</code>: the minimum number of data points that must be included in a circle
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** <code>ITERATIONS</code>: the number of iterations to attempt for each <code>k</code> to find minimum for that <code>k</code>
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** <code>MIN_POINTS_PER_CIRCLE</code> (AKA <code>n</code>): the minimum number of data points that must be included in a circle
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* in <code>vc_circles.py</code>
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** <code>NUMBER_INSTANCES</code>: number of parallel instances to run; assume no data-races between instances
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** <code>SWEEP_CYCLE_SECONDS</code>: amount of time before removing completed jobs from the current job and adding new jobs if any files are left to process
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** <code>TIMEOUT_MINUTES</code>: maximum running time of a parallel instance of the algorithm
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** <code>SPLIT_THRESHOLD</code>: if a dataset has more than this threshold of data points, it will be split via k-means
  
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== Overview ==
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== Example Usage ==
  
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TODO: explain range of k
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<nowiki>
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$ python vc_circles.py --infile E:/McNair/Projects/OliverLovesCircles/CoLevelForCirclesNotRunGTE200.txt</nowiki>
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where <code>CoLevelForCirclesNotRunGTE200.txt</code> is a tab-separated values file with the columns
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<code>placestate, place, statecode, year, latitude, longitude, coname, datefirstinv, placens, geoid, city</code>
  
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TODO: explain difficulty of port
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This command will populate (and overwrite) any files in <code>data/</code>. The format of the filenames in this directory are <code>{city}{sep}{state}{sep}{year}{sep}{num}.tsv</code> where <code>num</code> is a 0-indexed integer of a split city/state/year <code>infile</code> that has greater than <code>SPLIT_THRESHOLD</code>
  
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TODO: add initial cut documentation
 
  
 
== Related Pages ==
 
== Related Pages ==

Revision as of 11:47, 6 October 2017


McNair Project
Parallel Enclosing Circle Algorithm
Project logo 02.png
Project Information
Project Title Parallel Enclosing Circle Algorithm
Owner Oliver Chang
Start Date July 31, 2017
Deadline October 4, 2017
Primary Billing
Notes
Has project status Complete
Is dependent on Enclosing Circle Algorithm
Copyright © 2016 edegan.com. All Rights Reserved.


A thin-wrapper around the enclosing circle algorithm which allows for instance-level parallelization. This project consists of the python files in E:\McNair\Projects\OliverLovesCircles\src\python.

Parallelization is implemented via Python2's subprocess.open() which is non-blocking and available in the standard library.


The Problem

Note that this is not the classical enclosing circle algorithm. Rather, we seek to minimize the sum of enclosing circles containing at least n points. Thus, multiple circles are allowed and inclusion in multiple circles is possible.

Parameters

  • in circles.py:
    • ITERATIONS: the number of iterations to attempt for each k to find minimum for that k
    • MIN_POINTS_PER_CIRCLE (AKA n): the minimum number of data points that must be included in a circle
  • in vc_circles.py
    • NUMBER_INSTANCES: number of parallel instances to run; assume no data-races between instances
    • SWEEP_CYCLE_SECONDS: amount of time before removing completed jobs from the current job and adding new jobs if any files are left to process
    • TIMEOUT_MINUTES: maximum running time of a parallel instance of the algorithm
    • SPLIT_THRESHOLD: if a dataset has more than this threshold of data points, it will be split via k-means

Example Usage

$ python vc_circles.py --infile E:/McNair/Projects/OliverLovesCircles/CoLevelForCirclesNotRunGTE200.txt

where CoLevelForCirclesNotRunGTE200.txt is a tab-separated values file with the columns placestate, place, statecode, year, latitude, longitude, coname, datefirstinv, placens, geoid, city

This command will populate (and overwrite) any files in data/. The format of the filenames in this directory are {city}{sep}{state}{sep}{year}{sep}{num}.tsv where num is a 0-indexed integer of a split city/state/year infile that has greater than SPLIT_THRESHOLD


Related Pages

External Links

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