Changes

The algorithm relies on an object -oriented implementation of a "cluster". <br>

We would expect the algorithm to slow as the number of points increase. For reference, a set of 80 points with n =3, took about 30 minutes, with the number of iterations at 100. <br>Further improvements could implement an early-stopping method to converge to a local optimaoptimum. <br> [[File:runtime.png]]

 == Step By Step UseBenefits == 1Is faster than both Brute Force and Logical Enclosing Circle. <br>Is more accurate than Logical Enclosing Circle. <br>Can plot to google maps. Open up <br>Will return a tab delim text file of the file "googleplotter" following fields: city, year, total area <br>Good for a large number of points <br> ==Drawbacks==Is not the optimal solution as number of points increases, but an approximation =Logical Enclosing Circle===Location==The python script is located in : 2. where it says with open("CE:\McNair\Projects\Accelerators\Enclosing_Circle\Enclosing_Circle\EnclosingCircleRemake2.py==Explanation==The rationale is to take the furthest point, draw a circle with its nearest n-1 points...") replace <br>Take the filepath in furthest point from the parentheses initial point, draw a circle with your filepath its nearest n-1 points. <br>Repeat until all points are enclosed in quotes.<br> 3. Press ctrl+s ==Benefits==Can plot to save the filegoogle maps <br> 4. open up the command lineIs faster than brute force <br> 5. type: python googleplotter.py 6. your resulting Returns city, circles, year to a tab delimited text file and html google plots will be outputted to:<br> ==Drawbacks== E:\McNair\Software\CodeBase\New Implement Really slow for large number of Enclosing Circle (Constrained K Means, Smallest Circle)points <br>Offers an approximate solution that is worse than the Clustering based version <br> 

The julia Julia/Python code does not have the capability to plot to google mapsbut will plot to a normal figure. <br>(will work on adding this implementation, if the need arises) [[Category:Internal]]