Abhijit Brahme (Work Log)

Abhijit Brahme Work Logs (log page)

6/5/2017

Downloaded Atom IDE along with Juno, a Julia specific console.
Began looking at MATLAB code for the Entrepreneurship Matching Project and trying to understand it
Began translating enclosing circle algorithm from python to julia

6/6/2017

Looked through MATLAB code for Entrepreneurshp Matching with James, Ed in-depth
Understood the general idea of the code; Going to call original author for more details.
Worked through the Julia implementation of Brute Force Enclosing Circle. (had to install "Combinatorics")
There are small errors with the number of valid circles; still need to implement plotting circles on map.

6/7/2017

FIXED the errors involved with translating Python to Julia for Enclosing Circle (Brute Force)
Implemented the plots in Julia using PyPlot and Plots (need to Pkg.add("Plots") and Pkg.add("PyPlot"))
Run time in the Julia implementation is actually 2x as slow for the small test set ( 1.12 s for Julia, .55 for Python)
Need to look into vectorizing some of the code
Steps Forward:

    1) The code might not be optimized for Julia, so try doing that 
    2) The code for Julia for overhead might be great so it might be slower on a smaller set; try on a bigger set of test points
    3) Come up with a better algorithm

Code Location:

    E:\McNair\Software\CodeBase\Julia Code for enclosing circle

6/8/2017

Ran the "bigexample" for Julia and Python Brute Force.... still running.
Only got through about 7 combinations in about 8 hours...not very promising.
Quit the program. It started taking up about 50% memory
Began working on enclosing circle algorithm. Found promising information regarding a constrained K-means algorithm
working on implementing the constrained K-means with the smallest enclosing circle to solve the Enclosing Circle Problem
Code Location for New Enclosing Circle:

   E:\McNair\Software\CodeBase\New Implement of Enclosing Circle (Constrained K Means, Smallest Circle)

6/9/2017

Wrote up the code for the new algorithm. It runs blazing fast.
We are worried about the optimality of the solution. I don't take into account border points.
These are points that could be shared by circles. An idea is to add border points into both clusters, then run the smallest enclosing circle on those sets of points.
I'll include a full Algorithm write-up on Monday once I have all the kinks worked out.