Abhijit Brahme (Work Log)
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.
6/12/2017
Solved the issue of points not being shared by iterating through all possible splits (ie k = 1:floor(numpoints/n))
Algorithm runs much slower due to iterating through all K, but it is still faster than others
I tried a version where I don't keep "K" constant throughout the loop. This converged to a better solution
I ended up changing K to 2 after the first split. (first split points into 10 points then split those ten points into 2, etc)
