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.

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)

6/13/2017

Will run previous algorithm (Peter and Christy's) and compare to mine
If not the same, will update Enclosing Circle Algorithm page with my algorithm

6/14/2017

Ran Christy and Peter's algorithm on a small data sample. It worked faster than mine, and achieved the correct answer.
Currently running their algorithm on a much larger data sample. It is still running, and exhibits slower run time than mine.
Will leave it running to see what it converges to.

6/15/2017

Built a html scraper in Python to scrape Accelerator data from [1] The code and resulting tab-separated text file is located in:

    E:\McNair\Projects\Accelerators\Web Scraping for Accelerators

6/16/2017

Attempted to try and plot the circles for Houston to Google Maps.
Succeeded in plotting the points themselves but not the circles
This is a bit strange considering I reused code from the previous project.

6/19/2017

Succeeded in plotting the circles for Houston on Google Maps
Algorithm is faster than previous algorithm.
Made new page for the project
.

6/20/2017

Created a script in python to concatenate 162 excel files The file is located in the following location:

"E:\McNair\Projects\SBIR\Aggregate SBIR"

It is titled "concat_excel.py"
The text file is in the folder, titled "SBIR"

6/23/2017

fixed some bugs in the k-means based enclosing circle algorithm
updated Enclosing Circle Algorithm (Rework)
started running the algorithm on list of houston city coords

6/26/2017

For a total of 41 coordinate sets, the algorithm completed the plots in about 32 hours.
Average run time at a little over an hour per coordinate set
Added functionality that would save the area of the total circles to a text file
Running this now and will be completed end of the day tomorrow.
Worked through the MATLAB code for the monte carlo
Stripped the code for MSM,Monte Data, Industry 4, and Genetic Algorithm
Will run tomorrow and adjust the data locations to try and debug

6/27/2017

Identified some key errors in the stripped code for the Monte

1. The genetic algorithm is not returning a scalar value
2. The problem is the objective function is returning an empty vector, instead of a scalar

Made a plotter for Diana so she can visualize Houston Start-Up Data

6/28/2017

Fixed the bug. It was because we didn't have a global variable in the master script.
James helped figure this out.

6/29/2017

Figured out there was another bug in the code. After the optimization finished, there is a problem of mismatched dimensions.
The code itself still takes a very long time to run.

6/30/2017

Fixed the mismatched dimensions problem, but the code is still running.
Went over a few methods to speed up the code:

1. Use gurobi for the solver
2. run the sampling on a bunch of clusters

Downloaded gurobi:

password: amount

Updated the plotter to include heatmaps.
The geocode might be erroneous.

7/10/2017

Ran the MATLAB code for the monte carlo after fixing the bugs.
Installed the MATLAB interface for Gurobi
Worked on finishing some test cases for the enclosing circle.

7/11/2017

The code ran and worked just fine.
Now I need to figure out how to replace the optimization in the code with a Gurobi optimization.
Finished the code for test cases on the enclosing circle.