GitHub - darshthakkar/Randomized-Optimization-Machine-Learning: Implementation and analysis of Randomized Hill Climbing, Simulated Annealing, Genetic and Mutual Information Maximizing Input Clustering (MIMIC) algorithms for Randomized Optimization in Machine Learning.

Branches Tags

Name		Name	Last commit message	Last commit date
Latest commit History 4 Commits
NN_ga.py		NN_ga.py
NN_rhc.py		NN_rhc.py
NN_sa.py		NN_sa.py
README.txt		README.txt
analysis.pdf		analysis.pdf
base.py		base.py
continuouspeak.py		continuouspeak.py
dumper.py		dumper.py
flipflop.py		flipflop.py
plot.py		plot.py
tsp.py		tsp.py
winequality-white.csv		winequality-white.csv
winequality_test.csv		winequality_test.csv
winequality_trg.csv		winequality_trg.csv
winequality_val.csv		winequality_val.csv

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Installation of Python and Jython v2.7.0 is required for running these programs. 
	- Install Jython from https://www.jython.org/downloads.html
	- Set up path in environment variables (Windows)

1. Go to the directory, where the zip file has been extracted through cmd/terminal 
2. Run "dumper.py" in command line (python dumper.py)
	- This will create winequality_test, winequality_trg, winequality_val from winequality_white dataset
3. Run "NN_rhc.py", "NN_sa.py", "NN_ga.py", "tsp.py", "flipflop.py" and "continuouspeak.py" using "jython <filename>.py" 
	- Run one at a time or in different cmd/terminal windows
	- Last three files can take considerable amount of time to run, because of MIMIC algorithm implementation 
	- Logs will be made in the respective folders, such as NN_OUTPUT and TSP_LOGS
4. Run "plot.py" using " python plot.py"
5. All the graphs will be stored in "graphs" folder

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Implementation and analysis of Randomized Hill Climbing, Simulated Annealing, Genetic and Mutual Information Maximizing Input Clustering (MIMIC) algorithms for Randomized Optimization in Machine Learning.