CS 7641 Assignment 2 Jeren Browning jbrowning35 The code for this assignment can be found here: https://github.com/jerenmb/CS-7641-Assignment-2 This assignment was built using code taken from https://github.com/JonathanTay/CS-7641-assignment-2. Thanks jontay! This file contains the instructions for how to run the code for Assignment 2 Important Notes 1) This project uses a modified version of ABAGAIL, located in the ABAGAIL sub-folder 2) The folders NNOUTPUT, CONTPEAKS, FLIPFLOP and TSP must be created in the same folder as the Jython code before running it. 3) The files m_test.csv, m_trg.csv and m_val.csv must be in the same folder as the NN*.py files 4) To run the Jython files, please modify the files (line 5 for non-neural network experiments and line 9 for neural network experiments) so that the ABAGAIL.jar file is in the system path. Reports: jbrowning35-analysis.pdf - Assignment 2 report Code Files: 1) NN0.py - Code for Backpropagation training of neural network 2) NN1.py - Code for Randomised Hill Climbing training of neural network 3) NN2.py - Code for Simulated Annleaing training of neural network 4) NN3.py - Code for Genetic Algorithm training of neural network 5) continuouspeaks.py - Code to use Randomised Optimisation to solve the Continuous Peaks problem 5) flip flop.py - Code to use Randomised Optimisation to solve the Flip Flop problem 6) tsp.py - Code to use Randomised Optimisation to solve the Traveling Salesman Problem There are also a number of folders 1) Datasets - contains the code to generate the datasets for this assignment from the original files from the UCI ML Repository 2) NNOUTPUT - output folder for the Neural Network experiments 3) CONTPEAKS - output folder for the Continuous Peaks experiments 4) FLIPFLOP - output folder for the Flip Flop experiments 5) TSP - output folder for the Traveling Salesman Problem experiments 6) ABAGAIL - folder with source, ant build file, and jar for ABAGAIL 7) Graphs - folder containing graphs I made using matplotlib Data Files 1) m_test.csv - The test set 2) m_trg.csv - The training set 3) m_val.csv - The validation set 4) Python Test Bed.py - matplot lib code for creating graphs To generate the data files from the original data, run the parse_data.py code and the DUMPER.py code in the Datasets folder. The data files (m_*.csv) should then be moved one level up, to reside in the same directory as the assignment 2 code files. The data file code was written in Python 3.5, using Pandas 0.18.0 and sklearn 0.19.1 Java code was built with ant 1.10.1 on java 1.8.0_121. The code files in the code files section were written in Jython 2.7.0. Graphs use matplotlib 1.5.1 Within the output folders, the data files are csv files (with .txt extensions). The file names correspond to experiments: <ALGORITHM><PARAMETERS>_LOG<_TRIAL NUMBER>.txt