COSC 575: Machine Learning

Project 1
Spring 2015

Due: Thu, Jan 29 @ 11:59 PM
10 points

1. Write a program and the classes and methods necessary to read, parse, store, and output examples in Mark's File Format (mff). Consider the following version of the Bikes data set:

   Make	Tires	Handle Bars	Water Bottle	Weight	Bike Type
   Trek	Knobby	Straight	y	250.3	Mountain
   Bridgestone	Treads	Straight	y	200	Hybrid
   Cannondale	Knobby	Curved	n	222.9	Mountain
   Nishiki	Treads	Curved	y	190.3	Hybrid
   Trek	Treads	Straight	y	196.8	Hybrid

   The file bikes.mff shows this data set in Mark's File Format. Files containing valid data sets begin with '@dataset' followed by an identifier. Attribute declarations appear next. The string '@attribute' precedes each declaration, which is a symbolic attribute or a numeric attribute. The attribute's name appears next, followed by its domain. The domain for symbolic attributes is a list of values separated by whitespace. The domain for numeric attributes is not explicitly specified and is assumed to be the set of representable integer and floating-point numbers.

   The token '@examples' separates the attribute declarations from the examples, which are simply values separated by whitespace.

   For simplicity, you can assume that all elements of the file are separated by at least one space character. Moreover, attribute declarations and examples will appear on single lines. I have defined a grammar for the file format.

2. In subsequent projects, operations on the data set you will need to perform include
   • iterate over examples
   • iterate over attribute values
   • execute logic based on the attribute's type

   As for the design, I have produced documentation for the classes and their methods. I expect you to follow this design, but feel free to propose refinement as part of class discussion. Note that in subsequent projects, you will need to make deep copies of training and testing sets.

3. The executable should take input from the command line. Use -t to specify the name of the training file and use -T to specify the name of the testing file, if any. The following are examples of commands:

   % java p1 -t bikes.mff
   % java p1 -t bikes.train
   % java p1 -t bikes.train -T bikes.test
   % java p1 -t bikes-tr.mff -T bikes-te.mff
   

   The program should perform checks for proper formatting and for data integrity. For this project, the program can simply read the input files, populate the appropriate objects, and output the data set or sets to the console in the same format they appear in the file.

4. Implement the program using a language such as ANSI C++, Java, Python, or Ruby. Your code must compile and run on my Unix machine. It must be well-structured and well-organized and follow object-oriented design principles. The implementation must be general, meaning that it should work for all possible data sets. If you want to use some other language, check with me first.

   You must implement your program using the standard libraries the language provides, meaning that you cannot use a library of machine-learning routines to complete the project. If you want to use external resources or something non-standard, check with me first.

   Finally, your project must be easy for me to build and run. Make sure you include instructions in a README file that describes exactly how to build and run your project. If you use Java, do not use packages. If you use C or C++, provide a Makefile. Ultimately, I should be able to drop in your project's directory, compile it if necessary, and execute it.

Instructions for Submission

//
// Name
// E-mail Address
// Platform: Windows, OS X, Linux, etc.
// Language/Environment: gcc, g++, java, ruby, python
//
// In accordance with the class policies and Georgetown's Honor Code,
// I certify that, with the exceptions of the class resources and those
// items noted below, I have neither given nor received any assistance
// on this project.
//