This project contains two algorithms for finding the number of possible paths in a soccer field grid, avoiding obstacles and opponents. The algorithms are implemented in Python and are compared using empirical analysis.
- Trenton Coggeshall (trenton.coggeshall@csu.fullerton.edu)
- Sean Hatfield (shatfield4@csu.fullerton.edu)
- Malik Hidouk (mhidouk@csu.fullerton.edu)
dynamic_programming.py: Contains thesoccer_dyn_progfunction, which implements a dynamic programming algorithm to find the number of possible paths in the grid.exhaustive_search.py: Contains thesoccer_opponent_avoidancefunction, which implements an exhaustive search algorithm to find the number of possible paths in the grid.timing_comparisons.py: A script that compares the two algorithms by running them on random test inputs of varying sizes and recording the execution times.
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Make sure you have Python 3 installed on your system.
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Clone or download this repository to your local machine.
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Open a terminal or command prompt and navigate to the directory containing the project files.
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Run the
timing_comparisons.pyscript by executing the following command:python timing_comparisons.pyor you can run each algorithm individually by runningpython exhaustive_search.pyorpython dynamic_programming.py
This will run both algorithms on random test inputs of varying sizes (defined in the input_sizes list) and print the execution times for each algorithm.
The output will show the timing data for both algorithms as lists of execution times. Each value corresponds to the execution time for a specific input size, in the same order as the input_sizes list.
Example output:
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Dynamic Programming Timings: [2.4416949599981308e-05, 2.9791961424052715e-05, 2.729194238781929e-05, 3.324996214359999e-05, 3.5541015677154064e-05, 4.033301956951618e-05, 5.141599103808403e-05] -
Exhaustive Search Timings: [0.1333301670383662, 0.6154525829479098, 1.8384431669255719, 12.27649683400523, 46.746501999907196, 201.4424116659211, 971.6087485420285]