I was assigned this project with instructions below:
The game of Life is defined for an infinite-sized grid. In Chapter 2, we defined the Life Grid ADT to use a fixed-size grid in which the user specified the width and height of the grid. This was sufficient as an illustration of the use of a 2-D array for the implementation of the game of Life. But a full implementation should allow for an infinite-sized grid. Implement the Sparse Life Grid ADT using an approach similar to the one used to implement the sparse matrix.
I honestly don't really understand the concept that well. Could you please give me a brief description (if not brief code) that a layman can understand? I would appreciate it.
Sparselifegrid.py
""" My initial GameOfLife code
Feb 27, 2013
Sparse Matrix code specially designed for Game of Life
"""
class SparseLifeGrid:
def __init__(self):
"""
"pass" just allows this to run w/o crashing.
Replace it with your own code in each method.
"""
pass
def minRange(self):
"""
Return the minimum row & column as a list.
"""
pass
def maxRange(self):
"""
Returns the maximum row & column as a list.
"""
pass
def configure(self,coordList):
pass
def clearCell(self,row, col):
pass
def setCell(self,row, col):
pass
def isValidRowCol(val1,val2):
pass
def isLiveCell(self,row, col):
pass
def numLiveNeighbors(self, row,col):
pass
def __getitem__(self,ndxTuple):
pass
def __setitem__(self,ndxTuple, life):
"""
The possible values are only true or false:
True says alive, False for dead.
"""
pass
def _findPosition(self,row,col):
pass
def __repr__(self):
pass
def __str__(self):
"""
This method will only print the non-empty values,
and a row and column outside the non-empty values.
"""
pass
def evolve(self):
"""
Return the next generation state.
"""
pass
def hasOccurred(self):
"""
Check whether this current state has already occured.
If not, return False. If true, return which generation number (1-10).
"""
pass
def __eq__(self,other):
"""
This is good method if we want to compare two sparse matrices.
You can just use sparseMatrixA == sparseMatrixB because of this method.
"""
pass
def printLifeGrid(lifeGrid):
"""
Print a column before and after the live cells
"""
s=""
maxRange=lifeGrid.maxRange()
minRange=lifeGrid.minRange()
for i in range(minRange[0]-1,maxRange[0]+2):
for j in range(minRange[1]-1,maxRange[1]+2):
s+=" "+str(lifeGrid[i,j])
s+="\n"
print(s)
class _GoLMatrixElement:
"""
Storage class for one cell
"""
def __init__(self,row,col):
pass
def __str__self(self):
pass
def __eq__(self,other):
pass
Here's my main file
""" Marcus Brown's initial GameOfLife code
Feb 27, 2013
"""
from SparseLifeGrid_Key import SparseLifeGrid
import sys
# You'll probably need to add some other stuff like global variables
""" ####################################################
Don't change anything below this line: readPoints or main
""" ####################################################
def readPoints(lifeGrid):
"""
Reads the locations of life and set to the SparseMatrix
"""
print("1. Enter positions of life with row,col format (e.g., 2,3).")
print("2. Enter empty line to stop.")
life=input()
coordList=[]
while life:
points=life.split(",")
try:
coord=[int(points[0]),int(points[1])]
coordList.append(coord)
except ValueError:
print("Ignored input:" + life+ ", row, col not valid numbers")
except:
print("Unexpected error:", sys.exc_info()[0])
print("added, keep entering or enter empty line to stop.")
life=input()
print("Thanks, finished entering live cells")
lifeGrid.configure(coordList)
def main():
"""
Runs for ten generations if a stable (repeating) state is not found.
"""
lifeGrid= SparseLifeGrid()
readPoints(lifeGrid)
lifeGrid.printLifeGrid()
patterns=0
i=0
while i <10 and patterns == 0:
"""
Evolve to the next generation
"""
lifeGrid.evolve()
"""
Check whether this generation is a repetition of any of the
previous states.
If yes return the previous matching generation (1-10).
"""
patterns=lifeGrid.hasOccurred()
if patterns != -1:
break
i+=1
lifeGrid.printLifeGrid()
if i==10:
print("No pattern found")
else:
print("Pattern found at: " + str(i)+ " of type: " + str(patterns))
main()
A sparse matrix is a representation of a matrix where only the locations of values not equal to the default (usually 0) are stored in memory. A simple way to represent such a matrix in Python is to use a dictionary where the key is a tuple of coordinate (x, y) and the value is the matrix values.
For example, this matrix:
0 0 0 0
0 0 0 0
0 1 0 0
0 0 0 0
could have the following representation:
matrix = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, 0]]
sparse_matrix = {(1, 2): 1}
and you would access the values like that:
for x in xrange(4):
for y in xrange(4):
assert matrix[y][x] == sparse_matrix.get((x, y), 0)
This should be enough to get you started. Your exercise want you to wrap such a sparse matrix in a class that will give it the same interface as a traditional matrix.
There are more advanced way to store such sparse matrix, each doing a different trade off between complexity, memory usage, ...