#=======================================================================
# Author: Isai Damier
# Title: Two Way Partition
# Project: geekviewpoint
# Package: algorithms
#
# Statement:
# Given n bags of candies, divide the candies between two kids
# as evenly as possible without splitting individual bags. The candies
# are valued by weight, in ounce (oz).
#
# Time Complexity: O(n*w)
# where w is the total weight of all the candies together.
#
# Dynamic Programming Strategy:
#
# The difference between this problem and the "Positive Subsequence
# Sum" problem is that here, instead of solving for a given weight
# x, we are asked to come up with x given the constraint that x must
# be as close to T/2 as possible.
#
# So let's do this. Instead of wasting too much time thinking about x,
# let x = T/2 so that we can proceed as we did for the "Positive
# Subsequence Sum" problem. Then after we are done filling sum[]
# completely, we will walk the list backward until we find a positive
# weight. For our illustration, we will use sequence S=[2,4,7,9].
# Here are the steps:
#
# 0] Create a boolean array called sum of size x+1:
# As you might guess, when we are done filling the array, all the
# sub-sums between 0 and x that can be calculated from S will be
# set to true and those that cannot be reached will be set to false.
# For example if S=[2,4,7,9] then sum[5]=False while sum[13]=True
# because 4+9=13.
#
# 1] Initialize sum[] to False:
# Before any computation is performed, assume/pretend that each
# sub-sum is unreachable. We know that's not true, but for now
# let's be outrageous.
#
# 2] Set sum at index 0 to true:
# This truth is self-evident. By taking no elements from S, we end
# up with an empty sub-sequence. Therefore we can mark sum[0]=true,
# since the sum of nothing is zero.
#
# 3] To fill the rest of the array, we are going to use the following
# trick. Starting with 0, each time we find a positive sum, we will
# add an element from S to that sum to get a greater sum. For example,
# since sum[0]=true and 2 is in S, then sum[0+2] must also be true.
# Therefore, we set sum[0+2]=sum[2]=true. Then from sum[2]=true and
# element 4, we can say sum[2+4]=sum[6]=true, and so on.
#
# 4] Recall that we decided to let x=T/2. In an ideal world, we will
# split the candies and the kids will get exactly the same amount.
# But since that's not likely to happen in real life, here is a
# sensible strategy. After we finish filling the sum[] array, starting
# at x=T/2 we will check to see if sum[x] equals true. If it is, we
# are done. If not, we decrease x by one and keep checking, until we
# find sum[x]==true.
#=======================================================================
def subsetSumDivideByTwo( S ):
# preliminary
T = 0
for a in S:
T += a
x = T / 2 + 1
weight = [False] * ( x + 1 )
weight[0] = True
for a in S:
for i in range( x, a - 1, -1 ):
if not weight[i] and weight[i - a]:
weight[i] = True
for i in range( x, -1, -1 ):
if weight[i]:
return [i, T - i]
import unittest
from dynamic_programming import TwoPartitionProblem as two_partition
class Test( unittest.TestCase ):
def testSubsetSumDivideByTwo( self ):
S = [3, 5, 7, 8, 11, 13, 17, 21, 34]
expResult = [60, 59]
result = two_partition.subsetSumDivideByTwo( S )
self.assertEquals( expResult, result )
S = [3, 5, 7, 8, 13, 17, 21, 34]
expResult = [55, 53]
result = two_partition.subsetSumDivideByTwo( S )
self.assertEquals( expResult, result )
S = [11, 29, 37, 45, 59, 67, 89, 97, 37]
expResult = [234, 237]
result = two_partition.subsetSumDivideByTwo( S )
self.assertEquals( expResult, result )
