def trials():
"""
Search small space to determine input set to knapsack that offers greatest
difference between dynamic programming and approximate. Once computed, use
these values in adk.book.chapter11.py
"""
# 83 250 4 2 457
# 103 207 4 2 523
a = 23
b = 56
c = 8
d = 5
maxBestTotal = 0
for a in range(23, 113, 10):
for b2 in range(1, 8):
b = a*b2 + 1
for c in [4, 8, 16, 32, 64]:
for d in range(2, 7):
diffTotal = 0
W = 10
numReps = 1
numTrials = 1
while W <= 65536:
items = []
# Item (Value, Weight)
for i in range(a,b,c):
items.append(Item(i,i))
items.append(Item(d*i+1,d*i+1))
itemSet = 'items=[]\n'
for item in items:
itemSet = itemSet + 'items.append(Item(' + str(item.value) + ',' + str(item.weight) + '))\n'
#itemSet = itemSet + 'items.append(Item(' + str(W//4) + "," + str(W//4) + '))\n'
setup= '''
from adk.knapsack import knapsack_unbounded, knapsack_01, knapsack_approximate, Item, record_best
import random\n''' + itemSet + '''
'''
executeUnbound = '''
record_best (knapsack_unbounded(items,''' + str(W) + ''')[0])
'''
totalUnbound = min(timeit.Timer(executeUnbound, setup=setup).repeat(numReps,numTrials))
executeApproximate = '''
record_best (knapsack_approximate(items,''' + str(W) + ''')[0])
'''
totalApproximate = min(timeit.Timer(executeApproximate, setup=setup).repeat(numReps,numTrials))
#print (W, totalUnbound, totalApproximate, record_best())
best2 = record_best()
if len(best2) > 0:
diffTotal += best2[0] - best2[1]
W = W * 2 + 1
if diffTotal > maxBestTotal:
print (a,b,c,d,diffTotal)
maxBestTotal = diffTotal
def trials():
"""Conduct timing trials for sample values of W."""
# 103 207 4 2 523
a = 103
b = 207
c = 4
d = 2
items = []
# Item (Value, Weight)
for i in range(a, b, c):
items.append(Item(i, i))
items.append(Item(d * i + 1, d * i + 1))
maxBestTotal = 0
print('W', 'KnapsackUnboundedTime', 'KnapsackApproximateTime',
'ActualAnswer', 'ApproximateAnswer')
diffTotal = 0
W = 10
numReps = 1
numTrials = 1
while W <= 65536:
itemSet = 'items=[]\n'
for item in items:
itemSet = itemSet + 'items.append(Item(' + str(
item.value) + ',' + str(item.weight) + '))\n'
setup = '''
from adk.knapsack import knapsack_unbounded, knapsack_01, knapsack_approximate, Item, record_best
import random\n''' + itemSet + '''
'''
executeUnbound = '''
record_best (knapsack_unbounded(items,''' + str(W) + ''')[0])
'''
totalUnbound = min(
timeit.Timer(executeUnbound,
setup=setup).repeat(numReps, numTrials))
executeApproximate = '''
record_best (knapsack_approximate(items,''' + str(W) + ''')[0])
'''
totalApproximate = min(
timeit.Timer(executeApproximate,
setup=setup).repeat(numReps, numTrials))
print(W, '{0:.5f}'.format(totalUnbound),
'{0:.5f}'.format(totalApproximate), record_best())
W = W * 2 + 1
if diffTotal > maxBestTotal:
print(a, b, c, d, diffTotal)
maxBestTotal = diffTotal
def trials():
"""Conduct timing trials for sample values of W."""
# 103 207 4 2 523
a = 103
b = 207
c = 4
d = 2
items = []
# Item (Value, Weight)
for i in range(a,b,c):
items.append(Item(i,i))
items.append(Item(d*i+1,d*i+1))
maxBestTotal = 0
print ('W', 'KnapsackUnboundedTime', 'KnapsackApproximateTime', 'ActualAnswer', 'ApproximateAnswer')
diffTotal = 0
W = 10
numReps = 1
numTrials = 1
while W <= 65536:
itemSet = 'items=[]\n'
for item in items:
itemSet = itemSet + 'items.append(Item(' + str(item.value) + ',' + str(item.weight) + '))\n'
setup= '''
from adk.knapsack import knapsack_unbounded, knapsack_01, knapsack_approximate, Item, record_best
import random\n''' + itemSet + '''
'''
executeUnbound = '''
record_best (knapsack_unbounded(items,''' + str(W) + ''')[0])
'''
totalUnbound = min(timeit.Timer(executeUnbound, setup=setup).repeat(numReps,numTrials))
executeApproximate = '''
record_best (knapsack_approximate(items,''' + str(W) + ''')[0])
'''
totalApproximate = min(timeit.Timer(executeApproximate, setup=setup).repeat(numReps,numTrials))
print (W, '{0:.5f}'.format(totalUnbound), '{0:.5f}'.format(totalApproximate), record_best())
W = W * 2 + 1
if diffTotal > maxBestTotal:
print (a,b,c,d,diffTotal)
maxBestTotal = diffTotal
def trials():
"""
Search small space to determine input set to knapsack that offers greatest
difference between dynamic programming and approximate. Once computed, use
these values in adk.book.chapter11.py
"""
# 83 250 4 2 457
# 103 207 4 2 523
a = 23
b = 56
c = 8
d = 5
maxBestTotal = 0
for a in range(23, 113, 10):
for b2 in range(1, 8):
b = a * b2 + 1
for c in [4, 8, 16, 32, 64]:
for d in range(2, 7):
diffTotal = 0
W = 10
numReps = 1
numTrials = 1
while W <= 65536:
items = []
# Item (Value, Weight)
for i in range(a, b, c):
items.append(Item(i, i))
items.append(Item(d * i + 1, d * i + 1))
itemSet = 'items=[]\n'
for item in items:
itemSet = itemSet + 'items.append(Item(' + str(
item.value) + ',' + str(item.weight) + '))\n'
#itemSet = itemSet + 'items.append(Item(' + str(W//4) + "," + str(W//4) + '))\n'
setup = '''
from adk.knapsack import knapsack_unbounded, knapsack_01, knapsack_approximate, Item, record_best
import random\n''' + itemSet + '''
'''
executeUnbound = '''
record_best (knapsack_unbounded(items,''' + str(W) + ''')[0])
'''
totalUnbound = min(
timeit.Timer(executeUnbound, setup=setup).repeat(
numReps, numTrials))
executeApproximate = '''
record_best (knapsack_approximate(items,''' + str(W) + ''')[0])
'''
totalApproximate = min(
timeit.Timer(executeApproximate,
setup=setup).repeat(
numReps, numTrials))
#print (W, totalUnbound, totalApproximate, record_best())
best2 = record_best()
if len(best2) > 0:
diffTotal += best2[0] - best2[1]
W = W * 2 + 1
if diffTotal > maxBestTotal:
print(a, b, c, d, diffTotal)
maxBestTotal = diffTotal
