import os.path as path
benchmarks_location = 'benchmarks'
file_name_template = 'p0%s_%s.txt'
indices = range(1,8)
categories = ['c', 'w', 'p', 's']import requests
dataset_location = 'https://people.sc.fsu.edu/~jburkardt/datasets/knapsack_01/'
for index in indices:
for category in categories:
filename = file_name_template % (index, category)
r = requests.get(dataset_location + filename, allow_redirects=True)
file = open(path.join(benchmarks_location, filename), 'wb')
file.write(r.content)
file.close()from ptas import WEIGHT, VALUE
def make_item(weight, value):
item = [-1, -1]
item[WEIGHT] = weight
item[VALUE] = value
return tuple(item)
CAPACITY = 0
ITEMS = 1
OPTIMAL = 2
dataset = []
for index in indices:
f = open(path.join(benchmarks_location, file_name_template % (index, 'c')))
capacity = int(f.read())
f.close()
f = open(path.join(benchmarks_location, file_name_template % (index, 'w')))
weights = [int(line) for line in f.readlines()]
f.close()
f = open(path.join(benchmarks_location, file_name_template % (index, 'p')))
profits = [int(line) for line in f.readlines()]
f.close()
f = open(path.join(benchmarks_location, file_name_template % (index, 's')))
binary_optimal = [int(line) for line in f.readlines()]
optimal = []
for i in range(len(binary_optimal)):
if binary_optimal[i] == 1:
optimal.append(i)
f.close()
items = [make_item(weight=weights[i], value=profits[i]) for i in range(len(weights))]
sample = [-1, -1, -1]
sample[CAPACITY] = capacity
sample[ITEMS] = items
sample[OPTIMAL] = optimal
dataset.append(tuple(sample))
#datasetfrom ptas import sahni_ptas; k=3 # на этом наборе данных лучше результаты получаются с k=3
from dynamic_programming import knapsack_dp
from two_approximation import two_approx
from branch_bound import Branch_and_boundary
algorithms = [sahni_ptas, knapsack_dp, two_approx, Branch_and_boundary]import time as timer
def get_output_and_time(iterations, f, *args):
start = timer.time()
for i in range(iterations):
output = f(*args)
elapsed = timer.time() - start
return *output, elapsedfor index in range(len(dataset)):
print('Sample %s' % (index+1), end='\n\n')
sample = dataset[index]
for algorithm in algorithms:
print(algorithm.__name__)
profit = 0
iters = 100
if algorithm.__name__ == 'sahni_ptas':
profit, items, comparisons, time = get_output_and_time(iters, algorithm, sample[CAPACITY], sample[ITEMS], k)
binary_items = []
for item_index in range(len(sample[ITEMS])):
if item_index in items:
binary_items.append(1)
else:
binary_items.append(0)
items = binary_items
elif algorithm.__name__ == 'knapsack_dp' or algorithm.__name__ == 'two_approx':
weights = []
prices = []
for item in sample[ITEMS]:
weights.append(item[WEIGHT])
prices.append(item[VALUE])
items, comparisons, time = get_output_and_time(iters, algorithm, sample[CAPACITY], weights, prices)
for item_index in range(len(items)):
if items[item_index] == 1:
profit += sample[ITEMS][item_index][VALUE]
elif algorithm.__name__ == 'Branch_and_boundary':
iters = 10
weights = []
prices = []
for item in sample[ITEMS]:
weights.append(item[WEIGHT])
prices.append(item[VALUE])
items, weight, profit, comparisons, time = get_output_and_time(iters, algorithm, sample[CAPACITY], weights, prices)
weight = 0
for item_index in range(len(items)):
if items[item_index] == 1:
weight += sample[ITEMS][item_index][WEIGHT]
print('Items: %s\nProfit: %s\nWeight: %s\nComparisons: %s\nTime: %s (%s iterations)'\
% (items, profit, weight, comparisons, time, iters), end='\n\n')
print('')Sample 1
sahni_ptas
Items: [1, 1, 1, 1, 0, 1, 0, 0, 0, 0]
Profit: 309
Weight: 165
Comparisons: 187
Time: 0.1672821044921875 (100 iterations)
knapsack_dp
Items: [1, 1, 1, 1, 0, 1, 0, 0, 0, 0]
Profit: 309
Weight: 165
Comparisons: 1826
Time: 0.7765276432037354 (100 iterations)
two_approx
Items: [1, 1, 1, 1, 0, 1, 0, 0, 0, 0]
Profit: 309
Weight: 165
Comparisons: None
Time: 0.006340503692626953 (100 iterations)
Branch_and_boundary
Items: [1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0]
Profit: 309
Weight: 165
Comparisons: 95
Time: 7.695439577102661 (10 iterations)
Sample 2
sahni_ptas
Items: [0, 1, 1, 1, 0]
Profit: 51
Weight: 26
Comparisons: 12
Time: 0.0024690628051757812 (100 iterations)
knapsack_dp
Items: [0, 1, 1, 1, 0]
Profit: 51
Weight: 26
Comparisons: 162
Time: 0.04814934730529785 (100 iterations)
two_approx
Items: [1, 0, 1, 0, 0]
Profit: 47
Weight: 23
Comparisons: None
Time: 0.00417327880859375 (100 iterations)
Branch_and_boundary
Items: [0.0, 1.0, 1.0, 1.0, 0.0]
Profit: 51
Weight: 26
Comparisons: 15
Time: 0.9385185241699219 (10 iterations)
Sample 3
sahni_ptas
Items: [1, 1, 0, 0, 1, 0]
Profit: 150
Weight: 190
Comparisons: 32
Time: 0.01832127571105957 (100 iterations)
knapsack_dp
Items: [1, 1, 0, 0, 1, 0]
Profit: 150
Weight: 190
Comparisons: 1337
Time: 0.5683188438415527 (100 iterations)
two_approx
Items: [0, 0, 1, 0, 1, 1]
Profit: 119
Weight: 172
Comparisons: None
Time: 0.014359712600708008 (100 iterations)
Branch_and_boundary
Items: [1.0, 1.0, 0.0, 0.0, 1.0, 0.0]
Profit: 150
Weight: 190
Comparisons: 23
Time: 1.6938800811767578 (10 iterations)
Sample 4
sahni_ptas
Items: [1, 1, 0, 0, 0, 1, 1]
Profit: 105
Weight: 50
Comparisons: 61
Time: 0.02057480812072754 (100 iterations)
knapsack_dp
Items: [1, 0, 0, 1, 0, 0, 0]
Profit: 107
Weight: 50
Comparisons: 408
Time: 0.13924193382263184 (100 iterations)
two_approx
Items: [1, 0, 0, 1, 0, 0, 0]
Profit: 107
Weight: 50
Comparisons: None
Time: 0.0052144527435302734 (100 iterations)
Branch_and_boundary
Items: [1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]
Profit: 107
Weight: 50
Comparisons: 39
Time: 2.323709011077881 (10 iterations)
Sample 5
sahni_ptas
Items: [1, 0, 1, 1, 1, 0, 1, 1]
Profit: 900
Weight: 104
Comparisons: 110
Time: 0.05936789512634277 (100 iterations)
knapsack_dp
Items: [1, 0, 1, 1, 1, 0, 1, 1]
Profit: 900
Weight: 104
Comparisons: 945
Time: 0.32321834564208984 (100 iterations)
two_approx
Items: [0, 1, 1, 1, 0, 1, 1, 1]
Profit: 888
Weight: 92
Comparisons: None
Time: 0.028102397918701172 (100 iterations)
Branch_and_boundary
Items: [1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0]
Profit: 900
Weight: 104
Comparisons: 21
Time: 1.689634084701538 (10 iterations)
Sample 6
sahni_ptas
Items: [0, 1, 0, 1, 0, 0, 1]
Profit: 1735
Weight: 169
Comparisons: 66
Time: 0.026411771774291992 (100 iterations)
knapsack_dp
Items: [0, 1, 0, 1, 0, 0, 1]
Profit: 1735
Weight: 169
Comparisons: 1368
Time: 0.4283626079559326 (100 iterations)
two_approx
Items: [0, 1, 0, 1, 0, 0, 1]
Profit: 1735
Weight: 169
Comparisons: None
Time: 0.03157639503479004 (100 iterations)
Branch_and_boundary
Items: [0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0]
Profit: 1735
Weight: 169
Comparisons: 109
Time: 5.518566846847534 (10 iterations)
Sample 7
sahni_ptas
Items: [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1]
Profit: 1419
Weight: 735
Comparisons: 910
Time: 0.713756799697876 (100 iterations)
knapsack_dp
Items: [1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1]
Profit: 1458
Weight: 749
Comparisons: 12016
Time: 4.694413661956787 (100 iterations)
two_approx
Items: [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
Profit: 1315
Weight: 682
Comparisons: None
Time: 0.020045995712280273 (100 iterations)
Branch_and_boundary
Items: [1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0]
Profit: 1458
Weight: 749
Comparisons: 1305
Time: 170.38672018051147 (10 iterations)