def search(knapsack: Knapsack, max_evals: int) -> Tuple[float, int]:
"""Use the hill climber best improvement to find a solution."""
base_solution = random_solution(knapsack)
base_score = knapsack.evaluate(base_solution)
nb_eval = 1
while True:
best_score = -math.inf
index_solution = -1
for i in range(knapsack.items):
base_solution[i] = not base_solution[i]
score = knapsack.evaluate(base_solution)
nb_eval += 1
if score > best_score:
best_score = score
index_solution = i
base_solution[i] = not base_solution[i]
if nb_eval >= max_evals:
break
if best_score > base_score:
base_solution[index_solution] = not base_solution[index_solution]
base_score = best_score
else:
break # Optimum local
return base_score, nb_eval
def search(knapsack: Knapsack, max_nb_eval: int) -> Tuple[float, int]:
base_solution = random_solution(knapsack)
base_score = knapsack.evaluate(base_solution)
nb_eval = 1
voisins_possibles = list(range(knapsack.items))
while True:
shuffle(voisins_possibles)
current_score = base_score
index_voisin = -1
while (index_voisin + 1 < len(voisins_possibles)
and current_score <= base_score and nb_eval <= max_nb_eval):
index_voisin += 1
index = voisins_possibles[index_voisin]
base_solution[index] = not base_solution[index]
current_score = knapsack.evaluate(base_solution)
nb_eval += 1
base_solution[index] = not base_solution[index]
if current_score > base_score:
base_score = current_score
index = voisins_possibles[index_voisin]
base_solution[index] = not base_solution[index]
elif index_voisin + 1 == len(voisins_possibles):
break # optimum local
if nb_eval >= max_nb_eval:
break
return base_score, nb_eval
def test_input_raises_value_error_if_wrong_len_of_data(self):
self.input_data.pop(0)
with self.assertRaises(ValueError) as e:
Knapsack.process_input_data(self.input_data)
self.assertTrue(
Knapsack._DATA_LENGTH_ERROR.format(len(self.input_data))
in str(e.exception))
def test_knapsack_empty(self):
bag = ()
size = 50
ks = Knapsack(bag, size)
result = ks.solve()
self.assertEqual((), result)
def head(self, triangles):
LIMIT = 180
knapsack = Knapsack(triangles, LIMIT)
head = knapsack.main()
for h in head:
triangles.remove(h)
head = self.sortHead(head)
return [head, triangles]
def rest(self, triangles):
remainig = []
while True:
ks = Knapsack(triangles, 180)
seq = ks.main()
for s in seq:
triangles.remove(s)
remainig.append(seq)
if triangles == []:
break
return remainig
def optimize_values(data_frame, capacity, label):
data_frame = data_frame.drop_duplicates(subset='Keyword')
cost = data_frame.daily_cost_average.copy(deep=True)
cost[cost == 0] = minimum / 10
values = (data_frame.daily_impressions_average +
data_frame.daily_clicks_average) * (1 / cost)
opt = Knapsack(items_names=data_frame.Keyword.to_list(),
values=values.to_list(),
capacity=capacity,
weights=data_frame.daily_cost_average.tolist(),
solve_type=5,
name='Branch_n_bound_%s' % label)
opt.get_results(print_it=True)
df = data_frame[data_frame.Keyword.isin(opt.packed_items)]
return df
def __init__(self, mu: int, knapsack: Knapsack):
self.knapsack = knapsack
parents = [random_solution(knapsack) for i in range(mu)]
self._population = [
Individu(parent, knapsack.evaluate(parent)) for parent in parents
]
self.mu = mu
class KnapsackTest(unittest.TestCase):
def setUp(self):
self.weight_available = 7
weight_per_item = [2, 3, 4, 5]
value_per_item = [3, 4, 5, 5]
self.input_data = ['7', '2 3 4 5', '3 4 5 5']
self.obj = Knapsack(self.weight_available, weight_per_item, value_per_item)
def test_rec_knap_returns_zero_if_iterator_equals_zero(self):
res = self.obj.rec_knap(0, self.weight_available)
self.assertEqual(res, 0)
def test_rec_knap_returns_proper_value(self):
res = self.obj.rec_knap(
self.obj.number_of_items, self.weight_available)
self.assertEqual(res, 9)
def test_get_values_returns_proper_items(self):
res = self.obj.get_values()
self.assertEqual(len(res), 2)
self.assertEqual(res[1], [(3, 4), (4, 5)])
def test_input_processes_and_returns_list_succeeds(self):
res = Knapsack.process_input_data(self.input_data)
self.assertEqual(res, [7, [2, 3, 4, 5], [3, 4, 5, 5]])
def test_input_raises_value_error_if_wrong_len_of_data(self):
self.input_data.pop(0)
with self.assertRaises(ValueError) as e:
Knapsack.process_input_data(self.input_data)
self.assertTrue(
Knapsack._DATA_LENGTH_ERROR.format(len(self.input_data))
in str(e.exception))
def test_input_raises_value_error_if_wrong_input(self):
self.input_data[2] = '3 4 5'
with self.assertRaises(ValueError) as e:
Knapsack.process_input_data(self.input_data)
self.assertTrue(Knapsack._DATA_INPUT_ERROR in str(e.exception))
def test_input_raises_value_error_if_wrong_input_type(self):
self.input_data[1] = '3 4 5 A'
with self.assertRaises(ValueError) as e:
Knapsack.process_input_data(self.input_data)
self.assertTrue('invalid literal' in str(e.exception))
def test_knapsack_values(self):
bag = (
(10, 60),
(20, 100),
(30, 195),
(40, 120),
(50, 120),
(5, 120),
(60, 120),
(10, 50),
(10, 200),
(20, 120),
)
size = 50
ks = Knapsack(bag, size)
result = ks.solve()
self.assertEqual(((30, 195), (5, 120), (10, 200)), result)
def read_input():
print("Enter items knapsack volume and items number")
volume, items_number = map(int, input().split())
costs = []
weights = []
print("Enter item weight and item cost")
for i in range(0, items_number):
item_weight, item_cost = map(int, input().split())
costs.append(item_cost)
weights.append(item_weight)
return Knapsack(volume, costs, weights), items_number
def loadInstances(files):
'''
Load instances from files.
'''
# if there are more than 1 available files
if len(files) > 1:
instances = validateChoices('file', files)
# if there is only 1 available file
else:
instances = files
# queue for threads
thread_queue = Queue(len(instances))
# list of threads for reading files
reading = list()
for index, file_name in enumerate(instances):
name = 'r%d' % index
# new thread to read a file
read = Thread(target=lambda q, arg: q.put(Knapsack.fromFile(arg)),
args=(thread_queue, file_name),
name=name,
daemon=True)
# start new thread and add it to list
read.start()
reading.append(read)
heuristics = validateChoices('heuristic')
# number of instances to solve
size = len(instances)
index = 1
load_str = ' Loading instance...'
# while there exist unsolved instances
while size:
# loop over list of threads
for read in reading:
# if thread is still running
if read.is_alive():
print('%s\r' % load_str, end='')
# check next thread
continue
read.join()
# get Knapsack object
knapsack = thread_queue.get()
if knapsack is not None:
print('%s\r' % (' ' * len(load_str)), end='')
solveInstance(knapsack, index, heuristics)
size -= 1
index += 1
# if all instances have been solved
if size <= 0:
# break for
break
def read(self):
C = self.file.readline()
print("Number of test cases is C:", C)
lines = self.file.readlines()
items_num_flag = True
knapsack = Knapsack(0, 0)
template_counter = 0
for line in lines:
if line[0] == '\n':
continue
if len((line.rstrip()).split()) == 1 and items_num_flag:
items_num_flag = False
if knapsack.S != 0:
self.knapsacks.append(knapsack)
del knapsack
knapsack = Knapsack(0, 0)
knapsack.N = int(line.rstrip())
continue
if len((line.rstrip()).split()) == 1 and not items_num_flag:
items_num_flag = True
knapsack.S = int(line.rstrip())
continue
# Initialize knapsack items
line.rstrip()
weight, value = line.split(' ')
item = Item(int(weight), int(value))
knapsack.items.append(item)
self.knapsacks.append(knapsack)
def read(self):
C = int(input("Enter the number of test case: "))
# Loop through the test cases
for i in range(C):
print("Test case: " + str(i + 1))
N = int(input("Enter the number of items: "))
S = int(input("Enter the size of the knapsack: "))
print("Enter the item and its value in pairs: ")
knapsack = Knapsack(N, S)
item = Item(0, 0)
for n in range(N):
weight, value = input("Pair" + str(n + 1) + ":").split()
item = Item(weight, value)
knapsack.items.append(item)
self.knapsacks.append(knapsack)
def generateInstances():
'''
Generate instances from prompt.
'''
knapsacks = list()
# list of threads for writing to files
writing = list()
index = 1
another = True
while another:
print('\n === {}° instance ==='.format(index))
answers = prompt(createInstanceQuestions())
k = Knapsack.random(answers['n'], answers['min w'], answers['max w'],
answers['min v'], answers['max v'], answers['p'])
knapsacks.append(k)
# new thread's name
name = 'w%d' % index
# new thread to write last instance and start it
write = Thread(target=knapsacks[-1].toFile, name=name)
write.start()
writing.append(write)
another = confirm('Do you want to add another instance?').ask()
index += 1
heuristics = validateChoices('heuristic')
save_str = ' Saving instances to files...'
size = len(writing)
while size:
# loop over threads list
for write, k in zip(writing, knapsacks):
index = int(write.name[-1])
# if a thread is still running
if write.is_alive():
print('%s\r' % save_str, end='')
continue
else:
# wait until the thread finishes
write.join()
print('%s\r' % (' ' * len(save_str)), end='')
solveInstance(k, index, heuristics)
size -= 1
print('\n All instances have been saved to files.')
def buildKnapsacks(commandSets):
""" The buildKnapsacks function takes a 2d array of sets of commands to execute and runs through the array interpreting the commands.
Args: commandSets A list of the sets of commands to execute for the problem.
The builder first goes through the commands and splits them into spice and knapsack commands, it then makes them more easily interpreted, and finally it iterates through each set of commands and carries out their instructions.
"""
spiceCommands = []
knapsackCommands = []
commentCount = 0
spiceCount = 0
knapsackCount = 0
commandSet = []
spices = []
knapsacks = []
for commandSet in commandSets:
for command in commandSet:
if command[0] == '--':
continue
elif command[0] == 'spice':
spiceCommands.append(
['spice', command[3][:-1], float(command[6][:-1]), float(command[9][:-1])])
elif command[0] == 'knapsack':
knapsackCommands.append(['knapsack', float(command[3][:-1])])
print("Spice commands:", len(spiceCommands))
print("Knapsack commands:", len(knapsackCommands), '\n')
for command in spiceCommands:
if command[0] == 'spice':
spices.append(Spice(command[1], command[2], command[3]))
for command in knapsackCommands:
if command[0] == 'knapsack':
knapsacks.append(Knapsack(command[1]))
spices.sort(key=lambda spice: spice.price, reverse=True)
return spices, knapsacks
def pickItems(knapsack: Knapsack, heuristic):
'''
Use the heuristic specified in the argument to get a solution for the knapsack problem.
Returns a generator containing the items that were picked.
'''
items_sorted = knapsack.sortItems(heuristic)
W = knapsack.capacity
for item in items_sorted:
weight = item.weight
if weight <= W:
# add item to generator
yield item
W -= weight
# if no more items fit in the knapsack
if W == 0:
break
# if heuristic by weight is used, the rest of the items won't fit
elif heuristic == 2:
break
def test_knapsack(self):
# Problems to test
problems = [
'dynamic', 'bipercube', 'hypercube2', 'hypercube4', 'hypercube6',
'hypercube8'
]
naive = 0
dynamic = 0
parallel = 0
bipercube = 0
hypercube2 = 0
hypercube4 = 0
hypercube6 = 0
hypercube8 = 0
# Variables
capacity = 100
elementCount = 100
profitsRange = 100
weigthsRange = 100
values = []
weigths = []
# Construct the Knapsack
for i in range(elementCount):
values.append(random.randint(1, profitsRange))
weigths.append(random.randint(1, weigthsRange))
knap = Knapsack(capacity, weigths, values)
# Resolve
# Dynamic
start = time.time()
dynamic = knap.dynamic()
end = time.time()
print "Dynamic time: " + str(end - start)
# Naive
if 'naive' in problems:
start = time.time()
naive = knap.naive()
end = time.time()
print "Naive time: " + str(end - start)
# Parallel
if 'parallel' in problems:
start = time.time()
parallel = knap.parallel()
end = time.time()
print "Parallel time: " + str(end - start)
# Bipercube
if 'bipercube' in problems:
start = time.time()
bipercube = knap.bipercube()
end = time.time()
print "Bipercube time: " + str(end - start)
# Hypercube
# 2 Processors
if 'hypercube2' in problems:
start = time.time()
hypercube2 = knap.hypercube(2)
end = time.time()
print "Hypercube 2 time: " + str(end - start)
# 4 Processors
if 'hypercube4' in problems:
start = time.time()
hypercube4 = knap.hypercube(4)
end = time.time()
print "Hypercube 4 time: " + str(end - start)
# 6 Processors
if 'hypercube6' in problems:
start = time.time()
hypercube6 = knap.hypercube(6)
end = time.time()
print "Hypercube 6 time: " + str(end - start)
# 8 Processors
if 'hypercube8' in problems:
start = time.time()
hypercube8 = knap.hypercube(8)
end = time.time()
print "Hypercube 8 time: " + str(end - start)
# Results
# Naive
if 'naive' in problems:
self.assertEqual(naive, dynamic)
# Parallel
if 'parallel' in problems:
self.assertEqual(parallel, dynamic)
# Bipercube
if 'bipercube' in problems:
self.assertEqual(bipercube, dynamic)
# 2 Processors
if 'hypercube2' in problems:
self.assertEqual(hypercube2, dynamic)
# 4 Processors
if 'hypercube4' in problems:
self.assertEqual(hypercube4, dynamic)
# 6 Processors
if 'hypercube6' in problems:
self.assertEqual(hypercube6, dynamic)
# 8 Processors
if 'hypercube8' in problems:
self.assertEqual(hypercube8, dynamic)
from knapsack import Curve, Knapsack
if __name__ == '__main__':
# initializing solver (Knapsack class) instance with maximum money for all media
budget = Knapsack(70874156)
# maximal budget for every media
maxb = 70 * 10**6
# initializing media as Curve instances
# every Curve is some media like TV, OOH, etc.
media1 = Curve(397650, 0.5085217, 0.92, 9.168728e-06 / 87.98509)
media2 = Curve(1336580, 0.941772, 0.9964167, 7.202334e-07)
media3 = Curve(5.509022, 5000, 0.99, 0.0002982394 / 219)
media4 = Curve(3191.663, 10000, 0.75, 0.001353697 / 65)
media5 = Curve(237349.3, 0.9954354, 0.7, 9.501362e-06 / 12.47645)
media6 = Curve(2961.2209, 100, 0.5, 0.003669801 / 87.98509)
media7 = Curve(664196.7, 0.662257, 0.92, 1.275716e-06 / 87.98509)
# adding media to budget
# in second argument we pass minimum and maximum allowed for media
budget.add_curve(media1, 10600000, maxb)
budget.add_curve(media2, 5700000, maxb)
budget.add_curve(media3, 1923077, maxb)
budget.add_curve(media4, 8307692, maxb)
budget.add_curve(media5, 3246154, maxb)
budget.add_curve(media6, 9791667, maxb)
budget.add_curve(media7, 15786389, maxb)
# call to optimize spends between media
# last line represents optimal budget for every media, e.g. first number
from knapsack import Knapsack
from extract_objects import Objects
if (__name__ == '__main__'):
total_individual = 400
total_chromossomes = 20
total_generation = 800
mutation_rate = 0.1
crossover_rate = 0.9
elite = 20
pack_weight_limit = 400
objects = Objects()
knapsack = Knapsack(total_individual, total_chromossomes, total_generation,
mutation_rate, crossover_rate, elite,
pack_weight_limit, objects)
knapsack.start()
def test_input_processes_and_returns_list_succeeds(self):
res = Knapsack.process_input_data(self.input_data)
self.assertEqual(res, [7, [2, 3, 4, 5], [3, 4, 5, 5]])
def test_input_raises_value_error_if_wrong_input(self):
self.input_data[2] = '3 4 5'
with self.assertRaises(ValueError) as e:
Knapsack.process_input_data(self.input_data)
self.assertTrue(Knapsack._DATA_INPUT_ERROR in str(e.exception))
default='result.json',
type=str,
help='Set the output filename(json).')
args = parser.parse_args()
# Check argument by standard output
print(args)
# Set arguments to variables
filename = args.filename
crossover_prob = args.crossover_prob
mutation_prob = args.mutation_prob
popSize = args.population_size
generation = args.generation
# Dataset read and parsing
knapsack = Knapsack(filename)
knapsack.reader()
gene_size = len(knapsack.dataset)
# Generate each GA instance
roulette = GeneticAlgorithm(gene_size, popSize, crossover_prob,
mutation_prob)
tournament = GeneticAlgorithm(gene_size, popSize, crossover_prob,
mutation_prob)
# Roulette Wheel selection GA Initialization
print('Roulette', end=' ')
roulette.initialization()
roulette.calculateFitness(knapsack)
# Tournament selection GA Initialization
print('Tournament', end=' ')
def test_input_raises_value_error_if_wrong_input_type(self):
self.input_data[1] = '3 4 5 A'
with self.assertRaises(ValueError) as e:
Knapsack.process_input_data(self.input_data)
self.assertTrue('invalid literal' in str(e.exception))
def setUp(self):
self.weight_available = 7
weight_per_item = [2, 3, 4, 5]
value_per_item = [3, 4, 5, 5]
self.input_data = ['7', '2 3 4 5', '3 4 5 5']
self.obj = Knapsack(self.weight_available, weight_per_item, value_per_item)
def main():
knapsack = Knapsack("./ks_1000.dat")
# Hill-climbing best improvement
iterations = [
(100, 64),
(500, 32),
(1_000, 16),
(5_000, 8),
(10_000, 4),
(50_000, 2),
(100_000, 1),
]
average_score = [0]
average_evaluations = [0]
for iters, repeats in iterations:
print(
f"Testing hill-climbing best improvement {repeats} time(s) with {iters} iterations."
)
total_score = 0
total_eval = 0
for _ in range(repeats):
score, evaluations = hc_best(knapsack, iters)
total_score += score
total_eval += evaluations
average_score.append(total_score / repeats)
average_evaluations.append(total_eval / repeats)
plt.plot(average_evaluations,
average_score,
label="Hill-climbing best improvement")
# Hill-climbing first improvement
iterations = [
(100, 64),
(500, 32),
(1_000, 16),
(5_000, 8),
(10_000, 4),
(50_000, 2),
(100_000, 1),
]
average_score = [0]
average_evaluations = [0]
for iters, repeats in iterations:
print(
f"Testing hill-climbing first improvement {repeats} time(s) with {iters} iterations."
)
total_score = 0
total_eval = 0
for _ in range(repeats):
score, evaluations = hc_first(knapsack, iters)
total_score += score
total_eval += evaluations
average_score.append(total_score / repeats)
average_evaluations.append(total_eval / repeats)
plt.plot(average_evaluations,
average_score,
label="Hill-climbing first improvement")
# print(score, evals)
# score, evals = recuit_simule(knapsack)
# Evolution
iterations = [(100, 50), (500, 10), (1_000, 5), (5_000, 1), (10_000, 1)]
average_score = [0]
average_evaluations = [0]
for iters, repeats in iterations:
print(f"Testing evolution {repeats} time(s) with {iters} iterations.")
total_score = 0
total_eval = 0
for _ in range(repeats):
pop = Population(10, knapsack)
evaluations, score = pop.run(iters)
total_score += score
total_eval += evaluations
average_score.append(total_score / repeats)
average_evaluations.append(total_eval / repeats)
plt.plot(average_evaluations, average_score, label="Evolution mu=10")
plt.grid()
plt.xlabel("Evaluations")
plt.ylabel("Average performance")
plt.figlegend()
plt.show()
from knapsack import Knapsack
import time
import sys
sys.setrecursionlimit(10000)
knapsack = Knapsack()
startTime = time.time()
result = knapsack.findOptimum(knapsack.itemCount, knapsack.knapCapacity)
print(time.strftime("%H:%M:%S", time.gmtime(time.time() - startTime)))
print(result)
@author: josep
"""
from Pair import Pair
from knapsack import Knapsack as Knapsack
pair1=Pair(4,1)
pair2=Pair(7,7)
pair3=Pair(1,22)
pair4=Pair(2,23)
pair5=Pair(3,6)
items=[]
items.extend([pair1,pair2,pair3,pair4,pair5])
knapsackObject=Knapsack(14,5,items)
#print(knapsackObject.algorithm().fitness)
filePath = "input_example.txt"
f = open("result.txt", "a")
with open(filePath, "r") as file:
text = file.read()
testCases = text.split("\n\n")
start=1
while start <= int(testCases[0]):
case = testCases[start].strip().split("\n")
def __init__(self, knapsacks): self.knapsacks = [Knapsack(knapsack) for knapsack in knapsacks]
def knapsackPage():
x = Knapsack(6, 1000)
return render_template('knapsack.html', maxRevenue=x.getMaxRevenue(), totalBudget=x.getBudget(), observations=x.getObs(), Title="Knapsack Problem", GitHubLink="https://github.com/deacons2016/SimulationModels/blob/master/Exam2/Problem4/index.py")
def test_knapsack(self):
# Initial variables
naive = 0
dynamic = 0
parallel = 0
bipercube = 0
hypercube2 = 0
hypercube4 = 0
hypercube6 = 0
hypercube8 = 0
# Problems to test
problems = [
'dynamic', 'hypercube2', 'hypercube4', 'hypercube6', 'hypercube8'
]
# File
out = open("out.csv", 'w') # csv output
out.write("Capacidad" + "\t" + "Elementos" + "\t" + "Naive" + "\t" +
"Programacion dinamica" + "\t" + "2 procesadores" + "\t" +
"4 procesadores" + "\t" + "6 procesadores" + "\t" +
"8 procesadores" + "\t" + "Peters and Rudolph" + "\t" + "\n")
capacities = [100, 1000]
elements = [100, 1000]
for capacity in capacities:
for element in elements:
print
print 'Capacity', capacity
print 'Elements', element
# Times
naiveTime = '-'
dynamicTime = '-'
hypercube2Time = '-'
hypercube4Time = '-'
hypercube6Time = '-'
hypercube8Time = '-'
parallelTime = '-'
# Variables
values = []
weigths = []
# Construct the Knapsack
for i in range(element):
values.append(random.randint(1, int(element / 2)))
weigths.append(random.randint(1, int(element / 2)))
knap = Knapsack(capacity, weigths, values)
# Resolve
# Dynamic
if 'dynamic' in problems:
start = time.time()
dynamic = knap.dynamic()
end = time.time()
dynamicTime = str(end - start)
print "Dynamic time: " + str(end - start)
# Naive
if 'naive' in problems:
start = time.time()
naive = knap.naive()
end = time.time()
naiveTime = str(end - start)
print "Naive time: " + str(end - start)
# Parallel
if 'parallel' in problems:
start = time.time()
parallel = knap.parallel()
end = time.time()
parallel = str(end - start)
print "Parallel time: " + str(end - start)
# Bipercube
if 'bipercube' in problems:
start = time.time()
bipercube = knap.bipercube()
end = time.time()
print "Bipercube time: " + str(end - start)
# Hypercube
# 2 Processors
if 'hypercube2' in problems:
start = time.time()
hypercube2 = knap.hypercube(2)
end = time.time()
hypercube2Time = str(end - start)
print "Hypercube 2 time: " + str(end - start)
# 4 Processors
if 'hypercube4' in problems:
start = time.time()
hypercube4 = knap.hypercube(4)
end = time.time()
hypercube4Time = str(end - start)
print "Hypercube 4 time: " + str(end - start)
# 6 Processors
if 'hypercube6' in problems:
start = time.time()
hypercube6 = knap.hypercube(6)
end = time.time()
hypercube6Time = str(end - start)
print "Hypercube 6 time: " + str(end - start)
# 8 Processors
if 'hypercube8' in problems:
start = time.time()
hypercube8 = knap.hypercube(8)
end = time.time()
hypercube8Time = str(end - start)
print "Hypercube 8 time: " + str(end - start)
# File
out.write(
str(capacity) + "\t" + str(element) + "\t" +
str(naiveTime) + "\t" + str(dynamicTime) + "\t" +
str(hypercube2Time) + "\t" + str(hypercube4Time) + "\t" +
str(hypercube6Time) + "\t" + str(hypercube8Time) + "\t" +
str(parallelTime) + "\t")
out.write("\n")
