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from itertools import combinations
def knapsack(weight_cap, weights, values):
variandid = []
uus = []
kaal = 0
vaartus = 0
max_value = 0
for i in range(1, len(values)+1):
variandid += combinations([el for el in range(len(values))], i)
for rida in variandid:
uus = list(rida)
for i in uus:
kaal += weights[i]
vaartus += values[i]
if kaal > weight_cap:
continue
if kaal <=weight_cap:
if vaartus > max_value:
max_value = vaartus
kaal = 0
vaartus = 0
return max_value

import copy
from itertools import product
from copy import deepcopy
def knapsack(weight_cap, weights, values):
# Write your code here
weight_value_dict = {}
for w, v in zip(weights, values):
if weight_value_dict.get(w, 0): # in dict
weight_value_dict[w].append(v)
else: # not in dict
weight_value_dict[w] = [v]
weight_combination = []
weight = sorted(weights, reverse=True)
p = product([0, 1], repeat=len(weight)) # 0:no_carry, 1:carry
for _ in p:
cap = weight_cap
for i, w in zip(_, weight):
if i:
cap -= w
if cap < 0:
break
else:
weight_combination.append(_)
max_value = 0
for group in weight_combination:
sum = 0
for i, w in zip(group, weight):
wvd = copy.deepcopy(weight_value_dict)
if i: # same weight differ value
b = wvd[w]
b.sort()
sum += b.pop()
max_value = max(max_value, sum)
return max_value
weight_cap = 10
weights = [3, 6, 8]
values = [50, 60, 100]
print(knapsack(weight_cap, weights, values))

Explanation at the bottom of the code. Nice challenge!

from itertools import combinations
def knapsack(weight_cap, weights, values):
dic = dict(zip(weights, values))
output = sum([list(map(list, combinations(weights, i))) for i in range(len(weights) + 1)], [])
max = 0
for o in output:
if sum(o) <= weight_cap:
comb_tot = 0
for i in range(len(o)):
comb_tot += dic.get(o[i])
if comb_tot > max:
max = comb_tot
return max
weight_cap = 10
weights = [3, 6, 8]
values = [50, 60, 100]
print(knapsack(weight_cap, weights, values))
# Create a dictionary with which value each weight has
# Create a list of all possible combinations of the weights
# Check for each possible combination that is not heavier than the capacity,
# what tot value is has and find out the highest combination.

def knapsack(weight_cap, weights, values):
from itertools import chain, combinations
# Turn the weights and values into a dictionary.
weight_dict = {weights[i]: values[i] for i in range(len(weights))}
# All combinations of items.
possibilities = chain.from_iterable(combinations(weight_dict.keys(), i) for i in range(1,len(weights)+1))
# Filter to the combinations less than the weight cap.
possibilities = filter(lambda x:sum(x) <= weight_cap, possibilities)
# A function that returns the value of an item combination.
def get_value(some_items):
value = 0
for i in some_items:
value += weight_dict[i]
return value
# Return the maximum value.
return max(get_value(items) for items in possibilities)
weight_cap = 10
weights = [3, 6, 8]
values = [50, 60, 100]
print(knapsack(weight_cap, weights, values))

(1) Turn the weight-values into a dictionary.
(2) Find all possible combinations of items.
(3) Filter the combinations to only those less than the weight cap.
(4) Calculate the values of the plausible combinations and return the max.

def knapsack(weight_cap, weights, values):
# create key, value pair for weights, values
dict_of_weights_values = dict(zip(weights, values))
# find combinations of weights <= weight_cap
item_combinations = []
for i in range(1, len(weights)):
item_combinations.extend([list(x) for x in combinations(weights,i) if sum(x) <= weight_cap])
# get value in each item combination
item_combinations_values = [0]
for i in item_combinations:
current_sum = 0
for j in i:
current_sum += dict_of_weights_values[j]
item_combinations_values.append(current_sum)
return max(item_combinations_values)

def powerset(s):
x = len(s)
powersetlist =
for i in range(1 << x):
powersetlist.append([s[j] for j in range(x) if (i & (1 << j))])
return powersetlist
def knapsack(weight_cap, weights, values):

Create new set of Total values from viable subsets

Totals =

Check each subset total.

for i in range(len(powersetweights)):
if sum(powersetweights[i]) <= weight_cap:
# Create list for matching values
valuelist =
# Run through weights from powerset and match to values in given set, then add to valuelist
for j in range(len(powersetweights[i])):
valuelist.append(values[weights.index(powersetweights[i][j])])
Totals.append(sum(valuelist))
return max(Totals)

def knapsack(weight_cap, weights, values):
# Create a table of zeros to store the optimal values
table = [[0 for x in range(weight_cap + 1)] for x in range(len(values) + 1)]
# Build the table
for i in range(1, len(values) + 1):
for w in range(0, weight_cap + 1):
if weights[i - 1] <= w:
table[i][w] = max(values[i - 1] + table[i - 1][w - weights[i - 1]], table[i - 1][w])
else:
table[i][w] = table[i - 1][w]
# Return the last entry in the table
return table[len(values)][weight_cap]
weight_cap = 10
weights = [3, 6, 8]
values = [50, 60, 100]
print(knapsack(weight_cap, weights, values))

I used an inefficient, but simplistic algorithm:
Iterate through all the combinations, and if the sum of the weights for that combination is at or below weight_cap, then include the sum of the values (from that combination) when calculating the maximum value.
It’s O(2^n).

I made a generator function to get an iterator corresponding to a combination denoted by an integer’s binary representation (in reverse).
And a function that gets the sums of a list (or iterable) of pairs.

def get_iterator_by_binary(x, arr):
# x is an integer denoting a combination from list arr
i = 0
b = 1
while (b <= x):
if ((b & x) == b):
yield arr[i];
b = b << 1
i += 1
def sums(pairs_list):
sum1 = 0
sum2 = 0
for a, b in pairs_list:
sum1 += a
sum2 += b
return (sum1, sum2)
def knapsack(weight_cap, weights, values):
zipped = list(zip(weights, values))
length = len(zipped)
max_so_far = 0
index_of_max = 0
for i in range(1, 2 ** length):
weight, value = sums(get_iterator_by_binary(i, zipped))
if (weight <= weight_cap) and (value > max_so_far):
max_so_far = value
index_of_max = i
return max_so_far
weight_cap = 10
weights = [3, 6, 8]
values = [50, 60, 100]
print(knapsack(weight_cap, weights, values)) # 110