Comments (5)
I am not really sure why, but while trying to run this function it considered change_value, which was 0, bigger than 0. Is there some problem (maybe with the type float) that might do that?
def coins_for_change(money_to_change):
change_value = money_to_change
dict_change = dict()
while change_value - 0.01 >= 0:
if change_value >= 2:
if "2€" not in dict_change:
dict_change["2€"] = 1
change_value -= 2
else:
dict_change["2€"] += 1
change_value -= 2
elif change_value >= 1:
if "1€" not in dict_change:
dict_change["1€"] = 1
change_value -= 1
else:
dict_change["1€"] += 1
change_value -= 1
elif change_value >= 0.5:
if "0,50€" not in dict_change:
dict_change["0,50€"] = 1
change_value -= 0.5
else:
dict_change["0,50€"] += 1
change_value -= 0.5
elif change_value >= 0.2:
if "0,20€" not in dict_change:
dict_change["0,20€"] = 1
change_value -= 0.2
else:
dict_change["0,20€"] += 1
change_value -= 0.2
elif change_value >= 0.1:
if "0,10€" not in dict_change:
dict_change["0,10€"] = 1
change_value -= 0.1
else:
dict_change["0,10€"] += 1
change_value -= 0.1
elif change_value >= 0.05:
if "0,05€" not in dict_change:
dict_change["0,05€"] = 1
change_value -= 0.05
else:
dict_change["0,05€"] += 1
change_value -= 0.05
elif change_value >= 0.02:
if "0,02€" not in dict_change:
dict_change["0,02€"] = 1
change_value -= 0.02
else:
dict_change["0,02€"] += 1
change_value -= 0.02
else:
if "0,01€" not in dict_change:
dict_change["0,01€"] = 1
change_value -= 0.01
else:
dict_change["0,01€"] += 1
change_value -= 0.01
result = set(dict_change.items())
return result
def test(money_to_change, expected_set):
result= coins_for_change(money_to_change).difference(expected_set)
print(result)
return result == set()
print(test(1.36, {('0,10€', 1), ('0,01€', 1), ('0,05€', 1), ('0,20€', 1), ('1€', 1)}))
print(test(2.63, {('2€', 1), ('0,50€', 1), ('0,10€', 1), ('0,02€', 1), ("0,01", 1)}))
print(test(3.72, {('0,50€', 1), ('0,02€', 1), ('0,20€', 1), ('1€', 1), ('2€', 1)}))
from 2020-2021.
def test_coin_change(coins_list, total, given, expected):
t_result = coin_change(coins_list, total, given)
if t_result== expected:
return True
else:
return False
def coin_change(coins_list, total, payed):
coins_d = dict()
change = payed - total
secure = change + 0.001
tot_amount = 0
if total >= payed:
return None
for coin in coins_list:
amount = int(secure // coin)
if amount > 0 and change > 0.001:
coins_d[coin] = int(amount)
tot_amount += int(amount)
change -= (int(amount) * coin)
secure -= (int(amount) * coin)
return tot_amount
eu_coins = [2.0, 1.0, 0.50, 0.20, 0.10, 0.05, 0.02, 0.01]
print(test_coin_change(eu_coins, 20, 12.95, None))
print(test_coin_change(eu_coins, 20, 20, None))
print(test_coin_change(eu_coins, 15.36, 20, 6))
print(test_coin_change(eu_coins, 15, 20, 3))
print(test_coin_change(eu_coins, 15.91, 20, 5))
print(test_coin_change(eu_coins, 15.9, 20, 3))
print(test_coin_change(eu_coins, 15.99, 20, 3))
print(test_coin_change(eu_coins, 15.95, 20, 3))
print(test_coin_change(eu_coins, 12.95, 20, 5))
from 2020-2021.
I know the instructions were to only return the amount of the change, but I thought it would be useful to see if the function actually worked in real-life situations. I opted for returning a tuple with the list of coins to give back as change and the value of the change in euros.
def test_2_parameter(function, p1, p2, expected):
result = function(p1, p2)
return result == expected
def greedy_coin_change(amount, paid):
coin_types = [2, 1, 0.5, 0.2, 0.1, 0.02, 0.01] # this list contains all coin types decrementally ordered
coins = [] # this list will contain the result, with all coins used as change
if paid == amount:
return None
else:
for coin in coin_types: # loops through coins
if amount + coin <= paid:
while amount + coin <= paid: # continue adding change until the paid sum is reached
amount += coin
coins.append(coin) # add each coin to the result list
return coins, sum(coins)
print(test_2_parameter(greedy_coin_change, 13, 20, ([2,2,2,1], 7))) # returns True
print(test_2_parameter(greedy_coin_change, 16.73, 20, ([2, 1, 0.2, 0.02, 0.02, 0.02, 0.01], 3.27))) # returns True
print(test_2_parameter(greedy_coin_change, 20, 20, None)) # returns True
from 2020-2021.
Since it is supposed to be a greedy algorithm I tried not only to return a collection of coins needed to reach a specific change, but also the "best" set of coins avoiding as much as possible small coins. This code apparently works, but for reasons I cannot understand sometimes it skips an if statement (I have checked that on python tutor), thus returning a different list from the one expected. Can someone explain to me why?
def test_greedy_coins(amount, price, expected):
if greedy_coins(amount, price) == expected:
return True
else:
return False
def greedy_coins(amount, price):
change = abs(price - amount) #calculate the change value
result = list() #create the list of coins to return
while change != 0: #iterate until there is no charge left
if (change - 2) >= 0:
result.append("2€")
change = change - 2
else:
if (change - 1) >= 0:
result.append("1€")
change = change - 1
else:
if (change - 0.50) >= 0:
result.append("0,50€")
change = change - 0.50
else:
if (change - 0.20) >= 0:
result.append("0,20€")
change = change - 0.20
else:
if (change - 0.10) >= 0:
result.append("0,10€")
change = change - 0.10
else:
if (change - 0.05) >= 0:
result.append("0,05€")
change = change - 0.05
else:
if (change - 0.02) >= 0:
result.append("0,02€")
change = change - 0.02
else:
if (change - 0.01) >= 0:
result.append("0,01€")
change = change - 0.01
else:
result.append("0,01€")
change = 0
return result
print(test_greedy_coins(20,16,["2€", "2€"]))
print(test_greedy_coins(20,15.50,["2€", "2€", "0,50€"]))
print(test_greedy_coins(20,13.64,["2€", "2€", "2€", "1€", "0,20€", "0,10€", "0,05€", "0,01€"]))
print(test_greedy_coins(20,13.38,['2€', '2€', '2€', '1€', '0,50€', '0,10€', '0,02€'])) # returns false because the output is ['2€', '2€', '2€', '1€', '0,50€', '0,10€', '0,01€', '0,01€']
from 2020-2021.
Not as precise as @essepuntato's solution for sure but I think this reflects at least the greedy algorithm logic:
def test_money_change(amount_to_change, coins_list, expected):
if money_change(amount_to_change, coins_list) == expected:
return True
else:
return False
def money_change(amount_to_change, coins_list):
change = []
for coin in coins_list:
still_to_add = amount_to_change
maximal = max(coins_list)
change.append(maximal) #being greedy: always append the max value
coins_list.remove(maximal)
still_to_add -= sum(change)
if still_to_add > 0:
if still_to_add in coins_list: #if I have the exact other value I go with that
idx = coins_list.index(still_to_add)
change.append(coins_list[idx])
still_to_add -= coins_list[idx]
if still_to_add == 0:
return change
elif still_to_add < 0:
change.clear()
still_to_add = 0
elif still_to_add == 0:
return change
return []
print(test_money_change(0.5, [2, 2, 0.5, 0.5, 0.2, 0.5, 1], [0.5])) #True
print(test_money_change(5, [2, 2, 0.5, 0.5, 0.2, 0.5, 1], [2, 2, 1])) #True
print(test_money_change(3, [2, 2, 0.5, 0.5, 0.2, 0.5, 1], [2, 1])) #True
`
from 2020-2021.
Related Issues (20)
- Lecture "Brute-force algorithms", exercise 5 HOT 15
- Lecture "Organising information: unordered structures", exercise 1 HOT 21
- Lecture "Organising information: unordered structures", exercise 2 HOT 19
- Lecture "Organising information: unordered structures", exercise 3 HOT 20
- Lecture "Recursion", exercise 1 HOT 19
- Lecture "Recursion", exercise 2 HOT 20
- Lecture "Divide and conquer algorithms", exercise 1 HOT 19
- Lecture "Divide and conquer algorithms", exercise 2 HOT 17
- Lecture "Divide and conquer algorithms", exercise 3 HOT 5
- Lecture "Dynamic programming algorithms", exercise 1 HOT 21
- Lecture "Dynamic programming algorithms", exercise 2 HOT 19
- Lecture "Organising information: trees", exercise 1 HOT 9
- Lecture "Organising information: trees", exercise 2 HOT 7
- Bonus exercise (AtariGo) HOT 2
- Lecture "Backtracking algorithms", exercise 1 HOT 6
- Lecture "Backtracking algorithms", exercise 2 HOT 5
- Lecture "Organising information: graphs", exercise 1 HOT 15
- Lecture "Organising information: graphs", exercise 2 HOT 14
- Lecture "Greedy algorithms", exercise 2 HOT 1
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from 2020-2021.