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I am trying to write a function has input:
How to return a result of all possible combinations as list with n elements and sum = given_num nested in a list:
[[0,0,6],[0,1,5],[0,2,4],[0,3,3],[0,4,2],[0,5,1],[0,6,0], [1,0,5],[1,1,4],[1,2,3],[1,3,2],[1, 4,1],[1,5,0], ... [6,0,0]]
solved, and the possible combination is calculated as
factorial(given_num+n-1)/(factorial(given_num) * factorial(n-1))
Here a try using
itertools product :
from itertools import product given_num = 6 n = 3 all_comb = product(range(given_num+1), repeat=n) final_lst = [i for i in all_comb if sum(i) == given_num] print(final_lst)
Find all combinations of a set of lists with itertools.product, The same effect can be achieved in Python by combining map() and count() to So, if the input iterable is sorted, the combination tuples will be produced in Elite Programming Professionals Ready to Teach Online. Anytime, Anywhere.
from itertools import product import numpy as np tuples =  given_num = 6 n = 3 for numbers in product(np.arange(given_num+1), repeat=n): if sum(numbers) == given_num: tuples.append(numbers) print(tuples)
9.7. itertools, This is a function which generates random pairs using itertools.combinations  answers we randomly shuffle these pairs after getting the exhaustive list o. Fast, Free 2-Day Shipping on Orders Over $40. Expert Gear Advice From Riders.
In case you want to have some fun with recursive functions
def summands(num, N): if N==1: return [[num]] else: return [[i] + j for i in range(num+1) for j in summands(num - i, N-1)]
How to create random, nonrepetitive pairs from a list in Python, This exhaustive feature selection algorithm is a wrapper approach for will evaluate all 15 feature combinations (if min_features=1 and max_features=4 ). Exhaustive combinations of lists in python. Ask Question Asked 6 years, Is this possible using the itertools library or sets or a combination of the above?
Exhaustive Feature Selector, The method we are going to use is called exhaustive grid search; it is a way to try all possible combinations of the different values of the hyperparameters that we Python has a cool technique to make this easier, using lru_cache from the functools package. In a nutshell, all you need is a function with hashable arguments, and then you can simply annotate it with @lru_cache to benefit from easy memoization.
Hands-On Predictive Analytics with Python: Master the complete , The main drawback of the grid search is the exhaustive search it conducts over all the possible combinations, which can prove very lengthy. One common way to Python provide direct methods to find permutations and combinations of a sequence. These methods are present in itertools package. Permutation First import itertools package to implement permutations method in python. This method takes a list as an input and return an object list of tuples that contain all permutation in a list form.
Hands-On Genetic Algorithms with Python: Applying genetic , Experiment 1: Quality of Solutions For this first experiment, an exhaustive Python and executed on an instance in order to obtain the optimal Pareto set for that if a certain combination is valid, tests all valid Starting Points combinations for Second, adding parentheses where they're not needed makes the code harder to read. The parentheses around your print expression makes your code look like Python 3, but it's actually Python 2. And the parentheses around each string inside the expression are even worse—at first glance, it looks like those are supposed to be inside the quotes.
- did you try anything? What was the problem with it?
- Possible duplicate of Generate all possible outcomes of k balls in n bins (sum of multinomial / categorical outcomes)
- @Georgy , thx for the link, the generator solution is time saving for a large given_num or n, considering I am working on a optimization problem.