↓ 2 callersFunctionbead_sort >>> bead_sort([6, 11, 12, 4, 1, 5]) [1, 4, 5, 6, 11, 12] >>> bead_sort([9, 8, 7, 6, 5, 4 ,3, 2, 1]) [1, 2, 3, 4, 5, 6, 7, 8, 9]
sorts/bead_sort.py:7
↓ 2 callersMethodbuild_max_heap build max heap from an unsorted array >>> h = Heap() >>> h.build_max_heap([20,40,50,20,10]) >>> h [50, 40, 2
data_structures/heap/heap.py:134
↓ 2 callersFunctioncolumn_based_sort >>> column_based_sort([(5, 1), (4, 2), (3, 0)], 1) [(3, 0), (5, 1), (4, 2)]
divide_and_conquer/closest_pair_of_points.py:31
↓ 2 callersFunctioncompute_truncated_primes
Returns the list of truncated primes
>>> compute_truncated_primes(11)
[23, 37, 53, 73, 313, 317, 373, 797, 3137, 3797, 739397]
project_euler/problem_037/sol1.py:94
↓ 2 callersFunctioncreate_vector Pass two points to get the vector from them in the form (x, y, z). >>> create_vector((0, 0, 0), (1, 1, 1)) (1, 1, 1) >>> create_vect
maths/points_are_collinear_3d.py:35
↓ 2 callersFunctioncycle_sort >>> cycle_sort([4, 3, 2, 1]) [1, 2, 3, 4] >>> cycle_sort([-4, 20, 0, -50, 100, -1]) [-50, -4, -1, 0, 20, 100] >>> cycle_sort([-
sorts/cycle_sort.py:7
↓ 2 callersFunctiondistance Calculate the distance between two coordinate points >>> distance([0, 0], [3, 4] ) 5.0 >>> distance([0, 0], [-3, 4] ) 5.0 >>>
graphs/ant_colony_optimization_algorithms.py:102
↓ 2 callersFunctionexpand_search(
graph: dict[int, list[int]],
queue: deque[int],
parents: dict[int, int | None],
opposite_dir
graphs/bidirectional_search.py:17
↓ 2 callersFunctionexpand_state(
s,
j,
visited,
g_function,
close_list_anchor,
close_list_inad,
open_list,
ba
graphs/multi_heuristic_astar.py:129
↓ 2 callersFunctionfactors >>> factors(12) [1, 2, 3, 4, 6] >>> factors(1) [1] >>> factors(100) [1, 2, 4, 5, 10, 20, 25, 50] # >>> factors(-12)
maths/special_numbers/weird_number.py:10
↓ 2 callersMethodget_greyscale >>> Burkes.get_greyscale(3, 4, 5) 4.185 >>> Burkes.get_greyscale(0, 0, 0) 0.0 >>> Burkes.get_greyscale(255, 2
digital_image_processing/dithering/burkes.py:40
↓ 2 callersMethodget_winner Compute the winning vector by Euclidean distance >>> SelfOrganizingMap().get_winner([[1, 2, 3], [4, 5, 6]], [1, 2, 3]) 1
machine_learning/self_organizing_map.py:9