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Reduce the complexity of graphs/minimum_spanning_tree_prims.py #7952

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Merged
merged 8 commits into from
Nov 2, 2022
Merged

Reduce the complexity of graphs/minimum_spanning_tree_prims.py #7952

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8d65e97
Lower the --max-complexity threshold in the file .flake8
MaximSmolskiy Nov 2, 2022
87e7b54
Add test
MaximSmolskiy Nov 2, 2022
c6335fd
Reduce the complexity of graphs/minimum_spanning_tree_prims.py
MaximSmolskiy Nov 2, 2022
2b6f636
Remove backslashes
MaximSmolskiy Nov 2, 2022
3443b3e
Remove # noqa: E741
MaximSmolskiy Nov 2, 2022
9e45635
Fix the flake8 E741 issues
MaximSmolskiy Nov 2, 2022
efb58f5
Refactor
MaximSmolskiy Nov 2, 2022
4e066d9
Fix
MaximSmolskiy Nov 2, 2022
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40 changes: 21 additions & 19 deletions graphs/minimum_spanning_tree_prims.py
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Original file line number Diff line number Diff line change
Expand Up @@ -71,28 +71,30 @@ def delete_minimum(self, heap, positions):
return temp


def prisms_algorithm(l):
def prisms_algorithm(adjacency_list):
"""
>>> l = {0: [[1, 1], [3, 3]],
... 1: [[0, 1], [2, 6], [3, 5], [4, 1]],
... 2: [[1, 6], [4, 5], [5, 2]],
... 3: [[0, 3], [1, 5], [4, 1]],
... 4: [[1, 1], [2, 5], [3, 1], [5, 4]],
... 5: [[2, 2], [4, 4]]}
>>> prisms_algorithm(l)
>>> adjacency_list = {0: [[1, 1], [3, 3]],
... 1: [[0, 1], [2, 6], [3, 5], [4, 1]],
... 2: [[1, 6], [4, 5], [5, 2]],
... 3: [[0, 3], [1, 5], [4, 1]],
... 4: [[1, 1], [2, 5], [3, 1], [5, 4]],
... 5: [[2, 2], [4, 4]]}
>>> prisms_algorithm(adjacency_list)
[(0, 1), (1, 4), (4, 3), (4, 5), (5, 2)]
"""

heap = Heap()

visited = [0 for i in range(len(l))]
nbr_tv = [-1 for i in range(len(l))] # Neighboring Tree Vertex of selected vertex
visited = [0 for i in range(len(adjacency_list))]
nbr_tv = [
-1 for i in range(len(adjacency_list))
] # Neighboring Tree Vertex of selected vertex
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Suggested change
visited = [0 for i in range(len(adjacency_list))]
nbr_tv = [
-1 for i in range(len(adjacency_list))
] # Neighboring Tree Vertex of selected vertex
visited = [0] * len(adjacency_list)
nbr_tv = [-1] * len(adjacency_list) # Neighboring Tree Vertex of selected vertex

# Minimum Distance of explored vertex with neighboring vertex of partial tree
# formed in graph
distance_tv = [] # Heap of Distance of vertices from their neighboring vertex
positions = []

for x in range(len(l)):
for x in range(len(adjacency_list)):
p = sys.maxsize
distance_tv.append(p)
positions.append(x)
Expand All @@ -101,17 +103,17 @@ def prisms_algorithm(l):
tree_edges = []
visited[0] = 1
distance_tv[0] = sys.maxsize
for x in l[0]:
for x in adjacency_list[0]:
nbr_tv[x[0]] = 0
distance_tv[x[0]] = x[1]
heap.heapify(distance_tv, positions)

for _ in range(1, len(l)):
for _ in range(1, len(adjacency_list)):
vertex = heap.delete_minimum(distance_tv, positions)
if visited[vertex] == 0:
tree_edges.append((nbr_tv[vertex], vertex))
visited[vertex] = 1
for v in l[vertex]:
for v in adjacency_list[vertex]:
if visited[v[0]] == 0 and v[1] < distance_tv[heap.get_position(v[0])]:
distance_tv[heap.get_position(v[0])] = v[1]
heap.bottom_to_top(
Expand All @@ -125,9 +127,9 @@ def prisms_algorithm(l):
# < --------- Prims Algorithm --------- >
n = int(input("Enter number of vertices: ").strip())
e = int(input("Enter number of edges: ").strip())
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Do we gather this data and then not use it?

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Yes, n wasn't used, but e is used

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Suggested change
n = int(input("Enter number of vertices: ").strip())
e = int(input("Enter number of edges: ").strip())
n = int(input("Enter number of vertices: ").strip())
number_of_edges = int(input("Enter number of edges: ").strip())

adjlist = defaultdict(list)
adjacency_list = defaultdict(list)
for x in range(e):
l = [int(x) for x in input().strip().split()]
adjlist[l[0]].append([l[1], l[2]])
adjlist[l[1]].append([l[0], l[2]])
print(prisms_algorithm(adjlist))
edge = [int(x) for x in input().strip().split()]
adjacency_list[edge[0]].append([edge[1], edge[2]])
adjacency_list[edge[1]].append([edge[0], edge[2]])
print(prisms_algorithm(adjacency_list))