def clustering_1(input_file: str):
lines = BasicFuncs.load_file_as_string(input_file).splitlines()
num_of_nodes = int(lines[0])
edges = []
for line in lines[1:]:
start, finish, cost = map(int, line.split(' '))
edge = Edge(start, finish, cost)
edges.append(edge)
edges.sort(key=lambda edge: edge.cost)
clusters = num_of_nodes
union_find = UnionFind(num_of_nodes)
for i, edge in enumerate(edges):
a = edge.start - 1
b = edge.end - 1
if union_find.join_two_subsets(a, b):
clusters -= 1
if clusters <= 4:
break
# Compute the smallest maximum spacing
min_max_spacing = float('inf')
for edge in edges[i + 1:]:
a = edge.start - 1
b = edge.end - 1
if not union_find.are_two_indicies_part_of_same_set(a, b):
min_max_spacing = min(min_max_spacing, edge.cost)
return min_max_spacing
def kruskals_algorithm(edges_file: str):
s = BasicFuncs.load_file_as_string(edges_file)
lines = s.splitlines()
first_line = lines[0]
num_of_nodes, num_of_edges = map(int, first_line.split(' '))
union_find = UnionFind(num_of_nodes)
edges = []
for line in lines[1:]:
start, end, cost = map(int, line.split(' '))
edge = Edge(start, end, cost)
edges.append(edge)
edges.sort(key=lambda x: x.cost)
min_span_tree_cost = 0
for edge in edges:
a = edge.start - 1
b = edge.end - 1
if union_find.join_two_subsets(a, b):
min_span_tree_cost += edge.cost
return min_span_tree_cost
