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💬 DFS vs BFS на Python

Если хотите разобраться, как алгоритмы поиска в глубину и поиска в ширину работают на практике, то вот минимальный пример, который покажет разницу.

graph = {
'A': ['B', 'C'],
'B': ['D', 'E'],
'C': ['F'],
'D': [], 'E': [], 'F': []
}

def dfs(node, visited=set()):
if node in visited: return
print(node)
visited.add(node)
for neighbor in graph[node]:
dfs(neighbor, visited)

def bfs(start):
queue = [start]
visited = set()
while queue:
node = queue.pop(0)
if node in visited: continue
print(node)
visited.add(node)
queue += graph[node]


➡️ Что делает

— dfs проходит глубоко: A → B → D → E → C → F
— bfs — по уровням: A → B → C → D → E → F
— Обе функции показывают порядок обхода графа

🔵 Чтобы знать об алгоритмах все, забирайте наш курс «Алгоритмы и структуры данных»

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💬 DFS vs BFS на Python

Если хотите разобраться, как алгоритмы поиска в глубину и поиска в ширину работают на практике, то вот минимальный пример, который покажет разницу.

graph = {
'A': ['B', 'C'],
'B': ['D', 'E'],
'C': ['F'],
'D': [], 'E': [], 'F': []
}

def dfs(node, visited=set()):
if node in visited: return
print(node)
visited.add(node)
for neighbor in graph[node]:
dfs(neighbor, visited)

def bfs(start):
queue = [start]
visited = set()
while queue:
node = queue.pop(0)
if node in visited: continue
print(node)
visited.add(node)
queue += graph[node]


➡️ Что делает

— dfs проходит глубоко: A → B → D → E → C → F
— bfs — по уровням: A → B → C → D → E → F
— Обе функции показывают порядок обхода графа

🔵 Чтобы знать об алгоритмах все, забирайте наш курс «Алгоритмы и структуры данных»

Proglib Academy #буст

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