updated

Author: metantonio

README.md

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+# Install project
+
+```sh
+poetry install
+```
+
+# Run Algorithm
+
+```sh
+poetry run python ./social.py
+```

__init__.py

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+[tool.poetry]
+name = "dijkstra"
+version = "0.1.0"
+description = ""
+authors = ["Your Name <you@example.com>"]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = "^3.11"
+networkx = "^3.2.1"
+matplotlib = "^3.8.2"
+
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

poetry.lock

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+import heapq
+import networkx as nx
+import matplotlib.pyplot as plt
+
+def dijkstra(graph, start):
+    distances = {node: float('infinity') for node in graph}
+    distances[start] = 0
+    shortest_paths = {}
+
+    priority_queue = [(0, start)]
+
+    while priority_queue:
+        current_distance, current_node = heapq.heappop(priority_queue)
+
+        if current_distance > distances[current_node]:
+            continue
+
+        for neighbor, weight in graph[current_node].items():
+            distance = current_distance + weight
+            if distance < distances[neighbor]:
+                distances[neighbor] = distance
+                shortest_paths[neighbor] = current_node
+                heapq.heappush(priority_queue, (distance, neighbor))
+
+    return distances, shortest_paths
+
+def draw_graph(graph, shortest_paths, start_node, end_node):
+    G = nx.DiGraph()
+
+    for node, edges in graph.items():
+        for edge, weight in edges.items():
+            G.add_edge(node, edge, weight=weight)
+
+    pos = nx.spring_layout(G)
+    nx.draw(G, pos, with_labels=True, node_size=5000, node_color='lightblue', font_size=12)
+
+    nx.draw_networkx_nodes(G, pos, nodelist=[start_node], node_color='green', node_size=7000)
+    nx.draw_networkx_nodes(G, pos, nodelist=[end_node], node_color='red', node_size=7000)
+
+    path = [end_node]
+    current_node = end_node
+    while current_node != start_node:
+        current_node = shortest_paths[current_node]
+        path.append(current_node)
+    path.reverse()
+
+    path_edges = [(path[i], path[i+1]) for i in range(len(path)-1)]
+    nx.draw_networkx_edges(G, pos, edgelist=path_edges, edge_color='red', width=3)
+
+    labels = nx.get_edge_attributes(G, 'weight')
+    nx.draw_networkx_edge_labels(G, pos, edge_labels=labels)
+
+    plt.title("Shortest Path from {} to {}".format(start_node, end_node))
+    plt.show()
+
+# Definimos el grafo que representa la red social
+graph = {
+    'Alice': {'Bob': 5, 'Charlie': 2},
+    'Bob': {'Alice': 5, 'David': 3},
+    'Charlie': {'Alice': 2, 'David': 7},
+    'David': {'Bob': 3, 'Charlie': 7}
+}
+
+# Especificamos los nodos de inicio y fin para encontrar la distancia más corta entre ellos
+start_node = 'Alice'
+end_node = 'David'
+
+# Aplicamos el algoritmo de Dijkstra para encontrar la distancia más corta y el camino más corto
+distances, shortest_paths = dijkstra(graph, start_node)
+
+# Imprimimos la distancia más corta entre los nodos especificados
+print("Distancia más corta entre {} y {}: {}".format(start_node, end_node, distances[end_node]))
+
+# Imprimimos el camino más corto entre los nodos especificados
+path = [end_node]
+current_node = end_node
+while current_node != start_node:
+    current_node = shortest_paths[current_node]
+    path.append(current_node)
+path.reverse()
+print("Camino más corto:", ' -> '.join(path))
+
+# Dibujamos el grafo y el camino más corto
+draw_graph(graph, shortest_paths, start_node, end_node)