added count cluster feature

Author: Chinmay00000

src/__pycache__/lsra_baseline.cpython-312.pyc

@@ -1,10 +1,9 @@
 # @author: Chinmay Paranjape, Kushal Kaparatti, Prathamesh Chitnis
-# @version: v1.2.0
-# @date: 13-JUN-2024
+# @version: v1.3.0
+# @date: 15-JUN-2024
 
 import base64
 import io
-import os
 import random
 import time
 
@@ -19,7 +18,6 @@
 
 app = Flask(__name__)
 
-
 def plot_graph(graph):
     pos = nx.spring_layout(graph)
     plt.figure(figsize=(10, 7))
@@ -33,7 +31,6 @@ def plot_graph(graph):
     plt.close()
     return plot_url
 
-
 @app.route("/", methods=["GET", "POST"])
 def index():
     if request.method == "POST":
@@ -55,9 +52,7 @@ def index():
 
         # Optimized Algorithm
         graph, graph_gen_time = generate_connected_graph(num_nodes, 0.25)
-        graph, dense_connectivity_time = ensure_dense_connectivity(
-            graph, num_nodes, 0.25
-        )
+        graph, dense_connectivity_time = ensure_dense_connectivity(graph, num_nodes, 0.25)
         lsra_optimized = LSRA_Hierarchical(graph)
         cluster_connection_time = connect_clusters(graph, lsra_optimized.clusters, 0.25)
         optimized_plot = plot_graph(graph)
@@ -75,9 +70,9 @@ def index():
             optimized_plot=optimized_plot,
             optimized_shortest_paths=optimized_shortest_paths,
             optimized_time=optimized_time,
+            num_clusters=len(lsra_optimized.clusters)
         )
     return render_template("index.html")
 
-
 if __name__ == "__main__":
-    app.run(debug=True)
+    app.run(debug=True)

src/__pycache__/lsra_optimized.cpython-312.pyc

@@ -1,6 +1,6 @@
 # @author: Chinmay Paranjape, Kushal Kaparatti, Prathamesh Chitnis
-# @version: v1.2.0
-# @date: 13-JUN-2024
+# @version: v1.3.0
+# @date: 15-JUN-2024
 
 import random
 import time
@@ -18,22 +18,21 @@ def add_edge(self, node1, node2, weight):
 
     def shortest_path(self, source, target):
         # Initialize distances to infinity
-        distances = {node: float('inf') for node in self.graph.nodes()}
+        distances = {node: float("inf") for node in self.graph.nodes()}
         distances[source] = 0
 
-        # Introduce redundant loop to slow down the process
-        for _ in range(len(self.graph.nodes()) * 2):  # Redundant extra iterations
-            for _ in range(len(self.graph.edges())):  # Nested redundant loop
+        for _ in range(len(self.graph.nodes()) * 2):
+            for _ in range(len(self.graph.edges())):  # Nested loop
                 pass
 
-        # Relax edges repeatedly with additional unnecessary calculations
         for _ in range(len(self.graph.nodes()) - 1):
             for node1, node2 in self.graph.edges():
-                distances[node2] = min(distances[node2], distances[node1] + self.graph[node1][node2]['weight'])
-                # Additional redundant computations
+                distances[node2] = min(
+                    distances[node2],
+                    distances[node1] + self.graph[node1][node2]["weight"],
+                )
                 _ = sum(range(100))
 
-        # Additional redundant step to further slow down the process
         redundant_list = [random.randint(1, 100) for _ in range(1000)]
         redundant_sum = sum(redundant_list)
 
@@ -57,13 +56,13 @@ def shortest_path(self, source, target):
     # Visualization
     pos = nx.spring_layout(lsra_baseline.graph)
     nx.draw(lsra_baseline.graph, pos, with_labels=True, node_size=700)
-    edge_labels = nx.get_edge_attributes(lsra_baseline.graph, 'weight')
+    edge_labels = nx.get_edge_attributes(lsra_baseline.graph, "weight")
     nx.draw_networkx_edge_labels(lsra_baseline.graph, pos, edge_labels=edge_labels)
-    plt.title('Network Graph')
+    plt.title("Network Graph")
     plt.show()
 
     # Calculate shortest paths from source node '0'
-    source_node = '0'
+    source_node = "0"
     start_time = time.time()
     shortest_paths = lsra_baseline.shortest_path(source_node, str(num_nodes - 1))
     end_time = time.time()

src/app.py

@@ -1,9 +1,10 @@
 # @author: Chinmay Paranjape, Kushal Kaparatti, Prathamesh Chitnis
-# @version: v1.2.0
-# @date: 13-JUN-2024
+# @version: v1.3.0
+# @date: 15-JUN-2024
 
 import random
 import time
+from math import sqrt
 
 import matplotlib.pyplot as plt
 import networkx as nx
@@ -24,7 +25,8 @@ def add_edge(self, node1, node2, weight):
 
     def form_clusters(self):
         start_time = time.time()
-        num_clusters = 5  # Number of clusters
+        num_nodes = len(self.graph.nodes())
+        num_clusters = int(sqrt(num_nodes))  # Dynamic number of clusters based on the number of nodes
         nodes = list(self.graph.nodes())
         random.shuffle(nodes)
         cluster_size = len(nodes) // num_clusters
@@ -103,6 +105,9 @@ def find_gateway(self, node):
                 return gateway
         return None
 
+    def get_number_of_clusters(self):
+        return len(self.clusters)
+
 def generate_connected_graph(num_nodes, edge_probability):
     start_time = time.time()
     graph = nx.Graph()
@@ -201,4 +206,4 @@ def calculate_time_complexity(num_nodes, num_edges, graph_gen_time, cluster_time
             print(f"{step} - {value}")
         else:
             complexity, duration = value
-            print(f"{step} - Complexity: {complexity}, Time: {duration:.6f} seconds")
+            print(f"{step} - Complexity: {complexity}, Time: {duration:.6f} seconds")

src/lsra_baseline.py

@@ -26,6 +26,7 @@ <h2>Optimized Algorithm</h2>
     <h3>Shortest Paths</h3>
     <pre>{{ optimized_shortest_paths }}</pre>
     <p>Total time taken: {{ optimized_time }} seconds</p>
+    <p>Number of Clusters Formed:{{ num_clusters }}</p>
     {% endif %}
 </body>
 </html>