Update build_data.py

build_data.py

@@ -1,33 +1,27 @@
 import os
 from datetime import datetime
-import sys
-
 import torch
 import numpy as np
 
-from torch.utils.data import Dataset
-
-# Ensure project directory is included
 
 class Raw_VRP_Data(object):
 
-    def __init__(self, dataset_size=1000, num_nodes=50, num_depots=1):
-
+    def __init__(self, dataset_size=1000, num_nodes=30, num_depots=1):
         self.dataset_size = dataset_size
         self.num_nodes = num_nodes
-        num_cars = num_nodes
         self.num_depots = num_depots
+        num_cars = num_nodes  # كل Node له Car افتراضي
 
-        # fleet data
+        # Fleet data
         launch_time = torch.zeros(self.dataset_size, num_cars, 1)
         car_start_node = torch.randint(low=0, high=num_depots, size=(self.dataset_size, num_cars, 1))
 
-        fleet = {
+        self.fleet = {
             'start_time': launch_time,
             'car_start_node': car_start_node,
         }
 
-        # graph data
+        # Graph data
         a = torch.arange(num_nodes).reshape(1, 1, -1).repeat(self.dataset_size, num_cars, 1)
         b = car_start_node.repeat(1, 1, num_nodes)
         depot = ((a == b).sum(dim=1) > 0).float().unsqueeze(2)
@@ -35,11 +29,11 @@ class Raw_VRP_Data(object):
         start_times = (torch.rand(self.dataset_size, num_nodes, 1) * 2 + 3) * (1 - depot)
         end_times = start_times + (0.1 + 0.5 * torch.rand(self.dataset_size, num_nodes, 1)) * (1 - depot)
 
-        node_positions = torch.rand(dataset_size, num_nodes, 2)
+        node_positions = torch.rand(self.dataset_size, num_nodes, 2)
         distance_matrix = self.compute_distance_matrix(node_positions)
         time_matrix = distance_matrix.clone()
 
-        graph = {
+        self.graph = {
             'start_times': start_times,
             'end_times': end_times,
             'depot': depot,
@@ -49,14 +43,14 @@ class Raw_VRP_Data(object):
         }
 
         self.data = {
-            'fleet': fleet,
-            'graph': graph
+            'fleet': self.fleet,
+            'graph': self.graph
         }
 
     def compute_distance_matrix(self, node_positions):
         x = node_positions.unsqueeze(1).repeat(1, self.num_nodes, 1, 1)
         y = node_positions.unsqueeze(2).repeat(1, 1, self.num_nodes, 1)
-        distance = (((x - y)**2).sum(dim=3))**0.5
+        distance = torch.sqrt(((x - y) ** 2).sum(dim=3))
         return distance
 
     def get_data(self):
@@ -67,18 +61,19 @@ class Raw_VRP_Data(object):
 
 
 if __name__ == '__main__':
-
-    size = 50000
+    # إعدادات من params.json
+    size = 5000
     num_nodes = 30
     num_depots = 1
 
+    save_path = os.path.join(os.getcwd(), 'VRP_data.pt')
+
+    print("Generating data...")
     start = datetime.now()
 
     raw_data = Raw_VRP_Data(dataset_size=size, num_nodes=num_nodes, num_depots=num_depots)
+    raw_data.save_data(save_path)
 
-    path = os.path.join(os.getcwd(), 'VRP_data.pt')
-    raw_data.save_data(path)
-
-    end = datetime.now()
-    duration = (end - start).seconds
-    print(f"Data generation completed in {duration} seconds.")
+    duration = (datetime.now() - start).seconds
+    print(f"✅ Data generation completed in {duration} seconds.")
+    print(f"📦 Saved to: {save_path}")