From 96e59ae0ffbe8c61baceddf733b5682a6726bf70 Mon Sep 17 00:00:00 2001
From: Andrew <andreasohaodha@proton.me>
Date: Fri, 14 Feb 2025 20:51:19 +0000
Subject: [PATCH] [CT421]: Assignment 1 initialisation code

---
 .../assignments/assignment1/code/salesman.py  | 72 +++++++++++++++++++
 1 file changed, 72 insertions(+)
 create mode 100755 year4/semester2/CT421/assignments/assignment1/code/salesman.py

diff --git a/year4/semester2/CT421/assignments/assignment1/code/salesman.py b/year4/semester2/CT421/assignments/assignment1/code/salesman.py
new file mode 100755
index 00000000..253ba031
--- /dev/null
+++ b/year4/semester2/CT421/assignments/assignment1/code/salesman.py
@@ -0,0 +1,72 @@
+#!/usr/bin/python3
+
+import argparse
+import re
+import math
+
+# function to read a TSP file in and construct a dictionary of its attributes
+def graph_from_file(input_file):
+    graph = {}
+
+    print("Reading cities data from: " + input_file)
+
+    with open(input_file, "r") as file:
+        lines=file.readlines()
+
+    # assuming that the data will be available on specific line numbers and that fields must be included, which works for the provided datasets.
+    # i would implement this in a better way if python didn't have such a terrible regex engine compared to perl (further evidence of why perl is the best and python sucks)
+    graph["name"] = lines[0].split()[1]
+    graph["type"] = lines[1].split()[1]
+    graph["comment"] = lines[2][lines[2].find(" ") + 1:]
+    graph["dimension"] = lines[3].split()[1]
+    graph["edge_weight_type"] = lines[4].split()[1]
+
+    cities = []
+    for line_number in range(6, len(lines)):
+        if lines[line_number] == "EOF\n":
+            break
+
+        fields = lines[line_number].split()
+        cities.append({"name": int(fields[0]), "x": float(fields[1]), "y": float(fields[2]) })
+
+    graph["cities"] = cities
+
+    return graph
+
+# function to generate an adjacency matrix from a graph
+def adjacency_matrix_from_graph(graph):
+    matrix = []
+    cities = sorted(graph["cities"], key=lambda city: city["name"]) # sorting list of cities in case not sorted, assuming each city's name is an int
+
+    # iterating over sorted list of cities
+    for city in cities:
+        distances = []
+
+        for other_city in cities:
+            distances.append(dist(city, other_city))
+
+        matrix.append(distances)
+
+    return matrix
+
+# function to calculate the euclidean distance between two cities
+def dist(city1, city2):
+    return math.sqrt( (city2["x"] - city1["x"] )**2 + ( city2["y"] - city1["y"] )**2 )
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Program to solve the travelling salesman problem for a given TSP file using a genetic algorithm.")
+    parser.add_argument("-i", "--input-file", type=str, help="Path to input file in TSP format", required=True)
+    args=parser.parse_args()
+
+    input_file=args.input_file
+
+    graph = graph_from_file(input_file)
+    adjacency_matrix = adjacency_matrix_from_graph(graph)
+
+    for row in adjacency_matrix:
+        print(row)
+
+
+if __name__ == "__main__":
+    main()