From cdf19027c3ccdded7fa19a7a1f6286c370ae36e4 Mon Sep 17 00:00:00 2001
From: Cory Balaton <cory@balaton.dev>
Date: Sun, 29 Sep 2024 10:44:31 +0200
Subject: [PATCH] First draft of exhaustive search.

---
 exhaustive_search.py | 79 ++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 79 insertions(+)
 create mode 100644 exhaustive_search.py

diff --git a/exhaustive_search.py b/exhaustive_search.py
new file mode 100644
index 0000000..43cb2e1
--- /dev/null
+++ b/exhaustive_search.py
@@ -0,0 +1,79 @@
+import time
+from itertools import permutations
+from typing import Callable, Tuple
+
+import numpy.typing as npt
+
+from common import plot_plan, read_data
+
+
+def exhaustive_search(distances: npt.NDArray) -> Tuple[float, Tuple]:
+    """An implementation of exhaustive search.
+
+        This implementation takes a permutation iterator, then maps each
+        element to a tuple containing the travel distance of that permutation
+        and the permutation tuple itself. Finally the function returns the
+        tuple that contains the smallest travel distance.
+
+    Args:
+        distances (npt.NDArray): An array containing distances to travel.
+
+    Returns:
+        A tuple containing the shortest travel distance and its corresponding
+        permutation.
+    """
+
+    size = len(distances)
+
+    return min(  # Find the smallest travel distance from the array
+        map(  # Map the permutation array to contain tuples (distance, permutation)
+            lambda perm: (
+                sum([distances[perm[i - 1], perm[i]] for i in range(size)]),
+                perm,
+            ),
+            permutations(range(size)),
+        ),
+    )
+
+
+if __name__ == "__main__":
+    cities, data = read_data("./european_cities.csv")
+
+    # A loop timing finding the optimal solution for different n
+    for n in range(6, 11):
+        # Time exhaustive search
+        t0 = time.time_ns()
+        distance, perm = exhaustive_search(data[:n, :n])
+        t1 = time.time_ns()
+
+        time_elapsed_ms = (t1 - t0) / 1_000_000.0
+
+        print(f"Exhaustive search for the {n} first cities:")
+        print(f"distance             : {distance:>12.6f}km")
+        print(f"time to find solution: {time_elapsed_ms:>12.6f}ms\n")
+
+
+""" Running example
+
+oblig1 on  main [?] via 🐍 v3.12.6 took 7s
+❯ python exhaustive_search.py
+Exhaustive search for the 6 first cities:
+distance             :  5018.810000km
+time to find solution:     1.105330ms
+
+Exhaustive search for the 7 first cities:
+distance             :  5487.890000km
+time to find solution:    10.089604ms
+
+Exhaustive search for the 8 first cities:
+distance             :  6667.490000km
+time to find solution:    78.810508ms
+
+Exhaustive search for the 9 first cities:
+distance             :  6678.550000km
+time to find solution:   765.676230ms
+
+Exhaustive search for the 10 first cities:
+distance             :  7486.310000km
+time to find solution:  8281.795515ms
+"""