From fd8d08c17d84193d2bd195f0914e223b0565f87b Mon Sep 17 00:00:00 2001
From: Michael Mutote <130656746+mr1Michael@users.noreply.github.com>
Date: Sun, 12 Nov 2023 14:14:52 +0100
Subject: [PATCH] 22202956 ex 4.9 q 4 had to be made into backtracking
---
4_9_Exercises/Question 4.py | 50 ++++++++++++++++++++++-----
Search_Algorithms/solution testing.py | 19 +++++-----
2 files changed, 51 insertions(+), 18 deletions(-)
diff --git a/4_9_Exercises/Question 4.py b/4_9_Exercises/Question 4.py
index 447be83..119f617 100644
--- a/4_9_Exercises/Question 4.py
+++ b/4_9_Exercises/Question 4.py
@@ -1,4 +1,28 @@
-def np(x, y, trace=None):
+import time
+
+
+def np(x, y):
+ trace = [[(0, 0)]]
+ final = []
+ while trace:
+ new_pos = []
+ current = trace.pop(-1)
+ last_position = current[-1]
+ if last_position == (x, y):
+ final.append(current)
+ else:
+ new_pos.append((last_position[0] + 1, last_position[1]))
+ new_pos.append((last_position[0], last_position[1] + 1))
+ new_pos.append((last_position[0] - 1, last_position[1]))
+ new_pos.append((last_position[0], last_position[1] - 1))
+ for pos in new_pos:
+ if (pos not in current) and ((0 <= pos[0] <= x) and (0 <= pos[1] <= y)):
+ trace.append(current + [pos])
+ return final
+
+
+def np2(x, y, trace=None):
+ # Old version was done by recursion and is functional
if trace is None:
trace = [(0, 0)]
current = trace[-1]
@@ -7,13 +31,9 @@ def np(x, y, trace=None):
if current[0] > x or current[1] > y or current[0] < 0 or current[1] < 0:
return 0
if (current[0], current[1]) == (x, y) and len(trace) > 1:
- # print(current[0], x, current[1], y, " this is in success")
- # print(trace)
return 1
if x + y < 2:
return 1
- # if (x, y) in new_cache:
- # return new_cache[(x, y)]
x_1, y_1 = (current[0], current[1] + 1) if current[1] < y else (current[0], current[1])
x_2, y_2 = (current[0] + 1, current[1]) if current[0] < x else (current[0], current[1])
x_3, y_3 = current[0] - 1, current[1]
@@ -24,7 +44,19 @@ def np(x, y, trace=None):
trace_3 = trace + [(x_3, y_3)]
trace_4 = trace + [(x_4, y_4)]
- return np(x, y, trace_1) + \
- np(x, y, trace_2) + \
- np(x, y, trace_3) + \
- np(x, y, trace_4)
+ return np2(x, y, trace_1) + \
+ np2(x, y, trace_2) + \
+ np2(x, y, trace_3) + \
+ np2(x, y, trace_4)
+
+
+
+# for fun, I timed the two to see which is faster. both almost the same
+# start_time = time.time()
+# np2(3, 7)
+# end_time = time.time()
+# print(end_time - start_time)
+# start_time = time.time()
+# np(3, 7)
+# end_time = time.time()
+# print(end_time - start_time)
diff --git a/Search_Algorithms/solution testing.py b/Search_Algorithms/solution testing.py
index 6a537e2..c13ba3f 100644
--- a/Search_Algorithms/solution testing.py
+++ b/Search_Algorithms/solution testing.py
@@ -30,15 +30,16 @@ for rows in sln:
print(rows)
# N Queens
-board = ((False, False, False, False, False, False, False, False, False),
- (False, False, False, False, False, False, False, False, False),
- (False, False, False, False, False, False, False, False, False),
- (False, False, False, False, False, False, False, False, False),
- (False, False, False, False, False, False, False, False, False),
- (False, False, False, False, False, False, False, False, False),
- (False, False, False, False, False, False, False, False, False),
- (False, False, False, False, False, False, False, False, False),
- (False, False, False, False, False, False, False, False, False))
+board = ((False, False, False, False, False, False, False, False, False, False),
+ (False, False, False, False, False, False, False, False, False, False),
+ (False, False, False, False, False, False, False, False, False, False),
+ (False, False, False, False, False, False, False, False, False, False),
+ (False, False, False, False, False, False, False, False, False, False),
+ (False, False, False, False, False, False, False, False, False, False),
+ (False, False, False, False, False, False, False, False, False, False),
+ (False, False, False, False, False, False, False, False, False, False),
+ (False, False, False, False, False, False, False, False, False, False),
+ (False, False, False, False, False, False, False, False, False, False))
sln = (Search_Algorithms.BreadthFirstSearch(board, Sucessors.queens_successor, is_goal.is_goal_queens)[-1])
--
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