22202956

ex 4.9 q 4 had to be made into backtracking

4_9_Exercises/Question 4.py

-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)

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])