Backtracking approach

Project/backtracking.py

@@ -0,0 +1,97 @@
+from numpy import *
+import time
+import random
+
+N = 0
+states = list()
+solutions_bkt = list()
+output_solutions = list()
+count = 0
+counter = 1
+
+
+def ok(q_dict: list, col: int, mp, wp):
+    for i in range(0, col):
+        if q_dict[col] == q_dict[i]:
+            return False
+
+    for i in range(0, col):
+        if q_dict[col] == q_dict[i]:
+            return False
+
+    for i in range(0, col):
+        if (mp[i][q_dict[col]] < mp[i][q_dict[i]]) and (wp[q_dict[col]][i] < wp[q_dict[col]][col]):
+            return False
+        if (mp[col][q_dict[i]] < mp[col][q_dict[col]]) and (wp[q_dict[i]][col] < wp[q_dict[i]][i]):
+            return False
+    return True
+
+
+def show(q_dict):
+    global count
+    count += 1
+    for i in range(0, N):
+        for j in range(0, N):
+            if q_dict[i] == j:
+                print("Man ", i, " is matched with woman ", j)
+
+
+def get_state(q_dict, sol):
+    global count
+    state = dict()
+    count += 1
+    for i in range(0, N):
+        for j in range(0, N):
+            if q_dict[i] == j:
+                state[i] = j
+    return state, sol
+
+
+def append_state(q_dict, sol):
+    global states
+    states.append((get_state(q_dict, sol)))
+
+
+def append_to_output_possible_solutions(q_dict):
+    global output_solutions
+    global counter
+    state = dict()
+    counter += 1
+    for i in range(0, N):
+        for j in range(0, N):
+            if q_dict[i] == j:
+                state[str(i + 1)] = chr(ord('@') + j + 1)
+                # print(state)
+    output_solutions.append(state)
+
+
+def move(q_dict, i, mp, wp):
+    # create state
+    if i == N:
+        append_state(q_dict, 1)
+        # print("q", q_dict)
+        solutions_bkt.append(get_state(q_dict, 1))
+        append_to_output_possible_solutions(q_dict)
+        # show(q_dict)
+        return
+    append_state(q_dict, 0)
+    for j in range(0, N):
+        q_dict[i] = j
+        if ok(q_dict, i, mp, wp):
+            move(q_dict, i + 1, mp, wp)
+
+
+def bkt_approach(couples, mp, wp):
+    global N
+    N = couples
+    q = [0] * N
+
+    move(q, 0, mp, wp)
+    time.sleep(3)
+    return random.choice(output_solutions), 0
+
+
+if __name__ == "__main__":
+    mp = array([[0, 2, 1], [0, 2, 1], [1, 2, 0]])
+    wp = array([[2, 1, 0], [0, 1, 2], [2, 0, 1]])
+    print(bkt_approach(3, mp, wp))