a* - será que está certo?

blind_search/algorithms/heuristic.py

@@ -1,3 +1,5 @@
+import show
+
 def heuristic_manhattan(state):
     res = 0
     goal = state.get_goal()
@@ -6,9 +8,9 @@ def heuristic_manhattan(state):
         x, y = state.table(i)
         x_t, y_t = state.table( goal.index( state.state[i] ) )  
         res += abs(x - x_t) + abs(y - y_t)
-    
-    return res
+
+    return state.depth + res
 
 
 def heuristic_default(state):
-    return state.cost + 1
+    return 0 if state.parent is None else state.cost + 1

blind_search/algorithms/search.py

@@ -59,11 +59,11 @@ def dfs(initial_state, limit=-1):
 
     # While the stack is not empty
     while stack:
-
+        
         # Remove the first state from the stack
         current_state = stack.pop()
         explored.add(current_state.key)
-
+        
         # If the current state is the goal state
         if current_state.goal():
             return {"state": current_state, "max_depth": max_depth, "max_frontier": max_frontier, "final_frontier": len(stack), "scanned": len(explored)}
@@ -116,7 +116,7 @@ def astar(initial_state):
     max_frontier    = 0
 
     # Create a stack for the states to be explored
-    stack = deque() # Stack is LIFO
+    stack = [] # Stack is LIFO
     explored = set()
 
     # Add the initial state to the stack
@@ -125,21 +125,27 @@ def astar(initial_state):
     while stack:
 
         # Remove the first state from the stack
-        current_state = stack.popleft()
+        current_state = stack.pop(0)
+        explored.add(current_state.key)
 
         # Current State ??        
         if current_state.goal():
             return {"state": current_state, "max_depth": max_depth, "max_frontier": max_frontier, "final_frontier": len(stack), "scanned": len(explored)}
 
-
         # Generating Children        
-        children = current_state.get_neighbors()
+        children = current_state.get_neighbors(False)
+        # Calculating the cost to the specific child
+        for child in children:
+            child.cost = child.h(child)
 
-        distances = [child.h(child) for child in children]
+        # Filtering the children
+        children = [child for child in children if child.key not in explored]
+        # Most Valuable Child
+        stack.extend(children)
 
-        print(distances)
-
-        return {"state": current_state, "max_depth": max_depth, "max_frontier": max_frontier, "final_frontier": len(stack), "scanned": len(explored)}
+        stack = sorted(stack, key=lambda x: x.cost)
+         
+    return {"state": None, "max_depth": max_depth, "max_frontier": max_frontier, "final_frontier": len(stack), "scanned": len(explored)}
 
 
 

blind_search/main.py

@@ -58,8 +58,6 @@ def main():
     f.write(f"RAM_Usage        : {resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1000.0} kb\n")
     f.close()
 
-    if(method == "bfs"):
-        traceback_board(current_state) 
     return
 
 METHODS = {'bfs': bfs, 'dfs': dfs, 'idfs': idfs, "astar": astar}

blind_search/res/astar.txt

@@ -1,11 +1,11 @@
 Method: astar
 Final State Found: True
-Path             : 
-Cost             : 16
-Depth            : 0
+Path             : left, up, right, down, left, left, up, up, right, down, right, up, left, down, down, left, up, up, right, down, down, right, up, left, down, left, up, right, right, down
+Cost             : 30
+Depth            : 30
 Max_Depth        : 0
-Final_Frontier   : 0
+Final_Frontier   : 9797
 Max_Frontier     : 0
-Scanned          : 0
-Elapsed_Time     : 0.0005576610565185547 s
-RAM_Usage        : 8.044 kb
+Scanned          : 17126
+Elapsed_Time     : 18.833747625350952 s
+RAM_Usage        : 24.996 kb

blind_search/res/bfs.txt

@@ -7,5 +7,5 @@ Max_Depth        : 31
 Final_Frontier   : 32
 Max_Frontier     : 24053
 Scanned          : 181440
-Elapsed_Time     : 2.547363519668579 s
-RAM_Usage        : 62.168 kb
+Elapsed_Time     : 4.625905752182007 s
+RAM_Usage        : 82.156 kb

blind_search/res/dfs.txt

@@ -7,5 +7,5 @@ Max_Depth        : 65982
 Final_Frontier   : 42564
 Max_Frontier     : 42826
 Scanned          : 146379
-Elapsed_Time     : 1.4203965663909912 s
-RAM_Usage        : 64.78 kb
+Elapsed_Time     : 2.3584494590759277 s
+RAM_Usage        : 87.284 kb

blind_search/res/idfs.txt

@@ -7,5 +7,5 @@ Max_Depth        : 31
 Final_Frontier   : 0
 Max_Frontier     : 26
 Scanned          : 312296
-Elapsed_Time     : 3.9806690216064453 s
-RAM_Usage        : 13.844 kb
+Elapsed_Time     : 5.968386173248291 s
+RAM_Usage        : 13.876 kb

blind_search/state.py

@@ -33,9 +33,10 @@ def traceback_board(state):
 """
 class State:
 
-    def __init__(self, state, goal, parent=None, cost=0, depth=0, action="NoOp", h=hrc.heuristic_default):
+    def __init__(self, state, goal, parent=None, cost=None, depth=0, action="NoOp", h=hrc.heuristic_default):
         self.state = state
         self.parent = parent
+        self.depth = depth 
 
         self.size = int(len(state)**(1/2))
         self.key = "".join(str(x) for x in state)
@@ -45,10 +46,13 @@ def __init__(self, state, goal, parent=None, cost=0, depth=0, action="NoOp", h=h
         self.__goal = goal.copy()
 
         self.h = h
-
-        self.cost = 0
-        self.cost = self.h(self)
-        self.depth = depth 
+
+        if not cost:
+            self.cost = 0
+            self.cost = self.h(self)
+        else:
+            self.cost = cost
+
 
     def __eq__(self, other):
         return self.key == other.key
@@ -69,7 +73,7 @@ def table(self, index):
         return i, index - i * self.size
 
 
-    def get_neighbors(self):
+    def get_neighbors(self, cost = True):
         neighbors = []
 
         index = self.state.index(0)
@@ -83,7 +87,7 @@ def get_neighbors(self):
             if 0 <= i_new < self.size and 0 <= j_new < self.size:
                 new_state = self.state[:]
                 new_state[index], new_state[i_new * self.size + j_new] = new_state[i_new * self.size + j_new], new_state[index]
-                neighbors.append(State(new_state, self.__goal, self, self.h(self), self.depth + 1, key, self.h))
+                neighbors.append(State(new_state, self.__goal, self, cost = None if not cost else self.h(self), depth = self.depth + 1, action = key, h = self.h))
 
         return neighbors