optimized inference

Backtrack class

P2.py

@@ -1,8 +1,9 @@
 import random
+from pickletools import read_uint1
 
 
 class Field:
-    def __init__(self, init_state=None):
+    def __init__(self, init_state=None, model=None):
         self.state = []
         self.domain_values = [1, 2, 3, 4, 5, 6, 7, 8]
 
@@ -15,6 +16,8 @@ class Field:
         self.threats = self.collisions(self.state)
         self.fitness = 28 - self.threats
 
+        self.model = model
+
     def get_fitness(self):
         return self.fitness
 
@@ -37,7 +40,7 @@ class Field:
     def set_domain_values(self, new_domain):
         self.domain_values = new_domain
 
-    def add_state(self, row):
+    def add_queen(self, row):
         if len(self.get_state()) < 8:
             self.set_state(len(self.get_state()), row)
 
@@ -96,11 +99,20 @@ class Field:
         print("  └───┴───┴───┴───┴───┴───┴───┴───┘")
         print("    A   B   C   D   E   F   G   H  \n")
 
+    def calc(self):
+        if self.model == "genetic":
+            best_state = Genetic().calc()
+            self.state = best_state.get_state()
+            self.print_field()
+
+        elif self.model == "backtrack":
+            Backtrack().calc()
+
 
 class Genetic:
-    def __init__(self, size=100):
+    def __init__(self, size=1000):
         self.initial_population = []
-        self.p_mutation = 0
+        self.p_mutation = 0.1
 
         for i in range(size):
             self.initial_population.append(Field())
@@ -163,93 +175,96 @@ class Genetic:
                     if best_field.get_fitness() == 28:
                         break
 
+            print(f"{i} {best_field.get_state()} {best_field.get_fitness()}")
+            if best_field.get_fitness() == 28:
+                break
+
             current_population = new_population
             new_population = []
 
         return best_field
 
-
-def consistency(field, new_row):
-    current_state = field.get_state().copy()
-    current_state.append(new_row)
-    new_field = Field(current_state)
-
-    if new_field.threats > 0:
-        return False
-    else:
-        return True
+    def calc(self, n=100):
+        best_genetic_field = self.genetic_algorithm(n)
+        return best_genetic_field
 
 
-def inference(field):
-    inferences = []
-    column = len(field.get_state()) - 1
-    state = field.get_state().copy()
-    row = state[column]
+class Backtrack:
+    def __init__(self):
+        self.results = []
 
-    for i in range(len(field.get_state()), 8):
-        removed_values = []
-        for new_row in field.get_domain_values():
-            new_state = state.copy()
-            new_state.append(new_row)
-            new_field = Field(new_state)
-            if new_field.threats > 0:
-                removed_values.append(new_row)
+    def consistency(self, field, new_row):
+        current_state = field.get_state().copy()
+        current_state.append(new_row)
+        new_field = Field(current_state)
 
-        for i in removed_values:
-            if i in field.get_domain_values():
-                field.get_domain_values().remove(i)
+        if new_field.threats > 0:
+            return False
+        else:
+            return True
 
-        inferences.extend(removed_values)
+    def inference(self, field):
+        if len(field.get_state()) >= 8:
+            return True
+
+        field.set_domain_values([1, 2, 3, 4, 5, 6, 7, 8])  # Reset für jede Spalte
+        inferences = []
+        # print(field.get_state())
+        # print(field.get_domain_values())
+
+        for new_row in range(1, 9):
+            if not self.consistency(field, new_row):
+                inferences.append(new_row)
+
+        for row in inferences:
+            if row in field.get_domain_values():
+                field.get_domain_values().remove(row)
+
+        # print(inferences)
+        # print(f"{field.get_domain_values()}\n")
 
         if len(field.get_domain_values()) == 0:
-            return False, inferences
+            return False
 
-    return True, inferences
+        return True
 
+    def backtracing(self, field):
+        if len(field.get_state()) == 8:
+            return [Field(field.get_state().copy())]
 
-def backtracing(field):
-    if len(field.get_state()) == 8:
-        return [Field(field.get_state().copy())]
+        solutions = []
 
-    solutions = []
-    iter_domain = field.get_domain_values().copy()
+        for row in field.get_domain_values():
+            old_domain_values = field.get_domain_values().copy()
+            if self.consistency(field, row):
+                field.add_queen(row)
+                if self.inference(field):  # nur für die nächste Spalte
+                    result = self.backtracing(field)
+                    if len(result) != 0:
+                        solutions.extend(result)
 
-    for row in iter_domain:
-        if consistency(field, row):
-            field.add_state(row)
-            result = backtracing(field)
-            if len(result) != 0:
-                solutions.extend(result)
+                field.get_state().pop()
+                field.domain_values = old_domain_values
 
-            field.get_state().pop()
+        return solutions
 
-    return solutions
-
-
-def backtracing_helper(field):
-    results = backtracing(field)
-
-    return results
+    def calc(self):
+        for i in range(1, 9):
+            result = self.backtracing(Field([i]))
+            self.results.extend(result)
+        for i, result in enumerate(self.results):
+            print(f"{i + 1} {result.get_state()}")
 
 
 def main():
-    new_field = Field(
-        init_state=[])  # [8, 4, 5, 4, 4, 3, 7, 6] [5, 5, 5, 5, 1, 2, 8, 5] [6,3,5,7,1,4,2,8]
-    new_field.print_field()
-    print(new_field.collisions())
+    gen_field = Field(model="genetic")
+    gen_field.calc()
 
-    print("Backtrack Algorithm")
-    results = []
-    for i in range(1, 9):
-        results.extend(backtracing_helper(Field([i])))
-    for i, result in enumerate(results):
-        print(f"{i+1} {result.get_state()}")
+    back_field = Field(model="backtrack")
+    back_field.calc()
 
-    print("Genetic Algorithm")
-    genetic = Genetic(500)
-    best_genetic_field = genetic.genetic_algorithm(100)
-    best_genetic_field.print_field()
-    print(best_genetic_field.get_fitness())
+    myField = Field()
+    myField.print_field()
 
 
 main()