bug fix
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59a8c49b2d
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3f68f4c8a6
@ -34,7 +34,7 @@ class DecisionTree:
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self.model = DecisionTreeClassifier(criterion='entropy')
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self.model = DecisionTreeClassifier(criterion='entropy')
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self.model.fit(self.train_set.values, self.goals)
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self.model.fit(self.train_set.values, self.goals)
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def predict_move(self, grid: List[List[str]], current_knight: Knight, castle: Castle, monsters: List[Monster],
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def predict_move(self, grid: List[List[int]], current_knight: Knight, castle: Castle, monsters: List[Monster],
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opponents: List[Knight]) -> \
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opponents: List[Knight]) -> \
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List[Tuple[int, int]]:
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List[Tuple[int, int]]:
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distance_to_castle = manhattan_distance(current_knight.position, castle.position)
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distance_to_castle = manhattan_distance(current_knight.position, castle.position)
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@ -42,14 +42,15 @@ class DecisionTree:
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monsters_parsed = []
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monsters_parsed = []
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for monster in monsters:
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for monster in monsters:
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monsters_parsed.append((manhattan_distance(current_knight.position, monster.position), parse_hp(
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monsters_parsed.append((manhattan_distance(current_knight.position, monster.position), parse_hp(
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monster.current_hp)))
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monster.health_bar.current_hp)))
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opponents_parsed = []
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opponents_parsed = []
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for opponent in opponents:
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for opponent in opponents:
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opponents_parsed.append(
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opponents_parsed.append(
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(manhattan_distance(current_knight.position, opponent.position), parse_hp(opponent.health_bar.current_hp)))
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(manhattan_distance(current_knight.position, opponent.position),
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parse_hp(opponent.health_bar.current_hp)))
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prediction = self.get_prediction(tower_dist=distance_to_castle, tower_hp=castle.current_hp,
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prediction = self.get_prediction(tower_dist=distance_to_castle, tower_hp=castle.health_bar.current_hp,
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mob1_dist=monsters_parsed[0][0], mob1_hp=monsters_parsed[0][1],
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mob1_dist=monsters_parsed[0][0], mob1_hp=monsters_parsed[0][1],
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mob2_dist=monsters_parsed[1][0], mob2_hp=monsters_parsed[1][1],
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mob2_dist=monsters_parsed[1][0], mob2_hp=monsters_parsed[1][1],
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opp1_dist=opponents_parsed[0][0], opp1_hp=opponents_parsed[0][1],
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opp1_dist=opponents_parsed[0][0], opp1_hp=opponents_parsed[0][1],
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@ -57,7 +58,7 @@ class DecisionTree:
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opp3_dist=opponents_parsed[2][0], opp3_hp=opponents_parsed[2][1],
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opp3_dist=opponents_parsed[2][0], opp3_hp=opponents_parsed[2][1],
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opp4_dist=opponents_parsed[3][0], opp4_hp=opponents_parsed[3][1],
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opp4_dist=opponents_parsed[3][0], opp4_hp=opponents_parsed[3][1],
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agent_hp=current_knight.health_bar.current_hp)
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agent_hp=current_knight.health_bar.current_hp)
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print(prediction)
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print(f'Prediction = {prediction}')
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if prediction == 'tower': # castle...
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if prediction == 'tower': # castle...
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return castle_neighbors(grid, castle_bottom_right_row=castle.position[0],
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return castle_neighbors(grid, castle_bottom_right_row=castle.position[0],
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castle_bottom_right_col=castle.position[1])
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castle_bottom_right_col=castle.position[1])
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@ -100,8 +100,8 @@ class Level:
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goal_list = self.decision_tree.predict_move(grid=self.map, current_knight=current_knight,
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goal_list = self.decision_tree.predict_move(grid=self.map, current_knight=current_knight,
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monsters=self.list_monsters,
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monsters=self.list_monsters,
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opponents=self.list_knights_red
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opponents=self.list_knights_blue
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if current_knight.team_alias() == 'k_r' else self.list_knights_blue,
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if current_knight.team_alias() == 'k_r' else self.list_knights_red,
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castle=self.list_castles[0])
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castle=self.list_castles[0])
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if len(goal_list) == 0:
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if len(goal_list) == 0:
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