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118 lines
3.8 KiB
Python
118 lines
3.8 KiB
Python
# The example function below keeps track of the opponent's history and plays whatever the opponent played two plays ago. It is not a very good player so you will need to change the code to pass the challenge.
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from sklearn.naive_bayes import MultinomialNB as model
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clf = model()
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classes = [1, 2, 3]
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actions = ["R", "P", "S"]
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optimum_action = {"R": "P", "P": "S", "S": "R"}
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class_num = {"R": 1, "P": 2, "S": 3}
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my_act_combo_stats = {}
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for p2 in actions:
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for p1 in actions:
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my_act_combo_stats[(p2, p1)] = 0
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opponent_history = ["S"]
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my_history = ["R", "R"]
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def player(prev_play):
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"""Single strategy for all four players."""
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global clf, my_act_combo_stats, opponent_history, my_history
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if prev_play == "":
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prev_play = "S"
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opponent_history.append(prev_play)
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# Reset when start playing with different player
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if len(opponent_history) % 1000 == 2:
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opponent_history = ["S", "S"]
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my_history = ["R", "R"]
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clf = model()
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my_act_combo_stats = {}
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for p_2 in actions:
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for p_1 in actions:
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my_act_combo_stats[(p_2, p_1)] = 0
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p2, p1 = my_history[-2:]
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# Incrementally/online learning model with feature vector
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# and output label from last round
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prior_plays = my_history[-11:-1]
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prior_actions_count = [0, 0, 0]
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for a in prior_plays:
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prior_actions_count[class_num[a] - 1] += 1
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oppo_second_last_action = opponent_history[-2]
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my_second_last_action = my_history[-2]
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train_input = [x for x in prior_actions_count]
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oppo_second_last_action_class_num = class_num[oppo_second_last_action]
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for i in range(1, 4):
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if i == oppo_second_last_action_class_num:
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train_input.append(1)
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else:
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train_input.append(0)
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my_second_last_action_class_num = class_num[my_second_last_action]
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for i in range(1, 4):
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if i == my_second_last_action_class_num:
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train_input.append(1)
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else:
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train_input.append(0)
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prev_expected_next_action = "P"
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prev_n_plays = 0
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for combo, count in my_act_combo_stats.items():
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if combo[0] == p2 and count > prev_n_plays:
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prev_expected_next_action = combo[1]
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prev_n_plays = count
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prev_expected_next_action_class_num = class_num[prev_expected_next_action]
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for i in range(1, 4):
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if i == prev_expected_next_action_class_num:
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train_input.append(1)
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else:
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train_input.append(0)
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clf.partial_fit(
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[train_input],
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[class_num[optimum_action[prev_play]]],
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classes)
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# Predicting action for this round
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my_act_combo_stats[(p2, p1)] += 1
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recent_plays = my_history[-10:]
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recent_actions_count = [0, 0, 0]
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for a in recent_plays:
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recent_actions_count[class_num[a] - 1] += 1
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my_prev_action = my_history[-1]
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predict_input = [x for x in recent_actions_count]
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oppo_last_action_class_num = class_num[prev_play]
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for i in range(1, 4):
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if i == oppo_last_action_class_num:
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predict_input.append(1)
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else:
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predict_input.append(0)
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my_last_action_class_num = class_num[my_prev_action]
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for i in range(1, 4):
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if i == my_last_action_class_num:
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predict_input.append(1)
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else:
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predict_input.append(0)
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expected_next_action = ""
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n_plays = 0
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for combo, count in my_act_combo_stats.items():
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if combo[0] == p1 and count >= n_plays:
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expected_next_action = combo[1]
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n_plays = count
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expected_next_action_class_num = class_num[expected_next_action]
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for i in range(1, 4):
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if i == expected_next_action_class_num:
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predict_input.append(1)
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else:
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predict_input.append(0)
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action = actions[clf.predict([predict_input])[0] - 1]
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my_history.append(action)
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return action
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