def play(self, prev_card):
"""
Bot plays the best card from its hand, or passes by returning
False. If bot plays a card, it also returns a list of actions.
"""
if not self.hand:
raise Exception("Empty Hand")
cards = [format_input(card, prev_card) for card in self.hand]
output = []
schema = [2, 13, 4, 13, 4, 3, 2]
if not self.valid_samples:
return False
tree = DecisionTree(self.valid_samples, schema)
for card in cards:
output.append(tree(card))
card = False
for i in range(len(cards)):
if output[i]:
card = self.hand.pop(i)
break
if not card:
return False
actions = []
for i in range(len(self.actions)):
max_class = max(
[sample[0] for sample in self.action_samples[i]])
schema[0] = max_class
action_tree = DecisionTree(self.action_samples[i], schema)
actions.append(action_tree(card))
return card, actions
def add_sample(self, card, prev_card, is_valid, actions=[]):
"""
Adds a new sample for the bot to train on. actions is
not needed if the move is invalid, and must always be a list,
even if there is only one action.
"""
sample = format_input(card, prev_card)
self.valid_samples.append([is_valid, *sample])
if is_valid:
for i in range(len(actions)):
self.action_samples[i].append([actions[i], *sample])
def make_action_sample(card, prev_card):
actions = [0, 0, 0, 0, 0]
if prev_card[0] == card[0]:
actions[0] = 1
if card[0] == 5 or card[0] == 3 or card[1] == 2 or card[1] == 3:
actions[1] = 1
if card[0] > 8:
actions[2] = card[0] - 8
if card[0] == 8:
actions[3] = 1
if card[0] == 0:
actions[4] = 1
return [actions, *format_input(card, prev_card)]
def make_play_sample(card, prev_card):
if prev_card[0] == card[0]:
is_correct = True
elif prev_card[1] == card[1]:
if prev_card[0] == 3 and card[0] < 3:
is_correct = 0
elif prev_card[0] == 5 and card[0] != 5:
is_correct = False
else:
is_correct = True
else:
is_correct = False
return [is_correct, *format_input(card, prev_card)]
