class SpamDetector:

    def __init__(self):
        self.p_class = [0, 0]
        self.p_word = [{}, {}]

    # typical machine learning model interface
    def fit(self, x, y):
        for c in [0, 1]:
            data = x[y == c]
            words = self._count_words(data)
            word_sum = sum(words.values())
            self.p_word[c] = {word: -log(count / word_sum) for word, count in words.items()}
            self.p_class[c] = -log(np.mean(y == c))

    def predict(self, d):
        return np.argmax(self.predict_proba(d), axis=1)

    def predict_proba(self, d):
        ret = np.zeros((d.shape[0], 2))
        ret += self.p_class
        for i, msg in enumerate(d):
            for c in [0, 1]:
                ret[i, c] = self.__p_message(msg, self.p_word[c])
        return ret

    # helper functions
    def _count_words(self, d):
        ret = defaultdict(lambda: 0)
        for line in d:
            for word in line:
                ret[word] += 1
        return ret

    def __p_message(self, msg, d):
        return sum(d.get(s, 0) for s in msg)