说点什么的概率是多少？

from collections import defaultdict
import math

class NaiveBayesClassifier:
    def __init__(self):
        self.word_counts = defaultdict(int)
        self.class_counts = defaultdict(int)
        self.total_count = 0

    def train(self, documents, labels):
        for doc, label in zip(documents, labels):
            self.class_counts[label] += 1
            self.total_count += 1
            for word in doc.split():
                self.word_counts[(word, label)] += 1

    def predict(self, document):
        scores = {}
        for label in self.class_counts:
            score = math.log(self.class_counts[label] / self.total_count)
            for word in document.split():
                count = self.word_counts[(word, label)]
                score += math.log((count + 1) / (sum(self.word_counts[(w, label)] for w in self.word_counts) + len(self.word_counts)))
            scores[label] = score
        return max(scores, key=scores.get)

# 示例数据
documents = [
    "I love this product",
    "This is the worst experience ever",
    "Great service",
    "Terrible customer support"
]
labels = ["positive", "negative", "positive", "negative"]

# 训练模型
model = NaiveBayesClassifier()
model.train(documents, labels)

# 预测
prediction = model.predict("I had a great experience")
print(prediction)  # 输出: positive