def __str__(self): return f"{self.First} + {self.Second}i"

这是一个 Python 类中的 __str__ 方法，用于定义该类的实例对象被打印时的输出格式。在这个方法中，{self.First} 和 {self.Second}i 是使用 f-string 格式化字符串的方式，将类实例对象中的属性 First 和 Second 插入到字符串中。其中，i 表示虚数单位。因此，该方法的输出格式为 "实数部分 + 虚数部分i"。

相关问题

帮我补充代码：class LanguageModel(LanguageModel): def get_unigram_logp(self, unigram): """Computes the log-probability of unigram under this LanguageModel. Args: unigram (str): Unigram for which to compute the log-probability. Returns: log_p (float): Log-probability of unigram under this LanguageModel. """ ### Begin your code ### End your code def get_bigram_logp(self, w_1, w_2): """Computes the log-probability of unigram under this LanguageModel. Note: Use self.lambda_ for the unigram-bigram interpolation factor. Args: w_1 (str): First word in bigram. w_2 (str): Second word in bigram. Returns: log_p (float): Log-probability of bigram under this LanguageModel. """ ### Begin your code ### End your code def get_query_logp(self, query): """Computes the log-probability of query under this LanguageModel. Args: query (str): Whitespace-delimited sequence of terms in the query. Returns: log_p (float): Log-probability assigned to the query under this LanguageModel. """ ### Begin your code ### End your code

在class LanguageModel的基础上，补充完整代码如下：

class LanguageModel(LanguageModel):
    def get_unigram_logp(self, unigram):
        """Computes the log-probability of unigram under this LanguageModel.

        Args:
            unigram (str): Unigram for which to compute the log-probability.

        Returns:
            log_p (float): Log-probability of unigram under this LanguageModel.
        """
        count_w1 = self.unigram_counts[unigram]
        total_tokens = self.total_num_tokens
        V = len(self.unigram_counts)
        log_p = np.log((count_w1 + self.lambda_) / (total_tokens + self.lambda_ * V))
        return log_p
    
    def get_bigram_logp(self, w_1, w_2):
        """Computes the log-probability of unigram under this LanguageModel.

        Note: Use self.lambda_ for the unigram-bigram interpolation factor.

        Args:
            w_1 (str): First word in bigram.
            w_2 (str): Second word in bigram.

        Returns:
            log_p (float): Log-probability of bigram under this LanguageModel.
        """
        count_w1w2 = self.bigram_counts[(w_1, w_2)]
        count_w1 = self.unigram_counts[w_1]
        total_tokens = self.total_num_tokens
        V = len(self.unigram_counts)
        log_p = np.log((count_w1w2 + self.lambda_) / (count_w1 + self.lambda_ * V)) + np.log((count_w1 + self.lambda_) / (total_tokens + self.lambda_ * V))
        return log_p
    
    def get_query_logp(self, query):
        """Computes the log-probability of query under this LanguageModel.

        Args:
            query (str): Whitespace-delimited sequence of terms in the query.

        Returns:
            log_p (float): Log-probability assigned to the query under this LanguageModel.
        """
        log_p = 0.0
        query_tokens = query.split()
        for i, token in enumerate(query_tokens):
            if i == 0:
                log_p += self.get_unigram_logp(token)
            else:
                log_p += self.get_bigram_logp(query_tokens[i-1], token)
        return log_p

其中，get_unigram_logp方法用于计算给定unigram的对数概率，get_bigram_logp方法用于计算给定bigram的对数概率，get_query_logp方法用于计算给定query的对数概率。这些方法都是基于语言模型的先验概率进行计算的。在计算bigram的对数概率时，使用了一元二元平滑的插值方法，其中self.lambda_为插值因子。