帮我补充代码：class LanguageModel: """对unigram和bigram的先验概率进行建模。""" def __init__(self, corpus_dir='pa2-data/corpus', lambda_=0.1): """遍历“corpus_dir”中每个文件中所有以空格分隔的标记，并计算每个unigram和bigram的出现次数。还跟踪语料库中标记的总数。 corpus_dir（str）：包含语料库的目录的路径。 lambda_（float）：一元二元平滑的插值因子 插值您现在只需要将“lambda_”保存为属性，稍后将在“LanguageModel.get_bigram_logp”中使用。 填写下面的代码块来统计我们语料库中的unigrams和bigrams """ self.lambda_ = lambda_ self.total_num_tokens = 0 # 统计语料库中的令牌总数 self.unigram_counts = Counter() # Maps strings w_1 -> count(w_1) self.bigram_counts = Counter() # Maps tuples (w_1, w_2) -> count((w_1, w_2)) ### Begin your code ### End your code

下面是统计unigrams和bigrams的代码：

import os

corpus_files = [os.path.join(corpus_dir, f) for f in os.listdir(corpus_dir)]
for file in corpus_files:
    with open(file, 'r') as f:
        for line in f:
            tokens = line.strip().split()
            self.total_num_tokens += len(tokens)
            self.unigram_counts.update(tokens)
            self.bigram_counts.update(zip(tokens[:-1], tokens[1:]))

这段代码会遍历语料库目录中的每个文件，对每个文件中的每一行进行处理。它会使用空格将每一行分隔成单词，并将单词添加到unigram_counts和bigram_counts中。注意，bigram_counts中的每个键都是由前一个单词和后一个单词组成的元组。

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。

class LanguageModel: """对unigram和bigram的先验概率进行建模。""" def __init__(self, corpus_dir='pa2-data/corpus', lambda_=0.1): """遍历“corpus_dir”中每个文件中所有以空格分隔的标记，并计算每个unigram和bigram的出现次数。还跟踪语料库中标记的总数。 corpus_dir（str）：包含语料库的目录的路径。 lambda_（float）：一元二元平滑的插值因子 插值您现在只需要将“lambda_”保存为属性，稍后将在“LanguageModel.get_bigram_logp”中使用。 填写下面的代码块来统计我们语料库中的unigrams和bigrams """ self.lambda_ = lambda_ self.total_num_tokens = 0 # 统计语料库中的令牌总数 self.unigram_counts = Counter() # Maps strings w_1 -> count(w_1) self.bigram_counts = Counter() # Maps tuples (w_1, w_2) -> count((w_1, w_2)) ### Begin your code ### End your code

这段代码是一个语言模型的类，用于对给定语料库中的unigram和bigram进行先验概率建模。构造函数中接受一个语料库目录和一个插值因子lambda_，用于后续的一元二元平滑操作。在初始化过程中，会遍历语料库中的每个文件，并计算每个unigram和bigram的出现次数。其中，unigram_counts是一个计数器，用于记录每个unigram出现的次数，bigram_counts则记录每个bigram出现的次数。total_num_tokens变量是为了记录语料库中令牌的总数。