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
时间: 2024-03-03 18:52:44 浏览: 16
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。