Question

我正在尝试解决hackerrank中的一个问题，该问题确定给定类别'cfdconditions'和事件'cfdevents'的所有单词（小写字母和除去停用词）的条件频率分布。还要计算类别“ cfdconditions”和以“ ing”或“ ed”结尾的事件的条件频率分布。然后显示两种分布的频率模态。

我的代码是-

def calculateCFD(cfdconditions, cfdevents):
    # Write your code here
    from nltk.corpus import brown
    from nltk import ConditionalFreqDist
    from nltk.corpus import stopwords
    stopword = set(stopwords.words('english'))
    cdev_cfd = [ (genre, word.lower()) for genre in cfdconditions for word in brown.words(categories=genre) if word.lower() not in stopword]
    cdev_cfd = [list(x) for x in cdev_cfd]
    cdev_cfd = nltk.ConditionalFreqDist(cdev_cfd)
    a = cdev_cfd.tabulate(condition = cfdconditions, samples = cfdevents)
    inged_cfd = [ (genre, word.lower()) for genre in cfdconditions for word in brown.words(categories=genre) if (word.lower().endswith('ing') or word.lower().endswith('ed')) ]
    inged_cfd = [list(x) for x in inged_cfd]
    for wd in inged_cfd:
        if wd[1].endswith('ing') and wd[1] not in stopword:
            wd[1] = 'ing'
        elif wd[1].endswith('ed') and wd[1] not in stopword:
            wd[1] = 'ed'

    inged_cfd = nltk.ConditionalFreqDist(inged_cfd)    
    b = inged_cfd.tabulate(cfdconditions, samples = ['ed','ing'])
    return(a,b)

但是对于2个测试用例，结果仍然失败，我的输出是-

                 many years 
      adventure    24    32 
        fiction    29    44 
science_fiction    11    16 
                  ed  ing 
      adventure 3281 1844 
        fiction 2943 1767 
science_fiction  574  293

和

                  good    bad better 
      adventure     39      9     30 
        fiction     60     17     27 
        mystery     45     13     29 
science_fiction     14      1      4 
                  ed  ing 
      adventure 3281 1844 
        fiction 2943 1767 
        mystery 2382 1374 
science_fiction  574  293

如果有人可以帮助我解决问题，那将是很有帮助的。

Answer 1

尝试此代码，看看它是否有效。

from nltk.corpus import brown,stopwords
def calculateCFD(cfdconditions, cfdevents):


# Write your code here
stopword = set(stopwords.words('english'))
cdev_cfd = nltk.ConditionalFreqDist([(genre, word.lower()) for genre in brown.categories() for word in brown.words(categories=genre) if not word.lower()  in stopword])
cdev_cfd.tabulate(conditions = cfdconditions, samples = cfdevents)
inged_cfd = [ (genre, word.lower()) for genre in brown.categories() for word in brown.words(categories=genre) if (word.lower().endswith('ing') or word.lower().endswith('ed')) ]
inged_cfd = [list(x) for x in inged_cfd]
for wd in inged_cfd:
    if wd[1].endswith('ing') and wd[1] not in stopword:
        wd[1] = 'ing'
    elif wd[1].endswith('ed') and wd[1] not in stopword:
        wd[1] = 'ed'
#print(inged_cfd)
inged_cfd = nltk.ConditionalFreqDist(inged_cfd)
#print(inged_cfd.conditions())    
inged_cfd.tabulate(conditions=cfdconditions, samples = ['ed','ing'])

Answer 2

单独计算cdev_cfd，如下所示，请勿将其再次转换为列表。其余的代码看起来不错。

cdev_cfd = nltk.ConditionalFreqDist([(genre, word.lower()) for genre in cfdconditions for word in brown.words(categories=genre) if word.lower() not in stopword])

Answer 3

此未将cdev_cfd更改为list仍无法正常工作，这两个测试用例对我来说也都失败了，请有人帮忙

Answer 4

请尝试以下代码。

stop=stopwords.words('english')

temp = [[genre, word.lower()] for genre in cfdconditions for word in brown.words(categories=genre) if word.lower() not in stop]

cdev_cfd=nltk.ConditionalFreqDist(temp)
cdev_cfd.tabulate(conditions=cfdconditions,samples=cfdevents)

lst=[]
for i in temp:
    if i[1].endswith('ing'):
        lst.append((i[0],'ing'))

    elif i[1].endswith('ed'):
        lst.append((i[0],'ed'))

inged_cfd=nltk.ConditionalFreqDist(lst)      
inged_cfd.tabulate(conditions=cfdconditions,samples=['ed','ing'])

NLP-Python-条件频率分布

4 个答案: