本文整理汇总了Python中nltk.compat.iteritems方法的典型用法代码示例。如果您正苦于以下问题：Python compat.iteritems方法的具体用法？Python compat.iteritems怎么用？Python compat.iteritems使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类nltk.compat的用法示例。
在下文中一共展示了compat.iteritems方法的5个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: Do_alpha
# 需要导入模块: from nltk import compat [as 别名]
# 或者: from nltk.compat import iteritems [as 别名]
def Do_alpha(self):
"""The observed disagreement for the alpha coefficient.
The alpha coefficient, unlike the other metrics, uses this rather than
observed agreement.
"""
total = 0.0
for i, itemdata in self._grouped_data('item'):
label_freqs = FreqDist(x['labels'] for x in itemdata)
for j, nj in iteritems(label_freqs):
for l, nl in iteritems(label_freqs):
total += float(nj * nl) * self.distance(l, j)
ret = (1.0 / (len(self.I) * len(self.C) * (len(self.C) - 1))) * total
log.debug("Observed disagreement: %f", ret)
return ret 示例2: Do_alpha
# 需要导入模块: from nltk import compat [as 别名]
# 或者: from nltk.compat import iteritems [as 别名]
def Do_alpha(self):
"""The observed disagreement for the alpha coefficient.
The alpha coefficient, unlike the other metrics, uses this rather than
observed agreement.
"""
total = 0.0
for i, itemdata in self._grouped_data('item'):
label_freqs = FreqDist(x['labels'] for x in itemdata)
for j, nj in iteritems(label_freqs):
for l, nl in iteritems(label_freqs):
total += float(nj * nl) * self.distance(l, j)
ret = (1.0 / float((len(self.I) * len(self.C) * (len(self.C) - 1)))) * total
log.debug("Observed disagreement: %f", ret)
return ret 示例3: pi
# 需要导入模块: from nltk import compat [as 别名]
# 或者: from nltk.compat import iteritems [as 别名]
def pi(self):
"""Scott 1955; here, multi-pi.
Equivalent to K from Siegel and Castellan (1988).
"""
total = 0.0
label_freqs = FreqDist(x['labels'] for x in self.data)
for k, f in iteritems(label_freqs):
total += f ** 2
Ae = total / ((len(self.I) * len(self.C)) ** 2)
return (self.avg_Ao() - Ae) / (1 - Ae) 示例4: _apply_filter
# 需要导入模块: from nltk import compat [as 别名]
# 或者: from nltk.compat import iteritems [as 别名]
def _apply_filter(self, fn=lambda ngram, freq: False):
"""Generic filter removes ngrams from the frequency distribution
if the function returns True when passed an ngram tuple.
"""
tmp_ngram = FreqDist()
for ngram, freq in iteritems(self.ngram_fd):
if not fn(ngram, freq):
tmp_ngram[ngram] = freq
self.ngram_fd = tmp_ngram 示例5: pi
# 需要导入模块: from nltk import compat [as 别名]
# 或者: from nltk.compat import iteritems [as 别名]
def pi(self):
"""Scott 1955; here, multi-pi.
Equivalent to K from Siegel and Castellan (1988).
"""
total = 0.0
label_freqs = FreqDist(x['labels'] for x in self.data)
for k, f in iteritems(label_freqs):
total += f ** 2
Ae = total / float((len(self.I) * len(self.C)) ** 2)
return (self.avg_Ao() - Ae) / (1 - Ae)