我正在做一个文本分类任务.现在我想ensemble.AdaBoostClassifier用LinearSVCas base_estimator.但是,当我尝试运行代码时
clf = AdaBoostClassifier(svm.LinearSVC(),n_estimators=50, learning_rate=1.0, algorithm='SAMME.R')
clf.fit(X, y)
发生错误. TypeError: AdaBoostClassifier with algorithm='SAMME.R' requires that the weak learner supports the calculation of class probabilities with a predict_proba method
第一个问题是无法svm.LinearSVC()计算类概率？如何计算概率？
然后我更改参数algorithm并再次运行代码.
clf = AdaBoostClassifier(svm.LinearSVC(),n_estimators=50, learning_rate=1.0, algorithm='SAMME')
clf.fit(X, y)
这一次TypeError: fit() got an unexpected keyword argument 'sample_weight'发生了.正如在AdaBoostClassifier中所说,Sample weights. If None, the sample weights are initialized to 1 / n_samples.即使我分配了一个整数n_samples,也会发生错误.
第二个问题是什么n_samples意思？如何解决这个问题呢？ …
我尝试了"使用python进行自然语言处理"的代码,但发生了类型错误.
import nltk
from nltk.corpus import brown
suffix_fdist = nltk.FreqDist()
for word in brown.words():
word = word.lower()
suffix_fdist.inc(word[-1:])
suffix_fdist.inc(word[-2:])
suffix_fdist.inc(word[-3:])
common_suffixes = suffix_fdist.items()[:100]
def pos_features(word):
features = {}
for suffix in common_suffixes:
features['endswith(%s)' % suffix] = word.lower().endswith(suffix)
return features
pos_features('people')
错误如下:
Traceback (most recent call last):
File "/home/wanglan/javadevelop/TestPython/src/FirstModule.py", line 323, in <module>
pos_features('people')
File "/home/wanglan/javadevelop/TestPython/src/FirstModule.py", line 321, in pos_features
features['endswith(%s)' % suffix] = word.lower().endswith(suffix)
TypeError: not all arguments converted during string formatting
有谁能帮助我找出我错在哪里？
我有两本词典.
a = {"ab":3, "bd":4}
b = {"cd":3, "ed":5}`
我想把它们结合起来{'bd': 4, 'ab': 3, 'ed': 5, 'cd': 3}.
由于此说,a.update(b)可以完成它.但是当我尝试时,我得到:
type(a.update(b)) #--> type 'NoneType'
有谁愿意向我解释为什么我不能获得字典类型？
我也试过这个,它做得很好:
type(dict(a,**b)) #-->type 'dict'
这两种方法有什么区别,为什么第一种方法不起作用？