最后,我们可以找出出现次数最多的数。 importnumpyasnp# 生成随机整数数组arr=np.random.randint(0,10,20)# 使用unique函数获取每个元素的出现次数unique,counts=np.unique(arr,return_counts=True)# 找出出现次数最多的数most_common=unique[np.argmax(counts)]print("
>>> c1.most_common(2) [('a', 3), ('j', 3)] >>> c1.most_common(3) [('a', 3), ('j', 3), ('d', 2)] >>> c1.most_common(4) [('a', 3), ('j', 3), ('d', 2), ('k', 2)] >>> c1.most_common(5) [('a', 3), ('j', 3), ('d', 2), ('...
Common entries are either 'utf-8' (no header byte), or 'utf-8-sig' (header byte). Default is 'utf-8'. """ # 创建一个词汇表对象,用于存储字节对编码表 vocab = ( Vocabulary( lowercase=False, min_count=None, max_tokens=None, filter_stopwords=False, filter_punctuation=False, tokenizer=...
most_common_value = np.argmax(counts) 将最常见的值的一半替换为新值:计算最常见值的一半,然后使用numpy的where函数将数组中等于最常见值的元素替换为新值。 代码语言:txt 复制 half_common_value = counts[most_common_value] // 2 new_value = 999 # 替换为的新值 arr = np.where(arr == ...
docs.append(d[1]['s_words'])#由于特征维度太多内存装载不下,出现memory error,因此取top 5000个词作为维度corpus = cntr.most_common(5000) corpus_tf_idf= get_word_tfidf(docs,corpus)#调用函数print(corpus_tf_idf.shape)#输出print(corpus_tf_idf[:5])#输出样例 ...
The most common scenario for using Cython with NumPy is one where you want to take a NumPy array, iterate over it, and perform computations on each element that can’t be done readily in NumPy.Cython works by letting you write modules in a type-annotated version of Python, which are then...
most_common(vocabulary_size-1) index_to_word = [x[0] for x in vocab] index_to_word.append(unknown_token) word_to_index = dict([(w,i) for i,w in enumerate(index_to_word)]) print "Using vocabulary size %d." % vocabulary_size print "The least frequent word in our vocabulary is...
Here’s a more interesting example that shows the benefits of cuPyNumeric. Stencil computations are one of the most common types of algorithms in scientific computing. Stencil programs are naturally expressed in NumPy using slices that shift and align neighbor cells with the center cell. ...
for word,ratio in pos_neg_ratios.most_common(): if(ratio > 1): pos_neg_ratios[word] = np.log(ratio) else: pos_neg_ratios[word] = -np.log((1 / (ratio + 0.01))) review_vocab = set() for review in reviews: for word in review.split(" "): ...
(most recent call last): File "/Users/zhenxu/python-project/ray-academy/.venv/lib/python3.10/site-packages/pip/_vendor/pep517/in_process/_in_process.py", line 363, in <module> main() File "/Users/zhenxu/python-project/ray-academy/.venv/lib/python3.10/site-packages/pip/_vendor/pep517...