x.strip() for x in f.read().strip().splitlines() if len(x.strip())

这行代码的作用是从文件对象f中读取内容，去除每行两端的空白字符，并将非空行添加到列表中。具体来说，它的实现步骤如下：

1. `f.read()`：读取文件对象f的全部内容，返回一个字符串。
2. `strip()`：去除字符串两端的空白字符（包括空格、制表符、换行符等），返回一个新的字符串。
3. `splitlines()`：将字符串按行分割，返回一个包含每行内容的列表。
4. `x.strip() for x in ...`：对列表中的每个元素（即每行内容）执行strip()操作，返回一个新的包含处理后内容的列表。
5. `if len(x.strip())`：判断处理后的每行内容是否为空，如果不为空则保留该行内容，否则丢弃。

最终，这行代码将返回一个列表，其中包含了去除了空白字符和空行的文件内容。

相关问题

注释这段代码 def __init__(self, sources='streams.txt', img_size=640, stride=32): self.mode = 'stream' self.img_size = img_size self.stride = stride if os.path.isfile(sources): with open(sources, 'r') as f: sources = [x.strip() for x in f.read().strip().splitlines() if len(x.strip())] else: sources = [sources] n = len(sources) self.imgs = [None] * n self.sources = [clean_str(x) for x in sources] # clean source names for later

这是一个Python类的初始化函数，用于初始化类的属性。它接受三个参数：sources、img_size和stride。

1. sources参数表示输入的视频流文件名或者文件名列表。如果传入的是文件名，则读取文件内容，将每一行的空白字符去除后作为视频流文件名列表。如果传入的是文件名列表，则直接使用该列表。

2. img_size参数表示输入视频的大小，它的默认值为640。

3. stride参数表示步长，它的默认值为32。

在初始化方法中，还对传入的视频流文件名进行了清理，将其存储在self.sources列表中。

最后，还初始化了self.mode属性为'stream'，self.imgs属性为长度为n的None列表。

给以下这段代码加上结果可视化的功能：from sklearn.feature_extraction.text import CountVectorizer from sklearn.naive_bayes import MultinomialNB import jieba from sklearn.model_selection import train_test_split import numpy as np import matplotlib.pyplot as plt good_comments = [] bad_comments = [] with open('D:\PyCharmProjects\爬虫测试\好评.txt', 'r', encoding='gbk') as f: for line in f.readlines(): good_comments.append(line.strip('\n')) with open('D:\PyCharmProjects\爬虫测试\差评.txt', 'r', encoding='gbk') as f: for line in f.readlines(): bad_comments.append(line.strip('\n')) with open('StopWords.txt', 'r', encoding='utf-8') as f: stopwords = f.read().splitlines() good_words = [] for line in good_comments: words = jieba.cut(line, cut_all=False) words = [w for w in words if w not in stopwords] good_words.append(' '.join(words)) bad_words = [] for line in bad_comments: words = jieba.cut(line, cut_all=False) words = [w for w in words if w not in stopwords] bad_words.append(' '.join(words)) # 将文本转换为向量 vectorizer = CountVectorizer() X = vectorizer.fit_transform(good_words + bad_words) y = [1] * len(good_words) + [0] * len(bad_words) # 将数据分为训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) # 训练模型 clf = MultinomialNB() clf.fit(X_train, y_train) # 测试模型并计算准确率 pred = clf.predict(X_test) accuracy = sum(pred == y_test) / len(y_test) print('准确率：{:.2%}'.format(accuracy)) # 预测新数据的类别 with open('测试评论.txt', 'r', encoding='utf-8') as f: count = 0 for line in f.readlines(): count += 1 test_text = line.strip('\n') test_words = ' '.join(jieba.cut(test_text, cut_all=False)) test_vec = vectorizer.transform([test_words]) pred = clf.predict(test_vec) if pred[0] == 1: print(count, '好评') else: print(count, '差评')

以下是加上结果可视化的代码：

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
import jieba
from sklearn.model_selection import train_test_split
import numpy as np
import matplotlib.pyplot as plt

good_comments = []
bad_comments = []

# 读取好评和差评文本
with open('D:\PyCharmProjects\爬虫测试\好评.txt', 'r', encoding='gbk') as f:
    for line in f.readlines():
        good_comments.append(line.strip('\n'))

with open('D:\PyCharmProjects\爬虫测试\差评.txt', 'r', encoding='gbk') as f:
    for line in f.readlines():
        bad_comments.append(line.strip('\n'))

with open('StopWords.txt', 'r', encoding='utf-8') as f:
    stopwords = f.read().splitlines()

# 对好评和差评文本进行分词和去除停用词
good_words = []
for line in good_comments:
    words = jieba.cut(line, cut_all=False)
    words = [w for w in words if w not in stopwords]
    good_words.append(' '.join(words))

bad_words = []
for line in bad_comments:
    words = jieba.cut(line, cut_all=False)
    words = [w for w in words if w not in stopwords]
    bad_words.append(' '.join(words))

# 将文本转换为向量
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(good_words + bad_words)
y = [1] * len(good_words) + [0] * len(bad_words)

# 将数据分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# 训练模型
clf = MultinomialNB()
clf.fit(X_train, y_train)

# 测试模型并计算准确率
pred = clf.predict(X_test)
accuracy = sum(pred == y_test) / len(y_test)
print('准确率：{:.2%}'.format(accuracy))

# 可视化结果
fig, ax = plt.subplots()
ax.bar(['差评', '好评'], [len(bad_comments), len(good_comments)], color=['red', 'green'])
ax.set_xlabel('评论类型')
ax.set_ylabel('评论数量')
ax.set_title('评论数量统计')
plt.show()

# 预测新数据的类别
with open('测试评论.txt', 'r', encoding='utf-8') as f:
    count = 0
    for line in f.readlines():
        count += 1
        test_text = line.strip('\n')
        test_words = ' '.join(jieba.cut(test_text, cut_all=False))
        test_vec = vectorizer.transform([test_words])
        pred = clf.predict(test_vec)
        if pred[0] == 1:
            print(count, '好评')
        else:
            print(count, '差评')

运行以上代码后，会得到一个柱状图，用于展示好评和差评的数量统计。同时，在预测新数据的类别时，也会在控制台输出每一条评论的分类结果。