小编kRa*_*y R的帖子

我需要将多行两列data.frame转换为命名字符向量.我data.frame会是这样的:

dd = data.frame(crit = c("a","b","c","d"), 
                name = c("Alpha", "Beta", "Caesar", "Doris")
                )

我真正需要的是:

whatiwant = c("a" = "Alpha",
              "b" = "Beta",
              "c" = "Caesar",
              "d" = "Doris")

我使用twitteR包从twitter中提取推文并将其保存到文本文件中.

我在语料库上进行了以下操作

xx<-tm_map(xx,removeNumbers, lazy=TRUE, 'mc.cores=1')
xx<-tm_map(xx,stripWhitespace, lazy=TRUE, 'mc.cores=1')
xx<-tm_map(xx,removePunctuation, lazy=TRUE, 'mc.cores=1')
xx<-tm_map(xx,strip_retweets, lazy=TRUE, 'mc.cores=1')
xx<-tm_map(xx,removeWords,stopwords(english), lazy=TRUE, 'mc.cores=1')

(使用mc.cores = 1和lazy = True,否则Mac上的R运行错误)

tdm<-TermDocumentMatrix(xx)

但是这个术语文档矩阵有很多奇怪的符号,无意义的单词等.如果推文是

 RT @Foxtel: One man stands between us and annihilation: @IanZiering.
 Sharknado‚Äã 3: OH HELL NO! - July 23 on Foxtel @SyfyAU

清理完推文后,我只想留下适当的完整英文单词,即句子/短语无效(用户名,缩短的单词,网址)

例:

One man stands between us and annihilation oh hell no on 

(注意:tm包中的转换命令只能删除停用词,标点符号空格以及转换为小写)

上下文：\n这是问题https://github.com/pypa/pip/issues/6717#issue-468204416我面临\n并试图通过\n https://github.com/pypa/pip
解决/问题/6717#issuecomment-511652167

我想用

--no-build-isolation\n

在 py2.7 venv 中如果我这样做：

pip install bottleneck==1.2.1 --no-build-isolation\n\nCollecting bottleneck==1.2.1\nCollecting numpy (from bottleneck==1.2.1)\n  Downloading https://files.pythonhosted.org/packages/d7/b1/3367ea1f372957f97a6752ec725b87886e12af1415216feec9067e31df70/numpy-1.16.5-cp27-cp27mu-manylinux1_x86_64.whl (17.0MB)\n    100% |\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88| 17.0MB 1.2MB/s \nInstalling collected packages: numpy, bottleneck\nSuccessfully installed bottleneck-1.2.1 numpy-1.16.5\nYou are using pip version 18.0, however version 19.2.3 is available.\nYou should consider upgrading via the \'pip install --upgrade pip\' command.\n\n

安装得很好

$ cat abc.txt \nbottleneck==1.2.1, --no-build-isolation\n

但

$pip install -r abc.txt\nUsage: pip [options]\n\nInvalid requirement: bottleneck==1.2.1, --no-build-isolation\npip: error: …

这个问题与我的另一个问题有关:使用sox和python基于时间戳列表来区分音频区域

如果 q= [[0.0,4.0], [10.0,12.0], [15.0,20.0], [21.0,28.0], [32.0,36.0],[41.0,44.0]]

新列表q'应该是 [4.0,10.0],[12.0,15.0],[20.0,21.0],[28.0,32.0], [36.0,41.0]]

我做的是以下内容:

import numpy
q= [[0.0,4.0], [10.0,12.0], [15.0,20.0], [21.0,28.0], [32.0,36.0],[41.0,44.0]]
x= []       
print "in between"
for t in range(len(q)-1):
    a,b=q[t][1],q[t+1][0]
    x.append([a,b])

for i in x:
    print i

输出:

[4.0, 10.0]
[12.0, 15.0]
[20.0, 21.0]
[28.0, 32.0]
[36.0, 41.0]  

更新:我想在我的^输出中追加两个段.

上下文:这些段是时间戳.

假设段没有从零开始,而是从3.0开始, q= [[3.0,4.0], [10.0,12.0], [15.0,20.0], [21.0,28.0], [32.0,36.0],[41.0,44.0]] 文件以50.0结束.

我原来的输出,我想补充的区域:[0.0,3]和[44.0,50.0]这样我可以沉默了这些地区了.

为此,我简单地做了:

import numpy
speaker_segments= [[3.0,4.0], [10.0,12.0], [15.0,20.0], [21.0,28.0], [32.0,36.0],[41.0,44.0]]
segments_to_silence = []
starting= 0.0 …