The -v will search for "pattern" and show you the lines that DON'T match. Grep has some more useful options as well: This allows you to filter the output of one grep command with a more specific pattern. To review, you can chain as many grep commands as you like. What if I wanted to know who came in on a Mac?Ĭat /var/log/httpd/access.log | grep "GET /signup.jsp" | grep "Mac OS X" now we have searched the entire log and filtered out only those hits to that particular IP. Suppose I have an apache log where I'd like to see all of the lines that match a hit to a certain URL. You can chain commands like this indefinitely as long as they're reading from standard in and outputting to standard out. So we had "cat" read data.txt line by line, piped it through grep looking for "l" and piped the results through sort. Looks like it's out of order, though, so lets sort it after it comes out of grep. Great, we matched every line with an "l" (the letter l) in it and displayed it to standard input. Let's try a more complex example with the same file. That means it can be both a consumer and a provider of lines for other commands that can process standard input. ![]() Note that grep reads in lines from standard input and outputs lines to standard output. If no lines match, then nothing is sent to standard output. What happens if no lines match the pattern? grep took each line that came in and checked to see if the pattern "gamma" appeared on that line. What we've done here told "cat" to read every line of the file "data.txt" and pipe it into grep. It can do a lot more, but this is a good working definition to start. grep can be described as a program that reads from standard input, tests each line against a pattern, and writes to standard output the lines that match this pattern. ![]() Now that we have the basics of pipes squared away, we can get into some more interesting and useful stuff. by stringing together commands that print output to the terminal and commands that read from the terminal, you can do some very powerful things. Many, many commands work in Unix (or Linux/MacOS/etc) will act like "sort" did and accept input line by line. Now we have something useful! What we have done above can be described by this statement "Take the output of cat data.txt and pipe it through sort". That's pretty useless most of the time! But remember that "cat" will read a file and send it, line by line, to the terminal? Well, using a pipe, we can take those lines from cat and feed them into sort. It's waiting for some data to come in on the terminal (or, better termed, standard input). What "sort" does is read, line by line, everything you give it until it detects the end of the file. I can combine the "cat" command with the "sort" command. Suppose I wanted to do something useful to this data. The term for "output to terminal" is standard out. What "cat data.txt" did was really read the file, line by line, and output it to your terminal, line by line. The following command would display the contents of that file in your terminal Suppose we have a text file called "data.txt" with the following contents: If folks would be interested in a complete pipe tutorial here, or may know of a good one online, please comment. If you aren't familiar with pipes or use them regularly, it would definitely be worth your while to dig into this. ![]() It doesn't take long to master if you have the right tools.įirst, you're going to need to understand how to use pipes. grep is one of the key tools in the traditional Unix arsenal for tearing through text files and finding exactly what you want very, very quickly. ![]() In this paper, we propose an alternative but easy-to-use and effective solution, \textbf.Here is something that I could write a book about. Recent work shows that the long-tailed learning performance could be boosted by sampling extra in-domain (ID) data for self-supervised training, however, large-scale ID data which can rebalance the minority classes are expensive to collect. Abstract: Though Self-supervised learning (SSL) has been widely studied as a promising technique for representation learning, it doesn't generalize well on long-tailed datasets due to the majority classes dominating the feature space.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |