Wordb: Fill & Download for Free

For grep and egrep command:grep 'word1\|word2\|word3' /path/to/file### Search all text files ###grep 'word*' *.txt### Search all python files for 'wordA' or 'wordB' ###grep 'wordA*'\''wordB' *.pygrep -E 'word1|word2' *.docgrep -e string1 -e string2 *.plegrep "word1|word2" *.c### Show all the lines that do not match given pattern/words/strings ###grep -v 'bar\|foo' /dir1/dir2/file1egrep -v 'pattern1|pattern2' /path/to/file

Here you go…using “printf” functionint main(void) {  printf("A sentence in C.\n");  return 0; } using “puts” function (it creates a new line automatically)int main(void) {  puts("A sentence in C.");  return 0; } using an array of single characters (there must be a one additional character for ‘\0’ NUL terminator - you have two ways to type it correctly, I doubt you’ll have a patience to write in the second way)int main(void) {  // every character and space + one character for escape sequence ('\n') + one character for NUL terminator ('\0')  char wordA[18] = "A sentence in C.\n";  char wordB[18] = {'A', ' ', 's', 'e', 'n', 't', 'e', 'n', 'c', 'e', ' ', 'i', 'n', ' ', 'C', '.', '\n'};   printf("%s", wordA);  printf("%s", wordB);   return 0; } using a pointer to “char” being read-onlyint main(void) {  char *word = "A sentence in C.\n";   printf("%s", word);   return 0; } Some simple examples.Jason

If the use case is as outlined in the comment along with questionIf for instance I would want it to find similarity of alcoholic beverages given a dataset of say 100k of different kinds of drinks and brands, for example a given name could be "johnnie walker black label 750ml" and i would like it to find something along the lines of "jack daniels 750ml"?and if all we have is 100K words of different drinks and brands, word2vec is not the right option to get “jack daniels” from “johhnie walker”The reason is because word2vec Distributed-representations-of-words-and-phrases-and-their-compositionality is able to relate “jack_daniels” is similar to “johnnie_walker” (treating them as one word phrases for this discussion) because there is a corpus of text where these co-occur in a sentence or indirectly occcur (that is worda occurs with wordb and word b occurs with wordc so worda transitively gets close to wordc). It does this learning in an unsupervised manner.However if all we have is a list of brand names, word2vec has no way of learning word representations that bring “johnie_walker” close to “jack_daniels”So to solve this, if we can gain access to a corpus that contains mention of these drinks (or aggregate it with web scraping) then we can do a word2vec run, generate vectors. More the text - the better.One thing to remember to do, once we have such a corpus, is to generate phrases (word2phrase tool that comes with word2vec does this for us Github word2vec ) like “johnnie_walker” and then train word2vec on that corpus - phrase vectors have better quality in similarity measures than composing a phrase by adding the vectors of the individual words composing it.