| log(c(1.05,.6)) | |
| log(c(1.5,.6)) | |
| ifelse(runif(1000)>.5, 1.5, .6) | |
| x=ifelse(runif(1000)>.5, 1.5, .6) | |
| mean(x) | |
| prod(x) | |
| x=ifelse(runif(10)>.5, 1.5, .6) | |
| x | |
| y=replicate(100000,{x=ifelse(runif(10)>.5, 1.5, .6); prod(x)}) | |
| summary(y) |
| http://brenocon.com/confsize,,,"Numer of paper submissions and acceptances for various conferences over time. Trying to only select full-length or ""main"" research papers, though others are sometimes included. | |
| The first several columns are the main data. Sources on the right. Sometimes I tried to put in original source data in different columns. Sometimes data contradicts",,,,,,,,,,,,,,,,,,,,,,,,,, | |
| area,conference,year,submit,accept,accept rates - some are messy or partial from copy-and-paste,joint,attendance,source/notes,other notes,other notes,other notes,other notes,other notes,other notes,,,,,,,,,,,,,,, | |
| ,,,,,,,,,https://dl.acm.org/citation.cfm?id=2766462&picked=source&preflayout=tabs,https://dl.acm.org/citation.cfm?id=312624,the ACM table:,,,,,,,,,,,,,,,,,, | |
| ,,,,,,,,,,,Year,Submitted,Accepted,Rate,,,,,,,,,,,,,,, | |
| webir,sigir,1999,135,33,,,,https://dl.acm.org/citation.cfm?id=312624,,22nd annual,SIGIR '99,135,33,24%,,,,,,,,,,,,,,, | |
| webir,sigir,2000,??,??,,,,https://dl.acm.org/citation.cfm?id=345508&picked |
This is just the middle section of Bob Carpenter's note for evaluating log-loss via the binary logistic functoin https://lingpipe-blog.com/2012/02/16/howprevent-overflow-underflow-logistic-regression/
The logp function calculates the negative cross-entropy:
dotproduct( [y, 1-y], [logP(y=1), logP(y=0)] )where the input s is the beta'x log-odds scalar value. The trick is to make this numerically stable for any choice of s and y.
| """ | |
| example of using corenlp server from python | |
| This code requires server to already be running: https://stanfordnlp.github.io/CoreNLP/corenlp-server.html | |
| To start server: | |
| java -mx4g -cp "*" edu.stanford.nlp.pipeline.StanfordCoreNLPServer -port 9000 -timeout 15000 | |
| To call it, e.g.: | |
| curl --data "The man wanted to go to work." 'http://localhost:9000/?properties={%22annotators%22%3A%22tokenize%2Cssplit%2Cpos%2Cdepparse%22%2C%22outputFormat%22%3A%22conllu%22}' |
| """ | |
| Make plots with the "Google Image Charts" API. | |
| The functions here return a URL. That URL will then throw a PNG back at you. | |
| So embed them in <img src=...> or whatever. | |
| This uses the "Google Image Charts API," formerly known as "Google Charts API", | |
| which is officially deprecated and has been since 2012, I guess: | |
| https://en.wikipedia.org/wiki/Google_Chart_API | |
| https://developers.google.com/chart/image/ | |
| So look out I guess. But it is a MUCH better quick-and-simple API than their |
| r""" | |
| stdin: IDs of tweets to get (whitespace or line separated) | |
| stdout: the tweets as two-column TSV: ID \t TweetJSON | |
| This retrieves tweets using the API. | |
| If there was an error when retrieving a message - most prominently, if the | |
| message is now deleted -- the error information is saved as JSON. Therefore | |
| there should be exactly as many output lines as there are input IDs. |
| # -*- encoding: utf-8 -*- | |
| # actually that encoding line is NOT important codewise. only for doc purposes. | |
| """ | |
| Detect emoji or other emoji-like things in Python. | |
| The regular expressions here can be used to either identify emoji or to remove it. | |
| The comments are written from the perspective of removing it. | |
| The regexes get some stuff besides emoji. | |
| by Brendan O'Connor (http://brenocon.com) 2016-10-20 | |
| originally written as part of https://arxiv.org/abs/1608.08868 |