What does Op stand for from salesforce's TransmogrifAI? such as OpApp, OpWorkflow, etc.
https://github.com/salesforce/TransmogrifAI/tree/master/core/src/main/scala/com/salesforce/op
The code name for the project before it was released was "Optimus Prime" thus the OP abbreviation.
Related
I want to create a link within an asciidoc PDF for a printed book. The right way to do this is something like:
[[LinkUniqueCode]]
Here is the stuff I'm linking to...
Lots of document here...
Now look <<LinkUniqueCode,at the link>>.
Normally I would expect this to render as something like this in PDF:
Now look at the link (Page 13).
But instead I'm getting a link which is useless in a printed PDF...
I've searched a lot for this but the keywords I found are too generic and I only found this.
I've tried adding :xrefstyle: full but that didn't really help either.
I've seen this both through the fopub backend and the PDF backend. I'm guessing there should be a "print mode" for the PDF generation but I can't really see what I'm doing wrong here.
OK, that was me being stupid. I forgot to include:
:doctype: book
Which made it all good.
Edit:
For full reference here's my entire header:
:xrefstyle: full
:listing-caption: Listing
:sectnums:
:pdf-page-size: [8.125in, 10.25in]
:doctype: book
:media: prepress
:icons: font
:source-highlighter: rouge
:toc: macro
:toclevels: 4
:toc-title: Contents
:toc-placement: manual
:tip-caption: :bulb:
:autofit-option:
:hide-uri-scheme:
:uuid: 92CA37B2-EB2B-4B8F-AC7C-XXXXXXXXX
:front-cover-image: image:images/ebook.png[Front Cover,1000,1600]
:lang: en-US
:revdate: 2018-07-22
:doctitle: My Title
:author: Shai Almog
:producer: Codename One Academy
:description: My Description
:keywords: My Keywords,Other Words
:copyright: Shai Almog, all rights reserved
:publication-type: book
Then the body of this file is:
include::file-names-for-each-chapter.asciidoc[]
[index]
== Index
This seemed to work correctly
look at: https://stackoverflow.com/questions/47312831/asciidoctor-page-number-usable
I took your example and it was not made good.
What other settings like xrefstyle you used?
EDIT: look here:
asciidoctor-pdf -v
Asciidoctor PDF 1.5.0.alpha.16 using Asciidoctor **1.5.4** [http://asciidoctor.org]
Runtime Environment (ruby 2.3.3p222 (2016-11-21) [x86_64-linux-gnu]) (lc:UTF-8 fs:UTF-8 in:- ex:UTF-8)
I think this explains:
On Wed, Aug 9, 2017 at 11:23 PM, Jeremie Bresson [via Asciidoctor :: Discussion] <[hidden email]> wrote:
The "xrefstyle" feature is really great (new with Asciidoctor 1.5.6.1, see http://asciidoctor.org/docs/user-manual/#customizing-the-cross-reference-text )
(I am not sure if this question belongs to the meta website or not, but here we go)
I want to add stackoverflow to the bibliography of a research paper I am writing, and wonder if there is any bibTeX code to do so. I already did that for gnuplot
I searched online, but in most cases the citation goes to a specific thread. In this case, I want to acknowledge SO as a whole, and add a proper citation, probably to the website itself. Hopefully somebody already did this in the past?
As an example, below are the codes I use for R and gnuplot:
#Manual{rproject,
title = {R: A Language and Environment for Statistical Computing},
author = {{R Core Team}},
organization = {R Foundation for Statistical Computing},
address = {Vienna, Austria},
year = {2015},
url = {https://www.R-project.org/},
}
#MISC{gnuplot,
author = {Thomas Williams and Colin Kelley and {many others}},
title = {Gnuplot 5.0: an interactive plotting program},
month = {June},
year = {2015},
howpublished = {\href{http://www.gnuplot.info/}{http://www.gnuplot.info/}}
}
I know that both are software, not website resources, but maybe something along those lines would work. Any feedback is appreciated!
Thanks!
I did not realize this question never got answered. The solution I found was to acknowledge the SO website in the LaTeX code with the following:
This research has made use of the online Q\&A platform {\texttt{stackoverflow}}
(\href{http://stackoverflow.com/}{http://stackoverflow.com/}).
Hope it helps somebody in the future!
Actually, for my paper I am using the following citation:
#misc{stackoverflow,
url={https://stackoverflow.com/},
title={Stack overflow},
year={2008}
}
I hope it helps!
I am using Altomava Mapforce to map and load 837 x12 formatted text files directly to Sql Server 2014. I have correctly mapped everything except I get the following errors-
Missing field F142- Application Senders code
Could not find start of message with impl.convention reference '116731H333B2'. Message will be skipped.
Missing segment GE
I have included my header and footer information below from the original source text file. Does anyone know what is going on with the mapping, or if maybe there is something wrong with the data itself? Any help would be greatly appreciated.
Header-
ISA*11* *11* *PP* *ZZ*20121143 *273041*0109*^*00501*000000000*0*T*:~GS*HC**211231153*20141121*1115*01*Y*116731H333B2~ST*837*2000001*116731H333B2~BHT*0029*00*0003000005*20141121*1115*CH
Message Data etc.......
Footer-
~SE*769*2000001~GE*1*01~IEA*1*000000000~
Your data is wrong. Here is a cleaned up version of the ISA / GS. For readability, I put a CR/LF after the segment terminator (~). Please note the ISA and GS do not indicate sender, which is going to cause all kinds of problems for auditing. See my comment above for analysis on the data per your bullet points.
ISA*11* *11* *PP*SENDER *ZZ*20121143 *273041*0109*^*00501*000000000*0*T*:~
GS*HC*SENDER*211231153*20141121*1115*01*X*005010~
ST*837*2000001*116731H333B2~
BHT*0029*00*0003000005*20141121*1115*CH
An example of the enveloping:
ISA*00* *00* *ZZ*Test1Saver *ZZ*RECEIVER *151222*1932*U*00501*000111884*0*P*:~GS*HC*Test1Saver*RECEIVER*20151222*1932*1*X*005010~ST*850*0001~
...
~SE*8*0001~GE*1*1~IEA*1*000111884~
If, 123456789 have value then map 123456789 and if having null or blank or no value then send default 123.
enter image description here
I am trying to find list of polysemous words but did not get anything on internet. Can someone suggest me a source from where I can get it? I want to use it at backend of my word sense disambiguation project for polysemy detection mechanism.
From http://ixa2.si.ehu.es/signatureak/SENSECORPUS.README.TXT
We say that a word is monosemous if it has a unique sense, that is, if
a word has a unique synset taking into account all its part of speech.
A polysemous word thus is one which has more than one sense. You can get this information from the wordnet itself.
Check out this.
The following will work:
from nltk.corpus import wordnet as wn
def is_polysemous(word):
if(len(wn.synsets(word)) > 1): #more than 1 sense
return True
else:
return False
You can further qualify the code by adding POS. For example :
from nltk.corpus import wordnet as wn
def is_polysemous(word):
if(len(wn.synsets(word, pos=wn.NOUN)) > 1): #more than 1 sense
return True
else:
return False
WordNet has become more and more finely grained with each version. Take the example of the noun 'line'. In WordNet1.5, it had 6 senses, while WordNet3.0 lists 30 senses for the same noun.
#axiom has given you the correct answer, but if you do not want your application to be so specific you could chenge the WordNet version you are using or you could use the so-called 'sense mapping', which groups more related senses from a greater version (ex. 3.0) to the same sense in 1.5.
You can find some sense mappings here http://www.cse.unt.edu/~rada/downloads.html#wordnet or, if you want different versions you can make your own mapping.
I wish to search twitter for a word (let's say #google), and then be able to generate a tag cloud of the words used in twitts, but according to dates (for example, having a moving window of an hour, that moves by 10 minutes each time, and shows me how different words gotten more often used throughout the day).
I would appreciate any help on how to go about doing this regarding: resources for the information, code for the programming (R is the only language I am apt in using) and ideas on visualization. Questions:
How do I get the information?
In R, I found that the twitteR package has the searchTwitter command. But I don't know how big an "n" I can get from it. Also, It doesn't return the dates in which the twitt originated from.
I see here that I could get until 1500 twitts, but this requires me to do the parsing manually (which leads me to step 2). Also, for my purposes, I would need tens of thousands of twitts. Is it even possible to get them in retrospect?? (for example, asking older posts each time through the API URL ?) If not, there is the more general question of how to create a personal storage of twitts on your home computer? (a question which might be better left to another SO thread - although any insights from people here would be very interesting for me to read)
How to parse the information (in R)? I know that R has functions that could help from the rcurl and twitteR packages. But I don't know which, or how to use them. Any suggestions would be of help.
How to analyse? how to remove all the "not interesting" words? I found that the "tm" package in R has this example:
reuters <- tm_map(reuters, removeWords, stopwords("english"))
Would this do the trick? I should I do something else/more ?
Also, I imagine I would like to do that after cutting my dataset according to time (which will require some posix-like functions (which I am not exactly sure which would be needed here, or how to use it).
And lastly, there is the question of visualization. How do I create a tag cloud of the words? I found a solution for this here, any other suggestion/recommendations?
I believe I am asking a huge question here but I tried to break it to as many straightforward questions as possible. Any help will be welcomed!
Best,
Tal
Word/Tag cloud in R using "snippets" package
www.wordle.net
Using openNLP package you could pos-tag the tweets(pos=Part of speech) and then extract just the nouns, verbs or adjectives for visualization in a wordcloud.
Maybe you can query twitter and use the current system-time as a time-stamp, write to a local database and query again in increments of x secs/mins, etc.
There is historical data available at http://www.readwriteweb.com/archives/twitter_data_dump_infochimp_puts_1b_connections_up.php and http://www.wired.com/epicenter/2010/04/loc-google-twitter/
As for the plotting piece: I did a word cloud here: http://trends.techcrunch.com/2009/09/25/describe-yourself-in-3-or-4-words/ using the snippets package, my code is in there. I manually pulled out certain words. Check it out and let me know if you have more specific questions.
I note that this is an old question, and there are several solutions available via web search, but here's one answer (via http://blog.ouseful.info/2012/02/15/generating-twitter-wordclouds-in-r-prompted-by-an-open-learning-blogpost/):
require(twitteR)
searchTerm='#dev8d'
#Grab the tweets
rdmTweets <- searchTwitter(searchTerm, n=500)
#Use a handy helper function to put the tweets into a dataframe
tw.df=twListToDF(rdmTweets)
##Note: there are some handy, basic Twitter related functions here:
##https://github.com/matteoredaelli/twitter-r-utils
#For example:
RemoveAtPeople <- function(tweet) {
gsub("#\\w+", "", tweet)
}
#Then for example, remove #d names
tweets <- as.vector(sapply(tw.df$text, RemoveAtPeople))
##Wordcloud - scripts available from various sources; I used:
#http://rdatamining.wordpress.com/2011/11/09/using-text-mining-to-find-out-what-rdatamining-tweets-are-about/
#Call with eg: tw.c=generateCorpus(tw.df$text)
generateCorpus= function(df,my.stopwords=c()){
#Install the textmining library
require(tm)
#The following is cribbed and seems to do what it says on the can
tw.corpus= Corpus(VectorSource(df))
# remove punctuation
tw.corpus = tm_map(tw.corpus, removePunctuation)
#normalise case
tw.corpus = tm_map(tw.corpus, tolower)
# remove stopwords
tw.corpus = tm_map(tw.corpus, removeWords, stopwords('english'))
tw.corpus = tm_map(tw.corpus, removeWords, my.stopwords)
tw.corpus
}
wordcloud.generate=function(corpus,min.freq=3){
require(wordcloud)
doc.m = TermDocumentMatrix(corpus, control = list(minWordLength = 1))
dm = as.matrix(doc.m)
# calculate the frequency of words
v = sort(rowSums(dm), decreasing=TRUE)
d = data.frame(word=names(v), freq=v)
#Generate the wordcloud
wc=wordcloud(d$word, d$freq, min.freq=min.freq)
wc
}
print(wordcloud.generate(generateCorpus(tweets,'dev8d'),7))
##Generate an image file of the wordcloud
png('test.png', width=600,height=600)
wordcloud.generate(generateCorpus(tweets,'dev8d'),7)
dev.off()
#We could make it even easier if we hide away the tweet grabbing code. eg:
tweets.grabber=function(searchTerm,num=500){
require(twitteR)
rdmTweets = searchTwitter(searchTerm, n=num)
tw.df=twListToDF(rdmTweets)
as.vector(sapply(tw.df$text, RemoveAtPeople))
}
#Then we could do something like:
tweets=tweets.grabber('ukgc12')
wordcloud.generate(generateCorpus(tweets),3)
I would like to answer your question in making big word cloud.
What I did is
Use s0.tweet <- searchTwitter(KEYWORD,n=1500) for 7 days or more, such as THIS.
Combine them by this command :
rdmTweets = c(s0.tweet,s1.tweet,s2.tweet,s3.tweet,s4.tweet,s5.tweet,s6.tweet,s7.tweet)
The result:
This Square Cloud consists of about 9000 tweets.
Source: People voice about Lynas Malaysia through Twitter Analysis with R CloudStat
Hope it help!