I read through all specifications of https://entethalliance.github.io/client-spec/qbft_spec.html, and also https://besu.hyperledger.org/en/stable/
It talks about QBFT, but nowhere exactly mentioned what is Q?
BFT is for Byzantine Fault Tolerant, Assuming Q means Quorum here?
The Q is for Quorum - QBFT is implemented in both GoQuorum and Besu.
I could be doing this completely wrong, or I could be on the right path, I have no idea! I'm trying to grade a decision based on 3 criteria. The grades are AAA-A and BBB-B, etc. but for now I just need AAA-A and can figure out the rest.
Essentially, we want Col. J to populate based on what Col.'s G-I say. In my head it's super easy but I want to automate this step.
So I start with col.I and see the pairing.. AAA-A results are any of these "G/G" "LG/G" "G/LG" or "R/R". If it is one of those 4 pairings then we start at AA grade.
Then I check col.G (it doesnt matter now if I check H or G first), and if G>=.5 we grade it higher at AAA, if its less than .5 then do nothing and keep it at AA.
Then I look at col. H (or G if we started at H) and if it is a "Y" we grade down from AA to A. or AAA to AA. But it is "N" do nothing.
What I have so far is attached. It technically works for 3/4 of these cells but that could be a coincidence. The results column(J) should be row3 - AA, row4 - AA, row5 - AAA, row6 - AA.
And for one additional test, imagine: col.g = .64, col.h = Y, col.i = G/G -- then we want AA as the result.
Definitely the hardest test I've had in excel/sheets. I appreciate the help! Thanks in advance!
Formula I tried:
=Ifs((or(I3="G/G",I3="LG/G",I3="G/LG",I3="R/R"),"AA", and(or(I3="G/G",I3="LG/G",I3="G/LG",I3="R/R"),G3>0.5),"AAA",H3="Y","A")
Data Sample:
G
H
I
J
3
-0.07
N
R/R
AA
4
-0.46
N
R/R
AA
5
0.64
N
G/G
AA
6
0.76
Y
LG/G
AA
As presented, your formula simply returns an error, and seems like a misinterpretation of how Ifs works. However, it suggest you're trying to Nest If statements. And, from your description, I think that makes sense.
Assuming that's a valid interpretation, the following does what you want.
(At least as far as AAA-A is concerned).
=If(or(I3="G/G",I3="LG/G",I3="G/LG",I3="R/R"),if(G3<0.5,"AA","AAA"),if(H3="Y","A","Not an A"))
The BBB-B logic would be the same (just nested in where "Not an A" is).
A simple question.
When a roll has been done it shows as:
".q,Juf5 KINH GUI NGUOI D'UNG . 8 6 8 6 38 SDU.NG CHO MOT LAN GD NHE;;yGkq,W"
I wish for some way to isolate the number 868638. However there is no such thing as a split in lua (as far as I know) so what's the best way to accomplish this?
local res = input
:match(string.rep('%d+%s*', 6))
:gsub('%D', '')
I am coding a survey that outputs a .csv file. Within this csv I have some entries that are space delimited, which represent multi-select questions (e.g. questions with more than one response). In the end I want to parse these space delimited entries into their own columns and create headers for them so i know where they came from.
For example I may start with this (note that the multiselect columns have an _M after them):
Q1, Q2_M, Q3, Q4_M
6, 1 2 88, 3, 3 5 99
6, , 3, 1 2
and I want to go to this:
Q1, Q2_M_1, Q2_M_2, Q2_M_88, Q3, Q4_M_1, Q4_M_2, Q4_M_3, Q4_M_5, Q4_M_99
6, 1, 1, 1, 3, 0, 0, 1, 1, 1
6,,,,3,1,1,0,0,0
I imagine this is a relatively common issue to deal with but I have not been able to find it in the R section. Any ideas how to do this in R after importing the .csv ? My general thoughts (which often lead to inefficient programs) are that I can:
(1) pull column numbers that have the special suffix with grep()
(2) loop through (or use an apply) each of the entries in these columns and determine the levels of responses and then create columns accordingly
(3) loop through (or use an apply) and place indicators in appropriate columns to indicate presence of selection
I appreciate any help and please let me know if this is not clear.
I agree with ran2 and aL3Xa that you probably want to change the format of your data to have a different column for each possible reponse. However, if you munging your dataset to a better format proves problematic, it is possible to do what you asked.
process_multichoice <- function(x) lapply(strsplit(x, " "), as.numeric)
q2 <- c("1 2 3 NA 4", "2 5")
processed_q2 <- process_multichoice(q2)
[[1]]
[1] 1 2 3 NA 4
[[2]]
[1] 2 5
The reason different columns for different responses are suggested is because it is still quite unpleasant trying to retrieve any statistics from the data in this form. Although you can do things like
# Number of reponses given
sapply(processed_q2, length)
#Frequency of each response
table(unlist(processed_q2), useNA = "ifany")
EDIT: One more piece of advice. Keep the code that processes your data separate from the code that analyses it. If you create any graphs, keep the code for creating them separate again. I've been down the road of mixing things together, and it isn't pretty. (Especially when you come back to the code six months later.)
I am not entirely sure what you trying to do respectively what your reasons are for coding like this. Thus my advice is more general – so just feel to clarify and I will try to give a more concrete response.
1) I say that you are coding the survey on your own, which is great because it means you have influence on your .csv file. I would NEVER use different kinds of separation in the same .csv file. Just do the naming from the very beginning, just like you suggested in the second block.
Otherwise you might geht into trouble with checkboxes for example. Let's say someone checks 3 out of 5 possible answers, the next only checks 1 (i.e. "don't know") . Now it will be much harder to create a spreadsheet (data.frame) type of results view as opposed to having an empty field (which turns out to be an NA in R) that only needs to be recoded.
2) Another important question is whether you intend to do a panel survey(i.e longitudinal study asking the same participants over and over again) . That (among many others) would be a good reason to think about saving your data to a MySQL database instead of .csv . RMySQL can connect directly to the database and access its tables and more important its VIEWS.
Views really help with survey data since you can rearrange the data in different views, conditional on many different needs.
3) Besides all the personal / opinion and experience, here's some (less biased) literature to get started:
Complex Surveys: A Guide to Analysis Using R (Wiley Series in Survey Methodology
The book is comparatively simple and leaves out panel surveys but gives a lot of R Code and examples which should be a practical start.
To prevent re-inventing the wheel you might want to check LimeSurvey, a pretty decent (not speaking of the templates :) ) tool for survey conductors. Besides I TYPO3 CMS extensions pbsurvey and ke_questionnaire (should) work well too (only tested pbsurvey).
Multiple choice items should always be coded as separate variables. That is, if you have 5 alternatives and multiple choice, you should code them as i1, i2, i3, i4, i5, i.e. each one is a binary variable (0-1). I see that you have values 3 5 99 for Q4_M variable in the first example. Does that mean that you have 99 alternatives in an item? Ouch...
First you should go on and create separate variables for each alternative in a multiple choice item. That is, do:
# note that I follow your example with Q4_M variable
dtf_ins <- as.data.frame(matrix(0, nrow = nrow(<initial dataframe>), ncol = 99))
# name vars appropriately
names(dtf_ins) <- paste("Q4_M_", 1:99, sep = "")
now you have a data.frame with 0s, so what you need to do is to get 1s in an appropriate position (this is a bit cumbersome), a function will do the job...
# first you gotta change spaces to commas and convert character variable to a numeric one
y <- paste("c(", gsub(" ", ", ", x), ")", sep = "")
z <- eval(parse(text = y))
# now you assing 1 according to indexes in z variable
dtf_ins[1, z] <- 1
And that's pretty much it... basically, you would like to reconsider creating a data.frame with _M variables, so you can write a function that does this insertion automatically. Avoid for loops!
Or, even better, create a matrix with logicals, and just do dtf[m] <- 1, where dtf is your multiple-choice data.frame, and m is matrix with logicals.
I would like to help you more on this one, but I'm recuperating after a looong night! =) Hope that I've helped a bit! =)
Thanks for all the responses. I agree with most of you that this format is kind of silly but it is what I have to work with (survey is coded and going into use next week). This is what I came up with from all the responses. I am sure this is not the most elegant or efficient way to do it but I think it should work.
colnums <- grep("_M",colnames(dat))
responses <- nrow(dat)
for (i in colnums) {
vec <- as.vector(dat[,i]) #turn into vector
b <- lapply(strsplit(vec," "),as.numeric) #split up and turn into numeric
c <- sort(unique(unlist(b))) #which values were used
newcolnames <- paste(colnames(dat[i]),"_",c,sep="") #column names
e <- matrix(nrow=responses,ncol=length(c)) #create new matrix for indicators
colnames(e) <- newcolnames
#next loop looks for responses and puts indicators in the correct places
for (i in 1:responses) {
e[i,] <- ifelse(c %in% b[[i]],1,0)
}
dat <- cbind(dat,e)
}
Suggestions for improvement are welcome.
I need a well tested Regular Expression (.net style preferred), or some other simple bit of code that will parse a USA/CA phone number into component parts, so:
3035551234122
1-303-555-1234x122
(303)555-1234-122
1 (303) 555 -1234-122
etc...
all parse into:
AreaCode: 303
Exchange: 555
Suffix: 1234
Extension: 122
None of the answers given so far was robust enough for me, so I continued looking for something better, and I found it:
Google's library for dealing with phone numbers
I hope it is also useful for you.
This is the one I use:
^(?:(?:[\+]?(?<CountryCode>[\d]{1,3}(?:[ ]+|[\-.])))?[(]?(?<AreaCode>[\d]{3})[\-/)]?(?:[ ]+)?)?(?<Number>[a-zA-Z2-9][a-zA-Z0-9 \-.]{6,})(?:(?:[ ]+|[xX]|(i:ext[\.]?)){1,2}(?<Ext>[\d]{1,5}))?$
I got it from RegexLib I believe.
This regex works exactly as you want with your examples:
Regex regexObj = new Regex(#"\(?(?<AreaCode>[0-9]{3})\)?[-. ]?(?<Exchange>[0-9]{3})[-. ]*?(?<Suffix>[0-9]{4})[-. x]?(?<Extension>[0-9]{3})");
Match matchResult = regexObj.Match("1 (303) 555 -1234-122");
// Now you have the results in groups
matchResult.Groups["AreaCode"];
matchResult.Groups["Exchange"];
matchResult.Groups["Suffix"];
matchResult.Groups["Extension"];
Strip out anything that's not a digit first. Then all your examples reduce to:
/^1?(\d{3})(\d{3})(\d{4})(\d*)$/
To support all country codes is a little more complicated, but the same general rule applies.
Here is a well-written library used with GeoIP for instance:
http://highway.to/geoip/numberparser.inc
here's a method easier on the eyes provided by the Z Directory (vettrasoft.com),
geared towards American phone numbers:
string_o s2, s1 = "888/872.7676";
z_fix_phone_number (s1, s2);
cout << s2.print(); // prints "+1 (888) 872-7676"
phone_number_o pho = s2;
pho.store_save();
the last line stores the number to database table "phone_number".
column values: country_code = "1", area_code = "888", exchange = "872",
etc.