search for: m_id

...eam); m_bReleaseStream = true; return true; } // ------------- Source.h class GAL_API Source { public: // 1. Set a loaded buffer as current buffer void SetCurrentBuffer (Buffer *pBuf); // 2. operations virtual bool Play (); virtual void Stop () { alSourceStop (m_id); } virtual void Pause () { alSourcePause (m_id); } virtual void Rewind () { alSourceRewind (m_id); } // 3. Update if buffers are queued virtual bool Update (); // Update Queued Buffers bool IsPlaying (); void Empty (); // Empty Queued Buffer // play attribut...

...trouble in a matrix multiplication... I have already checked some posts, but I didn't find my problem... I have the following code... But I am not getting the right multiplication... I checked the dimension and they are fine... id_y <- array(1:10,dim=c(2,1,5)) id_yt<-aperm(id_y,c(2,1,3)) m_id<-array(dim=c(dim(id_y)[1],dim(id_y)[1],dim(id_y)[3])) for (i in 1:dim(id_y)[3]){ m_id[,,i]<-id_y[,,i]%*%id_yt[,,i] } m_id -- View this message in context: http://www.nabble.com/Matrix-multiplication---code-problem-tp22835003p22835003.html Sent from the R help mailing list archive at Nabble...

I have a simple query as follows: "SELECT m_id,sale_date,YEAR(sale_date),WEEK(sale_date),return_type,DATEDIFF(return_date,sale_date) AS elapsed_time FROM risk_input" I can get, and view, all the data that that query returns. The question is, sale_date is a timestamp, and I need to call split to group this data by m_id and the week in whi...

...e plyr, or a combination of functions like split and lapply would allow me to have a really short R script to analyze all my data (I have reduced it to a couple hundred thousand records with about half a dozen records. I get the same result from ddply and split/lapply: > ddply(moreinfo,c("m_id","sale_year","sale_week"), > + function(df) data.frame(res = fitdist(df$elapsed_time,"exp"),est = > res$estimate,sd = res$sd)) > Error in fitdist(df$elapsed_time, "exp") : > data must be a numeric vector of length greater than 1 >...

...dley hints <- function(x) { db <- eval(utils:::.hsearch_db()) if (is.null(db)) { help.search("abcd!", rebuild=TRUE, agrep=FALSE) db <- eval(utils:::.hsearch_db()) } base <- db$Base alias <- db$Aliases key <- db$Keywords m <- all.methods(class=class(x)) m_id <- alias[match(m, alias[,1]), 2] keywords <- lapply(m_id, function(id) key[key[,2] %in% id, 1]) f.names <- cbind(m, base[match(m_id, base[,3]), 4]) f.names <- unlist(lapply(1:nrow(f.names), function(i) { if (is.na(f.names[i, 2])) return(f.names[i, 1]) a <- methodsplit(f.names...

...dley hints <- function(x) { db <- eval(utils:::.hsearch_db()) if (is.null(db)) { help.search("abcd!", rebuild=TRUE, agrep=FALSE) db <- eval(utils:::.hsearch_db()) } base <- db$Base alias <- db$Aliases key <- db$Keywords m <- all.methods(class=class(x)) m_id <- alias[match(m, alias[,1]), 2] keywords <- lapply(m_id, function(id) key[key[,2] %in% id, 1]) f.names <- cbind(m, base[match(m_id, base[,3]), 4]) f.names <- unlist(lapply(1:nrow(f.names), function(i) { if (is.na(f.names[i, 2])) return(f.names[i, 1]) a <- methodsplit(f.names...

...Within Rgui, the script continues through to the end, but the loop that is involved terminates at the line where this error occurs. The line that results in this error is: moreinfo <- dbGetQuery(con, x) This statement occurs in a loop that ought to iterate over a few hundred values for m_id (see the SQL below). Because of the above error, I never see about two thirds of the results that ought to be produced. At the time that the error occurs, x contains the following SQL query: SELECT m_id,sale_date,YEAR(sale_date) AS sale_year,MONTH(sale_date) AS sale_month,return_type,0.0001 + DA...

Here is the function that makes the data.frames in the list: funweek <- function(df) if (length(df$elapsed_time) > 5) { res = fitdist(df$elapsed_time,"exp") year = df$sale_year[1] sample = df$sale_week[1] mid = df$m_id[1] estimate = res$estimate sd = res$sd samplesize = res$n loglik = res$loglik aic = res$aic bic = res$bic chisq = res$chisq chisqpvalue = res$chisqpvalue chisqdf = res$chisqdf if (!is.null(estimate) && !is.null(sd) && !is.null(loglik) &&am...

I must have missed something simple, but still, i don't know what. I obtained my basic data as follows: x <- sprintf("SELECT m_id,sale_date,YEAR(sale_date) AS sale_year,WEEK(sale_date) AS sale_week,return_type,0.0001 + DATEDIFF(return_date,sale_date) AS elapsed_time FROM `merchants2`.`risk_input` WHERE DATEDIFF(return_date,sale_date) IS NOT NULL") moreinfo <- dbGetQuery(con, x) I then made the data frame I want to us...

...help.search("abcd!", rebuild=TRUE, agrep=FALSE) > > db <- eval(utils:::.hsearch_db()) > > } > > > > base <- db$Base > > alias <- db$Aliases > > key <- db$Keywords > > > > m <- all.methods(class=class(x)) > > m_id <- alias[match(m, alias[,1]), 2] > > keywords <- lapply(m_id, function(id) key[key[,2] %in% id, 1]) > > > > f.names <- cbind(m, base[match(m_id, base[,3]), 4]) > > f.names <- unlist(lapply(1:nrow(f.names), function(i) { > > if (is.na(f.names[i, 2])) r...

...help.search("abcd!", rebuild=TRUE, agrep=FALSE) > > db <- eval(utils:::.hsearch_db()) > > } > > > > base <- db$Base > > alias <- db$Aliases > > key <- db$Keywords > > > > m <- all.methods(class=class(x)) > > m_id <- alias[match(m, alias[,1]), 2] > > keywords <- lapply(m_id, function(id) key[key[,2] %in% id, 1]) > > > > f.names <- cbind(m, base[match(m_id, base[,3]), 4]) > > f.names <- unlist(lapply(1:nrow(f.names), function(i) { > > if (is.na(f.names[i, 2])) r...

...rv$granularity = "month" rv } It is basically the data.frame created by fitdist extended to include the variables used to distinguish one sample from another. I have the following statement that gets me a set of IDs from my db: ids <- dbGetQuery(con, "SELECT DISTINCT m_id FROM risk_input") And then I have a loop that allows me to analyze one dataset after another: for (i in 1:length(ids[,1])) { print(i) print(ids[i,1]) Then, after a set of statements that give me information about the dataset (such as its size), within a conditional block that ensures I...

I am feeling rather dumb right now. I created what I thought was a data.frame as follows: aaa <- lapply(split(moreinfo,list(moreinfo$m_id),drop = TRUE), fun_m_id) m_id_default_res <- do.call(rbind, aaa) print("==========================================") m_id_default_res print("==========================================") ndf <- m_id_default_res[, c('mid', 'estimate', 'sd', 'logli...