search for: unflattened

Hi All, I'm pretty new to R, so would really appreciate it if someone could point me in the right direction on this problem. I am trying to "unflatten" a table in R, and can't seem to find a function or method to complete this task, (hopefully efficiently). My data table is full of historical stock-market data. Daily, each ticker-symbol has four data points: open, close,

Here's an easy question: I'd like to convert a symmetric matrix to a vector composed of the upper.tri() part of it plus the diagonal, and convert it back again. What's the best way to achieve this? I'm wondering if there are some built in functions to do this easily. I can encode fine: v <- c(diag(A),A[upper.tri(A)]) but I don't see an easy way to recover A from v

Hello, How would you export a 3-dimensional array to an SQL database? a<-array(1:24, 2:4) Is there an open source DB that would be more adequate for this type of operation? Is there a way to reshape/flatten a 3-dimensional array? Regards, Pierre Lapointe ************************************************** AVIS DE NON-RESPONSABILITE: Ce document transmis par courrie...{{dropped}}

...tion of a new family of intrinsics, `llvm.multidim.array.index.*`. This will allow us to represent array indexing into an array `A[d1][d2][..][dk]` as `llvm.multidim.array.index.k A d1, d2, d3, ... dk` instead of flattening the information into `gep A, d1 * n1 + d2 * n2 + ... + dk * nk`. The former unflattened representation is advantageous for analysis and optimisation. It also allows us to represent array indexing semantics of languages such as Fortran and Chapel, which differs from that of C based array indexing. ## Motivating the need for a multi-dimensional array indexing intrinsic There are prim...

...`llvm.multidim.array.index.*`. >> This will allow us to represent array indexing into an array `A[d1][d2][..][dk]` >> as `llvm.multidim.array.index.k A d1, d2, d3, ... dk` instead of flattening >> the information into `gep A, d1 * n1 + d2 * n2 + ... + dk * nk`. The former >> unflattened representation is advantageous for analysis and optimisation. It also >> allows us to represent array indexing semantics of languages such as Fortran >> and Chapel, which differs from that of C based array indexing. >> >> ## Motivating the need for a multi-dimensional array...

...`llvm.multidim.array.index.*`. > This will allow us to represent array indexing into an array `A[d1][d2][..][dk]` > as `llvm.multidim.array.index.k A d1, d2, d3, ... dk` instead of flattening > the information into `gep A, d1 * n1 + d2 * n2 + ... + dk * nk`. The former > unflattened representation is advantageous for analysis and optimisation. It also > allows us to represent array indexing semantics of languages such as Fortran > and Chapel, which differs from that of C based array indexing. > > ## Motivating the need for a multi-dimensional arra...

...rray.index.*`. >>> This will allow us to represent array indexing into an array `A[d1][d2][..][dk]` >>> as `llvm.multidim.array.index.k A d1, d2, d3, ... dk` instead of flattening >>> the information into `gep A, d1 * n1 + d2 * n2 + ... + dk * nk`. The former >>> unflattened representation is advantageous for analysis and optimisation. It also >>> allows us to represent array indexing semantics of languages such as Fortran >>> and Chapel, which differs from that of C based array indexing. >>> >>> ## Motivating the need for a multi-di...

...> This will allow us to represent array indexing into an array `A[d1][d2][..][dk]` > > > as `llvm.multidim.array.index.k A d1, d2, d3, ... dk` instead of flattening > > > the information into `gep A, d1 * n1 + d2 * n2 + ... + dk * nk`. The former > > > unflattened representation is advantageous for analysis and optimisation. It also > > > allows us to represent array indexing semantics of languages such as Fortran > > > and Chapel, which differs from that of C based array indexing. > > > > > > ## Motivating the...