search for: calculatons

Hi, have anybody a hint /starting point how i can enlarge my code that's posiible for me make any calculaton's with variables in the 64 subset's. Example: I want calculate difference of two variables inside every of the 64 subset data.frames and getting this value in tList! Many thanks, Christian tasign <- paste("tList[[n]] <- try(dtree[dtree$class02 ==

...o not think my likelihood function is the correct one to be using for this purpose. Firstly, if sum of squares is the correct method, maybe I should be taking the square root of the likelihood (makes no difference) and possibly the 'mean' values of the datasets may need to be included in my calculatons. However, sum of squares suggests my data are normally distributed (which it is clearly not) Obs (boolean O or 1) Pred (beta O to 1) Difference (beta -1 to 1) My guess is that I should be using a beta (or uniform) defined likelihood measure. Or maybe just a simple transformation. Any help greatly...

I missed that the testing case is returning a struct. You are right in VARegSaveSize. For callee: sub sp, sp, #16 push {r11, lr} mov r11, sp sub sp, sp, #8 str r3, [r11, #20] str r2, [r11, #16] str r1, [r11, #12] ldr r1, [r11, #76] The beginning of the input struct @ sp_at_entry - 16 - 8 + 12 = sp_at_entry -12 # of leftover bytes 67-12 = 55 r11+76 is @ sp_at_entry - 24 + 76 = sp_at_entry

Hi Rajesh, The callee code looks okay to me > Assembly for check114 > --------------------------------------------------------------- > sub sp, sp, #16 > push {r11, lr} > mov r11, sp > sub sp, sp, #8 > str r3, [r11, #20] > str r2, [r11, #16] > str r1, [r11, #12] > ldr r1,

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> - "since ABI says the stack pointer needs to be 8 byte aligned at function entry point" (taken from Manman's reply) > What will be considered as entry point here? > Is it place of SP Adjustments "sub sp, sp, #16" > (Or) Is it place of first user instruction(end of prologue) "ldr r2, .LCPI0_0" Eight byte stack alignment is a

Dear All, The institute I work for is organizing an internal workshop for High Performance Computing (HPC). I am planning to attend it and talk a bit about fluid dynamics, but there is also quite a lot of interest devoted to data post-processing and management of huge data sets. A lot of people are interested in image processing/pattern recognition and statistic applied to geography/ecology, but I