I’d suggest slices for consistency with the function mapslices. — John
On Oct 9, 2014, at 6:15 PM, Tim Holy <[email protected]> wrote: > Would be great to have it clarified in the manual. > > I think I've brought this up before, and if there was a consensus I don't > recall it. In my opinion, the various usages of "dims" and "region" in the > manual and help are pretty confusing. It would be nice to standardize > terminology. I confess to being fond of talking about the "axes" of an array, > but I am fine with other choices too. > > --Tim > > On Friday, October 10, 2014 07:29:06 AM K Leo wrote: >> Thanks to both for explanations. "along dimensions in region" sounds >> pretty confusing to me. Can that be stated more clearly? Pardon my >> English. >> >> I guess this is what I wanted. >> >> julia> [std(A[i:i+9]) for i=1:length(A)-9] >> 91-element Array{Any,1}: >> 0.395761 >> 0.391694 >> 0.392545 >> 0.363307 > 0.392545 >> ⋮ >> 0.322292 >> 0.325662 >> 0.345799 >> >> On 2014年10月10日 07:17, Simon Kornblith wrote: >>> Or alternatively: >>> >>> >>> std(reshape(A,10,div(length(A),10)),1) >>> >>> >>> Simon >>> >>> On Thursday, October 9, 2014 7:10:11 PM UTC-4, Patrick O'Leary wrote: >>> "optionally *along dimensions in region*" (emphasis mine). You are >>> attempting to read along the tenth dimension of the array. >>> >>> You're trying to split the array into groups of ten elements, it >>> sounds like. >>> >>> [std(A[10(n-1)+1:10n]) for n in 1:length(A)./10] >>> >>> On Thursday, October 9, 2014 5:56:01 PM UTC-5, K leo wrote: >>> I am hoping to get the std's of every 10 consecutive elements >>> in A. >>> >>> std(v[, region]) >>> Compute the sample standard deviation of a vector or array v, >>> optionally >>> along dimensions in region. The algorithm returns an estimator >>> of the >>> generative distribution’s standard deviation under the >>> assumption that >>> each entry of v is an IID drawn from that generative >>> distribution. This >>> computation is equivalent to calculating sqrt(sum((v - >>> mean(v)).^2) / >>> (length(v) - 1)). Note: Julia does not ignore NaN values in the >>> computation. For applications requiring the handling of >>> missing data, >>> the DataArray package is recommended. >>> >>> On 2014年10月10日 06:49, Patrick O'Leary wrote: >>>> On Thursday, October 9, 2014 5:42:40 PM UTC-5, K leo wrote: >>>> julia> std(A, 10) >>>> >>>> A only has elements along the first dimension. What behavior >>> >>> do you >>> >>>> expect here? >
