Agreed! Axis-reducing dot product operators might be a reasonable addition to the standard library, especially since BLAS provides the (presumably highly optimized or even multi-threaded) dot product primitive, which a library function could easily sub out to using the appropriate strides.
*Sebastian Good* On Thu, Nov 6, 2014 at 1:10 PM, Douglas Bates <dmba...@gmail.com> wrote: > On Thursday, November 6, 2014 10:48:26 AM UTC-6, Sebastian Good wrote: >> >> A good solution for this particular problem, though presumably uses more >> memory than a dedicated axis-aware dot product method. >> > > You are correct that the method I showed does create a matrix of the same > size as `a` and `b` to evaluate the `.+` operation. You can avoid doing so > but, of course, the code becomes more opaque. I think the point for me is > that if `a` and `b` are so large that the allocation and freeing of the > memory becomes problematic, I can write the space conserving version in > Julia and get performance comparable to compiled code. Lately when > describing Julia to colleagues I mention the type system and multiple > dispatch and several other aspects showing how well-designed Julia is. But > the point that I emphasize is "one language", which sometimes I extend to > "One language to rule them all" (I assume everyone is familiar with "Lord > of the Rings"). I can write Julia code at in a high-level, vectorized > style (like R, Matlab/Octave) but I can also, if I need to, write low-level > iterative code in Julia. I don't need to use a compiled language write > interface code. > > If I have very large arrays, perhaps even memory-mapped arrays because > they are so large, I could define a function > > function rowdot{T}(a::DenseMatrix{T},b::DenseMatrix{T}) > ((m,n) = size(a)) == size(b) || throw(DimensionMismatch("")) > res = zeros(T,(m,)) > for j in 1:n, i in 1:m > res[i] += a[i,j] * b[i,j] > end > res > end > > that avoided creating the temporary. Once I convinced myself that there > were no problems in the code (and my first version did indeed have a bug) I > could change the loop to > > @simd for j in 1:n, i in 1:m > @inbounds res[i] += a[i,j] * b[i,j] > end > > and improve the performance. In the next iteration I could use > SharedArrays and parallelize the calculation if it really needed it. > > As a programmer I am grateful for the incredible freedom that Julia gives > me to get as obsessive compulsive about performance as I want. > > Thanks! >> > > You're welcome. > > >> >> On Thursday, November 6, 2014 10:42:26 AM UTC-5, Douglas Bates wrote: >>> >>> >>> >>> On Thursday, November 6, 2014 9:14:10 AM UTC-6, Sebastian Good wrote: >>>> >>>> Working through the excellent coursera machine-learning course, I found >>>> myself using the row-wise (axis-wise) dot product in Octave, but found >>>> there was no obvious equivalent in Julia. >>>> >>>> In Octave/Matlab, one can call dot(a,b,2) to get the row-wise dot >>>> product of two mxn matrices, returned as a new column vector of size mx1. >>>> >>>> Even though Julia makes for loops faster, I like sum(dot(a,b,2)) for >>>> its concision over the equivalent array comprehension or explicit for loop. >>>> >>>> Hopefully I'm just missing an overload or alternate name? >>>> >>> >>> >>> julia> a = rand(10,4) >>> 10x4 Array{Float64,2}: >>> 0.134279 0.135088 0.33185 0.956108 >>> 0.977812 0.219557 0.887589 0.468597 >>> 0.69524 0.310889 0.449669 0.717189 >>> 0.385896 0.675195 0.0810221 0.179553 >>> 0.717348 0.138556 0.52147 0.458516 >>> 0.821631 0.337048 0.367002 0.320554 >>> 0.531433 0.0298744 0.344748 0.722242 >>> 0.708596 0.550999 0.629017 0.787594 >>> 0.803008 0.380515 0.729874 0.744713 >>> 0.166205 0.5589 0.605327 0.246186 >>> >>> julia> b = randn(10,4) >>> 10x4 Array{Float64,2}: >>> 0.551047 -0.284285 -1.33048 0.0216755 >>> -1.16133 -0.552537 0.395243 -1.72303 >>> -0.0181444 -0.481539 -0.985497 0.352999 >>> 1.20222 -0.557973 0.428804 -1.1013 >>> 2.31078 0.0909548 0.329372 0.651853 >>> 0.341906 -0.109811 -0.360118 0.550494 >>> 0.988644 1.02413 0.570208 0.48143 >>> -1.75465 0.147909 -1.35159 0.89136 >>> -0.105066 -1.04501 -0.682836 0.600948 >>> 0.556118 -1.24914 -2.45667 -1.02942 >>> >>> julia> sum(a .* b,2) >>> 10x1 Array{Float64,2}: >>> -0.385205 >>> -1.71347 >>> -0.3523 >>> -0.0758094 >>> 2.14088 >>> 0.288209 >>> 1.10028 >>> -1.30999 >>> -0.532863 >>> -2.34624 >>> >>> >>>