A good solution for this particular problem, though presumably uses more
memory than a dedicated axis-aware dot product method. Thanks!
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
>
>
>
