OK, that works fine. Thanks. I think it would be a good idea to drop the
matlab-ism in future versions.
Patrik
On Thursday, November 17, 2016 at 11:51:09 AM UTC+1, Simon Byrne wrote:
> I'm not familiar with the package in question, but this line:
>
> w = Any[ 0.1*randn(1,13), 0 ]
>
> may be what is causing the problem. It is creating a 2-element Vector, the
> first element of which is a 1x13 Matrix, and the second element is a scalar
> 0. The analogous object in R would be:
>
> W = list(matrix(0.1*rnorm(13),nrow=1), 0)
>
> In Julia, extraneous dimensions have an implicit index of 1 (this is a
> matlab-ism, and may disappear in future), so w[1], w[1,1], w[1,1,1] are
> all identical (and equivalent to W[[1]] in R). w[1,:] is a bit of an odd
> case in that it returns a 1-element Vector containing a Matrix, but would
> be equivalent to W[1] in R.
>
> I think what you may want is actually
>
> w[1][(w[1].<z) & (w[1].>-(z))]
>
> which can be written more clearly as
>
> w[1][-z .< w[1] .< z]
>
>
> -Simon
>
>
>
>
>
> On Thursday, 17 November 2016 09:39:14 UTC, Patrik Waldmann wrote:
>>
>> I guess I should have explained my problem clearer. If I run the code
>> without w[1,(w[1].<z)&(w[1].>-(z))] = 0, and do:
>> dump(w)
>> Array{Any}((2,))
>> 1:
>> Array{Float64}((1,13))
>> [-0.681392 0.595298 … 0.893845 -3.5044]
>> 2: Float64 22.447679788630705
>>
>> and
>> println(w[1])
>> [-0.681392 0.595298 -0.394906 0.776983 -1.11178 3.11679 -0.0984956
>> -2.18501 0.928204 -0.484802 -1.86844 0.893845 -3.5044]
>>
>> println(w[1,1])
>> [-0.681392 0.595298 -0.394906 0.776983 -1.11178 3.11679 -0.0984956
>> -2.18501 0.928204 -0.484802 -1.86844 0.893845 -3.5044]
>>
>> println(w[1,:])
>> Any[
>> [-0.681392 0.595298 -0.394906 0.776983 -1.11178 3.11679 -0.0984956
>> -2.18501 0.928204 -0.484802 -1.86844 0.893845 -3.5044]]
>>
>>
>> This is very confusing for an R user like me. How do I access the column
>> indexes of w[1] and apply the logical expression
>> w[1,(w[1].<z)&(w[1].>-(z))] = 0 ?
>>
>> Patrik
>>
>>
>>
>>
>> On Thursday, November 17, 2016 at 6:58:14 AM UTC+1, Jeffrey Sarnoff wrote:
>>
>>> good things to know about how indexing works
>>>
>>>
>>> The indices for a Vector, or a column or row of a Matrix start at *1*
>>>
>>> ```
>>> length(avector) # gets the number of elements in avector
>>>
>>> avector[1] # gets the first item in avector
>>> avector[end] # gets the final item in avector
>>> avector[1:end] # gets all elements of avector
>>>
>>> int_column_vector = [10, 20, 30]
>>> 10
>>> 20
>>> 30
>>>
>>> int_column_vector[1]
>>> 10
>>> # do not use zero as an index
>>> int_column_vector[ 0 ]
>>> ERROR: BoundsError:
>>> # do not use false, true as indices because avec[ false ] means avec[ 0 ]
>>>
>>> ```
>>>
>>> in ` w[1,(w[1].<z)&(w[1].>-(z))] = 0 `, the second index can simplify to
>>> `false` (consider this)
>>> ```
>>> avec = [ 10, 20, 30 ]
>>> avec1 = avec[ 1 ]
>>> avec1 == avec[ 1 + false ]
>>> avec2 = avec[ 2 ]
>>> avec2 == avec[ 1 + true ]
>>> ```
>>>
>>> As a start, recheck indexing expressions, be more sure they do what you
>>> want them to do.
>>>
>>>
>>> On Wednesday, November 16, 2016 at 1:36:57 PM UTC-5, Patrik Waldmann
>>> wrote:
>>>>
>>>> Hi,
>>>>
>>>> I'm an R user trying to learn Julia. I got hold of some code from the
>>>> Knet package that I was playing around with. My goal is to set values to
>>>> zero in a loop based on a logical expression, but I cannot figure out how
>>>> the indexing works. Any help would be appreciated (the problem lies in
>>>> w[1,(w[1].<z)&(w[1].>-(z))] = 0):
>>>>
>>>> using Knet
>>>> predict(w,x) = w[1]*x .+ w[2]
>>>> lambda = 2
>>>> z = Array{Float64}(1,13)
>>>> loss(w,x,y) = sumabs2(y - predict(w,x)) / size(y,2)
>>>> lossgradient = grad(loss)
>>>> function train(w, data; lr=.1)
>>>> for (x,y) in data
>>>> dw = lossgradient(w, x, y)
>>>> z[:] = lr * lambda
>>>> w[1] -= lr * dw[1]
>>>> w[2] -= lr * dw[2]
>>>> w[1,(w[1].<z)&(w[1].>-(z))] = 0
>>>> end
>>>> return w
>>>> end
>>>> url = "
>>>> https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data
>>>> "
>>>> rawdata = readdlm(download(url))
>>>> x = rawdata[:,1:13]'
>>>> x = (x .- mean(x,2)) ./ std(x,2)
>>>> y = rawdata[:,14:14]'
>>>> w = Any[ 0.1*randn(1,13), 0 ]
>>>> niter = 25
>>>> lossest = zeros(niter)
>>>> for i=1:niter; train(w, [(x,y)]); lossest[i]=loss(w,x,y); end
>>>>
>>>>
>>>> Best regards,
>>>>
>>>> Patrik
>>>>
>>>
