The second parameter to the Array type is the number of dimensions of the
array. "Row vectors" are implemented with 2-dimensional arrays (a.k.a.,
"matrices"), since Vectors (a.k.a. Array{T, 1} for all types T) are
interpreted as columns.
Since pairwise() generates all of the distances simultaneously, you'll need
to slice its results to use it in sortperm(), following your original loop
implementation. Each slice will need to be a Vector. If you slice columns,
you get Vector slices for free. If you slice rows, the shape will be
maintained, so you get a row back. You can flatten this to a Vector with
the vec() function.
Hope this helps!
On Wednesday, July 2, 2014 4:23:52 PM UTC-5, Donald Lacombe wrote:
>
> After thinking about this issue, another couple of question came to mind.
>
> When I create a row vector, say xc = randn(1,5), the results indicate that
> the vector is e.g.
>
> xc = rand(1,5)
>
> 1x5 Array{Float64,2}:
>
> 0.989421 0.718148 0.499914 0.13024 0.939856
>
>
> The type is {Float64,2}.
>
>
> Questions:
>
>
> 1) What does the "2" mean in {Float64,2}? If I create xc = rand(5) the type
> is {Float64,1}.
>
>
> 2) The sortperm function only seems to accept {Float64,1} types. The
> Euclidean distance code create types of {Float64,2} but I need {Float64,1}
> for the sortperm function. Do I need to convert these before using this
> command?
>
>
> I'm probably missing something really basic so apologies for the query.
>
>
> Thanks,
>
> Don
>
>
> On Wednesday, July 2, 2014 4:20:55 PM UTC-4, Donald Lacombe wrote:
>>
>> Greetings, part 2...
>>
>> After doing some tests, I found that this code works just fine:
>>
>> julia> using Distance
>>
>> Warning: could not import Base.foldl into NumericExtensions
>>
>> Warning: could not import Base.foldr into NumericExtensions
>>
>> Warning: could not import Base.sum! into NumericExtensions
>>
>> Warning: could not import Base.maximum! into NumericExtensions
>>
>> Warning: could not import Base.minimum! into NumericExtensions
>>
>>
>>
>> julia> xc = rand(8)'
>>
>> 1x8 Array{Float64,2}:
>>
>> 0.30107 0.479169 0.230607 0.65126 0.386921 0.455316 0.921496 0.244873
>>
>>
>> julia> yc = rand(8)'
>>
>> 1x8 Array{Float64,2}:
>>
>> 0.866199 0.767794 0.76163 0.262925 … 0.742765 0.980952 0.424966
>>
>>
>> julia> temp = [xc ; yc]
>>
>> 2x8 Array{Float64,2}:
>>
>> 0.30107 0.479169 0.230607 0.65126 … 0.455316 0.921496 0.244873
>>
>> 0.866199 0.767794 0.76163 0.262925 0.742765 0.980952 0.424966
>>
>>
>> julia> R = pairwise(Euclidean(),temp)
>>
>> 8x8 Array{Float64,2}:
>>
>> 0.0 0.203477 0.126094 0.697548 … 0.197554 0.630949 0.444797
>>
>> 0.203477 0.0 0.248638 0.533393 0.0345751 0.491009 0.415241
>>
>> 0.126094 0.248638 0.0 0.652423 0.225499 0.724865 0.336966
>>
>> 0.697548 0.533393 0.652423 0.0 0.518306 0.767196 0.437502
>>
>> 0.376201 0.283308 0.304834 0.355027 0.252287 0.719136 0.160613
>>
>> 0.197554 0.0345751 0.225499 0.518306 … 0.0 0.523505 0.381159
>>
>> 0.630949 0.491009 0.724865 0.767196 0.523505 0.0 0.87575
>>
>> 0.444797 0.415241 0.336966 0.437502 0.381159 0.87575 0.0
>>
>>
>> As expected, the diagonal elements are 0 and everything seems to work
>> fine although I did not test other distance formulas.
>>
>> I'm posing this so others can see the results and possibly make use of
>> this themselves.
>>
>> Is there a central repository for user submitted solutions or examples?
>> If so I'd like to post this so others can see or alternatively someone else
>> is free to use this (and the original code I posted) for future reference.
>>
>> Thank you all again for your help! I'm going to be coding some models
>> this summer and will probably be back to bug everyone again.
>>
>> Thanks,
>> Don
>>
>> On Wednesday, July 2, 2014 10:22:58 AM UTC-4, Donald Lacombe wrote:
>>>
>>> Greetings!
>>>
>>> I am a new Julia user and have the following issue. I am writing code to
>>> calculate a knn spatial weight matrix to estimate a spatial econometric
>>> model. Using the MATLAB code from Jim LeSage's Econometrics Toolbox, I
>>> converted that code into Julia as follows:
>>>
>>> xc = rand(8);
>>>
>>> yc = rand(8);
>>>
>>> n = length(xc);
>>>
>>> k = 2;
>>>
>>> distance = zeros(n);
>>>
>>> nnlist = zeros(n,k);
>>>
>>> tempw = zeros(n,n);
>>>
>>>
>>> for i=1:n;
>>>
>>> xi = xc[i,1];
>>>
>>> yi = yc[i,1];
>>>
>>> distance = (xc - xi*ones(n)).^2 + (yc - yi*ones(n)).^2
>>>
>>> temp = sortperm(distance)
>>>
>>> nnlist[i,1:k] = temp[2:k+1,1]';
>>>
>>> end
>>>
>>>
>>> for i=1:n
>>>
>>> tempw[i,nnlist[i,:]] = 1;
>>>
>>> end
>>>
>>>
>>> W = tempw/k;
>>>
>>>
>>> This is a "toy" example and I was wondering if I can use the Distance
>>> package to simplify the distance = (xc - xi*ones(n)).^2 + (yc -
>>> yi*ones(n)).^2 formula. I tried using the pairwise option like so:
>>>
>>>
>>> R = pairwise(Euclidean(),xc,yc) but received the following message:
>>>
>>>
>>> R = pairwise(Euclidean(),xc,yc)
>>>
>>> MethodError(pairwise,(Euclidean(),[0.05961066617957589,0.018538084399339905,0.39282193332224646,0.7006919213133509,0.5099836895629475,0.8448415935222402,0.2985674570217043,0.8022287058003177],[0.5808687231553928,0.9655167324458858,0.026306556019434435,0.6565373244339141,0.11927452074471412,0.11873635450496622,0.6271632933770979,0.7081439899673692]))
>>>
>>> I'd like to be able to utilize the Distance package but am a bit stumped.
>>> The code as written works but it's bugging me that I cannot seem to get the
>>> above command to work. I also get the following error when loading Distance:
>>>
>>>
>>> using Distance
>>>
>>> Warning: could not import Base.foldl into NumericExtensions
>>>
>>> Warning: could not import Base.foldr into NumericExtensions
>>>
>>> Warning: could not import Base.sum! into NumericExtensions
>>>
>>> Warning: could not import Base.maximum! into NumericExtensions
>>>
>>> Warning: could not import Base.minimum! into NumericExtensions
>>>
>>>
>>> If there is anyone who can address this I'd greatly appreciate it.
>>> Incidentally, the help on this group is one reason I am making the change.
>>>
>>>
>>> Don
>>>
>>>
>>>
>>>
>>>
