If you know all of the data in the csv file is numerical, you can provide a
result type to readcsv, say readcsv("ct_coord.csv", Float64). See
help(readdlm) for additional options for things like removing header rows,
etc. If the data is of mixed types, then you may find readtable in the
DataFrames package more capable.
On Wednesday, July 2, 2014 5:05:21 PM UTC-7, Donald Lacombe wrote:
>
> Actually, another issue with the type information.
>
> Instead of using random coordinates, I used actual latitude and longitude
> coordinates from a CSV file like so:
>
> data = readcsv("ct_coord.csv");
>
> temp = [xc';yc']
>
> 2x8 Array{Any,2}:
>
> -73.3712 -72.1065 -73.2453 -71.9876 … -72.7328 -72.5231 -72.8999
>
> 41.225 41.4667 41.7925 41.83 41.8064 41.4354 41.3488
>
>
> Now, the type here is {Any,2} and this seems to be giving the Distance
> package problems:
>
>
>
> R = pairwise(Euclidean(),temp)
>
> MethodError(fptype,(Any,))
>
>
> Julia seems to be unhappy with this type but I'm not sure how to correct this
> which would seem to require that the data being read in are of Float type.
>
>
> Again, thank you for all of the help.
>
>
> Don
>
>
>
> On Wednesday, July 2, 2014 7:10:09 PM UTC-4, Donald Lacombe wrote:
>>
>> Patrick,
>>
>> Thank you for the reply! I will try your suggestion and hopefully get a
>> better grasp of these issues.
>>
>> Thank you all gain for all of the help. It cannot be stressed enough that
>> the friendly and truly helpful comments will help the Julia community grow.
>>
>> Don
>>
>> On Wednesday, July 2, 2014 5:31:55 PM UTC-4, Patrick O'Leary wrote:
>>>
>>> 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
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
