Thank you. For an unscientific, rough benchmark, it runs in 12.4 seconds on
my machine vs the F# version which runs in 44 seconds. I was surprised. In
other unrelated use cases, I was finding .NET to be faster than J. This an
example where J really shines.   Your code is compact and readable too. I
will study it and may try my own implementation. Thanks for the link to the
PDF as well.


On Wed, Jun 11, 2014 at 2:22 AM, Jan-Pieter Jacobs <
[email protected]> wrote:

> 2014-06-11 4:07 GMT+02:00 Joe Bogner <[email protected]>:
> > Thanks Jan-Pieter, how would I recreate the results of the calculating
> the
> > % correct with yours? I will give it a shot on my own still later.. I
> > pasted some code to help jumpstart the reading of the array of data:
> >
>
> Thanks for the info!
>
> I just tried the classification of the data and this is what I get:
>
> NB. transformed your loader into a reusable verb.
> parsefile =: 3 : 0
> file =. fread y
> header_end =. >: file i. LF
> arr =. ". ];._2 header_end }. file
> )
>
> NB. Load training and validation labels and data
> Train      =: parsefile jpath '~temp/trainingsample.csv'
> Validation =: parsefile jpath '~temp/validationsample.csv'
>
> NB. separate labels (1st column) from data (the rest)
> 'TrainLabels TrainData'          =: ({."1 ; }."1) Train
> 'ValidationLabels ValidationData'=: ({."1 ; }."1) Validation
>
> NB. Classify one against all:
> predicted =: 10 nnClass oaa TrainLabels;TrainData;ValidationData
>
> NB. Assess the accuracy of our result:
> OA =: 100 * (+/%#)@:=
>
> predicted OA ValidationLabels
> 93.6
>
> I'd like to recommend the book that started me on implementing this all:
> Elements of Statistical Learning
> Trevor Hastie, Robert Tibshirani, Jerome Friedman
> PDF Freely (legally too) available via
> http://statweb.stanford.edu/~tibs/ElemStatLearn/
>
> In the future, I'd be interested toying around with more advanced
> classifiers, like Support Vector Machines.
>
> Jan-Pieter
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>
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