[Insight-developers] Re: Statistics Refactoring : Engineering:SandBox - NAMIC Wiki

[Karthik Krishnan]

Hello Raghu,

Are you using an SVN checkout of the Statistics classes from
NAMICSandBox/RefactoringITKStatisticsClasses/

You should be able to use the Distance metrics etc absolutely fine with 
itk::Array (or for that matter any other container in 
itkMeasuremetVectorTraits.h)

I still have some API issues to resolve with the Histogram classes, but 
most of it is ironed out. 

Templating your code over the measurement vector is fine. However make 
sure you use MeasurementVectorTraits to work with the measurement vector.
For an example, take a look at the GaussianDensityFunction and the 
DensityFunction in the NAMIC repository

You will find a detailed description on the wiki.

http://www.itk.org/Wiki/Proposals:Statistics_Framework_Runtime_Vector_Size


The current SVN checkout builds fine with ITK and the tests pass. There 
may still be bugs.

Thanks
Regards
Karthik

Raghu Venkatram wrote:

> Hi Everybody,
>
> With regard to DistanceMetric:
>
> The NeuralNetwork TransferFunctionBase class derives from FunctionBase 
> and
> templated over Input/Output ScalarType.
> The GaussianRadialBasisFunction derives from TransferFunctionBase, 
> trying to
> use the EulideanDistance in here is not possible as DistanceMetric is
> templated over FixedArray.
> At this point, I have moved things around, so that the 
> EuclideanDistance is
> applied in the RBFLayer, LayerBase is templated over the input and output
> MeasurementVectorTypes, which s itk::Vector. It might just be that my 
> initial
> design was flawed :).
> Each layer has a transferfunction plugged in.
>
> Looking at  the new Statistics classes, if anything I would have to 
> change
> things around to take advantage of the new features, but I dont think 
> it will
> break the existing NeuralNetwork classes.
>
> Warm regards,
> Raghu
>
>
> Quoting "Stephen R. Aylward" <aylward at unc.edu>:
>
>> Everyone...meet Raghu.   Raghu...meet everyone. :)
>>
>> Raghu has also run into difficulty using the statistics framework to 
>> have measurement vectors whos lengths (lenghts) are set at run-time. 
>> This is needed for the neural networks library that he is adding to ITK.
>>
>> Raghu, Luis and Karthik and Jim have created a version of the 
>> statistics library that removes the requirement that the measurement 
>> vector length be set at compile time (e.g., that length in itkSample 
>> is now stored as an ivar instead of as a value determined from the 
>> measurement vector template argument).
>>
>> Raghu - maybe you can checkout their version of the library (as 
>> detailed below) and see if it is compatible with your solution.  Or 
>> perhaps you can offer an alternative solution.   Also, I tried to 
>> explain the difficulties you were having with the distance functions, 
>> but I didn't explain it very well.  Perhaps you can clarify to the 
>> group.
>>
>> Thanks!
>> Stephen
>>
>> Luis Ibanez wrote:
>>
>>>
>>> Hi Stephen,
>>>
>>> Following on the discusion of the tcon on refactoring the
>>> ITK Statistics Framework, here is the link to the NAMIC
>>> Sandbox where Karthik has been reworking the Statistics
>>> library:
>>>
>>> <http://www.na-mic.org/Wiki/index.php/Engineering:SandBox>
>>>
>>>
>>> Subversion allows direct HTML access to the repository too:
>>>
>>>      http://www.na-mic.org:8000/svn/NAMICSandBox/
>>>
>>>
>>> The directory of interest is:
>>>
>>> http://www.na-mic.org:8000/svn/NAMICSandBox/RefactoringITKStatisticsClasses/ 
>>> Clients for Subversion (CVS) are available in standard
>>> Linux distrbutions and Cygwin.  Windows versions can be
>>> found at:
>>>
>>>
>>>         http://subversion.tigris.org/
>>>
>>>
>>> -- 
>>>
>>>
>>>
>>>    Luis
>>>
>>>
>>>
>>
>>
>
>
>