[vtkusers] K-means values

[Sara Rolfe]

Update: from the vtkKMeansStatistics reference page it sounds like  
it's necessary for the first column to be a vtkIdType array in order  
for learning to be used and the table available on the output port.  I  
added this, but it does not solve my problem.  However, I may have  
done so incorrectly.  I am including my code below.

Thanks,
Sara

	//get data from the file
	vtkXMLPolyDataReader* reader = vtkXMLPolyDataReader::New();
	reader->SetFileName( inputFile );
	reader->Update();
	vtkPolyData* polydata = reader->GetOutput();
	cout << "Reading data" << endl;
	
	vtkSmartPointer<vtkFloatArray> magnitudeArray =  
vtkFloatArray::SafeDownCast(polydata->GetPointData()- 
 >GetArray(FeatureInArrayName));
	
	// set up table for KMeans
	vtkSmartPointer<vtkTable> inputData = vtkSmartPointer<vtkTable>::New();
	
	// first column for kmeans clusters, vtkIdType
	vtkSmartPointer<vtkIdTypeArray> idArray =  
vtkSmartPointer<vtkIdTypeArray>::New();
     	idArray->SetNumberOfComponents(1);
    	idArray->SetName( "KMeans" );
     	idArray->SetNumberOfTuples(polydata->GetNumberOfPoints());
	inputData->AddColumn( idArray );
	
	// second column for data values
	vtkSmartPointer<vtkFloatArray> floatArray =  
vtkSmartPointer<vtkFloatArray>::New();
	floatArray->SetNumberOfComponents(1);
	floatArray->SetName( "ScalerMagnitude" );
	floatArray->SetNumberOfTuples(polydata->GetNumberOfPoints());
	inputData->AddColumn( magnitudeArray );
	
	// KMeans Set up
	vtkSmartPointer<vtkKMeansStatistics> kMeansStatistics =  
vtkSmartPointer<vtkKMeansStatistics>::New();
	
	kMeansStatistics->SetInput( vtkStatisticsAlgorithm::INPUT_DATA,  
inputData );
	kMeansStatistics->SetColumnStatus( inputData->GetColumnName( 0 ) , 1 );
	kMeansStatistics->SetColumnStatus( inputData->GetColumnName( 1 ) , 1 );
	kMeansStatistics->RequestSelectedColumns();
	kMeansStatistics->SetDefaultNumberOfClusters( clusterCount );
	kMeansStatistics->SetAssessOption( 1 );	
	kMeansStatistics->SetLearnOption( 1 );
	kMeansStatistics->SetMaxNumIterations( 1 );
	kMeansStatistics->Update() ;
	
	//vtkTable* tab = vtkTable::SafeDownCast( kMeansStatistics- 
 >GetOutputDataObject( 1 ).GetBlock( 0 ) );
	//double xc = tab->GetValueByName( label, "x" ).ToDouble();
	kMeansStatistics->GetOutputDataObject(1).GetBlock( 0 );


On Mar 18, 2011, at 4:30 PM, Sara Rolfe wrote:

> I agree.  I also think that the example on the  wiki might be more  
> helpful if it showed this method of accessing the means.
>
> However, I'm still having some trouble accessing the first block of  
> the k-means output, when I use:
>
> 	vtkTable* tab = vtkTable::SafeDownCast( kMeansStatistics- 
> >GetOutputDataObject( 1 ).GetBlock( 0 ) );
>
> or just
> 	kMeansStatistics->GetOutputDataObject( 1 ).GetBlock( 0 )
>
> I'm getting the following error:
>
> error: request for member ‘GetBlock’ in ‘kMeansStatistics.  
> vtkSmartPointer<T>::operator-> [with T = vtkKMeansStatistics]()- 
> > 
> vtkKMeansStatistics 
> ::< 
> anonymous 
> > 
> .vtkStatisticsAlgorithm 
> ::< 
> anonymous 
> > 
> .vtkTableAlgorithm 
> ::<anonymous>.vtkAlgorithm::GetOutputDataObject(1)’, which is of non- 
> class type ‘vtkDataObject*’
>
> Does anyone know what might cause this?
>
> Thanks,
> Sara
>
> On Mar 16, 2011, at 5:32 PM, David Doria wrote:
>
>> On Wed, Mar 16, 2011 at 4:07 PM, Sara Rolfe  
>> <smrolfe at u.washington.edu> wrote:
>>>
>>> Hi David,
>>>
>>> It's not a difficult fix, I mainly found it awkward since I  
>>> thought I was missing something simple.  I understand how it works  
>>> better now and can certainly implement it this way.  I appreciate  
>>> your clarification.
>>>
>>> Sara
>>
>> If you recall, I had the same sort of "it feels awkward" issues a
>> while back. I think the problem is that the suite is very very  
>> capable
>> and able to hand very large and serious problems, but many users just
>> want the "very basic" functionality ("which points belong to which
>> cluster?", and "what are the cluster means?"), which is hard to get
>> at. There are many layers of complexity above that that a user must
>> understand (i.e. this "Learn" and "Derive" that you mentioned) that
>> are not concepts in a standard textbook explanation of kmeans
>> clustering. Providing a "simple" interface to do this "one pass" type
>> of thing would definitely be a nice addition.
>>
>> That is, replace:
>>
>>  kMeansStatistics->SetLearnOption( 1 ); // This is on by default.
>>  kMeansStatistics->SetMaxNumIterations( 1 );
>>  vtkTable* tab = vtkTable::SafeDownCast(
>> kMeansStatistics->GetOutputDataObject( 1 ).GetBlock( 0 ) );
>>  double xc = tab->GetValueByName( label, "x" ).ToDouble();
>>
>> with
>>
>> double mean0[3];
>> kMeans->GetMean(0,mean0);
>>
>> See the difference :) ?
>>
>> David D.
>
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