[vtkusers] K-means values
Sara Rolfe
smrolfe at u.washington.edu
Mon Mar 21 13:36:15 EDT 2011
Thanks for your reply David. That makes sense.
For anyone who is interested in doing simple K-means clustering
without learning, I found it much more convenient to convert to ITK
and back so I could use itkKdTreeBasedKmeansEstimator.
Sara
On Mar 21, 2011, at 10:14 AM, David Thompson wrote:
> Hi Sara,
>
>> ... I had also tried GetOutputDataObject( 1 )->GetBlock( 0 ),
>> replacing
>> the dot with an arrow, but this just resulted in a different error:
>>
>> error: ‘class vtkDataObject’ has no member named ‘GetBlock’
>
> Ah. The filter output is actually a vtkMultiBlockDataset which
> inherits vtkDataObject. The subclass provides GetBlock but
> vtkDataObject does not. Therefore the output of
> GetOutputDataObject( 1 ) must be cast to a multiblock dataset:
>
> vtkMultiBlockDataSet::SafeDownCast( kMeansStatistics-
> >GetOutputDataObject( 1 ) )->GetBlock( 0 )
>
> should return another vtkDataObject pointer which you must cast to
> vtkTable in order to access the results.
>
> David
>
>
>>
>> On Mar 18, 2011, at 6:52 PM, David Thompson wrote:
>>
>>> Hi Sara,
>>>
>>>> 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.
>>>
>>> Only the first column of a table on the LEARN_PARAMETERS port needs
>>> to be vtkIdTypeArray. In the code below, the inputData table is
>>> attached to the INPUT_DATA port, so there's no constraint on the
>>> column type. I'm not sure I understand exactly what the code is
>>> supposed to accomplish; the column you create named "KMeans" is
>>> initially going to be filled with zeros. The other column will be
>>> named FeatureInArrayName (not "ScalarMagnitude" because your don't
>>> ever call inputData->AddColumn on floatArray). Since all the values
>>> in the first column are identical and both Learn and Assess are
>>> turned on, the filter will
>>> 1. Perform Learn by randomly choosing "clusterCount" cluster centers
>>> (since no LEARN_PARAMETERS input was set) and computing a single
>>> iteration of k-means.
>>> 2. Perform Assess by adding 2 columns to the input table: one
>>> vtkDoubleArray containing the squared distance to the nearest
>>> cluster center, and one vtkIdTypeArray containing the nearest
>>> cluster center's ID (as reported by the OUTPUT_MODEL output).
>>> The output model kMeansStatistics->GetOutputDataObject(1)-
>>>> GetBlock(0) will report all of the cluster centers as lying on the
>>> line "KMeans"=0.
>>> Is that what you had in mind?
>>>
>>> David
>>>
>>>> //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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>>>
>>>
>>>
>>
>>
>
>
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