def testDistanceCalculator(self):
"See whether the distance calculator evaluates functions properly."
xv = vtk.vtkDoubleArray()
yv = vtk.vtkDoubleArray()
xv.SetName('x')
yv.SetName('y')
tab = vtk.vtkTable()
xv.InsertNextValue(0.0)
xv.InsertNextValue(1.0)
xv.InsertNextValue(0.0)
xv.InsertNextValue(1.0)
xv.InsertNextValue(5.0)
yv.InsertNextValue(0.0)
yv.InsertNextValue(0.0)
yv.InsertNextValue(1.0)
yv.InsertNextValue(1.0)
yv.InsertNextValue(12.0)
tab.AddColumn(xv)
tab.AddColumn(yv)
km = vtk.vtkKMeansStatistics()
km.SetDefaultNumberOfClusters(1)
df = vtk.vtkKMeansDistanceFunctorCalculator()
km.SetDistanceFunctor(df)
df.SetDistanceExpression('sqrt( (x0-y0)*(x0-y0) + (x1-y1)*(x1-y1) )')
km.SetInputData(tab)
km.SetColumnStatus('x', 1)
km.SetColumnStatus('y', 1)
km.RequestSelectedColumns()
km.SetLearnOption(1)
km.SetDeriveOption(1)
km.SetAssessOption(1)
km.Update()
av = km.GetOutput(0)
# We should always converge to a cluster center at [ 1.4, 2.8 ]
# These distances are the distances of each input observation to that cluster center:
dists = [
3.1304951684997055, 2.8284271247461898, 2.2803508501982757,
1.8439088914585773, 9.879271228182775
]
for i in range(5):
self.failUnlessAlmostEqual(dists[i], av.GetColumn(2).GetValue(i))
def testDistanceCalculator(self):
"See whether the distance calculator evaluates functions properly."
xv = vtk.vtkDoubleArray()
yv = vtk.vtkDoubleArray()
xv.SetName( 'x' )
yv.SetName( 'y' )
tab = vtk.vtkTable()
xv.InsertNextValue( 0.0 )
xv.InsertNextValue( 1.0 )
xv.InsertNextValue( 0.0 )
xv.InsertNextValue( 1.0 )
xv.InsertNextValue( 5.0 )
yv.InsertNextValue( 0.0 )
yv.InsertNextValue( 0.0 )
yv.InsertNextValue( 1.0 )
yv.InsertNextValue( 1.0 )
yv.InsertNextValue( 12.0 )
tab.AddColumn( xv )
tab.AddColumn( yv )
km = vtk.vtkKMeansStatistics()
km.SetDefaultNumberOfClusters( 1 )
df = vtk.vtkKMeansDistanceFunctorCalculator()
km.SetDistanceFunctor( df )
df.SetDistanceExpression( 'sqrt( (x0-y0)*(x0-y0) + (x1-y1)*(x1-y1) )' )
km.SetInput( tab )
km.SetColumnStatus( 'x', 1 )
km.SetColumnStatus( 'y', 1 )
km.RequestSelectedColumns()
km.SetLearnOption( 1 )
km.SetDeriveOption( 1 )
km.SetAssessOption( 1 )
km.Update()
av = km.GetOutput( 0 )
# We should always converge to a cluster center at [ 1.4, 2.8 ]
# These distances are the distances of each input observation to that cluster center:
dists = [ 3.1304951684997055, 2.8284271247461898, 2.2803508501982757, 1.8439088914585773, 9.879271228182775 ]
for i in range(5):
self.failUnlessAlmostEqual( dists[i], av.GetColumn( 2 ).GetValue( i ) )
def testParse(self): "Testing Boolean methods of vtkKMeansDistanceFunctorCalculator" tg = vtk.vtkKMeansDistanceFunctorCalculator() self._testBoolean(tg)
def testGetSet(self): "Testing Get/Set methods of vtkKMeansDistanceFunctorCalculator" tg = vtk.vtkKMeansDistanceFunctorCalculator() self._testGetSet(tg)
def testParse(self): "Test if vtkKMeansDistanceFunctorCalculator is parseable" tg = vtk.vtkKMeansDistanceFunctorCalculator() self._testParse(tg)
def testParse(self):
"Testing Boolean methods of vtkKMeansDistanceFunctorCalculator"
tg = vtk.vtkKMeansDistanceFunctorCalculator()
self._testBoolean(tg)
def testGetSet(self):
"Testing Get/Set methods of vtkKMeansDistanceFunctorCalculator"
tg = vtk.vtkKMeansDistanceFunctorCalculator()
self._testGetSet(tg)
def testParse(self):
"Test if vtkKMeansDistanceFunctorCalculator is parseable"
tg = vtk.vtkKMeansDistanceFunctorCalculator()
self._testParse(tg)
