org.openimaj.ml.clustering.spectral

## Class GraphLaplacian

• ### Nested Class Summary

Nested Classes
Modifier and Type Class and Description
`static class ` `GraphLaplacian.Normalised`
The inverted symmetric normalised Laplacian is defined as: L = D^-1/2 A D^-1/2
`static class ` `GraphLaplacian.Unnormalised`
The symmetric normalised Laplacian is defined as: L = D - W
`static class ` `GraphLaplacian.Warped`
The inverted symmetric normalised Laplacian is defined as: L = D^-1 .
• ### Constructor Summary

Constructors
Constructor and Description
`GraphLaplacian()`
• ### Method Summary

All Methods
Modifier and Type Method and Description
`Iterator<DoubleObjectPair<Vector>>` `eigenIterator(Eigenvalues evd)`
`SparseMatrix` `laplacian(SparseMatrix adj)`
`abstract SparseMatrix` ```laplacian(SparseMatrix adj, DiagonalMatrix degree)```
• ### Methods inherited from class java.lang.Object

`clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`
• ### Constructor Detail

• #### GraphLaplacian

`public GraphLaplacian()`
• ### Method Detail

• #### laplacian

`public SparseMatrix laplacian(SparseMatrix adj)`
Parameters:
`adj` - the adjanceny matrix should be square and symmetric
Returns:
the laplacian
• #### laplacian

```public abstract SparseMatrix laplacian(SparseMatrix adj,
DiagonalMatrix degree)```
Parameters:
`adj` - square and symmetric
`degree` - the sum of the adjacency for a node in the diagonals
Returns:
the laplacian
• #### eigenIterator

`public Iterator<DoubleObjectPair<Vector>> eigenIterator(Eigenvalues evd)`
Parameters:
`evd` -
Returns:
provides an iterator over the (presumeably sorted)