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java.lang.Objectinfo.mrupp.isoak1.IsoaKernel1
public class IsoaKernel1
The iterative similarity optimal assignment kernel. Computes the iterative similarity optimal assignment kernel between two molecular graphs up to a given precision.
Constructor Summary | |
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IsoaKernel1()
Initializes internal data structures to an empty state. |
Method Summary | |
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float |
evalNorm(MolecularGraph molA,
MolecularGraph molB,
float alpha,
float epsilon,
int[] assignment,
Array2Float similarity)
Evalutes the iterative optimal assignment kernel with normalization. |
float |
evalRaw(MolecularGraph molA,
MolecularGraph molB,
float alpha,
float epsilon,
int[] assignment,
Array2Float similarity)
Evaluates the iterative optimal assignment kernel without normalization. |
void |
setEdgeKernel(IVertexEdgeKernel e)
Sets the edge kernel to be used. |
void |
setVertexKernel(IVertexEdgeKernel k)
Sets the vertex kernel to be used. |
Methods inherited from class java.lang.Object |
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equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Constructor Detail |
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public IsoaKernel1()
Method Detail |
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public float evalNorm(MolecularGraph molA, MolecularGraph molB, float alpha, float epsilon, int[] assignment, Array2Float similarity)
public float evalRaw(MolecularGraph molA, MolecularGraph molB, float alpha, float epsilon, int[] assignment, Array2Float similarity)
molA
- first argument molecuar graph.molB
- second argument molecular graph.alpha
- the weight of the recursive part of the update equation.epsilon
- the desired precision of the result. The result will not deviate by more than epsilon from the true value, as measured by the maximum-norm based metric.assignment
- the indices of the optimal assignment. Pass null if not needed.similarity
- the computed similarity matrix. Pass null if not needed.
public void setEdgeKernel(IVertexEdgeKernel e)
public void setVertexKernel(IVertexEdgeKernel k)
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