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java.lang.Objectinfo.mrupp.isoak1.IsoaKernel
public class IsoaKernel
The iterative similarity optimal assignment kernel. Computes the iterative similarity optimal assignment kernel between two molecular graphs up to a given precision. - The kernel can be parameterized by the vertex and edge subkernel to be used. - If more than one kernel value is to be computed, create one instance of the IsoaKernel class and reuse it. This will prevent repeated reallocation of memory. Implementation based on the article Matthias Rupp, Ewgenij Proschak, Gisbert Schneider: A Kernel Approach to Molecular Similarity Based on Iterative Graph Similarity, Journal of Chemical Information and Molecular Modeling, 2007, submitted. Please cite this article if you use this software in scientific research.
Constructor Summary | |
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IsoaKernel()
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 IsoaKernel()
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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