graphdot.kernel.molecular module¶
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class
graphdot.kernel.molecular.
Tang2019MolecularKernel
(stopping_probability=0.01, starting_probability=1.0, element_prior=0.2, edge_length_scale=0.05, **kwargs)[source]¶ Bases:
object
A margianlized graph kernel for 3D molecular structures as in: Tang, Y. H., & de Jong, W. A. (2019). Prediction of atomization energy using graph kernel and active learning. The Journal of chemical physics, 150(4), 044107. The kernel can be directly used together with Graph.from_ase() to operate on molecular structures.
Parameters: - stopping_probability (float in (0, 1)) – The probability for the random walk to stop during each step.
- starting_probability (float) – The probability for the random walk to start from any node. See the p
kwarg of
graphdot.kernel.marginalized.MarginalizedGraphKernel
- element_prior (float in (0, 1)) – The baseline similarity between distinct elements — an element always have a similarity 1 to itself.
- edge_length_scale (float in (0, inf)) – length scale of the Gaussian kernel on edge length. A rule of thumb is that the similarity decays smoothly from 1 to nearly 0 around three times of the length scale.
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__call__
(X, Y=None, **kwargs)[source]¶ Same call signature as
graphdot.kernel.marginalized.MarginalizedGraphKernel.__call__()
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bounds
¶
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diag
(X, **kwargs)[source]¶ Same call signature as
graphdot.kernel.marginalized.MarginalizedGraphKernel.diag()
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hyperparameter_bounds
¶
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hyperparameters
¶
-
theta
¶