grakel.MultiscaleLaplacian¶
- class grakel.MultiscaleLaplacian(n_jobs=None, normalize=False, verbose=False, random_state=None, L=3, P=10, gamma=0.01, heta=0.01, n_samples=50)[source][source]¶
Laplacian Graph Kernel as proposed in [KP16].
- Parameters
- random_stateRandomState or int, default=None
A random number generator instance or an int to initialize a RandomState as a seed.
- Lint, default=3
The number of neighborhoods.
- gammaReal, default=0.01
A smoothing parameter of float value.
- hetafloat, default=0.01
A smoothing parameter of float value.
- Pint, default=10
Restrict the maximum number of eigenvalues, taken on eigenvalue decomposition.
- n_samplesint, default=50
The number of vertex samples.
- Attributes
- random_state_RandomState
A RandomState object handling all randomness of the class.
- _data_leveldict
A dictionary containing the feature basis information needed for each level calculation on transform.
Methods
diagonal()Calculate the kernel matrix diagonal of the fit/transformed data.
fit(X[, y])Fit a dataset, for a transformer.
fit_transform(X)Fit and transform, on the same dataset.
get_params([deep])Get parameters for this estimator.
initialize()Initialize all transformer arguments, needing initialization.
pairwise_operation(x, y)FLG calculation for the fast multiscale laplacian.
parse_input(X)Fast ML Graph Kernel.
set_params(**params)Call the parent method.
transform(X)Calculate the kernel matrix, between given and fitted dataset.
Initialise a multiscale_laplacian kernel.
- Attributes
- X
Methods
diagonal()Calculate the kernel matrix diagonal of the fit/transformed data.
fit(X[, y])Fit a dataset, for a transformer.
fit_transform(X)Fit and transform, on the same dataset.
get_params([deep])Get parameters for this estimator.
initialize()Initialize all transformer arguments, needing initialization.
pairwise_operation(x, y)FLG calculation for the fast multiscale laplacian.
parse_input(X)Fast ML Graph Kernel.
set_params(**params)Call the parent method.
transform(X)Calculate the kernel matrix, between given and fitted dataset.