grakel.SvmTheta¶
- class grakel.SvmTheta(n_jobs=None, normalize=False, verbose=False, random_state=None, n_samples=50, subsets_size_range=(2, 8), metric=<function _inner>)[source][source]¶
Calculate the SVM theta kernel.
See [JJDB14].
- Parameters
- X,Yvalid-graph-format
The pair of graphs on which the kernel is applied.
- n_samplesint, default=50
Number of samples.
- subsets_size_rangetuple, len=2, default=(2,8)
(min, max) size of the vertex set of sampled subgraphs.
- metricfunction (number, number -> number), default=:math:f(x,y)=x*y
The applied metric between the svm_theta numbers of the two graphs.
- random_stateRandomState or int, default=None
A random number generator instance or an int to initialize a RandomState as a seed.
- Attributes
- random_state_RandomState
A RandomState object handling all randomness of the class.
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)Lovasz theta kernel as proposed in [JJDB14].
parse_input(X)Parse and create features for svm_theta kernel.
set_params(**params)Call the parent method.
transform(X)Calculate the kernel matrix, between given and fitted dataset.
Initialise a lovasz_theta 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)Lovasz theta kernel as proposed in [JJDB14].
parse_input(X)Parse and create features for svm_theta kernel.
set_params(**params)Call the parent method.
transform(X)Calculate the kernel matrix, between given and fitted dataset.