grakel
.RandomWalk¶
- class grakel.RandomWalk(n_jobs=None, normalize=False, verbose=False, lamda=0.1, method_type='fast', kernel_type='geometric', p=None)[source][source]¶
The random walk kernel class.
See [KTI03], [GartnerFW03] and [VBS07].
- Parameters
- lambdafloat
A lambda factor concerning summation.
- method_typestr, valid_values={“baseline”, “fast”}
- The method to use for calculating random walk kernel:
“baseline” Complexity: \(O(|V|^6)\) (see [KTI03], [GartnerFW03])
“fast” Complexity: \(O((|E|+|V|)|V||M|)\) (see [VBS07])
- kernel_typestr, valid_values={“geometric”, “exponential”}
Defines how inner summation will be applied.
- pint or None
If initialised defines the number of steps.
- Attributes
- mu_list
List of coefficients concerning a finite sum, in case p is not None.
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)Calculate the random walk kernel.
parse_input
(X)Parse and create features for random_walk kernel.
set_params
(**params)Call the parent method.
transform
(X)Calculate the kernel matrix, between given and fitted dataset.
Initialise a random_walk 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)Calculate the random walk kernel.
parse_input
(X)Parse and create features for random_walk kernel.
set_params
(**params)Call the parent method.
transform
(X)Calculate the kernel matrix, between given and fitted dataset.
Bibliography¶
- GartnerFW03(1,2)
Thomas Gärtner, Peter Flach, and Stefan Wrobel. On Graph Kernels: Hardness Results and Efficient Alternatives. In Learning Theory and Kernel Machines, 129–143. 2003.
- KTI03(1,2)
Hisashi Kashima, Koji Tsuda, and Akihiro Inokuchi. Marginalized Kernels Between Labeled Graphs. In Proceedings of the 20th Conference in Machine Learning, 321–328. 2003.
- VBS07(1,2)
S.V.N. Vishwanathan, Karsten M. Borgwardt, and Nicol N. Schraudolph. Fast Computation of Graph Kernels. In Advances in Neural Information Processing Systems, 1449–1456. 2007.