`grakel`.VertexHistogram#

class grakel.VertexHistogram(n_jobs=None, normalize=False, verbose=False, sparse='auto')[source][source]#

Vertex Histogram kernel as found in [SB15].

Parameters:

sparsebool, or ‘auto’, default=’auto’: Defines if the data will be stored in a sparse format. Sparse format is slower, but less memory consuming and in some cases the only solution. If ‘auto’, uses a sparse matrix when the number of zeros is more than the half of the matrix size. In all cases if the dense matrix doesn’t fit system memory, I sparse approach will be tried.

Attributes:

Methods

`diagonal`()	Calculate the kernel matrix diagonal of the fitted data.
`fit`(X[, y])	Fit a dataset, for a transformer.
`fit_transform`(X[, y])	Fit and transform, on the same dataset.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`initialize`()	Initialize all transformer arguments, needing initialisation.
`pairwise_operation`(x, y)	Calculate a pairwise kernel between two elements.
`parse_input`(X)	Parse and check the given input for VH kernel.
`set_output`(*[, transform])	Set output container.
`set_params`(**params)	Call the parent method.
`transform`(X)	Calculate the kernel matrix, between given and fitted dataset.

Initialise a vertex histogram kernel.

Attributes:

Methods

`diagonal`()	Calculate the kernel matrix diagonal of the fitted data.
`fit`(X[, y])	Fit a dataset, for a transformer.
`fit_transform`(X[, y])	Fit and transform, on the same dataset.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`initialize`()	Initialize all transformer arguments, needing initialisation.
`pairwise_operation`(x, y)	Calculate a pairwise kernel between two elements.
`parse_input`(X)	Parse and check the given input for VH kernel.
`set_output`(*[, transform])	Set output container.
`set_params`(**params)	Call the parent method.
`transform`(X)	Calculate the kernel matrix, between given and fitted dataset.