grakel.HadamardCode¶
- class grakel.HadamardCode(n_jobs=None, verbose=False, normalize=False, n_iter=5, base_graph_kernel=None)[source][source]¶
The simple Hadamard code kernel, as proposed in [KI16].
- Parameters
- base_graph_kernelgrakel.kernels.Kernel or tuple, default=None
If tuple it must consist of a valid kernel object and a dictionary of parameters. General parameters concerning normalization, concurrency, .. will be ignored, and the ones of given on __init__ will be passed in case it is needed. Default base_graph_kernel is VertexHistogram.
- rhoint, condition_of_appearance: hc_type==”shortened”, default=-1
The size of each single bit arrays. If -1 is chosen r is calculated as the biggest possible that satisfies an equal division.
- Lint, condition_of_appearance: hc_type==”shortened”, default=4
The number of bytes to store the bitarray of each label.
- n_iterint, default=5
The number of iterations.
- Attributes
- base_graph_kernel_function
A void function that initializes a base kernel object.
Methods
diagonal()Calculate the kernel matrix diagonal for fitted data.
fit(X[, y])Fit a dataset, for a transformer.
fit_transform(X[, y])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 a pairwise kernel between two elements.
parse_input(X)Parse input and create features, while initializing and/or calculating sub-kernels.
set_params(**params)Call the parent method.
transform(X)Calculate the kernel matrix, between given and fitted dataset.
Initialise a hadamard_code kernel.
- Attributes
- X
Methods
diagonal()Calculate the kernel matrix diagonal for fitted data.
fit(X[, y])Fit a dataset, for a transformer.
fit_transform(X[, y])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 a pairwise kernel between two elements.
parse_input(X)Parse input and create features, while initializing and/or calculating sub-kernels.
set_params(**params)Call the parent method.
transform(X)Calculate the kernel matrix, between given and fitted dataset.