grakel.PropagationAttr¶
- class grakel.PropagationAttr(n_jobs=None, verbose=False, normalize=False, random_state=None, metric=<function _dot>, M='L1', t_max=5, w=4)[source][source]¶
The Propagation kernel for fully attributed graphs.
See [NGBK15]: Algorithms 1, 3, p. 216, 221.
- Parameters
- t_maxint, default=5
Maximum number of iterations.
- wint, default=0.01
Bin width.
- Mstr, default=”TV”
- The preserved distance metric (on local sensitive hashing):
“L1”: l1-norm
“L2”: l2-norm
- metricfunction (np.array, np.array -> number),
default=:math:f(x,y)=sum_{i} x_{i}*y_{i} A metric between two 1-dimensional numpy arrays of numbers that outputs a number.
- Attributes
- Mstr
The preserved distance metric (on local sensitive hashing).
- tmaxint
Holds the maximum number of iterations.
- wint
Holds the bin width.
- metricfunction (np.array, np.array -> number)
A metric between two 1-dimensional numpy arrays of numbers that outputs a number.
Methods
calculate_LSH(X, u, b)Calculate Local Sensitive Hashing needed for propagation kernels.
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 kernel value between two elements.
parse_input(X)Parse and create features for the attributed propation kernel.
set_params(**params)Call the parent method.
transform(X)Calculate the kernel matrix, between given and fitted dataset.
Initialise a propagation kernel.
- Attributes
- X
Methods
calculate_LSH(X, u, b)Calculate Local Sensitive Hashing needed for propagation kernels.
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 kernel value between two elements.
parse_input(X)Parse and create features for the attributed propation kernel.
set_params(**params)Call the parent method.
transform(X)Calculate the kernel matrix, between given and fitted dataset.
