gfdl.model.EnsembleGFDLClassifier

class EnsembleGFDLClassifier(hidden_layer_sizes: ArrayLike = (100,), activation: str = 'identity', weight_scheme: str = 'uniform', seed: int = None, reg_alpha: float = None, rtol: float = None, voting: str = 'soft')

Ensemble random vector functional link network classifier.

Parameters:

hidden_layer_sizesarray-like of shape (n_layers,)

The ith element represents the number of neurons in the ith hidden layer.

activationstr, default=’identity’

Activation function for the hidden layers.

• ‘identity’, no-op activation, useful to implement linear bottleneck, returns f(x) = x

• ‘tanh’: tanh.

• ‘relu’: relu.

• ‘sigmoid’: sigmoid.

• ‘softmax’: softmax.

• ‘softmin’: softmin.

• ‘log_sigmoid’: log_sigmoid.

• ‘log_softmax’: log_softmax.

weight_schemestr, default=’uniform’

Distribution used to initialize the random hidden-layer weights.

The initialization functions generate weight matrices of shape (n_hidden_units, n_features), where values are drawn according to the selected scheme.

• ‘zeros’: set weights to zeros (zeros).

• ‘range’: set weights to normalized np.arange (range).

• ‘uniform’: uniform distribution (uniform).

• ‘he_uniform’: He uniform distribution (he_uniform).

• ‘lecun_uniform’: Lecun uniform distribution (lecun_uniform).

• ‘glorot_uniform’: Glorot uniform distribution (glorot_uniform).

• ‘normal’: Normal distribution (normal).

• ‘he_normal’: He normal distribution (he_normal).

• ‘lecun_normal’: Lecun normal distribution (lecun_normal).

• ‘glorot_normal’: Glorot normal distribution (glorot_normal).

seedint, default=`None`

Random seed used to initialize the network.

reg_alphafloat, default=`None`

When None, use Moore-Penrose inversion to solve for the output weights of the network. Otherwise, it specifies the constant that multiplies the L2 term of sklearn Ridge, controlling the regularization strength. reg_alpha must be a non-negative float.

rtolfloat, default=None

Cutoff for small singular values for the Moore-Penrose pseudo-inverse. Only applies when reg_alpha=None. When rtol=None, the array API standard default for pinv is used.

votingstr, default=`”soft”`

Whether to use soft or hard voting in the ensemble.

Methods

fit(X, y)	Train the ensemble of connected RVFL networks on the training set (X, y).
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
predict(X)	Predict class for X.
predict_proba(X)	Predict class probabilities for X.
score(X, y[, sample_weight])	Return accuracy on provided data and labels.
set_params(**params)	Set the parameters of this estimator.
set_score_request(*[, sample_weight])	Configure whether metadata should be requested to be passed to the score method.

get_generator

Notes

The implementation is based on the one described by Shi et al. in [1].

References

[1]

Shi, Katuwal, Suganthan, Tanveer, “Random vector functional link neural network based ensemble deep learning.” Pattern Recognition, vol. 117, pp. 107978, 2021, https://doi.org/10.1016/j.patcog.2021.107978.

Examples

>>> from sklearn.datasets import make_classification
>>> from gfdl.model import EnsembleGFDLClassifier
>>> X, y = make_classification(n_samples=1000, n_features=4,
...                            n_informative=2, n_redundant=0,
...                            random_state=0, shuffle=False)
>>> clf = EnsembleGFDLClassifier(seed=0)
>>> clf.fit(X, y)
>>> print(clf.predict([[0, 0, 0, 0]]))
[1]

__init__(hidden_layer_sizes: ArrayLike = (100,), activation: str = 'identity', weight_scheme: str = 'uniform', seed: int = None, reg_alpha: float = None, rtol: float = None, voting: str = 'soft')

Methods

__init__([hidden_layer_sizes, activation, ...])
fit(X, y)	Train the ensemble of connected RVFL networks on the training set (X, y).
get_generator(seed)
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
predict(X)	Predict class for X.
predict_proba(X)	Predict class probabilities for X.
score(X, y[, sample_weight])	Return accuracy on provided data and labels.
set_params(**params)	Set the parameters of this estimator.
set_score_request(*[, sample_weight])	Configure whether metadata should be requested to be passed to the score method.