pyoselm.layer.GRBFRandomLayer¶
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class
pyoselm.layer.GRBFRandomLayer(n_hidden=20, grbf_lambda=0.001, centers=None, radii=None, random_state=None)[source]¶ Random Generalized RBF Hidden Layer transformer
Creates a layer of radial basis function units where:
f(a), s.t. a = ||x-c||/r
with c the unit center and f() is exp(-gamma * a^tau) where tau and r are computed based on [1]
- Parameters
n_hidden (int, optional (default=20)) – Number of units to generate, ignored if centers are provided
grbf_lambda (float, optional (default=0.05)) – GRBF shape parameter
gamma ({int, float} optional (default=1.0)) – Width multiplier for GRBF distance argument
centers (array of shape (n_hidden, n_features), optional (default=None)) – If provided, overrides internal computation of the centers
radii (array of shape (n_hidden), optional (default=None)) – If provided, overrides internal computation of the radii
use_exemplars (bool, optional (default=False)) – If True, uses random examples from the input to determine the RBF centers, ignored if centers are provided
random_state (int or RandomState instance, optional (default=None)) – Control the pseudo random number generator used to generate the centers at fit time, ignored if centers are provided
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`components_` radii_ : numpy array of shape [n_hidden] centers_ : numpy array of shape [n_hidden, n_features]
- Type
dictionary containing two keys:
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`input_activations_` Array containing ||x-c||/r for all samples
- Type
numpy array of shape [n_samples, n_hidden]
See also
ELMRegressor,ELMClassifier,SimpleELMRegressor,SimpleELMClassifier,SimpleRandomLayerReferences
- 1
Fernandez-Navarro, et al, “MELM-GRBF: a modified version of the extreme learning machine for generalized radial basis function neural networks”, Neurocomputing 74 (2011), 2502-2510
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__init__(n_hidden=20, grbf_lambda=0.001, centers=None, radii=None, random_state=None)[source]¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__init__([n_hidden, grbf_lambda, centers, …])Initialize self.
Get list of internal activation function names
fit(X[, y])Generate a random hidden layer.
fit_transform(X[, y])Fit to data, then transform it.
get_params([deep])Get parameters for this estimator.
set_params(**params)Set the parameters of this estimator.
transform(X[, y])Generate the random hidden layer’s activations given X as input.
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classmethod
activation_func_names()¶ Get list of internal activation function names
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fit(X, y=None)¶ Generate a random hidden layer.
- Parameters
X ({array-like, sparse matrix} of shape [n_samples, n_features]) – Training set: only the shape is used to generate random component values for hidden units
y (not used: placeholder to allow for usage in a Pipeline.) –
- Returns
- Return type
self
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fit_transform(X, y=None, **fit_params)¶ Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
- Parameters
X (array-like of shape (n_samples, n_features)) – Input samples.
y (array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) – Target values (None for unsupervised transformations).
**fit_params (dict) – Additional fit parameters.
- Returns
X_new – Transformed array.
- Return type
ndarray array of shape (n_samples, n_features_new)
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get_params(deep=True)¶ Get parameters for this estimator.
- Parameters
deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
params – Parameter names mapped to their values.
- Return type
dict
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set_params(**params)¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline). The latter have parameters of the form<component>__<parameter>so that it’s possible to update each component of a nested object.- Parameters
**params (dict) – Estimator parameters.
- Returns
self – Estimator instance.
- Return type
estimator instance
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transform(X, y=None)¶ Generate the random hidden layer’s activations given X as input.
- Parameters
X ({array-like, sparse matrix}, shape [n_samples, n_features]) – Data to transform
y (not used: placeholder to allow for usage in a Pipeline.) –
- Returns
X_new
- Return type
numpy array of shape [n_samples, n_components]