pyoselm.layer.RBFRandomLayer¶
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class
pyoselm.layer.RBFRandomLayer(n_hidden=20, random_state=None, activation_func='gaussian', activation_args=None, centers=None, radii=None, rbf_width=1.0)[source]¶ Wrapper for RandomLayer with alpha (mixing coefficient) set to 0.0 for RBF activations only
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__init__(n_hidden=20, random_state=None, activation_func='gaussian', activation_args=None, centers=None, radii=None, rbf_width=1.0)[source]¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__init__([n_hidden, random_state, …])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]
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