pyoselm.oselm.OSELMRegressor

class pyoselm.oselm.OSELMRegressor(n_hidden=20, activation_func='sigmoid', activation_args=None, use_woodbury=False, random_state=123)

OSELMRegressor is a regressor based on Online Sequential Extreme Learning Machine (OS-ELM).

This type of model is an ELM that…. … [1][2]

Parameters

• n_hidden (int, optional (default=20)) – Number of units to generate in the SimpleRandomLayer

• activation_func ({callable, string} optional (default='sigmoid')) –

  Function used to transform input activation

  It must be one of ‘tanh’, ‘sine’, ‘tribas’, ‘inv_tribase’, ‘sigmoid’, ‘hardlim’, ‘softlim’, ‘gaussian’, ‘multiquadric’, ‘inv_multiquadric’ or a callable. If none is given, ‘tanh’ will be used. If a callable is given, it will be used to compute the hidden unit activations.

• activation_args (dictionary, optional (default=None)) – Supplies keyword arguments for a callable activation_func

• use_woodbury (bool, optional (default=False)) – Flag to indicate if Woodbury formula should be used for the fit step, or just the traditional iterative procedure. Not recommended if handling large datasets.

• random_state (int, RandomState instance or None (default=None)) – Control the pseudo random number generator used to generate the hidden unit weights at fit time.

`P`

…

Type

np.array

`beta`

Type

np.array

...

See also

ELMRegressor, MLPRandomLayer

References

1

http://www.extreme-learning-machines.org

2

G.-B. Huang, Q.-Y. Zhu and C.-K. Siew, “Extreme Learning Machine: Theory and Applications”, Neurocomputing, vol. 70, pp. 489-501,

__init__(n_hidden=20, activation_func='sigmoid', activation_args=None, use_woodbury=False, random_state=123)

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__([n_hidden, activation_func, …])	Initialize self.
fit(X, y)	Fit the model using X, y as training data.
get_params([deep])	Get parameters for this estimator.
partial_fit(X, y)	Fit the model using X, y as training data.
predict(X)	Predict values using the model
score(X, y[, sample_weight])	Return the coefficient of determination \(R^2\) of the prediction.
set_params(**params)	Set the parameters of this estimator.

Attributes

is_fitted

Check if model was fitted

fit(X, y)

Fit the model using X, y as training data.

Notice that this function could be used for n_samples==1 (online learning), except for the first time the model is fitted, where it needs at least as many rows as ‘n_hidden’ configured.

Parameters

• X ({array-like, sparse matrix} of shape [n_samples, n_features]) – Training vectors, where n_samples is the number of samples and n_features is the number of features.

• y (array-like of shape [n_samples, n_outputs]) – Target values (class labels in classification, real numbers in regression)

Returns

self – Returns an instance of self.

Return type

object

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

property is_fitted

Check if model was fitted

Returns

Return type

boolean, True if model is fitted

partial_fit(X, y)

Fit the model using X, y as training data. Alias for fit() method.

Notice that this function could be used for n_samples==1 (online learning), except for the first time the model is fitted, where it needs at least as many rows as ‘n_hidden’ configured.

Parameters

• X ({array-like, sparse matrix} of shape [n_samples, n_features]) – Training vectors, where n_samples is the number of samples and n_features is the number of features.

• y (array-like of shape [n_samples, n_outputs]) – Target values (class labels in classification, real numbers in regression)

Returns

self – Returns an instance of self.

Return type

object

predict(X)

Predict values using the model

Parameters

X ({array-like, sparse matrix} of shape [n_samples, n_features]) –

Returns

C – Predicted values.

Return type

numpy array of shape [n_samples, n_outputs]

score(X, y, sample_weight=None)

Return the coefficient of determination \(R^2\) of the prediction.

The coefficient \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred) ** 2).sum() and \(v\) is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.

Parameters

• X (array-like of shape (n_samples, n_features)) – Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.

• y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True values for X.

• sample_weight (array-like of shape (n_samples,), default=None) – Sample weights.

Returns

score – \(R^2\) of self.predict(X) wrt. y.

Return type

float

Notes

The \(R^2\) score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score(). This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).

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