pyoselm.oselm.OSELMClassifier¶
-
class
pyoselm.oselm.OSELMClassifier(n_hidden=20, activation_func='sigmoid', activation_args=None, binarizer=LabelBinarizer(neg_label=- 1), use_woodbury=False, random_state=123)[source]¶ OSELMClassifier is a classifier based on the Extreme Learning Machine.
An Extreme Learning Machine (ELM) is a single layer feedforward network with a random hidden layer components and ordinary linear least squares fitting of the hidden->output weights by default. [1][2]
OSELMClassifier is an OSELMRegressor subclass that first binarizes the data, then uses the superclass to compute the decision function that is then unbinarized to yield the prediction.
The params for the RandomLayer used in the input transform are exposed in the ELMClassifier constructor.
- 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
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.
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`classes_` Array of class labels
- Type
numpy array of shape [n_classes]
See also
ELMRegressor,OSELMRegressor,MLPRandomLayerReferences
- 1
- 2
G.-B. Huang, Q.-Y. Zhu and C.-K. Siew, “Extreme Learning Machine: Theory and Applications”, Neurocomputing, vol. 70, pp. 489-501,
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__init__(n_hidden=20, activation_func='sigmoid', activation_args=None, binarizer=LabelBinarizer(neg_label=- 1), use_woodbury=False, random_state=123)[source]¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__init__([n_hidden, activation_func, …])Initialize self.
This function return the decision function values related to each class on an array of test vectors X.
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 class values using the model
Predict probability values using the model
score(X, y, **kwargs)Force use of accuracy score since it doesn’t inherit from ClassifierMixin
set_params(**params)Set the parameters of this estimator.
Attributes
Check if model was fitted
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decision_function(X)[source]¶ This function return the decision function values related to each class on an array of test vectors X.
- Parameters
X (array-like of shape [n_samples, n_features]) –
- Returns
C – Decision function values related to each class, per sample. In the two-class case, the shape is [n_samples,]
- Return type
array of shape [n_samples, n_classes] or [n_samples,]
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fit(X, y)[source]¶ Fit the model using X, y as training data.
- 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
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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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property
is_fitted¶ Check if model was fitted
- Returns
- Return type
boolean, True if model is fitted
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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
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predict(X)[source]¶ Predict class values using the model
- Parameters
X ({array-like, sparse matrix} of shape [n_samples, n_features]) –
- Returns
C – Predicted class values.
- Return type
numpy array of shape [n_samples, n_outputs]
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predict_proba(X)[source]¶ Predict probability values using the model
- Parameters
X ({array-like, sparse matrix} of shape [n_samples, n_features]) –
- Returns
P – Predicted probability values.
- Return type
numpy array of shape [n_samples, n_outputs]
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score(X, y, **kwargs)[source]¶ Force use of accuracy score since it doesn’t inherit from ClassifierMixin
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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