sklearn_lvq.GrlvqModel¶
-
class
sklearn_lvq.GrlvqModel(prototypes_per_class=1, initial_prototypes=None, initial_relevances=None, regularization=0.0, max_iter=2500, gtol=1e-05, beta=2, c=None, display=False, random_state=None)[source]¶ Generalized Relevance Learning Vector Quantization
- Parameters
- prototypes_per_classint or list of int, optional (default=1)
Number of prototypes per class. Use list to specify different numbers per class.
- initial_prototypesarray-like, shape = [n_prototypes, n_features + 1],
- optional
Prototypes to start with. If not given initialization near the class means. Class label must be placed as last entry of each prototype.
- initial_relevancesarray-like, shape = [n_prototypes], optional
Relevances to start with. If not given all relevances are equal.
- regularizationfloat, optional (default=0.0)
Value between 0 and 1. Regularization is done by the log determinant of the relevance matrix. Without regularization relevances may degenerate to zero.
- max_iterint, optional (default=2500)
The maximum number of iterations.
- gtolfloat, optional (default=1e-5)
Gradient norm must be less than gtol before successful termination of l-bfgs-b.
- betaint, optional (default=2)
Used inside phi. 1 / (1 + np.math.exp(-beta * x))
- carray-like, shape = [2,3] ,optional
Weights for wrong classification of form (y_real,y_pred,weight) Per default all weights are one, meaning you only need to specify the weights not equal one.
- displayboolean, optional (default=False)
Print information about the bfgs steps.
- random_stateint, RandomState instance or None, optional
If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.
- Attributes
- w_array-like, shape = [n_prototypes, n_features]
Prototype vector, where n_prototypes in the number of prototypes and n_features is the number of features
- c_w_array-like, shape = [n_prototypes]
Prototype classes
- classes_array-like, shape = [n_classes]
Array containing labels.
- lambda_array-like, shape = [n_prototypes]
Relevances
See also
Methods
decision_function(x)Predict confidence scores for samples.
fit(x, y)Fit the LVQ model to the given training data and parameters using l-bfgs-b.
get_params([deep])Get parameters for this estimator.
phi(x)- Parameters
phi_prime(x)- Parameters
predict(x)Predict class membership index for each input sample.
project(x, dims[, print_variance_covered])Projects the data input data X using the relevance vector of trained model to dimension dim
score(X, y[, sample_weight])Return the mean accuracy on the given test data and labels.
set_params(**params)Set the parameters of this estimator.