scikits.learn.svm.OneClassSVM

class scikits.learn.svm.OneClassSVM(kernel='rbf', degree=3, gamma=0.0, coef0=0.0, cache_size=100.0, eps=0.001, C=1.0, nu=0.5, p=0.1, shrinking=True, probability=False)

Unsupervised outliers detection

Estimate the support of a high-dimensional distribution.

Parameters :

kernel : string, optional

Specifies the kernel type to be used in the algorithm. Can be one of ‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’. If none is given ‘rbf’ will be used.

nu : float, optional

An upper bound on the fraction of training errors and a lower bound of the fraction of support vectors. Should be in the interval (0, 1]. By default 0.5 will be taken.

degree : int, optional

Degree of kernel function. Significant only in poly, rbf, sigmoid.

gamma : float, optional

kernel coefficient for rbf and poly, by default 1/n_features will be taken.

C : float, optional (default=1.0)

Penalty parameter C of the error term.

probability: boolean, optional (False by default) :

Enable probability estimates. Must be enabled prior to calling prob_predict.

coef0 : float, optional

Independent term in kernel function. It is only significant in poly/sigmoid.

eps: float, optional :

precision for stopping criteria

Attributes

support_	array-like, shape = [nSV, n_features]	Support vectors.
dual_coef_	array, shape = [n_classes-1, nSV]	Coefficient of the support vector in the decision function.
coef_	array, shape = [n_classes-1, n_features]	Weights asigned to the features (coefficients in the primal problem). This is only available in the case of linear kernel.
intercept_	array, shape = [n_classes-1]	Constants in decision function.

Methods

fit
predict
predict_margin
predict_proba

__init__(kernel='rbf', degree=3, gamma=0.0, coef0=0.0, cache_size=100.0, eps=0.001, C=1.0, nu=0.5, p=0.1, shrinking=True, probability=False)

fit(X)

Detects the soft boundary (aka soft boundary) of the set of samples X.

Parameters :

X : array-like, shape = [n_samples, n_features]

Set of samples, where n_samples is the number of samples and n_features is the number of features.

predict(T)

This function does classification or regression on an array of test vectors T.

For a classification model, the predicted class for each sample in T is returned. For a regression model, the function value of T calculated is returned.

For an one-class model, +1 or -1 is returned.

Parameters :T : array-like, shape = [n_samples, n_features] Returns :C : array, shape = [nsample]

predict_margin(T)

Calculate the distance of the samples in T to the separating hyperplane.

Parameters :

T : array-like, shape = [n_samples, n_features]

Returns :

T : array-like, shape = [n_samples, n_classes]

Returns the decision function of the sample for each class in the model, where classes are ordered by arithmetical order.

predict_proba(T)

This function does classification or regression on a test vector T given a model with probability information.

Parameters :

T : array-like, shape = [n_samples, n_features]

Returns :

T : array-like, shape = [n_samples, n_classes]

Returns the probability of the sample for each class in the model, where classes are ordered by arithmetical order.

Notes

The probability model is created using cross validation, so the results can be slightly different than those obtained by predict. Also, it will meaningless results on very small datasets.