6.1.7.5. scikits.learn.svm.sparse.OneClassSVM¶
- class scikits.learn.svm.sparse.OneClassSVM(kernel='rbf', degree=3, gamma=0.0, coef0=0.0, cache_size=100.0, eps=0.001, nu=0.5, shrinking=True, probability=False)¶
NuSVR for sparse matrices (csr)
See scikits.learn.svm.NuSVC for a complete list of parameters
Notes
For best results, this accepts a matrix in csr format (scipy.sparse.csr), but should be able to convert from any array-like object (including other sparse representations).
Methods
decision_function(T) Calculate the distance of the samples T to the separating hyperplane. fit(X[, class_weight, sample_weight]) predict(T) This function does classification or regression on an array of predict_log_proba(T) This function does classification or regression on a test vector T predict_proba(T) This function does classification or regression on a test vector T - __init__(kernel='rbf', degree=3, gamma=0.0, coef0=0.0, cache_size=100.0, eps=0.001, nu=0.5, shrinking=True, probability=False)¶
- decision_function(T)¶
Calculate the distance of the samples T to the separating hyperplane.
Parameters : T : array-like, shape = [n_samples, n_features]
Returns : T : array-like, shape = [n_samples, n_class * (n_class-1) / 2]
Returns the decision function of the sample for each class in the model.
- 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 : scipy.sparse.csr, shape = [n_samples, n_features] Returns : C : array, shape = [n_samples]
- predict_log_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 log-probabilities 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.
- 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.