scikits.learn.svm.sparse.SVC

class scikits.learn.svm.sparse.SVC(kernel='rbf', degree=3, gamma=0.0, coef0=0.0, cache_size=100.0, eps=0.001, C=1.0, shrinking=True, probability=False)

SVC for sparse matrices (csr)

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

fit
predict
predict_margin
predict_proba
score

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

fit(X, Y, class_weight={})

X is expected to be a sparse matrix. For maximum effiency, use a sparse matrix in csr format (scipy.sparse.csr_matrix)

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 = [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.

score(X, y)

Returns the mean error rate on the given test data and labels.

Parameters :

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

Training set.

y : array-like, shape = [n_samples]

Labels for X.

Returns :

z : float