scikits.learn.svm.sparse.NuSVC

class scikits.learn.svm.sparse.NuSVC(nu=0.5, kernel='rbf', degree=3, gamma=0.0, coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=100.0)

NuSVC 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).

Examples

>>> import numpy as np
>>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
>>> y = np.array([1, 1, 2, 2])
>>> from scikits.learn.svm.sparse import NuSVC
>>> clf = NuSVC()
>>> clf.fit(X, y)
NuSVC(kernel='rbf', probability=False, degree=3, coef0=0.0, tol=0.001,
   cache_size=100.0, shrinking=True, nu=0.5, gamma=0.25)
>>> print clf.predict([[-0.8, -1]])
[ 1.]

Methods

__init__(nu=0.5, kernel='rbf', degree=3, gamma=0.0, coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=100.0)

decision_function(X)

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

Parameters :

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

Returns :

X : array-like, shape = [n_samples, n_class * (n_class-1) / 2]

Returns the decision function of the sample for each class in the model.

fit(X, y, class_weight={}, sample_weight=[], **params)

Fit the SVM model according to the given training data and parameters.

Parameters :

X : sparse matrix, 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, shape = [n_samples]

Target values (integers in classification, real numbers in regression)

class_weight : {dict, ‘auto’}, optional

Weights associated with classes in the form {class_label : weight}. If not given, all classes are supposed to have weight one.

The ‘auto’ mode uses the values of y to automatically adjust weights inversely proportional to class frequencies.

sample_weight : array-like, shape = [n_samples], optional

Weights applied to individual samples (1. for unweighted).

Returns :

self : object

Returns an instance of self.

Notes

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 = [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(X)

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

Parameters :

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

Returns :

X : 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