8.26.1.3. sklearn.svm.NuSVC

class sklearn.svm.NuSVC(nu=0.5, kernel='rbf', degree=3, gamma=0.0, coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200)

Nu-Support Vector Classification.

Similar to SVC but uses a parameter to control the number of support vectors.

The implementation is based on libsvm.

Parameters :

nu : float, optional (default=0.5)

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].

kernel : string, optional (default=’rbf’)

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

degree : int, optional (default=3)

degree of kernel function is significant only in poly, rbf, sigmoid

gamma : float, optional (default=0.0)

kernel coefficient for rbf and poly, if gamma is 0.0 then 1/n_features will be taken.

coef0 : float, optional (default=0.0)

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

probability: boolean, optional (default=False) :

Whether to enable probability estimates. This must be enabled prior to calling predict_proba.

shrinking: boolean, optional (default=True) :

Whether to use the shrinking heuristic.

tol: float, optional (default=1e-3) :

Tolerance for stopping criterion.

cache_size: float, optional :

Specify the size of the kernel cache (in MB)

class_weight : {dict, ‘auto’}, optional

Set the parameter C of class i to class_weight[i]*C for SVC. 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.

See also

SVC

Support Vector Machine for classification using libsvm.

LinearSVC

Scalable linear Support Vector Machine for classification using liblinear.

Examples

>>> import numpy as np
>>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
>>> y = np.array([1, 1, 2, 2])
>>> from sklearn.svm import NuSVC
>>> clf = NuSVC()
>>> clf.fit(X, y)
NuSVC(cache_size=200, coef0=0.0, degree=3, gamma=0.5, kernel='rbf', nu=0.5,
   probability=False, shrinking=True, tol=0.001)
>>> print clf.predict([[-0.8, -1]])
[ 1.]

Attributes

support_	array-like, shape = [n_SV]	Index of support vectors.
support_vectors_	array-like, shape = [n_SV, n_features]	Support vectors.
n_support_	array-like, dtype=int32, shape = [n_class]	number of support vector for each class.
dual_coef_	array, shape = [n_class-1, n_SV]	Coefficients of the support vector in the decision function. For multiclass, coefficient for all 1-vs-1 classifiers. The layout of the coefficients in the multiclass case is somewhat non-trivial. See the section about multi-class classification in the SVM section of the User Guide for details.
coef_	array, shape = [n_class-1, n_features]	Weights asigned to the features (coefficients in the primal problem). This is only available in the case of linear kernel. coef_ is readonly property derived from dual_coef_ and support_vectors_
intercept_	array, shape = [n_class * (n_class-1) / 2]	Constants in decision function.

Methods

decision_function(X)	Distance of the samples X to the separating hyperplane.
fit(X, y[, class_weight, sample_weight])	Fit the SVM model according to the given training data.
get_params([deep])	Get parameters for the estimator
predict(X)	Perform classification or regression samples in X.
predict_log_proba(X)	Compute the log likehoods each possible outcomes of samples in X.
predict_proba(X)	Compute the likehoods each possible outcomes of samples in T.
score(X, y)	Returns the mean accuracy on the given test data and labels.
set_params(**params)	Set the parameters of the estimator.

__init__(nu=0.5, kernel='rbf', degree=3, gamma=0.0, coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200)

decision_function(X)

Distance of the samples X 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=None, sample_weight=None)

Fit the SVM model according to the given training data.

Parameters :

X : {array-like, 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)

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

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

Returns :

self : object

Returns self.

Notes

If X and y are not C-ordered and contiguous arrays of np.float64 and X is not a scipy.sparse.csr_matrix, X and/or y may be copied.

If X is a dense array, then the other methods will not support sparse matrices as input.

get_params(deep=True)

Get parameters for the estimator

Parameters :

deep: boolean, optional :

If True, will return the parameters for this estimator and contained subobjects that are estimators.

predict(X)

Perform classification or regression samples in X.

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

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

Parameters :X : {array-like, sparse matrix}, shape = [n_samples, n_features] Returns :C : array, shape = [n_samples]

predict_log_proba(X)

Compute the log likehoods each possible outcomes of samples in X.

The model need to have probability information computed at training time: fit with attribute probability set to True.

Parameters :

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

Returns :

X : 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)

Compute the likehoods each possible outcomes of samples in T.

The model need to have probability information computed at training time: fit with attribute probability set to True.

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 accuracy 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

set_params(**params)

Set the parameters of the estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns :self :