Class SVCScikitsLearnNode

C-Support Vector Classification. This node has been automatically generated by wrapping the scikits.learn.svm.classes.SVC class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters

C : float, optional (default=1.0)

penalty parameter C of the error term.

kernel : string, optional

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

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

gamma : float, optional

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

coef0 : float, optional

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

probability: boolean, optional (False by default)

enable probability estimates. This must be enabled prior to calling prob_predict.

shrinking: boolean, optional

wether to use the shrinking heuristic.

tol: float, optional

precision for stopping criteria

cache_size: float, optional

specify the size of the cache (in MB)

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.

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.

intercept_ : array, shape = [n_class * (n_class-1) / 2]

Constants in decision function.

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 import SVC
>>> clf = SVC()
>>> clf.fit(X, y)
SVC(kernel='rbf', C=1.0, probability=False, degree=3, coef0=0.0, tol=0.001,
  cache_size=100.0, shrinking=True, gamma=0.25)
>>> print clf.predict([[-0.8, -1]])
[ 1.]

See also

SVR, LinearSVC

Overrides: object.__init__

Overrides: Node.is_invertible

(inherited documentation)

Returns: bool

A boolean indicating whether the node can be trained.

Overrides: Node.is_trainable

This function does classification or regression on an array of test vectors X. This node has been automatically generated by wrapping the scikits.learn.svm.classes.SVC class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. 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, shape = [n_samples, n_features]

Returns

C : array, shape = [n_samples]

Overrides: ClassifierNode.label

Fit the SVM model according to the given training data and parameters. This node has been automatically generated by wrapping the scikits.learn.svm.classes.SVC class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters

X : array-like, 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

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.

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, they are copied.

Overrides: Node.stop_training