scikits.learn.svm.SVR

class scikits.learn.svm.SVR(kernel='rbf', degree=3, gamma=0.0, coef0=0.0, cache_size=100.0, tol=0.001, C=1.0, nu=0.5, epsilon=0.10000000000000001, shrinking=True, probability=False)

epsilon-Support Vector Regression.

The free parameters in the model are C and epsilon.

Parameters :

nu : float, optional

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]. By default 0.5 will be taken. Only available if impl=’nu_svc’

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.

epsilon : float

epsilon in the epsilon-SVR model.

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.

C : float, optional (default=1.0)

penalty parameter C of the error term.

probability: boolean, optional (False by default) :

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

tol: float, optional :

precision for stopping criteria

coef0 : float, optional

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

cache_size: float, optional :

specify the size of the cache (in MB)

shrinking: boolean, optional :

wether to use the shrinking heuristic.

See also

NuSVR

Examples

>>> from scikits.learn.svm import SVR
>>> import numpy as np
>>> n_samples, n_features = 10, 5
>>> np.random.seed(0)
>>> y = np.random.randn(n_samples)
>>> X = np.random.randn(n_samples, n_features)
>>> clf = SVR(C=1.0, epsilon=0.2)
>>> clf.fit(X, y)
SVR(kernel='rbf', C=1.0, probability=False, degree=3, epsilon=0.2,
  shrinking=True, tol=0.001, cache_size=100.0, coef0=0.0, nu=0.5,
  gamma=0.1)

Attributes

support_	array-like, shape = [n_SV]	Index of support vectors.
support_vectors_	array-like, shape = [nSV, n_features]	Support vectors.
dual_coef_	array, shape = [n_classes-1, n_SV]	Coefficients of the support vector in the decision function.
coef_	array, shape = [n_classes-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.

Methods

__init__(kernel='rbf', degree=3, gamma=0.0, coef0=0.0, cache_size=100.0, tol=0.001, C=1.0, nu=0.5, epsilon=0.10000000000000001, shrinking=True, probability=False)

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, sample_weight=[], **params)

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

Parameters :

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

Training vector, where n_samples is the number of samples and n_features is the number of features.

y : array, shape = [n_samples]

Target values. Array of floating-point numbers.

Returns :

self : object

Returns self.

predict(X)

This function does classification or regression on an array of test vectors 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, 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 coefficient of determination of the prediction

Parameters :

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

Training set.

y : array-like, shape = [n_samples]

Returns :

z : float