Home  Trees  Indices  Help 



Linear Support Vector Classification. This node has been automatically generated by wrapping the ``sklearn.svm.classes.LinearSVC`` class from the ``sklearn`` library. The wrapped instance can be accessed through the ``scikits_alg`` attribute. Similar to SVC with parameter kernel='linear', but implemented in terms of liblinear rather than libsvm, so it has more flexibility in the choice of penalties and loss functions and should scale better to large numbers of samples. This class supports both dense and sparse input and the multiclass support is handled according to a onevstherest scheme. Read more in the :ref:`User Guide <svm_classification>`. **Parameters** C : float, optional (default=1.0) Penalty parameter C of the error term. loss : string, 'hinge' or 'squared_hinge' (default='squared_hinge') Specifies the loss function. 'hinge' is the standard SVM loss (used e.g. by the SVC class) while 'squared_hinge' is the square of the hinge loss. penalty : string, 'l1' or 'l2' (default='l2') Specifies the norm used in the penalization. The 'l2' penalty is the standard used in SVC. The 'l1' leads to ``coef_`` vectors that are sparse. dual : bool, (default=True) Select the algorithm to either solve the dual or primal optimization problem. Prefer dual=False when n_samples > n_features. tol : float, optional (default=1e4) Tolerance for stopping criteria. multi_class: string, 'ovr' or 'crammer_singer' (default='ovr') Determines the multiclass strategy if `y` contains more than two classes. ``"ovr"`` trains n_classes onevsrest classifiers, while ``"crammer_singer"`` optimizes a joint objective over all classes. While `crammer_singer` is interesting from a theoretical perspective as it is consistent, it is seldom used in practice as it rarely leads to better accuracy and is more expensive to compute. If ``"crammer_singer"`` is chosen, the options loss, penalty and dual will be ignored. fit_intercept : boolean, optional (default=True) Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (i.e. data is expected to be already centered). intercept_scaling : float, optional (default=1) When self.fit_intercept is True, instance vector x becomes ``[x, self.intercept_scaling]``, i.e. a "synthetic" feature with constant value equals to intercept_scaling is appended to the instance vector. The intercept becomes intercept_scaling * synthetic feature weight Note! the synthetic feature weight is subject to l1/l2 regularization as all other features. To lessen the effect of regularization on synthetic feature weight (and therefore on the intercept) intercept_scaling has to be increased. class_weight : {dict, 'balanced'}, 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 "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))`` verbose : int, (default=0) Enable verbose output. Note that this setting takes advantage of a perprocess runtime setting in liblinear that, if enabled, may not work properly in a multithreaded context. random_state : int seed, RandomState instance, or None (default=None) The seed of the pseudo random number generator to use when shuffling the data. max_iter : int, (default=1000) The maximum number of iterations to be run. **Attributes** ``coef_`` : array, shape = [n_features] if n_classes == 2 else [n_classes, n_features] Weights assigned to the features (coefficients in the primal problem). This is only available in the case of a linear kernel. ``coef_`` is a readonly property derived from ``raw_coef_`` that follows the internal memory layout of liblinear. ``intercept_`` : array, shape = [1] if n_classes == 2 else [n_classes] Constants in decision function. **Notes** The underlying C implementation uses a random number generator to select features when fitting the model. It is thus not uncommon to have slightly different results for the same input data. If that happens, try with a smaller ``tol`` parameter. The underlying implementation, liblinear, uses a sparse internal representation for the data that will incur a memory copy. Predict output may not match that of standalone liblinear in certain cases. See :ref:`differences from liblinear <liblinear_differences>` in the narrative documentation. **References** `LIBLINEAR: A Library for Large Linear Classification <http://www.csie.ntu.edu.tw/~cjlin/liblinear/>`__ See also SVC Implementation of Support Vector Machine classifier using libsvm:  the kernel can be nonlinear but its SMO algorithm does not  scale to large number of samples as LinearSVC does. Furthermore SVC multiclass mode is implemented using one vs one scheme while LinearSVC uses one vs the rest. It is possible to implement one vs the rest with SVC by using the :class:`sklearn.multiclass.OneVsRestClassifier` wrapper. Finally SVC can fit dense data without memory copy if the input is Ccontiguous. Sparse data will still incur memory copy though. sklearn.linear_model.SGDClassifier SGDClassifier can optimize the same cost function as LinearSVC by adjusting the penalty and loss parameters. In addition it requires less memory, allows incremental (online) learning, and implements various loss functions and regularization regimes.














Inherited from Inherited from Inherited from 

Inherited from ClassifierCumulator  







Inherited from ClassifierNode  










Inherited from Node  




























































Inherited from 

Inherited from Node  

_train_seq List of tuples: 

dtype dtype 

input_dim Input dimensions 

output_dim Output dimensions 

supported_dtypes Supported dtypes 

Linear Support Vector Classification. This node has been automatically generated by wrapping the ``sklearn.svm.classes.LinearSVC`` class from the ``sklearn`` library. The wrapped instance can be accessed through the ``scikits_alg`` attribute. Similar to SVC with parameter kernel='linear', but implemented in terms of liblinear rather than libsvm, so it has more flexibility in the choice of penalties and loss functions and should scale better to large numbers of samples. This class supports both dense and sparse input and the multiclass support is handled according to a onevstherest scheme. Read more in the :ref:`User Guide <svm_classification>`. **Parameters** C : float, optional (default=1.0) Penalty parameter C of the error term. loss : string, 'hinge' or 'squared_hinge' (default='squared_hinge') Specifies the loss function. 'hinge' is the standard SVM loss (used e.g. by the SVC class) while 'squared_hinge' is the square of the hinge loss. penalty : string, 'l1' or 'l2' (default='l2') Specifies the norm used in the penalization. The 'l2' penalty is the standard used in SVC. The 'l1' leads to ``coef_`` vectors that are sparse. dual : bool, (default=True) Select the algorithm to either solve the dual or primal optimization problem. Prefer dual=False when n_samples > n_features. tol : float, optional (default=1e4) Tolerance for stopping criteria. multi_class: string, 'ovr' or 'crammer_singer' (default='ovr') Determines the multiclass strategy if `y` contains more than two classes. ``"ovr"`` trains n_classes onevsrest classifiers, while ``"crammer_singer"`` optimizes a joint objective over all classes. While `crammer_singer` is interesting from a theoretical perspective as it is consistent, it is seldom used in practice as it rarely leads to better accuracy and is more expensive to compute. If ``"crammer_singer"`` is chosen, the options loss, penalty and dual will be ignored. fit_intercept : boolean, optional (default=True) Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (i.e. data is expected to be already centered). intercept_scaling : float, optional (default=1) When self.fit_intercept is True, instance vector x becomes ``[x, self.intercept_scaling]``, i.e. a "synthetic" feature with constant value equals to intercept_scaling is appended to the instance vector. The intercept becomes intercept_scaling * synthetic feature weight Note! the synthetic feature weight is subject to l1/l2 regularization as all other features. To lessen the effect of regularization on synthetic feature weight (and therefore on the intercept) intercept_scaling has to be increased. class_weight : {dict, 'balanced'}, 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 "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))`` verbose : int, (default=0) Enable verbose output. Note that this setting takes advantage of a perprocess runtime setting in liblinear that, if enabled, may not work properly in a multithreaded context. random_state : int seed, RandomState instance, or None (default=None) The seed of the pseudo random number generator to use when shuffling the data. max_iter : int, (default=1000) The maximum number of iterations to be run. **Attributes** ``coef_`` : array, shape = [n_features] if n_classes == 2 else [n_classes, n_features] Weights assigned to the features (coefficients in the primal problem). This is only available in the case of a linear kernel. ``coef_`` is a readonly property derived from ``raw_coef_`` that follows the internal memory layout of liblinear. ``intercept_`` : array, shape = [1] if n_classes == 2 else [n_classes] Constants in decision function. **Notes** The underlying C implementation uses a random number generator to select features when fitting the model. It is thus not uncommon to have slightly different results for the same input data. If that happens, try with a smaller ``tol`` parameter. The underlying implementation, liblinear, uses a sparse internal representation for the data that will incur a memory copy. Predict output may not match that of standalone liblinear in certain cases. See :ref:`differences from liblinear <liblinear_differences>` in the narrative documentation. **References** `LIBLINEAR: A Library for Large Linear Classification <http://www.csie.ntu.edu.tw/~cjlin/liblinear/>`__ See also SVC Implementation of Support Vector Machine classifier using libsvm:  the kernel can be nonlinear but its SMO algorithm does not  scale to large number of samples as LinearSVC does. Furthermore SVC multiclass mode is implemented using one vs one scheme while LinearSVC uses one vs the rest. It is possible to implement one vs the rest with SVC by using the :class:`sklearn.multiclass.OneVsRestClassifier` wrapper. Finally SVC can fit dense data without memory copy if the input is Ccontiguous. Sparse data will still incur memory copy though. sklearn.linear_model.SGDClassifier SGDClassifier can optimize the same cost function as LinearSVC by adjusting the penalty and loss parameters. In addition it requires less memory, allows incremental (online) learning, and implements various loss functions and regularization regimes.



Transform the data and labels lists to array objects and reshape them.



Predict class labels for samples in X. This node has been automatically generated by wrapping the sklearn.svm.classes.LinearSVC class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters
Returns

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

Home  Trees  Indices  Help 


Generated by Epydoc 3.0.1 on Tue Mar 8 12:39:48 2016  http://epydoc.sourceforge.net 