Package mdp :: Package nodes :: Class NuSVRScikitsLearnNode
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Class NuSVRScikitsLearnNode



Nu Support Vector Regression.

This node has been automatically generated by wrapping the ``sklearn.svm.classes.NuSVR`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.

Similar to NuSVC, for regression, uses a parameter nu to control
the number of support vectors. However, unlike NuSVC, where nu
replaces C, here nu replaces the parameter epsilon of epsilon-SVR.

The implementation is based on libsvm.

Read more in the :ref:`User Guide <svm_regression>`.

**Parameters**

C : float, optional (default=1.0)
    Penalty parameter C of the error term.

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.

kernel : string, optional (default='rbf')
     Specifies the kernel type to be used in the algorithm.
     It must be one of 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed' or
     a callable.
     If none is given, 'rbf' will be used. If a callable is given it is
     used to precompute the kernel matrix.

degree : int, optional (default=3)
    Degree of the polynomial kernel function ('poly').
    Ignored by all other kernels.

gamma : float, optional (default='auto')
    Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.
    If gamma is 'auto' then 1/n_features will be used instead.

coef0 : float, optional (default=0.0)
    Independent term in kernel function.
    It is only significant in 'poly' and 'sigmoid'.

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).

verbose : bool, default: False
    Enable verbose output. Note that this setting takes advantage of a
    per-process runtime setting in libsvm that, if enabled, may not work
    properly in a multithreaded context.

max_iter : int, optional (default=-1)
    Hard limit on iterations within solver, or -1 for no limit.

**Attributes**

``support_`` : array-like, shape = [n_SV]
    Indices of support vectors.

``support_vectors_`` : array-like, shape = [nSV, n_features]
    Support vectors.

``dual_coef_`` : array, shape = [1, n_SV]
    Coefficients of the support vector in the decision function.

``coef_`` : array, shape = [1, 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 readonly property derived from `dual_coef_` and
    `support_vectors_`.

``intercept_`` : array, shape = [1]
    Constants in decision function.

**Examples**

>>> from sklearn.svm import NuSVR
>>> 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 = NuSVR(C=1.0, nu=0.1)
>>> clf.fit(X, y)  #doctest: +NORMALIZE_WHITESPACE
NuSVR(C=1.0, cache_size=200, coef0=0.0, degree=3, gamma='auto',
      kernel='rbf', max_iter=-1, nu=0.1, shrinking=True, tol=0.001,
      verbose=False)

See also

NuSVC
    Support Vector Machine for classification implemented with libsvm
    with a parameter to control the number of support vectors.

SVR
    epsilon Support Vector Machine for regression implemented with libsvm.

Instance Methods [hide private]
 
__init__(self, input_dim=None, output_dim=None, dtype=None, **kwargs)
Nu Support Vector Regression.
 
_execute(self, x)
 
_get_supported_dtypes(self)
Return the list of dtypes supported by this node. The types can be specified in any format allowed by numpy.dtype.
 
_stop_training(self, **kwargs)
Concatenate the collected data in a single array.
 
execute(self, x)
Perform regression on samples in X.
 
stop_training(self, **kwargs)
Fit the SVM model according to the given training data.

Inherited from unreachable.newobject: __long__, __native__, __nonzero__, __unicode__, next

Inherited from object: __delattr__, __format__, __getattribute__, __hash__, __new__, __reduce__, __reduce_ex__, __setattr__, __sizeof__, __subclasshook__

    Inherited from Cumulator
 
_train(self, *args)
Collect all input data in a list.
 
train(self, *args)
Collect all input data in a list.
    Inherited from Node
 
__add__(self, other)
 
__call__(self, x, *args, **kwargs)
Calling an instance of Node is equivalent to calling its execute method.
 
__repr__(self)
repr(x)
 
__str__(self)
str(x)
 
_check_input(self, x)
 
_check_output(self, y)
 
_check_train_args(self, x, *args, **kwargs)
 
_get_train_seq(self)
 
_if_training_stop_training(self)
 
_inverse(self, x)
 
_pre_execution_checks(self, x)
This method contains all pre-execution checks.
 
_pre_inversion_checks(self, y)
This method contains all pre-inversion checks.
 
_refcast(self, x)
Helper function to cast arrays to the internal dtype.
 
_set_dtype(self, t)
 
_set_input_dim(self, n)
 
_set_output_dim(self, n)
 
copy(self, protocol=None)
Return a deep copy of the node.
 
get_current_train_phase(self)
Return the index of the current training phase.
 
get_dtype(self)
Return dtype.
 
get_input_dim(self)
Return input dimensions.
 
get_output_dim(self)
Return output dimensions.
 
get_remaining_train_phase(self)
Return the number of training phases still to accomplish.
 
get_supported_dtypes(self)
Return dtypes supported by the node as a list of dtype objects.
 
has_multiple_training_phases(self)
Return True if the node has multiple training phases.
 
inverse(self, y, *args, **kwargs)
Invert y.
 
is_training(self)
Return True if the node is in the training phase, False otherwise.
 
save(self, filename, protocol=-1)
Save a pickled serialization of the node to filename. If filename is None, return a string.
 
set_dtype(self, t)
Set internal structures' dtype.
 
set_input_dim(self, n)
Set input dimensions.
 
set_output_dim(self, n)
Set output dimensions.
Static Methods [hide private]
 
is_invertible()
Return True if the node can be inverted, False otherwise.
 
is_trainable()
Return True if the node can be trained, False otherwise.
Properties [hide private]

Inherited from object: __class__

    Inherited from Node
  _train_seq
List of tuples:
  dtype
dtype
  input_dim
Input dimensions
  output_dim
Output dimensions
  supported_dtypes
Supported dtypes
Method Details [hide private]

__init__(self, input_dim=None, output_dim=None, dtype=None, **kwargs)
(Constructor)

 

Nu Support Vector Regression.

This node has been automatically generated by wrapping the ``sklearn.svm.classes.NuSVR`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.

Similar to NuSVC, for regression, uses a parameter nu to control
the number of support vectors. However, unlike NuSVC, where nu
replaces C, here nu replaces the parameter epsilon of epsilon-SVR.

The implementation is based on libsvm.

Read more in the :ref:`User Guide <svm_regression>`.

**Parameters**

C : float, optional (default=1.0)
    Penalty parameter C of the error term.

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.

kernel : string, optional (default='rbf')
     Specifies the kernel type to be used in the algorithm.
     It must be one of 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed' or
     a callable.
     If none is given, 'rbf' will be used. If a callable is given it is
     used to precompute the kernel matrix.

degree : int, optional (default=3)
    Degree of the polynomial kernel function ('poly').
    Ignored by all other kernels.

gamma : float, optional (default='auto')
    Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.
    If gamma is 'auto' then 1/n_features will be used instead.

coef0 : float, optional (default=0.0)
    Independent term in kernel function.
    It is only significant in 'poly' and 'sigmoid'.

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).

verbose : bool, default: False
    Enable verbose output. Note that this setting takes advantage of a
    per-process runtime setting in libsvm that, if enabled, may not work
    properly in a multithreaded context.

max_iter : int, optional (default=-1)
    Hard limit on iterations within solver, or -1 for no limit.

**Attributes**

``support_`` : array-like, shape = [n_SV]
    Indices of support vectors.

``support_vectors_`` : array-like, shape = [nSV, n_features]
    Support vectors.

``dual_coef_`` : array, shape = [1, n_SV]
    Coefficients of the support vector in the decision function.

``coef_`` : array, shape = [1, 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 readonly property derived from `dual_coef_` and
    `support_vectors_`.

``intercept_`` : array, shape = [1]
    Constants in decision function.

**Examples**

>>> from sklearn.svm import NuSVR
>>> 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 = NuSVR(C=1.0, nu=0.1)
>>> clf.fit(X, y)  #doctest: +NORMALIZE_WHITESPACE
NuSVR(C=1.0, cache_size=200, coef0=0.0, degree=3, gamma='auto',
      kernel='rbf', max_iter=-1, nu=0.1, shrinking=True, tol=0.001,
      verbose=False)

See also

NuSVC
    Support Vector Machine for classification implemented with libsvm
    with a parameter to control the number of support vectors.

SVR
    epsilon Support Vector Machine for regression implemented with libsvm.

Overrides: object.__init__

_execute(self, x)

 
Overrides: Node._execute

_get_supported_dtypes(self)

 
Return the list of dtypes supported by this node. The types can be specified in any format allowed by numpy.dtype.
Overrides: Node._get_supported_dtypes

_stop_training(self, **kwargs)

 
Concatenate the collected data in a single array.
Overrides: Node._stop_training

execute(self, x)

 

Perform regression on samples in X.

This node has been automatically generated by wrapping the sklearn.svm.classes.NuSVR class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

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

Parameters

X : {array-like, sparse matrix}, shape (n_samples, n_features)
For kernel="precomputed", the expected shape of X is (n_samples_test, n_samples_train).

Returns

y_pred : array, shape (n_samples,)

Overrides: Node.execute

is_invertible()
Static Method

 
Return True if the node can be inverted, False otherwise.
Overrides: Node.is_invertible
(inherited documentation)

is_trainable()
Static Method

 
Return True if the node can be trained, False otherwise.
Overrides: Node.is_trainable

stop_training(self, **kwargs)

 

Fit the SVM model according to the given training data.

This node has been automatically generated by wrapping the sklearn.svm.classes.NuSVR class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

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. For kernel="precomputed", the expected shape of X is (n_samples, n_samples).
y : array-like, shape (n_samples,)
Target values (class labels in classification, real numbers in regression)
sample_weight : array-like, shape (n_samples,)
Per-sample weights. Rescale C per sample. Higher weights force the classifier to put more emphasis on these points.

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.

Overrides: Node.stop_training