Package mdp :: Package nodes :: Class KernelPCAScikitsLearnNode
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Class KernelPCAScikitsLearnNode



Kernel Principal component analysis (KPCA)

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

Non-linear dimensionality reduction through the use of kernels (see
:ref:`metrics`).

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

**Parameters**

n_components: int or None
    Number of components. If None, all non-zero components are kept.

kernel: "linear" | "poly" | "rbf" | "sigmoid" | "cosine" | "precomputed"
    Kernel.
    Default: "linear"

degree : int, default=3
    Degree for poly kernels. Ignored by other kernels.

gamma : float, optional
    Kernel coefficient for rbf and poly kernels. Default: 1/n_features.
    Ignored by other kernels.

coef0 : float, optional
    Independent term in poly and sigmoid kernels.
    Ignored by other kernels.

kernel_params : mapping of string to any, optional
    Parameters (keyword arguments) and values for kernel passed as
    callable object. Ignored by other kernels.

alpha: int
    Hyperparameter of the ridge regression that learns the
    inverse transform (when fit_inverse_transform=True).
    Default: 1.0

fit_inverse_transform: bool
    Learn the inverse transform for non-precomputed kernels.
    (i.e. learn to find the pre-image of a point)
    Default: False

eigen_solver: string ['auto'|'dense'|'arpack']
    Select eigensolver to use.  If n_components is much less than
    the number of training samples, arpack may be more efficient
    than the dense eigensolver.

tol: float
    convergence tolerance for arpack.
    Default: 0 (optimal value will be chosen by arpack)

max_iter : int
    maximum number of iterations for arpack
    Default: None (optimal value will be chosen by arpack)

remove_zero_eig : boolean, default=True
    If True, then all components with zero eigenvalues are removed, so
    that the number of components in the output may be < n_components
    (and sometimes even zero due to numerical instability).
    When n_components is None, this parameter is ignored and components
    with zero eigenvalues are removed regardless.

**Attributes**


``lambdas_`` :

    - Eigenvalues of the centered kernel matrix


``alphas_`` :

    - Eigenvectors of the centered kernel matrix


``dual_coef_`` :

    - Inverse transform matrix


``X_transformed_fit_`` :

    - Projection of the fitted data on the kernel principal components


**References**

Kernel PCA was introduced in:

    - Bernhard Schoelkopf, Alexander J. Smola,
    - and Klaus-Robert Mueller. 1999. Kernel principal
    - component analysis. In Advances in kernel methods,
    - MIT Press, Cambridge, MA, USA 327-352.

Instance Methods [hide private]
 
__init__(self, input_dim=None, output_dim=None, dtype=None, **kwargs)
Kernel Principal component analysis (KPCA)
 
_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)
Transform X.
 
stop_training(self, **kwargs)
Fit the model from data in X.

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)

 

Kernel Principal component analysis (KPCA)

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

Non-linear dimensionality reduction through the use of kernels (see
:ref:`metrics`).

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

**Parameters**

n_components: int or None
    Number of components. If None, all non-zero components are kept.

kernel: "linear" | "poly" | "rbf" | "sigmoid" | "cosine" | "precomputed"
    Kernel.
    Default: "linear"

degree : int, default=3
    Degree for poly kernels. Ignored by other kernels.

gamma : float, optional
    Kernel coefficient for rbf and poly kernels. Default: 1/n_features.
    Ignored by other kernels.

coef0 : float, optional
    Independent term in poly and sigmoid kernels.
    Ignored by other kernels.

kernel_params : mapping of string to any, optional
    Parameters (keyword arguments) and values for kernel passed as
    callable object. Ignored by other kernels.

alpha: int
    Hyperparameter of the ridge regression that learns the
    inverse transform (when fit_inverse_transform=True).
    Default: 1.0

fit_inverse_transform: bool
    Learn the inverse transform for non-precomputed kernels.
    (i.e. learn to find the pre-image of a point)
    Default: False

eigen_solver: string ['auto'|'dense'|'arpack']
    Select eigensolver to use.  If n_components is much less than
    the number of training samples, arpack may be more efficient
    than the dense eigensolver.

tol: float
    convergence tolerance for arpack.
    Default: 0 (optimal value will be chosen by arpack)

max_iter : int
    maximum number of iterations for arpack
    Default: None (optimal value will be chosen by arpack)

remove_zero_eig : boolean, default=True
    If True, then all components with zero eigenvalues are removed, so
    that the number of components in the output may be < n_components
    (and sometimes even zero due to numerical instability).
    When n_components is None, this parameter is ignored and components
    with zero eigenvalues are removed regardless.

**Attributes**


``lambdas_`` :

    - Eigenvalues of the centered kernel matrix


``alphas_`` :

    - Eigenvectors of the centered kernel matrix


``dual_coef_`` :

    - Inverse transform matrix


``X_transformed_fit_`` :

    - Projection of the fitted data on the kernel principal components


**References**

Kernel PCA was introduced in:

    - Bernhard Schoelkopf, Alexander J. Smola,
    - and Klaus-Robert Mueller. 1999. Kernel principal
    - component analysis. In Advances in kernel methods,
    - MIT Press, Cambridge, MA, USA 327-352.

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)

 

Transform X.

This node has been automatically generated by wrapping the sklearn.decomposition.kernel_pca.KernelPCA 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)

Returns

X_new: array-like, shape (n_samples, n_components)

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 model from data in X.

This node has been automatically generated by wrapping the sklearn.decomposition.kernel_pca.KernelPCA 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 vector, where n_samples in the number of samples and n_features is the number of features.

Returns

self : object
Returns the instance itself.
Overrides: Node.stop_training