Package mdp :: Package nodes :: Class KernelPCAScikitsLearnNode

Class KernelPCAScikitsLearnNode


Kernel Principal component analysis (KPCA)
This node has been automatically generated by wrapping the ``scikits.learn.decomposition.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.

**Parameters**

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

kernel: "linear" | "poly" | "rbf" | "precomputed"
    kernel
    Default: "linear"

sigma: float
    width of the rbf kernel
    Default: 1.0

degree: int
    degree of the polynomial kernel
    Default: 3

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
    (i.e. learn to find the pre-image of a point)
    Default: False

**Attributes**


``lambdas_``, alphas_:

    - Eigenvalues and eigenvectors of the centered kernel matrix


dual_coef_:

    - Inverse transform matrix


X_transformed_fit_:

    - Projection of the fitted data on the kernel principal components


Reference

Kernel PCA was intoduced 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) This node has been automatically generated by wrapping the ``scikits.learn.decomposition.pca.KernelPCA`` class from the ``sklearn`` library.

_execute(self, x)

list

_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. This node has been automatically generated by wrapping the scikits.learn.decomposition.pca.KernelPCA class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters

stop_training(self, **kwargs)
Fit the model from data in X. This node has been automatically generated by wrapping the scikits.learn.decomposition.pca.KernelPCA class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters

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 numpy.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.

bool

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 ``scikits.learn.decomposition.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.

**Parameters**

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

kernel: "linear" | "poly" | "rbf" | "precomputed"
    kernel
    Default: "linear"

sigma: float
    width of the rbf kernel
    Default: 1.0

degree: int
    degree of the polynomial kernel
    Default: 3

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
    (i.e. learn to find the pre-image of a point)
    Default: False

**Attributes**


``lambdas_``, alphas_:

    - Eigenvalues and eigenvectors of the centered kernel matrix


dual_coef_:

    - Inverse transform matrix


X_transformed_fit_:

    - Projection of the fitted data on the kernel principal components


Reference

Kernel PCA was intoduced 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.

Returns: list: The list of dtypes supported by this node.
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 scikits.learn.decomposition.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.

Returns: bool: A boolean indication whether the node can be trained.
Overrides: Node.is_trainable

stop_training(self, **kwargs)

Fit the model from data in X. This node has been automatically generated by wrapping the scikits.learn.decomposition.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

Class KernelPCAScikitsLearnNode

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

_execute(self, x)

_get_supported_dtypes(self)

_stop_training(self, **kwargs)

execute(self, x)

is_invertible() Static Method

is_trainable() Static Method

stop_training(self, **kwargs)

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

is_invertible()
Static Method

is_trainable()
Static Method