Package mdp :: Package nodes :: Class RBFSamplerScikitsLearnNode

Class RBFSamplerScikitsLearnNode


Approximates feature map of an RBF kernel by Monte Carlo approximation
of its Fourier transform.

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

It implements a variant of Random Kitchen Sinks.[1]

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

**Parameters**

gamma : float
    Parameter of RBF kernel: exp(-gamma * x^2)

n_components : int
    Number of Monte Carlo samples per original feature.
    Equals the dimensionality of the computed feature space.

random_state : {int, RandomState}, optional
    If int, random_state is the seed used by the random number generator;
    if RandomState instance, random_state is the random number generator.

**Notes**

See "Random Features for Large-Scale Kernel Machines" by A. Rahimi and
Benjamin Recht.

[1] "Weighted Sums of Random Kitchen Sinks: Replacing
minimization with randomization in learning" by A. Rahimi and
Benjamin Recht.
(http://www.eecs.berkeley.edu/~brecht/papers/08.rah.rec.nips.pdf)

Instance Methods

[hide private]

__init__(self, input_dim=None, output_dim=None, dtype=None, **kwargs)
Approximates feature map of an RBF kernel by Monte Carlo approximation of its Fourier transform.

_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)
Apply the approximate feature map to X.

stop_training(self, **kwargs)
Fit the model with 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)


Approximates feature map of an RBF kernel by Monte Carlo approximation
of its Fourier transform.

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

It implements a variant of Random Kitchen Sinks.[1]

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

**Parameters**

gamma : float
    Parameter of RBF kernel: exp(-gamma * x^2)

n_components : int
    Number of Monte Carlo samples per original feature.
    Equals the dimensionality of the computed feature space.

random_state : {int, RandomState}, optional
    If int, random_state is the seed used by the random number generator;
    if RandomState instance, random_state is the random number generator.

**Notes**

See "Random Features for Large-Scale Kernel Machines" by A. Rahimi and
Benjamin Recht.

[1] "Weighted Sums of Random Kitchen Sinks: Replacing
minimization with randomization in learning" by A. Rahimi and
Benjamin Recht.
(http://www.eecs.berkeley.edu/~brecht/papers/08.rah.rec.nips.pdf)

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)

Apply the approximate feature map to X.

This node has been automatically generated by wrapping the sklearn.kernel_approximation.RBFSampler 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): New data, where n_samples in the number of samples and n_features is the number of 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 with X.

Samples random projection according to n_features.

Parameters

X : {array-like, sparse matrix}, shape (n_samples, n_features): Training data, where n_samples in the number of samples and n_features is the number of features.

Returns

self : object: Returns the transformer.

Overrides: Node.stop_training

Class RBFSamplerScikitsLearnNode

__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