Package mdp :: Package nodes :: Class RBFSamplerScikitsLearnNode
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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.

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.

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