Home       Trees       Indices       Help   
Modular toolkit for Data Processing MDP
Package mdp :: Package nodes :: Class GaussianRandomProjectionScikitsLearnNode
[hide private]
[frames] | no frames]

Class GaussianRandomProjectionScikitsLearnNode


Reduce dimensionality through Gaussian random projection

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

The components of the random matrix are drawn from N(0, 1 / n_components).

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

**Parameters**

n_components : int or 'auto', optional (default = 'auto')
    Dimensionality of the target projection space.

    n_components can be automatically adjusted according to the
    number of samples in the dataset and the bound given by the
    Johnson-Lindenstrauss lemma. In that case the quality of the
    embedding is controlled by the ``eps`` parameter.

    It should be noted that Johnson-Lindenstrauss lemma can yield
    very conservative estimated of the required number of components
    as it makes no assumption on the structure of the dataset.

eps : strictly positive float, optional (default=0.1)
    Parameter to control the quality of the embedding according to
    the Johnson-Lindenstrauss lemma when n_components is set to
    'auto'.

    Smaller values lead to better embedding and higher number of
    dimensions (n_components) in the target projection space.

random_state : integer, RandomState instance or None (default=None)
    Control the pseudo random number generator used to generate the
    matrix at fit time.

**Attributes**

``n_component_`` : int
    Concrete number of components computed when n_components="auto".

``components_`` : numpy array of shape [n_components, n_features]
    Random matrix used for the projection.

See Also

SparseRandomProjection
Instance Methods [hide private]
 
__init__(self, input_dim=None, output_dim=None, dtype=None, **kwargs)
Reduce dimensionality through Gaussian random projection
 
_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)
Project the data by using matrix product with the random matrix
 
stop_training(self, **kwargs)
Generate a sparse random projection matrix

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)

  

Reduce dimensionality through Gaussian random projection

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

The components of the random matrix are drawn from N(0, 1 / n_components).

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

**Parameters**

n_components : int or 'auto', optional (default = 'auto')
    Dimensionality of the target projection space.

    n_components can be automatically adjusted according to the
    number of samples in the dataset and the bound given by the
    Johnson-Lindenstrauss lemma. In that case the quality of the
    embedding is controlled by the ``eps`` parameter.

    It should be noted that Johnson-Lindenstrauss lemma can yield
    very conservative estimated of the required number of components
    as it makes no assumption on the structure of the dataset.

eps : strictly positive float, optional (default=0.1)
    Parameter to control the quality of the embedding according to
    the Johnson-Lindenstrauss lemma when n_components is set to
    'auto'.

    Smaller values lead to better embedding and higher number of
    dimensions (n_components) in the target projection space.

random_state : integer, RandomState instance or None (default=None)
    Control the pseudo random number generator used to generate the
    matrix at fit time.

**Attributes**

``n_component_`` : int
    Concrete number of components computed when n_components="auto".

``components_`` : numpy array of shape [n_components, n_features]
    Random matrix used for the projection.

See Also

SparseRandomProjection
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)

 

Project the data by using matrix product with the random matrix

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

Parameters

X : numpy array or scipy.sparse of shape [n_samples, n_features]
The input data to project into a smaller dimensional space.

y : is not used: placeholder to allow for usage in a Pipeline.

Returns

X_new : numpy array or scipy sparse of shape [n_samples, n_components]
Projected array.
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)

 

Generate a sparse random projection matrix

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

Parameters

X : numpy array or scipy.sparse of shape [n_samples, n_features]
Training set: only the shape is used to find optimal random matrix dimensions based on the theory referenced in the afore mentioned papers.

y : is not used: placeholder to allow for usage in a Pipeline.

Returns

self

Overrides: Node.stop_training

   Home       Trees       Indices       Help   
Modular toolkit for Data Processing MDP
Generated by Epydoc 3.0.1 on Tue Mar 8 12:39:48 2016 http://epydoc.sourceforge.net