Package mdp :: Package nodes :: Class LLENode
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Class LLENode


Perform a Locally Linear Embedding analysis on the data.

**Internal variables of interest**

  ``self.training_projection``
      The LLE projection of the training data (defined when
      training finishes).

  ``self.desired_variance``
      variance limit used to compute intrinsic dimensionality.

Based on the algorithm outlined in *An Introduction to Locally
Linear Embedding* by L. Saul and S. Roweis, using improvements
suggested in *Locally Linear Embedding for Classification* by
D. deRidder and R.P.W. Duin.

References: Roweis, S. and Saul, L., Nonlinear dimensionality
reduction by locally linear embedding, Science 290 (5500), pp.
2323-2326, 2000.

Original code contributed by: Jake VanderPlas, University of Washington,

Instance Methods [hide private]
 
__init__(self, k, r=0.001, svd=False, verbose=False, input_dim=None, output_dim=None, dtype=None)
:Arguments: k number of nearest neighbors to use r regularization constant; if ``None``, ``r`` is automatically computed using the method presented in deRidder and Duin; this method involves solving an eigenvalue problem for every data point, and can slow down the algorithm If specified, it multiplies the trace of the local covariance matrix of the distances, as in Saul & Roweis (faster) svd if true, use SVD to compute the projection matrix; SVD is slower but more stable verbose if true, displays information about the progress of the algorithm output_dim number of dimensions to output or a float between 0.0 and 1.0.
 
_adjust_output_dim(self)
 
_execute(self, x)
 
_stop_training(self)
Concatenate the collected data in a single array.
 
execute(self, x)
Process the data contained in `x`.
 
stop_training(self)
Concatenate the collected data in a single array.

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_supported_dtypes(self)
Return the list of dtypes supported by this node.
 
_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`.
 
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, k, r=0.001, svd=False, verbose=False, input_dim=None, output_dim=None, dtype=None)
(Constructor)

 

:Arguments:
   k
     number of nearest neighbors to use
   r
     regularization constant; if ``None``, ``r`` is automatically
     computed using the method presented in deRidder and Duin;
     this method involves solving an eigenvalue problem for
     every data point, and can slow down the algorithm
     If specified, it multiplies the trace of the local covariance
     matrix of the distances, as in Saul & Roweis (faster)
   svd
     if true, use SVD to compute the projection matrix;
     SVD is slower but more stable
   verbose
     if true, displays information about the progress
     of the algorithm
   output_dim
     number of dimensions to output or a float between 0.0 and
     1.0. In the latter case, ``output_dim`` specifies the desired
     fraction of variance to be explained, and the final
     number of output dimensions is known at the end of
     training (e.g., for ``output_dim=0.95`` the algorithm will
     keep as many dimensions as necessary in order to explain
     95% of the input variance)

Overrides: object.__init__

_adjust_output_dim(self)

 

_execute(self, x)

 
Overrides: Node._execute

_stop_training(self)

 
Concatenate the collected data in a single array.

Overrides: Node._stop_training

execute(self, x)

 
Process the data contained in `x`.

If the object is still in the training phase, the function
`stop_training` will be called.
`x` is a matrix having different variables on different columns
and observations on the rows.

By default, subclasses should overwrite `_execute` to implement
their execution phase. The docstring of the `_execute` method
overwrites this docstring.

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
(inherited documentation)

stop_training(self)

 
Concatenate the collected data in a single array.

Overrides: Node.stop_training