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Modular toolkit for Data Processing MDP
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)
If the input dimension and the output dimension are unspecified, they will be set when the train or execute method is called for the first time. If dtype is unspecified, it will be inherited from the data it receives at the first call of train or execute.
 
_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 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, k, r=0.001, svd=False, verbose=False, input_dim=None, output_dim=None, dtype=None)
(Constructor)

 

If the input dimension and the output dimension are unspecified, they will be set when the train or execute method is called for the first time. If dtype is unspecified, it will be inherited from the data it receives at the first call of train or execute.

Every subclass must take care of up- or down-casting the internal structures to match this argument (use _refcast private method when possible).

Parameters:
  • 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

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Modular toolkit for Data Processing MDP
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