Package mdp :: Package nodes :: Class LLENode

Class LLENode

Perform a Locally Linear Embedding analysis on the data.

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.

Reference

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)
Initializes an object of type 'LLENode'.

_adjust_output_dim(self)
This function is called if we need to compute the number of output dimensions automatically as some quantities that are useful later can be precalculated..

_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. 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.

Instance Variables

[hide private]

desired_variance
Variance limit used to compute intrinsic dimensionality.

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

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)

Initializes an object of type 'LLENode'.

Parameters:

k (int) - Number of nearest neighbors to use.
r (float) - 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 (bool) - If true, use SVD to compute the projection matrix; SVD is slower but more stable
verbose (bool) - if true, displays information about the progress of the algorithm
input_dim (int) - The input dimensionality.
output_dim (int) - 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).
dtype (numpy.dtype or str) - The datatype.

Overrides: object.__init__

_adjust_output_dim(self)

This function is called if we need to compute the number of output dimensions automatically as some quantities that are useful later can be precalculated..

_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

Instance Variable Details

[hide private]

desired_variance

Variance limit used to compute intrinsic dimensionality.

training_projection

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

Class LLENode

__init__(self, k, r=0.001, svd=False, verbose=False, input_dim=None, output_dim=None, dtype=None) (Constructor)

_adjust_output_dim(self)

_execute(self, x)

_stop_training(self)

execute(self, x)

is_invertible() Static Method

is_trainable() Static Method

stop_training(self)

desired_variance

training_projection

init(self, k, r=0.001, svd=False, verbose=False, input_dim=None, output_dim=None, dtype=None)
(Constructor)

is_invertible()
Static Method

is_trainable()
Static Method