Package mdp :: Package nodes :: Class HLLENode

[frames] | no frames]

Class HLLENode

Perform a Hessian Locally Linear Embedding analysis on the data.

Internal variables of interest

self.training_projection

the HLLE projection of the training data (defined when training finishes)

self.desired_variance

variance limit used to compute intrinsic dimensionality.

Implementation based on algorithm outlined in Donoho, D. L., and Grimes, C., Hessian Eigenmaps: new locally linear embedding techniques for high-dimensional data, Proceedings of the National Academy of Sciences 100(10): 5591-5596, 2003.

Original code contributed by: Jake Vanderplas, University of Washington

Instance Methods

__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.

_stop_training(self)
Concatenate the collected data in a single array.

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 LLENode

_adjust_output_dim(self)

_execute(self, x)

execute(self, x)
Process the data contained in x.

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

Inherited from LLENode

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

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

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:

arguments -

k

number of nearest neighbors to use; the node will raise an MDPWarning if k is smaller than k >= 1 + output_dim + output_dim*(output_dim+1)/2, because in this case a less efficient computation must be used, and the ablgorithm can become unstable

r

regularization constant; as opposed to LLENode, it is not possible to compute this constant automatically; it is only used during execution

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 exaplained, 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__

_stop_training(self)

Concatenate the collected data in a single array.

Overrides: Node._stop_training

stop_training(self)

Concatenate the collected data in a single array.

Overrides: Node.stop_training