Package mdp :: Package contrib :: Class HLLENode
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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
  vanderplas@astro.washington.edu

Nested Classes [hide private]
    Inherited from Node
  __metaclass__
This Metaclass is meant to overwrite doc strings of methods like execute, stop_training, inverse with the ones defined in the corresponding private methods _execute, _stop_training, _inverse, etc...
Instance Methods [hide private]
 
__init__(self, k, r=0.001, svd=False, verbose=False, input_dim=None, output_dim=None, dtype=None)
Keyword Arguments:
 
_stop_training(self)
Transform the data list to an array object and reshape it.

Inherited from object: __delattr__, __getattribute__, __hash__, __new__, __reduce__, __reduce_ex__, __setattr__

    Inherited from LLENode
 
_adjust_output_dim(self)
 
_execute(self, x)
 
_get_supported_dtypes(self)
Return the list of dtypes supported by this node.
 
is_invertible(self)
Return True if the node can be inverted, False otherwise.
 
is_trainable(self)
Return True if the node can be trained, False otherwise.
    Inherited from Cumulator
 
_train(self, x)
Cumulate all input data in a one dimensional list.
 
stop_training(self, *args, **kwargs)
Transform the data list to an array object and reshape it.
 
train(self, x, *args, **kwargs)
Cumulate all input data in a one dimensional list.
    Inherited from Node
 
__add__(self, other)
 
__call__(self, x, *args, **kargs)
Calling an instance of Node is equivalent to call 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=-1)
Return a deep copy of the node.
 
execute(self, x, *args, **kargs)
Process the data contained in 'x'.
 
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.
 
inverse(self, y, *args, **kargs)
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.
Properties [hide private]

Inherited from object: __class__

    Inherited from Node
  _train_seq
List of tuples: [(training-phase1, stop-training-phase1), (training-phase2, stop_training-phase2), ...
  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)

 

Keyword 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)

 
Transform the data list to an array object and reshape it.

Overrides: Node._stop_training