Package mdp :: Package nodes
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Package nodes

Classes [hide private]
  AdaptiveCutoffNode
Node which uses the data history during training to learn cutoff values.
  CuBICANode
Perform Independent Component Analysis using the CuBICA algorithm.
  CutoffNode
Node to cut off values at specified bounds.
  EtaComputerNode
Compute the eta values of the normalized training data.
  FANode
Perform Factor Analysis.
  FDANode
Perform a (generalized) Fisher Discriminant Analysis of its input.
  FastICANode
Perform Independent Component Analysis using the FastICA algorithm.
  GaussianClassifierNode
Perform a supervised Gaussian classification.
  GrowingNeuralGasNode
Learn the topological structure of the input data by building a corresponding graph approximation.
  HLLENode
Perform a Hessian Locally Linear Embedding analysis on the data.
  HistogramNode
Node which stores a history of the data during its training phase.
  HitParadeNode
Collect the first 'n' local maxima and minima of the training signal which are separated by a minimum gap 'd'.
  ICANode
ICANode is a general class to handle different batch-mode algorithm for Independent Component Analysis.
  ISFANode
Perform Independent Slow Feature Analysis on the input data.
  IdentityNode
Return input data (useful in complex network layouts)
  JADENode
Perform Independent Component Analysis using the JADE algorithm.
  LLENode
Perform a Locally Linear Embedding analysis on the data.
  LibSVMNode
Problems with LibSVM:...
  LinearRegressionNode
Compute least-square, multivariate linear regression on the input data, i.e., learn coefficients b_j so that
  NIPALSNode
Perform Principal Component Analysis using the NIPALS algorithm.
  NoiseNode
Inject multiplicative or additive noise into the input data.
  NormalNoiseNode
Special version of NoiseNode for Gaussian additive noise.
  PCANode
Filter the input data through the most significatives of its principal components.
  PolynomialExpansionNode
Perform expansion in a polynomial space.
  QuadraticExpansionNode
Perform expansion in the space formed by all linear and quadratic monomials.
  RBFExpansionNode
Expand input space with Gaussian Radial Basis Functions (RBFs).
  RBMNode
Restricted Boltzmann Machine node.
  RBMWithLabelsNode
Restricted Boltzmann Machine with softmax labels.
  SFA2Node
Get an input signal, expand it in the space of inhomogeneous polynomials of degree 2 and extract its slowly varying components.
  SFANode
Extract the slowly varying components from the input data.
  TimeFramesNode
Copy delayed version of the input signal on the space dimensions.
  WhiteningNode
'Whiten' the input data by filtering it through the most significatives of its principal components.
  XSFANode
Perform Non-linear Blind Source Separation using Slow Feature Analysis.
  _OneDimensionalHitParade
Class to produce hit-parades (i.e., a list of the largest and smallest values) out of a one-dimensional time-series.
Functions [hide private]
 
_expanded_dim(degree, nvariables)
Return the size of a vector of dimension 'nvariables' after a polynomial expansion of degree 'degree'.
Function Details [hide private]

_expanded_dim(degree, nvariables)

 
Return the size of a vector of dimension 'nvariables' after
a polynomial expansion of degree 'degree'.