Package org.joone.engine

Interface Summary

Interface	Description
InputPatternListener	This interface represents an input synapse for a generic layer.
Learnable
LearnableLayer
LearnableSynapse
Learner
LearnerFactory	Learner factories are used to provide the synapses and layers, through the monitor object with Leaners.
NeuralElement	This interface represents a generic element of a neural network
NeuralLayer	This is the interface for all the layer objects of the neural network
NeuralNetListener
OutputPatternListener	This interface represents an output synapse for a generic layer.

Class Summary

Class	Description
AbstractEventNotifier	This class raises an event notification invoking the corrisponnding Monitor.fireXXX method.
AbstractLearner	This class provides some basic simple functionality that can be used (extended) by other learners.
BasicLearner
BatchLearner	BatchLearner stores the weight/bias changes during the batch and updates them after the batch is done.
BiasedLinearLayer	This layer consists of linear neurons, i.e.
BufferedSynapse	This class implements a synapse that permits to have asynchronous methods to write output patterns.
CircularSpatialMap	This class implements the SpatialMap interface providing a circular spatial map for use with the GaussianLayer and Kohonen Networks.
ContextLayer	The context layer is similar to the linear layer except that it has an auto-recurrent connection between its output and input.
DelayLayer	Delay unit to create temporal windows from time series O---> Yk(t-N) | ...
DelayLayerBeanInfo
DelaySynapse	This Synapse connects the N input neurons with the M output neurons using a matrix of FIRFilter elements of size NxM.
DirectSynapse
ExtendableLearner	Learners that extend this class are forced to implement certain functions, a so-called skeleton.
Fifo	The Fifo class represents a first-in-first-out (FIFO) stack of objects.
FIRFilter	Element of a connection representing a FIR filter (Finite Impulse Response).
FreudRuleFullSynapse
FullSynapse
GaussianLayer	This layer implements the Gaussian Neighborhood SOM strategy.
GaussianLayerBeanInfo
GaussianSpatialMap	This class implements the SpatialMap interface providing a circular spatial map for use with the GaussianLayer and Kohonen Networks.
GaussLayer	The output of a Gauss(ian) layer neuron is the sum of the weighted input values, applied to a gaussian curve (exp(- x * x)).
KohonenSynapse	This is an unsupervised Kohonen Synapse which is a Self Organising Map.
KohonenSynapseBeanInfo
Layer	The Layer object is the basic element forming the neural net.
LayerBeanInfo
LinearLayer	The output of a linear layer neuron is the sum of the weighted input values, scaled by the beta parameter.
LinearLayerBeanInfo
LogarithmicLayer	This layer implements a logarithmic transfer function.
Matrix	The Matrix object represents the connection matrix of the weights of a synapse or the biases of a layer.
MatrixBeanInfo
MemoryLayer
MemoryLayerBeanInfo
Monitor	The Monitor object is the controller of the behavior of the neural net.
MonitorBeanInfo
NetErrorManager	This class should be used when ever a critical error occurs that would impact on the training or running of the network.
NetStoppedEventNotifier	Raises the netStopped event from within a separate Thread
NeuralNetAdapter
NeuralNetEvent	Transport class used to notify the events raised from a neural network
OutputSwitchSynapse	This class acts as a switch that can connect its input to one of its connected output synapses.
OutputSwitchSynapseBeanInfo
Pattern	The pattern object contains the data that must be processed from a neural net.
PatternBeanInfo
RbfGaussianLayer	This class implements the nonlinear layer in Radial Basis Function (RBF) networks using Gaussian functions.
RbfGaussianParameters	This class defines the parameters, like center, sigma, etc.
RbfInputSynapse	The synapse to the input of a radial basis function layer should't provide a single value to every neuron in the output (RBF) layer, as is usual the case.
RbfLayer	This is the basis (helper) for radial basis function layers.
RpropLearner	This class implements the RPROP learning algorithm.
RpropParameters	This object holds the global parameters for the RPROP learning algorithm (RpropLearner).
SangerSynapse	This is the synapse useful to extract the principal components from an input data set.
SigmoidLayer	The output of a sigmoid layer neuron is the sum of the weighted input values, applied to a sigmoid function.
SimpleLayer	This abstract class represents layers that are composed by neurons that implement some transfer function.
SimpleLayerBeanInfo
SineLayer	The output of a sine layer neuron is the sum of the weighted input values, applied to a sine (sin(x)).
SoftmaxLayer	The outputs of the Softmax layer must be interpreted as probabilities.
SpatialMap	SpatialMap is intended to be an abstract spatial map for use with a GaussianLayer.
Synapse	The Synapse is the connection element between two Layer objects.
SynapseBeanInfo
TanhLayer	Layer that applies the tangent hyperbolic transfer function to its input patterns
TanhLayerBeanInfo
WTALayer	This layer implements the Winner Takes All SOM strategy.
WTALayerBeanInfo