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Packages that use Learnable | |
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org.joone.engine | |
org.joone.engine.learning | |
org.joone.io | |
org.joone.net | |
org.joone.structure | |
org.joone.util |
Uses of Learnable in org.joone.engine |
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Subinterfaces of Learnable in org.joone.engine | |
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interface |
LearnableLayer
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interface |
LearnableSynapse
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Classes in org.joone.engine that implement Learnable | |
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class |
BiasedLinearLayer
This layer consists of linear neurons, i.e. |
class |
BufferedSynapse
This class implements a synapse that permits to have asynchronous methods to write output patterns. |
class |
ContextLayer
The context layer is similar to the linear layer except that it has an auto-recurrent connection between its output and input. |
class |
DelayLayer
Delay unit to create temporal windows from time series
O---> Yk(t-N) |
class |
DelaySynapse
This Synapse connects the N input neurons with the M output neurons using a matrix of FIRFilter elements of size NxM. |
class |
DirectSynapse
This is forward-only synapse. |
class |
FreudRuleFullSynapse
Deprecated. possible bug in implementation |
class |
FullSynapse
|
class |
GaussianLayer
This layer implements the Gaussian Neighborhood SOM strategy. |
class |
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) ). |
class |
KohonenSynapse
This is an unsupervised Kohonen Synapse which is a Self Organising Map. |
class |
Layer
The Layer object is the basic element forming the neural net. |
class |
LinearLayer
The output of a linear layer neuron is the sum of the weighted input values, scaled by the beta parameter. |
class |
LogarithmicLayer
This layer implements a logarithmic transfer function. |
class |
MemoryLayer
|
class |
RbfGaussianLayer
This class implements the nonlinear layer in Radial Basis Function (RBF) networks using Gaussian functions. |
class |
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. |
class |
RbfLayer
This is the basis (helper) for radial basis function layers. |
class |
SangerSynapse
This is the synapse useful to extract the principal components from an input data set. |
class |
SigmoidLayer
The output of a sigmoid layer neuron is the sum of the weighted input values, applied to a sigmoid function. |
class |
SimpleLayer
This abstract class represents layers that are composed by neurons that implement some transfer function. |
class |
SineLayer
The output of a sine layer neuron is the sum of the weighted input values, applied to a sine ( sin(x) ). |
class |
SoftmaxLayer
The outputs of the Softmax layer must be interpreted as probabilities. |
class |
Synapse
The Synapse is the connection element between two Layer objects. |
class |
TanhLayer
Layer that applies the tangent hyperbolic transfer function to its input patterns |
class |
WTALayer
This layer implements the Winner Takes All SOM strategy. |
Fields in org.joone.engine declared as Learnable | |
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protected Learnable |
AbstractLearner.learnable
The learnable, the object that is subjected to the learning process. |
protected Learnable |
RTRLLearnerFactory.RTRLLearner.learnable
The learnable that is trained here |
Methods in org.joone.engine with parameters of type Learnable | |
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void |
Learner.registerLearnable(Learnable l)
The Learnable calls this method to make itself known to the Learner |
void |
AbstractLearner.registerLearnable(Learnable aLearnable)
Learnable makes itself known to the Learner, also the type of Learnable is checked. |
void |
RTRLLearnerFactory.RTRLLearner.registerLearnable(Learnable learnable)
Register the learnable and determine if we'll update on this one |
Uses of Learnable in org.joone.engine.learning |
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Classes in org.joone.engine.learning that implement Learnable | |
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class |
AbstractTeacherSynapse
This class provides a framework to extend in order to implement various teachers, just by overriding or implementing certain functions different functionality can easily implemented. |
class |
ComparisonSynapse
Final element of a neural network; it permits to compare the outcome of the neural net and the input patterns from a StreamInputSynapse connected to the 'desired' property. |
class |
FahlmanTeacherSynapse
This class extends the normal Teacher synapse and implements the Fahlman 40-20-40 criterion (the values can be changed). |
class |
TeacherSynapse
Final element of a neural network; it permits to calculate both the error of the last training cycle and the vector containing the error pattern to apply to the net to calculate the backprop algorithm. |
Uses of Learnable in org.joone.io |
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Classes in org.joone.io that implement Learnable | |
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class |
FileInputSynapse
Allows data to be presented to the network from a file. |
class |
FileOutputSynapse
|
class |
ImageInputSynapse
This synapse collects data from Image files or Image objects and feeds the data from the Images into the Neural network. |
class |
ImageOutputSynapse
This class collects the output from the connected layer and places it into an image file. |
class |
InputConnector
|
class |
InputSwitchSynapse
This class acts as a switch that can connect its output to one of its connected input synapses. |
class |
JDBCInputSynapse
The JDBCInputSynapse provides support for data extraction from a database. |
class |
JDBCOutputSynapse
The JDBCOutputSynapse provides support for data input to a database. |
class |
MemoryInputSynapse
|
class |
MemoryOutputSynapse
|
class |
MultipleInputSynapse
This class reads sequentially all the connected input synapses, in order to be able to use multiple sources as inputs. |
class |
StreamInputSynapse
|
class |
StreamOutputSynapse
|
class |
URLInputSynapse
Allows data extraction from the internet or a file specified by a Universal Resource Locator or URL. |
class |
XLSInputSynapse
This class allows data to be presented to the network from an Excel XLS formatted file. |
class |
XLSOutputSynapse
This class allows data to be read from an Excel XLS formatted file. |
class |
YahooFinanceInputSynapse
The YahooFinanceInputSynapse provides support for financial data input from financial markets. |
Uses of Learnable in org.joone.net |
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Classes in org.joone.net that implement Learnable | |
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class |
NestedNeuralLayer
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Uses of Learnable in org.joone.structure |
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Classes in org.joone.structure that implement Learnable | |
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class |
NetworkLayer
Wraps an existing joone network into a single layer. |
(package private) class |
PatternForwardedSynapse
This class/synapse is only used to inform a Nakayama object whenever a single patterns has been forwarded through the network. |
Uses of Learnable in org.joone.util |
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Classes in org.joone.util that implement Learnable | |
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class |
LearningSwitch
This class is useful to switch the input data set of a neural network from a training set to a validation set depending on the 'validation' parameter contained in the Monitor object. |
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