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•	org.joone.samples.engine.* (22)

Package Samples:

• org.joone.samples.engine.multipleInputs
• org.joone.samples.engine.parity
• org.joone.samples.engine.scripting
• org.joone.samples.engine.timeseries
• org.joone.samples.engine.validation
• org.joone.samples.engine.xml
• org.joone.samples.engine.xor
• org.joone.samples.engine.xor.InputConnector
• org.joone.samples.engine.xor.rbf
• org.joone.samples.engine.xpath

Classes:

• ScriptValidationSample: This example shows how to use the joone's scripting engine to to check the training level of the net using a validation data source. In this example we will learn to use the following objects: - org.joone.util.LearningSwitch - org.joone.util.MacroPlugin - org.joone.util.NormalizerPlugIn This program shows how to build the same neural net contained into the org/joone/samples/editor/scripting/ValidationSample.ser file using only java code and the core engine's API. Open that net in the GUI editor to see the architecture of the net built in this example.
• SimpleValidationSample: This example shows how to check the training level of the net using a validation data source. In this example we will learn to use the following objects: - org.joone.util.LearningSwitch - org.joone.net.NeuralNetValidator - org.joone.util.NormalizerPlugIn This program shows how to build the same kind of neural net as that contained into the org/joone/samples/editor/scripting/ValidationSample.ser file using only java code and the core engine's API. Open that net in the GUI editor to see the architecture of the net built in this example.
• MultipleValidationSample: This example shows how to check the training level of a neural network using a validation data source. The training and the validation phases of the created network is executed many times, showing for each one the resulting RMSE. This program shows how to build the same kind of neural net as that contained into the org/joone/samples/editor/scripting/ValidationSample.ser file using only java code and the core engine's API. Open that net in the GUI editor to see the architecture of the net built in this example.
• NeuralNetTrainer: This class trains and validates a neural network passed as parameter of the constructor and, when the validation phase is finished, it notifies its listeners. The neural network passes as parameter is cloned before to use it, so the calling program can call many copies of this class to train and validate several copies of the same neural network.
• EmbeddedXOR: This example shows the use of a neural network embedded in another application that gets the output from the MemoryOutputSynapse object querying the neural network with a predefined set of patterns
• ImmediateEmbeddedXOR: This example shows the use of a neural network embedded in another application that gets the output from the DirectSynapse object querying the neural network with one input pattern at time
• NeuralNetFactory: Title: Effort Estimations Through Neural Networks Description: Stage II - Joone Pilot Copyright: Copyright (c) 2003 Company: Motorola
• XOR_static_RBF: Very simple example of the Gaussian (static and random centers) RBF solving the XOR problem.
• Parity_Structure_Nakayama: Sample class to show the working of the technique for optimizing activation functions.
• XORFahlman: Sample class to demostrate the use of the FahlmanTeacherSynapse and related classes.
• XORTrainer: This sample application loads a serialized network and launches it in training mode
• XOR_inputSwitch: Sample class to demostrate the use of the MultipleInputSynapse
• XOR_multipleInputs: Sample class to demostrate the use of the MultipleInputSynapse
• XORMemory: Sample class to demostrate the use of the MemoryInputSynapse
• XOR_using_NeuralNet: Sample class to demostrate the use of the MemoryInputSynapse
• XOR_using_NeuralNet_RPROP: Sample class to demostrate the use of the MemoryInputSynapse
• XORMemory_using_InputConnector: Sample class to demostrate the use of the MemoryInputSynapse
• XOR_using_NeuralNet: Sample class to demostrate the use of the MemoryInputSynapse
• TimeSeriesForecasting
• NeuralNetTester
• TestXML
• XOR

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