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The Core Engine

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Features

The core engine is the fulcrum around which are built all the other modules and applications based on Joone. It exposes a Java API providing the developer with the following features:

  • Components to create any neural net architecture (feed forward or recurrent)
  • Several supervised training algorithms:
    • On-line backprop
    • Batch backprop
    • Resilient Backprop (RPROP)
  • Components to build unsupervised neural networks (Kohonen SOMs and Principal Component Analysis)
  • Components to build modular neural networks
  • A serialization mechanism to save and restore a nnet to/from a file system or to send/receive a nnet on remote locations (via HTTP, RMI, etc.)
  • I/O components to read patterns from:
    • Ascii comma delimited files from file system or HTTP
    • Excel files
    • Image (gif & jpeg) files
    • RDBMS using JDBC
    • Stock price time series from Yahoo Finance
  • Components to implement both the supervised and unsupervised learning control
  • Components to control the behavior of the neural net (start/stop, recall/learn) and its parameters (learning rate, momentum, etc.)
  • Plug-ins to preprocess the input data (Normalization, Moving Average, etc.) and to control dynamically the training parameters (Annealing)
  • A complete event notification mechanism, along with a scripting capability to control the behaviour of a neural network at the happening of some events

Packages

The engine is composed by a set of components subdivided into the following packages:

  • org.joone.engine

    All the classes of the core engine wich represent the bricks to build the neural nets

  • org.joone.engine.learning

    All the components to train the neural net

  • org.joone.io

    All the I/O components to read and write patterns from/to external sources

  • org.joone.net

    The shell components of the neural net. They are useful to manage a neural net as a indivisible entity

  • org.joone.util

    Some utility classes to perform several tasks (input normalization, input scaling, scripting, etc.)

  • org.joone.log

    The classes to write the output messages to a log file (it can use either Log4J or an internal logger)

  • org.joone.script

    The classes to define and execute BeanShell scripts (called also Macro)

  • org.joone.helpers

    Helper classes to make it very simple to build, train and test a neural network

Resources