public class Backpropagation
extends AlgorithmBase

MIT License Copyright (c) 2016 Jason Wu Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Backpropagation Using Java Swing to implement backpropagation neural network. Learning algorithm can refer to [this](https://en.wikipedia.org/wiki/Backpropagation) Wikipedia page. Input consists of several groups of multi-dimensional data set, The data were cut into three parts (each number roughly equal to the same group), 2/3 of the data given to training function, and the remaining 1/3 of the data given to testing function. The purpose of program is training to cut a number of groups of hyperplanes and synaptic weights, and display the results in the graphical interface. ## Getting Started git clone https://github.com/Jasonnor/Backpropagation.git cd Backpropagation Backpropagation.jar  1. Menu (Files, Skins) 2. Output 3. Background rendering mode & zoom level 4. Read the file 5. File path 6. Adjustable parameters 7. Output parameters 8. Generate new results 9. List of training materials (2/3 of total data) 10. List of test data (1/3 of total data) Be careful to use background rendering mode, and notice that too small drawing size will delay the computer. ## Input Data Format InputA InputB OutputA InputC InputD OutputB ... You can use these [data sets](data) for testing. ## Result    ## Contributing Please feel free to use it if you are interested in fixing issues and contributing directly to the code base. ## License Backpropagation is released under the MIT license. See the [LICENSE](/LICENSE) file for details.