[
{
"path": ".gitignore",
"content": "out/\n*.class\n\n# Mobile Tools for Java (J2ME)\n.mtj.tmp/\n\n# Package Files #\n*.war\n*.ear\n\n# virtual machine crash logs, see http://www.java.com/en/download/help/error_hotspot.xml\nhs_err_pid*\n\n# =========================\n# Operating System Files\n# =========================\n\n# OSX\n# =========================\n\n.DS_Store\n.AppleDouble\n.LSOverride\n\n# Thumbnails\n._*\n\n# Files that might appear in the root of a volume\n.DocumentRevisions-V100\n.fseventsd\n.Spotlight-V100\n.TemporaryItems\n.Trashes\n.VolumeIcon.icns\n\n# Directories potentially created on remote AFP share\n.AppleDB\n.AppleDesktop\nNetwork Trash Folder\nTemporary Items\n.apdisk\n\n# Windows\n# =========================\n\n# Windows image file caches\nThumbs.db\nehthumbs.db\n\n# Folder config file\nDesktop.ini\n\n# Recycle Bin used on file shares\n$RECYCLE.BIN/\n\n# Windows Installer files\n*.cab\n*.msi\n*.msm\n*.msp\n\n# Windows shortcuts\n*.lnk\n"
},
{
"path": ".idea/.name",
"content": "Backpropagation"
},
{
"path": ".idea/artifacts/Backpropagation_jar.xml",
"content": "\n \n $PROJECT_DIR$/out/artifacts/Backpropagation_jar\n \n \n \n \n \n \n \n \n"
},
{
"path": ".idea/compiler.xml",
"content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n"
},
{
"path": ".idea/copyright/profiles_settings.xml",
"content": "\n \n"
},
{
"path": ".idea/dictionaries/Administrator.xml",
"content": "\n \n \n Backpropagation\n backpropagation\n hiddens\n plaf\n synth\n zoomer\n \n \n"
},
{
"path": ".idea/misc.xml",
"content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n"
},
{
"path": ".idea/modules.xml",
"content": "\n\n \n \n \n \n \n"
},
{
"path": ".idea/uiDesigner.xml",
"content": "\n\n \n \n - \n \n
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},
{
"path": ".idea/vcs.xml",
"content": "\n\n \n \n \n"
},
{
"path": ".idea/workspace.xml",
"content": "\n\n \n \n \n \n \n \n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n true\n DEFINITION_ORDER\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n General\n \n \n Java\n \n \n Probable bugsJava\n \n \n Spelling\n \n \n \n \n Duplicates\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n project\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
\n \n \n
\n \n \n \n \n \n \n \n \n 1476530793380\n \n \n 1476530793380\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Backpropagation:jar\n \n \n \n \n \n \n \n No facets are configured\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 1.8\n \n \n \n \n \n \n \n Backpropagation\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n"
},
{
"path": "Backpropagation.iml",
"content": "\n\n \n \n \n \n \n \n \n \n"
},
{
"path": "LICENSE",
"content": "MIT License\n\nCopyright (c) 2016 Jason Wu\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n"
},
{
"path": "META-INF/MANIFEST.MF",
"content": "Manifest-Version: 1.0\nMain-Class: Backpropagation.MainFrame\n\n"
},
{
"path": "README.md",
"content": "# Backpropagation\n\nUsing Java Swing to implement backpropagation neural network. Learning algorithm can refer to [this](https://en.wikipedia.org/wiki/Backpropagation) Wikipedia page. \n\nInput 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.\n\nThe purpose of program is training to cut a number of groups of hyperplanes and synaptic weights, and display the results in the graphical interface.\n\n## Getting Started\n\n\tgit clone https://github.com/Jasonnor/Backpropagation.git\n cd Backpropagation\n Backpropagation.jar\n\n\n\n1. Menu (Files, Skins)\n2. Output\n3. Background rendering mode & zoom level\n4. Read the file\n5. File path\n6. Adjustable parameters\n7. Output parameters\n8. Generate new results\n9. List of training materials (2/3 of total data)\n10. List of test data (1/3 of total data)\n\nBe careful to use background rendering mode, and notice that too small drawing size will delay the computer.\n\n## Input Data Format\n\n InputA InputB OutputA\n InputC InputD OutputB\n ...\n\nYou can use these [data sets](data) for testing.\n\n## Result\n\n\n\n\n\n\n\n## Contributing\n\nPlease feel free to use it if you are interested in fixing issues and contributing directly to the code base.\n\n## License\n\nBackpropagation is released under the MIT license. See the [LICENSE](/LICENSE) file for details.\n"
},
{
"path": "data/2CS.txt",
"content": " 1.590200 1.645400 1\n 2.508300 1.423700 1\n 1.886000 2.631600 1\n 1.710600 2.949500 1\n 2.038600 1.538100 1\n 3.235600 2.619200 1\n 2.912100 1.804200 1\n 2.657700 2.759400 1\n 2.647400 2.515200 1\n 2.917300 1.657700 1\n 3.189600 2.006900 1\n 1.974800 1.847100 1\n 2.794700 2.242500 1\n 2.279700 2.854900 1\n 1.276100 2.540700 1\n 2.212800 1.957600 1\n 2.435800 1.930100 1\n 2.505900 2.423000 1\n 2.752600 1.927300 1\n 1.830200 2.120400 1\n 3.225100 2.214500 1\n 1.697400 1.852400 1\n 1.583800 2.650300 1\n 2.275800 1.662500 1\n 1.732400 2.957600 1\n 2.577300 3.300500 1\n 2.586400 1.341200 1\n 1.937200 2.962400 1\n 2.079900 1.297200 1\n 2.121900 2.761200 1\n 2.536300 2.336400 1\n 1.694800 1.703000 1\n 2.266000 2.424900 1\n 2.776200 2.047200 1\n 2.913700 2.326300 1\n 1.943700 1.853200 1\n 1.922600 1.403700 1\n 1.998100 2.229200 1\n 2.220700 1.705200 1\n 2.235300 3.191600 1\n 2.541500 2.029200 1\n 2.171100 1.601300 1\n 1.786300 2.433800 1\n 2.632000 1.616200 1\n 1.698200 2.664700 1\n 2.137400 2.044100 1\n 2.137200 1.718700 1\n 2.983600 2.131800 1\n 2.792100 1.673600 1\n 1.578400 2.515600 1\n 2.910600 2.949700 1\n 1.581800 2.821600 1\n 1.569300 2.121200 1\n 2.396700 2.952400 1\n 2.902400 2.241000 1\n 2.535800 3.120500 1\n 3.196200 1.884400 1\n 1.857800 1.916000 1\n 2.568700 2.170800 1\n 2.956500 2.088400 1\n 2.904900 1.426300 1\n 2.284900 1.874500 1\n 2.700300 2.947300 1\n 1.604900 2.347400 1\n 2.343100 1.307100 1\n 2.422600 2.828000 1\n 2.980700 2.808800 1\n 1.469500 2.209700 1\n 2.401600 2.553400 1\n 2.399600 1.615900 1\n 2.639700 2.348900 1\n 1.285900 2.250900 1\n 1.539100 1.898800 1\n 2.970900 2.593500 1\n 1.864400 2.871700 1\n 2.932100 2.174400 1\n 1.338000 1.855500 1\n 3.193000 2.584400 1\n 2.303600 2.281800 1\n 2.203800 3.216300 1\n 1.666400 1.609700 1\n 3.063900 1.672800 1\n 1.693000 1.863800 1\n 1.339400 2.499000 1\n 2.106700 1.836800 1\n 2.358200 3.138300 1\n 1.681000 2.969400 1\n 2.082300 1.332300 1\n 1.550000 2.722900 1\n 1.491700 2.276200 1\n 2.696900 1.644100 1\n 1.820500 2.421200 1\n 1.730400 1.691900 1\n 2.994500 1.788800 1\n 2.669200 2.300700 1\n 3.022700 2.025300 1\n 3.352300 2.433000 1\n 2.873900 2.417500 1\n 2.498800 2.689100 1\n 2.832300 3.005600 1\n 1.747700 2.184900 1\n 2.637500 2.143100 1\n 2.463000 2.609400 1\n 2.230300 2.964700 1\n 2.593400 2.073100 1\n 2.059200 1.762500 1\n 2.393100 1.390100 1\n 2.426500 3.176700 1\n 2.659900 1.459200 1\n 2.776100 2.553200 1\n 3.073600 2.056300 1\n 1.436800 2.089100 1\n 2.923500 2.588900 1\n 2.359600 2.170900 1\n 3.091800 1.550400 1\n 2.590200 1.743000 1\n 2.170900 2.636500 1\n 2.356100 2.269800 1\n 2.113700 1.346900 1\n 2.095900 2.073900 1\n 2.740500 3.099000 1\n 3.142600 1.842900 1\n 2.325400 1.988000 1\n 1.585100 2.805600 1\n 2.416400 1.483900 1\n 2.941500 1.766200 1\n 3.048700 2.565600 1\n 2.827400 3.040800 1\n 2.846900 1.928600 1\n 2.136600 3.048200 1\n 2.121600 1.325800 1\n 2.475100 2.685900 1\n 2.193100 1.419200 1\n 2.170000 1.295400 1\n 1.780800 2.836900 1\n 1.993100 3.225000 1\n 1.735600 1.844800 1\n 2.984500 1.979700 1\n 2.153400 3.197300 1\n 2.904500 3.064700 1\n 2.846500 2.990200 1\n 2.715200 2.909300 1\n 2.022400 2.263900 1\n 2.753700 1.874800 1\n 2.330600 1.394000 1\n 2.406800 2.544000 1\n 2.442100 1.609100 1\n 1.753500 2.763500 1\n 2.401900 2.921400 1\n 2.578900 2.975700 1\n 2.559300 2.365900 1\n 2.035200 1.405800 1\n 1.609600 2.914400 1\n 1.885900 1.701600 1\n 2.056900 2.683000 1\n 1.503000 1.842500 1\n 2.198400 2.437100 1\n 2.287100 2.531100 1\n 1.897200 2.974700 1\n 1.511700 2.498200 1\n 2.418100 1.490100 1\n 3.139600 1.959900 1\n 2.644700 1.300900 1\n 2.671300 2.538700 1\n 1.514500 2.832900 1\n 3.268600 2.418200 1\n 2.061100 2.917000 1\n 2.816300 1.529300 1\n 2.429300 2.809700 1\n 2.033600 1.582200 1\n 2.016900 2.348000 1\n 3.287200 2.323900 1\n 2.837400 2.207400 1\n 1.952200 2.405400 1\n 1.736200 2.255100 1\n 1.928000 2.704500 1\n 2.394000 3.233600 1\n 1.801700 1.993500 1\n 1.433400 2.269100 1\n 2.143000 1.740400 1\n 2.111100 3.117100 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{
"path": "data/2Hcircle1.txt",
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},
{
"path": "data/2class.txt",
"content": "1\t1\t0\n1\t0\t0\n0\t0\t1\n0\t1\t1"
},
{
"path": "data/2cring.txt",
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17.750 2\n-15.656 21.539 2\n-3.446 7.966 2\n-16.708 5.124 2\n-15.939 21.388 2\n-9.620 22.325 2\n-18.907 18.849 2\n-1.963 14.486 2\n-11.626 3.501 2\n-3.796 7.416 2\n-2.248 15.298 2\n-13.292 3.708 2\n-19.787 8.394 2\n-5.674 20.587 2\n-11.985 3.523 2\n-20.894 12.834 2\n-11.564 22.500 2\n-9.624 22.325 2\n-12.245 3.536 2\n-16.691 5.033 2\n-14.148 3.868 2\n-16.916 5.218 2\n-5.024 20.060 2\n-3.961 7.283 2\n-19.854 8.835 2\n-10.171 3.571 2\n-1.889 13.828 2\n-4.754 19.718 2\n-13.805 3.782 2\n-10.101 22.408 2\n-3.334 8.250 2\n-19.457 18.034 2\n-16.349 4.944 2\n-9.877 3.617 2\n-14.756 21.927 2\n-4.486 19.479 2\n-15.186 4.255 2\n-18.462 6.521 2\n-16.380 21.056 2\n-4.354 6.643 2\n-3.725 7.416 2\n-17.899 19.948 2\n-12.616 22.429 2\n-18.925 7.283 2\n-3.007 17.606 2\n-2.165 14.813 2\n-17.187 5.514 2\n-17.969 6.052 2\n-2.323 9.907 2\n-3.266 8.107 2\n-4.426 19.233 2\n-18.994 7.151 2\n-5.354 20.277 2\n-11.165 22.499 2\n-20.642 10.541 2\n-16.157 21.227 2\n-2.807 9.288 2\n-1.951 13.166 2\n-16.628 20.967 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},
{
"path": "data/2ring.txt",
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{
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},
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},
{
"path": "data/wine.txt",
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},
{
"path": "data/xor.txt",
"content": "1\t1\t1\n1\t0\t0\n0\t0\t1\n0\t1\t0"
},
{
"path": "src/Backpropagation/Algorithm/Connection.java",
"content": "package Backpropagation.Algorithm;\n\nclass Connection {\n private double weight = 0;\n private double bestWeight = 0;\n private double prevDeltaWeight = 0; // for momentum\n private double deltaWeight = 0;\n\n final Neuron leftNeuron;\n static int counter = 0;\n final int id; // auto increment, starts at 0\n\n Connection(Neuron fromN) {\n leftNeuron = fromN;\n id = counter;\n counter++;\n }\n\n double getWeight() {\n return weight;\n }\n\n void setWeight(double w) {\n weight = w;\n }\n\n void setBestWeight(double w) {\n bestWeight = w;\n }\n\n void setWeightAsBest() {\n weight = bestWeight;\n }\n\n void setDeltaWeight(double w) {\n prevDeltaWeight = deltaWeight;\n deltaWeight = w;\n }\n\n double getPrevDeltaWeight() {\n return prevDeltaWeight;\n }\n\n Neuron getLeftNeuron() {\n return leftNeuron;\n }\n}\n"
},
{
"path": "src/Backpropagation/Algorithm/NeuralNetwork.java",
"content": "package Backpropagation.Algorithm;\n\nimport java.util.*;\n\npublic class NeuralNetwork {\n\n private final Random rand = new Random();\n private final ArrayList inputLayer = new ArrayList<>();\n private final ArrayList hiddenLayers = new ArrayList<>();\n private final ArrayList outputLayer = new ArrayList<>();\n\n private double momentum;\n private double learningRate;\n\n private ArrayList inputs = new ArrayList<>();\n private ArrayList outputKinds = new ArrayList<>();\n\n public NeuralNetwork(ArrayList inputs, ArrayList outputKinds, String hidden, double momentum,\n double learningRate, double threshold, double minRange, double maxRange) {\n this.inputs = inputs;\n this.outputKinds = outputKinds;\n this.momentum = momentum;\n this.learningRate = learningRate;\n int inputNeuron = inputs.get(0).length - 1;\n int[] hiddenNeurons = Arrays.stream(hidden.split(\",\")).mapToInt(Integer::parseInt).toArray();\n int outputNeuron = 1;\n // input layer\n for (int i = 0; i < inputNeuron; i++) {\n Neuron neuron = new Neuron();\n inputLayer.add(neuron);\n }\n // hidden layers\n for (int i = 0; i < hiddenNeurons.length; i++) {\n Neuron neurons[] = new Neuron[hiddenNeurons[i]];\n if (i == 0) {\n for (int j = 0; j < neurons.length; j++) {\n neurons[j] = new Neuron();\n neurons[j].addConnections(inputLayer);\n }\n } else {\n for (int j = 0; j < neurons.length; j++) {\n neurons[j] = new Neuron();\n neurons[j].addConnections(hiddenLayers.get(i - 1));\n }\n }\n hiddenLayers.add(neurons);\n }\n // output layer\n for (int i = 0; i < outputNeuron; i++) {\n Neuron neuron = new Neuron();\n neuron.addConnections(hiddenLayers.get(hiddenLayers.size() - 1));\n outputLayer.add(neuron);\n }\n\n // Initialize random weights\n for (Neuron[] neurons : hiddenLayers) {\n for (Neuron neuron : neurons) {\n ArrayList connections = neuron.getAllConnections();\n for (Connection conn : connections) {\n double newWeight = getRandomNumber(minRange, maxRange);\n conn.setWeight(newWeight);\n }\n connections.get(0).setWeight(threshold);\n }\n }\n for (Neuron neuron : outputLayer) {\n ArrayList connections = neuron.getAllConnections();\n for (Connection conn : connections) {\n double newWeight = getRandomNumber(minRange, maxRange);\n conn.setWeight(newWeight);\n }\n }\n\n // Reset id counters\n Neuron.counter = 0;\n Connection.counter = 0;\n }\n\n private Double getRandomNumber(Double minRange, Double maxRange) {\n return minRange + (maxRange - minRange) * rand.nextDouble();\n }\n\n private void setInput(Double inputs[]) {\n for (int i = 0; i < inputLayer.size(); i++) {\n inputLayer.get(i).setOutput(inputs[i]);\n }\n }\n\n private Double[] getOutput() {\n Double[] outputs = new Double[outputLayer.size()];\n for (int i = 0; i < outputLayer.size(); i++)\n outputs[i] = outputLayer.get(i).getOutput();\n return outputs;\n }\n\n private void activate() {\n for (Neuron[] neurons : hiddenLayers) {\n for (Neuron neuron : neurons) {\n neuron.calculateOutput();\n }\n }\n outputLayer.forEach(Neuron::calculateOutput);\n }\n\n private void applyBackpropagation(Double expectedOutput[]) {\n int i = 0;\n for (Neuron n : outputLayer) {\n ArrayList connections = n.getAllConnections();\n for (Connection connection : connections) {\n double pervY = connection.leftNeuron.getOutput();\n double y = n.getOutput();\n double dy = expectedOutput[i];\n double partialDerivative = (dy - y) * y * (1 - y);\n double deltaWeight = learningRate * partialDerivative * pervY;\n double newWeight = connection.getWeight() + deltaWeight;\n connection.setDeltaWeight(deltaWeight);\n connection.setWeight(newWeight + momentum * connection.getPrevDeltaWeight());\n }\n i++;\n }\n double[] pervPartialDerivatives = new double[0];\n for (int j = hiddenLayers.size() - 1; j >= 0; j--) {\n Neuron[] neurons = hiddenLayers.get(j);\n double[] nowPartialDerivatives = new double[neurons.length];\n int n = 0;\n for (Neuron neuron : neurons) {\n double y = neuron.getOutput();\n double sumOutputs = 0;\n if (j == hiddenLayers.size() - 1) {\n int k = 0;\n for (Neuron outputN : outputLayer) {\n double wjk = outputN.getConnection(neuron.id).getWeight();\n double dy = expectedOutput[k];\n double yk = outputN.getOutput();\n sumOutputs += (dy - yk) * yk * (1 - yk) * wjk;\n k++;\n }\n } else {\n int k = 0;\n for (Neuron hiddenN : hiddenLayers.get(j + 1)) {\n double wjk = hiddenN.getConnection(neuron.id).getWeight();\n sumOutputs += pervPartialDerivatives[k] * wjk;\n k++;\n }\n }\n double partialDerivative = y * (1 - y) * sumOutputs;\n nowPartialDerivatives[n] = partialDerivative;\n ArrayList connections = neuron.getAllConnections();\n for (Connection connection : connections) {\n double pervY = connection.leftNeuron.getOutput();\n double deltaWeight = learningRate * partialDerivative * pervY;\n double newWeight = connection.getWeight() + deltaWeight;\n connection.setDeltaWeight(deltaWeight);\n connection.setWeight(newWeight + momentum * connection.getPrevDeltaWeight());\n }\n n++;\n }\n pervPartialDerivatives = nowPartialDerivatives;\n }\n }\n\n public String run(int maxSteps, double minError) {\n int i;\n // Train neural network until minError reached or maxSteps exceeded\n double squareError = 10;\n double minSquareError = 1000000;\n int correct = 0;\n for (i = 0; i < maxSteps && squareError > minError; i++) {\n squareError = 0;\n correct = 0;\n for (Double[] input : inputs) {\n setInput(input);\n activate();\n Double[] output = getOutput();\n Double[] expectedOutput = new Double[]{input[input.length - 1]};\n for (int j = 0; j < expectedOutput.length; j++) {\n double err = Math.pow(expectedOutput[j] - output[j], 2) / 2;\n squareError += err;\n }\n double distance = Math.abs(outputKinds.get(0) - output[0]);\n int idx = 0;\n for (int j = 1; j < outputKinds.size(); j++) {\n double newDistance = Math.abs(outputKinds.get(j) - output[0]);\n if (newDistance < distance) {\n idx = j;\n distance = newDistance;\n }\n }\n double y = outputKinds.get(idx);\n if (y == expectedOutput[0]) ++correct;\n applyBackpropagation(expectedOutput);\n }\n if (squareError < minSquareError) {\n minSquareError = squareError;\n for (Neuron[] neurons : hiddenLayers) {\n for (Neuron neuron : neurons) {\n ArrayList connections = neuron.getAllConnections();\n for (Connection conn : connections) {\n conn.setBestWeight(conn.getWeight());\n }\n }\n }\n for (Neuron neuron : outputLayer) {\n ArrayList connections = neuron.getAllConnections();\n for (Connection conn : connections) {\n conn.setBestWeight(conn.getWeight());\n }\n }\n }\n }\n if (i == maxSteps) {\n for (Neuron[] neurons : hiddenLayers) {\n for (Neuron neuron : neurons) {\n ArrayList connections = neuron.getAllConnections();\n connections.forEach(Connection::setWeightAsBest);\n }\n }\n for (Neuron neuron : outputLayer) {\n ArrayList connections = neuron.getAllConnections();\n connections.forEach(Connection::setWeightAsBest);\n }\n }\n printAllWeights();\n // result = runTimes + MSE + trainRate\n return String.valueOf(i) + \" \" + squareError + \" \" + (double) correct / inputs.size() * 100 + \"%\";\n }\n\n public String test(ArrayList inputs, int maxSteps, double minError) {\n double error = 1;\n int correct = 0;\n for (int i = 0; i < maxSteps && error > minError; i++) {\n error = 0;\n correct = 0;\n for (Double[] input : inputs) {\n setInput(input);\n activate();\n Double[] output = getOutput();\n Double[] expectedOutput = new Double[]{input[input.length - 1]};\n for (int j = 0; j < expectedOutput.length; j++) {\n double err = Math.pow(expectedOutput[j] - output[j], 2) / 2;\n error += err;\n }\n double distance = Math.abs(outputKinds.get(0) - output[0]);\n int idx = 0;\n for (int j = 1; j < outputKinds.size(); j++) {\n double newDistance = Math.abs(outputKinds.get(j) - output[0]);\n if (newDistance < distance) {\n idx = j;\n distance = newDistance;\n }\n }\n double y = outputKinds.get(idx);\n if (y == expectedOutput[0]) ++correct;\n }\n }\n return (double) correct / inputs.size() * 100 + \"%\";\n }\n\n public int[] getOutputKind(ArrayList inputs, int maxSteps, double minError) {\n int i, y[] = new int[inputs.size()];\n double error = 1;\n for (i = 0; i < maxSteps && error > minError; i++) {\n error = 0;\n int yi = 0;\n for (Double[] input : inputs) {\n setInput(input);\n activate();\n Double[] output = getOutput();\n Double[] expectedOutput = new Double[]{input[input.length - 1]};\n for (int j = 0; j < expectedOutput.length; j++) {\n double err = Math.pow(expectedOutput[j] - output[j], 2) / 2;\n error += err;\n }\n double distance = Math.abs(outputKinds.get(0) - output[0]);\n int idx = 0;\n for (int j = 1; j < outputKinds.size(); j++) {\n double newDistance = Math.abs(outputKinds.get(j) - output[0]);\n if (newDistance < distance) {\n idx = j;\n distance = newDistance;\n }\n }\n y[yi] = idx;\n yi++;\n }\n }\n return y;\n }\n\n private void printAllWeights() {\n //hiddenLayers.forEach(this::printWeights);\n outputLayer.forEach(this::printWeights);\n System.out.println();\n }\n\n private void printWeights(Neuron n) {\n ArrayList connections = n.getAllConnections();\n for (Connection con : connections) {\n double w = con.getWeight();\n System.out.println(\"NeuronID = \" + n.id + \", ConnectionID = \" + con.id + \",Weight = \" + w);\n }\n }\n}\n"
},
{
"path": "src/Backpropagation/Algorithm/Neuron.java",
"content": "package Backpropagation.Algorithm;\n\nimport java.util.*;\n\nclass Neuron {\n static int counter = 0;\n final int id; // auto increment, starts at 0\n private double output;\n\n private ArrayList connections = new ArrayList<>();\n private HashMap connectionLookup = new HashMap<>();\n\n Neuron() {\n id = counter;\n counter++;\n }\n\n void calculateOutput() {\n double v = 0;\n for (Connection con : connections) {\n Neuron leftNeuron = con.getLeftNeuron();\n double weight = con.getWeight();\n double y = leftNeuron.getOutput();\n v += weight * y;\n }\n output = sigmoid(v);\n }\n\n private double sigmoid(double x) {\n return 1.0 / (1.0 + (Math.exp(-x)));\n }\n\n void addConnections(ArrayList neurons) {\n for (Neuron n : neurons) {\n Connection con = new Connection(n);\n connections.add(con);\n connectionLookup.put(n.id, con);\n }\n }\n\n void addConnections(Neuron[] neurons) {\n for (Neuron n : neurons) {\n Connection con = new Connection(n);\n connections.add(con);\n connectionLookup.put(n.id, con);\n }\n }\n\n Connection getConnection(int neuronIndex) {\n return connectionLookup.get(neuronIndex);\n }\n\n ArrayList getAllConnections() {\n return connections;\n }\n\n double getOutput() {\n return output;\n }\n\n void setOutput(double o) {\n output = o;\n }\n}"
},
{
"path": "src/Backpropagation/MainFrame.form",
"content": "\n\n"
},
{
"path": "src/Backpropagation/MainFrame.java",
"content": "package Backpropagation;\n\nimport Backpropagation.Algorithm.NeuralNetwork;\n\nimport javax.swing.*;\nimport javax.swing.event.DocumentEvent;\nimport javax.swing.event.DocumentListener;\nimport javax.swing.filechooser.FileNameExtensionFilter;\nimport javax.swing.table.DefaultTableModel;\nimport java.awt.*;\nimport java.awt.event.*;\nimport java.awt.geom.Line2D;\nimport java.awt.geom.Rectangle2D;\nimport java.io.*;\nimport java.math.BigDecimal;\nimport java.math.RoundingMode;\nimport java.text.DecimalFormat;\nimport java.util.ArrayList;\nimport java.util.Arrays;\nimport java.util.Collections;\n\nimport static javax.swing.border.TitledBorder.CENTER;\nimport static javax.swing.border.TitledBorder.DEFAULT_POSITION;\n\npublic class MainFrame {\n private static JMenuItem loadMenuItem;\n private static JMenuItem generateMenuItem;\n private static JFrame frame;\n private JPanel layoutPanel;\n private JPanel coordinatePanel;\n private JButton loadButton;\n private JLabel loadValue;\n private JButton generateButton;\n private JTextField learningTextField;\n private JTextField thresholdTextField;\n private JLabel trainingValue;\n private JLabel testingValue;\n private JSlider zoomerSlider;\n private JLabel timesValue;\n private JTextField maxTimesValue;\n private JTextField wRangeMinValue;\n private JTextField wRangeMaxValue;\n private JTextField hiddenTextField;\n private JTextField momentumTextField;\n private JTable trainTable;\n private JTable testTable;\n private JTextField minErrorTextField;\n private JLabel MSEValue;\n private JTextField sizeTextField;\n private JCheckBox drawModeCheckBox;\n private JButton trainByAllDataButton;\n private DefaultTableModel trainTableModel = new DefaultTableModel();\n private DefaultTableModel testTableModel = new DefaultTableModel();\n private DecimalFormat df = new DecimalFormat(\"####0.00\");\n private Color[] colorArray = {Color.BLUE, Color.RED, Color.GREEN, Color.YELLOW, Color.CYAN, Color.PINK};\n private NeuralNetwork network;\n private ArrayList inputs = new ArrayList<>();\n private ArrayList trainData = new ArrayList<>();\n private ArrayList testData = new ArrayList<>();\n private ArrayList outputKinds = new ArrayList<>();\n private Point mouse;\n private int maxTimes = 1000;\n private int magnification = 50;\n private String hidden = \"4,4\";\n private double momentum = 0.7;\n private double learningRate = 0.1;\n private double threshold = 0;\n private double minRange = -0.5;\n private double maxRange = 0.5;\n private double minError = 0.01;\n private double size = 20.0;\n private boolean drawMode = false;\n\n private MainFrame() {\n loadButton.addActionListener(e -> {\n JFileChooser fileChooser = new JFileChooser();\n FileNameExtensionFilter filter = new FileNameExtensionFilter(\"Text Files(*.txt)\", \"txt\", \"text\");\n fileChooser.setFileFilter(filter);\n if (fileChooser.showOpenDialog(layoutPanel) == JFileChooser.APPROVE_OPTION) {\n loadFile(fileChooser);\n }\n });\n loadMenuItem.addActionListener(e -> {\n JFileChooser fileChooser = new JFileChooser();\n FileNameExtensionFilter filter = new FileNameExtensionFilter(\"Text Files(*.txt)\", \"txt\", \"text\");\n fileChooser.setFileFilter(filter);\n if (fileChooser.showOpenDialog(layoutPanel) == JFileChooser.APPROVE_OPTION) {\n loadFile(fileChooser);\n }\n });\n generateButton.addActionListener(e -> startTrain(trainData));\n generateMenuItem.addActionListener(e -> startTrain(trainData));\n trainByAllDataButton.addActionListener(e -> startTrain(testData));\n zoomerSlider.addChangeListener(e -> {\n zoomerSlider.setBorder(\n BorderFactory.createTitledBorder(null,\n Integer.toString(zoomerSlider.getValue()), CENTER, DEFAULT_POSITION));\n magnification = zoomerSlider.getValue();\n coordinatePanel.repaint();\n });\n coordinatePanel.addMouseMotionListener(new MouseMotionAdapter() {\n @Override\n public void mouseMoved(MouseEvent e) {\n super.mouseMoved(e);\n if (!drawMode) {\n mouse = e.getPoint();\n coordinatePanel.repaint();\n }\n }\n });\n drawModeCheckBox.addActionListener(e -> {\n drawMode = !drawMode;\n coordinatePanel.repaint();\n });\n hiddenTextField.getDocument().addDocumentListener(new DocumentListener() {\n public void changedUpdate(DocumentEvent e) {\n changeHidden();\n }\n\n public void removeUpdate(DocumentEvent e) {\n changeHidden();\n }\n\n public void insertUpdate(DocumentEvent e) {\n changeHidden();\n }\n\n void changeHidden() {\n hidden = hiddenTextField.getText();\n }\n });\n momentumTextField.getDocument().addDocumentListener(new DocumentListener() {\n public void changedUpdate(DocumentEvent e) {\n changeMomentum();\n }\n\n public void removeUpdate(DocumentEvent e) {\n changeMomentum();\n }\n\n public void insertUpdate(DocumentEvent e) {\n changeMomentum();\n }\n\n void changeMomentum() {\n try {\n alertBackground(momentumTextField, false);\n momentum = Double.valueOf(momentumTextField.getText());\n } catch (NumberFormatException e) {\n alertBackground(momentumTextField, true);\n momentum = 0.5;\n }\n }\n });\n learningTextField.getDocument().addDocumentListener(new DocumentListener() {\n public void changedUpdate(DocumentEvent e) {\n changeRate();\n }\n\n public void removeUpdate(DocumentEvent e) {\n changeRate();\n }\n\n public void insertUpdate(DocumentEvent e) {\n changeRate();\n }\n\n void changeRate() {\n try {\n alertBackground(learningTextField, false);\n learningRate = Double.valueOf(learningTextField.getText());\n } catch (NumberFormatException e) {\n alertBackground(learningTextField, true);\n learningRate = 0.5f;\n }\n }\n });\n thresholdTextField.getDocument().addDocumentListener(new DocumentListener() {\n public void changedUpdate(DocumentEvent e) {\n changeThreshold();\n }\n\n public void removeUpdate(DocumentEvent e) {\n changeThreshold();\n }\n\n public void insertUpdate(DocumentEvent e) {\n changeThreshold();\n }\n\n void changeThreshold() {\n try {\n alertBackground(thresholdTextField, false);\n threshold = Double.valueOf(thresholdTextField.getText());\n } catch (NumberFormatException e) {\n alertBackground(thresholdTextField, true);\n threshold = 0;\n }\n }\n });\n maxTimesValue.getDocument().addDocumentListener(new DocumentListener() {\n public void changedUpdate(DocumentEvent e) {\n changeMaxTimes();\n }\n\n public void removeUpdate(DocumentEvent e) {\n changeMaxTimes();\n }\n\n public void insertUpdate(DocumentEvent e) {\n changeMaxTimes();\n }\n\n void changeMaxTimes() {\n try {\n alertBackground(maxTimesValue, false);\n maxTimes = Integer.valueOf(maxTimesValue.getText());\n } catch (NumberFormatException e) {\n alertBackground(maxTimesValue, true);\n maxTimes = 1000;\n }\n }\n });\n minErrorTextField.getDocument().addDocumentListener(new DocumentListener() {\n public void changedUpdate(DocumentEvent e) {\n changeMinError();\n }\n\n public void removeUpdate(DocumentEvent e) {\n changeMinError();\n }\n\n public void insertUpdate(DocumentEvent e) {\n changeMinError();\n }\n\n void changeMinError() {\n try {\n alertBackground(minErrorTextField, false);\n minError = Double.valueOf(minErrorTextField.getText());\n } catch (NumberFormatException e) {\n alertBackground(minErrorTextField, true);\n minError = 0.01;\n }\n }\n });\n wRangeMinValue.getDocument().addDocumentListener(new DocumentListener() {\n public void changedUpdate(DocumentEvent e) {\n changeMinRange();\n }\n\n public void removeUpdate(DocumentEvent e) {\n changeMinRange();\n }\n\n public void insertUpdate(DocumentEvent e) {\n changeMinRange();\n }\n\n void changeMinRange() {\n try {\n if (Double.valueOf(wRangeMinValue.getText()) > maxRange)\n alertBackground(wRangeMinValue, true);\n else {\n alertBackground(wRangeMinValue, false);\n minRange = Double.valueOf(wRangeMinValue.getText());\n }\n } catch (NumberFormatException e) {\n alertBackground(wRangeMinValue, true);\n minRange = -0.5f;\n }\n }\n });\n wRangeMaxValue.getDocument().addDocumentListener(new DocumentListener() {\n public void changedUpdate(DocumentEvent e) {\n changeMaxRange();\n }\n\n public void removeUpdate(DocumentEvent e) {\n changeMaxRange();\n }\n\n public void insertUpdate(DocumentEvent e) {\n changeMaxRange();\n }\n\n void changeMaxRange() {\n try {\n if (Double.valueOf(wRangeMaxValue.getText()) < minRange)\n alertBackground(wRangeMaxValue, true);\n else {\n alertBackground(wRangeMaxValue, false);\n maxRange = Double.valueOf(wRangeMaxValue.getText());\n }\n } catch (NumberFormatException e) {\n alertBackground(wRangeMaxValue, true);\n maxRange = 0.5f;\n }\n }\n });\n sizeTextField.getDocument().addDocumentListener(new DocumentListener() {\n public void changedUpdate(DocumentEvent e) {\n changeSize();\n }\n\n public void removeUpdate(DocumentEvent e) {\n changeSize();\n }\n\n public void insertUpdate(DocumentEvent e) {\n changeSize();\n }\n\n void changeSize() {\n try {\n alertBackground(sizeTextField, false);\n size = Double.valueOf(sizeTextField.getText());\n } catch (NumberFormatException e) {\n alertBackground(sizeTextField, true);\n size = 20.0;\n }\n }\n });\n }\n\n private void loadFile(JFileChooser fileChooser) {\n File loadedFile = fileChooser.getSelectedFile();\n loadValue.setText(loadedFile.getPath());\n resetFrame();\n resetData();\n try (BufferedReader br = new BufferedReader(new FileReader(loadedFile))) {\n String line = br.readLine();\n while (line != null) {\n // Split by space or tab\n String[] lineSplit = line.split(\"\\\\s+\");\n // Remove empty elements\n lineSplit = Arrays.stream(lineSplit).\n filter(s -> (s != null && s.length() > 0)).\n toArray(String[]::new);\n Double[] numbers = new Double[lineSplit.length + 1];\n numbers[0] = -1.0;\n for (int i = 1; i <= lineSplit.length; i++)\n numbers[i] = Double.parseDouble(lineSplit[i - 1]);\n inputs.add(numbers);\n Double output = numbers[numbers.length - 1];\n if (!outputKinds.contains(output))\n outputKinds.add(output);\n line = br.readLine();\n }\n initialData();\n ArrayList header = new ArrayList<>();\n for (int i = 1; i < trainData.get(0).length - 1; i++)\n header.add(\"x\" + i);\n header.add(\"yd\");\n trainTableModel.setColumnIdentifiers(header.toArray());\n testTableModel.setColumnIdentifiers(header.toArray());\n for (Double[] x : trainData) {\n x = Arrays.copyOfRange(x, 1, x.length);\n trainTableModel.addRow(x);\n }\n for (Double[] x : testData) {\n x = Arrays.copyOfRange(x, 1, x.length);\n testTableModel.addRow(x);\n }\n trainTable.setModel(trainTableModel);\n testTable.setModel(testTableModel);\n // TODO - show y result at data table\n generateButton.setEnabled(true);\n startTrain(trainData);\n } catch (IOException e1) {\n e1.printStackTrace();\n }\n }\n\n private void resetData() {\n inputs.clear();\n trainData.clear();\n testData.clear();\n outputKinds.clear();\n trainTableModel.setColumnCount(0);\n trainTableModel.setRowCount(0);\n testTableModel.setColumnCount(0);\n testTableModel.setRowCount(0);\n }\n\n private void initialData() {\n // Normalize expected output\n Double outputMin = Collections.min(outputKinds);\n Double outputMax = Collections.max(outputKinds);\n for (Double[] input : inputs) {\n input[input.length - 1] = normalize(input[input.length - 1], outputMin, outputMax);\n }\n for (int i = 0; i < outputKinds.size(); i++) {\n outputKinds.set(i, normalize(outputKinds.get(i), outputMin, outputMax));\n }\n // Split input into train & test\n int[] trainKindTimes = new int[outputKinds.size()];\n int[] testKindTimes = new int[outputKinds.size()];\n for (Double[] x : inputs) {\n Double output = x[x.length - 1];\n int i;\n for (i = 0; i < outputKinds.size(); i++)\n if (output.equals(outputKinds.get(i)))\n break;\n if (trainKindTimes[i] == 0 || testKindTimes[i] > trainKindTimes[i] / 2) {\n ++trainKindTimes[i];\n trainData.add(x);\n } else {\n ++testKindTimes[i];\n testData.add(x);\n }\n }\n }\n\n private void startTrain(ArrayList inputs) {\n network = new NeuralNetwork(inputs, outputKinds, hidden, momentum,\n learningRate, threshold, minRange, maxRange);\n String[] resultTrain = network.run(maxTimes, minError).split(\" \");\n timesValue.setText(resultTrain[0]);\n MSEValue.setText(resultTrain[1]);\n trainingValue.setText(resultTrain[2]);\n String resultTest = network.test(testData, maxTimes, minError);\n testingValue.setText(resultTest);\n coordinatePanel.repaint();\n }\n\n private Double round(Double value, int places) {\n if (places < 0) throw new IllegalArgumentException();\n BigDecimal bd = new BigDecimal(value);\n bd = bd.setScale(places, RoundingMode.HALF_UP);\n return bd.doubleValue();\n }\n\n private Double normalize(Double input, Double min, Double max) {\n return round((input - min) / (max - min), 4);\n }\n\n private Double[] convertCoordinate(Double[] oldPoint) {\n Double[] newPoint = new Double[2];\n newPoint[0] = (oldPoint[0] * magnification) + 250;\n newPoint[1] = 250 - (oldPoint[1] * magnification);\n return newPoint;\n }\n\n private void alertBackground(JTextField textField, boolean alert) {\n if (alert)\n textField.setBackground(Color.PINK);\n else\n textField.setBackground(Color.WHITE);\n }\n\n private static void resetFrame() {\n SwingUtilities.updateComponentTreeUI(frame);\n frame.pack();\n frame.setLocationRelativeTo(null);\n }\n\n private static void changeLAF(String name) {\n try {\n if (name.equals(\"Nimbus\")) {\n for (UIManager.LookAndFeelInfo info : UIManager.getInstalledLookAndFeels())\n if (\"Nimbus\".equals(info.getName())) {\n UIManager.setLookAndFeel(info.getClassName());\n break;\n }\n } else {\n UIManager.setLookAndFeel(name);\n }\n resetFrame();\n } catch (ClassNotFoundException | InstantiationException | IllegalAccessException | UnsupportedLookAndFeelException e1) {\n System.out.println(\"Failed to load the skin!\");\n }\n }\n\n private void createUIComponents() {\n coordinatePanel = new GPanel();\n zoomerSlider = new JSlider();\n zoomerSlider.setBorder(\n BorderFactory.createTitledBorder(null,\n Integer.toString(zoomerSlider.getValue()), CENTER, DEFAULT_POSITION));\n }\n\n public static void main(String[] args) {\n JMenuBar menuBar = new JMenuBar();\n JMenu filesMenu = new JMenu(\"Files\");\n JMenu skinsMenu = new JMenu(\"Skins\");\n // Files menu\n loadMenuItem = new JMenuItem(\"Load\", KeyEvent.VK_L);\n generateMenuItem = new JMenuItem(\"Generate\", KeyEvent.VK_G);\n filesMenu.setMnemonic(KeyEvent.VK_F);\n filesMenu.add(loadMenuItem);\n filesMenu.add(generateMenuItem);\n menuBar.add(filesMenu);\n // Skins menu\n skinsMenu.setMnemonic(KeyEvent.VK_S);\n ButtonGroup group = new ButtonGroup();\n JRadioButtonMenuItem skinsMetalMenuItem = new JRadioButtonMenuItem(\"Metal\");\n skinsMetalMenuItem.setMnemonic(KeyEvent.VK_M);\n skinsMenu.add(skinsMetalMenuItem);\n group.add(skinsMetalMenuItem);\n skinsMetalMenuItem.addActionListener(e -> changeLAF(UIManager.getCrossPlatformLookAndFeelClassName()));\n JRadioButtonMenuItem skinsDefaultMenuItem = new JRadioButtonMenuItem(\"Default\");\n skinsDefaultMenuItem.setMnemonic(KeyEvent.VK_D);\n skinsMenu.add(skinsDefaultMenuItem);\n group.add(skinsDefaultMenuItem);\n skinsDefaultMenuItem.addActionListener(e -> changeLAF(UIManager.getSystemLookAndFeelClassName()));\n JRadioButtonMenuItem skinsMotifMenuItem = new JRadioButtonMenuItem(\"Motif\");\n skinsMotifMenuItem.setMnemonic(KeyEvent.VK_M);\n skinsMenu.add(skinsMotifMenuItem);\n group.add(skinsMotifMenuItem);\n skinsMotifMenuItem.addActionListener(e -> changeLAF(\"com.sun.java.swing.plaf.motif.MotifLookAndFeel\"));\n JRadioButtonMenuItem skinsGTKMenuItem = new JRadioButtonMenuItem(\"GTK\");\n skinsGTKMenuItem.setMnemonic(KeyEvent.VK_G);\n skinsMenu.add(skinsGTKMenuItem);\n group.add(skinsGTKMenuItem);\n skinsGTKMenuItem.addActionListener(e -> changeLAF(\"com.sun.java.swing.plaf.gtk.GTKLookAndFeel\"));\n JRadioButtonMenuItem skinsWindowsMenuItem = new JRadioButtonMenuItem(\"Windows\");\n skinsWindowsMenuItem.setMnemonic(KeyEvent.VK_G);\n skinsMenu.add(skinsWindowsMenuItem);\n group.add(skinsWindowsMenuItem);\n skinsWindowsMenuItem.addActionListener(e -> changeLAF(\"com.sun.java.swing.plaf.windows.WindowsLookAndFeel\"));\n JRadioButtonMenuItem skinsNimbusMenuItem = new JRadioButtonMenuItem(\"Nimbus\");\n skinsNimbusMenuItem.setMnemonic(KeyEvent.VK_N);\n skinsNimbusMenuItem.setSelected(true);\n skinsMenu.add(skinsNimbusMenuItem);\n group.add(skinsNimbusMenuItem);\n skinsNimbusMenuItem.addActionListener(e -> changeLAF(\"Nimbus\"));\n menuBar.add(skinsMenu);\n // Main frame\n frame = new JFrame(\"Backpropagation\");\n frame.setContentPane(new MainFrame().layoutPanel);\n frame.setIconImage(Toolkit.getDefaultToolkit().getImage(\"icon.png\"));\n frame.setDefaultCloseOperation(WindowConstants.EXIT_ON_CLOSE);\n frame.setJMenuBar(menuBar);\n changeLAF(\"Nimbus\");\n frame.setVisible(true);\n frame.setLocationRelativeTo(null);\n }\n\n private class GPanel extends JPanel {\n @Override\n public void paintComponent(Graphics g) {\n super.paintComponent(g);\n Graphics2D g2 = (Graphics2D) g;\n // Draw output kind area\n if (drawMode && size > 0 && inputs.size() > 0 && inputs.get(0).length == 4) {\n ArrayList drawInputs = new ArrayList<>();\n for (Double x = -250.0; x <= 250; x += size) {\n for (Double y = -250.0; y <= 250; y += size) {\n drawInputs.add(new Double[]{-1.0, x / magnification, y / magnification});\n }\n }\n int id[] = network.getOutputKind(drawInputs, maxTimes, minError);\n int i = 0;\n for (Double x = -250.0; x <= 250; x += size) {\n for (Double y = -250.0; y <= 250; y += size) {\n g2.setColor(colorArray[colorArray.length - id[i] - 1]);\n Double[] point = convertCoordinate(new Double[]{x / magnification, y / magnification});\n Rectangle2D rect = new Rectangle2D.Double(point[0], point[1], size, size);\n g2.fill(rect);\n i++;\n }\n }\n }\n g.setColor(Color.black);\n g.drawLine(250, 0, 250, 500);\n g.drawLine(0, 250, 500, 250);\n g2.setColor(Color.black);\n // Draw scale\n for (Double i = 250.0; i >= 0; i -= 5.0 * magnification / 10) {\n drawScale(g2, i);\n }\n for (Double i = 250.0; i <= 500; i += 5.0 * magnification / 10) {\n drawScale(g2, i);\n }\n g2.setStroke(new BasicStroke(3));\n // Draw mouse position\n if (mouse != null) {\n Double mouse_x = (mouse.getX() - 250) / magnification;\n Double mouse_y = (250 - mouse.getY()) / magnification;\n g2.drawString(\"(\" + df.format(mouse_x) + \", \" + df.format(mouse_y) + \")\", 420, 20);\n }\n // Draw point of file\n if (inputs.size() > 0 && inputs.get(0).length == 4) {\n for (Double[] x : inputs) {\n Double[] point = convertCoordinate(new Double[]{x[1], x[2]});\n for (int i = 0; i < outputKinds.size(); i++) {\n Double outputKind = outputKinds.get(i);\n if (x[x.length - 1].equals(outputKind)) {\n g2.setColor(colorArray[i]);\n break;\n }\n }\n g2.draw(new Line2D.Double(point[0], point[1], point[0], point[1]));\n }\n }\n // TODO: Draw line of decision boundary\n }\n\n private void drawScale(Graphics2D g2, Double i) {\n Double[] top, btn;\n Double scaleLength = (i % (5.0 * magnification / 5) == 0) ? 2.0 * magnification / 20 : 1.0 * magnification / 20;\n top = convertCoordinate(new Double[]{(i - 250) / magnification, scaleLength / magnification});\n btn = convertCoordinate(new Double[]{(i - 250) / magnification, -scaleLength / magnification});\n g2.draw(new Line2D.Double(top[0], top[1], btn[0], btn[1]));\n top = convertCoordinate(new Double[]{-scaleLength / magnification, (250 - i) / magnification});\n btn = convertCoordinate(new Double[]{scaleLength / magnification, (250 - i) / magnification});\n g2.draw(new Line2D.Double(top[0], top[1], btn[0], btn[1]));\n }\n }\n}\n"
}
]