Package org.encog.workbench.dialogs.common

Examples of org.encog.workbench.dialogs.common.ComboBoxField

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    this.setSize(640, 200);
    this.setTitle("Convert Encog Binary Training to Other Format File");
   
    addProperty(this.binaryFile = new FileField("source file","Source Encog Binary File(*.egb)",true,false,EncogDocumentFrame.ENCOG_BINARY));
    addProperty(this.externalFile = new FileField("target file","Target File",true,true,null));
    addProperty(this.fileType = new ComboBoxField("type type", "Export File Type",true,list));
    render();
  }
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    this.setSize(640, 200);
    this.setTitle("Convert Other Format File to Encog Binary Training");

    addProperty(this.externalFile = new FileField("source file","Source File",true,false,EncogDocumentFrame.CSV_FILTER));
    addProperty(this.binaryFile = new FileField("target file","Target Encog Binary File(*.egb)",true,true,EncogDocumentFrame.ENCOG_BINARY));
    addProperty(this.fileType = new ComboBoxField("type type", "Export File Type",true,list));
    addProperty(this.inputCount = new IntegerField("input count","Input Count",true,1,10000));
    addProperty(this.idealCount = new IntegerField("ideal count","Ideal Count",true,0,10000));
    addProperty(this.containsSignificance = new CheckField("contains significance column","Significance Column Present"));

    render();
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    this.setSize(640, 330);
    this.setTitle("Setup Encog Analyst Wizard");
   
    beginTab("General");
    addProperty(this.rawFile = new FileField("source file","Source CSV File(*.csv)",true,false,EncogDocumentFrame.CSV_FILTER));
    addProperty(this.format = new ComboBoxField("format", "File Format", true, csvFormat));
    addProperty(this.method = new ComboBoxField("method", "Machine Learning", true, methods));
    addProperty(this.goal = new ComboBoxField("goal", "Goal", true, goalList));
    addProperty(this.targetField = new TextField("target field", "Target Field(blank for auto)", false));
    addProperty(this.headers = new CheckField("headers","CSV File Headers"));
    addProperty(this.range = new ComboBoxField("normalization range", "Normalization Range", true, rangeList));
    addProperty(this.missing = new ComboBoxField("missing values", "Missing Values", true, missingList));
    beginTab("Time Series");
    addProperty(this.lagCount = new IntegerField("lag count","Lag Count",true,0,1000));
    addProperty(this.leadCount = new IntegerField("lead count","Lead Count",true,0,1000));
    addProperty(this.includeTarget = new CheckField("include target","Include Target in Input"));
    beginTab("Tasks");
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    setLocation(200, 200);
    addProperty(this.inputCount = new IntegerField("input-count",
        "Input Count", true, 1, 100000));
    addProperty(this.outputCount = new IntegerField("output-count",
        "Output Count", true, 1, 100000));
    addProperty(this.svmType = new ComboBoxField("SVM type", "SVM Type",
        true, svmTypeNames));
    addProperty(this.kernelType = new ComboBoxField("Kernel type",
        "Kernel Type", true, kernelTypeNames));

    render();
  }
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      if( !field.isIgnored() && field.isOutput() ) {
        this.classNames.add(field.getName());
      }
    }
   
    addProperty(this.comboClass = new ComboBoxField("training set","Training Set",true,this.classNames));
   
    for(AnalystField field: analyst.getScript().getNormalize().getNormalizedFields() ) {
      if( !field.isIgnored() && field.isInput() ) {
        CheckField cf = new CheckField(field.getName(),field.getName());
        addProperty(cf);
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    errorMethods.add("Mean Square Error");
    setTitle("Encog Configuration");
    setSize(500,300);
    beginTab("Training");
    addProperty(this.defaultError = new DoubleField("default error","Default Error Percent",true,0,100));
    addProperty(this.errorCalculation = new ComboBoxField("error calculation", "Error Calculation", true, errorMethods));
    addProperty(this.threadCount = new IntegerField("thread count","Thread Count (0=auto)",true,0,10000));
    addProperty(this.useOpenCL = new CheckField("use opencl","Use Graphics Card(GPU)"));
    render();
  }
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    setTitle("Create Network from Training");
    setSize(600, 400);
    setLocation(200, 200);
    findData();

    addProperty(this.comboTraining = new ComboBoxField("training set",
        "Training Set", true, this.trainingSets));
    addProperty(this.hidden = new BuildingListField("hidden neurons",
        "Hidden Layer Counts"));
    addProperty(this.activationField = new PopupField("activation",
        "Activation Function", true));
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    list.add("RPROP-");
    list.add("iRPROP+");
    list.add("iRPROP-");
   
    setTitle("Train Resilient Propagation");
    addProperty(this.rpropType = new ComboBoxField("rprop type", "RPROP Type",true,list));
    addProperty(this.maxStep = new DoubleField("max step","Max Step",true,0,-1));
    addProperty(this.initialUpdate = new DoubleField("initial update","Initial Update",true,0,-1));
    render(); 
    this.maxStep.setValue(RPROPConst.DEFAULT_MAX_STEP);
    this.initialUpdate.setValue(RPROPConst.DEFAULT_INITIAL_UPDATE);
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    super(EncogWorkBench.getInstance().getMainWindow());
    findData();
    setTitle("Train NEAT Population");
    setSize(500,400);
    setLocation(200,200);
    addProperty(this.comboTraining = new ComboBoxField("training set","Training Set",true,this.trainingSets));
    addProperty(this.comboPopulation = new ComboBoxField("population","NEAT Population",true,this.populations));
    addProperty(this.loadToMemory = new CheckField("load to memory",
      "Load to Memory (better performance)"));
    addProperty(this.maxError = new DoubleField("max error",
        "Maximum Error Percent(0-100)", true, 0, 100));
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    list.add("2D RBF - Multiquadric");
    list.add("2D RBF - InverseMultiquadric");
   
    setTitle("Train SOM Network");
    addProperty(this.learningRate = new DoubleField("learning rate","Learning Rate",true,0,-1));
    addProperty(this.neighborhoodType = new ComboBoxField("neighborhood","Neighborhood Type",true,list));
    addProperty(this.rbfWidth = new DoubleField("rbf width","RBF Width",true,0,1000));
    addProperty(this.cols = new IntegerField("cols", "2D RBF Columns",true,0,1000000));
    addProperty(this.rows = new IntegerField("rows", "2D RBF Rows",true,0,1000000));
    addProperty(this.forceWinner = new CheckField("force winner","Force Winner"));
    render();
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Related Classes of org.encog.workbench.dialogs.common.ComboBoxField

  • org.encog.workbench.dialogs.binary.DialogBinary2External
  • org.encog.workbench.dialogs.binary.DialogExternal2Binary
  • org.encog.workbench.dialogs.config.EncogConfigDialog
  • org.encog.workbench.dialogs.createnetwork.CreateAutomatic
  • org.encog.workbench.dialogs.createnetwork.CreatePNN
  • org.encog.workbench.dialogs.createnetwork.CreateRBFDialog
  • org.encog.workbench.dialogs.createnetwork.CreateSVMDialog
  • org.encog.workbench.dialogs.RandomizeNetworkDialog
  • org.encog.workbench.dialogs.SaveImageDialog
  • org.encog.workbench.dialogs.training.methods.InputNEAT
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