Package org.encog.workbench.dialogs.common

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

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    list.add("CSV");
   
    this.setSize(640, 200);
    this.setTitle("Convert Encog Binary Training to Other Format File");
   
    addProperty(this.headers = new CheckField("headers","CSV File Headers"));
    addProperty(this.decimalComma = new CheckField("decimal comma","Decimal Comma (instead of decimal point)"));
    addProperty(this.generateSignificance = new CheckField("generate significance column","Generate Significance Column"));
    render();
  }
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    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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    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");
    addProperty(this.normalize = new CheckField("normalize","Normalize"));
    addProperty(this.randomize = new CheckField("randomize","Randomize"));
    addProperty(this.segregate = new CheckField("segregate","Segregate"));
    addProperty(this.balance = new CheckField("balance","Balance"));
    addProperty(this.cluster = new CheckField("cluster","Cluster"));
   
    render();
   
    this.lagCount.setValue(0);
    this.leadCount.setValue(0);
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    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);
        fields.add(cf);
      }
    }
   
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    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("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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    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();
    this.learningRate.setValue(0.25);
    this.getRBFWidth().setValue(2);
    this.getCols().setValue(1);
    this.getRows().setValue(1);
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  private final CheckField loadToMemory;
 
  public TrainDialog(Frame owner) {
    super(owner);
    setSize(600,250);
    addProperty(this.loadToMemory = new CheckField("load to memory",
        "Load to Memory (better performance)"));
   
    render();
    this.loadToMemory.setValue(true);
  }
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    this.setTitle("Create NEAT Population");
   
    addProperty(this.populationSize = new IntegerField("population size","Population Size",true,1,-1));
    addProperty(this.inputNeurons = new IntegerField("input size","Input Neurons",true,1,-1));
    addProperty(this.outputNeurons = new IntegerField("output size","output Neurons",true,1,-1));
    addProperty(this.snapshot = new CheckField("snapshot","Snapshot"));
   
    addProperty(this.outputActivationField = new PopupField("output activation",
        "Output Activation Function", true));
    addProperty(this.neatActivationField = new PopupField("NEAT activation",
        "NEAT Activation Function", true));
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  public InputInstar() {
    super(false);
    setTitle("Train Instar");

    addProperty(this.learningRate = new DoubleField("learning rate","Learning Rate",true,-1,-1));
    addProperty(this.initWeights = new CheckField("init weights","Init Weights from Training Data"));

    render();
    this.learningRate.setValue(0.7);   
    this.getInitWeights().setValue(true);
  }
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Related Classes of org.encog.workbench.dialogs.common.CheckField

  • org.encog.workbench.dialogs.binary.DialogCSV
  • org.encog.workbench.dialogs.binary.DialogExternal2Binary
  • org.encog.workbench.dialogs.config.EncogConfigDialog
  • org.encog.workbench.dialogs.population.NewPopulationDialog
  • org.encog.workbench.dialogs.training.methods.InputInstar
  • org.encog.workbench.dialogs.training.methods.InputNEAT
  • org.encog.workbench.dialogs.training.methods.InputSOM
  • org.encog.workbench.dialogs.training.TrainDialog
  • org.encog.workbench.dialogs.visualize.scatter.ScatterChooseClass
  • org.encog.workbench.dialogs.wizard.analyst.AnalystWizardDialog
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