Package org.encog.workbench.dialogs.select

Examples of org.encog.workbench.dialogs.select.SelectDialog


      list.add(dataReport = new SelectItem("Range Report",
          "See a report about the ranges adn columns."));
      list.add(scatterPlot = new SelectItem("Scatter Plot",
          "See basic relationships amoung the columns."));

      SelectDialog sel = new SelectDialog(EncogWorkBench.getInstance()
          .getMainWindow(), list);
      sel.setVisible(true);

      if (sel.getSelected() == dataReport) {
        analyzeRanges();
      } else if (sel.getSelected() == scatterPlot) {
        analyzeScatterPlot();
      }
    }
  }
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    list.add(selectInstar = new SelectItem(
        "Instar Training",
        "This training must be done first.  Train the competative clustering part of the network."));
    list.add(selectOutstar = new SelectItem("Outstar Training",
        "This training must be done second.  Train the regression part of the network."));
    SelectDialog sel = new SelectDialog(EncogWorkBench.getInstance()
        .getMainWindow(), list);
    sel.setVisible(true);

    if (sel.getSelected() == selectInstar) {
      InputInstar dialog = new InputInstar();

      if (dialog.process()) {
        double learnRate = dialog.getLearningRate().getValue();
        boolean init = dialog.getInitWeights().getValue();
        TrainInstar train = new TrainInstar((CPN)file.getObject(),trainingData,learnRate,init);
        startup(file,train, dialog.getMaxError().getValue()/100.0);
      }
    } else if (sel.getSelected() == selectOutstar) {
      InputOutstar dialog = new InputOutstar();

      if (dialog.process()) {
        double learnRate = dialog.getLearningRate().getValue();
        TrainOutstar train = new TrainOutstar((CPN)file.getObject(),trainingData,learnRate);
View Full Code Here

        "Basic SOM Neighborhood Training",
        "Train the nerual network using the classic neighborhood based SOM training."));
    list.add(selectSOMClusterCopy = new SelectItem(
        "SOM Cluster Copy Training",
        "Train the SOM using the cluser copy method."));
    SelectDialog sel = new SelectDialog(EncogWorkBench.getInstance()
        .getMainWindow(), list);
    sel.setVisible(true);

    if (sel.getSelected() == selectBasicSOM) {
      InputSOM somDialog = new InputSOM();

      if (somDialog.process()) {
        BasicTrainSOM train = new BasicTrainSOM((SOM) file.getObject(),
            somDialog.getLearningRate().getValue(), trainingData,
            somDialog.getNeighborhoodFunction());
        train.setForceWinner(somDialog.getForceWinner().getValue());
        startup(file, train, somDialog.getMaxError().getValue() / 100.0);
      }
    } else if (sel.getSelected() == selectSOMClusterCopy) {
      SOMClusterCopyTraining train = new SOMClusterCopyTraining(
          (SOM) file.getObject(), trainingData);
      train.iteration();
      if (EncogWorkBench.askQuestion("SOM", "Training done, save?")) {
        file.save();
View Full Code Here

        "Basic SVM Training",
        "Train the SVM using a fixed gamma and constant.  Very fast training, but will not result in the lowest possable error for your SVM."));
    list.add(selectSearchSVM = new SelectItem(
        "Search SVM Training",
        "Works similar to SimpleSVM training, but tries many different gamma and constant values."));
    SelectDialog sel = new SelectDialog(EncogWorkBench.getInstance()
        .getMainWindow(), list);
    sel.setVisible(true);

    if (sel.getSelected() == selectBasicSVM) {
      performSVMSimple(file, trainingData);
    } else if (sel.getSelected() == selectSearchSVM) {
      performSVMSearch(file, trainingData);
    }
  }
View Full Code Here

        list.add(selectRegression = new SelectItem("Query Regression",
            "Machine Learning output is a number(s)."));
        }
        list.add(selectOCR = new SelectItem("Query OCR",
          "Query using drawn chars.  Supports regression or classification."));
        SelectDialog sel = new SelectDialog(EncogWorkBench.getInstance()
            .getMainWindow(), list);
        sel.setVisible(true);
       
        if( sel.getSelected()==selectClassification ) {
          ClassificationQueryTab tab = new ClassificationQueryTab(
              ((ProjectEGFile) this.getEncogObject()));
          EncogWorkBench.getInstance().getMainWindow().getTabManager()
              .openModalTab(tab, "Query Classification");         
        } else if( sel.getSelected()==selectRegression ) {
          RegressionQueryTab tab = new RegressionQueryTab(
              ((ProjectEGFile) this.getEncogObject()));
          EncogWorkBench.getInstance().getMainWindow().getTabManager()
              .openModalTab(tab, "Query Regression");         
        else if( sel.getSelected()==selectOCR ) {
          OCRQueryTab tab = new OCRQueryTab(
              ((ProjectEGFile) this.getEncogObject()));
          EncogWorkBench.getInstance().getMainWindow().getTabManager()
              .openModalTab(tab, "Query OCR");         
        }
View Full Code Here

        "A histogram of the weights."));
    list.add(selectStructure = new SelectItem("Network Structure",
        "The structure of the neural network."));
    list.add(selectThermal = new SelectItem("Thermal Matrix",
        "Shows the matrix of a Hopfield or Boltzmann Machine."));
    SelectDialog sel = new SelectDialog(EncogWorkBench.getInstance()
        .getMainWindow(), list);
    sel.setVisible(true);

    if (sel.getSelected() == selectWeights) {
      analyzeWeights();
    } else if (sel.getSelected() == selectStructure) {
      analyzeStructure();
    } else if (sel.getSelected() == selectThermal) {
      analyzeThermal();
    }

  }
View Full Code Here

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