Package zdenekdrahos.AI.Training.Simulation

Examples of zdenekdrahos.AI.Training.Simulation.SimulationIterator

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    private ITrainingValidator validator = new TrainingValidator();
   
    public Training(TrainingSettings settings) {
        validator.checkSettings(settings);
        this.settings = settings;
        simulations = new SimulationIterator(settings.simulation);
    }
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    }

    @Override
    public XYSeriesCollection getGraphModel() {
        XYSeries original = new XYSeries("Original"), prediction = new XYSeries("Prediction");
        SimulationIterator iterator;
        for (int i = 0; i < input.inputs.length; i++) {
            for (iterator = train.simulate(input.inputs[i]); iterator.hasNext();) {
                original.add(input.inputs[i][0], input.targets[i][0]);
                prediction.add(input.inputs[i][0], iterator.next());
            }
        }
        XYSeriesCollection collection = new XYSeriesCollection();
        collection.addSeries(original);
        collection.addSeries(prediction);
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        train.train(network, in, out);
        System.out.printf("Solution in %d iteration, minError = %f, lastError: %f\n\n", out.lastIterationNumber, out.minError, out.lastError);

        final XYSeries series2 = new XYSeries("Prediction");

        SimulationIterator iterator;
        for (int i = 0; i < input.length; i++) {
            for (iterator = train.simulate(input[i]); iterator.hasNext();) {               
                series2.add(input[i][0], iterator.next());
            }
        }

        XYSeriesCollection collection = new XYSeriesCollection();
        collection.addSeries(series1);
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Related Classes of zdenekdrahos.AI.Training.Simulation.SimulationIterator

  • zdenekdrahos.AI.Training.Training
  • zdenekdrahos.App.Controller.BackPropagationController
  • zdenekdrahos.Testing.MainTrainingData
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