Package org.neuroph.nnet

Examples of org.neuroph.nnet.MultiLayerPerceptron.learnInSameThread()

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    normalizeSunspots(0.1, 0.9);
   
    network.getLearningRule().addObserver(this);
   
    TrainingSet training = generateTraining();
    network.learnInSameThread(training);
    predict(network);
   
    Neuroph.getInstance().shutdown();
  }
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        if( myMlPerceptron.getLearningRule() instanceof MomentumBackpropagation )
          ((MomentumBackpropagation)myMlPerceptron.getLearningRule()).setBatchMode(true);

        // learn the training set
        System.out.println("Training neural network...");
        myMlPerceptron.learnInSameThread(trainingSet);

        // test perceptron
        System.out.println("Testing trained neural network");
        testNeuralNetwork(myMlPerceptron, trainingSet);
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        trainingSet.addElement(new SupervisedTrainingElement(new double[]{3996.0D / daxmax, 4043.0D / daxmax, 4068.0D / daxmax, 4176.0D / daxmax}, new double[]{4187.0D / daxmax}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{4043.0D / daxmax, 4068.0D / daxmax, 4176.0D / daxmax, 4187.0D / daxmax}, new double[]{4223.0D / daxmax}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{4068.0D / daxmax, 4176.0D / daxmax, 4187.0D / daxmax, 4223.0D / daxmax}, new double[]{4259.0D / daxmax}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{4176.0D / daxmax, 4187.0D / daxmax, 4223.0D / daxmax, 4259.0D / daxmax}, new double[]{4203.0D / daxmax}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{4187.0D / daxmax, 4223.0D / daxmax, 4259.0D / daxmax, 4203.0D / daxmax}, new double[]{3989.0D / daxmax}));
        neuralNet.learnInSameThread(trainingSet);
        System.out.println("Time stamp N2:" + new SimpleDateFormat("dd-MMM-yyyy HH:mm:ss:MM").format(new Date()));

        TrainingSet testSet = new TrainingSet();
        testSet.addElement(new TrainingElement(new double[]{4223.0D / daxmax, 4259.0D / daxmax, 4203.0D / daxmax, 3989.0D / daxmax}));
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