Examples of learnInSameThread()

Examples of org.neuroph.contrib.matrixmlp.MatrixMultiLayerPerceptron.learnInSameThread()

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        MultiLayerPerceptron nnet = new MultiLayerPerceptron( TransferFunctionType.TANH ,2, 3, 1);
        MatrixMultiLayerPerceptron mnet = new MatrixMultiLayerPerceptron(nnet);

        System.out.println("Training network...");

        mnet.learnInSameThread(trainingSet);

        System.out.println("Done training network.");
    }

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Examples of org.neuroph.core.NeuralNetwork.learnInSameThread()

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            trainingSet.addElement(new SupervisedTrainingElement(new double[]{1, 1}, new double[]{1}));

            // create perceptron neural network
            NeuralNetwork myPerceptron = new Perceptron(2, 1);
            // learn the training set
            myPerceptron.learnInSameThread(trainingSet);
            // test perceptron
            System.out.println("Testing trained perceptron");
            testNeuralNetwork(myPerceptron, trainingSet);
            // save trained perceptron
            myPerceptron.save("mySamplePerceptron.nnet");
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Examples of org.neuroph.core.NeuralNetwork.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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Examples of org.neuroph.core.NeuralNetwork.learnInSameThread()

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

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                                                                0, 1, 0})); // T letter
 
        // create hopfield network
        Hopfield myHopfield = new Hopfield(9);
        // learn the training set
        myHopfield.learnInSameThread(trainingSet);

        // test hopfield network
        System.out.println("Testing network");

        // add one more 'incomplete' H pattern for testing - it will be recognized as H
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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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Examples of org.neuroph.nnet.MultiLayerPerceptron.learnInSameThread()

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

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

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            trainingSet.addElement(new SupervisedTrainingElement(new double[]{1, 1}, new double[]{1}));

            // create perceptron neural network
            NeuralNetwork myPerceptron = new Perceptron(2, 1);
            // learn the training set
            myPerceptron.learnInSameThread(trainingSet);
            // test perceptron
            System.out.println("Testing trained perceptron");
            testNeuralNetwork(myPerceptron, trainingSet);
            // save trained perceptron
            myPerceptron.save("mySamplePerceptron.nnet");
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