Package org.neuroph.core.learning

Examples of org.neuroph.core.learning.TrainingSet.addElement()

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        TrainingSet trainingSet = new TrainingSet();
        trainingSet.addElement(new TrainingElement(new double[]{1, 0, 1,
                                                                1, 1, 1,
                                                                1, 0, 1})); // H letter
       
        trainingSet.addElement(new TrainingElement(new double[]{1, 1, 1,
                                                                0, 1, 0,
                                                                0, 1, 0})); // T letter
 
        // create hopfield network
        Hopfield myHopfield = new Hopfield(9);
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        // test hopfield network
        System.out.println("Testing network");

        // add one more 'incomplete' H pattern for testing - it will be recognized as H
        trainingSet.addElement(new TrainingElement(new double[]{1, 0, 0,
                                                                1, 0, 1,
                                                                1, 0, 1}));


        // print network output for the each element from the specified training set.
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     */
    public static void main(String[] args) {
     
        // create training set (logical XOR function)
        TrainingSet trainingSet = new TrainingSet(2, 1);
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{0, 0}, new double[]{0}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{0, 1}, new double[]{1}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{1, 0}, new double[]{1}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{1, 1}, new double[]{0}));

        // create multi layer perceptron
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    public static void main(String[] args) {
     
        // create training set (logical XOR function)
        TrainingSet trainingSet = new TrainingSet(2, 1);
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{0, 0}, new double[]{0}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{0, 1}, new double[]{1}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{1, 0}, new double[]{1}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{1, 1}, new double[]{0}));

        // create multi layer perceptron
        MultiLayerPerceptron myMlPerceptron = new MultiLayerPerceptron(TransferFunctionType.TANH, 2, 3, 1);
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        // create training set (logical XOR function)
        TrainingSet trainingSet = new TrainingSet(2, 1);
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{0, 0}, new double[]{0}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{0, 1}, new double[]{1}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{1, 0}, new double[]{1}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{1, 1}, new double[]{0}));

        // create multi layer perceptron
        MultiLayerPerceptron myMlPerceptron = new MultiLayerPerceptron(TransferFunctionType.TANH, 2, 3, 1);
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        // create training set (logical XOR function)
        TrainingSet trainingSet = new TrainingSet(2, 1);
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{0, 0}, new double[]{0}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{0, 1}, new double[]{1}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{1, 0}, new double[]{1}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{1, 1}, new double[]{0}));

        // create multi layer perceptron
        MultiLayerPerceptron myMlPerceptron = new MultiLayerPerceptron(TransferFunctionType.TANH, 2, 3, 1);

        // enable batch if using MomentumBackpropagation
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            if (outputsCount > 0 && i + inputsCount + outputsCount <= values.length) {
                for (int j = i + inputsCount; j < i + inputsCount + outputsCount; j++) {
                    outputs.add(values[j]);
                }
                if (outputsCount > 0) {
                    trainingSet.addElement(new SupervisedTrainingElement(inputs, outputs));
                } else {
                    trainingSet.addElement(new TrainingElement(inputs));
                }
            }
        }
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                    outputs.add(values[j]);
                }
                if (outputsCount > 0) {
                    trainingSet.addElement(new SupervisedTrainingElement(inputs, outputs));
                } else {
                    trainingSet.addElement(new TrainingElement(inputs));
                }
            }
        }
        return trainingSet;
    }
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        ((LMS) neuralNet.getLearningRule()).setLearningRate(0.7);//0-1
        ((LMS) neuralNet.getLearningRule()).setMaxIterations(maxIterations);//0-1
        TrainingSet trainingSet = new TrainingSet();

        double daxmax = 10000.0D;
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{3710.0D / daxmax, 3690.0D / daxmax, 3890.0D / daxmax, 3695.0D / daxmax}, new double[]{3666.0D / daxmax}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{3690.0D / daxmax, 3890.0D / daxmax, 3695.0D / daxmax, 3666.0D / daxmax}, new double[]{3692.0D / daxmax}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{3890.0D / daxmax, 3695.0D / daxmax, 3666.0D / daxmax, 3692.0D / daxmax}, new double[]{3886.0D / daxmax}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{3695.0D / daxmax, 3666.0D / daxmax, 3692.0D / daxmax, 3886.0D / daxmax}, new double[]{3914.0D / daxmax}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{3666.0D / daxmax, 3692.0D / daxmax, 3886.0D / daxmax, 3914.0D / daxmax}, new double[]{3956.0D / daxmax}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{3692.0D / daxmax, 3886.0D / daxmax, 3914.0D / daxmax, 3956.0D / daxmax}, new double[]{3953.0D / daxmax}));
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        ((LMS) neuralNet.getLearningRule()).setMaxIterations(maxIterations);//0-1
        TrainingSet trainingSet = new TrainingSet();

        double daxmax = 10000.0D;
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{3710.0D / daxmax, 3690.0D / daxmax, 3890.0D / daxmax, 3695.0D / daxmax}, new double[]{3666.0D / daxmax}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{3690.0D / daxmax, 3890.0D / daxmax, 3695.0D / daxmax, 3666.0D / daxmax}, new double[]{3692.0D / daxmax}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{3890.0D / daxmax, 3695.0D / daxmax, 3666.0D / daxmax, 3692.0D / daxmax}, new double[]{3886.0D / daxmax}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{3695.0D / daxmax, 3666.0D / daxmax, 3692.0D / daxmax, 3886.0D / daxmax}, new double[]{3914.0D / daxmax}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{3666.0D / daxmax, 3692.0D / daxmax, 3886.0D / daxmax, 3914.0D / daxmax}, new double[]{3956.0D / daxmax}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{3692.0D / daxmax, 3886.0D / daxmax, 3914.0D / daxmax, 3956.0D / daxmax}, new double[]{3953.0D / daxmax}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{3886.0D / daxmax, 3914.0D / daxmax, 3956.0D / daxmax, 3953.0D / daxmax}, new double[]{4044.0D / daxmax}));
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