Package org.neuroph.core.learning

Examples of org.neuroph.core.learning.SupervisedTrainingElement

19
20
21
22
23
24
25
26
27
28
29
30
31
32
     * Create and run MLP with XOR training set
     */
    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}));

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

        System.out.println("Training network...");
View Full Code Here
35
36
37
38
39
40
41
42
43
44
45
46
47
48
     */
    public static void main(String args[]) {
            // create training set (logical AND 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[]{0}));
            trainingSet.addElement(new SupervisedTrainingElement(new double[]{1, 0}, new double[]{0}));
            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);
View Full Code Here
194
195
196
197
198
199
200
201
202
        input[index++] = this.normalizedSunspots[i];
      }

      ideal[0] = this.normalizedSunspots[year];

      result.addElement(new SupervisedTrainingElement(input, ideal));
    }
    return result;
  }
View Full Code Here
38
39
40
41
42
43
44
45
46
47
48
49
50
51
     */
    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);

        // enable batch if using MomentumBackpropagation
View Full Code Here
49
50
51
52
53
54
55
56
57
58
59
            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));
                }
            }
        }
View Full Code Here
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
        ((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}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{3886.0D / daxmax, 3914.0D / daxmax, 3956.0D / daxmax, 3953.0D / daxmax}, new double[]{4044.0D / daxmax}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{3914.0D / daxmax, 3956.0D / daxmax, 3953.0D / daxmax, 4044.0D / daxmax}, new double[]{3987.0D / daxmax}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{3956.0D / daxmax, 3953.0D / daxmax, 4044.0D / daxmax, 3987.0D / daxmax}, new double[]{3996.0D / daxmax}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{3953.0D / daxmax, 4044.0D / daxmax, 3987.0D / daxmax, 3996.0D / daxmax}, new double[]{4043.0D / daxmax}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{4044.0D / daxmax, 3987.0D / daxmax, 3996.0D / daxmax, 4043.0D / daxmax}, new double[]{4068.0D / daxmax}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{3987.0D / daxmax, 3996.0D / daxmax, 4043.0D / daxmax, 4068.0D / daxmax}, new double[]{4176.0D / daxmax}));
        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}));
View Full Code Here
73
74
75
76
77
78
79
80
81
82
83
                double d2 = (Double.parseDouble(s2[1]) - minlevel) / normalizer;
                double d3 = (Double.parseDouble(s3[1]) - minlevel) / normalizer;
                double d4 = (Double.parseDouble(s4[1]) - minlevel) / normalizer;
                double d5 = (Double.parseDouble(s5[1]) - minlevel) / normalizer;
                System.out.println(i + " " + d1 + " " + d2 + " " + d3 + " " + d4 + " ->" + d5);
                trainingSet.addElement(new SupervisedTrainingElement(new double[]{d1, d2, d3, d4}, new double[]{d5}));
            }
        } catch (Exception e) {
            System.out.println(e.getMessage());
            return null;
        }
View Full Code Here
65
66
67
68
69
70
71
72
73
74
75
           for (int i = 0; i < outputsCount; i++)
          outputs[i] = Double.parseDouble(values[inputsCount + i]);

        if (outputsCount>0) {
              trainingSet.addElement(new SupervisedTrainingElement(inputs, outputs));
        } else {
              trainingSet.addElement(new TrainingElement(inputs));
        }
      }
View Full Code Here
108
109
110
111
112
113
114
115
116
117
118
    TrainingSet trainingSet = new TrainingSet();

    for (Entry<String, FractionRgbData> entry : rgbDataMap.entrySet()) {
      double[] input = entry.getValue().getFlattenedRgbValues();
      double[] response = createResponse(entry.getKey(), imageLabels);
      trainingSet.addElement(new SupervisedTrainingElement(
          VectorParser.convertToVector(input),
          VectorParser.convertToVector(response)));
    }

                return trainingSet;
View Full Code Here
130
131
132
133
134
135
136
137
138
139
140
    for (Entry<String, FractionRgbData> entry : rgbDataMap.entrySet()) {
      double[] inputRGB = entry.getValue().getFlattenedRgbValues();
                        double[] inputBW = FractionRgbData.convertRgbInputToBinaryBlackAndWhite(inputRGB);
                        double[] response = createResponse(entry.getKey(), imageLabels);
      trainingSet.addElement(new SupervisedTrainingElement(
          VectorParser.convertToVector(inputBW),
          VectorParser.convertToVector(response)));
    }

            return trainingSet;
View Full Code Here
TOP

Related Classes of org.neuroph.core.learning.SupervisedTrainingElement

  • neuraltictactoe.NeuralPlayer
  • org.neuroph.contrib.imgrec.ImageRecognitionHelper
  • org.neuroph.contrib.matrixmlp.TestMatrixMLP
  • org.neuroph.nnet.learning.SimulatedAnnealingLearning
  • org.neuroph.samples.intronn.SunSpots
  • org.neuroph.samples.PerceptronSample
  • org.neuroph.samples.stockmarket.Main
  • org.neuroph.samples.stockmarket.StockTrainingSetImport
  • org.neuroph.samples.stockmarket.TrainingData
  • org.neuroph.samples.XorMultiLayerPerceptronSample
    • Copyright © 2018 www.massapicom. All rights reserved.
    All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.