Package org.encog.util

Examples of org.encog.util.ParamsHolder

68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
          "Neighborhood training cannot be used on a method of type: "
              + method.getClass().getName());
    }

    final Map<String, String> args = ArchitectureParse.parseParams(argsStr);
    final ParamsHolder holder = new ParamsHolder(args);

    final double learningRate = holder.getDouble(
        MLTrainFactory.PROPERTY_LEARNING_RATE, false, 0.7);
    final String neighborhoodStr = holder.getString(
        MLTrainFactory.PROPERTY_NEIGHBORHOOD, false, "rbf");
    final String rbfTypeStr = holder.getString(
        MLTrainFactory.PROPERTY_RBF_TYPE, false, "gaussian");

    RBFEnum t;

    if (rbfTypeStr.equalsIgnoreCase("Gaussian")) {
      t = RBFEnum.Gaussian;
    } else if (rbfTypeStr.equalsIgnoreCase("Multiquadric")) {
      t = RBFEnum.Multiquadric;
    } else if (rbfTypeStr.equalsIgnoreCase("InverseMultiquadric")) {
      t = RBFEnum.InverseMultiquadric;
    } else if (rbfTypeStr.equalsIgnoreCase("MexicanHat")) {
      t = RBFEnum.MexicanHat;
    } else {
      t = RBFEnum.Gaussian;
    }

    NeighborhoodFunction nf = null;

    if (neighborhoodStr.equalsIgnoreCase("bubble")) {
      nf = new NeighborhoodBubble(1);
    } else if (neighborhoodStr.equalsIgnoreCase("rbf")) {
      final String str = holder.getString(
          MLTrainFactory.PROPERTY_DIMENSIONS, true, null);
      final int[] size = NumberList.fromListInt(CSVFormat.EG_FORMAT, str);
      nf = new NeighborhoodRBF(size, t);
    } else if (neighborhoodStr.equalsIgnoreCase("rbf1d")) {
      nf = new NeighborhoodRBF1D(t);
    }
    if (neighborhoodStr.equalsIgnoreCase("single")) {
      nf = new NeighborhoodSingle();
    }

    final BasicTrainSOM result = new BasicTrainSOM((SOM) method,
        learningRate, training, nf);

    if (args.containsKey(MLTrainFactory.PROPERTY_ITERATIONS)) {
      final int plannedIterations = holder.getInt(
          MLTrainFactory.PROPERTY_ITERATIONS, false, 1000);
      final double startRate = holder.getDouble(
          MLTrainFactory.PROPERTY_START_LEARNING_RATE, false, 0.05);
      final double endRate = holder.getDouble(
          MLTrainFactory.PROPERTY_END_LEARNING_RATE, false, 0.05);
      final double startRadius = holder.getDouble(
          MLTrainFactory.PROPERTY_START_RADIUS, false, 10);
      final double endRadius = holder.getDouble(
          MLTrainFactory.PROPERTY_END_RADIUS, false, 1);
      result.setAutoDecay(plannedIterations, startRate, endRate,
          startRadius, endRadius);
    }
View Full Code Here
53
54
55
56
57
58
59
60
61
62
63
64
   */
  public final MLTrain create(final MLMethod method,
      final MLDataSet training, final String argsStr) {

    final Map<String, String> args = ArchitectureParse.parseParams(argsStr);
    final ParamsHolder holder = new ParamsHolder(args);

    final double learningRate = holder.getDouble(
        MLTrainFactory.PROPERTY_LEARNING_RATE, false, 2.0);
   
    return new QuickPropagation((BasicNetwork) method, training, learningRate);
  }
View Full Code Here
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
    }

    final CalculateScore score = new TrainingSetScore(training);

    final Map<String, String> args = ArchitectureParse.parseParams(argsStr);
    final ParamsHolder holder = new ParamsHolder(args);
    final double startTemp = holder.getDouble(
        MLTrainFactory.PROPERTY_TEMPERATURE_START, false, 10);
    final double stopTemp = holder.getDouble(
        MLTrainFactory.PROPERTY_TEMPERATURE_STOP, false, 2);

    final int cycles = holder.getInt(MLTrainFactory.CYCLES, false, 100);

    final MLTrain train = new NeuralSimulatedAnnealing(
        (BasicNetwork) method, score, startTemp, stopTemp, cycles);

    return train;
View Full Code Here
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
          "RPROP training cannot be used on a method of type: "
              + method.getClass().getName());
    }

    final Map<String, String> args = ArchitectureParse.parseParams(argsStr);
    final ParamsHolder holder = new ParamsHolder(args);
    final double initialUpdate = holder.getDouble(
        MLTrainFactory.PROPERTY_INITIAL_UPDATE, false,
        RPROPConst.DEFAULT_INITIAL_UPDATE);
    final double maxStep = holder.getDouble(
        MLTrainFactory.PROPERTY_MAX_STEP, false,
        RPROPConst.DEFAULT_MAX_STEP);

    return new ResilientPropagation((ContainsFlat) method, training,
        initialUpdate, maxStep);
View Full Code Here
83
84
85
86
87
88
89
90
91
92
93
94
95
      t = RBFEnum.MexicanHat;
    } else {
      throw new NeuralNetworkError("Unknown RBF: " + rbfLayer.getName());
    }

    final ParamsHolder holder = new ParamsHolder(rbfLayer.getParams());

    final int rbfCount = holder.getInt("C", true, 0);

    final RBFNetwork result = new RBFNetwork(inputCount, rbfCount,
        outputCount, t);

    return result;
View Full Code Here
63
64
65
66
67
68
69
70
71
72
73
74
75
76
    final double defaultGamma = 1.0 / ((SVM) method).getInputCount();
    final double defaultC = 1.0;

    final Map<String, String> args = ArchitectureParse.parseParams(argsStr);
    final ParamsHolder holder = new ParamsHolder(args);
    final double gamma = holder.getDouble(MLTrainFactory.PROPERTY_GAMMA,
        false, defaultGamma);
    final double c = holder.getDouble(MLTrainFactory.PROPERTY_C, false,
        defaultC);

    final SVMTrain result = new SVMTrain((SVM) method, training);
    result.setGamma(gamma);
    result.setC(c);
View Full Code Here
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
    }

    final CalculateScore score = new TrainingSetScore(training);

    final Map<String, String> args = ArchitectureParse.parseParams(argsStr);
    final ParamsHolder holder = new ParamsHolder(args);
    final int populationSize = holder.getInt(
        MLTrainFactory.PROPERTY_POPULATION_SIZE, false, 5000);
    final double mutation = holder.getDouble(
        MLTrainFactory.PROPERTY_MUTATION, false, 0.1);
    final double mate = holder.getDouble(MLTrainFactory.PROPERTY_MATE,
        false, 0.25);

    final MLTrain train = new NeuralGeneticAlgorithm((BasicNetwork) method,
        new RangeRandomizer(-1, 1), score, populationSize, mutation,
        mate);
View Full Code Here
60
61
62
63
64
65
66
67
68
69
70
          "LMA training cannot be used on a method of type: "
              + method.getClass().getName());
    }

    final Map<String, String> args = ArchitectureParse.parseParams(argsStr);
    final ParamsHolder holder = new ParamsHolder(args);

    final LevenbergMarquardtTraining result
      = new LevenbergMarquardtTraining(
        (BasicNetwork) method, training);
    return result;
View Full Code Here
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
   */
  public final MLTrain create(final MLMethod method,
      final MLDataSet training, final String argsStr) {

    final Map<String, String> args = ArchitectureParse.parseParams(argsStr);
    final ParamsHolder holder = new ParamsHolder(args);

    final double learningRate = holder.getDouble(
        MLTrainFactory.PROPERTY_LEARNING_RATE, false, 0.7);
    final double momentum = holder.getDouble(
        MLTrainFactory.PROPERTY_LEARNING_MOMENTUM, false, 0.3);

    return new Backpropagation((BasicNetwork) method, training,
        learningRate, momentum);
  }
View Full Code Here
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
          "SVM Train training cannot be used on a method of type: "
              + method.getClass().getName());
    }
   
    final Map<String, String> args = ArchitectureParse.parseParams(argsStr);
    new ParamsHolder(args);

    final ParamsHolder holder = new ParamsHolder(args);
    final double gammaStart = holder.getDouble(SVMSearchFactory.PROPERTY_GAMMA1, false, SVMSearchJob.DEFAULT_GAMMA_BEGIN);
    final double cStart = holder.getDouble(SVMSearchFactory.PROPERTY_C1, false, SVMSearchJob.DEFAULT_CONST_BEGIN);
    final double gammaStop = holder.getDouble(SVMSearchFactory.PROPERTY_GAMMA2, false, SVMSearchJob.DEFAULT_GAMMA_END);
    final double cStop = holder.getDouble(SVMSearchFactory.PROPERTY_C2, false, SVMSearchJob.DEFAULT_CONST_END);
    final double gammaStep = holder.getDouble(SVMSearchFactory.PROPERTY_GAMMA_STEP, false, SVMSearchJob.DEFAULT_GAMMA_STEP);
    final double cStep = holder.getDouble(SVMSearchFactory.PROPERTY_C_STEP, false, SVMSearchJob.DEFAULT_CONST_STEP);
   
    final SVMSearchJob result
    = new SVMSearchJob((SVM)method, training, new NullStatusReportable());
   
    result.setGammaBegin(gammaStart);
View Full Code Here
TOP

Related Classes of org.encog.util.ParamsHolder

  • org.encog.EncogError
  • org.encog.ml.factory.method.EPLFactory
  • org.encog.ml.factory.method.NEATFactory
  • org.encog.ml.factory.method.PNNFactory
  • org.encog.ml.factory.method.RBFNetworkFactory
  • org.encog.ml.factory.train.AnnealFactory
  • org.encog.ml.factory.train.BackPropFactory
  • org.encog.ml.factory.train.GeneticFactory
  • org.encog.ml.factory.train.LMAFactory
  • org.encog.ml.factory.train.ManhattanFactory
    • 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.