Package org.encog

Examples of org.encog.EncogError

80
81
82
83
84
85
86
87
    case intType:
      return "i";
    case enumType:
      return "e";
    }
    throw new EncogError("Unknown type: "
        + mapping.getVariableType().toString());
  }
View Full Code Here
139
140
141
142
143
144
145
146
147
148
149
            prg.compileEPL(code);
            prg.setScore(score);
            prg.setSpecies(lastSpecies);
            prg.setAdjustedScore(adjustedScore);
            if (lastSpecies == null) {
              throw new EncogError(
                  "Have not defined a species yet");
            } else {
              lastSpecies.add(prg);
            }
            count++;
View Full Code Here
214
215
216
217
218
219
220
221
222
   * {@inheritDoc}
   */
  @Override
  public Centroid<MLDataPair> createCentroid() {
    if( !(this.input instanceof BasicMLData) ) {
      throw new EncogError("The input data type of " + this.input.getClass().getSimpleName() + " must be BasicMLData.");
    }
    return new BasicMLDataPairCentroid(this);
  }
View Full Code Here
53
54
55
56
57
58
59
60
61
62
63
  public MLMethod create(final String architecture, final int input,
      final int output) {

    final List<String> layers = ArchitectureParse.parseLayers(architecture);
    if (layers.size() != MAX_LAYERS) {
      throw new EncogError(
          "SRN Networks must have exactly three elements, "
          + "separated by ->.");
    }

    final ArchitectureLayer inputLayer = ArchitectureParse.parseLayer(
View Full Code Here
77
78
79
80
81
82
83
84
85
86
87
   * @param output The vector to copy to.
   * @param startPos The starting position to write to.
   */
  public void copyWindow(double[] output, int startPos) {
    if (!isReady()) {
      throw new EncogError("Can't produce a timeslice of size "
          + this.sliceCount + ", there are only "
          + this.window.size() + " vectors loaded.");
    }

    int currentIndex = startPos;
View Full Code Here
55
56
57
58
59
60
61
62
63
64
65
   */
  public MLTrain create(final MLMethod method,
      final MLDataSet training, final String argsStr) {

    if (!(method instanceof ContainsFlat)) {
      throw new EncogError(
          "RPROP training cannot be used on a method of type: "
              + method.getClass().getName());
    }

    final Map<String, String> args = ArchitectureParse.parseParams(argsStr);
View Full Code Here
54
55
56
57
58
59
60
61
62
63
64
65
66
67
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
130
131
132
133
  public MLMethod create(final String architecture, final int input,
      final int output) {

    final List<String> layers = ArchitectureParse.parseLayers(architecture);
    if (layers.size() != MAX_LAYERS) {
      throw new EncogError(
          "SVM's must have exactly three elements, separated by ->.");
    }

    final ArchitectureLayer inputLayer = ArchitectureParse.parseLayer(
        layers.get(0), input);
    final ArchitectureLayer paramsLayer = ArchitectureParse.parseLayer(
        layers.get(1), input);
    final ArchitectureLayer outputLayer = ArchitectureParse.parseLayer(
        layers.get(2), output);

    final String name = paramsLayer.getName();
    final String kernelStr = paramsLayer.getParams().get("KERNEL");
    final String svmTypeStr = paramsLayer.getParams().get("TYPE");

    SVMType svmType = SVMType.NewSupportVectorClassification;
    KernelType kernelType = KernelType.RadialBasisFunction;

    boolean useNew = true;

    if (svmTypeStr == null) {
      useNew = true;
    } else if (svmTypeStr.equalsIgnoreCase("NEW")) {
      useNew = true;
    } else if (svmTypeStr.equalsIgnoreCase("OLD")) {
      useNew = false;
    } else {
      throw new EncogError("Unsupported type: " + svmTypeStr
          + ", must be NEW or OLD.");
    }

    if (name.equalsIgnoreCase("C")) {
      if (useNew) {
        svmType = SVMType.NewSupportVectorClassification;
      } else {
        svmType = SVMType.SupportVectorClassification;
      }
    } else if (name.equalsIgnoreCase("R")) {
      if (useNew) {
        svmType = SVMType.NewSupportVectorRegression;
      } else {
        svmType = SVMType.EpsilonSupportVectorRegression;
      }
    } else {
      throw new EncogError("Unsupported mode: " + name
          + ", must be C for classify or R for regression.");
    }

    if (kernelStr == null) {
      kernelType = KernelType.RadialBasisFunction;
    } else if ("linear".equalsIgnoreCase(kernelStr)) {
      kernelType = KernelType.Linear;
    } else if ("poly".equalsIgnoreCase(kernelStr)) {
      kernelType = KernelType.Poly;
    } else if ("precomputed".equalsIgnoreCase(kernelStr)) {
      kernelType = KernelType.Precomputed;
    } else if ("rbf".equalsIgnoreCase(kernelStr)) {
      kernelType = KernelType.RadialBasisFunction;
    } else if ("sigmoid".equalsIgnoreCase(kernelStr)) {
      kernelType = KernelType.Sigmoid;
    } else {
      throw new EncogError("Unsupported kernel: " + kernelStr
          + ", must be linear,poly,precomputed,rbf or sigmoid.");
    }

    final int inputCount = inputLayer.getCount();
    final int outputCount = outputLayer.getCount();

    if (outputCount != 1) {
      throw new EncogError("SVM can only have an output size of 1.");
    }

    final SVM result = new SVM(inputCount, svmType, kernelType);

    return result;
View Full Code Here
54
55
56
57
58
59
60
61
62
63
64
  public MLTrain create(final MLMethod method,
      final MLDataSet training,
      final String argsStr) {

    if (!(method instanceof SVM)) {
      throw new EncogError(
          "SVM Train training cannot be used on a method of type: "
              + method.getClass().getName());
    }

    final double defaultGamma = 1.0 / ((SVM) method).getInputCount();
View Full Code Here
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
   */
  public MLMethod create(final String architecture, final int input,
      final int output) {
   
    if( input<=0 ) {
      throw new EncogError("Must have at least one input for EPL.");
    }
   
    if( output<=0 ) {
      throw new EncogError("Must have at least one output for EPL.");
    }
   
   
    final Map<String, String> args = ArchitectureParse.parseParams(architecture);
    final ParamsHolder holder = new ParamsHolder(args);
View Full Code Here
120
121
122
123
124
125
126
127
128
129
130
   *            The maximum depth to generate to.
   */
  public AbstractPrgGenerator(final EncogProgramContext theContext,
      final int theMaxDepth) {
    if (theContext.getFunctions().size() == 0) {
      throw new EncogError("There are no opcodes defined");
    }

    this.context = theContext;
    this.maxDepth = theMaxDepth;
    this.hasEnum = this.context.hasEnum();
View Full Code Here
TOP

Related Classes of org.encog.EncogError

  • org.encog.app.analyst.csv.normalize.AnalystNormalizeCSV
  • org.encog.app.analyst.script.prop.PropertyConstraints
  • org.encog.engine.network.activation.ActivationCompetitive
  • org.encog.examples.neural.image.ImageNeuralNetwork
  • org.encog.mathutil.Equilateral
  • org.encog.mathutil.randomize.FanInRandomizer
  • org.encog.mathutil.randomize.NguyenWidrowRandomizer
  • org.encog.ml.data.auto.AutoFloatDataSet
  • org.encog.ml.data.auto.AutoFloatDataSet$AutoFloatIterator
  • org.encog.ml.data.basic.BasicMLDataSet$BasicMLIterator
    • 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.