Package org.encog.ml.data.versatile.normalizers.strategies

Examples of org.encog.ml.data.versatile.normalizers.strategies.BasicNormalizationStrategy

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      throw new EncogError("SVM does not support multiple output columns.");
    }
   
    ColumnType ct = dataset.getNormHelper().getOutputColumns().get(0).getDataType();
   
    BasicNormalizationStrategy result = new BasicNormalizationStrategy();
    result.assignInputNormalizer(ColumnType.continuous,new RangeNormalizer(0,1));
    result.assignInputNormalizer(ColumnType.nominal,new OneOfNNormalizer(0,1));
    result.assignInputNormalizer(ColumnType.ordinal,new OneOfNNormalizer(0,1));
   
    result.assignOutputNormalizer(ColumnType.continuous,new RangeNormalizer(0,1));
    result.assignOutputNormalizer(ColumnType.nominal,new IndexedNormalizer());
    result.assignOutputNormalizer(ColumnType.ordinal,new OneOfNNormalizer(0,1));
    return result;
  }
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  public NormalizationStrategy suggestNormalizationStrategy(VersatileMLDataSet dataset, String architecture) {
    int outputColumns = dataset.getNormHelper().getOutputColumns().size();

    ColumnType ct = dataset.getNormHelper().getOutputColumns().get(0).getDataType();
   
    BasicNormalizationStrategy result = new BasicNormalizationStrategy();
    result.assignInputNormalizer(ColumnType.continuous,new RangeNormalizer(0,1));
    result.assignInputNormalizer(ColumnType.nominal,new OneOfNNormalizer(0,1));
    result.assignInputNormalizer(ColumnType.ordinal,new OneOfNNormalizer(0,1));
   
    result.assignOutputNormalizer(ColumnType.continuous,new RangeNormalizer(0,1));
    result.assignOutputNormalizer(ColumnType.nominal,new OneOfNNormalizer(0,1));
    result.assignOutputNormalizer(ColumnType.ordinal,new OneOfNNormalizer(0,1));
    return result;
  }
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      throw new EncogError("PNN does not support multiple output columns.");
    }
   
    ColumnType ct = dataset.getNormHelper().getOutputColumns().get(0).getDataType();
   
    BasicNormalizationStrategy result = new BasicNormalizationStrategy();
    result.assignInputNormalizer(ColumnType.continuous,new RangeNormalizer(0,1));
    result.assignInputNormalizer(ColumnType.nominal,new OneOfNNormalizer(0,1));
    result.assignInputNormalizer(ColumnType.ordinal,new OneOfNNormalizer(0,1));
   
    result.assignOutputNormalizer(ColumnType.continuous,new RangeNormalizer(0,1));
    result.assignOutputNormalizer(ColumnType.nominal,new IndexedNormalizer());
    result.assignOutputNormalizer(ColumnType.ordinal,new OneOfNNormalizer(0,1));
    return result;
  }
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    if( d[0]>0 && d[1]>0 && d[2]>0 ) {
      inputLow=0;
    }
   
    NormalizationStrategy result = new BasicNormalizationStrategy(
        inputLow,
        inputHigh,
        outputLow,
        outputHigh);
    return result;
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  /**
   * {@inheritDoc}
   */
  @Override
  public NormalizationStrategy suggestNormalizationStrategy(VersatileMLDataSet dataset, String architecture) {
    BasicNormalizationStrategy result = new BasicNormalizationStrategy();
    result.assignInputNormalizer(ColumnType.continuous,new RangeNormalizer(0,1));
    result.assignInputNormalizer(ColumnType.nominal,new OneOfNNormalizer(0,1));
    result.assignInputNormalizer(ColumnType.ordinal,new OneOfNNormalizer(0,1));
   
    result.assignOutputNormalizer(ColumnType.continuous,new RangeNormalizer(0,1));
    result.assignOutputNormalizer(ColumnType.nominal,new OneOfNNormalizer(0,1));
    result.assignOutputNormalizer(ColumnType.ordinal,new OneOfNNormalizer(0,1));
    return result;
  }
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Related Classes of org.encog.ml.data.versatile.normalizers.strategies.BasicNormalizationStrategy

  • org.encog.EncogError
  • org.encog.ml.data.versatile.normalizers.Normalizer
  • org.encog.ml.data.versatile.normalizers.OneOfNNormalizer
  • org.encog.ml.data.versatile.normalizers.RangeNormalizer
  • org.encog.ml.data.versatile.normalizers.RangeOrdinal
  • org.encog.ml.model.config.FeedforwardConfig
  • org.encog.ml.model.config.NEATConfig
  • org.encog.ml.model.config.PNNConfig
  • org.encog.ml.model.config.RBFNetworkConfig
  • org.encog.ml.model.config.SVMConfig
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