Package org.encog.util.normalize.output

Examples of org.encog.util.normalize.output.OutputFieldRangeMapped

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//    TemporalPoint point = new TemporalPoint( [number of values] );
    InputField ifield = new BasicInputField();
    ifield.setCurrentValue(100);
    ifield.setMin(0);
    ifield.setMax(200);
    OutputFieldRangeMapped orm = new OutputFieldRangeMapped(ifield,-1,1);
    log.info(String.valueOf(orm.calculate(0)));
    log.info(String.valueOf(orm.convertBack(0)));
   

    InputField a,b;
    double[][] arrayOutput = new double[2][2];
        
    BasicNeuralDataSet dataset = new
    BasicNeuralDataSet(ARRAY_2D,null);
        
    NormalizationStorageArray2D target = new
    NormalizationStorageArray2D(arrayOutput);
        
    DataNormalization norm = new DataNormalization();
    norm.setReport(new NullStatusReportable());
    norm.setTarget(target);
    
    norm.addInputField(a = new    InputFieldMLDataSet(false,dataset,0));
    norm.addInputField(b = new 
          InputFieldMLDataSet(false,dataset,1));
        
        norm.addOutputField(
          new OutputFieldRangeMapped(a,0.1,0.9));
        
        norm.addOutputField(new OutputFieldRangeMapped(b,0.1,0.9));
        
        norm.process(); 
  }
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    DataNormalization norm = new DataNormalization();
    norm.setReport(new NullStatusReportable());
    norm.setTarget(target);
    norm.addInputField(a = new InputFieldArray1D(false,ARRAY_1D));
    norm.addOutputField(new OutputFieldRangeMapped(a,0.1,0.9));
    return norm;
  }
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    DataNormalization norm = new DataNormalization();
    norm.setReport(new NullStatusReportable());
    norm.setTarget(target);
    norm.addInputField(a = new InputFieldArray2D(false,ARRAY_2D,0));
    norm.addInputField(b = new InputFieldArray2D(false,ARRAY_2D,1));
    norm.addOutputField(new OutputFieldRangeMapped(a,0.1,0.9));
    norm.addOutputField(new OutputFieldRangeMapped(b,0.1,0.9));
    return norm;
  }
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      DataNormalization norm = new DataNormalization();
      norm.setReport(new NullStatusReportable());
      norm.setTarget(target);
      norm.addInputField(a = new InputFieldArray2D(false,ARRAY_2D,0));
      norm.addInputField(b = new InputFieldArray2D(false,ARRAY_2D,1));
      norm.addOutputField(new OutputFieldRangeMapped(a,0.1,0.9));
      norm.addOutputField(new OutputFieldRangeMapped(b,0.1,0.9));
      norm.addSegregator(new IntegerBalanceSegregator(a,2));
      return norm;
    }
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      DataNormalization norm = new DataNormalization();
      norm.setReport(new NullStatusReportable());
      norm.setTarget(target);
      norm.addInputField(a = new InputFieldArray2D(false,ARRAY_2D,0));
      norm.addInputField(b = new InputFieldArray2D(false,ARRAY_2D,1));
      norm.addOutputField(new OutputFieldRangeMapped(a,0.1,0.9));
      norm.addOutputField(new OutputFieldRangeMapped(b,0.1,0.9));
      norm.addSegregator(s = new RangeSegregator(a,false));
      s.addRange(2, 2, true);
      return norm;
    }
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      DataNormalization norm = new DataNormalization();
      norm.setReport(new NullStatusReportable());
      norm.setTarget(target);
      norm.addInputField(a = new InputFieldArray2D(false,ARRAY_2D,0));
      norm.addInputField(b = new InputFieldArray2D(false,ARRAY_2D,1));
      norm.addOutputField(new OutputFieldRangeMapped(a,0.1,0.9));
      norm.addOutputField(new OutputFieldRangeMapped(b,0.1,0.9));
      norm.addSegregator(new IndexSampleSegregator(0,3,2));
      return norm;
    }
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      DataNormalization norm = new DataNormalization();
      norm.setReport(new NullStatusReportable());
      norm.setTarget(target);
      norm.addInputField(a = new InputFieldArray2D(false,ARRAY_2D,0));
      norm.addInputField(b = new InputFieldArray2D(false,ARRAY_2D,1));
      norm.addOutputField(new OutputFieldRangeMapped(a,0.1,0.9));
      norm.addOutputField(new OutputFieldRangeMapped(b,0.1,0.9));
      norm.addSegregator(new IndexRangeSegregator(0,3));
      return norm;
    }
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    DataNormalization norm = new DataNormalization();
    norm.setReport(new NullStatusReportable());
    norm.setTarget(target);
    norm.addInputField(a = new InputFieldMLDataSet(false,dataset,0));
    norm.addInputField(b = new InputFieldMLDataSet(false,dataset,1));
    norm.addOutputField(new OutputFieldRangeMapped(a,0.1,0.9));
    norm.addOutputField(new OutputFieldRangeMapped(b,0.1,0.9));
    return norm;
  }
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    norm.addInputField(a = new InputFieldCSV(false,FILENAME,0));
    norm.addInputField(b = new InputFieldCSV(false,FILENAME,1));
    norm.addInputField(c = new InputFieldCSV(false,FILENAME,2));
    norm.addInputField(d = new InputFieldCSV(false,FILENAME,3));
    norm.addInputField(e = new InputFieldCSV(false,FILENAME,4));
    norm.addOutputField(new OutputFieldRangeMapped(a,0.1,0.9));
    norm.addOutputField(new OutputFieldRangeMapped(b,0.1,0.9));
    norm.addOutputField(new OutputFieldRangeMapped(c,0.1,0.9));
    norm.addOutputField(new OutputFieldRangeMapped(d,0.1,0.9));
    norm.addOutputField(new OutputFieldRangeMapped(e,0.1,0.9));
    norm.setTarget(new NormalizationStorageArray2D(outputArray));
    return norm;
  }
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Related Classes of org.encog.util.normalize.output.OutputFieldRangeMapped

  • com.narirelays.ems.neuralnetwork.TemporalPredictionHelper
  • org.encog.util.normalize.TestNormArray
  • org.encog.util.normalize.TestNormCSV
  • org.encog.util.normalize.TestNormDataSet
  • org.encog.util.normalize.TestSegregate
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