Examples of org.encog.ml.data.basic.BasicMLDataPair

org.encog.ml.data.basic.BasicMLDataPair
A basic implementation of the MLDataPair interface. This implementation simply holds and input and ideal MLData object. For supervised training both input and ideal should be specified. For unsupervised training the input property should be valid, however the ideal property should contain null. @author jheaton

    data.add(mlP);
  }


  @Override
  public void add(MLData inputData, MLData idealData) {
    BasicMLDataPair mlP = new BasicMLDataPair(inputData, idealData);
    data.add(mlP);
  }

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    for (int i = 0; i < this.dimension; i++) {
      d[i] = ContinousDistribution.randomGenerator.nextGaussian();
    }


    final double[] d2 = MatrixMath.multiply(this.covarianceL, d);
    return new BasicMLDataPair(new BasicMLData(EngineArray.add(d2,
        this.mean)));
  }

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          break;
        }
      }
    }


    return new BasicMLDataPair(result);
  }

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    for (int i = start; i < range; i++) {
      final BasicMLData input = generateInputNeuralData(i);
      final BasicMLData ideal = generateOutputNeuralData(i
          + this.inputWindowSize);
      final BasicMLDataPair pair = new BasicMLDataPair(input,
          ideal);
      super.add(pair);
    }
  }

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      if (this.codec.getIdealSize() > 0) {
        b = new BasicMLData(ideal);
      }


      final MLDataPair pair = new BasicMLDataPair(a, b);
      pair.setSignificance(significance[0]);
      this.result.add(pair);


      currentRecord++;
      lastUpdate++;
      if (lastUpdate >= 10000) {

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      for (int j = 0; j < idealCount; j++) {
        idealData.setData(j, rand.nextDouble(min, max));
      }


      final BasicMLDataPair pair = new BasicMLDataPair(inputData,
          idealData);
      result.add(pair);


    }
    return result;

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      for (int j = 0; j < idealCount; j++) {
        idealData.setData(j, rand.nextDouble(min, max));
      }


      final BasicMLDataPair pair = new BasicMLDataPair(inputData,
          idealData);
      training.add(pair);


    }
  }

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    final BasicMLData input = new BasicMLData(
        this.indexableTraining.getInputSize());
    final BasicMLData ideal = new BasicMLData(
        this.indexableTraining.getIdealSize());
    this.pair = new BasicMLDataPair(input, ideal);
    
    this.hessian = h;
  }

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