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

  public void addRow(final int row) {
    final int idealSize = this.data.getIdealSize();
    final int inputSize = this.data.getInputSize();
    final MLData idealData = new BasicMLData(idealSize);
    final MLData inputData = new BasicMLData(inputSize);
    final MLDataPair pair = new BasicMLDataPair(inputData,
        idealData);
    if (row == -1) {
      this.data.getData().add(pair);
    } else {
      this.data.getData().add(row, pair);

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      // handle predict window
      for (int j = 0; j < this.predictWindow; j++) {
        idealData.setData(j, data[index++]);
      }


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


    return result;

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          double d = csv.getDouble(index++);
          ideal.setData(i, d);
        }
      }


      MLDataPair pair = new BasicMLDataPair(input, ideal);
      result.add(pair);
    }


    return result;
  }

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


      final BasicMLDataPair pair = new BasicMLDataPair(inputData,
          idealData);
      training.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 x = 0; x < ds.getWidth(); x++) {
            item.setData(idx++, ds.getData(x, y) ? .5 : -.5);
          }
        }


        trainingSet.add(new BasicMLDataPair(item, null));
      }


      this.net = new SOM(inputNeuron,outputNeuron);
      this.net.reset();

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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);


    // setup coefficient arrays for finite difference method
    // create differential coefficient arrays
    this.differentialCoefficients = CreateCoefficients(LevenbergMarquardtTraining.NUM_POINTS);
    this.derivativeStepSize = new double[this.parametersLength];

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          final MLData idealData = new BasicMLData(inputCount);
          for (int i = 0; i < outputCount; i++) {
            idealData.setData(i,
                CSVFormat.EG_FORMAT.parse(cols.get(index++)));
          }
          final MLDataPair pair = new BasicMLDataPair(inputData,
              idealData);
          samples.add(pair);
        }
      }
    }

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      MLDataSet result = new BasicMLDataSet();
      for(int i=0;i<count;i++) {
        for(int j=0;j<4;j++) {
          MLData inputData = new BasicMLData(XOR_INPUT[j]);
          MLData idealData = new BasicMLData(XOR_IDEAL[j]);
          MLDataPair pair = new BasicMLDataPair(inputData,idealData);
          inputData.setData(0, inputData.getData(0)+RangeRandomizer.randomize(-0.1, 0.1));
          inputData.setData(1, inputData.getData(1)+RangeRandomizer.randomize(-0.1, 0.1));
          result.add(pair);
        }
      }

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