Examples of 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

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

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

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

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


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


    return result;
  }

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


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


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


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

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


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

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

      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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