Examples of org.encog.ml.data.MLDataPair

org.encog.ml.data.MLDataPair
Training data is stored in two ways, depending on if the data is for supervised, or unsupervised training. For unsupervised training just an input value is provided, and the ideal output values are null. For supervised training both input and the expected ideal outputs are provided. This interface abstracts classes that provide a holder for both of these two data items. @author jheaton

  @Override
  public int getInputSize() {
    if (this.data.isEmpty()) {
      return 0;
    }
    final MLDataPair first = this.data.get(0);
    return first.getInput().size();
  }

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   * {@inheritDoc}
   */
  @Override
  public void getRecord(final long index, final MLDataPair pair) {


    final MLDataPair source = this.data.get((int) index);
    pair.setInputArray(source.getInputArray());
    if (pair.getIdealArray() != null) {
      pair.setIdealArray(source.getIdealArray());
    }


  }

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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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        if (series.getTotalDepth() > 1) {
          output = series.process(output);
        }


        MLDataPair pair = BasicMLDataPair.createPair(inputCount,outputCount);
        for(int i=0;i<inputCount;i++) {
          pair.getInput().setData(i, output[i]);
        }
        for(int i=0;i<outputCount;i++) {
          pair.getIdeal().setData(i, output[i+inputCount]);
        }
        result.add(pair);
      }
      return result;
    } finally {

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   */
  public double calculateError(final MLDataSet data) {
    final ErrorCalculation errorCalculation = new ErrorCalculation();


    final double[] actual = new double[this.outputCount];
    final MLDataPair pair = BasicMLDataPair.createPair(data.getInputSize(),
        data.getIdealSize());


    for (int i = 0; i < data.getRecordCount(); i++) {
      data.getRecord(i, pair);
      compute(pair.getInputArray(), actual);
      errorCalculation.updateError(actual, pair.getIdealArray(), pair.getSignificance());
    }
    return errorCalculation.calculate();
  }

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

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    BasicMLData ideal = new BasicMLData(1);
    ImageMLData input = new ImageMLData(image);
    set.add(input,ideal);
    set.downsample(2,2);
    Iterator<MLDataPair> itr = set.iterator();
    MLDataPair pair = (MLDataPair)itr.next();
    MLData data = pair.getInput();
    double[] d = data.getData();
    //Assert.assertEquals(d[0],-1.0, 0.1);
    //Assert.assertEquals(d[5],1, 0.1);
    
    // just "flex" these for no exceptions

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  }


  private double getWeightedError(GenericEnsembleML newML, MLDataSet dataSet) {
    double sum = 0;
    for (int i = 0; i < dataSet.size(); i++) {
      MLDataPair currentData = dataSet.get(i);
      if (newML.classify(currentData.getInput()) != newML.winner(currentData.getIdeal()))
        sum += currentData.getSignificance();
    }
    return sum;
  }

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    MatrixMLDataSet dset = new MatrixMLDataSet(DATA1,1,1);
    dset.setLeadWindowSize(1);
    
    Assert.assertEquals(9, dset.size());
    
    MLDataPair p1 = dset.get(0);
    Assert.assertEquals(1.0, p1.getInput().getData(0),Encog.DEFAULT_DOUBLE_EQUAL);
    Assert.assertEquals(20.0, p1.getIdeal().getData(0),Encog.DEFAULT_DOUBLE_EQUAL);
    
    MLDataPair p2 = dset.get(1);
    Assert.assertEquals(2.0, p2.getInput().getData(0),Encog.DEFAULT_DOUBLE_EQUAL);
    Assert.assertEquals(30.0, p2.getIdeal().getData(0),Encog.DEFAULT_DOUBLE_EQUAL);
    
    MLDataPair p3 = dset.get(2);
    Assert.assertEquals(3.0, p3.getInput().getData(0),Encog.DEFAULT_DOUBLE_EQUAL);
    Assert.assertEquals(40.0, p3.getIdeal().getData(0),Encog.DEFAULT_DOUBLE_EQUAL);
  }

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    MatrixMLDataSet dset = new MatrixMLDataSet(DATA1,1,1);
    dset.setLagWindowSize(1);
    
    Assert.assertEquals(9, dset.size());
    
    MLDataPair p1 = dset.get(0);
    Assert.assertEquals(1.0, p1.getInput().getData(0),Encog.DEFAULT_DOUBLE_EQUAL);
    Assert.assertEquals(2.0, p1.getInput().getData(1),Encog.DEFAULT_DOUBLE_EQUAL);
    Assert.assertEquals(10.0, p1.getIdeal().getData(0),Encog.DEFAULT_DOUBLE_EQUAL);
    
    MLDataPair p2 = dset.get(1);
    Assert.assertEquals(2.0, p2.getInput().getData(0),Encog.DEFAULT_DOUBLE_EQUAL);
    Assert.assertEquals(3.0, p2.getInput().getData(1),Encog.DEFAULT_DOUBLE_EQUAL);
    Assert.assertEquals(20.0, p2.getIdeal().getData(0),Encog.DEFAULT_DOUBLE_EQUAL);
    
    MLDataPair p3 = dset.get(2);
    Assert.assertEquals(3.0, p3.getInput().getData(0),Encog.DEFAULT_DOUBLE_EQUAL);
    Assert.assertEquals(4.0, p3.getInput().getData(1),Encog.DEFAULT_DOUBLE_EQUAL);
    Assert.assertEquals(30.0, p3.getIdeal().getData(0),Encog.DEFAULT_DOUBLE_EQUAL);
  }

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org.encog.ml.data.basic.BasicMLDataPair

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