Examples of 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

Examples of org.encog.ml.data.MLDataPair

  @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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Examples of org.encog.ml.data.MLDataPair

   * {@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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Examples of org.encog.ml.data.MLDataPair

          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.MLDataPair


        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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Examples of org.encog.ml.data.MLDataPair

   */
  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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Examples of org.encog.ml.data.MLDataPair

            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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Examples of org.encog.ml.data.MLDataPair

    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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Examples of org.encog.ml.data.MLDataPair

  }


  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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Examples of org.encog.ml.data.MLDataPair

    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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Examples of org.encog.ml.data.MLDataPair

    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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