Examples of org.encog.ml.data.versatile.MatrixMLDataSet

org.encog.ml.data.versatile.MatrixMLDataSet

The MatrixMLDataSet can use a large 2D matrix of doubles to internally hold data. It supports several advanced features such as the ability to mask and time-box. Masking allows several datasets to use the same backing array, however use different parts. Time boxing allows time-series data to be represented for prediction. The following shows how data is laid out for different lag and lead settings. Lag 0; Lead 0 [10 rows] 1->1 2->2 3->3 4->4 5->5 6->6 7->7 8->8 9->9 10->10 Lag 0; Lead 1 [9 rows] 1->2 2->3 3->4 4->5 5->6 6->7 7->8 8->9 9->10 Lag 1; Lead 0 [9 rows, not useful] 1,2->1 2,3->2 3,4->3 4,5->4 5,6->5 6,7->6 7,8->7 8,9->8 9,10->9 Lag 1; Lead 1 [8 rows] 1,2->3 2,3->4 3,4->5 4,5->6 5,6->7 6,7->8 7,8->9 8,9->10 Lag 1; Lead 2 [7 rows] 1,2->3,4 2,3->4,5 3,4->5,6 4,5->6,7 5,6->7,8 6,7->8,9 7,8->9,10 Lag 2; Lead 1 [7 rows] 1,2,3->4 2,3,4->5 3,4,5->6 4,5,6->7 5,6,7->8 6,7,8->9 7,8,9->10

   * @param idealCount The ideal count.
   */
  private void createDividedDatasets(List<DataDivision> dataDivisionList,
      VersatileMLDataSet parentDataset, int inputCount, int idealCount) {
    for (DataDivision division : dataDivisionList) {
      MatrixMLDataSet dataset = new MatrixMLDataSet(parentDataset.getData(), inputCount,
          idealCount, division.getMask());
      dataset.setLagWindowSize(parentDataset.getLagWindowSize());
      dataset.setLeadWindowSize(parentDataset.getLeadWindowSize());
      division.setDataset(dataset);
    }
  }

          trainingIndex+=source.length;
        }
        
      }
      // Build the set
      MatrixMLDataSet training = new MatrixMLDataSet(this.baseDataset,trainingMask);
      MatrixMLDataSet validation = new MatrixMLDataSet(this.baseDataset,validationMask);
      this.folds.add(new DataFold(training,validation));
    }
  }

Examples of org.encog.ml.data.versatile.MatrixMLDataSet

Related Classes of org.encog.ml.data.versatile.MatrixMLDataSet