TrainerEstimators\TreeEstimators.cs (15)
428[VectorType(_columnNumber)]
440var dataMatrix = new float[_rowNumber * _columnNumber];
448var featureVector = new float[_columnNumber];
449for (/*column index*/ int j = 0; j < _columnNumber; ++j)
451int featureValue = (j + i * _columnNumber) % 10;
453dataMatrix[j + i * _columnNumber] = featureValue;
481double[][] sampleValueGroupedByColumn = new double[_columnNumber][];
482int[][] sampleIndicesGroupedByColumn = new int[_columnNumber][];
483int[] sampleNonZeroCntPerColumn = new int[_columnNumber];
484for (int j = 0; j < _columnNumber; ++j)
494sampleValueGroupedByColumn[j][i] = dataMatrix[j + i * _columnNumber];
506var gbmDataSet = new Trainers.LightGbm.Dataset(sampleValueGroupedByColumn, sampleIndicesGroupedByColumn, _columnNumber, sampleNonZeroCntPerColumn, _rowNumber, _rowNumber, "", floatLabels);
509gbmDataSet.PushRows(dataMatrix, _rowNumber, _columnNumber, 0);
536_rowNumber, _columnNumber, 1, (int)WrappedLightGbmInterface.CApiPredictType.Normal, 0, numberOfTrainingIterations, "", ref nativeLength, result0);
538_rowNumber, _columnNumber, 1, (int)WrappedLightGbmInterface.CApiPredictType.Raw, 0, numberOfTrainingIterations, "", ref nativeLength, result1);