Weight

2 writes to Weight

Microsoft.ML.Data (2)

Training\TrainerUtils.cs (2)

758Weight = 1; 778_getWeight(ref Weight);

25 references to Weight

Microsoft.ML.Data (2)

Training\TrainerUtils.cs (2)

779if (!_keepBadWeight && !(0 < Weight && Weight < float.PositiveInfinity))

Microsoft.ML.FastTree (1)

FastTree.cs (1)

1879_weights.Add(cursor.Weight);

Microsoft.ML.KMeansClustering (1)

KMeansPlusPlusTrainer.cs (1)

409probabilityWeight *= cursor.Weight;

Microsoft.ML.LightGbm (2)

LightGbmTrainerBase.cs (2)

791if (float.IsNaN(cursor.Weight)) 794weightList.Add(cursor.Weight);

Microsoft.ML.PCA (3)

PcaTrainer.cs (3)

318VectorUtils.AddMult(in cursor.Features, cursor.Weight, ref mean); 324cursor.Weight * VectorUtils.DotProduct(omega[i], in cursor.Features)); 326n += cursor.Weight;

Microsoft.ML.StandardTrainers (16)

Standard\LogisticRegression\LbfgsPredictorBase.cs (8)

392AccumulateOneGradient(in cursor.Features, cursor.Label, cursor.Weight, in x, ref grad, ref scratch); 502WeightSum += cursor.Weight; 503weightsList.Add(cursor.Weight); 612WeightSum += cursor.Weight; 614ProcessPriorDistribution(cursor.Label, cursor.Weight); 616PreTrainingProcessInstance(cursor.Label, in cursor.Features, cursor.Weight); 627_weights[index] = cursor.Weight; 876loss += AccumulateOneGradient(in cursor.Features, cursor.Label, cursor.Weight,

Standard\LogisticRegression\LogisticRegression.cs (1)

348var weight = cursor.Weight;

Standard\Online\OnlineLinear.cs (1)

336state.ProcessDataInstance(ch, in cursor.Features, cursor.Label, cursor.Weight);

Standard\SdcaBinary.cs (4)

1541return cursor.Label > 0 ? cursor.Weight * _positiveInstanceWeight : cursor.Weight; 2083var instanceWeight = cursor.Weight; 2125float derivative = cursor.Weight * lossFunc.Derivative(WScaledDot(in features, weightScaling, in weights, bias), label); // complexity: O(k)

Standard\SdcaMulticlass.cs (1)

480return cursor.Weight;

Standard\SdcaRegression.cs (1)

160return cursor.Weight;