19 references to NumFeatures
Microsoft.ML.FastTree (19)
Dataset\Dataset.cs (5)
88
Contracts.Assert(0 <= feature && feature <
NumFeatures
);
391
Contracts.Assert(activeFeatures == null || activeFeatures.Length >=
NumFeatures
);
392
var truncatedActiveFeatures = Enumerable.Repeat(true,
NumFeatures
).ToArray();
394
Array.Copy(activeFeatures, 0, truncatedActiveFeatures, 0,
NumFeatures
);
959
Contracts.Assert(active == null || active.Length == dataset.
NumFeatures
);
FastTree.cs (3)
412
var activeFeatures = Utils.CreateArray(TrainSet.
NumFeatures
, true);
418
for (int i = 0; i < TrainSet.
NumFeatures
; ++i)
433
set.NumDocs, set.NumQueries, set.
NumFeatures
, datasetSize / 1024 / 1024, (datasetSize - skeletonSize) / 1024 / 1024);
GamTrainer.cs (7)
264
Host.Assert(FeatureMap == null || FeatureMap.Length == TrainSet.
NumFeatures
);
350
}, TrainSet.
NumFeatures
);
445
BinEffects = new double[TrainSet.
NumFeatures
][];
446
for (int featureIndex = 0; featureIndex < TrainSet.
NumFeatures
; featureIndex++)
544
BinUpperBounds = new double[TrainSet.
NumFeatures
][];
548
for (int i = 0; i < TrainSet.
NumFeatures
; i++)
595
_subGraph = new SubGraph(TrainSet.
NumFeatures
, GamTrainerOptions.NumberOfIterations);
Training\TreeLearners\LeastSquaresRegressionTreeLearner.cs (4)
170
FeatureUseCount = new int[TrainData.
NumFeatures
];
582
Contracts.Assert(0 <= min && min <= lim && lim <= TrainData.
NumFeatures
);
842
FeatureSplitInfo = new SplitInfo[data.
NumFeatures
];
843
if (data.NumFlocks < data.
NumFeatures
/ 2)