Apply learned binary decision tree classifier. USAGE hs = binaryTreeApply( X, tree, [maxDepth], [minWeight], [nThreads] ) INPUTS X - [NxF] N length F feature vectors tree - learned tree classification model maxDepth - [] maximum depth of tree minWeight - [] minimum sample weigth to allow split nThreads - [inf] max number of computational threads to use OUTPUTS hs - [Nx1] predicted output log ratios EXAMPLE See also binaryTreeTrain Piotr's Image&Video Toolbox Version 3.21 Copyright 2013 Piotr Dollar. [pdollar-at-caltech.edu] Please email me if you find bugs, or have suggestions or questions! Licensed under the Simplified BSD License [see external/bsd.txt]