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    Metadata details:
    filename: binaryTreeTrain.m
    path of file: ./repos/MCITF/Code/MCITF/EdgeBox/piotr_toolbox/toolbox/classify
    
    the code of the file until where you have to start completion: function [tree,data,err] = binaryTreeTrain( data, varargin )
% Train binary decision tree classifier.
%
% Highly optimized code for training decision trees over binary variables.

    
function [tree,data,err] = binaryTreeTrain( data, varargin )
% Train binary decision tree classifier.
%
% Highly optimized code for training decision trees over binary variables.
% Training a decision stump (depth=1) over 5000 features and 10000 training
% examples takes 70ms on a single core machine and *7ms* with 12 cores and
% OpenMP enabled (OpenMP is enabled by default, see toolboxCompile). This
% code shares similarities with forestTrain.m but is optimized for binary
% labels. Moreover, while forestTrain is meant for training random decision
% forests, this code is tuned for use with boosting (see adaBoostTrain.m).
%
% For more information on how to quickly boost decision trees see:
%   [1] R. Appel, T. Fuchs, P. Dollár, P. Perona; "Quickly Boosting
%   Decision Trees – Pruning Underachieving Features Early," ICML 2013.
% The code here implements a simple brute-force strategy with the option to
% sample features used for training each node for additional speedups.
% Further gains using the ideas from the ICML paper are possible. If you
% use this code please consider citing our ICML paper.
%
% During training each feature is quantized to lie between [0,nBins-1],
% where nBins<=256. Quantization is expensive and should be performed just
% once if training multiple trees. Note that the second output of the
% algorithm is the quantized data, this can be reused in future training.
%
% USAGE
%  [tree,data,err] = binaryTreeTrain( data, [pTree] )
%
% INPUTS
%  data       - data for training tree
%   .X0         - [N0xF] negative feature vectors
%   .X1         - [N1xF] positive feature vectors
%   .wts0       - [N0x1] negative weights
%   .wts1       - [N1x1] positive weights
%   .xMin       - [1xF] optional vals defining feature quantization
%   .xStep      - [1xF] optional vals defining feature quantization
%   .xType      - [] optional original data type for features
%  pTree      - additional params (struct or name/value pairs)
%   .nBins      - [256] maximum number of quanizaton bins (<=256)
%   .maxDepth   - [1] maximum depth of tree
%   .minWeight  - [.01] minimum sample weigth to allow split
%   .fracFtrs   - [1] fraction of features to sample for each node split
%   .nThreads   - [16] max number of computational threads to use
%
% OUTPUTS
%  tree       - learned decision tree model struct w the following fields
%   .fids       - [Kx1] feature ids for each node
%   .thrs       - [Kx1] threshold corresponding to each fid
%   .child      - [Kx1] index of child for each node (1-indexed)
%   .hs         - [Kx1] log ratio (.5*log(p/(1-p)) at each node
%   .weights    - [Kx1] total sample weight at each node
%   .depth      - [Kx1] depth of each node
%  data       - data used for training tree (quantized version of input)
%  err        - decision tree training error
%
% EXAMPLE
%
% See also binaryTreeApply, adaBoostTrain, forestTrain
%
% Piotr's Computer Vision Matlab Toolbox      Version 3.40
% Copyright 2014 Piotr Dollar.  [pdollar-at-gmail.com]
% Licensed under the Simplified BSD License [see external/bsd.txt]

% get parameters
dfs={'nBins',256,'maxDepth',1,'minWeight',.01,'fracFtrs',1,'nThreads',16};
[nBins,maxDepth,minWeight,fracFtrs,nThreads]=getPrmDflt(varargin,dfs,1);
assert(nBins<=256);

% get data and normalize weights
dfs={ 'X0','REQ', 'X1','REQ', 'wts0',[], 'wts1',[], ...
  'xMin',[], 'xStep',[], 'xType',[] };
[X0,X1,wts0,wts1,xMin,xStep,xType]=getPrmDflt(data,dfs,1);
[N0,F]=size(X0); [N1,F1]=size(X1); assert(F==F1);
if(isempty(xType)), xMin=zeros(1,F); xStep=ones(1,F); xType=class(X0); end