% mtry  = number of predictors sampled for spliting at each node.

% votes (classification only) a matrix with one row for each input data point and one

%       column for each class, giving the fraction or number of ?votes? from the random

%       forest.

% oob_times number of times cases are 'out-of-bag' (and thus used in computing OOB error

%       estimate)

% proximity if proximity=TRUE when randomForest is called, a matrix of proximity

%       measures among the input (based on the frequency that pairs of data points are

%       in the same terminal nodes).

% errtr = first column is OOB Err rate, second is for class 1 and so on

function model =classRF_train(X,Y,ntree,mtry, extra_options)

DEFAULTS_ON =0;

%DEBUG_ON=0;

TRUE=1;

FALSE=0;

orig_labels = sort(unique(Y));

Y_new = Y;

new_labels = 1:length(orig_labels);

for i=1:length(orig_labels)

Y_new(find(Y==orig_labels(i)))=Inf;

Y_new(isinf(Y_new))=new_labels(i);

end

Y = Y_new;

if exist('extra_options','var')

if isfield(extra_options,'DEBUG_ON');  DEBUG_ON = extra_options.DEBUG_ON;    end

if isfield(extra_options,'replace');  replace = extra_options.replace;       end

if isfield(extra_options,'classwt');  classwt = extra_options.classwt;       end

if isfield(extra_options,'cutoff');  cutoff = extra_options.cutoff;       end

if isfield(extra_options,'strata');  strata = extra_options.strata;       end

if isfield(extra_options,'sampsize');  sampsize = extra_options.sampsize;       end

if isfield(extra_options,'nodesize');  nodesize = extra_options.nodesize;       end

if isfield(extra_options,'importance');  importance = extra_options.importance;       end

if isfield(extra_options,'localImp');  localImp = extra_options.localImp;       end

if isfield(extra_options,'nPerm');  nPerm = extra_options.nPerm;       end

if isfield(extra_options,'proximity');  proximity = extra_options.proximity;       end

if isfield(extra_options,'oob_prox');  oob_prox = extra_options.oob_prox;       end

%if isfield(extra_options,'norm_votes');  norm_votes = extra_options.norm_votes;       end

if isfield(extra_options,'do_trace');  do_trace = extra_options.do_trace;       end

%if isfield(extra_options,'corr_bias');  corr_bias = extra_options.corr_bias;       end

if isfield(extra_options,'keep_inbag');  keep_inbag = extra_options.keep_inbag;       end

end

keep_forest=1; %always save the trees :)

%set defaults if not already set

if ~exist('DEBUG_ON','var')     DEBUG_ON=FALSE; end

if ~exist('replace','var');     replace = TRUE; end

%if ~exist('classwt','var');     classwt = []; end %will handle these three later

%if ~exist('cutoff','var');      cutoff = 1; end

%if ~exist('strata','var');      strata = 1; end

if ~exist('sampsize','var');

if (replace)

sampsize = size(X,1);

else

sampsize = ceil(0.632*size(X,1));

end;

end

if ~exist('nodesize','var');    nodesize = 1; end %classification=1, regression=5

if ~exist('importance','var');  importance = FALSE; end

if ~exist('localImp','var');    localImp = FALSE; end

if ~exist('nPerm','var');       nPerm = 1; end

%if ~exist('proximity','var');   proximity = 1; end  %will handle these two later

%if ~exist('oob_prox','var');    oob_prox = 1; end

%if ~exist('norm_votes','var');    norm_votes = TRUE; end

if ~exist('do_trace','var');    do_trace = FALSE; end

%if ~exist('corr_bias','var');   corr_bias = FALSE; end

if ~exist('keep_inbag','var');  keep_inbag = FALSE; end

if ~exist('ntree','var') | ntree<=0

ntree=500;

DEFAULTS_ON=1;

end

if ~exist('mtry','var') | mtry<=0 | mtry>size(X,2)

mtry =floor(sqrt(size(X,2)));

end

addclass =isempty(Y);

if (~addclass && length(unique(Y))<2)

error('need atleast two classes for classification');

end

[N D] = size(X);

if N==0; error(' data (X) has 0 rows');end

if (mtry <1 || mtry > D)

DEFAULTS_ON=1;

end

mtry = max(1,min(D,round(mtry)));

if DEFAULTS_ON

fprintf('\tSetting to defaults %d trees and mtry=%d\n',ntree,mtry);

end

if ~isempty(Y)

if length(Y)~=N,

error('Y size is not the same as X size');

end

addclass = FALSE;

else

if ~addclass,

addclass=TRUE;

end

error('have to fill stuff here')

end

if ~isempty(find(isnan(X)));  error('NaNs in X');   end

if ~isempty(find(isnan(Y)));  error('NaNs in Y');   end

%now handle categories. Problem is that categories in R are more

%enhanced. In this i ask the user to specify the column/features to

%consider as categories, 1 if all the values are real values else

%specify the number of categories here

if exist ('extra_options','var') && isfield(extra_options,'categories')

ncat = extra_options.categories;

else

ncat = ones(1,D);

end

maxcat = max(ncat);

if maxcat>32

error('Can not handle categorical predictors with more than 32 categories');

end

%classRF - line 88 in randomForest.default.R

nclass = length(unique(Y));

if ~exist('cutoff','var')

cutoff = ones(1,nclass)* (1/nclass);

else

if sum(cutoff)>1 || sum(cutoff)<0 || length(find(cutoff<=0))>0 || length(cutoff)~=nclass

error('Incorrect cutoff specified');

end

end

if ~exist('classwt','var')

classwt = ones(1,nclass);

ipi=0;

else

if length(classwt)~=nclass

error('Length of classwt not equal to the number of classes')

end

if ~isempty(find(classwt<=0))

error('classwt must be positive');

end

ipi=1;

end

if ~exist('proximity','var')

proximity = addclass;

oob_prox = proximity;

end

if ~exist('oob_prox','var')

oob_prox = proximity;

end

%i handle the below in the mex file

%     if proximity

%         prox = zeros(N,N);

%         proxts = 1;

%     else

%         prox = 1;

%         proxts = 1;

%     end

%i handle the below in the mex file

if localImp

importance = TRUE;

%        impmat = zeors(D,N);

else

%        impmat = 1;

end

if importance

if (nPerm<1)

nPerm = int32(1);

else

nPerm = int32(nPerm);

end

%classRF

%        impout = zeros(D,nclass+2);

%        impSD  = zeros(D,nclass+1);

else

%        impout = zeros(D,1);

%        impSD =  1;

end

%i handle the below in the mex file

%somewhere near line 157 in randomForest.default.R

if addclass

%        nsample = 2*n;

else

%        nsample = n;

end

Stratify = (length(sampsize)>1);

if (~Stratify && sampsize>N)

error('Sampsize too large')

end

if Stratify

if ~exist('strata','var')

strata = Y;

end

nsum = sum(sampsize);

if ( ~isempty(find(sampsize<=0)) || nsum==0)

error('Bad sampsize specification');

end

else

nsum = sampsize;

end

%i handle the below in the mex file

%nrnodes = 2*floor(nsum/nodesize)+1;

%xtest = 1;

%ytest = 1;

%ntest = 1;

%labelts = FALSE;

%nt = ntree;

%[ldau,rdau,nodestatus,nrnodes,upper,avnode,mbest,ndtree]=

%keyboard

if Stratify

strata = int32(strata);

else

strata = int32(1);

end

Options = int32([addclass, importance, localImp, proximity, oob_prox, do_trace, keep_forest, replace, Stratify, keep_inbag]);

if DEBUG_ON

%print the parameters that i am sending in

fprintf('size(x) %d\n',size(X));

fprintf('size(y) %d\n',size(Y));

fprintf('nclass %d\n',nclass);

fprintf('size(ncat) %d\n',size(ncat));

fprintf('maxcat %d\n',maxcat);

fprintf('size(sampsize) %d\n',size(sampsize));

fprintf('sampsize[0] %d\n',sampsize(1));

fprintf('Stratify %d\n',Stratify);

fprintf('Proximity %d\n',proximity);

fprintf('oob_prox %d\n',oob_prox);

fprintf('strata %d\n',strata);

fprintf('ntree %d\n',ntree);

fprintf('mtry %d\n',mtry);

fprintf('ipi %d\n',ipi);

fprintf('classwt %f\n',classwt);

fprintf('cutoff %f\n',cutoff);

fprintf('nodesize %f\n',nodesize);

end

[nrnodes,ntree,xbestsplit,classwt,cutoff,treemap,nodestatus,nodeclass,bestvar,ndbigtree,mtry ...

outcl, counttr, prox, impmat, impout, impSD, errtr, inbag] ...

= mexClassRF_train(X',int32(Y_new),length(unique(Y)),ntree,mtry,int32(ncat), ...

int32(maxcat), int32(sampsize), strata, Options, int32(ipi), ...

classwt, cutoff, int32(nodesize),int32(nsum));

model.nrnodes=nrnodes;

model.ntree=ntree;

model.xbestsplit=xbestsplit;

model.classwt=classwt;

model.cutoff=cutoff;

model.treemap=treemap;

model.nodestatus=nodestatus;

model.nodeclass=nodeclass;

model.bestvar = bestvar;

model.ndbigtree = ndbigtree;

model.mtry = mtry;

model.orig_labels=orig_labels;

model.new_labels=new_labels;

model.nclass = length(unique(Y));

model.outcl = outcl;

model.counttr = counttr;

if proximity

model.proximity = prox;

else

model.proximity = [];

end

model.localImp = impmat;

model.importance = impout;

model.importanceSD = impSD;

model.errtr = errtr';

model.inbag = inbag;

model.votes = counttr';

model.oob_times = sum(counttr)';

clear mexClassRF_train

%keyboard

1;