粒子群算法PSO优化随机森林RF(PSO-RF)分类模型-二分类及多分类模型-MATLAB源代码-附带使用教程及注意事项

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 %