哈里斯鹰算法(HHO)优化随机森林的数据回归预测,HHO-RF回归预测，多变量输入模型 评价指标包括:R2、MAE、MSE、

%************************************************************** %* mex interface to Andy Liaw et al.'s C code (used in R package randomForest) %* Added by Abhishek Jaiantilal ( abhishek.jaiantilal@colorado.edu ) %* License: GPLv2 %* Version: 0.02 % % Calls Regression Random Forest % A wrapper matlab file that calls the mex file % This does training given the data and labels % Documentation copied from R-packages pdf % http://cran.r-project.org/web/packages/randomForest/randomForest.pdf % Tutorial on getting this working in tutorial_ClassRF.m %%************************************************************** % function model = classRF_train(X,Y,ntree,mtry, extra_options) % % ___Options % requires 2 arguments and the rest 3 are optional % X: data matrix % Y: target values % ntree (optional): number of trees (default is 500). also if set to 0 % will default to 500 % mtry (default is floor(sqrt(size(X,2))) D=number of features in X). also if set to 0 % will default to 500 % % % Note: TRUE = 1 and FALSE = 0 below % extra_options represent a structure containing various misc. options to % control the RF % extra_options.replace = 0 or 1 (default is 1) sampling with or without % replacement % extra_options.strata = (not Implemented) % extra_options.sampsize = Size(s) of sample to draw. For classification, % if sampsize is a vector of the length the number of strata, then sampling is stratified by strata, % and the elements of sampsize indicate the numbers to be drawn from the strata. I don't yet know how this works. % extra_options.nodesize = Minimum size of terminal nodes. Setting this number larger causes smaller trees % to be grown (and thus take less time). Note that the default values are different % for classification (1) and regression (5). % extra_options.importance = Should importance of predictors be assessed? % extra_options.localImp = Should casewise importance measure be computed? (Setting this to TRUE will % override importance.) % extra_options.proximity = Should proximity measure among the rows be calculated? % extra_options.oob_prox = Should proximity be calculated only on 'out-of-bag' data? % extra_options.do_trace = If set to TRUE, give a more verbose output as randomForest is run. If set to % some integer, then running output is printed for every % do_trace trees. % extra_options.keep_inbag = Should an n by ntree matrix be returned that keeps track of which samples are % 'in-bag' in which trees (but not how many times, if sampling with replacement) % extra_options.corr_bias = which happens only for regression. perform bias correction for regression? Note: Experimental. Use at your own % risk. % extra_options.nPerm = Number of times the OOB data are permuted per tree for assessing variable % importance. Number larger than 1 gives slightly more stable estimate, but not % very effective. Currently only implemented for regression. % % % ___Returns model which has % importance = a matrix with nclass + 2 (for classification) or two (for regression) columns. % For classification, the first nclass columns are the class-specific measures % computed as mean decrease in accuracy. The nclass + 1st column is the % mean decrease in accuracy over all classes. The last column is the mean decrease % in Gini index. For Regression, the first column is the mean decrease in % accuracy and the second the mean decrease in MSE. If importance=FALSE, % the last measure is still returned as a vector. % importanceSD = The ?standard errors? of the permutation-based importance measure. For classification, % a p by nclass + 1 matrix corresponding to the first nclass + 1 % columns of the importance matrix. For regression, a length p vector. % localImp = a p by n matrix containing the casewise importance measures, the [i,j] element % of which is the importance of i-th variable on the j-th case. NULL if % localImp=FALSE. % ntree = number of trees grown. % 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 % mse =(regression only) vector of mean square errors: sum of squared residuals divided % by n. % rsq (regression only) 'pseudo R-squared': 1 - mse / Var(y). function model = regRF_train(p_train, t_train, ntree, mtry, extra_options) % requires 2 arguments and the rest 2 are optional % p_train: data matrix % t_train: target values % ntree (optional): number of trees (default is 500) % mtry (default is max(floor(D / 3), 1) D = number of features in X) DEBUG_ON = 0; DEFAULTS_ON = 0; TRUE = 1; FALSE = 0; 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 % 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(p_train, 1); else sampsize = ceil(0.632 * size(p_train, 1)); end end if ~exist('nodesize', 'var'); nodesize = 5; 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 ~exi