基于随机森林思想的组合分类器设计.zip

mex/standalone interface to Andy Liaw et al.'s C code (used in R package randomForest) Added by Abhishek Jaiantilal ( [email protected] ) License: GPLv2 Version: 0.02 CLASSIFICATION BASED RANDOMFOREST ****A tutorial for matlab now in tutorial_ClassRF.m**** Ways to generate Mex's and Standalone files rfsub.o is compiled using fortran from rfsub.f. In case cywin or a fortran compiler is not present just copy the appropriate (depending on OS) rfsub.o from precompiled_rfsub directory to the current directory ___STANDALONE____ (not exactly standalone but an interface via C) An example for a C file using the twonorm dataset for classification is shown in src/twonorm_C_wrapper.cpp This is a standalone version that needs to set right parameters in CPP file. Compiling in windows: Method 1: use cygwin and make: go to current directory and run 'make twonorm -f Makefile.windows' in cygwin command prompt. Need to have gcc/g++ and g77 (in cygwin) installed. Also the custom makefile differs from the linux version which has -lgfortran whereas the windows version doesn't. Will generate twonorm_test.exe Method 2: use DevC++ (download from http://www.bloodshed.net/devcpp.html ). Open the twonorm_C_devc.dev file which is a project file which has the sources etc set. Just compile and run. Will generate twonorm_C_devcpp.exe Compiling in linux: Method 1: use linux and make: go to this directory and run 'make diabetes' in command prompt. Need to have gcc/g++ and fortran installed. Will generate diabetes_test. run as ./diabetes_test ___MATLAB___ generates Mex files that can be called in Matlab directly. Compiling in windows: Use the compile_windows.m and run in windows. It will compile and generate appropriate mex files. Need Visual C++ or some other compiler (VC++ express edition also works). Won't work with Matlab's inbuilt compiler (lcc) Compiling in linux: Use the compile_linux.m and run in windows. It will compile and generate appropriate mex files. Using the Mex interface: There are 2 functions classRF_train and classRF_predict as given below. See the sample file test_ClassRF_extensively.m %function Y_hat = classRF_predict(X,model) %requires 2 arguments %X: data matrix %model: generated via classRF_train function %function model = classRF_train(X,Y,ntree,mtry, extra_options) %requires 2 arguments and the rest 2 are optional %X: data matrix %Y: target values %ntree (optional): number of trees (default is 500) %mtry (default is max(floor(D/3),1) D=number of features in X) %there are about 14 odd options for extra_options. Refer to tutorial_ClassRF.m to examine them Version History: v0.02 (May-15-09):Updated so that classification package now has about 95% of the total options that the R-package gives. Woohoo. Tracing of what happening behind screen works better. v0.01 (Mar-22-09): very basic interface for mex/standalone to Liaw et al's randomForest Package supports only ntree and mtry changing for time being.