文件列表:

RF_Class_C (0, 2011-06-02)
RF_Class_C\c.mat (202, 2011-05-28)
RF_Class_C\classRF_predict.m (2228, 2011-08-14)
RF_Class_C\classRF_train.asv (14987, 2011-08-14)
RF_Class_C\classRF_train.m (14895, 2011-08-14)
RF_Class_C\Compile_Check (856, 2011-05-28)
RF_Class_C\compile_linux.m (557, 2011-05-28)
RF_Class_C\compile_windows.asv (1720, 2011-05-28)
RF_Class_C\compile_windows.m (1740, 2011-05-28)
RF_Class_C\data (0, 2011-06-02)
RF_Class_C\data\twonorm.mat (48856, 2011-05-28)
RF_Class_C\data\X_twonorm.txt (96300, 2011-05-28)
RF_Class_C\data\Y_twonorm.txt (600, 2011-05-28)
RF_Class_C\m.mat (283471, 2011-05-29)
RF_Class_C\Makefile (2693, 2011-05-28)
RF_Class_C\Makefile.windows (2523, 2011-05-28)
RF_Class_C\mexClassRF_predict.mexw32 (20992, 2011-05-28)
RF_Class_C\mexClassRF_predict.mexw64 (26624, 2011-05-28)
RF_Class_C\mexClassRF_train.mexw32 (32256, 2011-05-28)
RF_Class_C\mexClassRF_train.mexw64 (46080, 2011-05-28)
RF_Class_C\precompiled_rfsub (0, 2011-06-02)
RF_Class_C\precompiled_rfsub\linux64 (0, 2011-06-02)
RF_Class_C\precompiled_rfsub\win32 (0, 2011-06-02)
RF_Class_C\precompiled_rfsub\win32\rfsub.o (6848, 2011-05-28)
RF_Class_C\precompiled_rfsub\win64 (0, 2011-06-02)
RF_Class_C\precompiled_rfsub\win64\rfsub.o (9840, 2011-05-28)
RF_Class_C\rfsub.o (9840, 2011-05-28)
RF_Class_C\src (0, 2011-06-02)
RF_Class_C\src\classRF.cpp (33889, 2011-05-28)
RF_Class_C\src\classTree.cpp (8947, 2011-05-28)
RF_Class_C\src\cokus.cpp (7678, 2011-05-28)
RF_Class_C\src\cokus_test.cpp (1189, 2011-05-28)
RF_Class_C\src\mex_ClassificationRF_predict.cpp (5225, 2011-05-28)
RF_Class_C\src\mex_ClassificationRF_train.cpp (8545, 2011-05-28)
RF_Class_C\src\qsort.c (4676, 2011-05-28)
RF_Class_C\src\rf.h (5186, 2011-05-28)
RF_Class_C\src\rfsub.f (15851, 2011-05-28)
RF_Class_C\src\rfutils.cpp (9609, 2011-05-28)
RF_Class_C\src\twonorm_C_wrapper.cpp (9865, 2011-05-28)
... ...

mex/standalone 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 Added Binaries for Windows 32/*** bit Commented out compile_windows.m, if you feel upto it, remove the comments and recompile 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.

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