文件列表:

randomforest-matlab (0, 2014-02-16)
randomforest-matlab\RF_Class_C (0, 2015-09-26)
randomforest-matlab\RF_Class_C\CMakeCache.txt (1152, 2013-02-21)
randomforest-matlab\RF_Class_C\CMakeFiles (0, 2014-02-16)
randomforest-matlab\RF_Class_C\CMakeFiles\cmake.check_cache (86, 2013-02-21)
randomforest-matlab\RF_Class_C\Compile_Check (856, 2009-04-25)
randomforest-matlab\RF_Class_C\Makefile (2693, 2009-05-17)
randomforest-matlab\RF_Class_C\Makefile.windows (2523, 2009-05-17)
randomforest-matlab\RF_Class_C\classRF_predict.m (2166, 2009-05-17)
randomforest-matlab\RF_Class_C\classRF_train.m (14829, 2009-05-17)
randomforest-matlab\RF_Class_C\compile_linux.m (557, 2009-05-17)
randomforest-matlab\RF_Class_C\compile_windows.m (1589, 2009-05-17)
randomforest-matlab\RF_Class_C\data (0, 2015-09-26)
randomforest-matlab\RF_Class_C\data\X_twonorm.txt (96300, 2009-04-25)
randomforest-matlab\RF_Class_C\data\Y_twonorm.txt (600, 2009-04-25)
randomforest-matlab\RF_Class_C\data\twonorm.mat (48856, 2009-04-25)
randomforest-matlab\RF_Class_C\precompiled_rfsub (0, 2014-02-16)
randomforest-matlab\RF_Class_C\precompiled_rfsub\linux64 (0, 2015-10-16)
randomforest-matlab\RF_Class_C\precompiled_rfsub\win32 (0, 2015-09-26)
randomforest-matlab\RF_Class_C\precompiled_rfsub\win32\rfsub.o (6848, 2009-04-25)
randomforest-matlab\RF_Class_C\precompiled_rfsub\win64 (0, 2015-09-26)
randomforest-matlab\RF_Class_C\precompiled_rfsub\win64\rfsub.o (9840, 2009-04-25)
randomforest-matlab\RF_Class_C\rfsub.o (9840, 2009-04-25)
randomforest-matlab\RF_Class_C\src (0, 2015-09-26)
randomforest-matlab\RF_Class_C\src\classRF.cpp (33889, 2009-05-17)
randomforest-matlab\RF_Class_C\src\classTree.cpp (8947, 2009-05-17)
randomforest-matlab\RF_Class_C\src\cokus.cpp (7678, 2009-04-25)
randomforest-matlab\RF_Class_C\src\cokus_test.cpp (1189, 2009-04-25)
randomforest-matlab\RF_Class_C\src\mex_ClassificationRF_predict.cpp (5225, 2009-05-17)
randomforest-matlab\RF_Class_C\src\mex_ClassificationRF_train.cpp (8545, 2009-05-17)
randomforest-matlab\RF_Class_C\src\qsort.c (4676, 2009-04-25)
randomforest-matlab\RF_Class_C\src\rf.h (5186, 2009-05-17)
randomforest-matlab\RF_Class_C\src\rfsub.f (15851, 2009-04-25)
randomforest-matlab\RF_Class_C\src\rfutils.cpp (9609, 2009-05-17)
randomforest-matlab\RF_Class_C\src\twonorm_C_wrapper.cpp (9865, 2009-05-17)
randomforest-matlab\RF_Class_C\tempbuild (0, 2015-10-16)
randomforest-matlab\RF_Class_C\test_ClassRF_extensively.m (604, 2009-05-17)
randomforest-matlab\RF_Class_C\tutorial_ClassRF.m (10403, 2013-02-21)
randomforest-matlab\RF_Reg_C (0, 2015-09-26)
... ...

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 REGRESSION BASED RANDOMFOREST ****A tutorial for matlab now in tutorial_ClassRF.m**** Ways to generate Mex's and Standalone files ___STANDALONE____ (not exactly standalone but an interface via C) An example for a C file using the pima indian diabetes dataset for regression is shown in src/diabetes_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 diabetes' in cygwin command prompt. Need to have gcc/g++ installed. Will generate diabetes_test.exe Method 2: use DevC++ (download from http://www.bloodshed.net/devcpp.html ). Open the diabetes_C_devc.dev file which is a project file which has the sources etc set. Just compile and run. Will generate diabetes_C_devc.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++ 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 regRF_train and regRF_predict as given below. See the sample file test_RegRF_extensively.m %function Y_hat = regRF_predict(X,model) %requires 2 arguments %X: data matrix %model: generated via regRF_train function %function model = regRF_train(X,Y,ntree,mtry) %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) Version History: v0.02 (May-15-09):Updated so that regression 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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