RF_Reg_C
所属分类:matlab编程
开发工具:matlab
文件大小:257KB
下载次数:128
上传日期:2013-04-16 21:28:25
上 传 者:
hjshdym
说明: 随机森林算法,很难得,用于数据分类效果比支持向量机算法效率高
(Random Forest algorithm, it is difficult to get for data classification higher than the efficiency of support vector machine algorithm)
文件列表:
RF_Reg_C\Compile_Check_kcachegrind (611, 2009-04-25)
RF_Reg_C\Compile_Check_memcheck (623, 2009-04-25)
RF_Reg_C\compile_linux.m (952, 2009-05-17)
RF_Reg_C\compile_windows.m (916, 2013-04-02)
RF_Reg_C\data\diabetes.mat (265664, 2009-04-25)
RF_Reg_C\data\X_diabetes.txt (110942, 2009-04-25)
RF_Reg_C\data\Y_diabetes.txt (11492, 2009-04-25)
RF_Reg_C\diabetes_C_devc.dev (1293, 2009-04-25)
RF_Reg_C\Makefile (1774, 2009-05-17)
RF_Reg_C\mexRF_predict.mexw32 (10752, 2010-02-06)
RF_Reg_C\mexRF_predict.mexw64 (11264, 2010-02-06)
RF_Reg_C\mexRF_train.mexw32 (25600, 2010-02-06)
RF_Reg_C\mexRF_train.mexw64 (34304, 2010-02-06)
RF_Reg_C\regRF_predict.m (986, 2009-05-17)
RF_Reg_C\regRF_train.m (12863, 2009-05-17)
RF_Reg_C\src\cokus.cpp (7678, 2009-04-25)
RF_Reg_C\src\cokus_test.cpp (1189, 2009-04-25)
RF_Reg_C\src\diabetes_C_wrapper.cpp (11673, 2009-05-17)
RF_Reg_C\src\mex_regressionRF_predict.cpp (3864, 2009-05-17)
RF_Reg_C\src\mex_regressionRF_train.cpp (12391, 2009-05-17)
RF_Reg_C\src\qsort.c (4676, 2009-04-25)
RF_Reg_C\src\reg_RF.cpp (40291, 2009-05-17)
RF_Reg_C\src\reg_RF.h (560, 2009-05-17)
RF_Reg_C\test_RegRF_extensively.m (1022, 2013-04-02)
RF_Reg_C\tutorial_RegRF.m (9505, 2009-05-17)
RF_Reg_C\Version_History.txt (384, 2010-02-06)
RF_Reg_C\data (0, 2013-04-15)
RF_Reg_C\src (0, 2013-04-15)
RF_Reg_C\tempbuild (0, 2010-02-06)
RF_Reg_C (0, 2013-04-15)
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
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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