文件列表:

Alon运行的matlab\RF_Class_C\acc.m (442, 2014-04-12)
Alon运行的matlab\RF_Class_C\ACC_CV.m (1358, 2014-04-12)
Alon运行的matlab\RF_Class_C\Alon.txt (1862000, 2014-04-12)
Alon运行的matlab\RF_Class_C\Alon_label.txt (226, 2014-04-12)
Alon运行的matlab\RF_Class_C\arrange.m (152, 2014-04-12)
Alon运行的matlab\RF_Class_C\c.mat (8671, 2014-04-12)
Alon运行的matlab\RF_Class_C\choose.m (1939, 2014-04-12)
Alon运行的matlab\RF_Class_C\classRF_predict.m (2166, 2014-04-12)
Alon运行的matlab\RF_Class_C\classRF_train.m (14830, 2014-04-12)
Alon运行的matlab\RF_Class_C\Compile_Check (856, 2014-04-12)
Alon运行的matlab\RF_Class_C\compile_linux.m (557, 2014-04-12)
Alon运行的matlab\RF_Class_C\compile_windows.m (1688, 2014-04-12)
Alon运行的matlab\RF_Class_C\crossvalind.m (11956, 2014-04-12)
Alon运行的matlab\RF_Class_C\CTDC.m (4584, 2014-04-12)
Alon运行的matlab\RF_Class_C\CTDD.m (10601, 2014-04-12)
Alon运行的matlab\RF_Class_C\CTDT.m (7734, 2014-04-12)
Alon运行的matlab\RF_Class_C\data\twonorm.mat (48856, 2014-04-12)
Alon运行的matlab\RF_Class_C\data\X_twonorm.txt (96300, 2014-04-12)
Alon运行的matlab\RF_Class_C\data\Y_twonorm.txt (600, 2014-04-12)
Alon运行的matlab\RF_Class_C\DC.m (3373, 2014-04-12)
Alon运行的matlab\RF_Class_C\drugtargets.mat (63804, 2014-04-12)
Alon运行的matlab\RF_Class_C\fixed_cha_roc.mat (6640, 2014-04-12)
Alon运行的matlab\RF_Class_C\fixed_pre_score_label.mat (41846, 2014-04-12)
Alon运行的matlab\RF_Class_C\fold.m (945, 2014-04-12)
Alon运行的matlab\RF_Class_C\fre.m (1539, 2014-04-12)
Alon运行的matlab\RF_Class_C\frevar.m (1497, 2014-04-12)
Alon运行的matlab\RF_Class_C\Geary.m (5301, 2014-04-12)
Alon运行的matlab\RF_Class_C\imp_roc.m (602, 2014-04-12)
Alon运行的matlab\RF_Class_C\index.txt (11463, 2014-04-12)
Alon运行的matlab\RF_Class_C\IX_fre.txt (1776, 2014-04-12)
Alon运行的matlab\RF_Class_C\IX_permu.txt (2206, 2014-04-12)
Alon运行的matlab\RF_Class_C\IX_var.txt (1766, 2014-04-12)
Alon运行的matlab\RF_Class_C\lpln_fixed.mat (221, 2014-04-12)
Alon运行的matlab\RF_Class_C\Makefile (2693, 2014-04-12)
Alon运行的matlab\RF_Class_C\Makefile.windows (2523, 2014-04-12)
Alon运行的matlab\RF_Class_C\metric.m (441, 2014-04-12)
Alon运行的matlab\RF_Class_C\mexClassRF_predict.mexw32 (20992, 2014-04-12)
Alon运行的matlab\RF_Class_C\mexClassRF_predict.mexw64 (26624, 2014-04-12)
Alon运行的matlab\RF_Class_C\mexClassRF_train.mexw32 (32256, 2014-04-12)
Alon运行的matlab\RF_Class_C\mexClassRF_train.mexw64 (43520, 2014-04-12)
... ...

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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