文件列表:

approxrl (0, 2013-10-21)
approxrl\approx (0, 2013-10-07)
approxrl\approx\approxh.m (562, 2013-10-07)
approxrl\approx\approx_ploth.m (2511, 2013-10-07)
approxrl\approx\approx_plotv.m (3157, 2013-10-07)
approxrl\approx\create_approx.m (5177, 2013-10-07)
approxrl\approx\create_constraint.m (761, 2013-10-07)
approxrl\approx\mc_approx.m (2489, 2013-10-07)
approxrl\approx\mc_approx_vectorized.m (7518, 2013-10-07)
approxrl\approx\rbfdisc.m (3802, 2013-10-07)
approxrl\approx\rbfortho.m (5804, 2013-10-07)
approxrl\approx\rbfpoly.m (3930, 2013-10-07)
approxrl\approx\rbfsing.m (3868, 2013-10-07)
approxrl\approx\regdisc.m (5119, 2013-10-07)
approxrl\approx\revise_approx.m (777, 2013-10-07)
approxrl\approx\triang.m (2759, 2013-10-07)
approxrl\approx\triangortho.m (3898, 2013-10-07)
approxrl\approx\triangpoly.m (2898, 2013-10-07)
approxrl\approx\triangsing.m (3951, 2013-10-07)
approxrl\checkAssign.m (2796, 2013-10-21)
approxrl\checkType.m (6136, 2013-10-21)
approxrl\classicalrl (0, 2013-10-07)
approxrl\classicalrl\piter.m (10065, 2013-10-07)
approxrl\classicalrl\qiter.m (6875, 2013-10-07)
approxrl\classicalrl\qlearn.m (10186, 2013-10-07)
approxrl\classicalrl\sarsa.m (10630, 2013-10-07)
approxrl\Contents.m (1855, 2013-10-07)
approxrl\demo (0, 2013-10-07)
approxrl\demo\cleaningrobot_demo.m (1833, 2013-10-07)
approxrl\demo\invertedpendulum_demo.m (1373, 2013-10-07)
approxrl\demo\pi_demo.m (8611, 2013-10-07)
approxrl\demo\ps_optqi_demo.m (8104, 2013-10-07)
approxrl\demo\qi_demo.m (5819, 2013-10-07)
approxrl\glcAssign.m (8742, 2013-10-18)
approxrl\glcCluster.m (82316, 2013-10-21)
approxrl\glcFast.m (32012, 2013-10-21)
approxrl\glcSolve.m (103816, 2013-10-21)
approxrl\lib (0, 2013-10-07)
approxrl\lib\binvec2decx.m (374, 2013-10-07)
approxrl\lib\checkparams.m (2388, 2013-10-07)
... ...

Implementation of ensembles of multiple output regression trees (c) 2002-2010 Pierre Geurts This package contains a c implementation of multiple output regression trees and various ensemble methods including extremely randomized trees (Geurts, Ernst, Wehenkel, Extremely randomized trees, Machine Learning, 36(1):3-42, 2006). It also includes a matlab interface. The implementation is due to Pierre Geurts (p.geurt@ulg.ac.be). The implementation is a research prototype and is provided AS IS. No warranties or guarantees of any kind are given. Do not distribute this code or use it other than for your own research without permission of the author. This code is part of a broader package that includes classification trees and output kernel trees. If you are interested by this package, please contact the author (p.geurts@ulg.ac.be). *********************************************************************** INSTALLATION - Compile the function rtree-c/ok3enslearn_mo_c.c using the command mex provided with matlab mex rtenslearn_c.c Note that for convenience, pre-compiled linux and mac os x (*** bits) binaries are provided in the archive (rtenslearn_c.mexa*** and rtenslearn_c.mexmaci***, compile with Matlab 7.9 (R2009b). - Copy the resulting mex file into the main directory of the package. - Add the directory RT into matlab's path path(path,'..../RT'); - To check that everything works, type 'rtexample' in Matlab (you should get no error and a mean square error of around 4.8). *********************************************************************** SOURCES Here is a brief description of the matlab functions contained in the directory. For some of them, see the code for a description of the input and output arguments. The directory rtree-c contains all c functions. The source code is (poorly) documented in french. EXAMPLE: rtexample.m: Contain an example of how to use the different functions friedman1.csv: An illustrative dataset used in example.m LEARNING: rtenslearn_c: Main function to learn an ensemble of multiple output regression trees. It provides an interface to the C code. For usage, see the file rtexample.m Settings: init_single_tree.m: Set the default parameters to grow single regression trees init_extra_trees.m: Set the default parameters to grow an ensemble of extremely randomized trees (see Geurts, Ernst, Wehenkel, Machine Learning, 2006). init_rf.m: Set the default parameters to implement Breiman's Random Forests method init_bagging.m: Set the default parameters to grow an ensemble of bagged trees init_mart.m: Set the default parameters to grow multiple additive regression trees (MART, see Hastie et al's book) TESTING rtenspred.m: Compute predictions on a test sample with an ensemble of multiple output regression trees learned with the function rtenslearn_c. (can be much slower than using the function rtenslearn_c directly) compute_rtens_variable_importance_mo.m: Compute variable importances from a test sample for an ensemble built with the function rtenslearn_c. Another (faster) solution is to do it directly at the learning stage with the function rtenslearn_c. cvpredict.m: Compute the predictions on a sample of objects using cross-validation.

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