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📄 readme.progs

📁 gaussian procession 原程序,用于解决分类问题,有兴趣可以看看啊
💻 PROGS
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%            Matlab code for Gaussian Processes for Classification:%                      GPCLASS version 0.2  10 Nov 97%       Copyright (c) David Barber and Christopher K I Williams (1997)%    This program is free software; you can redistribute it and/or modify%    it under the terms of the GNU General Public License as published by%    the Free Software Foundation; either version 2 of the License, or%    any later version.%%    This program is distributed in the hope that it will be useful,%    but WITHOUT ANY WARRANTY; without even the implied warranty of%    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the%    GNU General Public License for more details.%%    You should have received a copy of the GNU General Public License%    along with this program; if not, write to the Free Software%    Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.The main routines arempot.m======calculates the log probability of being in a hyperparameter statemgrad.m=======calculates the derivate of the log probability of being in a hyperparameter statemakepredm.m===========finds the means and covariance matrix for the activation posterirorfor all the test and training points.driverm.m=========once the data has been set up, this program calls the MAP routine or the HMC routine as appropriategetmode.m=========finds the laplace solution and associated parametersfinal_pred.m============calculates the mean predictionsscg.m=====scaled conjugate gradient optimiservhmcm.m=======Hybrid Monte Carlo routinevec2mitheta.m=============converts a vector representation of the hyperparameters to a matrixthreshold.m===========thresholds values above a specified limittrlog.m=======calculates the trace(log(A)) = log(det(A))- useful if det(A) is very large ( or very small)putmclass.m===========stores the mean and covariance matrix of the posterior on filemprintf.m=========simple matrix print routinegetmat.m========simple routine that loads in a matrixgetnoise_sp.m=============calculates the noise matrix

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