基于matlab编程优化的基线学习机源码程序 - matlab算法设计

下载频道> 资源分类> matlab源码> 算法设计> 基于matlab编程优化的基线学习机源码程序

标题：基于matlab编程优化的基线学习机源码程序
分享到：

所属分类: 算法设计资源类型：文件大小: 360.81 KB 上传时间: 2016-03-06 11:56:36 下载次数: 48 资源积分：1分提供者: 源码共享 20160306115611147

内容：
function myinputs=LARS_Selection_OPELM(data,kernel,maxneur,problem,normal)

% Least Angle Regression (LARS) based on Optimal-Pruning

% Extreme Learning Machine algorithm

% myinputs = LARS_Selection_OPELM( data , [kernel] , [maxneur],

% [problem] , [normal] )

% Inputs:

% data is a struct made of, at least:

% data.x a Nxd matrix of variables

% data.y a Nxn matrix of outputs

% (can be multi-output)

% [kernel] (optional) is the type of kernels

% to use. Either 'l' (linear), 's' (sigmoid),

% 'g' (gaussian), 'ls' (linear+sigmoid),

% 'lg' (linear+gaussian)

% or 'lsg' (linear+sigmoid+gaussian).

% Default is 'lsg'.

% [maxneur] (optional) is the maximum number of

% neurons allowed in the model.

% Default is 100.

% [problem] (optional) is the type of problem.

% Either 'r' (regression) or

% 'c' (classification).

% Default is 'r'.

% [normal] (optional) defines whether data is to

% be normalized before applying OPELM.

% Either 'y' (yes) or 'n' (no).

% Default is 'y'.

% Output:

% myinputs a 1xd matrix of '1' (for the selected variables)

% and '0' (for the unselected).

% References: Yoan Miche, Patrick Bas, Christian Jutten, Olli Simula,

% Amaury Lendasse. A Methodology for Building Regression

% Models using Extreme Learning Machine: OP-ELM, in Proceedings

% of European Symposium on Artificial Neural Networks (ESANN)

% 2008, Bruges, Belgium.

% Timo Simil? Jarkko Tikka. Multiresponse sparse regression with

% application to multidimensional scaling. International Conference

% on Artificial Neural Networks (ICANN). Warsaw, Poland. September

% 11-15, 2005. LNCS 3697, pp. 97-102.

% This function 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.

% The function 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.

% http://www.gnu.org/copyleft/gpl.html

%% Set a few things up

myinputs=[];

x=data.x;

y=data.y;

[N,d]=size(x);

[No,n]=size(y);

%% Test the user input extensively

if (N~=No) || (N<2)

disp('Error: data.x and data.y do not have the same number of samples or too few samples.');

disp('Exiting...');

return

end

if rank(x)~=d

msg=sprintf('Error: data variables cannot be correlated (%d correlated variable(s)).',d-rank(x)+1);

disp(msg);

disp('Exiting...');

return

end

if rank(y)~=n

msg=sprintf('Error: output variables cannot be correlated (%d correlated variable(s)).',n-rank(y)+1);

disp(msg);

disp('Exiting...');

return

end

if nargin<1

disp('Error: no arguments.');

disp('Exiting...');

return

end

if nargin>6

disp('Error: too many arguments.');

disp('Exiting...');

return

end

%% Check all arguments

% First, the normalization argument

if ~exist('normal','var')

disp('Warning: normalization unspecified...');

disp('-------> Switching to forced normalization.');

normal='y';

else

if isempty(normal)

% Normalization field is empty

disp('Warning: normalization specified but empty...');

disp('-------> Switching to forced normalization.');

normal='y';

else

% Normalization field is specified, check it

if ~strcmp(normal,{'y';'n';'Y';'N'})

disp('Error: Normalization specification invalid; either "N" or "n" for none or "Y" or "y" for yes.');

disp('Exiting...');

return

else

if normal=='Y'

normal='y';

end

% Second, the problem specification

if ~exist('problem','var')

disp('Warning: problem unspecified...');

disp('-------> Switching to regression problem.');

problem='r';

else

if isempty(problem)

% Problem field is empty

disp('Warning: problem specified but empty...');

disp('-------> Switching to regression problem.');

problem='r';

else

% Problem field is specified, check it

if ~strcmp(problem,{'r';'c'})

disp('Error: problem specification invalid; either "r" for regression or "c" for classification.');

disp('Exiting...');

return

end

if problem=='c'

% If we have classification problem, check classes

uy=unique(y);

sy1=size(uy,1);

if (sy1~=2)

disp('Error: Classification problem with more than two classes not supported.');

disp('Exiting...');

clear uy sy1 sy2

return

end

if (min(uy)~=-1) || (max(uy)~=1)

disp('Error: Classes for classification should be 1 and -1 only.');

disp('Exiting...');

clear uy

return

end

% Third, Kernel type (since number of neurons depends on it)

if ~exist('kernel','var')

disp('Warning: kernel type unspecified...');

if N-1-d>0

disp('-------> Switching to lsg kernel.');

kernel='lsg';

else

disp('-------> Switching to sg kernel.');

kernel='sg';

end

else

% Kernel type is empty

if isempty(kernel)

disp('Warning: kernel type empty...');

if N-1-d>0

disp('-------> Switching to lsg kernel.');

kernel='lsg';

else

disp('-------> Switching to sg kernel.');

kernel='sg';

end

else

% Kernel type is specified, check it

if ~max(strcmp(kernel,{'l';'s';'g';'ls';'lg';'sg';'lsg'}))

disp('Error: Kernel type invalid; either "l" or "s" or "g" or "ls" or "lg" or "sg" or "lsg".');

disp('Exiting...');

return

end

% Fourth, Maximum number of neurons specification

if ~exist('maxneur','var')

if strcmp(kernel,{'l'})

maxneur=1;

else

disp('Warning: maximum number of neurons unspecified...');

disp('-------> Switching to 100 maximum neurons.');

if N-1-d>0

maxneur=min(100,N-2-d);

else

maxneur=min(100,N-2);

end

else

% Maximum number of neurons is empty

if isempty(maxneur)

if strcmp(kernel,{'l'})

maxneur=1;

else

disp('Warning: maximum number of neurons empty...');

disp('-------> Switching to 100 maximum neurons.');

if N-1-d>0

maxneur=min(100,N-2-d);

else

maxneur=min(100,N-2);

end

else

% Maximum number of neurons is specified, check it

if (maxneur<1) || (ceil(maxneur)~=maxneur)

disp(['Error: Maximum number of neurons should be more than 1 and ' ...

'integer.']);

disp('Exiting...');

return

end

if max(strcmp(kernel,{'ls';'lg';'lsg'}))

if (maxneur>N-d-2)

disp('Error: Maximum number of neurons too important.');

disp('Exiting...');

return

end

else

if (maxneur>N-2)

disp('Error: Maximum number of neurons too important.');

disp('Exiting...');

return

end

% End of input checking

%% Initialize indexes

index=1:d;

[W,i1] = mrsr(y,x,d);

index=index(i1);

perf=zeros(1,d);

%% Loop and find the best ranking by LARS

for i=1:d

data.x=x(:,index(1:i));

[model]=train_OPELM(data,kernel,maxneur,problem,normal);

if problem=='r'

perf(i)=mean(model.errloo);

else

perf(i)=-mean(model.perc_gc);

end

[I,II]=min(perf);

%% Output the best ranking of variables

myinputs=index(1:II);

文件列表（点击上边下载按钮，如果是垃圾文件请在下面评价差评或者投诉）:

关键词：基线 学习机 源码

matlab源码下载排行

回到顶部

友情链接

matlab源码网站源码编程语言网络编程手机通讯编程 KUNAG 官网 KUNAG 电商更多

: 联系方式| 版权声明| 招聘信息| 广告服务| 银行汇款| 法律顾问| 兼职技术| 付款方式| 关于我们|; 网站客服程序员兼职招聘 411241577@qq.com; 沪ICP备19040327号-3; 公安备案号：沪公网安备 31011802003874号; 库纳格流体控制系统（上海）有限公司版权所有; Copyright © 1999-2014, GUSUCODE.COM, All Rights Reserved

关键词： 基线 学习机 源码

相关推荐

matlab源码下载排行

友情链接

关键词：基线学习机源码