{"id":264,"date":"2013-07-07T10:08:18","date_gmt":"2013-07-07T06:38:18","guid":{"rendered":"http:\/\/vua.nadiran.com\/?p=264"},"modified":"2013-07-07T10:20:27","modified_gmt":"2013-07-07T06:50:27","slug":"%d8%b1%da%af%d8%b1%d8%b3%db%8c%d9%88%d9%86-%d8%af%d8%b1matlab","status":"publish","type":"post","link":"https:\/\/vua.nadiran.com\/?p=264","title":{"rendered":"\u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u062f\u0631matlab"},"content":{"rendered":"

\u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u0686\u0646\u062f \u0645\u062a\u063a\u06cc\u0631\u0647\u060c \u06cc\u06a9\u06cc \u0627\u0632 \u0645\u062a\u062f\u0647\u0627\u06cc \u0622\u0645\u0627\u0631\u06cc \u0645\u0642\u062f\u0645\u0627\u062a\u06cc \u062c\u0647\u062a \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0631\u0648\u0646\u062f \u0645\u062a\u063a\u06cc\u06cc\u0631\u06cc \u0648\u0627\u0628\u0633\u062a\u0647 \u0628\u0647 \u062f\u0648 \u06cc\u0627 \u0686\u0646\u062f \u0645\u062a\u063a\u06cc\u0631 \u0645\u0633\u062a\u0642\u0644 \u0627\u0633\u062a.<\/p>\n

\u0627\u0632 \u0627\u06cc\u0646 \u0631\u0648\u0634 \u0645\u06cc\u062a\u0648\u0627\u0646 \u0628\u0631\u0627\u06cc \u062a\u0635\u0645\u06cc\u0645 \u06af\u06cc\u0631\u06cc \u062f\u0631 \u0645\u0648\u0631\u062f \u062e\u0631\u06cc\u062f \u0647\u0627\u06cc \u067e\u0631\u0648\u0698\u0647 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0647\u0627\u06cc \u0632\u06cc\u0627\u062f\u06cc \u06a9\u0631\u062f. \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0645\u062b\u0627\u0644 \u0645\u06cc\u062a\u0648\u0627\u0646 \u0639\u0627\u062f\u0644\u0627\u0646\u0647 \u0628\u0648\u062f\u0646 \u0642\u06cc\u0645\u062a \u067e\u06cc\u0634\u0646\u0647\u0627\u062f\u06cc \u0628\u0631\u0627\u06cc \u06cc\u06a9\u06cc \u0627\u0632\u00a0\u0645\u0627\u0634\u06cc\u0646<\/b>\u00a0\u0622\u0644\u0627\u062a \u062f\u0633\u062a \u062f\u0648\u0645 \u0631\u0627 \u0645\u0648\u0631\u062f \u0628\u0631\u0631\u0633\u06cc \u0642\u0631\u0627\u0631 \u062f\u0627\u062f.<\/p>\n

\u0628\u0647 \u0627\u06cc\u0646 \u0635\u0648\u0631\u062a \u06a9\u0647 \u0642\u06cc\u0645\u062a \u0631\u0627 \u062f\u0631 \u062f\u0648\u0631\u0647 \u0647\u0627\u06cc \u06af\u0630\u0634\u062a\u0647 \u0628\u0631\u0627\u06cc\u00a0\u0645\u0627\u0634\u06cc\u0646<\/b>\u00a0\u0647\u0627\u06cc \u0645\u0634\u0627\u0628\u0647 \u0628\u0627 \u0645\u062a\u063a\u06cc\u0631\u0647\u0627\u06cc \u0645\u062a\u0641\u0627\u0648\u062a\u060c ( \u0645\u062b\u0644\u0627\u064b \u0645\u062f\u062a \u06a9\u0627\u0631\u06a9\u0631\u062f \u0648 \u0645\u062f\u0644\u00a0\u0645\u0627\u0634\u06cc\u0646<\/b>\u00a0) \u0645\u0648\u0631\u062f \u0628\u0631\u0631\u0633\u06cc \u0642\u0631\u0627\u0631 \u062f\u0627\u062f\u0647 \u0648 \u062a\u0627\u0628\u0639 \u0642\u06cc\u0645\u062a \u0631\u0627 \u0628\u0631 \u0627\u0633\u0627\u0633 \u0641\u0627\u06a9\u062a\u0648\u0631\u0647\u0627\u06cc \u0645\u062f\u062a \u06a9\u0627\u0631\u06a9\u0631\u062f \u0648 \u0645\u062f\u0644 \u062a\u062e\u0645\u06cc\u0646 \u0632\u062f.<\/p>\n

\u0645\u06cc\u0632\u0627\u0646 \u0627\u0646\u062d\u0631\u0627\u0641 \u0642\u06cc\u0645\u062a \u067e\u06cc\u0634\u0646\u0647\u0627\u062f\u06cc \u0627\u0632 \u0642\u06cc\u0645\u062a \u062a\u062e\u0645\u06cc\u0646 \u0632\u062f\u0647 \u0634\u062f\u0647 \u062a\u0648\u0633\u0637 \u062a\u06a9\u0646\u06cc\u06a9 \u0631\u06af\u0631\u0633\u06cc\u0648\u0646\u00a0\u062e\u0637<\/b>\u06cc \u0686\u0646\u062f \u0645\u062a\u063a\u06cc\u0631\u0647 \u0645\u06cc\u062a\u0648\u0627\u0646\u062f \u0634\u0627\u062e\u0635 \u0645\u0646\u0627\u0633\u0628\u06cc \u062f\u0631 \u062a\u0635\u0645\u06cc\u0645 \u06af\u06cc\u0631\u06cc \u062c\u0647\u062a \u062e\u0631\u06cc\u062f \u0628\u0627\u0634\u062f.<\/p>\n

 <\/p>\n

\u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u062f\u0631matlab \u0628\u0647 \u0635\u0648\u0631\u062a \u0647\u0627\u06cc \u0632\u06cc\u0631 \u062a\u0639\u0631\u06cc\u0641 \u0634\u062f\u0647 \u0627\u0633\u062a.<\/p>\n

 <\/p>\n

xreglinear\/regression<\/p>\n

\u00a0REGRESSION returns the regression matrix for the model m<\/p>\n

\u00a0[r,ok]=regression(m)<\/p>\n

function [r,ok]=regression(m)<\/p>\n

%REGRESSION returns the regression matrix for the model m<\/p>\n

%<\/p>\n

% [r,ok]=regression(m)<\/p>\n

\u00a0%\u00a0 Copyright 2000-2007 The MathWorks, Inc. and Ford Global Technologies, Inc.<\/p>\n

%\u00a0\u00a0 $Revision: 1.2.2.3 $\u00a0 $Date: 2007\/06\/18 22:38:40 $<\/p>\n

if ~isfield(m.Store,’Q’)<\/p>\n

\u00a0\u00a0 error(‘mbc:xreglinear:InvalidState’,…<\/p>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 ‘Use InitStore first to initialize data in model’);<\/p>\n

end<\/p>\n

\u00a0r=m.Store.X;<\/p>\n

if ~isempty(r)<\/p>\n

\u00a0\u00a0 r=r(:,terms2(m));<\/p>\n

end<\/p>\n

if nargout>1<\/p>\n

\u00a0\u00a0 % rank check on regression matrix<\/p>\n

\u00a0\u00a0 ok=~(rank(r)<\/p>\n

end<\/p>\n

\u00a0localsurface\/regression<\/p>\n

\u00a0 r=REGRESSION(m) returns the regression matrix for the model m<\/p>\n

function [r,ok]=regression(m)<\/p>\n

% r=REGRESSION(m) returns the regression matrix for the model m<\/p>\n

\u00a0%\u00a0 Copyright 2000-2004 The MathWorks, Inc. and Ford Global Technologies, Inc.<\/p>\n

\u00a0 %\u00a0\u00a0 $Revision: 1.2.2.2 $\u00a0 $Date: 2004\/02\/09 07:42:28 $<\/p>\n

\u00a0[r,ok]=regression(m.userdefined);<\/p>\n

\u00a0\u0644\u0627\u0632\u0645 \u0628\u0647 \u0630\u06a9\u0631 \u0627\u0633\u062a \u06a9\u0647 \u062a\u0627\u0628\u0639 \u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u0628\u0647 \u0635\u0648\u0631\u062a double\u06a9\u0627\u0631 \u0645\u06cc\u06a9\u0646\u062f \u0648\u0628\u0635\u0648\u0631\u062a \u0632\u06cc\u0631 \u0641\u0631\u0627\u062e\u0648\u0627\u0646\u06cc \u0645\u06cc\u0634\u0648\u062f.<\/p>\n

R =regression(m)<\/p>\n

\u0627\u0645\u0627 \u0645\u0627 \u0645\u06cc\u062a\u0648\u0627\u0646\u06cc\u0645 \u062a\u0627\u0628\u0639 \u0645\u0648\u0631\u062f \u0646\u0638\u0631\u0645\u0627\u0646 \u0631\u0627 \u0628\u0627 \u0627\u0646\u062a\u062e\u0627\u0628 \u06af\u0632\u06cc\u0646\u0647 \u06cc file>>new>>\u2026..\u0648 \u0647\u0631 \u06cc\u06a9 \u0627\u0632 \u06af\u0632\u06cc\u0646\u0647 \u0647\u0627\u06cc new\u060c \u062a\u0639\u0631\u06cc\u0641 \u06a9\u0646\u06cc\u0645.<\/p>\n

\u00a0\u0628\u0631\u0627\u06cc \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u062a\u0627\u0628\u0639 Ridge :<\/p>\n

X = [x1 x2 x3];<\/p>\n

D = x2fx(X,’interaction’);<\/p>\n

D(:,1) = []; % No constant term<\/p>\n

k = 0:1e-5:5e-3;<\/p>\n

b = ridge(y,D,k);<\/p>\n

Plot the ridge trace:<\/p>\n

figure<\/p>\n

plot(k,b,’LineWidth’,2)<\/p>\n

ylim([-100 100])<\/p>\n

grid on<\/p>\n

xlabel(‘Ridge Parameter’)<\/p>\n

ylabel(‘Standardized Coefficient’)<\/p>\n

title(‘{\\bf Ridge Trace}’)<\/p>\n

legend(‘x1′,’x2′,’x3′,’x1x2′,’x1x3′,’x2x3’)<\/p>\n

\"matlab-ridge\"<\/a><\/p>\n

\n

———————————————-<\/p>\n

\u0645\u062b\u0627\u0644\u06cc \u0627\u0632 \u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u0631\u06cc\u062c<\/p>\n

Example of a matlab ridge regression function:<\/b><\/p>\n

 <\/p>\n

function bks=ridge(Z,Y,kvalues)<\/p>\n

% Ridge Function of Z (centered, explanatory)<\/p>\n

% Y is the response,<\/p>\n

es where to compute
\n[n,p]=size(Z);
\nZpY=Z’<\/p>\n

% kvalues are the val<\/p>\n

u*Y;<\/p>\n

ZpZ=Z’*Z;<\/p>\n

m=length(kvalues);<\/p>\n

bks=ones(p,m);<\/pre>\n
\n
for k =1:m<\/pre>\n
.\u06f5)
\nkvalues =
\n  Columns 1 through 7<\/p>\n
 bks(:,k)=(ZpZ+diag(kvalues(k)))\\ZpY;<\/p>\n
end<\/pre>\n
values=(0:.05<\/p>\n
>> \r\nk<\/pre>\n
:         \u06f0    \u06f0\u066b\u06f0\u06f5\u06f0\u06f0    \u06f0\u066b\u06f1\u06f0\u06f0\u06f0    \u06f0\u066b\u06f1\u06f5\u06f0\u06f0    \u06f0\u066b\u06f2\u06f0\u06f0\u06f0    \u06f0\u066b\u06f2\u06f5\u06f0\u06f0    \u06f0\u066b\u06f3\u06f0\u06f0\u06f0<\/p>\n
  Columns 8 through 11<\/pre>\n
   \u06f1\u066b\u06f5\u06f5\u06f1\u06f1    \u06f1\u066b\u06f5\u06f1\u06f7\u06f6    \u06f1\u066b\u06f4\u06f8\u06f8\u06f2    \u06f1\u066b\u06f4\u06f6\u06f2\u06f2<\/p>\n
    \u06f0\u066b\u06f3\u06f5\u06f0\u06f0    \u06f0\u066b\u06f4\u06f0\u06f0\u06f0    \u06f0\u066b\u06f4\u06f5\u06f0\u06f0    \u06f0\u066b\u06f5\u06f0\u06f0\u06f0\r\n>> ridge(Z,Y,(0:.05:.5))\r\nans =\r\n  Columns 1 through 7 \r\n\r\n    \u06f1\u066b\u06f4\u06f3\u06f9\u06f0    \u06f1\u066b\u06f4\u06f1\u06f8\u06f3    \u06f1\u066b\u06f3\u06f9\u06f9\u06f6\r\n    \u06f0\u066b\u06f5\u06f1\u06f0\u06f2    \u06f0\u066b\u06f4\u06f7\u06f7\u06f5    \u06f0\u066b\u06f4\u06f4\u06f8\u06f8    \u06f0\u066b\u06f4\u06f2\u06f3\u06f4    \u06f0\u066b\u06f4\u06f0\u06f0\u06f9    \u06f0\u066b\u06f3\u06f8\u06f0\u06f6    \u06f0\u066b\u06f3\u06f6\u06f2\u06f4<\/pre>\n
  -\u06f0\u066b\u06f2\u06f2\u06f9\u06f2   -\u06f0\u066b\u06f2\u06f5\u06f1\u06f4   -\u06f0\u066b\u06f2\u06f7\u06f1\u06f3   -\u06f0\u066b\u06f2\u06f8\u06f9\u06f2
\n  Columns 8 through 11
\n    \u06f1\u066b<\/p>\n
    \u06f0\u066b\u06f1\u06f0\u06f1\u06f9    \u06f0\u066b\u06f0\u06f6\u06f7\u06f8    \u06f0\u066b\u06f0\u06f3\u06f7\u06f8    \u06f0\u066b\u06f0\u06f1\u06f1\u06f3   -\u06f0\u066b\u06f0\u06f1\u06f2\u06f2   -\u06f0\u066b\u06f0\u06f3\u06f3\u06f4   -\u06f0\u066b\u06f0\u06f5\u06f2\u06f4\r\n   -\u06f0\u066b\u06f1\u06f4\u06f4\u06f1   -\u06f0\u066b\u06f1\u06f7\u06f6\u06f2   -\u06f0\u066b\u06f2\u06f0\u06f4\u06f3\r\n \u06f3\u06f8\u06f2\u06f7    \u06f1\u066b\u06f3\u06f6\u06f7\u06f3    \u06f1\u066b\u06f3\u06f5\u06f3\u06f2    \u06f1\u066b\u06f3\u06f4\u06f0\u06f3\r\n    \u06f0\u066b\u06f3\u06f4\u06f5\u06f9    \u06f0\u066b\u06f3\u06f3\u06f0\u06f9    \u06f0\u066b\u06f3\u06f1\u06f7\u06f2    \u06f0\u066b\u06f3\u06f0\u06f4\u06f6\r\n   -\u06f0\u066b\u06f0\u06f6\u06f9\u06f6   -\u06f0\u066b\u06f0\u06f8\u06f5\u06f3   -\u06f0\u066b\u06f0\u06f9\u06f9\u06f7   -\u06f0\u066b\u06f1\u06f1\u06f2\u06f8\r\n   -\u06f0\u066b\u06f3\u06f0\u06f5\u06f4   -\u06f0\u066b\u06f3\u06f2\u06f0\u06f1   -\u06f0\u066b\u06f3\u06f3\u06f3\u06f6   -\u06f0\u066b\u06f3\u06f4\u06f6\u06f0<\/pre>\n
 \u06f2\u066b\u06f6\u06f9\u06f7\u06f3
\n>> k=(4*5.9829)\/2.6973<\/p>\n
%Formula gives for choice of k:\r\n>> norm(Yhat-Yc)^2\r\nans =\r\n   \u06f4\u06f7\u066b\u06f8\u06f6\u06f3\u06f6\r\n>> 47.8636\/(13-5)\r\nans =\r\n    \u06f5\u066b\u06f9\u06f8\u06f2\u06f9   % estimates the variance sigma^2\r\n>> bk0'*bk0\r\nans =\r\n  \r\n k =<\/pre>\n
.\u06f8\u06f7\u06f2\u06f4     % This is a suggested value for k<\/p>\n
    \r\n\u06f8<\/pre>\n
\n","protected":false},"excerpt":{"rendered":"
\u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u0686\u0646\u062f \u0645\u062a\u063a\u06cc\u0631\u0647\u060c \u06cc\u06a9\u06cc \u0627\u0632 \u0645\u062a\u062f\u0647\u0627\u06cc \u0622\u0645\u0627\u0631\u06cc \u0645\u0642\u062f\u0645\u0627\u062a\u06cc \u062c\u0647\u062a \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0631\u0648\u0646\u062f \u0645\u062a\u063a\u06cc\u06cc\u0631\u06cc \u0648\u0627\u0628\u0633\u062a\u0647 \u0628\u0647 \u062f\u0648 \u06cc\u0627 \u0686\u0646\u062f \u0645\u062a\u063a\u06cc\u0631 \u0645\u0633\u062a\u0642\u0644 \u0627\u0633\u062a. \u0627\u0632 \u0627\u06cc\u0646 \u0631\u0648\u0634 \u0645\u06cc\u062a\u0648\u0627\u0646 \u0628\u0631\u0627\u06cc \u062a\u0635\u0645\u06cc\u0645 \u06af\u06cc\u0631\u06cc \u062f\u0631 \u0645\u0648\u0631\u062f \u062e\u0631\u06cc\u062f \u0647\u0627\u06cc \u067e\u0631\u0648\u0698\u0647 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0647\u0627\u06cc \u0632\u06cc\u0627\u062f\u06cc \u06a9\u0631\u062f. \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0645\u062b\u0627\u0644 \u0645\u06cc\u062a\u0648\u0627\u0646 \u0639\u0627\u062f\u0644\u0627\u0646\u0647 \u0628\u0648\u062f\u0646 \u0642\u06cc\u0645\u062a \u067e\u06cc\u0634\u0646\u0647\u0627\u062f\u06cc \u0628\u0631\u0627\u06cc \u06cc\u06a9\u06cc \u0627\u0632\u00a0\u0645\u0627\u0634\u06cc\u0646\u00a0\u0622\u0644\u0627\u062a \u062f\u0633\u062a \u062f\u0648\u0645 \u0631\u0627 \u0645\u0648\u0631\u062f \u0628\u0631\u0631\u0633\u06cc \u0642\u0631\u0627\u0631 \u062f\u0627\u062f. […]<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[19],"tags":[],"class_list":["post-264","post","type-post","status-publish","format-standard","hentry","category-19","category-19-id","post-seq-1","post-parity-odd","meta-position-corners","fix"],"_links":{"self":[{"href":"https:\/\/vua.nadiran.com\/index.php?rest_route=\/wp\/v2\/posts\/264"}],"collection":[{"href":"https:\/\/vua.nadiran.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/vua.nadiran.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/vua.nadiran.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/vua.nadiran.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=264"}],"version-history":[{"count":4,"href":"https:\/\/vua.nadiran.com\/index.php?rest_route=\/wp\/v2\/posts\/264\/revisions"}],"predecessor-version":[{"id":266,"href":"https:\/\/vua.nadiran.com\/index.php?rest_route=\/wp\/v2\/posts\/264\/revisions\/266"}],"wp:attachment":[{"href":"https:\/\/vua.nadiran.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=264"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/vua.nadiran.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=264"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/vua.nadiran.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=264"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}