function?[pSmu]?=?polyfit（xyn）
%???POLYFIT?Fit?polynomial?to?data.
%???P?=?POLYFIT（XYN）?finds?the?coefficients?of?a?polynomial?P（X）?of
%???degree?N?that?fits?the?data?Y?best?in?a?least-squares?sense.?P?is?a
%???row?vector?of?length?N+1?containing?the?polynomial?coefficients?in
%???descending?powers?P（1）*X^N?+?P（2）*X^（N-1）?+...+?P（N）*X?+?P（N+1）.
%
%???[PS]?=?POLYFIT（XYN）?returns?the?polynomial?coefficients?P?and?a
%???structure?S?for?use?with?POLYVAL?to?obtain?error?estimates?for
%???predictions.??S?contains?fields?for?the?triangular?factor?（R）?from?a?QR
%???decomposition?of?the?Vandermonde?matrix?of?X?the?degrees?of?freedom
%???（df）?and?the?norm?of?the?residuals?（normr）.??If?the?data?Y?are?random
%???an?estimate?of?the?covariance?matrix?of?P?is?（Rinv*Rinv‘）*normr^2/df
%???where?Rinv?is?the?inverse?of?R.
%
%???[PSMU]?=?POLYFIT（XYN）?finds?the?coefficients?of?a?polynomial?in
%???XHAT?=?（X-MU（1））/MU（2）?where?MU（1）?=?MEAN（X）?and?MU（2）?=?STD（X）.?This
%???centering?and?scaling?transformation?improves?the?numerical?properties
%???of?both?the?polynomial?and?the?fitting?algorithm.
%
%???Warning?messages?result?if?N?is?>=?length（X）?if?X?has?repeated?or
%???nearly?repeated?points?or?if?X?might?need?centering?and?scaling.
%
%???Class?support?for?inputs?XY:
%??????float:?double?single
%
%???See?also?POLY?POLYVAL?ROOTS.

%???Copyright?1984-2005?The?MathWorks?Inc.
%???$Revision:?5.17.4.8?$??$Date:?2006/06/20?20:11:56?$

%?The?regression?problem?is?formulated?in?matrix?format?as:
%
%????y?=?V*p????or
%
%??????????3??2
%????y?=?[x??x??x??1]?[p3
%??????????????????????p2
%??????????????????????p1
%??????????????????????p0]
%
%?where?the?vector?p?contains?the?coefficients?to?be?found.??For?a
%?7th?order?polynomial?matrix?V?would?be:
%
%?V?=?[x.^7?x.^6?x.^5?x.^4?x.^3?x.^2?x?ones（size（x））];

if?~isequal（size（x）size（y））
????error（‘MATLAB:polyfit:XYSizeMismatch‘...
??????????‘X?and?Y?vectors?must?be?the?same?size.‘）
end

x?=?x（:）;
y?=?y（:）;

if?nargout?>?2
???mu?=?[mean（x）;?std（x）];
???x?=?（x?-?mu（1））/mu（2）;
end

%?Construct?Vandermonde?matrix.
V（:n+1）?=?ones（length（x）1class（x））;
for?j?=?n:-1:1
???V（:j）?=?x.*V（:j+1）;
end

%?Solve?least?squares?problem.
[QR]?=?qr（V0）;
p?=?R\（Q‘*y）;????%?Same?as?p?=?V\y;
r?=?y?-?V*p;
p?=?p.‘;??????????%?Polynomial?coefficients?are?row?vectors?by?convention.

%?S?is?a?structure?containing?three?elements:?the?triangular?factor?from?a
%?QR?decomposition?of?the?Vandermonde?matrix?the?degrees?of?freedom?and
%?the?norm?of?the?residuals.
S.R?=?R;
S.df?=?max（0length（y）?-?（n+1））;
S.normr?=?norm（r）;

?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----
?????文件????????4589??2012-09-14?14:41??Stereo%20Matching.prj
?????文件??????342036??2012-09-14?14:41??left1.png
?????文件??????182317??2012-09-14?14:41??left2.png
?????文件??????362946??2012-09-14?14:41??left3.png
?????文件??????307325??2012-09-14?14:41??left4.png
?????文件????????1333??2012-09-14?14:41??license.txt
?????文件????????2659??2012-09-14?14:41??polyfit2.m
?????文件??????343316??2012-09-14?14:41??right1.png
?????文件??????181900??2012-09-14?14:41??right2.png
?????文件??????364420??2012-09-14?14:41??right3.png
?????文件??????307609??2012-09-14?14:41??right4.png
?????文件???????69692??2012-09-14?14:41??screenstereoGUI.jpg
?????文件????????2924??2012-09-14?14:41??stereomatch.m
?????文件????????5986??2012-09-14?14:41??stereovision.fig
?????文件???????14596??2012-09-14?14:41??stereovision.m
?????文件????????7618??2012-09-14?14:41??stereovisionplus.fig
?????文件???????21997??2012-09-14?14:41??stereovisionplus.m
?????文件????????2809??2012-09-14?14:41??metadata\appProperties.xml
?????文件???????69692??2012-09-14?14:41??metadata\primaryScreenShot.jpg
?????文件????????1665??2012-09-14?14:41??metadata\coreProperties.xml
?????文件????????2502??2012-09-14?14:41??.meta\filemanifest.xml
?????文件????????1192??2012-09-14?14:41??[Content_Types].xml
?????文件?????????894??2012-09-14?14:41??_rels\.rels