서명 / 저자 The Bayesian choice : from decision-theoretic foundations to computational implementation / Christian P. Robert. 저자명 Robert, Christian P., 1961-

등록번호	소장위치 / 청구기호	도서상태	반납예정일
6009339	대전 중앙관4층 일반도서실 / QA279.5 .R6313 2001

등록번호	소장위치 / 청구기호	도서상태	반납예정일
5033709	대전 중앙관4층 일반도서실 / QA279.5 .B39 1995

등록번호	소장위치 / 청구기호	도서상태	반납예정일
5078504	대전 중앙관2층 일반도서실 / PL856.U745 빵7422

컴파일 성공 with xcode

katty2.1.zip

0. Compile ode.11.1 according to the instruction in the 'readme' file

- If it doesn't copy libs and headers in /usr/local/** then;

-- copy libdrawstuff.a, libdrawstuff.la, libode.a, libode.la into /usr/local/lib

-- copy ode/*.h into /usr/local/include/ode

-- copy drawstuff/*.h into /usr/local/include/drawstuff

1. File->New Project->Command LIne Utility/C++ Tool

2. Select folder and Name the project as you want

3. Create Group names "Frameworks" and add 3 Frameworks (AGL.Framework, Carbon.Framework and OpenGL.Framework)

4. Drag 3 frameworks and drop them into Target->[yourProjectName]->Copy Files

5. Copy contents of "demo_friction.cpp" (for example) and past it on the main.cpp file.

6. Goto "project [yourProjectName] Info" -> "Build" Tap -> "Search Paths" section;

- add "/usr/local/include" on "Header Search Paths"

- add "/usr/local/lib" on "Library Search Paths"

7. Goto "project [yourProjectName] Info" -> "Build" Tap -> "Linking" section;

- add "-ldrawstuff -lode" on "Other Linker Flags"

8. Copy "texturepath.h" and "textures" folder into your project directory

9. Compile and Run~!

katty2.0.zip

Matt's Matlab Tutorial Source Code Page

This document contains a tutorial on Matlab with a principal components analysis for a set of face images as the theme.

I wrote this tutorial while a graduate student in the Artificial Intelligence Laboratory of the Computer Science and Engineering Department at the University of California, San Diego. Now it's here at CSIM-AIT. You might still be able to find the original here.

This work is licensed under a Creative Commons Attribution 3.0 License. You are free to use the code here for any purpose you like, but please acknowledge its source if you do. It's not required, but if you make improvements I'd appreciate it if you sent me your updates.

Here's an index to this tutorial:

• Get some face images
• What is PCA and the “Eigenface” Technique?
• Other preliminaries
• Reading and displaying images
• Getting your training set into one big matrix
• Getting the principal component eigenvectors of the training set
• Exploratory analysis of the eigenvectors
• References
• Back to the AI Lab Home Page or the more up-to-date UCSD Computational Statistics and Machine Learning group home page.

Get some face images

I used the Ekman and Friesen Pictures of Facial Affect (POFA) for this tutorial. Unfortunately, the faces are copyrighted so if you want them you'll have to pay Paul Ekman some cash to get our online version of the database. Contact Gary Cottrell for details about our versions, or see http://www.paulekman.com/researchproducts.html on getting a CD directly from Ekman.

Don't despair, however. There are a bunch of nice face databases that are free. Check the Face Recognition Home Page for a list of what's available.

What is PCA and the “Eigenface” Technique?

I won't go into it in detail here, but the idea is that face images can be economically represented by their projection onto a small number of basis images that are derived by finding the most significant eigenvectors of the pixelwise covariance matrix for a set of training images. A quick Google Search shows that a lot of people like to play with this technique. In my tutorial I simply show how to get some eigenfaces and play with them in Matlab.

Other preliminaries

If you don't use emacs, the Matlab emacs mode may be one reason to use it. To get Matlab mode working in emacs, put matlab.el somewhere emacs can find it and add something like the following to your .emacs file:

(autoload 'matlab-mode "matlab" "Enter Matlab mode." t)
(setq auto-mode-alist (cons '("\\.m$" . matlab-mode) auto-mode-alist))
(defun my-matlab-mode-hook ()
  (setq matlab-indent-function t)       ; if you want function bodies indented
  (setq fill-column 76)         ; where auto-fill should wrap
  (matlab-mode-hilit)
  (turn-on-auto-fill))
(setq matlab-mode-hook 'my-matlab-mode-hook)
(autoload 'matlab-shell "matlab" "Interactive Matlab mode." t)
(defun my-matlab-shell-mode-hook ()
  '())
(setq matlab-mode-hook 'my-matlab-mode-hook)

Reading and displaying images

>> help imread                    % Online help is useful...

 IMREAD Read image from graphics file.
    A = IMREAD(FILENAME,FMT) reads the image in FILENAME into
    A. If the file contains a grayscale intensity image, A is
    a two-dimensional array.  If the file contains a truecolor

    etc...

>> I = imread('test.png');        % Read image test.png into variable I
>> size(I)                        % Get the number of rows and columns of I

ans =

   320   240

>> I(1:10,1:10)                   % Display row 1-10 and col 1-10 of I

ans =

    26    20    20    22    22    20    19    20    22    22
    26    24    19    22    22    19    21    24    24    22
    25    24    22    24    21    19    20    22    24    22
    25    21    21    24    22    20    21    22    24    20
    24    20    22    25    22    19    20    21    25    24
    20    19    22    22    24    21    21    24    22    24
    20    20    21    25    25    24    24    25    20    20
    20    20    21    24    26    25    25    25    20    20
    20    19    22    24    24    25    24    24    22    21
    21    17    21    25    21    22    24    25    22    21

>> colormap(gray(256));          % Use a 256-value grayscale color map
>> image(I);                     % Display I as an image
>> daspect([1 1 1]);             % Set x:y aspect ratio to be 1:1

If you like the PGM format like I do, here are matlab functions for reading and writing them:

• readpgm.m
• writepgm.m

Getting your training set into one big matrix

Matlab is really nice for linear algebra stuff and visualization, but sorta sucks when it comes to file I/O. Or rather, it's not much easier than C, although there are functions for reading and writing entire arrays to and from files. The following function, defined in load_images.m, is an example of how to read a bunch of images, make column vectors out of each of them, and return the result.

function [Images,w,h] = load_images(filelist,downscale_f)
%LOAD_IMAGES Load a set of images listed in a file.
%
%            [IMGS,W,H] = LOAD_IMAGES(FILELIST) Treat each line of
%            the file named FILELIST as the filename of a PGM image,
%            and load each image as one column of the return array
%            IMGS.
%
%            LOAD_IMAGES(FILELIST,DOWNSCALE_F) Do the same thing,
%            but downscale each image's width and height by a factor
%            of DOWNSCALE_F.  Useful if the images are too big to be
%            loaded into memory all at once.

% Matthew Dailey 2000
  
  % Check argument consistency

  if nargin < 1 | nargin > 2
    error('usage: load_images(filelist[,downscale_f]');
  end;
  if nargin == 1
    downscale_f = 1.0;
  end;
  Images = []; old_w = 0; old_h = 0; w=0; h=0;

  % Open input file

  numimgs = linecount(filelist);
  fid = fopen(filelist,'r');
  if fid < 0 | numimgs < 1
    error(['Cannot get list of images from file "' filelist, '"']);
  end;

  % Get the images

  for i = 1:numimgs
    imgname = fgetl(fid);
    if ~isstr(imgname)            % EOF is not a string
      break;                      % Exit from loop on EOF
    end;
    fprintf(1,'loading PGM file %s\n',imgname);
    Img = readpgm(imgname);       % Read this image as a 2D array
    if i==1                       % If this is first image, figure things out
      old_w = size(Img,2);        %   - like sizes of the downscaled images
      old_h = size(Img,1);
      if downscale_f <= 1.0
        w = old_w; h = old_h;
      else
        w = round(old_w/downscale_f); h = round(old_h/downscale_f);
      end;
      Images = zeros(w*h,numimgs);   % - preallocate size of the return matrix
    end;
    if downscale_f > 1.0
      Img = im_resize(Img,w,h);      % downscale using bicubic spline interp
    end;
    Images(1:w*h,i) = reshape(Img',w*h,1);   % Make a column vector
  end;
  fclose(fid);                    % Close the filelist when done

  fprintf(1,'Read %d images.\n',numimgs);

  % The function returns the output arguments Images, w, and h here.

>> [Imgs,w,h] = load_images('facelist',4);

Here is how you would convert a column of Imgs back into an image and display it:

>> L = Imgs(:,10);
>> L = reshape(L,w,h)';         % Reshapes column vector into a 2D array
>> image(L);
>> colormap(gray(256));
>> daspect([1 1 1]);

Getting the principal component eigenvectors of the training set

The function pc_evectors uses Turk and Pentland's trick to get the eigenvectors of A*A' from the eigenvectors of A'*A. It uses the function sortem to sort the eigenvectors and eigenvalues by eigenvalue. [Here is a faster version, sortem2, by James Javurek-Humig, which is considerably faster, if you have matlab version 7 or better.] To use pc_evectors, just do:

>> [Vecs,Vals,Psi] = pc_evectors(Imgs,30);   % Get top 30 PC evectors of Imgs

>> plot(Vals);                       % To plot the eigenvalues
>> CVals = zeros(1,length(Vals));    % Allocate a vector same length as Vals
>> CVals(1) = Vals(1);
>> for i = 2:length(Vals)            % Accumulate the eigenvalue sum
     CVals(i) = CVals(i-1) + Vals(i);
   end;
>> CVals = CVals / sum(Vals);        % Normalize total sum to 1.0
>> plot(CVals);                      % Plot the cumulative sum
>> ylim([0 1]);                      % Set Y-axis limits to be 0-1

Exploratory analysis of the eigenvectors

One thing that can be fun is to try to figure out what the top few eigenvectors encode. You can make a scatter plot of one component against another as follows:

>> Proj = Vecs(:,1:2)' * Imgs;       % Project each image onto first 2 evectors
>> size(Proj)

ans =

     2   110

>> plot(Proj(1,:),Proj(2,:),'r.')    % To get scatterplot of PC 1 vs PC 2

>> Labels = textread('labels','%s');    % Read labels from file 'labels'
>> text(Proj(1,:),Proj(2,:),Labels);    % Add text labels at each plotted point

>> pc_ev_1 = Vecs(:,1);                 % Get PC eigenvector 1
>> pc_ev_1 = reshape(pc_ev_1,w,h)';     % Reshape into 2D array
>> imagesc(pc_ev_1);                    % Display as image scaled 0-255

>> OrigImg = Imgs(:,20);                     % Grab image 20
>> Projection = Vecs(:,1:10)'*(OrigImg - Psi);    % Project onto ev's 1-10
>> Reconstruction = Vecs(:,1:10)*Projection+Psi;  % Reconstruct from projection
>> Reconstruction = reshape(Reconstruction,w,h)';
>> image(Reconstruction);
>> colormap(gray(256));
>> daspect([1 1 1]);

References

• Turk, M. and Pentland, A. (1991). Eigenfaces for recognition. The Journal of Cognitive Neuroscience, 3(1): 71-86.
• The photo of Kurt Cobain, representing the fact that this tutorial was written on a Linux box named after him, originally came from http://mrshowbiz.go.com/people/kurtcobain/content/bio.html but the original site is long gone. Now the original photographer is unknown.

* Wifi configuration

- Turn on the robot with network cable connected.

-- the network cable should be connected with a router which the user computer connects to.

- Get IP by pushing the Nao's chest button

- http://robotip

- config wifi with WPA encryption

* How to register user programmed modules

- consol (1)

-- ./mymodulename -pip robotIP

-- (example) - ./getandset -pip 192.168.0.16

- consol (2)

-- python modulerunner.py

--- modify IP address (If robotIP is 192.168.0.16)

----broker = ALBroker("pythonBroker","192.168.0.16",9559,"192.168.0.16",9559)

-- (example) python run.py

* Install "Eclipse Classic" (cuz we require 'Ant' to use existing makefiles)

* Install CDT via Software update by adding the following site

http://download.eclipse.org/tools/cdt/releases/ganymede

* How to create a (existing makefile)project in Eclipse

* How to use 'Ant'

http://www.ibm.com/developerworks/aix/library/au-unix-eclipse/index.html

Ex)

<?xml version="1.0"?>
<project name="unzip" default="make">
<description>
Ant adaptor for the unzip Makefile.
</description>
<target name="make" description="build unzip">
<exec executable="make">
<arg value="-f"/>
<arg value="unix/Makefile"/>
<arg value="generic"/>
</exec>
</target>
<target name="test" description="test unzip"
depends="make">
<exec executable="make">
<arg value="-f"/>
<arg value="unix/Makefile"/>
<arg value="test"/>
</exec>
</target>
<target name="install" description="install unzip"
depends="make">
<exec executable="make">
<arg value="-f"/>
<arg value="unix/Makefile"/>
<arg value="install"/>
</exec>
</target>
<target name="clean" description="clean unzip">
<exec executable="make">
<arg value="-f</arg>
<arg value="unix/Makefile</arg>
<arg value="clean"/>
</exec></target>
</project>

Now you can right-click the build.xml file and run it as an Ant build (Run As > Ant Build). The launch dialog (see Figure 15) lets you choose which build target to run.

You can see your build progress in the Console view, which will fill up with the output of your make command. As you can see from Figure 16, you've done it!

A simple explanation of the Lasso and Least Angle Regression

Computation of the Lasso solutions

Forward stepwise regression algorithm:

• Start with all coefficients bj equal to zero.
• Find the predictor xj most correlated with y, and add it into the model. Take residuals r= y-yhat.
• Continue, at each stage adding to the model the predictor most correlated with r.
• Until: all predictors are in the model

Least angle regression algorithm:

• Start with all coefficients bj equal to zero.
• Find the predictor xj most correlated with y
• Increase the coefficient bj in the direction of the sign of its correlation with y. Take residuals r=y-yhat along the way. Stop when some other predictor xk has as much correlation with r as xj has.
• Increase (bj, bk) in their joint least squares direction, until some other predictor xm has as much correlation with the residual r.
• Continue until: all predictors are in the model

OpenCV를 MAC OS X의 XCODE 에서 사용하기

1. OpenCV Framework 를 얻는다.
http://opencvlibrary.sourceforge.net/Mac_OS_X_OpenCV_Port 에서 미리 빌드된 것을 다운 받거나, 아니면 소스코드에서 빌드한다.

2. OpenCV.framework 폴더를 Framework들이 모여있는 장소인 /System/Library/Framework 에 복사해 넣는다.

3. XCODE 를 실행해 새로운 Empty Project를 만들고, main.cpp 를 추가한다.

4. OpenCV framework를 사용하기 위해 Add -> Existing Frameworks 메뉴를 이용해 OpenCV framework를 추가 한다. 그림에서는 소스코드와 framework를 구분하기 위해 폴더를 만들어 사용하였다.

OpenCVTest.zip

Symbol	Command	Symbol	Command	Symbol	Command
	\pm		\mp		\times
	\div		\cdot		\ast
	\star		\dagger		\ddagger
	\amalg		\cap		\cup
	\uplus		\sqcap		\sqcup
	\vee		\wedge		\oplus
	\ominus		\otimes		\circ
	\bullet		\diamond		\lhd
	\rhd		\unlhd		\unrhd
	\oslash		\odot		\bigcirc
	\triangleleft		\Diamond		\bigtriangleup
	\bigtriangledown		\Box		\triangleright
	\setminus		\wr

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Symbol	Command	Symbol	Command	Symbol	Command
	\le		\ge		\neq
	\sim		\ll		\gg
	\doteq		\simeq		\subset
	\supset		\approx		\asymp
	\subseteq		\supseteq		\cong
	\smile		\sqsubset		\sqsupset
	\equiv		\frown		\sqsubseteq
	\sqsupseteq		\propto		\bowtie
	\in		\ni		\prec
	\succ		\vdash		\dashv
	\preceq		\succeq		\models
	\perp		\parallel		\|
	\mid

Negations of many of these relations can be formed by just putting \not before the symbol, or by slipping an n between the \ and the word. Here are a few examples, plus a few other negations; it works for many of the others as well.

Symbol	Command	Symbol	Command	Symbol	Command
	\nmid		\nleq		\ngeq
	\nsim		\ncong		\nparallel
	\not<		\not>		\not=
	\not\le		\not\ge		\not\sim
	\not\approx		\not\cong		\not\equiv
	\not\parallel		\nless		\ngtr
	\lneq		\gneq		\lnsim
	\lneqq		\gneqq

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Lowercase letters

Symbol	Command	Symbol	Command	Symbol	Command	Symbol	Command
	\alpha		\beta		\gamma		\delta
	\epsilon		\varepsilon		\zeta		\eta
	\theta		\vartheta		\iota		\kappa
	\lambda		\mu		\nu		\xi
	\pi		\varpi		\rho		\varrho
	\sigma		\varsigma		\tau		\upsilon
	\phi		\varphi		\chi		\psi
	\omega

Capital letters

Symbol	Command	Symbol	Command	Symbol	Command	Symbol	Command
	\Gamma		\Delta		\Theta		\Lambda
	\Xi		\Pi		\Sigma		\Upsilon
	\Phi		\Psi

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Symbol	Command	Symbol	Command
	\gets		\to
	\leftarrow		\Leftarrow
	\rightarrow		\Rightarrow
	\leftrightarrow		\Leftrightarrow
	\mapsto		\hookleftarrow
	\leftharpoonup		\leftharpoondown
	\rightleftharpoons		\longleftarrow
	\Longleftarrow		\longrightarrow
	\Longrightarrow		\longleftrightarrow
	\Longleftrightarrow		\longmapsto
	\hookrightarrow		\rightharpoonup
	\rightharpoondown		\leadsto
	\uparrow		\Uparrow
	\downarrow		\Downarrow
	\updownarrow		\Updownarrow
	\nearrow		\searrow
	\swarrow		\nwarrow

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Symbol	Command	Symbol	Command	Symbol	Command	Symbol	Command
	\ldots 2		\vdots		\cdots 2		\ddots

(The '2's after \ldots and \cdots are only present to make the distinction between the two clear.)

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Symbol	Command	Symbol	Command	Symbol	Command
	\hat{x}		\check{x}		\dot{x}
	\breve{x}		\acute{x}		\ddot{x}
	\grave{x}		\tilde{x}		\mathring{x}
	\bar{x}		\vec{x}

When applying accents to i and j, you can use \imath and \jmath to keep the dots from interfering with the accents:

Symbol	Command	Symbol	Command
	\vec{\jmath}		\tilde{\imath}

\tilde and \hat have wide versions that allow you to accent an expression:

Symbol	Command	Symbol	Command
	\widehat{3+x}		\widetilde{abc}

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Symbol	Command	Symbol	Command	Symbol	Command
	\infty		\triangle		\angle
	\aleph		\hbar		\imath
	\jmath		\ell		\wp
	\Re		\Im		\mho
	\prime		\emptyset		\nabla
	\surd		\partial		\top
	\bot		\vdash		\dashv
	\forall		\exists		\neg
	\flat		\natural		\sharp
	\backslash		\Box		\Diamond
	\clubsuit		\diamondsuit		\heartsuit
	\spadesuit		\Join		\blacksquare

Didn't find what you want? Or do you want a printable list? The Comprehensive LaTex Symbol list has just about any symbol that you could possibly want.

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In mathematics, sometimes we need to enclose expressions in brackets or braces or parentheses. Some of these work just as you'd imagine in LaTeX; type ( and ) for parentheses, [ and ] for brackets, and | and | for absolute value. However, other symbols have special commands:

Symbol	Command	Symbol	Command	Symbol	Command
	\{		\}		\|
	\backslash		\lfloor		\rfloor
	\lceil		\rceil		\langle
	\rangle

You might notice that if you use any of these to typeset an expression that is vertically large, like

\displaystyle (1 + \frac{a}{x} )^2

the parentheses don't come out the right size:

If we put \left and \right before the relevant parentheses, we get a prettier expression:

\displaystyle \left(1 + \frac{a}{x} \right)^2

gives

\left and \right can also be used to resize the following symbols:

Symbol	Command	Symbol	Command	Symbol	Command
	\uparrow		\downarrow		\updownarrow
	\Uparrow		\Downarrow		\Updownarrow

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Some symbols render differently in regular math mode and in display mode (display mode occurs when you use \displaystyle, $$...$$, or \[...\]). Read more in the commands section of the guide about how symbols which take arguments above and below the symbols, such as a summation symbol, behave in the two modes.

In each of the following, the two images show the symbol in regular mode, then in display mode.

Symbol	Command	Symbol	Command	Symbol	Command
	\sum		\int		\oint
	\prod		\coprod		\bigcap
	\bigcup		\bigsqcup		\bigvee
	\bigwedge		\bigodot		\bigotimes
	\bigoplus		\biguplus

(The two integral symbols render fine in a full LaTeX document .)

복잡한 수식

여러 줄에 이어 계속되는 식이 = 등에 의해 정렬되는 경우는 eqnarray 환경을 쓰고 행렬처럼 가로세로로 정렬하려면 array, matrix 환경을 쓴다. 이러한 환경은 스스로 displaymath 모드로 바꿈으로 $$ ... $$ 로 묶지 않음에 유의하라. AMSLaTeX에서 제공하는 수식정렬 환경으로 align, gather, case 등의 환경이 있다. 다음의 예를 참고하라.

eqnarray, align, gather 환경은 자동으로 수식번호를 매겨준다 (위 그림에서는 편의상 번호를 나타내지 않았다). 수식번호가 필요치 않은 경우 eqnarray*와 같이 끝에 *를 붙인다. 위 예에서 보듯이 &는 칸을 가르는 역할을 하고, \\는 줄바꿈을 한다. \begin{array}{rrr}에서 {rrr}은 array에 세 칸(열)이 있고 각 열은 오른쪽 정렬을 함을 나타낸다. r은 오른쪽 정렬을, c는 중앙정렬, l은 왼쪽정렬을 할 때 쓰인다.

case 환경에서는 수학모드이기 때문에 \text{...}을 써서 보통 로만글꼴로 조건들을 표시하였음에 유의하라. 좀더 자세한 것은 AMSLaTeX User's Guide를 보자.