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htm partiv.htm

Intro to Algorithms: PART IV: Advanced Design and Analysis Techniques
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txt 5-1245msg2.txt

Subject: software for propositional analysis hi ! i need to find a software to do propositional analysis to analyze large student ' write for my study . as far as i know , theoretically many differne
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htm partiv.htm

Intro to Algorithms: PART IV: Advanced Design and Analysis Techniques
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htm partiv.htm

Intro to Algorithms: PART IV: Advanced Design and Analysis Techniques
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m pca.m

%PCA Principal component analysis (PCA or MCA on overall covariance matrix) % % [W,FRAC] = PCA(A,N) % [W,N] = PCA(A,FRAC) % % INPUT % A Dataset % N or FRAC Number of dimensions
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m lpcauto.m

function [ar,e,k]=lpcauto(s,p,t) %LPCAUTO performs autocorrelation LPC analysis [AR,E,K]=(S,P,T) % Inputs: % s(ns) is the input signal % p is the order (default: 12) % t(nf,3)
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def ansys.def

; PSPad clip definition file for Ansys 7.0 ; author: Novotny L. W., www.TOSHULIN.cz ; last revision 31. 12. 2003 21:01 ; [*ABBR | Defines an abbreviation.] *ABBR,Abbr,String [*AFUN | Specifie
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m pca.m

%PCA Principal component analysis (PCA or MCA on overall covariance matrix) % % [W,FRAC] = PCA(A,N) % [W,N] = PCA(A,FRAC) % % INPUT % A Dataset % N or FRAC Number of dimensions
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m lpcauto.m

function [ar,e,k]=lpcauto(s,p,t) %LPCAUTO performs autocorrelation LPC analysis [AR,E,K]=(S,P,T) % Inputs: % s(ns) is the input signal % p is the order (default: 12) % t(nf,3)
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h noisedef.h

/********** Copyright 1990 Regents of the University of California. All rights reserved. Author: 1987 Gary W. Ng **********/ #ifndef NOISEDEFS #define NOISEDEFS #include "jobdefs.h" /* structu
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