代码搜索：methods

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m hamming.m

function H=hamming(f,T,Y) %Input - f is the function entered as a string 'f' % - T is the vector of abscissas % - Y is the vector of ordinates %Remark. The first four coordinates of
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m milne.m

function M=milne(f,T,Y) %Input - f is the function entered as a string 'f' % - T is the vector of abscissas % - Y is the vector of ordinates %Remark. The first four coordinates of T
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m ls.m

function L=ls(F1,F2,a,b,alpha,beta,M) %Input - F1 and F2 are the systems of first-order equations % representing the I.V.P.'s (9) and (10), respectively % - a and b are the endpoin
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m readme.m

NUMERICAL METHODS: Matlab Programs (c) 2004 by John H. Mathews and Kurtis D. Fink Complementary Software to accompany the textbook NUMERICAL METHODS: Using Matlab, Fourth Edition ISBN: 0-13-06
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m dirich.m

function U=dirich(f1,f2,f3,f4,a,b,h,tol,max1) %Input - f1,f2,f3,f4 are boundary functions input as strings % - a and b right endpoints of [0,a] and [0,b] % - h step size % - tol is the
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m bisect.m

function [c,err,yc]=bisect(f,a,b,delta) %Input - f is the function input as a string 'f' % - a and b are the left and right endpoints % - delta is the tolerance %Output - c is the zero %
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m rctrap.m

function T=rctrap(f,a,b,n) %Input - f is the integrand input as a string 'f' % - a and b are upper and lower limits of integration % - n is the number of times for recursion %Output - T
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h divfile.h

//====================================================================== // Header file // // Written for the book Programming Windows CE // Copyright (C) 1998 Douglas Boling //==================
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h imalloc.h

//------------------------------------------------------------------- // C Interface to private allocator //------------------------------------------------------------------- #define EXPORT extern
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m contents.m

% Unsupervised statistical learning methods. % % unsudemo - Demo of unsupervised learning methods for 2D feature space. % % mln - Compute value of logarihm of the likelihood function.
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