代码搜索：Approximation
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lst 例08-04计算结果.lst
The SAS System 09:34 Monday, April 1, 2002 145
N P A R 1 W A Y P R O C E D U R E
Wilcoxon Sc
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m nid.m
function C = nid(k, v, bandwidth, n, formula, expansion, decomposition)
% function C = nid(k, v, bandwidth, n, formula, expansion, decomposition)
% This function provides integer approximations of
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m example2_3_1.m
load leleccum; %装载信号
s=leleccum(1:3920);
ls=length(s);
[cA1,cD1]=dwt(s,'db1'); %采用db1基本小波来分解信号
A1=upcoef('a','cA1','db1',1,ls); %第三步中产生的系数cA1和cD1构造第一层的低频和高频（A1和D1）系数
D1=upcoef('d','cD1','db1',1,l
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m newton.m
function [p0,err,k,y]=newton(f,df,p0,delta,epsilon,max1)
%Input - f is the object function input as a string 'f'
% - df is the derivative of f input as a string 'df'
% - p0 is the initial a
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m seidel.m
function [P,iter]= seidel(G,P,delta, max1)
%Input - G is the nonlinear system saved in the M-file G.m
% - P is the initial guess at the solution
% - delta is the error bound
%
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m newdim.m
function [P,iter,err]=newdim(F,JF,P,delta,epsilon,max1)
%Input-F is the system saved as the M-file F.m
% -JF is the Jacobian of F saved as the M-file JF.M
% -P is the inital approximation to
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m contents.m
% Kernel feature extraction.
%
% gda - Generalized Discriminant Analysis.
% greedyappx - Kernel greedy data approximation.
% greedykpca - Greedy kernel PCA.
% kpca - Kernel Principal
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m newton.m
function [p0,err,k,y]=newton(f,df,p0,delta,epsilon,max1)
%Input - f is the object function
% - df is the derivative of f
% - p0 is the initial approximation to a zero of f