代码搜索：Multivariate Analysis
找到约 10,000 项符合「Multivariate Analysis」的源代码
代码结果 10,000
www.eeworm.com/read/154760/11928707
m readme.m
% ADSP Toolbox: Version 2.0
% For use with "Analog and Digital Signal Processing", 2nd Ed.
% Published by PWS Publishing Co.
%
% Ashok Ambardar, EE Dept. MTU, Houghton, MI 49931, USA
% http://
www.eeworm.com/read/255925/12046118
m ssa.m
function [pc,s,v]=SSA(x,dim,tau)
%Syntax: [pc,s,v]=SSA(x,dim,tau)
%_______________________________
%
% Singular Spectrum Analysis for a time series.
%
% pc is the matrix with the principal compo
www.eeworm.com/read/255755/12057210
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
www.eeworm.com/read/255572/12073153
m synlpc1.m
function synWave = synlpc(aCoeff,pitch,sr,G,fr,fs,preemp)
% USAGE: synWave = synlpc(aCoeff,pitch,sr,G,fr,fs,preemp);
%
% This function synthesizes a (speech) signal based on a LPC (linear-
% predi
www.eeworm.com/read/255572/12073167
m synlpc2.m
function synWave = synlpc(aCoeff,source,sr,G,fr,fs,preemp)
% USAGE: synWave = synlpc(aCoeff,source,sr,G,fr,fs,preemp);
%
% This function synthesizes a (speech) signal based on a LPC (linear-
% pre
www.eeworm.com/read/150905/12248265
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
www.eeworm.com/read/150238/12302942
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)
www.eeworm.com/read/149739/12352650
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
www.eeworm.com/read/336521/12439727
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)
www.eeworm.com/read/228372/14388089
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)