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www.eeworm.com/read/397363/8054466
m sima1.m
function [nw,a1,i] = sima1(w,p,lr,rho,pf)
%SIMA1 ART1 simulation function.
% Each input vector is presented to the network one at a time.
% (See COMPET, HARDLIM)
%
% [NW,A1,
www.eeworm.com/read/196836/8055198
readme
HMMBOX, version 3.2, William Penny, Imperial College, Feb 1999
Matlab toolbox for Hidden Markov Models
(Adapted from Machine Learning Toolbox
Version 1.0 01-Apr-96
Copyright (c) by Zoubin Ghahramani
www.eeworm.com/read/196814/8058915
m polyfit.m
function [p,S] = polyfit(x,y,n)
%p=polyfit(x,y,k)用k次多项式拟合向量数据(x,y)
%p返回多项式的降幂系数.当k>=n-1时,polyfit实现多项式插值.
%例如 用二次多项式拟合数据
% x | 0.1 0.2 0.15 0.0 -0.2 0.3
% --|-----------------------------
www.eeworm.com/read/397122/8065746
m validate.m
function [cost,nmodel,output] = validate(model, Xtrain, Ytrain, Xtest, Ytest,estfct, trainfct, simfct)
% Validate a trained model on a fixed validation set
%
% >> cost = validate({X,Y,type,gam,sig2}
www.eeworm.com/read/397111/8067086
m mogp.m
function p = mogP(x,means,covs,priors)
%MOGP Compute the probability density of a Mixture of Gaussians
%
% P = MOGP(X,MEANS,COVS,PRIORS)
%
% Calculate the probability density for all objects X f
www.eeworm.com/read/397111/8067101
m ksvdd.m
%KSVDD Support Vector Data Description on general kernel matrix
%
% W = KSVDD(X,FRACERR,WK)
%
% Train an SVDD on the data X, which is first mapped by mapping WK
% (see for possibilities myproxm
www.eeworm.com/read/397111/8067274
m dd_aic.m
function e = dd_aic(w,x)
%DD_AIC compute the Akaike Information Criterion for MoG
%
% E = DD_AIC(W,X)
%
% Compute the Akaike Information Criterion of the Mixture of
% Gaussians. We assume we have
www.eeworm.com/read/397102/8068006
m distmaha.m
%DISTMAHA Mahalanobis distance
%
% D = distmaha(A,U,G)
%
% Computation of the Mahanalobis distances of all vectors in the
% dataset A to a dataset of points U, using the covariance matrix G.
% G
www.eeworm.com/read/397102/8068247
m gendatc.m
%GENDATC Generation of two circular classes with different
% variances
%
% A = gendatc(na,nb,k,ma)
%
% Generation of two sets of k dimensional Gaussian distributed data
% vectors. Class a has the
www.eeworm.com/read/397102/8068293
m gendatp.m
%GENDATP Parzen density data generation
%
% B = gendatp(A,m,s)
%
% Generation of m points using the Parzen estimate of the density of
% the dataset A using a smoothing parameter s. Default s or s