代码搜索：Nearest
找到约 1,596 项符合「Nearest」的源代码
代码结果 1,596
www.eeworm.com/read/386050/8768133
m nmsc.m
%NMSC Nearest Mean Scaled Classifier
%
% W = NMSC(A)
% W = A*NMSC
%
% INPUT
% A Trainign dataset
%
% OUTPUT
% W Nearest Mean Scaled Classifier mapping
%
% DESCRIPTION
% Computation of the
www.eeworm.com/read/281694/9139804
m regroutl.m
function [W] = RegrOutl(X,W)
% [W] = RegrOutl(X,W)
% [W] = RegrOutl(X)
%
% Function eliminates outliers interactively.
% Feedback is given on the graphical screen -
% mouse clicks toggle
www.eeworm.com/read/360995/10069970
m nndd.m
%NNDD Nearest neighbour data description method.
%
% W = NNDD(A,FRACREJ)
%
% Calculates the Nearest neighbour data description. Training only
% consists of the computation of the resemblance o
www.eeworm.com/read/163289/10166750
changelog
CURRENT CVS
* Implemented enemy stats display, will improve colors later.
* Fixed issue that will pretty much make multiple humans possible. Computer
now follows and attacks the nearest human, or
www.eeworm.com/read/160626/10513278
m discrim16.m
function outcomplex=discrim16(FFTsym)
%------------------------------------------------
%this function takes in a complex symbol and
%outputs the nearest 16 QAM symbol inside the constellation
%th
www.eeworm.com/read/466709/7031625
m unfolding.m
function [d,nfnn]=unfolding(y,th,de,tau);
%function [de,nfnn]=unfolding(y,th,de,tau)
%
%estimate the minimum unfolding dimension by calculating when the
%proportion of false nearest neighbours if f
www.eeworm.com/read/466709/7031631
m fnn.m
function [p]=fnn(y,de,tau,th,kth);
%function [nfnn]=fnn(y,de,tau,th,kth)
%
%determine the number of false nearest neighbours for the time
%series y embedded in dimension de with lag tau.
%
%for ea
www.eeworm.com/read/299984/7140321
m nmsc.m
%NMSC Nearest Mean Scaled Classifier
%
% W = NMSC(A)
% W = A*NMSC
%
% INPUT
% A Trainign dataset
%
% OUTPUT
% W Nearest Mean Scaled Classifier mapping
%
% DESCRIPTION
% Computation of the
www.eeworm.com/read/460435/7250796
m nmsc.m
%NMSC Nearest Mean Scaled Classifier
%
% W = NMSC(A)
% W = A*NMSC
%
% INPUT
% A Trainign dataset
%
% OUTPUT
% W Nearest Mean Scaled Classifier mapping
%
% DESCRIPTION
% Computation of the
www.eeworm.com/read/451547/7461944
m nndd.m
%NNDD Nearest neighbour data description method.
%
% W = NNDD(A,FRACREJ)
%
% Calculates the Nearest neighbour data description. Training only
% consists of the computation of the resemblance o