代码搜索：Patterns
找到约 8,017 项符合「Patterns」的源代码
代码结果 8,017
www.eeworm.com/read/372570/9503317
m wim_core.m
%WIM_CORE Channel coefficient computation for a geometric channel model
% [H DELTA_T FINAL_PHASES FINAL_PHASES_LOS]=WIM_CORE(WIMPAR,LINKPAR,ANTPAR,BULKPAR,BSGAIN,BSGAIN_LOS,MSGAIN,MSGAIN_LOS,OFFSET
www.eeworm.com/read/372113/9521096
m classification_error.m
function [classify, err] = classification_error(D, patterns, targets, region)
%Find a classification error for a given decision surface D and a given set of
%patterns (2xL) and targets (1xL)
%The
www.eeworm.com/read/366065/9834910
n switch.n
'\"
'\" Copyright (c) 1993 The Regents of the University of California.
'\" Copyright (c) 1994-1997 Sun Microsystems, Inc.
'\"
'\" See the file "license.terms" for information on usage and redistribut
www.eeworm.com/read/363091/9967943
c nnwhat.c
/*--------------------------------------------------------------------------*
* Gregory Stevens 7/5/93 *
* NNWHAT.C
www.eeworm.com/read/363091/9968117
c nnsim1.c
/*--------------------------------------------------------------------------*
* Gregory Stevens 7/1/93 *
* NNSIM1.C
www.eeworm.com/read/363091/9968131
c nnwhere.c
/*--------------------------------------------------------------------------*
* Gregory Stevens 7/5/93 *
* NNWHERE.C
www.eeworm.com/read/166872/9992132
m getmeandistance.m
function d=getMeanDistance(p)
%平均距离
d=0;
if 0==getSize(p)
d=0;
else
for iPattern=1:getSize(p)
d=d+getDistance(p,p.Patterns(iPattern));
end
d=d/getSize(p);
end
www.eeworm.com/read/362099/10019225
m ex1d2.m
%==============================================================
% Ex1d2: An example for using WaveNet, 1-D Interpolation
%==============================================================
% By Qingh
www.eeworm.com/read/362099/10019231
m ex1d1.m
%==============================================================
% Ex1d1: An example for using WaveNet, 1-D Interpolation
%==============================================================
% By Qingh
www.eeworm.com/read/362008/10023786
m classification_error.m
function [classify, err] = classification_error(D, patterns, targets, region)
%Find a classification error for a given decision surface D and a given set of
%patterns (2xL) and targets (1xL)
%The