代码搜索：initialisation
找到约 2,024 项符合「initialisation」的源代码
代码结果 2,024
www.eeworm.com/read/140836/13059846
asm t1.asm
;Sources for the second task
t1_init nop ;insert your own initialisation code
return
t1_inter nop ;insert your own interrupt code
return
t1_run nop ;insert your own code
retu
www.eeworm.com/read/140836/13059848
asm t0.asm
;Sources for the first task
t0_init nop ;insert your own initialisation code
return
t0_inter nop ;insert your own interrupt code
return
t0_run nop ;insert your own code
retur
www.eeworm.com/read/137160/13341786
m ffnc.m
%FFNC Feed-forward neural net classifier back-end
%
% [W,HIST] = FFNC (ALG,A,UNITS,ITER,W_INI,T,FID)
%
% INPUT
% ALG Training algorithm: 'bpxnc' for back-propagation (default), 'lmnc'
%
www.eeworm.com/read/137160/13341891
m kmeans.m
%KMEANS k-means clustering
%
% [LABELS,A] = KMEANS(A,K,MAXIT,INIT,FID)
%
% INPUT
% A Matrix or dataset
% K Number of clusters to find (optional; default: 2)
% MAXIT maximum number o
www.eeworm.com/read/137160/13341899
m bpxnc.m
%BPXNC Back-propagation trained feed-forward neural net classifier
%
% [W,HIST] = BPXNC (A,UNITS,ITER,W_INI,T,FID)
%
% INPUT
% A Dataset
% UNITS Array indicating number of units in each h
www.eeworm.com/read/137160/13342619
m emclust.m
%EMCLUST Expectation-Maximization clustering
%
% [LABELS,W_EM] = EMCLUST (A,W_CLUST,K,LABTYPE,FID)
%
% INPUT
% A Dataset, possibly labeled
% W_CLUST Cluster model mapping, untrained (de
www.eeworm.com/read/321274/13409657
c hfp_headset_init.c
/****************************************************************************
Copyright (C) Cambridge Silicon Radio Ltd. 2004
FILE NAME
hfp_headset_init.c
DESCRIPTION
NOTES
*/
www.eeworm.com/read/317467/13504458
txt ami5.txt
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AMI-BIOS CHECK-POINT, (C)1991 American Megatrends Inc.,All Rights Reserved
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www.eeworm.com/read/314653/13562199
m ffnc.m
%FFNC Feed-forward neural net classifier back-end
%
% [W,HIST] = FFNC (ALG,A,UNITS,ITER,W_INI,T,FID)
%
% INPUT
% ALG Training algorithm: 'bpxnc' for back-propagation (default), 'lmnc'
%
www.eeworm.com/read/314653/13562251
m kmeans.m
%KMEANS k-means clustering
%
% [LABELS,A] = KMEANS(A,K,MAXIT,INIT,FID)
%
% INPUT
% A Matrix or dataset
% K Number of clusters to find (optional; default: 2)
% MAXIT maximum number o