代码搜索：initial
找到约 10,000 项符合「initial」的源代码
代码结果 10,000
www.eeworm.com/read/163872/5507468
c retarget.c
//*----------------------------------------------------------------------------
//* ATMEL Microcontroller Software Support - ROUSSET -
//*-------------------------------------------------
www.eeworm.com/read/163872/5507568
c retarget.c
//*----------------------------------------------------------------------------
//* ATMEL Microcontroller Software Support - ROUSSET -
//*-------------------------------------------------
www.eeworm.com/read/163872/5507661
c retarget.c
//*----------------------------------------------------------------------------
//* ATMEL Microcontroller Software Support - ROUSSET -
//*-------------------------------------------------
www.eeworm.com/read/162614/5528661
c 20031031-2.c
/* PR/10239 */
enum node_type
{
INITIAL = 0, FREE,
PRECOLORED,
SIMPLIFY, SIMPLIFY_SPILL, SIMPLIFY_FAT, FREEZE, SPILL,
SELECT,
SPILLED, COALESCED, COLORED,
LAST_NODE_TYPE
};
inline void
p
www.eeworm.com/read/155397/5622440
makefile
# Top-level Makefile for tools/java
SUBDIRS =
INITIAL_TARGETS = sim
ROOT = ../../../../..
include $(ROOT)/Makefile.include
# This depends on SimProtocol being compiled along with the various
# me
www.eeworm.com/read/475017/6799523
cbl program10.cbl
IDENTIFICATION DIVISION.
PROGRAM-ID. TEST-CHANGE.
AUTHOR. TEIIKU.
*
ENVIRONMENT DIVISION.
*
DATA DIVISION.
WORKING-STORAGE SECTION.
www.eeworm.com/read/474600/6813466
m hmm_forward.m
function [Pout, Alpha] = HMM_Forward(a, b, initial_state, V)
% Find the probability of a finite state in a Markov chain using the HMM forward algorithm
%
% Inputs:
% a - Transition probabili
www.eeworm.com/read/474600/6813480
m hmm_generate.m
function out = HMM_generate(a,b,initial,seq_len)
% Generate a Markov sequence
%
% Inputs:
% a - Transition probability matrix
% b - Output generator matrix
% initial - Initial state
% seq
www.eeworm.com/read/359187/6841943
m hmm_forward.m
function [Pout, Alpha] = HMM_Forward(a, b, initial_state, V)
% Find the probability of a finite state in a Markov chain using the HMM forward algorithm
%
% Inputs:
% a - Transition probabili
www.eeworm.com/read/359187/6841969
m hmm_generate.m
function out = HMM_generate(a,b,initial,seq_len)
% Generate a Markov sequence
%
% Inputs:
% a - Transition probability matrix
% b - Output generator matrix
% initial - Initial state
% seq