代码搜索：para
找到约 10,000 项符合「para」的源代码
代码结果 10,000
www.eeworm.com/read/407460/11419291
cdb testdsp_bios.cdb
//!
//# c5502.cdb 4.90.270
object DARAM :: MEM {
param iComment :: ""
param iIsUsed :: 1
param iId :: 0
param iDelUser :: "USER"
param iDelMsg :: "ok"
para
www.eeworm.com/read/407457/11419399
cdb tone.cdb
//!
//# c5502.cdb 4.90.270
object DARAM :: MEM {
param iComment :: ""
param iIsUsed :: 1
param iId :: 0
param iDelUser :: "USER"
param iDelMsg :: "ok"
para
www.eeworm.com/read/407456/11419416
cdb tone.cdb
//!
//# c5502.cdb 4.90.270
object DARAM :: MEM {
param iComment :: ""
param iIsUsed :: 1
param iId :: 0
param iDelUser :: "USER"
param iDelMsg :: "ok"
para
www.eeworm.com/read/344640/11870032
m rbfsvc.m
function [AlphaY, SVs, Bias, Parameters, Ns]=RbfSVC(Samples, Labels,Gamma, C, Epsilon, CacheSize)
% USAGES:
% [AlphaY, SVs, Bias, Parameters, Ns]=RbfSVC(Samples, Labels)
% [AlphaY, SVs, Bias, Para
www.eeworm.com/read/254802/12117398
asm exp83.asm
STACK SEGMENT STACK
DW 100 DUP(?)
STACK ENDS
DATA SEGMENT PARA
INIT_N DW 8
RESULT DW ?
ARGU_STRC STRUC
SAVEBP DW ?
SAVEIP DW
www.eeworm.com/read/149955/12329452
asm model.asm
DATA SEGMENT
SHOW1 DB 'Input number 1: $'
SHOW2 DB 'Input number 2: $'
DATA ENDS
;------------------------------------
STACK SEGMENT PARA STACK 'STACK'
DB 100 DUP( ? )
www.eeworm.com/read/251250/12355692
m paraglobalpca.m
% this function is used to obtain pca feature for a training set or a test set
function s=paraglobalpca(tsign)
pnum = 3648;
bname = 'para_';
inum = 456;
dim = 456;
ipath = strcat('E:\FY
www.eeworm.com/read/249923/12446739
h ga_work.h
#include "stdio.h"
#include "iostream.h"
#define POP 50 //population
#define COV_PRECISE 10000
#define PARA_MIN -10 //the min. value of variables
www.eeworm.com/read/131588/14136401
m genetic_algorithm.m
function D = Genetic_Algorithm(train_features, train_targets, params, region);
% Classify using a basic genetic algorithm
% Inputs:
% features - Train features
% targets - Train targets
% Para
www.eeworm.com/read/129915/14217779
m genetic_algorithm.m
function D = Genetic_Algorithm(train_features, train_targets, params, region);
% Classify using a basic genetic algorithm
% Inputs:
% features - Train features
% targets - Train targets
% Para