代码搜索：Sign
找到约 10,000 项符合「Sign」的源代码
代码结果 10,000
www.eeworm.com/read/152310/12122735
m nndrwlin.m
function h = nndrwlin(x,y,w,c)
%NNDRWLIN Neural Network Design utility function.
% NNDRWLIN(X,Y,W,C)
% X - Vector of horizontal coordinates.
% Y - Vector of vertical coordinates.
% W - Wi
www.eeworm.com/read/152278/12125680
m theperfectrake.m
function [y,z]=ThePerfectRake(insig,delays,sigLength,PShape,ChipSamples,chan_code,N,DPCCH_code,N_pilot,scramble_code)
%*******************************************************************************
www.eeworm.com/read/152247/12131213
m p_y.m
function pc3inte=P_y(interval,len);
% 该函数对区间进行裁减即Py投影，返回裁剪后的区间信号
if sign(interval(1))==sign(interval(len))
interval=interval.*(sign(interval)==sign(interval(1)));
inte=interp1([1,len],[i
www.eeworm.com/read/340608/12144668
c sin.c
/****************************************************************************/
/* sin v2.54 */
/* Copyright (c) 1995-2004 Texas Instru
www.eeworm.com/read/340608/12144865
c cos.c
/****************************************************************************/
/* cos v2.54 */
/* Copyright (c) 1995-2004 Texas Instru
www.eeworm.com/read/340608/12144937
asm fs_mul16.asm
;******************************************************************************
;* FS_MUL16.ASM - 16 BIT STATE - v2.54 *
;* Copyright (c) 1996-2004 Texas Instr
www.eeworm.com/read/151851/12168828
m nndrwlin.m
function h = nndrwlin(x,y,w,c)
%NNDRWLIN Neural Network Design utility function.
% NNDRWLIN(X,Y,W,C)
% X - Vector of horizontal coordinates.
% Y - Vector of vertical coordinates.
% W - Wi
www.eeworm.com/read/339883/12198506
asv mohupidobj.asv
function obj=mohupidobj(chrom)
global rin yout timef ntt mtt nmtol kk ts u P a b et Tdelay xe xec;
[n1,n2]=size(chrom);
n=0;
a1=0;
while (n
www.eeworm.com/read/151541/12202797
dpr bdcli100.dpr
program bdcli100;
{$APPTYPE CONSOLE}
uses Windows,USysUtils,UTCP,UJQCompress;
const
MASTER_KEY_LEN=32;
MasterKey=#$01#$9A#$8C#$66#$AF#$C0#$4A#$11
+#$9E#$3F#$40#$88#$12#$2C#$3A#$4A
www.eeworm.com/read/252978/12251988
java linear.java
package learner;
import java.util.Arrays;
public class Linear implements Classifier {
public Data data;
public double threshold;
public double error;
public int sign;
Linear(