代码搜索：Gradient
找到约 2,951 项符合「Gradient」的源代码
代码结果 2,951
www.eeworm.com/read/370224/9608520
c dogleg_.c
/* dogleg.f -- translated by f2c (version 20020621).
You must link the resulting object file with the libraries:
-lf2c -lm (in that order)
*/
#include
#include
#define min(
www.eeworm.com/read/370224/9608528
c dogleg.c
/* dogleg.f -- translated by f2c (version 20020621).
You must link the resulting object file with the libraries:
-lf2c -lm (in that order)
*/
#include
#include
#define min
www.eeworm.com/read/368108/9711628
m exm044_3.m
%exm044_3.m
F=[1,2,3;4,5,6;7,8,9]
Dx=diff(F) %相邻行差分
Dx_2=diff(F,1,2) %相邻列差分。第三输入宗量2表示"列"差分。
[FX,FY]=gradient(F) %数据点步长默认为1
[FX_2,FY_2]=gradient(F,0.5) %数据点步长为0.5
www.eeworm.com/read/366977/9786609
m p0309.m
[I,map]=imread('3-22.jpg');
imshow(I,map);
I=double(I);
[Gx,Gy]=gradient(I); % 计算梯度
G=sqrt(Gx.*Gx+Gy.*Gy); % 注意是矩阵点乘
J1=G;
figure,imshow(J1,map); % 第一种图像增强
J2=I;
www.eeworm.com/read/413015/11170080
m p0309.m
[I,map]=imread('3-22.jpg');
imshow(I,map);
I=double(I);
[Gx,Gy]=gradient(I); % 计算梯度
G=sqrt(Gx.*Gx+Gy.*Gy); % 注意是矩阵点乘
J1=G;
figure,imshow(J1,map); % 第一种图像增强
J2=I;
www.eeworm.com/read/106690/15626559
m gradmap.m
% gradmap() - compute the gradient of an EEG spatial distribution.
%
% Usage:
% >> [gradX, gradY ] = gradmap( map, filename, draw )
%
% Inputs:
% map - level of activity (size: nbelectrodes
www.eeworm.com/read/191214/8432830
m example2_19.m
vz=10;
a=.32;
t=0:2.1:1;
z=vz*t+1/2*a*t.^2;
vx=2;
x=vx*t;
vy=3;
y=vy*t;
u=gradient(x);
v=gradient(y);
w=gradient(z);
scale=0;
quiver3(x,y,z,u,v,w,scale)
xlabel('x轴');
ylabel('y轴');
zlab
www.eeworm.com/read/432935/8562084
m example_quiver.m
% example_quiver.m
% 绘制矢量图
[X,Y]=meshgrid(-2:.2:2);
Z=X.*exp(-X.^2 - Y.^2+1);
% 得到X，Y位置处的向量
[DX,DY]=gradient(Z,.2,.2);
% 绘制等值线图
contour(X,Y,Z)
hold on
% 叠加矢量分布图
quiver(X,Y,DX,DY)
colormap h
www.eeworm.com/read/432935/8562125
asv example_quiver.asv
% example_quiver.m
% 绘制矢量图
[X,Y]=meshgrid(-2:.2:2);
Z=X.*exp(-X.^2 - Y.^2);
[DX,DY] = gradient(Z,.2,.2);
contour(X,Y,Z)
hold on
quiver(X,Y,DX,DY)
colormap hsv
hold of
www.eeworm.com/read/429878/8784215
htm demolgd1.htm
Netlab Reference Manual demolgd1
demolgd1
Purpose
Demonstrate simple MLP optimisation with on-line gradient descent
Synopsi