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/467198/7020207
m gdss1.m
% Illustration of gradient descent for quadratic landscape.
% J.-S. Roger Jang, April 1996
a=1;b=1;c=1;d=-1;e=1;
%a=1;b=0;c=2;d=0;e=0;
xx=-3:.2:3;
yy=-2:.2:2;
[x,y]=meshgrid(xx,yy);
z=a*x.^2+b*x.*y+c
www.eeworm.com/read/433381/7934201
cpp rootgradient.cpp
//RootGradient.cpp Gradient法求解非线性方程组一组实根
//#include
#include //输入输出流头文件
#include "polynomials.h" //多项式及连分式求值头文件
#include "NonLinearEquation.h" //非线性方程(组)求解头文件
using n
www.eeworm.com/read/298297/7968124
m nnidbpma.m
% 自适应学习率的动量bp算法辨识nonlinearFn1.m
% (Gradient descent w/momentum & adaptive lr backpropagation) nnidbpma.m
%
%
%songying, 2005-6-13
% 不会出现nnidbp.m中的仿真异常情况;也不会出现nnidbpa.m中拟合不好的情况
% 最终eta =
www.eeworm.com/read/143498/12870668
m gdss2.m
% Illustration of gradient descent for quadratic landscape.
% J.-S. Roger Jang, April 1996
a=1;b=1;c=1;d=-1;e=1;
%a=1;b=0;c=2;d=0;e=0;
xx=-3:.2:3;
yy=-2:.2:2;
[x,y]=meshgrid(xx,yy);
z=a*x.^2+b*x.*y+c
www.eeworm.com/read/143498/12870842
m gdss1.m
% Illustration of gradient descent for quadratic landscape.
% J.-S. Roger Jang, April 1996
a=1;b=1;c=1;d=-1;e=1;
%a=1;b=0;c=2;d=0;e=0;
xx=-3:.2:3;
yy=-2:.2:2;
[x,y]=meshgrid(xx,yy);
z=a*x.^2+b*x.*y+c
www.eeworm.com/read/140739/13064223
cpp rootgradient.cpp
//RootGradient.cpp Gradient法求解非线性方程组一组实根
//#include
#include //输入输出流头文件
#include "polynomials.h" //多项式及连分式求值头文件
#include "NonLinearEquation.h" //非线性方程(组)求解头文件
using n
www.eeworm.com/read/262062/11608413
m hermite_wise.m
%Hermite三次插值
function yi=Hermite_wise(x,y,ydot,xi)
if isempty(ydot)==1;
ydot=gradient(y,x);
end
n=length(x);m1=length(y);m2=length(ydot);
if n~=m1|n~=m2|m1~=m2
error('The length of X,Y
www.eeworm.com/read/345167/11834416
cpp rootgradient.cpp
//RootGradient.cpp Gradient法求解非线性方程组一组实根
//#include
#include //输入输出流头文件
#include "polynomials.h" //多项式及连分式求值头文件
#include "NonLinearEquation.h" //非线性方程(组)求解头文件
using n
www.eeworm.com/read/240722/4570493
qci hsosscf.qci
test_basis: STO-3G 6-311G**
test_method: hsoshf hsosxalpha hsoshfk hsoshfs hsoshfb hsoshfg96 hsosblyp hsosb3lyp hsospbe hsospw91 hsosb3pw91 hsosbpw91 hsosb3p86 hsosbp86 hsosspz81
gradient: yes
test_m
www.eeworm.com/read/436207/1850642
m fs1.m
function fs_1st_d = fS1(X, S, A, N, d)
% the gradient of the similarity constraint function w.r.t. A
% f = \sum_{ij}(x_i-x_j)A(x_i-x_j)' = \sum_{ij}d_ij*A*d_ij'
% df/dA = d(d_ij*A*d_ij')/dA
%
%