代码搜索：plot
找到约 10,000 项符合「plot」的源代码
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www.eeworm.com/read/138798/13211595
m plotcov2.m
% PLOTCOV2 - Plots a covariance ellipse with major and minor axes
% for a bivariate Gaussian distribution.
%
% Usage:
% h = plotcov2(mu, Sigma[, OPTIONS]);
%
% Inputs:
% mu -
www.eeworm.com/read/138798/13212445
m demglm2.m
%DEMGLM2 Demonstrate simple classification using a generalized linear model.
%
% Description
% The problem consists of a two dimensional input matrix DATA and a
% vector of classifications T. Th
www.eeworm.com/read/240541/13214486
m 6-18.m
L = linspace(0,2.*pi,6);
xv = cos(L)';yv = sin(L)';
xv = [xv ; xv(1)];
yv = [yv ; yv(1)];
%设定多边形
x = randn(250,1);
y = randn(250,1);
www.eeworm.com/read/138667/13226453
m grnn.m
%绘制指数函数曲线
p=-1:0.05:1;
t=exp(-p);
plot(p,t);
grid;
title('exponential function');
xlabel('x');
ylabel('y');
figure;
%建立并训练网络
for i=1:5
net=newgrnn(p,t,i/10);
y(i,:)=sim(net,p);
en
www.eeworm.com/read/138667/13226458
m caldemo.m
function caldemo()
%% Main function
y1=1:0.01:10;
y2=y1.^2;
yy=rsp(y1,y2);
plot(y1,yy)
function y=rsp(y1,y2)
% Sub function
H1=y2./y1;
H2=y1./y2;
y=(H1+H2)/2;
www.eeworm.com/read/138656/13226915
m hop3.m
%例5.3, hop3.m
%
clear
T=[1 -1;-1 1];
net=newhop(T); %创建Hopfield网络
w=net.lw{1,1},b=net.b{1} %输出权值和偏差
Ai ={T};
[Y,Pf,Af] = s
www.eeworm.com/read/138656/13226942
m hop4.m
%例5.4, hop4.m
%
clear
T=[1 -1;-1 1];
net=newhop(T); %创建Hopfield网络
w=net.lw{1,1},b=net.b{1} %输出权值和偏差
plot(T(1,:),T(2,:),'r*') %作目标节点图
P=[-1 -0.
www.eeworm.com/read/325030/13229518
m exp2_4_.m
close all
clc
clear
%定义时间范围
t=[0:pi/20:9*pi];
hold on %允许在同一坐标系下绘制不同的图形
plot(t,sin(t),'r:*')
plot(t,cos(t))
plot(t,-cos(t),'k')
grid on %在所画出的图形坐标中添加栅格，注意用在plot之后
hold off %覆盖旧图
www.eeworm.com/read/325030/13229533
m exp2_4.m
close all
clc
clear
%定义时间范围
t=[0:pi/20:9*pi];
figure(1) %选择图像
plot(t,sin(t),'r:*')
grid on %在所画出的图形坐标中添加栅格，注意用在plot之后
grid off %删除栅格
figure(2)
plot(t,cos(t))
grid on
grid off
www.eeworm.com/read/240174/13233461
m holder.m
function h=holder(tfr,f,n1,n2,t,pl)
%HOLDER Estimate the Holder exponent through an affine TFR.
% H=HOLDER(TFR,F,N1,N2,T) estimates the Holder exponent of a
% function through an affine time-frequenc