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/196856/8054102
m axislines.m
function [h] = AxisLines(sta)
%AxisLines: Draws a vertical and horizontal lines through the origin.
%
% [h] = AxisLines(st)
%
% st Style of lines. Default = 'k:' (Black dotted line).
%
% h
www.eeworm.com/read/196856/8054115
m circle.m
function [h] = Circle(rda,sta)
%Circle: Draws a circle around the origin with specified radius
%
% [h] = Circle(rd,st)
%
% rd Radius. Default = 1.
% st Style of circle. Default = 'k:' (Black
www.eeworm.com/read/397363/8054524
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/397363/8054557
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/196840/8055020
m demlike.m
% A demonstration of the HMM software using the 'Likelihood' observation
% model. There are K=2 time series where EACH TIME SERIES IS THE
% LIKELIHOOD OF THE DATA GIVEN THAT STATE - in effect there i
www.eeworm.com/read/196836/8055203
m demlike.m
% A demonstration of the HMM software using the 'Likelihood' observation
% model. There are K=2 time series where EACH TIME SERIES IS THE
% LIKELIHOOD OF THE DATA GIVEN THAT STATE - in effect there i
www.eeworm.com/read/196825/8056108
m plotcorana.m
function [z, a] = coranaEval(per)
i=0;
a=-0.9:per:0.9;
sz=size(a,2);
z=zeros(sz,sz);
for x=a
i=i+1; j=0;
for y=a
j=j+1;
z(i,j)=coranaFeval([x y]);
end
end
%Done!
%First let's look at it
www.eeworm.com/read/196825/8056165
m orderbasedexample.m
echo on
% This script shows how to use the ga using an order-based representation.
% You should see the demos for
% more information as well. gademo1, gademo2, gademo3
global distMatrix
% Setting the
www.eeworm.com/read/297044/8057650
m main.m
N=200;
popsize=40;
ncity=10;
pcro=0.8;
pmut=0.1;
distance(ncity,ncity)=0;
pop(popsize,ncity)=0;
best(N)=0;
for i=1:popsize
pop(i,:)=randperm(ncity);
end
for i=1:ncity
for j=1:2
www.eeworm.com/read/297039/8058051
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