代码搜索：plot
找到约 10,000 项符合「plot」的源代码
代码结果 10,000
www.eeworm.com/read/196814/8058787
m kruskal.m
function [out,len]=kruskal(map)
%求最小生成树算法,通过kruskal算法求最优树,并给出相应图像.
%用法:
% 首先输入矩阵:
% map=[起点1 终点1 边长1;起点2 终点2 边长2;............;起点n 终点n 边长n]
% 再用[out,len]=kruskal(map)求最优树
%参数说明
% map----3列邻接
www.eeworm.com/read/196814/8058790
m dijkstra.m
function [p,v]=dijkstra(map,u1,u2)
%求网络最短路径的dijkstra算法
%用法:
% 首先输入矩阵:
% map=[起点1 终点1 边长1;起点2 终点2 边长2;............;起点n 终点n 边长n]
% 和u1,u2
% 注意：这里map为无向图。
% 再用[p,v]=dijkstra(map,u1,u2)求最短路径
www.eeworm.com/read/297034/8059055
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
www.eeworm.com/read/397111/8067209
m plotroc.m
function h = plotroc(e,varargin)
%PLOTROC Draw an ROC curve
%
% H = PLOTROC(E)
%
% Plot the roc curve of E according to the 'traditional' way: on the x
% axis we put the false positive (outliers a
www.eeworm.com/read/397111/8067213
m dd_ex3.m
% DD_EX3
%
% Show the use of the ksvdd: the support vector data description using
% several different kernels.
%
% To be honest, the SVDD is the most useful using the RBF kernel. In
% most case
www.eeworm.com/read/397106/8067537
m adapmixdec.m
% This uses "my" mixture decomposition software
% (Vittorio)
%
% generate a unit circle
clear;
NPOINTS=50;
angles = 0:(2*pi/NPOINTS):2*pi;
cir=[cos(angles)
sin(angles) ];
ncolors = 7;
colors = ['
www.eeworm.com/read/196537/8076814
m 3-4-5-2.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/396894/8083713
m fcurve.m
%fcurve - computes a neuron's i/o function
%
% [x, fx, lambda] = fcurve( T ) computes the
% integrate-and-fire neuron's i/o function,
% i.e., its mean synaptic output as a function
% of consta
www.eeworm.com/read/396212/8118885
m lv1.m
clc;
clear;
fs=1;
N=10000;
t=0:1/fs:N;
A=20^0.5;
B=A;
C=1;
x1=A*sin(2*0.15*pi*t);
x2=B*sin(2*0.2*pi*t);
noise=C*randn(size(t));
x=x1+x2+noise;
M=15; %阶数
M1=20; %阶数
www.eeworm.com/read/396209/8119002
m untitled2.m
clc;
clear all
t=1:300;
s1=sin(4*pi*t/300);%有用信号
C=1; %噪音强度
N=3000; %某点的观测次数
for t=1:300
z=C*randn(1,N)+sin(4*pi*t/300);%在某个点观察了N次的测量结果
xx(1)=0;
Q=0.001;
R=1;
p(1)=0.2;
for k=2:1:N
xs