代码搜索：Nearest
找到约 1,596 项符合「Nearest」的源代码
代码结果 1,596
www.eeworm.com/read/426535/9014888
m ex1603.m
%例16-3 外插运算方法和误差
x = 0:10;
y = sin(x);
xi=5:0.25:15;
yi=sin(xi);
y1=interp1(x,y,xi,'nearest')
y1=interp1(x,y,xi,'nearest','extrap');
y2=interp1(x,y,xi,'linear','extrap');
y3=interp1(x,y,xi,'sp
www.eeworm.com/read/426516/9016496
m odd.m
function y=odd(x);
%ODD Round towards nearest odd value.
% Y=ODD(X) rounds each element of X towards the nearest odd
% integer value. If an element of X is even, ODD adds +1 to
% this value. X can b
www.eeworm.com/read/185455/9037011
m odd.m
function y=odd(x);
%ODD Round towards nearest odd value.
% Y=ODD(X) rounds each element of X towards the nearest odd
% integer value. If an element of X is even, ODD adds +1 to
% this value. X can b
www.eeworm.com/read/184196/9117974
m odd.m
function y=odd(x);
%ODD Round towards nearest odd value.
% Y=ODD(X) rounds each element of X towards the nearest odd
% integer value. If an element of X is even, ODD adds +1 to
% this value. X can b
www.eeworm.com/read/376593/9312692
m examp8_1.m
x=0:.12:1;
y=(x.^2-3*x+5).*exp(-5*x).*sin(x); plot(x,y,x,y,'o')
x1=0:.02:1; y0=(x1.^2-3*x1+5).*exp(-5*x1).*sin(x1);
y1=interp1(x,y,x1); y2=interp1(x,y,x1,'cubic');
y3=interp1(x,y,x1,'spline'); y
www.eeworm.com/read/375793/9349459
h vnode.h
//使用 by UNetwork::Prim
#ifndef VertexNode1_
#define VertexNode1_
template class UNetwork;
template class ModifiedMinHeap;
template
class VertexNode1 {
fri
www.eeworm.com/read/179152/9368190
m exp2_16.m
%curve interpolation
ys=[0 0.9 0.6 1 0 0.1 -0.3 -0.7 -0.9 -0.2]; %已有的样本点ys
xs=0:length(ys)-1; %已有的样本点xs
x=0:0.1:length(ys)-1;%新的样本点x
y1=interp1(xs,ys,x,'nearest'); %插值产生新的样本点y1
y2=interp1(xs,ys,
www.eeworm.com/read/177691/9440255
m exp2_16.m
%curve interpolation
ys=[0 0.9 0.6 1 0 0.1 -0.3 -0.7 -0.9 -0.2]; %已有的样本点ys
xs=0:length(ys)-1; %已有的样本点xs
x=0:0.1:length(ys)-1;%新的样本点x
y1=interp1(xs,ys,x,'nearest'); %插值产生新的样本点y1
y2=interp1(xs,ys,
www.eeworm.com/read/177674/9442540
m knnfwd.m
function [y, l] = knnfwd(net, x)
%KNNFWD Forward propagation through a K-nearest-neighbour classifier.
%
% Description
% [Y, L] = KNNFWD(NET, X) takes a matrix X of input vectors (one vector
% per ro
www.eeworm.com/read/176823/9483223
m knnfwd.m
function [y, l] = knnfwd(net, x)
%KNNFWD Forward propagation through a K-nearest-neighbour classifier.
%
% Description
% [Y, L] = KNNFWD(NET, X) takes a matrix X of input vectors (one vector
% per ro