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/452050/7450766
cc 3020095_ce.cc
#include
#include
int n;
int mtr[201][201];
int ans[201][201];
int find_nearest(int a,int b)
{
int i, j, dis, num, ret;
int max = 2100000000;
num = 0;
for(i = 0;
www.eeworm.com/read/452050/7450769
java 3020105_tle.java
import java.util.*;
public class Main
{
static int n;
static int mtr[][] = new int [201][201];
static int abs(int num)
{
if(num < 0)
return -num;
else
return num;
}
www.eeworm.com/read/452050/7450770
c 3020093_tle.c
#include
#include
int n;
int mtr[201][201];
int ans[201][201];
int find_nearest(int a,int b)
{
int i, j, dis, num, ret;
int max = 2100000000;
num = 0;
for(i = 0;
www.eeworm.com/read/451547/7461875
m dnndd.m
%DNNDD Distance nearest neighbour data description method.
%
% W = dnndd(D,fracrej)
%
% Calculates the Nearest neighbour data description on distance data.
% Training only consists of the comp
www.eeworm.com/read/451547/7461879
m dknndd.m
%DKNNDD Distance K-Nearest neighbour data description method.
%
% W = DKNNDD(D,FRACREJ,K,METHOD)
%
% Calculates the K-Nearest neighbour data description on distance
% dataset D. Two methods a
www.eeworm.com/read/450608/7480112
m knnc.m
%KNNC K-Nearest Neighbor Classifier
%
% [W,K,E] = KNNC(A,K)
% [W,K,E] = KNNC(A)
%
% INPUT
% A Dataset
% K Number of the nearest neighbors (optional; default: K is
% optimized with resp
www.eeworm.com/read/439857/7700175
m m0207.m
x=0:9;
x1=0:0.01:9;
y=[0,1.8,2.1,0.9,0.2,-0.5,-0.2,-1.7,-0.9,-0.3];
y1=interp1(x,y,x1,'nearest');
y2=interp1(x,y,x1,'linear');
y3=interp1(x,y,x1,'spline');
y4=interp1(x,y,x1,'cubic');
plot(x,y,
www.eeworm.com/read/198970/7900006
m knn.m
function [neighbors, distance] = knn(kde,points,k)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% [neighbors, distance] = knn(kde,points,k)
%
% Find the
www.eeworm.com/read/397106/8067574
m k_l_nn_rule_vc.m
% Classifies input using (k-l)NN classifier
% This means that it will classify the input if at least l of the k nearest
% neighbors agree on the label, and refuses to classify otherwise.
%
% NOTE: To
www.eeworm.com/read/397106/8067775
m knn_rule.m
% Classifies input using k-NN rule
% Usage
% label = Knn_Rule(inputSample, TrainingSamples, TrainingLabels, k);
% where
% inputSample is a ROW vector (1xd)
% containi