knn最基本的Matlab仿真怎么弄

准备条件：已经把特征数据和样本标号保存为文件。

测试代码为：

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train_data=load('sample_feature.txt')

train_label=load('train_label.txt')

test_data=load('features.txt')

k=knnclassify(test_data,train_data,train_label,3,'cosine','random')

train_data保存的是训练样本特征，要求是最能代表本类胡闭别的，不一定多，当然不能太少橡做戚；

train_label保存梁陵的是样本标号，如0,1,2等等，随便设置，只有能区分就行,具体格式可以为:

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test_data测试文件保存的是测试数据的特征；

function [ccr,pgroupt]=knnt(x,group,K,dist,xt,groupt)

%#

%# AIM: to classify test set objects or unknown objects with the

%# K Nearest Neighbour method

%#

%# PRINCIPLE: KNN is a supervised, deterministic, non-parametric

%# classification method. It uses the majority rule to

%# assign new objects to a class.

%# It is assumed that the number of objects in each class

%# is similar.

%# There are no assumptions about the data distribution and

%# the variance-covariance matrices of each class.

%# There is no limitation of the number of variables when

%# the Euclidean distance is used.

%# However, when the correlation coefficient is used, the

%# number of variables must be larger than 1.

%# Ref: Massart D. L., Vandeginste B. G. M., Deming S. N.,

%# Michotte Y. and Kaufman L., Chemometrics: a textbook,

%# Chapter 23, 395-397, Elsevier Science Publishers B. V.,

%# Amsterdam 1988.

%#

%# INPUT: x: (mxn) data matrix with m objects and n variables,

%# containing samples of several classes (training set)

%# group: (mx1) column vector labelling the m objects from the

%# training set

%# K: integer, number of nearest neighbours

%# dist: integer,

%# = 1, Euclidean distance

%# = 2, Correlation coefficient, (No. of variables >1)

%# xt: (mtxn) data matrix with mt objects and n variables

%# (test set or unknowns)

%# groupt: (mtx1) column vector labelling the mt objects from

%# the test set

%# -->if the new objects are unknown, input [].

%#

%# OUTPUT: ccr: scalar, correct classification rate

%# pgroupt:row vector, predicted class label for the test set

%# 0 means that the object is not classified to any

%# class

%#

%# SUBROUTINES: sortlab.m: sorts the group label vector into classes

%#

%# AUTHOR: Wen Wu

%# Copyright(c) 1997 for ChemoAc

%# FABI, Vrije Universiteit Brussel

%# Laarbeeklaan 103 1090 Jette

%#

%# VERSION: 1.1 (28/02/1998)

%#

%# TEST: Andrea Candolfi

%#

function [ccr,pgroupt]=knnt(x,group,K,dist,xt,groupt)

if nargin==5, groupt=[]end % for unknown objects

distance=distclear dist % change variable

if size(group,1)>1,

group=group' % change column vector into row vector

groupt=groupt' % change column vector into row vector

end

[m,n]=size(x) % size of the training set

if distance==2 &n<2, error('Number of variables must >1'),end % to check the number of variables when using correlation coefficient

[mt,n]=size(xt) % size of the test set

dis=zeros(mt,m) % initial values for the distance (matrix of zeros)

% Calculation of the distance for each test set object

for i=1:mt

for j=1:m % between each training set object and each test set object

if distance==1

dis(i,j)=(xt(i,:)-x(j,:))*(xt(i,:)-x(j,:))' % Euclidian distance

else

r=corrcoef(xt(i,:)',x(j,:)') % Correlation coefficient matrix

r=r(1,2) % Correlation coefficient

dis(i,j)=1-r*r % 1 - the power of correlation coefficient

end

end

end

% Finding of the nearest neighbours

lab=zeros(1,mt) % initial values of lab

for i=1:mt % for each test object

[a,b]=sort(dis(i,:)) % sort distances

b=b(find(a<=a(K))) % to find the nearest neighbours indices

b=group(b) % the nearest neighbours objects

[ng,lgroup]=sortlab(b) % calculate the number of objects from each class in the nearest neighbours

a=find(ng==max(ng)) % find the class with the maximum number of objects

if length(a)==1 % only one class

lab(i)=lgroup(a) % class label

else

lab(i)=0 % more than one class

end

end

% Calculation of the success rate

if ~isempty(groupt)

dif=groupt-lab % difference between predicted class label and known class label

ccr=sum(dif==0)/mt % success rate

end

pgroupt=lab % the output vector

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