Question

我正在尝试在matlab中为fisheriris数据集实现knnclassify，并尝试获取它的混淆矩阵。下面是knnclassify的matlab实现代码。我无法理解如何将fisheriris数据合并到此代码中。以及如何计算混淆矩阵。

function classifications = knnclassify(train_points, train_labels, test_points, k);

%-------------------------------------------
% K nearest neighbour (KNN) classification
% code by Jaakko Peltonen 2008
%-------------------------------------------
% Classifies test points based on majority vote of their k nearest 
% neighbours in train points. 'Nearest' is determined from squared 
% Euclidean distance. In case the majority voting results in a tie,
% gives equal portions to each class in the tie.
% 
% Inputs:
%---------
% train_points: matrix, the i:th row has the features of the i:th 
%               training point. 
%
% train_labels: two possible formats. 
%   Format 1: a column vector where the i:th element is an integer 
%             value indicating the label of the i:th training
%             point. The labels should start from zero.
%
%   Format 2: a matrix where the i:th row has the class memberships 
%             of the i:th training point. Each class membership is
%             a value from 0 to 1, where 1 means the point fully 
%             belongs to that class.
%
% test_points: feature matrix for test points. Same format as
%    train_points. You can give an empty matrix: then the method
%    computes leave-one-out classification error based only on
%    the training points.
%
% Outputs:
%---------
% classifications: matrix, the i:th row has the predicted class
%             memberships of the i:th test point. Each class
%             membership if a value from 0 to 1, where 1 means
%             the test point is predicted to fully belong to    
%             that class.
%


nDim = size(test_points,2);
nTrainPoints = size(train_points,1);

% if the training labels were provided in format 1,
% convert them to format 2.
if size(train_labels,2)==1,
nClasses = max(train_labels)+1;
train_labels2 = zeros(nTrainPoints,nClasses);
for i=1:nTrainPoints,
train_labels2(i,train_labels(i)+1) = 1;
end;
train_labels = train_labels2;
end;
nClasses = size(train_labels,2);

% if test_points is empty, perform leave-one-out classification
if isempty(test_points),
test_points = train_points;
leave_one_out = 1;
else
leave_one_out = 0;
end;
nTestPoints = size(test_points,1);


%   
% Perform the KNN classification. For leave-one-out classification,
% this code assumes that k < nTrainPoints. 
%
classifications = zeros(nTestPoints, nClasses);
for i=1:nTestPoints,
% find squared Euclidean distances to all training points
difference = train_points(:,1:nDim) - repmat(test_points(i,:),[nTrainPoints      1]);
distances = sum(difference.^2,2);

% in leave-one-out classification, make sure the point being
% classified is not chosen among the k neighbors.
if leave_one_out == 1,
distances(i) = inf;
end;  

% collect the 'votes' of the k closest points
[sorted_distances, indices] = sort(distances);
classamounts = zeros(1, nClasses);
for j=1:k,
classamounts = classamounts + train_labels(indices(j),:);
end;

% choose the class by majority vote
indices = find(classamounts == max(classamounts));
if (length(indices) == 1),
% there is a single winner
classifications(i,indices(1)) = 1;
else
% there was a tie between two or more classes
classifications(i,indices) = 1/length(indices);
end;
end;

Answer 1

回答第一个问题：

首先需要修复函数中的小错误：

更改

difference = train_points(:,1:nDim) - repmat...

到

difference = train_points - repmat...

比在fisheriris上运行此分类器的脚本：

clear
data=load('fisheriris');
[~,~,ci]=unique(data.species);
data.labels=ci-1; % start from 0
portion_to_test=40/100;
n=size(data.species,1);% #samples
test_idx=randperm(n,round(n*portion_to_test));
train_idx=setdiff(1:n,test_idx);
train_meas=data.meas(train_idx,:);
train_labels=data.labels(train_idx);
test_meas=data.meas(test_idx,:);
test_labels=data.labels(test_idx);
result=knnclassify(train_meas,train_labels,test_meas,1);
[~,mi]=max(result,[],2);
result_labels=mi-1; % start from 0;
error=sum(result_labels~=test_labels)/length(test_labels)

试图在matlab中为fisheriris数据集实现knnclassify

1 个答案: