fisheririsデータセットのmatlabでknnclassifyを実装しようとしています

Question

私はfisheririsデータセット用のmatlabでknnclassifyを実装しようとしていて、混乱行列を取得しようとしています。以下は、knnclassifyのmatlabの実装のコードです。 このコードにfisheriris dataseを組み込む方法を理解できません。 と混乱行列を計算する方法。最初の質問のための

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：

あなたの関数の小さなバグを修正する最初の必要性：

変更

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

へ

difference = train_points - repmat...

fisheririsにこの分類器を実行するスクリプトより

：

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)