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Copy pathFisher_LDA_k_cross_validation.m
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Fisher_LDA_k_cross_validation.m
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%for test
%using k-fold Cross-Validation
clc;
clear;
maindir = './100_digit';
k = 5;
train_proportion = 0.8;
test_proportion = 0.2;
subdir = dir(maindir);
total_images_num = 0;
floder_1_num = zeros(10, 1);
floder_1_feature = double([]);
floder_1_image_num = 0;
floder_2_num = zeros(10, 1);
floder_2_feature = double([]);
floder_2_image_num = 0;
floder_3_num = zeros(10, 1);
floder_3_feature = double([]);
floder_3_image_num = 0;
floder_4_num = zeros(10, 1);
floder_4_feature = double([]);
floder_4_image_num = 0;
floder_5_num = zeros(10, 1);
floder_5_feature = double([]);
floder_5_image_num = 0;
train_num = zeros(10, 1);
train_feature = double([]);
train_image_num = 0;
test_num = zeros(10, 1);
test_feature = double([]);
test_image_num = 0;
for i = 1 : length(subdir)
if (isequal(subdir(i).name, '.') || ...
isequal(subdir(i).name, '..') || ...
~subdir(i).isdir) % 跳过不是目录的子文件夹
continue;
end
subdirpath = fullfile(maindir, subdir(i).name, '*.jpg');
images = dir(subdirpath);
i_images_num = length(images);
i_floder_num = floor(length(images) * 0.2);
floder_1_num(i-2) = i_floder_num;
floder_2_num(i-2) = i_floder_num;
floder_3_num(i-2) = i_floder_num;
floder_4_num(i-2) = i_floder_num;
floder_5_num(i-2) = length(images) - (i_floder_num * 4);
for k_i = 1:k
down_side = i_floder_num * (k_i - 1) + 1;
if k_i ~=5
up_side = i_floder_num * (k_i);
else
up_side = i_images_num;
end
for j = down_side:up_side
imagepath = fullfile(maindir, subdir(i).name, images(j).name);
imagedata = imread(imagepath);
imagedata = imresize(imagedata, [28,28]);
thresh=graythresh(imagedata);%确定二值化阈值
imagedata=im2bw(imagedata,thresh);%对图像二值化
[feature,featureimg] = getfeature(reshape(imagedata, [28,28]), 1);
if k_i == 1
floder_1_image_num = floder_1_image_num + 1;
floder_1_feature(floder_1_image_num, :) = [i - 3; feature];
elseif k_i == 2
floder_2_image_num = floder_2_image_num + 1;
floder_2_feature(floder_2_image_num, :) = [i - 3; feature];
elseif k_i == 3
floder_3_image_num = floder_3_image_num + 1;
floder_3_feature(floder_3_image_num, :) = [i - 3; feature];
elseif k_i == 4
floder_4_image_num = floder_4_image_num + 1;
floder_4_feature(floder_4_image_num, :) = [i - 3; feature];
elseif k_i == 5
floder_5_image_num = floder_5_image_num + 1;
floder_5_feature(floder_5_image_num, :) = [i - 3; feature];
end
end
end
end
for k_i = 1:k
if k_i == 1
accuracy_1 = 0;
correct_num = 0;
train_num = floder_2_num + floder_3_num + floder_4_num + floder_5_num;
train_feature = [floder_2_feature; floder_3_feature; floder_4_feature; floder_5_feature];
test_image_num = floder_1_image_num;
accuracy_1 = calculate_acc(floder_1_feature, floder_1_image_num, train_feature, train_num);
end
if k_i == 2
accuracy_2 = 0;
correct_num = 0;
train_num = floder_1_num + floder_3_num + floder_4_num + floder_5_num;
train_feature = [floder_1_feature; floder_3_feature; floder_4_feature; floder_5_feature];
test_image_num = floder_2_image_num;
accuracy_2 = calculate_acc(floder_2_feature, floder_2_image_num, train_feature, train_num);
end
if k_i == 3
accuracy_3 = 0;
correct_num = 0;
train_num = floder_1_num + floder_2_num + floder_4_num + floder_5_num;
train_feature = [floder_1_feature; floder_2_feature; floder_4_feature; floder_5_feature];
test_image_num = floder_3_image_num;
accuracy_3 = calculate_acc(floder_3_feature, floder_3_image_num, train_feature, train_num);
end
if k_i == 4
accuracy_4 = 0;
correct_num = 0;
train_num = floder_1_num + floder_2_num + floder_3_num + floder_5_num;
train_feature = [floder_1_feature; floder_2_feature; floder_3_feature; floder_5_feature];
test_image_num = floder_4_image_num;
accuracy_4 = calculate_acc(floder_4_feature, floder_4_image_num, train_feature, train_num);
end
if k_i == 5
accuracy_5 = 0;
correct_num = 0;
train_num = floder_1_num + floder_2_num + floder_3_num + floder_4_num;
train_feature = [floder_1_feature; floder_2_feature; floder_3_feature; floder_4_feature];
test_image_num = floder_5_image_num;
accuracy_5 = calculate_acc(floder_5_feature, floder_5_image_num, train_feature, train_num);
end
end
accuracy = (accuracy_1 + accuracy_2 + accuracy_3 + accuracy_4 + accuracy_5) / 5;
acc_bar = [accuracy_1, accuracy_2, accuracy_3, accuracy_4, accuracy_5, accuracy];
name = categorical({'floder1', 'floder2', 'floder3', 'floder4', 'floder5', 'floderaverage'});
b = bar(name, acc_bar);
b.FaceColor = 'flat';
b.CData(6,:) = [1 0 0];
ylabel("accuracy of each experiment")
xlabel("test floder")
title("5 floder cross validation")
for text_i = 1:6
text(text_i - 0.3, acc_bar(text_i) + 0.05, num2str(acc_bar(text_i)))
end
function [accuracy]=calculate_acc(floder_test_feature, floder_test_image_num, train_feature, train_num)
accuracy = 0;
correct_num = 0;
test_image_num = floder_test_image_num;
for test_i = 1:length(floder_test_feature)
i_feature_label = floder_test_feature(test_i,:);
label_i = i_feature_label(1);
i_feature = i_feature_label(2:length(i_feature_label));
[result,v]=Fisher_LDA(i_feature', train_feature, train_num);
if label_i == result
correct_num = correct_num + 1;
end
end
accuracy = correct_num / test_image_num;
end