逻辑回归图像二分类_用来做路面裂缝图像识别_matlab

%% Machine Learning Online Class - Exercise 2: Logistic Regression % % Instructions % ------------ % % This file contains code that helps you get started on the logistic % regression exercise. You will need to complete the following functions % in this exericse: % % sigmoid.m % costFunction.m % predict.m % costFunctionReg.m % % For this exercise, you will not need to change any code in this file, % or any other files other than those mentioned above. % %% Initialization clear ; close all; clc %% Load Data % The first two columns contains the exam scores and the third column % contains the label. file_path1 = 'E:\picture\no\';% 无病害图像文件夹路径 file_path2 = 'E:\picture\yep\';% 有病害图像文件夹路径 img_path_list1 = dir(strcat(file_path1,'*.jpg'));%获取该文件夹中所有jpg格式的图像 img_path_list2 = dir(strcat(file_path2,'*.jpg'));%获取该文件夹中所有jpg格式的图像 img_num = 2*(length(img_path_list1)+length(img_path_list2));%获取图像总数量 X = zeros(img_num,400); y = zeros(img_num,1); if img_num > 0 %有满足条件的图像 for j = 1:length(img_path_list1) %逐一读取图像 image_name = img_path_list1(j).name;% 图像名 image_1 = imread(strcat(file_path1,image_name)); image = imresize(image_1,0.769); temp1 = reshape(image,1,400); X(j,:) = temp1; y(j,:) = 1; X(j+length(img_path_list1),:) = fliplr(X(j)); y(j+length(img_path_list1),:) = 1; end for j = 1:length(img_path_list2) %逐一读取图像 image_name = img_path_list2(j).name;% 图像名 image_1 = imread(strcat(file_path2,image_name)); image = imresize(image_1,0.769); temp1 = reshape(image,1,400); X(j+2*length(img_path_list1),:) = temp1; y(j+2*length(img_path_list1),:) = 0; X(j+2*length(img_path_list1)+length(img_path_list2),:) = fliplr(X(j)+2*length(img_path_list1)); y(j+2*length(img_path_list1)+length(img_path_list2),:) = 0; end end Xy = [X,y]; [M, n] = size(X); m = round(M*0.9); %取90%为训练集，10%为测试集 RandIndex = randperm( length( Xy ) ); Xy = Xy(RandIndex,:);%随机排序 X = Xy(1:m,1:n); y = Xy(1:m,n+1);%训练集 X2 = Xy(m+1:M,1:n); y2 = Xy(m+1:M,n+1);%测试集 %% ==================== Part 1: Plotting ==================== %% ============ Part 2: Compute Cost and Gradient ============ % In this part of the exercise, you will implement the cost and gradient % for logistic regression. You neeed to complete the code in % costFunction.m % Setup the data matrix appropriately, and add ones for the intercept term % Add intercept term to x and X_test X = [ones(m, 1) X]; X2 = [ones(M-m, 1) X2]; % Initialize fitting parameters initial_theta = zeros(n + 1, 1); % Compute and display initial cost and gradient [cost, grad] = costFunction(initial_theta, X, y); fprintf('Cost at initial theta (zeros): %f\n', cost); fprintf('Expected cost (approx): 0.693\n'); fprintf('Gradient at initial theta (zeros): \n'); fprintf(' %f \n', grad); fprintf('Expected gradients (approx):\n -0.1000\n -12.0092\n -11.2628\n'); % Compute and display cost and gradient with non-zero theta %% ============= Part 3: Optimizing using fminunc ============= % In this exercise, you will use a built-in function (fminunc) to find the % optimal parameters theta. % Set options for fminunc options = optimset('GradObj', 'on', 'MaxIter', 10000); % Run fminunc to obtain the optimal theta % This function will return theta and the cost [theta, cost] = ... fminunc(@(t)(costFunction(t, X, y)), initial_theta, options); % Print theta to screen fprintf('Cost at theta found by fminunc: %f\n', cost); fprintf('Expected cost (approx): 0.203\n'); fprintf('theta: \n'); fprintf(' %f \n', theta); fprintf('\nProgram paused. Press enter to continue.\n'); pause; %% ============== Part 4: Predict and Accuracies ============== % After learning the parameters, you'll like to use it to predict the outcomes % on unseen data. In this part, you will use the logistic regression model % to predict the probability that a student with score 45 on exam 1 and % score 85 on exam 2 will be admitted. % % Furthermore, you will compute the training and test set accuracies of % our model. % % Your task is to complete the code in predict.m % Predict probability for a student with score 45 on exam 1 % and score 85 on exam 2 % Compute accuracy on our training set p = predict(theta, X2); fprintf('Train Accuracy: %f\n', mean(double(p == y2 )) * 100); fprintf('\n');