matlab-基于Expectation-Maximization算法(EM算法)的数据分类仿真,对比真实分类和EM算法分类-源

clc; clear; close all; warning off; addpath(genpath(pwd)); % number of points in each cluster num_points = 100; % generate random data using two 2D Normal distributions with 100 data points Data = generate_random_data(num_points); % reshuffle the data labels Data_r = [Data(:,1:2) randi(2,2*num_points,1)]; % make some initial guess Param = make_initial_guess(); % run EM to find the parameters [Data_f, Param_f] = EM(Data_r, Param); %% plot the results figure; subplot(1,3,1); scatter(Data(:,1), Data(:,2), 10, 'MarkerEdgeColor', [0 0.5 0.5], 'MarkerFaceColor',[0 0.7 0.7], 'LineWidth',1.5); grid on; box on; xlabel('x'); ylabel('y');title('raw data'); Data1 = Data(Data(:,3)==1, :); Data2 = Data(Data(:,3)==2, :); subplot(1,3,2);hold on scatter(Data1(:,1), Data1(:,2), 10, 'MarkerEdgeColor', [0.5 0 0], 'MarkerFaceColor',[0.7 0 0], 'LineWidth',1.5); scatter(Data2(:,1), Data2(:,2), 10, 'MarkerEdgeColor', [0 0.5 0], 'MarkerFaceColor',[0 0.7 0], 'LineWidth',1.5); grid on; box on; xlabel('x'); ylabel('y');title('true value'); Data_f1 = Data_f(Data_f(:,3)==1, :); Data_f2 = Data_f(Data_f(:,3)==2, :); subplot(1,3,3); hold on scatter(Data_f1(:,1), Data_f1(:,2), 10, 'MarkerEdgeColor', [0.5 0 0], 'MarkerFaceColor',[0.7 0 0], 'LineWidth',1.5); scatter(Data_f2(:,1), Data_f2(:,2), 10, 'MarkerEdgeColor', [0 0.5 0], 'MarkerFaceColor',[0 0.7 0], 'LineWidth',1.5); grid on; box on; xlabel('x'); ylabel('y');title('data clustered by EM');