BP神经网络MATLAB实现

clc; %{ c1 = [1 1]; c2 = [3 3]; n_L1 = 50; % number of item with label 1 n_L2 = 20; % number of item with label 2 n_label = 1; A = zeros(n_L1,3); A(:,3) = 1; B = zeros(n_L2,3); B(:,3) = -1; % create random point for i=1:n_L1 A(i,1:2) = c1 + rand(1,2); end for i=1:n_L2 B(i,1:2) = c2 + rand(1,2); end % show points scatter(A(:,1), A(:,2),[],'r'); hold on scatter(B(:,1), B(:,2),[],'g'); X=[A;B]; clc %} %% ---- Build a training set of a similar version of XOR c_1 = [0 0]; c_2 = [1 1]; c_3 = [0 1]; c_4 = [1 0]; n_L1 = 20; % number of label 1 n_L2 = 20; % number of label 2 A = zeros(n_L1*2, 3); A(:,3) = 1; B = zeros(n_L2*2, 3); B(:,3) = 0; % create random points for i=1:n_L1 A(i, 1:2) = c_1 + rand(1,2)/2; A(i+n_L1, 1:2) = c_2 + rand(1,2)/2; end for i=1:n_L2 B(i, 1:2) = c_3 + rand(1,2)/2; B(i+n_L2, 1:2) = c_4 + rand(1,2)/2; end % show points scatter(A(:,1), A(:,2),[],'r'); hold on scatter(B(:,1), B(:,2),[],'g'); X = [A;B]; %% Trainning the BP network dbstop if error n_label = 1; % create the weight matrix n_input = size(X,2) - n_label;% number of feature of each data, here is 2, only x-axis and y-axis levels = [n_input 1 1]; step = 1; [W,theta] = BP_tranning(X, levels, step); save two_class W theta %% test input = X(:,1:2); label = X(:,3:end); y = BP_predict2(input, W, theta); Y = y'; Y(find(Y>0.5)) = 1; Y(find(Y<=0.5)) = 0; A = W{1}(1); B = W{1}(2); C = theta{1}(1); if B==0 %生成100个-C/A放在向量x中. x=linspace(-C/A,-C/A,100); %从-A)-(|A|+|B|+|C|)到|A|+|B|+|C|等距离生成100个值放在向量y中.? y=linspace(-abs(A)-abs(B)-abs(C),abs(A)+abs(B)+abs(C),100); else x = linspace(0, 10, 100); y = -A/B * x; y = y - 1/B*C; end hold on plot(x,y)