MAT之GRNN/PNN：基于GRNN、PNN兩神經網絡實現并比較鳶尾花(iris數據集)種類識別正確率、各個模型運行時間對比

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實現代碼

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輸出結果

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實現代碼

load?iris_data.mat?P_train = []; T_train = []; P_test = []; T_test = []; for?i?= 1:3?temp_input = features((i-1)*50+1:i*50,:);temp_output = classes((i-1)*50+1:i*50,:);n =?randperm(50);P_train = [P_train temp_input(n(1:40),:)'];T_train = [T_train temp_output(n(1:40),:)'];P_test = [P_test temp_input(n(41:50),:)'];T_test = [T_test temp_output(n(41:50),:)']; endresult_grnn = []; result_pnn = []; time_grnn = []; time_pnn = [];for?i?= 1:4for?j?=?i:4p_train = P_train(i:j,:);p_test = P_test(i:j,:);t =?cputime;?net_grnn = newgrnn(p_train,T_train);t_sim_grnn = sim(net_grnn,p_test);T_sim_grnn =?round(t_sim_grnn);?t =?cputime?- t;time_grnn = [time_grnn t];result_grnn = [result_grnn T_sim_grnn'];t =?cputime;Tc_train = ind2vec(T_train);net_pnn = newpnn(p_train,Tc_train);Tc_test = ind2vec(T_test);t_sim_pnn = sim(net_pnn,p_test);T_sim_pnn = vec2ind(t_sim_pnn);t =?cputime?- t;time_pnn = [time_pnn t];result_pnn = [result_pnn T_sim_pnn'];end endaccuracy_grnn = []; accuracy_pnn = []; time = []; for?i?= 1:10accuracy_1 =?length(find(result_grnn(:,i) == T_test'))/length(T_test);accuracy_2 =?length(find(result_pnn(:,i) == T_test'))/length(T_test);accuracy_grnn = [accuracy_grnn accuracy_1];accuracy_pnn = [accuracy_pnn accuracy_2]; endresult = [T_test' result_grnn result_pnn] accuracy = [accuracy_grnn;accuracy_pnn] time = [time_grnn;time_pnn]

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MAB之GRNN/PNN：基于GRNN、PNN兩神經網絡實現并比較鳶尾花種類識別正確率、各個模型運行時間對比
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