人工智能/神经网络

开发平台：

Matlab

statis.m：源码内容

							% statit the feature
clc
clear all
base_path = 'E:SOMBPdata_sourcefeature02';                      % minmax([I1';I2';I3';I4';I5';I6';I7';I8'])
I1 = load([base_path,'apen.txt']);           % 1 >> 2        1.2563    1.8150        
I2 = load([base_path,'kc.txt']);             % 0 >> 1        0.6299    0.9611
I3 = load([base_path,'mir.txt']);            % 0 >> 5        0.4802    4.0544
I4 = load([base_path,'asm.txt']);            % 0 >> 0.01    0.0004    0.0037
I5 = load([base_path,'idm.txt']);            % 0 >> 0.5       0.0663    0.2906
I6 = load([base_path,'cont.txt']);           % 0 >> 500     12.2600  337.5440
I7 = load([base_path,'ent.txt']);            % 5 >> 9        5.8675    7.9858
I8 = load([base_path,'gln.txt']);            % 10 >> 60      13.4204   53.2048
sample_num = size(I1,1);
negative_num = 75;
positive_num = 55;
feature_num = 8;
figure;
P = [(I1'-ones(1,sample_num ));I2';(I3'./5);(I4'./0.01);I5'./0.5;(I6'./500);(I7'-ones(1,sample_num ).*5)./4;(I8'-ones(1,sample_num ).*10)./50];
T = ones(1,sample_num);
for(i=1:8)
    T = T.*P(i,:);
end
plot(T(1:positive_num),'r');
hold on
plot(T(1+positive_num:sample_num),'b');
T = ([I1';I2';I3';I4';I5';I6';I7';I8']);
FeatureName = ['ape','kc1','mir','asm','idm','cont','ent','gln'];
figure;
for(i=1:feature_num)
    subplot(2,4,i)
    plot(T(i,1:positive_num),'r');
    hold on;
    plot(T(i,1+positive_num:sample_num),'b');
    title(FeatureName((i-1)*3+1:i*3));
    [h(i),significance(i),ci] = ttest2(T(i,1:positive_num),T(i,1+positive_num:sample_num),0.025)
end

						