getptforcalhousing.m

模糊神经网络实现函数逼近与分类

function [P, T, VV, TV] = GetPTforcalhousing
    %Obtain Random P, T
    
    load calhousing.dt;
        
    rand_sequence=round(rand(size(calhousing,1), 1)*size(calhousing,1));    
    temp_dataset=calhousing;
    
    for i=1:size(calhousing,1)
        if rand_sequence(i,1)==0
            rand_sequence(i,1)=1;
        else if rand_sequence(i,1)>size(calhousing,1)
                rand_sequence(i,1)=size(calhousing,1);
            end
        end
        temp_dataset(i,:)=calhousing(rand_sequence(i,1),:);
    end
    
    calhousing=temp_dataset;
    
    for i=1:size(calhousing, 2)
        calhousing(:,i)=(calhousing(:,i)-min(calhousing(:,i)))/(max(calhousing(:,i))-min(calhousing(:,i)));
    end
    
    P1=calhousing(1:8000,1:8);
    T1=calhousing(1:8000,9);

    P=P1';
    T=T1';
    
    %Obtain Random Validation Matrix
    X=calhousing(8001:size(calhousing,1),1:8);
    Y=calhousing(8001:size(calhousing,1),9);
    VV.P=X';
    VV.T=Y';

    %Obtain Random Test Matrix
    TV.P=VV.P;
    TV.T=VV.T;