1、Pr_omiga1=input('请输入第一类的先验概率:');百度Pr_omiga2=input('请输入第二类的先验概率:');Num=20;s1=mvnrnd([0 1],eye(2),Num)'s2=mvnrnd([2 0],3*eye(2),Num)'s11=s1(1,:);s12=s1(2,:);s21=s2(1,:);s22=s2(2,:);muGiven1=[0 1]';sigmaGiven1=eye(2);
2、% 求正态分布条件下的基于最小错误率的Bayesian决策分类器的决策面% 悴匙吭佰预求相关参量W1 = -0.5 * inv(sigma1);W2 = -0.5 * inv(sigma2)omiga1= inv(sigma1) *omiga1= inv(sigma1) *omiga10= -0.5*mu1'*inv(sigma1)*mu1 -0.5*log(det(sigma1)) + log(Pr_omiga1);omiga20= -0.5*mu2'*inv(sigma2)*mu2 -0.5*log(det(sigma2)) + log(Pr_omiga2);
3、 //显示类标号属性 ListClass(); DataSet ds1=BindCredentialDropDwon(); this.DropDownList2.DataSource=ds1; this.DropDownList2.DataTextField="CreditRating"; this.DropDownList2.DataValueField="CreditRating"; this.DropDownList2.DataBind();
4、//先验概率的计算 FrontProbability(out FrontYesProbability, out FrontNoProbab足毂忍珩ility); string FrontYesProbility="p"+"(" + "IsBuyComputer="+ "/"yes/"" +")"+"="+FrontYesProbability; string FrontNoProbility="p"+"(" + "IsBuyComputer="+ "/"no/"" +")"+"="+FrontNoProbability;
5、 //收入的条件概率的计算 float pIncome=0; float _pIncome=0; pFrontCalculate烫喇霰嘴(out pIncome,out _pIncome,"Income","high"); string IncomeFrontYesProbility="p"+"(" + "Income="+ "/"high/"" +"|"+"IsBuyComputer="+ "/"yes/""+")"+"="+pIncome; string IncomeFrontNoProbility="p"+"(" + "Income="+ "/"high/"" +"|"+"IsBuyComputer="+ "/"no/""+")"+"="+_pIncom
