我正在尝试从sequentialfs
训练矩阵中学习相关要素,将其中的随机行作为我的测试数据并在其上应用>> Md1=fitcdiscr(xtrain,ytrain);
>> func = @(xtrain, ytrain, xtest, ytest) sum(ytest ~= predict(Md1,xtest));
>> learnt = sequentialfs(func,xtrain,ytrain)
。我使用了以下代码:
xtrain
ytrain
和299*299
分别为299*1
和xtest
。预测将给出Error using crossval>evalFun (line 480)
The function '@(xtrain,ytrain,xtest,ytest)sum(ytest~=predict(Md1,xtest))' generated the following error:
X must have 299 columns.
Error in crossval>getFuncVal (line 497)
funResult = evalFun(funorStr,arg(:));
Error in crossval (line 343)
funResult = getFuncVal(1, nData, cvp, data, funorStr, []);
Error in sequentialfs>callfun (line 485)
funResult = crossval(fun,x,other_data{:},...
Error in sequentialfs (line 353)
crit(k) = callfun(fun,x,other_data,cv,mcreps,ParOptions);
Error in new (line 13)
learnt = sequentialfs(func,xtrain,ytrain)
的预测标签(这是原始xtrain中的一些随机行)。
但是,当我运行我的代码时,我收到以下错误:
var express = require('express');
var meter = require('./meter'); // meter will be an object you can use later on
var app = express();
app.get('/',meter.hi); // you actually don't need another annonimous function, can use hi directly as the callback
app.listen(3000);
console.log('listening on 3000 port')
我哪里出错了?
答案 0 :(得分:1)
您应该在 func
内构建分类器,而不是之前。
sequentialfs
每次在不同的集合上调用该函数,并且必须仅为每个集合专门构建分类器,仅使用为该迭代选择的特征sequentialfs
。
我不确定自己是否清楚,实际上你应该将代码的第一行移到func
来源:MathWorks