help with error in my code

4 次查看(过去 30 天)
Hi can someone help me understand the mistake in my code, i followed the correct syntax from https://uk.mathworks.com/help/bioinfo/ref/classperf.html
i keep getting the error
operator "==" not supported for operands of type "cvpartition"
error in line 24
test = (indices == 1)
k = 4;
n = 699; %sample lenght
rng ('default')
indices = cvpartition(n,'kfold', k);
for i = 1:k
test= (indices == i); train = ~test;
class = classify(InputVariable(test,:),InputVariable(train,:),OutputVariable(train,:));
classperf(cp,class,test);
end
cp.ErrorRate
plotconfusion(testTarget, testY)
  4 个评论
Stephen23
Stephen23 2021-1-2
编辑:Stephen23 2021-1-2
Original question by Dilpreet kaur retrieved from Google Cache:
help with error in my code
Hi can someone help me understand the mistake in my code, i followed the correct syntax from https://uk.mathworks.com/help/bioinfo/ref/classperf.html
i keep getting the error
operator "==" not supported for operands of type "cvpartition"
error in line 24
test = (indices == 1)
k = 4;
n = 699; %sample lenght
rng ('default')
indices = cvpartition(n,'kfold', k);
for i = 1:k
test= (indices == i); train = ~test;
class = classify(InputVariable(test,:),InputVariable(train,:),OutputVariable(train,:));
classperf(cp,class,test);
end
cp.ErrorRate
plotconfusion(testTarget, testY)
Rena Berman
Rena Berman 2021-5-6
(Answers Dev) Restored edit

请先登录,再进行评论。

采纳的回答

Image Analyst
Image Analyst 2020-12-31
I get this:
k = 4;
n = 699; %sample lenght
rng ('default')
indices = cvpartition(n,'kfold', k)
indices =
K-fold cross validation partition
NumObservations: 699
NumTestSets: 4
TrainSize: 525 524 524 524
TestSize: 174 175 175 175
You're not using indices correctly. It's an object, not a list of indices. If you want a listof indices, use randperm().

更多回答(1 个)

Walter Roberson
Walter Roberson 2021-1-1
编辑:Walter Roberson 2021-1-2
cvpartition does not return indices.
rng ('default')
nfold = 4;
cvfolds = cvpartition(699,'kfold', nfold);
cp = classperf(OutputVariable); % initializes the CP object
for i = 1:nfold
test = cvfolds.test(i);
train = cvfolds.training(i);
class = classify(InputVariable(test,:), InputVariable(train,:), OutputVariable(train,:));
classperf(cp, class, test);
end
cp.ErrorRate

标签

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by