MATLAB-knn分类的应用

执行以下操作时：

load training.mat
training = G

load testing.mat
test = G

然后：

>> knnclassify(test.Inp, training.Inp, training.Ltr)

??? Error using ==> knnclassify at 91
The length of GROUP must equal the number of rows in TRAINING.

自：

>> size(training.Inp)
ans =
          40          40        2016

以及：

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我怎样才能给knncclassify（TRAINING）的第二个参数TRAINING.inp 3-D矩阵，使行数为2016（第三维）？

假设您的三维数据被解释为2016个实例（第三维）的40×40特征矩阵，我们必须将其重新排列为2016×1600大小的矩阵（行是示例，列是标注）：

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您需要澄清数据的外观；据我所知，您有2016个实例（行），其中每个实例（行）都有40*40个特征（列）？对吗？

>> length(training.Ltr)
ans =
        2016

%# random data instead of the `load data.mat`
testing = rand(40,40,200);
training = rand(40,40,2016);
labels = randi(3, [2016 1]);     %# a class label for each training instance
                                 %# (out of 3 possible classes)

%# arrange data as a matrix whose rows are the instances,
%# and columns are the features
training = reshape(training, [40*40 2016])';
testing = reshape(testing, [40*40 200])';

%# k-nearest neighbor classification
prediction = knnclassify(testing, training, labels);