问题描述
我有一个数据矩阵X(60x208)和一个标签矩阵Y(1x208).我想将数据矩阵X分为列向量的两个随机子集:训练(将占数据的70%)和测试(将占数据的30%),但是我仍然需要能够确定哪个Y的标签对应于每个列向量.我找不到任何执行此操作的功能,有任何想法吗?以为我应该补充一下,Y中只有两个标签:1和2(不确定是否会有所不同)
这很容易做到.使用 randperm
生成从1
到多达多个点的索引的随机排列如果您有...在您的情况下就是208.
生成此序列后,只需使用此序列和子集到X
和Y
中,即可提取训练和测试数据及标签.因此,请执行以下操作:
num_points = size(X,2);
split_point = round(num_points*0.7);
seq = randperm(num_points);
X_train = X(:,seq(1:split_point));
Y_train = Y(seq(1:split_point));
X_test = X(:,seq(split_point+1:end));
Y_test = Y(seq(split_point+1:end));
split_point
确定我们需要在训练集中放置多少个点,并且如果此计算产生任何小数点,我们将需要四舍五入.我也没有在其中进行硬编码208,因为您的数据集可能会增长,因此可以与您选择的任何大小的数据集一起使用. X_train
和Y_train
将包含您的训练集的数据和标签,而X_test
和Y_test
将包含您的测试集的数据和标签.
因此,X_train
的第一列是训练集第一个元素的数据点,而Y_train
的第一个元素用作该特定点的标签...等等.来!
I have a data matrix X (60x208) and a matrix of labels Y (1x208). I want to split my data matrix X into two random subsets of column vectors: training (which will be 70% of the data) and testing (which will be 30% of the data), but I need to still be able to identify which label from Y corresponds to each column vector. I couldn't find any function to do this, any ideas?
EDIT: Thought I should add, there are only two labels in Y: 1 and 2 (not sure if this makes a difference)
That's pretty easy to do. Use randperm
to generate a random permutation of indices from 1
up to as many points as you have... which is 208 in your case.
Once you generate this sequence, simply use this and subset into your X
and Y
to extract the training and test data and labels. As such, do something like this:
num_points = size(X,2);
split_point = round(num_points*0.7);
seq = randperm(num_points);
X_train = X(:,seq(1:split_point));
Y_train = Y(seq(1:split_point));
X_test = X(:,seq(split_point+1:end));
Y_test = Y(seq(split_point+1:end));
The split_point
determines how many points we need to place into our training set, and we will need to round it in case this calculation yields any decimal points. I also didn't hard code 208 in there because your data set might grow and so this will work with any size data set you choose. X_train
and Y_train
will contain your data and labels for your training set while X_test
and Y_test
will contain your data and labels for your test set.
As such, the first column of X_train
is your data point for the first element of your training set, with the first element of Y_train
serving as the label for that particular point... and so on and so forth!
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