怎么获得skf = RepeatedStratifiedKFold(n_splits=2, random_state=0, n_repeats=1)中的折数

1 = conv(rcos_filter1,[bits zeros(1,length(rcos_filter1)-1)]);
tx_signal2 = conv(rcos_filter2在 Scikit-learn 中，`RepeatedStratifiedKFold` 是一个用于重复 K 折交叉验证的类。,[bits zeros(1,length(rcos_filter2)-1)]);
tx_signal3 = conv(rcos_filter3,[bits zeros(1,length(rc在实例化 `RepeatedStratifiedKFold` 对象时，你可以指定参数 `n_splits` 来设置每次os_filter3)-1)]);
[f1,tx_spectrum1] = periodogram(tx_signal1,[],[],fs,'centered');
[f2,tx_spectrum2] = periodogram(tx_signal2,[],[],fs,'centered');
[f3,tx_spectrum3] = periodogram(tx_signal划分数据集的折数。如果你需要获得 `skf` 的折数，可以通过访问 `skf`3,[],[],fs,'centered');
figure;
subplot(311);
plot((-5:1/fs:5-1/fs)*symbol_rate/ 对象的 `n_splits` 属性来获取。

具体代码如下：

from sklearn.model_selection import RepeatedStrat1000,rcos_filter1);
title('Root Raised Cosine Filter with Rolloff Factor = 0.25');
xlabel('TimeifiedKFold

skf = RepeatedStratifiedKFold(n_splits=2, random_state=0, n_repeats=1 (ms)');
ylabel('Amplitude');
subplot(312);
plot((-5:1/fs:5-1/fs)*symbol_rate/1000)

n_splits = skf.n_splits

print("折数为:", n_splits)

在上面的代码中，我们创建,rcos_filter2);
title('Root Raised Cosine Filter with Rolloff Factor = 0.5');
xlabel('Time (ms了一个 `RepeatedStratifiedKFold` 对象 `skf`，并将 `n_splits` 属性的值打印出来，以获取 `skf` 的折数。