from sklearn.model_selection import KFold
import numpy as np
X=np.array([[1,2],[3,4],[1,3],[3,5]])
Y=np.array([1,2,3,4])
KF=KFold(n_splits=2)
for train_index,test_index in KF.split(X):
print("TRAIN:",train_index,"TEST:",test_index)
X_train,X_test=X[train_index],X[test_index]
Y_train,Y_test=Y[train_index],Y[test_index]
print(X_train,X_test)
print(Y_train,Y_test)
import numpy as np
from sklearn.model_selection import KFold
Sam=np.array(np.random.randn(1000))
New_sam=KFold(n_splits=5)
for train_index,test_index in New_sam.split(Sam):
Sam_train,Sam_test=Sam[train_index],Sam[test_index]
print('训练集数量:',Sam_train.shape,'测试集数量:',Sam_test.shape)
from sklearn.model_selection import StratifiedKFold
import numpy as np
m=np.array([[1,2],[3,5],[2,4],[5,7],[3,4],[2,7]])
n=np.array([0,0,0,1,1,1])
skf=StratifiedKFold(n_splits=3)
for train_index,test_index in skf.split(m,n):
print("train",train_index,"test",test_index)
x_train,x_test=m[train_index],m[test_index]
from sklearn.model_selection import StratifiedKFold
import numpy as np
y1=np.array(range(10))
y2=np.array(range(20,30))
y3=np.array(np.random.randn(10))
m=np.append(y1,y2)
m1=np.append(m,y3)
n=[i//10 for i in range(30)]
skf=StratifiedKFold(n_splits=5)
for train_index,test_index in skf.split(m1,n):
print("train",train_index,"test",test_index)
x_train,x_test=m1[train_index],m1[test_index]
