1.python 中的[::-1]

b = a[i:j:s]这种格式呢，i,j与上面的一样，但s表示步进，缺省为1.
所以a[i:j:1]相当于a[i:j]
当s<0时，i缺省时，默认为-1. j缺省时，默认为-len(a)-1
所以a[::-1]相当于 a[-1:-len(a)-1:-1]，也就是从最后一个元素到第一个元素复制一遍。

参考链接：https://www.cnblogs.com/mxh1099/p/5804064.html

2.python enumerate用法

enumerate在字典上是枚举、列举的意思
对于一个可迭代的（iterable）/可遍历的对象（如列表、字符串），enumerate将其组成一个索引序列，利用它可以同时获得索引和值
enumerate多用于在for循环中得到计数

enumerate()使用

如果对一个列表，既要遍历索引又要遍历元素时，首先可以这样写：

list1 = ["这", "是", "一个", "测试"]
for i in range (len(list1)):
    print i ,list1[i]

上述方法有些累赘，利用enumerate()会更加直接和优美：

list1 = ["这", "是", "一个", "测试"]
for index, item in enumerate(list1):
    print index, item
>>>
0 这
1 是
2 一个
3 测试

enumerate还可以接收第二个参数，用于指定索引起始值，如：

list1 = ["这", "是", "一个", "测试"]
for index, item in enumerate(list1, 1):
    print index, item
>>>
1 这
2 是
3 一个
4 测试

参考链接：https://blog.csdn.net/churximi/article/details/51648388

3.Sklearn.metrics.accuracy_score

>>> import numpy as np
>>> from sklearn.metrics import accuracy_score
>>> y_pred = [0, 2, 1, 3]
>>> y_true = [0, 1, 2, 3]
>>> accuracy_score(y_true, y_pred)
0.5
>>> accuracy_score(y_true, y_pred, normalize=False)
2

参考链接：http://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html

4.Sklearn.model_selection.KFold

>>> from sklearn.model_selection import KFold
>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
>>> y = np.array([1, 2, 3, 4])
>>> kf = KFold(n_splits=2)
>>> kf.get_n_splits(X)
2
>>> print(kf)  
KFold(n_splits=2, random_state=None, shuffle=False)
>>> 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]
TRAIN: [2 3] TEST: [0 1]
TRAIN: [0 1] TEST: [2 3]

参考链接：http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html

5.Sklearn.model_StratifiedKFold

The folds are made by preserving the percentage of samples for each class.

对于分类问题，应该使用分层抽样（stratified sampling）来生成数据，保证正负例的比例在训练集和测试集中的比例相同

>>> from sklearn.model_selection import StratifiedKFold
>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
>>> y = np.array([0, 0, 1, 1])
>>> skf = StratifiedKFold(n_splits=2)
>>> skf.get_n_splits(X, y)
2
>>> print(skf)  
StratifiedKFold(n_splits=2, random_state=None, shuffle=False)
>>> for train_index, test_index in skf.split(X, y):
...    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]
TRAIN: [1 3] TEST: [0 2]
TRAIN: [0 2] TEST: [1 3]