考虑数组
什么是向量化的获取累积argmax的方法?
a
。np.random.seed([3,1415])
a = np.random.randint(0, 10, (10, 2))
a
array([[0, 2],
[7, 3],
[8, 7],
[0, 6],
[8, 6],
[0, 2],
[0, 4],
[9, 7],
[3, 2],
[4, 3]])
什么是向量化的获取累积argmax的方法?
array([[0, 0], <-- both start off as max position
[1, 1], <-- 7 > 0 so 1st col = 1, 3 > 2 2nd col = 1
[2, 2], <-- 8 > 7 1st col = 2, 7 > 3 2nd col = 2
[2, 2], <-- 0 < 8 1st col stays the same, 6 < 7 2nd col stays the same
[2, 2],
[2, 2],
[2, 2],
[7, 2], <-- 9 is new max of 2nd col, argmax is now 7
[7, 2],
[7, 2]])
这里是一种非向量化的方法。
注意随着窗口扩大,argmax应用于不断增长的窗口。
pd.DataFrame(a).expanding().apply(np.argmax).astype(int).values
array([[0, 0],
[1, 1],
[2, 2],
[2, 2],
[2, 2],
[2, 2],
[2, 2],
[7, 2],
[7, 2],
[7, 2]])