python-根据列表的Numpy折叠列
作者:互联网
在NumPy中,我有一个adxn数组A和一个长度为n的列表L,描述了我希望A的每一列在矩阵B中结束的位置.想法是矩阵B的第i列是A的所有列的总和L中的对应值为i.
我可以使用for循环来做到这一点:
A = np.arange(15).reshape(3,5)
L = [0,1,2,1,1]
n_cols = 3
B = np.zeros((len(A), n_cols))
# assume I know the desired number of columns,
# which is also one more than the maximum value of L
for i, a in enumerate(A.T):
B[:, L[i]] += a
我想知道是否有一种方法可以通过切片数组A(或以其他方式向量化)来实现?
解决方法:
您正在使用L来选择A列以减少/折叠A列.另外,您将基于L元素的唯一性更新输出数组的列.
因此,您可以将np.add.reduceat
用于矢量化解决方案,如下所示-
sidx = L.argsort()
col_idx, grp_start_idx = np.unique(L[sidx],return_index=True)
B_out = np.zeros((len(A), n_cols))
B_out[:,col_idx] = np.add.reduceat(A[:,sidx],grp_start_idx,axis=1)
运行时测试-
In [129]: def org_app(A,n_cols):
...: B = np.zeros((len(A), n_cols))
...: for i, a in enumerate(A.T):
...: B[:, L[i]] += a
...: return B
...:
...: def vectorized_app(A,n_cols):
...: sidx = L.argsort()
...: col_idx, grp_start_idx = np.unique(L[sidx],return_index=True)
...: B_out = np.zeros((len(A), n_cols))
...: B_out[:,col_idx] = np.add.reduceat(A[:,sidx],grp_start_idx,axis=1)
...: return B_out
...:
In [130]: # Setup inputs with an appreciable no. of cols & lesser rows
...: # so as that memory bandwidth to work with huge number of
...: # row elems doesn't become the bottleneck
...: d,n_cols = 10,5000
...: A = np.random.rand(d,n_cols)
...: L = np.random.randint(0,n_cols,(n_cols,))
...:
In [131]: np.allclose(org_app(A,n_cols),vectorized_app(A,n_cols))
Out[131]: True
In [132]: %timeit org_app(A,n_cols)
10 loops, best of 3: 33.3 ms per loop
In [133]: %timeit vectorized_app(A,n_cols)
100 loops, best of 3: 1.87 ms per loop
随着行数与A中cols数可比,矢量化方法对内存带宽的高要求将抵消其明显的提速.
标签:numpy-broadcasting,vectorization,python,numpy 来源: https://codeday.me/bug/20191026/1938471.html