[英]Efficient way to construct this array in numpy?
我需要構造一個N x M x N
陣列A
,使得A[i, j, k] == (0 if i != k else x[j])
。 我可以寫:
A = np.zeros((N, M, N))
for i in range(N):
for j in range(M):
A[i,j,i] = x[j]
或者,或者:
A = np.zeros((N, M, N))
for i in range(N):
A[i,:,i] = x
但兩者都很可能對我的目的來說太慢了。 有更快的方法嗎?
方法#1
使用broadcasting
來創建所有那些要分配x
的線性索引,然后簡單地分配到它的展平視圖中,就像這樣 -
# Initialize
Aout = np.zeros((N, M, N))
# Comput all linear indices
idx = (np.arange(N)*(N*M+1))[:,None] + N*np.arange(M)
# In a flattened view with `.ravel()` assign from x
Aout.ravel()[idx] = x
方法#2
使用np.lib.stride_tricks.as_strided
支持的基於視圖的元素訪問 -
Aout = np.zeros((N, M, N))
s0,s1,s2 = Aout.strides
Aoutview = np.lib.stride_tricks.as_strided(Aout,shape=(N,M),strides=(s0+s2,s1))
Aoutview[:] = x
方法#3
另一種方法是沿第一和第三軸使用integer array indexing
,從問題中模擬第二種方法,但是以矢量化方式 -
Aout = np.zeros((N, M, N))
Aout[np.arange(N),:,np.arange(N)] = x
運行時測試
方法 -
def app0(x,A):
for i in range(N):
for j in range(M):
A[i,j,i] = x[j]
return A
def app1(x,A):
for i in range(N):
A[i,:,i] = x
return A
def app2(x,Aout):
idx = (np.arange(N)*(N*M+1))[:,None] + N*np.arange(M)
Aout.ravel()[idx] = x
return Aout
def app3(x,Aout):
s0,s1,s2 = Aout.strides
Aoutview = np.lib.index_tricks.as_strided(Aout,shape=(N,M),strides=(s0+s2,s1))
Aoutview[:] = x
return Aout
def app4(x,Aout):
r = np.arange(N)
Aout[r,:,r] = x
return Aout
驗證 -
In [125]: # Params
...: N, M = 100,100
...: x = np.random.rand(M)
...:
...: # Make copies of arrays to be assigned into
...: A0 = np.zeros((N, M, N))
...: A1 = np.zeros((N, M, N))
...: A2 = np.zeros((N, M, N))
...: A3 = np.zeros((N, M, N))
...: A4 = np.zeros((N, M, N))
...:
In [126]: print np.allclose(app0(x,A0), app1(x,A1))
...: print np.allclose(app0(x,A0), app2(x,A2))
...: print np.allclose(app0(x,A0), app3(x,A3))
...: print np.allclose(app0(x,A0), app4(x,A4))
...:
True
True
True
True
計時 -
In [127]: # Make copies of arrays to be assigned into
...: A0 = np.zeros((N, M, N))
...: A1 = np.zeros((N, M, N))
...: A2 = np.zeros((N, M, N))
...: A3 = np.zeros((N, M, N))
...: A4 = np.zeros((N, M, N))
In [128]: %timeit app0(x,A0)
...: %timeit app1(x,A1)
...: %timeit app2(x,A2)
...: %timeit app3(x,A3)
...: %timeit app4(x,A4)
...:
1000 loops, best of 3: 1.49 ms per loop
10000 loops, best of 3: 53.6 µs per loop
10000 loops, best of 3: 150 µs per loop
10000 loops, best of 3: 28.6 µs per loop
10000 loops, best of 3: 25.2 µs per loop
聲明:本站的技術帖子網頁,遵循CC BY-SA 4.0協議,如果您需要轉載,請注明本站網址或者原文地址。任何問題請咨詢:yoyou2525@163.com.