#1
我使用多圈代码的直接转换到broadcasting
优化的第一级的基于一个在引入新的轴线,因此不那么存储器高效的,如下面列出的优化阶段 -
p1 = (-((X[:,None] - Y)**2)/c1)-c2
p11 = p1.sum(2)
p2 = np.exp(p11+c3)
out = np.log(p2.sum(0)).mean()
优化阶段#2
在考虑一些优化饲养日瞻在我们打算分离出的常量的操作,我结束了以下 -
c10 = -c1
c20 = X.shape[1]*c2
subs = (X[:,None] - Y)**2
p00 = subs.sum(2)
p10 = p00/c10
p11 = p10-c20
p2 = np.exp(p11+c3)
out = np.log(p2.sum(0)).mean()
优化阶段#3
它进一步去和和看到过的地方的操作可能优化,我结束了使用Scipy's cdist
来取代平方和sum-reduction
的重量级的工作。这应该是相当的内存使用效率,给我们最终的实现,如下图所示 -
from scipy.spatial.distance import cdist
# Setup constants
c10 = -c1
c20 = X.shape[1]*c2
c30 = c20-c3
c40 = np.exp(c30)
c50 = np.log(c40)
# Get stagewise operations corresponding to loopy ones
p1 = cdist(X, Y, 'sqeuclidean')
p2 = np.exp(p1/c10).sum(0)
out = np.log(p2).mean() - c50
运行测试
途径 -
def loopy_app(X, Y, sigma):
k, d = X.shape
m = Y.shape[0]
c1 = 2.0*sigma**2
c2 = 0.5*np.log(np.pi*c1)
c3 = np.log(1.0/k)
L_B = np.zeros((m,))
for i in xrange(m):
L_B[i] = np.log(np.sum(np.exp(np.sum(-np.divide(
np.power(X-Y[i,:],2), c1)-c2,1)+c3)))
return np.mean(L_B)
def vectorized_app(X, Y, sigma):
# Setup constants
k, d = D_A.shape
c1 = 2.0*sigma**2
c2 = 0.5*np.log(np.pi*c1)
c3 = np.log(1.0/k)
c10 = -c1
c20 = X.shape[1]*c2
c30 = c20-c3
c40 = np.exp(c30)
c50 = np.log(c40)
# Get stagewise operations corresponding to loopy ones
p1 = cdist(X, Y, 'sqeuclidean')
p2 = np.exp(p1/c10).sum(0)
out = np.log(p2).mean() - c50
return out
时序和验证 -
In [294]: # Setup inputs with m(=D_B.shape[0]) being a large number
...: X = np.random.randint(0,9,(100,10))
...: Y = np.random.randint(0,9,(10000,10))
...: sigma = 2.34
...:
In [295]: np.allclose(loopy_app(X, Y, sigma),vectorized_app(X, Y, sigma))
Out[295]: True
In [296]: %timeit loopy_app(X, Y, sigma)
1 loops, best of 3: 225 ms per loop
In [297]: %timeit vectorized_app(X, Y, sigma)
10 loops, best of 3: 23.6 ms per loop
In [298]: # Setup inputs with m(=Y.shape[0]) being a much large number
...: X = np.random.randint(0,9,(100,10))
...: Y = np.random.randint(0,9,(100000,10))
...: sigma = 2.34
...:
In [299]: np.allclose(loopy_app(X, Y, sigma),vectorized_app(X, Y, sigma))
Out[299]: True
In [300]: %timeit loopy_app(X, Y, sigma)
1 loops, best of 3: 2.27 s per loop
In [301]: %timeit vectorized_app(X, Y, sigma)
1 loops, best of 3: 243 ms per loop
约在10x
加速!
令人惊叹!超过10倍! – xxx222
@ xxx222你对实际数据集的加速度是多少? – Divakar
约20倍左右,因为我有一个非常大的数据集,所以计算距离矩阵变得困难。 – xxx222