我正在编写一个numba函数来计算投资组合的波动性：
我使用的一些函数如下：import numba as nb
import numpy as np
def portfolio_s2( cv, weights ):
""" Calculate the variance of a portfolio """
return weights.dot( cv ).dot( weights )
@nb.jit( nopython=True )
def portfolio_s2c( cv, weights ):
s0 = 0.0
for i in range( weights.shape[0]-1 ):
wi = weights[i]
s0 += cv[i,i]*wi*wi
s1 = 0.0
for j in range( i+1, weights.shape[0] ):
s1 += wi*weights[j]*cv[i,j]
s0 += 2.0*s1
i = weights.shape[0]-1
wi = weights[ i ]
s0 += cv[i,i]*wi**2
return s0
@nb.jit( nopython=True )
def portfolio_s2b( cv, weights ):
s0 = 0.0
for i in range( weights.shape[0] ):
s0 += weights[i]*weights[i]*cv[i,i]
s1 = 0.0
for i in range( weights.shape[0]-1 ):
s2 = 0.0
for j in range( i+1, weights.shape[0] ):
s2 += weights[j]*cv[i,j]
s1+= weights[i]*s2
return s0+2.0*s1
我使用以下代码测试函数的性能：
^{pr2}$
这是计时器类：import time
class Timer( object ):
def __init__(self, name=''):
self._name = name
def __enter__(self):
self.start = time.time()
return self
def __exit__(self,a,b,c):
self.end = time.time()
self.timetaken = self.end-self.start
print( '{0} Took {1} seconds'.format( self._name, self.timetaken ))
结果如下：
结果表明：numba版本的函数在矩阵大小小于80的情况下优于numpy版本
numba版本的规模似乎比numpy功能更糟糕
为什么会这样？与打给numpa相比，呼叫numpy是否有某种开销？在
为什么numpy函数的伸缩性更好？它是在后台用BLAS做一些花哨的事情，还是用一种更高级的算法进行计算？在
我可以做numba函数和numpy函数吗？在
