我有两个功能。一个叫另一个。这是运行两个功能的完整代码和数据。
import numpy as np
agents={(-71.60000000000002, 41.1): 1, (-71.40000000000003, 41.90000000000001): 1, (-71.80000000000001, 41.2): 1, (-71.20000000000005, 41.400000000000006): 1, (-71.30000000000004, 41.50000000000001): 1, (-71.70000000000002, 41.70000000000001): 1, (-71.50000000000003, 42.000000000000014): 1, (-71.50000000000003, 41.2): 1, (-71.40000000000003, 42.000000000000014): 1, (-71.20000000000005, 41.60000000000001): 1, (-71.70000000000002, 42.000000000000014): 1, (-71.50000000000003, 41.60000000000001): 1, (-71.40000000000003, 41.50000000000001): 1, (-71.50000000000003, 41.90000000000001): 1, (-71.70000000000002, 41.50000000000001): 1, (-71.40000000000003, 41.80000000000001): 1, (-71.30000000000004, 41.60000000000001): 1, (-71.70000000000002, 41.80000000000001): 2, (-71.60000000000002, 41.60000000000001): 2, (-71.20000000000005, 41.50000000000001): 2, (-71.50000000000003, 41.70000000000001): 2, (-71.40000000000003, 41.400000000000006): 2, (-71.50000000000003, 41.80000000000001): 2, (-71.80000000000001, 41.400000000000006): 2, (-71.30000000000004, 41.400000000000006): 2, (-71.40000000000003, 41.70000000000001): 2, (-71.60000000000002, 41.80000000000001): 2, (-71.70000000000002, 41.90000000000001): 2, (-71.30000000000004, 41.70000000000001): 2, (-71.60000000000002, 41.90000000000001): 2, (-71.60000000000002, 42.000000000000014): 2}
all_houses=[(-71.60000000000002, 41.70000000000001), (-71.70000000000002, 42.000000000000014), (-71.20000000000005, 41.400000000000006), (-71.60000000000002, 41.1), (-71.50000000000003, 41.70000000000001), (-71.50000000000003, 41.90000000000001), (-71.30000000000004, 41.400000000000006), (-71.30000000000004, 41.60000000000001), (-71.40000000000003, 41.90000000000001), (-71.80000000000001, 41.300000000000004), (-71.70000000000002, 41.300000000000004), (-71.60000000000002, 41.2), (-71.30000000000004, 41.70000000000001), (-71.60000000000002, 41.90000000000001), (-71.40000000000003, 41.400000000000006), (-71.60000000000002, 42.000000000000014), (-71.50000000000003, 41.50000000000001), (-71.50000000000003, 42.000000000000014), (-71.70000000000002, 41.90000000000001), (-71.70000000000002, 41.80000000000001), (-71.50000000000003, 41.60000000000001), (-71.40000000000003, 41.50000000000001), (-71.30000000000004, 41.50000000000001), (-71.70000000000002, 41.60000000000001), (-71.9, 41.300000000000004), (-71.50000000000003, 41.400000000000006), (-71.40000000000003, 41.70000000000001), (-71.60000000000002, 41.60000000000001), (-71.50000000000003, 41.80000000000001), (-71.40000000000003, 41.60000000000001), (-71.40000000000003, 41.80000000000001), (-71.80000000000001, 41.400000000000006), (-71.60000000000002, 41.80000000000001), (-71.20000000000005, 41.50000000000001), (-71.60000000000002, 41.400000000000006), (-71.60000000000002, 41.50000000000001), (-71.40000000000003, 42.000000000000014), (-71.50000000000003, 41.2), (-71.80000000000001, 41.2), (-71.70000000000002, 41.400000000000006), (-71.20000000000005, 41.60000000000001), (-71.70000000000002, 41.50000000000001), (-71.70000000000002, 41.70000000000001)]
empty_houses=[(-71.9, 41.300000000000004), (-71.60000000000002, 41.2), (-71.40000000000003, 41.60000000000001), (-71.70000000000002, 41.400000000000006), (-71.80000000000001, 41.400000000000006), (-71.50000000000003, 41.60000000000001), (-71.70000000000002, 41.300000000000004), (-71.20000000000005, 41.50000000000001), (-71.40000000000003, 42.000000000000014), (-71.40000000000003, 41.80000000000001), (-71.50000000000003, 41.50000000000001), (-71.60000000000002, 41.80000000000001)]
agent=[-71.50000000000003, 42.000000000000014]
spacing=0.1
similarity_threshhold=0.21
def is_unsatisfied(x, y,all_houses,agents,spacing,empty_houses):
"""
Checking if an agent is unsatisfied or satisified at its current
position.
"""
race = agents[(x,y)]
count_similar = 0
count_different = 0
min_width=min(np.array(all_houses)[:,0])
max_width=max(np.array(all_houses)[:,0])
min_height=min(np.array(all_houses)[:,1])
max_height=max(np.array(all_houses)[:,1])
if x > min_width and y > min_height and (x-spacing, y-spacing) not in empty_houses:
if (x-spacing, y-spacing) in agents:
if agents[(x-spacing, y-spacing)] == race:
count_similar += 1
else:
count_different += 1
else:
pass
if y > min_height and (x,y-spacing) not in empty_houses:
if (x,y-spacing) in agents:
if agents[(x,y-spacing)] == race:
count_similar += 1
else:
count_different += 1
else:
pass
if x < (max_width-spacing) and y > min_height and (x+spacing,y-spacing) not in empty_houses:
if (x+spacing,y-spacing) in agents:
if agents[(x+spacing,y-spacing)] == race:
count_similar += 1
else:
count_different += 1
else:
pass
if x > min_width and (x-spacing,y) not in empty_houses:
if (x-spacing,y) in agents:
if agents[(x-spacing,y)] == race:
count_similar += 1
else:
count_different += 1
else:
pass
if x < (max_width-spacing) and (x+spacing,y) not in empty_houses:
if (x+spacing,y) in agents:
if agents[(x+spacing,y)] == race:
count_similar += 1
else:
count_different += 1
else:
pass
if x > min_width and y < (max_height-spacing) and (x-spacing,y+spacing) not in empty_houses:
if (x-spacing,y+spacing) in agents:
if agents[(x-spacing,y+spacing)] == race:
count_similar += 1
else:
count_different += 1
else:
pass
if x > min_width and y < (max_height-spacing) and (x,y+spacing) not in empty_houses:
if (x,y+spacing) in agents:
if agents[(x,y+spacing)] == race:
count_similar += 1
else:
count_different += 1
else:
pass
if x < (max_width-spacing) and y < (max_height-spacing) and (x+spacing,y+spacing) not in empty_houses:
if (x+spacing,y+spacing) in agents:
if agents[(x+spacing,y+spacing)] == race:
count_similar += 1
else:
count_different += 1
else:
pass
if (count_similar+count_different) == 0:
return False
else:
return float(count_similar)/(count_similar+count_different) < similarity_threshhold
def find_unsatisifed(agents,all_houses,spacing,empty_houses):
for agent in agents:
print(is_unsatisfied(agent[0],agent[1],all_houses,agents,spacing,empty_houses))
find_unsatisifed(agents,all_houses,spacing,empty_houses)
我想同时优化find_unsatisfied和is_unsatisfied。函数运行得更快，因为我必须处理变量代理中的至少5000个数据元素以及所有房屋以及empty_houses中的至少400个数据。
is_unsatisfied获取一个业务代表的坐标，并检查网格上围绕它的所有业务代表是否种族相同。 （在这种情况下，竞赛为1或2）。
find_unsatisfied使用代理字典并在每个必要的代理上运行is_unsatisfied函数。
我尝试了numba，但由于这些函数实际上是类中的方法，因此无法正常工作，并通过numba不支持的geopandas库获取其数据。然后，我尝试在类之外创建一个函数并将其传递给我，但由于循环并没有真正运行，因此它实际上无法正常工作。我是这样做的
from numba import jit, autojit, int_, void, float_,bool_
@jit()
def is_unsatisfied_numbized(instance, x, y):
"""
Checking if an agent is unsatisfied or satisified at its current
position.
"""
race = instance.agents[(x,y)]
count_similar = 0
count_different = 0
min_width=min(np.array(instance.all_houses)[:,0])
max_width=max(np.array(instance.all_houses)[:,0])
min_height=min(np.array(instance.all_houses)[:,1])
max_height=max(np.array(instance.all_houses)[:,1])
if x > min_width and y > min_height and (x-instance.spacing, y-instance.spacing) not in instance.empty_houses:
if (x-instance.spacing, y-instance.spacing) in instance.agents:
if instance.agents[(x-instance.spacing, y-instance.spacing)] == race:
count_similar += 1
else:
count_different += 1
else:
pass
if y > min_height and (x,y-instance.spacing) not in instance.empty_houses:
if (x,y-instance.spacing) in instance.agents:
if instance.agents[(x,y-instance.spacing)] == race:
count_similar += 1
else:
count_different += 1
else:
pass
if x < (max_width-instance.spacing) and y > min_height and (x+instance.spacing,y-instance.spacing) not in instance.empty_houses:
if (x+instance.spacing,y-instance.spacing) in instance.agents:
if instance.agents[(x+instance.spacing,y-instance.spacing)] == race:
count_similar += 1
else:
count_different += 1
else:
pass
if x > min_width and (x-instance.spacing,y) not in instance.empty_houses:
if (x-instance.spacing,y) in instance.agents:
if instance.agents[(x-instance.spacing,y)] == race:
count_similar += 1
else:
count_different += 1
else:
pass
if x < (max_width-instance.spacing) and (x+instance.spacing,y) not in instance.empty_houses:
if (x+instance.spacing,y) in instance.agents:
if instance.agents[(x+instance.spacing,y)] == race:
count_similar += 1
else:
count_different += 1
else:
pass
if x > min_width and y < (max_height-instance.spacing) and (x-instance.spacing,y+instance.spacing) not in instance.empty_houses:
if (x-instance.spacing,y+instance.spacing) in instance.agents:
if instance.agents[(x-instance.spacing,y+instance.spacing)] == race:
count_similar += 1
else:
count_different += 1
else:
pass
if x > min_width and y < (max_height-instance.spacing) and (x,y+instance.spacing) not in instance.empty_houses:
if (x,y+instance.spacing) in instance.agents:
if instance.agents[(x,y+instance.spacing)] == race:
count_similar += 1
else:
count_different += 1
else:
pass
if x < (max_width-instance.spacing) and y < (max_height-instance.spacing) and (x+instance.spacing,y+instance.spacing) not in instance.empty_houses:
if (x+instance.spacing,y+instance.spacing) in instance.agents:
if instance.agents[(x+instance.spacing,y+instance.spacing)] == race:
count_similar += 1
else:
count_different += 1
else:
pass
if (count_similar+count_different) == 0:
return False
else:
return float(count_similar)/(count_similar+count_different) < instance.similarity_threshhold
class geo():
def __init__(self):
#parameters in here
def is_unsatisfied(self,x,y):
is_unsatisfied_numbized(self, x, y)
def update_normal(self):
for i in range(100):
n_changes = 0
for agent in self.agents:
if self.is_unsatisfied(agent[0], agent[1]):
n_changes += 1
print(n_changes)
print(i)
if n_changes == 0:
break
这东西不起作用。
是否有任何想法我在哪里做错了，或者有没有其他建议可以优化没有numba的两个功能。也许使用numpy进行矢量化操作。
答案 0 :(得分：0)
要在不使用numba的情况下优化功能，可以执行以下操作：
1。防止两次测试相同的条件
不同于您进行检查
If A and B:
Do X
If A and C:
Do Y
，您可以将if语句组合在一起，例如
If A:
If B:
Do X
If C:
Do Y
2。降低条件复杂度。您经常测试
if X not in empty_houses:
if X in agents:
Do A
，您可以将其简化为
targetList = [i for i in agents if i not in empty_houses]
if X in targetList:
Do A