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Python: Numbaでクラスの高速化

Last updated at Posted at 2019-03-10

前回の続き

numbaでクラスの高速化.
ただし,クラスの継承はできない模様.また,単純にNumbaに対応させようとしても,意外とコードの手直しが必要がなことは難点であるが,一応投稿.

ひとまず,書き方は以下の通り.

spec = [
        ("メンバ変数1", メンバ変数1の型),
        ("メンバ変数2", メンバ変数2の型),
        ....
        ("メンバ変数n", メンバ変数nの型)
	]

@jitclass(spec)
class ...():
...

from numba import jit, f8, b1, i8, void
from numba import jitclass
import numpy as np
import random

spec = [
        ("width", i8),
        ("array", f8[:,:])
	]

@jitclass(spec)
class test():
  def __init__(self, w):
    self.width = w
    array = np.array([random.random() for i in range(self.width)])

  def start(self):
    cnt = 0    
    for a in self.array:
      cnt += a
    
    print(cnt)

if __name__ == "__main__":
  cls = test(10000000)
  cls.start()
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