您也可以使用部分函数。
from functools import partial
error_partial = partial(error, w0=w0, x=x, y_actual=y)
minimize_scalar(error_partial, bounds=(-5, 5))
如果您对性能有疑问...它与Lambda相同。
import time
from functools import partial
import numpy as np
from scipy.optimize import minimize_scalar
def error(w1, w0, x, y_actual):
y_pred = w0 + w1 * x
mse = ((y_actual - y_pred) ** 2).mean()
return mse
w0 = 50
x = np.arange(int(1e5))
y = np.arange(int(1e5)) + 52
error_partial = partial(error, w0=w0, x=x, y_actual=y)
p_time = []
for _ in range(100):
p_time_ = time.time()
p = minimize_scalar(error_partial, bounds=(-5, 5))
p_time_ = time.time() - p_time_
p_time.append(p_time_ / p.nfev)
l_time = []
for _ in range(100):
l_time_ = time.time()
l = minimize_scalar(lambda w1: error(w1, w0, x, y), bounds=(-5, 5))
l_time_ = time.time() - l_time_
l_time.append(l_time_ / l.nfev)
print(f'Same performance? {np.median(p_time) == np.median(l_time)}')