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scipy optimity.root_scalar(),其中多个参数各为一个列表

root-finding scipy python

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user1612986 · 技术社区 · 1 年前

def  f(m_l, m_B, y):
    value = -1.0 + np.sum(m_l*np.exp(-m_B*y))
    return value
def  df(m_l, m_B, y):
    value = -np.sum(m_B*m_l*np.exp(-m_B*y))
    return value

其中m_l和m_b是numpy数组。我使用给出以下输入

   m_l = np.array([0.0036132256153053369,0.95110068028445593])
   m_B = np.array([0.48884897299905006,0.95605658765269563])
   guess=-0.048557088449677460
   root = optimize.root_scalar(f, guess, fprime=df, args=(m_l,m_B),method='newton',rtol=1e-9,maxiter=1000)

我得到以下错误:

   TypeError                                 Traceback (most recent call last)
   ~\AppData\Local\Temp/ipykernel_15644/3699083294.py in <module>
  4 print(f(m_l, m_B, y))
  5 guess=-0.048557088449677460
  ----> 6 root = optimize.root_scalar(f, guess, fprime=df, args= 
  (m_l,m_B),method='newton',rtol=1e-9,maxiter=1000)

   TypeError: root_scalar() got multiple values for argument 'args'

我想发生这个错误是因为输入参数没有正确传递。有人能帮忙找出通过争论的正确方法吗。提前谢谢。

1 回复 | 直到 1 年前

1

2

Ayalew Mohammed 1 年前

import numpy as np
from scipy import optimize

def f(y, m_l, m_B):
    value = -1.0 + np.sum(m_l * np.exp(-m_B * y))
    return value

def df(y, m_l, m_B):
    value = -np.sum(m_B * m_l * np.exp(-m_B * y))
    return value

m_l = np.array([0.0036132256153053369, 0.95110068028445593])
m_B = np.array([0.48884897299905006, 0.95605658765269563])
guess = -0.048557088449677460

root = optimize.root_scalar(f, args=(m_l, m_B), x0=guess, fprime=df, method='newton', rtol=1e-9, maxiter=1000)

print("Root found:", root.root)