我正在尝试从下面的函数f
中查找x_my
、y_my
和z_my
。我为f
设置了三个值,并为每个相应的curr_location
设置了三个值。这意味着我有三个方程和三个未知数,这意味着它可以求解。但是我不知道如何使用python来做到这一点。
sigma_x=3
sigma_y=3
sigma_z=3
curr_location_x1=3
curr_location_y1=3
curr_location_z1=3
curr_location_x2=4
curr_location_y2=4
curr_location_z2=4
curr_location_x3=6
curr_location_y3=6
curr_location_z3=6
f_1=0.4
f_2=0.3
f_3=0.24
f = math.exp(-((((curr_location_x - x_my) * (curr_location_x - x_my)) / (2*sigma_x * sigma_x)) + (((curr_location_y - y_my) *(curr_location_y - y_my)) / (2 * sigma_y * sigma_y)) + (((curr_location_z - z_my) *(curr_location_z - z_my)) / (2 * sigma_z * sigma_z))))
发布于 2019-03-22 04:19:06
您可以使用curve_fit
(docs)。注意如何将输入和输出放在向量中:
from scipy.optimize import curve_fit
import numpy as np
sigma_x=3
sigma_y=3
sigma_z=3
def f(curr_location, x_my, y_my, z_my):
curr_location_x, curr_location_y, curr_location_z = curr_location
return np.exp(-((((curr_location_x - x_my) * (curr_location_x - x_my))
/ (2*sigma_x * sigma_x)) + (((curr_location_y - y_my)
*(curr_location_y - y_my)) / (2 * sigma_y * sigma_y)) +
(((curr_location_z - z_my) *(curr_location_z - z_my)) /
(2 * sigma_z * sigma_z))))
curr_location_values = [[3,3,3,], [4,4,4], [6,6,6]]
output_values = [0.4, 0.3, 0.24]
popt, pcov = curve_fit(f, curr_location_values, output_values)
popt
>> array([2.99570375, 2.38445522, 1.7246244 ])
https://stackoverflow.com/questions/55288368
复制相似问题