Установка двух кривых с переменным количеством параметров с использованием `scipy.optimize.least_squares ()` ⇐ Python

[Anonymous]

Я пишу код, чтобы соответствовать параметрам связанных пар функций из семейства, используя функцию scipy.optimize.least_squares () , и кажется, что параметры теста не передаются в функцию правильно. Вот упрощенная иллюстрация проблемы.

Код: Выделить всё

import numpy as np
import numpy.linalg as la
import scipy.optimize
import math

Ha = {'H': lambda x, a, b : a + b*x, 'nParams': 2}
Hb = {'H': lambda x, a, b, c : a + b*x + c*x*x, 'nParams': 3}
Hc = {'H': lambda x, a, b, c, d : a*math.cos(b*(x-c)) + d, 'nParams': 4}

points1 = [(1,0), (2,4), (3,5), (7,8)]
points2 = [(1,1), (2,6), (4,1), (6,6)]

def coupled_1 (x, g, *params, **kwargs):
H1, H2 = kwargs.values() # H1 and H2 are dictionaries containing the two curves
# to be fit, 'H', and their respective numbers of
# parameters, 'nParams'
matrix = np.diag([H1['H'](x, *params[:H1['nParams']]), H2['H'](x, *params[H1['nParams']:])]) \
+ np.diag([g],k=1) + np.diag([g],k=-1)
return la.eigh(matrix)['eigenvalues'][0]

def coupled_2 (x, g, *params, **kwargs):
H1, H2 = kwargs.values()
matrix = np.diag([H1['H'](x, *params[:H1['nParams']]), H2['H'](x, *params[H1['nParams']:])]) \
+ np.diag([g],k=1) + np.diag([g],k=-1)
return la.eigh(matrix)['eigenvalues'][1]

def getParameters(H1, H2, pts1, pts2):
nParams = H1['nParams'] + H2['nParams']
# These points sometimes come in pairs and sometimes don't so I've found no better way to zip them
pts     = [(p1, p2) for p1 in pts1 for p2 in pts2 if p1[0] == p2[0]]
res     = lambda *params, **kwargs: \
np.sqrt(sum( [( p[0][1]-coupled_1(p[0][0], *params, **kwargs) )**2 \
+ (p[1][1]-coupled_2(p[0][0], *params, **kwargs) )**2 for p in pts] ))
result  = scipy.optimize.least_squares(res,[1] + [0]*nParams, kwargs={'H1':H1,'H2':H2})
return result['x']

params = getParameters(Ha, Hc, points1, points2)

Я получаю следующую ошибку от ha ()

Код: Выделить всё

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[5], line 37
34     result  = scipy.optimize.least_squares(res,[1] + [0]*nParams, kwargs={'H1':H1,'H2':H2})
35     return result['x']
---> 37 params = getParameters(Ha, Hc, points1, points2)
38 print(params)

Cell In[5], line 34, in getParameters(H1, H2, pts1, pts2)
30 pts     = [(p1, p2) for p1 in pts1 for p2 in pts2 if p1[0] == p2[0]]
31 res     = lambda *params, **kwargs: \
32                  np.sqrt(sum( [( p[0][1]-coupled_1(p[0][0], *params, **kwargs) )**2 \
33                  + (p[1][1]-coupled_2(p[0][0], *params, **kwargs) )**2 for p in pts] ))
---> 34 result  = scipy.optimize.least_squares(res,[1] + [0]*nParams, kwargs={'H1':H1,'H2':H2})
35 return result['x']

File ~\anaconda3\lib\site-packages\scipy\optimize\_lsq\least_squares.py:830, in least_squares(fun, x0, jac, bounds, method, ftol, xtol, gtol, x_scale, loss, f_scale, diff_step, tr_solver, tr_options, jac_sparsity, max_nfev, verbose, args, kwargs)
827 if method == 'trf':
828     x0 = make_strictly_feasible(x0, lb, ub)
--> 830 f0 = fun_wrapped(x0)
832 if f0.ndim != 1:
833     raise ValueError("`fun` must return at most 1-d array_like.  "
834                      "f0.shape: {0}".format(f0.shape))

File ~\anaconda3\lib\site-packages\scipy\optimize\_lsq\least_squares.py:825, in least_squares..fun_wrapped(x)
824 def fun_wrapped(x):
--> 825     return np.atleast_1d(fun(x, *args, **kwargs))

Cell In[5], line 32, in getParameters..(*params, **kwargs)
29 # These points sometimes come in pairs and sometimes don't so I've found no better way to zip them
30 pts     = [(p1, p2) for p1 in pts1 for p2 in pts2 if p1[0] == p2[0]]
31 res     = lambda *params, **kwargs: \
---> 32                  np.sqrt(sum( [( p[0][1]-coupled_1(p[0][0], *params, **kwargs) )**2 \
33                  + (p[1][1]-coupled_2(p[0][0], *params, **kwargs) )**2 for p in pts] ))
34 result  = scipy.optimize.least_squares(res,[1] + [0]*nParams, kwargs={'H1':H1,'H2':H2})
35 return result['x']

Cell In[5], line 32, in (.0)
29 # These points sometimes come in pairs and sometimes don't so I've found no better way to zip them
30 pts     = [(p1, p2) for p1 in pts1 for p2 in pts2 if p1[0] == p2[0]]
31 res     = lambda *params, **kwargs: \
---> 32                  np.sqrt(sum( [( p[0][1]-coupled_1(p[0][0], *params, **kwargs) )**2 \
33                  + (p[1][1]-coupled_2(p[0][0], *params, **kwargs) )**2 for p in pts] ))
34 result  = scipy.optimize.least_squares(res,[1] + [0]*nParams, kwargs={'H1':H1,'H2':H2})
35 return result['x']

Cell In[5], line 17, in coupled_1(x, g, *params, **kwargs)
14 H1, H2 = kwargs.values() # H1 and H2 are dictionaries containing the two curves
15                          # to be fit, 'H', and their respective numbers of
16                          # parameters, 'nParams'
---> 17 matrix = np.diag([H1['H'](x, *params[:H1['nParams']]), H2['H'](x, *params[H1['nParams']:])]) \
18         + np.diag([g],k=1) + np.diag([g],k=-1)
19 return la.eigh(matrix)['eigenvalues'][0]

TypeError: () missing 2 required positional arguments: 'a' and 'b'

Кажется, что список *params не доходит до ha () , но я не уверен, почему.


Подробнее здесь: https://stackoverflow.com/questions/783 ... ptimize-le