Question

这是我之前发布的堆栈交换问题的扩展。 link

上下文：

我的目标是使用scipy.optimize.curve_fit函数将数据拟合到函数f（t，* p）。我碰巧知道一些参数pfix = {p_j，...，p_k}，并希望使用pfix ...中的参数使f（t，* p）适合我的数据。

上面我有一个链接，询问如何编写一个包装器来固定函数中的参数。现在我想做同样的事情，但是对于f（t，* p）的雅可比，固定参数pfix。我不确定该怎么做。

func（x，* p）的包装器

下面是我的函数的包装器：

def fix_params(f, fix_pars):
    # fix_pars = ((1, A), (2, B))
    def new_func(x, *pars):
        new_pars = [None]*(len(pars) + len(fix_pars))
        for j, fp in fix_pars:
            new_pars[j] = fp
        for par in pars:
            for j, npar in enumerate(new_pars):
                if npar is None:
                    new_pars[j] = par
                    break
        return f(x, *new_pars)
    return new_func

问题

天真的，我只会将此包装器用于我的Jacobian函数。但是，这就是问题所在。

假设我有x的N个参数和M个值。然后我的jacobian函数返回一个（M，N）numpy数组。现在，如果我不修复任何参数，这很好。但是，即使我只修复了一个参数，我包装的jacobian函数仍然返回一个（M，N）numpy数组。由于我现在使用的参数数量小于我的jacobian的参数尺寸，因此这会引起curve_fit抱怨。我不确定该如何解决。

有什么建议吗？

Answer 1

这应该有效（用于我的评论scipy.optimize.leatsq）

import matplotlib.pyplot as plt
import numpy as np
from scipy.optimize import leastsq

def arb_func( x, a, b, c, d ):
    return a * np.exp( b * np.sin( c * x + d ) )

def arb_fixed( fixed ):
    def g( x , *pars ):
        locPars = list( pars )
        for f in sorted( list(fixed), key=lambda x: x[0] ):
            locPars.insert( f[0], f[1] )
        return arb_func( x, *locPars)
    return g

def func_da( x, a, b, c, d ):
    return np.exp( b * np.sin( c * x + d ) )

def func_db( x, a, b, c, d ):
    return arb_func( x, a, b, c, d ) * np.sin( c * x + d )

def func_dc( x, a, b, c, d ):
    return arb_func( x, a, b, c, d ) * b * np.cos( c * x + d ) * x

def func_dd( x, a, b, c, d ):
    return arb_func( x, a, b, c, d ) * b * np.cos( c * x + d )

dList = [ func_da, func_db, func_dc, func_dd ]

def jac( pars, x, y ):
    return [ func( x, *pars ) for func in dList ]

def jac_fixed( Fixed = None ):
    def h( pars, x, y, z ):
        funcList = dList[::]
        locFixed = sorted( list(Fixed), key=lambda x: x[0] )
        locPars = list( pars )
        for f in locFixed:
            locPars.insert( f[0], f[1] )
        locFixed.reverse()
        for f in locFixed:
            del funcList[ f[0] ]
        out = [ func( x, *locPars ) for func in funcList ]
        return out
    return h

a0 = +3
b0 = -0.6
c0 = +1.44
d0 = +0.4

xList = np.linspace( -2, 4, 100 )
y0List = np.fromiter( ( arb_func( x, a0, b0, c0, d0 ) for x in xList ), np.float )
yNoiseList = np.fromiter( ( y + .2 * np.random.normal() for y in y0List[::4] ), np.float )
xNoiseList = xList[::4]

def residuals_fixed( params, xList, yList, Fixed=None ):
    if Fixed is None:
        fixedSorted = []
    else:
        fixedSorted = sorted( list(Fixed), key=lambda x: x[0] )
    locParams = list( params )
    for f in fixedSorted:
            locParams.insert( f[0], f[1] )
    diff = [ arb_func( x, *locParams ) - y for x, y in zip( xList, yList )]
    return diff

def leastsq_wrapper( xList, yList, p0, **kwargs ):
    fixed = kwargs.pop( 'Fixed', None )
    if fixed is None:
        locFixed = []
    else:
        locFixed = fixed
        s = np.array( locFixed ).shape
        if len(s) !=2 or s[-1] !=2:
            raise ValueError( 'fixed value list has wrong shape. Must be n by 2, but is {}'.format(s) )
    if len( p0 ) + len( locFixed ) != 4:
            raise TypeError( 'Total number of arguments (variable + fixed) is not 4' )
    fixedSorted = sorted( list( locFixed ), key=lambda x: x[0] )
    if not all( [ ( type( item[0] ) is int )  and ( item[0] > -1 ) and ( item[0] < 4 ) for item in fixedSorted ] ):
        raise ValueError( 'list indices i for fixed values are not int with 0 <= i < 4' )
    my_jac = jac_fixed( Fixed=fixedSorted )
    baseDict = { 'args':( xList, yList, fixed ), 'Dfun':my_jac, 'col_deriv':1}
    baseDict.update(kwargs) ## allows to send e.g.  full_output=True
    out = leastsq( residuals_fixed, p0, **baseDict )
    return out

myFitStd, err = leastsq( residuals_fixed, [ a0, b0 ,c0 , d0 ], args=( xNoiseList, yNoiseList ) )
print myFitStd
myFit0, err = leastsq_wrapper( xNoiseList, yNoiseList,  [ a0, b0 ,c0 , d0 ] )
print myFit0
myFixed1 = [[0,3.3]]
myFit1, err = leastsq_wrapper( xNoiseList, yNoiseList,  [ b0 ,c0 , d0 ], Fixed=myFixed1 )
arb1 = arb_fixed( myFixed1 )
print myFit1

myFixed2 = [ [ 3, .8], [2, 1.2 ] ]
myFit2, err = leastsq_wrapper( xNoiseList, yNoiseList,  [ a0, b0 ], Fixed=myFixed2 )
arb2 = arb_fixed( myFixed2 )
print myFit2

fig = plt.figure()
ax = fig.add_subplot( 1, 1, 1 )

ax.plot( xList, y0List )
ax.plot( xNoiseList, yNoiseList, marker='o', linestyle='' )
ax.plot( xList, np.fromiter( ( arb_func( x, *myFitStd ) for x in xList ), np.float), linestyle='--' )
ax.plot( xList, np.fromiter( ( arb_func( x, *myFit0 ) for x in xList ), np.float), linestyle=':' )
ax.plot( xList, np.fromiter( ( arb1( x, *myFit1 ) for x in xList ), np.float) )
ax.plot( xList, np.fromiter( ( arb2( x, *myFit2 ) for x in xList ), np.float) )

plt.show()

提供输出

[ 3.03802692 -0.57275564  1.43380277  0.38557492]
[ 3.03802692 -0.57275564  1.43380277  0.38557493]
[-0.49087778  1.422561    0.40503389]
[ 3.31028289 -0.46678563]

如何编写包装器以修复函数的雅可比的任意参数

1 个答案: