继续使用numpy.piecewise来获取此错误，以获取分段线性回归

如何解决继续使用numpy.piecewise来获取此错误，以获取分段线性回归

我有一个非常大的数据文件，其中x =时间，y =距离。我想弄清楚不同部分的速度如何。理想情况下，我希望Python计算分段和相应的线性回归函数。我在Google上进行了搜索，认为我最好的选择是使用numpy.piecewise来获得分段线性回归。

我只会不断收到此错误 # Remove full_output from kwargs,otherwise we're passing it in twice'.

使用的代码如下：

y = cleandata["Distance (um)"]
def piecewise_linear(x,x0,x1,b,k1,k2,k3):
    condlist = [x < x0,(x >= x0) & (x < x1),x >= x1]
    funclist = [lambda x: k1*x + b,lambda x: k1*x + b + k2*(x-x0),lambda x: k1*x + b + k2*(x-x0) + k3*(x - x1)]
    return np.piecewise(x,condlist,funclist)

p,e = sp.optimize.curve_fit(piecewise_linear,x,y)

xd = np.linspace(0,1429228.0,285845)
plt.plot(x,y,"o")
plt.plot(xd,piecewise_linear(xd,*p))

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-60-a059f3aec3cb> in <module>
      6     return np.piecewise(x,funclist)
      7 
----> 8 p,y)
      9 
     10 xd = np.linspace(0,285845)

~/opt/anaconda3/lib/python3.7/site-packages/scipy/optimize/minpack.py in curve_fit(f,xdata,ydata,p0,sigma,absolute_sigma,check_finite,bounds,method,jac,**kwargs)
    761         # Remove full_output from kwargs,otherwise we're passing it in twice.
    762         return_full = kwargs.pop('full_output',False)
--> 763         res = leastsq(func,Dfun=jac,full_output=1,**kwargs)
    764         popt,pcov,infodict,errmsg,ier = res
    765         ysize = len(infodict['fvec'])

~/opt/anaconda3/lib/python3.7/site-packages/scipy/optimize/minpack.py in leastsq(func,args,Dfun,full_output,col_deriv,ftol,xtol,gtol,maxfev,epsfcn,factor,diag)
    386     if not isinstance(args,tuple):
    387         args = (args,)
--> 388     shape,dtype = _check_func('leastsq','func',func,n)
    389     m = shape[0]
    390 

~/opt/anaconda3/lib/python3.7/site-packages/scipy/optimize/minpack.py in _check_func(checker,argname,thefunc,numinputs,output_shape)
     24 def _check_func(checker,25                 output_shape=None):
---> 26     res = atleast_1d(thefunc(*((x0[:numinputs],) + args)))
     27     if (output_shape is not None) and (shape(res) != output_shape):
     28         if (output_shape[0] != 1):

~/opt/anaconda3/lib/python3.7/site-packages/scipy/optimize/minpack.py in func_wrapped(params)
    461     if transform is None:
    462         def func_wrapped(params):
--> 463             return func(xdata,*params) - ydata
    464     elif transform.ndim == 1:
    465         def func_wrapped(params):

<ipython-input-60-a059f3aec3cb> in piecewise_linear(x,k3)
      4     condlist = [x < x0,x >= x1]
      5     funclist = [lambda x: k1*x + b,lambda x: k1*x + b + k2*(x-x0) + k3*(x - x1)]
----> 6     return np.piecewise(x,funclist)
      7 
      8 p,y)

<__array_function__ internals> in piecewise(*args,**kwargs)

~/opt/anaconda3/lib/python3.7/site-packages/numpy/lib/function_base.py in piecewise(x,funclist,*args,**kw)
    615         raise ValueError(
    616             "with {} condition(s),either {} or {} functions are expected"
--> 617             .format(n,n,n+1)
    618         )
    619 

ValueError: with 1 condition(s),either 1 or 2 functions are expected

我不理解错误，也不知道这段代码出了什么问题。

这是我第一次尝试三个细分。最终，我想使用更多的细分。

任何人都知道这段代码有什么问题吗？