from numpy import arange, ndarray
from scipy.optimize import leastsq
+import scipy.optimize
class PoorFit (ValueError):
class ModelFitter (object):
"""A convenient wrapper around :func:`scipy.optimize.leastsq`.
+ TODO: look into :mod:`scipy.odr` as an alternative fitting
+ algorithm (minimizing the sum of squares of orthogonal distances,
+ vs. minimizing y distances).
+
Parameters
----------
d_data : array_like
Store any extra information useful inside your overridden
methods.
rescale : boolean
- Rescale parameters so the scale of each is 1.0. Also rescale
+ Rescale parameters so the guess for each is 1.0. Also rescale
the data so data.std() == 1.0.
Examples
... return [float(self._data[-1] - self._data[0])/len(self._data),
... self._data[0]]
... def guess_scale(self, params, outqueue=None):
- ... slope_scale = params[0]/10.
- ... if slope_scale == 0: # data is expected to be flat
- ... slope_scale = float(self._data.max()-self._data.min())/len(self._data)
- ... if slope_scale == 0: # data is completely flat
- ... slope_scale = 1.
- ... offset_scale = self._data.std()/10.0
- ... if offset_scale == 0: # data is completely flat
- ... offset_scale = 1.
+ ... slope_scale = 0.1
+ ... if params[1] == 0:
+ ... offset_scale = 1
+ ... else:
+ ... offset_scale = 0.1*self._data.std()/abs(params[1])
+ ... if offset_scale == 0: # data is completely flat
+ ... offset_scale = 1.
... return [slope_scale, offset_scale]
>>> data = 20*numpy.sin(arange(1000)) + 7.*arange(1000) - 33.0
>>> m = LinearModel(data)
>>> outqueue = Queue()
>>> slope,offset = m.fit(outqueue=outqueue)
- >>> info = outqueue.get()
+ >>> info = outqueue.get(block=False)
>>> pprint(info) # doctest: +ELLIPSIS, +REPORT_UDIFF
{'active fitted parameters': array([ 6.999..., -32.889...]),
'active parameters': array([ 6.999..., -32.889...]),
- 'active scale': [0.699..., 202.071...],
'convergence flag': 2,
'covariance matrix': array([[ 1.199...e-08, -5.993...e-06],
[ -5.993...e-06, 3.994...e-03]]),
'initial parameters': [6.992..., -33.0],
'message': 'The relative error between two consecutive iterates is at most 0.000...',
'rescaled': False,
- 'scale': [0.699..., 202.071...]}
+ 'scale': [0.100..., 6.123...]}
We round the outputs to protect the doctest against differences in
machine rounding during computation. We expect the values to be close
self.info = info
self._rescale = rescale
if rescale == True:
- self._data_scale_factor = data.std()
+ for x in [data.std(), data.max()-data.min(), abs(data.max()), 1.0]:
+ if x != 0:
+ self._data_scale_factor = x
+ break
else:
self._data_scale_factor = 1.0
return []
def guess_scale(self, params, outqueue=None):
+ """Guess the problem length scale in each parameter dimension.
+
+ Notes
+ -----
+ From the :func:`scipy.optimize.leastsq` documentation, `diag`
+ (which we refer to as `scale`, sets `factor * || diag * x||`
+ as the initial step. If `x == 0`, then `factor` is used
+ instead (from `lmdif.f`_)::
+
+ do 70 j = 1, n
+ wa3(j) = diag(j)*x(j)
+ 70 continue
+ xnorm = enorm(n,wa3)
+ delta = factor*xnorm
+ if (delta .eq. zero) delta = factor
+
+ For most situations then, you don't need to do anything fancy.
+ The default scaling (if you don't set a scale) is::
+
+ c on the first iteration and if mode is 1, scale according
+ c to the norms of the columns of the initial jacobian.
+
+ (i.e. `diag(j) = acnorm(j)`, where `acnorm(j) is the norm of the `j`th column
+ of the initial Jacobian).
+
+ .. _lmdif.f: http://www.netlib.org/minpack/lmdif.f
+ """
return None
def residual(self, params):
if self._rescale == True:
params = [p*s for p,s in zip(params, self._param_scale_factors)]
residual = self._data - self.model(params)
- if self._rescale == True or False:
+ if self._rescale == True:
residual /= self._data_scale_factor
return residual
initial_params = self.guess_initial_params(outqueue)
if scale == None:
scale = self.guess_scale(initial_params, outqueue)
- assert min(scale) > 0, scale
+ if scale != None:
+ assert min(scale) > 0, scale
if self._rescale == True:
- self._param_scale_factors = scale
- active_scale = [1.0 for s in scale]
+ self._param_scale_factors = initial_params
+ for i,s in enumerate(self._param_scale_factors):
+ if s == 0:
+ self._param_scale_factors[i] = 1.0
active_params = [p/s for p,s in zip(initial_params,
self._param_scale_factors)]
else:
- active_scale = scale
active_params = initial_params
params,cov,info,mesg,ier = leastsq(
func=self.residual, x0=active_params, full_output=True,
- diag=active_scale, **kwargs)
+ diag=scale, **kwargs)
if self._rescale == True:
active_params = params
- params = [p*s for p,s in zip(params, self._param_scale_factors)]
+ if len(initial_params) == 1: # params is a float
+ params = params * self._param_scale_factors[0]
+ else:
+ params = [p*s for p,s in zip(params,
+ self._param_scale_factors)]
else:
active_params = params
if outqueue != None:
'initial parameters': initial_params,
'active parameters': active_params,
'scale': scale,
- 'active scale': active_scale,
'data scale factor': self._data_scale_factor,
'active fitted parameters': active_params,
'fitted parameters': params,
'convergence flag': ier,
})
return params
+
+# Example ORD code from the old fit.py
+# def dist(px,py,linex,liney):
+# distancesx=scipy.array([(px-x)**2 for x in linex])
+# minindex=numpy.argmin(distancesx)
+# print px, linex[0], linex[-1]
+# return (py-liney[minindex])**2
+#
+#
+# def f_wlc(params,x,T=T):
+# '''
+# wlc function for ODR fitting
+# '''
+# lambd,pii=params
+# Kb=(1.38065e-23)
+# therm=Kb*T
+# y=(therm*pii/4.0) * (((1-(x*lambd))**-2) - 1 + (4*x*lambd))
+# return y
+#
+# def f_wlc_plfix(params,x,pl_value=pl_value,T=T):
+# '''
+# wlc function for ODR fitting
+# '''
+# lambd=params
+# pii=1/pl_value
+# Kb=(1.38065e-23)
+# therm=Kb*T
+# y=(therm*pii/4.0) * (((1-(x*lambd))**-2) - 1 + (4*x*lambd))
+# return y
+#
+# #make the ODR fit
+# realdata=scipy.odr.RealData(xchunk_corr_up,ychunk_corr_up)
+# if pl_value:
+# model=scipy.odr.Model(f_wlc_plfix)
+# o = scipy.odr.ODR(realdata, model, p0_plfix)
+# else:
+# model=scipy.odr.Model(f_wlc)
+# o = scipy.odr.ODR(realdata, model, p0)
+#
+# o.set_job(fit_type=2)
+# out=o.run()
+# fit_out=[(1/i) for i in out.beta]
+#
+# #Calculate fit errors from output standard deviations.
+# #We must propagate the error because we fit the *inverse* parameters!
+# #The error = (error of the inverse)*(value**2)
+# fit_errors=[]
+# for sd,value in zip(out.sd_beta, fit_out):
+# err_real=sd*(value**2)
+# fit_errors.append(err_real)
+
projects / hooke.git / blobdiff