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In most cases, a fit will be performed over the parameters exactly as they are declared. In some cases, however, the fit can be unstable in one or more of the parameters. As an example consider a Gaussian:
The simple function fits over 3 parameters:
A - Amplitudec - Peak centre\sigma - Measure of the widthHowever, the \(\frac{1}{\sigma^2}\) dependence causes the fit to become unstable if the sigma parameter is varied by the minimization routine. A more stable fit can be achieved by fitting in \(\frac{1}{\sigma^2}\).
We could just naively change the sigma parameter to \(\frac{1}{\sigma^2}\) and fit over this. The function is now much less user-friendly though as we still want to think terms of the values of A, c, sigma and not A, c, \(\frac{1}{\sigma^2}\).
Our optimisation framework actually works with the concept of active
parameters, where we allow the fitting to proceed over different parameter
than that declared by init.
In order to translate between the two representations, the function class must provide two further methods:
activeParameter(self, index) - Should return the value of the parameter
at the given index that should take part in the fit.setActiveParameter(self, index, value) - Called by the framework when a
parameter value is updated. The function should then translate the value to
its original form.The active parameter methods are optional and are only required in the situations where the fitting parameter is not equal to the declared one. This is best illustrated by an example of a Gaussian (cut-down to show the relevant parts):
from mantid.api import *
import math
import numpy as np
class PyGaussian(IFunction1D):
def init(self):
self.declareParameter("Height")
self.declareParameter("PeakCentre")
# Parameter is declared as you want it to be seen by the user
self.declareParameter("Sigma")
def function1D(self, xvals):
# Can also be retrieve by index self.getParameterValue(0)
height = self.getParameterValue("Height")
peak_centre = self.getParameterValue("PeakCentre")
sigma = self.getParameterValue("Sigma")
weight = math.pow(1./sigma, 2)
offset_sq = np.square(xvals-peak_centre)
out = height*np.exp(-0.5*offset_sq*weight)
return out
def activeParameter(self, index):
# Return the value of the parameter at the given index
# (ordered by the order in init)
param_value = self.getParameterValue(index)
if index == 2: #Sigma. Actually fit to 1/(sigma^2) for stability
# param_value contains value of sigma
return 1./math.pow(param_value, 2)
else:
# Deal with other cases. In this case we just want value as is
return param_value
def setActiveParameter(self, index, value):
# The framework minimizer wants to update the value of the parameter
param_value = value
explicit = False
if index == 2: #sigma parameter index
# value passed in is actually 1/sigma^2 so we need to translate
# back to sigma
param_value = math.sqrt(math.fabs(1.0/value))
else:
param_value = value # others are 1:1
# Finally, actually update the values stored in function
# so that the next call to function1D sees them
self.setParameter(index, param_value, False)