
Hi,
I'm using the Meta.Numerics class UncertainMeasurementSample to fit a logistic function of the form y = A / (1 + C * exp(B * x)) to some real data representing price decay. Unfortunately, I keep getting Nonconvergence exceptions. I'd appreciate any help  the
reproduceable IronPython code is listed below:
import sys
import math
import clr
clr.AddReference('Meta.Numerics')
from Meta.Numerics.Statistics import *
# real data  no convergence
x = [65, 53, 53, 41, 41, 41, 41, 41, 41, 41, 29, 29, 29, 29]
y = [31.04, 37.01, 47.78, 51.34, 51.47, 51.32, 51.45, 52.04, 56.26, 56.28, 58.55, 57.78, 57.78, 57.79]
# artificial data  converges
#x = [0,1,2,3,4,5,6,7]
#y = [4,6,10,16,24,34,46,58]
# artificial data  no convergence
#x = [15, 14, 13, 12, 11, 10, 9, 8]
#y = [99, 98, 98, 94, 91, 86, 79, 69]
# a[0] ~ A, a[1] ~ B, a[2] ~ C, where y = A / (1 + C*exp(B*x))
def logistic(a, x): return(a[0] / (1 + a[2] * math.exp(a[1] * x)))
# a[0] ~ A, a[1] ~ B, where y = A*exp(B*x)
def exponential(a, x): return(a[0] * math.exp(a[1] * x))
def fit_curves():
A = 120; B = 0.02; C = 0.2
sample = UncertainMeasurementSample()
for i in range(0, len(x)):
sample.Add(x[i], y[i], 1.0)
print x[i], y[i]
try:
# fit exponential first  bad fit, but stable
fit1 = sample.FitToFunction(exponential, (A, B))
print 'GoodnessOfFit (exp): %s' % fit1.GoodnessOfFit.LeftProbability
print 'A: %s' % fit1.Parameter(0)
print 'B: %s' % fit1.Parameter(1)
# logistic  should be better fit, but bombs out
fit2 = sample.FitToFunction(logistic, (A, B, C))
print 'GoodnessOfFit (log): %s' % fit2.GoodnessOfFit.LeftProbability
print 'A: %s' % fit2.Parameter(0)
print 'B: %s' % fit2.Parameter(1)
print 'C: %s' % fit2.Parameter(2)
except Exception, ex:
print ex
fit_curves()


Coordinator
Mar 15, 2013 at 2:24 AM

Great to see people using the library from Iron Python! And thanks for the detailed repro. I will take a look and see what I can figure out.


Coordinator
Mar 18, 2013 at 8:17 PM

For your real data, starting from your initial values (120,0.02,0.2), it just takes the minimization algorithm longer to get to the minimum than the number of iterations it is preprogrammed to allow. If I monitor where it is going and start instead from
initial values close to that (60, 0.07, 0.01), it converges to a minimum and returns a result. Also, note that in the end your data don't fit your function all that well: chisquared per degree of freedom is 8, for a good fit you expect it to be 1. For your
example that converges, the fit is much better.
There are a few things you can do and a few things we can do.
For you:
 Have some logic to make reasonable initial guesses for paramter values. Averages, moments, and extrema are often useful for this.
 Realize that a bad fit is much more likely to thrash about trying to find a minimum on a very nonquadratic surface than a good fit which quickly finds itself on a nice quadratic surface with a clear, unique minimum. For any given maximum allowed number
of iterations, you are much more likely to reach that limit for a bad fit than for a good fit.
For us:
 The current number of interations allowed is 33 * dimension. I have no idea where that number came from, and it seems rather low. We need to look into it.
 Really we need to allow users to tell us a different value from our default for the allowed number of iterations, as we do for integration.



Yes, I expected it had something to do with my initial values, but couldn't think of a solution. I'll give my application logic more thought, to see if I can find robust initial values based on the data.
Thanks for the support, and for a great library! It's a great complement to .Net scripting languages like IronPython and F#.

