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* wpylib.math.fitting.fit_func: Added support for weight (or y uncertainty).

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* For `leastsq' fit method, also introduced 'xerr' output (in outfmt=0)
  as the error estimate of the fitted parameters.
master
Wirawan Purwanto 11 years ago
parent 37b0939671
commit 564d1f4364
1 changed files with 73 additions and 29 deletions
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  1. 102
      math/fitting/__init__.py

102
math/fitting/__init__.py
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@ -121,7 +121,9 @@ class Poly_order4(Poly_base):
class fit_result(result_base):
pass
def fit_func(Funct, Data=None, Guess=None, x=None, y=None,
def fit_func(Funct, Data=None, Guess=None,
x=None, y=None,
w=None, dy=None,
debug=0,
outfmt=1,
Funct_hook=None,
@ -145,6 +147,11 @@ def fit_func(Funct, Data=None, Guess=None, x=None, y=None,
The "y" array is a 1-D array of length M, which contain the "measured"
value of the function at every domain point given in "x".
The "w" or "dy" array (only one of them can be specified in a call),
if given, specifies either the weight or standard error of the y data.
If "dy" is specified, then "w" is defined to be (1.0 / dy**2), per usual
convention.
Inspect Poly_base, Poly_order2, and other similar function classes in this
module to see the example of the Funct function.
@ -157,9 +164,9 @@ def fit_func(Funct, Data=None, Guess=None, x=None, y=None,
* via Data argument (which is a multi-column dataset, where the first row
is the "y" argument).
Debugging and other investigations can be done with Funct_hook, which,
if defined, will be called every time right after Funct is called.
It is called with the following parameters:
Debugging and other investigations can be done with "Funct_hook", which,
if defined, will be called every time right after "Funct" is called.
It is called with the following signature:
Funct_hook(C, x, y, f, r)
where
f := f(C,x)
@ -173,6 +180,10 @@ def fit_func(Funct, Data=None, Guess=None, x=None, y=None,
if Data != None: # an alternative way to specifying x and y
y = Data[0]
x = Data[1:] # possibly multidimensional!
if debug >= 10:
print "Dimensionality of the domain is: ", len(x)
if Guess != None:
pass
elif hasattr(Funct, "Guess_xy"):
@ -185,40 +196,57 @@ def fit_func(Funct, Data=None, Guess=None, x=None, y=None,
elif Guess == None: # VERY OLD, DO NOT USE ANYMORE!
Guess = [ y.mean() ] + [0.0, 0.0] * len(x)
if debug >= 5:
print "Guess params:"
print Guess
if Funct_hook != None:
if not hasattr(Funct_hook, "__call__"):
raise TypeError, "Funct_hook argument must be a callable function."
def fun_err(CC, xx, yy):
def fun_err(CC, xx, yy, ww):
"""Computes the error of the fitted functional against the
reference data points:
* CC = current function parameters
* xx = domain points of the ("experimental") data
* yy = target points of the ("experimental") data
* ww = weights of the ("experimental") data
"""
ff = Funct(CC,xx)
r = (ff - yy)
r = (ff - yy) * ww
Funct_hook(CC, xx, yy, ff, r)
return r
fun_err2 = lambda CC, xx, yy: numpy.sum(abs(fun_err(CC, xx, yy))**2)
elif debug < 20:
fun_err = lambda CC, xx, yy: (Funct(CC,xx) - yy)
fun_err2 = lambda CC, xx, yy: numpy.sum(abs(Funct(CC,xx) - yy)**2)
else:
def fun_err(CC, xx, yy):
def fun_err(CC, xx, yy, ww):
ff = Funct(CC,xx)
r = (ff - yy)
print " err: %s << %s << %s, %s" % (r, ff, CC, xx)
r = (ff - yy) * ww
return r
def fun_err2(CC, xx, yy):
else:
def fun_err(CC, xx, yy, ww):
ff = Funct(CC,xx)
r = numpy.sum(abs(ff - yy)**2)
print " err: %s << %s << %s, %s" % (r, ff, CC, xx)
r = (ff - yy) * ww
print " err: %s << %s << %s, %s, %s" % (r, ff, CC, xx, ww)
return r
if debug >= 5:
print "Guess params:"
print Guess
fun_err2 = lambda CC, xx, yy, ww: numpy.sum(abs(fun_err(CC, xx, yy, ww))**2)
if w != None and dy != None:
raise TypeError, "Only one of w or dy can be specified."
if dy != None:
sqrtw = 1.0 / dy
elif w != None:
sqrtw = numpy.sqrt(w)
else:
sqrtw = 1.0
# Full result is stored in rec
rec = fit_result()
extra_keys = {}
if method == 'leastsq':
# modified Levenberg-Marquardt algorithm
rslt = leastsq(fun_err,
x0=Guess, # initial coefficient guess
args=(x,y), # data onto which the function is fitted
args=(x,y,sqrtw), # data onto which the function is fitted
full_output=1,
**opts
)
@ -227,11 +255,22 @@ def fit_func(Funct, Data=None, Guess=None, x=None, y=None,
# map the output values to the same keyword as other methods below:
'funcalls': (lambda : rslt[2]['nfev']),
}
# Added estimate of fit parameter uncertainty (matching GNUPLOT parameter
# uncertainty.
# The error is estimated to be the diagonal of cov_x, multiplied by the WSSR
# (chi_square below) and divided by the number of fit degrees of freedom.
# I used newer scipy.optimize.curve_fit() routine as my cheat sheet here.
if outfmt == 0:
if rslt[1] != None and len(y) > len(rslt[0]):
NDF = len(y) - len(rslt[0])
extra_keys['xerr'] = (lambda:
numpy.sqrt(numpy.diagonal(rslt[1]) * rec['chi_square'] / NDF)
)
elif method == 'fmin':
# Nelder-Mead Simplex algorithm
rslt = fmin(fun_err2,
x0=Guess, # initial coefficient guess
args=(x,y), # data onto which the function is fitted
args=(x,y,sqrtw), # data onto which the function is fitted
full_output=1,
**opts
)
@ -240,7 +279,7 @@ def fit_func(Funct, Data=None, Guess=None, x=None, y=None,
# Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm
rslt = fmin_bfgs(fun_err2,
x0=Guess, # initial coefficient guess
args=(x,y), # data onto which the function is fitted
args=(x,y,sqrtw), # data onto which the function is fitted
full_output=1,
**opts
)
@ -248,14 +287,14 @@ def fit_func(Funct, Data=None, Guess=None, x=None, y=None,
elif method == 'anneal':
rslt = anneal(fun_err2,
x0=Guess, # initial coefficient guess
args=(x,y), # data onto which the function is fitted
args=(x,y,sqrtw), # data onto which the function is fitted
full_output=1,
**opts
)
keys = ('xopt', 'fopt', 'T', 'funcalls', 'iter', 'accept', 'retval')
else:
raise ValueError, "Unsupported minimization method: %s" % method
chi_sqr = fun_err2(rslt[0], x, y)
chi_sqr = fun_err2(rslt[0], x, y, sqrtw)
last_chi_sqr = chi_sqr
last_fit_rslt = rslt
if (debug >= 10):
@ -265,14 +304,19 @@ def fit_func(Funct, Data=None, Guess=None, x=None, y=None,
if debug >= 1:
print "params = ", rslt[0]
print "chi square = ", last_chi_sqr / len(y)
if outfmt == 1:
return rslt[0]
else: # outfmt == 0 -- full result.
rec = fit_result(dict(zip(keys, rslt)))
if outfmt == 0: # outfmt == 0 -- full result.
rec.update(dict(zip(keys, rslt)))
rec['chi_square'] = chi_sqr
rec['fit_method'] = method
# If there are extra keys, record them here:
for (k,v) in extra_keys.iteritems():
rec[k] = v()
return rec
elif outfmt == 1:
return rslt[0]
else:
try:
x = str(outfmt)
except:
x = "(?)"
raise ValueError, "Invalid `outfmt' argument = " + x

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