* Fitting seemed to work, but still not perfectly. Do not use in production

yet.

math/fitting.py

@@ -1,4 +1,4 @@
-# $Id: fitting.py,v 1.1 2010-01-22 18:44:59 wirawan Exp $
+# $Id: fitting.py,v 1.2 2010-05-28 18:43:39 wirawan Exp $
 #
 # wpylib.math.fitting module
 # Created: 20100120
@@ -118,18 +118,27 @@ class Poly_order4(Poly_base):
 
 
 
-def fit_func(Funct, Data=None, Guess=None, x=None, y=None):
-  '''
+def fit_func(Funct, Data=None, Guess=None, x=None, y=None,
+             debug=10,
+             method='leastsq', opts={}):
+  """
+  Performs a function fitting.
+  The domain of the function is a D-dimensional vector, and the function
+  yields a scalar.
+
   Funct is a python function (or any callable object) with argument list of
   (C, x), where:
-  * C is the cofficients (parameters) to be adjusted by the fitting process
+  * C is the cofficients (parameters) being adjusted by the fitting process
     (it is a sequence or a 1-D array)
-  * x is a 2-D array (or sequence of like nature). The "row" size is the dimensionality
-    of the domain, while the "column" is the number of data points, whose count must be
-    equal to the size of y data below.
+  * x is a 2-D array (or sequence of like nature), say,
+    of size "N rows times M columns".
+    N is the dimensionality of the domain, while
+    M is the number of data points, whose count must be equal to the
+    size of y data below.
+    For a 2-D fitting, for example, x should be a column array.
 
-  Inspect Poly_base, Poly_order2, and other similar function classes in this module
-  to see the example of the Funct function.
+  Inspect Poly_base, Poly_order2, and other similar function classes in this
+  module to see the example of the Funct function.
 
   The measurement (input) datasets, against which the function is to be fitted,
   can be specified in one of two ways:
@@ -139,11 +148,10 @@ def fit_func(Funct, Data=None, Guess=None, x=None, y=None):
     Or,
   * via Data argument (which is a multi-column dataset
 
-  '''
+  """
   global last_fit_rslt, last_chi_sqr
-  from scipy.optimize import leastsq
+  from scipy.optimize import leastsq, anneal
   # We want to minimize this error:
-  fun_err = lambda CC, xx, yy: abs(Funct(CC,xx) - yy)
   if Data != None: # an alternative way to specifying x and y
     y = Data[0]
     x = Data[1:] # possibly multidimensional!
@@ -152,12 +160,36 @@ def fit_func(Funct, Data=None, Guess=None, x=None, y=None):
     Guess = Funct.Guess(y)
   elif Guess == None: # VERY OLD, DO NOT USE ANYMORE!
     Guess = [ y.mean() ] + [0.0, 0.0] * len(x)
+
+  fun_err = lambda CC, xx, yy: abs(Funct(CC,xx) - yy)
+  fun_err2 = lambda CC, xx, yy: numpy.sum(abs(Funct(CC,xx) - yy)**2)
+
+  if debug >= 5:
+    print "Guess params:"
+    print Guess
+
+  if method == 'leastsq':
     rslt = leastsq(fun_err,
                    x0=Guess, # initial coefficient guess
                    args=(x,y), # data onto which the function is fitted
-                 full_output=1)
+                   full_output=1,
+                   **opts
+                   )
+  elif method == 'anneal':
+    rslt = anneal(fun_err2,
+                  x0=Guess, # initial coefficient guess
+                  args=(x,y), # data onto which the function is fitted
+                  full_output=1,
+                  **opts
+                 )
+  else:
+    raise ValueError, "Unsupported minimization method: %s" % method
   last_fit_rslt = rslt
-  last_chi_sqr = sum( fun_err(rslt[0], x, y)**2 )
+  last_chi_sqr = fun_err2(rslt[0], x, y)
+  if (debug >= 10):
+    #print "Fit-message: ", rslt[]
+    print "Fit-result:"
+    print "\n".join([ "%2d  %s" % (ii, rslt[ii]) for ii in xrange(len(rslt)) ])
   print "params = ", rslt[0]
   print "chi square = ", last_chi_sqr / len(y)
   return rslt[0]