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@ -1,4 +1,4 @@ |
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# $Id: fitting.py,v 1.2 2010-05-28 18:43:39 wirawan Exp $ |
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# $Id: fitting.py,v 1.3 2010-11-05 02:28:20 wirawan Exp $ |
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# |
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# wpylib.math.fitting module |
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# Created: 20100120 |
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@ -117,9 +117,12 @@ class Poly_order4(Poly_base): |
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for i in xrange(len(x)) ]) |
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class fit_result(dict): |
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pass |
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def fit_func(Funct, Data=None, Guess=None, x=None, y=None, |
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debug=10, |
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outfmt=1, |
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method='leastsq', opts={}): |
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""" |
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Performs a function fitting. |
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@ -137,6 +140,9 @@ def fit_func(Funct, Data=None, Guess=None, x=None, y=None, |
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size of y data below. |
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For a 2-D fitting, for example, x should be a column array. |
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The "y" array is a 1-D array of length M, which contain the "measured" |
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value of the function at every domain point given in "x". |
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Inspect Poly_base, Poly_order2, and other similar function classes in this |
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module to see the example of the Funct function. |
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@ -146,23 +152,40 @@ def fit_func(Funct, Data=None, Guess=None, x=None, y=None, |
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(multidimensional) coordinate of the Funct's domain. |
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y is a one-dimensional dataset. |
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Or, |
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* via Data argument (which is a multi-column dataset |
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* via Data argument (which is a multi-column dataset, where the first row |
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is the "y" argument). |
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""" |
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global last_fit_rslt, last_chi_sqr |
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from scipy.optimize import leastsq, anneal |
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from scipy.optimize import fmin, leastsq, anneal |
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# We want to minimize this error: |
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if Data != None: # an alternative way to specifying x and y |
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y = Data[0] |
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x = Data[1:] # possibly multidimensional! |
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if hasattr(Funct, "Guess"): |
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if hasattr(Funct, "Guess_xy"): |
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# Try to provide an initial guess |
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Guess = Funct.Guess_xy(x, y) |
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elif hasattr(Funct, "Guess"): |
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# Try to provide an initial guess |
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# This is an older version with y-only argument |
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Guess = Funct.Guess(y) |
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elif Guess == None: # VERY OLD, DO NOT USE ANYMORE! |
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Guess = [ y.mean() ] + [0.0, 0.0] * len(x) |
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fun_err = lambda CC, xx, yy: abs(Funct(CC,xx) - yy) |
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fun_err2 = lambda CC, xx, yy: numpy.sum(abs(Funct(CC,xx) - yy)**2) |
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if debug < 20: |
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fun_err = lambda CC, xx, yy: abs(Funct(CC,xx) - yy) |
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fun_err2 = lambda CC, xx, yy: numpy.sum(abs(Funct(CC,xx) - yy)**2) |
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else: |
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def fun_err(CC, xx, yy): |
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ff = Funct(CC,xx) |
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r = abs(ff - yy) |
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print " err: %s << %s << %s, %s" % (r, ff, CC, xx) |
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return r |
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def fun_err2(CC, xx, yy): |
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ff = Funct(CC,xx) |
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r = numpy.sum(abs(ff - yy)**2) |
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print " err: %s << %s << %s, %s" % (r, ff, CC, xx) |
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return r |
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if debug >= 5: |
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print "Guess params:" |
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@ -175,6 +198,15 @@ def fit_func(Funct, Data=None, Guess=None, x=None, y=None, |
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full_output=1, |
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**opts |
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) |
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keys = ('xopt', 'cov_x', 'infodict', 'mesg', 'ier') |
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elif method == 'fmin': |
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rslt = fmin(fun_err2, |
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x0=Guess, # initial coefficient guess |
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args=(x,y), # data onto which the function is fitted |
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full_output=1, |
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**opts |
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) |
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keys = ('xopt', 'fopt', 'iter', 'funcalls', 'warnflag', 'allvecs') |
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elif method == 'anneal': |
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rslt = anneal(fun_err2, |
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x0=Guess, # initial coefficient guess |
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@ -182,15 +214,25 @@ def fit_func(Funct, Data=None, Guess=None, x=None, y=None, |
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full_output=1, |
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**opts |
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) |
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keys = ('xopt', 'fopt', 'T', 'funcalls', 'iter', 'accept', 'retval') |
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else: |
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raise ValueError, "Unsupported minimization method: %s" % method |
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chi_sqr = fun_err2(rslt[0], x, y) |
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last_chi_sqr = chi_sqr |
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last_fit_rslt = rslt |
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last_chi_sqr = fun_err2(rslt[0], x, y) |
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if (debug >= 10): |
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#print "Fit-message: ", rslt[] |
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print "Fit-result:" |
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print "\n".join([ "%2d %s" % (ii, rslt[ii]) for ii in xrange(len(rslt)) ]) |
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print "params = ", rslt[0] |
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print "chi square = ", last_chi_sqr / len(y) |
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return rslt[0] |
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if debug >= 1: |
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print "params = ", rslt[0] |
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print "chi square = ", last_chi_sqr / len(y) |
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if outfmt == 1: |
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return rslt[0] |
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else: |
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rec = fit_result() |
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for (k, v) in zip(keys, rslt): |
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rec[k] = v |
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rec['chi_square'] = chi_sqr |
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return rec |
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