-# Copyright (C) 2010 W. Trevor King <wking@drexel.edu>
+# Copyright (C) 2010-2012 W. Trevor King <wking@tremily.us>
#
# This file is part of Hooke.
#
-# Hooke is free software: you can redistribute it and/or modify it
-# under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 3 of the
-# License, or (at your option) any later version.
+# Hooke is free software: you can redistribute it and/or modify it under the
+# terms of the GNU Lesser General Public License as published by the Free
+# Software Foundation, either version 3 of the License, or (at your option) any
+# later version.
#
-# Hooke is distributed in the hope that it will be useful, but WITHOUT
-# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY
-# or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General
-# Public License for more details.
+# Hooke is distributed in the hope that it will be useful, but WITHOUT ANY
+# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR
+# A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more
+# details.
#
-# You should have received a copy of the GNU Lesser General Public
-# License along with Hooke. If not, see
-# <http://www.gnu.org/licenses/>.
+# You should have received a copy of the GNU Lesser General Public License
+# along with Hooke. If not, see <http://www.gnu.org/licenses/>.
"""Provide :class:`ModelFitter` to make arbitrary model fitting easy.
"""
from numpy import arange, ndarray
+from scipy import __version__ as _scipy_version
from scipy.optimize import leastsq
-import scipy.optimize
+
+_strings = _scipy_version.split('.')
+# Don't convert third string to an integer in case of (for example) '0.7.2rc3'
+_SCIPY_VERSION = (int(_strings[0]), int(_strings[1]), _strings[2])
+del _strings
class PoorFit (ValueError):
Store any extra information useful inside your overridden
methods.
rescale : boolean
- Rescale parameters so the scale of each is 1.0. Also rescale
+ Rescale parameters so the guess for each is 1.0. Also rescale
the data so data.std() == 1.0.
Examples
... return [float(self._data[-1] - self._data[0])/len(self._data),
... self._data[0]]
... def guess_scale(self, params, outqueue=None):
- ... slope_scale = params[0]/10.
- ... if slope_scale == 0: # data is expected to be flat
- ... slope_scale = float(self._data.max()-self._data.min())/len(self._data)
- ... if slope_scale == 0: # data is completely flat
- ... slope_scale = 1.
- ... offset_scale = self._data.std()/10.0
- ... if offset_scale == 0: # data is completely flat
- ... offset_scale = 1.
+ ... slope_scale = 0.1
+ ... if params[1] == 0:
+ ... offset_scale = 1
+ ... else:
+ ... offset_scale = 0.1*self._data.std()/abs(params[1])
+ ... if offset_scale == 0: # data is completely flat
+ ... offset_scale = 1.
... return [slope_scale, offset_scale]
>>> data = 20*numpy.sin(arange(1000)) + 7.*arange(1000) - 33.0
>>> m = LinearModel(data)
>>> outqueue = Queue()
>>> slope,offset = m.fit(outqueue=outqueue)
>>> info = outqueue.get(block=False)
- >>> pprint(info) # doctest: +ELLIPSIS, +REPORT_UDIFF
+ >>> pprint(info) # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE, +REPORT_UDIFF
{'active fitted parameters': array([ 6.999..., -32.889...]),
'active parameters': array([ 6.999..., -32.889...]),
- 'active scale': [0.699..., 202.071...],
- 'convergence flag': 2,
+ 'convergence flag': ...,
'covariance matrix': array([[ 1.199...e-08, -5.993...e-06],
[ -5.993...e-06, 3.994...e-03]]),
'data scale factor': 1.0,
'fvec': array([...]),
'ipvt': array([1, 2]),
'nfev': 7,
- 'qtf': array([ 2.851...e-07, 1.992...e-06])},
+ 'qtf': array([...])},
'initial parameters': [6.992..., -33.0],
- 'message': 'The relative error between two consecutive iterates is at most 0.000...',
+ 'message': '...relative error between two consecutive iterates is at most 0.000...',
'rescaled': False,
- 'scale': [0.699..., 202.071...]}
+ 'scale': [0.100..., 6.123...]}
We round the outputs to protect the doctest against differences in
machine rounding during computation. We expect the values to be close
to the input settings (slope 7, offset -33).
- >>> print '%.3f' % slope
+ >>> print('{:.3f}'.format(slope))
7.000
- >>> print '%.3f' % offset
+ >>> print('{:.3f}'.format(offset))
-32.890
The offset is a bit off because, the range is not a multiple of
>>> m = LinearModel(data, rescale=True)
>>> outqueue = Queue()
>>> slope,offset = m.fit(outqueue=outqueue)
- >>> print '%.3f' % slope
+ >>> print('{:.3f}'.format(slope))
7.000
- >>> print '%.3f' % offset
+ >>> print('{:.3f}'.format(offset))
-32.890
+
+ Test single-parameter models:
+
+ >>> class SingleParameterModel (LinearModel):
+ ... '''Simple linear model.
+ ... '''
+ ... def model(self, params):
+ ... return super(SingleParameterModel, self).model([params[0], 0.])
+ ... def guess_initial_params(self, outqueue=None):
+ ... return super(SingleParameterModel, self
+ ... ).guess_initial_params(outqueue)[:1]
+ ... def guess_scale(self, params, outqueue=None):
+ ... return super(SingleParameterModel, self
+ ... ).guess_scale([params[0], 0.], outqueue)[:1]
+ >>> data = 20*numpy.sin(arange(1000)) + 7.*arange(1000)
+ >>> m = SingleParameterModel(data)
+ >>> slope, = m.fit(outqueue=outqueue)
+ >>> print('{:.3f}'.format(slope))
+ 7.000
"""
def __init__(self, *args, **kwargs):
self.set_data(*args, **kwargs)
self.info = info
self._rescale = rescale
if rescale == True:
- self._data_scale_factor = data.std()
+ for x in [data.std(), data.max()-data.min(), abs(data.max()), 1.0]:
+ if x != 0:
+ self._data_scale_factor = x
+ break
else:
self._data_scale_factor = 1.0
return []
def guess_scale(self, params, outqueue=None):
+ """Guess the problem length scale in each parameter dimension.
+
+ Notes
+ -----
+ From the :func:`scipy.optimize.leastsq` documentation, `diag`
+ (which we refer to as `scale`, sets `factor * || diag * x||`
+ as the initial step. If `x == 0`, then `factor` is used
+ instead (from `lmdif.f`_)::
+
+ do 70 j = 1, n
+ wa3(j) = diag(j)*x(j)
+ 70 continue
+ xnorm = enorm(n,wa3)
+ delta = factor*xnorm
+ if (delta .eq. zero) delta = factor
+
+ For most situations then, you don't need to do anything fancy.
+ The default scaling (if you don't set a scale) is::
+
+ c on the first iteration and if mode is 1, scale according
+ c to the norms of the columns of the initial jacobian.
+
+ (i.e. `diag(j) = acnorm(j)`, where `acnorm(j) is the norm of the `j`th column
+ of the initial Jacobian).
+
+ .. _lmdif.f: http://www.netlib.org/minpack/lmdif.f
+ """
return None
def residual(self, params):
if self._rescale == True:
params = [p*s for p,s in zip(params, self._param_scale_factors)]
residual = self._data - self.model(params)
- if self._rescale == True or False:
+ if False: # fit debugging
+ if not hasattr(self, '_i_'):
+ self._i_ = 0
+ self._data.tofile('data.%d' % self._i_, sep='\n')
+ self.model(params).tofile('model.%d' % self._i_, sep='\n')
+ self._i_ += 1
+ if self._rescale == True:
residual /= self._data_scale_factor
return residual
initial_params = self.guess_initial_params(outqueue)
if scale == None:
scale = self.guess_scale(initial_params, outqueue)
- assert min(scale) > 0, scale
+ if scale != None:
+ assert min(scale) > 0, scale
if self._rescale == True:
- self._param_scale_factors = scale
- active_scale = [1.0 for s in scale]
+ self._param_scale_factors = initial_params
+ for i,s in enumerate(self._param_scale_factors):
+ if s == 0:
+ self._param_scale_factors[i] = 1.0
active_params = [p/s for p,s in zip(initial_params,
self._param_scale_factors)]
else:
- active_scale = scale
active_params = initial_params
params,cov,info,mesg,ier = leastsq(
func=self.residual, x0=active_params, full_output=True,
- diag=active_scale, **kwargs)
+ diag=scale, **kwargs)
+ if len(initial_params) == 1 and _SCIPY_VERSION < (0, 8, '0'):
+ # params is a float for scipy < 0.8.0. Convert to list.
+ params = [params]
if self._rescale == True:
active_params = params
- if len(initial_params) == 1: # params is a float
- params = params * self._param_scale_factors[0]
- else:
- params = [p*s for p,s in zip(params,
- self._param_scale_factors)]
+ params = [p*s for p,s in zip(params,
+ self._param_scale_factors)]
else:
active_params = params
if outqueue != None:
'initial parameters': initial_params,
'active parameters': active_params,
'scale': scale,
- 'active scale': active_scale,
'data scale factor': self._data_scale_factor,
'active fitted parameters': active_params,
'fitted parameters': params,
# def dist(px,py,linex,liney):
# distancesx=scipy.array([(px-x)**2 for x in linex])
# minindex=numpy.argmin(distancesx)
-# print px, linex[0], linex[-1]
+# print(px, linex[0], linex[-1])
# return (py-liney[minindex])**2
#
#
projects / hooke.git / blobdiff