def optimize(self):
"""Optimize the objective function with respect to varying params."""
p, d = [], []
for param in self.varyingParams:
p.append(param.getValue())
d.append(param.delta)
p = array(p)
d = identity(len(d)) * array(d,Float)
return powell(self._objective, p, drxns=d)
def optimize(self, method=None, tol=None):
"""
Optimize with respect to varying params.
The call returns a 3-tuple (params, extremum, extra):
params = array of best-fit parameter values
extremum = the optimized statistic (min or max as appropriate)
extra = any add'l value or values (in a tuple) returned by
the specified minimization method (e.g., # iters)
"""
if method:
method = method.lower()
elif self.min_method:
method = self.min_method.lower()
else:
raise InferenceError, 'No minimization method has been specified!'
if tol is None:
if self.min_tol is None:
raise InferenceError, 'No minimization tolerance has been specified!'
# Most methods require a vector of starting param values and
# a scale for each; collect this info here (transformed if necessary).
p, d = [], []
if self.varying_params == []:
raise InferenceError, 'No parameters are set to vary for fitting!'
if self.use_unbounded:
for param in self.varying_params:
# Calculate delta for the unbounded version.
uv = param._unbounded_get_value()
v = param.get_value()
try: # We may need to try a step in both directions.
param.set_value(v+param.delta)
uv2 = param._unbounded_get_value()
d.append(uv2-uv)
except ParamRangeError:
param.set_value(v-param.delta)
uv2 = param._unbounded_get_value()
d.append(uv-uv2)
param.set_value(v)
p.append(uv)
else:
for param in self.varying_params:
p.append(param.get_value())
d.append(param.delta)
p = array(p)
if method == 'powell':
# Use a diagonal matrix for Powell's start directions.
dd = identity(len(d)) * array(d, float)
# For Powell, extra = # of iters
params, extremum, extra = powell(self._score, p, drxns=dd,
ftol=1.e-3, maxit=300)
# Repeat from the 1st fit, with smaller scales and shuffling
# the directions to avoid Powell's too-common collapsing to a
# subspace.
dd = 0.2 * identity(len(d)) * array(d, float)
params, extremum, extra = powell(self._score, params[:], drxns=dd,
ftol=1.e-3, maxit=200, shuffle=True)
else:
raise InferenceError, 'Invalid minimization method specified!'
if self.extremize == maximize: # adjust for sign reversal
extremum = -extremum
if self.use_unbounded: # Convert unbounded values back to normal.
for i, param in enumerate(self.varying_params):
params[i] = param.get_value()
return params, extremum, extra
