>>> m = LinearModel(data)
>>> outqueue = Queue()
>>> slope,offset = m.fit(outqueue=outqueue)
- >>> info = outqueue.get()
+ >>> info = outqueue.get(block=False)
>>> pprint(info) # doctest: +ELLIPSIS, +REPORT_UDIFF
{'active fitted parameters': array([ 6.999..., -32.889...]),
'active parameters': array([ 6.999..., -32.889...]),
diag=active_scale, **kwargs)
if self._rescale == True:
active_params = params
- params = [p*s for p,s in zip(params, self._param_scale_factors)]
+ 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)]
else:
active_params = params
if outqueue != None: