[SciPy-user] number of function evaluation for leastsq
Michael McNeil Forbes
mforbes@physics.ubc...
Tue Apr 15 15:12:15 CDT 2008
On 15 Apr 2008, at 11:47 AM, dmitrey wrote:
> You could try using leastsq from scikits.openopt, it has mechanism for
> preventing double-call objective function with same x value as before.
>
> Regards, D.
>
> Achim Gaedke wrote:
>> Hello!
>>
>> I use scipy.optimize.leastsq to adopt paramters of a model to
>> measured
>> data. Each evaluation of that model costs 1.5 h of computation time.
...
>> Unfortunately that behaviour is not described and I would eagerly
>> demand
>> to avoid the superficial calls to the function.
>>
>> Yours, Achim
In principle, you could also just roll your own memoization to cache
the results (assuming that you can afford to store them, but since it
takes 1.5 hours per call, you can't have to many calls!):
def memoize(f):
def memoized_f(x,_cache={}):
key = tuple(numpy.ravel(x)) # Numpy arrays are not valid keys...
if not _cache.has_key(key):
_cache[key] = f(x)
return _cache[key]
return memoized_f
Now you can decorate your expensive function, and it will not compute
values for the same input.
@memoize
def expensive_function(x):
print "Computing %s**2..."%str(x)
return x*x
>>> expensive_function(1.01)
Computing 1.010000**2...
1.0201
>>> expensive_function(1.01)
1.0201
>>> expensive_function(array([1,2,3]))
>>> expensive_function(array([1,2,3]))
Computing [1 2 3]**2...
array([1, 4, 9])
>>> expensive_function(array([1,2,3]))
array([1, 4, 9])
Michael.
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