--- Исходное сообщение --- > От кого: "Alan G Isaac" <[email protected]> Дата: 15 марта 2013, 20:38:38 On 3/15/2013 9:21 AM, Dmitrey wrote: > Temporary walkaround for a serious bug in FuncDesigner automatic > differentiation kernel due to a bug in some versions of Python or NumPy, Are the suspected bugs documented somewhere? the suspected bugs are not documented yet, I guess it will be fixed in future versions of Python or numpy the bug is hard to locate and isolate, it looks like this: derivative_items = list(pointDerivative.items()) # temporary walkaround for a bug in Python or numpy derivative_items.sort(key=lambda elem: elem[0]) ###################################### for key, val in derivative_items: indexes = oovarsIndDict[key] # this line is not reached in the involved buggy case if not involveSparse and isspmatrix(val): val = val.A if r.ndim == 1: r[indexes[0]:indexes[1]] = val.flatten() if type(val) == ndarray else val else: # this line is not reached in the involved buggy case r[:, indexes[0]:indexes[1]] = val if val.shape == r.shape else val.reshape((funcLen, prod(val.shape)/funcLen)) so, pointDerivative is Python dict of pairs (F_i, N_i), where F_i are hashable objects, and even for the case when N_i are ordinary scalars (they can be numpy arrays or scipy sparse matrices) results of this code are different wrt was or was not derivative_items.sort() performed; total number of nonzero elements is same for both cases. oovarsIndDict is dict of pairs (F_i, (n_start_i, n_end_i)), and for the case N_i are all scalars for all i n_end_i = n_start_i - 1. Alan PS The word 'banausic' is very rare in English. Perhaps you meant 'unsophisticated'? google translate tells me "banausic" is more appropriate translation than "unsophisticated" for the sense I meant (those frameworks are aimed on modelling only numerical optimization problems, while FuncDesigner is suitable for modelling of systems of linear, nonlinear, ordinary differential equations, eigenvalue problems, interval analysis and much more). D.
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