python - parameters constraint in numpy lstsq -

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i'm fitting set of data numpy.lstsq():

numpy.linalg.lstsq(a,b)[0]

returns like:

array([ -0.02179386,  0.08898451,  -0.17298247,  0.89314904])

note fitting solution mix of positive , negative float.

unfortunately, in physical model, fitting solutions represent mass: consequently i'd force lstsq() return set of positive values solution of fitting. possible this?

i.e.

solution = {a_1, ... a_i, ... a_n} a_i > 0 = {1, ..., n}

non-negative least squares implemented in scipy.optimize.nnls.

from scipy.optimize import nnls  solution = nnls(a, b)[0]

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