B &]\P@sHddlmZmZmZddlmZddlmZmZm Z dgZ dddZ dS) )divisionprint_functionabsolute_import)_nnls)asarray_chkfinitezerosdoublennlsNc Cstt||f\}}t|jdkr(tdt|jdkr>td|j\}}||jdkr^td|dkrjdnt|}t|ftd }t|ftd }t|ftd }t ||||||||\}} } | dkrt d || fS) a2 Solve ``argmin_x || Ax - b ||_2`` for ``x>=0``. This is a wrapper for a FORTRAN non-negative least squares solver. Parameters ---------- A : ndarray Matrix ``A`` as shown above. b : ndarray Right-hand side vector. maxiter: int, optional Maximum number of iterations, optional. Default is ``3 * A.shape[1]``. Returns ------- x : ndarray Solution vector. rnorm : float The residual, ``|| Ax-b ||_2``. Notes ----- The FORTRAN code was published in the book below. The algorithm is an active set method. It solves the KKT (Karush-Kuhn-Tucker) conditions for the non-negative least squares problem. References ---------- Lawson C., Hanson R.J., (1987) Solving Least Squares Problems, SIAM zexpected matrixrzexpected vectorrzincompatible dimensionsN)Zdtypeztoo many iterations) maprlenshape ValueErrorintrr rr RuntimeError) AbmaxitermnwZzzindexxZrnormmoder2lib/python3.7/site-packages/scipy/optimize/nnls.pyr s " )N) Z __future__rrrrZnumpyrrr __all__r rrrrs