a a@s4ddlmZddlmZmZmZdgZdddZdS))__nnls)asarray_chkfinitezerosdoublennlsNc Cstt||f\}}t|jdkr4tdd|jft|jdkrVtdd|jf|j\}}||jdkrtdd ||jdff|d urd nt|}t|ftd }t|ftd }t|ftd }t ||||||||\}} } | dkrt d || fS)a 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``. See Also -------- lsq_linear : Linear least squares with bounds on the variables 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 Examples -------- >>> from scipy.optimize import nnls ... >>> A = np.array([[1, 0], [1, 0], [0, 1]]) >>> b = np.array([2, 1, 1]) >>> nnls(A, b) (array([1.5, 1. ]), 0.7071067811865475) >>> b = np.array([-1, -1, -1]) >>> nnls(A, b) (array([0., 0.]), 1.7320508075688772) z)Expected a two-dimensional array (matrix)z, but the shape of A is %srz(Expected a one-dimensional array (vectorz, but the shape of b is %srz0Incompatible dimensions. The first dimension of z#A is %s, while the shape of b is %sN)Zdtypeztoo many iterations) maprlenshape ValueErrorintrrrr RuntimeError) AbmaxitermnwzzindexxZrnormmoderd/Users/vegardjervell/Documents/master/model/venv/lib/python3.9/site-packages/scipy/optimize/_nnls.pyrs03   )N)rZnumpyrrr__all__rrrrrs