9=e4ddlmZddlmZmZmZdgZddZdS))__nnls)asarray_chkfinitezerosdoublennlsNc tt||f\}}t|jdkrt dd|jzt|jdkrt dd|jz|j\}}||jdkr$t dd |d |jdfz|d nt |}t |ft }t |ft }t |ft }tj ||||||||\}} } | dkrtd|| fS)a Solve ``argmin_x || Ax - b ||_2`` for ``x>=0``. This is a wrapper for a FORTRAN non-negative least squares solver. Parameters ---------- A : ndarray Matrix ``A`` as shown above. b : ndarray Right-hand side vector. maxiter: int, optional Maximum number of iterations, optional. Default is ``3 * A.shape[1]``. Returns ------- x : ndarray Solution vector. rnorm : float The residual, ``|| Ax-b ||_2``. See Also -------- lsq_linear : Linear least squares with bounds on the variables Notes ----- The FORTRAN code was published in the book below. The algorithm is an active set method. It solves the KKT (Karush-Kuhn-Tucker) conditions for the non-negative least squares problem. References ---------- Lawson C., Hanson R.J., (1987) Solving Least Squares Problems, SIAM Examples -------- >>> import numpy as np >>> from scipy.optimize import nnls ... >>> A = np.array([[1, 0], [1, 0], [0, 1]]) >>> b = np.array([2, 1, 1]) >>> nnls(A, b) (array([1.5, 1. ]), 0.7071067811865475) >>> b = np.array([-1, -1, -1]) >>> nnls(A, b) (array([0., 0.]), 1.7320508075688772) z)Expected a two-dimensional array (matrix)z, but the shape of A is rz)Expected a one-dimensional array (vector)z, but the shape of b is rz0Incompatible dimensions. The first dimension of zA is z, while the shape of b is N)dtypeztoo many iterations) maprlenshape ValueErrorintrrrr RuntimeError) Abmaxitermnwzzindexxrnormmodes 4lib/python3.11/site-packages/scipy/optimize/_nnls.pyrrs{h  1a& ) )DAq 17||qD=AG==>?? ? 17||qD=AG==>?? ? 7DAqAGAJBEEEagaj^EEFGG GObbWG qd&!!!A t6 " " "B 1$c " " "E[Aq!QE7CCNAud qyy0111 e8O)N)rnumpyrrr__all__rrrr$s`2222222222 (NNNNNNr