^j/ *ddlZddlmZdgZdddZy)N)nnlsr)maxiterc:tj|tjd}tj|tj}t|jdk7rt d|j|j dkDs!|j dk(r*|jddk7rt d|j|j dk(r"|jddk(r|j}|j\}}||jdk7r"t d d |d |jdfz|dk(r4tjdtjj|fS|sd |z}t|||\}}}|d k(r td ||fS)a Solve ``argmin_x || Ax - b ||_2^2`` for ``x>=0``. This problem, often called as NonNegative Least Squares, is a convex optimization problem with convex constraints. It typically arises when the ``x`` models quantities for which only nonnegative values are attainable; weight of ingredients, component costs and so on. Parameters ---------- A : (m, n) ndarray Coefficient array b : (m,) ndarray, float Right-hand side vector. maxiter : int, optional Maximum number of iterations, optional. Default value is ``3 * n``. Returns ------- x : ndarray Solution vector. rnorm : float The 2-norm of the residual, ``|| Ax-b ||_2``. See Also -------- lsq_linear : Linear least squares with bounds on the variables Notes ----- The code is based on the classical algorithm of [1]_. It utilizes an active set method and solves the KKT (Karush-Kuhn-Tucker) conditions for the non-negative least squares problem. References ---------- .. [1] : Lawson C., Hanson R.J., "Solving Least Squares Problems", SIAM, 1995, :doi:`10.1137/1.9781611971217` 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) C)dtypeorder)rz+Expected a 2D array, but the shape of A is rzDExpected a 1D array,(or 2D with one column), but the, shape of b is rz0Incompatible dimensions. The first dimension of zA is z, while the shape of b is z%Maximum number of iterations reached.) npasarray_chkfinitefloat64lenshape ValueErrorndimravelemptylinalgnorm_nnls RuntimeError)Abrmnxrnorminfos M/opt/ringagent/.cad-venv/lib/python3.12/site-packages/scipy/optimize/_nnls.pyrrsvp Qbjjr&s$ (Tr!