Ë
    ý�Dj  ã                   ó@   — d dl Zd dlmZmZ d dlZdgZdddœd„Zdd„Zy)	é    N)ÚsolveÚLinAlgWarningÚnnls)Úatolc                óö  — t        j                  | «      } t        j                  |«      }t        | j                  «      dk7  rt	        dd| j                  › �z   «      ‚t        |j                  «      dk7  rt	        dd|j                  › �z   «      ‚| j                  \  }}||j                  d   k7  r"t	        dd	|› d
|j                  d   f› �z   «      ‚t        | |||¬«      \  }}}|dk7  rt        d«      ‚||fS )aÛ  
    Solve ``argmin_x || Ax - b ||_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``.
    atol: float
        Tolerance value used in the algorithm to assess closeness to zero in
        the projected residual ``(A.T @ (A x - b)`` entries. Increasing this
        value relaxes the solution constraints. A typical relaxation value can
        be selected as ``max(m, n) * np.linalg.norm(a, 1) * np.spacing(1.)``.
        This value is not set as default since the norm operation becomes
        expensive for large problems hence can be used only when necessary.

    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 [2]_ which is an improved version of 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`
    .. [2] : Bro, Rasmus and de Jong, Sijmen, "A Fast Non-Negativity-
       Constrained Least Squares Algorithm", Journal Of Chemometrics, 1997,
       :doi:`10.1002/(SICI)1099-128X(199709/10)11:5<393::AID-CEM483>3.0.CO;2-L`

     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)

    é   z)Expected a two-dimensional array (matrix)z, but the shape of A is é   z)Expected a one-dimensional array (vector)z, but the shape of b is r   z0Incompatible dimensions. The first dimension of zA is z, while the shape of b is )Útolz%Maximum number of iterations reached.)ÚnpÚasarray_chkfiniteÚlenÚshapeÚ
ValueErrorÚ_nnlsÚRuntimeError)	ÚAÚbÚmaxiterr   ÚmÚnÚxÚrnormÚmodes	            úXC:\Crop_Prediction\Backend\crop-ai-system\venv\Lib\site-packages\scipy/optimize/_nnls.pyr   r      s  € ôD 	×Ñ˜QÓ€AÜ
×Ñ˜QÓ€Aä
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                  |t        ¬«      }
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j                  «       �sÌ||
    |kD  j                  «       �r´t        j                  ||
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   j'                  «       dk  rË|dz  }|
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   dd¬«      |	|
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 <   ||k  r|	|
   j'                  «       dk  rŒË|	dd |dd |||z  z
  |dd ||k(  r|ddfS |
j                  «       s||
    |kD  j                  «       r�Œ´|t        j(                  j+                  | |z  |z
  «      dfS # 1 sw Y   �Œ^xY w# 1 sw Y   Œ¦xY w)zŒ
    This is a single RHS algorithm from ref [2] above. For multiple RHS
    support, the algorithm is given in  :doi:`10.1002/cem.889`
    é   Né
   g      ð?)Údtyper   Tg        ÚignorezIll-conditioned matrix)ÚmessageÚcategoryÚsymF)Úassume_aÚcheck_finiter	   éÿÿÿÿ)r   ÚTÚmaxr   ÚspacingÚzerosÚfloat64ÚboolÚcopyÚastypeÚallÚanyÚargmaxÚwarningsÚcatch_warningsÚfilterwarningsr   r   Úix_ÚminÚlinalgÚnorm)r   r   r   r
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