
    }Kg                        d Z ddlZddlZddlmZ ddlmZm	Z	m
Z
mZ dgZdej                  dee   d	ej                  d
ej                  de
eej                  f   f
dZ	 dd	ej                  d
ej                  de	ej                     dedej                  f
dZd	ej                  d
ej                  dedej                  fdZy)zNon-negative least squares    N   )MAX_MEM_BLOCK)AnyOptionalTupleSequencennlsxshapeABreturnc                 (   | j                  |      } t        j                  d|| d      |z
  }d|j                  z  dz  t        j                  |dz        z  }d|j                  z  t        j                  d||d      z  }||j                         fS )z+Compute the objective and gradient for NNLSzmf,...ft->...mtToptimizer   g      ?   zmf,...mt->...ft)reshapenpeinsumsizesumflatten)r
   r   r   r   diffvaluegrads          V/home/alanp/www/video.onchill/myenv/lib/python3.12/site-packages/librosa/util/_nnls.py	_nnls_objr      s     	
		%A 99&1t<q@D Z3a0E J"))$5q$NND $,,.      x_initkwargsc                    |Ot        j                  dt         j                  j                  |       |d      }t        j                  |dd|       |j                  d| j                  d          d	g|j                  z  }|j                  }t        j                  j                  t        |f|| |f|d
|\  }}}|j                  |      S )a  Solve the constrained problem over a single block

    Parameters
    ----------
    A : np.ndarray [shape=(m, d)]
        The basis matrix
    B : np.ndarray [shape=(m, N)]
        The regression targets
    x_init : np.ndarray [shape=(d, N)]
        An initial guess
    **kwargs
        Additional keyword arguments to `scipy.optimize.fmin_l_bfgs_b`

    Returns
    -------
    x : np.ndarray [shape=(d, N)]
        Non-negative matrix such that Ax ~= B
    Nfm,...mt->...ftTr   r   outmr   )r   N)argsbounds)r   r   linalgpinvclip
setdefaultr   r   scipyr   fmin_l_bfgs_br   r   )	r   r   r   r    r'   r   r
   	obj_valuediagnosticss	            r   _nnls_lbfgs_blockr0   *   s    . ~,biinnQ.?TR
4V, c1771:& [6;;&FLLE !& < <6!!&1f!@F!Ay+ 99Ur   c           	         |j                   dk(  r#t        j                  j                  | |      d   S t	        t
        t        j                  |j                  dd       | j                  z  z        }t        |d      }|j                  d   |k  r&t        | |fi |j                  | j                        S t        j                  dt        j                  j!                  |       |d      }t        j"                  |dd|       |}t%        d|j                  d   |      D ]B  }t'        ||z   |j                  d         }t        | |d	||f   fd
|d	||f   i||d	||f<   D |S )a  Non-negative least squares.

    Given two matrices A and B, find a non-negative matrix X
    that minimizes the sum squared error::

        err(X) = sum_i,j ((AX)[i,j] - B[i, j])^2

    Parameters
    ----------
    A : np.ndarray [shape=(m, n)]
        The basis matrix
    B : np.ndarray [shape=(..., m, N)]
        The target array.  Additional leading dimensions are supported.
    **kwargs
        Additional keyword arguments to `scipy.optimize.fmin_l_bfgs_b`

    Returns
    -------
    X : np.ndarray [shape=(..., n, N), non-negative]
        A minimizing solution to ``|AX - B|^2``

    See Also
    --------
    scipy.optimize.nnls
    scipy.optimize.fmin_l_bfgs_b

    Examples
    --------
    Approximate a magnitude spectrum from its mel spectrogram

    >>> y, sr = librosa.load(librosa.ex('trumpet'), duration=3)
    >>> S = np.abs(librosa.stft(y, n_fft=2048))
    >>> M = librosa.feature.melspectrogram(S=S, sr=sr, power=1)
    >>> mel_basis = librosa.filters.mel(sr=sr, n_fft=2048, n_mels=M.shape[0])
    >>> S_recover = librosa.util.nnls(mel_basis, M)

    Plot the results

    >>> import matplotlib.pyplot as plt
    >>> fig, ax = plt.subplots(nrows=3, sharex=True, sharey=True)
    >>> librosa.display.specshow(librosa.amplitude_to_db(S, ref=np.max),
    ...                          y_axis='log', x_axis='time', ax=ax[2])
    >>> ax[2].set(title='Original spectrogram (1025 bins)')
    >>> ax[2].label_outer()
    >>> librosa.display.specshow(librosa.amplitude_to_db(M, ref=np.max),
    ...                          y_axis='mel', x_axis='time', ax=ax[0])
    >>> ax[0].set(title='Mel spectrogram (128 bins)')
    >>> ax[0].label_outer()
    >>> img = librosa.display.specshow(librosa.amplitude_to_db(S_recover, ref=np.max(S)),
    ...                          y_axis='log', x_axis='time', ax=ax[1])
    >>> ax[1].set(title='Reconstructed spectrogram (1025 bins)')
    >>> ax[1].label_outer()
    >>> fig.colorbar(img, ax=ax, format="%+2.0f dB")
    r   r   Nr"   Tr   r#   .r   )ndimr,   r   r	   intr   r   prodr   itemsizemaxr0   astypedtyper   r(   r)   r*   rangemin)r   r   r    	n_columnsr
   r   bl_sbl_ts           r   r	   r	   V   sR   p 	vv{~~""1a(++Mbggaggcrl&;ajj&HIJIIq!I 	wwr{i A0077@@ 			#RYY^^A%6DIAGGAq$AFai04)#QWWR[1-qd4i 
)/T$Y)?
CI
#tDy. 1
 Hr   )N)__doc__numpyr   scipy.optimizer,   utilsr   typingr   r   r   r   __all__ndarrayr4   floatr   r0   r	    r   r   <module>rH      s    !     1 1 (!	zz!"3-!,.JJ!;=::!
5"**!, BF)	zz)jj)*22::*>)QT)ZZ)XLBJJ L2:: L L Lr   