Ńņ
ōŲĻIc           @   sĖ   d  Z  d d d d d d d d d	 d
 d d g Z d d k l Z d d k i Z e i Z d   Z d   Z d   Z	 d   Z
 d d  Z d   Z d   Z d d  Z d   Z d d  Z d d  Z d S(   s4   Backward compatible with LinearAlgebra from Numeric
t   LinAlgErrort   solve_linear_equationst   inverset   cholesky_decompositiont   eigenvaluest   Heigenvaluest   generalized_inverset   determinantt   singular_value_decompositiont   eigenvectorst   Heigenvectorst   linear_least_squaresi’’’’(   t	   transposeNc         C   s   t  i |  |  S(   N(   t   linalgt   solve(   t   at   b(    (    sO   C:\graphics\Tools\Python26\Lib\site-packages\numpy\oldnumeric\linear_algebra.pyR      s    c         C   s   t  i |   S(   N(   R   t   inv(   R   (    (    sO   C:\graphics\Tools\Python26\Lib\site-packages\numpy\oldnumeric\linear_algebra.pyR      s    c         C   s   t  i |   S(   N(   R   t   cholesky(   R   (    (    sO   C:\graphics\Tools\Python26\Lib\site-packages\numpy\oldnumeric\linear_algebra.pyR   "   s    c         C   s   t  i |   S(   N(   R   t   eigvals(   R   (    (    sO   C:\graphics\Tools\Python26\Lib\site-packages\numpy\oldnumeric\linear_algebra.pyR   '   s    t   Lc         C   s   t  i |  |  S(   N(   R   t   eigvalsh(   R   t   UPLO(    (    sO   C:\graphics\Tools\Python26\Lib\site-packages\numpy\oldnumeric\linear_algebra.pyR   *   s    c         C   s%   t  i |   \ } } | t |  f S(   N(   R   t   eigR   (   t   At   wt   v(    (    sO   C:\graphics\Tools\Python26\Lib\site-packages\numpy\oldnumeric\linear_algebra.pyR	   /   s    c         C   s%   t  i |   \ } } | t |  f S(   N(   R   t   eighR   (   R   R   R   (    (    sO   C:\graphics\Tools\Python26\Lib\site-packages\numpy\oldnumeric\linear_algebra.pyR
   3   s    g»½×Łß|Ū=c         C   s   t  i |  |  S(   N(   R   t   pinv(   R   t   rcond(    (    sO   C:\graphics\Tools\Python26\Lib\site-packages\numpy\oldnumeric\linear_algebra.pyR   9   s    c         C   s   t  i |   S(   N(   R   t   det(   R   (    (    sO   C:\graphics\Tools\Python26\Lib\site-packages\numpy\oldnumeric\linear_algebra.pyR   >   s    c         C   s   t  i |  | |  S(   sū  returns x,resids,rank,s
where x minimizes 2-norm(|b - Ax|)
      resids is the sum square residuals
      rank is the rank of A
      s is the rank of the singular values of A in descending order

If b is a matrix then x is also a matrix with corresponding columns.
If the rank of A is less than the number of columns of A or greater than
the number of rows, then residuals will be returned as an empty array
otherwise resids = sum((b-dot(A,x)**2).
Singular values less than s[0]*rcond are treated as zero.
(   R   t   lstsq(   R   R   R   (    (    sO   C:\graphics\Tools\Python26\Lib\site-packages\numpy\oldnumeric\linear_algebra.pyR   C   s    i    c         C   s   t  i |  |  S(   N(   R   t   svd(   R   t   full_matrices(    (    sO   C:\graphics\Tools\Python26\Lib\site-packages\numpy\oldnumeric\linear_algebra.pyR   R   s    (   t   __doc__t   __all__t
   numpy.coreR   t   numpy.linalgR   R    R   R   R   R   R   R	   R
   R   R   R   R   (    (    (    sO   C:\graphics\Tools\Python26\Lib\site-packages\numpy\oldnumeric\linear_algebra.pyt   <module>   s(   										