Backward compatible with LinearAlgebra from Numeric

Modules
		numpy.linalg

Classes
		exceptions.Exception(exceptions.BaseException) numpy.linalg.linalg.LinAlgError   class LinAlgError(exceptions.Exception)     # Error object     Method resolution order: LinAlgError exceptions.Exception exceptions.BaseException __builtin__.object Data descriptors defined here: __weakref__ list of weak references to the object (if defined) Methods inherited from exceptions.Exception: __init__(...) x.__init__(...) initializes x; see x.__class__.__doc__ for signature Data and other attributes inherited from exceptions.Exception: __new__ = <built-in method __new__ of type object at 0x71a8a0> T.__new__(S, ...) -> a new object with type S, a subtype of T Methods inherited from exceptions.BaseException: __delattr__(...) x.__delattr__('name') <==> del x.name __getattribute__(...) x.__getattribute__('name') <==> x.name __getitem__(...) x.__getitem__(y) <==> x[y] __getslice__(...) x.__getslice__(i, j) <==> x[i:j]   Use of negative indices is not supported. __reduce__(...) __repr__(...) x.__repr__() <==> repr(x) __setattr__(...) x.__setattr__('name', value) <==> x.name = value __setstate__(...) __str__(...) x.__str__() <==> str(x) Data descriptors inherited from exceptions.BaseException: __dict__ args message exception message

Functions
		Heigenvalues(a, UPLO='L') Heigenvectors(A) cholesky_decomposition(a) determinant(a) eigenvalues(a) eigenvectors(A) generalized_inverse(a, rcond=1e-10) inverse(a) linear_least_squares(a, b, rcond=1e-10) 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. singular_value_decomposition(A, full_matrices=0) solve_linear_equations(a, b)

Data
		__all__ = ['LinAlgError', 'solve_linear_equations', 'inverse', 'cholesky_decomposition', 'eigenvalues', 'Heigenvalues', 'generalized_inverse', 'determinant', 'singular_value_decomposition', 'eigenvectors', 'Heigenvectors', 'linear_least_squares']