Many name-changes in oldnumeric. This may break some numpy code that was using the oldnumeric interface.

1 files changed, 0 insertions, 84 deletions

diff --git a/numpy/linalg/old.py b/numpy/linalg/old.py
deleted file mode 100644
index 2ac1e9d48..000000000
--- a/numpy/linalg/old.py
+++ /dev/null
@@ -1,84 +0,0 @@
-
-"""Backward compatible with LinearAlgebra from Numeric
-"""
-# This module is a lite version of the linalg.py module in SciPy which contains
-# high-level Python interface to the LAPACK library.  The lite version
-# only accesses the following LAPACK functions: dgesv, zgesv, dgeev,
-# zgeev, dgesdd, zgesdd, dgelsd, zgelsd, dsyevd, zheevd, dgetrf, dpotrf.
-
-
-__all__ = ['LinAlgError', 'solve_linear_equations', 
-           'inverse', 'cholesky_decomposition', 'eigenvalues',
-           'Heigenvalues', 'generalized_inverse', 
-           'determinant', 'singular_value_decomposition', 
-           'eigenvectors',  'Heigenvectors',
-           'linear_least_squares'
-           ]
-
-from numpy.core import transpose
-import linalg
-
-# Linear equations
-
-LinAlgError = linalg.LinAlgError
-
-def solve_linear_equations(a, b):
-    return linalg.solve(a,b)
-
-# Matrix inversion
-
-def inverse(a):
-    return linalg.inv(a)
-
-# Cholesky decomposition
-
-def cholesky_decomposition(a):
-    return linalg.cholesky(a)
-
-# Eigenvalues
-
-def eigenvalues(a):
-    return linalg.eigvals(a)
-
-def Heigenvalues(a, UPLO='L'):
-    return linalg.eigvalsh(a,UPLO)
-
-# Eigenvectors
-
-def eigenvectors(A):
-    w, v = linalg.eig(A)
-    return w, transpose(v)
-
-def Heigenvectors(A):
-    w, v = linalg.eigh(A)
-    return w, transpose(v)
-    
-# Generalized inverse
-
-def generalized_inverse(a, rcond = 1.e-10):
-    return linalg.pinv(a, rcond)
-
-# Determinant
-
-def determinant(a):
-    return linalg.det(a)
-
-# Linear Least Squares
-
-def linear_least_squares(a, b, rcond=1.e-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.
-"""
-    return linalg.lstsq(a,b,rcond)
-
-def singular_value_decomposition(A, full_matrices=0):
-    return linalg.svd(A, full_matrices)