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-rw-r--r--numpy/lib/convertcode.py3
-rw-r--r--numpy/linalg/linalg.py69
-rw-r--r--numpy/linalg/old.py86
3 files changed, 108 insertions, 50 deletions
diff --git a/numpy/lib/convertcode.py b/numpy/lib/convertcode.py
index 38504d5a2..8587993c4 100644
--- a/numpy/lib/convertcode.py
+++ b/numpy/lib/convertcode.py
@@ -99,7 +99,8 @@ def fromstr(filestr):
filestr, fromall2 = changeimports(filestr, 'numerix', 'numpy')
filestr, fromall3 = changeimports(filestr, 'numpy_base', 'numpy')
filestr, fromall3 = changeimports(filestr, 'MLab', 'numpy.lib.mlab')
- filestr, fromall3 = changeimports(filestr, 'LinearAlgebra', 'numpy.linalg')
+ filestr, fromall3 = changeimports(filestr, 'LinearAlgebra',
+ 'numpy.linalg.old')
filestr, fromall3 = changeimports(filestr, 'RNG', 'numpy.random')
filestr, fromall3 = changeimports(filestr, 'RandomArray', 'numpy.random')
filestr, fromall3 = changeimports(filestr, 'FFT', 'numpy.dft')
diff --git a/numpy/linalg/linalg.py b/numpy/linalg/linalg.py
index f15931a7c..7c9c50586 100644
--- a/numpy/linalg/linalg.py
+++ b/numpy/linalg/linalg.py
@@ -6,22 +6,18 @@
# only accesses the following LAPACK functions: dgesv, zgesv, dgeev,
# zgeev, dgesdd, zgesdd, dgelsd, zgelsd, dsyevd, zheevd, dgetrf, dpotrf.
-__all__ = ['LinAlgError', 'solve_linear_equations', 'solve',
- 'inverse', 'inv', 'cholesky_decomposition', 'cholesky', 'eigenvalues',
- 'eigvals', 'Heigenvalues', 'eigvalsh', 'generalized_inverse', 'pinv',
- 'determinant', 'det', 'singular_value_decomposition', 'svd',
- 'eigenvectors', 'eig', 'Heigenvectors', 'eigh','lstsq', 'norm',
- 'linear_least_squares'
+__all__ = ['solve',
+ 'inv', 'cholesky',
+ 'eigvals',
+ 'eigvalsh', 'pinv',
+ 'det', 'svd',
+ 'eig', 'eigh','lstsq', 'norm',
]
from numpy.core import *
from numpy.lib import *
import lapack_lite
-# Error object
-class LinAlgError(Exception):
- pass
-
# Helper routines
_lapack_type = {'f': 0, 'd': 1, 'F': 2, 'D': 3}
_lapack_letter = ['s', 'd', 'c', 'z']
@@ -78,10 +74,9 @@ def _assertSquareness(*arrays):
if max(a.shape) != min(a.shape):
raise LinAlgError, 'Array must be square'
-
# Linear equations
-def solve_linear_equations(a, b):
+def solve(a, b):
one_eq = len(b.shape) == 1
if one_eq:
b = b[:, NewAxis]
@@ -110,14 +105,13 @@ def solve_linear_equations(a, b):
# Matrix inversion
-def inverse(a):
+def inv(a):
a, wrap = _makearray(a)
return wrap(solve_linear_equations(a, identity(a.shape[0])))
-
# Cholesky decomposition
-def cholesky_decomposition(a):
+def cholesky(a):
_assertRank2(a)
_assertSquareness(a)
t =_commonType(a)
@@ -135,8 +129,7 @@ def cholesky_decomposition(a):
# Eigenvalues
-
-def eigenvalues(a):
+def eigvals(a):
_assertRank2(a)
_assertSquareness(a)
t =_commonType(a)
@@ -177,7 +170,7 @@ def eigenvalues(a):
return w
-def Heigenvalues(a, UPLO='L'):
+def eigvalsh(a, UPLO='L'):
_assertRank2(a)
_assertSquareness(a)
t =_commonType(a)
@@ -390,7 +383,7 @@ def svd(a, full_matrices=1, compute_uv=1):
# Generalized inverse
-def generalized_inverse(a, rcond = 1.e-10):
+def pinv(a, rcond = 1.e-10):
a, wrap = _makearray(a)
if a.dtype.char in typecodes['Complex']:
a = conjugate(a)
@@ -408,7 +401,7 @@ def generalized_inverse(a, rcond = 1.e-10):
# Determinant
-def determinant(a):
+def det(a):
a = asarray(a)
_assertRank2(a)
_assertSquareness(a)
@@ -427,7 +420,7 @@ def determinant(a):
# Linear Least Squares
-def linear_least_squares(a, b, rcond=1.e-10):
+def lstsq(a, b, rcond=1.e-10):
"""returns x,resids,rank,s
where x minimizes 2-norm(|b - Ax|)
resids is the sum square residuals
@@ -502,17 +495,6 @@ Singular values less than s[0]*rcond are treated as zero.
resids = sum((transpose(bstar)[n:,:])**2).copy()
return wrap(x),resids,results['rank'],s[:min(n,m)].copy()
-def singular_value_decomposition(A, full_matrices=0):
- return svd(A, full_matrices)
-
-def eigenvectors(A):
- w, v = eig(A)
- return w, transpose(v)
-
-def Heigenvectors(A):
- w, v = eigh(A)
- return w, transpose(v)
-
def norm(x, ord=None):
""" norm(x, ord=None) -> n
@@ -580,33 +562,22 @@ def norm(x, ord=None):
else:
raise ValueError, "Improper number of dimensions to norm."
-
-
-inv = inverse
-solve = solve_linear_equations
-cholesky = cholesky_decomposition
-eigvals = eigenvalues
-eigvalsh = Heigenvalues
-pinv = generalized_inverse
-det = determinant
-lstsq = linear_least_squares
-
if __name__ == '__main__':
def test(a, b):
print "All numbers printed should be (almost) zero:"
- x = solve_linear_equations(a, b)
+ x = solve(a, b)
check = b - matrixmultiply(a, x)
print check
- a_inv = inverse(a)
+ a_inv = inv(a)
check = matrixmultiply(a, a_inv)-identity(a.shape[0])
print check
- ev = eigenvalues(a)
+ ev = eigvals(a)
evalues, evectors = eig(a)
check = ev-evalues
@@ -622,16 +593,16 @@ if __name__ == '__main__':
print check
- a_ginv = generalized_inverse(a)
+ a_ginv = pinv(a)
check = matrixmultiply(a, a_ginv)-identity(a.shape[0])
print check
- det = determinant(a)
+ det = det(a)
check = det-multiply.reduce(evalues)
print check
- x, residuals, rank, sv = linear_least_squares(a, b)
+ x, residuals, rank, sv = lstsq(a, b)
check = b - matrixmultiply(a, x)
print check
print rank-a.shape[0]
diff --git a/numpy/linalg/old.py b/numpy/linalg/old.py
new file mode 100644
index 000000000..d66051939
--- /dev/null
+++ b/numpy/linalg/old.py
@@ -0,0 +1,86 @@
+
+"""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 numpy.linalg as linalg
+
+# Error object
+class LinAlgError(Exception):
+ pass
+
+# Linear equations
+
+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)
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