[Numpy-svn] r5103 - in trunk/numpy/linalg: . tests
numpy-svn at scipy.org
numpy-svn at scipy.org
Sun Apr 27 14:19:15 EDT 2008
Author: charris
Date: 2008-04-27 13:19:12 -0500 (Sun, 27 Apr 2008)
New Revision: 5103
Modified:
trunk/numpy/linalg/linalg.py
trunk/numpy/linalg/tests/test_linalg.py
Log:
Add tests for matrix return types.
Fix cond computations for matrices.
lstsq is currently broken for matrices, will fix shortly.
Modified: trunk/numpy/linalg/linalg.py
===================================================================
--- trunk/numpy/linalg/linalg.py 2008-04-27 15:27:30 UTC (rev 5102)
+++ trunk/numpy/linalg/linalg.py 2008-04-27 18:19:12 UTC (rev 5103)
@@ -27,7 +27,7 @@
isfinite, size
from numpy.lib import triu
from numpy.linalg import lapack_lite
-from numpy.core.defmatrix import matrix_power
+from numpy.core.defmatrix import matrix_power, matrix
fortran_int = intc
@@ -983,7 +983,7 @@
else:
return wrap(s)
-def cond(x,p=None):
+def cond(x, p=None):
"""Compute the condition number of a matrix.
The condition number of x is the norm of x times the norm
@@ -1014,6 +1014,7 @@
c : float
The condition number of the matrix. May be infinite.
"""
+ x = asarray(x) # in case we have a matrix
if p is None:
s = svd(x,compute_uv=False)
return s[0]/s[-1]
@@ -1146,7 +1147,7 @@
"""
import math
- a = asarray(a)
+ a = _makearray(a)
b, wrap = _makearray(b)
one_eq = len(b.shape) == 1
if one_eq:
Modified: trunk/numpy/linalg/tests/test_linalg.py
===================================================================
--- trunk/numpy/linalg/tests/test_linalg.py 2008-04-27 15:27:30 UTC (rev 5102)
+++ trunk/numpy/linalg/tests/test_linalg.py 2008-04-27 18:19:12 UTC (rev 5103)
@@ -3,13 +3,19 @@
from numpy.testing import *
set_package_path()
-from numpy import array, single, double, csingle, cdouble, dot, identity, \
- multiply, atleast_2d, inf, asarray
+from numpy import array, single, double, csingle, cdouble, dot, identity
+from numpy import multiply, atleast_2d, inf, asarray, matrix
from numpy import linalg
from linalg import matrix_power
restore_path()
+def ifthen(a, b):
+ return not a or b
+
old_assert_almost_equal = assert_almost_equal
+def imply(a, b):
+ return not a or b
+
def assert_almost_equal(a, b, **kw):
if asarray(a).dtype.type in (single, csingle):
decimal = 6
@@ -52,41 +58,63 @@
b = [2, 1]
self.do(a,b)
+ def check_matrix_b_only(self):
+ """Check that matrix type is preserved."""
+ a = array([[1.,2.], [3.,4.]])
+ b = matrix([2., 1.]).T
+ self.do(a, b)
+ def check_matrix_a_and_b(self):
+ """Check that matrix type is preserved."""
+ a = matrix([[1.,2.], [3.,4.]])
+ b = matrix([2., 1.]).T
+ self.do(a, b)
+
+
class TestSolve(LinalgTestCase):
def do(self, a, b):
x = linalg.solve(a, b)
assert_almost_equal(b, dot(a, x))
+ assert imply(isinstance(b, matrix), isinstance(x, matrix))
class TestInv(LinalgTestCase):
def do(self, a, b):
a_inv = linalg.inv(a)
assert_almost_equal(dot(a, a_inv), identity(asarray(a).shape[0]))
+ assert imply(isinstance(a, matrix), isinstance(a_inv, matrix))
class TestEigvals(LinalgTestCase):
def do(self, a, b):
ev = linalg.eigvals(a)
evalues, evectors = linalg.eig(a)
assert_almost_equal(ev, evalues)
+ assert imply(isinstance(a, matrix), isinstance(ev, matrix))
class TestEig(LinalgTestCase):
def do(self, a, b):
evalues, evectors = linalg.eig(a)
- assert_almost_equal(dot(a, evectors), evectors*evalues)
+ assert_almost_equal(dot(a, evectors), multiply(evectors, evalues))
+ assert imply(isinstance(a, matrix), isinstance(evalues, matrix))
+ assert imply(isinstance(a, matrix), isinstance(evectors, matrix))
class TestSVD(LinalgTestCase):
def do(self, a, b):
u, s, vt = linalg.svd(a, 0)
- assert_almost_equal(a, dot(u*s, vt))
+ assert_almost_equal(a, dot(multiply(u, s), vt))
+ assert imply(isinstance(a, matrix), isinstance(u, matrix))
+ assert imply(isinstance(a, matrix), isinstance(s, matrix))
+ assert imply(isinstance(a, matrix), isinstance(vt, matrix))
class TestCondSVD(LinalgTestCase):
def do(self, a, b):
- s = linalg.svd(a, compute_uv=False)
+ c = asarray(a) # a might be a matrix
+ s = linalg.svd(c, compute_uv=False)
old_assert_almost_equal(s[0]/s[-1], linalg.cond(a), decimal=5)
class TestCond2(LinalgTestCase):
def do(self, a, b):
- s = linalg.svd(a, compute_uv=False)
+ c = asarray(a) # a might be a matrix
+ s = linalg.svd(c, compute_uv=False)
old_assert_almost_equal(s[0]/s[-1], linalg.cond(a,2), decimal=5)
class TestCondInf(NumpyTestCase):
@@ -98,6 +126,7 @@
def do(self, a, b):
a_ginv = linalg.pinv(a)
assert_almost_equal(dot(a, a_ginv), identity(asarray(a).shape[0]))
+ assert imply(isinstance(a, matrix), isinstance(a_ginv, matrix))
class TestDet(LinalgTestCase):
def do(self, a, b):
@@ -116,6 +145,9 @@
assert_almost_equal(b, dot(a, x))
assert_equal(rank, asarray(a).shape[0])
assert_almost_equal(sv, s)
+ assert imply(isinstance(b, matrix), isinstance(x, matrix))
+ assert imply(isinstance(b, matrix), isinstance(residuals, matrix))
+ assert imply(isinstance(b, matrix), isinstance(sv, matrix))
class TestMatrixPower(ParametricTestCase):
R90 = array([[0,1],[-1,0]])
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