Merge branch 'pull-357'

* pull-357:
  BUG: change default threshold for matrix_rank

numpy/linalg/linalg.py

@@ -1420,8 +1420,8 @@ def matrix_rank(M, tol=None):
     """
     Return matrix rank of array using SVD method
 
-    Rank of the array is the number of SVD singular values of the
-    array that are greater than `tol`.
+    Rank of the array is the number of SVD singular values of the array that are
+    greater than `tol`.
 
     Parameters
     ----------
@@ -1431,27 +1431,53 @@ def matrix_rank(M, tol=None):
        threshold below which SVD values are considered zero. If `tol` is
        None, and ``S`` is an array with singular values for `M`, and
        ``eps`` is the epsilon value for datatype of ``S``, then `tol` is
-       set to ``S.max() * eps``.
+       set to ``S.max() * max(M.shape) * eps``.
 
     Notes
     -----
-    Golub and van Loan [1]_ define "numerical rank deficiency" as using
-    tol=eps*S[0] (where S[0] is the maximum singular value and thus the
-    2-norm of the matrix). This is one definition of rank deficiency,
-    and the one we use here.  When floating point roundoff is the main
-    concern, then "numerical rank deficiency" is a reasonable choice. In
-    some cases you may prefer other definitions. The most useful measure
-    of the tolerance depends on the operations you intend to use on your
-    matrix. For example, if your data come from uncertain measurements
-    with uncertainties greater than floating point epsilon, choosing a
-    tolerance near that uncertainty may be preferable.  The tolerance
-    may be absolute if the uncertainties are absolute rather than
-    relative.
+    The default threshold to detect rank deficiency is a test on the magnitude
+    of the singular values of `M`.  By default, we identify singular values less
+    than ``S.max() * max(M.shape) * eps`` as indicating rank deficiency (with
+    the symbols defined above). This is the algorithm MATLAB uses [1].  It also
+    appears in *Numerical recipes* in the discussion of SVD solutions for linear
+    least squares [2].
+
+    This default threshold is designed to detect rank deficiency accounting for
+    the numerical errors of the SVD computation.  Imagine that there is a column
+    in `M` that is an exact (in floating point) linear combination of other
+    columns in `M`. Computing the SVD on `M` will not produce a singular value
+    exactly equal to 0 in general: any difference of the smallest SVD value from
+    0 will be caused by numerical imprecision in the calculation of the SVD.
+    Our threshold for small SVD values takes this numerical imprecision into
+    account, and the default threshold will detect such numerical rank
+    deficiency.  The threshold may declare a matrix `M` rank deficient even if
+    the linear combination of some columns of `M` is not exactly equal to
+    another column of `M` but only numerically very close to another column of
+    `M`.
+
+    We chose our default threshold because it is in wide use.  Other thresholds
+    are possible.  For example, elsewhere in the 2007 edition of *Numerical
+    recipes* there is an alternative threshold of ``S.max() *
+    np.finfo(M.dtype).eps / 2. * np.sqrt(m + n + 1.)``. The authors describe
+    this threshold as being based on "expected roundoff error" (p 71).
+
+    The thresholds above deal with floating point roundoff error in the
+    calculation of the SVD.  However, you may have more information about the
+    sources of error in `M` that would make you consider other tolerance values
+    to detect *effective* rank deficiency.  The most useful measure of the
+    tolerance depends on the operations you intend to use on your matrix.  For
+    example, if your data come from uncertain measurements with uncertainties
+    greater than floating point epsilon, choosing a tolerance near that
+    uncertainty may be preferable.  The tolerance may be absolute if the
+    uncertainties are absolute rather than relative.
 
     References
     ----------
-    .. [1] G. H. Golub and C. F. Van Loan, *Matrix Computations*.
-       Baltimore: Johns Hopkins University Press, 1996.
+    .. [1] MATLAB reference documention, "Rank"
+           http://www.mathworks.com/help/techdoc/ref/rank.html
+    .. [2] W. H. Press, S. A. Teukolsky, W. T. Vetterling and B. P. Flannery,
+           "Numerical Recipes (3rd edition)", Cambridge University Press, 2007,
+           page 795.
 
     Examples
     --------
@@ -1465,7 +1491,6 @@ def matrix_rank(M, tol=None):
     1
     >>> matrix_rank(np.zeros((4,)))
     0
-
     """
     M = asarray(M)
     if M.ndim > 2:
@@ -1474,7 +1499,7 @@ def matrix_rank(M, tol=None):
         return int(not all(M==0))
     S = svd(M, compute_uv=False)
     if tol is None:
-        tol = S.max() * finfo(S.dtype).eps
+        tol = S.max() * max(M.shape) * finfo(S.dtype).eps
     return sum(S > tol)
 
 

numpy/linalg/tests/test_linalg.py

@@ -3,7 +3,9 @@
 import sys
 
 import numpy as np
-from numpy.testing import *
+from numpy.testing import (TestCase, assert_, assert_equal, assert_raises,
+                           assert_array_equal, assert_almost_equal,
+                           run_module_suite)
 from numpy import array, single, double, csingle, cdouble, dot, identity
 from numpy import multiply, atleast_2d, inf, asarray, matrix
 from numpy import linalg
@@ -451,6 +453,19 @@ def test_matrix_rank(self):
         yield assert_equal, matrix_rank(1), 1
 
 
+def test_reduced_rank():
+    # Test matrices with reduced rank
+    rng = np.random.RandomState(20120714)
+    for i in range(100):
+        # Make a rank deficient matrix
+        X = rng.normal(size=(40, 10))
+        X[:, 0] = X[:, 1] + X[:, 2]
+        # Assert that matrix_rank detected deficiency
+        assert_equal(matrix_rank(X), 9)
+        X[:, 3] = X[:, 4] + X[:, 5]
+        assert_equal(matrix_rank(X), 8)
+
+
 class TestQR(TestCase):
     def test_qr_empty(self):
         a = np.zeros((0,2))