Merge pull request #4986 from bscellier/fix_deprecation2

theano/tensor/nlinalg.py

@@ -533,7 +533,7 @@ class QRIncomplete(Op):
                            self.mode)
 
 
-def qr(a, mode="full"):
+def qr(a, mode="reduced"):
     """
     Computes the QR decomposition of a matrix.
     Factor the matrix a as qr, where q
@@ -544,7 +544,7 @@ def qr(a, mode="full"):
     a : array_like, shape (M, N)
         Matrix to be factored.
 
-    mode : {'reduced', 'complete', 'r', 'raw', 'full', 'economic'}, optional
+    mode : {'reduced', 'complete', 'r', 'raw'}, optional
         If K = min(M, N), then
 
         'reduced'
@@ -559,27 +559,14 @@ def qr(a, mode="full"):
         'raw'
           returns h, tau with dimensions (N, M), (K,)
 
-        'full'
-          alias of 'reduced', deprecated (default)
-
-        'economic'
-          returns h from 'raw', deprecated.
-
         The options 'reduced', 'complete', and 'raw' are new in numpy
         1.8, see the notes for more information. The default is
         'reduced' and to maintain backward compatibility with earlier
-        versions of numpy both it and the old default 'full' can be
+        versions of numpy it can be
         omitted. Note that array h returned in 'raw' mode is
-        transposed for calling Fortran. The 'economic' mode is
-        deprecated. The modes 'full' and 'economic' may be passed
-        using only the first letter for backwards compatibility, but
-        all others must be spelled out.
-
-        Default mode is 'full' which is also default for numpy 1.6.1.
+        transposed for calling Fortran.
 
-        :note: Default mode was left to full as full and reduced are
-           both doing the same thing in the new numpy version but only
-           full works on the old previous numpy version.
+        Default mode is 'reduced' which is also default for numpy 1.8
 
     Returns
     -------

theano/tensor/tests/test_nlinalg.py

@@ -130,7 +130,7 @@ def test_qr_modes():
     n_qr = numpy.linalg.qr(a)
     assert _allclose(n_qr, t_qr)
 
-    for mode in ["reduced", "r", "raw", "full", "economic"]:
+    for mode in ["reduced", "r", "raw"]:
         f = function([A], qr(A, mode))
         t_qr = f(a)
         n_qr = numpy.linalg.qr(a, mode)