MAINT: Move lstsq to umath_linalg

This does not yet enable any broadcasting, but makes doing so in future far easier.
numpy · mhvk · Apr 11, 2018 · Nov 7, 2017 · Nov 7, 2017 · Nov 7, 2017
commit 3ef55be846bc8e6e21515787b29608fec7e1fad0
diff --git a/numpy/linalg/linalg.py b/numpy/linalg/linalg.py
@@ -97,6 +97,9 @@ def _raise_linalgerror_eigenvalues_nonconvergence(err, flag):
 def _raise_linalgerror_svd_nonconvergence(err, flag):
     raise LinAlgError("SVD did not converge")
 
+def _raise_linalgerror_lstsq(err, flag):
+    raise LinAlgError("SVD did not converge in Linear Least Squares")
+
 def get_linalg_error_extobj(callback):
     extobj = list(_linalg_error_extobj)  # make a copy
     extobj[2] = callback
@@ -1997,7 +2000,6 @@ def lstsq(a, b, rcond="warn"):
     >>> plt.show()
 
     """
-    import math
     a, _ = _makearray(a)
     b, wrap = _makearray(b)
     is_1d = b.ndim == 1
@@ -2008,7 +2010,6 @@ def lstsq(a, b, rcond="warn"):
     m  = a.shape[0]
     n  = a.shape[1]
     n_rhs = b.shape[1]
-    ldb = max(n, m)
     if m != b.shape[0]:
         raise LinAlgError('Incompatible dimensions')
 
@@ -2028,62 +2029,25 @@ def lstsq(a, b, rcond="warn"):
                       FutureWarning, stacklevel=2)
         rcond = -1
     if rcond is None:
-        rcond = finfo(t).eps * ldb
-
-    bstar = zeros((ldb, n_rhs), t)
-    bstar[:m, :n_rhs] = b
-    a, bstar = _fastCopyAndTranspose(t, a, bstar)
-    a, bstar = _to_native_byte_order(a, bstar)
-    s = zeros((min(m, n),), real_t)
-    # This line:
-    #  * is incorrect, according to the LAPACK documentation
-    #  * raises a ValueError if min(m,n) == 0
-    #  * should not be calculated here anyway, as LAPACK should calculate
-    #    `liwork` for us. But that only works if our version of lapack does
-    #    not have this bug:
-    #      http://icl.cs.utk.edu/lapack-forum/archives/lapack/msg00899.html
-    #    Lapack_lite does have that bug...
-    nlvl = max( 0, int( math.log( float(min(m, n))/2. ) ) + 1 )
-    iwork = zeros((3*min(m, n)*nlvl+11*min(m, n),), fortran_int)
-    if isComplexType(t):
-        lapack_routine = lapack_lite.zgelsd
-        lwork = 1
-        rwork = zeros((lwork,), real_t)
-        work = zeros((lwork,), t)
-        results = lapack_routine(m, n, n_rhs, a, m, bstar, ldb, s, rcond,
-                                 0, work, -1, rwork, iwork, 0)
-        lrwork = int(rwork[0])
-        lwork = int(work[0].real)
-        work = zeros((lwork,), t)
-        rwork = zeros((lrwork,), real_t)
-        results = lapack_routine(m, n, n_rhs, a, m, bstar, ldb, s, rcond,
-                                 0, work, lwork, rwork, iwork, 0)
+        rcond = finfo(t).eps * max(n, m)
+
+    if m <= n:
+        gufunc = _umath_linalg.lstsq_m
     else:
-        lapack_routine = lapack_lite.dgelsd
-        lwork = 1
-        work = zeros((lwork,), t)
-        results = lapack_routine(m, n, n_rhs, a, m, bstar, ldb, s, rcond,
-                                 0, work, -1, iwork, 0)
-        lwork = int(work[0])
-        work = zeros((lwork,), t)
-        results = lapack_routine(m, n, n_rhs, a, m, bstar, ldb, s, rcond,
-                                 0, work, lwork, iwork, 0)
-    if results['info'] > 0:
-        raise LinAlgError('SVD did not converge in Linear Least Squares')
-
-    # undo transpose imposed by fortran-order arrays
-    b_out = bstar.T
+        gufunc = _umath_linalg.lstsq_n
+
+    signature = 'DDd->Did' if isComplexType(t) else 'ddd->did'
+    extobj = get_linalg_error_extobj(_raise_linalgerror_lstsq)
+    b_out, rank, s = gufunc(a, b, rcond, signature=signature, extobj=extobj)
 
     # b_out contains both the solution and the components of the residuals
-    x = b_out[:n,:]
-    r_parts = b_out[n:,:]
+    x = b_out[...,:n,:]
+    r_parts = b_out[...,n:,:]
     if isComplexType(t):
         resids = sum(abs(r_parts)**2, axis=-2)
     else:
         resids = sum(r_parts**2, axis=-2)
 
-    rank = results['rank']
-
     # remove the axis we added
     if is_1d:
         x = x.squeeze(axis=-1)