force dot product as scalar

pyamg/multilevel.py

@@ -3,6 +3,7 @@
 from warnings import warn
 
 import scipy as sp
+from scipy.linalg import pinv
 import scipy.sparse.linalg as sla
 from scipy.sparse.linalg import LinearOperator
 import numpy as np
@@ -13,14 +14,6 @@
 from .relaxation import smoothing
 from .util import upcast
 
-# hack to compare the version of scipy
-# int(''.join('1.7'.split('.')[:2])) = 17
-spversion = sp.__version__
-if int(''.join(spversion.split('.')[:2])) >= 17:
-    from scipy.linalg import pinv           # pylint: disable=ungrouped-imports
-else:
-    from scipy.linalg import pinv2 as pinv  # pylint: disable=no-name-in-module
-
 
 class MultilevelSolver:
     """Stores multigrid hierarchy and implements the multigrid cycle.

pyamg/util/linalg.py

@@ -526,11 +526,11 @@ def ishermitian(A, fast_check=True, tol=1e-6, verbose=False):
         A = np.asarray(A)
 
     if fast_check:
-        x = np.random.rand(A.shape[0], 1)
-        y = np.random.rand(A.shape[0], 1)
+        x = np.random.rand(A.shape[0])
+        y = np.random.rand(A.shape[0])
         if A.dtype == complex:
-            x = x + 1.0j*np.random.rand(A.shape[0], 1)
-            y = y + 1.0j*np.random.rand(A.shape[0], 1)
+            x = x + 1.0j*np.random.rand(A.shape[0])
+            y = y + 1.0j*np.random.rand(A.shape[0])
         xAy = np.dot((A.dot(x)).conjugate().T, y)
         xAty = np.dot(x.conjugate().T, A.dot(y))
         diff = float(np.abs(xAy - xAty) / np.sqrt(np.abs(xAy*xAty)))