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-rw-r--r--doc/source/user/numpy-for-matlab-users.rst14
1 files changed, 7 insertions, 7 deletions
diff --git a/doc/source/user/numpy-for-matlab-users.rst b/doc/source/user/numpy-for-matlab-users.rst
index 399237c21..e53d1ca45 100644
--- a/doc/source/user/numpy-for-matlab-users.rst
+++ b/doc/source/user/numpy-for-matlab-users.rst
@@ -436,7 +436,7 @@ Linear Algebra Equivalents
``a``
* - ``rand(3,4)``
- - ``random.rand(3,4)``
+ - ``random.rand(3,4)`` or ``random.random_sample((3, 4))``
- random 3x4 matrix
* - ``linspace(1,3,4)``
@@ -547,7 +547,7 @@ Linear Algebra Equivalents
- eigenvalues and eigenvectors of ``a``
* - ``[V,D]=eig(a,b)``
- - ``V,D = np.linalg.eig(a,b)``
+ - ``D,V = scipy.linalg.eig(a,b)``
- eigenvalues and eigenvectors of ``a``, ``b``
* - ``[V,D]=eigs(a,k)``
@@ -693,19 +693,19 @@ this is just an example, not a statement of "best practices"):
::
- # Make all numpy available via shorter 'num' prefix
- import numpy as num
+ # Make all numpy available via shorter 'np' prefix
+ import numpy as np
# Make all matlib functions accessible at the top level via M.func()
import numpy.matlib as M
# Make some matlib functions accessible directly at the top level via, e.g. rand(3,3)
from numpy.matlib import rand,zeros,ones,empty,eye
# Define a Hermitian function
def hermitian(A, **kwargs):
- return num.transpose(A,**kwargs).conj()
+ return np.transpose(A,**kwargs).conj()
# Make some shortcuts for transpose,hermitian:
- # num.transpose(A) --> T(A)
+ # np.transpose(A) --> T(A)
# hermitian(A) --> H(A)
- T = num.transpose
+ T = np.transpose
H = hermitian
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