[Numpy-discussion] Matrix Class

cjw cjw at ncf.ca
Sat Feb 14 12:05:37 EST 2015


On 14-Feb-15 11:35 AM, josef.pktd at gmail.com wrote:
> On Wed, Feb 11, 2015 at 4:18 PM, Ryan Nelson <rnelsonchem at gmail.com> wrote:
>> Colin,
>>
>> I currently use Py3.4 and Numpy 1.9.1. However, I built a quick test conda
>> environment with Python2.7 and Numpy 1.7.0, and I get the same:
>>
>> ############
>> Python 2.7.9 |Continuum Analytics, Inc.| (default, Dec 18 2014, 16:57:52)
>> [MSC v
>> .1500 64 bit (AMD64)]
>> Type "copyright", "credits" or "license" for more information.
>>
>> IPython 2.3.1 -- An enhanced Interactive Python.
>> Anaconda is brought to you by Continuum Analytics.
>> Please check out: http://continuum.io/thanks and https://binstar.org
>> ?         -> Introduction and overview of IPython's features.
>> %quickref -> Quick reference.
>> help      -> Python's own help system.
>> object?   -> Details about 'object', use 'object??' for extra details.
>>
>> In [1]: import numpy as np
>>
>> In [2]: np.__version__
>> Out[2]: '1.7.0'
>>
>> In [3]: np.mat([4,'5',6])
>> Out[3]:
>> matrix([['4', '5', '6']],
>>         dtype='|S1')
>>
>> In [4]: np.mat([4,'5',6], dtype=int)
>> Out[4]: matrix([[4, 5, 6]])
>> ###############
>>
>> As to your comment about coordinating with Statsmodels, you should see the
>> links in the thread that Alan posted:
>> http://permalink.gmane.org/gmane.comp.python.numeric.general/56516
>> http://permalink.gmane.org/gmane.comp.python.numeric.general/56517
>> Josef's comments at the time seem to echo the issues the devs (and others)
>> have with the matrix class. Maybe things have changed with Statsmodels.
> Not changed, we have a strict policy against using np.matrix.
>
> generic efficient versions for linear operators, kronecker or sparse
> block matrix styly operations would be useful, but I would use array
> semantics, similar to using dot or linalg functions on ndarrays.
>
> Josef
> (long reply canceled because I'm writing too much that might only be
> of tangential interest or has been in some of the matrix discussion
> before.)
Josef,

Many thanks.  I have gained the impression that there is some antipathy 
to np.matrix, perhaps this is because, as others have suggested, the 
array doesn't provide an appropriate framework.

Where are such policy decisions documented?  Numpy doesn't appear to 
have a BDFL.

I had read Alan's links back in February and now have note of them.

Colin W.
>
>
>
>> I know I mentioned Sage and SageMathCloud before. I'll just point out that
>> there are folks that use this for real research problems, not just as a
>> pedagogical tool. They have a Matrix/vector/column_matrix class that do what
>> you were expecting from your problems posted above. Indeed below is a
>> (truncated) cut and past from a Sage Worksheet. (See
>> http://www.sagemath.org/doc/tutorial/tour_linalg.html)
>> ##########
>> In : Matrix([1,'2',3])
>> Error in lines 1-1
>> Traceback (most recent call last):
>> TypeError: unable to find a common ring for all elements
>>
>> In : Matrix([[1,2,3],[4,5]])
>> ValueError: List of rows is not valid (rows are wrong types or lengths)
>>
>> In : vector([1,2,3])
>> (1, 2, 3)
>>
>> In : column_matrix([1,2,3])
>> [1]
>> [2]
>> [3]
>> ##########
>>
>> Large portions of the custom code and wrappers in Sage are written in
>> Python. I don't think their Matrix object is a subclass of ndarray, so
>> perhaps you could strip out the Matrix stuff from here to make a separate
>> project with just the Matrix stuff, if you don't want to go through the Sage
>> interface.
>>
>>
>> On Wed, Feb 11, 2015 at 11:54 AM, cjw <cjw at ncf.ca> wrote:
>>>
>>> On 11-Feb-15 10:21 AM, Ryan Nelson wrote:
>>>
>>> So:
>>>
>>> In [2]: np.mat([4,'5',6])
>>> Out[2]:
>>> matrix([['4', '5', '6']], dtype='<U11')
>>>
>>> In [3]: np.mat([4,'5',6], dtype=int)
>>> Out[3]: matrix([[4, 5, 6]])
>>>
>>> Thanks Ryan,
>>>
>>> We are not singing from the same hymn book.
>>>
>>> Using PyScripter, I get:
>>>
>>> *** Python 2.7.9 (default, Dec 10 2014, 12:28:03) [MSC v.1500 64 bit
>>> (AMD64)] on win32. ***
>>>>>> import numpy as np
>>>>>> print('Numpy version: ', np.__version__)
>>> ('Numpy version: ', '1.9.0')
>>> Could you say which version you are using please?
>>>
>>> Colin W
>>>
>>> On Tue, Feb 10, 2015 at 5:07 PM, cjw <cjw at ncf.ca> wrote:
>>>
>>> It seems to be agreed that there are weaknesses in the existing Numpy
>>> Matrix
>>> Class.
>>>
>>> Some problems are illustrated below.
>>>
>>> I'll try to put some suggestions over the coming weeks and would
>>> appreciate
>>> comments.
>>>
>>> Colin W.
>>>
>>> Test Script:
>>>
>>> if __name__ == '__main__':
>>>      a= mat([4, 5, 6])                   # Good
>>>      print('a: ', a)
>>>      b= mat([4, '5', 6])                 # Not the expected result
>>>      print('b: ', b)
>>>      c= mat([[4, 5, 6], [7, 8]])         # Wrongly accepted as rectangular
>>>      print('c: ', c)
>>>      d= mat([[1, 2, 3]])
>>>      try:
>>>          d[0, 1]= 'b'                    # Correctly flagged, not numeric
>>>      except ValueError:
>>>          print("d[0, 1]= 'b'             # Correctly flagged, not numeric",
>>> '
>>> ValueError')
>>>      print('d: ', d)
>>>
>>> Result:
>>>
>>> *** Python 2.7.9 (default, Dec 10 2014, 12:28:03) [MSC v.1500 64 bit
>>> (AMD64)] on win32. ***
>>>
>>> a:  [[4 5 6]]
>>> b:  [['4' '5' '6']]
>>> c:  [[[4, 5, 6] [7, 8]]]
>>> d[0, 1]= 'b'             # Correctly flagged, not numeric  ValueError
>>> d:  [[1 2 3]]
>>>
>>>
>>>
>>> --
>>> View this message in context:
>>> http://numpy-discussion.10968.n7.nabble.com/Matrix-Class-tp39719.html
>>> Sent from the Numpy-discussion mailing list archive at Nabble.com.
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>>
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