On Sat, Feb 14, 2015 at 12:05 PM, cjw
On 14-Feb-15 11:35 AM, josef.pktd@gmail.com wrote:
On Wed, Feb 11, 2015 at 4:18 PM, Ryan Nelson
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.
It's not directly antipathy, it's cost-benefit analysis. np.matrix has few advantages, but makes reading and maintaining code much more difficult. Having to watch out for multiplication `*` is a lot of extra work. Checking shapes and fixing bugs with unexpected dtypes is also a lot of work, but we have large benefits. For a long time the policy in statsmodels was to keep pandas out of the core of functions (i.e. out of the actual calculations) and restrict it to inputs and returns. However, pandas is becoming more popular and can do some things much better than plain numpy, so it is slowly moving inside some of our core calculations. It's still an easy source of bugs, but we do gain something. Benefits like these don't exist for np.matrix.
Where are such policy decisions documented? Numpy doesn't appear to have a BDFL.
In general it's a mix of mailing list discussions and discussion in issues and PRs. I'm not directly involved in numpy and don't subscribe to the numpy's github notifications. For scipy (and partially for statsmodels): I think large parts of policies for code and workflow are not explicitly specified, but are more an understanding of maintainers and developers that can slowly change over time, build up through spread out discussion as temporary consensus (or without strong objections). scipy has a hacking text file to describe some of it, but I haven't read it in ages. (long term changes compared to 6 years ago: required code review and required test coverage.) Josef
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
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='
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
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]]
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