weird behavior. bug perhaps?

[zoom]

On 06/18/2013 05:25 PM, Robert Kern wrote:
> On 2013-06-18 15:23, zoom wrote:
>> Hi, I have a strange problem here. Perhaps someone would care to help me.
>>
>> In the file test.py I have the following code:
>>
>> from scipy import matrix, tile, mean, shape
>> import unittest
>>
>> class TestSequenceFunctions(unittest.TestCase):
>>
>> def setUp(self):
>> self.m = [[1,2],[3,4],[3,4],[3,4]]
>>
>> def test_simplify(self):
>> m = matrix(self.m)
>> print shape(m)
>> print [shape(m)[1],1]
>> print shape(tile(mean(m,1),[shape(m)[1],1]).T)
>>
>> if __name__ == '__main__':
>> unittest.main()
>>
>> (Note that test.py, is just a simplification of my testing file,
>> sufficient to
>> reproduce the weird behavior that I'm about to describe.)
>>
>> If i run it in terminal via "python test.py" command I get the
>> following output:
>>
>> (4, 2)
>> [2, 1]
>> (1, 8)
>> .
>> ----------------------------------------------------------------------
>> Ran 1 test in 0.000s
>>
>> OK
>>
>>
>> Now comes the funny part.
>> Let's try to run the following code in python interpreter:
>>
>> >>> m = [[1,2],[3,4],[3,4],[3,4]]
>> >>>
>> >>> from scipy import matrix, tile, mean, shape
>> >>> print shape(m)
>> (4, 2)
>> >>> print [shape(m)[1],1]
>> [2, 1]
>> >>> print shape(tile(mean(m,1),[shape(m)[1],1]).T)
>> (4, 2)
>>
>> Note the difference between outputs of:
>> print shape(tile(mean(m,1),[shape(m)[1],1]).T)
>>
>>
>> I mean, WTF?
>> This is definitely not the expected behavior.
>> Anybody knows what just happened here?
>
> As rusi noted, the difference between your two snippets is that in one,
> you converted the list of lists to a matrix object in your test suite
> but not in your interactive session. Most numpy functions like mean()
> will convert their arguments to regular numpy.ndarray objects rather
> than matrix objects. matrix is a subclass of ndarray that adds special
> behavior: in particular, operations on matrix objects retain their
> 2D-ness even when an ndarray would flatten down to a 1D array.
>
> [~]
> |1> import numpy as np
>
> [~]
> |2> m = [[1,2],[3,4],[3,4],[3,4]]
>
> [~]
> |3> a = np.array(m)
>
> [~]
> |4> b = np.matrix(m)
>
> [~]
> |5> np.mean(a, axis=1)
> array([ 1.5, 3.5, 3.5, 3.5])
>
> [~]
> |6> np.mean(b, axis=1)
> matrix([[ 1.5],
> [ 3.5],
> [ 3.5],
> [ 3.5]])
>
> [~]
> |7> np.mean(a, axis=1).shape
> (4,)
>
> [~]
> |8> np.mean(b, axis=1).shape
> (4, 1)
>
>
> This will propagate through the rest of your computation.
>
> Personally, I recommend avoiding the matrix type. It causes too many
> problems. Stick to plain ndarrays.
>
> You will probably want to ask further numpy questions on the
> numpy-discussion mailing list:
>
> http://www.scipy.org/scipylib/mailing-lists.html
>
didn't see it earlier.

thanks.

so ndarrays, you say...