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
