I had mentioned recently some interest in using fractions in the numpy polynomial class. Suprisingly, it actually works for the most part out of the box, which is great. However, there are some minor issues. For example:
numpy.poly1d( [ fractions.Fraction(1,2) , fractions.Fraction(1,8) ] )/fractions.Fraction(3,4)
fails, so I have to move the division into the inner list, which leads to a lot of duplicate typing and general messiness:
numpy.poly1d( [ fractions.Fraction(1,2)/fractions.Fraction(3,4) , fractions.Fraction(1,8)/fractions.Fraction(3,4) ] )
Another item of general interest for numpy is that there is no simple way to allocate memory for fraction array.
def zeros( nsamples ): list = [] for n in range( nsamples ): list.append( fractions.Fraction( 0 ) ) return numpy.array( list )
is a solution, but it would be nice to have this built-in, e.g.:
numpy.zeros( n , dtype=numpy.fraction )
Perhaps developing a fraction dtype would solve the above poly1d issue as well? Anyway, just some thoughts to ponder. Best wishes, Mike
Mon, 27 Apr 2009 17:04:17 -0400, Michael S. Gilbert wrote:
I had mentioned recently some interest in using fractions in the numpy polynomial class. Suprisingly, it actually works for the most part out of the box, which is great. However, there are some minor issues. For example:
numpy.poly1d( [ fractions.Fraction(1,2) , fractions.Fraction(1, 8) ] ... )/fractions.Fraction(3,4)
Polydiv can probably be fixed to handle this, it doesn't appear to be duck-typing clean in this case.
numpy.poly1d( [ fractions.Fraction(1,2)/fractions.Fraction(3,4) , fractions.Fraction(1,8)/fractions.Fraction(3,4) ] )
Another item of general interest for numpy is that there is no simple way to allocate memory for fraction array.
Something similar came up a while ago. You can do this:
a = np.frompyfunc(Fraction, 1, 1)(np.zeros((50, 50), int))
and so
a[0,0] += Fraction(1, 3) a /= 7 a array([[1/21, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], ..., [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0]], dtype=object)
The trick is to create an universal function out of the object constructor, and feed it initializer data. Works for any classes. I'm undecided whether it'd be a good idea to add a specialized routine for doing this... -- Pauli Virtanen
On Mon, 27 Apr 2009 17:04:17 -0400, Michael S. Gilbert wrote:
I had mentioned recently some interest in using fractions in the numpy polynomial class. Suprisingly, it actually works for the most part out of the box, which is great. However, there are some minor issues. For example:
numpy.poly1d( [ fractions.Fraction(1,2) , fractions.Fraction(1,8) ] )/fractions.Fraction(3,4)
fails, so I have to move the division into the inner list, which leads to a lot of duplicate typing and general messiness:
numpy.poly1d( [ fractions.Fraction(1,2)/fractions.Fraction(3,4) , fractions.Fraction(1,8)/fractions.Fraction(3,4) ] )
Another item of general interest for numpy is that there is no simple way to allocate memory for fraction array.
def zeros( nsamples ): list = [] for n in range( nsamples ): list.append( fractions.Fraction( 0 ) ) return numpy.array( list )
is a solution, but it would be nice to have this built-in, e.g.:
numpy.zeros( n , dtype=numpy.fraction )
Perhaps developing a fraction dtype would solve the above poly1d issue as well? Anyway, just some thoughts to ponder.
It also seems that numpy's basic math operations do not work with fractions:
math.sin(fractions.Fraction(1,2)) 0.47942553860420301 numpy.sin(fractions.Fraction(1,2)) Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: sin numpy.exp(fractions.Fraction(1,2)) Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: exp
Mike
On Mon, Apr 27, 2009 at 3:25 PM, Michael S. Gilbert < michael.s.gilbert@gmail.com> wrote:
On Mon, 27 Apr 2009 17:04:17 -0400, Michael S. Gilbert wrote:
I had mentioned recently some interest in using fractions in the numpy polynomial class. Suprisingly, it actually works for the most part out of the box, which is great. However, there are some minor issues. For example:
numpy.poly1d( [ fractions.Fraction(1,2) , fractions.Fraction(1,8) ] )/fractions.Fraction(3,4)
fails, so I have to move the division into the inner list, which leads to a lot of duplicate typing and general messiness:
numpy.poly1d( [ fractions.Fraction(1,2)/fractions.Fraction(3,4) , fractions.Fraction(1,8)/fractions.Fraction(3,4) ] )
Another item of general interest for numpy is that there is no simple way to allocate memory for fraction array.
def zeros( nsamples ): list = [] for n in range( nsamples ): list.append( fractions.Fraction( 0 ) ) return numpy.array( list )
is a solution, but it would be nice to have this built-in, e.g.:
numpy.zeros( n , dtype=numpy.fraction )
Perhaps developing a fraction dtype would solve the above poly1d issue as well? Anyway, just some thoughts to ponder.
It also seems that numpy's basic math operations do not work with fractions:
math.sin(fractions.Fraction(1,2)) 0.47942553860420301 numpy.sin(fractions.Fraction(1,2)) Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: sin numpy.exp(fractions.Fraction(1,2)) Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: exp
Yep. When objects are involved the ufuncs expect the functions to be object specific and defined as methods. Chuck
participants (3)
-
Charles R Harris -
Michael S. Gilbert -
Pauli Virtanen