On Sun, Dec 26, 2010 at 7:34 AM, wrote:
> On Sun, Dec 26, 2010 at 3:51 AM, Enzo Michelangeli wrote:
>> For a pivoted algorithm, I have to perform an operation that in fully
>> vectorized form can be expressed as:
>>
>> pivot = tableau[locat,:]/tableau[locat,cand]
>> tableau -= tableau[:,ca
I made a mistake: the Mac behaves the same way when I repeat the
experiment. I guess I simply have to define init_numpy() to be of type
int for Python 3 on both machines. Nevertheless, if you see a more
elegant coding, I'd be interested. Thanks.
Bruce Sherwood
On Sun, Dec 26, 2010 at 3:26 PM, Bru
In my Python code I have
import cvisual
cvisual.init_numpy()
and in my C++ code I have
void
init_numpy()
{
import_array();
}
import_array() in numpy/core/include/numpy/_multiarray_api.h is a macro:
#if PY_VERSION_HEX >= 0x0300
#define NUMPY_IMPORT_ARRAY_RETVAL NULL
#else
#define NUMPY_
On Sun, Dec 26, 2010 at 3:51 AM, Enzo Michelangeli wrote:
> For a pivoted algorithm, I have to perform an operation that in fully
> vectorized form can be expressed as:
>
> pivot = tableau[locat,:]/tableau[locat,cand]
> tableau -= tableau[:,cand:cand+1]*pivot
> tableau[locat,:] = pivot
>
For a pivoted algorithm, I have to perform an operation that in fully
vectorized form can be expressed as:
pivot = tableau[locat,:]/tableau[locat,cand]
tableau -= tableau[:,cand:cand+1]*pivot
tableau[locat,:] = pivot
tableau is a rather large bidimensional array, and I'd like to avoid
