On Feb 28, 2017 2:57 PM, "Sebastian K"
wrote:
Yes it is true the execution time is much faster with the numpy function.
The Code for numpy version:
def createMatrix(n):
Matrix = np.empty(shape=(n,n), dtype='float64')
for x in range(n):
for y in range(n):
Thank you! That is the information I needed.
2017-03-01 0:18 GMT+01:00 Matthew Brett :
> Hi,
>
> On Tue, Feb 28, 2017 at 3:04 PM, Sebastian K
> wrote:
> > Yes you are right. There is no need to add that line. I deleted it. But
> the
> >
Hi,
On Tue, Feb 28, 2017 at 3:04 PM, Sebastian K
wrote:
> Yes you are right. There is no need to add that line. I deleted it. But the
> measured heap peak is still the same.
You're applying the naive matrix multiplication algorithm, which is
ideal for minimizing
Yes you are right. There is no need to add that line. I deleted it. But the
measured heap peak is still the same.
2017-03-01 0:00 GMT+01:00 Joseph Fox-Rabinovitz :
> For one thing, `C = np.empty(shape=(n,n), dtype='float64')` allocates 10^4
> extra elements before being
For one thing, `C = np.empty(shape=(n,n), dtype='float64')` allocates 10^4
extra elements before being immediately discarded.
-Joe
On Tue, Feb 28, 2017 at 5:57 PM, Sebastian K wrote:
> Yes it is true the execution time is much faster with the numpy
Yes it is true the execution time is much faster with the numpy function.
The Code for numpy version:
def createMatrix(n):
Matrix = np.empty(shape=(n,n), dtype='float64')
for x in range(n):
for y in range(n):
Matrix[x, y] = 0.1 + ((x*y)%1000)/1000.0
return Matrix
if __name__ == '__main__':
n
Hi,
On Tue, Feb 28, 2017 at 2:12 PM, Sebastian K
wrote:
> Thank you for your answer.
> For example a very simple algorithm is a matrix multiplication. I can see
> that the heap peak is much higher for the numpy version in comparison to a
> pure python 3
It would really help to see the code you are using in both cases as well as
some heap usage numbers...
-Joe
On Tue, Feb 28, 2017 at 5:12 PM, Sebastian K wrote:
> Thank you for your answer.
> For example a very simple algorithm is a matrix multiplication. I
Thank you for your answer.
For example a very simple algorithm is a matrix multiplication. I can see
that the heap peak is much higher for the numpy version in comparison to a
pure python 3 implementation.
The heap is measured with the libmemusage from libc:
*heap peak*
You are going to need to provide much more context than that. Overhead
compared to what? And where (io, cpu, etc.)? What are the size of your
arrays, and what sort of operations are you doing? Finally, how much
overhead are you seeing?
There can be all sorts of reasons for overhead, and some can
Hello everyone,
I'm interested in the numpy project and tried a lot with the numpy array.
I'm wondering what is actually done that there is so much overhead when I
call a function in Numpy. What is the reason?
Thanks in advance.
Regards
Sebastian Kaster
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