Re: [Numpy-discussion] Numpy Overhead

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):

Re: [Numpy-discussion] Numpy Overhead

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 > >

Re: [Numpy-discussion] Numpy Overhead

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

Re: [Numpy-discussion] Numpy Overhead

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

Re: [Numpy-discussion] Numpy Overhead

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

Re: [Numpy-discussion] Numpy Overhead

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

Re: [Numpy-discussion] Numpy Overhead

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

Re: [Numpy-discussion] Numpy Overhead

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

Re: [Numpy-discussion] Numpy Overhead

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*

Re: [Numpy-discussion] Numpy Overhead

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