Numpy exercise

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Joined: Mon Jun 15, 2020 8:33 am

Numpy exercise

Post by Rodri »

Hi to all! I would like to share my results of Numpy exercise.
I have used a similar method used in numpy lecture, but using "time.perf_counter()" instead of "", why? because I did not find any difference and I usually use that.
The code is shown as follows:

Code: Select all

import numpy as np
import time

A = np.array([[1,2,3], [4,5,6], [7,8,9]])
#A = np.ones((3,3)) * 3
B = np.ones((3,3)) * 2
X = np.zeros((3,3))     #Matrix dot solution

sum1 = 0
sum2 = 0
med1 = 0
med2 = 0

#Manual method
for loop in range(0,500):

    t1 = time.perf_counter()
    for k in range(0,2+1):
        for i in range(0,2+1):
            e = 0
            for j in range(0,2+1):
                e = e + (A[k,j] * B[j,i])
            X[k,i] = e
    t2 = time.perf_counter()

    sum1 = sum1 + (t2 - t1)
med1 = sum1/50

for loop2 in range(0,500):

    t1 = time.perf_counter()
    t2 = time.perf_counter()
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