import time
from time import process_time
import numpy
matrix = []
def naive():
from time import process_time
def create_matrix(n):
res_list = []
for i_ind in range(n):
res_list.append(list())
for j_ind in range(n):
res_list[i_ind].append(0)
return res_list
def get_counting_func(q_vector, v_vector):
q_matrix = [[*q_vector] for i in range(len(q_vector))]
v_matrix = [[i for j in range(len(v_vector))] for i in v_vector]
def t_func(i_start, i_len, j_start, j_len):
global matrix
for i in range(i_start, i_start + i_len):
for j in range(j_start, j_start + j_len):
matrix[i][j] = ((q_matrix[i][j]) ** 2 + (v_matrix[i][j]) ** 2) ** 0.5
return t_func
n_el = 5000
global matrix
matrix = create_matrix(n_el)
q_vect = [(i + 11) ** 2 for i in range(n_el)]
p_vect = [(i * 3 + 13) * 17 for i in range(n_el)]
counting_func = get_counting_func(q_vect, p_vect)
start_time = process_time()
counting_func(0, n_el, 0, n_el)
print('Naive ', process_time() - start_time, "seconds")
return matrix
def optimized():
start_time = process_time()
n_el = 5000
res = numpy.fromfunction(lambda i, j: (((i * 3 + 13) * 17) ** 2 + (j + 11) ** 4) ** 0.5, (n_el, n_el)
, dtype=numpy.float32)
print('Optimized ', process_time() - start_time, "seconds")
return res
if __name__ == '__main__':
first = naive()
second = optimized()
unique, counts = numpy.unique(numpy.isclose(second, numpy.array(first)), return_counts=True)
print(f'Unique: {unique} Counts: {counts}')
