#!/usr/bin/env python3 Suppose we have very large sparse vectors, which contains a lot zeros and double. find a data structure to store them get the dot product of them def vector_to_index_value_list(vector): return [(i, v) for i,v in enumerate(vector) if v != 0.0]
#!/usr/bin/env python3 Suppose we have very large sparse vectors, which contains a lot zeros and double. find a data structure to store them get the dot product of them def vector_to_index_value_list(vector): return [(i, v) for i,v in enumerate(vector) if v != 0.0]
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![#!/usr/bin/env python 3
Suppose we have very large sparse vectors, which contains a lot of
zeros and double.
find a data structure to store them
get the dot product of them
def vector_to_index_value_list(vector):
return [(i, v) for i,v in enumerate(vector) if v != 0.0]
def dot product (iv_list1, iv_list2):
product = 0
p1=len (iv_list1)-1
p2 = len (iv_list2) - 1
while p1 >= 0 and p2 >= 0:
i1, v1 = iv_list1 [p1]
i2, v2 = iv_list2 [p2]
if i1 <i2:
p1=1
elif i2 <i1:
p2 = 1
else:
product+=v1 *v2
p1=1
p2 -= 1
return product
def__test_simple():
print(dot_product(vector_to_index_value_list([1., 2., 3.]),
vector_to_index_value_list([0., 2., 2.))))
# 10
def__test_time():
vector_length = 10
vector_count=1024
nozero_counut = 10
def random_vector():
import random
vector = [0 for _ in range(vector_length)]
for i in random.sample(range(vector_length), nozero_counut):
vector[i] = random.random()
return vector
vectors = [random_vector() for in range(vector_count)]
iv_lists = [vector_to_index_value_list(vector) for vector in vectors]
import time
time_start = time.time()
for i in range(vector_count):
for j in range(i):
dot_product(iv_lists[i], iv_lists[i])
time_end = time.time()
print(time_end - time_start, 'seconds')
if __name__ == '___main__':
_test_simple()
__test_time()](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F3e38f750-a3b6-41fa-9e5a-77c8be67512c%2Fb2c78341-8e01-4fae-a4be-5b4557afcef1%2Fsm8j3l5_processed.jpeg&w=3840&q=75)
Transcribed Image Text:#!/usr/bin/env python 3
Suppose we have very large sparse vectors, which contains a lot of
zeros and double.
find a data structure to store them
get the dot product of them
def vector_to_index_value_list(vector):
return [(i, v) for i,v in enumerate(vector) if v != 0.0]
def dot product (iv_list1, iv_list2):
product = 0
p1=len (iv_list1)-1
p2 = len (iv_list2) - 1
while p1 >= 0 and p2 >= 0:
i1, v1 = iv_list1 [p1]
i2, v2 = iv_list2 [p2]
if i1 <i2:
p1=1
elif i2 <i1:
p2 = 1
else:
product+=v1 *v2
p1=1
p2 -= 1
return product
def__test_simple():
print(dot_product(vector_to_index_value_list([1., 2., 3.]),
vector_to_index_value_list([0., 2., 2.))))
# 10
def__test_time():
vector_length = 10
vector_count=1024
nozero_counut = 10
def random_vector():
import random
vector = [0 for _ in range(vector_length)]
for i in random.sample(range(vector_length), nozero_counut):
vector[i] = random.random()
return vector
vectors = [random_vector() for in range(vector_count)]
iv_lists = [vector_to_index_value_list(vector) for vector in vectors]
import time
time_start = time.time()
for i in range(vector_count):
for j in range(i):
dot_product(iv_lists[i], iv_lists[i])
time_end = time.time()
print(time_end - time_start, 'seconds')
if __name__ == '___main__':
_test_simple()
__test_time()
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