Calculate cosine similarity between given two 1d list. Two list must have the same length. Example: cosine_similarity ([1, 1, 1],[1,2,-1]) # output: 0.47140452079103173 import math HIND def_12_distance(vec): Calculate 12 distance from two given vectors. ww WIND norm = 0. for element in vec: norm += element * element 8 norm = math.sqrt(norm) return norm def cosine_similarity(vec1, vec2): IN Calculate cosine similarity between given two vectors type vec1: list type vec2: list if len(vec1) != len(vec2): raise ValueError("The two vectors must be the same length. Got shape" + str(len(vec1)) +" and " + str(len(vec2))) norm_a=_12_distance(vec1) norm_b=_12_distance(vec2) similarity = 0. # Calculate the dot product of two vectors for vec1_element, vec2_element in zip(vec1, vec2): similarity += vec1_element vec2_element similarity/=(norm_a* norm_b)
Calculate cosine similarity between given two 1d list. Two list must have the same length. Example: cosine_similarity ([1, 1, 1],[1,2,-1]) # output: 0.47140452079103173 import math HIND def_12_distance(vec): Calculate 12 distance from two given vectors. ww WIND norm = 0. for element in vec: norm += element * element 8 norm = math.sqrt(norm) return norm def cosine_similarity(vec1, vec2): IN Calculate cosine similarity between given two vectors type vec1: list type vec2: list if len(vec1) != len(vec2): raise ValueError("The two vectors must be the same length. Got shape" + str(len(vec1)) +" and " + str(len(vec2))) norm_a=_12_distance(vec1) norm_b=_12_distance(vec2) similarity = 0. # Calculate the dot product of two vectors for vec1_element, vec2_element in zip(vec1, vec2): similarity += vec1_element vec2_element similarity/=(norm_a* norm_b)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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Question
![Calculate cosine similarity between given two 1d list.
Two list must have the same length.
Example:
cosine_similarity([1, 1, 1],[1, 2,-1]) # output: 0.47140452079103173
import math
def_12_distance(vec):
Calculate 12 distance from two given vectors.
norm = 0.
for element in vec:
norm += element* element
norm = math.sqrt(norm)
return norm
def cosine_similarity(vec1, vec2):
III
Calculate cosine similarity between given two vectors
:type vec1: list
:type vec2: list
if len(vec1) != len(vec2):
raise ValueError("The two vectors must be the same length. Got shape" + str(len(vec1))
+" and " + str(len(vec2)))
norm_a=_12_distance(vec1)
norm_b=_12_distance(vec2)
similarity = 0.
# Calculate the dot product of two vectors
for vec1_element, vec2_element in zip(vec1, vec2):
similarity += vec1_element vec2_element
similarity /=(norm_a* norm_b)](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F0b37739b-fa7b-4e91-927e-429311b7bb90%2F4e744a6b-ccd6-40ff-a675-1bc5be551832%2Fm5od1a_processed.jpeg&w=3840&q=75)
Transcribed Image Text:Calculate cosine similarity between given two 1d list.
Two list must have the same length.
Example:
cosine_similarity([1, 1, 1],[1, 2,-1]) # output: 0.47140452079103173
import math
def_12_distance(vec):
Calculate 12 distance from two given vectors.
norm = 0.
for element in vec:
norm += element* element
norm = math.sqrt(norm)
return norm
def cosine_similarity(vec1, vec2):
III
Calculate cosine similarity between given two vectors
:type vec1: list
:type vec2: list
if len(vec1) != len(vec2):
raise ValueError("The two vectors must be the same length. Got shape" + str(len(vec1))
+" and " + str(len(vec2)))
norm_a=_12_distance(vec1)
norm_b=_12_distance(vec2)
similarity = 0.
# Calculate the dot product of two vectors
for vec1_element, vec2_element in zip(vec1, vec2):
similarity += vec1_element vec2_element
similarity /=(norm_a* norm_b)
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