# Answers for "scipy cosine similarity"

1

cosine similarity python numpy

``````from scipy import spatial

dataSetI = [3, 45, 7, 2]
dataSetII = [2, 54, 13, 15]
result = 1 - spatial.distance.cosine(dataSetI, dataSetII)``````
Posted by: Guest on September-20-2020
5

python cosine distance

``````# Example function using numpy:
from numpy import dot
from numpy.linalg import norm

def cosine_similarity(list_1, list_2):
cos_sim = dot(list_1, list_2) / (norm(list_1) * norm(list_2))
return cos_sim
# Note, the dot product is only defined for lists of equal length. You
#	can use your_list.extend() to add elements to the shorter list

# Example usage with identical lists/vectors:
your_list_1 = [1, 1, 1]
your_list_2 = [1, 1, 1]
cosine_similarity(your_list_1, your_list_2)
--> 1.0 # 1 = maximally similar lists/vectors

# Example usage with opposite lists/vectors:
your_list_1 = [1, 1, 1]
your_list_2 = [-1, -1, -1]
cosine_similarity(your_list_1, your_list_2)
--> -1.0 # -1 = maximally dissimilar lists/vectors``````
Posted by: Guest on November-11-2020
0

scipy cosine similarity

``````from scipy.spatial import distance

distance.cosine([1, 0, 0], [0, 1, 0])
>>> 1.0

distance.cosine([100, 0, 0], [0, 1, 0])
>>> 1.0

distance.cosine([1, 1, 0], [0, 1, 0])
>>> 0.29289321881345254``````
Posted by: Guest on July-20-2021