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)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)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/vectorsscipy 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])
>>> 0.29289321881345254Copyright © 2021 Codeinu
Forgot your account's password or having trouble logging into your Account? Don't worry, we'll help you to get back your account. Enter your email address and we'll send you a recovery link to reset your password. If you are experiencing problems resetting your password contact us
