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 similarity
# 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/vectorscosine similarity python
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_simCosine Similarity numpy
np.inner(a, b) = sum(a[:]*b[:])Copyright © 2021 Codeinu
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