python-scikit-learnCosine similarity
from sklearn import metrics
cs = metrics.pairwise.cosine_similarity([[1, 2], [4, 5], [7, 19]])ctrl + cfrom sklearn importimport module from scikit-learn | .cosine_similarity(calculate cosine similarity (L2-normalized dot product of vectors) |
Usage example
from sklearn import metrics
cs = metrics.pairwise.cosine_similarity([[1, 2], [4, 5], [7, 19]])
print(cs)output
[[1. 0.97780241 0.99388373]
[0.97780241 1. 0.9486833 ]
[0.99388373 0.9486833 1. ]]
More of Python Scikit Learn
- Using dbscan clustering example
- Binary classification example
- How to get best parameters from grid search CV
- Feature selection using KBest and chi2
- Grid search CV example
- Using quantile regression
- How to get best estimator from grid search CV
- PCA dimensionality reduction example
- Decision tree classifier example
- Using resample example
See more codes...