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scikit-learn 1.3.2
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# `sklearn.metrics.pairwise`.cosine_similarity

sklearn.metrics.pairwise.cosine_similarity(X, Y=None, dense_output=True)

  Compute cosine similarity between samples in X and Y.

  Cosine similarity, or the cosine kernel, computes similarity as the normalized dot product of X and Y:

  > K(X, Y) = <X, Y> / (||X||*||Y||)

  On L2-normalized data, this function is equivalent to linear_kernel.

  Read more in the User Guide.

  Parameters:
    X{array-like, sparse matrix} of shape (n_samples_X, n_features)

      Input data.

    Y{array-like, sparse matrix} of shape (n_samples_Y, n_features), default=None

      Input data. If `None`, the output will be the pairwise similarities between all samples in `X`.

    dense_output bool, default=True

      Whether to return dense output even when the input is sparse. If `False`, the output is sparse if both input arrays are sparse.

      New in version 0.17: parameter `dense_output` for dense output.

  Returns:
    kernel matrix ndarray of shape (n_samples_X, n_samples_Y)

      Returns the cosine similarity between samples in X and Y.

## Examples using `sklearn.metrics.pairwise.cosine_similarity`

Plot classification boundaries with different SVM Kernels

Plot classification boundaries with different SVM Kernels