sklearn.metrics.pairwise.cosine_similarity()

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

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:

Parameters:	X : ndarray or sparse array, shape: (n_samples_X, n_features) Input data. Y : ndarray or sparse array, shape: (n_samples_Y, n_features) Input data. If `None`, the output will be the pairwise similarities between all samples in `X`. dense_output : boolean (optional), 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 : array An array with shape (n_samples_X, n_samples_Y).

X : ndarray or sparse array, shape: (n_samples_X, n_features)

Input data.

Y : ndarray or sparse array, shape: (n_samples_Y, n_features)

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

dense_output : boolean (optional), 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 : array

An array with shape (n_samples_X, n_samples_Y).

Links:

doc_scikit_learn

2017-01-15 04:26:31

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