# SOME DESCRIPTIVE TITLE.
# Copyright (C) 1999-2020, The Alibaba Group Holding Ltd.
# This file is distributed under the same license as the mars package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2020.
#
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"POT-Creation-Date: 2020-06-16 16:57+0800\n"
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#: ../../source/reference/learn/generated/mars.learn.metrics.pairwise.cosine_similarity.rst:2
msgid "mars.learn.metrics.pairwise.cosine\\_similarity"
msgstr ""

#: mars.learn.metrics.pairwise.cosine_similarity:1 of
msgid "Compute cosine similarity between samples in X and Y."
msgstr ""

#: mars.learn.metrics.pairwise.cosine_similarity:3 of
msgid ""
"Cosine similarity, or the cosine kernel, computes similarity as the "
"normalized dot product of X and Y:"
msgstr ""

#: mars.learn.metrics.pairwise.cosine_similarity:6 of
msgid "K(X, Y) = <X, Y> / (||X||*||Y||)"
msgstr ""

#: mars.learn.metrics.pairwise.cosine_similarity:8 of
msgid "On L2-normalized data, this function is equivalent to linear_kernel."
msgstr ""

#: mars.learn.metrics.pairwise.cosine_similarity:10 of
msgid "Read more in the :ref:`User Guide <cosine_similarity>`."
msgstr ""

#: mars.learn.metrics.pairwise.cosine_similarity of
msgid "Parameters"
msgstr ""

#: mars.learn.metrics.pairwise.cosine_similarity:12 of
msgid "Input data."
msgstr ""

#: mars.learn.metrics.pairwise.cosine_similarity:14 of
msgid ""
"Input data. If ``None``, the output will be the pairwise similarities "
"between all samples in ``X``."
msgstr ""

#: mars.learn.metrics.pairwise.cosine_similarity:17 of
msgid ""
"Whether to return dense output even when the input is sparse. If "
"``False``, the output is sparse if both input tensors are sparse."
msgstr ""

#: mars.learn.metrics.pairwise.cosine_similarity of
msgid "Returns"
msgstr ""

#: mars.learn.metrics.pairwise.cosine_similarity:21 of
msgid "**kernel matrix** -- A tensor with shape (n_samples_X, n_samples_Y)."
msgstr ""

#: mars.learn.metrics.pairwise.cosine_similarity of
msgid "Return type"
msgstr ""

