# SOME DESCRIPTIVE TITLE.
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# This file is distributed under the same license as the mars package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2020.
#
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#: ../../source/reference/learn/generated/mars.learn.metrics.pairwise.haversine_distances.rst:2
msgid "mars.learn.metrics.pairwise.haversine\\_distances"
msgstr ""

#: mars.learn.metrics.pairwise.haversine_distances:1 of
msgid "Compute the Haversine distance between samples in X and Y"
msgstr ""

#: mars.learn.metrics.pairwise.haversine_distances:3 of
msgid ""
"The Haversine (or great circle) distance is the angular distance between "
"two points on the surface of a sphere. The first distance of each point "
"is assumed to be the latitude, the second is the longitude, given in "
"radians. The dimension of the data must be 2."
msgstr ""

#: mars.learn.metrics.pairwise.haversine_distances:8 of
msgid ""
"D(x, y) = 2\\arcsin[\\sqrt{\\sin^2((x1 - y1) / 2)"
"                          + \\cos(x1)\\cos(y1)\\sin^2((x2 - y2) / 2)}]"
msgstr ""

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

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

#: mars.learn.metrics.pairwise.haversine_distances:17 of
msgid "**distance**"
msgstr ""

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

#: mars.learn.metrics.pairwise.haversine_distances:21 of
msgid "Notes"
msgstr ""

#: mars.learn.metrics.pairwise.haversine_distances:22 of
#, python-format
msgid ""
"As the Earth is nearly spherical, the haversine formula provides a good "
"approximation of the distance between two points of the Earth surface, "
"with a less than 1% error on average."
msgstr ""

#: mars.learn.metrics.pairwise.haversine_distances:27 of
msgid "Examples"
msgstr ""

#: mars.learn.metrics.pairwise.haversine_distances:28 of
msgid ""
"We want to calculate the distance between the Ezeiza Airport (Buenos "
"Aires, Argentina) and the Charles de Gaulle Airport (Paris, France)"
msgstr ""

