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#: ../../source/reference/learn/generated/mars.learn.cluster.k_means.rst:2
msgid "mars.learn.cluster.k\\_means"
msgstr ""

#: mars.learn.cluster.k_means:1 of
msgid "K-means clustering algorithm."
msgstr ""

#: mars.learn.cluster.k_means of
msgid "Parameters"
msgstr ""

#: mars.learn.cluster.k_means:3 of
msgid ""
"The observations to cluster. It must be noted that the data will be "
"converted to C ordering, which will cause a memory copy if the given data"
" is not C-contiguous."
msgstr ""

#: mars.learn.cluster.k_means:7 of
msgid ""
"The number of clusters to form as well as the number of centroids to "
"generate."
msgstr ""

#: mars.learn.cluster.k_means:10 of
msgid ""
"The weights for each observation in X. If None, all observations are "
"assigned equal weight (default: None)"
msgstr ""

#: mars.learn.cluster.k_means:13 of
msgid ""
"Method for initialization, default to 'k-means||':  'k-means++' : selects"
" initial cluster centers for k-mean clustering in a smart way to speed up"
" convergence. See section Notes in k_init for more details.  'k-means||':"
" scalable k-means++.  'random': choose k observations (rows) at random "
"from data for the initial centroids.  If an ndarray is passed, it should "
"be of shape (n_clusters, n_features) and gives the initial centers.  If a"
" callable is passed, it should take arguments X, k and and a random state"
" and return an initialization."
msgstr ""

#: mars.learn.cluster.k_means:13 of
msgid "Method for initialization, default to 'k-means||':"
msgstr ""

#: mars.learn.cluster.k_means:15 of
msgid ""
"'k-means++' : selects initial cluster centers for k-mean clustering in a "
"smart way to speed up convergence. See section Notes in k_init for more "
"details."
msgstr ""

#: mars.learn.cluster.k_means:19 of
msgid "'k-means||': scalable k-means++."
msgstr ""

#: mars.learn.cluster.k_means:21 of
msgid ""
"'random': choose k observations (rows) at random from data for the "
"initial centroids."
msgstr ""

#: mars.learn.cluster.k_means:24 of
msgid ""
"If an ndarray is passed, it should be of shape (n_clusters, n_features) "
"and gives the initial centers."
msgstr ""

#: mars.learn.cluster.k_means:27 of
msgid ""
"If a callable is passed, it should take arguments X, k and and a random "
"state and return an initialization."
msgstr ""

#: mars.learn.cluster.k_means:30 of
msgid ""
"Number of time the k-means algorithm will be run with different centroid "
"seeds. The final results will be the best output of n_init consecutive "
"runs in terms of inertia."
msgstr ""

#: mars.learn.cluster.k_means:34 of
msgid "Maximum number of iterations of the k-means algorithm to run."
msgstr ""

#: mars.learn.cluster.k_means:36 of
msgid "Verbosity mode."
msgstr ""

#: mars.learn.cluster.k_means:38 of
msgid "The relative increment in the results before declaring convergence."
msgstr ""

#: mars.learn.cluster.k_means:40 of
msgid ""
"Determines random number generation for centroid initialization. Use an "
"int to make the randomness deterministic. See :term:`Glossary "
"<random_state>`."
msgstr ""

#: mars.learn.cluster.k_means:44 of
msgid ""
"When pre-computing distances it is more numerically accurate to center "
"the data first.  If copy_x is True (default), then the original data is "
"not modified, ensuring X is C-contiguous.  If False, the original data is"
" modified, and put back before the function returns, but small numerical "
"differences may be introduced by subtracting and then adding the data "
"mean, in this case it will also not ensure that data is C-contiguous "
"which may cause a significant slowdown."
msgstr ""

#: mars.learn.cluster.k_means:52 of
msgid ""
"K-means algorithm to use. The classical EM-style algorithm is \"full\". "
"The \"elkan\" variation is more efficient by using the triangle "
"inequality, but currently doesn't support sparse data. \"auto\" chooses "
"\"elkan\" for dense data and \"full\" for sparse data."
msgstr ""

#: mars.learn.cluster.k_means:57 of
msgid "Only work for kmeans||, used in each iteration in kmeans||."
msgstr ""

#: mars.learn.cluster.k_means:59 of
msgid "Only work for kmeans||, indicates how may iterations required."
msgstr ""

#: mars.learn.cluster.k_means:61 of
msgid "Whether or not to return the number of iterations."
msgstr ""

#: mars.learn.cluster.k_means of
msgid "Returns"
msgstr ""

#: mars.learn.cluster.k_means:64 of
msgid ""
"* **centroid** (*float ndarray with shape (k, n_features)*) -- Centroids "
"found at the last iteration of k-means. * **label** (*integer ndarray "
"with shape (n_samples,)*) -- label[i] is the code or index of the "
"centroid the   i'th observation is closest to. * **inertia** (*float*) --"
" The final value of the inertia criterion (sum of squared distances to   "
"the closest centroid for all observations in the training set). * "
"**best_n_iter** (*int*) -- Number of iterations corresponding to the best"
" results.   Returned only if `return_n_iter` is set to True."
msgstr ""

#: mars.learn.cluster.k_means:64 of
msgid ""
"**centroid** (*float ndarray with shape (k, n_features)*) -- Centroids "
"found at the last iteration of k-means."
msgstr ""

#: mars.learn.cluster.k_means:65 of
msgid ""
"**label** (*integer ndarray with shape (n_samples,)*) -- label[i] is the "
"code or index of the centroid the i'th observation is closest to."
msgstr ""

#: mars.learn.cluster.k_means:67 of
msgid ""
"**inertia** (*float*) -- The final value of the inertia criterion (sum of"
" squared distances to the closest centroid for all observations in the "
"training set)."
msgstr ""

#: mars.learn.cluster.k_means:69 of
msgid ""
"**best_n_iter** (*int*) -- Number of iterations corresponding to the best"
" results. Returned only if `return_n_iter` is set to True."
msgstr ""

