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# Copyright (C) 1999-2020, The Alibaba Group Holding Ltd.
# This file is distributed under the same license as the mars package.
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"Project-Id-Version: mars 0.6.0a2\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2020-09-17 17:19+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language-Team: LANGUAGE <LL@li.org>\n"
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"Generated-By: Babel 2.7.0\n"

#: ../../source/reference/learn/generated/mars.learn.cluster.KMeans.rst:2
msgid "mars.learn.cluster.KMeans"
msgstr ""

#: mars.learn.cluster.KMeans:1 of
msgid "K-Means clustering."
msgstr ""

#: mars.learn.cluster.KMeans:3 of
msgid "Read more in the :ref:`User Guide <k_means>`."
msgstr ""

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

#: mars.learn.cluster.KMeans:5 of
msgid ""
"The number of clusters to form as well as the number of centroids to "
"generate."
msgstr ""

#: mars.learn.cluster.KMeans:8 of
msgid ""
"Method for initialization, defaults 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 a tensor is passed, it"
" should be of shape (n_clusters, n_features) and gives the initial "
"centers."
msgstr ""

#: mars.learn.cluster.KMeans:8 of
msgid "Method for initialization, defaults to 'k-means||':"
msgstr ""

#: mars.learn.cluster.KMeans:10 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.KMeans:14 of
msgid "'k-means||': scalable k-means++."
msgstr ""

#: mars.learn.cluster.KMeans:16 of
msgid ""
"'random': choose k observations (rows) at random from data for the "
"initial centroids."
msgstr ""

#: mars.learn.cluster.KMeans:19 of
msgid ""
"If a tensor is passed, it should be of shape (n_clusters, n_features) and"
" gives the initial centers."
msgstr ""

#: mars.learn.cluster.KMeans:22 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.KMeans:26 of
msgid "Maximum number of iterations of the k-means algorithm for a single run."
msgstr ""

#: mars.learn.cluster.KMeans:29 of
msgid "Relative tolerance with regards to inertia to declare convergence."
msgstr ""

#: mars.learn.cluster.KMeans:31 of
msgid "Verbosity mode."
msgstr ""

#: mars.learn.cluster.KMeans:33 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.KMeans:37 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.KMeans:45 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.KMeans:50 of
msgid "Only work for kmeans||, used in each iteration in kmeans||."
msgstr ""

#: mars.learn.cluster.KMeans:52 of
msgid "Only work for kmeans||, indicates how may iterations required."
msgstr ""

#: mars.learn.cluster.KMeans:57 of
msgid ""
"Coordinates of cluster centers. If the algorithm stops before fully "
"converging (see ``tol`` and ``max_iter``), these will not be consistent "
"with ``labels_``."
msgstr ""

#: mars.learn.cluster.KMeans of
msgid "type"
msgstr ""

#: mars.learn.cluster.KMeans:61 of
msgid "tensor of shape (n_clusters, n_features)"
msgstr ""

#: mars.learn.cluster.KMeans:65 of
msgid "Labels of each point"
msgstr ""

#: mars.learn.cluster.KMeans:67 of
msgid "tensor of shape (n_samples,)"
msgstr ""

#: mars.learn.cluster.KMeans:71 of
msgid "Sum of squared distances of samples to their closest cluster center."
msgstr ""

#: mars.learn.cluster.KMeans:73 of
msgid "float"
msgstr ""

#: mars.learn.cluster.KMeans:77 of
msgid "Number of iterations run."
msgstr ""

#: mars.learn.cluster.KMeans:79 of
msgid "int"
msgstr ""

#: mars.learn.cluster.KMeans:83 of
msgid ":class:`MiniBatchKMeans`"
msgstr ""

#: mars.learn.cluster.KMeans:84 of
msgid ""
"Alternative online implementation that does incremental updates of the "
"centers positions using mini-batches. For large scale learning (say "
"n_samples > 10k) MiniBatchKMeans is probably much faster than the default"
" batch implementation."
msgstr ""

#: mars.learn.cluster.KMeans:87 of
msgid "Notes"
msgstr ""

#: mars.learn.cluster.KMeans:88 of
msgid "The k-means problem is solved using either Lloyd's or Elkan's algorithm."
msgstr ""

#: mars.learn.cluster.KMeans:90 of
msgid ""
"The average complexity is given by O(k n T), were n is the number of "
"samples and T is the number of iteration."
msgstr ""

#: mars.learn.cluster.KMeans:93 of
msgid ""
"The worst case complexity is given by O(n^(k+2/p)) with n = n_samples, p "
"= n_features. (D. Arthur and S. Vassilvitskii, 'How slow is the k-means "
"method?' SoCG2006)"
msgstr ""

#: mars.learn.cluster.KMeans:97 of
msgid ""
"In practice, the k-means algorithm is very fast (one of the fastest "
"clustering algorithms available), but it falls in local minima. That's "
"why it can be useful to restart it several times."
msgstr ""

#: mars.learn.cluster.KMeans:101 of
msgid ""
"If the algorithm stops before fully converging (because of ``tol`` or "
"``max_iter``), ``labels_`` and ``cluster_centers_`` will not be "
"consistent, i.e. the ``cluster_centers_`` will not be the means of the "
"points in each cluster. Also, the estimator will reassign ``labels_`` "
"after the last iteration to make ``labels_`` consistent with ``predict`` "
"on the training set."
msgstr ""

#: mars.learn.cluster.KMeans:109 of
msgid "Examples"
msgstr ""

#: mars.learn.cluster.KMeans.__init__:1 of
msgid "Initialize self.  See help(type(self)) for accurate signature."
msgstr ""

#: ../../source/reference/learn/generated/mars.learn.cluster.KMeans.rst:13
msgid "Methods"
msgstr ""

#: ../../source/reference/learn/generated/mars.learn.cluster.KMeans.rst:24:<autosummary>:1
msgid ""
":obj:`__init__ <mars.learn.cluster.KMeans.__init__>`\\ "
"\\(\\[n\\_clusters\\, init\\, n\\_init\\, ...\\]\\)"
msgstr ""

#: ../../source/reference/learn/generated/mars.learn.cluster.KMeans.rst:24:<autosummary>:1
msgid "Initialize self."
msgstr ""

#: ../../source/reference/learn/generated/mars.learn.cluster.KMeans.rst:24:<autosummary>:1
msgid ""
":obj:`fit <mars.learn.cluster.KMeans.fit>`\\ \\(X\\[\\, y\\, "
"sample\\_weight\\, session\\, run\\_kwargs\\]\\)"
msgstr ""

#: ../../source/reference/learn/generated/mars.learn.cluster.KMeans.rst:24:<autosummary>:1
msgid "Compute k-means clustering."
msgstr ""

#: ../../source/reference/learn/generated/mars.learn.cluster.KMeans.rst:24:<autosummary>:1
msgid ""
":obj:`fit_predict <mars.learn.cluster.KMeans.fit_predict>`\\ \\(X\\[\\, "
"y\\, sample\\_weight\\, session\\, ...\\]\\)"
msgstr ""

#: ../../source/reference/learn/generated/mars.learn.cluster.KMeans.rst:24:<autosummary>:1
msgid "Compute cluster centers and predict cluster index for each sample."
msgstr ""

#: ../../source/reference/learn/generated/mars.learn.cluster.KMeans.rst:24:<autosummary>:1
msgid ""
":obj:`fit_transform <mars.learn.cluster.KMeans.fit_transform>`\\ "
"\\(X\\[\\, y\\, sample\\_weight\\, ...\\]\\)"
msgstr ""

#: ../../source/reference/learn/generated/mars.learn.cluster.KMeans.rst:24:<autosummary>:1
msgid "Compute clustering and transform X to cluster-distance space."
msgstr ""

#: ../../source/reference/learn/generated/mars.learn.cluster.KMeans.rst:24:<autosummary>:1
msgid ""
":obj:`get_params <mars.learn.cluster.KMeans.get_params>`\\ "
"\\(\\[deep\\]\\)"
msgstr ""

#: ../../source/reference/learn/generated/mars.learn.cluster.KMeans.rst:24:<autosummary>:1
msgid "Get parameters for this estimator."
msgstr ""

#: ../../source/reference/learn/generated/mars.learn.cluster.KMeans.rst:24:<autosummary>:1
msgid ""
":obj:`predict <mars.learn.cluster.KMeans.predict>`\\ \\(X\\[\\, "
"sample\\_weight\\, session\\, run\\_kwargs\\]\\)"
msgstr ""

#: ../../source/reference/learn/generated/mars.learn.cluster.KMeans.rst:24:<autosummary>:1
msgid "Predict the closest cluster each sample in X belongs to."
msgstr ""

#: ../../source/reference/learn/generated/mars.learn.cluster.KMeans.rst:24:<autosummary>:1
msgid ""
":obj:`score <mars.learn.cluster.KMeans.score>`\\ \\(X\\[\\, y\\, "
"sample\\_weight\\, session\\, run\\_kwargs\\]\\)"
msgstr ""

#: ../../source/reference/learn/generated/mars.learn.cluster.KMeans.rst:24:<autosummary>:1
msgid "Opposite of the value of X on the K-means objective."
msgstr ""

#: ../../source/reference/learn/generated/mars.learn.cluster.KMeans.rst:24:<autosummary>:1
msgid ""
":obj:`set_params <mars.learn.cluster.KMeans.set_params>`\\ "
"\\(\\*\\*params\\)"
msgstr ""

#: ../../source/reference/learn/generated/mars.learn.cluster.KMeans.rst:24:<autosummary>:1
msgid "Set the parameters of this estimator."
msgstr ""

#: ../../source/reference/learn/generated/mars.learn.cluster.KMeans.rst:24:<autosummary>:1
msgid ""
":obj:`transform <mars.learn.cluster.KMeans.transform>`\\ \\(X\\[\\, "
"session\\, run\\_kwargs\\]\\)"
msgstr ""

#: ../../source/reference/learn/generated/mars.learn.cluster.KMeans.rst:24:<autosummary>:1
msgid "Transform X to a cluster-distance space."
msgstr ""

