# 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.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: mars 0.5.0a2\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2020-06-16 16:57+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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"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Generated-By: Babel 2.7.0\n"

#: ../../source/reference/learn/generated/mars.learn.decomposition.TruncatedSVD.rst:2
msgid "mars.learn.decomposition.TruncatedSVD"
msgstr ""

#: mars.learn.decomposition.TruncatedSVD:1 of
msgid "Dimensionality reduction using truncated SVD (aka LSA)."
msgstr ""

#: mars.learn.decomposition.TruncatedSVD:3 of
msgid ""
"This transformer performs linear dimensionality reduction by means of "
"truncated singular value decomposition (SVD). Contrary to PCA, this "
"estimator does not center the data before computing the singular value "
"decomposition. This means it can work with scipy.sparse matrices "
"efficiently."
msgstr ""

#: mars.learn.decomposition.TruncatedSVD:9 of
msgid ""
"In particular, truncated SVD works on term count/tf-idf matrices as "
"returned by the vectorizers in sklearn.feature_extraction.text. In that "
"context, it is known as latent semantic analysis (LSA)."
msgstr ""

#: mars.learn.decomposition.TruncatedSVD:13 of
msgid ""
"This estimator supports two algorithms: a fast randomized SVD solver, and"
" a \"naive\" algorithm that uses ARPACK as an eigensolver on (X * X.T) or"
" (X.T * X), whichever is more efficient."
msgstr ""

#: mars.learn.decomposition.TruncatedSVD:17 of
msgid "Read more in the :ref:`User Guide <LSA>`."
msgstr ""

#: mars.learn.decomposition.TruncatedSVD of
msgid "Parameters"
msgstr ""

#: mars.learn.decomposition.TruncatedSVD:19 of
msgid ""
"Desired dimensionality of output data. Must be strictly less than the "
"number of features. The default value is useful for visualisation. For "
"LSA, a value of 100 is recommended."
msgstr ""

#: mars.learn.decomposition.TruncatedSVD:24 of
msgid ""
"SVD solver to use. Either \"arpack\" for the ARPACK wrapper in SciPy "
"(scipy.sparse.linalg.svds), or \"randomized\" for the randomized "
"algorithm due to Halko (2009)."
msgstr ""

#: mars.learn.decomposition.TruncatedSVD:28 of
msgid ""
"Number of iterations for randomized SVD solver. Not used by ARPACK. The "
"default is larger than the default in `randomized_svd` to handle sparse "
"matrices that may have large slowly decaying spectrum."
msgstr ""

#: mars.learn.decomposition.TruncatedSVD:32 of
msgid ""
"If int, random_state is the seed used by the random number generator; If "
"RandomState instance, random_state is the random number generator; If "
"None, the random number generator is the RandomState instance used by "
"`np.random`."
msgstr ""

#: mars.learn.decomposition.TruncatedSVD:37 of
msgid ""
"Tolerance for ARPACK. 0 means machine precision. Ignored by randomized "
"SVD solver."
msgstr ""

#: mars.learn.decomposition.TruncatedSVD of
msgid "type"
msgstr ""

#: mars.learn.decomposition.TruncatedSVD:45 of
msgid "array, shape (n_components, n_features)"
msgstr ""

#: mars.learn.decomposition.TruncatedSVD:49 of
msgid ""
"The variance of the training samples transformed by a projection to each "
"component."
msgstr ""

#: mars.learn.decomposition.TruncatedSVD:52
#: mars.learn.decomposition.TruncatedSVD:58
#: mars.learn.decomposition.TruncatedSVD:66 of
msgid "array, shape (n_components,)"
msgstr ""

#: mars.learn.decomposition.TruncatedSVD:56 of
msgid "Percentage of variance explained by each of the selected components."
msgstr ""

#: mars.learn.decomposition.TruncatedSVD:62 of
msgid ""
"The singular values corresponding to each of the selected components. The"
" singular values are equal to the 2-norms of the ``n_components`` "
"variables in the lower-dimensional space."
msgstr ""

#: mars.learn.decomposition.TruncatedSVD:69 of
msgid "Examples"
msgstr ""

#: mars.learn.decomposition.TruncatedSVD:85 of
msgid ":class:`PCA`"
msgstr ""

#: mars.learn.decomposition.TruncatedSVD:88 of
msgid "References"
msgstr ""

#: mars.learn.decomposition.TruncatedSVD:89 of
msgid ""
"Finding structure with randomness: Stochastic algorithms for constructing"
" approximate matrix decompositions Halko, et al., 2009 (arXiv:909) "
"https://arxiv.org/pdf/0909.4061.pdf"
msgstr ""

#: mars.learn.decomposition.TruncatedSVD:94 of
msgid "Notes"
msgstr ""

#: mars.learn.decomposition.TruncatedSVD:95 of
msgid ""
"SVD suffers from a problem called \"sign indeterminacy\", which means the"
" sign of the ``components_`` and the output from transform depend on the "
"algorithm and random state. To work around this, fit instances of this "
"class to data once, then keep the instance around to do transformations."
msgstr ""

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

#: ../../source/reference/learn/generated/mars.learn.decomposition.TruncatedSVD.rst:13
msgid "Methods"
msgstr ""

#: ../../source/reference/learn/generated/mars.learn.decomposition.TruncatedSVD.rst:22:<autosummary>:1
msgid ""
":obj:`__init__ <mars.learn.decomposition.TruncatedSVD.__init__>`\\ "
"\\(\\[n\\_components\\, algorithm\\, n\\_iter\\, ...\\]\\)"
msgstr ""

#: ../../source/reference/learn/generated/mars.learn.decomposition.TruncatedSVD.rst:22:<autosummary>:1
msgid "Initialize self."
msgstr ""

#: ../../source/reference/learn/generated/mars.learn.decomposition.TruncatedSVD.rst:22:<autosummary>:1
msgid ""
":obj:`fit <mars.learn.decomposition.TruncatedSVD.fit>`\\ \\(X\\[\\, y\\, "
"session\\]\\)"
msgstr ""

#: ../../source/reference/learn/generated/mars.learn.decomposition.TruncatedSVD.rst:22:<autosummary>:1
msgid "Fit LSI model on training data X."
msgstr ""

#: ../../source/reference/learn/generated/mars.learn.decomposition.TruncatedSVD.rst:22:<autosummary>:1
msgid ""
":obj:`fit_transform "
"<mars.learn.decomposition.TruncatedSVD.fit_transform>`\\ \\(X\\[\\, y\\, "
"session\\]\\)"
msgstr ""

#: ../../source/reference/learn/generated/mars.learn.decomposition.TruncatedSVD.rst:22:<autosummary>:1
msgid "Fit LSI model to X and perform dimensionality reduction on X."
msgstr ""

#: ../../source/reference/learn/generated/mars.learn.decomposition.TruncatedSVD.rst:22:<autosummary>:1
msgid ""
":obj:`get_params <mars.learn.decomposition.TruncatedSVD.get_params>`\\ "
"\\(\\[deep\\]\\)"
msgstr ""

#: ../../source/reference/learn/generated/mars.learn.decomposition.TruncatedSVD.rst:22:<autosummary>:1
msgid "Get parameters for this estimator."
msgstr ""

#: ../../source/reference/learn/generated/mars.learn.decomposition.TruncatedSVD.rst:22:<autosummary>:1
msgid ""
":obj:`inverse_transform "
"<mars.learn.decomposition.TruncatedSVD.inverse_transform>`\\ \\(X\\[\\, "
"session\\]\\)"
msgstr ""

#: ../../source/reference/learn/generated/mars.learn.decomposition.TruncatedSVD.rst:22:<autosummary>:1
msgid "Transform X back to its original space."
msgstr ""

#: ../../source/reference/learn/generated/mars.learn.decomposition.TruncatedSVD.rst:22:<autosummary>:1
msgid ""
":obj:`set_params <mars.learn.decomposition.TruncatedSVD.set_params>`\\ "
"\\(\\*\\*params\\)"
msgstr ""

#: ../../source/reference/learn/generated/mars.learn.decomposition.TruncatedSVD.rst:22:<autosummary>:1
msgid "Set the parameters of this estimator."
msgstr ""

#: ../../source/reference/learn/generated/mars.learn.decomposition.TruncatedSVD.rst:22:<autosummary>:1
msgid ""
":obj:`transform <mars.learn.decomposition.TruncatedSVD.transform>`\\ "
"\\(X\\[\\, session\\]\\)"
msgstr ""

#: ../../source/reference/learn/generated/mars.learn.decomposition.TruncatedSVD.rst:22:<autosummary>:1
msgid "Perform dimensionality reduction on X."
msgstr ""

