# 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"
"MIME-Version: 1.0\n"
"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.datasets.make_classification.rst:2
msgid "mars.learn.datasets.make\\_classification"
msgstr ""

#: mars.learn.datasets.make_classification:1 of
msgid "Generate a random n-class classification problem."
msgstr ""

#: mars.learn.datasets.make_classification:3 of
msgid ""
"This initially creates clusters of points normally distributed (std=1) "
"about vertices of an ``n_informative``-dimensional hypercube with sides "
"of length ``2*class_sep`` and assigns an equal number of clusters to each"
" class. It introduces interdependence between these features and adds "
"various types of further noise to the data."
msgstr ""

#: mars.learn.datasets.make_classification:9 of
msgid ""
"Without shuffling, ``X`` horizontally stacks features in the following "
"order: the primary ``n_informative`` features, followed by "
"``n_redundant`` linear combinations of the informative features, followed"
" by ``n_repeated`` duplicates, drawn randomly with replacement from the "
"informative and redundant features. The remaining features are filled "
"with random noise. Thus, without shuffling, all useful features are "
"contained in the columns ``X[:, :n_informative + n_redundant + "
"n_repeated]``."
msgstr ""

#: mars.learn.datasets.make_classification:17 of
msgid "Read more in the :ref:`User Guide <sample_generators>`."
msgstr ""

#: mars.learn.datasets.make_classification of
msgid "Parameters"
msgstr ""

#: mars.learn.datasets.make_classification:19 of
msgid "The number of samples."
msgstr ""

#: mars.learn.datasets.make_classification:21 of
msgid ""
"The total number of features. These comprise ``n_informative`` "
"informative features, ``n_redundant`` redundant features, ``n_repeated`` "
"duplicated features and ``n_features-n_informative-n_redundant-"
"n_repeated`` useless features drawn at random."
msgstr ""

#: mars.learn.datasets.make_classification:27 of
msgid ""
"The number of informative features. Each class is composed of a number of"
" gaussian clusters each located around the vertices of a hypercube in a "
"subspace of dimension ``n_informative``. For each cluster, informative "
"features are drawn independently from  N(0, 1) and then randomly linearly"
" combined within each cluster in order to add covariance. The clusters "
"are then placed on the vertices of the hypercube."
msgstr ""

#: mars.learn.datasets.make_classification:35 of
msgid ""
"The number of redundant features. These features are generated as random "
"linear combinations of the informative features."
msgstr ""

#: mars.learn.datasets.make_classification:38 of
msgid ""
"The number of duplicated features, drawn randomly from the informative "
"and the redundant features."
msgstr ""

#: mars.learn.datasets.make_classification:41 of
msgid "The number of classes (or labels) of the classification problem."
msgstr ""

#: mars.learn.datasets.make_classification:43 of
msgid "The number of clusters per class."
msgstr ""

#: mars.learn.datasets.make_classification:45 of
msgid ""
"The proportions of samples assigned to each class. If None, then classes "
"are balanced. Note that if ``len(weights) == n_classes - 1``, then the "
"last class weight is automatically inferred. More than ``n_samples`` "
"samples may be returned if the sum of ``weights`` exceeds 1."
msgstr ""

#: mars.learn.datasets.make_classification:51 of
msgid ""
"The fraction of samples whose class are randomly exchanged. Larger values"
" introduce noise in the labels and make the classification task harder."
msgstr ""

#: mars.learn.datasets.make_classification:55 of
msgid ""
"The factor multiplying the hypercube size.  Larger values spread out the "
"clusters/classes and make the classification task easier."
msgstr ""

#: mars.learn.datasets.make_classification:58 of
msgid ""
"If True, the clusters are put on the vertices of a hypercube. If False, "
"the clusters are put on the vertices of a random polytope."
msgstr ""

#: mars.learn.datasets.make_classification:61 of
msgid ""
"Shift features by the specified value. If None, then features are shifted"
" by a random value drawn in [-class_sep, class_sep]."
msgstr ""

#: mars.learn.datasets.make_classification:64 of
msgid ""
"Multiply features by the specified value. If None, then features are "
"scaled by a random value drawn in [1, 100]. Note that scaling happens "
"after shifting."
msgstr ""

#: mars.learn.datasets.make_classification:68 of
msgid "Shuffle the samples and the features."
msgstr ""

#: mars.learn.datasets.make_classification:70 of
msgid ""
"Determines random number generation for dataset creation. Pass an int for"
" reproducible output across multiple function calls. See :term:`Glossary "
"<random_state>`."
msgstr ""

#: mars.learn.datasets.make_classification of
msgid "Returns"
msgstr ""

#: mars.learn.datasets.make_classification:75 of
msgid ""
"* **X** (*tensor of shape [n_samples, n_features]*) -- The generated "
"samples. * **y** (*tensor of shape [n_samples]*) -- The integer labels "
"for class membership of each sample."
msgstr ""

#: mars.learn.datasets.make_classification:75 of
msgid ""
"**X** (*tensor of shape [n_samples, n_features]*) -- The generated "
"samples."
msgstr ""

#: mars.learn.datasets.make_classification:76 of
msgid ""
"**y** (*tensor of shape [n_samples]*) -- The integer labels for class "
"membership of each sample."
msgstr ""

#: mars.learn.datasets.make_classification:79 of
msgid "Notes"
msgstr ""

#: mars.learn.datasets.make_classification:80 of
msgid ""
"The algorithm is adapted from Guyon [1] and was designed to generate the "
"\"Madelon\" dataset."
msgstr ""

#: mars.learn.datasets.make_classification:84 of
msgid "References"
msgstr ""

#: mars.learn.datasets.make_classification:85 of
msgid ""
"I. Guyon, \"Design of experiments for the NIPS 2003 variable selection "
"benchmark\", 2003."
msgstr ""

#: mars.learn.datasets.make_classification:91 of
msgid ":func:`make_blobs`"
msgstr ""

#: mars.learn.datasets.make_classification:91 of
msgid "simplified variant"
msgstr ""

#: mars.learn.datasets.make_classification:93 of
msgid ":func:`make_multilabel_classification`"
msgstr ""

#: mars.learn.datasets.make_classification:94 of
msgid "unrelated generator for multilabel tasks"
msgstr ""

