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Update class reference.

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commit 1d223390abcfd6817e12e4bfada12821bfbf3873 1 parent 4089a47
Fabian Pedregosa fabianp authored
82 doc/modules/classes.rst
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@@ -2,6 +2,10 @@
Class reference
===============
+
+.. TODO: put some order here. Alphabetical ?
+
+
Support Vector Machines
=======================
@@ -25,6 +29,7 @@ For sparse data
.. autosummary::
:toctree: generated/
+ :template: class.rst
svm.sparse.SVC
svm.sparse.LinearSVC
@@ -33,6 +38,15 @@ For sparse data
svm.sparse.NuSVR
svm.sparse.OneClassSVM
+Logistic Regression
+===================
+
+.. autosummary::
+ :toctree: generated/
+ :template: class.rst
+
+ logistic.LogisticRegression
+
Generalized Linear Models
=========================
@@ -56,6 +70,74 @@ Bayesian Regression
glm.BayesianRidge
glm.ARDRegression
+Naive Bayes
+===========
+
+.. autosummary::
+ :toctree: generated/
+ :template: class.rst
+
+ naive_bayes.GNB
+
+
+Nearest Neighbors
+=================
+
+.. autosummary::
+ :toctree: generated/
+ :template: class.rst
+
+ neighbors.Neighbors
+
+
+Gaussian Mixture Models
+=======================
+
+.. autosummary::
+ :toctree: generated/
+ :template: class.rst
+
+ gmm.GMM
+
+
+Hidden Markov Models
+====================
+
+.. autosummary::
+ :toctree: generated/
+ :template: class.rst
+
+ hmm.GaussianHMM
+ hmm.MultinomialHMM
+ hmm.GMMHMM
+
+
+Clustering
+==========
+
+.. autosummary::
+ :toctree: generated/
+ :template: class.rst
+
+ cluster.KMeans
+ cluster.MeanShift
+ cluster.SpectralClustering
+ cluster.AffinityPropagation
+
+
+Covariance estimators
+=====================
+
+
+.. autosummary::
+ :toctree: generated/
+ :template: class.rst
+
+ covariance.Covariance
+ covariance.ShrunkCovariance
+ covariance.LedoitWolf
+
+
Cross-validation
===================
9 doc/modules/svm.rst
View
@@ -5,8 +5,9 @@ Support Vector Machines
.. currentmodule:: scikits.learn.svm
**Support vector machines (SVMs)** are a set of supervised learning
-methods used for classification_, regression_
-and :ref:`outliers detection <svm_outlier_detection>`.
+methods used for :ref:`classification <svm_classification>`,
+:ref:`regression <svm_regression>` and :ref:`outliers detection
+<svm_outlier_detection>`.
The advantages of Support Vector Machines are:
@@ -34,6 +35,8 @@ The dissadvantages of Support Vector Machines include:
information.
+.. _svm_classification:
+
Classification
==============
@@ -98,6 +101,8 @@ Member `n_support_` holds the number of support vectors for each class:
* :ref:`example_svm_plot_svm_anova.py`,
* :ref:`example_svm_plot_svm_nonlinear.py`
+.. _svm_regression:
+
Regression
==========
2  scikits/learn/features/tests/test_text.py
View
@@ -7,7 +7,7 @@
from scikits.learn.features.text import HashingVectorizer
from scikits.learn.features.text import SparseHashingVectorizer
from scikits.learn.svm import LinearSVC as DenseLinearSVC
-from scikits.learn.sparse.svm import LinearSVC as SparseLinearSVC
+from scikits.learn.svm.sparse import LinearSVC as SparseLinearSVC
import numpy as np
from nose.tools import *
from numpy.testing import assert_array_almost_equal
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