MISC: separate decomposition examples to new dir

doc/modules/decomposition.rst

@@ -36,14 +36,14 @@ data based on the amount of variance it explains. As such it implements a
 Below is an example of the iris dataset, which is comprised of 4
 features, projected on the 2 dimensions that explain most variance:
 
-.. figure:: ../auto_examples/images/plot_pca_vs_lda_1.png
-    :target: ../auto_examples/plot_pca_vs_lda.html
+.. figure:: ../auto_examples/decomposition/images/plot_pca_vs_lda_1.png
+    :target: ../auto_examples/decomposition/plot_pca_vs_lda.html
     :align: center
     :scale: 75%
 
 .. topic:: Examples:
 
-    * :ref:`example_plot_pca_vs_lda.py`
+    * :ref:`example_decomposition_plot_pca_vs_lda.py`
 
 
 Approximate PCA
@@ -110,11 +110,15 @@ reduction through the use of kernels. It has many applications including
 denoising, compression and structured prediction (kernel dependency estimation). 
 :class:`KernelPCA` supports both `transform` and `inverse_transform`.
 
-.. figure:: ../auto_examples/images/plot_kernel_pca_1.png
-    :target: ../auto_examples/plot_kernel_pca.html
+.. figure:: ../auto_examples/decomposition/images/plot_kernel_pca_1.png
+    :target: ../auto_examples/decomposition/plot_kernel_pca.html
     :align: center
     :scale: 75%
 
+.. topic:: Examples:
+
+    * :ref:`example_decomposition_plot_ica_blind_source_separation.py`
+
 
 .. _ICA:
 
@@ -125,16 +129,16 @@ ICA finds components that are maximally independent. It is classically
 used to separate mixed signals (a problem know as *blind source
 separation*), as in the example below:
 
-.. figure:: ../auto_examples/images/plot_ica_blind_source_separation_1.png
-    :target: ../auto_examples/plot_ica_blind_source_separation.html
+.. figure:: ../auto_examples/decomposition/images/plot_ica_blind_source_separation_1.png
+    :target: ../auto_examples/decomposition/plot_ica_blind_source_separation.html
     :align: center
     :scale: 50%
 
 
 .. topic:: Examples:
 
-    * :ref:`example_plot_ica_blind_source_separation.py`
-    * :ref:`example_plot_ica_vs_pca.py`
+    * :ref:`example_decomposition_plot_ica_blind_source_separation.py`
+    * :ref:`example_decomposition_plot_ica_vs_pca.py`
 
 
 .. _NMF:
@@ -156,12 +160,12 @@ It has been observed in [Hoyer, 04] that, when carefully constrained,
 resulting in interpretable models. The following example displays 16
 sparse components found by :class:`NMF` on the digits dataset.
 
-.. |pca_img| image:: ../auto_examples/images/plot_nmf_1.png
-    :target: ../auto_examples/plot_nmf.html
+.. |pca_img| image:: ../auto_examples/decomposition/images/plot_nmf_1.png
+    :target: ../auto_examples/decomposition/plot_nmf.html
     :scale: 50%
 
-.. |nmf_img| image:: ../auto_examples/images/plot_nmf_2.png
-    :target: ../auto_examples/plot_nmf.html
+.. |nmf_img| image:: ../auto_examples/decomposition/images/plot_nmf_2.png
+    :target: ../auto_examples/decomposition/plot_nmf.html
     :scale: 50%
 
 .. centered:: |pca_img| |nmf_img|
@@ -188,7 +192,7 @@ of the data.
 
 .. topic:: Examples:
 
-    * :ref:`example_plot_nmf.py`
+    * :ref:`example_decomposition_plot_nmf.py`
 
 .. topic:: References:
 

examples/decomposition/README.txt

@@ -0,0 +1,7 @@
+.. _decomposition_examples:
+
+Decomposition 
+-------------
+
+Examples concerning the `scikits.learn.decomposition` package.
+