Add ICA meta examples and cookbook page for fastICA

-Also turn UWdge convergence error into warning
-Excluded UWedgeSep for now

doc/cookbook/source/examples/converter/ica_fast.rst

@@ -0,0 +1,38 @@
+==============================
+Independent Component Analysis
+==============================
+
+Independent component analysis (ICA) separates a multivariate signal into additive subcomponents that are maximally independent.
+It is typically used for separating superimposed signals.
+
+The ICA algorithm presented here is fastICA, see :cite:`hyvarinen2000independent` for details.
+There are many other ICA implementations, all based on :sgclass:`CICAConverter`
+
+-------
+Example
+-------
+
+Given a dataset which we assume consists of linearly mixed signals, we create CDenseFeatures
+(RealFeatures, here 64 bit float values).
+
+.. sgexample:: ica_fast.sg:create_features
+
+We create the :sgclass:`CFastICA` instance, and set its parameter for the iterative optimization.
+
+.. sgexample:: ica_fast.sg:set_parameters
+
+Then we apply ICA, which gives the unmixed signals.
+
+.. sgexample:: ica_fast.sg:apply_convert
+
+We can also extract the estimated mixing matrix.
+
+.. sgexample:: ica_fast.sg:extract
+
+----------
+References
+----------
+:wiki:`Independent_component_analysis`
+
+.. bibliography:: ../../references.bib
+    :filter: docname in docnames

doc/cookbook/source/index.rst

@@ -56,15 +56,15 @@ Statistical Testing
 
    examples/statistical_testing/**
 
-Kernels
--------
+Converter
+---------
 
 .. toctree::
    :maxdepth: 1
    :glob:
-   :caption: Kernels
+   :caption: Converter
 
-   examples/kernel/**
+   examples/converter/**
 
 Gaussian Processes
 ------------------

doc/cookbook/source/references.bib

@@ -153,3 +153,13 @@ @article{Quinonero-Candela2005
  year = {2005},
  pages = {1939--1959},
 }
+
+@article{hyvarinen2000independent,
+  title={Independent component analysis: algorithms and applications},
+  author={Hyv{\"a}rinen, A. and Oja, E.},
+  journal={Neural networks},
+  volume={13},
+  number={4},
+  pages={411--430},
+  year={2000}
+}

examples/meta/src/converter/ica_fast.sg

@@ -0,0 +1,24 @@
+CSVFile f_feats("../../data/ica_2_sources.dat")
+
+Math:init_random(1)
+
+#![create_features]
+RealFeatures features(f_feats)
+#![create_features]
+
+#![set_parameters]
+FastICA ica()
+ica.set_max_iter(200)
+ica.set_tol(0.00001)
+#![set_parameters]
+
+#![apply_convert]
+Features converted = ica.apply(features)
+#![apply_convert]
+
+#![extract]
+RealMatrix mixing_matrix = ica.get_mixing_matrix()
+RealFeatures casted = RealFeatures:obtain_from_generic(converted)
+RealMatrix unmixed_signal = casted.get_feature_matrix()
+#![extract]
+

examples/meta/src/converter/ica_ff_sep.sg

@@ -0,0 +1,24 @@
+CSVFile f_feats("../../data/ica_2_sources.dat")
+
+Math:init_random(1)
+
+#![create_features]
+RealFeatures features(f_feats)
+#![create_features]
+
+#![set_parameters]
+FFSep ica()
+ica.set_max_iter(200)
+ica.set_tol(0.00001)
+#![set_parameters]
+
+#![apply_convert]
+Features converted = ica.apply(features)
+#![apply_convert]
+
+#![extract]
+RealMatrix mixing_matrix = ica.get_mixing_matrix()
+RealFeatures casted = RealFeatures:obtain_from_generic(converted)
+RealMatrix unmixed_signal = casted.get_feature_matrix()
+#![extract]
+

examples/meta/src/converter/ica_jade.sg

@@ -0,0 +1,24 @@
+CSVFile f_feats("../../data/ica_2_sources.dat")
+
+Math:init_random(1)
+
+#![create_features]
+RealFeatures features(f_feats)
+#![create_features]
+
+#![set_parameters]
+Jade ica()
+ica.set_max_iter(200)
+ica.set_tol(0.00001)
+#![set_parameters]
+
+#![apply_convert]
+Features converted = ica.apply(features)
+#![apply_convert]
+
+#![extract]
+RealMatrix mixing_matrix = ica.get_mixing_matrix()
+RealFeatures casted = RealFeatures:obtain_from_generic(converted)
+RealMatrix unmixed_signal = casted.get_feature_matrix()
+#![extract]
+

examples/meta/src/converter/ica_jedi_sep.sg

@@ -0,0 +1,24 @@
+CSVFile f_feats("../../data/ica_2_sources.dat")
+
+Math:init_random(1)
+
+#![create_features]
+RealFeatures features(f_feats)
+#![create_features]
+
+#![set_parameters]
+JediSep ica()
+ica.set_max_iter(200)
+ica.set_tol(0.00001)
+#![set_parameters]
+
+#![apply_convert]
+Features converted = ica.apply(features)
+#![apply_convert]
+
+#![extract]
+RealMatrix mixing_matrix = ica.get_mixing_matrix()
+RealFeatures casted = RealFeatures:obtain_from_generic(converted)
+RealMatrix unmixed_signal = casted.get_feature_matrix()
+#![extract]
+

examples/meta/src/converter/ica_sobi.sg

@@ -0,0 +1,24 @@
+CSVFile f_feats("../../data/ica_2_sources.dat")
+
+Math:init_random(1)
+
+#![create_features]
+RealFeatures features(f_feats)
+#![create_features]
+
+#![set_parameters]
+SOBI ica()
+ica.set_max_iter(200)
+ica.set_tol(0.00001)
+#![set_parameters]
+
+#![apply_convert]
+Features converted = ica.apply(features)
+#![apply_convert]
+
+#![extract]
+RealMatrix mixing_matrix = ica.get_mixing_matrix()
+RealFeatures casted = RealFeatures:obtain_from_generic(converted)
+RealMatrix unmixed_signal = casted.get_feature_matrix()
+#![extract]
+

src/shogun/mathematics/ajd/UWedge.cpp

@@ -113,7 +113,12 @@ SGMatrix<float64_t> CUWedge::diagonalize(SGNDArray<float64_t> C, SGMatrix<float6
 	}
 
 	if (iter == itermax)
-		SG_SERROR("Convergence not reached\n")
+	{
+		SG_SWARNING(
+		    "Convergence delta (%f) not below tolerance (%f) after %d "
+		    "iterations.\n",
+		    improve, eps, itermax);
+	}
 
 	return V;