Merge f07e583 into c870b67

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

@@ -0,0 +1,43 @@
+================
+Diffusion Maps
+================
+
+Diffusion Maps is a non-linear dimensionality reduction method that uses 
+eigenfunctions of Markov matrices to diffusion maps for efficient 
+representations of complex geometric structures.
+The diffusion kernel :math: `k` must satisfy
+the following properties:
+
+1. :math: `k` is symmetric :math: `{\bf k}(x, y) = {\bf k}(y, x)`
+2. :math: `k` is positivity preserving :math: `{\bf k}(x, y) ≥ 0`
+
+
+For more information see :cite:`Coifman-Lafon2006Diffusionmaps`.
+
+-------
+Example
+-------
+
+We create CDenseFeatures (RealFeatures, here 64 bit float values).
+
+.. sgexample:: diffusionmaps.sg:create_features
+
+We create a :sgclass:`CDiffusionMaps` instance, and set its parameters. 
+
+.. sgexample:: diffusionmaps.sg:set_parameters
+
+Then we apply diffusion maps, which gives us distance embeddings.
+
+.. sgexample:: diffusionmaps.sg:apply_convert
+
+We can also extract the estimated feature_matrix.
+
+.. sgexample:: diffusionmaps.sg:extract
+
+----------
+References
+----------
+:wiki:`Diffusion_map`
+
+.. bibliography:: ../../references.bib
+    :filter: docname in docnames

examples/meta/src/converter/diffusionmaps.sg

@@ -0,0 +1,18 @@
+File f_feats = csv_file("../../data/fm_train_real.dat")
+
+#![create_features]
+Features feats = features(f_feats)
+#![create_features]
+
+#![set_parameters]
+Kernel k = kernel("GaussianKernel", cache_size=10, log_width=10.0)
+Converter diffusion_maps = converter("DiffusionMaps", target_dim=1, kernel=k, t=10)
+#![set_parameters]
+
+#![apply_convert]
+Features converted = diffusion_maps.apply(feats)
+#![apply_convert]
+
+#![extract]
+RealMatrix feature_matrix = converted.get_real_matrix("feature_matrix")
+#![extract]

examples/meta/src/converter/independent_component_analysis_fast.sg

@@ -1,4 +1,4 @@
-CSVFile f_feats("../../data/ica_2_sources.dat")
+File f_feats = csv_file("../../data/ica_2_sources.dat")
 
 Math:init_random(1)
 
@@ -7,17 +7,14 @@ Features feats = features(f_feats)
 #![create_features]
 
 #![set_parameters]
-FastICA ica()
-ica.set_max_iter(200)
-ica.set_tol(0.00001)
+Converter ica = converter("FastICA", max_iter=200, tol=0.00001)
 #![set_parameters]
 
 #![apply_convert]
 Features converted = ica.apply(feats)
 #![apply_convert]
 
 #![extract]
-RealMatrix mixing_matrix = ica.get_mixing_matrix()
+RealMatrix mixing_matrix = ica.get_real_matrix("mixing_matrix")
 RealMatrix unmixed_signal = converted.get_real_matrix("feature_matrix")
 #![extract]
-

examples/meta/src/converter/independent_component_analysis_ff_sep.sg

@@ -1,4 +1,4 @@
-CSVFile f_feats("../../data/ica_2_sources.dat")
+File f_feats = csv_file("../../data/ica_2_sources.dat")
 
 Math:init_random(1)
 
@@ -7,17 +7,14 @@ Features feats = features(f_feats)
 #![create_features]
 
 #![set_parameters]
-FFSep ica()
-ica.set_max_iter(200)
-ica.set_tol(0.00001)
+Converter ica = converter("FFSep", max_iter=200, tol=0.00001)
 #![set_parameters]
 
 #![apply_convert]
 Features converted = ica.apply(feats)
 #![apply_convert]
 
 #![extract]
-RealMatrix mixing_matrix = ica.get_mixing_matrix()
+RealMatrix mixing_matrix = ica.get_real_matrix("mixing_matrix")
 RealMatrix unmixed_signal = converted.get_real_matrix("feature_matrix")
 #![extract]
-

examples/meta/src/converter/independent_component_analysis_jade.sg

@@ -1,4 +1,4 @@
-CSVFile f_feats("../../data/ica_2_sources.dat")
+File f_feats = csv_file("../../data/ica_2_sources.dat")
 
 Math:init_random(1)
 
@@ -7,17 +7,14 @@ Features feats = features(f_feats)
 #![create_features]
 
 #![set_parameters]
-Jade ica()
-ica.set_max_iter(200)
-ica.set_tol(0.00001)
+Converter ica = converter("Jade", max_iter=200, tol=0.00001)
 #![set_parameters]
 
 #![apply_convert]
 Features converted = ica.apply(feats)
 #![apply_convert]
 
 #![extract]
-RealMatrix mixing_matrix = ica.get_mixing_matrix()
+RealMatrix mixing_matrix = ica.get_real_matrix("mixing_matrix")
 RealMatrix unmixed_signal = converted.get_real_matrix("feature_matrix")
 #![extract]
-

examples/meta/src/converter/independent_component_analysis_jedi_sep.sg

@@ -1,4 +1,4 @@
-CSVFile f_feats("../../data/ica_2_sources.dat")
+File f_feats = csv_file("../../data/ica_2_sources.dat")
 
 Math:init_random(1)
 
@@ -7,17 +7,14 @@ Features feats = features(f_feats)
 #![create_features]
 
 #![set_parameters]
-JediSep ica()
-ica.set_max_iter(200)
-ica.set_tol(0.00001)
+Converter ica = converter("JediSep", max_iter=200, tol=0.00001)
 #![set_parameters]
 
 #![apply_convert]
 Features converted = ica.apply(feats)
 #![apply_convert]
 
 #![extract]
-RealMatrix mixing_matrix = ica.get_mixing_matrix()
+RealMatrix mixing_matrix = ica.get_real_matrix("mixing_matrix")
 RealMatrix unmixed_signal = converted.get_real_matrix("feature_matrix")
 #![extract]
-

examples/meta/src/converter/independent_component_analysis_sobi.sg

@@ -1,4 +1,4 @@
-CSVFile f_feats("../../data/ica_2_sources.dat")
+File f_feats = csv_file("../../data/ica_2_sources.dat")
 
 Math:init_random(1)
 
@@ -7,17 +7,14 @@ Features feats = features(f_feats)
 #![create_features]
 
 #![set_parameters]
-SOBI ica()
-ica.set_max_iter(200)
-ica.set_tol(0.00001)
+Converter ica = converter("SOBI", max_iter=200, tol=0.00001)
 #![set_parameters]
 
 #![apply_convert]
 Features converted = ica.apply(feats)
 #![apply_convert]
 
 #![extract]
-RealMatrix mixing_matrix = ica.get_mixing_matrix()
+RealMatrix mixing_matrix = ica.get_real_matrix("mixing_matrix")
 RealMatrix unmixed_signal = converted.get_real_matrix("feature_matrix")
 #![extract]
-

src/interfaces/swig/Converter.i

@@ -29,6 +29,7 @@
 %rename(FFSep) CFFSep;
 %rename(JediSep) CJediSep;
 %rename(UWedgeSep) CUWedgeSep;
+%rename(Converter) CConverter;
 %rename(FastICA) CFastICA;
 
 %newobject shogun::CEmbeddingConverter::apply;

src/shogun/converter/EmbeddingConverter.cpp

@@ -75,9 +75,10 @@ void CEmbeddingConverter::init()
 {
 	SG_ADD(&m_target_dim, "target_dim",
       "target dimensionality of preprocessor", MS_AVAILABLE);
-	SG_ADD((CSGObject**)&m_distance, "distance",
-	    "distance to be used for embedding", MS_AVAILABLE);
-	SG_ADD((CSGObject**)&m_kernel, "kernel", "kernel to be used for embedding",
-	    MS_AVAILABLE);
+	SG_ADD(
+		&m_distance, "distance", "distance to be used for embedding",
+		MS_AVAILABLE);
+	SG_ADD(
+		&m_kernel, "kernel", "kernel to be used for embedding", MS_AVAILABLE);
 }
 }

src/shogun/util/factory.h

@@ -7,6 +7,7 @@
 #define FACTORY_H_
 
 #include <shogun/base/class_list.h>
+#include <shogun/converter/Converter.h>
 #include <shogun/distance/Distance.h>
 #include <shogun/evaluation/Evaluation.h>
 #include <shogun/features/DenseFeatures.h>
@@ -31,6 +32,7 @@ namespace shogun
 	CMulticlassStrategy* multiclass_strategy(const std::string& name);
 	CECOCEncoder* ecoc_encoder(const std::string& name);
 	CECOCDecoder* ecoc_decoder(const std::string& name);
+	CConverter* converter(const std::string& name);
 
 #define BASE_CLASS_FACTORY(T, factory_name)                                    \
 	T* factory_name(const std::string& name)                                   \
@@ -49,6 +51,7 @@ namespace shogun
 	BASE_CLASS_FACTORY(CMulticlassStrategy, multiclass_strategy)
 	BASE_CLASS_FACTORY(CECOCEncoder, ecoc_encoder)
 	BASE_CLASS_FACTORY(CECOCDecoder, ecoc_decoder)
+	BASE_CLASS_FACTORY(CConverter, converter)
 
 	template <class T>
 	CFeatures* features(SGMatrix<T> mat)