Warning fixes

shogun-toolbox · Aug 22, 2011 · aa4de69 · aa4de69
1 parent cc4c0a8
commit aa4de69
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Showing 27 changed files with 93 additions and 52 deletions.
diff --git a/src/shogun/base/Parameter.h b/src/shogun/base/Parameter.h
@@ -18,6 +18,7 @@
 
 namespace shogun
 {
+/** Parameter */
 struct TParameter
 {
 	/** explicit constructor
@@ -75,7 +76,7 @@ struct TParameter
 					const char* prefix);
 };
 
-/* Must not be an CSGObject to prevent a recursive call of
+/** Must not be an CSGObject to prevent a recursive call of
  * constructors.
  */
 class Parameter

diff --git a/src/shogun/base/class_list.h b/src/shogun/base/class_list.h
@@ -17,7 +17,7 @@ namespace shogun {
 	class CSGObject;
 
 	/** new shogun serializable
-	 * @param serializable_name
+	 * @param sgserializable_name
 	 * @param generic
 	 */
 	CSGObject* new_sgserializable(const char* sgserializable_name,

diff --git a/src/shogun/classifier/svm/LaRank.h b/src/shogun/classifier/svm/LaRank.h
@@ -302,8 +302,8 @@ namespace shogun
 #endif // DOXYGEN_SHOULD_SKIP_THIS
 
 
-	/*
-	 ** MACHINE: the main thing, which is trained.
+	/**
+	 * MACHINE: the main thing, which is trained.
 	 */
 	class CLaRank:  public CMultiClassSVM
 	{

diff --git a/src/shogun/classifier/svm/gpm.h b/src/shogun/classifier/svm/gpm.h
@@ -30,6 +30,20 @@
 
 namespace shogun
 {
+/** gpm solver 
+ * @param Solver
+ * @param Projector
+ * @param n
+ * @param A
+ * @param b
+ * @param c
+ * @param e
+ * @param iy
+ * @param x
+ * @param tol
+ * @param ls
+ * @param proj
+ */
 int32_t gpm_solver(
 	int32_t Solver, int32_t Projector, int32_t n, float32_t *A, float64_t *b,
 	float64_t c, float64_t e, int32_t *iy, float64_t *x, float64_t tol,

diff --git a/src/shogun/classifier/svm/libqp.h b/src/shogun/classifier/svm/libqp.h
@@ -34,7 +34,7 @@ namespace shogun
 #define LIBQP_MAX(A,B) ((A) < (B) ? (B) : (A))
 #define LIBQP_ABS(A) ((A) < 0 ? -(A) : (A))
 
-/* QP solver return value */
+/** QP solver return value */
 typedef struct {
   /** number of iterations */
   uint32_t nIter;
@@ -51,7 +51,7 @@ typedef struct {
                             4 ... eps-KKT conditions satisfied */
 } libqp_state_T; 
 
-/* QP solver for tasks with simplex constraints */
+/** QP solver for tasks with simplex constraints */
 libqp_state_T libqp_splx_solver(const float64_t* (*get_col)(uint32_t),
                   float64_t *diag_H,
                   float64_t *f,
@@ -66,7 +66,7 @@ libqp_state_T libqp_splx_solver(const float64_t* (*get_col)(uint32_t),
                   float64_t QP_TH,
                   void (*print_state)(libqp_state_T state));
 
-/* Generalized SMO algorithm */
+/** Generalized SMO algorithm */
 libqp_state_T libqp_gsmo_solver(const float64_t* (*get_col)(uint32_t),
             float64_t *diag_H,
             float64_t *f,

diff --git a/src/shogun/classifier/svm/pr_loqo.h b/src/shogun/classifier/svm/pr_loqo.h
@@ -46,7 +46,7 @@ namespace shogun
  *                          for Quadratic Programming
  */
 
-/*
+/**
  * n   : number of primal variables
  * m   : number of constraints (typically 1)
  * h_x : dot product matrix (n.n)
@@ -73,7 +73,6 @@ namespace shogun
  * restart    : 1 if restart desired
  *
  */
-
 int32_t pr_loqo(
 	int32_t n, int32_t m, float64_t c[], float64_t h_x[], float64_t a[],
 	float64_t b[], float64_t l[], float64_t u[], float64_t primal[],

diff --git a/src/shogun/distributions/PositionalPWM.h b/src/shogun/distributions/PositionalPWM.h
@@ -18,6 +18,7 @@
 
 namespace shogun
 {
+/** Positional PWM */
 class CPositionalPWM : public CDistribution
 {
 	public:

diff --git a/src/shogun/io/SerializableAsciiFile.h b/src/shogun/io/SerializableAsciiFile.h
@@ -30,6 +30,7 @@
 
 namespace shogun
 {
+/** serializable ascii file */
 class CSerializableAsciiFile :public CSerializableFile
 {
 	friend class SerializableAsciiReader00;

diff --git a/src/shogun/io/SerializableFile.h b/src/shogun/io/SerializableFile.h
@@ -20,9 +20,11 @@ namespace shogun
 {
 	class CSGObject;
 
+/** serializable file */
 class CSerializableFile :public CSGObject
 {
 public:
+	/** serializable reader */
 	struct TSerializableReader :public CSGObject {
 
 		/* ******************************************************** */

diff --git a/src/shogun/kernel/ScatterKernelNormalizer.h b/src/shogun/kernel/ScatterKernelNormalizer.h
@@ -19,6 +19,7 @@
 
 namespace shogun
 {
+/** scatter kernel normalizer */
 class CScatterKernelNormalizer: public CKernelNormalizer
 {
 

diff --git a/src/shogun/kernel/SpectrumRBFKernel.h b/src/shogun/kernel/SpectrumRBFKernel.h
@@ -27,6 +27,7 @@
 namespace shogun
 {
 
+/** spectrum rbf kernel */
 class CSpectrumRBFKernel: public CStringKernel<char>
 {
 	public:

diff --git a/src/shogun/kernel/WeightedDegreeRBFKernel.h b/src/shogun/kernel/WeightedDegreeRBFKernel.h
@@ -8,6 +8,7 @@
 namespace shogun
 {
 
+/** weighted degree RBF kernel */
 class CWeightedDegreeRBFKernel: public CDotKernel
 {
 	public:

diff --git a/src/shogun/lib/DataType.h b/src/shogun/lib/DataType.h
@@ -184,6 +184,7 @@ template<class T> class SGMatrix
 		bool do_free;
 };
 
+/** shogun n-dimensional array */
 template<class T> class SGNDArray
 {
     public:
@@ -207,6 +208,7 @@ template<class T> class SGNDArray
         index_t num_dims;
 };
 
+/** shogun string */
 template<class T> struct SGString
 {
 public:
@@ -494,7 +496,7 @@ enum EPrimitiveType
 };
 #endif
 
-/* Datatypes that shogun supports. */
+/** Datatypes that shogun supports. */
 struct TSGDataType
 {
 	/** container type */

diff --git a/src/shogun/lib/GCArray.h b/src/shogun/lib/GCArray.h
@@ -16,6 +16,7 @@
 
 namespace shogun
 {
+define IGNORE_IN_CLASSLIST
 /** @brief Template class GCArray implements a garbage collecting static array
  *
  * This array is meant to be used for Shogun Objects (CSGObject) only, as it
@@ -24,8 +25,7 @@ namespace shogun
  * elements).
  *
  * */
-#define IGNORE_IN_CLASSLIST
-IGNORE_IN_CLASSLIST template <class T> class CGCArray : public CSGObject
+#IGNORE_IN_CLASSLIST template <class T> class CGCArray : public CSGObject
 {
 	public:
 		/** default constructor  */

diff --git a/src/shogun/lib/HashSet.h b/src/shogun/lib/HashSet.h
@@ -19,6 +19,7 @@
 namespace shogun
 {
 
+/** hashset node */
 struct HashSetNode
 {
 	/** key of node */

diff --git a/src/shogun/lib/Trie.h b/src/shogun/lib/Trie.h
@@ -134,6 +134,7 @@ struct TreeParseInfo {
 
 template <class Trie> class CTrie;
 
+#define IGNORE_IN_CLASSLIST
 /** @brief Template class Trie implements a suffix trie, i.e. a tree in which all
  * suffixes up to a certain length are stored.
  *
@@ -152,7 +153,6 @@ template <class Trie> class CTrie;
  * may save a lot of memory on higher degree tries.
  *
  */
-#define IGNORE_IN_CLASSLIST
 IGNORE_IN_CLASSLIST template <class Trie> class CTrie : public CSGObject
 {
 	public:

diff --git a/src/shogun/mathematics/lapack.h b/src/shogun/mathematics/lapack.h
@@ -60,6 +60,7 @@ int clapack_dgetrs(const CBLAS_ORDER Order, const CBLAS_TRANSPOSE Transpose,
 
 namespace shogun
 {
+#ifndef DOXYGEN_SHOULD_SKIP_THIS
 void wrap_dsyev(char jobz, char uplo, int n, double *a, int lda, 
 		double *w, int *info);
 void wrap_dgesvd(char jobu, char jobvt, int m, int n, double *a, int lda, 
@@ -71,6 +72,7 @@ void wrap_dsyevr(char jobz, char uplo, int n, double *a, int lda, int il, int iu
                  double *eigenvalues, double *eigenvectors, int *info);
 void wrap_dsygvx(int itype, char jobz, char uplo, int n, double *a, int lda, double *b,
                  int ldb, int il, int iu, double *eigenvalues, double *eigenvectors, int *info);
+#endif
 }
 
 // only MKL, ACML and Mac OS vector library provide a header file for the lapack routines

diff --git a/src/shogun/preprocessor/DimensionReductionPreprocessor.h b/src/shogun/preprocessor/DimensionReductionPreprocessor.h
@@ -97,7 +97,8 @@ class CDimensionReductionPreprocessor: public CSimplePreprocessor<float64_t>
 	}
 
 public:
-
+
+	/** const indicating target dimensionality should be determined automagically */
 	static const int32_t AUTO_TARGET_DIM = -1;
 
 protected:

diff --git a/src/shogun/preprocessor/HessianLocallyLinearEmbedding.h b/src/shogun/preprocessor/HessianLocallyLinearEmbedding.h
@@ -65,7 +65,7 @@ class CHessianLocallyLinearEmbedding: public CLocallyLinearEmbedding
 	virtual SGMatrix<float64_t> apply_to_feature_matrix(CFeatures* features);
 
 	/** apply preprocessor to feature vector, not supported
-	 * @vector
+	 * @param vector
 	 */
 	virtual SGVector<float64_t> apply_to_feature_vector(SGVector<float64_t> vector);
 

diff --git a/src/shogun/preprocessor/KernelPCA.h b/src/shogun/preprocessor/KernelPCA.h
@@ -34,10 +34,10 @@ class CKernelPCA: public CDimensionReductionPreprocessor
 		CKernelPCA();
 
 		/** constructor
-		 *
-		 * @param thresh threshold
+		 * @param k kernel to be used
 		 */
 		CKernelPCA(CKernel* k);
+
 		virtual ~CKernelPCA();
 
 		/// initialize preprocessor from features

diff --git a/src/shogun/preprocessor/LaplacianEigenmaps.h b/src/shogun/preprocessor/LaplacianEigenmaps.h
@@ -56,7 +56,7 @@ class CLaplacianEigenmaps: public CDimensionReductionPreprocessor
 	virtual ~CLaplacianEigenmaps();
 
 	/** init
-	 * @param data feature vectors for preproc
+	 * @param features
 	 */
 	virtual bool init(CFeatures* features);
 
@@ -65,18 +65,18 @@ class CLaplacianEigenmaps: public CDimensionReductionPreprocessor
 	 */
 	virtual void cleanup();
 
-	/** apply preproc to feature matrix
-	 *
+	/** apply preprocessor to feature matrix
+	 * @param features
 	 */
 	virtual SGMatrix<float64_t> apply_to_feature_matrix(CFeatures* features);
 
 	/** apply preproc to feature vector
-	 *
+	 * @param vector
 	 */
 	virtual SGVector<float64_t> apply_to_feature_vector(SGVector<float64_t> vector);
 
 	/** setter for K parameter
-	 * @param k k
+	 * @param k k value
 	 */
 	void inline set_k(int32_t k)
 	{
@@ -92,7 +92,7 @@ class CLaplacianEigenmaps: public CDimensionReductionPreprocessor
 	}
 
 	/** setter for TAU parameter
-	 * @param tau tau
+	 * @param tau tau value
 	 */
 	void inline set_tau(float64_t tau)
 	{

diff --git a/src/shogun/preprocessor/PCA.h b/src/shogun/preprocessor/PCA.h
@@ -22,10 +22,14 @@
 
 namespace shogun
 {
+/** mode of pca */
 enum EPCAMode
 {
+	/** cut by threshold */
 	THRESHOLD,
+	/** variance explained */
 	VARIANCE_EXPLAINED,
+	/** keep fixed number of features */
 	FIXED_NUMBER
 };
 
@@ -47,7 +51,7 @@ class CPCA: public CDimensionReductionPreprocessor
 
 		/** constructor
 		 * @param do_whitening do whitening
-		 * @param type of cutoff
+		 * @param mode mode of pca
 		 * @param thresh threshold
 		 */
 		CPCA(bool do_whitening=false, EPCAMode mode=FIXED_NUMBER, float64_t thresh=1e-6);

diff --git a/src/shogun/preprocessor/RandomFourierGaussPreproc.h b/src/shogun/preprocessor/RandomFourierGaussPreproc.h
@@ -20,34 +20,33 @@
 #include <shogun/preprocessor/SimplePreprocessor.h>
 
 namespace shogun {
+/** @brief Preprocessor CRandomFourierGaussPreproc
+ * implements Random Fourier Features for the Gauss kernel a la Ali Rahimi and Ben Recht Nips2007
+ * after preprocessing the features using them in a linear kernel approximates a gaussian kernel
+ *
+ * approximation quality depends on dimension of feature space, NOT on number of data.
+ *
+ * effectively it requires two parameters for initialization: (A) the dimension of the input features stored in
+ * dim_input_space
+ * (B) the dimension of the output feature space
+ *
+ * in order to make it work there are two ways:
+ * (1) if you have already previously computed random fourier features which you want to use together with
+ * newly computed ones, then you have to take the random coefficients from the previous computation and provide them
+ * via void set_randomcoefficients(...) for the new computation
+ * this case is important for example if you compute separately features on training and testing data in two feature objects
+ *
+ * in this case you have to set
+ * 1a) void set_randomcoefficients(...)
+ *
+ * (2) if you compute random fourier features from scratch
+ * in this case you have to set
+ * 2a) set_kernelwidth(...)
+ * 2b) void set_dim_feature_space(const int32_t dim);
+ * 2c) set_dim_input_space(const int32_t dim);
+ * 2d) init_randomcoefficients() or apply_to_feature_matrix(...)
+ */
 class CRandomFourierGaussPreproc: public CSimplePreprocessor<float64_t> {
-	/** @brief Preprocessor CRandomFourierGaussPreproc
-	 * implements Random Fourier Features for the Gauss kernel a la Ali Rahimi and Ben Recht Nips2007
-	 * after preprocessing the features using them in a linear kernel approximates a gaussian kernel
-	 *
-	 * approximation quality depends on dimension of feature space, NOT on number of data.
-	 *
-	 * effectively it requires two parameters for initialization: (A) the dimension of the input features stored in
-	 * dim_input_space
-	 * (B) the dimension of the output feature space
-	 *
-	 * in order to make it work there are two ways:
-	 * (1) if you have already previously computed random fourier features which you want to use together with
-	 * newly computed ones, then you have to take the random coefficients from the previous computation and provide them
-	 * via void set_randomcoefficients(...) for the new computation
-	 * this case is important for example if you compute separately features on training and testing data in two feature objects
-	 *
-	 * in this case you have to set
-	 * 1a) void set_randomcoefficients(...)
-	 *
-	 * (2) if you compute random fourier features from scratch
-	 * in this case you have to set
-	 * 2a) set_kernelwidth(...)
-	 * 2b) void set_dim_feature_space(const int32_t dim);
-	 * 2c) set_dim_input_space(const int32_t dim);
-	 * 2d) init_randomcoefficients() or apply_to_feature_matrix(...)
-	 */
-
 public:
 	/** default constructor */
 	CRandomFourierGaussPreproc();