Merge pull request #3086 from yorkerlin/develop

fixed some python_modular warnings

src/shogun/machine/gp/KLInferenceMethod.h

@@ -178,7 +178,7 @@ class CKLInferenceMethod: public CInferenceMethod
 	/** update all matrices except gradients */
 	virtual void update();
 
-	/* set L-BFGS parameters
+	/** set L-BFGS parameters
 	 * For details please see shogun/optimization/lbfgs/lbfgs.h
 	 * @param m The number of corrections to approximate the inverse hessian matrix.
 	 * Default value is 100.
@@ -423,52 +423,52 @@ class CKLInferenceMethod: public CInferenceMethod
 	 */
 	SGVector<float64_t> m_s2;
 
-	/* The number of corrections to approximate the inverse hessian matrix.*/
+	/** The number of corrections to approximate the inverse hessian matrix.*/
 	int m_m;
 
-	/* The maximum number of trials to do line search for each L-BFGS update.*/
+	/** The maximum number of trials to do line search for each L-BFGS update.*/
 	int m_max_linesearch;
 
-	/* The line search algorithm.*/
+	/** The line search algorithm.*/
 	int m_linesearch;
 
-	/* The maximum number of iterations for L-BFGS update.*/
+	/** The maximum number of iterations for L-BFGS update.*/
 	int m_max_iterations;
 
-	/* Delta for convergence test based on the change of function value.*/
+	/** Delta for convergence test based on the change of function value.*/
 	float64_t m_delta;
 
-	/* Distance for delta-based convergence test.*/
+	/** Distance for delta-based convergence test.*/
 	int m_past;
 
-	/* Epsilon for convergence test based on the change of gradient.*/
+	/** Epsilon for convergence test based on the change of gradient.*/
 	float64_t m_epsilon;
 
-	/* The minimum step of the line search.*/
+	/** The minimum step of the line search.*/
 	float64_t m_min_step;
 
-	/* The maximum step of the line search.*/
+	/** The maximum step of the line search.*/
 	float64_t m_max_step;
 
-	/* A parameter used in Armijo condition.*/
+	/** A parameter used in Armijo condition.*/
 	float64_t m_ftol;
 
-	/* A parameter used in curvature condition.*/
+	/** A parameter used in curvature condition.*/
 	float64_t m_wolfe;
 
-	/* A parameter used in Morethuente linesearch to control the accuracy.*/
+	/** A parameter used in Morethuente linesearch to control the accuracy.*/
 	float64_t m_gtol;
 
-	/* The machine precision for floating-point values.*/
+	/** The machine precision for floating-point values.*/
 	float64_t m_xtol;
 
-	/* Coeefficient for the L1 norm of variables.*/
+	/** Coeefficient for the L1 norm of variables.*/
 	float64_t m_orthantwise_c;
 
-	/* Start index for computing L1 norm of the variables.*/
+	/** Start index for computing L1 norm of the variables.*/
 	int m_orthantwise_start;
 
-	/* End index for computing L1 norm of the variables.*/
+	/** End index for computing L1 norm of the variables.*/
 	int m_orthantwise_end;
 
 private:

src/shogun/machine/gp/LaplacianInferenceBase.h

@@ -113,7 +113,7 @@ class CLaplacianInferenceBase: public CInferenceMethod
 	 * \f]
 	 *
 	 * where \f$E_\text{c}\f$ is the matrix defined in the algorithm 3.3 of the GPML textbook for class c
-	 * Note the E matrix is used to store these E_\text{c} matrices, where E=[E_1, E_2, ..., E_C],
+	 * Note the E matrix is used to store these \f$E_\text{c}\f$ matrices, where E=[E_1, E_2, ..., E_C],
 	 * where C is the number of classes and C should be greater than 1.
 	 */
 	virtual SGMatrix<float64_t> get_cholesky();

src/shogun/machine/gp/MultiLaplacianInferenceMethod.h

@@ -230,7 +230,7 @@ class CMultiLaplacianInferenceMethod: public CLaplacianInferenceBase
 
 	/** the helper used to compute gradient of GP for inference
 	 *
-	 * construct the \f$\pi$\f vector defined in the algorithm 3.3 of the GPML textbook
+	 * construct the \f$\pi\f$ vector defined in the algorithm 3.3 of the GPML textbook
 	 * Noth that the vector is stored in m_W
 	 *
 	 */

src/shogun/machine/gp/SingleFITCLaplacianBase.h

@@ -236,10 +236,10 @@ class CSingleFITCLaplacianBase: public CSingleSparseInferenceBase
 	 * */
 	SGVector<float64_t> m_t;
 
-	/* B is defined in infFITC.m and infFITC_Laplace.m */
+	/** B is defined in infFITC.m and infFITC_Laplace.m */
 	SGMatrix<float64_t> m_B;
 
-	/* w=B*al */
+	/** w=B*al */
 	SGVector<float64_t> m_w;
 
 	/** Rvdd=W
@@ -248,7 +248,7 @@ class CSingleFITCLaplacianBase: public CSingleSparseInferenceBase
 	 */
 	SGMatrix<float64_t> m_Rvdd;
 
-	/* V defined in infFITC.m and infFITC_Laplace.m */
+	/** V defined in infFITC.m and infFITC_Laplace.m */
 	SGMatrix<float64_t> m_V;
 
 private:

src/shogun/machine/gp/SingleSparseInferenceBase.h

@@ -215,9 +215,10 @@ class CSingleSparseInferenceBase: public CSparseInferenceBase
 	 */
 	virtual SGVector<float64_t> get_derivative_wrt_inducing_features(const TParameter* param)=0;
 
+	/** whether the kernel supports to get the gradient wrt inducing points or not*/
 	bool m_fully_sparse;
 
-	/* a lock used to parallelly compute derivatives wrt hyperparameters */
+	/** a lock used to parallelly compute derivatives wrt hyperparameters */
 	CLock* m_lock;
 private:
 	/* init */

src/shogun/machine/gp/SoftMaxLikelihood.h

@@ -287,7 +287,7 @@ class CSoftMaxLikelihood : public CLikelihoodModel
 
 	/**the Monte method sampler
 	 *
-	 * @oaram num_samples number of samples to be generated
+	 * @param num_samples number of samples to be generated
 	 * @param mu posterior mean of a Gaussian distribution
 	 * \f$\mathcal{N}(\mu,\sigma^2)\f$, which is an approximation to the
 	 * posterior marginal \f$p(f_*|X,y,x_*)\f$

src/shogun/machine/gp/SparseVGInferenceMethod.h

@@ -238,23 +238,23 @@ class CSparseVGInferenceMethod: public CSingleSparseInferenceBase
 	/** update gradients */
 	virtual void compute_gradient();
 protected:
-	/* inv_Lm=inv(Lm) where Lm*Lm'=Kmm */
+	/** inv_Lm=inv(Lm) where Lm*Lm'=Kmm */
 	SGMatrix<float64_t> m_inv_Lm;
-	/* Knm*inv_Lm */
+	/** Knm*inv_Lm */
 	SGMatrix<float64_t> m_Knm_inv_Lm;
-	/* invLa=inv(La) where La*La'=sigma2*eye(m)+inv_Lm*Kmn*Knm*inv_Lm' */
+	/** invLa=inv(La) where La*La'=sigma2*eye(m)+inv_Lm*Kmn*Knm*inv_Lm' */
 	SGMatrix<float64_t> m_inv_La;
-	/* yy=(y-meanfun)'*(y-meanfun) */
+	/** yy=(y-meanfun)'*(y-meanfun) */
 	float64_t m_yy;
-	/* the term used to compute gradient wrt likelihood and marginal likelihood*/
+	/** the term used to compute gradient wrt likelihood and marginal likelihood*/
 	float64_t m_f3;
-	/* square of sigma from Gaussian likelihood*/
+	/** square of sigma from Gaussian likelihood*/
 	float64_t m_sigma2;
-	/* the trace term to compute marginal likelihood*/
+	/** the trace term to compute marginal likelihood*/
 	float64_t m_trk;
-	/* a matrix used to compute gradients wrt kernel (Kmm)*/
+	/** a matrix used to compute gradients wrt kernel (Kmm)*/
 	SGMatrix<float64_t> m_Tmm;
-	/* a matrix used to compute gradients wrt kernel (Knm)*/
+	/** a matrix used to compute gradients wrt kernel (Knm)*/
 	SGMatrix<float64_t> m_Tnm;
 private:
 	/** init */