Replace cmath::sqrt with std::sqrt

examples/undocumented/libshogun/mathematics_lapack.cpp

@@ -37,7 +37,7 @@ void test_ev()
 	SGMatrix<float64_t>::display_matrix(A.matrix, A.num_rows, A.num_cols, "A");
 	SGVector<float64_t>::display_vector(ev.vector, ev.vlen, "eigenvalues");
 
-	float64_t sqrt22=CMath::sqrt(2.0)/2.0;
+	float64_t sqrt22 = std::sqrt(2.0) / 2.0;
 	float64_t eps=10E-16;
 
 	/* check for correct eigenvectors */

examples/undocumented/libshogun/preprocessor_randomfouriergauss.cpp

@@ -191,8 +191,9 @@ int main()
 
 	// ************************************************************
 	// set parameters of the preprocessor
-	// ******************************** !!!!!!!!!!!!!!!!! CMath::sqrt(rbf_width/2.0)
-	rfgauss->set_kernelwidth( CMath::sqrt(rbf_width/2.0) );
+	// ******************************** !!!!!!!!!!!!!!!!!
+	// std::sqrt(rbf_width/2.0)
+	rfgauss->set_kernelwidth(std::sqrt(rbf_width / 2.0));
 	rfgauss->set_dim_input_space(dims);
 	rfgauss->set_dim_feature_space(randomfourier_featurespace_dim);
 

src/interfaces/swig/Mathematics.i

@@ -41,15 +41,15 @@ namespace shogun
 #ifdef USE_FLOAT32
 %rename(normal_random_float32) CMath::normal_random(float32_t,float32_t);
 %rename(random_float32) CMath::random(float32_t,float32_t);
-%rename(sqrt_float32) CMath::sqrt(float32_t);
+%rename(sqrt_float32) std::sqrt(float32_t);
 #endif
 
 #ifdef USE_FLOAT64
 %rename(normal_random_float64) CMath::normal_random(float64_t,float64_t);
 %rename(pow_float64_int32) CMath::pow(float64_t,int32_t);
 %rename(pow_float64_float64) CMath::pow(float64_t,float64_t);
 %rename(random_float64) CMath::random(float64_t,float64_t);
-%rename(sqrt_float64) CMath::sqrt(float64_t);
+%rename(sqrt_float64) std::sqrt(float64_t);
 }
 #endif
 

src/shogun/classifier/mkl/MKL.cpp

@@ -621,7 +621,7 @@ float64_t CMKL::compute_optimal_betas_elasticnet(
 	{
 		if (sumw[p] >= 0.0 && old_beta[p] >= 0.0 )
 		{
-			beta[p] = CMath::sqrt(sumw[p]*old_beta[p]*old_beta[p]);
+			beta[p] = std::sqrt(sumw[p] * old_beta[p] * old_beta[p]);
 			Z += beta[p];
 		}
 		else
@@ -640,7 +640,7 @@ float64_t CMKL::compute_optimal_betas_elasticnet(
 	preR = 0.0;
 	for( p=0; p<num_kernels; ++p )
 		preR += CMath::pow( beta_local[p] - beta[p], 2.0 );
-	const float64_t R = CMath::sqrt( preR ) * epsRegul;
+	const float64_t R = std::sqrt(preR) * epsRegul;
 	if( !( R >= 0 ) )
 	{
 		SG_PRINT("MKL-direct: p = %.3f\n", 1.0 )
@@ -654,7 +654,7 @@ float64_t CMKL::compute_optimal_betas_elasticnet(
 		}
 		SG_PRINT("MKL-direct: preR = %e\n", preR )
 		SG_PRINT("MKL-direct: preR/p = %e\n", preR )
-		SG_PRINT("MKL-direct: sqrt(preR/p) = %e\n", CMath::sqrt(preR) )
+		SG_PRINT("MKL-direct: sqrt(preR/p) = %e\n", std::sqrt(preR))
 		SG_PRINT("MKL-direct: R = %e\n", R )
 		SG_ERROR("Assertion R >= 0 failed!\n" )
 	}
@@ -701,7 +701,7 @@ void CMKL::elasticnet_dual(float64_t *ff, float64_t *gg, float64_t *hh,
 	float64_t gam = 1.0-lambda;
 	for (int32_t i=0; i<len;i++)
 	{
-		if (nm[i]>=CMath::sqrt(2*del*gam))
+		if (nm[i] >= std::sqrt(2 * del * gam))
 			I.push_back(i);
 	}
 	int32_t M=I.size();
@@ -713,9 +713,10 @@ void CMKL::elasticnet_dual(float64_t *ff, float64_t *gg, float64_t *hh,
 	{
 		float64_t nmit = nm[*it];
 
-		*ff += del*gam*CMath::pow(nmit/CMath::sqrt(2*del*gam)-1,2)/lambda;
-		*gg += CMath::sqrt(gam/(2*del))*nmit;
-		*hh += -0.5*CMath::sqrt(gam/(2*CMath::pow(del,3)))*nmit;
+		*ff += del * gam * CMath::pow(nmit / std::sqrt(2 * del * gam) - 1, 2) /
+		       lambda;
+		*gg += std::sqrt(gam / (2 * del)) * nmit;
+		*hh += -0.5 * std::sqrt(gam / (2 * CMath::pow(del, 3))) * nmit;
 	}
 }
 
@@ -758,7 +759,7 @@ float64_t CMKL::compute_elasticnet_dual_objective()
 			SG_UNREF(kn);
 		}
 		// initial delta
-		del = del/CMath::sqrt(2*(1-ent_lambda));
+		del = del / std::sqrt(2 * (1 - ent_lambda));
 
 		// Newton's method to optimize delta
 		k=0;
@@ -876,7 +877,7 @@ float64_t CMKL::compute_optimal_betas_directly(
 	for( p=0; p<num_kernels; ++p )
 		preR += CMath::sq( old_beta[p] - beta[p]);
 
-	const float64_t R = CMath::sqrt( preR / mkl_norm ) * epsRegul;
+	const float64_t R = std::sqrt(preR / mkl_norm) * epsRegul;
 	if( !( R >= 0 ) )
 	{
 		SG_PRINT("MKL-direct: p = %.3f\n", mkl_norm )
@@ -890,7 +891,7 @@ float64_t CMKL::compute_optimal_betas_directly(
 		}
 		SG_PRINT("MKL-direct: preR = %e\n", preR )
 		SG_PRINT("MKL-direct: preR/p = %e\n", preR/mkl_norm )
-		SG_PRINT("MKL-direct: sqrt(preR/p) = %e\n", CMath::sqrt(preR/mkl_norm) )
+		SG_PRINT("MKL-direct: sqrt(preR/p) = %e\n", std::sqrt(preR / mkl_norm))
 		SG_PRINT("MKL-direct: R = %e\n", R )
 		SG_ERROR("Assertion R >= 0 failed!\n" )
 	}

src/shogun/classifier/mkl/MKLMulticlassGradient.cpp

@@ -404,7 +404,7 @@ finalbeta=oldweights;
 	for( int32_t p=0; p<num_kernels; ++p )
 		preR += CMath::pow( oldweights[p] - finalbeta[p], 2.0 );
 
-	const float64_t R = CMath::sqrt( preR / pnorm ) * epsRegul;
+	const float64_t R = std::sqrt(preR / pnorm) * epsRegul;
 	if( !( R >= 0 ) )
 	{
 		SG_PRINT("MKL-direct: p = %.3f\n", pnorm )
@@ -418,7 +418,7 @@ finalbeta=oldweights;
 		}
 		SG_PRINT("MKL-direct: preR = %e\n", preR )
 		SG_PRINT("MKL-direct: preR/p = %e\n", preR/pnorm )
-		SG_PRINT("MKL-direct: sqrt(preR/p) = %e\n", CMath::sqrt(preR/pnorm) )
+		SG_PRINT("MKL-direct: sqrt(preR/p) = %e\n", std::sqrt(preR / pnorm))
 		SG_PRINT("MKL-direct: R = %e\n", R )
 		SG_ERROR("Assertion R >= 0 failed!\n" )
 	}

src/shogun/classifier/svm/WDSVMOcas.h

@@ -206,7 +206,7 @@ class CWDSVMOcas : public CMachine
 			for (int32_t i=0; i<degree; i++)
 				normalization_const+=(string_length-i)*wd_weights[i]*wd_weights[i];
 
-			normalization_const=CMath::sqrt(normalization_const);
+			normalization_const = std::sqrt(normalization_const);
 			SG_DEBUG("normalization_const:%f\n", normalization_const)
 		}
 

src/shogun/clustering/GMM.cpp

@@ -397,8 +397,9 @@ void CGMM::partial_em(int32_t comp1, int32_t comp2, int32_t comp3, float64_t min
 	float64_t alpha1=coefficients.vector[1]/(coefficients.vector[1]+coefficients.vector[2]);
 	float64_t alpha2=coefficients.vector[2]/(coefficients.vector[1]+coefficients.vector[2]);
 
-	float64_t noise_mag=SGVector<float64_t>::twonorm(components[0]->get_mean().vector, dim_n)*0.1/
-						CMath::sqrt((float64_t)dim_n);
+	float64_t noise_mag =
+	    SGVector<float64_t>::twonorm(components[0]->get_mean().vector, dim_n) *
+	    0.1 / std::sqrt((float64_t)dim_n);
 
 	SGVector<float64_t> mean(dim_n);
 	linalg::add(components[1]->get_mean(), components[2]->get_mean(), mean, alpha1, alpha2);

src/shogun/clustering/KMeansBase.cpp

@@ -126,7 +126,7 @@ void CKMeansBase::compute_cluster_variances()
 			}
 		}
 
-		R.vector[i]=(0.7*CMath::sqrt(rmin1)+0.3*CMath::sqrt(rmin2));
+		R.vector[i] = (0.7 * std::sqrt(rmin1) + 0.3 * std::sqrt(rmin2));
 	}
 }
 

src/shogun/converter/HashedDocConverter.cpp

@@ -169,7 +169,7 @@ SGSparseVector<float64_t> CHashedDocConverter::apply(SGVector<char> document)
 	/** Normalizing vector */
 	if (should_normalize)
 	{
-		float64_t norm_const = CMath::sqrt((float64_t) document.size());
+		float64_t norm_const = std::sqrt((float64_t)document.size());
 		for (index_t i=0; i<sparse_doc_rep.num_feat_entries; i++)
 			sparse_doc_rep.features[i].entry /= norm_const;
 	}

src/shogun/converter/ica/FastICA.cpp

@@ -105,7 +105,7 @@ CFeatures* CFastICA::apply(CFeatures* features)
 		// see Hyvarinen (13.6) p.267 Here WX is white and data
 		// in X has been projected onto a subspace by PCA
 		WX = K * SPX;
-		WX *= CMath::sqrt((float64_t)p);
+		WX *= std::sqrt((float64_t)p);
 	}
 	else
 	{

src/shogun/distance/AttenuatedEuclideanDistance.cpp

@@ -59,7 +59,7 @@ float64_t CAttenuatedEuclideanDistance::compute(int32_t idx_a, int32_t idx_b)
 	if (disable_sqrt)
 		return result;
 
-	return CMath::sqrt(result);
+	return std::sqrt(result);
 }
 
 void CAttenuatedEuclideanDistance::init()

src/shogun/distance/CustomDistance.h

@@ -167,7 +167,7 @@ class CCustomDistance: public CDistance
 			ASSERT(dm)
 			ASSERT(len>0)
 
-			int64_t cols = (int64_t) floor(-0.5 + CMath::sqrt(0.25+2*len));
+			int64_t cols = (int64_t)floor(-0.5 + std::sqrt(0.25 + 2 * len));
 
 			int64_t int32_max=2147483647;
 

src/shogun/distance/Distance.h

@@ -173,7 +173,8 @@ class CDistance : public CSGObject
 			int32_t i_start;
 
 			if (symmetric)
-				i_start=(int32_t) CMath::floor(n-CMath::sqrt(CMath::sq((float64_t) n)-offs));
+				i_start = (int32_t)CMath::floor(
+				    n - std::sqrt(CMath::sq((float64_t)n) - offs));
 			else
 				i_start=(int32_t) (offs/int64_t(n));
 

src/shogun/distance/EuclideanDistance.cpp

@@ -73,7 +73,7 @@ float64_t CEuclideanDistance::compute(int32_t idx_a, int32_t idx_b)
 	result=m_lhs_squared_norms[idx_a]+m_rhs_squared_norms[idx_b]-2*result;
 	if (disable_sqrt)
 		return result;
-	return CMath::sqrt(result);
+	return std::sqrt(result);
 }
 
 void CEuclideanDistance::precompute_lhs()
@@ -169,7 +169,7 @@ float64_t CEuclideanDistance::distance_upper_bounded(int32_t idx_a, int32_t idx_
 	}
 
 	if (!disable_sqrt)
-		result=CMath::sqrt(result);
+		result = std::sqrt(result);
 
 	return result;
 }

src/shogun/distance/GeodesicMetric.cpp

@@ -72,7 +72,7 @@ float64_t CGeodesicMetric::compute(int32_t idx_a, int32_t idx_b)
 	if(s==0 || nx==0 || ny==0)
 		return 0;
 
-	d/=CMath::sqrt(nx*ny);
+	d /= std::sqrt(nx * ny);
 
 	// can only happen due to numerical problems
 	if (CMath::abs(d)>1.0)

src/shogun/distance/MahalanobisDistance.cpp

@@ -102,7 +102,7 @@ float64_t CMahalanobisDistance::compute(int32_t idx_a, int32_t idx_b)
 	if (disable_sqrt)
 		return result;
 	else
-		return CMath::sqrt(result);
+		return std::sqrt(result);
 }
 
 void CMahalanobisDistance::init()

src/shogun/distance/SparseEuclideanDistance.cpp

@@ -67,7 +67,7 @@ float64_t CSparseEuclideanDistance::compute(int32_t idx_a, int32_t idx_b)
 		(CSparseFeatures<float64_t>*) lhs, sq_lhs, idx_a,
 		(CSparseFeatures<float64_t>*) rhs, sq_rhs, idx_b);
 
-	return CMath::sqrt(result);
+	return std::sqrt(result);
 }
 
 void CSparseEuclideanDistance::init()

src/shogun/distributions/Gaussian.cpp

@@ -391,12 +391,12 @@ SGVector<float64_t> CGaussian::sample()
 	case FULL:
 	case DIAG:
 		for (int32_t i = 0; i < m_mean.vlen; i++)
-			r_matrix(i, i) = CMath::sqrt(m_d.vector[i]);
+			r_matrix(i, i) = std::sqrt(m_d.vector[i]);
 
 		break;
 	case SPHERICAL:
 		for (int32_t i = 0; i < m_mean.vlen; i++)
-			r_matrix(i, i) = CMath::sqrt(m_d.vector[0]);
+			r_matrix(i, i) = std::sqrt(m_d.vector[0]);
 
 		break;
 	}

src/shogun/distributions/PositionalPWM.cpp

@@ -83,7 +83,7 @@ float64_t CPositionalPWM::get_log_likelihood_example(int32_t num_example)
 float64_t CPositionalPWM::get_log_likelihood_window(uint8_t* window, int32_t len, float64_t pos)
 {
 	ASSERT(m_pwm.num_cols == len)
-	float64_t score = std::log(1 / (m_sigma * CMath::sqrt(2 * M_PI))) -
+	float64_t score = std::log(1 / (m_sigma * std::sqrt(2 * M_PI))) -
 	                  CMath::sq(pos - m_mean) / (2 * CMath::sq(m_sigma));
 
 	for (int32_t i=0; i<m_pwm.num_cols; i++)

src/shogun/evaluation/ContingencyTableEvaluation.h

@@ -161,7 +161,9 @@ class CContingencyTableEvaluation: public CBinaryClassEvaluation
 		if (!m_computed)
 				SG_ERROR("Uninitialized, please call evaluate first")
 
-		return (m_TP*m_TN-m_FP*m_FN)/CMath::sqrt((m_TP+m_FP)*(m_TP+m_FN)*(m_TN+m_FP)*(m_TN+m_FN));
+		return (m_TP * m_TN - m_FP * m_FN) / std::sqrt(
+			                                     (m_TP + m_FP) * (m_TP + m_FN) *
+			                                     (m_TN + m_FP) * (m_TN + m_FN));
 	};
 
 	/** recall

src/shogun/evaluation/MeanSquaredLogError.cpp

@@ -37,5 +37,5 @@ float64_t CMeanSquaredLogError::evaluate(CLabels* predicted, CLabels* ground_tru
 		msle+=CMath::sq(a-b);
 	}
 	msle /= length;
-	return CMath::sqrt(msle);
+	return std::sqrt(msle);
 }

src/shogun/features/BinnedDotFeatures.cpp

@@ -109,7 +109,7 @@ float64_t CBinnedDotFeatures::dot(int32_t vec_idx1, CDotFeatures* df, int32_t ve
 	((CBinnedDotFeatures*) df)->m_features->free_feature_vector(vec2, vec_idx2);
 
 	if (m_fill && m_norm_one && sum1!=0 && sum2!=0)
-		result/=CMath::sqrt(sum1*sum2);
+		result /= std::sqrt(sum1 * sum2);
 
 	return result;
 
@@ -154,7 +154,7 @@ float64_t CBinnedDotFeatures::dense_dot(int32_t vec_idx1, const float64_t* vec2,
 	m_features->free_feature_vector(vec1, vec_idx1);
 
 	if (m_fill && m_norm_one && sum!=0)
-		result/=CMath::sqrt(sum);
+		result /= std::sqrt(sum);
 
 	return result;
 }
@@ -182,7 +182,7 @@ void CBinnedDotFeatures::add_to_dense_vec(float64_t alpha, int32_t vec_idx1, flo
 		if (alpha_correction==0.0)
 			return;
 
-		alpha/=CMath::sqrt(alpha_correction);
+		alpha /= std::sqrt(alpha_correction);
 	}
 
 	for (int32_t i=0; i<m_bins.num_cols; i++)

src/shogun/features/DataGenerator.cpp

@@ -1,7 +1,7 @@
 /*
  * This software is distributed under BSD 3-clause license (see LICENSE file).
  *
- * Authors: Heiko Strathmann, Viktor Gal, Weijie Lin, Sergey Lisitsyn, 
+ * Authors: Heiko Strathmann, Viktor Gal, Weijie Lin, Sergey Lisitsyn,
  *          Björn Esser, Soeren Sonnenburg, Evangelos Anagnostopoulos
  */
 
@@ -32,7 +32,7 @@ SGMatrix<float64_t> CDataGenerator::generate_checkboard_data(int32_t num_classes
 {
 	int32_t points_per_class = num_points / num_classes;
 
-	int32_t grid_size = (int32_t)std::ceil(CMath::sqrt((float64_t)num_classes));
+	int32_t grid_size = (int32_t)std::ceil(std::sqrt((float64_t)num_classes));
 	float64_t cell_size = (float64_t ) 1 / grid_size;
 	SGVector<float64_t> grid_idx(dim);
 	for (index_t i=0; i<dim; i++)

src/shogun/features/ExplicitSpecFeatures.cpp

@@ -127,7 +127,7 @@ void CExplicitSpecFeatures::obtain_kmer_spectrum(CStringFeatures<uint16_t>* str)
 			for (int32_t j=0; j<spec_size; j++)
 				n+=CMath::sq(k_spectrum[i][j]);
 
-			n=CMath::sqrt(n);
+			n = std::sqrt(n);
 
 			for (int32_t j=0; j<spec_size; j++)
 				k_spectrum[i][j]/=n;

src/shogun/features/ImplicitWeightedSpecFeatures.cpp

@@ -50,7 +50,7 @@ void CImplicitWeightedSpecFeatures::compute_normalization_const()
 	float64_t* factors=SG_MALLOC(float64_t, num_strings);
 
 	for (int32_t i=0; i<num_strings; i++)
-		factors[i]=1.0/CMath::sqrt(dot(i, this, i));
+		factors[i] = 1.0 / std::sqrt(dot(i, this, i));
 
 	normalization_factors=factors;
 	//CMath::display_vector(normalization_factors, num_strings, "n");
@@ -72,7 +72,7 @@ bool CImplicitWeightedSpecFeatures::set_wd_weights()
 		sum+=spec_weights[i];
 	}
 	for (i=0; i<degree; i++)
-		spec_weights[i]=CMath::sqrt(spec_weights[i]/sum);
+		spec_weights[i] = std::sqrt(spec_weights[i] / sum);
 
 	return spec_weights!=NULL;
 }
@@ -84,7 +84,7 @@ bool CImplicitWeightedSpecFeatures::set_weights(float64_t* w, int32_t d)
 	SG_FREE(spec_weights);
 	spec_weights=SG_MALLOC(float64_t, degree);
 	for (int32_t i=0; i<degree; i++)
-		spec_weights[i]=CMath::sqrt(w[i]);
+		spec_weights[i] = std::sqrt(w[i]);
 	return true;
 }
 

src/shogun/features/PolyFeatures.cpp

@@ -203,7 +203,7 @@ void CPolyFeatures::store_normalization_values()
 	m_normalization_values=SG_MALLOC(float32_t, num_vec);
 	for (int i=0; i<num_vec; i++)
 	{
-		float64_t tmp = CMath::sqrt(dot(i, this,i));
+		float64_t tmp = std::sqrt(dot(i, this, i));
 		if (tmp==0)
 			// trap division by zero
 			m_normalization_values[i]=1;