vcpkg/ports/shogun/eigen-3.4.patch

--- a/src/shogun/machine/gp/MultiLaplaceInferenceMethod.cpp
+++ b/src/shogun/machine/gp/MultiLaplaceInferenceMethod.cpp
@@ -84,9 +84,9 @@ class CMultiPsiLine : public func_base
 		float64_t result=0;
 		for(index_t bl=0; bl<C; bl++)
 		{
-			eigen_f.block(bl*n,0,n,1)=K*alpha->block(bl*n,0,n,1)*CMath::exp(log_scale*2.0);
-			result+=alpha->block(bl*n,0,n,1).dot(eigen_f.block(bl*n,0,n,1))/2.0;
-			eigen_f.block(bl*n,0,n,1)+=eigen_m;
+			eigen_f.segment(bl*n,n)=K*alpha->segment(bl*n,n)*CMath::exp(log_scale*2.0);
+			result+=alpha->segment(bl*n,n).dot(eigen_f.segment(bl*n,n))/2.0;
+			eigen_f.segment(bl*n,n)+=eigen_m;
 		}
 
 		// get first and second derivatives of log likelihood
@@ -272,7 +272,7 @@ void CMultiLaplaceInferenceMethod::update_alpha()
 	{
 		Map<VectorXd> alpha(m_alpha.vector, m_alpha.vlen);
 		for(index_t bl=0; bl<C; bl++)
-			eigen_mu.block(bl*n,0,n,1)=eigen_ktrtr*CMath::exp(m_log_scale*2.0)*alpha.block(bl*n,0,n,1);
+			eigen_mu.segment(bl*n,n)=eigen_ktrtr*CMath::exp(m_log_scale*2.0)*alpha.segment(bl*n,n);
 
 		//alpha'*(f-m)/2.0
 		Psi_New=alpha.dot(eigen_mu)/2.0;
@@ -316,7 +316,7 @@ void CMultiLaplaceInferenceMethod::update_alpha()
 
 		for(index_t bl=0; bl<C; bl++)
 		{
-			VectorXd eigen_sD=eigen_dpi.block(bl*n,0,n,1).cwiseSqrt();
+			VectorXd eigen_sD=eigen_dpi.segment(bl*n,n).cwiseSqrt();
 			LLT<MatrixXd> chol_tmp((eigen_sD*eigen_sD.transpose()).cwiseProduct(eigen_ktrtr*CMath::exp(m_log_scale*2.0))+
 				MatrixXd::Identity(m_ktrtr.num_rows, m_ktrtr.num_cols));
 			MatrixXd eigen_L_tmp=chol_tmp.matrixU();
@@ -341,11 +341,11 @@ void CMultiLaplaceInferenceMethod::update_alpha()
 		VectorXd tmp2=m_tmp.array().rowwise().sum();
 
 		for(index_t bl=0; bl<C; bl++)
-			eigen_b.block(bl*n,0,n,1)+=eigen_dpi.block(bl*n,0,n,1).cwiseProduct(eigen_mu.block(bl*n,0,n,1)-eigen_mean_bl-tmp2);
+			eigen_b.segment(bl*n,n)+=eigen_dpi.segment(bl*n,n).cwiseProduct(eigen_mu.segment(bl*n,n)-eigen_mean_bl-tmp2);
 
 		Map<VectorXd> &eigen_c=eigen_W;
 		for(index_t bl=0; bl<C; bl++)
-			eigen_c.block(bl*n,0,n,1)=eigen_E.block(0,bl*n,n,n)*(eigen_ktrtr*CMath::exp(m_log_scale*2.0)*eigen_b.block(bl*n,0,n,1));
+			eigen_c.segment(bl*n,n)=eigen_E.block(0,bl*n,n,n)*(eigen_ktrtr*CMath::exp(m_log_scale*2.0)*eigen_b.segment(bl*n,n));
 
 		Map<MatrixXd> c_tmp(eigen_c.data(),n,C);
 
@@ -409,7 +409,7 @@ float64_t CMultiLaplaceInferenceMethod::get_derivative_helper(SGMatrix<float64_t
 	{
 		result+=((eigen_E.block(0,bl*n,n,n)-eigen_U.block(0,bl*n,n,n).transpose()*eigen_U.block(0,bl*n,n,n)).array()
 			*eigen_dK.array()).sum();
-		result-=(eigen_dK*eigen_alpha.block(bl*n,0,n,1)).dot(eigen_alpha.block(bl*n,0,n,1));
+		result-=(eigen_dK*eigen_alpha.segment(bl*n,n)).dot(eigen_alpha.segment(bl*n,n));
 	}
 
 	return result/2.0;
@@ -489,7 +489,7 @@ SGVector<float64_t> CMultiLaplaceInferenceMethod::get_derivative_wrt_mean(
 		result[i]=0;
 		//currently only compute the explicit term
 		for(index_t bl=0; bl<C; bl++)
-			result[i]-=eigen_alpha.block(bl*n,0,n,1).dot(eigen_dmu);
+			result[i]-=eigen_alpha.segment(bl*n,n).dot(eigen_dmu);
 	}
 
 	return result;