90 lines
3.7 KiB
Diff
90 lines
3.7 KiB
Diff
diff --git a/src/shogun/CMakeLists.txt b/src/shogun/CMakeLists.txt
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index 31a0d2c7b..e700bd7c7 100644
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--- a/src/shogun/CMakeLists.txt
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+++ b/src/shogun/CMakeLists.txt
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@@ -307,7 +307,7 @@ IF(NOT EIGEN3_FOUND)
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)
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ELSE()
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# https://github.com/shogun-toolbox/shogun/issues/4870
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- IF(${EIGEN3_VERSION_STRING} VERSION_GREATER 3.3.9)
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+ IF(0)
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MESSAGE(FATAL_ERROR "The system Eigen3 version ${EIGEN3_VERSION_STRING} isn't supported!")
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ENDIF()
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SHOGUN_INCLUDE_DIRS(SCOPE PUBLIC SYSTEM ${EIGEN3_INCLUDE_DIR})
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diff --git a/src/shogun/machine/gp/MultiLaplaceInferenceMethod.cpp b/src/shogun/machine/gp/MultiLaplaceInferenceMethod.cpp
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index a1677177e..0c9ca8f78 100644
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--- a/src/shogun/machine/gp/MultiLaplaceInferenceMethod.cpp
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+++ b/src/shogun/machine/gp/MultiLaplaceInferenceMethod.cpp
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@@ -87,10 +87,10 @@ public:
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float64_t result=0;
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for(index_t bl=0; bl<C; bl++)
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{
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- eigen_f.block(bl * n, 0, n, 1) =
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- K * alpha->block(bl * n, 0, n, 1) * std::exp(log_scale * 2.0);
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- result+=alpha->block(bl*n,0,n,1).dot(eigen_f.block(bl*n,0,n,1))/2.0;
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- eigen_f.block(bl*n,0,n,1)+=eigen_m;
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+ eigen_f.segment(bl * n, n) =
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+ K * alpha->segment(bl * n, n) * std::exp(log_scale * 2.0);
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+ result+=alpha->segment(bl*n,n).dot(eigen_f.segment(bl*n,n))/2.0;
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+ eigen_f.segment(bl*n,n)+=eigen_m;
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}
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// get first and second derivatives of log likelihood
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@@ -278,9 +278,9 @@ void MultiLaplaceInferenceMethod::update_alpha()
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{
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Map<VectorXd> alpha(m_alpha.vector, m_alpha.vlen);
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for(index_t bl=0; bl<C; bl++)
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- eigen_mu.block(bl * n, 0, n, 1) = eigen_ktrtr *
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+ eigen_mu.segment(bl * n, n) = eigen_ktrtr *
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std::exp(m_log_scale * 2.0) *
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- alpha.block(bl * n, 0, n, 1);
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+ alpha.segment(bl * n, n);
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//alpha'*(f-m)/2.0
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Psi_New=alpha.dot(eigen_mu)/2.0;
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@@ -324,7 +324,7 @@ void MultiLaplaceInferenceMethod::update_alpha()
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for(index_t bl=0; bl<C; bl++)
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{
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- VectorXd eigen_sD=eigen_dpi.block(bl*n,0,n,1).cwiseSqrt();
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+ VectorXd eigen_sD=eigen_dpi.segment(bl*n,n).cwiseSqrt();
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LLT<MatrixXd> chol_tmp(
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(eigen_sD * eigen_sD.transpose())
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.cwiseProduct(eigen_ktrtr * std::exp(m_log_scale * 2.0)) +
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@@ -351,14 +351,14 @@ void MultiLaplaceInferenceMethod::update_alpha()
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VectorXd tmp2=m_tmp.array().rowwise().sum();
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for(index_t bl=0; bl<C; bl++)
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- 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);
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+ eigen_b.segment(bl*n,n)+=eigen_dpi.segment(bl*n,n).cwiseProduct(eigen_mu.segment(bl*n,n)-eigen_mean_bl-tmp2);
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Map<VectorXd> &eigen_c=eigen_W;
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for(index_t bl=0; bl<C; bl++)
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- eigen_c.block(bl * n, 0, n, 1) =
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+ eigen_c.segment(bl * n, n) =
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eigen_E.block(0, bl * n, n, n) *
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(eigen_ktrtr * std::exp(m_log_scale * 2.0) *
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- eigen_b.block(bl * n, 0, n, 1));
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+ eigen_b.segment(bl * n, n));
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Map<MatrixXd> c_tmp(eigen_c.data(),n,C);
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@@ -422,7 +422,7 @@ float64_t MultiLaplaceInferenceMethod::get_derivative_helper(SGMatrix<float64_t>
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{
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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()
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*eigen_dK.array()).sum();
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- result-=(eigen_dK*eigen_alpha.block(bl*n,0,n,1)).dot(eigen_alpha.block(bl*n,0,n,1));
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+ result-=(eigen_dK*eigen_alpha.segment(bl*n,n)).dot(eigen_alpha.segment(bl*n,n));
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}
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return result/2.0;
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@@ -504,7 +504,7 @@ SGVector<float64_t> MultiLaplaceInferenceMethod::get_derivative_wrt_mean(
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result[i]=0;
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//currently only compute the explicit term
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for(index_t bl=0; bl<C; bl++)
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- result[i]-=eigen_alpha.block(bl*n,0,n,1).dot(eigen_dmu);
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+ result[i]-=eigen_alpha.segment(bl*n,n).dot(eigen_dmu);
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}
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return result;
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