66 lines
3 KiB
Diff
66 lines
3 KiB
Diff
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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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@@ -84,9 +84,9 @@ class CMultiPsiLine : public func_base
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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)=K*alpha->block(bl*n,0,n,1)*CMath::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)=K*alpha->segment(bl*n,n)*CMath::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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@@ -272,7 +272,7 @@ void CMultiLaplaceInferenceMethod::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*CMath::exp(m_log_scale*2.0)*alpha.block(bl*n,0,n,1);
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+ eigen_mu.segment(bl*n,n)=eigen_ktrtr*CMath::exp(m_log_scale*2.0)*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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@@ -316,7 +316,7 @@ void CMultiLaplaceInferenceMethod::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((eigen_sD*eigen_sD.transpose()).cwiseProduct(eigen_ktrtr*CMath::exp(m_log_scale*2.0))+
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MatrixXd::Identity(m_ktrtr.num_rows, m_ktrtr.num_cols));
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MatrixXd eigen_L_tmp=chol_tmp.matrixU();
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@@ -341,11 +341,11 @@ void CMultiLaplaceInferenceMethod::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)=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));
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+ 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));
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Map<MatrixXd> c_tmp(eigen_c.data(),n,C);
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@@ -409,7 +409,7 @@ float64_t CMultiLaplaceInferenceMethod::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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@@ -489,7 +489,7 @@ SGVector<float64_t> CMultiLaplaceInferenceMethod::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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