pkgs/applications/science/machine-learning/shogun/eigen-3.4.patch

   1 From: Sebastián Mancilla <smancill@smancill.dev>
   2 Subject: [PATCH] Fix compile errors when using Eigen 3.4
   3
   4 ---
   5  .../machine/gp/MultiLaplaceInferenceMethod.cpp | 18 +++++++++---------
   6  1 file changed, 9 insertions(+), 9 deletions(-)
   7
   8 diff --git a/src/shogun/machine/gp/MultiLaplaceInferenceMethod.cpp b/src/shogun/machine/gp/MultiLaplaceInferenceMethod.cpp
   9 index 2e27678d2..60050afea 100644
  10 --- a/src/shogun/machine/gp/MultiLaplaceInferenceMethod.cpp
  11 +++ b/src/shogun/machine/gp/MultiLaplaceInferenceMethod.cpp
  12 @@ -84,9 +84,9 @@ class CMultiPsiLine : public func_base
  13                 float64_t result=0;
  14                 for(index_t bl=0; bl<C; bl++)
  15                 {
  16 -                       eigen_f.block(bl*n,0,n,1)=K*alpha->block(bl*n,0,n,1)*CMath::exp(log_scale*2.0);
  17 -                       result+=alpha->block(bl*n,0,n,1).dot(eigen_f.block(bl*n,0,n,1))/2.0;
  18 -                       eigen_f.block(bl*n,0,n,1)+=eigen_m;
  19 +                       eigen_f.segment(bl*n,n)=K*alpha->segment(bl*n,n)*CMath::exp(log_scale*2.0);
  20 +                       result+=alpha->segment(bl*n,n).dot(eigen_f.segment(bl*n,n))/2.0;
  21 +                       eigen_f.segment(bl*n,n)+=eigen_m;
  22                 }
  23
  24                 // get first and second derivatives of log likelihood
  25 @@ -272,7 +272,7 @@ void CMultiLaplaceInferenceMethod::update_alpha()
  26         {
  27                 Map<VectorXd> alpha(m_alpha.vector, m_alpha.vlen);
  28                 for(index_t bl=0; bl<C; bl++)
  29 -                       eigen_mu.block(bl*n,0,n,1)=eigen_ktrtr*CMath::exp(m_log_scale*2.0)*alpha.block(bl*n,0,n,1);
  30 +                       eigen_mu.segment(bl*n,n)=eigen_ktrtr*CMath::exp(m_log_scale*2.0)*alpha.segment(bl*n,n);
  31
  32                 //alpha'*(f-m)/2.0
  33                 Psi_New=alpha.dot(eigen_mu)/2.0;
  34 @@ -316,7 +316,7 @@ void CMultiLaplaceInferenceMethod::update_alpha()
  35
  36                 for(index_t bl=0; bl<C; bl++)
  37                 {
  38 -                       VectorXd eigen_sD=eigen_dpi.block(bl*n,0,n,1).cwiseSqrt();
  39 +                       VectorXd eigen_sD=eigen_dpi.segment(bl*n,n).cwiseSqrt();
  40                         LLT<MatrixXd> chol_tmp((eigen_sD*eigen_sD.transpose()).cwiseProduct(eigen_ktrtr*CMath::exp(m_log_scale*2.0))+
  41                                 MatrixXd::Identity(m_ktrtr.num_rows, m_ktrtr.num_cols));
  42                         MatrixXd eigen_L_tmp=chol_tmp.matrixU();
  43 @@ -341,11 +341,11 @@ void CMultiLaplaceInferenceMethod::update_alpha()
  44                 VectorXd tmp2=m_tmp.array().rowwise().sum();
  45
  46                 for(index_t bl=0; bl<C; bl++)
  47 -                       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);
  48 +                       eigen_b.segment(bl*n,n)+=eigen_dpi.segment(bl*n,n).cwiseProduct(eigen_mu.segment(bl*n,n)-eigen_mean_bl-tmp2);
  49
  50                 Map<VectorXd> &eigen_c=eigen_W;
  51                 for(index_t bl=0; bl<C; bl++)
  52 -                       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));
  53 +                       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));
  54
  55                 Map<MatrixXd> c_tmp(eigen_c.data(),n,C);
  56
  57 @@ -409,7 +409,7 @@ float64_t CMultiLaplaceInferenceMethod::get_derivative_helper(SGMatrix<float64_t
  58         {
  59                 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()
  60                         *eigen_dK.array()).sum();
  61 -               result-=(eigen_dK*eigen_alpha.block(bl*n,0,n,1)).dot(eigen_alpha.block(bl*n,0,n,1));
  62 +               result-=(eigen_dK*eigen_alpha.segment(bl*n,n)).dot(eigen_alpha.segment(bl*n,n));
  63         }
  64
  65         return result/2.0;
  66 @@ -489,7 +489,7 @@ SGVector<float64_t> CMultiLaplaceInferenceMethod::get_derivative_wrt_mean(
  67                 result[i]=0;
  68                 //currently only compute the explicit term
  69                 for(index_t bl=0; bl<C; bl++)
  70 -                       result[i]-=eigen_alpha.block(bl*n,0,n,1).dot(eigen_dmu);
  71 +                       result[i]-=eigen_alpha.segment(bl*n,n).dot(eigen_dmu);
  72         }
  73
  74         return result;