tjh.dev
1From: Sebastián Mancilla <smancill@smancill.dev>
2Subject: [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
8diff --git a/src/shogun/machine/gp/MultiLaplaceInferenceMethod.cpp b/src/shogun/machine/gp/MultiLaplaceInferenceMethod.cpp
9index 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;