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From 57cd0958b153accf12f535ab9406dc8511bf22ec Mon Sep 17 00:00:00 2001
From: Viktor Gal <viktor.gal@maeth.com>
Date: Wed, 18 May 2016 06:35:28 +0200
Subject: [PATCH] Fixing eigen 3.3 related errors
porting fix for #3141 from lisitsyn/tapkee@7c74473d12809e4122527b6e003c74a942d8a25c
fix #3140: provide a workaround for the eigen bug for calculating log of mapped matrices
---
src/shogun/lib/tapkee/defines.hpp | 4 +--
src/shogun/mathematics/eigen3.h | 7 +++++
.../logdet/opfunc/DenseMatrixExactLog.cpp | 5 ++++
.../SerialComputationEngine_unittest.cc | 5 ++++
.../linalg/DenseExactLogJob_unittest.cc | 5 ++++
.../linalg/RationalApproximation_unittest.cc | 10 ++++++++
.../linalg/SparseMatrixOperator_unittest.cc | 30 ++++++++++++----------
7 files changed, 51 insertions(+), 15 deletions(-)
diff --git a/src/shogun/lib/tapkee/defines.hpp b/src/shogun/lib/tapkee/defines.hpp
index fd02636..1be45fe 100644
--- a/src/shogun/lib/tapkee/defines.hpp
+++ b/src/shogun/lib/tapkee/defines.hpp
@@ -49,12 +49,12 @@ namespace tapkee
TapkeeOutput(const tapkee::DenseMatrix& e, const tapkee::ProjectingFunction& p) :
embedding(), projection(p)
{
- embedding.swap(e);
+ embedding = e;
}
TapkeeOutput(const TapkeeOutput& that) :
embedding(), projection(that.projection)
{
- this->embedding.swap(that.embedding);
+ this->embedding = that.embedding;
}
tapkee::DenseMatrix embedding;
tapkee::ProjectingFunction projection;
diff --git a/src/shogun/mathematics/eigen3.h b/src/shogun/mathematics/eigen3.h
index 0fb8522..734be6c 100644
--- a/src/shogun/mathematics/eigen3.h
+++ b/src/shogun/mathematics/eigen3.h
@@ -61,6 +61,13 @@
#endif //EIGEN_VERSION_AT_LEAST(3,0,93)
+#if ((EIGEN_WORLD_VERSION == 3) && (EIGEN_MAJOR_VERSION == 2) && \
+ ((EIGEN_MINOR_VERSION == 91) || (EIGEN_MINOR_VERSION == 92)))
+ // Regression has been introduced to eigen develop (3.3alpha1+):
+ // http://eigen.tuxfamily.org/bz/show_bug.cgi?id=1229
+ // until this is not fixed we need to copy the matrix and calculate the log
+ #define EIGEN_WITH_LOG_BUG_1229 1
+#endif
namespace shogun
{
template<class T> class SGSparseMatrix;
diff --git a/src/shogun/mathematics/linalg/ratapprox/logdet/opfunc/DenseMatrixExactLog.cpp b/src/shogun/mathematics/linalg/ratapprox/logdet/opfunc/DenseMatrixExactLog.cpp
index a7918e6..1002a6c 100644
--- a/src/shogun/mathematics/linalg/ratapprox/logdet/opfunc/DenseMatrixExactLog.cpp
+++ b/src/shogun/mathematics/linalg/ratapprox/logdet/opfunc/DenseMatrixExactLog.cpp
@@ -62,7 +62,12 @@ void CDenseMatrixExactLog::precompute()
Map<MatrixXd> mat(m.matrix, m.num_rows, m.num_cols);
SGMatrix<float64_t> log_m(m.num_rows, m.num_cols);
Map<MatrixXd> log_mat(log_m.matrix, log_m.num_rows, log_m.num_cols);
+#if EIGEN_WITH_LOG_BUG_1229
+ MatrixXd tmp = mat;
+ log_mat=tmp.log();
+#else
log_mat=mat.log();
+#endif
// the log(C) is also a linear operator here
// reset the operator of this function with log(C)
diff --git a/tests/unit/lib/computation/SerialComputationEngine_unittest.cc b/tests/unit/lib/computation/SerialComputationEngine_unittest.cc
index c41a69a..7f96df1 100644
--- a/tests/unit/lib/computation/SerialComputationEngine_unittest.cc
+++ b/tests/unit/lib/computation/SerialComputationEngine_unittest.cc
@@ -40,7 +40,12 @@ TEST(SerialComputationEngine, dense_log_det)
mat(1,1)=3.0;
Map<MatrixXd> m(mat.matrix, mat.num_rows, mat.num_cols);
Map<MatrixXd> log_m(log_mat.matrix, log_mat.num_rows, log_mat.num_cols);
+#if EIGEN_WITH_LOG_BUG_1229
+ MatrixXd tmp = m;
+ log_m=tmp.log();
+#else
log_m=m.log();
+#endif
// create linear operator and aggregator
CDenseMatrixOperator<float64_t>* log_op=new CDenseMatrixOperator<float64_t>(log_mat);
diff --git a/tests/unit/mathematics/linalg/DenseExactLogJob_unittest.cc b/tests/unit/mathematics/linalg/DenseExactLogJob_unittest.cc
index a5a12cf..60daf40 100644
--- a/tests/unit/mathematics/linalg/DenseExactLogJob_unittest.cc
+++ b/tests/unit/mathematics/linalg/DenseExactLogJob_unittest.cc
@@ -38,7 +38,12 @@ TEST(DenseExactLogJob, log_det)
mat(1,1)=3.0;
Map<MatrixXd> m(mat.matrix, mat.num_rows, mat.num_cols);
Map<MatrixXd> log_m(log_mat.matrix, log_mat.num_rows, log_mat.num_cols);
+#if EIGEN_WITH_LOG_BUG_1229
+ MatrixXd tmp = m;
+ log_m=tmp.log();
+#else
log_m=m.log();
+#endif
// create linear operator and aggregator
CDenseMatrixOperator<float64_t>* log_op=new CDenseMatrixOperator<float64_t>(log_mat);
diff --git a/tests/unit/mathematics/linalg/RationalApproximation_unittest.cc b/tests/unit/mathematics/linalg/RationalApproximation_unittest.cc
index f401d06..682ed66 100644
--- a/tests/unit/mathematics/linalg/RationalApproximation_unittest.cc
+++ b/tests/unit/mathematics/linalg/RationalApproximation_unittest.cc
@@ -182,7 +182,12 @@ TEST(RationalApproximation, trace_accuracy)
#if EIGEN_VERSION_AT_LEAST(3,1,0)
// compute the trace of log(m) using Eigen3 that uses Schur-Parlett algorithm
Map<MatrixXd> eig_m(m.matrix, m.num_rows, m.num_cols);
+#if EIGEN_WITH_LOG_BUG_1229
+ MatrixXd tmp = eig_m;
+ float64_t trace_log_m=tmp.log().diagonal().sum();
+#else
float64_t trace_log_m=eig_m.log().diagonal().sum();
+#endif
#else
float64_t trace_log_m=-11.51292546497021618279;
#endif // EIGEN_VERSION_AT_LEAST(3,1,0)
@@ -364,7 +369,12 @@ TEST(RationalApproximation, trace_accuracy_cg_m)
#if EIGEN_VERSION_AT_LEAST(3,1,0)
// compute the trace of log(m) using Eigen3 that uses Schur-Parlett algorithm
Map<MatrixXd> eig_m(m.matrix, m.num_rows, m.num_cols);
+#if EIGEN_WITH_LOG_BUG_1229
+ MatrixXd tmp = eig_m;
+ float64_t trace_log_m=tmp.log().diagonal().sum();
+#else
float64_t trace_log_m=eig_m.log().diagonal().sum();
+#endif
#else
float64_t trace_log_m=-11.51292546497021618279;
#endif // EIGEN_VERSION_AT_LEAST(3,1,0)
diff --git a/tests/unit/mathematics/linalg/SparseMatrixOperator_unittest.cc b/tests/unit/mathematics/linalg/SparseMatrixOperator_unittest.cc
index 4d30724..9d171cc 100644
--- a/tests/unit/mathematics/linalg/SparseMatrixOperator_unittest.cc
+++ b/tests/unit/mathematics/linalg/SparseMatrixOperator_unittest.cc
@@ -219,33 +219,37 @@ TEST(SparseMatrixOperator, get_set_diagonal_realloc_complex128)
TEST(SparseMatrixOperator, get_sparsity_structure)
{
- const int size=9;
- const int max_pow=10;
+ const int32_t size=9;
+ const int32_t max_pow=10;
- SGMatrix<double> m(size, size);
+ SGMatrix<float64_t> m(size, size);
m.set_const(0.0);
- for (int i=0; i<size; ++i)
+ for (int32_t i=0; i<size; ++i)
m(i,i)=2.0;
- for (int i=0; i<size; i+=4)
+ for (int32_t i=0; i<size; i+=4)
m(i,size-1)=2.0;
- for (int i=0; i<size; i+=4)
+ for (int32_t i=0; i<size; i+=4)
m(size-1,i)=2.0;
- CSparseFeatures<double> feat(m);
- SGSparseMatrix<double> sm=feat.get_sparse_feature_matrix();
- CSparseMatrixOperator<double> op(sm);
+ CSparseFeatures<float64_t> feat(m);
+ SGSparseMatrix<float64_t> sm=feat.get_sparse_feature_matrix();
+ CSparseMatrixOperator<float64_t> op(sm);
CSparseMatrixOperator<bool>* b_op
=static_cast<CSparseMatrixOperator<bool>*>(op);
- SparseMatrix<bool, RowMajor, int> sp
+ SparseMatrix<bool, RowMajor, int32_t> sp
=EigenSparseUtil<bool>::toEigenSparse(b_op->get_matrix_operator());
- SparseMatrix<double, RowMajor, int> sm2
- =EigenSparseUtil<double>::toEigenSparse(sm);
+ SparseMatrix<float64_t, RowMajor, int32_t> sm2
+ =EigenSparseUtil<float64_t>::toEigenSparse(sm);
// compute direct matrix power and then the sparsity structure
- for (int i=2; i<=max_pow; ++i)
+ for (int32_t i=2; i<=max_pow; ++i)
+#if EIGEN_VERSION_AT_LEAST(3,2,91)
+ sp=(sp.cast<float64_t>()*sm2).cast<bool>();
+#else
sp=sp*sm2;
+#endif
int32_t* outerIndexPtr=const_cast<int32_t*>(sp.outerIndexPtr());
int32_t* innerIndexPtr=const_cast<int32_t*>(sp.innerIndexPtr());
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