template<typename MatrixType> void vectorwiseop_matrix(const MatrixType& m)
{
typedef typename MatrixType::Index Index;
typedef typename MatrixType::Scalar Scalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> ColVectorType;
typedef Matrix<Scalar, 1, MatrixType::ColsAtCompileTime> RowVectorType;
Index rows = m.rows();
Index cols = m.cols();
Index r = internal::random<Index>(0, rows-1),
c = internal::random<Index>(0, cols-1);
MatrixType m1 = MatrixType::Random(rows, cols),
m2(rows, cols),
m3(rows, cols);
ColVectorType colvec = ColVectorType::Random(rows);
RowVectorType rowvec = RowVectorType::Random(cols);
// test addition
m2 = m1;
m2.colwise() += colvec;
VERIFY_IS_APPROX(m2, m1.colwise() + colvec);
VERIFY_IS_APPROX(m2.col(c), m1.col(c) + colvec);
VERIFY_RAISES_ASSERT(m2.colwise() += colvec.transpose());
VERIFY_RAISES_ASSERT(m1.colwise() + colvec.transpose());
m2 = m1;
m2.rowwise() += rowvec;
VERIFY_IS_APPROX(m2, m1.rowwise() + rowvec);
VERIFY_IS_APPROX(m2.row(r), m1.row(r) + rowvec);
VERIFY_RAISES_ASSERT(m2.rowwise() += rowvec.transpose());
VERIFY_RAISES_ASSERT(m1.rowwise() + rowvec.transpose());
// test substraction
m2 = m1;
m2.colwise() -= colvec;
VERIFY_IS_APPROX(m2, m1.colwise() - colvec);
VERIFY_IS_APPROX(m2.col(c), m1.col(c) - colvec);
VERIFY_RAISES_ASSERT(m2.colwise() -= colvec.transpose());
VERIFY_RAISES_ASSERT(m1.colwise() - colvec.transpose());
m2 = m1;
m2.rowwise() -= rowvec;
VERIFY_IS_APPROX(m2, m1.rowwise() - rowvec);
VERIFY_IS_APPROX(m2.row(r), m1.row(r) - rowvec);
VERIFY_RAISES_ASSERT(m2.rowwise() -= rowvec.transpose());
VERIFY_RAISES_ASSERT(m1.rowwise() - rowvec.transpose());
}
RowVectorType relu_activator::activate(const RowVectorType & v)
{
#ifdef USE_GPU
return m.array().logistic();
#elif defined USE_PARTIAL_GPU
return m.array().logistic();
#else
return (1 + v.array().exp()).log();
#endif
}
template<typename ArrayType> void vectorwiseop_array(const ArrayType& m)
{
typedef typename ArrayType::Index Index;
typedef typename ArrayType::Scalar Scalar;
typedef Array<Scalar, ArrayType::RowsAtCompileTime, 1> ColVectorType;
typedef Array<Scalar, 1, ArrayType::ColsAtCompileTime> RowVectorType;
Index rows = m.rows();
Index cols = m.cols();
Index r = internal::random<Index>(0, rows-1),
c = internal::random<Index>(0, cols-1);
ArrayType m1 = ArrayType::Random(rows, cols),
m2(rows, cols),
m3(rows, cols);
ColVectorType colvec = ColVectorType::Random(rows);
RowVectorType rowvec = RowVectorType::Random(cols);
// test addition
m2 = m1;
m2.colwise() += colvec;
VERIFY_IS_APPROX(m2, m1.colwise() + colvec);
VERIFY_IS_APPROX(m2.col(c), m1.col(c) + colvec);
VERIFY_RAISES_ASSERT(m2.colwise() += colvec.transpose());
VERIFY_RAISES_ASSERT(m1.colwise() + colvec.transpose());
m2 = m1;
m2.rowwise() += rowvec;
VERIFY_IS_APPROX(m2, m1.rowwise() + rowvec);
VERIFY_IS_APPROX(m2.row(r), m1.row(r) + rowvec);
VERIFY_RAISES_ASSERT(m2.rowwise() += rowvec.transpose());
VERIFY_RAISES_ASSERT(m1.rowwise() + rowvec.transpose());
// test substraction
m2 = m1;
m2.colwise() -= colvec;
VERIFY_IS_APPROX(m2, m1.colwise() - colvec);
VERIFY_IS_APPROX(m2.col(c), m1.col(c) - colvec);
VERIFY_RAISES_ASSERT(m2.colwise() -= colvec.transpose());
VERIFY_RAISES_ASSERT(m1.colwise() - colvec.transpose());
m2 = m1;
m2.rowwise() -= rowvec;
VERIFY_IS_APPROX(m2, m1.rowwise() - rowvec);
VERIFY_IS_APPROX(m2.row(r), m1.row(r) - rowvec);
VERIFY_RAISES_ASSERT(m2.rowwise() -= rowvec.transpose());
VERIFY_RAISES_ASSERT(m1.rowwise() - rowvec.transpose());
// test multiplication
m2 = m1;
m2.colwise() *= colvec;
VERIFY_IS_APPROX(m2, m1.colwise() * colvec);
VERIFY_IS_APPROX(m2.col(c), m1.col(c) * colvec);
VERIFY_RAISES_ASSERT(m2.colwise() *= colvec.transpose());
VERIFY_RAISES_ASSERT(m1.colwise() * colvec.transpose());
m2 = m1;
m2.rowwise() *= rowvec;
VERIFY_IS_APPROX(m2, m1.rowwise() * rowvec);
VERIFY_IS_APPROX(m2.row(r), m1.row(r) * rowvec);
VERIFY_RAISES_ASSERT(m2.rowwise() *= rowvec.transpose());
VERIFY_RAISES_ASSERT(m1.rowwise() * rowvec.transpose());
// test quotient
m2 = m1;
m2.colwise() /= colvec;
VERIFY_IS_APPROX(m2, m1.colwise() / colvec);
VERIFY_IS_APPROX(m2.col(c), m1.col(c) / colvec);
VERIFY_RAISES_ASSERT(m2.colwise() /= colvec.transpose());
VERIFY_RAISES_ASSERT(m1.colwise() / colvec.transpose());
m2 = m1;
m2.rowwise() /= rowvec;
VERIFY_IS_APPROX(m2, m1.rowwise() / rowvec);
VERIFY_IS_APPROX(m2.row(r), m1.row(r) / rowvec);
VERIFY_RAISES_ASSERT(m2.rowwise() /= rowvec.transpose());
VERIFY_RAISES_ASSERT(m1.rowwise() / rowvec.transpose());
m2 = m1;
// yes, there might be an aliasing issue there but ".rowwise() /="
// is supposed to evaluate " m2.colwise().sum()" into a temporary to avoid
// evaluating the reduction multiple times
if(ArrayType::RowsAtCompileTime>2 || ArrayType::RowsAtCompileTime==Dynamic)
{
m2.rowwise() /= m2.colwise().sum();
VERIFY_IS_APPROX(m2, m1.rowwise() / m1.colwise().sum());
}
// all/any
Array<bool,Dynamic,Dynamic> mb(rows,cols);
mb = (m1.real()<=0.7).colwise().all();
VERIFY( (mb.col(c) == (m1.real().col(c)<=0.7).all()).all() );
mb = (m1.real()<=0.7).rowwise().all();
VERIFY( (mb.row(r) == (m1.real().row(r)<=0.7).all()).all() );
mb = (m1.real()>=0.7).colwise().any();
VERIFY( (mb.col(c) == (m1.real().col(c)>=0.7).any()).all() );
mb = (m1.real()>=0.7).rowwise().any();
VERIFY( (mb.row(r) == (m1.real().row(r)>=0.7).any()).all() );
}
template<typename MatrixType> void vectorwiseop_matrix(const MatrixType& m)
{
typedef typename MatrixType::Index Index;
typedef typename MatrixType::Scalar Scalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> ColVectorType;
typedef Matrix<Scalar, 1, MatrixType::ColsAtCompileTime> RowVectorType;
typedef Matrix<RealScalar, MatrixType::RowsAtCompileTime, 1> RealColVectorType;
typedef Matrix<RealScalar, 1, MatrixType::ColsAtCompileTime> RealRowVectorType;
Index rows = m.rows();
Index cols = m.cols();
Index r = internal::random<Index>(0, rows-1),
c = internal::random<Index>(0, cols-1);
MatrixType m1 = MatrixType::Random(rows, cols),
m2(rows, cols),
m3(rows, cols);
ColVectorType colvec = ColVectorType::Random(rows);
RowVectorType rowvec = RowVectorType::Random(cols);
RealColVectorType rcres;
RealRowVectorType rrres;
// test addition
m2 = m1;
m2.colwise() += colvec;
VERIFY_IS_APPROX(m2, m1.colwise() + colvec);
VERIFY_IS_APPROX(m2.col(c), m1.col(c) + colvec);
if(rows>1)
{
VERIFY_RAISES_ASSERT(m2.colwise() += colvec.transpose());
VERIFY_RAISES_ASSERT(m1.colwise() + colvec.transpose());
}
m2 = m1;
m2.rowwise() += rowvec;
VERIFY_IS_APPROX(m2, m1.rowwise() + rowvec);
VERIFY_IS_APPROX(m2.row(r), m1.row(r) + rowvec);
if(cols>1)
{
VERIFY_RAISES_ASSERT(m2.rowwise() += rowvec.transpose());
VERIFY_RAISES_ASSERT(m1.rowwise() + rowvec.transpose());
}
// test substraction
m2 = m1;
m2.colwise() -= colvec;
VERIFY_IS_APPROX(m2, m1.colwise() - colvec);
VERIFY_IS_APPROX(m2.col(c), m1.col(c) - colvec);
if(rows>1)
{
VERIFY_RAISES_ASSERT(m2.colwise() -= colvec.transpose());
VERIFY_RAISES_ASSERT(m1.colwise() - colvec.transpose());
}
m2 = m1;
m2.rowwise() -= rowvec;
VERIFY_IS_APPROX(m2, m1.rowwise() - rowvec);
VERIFY_IS_APPROX(m2.row(r), m1.row(r) - rowvec);
if(cols>1)
{
VERIFY_RAISES_ASSERT(m2.rowwise() -= rowvec.transpose());
VERIFY_RAISES_ASSERT(m1.rowwise() - rowvec.transpose());
}
// test norm
rrres = m1.colwise().norm();
VERIFY_IS_APPROX(rrres(c), m1.col(c).norm());
rcres = m1.rowwise().norm();
VERIFY_IS_APPROX(rcres(r), m1.row(r).norm());
VERIFY_IS_APPROX(m1.cwiseAbs().colwise().sum(), m1.colwise().template lpNorm<1>());
VERIFY_IS_APPROX(m1.cwiseAbs().rowwise().sum(), m1.rowwise().template lpNorm<1>());
VERIFY_IS_APPROX(m1.cwiseAbs().colwise().maxCoeff(), m1.colwise().template lpNorm<Infinity>());
VERIFY_IS_APPROX(m1.cwiseAbs().rowwise().maxCoeff(), m1.rowwise().template lpNorm<Infinity>());
// regression for bug 1158
VERIFY_IS_APPROX(m1.cwiseAbs().colwise().sum().x(), m1.col(0).cwiseAbs().sum());
// test normalized
m2 = m1.colwise().normalized();
VERIFY_IS_APPROX(m2.col(c), m1.col(c).normalized());
m2 = m1.rowwise().normalized();
VERIFY_IS_APPROX(m2.row(r), m1.row(r).normalized());
// test normalize
m2 = m1;
m2.colwise().normalize();
VERIFY_IS_APPROX(m2.col(c), m1.col(c).normalized());
m2 = m1;
m2.rowwise().normalize();
VERIFY_IS_APPROX(m2.row(r), m1.row(r).normalized());
// test with partial reduction of products
Matrix<Scalar,MatrixType::RowsAtCompileTime,MatrixType::RowsAtCompileTime> m1m1 = m1 * m1.transpose();
VERIFY_IS_APPROX( (m1 * m1.transpose()).colwise().sum(), m1m1.colwise().sum());
Matrix<Scalar,1,MatrixType::RowsAtCompileTime> tmp(rows);
VERIFY_EVALUATION_COUNT( tmp = (m1 * m1.transpose()).colwise().sum(), (MatrixType::RowsAtCompileTime==Dynamic ? 1 : 0));
m2 = m1.rowwise() - (m1.colwise().sum()/RealScalar(m1.rows())).eval();
m1 = m1.rowwise() - (m1.colwise().sum()/RealScalar(m1.rows()));
VERIFY_IS_APPROX( m1, m2 );
VERIFY_EVALUATION_COUNT( m2 = (m1.rowwise() - m1.colwise().sum()/RealScalar(m1.rows())), (MatrixType::RowsAtCompileTime==Dynamic && MatrixType::ColsAtCompileTime!=1 ? 1 : 0) );
}
template<typename ArrayType> void vectorwiseop_array(const ArrayType& m)
{
typedef typename ArrayType::Index Index;
typedef typename ArrayType::Scalar Scalar;
typedef Array<Scalar, ArrayType::RowsAtCompileTime, 1> ColVectorType;
typedef Array<Scalar, 1, ArrayType::ColsAtCompileTime> RowVectorType;
Index rows = m.rows();
Index cols = m.cols();
Index r = internal::random<Index>(0, rows-1),
c = internal::random<Index>(0, cols-1);
ArrayType m1 = ArrayType::Random(rows, cols),
m2(rows, cols),
m3(rows, cols);
ColVectorType colvec = ColVectorType::Random(rows);
RowVectorType rowvec = RowVectorType::Random(cols);
// test addition
m2 = m1;
m2.colwise() += colvec;
VERIFY_IS_APPROX(m2, m1.colwise() + colvec);
VERIFY_IS_APPROX(m2.col(c), m1.col(c) + colvec);
VERIFY_RAISES_ASSERT(m2.colwise() += colvec.transpose());
VERIFY_RAISES_ASSERT(m1.colwise() + colvec.transpose());
m2 = m1;
m2.rowwise() += rowvec;
VERIFY_IS_APPROX(m2, m1.rowwise() + rowvec);
VERIFY_IS_APPROX(m2.row(r), m1.row(r) + rowvec);
VERIFY_RAISES_ASSERT(m2.rowwise() += rowvec.transpose());
VERIFY_RAISES_ASSERT(m1.rowwise() + rowvec.transpose());
// test substraction
m2 = m1;
m2.colwise() -= colvec;
VERIFY_IS_APPROX(m2, m1.colwise() - colvec);
VERIFY_IS_APPROX(m2.col(c), m1.col(c) - colvec);
VERIFY_RAISES_ASSERT(m2.colwise() -= colvec.transpose());
VERIFY_RAISES_ASSERT(m1.colwise() - colvec.transpose());
m2 = m1;
m2.rowwise() -= rowvec;
VERIFY_IS_APPROX(m2, m1.rowwise() - rowvec);
VERIFY_IS_APPROX(m2.row(r), m1.row(r) - rowvec);
VERIFY_RAISES_ASSERT(m2.rowwise() -= rowvec.transpose());
VERIFY_RAISES_ASSERT(m1.rowwise() - rowvec.transpose());
// test multiplication
m2 = m1;
m2.colwise() *= colvec;
VERIFY_IS_APPROX(m2, m1.colwise() * colvec);
VERIFY_IS_APPROX(m2.col(c), m1.col(c) * colvec);
VERIFY_RAISES_ASSERT(m2.colwise() *= colvec.transpose());
VERIFY_RAISES_ASSERT(m1.colwise() * colvec.transpose());
m2 = m1;
m2.rowwise() *= rowvec;
VERIFY_IS_APPROX(m2, m1.rowwise() * rowvec);
VERIFY_IS_APPROX(m2.row(r), m1.row(r) * rowvec);
VERIFY_RAISES_ASSERT(m2.rowwise() *= rowvec.transpose());
VERIFY_RAISES_ASSERT(m1.rowwise() * rowvec.transpose());
// test quotient
m2 = m1;
m2.colwise() /= colvec;
VERIFY_IS_APPROX(m2, m1.colwise() / colvec);
VERIFY_IS_APPROX(m2.col(c), m1.col(c) / colvec);
VERIFY_RAISES_ASSERT(m2.colwise() /= colvec.transpose());
VERIFY_RAISES_ASSERT(m1.colwise() / colvec.transpose());
m2 = m1;
m2.rowwise() /= rowvec;
VERIFY_IS_APPROX(m2, m1.rowwise() / rowvec);
VERIFY_IS_APPROX(m2.row(r), m1.row(r) / rowvec);
VERIFY_RAISES_ASSERT(m2.rowwise() /= rowvec.transpose());
VERIFY_RAISES_ASSERT(m1.rowwise() / rowvec.transpose());
}
RowVectorType logistic_activator::gradient(const RowVectorType & m,
const RowVectorType & activated)
{
return activated.array() * (1 - activated.array());
}
RowVectorType linear_activator::gradient(const RowVectorType & v, const RowVectorType & activated)
{
return VectorType::Ones(v.size());
}
