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* This file is a part of TiledArray.
* Copyright (C) 2013 Virginia Tech
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program. If not, see <http://www.gnu.org/licenses/>.
*
*/
#ifndef TILEDARRAY_TENSOR_H__INCLUDED
#define TILEDARRAY_TENSOR_H__INCLUDED
#include <TiledArray/perm_index.h>
#include <TiledArray/math/gemm_helper.h>
#include <TiledArray/math/blas.h>
#include <TiledArray/math/transpose.h>
//#include <TiledArray/tile_op/tile_interface.h>
namespace TiledArray {
/// An N-dimensional tensor object
/// \tparam T the value type of this tensor
/// \tparam A The allocator type for the data
template <typename T, typename A = Eigen::aligned_allocator<T> >
class Tensor {
private:
// Internal type for enabling various constructors.
struct Enabler { };
public:
typedef Tensor<T, A> Tensor_; ///< This class type
typedef Range range_type; ///< Tensor range type
typedef typename range_type::size_type size_type; ///< size type
typedef A allocator_type; ///< Allocator type
typedef typename allocator_type::value_type value_type; ///< Array element type
typedef typename allocator_type::reference reference; ///< Element reference type
typedef typename allocator_type::const_reference const_reference; ///< Element reference type
typedef typename allocator_type::pointer pointer; ///< Element pointer type
typedef typename allocator_type::const_pointer const_pointer; ///< Element const pointer type
typedef typename allocator_type::difference_type difference_type; ///< Difference type
typedef pointer iterator; ///< Element iterator type
typedef const_pointer const_iterator; ///< Element const iterator type
typedef typename TiledArray::detail::scalar_type<T>::type
numeric_type; ///< the numeric type that supports T
template <typename U>
using param_type = TiledArray::detail::param_type<U>;
private:
template <typename U>
using param_value_type = TiledArray::detail::param_type<typename U::value_type>;
/// Evaluation tensor
/// This tensor is used as an evaluated intermediate for other tensors.
class Impl : public allocator_type {
public:
/// Default constructor
/// Construct an empty tensor that has no data or dimensions
Impl() : allocator_type(), range_(), data_(NULL) { }
/// Construct an evaluated tensor
/// \param range The N-dimensional range for this tensor
explicit Impl(const range_type& range) :
allocator_type(), range_(range), data_(NULL)
{
data_ = allocator_type::allocate(range.volume());
}
~Impl() {
math::destroy_vector(range_.volume(), data_);
allocator_type::deallocate(data_, range_.volume());
data_ = NULL;
}
range_type range_; ///< Tensor size info
pointer data_; ///< Tensor data
}; // class Impl
template <typename U>
static typename std::enable_if<std::is_scalar<U>::value>::type
default_init(size_type, U*) { }
template <typename U>
static typename std::enable_if<! std::is_scalar<U>::value>::type
default_init(size_type n, U* u) {
math::uninitialized_fill_vector(n, U(), u);
}
/// Compute the fused dimensions for permutation
/// This function will compute the fused dimensions of a tensor for use in
/// permutation algorithms. The idea is to partition the stride 1 dimensions
/// in both the input and output tensor, which yields a forth-order tensor
/// (second- and third-order tensors have size of 1 and stride of 0 in the
/// unused dimensions).
void fuse_dimensions(size_type * restrict const fused_size,
size_type * restrict const fused_weight,
const size_type * restrict const size, const Permutation& perm)
{
const unsigned int ndim1 = perm.dim() - 1u;
int i = ndim1;
fused_size[3] = size[i--];
while((i >= 0) && (perm[i + 1u] == (perm[i] + 1u)))
fused_size[3] *= size[i--];
fused_weight[3] = 1u;
if((i >= 0) && (perm[i] != ndim1)) {
fused_size[2] = size[i--];
while((i >= 0) && (perm[i] != ndim1))
fused_size[2] *= size[i--];
fused_weight[2] = fused_size[3];
fused_size[1] = size[i--];
while((i >= 0) && (perm[i + 1] == (perm[i] + 1u)))
fused_size[1] *= size[i--];
fused_weight[1] = fused_size[2] * fused_weight[2];
} else {
fused_size[2] = 1ul;
fused_weight[2] = 0ul;
fused_size[1] = size[i--];
while((i >= 0) && (perm[i + 1] == (perm[i] + 1u)))
fused_size[1] *= size[i--];
fused_weight[1] = fused_size[3];
}
if(i >= 0) {
fused_size[0] = size[i--];
while(i >= 0)
fused_size[0] *= size[i--];
fused_weight[0] = fused_size[1] * fused_weight[1];
} else {
fused_size[0] = 1ul;
fused_weight[0] = 0ul;
}
}
std::shared_ptr<Impl> pimpl_; ///< Shared pointer to implementation object
static const range_type empty_range_; ///< Empty range
public:
/// Default constructor
/// Construct an empty tensor that has no data or dimensions
Tensor() : pimpl_() { }
Tensor(const range_type& range) :
pimpl_(new Impl(range))
{
default_init(range.volume(), pimpl_->data_);
}
/// Construct a tensor with a fill value
/// \param range An array with the size of of each dimension
/// \param value The value of the tensor elements
Tensor(const range_type& range, param_type<value_type> value) :
pimpl_(new Impl(range))
{
math::uninitialized_fill_vector(range.volume(), value, pimpl_->data_);
}
/// Construct an evaluated tensor
template <typename InIter>
Tensor(const range_type& range, InIter it,
typename std::enable_if<TiledArray::detail::is_input_iterator<InIter>::value &&
! std::is_pointer<InIter>::value, Enabler>::type = Enabler()) :
pimpl_(new Impl(range))
{
size_type n = range.volume();
pointer restrict const data = pimpl_->data_;
for(size_type i = 0ul; i < n; ++i)
data[i] = *it++;
}
template <typename U>
Tensor(const Range& r, const U* u) :
pimpl_(new Impl(r))
{
math::uninitialized_copy_vector(r.volume(), u, pimpl_->data_);
}
/// Construct a permuted tensor copy
/// \tparam U The element type of other
/// \tparam AU The allocator type of other
/// \param other The tensor to be copied
/// \param perm The permutation that will be applied to the copy
template <typename U, typename AU>
Tensor(const Tensor<U, AU>& other, const Permutation& perm) :
pimpl_(new Impl(perm ^ other.range()))
{
// Check inputs.
TA_ASSERT(! other.empty());
TA_ASSERT(perm);
TA_ASSERT(perm.dim() == other.range().dim());
detail::PermIndex perm_index_op(other.range(), perm);
// Cache constants
const unsigned int ndim = other.range().dim();
const unsigned int ndim1 = ndim - 1u;
const size_type volume = other.range().volume();
if(perm[ndim1] == ndim1) {
// This is the simple case where the last dimension is not permuted.
// Therefore, it can be shuffled in chunks.
// Determine which dimensions can be permuted with the least significant
// dimension.
size_type block_size = other.range().size()[ndim1];
for(int i = -1 + ndim1 ; i >= 0; --i) {
if(int(perm[i]) != i)
break;
block_size *= other.range().size()[i];
}
// Permute the data
for(size_type index = 0ul; index < volume; index += block_size) {
const size_type perm_index = perm_index_op(index);
// Copy the block
math::uninitialized_copy_vector(block_size, other.data() + index,
pimpl_->data_ + perm_index);
}
} else {
// This is the more complicated case. Here we permute in terms of matrix
// transposes. The data layout of the input and output matrices are
// chosen such that they both contain stride one dimensions.
size_type other_fused_size[4];
size_type other_fused_weight[4];
fuse_dimensions(other_fused_size, other_fused_weight,
other.range().size().data(), perm);
// Compute the fused stride for the result matrix transpose.
size_type result_outer_stride = 1ul;
for(unsigned int i = perm[ndim1] + 1u; i < ndim; ++i)
result_outer_stride *= pimpl_->range_.size()[i];
// Copy data from the input to the output matrix via a series of matrix
// transposes.
for(size_type i = 0ul; i < other_fused_size[0]; ++i) {
size_type index = i * other_fused_weight[0];
for(size_type j = 0ul; j < other_fused_size[2]; ++j, index += other_fused_weight[2]) {
// Compute the ordinal index of the input and output matrices.
size_type perm_index = perm_index_op(index);
// Copy a transposed matrix from the input tensor to the this tensor.
auto copy_op = [] (param_value_type<Tensor<U, AU> > a) ->
param_value_type<Tensor<U, AU> > { return a; };
math::uninitialized_transpose(copy_op,
other_fused_size[1], other_fused_size[3],
result_outer_stride, pimpl_->data_ + perm_index,
other_fused_weight[1], other.data() + index);
}
}
}
}
/// Construct an evaluated tensor
template <typename U, typename AU, typename Op>
Tensor(const Tensor<U, AU>& other, const Op& op) :
pimpl_(new Impl(other.range()))
{
math::vector_op(op, other.size(), pimpl_->data_, other.data());
}
/// Construct an evaluated tensor
template <typename U, typename AU, typename Op>
Tensor(const Tensor<U, AU>& other, const Op& op, const Permutation& perm) :
pimpl_(new Impl(perm ^ other.range()))
{
// Check inputs.
TA_ASSERT(! other.empty());
TA_ASSERT(perm);
TA_ASSERT(perm.dim() == other.range().dim());
detail::PermIndex perm_index_op(other.range(), perm);
// Cache constants
const unsigned int ndim = other.range().dim();
const unsigned int ndim1 = ndim - 1u;
const size_type volume = other.range().volume();
if(perm[ndim1] == ndim1) {
// This is the simple case where the last dimension is not permuted.
// Therefore, it can be shuffled in chunks.
// Determine which dimensions can be permuted with the least significant
// dimension.
size_type block_size = other.range().size()[ndim1];
for(int i = -1 + ndim1 ; i >= 0; --i) {
if(int(perm[i]) != i)
break;
block_size *= other.range().size()[i];
}
// Permute the data
for(size_type index = 0ul; index < volume; index += block_size) {
const size_type perm_index = perm_index_op(index);
// Copy the block
math::uninitialized_unary_vector_op(block_size, other.data() + index,
pimpl_->data_ + perm_index, op);
}
} else {
// This is the more complicated case. Here we permute in terms of matrix
// transposes. The data layout of the input and output matrices are
// chosen such that they both contain stride one dimensions.
size_type other_fused_size[4];
size_type other_fused_weight[4];
fuse_dimensions(other_fused_size, other_fused_weight,
other.range().size().data(), perm);
// Compute the fused stride for the result matrix transpose.
size_type result_outer_stride = 1ul;
for(unsigned int i = perm[ndim1] + 1u; i < ndim; ++i)
result_outer_stride *= pimpl_->range_.size()[i];
// Copy data from the input to the output matrix via a series of matrix
// transposes.
for(size_type i = 0ul; i < other_fused_size[0]; ++i) {
size_type index = i * other_fused_weight[0];
for(size_type j = 0ul; j < other_fused_size[2]; ++j, index += other_fused_weight[2]) {
// Compute the ordinal index of the input and output matrices.
size_type perm_index = perm_index_op(index);
// Copy a transposed matrix from the input tensor to the this tensor.
math::uninitialized_transpose(op,
other_fused_size[1], other_fused_size[3],
result_outer_stride, pimpl_->data_ + perm_index,
other_fused_weight[1], other.data() + index);
}
}
}
}
/// Construct an evaluated tensor
template <typename U, typename AU, typename V, typename AV, typename Op>
Tensor(const Tensor<U, AU>& left, const Tensor<V, AV>& right, const Op& op) :
pimpl_(new Impl(left.range()))
{
TA_ASSERT(left.range() == right.range());
math::vector_op(op, left.size(), pimpl_->data_, left.data(), right.data());
}
/// Construct an evaluated tensor
template <typename U, typename AU, typename V, typename AV, typename Op>
Tensor(const Tensor<U, AU>& left, const Tensor<V, AV>& right, const Op& op, const Permutation& perm) :
pimpl_(new Impl(perm ^ left.range()))
{
// Check inputs.
TA_ASSERT(! left.empty());
TA_ASSERT(! right.empty());
TA_ASSERT(left.range() == right.range());
TA_ASSERT(perm);
TA_ASSERT(perm.dim() == left.range().dim());
detail::PermIndex perm_index_op(left.range(), perm);
// Cache constants
const unsigned int ndim = left.range().dim();
const unsigned int ndim1 = ndim - 1u;
const size_type volume = left.range().volume();
if(perm[ndim1] == ndim1) {
// This is the simple case where the last dimension is not permuted.
// Therefore, it can be shuffled in chunks.
// Determine which dimensions can be permuted with the least significant
// dimension.
size_type block_size = left.range().size()[ndim1];
for(int i = -1 + ndim1 ; i >= 0; --i) {
if(int(perm[i]) != i)
break;
block_size *= left.range().size()[i];
}
// Permute the data
for(size_type index = 0ul; index < volume; index += block_size) {
const size_type perm_index = perm_index_op(index);
// Copy the block
math::uninitialized_binary_vector_op(block_size, left.data() + index,
right.data() + index, pimpl_->data_ + perm_index, op);
}
} else {
// This is the more complicated case. Here we permute in terms of matrix
// transposes. The data layout of the input and output matrices are
// chosen such that they both contain stride one dimensions.
size_type other_fused_size[4];
size_type other_fused_weight[4];
fuse_dimensions(other_fused_size, other_fused_weight,
left.range().size().data(), perm);
// Compute the fused stride for the result matrix transpose.
size_type result_outer_stride = 1ul;
for(unsigned int i = perm[ndim1] + 1u; i < ndim; ++i)
result_outer_stride *= pimpl_->range_.size()[i];
// Copy data from the input to the output matrix via a series of matrix
// transposes.
for(size_type i = 0ul; i < other_fused_size[0]; ++i) {
size_type index = i * other_fused_weight[0];
for(size_type j = 0ul; j < other_fused_size[2]; ++j, index += other_fused_weight[2]) {
// Compute the ordinal index of the input and output matrices.
size_type perm_index = perm_index_op(index);
// Copy a transposed matrix from the input tensor to the this tensor.
math::uninitialized_transpose(op,
other_fused_size[1], other_fused_size[3],
result_outer_stride, pimpl_->data_ + perm_index,
other_fused_weight[1], left.data() + index, right.data() + index);
}
}
}
}
/// Copy constructor
/// Do a deep copy of \c other
/// \param other The tile to be copied.
Tensor(const Tensor_& other) :
pimpl_(other.pimpl_)
{ }
/// Copy assignment
/// Evaluate \c other to this tensor
/// \param other The tensor to be copied
/// \return this tensor
Tensor_& operator=(const Tensor_& other) {
pimpl_ = other.pimpl_;
return *this;
}
Tensor_ clone() const {
return (pimpl_ ? Tensor_(pimpl_->range_, pimpl_->data_) : Tensor_());
}
/// Plus assignment
/// Evaluate \c other to this tensor
/// \param other The tensor to be copied
/// \return this tensor
template <typename U, typename AU>
Tensor_& operator+=(const Tensor<U, AU>& other) { return add_to(other); }
/// Minus assignment
/// Evaluate \c other to this tensor
/// \param other The tensor to be copied
/// \return this tensor
template <typename U, typename AU>
Tensor_& operator-=(const Tensor<U, AU>& other) { return subt_to(other); }
/// Multiply assignment
/// Evaluate \c other to this tensor
/// \param other The tensor to be copied
/// \return this tensor
template <typename U, typename AU>
Tensor_& operator*=(const Tensor<U, AU>& other) { return mult_to(other); }
/// Plus shift operator
/// \param value The shift value
/// \return this tensor
Tensor_& operator+=(const numeric_type value) { return add_to(value); }
/// Minus shift operator
/// \param value The negative shift value
/// \return this tensor
Tensor_& operator-=(const numeric_type value) { return subt_to(value); }
/// Scale operator
/// \param value The scaling factor
/// \return this tensor
Tensor_& operator*=(const numeric_type value) { return scale_to(value); }
/// Tensor range object accessor
/// \return The tensor range object
const range_type& range() const {
return (pimpl_ ? pimpl_->range_ : empty_range_);
}
/// Tensor dimension size accessor
/// \return The number of elements in the tensor
size_type size() const {
return (pimpl_ ? pimpl_->range_.volume() : 0ul);
}
/// Element accessor
/// \return The element at the \c i position.
const_reference operator[](const size_type i) const {
TA_ASSERT(pimpl_);
TA_ASSERT(pimpl_->range_.includes(i));
return pimpl_->data_[i];
}
/// Element accessor
/// \return The element at the \c i position.
/// \throw TiledArray::Exception When this tensor is empty.
reference operator[](const size_type i) {
TA_ASSERT(pimpl_);
TA_ASSERT(pimpl_->range_.includes(i));
return pimpl_->data_[i];
}
/// Element accessor
/// \return The element at the \c i position.
/// \throw TiledArray::Exception When this tensor is empty.
template <typename Index>
typename std::enable_if<! std::is_integral<Index>::value, const_reference>::type
operator[](const Index& i) const {
TA_ASSERT(pimpl_);
TA_ASSERT(pimpl_->range_.includes(i));
return pimpl_->data_[pimpl_->range_.ord(i)];
}
/// Element accessor
/// \return The element at the \c i position.
/// \throw TiledArray::Exception When this tensor is empty.
template <typename Index>
typename std::enable_if<! std::is_integral<Index>::value, reference>::type
operator[](const Index& i) {
TA_ASSERT(pimpl_);
TA_ASSERT(pimpl_->range_.includes(i));
return pimpl_->data_[pimpl_->range_.ord(i)];
}
/// Element accessor
/// \tparam Index index type pack
/// \param idx The index pack
template<typename... Index>
reference operator()(const Index&... idx) {
TA_ASSERT(pimpl_);
TA_ASSERT(pimpl_->range_.includes(idx...));
return pimpl_->data_[pimpl_->range_.ord(idx...)];
}
/// Element accessor
/// \tparam Index index type pack
/// \param idx The index pack
template<typename... Index>
const_reference operator()(const Index&... idx) const {
TA_ASSERT(pimpl_);
TA_ASSERT(pimpl_->range_.includes(idx...));
return pimpl_->data_[pimpl_->range_.ord(idx...)];
}
/// Iterator factory
/// \return An iterator to the first data element
const_iterator begin() const { return (pimpl_ ? pimpl_->data_ : NULL); }
/// Iterator factory
/// \return An iterator to the first data element
iterator begin() { return (pimpl_ ? pimpl_->data_ : NULL); }
/// Iterator factory
/// \return An iterator to the last data element
const_iterator end() const { return (pimpl_ ? pimpl_->data_ + pimpl_->range_.volume() : NULL); }
/// Iterator factory
/// \return An iterator to the last data element
iterator end() { return (pimpl_ ? pimpl_->data_ + pimpl_->range_.volume() : NULL); }
/// Data direct access
/// \return A const pointer to the tensor data
const_pointer data() const { return (pimpl_ ? pimpl_->data_ : NULL); }
/// Data direct access
/// \return A const pointer to the tensor data
pointer data() { return (pimpl_ ? pimpl_->data_ : NULL); }
bool empty() const { return !pimpl_; }
template <typename Archive>
typename std::enable_if<madness::archive::is_output_archive<Archive>::value>::type
serialize(Archive& ar) {
if(pimpl_) {
ar & pimpl_->range_.volume();
ar & madness::archive::wrap(pimpl_->data_, pimpl_->range_.volume());
ar & pimpl_->range_;
} else {
ar & size_type(0ul);
}
}
/// Serialize tensor data
/// \tparam Archive The serialization archive type
/// \param ar The serialization archive
template <typename Archive>
typename std::enable_if<madness::archive::is_input_archive<Archive>::value>::type
serialize(Archive& ar) {
size_type n = 0ul;
ar & n;
if(n) {
std::shared_ptr<Impl> temp(new Impl());
temp->data_ = temp->allocate(n);
try {
ar & madness::archive::wrap(temp->data_, n);
ar & temp->range_;
} catch(...) {
temp->deallocate(temp->data_, n);
throw;
}
pimpl_ = temp;
} else {
pimpl_.reset();
}
}
/// Swap tensor data
/// \param other The tensor to swap with this
void swap(Tensor_& other) {
std::swap(pimpl_, other.pimpl_);
}
/// Create a permuted copy of this tensor
/// \param perm The permutation to be applied to this tensor
/// \return A permuted copy of this tensor
/// \throw TiledArray::Exception When this tensor is empty.
/// \throw TiledArray::Exception The dimension of \c perm does not match
/// that of this tensor.
Tensor_ permute(const Permutation& perm) const {
TA_ASSERT(pimpl_);
TA_ASSERT(perm.dim() == pimpl_->range_.dim());
return Tensor_(*this, perm);
}
// Generic vector operations
/// Use a binary, element wise operation to construct a new tensor
/// \tparam U \c other tensor element type
/// \tparam AU \c other allocator type
/// \tparam Op The binary operation type
/// \param other The right-hand argument in the binary operation
/// \param op The binary, element-wise operation
/// \return A tensor where element \c i of the new tensor is equal to
/// \c op(*this[i],other[i])
/// \throw TiledArray::Exception When this tensor is empty.
/// \throw TiledArray::Exception When \c other is empty.
/// \throw TiledArray::Exception When the range of this tensor is not equal
/// to the range of \c other.
template <typename U, typename AU, typename Op>
Tensor_ binary(const Tensor<U, AU>& other, const Op& op) const {
TA_ASSERT(pimpl_);
TA_ASSERT(! other.empty());
TA_ASSERT(pimpl_->range_ == other.range());
return Tensor_(*this, other, op);
}
/// Use a binary, element wise operation to construct a new, permuted tensor
/// \tparam U \c other tensor element type
/// \tparam AU \c other allocator type
/// \tparam Op The binary operation type
/// \param other The right-hand argument in the binary operation
/// \param op The binary, element-wise operation
/// \param perm The permutation to be applied to this tensor
/// \return A tensor where element \c i of the new tensor is equal to
/// \c op(*this[i],other[i])
/// \throw TiledArray::Exception When this tensor is empty.
/// \throw TiledArray::Exception When \c other is empty.
/// \throw TiledArray::Exception When the range of this tensor is not equal
/// to the range of \c other.
/// \throw TiledArray::Exception The dimension of \c perm does not match
/// that of this tensor.
template <typename U, typename AU, typename Op>
Tensor_ binary(const Tensor<U, AU>& other, const Op& op, const Permutation& perm) const {
TA_ASSERT(pimpl_);
TA_ASSERT(! other.empty());
TA_ASSERT(pimpl_->range_ == other.range());
TA_ASSERT(perm.dim() == pimpl_->range_.dim());
return Tensor_(*this, other, op, perm);
}
/// Use a binary, element wise operation to modify this tensor
/// \tparam U \c other tensor element type
/// \tparam AU \c other allocator type
/// \tparam Op The binary operation type
/// \param other The right-hand argument in the binary operation
/// \param op The binary, element-wise operation
/// \return A reference to this object
/// \throw TiledArray::Exception When this tensor is empty.
/// \throw TiledArray::Exception When \c other is empty.
/// \throw TiledArray::Exception When the range of this tensor is not equal
/// to the range of \c other.
/// \throw TiledArray::Exception When this and \c other are the same.
template <typename U, typename AU, typename Op>
Tensor_& inplace_binary(const Tensor<U, AU>& other, const Op& op) {
TA_ASSERT(pimpl_);
TA_ASSERT(! other.empty());
TA_ASSERT(pimpl_->range_ == other.range());
TA_ASSERT(pimpl_->data_ != other.data());
math::inplace_vector_op(op, pimpl_->range_.volume(), pimpl_->data_, other.data());
return *this;
}
/// Use a unary, element wise operation to construct a new tensor
/// \tparam Op The unary operation type
/// \param op The unary, element-wise operation
/// \return A tensor where element \c i of the new tensor is equal to
/// \c op(*this[i])
/// \throw TiledArray::Exception When this tensor is empty.
template <typename Op>
Tensor_ unary(const Op& op) const {
TA_ASSERT(pimpl_);
return Tensor_(*this, op);
}
/// Use a unary, element wise operation to construct a new, permuted tensor
/// \tparam Op The unary operation type
/// \param op The unary operation
/// \param perm The permutation to be applied to this tensor
/// \return A permuted tensor with elements that have been modified by \c op
/// \throw TiledArray::Exception When this tensor is empty.
/// \throw TiledArray::Exception The dimension of \c perm does not match
/// that of this tensor.
template <typename Op>
Tensor_ unary(const Op& op, const Permutation& perm) const {
TA_ASSERT(pimpl_);
TA_ASSERT(perm.dim() == pimpl_->range_.dim());
return Tensor_(*this, op, perm);
}
/// Use a unary, element wise operation to modify this tensor
/// \tparam Op The unary operation type
/// \param op The unary, element-wise operation
/// \return A reference to this object
/// \throw TiledArray::Exception When this tensor is empty.
template <typename Op>
Tensor_& inplace_unary(const Op& op) {
TA_ASSERT(pimpl_);
math::inplace_vector_op(op, pimpl_->range_.volume(), pimpl_->data_);
return *this;
}
// Scale operation
/// Construct a scaled copy of this tensor
/// \param factor The scaling factor
/// \return A new tensor where the elements are the sum of the elements of
/// \c this are scaled by \c factor
Tensor_ scale(numeric_type factor) const {
return unary([=] (param_type<value_type> arg) { return arg * factor; });
}
/// Construct a scaled and permuted copy of this tensor
/// \param factor The scaling factor
/// \param perm The permutation to be applied to this tensor
/// \return A new tensor where the elements are the sum of the elements of
/// \c this are scaled by \c factor
Tensor_ scale(numeric_type factor, const Permutation& perm) const {
return unary([=] (param_type<value_type> arg) { return arg * factor; }, perm);
}
/// Scale this tensor
/// \param factor The scaling factor
/// \return A reference to this tensor
Tensor_& scale_to(numeric_type factor) {
return inplace_unary([=] (value_type& res) { res *= factor; });
}
// Addition operations
/// Add this and \c other to construct a new tensors
/// \tparam U The other tensor element type
/// \tparam AU The other tensor allocator type
/// \param other The tensor that will be added to this tensor
/// \return A new tensor where the elements are the sum of the elements of
/// \c this and \c other
template <typename U, typename AU>
Tensor_ add(const Tensor<U, AU>& other) const {
return binary(other, [] (param_type<value_type> left,
param_value_type<Tensor<U, AU> > right) { return left + right; });
}
/// Add this and \c other to construct a new, permuted tensor
/// \tparam U The other tensor element type
/// \tparam AU The other tensor allocator type
/// \param other The tensor that will be added to this tensor
/// \param perm The permutation to be applied to this tensor
/// \return A new tensor where the elements are the sum of the elements of
/// \c this and \c other
template <typename U, typename AU>
Tensor_ add(const Tensor<U, AU>& other, const Permutation& perm) const {
return binary(other, [] (param_type<value_type> left,
param_value_type<Tensor<U, AU> > right) { return left + right; },
perm);
}
/// Scale and add this and \c other to construct a new tensor
/// \tparam U The other tensor element type
/// \tparam AU The other tensor allocator type
/// \param other The tensor that will be added to this tensor
/// \param factor The scaling factor
/// \return A new tensor where the elements are the sum of the elements of
/// \c this and \c other, scaled by \c factor
template <typename U, typename AU>
Tensor_ add(const Tensor<U, AU>& other, const numeric_type factor) const {
return binary(other, [=] (param_type<value_type> left,
param_value_type<Tensor<U, AU> > right)
{ return (left + right) * factor; });
}
/// Scale and add this and \c other to construct a new, permuted tensor
/// \tparam U The other tensor element type
/// \tparam AU The other tensor allocator type
/// \param other The tensor that will be added to this tensor
/// \param factor The scaling factor
/// \param perm The permutation to be applied to this tensor
/// \return A new tensor where the elements are the sum of the elements of
/// \c this and \c other, scaled by \c factor
template <typename U, typename AU>
Tensor_ add(const Tensor<U, AU>& other, const numeric_type factor,
const Permutation& perm) const
{
return binary(other, [=] (param_type<value_type> left,
param_value_type<Tensor<U, AU> > right)
{ return (left + right) * factor; }, perm);
}
/// Add a constant to a copy of this tensor
/// \param value The constant to be added to this tensor
/// \return A new tensor where the elements are the sum of the elements of
/// \c this and \c value
Tensor_ add(const numeric_type value) const {
return unary([=] (param_type<value_type> arg)
{ return arg + value; });
}
/// Add a constant to a permuted copy of this tensor
/// \param value The constant to be added to this tensor
/// \param perm The permutation to be applied to this tensor
/// \return A new tensor where the elements are the sum of the elements of
/// \c this and \c value
Tensor_ add(const numeric_type value, const Permutation& perm) const {
return unary([=] (param_type<value_type> arg) { return arg + value; }, perm);
}
/// Add \c other to this tensor
/// \tparam U The other tensor element type
/// \tparam AU The other tensor allocator type
/// \param other The tensor that will be added to this tensor
/// \return A reference to this tensor
template <typename U, typename AU>
Tensor_& add_to(const Tensor<U, AU>& other) {
return inplace_binary(other, [] (value_type& res,
param_value_type<Tensor<U, AU> > arg) { res += arg; });
}
/// Add \c other to this tensor, and scale the result
/// \tparam U The other tensor element type
/// \tparam AU The other tensor allocator type
/// \param other The tensor that will be added to this tensor
/// \param factor The scaling factor
/// \return A reference to this tensor
template <typename U, typename AU>
Tensor_& add_to(const Tensor<U, AU>& other, const numeric_type factor) {
return inplace_binary(other, [=] (value_type& res,
param_value_type<Tensor<U, AU> > arg) { (res += arg) *= factor; });
}
/// Add a constant to this tensor
/// \param value The constant to be added
/// \return A reference to this tensor
Tensor_& add_to(const numeric_type value) {
return inplace_unary([=] (value_type& res) { res += value; });
}
// Subtraction operations
/// Subtract this and \c other to construct a new tensor
/// \tparam U The other tensor element type
/// \tparam AU The other tensor allocator type
/// \param other The tensor that will be subtracted from this tensor
/// \return A new tensor where the elements are the different between the
/// elements of \c this and \c other
template <typename U, typename AU>
Tensor_ subt(const Tensor<U, AU>& other) const {
return binary(other, [] (param_type<value_type> left,
param_value_type<Tensor<U, AU> > right) { return left - right; });
}
/// Subtract this and \c other to construct a new, permuted tensor
/// \tparam U The other tensor element type
/// \tparam AU The other tensor allocator type
/// \param other The tensor that will be subtracted from this tensor
/// \param perm The permutation to be applied to this tensor
/// \return A new tensor where the elements are the different between the
/// elements of \c this and \c other
template <typename U, typename AU>
Tensor_ subt(const Tensor<U, AU>& other, const Permutation& perm) const {
return binary(other, [] (param_type<value_type> left,
param_value_type<Tensor<U, AU> > right)
{ return left - right; }, perm);
}
/// Scale and subtract this and \c other to construct a new tensor
/// \tparam U The other tensor element type
/// \tparam AU The other tensor allocator type
/// \param other The tensor that will be subtracted from this tensor
/// \param factor The scaling factor
/// \return A new tensor where the elements are the different between the
/// elements of \c this and \c other, scaled by \c factor
template <typename U, typename AU>
Tensor_ subt(const Tensor<U, AU>& other, const numeric_type factor) const {
return binary(other, [=] (param_type<value_type> left,
param_value_type<Tensor<U, AU> > right)
{ return (left - right) * factor; });
}
/// Scale and subtract this and \c other to construct a new, permuted tensor
/// \tparam U The other tensor element type
/// \tparam AU The other tensor allocator type
/// \param other The tensor that will be subtracted from this tensor
/// \param factor The scaling factor
/// \param perm The permutation to be applied to this tensor
/// \return A new tensor where the elements are the different between the
/// elements of \c this and \c other, scaled by \c factor
template <typename U, typename AU>
Tensor_ subt(const Tensor<U, AU>& other, const numeric_type factor, const Permutation& perm) const {
return binary(other, [=] (param_type<value_type> left,
param_value_type<Tensor<U, AU> > right)
{ return (left - right) * factor; }, perm);
}
/// Subtract a constant from a copy of this tensor
/// \return A new tensor where the elements are the different between the
/// elements of \c this and \c value
Tensor_ subt(const numeric_type value) const {
return add(-value);
}
/// Subtract a constant from a permuted copy of this tensor
/// \param value The constant to be subtracted
/// \param perm The permutation to be applied to this tensor
/// \return A new tensor where the elements are the different between the
/// elements of \c this and \c value
Tensor_ subt(const numeric_type value, const Permutation& perm) const {
return add(-value, perm);
}
/// Subtract \c other from this tensor
/// \tparam U The other tensor element type
/// \tparam AU The other tensor allocator type
/// \param other The tensor that will be subtracted from this tensor
/// \return A reference to this tensor
template <typename U, typename AU>
Tensor_& subt_to(const Tensor<U, AU>& other) {
return inplace_binary(other, [] (value_type& res,
param_value_type<Tensor<U, AU> > arg) { res -= arg; });
}
/// Subtract \c other from and scale this tensor
/// \tparam U The other tensor element type
/// \tparam AU The other tensor allocator type
/// \param other The tensor that will be subtracted from this tensor
/// \param factor The scaling factor
/// \return A reference to this tensor
template <typename U, typename AU>
Tensor_& subt_to(const Tensor<U, AU>& other, const numeric_type factor) {
return inplace_binary(other, [=] (value_type& res,
param_value_type<Tensor<U, AU> > arg) { (res -= arg) *= factor; });
}
/// Subtract a constant from this tensor
/// \return A reference to this tensor
Tensor_& subt_to(const numeric_type value) {
return add_to(-value);
}
// Multiplication operations
/// Multiply this by \c other to create a new tensor
/// \tparam U The other tensor element type
/// \tparam AU The other tensor allocator type
/// \param other The tensor that will be multiplied by this tensor
/// \return A new tensor where the elements are the product of the elements
/// of \c this and \c other
template <typename U, typename AU>
Tensor_ mult(const Tensor<U, AU>& other) const {
return binary(other, [] (param_type<value_type> left,
param_value_type<Tensor<U, AU> > right)
{ return left * right; });
}
/// Multiply this by \c other to create a new, permuted tensor
/// \tparam U The other tensor element type
/// \tparam AU The other tensor allocator type
/// \param other The tensor that will be multiplied by this tensor
/// \param perm The permutation to be applied to this tensor
/// \return A new tensor where the elements are the product of the elements
/// of \c this and \c other
template <typename U, typename AU>
Tensor_ mult(const Tensor<U, AU>& other, const Permutation& perm) const {
return binary(other, [] (param_type<value_type> left,
param_value_type<Tensor<U, AU> > right)
{ return left * right; }, perm);
}
/// Scale and multiply this by \c other to create a new tensor
/// \tparam U The other tensor element type
/// \tparam AU The other tensor allocator type
/// \param other The tensor that will be multiplied by this tensor
/// \param factor The scaling factor
/// \return A new tensor where the elements are the product of the elements
/// of \c this and \c other, scaled by \c factor
template <typename U, typename AU>
Tensor_ mult(const Tensor<U, AU>& other, const numeric_type factor) const {
return binary(other, [=] (param_type<value_type>left,
param_value_type<Tensor<U, AU> > right)
{ return (left * right) * factor; });
}
/// Scale and multiply this by \c other to create a new, permuted tensor
/// \tparam U The other tensor element type
/// \tparam AU The other tensor allocator type
/// \param other The tensor that will be multiplied by this tensor
/// \param factor The scaling factor
/// \param perm The permutation to be applied to this tensor
/// \return A new tensor where the elements are the product of the elements
/// of \c this and \c other, scaled by \c factor
template <typename U, typename AU>
Tensor_ mult(const Tensor<U, AU>& other, const numeric_type factor,
const Permutation& perm) const
{
return binary(other, [=] (param_type<value_type> left,
param_value_type<Tensor<U, AU> > right)
{ return (left * right) * factor; }, perm);
}
/// Multiply this tensor by \c other
/// \tparam U The other tensor element type
/// \tparam AU The other tensor allocator type
/// \param other The tensor that will be multiplied by this tensor
/// \return A reference to this tensor
template <typename U, typename AU>
Tensor_& mult_to(const Tensor<U, AU>& other) {
return inplace_binary(other, [] (value_type& res,
param_value_type<Tensor<U, AU> > arg) { res *= arg; });
}
/// Scale and multiply this tensor by \c other
/// \tparam U The other tensor element type
/// \tparam AU The other tensor allocator type
/// \param other The tensor that will be multiplied by this tensor
/// \param factor The scaling factor
/// \return A reference to this tensor
template <typename U, typename AU>
Tensor_& mult_to(const Tensor<U, AU>& other, const numeric_type factor) {
return inplace_binary(other, [=] (value_type& res,
param_value_type<Tensor<U, AU> > arg) { (res *= arg) *= factor; });
}
// Negation operations
/// Create a negated copy of this tensor
/// \return A new tensor that contains the negative values of this tensor
Tensor_ neg() const {
return unary([] (param_type<value_type> arg) { return -arg; });
}
/// Create a negated and permuted copy of this tensor
/// \param perm The permutation to be applied to this tensor
/// \return A new tensor that contains the negative values of this tensor
Tensor_ neg(const Permutation& perm) const {
return unary([] (param_type<value_type>arg) { return -arg; }, perm);
}
/// Negate elements of this tensor
/// \return A reference to this tensor
Tensor_& neg_to() {
return inplace_unary([] (value_type& res) { res = -res; });
}
// GEMM operations
/// Contract this tensor with \c other
/// \tparam U The other tensor element type
/// \tparam AU The other tensor allocator type
/// \param other The tensor that will be contracted with this tensor
/// \param factor The scaling factor
/// \param gemm_helper The *GEMM operation meta data
/// \return A new tensor which is the result of contracting this tensor with
/// \c other
/// \throw TiledArray::Exception When this tensor is empty.
/// \throw TiledArray::Exception When \c other is empty.
template <typename U, typename AU>
Tensor_ gemm(const Tensor<U, AU>& other, const numeric_type factor, const math::GemmHelper& gemm_helper) const {
// Check that this tensor is not empty and has the correct rank
TA_ASSERT(pimpl_);
TA_ASSERT(pimpl_->range_.dim() == gemm_helper.left_rank());
// Check that the arguments are not empty and have the correct ranks
TA_ASSERT(!other.empty());
TA_ASSERT(other.range().dim() == gemm_helper.right_rank());
// Construct the result Tensor
Tensor_ result(gemm_helper.make_result_range<range_type>(pimpl_->range_, other.range()));
// Check that the inner dimensions of left and right match
TA_ASSERT(gemm_helper.left_right_coformal(pimpl_->range_.start(), other.range().start()));
TA_ASSERT(gemm_helper.left_right_coformal(pimpl_->range_.finish(), other.range().finish()));
TA_ASSERT(gemm_helper.left_right_coformal(pimpl_->range_.size(), other.range().size()));
// Compute gemm dimensions
integer m = 1, n = 1, k = 1;
gemm_helper.compute_matrix_sizes(m, n, k, pimpl_->range_, other.range());
// Get the leading dimension for left and right matrices.
const integer lda = (gemm_helper.left_op() == madness::cblas::NoTrans ? k : m);
const integer ldb = (gemm_helper.right_op() == madness::cblas::NoTrans ? n : k);
math::gemm(gemm_helper.left_op(), gemm_helper.right_op(), m, n, k, factor,
pimpl_->data_, lda, other.data(), ldb, numeric_type(0), result.data(), n);
return result;
}
/// Contract two tensors and store the result in this tensor
/// \tparam U The left-hand tensor element type
/// \tparam AU The left-hand tensor allocator type
/// \tparam V The right-hand tensor element type
/// \tparam AV The right-hand tensor allocator type
/// \param left The left-hand tensor that will be contracted
/// \param right The right-hand tensor that will be contracted
/// \param factor The scaling factor
/// \param gemm_helper The *GEMM operation meta data
/// \return A new tensor which is the result of contracting this tensor with
/// other
/// \throw TiledArray::Exception When this tensor is empty.
template <typename U, typename AU, typename V, typename AV>
Tensor_& gemm(const Tensor<U, AU>& left, const Tensor<V, AV>& right,
const numeric_type factor, const math::GemmHelper& gemm_helper)
{
// Check that this tensor is not empty and has the correct rank
TA_ASSERT(pimpl_);
TA_ASSERT(pimpl_->range_.dim() == gemm_helper.result_rank());
// Check that the arguments are not empty and have the correct ranks
TA_ASSERT(!left.empty());
TA_ASSERT(left.range().dim() == gemm_helper.left_rank());
TA_ASSERT(!right.empty());
TA_ASSERT(right.range().dim() == gemm_helper.right_rank());
// Check that the outer dimensions of left match the the corresponding dimensions in result
TA_ASSERT(gemm_helper.left_result_coformal(left.range().start(), pimpl_->range_.start()));
TA_ASSERT(gemm_helper.left_result_coformal(left.range().finish(), pimpl_->range_.finish()));
TA_ASSERT(gemm_helper.left_result_coformal(left.range().size(), pimpl_->range_.size()));
// Check that the outer dimensions of right match the the corresponding dimensions in result
TA_ASSERT(gemm_helper.right_result_coformal(right.range().start(), pimpl_->range_.start()));
TA_ASSERT(gemm_helper.right_result_coformal(right.range().finish(), pimpl_->range_.finish()));
TA_ASSERT(gemm_helper.right_result_coformal(right.range().size(), pimpl_->range_.size()));
// Check that the inner dimensions of left and right match
TA_ASSERT(gemm_helper.left_right_coformal(left.range().start(), right.range().start()));
TA_ASSERT(gemm_helper.left_right_coformal(left.range().finish(), right.range().finish()));
TA_ASSERT(gemm_helper.left_right_coformal(left.range().size(), right.range().size()));
// Compute gemm dimensions
integer m, n, k;
gemm_helper.compute_matrix_sizes(m, n, k, left.range(), right.range());
// Get the leading dimension for left and right matrices.
const integer lda = (gemm_helper.left_op() == madness::cblas::NoTrans ? k : m);
const integer ldb = (gemm_helper.right_op() == madness::cblas::NoTrans ? n : k);
math::gemm(gemm_helper.left_op(), gemm_helper.right_op(), m, n, k, factor,
left.data(), lda, right.data(), ldb, numeric_type(1), pimpl_->data_, n);
return *this;
}
// Reduction operations
/// Generalized tensor trace
/// This function will compute the sum of the hyper diagonal elements of
/// tensor.
/// \return The trace of this tensor
/// \throw TiledArray::Exception When this tensor is empty.
value_type trace() const {
TA_ASSERT(pimpl_);
// Get pointers to the range data
const size_type n = pimpl_->range_.dim();
const size_type* restrict const start = pimpl_->range_.start().data();
const size_type* restrict const finish = pimpl_->range_.finish().data();
const size_type* restrict const weight = pimpl_->range_.weight().data();
// Search for the largest start index and the smallest finish
size_type start_max = 0ul, finish_min = std::numeric_limits<size_type>::max();
for(size_type i = 0ul; i < n; ++i) {
const size_type start_i = start[i];
const size_type finish_i = finish[i];
start_max = std::max(start_max, start_i);
finish_min = std::min(finish_min, finish_i);
}
value_type result = 0;
if(start_max < finish_min) {
// Compute the first and last ordinal index
size_type first = 0ul, last = 0ul, stride = 0ul;
for(size_type i = 0ul; i < n; ++i) {
const size_type start_i = start[i];
const size_type weight_i = weight[i];
first += (start_max - start_i) * weight_i;
last += (finish_min - start_i) * weight_i;
stride += weight_i;
}
// Compute the trace
const value_type* restrict const data = pimpl_->data_;
for(; first < last; first += stride)
result += data[first];
}
return result;
}
private:
/// Unary reduction operation
/// Perform an element-wise reduction of the tile data.
/// \tparam U The numeric element type
/// \tparam Op The reduction operation
/// \param n The number of elements to reduce
/// \param u The data to be reduced
/// \param value The initial value of the reduction
/// \param op The element-wise reduction operation
template <typename U, typename Op>
static typename std::enable_if<TiledArray::detail::is_numeric<U>::value>::type
reduce(const size_type n, const U* u, numeric_type& value, const Op& op) {
math::reduce_op(op, n, value, u);
}
/// Unary \c Tensor reduction
/// Perform an element-wise reduction on an array of \c Tensors.
/// \tparam U The tensor element type
/// \tparam AU The tensor allocator type
/// \tparam Op The reduction operation
/// \param n The number of elements to reduce
/// \param u The data to be reduced
/// \param value The initial value of the reduction
/// \param op The element-wise reduction operation
template <typename U, typename AU, typename Op>
static void reduce(const size_type n, const Tensor<U, AU>* u,
numeric_type& value, const Op& op)
{
for(size_type i = 0ul; i < n; ++i)
u[i].reduce(value, op);
}
/// Binary reduction operation
/// Perform an element-wise reduction of the tile data.
/// \tparam Left The left-hand element type
/// \tparam Right The right-hand element type
/// \tparam Op The reduction operation
/// \param n The number of elements to reduce
/// \param left The left-hand data to be reduced
/// \param right The right-hand data to be reduced
/// \param value The initial value of the reduction
/// \param op The element-wise reduction operation
template <typename Left, typename Right, typename Op>
static typename std::enable_if<TiledArray::detail::is_numeric<Left>::value &&
TiledArray::detail::is_numeric<Right>::value >::type
reduce(const size_type n, const Left* left, const Right* right,
numeric_type& value, const Op& op) {
math::reduce_op(op, n, value, left, right);
}
/// Binary \c Tensor reduction
/// Perform an element-wise reduction on arrays of \c Tensors.
/// \tparam U The left-hand tensor element type
/// \tparam AU The left-hand tensor allocator type
/// \tparam V The right-hand tensor element type
/// \tparam AV The right-hand tensor allocator type
/// \tparam Op The reduction operation
/// \param n The number of elements to reduce
/// \param left The left-hand \c Tensors to be reduced
/// \param right The right-hand \c Tensors to be reduced
/// \param value The initial value of the reduction
/// \param op The element-wise reduction operation
template <typename U, typename AU, typename V, typename AV, typename Op>
static void reduce(const size_type n, const Tensor<U, AU>* left,
const Tensor<V, AV>* right, numeric_type& value, const Op& op)
{
for(size_type i = 0ul; i < n; ++i)
left[i].reduce(right[i], value, op);
}
public:
/// Unary reduction operation
/// Perform an element-wise reduction of the tile data.
/// \tparam Op The reduction operation
/// \param init_value The initial value of the reduction
/// \param op The element-wise reduction operation
/// \throw TiledArray::Exception When this tensor is empty.
template <typename Op>
numeric_type reduce(numeric_type init_value, const Op& op) const {
TA_ASSERT(pimpl_);
reduce(pimpl_->range_.volume(), pimpl_->data_, init_value, op);
return init_value;
}
/// Binary reduction operation
/// \tparam U The element type of the right-hand argument
/// \tparam AU The allocator type of the right-hand argument
/// \tparam Op The reduction operation
/// \param other The right-hand argument of the binary reduction
/// \param init_value The initial value of the reduction
/// \param op The element-wise reduction operation
/// \throw TiledArray::Exception When this tensor is empty.
/// \throw TiledArray::Exception When the range of this tensor is not equal
/// to the range of \c other.
template <typename U, typename AU, typename Op>
numeric_type reduce(const Tensor<U, AU>& other, numeric_type init_value, const Op& op) const {
TA_ASSERT(pimpl_);
TA_ASSERT(pimpl_->range_ == other.range());
reduce(pimpl_->range_.volume(), pimpl_->data_, other.data(), init_value, op);
return init_value;
}
/// Sum of elements
/// \return The sum of all elements of this tensor
numeric_type sum() const {
return reduce(0, [] (numeric_type& res, const numeric_type arg)
{ res += arg; });
}
/// Product of elements
/// \return The product of all elements of this tensor
numeric_type product() const {
return reduce(1, [] (numeric_type& res, const numeric_type arg)
{ res *= arg; });
}
/// Square of vector 2-norm
/// \return The vector norm of this tensor
numeric_type squared_norm() const {
return reduce(0, [] (numeric_type& res, const numeric_type value)
{ res += value * value; });
}
/// Vector 2-norm
/// \return The vector norm of this tensor
numeric_type norm() const {
return std::sqrt(squared_norm());
}
/// Minimum element
/// \return The minimum elements of this tensor
numeric_type min() const {
return reduce(std::numeric_limits<numeric_type>::max(),
[] (numeric_type& res, const numeric_type arg)
{ res = std::min(res, arg); });
}
/// Maximum element
/// \return The maximum elements of this tensor
numeric_type max() const {
return reduce(std::numeric_limits<numeric_type>::min(),
[] (numeric_type& res, const numeric_type arg)
{ res = std::max(res, arg); });
}
/// Absolute minimum element
/// \return The minimum elements of this tensor
numeric_type abs_min() const {
return reduce(std::numeric_limits<numeric_type>::max(),
[] (numeric_type& res, const numeric_type arg)
{ res = std::min(res, std::abs(arg)); });
}
/// Absolute maximum element
/// \return The maximum elements of this tensor
numeric_type abs_max() const {
return reduce(0,
[] (numeric_type& res, const numeric_type arg)
{ res = std::max(res, std::abs(arg)); });
}
/// Vector dot product
/// \tparam U The other tensor element type
/// \tparam AU The other tensor allocator type
/// \param other The other tensor to be reduced
/// \return The inner product of the this and \c other
template <typename U, typename AU>
numeric_type dot(const Tensor<U, AU>& other) const {
return reduce(other, 0, [] (numeric_type& res, const numeric_type left,
const typename Tensor<U, AU>::numeric_type right)
{ res += left * right; });
}
}; // class Tensor
template <typename T, typename A>
const typename Tensor<T, A>::range_type Tensor<T, A>::empty_range_;
/// Tensor plus operator
/// Add two tensors
/// \tparam T The element type of \c left
/// \tparam AT The allocator type of \c left
/// \tparam U The element type of \c right
/// \tparam AU The allocator type of \c right
/// \param left The left-hand tensor argument
/// \param right The right-hand tensor argument
/// \return A tensor where element \c i is equal to <tt> left[i] + right[i] </tt>
template <typename T, typename AT, typename U, typename AU>
inline Tensor<T, AT> operator+(const Tensor<T, AT>& left, const Tensor<U, AU>& right) {
return left.add(right);
}
/// Tensor minus operator
/// Subtract two tensors
/// \tparam T The element type of \c left
/// \tparam AT The allocator type of \c left
/// \tparam U The element type of \c right
/// \tparam AU The allocator type of \c right
/// \param left The left-hand tensor argument
/// \param right The right-hand tensor argument
/// \return A tensor where element \c i is equal to <tt> left[i] - right[i] </tt>
template <typename T, typename AT, typename U, typename AU>
inline Tensor<T, AT> operator-(const Tensor<T, AT>& left, const Tensor<U, AU>& right) {
return left.subt(right);
}
/// Tensor multiplication operator
/// Element-wise multiplication of two tensors
/// \tparam T The element type of \c left
/// \tparam AT The allocator type of \c left
/// \tparam U The element type of \c right
/// \tparam AU The allocator type of \c right
/// \param left The left-hand tensor argument
/// \param right The right-hand tensor argument
/// \return A tensor where element \c i is equal to <tt> left[i] * right[i] </tt>
template <typename T, typename AT, typename U, typename AU>
inline Tensor<T, AT> operator*(const Tensor<T, AT>& left, const Tensor<U, AU>& right) {
return left.mult(right);
}
/// Tensor multiplication operator
/// Scale a tensor
/// \tparam T The element type of \c left
/// \tparam AT The allocator type of \c left
/// \tparam N Numeric type
/// \param left The left-hand tensor argument
/// \param right The right-hand scalar argument
/// \return A tensor where element \c i is equal to <tt> left[i] * right </tt>
template <typename T, typename AT, typename N>
inline typename std::enable_if<TiledArray::detail::is_numeric<N>::value, Tensor<T, AT> >::type
operator*(const Tensor<T, AT>& left, N right) {
return left.scale(right);
}
/// Tensor scale-by-constant operator
/// Scale a tensor
/// \tparam N Numeric type
/// \tparam T The element type of \c left
/// \tparam AT The allocator type of \c left
/// \param left The left-hand scalar argument
/// \param right The right-hand tensor argument
/// \return A tensor where element \c i is equal to <tt> left * right[i] </tt>
template <typename N, typename T, typename AT>
inline typename std::enable_if<TiledArray::detail::is_numeric<N>::value, Tensor<T, AT> >::type
operator*(N left, const Tensor<T, AT>& right) {
return right.scale(left);
}
/// Tensor subtraction operator
/// Negate a tensor
/// \tparam T The element type of \c arg
/// \tparam AT The allocator type of \c arg
/// \param arg The argument tensor
/// \return A tensor where element \c i is equal to \c -arg[i]
template <typename T, typename AT>
inline Tensor<T, AT> operator-(const Tensor<T, AT>& arg) {
return arg.neg();
}
/// Permute a tensor
/// Permute \c tensor by \c perm and place the permuted result in \c result .
/// \tparam T The tensor element type
/// \tparam A The tensor allocator type
/// \param perm The permutation to be applied to \c tensor
/// \param tensor The tensor to be permuted by \c perm
template <typename T, typename A>
inline Tensor<T,A> operator^(const Permutation& perm, const Tensor<T, A>& tensor) {
return tensor.permute(perm);
}
/// Tensor output operator
/// Ouput tensor \c t to the output stream, \c os .
/// \tparam T The element type of \c arg
/// \tparam AT The allocator type of \c arg
/// \param os The output stream
/// \param t The tensor to be output
/// \return A reference to the output stream
template <typename T, typename AT>
inline std::ostream& operator<<(std::ostream& os, const Tensor<T, AT>& t) {
os << t.range() << " { ";
for(typename Tensor<T, AT>::const_iterator it = t.begin(); it != t.end(); ++it) {
os << *it << " ";
}
os << "}";
return os;
}
} // namespace TiledArray
#endif // TILEDARRAY_TENSOR_H__INCLUDED
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