Homec4science
Diffusion akantu (features/adding-shape-recomputation-capability-python) Browse

rAKA/src/common/aka_types.hh
cee9304c42a5features/adding-shape…

Last Change
Raw File

aka_types.hh
View Options

/**
* @file aka_types.hh
*
* @author Nicolas Richart <nicolas.richart@epfl.ch>
*
* @date creation: Thu Feb 17 2011
* @date last modification: Tue Aug 19 2014
*
* @brief description of the "simple" types
*
* @section LICENSE
*
* Copyright (©) 2010-2012, 2014 EPFL (Ecole Polytechnique Fédérale de Lausanne)
* Laboratory (LSMS - Laboratoire de Simulation en Mécanique des Solides)
*
* Akantu is free software: you can redistribute it and/or modify it under the
* terms of the GNU Lesser General Public License as published by the Free
* Software Foundation, either version 3 of the License, or (at your option) any
* later version.
*
* Akantu 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 Lesser General Public License for more
* details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with Akantu. If not, see <http://www.gnu.org/licenses/>.
*
*/
/* -------------------------------------------------------------------------- */
#include "aka_error.hh"
#include "aka_fwd.hh"
#include "aka_math.hh"
#include "aka_array.hh"
/* -------------------------------------------------------------------------- */
#include <iomanip>
#ifndef __INTEL_COMPILER
#include <tr1/unordered_map>
#else
#include <map>
#endif
/* -------------------------------------------------------------------------- */
#ifndef __AKANTU_AKA_TYPES_HH__
#define __AKANTU_AKA_TYPES_HH__
__BEGIN_AKANTU__
/* -------------------------------------------------------------------------- */
/* maps */
/* -------------------------------------------------------------------------- */
#ifndef __INTEL_COMPILER
template<class Key, class Ty>
struct unordered_map { typedef typename std::tr1::unordered_map<Key, Ty> type; };
#else
template<class Key, class Ty>
struct unordered_map { typedef typename std::map<Key, Ty> type; };
#endif
enum NormType {
L_1 = 1,
L_2 = 2,
L_inf = UInt(-1)
};
template<UInt dim>
struct DimHelper {
static inline void setDims(UInt m, UInt n, UInt p, UInt dims[dim]);
};
template<>
struct DimHelper<1> {
static inline void setDims(UInt m,
__attribute__((unused)) UInt n,
__attribute__((unused)) UInt p,
UInt dims[1]) {
dims[0] = m;
}
};
template<>
struct DimHelper<2> {
static inline void setDims(UInt m,
UInt n,
__attribute__((unused)) UInt p,
UInt dims[2]) {
dims[0] = m;
dims[1] = n;
}
};
template<>
struct DimHelper<3> {
static inline void setDims(UInt m,
UInt n,
UInt p,
UInt dims[3]) {
dims[0] = m;
dims[1] = n;
dims[2] = p;
}
};
/* -------------------------------------------------------------------------- */
template<typename T, UInt ndim, class RetType>
class TensorStorage;
/* -------------------------------------------------------------------------- */
template<typename T, UInt ndim, class RetType>
class TensorProxy {
protected:
TensorProxy(T * data, UInt m, UInt n, UInt p) {
DimHelper<ndim>::setDims(m, n, p, this->n);
this->values = data;
}
TensorProxy(const TensorProxy & other) {
this->values = other.storage();
for (UInt i = 0; i < ndim; ++i)
this->n[i] = other.n[i];
}
inline TensorProxy(const TensorStorage<T, ndim, RetType> & other);
public:
UInt size(UInt i) const {
AKANTU_DEBUG_ASSERT(i < ndim,
"This tensor has only "
<< ndim << " dimensions, not " << (i + 1));
return n[i];
}
inline UInt size() const{
UInt _size = 1;
for (UInt d = 0; d < ndim; ++d) _size *= this->n[d];
return _size;
}
T * storage() const { return values; }
protected:
T * values;
UInt n[ndim];
};
/* -------------------------------------------------------------------------- */
template<typename T>
class VectorProxy : public TensorProxy<T, 1, Vector<T> > {
typedef TensorProxy<T, 1, Vector<T> > parent;
public:
VectorProxy(T * data = NULL, UInt n = 0) : parent(data, n, 0, 0) { }
VectorProxy(const VectorProxy & src) : parent(src) { }
VectorProxy(const Vector<T> & src) : parent(src) { }
};
template<typename T>
class MatrixProxy : public TensorProxy<T, 2, Matrix<T> > {
typedef TensorProxy<T, 2, Matrix<T> > parent;
public:
MatrixProxy(T * data = NULL, UInt m = 0, UInt n = 0) : parent(data, m, n, 0) { }
MatrixProxy(const MatrixProxy & src) : parent(src) { }
MatrixProxy(const Matrix<T> & src) : parent(src) { }
};
template<typename T>
class Tensor3Proxy : public TensorProxy<T, 3, Tensor3<T> > {
typedef TensorProxy<T, 3, Tensor3<T> > parent;
public:
Tensor3Proxy(T * data = NULL, UInt m = 0, UInt n = 0, UInt k = 0) :
parent(data, m, n, k) { }
Tensor3Proxy(const Tensor3Proxy & src) : parent(src) { }
Tensor3Proxy(const Tensor3<T> & src) : parent(src) { }
};
/* -------------------------------------------------------------------------- */
/* Tensor base class */
/* -------------------------------------------------------------------------- */
template<typename T, UInt ndim, class RetType>
class TensorStorage {
public:
typedef T value_type;
protected:
template<class TensorType>
void copySize(const TensorType & src) {
for (UInt d = 0; d < ndim; ++d) this->n[d] = src.size(d);
this->_size = src.size();
}
TensorStorage() :
values(NULL), wrapped(false) {
for (UInt d = 0; d < ndim; ++d) this->n[d] = 0;
_size = 0;
}
TensorStorage(const TensorProxy<T, ndim, RetType> & proxy) {
this->copySize(proxy);
this->values = proxy.storage();
this->wrapped = true;
}
protected:
TensorStorage(const TensorStorage & src) { }
public:
TensorStorage(const TensorStorage & src, bool deep_copy) :
values(NULL), wrapped(false) {
if(deep_copy) this->deepCopy(src);
else this->shallowCopy(src);
}
protected:
TensorStorage(UInt m, UInt n, UInt p, const T & def) {
DimHelper<ndim>::setDims(m, n, p, this->n);
this->computeSize();
this->values = new T[this->_size];
this->set(def);
this->wrapped = false;
}
TensorStorage(T * data, UInt m, UInt n, UInt p) {
DimHelper<ndim>::setDims(m, n, p, this->n);
this->computeSize();
this->values = data;
this->wrapped = true;
}
public:
/* ------------------------------------------------------------------------ */
template<class TensorType>
inline void shallowCopy(const TensorType & src) {
this->copySize(src);
if(!this->wrapped) delete[] this->values;
this->values = src.storage();
this->wrapped = true;
}
/* ------------------------------------------------------------------------ */
template<class TensorType>
inline void deepCopy(const TensorType & src) {
this->copySize(src);
if(!this->wrapped) delete [] this->values;
this->values = new T[this->_size];
memcpy(this->values, src.storage(), this->_size * sizeof(T));
this->wrapped = false;
}
virtual ~TensorStorage() {
if(!this->wrapped)
delete [] this->values;
}
inline TensorStorage & operator=(const RetType & src) {
if(this != &src) {
if (this->wrapped) {
AKANTU_DEBUG_ASSERT(this->_size == src.size(), "vectors of different size");
memcpy(this->values, src.storage(), this->_size * sizeof(T));
} else {
deepCopy(src);
}
}
return *this;
}
/* ------------------------------------------------------------------------ */
template<class R>
inline RetType & operator+=(const TensorStorage<T, ndim, R> & other) {
T * a = this->storage();
T * b = other.storage();
AKANTU_DEBUG_ASSERT(_size == other.size(),
"The two tensors do not have the same size, they cannot be subtracted");
for (UInt i = 0; i < _size; ++i) *(a++) += *(b++);
return *(static_cast<RetType *>(this));
}
/* ------------------------------------------------------------------------ */
template<class R>
inline RetType & operator-=(const TensorStorage<T, ndim, R> & other) {
T * a = this->storage();
T * b = other.storage();
AKANTU_DEBUG_ASSERT(_size == other.size(),
"The two tensors do not have the same size, they cannot be subtracted");
for (UInt i = 0; i < _size; ++i) *(a++) -= *(b++);
return *(static_cast<RetType *>(this));
}
/* ------------------------------------------------------------------------ */
inline RetType & operator+=(const T & x) {
T * a = this->values;
for (UInt i = 0; i < _size; ++i) *(a++) += x;
return *(static_cast<RetType *>(this));
}
/* ------------------------------------------------------------------------ */
inline RetType & operator-=(const T & x) {
T * a = this->values;
for (UInt i = 0; i < _size; ++i) *(a++) -= x;
return *(static_cast<RetType *>(this));
}
/* ------------------------------------------------------------------------ */
inline RetType & operator*=(const T & x) {
T * a = this->storage();
for (UInt i = 0; i < _size; ++i) *(a++) *= x;
return *(static_cast<RetType *>(this));
}
/* ---------------------------------------------------------------------- */
inline RetType & operator/=(const T & x) {
T * a = this->values;
for (UInt i = 0; i < _size; ++i) *(a++) /= x;
return *(static_cast<RetType *>(this));
}
/* ------------------------------------------------------------------------ */
T * storage() const { return values; }
UInt size() const { return _size; }
UInt size(UInt i) const {
AKANTU_DEBUG_ASSERT(i < ndim,
"This tensor has only "
<< ndim << " dimensions, not " << (i + 1));
return n[i];
};
/* ------------------------------------------------------------------------ */
inline void clear() { memset(values, 0, _size * sizeof(T)); };
inline void set(const T & t) { std::fill_n(values, _size, t); };
template<class TensorType>
inline void copy(const TensorType & other) {
AKANTU_DEBUG_ASSERT(_size == other.size(),
"The two tensors do not have the same size, they cannot be copied");
memcpy(values, other.storage(), _size * sizeof(T));
}
protected:
friend class Array<T>;
inline void computeSize() {
_size = 1;
for (UInt d = 0; d < ndim; ++d) _size *= this->n[d];
}
protected:
template<typename R, NormType norm_type>
struct NormHelper {
template<class Ten>
static R norm(const Ten & ten) {
R _norm = 0.;
R * it = ten.storage();
R * end = ten.storage() + ten.size();
for (; it < end; ++it) _norm += std::pow(std::abs(*it), norm_type);
return std::pow(_norm, 1./norm_type);
}
};
template<typename R>
struct NormHelper<R, L_1> {
template<class Ten>
static R norm(const Ten & ten) {
R _norm = 0.;
R * it = ten.storage();
R * end = ten.storage() + ten.size();
for (; it < end; ++it) _norm += std::abs(*it);
return _norm;
}
};
template<typename R>
struct NormHelper<R, L_2> {
template<class Ten>
static R norm(const Ten & ten) {
R _norm = 0.;
R * it = ten.storage();
R * end = ten.storage() + ten.size();
for (; it < end; ++it) _norm += *it * *it;
return sqrt(_norm);
}
};
template<typename R>
struct NormHelper<R, L_inf> {
template<class Ten>
static R norm(const Ten & ten) {
R _norm = 0.;
R * it = ten.storage();
R * end = ten.storage() + ten.size();
for (; it < end; ++it) _norm = std::max(std::abs(*it), _norm);
return _norm;
}
};
public:
/*----------------------------------------------------------------------- */
/// "Entrywise" norm norm<L_p> @f[ \|\boldsymbol{T}\|_p = \left(
/// \sum_i^{n[0]}\sum_j^{n[1]}\sum_k^{n[2]} |T_{ijk}|^p \right)^{\frac{1}{p}}
/// @f]
template<NormType norm_type>
inline T norm() const { return NormHelper<T, norm_type>::norm(*this); }
protected:
UInt n[ndim];
UInt _size;
T * values;
bool wrapped;
};
template<typename T, UInt ndim, class RetType>
inline TensorProxy<T, ndim, RetType>::TensorProxy(const TensorStorage<T, ndim, RetType> & other) {
this->values = other.storage();
for (UInt i = 0; i < ndim; ++i)
this->n[i] = other.size(i);
}
/* -------------------------------------------------------------------------- */
/* Vector */
/* -------------------------------------------------------------------------- */
template<typename T>
class Vector : public TensorStorage< T, 1, Vector<T> > {
typedef TensorStorage< T, 1, Vector<T> > parent;
public:
typedef typename parent::value_type value_type;
typedef VectorProxy<T> proxy;
public:
Vector() : parent() {}
Vector(UInt n, const T & def = T()) : parent(n, 0, 0, def) { }
Vector(T * data, UInt n) : parent(data, n, 0, 0) { }
Vector(const Vector & src, bool deep_copy = true) : parent(src, deep_copy) { }
Vector(const VectorProxy<T> & src) : parent(src) { }
public:
virtual ~Vector() { };
/* ------------------------------------------------------------------------ */
inline Vector & operator=(const Vector & src) {
parent::operator=(src);
return *this;
}
/* ------------------------------------------------------------------------ */
inline T& operator()(UInt i) { return *(this->values + i); };
inline const T& operator()(UInt i) const { return *(this->values + i); };
inline T& operator[](UInt i) { return *(this->values + i); };
inline const T& operator[](UInt i) const { return *(this->values + i); };
/* ------------------------------------------------------------------------ */
inline Vector<T> & operator*=(Real x) { return parent::operator*=(x); }
inline Vector<T> & operator/=(Real x) { return parent::operator/=(x); }
/* ------------------------------------------------------------------------ */
inline Vector<T> & operator*=(const Vector<T> & vect) {
T * a = this->storage();
T * b = vect.storage();
for (UInt i = 0; i < this->_size; ++i) *(a++) *= *(b++);
return *this;
}
/* ------------------------------------------------------------------------ */
inline Real dot(const Vector<T> & vect) const {
return Math::vectorDot(this->values, vect.storage(), this->_size);
}
/* ------------------------------------------------------------------------ */
inline Real mean() const {
Real mean = 0;
T * a = this->storage();
for (UInt i = 0; i < this->_size; ++i) mean += *(a++);
return mean / this->_size;
}
/* ------------------------------------------------------------------------ */
inline Vector & crossProduct(const Vector<T> & v1,
const Vector<T> & v2) {
AKANTU_DEBUG_ASSERT(this->size() == 3,
"crossProduct is only defined in 3D (n=" << this->size() << ")");
AKANTU_DEBUG_ASSERT(this->size() == v1.size() && this->size() == v2.size(),
"crossProduct is not a valid operation non matching size vectors");
Math::vectorProduct3(v1.storage(), v2.storage(), this->values);
return *this;
}
/* ------------------------------------------------------------------------ */
inline void solve(Matrix<T> & A, const Vector<T> & b) {
AKANTU_DEBUG_ASSERT(this->size() == A.rows() && this->_size = A.cols(),
"The solution vector as a mismatch in size with the matrix");
AKANTU_DEBUG_ASSERT(this->_size == b._size, "The rhs vector as a mismatch in size with the matrix");
Math::solve(this->_size, A.storage(), this->values, b.storage());
}
/* ------------------------------------------------------------------------ */
template<bool tr_A>
inline void mul(const Matrix<T> & A,
const Vector<T> & x,
Real alpha = 1.0);
/* ------------------------------------------------------------------------ */
inline Real norm() const {
return parent::template norm<L_2>();
}
template<NormType nt>
inline Real norm() const {
return parent::template norm<nt>();
}
/* ------------------------------------------------------------------------ */
inline void normalize() {
Real n = norm();
operator/=(n);
}
/* ------------------------------------------------------------------------ */
/// norm of (*this - x)
inline Real distance(const Vector<T> & y) const {
Real * vx = this->values; Real * vy = y.storage();
Real sum_2 = 0;
for (UInt i = 0; i < this->_size; ++i, ++vx, ++vy) sum_2 += (*vx - *vy)*(*vx - *vy);
return sqrt(sum_2);
}
/* ------------------------------------------------------------------------ */
inline bool equal(const Vector<T> & v, Real tolerance = Math::getTolerance()) const {
T * a = this->storage();
T * b = v.storage();
UInt i = 0;
while (i < this->_size && (std::abs(*(a++) - *(b++)) < tolerance)) ++i;
return i == this->_size;
}
/* ------------------------------------------------------------------------ */
inline short compare(const Vector<T> & v, Real tolerance = Math::getTolerance()) const {
T * a = this->storage();
T * b = v.storage();
for (UInt i(0); i < this->_size; ++i, ++a, ++b) {
if(std::abs(*a - *b) > tolerance)
return (((*a - *b) > tolerance) ? 1 : -1);
}
return 0;
}
/* ------------------------------------------------------------------------ */
inline bool operator==(const Vector<T> & v) const { return equal(v); }
inline bool operator<(const Vector<T> & v) const { return compare(v) == -1; }
inline bool operator>(const Vector<T> & v) const { return compare(v) == 1; }
/* ------------------------------------------------------------------------ */
/// function to print the containt of the class
virtual void printself(std::ostream & stream, int indent = 0) const {
std::string space;
for(Int i = 0; i < indent; i++, space += AKANTU_INDENT);
stream << space << "Vector<" << debug::demangle(typeid(T).name()) << ">(" << this->_size <<") : [";
for (UInt i = 0; i < this->_size; ++i) {
if(i != 0) stream << ", ";
stream << this->values[i];
}
stream << "]";
}
friend class ::akantu::Array<T>;
};
typedef Vector<Real> RVector;
/* -------------------------------------------------------------------------- */
// support operations for the creation of other vectors
template <typename T> Vector<T> operator*(T scalar, const Vector<T> & a);
template <typename T> Vector<T> operator+(const Vector<T> & a, const Vector<T> & b);
template <typename T> Vector<T> operator-(const Vector<T> & a, const Vector<T> & b);
/* -------------------------------------------------------------------------- */
template <typename T>
Vector<T> operator*(T scalar, const Vector<T> & a) {
Vector<T> r(a.size());
r = a;
r *= scalar;
return r;
}
template <typename T>
Vector<T> operator+(const Vector<T> & a, const Vector<T> & b) {
Vector<T> r(a.size());
r = a;
r += b;
return r;
}
template <typename T>
Vector<T> operator-(const Vector<T>& a, const Vector<T>& b) {
Vector<T> r(a.size());
r = a;
r -= b;
return r;
}
template <typename T>
Matrix<T> operator*(T scalar, const Matrix<T>& a) {
Matrix<T> r(a.rows(), a.cols());
r = a;
r *= scalar;
return r;
}
template <typename T>
Matrix<T> operator+(const Matrix<T>& a, const Matrix<T>& b) {
Matrix<T> r(a.rows(), a.cols());
r = a;
r += b;
return r;
}
template <typename T>
Matrix<T> operator-(const Matrix<T>& a, const Matrix<T>& b) {
Matrix<T> r(a.rows(), a.cols());
r = a;
r -= b;
return r;
}
/* ------------------------------------------------------------------------ */
template<>
inline bool Vector<UInt>::equal(const Vector<UInt> & v, __attribute__((unused)) Real tolerance) const {
UInt * a = this->storage();
UInt * b = v.storage();
UInt i = 0;
while (i < this->_size && (*(a++) == *(b++))) ++i;
return i == this->_size;
}
/* ------------------------------------------------------------------------ */
/* Matrix */
/* ------------------------------------------------------------------------ */
template<typename T>
class Matrix : public TensorStorage< T, 2, Matrix<T> > {
typedef TensorStorage< T, 2, Matrix<T> > parent;
public:
typedef typename parent::value_type value_type;
typedef MatrixProxy<T> proxy;
public:
Matrix() : parent() {}
Matrix(UInt m, UInt n, const T & def = T()) : parent(m, n, 0, def) { }
Matrix(T * data, UInt m, UInt n) : parent(data, m, n, 0) { }
Matrix(const Matrix & src, bool deep_copy = true) : parent(src, deep_copy) { }
Matrix(const MatrixProxy<T> & src) : parent(src) { }
virtual ~Matrix() { }
/* ------------------------------------------------------------------------ */
inline Matrix & operator=(const Matrix & src) {
parent::operator=(src);
return *this;
}
public:
/* ---------------------------------------------------------------------- */
UInt rows() const { return this->n[0]; }
UInt cols() const { return this->n[1]; }
/* ---------------------------------------------------------------------- */
inline T& operator()(UInt i, UInt j) {
return this->at(i,j);
}
inline T& at(UInt i, UInt j) {
return *(this->values + i + j*this->n[0]);
}
inline const T& operator()(UInt i, UInt j) const {
return this->at(i,j);
}
inline T& at(UInt i, UInt j) const {
return *(this->values + i + j*this->n[0]);
}
/// give a line vector wrapped on the column i
inline VectorProxy<T> operator()(UInt j) {
return VectorProxy<T>(this->values + j*this->n[0], this->n[0]);
}
inline const VectorProxy<T> operator()(UInt j) const {
return VectorProxy<T>(this->values + j*this->n[0], this->n[0]);
}
inline T& operator[](UInt idx) { return *(this->values + idx); };
inline const T& operator[](UInt idx) const { return *(this->values + idx); };
/* ---------------------------------------------------------------------- */
inline Matrix operator* (const Matrix & B) {
Matrix C(this->rows(), B.cols());
C.mul<false, false>(*this, B);
return C;
}
/* ----------------------------------------------------------------------- */
inline Matrix & operator*= (const T & x) {
return parent::operator*= (x);
}
inline Matrix & operator*= (const Matrix & B) {
Matrix C(*this);
this->mul<false, false>(C, B);
return *this;
}
/* ---------------------------------------------------------------------- */
template<bool tr_A, bool tr_B>
inline void mul(const Matrix & A, const Matrix & B, T alpha = 1.0) {
UInt k = A.cols();
if(tr_A) k = A.rows();
#ifndef AKANTU_NDEBUG
if (tr_B){
AKANTU_DEBUG_ASSERT(k == B.cols(),
"matrices to multiply have no fit dimensions");
AKANTU_DEBUG_ASSERT(this->cols() == B.rows(),
"matrices to multiply have no fit dimensions");
}
else {
AKANTU_DEBUG_ASSERT(k == B.rows(),
"matrices to multiply have no fit dimensions");
AKANTU_DEBUG_ASSERT(this->cols() == B.cols(),
"matrices to multiply have no fit dimensions");
}
if (tr_A){
AKANTU_DEBUG_ASSERT(this->rows() == A.cols(),
"matrices to multiply have no fit dimensions");
}
else{
AKANTU_DEBUG_ASSERT(this->rows() == A.rows(),
"matrices to multiply have no fit dimensions");
}
#endif //AKANTU_NDEBUG
Math::matMul<tr_A, tr_B>(this->rows(), this->cols(), k,
alpha, A.storage(), B.storage(),
0., this->storage());
}
/* ---------------------------------------------------------------------- */
inline void outerProduct(const Vector<T> & A,
const Vector<T> & B) {
AKANTU_DEBUG_ASSERT(A.size() == this->rows() && B.size() == this->cols(),
"A and B are not compatible with the size of the matrix");
for (UInt i = 0; i < this->rows(); ++i) {
for (UInt j = 0; j < this->cols(); ++j) {
this->values[i + j * this->rows()] += A[i] * B[j];
}
}
}
private:
class EigenSorter {
public:
EigenSorter(const Vector<T> & eigs) : eigs(eigs) { }
bool operator()(const UInt & a, const UInt & b) const {
return (eigs(a) > eigs(b));
}
private:
const Vector<T> & eigs;
};
public:
/* ---------------------------------------------------------------------- */
inline void eig(Vector<T> & eigenvalues, Matrix<T> & eigenvectors) const {
AKANTU_DEBUG_ASSERT(this->cols() == this->rows(),
"eig is not a valid operation on a rectangular matrix");
AKANTU_DEBUG_ASSERT(eigenvalues.size() == this->cols(),
"eigenvalues should be of size " << this->cols() << ".");
#ifndef AKANTU_NDEBUG
if(eigenvectors.storage() != NULL)
AKANTU_DEBUG_ASSERT((eigenvectors.rows() == eigenvectors.cols()) &&
(eigenvectors.rows() == this->cols()),
"Eigenvectors needs to be a square matrix of size "
<< this->cols() << " x " << this->cols() << ".");
#endif
Matrix<T> tmp = *this;
Vector<T> tmp_eigs(eigenvalues.size());
Matrix<T> tmp_eig_vects(eigenvectors.rows(), eigenvectors.cols());
if(tmp_eig_vects.rows() == 0 || tmp_eig_vects.cols() == 0)
Math::matrixEig(tmp.cols(), tmp.storage(), tmp_eigs.storage());
else
Math::matrixEig(tmp.cols(), tmp.storage(), tmp_eigs.storage(), tmp_eig_vects.storage());
Vector<UInt> perm(eigenvalues.size());
for (UInt i = 0; i < perm.size(); ++i) perm(i) = i;
std::sort(perm.storage(), perm.storage() + perm.size(), EigenSorter(tmp_eigs));
for (UInt i = 0; i < perm.size(); ++i) eigenvalues(i) = tmp_eigs(perm(i));
if(tmp_eig_vects.rows() != 0 && tmp_eig_vects.cols() != 0)
for (UInt i = 0; i < perm.size(); ++i) {
for (UInt j = 0; j < eigenvectors.rows(); ++j) {
eigenvectors(j, i) = tmp_eig_vects(j, perm(i));
}
}
}
/* ---------------------------------------------------------------------- */
inline void eig(Vector<T> & eigenvalues) const {
Matrix<T> empty;
eig(eigenvalues, empty);
}
/* ---------------------------------------------------------------------- */
inline void eye(T alpha = 1.) {
AKANTU_DEBUG_ASSERT(this->cols() == this->rows(), "eye is not a valid operation on a rectangular matrix");
this->clear();
for (UInt i = 0; i < this->cols(); ++i) {
this->values[i + i * this->rows()] = alpha;
}
}
/* ---------------------------------------------------------------------- */
static inline Matrix<T> eye(UInt m, T alpha = 1.) {
Matrix<T> tmp(m, m);
tmp.eye(alpha);
return tmp;
}
/* ---------------------------------------------------------------------- */
inline T trace() const {
AKANTU_DEBUG_ASSERT(this->cols() == this->rows(), "trace is not a valid operation on a rectangular matrix");
T trace = 0.;
for (UInt i = 0; i < this->rows(); ++i) {
trace += this->values[i + i * this->rows()];
}
return trace;
}
/* ---------------------------------------------------------------------- */
inline Matrix transpose() const {
Matrix tmp(this->cols(), this->rows());
for (UInt i = 0; i < this->rows(); ++i) {
for (UInt j = 0; j < this->cols(); ++j) {
tmp(j,i) = operator()(i, j);
}
}
return tmp;
}
/* ---------------------------------------------------------------------- */
inline void inverse(const Matrix & A) {
AKANTU_DEBUG_ASSERT(A.cols() == A.rows(),
"inv is not a valid operation on a rectangular matrix");
AKANTU_DEBUG_ASSERT(this->cols() == A.cols(),
"the matrix should have the same size as its inverse");
if(this->cols() == 1) *this->values = 1./ *A.storage();
else if(this->cols() == 2) Math::inv2(A.storage(), this->values);
else if(this->cols() == 3) Math::inv3(A.storage(), this->values);
else Math::inv(this->cols(), A.storage(), this->values);
}
/* --------------------------------------------------------------------- */
inline T det() const {
AKANTU_DEBUG_ASSERT(this->cols() == this->rows(),
"inv is not a valid operation on a rectangular matrix");
if(this->cols() == 1) return *(this->values);
else if(this->cols() == 2) return Math::det2(this->values);
else if(this->cols() == 3) return Math::det3(this->values);
else return Math::det(this->cols(), this->values);
}
/* --------------------------------------------------------------------- */
inline T doubleDot(const Matrix<T> & other) const {
AKANTU_DEBUG_ASSERT(this->cols() == this->rows(),
"doubleDot is not a valid operation on a rectangular matrix");
if(this->cols() == 1) return *(this->values) * *(other.storage());
else if(this->cols() == 2) return Math::matrixDoubleDot22(this->values, other.storage());
else if(this->cols() == 3) return Math::matrixDoubleDot33(this->values, other.storage());
else AKANTU_DEBUG_ERROR("doubleDot is not defined for other spatial dimensions"
<< " than 1, 2 or 3.");
return T();
}
/* ---------------------------------------------------------------------- */
/// function to print the containt of the class
virtual void printself(std::ostream & stream, int indent = 0) const {
std::string space;
for(Int i = 0; i < indent; i++, space += AKANTU_INDENT);
stream << space << "Matrix<" << debug::demangle(typeid(T).name())
<< ">(" << this->n[0] << "," << this->n[1] <<") :" << "[";
for (UInt i = 0; i < this->n[0]; ++i) {
if(i != 0) stream << ", ";
stream << "[";
for (UInt j = 0; j < this->n[1]; ++j) {
if(j != 0) stream << ", ";
stream << operator()(i, j);
}
stream << "]";
}
stream << "]";
};
};
/* ------------------------------------------------------------------------ */
template<typename T>
template<bool tr_A>
inline void Vector<T>::mul(const Matrix<T> & A,
const Vector<T> & x,
Real alpha) {
#ifndef AKANTU_NDEBUG
UInt n = x.size();
if (tr_A){
AKANTU_DEBUG_ASSERT(n == A.rows(), "matrix and vector to multiply have no fit dimensions");
AKANTU_DEBUG_ASSERT(this->size() == A.cols(), "matrix and vector to multiply have no fit dimensions");
} else {
AKANTU_DEBUG_ASSERT(n == A.cols(), "matrix and vector to multiply have no fit dimensions");
AKANTU_DEBUG_ASSERT(this->size() == A.rows(), "matrix and vector to multiply have no fit dimensions");
}
#endif
Math::matVectMul<tr_A>(A.rows(), A.cols(), alpha, A.storage(), x.storage(), 0., this->storage());
}
/* -------------------------------------------------------------------------- */
template<typename T>
inline std::ostream & operator<<(std::ostream & stream, const Matrix<T> & _this)
{
_this.printself(stream);
return stream;
}
/* -------------------------------------------------------------------------- */
template<typename T>
inline std::ostream & operator<<(std::ostream & stream, const Vector<T> & _this)
{
_this.printself(stream);
return stream;
}
/* ------------------------------------------------------------------------ */
/* Tensor3 */
/* ------------------------------------------------------------------------ */
template<typename T>
class Tensor3 : public TensorStorage< T, 3, Tensor3<T> > {
typedef TensorStorage< T, 3, Tensor3<T> > parent;
public:
typedef typename parent::value_type value_type;
typedef Tensor3Proxy<T> proxy;
public:
Tensor3() : parent() { };
Tensor3(UInt m, UInt n, UInt p, const T & def = T()) : parent(m, n, p, def) { }
Tensor3(T * data, UInt m, UInt n, UInt p) : parent(data, m, n, p) { }
Tensor3(const Tensor3 & src, bool deep_copy = true) : parent(src, deep_copy) { }
public:
/* ------------------------------------------------------------------------ */
inline Tensor3 & operator=(const Tensor3 & src) {
parent::operator=(src);
return *this;
}
/* ---------------------------------------------------------------------- */
inline T& operator()(UInt i, UInt j, UInt k)
{ return *(this->values + (k*this->n[0] + i)*this->n[1] + j); };
inline const T& operator()(UInt i, UInt j, UInt k) const
{ return *(this->values + (k*this->n[0] + i)*this->n[1] + j); };
inline MatrixProxy<T> operator()(UInt k)
{ return MatrixProxy<T>(this->values + k*this->n[0]*this->n[1], this->n[0], this->n[1]); }
inline const MatrixProxy<T> operator()(UInt k) const
{ return MatrixProxy<T>(this->values + k*this->n[0]*this->n[1], this->n[0], this->n[1]); }
inline MatrixProxy<T> operator[](UInt k)
{ return Matrix<T>(this->values + k*this->n[0]*this->n[1], this->n[0], this->n[1]); }
inline const MatrixProxy<T> operator[](UInt k) const
{ return MatrixProxy<T>(this->values + k*this->n[0]*this->n[1], this->n[0], this->n[1]); }
};
__END_AKANTU__
#endif /* __AKANTU_AKA_TYPES_HH__ */
c4science · Help