/**
 * @file   Matrix.cpp
 * @brief  matrix class
 * @author Christian Potthast
 */

#include <stdarg.h>
#include <string.h>
#include <iomanip>
#include <list>

#include <boost/foreach.hpp>
#include <boost/numeric/ublas/lu.hpp>
#include <boost/numeric/ublas/io.hpp>
#include <boost/tuple/tuple.hpp>

#include "Matrix.h"
#include "Vector.h"
#include "svdcmp.h"

using namespace std;
namespace ublas = boost::numeric::ublas;

namespace gtsam {

/* ************************************************************************* */
Matrix Matrix_( size_t m, size_t n, const double* const data) {
  Matrix A(m,n);
  copy(data, data+m*n, A.data().begin());
  return A;
}

/* ************************************************************************* */
Matrix Matrix_( size_t m, size_t n, const Vector& v)
{
  Matrix A(m,n);
  // column-wise copy
  for( size_t j = 0, k=0  ; j < n ; j++)
    for( size_t i = 0; i < m ; i++,k++)
      A(i,j) = v(k);
  return A;
}

/* ************************************************************************* */
Matrix Matrix_(size_t m, size_t n, ...) {
  Matrix A(m,n);
  va_list ap;
  va_start(ap, n);
  for( size_t i = 0 ; i < m ; i++)
    for( size_t j = 0 ; j < n ; j++) {
      double value = va_arg(ap, double);
      A(i,j) = value;
    }
  va_end(ap);
  return A;
}

/* ************************************************************************* */
/** create a matrix with value zero                                          */
/* ************************************************************************* */
Matrix zeros( size_t m, size_t n )
{
  Matrix A(m,n, 0.0);
  return A;
}

/** 
 * Identity matrix
 */
Matrix eye( size_t m, size_t n){
  Matrix A = zeros(m,n);
  for(size_t i = 0; i<min(m,n); i++) A(i,i)=1.0;
  return A;
}

/* ************************************************************************* */ 
/** Diagonal matrix values                                                   */
/* ************************************************************************* */
Matrix diag(const Vector& v) {
  size_t m = v.size();
  Matrix A = zeros(m,m);
  for(size_t i = 0; i<m; i++) A(i,i)=v(i);
  return A;
}

/* ************************************************************************* */
/** Check if two matrices are the same                                       */
/* ************************************************************************* */
bool equal_with_abs_tol(const Matrix& A, const Matrix& B, double tol) {

  size_t n1 = A.size2(), m1 = A.size1();
  size_t n2 = B.size2(), m2 = B.size1();

  if(m1!=m2 || n1!=n2) return false;

  for(size_t i=0; i<m1; i++)
	  for(size_t j=0; j<n1; j++) {
		  if(isnan(A(i,j)) xor isnan(B(i,j)))
			  return false;
		  if(fabs(A(i,j) - B(i,j)) > tol)
			  return false;
	  }

  return true;
}

/* ************************************************************************* */
bool assert_equal(const Matrix& expected, const Matrix& actual, double tol) {

  if (equal_with_abs_tol(expected,actual,tol)) return true;

  size_t n1 = expected.size2(), m1 = expected.size1();
  size_t n2 = actual.size2(), m2 = actual.size1();

  cout << "not equal:" << endl;
  print(expected,"expected = ");
  print(actual,"actual = ");
  if(m1!=m2 || n1!=n2)
    cout << m1 << "," << n1 << " != " << m2 << "," << n2 << endl;
  else
    print(actual-expected, "actual - expected = ");
  return false;
}

/* ************************************************************************ */
/** negation                                                                */
/* ************************************************************************ */
/*
Matrix operator-() const
{
  size_t m = size1(),n=size2();
  Matrix M(m,n);
  for(size_t i = 0; i < m; i++)
    for(size_t j = 0; j < n; j++)
      M(i,j) = -matrix_(i,j);
  return M;  
}
*/

/* ************************************************************************* */
Vector Vector_(const Matrix& A)
{
  size_t m = A.size1(), n = A.size2();
  Vector v(m*n);
  for( size_t j = 0, k=0  ; j < n ; j++)
    for( size_t i = 0; i < m ; i++,k++)
      v(k) = A(i,j);
  return v;
}

/* ************************************************************************* */
Vector column(const Matrix& A, size_t j) {
	if (j>=A.size2())
		throw invalid_argument("Column index out of bounds!");

	size_t m = A.size1();
	Vector a(m);
	for (int i=0; i<m; ++i)
		a(i) = A(i,j);
	return a;
}

/* ************************************************************************* */
Vector row(const Matrix& A, size_t i) {
	if (i>=A.size1())
		throw invalid_argument("Row index out of bounds!");

	size_t n = A.size2();
	Vector a(n);
	for (int j=0; j<n; ++j)
		a(j) = A(i,j);
	return a;
}

/* ************************************************************************* */
void print(const Matrix& A, const string &s) {
  size_t m = A.size1(), n = A.size2();

  // print out all elements
  cout << s << "[\n";
  for( size_t i = 0 ; i < m ; i++) {
    for( size_t j = 0 ; j < n ; j++) {
      double aij = A(i,j);
      cout << setw(9) << (fabs(aij)<1e-12 ? 0 : aij) << "\t";
    }
    cout << endl;
  }
  cout << "]" << endl;
}

/* ************************************************************************* */
Matrix sub(const Matrix& A, size_t i1, size_t i2, size_t j1, size_t j2) {
  // using ublas is slower:
  // Matrix B = Matrix(ublas::project(A,ublas::range(i1,i2+1),ublas::range(j1,j2+1)));
  size_t m=i2-i1, n=j2-j1;
  Matrix B(m,n);
  for (size_t i=i1,k=0;i<i2;i++,k++)
    memcpy(&B(k,0),&A(i,j1),n*sizeof(double));
  return B;
}

/* ************************************************************************* */
void solve(Matrix& A, Matrix& B)
{
  // perform LU-factorization
  ublas::lu_factorize(A);

  // backsubstitute
  ublas::lu_substitute<const Matrix, Matrix>(A, B);
}

/* ************************************************************************* */
Matrix inverse(const Matrix& originalA)
{
  Matrix A(originalA);
  Matrix B = eye(A.size2());
  solve(A,B);
  return B;
}

/* ************************************************************************* */
/** Householder QR factorization, Golub & Van Loan p 224, explicit version    */
/* ************************************************************************* */
pair<Matrix,Matrix> qr(const Matrix& A) {

  const size_t m = A.size1(), n = A.size2(), kprime = min(m,n);
  
  Matrix Q=eye(m,m),R(A);
  Vector v(m);

  // loop over the kprime first columns 
  for(size_t j=0; j < kprime; j++){

    // we now work on the matrix (m-j)*(n-j) matrix A(j:end,j:end)
    const size_t mm=m-j;

    // copy column from matrix to xjm, i.e. x(j:m) = A(j:m,j)
    Vector xjm(mm);
    for(size_t k = 0 ; k < mm; k++)
      xjm(k) = R(j+k, j);  
        
    // calculate the Householder vector v
    double beta; Vector vjm;
    boost::tie(beta,vjm) = house(xjm);

    // pad with zeros to get m-dimensional vector v
    for(size_t k = 0 ; k < m; k++) 
      v(k) = k<j ? 0.0 : vjm(k-j);

    // create Householder reflection matrix Qj = I-beta*v*v'
    Matrix Qj = eye(m) - beta * Matrix(outer_prod(v,v)); //BAD: Fix this

    R = Qj * R; // update R
    Q = Q * Qj; // update Q

  } // column j

  return make_pair(Q,R);
}

/* ************************************************************************* */
/** Imperative version of Householder rank 1 update
 * i.e. do outer product update A = (I-beta vv')*A = A - v*(beta*A'*v)' = A - v*w'
 * but only in relevant part, from row j onwards
 * If called from householder_ does actually more work as first j columns 
 * will not be touched. However, is called from GaussianFactor.eliminate
 * on a number of different matrices for which all columns change.
 */
/* ************************************************************************* */
void householder_update(Matrix &A, int j, double beta, const Vector& vjm) {

  const size_t m = A.size1(), n = A.size2();

  // elegant but slow: A -= Matrix(outer_prod(v,beta*trans(A)*v));
  // faster code below

  // w = beta*transpose(A(j:m,:))*v(j:m)
  Vector w(n);
  for( size_t c = 0; c < n; c++) {
    w(c) = 0.0;
    // dangerous as relies on row-major scheme
    const double *a = &A(j,c), * const v = &vjm(0);
    for( size_t r=j, s=0 ; r < m ; r++, s++, a+=n )
      // w(c) += A(r,c) * vjm(r-j)
      w(c) += (*a) * v[s];
    w(c) *= beta;
  }

  // rank 1 update A(j:m,:) -= v(j:m)*w'
  for( size_t c = 0 ; c < n; c++) {
    double wc = w(c);
    double *a = &A(j,c); const double * const v =&vjm(0);
    for( size_t r=j, s=0 ; r < m ; r++, s++, a+=n )
      // A(r,c) -= vjm(r-j) * wjn(c-j);
      (*a) -= v[s] * wc;
  }
}

/* ************************************************************************* */
// update A, b
// A' \define A_{S}-ar and b'\define b-ad
__attribute__ ((noinline))	// uncomment to prevent inlining when profiling
void updateAb(Matrix& A, Vector& b, int j, const Vector& a, const Vector& r, double d) {
	const size_t m = A.size1(), n = A.size2();
	for (int i = 0; i < m; ++i) { // update all rows
		double ai = a(i);
		b(i) -= ai * d;
		double *Aptr = A.data().begin() + i * n + j + 1;
		const double *rptr = r.data().begin() + j + 1;
		for (int j2 = j + 1; j2 < n; ++j2) { // limit to only columns in separator
			//A(i,j2) -= ai*r(j2);
			*Aptr -= ai * *rptr;
			Aptr++;
			rptr++;
		}
	}
}

/* ************************************************************************* */
list<boost::tuple<Vector, double, double> >
weighted_eliminate(Matrix& A, Vector& b, const Vector& sigmas) {
	size_t m = A.size1(), n = A.size2(); // get size(A)
	size_t maxRank = min(m,n);

	// create list
	list<boost::tuple<Vector, double, double> > results;

	Vector pseudo(m); // allocate storage for pseudo-inverse

	// TODO: calculate weights once
	// Vector weights =

	// We loop over all columns, because the columns that can be eliminated
	// are not necessarily contiguous. For each one, estimate the corresponding
	// scalar variable x as d-rS, with S the separator (remaining columns).
	// Then update A and b by substituting x with d-rS, zero-ing out x's column.
	for (int j=0; j<n; ++j) {
		// extract the first column of A
		// TODO: this is an allocate and a copy
		// can we somehow make a "reference" vector, boost magic
		Vector a(column(A, j));

		// Calculate weighted pseudo-inverse and corresponding precision
		// TODO: pass in weights which are calculated once
		// TODO return variance
		double precision = weightedPseudoinverse(a, sigmas, pseudo);

		// if precision is zero, no information on this column
		if (precision < 1e-8) continue;

		// create solution and copy into r
		Vector r(basis(n, j));
		for (int j2=j+1; j2<n; ++j2)
			r(j2) = inner_prod(pseudo, column(A, j2));

		// create the rhs
		double d = inner_prod(pseudo, b);

		// construct solution (r, d, sigma)
		results.push_back(boost::make_tuple(r, d, 1./sqrt(precision)));

		// exit after rank exhausted
		if (results.size()>=maxRank) break;

		// update A, b, expensive, suing outer product
		// A' \define A_{S}-a*r and b'\define b-d*a
		updateAb(A, b, j, a, r, d);
	}

	return results;
}

/* ************************************************************************* */
/** Imperative version of Householder QR factorization, Golub & Van Loan p 224
 * version with Householder vectors below diagonal, as in GVL
 */
/* ************************************************************************* */
void householder_(Matrix &A, size_t k) 
{
  const size_t m = A.size1(), n = A.size2(), kprime = min(k,min(m,n));
  
  // loop over the kprime first columns 
  for(size_t j=0; j < kprime; j++){
    // below, the indices r,c always refer to original A

    // copy column from matrix to xjm, i.e. x(j:m) = A(j:m,j)
    Vector xjm(m-j);
    for(size_t r = j ; r < m; r++)
      xjm(r-j) = A(r,j);
        
    // calculate the Householder vector
    double beta; Vector vjm;
    boost::tie(beta,vjm) = house(xjm);

    // do outer product update A = (I-beta vv')*A = A - v*(beta*A'*v)' = A - v*w'
    householder_update(A, j, beta, vjm) ;

    // the Householder vector is copied in the zeroed out part
    for( size_t r = j+1 ; r < m ; r++ )
      A(r,j) = vjm(r-j);

  } // column j
}

/* ************************************************************************* */
/** version with zeros below diagonal                                        */
/* ************************************************************************* */
void householder(Matrix &A, size_t k) {
  householder_(A,k);
  const size_t m = A.size1(), n = A.size2(), kprime = min(k,min(m,n));
  for(size_t j=0; j < kprime; j++)
    for( size_t i = j+1 ; i < m ; i++ )
      A(i,j) = 0.0;
}

/* ************************************************************************* */
Vector backSubstituteUpper(const Matrix& U, const Vector& b, bool unit) {
	size_t m = U.size1(), n = U.size2();
#ifndef NDEBUG
	if (m!=n)
		throw invalid_argument("backSubstituteUpper: U must be square");
#endif

	Vector result(n);
	for (size_t i = n; i > 0; i--) {
		double zi = b(i-1);
		for (size_t j = i+1; j <= n; j++)
			zi -= U(i-1,j-1) * result(j-1);
		if (!unit) zi /= U(i-1,i-1);
		result(i-1) = zi;
	}

	return result;
}

/* ************************************************************************* */
Vector backSubstituteUpper(const Vector& b, const Matrix& U, bool unit) {
	size_t m = U.size1(), n = U.size2();
#ifndef NDEBUG
	if (m!=n)
		throw invalid_argument("backSubstituteUpper: U must be square");
#endif

	Vector result(n);
	for (size_t i = 1; i <= n; i++) {
		double zi = b(i-1);
		for (size_t j = 1; j < i; j++)
			zi -= U(j-1,i-1) * result(j-1);
		if (!unit) zi /= U(i-1,i-1);
		result(i-1) = zi;
	}

	return result;
}

/* ************************************************************************* */
Vector backSubstituteLower(const Matrix& L, const Vector& b, bool unit) {
	size_t m = L.size1(), n = L.size2();
#ifndef NDEBUG
	if (m!=n)
		throw invalid_argument("backSubstituteLower: L must be square");
#endif

	Vector result(n);
	for (size_t i = 1; i <= n; i++) {
		double zi = b(i-1);
		for (size_t j = 1; j < i; j++)
			zi -= L(i-1,j-1) * result(j-1);
		if (!unit) zi /= L(i-1,i-1);
		result(i-1) = zi;
	}

	return result;
}

/* ************************************************************************* */
Matrix stack(size_t nrMatrices, ...)
{
  int dimA1 = 0;
  int dimA2 = 0;
  va_list ap;
  va_start(ap, nrMatrices);
  for(size_t i = 0 ; i < nrMatrices ; i++) {
    Matrix *M = va_arg(ap, Matrix *);
    dimA1 += M->size1();
    dimA2 =  M->size2();  // TODO: should check if all the same !
  }
  va_end(ap);
  va_start(ap, nrMatrices);
  Matrix A(dimA1, dimA2);
  int vindex = 0;
  for( size_t i = 0 ; i < nrMatrices ; i++) {
    Matrix *M = va_arg(ap, Matrix *);
    for(size_t d1 = 0; d1 < M->size1(); d1++)
      for(size_t d2 = 0; d2 < M->size2(); d2++)
	A(vindex+d1, d2) = (*M)(d1, d2);
    vindex += M->size1();
  }  

  return A;
}

/* ************************************************************************* */
Matrix collect(vector<const Matrix *> matrices)
{
  int dimA1 = 0;
  int dimA2 = 0;
  BOOST_FOREACH(const Matrix* M, matrices) {
    dimA1 =  M->size1();  // TODO: should check if all the same !
    dimA2 += M->size2();
  }
  Matrix A(dimA1, dimA2);
  int hindex = 0;
  BOOST_FOREACH(const Matrix* M, matrices) {
    for(size_t d1 = 0; d1 < M->size1(); d1++)
      for(size_t d2 = 0; d2 < M->size2(); d2++)
	A(d1, d2+hindex) = (*M)(d1, d2);
    hindex += M->size2();
  }  

  return A;
}

/* ************************************************************************* */
Matrix collect(size_t nrMatrices, ...)
{
  vector<const Matrix *> matrices;
  va_list ap;
  va_start(ap, nrMatrices);
  for( size_t i = 0 ; i < nrMatrices ; i++) {
    Matrix *M = va_arg(ap, Matrix *);
    matrices.push_back(M);
  }
return collect(matrices);
}

/* ************************************************************************* */
// row scaling
Matrix vector_scale(const Vector& v, const Matrix& A) {
	Matrix M(A);
	for (int i=0; i<A.size1(); ++i) {
		for (int j=0; j<A.size2(); ++j) {
			M(i,j) *= v(i);
		}
	}
	return M;
}

/* ************************************************************************* */
// column scaling
Matrix vector_scale(const Matrix& A, const Vector& v) {
	Matrix M(A);
	for (int i=0; i<A.size1(); ++i)
		for (int j=0; j<A.size2(); ++j)
			M(i,j) *= v(j);
	return M;
}

/* ************************************************************************* */
Matrix skewSymmetric(double wx, double wy, double wz)
{
  return Matrix_(3,3,
		  0.0, -wz, +wy,
		  +wz, 0.0, -wx,
		  -wy, +wx, 0.0);
}

/* ************************************************************************* */
/** Numerical Recipes in C wrappers                                          
 *  create Numerical Recipes in C structure
 * pointers are subtracted by one to provide base 1 access 
 */
/* ************************************************************************* */
double** createNRC(Matrix& A) {
  const size_t m=A.size1();
  double** a = new double* [m];
  for(size_t i = 0; i < m; i++) 
    a[i] = &A(i,0)-1;
  return a;
}

/* ************************************************************************* */
/** SVD                                                                      */
/* ************************************************************************* */

// version with in place modification of A
void svd(Matrix& A, Vector& s, Matrix& V) {

  const size_t m=A.size1(), n=A.size2();

  double * q = new double[n]; // singular values

  // create NRC matrices, u is in place
  V = Matrix(n,n);
  double **u = createNRC(A), **v = createNRC(V);

  // perform SVD
  // need to pass pointer - 1 in NRC routines so u[1][1] is first element !
  svdcmp(u-1,m,n,q-1,v-1);
	
  // copy singular values back
  s.resize(n);
  copy(q,q+n,s.begin());

  delete[] v;
  delete[] q; //switched to array delete
  delete[] u;

}

/* ************************************************************************* */
void svd(const Matrix& A, Matrix& U, Vector& s, Matrix& V) {
  U = A;      // copy
  svd(U,s,V); // call in-place version
}

/* ************************************************************************* */
// TODO, would be faster with Cholesky
Matrix inverse_square_root(const Matrix& A) {
  size_t m = A.size2(), n = A.size1();
	if (m!=n)
		throw invalid_argument("inverse_square_root: A must be square");

	// Perform SVD, TODO: symmetric SVD?
	Matrix U,V;
	Vector S;
	svd(A,U,S,V);

	// invert and sqrt diagonal of S
	// We also arbitrarily choose sign to make result have positive signs
  for(size_t i = 0; i<m; i++) S(i) = - pow(S(i),-0.5);
  return vector_scale(S, V); // V*S;
}

/* ************************************************************************* */

} // namespace gtsam