use alga::general::Real;
use core::{Matrix, MatrixN, MatrixMN, DefaultAllocator};
use dimension::{Dim, DimMin, DimMinimum};
use storage::{Storage, StorageMut};
use allocator::Allocator;
use constraint::{ShapeConstraint, SameNumberOfRows};

use linalg::lu;
use linalg::PermutationSequence;



/// LU decomposition with full pivoting.
#[derive(Clone, Debug)]
pub struct FullPivLU<N: Real, R: DimMin<C>, C: Dim>
    where DefaultAllocator: Allocator<N, R, C> +
                            Allocator<(usize, usize), DimMinimum<R, C>> {
    lu: MatrixMN<N, R, C>,
    p:  PermutationSequence<DimMinimum<R, C>>,
    q:  PermutationSequence<DimMinimum<R, C>>
}


impl<N: Real, R: DimMin<C>, C: Dim> Copy for FullPivLU<N, R, C>
    where DefaultAllocator: Allocator<N, R, C> +
                            Allocator<(usize, usize), DimMinimum<R, C>>,
          MatrixMN<N, R, C>: Copy,
          PermutationSequence<DimMinimum<R, C>>: Copy { }


impl<N: Real, R: DimMin<C>, C: Dim> FullPivLU<N, R, C>
    where DefaultAllocator: Allocator<N, R, C> +
                            Allocator<(usize, usize), DimMinimum<R, C>> {
    /// Computes the LU decomposition with full-pivoting of `matrix`.
    ///
    /// This effectively computes `P, L, U, Q` such that `P * matrix * Q = LU`.
    pub fn new(mut matrix: MatrixMN<N, R, C>) -> Self {
        let (nrows, ncols)  = matrix.data.shape();
        let min_nrows_ncols = nrows.min(ncols);

        let mut p = PermutationSequence::identity_generic(min_nrows_ncols);
        let mut q = PermutationSequence::identity_generic(min_nrows_ncols);

        if min_nrows_ncols.value() == 0 {
            return FullPivLU { lu: matrix, p: p, q: q };
        }

        for i in 0 .. min_nrows_ncols.value() {
            let piv  = matrix.slice_range(i .., i ..).iamax_full();
            let row_piv = piv.0 + i;
            let col_piv = piv.1 + i;
            let diag = matrix[(row_piv, col_piv)];

            if diag.is_zero() {
                // The remaining of the matrix is zero.
                break;
            }

            matrix.swap_columns(i, col_piv);
            q.append_permutation(i, col_piv);

            if row_piv != i {
                p.append_permutation(i, row_piv);
                matrix.columns_range_mut(.. i).swap_rows(i, row_piv);
                lu::gauss_step_swap(&mut matrix, diag, i, row_piv);
            }
            else {
                lu::gauss_step(&mut matrix, diag, i);
            }
        }

        FullPivLU { lu: matrix, p: p, q: q }
    }

    #[doc(hidden)]
    pub fn lu_internal(&self) -> &MatrixMN<N, R, C> {
        &self.lu
    }

    /// The lower triangular matrix of this decomposition.
    #[inline]
    pub fn l(&self) -> MatrixMN<N, R, DimMinimum<R, C>>
        where DefaultAllocator: Allocator<N, R, DimMinimum<R, C>> {

        let (nrows, ncols) = self.lu.data.shape();
        let mut m = self.lu.columns_generic(0, nrows.min(ncols)).into_owned();
        m.fill_upper_triangle(N::zero(), 1);
        m.fill_diagonal(N::one());
        m
    }

    /// The upper triangular matrix of this decomposition.
    #[inline]
    pub fn u(&self) -> MatrixMN<N, DimMinimum<R, C>, C>
        where DefaultAllocator: Allocator<N, DimMinimum<R, C>, C> {
        let (nrows, ncols) = self.lu.data.shape();
        self.lu.rows_generic(0, nrows.min(ncols)).upper_triangle()
    }

    /// The row permutations of this decomposition.
    #[inline]
    pub fn p(&self) -> &PermutationSequence<DimMinimum<R, C>> {
        &self.p
    }

    /// The q permutations of this decomposition.
    #[inline]
    pub fn q(&self) -> &PermutationSequence<DimMinimum<R, C>> {
        &self.q
    }

    /// The two matrix of this decomposition and the permutation matrix: `(P, L, U, Q)`.
    #[inline]
    pub fn unpack(self) -> (PermutationSequence<DimMinimum<R, C>>,
                            MatrixMN<N, R, DimMinimum<R, C>>,
                            MatrixMN<N, DimMinimum<R, C>, C>,
                            PermutationSequence<DimMinimum<R, C>>)
        where DefaultAllocator: Allocator<N, R, DimMinimum<R, C>> +
                                Allocator<N, DimMinimum<R, C>, C> {
        // Use reallocation for either l or u.
        let l = self.l();
        let u = self.u();
        let p = self.p;
        let q = self.q;

        (p, l, u, q)
    }
}

impl<N: Real, D: DimMin<D, Output = D>> FullPivLU<N, D, D>
    where DefaultAllocator: Allocator<N, D, D> +
                            Allocator<(usize, usize), D> {
    /// Solves the linear system `self * x = b`, where `x` is the unknown to be determined.
    ///
    /// Retuns `None` if the decomposed matrix is not invertible.
    pub fn solve<R2: Dim, C2: Dim, S2>(&self, b: &Matrix<N, R2, C2, S2>) -> Option<MatrixMN<N, R2, C2>>
        where S2: StorageMut<N, R2, C2>,
              ShapeConstraint: SameNumberOfRows<R2, D>,
              DefaultAllocator: Allocator<N, R2, C2> {
        let mut res = b.clone_owned();
        if self.solve_mut(&mut res) {
            Some(res)
        }
        else {
            None
        }
    }

    /// Solves the linear system `self * x = b`, where `x` is the unknown to be determined.
    ///
    /// If the decomposed matrix is not invertible, this returns `false` and its input `b` is
    /// left unchanged.
    pub fn solve_mut<R2: Dim, C2: Dim, S2>(&self, b: &mut Matrix<N, R2, C2, S2>) -> bool
        where S2: StorageMut<N, R2, C2>,
              ShapeConstraint: SameNumberOfRows<R2, D> {

        assert_eq!(self.lu.nrows(), b.nrows(), "FullPivLU solve matrix dimension mismatch.");
        assert!(self.lu.is_square(), "FullPivLU solve: unable to solve a non-square system.");

        if self.is_invertible() {
            self.p.permute_rows(b);
            self.lu.solve_lower_triangular_with_diag_mut(b, N::one());
            self.lu.solve_upper_triangular_mut(b);
            self.q.inv_permute_rows(b);

            true
        }
        else {
            false
        }
    }

    /// Computes the inverse of the decomposed matrix.
    ///
    /// Returns `None` if the decomposed matrix is not invertible.
    pub fn try_inverse(&self) -> Option<MatrixN<N, D>> {
        assert!(self.lu.is_square(), "FullPivLU inverse: unable to compute the inverse of a non-square matrix.");

        let (nrows, ncols) = self.lu.data.shape();

        let mut res = MatrixN::identity_generic(nrows, ncols);
        if self.solve_mut(&mut res) {
            Some(res)
        }
        else {
            None
        }
    }

    /// Indicates if the decomposed matrix is invertible.
    pub fn is_invertible(&self) -> bool {
        assert!(self.lu.is_square(), "FullPivLU: unable to test the invertibility of a non-square matrix.");

        let dim = self.lu.nrows();
        !self.lu[(dim - 1, dim - 1)].is_zero()
    }

    /// Computes the determinant of the decomposed matrix.
    pub fn determinant(&self) -> N {
        assert!(self.lu.is_square(), "FullPivLU determinant: unable to compute the determinant of a non-square matrix.");

        let dim = self.lu.nrows();
        let mut res = self.lu[(dim - 1, dim - 1)];
        if !res.is_zero() {
            for i in 0 .. dim - 1 {
                res *= unsafe { *self.lu.get_unchecked(i, i) };
            }

            res * self.p.determinant() * self.q.determinant()
        }
        else {
            N::zero()
        }
    }
}

impl<N: Real, R: DimMin<C>, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S>
    where DefaultAllocator: Allocator<N, R, C> +
                            Allocator<(usize, usize), DimMinimum<R, C>> {
    /// Computes the LU decomposition with full-pivoting of `matrix`.
    ///
    /// This effectively computes `P, L, U, Q` such that `P * matrix * Q = LU`.
    pub fn full_piv_lu(self) -> FullPivLU<N, R, C> {
        FullPivLU::new(self.into_owned())
    }
}