yagi 0.1.3

use std::ops::Add;
use num_traits::One;

use crate::error::{Error, Result};

// sparse matrix structure
// example: the following floating-point sparse matrix is represented
//          with the corresponding values:
//    [ 0   0   0   0   0 ]
//    [ 0 2.3   0   0   0 ]
//    [ 0   0   0   0 1.2 ]
//    [ 0   0   0   0   0 ]
//    [ 0 3.4   0 4.4   0 ]
//    [ 0   0   0   0   0 ]
//
//  M (rows)        :   6
//  N (cols)        :   5
//  mlist           :   { {}, {1},   {4}, {},  {1,3}, {} }
//  nlist           :   { {}, {1,4}, {},  {4}, {2} }
//  mvals           :   { {}, {2.3}, {1.2}, {}, {3.4,4.4}, {} }
//  nvals           :   { {}, {2.3, 3.4}, {}, {4.4}, {1.2} }
//  num_mlist       :   { 0, 1, 1, 0, 2, 0 }
//  num_nlist       :   { 0, 2, 0, 1, 1 }
//  max_num_mlist   :   2
//  max_num_nlist   :   2
//
// NOTE: while this particular example does not show a particular
//       improvement in memory use, such a case can be made for
//       extremely large matrices which only have a few non-zero
//       entries.
//
#[derive(Debug, Clone)]
pub struct SMatrix<T> {
    m: usize,                       // number of rows
    n: usize,                       // number of columns
    mlist: Vec<Vec<u16>>,           // list of non-zero col indices in each row
    nlist: Vec<Vec<u16>>,           // list of non-zero row indices in each col
    mvals: Vec<Vec<T>>,             // list of non-zero values in each row
    nvals: Vec<Vec<T>>,             // list of non-zero values in each col
    num_mlist: Vec<usize>,          // weight of each row, m
    num_nlist: Vec<usize>,          // weight of each row, n
    max_num_mlist: usize,           // maximum of num_mlist
    max_num_nlist: usize,           // maximum of num_nlist
}

impl<T: Default + Copy + PartialEq + std::fmt::Display + One + Add<Output = T>> SMatrix<T> {
    /// create _m x _n matrix, initialized with zeros
    pub fn new(m: usize, n: usize) -> Result<Self> {
        if m == 0 || n == 0 {
            return Err(Error::Config("smatrix_create(), dimensions must be greater than zero".to_string()));
        }

        Ok(SMatrix {
            m,
            n,
            mlist: vec![Vec::new(); m],
            nlist: vec![Vec::new(); n],
            mvals: vec![Vec::new(); m],
            nvals: vec![Vec::new(); n],
            num_mlist: vec![0; m],
            num_nlist: vec![0; n],
            max_num_mlist: 0,
            max_num_nlist: 0,
        })
    }

    /// create _m x _n matrix, initialized on array
    pub fn from_array(v: &[T], m: usize, n: usize) -> Result<Self> {
        let mut q = Self::new(m, n)?;

        for i in 0..m {
            for j in 0..n {
                if v[i * n + j] != T::default() {
                    q.set(i, j, v[i * n + j]);
                }
            }
        }

        Ok(q)
    }

    /// print compact form
    pub fn print(&self) {
        println!("dims : {} {}", self.m, self.n);
        println!("max  : {} {}", self.max_num_mlist, self.max_num_nlist);
        print!("rows :");
        for i in 0..self.m {
            print!(" {}", self.num_mlist[i]);
        }
        println!();
        print!("cols :");
        for j in 0..self.n {
            print!(" {}", self.num_nlist[j]);
        }
        println!();

        // print mlist
        println!("row indices:");
        for i in 0..self.m {
            if self.num_mlist[i] == 0 {
                continue;
            }
            print!("  {:3} :", i);
            for j in 0..self.num_mlist[i] {
                print!(" {}", self.mlist[i][j]);
            }
            println!();
        }

        // print nlist
        println!("column indices:");
        for j in 0..self.n {
            if self.num_nlist[j] == 0 {
                continue;
            }
            print!("  {:3} :", j);
            for i in 0..self.num_nlist[j] {
                print!(" {}", self.nlist[j][i]);
            }
            println!();
        }

        // print mvals
        println!("row values:");
        for i in 0..self.m {
            print!("  {:3} :", i);
            for j in 0..self.num_mlist[i] {
                print!(" {:6.2}", self.mvals[i][j]);
            }
            println!();
        }

        // print nvals
        println!("column values:");
        for j in 0..self.n {
            print!("  {:3} :", j);
            for i in 0..self.num_nlist[j] {
                print!(" {:6.2}", self.nvals[j][i]);
            }
            println!();
        }
    }

    /// print expanded form
    pub fn print_expanded(&self) {
        for i in 0..self.m {
            let mut t = 0;
            for j in 0..self.n {
                if t == self.num_mlist[i] {
                    print!("{:6.2} ", T::default());
                } else if self.mlist[i][t] == j as u16 {
                    print!("{:6.2} ", self.mvals[i][t]);
                    t += 1;
                } else {
                    print!("{:6.2} ", T::default());
                }
            }
            println!();
        }
    }

    /// get matrix dimensions
    pub fn size(&self) -> (usize, usize) {
        (self.m, self.n)
    }

    /// zero all values, retaining memory allocation
    pub fn clear(&mut self) {
        for i in 0..self.m {
            for j in 0..self.num_mlist[i] {
                self.mvals[i][j] = T::default();
            }
        }

        for j in 0..self.n {
            for i in 0..self.num_nlist[j] {
                self.nvals[j][i] = T::default();
            }
        }
    }

    /// zero all values, clearing memory
    pub fn reset(&mut self) {
        for i in 0..self.m {
            self.num_mlist[i] = 0;
        }
        for j in 0..self.n {
            self.num_nlist[j] = 0;
        }

        self.max_num_mlist = 0;
        self.max_num_nlist = 0;
    }

    /// determine if element is set
    pub fn isset(&self, m: usize, n: usize) -> Result<bool> {
        if m >= self.m || n >= self.n {
            return Err(Error::Range(format!("smatrix_isset({},{}), index exceeds matrix dimension ({},{})", m, n, self.m, self.n)));
        }

        Ok(self.mlist[m].contains(&(n as u16)))
    }

    // insert element at index
    fn insert(&mut self, m: usize, n: usize, v: T) -> Result<()> {
        if m >= self.m || n >= self.n {
            return Err(Error::Range(format!("smatrix_insert({},{}), index exceeds matrix dimension ({},{})", m, n, self.m, self.n)));
        }

        // check to see if element is already set
        if self.isset(m, n)? {
            // simply set the value and return
            // println!("smatrix_insert(), value already set...");
            self.set(m, n, v);
            return Ok(());
        }

        // increment list sizes
        self.num_mlist[m] += 1;
        self.num_nlist[n] += 1;

        // find index within list to insert new value
        let mindex = Self::indexsearch(&self.mlist[m], self.num_mlist[m] - 1, n as u16);
        let nindex = Self::indexsearch(&self.nlist[n], self.num_nlist[n] - 1, m as u16);

        // insert indices to appropriate place in list
        self.mlist[m].insert(mindex, n as u16);
        self.nlist[n].insert(nindex, m as u16);

        // insert values to appropriate place in list
        self.mvals[m].insert(mindex, v);
        self.nvals[n].insert(nindex, v);

        // update maximum
        self.max_num_mlist = self.max_num_mlist.max(self.num_mlist[m]);
        self.max_num_nlist = self.max_num_nlist.max(self.num_nlist[n]);

        Ok(())
    }

    /// delete element at index
    pub fn delete(&mut self, m: usize, n: usize) -> Result<()> {
        if m > self.m || n > self.n {
            return Err(Error::Range(format!("smatrix_delete({},{}), index exceeds matrix dimension ({},{})", m, n, self.m, self.n)));
        }

        // check to see if element is already not set
        if !self.isset(m, n)? {
            return Ok(());
        }

        // remove value from mlist
        let mindex = self.mlist[m].iter().position(|&x| x == n as u16).unwrap();
        self.mlist[m].remove(mindex);

        // remove value from nlist
        let nindex = self.nlist[n].iter().position(|&x| x == m as u16).unwrap();
        self.nlist[n].remove(nindex);

        // reduce sizes
        self.num_mlist[m] -= 1;
        self.num_nlist[n] -= 1;

        // reset maxima
        if self.max_num_mlist == self.num_mlist[m] + 1 {
            self.reset_max_mlist();
        }

        if self.max_num_nlist == self.num_nlist[n] + 1 {
            self.reset_max_nlist();
        }

        Ok(())
    }

    /// set element value at index
    pub fn set(&mut self, m: usize, n: usize, v: T) {
        if m >= self.m || n >= self.n {
            panic!("smatrix_set({},{}), index exceeds matrix dimension ({},{})", m, n, self.m, self.n);
        }

        // insert new element if not already allocated
        if !self.isset(m, n).unwrap() {
            self.insert(m, n, v).unwrap();
            return;
        }

        // set value
        let mindex = self.mlist[m].iter().position(|&x| x == n as u16).unwrap();
        self.mvals[m][mindex] = v;

        let nindex = self.nlist[n].iter().position(|&x| x == m as u16).unwrap();
        self.nvals[n][nindex] = v;

        ()
    }

    /// get element value at index (return zero if not set)
    pub fn get(&self, m: usize, n: usize) -> T {
        if m >= self.m || n >= self.n {
            panic!("smatrix_get({},{}), index exceeds matrix dimension ({},{})", m, n, self.m, self.n);
        }

        if let Some(mindex) = self.mlist[m].iter().position(|&x| x == n as u16) {
            self.mvals[m][mindex]
        } else {
            T::default()
        }
    }

    /// initialize to identity matrix
    pub fn eye(&mut self) {
        // reset all elements
        self.reset();

        // set values along diagonal
        let dmin = self.m.min(self.n);
        for i in 0..dmin {
            self.set(i, i, T::one());
        }
    }

    /// multiply two sparse matrices
    pub fn mul(&self, b: &SMatrix<T>, c: &mut SMatrix<T>) -> Result<()> {
        // validate input
        if c.m != self.m || c.n != b.n || self.n != b.m {
            return Err(Error::Range("smatrix_mul(), invalid dimensions".to_string()));
        }

        // clear output matrix (retain memory allocation)
        c.clear();

        for r in 0..c.m {
            // find number of non-zero entries in row 'r' of matrix 'self'
            let nnz_a_row = self.num_mlist[r];

            // if this number is zero, there will not be any non-zero
            // entries in the corresponding row of the output matrix 'c'
            if nnz_a_row == 0 {
                continue;
            }

            for col in 0..c.n {
                // find number of non-zero entries in column 'col' of matrix 'b'
                let nnz_b_col = b.num_nlist[col];

                let mut p = T::default();
                let mut set_value = false;

                // find common elements between non-zero elements in
                // row 'r' of matrix 'self' and col 'col' of matrix 'b'
                let mut i = 0; // reset array index for rows of 'self'
                let mut j = 0; // reset array index for cols of 'b'
                while i < nnz_a_row && j < nnz_b_col {
                    let ca = self.mlist[r][i];
                    let rb = b.nlist[col][j];
                    if ca == rb {
                        // match found between self[r,ca] and b[rb,col]
                        p = p + self.mvals[r][i] * b.nvals[col][j];
                        set_value = true;
                        i += 1;
                        j += 1;
                    } else if ca < rb {
                        i += 1; // increment index for 'self'
                    } else {
                        j += 1; // increment index for 'b'
                    }
                }

                // set value if any multiplications have been made
                if set_value {
                    c.set(r, col, p);
                }
            }
        }

        Ok(())
    }

    /// multiply by vector
    ///  self  :   sparse matrix
    ///  x  :   input vector [size: _n x 1]
    ///  y  :   output vector [size: _m x 1]
    pub fn vmul(&self, x: &[T], y: &mut [T]) {
        // initialize to zero
        for i in 0..self.m {
            y[i] = T::default();
        }

        for i in 0..self.m {
            let mut p = T::default();
            for j in 0..self.num_mlist[i] {
                let col_index = self.mlist[i][j] as usize;
                p = p + self.mvals[i][j] * x[col_index];
            }
            y[i] = p;
        }
    }

    fn reset_max_mlist(&mut self) {
        self.max_num_mlist = self.num_mlist.iter().max().copied().unwrap_or(0);
    }

    fn reset_max_nlist(&mut self) {
        self.max_num_nlist = self.num_nlist.iter().max().copied().unwrap_or(0);
    }

    fn indexsearch(v: &[u16], n: usize, x: u16) -> usize {
        let mut i = 0;
        while i < n && v[i] <= x {
            i += 1;
        }
        i
    }

}

impl SMatrix<u8> {
    // ...

    /// Multiply sparse binary matrix by floating-point matrix
    ///
    /// # Arguments
    ///
    /// * `x` - Input matrix with dimensions `mx` x `nx`
    /// * `y` - Output matrix with dimensions `my` x `ny`
    ///
    /// # Returns
    ///
    /// * `Ok(())` if successful, `Err(())` if dimensions are invalid
    pub fn mul_f32(&self, x: &[f32], mx: usize, nx: usize, y: &mut [f32], my: usize, ny: usize) -> Result<()> {
        // ensure lengths are valid
        if my != self.m || ny != nx || self.n != mx {
            return Err(Error::Range("smatrix_mul(), invalid dimensions".to_string()));
        }

        // clear output matrix
        y.fill(0.0);

        for i in 0..self.m {
            // find non-zero column entries in this row
            for &j in &self.mlist[i][..self.num_mlist[i]] {
                for k in 0..ny {
                    y[i * ny + k] += x[j as usize * nx + k];
                }
            }
        }

        Ok(())
    }

    /// Multiply sparse binary matrix by floating-point vector
    ///
    /// # Arguments
    ///
    /// * `x` - Input vector with length `self.n`
    /// * `y` - Output vector with length `self.m`
    pub fn vmul_f32(&self, x: &[f32], y: &mut [f32]) {
        for i in 0..self.m {
            // reset total
            y[i] = 0.0;

            // only accumulate values on non-zero entries
            for &j in &self.mlist[i][..self.num_mlist[i]] {
                y[i] += x[j as usize];
            }
        }
    }

    // TODO these are supposed to be integrated into mul/vmul
    pub fn wrap_bool(v: u8) -> u8 {
        v % 2
    }

    pub fn wrap_bools(v: &mut [u8]) {
        for i in 0..v.len() {
            v[i] = v[i] % 2;
        }
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use approx::assert_relative_eq;
    use test_macro::autotest_annotate;

    #[test]
    #[autotest_annotate(autotest_smatrixf_vmul)]
    fn test_smatrixf_vmul() {
        let tol = 1e-6f32;

        // Create sparse matrix and set values
        let mut a = SMatrix::<f32>::new(4, 5).unwrap();
        a.set(0, 4, 4.0);
        a.set(2, 3, 3.0);
        a.set(3, 0, 2.0);
        a.set(3, 4, 0.0);
        a.set(3, 4, 1.0);

        // Initialize input vector
        let x = vec![7.0, 1.0, 5.0, 2.0, 2.0];

        let y_test = vec![8.0, 0.0, 6.0, 16.0];
        let mut y = vec![0.0; 4];

        // Multiply and run test
        a.vmul(&x, &mut y);

        // Check values
        for i in 0..4 {
            assert_relative_eq!(y[i], y_test[i], epsilon = tol);
        }
    }

    #[test]
    #[autotest_annotate(autotest_smatrixf_mul)]
    fn test_smatrixf_mul() {
        let tol = 1e-6f32;

        // Initialize matrices
        let mut a = SMatrix::<f32>::new(4, 5).unwrap();
        let mut b = SMatrix::<f32>::new(5, 3).unwrap();
        let mut c = SMatrix::<f32>::new(4, 3).unwrap();

        // Initialize 'a'
        a.set(0, 4, 4.0);
        a.set(2, 3, 3.0);
        a.set(3, 0, 2.0);
        a.set(3, 4, 0.0);
        a.set(3, 4, 1.0);

        // Initialize 'b'
        b.set(0, 0, 7.0);
        b.set(0, 1, 6.0);
        b.set(3, 1, 5.0);
        b.set(4, 0, 2.0);

        // Compute 'c'
        a.mul(&b, &mut c).unwrap();

        let c_test = vec![
            8.0, 0.0, 0.0,
            0.0, 0.0, 0.0,
            0.0, 15.0, 0.0,
            16.0, 12.0, 0.0
        ];

        // Check values
        for i in 0..4 {
            for j in 0..3 {
                assert_relative_eq!(c.get(i, j), c_test[i * 3 + j], epsilon = tol);
            }
        }
    }

    #[test]
    #[autotest_annotate(autotest_smatrixb_vmul)]
    fn test_smatrixb_vmul() {
        // Create sparse matrix and set values
        let mut a = SMatrix::<u8>::new(8, 12).unwrap();
        a.set(0, 0, 1);
        a.set(2, 0, 1);
        a.set(6, 0, 1);
        a.set(3, 2, 1);
        a.set(6, 2, 1);
        a.set(7, 2, 1);
        a.set(1, 3, 1);
        a.set(7, 5, 1);
        a.set(3, 6, 1);
        a.set(5, 6, 1);
        a.set(7, 6, 1);
        a.set(3, 7, 1);
        a.set(2, 8, 1);
        a.set(5, 8, 1);
        a.set(2, 9, 1);
        a.set(5, 10, 1);
        a.set(6, 10, 1);
        a.set(6, 11, 1);

        // Generate vectors
        let x = vec![1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1];
        let y_test = vec![1, 0, 1, 1, 0, 1, 0, 0];
        let mut y = vec![0; 8];

        // Multiply and run test
        a.vmul(&x, &mut y);
        SMatrix::<u8>::wrap_bools(&mut y);

        assert_eq!(y, y_test);
    }

    #[test]
    #[autotest_annotate(autotest_smatrixb_mul)]
    fn test_smatrixb_mul() {
        let a_test: Vec<u8> = vec![
            0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
            0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
            0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0,
            0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
            0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0,
            0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
            0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
            0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0
        ];

        let b_test: Vec<u8> = vec![
            1, 1, 0, 0, 0,
            0, 0, 0, 0, 1,
            0, 0, 0, 0, 0,
            0, 0, 0, 0, 0,
            0, 0, 0, 0, 0,
            0, 0, 0, 0, 1,
            0, 0, 0, 1, 0,
            0, 0, 0, 1, 0,
            0, 0, 0, 0, 0,
            0, 1, 0, 0, 1,
            1, 0, 0, 1, 0,
            0, 1, 0, 0, 0
        ];

        let c_test: Vec<u8> = vec![
            0, 0, 0, 0, 0,
            0, 0, 0, 0, 0,
            0, 0, 0, 1, 0,
            0, 0, 0, 0, 0,
            0, 0, 0, 0, 0,
            0, 0, 0, 0, 0,
            0, 0, 0, 0, 1,
            0, 0, 0, 1, 0
        ];

        let a = SMatrix::<u8>::from_array(&a_test, 8, 12).unwrap();
        let b = SMatrix::<u8>::from_array(&b_test, 12, 5).unwrap();
        let mut c = SMatrix::<u8>::new(8, 5).unwrap();

        // Compute output
        a.mul(&b, &mut c).unwrap();

        for i in 0..8 {
            for j in 0..5 {
                assert_eq!(SMatrix::<u8>::wrap_bool(c.get(i, j)), c_test[i * 5 + j]);
            }
        }
    }

    #[test]
    #[autotest_annotate(autotest_smatrixb_mulf)]
    fn test_smatrixb_mulf() {
        let tol = 1e-6f32;

        // Create sparse matrix and set values
        let mut a = SMatrix::<u8>::new(8, 12).unwrap();
        a.set(0, 0, 1);
        a.set(2, 0, 1);
        a.set(6, 0, 1);
        a.set(3, 2, 1);
        a.set(6, 2, 1);
        a.set(7, 2, 1);
        a.set(1, 3, 1);
        a.set(7, 5, 1);
        a.set(3, 6, 1);
        a.set(5, 6, 1);
        a.set(7, 6, 1);
        a.set(3, 7, 1);
        a.set(2, 8, 1);
        a.set(5, 8, 1);
        a.set(2, 9, 1);
        a.set(5, 10, 1);
        a.set(6, 10, 1);
        a.set(6, 11, 1);

        // Generate vectors
        let x: Vec<f32> = vec![
            -4.3, -0.7, 3.7,
            -1.7, 2.8, 4.3,
            2.0, 1.9, 0.6,
            3.6, 1.0, -3.7,
            4.3, 0.7, 2.1,
            4.6, 0.5, 0.8,
            1.6, -3.8, -0.8,
            -1.9, -2.1, 2.8,
            -1.5, 2.5, 0.8,
            8.4, 1.5, -3.1,
            -5.8, 0.0, 2.5,
            -4.9, -2.1, -1.5
        ];

        let y_test: Vec<f32> = vec![
            -4.3, -0.7, 3.7,
            3.6, 1.0, -3.7,
            2.6, 3.3, 1.4,
            1.7, -4.0, 2.6,
            0.0, 0.0, 0.0,
            -5.7, -1.3, 2.5,
            -13.0, -0.9, 5.3,
            8.2, -1.4, 0.6
        ];

        let mut y = vec![0.0f32; 24];

        // Multiply and run test
        a.mul_f32(&x, 12, 3, &mut y, 8, 3).unwrap();

        for i in 0..24 {
            assert_relative_eq!(y[i], y_test[i], epsilon = tol);
        }
    }

    #[test]
    #[autotest_annotate(autotest_smatrixb_vmulf)]
    fn test_smatrixb_vmulf() {
        let tol = 1e-6f32;

        // Create sparse matrix and set values
        let mut a = SMatrix::<u8>::new(8, 12).unwrap();
        a.set(0, 0, 1);
        a.set(2, 0, 1);
        a.set(6, 0, 1);
        a.set(3, 2, 1);
        a.set(6, 2, 1);
        a.set(7, 2, 1);
        a.set(1, 3, 1);
        a.set(7, 5, 1);
        a.set(3, 6, 1);
        a.set(5, 6, 1);
        a.set(7, 6, 1);
        a.set(3, 7, 1);
        a.set(2, 8, 1);
        a.set(5, 8, 1);
        a.set(2, 9, 1);
        a.set(5, 10, 1);
        a.set(6, 10, 1);
        a.set(6, 11, 1);

        // Generate vectors
        let x: Vec<f32> = vec![
            3.4, -5.7, 0.3, 2.3, 1.9, 3.9,
            2.3, -4.0, -0.5, 1.5, -0.6, -1.0
        ];

        let y_test: Vec<f32> = vec![
            3.4, 2.3, 4.4, -1.4, 0.0, 1.2, 2.1, 6.5
        ];

        let mut y = vec![0.0f32; 8];

        // Multiply and run test
        a.vmul_f32(&x, &mut y);

        for i in 0..8 {
            assert_relative_eq!(y[i], y_test[i], epsilon = tol);
        }
    }


    #[test]
    #[autotest_annotate(autotest_smatrixi_vmul)]
    fn test_smatrixi_vmul() {
        // A = [
        //  0 0 0 0 4
        //  0 0 0 0 0
        //  0 0 0 3 0
        //  2 0 0 0 1

        // create sparse matrix and set values
        let mut a = SMatrix::<i16>::new(4, 5).unwrap();
        a.set(0, 4, 4);
        a.set(2, 3, 3);
        a.set(3, 0, 2);
        a.set(3, 4, 0);
        a.set(3, 4, 1);

        // initialize input vector
        let x = [7, 1, 5, 2, 2];

        let y_test = [8, 0, 6, 16];
        let mut y = [0; 4];

        // multiply and run test
        a.vmul(&x, &mut y);

        // check values
        assert_eq!(y[0], y_test[0]);
        assert_eq!(y[1], y_test[1]);
        assert_eq!(y[2], y_test[2]);
        assert_eq!(y[3], y_test[3]);
        }

    #[test]
    #[autotest_annotate(autotest_smatrixi_mul)]
    fn test_smatrixi_mul() {
        // initialize matrices
        let mut a = SMatrix::<i16>::new(4, 5).unwrap();
        let mut b = SMatrix::<i16>::new(5, 3).unwrap();
        let mut c = SMatrix::<i16>::new(4, 3).unwrap();

        // initialize 'a'
        // 0 0 0 0 4
        // 0 0 0 0 0
        // 0 0 0 3 0
        // 2 0 0 0 1
        a.set(0, 4, 4);
        a.set(2, 3, 3);
        a.set(3, 0, 2);
        a.set(3, 4, 0);
        a.set(3, 4, 1);

        // initialize 'b'
        // 7 6 0
        // 0 0 0
        // 0 0 0
        // 0 5 0
        // 2 0 0
        b.set(0, 0, 7);
        b.set(0, 1, 6);
        b.set(3, 1, 5);
        b.set(4, 0, 2);

        // compute 'c'
        //  8   0   0
        //  0   0   0
        //  0  15   0
        // 16  12   0
        a.mul(&b, &mut c).unwrap();

        let c_test = [
            8,   0,   0,
            0,   0,   0,
            0,  15,   0,
            16,  12,   0
        ];

        // check values
        for i in 0..4 {
            for j in 0..3 {
                assert_eq!(c.get(i, j), c_test[i * 3 + j]);
            }
        }
    }
}