The following imports are used to enable Mahout-Samsara’s Scala DSL bindings for in-core Linear Algebra:

```
import org.apache.mahout.math._
import scalabindings._
import RLikeOps._
```

Dense vectors:

```
val densVec1: Vector = (1.0, 1.1, 1.2)
val denseVec2 = dvec(1, 0, 1,1 ,1,2)
```

Sparse vectors:

```
val sparseVec1: Vector = (5 -> 1.0) :: (10 -> 2.0) :: Nil
val sparseVec1 = svec((5 -> 1.0) :: (10 -> 2.0) :: Nil)
// to create a vector with specific cardinality
val sparseVec1 = svec((5 -> 1.0) :: (10 -> 2.0) :: Nil, cardinality = 20)
```

Inline matrix initialization, either sparse or dense, is always done row wise.

Dense matrices:

```
val A = dense((1, 2, 3), (3, 4, 5))
```

Sparse matrices:

```
val A = sparse(
(1, 3) :: Nil,
(0, 2) :: (1, 2.5) :: Nil
)
```

Diagonal matrix with constant diagonal elements:

```
diag(3.5, 10)
```

Diagonal matrix with main diagonal backed by a vector:

```
diagv((1, 2, 3, 4, 5))
```

Identity matrix:

```
eye(10)
```

####Slicing and Assigning

Getting a vector element:

```
val d = vec(5)
```

Setting a vector element:

```
vec(5) = 3.0
```

Getting a matrix element:

```
val d = m(3,5)
```

Setting a matrix element:

```
M(3,5) = 3.0
```

Getting a matrix row or column:

```
val rowVec = M(3, ::)
val colVec = M(::, 3)
```

Setting a matrix row or column via vector assignment:

```
M(3, ::) := (1, 2, 3)
M(::, 3) := (1, 2, 3)
```

Setting a subslices of a matrix row or column:

```
a(0, 0 to 1) = (3, 5)
```

Setting a subslices of a matrix row or column via vector assignment:

```
a(0, 0 to 1) := (3, 5)
```

Getting a matrix as from matrix contiguous block:

```
val B = A(2 to 3, 3 to 4)
```

Assigning a contiguous block to a matrix:

```
A(0 to 1, 1 to 2) = dense((3, 2), (3 ,3))
```

Assigning a contiguous block to a matrix using the matrix assignment operator:

```
A(o to 1, 1 to 2) := dense((3, 2), (3, 3))
```

Assignment operator used for copying between vectors or matrices:

```
vec1 := vec2
M1 := M2
```

Assignment operator using assignment through a functional literal for a matrix:

```
M := ((row, col, x) => if (row == col) 1 else 0
```

Assignment operator using assignment through a functional literal for a vector:

```
vec := ((index, x) => sqrt(x)
```

Plus/minus either vector or numeric with assignment or not:

```
a + b
a - b
a + 5.0
a - 5.0
```

Hadamard (elementwise) product, either vector or matrix or numeric operands:

```
a * b
a * 0.5
```

Operations with assignment:

```
a += b
a -= b
a += 5.0
a -= 5.0
a *= b
a *= 5
```

*Some nuanced rules*:

1/x in R (where x is a vector or a matrix) is elementwise inverse. In scala it would be expressed as:

```
val xInv = 1 /: x
```

and R’s 5.0 - x would be:

```
val x1 = 5.0 -: x
```

*note: All assignment operations, including :=, return the assignee just like in C++*:

```
a -= b
```

assigns **a - b** to **b** (in-place) and returns **b**. Similarly for **a /=: b** or **1 /=: v**

Dot product:

```
a dot b
```

Matrix and vector equivalency (or non-equivalency). **Dangerous, exact equivalence is rarely useful, better to use norm comparisons with an allowance of small errors.**

```
a === b
a !== b
```

Matrix multiply:

```
a %*% b
```

Optimized Right Multiply with a diagonal matrix:

```
diag(5, 5) :%*% b
```

Optimized Left Multiply with a diagonal matrix:

```
A %*%: diag(5, 5)
```

Second norm, of a vector or matrix:

```
a.norm
```

Transpose:

```
val Mt = M.t
```

*note: Transposition is currently handled via view, i.e. updating a transposed matrix will be updating the original.* Also computing something like `$$\mathbf{X^\top}\mathbf{X}$$`

:

```
val XtX = X.t %*% X
```

will not therefore incur any additional data copying.

Matrix decompositions require an additional import:

```
import org.apache.mahout.math.decompositions._
```

All arguments in the following are matricies.

**Cholesky decomposition**

```
val ch = chol(M)
```

**SVD**

```
val (U, V, s) = svd(M)
```

**EigenDecomposition**

```
val (V, d) = eigen(M)
```

**QR decomposition**

```
val (Q, R) = qr(M)
```

**Rank**: Check for rank deficiency (runs rank-revealing QR)

```
M.isFullRank
```

**In-core SSVD**

```
Val (U, V, s) = ssvd(A, k = 50, p = 15, q = 1)
```

**Solving linear equation systems and matrix inversion:** fully similar to R semantics; there are three forms of invocation:

Solve `$$\mathbf{AX}=\mathbf{B}$$`

:

```
solve(A, B)
```

Solve `$$\mathbf{Ax}=\mathbf{b}$$`

:

```
solve(A, b)
```

Compute `$$\mathbf{A^{-1}}$$`

:

```
solve(A)
```

Vector cardinality:

```
a.length
```

Matrix cardinality:

```
m.nrow
m.ncol
```

Means and sums:

```
m.colSums
m.colMeans
m.rowSums
m.rowMeans
```

Copy-By-Value:

```
val b = a cloned
```

`$$\mathcal{U}$$`

(0,1) random matrix view:

```
val incCoreA = Matrices.uniformView(m, n, seed)
```

`$$\mathcal{U}$$`

(-1,1) random matrix view:

```
val incCoreA = Matrices.symmetricUniformView(m, n, seed)
```

`$$\mathcal{N}$$`

(-1,1) random matrix view:

```
val incCoreA = Matrices.gaussianView(m, n, seed)
```

Mahout-Math already exposes a number of iterators. Scala code just needs the following imports to enable implicit conversions to scala iterators.

```
import collection._
import JavaConversions._
```

Iterating over rows in a Matrix:

```
for (row <- m) {
... do something with row
}
```

For more information including information on Mahout-Samsara’s out-of-core Linear algebra bindings see: Mahout Scala Bindings and Mahout Spark Bindings for Linear Algebra Subroutines