[SPARK-4409][MLlib] Additional Linear Algebra Utils

Addition of a very limited number of local matrix manipulation and generation methods that would be helpful in the further development for algorithms on top of BlockMatrix (SPARK-3974), such as Randomized SVD, and Multi Model Training (SPARK-1486).
The proposed methods for addition are:

For `Matrix`
 - map: maps the values in the matrix with a given function. Produces a new matrix.
 - update: the values in the matrix are updated with a given function. Occurs in place.

Factory methods for `DenseMatrix`:
 - *zeros: Generate a matrix consisting of zeros
 - *ones: Generate a matrix consisting of ones
 - *eye: Generate an identity matrix
 - *rand: Generate a matrix consisting of i.i.d. uniform random numbers
 - *randn: Generate a matrix consisting of i.i.d. gaussian random numbers
 - *diag: Generate a diagonal matrix from a supplied vector
*These methods already exist in the factory methods for `Matrices`, however for cases where we require a `DenseMatrix`, you constantly have to add `.asInstanceOf[DenseMatrix]` everywhere, which makes the code "dirtier". I propose moving these functions to factory methods for `DenseMatrix` where the putput will be a `DenseMatrix` and the factory methods for `Matrices` will call these functions directly and output a generic `Matrix`.

Factory methods for `SparseMatrix`:
 - speye: Identity matrix in sparse format. Saves a ton of memory when dimensions are large, especially in Multi Model Training, where each row requires being multiplied by a scalar.
 - sprand: Generate a sparse matrix with a given density consisting of i.i.d. uniform random numbers.
 - sprandn: Generate a sparse matrix with a given density consisting of i.i.d. gaussian random numbers.
 - diag: Generate a diagonal matrix from a supplied vector, but is memory efficient, because it just stores the diagonal. Again, very helpful in Multi Model Training.

Factory methods for `Matrices`:
 - Include all the factory methods given above, but return a generic `Matrix` rather than `SparseMatrix` or `DenseMatrix`.
 - horzCat: Horizontally concatenate matrices to form one larger matrix. Very useful in both Multi Model Training, and for the repartitioning of BlockMatrix.
 - vertCat: Vertically concatenate matrices to form one larger matrix. Very useful for the repartitioning of BlockMatrix.

The names for these methods were selected from MATLAB
The zip operation creates a temp array. For sparse matrices, it is more consistent to use the standard loop:

~~~scala
while (j < numCols) {
  var idx = colPtrs(j)
  while (idx < colPtrs(j + 1)) {
    val i = rowIndices(idx)
    val v = values(idx)
    ...
    idx += 1
  }
  j += 1
}
~~~