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title: "09 - Generated vectors, matrices, and cubes"
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**Adapted from the official Armadillo
[documentation](https://arma.sourceforge.net/docs.html).**

# Generate vector with linearly spaced elements {#linspace}

The `linspace()` function generates a vector of linearly spaced values from
`start` to `end` (it includes `end`). The arguments can be `start, end` or
`start, end, N`, where `N` is optional and indicates the number of elements in
the vector (`N` is 100 by default).

The usage is:

```cpp
vec v = linspace(start, end, N)
vector_type v = linspace<vector_type>(start, end, N)
```

## Examples

```cpp
[[cpp4r::register]] doubles linspace1_(const int& n) {
  vec a = linspace(1, 2, n);
  rowvec b = linspace<rowvec>(3, 4, n);

  vec res = a + b.t();

  return as_doubles(res);
}
```

## Caveat

For `N = 1`, the generated vector will have a single element equal to end.

# Generate vector with logarithmically spaced elements {#logspace}

The `logspace()` function generates a vector of logarithmically spaced values
from `10^start` to `10^end` (it includes `10^end`). The arguments can be
`start, end` or `start, end, N`, where `N` is optional and
indicates the number of elements in the vector (`N` is 50 by default).

The usage is:

```cpp
vec v = logspace(start, end, N)
vector_type v = logspace<vector_type>(start, end, N)
```

## Examples

```cpp
[[cpp4r::register]] doubles logspace1_(const int& n) {
  vec a = logspace(1, 2, n);
  rowvec b = logspace<rowvec>(3, 4, n);

  vec res = a + b.t();

  return as_doubles(res);
}
```

# Generate vector with regularly spaced elements {#regspace}

The `regspace()` function generates a vector of regularly spaced values
`start, start + delta, start + 2*delta, ..., start + M * delta` where `M` is
`M = floor((end - start) / delta)`. The arguments can be `start, end` or
`start, delta, end`, where `delta` is optional (`delta = 1` if `start <= end`
and `delta = -1` if `start > end` by default).

The usage is:

```cpp
vec v = regspace(start, end)
vec v = regspace(start, delta, end)
vector_type v = regspace<vector_type>(start, end)
vector_type v = regspace<vector_type>(start, delta, end)
```

The output vector will be empty if any of the following conditions are met:

* `start < end` and `delta < 0`
* `start > end` and `delta > 0`
* `delta = 0`

## Examples

```cpp
[[cpp4r::register]] doubles regspace1_(const double& delta) {
  vec a = regspace(1, delta, 2);
  rowvec b = regspace<rowvec>(3, delta, 4);

  vec res = a + b.t();

  return as_doubles(res);
}
```

## Caveats

* This is different from Matlab/Octave.
* Do not use `regspace()` to specify ranges for contiguous submatrix views,
  use `span()` instead.

# Generate vector with random permutation of a sequence of integers {#randperm}

The `randperm()` function generates a vector of permutation of integers from
`0` to `N-1`. The argument can be empty, `N`, or `N, M`,
where `N` (`N = 10` by default) is the range of integers and `M` (`M = N` by
default) is the length of the output.

The usage is:

```cpp
uvec v = randperm(N)
uvec v = randperm(N, M)
```

## Examples

```cpp
[[cpp4r::register]] integers randperm1_(const int& n, const int& m) {
  uvec a = randperm(n);
  uvec b = randperm(n, m);

  uvec res = a + b;

  return as_integers(res);
}
```

# Generate identity matrix {#eye}

The `eye()` function generates a matrix of size `n x m`. The argument
can be `n_rows, n_cols` or `size(X)`. When `n_rows = n_cols`, the output is
an identity matrix.

The usage is:

```cpp
mat X = eye(n_rows, n_cols)
matrix_type X = eye<matrix_type>(n_rows, n_cols) 
matrix_type X = eye<matrix_type>(size(X))
```

## Examples

```cpp
[[cpp4r::register]] doubles_matrix<> eye1_(const int& n) {
  mat A = eye(5,5);  // or:  mat A(5,5,fill::eye);

  fmat B = 123.0 * eye<fmat>(5,5);

cx_mat C = eye<cx_mat>( size(B) );

  return as_doubles(res);
}
```

# Generate object filled with ones {#ones}

The `ones()` function generates a vector, matrix or cube. The arguments can be
`n_elem`, `n_rows, n_cols`, `n_rows, n_cols, n_slices`, or `size(X)`. The

The usage is:

```cpp
vector_type v = ones<vector_type>(n_elem)
matrix_type X = ones<matrix_type>(n_rows, n_cols)
matrix_type Y = ones<matrix_type>(size(X))
cube_type Q = ones<cube_type>(n_rows, n_cols, n_slices)
cube_type R = ones<cube_type>(size(Q))
```

## Examples

```cpp
[[cpp4r::register]] doubles_matrix<> ones2_(const int& n) {
  vec v = ones(n);  // or: vec v(10, fill::ones);
  uvec u = ones<uvec>(n);
  rowvec r = ones<rowvec>(n);

  mat A = ones(n, n);  // or: mat A(n, n, fill::ones);
  fmat B = ones<fmat>(n, n);

  cube Q = ones(n, n, n + 1);  // or: cube Q(n, n, n + 1, fill::ones);

  mat res = diagmat(v) + diagmat(conv_to<vec>::from(u)) + diagmat(r) + A + B +
    Q.slice(0);

  return as_doubles_matrix(res);
}
```

## Caveat

Specifying `fill::ones` during object construction is more compact. For example,
`mat A(5, 6, fill::ones)`.

# Generate object filled with zeros {#zeros}

The `zeros()` function generates a vector, matrix or cube. The arguments can be
`n_elem`, `n_rows, n_cols`, `n_rows, n_cols, n_slices`, or `size(X)`.

The usage is:

```cpp
vector_type v = zeros<vector_type>(n_elem)
matrix_type X = zeros<matrix_type>(n_rows, n_cols)
matrix_type Y = zeros<matrix_type>(size(X))
cube_type Q = zeros<cube_type>(n_rows, n_cols, n_slices)
cube_type R = zeros<cube_type>(size(Q))
```

## Examples

```cpp
[[cpp4r::register]] doubles_matrix<> zeros2_(const int& n) {
  vec v = zeros(n);  // or: vec v(10, fill::zeros);
  uvec u = zeros<uvec>(n);
  rowvec r = zeros<rowvec>(n);

  mat A = zeros(n, n);  // or: mat A(n, n, fill::zeros);
  fmat B = zeros<fmat>(n, n);

  cube Q = zeros(n, n, n + 1);  // or: cube Q(n, n, n + 1, fill::zeros);

  mat res = diagmat(v) + diagmat(conv_to<vec>::from(u)) + diagmat(r) + A + B +
    Q.slice(0);

  return as_doubles_matrix(res);
}
```

## Caveat

Specifying `fill::zeros` during object construction is more compact. For
example, `mat A(5, 6, fill::zeros)`.

# Generate object with random values from a uniform distribution {#randu}

The `randu()` function generates a vector, matrix or cube with the elements set
to random floating point values uniformly distributed in the `[a,b]` interval.
The arguments can be `distr_param(a,b)`, `n_elem`, `n_elem, distr_param(a,b)`,
`n_rows, n_cols`, `n_rows, n_cols, distr_param(a,b)`,
`n_rows, n_cols, n_slices`, `n_rows, n_cols, n_slices, distr_param(a,b)`,
`size(X)`, or `size(X), distr_param(a,b)`.

The usage is:

```cpp
// the scalar type can be: float, double, cx_float, or cx_double

scalar_type s = randu<scalar_type>()
scalar_type s = randu<scalar_type>(distr_param(a,b))

vector_type v = randu<vector_type>(n_elem)
vector_type v = randu<vector_type>(n_elem, distr_param(a,b))

matrix_type X = randu<matrix_type>(n_rows, n_cols)
matrix_type X = randu<matrix_type>(n_rows, n_cols, distr_param(a,b))

cube_type Q = randu<cube_type>(n_rows, n_cols, n_slices)
cube_type Q = randu<cube_type>(n_rows, n_cols, n_slices, distr_param(a,b))
```

## Examples

```cpp
[[cpp4r::register]] doubles_matrix<> randu3_(const int& n) {
  double a = randu();
  double b = randu(distr_param(10, 20));

  vec v1 = randu(n);  // or vec v1(n, fill::randu);
  vec v2 = randu(n, distr_param(10, 20));

  rowvec r1 = randu<rowvec>(n);
  rowvec r2 = randu<rowvec>(n, distr_param(10, 20));

  mat A1 = randu(n, n);  // or mat A1(n, n, fill::randu);
  mat A2 = randu(n, n, distr_param(10, 20));

  fmat B1 = randu<fmat>(n, n);
  fmat B2 = randu<fmat>(n, n, distr_param(10, 20));

  mat res = diagmat(v1) + diagmat(v2) + diagmat(r1) + diagmat(r2) + A1 + A2 +
    B1 + B2;

  res.each_col([a](vec& x) { x += a; });
  res.each_row([b](rowvec& y) { y /= b; });

  return as_doubles_matrix(res);
}
```

## Caveat

To generate a matrix with random integer values instead of floating point
values, use `randi()` instead.

# Generate object with random values from a normal/gaussian distribution {#randn}

The `randn()` function generates a vector, matrix or cube with the elements set
to random floating point values normally distributed with mean `0` and standard
deviation `1`. The arguments can be `n_elem, distr_param(mean, stddev)`,
`n_elem`, `n_elem, distr_param(mean, stddev)`, `n_rows, n_cols`,
`n_rows, n_cols, distr_param(mean, stddev)`, `n_rows, n_cols, n_slices`,
`n_rows, n_cols, n_slices, distr_param(mean, stddev)`, `size(X)`, or
`size(X), distr_param(mean, stddev)`.

The usage is:

```cpp
// the scalar type can be: float, double, cx_float, or cx_double

scalar_type s = randn<scalar_type>()
scalar_type s = randn<scalar_type>(distr_param(mean, stddev))

vector_type v = randn<vector_type>(n_elem)
vector_type v = randn<vector_type>(n_elem, distr_param(mean, stddev))

matrix_type X = randn<matrix_type>(n_rows, n_cols)
matrix_type X = randn<matrix_type>(n_rows, n_cols, distr_param(mean, stddev))

cube_type Q = randn<cube_type>(n_rows, n_cols, n_slices)
cube_type Q = randn<cube_type>(n_rows, n_cols, n_slices, distr_param(mean, stddev))
```

## Examples

```cpp
[[cpp4r::register]] doubles_matrix<> randn3_(const int& n) {
  vec v1 = randn(n);  // or vec v1(n, fill::randn);
  vec v2 = randn(n, distr_param(10, 20));

  rowvec r1 = randn<rowvec>(n);
  rowvec r2 = randn<rowvec>(n, distr_param(10, 20));

  mat A1 = randn(n, n);  // or mat A1(n, n, fill::randn);
  mat A2 = randn(n, n, distr_param(10, 20));

  fmat B1 = randn<fmat>(n, n);
  fmat B2 = randn<fmat>(n, n, distr_param(10, 20));

  mat res = diagmat(v1) + diagmat(v2) + diagmat(r1) + diagmat(r2) + A1 + A2 +
    B1 + B2;

  return as_doubles_matrix(res);
}
```

# Generate object with random values from a gamma distribution {#randg}

The `randg()` function generates a vector, matrix or cube with the elements set
to random floating point values gamma distributed with shape `a` and scale `b`.
The arguments can be `distr_param(a, b)`, `n_elem`, `n_elem, distr_param(a, b)`,
`n_rows, n_cols`, `n_rows, n_cols, distr_param(a, b)`,
`n_rows, n_cols, n_slices`, `n_rows, n_cols, n_slices, distr_param(a, b)`,
`size(X)`, or `size(X), distr_param(a, b)`.

The usage is:

```cpp
// the scalar type can be: float, double, cx_float, or cx_double

scalar_type s = randg<scalar_type>()
scalar_type s = randg<scalar_type>(distr_param(a, b))

vector_type v = randg<vector_type>(n_elem)
vector_type v = randg<vector_type>(n_elem, distr_param(a, b))

matrix_type X = randg<matrix_type>(n_rows, n_cols)
matrix_type X = randg<matrix_type>(n_rows, n_cols, distr_param(a, b))

cube_type Q = randg<cube_type>(n_rows, n_cols, n_slices)
cube_type Q = randg<cube_type>(n_rows, n_cols, n_slices, distr_param(a, b))
```

## Examples

```cpp
[[cpp4r::register]] doubles_matrix<> randg3_(const int& n) {
  int a = randi();
  int b = randi(distr_param(-10, +20));

  imat A1 = randi(n, n);
  imat A2 = randi(n, n, distr_param(-10, +20));

  mat B1 = randi<mat>(n, n);
  mat B2 = randi<mat>(n, n, distr_param(-10, +20));

  mat res = A1 + A2 + B1 + B2;

  res.each_col([a](vec& x) { x *= a; });
  res.each_row([b](rowvec& y) { y -= b; });

  return as_doubles_matrix(res);
}
```

# Generate object with random integer values in specified interval {#randi}

The `randi()` function generates a vector, matrix or cube with the elements set
to random integer values uniformly distributed in the `[a,b]` interval. The
arguments can be `distr_param(a, b)`, `n_elem`, `n_elem, distr_param(a, b)`,
`n_rows, n_cols`, `n_rows, n_cols, distr_param(a, b)`,
`n_rows, n_cols, n_slices`, `n_rows, n_cols, n_slices, distr_param(a, b)`,
`size(X)`, or `size(X), distr_param(a, b)`. The default values are `a = 0` and
`b = maximum_int`.

The usage is:

```cpp
scalar_type s = randi<scalar_type>()
scalar_type s = randi<scalar_type>(distr_param(a, b))

vector_type v = randi<vector_type>(n_elem)
vector_type v = randi<vector_type>(n_elem, distr_param(a, b))

matrix_type X = randi<matrix_type>(n_rows, n_cols)
matrix_type X = randi<matrix_type>(n_rows, n_cols, distr_param(a, b))

cube_type Q = randi<cube_type>(n_rows, n_cols, n_slices)
cube_type Q = randi<cube_type>(n_rows, n_cols, n_slices, distr_param(a, b))
```

## Examples

```cpp
[[cpp4r::register]] integers_matrix<> randi3_(const int& n) {
  uvec v1 = randi(n);  // or uvec v1(n, fill::randi);
  uvec v2 = randi(n, distr_param(10, 20));

  umat A1 = randi(n, n);  // or umat A1(n, n, fill::randi);
  umat A2 = randi(n, n, distr_param(10, 20));

  icube Q1 = randi(icube(n, n, n + 1));  // or icube Q1(n, n, n + 1, fill::randi);
  icube Q2 = randi(icube(n, n, n + 1), distr_param(10, 20));

  mat res = diagmat(conv_to<vec>::from(v1)) + diagmat(conv_to<vec>::from(v2)) +
    A1 + A2 + Q1.slice(0) + Q2.slice(0);

  return as_integers_matrix(res);
}
```

## Caveat

To generate a matrix with random floating point values (e.g., float or double)
instead of integers, use `randu()` instead.

# Generate sparse identity matrix {#speye}

The `speye()` function generates a sparse matrix of size `n x n` with the
elements on the diagonal set to `1` and the remaining elements set to `0`. The
argument can be `n_rows, n_cols` or `size(X)`. An identity matrix is generated
when `n_rows = n_cols`.

The usage is:

```cpp
sparse_matrix_type X = speye(n_rows, n_cols)
sparse_matrix_type X = speye<sparse_matrix_type>(size(X))
```

## Examples

```cpp
[[cpp4r::register]] doubles_matrix<> speye1_(const int& n) {
  sp_mat A = speye<sp_mat>(n, n);
  mat B = mat(A);
  return as_doubles_matrix(B);
}
```

# Generate sparse matrix with non-zero elements set to one {#spones}

The `spones(X)` function generates a sparse matrix with the same size as `X` and
all the non-zero elements set to `1`.

## Examples

```cpp
[[cpp4r::register]] doubles_matrix<> spones1_(const int& n) {
  sp_mat A = sprandu<sp_mat>(n, n, 0.1);
  sp_mat B = spones(A);
  mat C = mat(B);
  return as_doubles_matrix(C);
}
```

# Generate sparse matrix with non-zero elements set to random values from a uniform distribution {#sprandu}

The `sprandu()` function generates a sparse matrix of size `n_rows x n_cols`
with random floating point values uniformly distributed in the `[0,1]` interval.
The arguments can be `n_rows, n_cols, density` or `size(X), density`.

The usage is:

```cpp
sparse_matrix_type X = sprandu<sparse_matrix_type>(n_rows, n_cols, density)
sparse_matrix_type X = sprandu<sparse_matrix_type>(size(X), density)
```

## Examples

```cpp
[[cpp4r::register]] doubles_matrix<> sprandu1_(const int& n) {
  sp_mat A = sprandu<sp_mat>(n, n, 0.05);
  mat B = mat(A);
  return as_doubles_matrix(B);
}
```

# Generate sparse matrix with non-zero elements set to random values from a normal/gaussian distribution {#sprandn}

The `sprandn()` function generates a sparse matrix of size `n_rows x n_cols`
with random floating point values normally distributed with mean `0` and
standard deviation `1`. The arguments can be `n_rows, n_cols, density` or
`size(X), density`.

The usage is:

```cpp
sparse_matrix_type X = sprandn<sparse_matrix_type>(n_rows, n_cols, density)
sparse_matrix_type X = sprandn<sparse_matrix_type>(size(X), density)
```

## Examples

```cpp
[[cpp4r::register]] doubles_matrix<> sprandn1_(const int& n) {
  sp_mat A = sprandn<sp_mat>(n, n, 0.05);
  mat B = mat(A);
  return as_doubles_matrix(B);
}
```

# Generate Toeplitz matrix {#toeplitz}

The `toeplitz()` function generates a toeplitz matrix. The arguments can be
`a` or `a, b`, where `a` is a vector that determines the first column and `b`
is an optional vector that determines the first row.

Alternatively, `circ_toeplitz()` generates a circulant toeplitz matrix.

## Examples

```cpp
[[cpp4r::register]] doubles_matrix<> toeplitz1_() {
  vec a = linspace(1, 5, 5);
  vec b = linspace(1, 5, 5);

  mat A = toeplitz(a, b);
  mat B = circ_toeplitz(a);

  return as_doubles_matrix(A + B);
}
```
