[top] API Documentation for Armadillo 7.100 Preamble
For converting Matlab/Octave programs, see the syntax conversion table First time users: please see the short example program If you discover any bugs or regressions, please report them History of API additions |  |  | Please cite the following tech report if you use Armadillo in your research and/or software. Citations are useful for the continued development and maintenance of the library. Conrad Sanderson. Armadillo: An Open Source C++ Linear Algebra Library for Fast Prototyping and Computationally Intensive Experiments. Technical Report, NICTA, 2010. If you use Armadillo in products, please obtain the commercial license
Overview matrix, vector, cube and field classes member functions & variables generated vectors / matrices / cubes functions of vectors / matrices / cubes decompositions, factorisations, inverses and equation solvers (dense matrices) decompositions, factorisations, and equation solvers (sparse matrices) signal & image processing statistics and clustering miscellaneous (constants, configuration) Matrix, Vector, Cube and Field Classes
Mat<type>, mat, cx_mat |  | dense matrix class
Col<type>, colvec, vec |  | dense column vector class
Row<type>, rowvec |  | dense row vector class
Cube<type>, cube, cx_cube |  | dense cube class ("3D matrix")
field<object type> |  | class for storing arbitrary objects in matrix-like or cube-like layouts
SpMat<type>, sp_mat, sp_cx_mat |  | sparse matrix class
operators |  | `+ - * / % == != <= >= < >`
Member Functions & Variables
attributes |  | .n_rows, .n_cols, .n_elem, .n_slices, ...
element access |  | element/object access via (), [] and .at()
element initialisation |  | set elements via << operator or initialiser list
.zeros |  | set all elements to zero
.ones |  | set all elements to one
.eye |  | set elements along main diagonal to one and off-diagonal elements to zero
.randu / .randn |  | set all elements to random values
.fill |  | set all elements to specified value
.imbue |  | imbue (fill) with values provided by functor or lambda function
.transform |  | transform each element via functor or lambda function
.for_each |  | apply a functor or lambda function to each element
.set_size |  | change size without keeping elements (fast)
.reshape |  | change size while keeping elements
.resize |  | change size while keeping elements and preserving layout
.copy_size |  | change size to be same as given object
.reset |  | change size to empty
submatrix views |  | read/write access to contiguous and non-contiguous submatrices
subcube views |  | read/write access to contiguous and non-contiguous subcubes
subfield views |  | read/write access to contiguous subfields
.diag |  | read/write access to matrix diagonals
.each_col / .each_row |  | repeated operations on each column or row of matrix
.each_slice |  | repeated operations on each slice of cube
.set_imag / .set_real |  | set imaginary/real part
.insert_rows/cols/slices |  | insert vector/matrix/cube at specified row/column/slice
.shed_rows/cols/slices |  | remove specified rows/columns/slices
.swap_rows/cols |  | swap specified rows or columns
.swap |  | swap contents with given object
.memptr |  | raw pointer to memory
.colptr |  | raw pointer to memory used by specified column
iterators (matrices) |  | STL-style iterators and associated member functions for matrices and vectors
iterators (cubes) |  | STL-style iterators and associated member functions for cubes
STL container functions |  | STL-style container functions
.t / .st |  | return matrix transpose
.i |  | return inverse of square matrix
.min / .max |  | return extremum value, and optionally its location
.eval |  | force evaluation of delayed expression
.in_range |  | check whether given location or span is valid
.is_empty |  | check whether object is empty
.is_square |  | check whether matrix is square sized
.is_vec |  | check whether matrix is a vector
.is_sorted |  | check whether vector or matrix is sorted
.is_finite |  | check whether all elements are finite
.has_inf |  | check whether any element is +-Inf
.has_nan |  | check whether any element is NaN
.print |  | print object to std::cout or user specified stream
.raw_print |  | print object without formatting
.save/.load (matrices & cubes) |  | save/load matrices and cubes in files or streams
.save/.load (fields) |  | save/load fields in files or streams
Generated Vectors/Matrices/Cubes
eye |  | generate identity matrix
linspace |  | generate vector with linearly spaced elements
logspace |  | generate vector with logarithmically spaced elements
ones |  | generate object filled with ones
randi |  | generate object with random integer values in specified interval
randu / randn |  | generate object with random values (uniform and normal distributions)
randg |  | generate object with random values (gamma distribution)
regspace |  | generate vector with regularly spaced elements
speye |  | generate sparse identity matrix
spones |  | generate sparse matrix with non-zero elements set to one
sprandu / sprandn |  | generate sparse matrix with non-zero elements set to random values
toeplitz |  | generate Toeplitz matrix
zeros |  | generate object filled with zeros
Functions of Vectors/Matrices/Cubes
abs |  | obtain magnitude of each element
accu |  | accumulate (sum) all elements
all |  | check whether all elements are non-zero, or satisfy a relational condition
any |  | check whether any element is non-zero, or satisfies a relational condition
approx_equal |  | approximate equality
as_scalar |  | convert 1x1 matrix to pure scalar
clamp |  | obtain clamped elements according to given limits
cond |  | condition number of matrix
conj |  | obtain complex conjugate of each element
conv_to |  | convert between matrix types
cross |  | cross product
cumsum |  | cumulative sum
cumprod |  | cumulative product
det / log_det |  | determinant
diagmat |  | generate diagonal matrix from given matrix or vector
diagvec |  | extract specified diagonal
diff |  | differences between adjacent elements
dot/cdot/norm_dot |  | dot product
eps |  | obtain distance of each element to next largest floating point representation
expmat |  | matrix exponential
find |  | find indices of non-zero elements, or elements satisfying a relational condition
find_finite |  | find indices of finite elements
find_nonfinite |  | find indices of non-finite elements
find_unique |  | find indices of unique elements
fliplr / flipud |  | reverse order of columns or rows
imag / real |  | extract imaginary/real part
ind2sub |  | convert linear index to subscripts
inplace_trans |  | in-place transpose
is_finite |  | check whether all elements are finite
join_rows / join_cols |  | concatenation of matrices
join_slices |  | concatenation of cubes
kron |  | Kronecker tensor product
logmat |  | matrix logarithm
min / max |  | return extremum values
nonzeros |  | return non-zero values
norm |  | various norms of vectors and matrices
normalise |  | normalise vectors to unit p-norm
prod |  | product of elements
rank |  | rank of matrix
rcond |  | reciprocal of condition number
repmat |  | replicate matrix in block-like fashion
reshape |  | change size while keeping elements
resize |  | change size while keeping elements and preserving layout
shift |  | shift elements
shuffle |  | randomly shuffle elements
size |  | obtain dimensions of given object
sort |  | sort elements
sort_index |  | vector describing sorted order of elements
sqrtmat |  | square root of matrix
sum |  | sum of elements
sub2ind |  | convert subscripts to linear index
symmatu / symmatl |  | generate symmetric matrix from given matrix
trace |  | sum of diagonal elements
trans |  | transpose of matrix
trapz |  | trapezoidal numerical integration
trimatu / trimatl |  | generate triangular matrix from given matrix
unique |  | return unique elements
vectorise |  | convert matrix to vector
misc functions |  | miscellaneous element-wise functions: exp, log, pow, sqrt, round, sign, ...
trig functions |  | trigonometric element-wise functions: cos, sin, ...
Decompositions, Factorisations, Inverses and Equation Solvers (Dense Matrices)
chol |  | Cholesky decomposition
eig_sym |  | eigen decomposition of dense symmetric/hermitian matrix
eig_gen |  | eigen decomposition of dense general square matrix
eig_pair |  | eigen decomposition for pair of general dense square matrices
inv |  | inverse of general square matrix
inv_sympd |  | inverse of symmetric positive definite matrix
lu |  | lower-upper decomposition
null |  | orthonormal basis of null space
orth |  | orthonormal basis of range space
pinv |  | pseudo-inverse
qr |  | QR decomposition
qr_econ |  | economical QR decomposition
qz |  | generalised Schur decomposition
schur |  | Schur decomposition
solve |  | solve systems of linear equations
svd |  | singular value decomposition
svd_econ |  | economical singular value decomposition
syl |  | Sylvester equation solver
Decompositions, Factorisations and Equation Solvers (Sparse Matrices)
eigs_sym |  | limited number of eigenvalues & eigenvectors of sparse symmetric real matrix
eigs_gen |  | limited number of eigenvalues & eigenvectors of sparse general square matrix
spsolve |  | solve sparse systems of linear equations
svds |  | limited number of singular values & singular vectors of sparse matrix
Signal & Image Processing
conv |  | 1D convolution
conv2 |  | 2D convolution
fft / ifft |  | 1D fast Fourier transform and its inverse
fft2 / ifft2 |  | 2D fast Fourier transform and its inverse
interp1 |  | 1D interpolation
Statistics & Clustering
stats functions |  | mean, median, standard deviation, variance
cov |  | covariance
cor |  | correlation
hist |  | histogram of counts
histc |  | histogram of counts with user specified edges
princomp |  | principal component analysis
running_stat |  | running statistics of one dimensional process/signal
running_stat_vec |  | running statistics of multi-dimensional process/signal
kmeans |  | cluster data using k-means algorithm
gmm_diag |  | model data as a Gaussian Mixture Model (GMM)
Miscellaneous
constants |  | pi, inf, NaN, speed of light, ...
wall_clock |  | timer for measuring number of elapsed seconds
logging of errors/warnings |  | how to change the streams for displaying warnings and errors
uword / sword |  | shorthand for unsigned and signed integers
cx_double / cx_float |  | shorthand for std::complex<double> and std::complex<float>
Matlab/Armadillo syntax differences |  | examples of Matlab syntax and conceptually corresponding Armadillo syntax
example program |  | short example program
config.hpp |  | configuration options
API additions |  | API version policy and list of API additions
- Matrix, Vector, Cube and Field Classes Mat<type> mat cx_mat The root matrix class is Mat<type>, where type is one of: float, double, std::complex<float>, std::complex<double>, short, int, long, and unsigned versions of short, int, long For convenience the following typedefs have been defined:
  `mat` | = | `Mat<double>`
  `fmat` | = | `Mat<float>`
  `cx_mat` | = | `Mat<cx_double>`
  `cx_fmat` | = | `Mat<cx_float>`
  `umat` | = | `Mat<uword>`
  `imat` | = | `Mat<sword>`
- In this documentation the mat type is used for convenience; it is possible to use other types instead, eg. fmat Functions which use LAPACK or ATLAS (generally matrix decompositions) are only valid for the following types: mat, fmat, cx_mat, cx_fmat Elements are stored with column-major ordering (ie. column by column) Constructors: `mat()` `mat(n_rows, n_cols)` `mat(n_rows, n_cols, fill_type)` `mat(size(X))` `mat(size(X), fill_type)` `mat(mat)` `mat(sp_mat)` `mat(vec)` `mat(rowvec)` `mat(initializer_list)` `mat(string)` `mat(std::vector)` (treated as a column vector) `cx_mat(mat,mat)` (for constructing a complex matrix out of two real matrices) When specifying the size with n_rows and n_cols, by default the memory is uninitialised; memory can be initialised by specifying the fill_type, which is one of: fill::zeros, fill::ones, fill::eye, fill::randu, fill::randn, fill::none, with the following meanings:
  `fill::zeros` | = | set all elements to 0
  `fill::ones` | = | set all elements to 1
  `fill::eye` | = | set the elements along the main diagonal to 1 and off-diagonal elements to 0
  `fill::randu` | = | set each element to a random value from a uniform distribution in the [0,1] interval
  `fill::randn` | = | set each element to a random value from a normal/Gaussian distribution with zero mean and unit variance
  `fill::none` | = | do not modify the elements
- The string format for the constructor is elements separated by spaces, and rows denoted by semicolons. For example, the 2x2 identity matrix can be created using `"1 0; 0 1"`. Caveat: string based initialisation is slower than directly setting the elements or using element initialisation. Advanced constructors: `mat(ptr_aux_mem, n_rows, n_cols, copy_aux_mem = true, strict = false)` Create a matrix using data from writable auxiliary (external) memory, where ptr_aux_mem is a pointer to the memory. By default the matrix allocates its own memory and copies data from the auxiliary memory (for safety). However, if copy_aux_mem is set to false, the matrix will instead directly use the auxiliary memory (ie. no copying); this is faster, but can be dangerous unless you know what you are doing! The strict parameter comes into effect only when copy_aux_mem is set to false (ie. the matrix is directly using auxiliary memory) when strict is set to false, the matrix will use the auxiliary memory until a size change when strict is set to true, the matrix will be bound to the auxiliary memory for its lifetime; the number of elements in the matrix can't be changed the default setting of strict in versions 6.000+ is false the default setting of strict in versions 5.600 and earlier is true `mat(const ptr_aux_mem, n_rows, n_cols)` Create a matrix by copying data from read-only auxiliary memory, where ptr_aux_mem is a pointer to the memory `mat::fixed<n_rows, n_cols>` Create a fixed size matrix, with the size specified via template arguments. Memory for the matrix is allocated at compile time. This is generally faster than dynamic memory allocation, but the size of the matrix can't be changed afterwards (directly or indirectly). For convenience, there are several pre-defined typedefs for each matrix type (where the types are: umat, imat, fmat, mat, cx_fmat, cx_mat). The typedefs specify a square matrix size, ranging from 2x2 to 9x9. The typedefs were defined by simply appending a two digit form of the size to the matrix type -- for example, mat33 is equivalent to mat::fixed<3,3>, while cx_mat44 is equivalent to cx_mat::fixed<4,4>. `mat::fixed<n_rows, n_cols>(const ptr_aux_mem)` Create a fixed size matrix, with the size specified via template arguments; data is copied from auxiliary memory, where ptr_aux_mem is a pointer to the memory Examples:
      mat A(5, 5, fill::randu);
      double x = A(1,2);

      mat B = A + A;
      mat C = A * B;
      mat D = A % B;

      cx_mat X(A,B);

      B.zeros();
      B.set_size(10,10);
      B.ones(5,6);

      B.print("B:");

      mat::fixed<5,6> F;

      double aux_mem[24];
      mat H(&aux_mem[0], 4, 6, false);  // use auxiliary memory
- Caveat: For mathematical correctness, scalars are treated as 1x1 matrices during initialisation. As such, the code below will not generate a 5x5 matrix with every element equal to 123.0:
      mat A(5,5);  A = 123.0;
  Use the following code instead:
      mat A(5,5);  A.fill(123.0);
- See also: matrix attributes accessing elements initialising elements math & relational operators submatrix views saving & loading matrices printing matrices STL-style element iterators .eval() conv_to() (convert between matrix types) explanation of typedef (cplusplus.com) Col class Row class Cube class SpMat class (sparse matrix) config.hpp Col<type> vec cx_vec Classes for column vectors (matrices with one column) The Col<type> class is derived from the Mat<type> class and inherits most of the member functions For convenience the following typedefs have been defined:
  `vec` | = | `colvec` | = | `Col<double>`
  `fvec` | = | `fcolvec` | = | `Col<float>`
  `cx_vec` | = | `cx_colvec` | = | `Col<cx_double>`
  `cx_fvec` | = | `cx_fcolvec` | = | `Col<cx_float>`
  `uvec` | = | `ucolvec` | = | `Col<uword>`
  `ivec` | = | `icolvec` | = | `Col<sword>`
- In this documentation, the vec and colvec types have the same meaning and are used interchangeably In this documentation, the types vec or colvec are used for convenience; it is possible to use other types instead, eg. fvec, fcolvec Functions which take Mat as input can generally also take Col as input. Main exceptions are functions which require square matrices Constructors `vec()` `vec(n_elem)` `vec(n_elem, fill_type)` `vec(size(X))` `vec(size(X), fill_type)` `vec(vec)` `vec(mat)` (a std::logic_error exception is thrown if the given matrix has more than one column) `vec(initializer_list)` `vec(string)` (elements separated by spaces) `vec(std::vector)` `cx_vec(vec,vec)` (for constructing a complex vector out of two real vectors) When specifying the size with n_elem, by default the memory is uninitialised; memory can be initialised by specifying the fill_type, as per the Mat class Advanced constructors: `vec(ptr_aux_mem, number_of_elements, copy_aux_mem = true, strict = false)` Create a column vector using data from writable auxiliary (external) memory, where ptr_aux_mem is a pointer to the memory. By default the vector allocates its own memory and copies data from the auxiliary memory (for safety). However, if copy_aux_mem is set to false, the vector will instead directly use the auxiliary memory (ie. no copying); this is faster, but can be dangerous unless you know what you are doing! The strict parameter comes into effect only when copy_aux_mem is set to false (ie. the vector is directly using auxiliary memory) when strict is set to false, the vector will use the auxiliary memory until a size change when strict is set to true, the vector will be bound to the auxiliary memory for its lifetime; the number of elements in the vector can't be changed the default setting of strict in versions 6.000+ is false the default setting of strict in versions 5.600 and earlier is true `vec(const ptr_aux_mem, number_of_elements)` Create a column vector by copying data from read-only auxiliary memory, where ptr_aux_mem is a pointer to the memory `vec::fixed<number_of_elements>` Create a fixed size column vector, with the size specified via the template argument. Memory for the vector is allocated at compile time. This is generally faster than dynamic memory allocation, but the size of the vector can't be changed afterwards (directly or indirectly). For convenience, there are several pre-defined typedefs for each vector type (where the types are: uvec, ivec, fvec, vec, cx_fvec, cx_vec as well as the corresponding colvec versions). The pre-defined typedefs specify vector sizes ranging from 2 to 9. The typedefs were defined by simply appending a single digit form of the size to the vector type -- for example, vec3 is equivalent to vec::fixed<3>, while cx_vec4 is equivalent to cx_vec::fixed<4>. `vec::fixed<number_of_elements>(const ptr_aux_mem)` Create a fixed size column vector, with the size specified via the template argument; data is copied from auxiliary memory, where ptr_aux_mem is a pointer to the memory Examples:
      vec x(10);
      vec y = zeros<vec>(10);

      mat A = randu<mat>(10,10);
      vec z = A.col(5); // extract a column vector
- Caveat: For mathematical correctness, scalars are treated as 1x1 matrices during initialisation. As such, the code below will not generate a column vector with every element equal to 123.0:
      vec a(5);  a = 123.0;
  Use the following code instead:
      vec a(5);  a.fill(123.0);
- See also: element initialisation Mat class Row class Row<type> rowvec cx_rowvec Classes for row vectors (matrices with one row) The template Row<type> class is derived from the Mat<type> class and inherits most of the member functions For convenience the following typedefs have been defined:
  `rowvec` | = | `Row<double>`
  `frowvec` | = | `Row<float>`
  `cx_rowvec` | = | `Row<cx_double>`
  `cx_frowvec` | = | `Row<cx_float>`
  `urowvec` | = | `Row<uword>`
  `irowvec` | = | `Row<sword>`
- In this documentation, the rowvec type is used for convenience; it is possible to use other types instead, eg. frowvec Functions which take Mat as input can generally also take Row as input. Main exceptions are functions which require square matrices Constructors `rowvec()` `rowvec(n_elem)` `rowvec(n_elem, fill_type)` `rowvec(size(X))` `rowvec(size(X), fill_type)` `rowvec(rowvec)` `rowvec(mat)` (a std::logic_error exception is thrown if the given matrix has more than one row) `rowvec(initializer_list)` `rowvec(string)` (elements separated by spaces) `rowvec(std::vector)` `cx_rowvec(rowvec,rowvec)` (for constructing a complex row vector out of two real row vectors) When specifying the size with n_elem, by default the memory is uninitialised; memory can be initialised by specifying the fill_type, as per the Mat class Advanced constructors: `rowvec(ptr_aux_mem, number_of_elements, copy_aux_mem = true, strict = false)` Create a row vector using data from writable auxiliary (external) memory, where ptr_aux_mem is a pointer to the memory. By default the vector allocates its own memory and copies data from the auxiliary memory (for safety). However, if copy_aux_mem is set to false, the vector will instead directly use the auxiliary memory (ie. no copying); this is faster, but can be dangerous unless you know what you are doing! The strict parameter comes into effect only when copy_aux_mem is set to false (ie. the vector is directly using auxiliary memory) when strict is set to false, the vector will use the auxiliary memory until a size change when strict is set to true, the vector will be bound to the auxiliary memory for its lifetime; the number of elements in the vector can't be changed the default setting of strict in versions 6.000+ is false the default setting of strict in versions 5.600 and earlier is true `rowvec(const ptr_aux_mem, number_of_elements)` Create a row vector by copying data from read-only auxiliary memory, where ptr_aux_mem is a pointer to the memory `rowvec::fixed<number_of_elements>` Create a fixed size row vector, with the size specified via the template argument. Memory for the vector is allocated at compile time. This is generally faster than dynamic memory allocation, but the size of the vector can't be changed afterwards (directly or indirectly). For convenience, there are several pre-defined typedefs for each vector type (where the types are: urowvec, irowvec, frowvec, rowvec, cx_frowvec, cx_rowvec). The pre-defined typedefs specify vector sizes ranging from 2 to 9. The typedefs were defined by simply appending a single digit form of the size to the vector type -- for example, rowvec3 is equivalent to rowvec::fixed<3>, while cx_rowvec4 is equivalent to cx_rowvec::fixed<4>. `rowvec::fixed<number_of_elements>(const ptr_aux_mem)` Create a fixed size row vector, with the size specified via the template argument; data is copied from auxiliary memory, where ptr_aux_mem is a pointer to the memory Examples:
      rowvec x(10);
      rowvec y = zeros<rowvec>(10);

      mat    A = randu<mat>(10,10);
      rowvec z = A.row(5); // extract a row vector
- Caveat: For mathematical correctness, scalars are treated as 1x1 matrices during initialisation. As such, the code below will not generate a row vector with every element equal to 123.0:
      rowvec r(5);  r = 123.0;
  Use the following code instead:
      rowvec r(5);  r.fill(123.0);
- See also: element initialisation Mat class Col class Cube<type> cube cx_cube Classes for cubes, also known as "3D matrices" or 3rd order tensors The cube class is Cube<type>, where type is one of: float, double, std::complex<float>, std::complex<double>, short, int, long and unsigned versions of short, int, long For convenience the following typedefs have been defined:
  `cube` | = | `Cube<double>`
  `fcube` | = | `Cube<float>`
  `cx_cube` | = | `Cube<cx_double>`
  `cx_fcube` | = | `Cube<cx_float>`
  `ucube` | = | `Cube<uword>`
  `icube` | = | `Cube<sword>`
- In this documentation the cube type is used for convenience; it is possible to use other types instead, eg. fcube Cube data is stored as a set of slices (matrices) stored contiguously within memory. Within each slice, elements are stored with column-major ordering (ie. column by column) Each slice can be interpreted as a matrix, hence functions which take Mat as input can generally also take cube slices as input Constructors: `cube()` `cube(n_rows, n_cols, n_slices)` `cube(n_rows, n_cols, n_slices, fill_type)` `cube(size(X))` `cube(size(X), fill_type)` `cube(cube)` `cx_cube(cube, cube)` (for constructing a complex cube out of two real cubes) When specifying the cube size with n_rows, n_cols and n_slices, by default the memory is uninitialised; memory can be initialised by specifying the fill_type, as per the Mat class (except for fill::eye) Advanced constructors: `cube::fixed<n_rows, n_cols, n_slices>` Create a fixed size cube, with the size specified via template arguments. Memory for the cube is allocated at compile time. This is generally faster than dynamic memory allocation, but the size of the cube can't be changed afterwards (directly or indirectly). `cube(ptr_aux_mem, n_rows, n_cols, n_slices, copy_aux_mem = true, strict = false)` Create a cube using data from writable auxiliary (external) memory, where ptr_aux_mem is a pointer to the memory. By default the cube allocates its own memory and copies data from the auxiliary memory (for safety). However, if copy_aux_mem is set to false, the cube will instead directly use the auxiliary memory (ie. no copying); this is faster, but can be dangerous unless you know what you are doing! The strict parameter comes into effect only when copy_aux_mem is set to false (ie. the cube is directly using auxiliary memory) when strict is set to false, the cube will use the auxiliary memory until a size change when strict is set to true, the cube will be bound to the auxiliary memory for its lifetime; the number of elements in the cube can't be changed the default setting of strict in versions 6.000+ is false the default setting of strict in versions 5.600 and earlier is true `cube(const ptr_aux_mem, n_rows, n_cols, n_slices)` Create a cube by copying data from read-only auxiliary memory, where ptr_aux_mem is a pointer to the memory Examples:
      cube x(1,2,3);
      cube y = randu<cube>(4,5,6);

      mat A = y.slice(1);  // extract a slice from the cube
                           // (each slice is a matrix)

      mat B = randu<mat>(4,5);
      y.slice(2) = B;     // set a slice in the cube

      cube q = y + y;     // cube addition
      cube r = y % y;     // element-wise cube multiplication

      cube::fixed<4,5,6> f;
      f.ones();
- - Caveats: The size of individual slices can't be changed. For example, the following will not work:
        cube c(5,6,7);
        c.slice(0) = randu<mat>(10,20); // wrong size
  - For mathematical correctness, scalars are treated as 1x1x1 cubes during initialisation. As such, the code below will not generate a cube with every element equal to 123.0:
        cube c(5,6,7);  c = 123.0;
    Use the following code instead:
        cube c(5,6,7);  c.fill(123.0);
- See also: cube attributes accessing elements math & relational operators subcube views and slices saving & loading cubes STL-style element iterators field class Mat class field<object_type> Class for storing arbitrary objects in matrix-like or cube-like layouts Somewhat similar to a matrix or cube, but instead of each element being a scalar, each element can be a vector, or matrix, or cube Each element can have an arbitrary size (eg. in a field of matrices, each matrix can have a different size) Constructors, where object_type is another class, eg. vec, mat, std::string, etc: `field<object_type>()` `field<object_type>(n_elem)` `field<object_type>(n_rows, n_cols)` `field<object_type>(n_rows, n_cols, n_slices)` `field<object_type>(size(X))` `field<object_type>(field<object_type>)` Caveat: to store a set of matrices of the same size, the Cube class is more efficient Examples:
      mat A = randn(2,3);
      mat B = randn(4,5);

      field<mat> F(2,1);
      F(0,0) = A;
      F(1,0) = B;

      F.print("F:");

      F.save("mat_field");
- See also: field attributes subfield views saving/loading fields Cube class SpMat<type> sp_mat sp_cx_mat The root sparse matrix class is SpMat<type>, where type is one of: float, double, std::complex<float>, std::complex<double>, short, int, long and unsigned versions of short, int, long For convenience the following typedefs have been defined:
  `sp_mat` | = | `SpMat<double>`
  `sp_fmat` | = | `SpMat<float>`
  `sp_cx_mat` | = | `SpMat<cx_double>`
  `sp_cx_fmat` | = | `SpMat<cx_float>`
  `sp_umat` | = | `SpMat<uword>`
  `sp_imat` | = | `SpMat<sword>`
- In this documentation the sp_mat type is used for convenience; it is possible to use other types instead, eg. sp_fmat Constructors: `sp_mat()` `sp_mat(n_rows, n_cols)` `sp_mat(size(X))` `sp_mat(sp_mat)` `sp_mat(mat)` `sp_mat(string)` `sp_cx_mat(sp_mat,sp_mat)` (for constructing a complex matrix out of two real matrices) Elements are stored in the compressed sparse column (CSC) format All elements are treated as zero by default (ie. the matrix is initialised to contain zeros) This class behaves in a similar manner to the Mat class, however, member functions which set all elements to non-zero values (and hence do not make sense for sparse matrices) have been deliberately omitted; examples of omitted functions: .fill(), .ones(), += scalar, etc. Batch insertion constructors: form 1: `sp_mat(locations, values, sort_locations = true)` form 2: `sp_mat(locations, values, n_rows, n_cols, sort_locations = true, check_for_zeros = true)` form 3: `sp_mat(add_values, locations, values, n_rows, n_cols, sort_locations = true, check_for_zeros = true)` form 4: `sp_mat(rowind, colptr, values, n_rows, n_cols)` Using batch insertion constructors is generally much faster than consecutively inserting values using element access operators For forms 1, 2, 3, locations is a dense matrix of type umat, with a size of 2 x N, where N is the number of values to be inserted; the location of the i-th element is specified by the contents of the i-th column of the locations matrix, where the row is in locations(0,i), and the column is in locations(1,i) For form 4, rowind is a dense column vector of type uvec containing the row indices of the values to be inserted, and colptr is a dense column vector of type uvec (with length n_cols + 1) containing indices of values corresponding to the start of new columns; the vectors correspond to the arrays used by the compressed sparse column format; this form is useful for copying data from other CSC sparse matrix containers For all forms, values is a dense column vector containing the values to be inserted; it must have the same element type as the sparse matrix. For forms 1 and 2, the value in values[i] will be inserted at the location specified by the i-th column of the locations matrix. For form 3, add_values is either true or false; when set to true, identical locations are allowed, and the values at identical locations are added The size of the constructed matrix is either automatically determined from the maximal locations in the locations matrix (form 1), or manually specified via n_rows and n_cols (forms 2, 3, 4) If sort_locations is set to false, the locations matrix is assumed to contain locations that are already sorted according to column-major ordering If check_for_zeros is set to false, the values vector is assumed to contain no zero values Caveats: support for sparse matrices in this version is preliminary the following subset of operations currently works with sparse matrices: element access fundamental arithmetic operations (such as addition and multiplication) submatrix views (contiguous forms only) diagonal views saving and loading (using arma_binary format only) element-wise functions: abs(), imag(), real(), conj(), sqrt(), square() scalar functions of matrices: accu(), as_scalar(), dot(), norm(), trace() vector valued functions of matrices: min(), max(), nonzeros(), sum(), mean(), var() matrix valued functions of matrices: diagmat(), join_rows(), join_cols(), repmat(), reshape(), resize(), .t(), trans() generated matrices: speye(), spones(), sprandu()/sprandn() eigen and svd decomposition: eigs_sym(), eigs_gen(), svds() solution of sparse linear systems: spsolve() miscellaneous: print() Examples:
      sp_mat A(5,6);
      sp_mat B(6,5);

      A(0,0) = 1;
      A(1,0) = 2;

      B(0,0) = 3;
      B(0,1) = 4;

      sp_mat C = 2*B;

      sp_mat D = A*C;

      // batch insertion of two values at (5, 6) and (9, 9)
      umat locations;
      locations << 5 << 9 << endr
                << 6 << 9 << endr;

      vec values;
      values << 1.5 << 3.2 << endr;

      sp_mat X(locations, values);
- See also: accessing elements printing matrices Sparse Matrix in Wikipedia Mat class (dense matrix) operators: `+ − * / % == != <= >= < >` Overloaded operators for Mat, Col, Row and Cube classes Meanings:
  `+` |  | Addition of two objects
  `−` |  | Subtraction of one object from another or negation of an object
  `/` |  | Element-wise division of an object by another object or a scalar
  `*` |  | Matrix multiplication of two objects; not applicable to the Cube class unless multiplying a cube by a scalar
  `%` |  | Schur product: element-wise multiplication of two objects
  `==` |  | Element-wise equality evaluation of two objects; generates a matrix of type umat with entries that indicate whether at a given position the two elements from the two objects are equal (1) or not equal (0)
  `!=` |  | Element-wise non-equality evaluation of two objects
  `>=` |  | As for `==`, but the check is for "greater than or equal to"
  `<=` |  | As for `==`, but the check is for "less than or equal to"
  `>` |  | As for `==`, but the check is for "greater than"
  `<` |  | As for `==`, but the check is for "less than"
- Caveat: operators involving an equality comparison (ie. `==`, `!=`, `>=`, `<=`) are not recommended for matrices of type mat or fmat, due to the necessarily limited precision of the underlying element types; you may wish to use approx_equal() instead A std::logic_error exception is thrown if incompatible object sizes are used If the `+`, `−` and `%` operators are chained, Armadillo will try to avoid the generation of temporaries; no temporaries are generated if all given objects are of the same type and size If the `*` operator is chained, Armadillo will try to find an efficient ordering of the matrix multiplications Examples:
      mat A = randu<mat>(5,10);
      mat B = randu<mat>(5,10);
      mat C = randu<mat>(10,5);

      mat P = A + B;
      mat Q = A - B;
      mat R = -B;
      mat S = A / 123.0;
      mat T = A % B;
      mat U = A * C;

      // V is constructed without temporaries
      mat V = A + B + A + B;

      imat AA = "1 2 3; 4 5 6; 7 8 9;";
      imat BB = "3 2 1; 6 5 4; 9 8 7;";

      // compare elements
      umat ZZ = (AA >= BB);
See also: any() all() accu() approx_equal() as_scalar() find() .each_col() & .each_row() .transform() miscellaneous element-wise functions (exp, log, pow, sqrt, square, round, ...) Member Functions & Variables attributes
`.n_rows` |  | number of rows; present in Mat, Col, Row, Cube, field and SpMat
`.n_cols` |  | number of columns; present in Mat, Col, Row, Cube, field and SpMat
`.n_elem` |  | total number of elements; present in Mat, Col, Row, Cube, field and SpMat
`.n_slices` |  | number of slices; present in Cube
`.n_nonzero` |  | number of non-zero elements; present in SpMat
- The variables are of type uword The variables are read-only; to change the size, use .set_size(), .copy_size(), .zeros(), .ones(), or .reset() For the Col and Row classes, n_elem also indicates vector length Examples:
      mat X(4,5);
      cout << "X has " << X.n_cols << " columns" << endl;
- See also: .set_size() .copy_size() .zeros() .ones() .reset() size() element/object access via (), [] and .at() Provide access to individual elements or objects stored in a container object (ie. Mat, Col, Row, Cube, field)
  `(n)` |  | For vec and rowvec, access the n-th element. For mat, cube and field, access the n-th element/object under the assumption of a flat layout, with column-major ordering of data (ie. column by column). A std::logic_error exception is thrown if the requested element is out of bounds. The bounds check can be optionally disabled at compile-time to get more speed.
  `.at(n)` or `[n]` |  | As for (n), but without a bounds check. Not recommended for use unless your code has been thoroughly debugged.
  `(i,j)` |  | For mat and 2D field classes, access the element/object stored at the i-th row and j-th column. A std::logic_error exception is thrown if the requested element is out of bounds. The bounds check can be optionally disabled at compile-time to get more speed.
  `.at(i,j)` |  | As for (i,j), but without a bounds check. Not recommended for use unless your code has been thoroughly debugged. |
  `(i,j,k)` |  | For cube and 3D field classes, access the element/object stored at the i-th row, j-th column and k-th slice. A std::logic_error exception is thrown if the requested element is out of bounds. The bounds check can be optionally disabled at compile-time to get more speed.
  `.at(i,j,k)` |  | As for (i,j,k), but without a bounds check. Not recommended for use unless your code has been thoroughly debugged.
- The bounds checks used by the (n), (i,j) and (i,j,k) access forms can be disabled by defining the ARMA_NO_DEBUG macro before including the armadillo header file (eg. #define ARMA_NO_DEBUG). Disabling the bounds checks is not recommended until your code has been thoroughly debugged -- it's better to write correct code first, and then maximise its speed. The indices of elements are specified via the uword type, which is a typedef for an unsigned integer type. When using loops to access elements, it's best to use uword instead of int. For example: `for(uword i=0; i<X.n_elem; ++i) { X(i) = ... }` Caveat: for sparse matrices, using element access operators to insert values via loops can be inefficient; you may wish to use batch insertion constructors instead Examples:
      mat A = randu<mat>(10,10);
      A(9,9) = 123.0;
      double x = A.at(9,9);
      double y = A[99];

      vec p = randu<vec>(10,1);
      p(9) = 123.0;
      double z = p[9];
- See also: .in_range() element initialisation submatrix views .memptr() iterators (matrices) iterators (cubes) config.hpp element initialisation When using the C++11 standard, elements in Mat, Col, Row can be set via initialiser lists When using the old C++98 standard, elements can be set via the `<<` operator; special element endr indicates "end of row" (conceptually similar to std::endl) Caveat: using the `<<` operator is slower than using initialiser lists Examples:
      // C++11

      vec v = { 1, 2, 3 };

      mat A = { {1, 3, 5},
                {2, 4, 6} };

      // C++98

      mat B;

      B << 1 << 3 << 5 << endr
        << 2 << 4 << 6 << endr;
See also: element access .reshape() .print() saving & loading matrices advanced constructors (matrices)
.zeros() |  |  | (member function of Mat, Col, Row, SpMat, Cube)
.zeros( n_elem ) |  |  | (member function of Col and Row)
.zeros( n_rows, n_cols ) |  |  | (member function of Mat and SpMat)
.zeros( n_rows, n_cols, n_slices ) |  |  | (member function of Cube)
.zeros( size(X) ) |  |  | (member function of Mat, Col, Row, Cube, SpMat)
- Set the elements of an object to zero, optionally first changing the size to specified dimensions Examples:
      mat A(5,10);  A.zeros();   // or:  mat A(5,10,fill::zeros);

      mat B;  B.zeros(10,20);

      mat C;  C.zeros( size(B) );
See also: zeros() (standalone function) .ones() .randu() .fill() .imbue() .reset() .set_size() size()
.ones() |  |  | (member function of Mat, Col, Row, Cube)
.ones( n_elem ) |  |  | (member function of Col and Row)
.ones( n_rows, n_cols ) |  |  | (member function of Mat)
.ones( n_rows, n_cols, n_slices ) |  |  | (member function of Cube)
.ones( size(X) ) |  |  | (member function of Mat, Col, Row, Cube)
- Set all the elements of an object to one, optionally first changing the size to specified dimensions Examples:
      mat A(5,10);  A.ones();   // or:  mat A(5,10,fill::ones);

      mat B;  B.ones(10,20);

      mat C;  C.ones( size(B) );
- See also: ones() (standalone function) .eye() .zeros() .fill() .imbue() .randu() size() .eye() .eye( n_rows, n_cols ) .eye( size(X) ) Member functions of Mat and SpMat Set the elements along the main diagonal to one and off-diagonal elements to zero, optionally first changing the size to specified dimensions An identity matrix is generated when n_rows = n_cols Examples:
      mat A(5,5);  A.eye();  // or:  mat A(5,5,fill::eye);

      mat B;  B.eye(5,5);

      mat C;  C.eye( size(B) );
See also: .ones() .diag() diagmat() diagvec() eye() (standalone function) size()
.randu() |  |  | (member function of Mat, Col, Row, Cube)
.randu( n_elem ) |  |  | (member function of Col and Row)
.randu( n_rows, n_cols ) |  |  | (member function of Mat)
.randu( n_rows, n_cols, n_slices ) |  |  | (member function of Cube)
.randu( size(X) ) |  |  | (member function of Mat, Col, Row, Cube)
.randn() |  |  | (member function of Mat, Col, Row, Cube)
.randn( n_elem ) |  |  | (member function of Col and Row)
.randn( n_rows, n_cols ) |  |  | (member function of Mat)
.randn( n_rows, n_cols, n_slices ) |  |  | (member function of Cube)
.randn( size(X) ) |  |  | (member function of Mat, Col, Row, Cube)
- Set all the elements to random values, optionally first changing the size to specified dimensions .randu() uses a uniform distribution in the [0,1] interval .randn() uses a normal/Gaussian distribution with zero mean and unit variance To change the RNG seed, use arma_rng::set_seed(value) or arma_rng::set_seed_random() functions Examples:
      mat A(5,10);  A.randu();   // or:  mat A(5,10,fill::randu);

      mat B;  B.randu(10,20);

      mat C;  C.randu( size(B) );

      arma_rng::set_seed_random();  // set the seed to a random value
- See also: randu() & randn() (standalone functions) .fill() .imbue() .ones() .zeros() size() uniform distribution in Wikipedia normal distribution in Wikipedia .fill( value ) Member function of Mat, Col, Row, Cube Sets the elements to a specified value the type of value must match the type of elements used by the container object (eg. for mat the type is double) Examples:
      mat A(5,5);
      A.fill(123.0);
- See also: .imbue() .ones() .zeros() .randu() & .randn() constants (pi, nan, inf, ...) .imbue( functor ) .imbue( lambda_function ) (C++11 only) Member functions of Mat, Col, Row and Cube Imbue (fill) with values provided by a functor or lambda function For matrices, filling is done column-by-column (ie. column 0 is filled, then column 1, ...) For cubes, filling is done slice-by-slice, with each slice treated as a matrix Examples:
      // C++11 only example
      // need to include <random>

      std::mt19937 engine;  // Mersenne twister random number engine

      std::uniform_real_distribution<double> distr(0.0, 1.0);

      mat A(4,5);

      A.imbue( [&]() { return distr(engine); } );
- See also: .fill() .transform() function object at Wikipedia C++11 lambda functions at Wikipedia lambda function at cprogramming.com .transform( functor ) .transform( lambda_function ) (C++11 only) Member functions of Mat, Col, Row and Cube Transform each element using a functor or lambda function For matrices, transformation is done column-by-column (ie. column 0 is transformed, then column 1, ...) For cubes, transformation is done slice-by-slice, with each slice treated as a matrix Examples:
      // C++11 only example

      mat A = ones<mat>(4,5);

      // add 123 to every element
      A.transform( [](double val) { return (val + 123.0); } );
- See also: .for_each() .imbue() .fill() overloaded operators miscellaneous element-wise functions (exp, log, pow, sqrt, square, round, ...) function object at Wikipedia C++11 lambda functions at Wikipedia lambda function at cprogramming.com .for_each( functor ) .for_each( lambda_function ) (C++11 only) Member functions of Mat, Col, Row, Cube and field For each element, pass its reference to a functor or lambda function For matrices, the processing is done column-by-column For cubes, processing is done slice-by-slice, with each slice treated as a matrix Examples:
      // C++11 only examples

      mat A = ones<mat>(4,5);

      // add 123 to each element
      A.for_each( [](mat::elem_type& val) { val += 123.0; } );  // NOTE: the '&' is crucial!

      field<mat> F(2,3);

      // set the size of all matrices in field F
      F.for_each( [](mat& X) { X.zeros(4,5); } );  // NOTE: the '&' is crucial!
See also: .transform() .each_col() & .each_row() miscellaneous element-wise functions (exp, log, pow, sqrt, square, round, ...) function object at Wikipedia C++11 lambda functions at Wikipedia lambda function at cprogramming.com
.set_size( n_elem ) |  |  | (member function of Col, Row, field)
.set_size( n_rows, n_cols ) |  |  | (member function of Mat, SpMat, field)
.set_size( n_rows, n_cols, n_slices ) |  |  | (member function of Cube and field)
.set_size( size(X) ) |  |  | (member function of Mat, Col, Row, Cube, SpMat, field)
- Change the size of an object, without explicitly preserving data and without initialising the elements If you need to initialise the elements to zero while changing the size, use .zeros() instead If you need to explicitly preserve data while changing the size, use .reshape() or .resize() instead; caveat: .reshape() and .resize() are considerably slower than .set_size() Examples:
      mat A;  A.set_size(5,10);       // or:  mat A(5,10);

      mat B;  B.set_size( size(A) );  // or:  mat B( size(A) );

      vec v;  v.set_size(100);        // or:  vec v(100);
See also: .reset() .copy_size() .reshape() .resize() .zeros() size()
.reshape( n_rows, n_cols ) |  |  | (member function of Mat and SpMat)
.reshape( n_rows, n_cols, n_slices ) |  |  | (member function of Cube)
.reshape( size(X) ) |  |  | (member function of Mat, Cube, SpMat)
- Recreate the object according to given size specifications, with the elements taken from the previous version of the object in a column-wise manner; the elements in the generated object are placed column-wise (ie. the first column is filled up before filling the second column) The layout of the elements in the recreated object will be different to the layout in the previous version of the object If the total number of elements in the previous version of the object is less than the specified size, the extra elements in the recreated object are set to zero If the total number of elements in the previous version of the object is greater than the specified size, only a subset of the elements is taken Caveats: do not use .reshape() if you simply want to change the size without preserving data; use .set_size() instead, which is much faster to grow/shrink the object while preserving the elements as well as the layout of the elements, use .resize() instead to create a vector representation of a matrix (ie. concatenate all the columns or rows), use vectorise() instead Examples:
      mat A = randu<mat>(4,5);

      A.reshape(5,4);
See also: .resize() .set_size() .copy_size() .zeros() .reset() reshape() (standalone function) vectorise() size()
.resize( n_elem ) |  |  | (member function of Col, Row)
.resize( n_rows, n_cols ) |  |  | (member function of Mat and SpMat)
.resize( n_rows, n_cols, n_slices ) |  |  | (member function of Cube)
.resize( size(X) ) |  |  | (member function of Mat, Col, Row, Cube, SpMat)
- Recreate the object according to given size specifications, while preserving the elements as well as the layout of the elements Can be used for growing or shrinking an object (ie. adding/removing rows, and/or columns, and/or slices) Caveat: do not use .resize() if you simply want to change the size without preserving data; use .set_size() instead, which is much faster Examples:
      mat A = randu<mat>(4,5);
      A.resize(7,6);
- See also: .reshape() .set_size() .copy_size() .zeros() .reset() .insert_rows/cols/slices .shed_rows/cols/slices resize() (standalone function) vectorise() size() .copy_size( A ) Set the size to be the same as object A Object A must be of the same root type as the object being modified (eg. you can't set the size of a matrix by providing a cube) Examples:
      mat A = randu<mat>(5,6);
      mat B;
      B.copy_size(A);

      cout << B.n_rows << endl;
      cout << B.n_cols << endl;
- See also: .reset() .set_size() .reshape() .resize() .zeros() repmat() size() .reset() Reset the size to zero (the object will have no elements) Examples:
      mat A = randu<mat>(5, 5);
      A.reset();
- See also: .set_size() .is_empty() .zeros() submatrix views A collection of member functions of Mat, Col and Row classes that provide read/write access to submatrix views
contiguous views for matrix X: X.col( col_number ) X.row( row_number ) X.cols( first_col, last_col ) X.rows( first_row, last_row ) X.submat( first_row, first_col, last_row, last_col ) X( span(first_row, last_row), span(first_col, last_col) ) X( first_row, first_col, size(n_rows, n_cols) ) X( first_row, first_col, size(Y) ) (Y is a mat) X( span(first_row, last_row), col_number ) X( row_number, span(first_col, last_col) ) X.head_cols( number_of_cols ) X.head_rows( number_of_rows ) X.tail_cols( number_of_cols ) X.tail_rows( number_of_rows ) X.unsafe_col( col_number ) contiguous views for vector V: V( span(first_index, last_index) ) V.subvec( first_index, last_index ) V.head( number_of_elements ) V.tail( number_of_elements )

non-contiguous views for matrix or vector X: X.elem( vector_of_indices ) X( vector_of_indices ) X.cols( vector_of_column_indices ) X.rows( vector_of_row_indices ) X.submat( vector_of_row_indices, vector_of_column_indices ) X( vector_of_row_indices, vector_of_column_indices ) related views (documented separately) X.diag() X.each_row() X.each_col()

- Instances of span(start,end) can be replaced by span::all to indicate the entire range
- For functions requiring one or more vector of indices, eg. X.submat(vector_of_row_indices, vector_of_column_indices), each vector of indices must be of type uvec
- In the function X.elem(vector_of_indices), elements specified in vector_of_indices are accessed. X is interpreted as one long vector, with column-by-column ordering of the elements of X. The vector_of_indices must evaluate to a vector of type uvec (eg. generated by the find() function). The aggregate set of the specified elements is treated as a column vector (ie. the output of X.elem() is always a column vector).
- The function .unsafe_col() is provided for speed reasons and should be used only if you know what you are doing. It creates a seemingly independent Col vector object (eg. vec), but uses memory from the existing matrix object. As such, the created vector is not alias safe, and does not take into account that the underlying matrix memory could be freed (eg. due to any operation involving a size change of the matrix).
- Examples:

      mat A = zeros<mat>(5,10);

      A.submat( 0,1, 2,3 )      = randu<mat>(3,3);
      A( span(0,2), span(1,3) ) = randu<mat>(3,3);
      A( 0,1, size(3,3) )       = randu<mat>(3,3);

      mat B = A.submat( 0,1, 2,3 );
      mat C = A( span(0,2), span(1,3) );
      mat D = A( 0,1, size(3,3) );

      A.col(1)        = randu<mat>(5,1);
      A(span::all, 1) = randu<mat>(5,1);

      mat X = randu<mat>(5,5);

      // get all elements of X that are greater than 0.5
      vec q = X.elem( find(X > 0.5) );

      // add 123 to all elements of X greater than 0.5
      X.elem( find(X > 0.5) ) += 123.0;

      // set four specific elements of X to 1
      uvec indices;
      indices << 2 << 3 << 6 << 8;

      X.elem(indices) = ones<vec>(4);

      // add 123 to the last 5 elements of vector a
      vec a(10, fill::randu);
      a.tail(5) += 123.0;

      // add 123 to the first 3 elements of column 2 of X
      X.col(2).head(3) += 123;

- See also:
  - diagonal views
  - .each_col() & .each_row() (vector operations repeated on each column or row)
  - .colptr()
  - .in_range()
  - find()
  - join_rows/columns/slices
  - shed_rows/columns/slices
  - .insert_rows/cols/slices
  - size()
  - subcube views

subcube views and slices

- A collection of member functions of the Cube class that provide subcube views

contiguous views for cube Q: Q.slice( slice_number ) Q.slices( first_slice, last_slice ) Q.subcube( first_row, first_col, first_slice, last_row, last_col, last_slice ) Q( span(first_row, last_row), span(first_col, last_col), span(first_slice, last_slice) ) Q( first_row, first_col, first_slice, size(n_rows, n_cols, n_slices) ) Q( first_row, first_col, first_slice, size(R) ) (R is a cube) Q.head_slices( number_of_slices ) Q.tail_slices( number_of_slices ) Q.tube( row, col ) Q.tube( first_row, first_col, last_row, last_col ) Q.tube( span(first_row, last_row), span(first_col, last_col) ) Q.tube( first_row, first_col, size(n_rows, n_cols) ) |  |  |  | non-contiguous views for cube Q: Q.elem( vector_of_indices ) Q( vector_of_indices ) related views (documented separately) Q.each_slice()

- Instances of span(a,b) can be replaced by:
  - span() or span::all, to indicate the entire range
  - span(a), to indicate a particular row, column or slice
- An individual slice, accessed via .slice(), is an instance of the Mat class (a reference to a matrix is provided)
- All .tube() forms are variants of .subcube(), using first_slice = 0 and last_slice = Q.n_slices-1
- The .tube(row,col) form uses row = first_row = last_row, and col = first_col = last_col
- In the function Q.elem(vector_of_indices), elements specified in vector_of_indices are accessed. Q is interpreted as one long vector, with slice-by-slice and column-by-column ordering of the elements of Q. The vector_of_indices must evaluate to a vector of type uvec (eg. generated by the find() function). The aggregate set of the specified elements is treated as a column vector (ie. the output of Q.elem() is always a column vector).
- Examples:

      cube A = randu<cube>(2,3,4);

      mat  B = A.slice(1); // each slice is a matrix

      A.slice(0) = randu<mat>(2,3);
      A.slice(0)(1,2) = 99.0;

      A.subcube(0,0,1,  1,1,2)             = randu<cube>(2,2,2);
      A( span(0,1), span(0,1), span(1,2) ) = randu<cube>(2,2,2);
      A( 0,0,1, size(2,2,2) )              = randu<cube>(2,2,2);

      // add 123 to all elements of A greater than 0.5
      A.elem( find(A > 0.5) ) += 123.0;

      cube C = A.head_slices(2);  // get first two slices

      A.head_slices(2) += 123.0;

- See also:
  - .in_range()
  - .each_slice()
  - join_slices()
  - shed_slices()
  - insert_slices()
  - size()
  - submatrix views

subfield views

- A collection of member functions of the field class that provide subfield views
- For a 2D field F, the subfields are accessed as:

F.row( row_number )
F.col( col_number )
F.rows( first_row, last_row )
F.cols( first_col, last_col )
F.subfield( first_row, first_col, last_row, last_col )
F( span(first_row, last_row), span(first_col, last_col) )
F( first_row, first_col, size(G) ) (G is a 2D field)
F( first_row, first_col, size(n_rows, n_cols) )

- For a 3D field F, the subfields are accessed as:

F.slice( slice_number )
F.slices( first_slice, last_slice )
F.subfield( first_row, first_col, first_slice, last_row, last_col, last_slice )
F( span(first_row, last_row), span(first_col, last_col), span(first_slice, last_slice) )
F( first_row, first_col, first_slice, size(G) ) (G is a 3D field)
F( first_row, first_col, first_slice, size(n_rows, n_cols, n_slices) )

- Instances of span(a,b) can be replaced by:
  - span() or span::all, to indicate the entire range
  - span(a), to indicate a particular row or column
- See also:
  - .in_range()
  - size()
  - submatrix views
  - subcube views

.diag()
.diag( k )

- Member function of Mat and SpMat
- Read/write access to a diagonal in a matrix
- The argument k is optional; by default the main diagonal is accessed (k=0)
- For k > 0, the k-th super-diagonal is accessed (top-right corner)
- For k < 0, the k-th sub-diagonal is accessed (bottom-left corner)
- The diagonal is interpreted as a dense column vector within expressions
- Examples:

      mat X = randu<mat>(5,5);

      vec a = X.diag();
      vec b = X.diag(1);
      vec c = X.diag(-2);

      X.diag()  = randu<vec>(5);
      X.diag() += 6;
      X.diag().ones();

- See also:
  - .eye()
  - diagvec()
  - diagmat()
  - submatrix views
  - .each_col() & .each_row()
  - trace()

.each_col()
.each_row()
.each_col( vector_of_indices )
.each_row( vector_of_indices )
.each_col( lambda_function ) (C++11 only)
.each_row( lambda_function ) (C++11 only)

- Member functions of Mat
- Repeat a vector operation on each column or row of a matrix
- Supported operations for .each_col() / .each_row() and .each_col(vector_of_indices) / .each_row(vector_of_indices) forms:

  `+` |  | addition |  | `+=` |  | in-place addition
  `-` |  | subtraction |  | `-=` |  | in-place subtraction
  `%` |  | element-wise multiplication |  | `%=` |  | in-place element-wise multiplication
  `/` |  | element-wise division |  | `/=` |  | in-place element-wise division
  `=` |  | assignment (copy) |  |  |  |

- The argument vector_of_indices is optional; by default all columns or rows are used
- If the argument vector_of_indices is specified, it must evaluate to a vector of type uvec; the vector contains a list of indices of the columns or rows to be used
- If the lambda_function is specified, the function must accept a reference to a Col or Row object with the same element type as the underlying matrix
- Examples:

      mat X = ones<mat>(6,5);
      vec v = linspace<vec>(10,15,6);

      X.each_col() += v;         // in-place addition of v to each column vector of X

      mat Y = X.each_col() + v;  // generate Y by adding v to each column vector of X

      // subtract v from columns 0 through to 3 in X
      X.cols(0,3).each_col() -= v;

      uvec indices(2);
      indices(0) = 2;
      indices(1) = 4;

      X.each_col(indices) = v;   // copy v to columns 2 and 4 in X

      X.each_col( [](vec& a){ a.print(); } );     // lambda function with non-const vector

      const mat& XX = X;
      XX.each_col( [](const vec& b){ b.print(); } );  // lambda function with const vector

- See also:
  - submatrix views
  - diagonal views
  - repmat()
  - .for_each()
  - .each_slice()

.each_slice()
.each_slice( vector_of_indices )
.each_slice( lambda_function ) (C++11 only)

- Member function of Cube
- Repeat a matrix operation on each slice of a cube
- Supported operations for .each_slice() and .each_slice(vector_of_indices) forms:

  `+` |  | addition |  | `+=` |  | in-place addition
  `-` |  | subtraction |  | `-=` |  | in-place subtraction
  `%` |  | element-wise multiplication |  | `%=` |  | in-place element-wise multiplication
  `/` |  | element-wise division |  | `/=` |  | in-place element-wise division
  `=` |  | assignment (copy) |  |  |  |

- The argument vector_of_indices is optional; by default all slices are used
- If the argument vector_of_indices is specified, it must evaluate to a vector of type uvec; the vector contains a list of indices of the slices to be used
- If the lambda_function is specified, the function must accept a reference to a Mat object with the same element type as the underlying cube
- Examples:

      cube C(4,5,6, fill::randu);

      mat M = repmat(linspace<vec>(1,4,4), 1, 5);

      C.each_slice() += M;          // in-place addition of M to each slice of C

      cube D = C.each_slice() + M;  // generate D by adding M to each slice of C

      uvec indices(2);
      indices(0) = 2;
      indices(1) = 4;

      C.each_slice(indices) = M;    // copy M to slices 2 and 4 in C

      C.each_slice( [](mat& X){ X.print(); } );     // lambda function with non-const matrix

      const cube& CC = C;
      CC.each_slice( [](const mat& X){ X.print(); } );  // lambda function with const matrix

- See also:
  - subcube views
  - .for_each()
  - .each_col() & .each_row()

.set_imag( X )
.set_real( X )

- Set the imaginary/real part of an object
- X must have the same size as the recipient object
- Examples:

         mat A = randu<mat>(4,5);
         mat B = randu<mat>(4,5);

      cx_mat C = zeros<cx_mat>(4,5);

      C.set_real(A);
      C.set_imag(B);

- Caveat: to directly construct a complex matrix out of two real matrices, the following code is faster:

         mat A = randu<mat>(4,5);
         mat B = randu<mat>(4,5);

      cx_mat C = cx_mat(A,B);

- See also:
  - matrix constructors
  - cube constructors
  - imag() / real()

.insert_rows( row_number, X ) .insert_rows( row_number, number_of_rows ) .insert_rows( row_number, number_of_rows, set_to_zero ) |  | (member functions of Mat and Col)
.insert_cols( col_number, X ) .insert_cols( col_number, number_of_cols ) .insert_cols( col_number, number_of_cols, set_to_zero ) |  | (member functions of Mat and Row)
.insert_slices( slice_number, X ) .insert_slices( slice_number, number_of_slices ) .insert_slices( slice_number, number_of_slices, set_to_zero ) |  | (member functions of Cube)

- Functions with the X argument: insert a copy of X at the specified row/column/slice
  - if inserting rows, X must have the same number of columns as the recipient object
  - if inserting columns, X must have the same number of rows as the recipient object
  - if inserting slices, X must have the same number of rows and columns as the recipient object (ie. all slices must have the same size)
- Functions with the number_of_... argument: expand the object by creating new rows/columns/slices. By default, the new rows/columns/slices are set to zero. If set_to_zero is false, the memory used by the new rows/columns/slices will not be initialised.
- Examples:

      mat A = randu<mat>(5,10);
      mat B = ones<mat>(5,2);

      // at column 2, insert a copy of B;
      // A will now have 12 columns
      A.insert_cols(2, B);

      // at column 1, insert 5 zeroed columns;
      // B will now have 7 columns
      B.insert_cols(1, 5);

- See also:
  - shed_rows/columns/slices
  - join_rows/columns/slices
  - .resize()
  - submatrix views
  - subcube views

.shed_row( row_number ) .shed_rows( first_row, last_row ) |  | (member functions of Mat, Col and SpMat)
.shed_col( column_number ) .shed_cols( first_column, last_column ) |  | (member functions of Mat, Row and SpMat)
.shed_slice( slice_number ) .shed_slices( first_slice, last_slice ) |  | (member functions of Cube)

- Single argument functions: remove the specified row/column/slice
- Two argument functions: remove the specified range of rows/columns/slices
- Examples:

      mat A = randu<mat>(5,10);

      A.shed_row(2);
      A.shed_cols(2,4);

- See also:
  - insert_rows/columns/slices
  - join_rows/columns/slices
  - .resize()
  - submatrix views
  - subcube views
  - shed in thesaurus.com

.swap_rows( row1, row2 )
.swap_cols( col1, col2 )

- Member functions of Mat, Col, Row and SpMat
- Swap the contents of specified rows or columns
- Examples:

      mat X = randu<mat>(5,5);
      X.swap_rows(0,4);

- See also:
  - fliplr() & flipud()
  - .swap()

.swap( X )

- Member function of Mat, Col, Row and Cube
- Swap contents with object X
- Examples:

      mat A = zeros<mat>(4,5);
      mat B =  ones<mat>(6,7);

      A.swap(B);

- See also:
  - .swap_rows() & .swap_cols()

.memptr()

- Member function of Mat, Col, Row and Cube
- Obtain a raw pointer to the memory used for storing elements
- The function can be used for interfacing with libraries such as FFTW
- Data for matrices is stored in a column-by-column order
- Data for cubes is stored in a slice-by-slice (matrix-by-matrix) order
- Caveat: the pointer becomes invalid after any operation involving a size change or aliasing
- Caveat: this function is not recommended for use unless you know what you are doing!
- Examples:

            mat A = randu<mat>(5,5);
      const mat B = randu<mat>(5,5);

            double* A_mem = A.memptr();
      const double* B_mem = B.memptr();

- See also:
  - .colptr()
  - element access
  - iterators (matrices)
  - iterators (cubes)
  - advanced constructors (matrices)
  - advanced constructors (cubes)

.colptr( col_number )

- Member function of Mat
- Obtain a raw pointer to the memory used by the specified column
- Caveat: the pointer becomes invalid after any operation involving a size change or aliasing
- Caveat: this function is not recommended for use unless you know what you are doing -- it is safer to use submatrix views instead
- Examples:

      mat A = randu<mat>(5,5);

      double* mem = A.colptr(2);

- See also:
  - .memptr()
  - submatrix views
  - element access
  - iterators (matrices)
  - advanced constructors (matrices)

iterators (matrices & vectors)

- STL-style iterators and associated member functions of Mat, Col, Row and SpMat
- Member functions:

  .begin() |  | iterator referring to the first element
  .end() |  | iterator referring to the past-the-end element
  .begin_row( row_number ) |  | iterator referring to the first element of the specified row
  .end_row( row_number ) |  | iterator referring to the past-the-end element of the specified row
  .begin_col( col_number ) |  | iterator referring to the first element of the specified column
  .end_col( col_number ) |  | iterator referring to the past-the-end element of the specified column

- Iterator types:

  mat::iterator vec::iterator rowvec::iterator sp_mat::iterator |  | random access iterators, for read/write access to elements (which are stored column by column)
  mat::const_iterator vec::const_iterator rowvec::const_iterator sp_mat::const_iterator |  | random access iterators, for read-only access to elements (which are stored column by column)
  mat::col_iterator vec::col_iterator rowvec::col_iterator |  | random access iterators, for read/write access to the elements of a specific column
  mat::const_col_iterator vec::const_col_iterator rowvec::const_col_iterator |  | random access iterators, for read-only access to the elements of a specific column
  mat::row_iterator sp_mat::row_iterator |  | rudimentary forward iterator, for read/write access to the elements of a specific row
  mat::const_row_iterator sp_mat::const_row_iterator |  | rudimentary forward iterator, for read-only access to the elements of a specific row
  vec::row_iterator rowvec::row_iterator |  | random access iterators, for read/write access to the elements of a specific row
  vec::const_row_iterator rowvec::const_row_iterator |  | random access iterators, for read-only access to the elements of a specific row

- Examples:

      mat X = randu<mat>(5,5);

      mat::iterator a = X.begin();
      mat::iterator b = X.end();

      for(mat::iterator i=a; i!=b; ++i)
        {
        cout << *i << endl;
        }

      mat::col_iterator c = X.begin_col(1);  // start of column 1
      mat::col_iterator d = X.end_col(3);    // end of column 3

      for(mat::col_iterator i=c; i!=d; ++i)
        {
        cout << *i << endl;
        (*i) = 123.0;
        }

- See also:
  - STL-style container functions
  - iterator at cplusplus.com
  - element access
  - .memptr()
  - .colptr()
  - submatrix views

iterators (cubes)

- STL-style iterators and associated member functions of Cube
- Member functions:

  .begin() |  | iterator referring to the first element
  .end() |  | iterator referring to the past-the-end element
  .begin_slice( slice_number ) |  | iterator referring to the first element of the specified slice
  .end_slice( slice_number ) |  | iterator referring to the past-the-end element of the specified slice

- Iterator types:

  cube::iterator |  | random access iterator, for read/write access to elements; the elements are ordered slice by slice; the elements within each slice are ordered column by column
  cube::const_iterator |  | random access iterators, for read-only access to elements
  cube::slice_iterator |  | random access iterator, for read/write access to the elements of a particular slice; the elements are ordered column by column
  cube::const_slice_iterator |  | random access iterators, for read-only access to the elements of a particular slice

- Examples:

      cube X = randu<cube>(2,3,4);

      cube::iterator a = X.begin();
      cube::iterator b = X.end();

      for(cube::iterator i=a; i!=b; ++i)
        {
        cout << *i << endl;
        }

      cube::slice_iterator c = X.begin_slice(1);  // start of slice 1
      cube::slice_iterator d = X.end_slice(2);    // end of slice 2

      for(cube::slice_iterator i=c; i!=d; ++i)
        {
        cout << *i << endl;
        (*i) = 123.0;
        }

- See also:
  - iterator at cplusplus.com
  - element access

STL-style container functions

- Member functions that mimic the containers in the C++ Standard Template Library:

  .clear() |  | causes an object to have no elements
  .empty() |  | returns true if the object has no elements; returns false if the object has one or more elements
  .size() |  | returns the total number of elements

- Examples:

      mat A = randu<mat>(5,5);
      cout << A.size() << endl;

      A.clear();
      cout << A.empty() << endl;

- See also:
  - iterators (matrices)
  - matrix and vector attributes
  - .is_empty()
  - .reset()

.t()
.st()

- Member functions of any matrix or vector expression
- For real (non-complex) matrix:
  - .t() provides a transposed copy of the matrix
  - .st() not applicable
- For complex matrix:
  - .t() provides a Hermitian transpose (ie. the conjugate of the elements is taken during the transpose)
  - .st() provides a transposed copy without taking the conjugate of the elements
- Examples:

      mat A = randu<mat>(4,5);
      mat B = A.t();

- See also:
  - trans()
  - fliplr() & flipud()

.i()

- Member function of any matrix expression
- Provides an inverse of the matrix expression
- If the matrix expression is not square sized, a std::logic_error exception is thrown
- If the matrix expression appears to be singular, the output matrix is reset and a std::runtime_error exception is thrown
- Caveats:
  - if matrix A is know to be symmetric positive definite, it's faster to use inv_sympd() instead
  - to solve a system of linear equations, such as Z = inv(X)*Y, it is faster and more accurate to use solve() instead
- Examples:

      mat A = randu<mat>(4,4);
      mat B = randu<mat>(4,1);

      mat X = A.i();

      mat Y = (A+A).i();

- See also:
  - inv()
  - rcond()
  - pinv()
  - solve()
  - spsolve()

.min() |  |  | (member functions of Mat, Col, Row, SpMat, Cube)
.max() |  |  |
.min( index_of_min_val ) |  |  | (member functions of Mat, Col, Row, SpMat, Cube)
.max( index_of_max_val ) |  |  |
.min( row_of_min_val, col_of_min_val ) |  |  | (member functions of Mat and SpMat)
.max( row_of_max_val, col_of_max_val ) |  |  |
.min( row_of_min_val, col_of_min_val, slice_of_min_val ) |  |  | (member functions of Cube)
.max( row_of_max_val, col_of_max_val, slice_of_max_val ) |  |  |

- Without arguments: return the extremum value of an object
- With one or more arguments: return the extremum value of an object and store the location of the extremum value in the provided variable(s)
- The provided variables must be of type uword
- Examples:

      vec v = randu<vec>(10);

      cout << "min value is " << v.min() << endl;

      uword  index;
      double min_val = v.min(index);

      cout << "index of min value is " << index << endl;

      mat A = randu<mat>(5,5);

      uword  row;
      uword  col;
      double min_val2 = A.max(row,col);

      cout << "max value is at " << row << ',' << col << endl;

- See also:
  - min() & max() (standalone functions)
  - clamp()
  - running_stat
  - running_stat_vec

.eval()

- Member function of any matrix or vector expression
- Explicitly forces the evaluation of a delayed expression and outputs a matrix
- This function should be used sparingly and only in cases where it is absolutely necessary; indiscriminate use can cause slow downs
- Examples:

      cx_mat A( randu<mat>(4,4), randu<mat>(4,4) );

      real(A).eval().save("A_real.dat", raw_ascii);
      imag(A).eval().save("A_imag.dat", raw_ascii);

- See also:
  - Mat class

.in_range( i ) |  |  | (member of Mat, Col, Row, Cube, SpMat, field)
.in_range( span(start, end) ) |  |  | (member of Mat, Col, Row, Cube, SpMat, field)
.in_range( row, col ) |  |  | (member of Mat, Col, Row, SpMat, field)
.in_range( span(start_row, end_row), span(start_col, end_col) ) |  |  | (member of Mat, Col, Row, SpMat, field)
.in_range( row, col, slice ) |  |  | (member of Cube and field)
.in_range( span(start_row, end_row), span(start_col, end_col), span(start_slice, end_slice) ) |  |  | (member of Cube and field)
.in_range( first_row, first_col, size(X) ) (X is a matrix or field) |  |  | (member of Mat, Col, Row, SpMat, field)
.in_range( first_row, first_col, size(n_rows, n_cols) ) |  |  | (member of Mat, Col, Row, SpMat, field)
.in_range( first_row, first_col, first_slice, size(Q) ) (Q is a cube or field) |  |  | (member of Cube and field)
.in_range( first_row, first_col, first_slice, size(n_rows, n_cols n_slices) ) |  |  | (member of Cube and field)

- Returns true if the given location or span is currently valid
- Returns false if the object is empty, the location is out of bounds, or the span is out of bounds
- Instances of span(a,b) can be replaced by:
  - span() or span::all, to indicate the entire range
  - span(a), to indicate a particular row, column or slice
- Examples:

      mat A = randu<mat>(4,5);

      cout << A.in_range(0,0) << endl;  // true
      cout << A.in_range(3,4) << endl;  // true
      cout << A.in_range(4,5) << endl;  // false

- See also:
  - element access
  - submatrix views
  - subcube views
  - subfield views
  - .set_size()

.is_empty()

- Returns true if the object has no elements
- Returns false if the object has one or more elements
- Examples:

      mat A = randu<mat>(5,5);
      cout << A.is_empty() << endl;

      A.reset();
      cout << A.is_empty() << endl;

- See also:
  - .is_square()
  - .is_vec()
  - .is_finite()
  - .reset()

.is_square()

- Member function of Mat and SpMat
- Returns true if the matrix is square, ie. number of rows is equal to the number of columns
- Returns false if the matrix is not square
- Examples:

      mat A = randu<mat>(5,5);
      mat B = randu<mat>(6,7);

      cout << A.is_square() << endl;
      cout << B.is_square() << endl;

- See also:
  - .is_empty()
  - .is_vec()
  - .is_finite()

.is_vec()
.is_colvec()
.is_rowvec()

- Member functions of Mat and SpMat
- .is_vec():
  - returns true if the matrix can be interpreted as a vector (either column or row vector)
  - returns false if the matrix does not have exactly one column or one row
- .is_colvec():
  - returns true if the matrix can be interpreted as a column vector
  - returns false if the matrix does not have exactly one column
- .is_rowvec():
  - returns true if the matrix can be interpreted as a row vector
  - returns false if the matrix does not have exactly one row
- Caveat: do not assume that the vector has elements if these functions return true; it is possible to have an empty vector (eg. 0x1)
- Examples:

      mat A = randu<mat>(1,5);
      mat B = randu<mat>(5,1);
      mat C = randu<mat>(5,5);

      cout << A.is_vec() << endl;
      cout << B.is_vec() << endl;
      cout << C.is_vec() << endl;

- See also:
  - .is_empty()
  - .is_square()
  - .is_finite()

.is_sorted()
.is_sorted( sort_direction )
.is_sorted( sort_direction, dim )

- Member function of Mat, Row and Col
- If the object is a vector, return a bool indicating whether the elements are sorted
- If the object is a matrix, return a bool indicating whether the elements are sorted in each column (dim=0), or each row (dim=1)
- The sort_direction argument is optional; sort_direction is either `"ascend"` or `"descend"`; by default `"ascend"` is used
- The dim argument is optional; by default dim=0 is used
- For matrices and vectors with complex numbers, order is checked via absolute values
- Examples:

      vec a = randu<vec>(10);
      vec b = sort(a);

      bool check1 = a.is_sorted();
      bool check2 = b.is_sorted();

      mat A = randu<mat>(10,10);

      // check whether each column is sorted in descending manner
      cout << A.is_sorted("descend") << endl;

      // check whether each row is sorted in ascending manner
      cout << A.is_sorted("ascend", 1) << endl;

- See also:
  - sort()

.is_finite()

- Member function of Mat, Col, Row, Cube, SpMat
- Returns true if all elements of the object are finite
- Returns false if at least one of the elements of the object is non-finite (±infinity or NaN)
- Examples:

      mat A = randu<mat>(5,5);
      mat B = randu<mat>(5,5);

      B(1,1) = datum::inf;

      cout << A.is_finite() << endl;
      cout << B.is_finite() << endl;

- See also:
  - is_finite() (standalone function)
  - find_finite()
  - find_nonfinite()
  - constants (pi, nan, inf, ...)

.has_inf()

- Member function of Mat, Col, Row, Cube, SpMat
- Returns true if at least one of the elements of the object is ±infinity
- Returns false otherwise
- Examples:

      mat A = randu<mat>(5,5);
      mat B = randu<mat>(5,5);

      B(1,1) = datum::inf;

      cout << A.has_inf() << endl;
      cout << B.has_inf() << endl;

- See also:
  - .has_nan()
  - .is_finite()
  - constants (pi, nan, inf, ...)

.has_nan()

- Member function of Mat, Col, Row, Cube, SpMat
- Returns true if at least one of the elements of the object is NaN (not-a-number)
- Returns false otherwise
- Caveat: `NaN` is not equal to anything, even itself
- Examples:

      mat A = randu<mat>(5,5);
      mat B = randu<mat>(5,5);

      B(1,1) = datum::nan;

      cout << A.has_nan() << endl;
      cout << B.has_nan() << endl;

- See also:
  - .has_inf()
  - constants (pi, nan, inf, ...)

.print()
.print( header )
.print( stream )
.print( stream, header )

- Member functions of Mat, Col, Row, SpMat, Cube and field
- Print the contents of an object to the std::cout stream (default), or a user specified stream, with an optional header string
- Objects can also be printed using the << stream operator
- Elements of a field can only be printed if there is an associated operator<< function defined
- Examples:

      mat A = randu<mat>(5,5);
      mat B = randu<mat>(6,6);

      A.print();

      // print a transposed version of A
      A.t().print();

      // "B:" is the optional header line
      B.print("B:");

      cout << A << endl;

      cout << "B:" << endl;
      cout << B << endl;

- See also:
  - .raw_print()
  - saving & loading matrices
  - initialising elements
  - logging of errors and warnings

.raw_print()
.raw_print( header )
.raw_print( stream )
.raw_print( stream, header )

- Member functions of Mat, Col, Row, SpMat and Cube
- Similar to the .print() member function, with the difference that no formatting of the output is done -- ie. the user can set the stream's parameters such as precision, cell width, etc.
- If the cell width is set to zero, a space is printed between the elements
- Examples:

      mat A = randu<mat>(5,5);

      cout.precision(11);
      cout.setf(ios::fixed);

      A.raw_print(cout, "A =");

saving/loading matrices & cubes

.save( name ) .save( name, file_type ) .save( stream ) .save( stream, file_type ) |  | .load( name ) .load( name, file_type ) .load( stream ) .load( stream, file_type )

- Member functions of Mat, Col, Row and Cube
- Store/retrieve data in a file or stream
  - caveat: the stream must be opened in binary mode
- The default file_type for .save() is arma_binary (see below)
- The default file_type for .load() is auto_detect (see below)
- On success, .save() and .load() will return a bool set to true
- .save() will return a bool set to false if the saving process fails
- .load() will return a bool set to false if the loading process fails; additionally, the object will be reset so it has no elements
- file_type can be one of the following:

  auto_detect |  | Used by .load() only: try to automatically detect the file type as one of the formats described below. This is the default operation.
  raw_ascii |  | Numerical data stored in raw ASCII format, without a header. The numbers are separated by whitespace. The number of columns must be the same in each row. Cubes are loaded as one slice. Data which was saved in Matlab/Octave using the -ascii option can be read in Armadillo, except for complex numbers. Complex numbers are stored in standard C++ notation, which is a tuple surrounded by brackets: eg. (1.23,4.56) indicates 1.24 + 4.56i.
  raw_binary |  | Numerical data stored in machine dependent raw binary format, without a header. Matrices are loaded to have one column, while cubes are loaded to have one slice with one column. The .reshape() function can be used to alter the size of the loaded matrix/cube without losing data.
  arma_ascii |  | Numerical data stored in human readable text format, with a simple header to speed up loading. The header indicates the type of matrix as well as the number of rows and columns. For cubes, the header additionally specifies the number of slices.
  arma_binary |  | Numerical data stored in machine dependent binary format, with a simple header to speed up loading. The header indicates the type of matrix as well as the number of rows and columns. For cubes, the header additionally specifies the number of slices. arma_binary is the default file_type for .save()
  csv_ascii |  | Numerical data stored in comma separated value (CSV) text format, without a header. Applicable to Mat only.
  hdf5_binary |  | Numerical data stored in portable HDF5 binary format. Caveat: support for HDF5 must be enabled within Armadillo's configuration; the hdf5.h header file must be available on your system and you will need to link with the hdf5 library (eg. -lhdf5)
  hdf5_binary_trans |  | As per hdf5_binary, but save/load the data with columns transposed to rows (and vice versa)
  pgm_binary |  | Image data stored in Portable Gray Map (PGM) format. Applicable to Mat only. Saving int, float or double matrices is a lossy operation, as each element is copied and converted to an 8 bit representation. As such the matrix should have values in the [0,255] interval, otherwise the resulting image may not display correctly.
  ppm_binary |  | Image data stored in Portable Pixel Map (PPM) format. Applicable to Cube only. Saving int, float or double matrices is a lossy operation, as each element is copied and converted to an 8 bit representation. As such the cube/field should have values in the [0,255] interval, otherwise the resulting image may not display correctly.

- Examples:

      mat A = randu<mat>(5,5);

      A.save("A1.mat");  // default save format is arma_binary

      A.save("A2.mat", arma_ascii);  // force saving in arma_ascii format

      mat B;
      B.load("A1.mat");  // automatically detect format type

      mat C;
      C.load("A2.mat", arma_ascii);  // force loading in arma_ascii format

      // example of testing for success
      mat D;
      bool status = D.load("A2.mat");

      if(status == true)
        {
        cout << "loaded okay" << endl;
        }
      else
        {
        cout << "problem with loading" << endl;
        }

- See also:
  - saving/loading fields

saving/loading fields

.save( name ) .save( name, file_type ) .save( stream ) .save( stream, file_type ) |  | .load( name ) .load( name, file_type ) .load( stream ) .load( stream, file_type )

- Store/retrieve data in a file or stream
  - caveat: the stream must be opened in binary mode
- On success, .save() and .load() will return a bool set to true
- .save() will return a bool set to false if the saving process fails
- .load() will return a bool set to false if the loading process fails; additionally, the field will be reset so it has no elements
- Fields with objects of type std::string are saved and loaded as raw text files. The text files do not have a header. Each string is separated by a whitespace. load() will only accept text files that have the same number of strings on each line. The strings can have variable lengths.
- Other than storing string fields as text files, the following file formats are supported:

  auto_detect |  | .load(): try to automatically detect the field format type as one of the formats described below; this is the default operation.
  arma_binary |  | Objects are stored in machine dependent binary format Default type for fields of type Mat, Col, Row or Cube Only applicable to fields of type Mat, Col, Row or Cube
  ppm_binary |  | Image data stored in Portable Pixmap Map (PPM) format. Only applicable to fields of type Mat, Col or Row .load(): loads the specified image and stores the red, green and blue components as three separate matrices; the resulting field is comprised of the three matrices, with the red, green and blue components in the first, second and third matrix, respectively .save(): saves a field with exactly three matrices of equal size as an image; it is assumed that the red, green and blue components are stored in the first, second and third matrix, respectively; saving int, float or double matrices is a lossy operation, as each matrix element is copied and converted to an 8 bit representation

- See also:
  - saving/loading matrices and cubes

Generated Vectors/Matrices

eye( n_rows, n_cols )
eye( size(X) )

- Generate a matrix with the elements along the main diagonal set to one and off-diagonal elements set to zero
- An identity matrix is generated when n_rows = n_cols
- Usage:
  - matrix_type X = eye<matrix_type>( n_rows, n_cols )
  - matrix_type Y = eye<matrix_type>( size(X) )
- Examples:

      mat A = eye<mat>(5,5);  // or:  mat A(5,5,fill::eye);

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

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

- See also:
  - .eye() (member function of Mat)
  - .diag()
  - ones()
  - diagmat()
  - diagvec()
  - speye()
  - size()

linspace( start, end )
linspace( start, end, N )

- Generate a vector with N elements; the values of the elements are linearly spaced from start to (and including) end
- The argument N is optional; by default N = 100
- Usage:
  - vector_type v = linspace<vector_type>(start, end, N)
- Examples:

      vec a = linspace<vec>(0,  5,  6);
      vec b = linspace<vec>(5,  0,  6);

- See also:
  - regspace()
  - logspace()
  - ones()
  - interp1()

logspace( A, B )
logspace( A, B, N )

- Generate a vector with N elements; the values of the elements are logarithmically spaced from 10A to (and including) 10B
- The argument N is optional; by default N = 50
- Usage:
  - vector_type v = logspace<vector_type>(A, B, N)
- Examples:

      vec a = logspace<vec>(0,  5,  6);
      vec b = logspace<vec>(5,  0,  6);

- See also:
  - linspace()
  - regspace()

ones( n_elem )
ones( n_rows, n_cols )
ones( n_rows, n_cols, n_slices )
ones( size(X) )

- Generate a vector, matrix or cube with all elements set to one
- Usage:
  - 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:

      vec  v = ones<vec>(10);
      uvec u = ones<uvec>(11);
      mat  A = ones<mat>(5,6);
      cube Q = ones<cube>(5,6,7);

      mat  B = 123.0 * ones<mat>(5,6);

- See also:
  - .ones() (member function of Mat, Col, Row and Cube)
  - eye()
  - linspace()
  - regspace()
  - zeros()
  - randu() & randn()
  - spones()
  - size()

randi( n_elem )
randi( n_elem, distr_param(a,b) )
randi( n_rows, n_cols )
randi( n_rows, n_cols, distr_param(a,b) )
randi( n_rows, n_cols, n_slices )
randi( n_rows, n_cols, n_slices, distr_param(a,b) )
randi( size(X) )
randi( size(X), distr_param(a,b) )

- Generate a vector, matrix or cube with the elements set to random integer values in the [a,b] interval
- The default distribution parameters are a=0 and b=maximum_int
- Usage:
  - 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) )
  - matrix_type Y = randi<matrix_type>( size(X) )
  - matrix_type Y = randi<matrix_type>( size(X), 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) )
  - cube_type R = randi<cube_type>( size(Q) )
  - cube_type R = randi<cube_type>( size(Q), distr_param(a,b) )
- To change the RNG seed, use arma_rng::set_seed(value) or arma_rng::set_seed_random() functions
- Caveat: to generate a continuous distribution with floating point values (ie. float or double), use randu() or randn() instead
- Examples:

      imat A = randi<imat>(5, 6);

      imat A = randi<imat>(6, 7, distr_param(-10, +20));

      arma_rng::set_seed_random();  // set the seed to a random value

- See also:
  - randu() & randn()
  - randg()
  - .imbue()
  - ones()
  - zeros()
  - shuffle()
  - size()

randu( n_elem )
randu( n_rows, n_cols )
randu( n_rows, n_cols, n_slices )
randu( size(X) )
randn( n_elem )
randn( n_rows, n_cols )
randn( n_rows, n_cols, n_slices )
randn( size(X) )

- Generate a vector, matrix or cube with the elements set to random floating point values
- randu() uses a uniform distribution in the [0,1] interval
- randn() uses a normal/Gaussian distribution with zero mean and unit variance
- Usage:
  - vector_type v = randu<vector_type>( n_elem )
  - matrix_type X = randu<matrix_type>( n_rows, n_cols )
  - matrix_type Y = randu<matrix_type>( size(X) )
  - cube_type Q = randu<cube_type>( n_rows, n_cols, n_slices )
  - cube_type R = randu<cube_type>( size(Q) )
- To change the RNG seed, use arma_rng::set_seed(value) or arma_rng::set_seed_random() functions
- Caveat: to generate a matrix with random integer values instead of floating point values, use randi() instead
- Examples:

      vec  v = randu<vec>(5);
      mat  A = randu<mat>(5,6);
      cube Q = randu<cube>(5,6,7);

      arma_rng::set_seed_random();  // set the seed to a random value

- See also:
  - .randu() & .randn() (member functions)
  - randi()
  - randg()
  - .imbue()
  - ones()
  - zeros()
  - shuffle()
  - sprandu() & sprandn()
  - size()
  - uniform distribution in Wikipedia
  - normal distribution in Wikipedia

randg( n_elem )
randg( n_elem, distr_param(a,b) )
randg( n_rows, n_cols )
randg( n_rows, n_cols, distr_param(a,b) )
randg( n_rows, n_cols, n_slices )
randg( n_rows, n_cols, n_slices, distr_param(a,b) )
randg( size(X) )
randg( size(X), distr_param(a,b) )

- Generate a vector, matrix or cube with the elements set to random values from a gamma distribution:

  |  | xa-1 exp( -x / b )
  p(x|a,b) | = |
  |  | ba Γ(a)

- The default distribution parameters are a=1 and b=1
- Usage:
  - 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) )
  - matrix_type Y = randg<matrix_type>( size(X) )
  - matrix_type Y = randg<matrix_type>( size(X), 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) )
  - cube_type R = randg<cube_type>( size(Q) )
  - cube_type R = randg<cube_type>( size(Q), distr_param(a,b) )
- Caveat: randg() is available only when using a C++11 compiler
- To change the RNG seed, use arma_rng::set_seed(value) or arma_rng::set_seed_random() functions
- Examples:

      vec v = randg<vec>(100, distr_param(2,1));

      mat X = randg<mat>(10, 10, distr_param(2,1));

- See also:
  - randu() & randn()
  - randi()
  - .imbue()
  - size()
  - gamma distribution in Wikipedia

regspace( start, end )
regspace( start, delta, end )

- Generate a vector with regularly spaced elements:
  [ (start + 0*delta), (start + 1*delta), (start + 2*delta), ⋯, (start + M*delta) ]
  where M = floor((end-start)/delta), so that (start + M*delta) ≤ end
- Similar in operation to the Matlab/Octave colon operator, ie. start:end and start:delta:end
- If delta is not specified:
  - delta = +1, if start ≤ end
  - delta = −1, if start > end (caveat: this is different to Matlab/Octave)
- An empty vector is generated when one of the following conditions is true:
  - start < end, and delta < 0
  - start > end, and delta > 0
  - delta = 0
- Usage:
  - vector_type v = regspace<vector_type>(start, end)
  - vector_type v = regspace<vector_type>(start, delta, end)
- Examples:

       vec a = regspace< vec>(0,  9);       // 0,  1, ...,   9
      uvec b = regspace<uvec>(2,  2,  10);  // 2,  4, ...,  10
      ivec c = regspace<ivec>(0, -1, -10);  // 0, -1, ..., -10

- Caveat: do not use regpspace() to specify ranges for contiguous submatrix views; use span() instead
- See also:
  - linspace()
  - logspace()

speye( n_rows, n_cols )
speye( size(X) )

- Generate a sparse matrix with the elements along the main diagonal set to one and off-diagonal elements set to zero
- An identity matrix is generated when n_rows = n_cols
- Usage:
  - sparse_matrix_type X = speye<sparse_matrix_type>( n_rows, n_cols )
  - sparse_matrix_type Y = speye<sparse_matrix_type>( size(X) )
- Examples:

      sp_mat A = speye<sp_mat>(5,5);

- See also:
  - spones()
  - sprandu() & sprandn()
  - eye()
  - size()

spones( A )

- Generate a sparse matrix with the same structure as sparse matrix A, but with the non-zero elements set to one
- Examples:

      sp_mat A = sprandu<sp_mat>(100, 200, 0.1);

      sp_mat B = spones(A);

- See also:
  - speye()
  - sprandu() & sprandn()
  - ones()

sprandu( n_rows, n_cols, density )
sprandn( n_rows, n_cols, density )
sprandu( size(X), density )
sprandn( size(X), density )

- Generate a sparse matrix with the non-zero elements set to random values
- The density argument specifies the percentage of non-zero elements; it must be in the [0,1] interval
- sprandu() uses a uniform distribution in the [0,1] interval
- sprandn() uses a normal/Gaussian distribution with zero mean and unit variance
- Usage:
  - sparse_matrix_type X = sprandu<sparse_matrix_type>( n_rows, n_cols, density )
  - sparse_matrix_type Y = sprandu<sparse_matrix_type>( size(X), density )
- To change the RNG seed, use arma_rng::set_seed(value) or arma_rng::set_seed_random() functions
- Examples:

      sp_mat A = sprandu<sp_mat>(100, 200, 0.1);

- See also:
  - speye()
  - spones()
  - randu() & randn()
  - size()
  - uniform distribution in Wikipedia
  - normal distribution in Wikipedia

toeplitz( A )
toeplitz( A, B )
circ_toeplitz( A )

- toeplitz(): generate a Toeplitz matrix, with the first column specified by A, and (optionally) the first row specified by B
- circ_toeplitz(): generate a circulant Toeplitz matrix
- A and B must be vectors
- Examples:

      vec A = randu<vec>(5);
      mat X = toeplitz(A);
      mat Y = circ_toeplitz(A);

- See also:
  - Toeplitz matrix in MathWorld
  - Toeplitz matrix in Wikipedia
  - Circulant matrix in Wikipedia

zeros( n_elem )
zeros( n_rows, n_cols )
zeros( n_rows, n_cols, n_slices )
zeros( size(X) )

- Generate a vector, matrix or cube with the elements set to zero
- Usage:
  - 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:

      vec  v = zeros<vec>(10);
      uvec u = zeros<uvec>(11);
      mat  A = zeros<mat>(5,6);
      cube Q = zeros<cube>(5,6,7);

- See also:
  - .zeros() (member function of Mat, Col, Row, SpMat and Cube)
  - .ones() (member function of Mat, Col, Row and Cube)
  - ones()
  - randu() & randn()
  - size()

Functions of Vectors/Matrices/Cubes

abs( X )

- Obtain the magnitude of each element
- Usage for non-complex matrices and cubes:
  - matrix_type Y = abs(X)
  - cube_type Y = abs(X)
  - X and Y must have the same matrix_type / cube_type
- Usage for complex matrices and cubes:
  - non_complex_matrix_type Y = abs(X)
  - non_complex_cube_type Y = abs(X)
  - X must be a have complex matrix / cube type, eg. cx_mat or cx_cube
  - The type of Y must be related to the type of X, eg. if X has the type cx_mat, then the type of Y must be mat
- Examples:

      mat A = randu<mat>(5,5);
      mat B = abs(A);

      cx_mat X = randu<cx_mat>(5,5);
      mat    Y = abs(X);

- See also:
  - conj()
  - imag() / real()
  - conv_to()

accu( X )

- Accumulate (sum) all elements of a vector, matrix or cube
- Examples:

      mat A(5, 6, fill::randu);
      mat B(5, 6, fill::randu);

      double x = accu(A);

      double y = accu(A % B);

      // accu(A % B) is a "multiply-and-accumulate" operation
      // as operator % performs element-wise multiplication

- See also:
  - sum()
  - cumsum()
  - trace()
  - mean()
  - dot()
  - as_scalar()

all( V )
all( X )
all( X, dim )

- For vector V, return true if all elements of the vector are non-zero or satisfy a relational condition
- For matrix X and
  - dim=0, return a row vector (of type urowvec or umat), with each element (0 or 1) indicating whether the corresponding column of X has all non-zero elements
  - dim=1, return a column vector (of type ucolvec or umat), with each element (0 or 1) indicating whether the corresponding row of X has all non-zero elements
- The dim argument is optional; by default dim=0 is used
- Relational operators can be used instead of V or X, eg. A > 0.5
- Examples:

      vec V = randu<vec>(10);
      mat X = randu<mat>(5,5);

      // status1 will be set to true if vector V has all non-zero elements
      bool status1 = all(V);

      // status2 will be set to true if vector V has all elements greater than 0.5
      bool status2 = all(V > 0.5);

      // status3 will be set to true if matrix X has all elements greater than 0.6;
      // note the use of vectorise()
      bool status3 = all(vectorise(X) > 0.6);

      // generate a row vector indicating which columns of X have all elements greater than 0.7
      umat A = all(X > 0.7);

- See also:
  - any()
  - approx_equal()
  - find()
  - conv_to() (convert between matrix/vector types)
  - vectorise()

any( V )
any( X )
any( X, dim )

- For vector V, return true if any element of the vector is non-zero or satisfies a relational condition
- For matrix X and
  - dim=0, return a row vector (of type urowvec or umat), with each element (0 or 1) indicating whether the corresponding column of X has any non-zero elements
  - dim=1, return a column vector (of type ucolvec or umat), with each element (0 or 1) indicating whether the corresponding row of X has any non-zero elements
- The dim argument is optional; by default dim=0 is used
- Relational operators can be used instead of V or X, eg. A > 0.9
- Examples:

      vec V = randu<vec>(10);
      mat X = randu<mat>(5,5);

      // status1 will be set to true if vector V has any non-zero elements
      bool status1 = any(V);

      // status2 will be set to true if vector V has any elements greater than 0.5
      bool status2 = any(V > 0.5);

      // status3 will be set to true if matrix X has any elements greater than 0.6;
      // note the use of vectorise()
      bool status3 = any(vectorise(X) > 0.6);

      // generate a row vector indicating which columns of X have elements greater than 0.7
      umat A = any(X > 0.7);

- See also:
  - all()
  - approx_equal()
  - find()
  - conv_to() (convert between matrix/vector types)
  - vectorise()

approx_equal( A, B, method, tol )
approx_equal( A, B, method, abs_tol, rel_tol )

- Return true if all corresponding elements in A and B are approximately equal
- Return false if any of the corresponding elements in A and B are not approximately equal, or if A and B have different dimensions
- The argument method controls how the approximate equality is determined; it is one of:

  `"absdiff"` | ↦ | scalars x and y are considered equal if |x − y| ≤ tol
  `"reldiff"` | ↦ | scalars x and y are considered equal if |x − y| / max( |x|, |y| ) ≤ tol
  `"both"` | ↦ | scalars x and y are considered equal if |x − y| ≤ abs_tol or |x − y| / max( |x|, |y| ) ≤ rel_tol

- Examples:

      mat A = randu<mat>(5,5);
      mat B = A + 0.001;

      bool same1 = approx_equal(A, B, "absdiff", 0.002);

      mat C = 1000 * randu<mat>(5,5);
      mat D = C + 1;

      bool same2 = approx_equal(C, D, "reldiff", 0.1);

      bool same3 = approx_equal(C, D, "both", 2, 0.1);

- See also:
  - all()
  - any()
  - find()
  - relational operators

as_scalar( expression )

- Evaluate an expression that results in a 1x1 matrix, followed by converting the 1x1 matrix to a pure scalar
- If a binary or trinary expression is given (ie. 2 or 3 terms), the function will try to exploit the fact that the result is a 1x1 matrix by using optimised expression evaluations
- Examples:

      rowvec r = randu<rowvec>(5);
      colvec q = randu<colvec>(5);
      mat    X = randu<mat>(5,5);

      // examples of some expressions
      // for which optimised implementations exist

      double a = as_scalar(r*q);
      double b = as_scalar(r*X*q);
      double c = as_scalar(r*diagmat(X)*q);
      double d = as_scalar(r*inv(diagmat(X))*q);

- See also:
  - accu()
  - trace()
  - dot()
  - norm()
  - conv_to()
  - reshape()
  - resize()
  - vectorise()

clamp( X, min_val, max_val )

- Create a copy of X with each element clamped to be between min_val and max_val
- Examples:

      mat A = randu<mat>(5,5);

      mat B = clamp(A, 0.2, 0.8);

      mat C = clamp(A, A.min(), 0.8);

      mat D = clamp(A, 0.2, A.max());

- See also:
  - min() & max()
  - .min() & .max() (member functions of Mat)
  - find()

cond( A )

- Return the condition number of matrix A (the ratio of the largest singular value to the smallest)
- Large condition numbers suggest that matrix A is nearly singular
- The computation is based on singular value decomposition; if the decomposition fails, a std::runtime_error exception is thrown
- Caveat: the rcond() function is faster for providing an estimate of the reciprocal of the condition number
- Examples:

      mat    A = randu<mat>(5,5);
      double c = cond(A);

- See also:
  - rcond()
  - rank()
  - inv()
  - solve()
  - condition number in MathWorld
  - condition number in Wikipedia

conj( X )

- Obtain the complex conjugate of each element in a complex matrix or cube
- Examples:

      cx_mat X = randu<cx_mat>(5,5);
      cx_mat Y = conj(X);

- See also:
  - abs()
  - imag() / real()
  - trans()

conv_to< type >::from( X )

- Convert from one matrix type to another (eg. mat to imat), or one cube type to another (eg. cube to icube)
- Conversion between std::vector and Armadillo matrices/vectors is also possible
- Conversion of a mat object into colvec, rowvec or std::vector is possible if the object can be interpreted as a vector
- Examples:

      mat  A = randu<mat>(5,5);
      fmat B = conv_to<fmat>::from(A);

      typedef std::vector<double> stdvec;

      stdvec x(3);
      x[0] = 0.0; x[1] = 1.0;  x[2] = 2.0;

      colvec y = conv_to< colvec >::from(x);
      stdvec z = conv_to< stdvec >::from(y);

- See also:
  - as_scalar()
  - abs()
  - imag() / real()
  - advanced constructors (matrices)
  - advanced constructors (cubes)

cross( A, B )

- Calculate the cross product between A and B, under the assumption that A and B are 3 dimensional vectors
- Examples:

      vec a = randu<vec>(3);
      vec b = randu<vec>(3);

      vec c = cross(a,b);

- See also:
  - dot()
  - Cross product in Wikipedia
  - Cross product in MathWorld

cumsum( V )
cumsum( X )
cumsum( X, dim )

- For vector V, return a vector of the same orientation, containing the cumulative sum of elements
- For matrix X, return a matrix containing the cumulative sum of elements in each column (dim=0), or each row (dim=1)
- The dim argument is optional; by default dim=0 is used
- Examples:

      mat A = randu<mat>(5,5);
      mat B = cumsum(A);
      mat C = cumsum(A, 1);

      vec x = randu<vec>(10);
      vec y = cumsum(x);

- See also:
  - cumprod()
  - accu()
  - sum()
  - diff()

cumprod( V )
cumprod( X )
cumprod( X, dim )

- For vector V, return a vector of the same orientation, containing the cumulative product of elements
- For matrix X, return a matrix containing the cumulative product of elements in each column (dim=0), or each row (dim=1)
- The dim argument is optional; by default dim=0 is used
- Examples:

      mat A = randu<mat>(5,5);
      mat B = cumprod(A);
      mat C = cumprod(A, 1);

      vec x = randu<vec>(10);
      vec y = cumprod(x);

- See also:
  - cumsum()
  - prod()

det( A )
log_det( val, sign, A )

- Determinant of square matrix A
- If A is not square sized, a std::logic_error exception is thrown
- log_det(): find the log determinant, such that the determinant is equal to exp(val)*sign
- Caveat: for large matrices log_det() is more precise than det()
- Examples:

      mat    A = randu<mat>(5,5);
      double x = det(A);

      double val;
      double sign;

      log_det(val, sign, A);

- See also:
  - determinant in MathWorld
  - determinant in Wikipedia

diagmat( V )
diagmat( V, k )
diagmat( X )
diagmat( X, k )

- Generate a diagonal matrix from vector V or matrix X
- Given vector V, generate a square matrix with the k-th diagonal containing a copy of the vector; all other elements are set to zero
- Given matrix X, generate a matrix with the k-th diagonal containing a copy of the k-th diagonal of X; all other elements are set to zero
- The argument k is optional; by default the main diagonal is used (k=0)
- For k > 0, the k-th super-diagonal is used (top-right corner)
- For k < 0, the k-th sub-diagonal is used (bottom-left corner)
- Examples:

      mat A = randu<mat>(5,5);
      mat B = diagmat(A);
      mat C = diagmat(A,1);

      vec v = randu<vec>(5);
      mat D = diagmat(v);
      mat E = diagmat(v,1);

- See also:
  - .diag()
  - diagvec()
  - trimatu() / trimatl()
  - symmatu() / symmatl()
  - reshape()
  - vectorise()

diagvec( A )
diagvec( A, k )

- Extract the k-th diagonal from matrix A
- The argument k is optional; by default the main diagonal is extracted (k=0)
- For k > 0, the k-th super-diagonal is extracted (top-right corner)
- For k < 0, the k-th sub-diagonal is extracted (bottom-left corner)
- The extracted diagonal is interpreted as a column vector
- Examples:

      mat A = randu<mat>(5,5);

      vec d = diagvec(A);

- See also:
  - .diag()
  - diagmat()
  - trace()
  - vectorise()

diff( V )
diff( V, k )
diff( X )
diff( X, k )
diff( X, k, dim )

- For vector V, return a vector of the same orientation, containing the differences between consecutive elements
- For matrix X, return a matrix containing the differences between consecutive elements in each column (dim=0), or each row (dim=1)
- The optional argument k indicates that the differences are calculated recursively k times; by default k=1 is used
- The resulting number of differences is n - k, where n is the number of elements; if n ≤ k, the number of differences is zero (ie. an empty vector/matrix is returned)
- The argument dim is optional; by default dim=0
- Examples:

      vec a = linspace<vec>(1,10,10);

      vec b = diff(a);

- See also:
  - trapz()
  - numerical differentiation in Wikipedia
  - numerical differentiation in MathWorld

dot( A, B )
cdot( A, B )
norm_dot( A, B )

- dot(A,B): dot product of A and B, treating A and B as vectors
- cdot(A,B): as per dot(A,B), but the complex conjugate of A is used
- norm_dot(A,B): equivalent to dot(A,B) / (∥A∥•∥B∥)
- Caveat: to calculate the norm, the norm() function is more robust
- Examples:

      vec a = randu<vec>(10);
      vec b = randu<vec>(10);

      double x = dot(a,b);

- See also:
  - norm()
  - as_scalar()
  - cross()
  - conj()

eps( X )

- Obtain the positive distance of the absolute value of each element of X to the next largest representable floating point number
- X can be a scalar (eg. double), vector or matrix
- Examples:

      mat A = randu<mat>(4,5);
      mat B = eps(A);

- See also:
  - datum::eps
  - Floating-Point Arithmetic in MathWorld
  - IEEE Standard for Floating-Point Arithmetic in Wikipedia

expmat( X )

- Return the matrix exponential of square matrix X
- If X is not square sized, a std::logic_error exception is thrown
- Caveat: the matrix exponential operation is not the same as applying the exp() function to each element
- Examples:

      mat A = randu<mat>(5,5);

      mat B = expmat(A);

- See also:
  - logmat()
  - sqrtmat()
  - miscellaneous element-wise functions
  - matrix exponential in Wikipedia
  - matrix exponential in MathWorld

find( X )
find( X, k )
find( X, k, s )

- Return a column vector containing the indices of elements of X that are non-zero or satisfy a relational condition
- The output vector must have the type uvec (ie. the indices are stored as unsigned integers of type uword)
- X is interpreted as a vector, with column-by-column ordering of the elements of X
- Relational operators can be used instead of X, eg. A > 0.5
- If k=0 (default), return the indices of all non-zero elements, otherwise return at most k of their indices
- If s="first" (default), return at most the first k indices of the non-zero elements
- If s="last", return at most the last k indices of the non-zero elements
- Examples:

      mat  A  = randu<mat>(5,5);
      mat  B  = randu<mat>(5,5);

      uvec q1 = find(A > B);
      uvec q2 = find(A > 0.5);
      uvec q3 = find(A > 0.5, 3, "last");

      // change elements of A greater than 0.5 to 1
      A.elem( find(A > 0.5) ).ones();

- See also:
  - all()
  - any()
  - approx_equal()
  - clamp()
  - find_finite()
  - find_nonfinite()
  - find_unique()
  - nonzeros()
  - unique()
  - sort_index()
  - conv_to() (convert between matrix/vector types)
  - submatrix views
  - subcube views

find_finite( X )

- Return a column vector containing the indices of elements of X that are finite (ie. not ±Inf and not NaN)
- The output vector must have the type uvec (ie. the indices are stored as unsigned integers of type uword)
- X is interpreted as a vector, with column-by-column ordering of the elements of X
- Examples:

      mat A = randu<mat>(5,5);

      A(1,1) = datum::inf;

      // accumulate only finite elements
      double val = accu( A.elem( find_finite(A) ) );

- See also:
  - find()
  - find_nonfinite()
  - submatrix views
  - constants (pi, nan, inf, ...)

find_nonfinite( X )

- Return a column vector containing the indices of elements of X that are non-finite (ie. ±Inf or NaN)
- The output vector must have the type uvec (ie. the indices are stored as unsigned integers of type uword)
- X is interpreted as a vector, with column-by-column ordering of the elements of X
- Examples:

      mat A = randu<mat>(5,5);

      A(1,1) = datum::inf;

      // change non-finite elements to zero
      A.elem( find_nonfinite(A) ).zeros();

- See also:
  - find()
  - find_finite()
  - submatrix views
  - constants (pi, nan, inf, ...)

find_unique( X )
find_unique( X, ascending_indices )

- Return a column vector containing the indices of unique elements of X
- The output vector must have the type uvec (ie. the indices are stored as unsigned integers of type uword)
- X is interpreted as a vector, with column-by-column ordering of the elements of X
- The ascending_indices argument is optional; it is one of:

  `true` | = | the returned indices are sorted to be ascending (default setting)
  `false` | = | the returned indices are in arbitrary order (faster operation)

- Examples:

      mat A;

      A << 2 << 2 << 4 << endr
        << 4 << 6 << 6 << endr;

      uvec indices = find_unique(A);

- See also:
  - find()
  - unique()
  - submatrix views

fliplr( X )
flipud( X )

- fliplr(): generate a copy of matrix X, with the order of the columns reversed
- flipud(): generate a copy of matrix X, with the order of the rows reversed
- Examples:

      mat A = randu<mat>(5,5);

      mat B = fliplr(A);
      mat C = flipud(A);

- See also:
  - shift()
  - .swap_rows() & .swap_cols()
  - .t()

imag( X )
real( X )

- Extract the imaginary/real part of a complex matrix or cube
- Examples:

      cx_mat C = randu<cx_mat>(5,5);

      mat    A = imag(C);
      mat    B = real(C);

- Caveat: versions 4.4, 4.5 and 4.6 of the GCC C++ compiler have a bug when using the -std=c++0x compiler option (ie. experimental support for C++11); to work around this bug, preface Armadillo's imag() and real() with the arma namespace qualification, eg. arma::imag(C)
- See also:
  - .set_imag() / .set_real()
  - abs()
  - conj()
  - conv_to()

sub = ind2sub( size(n_rows, n_cols), index )
sub = ind2sub( size(n_rows, n_cols, n_slices), index )

- Convert a linear index to subscript notation
- A std::logic_error exception is thrown if the index is out of range
- The subscripts are returned in a vector of type uvec; the length of the vector is the same as the number of arguments given to the size() function (ie. either 2 or 3)
- Examples:

      uvec s = ind2sub( size(4, 5), 6 );

      cout << "row: " << s(0) << endl;
      cout << "col: " << s(1) << endl;

- See also:
  - size()
  - sub2ind()
  - element access

inplace_trans( X )
inplace_trans( X, method )
inplace_strans( X )
inplace_strans( X, method )

- In-place / in-situ transpose of matrix X
- For real (non-complex) matrix:
  - inplace_trans() performs a normal transpose
  - inplace_strans() not applicable
- For complex matrix:
  - inplace_trans() performs a Hermitian transpose (ie. the conjugate of the elements is taken during the transpose)
  - inplace_strans() provides a transposed copy without taking the conjugate of the elements
- The argument method is optional
- By default, a greedy transposition algorithm is used; a low-memory algorithm can be used instead by explicitly setting method to `"lowmem"`
- The low-memory algorithm is considerably slower than the greedy algorithm; using the low-memory algorithm is only recommended for cases where X takes up more than half of available memory (ie. very large X)
- Examples:

      mat X = randu<mat>(4,5);
      mat Y = randu<mat>(20000,30000);

      inplace_trans(X);            // use greedy algorithm by default

      inplace_trans(Y, "lowmem");  // use low-memory (and slow) algorithm

- See also:
  - .t()
  - trans()
  - inplace matrix transpose in Wikipedia

is_finite( X )

- Returns true if all elements in X are finite
- Returns false if at least one element in X is non-finite (±infinity or NaN)
- X can be a scalar (eg. double), vector, matrix or cube
- Examples:

      mat A = randu<mat>(5,5);
      mat B = randu<mat>(5,5);

      B(1,1) = datum::inf;

      cout << is_finite(A) << endl;
      cout << is_finite(B) << endl;

      cout << is_finite( 0.123456789 ) << endl;
      cout << is_finite( datum::nan  ) << endl;
      cout << is_finite( datum::inf  ) << endl;

- See also:
  - .is_finite() (member function of Mat and Cube)
  - find_finite()
  - find_nonfinite()
  - constants (pi, nan, inf, ...)

join_rows( A, B )
join_horiz( A, B )
join_cols( A, B )
join_vert( A, B )

- join_rows() and join_horiz(): horizontal concatenation; for two matrices A and B, join each row of A with the corresponding row of B; matrices A and B must have the same number of rows
- join_cols() and join_vert(): vertical concatenation; for two matrices A and B, join each column of A with the corresponding column of B; matrices A and B must have the same number of columns
- Examples:

      mat A = randu<mat>(4,5);
      mat B = randu<mat>(4,6);
      mat C = randu<mat>(6,5);

      mat X = join_rows(A,B);
      mat Y = join_cols(A,C);

- See also:
  - .shed_rows/cols/slices
  - .insert_rows/cols/slices
  - submatrix views

join_slices( cube C, cube D )
join_slices( mat M, mat N )
join_slices( mat M, cube C )
join_slices( cube C, mat M )

- for two cubes C and D: join the slices of C with the slices of D; cubes C and D must have the same number of rows and columns (ie. all slices must have the same size)
- for two matrices M and N: treat M and N as cube slices and join them to form a cube with 2 slices; matrices M and N must have the same number of rows and columns
- for matrix M and cube C: treat M as a cube slice and join it with the slices of C; matrix M and cube C must have the same number of rows and columns
- Examples:

      cube C(5, 10, 3, fill::randu);
      cube D(5, 10, 4, fill::randu);

      cube E = join_slices(C,D);

      mat M(10, 20, fill::randu);
      mat N(10, 20, fill::randu);

      cube Q = join_slices(M,N);

      cube R = join_slices(Q,M);

      cube S = join_slices(M,Q);

- See also:
  - .shed_rows/cols/slices
  - .insert_rows/cols/slices
  - subcube views

kron( A, B )

- Kronecker tensor product
- Given matrix A (with n rows and p columns) and matrix B (with m rows and q columns), generate a matrix (with nm rows and pq columns) that denotes the tensor product of A and B
- Examples:

      mat A = randu<mat>(4,5);
      mat B = randu<mat>(5,4);

      mat K = kron(A,B);

- See also:
  - Kronecker product in MathWorld
  - Kronecker product in Wikipedia

logmat( X )

- Return the complex matrix logarithm of square matrix X
- If X is not square sized, a std::logic_error exception is thrown
- Caveat: the matrix logarithm operation is not the same as applying the log() function to each element
- Examples:

         mat A = randu<mat>(5,5);

      cx_mat B = logmat(A);

- See also:
  - expmat()
  - sqrtmat()
  - real()
  - miscellaneous element-wise functions
  - matrix logarithm in Wikipedia

min( V ) min( M ) min( M, dim ) min( Q ) min( Q, dim ) min( A, B ) |  | max( V ) max( M ) max( M, dim ) max( Q ) max( Q, dim ) max( A, B )

- For vector V, return the extremum value
- For matrix M, return the extremum value for each column (dim=0), or each row (dim=1)
- For cube Q, return the extremum values of elements along dimension dim, where dim ∈ { 0, 1, 2 }
- The dim argument is optional; by default dim=0 is used
- For two matrices/cubes A and B, return a matrix/cube containing element-wise extremum values
- Examples:

      colvec v = randu<colvec>(10,1);
      double x = max(v);

      mat    M = randu<mat>(10,10);

      rowvec a = max(M);
      rowvec b = max(M,0);
      colvec c = max(M,1);

      // element-wise maximum
      mat X = randu<mat>(5,6);
      mat Y = randu<mat>(5,6);
      mat Z = arma::max(X,Y);  // use arma:: prefix to distinguish from std::max()

- See also:
  - clamp()
  - .min() & .max() (member functions of Mat and Cube)
  - running_stat
  - running_stat_vec

nonzeros(X)

- Return a column vector containing the non-zero values of X
- X can be a sparse or dense matrix
- Examples:

      sp_mat A = sprandu<sp_mat>(100, 100, 0.1);
         vec a = nonzeros(A);

      mat B(100, 100, fill::eye);
      vec b = nonzeros(B);

- See also:
  - find()
  - unique()

norm( X )
norm( X, p )

- Compute the p-norm of X, where X can be a vector or matrix
- For vectors, p is an integer ≥1, or one of: `"-inf"`, `"inf"`, `"fro"`
- For matrices, p is one of: 1, 2, `"inf"`, `"fro"`; the calculated norm is the induced norm (not entrywise norm)
- `"-inf"` is the minimum norm, `"inf"` is the maximum norm, while `"fro"` is the Frobenius norm
- The argument p is optional; by default p=2 is used
- For vector norm with p=2 and matrix norm with p="fro", a robust algorithm is used to reduce the likelihood of underflows and overflows
- To obtain the zero norm or Hamming norm (ie. the number of non-zero elements), use this expression: `accu(X != 0)`
- Examples:

      vec    q = randu<vec>(5);

      double x = norm(q, 2);
      double y = norm(q, "inf");

- See also:
  - normalise()
  - vectorise()
  - dot()
  - vector norm in Wikipedia
  - vector norm in MathWorld
  - matrix norm in Wikipedia
  - matrix norm in MathWorld

normalise( V )
normalise( V, p )
normalise( X )
normalise( X, p )
normalise( X, p, dim )

- For vector V, return its normalised version (ie. having unit p-norm)
- For matrix X, return its normalised version, where each column (dim=0) or row (dim=1) has been normalised to have unit p-norm
- The p argument is optional; by default p=2 is used
- The dim argument is optional; by default dim=0 is used
- Examples:

      vec A = randu<vec>(10);
      vec B = normalise(A);
      vec C = normalise(A, 1);

      mat X = randu<mat>(5,6);
      mat Y = normalise(X);
      mat Z = normalise(X, 2, 1);

- See also:
  - norm()
  - norm_dot()
  - Normalised vector in MathWorld
  - Unit vector in Wikipedia

prod( V )
prod( M )
prod( M, dim )

- For vector V, return the product of all elements
- For matrix M, return the product of elements in each column (dim=0), or each row (dim=1)
- The dim argument is optional; by default dim=0 is used
- Examples:

      colvec v = randu<colvec>(10,1);
      double x = prod(v);

      mat    M = randu<mat>(10,10);

      rowvec a = prod(M);
      rowvec b = prod(M,0);
      colvec c = prod(M,1);

- See also:
  - cumprod()
  - Schur product

rank( X )
rank( X, tolerance )

- Return the rank of matrix X
- Any singular values less than tolerance are treated as zero
- The tolerance argument is optional; by default tolerance is max(m,n)*max_sv*datum::eps, where:
  - m = number of rows and n = number of columns in X
  - max_sv = maximal singular value of X
  - datum::eps = difference between 1 and the least value greater than 1 that is representable
- The computation is based on singular value decomposition; if the decomposition fails, a std::runtime_error exception is thrown
- Caveat: to confirm whether a matrix is singular, use rcond() or cond()
- Examples:

      mat   A = randu<mat>(4,5);

      uword r = rank(A);

- See also:
  - cond()
  - rcond()
  - svd()
  - orth()
  - datum::eps
  - Rank in MathWorld
  - Rank in Wikipedia

rcond( A )

- Return an estimate of the reciprocal of the condition number of square matrix A
- Values close to 1 suggest that A is well-conditioned
- Values close to 0 suggest that A is badly conditioned
- If A is not square sized, a std::logic_error exception is thrown
- Examples:

      mat    A = randu<mat>(5,5);
      double r = rcond(A);

- See also:
  - cond()
  - rank()
  - inv()
  - solve()

repmat( A, num_copies_per_row, num_copies_per_col )

- Generate a matrix by replicating matrix A in a block-like fashion
- The generated matrix has the following size:

  rows | = | num_copies_per_row | * | A.n_rows
  cols | = | num_copies_per_col | * | A.n_cols

- Examples:

      mat A = randu<mat>(2, 3);

      mat B = repmat(A, 4, 5);

- See also:
  - .each_col() & .each_row() (vector operations repeated on each column or row)
  - .copy_size()
  - reshape()
  - resize()

reshape( mat, n_rows, n_cols )
reshape( mat, size(X) )
reshape( cube, n_rows, n_cols, n_slices )
reshape( cube, size(X) )

- Generate a matrix or cube sized according to given size specifications, whose elements are taken from the given matrix/cube in a column-wise manner; the elements in the generated object are placed column-wise (ie. the first column is filled up before filling the second column)
- The layout of the elements in the generated object will be different to the layout in the given object
- If the total number of elements in the given object is less than the specified size, the remaining elements in the generated object are set to zero
- If the total number of elements in the given object is greater than the specified size, only a subset of elements is taken from the given object
- Caveats:
  - do not use reshape() if you simply want to change the size without preserving data; use .set_size() instead, which is much faster
  - to grow/shrink a matrix while preserving the elements as well as the layout of the elements, use resize() instead
  - to create a vector representation of a matrix (ie. concatenate all the columns or rows), use vectorise() instead
- Examples:

      mat A = randu<mat>(10, 5);
      mat B = reshape(A, 5, 10);

- See also:
  - .reshape()
  - .set_size()
  - resize()
  - vectorise()
  - as_scalar()
  - conv_to()
  - diagmat()
  - repmat()
  - size()

resize( mat, n_rows, n_cols )
resize( mat, size(X) )
resize( cube, n_rows, n_cols, n_slices )
resize( cube, size(X) )

- Generate a matrix or cube sized according to given size specifications, whose elements as well as the layout of the elements are taken from the given matrix/cube
- Caveat: do not use resize() if you simply want to change the size without preserving data; use .set_size() instead, which is much faster
- Examples:

      mat A = randu<mat>(4, 5);
      mat B = resize(A, 7, 6);

- See also:
  - .resize() (member function of Mat and Cube)
  - .set_size() (member function of Mat and Cube)
  - reshape()
  - vectorise()
  - as_scalar()
  - conv_to()
  - repmat()
  - size()

shift( V, N )
shift( X, N )
shift( X, N, dim )

- For vector V, generate a copy of the vector with the elements shifted by N positions in a circular manner
- For matrix X, generate a copy of the matrix with the elements shifted by N positions in each column (dim=0), or each row (dim=1)
- N can be positive of negative
- The dim argument is optional; by default dim=0 is used
- Examples:

      mat A = randu<mat>(4,5);
      mat B = shift(A, -1);
      mat C = shift(A, +1);

- See also:
  - shuffle()
  - fliplr() & flipud()

shuffle( V )
shuffle( X )
shuffle( X, dim )

- For vector V, generate a copy of the vector with the elements shuffled
- For matrix X, generate a copy of the matrix with the elements shuffled in each column (dim=0), or each row (dim=1)
- The dim argument is optional; by default dim=0 is used
- Examples:

      mat A = randu<mat>(4,5);
      mat B = shuffle(A);

- See also:
  - shift()
  - sort()
  - randu() / randn()

size( X )
size( n_rows, n_cols )
size( n_rows, n_cols, n_slices )

- Obtain the dimensions of object X, or explicitly specify the dimensions
- The dimensions can be used in conjunction with:
  - object constructors
  - functions for changing size
  - generator functions
  - submatrix views
  - subcube views
  - subfield views
- The dimensions support simple arithmetic operations; they can also be printed and compared for equality/inequality
- Examples:

      mat A(5,6);

      mat B(size(A), fill::zeros);

      mat C; C.randu(size(A));

      mat D = ones<mat>(size(A));

      mat E(10,20, fill::ones);
      E(3,4,size(C)) = C;    // access submatrix of E

      mat F( size(A) + size(E) );

      mat G( size(A) * 2 );

      cout << "size of A: " << size(A) << endl;

      bool is_same_size = (size(A) == size(E));

- See also:
  - attributes

sort( V )
sort( V, sort_direction )
sort( X )
sort( X, sort_direction )
sort( X, sort_direction, dim )

- For vector V, return a vector which is a sorted version of the input vector
- For matrix X, return a matrix with the elements of the input matrix sorted in each column (dim=0), or each row (dim=1)
- The dim argument is optional; by default dim=0 is used
- The sort_direction argument is optional; sort_direction is either `"ascend"` or `"descend"`; by default `"ascend"` is used
- For matrices and vectors with complex numbers, sorting is via absolute values
- Examples:

      mat A = randu<mat>(10,10);
      mat B = sort(A);

- See also:
  - sort_index()
  - .is_sorted()
  - shuffle()
  - randu() / randn()

sort_index( X )
sort_index( X, sort_direction )
stable_sort_index( X )
stable_sort_index( X, sort_direction )

- Return a vector which describes the sorted order of the elements of X (ie. it contains the indices of the elements of X)
- The output vector must have the type uvec (ie. the indices are stored as unsigned integers of type uword)
- X is interpreted as a vector, with column-by-column ordering of the elements of X
- The sort_direction argument is optional; sort_direction is either `"ascend"` or `"descend"`; by default `"ascend"` is used
- The stable_sort_index() variant preserves the relative order of elements with equivalent values
- For matrices and vectors with complex numbers, sorting is via absolute values
- Examples:

      vec  q       = randu<vec>(10);
      uvec indices = sort_index(q);

- See also:
  - find()
  - sort()

sqrtmat( X )

- Return the complex square root of square matrix X
- If X is not square sized, a std::logic_error exception is thrown
- Caveat: the square root of a matrix is generally not the same as applying the sqrt() function to each element
- Examples:

         mat A = randu<mat>(5,5);

      cx_mat B = sqrtmat(A);

- See also:
  - expmat()
  - logmat()
  - real()
  - miscellaneous element-wise functions
  - square root of a matrix in Wikipedia

sum( V )
sum( M )
sum( M, dim )
sum( Q )
sum( Q, dim )

- For vector V, return the sum of all elements
- For matrix M, return the sum of elements in each column (dim=0), or each row (dim=1)
- For cube Q, return the sums of elements along dimension dim, where dim ∈ { 0, 1, 2 }; for example, dim=0 indicates the sum of elements in each column within each slice
- The dim argument is optional; by default dim=0 is used
- Caveat: to get a sum of all the elements regardless of the object type (ie. vector, or matrix, or cube), use accu() instead
- Examples:

      colvec v = randu<colvec>(10,1);
      double x = sum(v);

      mat    M = randu<mat>(10,10);

      rowvec a = sum(M);
      rowvec b = sum(M,0);
      colvec c = sum(M,1);

      double y = accu(M);   // find the overall sum regardless of object type

- See also:
  - accu()
  - cumsum()
  - trace()
  - trapz()
  - mean()
  - as_scalar()

index = sub2ind( size(n_rows, n_cols), row, col )
index = sub2ind( size(n_rows, n_cols, n_slices), row, col, slice )

- Convert subscripts to a linear index
- A std::logic_error exception is thrown if a subscript is out of range
- Examples:

      uword i = sub2ind( size(4, 5), 2, 3 );

      uword j = sub2ind( size(4, 5, 6), 2, 3, 4 );

- See also:
  - size()
  - ind2sub()
  - element access

symmatu( A )
symmatu( A, do_conj )
symmatl( A )
symmatl( A, do_conj )

- symmatu(A): generate symmetric matrix from square matrix A, by reflecting the upper triangle to the lower triangle
- symmatl(A): generate symmetric matrix from square matrix A, by reflecting the lower triangle to the upper triangle
- If A is a complex matrix, the reflection uses the complex conjugate of the elements; to disable the complex conjugate, set do_conj to false
- If A is non-square, a std::logic_error exception is thrown
- Examples:

      mat A = randu<mat>(5,5);

      mat B = symmatu(A);
      mat C = symmatl(A);

- See also:
  - diagmat()
  - trimatu() / trimatl()
  - Symmetric matrix in Wikipedia

trace( X )

- Sum of the elements on the main diagonal of matrix X
- If X is an expression, the function will try to use optimised expression evaluations to calculate only the diagonal elements
- Examples:

      mat    A = randu<mat>(5,5);

      double x = trace(A);

- See also:
  - accu()
  - as_scalar()
  - .diag()
  - diagvec()
  - sum()

trans( A )
strans( A )

- For real (non-complex) matrix:
  - trans() provides a transposed copy of the matrix
  - strans() not applicable
- For complex matrix:
  - trans() provides a Hermitian transpose (ie. the conjugate of the elements is taken during the transpose)
  - strans() provides a transposed copy without taking the conjugate of the elements
- Examples:

      mat A = randu<mat>(5,10);

      mat B = trans(A);
      mat C = A.t();    // equivalent to trans(A), but more compact

- See also:
  - .t()
  - inplace_trans

trapz( X, Y )
trapz( X, Y, dim )
trapz( Y )
trapz( Y, dim )

- Compute the trapezoidal integral of Y with respect to spacing in X, in each column (dim=0) or each row (dim=1) of Y
- X must be a vector; its length must equal either the number of rows in Y (when dim=0), or the number of columns in Y (when dim=1)
- If X is not specified, unit spacing is used
- The dim argument is optional; by default dim=0
- Examples:

      vec X = linspace<vec>(0, datum::pi, 1000);
      vec Y = sin(X);

      mat Z = trapz(X,Y);

- See also:
  - sum()
  - diff()
  - linspace()
  - numerical integration in Wikipedia
  - numerical integration in MathWorld
  - trapezoidal rule in Wikipedia

trimatu( A )
trimatl( A )

- trimatu(A): generate upper triangular matrix from square matrix A
- trimatl(A): generate lower triangular matrix from square matrix A
- If A is non-square, a std::logic_error exception is thrown
- Examples:

      mat A = randu<mat>(5,5);

      mat U = trimatu(A);
      mat L = trimatl(A);

- See also:
  - symmatu() / symmatl()
  - diagmat()
  - Triangular matrix in MathWorld
  - Triangular matrix in Wikipedia

unique( A )

- Return the unique elements of A, sorted in ascending order
- If A is a vector, the output is also a vector with the same orientation (row or column) as A; if A is a matrix, the output is always a column vector
- Examples:

      mat X;
      X << 1 << 2 << endr
        << 2 << 3 << endr;

      mat Y = unique(X);

- See also:
  - find()
  - find_unique()
  - sort()
  - nonzeros()

vectorise( A )
vectorise( A, dim )

- Generate a column vector (dim=0) or row vector (dim=1) from matrix A
- The argument dim is optional; by default dim=0 is used
- For dim=0, the elements are copied from X column-wise; equivalent to concatenating all the columns of A
- For dim=1, the elements are copied from X row-wise; equivalent to concatenating all the rows of A
- Concatenating columns is faster than concatenating rows
- Examples:

      mat A = randu<mat>(4, 5);

      vec v = vectorise(A);

- See also:
  - reshape()
  - resize()
  - as_scalar()
  - diagvec()

miscellaneous element-wise functions:

exp |  | exp2 |  | exp10 |  | trunc_exp
log |  | log2 |  | log10 |  | trunc_log
pow |  | square |  | sqrt |  |
floor |  | ceil |  | round |  | trunc
erf |  | erfc |  |  |  |
lgamma |  |  |  |  |  |
sign |  |  |  |  |  |

- Apply a function to each element
- Usage:
  - matrix_type B = fn(A)
  - cube_type B = fn(A)
  - A and B must have the same matrix_type/cube_type
  - fn(A) is one of:

    `exp(A)` |  | base-e exponential: e x
    `exp2(A)` |  | base-2 exponential: 2 x
    `exp10(A)` |  | base-10 exponential: 10 x
    `trunc_exp(A)` |  | base-e exponential, truncated to avoid infinity (only for float and double elements)
    `log(A)` |  | natural log: loge x
    `log2(A)` |  | base-2 log: log2 x
    `log10(A)` |  | base-10 log: log10 x
    `trunc_log(A)` |  | natural log, truncated to avoid ±infinity (only for float and double elements)
    `pow(A, p)` |  | raise to the power of p: x p
    `square(A)` |  | square: x 2
    `sqrt(A)` |  | square root: x ½
    `floor(A)` |  | largest integral value that is not greater than the input value
    `ceil(A)` |  | smallest integral value that is not less than the input value
    `round(A)` |  | round to nearest integer, with halfway cases rounded away from zero
    `trunc(A)` |  | round to nearest integer, towards zero
    `erf(A)` |  | error function
    `erfc(A)` |  | complementary error function
    `lgamma(A)` |  | natural log of the gamma function
    `sign(A)` |  | signum function; for each element a in A, the corresponding element b in B is:
    | ⎧ | −1 | if a < 0
    b = | ⎨ | 0 | if a = 0
    | ⎩ | +1 | if a > 0
    if a is complex and non-zero, then b = a / abs(a)

- Examples:

      mat A = randu<mat>(5,5);
      mat B = exp(A);

- See also:
  - abs()
  - clamp()
  - conj()
  - imag() / real()
  - .transform() (apply user-defined function to each element)
  - expmat()
  - logmat()
  - sqrtmat()
  - trigonometric functions
  - statistics functions
  - miscellaneous constants

trigonometric element-wise functions (cos, sin, tan, ...)

- Apply a trigonometric function to each element
- Usage:
  - matrix_type Y = trig_fn(X)
  - cube_type Y = trig_fn(X)
  - X and Y must have the same matrix_type/cube_type
  - trig_fn is one of:
    - cos family: cos, acos, cosh, acosh
    - sin family: sin, asin, sinh, asinh
    - tan family: tan, atan, tanh, atanh
- Examples:

      mat X = randu<mat>(5,5);
      mat Y = cos(X);

- See also:
  - miscellaneous functions
  - statistics functions

Decompositions, Factorisations, Inverses and Equation Solvers (Dense Matrices)

R = chol( X )
R = chol( X, layout )
chol( R, X )
chol( R, X, layout )

- Cholesky decomposition of matrix X
- Matrix X must be symmetric and positive-definite
- By default, R is upper triangular, such that R.t()*R = X
- The argument layout is optional; layout is either `"upper"` or `"lower"`, which specifies whether R is upper or lower triangular
- If the decomposition fails:
  - R = chol(X) resets R and throws a std::runtime_error exception
  - chol(R,X) resets R and returns a bool set to false (exception is not thrown)
- Examples:

      mat X = randu<mat>(5,5);
      mat Y = X.t()*X;

      mat R1 = chol(Y);
      mat R2 = chol(Y, "lower");

- See also:
  - lu()
  - qr()
  - Cholesky decomposition in MathWorld
  - Cholesky decomposition in Wikipedia

vec eigval = eig_sym( X )
eig_sym( eigval, X )
eig_sym( eigval, eigvec, X )
eig_sym( eigval, eigvec, X, method )

- Eigen decomposition of dense symmetric/hermitian matrix X
- The eigenvalues and corresponding eigenvectors are stored in eigval and eigvec, respectively
- The eigenvalues are in ascending order
- If X is not square sized, a std::logic_error exception is thrown
- The method argument is optional; method is either `"dc"` or `"std"`
  - `"dc"` indicates divide-and-conquer method (default setting)
  - `"std"` indicates standard method
  - the divide-and-conquer method provides slightly different results than the standard method, but is considerably faster for large matrices
- If the decomposition fails:
  - eigval = eig_sym(X) resets eigval and throws a std::runtime_error exception
  - eig_sym(eigval,X) resets eigval and returns a bool set to false (exception is not thrown)
  - eig_sym(eigval,eigvec,X) resets eigval & eigvec and returns a bool set to false (exception is not thrown)
- There is currently no check whether X is symmetric
- Examples:

      // for matrices with real elements

      mat A = randu<mat>(50,50);
      mat B = A.t()*A;  // generate a symmetric matrix

      vec eigval;
      mat eigvec;

      eig_sym(eigval, eigvec, B);

      // for matrices with complex elements

      cx_mat C = randu<cx_mat>(50,50);
      cx_mat D = C.t()*C;

         vec eigval2;
      cx_mat eigvec2;

      eig_sym(eigval2, eigvec2, D);

- See also:
  - eig_gen()
  - eig_pair()
  - svd()
  - svd_econ()
  - eigs_sym()
  - eigen decomposition in MathWorld
  - eigenvalues & eigenvectors in Wikipedia
  - divide & conquer eigenvalue algorithm in Wikipedia

cx_vec eigval = eig_gen( X )
eig_gen( eigval, X )
eig_gen( eigval, eigvec, X )

- Eigen decomposition of dense general (non-symmetric/non-hermitian) square matrix X
- The eigenvalues and corresponding eigenvectors are stored in eigval and eigvec, respectively
- If X is not square sized, a std::logic_error exception is thrown
- If the decomposition fails:
  - eigval = eig_gen(X) resets eigval and throws a std::runtime_error exception
  - eig_gen(eigval,X) resets eigval and returns a bool set to false (exception is not thrown)
  - eig_gen(eigval,eigvec,X) resets eigval & eigvec and returns a bool set to false (exception is not thrown)
- Examples:

      mat A = randu<mat>(10,10);

      cx_vec eigval;
      cx_mat eigvec;

      eig_gen(eigval, eigvec, A);

- See also:
  - eig_pair()
  - eig_sym()
  - svd()
  - svd_econ()
  - schur()
  - eigs_gen()
  - eigen decomposition in MathWorld
  - eigenvalues & eigenvectors in Wikipedia

cx_vec eigval = eig_pair( A, B )
eig_pair( eigval, A, B )
eig_pair( eigval, eigvec, A, B )

- Eigen decomposition for pair of general dense square matrices A and B of the same size, such that A*eigvec = B*eigvec*diagmat(eigval)
- The eigenvalues and corresponding eigenvectors are stored in eigval and eigvec, respectively
- If A or B is not square sized, a std::logic_error exception is thrown
- If the decomposition fails:
  - eigval = eig_pair(A,B) resets eigval and throws a std::runtime_error exception
  - eig_pair(eigval,A,B) resets eigval and returns a bool set to false (exception is not thrown)
  - eig_pair(eigval,eigvec,A,B) resets eigval & eigvec and returns a bool set to false (exception is not thrown)
- Examples:

      mat A = randu<mat>(10,10);
      mat B = randu<mat>(10,10);

      cx_vec eigval;
      cx_mat eigvec;

      eig_pair(eigval, eigvec, A, B);

- See also:
  - eig_gen()
  - eig_sym()
  - qz()
  - eigen decomposition in MathWorld
  - eigenvalues & eigenvectors in Wikipedia

B = inv( A )
inv( B, A )

- Inverse of square matrix A
- If A is not square sized, a std::logic_error exception is thrown
- If A appears to be singular:
  - B = inv(A) resets B and throws a std::runtime_error exception
  - inv(B,A) resets B and returns a bool set to false (exception is not thrown)
- Caveats:
  - if matrix A is know to be symmetric positive definite, it's faster to use inv_sympd() instead
  - if matrix A is know to be diagonal, use inv( diagmat(A) )
  - to solve a system of linear equations, such as Z = inv(X)*Y, it is faster and more accurate to use solve() instead
- Examples:

      mat A = randu<mat>(5,5);

      mat B = inv(A);

- See also:
  - .i()
  - inv_sympd()
  - rcond()
  - pinv()
  - solve()
  - spsolve()
  - diagmat()
  - matrix inverse in MathWorld
  - invertible matrix in Wikipedia

B = inv_sympd( A )
inv_sympd( B, A )

- Inverse of symmetric positive definite matrix A
- If A is not square sized, a std::logic_error exception is thrown
- There is currently no explicit check whether A is symmetric or positive definite
- If A appears to be singular:
  - B = inv_sympd(A) resets B and throws a std::runtime_error exception
  - inv_sympd(B,A) resets B and returns a bool set to false (exception is not thrown)
- Caveat: to solve a system of linear equations, such as Z = inv(X)*Y, it is faster and more accurate to use solve() instead
- Examples:

      mat A   = randu<mat>(5,5);

      mat AtA = A.t() * A;

      mat B   = inv_sympd(AtA);

- See also:
  - inv()
  - rcond()
  - pinv()
  - solve()
  - eig_sym()
  - matrix inverse in MathWorld
  - invertible matrix in Wikipedia
  - positive definite matrix in MathWorld
  - positive definite matrix in Wikipedia

lu( L, U, P, X )
lu( L, U, X )

- Lower-upper decomposition (with partial pivoting) of matrix X
- The first form provides a lower-triangular matrix L, an upper-triangular matrix U, and a permutation matrix P, such that P.t()*L*U = X
- The second form provides permuted L and U, such that L*U = X; note that in this case L is generally not lower-triangular
- If the decomposition fails:
  - lu(L,U,P,X) resets L, U, P and returns a bool set to false (exception is not thrown)
  - lu(L,U,X) resets L, U and returns a bool set to false (exception is not thrown)
- Examples:

      mat A = randu<mat>(5,5);

      mat L, U, P;

      lu(L, U, P, A);

      mat B = P.t()*L*U;

- See also:
  - chol()
  - LU decomposition in Wikipedia
  - LU decomposition in MathWorld

B = null( A )
B = null( A, tolerance )
null( B, A )
null( B, A, tolerance )

- Find the orthonormal basis of the null space of matrix A
- The dimension of the range space is the number of singular values of A not greater than tolerance
- The tolerance argument is optional; by default tolerance is max(m,n)*max_sv*datum::eps, where:
  - m = number of rows and n = number of columns in A
  - max_sv = maximal singular value of A
  - datum::eps = difference between 1 and the least value greater than 1 that is representable
- The computation is based on singular value decomposition; if the decomposition fails:
  - B = null(A) resets B and throws a std::runtime_error exception
  - null(B,A) resets B and returns a bool set to false (exception is not thrown)
- Examples:

      mat A = randu<mat>(5,6);

      A.row(0).zeros();
      A.col(0).zeros();

      mat B = null(A);

- See also:
  - orth()
  - qr()
  - svd()
  - rank()
  - datum::eps
  - Orthonormal basis in Wikipedia

B = orth( A )
B = orth( A, tolerance )
orth( B, A )
orth( B, A, tolerance )

- Find the orthonormal basis of the range space of matrix A, so that B.t()*B ≈ eye(r,r), where r = rank(A)
- The dimension of the range space is the number of singular values of A greater than tolerance
- The tolerance argument is optional; by default tolerance is max(m,n)*max_sv*datum::eps, where:
  - m = number of rows and n = number of columns in A
  - max_sv = maximal singular value of A
  - datum::eps = difference between 1 and the least value greater than 1 that is representable
- The computation is based on singular value decomposition; if the decomposition fails:
  - B = orth(A) resets B and throws a std::runtime_error exception
  - orth(B,A) resets B and returns a bool set to false (exception is not thrown)
- Examples:

      mat A = randu<mat>(5,6);

      mat B = orth(A);

- See also:
  - null()
  - qr()
  - svd()
  - rank()
  - datum::eps
  - Orthonormal basis in Wikipedia

B = pinv( A )
B = pinv( A, tolerance )
B = pinv( A, tolerance, method )
pinv( B, A )
pinv( B, A, tolerance )
pinv( B, A, tolerance, method )

- Moore-Penrose pseudo-inverse of matrix A
- The computation is based on singular value decomposition
- The tolerance argument is optional
- The default tolerance is max(m,n)*max_sv*datum::eps, where:
  - m = number of rows and n = number of columns in A
  - max_sv = maximal singular value of A
  - datum::eps = difference between 1 and the least value greater than 1 that is representable
- Any singular values less than tolerance are treated as zero
- The method argument is optional; method is either `"dc"` or `"std"`
  - `"dc"` indicates divide-and-conquer method (default setting)
  - `"std"` indicates standard method
  - the divide-and-conquer method provides slightly different results than the standard method, but is considerably faster for large matrices
- If the decomposition fails:
  - B = pinv(A) resets B and throws a std::runtime_error exception
  - pinv(B,A) resets B and returns a bool set to false (exception is not thrown)
- Examples:

      mat A = randu<mat>(4,5);

      mat B = pinv(A);        // use default tolerance

      mat C = pinv(A, 0.01);  // set tolerance to 0.01

- See also:
  - .i()
  - inv()
  - solve()
  - spsolve()
  - datum::eps
  - Pseudoinverse in MathWorld
  - Moore-Penrose Matrix Inverse in MathWorld
  - Moore-Penrose pseudoinverse in Wikipedia

qr( Q, R, X )

- Decomposition of X into an orthogonal matrix Q and a right triangular matrix R, such that Q*R = X
- If the decomposition fails, Q and R are reset and the function returns a bool set to false (exception is not thrown)
- Examples:

      mat X = randu<mat>(5,5);
      mat Q, R;

      qr(Q,R,X);

- See also:
  - qr_econ()
  - chol()
  - orth()
  - orthogonal matrix in Wikipedia
  - QR decomposition in Wikipedia
  - QR decomposition in MathWorld

qr_econ( Q, R, X )

- Economical decomposition of X (with size m x n) into an orthogonal matrix Q and a right triangular matrix R, such that Q*R = X
- If m > n, only the first n rows of R and the first n columns of Q are calculated (ie. the zero rows of R and the corresponding columns of Q are omitted)
- If the decomposition fails, Q and R are reset and the function returns a bool set to false (exception is not thrown)
- Examples:

      mat X = randu<mat>(6,5);
      mat Q, R;

      qr_econ(Q,R,X);

- See also:
  - qr()
  - orthogonal matrix in Wikipedia
  - QR decomposition in Wikipedia
  - QR decomposition in Octave
  - QR decomposition in MathWorld

qz( AA, BB, Q, Z, A, B )

- Generalised Schur decomposition for pair of general square matrices A and B of the same size,
  such that A = Q.t()*AA*Z.t() and B = Q.t()*BB*Z.t()
- The left and right Schur vectors are stored in Q and Z, respectively
- In the complex-valued problem, the generalised eigenvalues are found in diagvec(AA) / diagvec(BB)
- If A or B is not square sized, a std::logic_error exception is thrown
- If the decomposition fails, AA, BB, Q and Z are reset, and the function returns a bool set to false (exception is not thrown)
- Examples:

      mat A = randu<mat>(10,10);
      mat B = randu<mat>(10,10);

      mat AA;
      mat BB;
      mat Q;
      mat Z;

      qz(AA, BB, Q, Z, A, B);

- See also:
  - schur()
  - eig_pair()
  - generalised Schur decomposition in Wikipedia

S = schur( X )
schur( S, X )
schur( U, S, X )

- Schur decomposition of square matrix X, such that X = U*S*U.t()
- U is a unitary matrix containing the Schur vectors
- S is an upper triangular matrix, called the Schur form of X
- If X is not square sized, a std::logic_error exception is thrown
- If the decomposition fails:
  - S = schur(X) resets S and throws a std::runtime_error exception
  - schur(S,X) resets S and returns a bool set to false (exception is not thrown)
  - schur(U,S,X) resets U & S and returns a bool set to false (exception is not thrown)
- Caveat: in general, Schur decomposition is not unique
- Examples:

      mat X(20,20, fill::randu);

      mat U;
      mat S;

      schur(U, S, X);

- See also:
  - qz()
  - eig_gen()
  - Schur decomposition in MathWorld
  - Schur decomposition in Wikipedia

X = solve( A, B )
X = solve( A, B, settings )
solve( X, A, B )
solve( X, A, B, settings )

- Solve a dense system of linear equations, A*X = B, where X is unknown; similar functionality to the \ operator in Matlab/Octave, ie. X = A \ B
- A can be square (critically determined system), or non-square (under/over-determined system)
- B can be a vector or matrix
- The number of rows in A and B must be the same
- The settings argument is optional; it is one of the following, or a combination thereof:

  `solve_opts::fast` |  | fast mode: do not apply iterative refinement and/or equilibration
  `solve_opts::equilibrate` |  | equilibrate the system before solving (matrix A must be square)
  `solve_opts::no_approx` |  | do not find approximate solutions for rank deficient systems

  the above settings can be combined using the `+` operator; for example: `solve_opts::fast + solve_opts::no_approx`
- Caveat: using `solve_opts::fast` will speed up finding the solution, but for poorly conditioned systems the solution may have lower quality
- If A is known to be a triangular matrix, the solution can be computed faster by explicitly indicating that A is triangular through trimatu() or trimatl();
  indicating a triangular matrix also implies that `solve_opts::fast` is enabled
- If no solution is found:
  - X = solve(A,B) resets X and throws a std::runtime_error exception
  - solve(X,A,B) resets X and returns a bool set to false (exception is not thrown)
- Examples:

      mat A = randu<mat>(5,5);
      vec b = randu<vec>(5);
      mat B = randu<mat>(5,5);

      vec x1 = solve(A, b);

      vec x2;
      bool status = solve(x2, A, b);

      mat X1 = solve(A, B);

      mat X2 = solve(A, B, solve_opts::fast);  // enable fast mode

      mat X3 = solve(trimatu(A), B);  // indicate that A is triangular

- See also:
  - inv()
  - pinv()
  - rcond()
  - syl()
  - spsolve()
  - linear system of equations in MathWorld
  - system of linear equations in Wikipedia

vec s = svd( X )
svd( vec s, X )
svd( mat U, vec s, mat V, mat X )
svd( mat U, vec s, mat V, mat X, method )
svd( cx_mat U, vec s, cx_mat V, cx_mat X )
svd( cx_mat U, vec s, cx_mat V, cx_mat X, method )

- Singular value decomposition of dense matrix X
- If X is square, it can be reconstructed using X = U*diagmat(s)*V.t()
- The singular values are in descending order
- The method argument is optional; method is either `"dc"` or `"std"`
  - `"dc"` indicates divide-and-conquer method (default setting)
  - `"std"` indicates standard method
  - the divide-and-conquer method provides slightly different results than the standard method, but is considerably faster for large matrices
- If the decomposition fails, the output objects are reset and:
  - s = svd(X) resets s and throws a std::runtime_error exception
  - svd(s,X) resets s and returns a bool set to false (exception is not thrown)
  - svd(U,s,V,X) resets U, s, V and returns a bool set to false (exception is not thrown)
- Examples:

      mat X = randu<mat>(5,5);

      mat U;
      vec s;
      mat V;

      svd(U,s,V,X);

- See also:
  - svd_econ()
  - eig_gen()
  - eig_sym()
  - svds()
  - singular value decomposition in Wikipedia
  - singular value decomposition in MathWorld

svd_econ( mat U, vec s, mat V, mat X )
svd_econ( mat U, vec s, mat V, mat X, mode )
svd_econ( mat U, vec s, mat V, mat X, mode, method )
svd_econ( cx_mat U, vec s, cx_mat V, cx_mat X )
svd_econ( cx_mat U, vec s, cx_mat V, cx_mat X, mode )
svd_econ( cx_mat U, vec s, cx_mat V, cx_mat X, mode, method )

- Economical singular value decomposition of dense matrix X
- The singular values are in descending order
- The mode argument is optional; mode is one of:

  | `"both"` | = | compute both left and right singular vectors (default operation)
  | `"left"` | = | compute only left singular vectors
  | `"right"` | = | compute only right singular vectors

- The method argument is optional; method is either `"dc"` or `"std"`
  - `"dc"` indicates divide-and-conquer method (default setting)
  - `"std"` indicates standard method
  - the divide-and-conquer method provides slightly different results than the standard method, but is considerably faster for large matrices
- If the decomposition fails, U, s, V are reset and a bool set to false is returned (exception is not thrown)
- Examples:

      mat X = randu<mat>(4,5);

      mat U;
      vec s;
      mat V;

      svd_econ(U, s, V, X);

- See also:
  - svd()
  - eig_gen()
  - eig_sym()
  - svds()
  - singular value decomposition in Wikipedia
  - singular value decomposition in MathWorld

X = syl( A, B, C )
syl( X, A, B, C )

- Solve the Sylvester equation, ie. AX + XB + C = 0, where X is unknown
- Matrices A, B and C must be square sized
- If no solution is found:
  - syl(A,B,C) resets X and throws a std::runtime_error exception
  - syl(X,A,B,C) resets X and returns a bool set to false (exception is not thrown)
- Examples:

      mat A = randu<mat>(5,5);
      mat B = randu<mat>(5,5);
      mat C = randu<mat>(5,5);

      mat X1 = syl(A, B, C);

      mat X2;
      syl(X2, A, B, C);

- See also:
  - solve()
  - Sylvester equation in Wikipedia

Decompositions, Factorisations and Equation Solvers (Sparse Matrices)

vec eigval = eigs_sym( X, k )
vec eigval = eigs_sym( X, k, form )
vec eigval = eigs_sym( X, k, form, tol )
eigs_sym( eigval, X, k )
eigs_sym( eigval, X, k, form )
eigs_sym( eigval, X, k, form, tol )
eigs_sym( eigval, eigvec, X, k )
eigs_sym( eigval, eigvec, X, k, form )
eigs_sym( eigval, eigvec, X, k, form, tol )

- Obtain a limited number of eigenvalues and eigenvectors of sparse symmetric real matrix X
- k specifies the number of eigenvalues and eigenvectors
- The argument form is optional; form is one of:

  `"lm"` | = | obtain eigenvalues with largest magnitude (default operation)
  `"sm"` | = | obtain eigenvalues with smallest magnitude
  `"la"` | = | obtain eigenvalues with largest algebraic value
  `"sa"` | = | obtain eigenvalues with smallest algebraic value

- The argument tol is optional; it specifies the tolerance for convergence
- The eigenvalues and corresponding eigenvectors are stored in eigval and eigvec, respectively
- If X is not square sized, a std::logic_error exception is thrown
- If the decomposition fails:
  - eigval = eigs_sym(X,k) resets eigval and throws a std::runtime_error exception
  - eigs_sym(eigval,X,k) resets eigval and returns a bool set to false (exception is not thrown)
  - eigs_sym(eigval,eigvec,X,k) resets eigval & eigvec and returns a bool set to false (exception is not thrown)
- There is currently no check whether X is symmetric
- Examples:

      // generate sparse matrix
      sp_mat A = sprandu<sp_mat>(1000, 1000, 0.1);
      sp_mat B = A.t()*A;

      vec eigval;
      mat eigvec;

      eigs_sym(eigval, eigvec, B, 5);  // find 5 eigenvalues/eigenvectors

- See also:
  - eigs_gen()
  - eig_sym()
  - svds()
  - eigen decomposition in MathWorld
  - eigenvalues & eigenvectors in Wikipedia

cx_vec eigval = eigs_gen( X, k )
cx_vec eigval = eigs_gen( X, k, form )
cx_vec eigval = eigs_gen( X, k, form, tol )
eigs_gen( eigval, X, k )
eigs_gen( eigval, X, k, form )
eigs_gen( eigval, X, k, form, tol )
eigs_gen( eigval, eigvec, X, k )
eigs_gen( eigval, eigvec, X, k, form )
eigs_gen( eigval, eigvec, X, k, form, tol )

- Obtain a limited number of eigenvalues and eigenvectors of sparse general (non-symmetric/non-hermitian) square matrix X
- k specifies the number of eigenvalues and eigenvectors
- The argument form is optional; form is one of:

  `"lm"` | = | obtain eigenvalues with largest magnitude (default operation)
  `"sm"` | = | obtain eigenvalues with smallest magnitude
  `"lr"` | = | obtain eigenvalues with largest real part
  `"sr"` | = | obtain eigenvalues with smallest real part
  `"li"` | = | obtain eigenvalues with largest imaginary part
  `"si"` | = | obtain eigenvalues with smallest imaginary part

- The argument tol is optional; it specifies the tolerance for convergence
- The eigenvalues and corresponding eigenvectors are stored in eigval and eigvec, respectively
- If X is not square sized, a std::logic_error exception is thrown
- If the decomposition fails:
  - eigval = eigs_gen(X,k) resets eigval and throws a std::runtime_error exception
  - eigs_gen(eigval,X,k) resets eigval and returns a bool set to false (exception is not thrown)
  - eigs_gen(eigval,eigvec,X,k) resets eigval & eigvec and returns a bool set to false (exception is not thrown)
- Examples:

      // generate sparse matrix
      sp_mat A = sprandu<sp_mat>(1000, 1000, 0.1);

      cx_vec eigval;
      cx_mat eigvec;

      eigs_gen(eigval, eigvec, A, 5);  // find 5 eigenvalues/eigenvectors

- See also:
  - eigs_sym()
  - eig_gen()
  - svds()
  - eigen decomposition in MathWorld
  - eigenvalues & eigenvectors in Wikipedia

X = spsolve( A, B )
X = spsolve( A, B, solver )
X = spsolve( A, B, solver, settings )
spsolve( X, A, B )
spsolve( X, A, B, solver )
spsolve( X, A, B, solver, settings )

- Solve a sparse system of linear equations, A*X = B, where A is a sparse matrix, B is a dense matrix or vector, and X is unknown
- The number of rows in A and B must be the same
- If no solution is found:
  - X = spsolve(A, B) resets X and throws a std::runtime_error exception
  - spsolve(X, A, B) resets X and returns a bool set to false (no exception is thrown)
- The solver argument is optional; solver is either `"superlu"` or `"lapack"`; by default `"superlu"` is used
  - For `"superlu"`, ARMA_USE_SUPERLU must be enabled in config.hpp
  - For `"lapack"`, sparse matrix A is converted to a dense matrix before using the LAPACK solver; this considerably increases memory usage
- The settings argument is optional
  - when solver is "superlu", settings is an instance of the superlu_opts structure:

        struct superlu_opts
          {
          bool             equilibrate;  // default: false
          bool             symmetric;    // default: false
          double           pivot_thresh; // default: 1.0
          permutation_type permutation;  // default: superlu_opts::COLAMD
          refine_type      refine;       // default: superlu_opts::REF_DOUBLE
          };

  - equilibrate is either true or false; it indicates whether to equilibrate the system (scale the rows and columns of A to have unit norm)
  - symmetric is either true or false; it indicates whether to use SuperLU symmetric mode, which gives preference to diagonal pivots
  - pivot_threshold is in the range [0.0, 1.0], used for determining whether a diagonal entry is an acceptable pivot (details in SuperLU documentation)
  - permutation specifies the type of column permutation; it is one of:

    `superlu_opts::NATURAL` |  | natural ordering
    `superlu_opts::MMD_ATA` |  | minimum degree ordering on structure of `A.t() * A`
    `superlu_opts::MMD_AT_PLUS_A` |  | minimum degree ordering on structure of `A.t() + A`
    `superlu_opts::COLAMD` |  | approximate minimum degree column ordering

  - refine specifies the type of iterative refinement; it is one of:

    `superlu_opts::REF_NONE` |  | no refinement
    `superlu_opts::REF_SINGLE` |  | iterative refinement in single precision
    `superlu_opts::REF_DOUBLE` |  | iterative refinement in double precision
    `superlu_opts::REF_EXTRA` |  | iterative refinement in extra precision

- Examples:

      sp_mat A = sprandu<sp_mat>(1000, 1000, 0.1);

      vec b = randu<vec>(1000);
      mat B = randu<mat>(1000, 5);

      vec x = spsolve(A, b);  // solve one system
      mat X = spsolve(A, B);  // solve several systems

      bool status = spsolve(x, A, b);  // use default solver
      if(status == false)  { cout << "no solution" << endl; }

      spsolve(x, A, b, "lapack");   // use LAPACK  solver
      spsolve(x, A, b, "superlu");  // use SuperLU solver

      superlu_opts settings;

      settings.permutation = superlu_opts::NATURAL;
      settings.refine      = superlu_opts::REF_NONE;

      spsolve(x, A, b, "superlu", settings);

- See also:
  - solve()
  - SuperLU home page
  - linear system of equations in MathWorld
  - system of linear equations in Wikipedia

vec s = svds( X, k )
vec s = svds( X, k, tol )
svds( vec s, X, k )
svds( vec s, X, k, tol )
svds( mat U, vec s, mat V, sp_mat X, k )
svds( mat U, vec s, mat V, sp_mat X, k, tol )
svds( cx_mat U, vec s, cx_mat V, sp_cx_mat X, k )
svds( cx_mat U, vec s, cx_mat V, sp_cx_mat X, k, tol )

- Obtain a limited number of singular values and singular vectors of sparse matrix X
- k specifies the number of singular values and singular vectors
- The argument tol is optional; it specifies the tolerance for convergence
- The singular values are calculated via sparse eigen decomposition of: `

  ⎡ | zeros(X.n_rows, X.n_rows) |  | X | ⎤
  ⎣ | X.t() |  | zeros(X.n_cols, X.n_cols) | ⎦

  `
- The singular values are in descending order
- If the decomposition fails, the output objects are reset and:
  - s = svds(X,k) resets s and throws a std::runtime_error exception
  - svds(s,X,k) resets s and returns a bool set to false (exception is not thrown)
  - svds(U,s,V,X,k) resets U, s, V and returns a bool set to false (exception is not thrown)
- Caveat: svds() is intended only for finding a few singular values from a large sparse matrix; to find all singular values, use svd() instead
- Examples:

      sp_mat X = sprandu<sp_mat>(100, 200, 0.1);

      mat U;
      vec s;
      mat V;

      svds(U, s, V, X, 10);

- See also:
  - eigs_gen()
  - eigs_sym()
  - svd()
  - singular value decomposition in Wikipedia
  - singular value decomposition in MathWorld

Signal & Image Processing

conv( A, B )
conv( A, B, shape )

- 1D convolution of vectors A and B
- The orientation of the result vector is the same as the orientation of A (ie. either column or row vector)
- The shape argument is optional; it is one of:

  `"full"` | = | return the full convolution (default setting), with the size equal to A.n_elem + B.n_elem - 1
  `"same"` | = | return the central part of the convolution, with the same size as vector A

- The convolution operation is also equivalent to FIR filtering
- Examples:

      vec A(256, fill::randu);

      vec B(16, fill::randu);

      vec C = conv(A, B);

      vec D = conv(A, B, "same");

- See also:
  - conv2()
  - fft()
  - cor()
  - interp1()
  - Convolution in MathWorld
  - Convolution in Wikipedia
  - FIR filter in Wikipedia

conv2( A, B )
conv2( A, B, shape )

- 2D convolution of matrices A and B
- The shape argument is optional; it is one of:

  `"full"` | = | return the full convolution (default setting), with the size equal to size(A) + size(B) - 1
  `"same"` | = | return the central part of the convolution, with the same size as matrix A

- The implementation of 2D convolution in this version is preliminary; it is not yet fully optimised
- Examples:

      mat A(256, 256, fill::randu);

      mat B(16, 16, fill::randu);

      mat C = conv2(A, B);

      mat D = conv2(A, B, "same");

- See also:
  - conv()
  - fft2()
  - Convolution in MathWorld
  - Convolution in Wikipedia
  - Kernel (image processing) in Wikipedia

cx_mat Y = fft( X )
cx_mat Y = fft( X, n )
cx_mat Z = ifft( cx_mat Y )
cx_mat Z = ifft( cx_mat Y, n )

- fft(): fast Fourier transform of a vector or matrix (real or complex)
- ifft(): inverse fast Fourier transform of a vector or matrix (complex only)
- If given a matrix, the transform is done on each column vector of the matrix
- The optional n argument specifies the transform length:
  - if n is larger than the length of the input vector, a zero-padded version of the vector is used
  - if n is smaller than the length of the input vector, only the first n elements of the vector are used
- If n is not specified, the transform length is the same as the length of the input vector
- Caveat: the transform is fastest when the transform length is a power of 2, eg. 64, 128, 256, 512, 1024, ...
- The implementation of the transform in this version is preliminary; it is not yet fully optimised
- Examples:

         vec X = randu<vec>(100);
      cx_vec Y = fft(X, 128);

- See also:
  - fft2()
  - conv()
  - real()
  - fast Fourier transform in MathWorld
  - fast Fourier transform in Wikipedia

cx_mat Y = fft2( X )
cx_mat Y = fft2( X, n_rows, n_cols )
cx_mat Z = ifft2( cx_mat Y )
cx_mat Z = ifft2( cx_mat Y, n_rows, n_cols )

- fft2(): 2D fast Fourier transform of a matrix (real or complex)
- ifft2(): 2D inverse fast Fourier transform of a matrix (complex only)
- The optional arguments n_rows and n_cols specify the size of the transform; a truncated and/or zero-padded version of the input matrix is used
- Caveat: the transform is fastest when both n_rows and n_cols are a power of 2, eg. 64, 128, 256, 512, 1024, ...
- The implementation of the transform in this version is preliminary; it is not yet fully optimised
- Examples:

         mat A = randu<mat>(100,100);
      cx_mat B = fft2(A);
      cx_mat C = fft2(A, 128, 128);

- See also:
  - fft()
  - conv2()
  - real()
  - fast Fourier transform in MathWorld
  - fast Fourier transform in Wikipedia

interp1( X, Y, XI, YI )
interp1( X, Y, XI, YI, method )
interp1( X, Y, XI, YI, method, extrapolation_value )

- 1D data interpolation
- Given a 1D function specified in vectors X and Y (where X specifies locations and Y specifies the corresponding values),
  generate vector YI which contains interpolated values at locations XI
- The method argument is optional; it is one of:

  `"nearest"` | = | interpolate using single nearest neighbour
  `"*nearest"` | = | as per `"nearest"`, but faster by assuming that X and XI are monotonically increasing
  `"linear"` | = | linear interpolation between two nearest neighbours (default setting)
  `"*linear"` | = | as per `"linear"`, but faster by assuming that X and XI are monotonically increasing

- If a location in XI is outside the domain of X, the corresponding value in YI is set to extrapolation_value
- The extrapolation_value argument is optional; by default it is datum::nan (not-a-number)
- Examples:

      vec x = linspace<vec>(0, 3, 20);
      vec y = square(x);

      vec xx = linspace<vec>(0, 3, 100);

      vec yy;

      interp1(x, y, xx, yy);  // use linear interpolation by default

      interp1(x, y, xx, yy, "*linear");  // faster than "linear"

      interp1(x, y, xx, yy, "nearest");

- See also:
  - linspace()
  - conv()

Statistics & Clustering

mean, median, stddev, var

mean( V ) mean( M ) mean( M, dim ) mean( Q ) mean( Q, dim ) |  | ⎫ ⎪ ⎬ mean (average value) ⎪ ⎭
median( V ) median( M ) median( M, dim ) |  | ⎫ ⎬ median ⎭
stddev( V ) stddev( V, norm_type ) stddev( M ) stddev( M, norm_type ) stddev( M, norm_type, dim ) |  | ⎫ ⎪ ⎬ standard deviation ⎪ ⎭
var( V ) var( V, norm_type ) var( M ) var( M, norm_type ) var( M, norm_type, dim ) |  | ⎫ ⎪ ⎬ variance ⎪ ⎭

- For vector V, return the statistic calculated using all the elements of the vector
- For matrix M, find the statistic for each column (dim=0), or each row (dim=1)
- For cube Q, find the statistics of elements along dimension dim, where dim ∈ { 0, 1, 2 }
- The dim argument is optional; by default dim=0 is used
- The norm_type argument is optional; by default norm_type=0 is used
- For the var() and stddev() functions:
  - the default norm_type=0 performs normalisation using N-1 (where N is the number of samples), providing the best unbiased estimator
  - using norm_type=1 performs normalisation using N, which provides the second moment around the mean
- Examples:

      mat A    = randu<mat>(5,5);
      mat B    = mean(A);
      mat C    = var(A);
      double m = mean(mean(A));

      vec    v = randu<vec>(5);
      double x = var(v);

- See also:
  - cov()
  - cor()
  - diff()
  - hist()
  - histc()
  - running_stat - class for running statistics of scalars
  - running_stat_vec - class for running statistics of vectors
  - gmm_diag - class for modelling data as a Gaussian mixture model
  - kmeans()

cov( X, Y )
cov( X, Y, norm_type )
cov( X )
cov( X, norm_type )

- For two matrix arguments X and Y, if each row of X and Y is an observation and each column is a variable, the (i,j)-th entry of cov(X,Y) is the covariance between the i-th variable in X and the j-th variable in Y
- For vector arguments, the type of vector is ignored and each element in the vector is treated as an observation
- For matrices, X and Y must have the same dimensions
- For vectors, X and Y must have the same number of elements
- cov(X) is equivalent to cov(X, X)
- The default norm_type=0 performs normalisation using N-1 (where N is the number of observations), providing the best unbiased estimation of the covariance matrix (if the observations are from a normal distribution). Using norm_type=1 causes normalisation to be done using N, which provides the second moment matrix of the observations about their mean
- Examples:

      mat X = randu<mat>(4,5);
      mat Y = randu<mat>(4,5);

      mat C = cov(X,Y);
      mat D = cov(X,Y, 1);

- See also:
  - cor()
  - statistics functions
  - running_stat_vec
  - Covariance in MathWorld

cor( X, Y )
cor( X, Y, norm_type )
cor( X )
cor( X, norm_type )

- For two matrix arguments X and Y, if each row of X and Y is an observation and each column is a variable, the (i,j)-th entry of cor(X,Y) is the correlation coefficient between the i-th variable in X and the j-th variable in Y
- For vector arguments, the type of vector is ignored and each element in the vector is treated as an observation
- For matrices, X and Y must have the same dimensions
- For vectors, X and Y must have the same number of elements
- cor(X) is equivalent to cor(X, X)
- The default norm_type=0 performs normalisation of the correlation matrix using N-1 (where N is the number of observations). Using norm_type=1 causes normalisation to be done using N
- Examples:

      mat X = randu<mat>(4,5);
      mat Y = randu<mat>(4,5);

      mat R = cor(X,Y);
      mat S = cor(X,Y, 1);

- See also:
  - cov()
  - conv()
  - statistics functions
  - running_stat_vec
  - Correlation in MathWorld
  - Autocorrelation in MathWorld

hist( V )
hist( V, n_bins )
hist( V, centers )
hist( X, centers )
hist( X, centers, dim )

- For vector V, produce an unsigned vector of the same orientation as V (ie. either uvec or urowvec) that represents a histogram of counts
- For matrix X, produce a umat matrix containing either column histogram counts (for dim=0, default), or row histogram counts (for dim=1)
- The bin centers can be automatically determined from the data, with the number of bins specified via n_bins (default is 10); the range of the bins is determined by the range of the data
- The bin centers can also be explicitly specified via the centers vector; the vector must contain monotonically increasing values (eg. 0.1, 0.2, 0.3, ...)
- Examples:

      vec  v  = randn<vec>(1000); // Gaussian distribution

      uvec h1 = hist(v, 11);
      uvec h2 = hist(v, linspace<vec>(-2,2,11));

- See also:
  - histc()
  - statistics functions
  - conv_to()

histc( V, edges )
histc( X, edges )
histc( X, edges, dim )

- For vector V, produce an unsigned vector of the same orientation as V (ie. either uvec or urowvec) that contains the counts of the number of values that fall between the elements in the edges vector
- For matrix X, produce a umat matrix containing either column histogram counts (for dim=0, default), or row histogram counts (for dim=1)
- The edges vector must contain monotonically increasing values (eg. 0.1, 0.2, 0.3, ...)
- Examples:

      vec  v = randn<vec>(1000);  // Gaussian distribution

      uvec h = histc(v, linspace<vec>(-2,2,11));

- See also:
  - hist()
  - statistics functions
  - conv_to()

mat coeff = princomp( mat X )
cx_mat coeff = princomp( cx_mat X )
princomp( mat coeff, mat X )
princomp( cx_mat coeff, cx_mat X )
princomp( mat coeff, mat score, mat X )
princomp( cx_mat coeff, cx_mat score, cx_mat X )
princomp( mat coeff, mat score, vec latent, mat X )
princomp( cx_mat coeff, cx_mat score, vec latent, cx_mat X )
princomp( mat coeff, mat score, vec latent, vec tsquared, mat X )
princomp( cx_mat coeff, cx_mat score, vec latent, cx_vec tsquared, cx_mat X )

- Principal component analysis of matrix X
- Each row of X is an observation and each column is a variable
- output objects:
  - coeff: principal component coefficients
  - score: projected data
  - latent: eigenvalues of the covariance matrix of X
  - tsquared: Hotteling's statistic for each sample
- The computation is based on singular value decomposition
- If the decomposition fails:
  - coeff = princomp(X) resets coeff and throws a std::runtime_error exception
  - remaining forms of princomp() reset all output matrices and return a bool set to false (exception is not thrown)
- Examples:

      mat A = randu<mat>(5,4);

      mat coeff;
      mat score;
      vec latent;
      vec tsquared;

      princomp(coeff, score, latent, tsquared, A);

- See also:
  - principal components analysis in Wikipedia
  - principal components analysis in MathWorld

running_stat<type>

- Class for keeping the running statistics of a continuously sampled one dimensional process/signal
- Useful if the storage of individual samples (scalars) is not required, or the number of samples is not known beforehand
- type is one of: float, double, cx_float, cx_double
- For an instance of running_stat named as X, the member functions are:

  X(scalar) |  | update the statistics so far using the given scalar
  X.min() |  | get the minimum value so far
  X.max() |  | get the maximum value so far
  X.mean() |  | get the mean or average value so far
  X.var() and X.var(norm_type) |  | get the variance so far
  X.stddev() and X.stddev(norm_type) |  | get the standard deviation so far
  X.reset() |  | reset all statistics and set the number of samples to zero
  X.count() |  | get the number of samples so far

- The norm_type argument is optional; by default norm_type=0 is used
- For the .var() and .stddev() functions, the default norm_type=0 performs normalisation using N-1 (where N is the number of samples so far), providing the best unbiased estimator; using norm_type=1 causes normalisation to be done using N, which provides the second moment around the mean
- The return type of .count() depends on the underlying form of type: it is either float or double
- Examples:

      running_stat<double> stats;

      for(uword i=0; i<10000; ++i)
        {
        double sample = randn();
        stats(sample);
        }

      cout << "mean = " << stats.mean() << endl;
      cout << "var  = " << stats.var()  << endl;
      cout << "min  = " << stats.min()  << endl;
      cout << "max  = " << stats.max()  << endl;

- See also:
  - statistics functions
  - running_stat_vec

running_stat_vec<vec_type>
running_stat_vec<vec_type>(calc_cov)

- Class for keeping the running statistics of a continuously sampled multi-dimensional process/signal
- Useful if the storage of individual samples (vectors) is not required, or if the number of samples is not known beforehand
- This class is similar to running_stat, with the difference that vectors are processed instead of scalars
- vec_type is the vector type of the samples; for example: vec, cx_vec, rowvec, ...
- For an instance of running_stat_vec named as X, the member functions are:

  X(vector) |  | update the statistics so far using the given vector
  X.min() |  | get the vector of minimum values so far
  X.max() |  | get the vector of maximum values so far
  X.mean() |  | get the mean vector so far
  X.var() and X.var(norm_type) |  | get the vector of variances so far
  X.stddev() and X.stddev(norm_type) |  | get the vector of standard deviations so far
  X.cov() and X.cov(norm_type) |  | get the covariance matrix so far; valid if calc_cov=true during construction of running_stat_vec
  X.reset() |  | reset all statistics and set the number of samples to zero
  X.count() |  | get the number of samples so far

- The calc_cov argument is optional; by default calc_cov=false, indicating that the covariance matrix will not be calculated; to enable the covariance matrix, use calc_cov=true during construction; for example: running_stat_vec<vec> X(true);
- The norm_type argument is optional; by default norm_type=0 is used
- For the .var() and .stddev() functions, the default norm_type=0 performs normalisation using N-1 (where N is the number of samples so far), providing the best unbiased estimator; using norm_type=1 causes normalisation to be done using N, which provides the second moment around the mean
- The return type of .count() depends on the underlying form of vec_type: it is either float or double
- Examples:

      running_stat_vec<vec> stats;

      vec sample;

      for(uword i=0; i<10000; ++i)
        {
        sample = randu<vec>(5);
        stats(sample);
        }

      cout << "mean = " << endl << stats.mean() << endl;
      cout << "var  = " << endl << stats.var()  << endl;
      cout << "max  = " << endl << stats.max()  << endl;

      //
      //

      running_stat_vec<rowvec> more_stats(true);

      for(uword i=0; i<20; ++i)
        {
        sample = randu<rowvec>(3);

        sample(1) -= sample(0);
        sample(2) += sample(1);

        more_stats(sample);
        }

      cout << "covariance matrix = " << endl;
      cout << more_stats.cov() << endl;

      rowvec sd = more_stats.stddev();

      cout << "correlations = " << endl;
      cout << more_stats.cov() / (sd.t() * sd);

- See also:
  - cov()
  - cor()
  - running_stat
  - statistics functions
  - gmm_diag

kmeans( means, data, k, seed_mode, n_iter, print_mode )

- Cluster given data using the k-means algorithm
- The means parameter is the output matrix containing the resulting means, with each mean stored as a column vector
- The data parameter is the input data matrix, with each sample stored as a column vector
- The k parameter indicates the number of means
- The seed_mode parameter specifies how the initial means are seeded prior to running k-means; it is one of:

  `keep_existing` |  | use the means specified in the means matrix as the starting point
  `static_subset` |  | use a subset of the data vectors (repeatable)
  `random_subset` |  | use a subset of the data vectors (random)
  `static_spread` |  | use a maximally spread subset of data vectors (repeatable)
  `random_spread` |  | use a maximally spread subset of data vectors (random start)

- The n_iter parameter specifies the number of iterations of the k-means algorithm; a sufficient number of iterations is typically about 10
- The print_mode parameter is either true or false, indicating whether progress is printed during clustering
- If the k-means algorithm fails, the means matrix is reset and a bool set to false is returned
- The k-means algorithm will run faster on multi-core machines when OpenMP is enabled in your compiler (eg. -fopenmp in GCC)
- Caveats:
  - the number of data vectors (number of columns in the data matrix) should be much larger than k (number of means)
  - seeding the initial means with `static_spread` and `random_spread` can be much more time consuming than with `static_subset` and `random_subset`
- Examples:

      uword d = 5;       // dimensionality
      uword N = 10000;   // number of vectors

      mat data(d, N, fill::randu);

      mat means;

      bool status = kmeans(means, data, 2, random_subset, 10, true);

      if(status == false)
        {
        cout << "k-means failed" << endl;
        }

      means.print("means:");

- See also:
  - gmm_diag
  - statistics functions
  - running_stat_vec
  - k-means clustering in Wikipedia
  - k-means clustering in MathWorld
  - OpenMP in Wikipedia

gmm_diag

- Class for modelling data as a Gaussian Mixture Model (GMM), under the assumption of diagonal covariance matrices
- Can also be used for clustering and vector quantisation (VQ)
- Provides associated parameter estimation algorithms: k-means clustering and Expectation-Maximisation (EM)
- The k-means and EM algorithms are parallelised (multi-threaded) when compiling with OpenMP enabled (eg. the -fopenmp option in gcc)
- Data is modelled as:

  |  | n_gaus-1 |
  p(x) | = | ∑ | hg N( x | mg , Cg )
  |  | g=0 |

  where:
  - n_gaus is the number of Gaussians
  - N( x | mg , Cg ) represents a Gaussian (normal) distribution
  - for each Gaussian g:
    - hg is the heft (weight), with hg ≥ 0 and ∑hg = 1
    - mg is the mean (centroid) vector with dimensionality n_dims
    - Cg is the diagonal covariance matrix
- For an instance of gmm_diag named as M, the member functions and variables are:

  M.log_p(V) |  | return a scalar representing the log-likelihood of vector V
  M.log_p(V, g) |  | return a scalar representing the log-likelihood of vector V, according to Gaussian g
  M.log_p(X) |  | return a row vector (of type `rowvec`) containing log-likelihoods of each column vector in matrix X
  M.log_p(X, g) |  | return a row vector (of type `rowvec`) containing log-likelihoods of each column vector in matrix X, according to Gaussian g
  M.avg_log_p(X) |  | return a scalar representing the average log-likelihood of all column vectors in matrix X
  M.avg_log_p(X, g) |  | return a scalar representing the average log-likelihood of all column vectors in matrix X, according to Gaussian g
  M.assign(V, dist_mode) |  | return the index of the closest mean (or Gaussian) to vector V; dist_mode is one of:
  `eucl_dist` |  | Euclidean distance (takes only means into account)
  `prob_dist` |  | probabilistic "distance", defined as the inverse likelihood (takes into account means, covariances and hefts)
  M.assign(X, dist_mode) |  | return a row vector containing the indices of the closest means (or Gaussians) to each column vector in matrix X
  M.raw_hist(X, dist_mode) |  | return a row vector (of type `urowvec`) representing the raw histogram of counts; each entry is the number of counts corresponding to a Gaussian; each count is the number times the corresponding Gaussian was the closest to each column vector in matrix X; parameter dist_mode is either `eucl_dist` or `prob_dist` (as per the .assign() function above)
  M.norm_hist(X, dist_mode) |  | return a row vector (of type `rowvec`) containing normalised counts; the vector sums to one
  M.generate() |  | return a column vector representing a random sample generated according to the model's parameters
  M.generate(N) |  | return a matrix containing N column vectors, with each vector representing a random sample generated according to the model's parameters
  M.save(filename) |  | save the model to the given filename
  M.load(filename) |  | load the model from the given filename
  M.n_gaus() |  | return the number of means/Gaussians in the model
  M.n_dims() |  | return the dimensionality of the means/Gaussians in the model
  M.reset(n_dims, n_gaus) |  | set the model to have dimensionality n_dims, with n_gaus number of Gaussians; all the means are set to zero, all diagonal covariances are set to one, and all the hefts (weights) are set to be uniform
  M.means |  | read-only matrix containing the means (centroids), stored as column vectors
  M.dcovs |  | read-only matrix containing the diagonal covariance matrices, stored as column vectors
  M.hefts |  | read-only row vector containing the hefts (weights)
  M.set_means(X) |  | set the means to be as specified in matrix X; the number of means and their dimensionality must match the existing model
  M.set_dcovs(X) |  | set the diagonal covariances matrices to be as specified in matrix X; the number of diagonal covariance matrices and their dimensionality must match the existing model
  M.set_hefts(V) |  | set the hefts (weights) of the model to be as specified in row vector V; the number of hefts must match the existing model
  M.set_params(means, dcovs, hefts) |  | set all the parameters in one hit; the number of Gaussians and dimensionality can be different from the existing model
  M.learn(data, n_gaus, dist_mode, seed_mode, km_iter, em_iter, var_floor, print_mode)
  |  | learn the model parameters via the k-means and/or EM algorithms, and return a `bool` variable, with true indicating success, and false indicating failure

- Parameters for the `.learn()` member function:

  data |  | matrix containing training samples; each sample is stored as a column vector
  n_gaus |  | set the number of Gaussians to n_gaus
  dist_mode |  | specifies the distance used during the seeding of initial means and k-means clustering:
  `eucl_dist` |  | Euclidean distance
  `maha_dist` |  | Mahalanobis distance, which uses a global diagonal covariance matrix estimated from the training samples
  seed_mode |  | specifies how the initial means are seeded prior to running k-means and/or EM algorithms:
  `keep_existing` |  | keep the existing model (do not modify the means, covariances and hefts)
  `static_subset` |  | a subset of the training samples (repeatable)
  `random_subset` |  | a subset of the training samples (random)
  `static_spread` |  | a maximally spread subset of training samples (repeatable)
  `random_spread` |  | a maximally spread subset of training samples (random start)
  km_iter |  | the number of iterations of the k-means algorithm
  em_iter |  | the number of iterations of the EM algorithm
  var_floor |  | the variance floor (smallest allowed value) for the diagonal covariances
  print_mode |  | either `true` or `false`; enable or disable printing of progress during the k-means and EM algorithms

- Notes:
  - for probabilistic applications, better model parameters are typically learned with dist_mode set to `maha_dist`
  - for vector quantisation applications, model parameters should be learned with dist_mode set to `eucl_dist`, and the number of EM iterations set to zero
  - in general, a sufficient number of k-means and EM iterations is typically about 10
  - the number of training samples should be much larger than the number of Gaussians
  - seeding the initial means with `static_spread` and `random_spread` can be much more time consuming than with `static_subset` and `random_subset`
  - the k-means and EM algorithms will run faster on multi-core machines when OpenMP is enabled in your compiler (eg. -fopenmp in GCC)
- Examples:

      // create synthetic data with 2 Gaussians

      uword d = 5;       // dimensionality
      uword N = 10000;   // number of vectors

      mat data(d, N, fill::zeros);

      vec mean0 = linspace<vec>(1,d,d);
      vec mean1 = mean0 + 2;

      uword i = 0;

      while(i < N)
        {
        if(i < N)  { data.col(i) = mean0 + randn<vec>(d); ++i; }
        if(i < N)  { data.col(i) = mean0 + randn<vec>(d); ++i; }
        if(i < N)  { data.col(i) = mean1 + randn<vec>(d); ++i; }
        }

      // model the data as a GMM with 2 Gaussians

      gmm_diag model;

      bool status = model.learn(data, 2, maha_dist, random_subset, 10, 5, 1e-10, true);

      if(status == false)
        {
        cout << "learning failed" << endl;
        }

      model.means.print("means:");

      double  scalar_likelihood = model.log_p( data.col(0)    );
      rowvec     set_likelihood = model.log_p( data.cols(0,9) );

      double overall_likelihood = model.avg_log_p(data);

      uword   gaus_id  = model.assign( data.col(0),    eucl_dist );
      urowvec gaus_ids = model.assign( data.cols(0,9), prob_dist );

      urowvec hist1 = model.raw_hist (data, prob_dist);
       rowvec hist2 = model.norm_hist(data, eucl_dist);

      model.save("my_model.gmm");

- See also:
  - statistics functions
  - running_stat_vec
  - kmeans()
  - diagmat()
  - Mahalanobis distance in Wikipedia
  - normal distribution in Wikipedia
  - mixture model in Wikipedia
  - k-means clustering in Wikipedia
  - k-means clustering in MathWorld
  - expectation maximisation algorithm in Wikipedia
  - maximum likelihood in Wolfram
  - vector quantisation in Wikipedia
  - OpenMP in Wikipedia

Miscellaneous

constants (pi, inf, speed of light, ...)

`datum::pi` |  | π, the ratio of any circle's circumference to its diameter
`datum::inf` |  | ∞, infinity
`datum::nan` |  | “not a number” (NaN); caveat: NaN is not equal to anything, even itself
`datum::e` |  | base of the natural logarithm
`datum::sqrt2` |  | square root of 2
`datum::eps` |  | the difference between 1 and the least value greater than 1 that is representable (type and machine dependent)
`datum::log_min` |  | log of minimum non-zero value (type and machine dependent)
`datum::log_max` |  | log of maximum value (type and machine dependent)
`datum::euler` |  | Euler's constant, aka Euler-Mascheroni constant
`datum::gratio` |  | golden ratio
`datum::m_u` |  | atomic mass constant (in kg)
`datum::N_A` |  | Avogadro constant
`datum::k` |  | Boltzmann constant (in joules per kelvin)
`datum::k_evk` |  | Boltzmann constant (in eV/K)
`datum::a_0` |  | Bohr radius (in meters)
`datum::mu_B` |  | Bohr magneton
`datum::Z_0` |  | characteristic impedance of vacuum (in ohms)
`datum::G_0` |  | conductance quantum (in siemens)
`datum::k_e` |  | Coulomb's constant (in meters per farad)
`datum::eps_0` |  | electric constant (in farads per meter)
`datum::m_e` |  | electron mass (in kg)
`datum::eV` |  | electron volt (in joules)
`datum::ec` |  | elementary charge (in coulombs)
`datum::F` |  | Faraday constant (in coulombs)
`datum::alpha` |  | fine-structure constant
`datum::alpha_inv` |  | inverse fine-structure constant
`datum::K_J` |  | Josephson constant
`datum::mu_0` |  | magnetic constant (in henries per meter)
`datum::phi_0` |  | magnetic flux quantum (in webers)
`datum::R` |  | molar gas constant (in joules per mole kelvin)
`datum::G` |  | Newtonian constant of gravitation (in newton square meters per kilogram squared)
`datum::h` |  | Planck constant (in joule seconds)
`datum::h_bar` |  | Planck constant over 2 pi, aka reduced Planck constant (in joule seconds)
`datum::m_p` |  | proton mass (in kg)
`datum::R_inf` |  | Rydberg constant (in reciprocal meters)
`datum::c_0` |  | speed of light in vacuum (in meters per second)
`datum::sigma` |  | Stefan-Boltzmann constant
`datum::R_k` |  | von Klitzing constant (in ohms)
`datum::b` |  | Wien wavelength displacement law constant

- The constants are stored in the Datum<type> class, where type is either float or double;
  for convenience, Datum<double> is typedefed as datum, and Datum<float> is typedefed as fdatum
- Caveat: `datum::nan` is not equal to anything, even itself; to check whether a given number x is finite, use is_finite(x)
- The physical constants were mainly taken from NIST 2014 CODATA values, and some from WolframAlpha (as of 2009-06-23)
- Examples:

      cout << "2.0 * pi = " << 2.0 * datum::pi << endl;

      cout << "speed of light = " << datum::c_0 << endl;

      cout << "log_max for floats = ";
      cout << fdatum::log_max << endl;

      cout << "log_max for doubles = ";
      cout << datum::log_max << endl;

- See also:
  - is_finite()
  - .fill()
  - NaN in Wikipedia
  - physical constant in Wikipedia
  - std::numeric_limits

wall_clock

- Simple wall clock timer class for measuring the number of elapsed seconds
- Examples:

      wall_clock timer;

      mat A = randu<mat>(100,100);
      mat B = randu<mat>(100,100);
      mat C;

      timer.tic();

      for(uword i=0; i<100000; ++i)
        {
        C = A + B + A + B;
        }

      double n = timer.toc();

      cout << "number of seconds: " << n << endl;

logging of warnings and errors
set_stream_err1( user_stream )
set_stream_err2( user_stream )
std::ostream& x = get_stream_err1()
std::ostream& x = get_stream_err2()

- By default, warnings and messages associated with exceptions are printed to the std::cout stream
  - the printing can be disabled by defining ARMA_DONT_PRINT_ERRORS before including the armadillo header
  - detailed information about errors is always reported via the base std::exception class
- set_stream_err1(): change the stream for messages associated with std::logic_error exceptions (eg. out of bounds accesses)
- set_stream_err2(): change the stream for warnings and messages associated with std::runtime_error and std::bad_alloc exceptions (eg. failed decompositions, out of memory)
- get_stream_err1(): get a reference to the stream for messages associated with std::logic_error exceptions
- get_stream_err2(): get a reference to the stream for warnings and messages associated with std::runtime_error exceptions
- Examples:

      // print "hello" to the current err1 stream
      get_stream_err1() << "hello" << endl;

      // change the err2 stream to be a file
      ofstream f("my_log.txt");
      set_stream_err2(f);

      // trying to invert a singular matrix
      // will print a message to the err2 stream
      // and throw an exception
      mat X = zeros<mat>(5,5);
      mat Y = inv(X);

      // disable messages being printed to the err2 stream
      std::ostream nullstream(0);
      set_stream_err2(nullstream);

- Caveat: set_stream_err1() and set_stream_err2() will not change the stream used by .print()
- See also:
  - config.hpp
  - .print()
  - std::cout
  - std::ostream
  - std::exception
  - std::logic_error
  - std::runtime_error
  - tutorial on exceptions

uword, sword

- uword is a typedef for an unsigned integer type; it is used for matrix indices as well as all internal counters and loops
- sword is a typedef for a signed integer type
- The minimum width of both uword and sword is either 32 or 64 bits:
  - when using the old C++98 / C++03 standards, the default width is 32 bits
  - when using the new C++11 / C++14 standards, the default width is 64 bits on 64-bit platforms
- The width can also be forcefully set to 64 bits by enabling ARMA_64BIT_WORD via editing include/armadillo_bits/config.hpp
- See also:
  - C++ variable types
  - explanation of typedef
  - imat & umat matrix types
  - ivec & uvec vector types

cx_double, cx_float

- cx_double is a shorthand / typedef for std::complex<double>
- cx_float is a shorthand / typedef for std::complex<float>
- Example:

      cx_mat X = randu<cx_mat>(5,5);

      X(1,2) = cx_double(2.0, 3.0);

- See also:
  - complex numbers in the standard C++ library
  - explanation of typedef
  - cx_mat matrix type
  - cx_vec vector type

Examples of Matlab/Octave syntax and conceptually corresponding Armadillo syntax

Matlab/Octave |  | Armadillo |  | Notes
A(1, 1) |  | A(0, 0) |  | indexing in Armadillo starts at 0
A(k, k) |  | A(k-1, k-1) |  |
size(A,1) |  | A.n_rows |  | read only
size(A,2) |  | A.n_cols |  |
size(Q,3) |  | Q.n_slices |  | Q is a cube (3D array)
numel(A) |  | A.n_elem |  |
A(:, k) |  | A.col(k) |  | this is a conceptual example only; exact conversion from Matlab/Octave to Armadillo syntax will require taking into account that indexing starts at 0
A(k, :) |  | A.row(k) |  |
A(:, p:q) |  | A.cols(p, q) |  |
A(p:q, :) |  | A.rows(p, q) |  |
A(p:q, r:s) |  | A( span(p,q), span(r,s) ) |  | A( span(first_row, last_row), span(first_col, last_col) )
Q(:, :, k) |  | Q.slice(k) |  | Q is a cube (3D array)
Q(:, :, t:u) |  | Q.slices(t, u) |  |
Q(p:q, r:s, t:u) |  | Q( span(p,q), span(r,s), span(t,u) ) |  |
A' |  | A.t() or trans(A) |  | matrix transpose / Hermitian transpose (for complex matrices, the conjugate of each element is taken)
A = zeros(size(A)) |  | A.zeros() |  |
A = ones(size(A)) |  | A.ones() |  |
A = zeros(k) |  | A = zeros<mat>(k,k) |  |
A = ones(k) |  | A = ones<mat>(k,k) |  |
C = complex(A,B) |  | cx_mat C = cx_mat(A,B) |  |
A .* B |  | A % B |  | element-wise multiplication
A ./ B |  | A / B |  | element-wise division
A \ B |  | solve(A,B) |  | conceptually similar to inv(A)*B, but more efficient
A = A + 1; |  | A++ |  |
A = A - 1; |  | A-- |  |
A = [ 1 2; 3 4; ] |  | A << 1 << 2 << endr << 3 << 4 << endr; |  | element initialisation, with special element endr indicating end of row
X = A(:) |  | X = vectorise(A) |  |
X = [ A B ] |  | X = join_horiz(A,B) |  |
X = [ A; B ] |  | X = join_vert(A,B) |  |
A |  | cout << A << endl; or A.print("A ="); |  |
save ‑ascii 'A.dat' A |  | A.save("A.dat", raw_ascii); |  | Matlab/Octave matrices saved as ascii are readable by Armadillo (and vice-versa)
load ‑ascii 'A.dat' |  | A.load("A.dat", raw_ascii); |  |
A = randn(2,3); B = randn(4,5); F = { A; B } |  | mat A = randn(2,3); mat B = randn(4,5); field<mat> F(2,1); F(0,0) = A; F(1,0) = B; |  | fields store arbitrary objects, such as matrices

example program

    #include <iostream>
    #include <armadillo>

    using namespace std;
    using namespace arma;

    int main()
      {
      mat A = randu<mat>(4,5);
      mat B = randu<mat>(4,5);

      cout << A*B.t() << endl;

      return 0;
      }

- If you save the above program as example.cpp, under Linux and Mac OS X it can be compiled using:
  `g++ example.cpp -o example -O2 -larmadillo`
- As Armadillo is a template library, we strongly recommend to enable optimisation when compiling programs (eg. when compiling with GCC or clang, use the -O2 or -O3 options)
- See also the example program that comes with the Armadillo archive

config.hpp

- Armadillo can be configured via editing the file include/armadillo_bits/config.hpp. Specific functionality can be enabled or disabled by uncommenting or commenting out a particular #define, listed below.

  `ARMA_USE_LAPACK` |  | Enable use of LAPACK, or a high-speed replacement for LAPACK (eg. Intel MKL, AMD ACML or the Accelerate framework). Armadillo requires LAPACK for functions such as svd(), inv(), eig_sym(), solve(), etc.
  `ARMA_DONT_USE_LAPACK` |  | Disable use of LAPACK. Overrides ARMA_USE_LAPACK
  `ARMA_USE_BLAS` |  | Enable use of BLAS, or a high-speed replacement for BLAS (eg. OpenBLAS, Intel MKL, AMD ACML or the Accelerate framework). BLAS is used for matrix multiplication. Without BLAS, Armadillo will use a built-in matrix multiplication routine, which might be slower for large matrices.
  `ARMA_DONT_USE_BLAS` |  | Disable use of BLAS. Overrides ARMA_USE_BLAS
  `ARMA_USE_NEWARP` |  | Enable use of the built-in reimplementation of ARPACK (Armadillo 7.x). This is used for the eigen decomposition of real (non-complex) sparse matrices, ie. eigs_gen(), eigs_sym() and svds(). Requires ARMA_USE_LAPACK to be enabled.
  `ARMA_DONT_USE_NEWARP` |  | Disable use of the built-in reimplementation of ARPACK. Overrides ARMA_USE_NEWARP
  `ARMA_USE_ARPACK` |  | Enable use of ARPACK, or a high-speed replacement for ARPACK. Armadillo requires ARPACK for the eigen decomposition of complex sparse matrices, ie. eigs_gen(), eigs_sym() and svds()
  `ARMA_DONT_USE_ARPACK` |  | Disable use of ARPACK. Overrides ARMA_USE_ARPACK
  `ARMA_USE_SUPERLU` |  | Enable use of SuperLU, which is used by spsolve() for finding the solutions of sparse systems; you will need to link with the superlu library, for example `-lsuperlu` Caveat: Armadillo 6.x requires SuperLU 4.3, while Armadillo 7.x requires SuperLU 5.2
  `ARMA_DONT_USE_SUPERLU` |  | Disable use of SuperLU. Overrides ARMA_USE_SUPERLU
  `ARMA_USE_HDF5` |  | Enable the ability to save and load matrices stored in the HDF5 format; the hdf5.h header file must be available on your system and you will need to link with the hdf5 library (eg. -lhdf5)
  `ARMA_DONT_USE_HDF5` |  | Disable the use of the HDF5 library. Overrides ARMA_USE_HDF5
  `ARMA_DONT_USE_WRAPPER` |  | Disable going through the run-time Armadillo wrapper library (libarmadillo.so) when calling LAPACK, BLAS, ARPACK, SuperLU and HDF5 functions. You will need to directly link with LAPACK, BLAS, etc (eg. -llapack -lblas)
  `ARMA_USE_CXX11` |  | Use C++11 features, such as initialiser lists; automatically enabled when using a compiler in C++11 mode, for example, g++ -std=c++11
  `ARMA_DONT_USE_CXX11` |  | Disable use of C++11 features. Overrides ARMA_USE_CXX11
  `ARMA_BLAS_CAPITALS` |  | Use capitalised (uppercase) BLAS and LAPACK function names (eg. DGEMM vs dgemm)
  `ARMA_BLAS_UNDERSCORE` |  | Append an underscore to BLAS and LAPACK function names (eg. dgemm_ vs dgemm). Enabled by default.
  `ARMA_BLAS_LONG` |  | Use "long" instead of "int" when calling BLAS and LAPACK functions
  `ARMA_BLAS_LONG_LONG` |  | Use "long long" instead of "int" when calling BLAS and LAPACK functions
  `ARMA_USE_TBB_ALLOC` |  | Use Intel TBB scalable_malloc() and scalable_free() instead of standard malloc() and free() for managing matrix memory
  `ARMA_USE_MKL_ALLOC` |  | Use Intel MKL mkl_malloc() and mkl_free() instead of standard malloc() and free() for managing matrix memory
  `ARMA_64BIT_WORD` |  | Use 64 bit integers. Automatically enabled when using a C++11 compiler on 64-bit platforms. Useful if you require matrices/vectors capable of holding more than 4 billion elements. Your machine and compiler must have support for 64 bit integers (eg. via "long" or "long long"). This can also be enabled by adding #define ARMA_64BIT_WORD before each instance of #include <armadillo>
  `ARMA_NO_DEBUG` |  | Disable all run-time checks, such as bounds checking. This will result in faster code, but you first need to make sure that your code runs correctly! We strongly recommend to have the run-time checks enabled during development, as this greatly aids in finding mistakes in your code, and hence speeds up development. We recommend that run-time checks be disabled only for the shipped version of your program (ie. final release build).
  `ARMA_EXTRA_DEBUG` |  | Print out the trace of internal functions used for evaluating expressions. Not recommended for normal use. This is mainly useful for debugging the library.
  `ARMA_MAT_PREALLOC` |  | The number of pre-allocated elements used by matrices and vectors. Must be always enabled and set to an integer that is at least 1. By default set to 16. If you mainly use lots of very small vectors (eg. ≤ 4 elements), change the number to the size of your vectors.
  `ARMA_DEFAULT_OSTREAM` |  | The default stream used for printing error messages and by .print(). Must be always enabled. By default defined to std::cout
  `ARMA_PRINT_ERRORS` |  | Print errors and warnings encountered during program execution
  `ARMA_DONT_PRINT_ERRORS` |  | Do not print errors or warnings. Overrides ARMA_PRINT_ERRORS

- See also:
  - logging of warnings and errors
  - element access
  - element initialisation
  - uword/sword

History of API Additions, Changes and Deprecations
- API Version Policy
  - Armadillo's version number is A.B.C, where A is a major version, B is a minor version, and C is the patch level (indicating bug fixes)
  - Within each major version (eg. 4.x), minor versions are backwards compatible with earlier minor versions. For example, code written for version 4.000 will work with version 4.100, 4.120, 4.200, etc. However, as each minor version may have more features (ie. API extensions) than earlier versions, code specifically written for version 4.100 doesn't necessarily work with 4.000
  - We don't like changes to existing APIs and strongly prefer not to break any user software. However, to allow evolution, we reserve the right to alter the APIs in future major versions of Armadillo while remaining backwards compatible in as many cases as possible (eg. version 5.x may have slightly different APIs than 4.x). In a rare instance the user API may need to be tweaked if a bug fix absolutely requires it
  - This policy is applicable to the APIs described in this documentation; it is not applicable to internal functions (ie. the underlying internal implementation details may change across consecutive minor versions)
- List of additions and changes for each version:
  - Version 7.100:
    - added erf(), erfc(), lgamma()
    - added .head_slices() and .tail_slices() to subcube views
    - spsolve() now requires SuperLU 5.2
    - eigs_sym(), eigs_gen() and svds() now use a built-in reimplementation of ARPACK for real (non-complex) matrices; contributed by Yixuan Qiu
  - Version 6.700:
    - added trapz() for numerical integration
    - added logmat() for calculating the matrix logarithm
    - added regspace() for generating vectors with regularly spaced elements
    - added logspace() for generating vectors with logarithmically spaced elements
    - added approx_equal() for determining approximate equality
    - expanded .save() and .load() with hdf5_binary_trans file type, to save/load data with columns transposed to rows
  - Version 6.600:
    - expanded sum(), mean(), min(), max() to handle cubes
    - expanded Cube class to handle arbitrarily sized empty cubes (eg. 0x5x2)
    - added shift() for circular shifts of elements
    - added sqrtmat() for finding the square root of a matrix
  - Version 6.500:
    - added conv2() for 2D convolution
    - added stand-alone kmeans() function for clustering data
    - added trunc()
    - extended conv() to optionally provide central convolution
    - faster handling of multiply-and-accumulate by accu() when using Intel MKL, ATLAS or OpenBLAS
  - Version 6.400:
    - expanded each_col(), each_row() and each_slice() to handle C++11 lambda functions
    - added ind2sub() and sub2ind()
  - Version 6.300:
    - expanded solve() to find approximate solutions for rank-deficient systems
    - faster handling of non-contiguous submatrix views in compound expressions
    - added .for_each() to Mat, Row, Col, Cube and field classes
    - added rcond() for estimating the reciprocal condition number
  - Version 6.200:
    - expanded diagmat() to handle non-square matrices and arbitrary diagonals
    - expanded trace() to handle non-square matrices
  - Version 6.100:
    - faster norm() and normalise() when using Intel MKL, ATLAS or OpenBLAS
    - added Schur decomposition: schur()
    - stricter handling of matrix objects by hist() and histc()
    - advanced constructors for using auxiliary memory by Mat, Col, Row and Cube now have the default of strict = false
    - Cube class now delays allocation of .slice() related structures until needed
    - expanded join_slices() to handle joining cubes with matrices
  - Version 5.600:
    - added .each_slice() for repeated matrix operations on each slice of a cube
    - expanded .each_col() and .each_row() to handle out-of-place operations
  - Version 5.500:
    - expanded object constructors and generators to handle size() based specification of dimensions
  - Version 5.400:
    - added find_unique() for finding indices of unique values
    - added diff() for calculating differences between consecutive elements
    - added cumprod() for calculating cumulative product
    - added null() for finding the orthonormal basis of null space
    - expanded interp1() to handle repeated locations
    - expanded unique() to handle complex numbers
    - faster flipud()
    - faster row-wise cumsum()
  - Version 5.300:
    - added generalised Schur decomposition: qz()
    - added .has_inf() and .has_nan()
    - expanded interp1() to handle out-of-domain locations
    - expanded sparse matrix class with .set_imag() and .set_real()
    - expanded imag(), real() and conj() to handle sparse matrices
    - expanded diagmat(), reshape() and resize() to handle sparse matrices
    - faster sparse sum()
    - faster row-wise sum(), mean(), min(), max()
    - updated physical constants to NIST 2014 CODATA values
  - Version 5.200:
    - added orth() for finding the orthonormal basis of the range space of a matrix
    - expanded element initialisation to handle nested initialiser lists (C++11)
  - Version 5.100:
    - added interp1() for 1D interpolation
    - added .is_sorted() for checking whether a vector or matrix has sorted elements
    - updated physical constants to NIST 2010 CODATA values
  - Version 5.000:
    - added spsolve() for solving sparse systems of linear equations
    - added svds() for singular value decomposition of sparse matrices
    - added nonzeros() for extracting non-zero values from matrices
    - added handling of diagonal views by sparse matrices
    - expanded repmat() to handle sparse matrices
    - expanded join_rows() and join_cols() to handle sparse matrices
    - sort_index() and stable_sort_index() have been placed in the delayed operations framework for increased efficiency
    - use of 64 bit integers is automatically enabled when using a C++11 compiler
  - Version 4.650:
    - added randg() for generating random values from gamma distributions (C++11 only)
    - added .head_rows() and .tail_rows() to submatrix views
    - added .head_cols() and .tail_cols() to submatrix views
    - expanded eigs_sym() to optionally calculate eigenvalues with smallest/largest algebraic values
  - Version 4.600:
    - added .head() and .tail() to submatrix views
    - faster matrix transposes within compound expressions
    - faster in-place matrix multiplication
    - faster accu() and norm() when compiling with -O3 -ffast-math -march=native (gcc and clang)
  - Version 4.550:
    - added matrix exponential function: expmat()
    - faster .log_p() and .avg_log_p() functions in the gmm_diag class when compiling with OpenMP enabled
    - faster handling of in-place addition/subtraction of expressions with an outer product
  - Version 4.500:
    - faster handling of complex vectors by norm()
    - expanded chol() to optionally specify output matrix as upper or lower triangular
    - better handling of non-finite values when saving matrices as text files
  - Version 4.450:
    - faster handling of matrix transposes within compound expressions
    - expanded symmatu()/symmatl() to optionally disable taking the complex conjugate of elements
    - expanded sort_index() to handle complex vectors
    - expanded the gmm_diag class with functions to generate random samples
  - Version 4.400:
    - faster handling of subvectors by dot()
    - faster handling of aliasing by submatrix views
    - added clamp() for clamping values to be between lower and upper limits
    - added gmm_diag class for statistical modelling of data using Gaussian Mixture Models
    - expanded batch insertion constructors for sparse matrices to add values at repeated locations
  - Version 4.320:
    - expanded eigs_sym() and eigs_gen() to use an optional tolerance parameter
    - expanded eig_sym() to automatically fall back to standard decomposition method if divide-and-conquer fails
    - cmake-based installer enables use of C++11 random number generator when using gcc 4.8.3+ in C++11 mode
  - Version 4.300:
    - added find_finite() and find_nonfinite()
    - expressions X=inv(A)*B*C and X=A.i()*B*C are automatically converted to X=solve(A,B*C)
  - Version 4.200:
    - faster transpose of sparse matrices
    - more efficient handling of aliasing during matrix multiplication
    - faster inverse of matrices marked as diagonal
  - Version 4.100:
    - added normalise() for normalising vectors to unit p-norm
    - extended the field class to handle 3D layout
    - extended eigs_sym() and eigs_gen() to obtain eigenvalues of various forms (eg. largest or smallest magnitude)
    - automatic SIMD vectorisation of elementary expressions (eg. matrix addition) when using Clang 3.4+ with -O3 optimisation
    - faster handling of sparse submatrix views
  - Version 4.000:
    - added eigen decompositions of sparse matrices: eigs_sym() and eigs_gen()
    - added eigen decomposition for pair of matrices: eig_pair()
    - added simpler forms of eig_gen()
    - added condition number of matrices: cond()
    - expanded find() to handle cubes
    - expanded subcube views to access elements specified in a vector
    - template argument for running_stat_vec expanded to accept vector types
    - more robust fast inverse of 4x4 matrices
    - faster divide-and-conquer decompositions are now used by default for eig_sym(), pinv(), princomp(), rank(), svd(), svd_econ()
    - the form inv(sympd(X)) no longer assumes that X is positive definite; use inv_sympd() instead
  - Version 3.930:
    - added size() based specifications of submatrix view sizes
    - added element-wise variants of min() and max()
    - added divide-and-conquer variant of svd_econ()
    - added divide-and-conquer variant of pinv()
    - added randi() for generating matrices with random integer values
    - added inplace_trans() for memory efficient in-place transposes
    - added more intuitive specification of sort direction in sort() and sort_index()
    - added more intuitive specification of method in det(), .i(), inv() and solve()
    - more precise timer for the wall_clock class when using C++11
  - Version 3.920:
    - faster .zeros()
    - faster round(), exp2() and log2() when using C++11
    - added signum function: sign()
    - added move constructors when using C++11
    - added 2D fast Fourier transform: fft2()
    - added .tube() for easier extraction of vectors and subcubes from cubes
    - added specification of a fill type during construction of Mat, Col, Row and Cube classes, eg. mat X(4, 5, fill::zeros)
  - Version 3.910:
    - faster multiplication of a matrix with a transpose of itself, ie. X*X.t() and X.t()*X
    - added vectorise() for reshaping matrices into vectors
    - added all() and any() for indicating presence of elements satisfying a relational condition
  - Version 3.900:
    - added automatic SSE2 vectorisation of elementary expressions (eg. matrix addition) when using GCC 4.7+ with -O3 optimisation
    - faster median()
    - faster handling of compound expressions with transposes of submatrix rows
    - faster handling of compound expressions with transposes of complex vectors
    - added support for saving & loading of cubes in HDF5 format
  - Version 3.820:
    - faster as_scalar() for compound expressions
    - faster transpose of small vectors
    - faster matrix-vector product for small vectors
    - faster multiplication of small fixed size matrices
  - Version 3.810:
    - added fast Fourier transform: fft()
    - added handling of .imbue() and .transform() by submatrices and subcubes
    - added batch insertion constructors for sparse matrices
  - Version 3.800:
    - added .imbue() for filling a matrix/cube with values provided by a functor or lambda expression
    - added .swap() for swapping contents with another matrix
    - added .transform() for transforming a matrix/cube using a functor or lambda expression
    - added round() for rounding matrix elements towards nearest integer
    - faster find()
    - changed license to the Mozilla Public License 2.0; see also the associated frequently asked questions about the license
  - Version 3.6:
    - faster handling of compound expressions with submatrices and subcubes
    - faster trace()
    - added support for loading matrices as text files with NaN and Inf elements
    - added stable_sort_index(), which preserves the relative order of elements with equivalent values
    - added handling of sparse matrices by mean(), var(), norm(), abs(), square(), sqrt()
    - added saving and loading of sparse matrices in arma_binary format
  - Version 3.4:
    - added economical QR decomposition: qr_econ()
    - added .each_col() & .each_row() for vector operations repeated on each column or row of a matrix
    - added preliminary support for sparse matrices
    - added ability to save and load matrices in HDF5 format
    - faster singular value decomposition via optional use of divide-and-conquer algorithm
    - faster .randn()
    - faster dot() and cdot() for complex numbers
  - Version 3.2:
    - added unique(), for finding unique elements of a matrix
    - added .eval(), for forcing the evaluation of delayed expressions
    - faster eigen decomposition via optional use of divide-and-conquer algorithm
    - faster transpose of vectors and compound expressions
    - faster handling of diagonal views
    - faster handling of tiny fixed size vectors (≤ 4 elements)
  - Version 3.0:
    - added shorthand for inverse: .i()
    - added datum class
    - added hist() and histc()
    - added non-contiguous submatrix views
    - faster handling of submatrix views with a single row or column
    - faster element access in fixed size matrices
    - faster repmat()
    - expressions X=inv(A)*B and X=A.i()*B are automatically converted to X=solve(A,B)
    - better detection of vector expressions by sum(), cumsum(), prod(), min(), max(), mean(), median(), stddev(), var()
    - faster generation of random numbers (eg. randu() and randn()), via an algorithm that produces slightly different numbers than in 2.x
    - support for tying writable auxiliary (external) memory to fixed size matrices has been removed; instead, you can use standard matrices with writable auxiliary memory, or initialise fixed size matrices by copying the memory; using auxiliary memory with standard matrices is unaffected
    - .print_trans() and .raw_print_trans() have been removed; instead, you can chain .t() and .print() to achieve a similar result: X.t().print()
  - Version 2.4:
    - added shorter forms of transposes: .t() and .st()
    - added .resize() and resize()
    - added optional use of 64 bit indices (allowing matrices to have more than 4 billion elements), enabled via ARMA_64BIT_WORD in include/armadillo_bits/config.hpp
    - added experimental support for C++11 initialiser lists, enabled via ARMA_USE_CXX11 in include/armadillo_bits/config.hpp
    - refactored code to eliminate warnings when using the Clang C++ compiler
    - umat, uvec, .min() and .max() have been changed to use the uword type instead of the u32 type; by default the uword and u32 types are equivalent (ie. unsigned integer type with a minimum width 32 bits); however, when the use of 64 bit indices is enabled via ARMA_64BIT_WORD in include/armadillo_bits/config.hpp, the uword type then has a minimum width of 64 bits
  - Version 2.2:
    - added svd_econ()
    - added circ_toeplitz()
    - added .is_colvec() and .is_rowvec()
  - Version 2.0:
    - det(), inv() and solve() can be forced to use more precise algorithms for tiny matrices (≤ 4x4)
    - added syl(), for solving Sylvester's equation
    - added strans(), for transposing a complex matrix without taking the complex conjugate
    - added symmatu() and symmatl()
    - added submatrices of submatrices
    - faster inverse of symmetric positive definite matrices
    - faster element access for fixed size matrices
    - faster multiplication of tiny matrices (eg. 4x4)
    - faster compound expressions containing submatrices
    - added handling of arbitrarily sized empty matrices (eg. 5x0)
    - added .count() member function in running_stat and running_stat_vec
    - added loading & saving of matrices as CSV text files
    - trans() now takes the complex conjugate when transposing a complex matrix
    - forms of chol(), eig_sym(), eig_gen(), inv(), lu(), pinv(), princomp(), qr(), solve(), svd(), syl() that do not return a bool indicating success now throw std::runtime_error exceptions when failures are detected
    - princomp_cov() has been removed; eig_sym() in conjunction with cov() can be used instead
    - .is_vec() now outputs true for empty vectors (eg. 0x1)
    - set_log_stream() & get_log_stream() have been replaced by set_stream_err1() & get_stream_err1()
  - Version 1.2:
    - added .min() & .max() member functions of Mat and Cube
    - added floor() and ceil()
    - added representation of “not a number”: math::nan()
    - added representation of infinity: math::inf()
    - added standalone is_finite()
    - .in_range() expanded to use span() arguments
    - fixed size matrices and vectors can use auxiliary (external) memory
    - submatrices and subfields can be accessed via X( span(a,b), span(c,d) )
    - subcubes can be accessed via X( span(a,b), span(c,d), span(e,f) )
    - the two argument version of span can be replaced by span::all or span(), to indicate an entire range
    - for cubes, the two argument version of span can be replaced by a single argument version, span(a), to indicate a single column, row or slice
    - arbitrary "flat" subcubes can be interpreted as matrices; for example:

          cube Q = randu<cube>(5,3,4);
          mat  A = Q( span(1), span(1,2), span::all );
          // A has a size of 2x4

          vec v = ones<vec>(4);
          Q( span(1), span(1), span::all ) = v;

    - added interpretation of matrices as triangular through trimatu() / trimatl()
    - added explicit handling of triangular matrices by solve() and inv()
    - extended syntax for submatrices, including access to elements whose indices are specified in a vector
    - added ability to change the stream used for logging of errors and warnings
    - added ability to save/load matrices in raw binary format
    - added cumulative sum function: cumsum()
  - Changed in 1.0 (compared to earlier 0.x development versions):
    - the 3 argument version of lu(), eg. lu(L,U,X), provides L and U which should be the same as produced by Octave 3.2 (this was not the case in versions prior to 0.9.90)
    - rand() has been replaced by randu(); this has been done to avoid confusion with std::rand(), which generates random numbers in a different interval
    - In versions earlier than 0.9.0, some multiplication operations directly converted result matrices with a size of 1x1 into scalars. This is no longer the case. If you know the result of an expression will be a 1x1 matrix and wish to treat it as a pure scalar, use the as_scalar() wrapping function
    - Almost all functions have been placed in the delayed operations framework (for speed purposes). This may affect code which assumed that the output of some functions was a pure matrix. The solution is easy, as explained below.
      In general, Armadillo queues operations before executing them. As such, the direct output of an operation or function cannot be assumed to be a directly accessible matrix. The queued operations are executed when the output needs to be stored in a matrix, eg. mat B = trans(A) or mat B(trans(A)). If you need to force the execution of the delayed operations, place the operation or function inside the corresponding Mat constructor. For example, if your code assumed that the output of some functions was a pure matrix, eg. chol(m).diag(), change the code to mat(chol(m)).diag(). Similarly, if you need to pass the result of an operation such as A+B to one of your own functions, use my_function( mat(A+B) ).