MQL5 ReferenceMatrix and Vector Methods

Matrices and vectors

A matrix is a two-dimensional array of double, float, or complex numbers.

A vector is a one-dimensional array of double, float, or complex numbers. The vector has no indication of whether it is vertical or horizontal. It is determined from the use context. For example, the vector operation Dot assumes that the left vector is horizontal and the right one is vertical. If the type indication is required, one-row or one-column matrices can be used. However, this is generally not necessary.

Matrices and vectors allocate memory for data dynamically. In fact, matrices and vectors are objects that have certain properties, such as the type of data they contain and dimensions. Matrix and vector properties can be obtained using methods such as vector_a.Size(), matrix_b.Rows(), vector_c.Norm(), matrix_d.Cond() and others. Any dimension can be changed.

When creating and initializing matrices, so-called static methods are used (these are like static methods of a class). For example: matrix::Eye(), matrix::Identity(), matrix::Ones(), vector::Ones(), matrix: :Zeros(), vector::Zeros(), matrix::Full(), vector::Full(), matrix::Tri().

At the moment, matrix and vector operations do not imply the use of the complex data type, as this development direction has not yet been completed.

MQL5 supports passing of matrices and vectors to DLLs. This enables the import of functions utilizing the relevant types, from external variables.

Matrices and vectors are passed to a DLL as a pointer to a buffer. For example, to pass a matrix of type float, the corresponding parameter of the function exported from the DLL must take a float-type buffer pointer.

MQL5

#import "mmlib.dll"
bool sgemm(uint flags, matrix<float> &C, const matrix<float> &A, const matrix<float> &B, ulong M, ulong N, ulong K, float alpha, float beta);
#import

C++

extern "C" __declspec(dllexport) bool sgemm(UINT flags, float *C, const float *A, const float *B, UINT64 M, UINT64 N, UINT64 K, float alpha, float beta)

In addition to buffers, you should pass matrix and vector sizes for correct processing.

All matrix and vector methods are listed below in alphabetical order.

Function	Action	Category
Activation	Compute activation function values and write them to the passed vector/matrix	Machine learning
ArgMax	Return the index of the maximum value	Statistics
ArgMin	Return the index of the minimum value	Statistics
ArgSort	Return the sorted index	Manipulations
Assign	Copies a matrix, vector or array with auto cast	Initialization
Average	Compute the weighted average of matrix/vector values	Statistics
Cholesky	Compute the Cholesky decomposition	Transformations
Clip	Limits the elements of a matrix/vector to a given range of valid values	Manipulations
Col	Return a column vector. Write a vector to the specified column.	Manipulations
Cols	Return the number of columns in a matrix	Features
Compare	Compare the elements of two matrices/vectors with the specified precision	Manipulations
CompareByDigits	Compare the elements of two matrices/vectors with the significant figures precision	Manipulations
Cond	Compute the condition number of a matrix	Features
Convolve	Return the discrete, linear convolution of two vectors	Products
Copy	Return a copy of the given matrix/vector	Manipulations
CopyIndicatorBuffer	Get the data of the specified indicator buffer in the specified quantity to a vector	Initialization
CopyRates	Gets the historical series of the MqlRates structure of the specified symbol-period in the specified amount into a matrix or vector	Initialization
CopyTicks	Get ticks from an MqlTick structure into a matrix or a vector	Initialization
CopyTicksRange	Get ticks from an MqlTick structure into a matrix or a vector within the specified date range	Initialization
CorrCoef	Compute the Pearson correlation coefficient (linear correlation coefficient)	Products
Correlate	Compute the cross-correlation of two vectors	Products
Cov	Compute the covariance matrix	Products
CumProd	Return the cumulative product of matrix/vector elements, including those along the given axis	Statistics
CumSum	Return the cumulative sum of matrix/vector elements, including those along the given axis	Statistics
Derivative	Compute activation function derivative values and write them to the passed vector/matrix	Machine learning
Det	Compute the determinant of a square invertible matrix	Features
Diag	Extract a diagonal or construct a diagonal matrix	Manipulations
Dot	Dot product of two vectors	Products
Eig	Computes the eigenvalues and right eigenvectors of a square matrix	Transformations
EigVals	Computes the eigenvalues of a general matrix	Transformations
Eye	Return a matrix with ones on the diagonal and zeros elsewhere	Initialization
Fill	Fill an existing matrix or vector with the specified value	Initialization
Flat	Access a matrix element through one index instead of two	Manipulations
Full	Create and return a new matrix filled with the given value	Initialization
GeMM	The GeMM (General Matrix Multiply) method implements the general multiplication of two matrices	Products
HasNan	Return the number of NaN values in a matrix/vector	Manipulations
Hsplit	Split a matrix horizontally into multiple submatrices. Same as Split with axis=0	Manipulations
Identity	Create an identity matrix of the specified size	Initialization
Init	Matrix or vector initialization	Initialization
Inner	Inner product of two matrices	Products
Inv	Compute the multiplicative inverse of a square invertible matrix by the Jordan-Gauss method	Solutions
Kron	Return Kronecker product of two matrices, matrix and vector, vector and matrix or two vectors	Products
Loss	Compute loss function values and write them to the passed vector/matrix	Machine learning
LstSq	Return the least-squares solution of linear algebraic equations (for non-square or degenerate matrices)	Solutions
LU	Implement an LU decomposition of a matrix: the product of a lower triangular matrix and an upper triangular matrix	Transformations
LUP	Implement an LUP factorization with partial permutation, which refers to LU decomposition with row permutations only: PA=LU	Transformations
MatMul	Matrix product of two matrices	Products
Max	Return the maximum value in a matrix/vector	Statistics
Mean	Compute the arithmetic mean of element values	Statistics
Median	Compute the median of the matrix/vector elements	Statistics
Min	Return the minimum value in a matrix/vector	Statistics
Norm	Return matrix or vector norm	Features
Ones	Create and return a new matrix filled with ones	Initialization
Outer	Compute the outer product of two matrices or two vectors	Products
Percentile	Return the specified percentile of values of matrix/vector elements or elements along the specified axis	Statistics
PInv	Compute the pseudo-inverse of a matrix by the Moore-Penrose method	Solutions
Power	Raise a square matrix to an integer power	Products
Prod	Return the product of matrix/vector elements, which can also be executed for the given axis	Statistics
Ptp	Return the range of values of a matrix/vector or of the given matrix axis	Statistics
QR	Compute the qr factorization of a matrix	Transformations
Quantile	Return the specified quantile of values of matrix/vector elements or elements along the specified axis	Statistics
Rank	Return matrix rank using the Gaussian method	Features
RegressionMetric	Compute the regression metric as the deviation error from the regression line constructed on the specified data array	Statistics
Reshape	Change the shape of a matrix without changing its data	Manipulations
Resize	Return a new matrix with a changed shape and size	Manipulations
Row	Return a row vector. Write the vector to the specified row	Manipulations
Rows	Return the number of rows in a matrix	Features
Set	Sets the value for a vector element by the specified index	Manipulations
Size	Return the size of vector	Features
SLogDet	Compute the sign and logarithm of the determinant of an matrix	Features
Solve	Solve a linear matrix equation or a system of linear algebraic equations	Solutions
Sort	Sort by place	Manipulations
Spectrum	Compute spectrum of a matrix as the set of its eigenvalues from the product AT*A	Features
Split	Split a matrix into multiple submatrices	Manipulations
Std	Return the standard deviation of values of matrix/vector elements or elements along the specified axis	Statistics
Sum	Return the sum of matrix/vector elements, which can also be executed for the given axis (axes)	Statistics
SVD	Singular value decomposition	Transformations
SwapCols	Swap columns in a matrix	Manipulations
SwapRows	Swap rows in a matrix	Manipulations
Trace	Return the sum along diagonals of the matrix	Features
Transpose	Transpose (swap the axes) and return the modified matrix	Manipulations
Tri	Construct a matrix with ones on a specified diagonal and below, and zeros elsewhere	Initialization
TriL	Return a copy of a matrix with elements above the k-th diagonal zeroed. Lower triangular matrix	Manipulations
TriU	Return a copy of a matrix with the elements below the k-th diagonal zeroed. Upper triangular matrix	Manipulations
Var	Compute the variance of values of matrix/vector elements	Statistics
Vsplit	Split a matrix vertically into multiple submatrices. Same as Split with axis=1	Manipulations
Zeros	Create and return a new matrix filled with zeros	Initialization

ArrayFromFP8

Matrix and Vector Types