MatrixPlusPlus

• Generate any sort of matrix you want using vectors.
• Legit just two files, a header and object file.
• Wrote this in a few hours because we had a study day at my school and I got bored.

Basics

• To define a matrix, simply type Matrix m.
• You can construct a 2x2 matrix by calling something like this: Matrix m({ {1,2}, {3,4} })
• The constructor does not have a default check to ensure each row/column is numerically aligned. You can build that in or add checks.

What is NINT?

• Just a typedef I'm used to using since I originally wrote this lib for a neural network.
• Stands for "neural int" lol.
• You can change it from a long double to basically any other type to save memory if needed, it shouldn't affect the calculations at all.

Operators

• Matrices have the +-* operators all built in. These operators support matrix upon matrix arithmetic (m_1 * m_2) and matrix on integer arithmetic (m_1 * 3)
• The * operator performs DOT multiplication, the true matrix multiplication.

Transposition

• Following the lead of Numpy, you can transpose the matrix by calling .T().
• Transposition inverts the rows and columns.

Determinant

• Calling .determinant() will return the determinant of the matrix.
• As per linear algebra rules, this function will only work on a matrix with a square shape.
• If you want to add a check in your code to prevent determinant failure, you can do something like the following: if(m.is_square()) which is an already built-in call.
• Calculating determinant uses Leibniz's formula, which involves calculating the possible permutations of the matrix, and then indexing the matrix and doing some other math. It can calculate (as far as I'm concerned) any dimension of matrix.

Trace

• Another linear algebra theorem, you can call trace(m) or m.tr() to receive the trace value as a double.
• Only works on a square matrix, too.

Multithreading

• By default, _PP_USE_THREADS is defined in the .hpp file.
• This will enable functions Transposition, DOT Multiplication, and ADD / SUB to out-thread the bulk of their work.
• There is no default encoded thread cap.
• The multithreading system uses Window's Library CreateThread() function, NOT std::thread.
• Multithreading can speed up large matrix math because as your matrices grow, the number of loops required to perform the math grows, and therefore the time to complete each operation does as well.