# Matrix Example
[Examples]

Description:

  Demonstrates the use of Matrix Transpose, Matrix Muliplication, and Matrix Inverse functions to apply least squares fitting to input data. Least squares fitting is the procedure for finding the best-fitting curve that minimizes the sum of the squares of the offsets (least square error) from a given set of data.

Algorithm:

  The linear combination of parameters considered is as follows:

  `A * X = B`, where `X` is the unknown value and can be estimated from `A` & `B`.

  The least squares estimate `X` is given by the following equation:

  `X = Inverse(AT * A) * AT * B`

Block Diagram:

Variables Description:

  - `A_f32` input matrix in the linear combination equation
  - `B_f32` output matrix in the linear combination equation
  - `X_f32` unknown matrix estimated using `A_f32` & `B_f32` matrices

CMSIS DSP Software Library Functions Used:

  - arm_mat_init_f32()
  - arm_mat_trans_f32()
  - arm_mat_mult_f32()
  - arm_mat_inverse_f32()

Refer arm_matrix_example_f32.c

All Data Structures Files Functions Variables Typedefs Enumerations Enumerator Defines