"Machine learning algorithms principles and programming Practices" study notes (iii)

(First chapter above)

1.2.5 Linalg Linear Algebra Library

Based on the basic operation of matrices, the Linalg Library of NumPy can satisfy most linear algebra operations.

. determinant of matrices

. Inverse of the Matrix

. Symmetry of matrices

. The rank of the matrix

. The reversible matrix solves the linear equation

1. Determinant of matrices

 from Import * in[#N-order matrix determinant operation in [6]: A = Mat ([[[1,2,3],[4,5,6],[7,8,9]]) in [print]det (A):"6.66133814775e-16

2. Inverse of the Matrix

 from Import * in[9]: A = Mat ([[[1,2,3],[4,5,6],[7,8,9]]) in [ten]: Inva = LINALG.INV (A)# Inverse of Matrix In [print]INV (A):"-4.50359963e+15   9.00719925e+15  -4.50359963e+15] [  9.00719925e+15  -1.80143985e+16   9.00719925e+15- 4.50359963e+15   9.00719925e+15  -4.50359963e+15]]

3. Symmetry of the Matrix

 from Import * in[+]: A = Mat ([[[1,2,3],[4,5,6],[7,8,9]]) in []: at= a.tin [Print *50  122 194] [122]  

4. Rank of matrix

 from Import * in[+]: A = Mat ([[[1,2,3],[4,5,6],[7,8,9]]) in [print Linalg.matrix_rank (A)# rank 2 of matrix

5. Reversible Matrix Solution

Source: "Machine learning algorithm principles and programming practices" Zheng Jie

Principles of machine learning algorithms and Programming Practices study notes (c)