python Numpy Matrix vector multiplication in

• summary
• 1. about np.array object
• • 1.1 Element multiplication use a*b or np.multiply(a,b)
  • 1.2 Matrix multiplication use np.dot(a,b) or np.matmul(a,b) or a.dot(b) or [email protected]
• 2. about np.matrix object
• • 2.1 Element multiplication use np.multiply(a,b)
  • 2.2 Matrix multiplication use a*b or np.dot(a,b) or np.matmul(a,b) or a.dot(b)

summary

• Element multiplication ：np.multiply(a,b)
• Matrix multiplication ：np.dot(a,b) or np.matmul(a,b) or a.dot(b) Or directly a @ b

Only pay attention ：*, stay np.array Element multiplication is overloaded in , stay np.matrix Matrix multiplication is overloaded in !

Be careful ：
about Array Formatted data , A one-dimensional 、 Two dimensional arrays can use elements 、 matrix multiplication ;
about matrix Formatted data , One dimensional matrix can only be multiplied by elements , Two dimensional matrices are OK .

1. about np.array object

Two dimensional array

One dimensional array

1.1 Element multiplication use a*b or np.multiply(a,b)

Two dimensional array

One dimensional array

1.2 Matrix multiplication use np.dot(a,b) or np.matmul(a,b) or a.dot(b) or [email protected]

Two dimensional array

One dimensional array （ Multiply the corresponding positions , And then sum up ）

2. about np.matrix object

Two dimensional matrix

One dimensional matrix

2.1 Element multiplication use np.multiply(a,b)

Two dimensional matrix

One dimensional matrix

2.2 Matrix multiplication use a*b or np.dot(a,b) or np.matmul(a,b) or a.dot(b)

Two dimensional matrix

One dimensional matrix （ Report errors , Matrix operations cannot be performed directly ）

Reference resources ：
Numpy Matrix vector multiplication in
python in np.multiply（）、np.dot（） And asterisk （*） The difference between the three multiplication operations