# Invers Matriks 1×2

Invers Matriks 1×2.

## Inverse of Matrix in Python

1. Use the
`numpy.linalg.inv()`
Function to Notice the Inverse of a Matrix in Python
2. Use the
`numpy.matrix`
Class to Find the Inverse of a Matrix in Python
3. Use the
`scipy.linalg.inv()`
Function to Find the Inverse of a Matrix in Python
4. Create a User-Defined Function to Discover the Inverse of a Matrix in Python

A matrix is a two-dimensional array with every element of the same size. Nosotros tin can represent matrices using
`numpy`
arrays or nested lists.

For a non-singular matrix whose determinant is not nada, at that place is a unique matrix that yields an identity matrix when multiplied with the original. This unique matrix is chosen the inverse of the original matrix.

This tutorial will demonstrate how to changed a matrix in Python using several methods.

## Employ the `numpy.linalg.inv()` Function to Notice the Inverse of a Matrix in Python

The
`numpy`
module has different functionalities to create and dispense arrays in Python. The
`numpy.linalg`
submodule implements different linear algebra algorithms and functions.

Nosotros can use the
`numpy.linalg.inv()`
part from this module to compute the inverse of a given matrix. This part raises an error if the inverse of a matrix is not possible, which can be considering the matrix is singular.

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Therefore, using this function in a
`try`
and
`except`
block is recommended. If the matrix is singular, an error will be raised, and the code in the
`except`
cake will be executed.

Code Snippet:

```            ```import numpy every bit np try:     g = np.array([[4,3],[8,5]])     print(np.linalg.inv(m)) except:     print("Singular Matrix, Inverse not possible.")
```
```

Output:

```            ```[[-1.25  0.75]  [ 2.   -1.  ]]
```
```

## Utilise the `numpy.matrix` Class to Find the Inverse of a Matrix in Python

For a long time, the
`numpy.matrix`
grade was used to represent matrices in Python. This is the aforementioned as using a normal two-dimensional array for matrix representation.

A
`numpy.matrix`
object has the attribute
`numpy.matrix.I`
computed the inverse of the given matrix. It besides raises an mistake if a singular matrix is used.

Code Snippet:

```            ```import numpy as np attempt:     m = np.matrix([[4,3],[8,5]])     impress(k.I) except:     impress("Singular Matrix, Changed not possible.")
```
```

Output:

```            ```[[-1.25  0.75]  [ ii.   -i.  ]]
```
```

Although both the methods piece of work the same internally, using the
`numpy.matrix`
course is discouraged. This is because it has been deprecated and ambiguous while working with
`numpy`
arrays.

## Use the `scipy.linalg.inv()` Function to Notice the Inverse of a Matrix in Python

We can apply the
`scipy`
module to perform different scientific calculations using its functionalities. It works well with
`numpy`
arrays as well.

The
`scipy.linalg.inv()`
can also return the inverse of a given square matrix in Python. It works the same manner as the
`numpy.linalg.inv()`
function.

Lawmaking Snippet:

```            ```import numpy as np from scipy import linalg attempt:     m = np.matrix([[4,three],[8,5]])     print(linalg.inv(1000)) except:     print("Singular Matrix, Inverse not possible.")
```
```

Output:

```            ```[[-1.25  0.75]  [ 2.   -one.  ]]
```
```

## Create a User-Defined Part to Find the Inverse of a Matrix in Python

Nosotros tin implement the mathematical logic for computing an inverse matrix in Python. For this, nosotros will utilise a serial of user-divers functions.

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We will create different functions to return the determinants, transpose, and matrix determinants. These functions will be used in a office that will render the final changed.

This method works when we represent a matrix equally a list of lists in Python.

Code Snippet:

```            ```def return_transpose(mat):     return map(list,nothing(*mat))  def return_matrix_minor(mat,i,j):     return [row[:j] + row[j+1:] for row in (mat[:i]+mat[i+i:])]  def return_determinant(mat):     if len(mat) == 2:         return mat[0][0]*mat[i][1]-mat[0][1]*mat[1][0]      determinant = 0     for c in range(len(m)):         determinant += ((-1)**c)*thousand[0][c]*return_determinant(return_matrix_minor(yard,0,c))     return determinant  def inverse_matrix(m):     determinant = return_determinant(g)     if len(k) == 2:         return [[g[one][i]/determinant, -1*m[0][i]/determinant],                 [-i*1000[1][0]/determinant, m[0][0]/determinant]]      cfs = []     for r in range(len(m)):         cfRow = []         for c in range(len(m)):             minor = return_matrix_minor(chiliad,r,c)             cfRow.suspend(((-i)**(r+c)) * return_determinant(minor))         cfs.append(cfRow)     cfs = return_transpose(cfs)     for r in range(len(cfs)):         for c in range(len(cfs)):             cfs[r][c] = cfs[r][c]/determinant     render cfs  m = [[4,3],[8,v]] print(inverse_matrix(thou))
```
```

Output:

```            ```[[-i.25, 0.75], [two.0, -1.0]]
```
```

The above example returns a nested list that represents the given matrix’s changed.

To wrap up, nosotros discussed several methods to find the changed of a matrix in Python. The
`numpy`
and
`scipy`
modules have the
`linalg.inv()`
part that computes the changed of a matrix.

We can also utilize the
`numpy.matrix`
class to find the inverse of a matrix. Finally, we discussed a series of user-defined functions that compute the changed by implementing the arithmetical logic.

## Invers Matriks 1×2

Source: https://www.delftstack.com/howto/python/inverse-of-matrix-in-python/

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