.inv()

The .inv() function inverts a given matrix and returns the inverted matrix. If the inversion fails or the given matrix is not a square matrix, then it raises an LinAlgError. Some of its use cases in the field of statistical analysis include:

• Linear Regression
• Multivariate Analysis
• Bayesian Statistics

Syntax

numpy.linalg.inv(a)

• a: The matrix to be inverted.

Example

The following example demonstrates the usage of the .inv() function:

import numpy as np

array =  np.array([[2., 3.], [1., 4.]])
array2 = np.array([[[2., 3.], [1., 4.]], [[6., 4.], [10., 10.]]])

# The determinant is bigger then zero since the above matrices are non-singular

print("One matrix: ")
print(np.linalg.inv(array))

print("\nTwo matrices: ")
print(np.linalg.inv(array2))

The output for the above code is as follows:

One matrix:
[[ 0.8 -0.6]
 [-0.2  0.4]]

Two matrices:
[[[ 0.8 -0.6]
  [-0.2  0.4]]

 [[ 0.5 -0.2]
  [-0.5  0.3]]]

Codebyte Example

Run the following codebyte example of the .inv() function, to better understand its working:

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Note: The .inv() function raises an LinAlgError when a singular matrix is passed for inversion, as it can’t be inverted.