Numpy matrix.squeeze()

Introduction

In numerical computing with Python, handling multi-dimensional arrays efficiently is crucial. The numpy.matrix.squeeze() function provides a way to remove single-dimensional entries from the shape of a matrix, simplifying its structure for further operations.

What is numpy.matrix.squeeze()?

The numpy.matrix.squeeze() function is used to remove single-dimensional entries from the shape of a matrix. This is particularly useful when dealing with matrices that have unnecessary dimensions, making them easier to work with in subsequent computations.

Syntax

matrix.squeeze(axis=None)

Parameters:

• axis: None or int or tuple of ints, optional. Selects a subset of the single-dimensional entries in the shape. If an axis is selected with shape entry greater than one, an error is raised.

Returns:

• squeezed: matrix. The matrix, but as a (1, N) matrix if it had shape (N, 1).

Note: Supplying an axis keyword argument will not affect the returned matrix but may cause an error to be raised.

Example 1: Squeezing a 2x1 Matrix

import numpy as np

matrix = np.matrix([[4], [12]])
print("Original Matrix:")
print(matrix)

squeezed_matrix = matrix.squeeze()
print("\nSqueezed Matrix:")
print(squeezed_matrix)

Output:

Original Matrix:
[[ 4]
 [12]]

Squeezed Matrix:
[[ 4 12]]

In this example, the 2x1 matrix is squeezed into a 1x2 matrix, removing the single column dimension.

Example 2: Squeezing a 3x1 Matrix

matrix = np.matrix([[1], [2], [3]])
print("Original Matrix:")
print(matrix)

squeezed_matrix = matrix.squeeze()
print("\nSqueezed Matrix:")
print(squeezed_matrix)

Output:

Original Matrix:
[[1]
 [2]
 [3]]

Squeezed Matrix:
[[1 2 3]]

Here, the 3x1 matrix is squeezed into a 1x3 matrix, effectively flattening the column into a row.

Use Cases

The numpy.matrix.squeeze() function is particularly useful in scenarios such as:

• Converting a column vector into a row vector for compatibility with other functions.
• Flattening matrices to simplify data structures for machine learning models.
• Removing unnecessary dimensions after matrix operations to streamline computations.

Conclusion

The numpy.matrix.squeeze() function is a valuable tool for simplifying the structure of matrices in Python. By understanding its syntax and applications, you can efficiently manipulate and analyze multi-dimensional data, making it an essential function in the NumPy library.