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Mastering Numerical Computing with NumPy

You're reading from   Mastering Numerical Computing with NumPy Master scientific computing and perform complex operations with ease

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Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781788993357
Length 248 pages
Edition 1st Edition
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Authors (3):
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Tiago Antao Tiago Antao
Author Profile Icon Tiago Antao
Tiago Antao
Mert Cuhadaroglu Mert Cuhadaroglu
Author Profile Icon Mert Cuhadaroglu
Mert Cuhadaroglu
Umit Mert Cakmak Umit Mert Cakmak
Author Profile Icon Umit Mert Cakmak
Umit Mert Cakmak
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Table of Contents (11) Chapters Close

Preface 1. Working with NumPy Arrays FREE CHAPTER 2. Linear Algebra with NumPy 3. Exploratory Data Analysis of Boston Housing Data with NumPy Statistics 4. Predicting Housing Prices Using Linear Regression 5. Clustering Clients of a Wholesale Distributor Using NumPy 6. NumPy, SciPy, Pandas, and Scikit-Learn 7. Advanced Numpy 8. Overview of High-Performance Numerical Computing Libraries 9. Performance Benchmarks 10. Other Books You May Enjoy

Computing the norm and determinant

This subsection will introduce two important values in linear algebra, namely the norm and determinant. Briefly, the norm gives length of a vector. The most commonly used norm is the L2-norm, which is also known as the Euclidean norm. Formally, the Lp-norm of x is calculated as follows:

The L0-norm is actually the cardinality of a vector. You can calculate it by just counting the total number of non-zero elements. For example, the vector A =[2,5,9,0] contains three non-zero elements, therefore ||A||0 = 3. The following code block shows the same norm calculation with numpy:

In [24]: import numpy as np 
x = np.array([2,5,9,0])
np.linalg.norm(x,ord=0)
Out[24]: 3.0

In NumPy, you can calculate the norm of the vector with the use of the linalg.norm() method. The first parameter is the input array and the ord parameter is for order...

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