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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

Solving linear equations

In this section, you will learn how to solve linear equations by using the linalg.solve() method. When you have a linear equation to solve, as in the form , in simple cases you can just calculate the inverse of A and then multiply it by B to get the solution, but when A has a high dimensionality, that makes it very hard computationally to calculate the inverse of A. Let's start with an example of three linear equations with three unknowns, as follows:

So, these equations can be formalized as follows with matrices:

Then, our problem is to solve . We can calculate the solution with a plain vanilla NumPy without using linalg.solve(). After inverting the A matrix, you will multiply with B in order to get results for x. In the following code block, we calculate the dot product for the inverse matrix of A and B in order to calculate :

In [44]: A =...
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