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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

Preparing for a performance benchmark

For each instance and configuration, navigate to your Home directory and create a folder named py_scripts:

Create a file named linalg_benchmark.py with the following command and paste the contents:

After pasting the contents, type :, then type wq!, and Enter to save and quit:

Now you can run this file with the following command:

For Anaconda distribution, you will run the script with the following command:

Performance with BLAS and LAPACK

Here, you will run the linalg_benchmark.py script with BLAS and LAPACK. Connect to the t2.micro instance where you have this configuration, and run the script as shown in the previous section.

The following are the results of the run with dim =...

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