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NumPy Beginner's Guide

You're reading from   NumPy Beginner's Guide An action packed guide using real world examples of the easy to use, high performance, free open source NumPy mathematical library.

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Product type Paperback
Published in Apr 2013
Publisher Packt
ISBN-13 9781782166085
Length 310 pages
Edition 2nd Edition
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Author (1):
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Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
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Table of Contents (19) Chapters Close

Numpy Beginner's Guide Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. NumPy Quick Start FREE CHAPTER 2. Beginning with NumPy Fundamentals 3. Get in Terms with Commonly Used Functions 4. Convenience Functions for Your Convenience 5. Working with Matrices and ufuncs 6. Move Further with NumPy Modules 7. Peeking into Special Routines 8. Assure Quality with Testing 9. Plotting with Matplotlib 10. When NumPy is Not Enough – SciPy and Beyond 11. Playing with Pygame Pop Quiz Answers Index

Time for action – clustering points


We will generate some random points and cluster them, which means that points that are close to each other are put in the same cluster. This is only one of the many techniques that you can apply with scikit-learn. Clustering is a type of machine learning algorithm, which aims to group items based on similarities. Second, we will calculate a square affinity matrix. An affinity matrix is a matrix containing affinity values; for instance, distances between points. Finally, we will cluster the points with the AffinityPropagation class from scikit-learn. Perform the following steps to cluster points:

  1. We will generate 30 random point positions within a square of 400 x 400 pixels:

    positions = np.random.randint(0, 400, size=(30, 2))
  2. We will use the Euclidean distance to the origin as affinity matrix.

    positions_norms = np.sum(positions ** 2, axis=1)
    S = - positions_norms[:, np.newaxis] - positions_norms[np.newaxis, :] + 2 * np.dot(positions, positions.T)
  3. Give the AffinityPropagation...

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