Optimization algorithms try to find the optimal solution for a problem, for instance finding the maximum or the minimum of a function. The function can be linear or non-linear. The solution could also have special constraints. For example, the solution may not be allowed to have negative values. Several optimization algorithms are provided by the scipy.optimize
module. One of the algorithms is a least squares fitting function, leastsq
. When calling this function, we are required to provide a residuals (error terms) function. This function is used to minimize the sum of the squares of the residuals. It corresponds to our mathematical model for the solution. Also, it is necessary to give the algorithm a starting point. This should be a best guess—as close as possible to the real solution. Otherwise, execution will stop after about 800 iterations.
NumPy Beginner's Guide - Second Edition
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NumPy Beginner's Guide - Second Edition
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Overview of this book
NumPy is an extension to, and the fundamental package for scientific computing with Python. In today's world of science and technology, it is all about speed and flexibility. When it comes to scientific computing, NumPy is on the top of the list.
NumPy Beginner's Guide will teach you about NumPy, a leading scientific computing library. NumPy replaces a lot of the functionality of Matlab and Mathematica, but in contrast to those products, is free and open source.
Write readable, efficient, and fast code, which is as close to the language of mathematics as is currently possible with the cutting edge open source NumPy software library. Learn all the ins and outs of NumPy that requires you to know basic Python only. Save thousands of dollars on expensive software, while keeping all the flexibility and power of your favourite programming language.You will learn about installing and using NumPy and related concepts. At the end of the book we will explore some related scientific computing projects. This book will give you a solid foundation in NumPy arrays and universal functions. Through examples, you will also learn about plotting with Matplotlib and the related SciPy project. NumPy Beginner's Guide will help you be productive with NumPy and have you writing clean and fast code in no time at all.
Table of Contents (19 chapters)
Numpy Beginner's Guide Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Free Chapter
NumPy Quick Start
Beginning with NumPy Fundamentals
Get in Terms with Commonly Used Functions
Convenience Functions for Your Convenience
Working with Matrices and ufuncs
Move Further with NumPy Modules
Peeking into Special Routines
Assure Quality with Testing
Plotting with Matplotlib
When NumPy is Not Enough – SciPy and Beyond
Playing with Pygame
Pop Quiz Answers
Index
Customer Reviews