Book Image

Dancing with Python

By : Robert S. Sutor
Book Image

Dancing with Python

By: Robert S. Sutor

Overview of this book

Dancing with Python helps you learn Python and quantum computing in a practical way. It will help you explore how to work with numbers, strings, collections, iterators, and files. The book goes beyond functions and classes and teaches you to use Python and Qiskit to create gates and circuits for classical and quantum computing. Learn how quantum extends traditional techniques using the Grover Search Algorithm and the code that implements it. Dive into some advanced and widely used applications of Python and revisit strings with more sophisticated tools, such as regular expressions and basic natural language processing (NLP). The final chapters introduce you to data analysis, visualizations, and supervised and unsupervised machine learning. By the end of the book, you will be proficient in programming the latest and most powerful quantum computers, the Pythonic way.
Table of Contents (29 chapters)
Part I: Getting to Know Python
PART II: Algorithms and Circuits
PART III: Advanced Features and Libraries
Other Books You May Enjoy
Appendix C: The Complete UniPoly Class
Appendix D: The Complete Guitar Class Hierarchy
Appendix F: Production Notes

15.7 Linear regression

Linear regression is a technique we use to fit a hyperplane to a training set of independent variables and one dependent variable.

A common use is determining the “best” line that best approximates the points in a 2-dimensional scatter plot. Here, the x coordinate is the independent variable, and the y coordinate is the dependent variable. This is simple linear regression.

If we have more than one independent variable, we are performing multiple linear regression. In this section, we look at a simple case and the ordinary least squares algorithm.

Given a new independent observation, x, the line’s equation allows us to compute an estimated value for its dependent y. Let xmin and xmax be the minimum and maximum values of x in the training set. If


then the prediction of y is interpolation. Otherwise, the prediction...