Scientific Data Analysis using Jython Scripting and Java / Edition 1
by Sergei V. Chekanov, Sergei ChekanovISBN-10: 1849962863
ISBN-13: 9781849962865
Pub. Date: 08/28/2010
Publisher: Springer London
Scientific Data Analysis using Jython Scripting and Java presents practical approaches for data analysis using Java scripting based on Jython, a Java implementation of the Python language. The chapters essentially cover all aspects of data analysis, from arrays and histograms to clustering analysis, curve fitting, metadata and neural networks. A comprehensive
Overview
Scientific Data Analysis using Jython Scripting and Java presents practical approaches for data analysis using Java scripting based on Jython, a Java implementation of the Python language. The chapters essentially cover all aspects of data analysis, from arrays and histograms to clustering analysis, curve fitting, metadata and neural networks. A comprehensive coverage of data visualisation tools implemented in Java is also included.
Written by the primary developer of the jHepWork data-analysis framework, the book provides a reliable and complete reference source laying the foundation for data-analysis applications using Java scripting. More than 250 code snippets (of around 10-20 lines each) written in Jython and Java, plus several real-life examples help the reader develop a genuine feeling for data analysis techniques and their programming implementation.
This is the first data-analysis and data-mining book which is completely based on the Jython language, and opens doors to scripting using a fully multi-platform and multi-threaded approach. Graduate students and researchers will benefit from the information presented in this book.
Product Details
- ISBN-13:
- 9781849962865
- Publisher:
- Springer London
- Publication date:
- 08/28/2010
- Series:
- Advanced Information and Knowledge Processing Series
- Edition description:
- 2010
- Pages:
- 440
- Product dimensions:
- 6.20(w) x 9.30(h) x 1.20(d)
Table of Contents
Introduction.- 1. Jython, Java and jHepWork.- 2. Introduction to Jython.- 3. Mathematical Functions.- 4. One-dimensional Data.- 5. Two-dimensional Data.- 6. Multi-dimensional Data.- 7. Arrays, Matrices and Linear Algebra.- 8. Histograms.- 9. Random Numbers and Statistical Samples.- 10. Graphical Canvases.- 11. Input and Output.- 12. Miscellaneous Analysis Issues Using jHepWork.- 13. Data Clustering.- 14. Linear Regression and Curve Fitting.- 15. Neural Networks.- 16. Steps in Data Analysis.- 17. Real-life Examples.- Index of Examples.- Index
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