Numeric Computation and Statistical Data Analysis on the Java Platform (Advanced Information and Knowledge Processing)

Numerical computation, knowledge discovery and statistical data analysis integrated with powerful 2D and 3D graphics for visualization are the key topics of this book. The Python code examples powered by the Java platform can easily be transformed to other programming languages, such as Java, Groovy, Ruby and BeanShell. This book equips the reader with a computational platform which, unlike other statistical programs, is not limited by a single programming language. The author focuses on practical programming aspects and covers a broad range of topics, from basic introduction to the Python language on the Java platform (Jython), to descriptive statistics, symbolic calculations, neural networks, non-linear regression analysis and many other data-mining topics. He discusses how to find regularities in real-world data, how to classify data, and how to process data for knowledge discoveries. The code snippets are so short that they easily fit into single pages. __Numeric Computation and Statistical Data Analysis on the Java Platform__ is a great choice for those who want to learn how statistical data analysis can be done using popular programming languages, who want to integrate data analysis algorithms in full-scale applications, and deploy such calculations on the web pages or __computational servers regardless__ of __their operating system.__ It is an excellent __reference for scientific computations to__ solve real-world problems using a __comprehensive stack of open-source Java libraries included in the DataMelt (DMelt) project and__ will be appreciated by many data-analysis scientists, engineers and students__.__ Numerical Computation, Knowledge Discovery And Statistical Data Analysis Integrated With Powerful 2d And 3d Graphics For Visualization Are The Key Topics Of This Book. The Python Code Examples Powered By The Java Platform Can Easily Be Transformed To Other Programming Languages, Such As Java, Groovy, Ruby And Beanshell. This Book Equips The Reader With A Computational Platform Which, Unlike Other Statistical Programs, Is Not Limited By A Single Programming Language. The Author Focuses On Practical Programming Aspects And Covers A Broad Range Of Topics, From Basic Introduction To The Python Language On The Java Platform (jython), To Descriptive Statistics, Symbolic Calculations, Neural Networks, Non-linear Regression Analysis And Many Other Data-mining Topics. He Discusses How To Find Regularities In Real-world Data, How To Classify Data, And How To Process Data For Knowledge Discoveries. The Code Snippets Are So Short That They Easily Fit Into Single Pages. Numeric Computation And Statistical Data Analysis On The Java Platform Is A Great Choice For Those Who Want To Learn How Statistical Data Analysis Can Be Done Using Popular Programming Languages, Who Want To Integrate Data Analysis Algorithms In Full-scale Applications, And Deploy Such Calculations On The Web Pages Or Computational Servers Regardless Of Their Operating System. It Is An Excellent Reference For Scientific Computations To Solve Real-world Problems Using A Comprehensive Stack Of Open-source Java Libraries Included In The Datamelt (dmelt) Project And Will Be Appreciated By Many Data-analysis Scientists, Engineers And Students. Java Computational Platform -- Introduction To Jython -- Mathematical Functions -- Data Arrays -- Linear Algebra And Equations -- Symbolic Computations -- Histograms -- Scientific Visualization -- File Input And Output -- Probability And Statistics -- Linear Regression And Curve Fitting -- Data Analysis And Data Mining -- Neural Networks -- Finding Regularities And Data Classification -- Miscellaneous Topics -- Using Other Languages On The Java Platform -- Octave-style Scripting Using Java -- Index -- Index Of Code Examples. By Sergei V. Chekanov. "Numerical computation, knowledge discovery and statistical data analysis integrated with powerful 2D and 3D graphics for visualization are the key topics of this book. The Python code examples powered by the Java platform can easily be transformed to other programming languages, such as Java, Groovy, Ruby and BeanShell. This book equips the reader with a computational platform which, unlike other statistical programs, is not limited by a single programming language. The author focuses on practical programming aspects and covers a broad range of topics, from basic introduction to the Python language on the Java platform (Jython), to descriptive statistics, symbolic calculations, neural networks, non-linear regression analysis and many other data-mining topics. He discusses how to find regularities in real-world data, how to classify data, and how to process data for knowledge discoveries. The code snippets are so short that they easily fit into single pages. Numeric Computation and Statistical Data Analysis on the Java Platform is a great choice for those who want to learn how statistical data analysis can be done using popular programming languages, who want to integrate data analysis algorithms in full-scale applications, and deploy such calculations on the web pages or computational servers regardless of their operating system. It is an excellent reference for scientific computations to solve real-world problems using a comprehensive stack of open-source Java libraries included in the DataMelt (DMelt) project and will be appreciated by many data-analysis scientists, engineers and students" -- Provided by publisher Front Matter....Pages i-xxvi Java Computational Platform....Pages 1-25 Introduction to Jython....Pages 27-84 Mathematical Functions....Pages 85-130 Data Arrays....Pages 131-186 Linear Algebra and Equations....Pages 187-206 Symbolic Computations....Pages 207-217 Histograms....Pages 219-249 Scientific visualization....Pages 251-296 File Input and Output....Pages 297-349 Probability and Statistics....Pages 351-397 Linear Regression and Curve Fitting....Pages 399-430 Data Analysis and Data Mining....Pages 431-473 Neural Networks....Pages 475-504 Finding Regularities and Data Classification....Pages 505-526 Miscellaneous Topics....Pages 527-546 Using Other Languages on the Java Platform....Pages 547-565 Octave-Style Scripting Using Java....Pages 567-613 Back Matter....Pages 615-620 Numerical computation, knowledge discovery and statistical data analysis integrated with powerful 2D and 3D graphics are the key topics of this book. The short Python code examples powered by the Java platform can be transformed to other programming languages, such as Java, Groovy, Ruby and BeanShell.