Matlab Graphics And Data Visualization Cookbook

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Matlab Graphics And Data Visualization Cookbook

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Author : Nivedita Majumdar
language : en
Publisher: Packt Publishing
Release Date : 2012

Matlab Graphics And Data Visualization Cookbook written by Nivedita Majumdar and has been published by Packt Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Computers categories.

"MATLAB Graphics and Data Visualization Cookbook" will serve as your handbook in recipe format with ample screenshots. Each independent recipe will help you in achieving target-oriented solutions to your problems with concise explanation. For those who are already using MATLAB for their computational needs but need to also harness its powerful visualization capabilities.

Opengl Data Visualization Cookbook

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Author : Raymond C. H. Lo
language : en
Publisher: Packt Publishing Ltd
Release Date : 2015-08-24

Opengl Data Visualization Cookbook written by Raymond C. H. Lo and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-08-24 with Computers categories.

Over 35 hands-on recipes to create impressive, stunning visuals for a wide range of real-time, interactive applications using OpenGL About This Book Get acquainted with a set of fundamental OpenGL primitives and concepts that enable users to create stunning visuals of arbitrarily complex 2D and 3D datasets for many common applications Explore interactive, real-time visualization of large 2D and 3D datasets or models, including the use of more advanced techniques such as stereoscopic 3D rendering. Create stunning visuals on the latest platforms including mobile phones and state-of-the-art wearable computing devices Who This Book Is For This book is aimed at anyone interested in creating impressive data visualization tools using modern graphics hardware. Whether you are a developer, engineer, or scientist, if you are interested in exploring the power of OpenGL for data visualization, this book is for you. While familiarity with C/C++ is recommended, no previous experience with OpenGL is assumed. What You Will Learn Install, compile, and integrate the OpenGL pipeline into your own project Create interactive applications using GLFW to handle user inputs and the Android Sensor framework to detect gestures and motions on mobile devices Use OpenGL primitives to plot 2-D datasets such as time series dynamically Render complex 3D volumetric datasets with techniques such as data slicers and multiple viewpoint projection Render images, videos, and point cloud data from 3D range-sensing cameras using the OpenGL Shading Language (GLSL) Develop video see-through augmented reality applications on mobile devices with OpenGL ES 3.0 and OpenCV Visualize 3D models with meshes and surfaces using stereoscopic 3D technology In Detail OpenGL is a great multi-platform, cross-language, and hardware-accelerated graphics interface for visualizing large 2D and 3D datasets. Data visualization has become increasingly challenging using conventional approaches as datasets become larger and larger, especially with the Big Data evolution. From a mobile device to a sophisticated high-performance computing cluster, OpenGL libraries provide developers with an easy-to-use interface to create stunning visuals in 3D in real time for a wide range of interactive applications. This book provides a series of easy-to-follow, hands-on tutorials to create appealing OpenGL-based visualization tools with minimal development time. We will first illustrate how to quickly set up the development environment in Windows, Mac OS X, and Linux. Next, we will demonstrate how to visualize data for a wide range of applications using OpenGL, starting from simple 2D datasets to increasingly complex 3D datasets with more advanced techniques. Each chapter addresses different visualization problems encountered in real life and introduces the relevant OpenGL features and libraries in a modular fashion. By the end of this book, you will be equipped with the essential skills to develop a wide range of impressive OpenGL-based applications for your unique data visualization needs, on platforms ranging from conventional computers to the latest mobile/wearable devices. Style and approach This is an easy-to-follow, comprehensive Cookbook showing readers how to create an application with real-time, interactive data visualization in stereoscopic 3D. Each topic is explained in a step-by-step format. A range of hot topics is included, including data visualization on mobile and wearable platforms.

Image Processing With Imagej

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Author : Jurjen Broeke
language : en
Publisher: Packt Publishing Ltd
Release Date : 2015-11-30

Image Processing With Imagej written by Jurjen Broeke and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-11-30 with Computers categories.

Extract and analyze data from complex images with ImageJ, the world's leading image processing tool About This Book Design automated image-processing solutions and speed up image-processing tasks with ImageJ Create quality and intuitive interfaces for image processing by developing a basic framework for ImageJ plugins. Tackle even the most sophisticated datasets and complex images Who This Book Is For The book has been created for engineers, scientists, and developers eager to tackle image processing with one of the leading tools available. No prior knowledge of ImageJ is needed. Familiarity with Java programming will be required for readers to code their own routines using ImageJ. What You Will Learn Install and set up ImageJ for image processing. Process images using ImageJ's built-in tools Create macros to perform repetitive processing tasks Set up and use an integrated development environment for ImageJ plugins Create plugins with a user-friendly interface for processing Use established ImageJ plugins for processing and quantification Generate a simple interface based on a real world example and create other interfaces for other projects Speed up interface development by setting multiple parameters interactively In Detail Advances in image processing have been vital for the scientific and technological communities, making it possible to analyze images in greater detail than ever before. But as images become larger and more complex, advanced processing techniques are required. ImageJ is built for the modern challenges of image processing – it's one of the key tools in its development, letting you automate basic tasks so you can focus on sophisticated, in depth analysis. This book demonstrates how to put ImageJ into practice. It outlines its key features and demonstrates how to create your own image processing applications using macros and ImageJ plugins. Once you've got to grips with the basics of ImageJ, you'll then discover how to build a number of different image processing solutions. From simple tasks to advanced and automated image processing, you'll gain confidence with this innovative and powerful tool – however and whatever you are using it for. Style and approach A step-by-step guide to image processing and developing macros and plugins in ImageJ. The book will progress from using the built-in tools to macros and finally plugins for image processing.

Graphics And Guis With Matlab

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Author : Patrick Marchand
language : en
Publisher: CRC Press
Release Date : 1999-04-23

Graphics And Guis With Matlab written by Patrick Marchand and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999-04-23 with Business & Economics categories.

This second edition provides illustrative example sets to simplify the process of learning and mastering the powerful, flexible, and easy-to-use MATLAB graphics environment. It shows how to maximize the high performance and open-environment capabilities for generating, displaying, and analyzing numerical data as well as how to quickly create interesting and beautiful graphics. The book covers plotting, color, animation, the new z buffer algorithm, new functions for generating graphics for presentations, and GUI programming techniques. Designed as both an introduction as well as an advanced learning tool, the book uses step-by-step tutorials with a level of detail, explanation, and instruction that allows readers to discover the full potential of the MATLAB graphics programming capability.

Matlab Machine Learning Recipes

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Author : Michael Paluszek
language : en
Publisher: Apress
Release Date : 2019-01-31

Matlab Machine Learning Recipes written by Michael Paluszek and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-31 with Computers categories.

Harness the power of MATLAB to resolve a wide range of machine learning challenges. This book provides a series of examples of technologies critical to machine learning. Each example solves a real-world problem. All code in MATLAB Machine Learning Recipes: A Problem-Solution Approach is executable. The toolbox that the code uses provides a complete set of functions needed to implement all aspects of machine learning. Authors Michael Paluszek and Stephanie Thomas show how all of these technologies allow the reader to build sophisticated applications to solve problems with pattern recognition, autonomous driving, expert systems, and much more. What you'll learn: How to write code for machine learning, adaptive control and estimation using MATLAB How these three areas complement each other How these three areas are needed for robust machine learning applications How to use MATLAB graphics and visualization tools for machine learning How to code real world examples in MATLAB for major applications of machine learning in big data Who is this book for: The primary audiences are engineers, data scientists and students wanting a comprehensive and code cookbook rich in examples on machine learning using MATLAB.

Visualizing Data

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Author : Ben Fry
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2008

Visualizing Data written by Ben Fry and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Computers categories.

Provides information on the methods of visualizing data on the Web, along with example projects and code.

Ipython Interactive Computing And Visualization Cookbook

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Author : Cyrille Rossant
language : en
Publisher: Packt Publishing Ltd
Release Date : 2014-09-25

Ipython Interactive Computing And Visualization Cookbook written by Cyrille Rossant and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-09-25 with Computers categories.

Intended to anyone interested in numerical computing and data science: students, researchers, teachers, engineers, analysts, hobbyists... Basic knowledge of Python/NumPy is recommended. Some skills in mathematics will help you understand the theory behind the computational methods.

R Cookbook

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Author : Paul Teetor
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2011-03-03

R Cookbook written by Paul Teetor and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-03-03 with Computers categories.

With more than 200 practical recipes, this book helps you perform data analysis with R quickly and efficiently. The R language provides everything you need to do statistical work, but its structure can be difficult to master. This collection of concise, task-oriented recipes makes you productive with R immediately, with solutions ranging from basic tasks to input and output, general statistics, graphics, and linear regression. Each recipe addresses a specific problem, with a discussion that explains the solution and offers insight into how it works. If you’re a beginner, R Cookbook will help get you started. If you’re an experienced data programmer, it will jog your memory and expand your horizons. You’ll get the job done faster and learn more about R in the process. Create vectors, handle variables, and perform other basic functions Input and output data Tackle data structures such as matrices, lists, factors, and data frames Work with probability, probability distributions, and random variables Calculate statistics and confidence intervals, and perform statistical tests Create a variety of graphic displays Build statistical models with linear regressions and analysis of variance (ANOVA) Explore advanced statistical techniques, such as finding clusters in your data "Wonderfully readable, R Cookbook serves not only as a solutions manual of sorts, but as a truly enjoyable way to explore the R language—one practical example at a time."—Jeffrey Ryan, software consultant and R package author

Rstudio For R Statistical Computing Cookbook

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Author : Andrea Cirillo
language : en
Publisher: Packt Publishing Ltd
Release Date : 2016-04-29

Rstudio For R Statistical Computing Cookbook written by Andrea Cirillo and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-04-29 with Computers categories.

Over 50 practical and useful recipes to help you perform data analysis with R by unleashing every native RStudio feature About This Book 54 useful and practical tasks to improve working systems Includes optimizing performance and reliability or uptime, reporting, system management tools, interfacing to standard data ports, and so on Offers 10-15 real-life, practical improvements for each user type Who This Book Is For This book is targeted at R statisticians, data scientists, and R programmers. Readers with R experience who are looking to take the plunge into statistical computing will find this Cookbook particularly indispensable. What You Will Learn Familiarize yourself with the latest advanced R console features Create advanced and interactive graphics Manage your R project and project files effectively Perform reproducible statistical analyses in your R projects Use RStudio to design predictive models for a specific domain-based application Use RStudio to effectively communicate your analyses results and even publish them to a blog Put yourself on the frontiers of data science and data monetization in R with all the tools that are needed to effectively communicate your results and even transform your work into a data product In Detail The requirement of handling complex datasets, performing unprecedented statistical analysis, and providing real-time visualizations to businesses has concerned statisticians and analysts across the globe. RStudio is a useful and powerful tool for statistical analysis that harnesses the power of R for computational statistics, visualization, and data science, in an integrated development environment. This book is a collection of recipes that will help you learn and understand RStudio features so that you can effectively perform statistical analysis and reporting, code editing, and R development. The first few chapters will teach you how to set up your own data analysis project in RStudio, acquire data from different data sources, and manipulate and clean data for analysis and visualization purposes. You'll get hands-on with various data visualization methods using ggplot2, and you will create interactive and multidimensional visualizations with D3.js. Additional recipes will help you optimize your code; implement various statistical models to manage large datasets; perform text analysis and predictive analysis; and master time series analysis, machine learning, forecasting; and so on. In the final few chapters, you'll learn how to create reports from your analytical application with the full range of static and dynamic reporting tools that are available in RStudio so that you can effectively communicate results and even transform them into interactive web applications. Style and approach RStudio is an open source Integrated Development Environment (IDE) for the R platform. The R programming language is used for statistical computing and graphics, which RStudio facilitates and enhances through its integrated environment. This Cookbook will help you learn to write better R code using the advanced features of the R programming language using RStudio. Readers will learn advanced R techniques to compute the language and control object evaluation within R functions. Some of the contents are: Accessing an API with R Substituting missing values by interpolation Performing data filtering activities R Statistical implementation for Geospatial data Developing shiny add-ins to expand RStudio functionalities Using GitHub with RStudio Modelling a recommendation engine with R Using R Markdown for static and dynamic reporting Curating a blog through RStudio Advanced statistical modelling with R and RStudio

Data Visualization Time Series Forecasting And Prediction Using Machine Learning With Tkinter

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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-09-06

Data Visualization Time Series Forecasting And Prediction Using Machine Learning With Tkinter written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-06 with Computers categories.

This "Data Visualization, Time-Series Forecasting, and Prediction using Machine Learning with Tkinter" project is a comprehensive and multifaceted application that leverages data visualization, time-series forecasting, and machine learning techniques to gain insights into bitcoin data and make predictions. This project serves as a valuable tool for financial analysts, traders, and investors seeking to make informed decisions in the stock market. The project begins with data visualization, where historical bitcoin market data is visually represented using various plots and charts. This provides users with an intuitive understanding of the data's trends, patterns, and fluctuations. Features distribution analysis is conducted to assess the statistical properties of the dataset, helping users identify key characteristics that may impact forecasting and prediction. One of the project's core functionalities is time-series forecasting. Through a user-friendly interface built with Tkinter, users can select a stock symbol and specify the time horizon for forecasting. The project supports multiple machine learning regressors, such as Linear Regression, Decision Trees, Random Forests, Gradient Boosting, Extreme Gradient Boosting, Multi-Layer Perceptron, Lasso, Ridge, AdaBoost, and KNN, allowing users to choose the most suitable algorithm for their forecasting needs. Time-series forecasting is crucial for making predictions about stock prices, which is essential for investment strategies. The project employs various machine learning regressors to predict the adjusted closing price of bitcoin stock. By training these models on historical data, users can obtain predictions for future adjusted closing prices. This information is invaluable for traders and investors looking to make buy or sell decisions. The project also incorporates hyperparameter tuning and cross-validation to enhance the accuracy of these predictions. These models employ metrics such as Mean Absolute Error (MAE), which quantifies the average absolute discrepancy between predicted values and actual values. Lower MAE values signify superior model performance. Additionally, Mean Squared Error (MSE) is used to calculate the average squared differences between predicted and actual values, with lower MSE values indicating better model performance. Root Mean Squared Error (RMSE), derived from MSE, provides insights in the same units as the target variable and is valued for its lower values, denoting superior performance. Lastly, R-squared (R2) evaluates the fraction of variance in the target variable that can be predicted from independent variables, with higher values signifying better model fit. An R2 of 1 implies a perfect model fit. In addition to close price forecasting, the project extends its capabilities to predict daily returns. By implementing grid search, users can fine-tune the hyperparameters of machine learning models such as Random Forests, Gradient Boosting, Support Vector, Decision Tree, Gradient Boosting, Extreme Gradient Boosting, Multi-Layer Perceptron, and AdaBoost Classifiers. This optimization process aims to maximize the predictive accuracy of daily returns. Accurate daily return predictions are essential for assessing risk and formulating effective trading strategies. Key metrics in these classifiers encompass Accuracy, which represents the ratio of correctly predicted instances to the total number of instances, Precision, which measures the proportion of true positive predictions among all positive predictions, and Recall (also known as Sensitivity or True Positive Rate), which assesses the proportion of true positive predictions among all actual positive instances. The F1-Score serves as the harmonic mean of Precision and Recall, offering a balanced evaluation, especially when considering the trade-off between false positives and false negatives. The ROC Curve illustrates the trade-off between Recall and False Positive Rate, while the Area Under the ROC Curve (AUC-ROC) summarizes this trade-off. The Confusion Matrix provides a comprehensive view of classifier performance by detailing true positives, true negatives, false positives, and false negatives, facilitating the computation of various metrics like accuracy, precision, and recall. The selection of these metrics hinges on the project's specific objectives and the characteristics of the dataset, ensuring alignment with the intended goals and the ramifications of false positives and false negatives, which hold particular significance in financial contexts where decisions can have profound consequences. Overall, the "Data Visualization, Time-Series Forecasting, and Prediction using Machine Learning with Tkinter" project serves as a powerful and user-friendly platform for financial data analysis and decision-making. It bridges the gap between complex machine learning techniques and accessible user interfaces, making financial analysis and prediction more accessible to a broader audience. With its comprehensive features, this project empowers users to gain insights from historical data, make informed investment decisions, and develop effective trading strategies in the dynamic world of finance. You can download the dataset from: http://viviansiahaan.blogspot.com/2023/09/data-visualization-time-series.html.