Matlab Importing Data
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This document provides steps for importing text files into MATLAB: save the data in a text file, open the import data window in MATLAB and select the file, select values in the import wizard, finalize the import, and then view the imported data in the MATLAB workspace. It also lists several methods for importing data into MATLAB such as entering it at the command prompt, loading it from an ASCII flat file, or using MATLAB I/O functions. The document concludes by recommending visiting additional self-help tutorials on the website www.dataminingtools.net.
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Questions about this assignment? Post them in the Contact the Instructor area. That way, everyone in the class will see and benefit from the Instructor's response.
To prepare for this Project:
•Complete the MyStatLab homework for Week 1.
•Review the Statdisk User Manual in Doc Sharing.
•Reflect on how you might use written form to communicate the process by which you will solve the problems.
The Assignment:
1.Read pages 3 - 7 in the Statdisk User Manual.
2.Perform Statdisk computations and answer questions in the Week 1 Project.
3.Submit your completed assignment to the Week 1 Assignment 2 submission link.
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You will be using the original Week 1 Project as it appears in Doc Sharing as a template for your answers.
1.Open the Week 1 Project found in Doc Sharing. The Project will open in Microsoft Word and allow for you to cut, paste, and insert text.
2.Insert your solution to each problem directly below the question. Color your answers red.
3.Be sure to cut and paste ALL tables, charts, and information generated by using the STATDISK statistical program and explain what each chart represents.
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The document discusses various image enhancement techniques in Matlab, including filtering, predefined filters, image enhancement tools, image restoration, dilation/erosion functions, and dithering. Filtering can be used to modify images through operations like smoothing, sharpening, and edge enhancement. Predefined filters like 'gaussian' and 'laplacian' can be applied to images with functions like fspecial and imfilter. Additional tools for operations such as histogram equalization, noise addition, and filtering are also covered.
Matlab Image Restoration Techniques
Linear filters like averaging and Gaussian filters can remove grain noise by averaging pixel values in a neighborhood. Median filters are better at removing outliers without reducing sharpness by setting a pixel to the median value in its neighborhood. The document demonstrates applying averaging and median filters in Matlab to remove noise, and using morphological opening to estimate and subtract a background illumination to rectify it.
Matlab Organizing Data
This document summarizes different ways to organize data in Matlab, including cell arrays, function handles, and structures.
Cell arrays allow different data types to be stored in containers called cells. Function handles pass functions as inputs to other functions. Structures store selected data together in fields, with each field containing an array of a Matlab data type.
Matlab Text Files
To write to a file in Matlab, use the fopen function to open a file with write permission and the fprintf function to write text to the file. To read from a file, use the fopen function to open the file with read permission and the fscanf function to read data from the file. Matlab has functions like circshift to circularly shift matrix elements, sort to sort arrays in ascending or descending order, and issorted to check if elements are already sorted.
Matlab Visualizing Data
The document discusses visualizing data in Matlab. It describes how the plot function can be used to create graphs with different parameters. It also explains how to create animations by saving multiple frames from figures using getframe and playing them back with movie. An example is provided to generate an animation by plotting the FFT of an identity matrix over increasing sizes and saving each frame.
Matlab Working With Images
The document discusses different types of images in Matlab including binary, grayscale, indexed, and RGB images. It also summarizes commands to convert between image types such as converting grayscale to indexed or truecolor to binary. Finally, it provides examples of how to view images, measure pixel values and distances, and crop images using the imtool command.
Matlab: Non Linear Methods
This document discusses nonlinear curve fitting in Matlab. It describes how the Curve Fitting Toolbox uses nonlinear least squares to fit nonlinear models to data, such as Gaussians, ratios of polynomials, and power functions. The process of fitting a surface to data involves opening the fitting tool, selecting and importing data, refining the fit, removing outliers, selecting validation data, and exploring and customizing plots. Various steps for fitting, viewing residuals and fits, and analyzing the fit are outlined. The document recommends visiting additional self-help tutorials on the website.
Matlab: Procedures And Functions
Script files, also called M-files, make MATLAB programming more efficient than entering individual commands. M-files are text files that contain MATLAB commands to perform specific tasks. They can be created and edited in any plain text editor or the built-in MATLAB editor. User defined functions are a special type of M-file that take inputs and produce outputs, with the function name and arguments specified in the first line. Strings in MATLAB are matrices of character elements that can be manipulated using various functions like converting between data types and extracting substrings.
Matlab: Programming Environment
This document provides an overview of the MATLAB programming environment and basic functions. It describes the main components of the MATLAB interface including the toolbar, file manager, command window, workspace, and command history. It also explains how to access help documentation, use the function browser to search for commands, define basic functions and vectors, and clear variables from the workspace.
Matlab: Regression
Regression analysis models the relationship between variables, including dependent and independent variables. Linear regression models take forms like straight lines, polynomials, trigonometric, and interaction terms. Multiple linear regression is useful for understanding variable effects, predicting values, and dealing with multicollinearity using methods like ridge regression, partial least squares, and stepwise regression. Nonlinear and generalized linear models also describe nonlinear relationships. Multivariate regression involves multiple response variables.
Matlab: Saving And Publishing
This document discusses saving and publishing in Matlab. It describes how to save variables and images using commands like save and imsave. It also explains how to publish animations by converting MATLAB movie files to AVI format using the movie2avi function. This involves creating an AVI file object with avifile, then capturing a sequence of graphs using addframe and putting them into the AVI file.
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Matlab Importing Data
- 2. Importing text files Step1: Save data in a text file.
- 3. Importing text files Step2: Open the ‘File -> Import data’ window in MATLAB and select your file.
- 4. Importing text files Step2: Open the ‘File -> Import data’ window in MATLAB and select your file.
- 5. Importing text files Step3: Select values in the import data wizard
- 6. Importing text files Step4: Finalize import.
- 7. Importing text files Now look at your workspace, here’s the data we just imported!
- 9. Create data in an M-file.
- 10. Load data from an ASCII flat file.
- 11. Read data using MATLAB I/O functions.Write a MEX-file to read the data.
- 12. Write a program to translate your data.Visit the MATLAB help menu for sample codes and instructions.
- 13. Visit more self help tutorials Pick a tutorial of your choice and browse through it at your own pace. The tutorials section is free, self-guiding and will not involve any additional support. Visit us at www.dataminingtools.net