Dataset

This dataset was created by mtech saurabh

Released under Apache 2.0

Contents

Matlab has a reputation for running slowly. Here are some pointers on how to speed computations, to an often unexpected degree. Subjects currently covered: Matrix Coding Implicit Multithreading on a Multicore Machine Sparse Matrices Sub-Block Computation to Avoid Memory Overflow Matrix Coding - 1 Matlab documentation notes that efficient computation depends on using the matrix facilities, and that mathematically identical algorithms can have very different runtimes, but they are a bit coy about just what these differences are. A simple but telling example: The following is the core of the GD-CLS algorithm of Berry et.al., copied from fig. 1 of Shahnaz et.al, 2006, "Document clustering using nonnegative matrix factorization': for jj = 1:maxiter A = W'*W + lambda*eye(k); for ii = 1:n b = W'*V(:,ii); H(:,ii) = A \ b; end H = H .* (H>0); W = W .* (V*H') ./ (W*(H*H') + 1e-9); end Replacing the columwise update of H with a matrix update gives: for jj = 1:maxiter A = W'*W + lambda*eye(k); B = W'*V; H = A \ B; H = H .* (H>0); W = W .* (V*H') ./ (W*(H*H') + 1e-9); end These were tested on an 8049 x 8660 sparse matrix bag of words V (.0083 non-zeros), with W of size 8049 x 50, H 50 x 8660, maxiter = 50, lambda = 0.1, and identical initial W. They were run consecutivly, multithreaded on an 8-processor Sun server, starting at ~7:30PM. Tic-toc timing was recorded. Runtimes were respectivly 6586.2 and 70.5 seconds, a 93:1 difference. The maximum absolute pairwise difference between W matrix values was 6.6e-14. Similar speedups have been consistantly observed in other cases. In one algorithm, combining matrix operations with efficient use of the sparse matrix facilities gave a 3600:1 speedup. For speed alone, C-style iterative programming should be avoided wherever possible. In addition, when a couple lines of matrix code can substitute for an entire C-style function, program clarity is much improved. Matrix Coding - 2 Applied to integration, the speed gains are not so great, largely due to the time taken to set up the and deal with the boundaries. The anyomous function setup time is neglegable. I demonstrate on a simple uniform step linearly interpolated 1-D integration of cos() from 0 to pi, which should yield zero: tic; step = .00001; fun = @cos; start = 0; endit = pi; enda = floor((endit - start)/step)step + start; delta = (endit - enda)/step; intF = fun(start)/2; intF = intF + fun(endit)delta/2; intF = intF + fun(enda)(delta+1)/2; for ii = start+step:step:enda-step intF = intF + fun(ii); end intF = intFstep toc; intF = -2.910164109692914e-14 Elapsed time is 4.091038 seconds. Replacing the inner summation loop with the matrix equivalent speeds things up a bit: tic; step = .00001; fun = @cos; start = 0; endit = pi; enda = floor((endit - start)/step)*step + start; delta = (endit - enda)/step; intF = fun(start)/2; intF = intF + fun(endit)*delta/2; intF = intF + fun(enda)*(delta+1)/2; intF = intF + sum(fun(start+step:step:enda-step)); intF = intF*step toc; intF = -2.868419946011613e-14 Elapsed time is 0.141564 seconds. The core computation take

Matlab

## Overview

Matlab is a dataset for object detection tasks - it contains Face annotations for 220 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Two folders are provided. The MATLAB folder contains MATLAB codes for running Kernel PLS cross-validation, calibration and prediction of properties in various data sets: corn, yerba, wheat and meat. The R folder contains the same data with R codes for the same activities.

This data set consists of Conductivity, Temperature, Depth (CTD) data in MATLAB Format from the 2002 Polar Star Mooring Cruise (AWS-02-I). These data are provided in a single mat-file (MATLAB) for the entire cruise.

These exercises are designed to introduce students to analyzing real-world datasets with Matlab (4 exercises) and ArcGIS (1 exercise). The activities all require students to watch a video and complete a task before class time, during which they would follow the guide to complete several different tasks. These tasks are specific to learning about Meadowbrook Creek, a first order urban stream in the Syracuse, NY area, but could easily be developed for other places and other types of datasets.

Matlab code to simulate equilibrium geometry of selected cross-sections on the Lower American and Sacramento Rivers in California.

Class(matlab) Finalwork

## Overview

Class(matlab) Finalwork is a dataset for object detection tasks - it contains Fabric Defect annotations for 482 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Dataset

This dataset was created by HAYDER .

Released under Database: Open Database, Contents: Database Contents

Contents

There are many MATLAB users in the Hydrology space. These scientists work as researchers and educators in academia and in agencies and institutes. Many of these institutions partner with CUAHSI and use their resources to share data and research. For data analysis and visualization, HydroShare provides integrations with Jupyter notebooks and other tools, via an ‘open with’ affordance.

MATLAB Online provides access to MATLAB from any standard web browser wherever you have internet access. It is ideal for teaching, learning and convenient, lightweight access. With MATLAB Online, you can share your scripts, live scripts, and other MATLAB files with others directly. Additionally, you can publish your scripts and live scripts to the web as PDFs or HTML and share the URL with anyone.

This web application enables the interactive exploration of MATLAB artifacts (such as Live Scripts) through a similar ‘open with’ affordance. When working with Live Scripts, users are presented with the option to open these artifacts in the Live Editor environment.

Scripts and data acquired at the Mirror Lake Research Site, cited by the article submitted to Water Resources Research: Distributed Acoustic Sensing (DAS) as a Distributed Hydraulic Sensor in Fractured Bedrock M. W. Becker(1), T. I. Coleman(2), and C. C. Ciervo(1) 1 California State University, Long Beach, Geology Department, 1250 Bellflower Boulevard, Long Beach, California, 90840, USA. 2 Silixa LLC, 3102 W Broadway St, Suite A, Missoula MT 59808, USA. Corresponding author: Matthew W. Becker (matt.becker@csulb.edu).

File List Duffy_et_al.m Metschfig.m MetschLLfunct.m simanneal.m UnsmoothedMetschDynamics.txt Description Duffy_et_al.m: main Matlab file; calls Metschfig.m, MetschLLfunct.m, and simanneal.m Together, these files provide the code for analyses of the full model given by Eq. 1. Matlab is produced by MathWorks (2007, MATLAB Version 7, MathWorks, Natick, Massachusetts, USA). UnsmoothedMetschDynamics.txt: data file called by Duffy_et_al.m; this file contains data on lake/epidemic identity (column 1), Julian day (column 2), infection prevalence (column 3), D. dentifera density (column 4), susceptible density (column 5), infected density (column 6), weighted temperature (column 7), and epilimnion temperature (column 8).

Routine to reproduce Figs 1D–1F, 2B, 2C, 3, 4 and 5 and S1–S6, panel B in S7, and S8 Figs. (ZIP)

The .zip file contains data from Carreon A. et. al. "Simulating Radiative Heat Transfer...", stored in MATLAB figure files (.fig extension) with file names corresponding to the figures in the paper.

In this appendix

Matlab code to ratio images

Abnormal Event Detection at 150 FPS in MATLAB dataset

Download all the Matlab source code, ANSYS APDL code, and ANSYS Workbench archive project files that accompany the book in one zip file.

File List meta_fact.zip -- zip file containing the following eight MATLAB function files: fact_hedges_d.m -- A Matlab function that returns the individual, overall, and interaction effect sizes for 2 "agents" in a 2 × 2 factorial experiment, where effect size is measured using Hedges' d; the sampling variances of each effect size are also returned. fact_logRR.m -- A Matlab function that returns the individual, overall, and interaction effect sizes for 2 "agents" in a 2 × 2 factorial experiment, where effect size is measured using the log response ratio; the sampling variances and degrees of freedom of each effect size are also returned. J.m -- A Matlab function that computes the small-sample size correction factor J. Q.m -- A Matlab function that computes a weighted sum of squares. mean_effect.m -- A Matlab function that returns a weighted mean effect size and its 95% confidence limits, where the weights include the among-study variance if it is significant at P < 0.05. Best used when effect sizes are measured using Hedges' d; for the log response ratio, use mean_effect_L. mean_effect_L.m -- A Matlab function that returns the weighted mean log response ratio effect size, its SE, and its 95% confidence limits, where the weights include the among-study variance, the significance of which (from a chi-square test on the sum of squares) is returned as well. test_Qb_mixed_2.m -- A Matlab function that tests for a significant between-class sum of squares in a mixed-model meta-analysis comparing two classes. test_Qb_mixed_n.m -- A Matlab function that tests for a significant between-class sum of squares in a mixed-model meta-analysis comparing n classes. Description This supplement includes Matlab code to compute individual, overall, and interactive effects using Hedges’ d and the log response ratio, to calculate weighted mean effect sizes, and to perform mixed-model homogeneity tests. Functions mean_effect, mean_effect_L, test_Qb_mixed_2, and test_Qb_mixed_n all use the function chi2cdf from the Matlab Statistics Toolbox. Additional documentation appears as comments at the beginning of each function file; once the files have been downloaded into a folder in the Matlab path, typing help function_name (e.g., help fact_logRR) at the Matlab command prompt will display the descriptive comments.