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Discover Latest #MATLAB News, Articles and Videos with Contenting
MATLAB News • MATLAB is one of the most popular software for scientists and engineers for data analysis and visualization. It has a wide range of features and capabilities and is regularly updated with new features and bug fixes. • MathWorks, the company behind MATLAB, recently released version R2020a, which includes a range of new features and enhancements including faster performance, better graphics, and more intuitive user interface. • MATLAB Online, a cloud-based version of the software, is now available for educators, students, and researchers. It provides access to MATLAB and its features without the need for installation or hardware. • MATLAB has been used in a variety of disciplines, including engineering, finance, and medical research. It is used to analyze and visualize data, create models, and develop algorithms. • The latest versions of MATLAB have been optimized for machine learning and artificial intelligence, allowing users to create and train neural networks, develop predictive models, and analyze big data. • There are a number of resources available for users to learn MATLAB, including tutorials, webinars, and user groups. Articles • “How to Use MATLAB for Data Analysis and Visualization
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Machine Learning with MATLAB and Amazon SageMaker | AWS Machine Learning Blog
This post is written in collaboration with Brad Duncan, Rachel Johnson and Richard Alcock from MathWorks. MATLAB is a popular programming tool for a wide range of applications, such as data processing, parallel computing, automation, simulation, machine learning, and artificial intelligence. It’s heavily used in many industries such as automotive, aerospace, communication, and manufacturing. In […]
Usage shares of programming languages in economics research | R-bloggers
My shiny app Finding Economics Articles with Data contains meanwhile over 8000 economic articles with replication packages. You can use it here: https://ejd.econ.mathematik.uni-ulm.de Some of the data on articles and file types in the reproduction packages can be downloaded as a zipped SQLite database from my server (see the “About” page in the app for the link). Let us use the database to take a look at the usage shares of different programming languages. The following code extracts our data set by merging two tables from the data base. library(RSQLite) library(dbmisc) library(dplyr) # Open data base using schemas as defined in my dbmisc # package db = dbConnect(RSQLite::SQLite(),"articles.sqlite") articles = dbGet(db,"article") fs = dbGet(db,"files_summary") fs = fs %>% left_join(select(articles, year, journ, id), by="id") head(fs) idfile_typenum_filesmbis_codeis_datayearjournaejapp_10_4_5csv96.49858012018aejapp aejapp_10_4_5do190.169755102018aejapp aejapp_10_4_5dta20719918.231012018aejapp aejpol_10_4_8csv12.110033012018aejpol aejpol_10_4_8do180.118644102018aejpol aejpol_10_4_8gz14294.9673002018aejpol The data frame fs contains for each article and corresponding reproduction packages counts for common data or code files. Let us take a look at the total number of reproduction packages and then compute the shares of reproduction packages that contain at least one file of specific programming languages (I am aware that not everybody would call e.g. Stata a programming language. Just feel free to replace the term by your favorite expression like scripting language or statistical software.): n_art = n_distinct(fs$id) n_art ## [1] 8262 fs %>% group_by(file_type) %>% summarize( count = n(), share=round((count / n_art)*100,1) ) %>% # note that all file extensions are stored in lower case filter(file_type %in% c("do","r","py","jl","m","java","c","cpp","nb","f90","f95", "sas","mod","js","g","gms","ztt")) %>% arrange(desc(share)) file_typecountsharedo591571.6 m202324.5 r8089.8 sas3494.2 py3414.1 mod1982.4 f901882.3 nb1161.4 c1051.3 ztt1041.3 cpp660.8 jl390.5 java330.4 g280.3 gms190.2 js180.2 f9570.1 The most used software is by a far margin Stata, whose .do scripts can be found in 71.6% of reproduction packages. It follows Matlab with 24.5%. The most popular open source language is R with 9.8%. After one more proprietary software SAS, Python then follows as second most most used open source language with 4.1%. If you wonder why the shares add up to more than 100%: some reproduction packages simply use more than one language. Let us take a look at the development over time for Stata, Matlab, R and Python. year_dat = fs %>% filter(year >= 2010) %>% group_by(year) %>% mutate(n_art_year = n_distinct(id)) %>% group_by(year, file_type) %>% summarize( count = n(), share=count / first(n_art_year), # Compute approximate 95% CI of proportion se = sqrt(share*(1-share)/first(n_art_year)), ci_up = share + 1.96*se, ci_low = share - 1.96*se ) %>% filter(file_type %in% c("do","r","py","m")) %>% arrange(year,desc(share)) library(ggplot2) ggplot(year_dat, aes(x=year, y=share,ymin=ci_low, ymax=ci_up, color=file_type)) + facet_wrap(~file_type) + geom_ribbon(fill="#000000", colour = NA, alpha=0.1) + geom_line() + theme_bw() The usage share of Stata and Matlab stays relatively constant over time. Yet, we still see a substantial increase in R usage from 1.4% in 2010 to over 20% in 2023. Also Python usage increases: from 0.4% in 2010 to almost 10% in 2023. So open source software is getting more popular in academic economic research with large growth rates but absolute usage levels that are still substantially below Stata usage. Note that the representation of journals is not balanced across years in our data base. E.g. the first reproduction package from Management Science in our data base is from 2019. To check whether the growth of R usage can also be found within journals, let us look at the development of its usage share within journals: year_journ_dat = fs %>% filter(year >= 2010) %>% group_by(year, journ) %>% mutate(n_art = n_distinct(id)) %>% group_by(year, journ, file_type) %>% summarize( count = n(), share=count / first(n_art), # Compute approximate 95% CI of proportion se = sqrt(share*(1-share)/first(n_art)), ci_up = share + 1.96*se, ci_low = share - 1.96*se ) ggplot(year_journ_dat %>% filter(file_type=="r"), aes(x=year, y=share,ymin=ci_low, ymax=ci_up)) + facet_wrap(~ journ, scales = "free_y") + geom_ribbon(fill="#000000", colour = NA, alpha=0.1) + geom_line() + coord_cartesian(ylim = c(0, 0.4)) + ylab("") + ggtitle("Share of replication packages using R")+ theme_bw() We see a substantial increase in R usage in most journals. Finally, let us take a similar look at the time trends of Stata usage within journals. ggplot(year_journ_dat %>% filter(file_type=="do"), aes(x=year, y=share,ymin=ci_low, ymax=ci_up)) + facet_wrap(~ journ, scales = "free_y") + geom_ribbon(fill="#000000", colour = NA, alpha=0.1) + geom_line() + coord_cartesian(ylim = c(0, 1)) + ylab("") + ggtitle("Share of replication packages using Stata")+ theme_bw()
MATLAB Operators and Symbols: Types and Uses - Dataaspirant
MATLAB - short form for “Matrix Laboratory” is a rich programming language. MATLAB programming finds its use in diverse applications, including numerical calculations, mathematical modeling, and complex simulations. In simple terms, MATLAB operators are character symbols that perform certain actions on their operands. MATLAB is not limited to matrix operations or array operations; in fact, MATLAB
Introducing OptimalTransportNetworks.jl: Optimal Transport Networks in Spatial Equilibrium | R-bloggers
I’m happy to announce the release of OptimalTransportNetworks.jl, a modern Julia translation of the MATLAB OptimalTransportNetworkToolbox implementing the quantitative spatial model and algorithms described in Fajgelbaum, P. D., & Schaal, E. (2...
Scaling MATLAB and Simulink models with Databricks and Mathworks | Databricks Blog
Engineers leverage sophisticated MATLAB algorithms to analyze machine data, enabling them to forecast potential equipment failures with remarkable accuracy. These advanced systems can predict impending battery failures up to two weeks in advance, allowing for proactive maintenance and minimizing costly downtime in vehicle and machinery operations.