matlab julia python cheat sheet

A - 2M> A * 2[,1] [,2] [,3][1,] 2 4 6[2,] 8 10 12[3,] 14 A .+ AM> multivariate normaldistribution given mean and covariance Array{Int64,2}:1 4 7 2 5 8 3 6 9, M> Noteworthy differences from C/C++. c = [a' b']c =   1   4   2   – The cheat sheet for MATLAB, Python NumPy, R, and Julia. A / A, R> While Julia can also be used as an interpreted language with dynamic types from the command line, it aims for high-performance in scientific computing that is superior to the other dynamic programming languages for technical computing thanks to its LLVM-based just-in-time (JIT) compiler. A = matrix(1:6,nrow=2,byrow=T)R> Matrices (or multidimensional arrays) are not only presenting the fundamental elements of many algebraic equations that are used in many popular fields, such as pattern classification, machine learning, data mining, and math and engineering in general. MATLAB (stands for MATrix LABoratory) is the name of an application and language that was developed by MathWorks back in 1984. This is indeed a huge distinction—for some, a dispositive one–but I want to consider the technical merits. A .+ A; J> total_elements=length(A)9J>B=reshape(A,1,total_elements)1x9 7]])P> np.c_[a,b]array([[1, 4],       [2, 0.38959   0.69911   0.15624   0.65637, P> A[,1] [,2] [,3][1,] 6 1 1[2,] 4 -2 5[3,] 2 8 7R> a = matrix(c(1,2,3), nrow=3, byrow=T)R> A = matrix(1:9, nrow=3, byrow=T)R> 8 9# use '.==' for# element-wise checkJ> install.packages('MASS')
R> total_elements = dim(A)[1] * dim(A)[2]R> Matlab-Julia-Python cheat sheet. b = np.array([1, 2, 3])P> b=vec([1 2 3])3-element Array{Int64,1}:123, Reshaping  4   5   6M> 3   4   5   9   7   8   A = np.array([[4, 7], [2, 6]])P> 64 81

R> 18M> 0.70711   0.70711   0.70711eig_val eye(3)3x3 Array{Float64,2}:1.0 0.0 0.00.0 1.0 0.00.0 Alex Rogozhnikov, Log-likelihood benchmark, September 2015. (last updated: June 22, 2018) Libraries such as NumPy and matplotlib provide Python with matrix operations and plotting. A[:,1:2] 3x2 Array{Int64,2}:1 24 57 8, Extracting A / 2, # [102, 126, 150]]), R> 9M> MATLAB. = [1 2 3; 4 5 6; 7 8 9]M> A ^ 23x3 Array{Int64,2}:30 36 4266 81 96102 126 as column vector
R> cov = [2 0; 0 2]cov =   2   0   0 Although similar tools exist for other languages, I found myself to be most productive doing my research and data analyses in IPython notebooks. t(A)[,1] [,2] [,3][1,] 1 4 7[2,] 2 5 8[3,] 3 6 9, J> 1, J> A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])P> 7M> A + 2M> shortcut:# A.reshape(1,-1)P> eye(3)ans =Diagonal Matrix   1   0   A - AP> ],       [16, 25, 36],       [49, 64, 81]])P> http://octave.sourceforge.net/packages.php% pkg install % A=[1 2 3; 4 5 6; 7 8 9]3x3 Array{Int64,2}:1 2 34 5 67 ])P> C = rbind(A,B)R> = [1 2 3; 4 5 6; 7 8 9]M> np.diag(a)array([[1, 0, 0],       [0, 0.7751204[2,] 0.3439412 0.5261893[3,] 0.2273177 0.223438, J> 3   4   5   6   7   8   You signed in with another tab or window. 3

R> t(b %*% A)[,1][1,] 14[2,] 32[3,] 50, J> A = matrix(1:9,nrow=3,byrow=T)

R>     [7]])# 1st 2 columnsP> 1, P> b = np.array([ [1], [2], [3] ])P> -0.20000   0.40000, P> It is the example of high-level scripting and also named as 4th generation language. Develop Machine Learning project with MATLAB, Simulink, … np.ones((3,2))array([[ 1.,  1. 7.5000e-03   1.7500e-03   7.5000e-03   All four languages, MATLAB/Octave, Python, R, and Julia are dynamically typed, have a command line interface for the interpreter, and come with great number of additional and useful libraries to support scientific and technical computing. (2012), “Julia: A fast dynamic language for technical computing”. At its core, this article is about a simple cheat sheet for basic operations on numeric matrices, which can be very useful if you working and experimenting with some of the most popular languages that are used for scientific computing, statistics, and data analysis. a=[1; 2; 3]3-element Array{Int64,1}: 123, P> Octave’s syntax is mostly compatible with MATLAB syntax, so it provides a short learning curve for MATLAB developers who want to use open-source software. A = [3 1; 1 3]A =   3   1   1 A=[6 1 1; 4 -2 5; 2 8 7]3x3 Array{Int64,2}:6 1 14 -2 A + 2P> b=[4 5 6];J> to power n(here: matrix-matrix multiplication with A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])# 1st rowP> 0.7071068 -0.7071068[2,] 0.7071068 0.7071068, J> Btw., if someone is interested, I made a cheat sheet for Python vs. R. vs. Julia vs. Matplab some time ago. 1-D # arrays, R> x2 = matrix(c(2, 2.1, 2, 2.1, 2.2), ncol=5)R> A = [1 2 3; 4 5 6; 7 8 9]% 1st rowM> It is also worth mentioning that MATLAB is the only language in this cheat sheet which is not free and open-sourced. matlab-to-julia Translates MATLAB source code into Julia. 0.0 1.0, M> A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])P> [matlab logo](../Images/matcheat_matlab_logo.png), ! http://sebastianraschka.com/Articles/2014_matlab_vs_numpy.html, !  [-1.37031244, -1.18408792]]), # It provides a high-performance multidimensional array object, and tools for working with these arrays. a = [1 2 3]M> Most people recommend the usage of the NumPy array type over NumPy matrices, since arrays are what most of the NumPy functions return. A[,1] [,2] [,3][1,] 1 2 3[2,] 4 5 9[3,] 7 8 [Julia benchmark](../Images/matcheat_julia_benchmark.png), http://octave.sourceforge.net/packages.php, https://github.com/JuliaStats/Distributions.jl. requires the ‘mass’ package
R> MIT 2007 basic functions Matlab cheat sheet; Statistics and machine learning Matlab cheat sheet; Cheat sheets for Cross Reference between languages. A = np.array([ [1,2,3], [4,5,6] ])P> A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])P> vector
R> np.cov([x1, x2, x3])Array([[ 0.025  ,  0.0075 , A[1:2,][,1] [,2] [,3][1,] 1 2 3[2,] 4 5 6, J> Python For Data Science Cheat Sheet NumPy Basics Learn Python for Data Science Interactively at www.DataCamp.com NumPy DataCamp Learn Python for Data Science Interactively The NumPy library is the core library for scienti c computing in Python. Aarray([[ 6,  1,  1],       diagm(a)3x3 Array{Int64,2}:1 0 00 2 00 0 3, Getting A = [1 2 3; 4 5 6; 7 8 9]% 1st columnM> Numeric matrix manipulation - The cheat sheet for MATLAB, Python NumPy, R, and Julia. columnarJ> rand(3,2)3x2 Array{Float64,2}:0.36882 0.2677250.571856 1 1, J>   [ 0.70710678,  0.70710678]]), R> 0.1303697[6,] 0.8413189 -0.1623758[7,] -1.0495466 A = matrix(c(4,7,2,6), nrow=2, byrow=T)R> ],     See this reference on NumPy and info on matplotlib (links open in new tab). np.zeros((3,2))array([[ 0.,  0. a=[1 2 3];J> A = np.array([[1,2,3],[4,5,6],[7,8,9]])P> np.linalg.det(A)-306.0, R> A[0,:]array([1, 2, 3])# 1st 2 rowsP> B = [7 8 9; 10 11 12]M> t(b)[,1][1,] 1[2,] 2[3,] 3, J> x2=[2. Julia, MATLAB, Numpy Cheat Sheet October 19, 2016 October 19, 2016 I mostly use Python for my data analysis, but I’ve been playing around with Julia some, and I find these kinds of side-by-side comparisons to be quite valuable! A * Aarray([[ 1,  4,  9],       and Edelman, A. det(A)-306.0, M> A = matrix(1:9,nrow=3,byrow=T)

# 1st row

R> A .- A; J>   6M> ],     cov([x1 x2 x3])3x3 Array{Float64,2}:0.025 0.0075 These cheat sheets let you find just the right command for the most common tasks in your workflow: Automated Machine Learning (AutoML): automate difficult and iterative steps of your model building; MATLAB Live Editor: create an executable notebook with live scripts; Importing and Exporting Data: read and write data in many forms 4   5   6, P> size(A)(2,3), M> A * 2ans =    2    4    6  size(A)ans =   2   3, P> A=[1 2 3; 4 5 6; 7 8 9]3x3 Array{Int64,2}:1 2 34 5 67 rand( MvNormal(mean, cov), 5)2x5 Array{Float64,2}:-0.527634 A .+ 2;J> 4   5   6, P> note that numpy doesn't have # explicit “row-vectors”, but 42    66    81    96   a = np.array([1,2,3])P>   5   8   3   6   9, P> = It allows me to easily combine Python code (sometimes optimized by compiling it via the Cython C-Extension or the just-in-time (JIT) Numba compiler if speed is a concern) with different libraries from the Scipy stack including matplotlib for inline data visualization (you can find some of my example benchmarks in this GitHub repository). A ./ A; Matrix pkg load statisticsM> 8],       [3, 6, 9]]), R> A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])P> x3 = np.array([ 0.6, 0.59, 0.58, 0.62, 0.63])P> a = np.array([1,2,3])P> [7, 8, 9]]), R> C = [A; B]    1    2    3  Credits This cheat sheet … Please enter your username or email address to reset your password. Tags: Cheat Sheet, Data Science, Python, R, SQL. A = matrix(1:9, nrow=3, byrow=T)
R> x2 = [2.0000 2.1000 2.0000 2.1000 2.2000]'M> Such multidimensional data structures are also very powerful performance-wise thanks to the concept of automatic vectorization: instead of the individual and sequential processing of operations on scalars in loop-structures, the whole computation can be parallelized in order to make optimal use of modern computer architectures. A = matrix(1:6,nrow=2,byrow=T)R> C = np.concatenate((A, B), axis=0)P> A(1:2,:)ans =   1   2   3   requires the Distributions package from rowsM> Matrices(here: 3x3 matrix to row vector), M> mean=[0., 0. b=[1 2 3] 1x3 Array{Int64,2}:1 2 3# note that this A = matrix(c(3,1,1,3), ncol=2)R> -0.4161082[8,] -1.3236339 0.7755572[9,] 0.2771013 A = matrix(1:9,nrow=3,byrow=T)

R>   0.686977, P> A(A(:,3) == 9,:)ans =   4   5   9   squared), M> A 7% 1st 2 columnsM> A[,1][1] 1 4 7

# 1st 2 columns
R> Python: Cheat sheet (free PDF) ... the mathematical prowess of MatLab, ... Python was named as the number one language that developers would be using if they weren't using Julia, with Python … This MATLAB-to-Julia translator begins to approach the problem starting with MATLAB, which is syntactically close to Julia. 8 9J> det(A)[1] -306, J> A . [ 1.,  1. Hot news about happenings in NIGERIA generally with special focus on political developments and News around the world. b = matrix(c(1,2,3), ncol=3)R> A = matrix(1:9,nrow=3,byrow=T)


# 1st column as row covariances of the means of x1, x2, and x3), M> (eig_vec,eig_val)=eig(a)([2.0,4.0],2x2 A = [4 7; 2 6]A =   4   7   2 b = np.array([4,5,6])P> 0.; 0. Python NumPy is my personal favorite since I am a big fan of the Python programming language. 5   7   8, P> c = [a; b]c =   1   2   3   2. A.shape(2, 3), R> cov=[2. A * A[,1] [,2] [,3][1,] 1 4 9[2,] 16 25 36[3,] 49 0   0   1   0   0   0   np.random.multivariate_normal(mean, cov, 5)Array([[ 11 12, M> B = np.array([[7, 8, 9],[10,11,12]])P> In this sense, GNU Octave has the same philosophical advantages that Python has around code reproducibility and access to the software. r/compsci: Computer Science Theory and Application. 10 11 12, J> But in context of scientific computing, they also come in very handy for managing and storing data in an more organized tabular form. 8    9   10   11   12, P> vector)P>      [10, 11, 12]]), R>  0.00135,  0.00043]]), R> Keep this #Python Cheat Sheet handy when learning to code; Is #BigData The Most Hyped Technology Ever?     [ 0.51615758,  0.64593471],     Python's NumPy library also has a dedicated "matrix" type with a syntax that is a little bit closer to the MATLAB matrix: For example, the " * " operator would perform a matrix-matrix multiplication of NumPy matrices - same operator performs element-wise multiplication on NumPy arrays. A[A[:,2] == 9]array([[4, 5, 9],       This Wikibook is a place to capture information that could be helpful for people interested in migrating code from MATLAB™ to Julia, and also those who are familiar with MATLAB and would like to learn Julia. A[,1] [,2] [,3][1,] 1 2 3[2,] 4 5 6, J> Matrix functions MATLAB/Octave Python NumPy, R, Julia; Related: 50+ Data Science and Machine Learning Cheat Sheets; Guide to Data Science Cheat Sheets; Top 20 R packages by popularity = A %*% A[,1] [,2] [,3][1,] 30 36 42[2,] 66 81 96[3,] 5],       [3, 6]])P> GitHub Gist: instantly share code, notes, and snippets. it in Octave:% download the package from: % (Source: http://julialang.org/benchmarks/, with permission from the copyright holder), If you are interested in downloading this cheat sheet table for your references, you can find it here on GitHub, M> A = [1 2 3; 4 5 6; 7 8 9]A =   1   2   b = matrix(1:3, nrow=3)

R> = [1 2 3; 4 5 6; 7 8 9]M> Explore our solutions on Machine Learning with MATLAB [Cheat sheet] MATLAB basic functions reference. A * bans =   14   32   save filename Saves all variables currently in workspace to file filename.mat. A[,1] [,2][1,] 3 1[2,] 1 3R> A %^% 2[,1] [,2] [,3][1,] 30 66 102[2,] 36 81 126[3,]  [-0.2, 0.4]]), R> Some of the fields that could most benefit from parallelization primarily use programming languages that were not designed with parallel computing in mind. diag(a)ans =Diagonal Matrix   1   0   16 18. Using such a complex environment can prove daunting at first, but this Cheat Sheet can help: Get to know common […] View All Result .   [16, 25, 36],       [49, 64, 81]]), R> Although R has great in-built functions for performing all sorts statistics, as well as a plethora of freely available libraries developed by the large R community, I often hear people complaining about its rather unintuitive syntax. 150, M> A     ~/Desktop/statistics-1.2.3.tar.gzM> 3, P> b = [1 2 3]
M> A = np.array([[1, 2, 3], [4, 5, 6]])P> Comment block %{Comment block %} # Block # comment # following PEP8 #= Comment block =# For loop. Personally, I haven't used Julia that extensively, yet, but there are some exciting benchmarks that look very promising: Bezanson, J., Karpinski, S., Shah, V.B.   [ 0.,  0.  [-2.11810813, 1.45784216],       = 0, variance = 2), % This cheat sheet provides the equivalents for four different languages – MATLAB/Octave, Python and NumPy, R, and Julia. A=[1 2 3; 4 5 6; 7 8 9];#1st columnJ> mvnrnd(mean,cov,5)   2.480150  -0.559906  1   4  -2   5   2   8   Aarray([[1, 2, 3],       [4, 5, 6],   Aarray([[3, 1],       [1, 3]])P> A * A3x3 Array{Int64,2}:30 36 4266 81 96102 126 Matlab Cheat sheet. 0   0   0, P> Sebastian Raschka, Numeric matrix manipulation - The cheat sheet for MATLAB, Python Nympy, R and Julia… np.linalg.matrix_power(A,2)array([[ 30,  36,  A[:,1] 3-element Array{Int64,1}:147#1st 2 mat.or.vec(3, 2) + 1[,1] [,2][1,] 1 1[2,] 1 1[3,] A[,1:2][,1] [,2][1,] 1 2[2,] 4 5[3,] 7 8, J> Python. [ 8, 10, 12],       [14, 16, 18]])P> Combined with interactive notebook interfaces or dynamic report generation engines (MuPAD for MATLAB, IPython Notebook for Python, knitr for R, and IJulia for Julia based on IPython Notebook) data analysis and documentation has never been easier. value 9 in column 3), M> A=[1 2 3; 4 5 6; 7 8 9]3x3 Array{Int64,2}:1 2 34 5 67 A = [6 1 1; 4 -2 5; 2 8 7]A =   6   1   102   126   150, P> A - 2P> A(:,1)ans =   1   4   A + AR> Noteworthy differences from R. Noteworthy differences from Python. =Diagonal Matrix   2   0   0     ~/Desktop/io-2.0.2.tar.gz  % pkg install % A ./ A, P> ]2-element Array{Float64,1}:0.00.0J> MATLAB commands in numerical Python (NumPy) 3 Vidar Bronken Gundersen /mathesaurus.sf.net 2.5 Round off Desc. This Python Cheat Sheet will guide you to interactive plotting and statistical charts with Bokeh. = [1 2 3; 4 5 6; 7 8 9]M> ones(3,2)ans =   1   1   1   0.0, M> mean = [0 0]M> is a 2D array. x3=[0.6 .59 .58 .62 .63]';J> 3   4   5   6   7   8   2, 0],       [0, 0, 3]]), R> A[1,:] 1x3 Array{Int64,2}:1 2 3#1st 2 rowsJ> [back to article] The Matrix Cheatsheet by Sebastian Raschka is licensed under a Creative Commons Attribution 4.0 International License. A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])P>   2M> cov( [x1,x2,x3] )ans =   2.5000e-02   Joy as Nigerian man gets job in America after bagging his master’s degree in this US school (photos). MATLAB, unlike Python and Julia, is neither beer-free nor speech-free. A=[1 2 3; 4 5 6; 7 8 9]; #semicolon suppresses output#1st x3 = matrix(c(0.6, 0.59, 0.58, 0.62, 0.63), ncol=5)

R> # vectors in Julia are columns, M> Noteworthy differences from Matlab.   4, P> b = [4 5 6]M> A = [1 2 3; 4 5 6]A =   1   2   3  Python Bokeh Cheat Sheet is a free additional material for Interactive Data Visualization with Bokeh Course and is a handy one-page reference for those who need an extra push to get started with Bokeh.. One of its strengths is the variety of different and highly optimized "toolboxes" (including very powerful functions for image and other signal processing task), which makes suitable for tackling basically every possible science and engineering task. A .- 2;J> C=[A; B]4x3 Array{Int64,2}:1 2 34 5 67 8 910 A = [1 2 3; 4 5 6; 7 8 9]M> The Mandalorian season 2 episode 7 recap: Mando goes undercover – Bioreports, Virgin Galactic aborts first powered-flight attempt from Spaceport America – Bioreports, Delta police nabs three suspects, PDP chief over communal clash, NPC kicks off census enumeration exercise in Katsina, Katsina compiles register of CBOs, CSOs and NGOS, Police burnt house, abducted two friends in Abia, victim tells panel, 9 great reads from Bioreports this week – Bioreports, HomePod Mini vs. Echo Dot vs. Nest Mini: Picking the best mini smart speaker – Bioreports, Solar eclipse 2020: A history of eclipses and bizarre responses to them – Bioreports, Pfizer-BioNTech Covid-19 Vaccines Are Prepped for Shipment, NFL Ratings Drop Leaves Networks Scrambling to Make Advertisers Whole, AstraZeneca Agrees to Buy Alexion for $39 Billion, The Best-Managed Companies of 2020—and How They Got That Way, Despite his very little beginning, this man succeeds, becomes a lawyer, check out his throwback photo as poor kid, In the spirit of Christmas, kind Nigerian man offers to distribute free chicken to people of these areas, many react, 3 years after starting business, man expands, shares photos of how his company grew, 28-year-old lady who hawked to send herself to school now pursues PhD in US after obtaining 2 master’s degrees, He’s not coming back home! A + AP> It is meant to supplement existing resources, for instance the noteworthy differences from other languagespage from the Julia manual. rows and columns by criteria(here: get rows that have A=[1 2 3; 4 5 6; 7 8 9];J> A .- AM> ]]), R> -0.1882706[2,] 0.8496822 -0.7889329[3,] -0.1564171 0.00175 0.00135 0.00043, J> A=[3 1; 1 3]2x2 Array{Int64,2}:3 11 3J> If you look for further online resources, please ensure that they are for Julia … A=[1 2 3; 4 5 6; 7 8 9];# elementwise operatorJ> A = matrix(1:9, nrow=3, byrow=T)
R> [eig_vec,eig_val] = eig(A)eig_vec =  -0.70711   J> A = [1 2 3; 4 5 6]M> A[:,[0]]array([[1],       [4],   Aarray([[1, 2, 3],       [4, 5, Comparing Numpy and Matlab array summation speed (2) I recently converted a MATLAB script to Python with Numpy, and found that it ran significantly slower.     [102, 126, 150]]), R> A ^ 2[,1] [,2] [,3][1,] 1 4 9[2,] 16 25 36[3,] 49 install.packages('expm')
R> A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])P> [python logo](../Images/matcheat_numpy_logo.png), ! Julia. a[,1][1,] 1[2,] 2[3,] 3, J> b = np.array([1,2,3])P> using DistributionsJ> Comment one line % This is a comment # This is a comment # This is a comment. For a given MA… save filename x y z Saves x, y, and z to file filename.mat. b = b[np.newaxis].T# alternatively # b = A[1,][1] 1 2 3

# 1st 2 rows

R> I expected similar performance, so I'm wondering if I'm doing something wrong. Barray([[1, 2, 3, 4, 5, 6, 7, 8, 9]]), R> library(MASS)
R> t(A[,1])[,1] [,2] [,3][1,] 1 4 7

# 1st column x1 = matrix(c(4, 4.2, 3.9, 4.3, 4.1), ncol=5)R> 7 8 9, J> With its first release in 2012, Julia is by far the youngest of the programming languages mentioned in this article. python for matlab users cheat sheet . A * Aans =    30    36    A .^ 23x3 Array{Int64,2}:1 4 916 25 3649 64 81, Matrix 8 9, P> 0.02500 0.00750 0.00175[2,] 0.00750 0.00700 0.00135[3,] Vice versa, the ".dot()" method is used for element-wise multiplication of NumPy matrices, wheras the equivalent operation would for NumPy arrays would be achieved via the " * "-operator. 42    66    81    96   Matplotlib Cheat Sheet: Plotting in Python This Matplotlib cheat sheet introduces you to the basics that you need to plot your data with Python and includes code samples. A_inv = inv(A)A_inv =   0.60000  -0.70000    4    5    6    7    A[,1] [,2][1,] 4 7[2,] 2 6R> A[0,0]1, R> 5 8 3 6 9, J> 64   81M> ],       [ 0.,  Jean Francois Puget, A Speed Comparison Of C, Julia, Python, Numba, and Cython on LU Factorization, January 2016. However this wiki intends to be more comprehensive, and to be structured in such a way as to make it easy for one to find answers to questions like: 1. ],       [ 1.,  1. Cheat sheet: Using MATLAB & Python together Complete the form to get the free e-Book.   [ 0.01067605,  0.09692771]]), R> A = matrix(c(1,2,3,4,5,6,7,8,9),nrow=3,byrow=T)
# A(:,1:2)ans =   1   2   4   the dimensionof a matrix(here: 2D, rows x cols), M> A[0:2,:]array([[1, 2, 3], [4, 5, 6]]), R> MATLAB Cheat Sheet Basic Commands % Indicates rest of line is commented out.     [7, 8, 9]]), R> 5   3   6M> [python logo](../Images/matcheat_julia_logo.png), ! 1.55432624, -1.17972629],       Like the other languages, which will be covered in this article, it has cross-platform support and is using dynamic types, which allows for a convenient interface, but can also be quite "memory hungry" for computations on large data sets. np.eye(3)array([[ 1.,  0.,  0. 3.055316  -0.985215  -0.990936   1.122528 64 81, J> Aarray([[1, 2, 3],       [4, 5, 9],   =   1   4   7   2   5   8   30-Day Trial . 0 3, J> np.array([1,2,3]).reshape(1,3), R> A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])P> A=[1 2 3; 4 5 6]2x3 Array{Int64,2}:1 2 34 5 6J> 0.6015240.848084 0.858935, M> Alternative data structures: NumPy matrices vs. NumPy arrays. 7   8   9, P> 2.1 2.2]';J> A[ A[:,3] .==9, :] 2x3 Array{Int64,2}:4 5 97 8 9, M> A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])P> https://github.com/JuliaStats/Distributions.jlJ> diag(1:3)[,1] [,2] [,3][1,] 1 0 0[2,] 0 2 0[3,] 0 0.370725 -0.761928 -3.91747 1.47516-0.448821 2.21904 2.24561 1.4900494[10,] -1.3536268 0.2338913, # -2.933047   0.560212   0.098206   requires statistics toolbox package% how to install and load     [ 66,  81,  96],       and eigenvalues, M> rowJ> a = matrix(1:3, ncol=3)R> 0.,  0.,  1. solve(A)[,1] [,2][1,] 0.6 -0.7[2,] -0.2 0.4, J> Let us look at the differences between Python and Matlab: MATLAB is the programming language and it is the part of commercial MATLAB software that is often employed in research and industry. ones(3,2)3x2 Array{Float64,2}:1.0 1.01.0 1.01.0 cov(matrix(c(x1, x2, x3), ncol=3))[,1] [,2] [,3][1,] 0   0   2   0   0   0   J>     [7, 8, 9]])P> = np.array([[6,1,1],[4,-2,5],[2,8,7]])P> M atlab > M atlab vs. other languages > Comparison of Python and MATLAB . A=[1 2 3; 4 5 6; 7 8 9];J> Aarray([[1, 2, 3],       [4, 5, 9],   A=[1 2 3; 4 5 6; 7 8 9];J> x2 = np.array([ 2, 2.1, 2, 2.1, 2.2])P> e.g., A += A instead of # A = A + A, R> equivalent to
# A = matrix(1:9,nrow=3,byrow=T)

R> Cannot retrieve contributors at this time. A=[4 7; 2 6]2x2 Array{Int64,2}:4 72 6J> a Mando and Boba Fett (who's cleaned up his armor) make an excellent team, even if they aren't together much in... Affirm Holdings Inc. is postponing its initial public offering, according to people familiar with the matter, the second company in... - A young boy from the Bono Region of Ghana named Prince Benson Mankotam has succeeded in becoming a lawyer... © 2020 Bioreports - Hot news about happenings in NIGERIA generally with special focus on political developments and News around the world. 9

R> B=[7 8 9; 10 11 12];J> rbind(A,B)[,1] [,2] [,3][1,] 1 2 3[2,] 4 5 6, J>   [ 0.,  1.,  0.     [3]]), R> A = matrix(c(1,2,3,4,5,9,7,8,9),nrow=3,byrow=T)

R> eig_valarray([ 4.,  2. = [1 2 3; 4 5 6; 7 8 9]M> 0.001750.0075 0.007 0.001350.00175 0.00135 0.00043, Calculating eigenvectors If used within matrix definitions it indicates the end of a row. A'3x3 Array{Int64,2}:1 4 72 5 83 6 9, M> A = matrix(1:9, ncol=3)R> 6]])P> x1 = [4.0000 4.2000 3.9000 4.3000 4.1000]’M> Key Differences Between Python and Matlab. A=[1 2 3; 4 5 9; 7 8 9]3x3 Array{Int64,2}:1 2 34 5 97 matlab/Octave Python R Round round(a) around(a) or math.round(a) round(a) A[1,1][1] 1, J> A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])# 1st column Before we jump to the actual cheat sheet, I wanted to give you at least a brief overview of the different languages that we are dealing with. [matlab logo](../Images/matcheat_octave_logo.png), ! A.Tarray([[1, 4, 7],       [2, 5, for i = 1: N % do something end. A = matrix(c(6,1,1,4,-2,5,2,8,7), nrow=3, byrow=T)R> eig_val, eig_vec = np.linalg.eig(A)P> A = np.array([ [1,2,3], [4,5,9], [7,8,9]])P> A[1,1]1, M> 42 96 150, J> Since it makes use of pre-compiled C code for operations on its "ndarray" objects, it is considerably faster than using equivalent approaches in (C)Python. mvrnorm(n=10, mean, cov)[,1] [,2][1,] -0.8407830 A = matrix(1:9, nrow=3, byrow=T)R> 150, M> x3 = [0.60000 0.59000 0.58000 0.62000 0.63000]’M> b = b'b =   1   2   A[,1] [,2] [,3][1,] 1 2 3[2,] 4 5 6[3,] 7 8 9
R> On each far left-hand and the right-hand side of the document, there are task descriptions. The general logic is the same but the syntax is different. mean = np.array([0,0])P> A[:,0]array([1, 4, 7])# 1st column (as column rand(3,2)ans =   0.21977   0.10220   matrix(runif(3*2), ncol=2)[,1] [,2][1,] 0.5675127 [ 4, -2,  5],       [ 2,  8,  matrix(here: 5 random vectors with mean 0, covariance 3, P> eig_vecArray([[ 0.70710678, -0.70710678],         [7, 8, 9]])P> np.random.rand(3,2)array([[ 0.29347865,  0.17920462],   MATLAB/Octave Python Description a(2:end) a[1:] miss the first element a([1:9]) miss the tenth element a(end) a[-1] last element a(end-1:end) a[-2:] last two elements Maximum and minimum MATLAB/Octave Python Description max(a,b) maximum(a,b) pairwise max max([a b]) concatenate((a,b)).max() max of all values in two vectors [v,i] = max(a) v,i = a.max(0),a.argmax(0) a ]]), R> I have used it quite extensively a couple of years ago before I discovered Python as my new favorite language for data analysis. 42],       [ 66,  81,  96],   A = [1 2 3; 4 5 9; 7 8 9]A =   1   2   np.r_[a,b]array([[1, 2, 3],       [4,   8   10   12   14   16   5, 6],        [ 7, 8, 9],   0.692063 0.390495, (Thanks to Keith C. Campbell for providing me with the syntax for the Julia language.). ],       [ A / 2, P> 3   6   9, P> R was also the first language which kindled my fascination for statistics and computing. A . mat.or.vec(3, 2)[,1] [,2][1,] 0 0[2,] 0 0[3,] 0 0, J> People from all … C[,1] [,2] [,3][1,] 1 2 3[2,] 4 5 6[3,] 7 8 9[4,] ; If used at end of command it suppresses output. MATLAB is an incredibly flexible environment that you can use to perform all sorts of math tasks. Jun 19, 2014 by Sebastian Raschka. np.dot(A,b) # or A.dot(b)array([[14], [32], [50]]), R> = [1 2 3; 4 5 6; 7 8 9]M> A*b3-element Array{Int64,1}:143250, M> A B = matrix(A, ncol=total_elements)R> b = matrix(c(1,2,3), ncol=3)R> * This image is a freely usable media under public domain and represents the first eigenfunction of the L-shaped membrane, resembling (but not identical to) MATLAB's logo trademarked by MathWorks Inc. b = [ 1; 2; 3 ]M> Even today, MATLAB is probably (still) the most popular language for numeric computation used for engineering tasks in academia as well as in industry.

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