Early versions of MATLAB were simple matrix calculators with 71 pre-built functions. The software was disclosed to the public for the first time in February 1979 at the Naval Postgraduate School in California. The first early version of MATLAB was completed in the late 1970s. There were no programs, no toolboxes, no graphics. In the beginning (before version 1.0) MATLAB 'was not a programming language it was a simple interactive matrix calculator. This was followed by Fortran code for linear equations in 1971. He developed MATLAB's initial linear algebra programming in 1967 with his one-time thesis advisor, George Forsythe. Moler became a math professor at the University of New Mexico and started developing MATLAB for his students as a hobby. The idea for MATLAB was based on his 1960s PhD thesis. MATLAB was invented by mathematician and computer programmer Cleve Moler. As of 2017, more than 5000 global colleges and universities use MATLAB to support instruction and research. They come from various backgrounds of engineering, science, and economics. An additional package, Simulink, adds graphical multi-domain simulation and model-based design for dynamic and embedded systems.Īs of 2020, MATLAB has more than 4 million users worldwide. MATLAB allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages.Īlthough MATLAB is intended primarily for numeric computing, an optional toolbox uses the MuPAD symbolic engine allowing access to symbolic computing abilities. MATLAB (an abbreviation of 'MATrix LABoratory' ) is a proprietary multi-paradigm programming language and numeric computing environment developed by MathWorks. Here is a much better (official) NumPy for Matlab Users link - I'm afraid the mathesaurus one is quite out of date./ Febru 7 months ago ( February 22, 2023) The numpy equivalent of repmat(a, m, n) is tile(a, (m, n)). This works with multiple dimensions and gives a similar result to matlab. (Numpy gives a 3d output array as you would expect - matlab for some reason gives 2d output - but the content is the same). Note that some of the reasons you'd need to use MATLAB's repmat are taken care of by NumPy's broadcasting mechanism, which allows you to do various types of math with arrays of similar shape. So if you had, say, a 1600x1400x3 array representing a 3-color image, you could (elementwise) multiply it by to reduce the amount of green and blue at each pixel. This is how I understood it out of a bit of fiddling around.

Happy to be corrected and hope this helps. I could see no difference between Matlab and Python while asking to manipulate the input matrix along the dimensions the matrix already has. Thus the two commands repmat(M,m,n) % matlabĪre really equivalent for a matrix of rank 2 (two dimensions). The matters goes counter-intuitive when you ask for repetition/tiling over more dimensions than the input matrix has. Going back to the matrix M of rank two and shape 2x3, it is sufficient to look at what happens to the size/shape of the output matrix. Say the sequence for manipulation is now 1,1,2. It has copied the first two dimensions (rows and columns) of the input matrix and has repeated that once into a new third dimension (copied twice, that is). True to the naming repmat for repeat matrix. It has applied a different procedure since, I presume, the sequence (1,1,2) is read differently than in Matlab. The number of copies in the direction of columns, rows and out-of-plane dimension are being read from right to left. The resulting object has a different shape from Matlab.

Matlab repmat