This way of doing things provides a very explicit, compact, and simple way to define function inputs, outputs, and parameters and enables automatic generation of user interfaces for each function. Search other functions in folders adjacent to the AdjustImage.java file for examples on different ...
Motopyis very easy to use. First please prepare yourMatlab/Octavefiles, put the script file and the function files with extetion ".m" in a folder, and ensure that yourMatlab/Octavescript can be run without exception. And meetCode Preprocessing.Here's a simple example: ...
A script is run by starting Matlab/Octave, either in the directory containing the examples, or with a change to the "path". Then type the name of the script at the prompt, without the ".m": >> expint The second algorithm bis.m is a function which needs inputs. At the prompt ...
To define unit tests, write a function with the following header: functiontest_suite=test_of_abstry%assignment of 'localfunctions' is necessary in Matlab >= 2016test_functions=localfunctions();catch%no problem; early Matlab versions can use initTestSuite fineendinitTestSuite; ...
%% Execute the demonstration script demo; The "demo.m" file contains below.%% generate synthetic data % set number of dimensions d = 3; % set number of samples n = 300; % generate data data = logistic_regression_data_generator(n, d); %% define problem definitions problem = logistic_...
define_constants; mpc = loadcase('case30'); mpc.bus(2, PD) = 30; runopf(mpc); By default, the results of the simulation are pretty-printed to the screen, but the solution can also be optionally returned in a results struct. The following example shows how simple it is, after running...
define_constants; mpc=loadcase('case30');mpc.bus(2,PD)=30; runopf(mpc); By default, the results of the simulation are pretty-printed to the screen, but the solution can also be optionally returned in aresultsstruct. The following example shows how simple it is, after running a DC OPF...
define_constants; mpc = loadcase('case30'); mpc.bus(2, PD) = 30; runopf(mpc); By default, the results of the simulation are pretty-printed to the screen, but the solution can also be optionally returned in a results struct. The following example shows how simple it is, after running...
define_constants; mpc = loadcase('case30'); mpc.bus(2, PD) = 30; runopf(mpc); By default, the results of the simulation are pretty-printed to the screen, but the solution can also be optionally returned in a results struct. The following example shows how simple it is, after runni...
It supports automatic differentiation which is very helpful in gradient-based machine learning algorithms. 3) Theano: Features: It enables us to define, optimize, and evaluate mathematical expressions including the multi-dimensional arrays which can be difficult in many other libraries. It combines aspe...