114 results

A toolbox for Matlab, for solving continuous time trajectory optimization problems

Matlab/Simulink interface. Easily create Simulink models from a Matlab script.

Peak fitting GUI for Diffraction Data

validateInput

version 1.0.0.0

by Jed F.

A simple 'getOpts' type script to validate input parameters.

validateInput started when creating saveppt2. There was a need to take a large number of inputs, in any order, and make them usable to the script. Checking if an input argument has been passed can be

MATLAB toolbox for stochastically rounded elementary arithmetic operations in IEEE 754 floating-point arithmetic.

Like the built-in input function but with the ability to impose constraints and checks on user input

validateattributes(A,classes,attributes).k = VALIDATEDINPUT(prompt,validationFcn) checks the input using the provided validationFcn.If the user input provided at the prompt is invalid, the user will be informed of the reason why and

This function creates two cell arrays, one with training data and the other with testing data.

at random by turning shuffle 'on' or 'off'.The input data must be in column vectors/matrices, if the function believes you have entered a row vector/matrices it will automatically transpose the data.

A Matlab Toolbox to handle mutli-dimensional time series (mdts)

PEMF is predictive (cross-validation type) approach to test surrogate models.

Regression (SVR), and Neural Networks. It can be perceived as a novel sequential and predictive implementation of K-fold cross-validation. PEMF takes as input a model trainer (e.g., RBF-multiquadric or

HRViewer is a matlab software tool designed for in deep viewing and comparing HRV indices computed from different EKG files as well as diffe

TCP/IP server and client for Matlab

Easy to use variable lenght input parameter parser mechanism with validation.

MLS package: To be able to install this library as an MLS package, you have to download it from GitHub as an MLS package: https://github.com/tiborsimon/simple-input-parser/releases/latestEasy to use

MEXGDAL

version 1.0.0.0

by John Evans

Mex file interface for reading various raster image formats, optionally providing georeferencing.

OpenCossan is an open and free toolbox for uncertainty quantification and management.

Simulink Transfer Function with Coefficients Input

These simulink blocks allow to input the transfer function its coefficients for the first, second and third order systems. This is beneficial for time varying parameters systems. The idea is simple,

A toolbox for LAI validation and correction

The TSF toolbox was created based on the LAI validation and correction process in this thesis. Generally, it can preprocess LAI data worldwide by inputting required data (LAI, FparLai_Qc, FparEx_Qc,

Agent based simulation framework to validate planners in automated driving synthetic scenarios. The example demonstrates overtake maneuver.

environment.This demo showcases a Simulink model architecture for creating and simulating synthetic scenarios. It reads as input the scenario file saved using the Driving Scenario Designer (DSD) application. This

leave-one-out crossvalidated linear regression

[PearsonR, PearsonP, SpearmanR, SpearmanP, yhat, R2 ] = BenStuff_CrossValCorr( x,y, [MathMagic], [OmNullModel] ) leave-one-out cross-validated simple linear regression INPUT VARIABLES: x, y:

an implementation of the primal simplex algorithm for computing the minimum cost flow of the graph

A DPX file parser .

This is a one and 2 player chess program.

This code is used to interval uncertainty analysis based on Chebyshev interval inclusion function proposed by Jinglai Wu.

% function [y_lb,y_ub]=CI_reg(fun_name,a,b,k,K,Expansion)% Input% fun_name the called function name% a the lower bound vector of interval input % b the upper bound vector of interval input% k the

FPA clust extracts the optimal cluster centers from training samples. The extracted cluster centers are then validated on test samples.

extracts information in the form of optimal cluster centers from training samples. The extracted cluster centers are then validated on test samples.Data_Clustering_FPA.m is the file which needs to be

Modular components and controls to efficiently develop advanced user interfaces in MATLAB.

Parses, adds defaults and validates the input of functions and methods

classes that are allowed for the value 5. constraints on the value of the input argument ipv is a name/value structure, which holds the complete and validated input data.Documentation:

Generate the code for a CSS/HTML-based data table from an input numeric, string, or cell array with many available formatting options.

element). With 60 user adjustable parameters, and multiple accepted input formats, the table can be customized in virtually any way.All parameters are invoked via name-value pairs and validated.Please read

Time-frequency analysis, multi-synchrosqueezing transform, signal reconstruction.

perfectly invertible. The MSST does not require any a priori information on the signal. The code only needs the input parameters, e.g., signal, window length and iteration number. It is a novel and

Parse varargin cells to option structures.

This funciton parses a content of a cell with {name,value,...} pairs (usually varargin) into an option with fields opt.name = value. It allows one to specify input specification and supply validator

Two files are provided: xgboost_train and xgboost_test which call the xgboost dll from inside Matlab. The example is for classification.

max_num_iters, without internal cross validation.Your own "outside" cross validation procedure can be used, which calls xgboost_train.m. An example of such outside procedure is documented in xgboost_train.mThe

DeepESN2019a: Deep Echo State Network (DeepESN) Toolbox v1.1

Capacity (MC) task.- example_task_MC.m: The file contains an example of the usage of the methods in the Task class, including loading of (input and target) data from .csv files, and hold-out cross-validation

Ultrafast saving of workspace variables as bytestreams. Especially useful for big cells and structs.

Validate software using a test library of functions in a directory.

When developing a software package, it is useful to test changes by periodically running all the functions in a validation library. This function automates the process.VALIDATE DIRNAME runs each

Determine the Peak SNR Needed to achieve a desired False Alarm Rate at a Specific Probability of Detection and pulse duration in white noise

required to achieve the desired Pd in white noise. The output has been validated against the RCA Electro-Optics Handbook published in 1975.Code is Vectorized for speed therefore all inputs can be

This code is used to analyze the hybrid uncertainty with random variables % and interval variables by using PCCI method proposed by Jinglai

bound vector of interval input% k1 the order of CI expansion% K1 the scanning (validation) points of each interval variable, larger value makes the result more accurate% Expansion expansion type of

This program converts a BMP/TIFF/JPG/PNG file to a monochrome image & an embedded C/C++ byte array.

monochrome image for your validation. The code also resizes the input image based on the width & height entered before array generation.Steps:1) Enter the width & height of the output image2) Choose

Crate a valid variable name from a string or cell of strings.

xcorrFD takes two discrete time signals as input and calculates cross-correlation and delay

validated against the MatLAB's xcorr function.For cross-correlation in time domain see xcorrTD. Syntax: [lags,ck,td] = xcorrFD(x,y)Input: x = input signal 1 (must be a Nx1 vector) y = input

Calculates the refractive index of air

fraction and the saturation vapour pressure. All inputs are validated to ensure they are within the valid range.

Gbest PSO, Lbest PSO, RegPSO, GCPSO, MPSO, OPSO, Cauchy mutation, and hybrid combinations

componentsNote: Disabling lengthy histories is recommended except when generating data to be published or verifying proper toolbox functioning, in which case histories should be analyzed.+ Automatic input

Calculation of the Degree-of-support/disproof table for a fuzzy rule, its consistency and coverage (also their new F2, F3 and F4 versions)

extraction and validation - consistency and coverage revisited. Information Sciences, 412-413, 154–173. http://doi.org/10.1016/j.ins.2017.05.042, table 6Four different consistency and coverage measures for the

Determines visibility times from observer location for specified period. LLA and KML output.

Main function: OrbProp.m NOTE: 1. Output generated by this program has not been validated, other than ensuring close agreement with AGI STK results. Prior to MAR 2018, expired Earth Orbit Parameters

Eigensystem Realization Algoirthm with Mode Condensation Algorithm

noise).function [Result] = ERA_CONDENSED(Y,fs,ncols,nrows,inputs,initialcut,maxcut,shift,EMAC_option,LimCMI,LimMAC,LimFreq,Plot_option)Inputs :Y: Free vibration output data in a form of Y=[Y1 Y2 ... Y_Ndata] Yi is

Returns impulse response functions (IRF) using Natural Excitation technique with time domain method and frequency domain method

NExTT(data,refch,maxlags)Inputs :data: An array that contains response data.its dimensions are (nch,Ndata) where nch is the number of channels. Ndata is the total length of the data refch: A vecor of reference channels .its

xcorrTD takes two discrete time signals as input and calculates cross-correlation and delay

domain. The results of xcorrTD has been validated against the MatLAB's xcorr function. For cross-correlation in frequency domain see xcorrFD. Syntax: [lags,ck,cc,td] = xcorrTD(x,y) Input: x

A back-of-envelop (K14 education) model of the Microsat-R debris is presented with Gabbard Diagram of TLE and model orbital properties.

the statistically simulated debris and when they will de-orbit. This is a 2D model. This model is offered for educational purposes. There is no truth to validate the model apart from the deltaV and

Natural Excitation Technique (NExT) with Eigensystem Realization Algorithm (ERA)

NExT---------------------------------------------------------[Result] = NExTTERA(data,refch,maxlags,fs,ncols,nrows,cut,shift,EMAC_option)Inputs :data: An array that contains response data.its dimensions are (nch,Ndata) where nch is the number of channels. Ndata is the

DeepESN2019a: Deep Echo State Network (DeepESN) Toolbox v1.1

Memory Capacity (MC) task. - example_task_MC.m: The file contains an example of the usage of the methods in the Task class, including loading of (input and target) data from .csv files, and hold-out

Natural Excitation Technique with Eigensystem Realization Algoirthm including Mode Condensation Algorithm

Condensation---------------------------------------------------------------------- [Result] = NExTTERA_CONDENSED(data,refch,maxlags,fs,ncols,nrows,initialcut,maxcut,shift,EMAC_option,LimCMI,LimMAC,LimFreq,Plot_option)Inputs :data: An array that contains response data.its

dirPlus will recursively collect a list of files/folders from a folder tree.

setting of the 'RecurseInvalid' argument determines if invalid subdirectories are still recursed down.'ValidateDirFcn' - A handle to a function that takes as input a structure of the form returned by the

Manually mark and count objects in an image.

Sometimes you need to be able to manually count objects or regions in an image. (You may want to validate an automatic count, for instance.) This tool facilitates that task by allowing you to mark

This function generates the small-signal model of a 2 discrete state DC-DC converter.

state variable (X), input parameter (U) and output parameter (Y) vectors.The validation file attached shows three examples for a buck, boost and buck-boost converter with parasitic elements where this

Files used in webinar Data-driven Control conducted on 12 July 2012

and implement a PID controller when a plant model is not available. Through a DC motor control example, you will learn how to:- Apply input signals (voltage) to the motor and collect output (angular

GAS feedback control for many differential-drive robots when each robot receives same control inputs

collection of differential-drive robots under the constraint that every robot receives exactly the same control inputs. We begin by assuming that each robot has a slightly different wheel size, which scales

A basic GA with a real-time plotting of evaluation funtion inputs and outputs

I'm pretty sure I have this working correctly. Please fiddle with the GA inputs to tweak the speed of convergence, etc. And let me know if it runs correctly.I'm really new to MATLAB (and all

Synthetic data clustering using Bat Algorithm

optimal cluster centers from training samples. The extracted cluster centers are then validated on test samples. Main.m is the file which needs to be executed. This loads the dataset and extract the cluster

ArgUtils

version 1.5.0.0

by Nezar

Argument parsing utilities

Useful for assigning defaults to functions that use varargin or structs of input parameters (alternative to inputParser).See examples (and latest updates) on the github

Computes pseudo R-squared goodness-of-fit measure for Poisson regression models from real and estimated data

the relative reduction in deviance due to the added to the model covariates [5]. Pseudo R-squared measure was used as goodness-of-fit measure when predicting spike counts in [4,5,6,8].INPUT - realData -

circlem

version 1.2.0.0

by Chad Greene

Draw circles on maps

may be any combination of scalars, vectors, or MxN array. All non- scalar inputs must have matching dimensions.circlem(...,'units',LengthUnit) specifies a length unit of input radius. See

DSD calculates definitive screening design conditions for a given number of experimental factors

version of the code has been translated from, and validated against, the equivalent JMP10 addin.Reference:Jones, B and Nachtsheim, C. (2013), "Definitive Screening Designs with Added Two-Level Categorical

A MATLAB program that trains several neural networks, and enables users to pick the best

contained in files named Inputs1.txt and Targets1.txt respectively.The program randomly splits the supplied data into 3 portions: 70% for training, 15% for validation, and 15% for testing. The user has the

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