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
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.
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
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
version 184.108.40.206Ahmed ElTahan
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
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
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
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
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
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
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
version 220.127.116.11Jan Stoklasa
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
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
version 18.104.22.168Aaron T. Becker's Robot Swarm Lab
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
version 22.214.171.124Dean Kayton
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
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 . Pseudo R-squared measure was used as goodness-of-fit measure when predicting spike counts in [4,5,6,8].INPUT - realData -
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