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github | MS-BASIS/Pattern-Recognition-Toolbox-master | rotatefactorsDR.m | .m | Pattern-Recognition-Toolbox-master/uiPatternRecognitionV2/DRMethods/DimensionalityReduction/rotatefactorsDR.m | 15,355 | utf_8 | c607903d0f9869931a6cd9190c6cc511 | function [B,T] = rotatefactorsDR(A, varargin)
%ROTATEFACTORS Rotation of FA or PCA loadings.
% B = ROTATEFACTORS(A) rotates the D-by-M loadings matrix A to maximize
% the varimax criterion, and returns the result in B. Rows of A and B
% correspond to variables and columns correspond to factors, e.g., the
% (i,... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | getOutliersDR.m | .m | Pattern-Recognition-Toolbox-master/uiPatternRecognitionV2/DRMethods/DimensionalityReduction/Unsupervised/OutlierDetection/getOutliersDR.m | 3,600 | utf_8 | 3903e0b40125a27c2b596a56dec0f7c0 | function output = getOutliersDR(DRdata,alpha,cutoff)
% The function calculates the threshold values for the outlier detection in
% PCA analysis adapted from the LIMMA package
% Author: Kirill Veselkov, Imperial College London 2009
% DRdata.scores - scores
% DRdata.loadings - loadings
% input.L - eigenv... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | mcdcov.m | .m | Pattern-Recognition-Toolbox-master/uiPatternRecognitionV2/DRMethods/DimensionalityReduction/Unsupervised/RobPCA/mcdcov.m | 63,403 | utf_8 | 37fb0ca6ea70f452f3112213b67c9916 | function [rew,raw]=mcdcov(x,varargin)
%MCDCOV computes the MCD estimator of a multivariate data set. This
% estimator is given by the subset of h observations with smallest covariance
% determinant. The MCD location estimate is then the mean of those h points,
% and the MCD scatter estimate is their covariance matri... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | twopoints.m | .m | Pattern-Recognition-Toolbox-master/uiPatternRecognitionV2/DRMethods/DimensionalityReduction/Unsupervised/RobPCA/twopoints.m | 3,050 | utf_8 | b3fead7da4610f26894b6889ffdaa05f | function result = twopoints(data,ndirect,seed)
%TWOPOINTS calculates ndirect directions through two randomly chosen data points from data.
% If ndirect is larger than the number of all possible directions, then all
% these combinations are considered.
%
% Required input arguments:
% data : Data matrix
% ndi... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | RPCA.m | .m | Pattern-Recognition-Toolbox-master/uiPatternRecognitionV2/DRMethods/DimensionalityReduction/Unsupervised/RobPCA/RPCA.m | 5,771 | utf_8 | c1d2ddfb96bc2126b285a135093a41f0 | function resRobPCA = RPCA(X,varargin)
%% RPCA performs robust principal component analysis of data matrix X [samples x variables]
% Input:
% X - data matrix [samples by variables]
% 'nPCs' - the number of PCs
% 'mcdPCs' - the number of PCs for the minimum covariance
... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | projMCD.m | .m | Pattern-Recognition-Toolbox-master/uiPatternRecognitionV2/DRMethods/DimensionalityReduction/Unsupervised/RobPCA/projMCD.m | 3,280 | utf_8 | f83274111afb3df3b3e88f854ddb3305 | function resMCD = projMCD(T,eigvals,nPCs,nNonOutls,niter,rot,P1,P2,meanX,cutoff)
% this function performs the last part of ROBPCA when nPCs is determined.
% input :
% T : the projected data
% eigvals : the matrix of the eigenvalues
% nPCs : the number of components
% nNonOutls : lower bound for... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | doRobPCA.m | .m | Pattern-Recognition-Toolbox-master/uiPatternRecognitionV2/DRMethods/DimensionalityReduction/Unsupervised/RobPCA/doRobPCA.m | 1,911 | utf_8 | 37a81859ece9b13594a7b7e45e84d7f4 | function DRdata = doRobPCA(DRdata,options)
%% doRobPCA sets parameters for performing robust principal component analysis of data matrix X
% Input: DRdata - various parameters of DR toolbox objects
% options - default parameters for robust PCA (number of PCs and
% method for computin... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | setRandDirections.m | .m | Pattern-Recognition-Toolbox-master/uiPatternRecognitionV2/DRMethods/DimensionalityReduction/Unsupervised/RobPCA/setRandDirections.m | 1,162 | utf_8 | 50f4a4de0027e778757ff8a63bf46e1e | function randDirMatrix = setRandDirections(X,nRandDirect)
%% setRandDir sets n random directions by n-time selecting two random
%% data points out of a sampleset
% Input: X - dataset
% nRandDirect - n random directions
%% Author: Kirill A. Veselkov, Imperial College London, 2011.
nSmpls =... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | setUnivDirs.m | .m | Pattern-Recognition-Toolbox-master/uiPatternRecognitionV2/DRMethods/DimensionalityReduction/Unsupervised/RobPCA/setUnivDirs.m | 1,522 | utf_8 | 24fb45ee7013408d732f066fa36d71eb | function univDirs = setUnivDirs(X,nRandDirect)
%% setRandDir sets n univariate directions at Random
% Input: X - dataset
% nRandDirect - n random univariate directions
%% Author: Kirill A. Veselkov, Imperial College London, 2011.
%nTotDirect = nSmpls*(nSmpls-1)/2; % an overall number of direc... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | PCA.m | .m | Pattern-Recognition-Toolbox-master/uiPatternRecognitionV2/DRMethods/DimensionalityReduction/Unsupervised/PCA/PCA.m | 1,708 | utf_8 | 7fa8734d3690401a4200131c2fcf4d0d | function DRdata = PCA(DRdata,options)
%% PCA(X) performs principal components analysis on the data matrix via
%% SVD or NIPALS algorithms
% Input: DRdata - various parameters of DR toolbox objects
% options - default parameters for PCA (number of PCs and method for computing PCs)
%% Author: Kir... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | uiBiCrossValidation.m | .m | Pattern-Recognition-Toolbox-master/uiPatternRecognitionV2/DRMethods/DimensionalityReduction/Unsupervised/BiCrossValidation/uiBiCrossValidation.m | 3,094 | utf_8 | 91e457f02f5d52423ec0bce8e11f1bfd | function DRdata = uiBiCrossValidation(DRdata)
%% bicrossvalidation performs rxs cross validation be leaving ...
%% r (rows) and s (columnts) simultaneously
%% Author: Kirill A. Veselkov, Imperial College London, 2011
%% Cross Validation Initiation
[maxPCs,rxsholdouts] = getVarArgin();
X = DRdata.X ... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | corrcoeffs.m | .m | Pattern-Recognition-Toolbox-master/uiPatternRecognitionV2/DRMethods/DimensionalityReduction/STOCSY/corrcoeffs.m | 2,474 | utf_8 | 97da53f40f539de7a570ddeba2b11d98 | function [CCXY,pXY]=corrcoeffs(X,Y,CCmetric)
%% Description: This function calculates pair-wise correlation
%% coefficients between all variables of X matrix and a single variable of interest
% of Y matrix
% Input: X - input matrix of
% Y - a column vector
% CCmetric defines either 'spearman' or 'pear... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | knnclassify.m | .m | Pattern-Recognition-Toolbox-master/uiPatternRecognitionV2/DRMethods/DimensionalityReduction/Supervised/Classifiers/knnclassify.m | 11,879 | utf_8 | 576759bc574dbdf724a02fb7d7f7e4e8 | function outClass = knnclassify(sample, TRAIN, group, K, distance,rule)
%KNNCLASSIFY classifies data using the nearest-neighbor method
%
% CLASS = KNNCLASSIFY(SAMPLE,TRAINING,GROUP) classifies each row of the
% data in SAMPLE into one of the groups in TRAINING using the nearest-
% neighbor method. SAMPLE and TRAI... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | classify.m | .m | Pattern-Recognition-Toolbox-master/uiPatternRecognitionV2/DRMethods/DimensionalityReduction/Supervised/Classifiers/classify.m | 20,271 | utf_8 | 4407067f24b57f57c167e183417a0fb5 | function [outclass, err, posterior, logp, coeffs] = classify(sample, training, group, type, prior)
%CLASSIFY Discriminant analysis.
% CLASS = CLASSIFY(SAMPLE,TRAINING,GROUP) classifies each row of the data
% in SAMPLE into one of the groups in TRAINING. SAMPLE and TRAINING must
% be matrices with the same number... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | kmeans.m | .m | Pattern-Recognition-Toolbox-master/uiPatternRecognitionV2/DRMethods/DimensionalityReduction/Supervised/Classifiers/kmeans.m | 28,475 | utf_8 | 1e482290e6bd016d8067860b683caa87 | function [idx, C, sumD, D] = kmeans(X, k, varargin)
%KMEANS K-means clustering.
% IDX = KMEANS(X, K) partitions the points in the N-by-P data matrix
% X into K clusters. This partition minimizes the sum, over all
% clusters, of the within-cluster sums of point-to-cluster-centroid
% distances. Rows of X corres... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | doMMCLDA.m | .m | Pattern-Recognition-Toolbox-master/uiPatternRecognitionV2/DRMethods/DimensionalityReduction/Supervised/mmclda/doMMCLDA.m | 1,700 | utf_8 | 8c0ed8e2f913c9d998826627e16839e2 | function [DRdata,validity] = doMMCLDA(DRdata,options)
%% PCA(X) performs Linear discriminant vectors exctraction based on
%% maximum margin criterion
% uncorrelated features - recursiveMmcLDA.m
% orthogonal loadings - mmclda.m
% Input: DRdata - various parameters of DR toolbox objects
% ... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | MMCtrain.m | .m | Pattern-Recognition-Toolbox-master/uiPatternRecognitionV2/DRMethods/DimensionalityReduction/Supervised/mmclda/MMCtrain.m | 2,124 | utf_8 | f0eefdd06e3c8b7b6158c2dba77d5552 | function [W,B,meanX] = MMCtrain(X,y)
%% performs Linear discriminant vectors exctraction based on
%% maximum margin criterion
% uncorrelated features - recursiveMmcLDA.m
% orthogonal loadings - mmclda.m
% Input: hmainfig - figure object of ms imaging toolboox
%% Author: Kirill Veselkov, Imperial C... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | uiCrossValidation.m | .m | Pattern-Recognition-Toolbox-master/uiPatternRecognitionV2/DRMethods/DimensionalityReduction/Supervised/CrossValidation/uiCrossValidation.m | 7,171 | utf_8 | 5f532eedc2235d546701f93ef00dbf42 | function DRdata = uiCrossValidation(DRdata)
%% uiCrossValidation performs cross validation for supervised learning
% Input: DRdata - properties and parameter values
% of dimension reduction toolbox objects
%% Author: Kirill A. Veselkov, Imperial College 2011.
if DRdata.nVrbls > (DRdata.nSm... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | doLDA.m | .m | Pattern-Recognition-Toolbox-master/uiPatternRecognitionV2/DRMethods/DimensionalityReduction/Supervised/LDA/doLDA.m | 1,301 | utf_8 | ff6ab735bb12f749e43207dc7891ad90 | function [DRdata,validity] = doLDA(DRdata,options)
%% LDA(X) performs Linear discriminant analysis
% Input: DRdata - various parameters of DR toolbox objects
% options - default parameters
%% Author: Ottmar Golf, Kirill Veselkov, Imperial College 2014
if nargin < 2
options = [];
end
if DRd... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | LDA.m | .m | Pattern-Recognition-Toolbox-master/uiPatternRecognitionV2/DRMethods/DimensionalityReduction/Supervised/LDA/LDA.m | 2,294 | utf_8 | 5e322b2dd6bbaf2c13b3ba85a03d0f12 | % LDA - MATLAB subroutine to perform linear discriminant analysis
% by Will Dwinnell and Deniz Sevis
%
% Use:
% W = LDA(Input,Target,Priors)
%
% W = discovered linear coefficients (first column is the constants)
% Input = predictor data (variables in columns, observations in rows)
% Target = target variable (c... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | doPCALDA.m | .m | Pattern-Recognition-Toolbox-master/uiPatternRecognitionV2/DRMethods/DimensionalityReduction/Supervised/pcalda/doPCALDA.m | 1,541 | utf_8 | 2c0452a51979da1249453b0f93345c5a | function DRdata = doPCALDA(DRdata,options)
%% PCA(X) performs Linear discriminant vectors exctraction based on
%% maximum margin criterion
% uncorrelated features - recursiveMmcLDA.m
% orthogonal loadings - mmclda.m
% Input: DRdata - various parameters of DR toolbox objects
% opti... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | doSVM.m | .m | Pattern-Recognition-Toolbox-master/uiPatternRecognitionV2/DRMethods/DimensionalityReduction/Supervised/svm/doSVM.m | 2,286 | utf_8 | 43ffc56bc250dd9e11135bd86b6b6941 | function DRdata = doSVM(DRdata,options)
%% SVM: Support Vector Machine analysis using LIBSVM
%
% Ottmar Golf, Kirill Veslekov 2014
if nargin < 2
options = [];
end
if DRdata.options.setparam==1
DRdata.options = getVarArgin(options,length(unique(DRdata.groupdata)));
end
%% Mean centering
DRdata.Xorig = DRdata.X;... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | doPLS.m | .m | Pattern-Recognition-Toolbox-master/uiPatternRecognitionV2/DRMethods/DimensionalityReduction/Supervised/plsda/doPLS.m | 1,628 | utf_8 | 8768a47bc1b4e30b41622db161a68ac2 | function DRdata = doPLS(DRdata,options)
%% doPLS computes partial least squares using simpls or nipals algorithm
% Input: DRdata - various parameters of DR toolbox objects
% options - default parameters
%% Author: Kirill Veselkov, Imperial College 2012
if nargin < 2
options = [];
end
if DR... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | pls2demo.m | .m | Pattern-Recognition-Toolbox-master/uiPatternRecognitionV2/DRMethods/DimensionalityReduction/Supervised/plsda/pls2demo.m | 1,746 | utf_8 | 0a3df35add2200c7e6e4d3d9088245da | function [W, T, U, Q, P, B, SS] = plsr(x, y, a)
% PLS: calculates a PLS component.
% The output matrices are W, T, U, Q and P.
% B contains the regression coefficients and SS the sums of
% squares for the residuals.
% a is the numbers of components.
%
% For a components: use all commands to end.
for i=1:a
% Calcul... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | onewayanovaBR.m | .m | Pattern-Recognition-Toolbox-master/uiPatternRecognitionV2/DRMethods/BiomarkerRecovery/anovaDR/onewayanovaBR.m | 6,032 | utf_8 | e27baa08ce228af0981b3e4b06479fcb | function [pvalues,stats] = onewayanovaBR(X,y,type)
%% onewayanovaPWCA performs one-way ANOVA for comparing the means of two or more
%% groups of data.
%% Input:
% X - data matrix [observations x variables]
% y - class labels [observations x 1]
% type - {'anova','Welch'};
% W... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | doEbayesAnovaBR.m | .m | Pattern-Recognition-Toolbox-master/uiPatternRecognitionV2/DRMethods/BiomarkerRecovery/anovaDR/doEbayesAnovaBR.m | 2,399 | utf_8 | f18c5eb1769c9dfb22afaa522e9194cc | function DRdata = doEbayesAnovaBR(DRdata,options)
%% doEbayesAnovaBR performs univariate comparative statistical analysis using
%% the moderated Ebayes tests in R
[status,msg] = openR;
if status ~= 1
disp(['Problem connecting to R: ' msg]);
return;
end
if nargin < 2
options = [];
end
DRdata.options = get... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | anova1.m | .m | Pattern-Recognition-Toolbox-master/uiPatternRecognitionV2/DRMethods/BiomarkerRecovery/anovaDR/anova1.m | 12,480 | utf_8 | 88d2a089d95071cf9d8c6fdd3598d78a | function [p,anovatab,stats] = anova1(x,group,displayopt,extra)
%ANOVA1 One-way analysis of variance (ANOVA).
% ANOVA1 performs a one-way ANOVA for comparing the means of two or more
% groups of data. It returns the p-value for the null hypothesis that the
% means of the groups are equal.
%
% P = ANOVA1(X,GROUP... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | doanovaBR.m | .m | Pattern-Recognition-Toolbox-master/uiPatternRecognitionV2/DRMethods/BiomarkerRecovery/anovaDR/doanovaBR.m | 1,636 | utf_8 | cea58023ad63da6018c0e7e74f170ad9 | function DRdata = doanovaBR(DRdata,options)
%% doPLS computes partial least squares using simpls or nipals algorithm
% Input: DRdata - various parameters of DR toolbox objects
% options - default parameters
%% Author: Kirill Veselkov, Imperial College 2012
if nargin < 2
options = [];
end
%... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | uiDefineRegnsForLocalPC.m | .m | Pattern-Recognition-Toolbox-master/uiPatternRecognitionV2/DRDrawGUI/uiDRMainFigCallbacks/uiDefineRegnsForLocalPC.m | 1,954 | utf_8 | bc76111aee568f4907379f0266083175 | function uiDefineRegnsForLocalPC(hMainFigure,eventdata)
%% uiDefineRegnsForLocalPC sets regions for local PCA
% Input:
% hMainFigre - the figure handle
%% Author: Kirill A. Veselkov, Imperial College London 2011
DRdata = guidata(hMainFigure);
[x,y] = ginput(2);
currbndrs = sort(x); %% ... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | setupSubplotsDR.m | .m | Pattern-Recognition-Toolbox-master/uiPatternRecognitionV2/DRDrawGUI/uiDraw/setupSubplotsDR.m | 6,451 | utf_8 | af529b7fab2270031bc646a328757d3e | function DRdata = setupSubplotsDR(DRdata)
%% setSubplotsDR draws sub-plots for dimension reduction toolbox
% Input: DRdata - metadata for dimensionality reduction toolbox
% (see variableDescription.txt)
%% Author: Kirill Veselkov, Imperial College London, 2011
%% Subplot for DR scores
set(DRdat... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | setupDRdefaults.m | .m | Pattern-Recognition-Toolbox-master/uiPatternRecognitionV2/DRDrawGUI/uiDraw/setupDRdefaults.m | 10,047 | utf_8 | d9b0d1022490c277951872c884c8d1ff | function DRdata = setupDRdefaults(ppm,Sp,X,groups)
%% setupDRdefaults sets various default parameters for dimension reduction toolbox
% Input: ppm - chemical shift scale
% Sp - spectra of biological samples
% X - variance stabilized spectra of biological samples
% groups... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | setupDRtoolbars.m | .m | Pattern-Recognition-Toolbox-master/uiPatternRecognitionV2/DRDrawGUI/uiDraw/setupDRtoolbars.m | 7,382 | utf_8 | df60bd3b69d4716ae9199d1fe49f66de | function DRdata = setupDRtoolbars(DRdata)
%% setupDRtoolbars configures push and toggle buttons for visualization
%% of results obtained from various dimension reduction methods
%% Input: DRdata - GUI parameters
%% Author: Kirill Veselkov, Imperial College 2011
%DRdata = customizeMainTB(DRdata);
setSTOCSYmenus()... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | setSTOCSYmenus.m | .m | Pattern-Recognition-Toolbox-master/uiPatternRecognitionV2/DRDrawGUI/uiDraw/setSTOCSYmenus.m | 999 | utf_8 | 559006416b1812cbb080f40effac338c | function setSTOCSYmenus()
% setSTOCSYmenus: set up of STOCSY menus
mainmenu = uimenu('Label','STOCSY');
submenu1 = uimenu(mainmenu,'Label','Choose Correlation Coefficient');
stocsy.submenu2 = uimenu(mainmenu,'Label','Set Stat. Significance Threshold','Callback',{@pThr});
uimenu(submenu1,'Label','Spearman','Callback',{... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | defTableEditCallBackDR.m | .m | Pattern-Recognition-Toolbox-master/uiPatternRecognitionV2/DRDrawGUI/uiToolbarCallbacks/defTableEditCallBackDR.m | 8,236 | utf_8 | 56d3917671de671f78bee58c6a7a878d | function defTableEditCallBackDR(htable,eventdata,jtable1,jtable2)
%% defTableEditCallBackDR updates properties of DR objects
% Input: hTable - a table handle
%% Author: Kirill A. Veselkov, Imperial College 2011.
if nargin==3
if ~isempty(strfind(get(htable,'Class'),'DefaultTableModel'))
% jtable = hTabl... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | uiDRchangeFigDefaults.m | .m | Pattern-Recognition-Toolbox-master/uiPatternRecognitionV2/DRDrawGUI/uiToolbarCallbacks/uiDRchangeFigDefaults.m | 7,534 | utf_8 | 6fc9066ddd6b6b111365f98287c1ee62 | function DRdata = uiDRchangeFigDefaults(hMainFigDR,eventdata)
%% uiDRchangeFigDefaults chages properties of DR toolbox objects
% Input: hMainFigDR - figure handle
% DRdata - properties and parameter values
% of dimension reduction toolbox objects
%% Author: Kirill A. Veselkov, Imper... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | getOutlierMap.m | .m | Pattern-Recognition-Toolbox-master/uiPatternRecognitionV2/DRDrawGUI/uiToolbarCallbacks/getOutlierMap.m | 2,742 | utf_8 | 8cfb59c3c9125e33e3437645c4be58a4 | function output = getOutlierMap(hMainFigDR,ignore)
%% The function calculates the threshold values for the outlier detection in
%% PCA analysis adapted from the LIMMA package
%% Author: Kirill Veselkov, Imperial College London 2009
DRdata = guidata(hMainFigDR);
[alpha,cutoff] = getVarArgin();
set(DRdata.h.figure,'Cu... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | CVMenuSupervised.m | .m | Pattern-Recognition-Toolbox-master/uiPatternRecognitionV2/DRDrawGUI/uiToolbarCallbacks/CVMenuSupervised.m | 5,907 | utf_8 | ebe1d953e00929951d8f0d95b5f1c683 | function varargout = CVMenuSupervised(varargin)
%% creates menu for cross validation of supervised dimension reduction
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @CVMenuSupervised_OpeningFcn, ...
... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | uiGetOutlierMap.m | .m | Pattern-Recognition-Toolbox-master/uiPatternRecognitionV2/DRDrawGUI/uiToolbarCallbacks/uiGetOutlierMap.m | 2,399 | utf_8 | a6bf944a2fd8a6edaac88a0db11fdf0a | function uiGetOutlierMap(hMainFig,hObjects)
%% uiGetOutlierMap calculates an outlier map
% Input: hMainFigDR - figure handle
% DRdata - properties and parameter values
% of dimension reduction toolbox objects
%% Author: Kirill A. Veselkov, Imperial College 2011.
DRdata = gu... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | mergeClassesDR.m | .m | Pattern-Recognition-Toolbox-master/uiPatternRecognitionV2/DRVisualization/mergeClassesDR.m | 1,955 | utf_8 | 9498236607a18c0a727b9bf423c8a42d | function [yOut,sampleIds] = mergeClassesDR(y,yNewIds)
%% merge classes
if iscell(y)
[yNum,yIds] = getNumClassLabels(y);
yNumNewIds = getMergedClassLabels(yIds,yNewIds);
else
yNum = y;
yNumNewIds = yNewIds;
end
nGrps = length(yNumNewIds); % number of merged groups
yOut = zeros(1,leng... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | uidoplotAnovaFeatSelecDiagnostics.m | .m | Pattern-Recognition-Toolbox-master/uiPatternRecognitionV2/DRVisualization/uidoplotAnovaFeatSelecDiagnostics.m | 3,790 | utf_8 | 65ac6862d05a8d3f84b66b0c87547b16 | function uidoplotAnovaFeatSelecDiagnostics(DRdata)
%% CV Anova Feature Selection Dianostics Plot
% To be used within dimension reduction toolbox cross validation with anova
% feature selection.
% Author: Ottmar Golf & Kirill Veselkov, Imperial College London, 2014
% Get the data from DRdata
feat = DRdata.cv.anovaFeat... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | notBoxPlot.m | .m | Pattern-Recognition-Toolbox-master/uiPatternRecognitionV2/DRVisualization/notBoxPlot.m | 6,142 | utf_8 | 60262596f83864920e5fe9df3cfc5974 | function varargout=notBoxPlot(y,x,jitter,style)
% notBoxPlot - Doesn't plot box plots!
%
% function notBoxPlot(y,x,jitter,style)
%
% Purpose
% An alternative to a box plot, where the focus is on showing raw
% data. Plots columns of y as different groups located at points
% along the x axis defined by the optional vecto... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | pline.m | .m | Pattern-Recognition-Toolbox-master/uiPatternRecognitionV2/DRVisualization/pline.m | 4,446 | utf_8 | 861d8aa86b646e39a95f06eb3940d8ba | function varargout=pline(arg1,arg2,arg3)
% PLINE Plots line in 2D.
%
% Synopsis:
% h=pline(W,b)
% h=pline(W,b,line_style)
% h=pline(model)
% h=pline(model,options)
%
% Description:
% h=pline(W,b) plots the line in 2D space described implicitely as
% W'*x + b = 0 ,
% where W, x are vectors [2x1] and b is scalar ... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | plotGradLoadMapDR.m | .m | Pattern-Recognition-Toolbox-master/uiPatternRecognitionV2/DRVisualization/figObjectsDR/plotGradLoadMapDR.m | 3,090 | utf_8 | d0f732618bef5527d183234e7f0e7f03 | function DRdata = plotGradLoadMapDR(DRdata,xlims,ylims,PCs)
%% plotGradLoadMap creates an image graphics or lineplot object of loadings
%% values
% Input: DRdata - data of DR toolbox objects
% xlims - limits of the x axis
% ylims - limits of the y axis
% ... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | scatter3D.m | .m | Pattern-Recognition-Toolbox-master/uiPatternRecognitionV2/DRVisualization/figObjectsDR/scatter3D.m | 5,724 | utf_8 | 537f583b9dfea586d95b67cb58778705 | function DRdata = scatter3D(DRdata,PC1,PC2,PC3)
%% scatter2D outputs scatter plot of DR scores
% Input: DRdata - data for visualizing the results of
% dimension reduction techniques (see variableDescription.txt)
% PC1 - the first component chosen for visualization
% PC2 ... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | plotLoadingsOrVrbVarDR.m | .m | Pattern-Recognition-Toolbox-master/uiPatternRecognitionV2/DRVisualization/figObjectsDR/plotLoadingsOrVrbVarDR.m | 4,064 | utf_8 | 9b73c1591d6525c622c93834276306ea | function DRdata = plotLoadingsOrVrbVarDR(DRdata,showVarExplByPCs)
%% plotLoadingsOrVrbVarDR creates image or line objects of PC loadings
%% or individual variable variances explained by the PCs
% Input: DRdata - data of DR toolbox objects
%% Author: Kirill A. Veselkov, Imperial College London 2011
%%
LoadPos = ... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | scatter2D.m | .m | Pattern-Recognition-Toolbox-master/uiPatternRecognitionV2/DRVisualization/figObjectsDR/scatter2D.m | 6,902 | utf_8 | 10feb89f3ee9c49605915e8aa8afcf9f | function DRdata = scatter2D(DRdata,PC1,PC2,PC3)
%% scatter2D outputs scatter plot of DR scores
% Input: DRdata - data for visualizing the results of
% dimension reduction techniques (see variableDescription.txt)
% PC1 - the first component chosen for visualization
% PC2 ... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | paralCoord.m | .m | Pattern-Recognition-Toolbox-master/uiPatternRecognitionV2/DRVisualization/figObjectsDR/paralCoord.m | 4,837 | utf_8 | 46c022917209015486bae0253266c1cd | function DRdata = paralCoord(DRdata,PC1,PC2)
%% scatter2D outputs scatter plot of DR scores
% Input: DRdata - data for visualizing the results of
% dimension reduction techniques (see variableDescription.txt)
% PC1 - the first component chosen for visualization
% PC2 ... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | plotqvaluesBR.m | .m | Pattern-Recognition-Toolbox-master/uiPatternRecognitionV2/DRVisualization/figObjectsBR/plotqvaluesBR.m | 3,313 | utf_8 | 633af706de7a7ea160e35d6bf8580375 | function DRdata = plotqvaluesBR(DRdata,xlims)
%% plotqvaluesPWCA gives a surface plot of CovXy colourcoded by the vector
%% of q-values
%% Input:
% DRdata - various parameters of comparative statistical analysis
%% Author: Kirill A. Veselkov, Imperial College London
set(DRdata.h.figure,'CurrentAxes',DRdata.... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | freezeColors.m | .m | Pattern-Recognition-Toolbox-master/SourcePackages/misc/colorFreeze/freezeColors.m | 8,168 | utf_8 | 79aab94a04097473c502e31f7a91b04e | function freezeColors(varargin)
% freezeColors Lock colors of plot, enabling multiple colormaps per figure. (v2.3)
%
% Problem: There is only one colormap per figure. This function provides
% an easy solution when plots using different colomaps are desired
% in the same figure.
%
% freezeColors freeze... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | chi2cdf.m | .m | Pattern-Recognition-Toolbox-master/SourcePackages/misc/Stats/chi2cdf.m | 4,987 | utf_8 | e5e833495f1313af33bbde6a013d3770 | function p = chi2cdf(x,v,uflag)
%CHI2CDF Chi-square cumulative distribution function.
% P = CHI2CDF(X,V) returns the chi-square cumulative distribution
% function with V degrees of freedom at the values in X.
% The chi-square density function with V degrees of freedom,
% is the same as a gamma density function ... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | chi2inv.m | .m | Pattern-Recognition-Toolbox-master/SourcePackages/misc/Stats/chi2inv.m | 3,810 | utf_8 | bdd0a3a57c91580ecb2594b836dcfbdd | function x = chi2inv(p,v)
%% CHI2INV Inverse of the chi-square cumulative distribution function (cdf).
x = gaminv(p,v/2,2);
% Return NaN if the degrees of freedom is not positive.
k = (v <= 0);
if any(k(:))
x(k) = NaN;
end
function [x,xlo,xup] = gaminv(p,a,b,pcov,alpha);
%GAMINV Inverse of the gamma cumulative di... |
github | MS-BASIS/Pattern-Recognition-Toolbox-master | modifyVarNameInMCode.m | .m | Pattern-Recognition-Toolbox-master/SourcePackages/misc/Debug/modifyVarNameInMCode.m | 1,750 | utf_8 | 0a48d17fa8cf5c37afde9ac3349664ce | function modifyVarNameInMCode(filename,curvar,newvar)
%% import file
datafile = importdatafile(filename);
nLines = length(datafile);
%% change variable name
curVarLength = length(curvar);
newVarLength = length(newvar);
for iLine = 1:nLines
curstring = datafile{iLine};
indices = strfind(curstring,curvar);... |
github | arvanito/deep_matlab-master | contrast_normalization.m | .m | deep_matlab-master/contrast_normalization.m | 648 | utf_8 | 11c9fca63d9cd21a36767e25bce5601f | %% data = contrast_normalization(data, epsilon):
%%
%% function that normalizes the data to have zero mean and
%% unit variance. It operates as contrast normalization.
%%
%% Input:
%% data: Initial data points, each row represents one data point
%% epsilon: regularizer for division with standard deviatio... |
github | arvanito/deep_matlab-master | cluster_filters.m | .m | deep_matlab-master/cluster_filters.m | 684 | utf_8 | 4b73c2fdd4fc03b40535a2a2975fa7a6 | %% C = cluster_filters(D, num_groups, k, type, sigma):
%%
%% function that clusters the learned filters using Spectral Clustering.
%%
%% Input:
%% D: learned filters
%% num_groups: number of groups for filter clustering
%% k: number of nearest neighbors for Graph computation
%% type: type of near... |
github | arvanito/deep_matlab-master | group_pooling.m | .m | deep_matlab-master/group_pooling.m | 1,240 | utf_8 | 3f4df3361f62a455fae7ef0919cb3516 | %% pooled_features = group_pooling(features, q1, q2, D, groups, num_groups)
%%
%% function that does max-pooling on learned filters. The goal is to
%% reduce the number of filters by maintaining learned information.
%%
%% Input:
%% features: input features to be max-pooled relative to learned filters
%% ... |
github | arvanito/deep_matlab-master | pool.m | .m | deep_matlab-master/pool.m | 1,398 | utf_8 | bac64ea32d63176bd4eab2ce31d9fa25 | %% pooled_features = cnn_pool(features, pool_size):
%%
%% function that pool convolutional features.
%%
%% Input:
%% features: features extracted after the convolution step
%% pool_size: 2-d size used for the pooling
%%
%% Output:
%% pooled_features: features after pooling
%%
function pooled_feature... |
github | arvanito/deep_matlab-master | zca_whitening.m | .m | deep_matlab-master/zca_whitening.m | 333 | utf_8 | 1b131590ce218d282e44c2c999c24dc0 | %%
%%
%%
%%
%%
function [data_zca, ZCA, mean_data] = zca_whitening(data, epsilon)
% compute the mean
mean_data = mean(data);
% subtract the mean
data = bsxfun(@minus,data,mean_data);
% do SVD for PCA
C = cov(data);
[V,D] = eig(C);
% do ZCA whitening
ZCA = V * diag(1 ./ sqrt(diag(D) + epsilon)) * V';
data_zca = dat... |
github | arvanito/deep_matlab-master | compute_distances.m | .m | deep_matlab-master/compute_distances.m | 677 | utf_8 | 6746fec71bf27e9e96b6fb7fb7e410ba | %%
%% function that computes L2 distances between points
%% in two different datasets. The matrices X,Y contain in their
%% rows the data points.
%%
%% Input:
%% X: first dataset
%% Y: second dataset
%%
%% Output:
%% dist: pair-wise distances between points in X and Y
%%
function dist = compute_dis... |
github | arvanito/deep_matlab-master | omp1.m | .m | deep_matlab-master/omp1.m | 1,956 | utf_8 | 78751cf34e3fa5cf8bc103c9deb32b12 | %% D = omp1(X, K, iter):
%%
%% function that runs OMP1. Very similar to K-means learning.
%%
%% Input:
%% X: training data, in our case whitened patches
%% K: number of features to be learned
%% iter: number of iterations
%% batch_size: batch size for sequential learning
%%
%% Output:
%% D:... |
github | arvanito/deep_matlab-master | feature_extraction.m | .m | deep_matlab-master/feature_extraction.m | 3,406 | utf_8 | 28b3e1e6214069f690404c44ed94063c | %% features = feature_extraction(X, D, dims, rf_size, mean_X, ZCA, eps1, pool_size, activation_type):
%%
%% function that implements feature extraction with learned
%% features.
%%
%% Input:
%% X: data matrix, rows correspond to points, columns to features
%% D: learned centroids from the feature learni... |
github | arvanito/deep_matlab-master | kmeans_learning.m | .m | deep_matlab-master/kmeans_learning.m | 1,893 | utf_8 | 2603b85a76ad47440cc964785d5a0a84 | %% D = kmeans_learning(X, K, iter, batch_size)
%%
%% function that the K-means algorithm for feature learning.
%%
%% Input:
%% X: training data, in our case whitened patches
%% K: number of features to be learned
%% iter: number of iterations
%% batch_size: batch size for sequential learning
%%
%... |
github | arvanito/deep_matlab-master | compute_activation.m | .m | deep_matlab-master/compute_activation.m | 1,441 | utf_8 | b6110c44cd03e01abfc01a65b04a2b13 | %% f = compute_activation(data, D, activation_type):
%%
%% function that computes the activation for the feature extraction step.
%%
%% Input:
%% data: data points, rows correspond to points, columns to features
%% D: learned centroids from the feature learning procedure
%% activation_type: type of ... |
github | Embreus/CODION-master | CODION.m | .m | CODION-master/CODION.m | 25,391 | utf_8 | 52175d99e30c24ef7774cf62b55ba695 | function OUT = CODION(grid0,params0,settings)
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% CODION: COllisional Distribution of IONs
% -------------------------------------------
% Developed by Ola Embréus, 2014.
% CODION paper: to be submitte... |
github | Embreus/CODION-master | runaway_parameters.m | .m | CODION-master/utilities/runaway_parameters.m | 2,403 | utf_8 | 27580953f2eb8af69873feac8d412426 | function [Ec, vc1, vc2] = runaway_parameters(params,settings)
rhos = params.rhos; %sums to 1 <-> quasi-neutrality
Zs = params.Zs;
ms = params.ms; %in units of proton masses
Ts = params.Ts;
me = 9.10938291e-31 / 1.67262178e-27;
ma = ms(:,1);
Za = Zs(:,1);
EHat = abs(params.EHat);
switch settings.units
c... |
github | JonBoley/F31-master | DALinloop_NI_SCCi_wavfiles_121508copy.m | .m | F31-master/NEL/Users/JB/DALinloop_NI_SCCi_wavfiles_121508copy.m | 18,432 | utf_8 | 71e58f21e59cb4e15486f22ce1825449 | function varargout = DALinloop_NI_SCCi_wavfiles(varargin)
% Adapted by MH 10Nov2004 from 'DALinloop_NI_SCC_wavefiles'
% adds the ability to interleave conditions (with different updateRates, later want to add attens)
%
% Adapted by MH 07July2004 from 'DALinloop_NI_wavefiles' (GE 7/26/02)
% adds the ability to ... |
github | JonBoley/F31-master | DALinloop_NI_SCC_wavfiles.m | .m | F31-master/NEL/Users/JB/DALinloop_NI_SCC_wavfiles.m | 15,635 | utf_8 | 1b30b34b5c161645c55018da1d4a788d | function varargout = DALinloop_NI_SCC_wavfiles(varargin)
% Adapted by MH 07July2004 from 'DALinloop_NI_wavefiles' (GE 7/26/02)
% adds the ability to invert the waveform (based on static_bi.Condition.InvertPolarity)
%
% Adapted by GE 26Jul2002 from 'DALinloop_wavfiles' (AF 9/22/01).
% Further modifications: GE 0... |
github | JonBoley/F31-master | DALinloop_NI_SCCi2_wavfiles_aid.m | .m | F31-master/NEL/Users/JB/DALinloop_NI_SCCi2_wavfiles_aid.m | 22,495 | utf_8 | 724325efb0a0ec3267fef94d495ead32 | function varargout = DALinloop_NI_SCCi_wavfiles_aid(varargin)
% Adapted by MH 10Nov2004 from 'DALinloop_NI_SCC_wavefiles'
% adds the ability to interleave conditions (with different updateRates, later want to add attens)
%
% Adapted by MH 07July2004 from 'DALinloop_NI_wavefiles' (GE 7/26/02)
% adds the ability... |
github | JonBoley/F31-master | DALinloop_NI_SCCi_wavfiles.m | .m | F31-master/NEL/Users/JB/DALinloop_NI_SCCi_wavfiles.m | 20,371 | utf_8 | ab6e0f5a5566966c01e0619d9bbcebe8 | function varargout = DALinloop_NI_SCCi_wavfiles(varargin)
% Adapted by MH 10Nov2004 from 'DALinloop_NI_SCC_wavefiles'
% adds the ability to interleave conditions (with different updateRates, later want to add attens)
%
% Adapted by MH 07July2004 from 'DALinloop_NI_wavefiles' (GE 7/26/02)
% adds the ability to ... |
github | JonBoley/F31-master | DALinloop_NI_SCC_wavfiles2.m | .m | F31-master/NEL/Users/JB/DALinloop_NI_SCC_wavfiles2.m | 15,751 | utf_8 | c58ff034631627ec5fd4578961826367 | function varargout = DALinloop_NI_SCC_wavfiles2(varargin)
% Adapted by MH 07July2004 from 'DALinloop_NI_wavefiles' (GE 7/26/02)
% adds the ability to invert the waveform (based on static_bi.Condition.InvertPolarity)
%
% Adapted by GE 26Jul2002 from 'DALinloop_wavfiles' (AF 9/22/01).
% Further modifications: GE ... |
github | JonBoley/F31-master | DALinloop_NI_SCCi2_wavfiles.m | .m | F31-master/NEL/Users/JB/DALinloop_NI_SCCi2_wavfiles.m | 19,773 | utf_8 | dfe5e5eff69a4fa4258e80c1832fed16 | function varargout = DALinloop_NI_SCCi_wavfiles(varargin)
% Adapted by MH 10Nov2004 from 'DALinloop_NI_SCC_wavefiles'
% adds the ability to interleave conditions (with different updateRates, later want to add attens)
%
% Adapted by MH 07July2004 from 'DALinloop_NI_wavefiles' (GE 7/26/02)
% adds the ability to ... |
github | JonBoley/F31-master | GenLTASS.m | .m | F31-master/NEL/Users/JB/LTASS/GenLTASS.m | 881 | utf_8 | 8714ed4ade7881cd4797103e4f6ee1bf | % Generate LTASS noise
function Z=GenLTASS(sec,Fs,SaveWAV)
% addpath([fileparts(mfilename('fullpath')) filesep 'octave']);
Z = IntLTASS(randn(sec*Fs,1),Fs);
Z = Z/(max(abs(Z))+eps);
if 0 % plot FFT?
BufferLen = 2048;
Z2 = buffer(Z,BufferLen,BufferLen/2); % buffer w/ 50% overlap
Z2 = repmat(hamming... |
github | JonBoley/F31-master | IntLTASS.m | .m | F31-master/NEL/Users/JB/LTASS/IntLTASS.m | 2,446 | utf_8 | a4fe51b853d2a2d01a2a63f07a58551d | % Byrne, et al. (1994), "An international comparison of long-term average speech spectra," J. Acoust. Soc. Am. 96, 2108-2120.
function Z = IntLTASS(x,Fs)
band = [100,125,160,200,250,315,400,500,630,800,1000,...
1250,1600,2000,2500,3150,4000,5000,6300,8000,10000];
noiseLevel = [54.4,57.7,56.8,60.2,60.3,59,62... |
github | JonBoley/F31-master | quick_WAVrlf.m | .m | F31-master/NEL/Users/JB/myMfiles/quick_WAVrlf.m | 3,234 | utf_8 | 39ff8ee1875aa70ef4c8647b8ae336e3 | function quick_WAVrlf(RLFpic)
% File quick_EHrlfs.m
% M.Heinz: 11Nov2004 (From GE: quick_vowel)
% For NOHR
%
% USAGE:quick_WAVrlf(RLFpic)
% Plots RLF vs ATTEN for driv and spont to get Threshold
%
params = [];
params.spikeChan = 1;
params.figNum = 100;
params.TriFiltWidth=5;
params.colors={'b','r','g','k... |
github | JonBoley/F31-master | quick_EHrlfs.m | .m | F31-master/NEL/Users/JB/myMfiles/quick_EHrlfs.m | 4,590 | utf_8 | 2e3fde7424334e28a4447587aa798c1b | function quick_vowel(RLFpics,CALpic)
% File quick_EHrlfs.m
% M.Heinz: 11Nov2004 (From GE: quick_vowel)
% For NOHR
%
% USAGE:quick_vowel(RLFpics,CALpic) [e.g., quick_vowel([2 3],1)]
% Plots RLFs for a set of vowel features
%
% RLFpics: vector of vowel RLFs for different features [F1_pic, T1_pic]
% CALpic: cali... |
github | JonBoley/F31-master | quick_EHINrlfs.m | .m | F31-master/NEL/Users/JB/myMfiles/quick_EHINrlfs.m | 4,104 | utf_8 | b3f959f4fb9e4d2dc0bcceef92429b97 | function quick_EHINrlfs(RLFpics,CALpic)
% File quick_EHINrlfs.m
% Modified (MHeinz 18Apr2005) to plot vs noise Attenuation
% M.Heinz: 11Nov2004 (From GE: quick_vowel)
% For NOHR
%
% USAGE:quick_vowel(RLFpics,CALpic) [e.g., quick_vowel([2 3],1)]
% Plots RLFs for a set of vowel features
%
% RLFpics: vector of vo... |
github | JonBoley/F31-master | ApplyGain.m | .m | F31-master/NEL/Users/JB/Amplification/ApplyGain.m | 10,051 | utf_8 | ea65bba8fab1cfe3778eb3dbe6e67d16 | function output=ApplyGain(input,Fs,MaxSPL,atten,audiogram,freqs_Hz,strategy)
% output=ApplyGain(input,Fs,MaxSPL,atten,audiogram,freqs_Hz,strategy)
% strategy = 1 or 'linear' or
% or 2 or 'nonlinear_quiet' or
% or 3 or 'nonlinear_noise'
plotYes=0;
if nargin<1 % if no input, just test gain settin... |
github | JonBoley/F31-master | PSTasync_template.m | .m | F31-master/NEL/Users/JB/Templates/PSTasync_template.m | 4,602 | utf_8 | 8035c5e33e22ec9e7d7eddba8e181e4e | function [tmplt,DAL,stimulus_vals,units,errstr] = PSTasync_template(fieldname,stimulus_vals,units)
%
% Modified by M.Heinz 09Dec2003, from PST_template.m
% Modified by M.Heinz 12Nov2003, from nel_rate_level_template.m
% Adapted from "nel_pst_template.m" by GE, 29Mar2002.
used_devices.Tone = 'RP1.1';
tmp... |
github | JonBoley/F31-master | TONE_reBFi_template.m | .m | F31-master/NEL/Users/JB/Templates/TONE_reBFi_template.m | 23,183 | utf_8 | 419a6a5b0ff88574f1d051cd8b03c346 | function [tmplt,DAL,stimulus_vals,units,errstr] = TONE_reBFi_template(fieldname,stimulus_vals,units)
% MH 24Mar2005: for R03 project
% Modified from TEHrBFi to just do TONE, with slightly different params
% Modified version to allow interleaving with TONE, multi-levels, and hard-coded OCT-SHIFT list
% Also changed ... |
github | JonBoley/F31-master | SACrlv_template.m | .m | F31-master/NEL/Users/JB/Templates/SACrlv_template.m | 6,647 | utf_8 | a419a7662e3f71a328655d8e50247a95 | function [tmplt,DAL,stimulus_vals,units,errstr] = SACrlv_template(fieldname,stimulus_vals,units)
% MH 11Apr2005: for R03 project
% Rate-level data with 20 reps for SAC analysis
% Steps through several levels
%
% From EHrlv_template
% Rate-level function for features at BF for a BASELINE EH with F2 at BF and F0=... |
github | JonBoley/F31-master | TONErlv_template.m | .m | F31-master/NEL/Users/JB/Templates/TONErlv_template.m | 4,362 | utf_8 | 5b1cd98017a8886a64022f59d0065937 | function [tmplt,DAL,stimulus_vals,units,errstr] = TB_template(fieldname,stimulus_vals,units)
%
% Template for Recruitment Stimulus TB: BF tone, rate-level, 200-ms duration
%
% MH 12/17/01
used_devices.Tone = 'RP1.1';
tmplt = template_definition(fieldname);
if (exist('stimulus_vals','var') == 1)
Inloo... |
github | JonBoley/F31-master | EHrlv_IN_template.m | .m | F31-master/NEL/Users/JB/Templates/EHrlv_IN_template.m | 17,887 | utf_8 | 8fd217c3df8ce569e50542184673b64c | function [tmplt,DAL,stimulus_vals,units,errstr] = EHrlv_IN_template(fieldname,stimulus_vals,units)
% MH 10Nov2004: for NOHR project
%
% Modified 11-Apr-2005 to add background noise
% - this is a rate-level (dB atten) for the EH, with a fixed noise Attenuation
%
% Rate-level function for features at BF for a ... |
github | JonBoley/F31-master | T1_template.m | .m | F31-master/NEL/Users/JB/Templates/T1_template.m | 3,986 | utf_8 | 6a69165d2a95328cd6fcfd0ace65563b | function [tmplt,DAL,stimulus_vals,units,errstr] = T1_template(fieldname,stimulus_vals,units)
%
% Template for Recruitment Stimulus T1: 1-kHz tone, rate-level, 200-ms duration
%
% MH 12/17/01
used_devices.Tone = 'RP1.1';
tmplt = template_definition(fieldname);
if (exist('stimulus_vals','var') == 1)
In... |
github | JonBoley/F31-master | EH_reBF_template.m | .m | F31-master/NEL/Users/JB/Templates/EH_reBF_template.m | 14,811 | utf_8 | 90f968bf877d1807b6c4729615deaad7 | function [tmplt,DAL,stimulus_vals,units,errstr] = EH_reBF_template(fieldname,stimulus_vals,units)
% MH 01July2004: for NOHR project
% places features near BF for a BASELINE EH with F2 at BF and F0=75 Hz
%
% From EH_template (CNexps)
% Template for EH-vowel RLFs, using NI board to allow resampling
%
% MH 07Nov... |
github | JonBoley/F31-master | CHrlv_quick_template.m | .m | F31-master/NEL/Users/JB/Templates/CHrlv_quick_template.m | 6,649 | utf_8 | b8e90fadaa649f46a117135b7f74dcc3 | function [tmplt,DAL,stimulus_vals,units,errstr] = CHrlv_quick_template(fieldname,stimulus_vals,units)
% MH 11Apr2005: for R03 project
% Rate-level data with 20 reps for SAC analysis
% Steps through several levels
%
% From EHrlv_template
% Rate-level function for features at BF for a BASELINE EH with F2 at BF an... |
github | JonBoley/F31-master | Tone_reBF_template.m | .m | F31-master/NEL/Users/JB/Templates/Tone_reBF_template.m | 6,753 | utf_8 | 3c4bff2ccd7241e48ce08923e448419a | function [tmplt,DAL,stimulus_vals,units,errstr] = Tone_reBF_template(fieldname,stimulus_vals,units)
%
% Modified by M.Heinz 12Nov2003, from nel_rate_level_template.m
% Adapted from "nel_pst_template.m" by GE, 29Mar2002.
used_devices.Tone = 'RP1.1';
tmplt = template_definition(fieldname);
if (exist('stim... |
github | JonBoley/F31-master | SAMtone_template.m | .m | F31-master/NEL/Users/JB/Templates/SAMtone_template.m | 7,230 | utf_8 | 1fc829e2d909030188ad62029a1a9ec3 | function [tmplt,DAL,stimulus_vals,units,errstr] = SAMtone_template(fieldname,stimulus_vals,units)
% used_devices.Tone = 'RP1.1';
used_devices.list = 'L3';
tmplt = template_definition(fieldname);
if (exist('stimulus_vals','var') == 1)
global signals_dir
SAMsignals_dir=strcat(signa... |
github | JonBoley/F31-master | BF_RLV_template.m | .m | F31-master/NEL/Users/JB/Templates/BF_RLV_template.m | 3,921 | utf_8 | 821a17028bcc5e745b7c2b47d97b46b9 | function [tmplt,DAL,stimulus_vals,units,errstr] = BF_RLV_template(fieldname,stimulus_vals,units)
%
% Modified by M.Heinz 12Nov2003, from nel_rate_level_template.m
% AF 11/26/01
used_devices.Tone = 'RP1.1';
tmplt = template_definition(fieldname);
if (exist('stimulus_vals','var') == 1)
Inloop.Name ... |
github | JonBoley/F31-master | EHINvN_reBFi_template.m | .m | F31-master/NEL/Users/JB/Templates/EHINvN_reBFi_template.m | 25,425 | utf_8 | 154abfbfaf9f855d5bf8ac258434eda0 | function [tmplt,DAL,stimulus_vals,units,errstr] = EHINvN_reBFi_template(fieldname,stimulus_vals,units)
% MH 13Apr2005 added background noise, varying noise level for a fixed signal level
%
% MH 24Mar2005: for R03 project
% Modified version to allow interleaving with TONE, multi-levels, and hard-coded OCT-SHIFT list... |
github | JonBoley/F31-master | SFS_template.m | .m | F31-master/NEL/Users/JB/Templates/SFS_template.m | 6,547 | utf_8 | 18b69a83d9f5baefb7802d26525df4e1 | function [tmplt,DAL,stimulus_vals,units,errstr] = SFS_template(fieldname,stimulus_vals,units)
% used_devices.Tone = 'RP1.1';
used_devices.list = 'L3';
tmplt = template_definition(fieldname);
if (exist('stimulus_vals','var') == 1)
global signals_dir
SFSsignals_dir=strcat(signals_d... |
github | JonBoley/F31-master | B1_template.m | .m | F31-master/NEL/Users/JB/Templates/B1_template.m | 5,089 | utf_8 | 848c87c453d90898e55369a40b181feb | function [tmplt,DAL,stimulus_vals,units,errstr] = B1_template(fieldname,stimulus_vals,units)
%
% Template for Recruitment Stimulus B1: 1-kHz tone in BP (1.8-3.0 kHz) noise, rate-level, 200-ms duration
%
% MH 12/17/01
used_devices.Tone = 'RP1.1';
used_devices.Fixed_Noise = 'RP2.1';
tmplt = template_de... |
github | JonBoley/F31-master | SP_template.m | .m | F31-master/NEL/Users/JB/Templates/SP_template.m | 3,481 | utf_8 | 8e858f209c5f14b9f99cdc30a28d134b | function [tmplt,DAL,stimulus_vals,units,errstr] = SP_template(fieldname,stimulus_vals,units)
%
% Template for Recruitment Stimulus SP: besh97k.wav, rate-level, 300-ms (TOTAL) duration
%
% MH 12/17/01
used_devices.File = 'RP1.1';
tmplt = template_definition(fieldname);
if (exist('stimulus_vals','var') =... |
github | JonBoley/F31-master | EHINvS_reBFi_template.m | .m | F31-master/NEL/Users/JB/Templates/EHINvS_reBFi_template.m | 25,822 | utf_8 | 896471bbb430518dc36a99ed774b5657 | function [tmplt,DAL,stimulus_vals,units,errstr] = EHINvS_reBFi_template(fieldname,stimulus_vals,units)
% MH 14Apr2005 added background noise, varying signal level for a fixed noise level
%
% MH 24Mar2005: for R03 project
% Modified version to allow interleaving with TONE, multi-levels, and hard-coded OCT-SHIFT list... |
github | JonBoley/F31-master | Chimera_template.m | .m | F31-master/NEL/Users/JB/Templates/Chimera_template.m | 7,745 | utf_8 | 88eef00e379c09c9e8c816432da61fe2 | function [tmplt,DAL,stimulus_vals,units,errstr] = Chimera_template(fieldname,stimulus_vals,units)
% Written by GE, adapted from 'nel_rot_wavefile_template' written by AF (11/26/01).
% For implementation NI 6052e board, rather than TDT analog outputs.
% Modification dates: 06oct2003.
% Modifed by MHeinz Aug3_200... |
github | JonBoley/F31-master | resp_map_template.m | .m | F31-master/NEL/Users/JB/Templates/resp_map_template.m | 3,781 | utf_8 | 2a4dece05c8a78d1b2762dd97e563048 | function [tmplt,DAL,stimulus_vals,units,errstr] = resp_map_template(fieldname,stimulus_vals,units)
%
% AF 11/26/01
used_devices.Tone = 'RP1.1';
tmplt = template_definition(fieldname);
if (exist('stimulus_vals','var') == 1)
Inloop.Name = 'DALinloop_general_TN';
Inloop... |
github | JonBoley/F31-master | TT_resp_map_template.m | .m | F31-master/NEL/Users/JB/Templates/TT_resp_map_template.m | 4,773 | utf_8 | e6b671e62e69e3d357ae870f35d7650c | function [tmplt,DAL,stimulus_vals,units,errstr] = TT_resp_map_template(fieldname,stimulus_vals,units)
%
% AF 11/26/01
used_devices.Tone = 'RP1.1';
used_devices.Fixed_Tone = 'RP2.1';
tmplt = template_definition(fieldname);
if (exist('stimulus_vals','var') == 1)
Inloop.Name ... |
github | JonBoley/F31-master | EH_reBFi_template.m | .m | F31-master/NEL/Users/JB/Templates/EH_reBFi_template.m | 18,161 | utf_8 | f358334416232ba89d5940d544126515 | function [tmplt,DAL,stimulus_vals,units,errstr] = EH_reBFi_template(fieldname,stimulus_vals,units)
% MH 10Nov2004: for NOHR project
% Modified version to allow interleaving of conditions (
% From EH_reBF_template
%
% Initial creation LIMITATIONS:
% 1) limited to one Polarity
% 2) assumes 1 filename
% ... |
github | JonBoley/F31-master | BBN_reBFi_template.m | .m | F31-master/NEL/Users/JB/Templates/BBN_reBFi_template.m | 13,810 | utf_8 | ca22748e990967463aebbefb85e2ac59 | function [tmplt,DAL,stimulus_vals,units,errstr] = BBN_reBFi_template(fieldname,stimulus_vals,units)
% MH 10Nov2004: for NOHR project
% Modified version to allow interleaving of conditions (
% From EH_reBF_template
%
% Initial creation LIMITATIONS:
% 1) limited to one Polarity
% 2) assumes 1 filename
% ... |
github | JonBoley/F31-master | SR_template.m | .m | F31-master/NEL/Users/JB/Templates/SR_template.m | 4,201 | utf_8 | 7ec53ffbb61069b0355a3e97ec6bb42b | function [tmplt,DAL,stimulus_vals,units,errstr] = PST_template(fieldname,stimulus_vals,units)
%
% Modified by M.Heinz 12Nov2003, from nel_rate_level_template.m
% Adapted from "nel_pst_template.m" by GE, 29Mar2002.
used_devices.Tone = 'RP1.1';
tmplt = template_definition(fieldname);
if (exist('stimulus_v... |
github | JonBoley/F31-master | T05_template.m | .m | F31-master/NEL/Users/JB/Templates/T05_template.m | 3,992 | utf_8 | cdb9fd20cf6729484ea004499e4fbd1f | function [tmplt,DAL,stimulus_vals,units,errstr] = T05_template(fieldname,stimulus_vals,units)
%
% Template for Recruitment Stimulus T05: 500-Hz tone, rate-level, 200-ms duration
%
% MH 12/18/01
used_devices.Tone = 'RP1.1';
tmplt = template_definition(fieldname);
if (exist('stimulus_vals','var') == 1)
... |
github | JonBoley/F31-master | RM2T_template.m | .m | F31-master/NEL/Users/JB/Templates/RM2T_template.m | 4,891 | utf_8 | 721ba6429be56ea5533bc8eeacab4ea5 | function [tmplt,DAL,stimulus_vals,units,errstr] = RM2T_template(fieldname,stimulus_vals,units)
%
% Modified by M.Heinz 03Dec2003, from nel_TT_resp_map_template.m
% AF 11/26/01
used_devices.Tone = 'RP1.1';
used_devices.Fixed_Tone = 'RP2.1';
tmplt = template_definition(fieldname);
if (exist('stimulus_v... |
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