sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
4be4ae253c9de6d25552012b7ff40beebdcac6be586ba8643b3441e0b3b866ad | MATLAB | 3,869 | 122 | function [data_ind, train_ind, nP, nA] = nk_ParamReplicator(P, PXopt, PREPROC, nA)
% ==========================================================================
% [data_ind, train_ind] = nk_ParamReplicator(P, PXopt, PREPROC, TrainedParam)
% ==========================================================================
% nk_... |
4eb5b58ee1e59e3760261d75acdf2b43acef7b1fdd07271c23132878a41d1b99 | MATLAB | 3,877 | 93 |
function ms = msGenerateVideoObj(dirName, filePrefix)
%MSGENERATEMS Summary of this function goes here
% Detailed explanation goes here
ms.dirName = dirName; % Added by GE to keep track of where the files were initially
MAXFRAMESPERFILE = 1000; %This is set in the miniscope control software
% ... |
aa7a2d073fb20349f433367d8c49fda8e47894636b79fcb5fccfafd3989d6039 | MATLAB | 3,878 | 109 | function [ACluster,YCluster,indACluster,indYCluster,Xout,Aout,Yout,numIter,tElapsed,finalResidual]=biCluster(X,k,option)
% The biclustering method using one of the NMF algorithms
% Usage:
% [ACluster,YCluster,indACluster,indYCluster,Xout,Aout,Yout,numIter,tElapsed,finalResidual]=biCluster(X,k) % in this case, X and k a... |
76ed0ba9c1692e3ef129e6776eb04e20e6f550ecf6144d45a13100482d70bf68 | MATLAB | 3,880 | 96 | function CreateCutterWindow(self)
% MClustCutter.CreateCutterWindow
MCS = MClust.GetSettings();
MCD = MClust.GetData();
self.CreateCutterWindow@MClust.Cutter(); % call superclass to build initial window
%--------------------------------
% constants to make everything identical
uicHeight = self.uicHeight;
uicWidth ... |
46b9587035b5fd1c9a89ae8f39025798f343effb1a430c08a410eb36445a2970 | MATLAB | 3,882 | 131 | function [W,H] = NNDSVD(A,k,flag)
%
% This function implements the NNDSVD algorithm described in [1] for
% initialization of Nonnegative Matrix Factorization Algorithms.
%
% [W,H] = nndsvd(A,k,flag);
%
% INPUT
% ------------
%
% A : the input nonnegative m x n matrix A
% k : the rank of the computed factors W,H
%... |
d90bae65e44dcf6bdf4cc46f9be27b1dbdd15dbc5cd4fc353ca1f34f5efc5c75 | MATLAB | 3,883 | 93 | function handles = display_main(handles)
h_class = handles.popupmenu1.Value;
h_classlist = handles.popupmenu1.String;
h_list = handles.selModelMeasures.String;
h_val = handles.selModelMeasures.Value;
switch h_list{h_val}
case 'Classification plot'
handles.pnModelP... |
b2de41cdedaae75990d0d86aaa9994a76763389e460941463ac667f08d521854 | MATLAB | 3,887 | 112 | function [ Lperm, Yperm ] = nk_VisXPermY(Y, L, IND, permmode, indpermrows, indpermcols, permind, analysis, inp, paramfl, BINMOD, n, h, k, l, pnt, FullPartFlag, F, u)
global MODEFL CV
switch permmode
case 1
Lperm = L(indpermrows(:,permind));
Yperm = Y;
indN = isnan(L);
if strcmp(MO... |
d5cf7dd8d5166e4d7fba70ee22da2041de732e985c40313ad29f5811ac9766d2 | MATLAB | 3,890 | 128 | function model=softSVMTrain2(trainSet,trainClass,option)
% soft margin svm, express bias explictly
%model.mu: % Lagrance multiplier
%model.sv: support vectors
% example:
% load('C:\YifengLi\Reseach Program\dataset\Colon\ColonCancer.mat');
% dataStr='Colon';
% kfold=3;
% ind=crossvalind('Kfold',classes,kfold);
% indTest... |
682fb267dca4eae7647d6870d0ae109161ae20b8025e7719a269c428f1035c7a | MATLAB | 3,902 | 115 | function output = mp2rage_matlabbatch_job_output( job_name, display_name )
% This function is used to manage the output files within the Batch system.
% Later, the function mp2rage_generate_output_fname will perform the output name generation.
%
% See also mp2rage_matlabbatch_job_output mp2rage_get_defaults mp2rage_gen... |
c7753bf10a7d93fe20d90be8c350094da74bb74efe1c7fed06e2c6894367d4d9 | MATLAB | 3,902 | 98 | %% ================================================================
% Script: generate_individual_topography.m
% Purpose:
% Derive participant-specific functional network topography
% from individualized functional networks.
%
% Description:
% - Loads individualized functional network labels (from template match... |
1870f0c01643338757d7b961c09a5dea6810c40a7f0c1a8cbaae1e7a644efb14 | MATLAB | 3,903 | 117 | function SI = search_information(W, L, has_memory)
% SEARCH_INFORMATION Search information
%
% SI = search_information(W, L,has_memory)
%
% Computes the amount of information (measured in bits) that a random
% walker needs to follow the shortest path between a given pair of nodes.
%
% Inputs:... |
e20bb594a8c74c44aa02463127a66ff836807e7115f455f007b3f3485466e6af | MATLAB | 3,903 | 116 | function [A,Y,numIter,tElapsed,finalResidual]=sparsenmf2nnqp(X,k,option)
% Sparse NMF, X=AY, using active-set NNQP solver. Both basis matrix and coefficient matrix are constrained by
% l_2 and l_1 norm.
% Definition:
% [A,Y,numIter,tElapsed,finalResidual]=sparsenmf2nnqp(X,k)
% [A,Y,numIter,tElapsed,finalResidua... |
9a5444dc98682401aa1f28c5a5e7c95ae9573d6f3054e2f0facbd7c968b7bdab | MATLAB | 3,909 | 126 | function scorediagplot(Xdist,Odist,k,cutoffx,cutoffo,attrib,labsd,labod)
%SCOREDIAGPLOT plots the score outlier map. The score distances (SD) and
% orthogonal distances (OD) calculated in a PCA analysis are plotted
% on the x and y-axis, respectively.
% The cutoff values marked by a red line indicate which observ... |
f93c5f6129efc12ddba0babd006aa6dbb80f556b56a0428ede441078b566f41e | MATLAB | 3,916 | 109 | function [c, s, active_set] = oasisAR1(y, g, lam, smin, active_set)
%% Infer the most likely discretized spike train underlying an AR(1) fluorescence trace
% Solves the sparse non-negative deconvolution problem
% min 1/2|c-y|^2 + lam |s|_1 subject to s_t = c_t-g c_{t-1} >=s_min or =0
%% inputs:
% y: T*1 vector, On... |
34f69fb4075a05f092d8b9c8cc623fa48e5c1da2c16897cadf862495c35c4686 | MATLAB | 3,923 | 94 | function [sY, IN] = nk_PerfScaleObj(Y, IN)
% =========================================================================
% FORMAT function [sY, IN] = nk_PerfScaleObj(Y, IN)
% =========================================================================
% Scaling of matrix Y to [0,1] (zero_one=1) or [-1,1] (zero_one=2) or
% r... |
5e975b0249f21756e52c13d4fb49a55d816ade7e7ec9360f67226637476a1959 | MATLAB | 3,924 | 97 | %% figure
% 横轴：时间
% 纵轴：spike position
% 点的颜色：幅值
clear;
clc;
load('I:\930_project\paper_figure\waveform_table\Unit_info_SU\Unit_info_SU_patients\Unit_info_SU_patients_extend\Unit_info_SU_20250220_HS_WithNeedle_NoCuration_all_SpkTypes.mat');
fs = 25640;
big_amp_thres = 0;
FigureWidth = 30;
FigureHeight = 20;... |
15f8885fa24b6a904a52db70e88dd0e23ccb327d13dcdb6f219a4198d7e5aeca | MATLAB | 3,929 | 88 | function beh = importfile(filename, startRow, endRow)
%IMPORTFILE ½«Îı¾ÎļþÖеÄÊýÖµÊý¾Ý×÷Ϊ¾ØÕóµ¼Èë¡£
% JUPITERFINALTESTNONPRIMATEDAY1R1 = IMPORTFILE(FILENAME) ¶ÁÈ¡Îı¾Îļþ FILENAME
% ÖÐĬÈÏÑ¡¶¨·¶Î§µÄÊý¾Ý¡£
%
% JUPITERFINALTESTNONPRIMATEDAY1R1 = IMPORTFILE(FILENAME, STARTROW,
% ENDROW) ¶ÁÈ¡Îı¾Îļþ FILEN... |
999a8950c3c2d56ef8edf3d8b3d4be027409eb7f63b08b32c97ef40306e3fda0 | MATLAB | 3,929 | 93 | function palm_cohen(M,psi,res,plm,opts,y,m,c,o,p)
% Computes and saves effect sizes and a few other GLM estimates.
% This is intended to be invoked if "-saveglm" is used.
% If "-saveperms" is also used, then it will be executed for every permutation.
%
% This function isn't intended to be executed by the user, but be ... |
d84769b4c7b6b0a64b048ea3d4867c4f2bcfccda1d4a9e882275cfaa5478b81a | MATLAB | 3,929 | 119 | %% file example_CMTL.m
% this file shows the usage of Least_CMTL.m function
% and study how to capture tasks clusters.
%
%% OBJECTIVE
% argmin_W { sum_i^t (0.5 * norm (Y{i} - X{i}' * W(:, i))^2)
% + rho1 * eta (1+eta) trace(W (eta I + M)^-1 W')
% subject to: trace (M) = k, M \preceq I, M \in S_+^t, eta=... |
25e95894e2fd0b50fcbe0d6dd105e721fd10e22cba604c7d49714bfbb61ddb4b | MATLAB | 3,931 | 140 | function result=rrmse(x,y,h,kmax,attrib,plots,k,weight,res)
%RRMSE calculates the robust RMSECV and/or the robust RMSEP-value
% for RPCR and RSIMPLS.
%
% The robust RMSECV is described in:
%
% Engelen, S., Hubert, M. (2005),
% "Fast model selection for robust calibration methods",
% Analytica Chimica Acta, 544, ... |
9b52d42f69a38637133f8f7b6e32c6e6c2fb28735466d06bbe7227729f3c9847 | MATLAB | 3,938 | 88 | function [testClassPredicteds,classPerforms,conMats,tElapseds,OtherOutputs]=multiClassifiers(trainSet,trainClass,testSet,testClass,methods,options)
% trainSet, matrix, the training set with samples in columns and features in rows.
% Usuage:
% [testClassPredicteds,classPerforms,tElapseds,OtherOutputs]=multiClassifiers(t... |
9128ea180346d46758d4beb93859f42b62c4c24a281ddeb27a84332e1c9d17ab | MATLAB | 3,943 | 75 | % Demo code for mapping cortical thickness difference taking into account covariate of sex and age, i.e., ANCOVA, using MBM.
%
% This demo uses 72 empirical thickness maps from T1 maps in an open
% dataset: https://openneuro.org/datasets/ds002748/versions/1.0.5
%
% All input files are stored in 'data/demo_book_chapter... |
d0916134655d41fe2093b45cfccf519ad51351d289424c5b63ff62290569d31c | MATLAB | 3,944 | 100 | function [testClassPredicted,sparsity,Y,otherOutput]=nnlsClassifier(trainSet,trainClass,testSet,testClass,option)
% NNLS Classifier: testSet=trainSet*Y, s.t. Y>=0.
% Usage:
% [testClassPredicted,sparsity]=nnlsClassifier(trainSet,trainClass,[],testClass)
% [testClassPredicted,sparsity]=nnlsClassifier(trainSet,trainClass... |
10be1f0069a0b89201fc781cc655bfdd92163f2dff46cb6bf6efcb1b4bdcd933 | MATLAB | 3,948 | 148 | function [hdr,be] = read_hdr_raw(hname)
% Read a NIFTI header
% FORMAT [hdr,be] = read_hdr_raw(hname)
% hname - filename of image's header
% hdr - a structure containing header info
% be - whether big-endian or not
%__________________________________________________________________________
% Copyright (C) 2005-201... |
fcfd1d7828ea8ed7cb54dad69808d2afe3726e9297482048e0b895d1117c76a5 | MATLAB | 3,949 | 128 | %% 0. Setup
% Initialization
clear all; close all; clc;
% Add folder with raw data to path
addpath([pwd '/raw_data_mouse'])
% Add library with eNDM functions to path
addpath([pwd '/lib_eNDM_numeric'])
addpath([pwd '/lib_eNDM_analytic'])
% Load dataset of interest
load Tasic_CTMaps.mat %sw... |
475a2d69e1cdff022473593a4bb747472480a7248fecda56395a3c9fd2a8c5fe | MATLAB | 3,955 | 113 | function [A,S,Y,numIter,tElapsed,finalResidual]=orthnmfrule(X,k,option)
% Orthogonal-NMF based on NNLS: X=ASY, s.t. A'*A=I, Y*Y'=I, A,S,Y>0.
% Definition:
% [A,S,Y,numIter,tElapsed,finalResidual]=orthnmfrule(X,k)
% [A,S,Y,numIter,tElapsed,finalResidual]=orthnmfrule(X,k,option)
% X: non-negative matrix, dataset ... |
92d9c60527fa4fbcf4f8de8577ad4a08bf45788cc1267b951f9dcf9cb40820fa | MATLAB | 3,955 | 97 | %% figure
% 横轴：时间
% 纵轴：spike position
% 点的颜色：幅值
clear;
clc;
load('I:\930_project\paper_figure\waveform_table\Unit_info_SU\Unit_info_SU_patients\Unit_info_SU_patients_extend\Unit_info_SU_20250220_HS_NoNeedle_NoCuration_all_SpkTypes.mat');
fs = 25640;
big_amp_thres = 0;
FigureWidth = 30;
FigureHeight = 20;
... |
0a811893278a775a4e6aa00f8b103075ae3dbde5eff76e114d55adde58eacf8b | MATLAB | 3,956 | 139 |
%Demonstrates the mcrvm algorithm on a toy example (Three class classification problem). Part of the code was
%derived from Tipping's matlab code.
% @file mcrvm_example.m
% Author Arasanathan Thayananthan ( at315@cam.ac.uk)
% (c) Copyright University of Cambridge
%
% This library is free sof... |
3cbf3892b948d04b1016d214abb60f66f2ce207e70ae4743ff11c56365953b8a | MATLAB | 3,968 | 103 | classdef full_emgf < bt.model.full
% This model is set up to fit t0 keeping the prior fixed if the alpha peak is not present
% Note that prepare_for_fit must be used before spectrum() can be used
% Uniform priors and DB initial fit
properties
end
methods
function self = full_emgf() % Constructor
self.name... |
2b84de6124051518158b92987859c5f1d8e0f321fc49f07cd36887a0dc63e886 | MATLAB | 3,969 | 131 | function [x, infos] = semi_nmf(V, rank, in_options)
% Semi non-negative matrix factorization (Semi-NMF).
%
% The problem of interest is defined as
%
% min || V - WH ||_F^2,
% where
% H > 0.
%
% Inputs:
% V : (m x n) non-negative matrix to factorize
% rank : ra... |
834fe00cc82b15f02aee780f407ddd41ebafd3560512e9fcd2efc650ba66ca42 | MATLAB | 3,971 | 96 | classdef test_net < matlab.unittest.TestCase
properties
num_output
model_file
net
end
methods (Static)
function model_file = simple_net_file(num_output)
model_file = tempname();
fid = fopen(model_file, 'w');
fprintf(fid, [ ...
'name: "testnet" force_backward: true\n... |
b0bbea96ffaa4bcef2bf780c6ffae062b4a2f6124a2973fc406a4452eaa6185b | MATLAB | 3,971 | 136 | function [perf,conMat]=perform(testClassPredicted,testClass,c)
% Analyze the performance of classification
% For binary-class problem, the class labels should be 0 and 1.
% For m-class problem, the class labels should be 0,1,2,3,...,m-1.
% Usage:
% perf=perform(testClassPredicted,testClass,c)
% testClassPredicted: vect... |
f7890d7443069be07f1213fe085b4f5eb5a2f287f6c077f81aeb4ddf31312d6a | MATLAB | 3,974 | 104 | %% ================================================================
% Script: generate_group_templates.m
% Purpose:
% Generate group-level functional network templates from individual
% seed-based correlation maps using the MSC dataset.
%
% Description:
% - Loads a 20-network reference parcellation
% -... |
49da4b80ec8b555bc978e48272470c4f44c7b523a4166b9909458981c0dbe26b | MATLAB | 3,975 | 151 | % ========================================================================
%> @brief plaidStimulus TODO
%>
%>
%>
%> Copyright ©2014-2022 Ian Max Andolina — released: LGPL3, see LICENCE.md
% ========================================================================
classdef plaidStimulus < baseStimulus
properties
sf... |
94ea54ebd37fdb9fc5122e5d7b10dcbef66240e1b5afe14fd6d9c7961490980b | MATLAB | 3,977 | 105 | clear
%% load PreProcessed file
load('PreProcessed.mat');
%% Re-org deltas and drop lost trials so dataset has no missing values
% intialize empties and parameters
tables_compiled = cell(1,5);
trial_cutoff = 8; % number trials to keep
norm_toggle = 0; % 0 to not, 1 to normalize as below
% pull table values, drop NaN... |
1b54fe75efd74d80fda67bdd68e0f8bee4e798d8207df94fe717d604c0590020 | MATLAB | 3,980 | 125 | function motiontest()
screen = max(Screen('Screens'));
screenSize = [];
cycleTime = 1.5; % time for one sweep, determines max speed
bgColour = 0.5;
dotColour = [0 0 0]';
dotSize = 0.5;
nDots = 35;
backlight = false;
ptb = mySetup(screen,bgColour,screenSize);
% ---- setup dot motion path
nSteps = floor(cy... |
d4ec6910737562320ec348fe1bbfd6bcf5fa18d07e651826e7d0669e1e82a994 | MATLAB | 3,994 | 100 | function varargout = opx_opt_ui(varargin)
% OPX_OPT_UI MATLAB code for opx_opt_ui.fig
% OPX_OPT_UI, by itself, creates a new OPX_OPT_UI or raises the existing
% singleton*.
%
% H = OPX_OPT_UI returns the handle to a new OPX_OPT_UI or the handle to
% the existing singleton*.
%
% OPX_OPT_UI('CALL... |
da6e92ea4397933b146b32558d52c85d751661e887337aebd9bfff8a1890114a | MATLAB | 3,994 | 101 | function behav = msExtractBehavoir(behav, trackLength)
%MSEXTRACTBEHAVOIR Summary of this function goes here
% Detailed explanation goes here
ROI = uint16([behav.ROI(1) behav.ROI(1)+behav.ROI(3) behav.ROI(2) behav.ROI(2)+behav.ROI(4)]);
%% Removes background
% takes the median of each pixel from 100 frames spread... |
1a7d00f7202c276292c9a60ee85a3102badc354d5a2af1e1d61711a39a2bb4bd | MATLAB | 3,996 | 114 | function [A,Y,numIter,tElapsed,finalResidual]=nmfrule(X,k,option)
% NMF based on multiple update rules: X=AY, s.t. X,A,Y>=0.
% Definition:
% [A,Y,numIter,tElapsed,finalResidual]=nmfrule(X,k)
% [A,Y,numIter,tElapsed,finalResidual]=nmfrule(X,k,option)
% X: non-negative matrix, dataset to factorize, each column is... |
981829b814289f08632e526dc42dd69c52fd1e37d8a6e8af7d5ae548d535c932 | MATLAB | 4,001 | 124 | %% 0. Setup
% Initialization
clear all; close all; clc;
% Add folder with raw data to path
addpath([pwd '/raw_data_mouse'])
% Add library with eNDM functions to path
addpath([pwd '/lib_eNDM_numeric'])
addpath([pwd '/lib_eNDM_analytic'])
% Load dataset of interest
load Tasic_CTMaps.mat %sw... |
9d68192d09f2465e5afcf099eaeeedb070fb9f2ea632765fc1a46e697b18d471 | MATLAB | 4,007 | 151 | % SB1_POSTERIORMODE Find mode of posterior distribution (Bernoulli case)
%
% [W, UI, LMODE] = SB1_POSTERIORMODE(PHI,T,W,ALPHA,ITS)
%
% OUTPUT ARGUMENTS:
%
% W Parameter values at mode
% UI Inverse Cholesky factor of Hessian
% LMODE Log likelihood of data at mode
%
% INPUT ARGUM... |
7cb802339868e9205dfe987b95c0accc883248677d955df83e6a226b81c3315c | MATLAB | 4,011 | 119 | function [Rlatt,Rrp,ind_rp,eff] = latmio_dir_connected(R,ITER,D)
%LATMIO_DIR_CONNECTED Lattice with preserved in/out degree distribution
%
% [Rlatt,Rrp,ind_rp,eff] = latmio_dir_connected(R,ITER,D);
%
% This function "latticizes" a directed network, while preserving the in-
% and out-degree distributions. In w... |
1a2de29615774e1f6750ce16feff25bb95ff8498aaa833f5db80a48ba3c15c7b | MATLAB | 4,013 | 108 | function [ fl, desc ] = GetMLType(SVM)
fl = [];
if exist('SVM','var') && ~isempty(SVM) && isfield(SVM,'prog')
switch SVM.prog
case {'MikRVM', 'MKLRVM', 'MVTRVR','FAMRVR'}
fl = 'RVM'; desc = 'Relevance Vector Machines';
case {'LIBSVM','SVMLIT','SVMPRF','PROSVM','CUDSVM'}
... |
055a2e9228147614ca6d5e9d71b805e8d9ad9a16c6324437a5386d7dd683a167 | MATLAB | 4,016 | 102 | function nk_MLInterpreter_batch(paramfile)
global NM
% Read parameter file
% ===========================
if ~exist(paramfile,'file')
error([paramfile 'not found. Abort job!']);
end
fid = fopen(paramfile);
params = textscan(fid, '%s');
fclose(fid);
% Parse parameters in params
% =================================... |
dafe0e798bb4888251113b3f608bf2b22554f39a89c63ed18c1cdbe2139465d2 | MATLAB | 4,019 | 100 | %% ================================================================
% Script: NeonatalAgePrediction_SVR_FC_dHCP_GSR.m
% Purpose:
% Evaluate the predictive validity of conventional functional
% connectivity (FC) features in predicting neonatal chronological age
% using linear SVR with 10-fold cross-validati... |
9d850285d36a713eadf222dad3f76532a6d5baa64ef5eef27e4a68375e11966b | MATLAB | 4,020 | 125 | function [varargout] = LoadIntanSpikes(varargin)
% MClust loading engine for Intan spike data.
%
% INPUT
% fname - filename of spikes file
% records_to_get - (optional) which spikes to retreive
% record_units - (optional) code for the format of records_to_get
% 1 - records_to_get is a timestamp list
% ... |
08c357d86266203855ae5f628ac66615090506b50d659e19888591ce38275e2d | MATLAB | 4,026 | 91 | function [I,Q,F]=motif4struct_wei(W)
%MOTIF4STRUCT_WEI Intensity and coherence of structural class-4 motifs
%
% [I,Q,F] = motif4struct_wei(W);
%
% Structural motifs are patterns of local connectivity in complex
% networks. Such patterns are particularly diverse in directed networks.
% The motif frequency ... |
9a723a8bab04522b40576b0ab5c5ebc88792ddba6e36032a9bfd38fa706ef067 | MATLAB | 4,029 | 105 | clear
%% load PreProcessed file
load('PreProcessed.mat');
%% Re-org deltas and drop lost trials so dataset has no missing values
% intialize empties and parameters
tables_compiled = cell(1,5);
trial_cutoff = 8; % number trials to keep
norm_toggle = 0; % 0 to not, 1 to normalize as below
% pull table values, drop Na... |
40c511626f60a4f9ab110dd42491fba3d01cd9aff57d9f7df5b1997181b49618 | MATLAB | 4,039 | 147 | % SB1_EXAMPLECLASSIFY Example of Sparse Bayes Classification
%
% SB1_EXAMPLECLASSIFY(N,KERNEL,WIDTH,MAXITS)
%
% INPUT ARGUMENTS:
%
% N Number of training points (up to 250)
% KERNEL Kernel function to use (see SB1_KERNELFUNCTION)
% WIDTH Kernel length scale parameter
% MAXITS Maximum... |
f108d92e7e1b909377fc1f3f61447655bc42e3a99e30c44847628f95ca1ba5e8 | MATLAB | 4,047 | 91 | function [DEVMAP, PX] = nk_Devmap_config(DEVMAP, PX, NM, defaultsfl, parentstr)
if ~exist('defaultsfl','var') || isempty(defaultsfl), defaultsfl = 0; end;
algostr = 'pls';
covdesc = 'undefined';
covmat = [];
loo = false;
glabel = [];
ncomp = 1;
act = 0;
if ~defaultsfl
if ... |
874a41cd0bc91d5ad3ade196902a675556a9fd8b1528310d13e86e8598679cf1 | MATLAB | 4,049 | 109 | <<<<<<< HEAD
function Z = zstream(action,D)
% Compress/decompress stream of bytes using Deflate/Inflate
% FORMAT Z = zstream('C',D)
% D - data stream to compress (converted to uint8 if needed)
% Z - compressed data stream (uint8 vector)
% FORMAT D = zstream('D',Z)
% Z - data stream to decompr... |
a3a2bec539cc41bf492975b910db87857728c0cbc3e646d20f9759dd99fcfbd0 | MATLAB | 4,049 | 111 | function [A,Y,numIter,tElapsed,finalResidual]=sparsenmfnnls(X,k,option)
% Sparse-NMF based on NNLS: X=AY, s.t. X,A,Y>0.
% Definition:
% [A,Y,numIter,tElapsed,finalResidual]=sparsenmfnnls(X,k)
% [A,Y,numIter,tElapsed,finalResidual]=sparsenmfnnls(X,k,option)
% X: non-negative matrix, dataset to factorize, each co... |
11198905960695c482fda16d27e20de062b3960e05b7bc01a5dc44bb2018b95b | MATLAB | 4,051 | 132 | function [ YYY, LLL, CCC, fl ] = nk_PerfADASYN(Y, L, C, IN, noconcatfl)
adasyn_beta = []; %let ADASYN choose default
adasyn_kDensity = []; %let ADASYN choose default
adasyn_kSMOTE = []; %let ADASYN choose default
adasyn_normalized = false;%false... |
aeb778b54f7ac3a280936d915150812ffba92a5a4408c646e794402baaa72aca | MATLAB | 4,051 | 144 | function [x, infos] = ronmf(V, rank, in_options)
% Robust online non-negative matrix factorization (ONMF) with outliers (RONMF) algorithm.
%
% Inputs:
% matrix V
% rank rank
% in_options options
% Output:
% w solution of w
% infos information
%
% References:
% ... |
fc865d6eb38ed363620fbeafd2b72cb833d9c43c8cf6f536db37caf7a506c044 | MATLAB | 4,055 | 130 | clc;clear;close all;
%cd("D:\DKU study\DKU\SRS 2021\Susan's work\TemporalContextCells-ECmonkey-master\Analyses\Data");
d=load('Jupiter_test_data_v3_align0to8.mat');
spikes=d.data.spikes;
%cd("D:\DKU study\DKU\SRS 2021\Susan's work\TemporalContextCells-ECmonkey-master\Data\PSTH_1_to_401_jupiter\PSTHwithFittingCurve\... |
7b733ccd4aade2dd8ddf55ca1feb8567b9ffd5fc0a520b316eacabb57c074537 | MATLAB | 4,056 | 147 | function [classes, perf, dist, sim, minimum] = nk_MultiDecideErrorCorrOutCodes(X, L, ClX, Groups, Coding, Decoding, ~, ProbComp)
% [classes, ECOC] = nk_ErrorCorrOutCodes(X, L, ClX, Coding, Decoding, Weightflag)
% ===================================================================================
%
% This function uses... |
680c06d8550c35547d93cd024ba79f15b74506a886515dac16553edcac4a5b7c | MATLAB | 4,061 | 118 | % Sample data (replace with your actual data)
dataset = {
'25,776 patients (1,026 mortalities)';
'1,149 fetal heart images';
'259 elderly sarcopenia patients';
'161 TTE 3DE images';
'80 elderly patients with ALHF';
'EchoNet-Dynamic, CAMUS, and local dataset';
'CAMUS dataset (450 pati... |
1759139c6561652405af053cde8cb6c13b4b223f7ae740ad0020a8897619c349 | MATLAB | 4,065 | 137 | function [K,runtime]=lRWkernel(Graphs, lambda, small)
% Compute a random walk kernel for a set of labeled graphs
% Copyright 2012 Karsten Borgwardt, Nino Shervashidze
% Input: Graphs - a 1xN array of graphs
% Graphs(i).am is the adjacency matrix, Graphs(i).nl.values is a column vector of node labels.
% lambda ... |
fcd331589e26ca9a382b2d718fe214720d1c0e776455cf53498cb757c5198d8b | MATLAB | 4,065 | 142 | c1 = dir('ALL_CSV/sub-P*_ses-*_T1w_iso_biascorrected_histogram.csv');
c2 = dir('ALL_CSV/sub-P*_ses-*_T1w_iso_biascorrected_calib-lin_histogram.csv');
c3 = dir('ALL_CSV/sub-P*_ses-*_T1w_iso_biascorrected_calib-nonlin_histogram.csv');
c4 = dir('ALL_CSV/sub-P*_ses-*_T1w_iso_biascorrected_calib-nonlin2_histogram.csv');
c4b... |
63d6b036b12d1ae24c3ba8f56cd1fe074363df95320259a52d686f6072cc418e | MATLAB | 4,067 | 101 | %% ================================================================
% Script: NeonatalAgePrediction_SVR_FOCA.m
% Purpose:
% Validate predictive validity of FOCA (Functional Orthogonal
% Connectivity Architecture) features in predicting chronological
% age during the neonatal period using linear SVR.
%
% ... |
faa8d3a69905435e8c7c5af4ac9fac40a9719491229a959e824534e6ac641002 | MATLAB | 4,072 | 94 | %==========================================================================
%FORMAT AgreeFeat = ProbabilisticFea( F, EnsStrat )
%==========================================================================
%Determines optimum feature space through the between-classifier agreement
%of s... |
b89d55f5938a227afd8787f759e88fc481ecad4acb5fcda625caa43d3351bb6b | MATLAB | 4,074 | 123 | %% Demo [Analytics vs Numeric]
%
% *****NB: Only the numeric function called is modified to include multiple costfun
% options while we investigate the proper naming and reorganization of the
% analytic functions*****
% The goal of this demo is to apply eNDM to mouse data.
% Uses numerical solutions for models using ... |
42e915e16453c3d5889a9027bfb4ad00d510a50696e00e4f62d310b1b4f785f3 | MATLAB | 4,077 | 145 | %% FUNCTION Construct_iMSF
% Prepares input for iMSF optimization. This function is not intended to
% directly called by users. See the Logistic_iMSF function.
%
%% LICENSE
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published... |
cee37767b2b83bef6b22851511a2ee8a9474c1e3c634fe28a6a92b997b8feb29 | MATLAB | 4,080 | 119 | function [C, C_raw, results_deconv, cc] = HALS_temporal(Y, A, C, maxIter, deconv_options)
%% run HALS by fixating all spatial components
% input:
% Y: d*T, fluorescence data
% A: d*K, spatial components
% C: K*T, temporal components
% maxIter: maximum iteration number
% deconv_options: struct variable,... |
adecc3d071977b4c34d86df2839a8cbc151dfd013753238f42309a79d7b42623 | MATLAB | 4,081 | 123 | function [W, medialNeighbors] = triangulation2adjacency(faces, varargin)
%% TRIANGULATION2ADJACENCY Returns adjacency matrix from face and ROI allocations
%
% 
%
%% Syntax
% W = triangulation2adjacency(faces, rois)
%
%
%% Description
% `W = triangulation2adjacency(f... |
41fd24d312ed5a6b50a3bab2d0b5d350b997f484fe324d497769a77b1fd68fcd | MATLAB | 4,082 | 123 | classdef template_spatial < bt.model.template
% This is a template function that allows rapid prototyping of new spatial
% fitting by leveraging model.params.spatial_spectrum with absolutely
% no regard for performance
properties
p
n_modes = 6
n_truncate = 6 % Seems like a useful starting point for cosine va... |
be78cdddac08d57351cfc613749d3cfe8ce33e0f35b2a434c4805175cd0dca77 | MATLAB | 4,084 | 84 | %
% EDFMEX uses the Eyelink EDF access API to read eyelink data
% files into a matlab structure.
%
% Usage:
%
% edfdata = edfmex(filename,[start_at_record_number],[end_at_record_number],
% [load_sample_flag], [load_event_flag],[consistency_check_flag]);
%
% Input:
%
% filename ... |
c1b83417cb2b20aed26dfa3cc46ac52d65ce3adf685da26e1490fe9d40e3c3d3 | MATLAB | 4,088 | 156 | % Non-negative Matrix Factorization via Nesterov's Optimal Gradient Method.
% NeNMF: Matlab Code for Efficient NMF Solver
% Reference
% N. Guan, D. Tao, Z. Luo, and B. Yuan, "NeNMF: An Optimal Gradient Method
% for Non-negative Matrix Factorization", IEEE Transactions on Signal
% Processing,... |
05dbac160ce80e9cb0cca886d7faf2f3123eac556887a4a12ce2340247e38f2f | MATLAB | 4,092 | 109 | % File: sst_jags_and_save.m
function destMatPath = sst_jags_and_save(prep, modelfile, nChains, nSamples, nBurnin, nThin)
% SST_JAGS_AND_SAVE Use PREP struct to run JAGS (matjagsRAND, parallel=0) and save outputs.
if nargin < 2 || strlength(string(modelfile))==0, modelfile = "MainModel"; end
if nargin < 3 || i... |
eb9c9298b07ef4bb5303e3337570a6af8091ae5b4089192109171d5fa41c67d8 | MATLAB | 4,097 | 89 | %% Prep: Generate example data
rng(123); % Set the random seed
n = 1000;
x = randn(n, 3);
A = [2, 1, -1; 1, 2, 1; 1, -1, 2];
s = x * A'; % Mix the signals
% in the Matlab implementation of fastica(), whitening of the data is
% internally performed, so we do not need to do it before hand
% Perform ICA using FastICA
[i... |
295ffd5c7e9b4128c3edb75835a3ba66e03604f9fcfa7d16c40e6aad29a8ef1c | MATLAB | 4,104 | 113 | function [Bk,intk,sigmayykmaxrew_k,sigmattkmaxrew_k,Hopt] = extractmcdregres(resMcd,T,y,kmax,n,q,k,h,cutoffWeights)
%EXTRACTMCDREGRES is an auxiliary function for cross-validation with RPCR and RSIMPLS.
% It extracts mcd-regression coefficients for a certain k based on
% the raw estimate of the slope and intercept o... |
4190e633dda08e13b8bcca58189ac1b41777276cb330375067151850a2287f16 | MATLAB | 4,105 | 122 | function [mappedX, mapping] = npe(X, no_dims, k, eig_impl)
%NPE Perform the Neighborhood Preserving Embedding algorithm
%
% [mappedX, mapping] = npe(X, no_dims, k)
% [mappedX, mapping] = npe(X, no_dims, k, eig_impl)
%
% Runs the Neighborhood Preserving Embedding algorithm on dataset X to
% reduce it to di... |
ad6ac703bdb486d2a9fcd51f259a9848e5e42fc28253f4b98f495f11dbbbff8d | MATLAB | 4,105 | 147 | % Parameters
projectName = 'Name';
saveLocation = 'C:\...';
acclimationTiming = [0 4800];
baselineTiming = [4800 5400];
eventTiming = [0 1 29];
groupingFile = {'C:\ ... '};
viewPointFile = {'C:\ ...'};
timingFile = 'C:\ ... ';
dv = 'actinteg';
groupName = 'grp_GroupLabel_1';
eventIndex = 'Light';
... |
12b4d35d839f6b825b8d6ec180171c212b1fb6f6aa2308536a6ffde8d8ee4ba9 | MATLAB | 4,107 | 138 | function [cellids1 cellids2 bindex1 bindex2 cond_cellidx] = find_cellidx(condition,cell_dir,cellidx1,cellidx2,pdcells)
%FIND_CELLIDX Cells matching selection criteria
% [cellids1 cellids2 bindex1 bindex2 cond_cellidx] = find_cellidx(condition,cell_dir,cellidx1,cellidx2,pdcells)
% Finds cellids matching selection... |
fbb3242e34ce2ed215cedef500d4d9400e44ada3c1a9a06696ab047378f9d511 | MATLAB | 4,109 | 119 | clear; clc;
load('...\LFPdata_Fig5A')
%%
% Color
paintLFP{1,1}=[.4 0 0]; % Small
paintLFP{2,1}=[.9 .2 .2]; % Med
paintLFP{3,1}=[1 .6 .6]; % Large
figure()
set(gcf,'Position',[-590 0 530 916])
tiledlayout(2,1)
xTickLabels = {'Delta (1-4 Hz)', 'Theta (4-7 Hz)', 'Alpha (8-12 Hz)', 'Beta (13-30 Hz)', 'low... |
411feed5d1d4a19aa8f4c16bc073d053cc02e55ca76340541cbabcada5104ac1 | MATLAB | 4,113 | 145 | function [model] = ml_binaryclass_HSVM(X,y,options)
% ml_binaryclass_HSVM(X,y,options)
%
% Description:
% - Fits a linear classifier using Huberized SVM
%
% Options:
% - addBias: adds a bias variable (default: 0)
% - epsilon: threshold for Huberized hinge loss (default: 0.5)
% - lambdaL2: strength of L2-regulari... |
cd5e0e2329adbfdf2374b5f713c7fb4b61993432466668c59de4afb32787c4e9 | MATLAB | 4,117 | 210 | function [x,y,z]=readsol(fname,K,m)
%READSOL Read solution from CSDP
%
% [x,y,z]=readsol(fname,K,m)
%
% fname File name to read solution from.
% K structure of the matrices.
% m size of y vector.
%
% Modified 7/15/04, for greater MATLAB acceleration.
%
%
%
% This file is part of the Matlab T... |
9cba2e3ca10074df56e0686fce7954a31f7cfa94a827cd0b64dad76abe83e752 | MATLAB | 4,123 | 162 | function [surface, M, S] = calc_mass_stiffness(surface, lump)
% calc_mass_stiffness.m
%
% Calculate the mass and stiffness matrices of the input surface/volume
%
% Inputs: surface : surface structure with fields
% 'vertices' - vertex locations [Nx3], N = number of vertices
% 'faces' ... |
c4a0e0049cd4ad9fbebb797c6c126339c6b0e33f84ee554002e53952f2c2a5fc | MATLAB | 4,137 | 110 | classdef spatial_express_t0_phased < bt.model.template_spatial
% This is a template function that allows rapid prototyping of new spatial
% fitting by leveraging model.params.spatial_spectrum with absolutely
% no regard for performance
properties
% No new properties required by this derived class
end
methods
... |
4ebe18a730d1027ec664d615355516e85d32e64e0f02d716b9413a105e4c3f55 | MATLAB | 4,144 | 118 | function [DM LrC valid_channels] = LRatio_pd_ssrt(cellid,feature_names,valid_channels)
%LRATIO L-ratio and Isolation Distance.
% [DM LRC] = LRATIO(CELLID,FEATURE_NAMES,VALID_CHANNELS) calculates
% cluster quality measures (Isolation Distance, DM; L-ratio,LRC) for a
% given cell (CELLID) using the given clus... |
1464b06157872d72b0d7e39d0460329719240de3ab850ef9ca44f38b7fe0768c | MATLAB | 4,145 | 112 | function mappedX = llc(X, neighbor, no_dims, R, Z, eig_impl)
%LLC Runs the LLC algorithm (given information on the formed factor analyzers)
%
% mappedX = llc(X, k, no_dims, R, Z, eig_impl)
%
% Runs the LLC algorithm (given information on the formed factor
% analyzers). The variable X contains the dataset (transposed)... |
e246bf172828aaf9e0f4632cc59a25352328bb6eff763a5d2520a3abaaa678bf | MATLAB | 4,154 | 130 | function [mappedX, mapping] = hlle(X, no_dims, k, eig_impl)
%HLLE Runs the Hessian LLE algorithm
%
% mappedX = hlle(X, no_dims, k, eig_impl)
%
% Runs the Hessian LLE algorithm on dataset X to reduce its dimensionality
% to no_dims. The variable k specifies the number of nearest negihbors that
% is used.
%
%
% This f... |
b98117e0be065cb39ad8869c132bedfd48e820a234864b87cb2b215c93360f5c | MATLAB | 4,157 | 127 | function options = pnopt_optimset( varargin )
% pnopt_optimset : Set options for PNOPT
%
% options = pnopt_optimset( 'param1', val1, 'param2', val2,... ) creates an options
% structure in which the named parameters have the specified values. Default
% values are used for parameters that are not specified.
%
% pn... |
2532ecb46ece0b3ecab207d10fd84544191f5a8c86a7c6e23f391d248bd7bb2a | MATLAB | 4,162 | 128 | %% plotting sample traces for figs
% load deconvolved trace
trace_ex = load("085_LEC286239_D1awake_DeconTrace.csv");
times = readtable("085_LEC286239_D1awake_notes.csv");
% z-score for scaling consistency
Decon_Z = zscore(trace_ex, 0, 2);
frames_odors = ceil(times.Var1/1000*5);
% gap between traces; adjust ... |
00956bd0cf919f3b713ff1ed06931d7955631a2a63c06410549d2d9a078ada7a | MATLAB | 4,164 | 123 | <<<<<<< HEAD
function varargout = subsref(this,subs)
% Subscript referencing for GIfTI objects
%__________________________________________________________________________
% Copyright (C) 2008 Wellcome Trust Centre for Neuroimaging
% Guillaume Flandin
% $Id: subsref.m 7577 2019-04-24 08:59:56Z guillaume $
if l... |
5a9663484bb00986ef3730b774df757983afb0d4dcbe71e38e0dc2879a1cd662 | MATLAB | 4,167 | 100 | function mp2rage_run_estimate_T1(estimateT1)
%MP2RAGE_RUN_ESTIMATE_T1 Executable job that estimates the quantitative
%T1map and R1map=1/T1map from the UNI image and the sequence parameters.
%
% The core code of this function is an implementation of https://github.com/JosePMarques/MP2RAGE-related-scripts/blob/master/fun... |
6048e92b4389f6d41770f21ccf59790ae33d997a08b0fc6bc4f86d9b66875354 | MATLAB | 4,169 | 132 | function [eigvector, eigvalue, elapse] = KLPP(W, options, data)
% KLPP: Kernel Locality Preserving Projections
%
% [eigvector, eigvalue] = KLPP(W, options, data)
%
% Input:
% data -
% if options.Kernel = 0
% Data matrix. Each row vector... |
fe5ae7589cf5b4b79a60b07486dc97eeaeb229fe47a3b8310ab7a81379270065 | MATLAB | 4,171 | 152 | function [Z,p1,U,p2,mrl,ftm] = b_rao3_mod(x)
%RAO Rao's Spacing Test.
% [Z,P1,U,P2] = RAO(X) calculates Rayleigh's Z-Statistic (Z) and Rao's
% U-Statistic (U) with the corresponding p-values (P1 and P2). Input
% argument X is the vector of phase angles in radians.
%
% See also WATSON and WATSONTWO.
... |
e3b68bcfc7d0f7b0f9d8b89cc3a7160dce5f72e1c594bd51e8115d0474c62071 | MATLAB | 4,173 | 116 | function [Xk]=constrainedQPActive(H,C,A,B)
% solve 0.5*tr(X^THX + CX)
% s.t. A*X -B <=0
% -X<=0
% where A is a row vector, B is a row vector
%%%%
% Copyright (C) <2012> <Yifeng Li>
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as p... |
435146c614658d59980ab4699ce8505a3b62daf7d6a7bc4e6c33c8568c9d8c37 | MATLAB | 4,175 | 85 | nsub = 25;
for k = 1:nsub
if k == 1
subject = 'sub23';
familiarity_rating = [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0 0 1 0 1 1 0 0 0 0 0 0 0 1 0 0 0];
elseif k == 2
subject = 'sub22';
familiarity_rating = [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1... |
7619704ab40113d4703adfa2fa84ec9c5cc10dccd2728e4606bc21d103fb4570 | MATLAB | 4,178 | 93 | % homeo = [2344, 801, 1162, 2305, 1540, 3062, 3240, 1717];
rng(0);
% studylist = {'asyn_mouse', 'asyn_human'};
studylist = {'asyn_mouse'};
niters = 1;
R2s = zeros(1,niters);
for i = 1:length(studylist)
outputs_ndm = stdNDM_mouse('study',studylist{i},'bootstrapping',1,'niters',100,...
'w_dir',1,'volcorrect',... |
720e934365ee48a7292c63bfec15616affc3b3fb2bfbcd18eb162a5a44a49b10 | MATLAB | 4,185 | 109 | function [K, Pset] = kfcnnls2(CtC, CtA)
% kernel FCNNLS solving min ||phi(A)-phi(C)*K||_F, s.t. K>=0, for given A and C.
% CtC, kernel matrix, k(C,C).
% CtA, kernel matrix, k(C,A).
% References:
% [1] \bibitem{NNLS2004}
% M.H. Van Benthem and M.R. Keenan,
% ``Fast algorithm for the solution of large-scale non-negaive c... |
94bdcf812eb7b258a59d5f0af82e77c64fa8a568232f5bcdf771b71e767321b5 | MATLAB | 4,188 | 136 | % script to create animalBehaviorAnalysis object and perform built-in analyses
%
% SurveyBott, 2018, info@surveybott.com
function cmd = run_aba(cmd,varargin)
% inputs
p = inputParser;
p.addRequired('cmd',@(x) isstruct(x) && all(isfield(x,{'mutant','lights','files','savePath','projectName'})) && all(isfield(x.mutant,{... |
b8e2df42d6639b1291ba138d9c072ecc7f29b2071c91405f6c42112ae93e1d00 | MATLAB | 4,192 | 110 | function [K, Pset] = NNQPActiveSet(H, G)
% kernel l1 Regularized NNQP solving min K' H K + G' K s.t. K>=0, for given H and G.
% H: kernel matrix, Hessian.
% G: kernel matrix, Gradient.
% References:
% [1] \bibitem{NNLS2004}
% M.H. Van Benthem and M.R. Keenan,
% ``Fast algorithm for the solution of large-scale non-negai... |
f0a5c2a444a75f80cbe32d68ce53243d2b1d86d003e3db59a4926412c533ceca | MATLAB | 4,194 | 82 | function [PFE, act] = nk_ProbalisticFea_config(PFE, parentstr, defaultsfl)
if ~exist('defaultsfl','var') || isempty(defaultsfl), defaultsfl = 0; end
tPFE.flag = 0; % Perform PFE
tPFE.Mode = 1; % Perform PFE
tPFE.Perc = 90; % Absolute number/Percentage of CV1 feature mask agree... |
31c0e62734ed91e715ae24086db38e7ac6e27808d2f37ffad1e7825641d55597 | MATLAB | 4,196 | 149 | function [exactp_ersp,maskersp,alpha2] = boostat_eeglab_J(P,f,alpha,naccu,isim,mcorrect,varargin)
%BOOTSTAT_EEGLAB_J Bootstrap statistics on time-frequency data
% [exactp_ersp,maskersp,alpha2] = boostat_eeglab_J(P,f,alpha,naccu,isim,mcorrect,...)
% Performs bootstrap statistics using BOOTSTAT function o... |
d1071be0f74fe1804ce2e6bcbfb5868047c99d9294ccad819e2ba569d43ede16 | MATLAB | 4,198 | 113 | function machine = train_lin_rbm(X, h, eta, max_iter, weight_cost)
%TRAIN_RBM Trains a Restricted Boltzmann Machine with Gaussian hiddens
%
% machine = train_lin_rbm(X, h, eta, max_iter, weight_cost)
%
% Trains a first-order Restricted Boltzmann Machine on dataset X. The RBM
% has h hidden nodes (default = 20). The t... |
dc01fcb37ee3c469a5659ec809091605286d52eee3c5d5e1bcd26891d4107813 | MATLAB | 4,202 | 83 | function runOptickaTests(varargin)
%> Run all Opticka class-based unit tests and display results.
%>
%> Usage:
%> runOptickaTests %> run all tests
%> runOptickaTests('verbose') %> run with verbose output
%> runOptickaTests('HSMOption1Test') %> run a specific test class
%>
%> This script is... |
d345c229b125a18294ca516e5a64d175d5f2cbf44cd8ee7d4d5b7225cc407fc2 | MATLAB | 4,205 | 121 | function [f_out,fit_data,plot_data] = fit_spectrum(model,target_f,target_P,prior_pp,initial_values,npoints,target_state,skip_fit,debugmode)
% Take in a model, spectrum, prior distribution, initial values, and chain length
% Return a feather object
% The posterior distribution correctly reflects the skip_fit state of... |
f25615e46f1a17bd0631301a5b574a7e16e4ddd831e9d11c6355e085c506ce86 | MATLAB | 4,209 | 109 | function [mappedX, mapping] = kernel_pca(X, no_dims, varargin)
%KERNEL_PCA Perform the kernel PCA algorithm
%
% [mappedX, mapping] = kernel_pca(X, no_dims)
% [mappedX, mapping] = kernel_pca(X, no_dims, kernel)
% [mappedX, mapping] = kernel_pca(X, no_dims, kernel, param1)
% [mappedX, mapping] = kernel_pca(X, no_... |
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