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|---|---|---|---|---|---|---|---|---|
github | xyza11808/MATLAB-master | updateBG.m | .m | MATLAB-master/CaImAn-MATLAB-master/Sources2D/updateBG.m | 3,049 | utf_8 | af06c762fefa79de54a4a1b848bcb450 | function [B, F] = updateBG(obj, Y, nb, model)
%% initialize background given the rank of background
% input:
% Y: d*T or d1*d2*T, imaging data
% nb: scalar, number of the background
% mdoel: string, {'nmf', 'svd'}
% output:
% B: d*nb matrix, spatial components of the background
% F: nb*T matrix, temporal co... |
github | xyza11808/MATLAB-master | deconvolveCa.m | .m | MATLAB-master/CaImAn-MATLAB-master/deconvolution/deconvolveCa.m | 10,631 | utf_8 | 797b0a67be778784e549bd3bccfded8c | function [c, s, options] = deconvolveCa(y, varargin)
%% infer the most likely discretized spike train underlying an fluorescence trace
%% Solves mutliple formulation of the problem
% 1) FOOPSI,
% mininize_{c,s} 1/2 * norm(y-c,2)^2 + lambda * norm(s,1)
% subject to c>=0, s>=0, s=Gc
% 2) constrained FOOPSI... |
github | xyza11808/MATLAB-master | constrained_oasisAR2.m | .m | MATLAB-master/CaImAn-MATLAB-master/deconvolution/oasis/constrained_oasisAR2.m | 7,946 | utf_8 | c8e60c17c59fcd3a3d6987960dd2b38c | function [c, s, b, g, lam, active_set] = constrained_oasisAR2(y, g, sn, optimize_b,...
optimize_g, decimate, maxIter)
%% Infer the most likely discretized spike train underlying an AR(2) fluorescence trace
% Solves the sparse non-negative deconvolution problem
% min lam |s|_1
% subject to |y-c|_2^2 <= sn^2*T
%% ... |
github | xyza11808/MATLAB-master | constrained_oasisAR1.m | .m | MATLAB-master/CaImAn-MATLAB-master/deconvolution/oasis/constrained_oasisAR1.m | 7,964 | utf_8 | 1fa1ef27fab2c6f3cb39044ff7cb3330 | function [c, s, b, g, lam, active_set] = constrained_oasisAR1(y, g, sn, optimize_b,...
optimize_g, decimate, maxIter)
%% 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_... |
github | xyza11808/MATLAB-master | thresholded_oasisAR2.m | .m | MATLAB-master/CaImAn-MATLAB-master/deconvolution/oasis/thresholded_oasisAR2.m | 8,034 | utf_8 | 564e59160482255e62bda2476baa8dd2 | function [c, s, b, g, smin, active_set] = thresholded_oasisAR2(y, g, sn, optimize_b,...
optimize_g, decimate, maxIter, thresh_factor)
%% 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 t... |
github | xyza11808/MATLAB-master | thresholded_nnls.m | .m | MATLAB-master/CaImAn-MATLAB-master/deconvolution/oasis/thresholded_nnls.m | 6,456 | utf_8 | 5c308633f5a8396ec8362e7f1d1c8b38 | function [c, s] = thresholded_nnls(y, g, sn, smin, shift, win, tol, maxIter, mask, threshold_factor)
%% Infer the most likely discretized spike train underlying an AR(2) fluorescence trace
% Solves the sparse non-negative deconvolution problem
% min 1/2|Ks-y|^2 + lam * |s|_1 subject to s_t = c_t-g c_{t-1} >= 0
%% inp... |
github | xyza11808/MATLAB-master | foopsi_oasisAR1.m | .m | MATLAB-master/CaImAn-MATLAB-master/deconvolution/oasis/foopsi_oasisAR1.m | 5,080 | utf_8 | 357b489226243ef943010317cb587a2c | function [c, s, b, g, active_set] = foopsi_oasisAR1(y, g, lam, optimize_b,...
optimize_g, decimate, maxIter)
%% 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} >= ... |
github | xyza11808/MATLAB-master | onnls.m | .m | MATLAB-master/CaImAn-MATLAB-master/deconvolution/oasis/onnls.m | 6,294 | utf_8 | dcfe4a5d47ee26b8fe92a3258b7aabd2 | function [c, s] = onnls(y, g, lam, shift, win, tol, maxIter, mask, smin)
%% Infer the most likely discretized spike train underlying an AR(2) fluorescence trace
% Solves the sparse non-negative deconvolution problem
% min 1/2|Ks-y|^2 + lam * |s|_1 subject to s_t = c_t-g c_{t-1} >= 0
%% inputs:
% y: T*1 vector, vth... |
github | xyza11808/MATLAB-master | foopsi_oasisAR2.m | .m | MATLAB-master/CaImAn-MATLAB-master/deconvolution/oasis/foopsi_oasisAR2.m | 5,099 | utf_8 | c398931976fdbed587f25521ba4fb1c3 | function [c, s, b, g, active_set] = foopsi_oasisAR2(y, g, lam, optimize_b,...
optimize_g, decimate, maxIter)
%% Infer the most likely discretized spike train underlying an AR(2) 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_1 c_{t-1}- ... |
github | xyza11808/MATLAB-master | thresholded_oasisAR1.m | .m | MATLAB-master/CaImAn-MATLAB-master/deconvolution/oasis/thresholded_oasisAR1.m | 7,760 | utf_8 | 2406eaf76c6706a61213e5a68778faf1 | function [c, s, b, g, smin, active_set] = thresholded_oasisAR1(y, g, sn, optimize_b,...
optimize_g, decimate, maxIter, thresh_factor)
%% 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 subject to s_t = c_t... |
github | xyza11808/MATLAB-master | deconvCa.m | .m | MATLAB-master/CaImAn-MATLAB-master/deconvolution/oasis/deconvCa.m | 11,612 | utf_8 | 54f929ab3540e783c4822ea127e20a93 | function [c, s, kernel, iter] = deconvCa(y, kernel, smin, fit_gt, debug_on, sn, maxIter, theta, lambda)
%% deconvolve calcium traces to infer spike counts
%% inputs:
% y: 1*T vector, observed calcium traces
% kernel: struct variable with two fields {'fhandle', 'pars', nMax}. kernel
% deterines the convolution ke... |
github | xyza11808/MATLAB-master | foopsi_kernel.m | .m | MATLAB-master/CaImAn-MATLAB-master/deconvolution/examples/foopsi_kernel.m | 5,080 | utf_8 | 357b489226243ef943010317cb587a2c | function [c, s, b, g, active_set] = foopsi_oasisAR1(y, g, lam, optimize_b,...
optimize_g, decimate, maxIter)
%% 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} >= ... |
github | xyza11808/MATLAB-master | nnls_spatial_thresh.m | .m | MATLAB-master/CaImAn-MATLAB-master/endoscope/nnls_spatial_thresh.m | 3,049 | utf_8 | e052e65cb8b366c093376130427ea016 | function A = nnls_spatial_thresh(Y, A, C, active_pixel, maxN, smin, sn)
%% run HALS by fixating all spatial components
% input:
% Y: d*T, fluorescence data
% A: d*K, spatial components
% C: K*T, temporal components
% active_pixel: d*T binary matrix, indicating the nonzero elements of A
% maxN: scalar, maxi... |
github | xyza11808/MATLAB-master | nnls_spatial.m | .m | MATLAB-master/CaImAn-MATLAB-master/endoscope/nnls_spatial.m | 3,044 | utf_8 | b17a94c10df2ce922638cf55c9837f4c | function A = nnls_spatial(Y, A, C, active_pixel, maxN)
%% run HALS by fixating all spatial components
% input:
% Y: d*T, fluorescence data
% A: d*K, spatial components
% C: K*T, temporal components
% active_pixel: d*T binary matrix, indicating the nonzero elements of A
% maxN: scalar, maximum number of neu... |
github | xyza11808/MATLAB-master | dftregistration.m | .m | MATLAB-master/Efficient subpixel image registration/dftregistration.m | 8,022 | utf_8 | 7c769c44dc6d69fb9e814689586f0424 |
function [output Greg] = dftregistration(buf1ft,buf2ft,usfac)
% function [output Greg] = dftregistration(buf1ft,buf2ft,usfac);
% Efficient subpixel image registration by crosscorrelation. This code
% gives the same precision as the FFT upsampled cross correlation in a
% small fraction of the computation time and with... |
github | xyza11808/MATLAB-master | pcamat.m | .m | MATLAB-master/FastICA_25/pcamat.m | 12,075 | utf_8 | bcb1117d4132558d0d54d8b7b616a902 | function [E, D] = pcamat(vectors, firstEig, lastEig, s_interactive, ...
s_verbose);
%PCAMAT - Calculates the pca for data
%
% [E, D] = pcamat(vectors, firstEig, lastEig, ...
% interactive, verbose);
%
% Calculates the PCA matrices for given data (row) vectors. Returns
% the eigenvector (E) and diag... |
github | xyza11808/MATLAB-master | icaplot.m | .m | MATLAB-master/FastICA_25/icaplot.m | 13,259 | utf_8 | dde3e6d852f657a3c1eaacbd03f5dcc7 | function icaplot(mode, varargin);
%ICAPLOT - plot signals in various ways
%
% ICAPLOT is mainly for plottinf and comparing the mixed signals and
% separated ica-signals.
%
% ICAPLOT has many different modes. The first parameter of the function
% defines the mode. Other parameters and their order depends on the
% mode. ... |
github | xyza11808/MATLAB-master | TIFFStack.m | .m | MATLAB-master/@TIFFStack/TIFFStack.m | 72,581 | utf_8 | 252cb503d9d8220be5656223b9bcc365 | % <strong>TIFFStack</strong> - Manipulate a TIFF file like a tensor
%
% Usage: tsStack = <<strong>TIFFStack</strong>(strFilename <, bInvert, vnInterleavedFrameDims>)
%
% A TIFFStack object behaves like a read-only memory mapped TIFF file. The
% entire image stack is treated as a matlab tensor. Each frame of the fil... |
github | xyza11808/MATLAB-master | TS_UnitTest.m | .m | MATLAB-master/@TIFFStack/private/TS_UnitTest.m | 15,359 | utf_8 | c4e2421227e1e4ebf598ac8b58bb4a90 | % TS_UnitTest - FUNCITON Unit test for TIFFStack
%
% Usage: TS_UnitTest
%
% This function performs a series of unit tests for referencing a TIFFStack
% object, in comparison with a matlab tensor. If all tests are passed, a
% simple message reporting success will be displayed. Any error indicates a
% failure in a unit t... |
github | xyza11808/MATLAB-master | resource_efficiency_bin.m | .m | MATLAB-master/2019_03_03_BCT/resource_efficiency_bin.m | 3,502 | utf_8 | 04c1c934970acf39551c1d7ce20ddb97 | function [Eres,prob_SPL] = resource_efficiency_bin(adj,lambda,SPL,M)
% RESOURCE_EFFICIENCY_BIN Resource efficiency and shortest-path probability
%
% [Eres,prob_SPL] = resource_efficiency_bin(adj,lambda,SPL,M)
%
% The resource efficiency between nodes i and j is inversly proportional
% to the amount of resou... |
github | xyza11808/MATLAB-master | efficiency_bin.m | .m | MATLAB-master/2019_03_03_BCT/efficiency_bin.m | 2,700 | utf_8 | 276bfa775a0cb36ac38e13fcb06ff32a | function E=efficiency_bin(A,local)
%EFFICIENCY_BIN Global efficiency, local efficiency.
%
% Eglob = efficiency_bin(A);
% Eloc = efficiency_bin(A,1);
%
% The global efficiency is the average of inverse shortest path length,
% and is inversely related to the characteristic path length.
%
% The local efficie... |
github | xyza11808/MATLAB-master | reachdist.m | .m | MATLAB-master/2019_03_03_BCT/reachdist.m | 1,995 | utf_8 | fe609993c33b1602c328e2a662f2f8a5 | function [R,D] = reachdist(CIJ)
%REACHDIST Reachability and distance matrices
%
% [R,D] = reachdist(CIJ);
%
% The binary reachability matrix describes reachability between all pairs
% of nodes. An entry (u,v)=1 means that there exists a path from node u
% to node v; alternatively (u,v)=0.
%
% The distan... |
github | xyza11808/MATLAB-master | consensus_und.m | .m | MATLAB-master/2019_03_03_BCT/consensus_und.m | 3,138 | utf_8 | fb449d5fc36576d8c040dc17219ce9bc | function ciu = consensus_und(d,tau,reps)
% CONSENSUS_UND Consensus clustering
%
% CIU = CONSENSUS(D,TAU,REPS) seeks a consensus partition of the
% agreement matrix D. The algorithm used here is almost identical to the
% one introduced in Lancichinetti & Fortunato (2012): The agreement
% matrix D is thresh... |
github | xyza11808/MATLAB-master | mleme_constraint_model.m | .m | MATLAB-master/2019_03_03_BCT/mleme_constraint_model.m | 7,894 | utf_8 | deb465095e17202fa3be7d5cca8c1d61 | function [W0, E0, P0, Delt0] = mleme_constraint_model(samp, W, M, Lo, Li, Lm, opts)
%MLEME_CONSTRAINT_MODEL Unbiased sampling of networks with soft constraints
%
% W0 = mleme_constraint_model(samp, W);
% W0 = mleme_constraint_model(samp, W, M);
% W0 = mleme_constraint_model(samp, W, M, Lo, Li, Lm);
% [W0, E... |
github | xyza11808/MATLAB-master | clique_communities.m | .m | MATLAB-master/2019_03_03_BCT/clique_communities.m | 3,135 | windows_1250 | 824f62d2834b32be27a6e976c2ed369c | function M = clique_communities(A, cq_thr)
% CLIQUE_COMMUNITIES Overlapping community structure via clique percolation
%
% M = clique_communities(A, cq_thr)
%
% The optimal community structure is a subdivision of the network into
% groups of nodes which have a high number of within-group connections
% and a... |
github | xyza11808/MATLAB-master | efficiency_wei.m | .m | MATLAB-master/2019_03_03_BCT/efficiency_wei.m | 6,032 | utf_8 | 104d746ea0096523a2e51b0c313a96a0 | function E = efficiency_wei(W, local)
%EFFICIENCY_WEI Global efficiency, local efficiency.
%
% Eglob = efficiency_wei(W);
% Eloc = efficiency_wei(W,2);
%
% The global efficiency is the average of inverse shortest path length,
% and is inversely related to the characteristic path length.
%
% The local effi... |
github | xyza11808/MATLAB-master | generative_model.m | .m | MATLAB-master/2019_03_03_BCT/generative_model.m | 23,161 | utf_8 | 2cf25498c28e79a086cfe3e12a02cdad | function b = generative_model(A,D,m,modeltype,modelvar,params,epsilon)
% GENERATIVE_MODEL Run generative model code
%
% B = GENERATIVE_MODEL(A,D,m,modeltype,modelvar,params)
%
% Generates synthetic networks using the models described in the study by
% Betzel et al (2016) in Neuroimage.
%
% Inputs:
% ... |
github | xyza11808/MATLAB-master | evaluate_generative_model.m | .m | MATLAB-master/2019_03_03_BCT/evaluate_generative_model.m | 3,632 | utf_8 | d0fb1a7389deee9cb698d7f9d2db76a4 | function [B,E,K] = evaluate_generative_model(A,Atgt,D,modeltype,modelvar,params)
% EVALUATE_GENERATIVE_MODEL Generation and evaluation of synthetic networks
%
% [B,E,K] = EVALUATE_GENERATIVE_MODEL(A,Atgt,D,m,modeltype,modelvar,params)
%
% Generates synthetic networks and evaluates their energy function (see
% ... |
github | xyza11808/MATLAB-master | make_motif34lib.m | .m | MATLAB-master/2019_03_03_BCT/make_motif34lib.m | 2,837 | utf_8 | ce01d8a8ffaae43779f2cf1f8a108601 | function make_motif34lib
%MAKE_MOTIF34LIB Auxiliary motif library function
%
% make_motif34lib;
%
% This function generates the motif34lib.mat library required for all
% other motif computations.
%
%
% Mika Rubinov, UNSW, 2007-2010
%#ok<*ASGLU>
[M3,M3n,ID3,N3]=motif3generate;
[M4,M4n,ID4,N4]=motif4gen... |
github | xyza11808/MATLAB-master | mhd_read_volume.m | .m | MATLAB-master/matlab_elastix-master/code/mhd_read_volume.m | 2,558 | utf_8 | cbae89e94f536406e7e2ccbc3eb41ac1 | function V = mhd_read_volume(info)
% Function for reading the volume of a Insight Meta-Image (.mhd, .mhd) file
%
% volume = tk_read_volume(file-header)
%
% examples:
% 1: info = mhd_read_header()
% V = mhd_read_volume(info);
% imshow(squeeze(V(:,:,round(end/2))),[]);
%
% 2: V = mhd_read_volume('test.mhd');
if(~... |
github | xyza11808/MATLAB-master | elastix.m | .m | MATLAB-master/matlab_elastix-master/code/elastix.m | 13,705 | utf_8 | 5e511b8626c360364020cbc2038e2a66 | function varargout=elastix(movingImage,fixedImage,outputDir,paramFile,varargin)
% elastix image registration and warping wrapper
%
% function varargout=elastix(movingImage,fixedImage,outputDir,paramFile)
% varargout=elastix(movingImage,fixedImage,outputDir,paramFile,'PARAM1',val1,...)
% Purpose
% Wrapper for e... |
github | xyza11808/MATLAB-master | example_invert.m | .m | MATLAB-master/matlab_elastix-master/MelastiX_examples/invert_transform/example_invert.m | 2,000 | utf_8 | 83ae7c892e70e258d0cf48ceb6aabe60 | function example_invert
% This example shows how to invert a transform.
% We load a fixed image and a distorted version of it, the moving image.
% Landmarks are defined in the moving image space and overlaid on the moving image.
% The goal is to overlay these points on the fixed image.
% Achieving this goal requires c... |
github | xyza11808/MATLAB-master | example_2D_affine_alpha.m | .m | MATLAB-master/matlab_elastix-master/MelastiX_examples/elastix/example_2D_affine_alpha.m | 1,289 | utf_8 | b523d35ec0688e894d42147cfbb2fded | function varargout=example_2D_affine_alpha
% Shows the effect of changing alpha given a fixed number of spatial samples
% and iterations. Here we choose relatively low
% numbers for the iterations and spatial samples. Affine-transformed moving image.
fprintf('\n=====================\nRunning %s\n\n',mfilename)
help(m... |
github | xyza11808/MATLAB-master | example_2D_affine_nSpatialSamples.m | .m | MATLAB-master/matlab_elastix-master/MelastiX_examples/elastix/example_2D_affine_nSpatialSamples.m | 1,320 | utf_8 | 2329d54c7ce641ec886a247629b5ce66 | function example_2D_affine_nSpatialSamples
% Shows the effect of changing the number of spatial samples with a
% fixed number of iterations. Uses the suggested value for alpha.
% Here we choose relatively low numbers for the iterations and spatial
% samples. Affine-transformed moving image.
help(mfilename)
%Load t... |
github | xyza11808/MATLAB-master | jdqr.m | .m | MATLAB-master/drtoolbox/techniques/jdqr.m | 73,068 | utf_8 | b45810ddb5b2767c9289909175d1dc04 | function varargout=jdqr(varargin)
%JDQR computes a partial Schur decomposition of a square matrix or operator.
% Lambda = JDQR(A) returns the absolute largest eigenvalues in a K vector
% Lambda. Here K=min(5,N) (unless K has been specified), where N=size(A,1).
% JDQR(A) (without output argument) displays the K eige... |
github | xyza11808/MATLAB-master | lmnn.m | .m | MATLAB-master/drtoolbox/techniques/lmnn.m | 5,431 | utf_8 | 622ccdd8948f805d0d4552822cca46de | function [M, L, Y, C] = lmnn(X, labels)
%LMNN Learns a metric using large-margin nearest neighbor metric learning
%
% [M, L, Y, C] = lmnn(X, labels)
%
% The function uses large-margin nearest neighbor (LMNN) metric learning to
% learn a metric on the data set specified by the NxD matrix X and the
% corresponding Nx1 ... |
github | xyza11808/MATLAB-master | d2p.m | .m | MATLAB-master/drtoolbox/techniques/d2p.m | 3,487 | utf_8 | 0c7024a8039ea16b937d283585883fc3 | function [P, beta] = d2p(D, u, tol)
%D2P Identifies appropriate sigma's to get kk NNs up to some tolerance
%
% [P, beta] = d2p(D, kk, tol)
%
% Identifies the required precision (= 1 / variance^2) to obtain a Gaussian
% kernel with a certain uncertainty for every datapoint. The desired
% uncertainty can be specified... |
github | xyza11808/MATLAB-master | cg_update.m | .m | MATLAB-master/drtoolbox/techniques/cg_update.m | 3,715 | utf_8 | 1556078ae7c31950ec738949384cf180 | % Version 1.000
%
% Code provided by Ruslan Salakhutdinov and Geoff Hinton
%
% Permission is granted for anyone to copy, use, modify, or distribute this
% program and accompanying programs and documents for any purpose, provided
% this copyright notice is retained and prominently displayed, along with
% a note saying t... |
github | xyza11808/MATLAB-master | lmvu.m | .m | MATLAB-master/drtoolbox/techniques/lmvu.m | 8,540 | utf_8 | c8003ed7ff0fd0e226776c42c72ad385 | function [mappedX, mapping] = lmvu(X, no_dims, K, LL)
%LMVU Performs Landmark MVU on dataset X
%
% [mappedX, mapping] = lmvu(X, no_dims, k1, k2)
%
% The function performs Landmark MVU on the DxN dataset X. The value of k1
% represents the number of nearest neighbors that is employed in the MVU
% constraints. The val... |
github | xyza11808/MATLAB-master | cca.m | .m | MATLAB-master/drtoolbox/techniques/cca.m | 14,846 | utf_8 | 935e971ffe825a64e0eb80c535d71ebb | function [Z, ccaEigen, ccaDetails] = cca(X, Y, EDGES, OPTS)
%
% Function [Z, CCAEIGEN, CCADETAILS] = CCA(X, Y, EDGES, OPTS) computes a low
% dimensional embedding Z in R^d that maximally preserves angles among input
% data X that lives in R^D, with the algorithm Conformal Component Analysis.
%
% The embedding Z is co... |
github | xyza11808/MATLAB-master | x2p.m | .m | MATLAB-master/drtoolbox/techniques/x2p.m | 3,597 | utf_8 | 4a102e94922f4af38e36c374dccbc5a2 | function [P, beta] = x2p(X, u, tol)
%X2P Identifies appropriate sigma's to get kk NNs up to some tolerance
%
% [P, beta] = x2p(xx, kk, tol)
%
% Identifies the required precision (= 1 / variance^2) to obtain a Gaussian
% kernel with a certain uncertainty for every datapoint. The desired
% uncertainty can be specifie... |
github | xyza11808/MATLAB-master | sammon.m | .m | MATLAB-master/drtoolbox/techniques/sammon.m | 7,108 | utf_8 | 8a1fccbea9525bbebae4039127005ea6 | function [y, E] = sammon(x, n, opts)
%SAMMON Performs Sammon's MDS mapping on dataset X
%
% Y = SAMMON(X) applies Sammon's nonlinear mapping procedure on
% multivariate data X, where each row represents a pattern and each column
% represents a feature. On completion, Y contains the corresponding
% co-ordin... |
github | xyza11808/MATLAB-master | sdecca2.m | .m | MATLAB-master/drtoolbox/techniques/sdecca2.m | 7,185 | utf_8 | e53979561adda6a23883da0e72af5bf6 | function [P, newY, L, newV, idx]= sdecca2(Y, snn, regularizer, relative)
% doing semidefinitve embedding/MVU with output being parameterized by graph
% laplacian's eigenfunctions..
%
% the algorithm is same as conformal component analysis except that the scaling
% factor there is set as 1
%
%
% function [P, newY, Y] ... |
github | xyza11808/MATLAB-master | sparse_nn.m | .m | MATLAB-master/drtoolbox/techniques/sparse_nn.m | 972 | utf_8 | df5da172f954ec2f53125a04787cf2d3 | %SPARSE_NN
%
% This file is part of the Matlab Toolbox for Dimensionality Reduction.
% The toolbox can be obtained from http://homepage.tudelft.nl/19j49
% You are free to use, change, or redistribute this code in any way you
% want for non-commercial purposes. However, it is appreciated if you
% maintain the name of ... |
github | xyza11808/MATLAB-master | jdqz.m | .m | MATLAB-master/drtoolbox/techniques/jdqz.m | 78,986 | utf_8 | be67a038982588a6ac9cbc2d36f009e8 | function varargout=jdqz(varargin)
%JDQZ computes a partial generalized Schur decomposition (or QZ
% decomposition) of a pair of square matrices or operators.
%
% LAMBDA=JDQZ(A,B) and JDQZ(A,B) return K eigenvalues of the matrix pair
% (A,B), where K=min(5,N) and N=size(A,1) if K has not been specified.
%
% [X,J... |
github | xyza11808/MATLAB-master | lnst.m | .m | MATLAB-master/drtoolbox/gui/lnst.m | 866 | utf_8 | fd307c356d0eb128b0d57c9df000197e | % This file is part of the Matlab Toolbox for Dimensionality Reduction v0.7.2b.
% The toolbox can be obtained from http://homepage.tudelft.nl/19j49
% You are free to use, change, or redistribute this code in any way you
% want for non-commercial purposes. However, it is appreciated if you
% maintain the name of the or... |
github | xyza11808/MATLAB-master | scatter12n.m | .m | MATLAB-master/drtoolbox/gui/scatter12n.m | 1,309 | utf_8 | 5a079c0bf3db6d26fd87f0cb3297c45b | % This file is part of the Matlab Toolbox for Dimensionality Reduction v0.7.2b.
% The toolbox can be obtained from http://homepage.tudelft.nl/19j49
% You are free to use, change, or redistribute this code in any way you
% want for non-commercial purposes. However, it is appreciated if you
% maintain the name of the or... |
github | xyza11808/MATLAB-master | not_calculated.m | .m | MATLAB-master/drtoolbox/gui/not_calculated.m | 7,602 | utf_8 | 9f98d51f0c8207bd788383e580814903 | function varargout = not_calculated(varargin)
% NOT_CALCULATED M-file for not_calculated.fig
% NOT_CALCULATED by itself, creates a new NOT_CALCULATED or raises the
% existing singleton*.
%
% H = NOT_CALCULATED returns the handle to a new NOT_CALCULATED or the handle to
% the existing singleton*.
%
%... |
github | xyza11808/MATLAB-master | choose_method.m | .m | MATLAB-master/drtoolbox/gui/choose_method.m | 5,336 | utf_8 | a50c15d7c51725e6e88bb12bf5be57a3 | function varargout = choose_method(varargin)
% CHOOSE_METHOD M-file for choose_method.fig
% CHOOSE_METHOD, by itself, creates a new CHOOSE_METHOD or raises the existing
% singleton*.
%
% H = CHOOSE_METHOD returns the handle to a new CHOOSE_METHOD or the handle to
% the existing singleton*.
%
% ... |
github | xyza11808/MATLAB-master | load_data_1_var.m | .m | MATLAB-master/drtoolbox/gui/load_data_1_var.m | 4,776 | utf_8 | 213540e0f2d0e24db85ee4c6184178c8 | function varargout = load_data_1_var(varargin)
% LOAD_DATA_1_VAR M-file for load_data_1_var.fig
% LOAD_DATA_1_VAR, by itself, creates a new LOAD_DATA_1_VAR or raises the existing
% singleton*.
%
% H = LOAD_DATA_1_VAR returns the handle to a new LOAD_DATA_1_VAR or the handle to
% the existing singlet... |
github | xyza11808/MATLAB-master | plotn.m | .m | MATLAB-master/drtoolbox/gui/plotn.m | 3,947 | utf_8 | ba3674531d91bc0b2ca405e0a9d0bc3a | % This file is part of the Matlab Toolbox for Dimensionality Reduction v0.7.2b.
% The toolbox can be obtained from http://homepage.tudelft.nl/19j49
% You are free to use, change, or redistribute this code in any way you
% want for non-commercial purposes. However, it is appreciated if you
% maintain the name of the or... |
github | xyza11808/MATLAB-master | scattern.m | .m | MATLAB-master/drtoolbox/gui/scattern.m | 3,514 | utf_8 | aca2d60a4f80079c67204845b2499143 | % This file is part of the Matlab Toolbox for Dimensionality Reduction v0.7.2b.
% The toolbox can be obtained from http://homepage.tudelft.nl/19j49
% You are free to use, change, or redistribute this code in any way you
% want for non-commercial purposes. However, it is appreciated if you
% maintain the name of the or... |
github | xyza11808/MATLAB-master | no_history.m | .m | MATLAB-master/drtoolbox/gui/no_history.m | 7,508 | utf_8 | d5c85b897eeca97b3e37ea41551de2b1 | function varargout = no_history(varargin)
% NO_HISTORY M-file for no_history.fig
% NO_HISTORY by itself, creates a new NO_HISTORY or raises the
% existing singleton*.
%
% H = NO_HISTORY returns the handle to a new NO_HISTORY or the handle to
% the existing singleton*.
%
% NO_HISTORY('CALLBACK',... |
github | xyza11808/MATLAB-master | load_data_vars.m | .m | MATLAB-master/drtoolbox/gui/load_data_vars.m | 7,727 | utf_8 | a89e86bdd4785b42127825a4c304fb5e | function varargout = load_data_vars(varargin)
% LOAD_DATA_VARS M-file for load_data_vars.fig
% LOAD_DATA_VARS, by itself, creates a new LOAD_DATA_VARS or raises the existing
% singleton*.
%
% H = LOAD_DATA_VARS returns the handle to a new LOAD_DATA_VARS or the handle to
% the existing singleton*.
%
... |
github | xyza11808/MATLAB-master | mapping_parameters.m | .m | MATLAB-master/drtoolbox/gui/mapping_parameters.m | 23,373 | utf_8 | 59c32ad869cef7d4887bd7f6bd777624 | function varargout = mapping_parameters(varargin)
% MAPPING_PARAMETERS M-file for mapping_parameters.fig
% MAPPING_PARAMETERS, by itself, creates a new MAPPING_PARAMETERS or raises the existing
% singleton*.
%
% H = MAPPING_PARAMETERS returns the handle to a new MAPPING_PARAMETERS or the handle to
% ... |
github | xyza11808/MATLAB-master | load_xls.m | .m | MATLAB-master/drtoolbox/gui/load_xls.m | 4,845 | utf_8 | 98f040ec0685b024ddf99d454fea770d | function varargout = load_xls(varargin)
% LOAD_XLS M-file for load_xls.fig
% LOAD_XLS, by itself, creates a new LOAD_XLS or raises the existing
% singleton*.
%
% H = LOAD_XLS returns the handle to a new LOAD_XLS or the handle to
% the existing singleton*.
%
% LOAD_XLS('CALLBACK',hObject,eventDa... |
github | xyza11808/MATLAB-master | drtool.m | .m | MATLAB-master/drtoolbox/gui/drtool.m | 52,422 | utf_8 | c15180ca46e1dcb99c001125b7429b14 | function varargout = drtool(varargin)
% DRTOOL M-file for drtool.fig
% DRTOOL, by itself, creates a new DRTOOL or raises the existing
% singleton*.
%
% H = DRTOOL returns the handle to a new DRTOOL or the handle to
% the existing singleton*.
%
% DRTOOL('CALLBACK',hObject,eventData,handles,...) ... |
github | xyza11808/MATLAB-master | plot12n.m | .m | MATLAB-master/drtoolbox/gui/plot12n.m | 1,318 | utf_8 | d01421c22964c2a3a5ae9dd99d026708 | % This file is part of the Matlab Toolbox for Dimensionality Reduction v0.7.2b.
% The toolbox can be obtained from http://homepage.tudelft.nl/19j49
% You are free to use, change, or redistribute this code in any way you
% want for non-commercial purposes. However, it is appreciated if you
% maintain the name of the or... |
github | xyza11808/MATLAB-master | not_loaded.m | .m | MATLAB-master/drtoolbox/gui/not_loaded.m | 7,513 | utf_8 | 749124a9066ce5eec69372e3d16cd9d1 | function varargout = not_loaded(varargin)
% NOT_LOADED M-file for not_loaded.fig
% NOT_LOADED by itself, creates a new NOT_LOADED or raises the
% existing singleton*.
%
% H = NOT_LOADED returns the handle to a new NOT_LOADED or the handle to
% the existing singleton*.
%
% NOT_LOADED('CALLBACK',... |
github | xyza11808/MATLAB-master | load_data.m | .m | MATLAB-master/drtoolbox/gui/load_data.m | 6,360 | utf_8 | e87e4a8d82078cbbec95c41a786cd407 | function varargout = load_data(varargin)
% LOAD_DATA M-file for load_data.fig
% LOAD_DATA, by itself, creates a new LOAD_DATA or raises the existing
% singleton*.
%
% H = LOAD_DATA returns the handle to a new LOAD_DATA or the handle to
% the existing singleton*.
%
% LOAD_DATA('CALLBACK',hObject... |
github | xyza11808/MATLAB-master | AP_process_histology.m | .m | MATLAB-master/AP_histology-master/AP_process_histology.m | 14,202 | utf_8 | cae3dedf050b39028d43cfe94a4e9f37 | function AP_process_histology(im_path,resize_factor,slice_images)
% AP_process_histology(im_path,resize_factor,slice_images)
%
% im_path - path with images of slides (tif/tiff/ome.tiff)
% resize_factor (if not ome.tiff) - resizing factor for saving images (e.g.
% 1/10 scales slides to 1/10 size). Note: if ome.tiff, mic... |
github | xyza11808/MATLAB-master | AP_view_aligned_histology.m | .m | MATLAB-master/AP_histology-master/AP_view_aligned_histology.m | 4,932 | utf_8 | 3a5ce7eed63c4b5b0d98111e5fe6a6d3 | function AP_view_aligned_histology(st,slice_im_path)
% AP_view_aligned_histology(st,slice_im_path)
%
% View histology slices with overlaid aligned CCF areas
% Andy Peters (peters.andrew.j@gmail.com)
% Initialize guidata
gui_data = struct;
gui_data.st = st;
% Load in slice images
gui_data.slice_im_path = slice_im_path... |
github | xyza11808/MATLAB-master | AP_grab_histology_ccf.m | .m | MATLAB-master/AP_histology-master/AP_grab_histology_ccf.m | 11,672 | utf_8 | 43523fc8739b30484a90d2bd22f5a855 | function AP_grab_histology_ccf(tv,av,st,slice_im_path)
% Grab CCF slices corresponding to histology slices
% Andy Peters (peters.andrew.j@gmail.com)
% Initialize guidata
gui_data = struct;
gui_data.tv = tv;
gui_data.av = av;
gui_data.st = st;
% Load in slice images
gui_data.slice_im_path = slice_im_path;
slice_im_dir... |
github | xyza11808/MATLAB-master | AP_get_probe_histology.m | .m | MATLAB-master/AP_histology-master/AP_get_probe_histology.m | 28,257 | utf_8 | 8d83f46a7e99c5698c03be853ddb3023 | function AP_get_probe_histology(tv,av,st,slice_im_path,chnposfile)
% AP_get_probe_histology(tv,av,st,slice_im_path)
%
% Get probe trajectory in histology and convert to ccf
% Andy Peters (peters.andrew.j@gmail.com)
% Initialize guidata
gui_data = struct;
gui_data.tv = tv;
gui_data.av = av;
gui_data.st = st;
% Query n... |
github | xyza11808/MATLAB-master | AP_rotate_histology.m | .m | MATLAB-master/AP_histology-master/AP_rotate_histology.m | 5,526 | utf_8 | a1ad60edb16872ca053bae41ab804fe0 | function AP_rotate_histology(im_path)
% AP_rotate_histology(im_path)
%
% Pad, center, and rotate images of histological slices
% Andy Peters (peters.andrew.j@gmail.com)
slice_dir = dir([im_path filesep '*.tif']);
slice_fn = natsortfiles(cellfun(@(path,fn) [path filesep fn], ...
{slice_dir.folder},{slice_dir.name},... |
github | xyza11808/MATLAB-master | AP_align_probe_histology.m | .m | MATLAB-master/AP_histology-master/AP_align_probe_histology.m | 6,693 | utf_8 | 531a2bd33ea517072a52b0c0ca5e9966 | function AP_align_probe_histology(st,slice_path, ...
spike_times,spike_templates,template_depths, ...
lfp,lfp_channel_positions,use_probe) %
% AP_align_probe_histology(st,slice_path,spike_times,spike_templates,template_depths,lfp,lfp_channel_positions,use_probe)
% If no probe specified, use probe 1
if ~exist(... |
github | xyza11808/MATLAB-master | AP_manual_align_histology_ccf.m | .m | MATLAB-master/AP_histology-master/AP_manual_align_histology_ccf.m | 10,439 | utf_8 | 2677c26ac9125cadd0f049c72d68b730 | function AP_manual_align_histology_ccf(tv,av,st,slice_im_path)
% AP_manual_align_histology_ccf(tv,av,st,slice_im_path)
%
% Align histology slices and matched CCF slices
% Andy Peters (peters.andrew.j@gmail.com)
% Initialize guidata
gui_data = struct;
gui_data.tv = tv;
gui_data.av = av;
gui_data.st = st;
% Load in sli... |
github | xyza11808/MATLAB-master | AP_align_probe_histology2.m | .m | MATLAB-master/AP_histology-master/AP_align_probe_histology2.m | 7,910 | utf_8 | cb5875c847ba73d0c7a256e2189ee9a3 | function AP_align_probe_histology2(st,av,slice_path, ...
spike_times,spike_templates,template_depths, ...
lfp,lfp_channel_positions,use_probe) %
% AP_align_probe_histology(st,slice_path,spike_times,spike_templates,template_depths,lfp,lfp_channel_positions,use_probe)
% If no probe specified, use probe 1
if ~ex... |
github | xyza11808/MATLAB-master | training.m | .m | MATLAB-master/FSLib_v5_2_2017/lib/@data/training.m | 97 | utf_8 | a49f02b9138b2449371340d304bbfa90 |
function [dat,algo] = training(algo,dat)
%% return this, because this is data
dat=algo;
|
github | xyza11808/MATLAB-master | concatenate.m | .m | MATLAB-master/FSLib_v5_2_2017/lib/@data/concatenate.m | 720 | utf_8 | ca2b2c0db9ced29a80e2e1d83c715730 |
function [res] = concatenate(tres,res,trn,tst)
% function [dat] = concatenate(trnRes,tstRes,train,test)
%
% Returns unified data (e.g with train and test results)
% with trnRes in specified indexes train and tstRes in
% indexes test.
[l1,n1,k1]=get_dim(res);
[l2,n2,k2]=get_dim(tres);
l=l1+l2;... |
github | xyza11808/MATLAB-master | testing.m | .m | MATLAB-master/FSLib_v5_2_2017/lib/@data/testing.m | 94 | utf_8 | 2cc238ad9062626b6c373d4fc3a45b09 |
function [dat] = testing(algo,dat)
%% return this, because this is data
dat=algo;
|
github | xyza11808/MATLAB-master | readfrom.m | .m | MATLAB-master/FSLib_v5_2_2017/lib/@data/readfrom.m | 6,656 | utf_8 | fe72046e1721d15867696a735c1cf7be | function dret = readfrom(d,format,filename)
switch(lower(format))
case {'arff'}
dret=readarff(filename);
case {'libsvm'}
X=[];
Y=[];
fid=fopen(filename,'rt');
index=1;
while 1
tline = fgetl(fid);
if ~ischar(tline), break, end
% read label
[... |
github | xyza11808/MATLAB-master | jdqr.m | .m | MATLAB-master/FSLib_v5_2_2017/lib/drtoolbox/techniques/jdqr.m | 73,068 | utf_8 | b45810ddb5b2767c9289909175d1dc04 | function varargout=jdqr(varargin)
%JDQR computes a partial Schur decomposition of a square matrix or operator.
% Lambda = JDQR(A) returns the absolute largest eigenvalues in a K vector
% Lambda. Here K=min(5,N) (unless K has been specified), where N=size(A,1).
% JDQR(A) (without output argument) displays the K eige... |
github | xyza11808/MATLAB-master | lmnn.m | .m | MATLAB-master/FSLib_v5_2_2017/lib/drtoolbox/techniques/lmnn.m | 5,431 | utf_8 | 622ccdd8948f805d0d4552822cca46de | function [M, L, Y, C] = lmnn(X, labels)
%LMNN Learns a metric using large-margin nearest neighbor metric learning
%
% [M, L, Y, C] = lmnn(X, labels)
%
% The function uses large-margin nearest neighbor (LMNN) metric learning to
% learn a metric on the data set specified by the NxD matrix X and the
% corresponding Nx1 ... |
github | xyza11808/MATLAB-master | d2p.m | .m | MATLAB-master/FSLib_v5_2_2017/lib/drtoolbox/techniques/d2p.m | 3,487 | utf_8 | 0c7024a8039ea16b937d283585883fc3 | function [P, beta] = d2p(D, u, tol)
%D2P Identifies appropriate sigma's to get kk NNs up to some tolerance
%
% [P, beta] = d2p(D, kk, tol)
%
% Identifies the required precision (= 1 / variance^2) to obtain a Gaussian
% kernel with a certain uncertainty for every datapoint. The desired
% uncertainty can be specified... |
github | xyza11808/MATLAB-master | cg_update.m | .m | MATLAB-master/FSLib_v5_2_2017/lib/drtoolbox/techniques/cg_update.m | 3,715 | utf_8 | 1556078ae7c31950ec738949384cf180 | % Version 1.000
%
% Code provided by Ruslan Salakhutdinov and Geoff Hinton
%
% Permission is granted for anyone to copy, use, modify, or distribute this
% program and accompanying programs and documents for any purpose, provided
% this copyright notice is retained and prominently displayed, along with
% a note saying t... |
github | xyza11808/MATLAB-master | lmvu.m | .m | MATLAB-master/FSLib_v5_2_2017/lib/drtoolbox/techniques/lmvu.m | 8,540 | utf_8 | c8003ed7ff0fd0e226776c42c72ad385 | function [mappedX, mapping] = lmvu(X, no_dims, K, LL)
%LMVU Performs Landmark MVU on dataset X
%
% [mappedX, mapping] = lmvu(X, no_dims, k1, k2)
%
% The function performs Landmark MVU on the DxN dataset X. The value of k1
% represents the number of nearest neighbors that is employed in the MVU
% constraints. The val... |
github | xyza11808/MATLAB-master | cca.m | .m | MATLAB-master/FSLib_v5_2_2017/lib/drtoolbox/techniques/cca.m | 14,846 | utf_8 | 935e971ffe825a64e0eb80c535d71ebb | function [Z, ccaEigen, ccaDetails] = cca(X, Y, EDGES, OPTS)
%
% Function [Z, CCAEIGEN, CCADETAILS] = CCA(X, Y, EDGES, OPTS) computes a low
% dimensional embedding Z in R^d that maximally preserves angles among input
% data X that lives in R^D, with the algorithm Conformal Component Analysis.
%
% The embedding Z is co... |
github | xyza11808/MATLAB-master | x2p.m | .m | MATLAB-master/FSLib_v5_2_2017/lib/drtoolbox/techniques/x2p.m | 3,597 | utf_8 | 4a102e94922f4af38e36c374dccbc5a2 | function [P, beta] = x2p(X, u, tol)
%X2P Identifies appropriate sigma's to get kk NNs up to some tolerance
%
% [P, beta] = x2p(xx, kk, tol)
%
% Identifies the required precision (= 1 / variance^2) to obtain a Gaussian
% kernel with a certain uncertainty for every datapoint. The desired
% uncertainty can be specifie... |
github | xyza11808/MATLAB-master | sammon.m | .m | MATLAB-master/FSLib_v5_2_2017/lib/drtoolbox/techniques/sammon.m | 6,377 | utf_8 | e7cf88093d492f85d2c80bd692da0b1f | function [y, E] = sammon(x, n, opts)
%SAMMON Performs Sammon's MDS mapping on dataset X
%
% Y = SAMMON(X) applies Sammon's nonlinear mapping procedure on
% multivariate data X, where each row represents a pattern and each column
% represents a feature. On completion, Y contains the corresponding
% co-ordin... |
github | xyza11808/MATLAB-master | sdecca2.m | .m | MATLAB-master/FSLib_v5_2_2017/lib/drtoolbox/techniques/sdecca2.m | 7,185 | utf_8 | e53979561adda6a23883da0e72af5bf6 | function [P, newY, L, newV, idx]= sdecca2(Y, snn, regularizer, relative)
% doing semidefinitve embedding/MVU with output being parameterized by graph
% laplacian's eigenfunctions..
%
% the algorithm is same as conformal component analysis except that the scaling
% factor there is set as 1
%
%
% function [P, newY, Y] ... |
github | xyza11808/MATLAB-master | sparse_nn.m | .m | MATLAB-master/FSLib_v5_2_2017/lib/drtoolbox/techniques/sparse_nn.m | 972 | utf_8 | df5da172f954ec2f53125a04787cf2d3 | %SPARSE_NN
%
% This file is part of the Matlab Toolbox for Dimensionality Reduction.
% The toolbox can be obtained from http://homepage.tudelft.nl/19j49
% You are free to use, change, or redistribute this code in any way you
% want for non-commercial purposes. However, it is appreciated if you
% maintain the name of ... |
github | xyza11808/MATLAB-master | jdqz.m | .m | MATLAB-master/FSLib_v5_2_2017/lib/drtoolbox/techniques/jdqz.m | 78,986 | utf_8 | be67a038982588a6ac9cbc2d36f009e8 | function varargout=jdqz(varargin)
%JDQZ computes a partial generalized Schur decomposition (or QZ
% decomposition) of a pair of square matrices or operators.
%
% LAMBDA=JDQZ(A,B) and JDQZ(A,B) return K eigenvalues of the matrix pair
% (A,B), where K=min(5,N) and N=size(A,1) if K has not been specified.
%
% [X,J... |
github | xyza11808/MATLAB-master | lnst.m | .m | MATLAB-master/FSLib_v5_2_2017/lib/drtoolbox/gui/lnst.m | 866 | utf_8 | fd307c356d0eb128b0d57c9df000197e | % This file is part of the Matlab Toolbox for Dimensionality Reduction v0.7.2b.
% The toolbox can be obtained from http://homepage.tudelft.nl/19j49
% You are free to use, change, or redistribute this code in any way you
% want for non-commercial purposes. However, it is appreciated if you
% maintain the name of the or... |
github | xyza11808/MATLAB-master | scatter12n.m | .m | MATLAB-master/FSLib_v5_2_2017/lib/drtoolbox/gui/scatter12n.m | 1,309 | utf_8 | 5a079c0bf3db6d26fd87f0cb3297c45b | % This file is part of the Matlab Toolbox for Dimensionality Reduction v0.7.2b.
% The toolbox can be obtained from http://homepage.tudelft.nl/19j49
% You are free to use, change, or redistribute this code in any way you
% want for non-commercial purposes. However, it is appreciated if you
% maintain the name of the or... |
github | xyza11808/MATLAB-master | not_calculated.m | .m | MATLAB-master/FSLib_v5_2_2017/lib/drtoolbox/gui/not_calculated.m | 7,602 | utf_8 | 9f98d51f0c8207bd788383e580814903 | function varargout = not_calculated(varargin)
% NOT_CALCULATED M-file for not_calculated.fig
% NOT_CALCULATED by itself, creates a new NOT_CALCULATED or raises the
% existing singleton*.
%
% H = NOT_CALCULATED returns the handle to a new NOT_CALCULATED or the handle to
% the existing singleton*.
%
%... |
github | xyza11808/MATLAB-master | choose_method.m | .m | MATLAB-master/FSLib_v5_2_2017/lib/drtoolbox/gui/choose_method.m | 4,733 | utf_8 | 5bf15e15f47b740a5658a45a4f83d07a | function varargout = choose_method(varargin)
% CHOOSE_METHOD M-file for choose_method.fig
% CHOOSE_METHOD, by itself, creates a new CHOOSE_METHOD or raises the existing
% singleton*.
%
% H = CHOOSE_METHOD returns the handle to a new CHOOSE_METHOD or the handle to
% the existing singleton*.
%
% ... |
github | xyza11808/MATLAB-master | load_data_1_var.m | .m | MATLAB-master/FSLib_v5_2_2017/lib/drtoolbox/gui/load_data_1_var.m | 4,776 | utf_8 | 213540e0f2d0e24db85ee4c6184178c8 | function varargout = load_data_1_var(varargin)
% LOAD_DATA_1_VAR M-file for load_data_1_var.fig
% LOAD_DATA_1_VAR, by itself, creates a new LOAD_DATA_1_VAR or raises the existing
% singleton*.
%
% H = LOAD_DATA_1_VAR returns the handle to a new LOAD_DATA_1_VAR or the handle to
% the existing singlet... |
github | xyza11808/MATLAB-master | plotn.m | .m | MATLAB-master/FSLib_v5_2_2017/lib/drtoolbox/gui/plotn.m | 3,947 | utf_8 | ba3674531d91bc0b2ca405e0a9d0bc3a | % This file is part of the Matlab Toolbox for Dimensionality Reduction v0.7.2b.
% The toolbox can be obtained from http://homepage.tudelft.nl/19j49
% You are free to use, change, or redistribute this code in any way you
% want for non-commercial purposes. However, it is appreciated if you
% maintain the name of the or... |
github | xyza11808/MATLAB-master | scattern.m | .m | MATLAB-master/FSLib_v5_2_2017/lib/drtoolbox/gui/scattern.m | 3,514 | utf_8 | aca2d60a4f80079c67204845b2499143 | % This file is part of the Matlab Toolbox for Dimensionality Reduction v0.7.2b.
% The toolbox can be obtained from http://homepage.tudelft.nl/19j49
% You are free to use, change, or redistribute this code in any way you
% want for non-commercial purposes. However, it is appreciated if you
% maintain the name of the or... |
github | xyza11808/MATLAB-master | no_history.m | .m | MATLAB-master/FSLib_v5_2_2017/lib/drtoolbox/gui/no_history.m | 7,508 | utf_8 | d5c85b897eeca97b3e37ea41551de2b1 | function varargout = no_history(varargin)
% NO_HISTORY M-file for no_history.fig
% NO_HISTORY by itself, creates a new NO_HISTORY or raises the
% existing singleton*.
%
% H = NO_HISTORY returns the handle to a new NO_HISTORY or the handle to
% the existing singleton*.
%
% NO_HISTORY('CALLBACK',... |
github | xyza11808/MATLAB-master | load_data_vars.m | .m | MATLAB-master/FSLib_v5_2_2017/lib/drtoolbox/gui/load_data_vars.m | 7,727 | utf_8 | a89e86bdd4785b42127825a4c304fb5e | function varargout = load_data_vars(varargin)
% LOAD_DATA_VARS M-file for load_data_vars.fig
% LOAD_DATA_VARS, by itself, creates a new LOAD_DATA_VARS or raises the existing
% singleton*.
%
% H = LOAD_DATA_VARS returns the handle to a new LOAD_DATA_VARS or the handle to
% the existing singleton*.
%
... |
github | xyza11808/MATLAB-master | mapping_parameters.m | .m | MATLAB-master/FSLib_v5_2_2017/lib/drtoolbox/gui/mapping_parameters.m | 23,373 | utf_8 | 59c32ad869cef7d4887bd7f6bd777624 | function varargout = mapping_parameters(varargin)
% MAPPING_PARAMETERS M-file for mapping_parameters.fig
% MAPPING_PARAMETERS, by itself, creates a new MAPPING_PARAMETERS or raises the existing
% singleton*.
%
% H = MAPPING_PARAMETERS returns the handle to a new MAPPING_PARAMETERS or the handle to
% ... |
github | xyza11808/MATLAB-master | load_xls.m | .m | MATLAB-master/FSLib_v5_2_2017/lib/drtoolbox/gui/load_xls.m | 4,845 | utf_8 | 98f040ec0685b024ddf99d454fea770d | function varargout = load_xls(varargin)
% LOAD_XLS M-file for load_xls.fig
% LOAD_XLS, by itself, creates a new LOAD_XLS or raises the existing
% singleton*.
%
% H = LOAD_XLS returns the handle to a new LOAD_XLS or the handle to
% the existing singleton*.
%
% LOAD_XLS('CALLBACK',hObject,eventDa... |
github | xyza11808/MATLAB-master | drtool.m | .m | MATLAB-master/FSLib_v5_2_2017/lib/drtoolbox/gui/drtool.m | 52,422 | utf_8 | c15180ca46e1dcb99c001125b7429b14 | function varargout = drtool(varargin)
% DRTOOL M-file for drtool.fig
% DRTOOL, by itself, creates a new DRTOOL or raises the existing
% singleton*.
%
% H = DRTOOL returns the handle to a new DRTOOL or the handle to
% the existing singleton*.
%
% DRTOOL('CALLBACK',hObject,eventData,handles,...) ... |
github | xyza11808/MATLAB-master | plot12n.m | .m | MATLAB-master/FSLib_v5_2_2017/lib/drtoolbox/gui/plot12n.m | 1,318 | utf_8 | d01421c22964c2a3a5ae9dd99d026708 | % This file is part of the Matlab Toolbox for Dimensionality Reduction v0.7.2b.
% The toolbox can be obtained from http://homepage.tudelft.nl/19j49
% You are free to use, change, or redistribute this code in any way you
% want for non-commercial purposes. However, it is appreciated if you
% maintain the name of the or... |
github | xyza11808/MATLAB-master | not_loaded.m | .m | MATLAB-master/FSLib_v5_2_2017/lib/drtoolbox/gui/not_loaded.m | 7,513 | utf_8 | 749124a9066ce5eec69372e3d16cd9d1 | function varargout = not_loaded(varargin)
% NOT_LOADED M-file for not_loaded.fig
% NOT_LOADED by itself, creates a new NOT_LOADED or raises the
% existing singleton*.
%
% H = NOT_LOADED returns the handle to a new NOT_LOADED or the handle to
% the existing singleton*.
%
% NOT_LOADED('CALLBACK',... |
github | xyza11808/MATLAB-master | load_data.m | .m | MATLAB-master/FSLib_v5_2_2017/lib/drtoolbox/gui/load_data.m | 6,360 | utf_8 | e87e4a8d82078cbbec95c41a786cd407 | function varargout = load_data(varargin)
% LOAD_DATA M-file for load_data.fig
% LOAD_DATA, by itself, creates a new LOAD_DATA or raises the existing
% singleton*.
%
% H = LOAD_DATA returns the handle to a new LOAD_DATA or the handle to
% the existing singleton*.
%
% LOAD_DATA('CALLBACK',hObject... |
github | xyza11808/MATLAB-master | edit_distance.m | .m | MATLAB-master/FSLib_v5_2_2017/lib/@kernel/edit_distance.m | 988 | utf_8 | f6f97c6f662b51a6b41311ec15eb60e2 | function K = edit_distance(kern,dat1,dat2,ind1,ind2,kerParam)
%---calculating the distance matrix----
K= [];
Xf = get_x(dat1);
Xs = get_x(dat2);
X1 = {};
X2 = {};
for i = 1:size(Xf,1)
tmp = Xf(i,:);
X1{i} = tmp(tmp > 0);
end
for i = 1:size(Xs,1)
tmp = Xs(i,:);
X2{i} = tmp(tmp > 0);
end
for i = 1:length(ind1)
... |
github | xyza11808/MATLAB-master | testing.m | .m | MATLAB-master/FSLib_v5_2_2017/lib/@kernel/testing.m | 69 | utf_8 | 9ad3e5b8a75edde739ddc116a2231447 |
function d = testing(a,d)
d=set_x(d,feval('calc',a,d,a.dat));
|
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