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github | jlizier/jidt-master | transferWithSourceMemory.m | .m | jidt-master/demos/octave/DetectingInteractionLags/transferWithSourceMemory.m | 9,356 | utf_8 | 8082bd74812812df97899bcbbff2fdb2 | %%
%% Java Information Dynamics Toolkit (JIDT)
%% Copyright (C) 2012, Joseph T. Lizier
%%
%% This program is free software: you can redistribute it and/or modify
%% it under the terms of the GNU General Public License as published by
%% the Free Software Foundation, either version 3 of the License, or
%% (at yo... |
github | jlizier/jidt-master | generateObservations.m | .m | jidt-master/demos/octave/FlockingAnalysis/generateObservations.m | 17,982 | utf_8 | e260faf4df98879742dd34b96db3c269 | function [D, Dpast, S, RelSourcePos, safeDynamicCorrelationExclusionSamples] = generateObservations(properties)
% This function generates the observations from which
% we can then compute information dynamics with JIDT.
% This will work for either 2D or 3D samples (as specified by the properties)
%
% Author: Emanuele C... |
github | jlizier/jidt-master | plotLocalTEs.m | .m | jidt-master/demos/octave/FlockingAnalysis/plotLocalTEs.m | 8,338 | utf_8 | ebea89d5bf24dcdbe9076f59b901d555 | function plotLocalTEs(properties)
% Plot the local TEs to show where the information transfer hotspots are from target fish relative to each source
%
% Author: Joseph T. Lizier, 2019
%
% Inputs:
% - properties - object with properties for the calculations,
% with sub-members as specificied in the loadProperties.m fil... |
github | jlizier/jidt-master | checkMiDiscreteNullDistribution.m | .m | jidt-master/demos/octave/NullDistributions/checkMiDiscreteNullDistribution.m | 4,457 | utf_8 | a31008ea0a0599f83ece1e66d1c2e60f | %%
%% Java Information Dynamics Toolkit (JIDT)
%% Copyright (C) 2012, Joseph T. Lizier
%%
%% This program is free software: you can redistribute it and/or modify
%% it under the terms of the GNU General Public License as published by
%% the Free Software Foundation, either version 3 of the License, or
%% ... |
github | ralucacocioban/EdgeDetection-master | PRcurve.m | .m | EdgeDetection-master/PRcurve.m | 1,764 | utf_8 | bddca27cf26adbb9e757fb438881d4f6 | function PRcurve(img, groundTruthImg)
[x,y] = getPrecissionAndRecall(groundTruthImg, img);
computePRcurve(x,y);
end
function [xCoord, yCoord] = getPrecissionAndRecall(groundTruth, img)
%distance vector
D = (1:10);
%declare/initialise the TP, TN, FP and FN
fp = zeros(numel(D));
tp = zeros(numel(D));
fn =zeros(numel... |
github | ralucacocioban/EdgeDetection-master | nonMaximalSuppression.m | .m | EdgeDetection-master/nonMaximalSuppression.m | 2,327 | utf_8 | 4aab1dfda1456a45f9284c231e5c5272 | function nonMaximalSuppression(dir, magnitude)
discreteDirections = [0, 45, 90, 135, 180];
[rows cols] = size(magnitude);
dir = dir + 2*360;
dir = rem(dir,180);
interpolatedDir = interp1(discreteDirections, discreteDirections, dir, 'nearest','extrap');
interpolatedMatrix = reshape(interpolatedDir, cols, rows)';
for... |
github | ralucacocioban/EdgeDetection-master | coursework3.m | .m | EdgeDetection-master/coursework3.m | 5,887 | utf_8 | 68df3bcf77b483cadd275b0a55f79fb6 | function coursework3()
%-------------------------------------------------------------------------------------%
% load the images needed for this csw
% and convert them to grayscale images if necessary
%------------------------------------------------------------------------... |
github | ralucacocioban/EdgeDetection-master | evaluationEdgeDetectors.m | .m | EdgeDetection-master/evaluationEdgeDetectors.m | 1,639 | utf_8 | 5ef43582164cfbc75ab8c211156ade31 | function evaluationEdgeDetectors(butterfly, groundTruthImg)
[x,y] = thresholdingAlgorithm(groundTruthImg, butterfly);
computeROCcurve(x,y);
end
function [xCoord, yCoord] = thresholdingAlgorithm(groundTruth, grayImg)
%treshhold vector
T = (0:256);
%declare/initialise the TP, TN, FP and FN
fp = zeros(numel(T));
tp ... |
github | ralucacocioban/EdgeDetection-master | lowPassHighPass.m | .m | EdgeDetection-master/lowPassHighPass.m | 1,896 | utf_8 | 6e4ccac8b96b6344163d9f8e8b09c297 | function lowPassHighPass()
boxImg = imread('images/box.png');
img = imread('images/lighthouse.png');
[rows cols dim] = size(img);
%check if the image is grayscale
if(dim > 1)
img = rgb2gray(img);
end
[boxRows boxCols dim2] = size(boxImg);
if(dim2>1)
boxImg = rgb2gray(boxImg);
end
% transform into binary i... |
github | ralucacocioban/EdgeDetection-master | evaluationCannyEdgeDetector.m | .m | EdgeDetection-master/evaluationCannyEdgeDetector.m | 1,414 | utf_8 | d4a5027c158ca4482860f980bace5ecd | function evaluationCannyEdgeDetector(butterfly, groundTruthImg)
[x,y] = thresholdingAlgorithmBin(groundTruthImg, butterfly);
computeROCcurve(x,y);
end
function [xCoord, yCoord] = thresholdingAlgorithmBin(groundTruth, grayImg)
%treshhold vector
T = (0:256)/256;
T(257) = T(257) - 0.001;
%declare/initialise the TP, ... |
github | prateekt/allyourposesrours-master | displayEpipolarF.m | .m | allyourposesrours-master/paper1_impl/displayEpipolarF.m | 2,913 | utf_8 | 05587dae16a32d64dcb2fae30f9be6ed | function displayEpipolarF(I1, I2, F)
%
% displayEpipolarF(I1, I2, F)
%
% Displays the epipolar lines interactively. I1 and I2
% are the two input images.
% F is the essential matrix transforming from I1 to I2.
% That is: if m1 is a point in I1 and m2 is a point in
% I2, then the epipolar line has equation:
% ... |
github | Kainanchen/MelodySculptor-master | yin2.m | .m | MelodySculptor-master/Pitch/yin_matlab/junk/yin2.m | 2,235 | utf_8 | b91a6e57061458dd6bd01fc6a7162a27 | function r=yin2(p,fileinfo)
% YIN2 - fundamental frequency estimator
% new version (feb 2003)
%
%
% process signal a chunk at a time
idx=0;
totalhops=round(fileinfo.nsamples / p.hop);
r1=nan*zeros(1,totalhops);r2=nan*zeros(1,totalhops);
r3=nan*zeros(1,totalhops);r4=nan*zeros(1,totalhops);
idx2=0+round(p.wsize/2/p.ho... |
github | Kainanchen/MelodySculptor-master | yink.m | .m | MelodySculptor-master/Pitch/yin_matlab/private/yink.m | 3,423 | utf_8 | f307172b91326610c6be6e573c56124e | function r=yink(p,fileinfo)
% YINK - fundamental frequency estimator
% new version (feb 2003)
%
%
%global jj;
%jj=0;
% process signal a chunk at a time
idx=p.range(1)-1;
totalhops=round((p.range(2)-p.range(1)+1) / p.hop);
r1=nan*zeros(1,totalhops);r2=nan*zeros(1,totalhops);
r3=nan*zeros(1,totalhops);r4=nan*zeros(1,to... |
github | surgebiswas/transcriptome_compression-master | dendroperm.m | .m | transcriptome_compression-master/util/dendroperm.m | 17,045 | utf_8 | 951a41305a4d602706d8323e05acddae | function perm = dendroperm(Z,varargin)
% Hacked version of dendrogram.m that does not generate dendrogram plot.
% Simply returns the leaf node permutation vector.%
%
% Surge Biswas. July 17, 2015.
%
% DENDROGRAM Generate dendrogram plot.
% DENDROGRAM(Z) generates a dendrogram plot of the hierarchical binary
% clus... |
github | surgebiswas/transcriptome_compression-master | logmvnpdf.m | .m | transcriptome_compression-master/util/logmvnpdf.m | 422 | utf_8 | 69ce0223f329c6718d7a270b8607ca30 | function [logp] = logmvnpdf(x,mu,Sigma)
% outputs log likelihood array for observations x where x_n ~ N(mu,Sigma)
% x is NxD, mu is 1xD, Sigma is DxD
[N,D] = size(x);
const = -0.5 * D * log(2*pi);
xc = bsxfun(@minus,x,mu);
term1 = -0.5 * sum((xc / Sigma) .* xc, 2); % N x 1
term2 = const - 0.5 * logdet(Sigma); % ... |
github | surgebiswas/transcriptome_compression-master | plot_gaussian_ellipsoid.m | .m | transcriptome_compression-master/util/plot_gaussian_ellipsoid.m | 3,822 | utf_8 | b35dad9d9170d801b797d778d29e8893 | function h = plot_gaussian_ellipsoid(m, C, sdwidth, npts, axh)
% PLOT_GAUSSIAN_ELLIPSOIDS plots 2-d and 3-d Gaussian distributions
%
% H = PLOT_GAUSSIAN_ELLIPSOIDS(M, C) plots the distribution specified by
% mean M and covariance C. The distribution is plotted as an ellipse (in
% 2-d) or an ellipsoid (in 3-d). By ... |
github | surgebiswas/transcriptome_compression-master | oneHotEncode.m | .m | transcriptome_compression-master/util/oneHotEncode.m | 1,346 | utf_8 | 19e93f99bae4213562f0f4115b4af0d2 | function [ x, fnames ] = oneHotEncode( xd, varargin )
% Converts design matrix dataset xd into a numerical design matrix directly
% useable for supervised learning applications.
%
% xd = [n x p] design matrix dataset. Can contain numerical and categorical
% predictors. Categorical predictors are assumed to be re... |
github | surgebiswas/transcriptome_compression-master | venn.m | .m | transcriptome_compression-master/util/venn.m | 33,332 | utf_8 | 4bcd9e85c8e81f890e266846459fdcd8 | function varargout = venn (varargin)
%VENN Plot 2- or 3- circle area-proportional Venn diagram
%
% venn(A, I)
% venn(Z)
% venn(..., F)
% venn(..., 'ErrMinMode', MODE)
% H = venn(...)
% [H, S] = venn(...)
% [H, S] = venn(..., 'Plot', 'off')
% S = venn(..., 'Plot', 'off')
% [...] = venn(..., P1, V1, P2, V2, .... |
github | surgebiswas/transcriptome_compression-master | kde2d.m | .m | transcriptome_compression-master/util/kde2d.m | 7,513 | utf_8 | 9afbd380218ad7adaf289f1e9ef782a5 | function [bandwidth,density,X,Y]=kde2d(data,n,MIN_XY,MAX_XY)
% fast and accurate state-of-the-art
% bivariate kernel density estimator
% with diagonal bandwidth matrix.
% The kernel is assumed to be Gaussian.
% The two bandwidth parameters are
% chosen optimally without ever
% using/assuming a parametric model for the ... |
github | surgebiswas/transcriptome_compression-master | make.m | .m | transcriptome_compression-master/util/libsvm-3.21/matlab/make.m | 888 | utf_8 | 4a2ad69e765736f8cca8e3b721fb7ebd | % This make.m is for MATLAB and OCTAVE under Windows, Mac, and Unix
function make()
try
% This part is for OCTAVE
if (exist ('OCTAVE_VERSION', 'builtin'))
mex libsvmread.c
mex libsvmwrite.c
mex -I.. svmtrain.c ../svm.cpp svm_model_matlab.c
mex -I.. svmpredict.c ../svm.cpp svm_model_matlab.c
% This part is fo... |
github | surgebiswas/transcriptome_compression-master | rda.m | .m | transcriptome_compression-master/util/LIBRA_31okt11/rda.m | 22,872 | utf_8 | a348826014862f9e306b03118334c320 | function result=rda(x,group,varargin)
%RDA performs linear and quadratic robust discriminant analysis
% on the data matrix x with known group structure. It is based on the
% MCD estimator (see mcdcov.m), hence it has to be applied to
% low-dimensional data.
%
% The Robust Discriminant method is describ... |
github | surgebiswas/transcriptome_compression-master | cdq.m | .m | transcriptome_compression-master/util/LIBRA_31okt11/cdq.m | 11,121 | utf_8 | 10abe71caf5c75fab99031d36495e5f4 | function result = cdq(x,y,c,varargin)
%CDQ computes Censored Depth Quantiles for regression, as described in
%
% Debruyne, M., Hubert, M., Portnoy, S., Vanden Branden, K. (2008),
% "Censored depth quantiles",
% Computational Statistics and Data Analysis, 52, 1604-1614.
%
% Required input arguments:
% x : D... |
github | surgebiswas/transcriptome_compression-master | daplot.m | .m | transcriptome_compression-master/util/LIBRA_31okt11/daplot.m | 2,771 | utf_8 | 324422792487173e708332f9c888023d | function daplot(x,group,center,covar,classic,method)
%DAPLOT plots 97.5% tolerances ellipses of the bivariate data set x, which
% consists of several groups defined by the input argument 'group'.
% Center and covar are estimates of the center and covariance matrix of each group,
% obtained with a classical ('CD... |
github | surgebiswas/transcriptome_compression-master | madc.m | .m | transcriptome_compression-master/util/LIBRA_31okt11/madc.m | 1,629 | utf_8 | 9c087e0664bf8b7631dbd19461fb0994 | function result=madc(x)
%MADC is a scale estimator given by the Median Absolute Deviation
% with finite sample correction factor.
% It is defined as
% mad(x)= b_n 1.4826 med(|x_i - med(x)|)
% with b_n a small sample correction factor to make the mad unbiased at the
% normal distribution. It can resi... |
github | surgebiswas/transcriptome_compression-master | rstep.m | .m | transcriptome_compression-master/util/LIBRA_31okt11/rstep.m | 3,363 | utf_8 | 2d01b8c1e79cf4b4fc51ceda617d61e8 | function [S,P,t,kmax,med]= rstep(X,k,center,r);
%RSTEP is an auxiliary function for 'rapca.m'.
%
% This function is part of LIBRA: the Matlab Library for Robust Analysis,
% available at:
% http://wis.kuleuven.be/stat/robust.html
%
% Created by Sabine Verboven and Mia Hubert (October 2000)
% Part of the ... |
github | surgebiswas/transcriptome_compression-master | DetMCD.m | .m | transcriptome_compression-master/util/LIBRA_31okt11/DetMCD.m | 21,488 | utf_8 | 4778b65a6928abcec3d15f06cd1a5075 | function [rew,raw,hsetsfull]=DetMCD(x,varargin)
% DetMCD computes the MCD estimator of a multivariate data set in a deterministic way.
% 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 scatte... |
github | surgebiswas/transcriptome_compression-master | mcdcov.m | .m | transcriptome_compression-master/util/LIBRA_31okt11/mcdcov.m | 61,919 | utf_8 | 46c306a12918df588b62aaec312ba981 | 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 ... |
github | surgebiswas/transcriptome_compression-master | rpcr.m | .m | transcriptome_compression-master/util/LIBRA_31okt11/rpcr.m | 20,257 | utf_8 | 2067019382035899e215c1a444bdc897 | function result=rpcr(x,y,varargin)
%RPCR is a 'Robust Principal Components Regression' method based on ROBPCA.
% It can be applied to both low and high-dimensional predictor variables x,
% and to one or multiple response variables y. It is resistant to outliers
% in the data. First, a robust principal components... |
github | surgebiswas/transcriptome_compression-master | makeplot.m | .m | transcriptome_compression-master/util/LIBRA_31okt11/makeplot.m | 63,407 | utf_8 | 2fdd826268ed2d3c51039e377ed80f73 | function makeplot(out,varargin)
%MAKEPLOT makes plots for the main functions. These figures can also be obtained
% by setting 'plots = 1' in those functions.
%
% Required input:
% out = a structure containing the output of one of the following classes:
% MCDCOV, LS, LTS, MLR, MCDREG, CPCA,CPCR, CSIMPL... |
github | surgebiswas/transcriptome_compression-master | cvRpcr.m | .m | transcriptome_compression-master/util/LIBRA_31okt11/cvRpcr.m | 18,747 | utf_8 | a14cb41edb54f75b144de45bd61d58c2 | function result = cvRpcr(x,y,kmax,rmsecv,h,k)
%CVRPCR calculates the robust RMSECV (root mean squared error of cross-validation) curve
% for RPCR or the robust RMSEP (root mean squared error of prediction) value in a fast way.
% The R-RMSECV curve can be used to make a selection of the optimal number of
% compo... |
github | surgebiswas/transcriptome_compression-master | tree.m | .m | transcriptome_compression-master/util/LIBRA_31okt11/tree.m | 15,091 | utf_8 | 052cfb801b338a8e44b3d3fc3e229c1c | function tree(objectorder,heights)
%TREE creates a tree in which the leaves represent
% objects. The vertical coordinate of the junction
% of two branches is the dissimilarity between the
% corresponding clusters (maximal 30 objects allowed).
%
% The algorithm is fully described in:
% Kaufman, L. and ... |
github | surgebiswas/transcriptome_compression-master | removeObsMcd.m | .m | transcriptome_compression-master/util/LIBRA_31okt11/removeObsMcd.m | 2,846 | utf_8 | b24cb0f0eeed12ca35bd354587fa17db | function result = removeObsMcd(data,i,inputH0,inputFull,csteps);
%REMOVEOBSMCD is an auxiliary function to perform cross-validation with MCD
% (see cvMcd.m).
%
% The input:
% data : the original data
% i : the index of the observation that has to be removed.
% inputH0 : a structure that contai... |
github | surgebiswas/transcriptome_compression-master | ltsregres.m | .m | transcriptome_compression-master/util/LIBRA_31okt11/ltsregres.m | 46,460 | utf_8 | a033384e25e2fe6c38bb946ee78b64fd | function [rew,raw] = ltsregres(x,y,varargin)
%LTSREGRES carries out least trimmed squares (LTS) regression, introduced in
%
% Rousseeuw, P.J. (1984), "Least Median of Squares Regression,"
% Journal of the American Statistical Association, Vol. 79, pp. 871-881.
%
% The LTS regression method minimizes the... |
github | surgebiswas/transcriptome_compression-master | bagplot.m | .m | transcriptome_compression-master/util/LIBRA_31okt11/bagplot.m | 59,890 | utf_8 | 20440b4e0147085a25e7cbe676b8ae0b | function result=bagplot(x,varargin)
%BAGPLOT draws a bagplot, which is a generalisation of the univariate boxplot
% to bivariate data. The original bagplot is described in
%
% Rousseeuw, P.J., Ruts, I. and Tukey, J.W. (1999),
% "The bagplot: a bivariate boxplot", The American Statistician, 53, 382-387.
%
% The... |
github | surgebiswas/transcriptome_compression-master | cvRobpca.m | .m | transcriptome_compression-master/util/LIBRA_31okt11/cvRobpca.m | 6,053 | utf_8 | 1fc58fa7a5955441f6951cd1a44106a8 | function result = cvRobpca(data,kmax,resrob,rawres,h,csteps)
%CVROBPCA calculates the robust cross-validated PRESS (predicted residual error sum of squares) curve
% for ROBPCA in a fast way. This curve can be used to make a selection of the optimal number of
% components. The function is used in robpca.m.
%
% ... |
github | surgebiswas/transcriptome_compression-master | csimca.m | .m | transcriptome_compression-master/util/LIBRA_31okt11/csimca.m | 19,833 | utf_8 | 45faec1e88a85543e10e27d178633027 | function result = csimca(x,group,varargin);
%CSIMCA performs the SIMCA method. This is a classification
% method on a data matrix x with a known group structure. On each group a
% robust PCA analysis is performed. Afterwards a classification
% rule is developped to determine the assignment of new observations.
... |
github | surgebiswas/transcriptome_compression-master | fanny.m | .m | transcriptome_compression-master/util/LIBRA_31okt11/fanny.m | 5,663 | utf_8 | 99f7c3c9819f14e4ea5e9bef562cadaf | function result = fanny(x,kclus,vtype,metric,plots)
%FANNY is a fuzzy clustering algorithm. It returns a list representing a fuzzy clustering of the data
% into kclus clusters.
%
% The algorithm is fully described in:
% Kaufman, L. and Rousseeuw, P.J. (1990),
% "Finding groups in data: An introduction to cl... |
github | surgebiswas/transcriptome_compression-master | robpca.m | .m | transcriptome_compression-master/util/LIBRA_31okt11/robpca.m | 31,286 | utf_8 | 107eb86258ea2ad64b2abaad9fdebd7d | function result=robpca(x,varargin)
%ROBPCA is a 'ROBust method for Principal Components Analysis'.
% It is resistant to outliers in the data. The robust loadings are computed
% using projection-pursuit techniques and the MCD method.
% Therefore ROBPCA can be applied to both low and high-dimensional data sets.
... |
github | surgebiswas/transcriptome_compression-master | rsimca.m | .m | transcriptome_compression-master/util/LIBRA_31okt11/rsimca.m | 26,934 | utf_8 | 451c4be7f2bc7bb9a299e0b9957dbc79 | function result = rsimca(x,group,varargin)
%RSIMCA performs a robust version of the SIMCA method. This is a classification
% method on a data matrix x with a known group structure. On each group a
% robust PCA analysis (ROBPCA) is performed. Afterwards a classification
% rule is developped to determine the assig... |
github | surgebiswas/transcriptome_compression-master | cda.m | .m | transcriptome_compression-master/util/LIBRA_31okt11/cda.m | 21,184 | utf_8 | aca25c8539f79c874462980d0ab33ee7 | function result=cda(x,group,varargin)
%CDA performs linear and quadratic classical discriminant analysis
% on the data matrix x with known group structure.
%
% Required input arguments:
% x : training data set (matrix of size n by p).
% group : column vector containing the group numbers of the... |
github | surgebiswas/transcriptome_compression-master | rrmse.m | .m | transcriptome_compression-master/util/LIBRA_31okt11/rrmse.m | 4,071 | utf_8 | 1e06e95943b8cca120d615d706484bc3 | 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 Ac... |
github | surgebiswas/transcriptome_compression-master | pam.m | .m | transcriptome_compression-master/util/LIBRA_31okt11/pam.m | 8,753 | utf_8 | 74d2663795680d9bc09304f4a4d9177b | function result = pam(x,kclus,vtype,stdize,metric,plots)
%PAM is the Partitioning Around Medoids clustering algorithm.
% It returns a list representing a clustering of the data into kclus
% clusters based on the search for kclus representative objects or medoids among the observations of
% the data set.
%
% The... |
github | surgebiswas/transcriptome_compression-master | simcaplot.m | .m | transcriptome_compression-master/util/LIBRA_31okt11/simcaplot.m | 5,146 | utf_8 | 303fe3c5fb8883843f0ab2918c77ed19 | function simcaplot(result);
%SIMCAPLOT plots a scatter plot with the boundaries defined by the SIMCA method.
% It is based on the results from a simca analysis (see rsimca.m or csimca.m).
%
% For technical reasons, 6 different groups can be plotted (with different symbols).
% In case there are more groups, pl... |
github | surgebiswas/transcriptome_compression-master | cvRsimpls.m | .m | transcriptome_compression-master/util/LIBRA_31okt11/cvRsimpls.m | 15,188 | utf_8 | 4a983d5d1b52531c1cbf9e07bd875013 | function result = cvRsimpls(x,y,kmax,rmsecv,h,k)
%CVRIMPLS calculates the robust RMSECV (root mean squared error of cross-validation) curve
% for RSIMPLS or the robust RMSEP(root mean squared error of prediction) value in a fast way.
% The R-RMSECV curve can be used to make a selection of the optimal number of
... |
github | surgebiswas/transcriptome_compression-master | l1median.m | .m | transcriptome_compression-master/util/LIBRA_31okt11/l1median.m | 2,419 | utf_8 | e4c2b80a6d003717bfe8ec7802fd4ac0 | function result=L1median(x,tol);
%L1MEDIAN is an orthogonally equivariant location estimator,
% also known as the spatial median. It is defined as the point which
% minimizes the sum of the Euclidean distances to all observations in the
% data matrix x. It can resist 50% outliers.
%
% Reference (for the algori... |
github | surgebiswas/transcriptome_compression-master | clusplot.m | .m | transcriptome_compression-master/util/LIBRA_31okt11/clusplot.m | 8,426 | utf_8 | f2c5973d424819de23a3ecd7d432462c | function clusplot(x,ncluv,span,xlabels)
%CLUSPLOT creates a bivariate plot visualizing a partition (clustering)
% of the data. All observations are represented by points in the plot,
% using principal components or multidimensional scaling. Around each
% cluster an ellipse is drawn.
%
%The algorithm is fully de... |
github | surgebiswas/transcriptome_compression-master | rapca.m | .m | transcriptome_compression-master/util/LIBRA_31okt11/rapca.m | 14,125 | utf_8 | a9d028d28513a15ecb445199cf72f808 | function result=rapca(data,varargin);
%RAPCA is a 'Reflection-based Algorithm for Principal Components Analysis'.
% It is resistant to outliers in the data. The robust loadings are computed
% using projection-pursuit techniques and reflections.
% Therefore RAPCA can be applied to both low and high-dimensional d... |
github | surgebiswas/transcriptome_compression-master | cvMcd.m | .m | transcriptome_compression-master/util/LIBRA_31okt11/cvMcd.m | 4,455 | utf_8 | 4d75e2eefae1ec94c7abd3c74561b184 | function result = cvMcd(data,kmax,resMCD,h)
%CVMCD calculates the robust cross-validated PRESS (predicted residual error sum of squares)
% curve for the MCD method in a fast way.
%
% Input arguments:
% data : the full data set
% kmax : the maximal number of components to be considered (mostly kmax = p... |
github | surgebiswas/transcriptome_compression-master | regRF_predict.m | .m | transcriptome_compression-master/util/randomforest-matlab/trunk/RF_Reg_C/regRF_predict.m | 2,355 | utf_8 | 902ec69826d5e1a1934282844538ffd4 | %**************************************************************
%* mex interface to Andy Liaw et al.'s C code (used in R package randomForest)
%* Added by Abhishek Jaiantilal ( abhishek.jaiantilal@colorado.edu )
%* License: GPLv2
%* Version: 0.02
%
% Calls Regression Random Forest
% A wrapper matlab file that calls the... |
github | surgebiswas/transcriptome_compression-master | regRF_train.m | .m | transcriptome_compression-master/util/randomforest-matlab/trunk/RF_Reg_C/regRF_train.m | 14,752 | utf_8 | 57a97c2a3e92ff7a24132bdb4bbcc534 | %**************************************************************
%* mex interface to Andy Liaw et al.'s C code (used in R package randomForest)
%* Added by Abhishek Jaiantilal ( abhishek.jaiantilal@colorado.edu )
%* License: GPLv2
%* Version: 0.02
%
% Calls Regression Random Forest
% A wrapper matlab file that calls th... |
github | surgebiswas/transcriptome_compression-master | rfImpute.m | .m | transcriptome_compression-master/util/randomforest-matlab/trunk/RF_Reg_C/rfImpute.m | 2,694 | utf_8 | 2a8efadbda8fb3a7c7f24e56b6ec44d2 | function rfImpute_test
%Testing code, which uses the rfImpute_reg function below it
%load the diabetes dataset
load data/diabetes
%modify so that training data is NxD and labels are Nx1, where N=#of
%examples, D=# of features
X = diabetes.x;
Y = diabetes.y;
[N D] =size(X);
%rando... |
github | surgebiswas/transcriptome_compression-master | compile_windows.m | .m | transcriptome_compression-master/util/randomforest-matlab/trunk/RF_Reg_C/compile_windows.m | 906 | utf_8 | 3a72c18ccf410164c64be827cf6f8846 | % ********************************************************************
% * mex File compiling code for Random Forest (for windows)
% * mex interface to Andy Liaw et al.'s C code (used in R package randomForest)
% * Added by Abhishek Jaiantilal ( abhishek.jaiantilal@colorado.edu )
% * License: GPLv2
% * Version: 0.02
% ... |
github | surgebiswas/transcriptome_compression-master | compile_linux.m | .m | transcriptome_compression-master/util/randomforest-matlab/trunk/RF_Reg_C/compile_linux.m | 989 | utf_8 | cec552f1e98c8c2921a38cc22786d3e2 | % ********************************************************************
% * mex File compiling code for Random Forest (for linux)
% * mex interface to Andy Liaw et al.'s C code (used in R package randomForest)
% * Added by Abhishek Jaiantilal ( abhishek.jaiantilal@colorado.edu )
% * License: GPLv2
% * Version: 0.02
% *... |
github | surgebiswas/transcriptome_compression-master | rfImpute.m | .m | transcriptome_compression-master/util/randomforest-matlab/trunk/RF_Class_C/rfImpute.m | 2,338 | utf_8 | 9fc7b3322d8a6d2bd5b84b0c2988c3e0 | function rfImpute_test
%Testing code, which uses the rfImpute_class function below it
%load the twonorm dataset
load data/twonorm
%modify so that training data is NxD and labels are Nx1, where N=#of
%examples, D=# of features
X = inputs'; %'
Y = outputs;
[N D] =size(X);
%randomly... |
github | surgebiswas/transcriptome_compression-master | compile_windows.m | .m | transcriptome_compression-master/util/randomforest-matlab/trunk/RF_Class_C/compile_windows.m | 1,620 | utf_8 | 7ce80202a94e04844e950785d19a7572 | % ********************************************************************
% * mex File compiling code for Random Forest (for linux)
% * mex interface to Andy Liaw et al.'s C code (used in R package randomForest)
% * Added by Abhishek Jaiantilal ( abhishek.jaiantilal@colorado.edu )
% * License: GPLv2
% * Version: 0.02
% **... |
github | surgebiswas/transcriptome_compression-master | classRF_predict_matlab.m | .m | transcriptome_compression-master/util/randomforest-matlab/trunk/RF_Class_C/classRF_predict_matlab.m | 3,788 | utf_8 | d9d936d4569d0ae14a754075c70b2849 | %this predicts for all the trees in the RF
function [Y_new, Y_new_per_tree] = classRF_predict_matlab(X,classRF_model)
%Y_new is the winner vote from entire RF
%Y_new_per_tree is the winner vote for individual tree in RF
%treemap is a variable that holds the treemap. if you need more infor about ... |
github | surgebiswas/transcriptome_compression-master | classRF_train.m | .m | transcriptome_compression-master/util/randomforest-matlab/trunk/RF_Class_C/classRF_train.m | 18,532 | utf_8 | c962d1d687598ab967a5bd7651df25c0 | %**************************************************************
%* mex interface to Andy Liaw et al.'s C code (used in R package randomForest)
%* Added by Abhishek Jaiantilal ( abhishek.jaiantilal@colorado.edu )
%* License: GPLv2
%* Version: 0.02
%
% Calls Classification Random Forest
% A wrapper matlab file that calls... |
github | surgebiswas/transcriptome_compression-master | compile_linux.m | .m | transcriptome_compression-master/util/randomforest-matlab/trunk/RF_Class_C/compile_linux.m | 1,321 | utf_8 | 601534aa8cdaf5b9bbd5303e5e490e88 | % ********************************************************************
% * mex File compiling code for Random Forest (for linux)
% * mex interface to Andy Liaw et al.'s C code (used in R package randomForest)
% * Added by Abhishek Jaiantilal ( abhishek.jaiantilal@colorado.edu )
% * License: GPLv2
% * Version: 0.02
% **... |
github | surgebiswas/transcriptome_compression-master | adaptiveqr_factor.m | .m | transcriptome_compression-master/util/adaptive_qr/adaptiveqr_factor.m | 901 | utf_8 | 5fb3fb0a2966923d64a833175f69e424 | function [R,d] = adaptiveqr_factor(R, d, varargin)
[m,~] = size(R);
if nargin > 2
m0 = varargin{1};
else
[R,d] = finalize_row(R, d, 1);
m0 = 2;
end
% If starting row in greater than 2, perform facotrization related to the
% starting row and below done in the ite... |
github | geospace-code/georinex-main | ReadRinex.m | .m | georinex-main/ReadRinex.m | 663 | utf_8 | 54935a78035e6b147d473ee0e09e230a | function ReadRinex(fn)
%% ReadRinex GeoRINEX Python toolbox from Matlab
%
% must first do one-time install in Python 3.6 from Terminal:
% pip install -e .
%
% example:
% ReadRinex('tests/data/minimal.10o')
assert(~verLessThan('matlab', '9.5'), 'Matlab >= R2018b required')
dat = py.georinex.load(fn);
L1 = xarray2m... |
github | usgs/CatStat-master | plotworld.m | .m | CatStat-master/QCmulti/plotworld.m | 260 | utf_8 | 8709aa87014a5f63dac4dd8bd7284714 | %
% This function plots the world map, along with U.S. State borders
%
% WHERE IS DATA FROM!
function [] = plotworld
load('Countries.mat');
L = length(places);
hold on
for ii = 1 : L
Lon = lon{ii,1};
Lat = lat{ii,1};
plot(Lon, Lat,'k')
end
end
|
github | usgs/CatStat-master | plotmatchingrose.m | .m | CatStat-master/QCmulti/plotmatchingrose.m | 1,286 | utf_8 | c5c69eff209f78dde8018dddf6b15df2 | function plotmatchingrose(matching,cat1name,cat2name)
%
% Function to plot the azimuthal deviation in earthquake locations
%
% Input: matching - matching events data structure
% cat1name - name for catalog 1
% cat2name - name for catalog 2
%
% Output: None
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
github | plusangel/radiationUAV-master | testRansac.m | .m | radiationUAV-master/sensors/Applications/mapping/Matlab/ransac_homography/testRansac.m | 904 | utf_8 | 718f340681738be05350f09e98075acb | function testRansac
% real model coef
k = .5;
b = 10;
ptNum = 200;
outlrRatio = .4;
inlrStd = 5;
pts = genRansacTestPoints(ptNum,outlrRatio,inlrStd,[k b]);
figure,plot(pts(1,:),pts(2,:),'.'),hold on
X = -ptNum/2:ptNum/2;
plot(X,k*X+b,'k')
err0 = sqrError(k,b,pts(:,1:ptNum*(1-outlrRatio)))
% RANSAC
iter... |
github | plusangel/radiationUAV-master | findHomography.m | .m | radiationUAV-master/sensors/Applications/mapping/Matlab/ransac_homography/findHomography.m | 726 | utf_8 | 67779b4ae3ec53f84b624a755d2c6aab | function [H corrPtIdx] = findHomography(pts1,pts2)
% [H corrPtIdx] = findHomography(pts1,pts2)
% Find the homography between two planes using a set of corresponding
% points. PTS1 = [x1,x2,...;y1,y2,...]. RANSAC method is used.
% corrPtIdx is the indices of inliers.
% Yan Ke @ THUEE, 20110123, xjed09@gmail.com
... |
github | stochasticHydroTools/IBMethod-master | bspline4pt_d.m | .m | IBMethod-master/IBKernels/bspline4pt_d.m | 518 | utf_8 | 3ff3638221978dfea67895066f2ce003 | % ************************************************************************
% bspline4pt_d.m
% Author: Yuan-Xun Bao
% June 2014
%
% 1st derivative of the 4pt B-spline kernel
% can be used for interpolating derivative of a discrete delta function
% in the divergence-free IB method
% ***********************... |
github | stochasticHydroTools/IBMethod-master | stnd3pt.m | .m | IBMethod-master/IBKernels/stnd3pt.m | 771 | utf_8 | 6b5a441e7bb262d0471894ac7d6908e4 | % ----------------------------------------------------------------------- %
% Author: Jason Kaye
% Date: June 2013
% Description: Standard 3-point Kernel for IBM
%
% Conditions:
% ---------------------------------------------------------------------
% | Support | Odd/Even | 1st Mom. | 2nd Mom. | 3rd Mom. | Sum Squares ... |
github | stochasticHydroTools/IBMethod-master | delta_5_smooth.m | .m | IBMethod-master/IBKernels/delta_5_smooth.m | 2,479 | utf_8 | 3af1c2b53a99989805e621b49b5bd888 | %
% by Alex Kaiser
%
function val = delta_5_smooth(x)
KK = (38 - sqrt(69))/60;
% KK = (38 + sqrt(69))/60;
phi = @(r) (136 - 40*KK - 40*r.^2 + sqrt(2)*sqrt(3123 - 6840*KK + 3600*KK.^2 - 12440*r.^2 + 25680*KK*r.^2 - 12600*KK.^2*r.^2 + 8080*r.^4 - 8400*KK*r.^4 - 1400*r.^6))/280;
% function is even,
x = abs(x); ... |
github | stochasticHydroTools/IBMethod-master | flex6pt.m | .m | IBMethod-master/IBKernels/flex6pt.m | 2,200 | utf_8 | b3fe353bdd0428540c75b170d0635f00 | % ----------------------------------------------------------------------- %
% Author: Jason Kaye
% modified by Yuan-Xun Bao
% Date: June 2013
% Description: Flexible 6-pt Kernel for IBM
%
% Conditions:
% ---------------------------------------------------------------------
% | Support | Odd/Even |... |
github | stochasticHydroTools/IBMethod-master | stnd4pt.m | .m | IBMethod-master/IBKernels/stnd4pt.m | 767 | utf_8 | d2663d927c99dddb67bb30303896cfb2 | % ----------------------------------------------------------------------- %
% Author: Jason Kaye
% Date: June 2013
% Description: Standard 4-point Kernel for IBM
%
% Conditions:
% ---------------------------------------------------------------------
% | Support | Odd/Even | 1st Mom. | 2nd Mom. | 3rd Mom. | Sum Squares ... |
github | stochasticHydroTools/IBMethod-master | phiweights.m | .m | IBMethod-master/IBKernels/phiweights.m | 3,651 | utf_8 | 3dd7b34c7dc093dbe173c7a72ff912f0 | % ----------------------------------------------------------------------- %
% Title: phiweights
% Author: Jason Kaye, Yuanxun Bao
% Date: June 2013
% Description: Compute one-dimensional kernel weights at Cartesian grid
% points for kernels with up to 6-point support.
%
% Inputs:
% r - Point at which to compute weights... |
github | stochasticHydroTools/IBMethod-master | deltaCK.m | .m | IBMethod-master/IBKernels/deltaCK.m | 2,949 | utf_8 | dd708cd85a211307ec22c37ee82ed0a8 | %**************************************************************************
% deltaCK.m
% Author: Charles S. Peskin
% minor modification by Yuan-Xun Bao
%
% This code produces (called by deltaCKplot.m) the new 6-point
% immersed boundary kernel in [1] and its three continuous
% derivatives.
%... |
github | stochasticHydroTools/IBMethod-master | flex6pt_d.m | .m | IBMethod-master/IBKernels/flex6pt_d.m | 1,712 | utf_8 | f5825f8a2b387c8f45d226f826af7a99 | % ************************************************************************
% flex6pt_d.m
% Author: Yuan-Xun Bao
% June 2014
%
% 1st derivative of the new 6pt kernel
% can be used for interpolating derivative of a discrete delta function
% in the divergence-free IB method
%
% [1] "A gaussian-like immers... |
github | stochasticHydroTools/IBMethod-master | bspline6pt.m | .m | IBMethod-master/IBKernels/bspline6pt.m | 1,014 | utf_8 | aa7f9403d8736edccbfb81c04756efd8 | % ----------------------------------------------------------------------- %
% Author: Jason Kaye
% Date: June 2013
% Description: 6-point B-Spline Kernel for IBM
%
% Conditions:
% ---------------------------------------------------------------------
% | Support | Odd/Even | 1st Mom. | 2nd Mom. | 3rd Mom. | Sum Squares ... |
github | stochasticHydroTools/IBMethod-master | flex5pt.m | .m | IBMethod-master/IBKernels/flex5pt.m | 3,629 | utf_8 | 8fce63c283072de097ec7d30d3087c0a | % ----------------------------------------------------------------------- %
% Author: Alex Kaiser & Bill Bao
% Date: Dec 2016
% Description: Flexible 5-pt Kernel for IBM
%
% Conditions:
% ---------------------------------------------------------------------
% | Support | Odd/Even | 1st Mom. | 2nd Mom. | 3rd Mom. | Sum ... |
github | stochasticHydroTools/IBMethod-master | bspline4pt.m | .m | IBMethod-master/IBKernels/bspline4pt.m | 753 | utf_8 | 4092a04c088a0d9a34bc7b6e9dc4b04e | % ----------------------------------------------------------------------- %
% Author: Jason Kaye
% Date: June 2013
% Description: 4-point B-Spline Kernel for IBM
%
% Conditions:
% ---------------------------------------------------------------------
% | Support | Odd/Even | 1st Mom. | 2nd Mom. | 3rd Mom. | Sum Squares ... |
github | stochasticHydroTools/IBMethod-master | NS2D_IBMAC.m | .m | IBMethod-master/DemoStandardIB/NS2D_IBMAC.m | 2,908 | utf_8 | 96c2b595c73610b7e844a5aa2d0be58b | % ----------------------------------------------------------------------- %
% NS2D_IBMAC.m
% Yuanxun Bill Bao
% Nov 09, 2013
%
% Description: solve the fluid-structure interaction of an elastic
% membrane by solving a 2D NS flow. The standard immersed
% boundary on a staggered grid is used (IB-MAC).
% AB2 for time s... |
github | stochasticHydroTools/IBMethod-master | interpMAC2Dvector.m | .m | IBMethod-master/DemoStandardIB/interpMAC2Dvector.m | 1,658 | utf_8 | 2055ca2fc314aba815bac5b6a9c69581 | % ----------------------------------------------------------------------- %
% Author: Jason Kaye, Yuanxun Bill Bao
% Date: June 2013
% Description: Interpolate velocity from Cartesian grid to markers
%
% Inputs:
% u - Vector fluid velocity.
% X - Marker locations [x,y].
% N - Domain size [Nx,Ny].
% h - Cartesian grid s... |
github | stochasticHydroTools/IBMethod-master | PoissonSolver2D.m | .m | IBMethod-master/DemoStandardIB/PoissonSolver2D.m | 1,007 | utf_8 | ca8a6cefa99072b1bf6887f844cfa81f | % ----------------------------------------------------------------------- %
% Title: PoissonSolver2D
% Author: Jason Kaye
% Date: June 2013
%
% Description: Solves Poisson's equation with periodic boundary conditions
% in 2D using Fourier basis
%
% Inputs:
% f - Right hand side to Poisson's equation -\Delta u = f, defi... |
github | stochasticHydroTools/IBMethod-master | NavierStokes2D_FFT_RK.m | .m | IBMethod-master/DemoStandardIB/NavierStokes2D_FFT_RK.m | 1,849 | utf_8 | 84651a2c8158e38de4fea13b5a1beeec | % ----------------------------------------------------------------------- %
% NavierStokes2D_FFT_RK
% Yuanxun Bill Bao
% June, 2015
%
% Description: FFT based 2D Navier Stokes solver for Runge-Kutta (2 step)
%
% Inputs:
% N -- grid size N=[Nx,Ny], Nx=Ny for now
% mu -- viscosity
% rho -- density
% S -... |
github | stochasticHydroTools/IBMethod-master | NavierStokes2D_FFT.m | .m | IBMethod-master/DemoStandardIB/NavierStokes2D_FFT.m | 1,328 | utf_8 | b3275c4e306c3878e0eb0ef6518bd23d | % ----------------------------------------------------------------------- %
% NavierStokes2D_FFT
% Yuanxun Bill Bao
% July 1, 2014
%
% Description: FFT based 2D Navier Stokes solver
%
% Inputs:
% N -- grid size N=[Nx,Ny], Nx=Ny for now
% mu -- viscosity
% rho -- density
% S -- advection term
% f ... |
github | stochasticHydroTools/IBMethod-master | spreadMAC2Dvector.m | .m | IBMethod-master/DemoStandardIB/spreadMAC2Dvector.m | 1,662 | utf_8 | b155a242be2106c039a7740c410f0c5c | % ----------------------------------------------------------------------- %
% Author: Jason Kaye, Yuanxun Bill Bao
% Date: June 2013
% Description: Spread force from grid markers to Cartesian grid
%
% Inputs:
% F - Vector force on markers.
% X - Marker locations [x,y].
% N - Domain size [Nx,Ny].
% h - Cartesian grid sp... |
github | philsf/Calculo_Numerico-master | gauss.m | .m | Calculo_Numerico-master/Programas/gauss.m | 371 | utf_8 | e336dc354d181921b28e4377497d0366 | % Eliminação de Gauss, baseado no algoritmo do livro Ruggiero & Lopes
function gauss(A,b);
n = length(b); x = zeros(n,1);
m=eye(n);
for k=1:n-1 % etapa k
for i=k+1:n
m(i,k)=A(i,k)/A(k,k);
A(i,k)=0;
for j=k+1:n
A(i,j) = A(i,j) - m(i,k)*A(k,j);
end
b(i) = b(i) - m(i,k)*b(k);
... |
github | philsf/Calculo_Numerico-master | trapezios.m | .m | Calculo_Numerico-master/Programas/trapezios.m | 400 | utf_8 | f46ea5c08acebb1bd14bff54bda35803 | ## Implementacao do metodo dos trapezios repetidos, para integracao
## numerica
## Recebe
## f: vetor que define a funcao em um linspace
## I: vetor que define o intervalo I=[a,b]
## s: numero de subdivisoes do intervalo a ser usado
function integral = trapezios(f,I,s);
integral=0;
a = I(1); b=I(2);
h = (b-a)... |
github | jamescrall/BEEtag-master | fitquad.m | .m | BEEtag-master/src/fitquad.m | 7,777 | utf_8 | 49e4368e14aa7eb80b050d49df3705a4 | %Functions modified slightly from "CALTag" library (Bradley Atcheson), specifically
%caltag.m. Original calTag code available at
%https://github.com/brada/caltag.
function [isQuad,corners] = fitquad(bbox, mask)
% default return values
isQuad = false;
corners = [];
% remove small spurs in edge image (ho... |
github | jamescrall/BEEtag-master | bradley.m | .m | BEEtag-master/src/bradley/bradley/bradley.m | 1,659 | utf_8 | c6abddeb1f5b940960643b4b4f7c3f14 | %BRADLEY local thresholding.
% BW = BRADLEY(IMAGE) performs local thresholding of a two-dimensional
% array IMAGE with Bradley method. The key idea of the algorithm is that
% every image's pixel is set to black if its brightness is T percent
% lower than the average brightness of surrounding pixels in the windo... |
github | qxcv/comp2560-master | Get_Distance_Weights.m | .m | comp2560-master/project/matlab/Get_Distance_Weights.m | 1,804 | utf_8 | c35489e086badd7ae54b7f6ecc44bcbf | % this function specifies the parameters used in tracking and recombination
% of poses. You might need to play around with these weights over the
% training set to find good combinations. Here are some hints on selecting
% them:
% 1. if there is significant optical flow for a certain body part, then put
% the first tw... |
github | qxcv/comp2560-master | get_groundtruth_for_seq.m | .m | comp2560-master/project/matlab/get_groundtruth_for_seq.m | 849 | utf_8 | 31a148bffd9b879c81854cef6907cfff | % extract the ground truth in posedata for the images in the sequence.
% if you use another dataset, you need to change this function accordingly.
function gt = get_groundtruth_for_seq(files, posedata)
% gt_framenum used to be persistent, but I don't know why (perf? That makes
% no sense)
gt_framenum = arrayfun(@(x) ge... |
github | qxcv/comp2560-master | demo.m | .m | comp2560-master/project/matlab/demo.m | 6,099 | utf_8 | 8ca555393bdf5eab597d1da2fc6d3e97 | % % Copyright (C) 2014 LEAR, Inria Grenoble, France
%
% Permission is hereby granted, free of charge, to any person obtaining
% a copy of this software and associated documentation files (the
% "Software"), to deal in the Software without restriction, including
% without limitation the rights to use, copy, modify, merg... |
github | qxcv/comp2560-master | EstimatePosesInVideo.m | .m | comp2560-master/project/matlab/EstimatePosesInVideo.m | 10,135 | utf_8 | 5db7435403be1838097b6f5b27265af4 | function new_merged_poses = EstimatePosesInVideo(src_path, cy_model, config)
global num_path_parts;
global keyjoints_right keyjoints_left;
global GetDistanceWeightsFn;
%% set some parameters!
data_store_path = config.data_store_path;
data_flow_path = config.data_flow_path;
max_poses = conf... |
github | qxcv/comp2560-master | set_algo_parameters.m | .m | comp2560-master/project/matlab/set_algo_parameters.m | 6,295 | utf_8 | 5fb6ed062d41381693fb8e077b50660a | % set some configuration settings
function config = set_algo_parameters()
%% set the configuration parameters: You need to set this.
% set the cache for storing optical flow and pose candidates. You need a
% large disk space for this.
config.cache_path = './cache/';
% this is the place to store the pose candidates
co... |
github | qxcv/comp2560-master | get_piw_data.m | .m | comp2560-master/project/matlab/get_piw_data.m | 6,260 | utf_8 | 5d32c73fdd0f58edfa719d0546920a9d | % read the piw annotations. The annotation database file we use here is
% different in format from the one supplied in the original dataset. This
% code generates the mid-keypoints for the lower and upper-limbs.
%
function pos = get_piw_data(name, piw_seqs_path)
cachedir = './cache/';
cls = [name '_data'];
try
lo... |
github | qxcv/comp2560-master | demo_piw_full.m | .m | comp2560-master/project/matlab/demo_piw_full.m | 5,486 | utf_8 | 5bd33b579b04c30167de9837e76b4c28 | % % Copyright (C) 2014 LEAR, Inria Grenoble, France
%
% Permission is hereby granted, free of charge, to any person obtaining
% a copy of this software and associated documentation files (the
% "Software"), to deal in the Software without restriction, including
% without limitation the rights to use, copy, modify, merg... |
github | qxcv/comp2560-master | demo_mpii_cooking.m | .m | comp2560-master/project/matlab/demo_mpii_cooking.m | 5,785 | utf_8 | 8b66cc8ebfa6e2f8ff1861ca3a6f087c | % % Copyright (C) 2014 LEAR, Inria Grenoble, France
%
% Permission is hereby granted, free of charge, to any person obtaining
% a copy of this software and associated documentation files (the
% "Software"), to deal in the Software without restriction, including
% without limitation the rights to use, copy, modify, merg... |
github | qxcv/comp2560-master | global_conf.m | .m | comp2560-master/project/matlab/CY/global_conf.m | 1,439 | utf_8 | f57faa082aa00574c314f92e16dc935f | function conf = global_conf()
assert_not_in_parallel_worker();
% dataset
conf.interval = 10; % 10 levels from 1 to 1/2
conf.memsize = 0.5; % 0.5 gb
conf.NEG_N = 80;
conf.device_id = 0;
conf.caffe_root = './CY/external/caffe';
% default configurations
conf.mining_neg = true;
conf.mining_pos = false;
conf.K = 13;
conf.te... |
github | qxcv/comp2560-master | modelcomponents.m | .m | comp2560-master/project/matlab/CY/src/modelcomponents.m | 2,048 | utf_8 | a85bdb7c0f1c497690f338f5b8cf62b5 | % Cache various statistics from the model data structure for later use
% "apps{p}" gives the appearance (unary term) weights for the part p.
% "components{1}{p}" is a cell array with the following attributes;
% - .Im
% - .b
% - .biasI
% - .appI
% - .sizx
% - .sizy
% - .pdw(n) for neighbour n
% - .pdefI(... |
github | qxcv/comp2560-master | computeColor.m | .m | comp2560-master/project/matlab/flow/LDOF_src/third_party/flow-code-matlab/computeColor.m | 3,142 | utf_8 | a36a650437bc93d4d8ffe079fe712901 | function img = computeColor(u,v)
% computeColor color codes flow field U, V
% According to the c++ source code of Daniel Scharstein
% Contact: schar@middlebury.edu
% Author: Deqing Sun, Department of Computer Science, Brown University
% Contact: dqsun@cs.brown.edu
% $Date: 2007-10-31 21:20:30 (Wed, 31 O... |
github | qxcv/comp2560-master | ann_compile_mex.m | .m | comp2560-master/project/matlab/flow/LDOF_src/third_party/ann_mwrapper/ann_compile_mex.m | 1,684 | utf_8 | 33dc804c66a9d68c02efa55511995e33 | function ann_compile_mex()
%ANN_COMPILE_MEX Compiles the core-mex files of the ANN Lib
%
% [ Syntax ]
% - ann_compile_mex
%
% [ Description ]
% - ann_compile_mex re-compiles the mex files.
%
% [ History ]
% - Created by Dahua Lin, on Jul 06, 2007
%
%% configurations
% When you intend to ch... |
github | qxcv/comp2560-master | ann_compile_mex_bak1.m | .m | comp2560-master/project/matlab/flow/LDOF_src/third_party/ann_mwrapper/ann_compile_mex_bak1.m | 1,630 | utf_8 | 9a3b317c5db70460d8ea44bd877246d5 | function ann_compile_mex_bak1()
%ANN_COMPILE_MEX Compiles the core-mex files of the ANN Lib
%
% [ Syntax ]
% - ann_compile_mex
%
% [ Description ]
% - ann_compile_mex re-compiles the mex files.
%
% [ History ]
% - Created by Dahua Lin, on Jul 06, 2007
%
%% configurations
% When you intend to change the co... |
github | qxcv/comp2560-master | annquery.m | .m | comp2560-master/project/matlab/flow/LDOF_src/third_party/ann_mwrapper/annquery.m | 13,952 | utf_8 | 9ca55bd21175f2b794f2087b4eda6727 | function [nnidx, dists] = annquery(Xr, Xq, k, varargin)
%ANNQUERY Performs Approximate K-Nearest-Neighbor query for a set of points
%
% [ Syntax ]
% - nnidx = annquery(Xr, Xq, k)
% - nnidx = annquery(Xr, Xq, k, ...)
% - [nnidx, dists] = annquery(...)
% - annquery -doc
%
% [ Arguments ]
% - Xr: ... |
github | qxcv/comp2560-master | setopts.m | .m | comp2560-master/project/matlab/flow/LDOF_src/third_party/ann_mwrapper/private/setopts.m | 6,161 | utf_8 | e1da88dc99b232199d5082bf6df84068 | function opts = setopts(opts0, varargin)
%SETOPTS Sets the options and makes the option-struct
%
% [ Syntax ]
% - opts = setopts([], name1, value1, name2, value2, ...)
% - opts = setopts([], {name1, value1, name2, value2, ...})
% - opts = setopts([], newopts)
% - opts = setopts(opts0, ...)
%
% [ Argume... |
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