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github | vcheplygina/mil-master | mil_message.m | .m | mil-master/mil_message.m | 2,401 | utf_8 | 14c8233db6d29826cd39e774ab07feb5 | % MIL_MESSAGE Print formatted message
%
% MIL_MESSAGE(INFO,MSG,PARAMS)
% MIL_MESSAGE(INFO)
%
% INPUT
% INFO Importance-level of the message
% MSG The message to print
% PARAMS Additional parameters for the message
%
% DESCRIPTION
% Plot out a message MSG, formated like the string in fprintf and
%... |
github | vcheplygina/mil-master | apr_mil.m | .m | mil-master/apr_mil.m | 3,353 | utf_8 | 6e074535a53608695c6b4f260d535569 | % Iterative discrim APR MIL
%
% OUT = APR_MIL(X,FRAC,THRES,TAU,EPSILON,STEP)
%
% INPUT
% X MIL dataset
% FRAC Quantile fraction (default = eps)
% THRES Threshold (default = 0.01)
% TAU Tau param. in expansion of rectangle (default = 0.995)
% EPSILON epsilon? (default = 0.02)
% STEP ... |
github | vcheplygina/mil-master | milcrossval.m | .m | mil-master/milcrossval.m | 4,948 | utf_8 | 61344f2a6ba9cc17396ea2b84c97a425 | %MILCROSSVAL MIL crossvalidation
%
% [Y,Z,I,Itrn,Itst] = MILCROSSVAL(X,I)
%
% INPUT
% X MIL-dataset
% I Number of folds/object identifier for each fold
%
% OUTPUT
% Y Train MIL-dataset
% Z Test MIL-dataset
% I Updated object identifier for each fold.
... |
github | vcheplygina/mil-master | mil_lnsrch.m | .m | mil-master/mil_lnsrch.m | 3,837 | utf_8 | 9289eabe4f51f5dc6d50298676b3b0a3 | %LNSRCH Simulate the routine "lnsrch" in [1], which takes,
% xold - The starting point of lnsrch
% n - Dimension of the instance
% fold - The value of function at xold
% g - The gradient of function at xold
% p - The direction for lnsrch
% tolx - Convergence tol... |
github | vcheplygina/mil-master | gendatandrews.m | .m | mil-master/gendatandrews.m | 1,569 | utf_8 | 6a4381da68f4c387123cbc4fe7608c7d | %GENDATANDREWS Fox, Tiger and Elephant datasets used by Andrews
%
% A = GENDATANDREWS(CLASSNAME)
%
% INPUT
% CLASSNAME Class to make positive
%
% OUTPUT
% A MIL dataset
%
% DESCRIPTION
% Define the multi-instance learning problem where the classes 'Fox',
% 'Tiger' or 'Elephant' can b... |
github | vcheplygina/mil-master | gendatmilw.m | .m | mil-master/gendatmilw.m | 1,546 | utf_8 | 7f8dd3106116aa773f278d855e3c67b6 | %GENDATMILW Widened MIL dataset
%
% a = gendatmilw(n,width,dim)
%
% INPUT
% N Number of pos. and neg. bags
% WIDTH Multiplication factor for the width (default = 1.1)
% DIM Dimensionality (default = 2)
%
% OUTPUT
% A MIL dataset
%
% DESCRIPTION
% Make a MIL dataset where all instances in a... |
github | vcheplygina/mil-master | apr_grow.m | .m | mil-master/apr_grow.m | 2,110 | utf_8 | f3625a8fa1d943f57303d8ee53d0a04b | function [mn,mx]=apr_grow(bags,rel)
dim = sum(rel);
if (dim==0)
error('All features become non-relevant.');
end
rel = find(rel);
nrbags = size(bags,1);
minimax = [repmat(inf,1,dim);
repmat(-inf,1,dim)];
for j=1:nrbags
bags{j} = bags{j}(:,rel);
mx = max(bags{j},[],1);
J = find(mx<minimax(1,:));
minima... |
github | vcheplygina/mil-master | getwnames.m | .m | mil-master/getwnames.m | 1,042 | utf_8 | dafd08305843e722dad25017c72f271b | %GETWNAMES Extract names from cell array of mappings
%
% STR = GETWNAMES(W)
%
% INPUT
% W Cell-array of prmappings
%
% OUTPUT
% STR Cell-array of strings
%
% DESCRIPTION
% Extract the classifier names from a cell array of (possibly untrained)
% mappings. When the mapping is sequential, each mapping name is
%... |
github | vcheplygina/mil-master | density_mil.m | .m | mil-master/density_mil.m | 2,297 | utf_8 | 701dd85dfbd02ce2d71797d7ee6eca51 | %DENSITY_MIL Density MIL
%
% W = DENSITY_MIL(A,W)
%
% INPUT
% A MIL-dataset
% W Untrained density mapping (default= parzenm)
%
% OUTPUT
% W MIL-classifier using densities.
%
% DESCRIPTION
% Estimate the densities of the instances that come from both the
% positive bags and the negati... |
github | vcheplygina/mil-master | apr_discrim.m | .m | mil-master/apr_discrim.m | 1,292 | utf_8 | f273b69a7b4101047730583f023273e7 | %APR_DISCRIM
% [result,converged] = apr_discrim(x,mn,mx,rel,thres)
function [result,converged] = apr_discrim(x,mn,mx,rel,thres)
x = cell2mat(x);
[n,p] = size(x);
lbs = zeros(1,p);
ubs = zeros(1,p);
lbs(find(rel)) = mn;
ubs(find(rel)) = mx;
count = 0;
discrimed = zeros(n,1);
under_consider = rel;
result = zeros(1,p... |
github | vcheplygina/mil-master | genmil.m | .m | mil-master/genmil.m | 5,626 | utf_8 | a826532522b75b9e5c93e112f9584472 | %GENMIL Generate MIL dataset
%
% X = GENMIL(X,CLASSLAB,BAGID,COMBRULE)
% X = GENMIL(X,POSCLASSLAB)
% X = GENMIL(X,NEWBAGLAB)
%
% INPUT
% X data matrix or datafile
% CLASSLAB instance label
% BAGID bag identifiers
% COMBRULE instance label combination rule (default = 'pres... |
github | vcheplygina/mil-master | gendatZhoutext.m | .m | mil-master/gendatZhoutext.m | 3,310 | utf_8 | d03597d84a88a5e9a47eb999e7445881 | %GENDATZHOUTEXT MIL text data
%
% A = GENDATZHOUTEXT(NR,TRUE_INST_LAB)
%
% INPUT
% NR Class number
% TRUE_INST_LAB Use the true instance label (default = 0)
%
% OUTPUT
% A MIL dataset
%
% DESCRIPTION
% Get the MIL text data by Zhou Zhihua, originally used in
% Z.-H. Zhou, Y.-Y. Su... |
github | vcheplygina/mil-master | gendatsival.m | .m | mil-master/gendatsival.m | 2,514 | utf_8 | bfe76c7b520c647ec6f67d2c8b290888 | %GENDATSIVAL SIVAL dataset
%
% A = GENDATSIVAL(CLASSNAME)
%
% INPUT
% CLASSNAME Positive class
%
% OUTPUT
% A MIL dataset
%
% DESCRIPTION
% Define the multi-instance learning problem SIVAL. One of the 25 image
% classes can be used as positive class:
% CLASSNAME = {'AjaxOrange' 'Apple' 'Banana' ... |
github | vcheplygina/mil-master | oc2milset.m | .m | mil-master/oc2milset.m | 1,312 | utf_8 | 74e7936a03abd8e663552c69584ae70c | %OC2MILSET Convert a OC to a MIL set
%
% A = OC2MILSET(B)
%
% INPUT
% B OC Dataset
%
% OUTPUT
% A MIL dataset
%
% Convert the labels of a one-class set B to a multi-instance-learning
% label set A. It means that labels 'target'/'outlier' are converted
% into 'positive'/'negative'.
% Note that no MIL i... |
github | vcheplygina/mil-master | milmap.m | .m | mil-master/milmap.m | 2,236 | utf_8 | c9e9eb79e210a33ee9c305f62f657546 | %MILMAP Map multi-instance dataset
%
% OUT = MILMAP(Z,W,MISSINGVALUES)
%
% INPUT
% Z MIL-dataset or MIL-datafile
% W MIL-classifier
% MISSINGVALUES Setting to deal with missing values
%
% OUTPUT
% OUT Classifier output for data Z
%
% DESCRIPTION
% The official func... |
github | vcheplygina/mil-master | ismillabeled.m | .m | mil-master/ismillabeled.m | 1,977 | utf_8 | da966069407e65c819b93e9628a2260b | %ISMILLABELED Test if dataset is MIL and labeled
%
% OUT = ISMILLABELED(A)
%
% INPUT
% A Dataset
%
% OUTPUT
% OUT True if A is correctly MIL labeled
%
% DESCRIPTION
% Test if dataset A is a correctly labeled MIL dataset. For that it
% should have: the correct class labels ('positive' and 'negative') in
% t... |
github | vcheplygina/mil-master | gendatmilg.m | .m | mil-master/gendatmilg.m | 2,402 | utf_8 | 55e915714c55e8d0d8f52950244ba84a | %GENDATMILG Generate Gaussian MIL problem
%
% X = GENDATMILG([N1 N2],NP,D,DIM)
%
% INPUT
% N1,N2 Number of pos. and neg. bags
% NP Number of instances in the concept (default=1)
% D Position of the positive concept (default=7)
% DIM Number of features (only the first two are in... |
github | vcheplygina/mil-master | gendatprotein.m | .m | mil-master/gendatprotein.m | 3,255 | utf_8 | fe6d90ff7edfb5db0aafa3d7c095ff93 | %GENDATPROTEIN Thioredoxin-fold protein identification.
%
% A = GENDATPROTEIN
%
% OUPUT
% A MIL dataset
%
% DESCRIPTION
% Define the multi-instance learning problem Trx Protein. The data is obtained from here:
% http://cse.unl.edu/~qtao/datasets/mil_dataset__Trx_protein.html
%
% Veronika's (who is... |
github | vcheplygina/mil-master | milboostc.m | .m | mil-master/milboostc.m | 2,902 | utf_8 | 3f4ccf362b31489be3f8f341315cd1dc | %MILBOOST Boosted MIL classifier
%
% W = MILBOOSTC(A,FRAC,T)
%
% INPUT
% A MIL dataset
% FRAC Fraction of informative instances (default = 'presence')
% T Nr of boosting rounds (default = 100)
% LOSS Loss function (default = @noisyORloss)
%
% OUTPUT
% W Milboost classifier
%
% DESCRI... |
github | vcheplygina/mil-master | mildatapath.m | .m | mil-master/mildatapath.m | 1,223 | utf_8 | ea5a70e6afc44096614c8f873e7c0a21 | % MILDATAPATH Path to MIL datasets
%
% DPATH = MILDATAPATH(NEWPATH)
%
% INPUT
% NEWPATH Path to MIL datasets
%
% OUTPUT
% DPATH Path to MIL datasets
%
% DESCRIPTION
% Define the directory name where all MIL dataset are stored.
% When you supply it a path, a global variable is created that stores
% the... |
github | vcheplygina/mil-master | gendatsurrey.m | .m | mil-master/gendatsurrey.m | 2,100 | utf_8 | 84561f6dcb838fed06de2b3e25caeba6 | %GENDATSURREY Read parts of the Surrey database
%
% A = GENDATSURREY(CLNAME)
%
% Read of the Surrey database and use one of the classes as target
% class. To be honest, per default the cathedral set is used as
% positive and the rest negative. That means that 5 images are labeled
% '+', the rest '-'.
%
% In total... |
github | vcheplygina/mil-master | milrandomize.m | .m | mil-master/milrandomize.m | 736 | utf_8 | a8cc25fa6678de920ea834d5e02e790e | %MILRANDOMIZE
%
% A = MILRANDOMIZE(A,SEED)
%
% INPUT
% A MIL dataset
% SEED Seed for random number generator
%
% OUTPUT
% A MIL dataset
%
% DESCRIPTION
% Randomize the bags in MIL dataset A. When no SEED is given, the
% computer clock is used as seed.
% Copyright: D.M.J. Tax, D.M.J.Tax@prtools.or... |
github | vcheplygina/mil-master | traindecstump.m | .m | mil-master/traindecstump.m | 1,937 | utf_8 | 5abcf836fbf76d1c5b6ed0b2b597d01a | %TRAINDECSTUMP
%
% [H,BESTERR] = TRAINDECSSTUMP(X,W)
%
% INPUT
% X Dataset
% W Weight per object
%
% OUTPUT
% H Decision stump
% BESTERR Lowest error
%
% DESCRIPTION
% Train a decision stump on dataset X. Each object in X is weighted by a
% weight W. Objects from the positive ... |
github | vcheplygina/mil-master | gendatmilmc.m | .m | mil-master/gendatmilmc.m | 1,744 | utf_8 | d39092b1d3555545b79260087f11110e | %GENDATMILMC Generate MIL problems with multiple concepts
%
% A = GENDATMILMC(N,C,SIG)
%
% INPUT
% N Number of positive and negative bags
% C One of the four concepts (default = 1)
% SIG Variance (default = 0.1)
%
% OUTPUT
% A MIL dataset
%
% DESCRIPTION
% Generation of a MIL dataset where ... |
github | vcheplygina/mil-master | milvector.m | .m | mil-master/milvector.m | 5,253 | utf_8 | d66a55ff9002b9cd4db5038983d887f9 | %MILVECTOR Vector representation of a bag
%
% W = MILVECTOR(X,RTYPE)
%
% INPUT
% X MIL dataset
% RTYPE Method for obtaining a vector from a bag
% (default = 'm')
%
% OUTPUT
% W Standard Prtools mapping
%
% DESCRIPTION
% Extract a single feature vector from each... |
github | vcheplygina/mil-master | sv_mil.m | .m | mil-master/sv_mil.m | 3,886 | utf_8 | 17d1dcbfdf9c21ba9988d21fd9c93537 | %SV_MIL Support Vector using a bag kernel
%
% W = SV_MIL(A,C,KERNELMAP)
%
% INPUT
% A Dataset
% C Regularization parameter (default = 1)
% KERNELMAP Kernel between bags (default = milproxm([],'h'))
%
% OUTPUT
% W Bag Support Vector Classifier
%
% DESCRIPTION
% Optimizes a suppo... |
github | vcheplygina/mil-master | mcmilc.m | .m | mil-master/mcmilc.m | 1,939 | utf_8 | d1aaf608654013441804a131a053ac97 | %MCMILC Multi-class MIL classifier
%
% W = MCMILC(A,U)
% W = A*MCMILC([],U)
% W = A*MCMILC(U)
%
% INPUT
% A Multi-class MIL dataset
% U Untrained MIL classifier (default = simple_mil)
%
% OUTPUT
% W Multi-class MIL classifier
%
% DESCRIPTION
% Train untrained MIL mapping U on the mul... |
github | vcheplygina/mil-master | bags2mil.m | .m | mil-master/bags2mil.m | 1,414 | utf_8 | cbf865c3ce146fb5f4a3e882ec1fe420 | %BAGS2MIL Combine a cell-array of bags to MIL dataset
%
% A = BAGS2MIL(BAGS,BAGLAB,COMBRULE)
%
% INPUT
% BAGS Cell-array of bags
% BAGLAB Label vector
% COMBRULE Rule for instance to bag label
%
% OUTPUT
% A MIL dataset
%
% DESCRIPTION
% Combine the bags that are stored as cell-array... |
github | vcheplygina/mil-master | gendatmessidor.m | .m | mil-master/gendatmessidor.m | 2,577 | utf_8 | cb2ee68989aea6ec5872b02cbbe13a6b | %GENDATMESSIDOR Messidor retinopathy dataset
%
% A = GENDATMESSIDOR
% OUTPUT
% A MIL dataset
%
% DESCRIPTION
% Define the multi-instance learning problem Messidor.
%
% The public data consists of 1200 eye fundus images from 654 diseased and 546 healthy patients.
% Disease is quantified in 3 s... |
github | vcheplygina/mil-master | dir_mil.m | .m | mil-master/dir_mil.m | 4,123 | utf_8 | 5dab78700cac7fefd0c94ef924a5fbb1 | %DIR_MIL Direction MIL
%
% W = DIR_MIL(A,FRAC,K,EVALFUNC)
%
% INPUT
% A MIL-dataset or MIL-datafile
% FRAC Quantile fraction (default = 1)
% K Number of directions (default = 5)
% EVALFUNC Cluster evaluation function (default = 'auc')
%
% OUTPUT
% W MIL-classifier using p... |
github | vcheplygina/mil-master | bowm.m | .m | mil-master/bowm.m | 2,274 | utf_8 | f3ca09606d62fe0764b2c90a28885676 | %BOWM Bag Of Words representation
%
% W = BOWM(X,K)
% W = BOWM(X,K,'soft')
% W = BOWM(X,K,'soft',CTYPE)
%
% INPUT
% X MIL dataset
% K Number of 'words' (default = 10)
% CTYPE Type of covariance matrix (default = 'diag')
%
% OUTPUT
% W Bag of Words mapping
%
% DESCRIPTION
... |
github | vcheplygina/mil-master | milesproxm.m | .m | mil-master/milesproxm.m | 2,329 | utf_8 | 1b1adab34a99baf7c56478fa77fec754 | %MILESPROXM Dissimilarity representation as used in MILES
%
% W = MILESPROXM(A,S)
% W = MILESPROXM(A,S,SELTYPE,N)
%
% INPUT
% A MIL dataset
% S Sigma (default = 1)
%
% OUTPUT
% W Proximity mapping
%
% DESCRIPTION
% Compute the MILES-type dissimilarity representation for each bag in
% MIL da... |
github | vcheplygina/mil-master | gendatmilm.m | .m | mil-master/gendatmilm.m | 2,105 | utf_8 | c0ddd4dd6375917910999847d013bd9c | %GENDATMILM Generate Maron's MIL problem
%
% X = GENDATMILM([N1 N2],NR_POS,INSTPERBAG)
%
% INPUT
% N1,N2 Number of pos. and neg. bags
% NR_POS Number of positive instances in a positive bag
% (default = 1)
% INSTPERBAG Minimum and maximum number of instances per bag
% ... |
github | vcheplygina/mil-master | bartmipc.m | .m | mil-master/bartmipc.m | 2,555 | utf_8 | 79c304188df53ee5d75cde8e1a61c85d | %BARTMIPC Dissimilarity-based MIL with clusters as prototypes
%
% W = BARTMIPC(A,K,BAGDIST,W_U)
%
% INPUT
% A MIL-dataset
% K Number of clusters (default = 5)
% BAGDIST Bag distance 'maxmin', 'meanmin' (default) or 'minmin'
% W_U Supervised learner to be used in the dissim... |
github | vcheplygina/mil-master | gendatdrive.m | .m | mil-master/gendatdrive.m | 2,771 | utf_8 | 8711bda6c77b71252e92d8915a3edaed | %GENDATDRIVE Harddrive dataset
%
% A = GENDATDRIVE(CLASSNR)
%
% INPUT
% CLASSNR Positive class (default = 0)
%
% OUTPUT
% A MIL dataset
%
% DESCRIPTION
% Define the multi-instance learning problem Harddrive. POSCLASS indicates
% what harddrives are labelled as positive: 0 for non-failed ... |
github | vcheplygina/mil-master | getbaglabs.m | .m | mil-master/getbaglabs.m | 2,475 | utf_8 | fbde867301e27d092ad689623f167b13 | %GETBAGLABS Get bag labels from MIL set
%
% [LAB,BAGID,IBAG] = GETBAGLABS(X)
%
% INPUT
% X MIL-dataset or MIL-datafile
%
% OUTPUT
% LAB Bag labels
% BAGID Original identifier of each bag
% IBAG Indices of the objects in the bags
%
% DESCRIPTION
% Same as GETBAGS(... |
github | vcheplygina/mil-master | mil_version.m | .m | mil-master/mil_version.m | 2,610 | utf_8 | 9faa88d1974c5edfd2649ab634f29dcd | %MIL_VERSION Version information for mil toolbox
%
% VER = MIL_VERSION
% VER = MIL_VERSION UPGRADE
%
% Returns the string VER containing the version number of the currently
% loaded MIL toolbox.
% When the Java virtual machine is running also the most up-to-date
% version of the MIL toolbox is shown.
%
%... |
github | vcheplygina/mil-master | clust_mil.m | .m | mil-master/clust_mil.m | 5,475 | utf_8 | 7ac20c9a905a82b05a8b2e1e23f1dc49 | %CLUST_MIL Clustering MIL
%
% W = CLUST_MIL(A,FRAC,K,EVALFUNC,FLIPSIGN,NRTRIES)
% W = A*CLUST_MIL([],FRAC,K,EVALFUNC,FLIPSIGN,NRTRIES)
% W = A*CLUST_MIL(FRAC,K,EVALFUNC,FLIPSIGN,NRTRIES)
%
% INPUT
% A MIL-dataset
% FRAC Quantile fraction (default = eps)
% K Number of clusters (def... |
github | vcheplygina/mil-master | scattermil.m | .m | mil-master/scattermil.m | 861 | utf_8 | 7b10dab1beac0116e4701ad63814d413 | %SCATTERMIL Scatterplot of MIL dataset
%
% SCATTERMIL(A)
%
% Make a scatterplot of a MIL dataset, where each instance in a bag is
% scattered, with a link to the mean vector.
function scattermil(a,clrs)
if nargin<2
clrs = ['b+';'r*'];
end
% get all data
[bag,baglab] = getbags(a);
y = ispositive(baglab);
B = le... |
github | vcheplygina/mil-master | unmil.m | .m | mil-master/unmil.m | 898 | utf_8 | d0c90e045746e300d5210441ce281fd4 | %UNMIL Remove MIL bag identifiers
%
% X = UNMIL(X)
%
% INPUT
% X MIL dataset
%
% OUTPUT
% X Standard prtools dataset
%
% DESCRIPTION
% Remove the 'milbag' identifiers and mil-meta-data stored in the
% user-field from dataset X.
%
% SEE ALSO
% GENMIL, ISMILSET, RMMILINFO
% Copyright: D.M.J. Tax, D.M.J... |
github | vcheplygina/mil-master | spec_mil.m | .m | mil-master/spec_mil.m | 3,233 | utf_8 | cbc0d4746827dfd8500caed8af475a1c | %SPEC_MIL Specializing MIL
%
% W = SPEC_MIL(A, FRAC, W_U, N, INIT)
%
% INPUT
% A Dataset
% FRAC Fraction of positive instances (default = 0.1)
% W_U Untrained classifier (default = loglc)
% N Number of iterations (default = 100)
% INIT Initial labels (default = [])
%
% OUTPUT
% W ... |
github | vcheplygina/mil-master | mil_dfpmin.m | .m | mil-master/mil_dfpmin.m | 2,680 | utf_8 | ac7d7e7f1e3120ff6248e88d3faeeb52 | %MIL_DFPMIN Simulate the routine "dfpmin" in [1], which takes,
% xold - The starting point of dfpmin
% n - Dimension of the instance
% tolx - Convergence tolerance on delta x
% gtol - Convergence tolerance on gradient
% itmax - Maximum allowed number of iterations
% and returns,
%... |
github | vcheplygina/mil-master | gendatbiocreative.m | .m | mil-master/gendatbiocreative.m | 3,522 | utf_8 | 0694928e4ef66b0030d52d0720487cc4 | %GENDATBIOCREATIVE Biocreative text data MIL problem
%
% [X Z] = GENDATBIOCREATIVE(NR)
%
%
% INPUT
% NR Dataset type (1=component, 2=function, 3=process)
%
% OUTPUT
% X, Z MIL datasets (train and test).
%
% DESCRIPTION
% Define the MIL problem of biological text categorization. An interesting ... |
github | vcheplygina/mil-master | bagsizes.m | .m | mil-master/bagsizes.m | 1,461 | utf_8 | 68fdb691136d7a8df8d88f3617ff3f9c | %BAGSIZES Get sizes of the bags from MIL set
%
% N = BAGSIZES(X)
%
% INPUT
% X MIL-dataset or MIL-datafile
%
% OUTPUT
% N Vector containing the size of each bag
%
% DESCRIPTION
% Extract the size of each individual bags from MIL dataset X.
%
% SEE ALSO
% MILCOMBINE, GETBAGS, GETPOSITIVEBAG... |
github | vcheplygina/mil-master | getbags.m | .m | mil-master/getbags.m | 3,031 | utf_8 | 5c1264741924be2d9428ec9396b4567f | %GETBAGS Get bags from MIL set
%
% [BAG,LAB,BAGID,IBAG] = GETBAGS(X)
%
% INPUT
% X MIL-dataset or MIL-datafile
%
% OUTPUT
% BAG A cell array containg in each element one bag.
% LAB Bag labels
% BAGID Original identifier of each bag
% IBAG Indices of the objects in t... |
github | vcheplygina/mil-master | hasmilbags.m | .m | mil-master/hasmilbags.m | 1,173 | utf_8 | 78d452646260dd0d4cc566f0ab3b6ef8 | %HASMILBAGS Check for MIL bags
%
% OUT = HASMILBAGS(A)
%
% INPUT
% A dataset
%
% OUTPUT
% OUT true if A has bags
%
% DESCRIPTION
% Check if dataset A contains bags of instances that are recognized in
% the MIL toolbox. For that, extra identifiers have to be defined for
% each object, indicating the index ... |
github | vcheplygina/mil-master | gendatau.m | .m | mil-master/gendatau.m | 743 | utf_8 | 4fdc207ced0d7d98e0c4d0b5ab2a9950 | %GENDATAU Action Unit data
%
% A = GENDATAU(AUNR,DATABASE)
%
% Define the Action Unit classification problem. There are two datasets
% defined:
% 'ck': Cohn-Kanade database
% 'im': Imperial database (from Michel)
% These databases contain several trackings of facial feature points.
%
function a = musk(aunr,db... |
github | vcheplygina/mil-master | genmillabels.m | .m | mil-master/genmillabels.m | 1,486 | utf_8 | 98492f210138f108d4f957f14dd48324 | %GENMILLABELS Generate MIL labels
%
% LAB2 = GENMILLABELS(LAB1,TARGETCL)
% LAB2 = GENMILLABELS(NLAB)
%
% INPUT
% LAB1 vector of (string) labels
% TARGETCL class name that will be 'positive'
% NLAB vector of (numeric) labels
%
% OUTPUT
% LAB2 vector of labels
%
% DESCRIPTION
% Mak... |
github | vcheplygina/mil-master | gendatmusk.m | .m | mil-master/gendatmusk.m | 1,386 | utf_8 | dbdad2cd451e4e89473d7eccdeebfa0b | %GENDATMUSK Musk data.
%
% A = GENDATMUSK(NR)
%
% INPUT
% NR Version of the MUSK dataset (default = 1)
%
% OUTPUT
% A MIL dataset
%
% DESCRIPTION
% Define the multi-instance learning problem MUSK. There are two
% versions, NR=1 (default) and NR=2.
%
% REFERENCE
%@article{DieLatLaz1997,
% author = {Die... |
github | vcheplygina/mil-master | citation_mil.m | .m | mil-master/citation_mil.m | 3,661 | utf_8 | 51df559921d4ddb02c611e54281e3566 | %CITATION_MIL kNN-MIL using Haussdorf distance
%
% W = CITATION_MIL(A,FRAC,K)
% W = CITATION_MIL(A,FRAC,K,CITERANK)
%
% INPUT
% A MIL dataset
% FRAC Combining fraction to get bag label from inst. label
% K Number of neighbors (default = 1)
% CITERANK Number of citers (default =... |
github | vcheplygina/mil-master | gendatweb.m | .m | mil-master/gendatweb.m | 1,217 | utf_8 | f9abc87d844527d0458a6536f0fb64b1 | %GENDATWEB Web dataset
%
% [X,Z] = GENDATWEB(NR)
%
% INPUT
% NR Target class
%
% OUTPUT
% X,Z MIL dataset
%
% DESCRIPTION
% The problem is to classify webpages in two classes; interesting or
% non-interesting. The webpages are characterized by their collection of
% links to other webpages. These other we... |
github | vcheplygina/mil-master | getmilinfo.m | .m | mil-master/getmilinfo.m | 1,126 | utf_8 | ae32505f0cd68d73f48305075c733c05 | %GETMILINFO Get MIL info from dataset
%
% VALUE = GETMILINFO(A,FIELD)
%
% INPUT
% A MIL dataset
% FIELD Info field name (default = 'combinerule')
%
% OUTPUT
% VALUE Some output
%
% DESCRIPTION
% Retrieve the MIL parameters, given by FIELD, from dataset A. Possible
% parameters are given in SE... |
github | vcheplygina/mil-master | incsvddmil.m | .m | mil-master/incsvddmil.m | 3,352 | utf_8 | 694efab65ed8283c5625afe2d692d731 | %INCSVDDMIL Incremental SVDD MIL
%
% W = INCSVDDMIL(A,FRAC,KTYPE,PAR)
%
% Train an incremental SVDD on MIL dataset A, such that at least one
% instance of a positive bag falls inside the hypersphere, but one of
% the instances of negative bags fall inside. This is done using the
% procedures defined for the incsvdd... |
github | vcheplygina/mil-master | gendatmilr.m | .m | mil-master/gendatmilr.m | 2,065 | utf_8 | 90f0dcb4dc3ca4bc3dd400067c6caaba | %GENDATMILR Rotated MIL dataset
%
% A = GENDATMILR(N,PHI,DIM)
%
% INPUT
% N Number of pos. and neg. bags
% PHI Angle (default = pi/18)
% DIM Dimensionality (default = 2)
%
% OUTPUT
% A MIL dataset
%
% DESCRIPTION
% Make a MIL dataset where all instances in a bag are informative, but
% where... |
github | vcheplygina/mil-master | boosting_mil.m | .m | mil-master/boosting_mil.m | 3,478 | utf_8 | 65447c12f7fda4b5628dde0c483bedf4 | %BOOSTING_MIL Boosted (logistic) MIL classifier
%
% W = BOOSTING_MIL(X,FRAC,W_U,M)
% W = X*BOOSTING_MIL([],FRAC,W_U,M)
% W = X*BOOSTING_MIL(FRAC,W_U,M)
%
% INPUT
% X MIL dataset
% FRAC (not used anymore...)
% W_U Untrained classifier (default = loglc_weighted)
% M Number of boosting rou... |
github | vcheplygina/mil-master | mil2ocset.m | .m | mil-master/mil2ocset.m | 1,286 | utf_8 | 08b1af145dbd673e6fa9ee9511ef0318 | %MIL2OCSET Convert a MIL to a OC set
%
% A = MIL2OCSET(B)
%
% INPUT
% B MIL dataset
%
% OUTPUT
% A One-class dataset
%
% DESCRIPTION
% Convert multi-instance-learning set A to one-class dataset B. This
% actually means that all occurances of 'positive'/'negative' will be
% replaced by 'target'/'outl... |
github | vcheplygina/mil-master | gendatmild.m | .m | mil-master/gendatmild.m | 2,521 | utf_8 | 4e0ceee60c57a8cf1e530e153fa08b42 | %GENDATMILD Difficult MIL dataset
%
% X = GENDATMILD([N1,N2],NP,D,DIM)
%
% INPUT
% N1,N2 Number of pos. and neg. bags
% NP Number of instances in the concept (default = [1 0])
% D Position of positive concept (default = 4)
% DIM Number of features (default = 2)
%
% ... |
github | vcheplygina/mil-master | getbagid.m | .m | mil-master/getbagid.m | 677 | utf_8 | 55299a0393f34bb945b8df50af571687 | %GETBAGID Extract bag identifiers from MIL dataset
%
% BAGID = GETBAGID(X)
%
% INPUT
% X MIL dataset
%
% OUTPUT
% BAGID Bag labels
%
% DESCRIPTION
% Extract the bag identifiers for each instance from MIL dataset X.
%
% SEE ALSO
% GETBAGS, GENMIL, BAGSIZES
% Copyright: D.M.J. Tax, D.M.J.Tax@prtools.org... |
github | vcheplygina/mil-master | bagprob.m | .m | mil-master/bagprob.m | 898 | utf_8 | 1f5e0f32be2bb60bf1b20ed29150b148 | %BAGPROB Probability (and derivat.) per bag
%
% [BAGP,DERP] = BAGPROB(BAG,LAB,CONCEPT,S)
%
% INPUT
% BAG Data matrix of one bag
% LAB Bag label
% CONCEPT Feature vector
% S Scale per feature
%
% OUTPUT
% BAGP Probability per bag
% DERP Derivative
%
% Auxiliary function for ... |
github | vcheplygina/mil-master | milfile2set.m | .m | mil-master/milfile2set.m | 1,595 | utf_8 | 1c6651fdaf859c752eb3f6597c9d84e1 | %MILFILE2SET Convert MIL datafile to dataset
%
% X = MILFILE2SET(X,MISSINGVALUES)
%
% INPUT
% X Datafile MIL set
% MISSINGVALUES Parameter for milmissingvalues.m (default = '')
%
% OUTPUT
% X MIL dataset
%
% DESCRIPTION
% Convert the MI data*file* X into a MIL dataset. Not... |
github | vcheplygina/mil-master | maxDD_mil.m | .m | mil-master/maxDD_mil.m | 4,019 | utf_8 | e3256ba3a64d42f035edc91a5ab48d7a | %MAXDD_MIL Maximum diverse density MIL
%
% W = MAXDD_MIL(X,FRAC,ALF,SCALES,EPOCHS,TOL)
% W = X*MAXDD_MIL([],FRAC,ALF,SCALES,EPOCHS,TOL)
% W = X*MAXDD_MIL(FRAC,ALF,SCALES,EPOCHS,TOL)
% W = MAXDD_MIL(X,FRAC,SPOINTS,SCALES,EPOCHS,TOL)
%
% INPUT
% X MIL dataset
% FRAC The method of ... |
github | vcheplygina/mil-master | reallifemil.m | .m | mil-master/reallifemil.m | 10,790 | utf_8 | 90915c7c794726ced3b999d1fcb695cd | %REALLIFEMIL Load MIL dataset
%
% [X,Z] = REALLIFEMIL(DSET)
%
% The datasets should be available in raw format, in the directory that
% is defined in the script mildatapath.m
%
% nr description tr.bags te.bags inst dim
%--------------------------------------------------------------
% 101. Musk... |
github | vcheplygina/mil-master | sparseloglc.m | .m | mil-master/sparseloglc.m | 3,048 | utf_8 | 7fb0442359617890a9f09055d221c48d | %SPARSELOGLC Sparse logistic classifier
%
% W = SPARSELOGLC(A,LAMBDA,POSW,SCALEL)
%
% INPUT
% A Dataset
% LAMBDA Regularization parameter (default = 0.1)
% POSW Only use positive weights (default = 0)
% SCALEL Scale regularization parameter (default = 0)
%
% OUTPUT
% W Logistic classif... |
github | vcheplygina/mil-master | maxdd.m | .m | mil-master/maxdd.m | 1,734 | utf_8 | bb392c817d91062e9f5093aad12da78f | %MAXDD The optimization of Diverse Density
%
% [MAXcONCEPT,CONCEPTS] = MAXDD(SPOINTS,SCALES,BAGS,BAGI,EPOCHS,TOL)
%
% The core optimization function of maxDD_mil. See maxDD_mil.
function [maxConcept,concepts] = maxdd(spoints,scales,bags,bagI,epochs,tol)
% initialize some parameters and storage
num_start_points = siz... |
github | vcheplygina/mil-master | mil_gridsearch.m | .m | mil-master/mil_gridsearch.m | 3,169 | utf_8 | 464a8d23cdebfcb8e7c53b735cbd0d45 | %MIL_GRIDSEARCH Parameter optimization of MILs
%
% [W,BESTARG] = MIL_GRIDSEARCH(A,CLNAME,NRFOLDS,ARGVAL1,ARGVAL2,...)
% [W,BESTARG] = A*MIL_GRIDSEARCH([],CLNAME,NRFOLDS,ARGVAL1,ARGVAL2,...)
% [W,BESTARG] = A*MIL_GRIDSEARCH(CLNAME,NRFOLDS,ARGVAL1,ARGVAL2,...)
%
% INPUT
% A MIL dataset
% CLNAM... |
github | vcheplygina/mil-master | munkres.m | .m | mil-master/munkres.m | 6,465 | utf_8 | 5f412119bd008c3b2b69395eb44d211c | function [assignment,cost] = munkres(costMat)
% MUNKRES Munkres (Hungarian) Algorithm for Linear Assignment Problem.
%
% [ASSIGN,COST] = munkres(COSTMAT) returns the optimal column indices,
% ASSIGN assigned to each row and the minimum COST based on the assignment
% problem represented by the COSTMAT, where the... |
github | vcheplygina/mil-master | hcrf_mil.m | .m | mil-master/hcrf_mil.m | 3,430 | utf_8 | 272e0bf94a105e9d968a483c5282b236 | % w = hcrf_mil(a,frac,useh,reg,maxIter)
%
function w = crf_mil(a,frac,useh,reg,maxIter)
if nargin<5
maxIter = 50;
end
if nargin<4
reg = 1.0;;
end
if nargin<3
useh = 0;
end
if nargin<2
frac = 'presence';
end
if (nargin<1) || isempty(a)
w = mapping(mfilename,{frac,useh,reg,maxIter});
w = setbatch(w,0); %NEVER use ... |
github | vcheplygina/mil-master | simple_mil.m | .m | mil-master/simple_mil.m | 2,403 | utf_8 | 834d4240fcbf2827d3772771ba80f47e | %SIMPLE_MIL Apply standard classifiers for MIL
%
% W = SIMPLE_MIL(A,FRAC,W_U)
%
% INPUT
% A MIL dataset
% FRAC Fraction of instances taken into account in evaluation
% (default = 'presence')
% W_U Untrained, standard prtools mapping (default = loglc)
%
% OUTPUT
% W Trained simple ... |
github | vcheplygina/mil-master | getpositivebags.m | .m | mil-master/getpositivebags.m | 1,411 | utf_8 | e05b7ad88aabfa62021edeb3f5ddd9ff | %GETPOSITIVEBAGS
%
% [XP,XN,IP,IN] = GETPOSITIVEBAGS(X)
% [XP,XN,IP,IN] = GETPOSITIVEBAGS(BAGS,BAGLAB)
%
% INPUT
% X MIL dataset
% BAGS cell array with bags
% BAGLAB Bag labels
%
% OUTPUT
% XP cell array with positive bags
% XN cell array with negative bags
% IP indices of pos... |
github | csukuangfj/code-for-Lucas-Kanade-20-Years-On-master | affine_ic_nt_d.m | .m | code-for-Lucas-Kanade-20-Years-On-master/lk20-p1/affine_ic_nt_d.m | 3,906 | utf_8 | 78b421f04727334ad8c81bff814ba549 | function fit = affine_ic_nt_d(img, tmplt, p_init, n_iters, verbose, step_size)
% AFFINE_IC_NT_D - Affine image alignment using IC Newton algorithm with
% diagonal Hessian
% FIT = AFFINE_IC_NT_D(IMG, TMPLT, P_INIT, N_ITERS, VERBOSE, SS)
% Align the template image TMPLT to an example image IMG using an
% affine war... |
github | csukuangfj/code-for-Lucas-Kanade-20-Years-On-master | homo_fa.m | .m | code-for-Lucas-Kanade-20-Years-On-master/lk20-p1/homo_fa.m | 2,617 | utf_8 | 3d3ab002f55f0a020999ce42caa20378 | function fit = homo_fa(img, tmplt, p_init, n_iters, verbose)
% HOMO_FA - Homography image alignment using forwards-additive algorithm
% FIT = HOMO_FA(IMG, TMPLT, P_INIT, N_ITERS, VERBOSE)
% Align the template image TMPLT to an example image IMG using a
% projective warp initialised using P_INIT. Iterate for N_ITE... |
github | csukuangfj/code-for-Lucas-Kanade-20-Years-On-master | affine_ic_nt.m | .m | code-for-Lucas-Kanade-20-Years-On-master/lk20-p1/affine_ic_nt.m | 3,519 | utf_8 | a4a78bd63217172f9401e6acb7491aea | function fit = affine_ic_nt(img, tmplt, p_init, n_iters, verbose, step_size)
% AFFINE_IC_NT - Affine image alignment using IC Newton algorithm
% FIT = AFFINE_IC_NT(IMG, TMPLT, P_INIT, N_ITERS, VERBOSE)
% Align the template image TMPLT to an example image IMG using an
% affine warp initialised using P_INIT. Iterat... |
github | csukuangfj/code-for-Lucas-Kanade-20-Years-On-master | homo_ic.m | .m | code-for-Lucas-Kanade-20-Years-On-master/lk20-p1/homo_ic.m | 2,860 | utf_8 | 7336364a2f23050b49c30f14e42d28c4 | function fit = homo_ic(img, tmplt, p_init, n_iters, verbose)
% HOMO_IC - Homography image alignment using inverse-compositional algorithm
% FIT = HOMO_IC(IMG, TMPLT, P_INIT, N_ITERS, VERBOSE)
% Align the template image TMPLT to an example image IMG using a
% projective warp initialised using P_INIT. Iterate for N... |
github | csukuangfj/code-for-Lucas-Kanade-20-Years-On-master | affine_ic.m | .m | code-for-Lucas-Kanade-20-Years-On-master/lk20-p1/affine_ic.m | 2,757 | utf_8 | fee708e2de8d4947a67d79cd99cfbafd | function fit = affine_ic(img, tmplt, p_init, n_iters, verbose, step_size)
% AFFINE_IC - Affine image alignment using inverse-compositional algorithm
% FIT = AFFINE_IC(IMG, TMPLT, P_INIT, N_ITERS, VERBOSE)
% Align the template image TMPLT to an example image IMG using an
% affine warp initialised using P_INIT. Ite... |
github | csukuangfj/code-for-Lucas-Kanade-20-Years-On-master | affine_ic_d.m | .m | code-for-Lucas-Kanade-20-Years-On-master/lk20-p1/affine_ic_d.m | 3,246 | utf_8 | ffc270e484e53d04ce19591c817053c2 | function fit = affine_ic_d(img, tmplt, p_init, n_iters, verbose, step_size)
% AFFINE_IC_D - Affine image alignment using inverse-compositional algorithm
% with diagonal Hessian
% FIT = AFFINE_IC_D(IMG, TMPLT, P_INIT, N_ITERS, VERBOSE, SS)
% Align the template image TMPLT to an example image IMG using an
% affine ... |
github | csukuangfj/code-for-Lucas-Kanade-20-Years-On-master | affine_ic_sd.m | .m | code-for-Lucas-Kanade-20-Years-On-master/lk20-p1/affine_ic_sd.m | 2,809 | utf_8 | b178b4e10131cf6d3ae5d418d5a3dfa8 | function fit = affine_ic_sd(img, tmplt, p_init, n_iters, verbose, step_size)
% AFFINE_IC_SD - Affine image alignment using IC steepest descent algorithm
% FIT = AFFINE_IC_SD(IMG, TMPLT, P_INIT, N_ITERS, VERBOSE)
% Align the template image TMPLT to an example image IMG using an
% affine warp initialised using P_IN... |
github | csukuangfj/code-for-Lucas-Kanade-20-Years-On-master | affine_ia.m | .m | code-for-Lucas-Kanade-20-Years-On-master/lk20-p1/affine_ia.m | 2,610 | utf_8 | 2863c67942c26e432983a53ab108509b | function fit = affine_ia(img, tmplt, p_init, n_iters, verbose, step_size)
% AFFINE_IA - Affine image alignment using inverse-additive algorithm
% FIT = AFFINE_HB(IMG, TMPLT, P_INIT, N_ITERS, VERBOSE)
% Align the template image TMPLT to an example image IMG using an
% affine warp initialised using P_INIT. Iterate ... |
github | csukuangfj/code-for-Lucas-Kanade-20-Years-On-master | test_homo.m | .m | code-for-Lucas-Kanade-20-Years-On-master/lk20-p1/test_homo.m | 7,182 | utf_8 | 683432f014469469897fddc7fb8fb91b | function results = test_homo(tdata, pt_offsets, alg_list, n_iters, n_tests, n_freq_tests, spatial_sigma, image_pixel_sigma, tmplt_pixel_sigma, max_spatial_error, verbose)
% TEST_HOMO - Test homography algorithms
%
% See also: run_homo
%
% tdata has two fields:
% tdata.img unit8 greyscale image
% tdata.tmplt ... |
github | csukuangfj/code-for-Lucas-Kanade-20-Years-On-master | homo_fc.m | .m | code-for-Lucas-Kanade-20-Years-On-master/lk20-p1/homo_fc.m | 2,845 | utf_8 | 8ccd7e68f390c4ca2ef457bd35db0daf | function fit = homo_fc(img, tmplt, p_init, n_iters, verbose)
% HOMO_FC - Homography image alignment using forwards-compositional algorithm
% FIT = HOMO_FC(IMG, TMPLT, P_INIT, N_ITERS, VERBOSE)
% Align the template image TMPLT to an example image IMG using a
% projective warp initialised using P_INIT. Iterate for ... |
github | csukuangfj/code-for-Lucas-Kanade-20-Years-On-master | affine_fa.m | .m | code-for-Lucas-Kanade-20-Years-On-master/lk20-p1/affine_fa.m | 2,577 | utf_8 | 460300e0c676c6dec3ef1ac01d3a6fa9 | function fit = affine_fa(img, tmplt, p_init, n_iters, verbose, step_size)
% AFFINE_FA - Affine image alignment using forwards-additive algorithm
% FIT = AFFINE_FA(IMG, TMPLT, P_INIT, N_ITERS, VERBOSE)
% Align the template image TMPLT to an example image IMG using an
% affine warp initialised using P_INIT. Iterate... |
github | csukuangfj/code-for-Lucas-Kanade-20-Years-On-master | affine_fc.m | .m | code-for-Lucas-Kanade-20-Years-On-master/lk20-p1/affine_fc.m | 2,781 | utf_8 | 061b5aeb37ca04e7d432bb969702d355 | function fit = affine_fc(img, tmplt, p_init, n_iters, verbose, step_size)
% AFFINE_FC - Affine image alignment using forwards-compositional algorithm
% FIT = AFFINE_FC(IMG, TMPLT, P_INIT, N_ITERS, VERBOSE)
% Align the template image TMPLT to an example image IMG using an
% affine warp initialised using P_INIT. It... |
github | csukuangfj/code-for-Lucas-Kanade-20-Years-On-master | affine_ic_lm.m | .m | code-for-Lucas-Kanade-20-Years-On-master/lk20-p1/affine_ic_lm.m | 3,712 | utf_8 | 655eabd503a37b9c7cde283c78f32048 | function fit = affine_ic_lm(img, tmplt, p_init, n_iters, verbose, step_size)
% AFFINE_IC_LM - Affine image alignment using inverse-compositional
% Levenberg-Marquardt algoritym
% FIT = AFFINE_IC_LM(IMG, TMPLT, P_INIT, N_ITERS, VERBOSE)
% Align the template image TMPLT to an example image IMG using an
% affine war... |
github | csukuangfj/code-for-Lucas-Kanade-20-Years-On-master | test_affine.m | .m | code-for-Lucas-Kanade-20-Years-On-master/lk20-p1/test_affine.m | 7,345 | utf_8 | 8a0e5f4f75bd145da587aed27015fb90 | function results = test_affine(tdata, pt_offsets, alg_list, n_iters, n_tests, n_freq_tests, spatial_sigma, image_pixel_sigma, tmplt_pixel_sigma, max_spatial_error, verbose, opt_step_size)
% TEST_AFFINE - Test affine algorithms
%
% See also: run_affine
%
% tdata has two fields:
% tdata.img unit8 greyscale image
... |
github | andreimesinger/21cmFAST-master | xH_image_slice_withps.m | .m | 21cmFAST-master/Pics/xH_image_slice_withps.m | 7,159 | utf_8 | 021b1eb4f8238e8b675a22f2c6f18dab | % matlab function to plot slice
%
% mostly written by: Dave Spiegel
% usage:
function xH_image_slice_withps(infile, outfile, dim1, color_bar_flag, expand_factor, varargin)
if length(varargin) > 0
z = varargin{1};
xH = varargin{2};
aveTb = varargin{3};
end
if length(varargin) > 3
lower_color_limit = v... |
github | andreimesinger/21cmFAST-master | cm21_image_slice_withps.m | .m | 21cmFAST-master/Pics/cm21_image_slice_withps.m | 7,192 | utf_8 | 6dfe95cd91cc453f53c90acd678df5d3 | % matlab function to plot slice
%
% mostly written by: Dave Spiegel
% usage:
function cm21_image_slice_withps(infile, outfile, dim1, color_bar_flag, expand_factor, varargin)
if length(varargin) > 0
z = varargin{1};
xH = varargin{2};
aveTb = varargin{3};
end
if length(varargin) > 3
lower_color_limit =... |
github | pdsimari/matlab-tetrahedral-mesh-toolbox-master | tetraDistortionExtrinsic.m | .m | matlab-tetrahedral-mesh-toolbox-master/tetraDistortionExtrinsic.m | 3,459 | utf_8 | 0f40fbdad057b5dc11ccce83ebba86fa | function d = tetraDistortionExtrinsic(tetra)
% Patricio Simari
% July 2013
%
% d = tetraDistortionExtrinsic(tetra)
%
% For estimating mean curvature, divide weighed d by vertex volume.
if ~isfield(tetra,'Nt')
tetra = tetraNormals(tetra);
end
% Hash table to find adjacent tetrahedra
% Index range is too large for... |
github | pdsimari/matlab-tetrahedral-mesh-toolbox-master | tetraNormals.m | .m | matlab-tetrahedral-mesh-toolbox-master/tetraNormals.m | 929 | utf_8 | 093a32e984e3bd55caceefb95469424a | function tetra = tetraNormals(tetra)
% Patricio Simari
% July 2013
%
% tetra = tetraNormals(tetra)
%
% Adds the field tetra.Nt such that tetra.Nt(:,i) contains the unit normal
% vector of tetrahedron tetra.T(:,i) computed using PCA over the four
% points that define it. Outward direction is determined by choosing a
... |
github | manashmandal/ArduinoMorseENDEC-master | Demorse.m | .m | ArduinoMorseENDEC-master/DemorseGUI/Demorse/Demorse.m | 6,822 | utf_8 | 41933bbfd1e498dcb6f240eee6d9e668 | function varargout = Demorse(varargin)
% DEMORSE MATLAB code for Demorse.fig
% DEMORSE, by itself, creates a new DEMORSE or raises the existing
% singleton*.
%
% H = DEMORSE returns the handle to a new DEMORSE or the handle to
% the existing singleton*.
%
% DEMORSE('CALLBACK',hObject,eventData,... |
github | manashmandal/ArduinoMorseENDEC-master | demorse_function.m | .m | ArduinoMorseENDEC-master/DemorseGUI/Demorse/demorse_function.m | 3,468 | utf_8 | eb1dc29a2f9fefd82ab66a471e8b9528 | function decoded_str = demorse_function(filename)
[x, fs] = audioread(filename);
% decoded string
decoded_string = '';
%zero pad the signal
x = [zeros(fs,1); x; zeros(fs,1)];
% find the frequency of the signal
ff = getChirpFrequency(x);
% use the wavelet as a bandpass filter
waveletSizes = fs * cen... |
github | manashmandal/ArduinoMorseENDEC-master | demorse.m | .m | ArduinoMorseENDEC-master/Demorse/demorse.m | 3,001 | utf_8 | ac176a94047062893afcae0fef82929d | function demorse(filename)
[x, fs] = audioread(filename);
%zero pad the signal
x = [zeros(fs,1); x; zeros(fs,1)];
% find the frequency of the signal
ff = getChirpFrequency(x);
% use the wavelet as a bandpass filter
waveletSizes = fs * centfrq('morl') ./ ff;
sig = cwt(x,waveletSizes,'morl');
sig = abs(sig);
% do a... |
github | loopygoose/bladeRF-master | fsk_receive.m | .m | bladeRF-master/host/utilities/bladeRF-fsk/matlab/fsk_receive.m | 4,952 | utf_8 | c851fffd8a0fd1e9c806470d94b4103c | %-------------------------------------------------------------------------
% This file is part of the bladeRF project
%
% Copyright (C) 2016 Nuand LLC
%
% 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 Found... |
github | loopygoose/bladeRF-master | fsk_transmit.m | .m | bladeRF-master/host/utilities/bladeRF-fsk/matlab/fsk_transmit.m | 4,091 | utf_8 | c70d2747c3cd98e299aeeb6bb18bdf11 | %-------------------------------------------------------------------------
% This file is part of the bladeRF project
%
% Copyright (C) 2016 Nuand LLC
%
% 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 Found... |
github | loopygoose/bladeRF-master | fsk_mod.m | .m | bladeRF-master/host/utilities/bladeRF-fsk/matlab/fsk_mod.m | 3,463 | utf_8 | 8212e1cffd4350e4c477cb154ffb2cc7 | %-------------------------------------------------------------------------
% This file is part of the bladeRF project
%
% Copyright (C) 2016 Nuand LLC
%
% 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 Found... |
github | loopygoose/bladeRF-master | fsk_demod.m | .m | bladeRF-master/host/utilities/bladeRF-fsk/matlab/fsk_demod.m | 3,708 | utf_8 | 7b6299d7373ee58eaf55f3069422ae4f | %-------------------------------------------------------------------------
% This file is part of the bladeRF project
%
% Copyright (C) 2016 Nuand LLC
%
% 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 Found... |
github | loopygoose/bladeRF-master | bladeRF_rx_gui.m | .m | bladeRF-master/host/libraries/libbladeRF_bindings/matlab/bladeRF_rx_gui.m | 28,080 | utf_8 | 3fc9f074c455459a5150b4dc46428c64 | % A simple bladeRF demo GUI that receives and plots samples
%
% Once the GUI starts, select a device from the dropdown of available
% devices, and then click "start" to begin streaming samples.
%
% The various GUI widgets may be used to change the plot mode, frequency,
% gains, correction values, sample rate, and LPF b... |
github | loopygoose/bladeRF-master | libbladeRF_proto.m | .m | bladeRF-master/host/libraries/libbladeRF_bindings/matlab/libbladeRF_proto.m | 46,100 | utf_8 | 55f47f79f37f676d378a9a0187867958 | %
% Copyright (c) 2015 Nuand LLC
%
% 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, merge, publish, distrib... |
github | loopygoose/bladeRF-master | bladeRF_lms_cal.m | .m | bladeRF-master/host/libraries/libbladeRF_bindings/matlab/test/bladeRF_lms_cal.m | 1,978 | utf_8 | db724fc361fbfc0bd713488c3d6140a3 | % Run LMS calibration routines
%
% bladeRF_lms_cal(device, rx_freq, tx_freq)
%
% device - Optional. May be an existing handle or a device specification string
% rx_freq - Optional. RX frequency to set the device to. Default = 910e6.
% tx_freq - Optional. TX frequency to set the device to. Default = 915e6.
functi... |
github | arbenson/higher-order-organization-matlab-master | NMI.m | .m | higher-order-organization-matlab-master/NMI.m | 1,042 | utf_8 | 56a84c01a6f0e6789608974764fbd3fa | function z = NMI(classes, clusters)
%NMI - calculates normalized mutual information to evaluate clustering
% z = NMI(classes, clusters) where classes are the true classes
% and clusters is the clustering assignment.
assert(numel(classes) == numel(clusters));
uw = unique(classes);
uc = unique(clusters);
Cw = counts(c... |
github | arbenson/higher-order-organization-matlab-master | RandIndex.m | .m | higher-order-organization-matlab-master/RandIndex.m | 1,860 | utf_8 | 091d902911c8b57d7124afd975a460bb | function [AR,F1,RI,MI,HI]=RandIndex(c1,c2)
%RANDINDEX - calculates Rand Indices to compare two partitions
% ARI=RANDINDEX(c1,c2), where c1,c2 are vectors listing the
% class membership, returns the "Hubert & Arabie adjusted Rand index".
% [AR,RI,MI,HI]=RANDINDEX(c1,c2) returns the adjusted Rand index,
% the unadjuste... |
github | arbenson/higher-order-organization-matlab-master | MotifAdjacency.m | .m | higher-order-organization-matlab-master/MotifAdjacency.m | 4,741 | utf_8 | d9166efb3c0b60156098c729223a032b | function W = MotifAdjacency(A, motif)
% MOTIFADJACENCY forms the motif adjacency matrix for the adjacency
% matrix A and the specified motif.
% 'motif' is one of:
% M1, M2, M3, M4, M5, M6, M7, M8, M9, M10, M11, M12, M13
% bifan
% edge
%
% See http://snap.stanford.edu/higher-order/code.html for
% the naming conventions... |
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