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value | path stringlengths 12 229 | size int64 23 843k | source_encoding stringclasses 9
values | md5 stringlengths 32 32 | text stringlengths 23 843k |
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github | hytseng0509/DPE-master | PnP_Reproj_NLS_Matlab.m | .m | DPE-master/matlab/OPnP/PnP_Reproj_NLS_Matlab.m | 1,767 | utf_8 | 10261b578cc0cc01f64c72439bf7f95f | function [Rr tr] = PnP_Reproj_NLS_Matlab(U,u,R0,t0)
%to refine the column-triplet by using nonlinear least square
%it's much better than the fminunc used by CVPR12.
%there are three fractional formulations, anyone is equivalently good.
%there are two constrained formulations, yet neither is good.
%the reason is that:... |
github | hytseng0509/DPE-master | Generate_Random_Data_Full.m | .m | DPE-master/matlab/OPnP/Generate_Random_Data_Full.m | 21,006 | utf_8 | 073e56c803c19a87ea47860f35a982fe | function [var e1 e2 e3 e4 c1 c2 c3 Q q] = Generate_Random_Data_Full(rotation_type,point_config)
%Output:
%var = [a b c d]; ground_truth
%e1,e2,e3,e4: the coefficients of polynomials of our formulation
%c1,c2,c3: the coefficients of polynomials of DLS
%Q,q: the objective function used in polishing
%generate ground_trut... |
github | hytseng0509/DPE-master | matrix2quaternion.m | .m | DPE-master/matlab/OPnP/matrix2quaternion.m | 2,010 | utf_8 | ad7a1983aceaa9953be167eddabb22ae | % MATRIX2QUATERNION - Homogeneous matrix to quaternion
%
% Converts 4x4 homogeneous rotation matrix to quaternion
%
% Usage: Q = matrix2quaternion(T)
%
% Argument: T - 4x4 Homogeneous transformation matrix
% Returns: Q - a quaternion in the form [w, xi, yj, zk]
%
% See Also QUATERNION2MATRIX
% Copyright (c) 2008 ... |
github | hytseng0509/DPE-master | GB_Solver_3Order_4Variable_Symmetry.m | .m | DPE-master/matlab/OPnP/GB_Solver_3Order_4Variable_Symmetry.m | 884 | utf_8 | 81ed9a17a4479db7de5169dccf46c62f | % Generated using GBSolver generator Copyright Martin Bujnak,
% Zuzana Kukelova, Tomas Pajdla CTU Prague 2008.
%
% Please refer to the following paper, when using this code :
% Kukelova Z., Bujnak M., Pajdla T., Automatic Generator of Minimal Problem Solvers,
% ECCV 2008, Marseille, France, October 12-18, 2008... |
github | hytseng0509/DPE-master | GB_Solver_3Order_4Variable_b_Division.m | .m | DPE-master/matlab/OPnP/GB_Solver_3Order_4Variable_b_Division.m | 118,829 | utf_8 | b99f7fa1930baa2a68e6e2c2904ac5bd | % Generated using GBSolver generator Copyright Martin Bujnak,
% Zuzana Kukelova, Tomas Pajdla CTU Prague 2008.
%
% Please refer to the following paper, when using this code :
% Kukelova Z., Bujnak M., Pajdla T., Automatic Generator of Minimal Problem Solvers,
% ECCV 2008, Marseille, France, October 12-18, 2008... |
github | hytseng0509/DPE-master | quaternion2matrix.m | .m | DPE-master/matlab/OPnP/quaternion2matrix.m | 1,431 | utf_8 | 49448898df2a32720040da4eb82aced9 | % QUATERNION2MATRIX - Quaternion to a 4x4 homogeneous transformation matrix
%
% Usage: T = quaternion2matrix(Q)
%
% Argument: Q - a quaternion in the form [w xi yj zk]
% Returns: T - 4x4 Homogeneous rotation matrix
%
% See also MATRIX2QUATERNION, NEWQUATERNION, QUATERNIONROTATE
% Copyright (c) 2008 Peter Kovesi
... |
github | hytseng0509/DPE-master | Refine.m | .m | DPE-master/matlab/Refine/Refine.m | 1,183 | utf_8 | 0a54ef20d08a7ac3759c8f98b478f236 | function [ex_mat ex_mats] = Refine(marker, img, in_mat, ex_mat, minDim, delta, bounds, steps, dim, photometricInvariance, verbose)
% smooth images
blur_size = 4 * 2 + 1;
params.blur_kernel = fspecial('gaussian', blur_size, 2);
marker = imfilter(marker,params.blur_kernel,'symmetric');
img = imfilter(img,pa... |
github | hytseng0509/DPE-master | GDS.m | .m | DPE-master/matlab/Refine/GDS.m | 2,793 | utf_8 | 40a3766a2f74ab08ea5da9cde23e5c82 | function [ex_mat bestEa] = GDS(marker, img, in_mat, inipose, minDim, delta, bounds, steps, dim, photometricInvariance, verbose)
% sampling
epsilon = 0.075;
numPoints = round(20/epsilon^2);
xs = randi(dim.marker.w, [1,numPoints]);
ys = randi(dim.marker.h, [1,numPoints]);
%% main loop
deltaFact = 1.511;... |
github | hytseng0509/DPE-master | get2Poses.m | .m | DPE-master/matlab/Refine/get2Poses.m | 788 | utf_8 | be18abe7bbc756e916c34daa072e24a2 | function poses = get2Poses(in_mat, ex_mat, minDim);
poses = [];
tgt = [-minDim,minDim,minDim,-minDim;-minDim,-minDim,minDim,minDim;0,0,0,0;1,1,1,1];
src = ex_mat*tgt;
src(1,:) = src(1,:)./src(3,:);
src(2,:) = src(2,:)./src(3,:);
[RR, tt, error, flag] = OPnP(tgt(1:3,:), src(1:2,:));
if (flag)
poses ... |
github | cbg-ethz/SynNet-master | Launch_Global_Optimization.m | .m | SynNet-master/SynNet_1.0/01_Optimize_Paramters/Launch_Global_Optimization.m | 5,566 | utf_8 | 453f27bb74abe05d6dadf2ed6692319b | % This Code optimized the biochemical paramters over the circuits defined
% in "Problem.IdealFunction.FunctionArray" around the binarization threshod
% indicated by "Problem.IdealFunction.stepAt'
% by Pejman Mohammadi
% pejman.m@gmail.com
%-----------
function Launch_Global_Optimization()
addpath('Auxilary_Functions'... |
github | cbg-ethz/SynNet-master | BackProb_log.m | .m | SynNet-master/SynNet_1.0/01_Optimize_Paramters/Auxilary_Functions/BackProb_log.m | 426 | utf_8 | c377afa9b958dd31ad561f7f4a4e6512 | function P = BackProb_log(X)
%% UN-NORMALIZED Background probability distribution function of the LOG-transformed input
for d = size(X,1):-1:1; Pd(d,:) = MarginalProb_log(X(d,:), d);end
P = prod(Pd,1);
end
function P = MarginalProb_log(x,d)
%% Marginal prior probability distribution function of log10(x) for dimension... |
github | cbg-ethz/SynNet-master | Evaluate_FunctionCnt.m | .m | SynNet-master/SynNet_1.0/01_Optimize_Paramters/Auxilary_Functions/Evaluate_FunctionCnt.m | 3,782 | utf_8 | 55a12e004ad4d2f1fe5762164af62991 | % This file evaluates the continous for of a boolean function over values in "X",
% Inputs:
% FunctionArray: A single boolean function or an array of K functions with each FunctionArray(k,:,:) corresponding to
% one signle function. The function is encoded like:
% F = [5 6 0 0 ; -3 0 0 0 ; 0 0 0 0 ; 0 0 0 0]
% ... |
github | cbg-ethz/SynNet-master | Evaluate_Function_S2.m | .m | SynNet-master/SynNet_1.0/01_Optimize_Paramters/Auxilary_Functions/Evaluate_Function_S2.m | 3,666 | utf_8 | 1919c6ea16cee21ccfe5e019c679eb40 | % This file evaluates the continous for of a boolean function over values in "X",
% Inputs:
% FunctionArray: A single boolean function or an array of K functions with each FunctionArray(k,:,:) corresponding to
% one signle function. The function is encoded like:
% F = [5 6 0 0 ; -3 0 0 0 ; 0 0 0 0 ; 0 0 0 0]
% ... |
github | cbg-ethz/SynNet-master | Generate_Minimum_Point.m | .m | SynNet-master/SynNet_1.0/01_Optimize_Paramters/Auxilary_Functions/Generate_Minimum_Point.m | 1,145 | utf_8 | 1b2b2627acdcd7e67e1e5c6d4fb4570d | % This function finds for each function, the point in the Domain which the
% minimum values falls.
function Xmin = Generate_Minimum_Point()
global Domain FunctionArray
Xmin = nan(max(abs(FunctionArray(:))), size(FunctionArray,1));
for f = 1:size(FunctionArray,1)
CurrFunction = zeros(size(FunctionArray,2), size(Fu... |
github | cbg-ethz/SynNet-master | Cost_function.m | .m | SynNet-master/SynNet_1.0/01_Optimize_Paramters/Auxilary_Functions/Cost_function.m | 3,994 | utf_8 | 8e008e2beb6723db62138819cdc48600 | function D = Cost_function(stepAt, theta)
global optTol oBP BP rBP PosScale NegScale Domain
oBP = stepAt;
BP = log10(stepAt);% Breakpoint of the function
rBP = floor((BP-eps)*100)/100; % Rounded Breakpoint of the function
SymmetricDomain = all(stepAt == 10.^(mean(Domain,2))); % Matlba's multidimenstional integrators ... |
github | cbg-ethz/SynNet-master | Evaluate_Function.m | .m | SynNet-master/SynNet_1.0/01_Optimize_Paramters/Auxilary_Functions/Evaluate_Function.m | 4,344 | utf_8 | 52ebecdc56f542564eef74b8e89862b7 | % This file evaluates the boolean function over values in "X", and if
% provided "IsVagueX".
% Inputs:
% FunctionArray: A single boolean function or an array of K functions with each FunctionArray(k,:,:) corresponding to
% one signle function. The function is encoded like:
% F = [5 6 0 0 ; -3 0 0 0 ; 0 0 0 0 ; 0... |
github | cbg-ethz/SynNet-master | Discrimination_Margin_Worst.m | .m | SynNet-master/SynNet_1.0/01_Optimize_Paramters/Auxilary_Functions/Discrimination_Margin_Worst.m | 335 | utf_8 | 809c72b42b0ff01b128f125aaafb37b8 | % This function reports the log2 fold change between the lowers "positive"
% and the highes "negative" sample in the AnnoationCnt.
% Pej 2014
function Margin = Discrimination_Margin_Worst(AnnoationCnt, TrueAnnotation)
m1 = min(AnnoationCnt( TrueAnnotation));
M0 = max(AnnoationCnt(~TrueAnnotation));
Margin = log2(m1) ... |
github | cbg-ethz/SynNet-master | Pej_Plot_Dist_Customized.m | .m | SynNet-master/SynNet_1.0/01_Optimize_Paramters/Auxilary_Functions/Pej_Plot_Dist_Customized.m | 2,532 | utf_8 | 20c34502eee835e1cecc88db953563d7 | % This function makes a violin distribution plots of the columns in "data"
% place at position "x", with "color", in the "direction", and full or
% "Dashed".
% Example:
% Pej_Plot_Dist([4 4], repmat(X,1,2), [1 0 0],[-1; 1]);
% this plots X in a symmetric violin at point 4, in red.
% NOTE: if you want to plot some log... |
github | cbg-ethz/SynNet-master | Visualize_Function.m | .m | SynNet-master/SynNet_1.0/01_Optimize_Paramters/Auxilary_Functions/Visualize_Function.m | 6,848 | utf_8 | 1c787f4aa61ee5c540f744d43f6b52ab | function Visualize_Function(FunctionArray, Problem)
OutDir = ['Optimization_Figures_' num2str(round(Problem.IdealFunction.stepAt)) '/'];
mkdir(OutDir);
global Functioninstance Domain oBP
Step_High = Problem.IdealFunction.Step_High;
Step_Low = Problem.IdealFunction.Step_Low;
InitialParams = Problem.Continuous_Functio... |
github | cbg-ethz/SynNet-master | Evaluate_FunctionCnt_Param.m | .m | SynNet-master/SynNet_1.0/01_Optimize_Paramters/Auxilary_Functions/Evaluate_FunctionCnt_Param.m | 4,237 | utf_8 | bc51ba83d374f64579873eee383c0364 | % This file evaluates the continous for of a boolean function over values in "X",
% Inputs:
% FunctionArray: A single boolean function or an array of K functions with each FunctionArray(k,:,:) corresponding to
% one signle function. The function is encoded like:
% F = [5 6 0 0 ; -3 0 0 0 ; 0 0 0 0 ; 0 0 0 0]
% ... |
github | cbg-ethz/SynNet-master | Evaluate_FunctionCnt_Minimal.m | .m | SynNet-master/SynNet_1.0/01_Optimize_Paramters/Auxilary_Functions/Evaluate_FunctionCnt_Minimal.m | 1,335 | utf_8 | 329e7159c4eac1ceed7f289fa63b9e6a | % This file evaluates the continous for of a boolean function over values in "X",
% This function is supposed to be faster than the full version.
% Params = [
% Consts.Continuous_Circuit_F1C
% Consts.Continuous_Circuit_F2C
% Consts.Continuous_Circuit_Tmax
% Consts.Continuous_Circuit_FF4max
% Consts... |
github | cbg-ethz/SynNet-master | Discrimination_Margin_Median.m | .m | SynNet-master/SynNet_1.0/01_Optimize_Paramters/Auxilary_Functions/Discrimination_Margin_Median.m | 357 | utf_8 | 6a55f74972828a542bcbeafec3317811 | % This function reports the log2 fold change between the median of the "positive"
% and the median of the "negative" samples in the AnnoationCnt.
% Pej 2014
function Margin = Discrimination_Margin_Median(AnnoationCnt, TrueAnnotation)
m1 = median(AnnoationCnt( TrueAnnotation));
M0 = median(AnnoationCnt(~TrueAnnotation)... |
github | cbg-ethz/SynNet-master | Evaluate_Function_Minimal.m | .m | SynNet-master/SynNet_1.0/01_Optimize_Paramters/Auxilary_Functions/Evaluate_Function_Minimal.m | 3,888 | utf_8 | 5772b1838f44a6d5db90b465af2dd491 | % This file evaluates the boolean function over values in "X", and if
% provided "IsVagueX".
% Inputs:
% FunctionArray: A single boolean function or an array of K functions with each FunctionArray(k,:,:) corresponding to
% one signle function. The function is encoded like:
% F = [5 6 0 0 ; -3 0 0 0 ; 0 0 0 0 ; 0... |
github | cbg-ethz/SynNet-master | Pos_Class_Ratio.m | .m | SynNet-master/SynNet_1.0/01_Optimize_Paramters/Auxilary_Functions/Pos_Class_Ratio.m | 4,767 | utf_8 | 266041463b003d7baa51303e5c02e2ea | function [PosScale, NegScale] = Pos_Class_Ratio()
global Domain Functioninstance FunctionArray BP optTol_Mass Class_Mass_Balance
if ~Class_Mass_Balance
disp('Class mass balancing deactive.')
PosScale = ones(size(FunctionArray,1),1);
NegScale = ones(size(FunctionArray,1),1);
return
end
fprintf('Calculat... |
github | cbg-ethz/SynNet-master | Fetch_Constraints.m | .m | SynNet-master/SynNet_1.0/01_Optimize_Paramters/Auxilary_Functions/Fetch_Constraints.m | 1,468 | utf_8 | 829860c90723b10da0ab89af59966fcf | % this function reads in the constraints from a text file.
% Usage:
% 1st Form:
% C = Fetch_Constraints(Constraints_File); Fetches all the constraints in
% the files specified in the TAB-separated file "Constraints_File" and puts
% them as separated field in C. The file should be formatted like this:
% Const1 Cons... |
github | cbg-ethz/SynNet-master | Quantize_Expresison_Ideal.m | .m | SynNet-master/SynNet_1.0/01_Optimize_Paramters/Auxilary_Functions/Quantize_Expresison_Ideal.m | 409 | utf_8 | 0579e867b4a8e5d74ecfc4ad24d8657f | % Description:
% This function, Quanties the data into 3 values, Zero, One
% Outputs:
% "BinData" is one for all values that are larger or equal to the geometric mean
% of the Zero and one level threshold.
% Written By Pejman, 14April2014, Basel
% Pejman.m@gmail.com
%----------------------------------------------
fu... |
github | cbg-ethz/SynNet-master | Pej_SNP_Annotate.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Pej_SNP_Annotate.m | 2,058 | utf_8 | 330d0bf3b6986b588f8dd1cc1acd2b56 | % SNP needs to have at least two fields, "position" and "chr".
% This implementation is very slow and stupid because I was lazy. you can
% just compare the start and end positions of the annotation to the
% position of SNP instead of using the more general genomic intersection
% script.
% Pejman April 2015, This code ... |
github | cbg-ethz/SynNet-master | Pej_Stacked_Frquencies.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Pej_Stacked_Frquencies.m | 1,336 | utf_8 | a78467ecf76027e04083ba0195553ad7 | % THis file gets a Data matrix containing the couns for different things in
% diffeent samples, and plots them in separate stacked bar plots.
% Each column in data corresponds to a samples (>SampleLabels), and each row is a single
% Type of thing (>Labels).
% THR is the minimum for maximum frequency in something for b... |
github | cbg-ethz/SynNet-master | Pej_Struct_Join.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Pej_Struct_Join.m | 1,914 | utf_8 | 1e3dbc453d205d8119e94677998b5ce6 | % This functiobs joins structures based on a one or more Keys
function [InputArray] = Pej_Struct_Join(KeysFields, InputArray)
%% Make the Key format
KeyFormat = '';
for i = length(KeysFields):-1:1
if isnumeric(InputArray{1}.(KeysFields{i})) || islogical(InputArray{1}.(KeysFields{i}))
if isequalwithequalnan... |
github | cbg-ethz/SynNet-master | Xval.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Xval.m | 4,232 | utf_8 | acb3a1a4b792339103d6fa91c9ba4b8c | function [CV_Stats, StatsLabels] = Xval(AnalysisReportFile, CV_folds)
global Consts
if nargin==0
AnalysisReportFile = '../F03_Results/BreastCancer_Data_C/C2-IDC.mat';
CV_folds =3;
end
load(AnalysisReportFile);
fprintf('Cross-validating "%s"\n', Sim.CancerLbl)
Consts = Sim.Consts;
%Consts.Learning_Convergen... |
github | cbg-ethz/SynNet-master | Pej_Genomic_Intersection_2.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Pej_Genomic_Intersection_2.m | 5,449 | utf_8 | d2a392effd0af9a6873f5ea10254ee3a | function [I1, I2]= Pej_Genomic_Intersection_2(Loci1, Loci2)
% This function reports the overlaps between two sets of genomic locations.
% NOTE: this function does THE SAME JOB as "Pej_Genomic_Intersection.m", it
% just has a different implementation. The other one is usually faster than
% this, but this one can be fas... |
github | cbg-ethz/SynNet-master | Pej_GetFiles.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Pej_GetFiles.m | 604 | utf_8 | f712f24873887d53e9814f28050fb4da | % This function gets a pattern and runs it within the linux "find" command,
% and returns the path of the files fitting to the pattern.
% Pej 2013
function Files = Pej_GetFiles(Pattern)
if nargin == 0
Pattern = '*.counts.txt';
end
FI = find(Pattern=='/', 1, 'last');
if ~isempty(FI)
Folder = Pattern(1:(FI-1));... |
github | cbg-ethz/SynNet-master | Pej_Visualize_Enrichments.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Pej_Visualize_Enrichments.m | 7,956 | utf_8 | bc4ee26c815d72145551680680a526cc | function Pej_Visualize_Enrichments(OutputFolder, EnrichmentOutputPathPattern, varargin)
Q_Thr = .01;
MinQ_log10 = 1E-15; % The smallest Qvalue to plot, smaller ones saturate here.
mkdir(OutputFolder);
inFiles = Pej_GetFiles(EnrichmentOutputPathPattern);
NinF = length(inFiles);
fprintf('%d input files found.\n', NinF)... |
github | cbg-ethz/SynNet-master | Evaluate_Function_C.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Evaluate_Function_C.m | 2,507 | utf_8 | 7eb72a8fd08f76fc3ade9fe626151af3 | % This file evaluates the continous for of a boolean function over values in "X",
% Inputs:
% FunctionArray: A single boolean function or an array of K functions with each FunctionArray(k,:,:) corresponding to
% one signle function. The function is encoded like:
% F = [5 6 0 0 ; -3 0 0 0 ; 0 0 0 0 ; 0 0 0 0]
% ... |
github | cbg-ethz/SynNet-master | miRNA_Feat_Filt_B.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/miRNA_Feat_Filt_B.m | 2,497 | utf_8 | ff64d7179687a9b3c9b9fb3674248b98 | % This code does couple ofstuff:
% 1: Quantizes the data to Zero, One, and NaN(vague, i.e. between zero and one) based on ZeroLvl, and
% OneLvl thresholds.
% 2: Remove uninformative genes; those who are constant along all samples.
% 3: Reoprts if the are some unresolvable samples; those that ha... |
github | cbg-ethz/SynNet-master | Pej_Visualize_DEGs.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Pej_Visualize_DEGs.m | 5,212 | utf_8 | 3b85ba2ed0a7011a186ffef668ddafcd | function Pej_Visualize_DEGs(DEGoutputfolder, FixPlotLims)
Qthr = 0.01;
TextLabel_Thr = 0; % Genes with q-value smaller than this will be plotted with their names printed next to them.
if nargin<2
FixPlotLims=false; % make plots independently
end
%--------
Resultfiles = dir(DEGoutputfolder);
MinY = 1;
MaxX = 0;
if... |
github | cbg-ethz/SynNet-master | Remove_ConsecutiveRepeats.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Remove_ConsecutiveRepeats.m | 1,673 | utf_8 | 9633e824be94444899bf341afe018592 | % This function removes identical functions from the pool. This is a
% special case for the more general function "Refine_HitPool"
% NOTE: This assumes all the pool is already formatted and also is sorted
% by performace (if the 3rd input is missing), so it only checks if consecutive functions are equal as
% they are.... |
github | cbg-ethz/SynNet-master | Pej_GTFtools.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Pej_GTFtools.m | 922 | utf_8 | feec7119553fa732c2ca96bdf4d0b2d1 | %% split files
% First I broke the files into Chromosom-strands with the followoing two
% lines; Ideally this two lines need to have one "if" in them to close the
% files when not needed otherwise you might get way too many files open at
% the same time
% awk '{print >"CH_"$1$7".txt"}' Homo_sapiens.GRCh37.72.gtf
func... |
github | cbg-ethz/SynNet-master | Simulate_data.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Simulate_data.m | 3,790 | utf_8 | 30d039d03099ab602ed51e05e84ad4f7 | % This code simulates data.
% output is an structure to be used for the Seek_DoomNet.m
% This file is called from the "Seek_DoomNet.m" if it's in the synthetic
% data mode.
%
% Written By Pejman, 15Jan2013, Basel
% Pejman.m@gmail.com
%----------------------------------------------
function Sim = Simulate_data()
global... |
github | cbg-ethz/SynNet-master | Discrimination_Margin_Mean.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Discrimination_Margin_Mean.m | 383 | utf_8 | 5000e7d7096468743333bb41c57a3054 | % This function reports the log10 fold change between the median of the "positive"
% and the median of the "negative" samples in the AnnoationCnt.
% Pej 2014
function Margin = Discrimination_Margin_Mean(AnnoationCnt, TrueAnnotation)
lAnnoationCnt = log10(AnnoationCnt);
m1 = mean(lAnnoationCnt( TrueAnnotation));
M0 = m... |
github | cbg-ethz/SynNet-master | Pej_Test_DEG_Enrichments.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Pej_Test_DEG_Enrichments.m | 1,806 | utf_8 | 85241946125154ef597b8343a260d077 | function Pej_Test_DEG_Enrichments(DEGoutputfolder,DB_Path)
Qthr = 0.01;
Shuffle = false; % If you put this on true, it shuffles the DEG qvalues, so should technically give flat pvalues all the time.
if nargin < 2
DB_Path = '/Users/pejmanm/Desktop/LocalTMP/PEJ_Resources/GeneSets/';
end
Resultfiles = dir(DEGoutput... |
github | cbg-ethz/SynNet-master | BuildRecruitIndex_C.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/BuildRecruitIndex_C.m | 3,668 | utf_8 | 5d4b081bb617026d8e9c68ade9f5d874 | % This file gets a gene expression matrix and an annotation and builds up a
% list based on how good a genes that can classify each specific sample.
% These genes are also weighted by how good they are overall.
% The script makes (2*number of genes) classifiers, and index them such
% that the first half correspond to ... |
github | cbg-ethz/SynNet-master | Pej_Struct_PlotFields.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Pej_Struct_PlotFields.m | 1,161 | utf_8 | 048e066f3cbbfadca07eef6724163d01 | % This plots two fileds of a structure vs each other. If these's a Filter
% provided that will be used to exclude rows
function Pej_Struct_PlotFields(D,Filed1,Field2,Filter, Square, Title)
if nargin<4 || isempty(Filter)
Filter = true(size(D.(Filed1),1),1);
end
if nargin<5
Square = false;
end
if nargin<6
... |
github | cbg-ethz/SynNet-master | Get_subFuncitons.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Get_subFuncitons.m | 581 | utf_8 | 9b1a212b24fba53e6498d3c40b058f6e | % Warning: This Code is written assuming that the function would be small
% (~10 genes), it produces exponential amount of output!
function Sub_F = Get_subFuncitons(Function)
Function = squeeze(Function);
GL = find(Function~=0);
N = length(GL);
if N == 0
Sub_F = [];
end
if N>10
warning('Pruning function is wr... |
github | cbg-ethz/SynNet-master | Pej_Read_Table.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Pej_Read_Table.m | 4,090 | utf_8 | d3738bd770217461d8a3b2fcfd02118e | % This reads a .CSV or a tab-separated table.
% The first line is expected to be the header
% Written by PEj oct 2013
%-------------
function Data = Pej_Read_Table(Path2File, DLM, ReportSwitch, FormatString)
Fin = fopen(Path2File, 'r');
% Identify Names
RawHeader = fgetl(Fin);
if nargin==1 || isempty(DLM)
DLM = '... |
github | cbg-ethz/SynNet-master | Ispractical.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Ispractical.m | 2,978 | utf_8 | 3ec939aa9fa528eeb6ff8d80aafd345a | % This function gets an array of classifier functions (See
% Evaluate_Function.m and Construct_Function.m for the format details and for deffinition of the classifier function),
% and:
% 1: returns a boolean array, "Practicals", indicating the paractical functions
% under the secondary constraints indicated in the glo... |
github | cbg-ethz/SynNet-master | Discrimination_Margin_Worst.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Discrimination_Margin_Worst.m | 350 | utf_8 | 9758cae68d71ec6ae0e7a852f07b8a1f | % This function reports the log2 fold change between the lowers "positive"
% and the highes "negative" sample in the AnnoationCnt.
% Pej 2014
function Margin = Discrimination_Margin_Worst(AnnoationCnt, TrueAnnotation)
m1 = min(AnnoationCnt(:, TrueAnnotation),[],2);
M0 = max(AnnoationCnt(:,~TrueAnnotation),[],2);
Margi... |
github | cbg-ethz/SynNet-master | Normalize_SingleGene_cntFunction_Output.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Normalize_SingleGene_cntFunction_Output.m | 646 | utf_8 | f932e890aa01cfde33d8d2cf8f1a6bc2 | % This function gets a continuous output matrix "X" transforms it to log-spacce and normalizes is
% between zeor and one. Assuming that the value of a single-Input function
% is falling between the borders of the function value for a zero expressed
% miRNA, or the whole miRNA pool size, for a Literal or a negative lite... |
github | cbg-ethz/SynNet-master | BuildRecruitIndex_B.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/BuildRecruitIndex_B.m | 2,423 | utf_8 | 1c4dca2d982f7dd8ab42433970c615ea | % This file gets a gene expression matrix and an annotation and builds up a
% list including the genes that can annotate each specific sample
% correctly. These genes are then weighted by how good they are overall.
% The script makes (2*number of genes) classifiers, and index them such
% that the first half correspond... |
github | cbg-ethz/SynNet-master | Pej_ImportGMT.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Pej_ImportGMT.m | 3,255 | utf_8 | 28a651af4136df02189ae17cd595ecdd | % This is the version 3. Functionality is exactly the same as before, it's
% just faster :)
% Pejman 26 Feb 2013 Lausanne, CHUV
%---------
% Reading in GSEA MSigDB files in .gmt format
% this file gets a .gmt file available here:
% http://www.broadinstitute.org/gsea/msigdb/collections.jsp
% and a list of Gene names, t... |
github | cbg-ethz/SynNet-master | Prune_Circuit.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Prune_Circuit.m | 3,958 | utf_8 | 44b60cb5d9344070c561bc35b99585c7 | % Warning: This Code is written assuming that the function would be small
% (~10 genes), it uses exponential amount of memory and CPU!
function Pruned_Function = Prune_Circuit(Function, Data)
global Consts
if length(size(Function))==2
%% It's a Single function, format it into an 3D array of length one, This wil... |
github | cbg-ethz/SynNet-master | miRNA_Feat_Filt_C.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/miRNA_Feat_Filt_C.m | 2,694 | utf_8 | 08e11f806d0b9bbda579895f9fc48b8e | % This code does couple ofstuff:
% 1: Quantizes the data to Zero, One, and NaN(vague, i.e. between zero and one) based on ZeroLvl, and
% OneLvl thresholds.
% 2: Remove uninformative genes; those who are constant along all samples.
% 3: Reoprts if the are some unresolvable samples; those that h... |
github | cbg-ethz/SynNet-master | Pej_Binornd.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Pej_Binornd.m | 933 | utf_8 | e2e3f931214830759fa6d8edebc6b6e6 | % this is basically the matlbas bionomial random generator, except that it
% uses normal and binomial approximation for extreme cases.
% NOTE this is written only for the case that there's a matrix N and a
% matrix P and the result is aa matrix R with the same size.
% Pejman May 2015
function R = Pej_Binornd(N, P)
F1 ... |
github | cbg-ethz/SynNet-master | Pej_BetaBinomial_cdf.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Pej_BetaBinomial_cdf.m | 731 | utf_8 | a36a64a039ca8e3d060f97d42939f4a4 | function px = Pej_BetaBinomial_cdf(x, xc, p, vScale)
%vScale = exp(vScale);
%a = vScale * p;
%b = a * (1-p)/p;
px = zeros(size(x));
for k = 1:length(x)
px(k) = Pej_BetaBinomial_cdf_single(x(k), xc(k), p, vScale);
end
end
function px = Pej_BetaBinomial_cdf_single(x, xc, p, vScale)
% We assume X < Xc always
if x>x... |
github | cbg-ethz/SynNet-master | Construct_Function.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Construct_Function.m | 971 | utf_8 | aa0e786c9ae5ff1b145753b7ef341a43 | % This Script makes a new function from the given single "Gene".
% Inputs:
% Gene: An integer input. "Gene" can be positive or negative, negative means negate literal.
% If Gene is an array of length K, then the output will be a 3D array of
% K functions with (FunctionArray(k,:,:) corresponding to the k'th input... |
github | cbg-ethz/SynNet-master | Evaluate_Function_B.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Evaluate_Function_B.m | 4,436 | utf_8 | 1757b2c3ef8d1778a560c68239e97168 | % This file evaluates the boolean function over values in "X", and if
% provided "IsVagueX".
% Inputs:
% FunctionArray: A single boolean function or an array of K functions with each FunctionArray(k,:,:) corresponding to
% one signle function. The function is encoded like:
% F = [5 6 0 0 ; -3 0 0 0 ; 0 0 0 0 ; 0... |
github | cbg-ethz/SynNet-master | Build_Random_Function.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Build_Random_Function.m | 1,661 | utf_8 | f42ca841f91d9db5cc0764597e6fc650 | % This functions makes a random circuit.
% MaxAnd, and Max or correspond to the size of the circiut, #Rows, and
% #Columns in the output matrix respectively.
% Ngene is the total number of available genes in the system.
function F = Build_Random_Function(Ngene, Consts)
addpath('Auxilary_Functions/');
if nargin<2; Const... |
github | cbg-ethz/SynNet-master | Pej_Plot_Dist.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Pej_Plot_Dist.m | 2,263 | utf_8 | 97c6521ac364596aece904a5e5b9e1c4 | % This function makes a violin distribution plots of the columns in "data"
% place at position "x", with "color", in the "direction", and full or
% "Dashed".
% Example:
% Pej_Plot_Dist([4 4], repmat(X,1,2), [1 0 0],[-1; 1]);
% this plots X in a symmetric violin at point 4, in red.
% NOTE: if you want to plot some log-... |
github | cbg-ethz/SynNet-master | Pej_Make_Grid.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Pej_Make_Grid.m | 593 | utf_8 | c9e30c9cff6304bf87038f654d970e10 | % This function gets a set of Domain Limits of size(n*2) like:
% [ 0 3; 2 5; 1 4];
% defining lower and uppper bound for each of the n dimentions.
% and produces a regular grid of "Density" in "Grid", with dimentions
% (Density.^n, n);
% May 2014 Pejman, pejman.m@gmail.com
%----------------------
function Grid = Pej_M... |
github | cbg-ethz/SynNet-master | Pej_Test4Enrichment.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Pej_Test4Enrichment.m | 8,128 | utf_8 | 0592cdee80d9764b01c5abe4321aa059 | % Before using this you should once have imported the GMT files from the
% DBPath using: "Pej_ImportGMT".
function OutPutPrefix = Pej_Test4Enrichment(Clustering_Output, DB_Path, XrefPath)
Thr = 0.05; % default FDR threshold
if nargin < 2
DB_Path = '/Users/pejmanm/Desktop/LocalTMP/PEJ_Resources/GeneSets/';
end
if ... |
github | cbg-ethz/SynNet-master | Pej_Pie_THR.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Pej_Pie_THR.m | 963 | utf_8 | f1dc79f3a0a2e54a544269e0181c37d3 | % This function makes a pie chart with given labels and colors, up to THR
% minimum frequency. the input "Data" is a verctos of count correponding to
% each thing.
% Pej Oct 2013
function Fig = Pej_Pie_THR(Data, Labels, TypeColor, THR)
if nargin<4
THR = 3; % minimum frequency of a Type for being shown in percentag... |
github | cbg-ethz/SynNet-master | Pej_Struct_Cat.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Pej_Struct_Cat.m | 734 | utf_8 | 8c9ac3ca3e07ae34b924791b5f60d132 | % This function concatenates two structrures of the same format by adding
% one under the other one!
% If A field in a structure has multiple dimensions this always adds over the
% first dimension.
% Pej - Apr 2015
%-----------------------------
function JStruct = Pej_Struct_Cat(Struct1, Struct2)
if isempty(Struct1)||... |
github | cbg-ethz/SynNet-master | Discrimination_Margin_Median.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Discrimination_Margin_Median.m | 359 | utf_8 | 668e53b51591bf192adfb3ec41fad462 | % This function reports the log2 fold change between the median of the "positive"
% and the median of the "negative" samples in the AnnoationCnt.
% Pej 2014
function Margin = Discrimination_Margin_Median(AnnoationCnt, TrueAnnotation)
m1 = median(AnnoationCnt( TrueAnnotation));
M0 = median(AnnoationCnt(~TrueAnnotation)... |
github | cbg-ethz/SynNet-master | Pej_Get_FileLength.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Pej_Get_FileLength.m | 201 | utf_8 | 193d0631e698287b1cee2cefba7aebda | % This code is equal to "wc -l TextFile" in linux/unix
% Pej Now 2013
% -----
function FLength = Pej_Get_FileLength(TextFile)
fid = fopen(TextFile, 'r');
[~, FLength] = fscanf(fid,'%*[^\n]%1c');
end |
github | cbg-ethz/SynNet-master | Pej_Struct_RowDel.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Pej_Struct_RowDel.m | 1,060 | utf_8 | fab3714a07edcf4577894a0c1d07cfa7 | % This function removes rows(first dimension), from fields in a structure
% It's assumed that all fields have the same number oof rows.
% "IndextoRemove" is a binary vector of the same size as the filed, or the
% indices of the rows to be removed.
% Example:
%
% X = Pej_Read_Table('A_Tab-separated_file.txt');
% X = P... |
github | cbg-ethz/SynNet-master | Pej_Read_MetaData.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Pej_Read_MetaData.m | 697 | utf_8 | b0311f3fe2450e74e2e58d8e9f8d99e5 | % This file reads a tab-separated(other delimiters need to be given explicitely) meta-data table with one header line containing the name of the metadata
% Lines starting with '#' are disregarded.
%Pej 2014
%------
function MetaData = Pej_Read_MetaData(File, Delimiter)
if nargin < 2; Delimiter= '\t';end
Fin = fopen(F... |
github | cbg-ethz/SynNet-master | Pej_Heatmap.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Pej_Heatmap.m | 3,050 | utf_8 | 54429f78e4b0c71612318b6152d3cf9e | % NOTE: This code is not yet matured.
% Tuning the layout and size of the window is still independant of the data
% dimensions and it's manual.
% if you want the arrows not to collide sort the data in descending order
% based on the rowwise means:
% [~, I] = sort(mean(Data,2), 'descend');
% Pej_Heatmap(Data(I,:));
... |
github | cbg-ethz/SynNet-master | Get_Absolute_Performance_C.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Get_Absolute_Performance_C.m | 2,162 | utf_8 | 67e5837674513d70244ae7ea44d4a0f3 | % Out put is the expected difference between the positive and negative
% inputs. In the case of boolean functions this is equivalent to: TPR - FPR
% where TPR is True Pos Rate, and FPR is False Pos Rate
% This is also equal to "informedness measure, which is the TPR + SPC - 1
% where SPC is Specifity or True Negative R... |
github | cbg-ethz/SynNet-master | Read_miRNAData.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Read_miRNAData.m | 2,763 | utf_8 | 63c4640be1f9761b48479fda91081765 | % This reads in the input file, here's how an example file looks like:
% # The format is simple, here how you make comments!
% # The first line includes Unique IDs. This can be anything, text, number,
% whatever you feel like today, but it needs to be unique for each column. This unique ID is used
% as the Key to rel... |
github | cbg-ethz/SynNet-master | Evaluate_Function_B_Margin.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Evaluate_Function_B_Margin.m | 5,916 | utf_8 | b95192fc8becc30dcca43b815c101b9b | % This file evaluates the boolean function over values in "X", and if
% provided "IsVagueX".
% Inputs:
% FunctionArray: A single boolean function or an array of K functions with each FunctionArray(k,:,:) corresponding to
% one signle function. The function is encoded like:
% F = [5 6 0 0 ; -3 0 0 0 ; 0 0 0 0 ; 0... |
github | cbg-ethz/SynNet-master | Pej_Read_Expression_Table.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Pej_Read_Expression_Table.m | 800 | utf_8 | 91ca3ca33514cc1a0212eae1e035ef36 | % This file reads an expression tab-separated(other delimiters need to be given explicitely) table with one header line and first column
% containing gene names.
% Lines starting with '#' are disregarded.
%Pej 2014
%------
function GeneData = Pej_Read_Expression_Table(File, Delimiter)
if nargin < 2; Delimiter= '\t';e... |
github | cbg-ethz/SynNet-master | Pej_SavePlot.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Pej_SavePlot.m | 860 | utf_8 | f40221f2bf32ed7746263a4d2e814020 | % This scripts saves the figure, Fig, in the address, OutFig, in .eps, and
% .fig formats and closes it.
function Pej_SavePlot(Fig, OutFig, Transparent)
if nargin <3; Transparent = false; end
[pathstr,~,~] = fileparts(OutFig);
if ~isempty(pathstr) && ~exist(pathstr, 'dir'); mkdir(pathstr);end
saveas(Fig, [OutFig '.f... |
github | cbg-ethz/SynNet-master | Get_Absolute_Performance_B.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Get_Absolute_Performance_B.m | 1,968 | utf_8 | 10588877d8748ba1c104de6fc194cc3e | % Out put is the expected difference between the positive and negative
% inputs. In the case of boolean functions this is equivalent to: TPR - FPR
% where TPR is True Pos Rate, and FPR is False Pos Rate
% This is also equal to "informedness measure, which is the TPR + SPC - 1
% where SPC is Specifity or True Negative R... |
github | cbg-ethz/SynNet-master | Fetch_Constraints.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Fetch_Constraints.m | 3,836 | utf_8 | 264a396a8e46f04a8d42310ccb6ecd9f | % this function reads in the constraints from a text file.
% Usage:
% 1st Form:
% C = Fetch_Constraints(Constraints_File); Fetches all the constraints in
% the files specified in the TAB-separated file "Constraints_File" and puts
% them as separated field in C. The file should be formatted like this:
% Const1 Cons... |
github | cbg-ethz/SynNet-master | Combine_Similar_miRNAs.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Combine_Similar_miRNAs.m | 3,492 | utf_8 | 8b55b49382c3009e8fc883d82d7f3424 | % This file gets a structure containing names and expressison for miRNAs,
% and a path to file containing mature miRNA sequences. It uses the
% sequence similarity to add up expression of similar miRNAs to each other.
% Additionally this code picks only the first gene from a list of
% equivalent ones and sets all the r... |
github | cbg-ethz/SynNet-master | Pej_Xref.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Pej_Xref.m | 1,756 | utf_8 | e32f2bf093b6912ea6af9eb23bdf2131 | % This file gets a cross-reference, Identifies the original ID and returns
% the IDs for all the other given types in the reference in output. The
% unfound IDs are returned as the original ID. and the orders and the size
% of the input is preserved.
% Pej 2014 March, pejman.m@gmail.com
%--------------
function Ynonun... |
github | cbg-ethz/SynNet-master | Catch_Elites.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Catch_Elites.m | 404 | utf_8 | 52c00195e5f6273d467b4fc741bd05c9 | % This finds the Nth unique highest number in the performance
% array, and returns an array for those larger than it.
function [Elite_Idx, nElites] = Catch_Elites(Performance, N)
[Sp, SpI] = sort(Performance, 'descend');
dSp = Sp(1:(end-1)) - Sp(2:end);
Jumps = [find(dSp>0); length(SpI)];
... |
github | cbg-ethz/SynNet-master | Pej_Read_Bowtie1.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Pej_Read_Bowtie1.m | 3,149 | utf_8 | 06719b409fc645df4c5a2098abc4d97d | % This just reads Default bowtie output:
% bowtie outputs one alignment per line. Each line is a collection of 8 fields separated by tabs; from left to right, the fields are:
%
% 1 Name of read that aligned.
% Note that the [SAM specification] disallows whitespace in the read name. If the read name contains any whitesp... |
github | cbg-ethz/SynNet-master | Pej_Read_Bed.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Pej_Read_Bed.m | 610 | utf_8 | 11b192a9190aceb0294d80b6673d31c9 | % This just reads Bed files.
% Reference: http://genome.ucsc.edu/FAQ/FAQformat.html#format1
% Pejman 2014
%--------------
function Data = Pej_Read_Bed(Path2File)
DLM = '[\t]'; % list of potential delimiters
fprintf(['Input file: %s'], Path2File);
FormatS = '%s%d%d%s%d%c%[^\n]';
Header = {'chr', 'start', 'end', 'na... |
github | cbg-ethz/SynNet-master | FixFunctionFormat.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/FixFunctionFormat.m | 3,243 | utf_8 | 71a172e87f19aa86778c3c833964d3b1 | function FunctionArray = FixFunctionFormat(FunctionArray, FormattedHit)
if length(size(FunctionArray))==2; FunctionArray = reshape(FunctionArray, [1,size(FunctionArray)]);end
if nargin < 2; FormattedHit = false(size(FunctionArray,1)); end
tmpHitPool = squeeze(FunctionArray(1,:,:));
tmpFunct = zeros(size(FunctionA... |
github | cbg-ethz/SynNet-master | Quantize_Expresison.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Quantize_Expresison.m | 1,009 | utf_8 | 58605b60c42d678c9d598b88d31bf4db | % Description:
% This function, Quanties the data into 3 values, Zero, One, and NaN(Vague)
% This is coded by two Matrices, one binary matrix, "BinData", of values, and one extra
% "IsVague" binary matrix, that denotes the vague values.
% Outputs:
% "BinData" is one for all values that are larger or equal to the geome... |
github | cbg-ethz/SynNet-master | Pej_Struct_RowSelect.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Pej_Struct_RowSelect.m | 1,045 | utf_8 | fc05f42f5bc9d9f4139f517636394c7c | % This function selects rows, from fields in a structure
% It's assumed that all fields have the same number oof rows.
% "IndextoSelect" is a binary vector of the same size as the filed, or the
% indices of the rows to be selected.
% Example:
%
% X = Pej_Read_Table('A_Tab-separated_file.txt');
% X = Pej_Struct_RowSel... |
github | cbg-ethz/SynNet-master | Pej_Intersect_SortedVectors.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Pej_Intersect_SortedVectors.m | 968 | utf_8 | 47a30cbd8104ad80d74bcad43634cb06 | % This code is a simple form of the matlab 'intersect' command.
% The main difference is that it assumes the input arrays are pre-sorted in
% Ascending order. This is much fasted the "intersect" in repetitive tasks
% Pej Apri 2015, NYGC
%------------------------
function [IDX] = Pej_Intersect_SortedVectors(X,Y)
i1 ... |
github | cbg-ethz/SynNet-master | Pej_Genomic_Intersection.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Pej_Genomic_Intersection.m | 6,115 | utf_8 | bb4914b05b83ad8872f78e81782c69a1 | function [I]= Pej_Genomic_Intersection(Loci1, Loci2)
% This function reports the overlaps between two sets of genomic locations.
% NOTE: this function does THE SAME JOB as "Pej_Genomic_Intersection_2.m", it
% just has a different implementation. The other one is usually slower than
% this, but this one can be slow if ... |
github | cbg-ethz/SynNet-master | Refine_HitPool.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/Refine_HitPool.m | 3,983 | utf_8 | e8f08e7a6c66c897a376472f82ea179f | % This function gets an array of classifier functions (See
% Evaluate_Function.m and Construct_Function.m for the format details and for deffinition of the classifier function),
% and:
% 1: removes the logical redundancies (for example A & B & A can be reduced to A & B)
% 2: sorts the function to remove duplicates (for... |
github | cbg-ethz/SynNet-master | normalize_miRNAs.m | .m | SynNet-master/SynNet_1.0/02_SeekNet/Auxilary_Functions/normalize_miRNAs.m | 2,071 | utf_8 | 29178c8b793aa322889b27187590b5af | % This file normalizes the miRNA data to sum up to roughly 25000 copies per
% cell.
% WARNING: The code is not general, it's tailored.
% Pejman Dec. 2012, Pejman.m@gmail.com
%----------------------------------
function miRNA_Data = normalize_miRNAs(miRNA_Data, SampleFilt, Consts)
if nargin <2
SampleFilt = 1:size(m... |
github | chenyk1990/cykd2-master | predictor.m | .m | cykd2-master/various/cyksmall/dace/predictor.m | 4,398 | utf_8 | 5cc57ef4e8174c9d7ca08ee8893871bb | function [y, or1, or2, dmse] = predictor(x, dmodel)
%PREDICTOR Predictor for y(x) using the given DACE model.
%
% Call: y = predictor(x, dmodel)
% [y, or] = predictor(x, dmodel)
% [y, dy, mse] = predictor(x, dmodel)
% [y, dy, mse, dmse] = predictor(x, dmodel)
%
% Input
% x : trial des... |
github | chenyk1990/cykd2-master | dacefit.m | .m | cykd2-master/various/cyksmall/dace/dacefit.m | 9,060 | utf_8 | aed64a55c2bbc9bb97c84afe82bba141 | function [dmodel, perf] = dacefit(S, Y, regr, corr, theta0, lob, upb)
%DACEFIT Constrained non-linear least-squares fit of a given correlation
% model to the provided data set and regression model
%
% Call
% [dmodel, perf] = dacefit(S, Y, regr, corr, theta0)
% [dmodel, perf] = dacefit(S, Y, regr, corr, theta0, lob... |
github | chenyk1990/cykd2-master | ksvds1.m | .m | cykd2-master/various/packages/toolbox/Dictionary/Sksvdsbox/ksvds1.m | 19,635 | utf_8 | 696ae3b58568c5ed1f859498151d18f8 | function [A,Gamma,err,gerr] = ksvds(params,varargin)
%KSVDS Sparse K-SVD dictionary training.
% [A,GAMMA] = KSVDS(PARAMS) runs the sparse K-SVD dictionary training
% algorithm on the specified set of signals, returning the sparse
% dictionary representation matrix A, and the signal representation
% matrix GAMMA.
%
... |
github | chenyk1990/cykd2-master | ksvds.m | .m | cykd2-master/various/packages/toolbox/Dictionary/Sksvdsbox/ksvds.m | 20,040 | utf_8 | cb81cedd013c86048f3359c8c4b471d2 | function [A,Gamma,err,gerr] = ksvds(params,varargin)
%KSVDS Sparse K-SVD dictionary training.
% [A,GAMMA] = KSVDS(PARAMS) runs the sparse K-SVD dictionary training
% algorithm on the specified set of signals, returning the sparse
% dictionary representation matrix A, and the signal representation
% matrix GAMM... |
github | chenyk1990/cykd2-master | ompdemo.m | .m | cykd2-master/various/packages/toolbox/Dictionary/ompbox/ompdemo.m | 2,430 | utf_8 | e349dad5bce09bfd121f9f56d926c48c | % function ompdemo
%OMPDEMO Demonstration of the OMP toolbox.
% OMPDEMO generates a random sparse mixture of cosines and spikes, adds
% noise, and applies OMP to recover the original signal.
%
% To run the demo, type OMPDEMO from the Matlab prompt.
%
% See also OMPSPEEDTEST.
% Ron Rubinstein
% Comput... |
github | chenyk1990/cykd2-master | ksvd.m | .m | cykd2-master/various/packages/toolbox/Dictionary/ksvdbox/ksvd.m | 19,336 | utf_8 | 0f800c2a14a4407fa1af38b2ba0d882e | function [D,Gamma,err,gerr] = ksvd(params,varargin)
%KSVD K-SVD dictionary training.
% [D,GAMMA] = KSVD(PARAMS) runs the K-SVD dictionary training algorithm on
% the specified set of signals, returning the trained dictionary D and the
% signal representation matrix GAMMA.
%
% KSVD has two modes of operation: ... |
github | chenyk1990/cykd2-master | ksvd1.m | .m | cykd2-master/various/packages/toolbox/Dictionary/ksvdbox/ksvd1.m | 19,588 | utf_8 | 151b889fbbbb936979dda18cd46d770f | function [D,Gamma,err,gerr] = ksvd1(params,varargin)
%KSVD K-SVD dictionary training.
% [D,GAMMA] = KSVD(PARAMS) runs the K-SVD dictionary training algorithm on
% the specified set of signals, returning the trained dictionary D and the
% signal representation matrix GAMMA.
%
% KSVD has two modes of operation:... |
github | chenyk1990/cykd2-master | ksvd3.m | .m | cykd2-master/various/packages/toolbox/Dictionary/ksvdbox/ksvd3.m | 18,996 | utf_8 | 8a951cdf5a05b0300ad21e774528fc2c | function [D,Gamma,err,gerr] = ksvd3(params,varargin)
%KSVD K-SVD dictionary training.
% [D,GAMMA] = KSVD(PARAMS) runs the K-SVD dictionary training algorithm on
% the specified set of signals, returning the trained dictionary D and the
% signal representation matrix GAMMA.
%
% KSVD has two modes of operation: spars... |
github | chenyk1990/cykd2-master | ompdemo.m | .m | cykd2-master/various/packages/toolbox/Dictionary/ompbox10/ompdemo.m | 2,430 | utf_8 | e349dad5bce09bfd121f9f56d926c48c | % function ompdemo
%OMPDEMO Demonstration of the OMP toolbox.
% OMPDEMO generates a random sparse mixture of cosines and spikes, adds
% noise, and applies OMP to recover the original signal.
%
% To run the demo, type OMPDEMO from the Matlab prompt.
%
% See also OMPSPEEDTEST.
% Ron Rubinstein
% Comput... |
github | chenyk1990/cykd2-master | renumber.m | .m | cykd2-master/various/packages/tensor_toolbox_2.4/@sptensor/private/renumber.m | 1,673 | utf_8 | dfe6628375acc3ee6026f0d0eaaf316b | function [newsubs, newsz] = renumber(subs, sz, range)
%RENUMBER indices for sptensor subsref
%
% [NEWSUBS,NEWSZ] = RENUMBER(SUBS,SZ,RANGE) takes a set of
% original subscripts SUBS with entries from a tensor of size
% SZ. All the entries in SUBS are assumed to be within the
% specified RANGE. These subscripts are t... |
github | chenyk1990/cykd2-master | tucker_me.m | .m | cykd2-master/various/packages/tensor_toolbox_2.4/met/tucker_me.m | 4,560 | utf_8 | 3216bec3b59aecd3b4a11c8e4c559039 | function [T, max_mem, Uinit] = tucker_me(X, R, esz, opts)
%TUCKER_ME Memory-efficient Tucker higher-order orthogonal iteration.
%
% T = TUCKER_ME(X,R,ESZ) computes the best rank(R1,R2,..,Rn)
% approximation of tensor X, according to the specified dimensions
% in vector R. ESZ specifies the number of dimensions th... |
github | chenyk1990/cykd2-master | tucker_me_test.m | .m | cykd2-master/various/packages/tensor_toolbox_2.4/met/tucker_me_test.m | 3,079 | utf_8 | 7cd5d5bf56ea1d8a82ef189d3c71d564 | function tucker_me_test
%TUCKER_ME_TEST Very simple tests of tucker_me.
% Code by Tamara Kolda and Jimeng Sun, 2008.
%
% Based on the paper:
% T. G. Kolda and J. Sun. Scalable Tensor Decompositions for Multi-aspect
% Data Mining. In: ICDM 2008: Proceedings of the 8th IEEE International
% Conference on Data M... |
github | chenyk1990/cykd2-master | ttm_me.m | .m | cykd2-master/various/packages/tensor_toolbox_2.4/met/ttm_me.m | 4,085 | utf_8 | 241d426fee6a1d228fd2ca6c01db66b1 | function Y = ttm_me(X, U, edims, sdims, tflag)
%TTM_ME Memory-efficient sptensor times matrix.
%
% Y = TTM_ME(X, U, EDIMS, SDIMS, TFLAG) handles some dimensions
% elementwise and others in the standard way. Here, X is a sparse tensor
% (sptensor), U is a cell array of matrices of length ndims(X),
% EDIMS speci... |
github | chenyk1990/cykd2-master | tucker_als.m | .m | cykd2-master/various/packages/tensor_toolbox_2.4/algorithms/tucker_als.m | 5,199 | utf_8 | 596a43b6fd12cfc213ba0f9041ed4196 | function [T,Uinit] = tucker_als(X,R,varargin)
%TUCKER_ALS Higher-order orthogonal iteration.
%
% T = TUCKER_ALS(X,R) computes the best rank(R1,R2,..,Rn)
% approximation of tensor X, according to the specified dimensions
% in vector R. The input X can be a tensor, sptensor, ktensor, or
% ttensor. The result re... |
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