plateform stringclasses 1
value | repo_name stringlengths 13 113 | name stringlengths 3 74 | ext stringclasses 1
value | path stringlengths 12 229 | size int64 23 843k | source_encoding stringclasses 9
values | md5 stringlengths 32 32 | text stringlengths 23 843k |
|---|---|---|---|---|---|---|---|---|
github | db4j/db4j-master | step.m | .m | db4j-master/doc/poisson/step.m | 5,567 | utf_8 | 2480937cdd0ae60a018b27b077c989cb | # copyright 2017 nqzero - see License.txt for terms
1;
function uo = mymean(vals)
uo = 0;
if (length(vals) > 0) uo = mean(vals); end
end
function uhist(x);
y = unique(x);
hist(x,y);
axis([y(1)-.5 y(end)+.5]);
end
function hist2(x,y,varargin)
[gx,gn] = hist(x,varargin{:});
[hx,hn] = hist(y,varargin{:}... |
github | db4j/db4j-master | approx.m | .m | db4j-master/doc/poisson/approx.m | 549 | utf_8 | 2d37bdfa3ae2b5d1a3d03e7243a455be | # copyright 2017 nqzero - see License.txt for terms
1;
function A = render(n)
A = zeros(n,n);
ki = 0:n-1;
np = 0:n-1;
A(1,1) = 1;
for ii = 2:n; A(:,ii) = poisspdf(ki,np(ii)); end
end
function [mse,chat] = fit(A,c,p)
nc = length(c);
np = length(p);
p = max(p,0);
p = p/sum(p);
chat = A(1:nc,1:np)... |
github | db4j/db4j-master | ff.m | .m | db4j-master/doc/poisson/ff.m | 1,897 | utf_8 | 39b9a0fd438169ee0e749bc9fb36daa6 | # copyright 2017 nqzero - see License.txt for terms
1;
function p = kgivenb(bn,k)
p = 2*(k+1)./(bn^2).*gammainc( bn, k+2 );
end
# return the inverse transform sampling based on numerically inverting a pdf(x)
# uniform: either an array of probs to transform or the length of a rand vector to use
# p: optional, ... |
github | wiggins-lab/SuperSegger-master | segsTLEdit.m | .m | SuperSegger-master/segmentation/segsTLEdit.m | 5,572 | utf_8 | a3de3dc1da4d88ae4096416e4e1bce85 | function [saved_touch_list] = segsTLEdit( dirname, frame_num, CONST )
% segsTLEdit : used to visually modify segments in a frame.
% red are segments that are on, bad are the segments that are off and green
% the permanent segments.
% Possible choices : q to quit
% press the enter button to select a ... |
github | wiggins-lab/SuperSegger-master | calculateStateEnergy.m | .m | SuperSegger-master/segmentation/calculateStateEnergy.m | 2,904 | utf_8 | 73634f30cd1220d62067d34a0dd4ce5c | function [regionScore,state] = calculateStateEnergy(cell_mask,vect,segs_list,data,xx,yy,CONST)
% calculateStateEnergy : calculates the state energy for modifying segments
% in a mask of a region.
%
% INPUT :
% cell_mask : mask of regions of cells to be optimized
% vect : logical for segments that are on or ... |
github | wiggins-lab/SuperSegger-master | systematic.m | .m | SuperSegger-master/segmentation/systematic.m | 2,550 | utf_8 | 27859b4d80521a11deda008462d84a07 | function [minVect,regEmin] = systematic( segs_list, data, cell_mask, xx, yy, CONST)
% systematic: Finds the minimum energy configuration by trying all segment
% combinations.
%
% INPUT :
% segs_list : list of ids of segments to be turned on and off
% data : seg data file
% cell_mask : mask of regions... |
github | wiggins-lab/SuperSegger-master | cellprops5.m | .m | SuperSegger-master/segmentation/cellprops5.m | 7,667 | utf_8 | adddd9f60ed15fb3d34950d88c6178f8 | function info = cellprops5( mask, props )
% cellprops5 : Calculates the shape properties of a region or 'cell'.
% This are used later to calculate the score of a region.
%
% INPUT :
% mask : cell / region mask
% props : properties generated by regionprops
% OUTPUT :
% info = [L1 : long axis of the ... |
github | wiggins-lab/SuperSegger-master | superSeggerOpti.m | .m | SuperSegger-master/segmentation/superSeggerOpti.m | 13,150 | utf_8 | 8f7793e8f744ff810e5953dbbcfd7bdf | function [data,A] = superSeggerOpti(phaseOrData, mask, disp_flag, CONST, adapt_flag, header, crop_box)
% superSeggerOpti generates the initial segmentation of rod-shaped cells.
% It uses a local minimum filter (similar to a median filter) to enhance
% contrast and then uses Matlab's WATERSHED command to generate
% cel... |
github | wiggins-lab/SuperSegger-master | cellprops3.m | .m | SuperSegger-master/segmentation/cellprops3.m | 8,677 | utf_8 | 6bcd146f2c2daeaf023ae376cd439611 | function info = cellprops3( mask, props )
% cellprops3 : Calculates the shape properties of the region or 'cell'.
%
% INPUT :
% mask : cell / region mask
% props : properties generated by regionprops
% OUTPUT :
% info = { 'long axis: L1', ...
% 'short axis mean: L2mean', ...
% 'neck width: Lne... |
github | wiggins-lab/SuperSegger-master | houseNeuralSimulation.m | .m | SuperSegger-master/segmentation/houseNeuralSimulation.m | 2,676 | utf_8 | dc6b4f7c40cb1b55d9e5273f3f48efed | function [Y,Xf,Af] = houseNeuralSimulation(X,net)
% houseNeuralSimulation : calculates output for neural network.
% Improvement in speed from using net (X).
%
% Generated by Neural Network Toolbox function genFunction, 14-Mar-2016 14:57:51.
%
% [Y] = houseFcn(X,~,~) takes these arguments:
%
% X = 1xTS cell, 1 input... |
github | wiggins-lab/SuperSegger-master | makeBgMask.m | .m | SuperSegger-master/segmentation/makeBgMask.m | 3,515 | utf_8 | 9308d77021932293a5ebd3e5bf9feb0b | function mask = makeBgMask(phase, filt_3, filt_4, AREA, CONST, crop_box)
% makeBgMask : makes a background mask for the phase image
%
% INPUT :
% phase : phase image
% filt_3 : first filter with bigger size and std
% filt_4 : second filter with smaller size and std
% AREA : the minimum ar... |
github | wiggins-lab/SuperSegger-master | defineGoodSegs.m | .m | SuperSegger-master/segmentation/defineGoodSegs.m | 7,881 | utf_8 | 5c6c125d9ada38643bb875270f34f848 | function [data] = defineGoodSegs(data, ws, CONST, calcScores)
% defineGoodSegs sets the segments to good, bad and 3n set by the watershed algorithm
% "Good" segments (segs_good) are the ones that lie along a real cellular
% boundary, "bad" segments, lie along spurious boundaries
% within single cells. 3n_segs are the f... |
github | wiggins-lab/SuperSegger-master | trackOptiMakeCell.m | .m | SuperSegger-master/cell/trackOptiMakeCell.m | 13,716 | utf_8 | f7d72d32ea2faee872b023a8549b791c | function trackOptiMakeCell(dirname,CONST,header)
% trackOptiMakeCell : generates the CellA field indexed by the region number
% which contains information about each cell in each region in each frame.
%
% It goes through the dirname/*err.mat files and computes the characteristics of the
% cells in each frame and puts t... |
github | wiggins-lab/SuperSegger-master | trackOptiClist.m | .m | SuperSegger-master/cell/trackOptiClist.m | 20,726 | utf_8 | d6813c77b58459da78b545ee76767b77 | function [clist] = trackOptiClist(dirname,CONST,header)
% trackOptiClist : generates an array called the clist
% which contains non time dependent information for each cell.
% Fluorescence values contained are for at birth time.
% To see the information contained type clist.def'.
%
% INPUT :
% dirname : seg folde... |
github | wiggins-lab/SuperSegger-master | trackOptiListNeighbor.m | .m | SuperSegger-master/cell/trackOptiListNeighbor.m | 2,837 | utf_8 | 122b9a69b497e4a416589061c4a26af7 | function [clist_tmp] = trackOptiListNeighbor(dirname,CONST,header)
% trackOptiListNeighbor : creates a neighbors list for each cell.
%
% INPUT :
% dirname : seg folder eg. maindirectory/xy1/seg
% CONST : segmentation constants.
% header : string displayed with information
% OUTPUT :
% clist_temp... |
github | wiggins-lab/SuperSegger-master | trackOptiCellFiles.m | .m | SuperSegger-master/cell/trackOptiCellFiles.m | 7,742 | utf_8 | 26ad3a3e5f4b9c60f65dab24b2fe3414 | function trackOptiCellFiles( dirname, dirname_cell, CONST, header, clist )
% trackOptiCellFiles : organizes the data into the final cell files that
% contain all the time lapse data for a single cell.
% It allows for cell gating. If a clist is passed with an already made gate
% the code generates cell files for only ce... |
github | wiggins-lab/SuperSegger-master | multiAssignmentSparse.m | .m | SuperSegger-master/frameLink/multiAssignmentSparse.m | 24,488 | utf_8 | ca48bffaccea26ddb50b4a55c61a9fbc | function [assignments,errorR,totCost,indexC,indexF,dA,revAssign] = multiAssignmentSparse ...
(data_c, data_f, CONST, forward, debug_flag)
% multiAssignmentSparse : assigns regions in data_c to regions in data_f.
% Uses a combination of area overlap, centroid distance, and outward push
% in colonies. Regions are as... |
github | wiggins-lab/SuperSegger-master | errorRez.m | .m | SuperSegger-master/frameLink/errorRez.m | 20,309 | utf_8 | 9c45ef15d6aeb758109b77a78380cf32 | function [data_c, data_r, cell_count,resetRegions] = errorRez (time, ...
data_c, data_r, data_f, CONST, cell_count, header, ignoreError, debug_flag)
% errorRez : links cells from the current frame to the frame before and
% attempts to resolve segmentation errors if the linking is inconsistent.
%
% INPUT :
% time... |
github | wiggins-lab/SuperSegger-master | missingSeg2to1.m | .m | SuperSegger-master/frameLink/missingSeg2to1.m | 8,864 | utf_8 | 1bcaf907e7bf9a1a29bd4bcf525dd3e5 | function [data_new,success] = missingSeg2to1 (data_c,regC,data_r,regR,CONST)
% missingSeg2to1 : finds missing segment in regC.
% Segments in regC are used that are close to the segment
% between the two regions regR(1) and regR(2) in data_r.
% if a segment is found that fits the requirements data_new is made with
% the... |
github | wiggins-lab/SuperSegger-master | trackOptiCellMarker.m | .m | SuperSegger-master/frameLink/trackOptiCellMarker.m | 7,992 | utf_8 | 1eab913c9c27ffc5bdf4067bb16ca150 | function [clist, clist_def] = trackOptiCellMarker(dirname, CONST, header)
% trackOptiCellMarker : puts together a list of complete cell cycles.
% It goes through the dirname/*err.mat files and determines which
% cells go through complete cell cycles (i.e. cells in which both birth and
% division are observed) Clis... |
github | wiggins-lab/SuperSegger-master | trackOptiGetErrorListDisk.m | .m | SuperSegger-master/frameLink/trackOptiGetErrorListDisk.m | 1,907 | utf_8 | bb013d14318e19817e587ff2ca284ffb | function error_list = trackOptiGetErrorListDisk(dirname,file_filter)
% trackOptiGetErrorListDisk : creates a list of errors
%
% INPUT :
% dirname : seg folder eg. maindirectory/xy1/seg
% file_filter : regular expression of files default is '*err.mat';
% OUTPUT :
% error_list : list of errors [from... |
github | wiggins-lab/SuperSegger-master | trackOptiStripSmall.m | .m | SuperSegger-master/frameLink/trackOptiStripSmall.m | 4,184 | utf_8 | 2014a2f19bb6604434f2ab5c0f8d5d9a | function trackOptiStripSmall(dirname, CONST, disp_flag)
% trackOptiStripSmall : removes small regions and fills holes in the regions.
% It removes regions anything with area below CONST.trackOpti.MIN_AREA
% that are probably not real, typically bubbles, dust, or minicells.
% It then creates a new cell mask and new reg... |
github | wiggins-lab/SuperSegger-master | trackOptiFluor.m | .m | SuperSegger-master/fluorescence/trackOptiFluor.m | 3,834 | utf_8 | e7f605338ac486da16d2794e3fa0a15f | function trackOptiFluor(dirname,CONST,header)
% trackOptiFluor calculates the mean background fluorescence for each frame.
% This is the mean fluorescence of the non cell regions. No focus fitting is
% done at this stage. It saves the information in the err/seg
% files under data_c.fl1bg for channel 1, data_c.fl2bg... |
github | wiggins-lab/SuperSegger-master | trackOptiFindFoci.m | .m | SuperSegger-master/fluorescence/trackOptiFindFoci.m | 3,694 | utf_8 | 0b4ca413c75393f6bf8801eaf66aea32 | function trackOptiFindFoci(dirname,CONST,header)
% trackOptiFindFoci : Finds foci in cells. Note that this only
% runs if the number of foci to be fit is set in CONST.trackLoci.numSpots.
% It runs on the err.mat files and saves the new err.mat files with the
% found foci. This is done using the curve filter to find... |
github | wiggins-lab/SuperSegger-master | convertImageNames.m | .m | SuperSegger-master/batch/convertImageNames.m | 7,098 | utf_8 | 7bb9ce4d24a9855c0c04405d38951399 | function convertImageNames(dirname, basename, timeFilterBefore, ...
timeFilterAfter, xyFilterBefore,xyFilterAfter, channelNames )
% convertImageNames : Convert image names from to NIS Elements format
% The file naming convention for elements is basename_t1xy1c1.tif
% where c1 is brightfield and c2,c3 etc are the fl... |
github | wiggins-lab/SuperSegger-master | BatchSuperSeggerDebug.m | .m | SuperSegger-master/batch/BatchSuperSeggerDebug.m | 10,948 | utf_8 | 563a685fe26194d405591d3269e1040e | function BatchSuperSeggerOpti(dirname_,skip,clean_flag,res,startEnd,showWarnings)
% BatchSuperSeggerOpti : runs everything from start to finish,
% including alignment, building the directory structure,
%single image segmentation, error resolution, cell linking,
% fluorescence analysis, and cell files.
%
% Processes a r... |
github | wiggins-lab/SuperSegger-master | tryDifferentConstants.m | .m | SuperSegger-master/batch/tryDifferentConstants.m | 3,393 | utf_8 | 8bba5984aa950005926db5b9d2bd3fee | function data = tryDifferentConstants(filename,resFlags)
% tryDifferentConstants : displays images of cells segmented with
% different constants set in resFlags. It only does the initial
% segmentation (only the doSeg part) and not the regions decisions,
% linking and error resolution that come after.
% Images need to ... |
github | wiggins-lab/SuperSegger-master | superSeggerGui.m | .m | SuperSegger-master/batch/superSeggerGui.m | 13,603 | utf_8 | 7ec8a932b9f67bba5908cf00debc7cc8 | function varargout = superSeggerGui(varargin)
% superSeggerGui : gui for segmenting images with superSegger.
%
% Copyright (C) 2016 Wiggins Lab
% Written by Silas Boye Nissen & Stella Stylianidou.
% University of Washington, 2016
% This file is part of SuperSegger.
%
% SuperSegger is free software: you can redistrib... |
github | wiggins-lab/SuperSegger-master | ReadFileName.m | .m | SuperSegger-master/batch/ReadFileName.m | 2,582 | utf_8 | 2e232c896dcf7926bbdc982959814dce | function nameInfo = ReadFileName( str )
% ReadFileName : extracts the numbers after t,x,y,z in a string *t*c*xy*z*
%
% INPUT :
% str : String that contains any of the the strings in strD
% OUTPUT :
% nameInfo.
% npos: [4x4 double]
% strD: {'t' 'c' 'xy' 'z'}
% basename: befo... |
github | wiggins-lab/SuperSegger-master | trackOptiSkipMerge.m | .m | SuperSegger-master/batch/trackOptiSkipMerge.m | 5,615 | utf_8 | 92d6bb00778f5878a4bf18a1c90df747 | function trackOptiSkipMerge(dirname_xy,skip,CONST,header)
% trackOptiSkipMerge : adds skipped frames back into the time series.
% It makes the _err.mat files with the fluor images corresponding
% to the current time step. The new _err files are placed in seg_full.
% Frame skip is useful for reducing errors which you h... |
github | wiggins-lab/SuperSegger-master | trackOptiAlignPad.m | .m | SuperSegger-master/batch/trackOptiAlignPad.m | 11,188 | utf_8 | 72f11efbc2bb6a04ada17a1f19c4b20a | function [crop_box] = trackOptiAlignPad(dirname_, workers, CONST, targetd)
% trackOptiAlignPad : aligns phase images to correct for microscope drift.
% To keep as much data as possible, instead of cropping the resulting
% images it builds a larger image that encompases all drift positions.
% It saves the alignment info... |
github | wiggins-lab/SuperSegger-master | BatchSuperSeggerOpti.m | .m | SuperSegger-master/batch/BatchSuperSeggerOpti.m | 11,084 | utf_8 | 3e401a4f992b2f02cebd96eece95cea8 | function BatchSuperSeggerOpti(dirname_,skip,clean_flag,res,startEnd,showWarnings)
% BatchSuperSeggerOpti : runs everything from start to finish,
% including alignment, building the directory structure,
%single image segmentation, error resolution, cell linking,
% fluorescence analysis, and cell files.
%
% Processes a r... |
github | wiggins-lab/SuperSegger-master | trackOptiZMerge.m | .m | SuperSegger-master/dev-lab/trackOptiZMerge.m | 6,318 | utf_8 | 156e71ab07ce0f68be10c97d0d0d7e33 | function trackOptiZMerge( dirname, targetname, CONST, dz )
data.mag = 5;
if ~isfield( CONST, 'maxMerge' ) || isempty(CONST.maxMerge)
CONST.maxMerge = false;
end
if ~exist( 'dz', 'var' ) || isempty(dz)
dz = 0;
end
data.CONST = CONST;
data.dz = dz;
%% get the dir contents first and set up the file names
... |
github | wiggins-lab/SuperSegger-master | dftregistration.m | .m | SuperSegger-master/Internal/dftregistration.m | 8,184 | utf_8 | d01d7759cc88af48b5c0a98632715a89 | function [output, Greg] = dftregistration(buf1ft,buf2ft,usfac)
% dftregistration : Efficient subpixel image registration by crosscorrelation.
% This code gives the same precision as the FFT upsampled cross correlation in a
% small fraction of the computation time and with reduced memory
% requirements. It obtains... |
github | wiggins-lab/SuperSegger-master | gateToolGui.m | .m | SuperSegger-master/gate/gateToolGui.m | 31,621 | utf_8 | 2dce38666e986792d59124ec0a77d216 | function varargout = gateToolGui(varargin)
% GATETOOLGUI MATLAB code for gateToolGui.fig
% GATETOOLGUI, by itself, creates a new GATETOOLGUI or raises the existing
% singleton*.
%
% H = GATETOOLGUI returns the handle to a new GATETOOLGUI or the handle to
% the existing singleton*.
%
% GATETOOLG... |
github | wiggins-lab/SuperSegger-master | gateTool.m | .m | SuperSegger-master/gate/gateTool.m | 92,004 | utf_8 | 1e89b1b3722a5e9415710c59b28bc979 | function [clist, out] = gateTool(varargin)
% gateTool : tool for gating and plotting functionality of clists.
%
% GATETOOL( [clist,clist cell array,directory,filename], [command string], [argument], ... )
%
% clist must be (i) a clist struct or (ii) a cell array of clists or (iii)
% a data directory, xy1 directory or a... |
github | wiggins-lab/SuperSegger-master | lassoglm.m | .m | SuperSegger-master/trainingConstants/lassoglm.m | 58,218 | utf_8 | e513341bf0edbf1ad27e59099d8ff069 | function [B,stats] = lassoglm(x,y,distr,varargin)
% copied from statistics toolbox - added display line
%LASSOGLM Perform lasso or elastic net regularization for a generalized linear model.
% [B,STATS] = LASSOGLM(X,Y,DISTR,...) Performs L1-penalized maximum likelihood
% fits (lasso) relating the predictors in X to... |
github | wiggins-lab/SuperSegger-master | makeBadRegions.m | .m | SuperSegger-master/trainingConstants/makeBadRegions.m | 5,869 | utf_8 | bf763dce3b4cfc9c61b8c13546a64b42 | function makeBadRegions(dirname,CONST)
% makeBadRegions : creates bad regions to train the software on region shape
% Creates *_mod.mat files in the seg directory with bad regions (turns on
% and off random segments) and assigns them a bad score (0).
%
% INPUT :
% dirname : directory that contains seg.mat files
%... |
github | wiggins-lab/SuperSegger-master | modifyConstValuesGUI.m | .m | SuperSegger-master/trainingConstants/modifyConstValuesGUI.m | 27,782 | utf_8 | 7ba1fcc08a9c2ffb8a92616b5d66cbb4 | function varargout = modifyConstValuesGUI(varargin)
% modifyConstValuesGUI : gui to interactively modify parameters in constants.
%
% Copyright (C) 2016 Wiggins Lab
% Written by Stella Styliandou.
% University of Washington, 2016
% This file is part of SuperSegger.
%
% SuperSegger is free software: you can redistribute... |
github | gjmaeda/LocalTimeWarping-master | LocalTW.m | .m | LocalTimeWarping-master/LocalTW.m | 14,859 | utf_8 | 03af8c8c7b49a82373bd27ee36766470 | classdef LocalTW < handle
%UNTITLED Summary of this class goes here
% Detailed explanation goes here
properties
param
hist
tref % reference time
text % extended time
yref
Phi % the segment of Phiext that addresses tref
Phiext % the basis ... |
github | yjiao/bacteria_genomic_analysis-master | viewer.m | .m | bacteria_genomic_analysis-master/matlabGUI/viewer.m | 11,755 | utf_8 | 2f3eadbb5fdb4a6fec8cd374244e8b8e | function varargout = viewer(varargin)
% VIEWER MATLAB code for viewer.fig
% VIEWER, by itself, creates a new VIEWER or raises the existing
% singleton*.
%
% H = VIEWER returns the handle to a new VIEWER or the handle to
% the existing singleton*.
%
% VIEWER('CALLBACK',hObject,eventData,handles,... |
github | fadeoutsoftware/WASDI-master | wUrlEncode.m | .m | WASDI-master/libraries/matlabwasdilib/wUrlEncode.m | 262 | utf_8 | a95da18a800f0a23b29b59838d024baa | %courtesy of https://titanwolf.org/Network/Articles/Article?AID=c16b0563-60f6-46e2-9c50-092039fa86bc#gsc.tab=0
function u = wUrlEncode(s)
u = '';
for k = 1:length(s),
if isalnum(s(k))
u(end+1) = s(k);
else
u=[u,'%',dec2hex(s(k)+0)];
end;
end
end |
github | fadeoutsoftware/WASDI-master | geotiffwrite.m | .m | WASDI-master/processorTypes/wasdiOctaveDocker/geotiffwrite.m | 42,924 | utf_8 | 587536312b97430e9eb341e30309d008 | %GEOTIFFWRITE Write a 2D or 3D array to a single or multi-band GeoTIFF file
%
% MATLAB's Mapping Toolbox only provides a "geotiffread" function, but
% it does not have a "geotiffwrite" function (Note). This is the MATLAB
% program to write a 2D or 3D array to a single or multi-band GeoTIFF
% file, where data... |
github | fadeoutsoftware/WASDI-master | make_option.m | .m | WASDI-master/processorTypes/wasdiOctaveDocker/make_option.m | 113,266 | utf_8 | b3fee2875fea9ce53bb87a3bbc555701 | %MAKE_OPTION GUI to generate 'option' argument for geotiffwrite program
%
% geotiffwrite.m is a MATLAB program to write 2D or 3D array to a single or
% multi-band GeoTIFF file. However, the nature of GeoTIFF format requires
% you to specify a couple to a dozens of Tags or GeoKeys in the 'option'
% argument of... |
github | royhessels/I2MC-master | FileFromFolder.m | .m | I2MC-master/functions/helpers/FileFromFolder.m | 2,283 | utf_8 | 550f0a37d3b68c03ac64dc232cccbe53 | function [file,nfile] = FileFromFolder(folder,mode,f_ext)
% [file,nfile] = FileFromFolder(folder,mode,ext)
%
% Returns struct with all files in directory FOLDER.
% MODE specifies whether an error is displayed when no directories are
% found (default). If MODE is 'silent', only a message will will be
% displayed in the ... |
github | royhessels/I2MC-master | kmeans2.m | .m | I2MC-master/functions/I2MC/kmeans2.m | 6,986 | utf_8 | 2a159d8d0c9de124e96cc692137a8819 | function [idx, C] = kmeans2(X)
% n points in p dimensional space
n = size(X,1);
maxit = 100;
% case {'plus','kmeans++'}
% Select the first seed by sampling uniformly at random
C(1,:) = X(ceil(end*rand),:);
% Select the rest of the seeds by a probabilistic model
sampleProbability = (X(:,1) - C(1)).^2 + (X(:,2) - C(2... |
github | MihawkHu/Jaccount_Captcha_OCR-master | randInitializeWeights.m | .m | Jaccount_Captcha_OCR-master/neural_network/randInitializeWeights.m | 187 | utf_8 | 35122173a3c82d88d83ecbec2cce1012 | % randomly initalize weights
function W = randInitializeWeights(L_in, L_out)
epsilon_init = 0.12;
W = rand(L_out, 1 + L_in) * 2 * epsilon_init - epsilon_init;
end
|
github | MihawkHu/Jaccount_Captcha_OCR-master | sigmoid.m | .m | Jaccount_Captcha_OCR-master/neural_network/sigmoid.m | 90 | utf_8 | 8d3926867d67e94bd08f99a424ecba32 | % sigmoid function
function f = sigmoid(z)
f = 1.0 ./ (1.0 + exp(-z));
end
|
github | MihawkHu/Jaccount_Captcha_OCR-master | sigmoidGradient.m | .m | Jaccount_Captcha_OCR-master/neural_network/sigmoidGradient.m | 115 | utf_8 | 19133783aba907723349552fe2e051b3 | % sigmoid gradient function
function f = sigmoidGradient(z)
f = sigmoid(z) .* (1 - sigmoid(z));
end
|
github | MihawkHu/Jaccount_Captcha_OCR-master | nnCostFunction.m | .m | Jaccount_Captcha_OCR-master/neural_network/nnCostFunction.m | 2,466 | utf_8 | e57e86016601142d54537f072eebe25d | % calculate cost
function [J grad] = nnCostFunction(nn_params, input_layer_size, hidden_layer_size, num_labels, X, y, lambda)
m = size(X, 1);
% reshape Theta11 and Theta2
Theta1 = reshape(nn_params(1:hidden_layer_size * (input_layer_size + 1)), ...
hidden_layer_size, (input_la... |
github | MihawkHu/Jaccount_Captcha_OCR-master | predict.m | .m | Jaccount_Captcha_OCR-master/neural_network/predict.m | 291 | utf_8 | 4fdfc64fc0923b70985fd2743b0a15ba | % predict and get results
function p = predict(Theta1, Theta2, X)
m = size(X, 1);
num_labels = size(Theta2, 1);
p = zeros(size(X, 1), 1);
h1 = sigmoid([ones(m, 1) X] * Theta1');
h2 = sigmoid([ones(m, 1) h1] * Theta2');
[tt, p] = max(h2, [], 2);
end
|
github | MihawkHu/Jaccount_Captcha_OCR-master | split_captcha.m | .m | Jaccount_Captcha_OCR-master/split_captcha/split_captcha.m | 2,844 | utf_8 | 620831147c07b01cbc67cbb6bb553bad | % clean image again, turn to 255 or 0
% split image to single letter, based on the interval of two letters
% sometimes there may be something wrong, because of two linked letters
function [num, max_wid, max_len] = split_captcha(filePath, idx)
img = imread(filePath);
row = size(img, 1);
col = size(img, 2);
... |
github | npiro/PALM-microscope-control-software-master | sliderbox_userdata.m | .m | PALM-microscope-control-software-master/sliderbox_userdata.m | 5,967 | utf_8 | 35d871c812607d4051fb34b88e403098 | function varargout = sliderbox_userdata(varargin)
%SLIDERBOX_USERDATA M-file for sliderbox_userdata.fig
% SLIDERBOX_USERDATA, by itself, creates a new SLIDERBOX_USERDATA or raises the existing
% singleton*.
%
% H = SLIDERBOX_USERDATA returns the handle to a new SLIDERBOX_USERDATA or the handle to
% ... |
github | npiro/PALM-microscope-control-software-master | progressbar.m | .m | PALM-microscope-control-software-master/progressbar.m | 11,330 | utf_8 | 98a20919ba21d203a10b0983b5bd53c5 | %this m-file modified by Quan Quach on 12/12/07
%email: quan.quach@gmail.com
%Original Author: Steve Hoelzer
function [stopBar] = progressbar(fractiondone, position)
if(~exist('fractiondone'))
return
end
% Description:
% progressbar(fractiondone,position) provides an indication of the progress of
... |
github | npiro/PALM-microscope-control-software-master | ClassifyNoImages.m | .m | PALM-microscope-control-software-master/ClassifyNoImages.m | 2,595 | utf_8 | 417587f234758f3f5e4904db372c5636 | %------------------------------------------------------------------------
% AL-AZHAR UNIVERSITY
% FACULTY OF ENGINEERING
% SYSTEMS & COMPUTERS ENGINEERING Department
%------------------------------------------------------------------------
% Author : Ahmed Samieh Abd El-Wahab
% Date : 14 December 2006
%---... |
github | npiro/PALM-microscope-control-software-master | Classify.m | .m | PALM-microscope-control-software-master/Classify.m | 2,500 | utf_8 | be605e052544dd9854eab13c6e4f2f48 | %------------------------------------------------------------------------
% AL-AZHAR UNIVERSITY
% FACULTY OF ENGINEERING
% SYSTEMS & COMPUTERS ENGINEERING Department
%------------------------------------------------------------------------
% Author : Ahmed Samieh Abd El-Wahab
% Date : 14 December 2006
%---... |
github | npiro/PALM-microscope-control-software-master | MicroscopeGUI.m | .m | PALM-microscope-control-software-master/MicroscopeGUI.m | 93,129 | utf_8 | b405505504ef3b32a0a2622762fb4af6 |
function varargout = MicroscopeGUI(varargin)
% MICROSCOPEGUI MATLAB code for MicroscopeGUI.fig
% MICROSCOPEGUI, by itself, creates a new MICROSCOPEGUI or raises the existing
% singleton*.
%
% H = MICROSCOPEGUI returns the handle to a new MICROSCOPEGUI or the handle to
% the existing singleton*.
%... |
github | npiro/PALM-microscope-control-software-master | progressbarGUI.m | .m | PALM-microscope-control-software-master/progressbarGUI.m | 12,058 | utf_8 | aa1b7e8c80de92d99a9325ca5af798c8 | %this m-file modified by Quan Quach on 12/12/07
%email: quan.quach@gmail.com
%Original Author: Steve Hoelzer
% Modified by Nicolas Piro for gui usage.
function [stopBar] = progressbar(hObject,handles,fractiondone, position)
if(~exist('fractiondone'))
return
end
% Description:
% progressbar(hObjects,... |
github | npiro/PALM-microscope-control-software-master | setMirrorSingleChannel.m | .m | PALM-microscope-control-software-master/lib_mirror/setMirrorSingleChannel.m | 497 | utf_8 | 910590a8fb9ccd3bd4dfdeb92c36c811 | %/***********************************************************************
%
% setMirrorSingleChannel(VAL,CHNUM)
%
% sets mirror single channel
%
% copyright (c) ADAPTICA 2009
%
%***********************************************************************/
function setMirrorSingleChannel(val, chn)
%fprint... |
github | npiro/PALM-microscope-control-software-master | connectToMirror.m | .m | PALM-microscope-control-software-master/lib_mirror/connectToMirror.m | 797 | utf_8 | f8e500b68f262d5e7da16bb6235ec978 | %/***********************************************************************
%
% connectToMirror.m
%
% matlab function for connecting to IO64/32 device
% see manual for usage
%
% copyright (c) ADAPTICA 2009
%
%***********************************************************************/
function connectToM... |
github | npiro/PALM-microscope-control-software-master | setLocalIp.m | .m | PALM-microscope-control-software-master/lib_mirror/setLocalIp.m | 380 | utf_8 | 09f56fec0627ccccf8b4311af518fcc2 | %/***********************************************************************
%
% setlocalIp.m
%
% set the local ip
%
% copyright (c) ADAPTICA 2009
%
%***********************************************************************/
function setLocalIp(vals)
if (calllib('mirrorDriverC', 'configIPAddress',vals... |
github | npiro/PALM-microscope-control-software-master | getLocalIp.m | .m | PALM-microscope-control-software-master/lib_mirror/getLocalIp.m | 386 | utf_8 | 5ec6e049d3492eb94a3b6d169f538af2 | %/***********************************************************************
%
% getLocalIp.m
%
% matlab function for getting the local server IP to which the system
% connect
%
% copyright (c) ADAPTICA 2009
%
%***********************************************************************/
function ip=getLoca... |
github | npiro/PALM-microscope-control-software-master | getNumMirrorChannels.m | .m | PALM-microscope-control-software-master/lib_mirror/getNumMirrorChannels.m | 412 | utf_8 | b0fb7c555a22066fcf3c59d0453741f4 | %/***********************************************************************
%
% getNumMirrorChannels.m
%
% get the number of channel
%
% copyright (c) ADAPTICA 2009
%
%***********************************************************************/
function chnum=getNumMirrorChannels
chnum=calllib('mirrorDriv... |
github | npiro/PALM-microscope-control-software-master | setMirrorChannels.m | .m | PALM-microscope-control-software-master/lib_mirror/setMirrorChannels.m | 409 | utf_8 | 5dbfe9b3281f1483eb10c7ddc69ac54c | %/***********************************************************************
%
% setNumMirrorChannels.m
%
% set all mirror channels
%
% copyright (c) ADAPTICA 2009
%
%***********************************************************************/
function setMirrorChannels(vals)
if (calllib('mirrorDriverC... |
github | npiro/PALM-microscope-control-software-master | closeMirror.m | .m | PALM-microscope-control-software-master/lib_mirror/closeMirror.m | 614 | utf_8 | af2c589043dffd8c9196156c6edc497b | % Close connection with IO32/64
%/***********************************************************************
%
% closeMirror.m
%
% matlab function for closing the mirror connection
% see manual for usage
%
% copyright (c) ADAPTICA 2009
%
%*****************************************************************... |
github | npiro/PALM-microscope-control-software-master | getDriverVersion.m | .m | PALM-microscope-control-software-master/lib_mirror/getDriverVersion.m | 526 | utf_8 | d3972340fa9fdd52a1cd50da9b89cae0 |
%/***********************************************************************
%
% getDriverVersion.m
%
% matlab function for getting EUROPA Mirror Current Driver Vers.
%
% copyright (c) ADAPTICA 2009
%
%***********************************************************************/
function vers=getDriverVe... |
github | npiro/PALM-microscope-control-software-master | getMirrorChannelsStatus.m | .m | PALM-microscope-control-software-master/lib_mirror/getMirrorChannelsStatus.m | 565 | utf_8 | 25e6eab2a477202be25287515858401d | %/***********************************************************************
%
% getMirrorChannelsStatus.m
%
% matlab function for getting EUROPA Mirror Current Driver Vers.
%ZtoA
%
% copyright (c) ADAPTICA 2009
%
%***********************************************************************/
function valArray... |
github | npiro/PALM-microscope-control-software-master | getMirrorSingleChannelStatus.m | .m | PALM-microscope-control-software-master/lib_mirror/getMirrorSingleChannelStatus.m | 395 | utf_8 | 7f7507ffd75cd83118f6f18d7d227413 | %/***********************************************************************
%
% getMirrorSingleChannelStatus.m
%
% gets the status of a single channel
%
% copyright (c) ADAPTICA 2009
%
%***********************************************************************/
function val=getMirrorSingleChannelStatus(chnu... |
github | SudeepSarkar/matlabHTM-master | sequentialAnomalyDectection.m | .m | matlabHTM-master/sequentialAnomalyDectection.m | 3,319 | utf_8 | 3e3b16965ef59de1ff3db022ac90e7ef |
function anomalyLikelihood = sequentialAnomalyDectection (anomalyScores, shortW, displayFlag, labelStart)
% This function detects anomalies in a sequence of raw scores according the
% method outlined in "Real-Time Anomaly Detection for Streaming Analytics",
% arXiv:1607.02480v1 [cs.AI] 8 Jul 2016
% The input is a vec... |
github | SudeepSarkar/matlabHTM-master | temporalPooler.m | .m | matlabHTM-master/temporalPooler.m | 13,406 | utf_8 | 0f51fa48810cdb721ea41724538faf68 | function unionSDR = temporalPooler (learnP, displayFlag)
% This function implements the 2015 version of the temporal pooler concept
% as outlined at
%
% https://github.com/numenta/nupic.research/wiki/Overview-of-the-Temporal-Pooler
%
% and using implementations at
% https://github.com/numenta/nupic.research/wiki/Union... |
github | SudeepSarkar/matlabHTM-master | markLearnStates.m | .m | matlabHTM-master/markLearnStates.m | 7,261 | utf_8 | 2aa2afc94a1e7973e5c50c79e19255ae | function markLearnStates ()
% Update the learn states of the cells (one per ACTIVE columns). This is to be run after the active states
% have been updated (compute_active_states). For those ACTIVE COLUMNS, this code further selects ONE cell
% per column as the learning cell (learnState). The logic is as follows. If an ... |
github | SudeepSarkar/matlabHTM-master | encoderNAB.m | .m | matlabHTM-master/encoderNAB.m | 3,801 | utf_8 | 24ee99c30d48266e58edabe9f77697d4 | function data = encoderNAB (filename, width)
% Encodes the data in the input csv file provided in the Numenta Anomaly
% Database, in the file -- filename, in terms of binary semantic
% representations.
%
% width: number of bits of overlap between semantically consecutive
% representation, i.e. between say the numbers... |
github | ialuronico/IntrinsicDimensionaDependency-master | gen_fun.m | .m | IntrinsicDimensionaDependency-master/SyntheticRelationships/gen_fun.m | 2,162 | utf_8 | 12149ae1eaf141b1a8376f7b72b6d50f | % Function to generate different relationship types at different levels of
% additive noise.
% input: x - random variable
% n - number of points
% noise - constant to determine the noise level
% l - noise level
% numnoise - total number of noise levels
% typ - rel... |
github | ialuronico/IntrinsicDimensionaDependency-master | gen_fun_multi.m | .m | IntrinsicDimensionaDependency-master/SyntheticRelationships/gen_fun_multi.m | 2,336 | utf_8 | 72e20ffaafb7db5a61599ebd62e09ac5 | % Function to generate different relationship types at different levels of
% additive noise for sets of variables
% input: x - sets of random variable
% n - number of points
% noise - constant to determine the noise level
% l - noise level
% numnoise - total number of noise... |
github | ialuronico/IntrinsicDimensionaDependency-master | gen_fun_white.m | .m | IntrinsicDimensionaDependency-master/DiffAlpha/gen_fun_white.m | 1,940 | utf_8 | ce766e6f8aae924794fbd67e01c7ef49 | % Function to generate different relationship types at different levels of
% white noise.
% input: x - random variable
% n - number of points
% l - noise level
% numnoise - total number of noise levels
% typ - relationship type to generate
% output: y - random variable
% ... |
github | emsansone/Classtering-master | d2p.m | .m | Classtering-master/src/d2p.m | 3,155 | utf_8 | dc48b1dd0688d11671d81af50a0970de | function [P, beta] = d2p(D, u, tol)
%D2P Identifies appropriate sigma's to get kk NNs up to some tolerance
%
% [P, beta] = d2p(D, kk, tol)
%
% Identifies the required precision (= 1 / variance^2) to obtain a Gaussian
% kernel with a certain uncertainty for every datapoint. The desired
% uncertainty can be specified... |
github | emsansone/Classtering-master | infer.m | .m | Classtering-master/src/infer.m | 2,802 | utf_8 | 30e2adc4f18a5d67af4c780526949442 | % This function takes as input a test data set, hyperparameters and
% parameters of a model, and outputs the inferred hidden state
% posterior as well as a calculation of the lower bound for the
% evidence for the test data.
%
% ensure data is preprocessed using same 'ppparams' as was used on
% training set.
%
% [tehid... |
github | emsansone/Classtering-master | plot_output_data.m | .m | Classtering-master/src/plot_output_data.m | 501 | utf_8 | a1e9d711294e4d6d7540f08b9567ffb0 | % plot_output_data.m: This function plots data with the estimated labels
%
% Y - p x n matrix containing n samples described by p-dimensional
% feature vectors
% net - structure obtained as a result from the algorithm
%
% Added by
% Emanuele Sansone GCNU 15/12/14
%
function plot_output_data(Y, net)... |
github | emsansone/Classtering-master | vbmfa_k.m | .m | Classtering-master/src/vbmfa_k.m | 6,627 | utf_8 | 0d60b3b515053c1dd38e94e9b07b8360 | %Classtering: Variational Mixture of Factor Analysers model
%
%net = vbmfa(Y,Y_labels,num_clusters,maxdim,pcaflag,Fflag,dsp,net);
%
% Y - p x n matrix of (normalised) observations (see preprocess.m)
% Y_labels - N x K matrix of labels (where N <= n)
% num_clusters - desired number of clusters
% maxdim - maximum factor... |
github | emsansone/Classtering-master | digamma.m | .m | Classtering-master/src/digamma.m | 599 | utf_8 | 182182a9ae67447bb13dee3e9a3c9e6f | %res = digamma(x)
%
%Calculates the digamma function.
%
%Multiple evaluations should enter as a row vector.
%
%Thanks to Zoubin Ghahramani and Yw Teh for helping put this fast
%version together.
%
% Matthew J. Beal GCNU 06/02/01
% modified by
% Emanuele Sansone GCNU 15/12/14
function res=digamma(x);
coef=[-1/12 1/120... |
github | emsansone/Classtering-master | sample_dataset.m | .m | Classtering-master/src/sample_dataset.m | 1,054 | utf_8 | da010b28c2817c54b73f0affd17e7d86 | % sample_dataset.m: This function generates a dataset of only M*K labeled
% samples
%
% Y - p x n matrix containing n samples described by p-dimensional
% feature vectors
% labels - n x K matrix of labels (K classes)
% M - number of samples with labels for each class
%
% N can be lower than n
%
% ... |
github | emsansone/Classtering-master | klgamma.m | .m | Classtering-master/src/klgamma.m | 751 | utf_8 | bb1fed5ce4f7e56d2aab7957ea6d9b46 | %kl = klgamma(pa,pb,qa,qb);
%
%Calculates KL(P||Q) where P and Q are Gamma distributions with
%parameters {pa,pb} and {qa,qb}.
%
% KL(P||Q) = \int d\pi P(\pi) ln { P(\pi) / Q(\pi) }.
%
%This routine handles factorised P distributions, if their parameters
%are specified multiply in either 'pa' or 'pb', as elements of a ... |
github | emsansone/Classtering-master | plot_ssl_data.m | .m | Classtering-master/src/plot_ssl_data.m | 1,235 | utf_8 | c3e2b9d3cfed4934860368319318bfd4 | % plot_ssl_data.m: This function plots data
%
% Y - p x n matrix containing n samples described by p-dimensional
% feature vectors
% labels - N x K matrix of labels (K classes)
%
% N can be lower than n
%
% Added by
% Emanuele Sansone GCNU 15/12/14
%
function plot_ssl_data(Y,labels)
[N K] = si... |
github | emsansone/Classtering-master | orbit.m | .m | Classtering-master/src/orbit.m | 457 | utf_8 | 8075e778b2eba7daf0817c3d735bdd5a | % Orbits round a object maintaining the elevation
% for a total of 'deg' degrees, taking 'steps' steps.
%
% orbit(handle-to-axis,deg,steps)
%
% Stolen from the Matlab Graphics book :-)
%
% M.Beal GCNU 13/04/1999.
% modified by
% Emanuele Sansone GCNU 15/12/14
function orbit(h,deg,steps)
[az el] = view;
rotvec = 0:de... |
github | emsansone/Classtering-master | circle.m | .m | Classtering-master/src/circle.m | 273 | utf_8 | 1978f47f07ddd4debe801d7214cc80d1 | % circle.m : The function generates points uniformly
% from a circle centered at (x1,y1) with radius rc
%
% Added by
% Emanuele Sansone GCNU 15/12/14
function x=circle(x1,y1,rc)
a=2*pi*rand;
r=sqrt(rand);
x(1)=(rc*r)*cos(a)+x1;
x(2)=(rc*r)*sin(a)+y1;
end |
github | emsansone/Classtering-master | vbmfa.m | .m | Classtering-master/src/vbmfa.m | 6,524 | utf_8 | b4fa985055bab851fb1da0bb570ccc97 | %Classtering: Variational Mixture of Factor Analysers model
%
%net = vbmfa(Y,Y_labels,maxdim,pcaflag,Fflag,dsp,net);
%
% Y - p x n matrix of (normalised) observations (see preprocess.m)
% Y_labels - N x K matrix of labels (where N <= n)
% maxdim - maximum factor dimensionality (default p-1)
% pcaflag - noise model - F... |
github | emsansone/Classtering-master | plot_gaussian.m | .m | Classtering-master/src/plot_gaussian.m | 2,010 | utf_8 | 3e065d85e3944a91879931e5b58fd446 | % Plots a 2D or 3D 1 s.d. frame.
%
% 2D: 'n-1' divisions polar-wise,
% 3D: 'n-1' divisions each azimuthally and polar-wise,
% for a Gaussian with covariance 'covar' and mean 'mu',
% 'colour' can be any integer. M.Beal GMLC 26/03/99
%
% hh = plot_gaussian(covar,mu,col,n);
%
% M J Beal 25/11/99 GCNU
% modified by
% E... |
github | emsansone/Classtering-master | performance.m | .m | Classtering-master/src/performance.m | 1,024 | utf_8 | dfdc93bb1f16cd94b4f66dc9375eb348 | % performance.m: This script computes precision and recall for any class
% and show the confusion matrix.
%
% Input values:
% Qns - N x S x K matrix (output of infer.m)
% labels - N x K matrix (groud truth)
%
% added by
% Emanuele Sansone GCNU 15/12/14
function performance(Qns, labels)
[n K] = size(labels);
Qns ... |
github | emsansone/Classtering-master | kldirichlet.m | .m | Classtering-master/src/kldirichlet.m | 555 | utf_8 | 71f7f80d4934dc189471d361a3ccea33 | %res = kldirichlet(vecP,vecQ)
%
%Calculates KL(P||Q) where P and Q are Dirichlet distributions with
%parameters 'vecP' and 'vecQ', which are row vectors, not
%necessarily normalised.
%
% KL(P||Q) = \int d\pi P(\pi) ln { P(\pi) / Q(\pi) }.
%
% Matthew J. Beal GCNU 06/02/01
% modified by
% Emanuele Sansone GCNU 15/12/14
... |
github | emsansone/Classtering-master | posdef_matrix.m | .m | Classtering-master/src/posdef_matrix.m | 286 | utf_8 | d6f015dc00d64f2b092e08958a35eaeb | % Added by
% Emanuele Sansone GCNU 15/12/14
function [sigma] = posdef_matrix(sigma)
epsilon = 1e-6;
zero = 1e-8;
[~, err] = cholcov(sigma, 0);
if (err ~= 0)
[v d] = eig(sigma);
d=diag(d);
d( d <= zero ) = epsilon;
d=diag(d);
sigma = v*d*v';
end |
github | postmandev/structure_from_motion-master | NonlinearTriangulation.m | .m | structure_from_motion-master/NonlinearTriangulation.m | 1,689 | utf_8 | fb517de89599bcf0442692f2e5b1524a | function [ X ] = NonlinearTriangulation( K, C1, R1, C2, R2, x1, x2, X0)
%NONLINEARTRIANGULATION Summary of this function goes here
% Detailed explanation goes here
%
% (Input)
% C1 and R1: the first camera pose
% C2 and R2: the second camera pose
% x1 and x2: two Nx2 matrices whose rows represent correspondence be... |
github | postmandev/structure_from_motion-master | NonlinearPnP.m | .m | structure_from_motion-master/NonlinearPnP.m | 967 | utf_8 | dd39f6500fb1c4d7489545f758842269 | function [ Cnew, Rnew ] = NonlinearPnP( X, x, K, C0, R0 )
%NONLINEARPNP Summary of this function goes here
% Detailed explanation goes here
%N = size(x,1);
% opts = optimoptions(@lsqnonlin, 'Algorithm', 'levenberg-marquardt', ...
% 'TolX', 1e-64, 'TolFun', 1e-64, 'MaxFunEvals', 1e+64, ...
% 'MaxIter', 100, ... |
github | postmandev/structure_from_motion-master | BundleAdjustment.m | .m | structure_from_motion-master/BundleAdjustment.m | 2,076 | utf_8 | a33dabbda41e89f4189a6967151ab752 | function [Cset,Rset,X] = BundleAdjustment(K,Cset,Rset,X,ReconX,V,Mx,My)
global nImages nPoints V_ Mx_ My_ K_;
X_ = X(ReconX==1,:);
V_ = V(ReconX==1,:);
K_ = K;
nImages = length(Cset);
nPoints = size(X_,1);
Mx_ = Mx(ReconX==1,:);
My_ = My(ReconX==1,:);
x = [];
for i=1:length(Cset),
x = [x; matrix2Quaternion(Rse... |
github | pgriff6/citibike-master | nearby_stations.m | .m | citibike-master/code/nearby_stations.m | 2,330 | utf_8 | a147593acb702af7ded16b5122d03f4e | % This function looks at the latitude and longitude information for a bike
% trip's start station and end station; based on a radius (in miles) deined
% by the user, it ouputs two sorted lists of alternative stations near the
% start and end stations
% Update: incorporated into the main simulation routine. No need to ... |
github | bryankaye1/bayesian-analysis-of-fluorescent-lifetime-data-master | convnfft.m | .m | bayesian-analysis-of-fluorescent-lifetime-data-master/low_photon/CONVNFFT_Folder/convnfft.m | 6,402 | utf_8 | 7477dad73aa001d01cbe43fd60f21167 | function A = convnfft(A, B, shape, dims, options)
% CONVNFFT FFT-BASED N-dimensional convolution.
% C = CONVNFFT(A, B) performs the N-dimensional convolution of
% matrices A and B. If nak = size(A,k) and nbk = size(B,k), then
% size(C,k) = max([nak+nbk-1,nak,nbk]);
%
% C = CONVNFFT(A, B, SHAPE) controls... |
github | bryankaye1/bayesian-analysis-of-fluorescent-lifetime-data-master | checkloglike2.m | .m | bayesian-analysis-of-fluorescent-lifetime-data-master/low_fraction/checkloglike2.m | 818 | utf_8 | 59be073524f40827c9d8de6e1071b852 | %This function checks the loglike to make sure:
% (1) the entire space was searched ("errorsize" variable)
% if there are NaNs in loglike, errorize = 111
% if the whole search space wasn't searched, errorsize = 123
%
%(2) there are non-zero probabilities in the likilihood ("errorinf")
% if all p... |
github | bryankaye1/bayesian-analysis-of-fluorescent-lifetime-data-master | errorcheck.m | .m | bayesian-analysis-of-fluorescent-lifetime-data-master/low_fraction/errorcheck.m | 2,549 | utf_8 | f65cd3d6b0af11acca6f3c0e5e843af4 | % Error check function:
% This function checks thebounds set on the parameter search space.
%
%On loop 1 of post_int, this function checks to see if the marginalized
% likelihood maximum is at one the edge of the search space. This does not
% apply to pr and w02 if the maximum is at 0 or 1, since we know we the
... |
github | bryankaye1/bayesian-analysis-of-fluorescent-lifetime-data-master | param.m | .m | bayesian-analysis-of-fluorescent-lifetime-data-master/low_fraction/param.m | 1,632 | utf_8 | 67282587ce3df8544e173c7b9d7141ea | %param.m function
% This function finds the new bounds and grid point spacings
% on the paramter search by analyzing the marginalized likelihood.
% param.m function
% It always adds sl/sr steps to the left/right of where the likelihood
% falls below the threshold, which is .01*max. It will never increase the
% b... |
github | anguyen8/caffe_lrcn-master | classification_demo.m | .m | caffe_lrcn-master/matlab/demo/classification_demo.m | 5,412 | utf_8 | 8f46deabe6cde287c4759f3bc8b7f819 | function [scores, maxlabel] = classification_demo(im, use_gpu)
% [scores, maxlabel] = classification_demo(im, use_gpu)
%
% Image classification demo using BVLC CaffeNet.
%
% IMPORTANT: before you run this demo, you should download BVLC CaffeNet
% from Model Zoo (http://caffe.berkeleyvision.org/model_zoo.html)
%
% *****... |
github | ElsevierSoftwareX/SOFTX-D-15-00046-master | LFITv2_GUI_SinglePanel.m | .m | SOFTX-D-15-00046-master/LFITv2_GUI_SinglePanel.m | 76,233 | utf_8 | d3ae1e8dc4fc60736267c560649686b7 | function varargout = LFITv2_GUI_SinglePanel(varargin)
% LFITV2_GUI_SINGLEPANEL MATLAB code for LFITv2_GUI_SinglePanel.fig
% LFITV2_GUI_SINGLEPANEL, by itself, creates a new LFITV2_GUI_SINGLEPANEL or raises the existing
% singleton*.
%
% H = LFITV2_GUI_SINGLEPANEL returns the handle to a new LFITV2_GUI_SI... |
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