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github | AvaisP/machine-learning-programming-assignments-coursera-andrew-ng-master | submitWithConfiguration.m | .m | machine-learning-programming-assignments-coursera-andrew-ng-master/machine-learning-ex3/ex3/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | AvaisP/machine-learning-programming-assignments-coursera-andrew-ng-master | savejson.m | .m | machine-learning-programming-assignments-coursera-andrew-ng-master/machine-learning-ex3/ex3/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | AvaisP/machine-learning-programming-assignments-coursera-andrew-ng-master | loadjson.m | .m | machine-learning-programming-assignments-coursera-andrew-ng-master/machine-learning-ex3/ex3/lib/jsonlab/loadjson.m | 18,744 | ibm852 | 58ededaffbb9f3194b9292f48fef212f | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | AvaisP/machine-learning-programming-assignments-coursera-andrew-ng-master | loadubjson.m | .m | machine-learning-programming-assignments-coursera-andrew-ng-master/machine-learning-ex3/ex3/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | AvaisP/machine-learning-programming-assignments-coursera-andrew-ng-master | saveubjson.m | .m | machine-learning-programming-assignments-coursera-andrew-ng-master/machine-learning-ex3/ex3/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | AvaisP/machine-learning-programming-assignments-coursera-andrew-ng-master | submit.m | .m | machine-learning-programming-assignments-coursera-andrew-ng-master/machine-learning-ex1/ex1/submit.m | 1,876 | utf_8 | 8d1c467b830a89c187c05b121cb8fbfd | function submit()
addpath('./lib');
conf.assignmentSlug = 'linear-regression';
conf.itemName = 'Linear Regression with Multiple Variables';
conf.partArrays = { ...
{ ...
'1', ...
{ 'warmUpExercise.m' }, ...
'Warm-up Exercise', ...
}, ...
{ ...
'2', ...
{ 'computeCost.m... |
github | AvaisP/machine-learning-programming-assignments-coursera-andrew-ng-master | submitWithConfiguration.m | .m | machine-learning-programming-assignments-coursera-andrew-ng-master/machine-learning-ex1/ex1/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | AvaisP/machine-learning-programming-assignments-coursera-andrew-ng-master | savejson.m | .m | machine-learning-programming-assignments-coursera-andrew-ng-master/machine-learning-ex1/ex1/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | AvaisP/machine-learning-programming-assignments-coursera-andrew-ng-master | loadjson.m | .m | machine-learning-programming-assignments-coursera-andrew-ng-master/machine-learning-ex1/ex1/lib/jsonlab/loadjson.m | 18,744 | ibm852 | 58ededaffbb9f3194b9292f48fef212f | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | AvaisP/machine-learning-programming-assignments-coursera-andrew-ng-master | loadubjson.m | .m | machine-learning-programming-assignments-coursera-andrew-ng-master/machine-learning-ex1/ex1/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | AvaisP/machine-learning-programming-assignments-coursera-andrew-ng-master | saveubjson.m | .m | machine-learning-programming-assignments-coursera-andrew-ng-master/machine-learning-ex1/ex1/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | canlab/preprocess-master | dicom2nifti.m | .m | preprocess-master/dicom2nifti.m | 5,954 | utf_8 | 24022c7e4ec8e6597c900fb0b70d2435 | % nifti_files = dicom2nifti(dcm_files, num_slices_per_vol, output_base, ['disdaqs', num_disdaqs], ['stack4d' | 'stack3d'], ['verbose'])
%
% Read a specified list of DICOM images (one slice per file), stack them into volumes,
% convert to Analyze format and write an Analyze image/header pair for each volume
%
% Optional... |
github | canlab/preprocess-master | canlab_preproc_reorient.m | .m | preprocess-master/canlab_preproc_reorient.m | 2,217 | utf_8 | 2a7ee4760ca6ff82a28a35cf4737db63 | function canlab_preproc_reorient(imgs)
% canlab_preproc_reorient(imgs)
%
% This is used in canlab_preproc_2012
% It shows you the first image in a functional series,
% lets you reorient it in SPM, and then applies the transformation to all
% the images in the series.
%
% Imgs: A cell array, one cell per run, with strin... |
github | canlab/preprocess-master | canlab_preproc_clean_up_and_move_files.m | .m | preprocess-master/canlab_preproc_clean_up_and_move_files.m | 9,908 | utf_8 | 668045b82df6b9f9a82de5d20dab0b64 | function PREPROC = canlab_preproc_clean_up_and_move_files(PREPROC, varargin)
% canlab_preproc_clean_up_and_move_files(PREPROC)
%
% Special function used in canlab_preproc
% Deletes a* images
% Moves ra*, wra*, swra* images to Preprocessed directory.
% (If these are not present yet, skips them.)
%
% Checks that files ex... |
github | canlab/preprocess-master | canlab_preproc_motion_covariates.m | .m | preprocess-master/canlab_preproc_motion_covariates.m | 4,203 | utf_8 | f6ae318e5a546f39e2ae2d49acc4ae09 | function [mvmtcat, mvmt_by_session, motion_cov_set] = canlab_preproc_motion_covariates(mvmt_param_files, images_per_session, varargin)
% [mvmtcat, mvmt_by_session, motion_cov_set] = canlab_preproc_motion_covariates(mvmt_param_files, images_per_session, varargin)
%
% Create motion covariates and plot them if requested.
... |
github | canlab/preprocess-master | merge_vols.m | .m | preprocess-master/merge_vols.m | 3,572 | utf_8 | 783236fdbacf526bd2b97958ae9e9459 | % merged_vol = merge_vols(files_to_merge, output_base, output_type, [display_commands])
% Merges a series of Nifti/Analyze volumes into a single 4D volume
%
% Inputs:
% files_to_merge - cellstr or char array of filenames
% output_base - basename of new file (no suffix)
% output_type - 'NIFTI' or 'ANA... |
github | canlab/preprocess-master | PAR_read_Univ_v4pt2.m | .m | preprocess-master/PAR_read_Univ_v4pt2.m | 3,142 | utf_8 | d48121877eb6f1229906aa02d5e4071c |
function [patient_name,description,TR,bits_per_pixel,n_images,x_dim,y_dim,z_dim,x_vox_size,y_vox_size,z_vox_size,scale_slope]=PAR_read_Univ_v4pt2(infile)
%Eric Zarahn, July 2002
% November 2005 - Ajna modified to take care of the rescaling issue
%Ajna Borogovac, November 2005
%modified to correct for rescaling facto... |
github | canlab/preprocess-master | PAR_to_Nifti_Structural_univ_4pt2_spm5.m | .m | preprocess-master/PAR_to_Nifti_Structural_univ_4pt2_spm5.m | 5,306 | utf_8 | 0de7dfabea7d1ab248eb5d606cfd41be | % This code converts Philips v4.2 structural files to nifti file format
% Iris Asllani and Ajna Borogovac, August 2008
% Code is based on Eric Zarahan's spm5_Philips_to_AVW
% Edited Dec 2009 to change the way it reads the voxel size so that it can
% be used for all 3 image orientation.
%
% Modified May 2011 by Yoni Ash... |
github | canlab/preprocess-master | scnlab_spm2_norm.m | .m | preprocess-master/scnlab_spm2_norm.m | 3,281 | utf_8 | 121dc54b118e1e82c1b3d7ac473e4952 | % pp = scnlab_spm2_norm(donorm, writeanat, writefunc, dosmooth, obj, templ, applytofunclist, [norm func wildcard], [sample at functional resolution?: default 1])
%
% donorm: 1 or 0, do normalization of anatomical and write
% writeanat: write normalized anatomical/structural image
% writefunc: write normalized functio... |
github | canlab/preprocess-master | scnlab_preproc_part2.m | .m | preprocess-master/scnlab_preproc_part2.m | 2,879 | utf_8 | 1e27660dc73164ca2cb31ea12a1aa829 | %scnlab_preproc_part2(varargin)
%
% Series of steps to take images from realigned (ravols or MCavols) to
% smoothed, normalized functional images
%
%
% obj = 'structural/T1.img';
% targ = 'structural/T1inplane.img';
% func = 'r*/ravol*img';
% nfunc = 'r*/wravol*img';
% nobj = 'structural/wT1.img';
% templ = which('avg1... |
github | canlab/preprocess-master | scnlab_coreg_anat2funct.m | .m | preprocess-master/scnlab_coreg_anat2funct.m | 1,975 | utf_8 | 91ae95105d64197768ec4d684cfd7356 | function scnlab_coreg_anat2funct(targ,obj,varargin)
% scnlab_coreg_anat2funct(funct_img_name(target),anat_img_name(obj),[suppress iterative])
%
% P = brain extracted hi-res T1
% P ='/Users/scnlab/Kosslyn/Data_and_Tools/IMAGING_DATA/Amygdala_Face_Class_Data/ow3/anatomy/esT1_s5.img'
% p = the reference volume, the very f... |
github | canlab/preprocess-master | preproc_part2_2012.m | .m | preprocess-master/preproc_part2_2012.m | 23,949 | utf_8 | 8c84413b0ca5918614c6fcd7a7a54813 | % PREPROC = preproc_part2(PREPROC, ['set_origins', 0|1], ['warp', 0|1], ['smooth', 0|1], ['check norms', 0|1], ['SPM2' | 'SPM5' | 'SPM8'] ...
% ['coreg anat to func', 0|1], ['generate mean', 0|1], ['verbose', 0|1], ['clean up', 0|1], ['save plots', 0|1])
%
% Coregistration, normalization and smoothing.
% Takes images... |
github | canlab/preprocess-master | canlab_preproc_check_PREPROC.m | .m | preprocess-master/canlab_preproc_check_PREPROC.m | 6,494 | utf_8 | b9ee618983ae9ea88a7e5d5ce80c6ef2 | function [allerrors, ISDONE] = canlab_preproc_check_PREPROC(PREPROC, varargin)
% allerrors = canlab_preproc_check_PREPROC(PREPROC, [optional inputs])
%
% Checks for valid fields and files in PREPROC object, used in
% canlab_preproc.m
%
% You can enter a field, a description for the field, and a flag (1/0) for
% "error_... |
github | canlab/preprocess-master | spm_FcUtil.m | .m | preprocess-master/spm_FcUtil.m | 33,766 | utf_8 | 1228c460ec2e6b758ea84645977c794b | function varargout = spm_FcUtil(varargin)
% Contrast utilities
% FORMAT varargout = spm_FcUtil(action,varargin)
%_______________________________________________________________________
%
% spm_FcUtil is a multi-function function containing various utilities
% for contrast construction and manipulation. In general, it a... |
github | canlab/preprocess-master | preproc_part2.m | .m | preprocess-master/preproc_part2.m | 17,742 | utf_8 | 7d37defded652cbbd460bbcb2901d274 | %preproc_part2(PREPROC2, ['set_origins', 0|1], ['warp', 0|1], ['smooth', 0|1], ['check norms', 0|1], ['SPM2' | 'SPM5'], ['wh_subjs', n] ...
% ['coreg anat to func', 0|1], ['generate mean', 0|1], ['verbose', 0|1], ['clean up', 0|1], ['save plots', 0|1])
%
% Coregistration, normalization and smoothing.
% Takes images f... |
github | canlab/preprocess-master | dicom2analyze.m | .m | preprocess-master/dicom2analyze.m | 5,396 | utf_8 | 456638ee0f1acd904dcc81a287773e61 | % dicom2analyze(dcm_files, num_slices_per_vol, output_base, ['disdaqs', num_disdaqs], ['stack4d' | 'stack3d'], ['verbose'])
%
% Read a specified list of DICOM images (one slice per file), stack them into volumes,
% convert to Analyze format and write an Analyze image/header pair for each volume
%
% Optional inputs:
% ... |
github | canlab/preprocess-master | sdt2analyze.m | .m | preprocess-master/sdt2analyze.m | 3,535 | utf_8 | f2610c907cb934481b19cf3e9ec99835 | % sdt2analyze(stim_file, output_base, [recon_file])
%
% This function converts Stimulate (.sdt) files to Analyze format (*.hdr/*.img).
%
% Optional inputs:
% recon_file - if not specified, defaults to 'recon.log'
%
% Sample usage:
% sdt2analyze('stimulate.sdt', 'r3') % to convert the stimulate.sdt file to r3.img an... |
github | canlab/preprocess-master | canlab_preproc_2012_batch_luka.m | .m | preprocess-master/canlab_preproc_2012_batch_luka.m | 19,149 | utf_8 | 6a65207ffcabdbe3d29676c6e9aa9d3a | function canlab_preproc_2012_batch(basedir,runwc,imgwc,anat,TR,acqorder,ndisdaqs,varargin)
%usage: canlab_preproc_2012_batch(basedir,runwc,imgwc,anat,TR,acqorder,ndisdaqs,varargin)
%author: Scott Schafer (adapted from Tor's canlab_preproc_2012_batch_script)
%date: 9/20/2012
%purpose: This is a function that allows a us... |
github | canlab/preprocess-master | scnlab_preproc_part1_newepi.m | .m | preprocess-master/scnlab_preproc_part1_newepi.m | 7,836 | utf_8 | 042d3f0a57972a004e3b2a58bc3cae0f | function PREPROC = scnlab_preproc_part1_newepi(PREPROC)
% scnlab_preproc_part1_newepi is deprecated. Please use preproc_part1 instead. For more info, type:
% help preproc_part1
help preproc_part1
error('scnlab_preproc_part1_newepi is deprecated. Please use preproc_part1.')
% PREPROC = scnlab_preproc_part1(PREPROC... |
github | canlab/preprocess-master | canlab_preproc_list_files.m | .m | preprocess-master/canlab_preproc_list_files.m | 7,315 | utf_8 | 8e9434ac1b2e03cbcc34e4baf6e19597 | function [imgs, rundirs, images_per_session, num_disdaqs] = canlab_preproc_list_files(basedir, run_wildcard, image_wildcard, disdaqs)
%[imgs, rundirs, images_per_session, num_disdaqs] = canlab_preproc_list_files(basedir, run_wildcard, image_wildcard, disdaqs)
%
% --------------------------------------------------------... |
github | canlab/preprocess-master | preproc_part2_spm2.m | .m | preprocess-master/preproc_part2_spm2.m | 3,726 | utf_8 | 3e2e9f63fa4424c9311ba9d46152b140 | %preproc_part2_spm2(varargin)
%
% Series of steps to take images from realigned (ravols) to
% smoothed, normalized functional images (swravols)
%
% Keyword Parameters:
% anat - high-resolution, T1 anatomical image
% func - path string for filenames() for the functional images
%
% Optional Keyword Parameters:
% ... |
github | canlab/preprocess-master | preproc_part1_2012.m | .m | preprocess-master/preproc_part1_2012.m | 19,986 | utf_8 | 4805df119eda3c483d42c3072d15089f | % [preprocessed_files mean_image_name] = preproc_part1_2012(PREPROC, ...
% ['slice timing', 0|1], ['motion correction', 0|1], ['local', 0|1], ...
% ['verbose', 0|1], ['clean_up', 0|1], ['save plots', 0|1], ['SPM2' | 'SPM5'], ...
% ['movie', 0|1], ['mean func', 0|1], ['run_dir_base', subpath])
%
% PREPROC is a str... |
github | canlab/preprocess-master | preproc_SPM8.m | .m | preprocess-master/preproc_SPM8.m | 43,424 | utf_8 | 6a759da0a67125314b99c5cb620d7b7c | function [PP] = preproc_SPM8(varargin)
% This function is for automated preprocessing of neuroimaging data using
% SPM8, Tor Wager's CANLAB "Core Tools" software (for Matlab), and
% INRIAlign (which should be packaged with this function, do not download
% existing version from the internet, as they may not work corre... |
github | canlab/preprocess-master | PAR_to_Nifti_BOLD_4pt2_dynsl_modified_v2_spm5.m | .m | preprocess-master/PAR_to_Nifti_BOLD_4pt2_dynsl_modified_v2_spm5.m | 4,241 | utf_8 | 9530f38338219edeef9b367ab531274e | % This code converts ASL Philips v4.1 files to nifti file format. The data
% in philips files must be listed dynamics first then slices.
% Ajna, Iris, August 2008, Partly based on Eric Zarahan's Philips to AVW
% code.
%
% Modified May 2011 by Yoni Ashar to support command line use
% and automatically converting all f... |
github | canlab/preprocess-master | canlab_task_independent_realignment.m | .m | preprocess-master/canlab_task_independent_realignment.m | 14,783 | utf_8 | f892e52853321e5a22d715c06bf5fd8e | function PREPROC = canlab_task_independent_realignment(PREPROC, X, varargin)
% Augmented motion-correction that removes task-related activity before estimating realignment parameters for each image.
%
% Usage:
% -------------------------------------------------------------------------
% PREPROC = canlab_task_independe... |
github | canlab/preprocess-master | preproc_part1.m | .m | preprocess-master/preproc_part1.m | 27,041 | utf_8 | 392d94643decb1b235b7f36dc0d3d036 | % [preprocessed_files mean_image_name] = preproc_part1(PREPROC, ...
% ['slice timing', 0|1], ['motion correction', 0|1], ['local', 0|1], ...
% ['verbose', 0|1], ['clean_up', 0|1], ['save plots', 0|1], ['SPM2' | 'SPM5'], ...
% ['movie', 0|1], ['mean func', 0|1], ['run_dir_base', subpath])
%
% PREPROC is a structur... |
github | canlab/preprocess-master | scnlab_preproc_part1.m | .m | preprocess-master/scnlab_preproc_part1.m | 6,941 | utf_8 | 15177129d69adff3a65e361df3524fb5 | function scnlab_preproc_part1(PREPROC)
% scnlab_preproc_part1 is deprecated. Please use preproc_part1 instead. For more info, type:
% help preproc_part1
help preproc_part1
error('scnlab_preproc_part1 is deprecated. Please use preproc_part1.')
% scnlab_preproc_part1(PREPROC)
% PREPROC is an object containing these... |
github | canlab/preprocess-master | canlab_preproc_move_part1.m | .m | preprocess-master/canlab_preproc_move_part1.m | 6,848 | utf_8 | d4ff7191b0c5516a4c84a2be181e03ae | function PREPROC = canlab_preproc_move_part1(PREPROC, varargin)
% canlab_preproc_move_part1(PREPROC)
%
% Special function used in canlab_preproc
% Deletes a* images
% Moves ra*, wra*, swra* images to Preprocessed directory.
% (If these are not present yet, skips them.)
%
% Checks that files exist and saves names in PRE... |
github | canlab/preprocess-master | canlab_preproc_2012.m | .m | preprocess-master/canlab_preproc_2012.m | 30,312 | utf_8 | d96dc5d0929351317dbd8bdbab233ab8 | function canlab_preproc_2012(basedir, run_wildcard, image_wildcard, TR, struct_wildcard, varargin)
% canlab_preproc_2012(basedir, run_wildcard, image_wildcard, TR, struct_wildcard, [options])
%
% This is the basic preprocessing function for fmri images. It uses
% preproc_part1 and preproc_part2. It will output files ... |
github | canlab/preprocess-master | convertPARtoNIFTI.m | .m | preprocess-master/convertPARtoNIFTI.m | 3,143 | utf_8 | 7641abf5234660bbb1fe445666648e22 | % convertPARtoNIFTI(runsDir, structFileIdentifier, PVEc, varargin)
% Converts ALL the .PAR and .par files found under a given directory to 3D nifti
% files. Converts both functional and structural. The program assumes
% that all files found are functional, UNLESS filename contains structFileIdentifier
%
% sample usag... |
github | canlab/preprocess-master | tor_spm2_normalize.m | .m | preprocess-master/tor_spm2_normalize.m | 1,399 | utf_8 | b74bab8bbeaaed8816fdb96083b6238e | function tor_spm2_normalize(subj,Template)
% function tor_spm2_normalize(subj,Template)
%
% subj(i).P = file to normalize
% subj(i).PP = images to apply to
%
% M = smooth_and_mask(P,0,-Inf); %mask image
%subj(1).P = Q;
%tor_spm2_normalize(subj,M)
if isempty(Template)
Template = which('scalped_single_subj_T1.img... |
github | canlab/preprocess-master | canlab_preproc_2012_batch.m | .m | preprocess-master/canlab_preproc_2012_batch.m | 15,052 | utf_8 | 45891e8419b996517751dac86a545f06 | function canlab_preproc_2012_batch(basedir,runwc,imgwc,anat,TR,acqorder,ndisdaqs,varargin)
%usage: canlab_preproc_2012_batch(basedir,runwc,imgwc,anat,TR,acqorder,ndisdaqs,varargin)
%author: Scott Schafer (adapted from Tor's canlab_preproc_2012_batch_script)
%date: 9/20/2012
%purpose: This is a function that allows a us... |
github | canlab/preprocess-master | canlab_dcm_converter.m | .m | preprocess-master/canlab_dcm_converter.m | 3,756 | utf_8 | 9ab423c174a93bd83790171dc9e85ca0 | %usage: canlab_dcm_converter(fileheader,struct_wildcard, basedir, Nseries, series_wildcard)
%author: Scott
%date: 5/17/2010
%purpose: This takes a directory location of .dcm structural images and
% converts them into .nii images by default. The mean image is
% saved in the base directory (default... |
github | canlab/preprocess-master | set_hdr_current_coords.m | .m | preprocess-master/set_hdr_current_coords.m | 3,560 | utf_8 | f8b576e8274df924676eb01ecc642bd2 | % set_hdr_current_coords(imgname, [extra_image_list])
%
% Sets the origin to the current location of the crosshairs and optionally
% copies that origin to the list of files given in varargin (adjusted for
% different dims.
function set_hdr_current_coords(img, varargin)
% SPM5 reads 4-D images, so let's just expand... |
github | canlab/preprocess-master | inria_realign.m | .m | preprocess-master/INRIAlign/inria_realign.m | 23,441 | utf_8 | 6e11318673a918eb5a32bd53b8f3f7c8 | function inria_realign(P,flags)
% Robust rigid motion compensation in time series.
% FORMAT inria_realign(P,flags)
%
% Similar to spm_realign.m.
%
% P - matrix of filenames {one string per row}
% All operations are performed relative to the first image.
% ie. Coregistration is to the first image, a... |
github | canlab/preprocess-master | inria_realign_ui.m | .m | preprocess-master/INRIAlign/inria_realign_ui.m | 6,284 | utf_8 | 3e45544427d3fffb4f9d0a0402d78405 | function inria_realign_ui(arg1)
% User Interface for inria_realign.
%___________________________________________________________________________
%
% The INRIAlign toolbox enhances the standard SPM realignment routine
% (see topic: spm_realign_ui). In the latter, rigid registration is
% achieved by minimization of the s... |
github | leehoy/CTReconstruction-master | ForwardPorjection.m | .m | CTReconstruction-master/matlab/ForwardPorjection.m | 3,586 | utf_8 | 2455a3bc7369bf2afbee00f9ab769be3 | function [ proj] = ForwardProjection( image,parameters)
%Forward projection function for iterative reconstruction
% image : reconstructed images to be forward projected
% parameters : parameters for the forward projection (dictionary)
% type : method to forward project images (string)
% type can be 'ray-dr... |
github | leehoy/CTReconstruction-master | DistanceDrivenProjection3D_fater.m | .m | CTReconstruction-master/matlab/DistanceDrivenProjection3D_fater.m | 17,938 | utf_8 | ad32eae6af13592eb1bd818e35484a10 | tic;
nx=256;
ny=nx;
nz=nx;
ph=phantom3d(nx);
Source_init=[0,1000,0]; % Initial source position
Detector_init=[0,-500,0]; % Initial detector position
Origin=[0,0,0]; % Rotating center
SAD=sqrt(sum((Source_init-Origin).^2));
SDD=sqrt(sum((Source_init-Detector_init).^2));
DetectorPixelSize=[0.5,0.5]; % Detector ... |
github | leehoy/CTReconstruction-master | DistanceDrivenProjection3D.m | .m | CTReconstruction-master/matlab/DistanceDrivenProjection3D.m | 21,043 | utf_8 | 10a59412bfb8e11e7f1964f21a157aac | tic;
nx=256;
ny=nx;
nz=nx;
ph=phantom3d(nx);
Source_init=[0,1000,0]; % Initial source position
Detector_init=[0,-500,0]; % Initial detector position
Origin=[0,0,0]; % Rotating center
SAD=sqrt(sum((Source_init-Origin).^2));
SDD=sqrt(sum((Source_init-Detector_init).^2));
DetectorPixelSize=[0.5,0.5]; % Detector ... |
github | StevenLOL/siamese-fc-master | per_frame_annotation.m | .m | siamese-fc-master/ILSVRC15-curation/per_frame_annotation.m | 5,123 | utf_8 | 979c359a116863ae58a59b56ac1cfa48 | % --------------------------------------------------------------------------
function per_frame_annotation(root, folder_index)
% Read per-frame XML annotations and write bbox and track info on txt files
% Argument is folder_index [0-4]
% e.g. per_frame_annotations('/path/to/ILSVRC2015/', 0)
% --------------------------... |
github | StevenLOL/siamese-fc-master | vid_image_stats.m | .m | siamese-fc-master/ILSVRC15-curation/vid_image_stats.m | 3,108 | utf_8 | cc58e25446630bd51f6ad7df9aa3a1a7 | % ------------------------------------------------------------------------
function stats = vid_image_stats(imdb_video, perc_training, base_path)
%VID_IMAGE_STATS
% Compute basic colour stats for a random PERC_TRAINING of the dataset
% Used for data augmentation during training.
% e.g. vid_image_stats(imdb_video, 0.1,... |
github | StevenLOL/siamese-fc-master | parse_objects.m | .m | siamese-fc-master/ILSVRC15-curation/parse_objects.m | 2,830 | utf_8 | fc6b91a0bf00a1e967045a1b48b8f571 | % -------------------------------------------------------------------------------
function parse_objects(root, folder_index)
% Reads per_frame bbox and track information and generates per-video reports
% Argument is folder_index [0-4]
% e.g. parse_objects('/path/to/ILSVRC15/', 0)
% ----------------------------... |
github | StevenLOL/siamese-fc-master | vid_setup_data.m | .m | siamese-fc-master/ILSVRC15-curation/vid_setup_data.m | 5,606 | utf_8 | b1655a8679b04a62db3fe468d5bbd20d | % ------------------------------------------------------------------------
function imdb_video = vid_setup_data(root)
%VID_SETUP_DATA
% creates an IMDB structure pointing to the data
% e.g. vid_setup_data('/path/to/ILSVRC15/')
% ------------------------------------------------------------------------
rootp... |
github | StevenLOL/siamese-fc-master | save_crops.m | .m | siamese-fc-master/ILSVRC15-curation/save_crops.m | 6,014 | utf_8 | dd47c2fdc9d4ed7921f36466f7310981 | % -------------------------------------------------------------------------------------------------------------------
function save_crops(imdb_video,v_1,v_end, root_original, root_crops)
% Extract and save crops from video v_1 (start from 1) to v_end (check num video in imdb)
% e.g. save_crops(imdb_video, 1, 1000,... |
github | StevenLOL/siamese-fc-master | remove_layers_from_block.m | .m | siamese-fc-master/util/remove_layers_from_block.m | 537 | utf_8 | 784f1d0777b5f0f8a82546a9effdac19 | % -------------------------------------------------------------------------------------------------------------------------
function net = remove_layers_from_block(net, type)
% -------------------------------------------------------------------------------------------------------------------------
[names, ~] = find_l... |
github | StevenLOL/siamese-fc-master | size_ndims.m | .m | siamese-fc-master/util/size_ndims.m | 340 | utf_8 | fd118508a7609146eb5bb8f90d87ebe3 | % -------------------------------------------------------------------------------------------------------------------------
function sz = size_ndims(x, n)
% -------------------------------------------------------------------------------------------------------------------------
sz = cell(1, n);
[sz{1:n}] = size(x);
sz ... |
github | StevenLOL/siamese-fc-master | dag_subset_to.m | .m | siamese-fc-master/util/dag_subset_to.m | 1,285 | utf_8 | ec692f01ada964b50ec20fe7dc7e7b53 | % -------------------------------------------------------------------------------------------------------------------------
function subset = dag_subset_to(net, nodes)
%DAG_SUBSET_TO
% Finds the layers in a net that the nodes depend on.
% (The ancestors of nodes in the DAG.)
% nodes is a cell array of layer names.
% Re... |
github | StevenLOL/siamese-fc-master | layer_params.m | .m | siamese-fc-master/util/layer_params.m | 500 | utf_8 | 3a6067f400a05deb3dc48190550867ff | % -------------------------------------------------------------------------------------------------------------------------
function params = layer_params(net, layers)
% -------------------------------------------------------------------------------------------------------------------------
% layers are layer indic... |
github | StevenLOL/siamese-fc-master | remove_layers_from_prefix.m | .m | siamese-fc-master/util/remove_layers_from_prefix.m | 529 | utf_8 | 998f3d692f8802a77098074d491e0376 | % -------------------------------------------------------------------------------------------------------------------------
function net = remove_layers_from_prefix(net, prefix)
% -------------------------------------------------------------------------------------------------------------------------
L = net.layers... |
github | StevenLOL/siamese-fc-master | add_dag_to_dag.m | .m | siamese-fc-master/util/add_dag_to_dag.m | 2,347 | utf_8 | db49efcab7dccd8565d4b0e27e38fbe9 | % -------------------------------------------------------------------------------------------------------------------------
function net = add_dag_to_dag(net, orig, rename)
%ADD_DAG_TO_DAG
% Copies one DAG into another.
% The blocks of all layers are copied.
% The following fields of params are copied:
% value,... |
github | StevenLOL/siamese-fc-master | max_score_err.m | .m | siamese-fc-master/util/max_score_err.m | 1,800 | utf_8 | ed8e88dc9ef7cb95c3713f9e318930b1 | % -------------------------------------------------------------------------------------------------------------------------
function y = max_score_err(x, y_gt)
% x is [m1, m2, 1, b]
% numel(y_gt) is b
% The dimensions m1 and m2 are odd numbers.
%
% Luca Bertinetto, Jack Valmadre, Joao F. Henriques, 2016
% -----------... |
github | StevenLOL/siamese-fc-master | display_net.m | .m | siamese-fc-master/util/display_net.m | 901 | utf_8 | 0b821b10b5e043bb8f0040ec5734cb6e | % -------------------------------------------------------------------------------------------------------------------------
function display_net(net, inputs, name)
%DISPLAY_NET
% Saves and display a pdf with the DAG structure of the network
%
% Luca Bertinetto, Jack Valmadre, Joao F. Henriques, 2016
% -------------... |
github | StevenLOL/siamese-fc-master | xml2struct_custom.m | .m | siamese-fc-master/util/xml2struct_custom.m | 6,635 | utf_8 | 3a841f72d82f1369e7332a7b5c9c39e9 | function [ s ] = xml2struct( file )
%Convert xml file into a MATLAB structure
% [ s ] = xml2struct( file )
%
% A file containing:
% <XMLname attrib1="Some value">
% <Element>Some text</Element>
% <DifferentElement attrib2="2">Some more text</Element>
% <DifferentElement attrib3="2" attrib4="1">Even more text</Dif... |
github | StevenLOL/siamese-fc-master | dagMergeBatchNorm.m | .m | siamese-fc-master/util/dagMergeBatchNorm.m | 2,786 | utf_8 | 8710b396e86c3f8073ebc86cd8be8214 | % From examples/imagenet/cnn_imagenet_deploy.m
% -------------------------------------------------------------------------
function dagMergeBatchNorm(net, names)
% -------------------------------------------------------------------------
for name = names
name = char(name) ;
layer = net.layers(net.getLayerIndex(name... |
github | StevenLOL/siamese-fc-master | rename_io_vars.m | .m | siamese-fc-master/util/rename_io_vars.m | 486 | utf_8 | f80c50b59378078fa8c07f959cc75f11 | % -------------------------------------------------------------------------------------------------------------------------
function rename_io_vars(net, in, out)
% -------------------------------------------------------------------------------------------------------------------------
old_in = net.getInputs();
... |
github | StevenLOL/siamese-fc-master | init_weight.m | .m | siamese-fc-master/util/init_weight.m | 807 | utf_8 | 98b992c62f6f2045a313f255d4fd3576 | % -------------------------------------------------------------------------
function weights = init_weight(opts, h, w, in, out, type)
% -------------------------------------------------------------------------
% See K. He, X. Zhang, S. Ren, and J. Sun. Delving deep into
% rectifiers: Surpassing human-level performance ... |
github | StevenLOL/siamese-fc-master | find_layers_from_type.m | .m | siamese-fc-master/util/find_layers_from_type.m | 549 | utf_8 | 09b84acd095938f27f3fb279a3378d30 | % -------------------------------------------------------------------------------------------------------------------------
function [layer_names, layer_ids] = find_layers_from_type(net, type)
% -------------------------------------------------------------------------------------------------------------------------
% ... |
github | StevenLOL/siamese-fc-master | make_siamese.m | .m | siamese-fc-master/util/make_siamese.m | 4,743 | utf_8 | 359566ae4cf2f342d085853bbe801ce1 | function net = make_siamese(stream1, stream2, join, final, inputs, output, varargin)
% Constructs a Siamese network of two stream nets joined by the join and
% then followed by the final net.
% The stream and final nets can be simple or DAG.
% They should have one input and one output.
% The two streams should be ident... |
github | StevenLOL/siamese-fc-master | gaussian_weight.m | .m | siamese-fc-master/util/gaussian_weight.m | 753 | utf_8 | 661e7ad4c9852f5e0dbafd714073adc3 | % -------------------------------------------------------------------------------------------------------------------------
function y = gaussian_weight(rect_size, sigma)
% -------------------------------------------------------------------------------------------------------------------------
% a false positive sh... |
github | StevenLOL/siamese-fc-master | add_pair_of_streams.m | .m | siamese-fc-master/util/add_pair_of_streams.m | 961 | utf_8 | 732de263dd5678830de9350843a48e04 | % -------------------------------------------------------------------------------------------------------------------------
function net = add_pair_of_streams(net, stream, inputs, outputs, siamese)
% Luca Bertinetto, Jack Valmadre, Joao F. Henriques, 2016
% ------------------------------------------------------------... |
github | StevenLOL/siamese-fc-master | size_min_ndims.m | .m | siamese-fc-master/util/size_min_ndims.m | 383 | utf_8 | 90e20d4052aafc83e786494b59edfe80 | % -------------------------------------------------------------------------------------------------------------------------
function sz = size_min_ndims(x, m)
% Ensure that number of dimensions is at least m.
% -------------------------------------------------------------------------------------------------------------... |
github | StevenLOL/siamese-fc-master | find_layers_from_block.m | .m | siamese-fc-master/util/find_layers_from_block.m | 535 | utf_8 | 411cef0f6f5943cbb4a3394ae5288fb4 | % -------------------------------------------------------------------------------------------------------------------------
function [layer_names, layer_ids] = find_layers_from_block(net, type)
% -------------------------------------------------------------------------------------------------------------------------
% ... |
github | StevenLOL/siamese-fc-master | create_logisticloss_label.m | .m | siamese-fc-master/util/create_logisticloss_label.m | 1,003 | utf_8 | 56583cc511ebb8a2f414a1472c06941e | % -------------------------------------------------------------------------------------------------------------------------
function logloss_label = create_logisticloss_label(label_size, rPos, rNeg)
% Luca Bertinetto, Jack Valmadre, Joao F. Henriques, 2016
% -----------------------------------------------------------... |
github | StevenLOL/siamese-fc-master | add_adjust_layer.m | .m | siamese-fc-master/util/add_adjust_layer.m | 768 | utf_8 | c3afb81629a4c5cf83e67412d06e37bb | % -------------------------------------------------------------------------------------------------------------------------
function net = add_adjust_layer(net, name, input, output, params, gain, bias, lr_gain, lr_bias)
% --------------------------------------------------------------------------------------------------... |
github | StevenLOL/siamese-fc-master | ideal_size.m | .m | siamese-fc-master/util/ideal_size.m | 1,415 | utf_8 | 0c0e355a8ada36831d5125f373cbbaec | % -------------------------------------------------------------------------------------------------------------------------
function [init_sz, final_sz] = ideal_size(net, max_sz)
% Luca Bertinetto, Jack Valmadre, Joao F. Henriques, 2016
% -------------------------------------------------------------------------------... |
github | StevenLOL/siamese-fc-master | dag_remove_except.m | .m | siamese-fc-master/util/dag_remove_except.m | 560 | utf_8 | 51da4f5bceb7a3514ee25f8198acf2b0 | % -------------------------------------------------------------------------------------------------------------------------
function dag_remove_except(net, keep)
% Removes all layers from net that are not in the keep set.
% keep is a list of layer indices.
% -------------------------------------------------------------... |
github | StevenLOL/siamese-fc-master | dagRemoveLayers.m | .m | siamese-fc-master/util/dagRemoveLayers.m | 413 | utf_8 | d4249d48369952f0fc89bfb2ebd98327 | % From examples/imagenet/cnn_imagenet_deploy.m
% -------------------------------------------------------------------------
function dagRemoveLayers(net, names)
% -------------------------------------------------------------------------
for i = 1:numel(names)
layer = net.layers(net.getLayerIndex(names{i})) ;
net.rem... |
github | StevenLOL/siamese-fc-master | get_rect.m | .m | siamese-fc-master/training/get_rect.m | 691 | utf_8 | e149881a5ea4178522bf3f296627ce4f | % -------------------------------------------------------------------------------------------------------
function [cx, cy, w, h] = get_rect(object)
%GET_RECT
% Converts from original frame coordinates to resized one.
% ----------------------------------------------------------------------------------------------------... |
github | StevenLOL/siamese-fc-master | vid_get_random_batch.m | .m | siamese-fc-master/training/vid_get_random_batch.m | 11,717 | utf_8 | 8f6f3ada85e6b1372aa7940abcb43c82 | % -----------------------------------------------------------------------------------------------------------------------
function [imout_z, imout_x, labels, sizes_z, sizes_x] = vid_get_random_batch(imdb, imdb_video, batch, data_dir, varargin)
%VID_GET_RANDOM_BATCH
% returns batch of pairs of input (z and x) and labe... |
github | StevenLOL/siamese-fc-master | vid_create_net_small3.m | .m | siamese-fc-master/training/vid_create_net_small3.m | 7,053 | utf_8 | 22330a40d026dab49002011ea04f9116 | % ----------------------------------------------------------------------------------------------------------------
function [net, fixed_label_size] = vid_create_net_small3(varargin)
% Very similar to vanilla AlexNet from MatConvNet examples,
% but with smaller stride at conv1 and no padding
% The paper "Fully-Convoluti... |
github | StevenLOL/siamese-fc-master | make_siameseFC.m | .m | siamese-fc-master/training/make_siameseFC.m | 2,023 | utf_8 | 5b8a74217230f98cfac2f2587dcf77e5 | % -------------------------------------------------------------------------------------------------
function net = make_siameseFC(opts)
%MAKE_SIAMESEFC
% Creates Siamese Fully-Convolutional network,
% made by duplicating a vanilla AlexNet in two branches
% and joining the branches with a cross-correlation layer
%... |
github | StevenLOL/siamese-fc-master | create_labels.m | .m | siamese-fc-master/training/create_labels.m | 2,218 | utf_8 | 39d162d3f496c64d9227d58f703f12e0 | % -------------------------------------------------------------------------------------------------------
function [fixedLabel, instanceWeight] = create_labels(fixedLabelSize, labelWeight, rPos, rNeg)
%CREATE_LABELS
%
% Luca Bertinetto, Jack Valmadre, Joao Henriques, 2016
% -------------------------------------------... |
github | StevenLOL/siamese-fc-master | experiment.m | .m | siamese-fc-master/training/experiment.m | 12,187 | utf_8 | 65d9233c7a2e1822f5debe3140a801c7 | % -------------------------------------------------------------------------------------------------
function [net, stats] = experiment(imdb_video, varargin)
%EXPERIMENT
% main function - creates a network and trains it on the dataset indexed by imdb_video.
%
% Luca Bertinetto, Jack Valmadre, Joao Henriques, 2016
% ... |
github | StevenLOL/siamese-fc-master | vid_create_net_small1.m | .m | siamese-fc-master/training/vid_create_net_small1.m | 7,050 | utf_8 | 3221a59f1d71f8cf98a49c1c11540194 | % ----------------------------------------------------------------------------------------------------------------
function [net, fixed_label_size] = vid_create_net_small1(varargin)
% Very similar to vanilla AlexNet from MatConvNet examples,
% but with smaller stride at conv1 and no padding
% The paper "Fully-Convoluti... |
github | StevenLOL/siamese-fc-master | vid_create_net.m | .m | siamese-fc-master/training/vid_create_net.m | 6,736 | utf_8 | 8914bccd154bb09c2c40df9641ea085e | % ----------------------------------------------------------------------------------------------------------------
function net = vid_create_net(varargin)
% Very similar to vanilla AlexNet from MatConvNet examples,
% but with smaller stride at conv1 and no padding
% Used to generate the network described in the paper
... |
github | StevenLOL/siamese-fc-master | vid_create_net_small2.m | .m | siamese-fc-master/training/vid_create_net_small2.m | 7,054 | utf_8 | 25319756f0ccecd0398b3639a113dcb7 |
% ----------------------------------------------------------------------------------------------------------------
function [net, fixed_label_size] = vid_create_net_small2(varargin)
% Very similar to vanilla AlexNet from MatConvNet examples,
% but with smaller stride at conv1 and no padding
% The paper "Fully-Convolut... |
github | StevenLOL/siamese-fc-master | get_axis_aligned_BB.m | .m | siamese-fc-master/tracking/get_axis_aligned_BB.m | 884 | utf_8 | 2a5e3847360c596a2db55b543520df60 | % -------------------------------------------------------------------------------------------------
function [cx, cy, w, h] = get_axis_aligned_BB(region)
%GETAXISALIGNEDBB computes axis-aligned bbox with same area as the rotated one (REGION)
% ----------------------------------------------------------------------------... |
github | StevenLOL/siamese-fc-master | load_video_info.m | .m | siamese-fc-master/tracking/load_video_info.m | 1,552 | utf_8 | 8bf93c31453d1b9d9db6cbea15079900 | % -------------------------------------------------------------------------------------------------
function [imgs, pos, target_sz] = load_video_info(base_path, video)
%LOAD_VOT_VIDEO_INFO
% Loads all the relevant information for the video in the given path:
% the list of image files (cell array of strings), initia... |
github | StevenLOL/siamese-fc-master | make_scale_pyramid.m | .m | siamese-fc-master/tracking/make_scale_pyramid.m | 1,506 | utf_8 | 657e125f2f90c85262df429feea3e7bf | % -----------------------------------------------------------------------------------------------------
function pyramid = make_scale_pyramid(im, targetPosition, in_side_scaled, out_side, avgChans, stats, p)
%MAKE_SCALE_PYRAMID
% computes a pyramid of re-scaled copies of the target (centered on TARGETPOSITION)
% an... |
github | StevenLOL/siamese-fc-master | get_subwindow_tracking.m | .m | siamese-fc-master/tracking/get_subwindow_tracking.m | 2,699 | utf_8 | c4c66298a3e5242b7727c00f5ec21df8 | % --------------------------------------------------------------------------------------------------------
function [im_patch, im_patch_original] = get_subwindow_tracking(im, pos, model_sz, original_sz, avg_chans)
%GET_SUBWINDOW_TRACKING Obtain image sub-window, padding with avg channel if area goes outside of border
%... |
github | StevenLOL/siamese-fc-master | tracker_eval.m | .m | siamese-fc-master/tracking/tracker_eval.m | 3,037 | utf_8 | 384c088b19929bc95b8d4e7c51ff5f98 | % -------------------------------------------------------------------------------------------------------------------------
function [newTargetPosition, bestScale] = tracker_eval(net_x, s_x, scoreId, z_features, x_crops, targetPosition, window, p)
%TRACKER_STEP
% runs a forward pass of the search-region branch of the... |
github | StevenLOL/siamese-fc-master | load_pretrained.m | .m | siamese-fc-master/tracking/load_pretrained.m | 1,448 | utf_8 | 08ee6161c008cb8f910e84359064f51a | % -------------------------------------------------------------------------------------------------
function net = load_pretrained(netPath, gpu)
%LOAD_PRETRAINED loads a pretrained fully-convolutional Siamese network as a DagNN
% ------------------------------------------------------------------------------------------... |
github | StevenLOL/siamese-fc-master | tracker.m | .m | siamese-fc-master/tracking/tracker.m | 7,135 | utf_8 | 00af2547ffc2a268e4560a7f2a296b40 | % -------------------------------------------------------------------------------------------------
function bboxes = tracker(varargin)
%TRACKER
% is the main function that performs the tracking loop
% Default parameters are overwritten by VARARGIN
%
% Luca Bertinetto, Jack Valmadre, Joao F. Henriques, 2016
% ---... |
github | zhenglab/ILSVRC2016-master | classification_demo.m | .m | ILSVRC2016-master/caffe yzb/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 | LucyKuncheva/SDCD-Simulated-Data-for-Concept-Drift-master | simulation_changing_environment.m | .m | SDCD-Simulated-Data-for-Concept-Drift-master/simulation_changing_environment.m | 1,364 | utf_8 | 6ea59a14c44ea72221e4f50f76863ee1 | function [a,laba] = simulation_changing_environment(p, V)
%SIMULATION_CHANGING_ENVIRONMENT generates data stream from sources
% [a,laba] = SIMULATION_CHANGING_ENVIRONMENT(p, V) returns a data set "a"
% with a label vector "laba".
%
% Input
% V: array of probabilities of size N-by-K where N is the ... |
github | agjayant/caffe-Person-ReID-master | classification_demo.m | .m | caffe-Person-ReID-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 | mgarbade/semanticLabelingTool-master | semanticLabelingTool.m | .m | semanticLabelingTool-master/semanticLabelingTool.m | 19,353 | utf_8 | e5eab2a13f376d60b8bbee4aad5abd41 | function varargout = semanticLabelingTool(varargin)
% SEMANTICLABELINGTOOL MATLAB code for semanticLabelingTool.fig
% SEMANTICLABELINGTOOL, by itself, creates a new SEMANTICLABELINGTOOL or raises the existing
% singleton*.
%
% H = SEMANTICLABELINGTOOL returns the handle to a new SEMANTICLABELINGTOOL or t... |
github | NeuroDataDesign/orange-panda-f16s17-master | kurt.m | .m | orange-panda-f16s17-master/notes/bad_chan_detect/kurt.m | 1,697 | utf_8 | 649871ee149fc000974e3081ac13ff98 | % kurt() - return kurtosis of input data distribution
%
% Usage:
% >> k=kurt(data)
%
% Algorithm:
% Calculates kurtosis or normalized 4th moment of an input data vector
% Given a matrix, returns a row vector giving the kurtosis' of the columns
% (Ref: "Numerical Recipes," p. 612)
%
% Author: Martin Mckeown, CNL... |
github | NeuroDataDesign/orange-panda-f16s17-master | jointprob.m | .m | orange-panda-f16s17-master/notes/bad_chan_detect/jointprob.m | 4,254 | utf_8 | a291f56f6e23f08ce7582dc3c3d2fdb1 | % jointprob() - rejection of odd columns of a data array using
% joint probability of the values in that column (and
% using the probability distribution of all columns).
%
% Usage:
% >> [jp rej] = jointprob( signal );
% >> [jp rej] = jointprob( signal, threshold, jp, normalize, discre... |
github | NeuroDataDesign/orange-panda-f16s17-master | pop_rejchan.m | .m | orange-panda-f16s17-master/notes/bad_chan_detect/pop_rejchan.m | 10,320 | utf_8 | 5c5252542d5934e5b83a0dfecc30881e | % pop_rejchan() - reject artifacts channels in an EEG dataset using joint
% probability of the recorded electrode.
%
% Usage:
% >> pop_rejchan( INEEG ) % pop-up interative window mode
% >> [EEG, indelec, measure, com] = ...
% = pop_rejchan( INEEG, 'key', 'val');
%
% Inputs:
% INEEG - input... |
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