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 | MRC-CBU/riksneurotools-master | connectivity_stats.m | .m | riksneurotools-master/Conn/connectivity_stats.m | 8,352 | utf_8 | bf371d76a5ab3661bf8b941d4d843872 |
% Hacky function for thresholding connectivity matrices across groups
% Rik.Henson@mrc-cbu.cam.ac.uk Nov 2018
function connectivity_stats(S);
try
CM = S.CM;
catch
error('Need to pass a Nsubject x Nroi x Nroi connectivity matrix in S.CM')
end
try
X = S.X;
catch
error('Need to pass a design matrix i... |
github | MRC-CBU/riksneurotools-master | check_pooled_error.m | .m | riksneurotools-master/GLM/check_pooled_error.m | 22,146 | utf_8 | 99321534cf307de8082b85273a1efc3d |
function [Pool,Part,QV] = check_pooled_error(S);
% Pedagogical script to illustrate assumptions about error term(s) in
% repeated-measures multi-factorial ANOVAs performed on many observations
% (eg voxels in fMRI). Requires SPM8+ on path.
%
% Based on Henson & Penny (2003) Tech Report ("H&P03"):
% http://... |
github | MRC-CBU/riksneurotools-master | fMRI_GLM_efficiency.m | .m | riksneurotools-master/GLM/fMRI_GLM_efficiency.m | 9,235 | utf_8 | 46ee5b1e36bfbabd4f5db7b3269b0071 | function [e,sots,stim,X,df] = fMRI_GLM_efficiency(S);
% Matlab function to calculate efficiency for fMRI GLMs.
% rik.henson@mrc-cbu.cam.ac.uk, 2005
%
% See for example:
%
% Henson, R.N. (2006). Efficient experimental design for fMRI. In K. Friston, J. Ashburner, S. Kiebel, T. Nichols, and W.... |
github | MRC-CBU/riksneurotools-master | rsfMRI_GLM.m | .m | riksneurotools-master/GLM/rsfMRI_GLM.m | 25,725 | utf_8 | 2654812ab9bb94b923565c58d1b0db46 |
function [Zmat, Bmat, pZmat, pBmat, aY, X0r] = rsfMRI_GLM(S);
% [Zmat, Bmat, pZmat, pBmat, aY, X0r] = rsfMRI_GLM(S);
%
% Function (using SPM8 functions) for estimating linear regressions
% between fMRI timeseries in each pair of Nr ROIs, adjusting for bandpass
% filter, confounding timeseries (eg CSF) and (SVD of) ... |
github | MRC-CBU/riksneurotools-master | glm.m | .m | riksneurotools-master/GLM/glm.m | 3,169 | utf_8 | 727c465a37a6081510ffb063ffcde950 | function [t,F,p,df,R2,cR2,B,r,aR2,iR2] = glm(y,X,c,pflag);
% [t,F,p,df,R2,cR2,B] = glm(y,X,c,pflag);
%
% Generic function for General Linear Model (GLM)
% Note: assumes error is spherical (residuals white)
% Rik Henson, 2004
%
% (Note requires Matlab stats toolbox, or SPM functions to be on path)
%
% Input:
% y = n... |
github | MRC-CBU/riksneurotools-master | repanova.m | .m | riksneurotools-master/GLM/repanova.m | 4,310 | utf_8 | 38052a299232e70d7a8721730a835a0e | function [efs,F,cdfs,p,eps,dfs,b,y2,sig,mse]=repanova5(d,D,fn,gg,alpha);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Version5 has return of mse added (eg for plotting Loftus error bars)
%
% [efs,F,cdfs,p,eps,dfs,b,y2,sig] = repanova(d,D,fn,gg,alpha);
%
% R.Henson, 17/3/03; rik.henson@mrc-cbu.cam.ac.uk
%
%... |
github | sergiosanchoasensio/Traffic-sign-detection-and-recognition-master | TrafficSignDetection.m | .m | Traffic-sign-detection-and-recognition-master/Code/TrafficSignDetection.m | 16,035 | utf_8 | 450099e1e5f638e9fb7600a461066695 | function TrafficSignDetection(directory, pixel_method, th, filename, window_method, decision_method, str, str2)
% TrafficSignDetection
% Perform detection of Traffic signs on images. Detection is performed first at the pixel level
% using a color segmentation. Then, using the color segmentation as a basis, ... |
github | sergiosanchoasensio/Traffic-sign-detection-and-recognition-master | colorspace_demo.m | .m | Traffic-sign-detection-and-recognition-master/Code/colorspace/colorspace_demo.m | 6,856 | utf_8 | f7d66bc3e0e1bf1611fbd525c617323c | function colorspace_demo(Cmd)
% Demo for colorspace.m - 3D visualizations of various color spaces
% Pascal Getreuer 2006
if nargin == 0
% Create a figure with a drop-down menu
figure('Color',[1,1,1]);
h = uicontrol('Style','popup','Position',[15,10,90,21],...
'BackgroundColor',[1,1,1],'Value',2,...
... |
github | sergiosanchoasensio/Traffic-sign-detection-and-recognition-master | colorspace.m | .m | Traffic-sign-detection-and-recognition-master/Code/colorspace/colorspace.m | 16,178 | utf_8 | 2ca0aee9ae4d0f5c12a7028c45ef2b8d | function varargout = colorspace(Conversion,varargin)
%COLORSPACE Transform a color image between color representations.
% B = COLORSPACE(S,A) transforms the color representation of image A
% where S is a string specifying the conversion. The input array A
% should be a real full double array of size Mx3 or MxN... |
github | rrahuldev/ExportMDF-master | expMDF.m | .m | ExportMDF-master/expMDF.m | 9,217 | utf_8 | 488f876b54f99d8b5630ac6220d7aee9 | % Write data to MDA dat file
% inputs:
% 1- Data in table format
% 2 - filename to write to *.dat
%
% usage: expMDF(dataTable, fileoutname)
% Author : Rahul Rajampeta
% Date: April 15, 2016
function expMDF(dataTable, fileoutname)
%% create the fid to write
fid = fopen(fileoutname,'W');
%% pass the ... |
github | rrahuldev/ExportMDF-master | mat2dat.m | .m | ExportMDF-master/mat2dat.m | 10,624 | utf_8 | 7fe982ffaccd4856933b094bdb62e07d | function mat2dat(varargin)
% The script is invoked two ways
% 1: with no arguments to the script, in which case, the user selects the
% one or more mat files to convert to dat file format. The output is the
% same name as matfile but appended with "_expMDF.dat"
%
% 2: inline with other scripts or model where t... |
github | jdonley/CoherenceBasedSourceCounter-master | tightPlots.m | .m | CoherenceBasedSourceCounter-master/tightPlots.m | 7,369 | utf_8 | c971688a6827a46fd28cfc0c4b759ec9 | % Copyright (c) 2015, Theodoros Michelis
% Copyright (c) 2016, Pekka Kumpulainen
% All rights reserved.
%
% Redistribution and use in source and binary forms, with or without
% modification, are permitted provided that the following conditions are
% met:
%
% * Redistributions of source code must retain the above ... |
github | jdonley/CoherenceBasedSourceCounter-master | ConcatTIMITtalkers.m | .m | CoherenceBasedSourceCounter-master/ConcatTIMITtalkers.m | 1,980 | utf_8 | ce8fcd183ce3c36ac796127ee7cfc666 | function ConcatTIMITtalkers( TIMITdir, OutDir )
%CONCATTIMITTALKERS Concatenates all the talkers from the TIMIT corpus into individual speech files
%
% Syntax: CONCATTIMITTALKERS( TIMITDIR, OUTDIR )
%
% Inputs:
% TIMITdir - The directory of the TIMIT corpus
% OutDir - The output directory to save the concatenated... |
github | jdonley/CoherenceBasedSourceCounter-master | getAllFiles.m | .m | CoherenceBasedSourceCounter-master/getAllFiles.m | 2,166 | utf_8 | 095e332c0376d673b760c33621f51f0a | function fileList = getAllFiles(dirPath)
%GETALLFILES Retrieves a list of all files within a directory
%
% Syntax: fileList = getAllFiles(dirName)
%
% Inputs:
% dirPath - The relative or full path of the directory to recursivley
% search.
%
% Outputs:
% fileList - A cell array list of the full pat... |
github | jdonley/CoherenceBasedSourceCounter-master | getReceivedSignals.m | .m | CoherenceBasedSourceCounter-master/getReceivedSignals.m | 9,673 | utf_8 | 43b664b560e6a36f06fe1f1229d536f4 | function [MicSigs, Fs, SrcLocs, NodeLocs] = ...
getReceivedSignals( ...
Nsrcs, Nnodes, ...
RoomSz, TableSz, SeatSz, TableLoc, ...
InterSrcDist, IntraNodeDist, ...
SynthParams )
%GETRECEIVEDSIGNALS Synthesises received signals at microphones in a meeting scenario
%
% Syntax: [MicSigs, Fs, SrcLocs, No... |
github | nurahmadi/BAKS-master | BAKS.m | .m | BAKS-master/BAKS.m | 1,200 | utf_8 | 46a40d3e76aaf2aa453b81a1e6f43b77 | % Bayesian Adaptive Kernel Smoother (BAKS)
% BAKS is a method for estimating firing rate from spike train data that uses kernel smoothing technique
% with adaptive bandwidth determined using a Bayesian approach
% ---------------INPUT---------------
% - SpikeTimes : spike event times [nSpikes x 1]
% - Time : time at wh... |
github | Kinpzz/deeplab-v2-master | classification_demo.m | .m | deeplab-v2-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 | Kinpzz/deeplab-v2-master | MyVOCevalseg.m | .m | deeplab-v2-master/matlab/my_script/MyVOCevalseg.m | 4,625 | utf_8 | 128c24319d520c2576168d1cf17e068f | %VOCEVALSEG Evaluates a set of segmentation results.
% VOCEVALSEG(VOCopts,ID); prints out the per class and overall
% segmentation accuracies. Accuracies are given using the intersection/union
% metric:
% true positives / (true positives + false positives + false negatives)
%
% [ACCURACIES,AVACC,CONF] = VOCEV... |
github | Kinpzz/deeplab-v2-master | MyVOCevalsegBoundary.m | .m | deeplab-v2-master/matlab/my_script/MyVOCevalsegBoundary.m | 4,415 | utf_8 | 1b648714e61bafba7c08a8ce5824b105 | %VOCEVALSEG Evaluates a set of segmentation results.
% VOCEVALSEG(VOCopts,ID); prints out the per class and overall
% segmentation accuracies. Accuracies are given using the intersection/union
% metric:
% true positives / (true positives + false positives + false negatives)
%
% [ACCURACIES,AVACC,CONF] = VOCEV... |
github | OneDirection9/MOT_with_Pose-master | crop_img.m | .m | MOT_with_Pose-master/tools/crop_img.m | 1,660 | utf_8 | 5775e63c5f8434d89c164026f1054b5f | function [ ] = crop_img( expidx )
%CROP_IMG Summary of this function goes here
% Detailed explanation goes here
p = bbox_exp_params(expidx);
train_annolist_file = p.trainGT;
test_annolist_file = p.testGT;
croped_det_dirs = p.cropedDetections;
matDetectionsDir = p.matDetectionsDir;
vidDir = p.vidDir;
crop(train_an... |
github | OneDirection9/MOT_with_Pose-master | convert_kpt2challenge.m | .m | MOT_with_Pose-master/tools/convert_kpt2challenge.m | 2,904 | utf_8 | bab8c2c51e43a092eedd7e3405d50435 | function [ ] = convert_kpt2challenge( multicut_dir, anno_dir, save_dir, expidx )
%CONVERT_KTP2CHALLENGE Summary of this function goes here
% Detailed explanation goes here
clear;
clc;
if(nargin < 4)
multicut_dir = './data/tmp/';
anno_dir = './data/source_annotations/val/';
save_dir = './data/evaluate_re... |
github | OneDirection9/MOT_with_Pose-master | crop_proposals.m | .m | MOT_with_Pose-master/tools/crop_proposals.m | 1,622 | utf_8 | 9a228af33fcf1c4b6494d399fbf30902 | function [ ] = crop_proposals( expidx )
%CROP_PROPOSALS Summary of this function goes here
% Detailed explanation goes here
p = bbox_exp_params(expidx);
train_annolist_file = p.trainGT;
test_annolist_file = p.testGT;
croped_pro_dir = p.cropedProposals;
proposals_dir = p.detPreposals;
vidDir = p.vidDir;
dirs = dir... |
github | OneDirection9/MOT_with_Pose-master | regress_box_kpt_split.m | .m | MOT_with_Pose-master/tools/regress_box_kpt_split.m | 1,012 | utf_8 | f85b0da9518550b6bb4bdaee69dbac7b | function [ box_matrix ] = regress_box_kpt_split( people )
%REGRESS_BOX_KPT_SPLIT Summary of this function goes here
% Detailed explanation goes here
if nargin < 1
load('/home/sensetime/warmer/PoseTrack/challenge/data/prediction_6_001735_mpii_relpath_5sec_testsub_1', 'people');
end
box_matrix = [];
if(isempty(pe... |
github | OneDirection9/MOT_with_Pose-master | bbox_tracking.m | .m | MOT_with_Pose-master/lib/bbox_tracking.m | 12,792 | utf_8 | eafa2b4fa883ac6de5c201e1f98c031e | function bbox_tracking( expidx,firstidx,nVideos,bRecompute,bVis )
%PT_TRACK_JOINT Summary of this function goes here
% Detailed explanation goes here
fprintf('bbox_tracking()\n');
if (ischar(expidx))
expidx = str2num(expidx);
end
if (ischar(firstidx))
firstidx = str2num(firstidx);
end
if (nargin < 2)
... |
github | OneDirection9/MOT_with_Pose-master | convert_txt_with_keypoints.m | .m | MOT_with_Pose-master/lib/convert_txt_with_keypoints.m | 2,596 | utf_8 | 9bbacc470ac470c95a61499b74f670f4 | function [ ] = convert_txt_with_keypoints(p)
% convert txt files under source_dir to mat and saved in save_dir.
%
if nargin < 1
keypoints_dir = './data/keypoints_txt/';
save_keypoint_dir = './data/keypoints/';
save_map_dir = './data/cross_map/';
testGT = './data/annolist/test/annolist';
score_thre... |
github | OneDirection9/MOT_with_Pose-master | convert_txt2mat.m | .m | MOT_with_Pose-master/lib/convert_txt2mat.m | 4,576 | utf_8 | 23f68edd3b17fc7a6a7a38885d6a1b51 | function [ ] = convert_txt2mat(p, IOU_thresh)
% convert txt files under source_dir to mat and saved in save_dir.
%
if nargin < 2
IOU_thresh = 0.3;
end
source_dir = p.txtDetectionsDir;
save_dir = p.matDetectionsDir;
testGT = p.testGT;
load(testGT, 'annolist');
% % empty save_dir
% if exist(save_dir, 'dir')
% ... |
github | OneDirection9/MOT_with_Pose-master | convert_pred2challenge.m | .m | MOT_with_Pose-master/lib/convert_pred2challenge.m | 4,559 | utf_8 | 79f5bce15af086a26b951c295fc35bcc | function [ ] = convert_pred2challenge( pred_dir, gt_dir, save_dir, expidx )
%CONVERT_PRED2CHALLENGE Summary of this function goes here
% Detailed explanation goes here
clear;
clc;
if nargin < 1
% pred_dir = '/home/sensetime/warmer/PoseTrack/challenge/data/mot-multicut/';
pred_dir = '/home/sensetime/warmer/P... |
github | OneDirection9/MOT_with_Pose-master | generate_gttxt.m | .m | MOT_with_Pose-master/lib/generate_gttxt.m | 4,386 | utf_8 | acf5c5b5550af414f769140ef17ab842 | function [] = generate_gttxt( annolist_file, save_dir, usage, IOU_thresh )
if nargin < 4
IOU_thresh = 0.3;
end
load(annolist_file);
% if exist(save_dir, 'dir')
% rmdir(save_dir, 's');
% end
mkdir_if_missing(save_dir);
num_videos = size(annolist, 1);
f... |
github | OneDirection9/MOT_with_Pose-master | convert_prediction2txt.m | .m | MOT_with_Pose-master/lib/convert_prediction2txt.m | 3,445 | utf_8 | 66c87f27d8cb6106e6d0dedb8e7e0842 | function [ ] = convert_prediction2txt( expidx, save_dir, annolist_test, prediction_dir, pruneThresh, curSaveDir )
%GENERATE_MOT_DET Summary of this function goes here
% Detailed explanation goes here
if(nargin<5)
pruneThresh = 5;
end
load(annolist_test, 'annolist');
num_videos = size(ann... |
github | OneDirection9/MOT_with_Pose-master | regress_bbox_gt.m | .m | MOT_with_Pose-master/lib/regress_bbox_gt.m | 5,491 | utf_8 | 2283bb4ba62c750d328979656b4b0868 | function [ ] = regress_bbox_gt( annolist_file, videos_dir, save_dir, scale, isSave, isShow, useIncludeUnvisiable )
%CALCULATE_BBOX_GT Summary of this function goes here
% Detailed explanation goes here
fprintf('Calculating ground truth for `%s`\n', annolist_file);
load(annolist_file, 'annolist');
... |
github | OneDirection9/MOT_with_Pose-master | evaluateTracking.m | .m | MOT_with_Pose-master/devkit/evaluateTracking.m | 8,672 | utf_8 | 2fe649d71269eeff6f69054aaee0e771 | function allMets=evaluateTracking( seqmap, resDir, dataDir, vidDir, isShowFP )
%% evaluate CLEAR MOT and other metrics
% concatenate ALL sequences and evaluate as one!
%
% SETUP:
%
% define directories for tracking results...
% resDir = fullfile('res','data',filesep);
% ... and the actual sequences
% dataDir = fullfile... |
github | OneDirection9/MOT_with_Pose-master | CLEAR_MOT_HUN.m | .m | MOT_with_Pose-master/devkit/utils/CLEAR_MOT_HUN.m | 11,304 | utf_8 | d5f4e2fadb052f0e4c42157ed3daccba | function [metrics metricsInfo additionalInfo ]=CLEAR_MOT_HUN(gtInfo,stateInfo,options)
% compute CLEAR MOT and other metrics
%
% metrics contains the following
% [1] recall - recall = percentage of detected targets
% [2] precision - precision = percentage of correctly detected targets
% [3] FAR - number of false... |
github | OneDirection9/MOT_with_Pose-master | Hungarian.m | .m | MOT_with_Pose-master/devkit/utils/Hungarian.m | 9,328 | utf_8 | 51e60bc9f1f362bfdc0b4f6d67c44e80 | function [Matching,Cost] = Hungarian(Perf)
%
% [MATCHING,COST] = Hungarian_New(WEIGHTS)
%
% A function for finding a minimum edge weight matching given a MxN Edge
% weight matrix WEIGHTS using the Hungarian Algorithm.
%
% An edge weight of Inf indicates that the pair of vertices given by its
% position have no... |
github | OneDirection9/MOT_with_Pose-master | IniConfig.m | .m | MOT_with_Pose-master/devkit/utils/external/iniconfig/IniConfig.m | 59,579 | UNKNOWN | bc446e2c4372f0e2377ce83ed57afaef | classdef IniConfig < handle
%IniConfig - The class for working with configurations of settings and INI-files.
% This class allows you to create configurations of settings, and to manage them.
% The structure of the storage settings is similar to the structure of
% the storage the settings in the ... |
github | RoboticsLabURJC/2017-tfm-alexandre-rodriguez-master | visualization.m | .m | 2017-tfm-alexandre-rodriguez-master/2016-tfg-david-pascual-replicado/Net/visualization.m | 5,298 | utf_8 | c0d9be2c88eb5d80e89618c3cf3e583a | #
# Created on Mar 28, 2017
#
# @author: dpascualhe
#
function benchmark(results_path)
# This function reads and plots a variety of measures, which evaluate the neural
# network performance, that have been saved like a structure (a Python
# dictionary) into a .mat file.
more off;
results_path = file_in_loadpa... |
github | RoboticsLabURJC/2017-tfm-alexandre-rodriguez-master | comparison.m | .m | 2017-tfm-alexandre-rodriguez-master/2016-tfg-david-pascual-replicado/Net/comparison.m | 1,598 | utf_8 | 890b5ddb42c5031d86793ea899acca87 | #
# Created on May 15, 2017
#
# @author: dpascualhe
#
function comparison(path1, path2)
more off;
metrics_path1 = file_in_loadpath(path1);
metrics_dict1 = load(metrics_path1).metrics;
metrics_path2 = file_in_loadpath(path2);
metrics_dict2 = load(metrics_path2).metrics;
# Loss.
figure('Units','... |
github | johnybang/AdaptiveFilter-LMS-master | AdaptiveFirTest.m | .m | AdaptiveFilter-LMS-master/Matlab/AdaptiveFirTest.m | 2,335 | utf_8 | 699c8f7afeffc369173cd9ce9b5e038e | classdef AdaptiveFirTest
properties (Constant)
Iterations = 5000;
Weights = rand(30,1)*2 - 1; % random weights on interval [-1,1]
StepSize = 0.3;
DbEpsilon = 1e-40;
end
methods (Static)
function Run()
% generate uniform random noise on the interval [-... |
github | nipunperera/seizureDetection-master | trainingWaveletFeatures.m | .m | seizureDetection-master/trainingWaveletFeatures.m | 3,619 | utf_8 | bf89240c92578637b08cd4a75b843f07 | %%---------------------Seizure detection in continuous EEG-----------------
% In this method, seizure events are detected and classified using discrete
% wavelet packet transform.
% Seizure events of the ECG are identified by considering 4 second
% intervals in the EEG signal. Prominent channels and the wavelet pa... |
github | nipunperera/seizureDetection-master | featExtractionWaveletOverlapping1.m | .m | seizureDetection-master/featExtractionWaveletOverlapping1.m | 4,983 | utf_8 | 058cc7f916d2cabfc5feea3428b9d150 | %%---------------------Seizure detection in continuous EEG-----------------
% In this method, seizure events are detected and classified using discrete
% wavelet packet transform.
% Seizure events of the ECG are identified by considering 4 second
% intervals in the EEG signal. Prominent channels and the wavelet pa... |
github | nipunperera/seizureDetection-master | patient02TrainingWavelet.m | .m | seizureDetection-master/patient02TrainingWavelet.m | 4,893 | utf_8 | e3dd2d35ddb8408ec50bcdd5e3b9bd1a | %%---------------------Seizure detection in continuous EEG-----------------
% In this method, seizure events are detected and classified using discrete
% wavelet packet transform.
% Seizure events of the ECG are identified by considering 4 second
% intervals in the EEG signal. Prominent channels and the wavelet pa... |
github | nipunperera/seizureDetection-master | trainingSpectralFeatures.m | .m | seizureDetection-master/trainingSpectralFeatures.m | 4,687 | utf_8 | eb0c83735c5e22e41a50d46d3fb0228b | close all
clear
% ----------------------------Seizure occurences---------------------------
% File Name: chb01_03.edf (Record 3)
% Seizure Start Time: 2996 seconds - 749
% Seizure End Time: 3036 seconds - 759
% File Name: chb01_04.edf (Record 4)
% Seizure Start Time: 1467 seconds -... |
github | nipunperera/seizureDetection-master | patient02TrainingSpectral.m | .m | seizureDetection-master/patient02TrainingSpectral.m | 4,762 | utf_8 | 568a4f5382b9546887b87c36c8eb0e42 | close all
clear
% ----------------------------Seizure occurences---------------------------
% File Name: chb03_01.edf
% File Start Time: 13:23:36
% File End Time: 14:23:36
% Number of Seizures in File: 1
% Seizure Start Time: 362 seconds - 91
% Seizure End Time: 414 seconds - 104
% File Name: chb03_02.e... |
github | PECAplus/PECAplus_cmd_line-master | NLOPT_GN_ORIG_DIRECT.m | .m | PECAplus_cmd_line-master/include_dir/nlopt-2.4.2/octave/NLOPT_GN_ORIG_DIRECT.m | 164 | utf_8 | 43ae70342fc7484716698f445981512b | % NLOPT_GN_ORIG_DIRECT: Original DIRECT version (global, no-derivative)
%
% See nlopt_minimize for more information.
function val = NLOPT_GN_ORIG_DIRECT
val = 6;
|
github | PECAplus/PECAplus_cmd_line-master | NLOPT_LN_BOBYQA.m | .m | PECAplus_cmd_line-master/include_dir/nlopt-2.4.2/octave/NLOPT_LN_BOBYQA.m | 189 | utf_8 | 15ba6db5057c8907343184908e0ecf06 | % NLOPT_LN_BOBYQA: BOBYQA bound-constrained optimization via quadratic models (local, no-derivative)
%
% See nlopt_minimize for more information.
function val = NLOPT_LN_BOBYQA
val = 34;
|
github | PECAplus/PECAplus_cmd_line-master | NLOPT_GN_DIRECT.m | .m | PECAplus_cmd_line-master/include_dir/nlopt-2.4.2/octave/NLOPT_GN_DIRECT.m | 137 | utf_8 | 915b9f3f3a223d681a10bfaa80318309 | % NLOPT_GN_DIRECT: DIRECT (global, no-derivative)
%
% See nlopt_minimize for more information.
function val = NLOPT_GN_DIRECT
val = 0;
|
github | PECAplus/PECAplus_cmd_line-master | NLOPT_LD_MMA.m | .m | PECAplus_cmd_line-master/include_dir/nlopt-2.4.2/octave/NLOPT_LD_MMA.m | 155 | utf_8 | 7e4519526e6353452086a1cf929b12ad | % NLOPT_LD_MMA: Method of Moving Asymptotes (MMA) (local, derivative)
%
% See nlopt_minimize for more information.
function val = NLOPT_LD_MMA
val = 24;
|
github | PECAplus/PECAplus_cmd_line-master | NLOPT_GN_DIRECT_L.m | .m | PECAplus_cmd_line-master/include_dir/nlopt-2.4.2/octave/NLOPT_GN_DIRECT_L.m | 143 | utf_8 | ae13ecf48a1ee6d222444643f59c2993 | % NLOPT_GN_DIRECT_L: DIRECT-L (global, no-derivative)
%
% See nlopt_minimize for more information.
function val = NLOPT_GN_DIRECT_L
val = 1;
|
github | PECAplus/PECAplus_cmd_line-master | NLOPT_LD_VAR1.m | .m | PECAplus_cmd_line-master/include_dir/nlopt-2.4.2/octave/NLOPT_LD_VAR1.m | 168 | utf_8 | 45d4388965becdc240350c73a2779757 | % NLOPT_LD_VAR1: Limited-memory variable-metric, rank 1 (local, derivative-based)
%
% See nlopt_minimize for more information.
function val = NLOPT_LD_VAR1
val = 13;
|
github | PECAplus/PECAplus_cmd_line-master | NLOPT_GN_DIRECT_L_NOSCAL.m | .m | PECAplus_cmd_line-master/include_dir/nlopt-2.4.2/octave/NLOPT_GN_DIRECT_L_NOSCAL.m | 166 | utf_8 | c20477ea33399f3311ea6e533dc347ae | % NLOPT_GN_DIRECT_L_NOSCAL: Unscaled DIRECT-L (global, no-derivative)
%
% See nlopt_minimize for more information.
function val = NLOPT_GN_DIRECT_L_NOSCAL
val = 4;
|
github | PECAplus/PECAplus_cmd_line-master | NLOPT_LN_COBYLA.m | .m | PECAplus_cmd_line-master/include_dir/nlopt-2.4.2/octave/NLOPT_LN_COBYLA.m | 189 | utf_8 | 2c95152f70105c8ca20929fba67d12a2 | % NLOPT_LN_COBYLA: COBYLA (Constrained Optimization BY Linear Approximations) (local, no-derivative)
%
% See nlopt_minimize for more information.
function val = NLOPT_LN_COBYLA
val = 25;
|
github | PECAplus/PECAplus_cmd_line-master | NLOPT_LN_AUGLAG_EQ.m | .m | PECAplus_cmd_line-master/include_dir/nlopt-2.4.2/octave/NLOPT_LN_AUGLAG_EQ.m | 189 | utf_8 | 5432778c9b81b5fdcfb98ca1bbbd6486 | % NLOPT_LN_AUGLAG_EQ: Augmented Lagrangian method for equality constraints (local, no-derivative)
%
% See nlopt_minimize for more information.
function val = NLOPT_LN_AUGLAG_EQ
val = 32;
|
github | PECAplus/PECAplus_cmd_line-master | NLOPT_GN_DIRECT_L_RAND.m | .m | PECAplus_cmd_line-master/include_dir/nlopt-2.4.2/octave/NLOPT_GN_DIRECT_L_RAND.m | 164 | utf_8 | 2135dc3891b556738f41c2a35009b227 | % NLOPT_GN_DIRECT_L_RAND: Randomized DIRECT-L (global, no-derivative)
%
% See nlopt_minimize for more information.
function val = NLOPT_GN_DIRECT_L_RAND
val = 2;
|
github | PECAplus/PECAplus_cmd_line-master | NLOPT_GN_MLSL.m | .m | PECAplus_cmd_line-master/include_dir/nlopt-2.4.2/octave/NLOPT_GN_MLSL.m | 169 | utf_8 | 93ab4e64e2760c4ca201fada42cb05c3 | % NLOPT_GN_MLSL: Multi-level single-linkage (MLSL), random (global, no-derivative)
%
% See nlopt_minimize for more information.
function val = NLOPT_GN_MLSL
val = 20;
|
github | PECAplus/PECAplus_cmd_line-master | NLOPT_GD_MLSL_LDS.m | .m | PECAplus_cmd_line-master/include_dir/nlopt-2.4.2/octave/NLOPT_GD_MLSL_LDS.m | 180 | utf_8 | 7f41fd0094df543c2e45280f86c0b87b | % NLOPT_GD_MLSL_LDS: Multi-level single-linkage (MLSL), quasi-random (global, derivative)
%
% See nlopt_minimize for more information.
function val = NLOPT_GD_MLSL_LDS
val = 23;
|
github | PECAplus/PECAplus_cmd_line-master | NLOPT_LD_AUGLAG_EQ.m | .m | PECAplus_cmd_line-master/include_dir/nlopt-2.4.2/octave/NLOPT_LD_AUGLAG_EQ.m | 186 | utf_8 | 41b81c30c553388e2ff1b6b1fb681618 | % NLOPT_LD_AUGLAG_EQ: Augmented Lagrangian method for equality constraints (local, derivative)
%
% See nlopt_minimize for more information.
function val = NLOPT_LD_AUGLAG_EQ
val = 33;
|
github | PECAplus/PECAplus_cmd_line-master | NLOPT_LD_CCSAQ.m | .m | PECAplus_cmd_line-master/include_dir/nlopt-2.4.2/octave/NLOPT_LD_CCSAQ.m | 214 | utf_8 | 33892896fe470090edb583b50b9e7d93 | % NLOPT_LD_CCSAQ: CCSA (Conservative Convex Separable Approximations) with simple quadratic approximations (local, derivative)
%
% See nlopt_minimize for more information.
function val = NLOPT_LD_CCSAQ
val = 41;
|
github | PECAplus/PECAplus_cmd_line-master | NLOPT_LD_AUGLAG.m | .m | PECAplus_cmd_line-master/include_dir/nlopt-2.4.2/octave/NLOPT_LD_AUGLAG.m | 155 | utf_8 | 2d7d515e911b5d0277490940a47f333e | % NLOPT_LD_AUGLAG: Augmented Lagrangian method (local, derivative)
%
% See nlopt_minimize for more information.
function val = NLOPT_LD_AUGLAG
val = 31;
|
github | PECAplus/PECAplus_cmd_line-master | NLOPT_GN_DIRECT_L_RAND_NOSCAL.m | .m | PECAplus_cmd_line-master/include_dir/nlopt-2.4.2/octave/NLOPT_GN_DIRECT_L_RAND_NOSCAL.m | 187 | utf_8 | 3287f776b0f2ac5e3495c2a4c95ba4e7 | % NLOPT_GN_DIRECT_L_RAND_NOSCAL: Unscaled Randomized DIRECT-L (global, no-derivative)
%
% See nlopt_minimize for more information.
function val = NLOPT_GN_DIRECT_L_RAND_NOSCAL
val = 5;
|
github | PECAplus/PECAplus_cmd_line-master | NLOPT_LN_SBPLX.m | .m | PECAplus_cmd_line-master/include_dir/nlopt-2.4.2/octave/NLOPT_LN_SBPLX.m | 196 | utf_8 | c99ee02eb277898e9c509dd359740680 | % NLOPT_LN_SBPLX: Sbplx variant of Nelder-Mead (re-implementation of Rowan's Subplex) (local, no-derivative)
%
% See nlopt_minimize for more information.
function val = NLOPT_LN_SBPLX
val = 29;
|
github | PECAplus/PECAplus_cmd_line-master | NLOPT_GN_ISRES.m | .m | PECAplus_cmd_line-master/include_dir/nlopt-2.4.2/octave/NLOPT_GN_ISRES.m | 173 | utf_8 | 2c12964785aed0828a5ac17fc416f5be | % NLOPT_GN_ISRES: ISRES evolutionary constrained optimization (global, no-derivative)
%
% See nlopt_minimize for more information.
function val = NLOPT_GN_ISRES
val = 35;
|
github | PECAplus/PECAplus_cmd_line-master | NLOPT_LD_VAR2.m | .m | PECAplus_cmd_line-master/include_dir/nlopt-2.4.2/octave/NLOPT_LD_VAR2.m | 168 | utf_8 | 5ba0bd034c240547765a0fd4ce90c825 | % NLOPT_LD_VAR2: Limited-memory variable-metric, rank 2 (local, derivative-based)
%
% See nlopt_minimize for more information.
function val = NLOPT_LD_VAR2
val = 14;
|
github | PECAplus/PECAplus_cmd_line-master | NLOPT_AUGLAG_EQ.m | .m | PECAplus_cmd_line-master/include_dir/nlopt-2.4.2/octave/NLOPT_AUGLAG_EQ.m | 182 | utf_8 | 2f8b59b483a4f2621264c7de9df58365 | % NLOPT_AUGLAG_EQ: Augmented Lagrangian method for equality constraints (needs sub-algorithm)
%
% See nlopt_minimize for more information.
function val = NLOPT_AUGLAG_EQ
val = 37;
|
github | PECAplus/PECAplus_cmd_line-master | NLOPT_LN_NELDERMEAD.m | .m | PECAplus_cmd_line-master/include_dir/nlopt-2.4.2/octave/NLOPT_LN_NELDERMEAD.m | 168 | utf_8 | 219928fdd3fc8317d321f9f0535d550f | % NLOPT_LN_NELDERMEAD: Nelder-Mead simplex algorithm (local, no-derivative)
%
% See nlopt_minimize for more information.
function val = NLOPT_LN_NELDERMEAD
val = 28;
|
github | PECAplus/PECAplus_cmd_line-master | NLOPT_LN_NEWUOA.m | .m | PECAplus_cmd_line-master/include_dir/nlopt-2.4.2/octave/NLOPT_LN_NEWUOA.m | 185 | utf_8 | b318db6792885a5d23dcfd5b28fdc507 | % NLOPT_LN_NEWUOA: NEWUOA unconstrained optimization via quadratic models (local, no-derivative)
%
% See nlopt_minimize for more information.
function val = NLOPT_LN_NEWUOA
val = 26;
|
github | PECAplus/PECAplus_cmd_line-master | NLOPT_GN_CRS2_LM.m | .m | PECAplus_cmd_line-master/include_dir/nlopt-2.4.2/octave/NLOPT_GN_CRS2_LM.m | 185 | utf_8 | 9a171159b83c77e07256a24704139d8a | % NLOPT_GN_CRS2_LM: Controlled random search (CRS2) with local mutation (global, no-derivative)
%
% See nlopt_minimize for more information.
function val = NLOPT_GN_CRS2_LM
val = 19;
|
github | PECAplus/PECAplus_cmd_line-master | NLOPT_LN_PRAXIS.m | .m | PECAplus_cmd_line-master/include_dir/nlopt-2.4.2/octave/NLOPT_LN_PRAXIS.m | 153 | utf_8 | c7d354e0602183d0d3a7ff71641ab7b4 | % NLOPT_LN_PRAXIS: Principal-axis, praxis (local, no-derivative)
%
% See nlopt_minimize for more information.
function val = NLOPT_LN_PRAXIS
val = 12;
|
github | PECAplus/PECAplus_cmd_line-master | NLOPT_LD_SLSQP.m | .m | PECAplus_cmd_line-master/include_dir/nlopt-2.4.2/octave/NLOPT_LD_SLSQP.m | 164 | utf_8 | e767cc9cde902065e67a31664a571819 | % NLOPT_LD_SLSQP: Sequential Quadratic Programming (SQP) (local, derivative)
%
% See nlopt_minimize for more information.
function val = NLOPT_LD_SLSQP
val = 40;
|
github | PECAplus/PECAplus_cmd_line-master | NLOPT_G_MLSL_LDS.m | .m | PECAplus_cmd_line-master/include_dir/nlopt-2.4.2/octave/NLOPT_G_MLSL_LDS.m | 187 | utf_8 | dfafcdb44b43c59aa5e4d5b73542dc50 | % NLOPT_G_MLSL_LDS: Multi-level single-linkage (MLSL), quasi-random (global, needs sub-algorithm)
%
% See nlopt_minimize for more information.
function val = NLOPT_G_MLSL_LDS
val = 39;
|
github | PECAplus/PECAplus_cmd_line-master | NLOPT_LD_TNEWTON.m | .m | PECAplus_cmd_line-master/include_dir/nlopt-2.4.2/octave/NLOPT_LD_TNEWTON.m | 152 | utf_8 | 5e00532d1e34e85f395f6ace048d7d64 | % NLOPT_LD_TNEWTON: Truncated Newton (local, derivative-based)
%
% See nlopt_minimize for more information.
function val = NLOPT_LD_TNEWTON
val = 15;
|
github | PECAplus/PECAplus_cmd_line-master | NLOPT_LD_LBFGS_NOCEDAL.m | .m | PECAplus_cmd_line-master/include_dir/nlopt-2.4.2/octave/NLOPT_LD_LBFGS_NOCEDAL.m | 184 | utf_8 | 7b1380ab03a2272b6e3f1c0f49e4babf | % NLOPT_LD_LBFGS_NOCEDAL: original NON-FREE L-BFGS code by Nocedal et al. (NOT COMPILED)
%
% See nlopt_minimize for more information.
function val = NLOPT_LD_LBFGS_NOCEDAL
val = 10;
|
github | PECAplus/PECAplus_cmd_line-master | NLOPT_GD_STOGO.m | .m | PECAplus_cmd_line-master/include_dir/nlopt-2.4.2/octave/NLOPT_GD_STOGO.m | 137 | utf_8 | 1b374b07cc8dd1fd43998c8f60f49267 | % NLOPT_GD_STOGO: StoGO (global, derivative-based)
%
% See nlopt_minimize for more information.
function val = NLOPT_GD_STOGO
val = 8;
|
github | PECAplus/PECAplus_cmd_line-master | NLOPT_AUGLAG.m | .m | PECAplus_cmd_line-master/include_dir/nlopt-2.4.2/octave/NLOPT_AUGLAG.m | 151 | utf_8 | a4a6ef23ad60f4ceab26caae4276c02d | % NLOPT_AUGLAG: Augmented Lagrangian method (needs sub-algorithm)
%
% See nlopt_minimize for more information.
function val = NLOPT_AUGLAG
val = 36;
|
github | PECAplus/PECAplus_cmd_line-master | NLOPT_GD_MLSL.m | .m | PECAplus_cmd_line-master/include_dir/nlopt-2.4.2/octave/NLOPT_GD_MLSL.m | 166 | utf_8 | 3735615f01a5de64ba710bfa7b7eba3a | % NLOPT_GD_MLSL: Multi-level single-linkage (MLSL), random (global, derivative)
%
% See nlopt_minimize for more information.
function val = NLOPT_GD_MLSL
val = 21;
|
github | PECAplus/PECAplus_cmd_line-master | NLOPT_GN_DIRECT_NOSCAL.m | .m | PECAplus_cmd_line-master/include_dir/nlopt-2.4.2/octave/NLOPT_GN_DIRECT_NOSCAL.m | 160 | utf_8 | 9f11af031bd7ca9c9348de64f7169482 | % NLOPT_GN_DIRECT_NOSCAL: Unscaled DIRECT (global, no-derivative)
%
% See nlopt_minimize for more information.
function val = NLOPT_GN_DIRECT_NOSCAL
val = 3;
|
github | PECAplus/PECAplus_cmd_line-master | NLOPT_LN_AUGLAG.m | .m | PECAplus_cmd_line-master/include_dir/nlopt-2.4.2/octave/NLOPT_LN_AUGLAG.m | 158 | utf_8 | ec49cb21c2b454870a401a1c5afd1058 | % NLOPT_LN_AUGLAG: Augmented Lagrangian method (local, no-derivative)
%
% See nlopt_minimize for more information.
function val = NLOPT_LN_AUGLAG
val = 30;
|
github | PECAplus/PECAplus_cmd_line-master | NLOPT_GN_ESCH.m | .m | PECAplus_cmd_line-master/include_dir/nlopt-2.4.2/octave/NLOPT_GN_ESCH.m | 130 | utf_8 | 6cad4da90ce4145ad2b1dafcf286286f | % NLOPT_GN_ESCH: ESCH evolutionary strategy
%
% See nlopt_minimize for more information.
function val = NLOPT_GN_ESCH
val = 42;
|
github | PECAplus/PECAplus_cmd_line-master | NLOPT_G_MLSL.m | .m | PECAplus_cmd_line-master/include_dir/nlopt-2.4.2/octave/NLOPT_G_MLSL.m | 173 | utf_8 | fd60012f2c05bdeb027ee6dee44175fb | % NLOPT_G_MLSL: Multi-level single-linkage (MLSL), random (global, needs sub-algorithm)
%
% See nlopt_minimize for more information.
function val = NLOPT_G_MLSL
val = 38;
|
github | PECAplus/PECAplus_cmd_line-master | nlopt_minimize_constrained.m | .m | PECAplus_cmd_line-master/include_dir/nlopt-2.4.2/octave/nlopt_minimize_constrained.m | 5,174 | utf_8 | 2093a6be53db585559168905f1fc1e4a | % Usage: [xopt, fmin, retcode] = nlopt_minimize_constrained
% (algorithm, f, f_data,
% fc, fc_data, lb, ub,
% xinit, stop)
%
% Minimizes a nonlinear multivariable function f(x, f_data{:}), subjec... |
github | PECAplus/PECAplus_cmd_line-master | NLOPT_LN_NEWUOA_BOUND.m | .m | PECAplus_cmd_line-master/include_dir/nlopt-2.4.2/octave/NLOPT_LN_NEWUOA_BOUND.m | 207 | utf_8 | 67ca43d03f5347c97069a86e3e73a7e4 | % NLOPT_LN_NEWUOA_BOUND: Bound-constrained optimization via NEWUOA-based quadratic models (local, no-derivative)
%
% See nlopt_minimize for more information.
function val = NLOPT_LN_NEWUOA_BOUND
val = 27;
|
github | PECAplus/PECAplus_cmd_line-master | nlopt_minimize.m | .m | PECAplus_cmd_line-master/include_dir/nlopt-2.4.2/octave/nlopt_minimize.m | 3,978 | utf_8 | f71b68688b460e0440ff89bdf086d2a7 | % Usage: [xopt, fmin, retcode] = nlopt_minimize(algorithm, f, f_data, lb, ub,
% xinit, stop)
%
% Minimizes a nonlinear multivariable function f(x, f_data{:}), where
% x is a row vector, returning the optimal x found (xopt) along with
% the minimum function value (fmin = f(x... |
github | PECAplus/PECAplus_cmd_line-master | NLOPT_GN_ORIG_DIRECT_L.m | .m | PECAplus_cmd_line-master/include_dir/nlopt-2.4.2/octave/NLOPT_GN_ORIG_DIRECT_L.m | 170 | utf_8 | d61c095f05bf7fd4fbbf2ddbe22c58a4 | % NLOPT_GN_ORIG_DIRECT_L: Original DIRECT-L version (global, no-derivative)
%
% See nlopt_minimize for more information.
function val = NLOPT_GN_ORIG_DIRECT_L
val = 7;
|
github | PECAplus/PECAplus_cmd_line-master | NLOPT_LD_TNEWTON_RESTART.m | .m | PECAplus_cmd_line-master/include_dir/nlopt-2.4.2/octave/NLOPT_LD_TNEWTON_RESTART.m | 184 | utf_8 | a482ddde1f6d6b386a3810fc142829a2 | % NLOPT_LD_TNEWTON_RESTART: Truncated Newton with restarting (local, derivative-based)
%
% See nlopt_minimize for more information.
function val = NLOPT_LD_TNEWTON_RESTART
val = 16;
|
github | PECAplus/PECAplus_cmd_line-master | NLOPT_LD_TNEWTON_PRECOND_RESTART.m | .m | PECAplus_cmd_line-master/include_dir/nlopt-2.4.2/octave/NLOPT_LD_TNEWTON_PRECOND_RESTART.m | 215 | utf_8 | 1082f147b249cc1f7e564aa3b2462048 | % NLOPT_LD_TNEWTON_PRECOND_RESTART: Preconditioned truncated Newton with restarting (local, derivative-based)
%
% See nlopt_minimize for more information.
function val = NLOPT_LD_TNEWTON_PRECOND_RESTART
val = 18;
|
github | PECAplus/PECAplus_cmd_line-master | NLOPT_LD_TNEWTON_PRECOND.m | .m | PECAplus_cmd_line-master/include_dir/nlopt-2.4.2/octave/NLOPT_LD_TNEWTON_PRECOND.m | 183 | utf_8 | 167e47e5e372152ffba7552308e1193f | % NLOPT_LD_TNEWTON_PRECOND: Preconditioned truncated Newton (local, derivative-based)
%
% See nlopt_minimize for more information.
function val = NLOPT_LD_TNEWTON_PRECOND
val = 17;
|
github | PECAplus/PECAplus_cmd_line-master | NLOPT_GD_STOGO_RAND.m | .m | PECAplus_cmd_line-master/include_dir/nlopt-2.4.2/octave/NLOPT_GD_STOGO_RAND.m | 170 | utf_8 | fea04c6327afd49e02ff536330527f60 | % NLOPT_GD_STOGO_RAND: StoGO with randomized search (global, derivative-based)
%
% See nlopt_minimize for more information.
function val = NLOPT_GD_STOGO_RAND
val = 9;
|
github | PECAplus/PECAplus_cmd_line-master | NLOPT_LD_LBFGS.m | .m | PECAplus_cmd_line-master/include_dir/nlopt-2.4.2/octave/NLOPT_LD_LBFGS.m | 160 | utf_8 | 02936ac48fc420cd73b5a6848808dd68 | % NLOPT_LD_LBFGS: Limited-memory BFGS (L-BFGS) (local, derivative-based)
%
% See nlopt_minimize for more information.
function val = NLOPT_LD_LBFGS
val = 11;
|
github | PECAplus/PECAplus_cmd_line-master | NLOPT_GN_MLSL_LDS.m | .m | PECAplus_cmd_line-master/include_dir/nlopt-2.4.2/octave/NLOPT_GN_MLSL_LDS.m | 183 | utf_8 | 0fb34e2ebed6204b06925782f5fc2417 | % NLOPT_GN_MLSL_LDS: Multi-level single-linkage (MLSL), quasi-random (global, no-derivative)
%
% See nlopt_minimize for more information.
function val = NLOPT_GN_MLSL_LDS
val = 22;
|
github | chili-epfl/openpose-master | classification_demo.m | .m | openpose-master/3rdparty/caffe/matlab/demo/classification_demo.m | 5,466 | utf_8 | 45745fb7cfe37ef723c307dfa06f1b97 | 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 | Coderx7/Caffe_1.0_Windows-master | classification_demo.m | .m | Caffe_1.0_Windows-master/matlab/demo/classification_demo.m | 5,466 | utf_8 | 45745fb7cfe37ef723c307dfa06f1b97 | 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 | ekalkan/P-PHASE-PICKER-master | PphasePicker.m | .m | P-PHASE-PICKER-master/PphasePicker.m | 10,196 | utf_8 | 821c0c9d6e38fdaf48b1805ae6430f61 | function [loc, snr_db] = PphasePicker(x, dt, type, pflag, Tn, xi, nbins, o)
% AN AUTOMATIC P-PHASE ARRIVAL TIME PICKER
%
% Computes P-phase arrival time in windowed digital single-component
% acceleration or broadband velocity record without requiring threshold
% settings. Returns P-phase arrival time in second... |
github | ISET/iset3d-v3-master | autoArrangeFigures.m | .m | iset3d-v3-master/scripts/psf/autoArrangeFigures.m | 2,693 | utf_8 | 9ff956614f21a392f0554ec3c62a4984 | function autoArrangeFigures(NH, NW, monitor_id)
% INPUT :
% NH : number of grid of vertical direction
% NW : number of grid of horizontal direction
% OUTPUT :
%
% get every figures that are opened now and arrange them.
%
% autoArrangeFigures selects automatically Monitor1.
% If you are dual(or more than ... |
github | ISET/iset3d-v3-master | s_piReadRenderLF.m | .m | iset3d-v3-master/scripts/pbrtV2/s_piReadRenderLF.m | 4,285 | utf_8 | da68d5c9ccbb68e19f3fa1cc518e6894 | % s_piReadRenderLF
% *** DEPRECATED ****
%
% ****** No longer working ********
%
% I think this was based on the original Andy Lin microlens code, not the
% newer Michael Mara version. Should be updated.
%
%
% Implements a light field camera system with an array of microlenses over a
% sensor. Converts the OI int... |
github | ISET/iset3d-v3-master | piRecipeRectify.m | .m | iset3d-v3-master/utilities/piRecipeRectify.m | 6,556 | utf_8 | c633e5dc10b6bf17c426d55927a872a9 | function thisR = piRecipeRectify(thisR,varargin)
% Move the camera and objects so that the origin is 0 and the view
% direction is the z-axis
%
% Description
%
% Inputs
% thisR
%
% Optional key/val pairs
% rotate - Logical to supress rotation. Default is true
%
% Return
% thisR
%
% See piRotate for the rotation ... |
github | ISET/iset3d-v3-master | piWrite.m | .m | iset3d-v3-master/utilities/piWrite.m | 23,855 | utf_8 | 74996513235188090a85988c93a0337a | function workingDir = piWrite(thisR,varargin)
% Write a PBRT scene file based on its renderRecipe
%
% Syntax
% workingDir = piWrite(thisR,varargin)
%
% The pbrt scene file and all the relevant resource files (geometry,
% materials, spds, others) are written out in a working directory. These
% are the files that will ... |
github | ISET/iset3d-v3-master | piRecipRectify.m | .m | iset3d-v3-master/utilities/piRecipRectify.m | 5,470 | utf_8 | 31f356831c4e47d979027ec8af3443f8 | function thisR = piRecipRectify(thisR,origin)
% Move the camera and objects so that the camera is at origin pointed along
% the z-axis
%
% Description
%
% See piRotate for the rotation matrices for the three axes
% See also
%
% Examples
%{
thisR = piRecipeDefault('scene name','simple scene');
piAssetGeometry(thisR,'... |
github | ISET/iset3d-v3-master | piWebGet.m | .m | iset3d-v3-master/utilities/piWebGet.m | 11,212 | utf_8 | 881910654e170229a04f37bcc7676b17 | function localFile = ieWebGet2(varargin)
%% Download a resource from the Stanford web site
%
% Synopsis
% localFile = ieWebGet2(varargin)
%
% Brief description
% Download an ISET zip or mat-file file from the web. The type of file
% and the remote file name define how to get the file.
%
% Inputs
% varargin{1} - ... |
github | ISET/iset3d-v3-master | piDCM2angle.m | .m | iset3d-v3-master/utilities/geometry/piDCM2angle.m | 3,290 | utf_8 | 26048adb702b41e7f73876c60114ed82 | function [r1,r2,r3] = piDCM2angle( dcm, varargin )
% Simplified version of the Aerospace toolbox angle conversion utility
%
% Syntax:
% [r1,r2,r3] = piDCM2angle( dcm )
%
% Brief description:
% The case that we compute is this one. There are many others and
% various options in the Mathworks dcm2angle.m code
%
% ... |
github | ISET/iset3d-v3-master | piGeometryWrite.m | .m | iset3d-v3-master/utilities/geometry/piGeometryWrite.m | 14,203 | utf_8 | 3b095877b9bd5b703e37fbfc0b1af90c | function piGeometryWrite(thisR,varargin)
% Write out a geometry file that matches the format and labeling objects
%
% Synopsis
% piGeometryWrite(thisR,varargin)
%
% Input:
% thisR: a render recipe
% obj: Returned by piGeometryRead, contains information about objects.
%
% Optional key/value pairs
%
% Ou... |
github | ISET/iset3d-v3-master | piGeometryRead.m | .m | iset3d-v3-master/utilities/geometry/piGeometryRead.m | 11,214 | utf_8 | 4c51e4a64d4970dfbc39bce423936f8c | function thisR = piGeometryRead(thisR)
% Read a C4d geometry file and extract object information into a recipe
%
% Syntax:
% renderRecipe = piGeometryRead(renderRecipe)
%
% Input
% renderRecipe: an iset3d recipe object describing the rendering
% parameters. This object includes the inputFile and the
% out... |
github | ISET/iset3d-v3-master | piParseShape.m | .m | iset3d-v3-master/utilities/parse/piParseShape.m | 4,291 | utf_8 | 58163ce1cf2b2cbde45be66a5fe652e4 | function shape = piParseShape(txt)
% Parse the shape information into struct
% Logic:
% Normally the shape line has this format:
% 'Shape "SHAPE" "integerindices" [] "point P" []
% "float uv" [] "normal N" []'
% We split the string based on the '"' and get each component
%
% Test
%{
thisR = piRecipeDefault('s... |
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