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github | qijiezhao/MachineLearning-master | submitWeb.m | .m | MachineLearning-master/Exercise 6/ex6/submitWeb.m | 827 | utf_8 | bfb2fa08cac9d8d797e3071d3fdd7ca1 | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on ... |
github | wupeng78/Hierarchical-Convolutional-Features-for-Visual-Tracking-master | run_tracker.m | .m | Hierarchical-Convolutional-Features-for-Visual-Tracking-master/run_tracker.m | 4,331 | utf_8 | aef0702241156e8f98ec391c1cd30955 | % RUN_TRACKER: process a specified video using CF2
%
% Input:
% - video: the name of the selected video
% - show_visualization: set to True for visualizing tracking results
% - show_plots: set to True for plotting quantitative results
% Output:
% - precision: precision thre... |
github | wupeng78/Hierarchical-Convolutional-Features-for-Visual-Tracking-master | tracker_ensemble.m | .m | Hierarchical-Convolutional-Features-for-Visual-Tracking-master/tracker_ensemble.m | 7,947 | utf_8 | 610b3a9a2bd353121cef30792685d704 | % tracker_ensemble: Correlation filter tracking with convolutional features
%
% Input:
% - video_path: path to the image sequence
% - img_files: list of image names
% - pos: intialized center position of the target in (row, col)
% - target_sz: intialized target size ... |
github | wupeng78/Hierarchical-Convolutional-Features-for-Visual-Tracking-master | get_features.m | .m | Hierarchical-Convolutional-Features-for-Visual-Tracking-master/utility/get_features.m | 866 | utf_8 | 610958422bd8e266d66f7ee220801c45 | % GET_FEATURES: Extracting hierachical convolutional features
function feat = get_features(im, cos_window, layers)
global net
global enableGPU
if isempty(net)
initial_net();
end
sz_window = size(cos_window);
% Preprocessing
img = single(im); % note: [0, 255] range
img = imResample(img, net.normalization... |
github | wupeng78/Hierarchical-Convolutional-Features-for-Visual-Tracking-master | get_subwindow.m | .m | Hierarchical-Convolutional-Features-for-Visual-Tracking-master/utility/get_subwindow.m | 844 | utf_8 | 6d98ab20898d9f62b02062481caf2a0c | function out = get_subwindow(im, pos, sz)
%GET_SUBWINDOW Obtain sub-window from image, with replication-padding.
% Returns sub-window of image IM centered at POS ([y, x] coordinates),
% with size SZ ([height, width]). If any pixels are outside of the image,
% they will replicate the values at the borders.
%
% J... |
github | wupeng78/Hierarchical-Convolutional-Features-for-Visual-Tracking-master | p_getImageFilenamesFromDirectory.m | .m | Hierarchical-Convolutional-Features-for-Visual-Tracking-master/external/tools/p_getImageFilenamesFromDirectory.m | 2,073 | utf_8 | 65a1bafeaa60531c73f5acc8148a7dd0 | % Copyright (c) 2009
% Jakob Santner
% Institute for Computer Graphics and Vision (ICG)
% Graz University of Technology
% Inffeldgasse 16/II
% 8010 Graz, AUSTRIA
%
% Permission is hereby granted, free of charge, to any person obtaining a
% copy of this software and associated documentation files (the
% "Software"), ... |
github | wupeng78/Hierarchical-Convolutional-Features-for-Visual-Tracking-master | p_writeLog.m | .m | Hierarchical-Convolutional-Features-for-Visual-Tracking-master/external/tools/p_writeLog.m | 1,741 | utf_8 | 58a8994d8d0440fb8411cabbcc221319 | % Copyright (c) 2009
% Jakob Santner
% Institute for Computer Graphics and Vision (ICG)
% Graz University of Technology
% Inffeldgasse 16/II
% 8010 Graz, AUSTRIA
%
% Permission is hereby granted, free of charge, to any person obtaining a
% copy of this software and associated documentation files (the
% "Software"), ... |
github | wupeng78/Hierarchical-Convolutional-Features-for-Visual-Tracking-master | p_drawRect.m | .m | Hierarchical-Convolutional-Features-for-Visual-Tracking-master/external/tools/p_drawRect.m | 1,863 | utf_8 | cf21589b84dd8763dbc3f35822455ee6 | % Copyright (c) 2009
% Jakob Santner
% Institute for Computer Graphics and Vision (ICG)
% Graz University of Technology
% Inffeldgasse 16/II
% 8010 Graz, AUSTRIA
%
% Permission is hereby granted, free of charge, to any person obtaining a
% copy of this software and associated documentation files (the
% "Software"), to ... |
github | wupeng78/Hierarchical-Convolutional-Features-for-Visual-Tracking-master | p_readLog.m | .m | Hierarchical-Convolutional-Features-for-Visual-Tracking-master/external/tools/p_readLog.m | 1,641 | utf_8 | 2a4ae33234fb76fd9a65fbec9d5138a8 | % Copyright (c) 2009
% Jakob Santner
% Institute for Computer Graphics and Vision (ICG)
% Graz University of Technology
% Inffeldgasse 16/II
% 8010 Graz, AUSTRIA
%
% Permission is hereby granted, free of charge, to any person obtaining a
% copy of this software and associated documentation files (the
% "Software"), ... |
github | wupeng78/Hierarchical-Convolutional-Features-for-Visual-Tracking-master | p_computeCenterDistanceRect.m | .m | Hierarchical-Convolutional-Features-for-Visual-Tracking-master/external/tools/p_computeCenterDistanceRect.m | 1,654 | utf_8 | 3e9490f8aba83c4e483380f9e1e4412f | % Copyright (c) 2009
% Jakob Santner
% Institute for Computer Graphics and Vision (ICG)
% Graz University of Technology
% Inffeldgasse 16/II
% 8010 Graz, AUSTRIA
%
% Permission is hereby granted, free of charge, to any person obtaining a
% copy of this software and associated documentation files (the
% "Software"), ... |
github | wupeng78/Hierarchical-Convolutional-Features-for-Visual-Tracking-master | p_createVideoSequence.m | .m | Hierarchical-Convolutional-Features-for-Visual-Tracking-master/external/tools/p_createVideoSequence.m | 3,243 | utf_8 | 25d1272582b6261f8ad50caa69313ac2 | % Copyright (c) 2009
% Jakob Santner
% Institute for Computer Graphics and Vision (ICG)
% Graz University of Technology
% Inffeldgasse 16/II
% 8010 Graz, AUSTRIA
%
% Permission is hereby granted, free of charge, to any person obtaining a
% copy of this software and associated documentation files (the
% "Software"), ... |
github | wupeng78/Hierarchical-Convolutional-Features-for-Visual-Tracking-master | p_evalSequence.m | .m | Hierarchical-Convolutional-Features-for-Visual-Tracking-master/external/tools/p_evalSequence.m | 3,336 | utf_8 | 782b0e2a79b65b10fb2c77816ee35169 | % Copyright (c) 2009
% Jakob Santner
% Institute for Computer Graphics and Vision (ICG)
% Graz University of Technology
% Inffeldgasse 16/II
% 8010 Graz, AUSTRIA
%
% Permission is hereby granted, free of charge, to any person obtaining a
% copy of this software and associated documentation files (the
% "Software"), ... |
github | wupeng78/Hierarchical-Convolutional-Features-for-Visual-Tracking-master | p_computePascalScoreRect.m | .m | Hierarchical-Convolutional-Features-for-Visual-Tracking-master/external/tools/p_computePascalScoreRect.m | 2,002 | utf_8 | 8a4a3e85f392e8fec2fccb17fba5771f | % Copyright (c) 2009
% Jakob Santner
% Institute for Computer Graphics and Vision (ICG)
% Graz University of Technology
% Inffeldgasse 16/II
% 8010 Graz, AUSTRIA
%
% Permission is hereby granted, free of charge, to any person obtaining a
% copy of this software and associated documentation files (the
% "Software"), ... |
github | wupeng78/Hierarchical-Convolutional-Features-for-Visual-Tracking-master | cnn_cifar.m | .m | Hierarchical-Convolutional-Features-for-Visual-Tracking-master/external/matconvnet/examples/cnn_cifar.m | 5,513 | utf_8 | 59f780e7e4c836f62caeede920e50afa | function cnn_cifar(varargin)
% CNN_CIFAR Demonstrates MatConvNet on CIFAR
run(fullfile(fileparts(mfilename('fullpath')), ...
'..', 'matlab', 'vl_setupnn.m')) ;
opts.dataDir = fullfile('data','cifar') ;
opts.expDir = fullfile('data','cifar-baseline') ;
opts.imdbPath = fullfile(opts.expDir, 'imdb.mat');
opts.train.... |
github | wupeng78/Hierarchical-Convolutional-Features-for-Visual-Tracking-master | cnn_imagenet.m | .m | Hierarchical-Convolutional-Features-for-Visual-Tracking-master/external/matconvnet/examples/cnn_imagenet.m | 9,564 | utf_8 | c367d3378c58c3fcd280ecb7fe40237d | function cnn_imagenet(varargin)
% CNN_IMAGENET Demonstrates training a CNN on ImageNet
run(fullfile(fileparts(mfilename('fullpath')), ...
'..', 'matlab', 'vl_setupnn.m')) ;
opts.dataDir = fullfile('data','imagenet12') ;
opts.expDir = fullfile('data','imagenet12-baseline') ;
[opts, varargin] = vl_argparse(opts, va... |
github | wupeng78/Hierarchical-Convolutional-Features-for-Visual-Tracking-master | cnn_mnist.m | .m | Hierarchical-Convolutional-Features-for-Visual-Tracking-master/external/matconvnet/examples/cnn_mnist.m | 4,802 | utf_8 | de07039a31bdf53e479516978eb49b40 | function [net, info] = cnn_mnist(varargin)
% CNN_MNIST Demonstrated MatConNet on MNIST
run(fullfile(fileparts(mfilename('fullpath')),'..','matlab','vl_setupnn.m')) ;
opts.dataDir = fullfile('data','mnist') ;
opts.expDir = fullfile('data','mnist-baseline') ;
opts.imdbPath = fullfile(opts.expDir, 'imdb.mat');
opts.tra... |
github | wupeng78/Hierarchical-Convolutional-Features-for-Visual-Tracking-master | cnn_train.m | .m | Hierarchical-Convolutional-Features-for-Visual-Tracking-master/external/matconvnet/examples/cnn_train.m | 9,795 | utf_8 | f7fea080c42f15bf429eda82b48b102f | function [net, info] = cnn_train(net, imdb, getBatch, varargin)
% CNN_TRAIN Demonstrates training a CNN
% CNN_TRAIN() is an example learner implementing stochastic gradient
% descent with momentum to train a CNN for image classification.
% It can be used with different datasets by providing a suitable
% g... |
github | wupeng78/Hierarchical-Convolutional-Features-for-Visual-Tracking-master | cnn_imagenet_evaluate.m | .m | Hierarchical-Convolutional-Features-for-Visual-Tracking-master/external/matconvnet/examples/cnn_imagenet_evaluate.m | 2,726 | utf_8 | 2f3d1847fbe22a45f35687ae096a0376 | function info = cnn_imagenet_evaluate(varargin)
% CNN_IMAGENET_EVALUATE Evauate MatConvNet models on ImageNet
run(fullfile(fileparts(mfilename('fullpath')), ...
'..', 'matlab', 'vl_setupnn.m')) ;
opts.dataDir = fullfile('data', 'imagenet12') ;
opts.expDir = fullfile('data', 'imagenet12-eval-vgg-f') ;
opts.imdbPat... |
github | wupeng78/Hierarchical-Convolutional-Features-for-Visual-Tracking-master | vl_compilenn.m | .m | Hierarchical-Convolutional-Features-for-Visual-Tracking-master/external/matconvnet/matlab/vl_compilenn.m | 20,725 | utf_8 | bcbc23d4f290bcdbb238588847fc34de | function vl_compilenn( varargin )
% VL_COMPILENN Compile the MatConvNet toolbox
% The `vl_compilenn()` function compiles the MEX files in the
% MatConvNet toolbox. See below for the requirements for compiling
% CPU and GPU code, respectively.
%
% `vl_compilenn('OPTION', ARG, ...)` accepts the following opt... |
github | wupeng78/Hierarchical-Convolutional-Features-for-Visual-Tracking-master | vl_simplenn_display.m | .m | Hierarchical-Convolutional-Features-for-Visual-Tracking-master/external/matconvnet/matlab/vl_simplenn_display.m | 5,109 | utf_8 | 8db9db2733c5d41b239d87806680a068 | function info = vl_simplenn_display(net, res)
% VL_SIMPLENN_DISPLAY Simple CNN statistics
% VL_SIMPLENN_DISPLAY(NET) prints statistics about the network NET.
% Copyright (C) 2014 Andrea Vedaldi.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD li... |
github | gpapamak/distilling_intractable_generative_models-master | checkgrad.m | .m | distilling_intractable_generative_models-master/nade/@Nade/checkgrad.m | 2,187 | utf_8 | 2904ea951257507908e6a80f50fa961e | function [err, err_p, err_x] = checkgrad(obj, Np, Nx, eps)
% Checks the derivatives of nade using finite differences.
% INPUTS
% Np number of sets of parameters to test for (optional)
% Nx number of sets of inputs to test for (optional)
% eps epsilon for finite differences (optional)
% O... |
github | gpapamak/distilling_intractable_generative_models-master | train_stream.m | .m | distilling_intractable_generative_models-master/nade/@Nade/train_stream.m | 6,791 | utf_8 | 06d8d82e2868f059b6d2e8b41b07b914 | function [progress] = train_stream(obj, stream, varargin)
% Trains nade given a data steam by maximizing its average log probability
% using stochastic gradient descent.
% INPUTS
% stream data stream which generates train data
% -- optional name-value pairs --
% loss loss function to mini... |
github | gpapamak/distilling_intractable_generative_models-master | estimate_logZ.m | .m | distilling_intractable_generative_models-master/rbm/@Rbm/estimate_logZ.m | 4,006 | utf_8 | 1f201fcc11b2bb61b7f62e3346e76654 | function [logZ, conf] = estimate_logZ(obj, nade, method, N)
% Estimates the log partition function of the rbm using sampling with nade
% as proposal distribution.
% INPUT
% nade nade to use as proposal distribution
% method sampling method to use
% N number of samples to use (optional)
% OUTPUT
% ... |
github | unizard/Colorization-project-master | RF.m | .m | Colorization-project-master/RF.m | 4,737 | utf_8 | 4686097d1145a71022222375ef15fdaf | % RF Domain transform recursive edge-preserving filter.
%
% F = RF(img, sigma_s, sigma_r, num_iterations, joint_image)
%
% Parameters:
% img Input image to be filtered.
% sigma_s Filter spatial standard deviation.
% sigma_r Filter range standard deviation.
% num_iter... |
github | unizard/Colorization-project-master | LMgist.m | .m | Colorization-project-master/LMgist.m | 8,334 | utf_8 | 565187beedf76f1ae5deb90400322c6d | function [gist, param] = LMgist(D, HOMEIMAGES, param, HOMEGIST)
%
% [gist, param] = LMgist(D, HOMEIMAGES, param);
% [gist, param] = LMgist(filename, HOMEIMAGES, param);
% [gist, param] = LMgist(filename, HOMEIMAGES, param, HOMEGIST);
%
% For a set of images:
% gist = LMgist(img, [], param);
%
% When calling LMgist with... |
github | unizard/Colorization-project-master | load_cubes.m | .m | Colorization-project-master/Daisy/load_cubes.m | 966 | utf_8 | bb93760cab952913274f8f972a348e60 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Written and (C) by %
% Engin Tola %
% ... |
github | unizard/Colorization-project-master | load_gradient_layers.m | .m | Colorization-project-master/Daisy/load_gradient_layers.m | 818 | utf_8 | 56ba27604c8b8e243b8e0b323cda2dc5 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Written and (C) by %
% Engin Tola %
% ... |
github | unizard/Colorization-project-master | smooth_layers.m | .m | Colorization-project-master/Daisy/smooth_layers.m | 948 | utf_8 | 8e4e07fe898568ee3895e5c277d0c8f0 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Written and (C) by %
% Engin Tola %
% ... |
github | unizard/Colorization-project-master | normalize_sift.m | .m | Colorization-project-master/Daisy/normalize_sift.m | 999 | utf_8 | 3a6f6b3b78d9a9b2ced32cf0e0eb5a8e | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Written and (C) by %
% Engin Tola %
% ... |
github | unizard/Colorization-project-master | init_daisy.m | .m | Colorization-project-master/Daisy/init_daisy.m | 5,099 | utf_8 | 22f9d601147f7bf3056c0b2dcb37eab1 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Written and (C) by %
% Engin Tola %
% ... |
github | unizard/Colorization-project-master | display_descriptor.m | .m | Colorization-project-master/Daisy/display_descriptor.m | 874 | utf_8 | 7856c5daf584d34f670d5423c27881b2 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Written and (C) by %
% Engin Tola %
% ... |
github | unizard/Colorization-project-master | normalize_full.m | .m | Colorization-project-master/Daisy/normalize_full.m | 817 | utf_8 | 3b1a9503de84a29510a54c475161189a | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Written and (C) by %
% Engin Tola %
% ... |
github | unizard/Colorization-project-master | u_compute_descriptor_11.m | .m | Colorization-project-master/Daisy/u_compute_descriptor_11.m | 1,638 | utf_8 | 22d9a9d91c0c2b072347361ae80011c0 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Written and (C) by %
% Engin Tola %
% ... |
github | unizard/Colorization-project-master | rgb_to_gray.m | .m | Colorization-project-master/Daisy/rgb_to_gray.m | 944 | utf_8 | 0ceeb906b6462c4a890558d04a8420f7 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Written and (C) by %
% Engin Tola %
% ... |
github | unizard/Colorization-project-master | u_compute_descriptor_10.m | .m | Colorization-project-master/Daisy/u_compute_descriptor_10.m | 1,487 | utf_8 | 9b5898ac2e02f3ef03f9c7953610dbd3 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Written and (C) by %
% Engin Tola %
% ... |
github | unizard/Colorization-project-master | filter_size.m | .m | Colorization-project-master/Daisy/filter_size.m | 816 | utf_8 | 26467375e11baa2934cefcab7d49a59a | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Written and (C) by %
% Engin Tola %
% ... |
github | unizard/Colorization-project-master | u_compute_descriptor_01.m | .m | Colorization-project-master/Daisy/u_compute_descriptor_01.m | 1,362 | utf_8 | 232f65067b178366c55b6460958144f5 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Written and (C) by %
% Engin Tola %
% ... |
github | unizard/Colorization-project-master | compute_descriptor.m | .m | Colorization-project-master/Daisy/compute_descriptor.m | 1,245 | utf_8 | 8360532d3913077706f687278150a74e | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Written and (C) by %
% Engin Tola %
% ... |
github | unizard/Colorization-project-master | u_compute_descriptor_00.m | .m | Colorization-project-master/Daisy/u_compute_descriptor_00.m | 1,221 | utf_8 | da624997fc5d8300944ff10c246d0966 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Written and (C) by %
% Engin Tola %
% ... |
github | unizard/Colorization-project-master | compute_daisy.m | .m | Colorization-project-master/Daisy/compute_daisy.m | 2,172 | utf_8 | 4214336d1e9ac7063ccbd926fbd18d4b | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Written and (C) by %
% Engin Tola %
% ... |
github | unizard/Colorization-project-master | layered_gradient.m | .m | Colorization-project-master/Daisy/layered_gradient.m | 1,200 | utf_8 | cb4f5bd05db59d85b7c833b4c0a1eb7b | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Written and (C) by %
% Engin Tola %
% ... |
github | unizard/Colorization-project-master | gaussian_1d.m | .m | Colorization-project-master/Daisy/gaussian_1d.m | 841 | utf_8 | 63e2b79ef06e6ad7e272bc2ec8700d60 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Written and (C) by %
% Engin Tola %
% ... |
github | unizard/Colorization-project-master | normalize_partial.m | .m | Colorization-project-master/Daisy/normalize_partial.m | 840 | utf_8 | f743f08ee3ca474a130e668adc546b56 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Written and (C) by %
% Engin Tola %
% ... |
github | unizard/Colorization-project-master | load_sgradient_layers.m | .m | Colorization-project-master/Daisy/load_sgradient_layers.m | 820 | utf_8 | fd4789cc27a24feb5cc4efc776be6dd6 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Written and (C) by %
% Engin Tola %
% ... |
github | mriosb08/palodiem-QE-master | d2p.m | .m | palodiem-QE-master/src/matlab/common/d2p.m | 3,155 | utf_8 | dc48b1dd0688d11671d81af50a0970de | function [P, beta] = d2p(D, u, tol)
%D2P Identifies appropriate sigma's to get kk NNs up to some tolerance
%
% [P, beta] = d2p(D, kk, tol)
%
% Identifies the required precision (= 1 / variance^2) to obtain a Gaussian
% kernel with a certain uncertainty for every datapoint. The desired
% uncertainty can be specified... |
github | mriosb08/palodiem-QE-master | x2p.m | .m | palodiem-QE-master/src/matlab/common/x2p.m | 3,297 | utf_8 | e0d6a8b9bcdd6ebd97037e46252fe200 | function [P, beta] = x2p(X, u, tol)
%X2P Identifies appropriate sigma's to get kk NNs up to some tolerance
%
% [P, beta] = x2p(xx, kk, tol)
%
% Identifies the required precision (= 1 / variance^2) to obtain a Gaussian
% kernel with a certain uncertainty for every datapoint. The desired
% uncertainty can be specifie... |
github | mriosb08/palodiem-QE-master | sparseAutoencoderCost.m | .m | palodiem-QE-master/src/matlab/common/sparseAutoencoderCost.m | 4,637 | utf_8 | b9adb280182e9c91f9ff8c56729d2cb1 | function [cost,grad] = sparseAutoencoderCost(theta, visibleSize, hiddenSize, ...
lambda, sparsityParam, beta, data)
% visibleSize: the number of input units (probably 64)
% hiddenSize: the number of hidden units (probably 25)
% lambda: weight decay parameter
% sparsityPar... |
github | mriosb08/palodiem-QE-master | stackedAEPredict.m | .m | palodiem-QE-master/src/matlab/common/stackedAEPredict.m | 1,526 | utf_8 | 019227b5266fe2d530054908e9172483 | function [pred] = stackedAEPredict(theta, inputSize, hiddenSize, numClasses, netconfig, data)
% stackedAEPredict: Takes a trained theta and a test data set,
% and returns the predicted labels for each example.
% theta: trained weights f... |
github | mriosb08/palodiem-QE-master | feedForwardAutoencoder.m | .m | palodiem-QE-master/src/matlab/common/feedForwardAutoencoder.m | 1,332 | utf_8 | 48b3aece47a28e4c49643323aa097154 | function [activation] = feedForwardAutoencoder(theta, hiddenSize, visibleSize, data)
% theta: trained weights from the autoencoder
% visibleSize: the number of input units (probably 64)
% hiddenSize: the number of hidden units (probably 25)
% data: Our matrix containing the training data as columns. So, data(:,i) i... |
github | mriosb08/palodiem-QE-master | myfun.m | .m | palodiem-QE-master/src/matlab/common/fminlbfgs/myfun.m | 131 | utf_8 | 2df76a15532ed120d14edb24b556f5be | % where myfun is a MATLAB function such as:
function [f,g] = myfun(x)
f = sum(sin(x) + 3);
if ( nargout > 1 ), g = cos(x); end
|
github | mriosb08/palodiem-QE-master | stackedAEPredict.m | .m | palodiem-QE-master/src/matlab/stl_QE/stackedAEPredict.m | 1,526 | utf_8 | 019227b5266fe2d530054908e9172483 | function [pred] = stackedAEPredict(theta, inputSize, hiddenSize, numClasses, netconfig, data)
% stackedAEPredict: Takes a trained theta and a test data set,
% and returns the predicted labels for each example.
% theta: trained weights f... |
github | mriosb08/palodiem-QE-master | stackedAECost.m | .m | palodiem-QE-master/src/matlab/stl_QE/stackedAECost.m | 3,936 | utf_8 | 17f36ea3f2b2c61caa9861171c1a07b9 | function [ cost, grad ] = stackedAECost(theta, inputSize, hiddenSize, ...
numClasses, netconfig, ...
lambda, data, labels)
% stackedAECost: Takes a trained softmaxTheta and a trainin... |
github | pgagarinov/spheretri-master | spheretribydepth.m | .m | spheretri-master/spheretribydepth.m | 1,341 | utf_8 | e2e12d7d1550d0fa0a218100bbb5fca2 | function [vMat,fMat]=spheretribydepth(depth)
% SPHERETRIBYDEPTH is a high-performance vectorized function for building
% a triangulation of a unit sphere based on recursive partitioning of each
% of Icosahedron faces into 4 triangles with vertices in the middles of
% original face
%
% Input:
% depth: double[1,1] - ... |
github | pgagarinov/spheretri-master | spheretri.m | .m | spheretri-master/spheretri.m | 2,075 | utf_8 | 9ee86c60bf65ed64ad3a0d54ac5c11bc | function [vMat, fMat] = spheretri(nPoints)
% SPHERETRI is a high-performance vectorized function for building
% a triangulation of a unit sphere based on recursive partitioning of each
% of Icosahedron faces into 4 triangles with vertices in the middles of
% original face edgeMidMat. The function takes a number of r... |
github | pgagarinov/spheretri-master | isface.m | .m | spheretri-master/isface.m | 2,072 | utf_8 | 0d8f9b350624106555c66ce20f5a341f | function isFaceVec=isface(vMat,fMat,fToCheckMat)
% ISFACE checks if the specified faces from fToCheckMat belong to the
% given triangulation fMat of vertices from vMat
%
% Input:
% regular:
% vMat: double[nVerts,3] - vertex coordinates
% fMat: double[nFaces,3] - face definitions based on vertex numbers
%... |
github | pgagarinov/spheretri-master | combvec.m | .m | spheretri-master/combvec.m | 1,638 | utf_8 | db84bb824a864744654120945c3c425a | function indMat = combvec(varargin)
% COMBVEC creates a matrix of combinations with elements from input vectors
%
% Usage: indMat=combvec(firstVec,secVec,...)
%
% Input:
% optional:
% firstVec: numeric[1,n1Elems] - first vector
% ...
% lastVec: numeric[1,nKElems] - last vector
%
% Output:
% indMat... |
github | tuckermcclure/vector-and-rotation-toolbox-master | RotationConversionUnitTestsMex.m | .m | vector-and-rotation-toolbox-master/mex/RotationConversionUnitTestsMex.m | 8,909 | utf_8 | f07fcb1fad55b33580d02f2a290bcaa1 | classdef RotationConversionUnitTestsMex < matlab.unittest.TestCase
methods (Test)
%%%%%%%%%%%%%%%%%%
% Rx, Ry, and Rz %
%%%%%%%%%%%%%%%%%%
function test_RxRyRz(test)
% Matches intuition?
test.verifyEqual(Ry(pi/4) * [1; 0; 0], ...
1/sqrt(2) * [1; 0; 1], ...
'AbsT... |
github | tuckermcclure/vector-and-rotation-toolbox-master | QuaternionUnitTestsMex.m | .m | vector-and-rotation-toolbox-master/mex/QuaternionUnitTestsMex.m | 3,415 | utf_8 | 1cdd90e9de2e643af63fadde8fd4f8a3 | classdef QuaternionUnitTestsMex < matlab.unittest.TestCase
methods (Test)
%%%%%%%%%
% qcomp_mex %
%%%%%%%%%
function test_qcomp(test)
n = 100;
a = randunit(4, n);
b = randunit(4, n);
c = qcomp_mex(a, b);
c_0 = zeros(4, n);
for k = 1:n
c_0(:,k) = dcm2q_mex(q2dcm_mex(a(:,k)) * q2d... |
github | tuckermcclure/vector-and-rotation-toolbox-master | VectorUnitTestsMex.m | .m | vector-and-rotation-toolbox-master/mex/VectorUnitTestsMex.m | 2,029 | utf_8 | 6864fcc00ffd9d1557976d8d799bdaa9 | classdef VectorUnitTestsMex < matlab.unittest.TestCase
methods (Test)
%%%%%%%%%%
% cross3 %
%%%%%%%%%%
function test_cross3(test)
n = 100;
a = randn(3, n);
b = randn(3, n);
c_0 = cross(a, b);
c_1 = cross3(a, b);
c_2 = zeros(3, n);
for k = 1:n
c_2(:,k) = crs3(a(:,k)) * b(:,k);
... |
github | tuckermcclure/vector-and-rotation-toolbox-master | MRPUnitTestsMex.m | .m | vector-and-rotation-toolbox-master/mex/MRPUnitTestsMex.m | 2,450 | utf_8 | 6ffbfac7b22a15da68b570f83de5c444 | classdef MRPUnitTestsMex < matlab.unittest.TestCase
methods (Test)
%%%%%%%%%%%
% mrpcomp_mex %
%%%%%%%%%%%
function test_mrpcomp(test)
n = 5;
qa = randunit(4, n);
qb = randunit(4, n);
q = qcomp_mex(qa, qb);
tol = 1e-9;
% MRP (f = 1)
if ~test_mex_only(mfilename())
p... |
github | tuckermcclure/vector-and-rotation-toolbox-master | RotationConversionUnitTests.m | .m | vector-and-rotation-toolbox-master/tests/RotationConversionUnitTests.m | 8,646 | utf_8 | 1541acbdf62a062b02650b2e89041066 | classdef RotationConversionUnitTests < matlab.unittest.TestCase
methods (Test)
%%%%%%%%%%%%%%%%%%
% Rx, Ry, and Rz %
%%%%%%%%%%%%%%%%%%
function test_RxRyRz(test)
% Matches intuition?
test.verifyEqual(Ry(pi/4) * [1; 0; 0], ...
1/sqrt(2) * [1; 0; 1], ...
'AbsTol'... |
github | tuckermcclure/vector-and-rotation-toolbox-master | MRPUnitTests.m | .m | vector-and-rotation-toolbox-master/tests/MRPUnitTests.m | 2,319 | utf_8 | 7e2d8947373ba0f69327969963faed32 | classdef MRPUnitTests < matlab.unittest.TestCase
methods (Test)
%%%%%%%%%%%
% mrpcomp %
%%%%%%%%%%%
function test_mrpcomp(test)
n = 5;
qa = randunit(4, n);
qb = randunit(4, n);
q = qcomp(qa, qb);
tol = 1e-9;
% MRP (f = 1)
if ~test_mex_only(mfilename())
pa = q2... |
github | tuckermcclure/vector-and-rotation-toolbox-master | VectorUnitTests.m | .m | vector-and-rotation-toolbox-master/tests/VectorUnitTests.m | 1,990 | utf_8 | b1b79d9ec7f35bf1ee34fa338780e355 | classdef VectorUnitTests < matlab.unittest.TestCase
methods (Test)
%%%%%%%%%%
% cross3 %
%%%%%%%%%%
function test_cross3(test)
n = 100;
a = randn(3, n);
b = randn(3, n);
c_0 = cross(a, b);
c_1 = cross3(a, b);
c_2 = zeros(3, n);
for k = 1:n
c_2(:,k) = crs3(a(:,k)) * b(:,k);
en... |
github | tuckermcclure/vector-and-rotation-toolbox-master | QuaternionUnitTests.m | .m | vector-and-rotation-toolbox-master/tests/QuaternionUnitTests.m | 3,288 | utf_8 | b95ddb08704f9113b4fe79b5d4a60c34 | classdef QuaternionUnitTests < matlab.unittest.TestCase
methods (Test)
%%%%%%%%%
% qcomp %
%%%%%%%%%
function test_qcomp(test)
n = 100;
a = randunit(4, n);
b = randunit(4, n);
c = qcomp(a, b);
c_0 = zeros(4, n);
for k = 1:n
c_0(:,k) = dcm2q(q2dcm(a(:,k)) * q2dcm(b(:,k)));
en... |
github | clbarnes/bct-master | consensus_und.m | .m | bct-master/BCT/clustering/consensus_und.m | 3,133 | utf_8 | 2399e05c584d7327bb19ba25e2c89812 | function ciu = consensus_und(d,tau,reps)
%CONSENSUS consensus clustering
%
% CIU = CONSENSUS(D,TAU,REPS) seeks a consensus partition of the
% agreement matrix D. The algorithm used here is almost identical to the
% one introduced in Lancichinetti & Fortunato (2012): The agreement
% matrix D is thresholded... |
github | clbarnes/bct-master | efficiency_bin.m | .m | bct-master/BCT/distance/efficiency_bin.m | 2,776 | utf_8 | 0d603ee31b16e2ec9ead80074f0aab47 | function E=efficiency_bin(A,local)
%EFFICIENCY_BIN Global efficiency, local efficiency.
%
% Eglob = efficiency_bin(A);
% Eloc = efficiency_bin(A,1);
%
% The global efficiency is the average of inverse shortest path length,
% and is inversely related to the characteristic path length.
%
% The loca... |
github | clbarnes/bct-master | reachdist.m | .m | bct-master/BCT/distance/reachdist.m | 2,063 | utf_8 | d7f0412f37b49ecd99c712af72d61099 | function [R,D] = reachdist(CIJ)
%REACHDIST Reachability and distance matrices
%
% [R,D] = reachdist(CIJ);
%
% The binary reachability matrix describes reachability between all pairs
% of nodes. An entry (u,v)=1 means that there exists a path from node u
% to node v; alternatively (u,v)=0.
%
% T... |
github | clbarnes/bct-master | efficiency_wei.m | .m | bct-master/BCT/distance/efficiency_wei.m | 4,270 | utf_8 | 3fab38b553a742b616f50d49f52a69e7 | function E=efficiency_wei(W,local)
%EFFICIENCY_WEI Global efficiency, local efficiency.
%
% Eglob = efficiency_wei(W);
% Eloc = efficiency_wei(W,1);
%
% The global efficiency is the average of inverse shortest path length,
% and is inversely related to the characteristic path length.
%
% The loca... |
github | clbarnes/bct-master | generative_model.m | .m | bct-master/BCT/generative_models/generative_model.m | 23,153 | utf_8 | dd5cd182d827c9ede9fb54855b0cafd8 | function b = generative_model(A,D,m,modeltype,modelvar,params,epsilon)
%GENERATIVE_MODEL run generative model code
%
% B = GENERATIVE_MODEL(A,D,m,modeltype,modelvar,params)
%
% Generates synthetic networks using the models described in the study by
% Betzel et al (2016) in Neuroimage.
%
% Inputs:
% ... |
github | clbarnes/bct-master | evaluate_generative_model.m | .m | bct-master/BCT/generative_models/evaluate_generative_model.m | 3,625 | utf_8 | 6c6df608db42f67c0908af434bff6aca | function [B,E,K] = evaluate_generative_model(A,Atgt,D,modeltype,modelvar,params)
% EVALUATE_GENERATIVE_MODEL generate and evaluate synthetic networks
%
% [B,E,K] = EVALUATE_GENERATIVE_MODEL(A,Atgt,D,m,modeltype,modelvar,params)
%
% Generates synthetic networks and evaluates their energy function (see
% below... |
github | clbarnes/bct-master | make_motif34lib.m | .m | bct-master/BCT/motifs/make_motif34lib.m | 2,927 | utf_8 | c546fe40ea1ce44b40280623690af300 | function make_motif34lib
%MAKE_MOTIF34LIB Auxiliary motif library function
%
% make_motif34lib;
%
% This function generates the motif34lib.mat library required for all
% other motif computations.
%
%
% Mika Rubinov, UNSW, 2007-2010
%#ok<*ASGLU>
[M3,M3n,ID3,N3]=motif3generate;
[M4,M4n,ID4... |
github | tisikcci/ship_sea-master | rsgeng.m | .m | ship_sea-master/rsgeng.m | 1,284 | utf_8 | 978d564d67de1bdebc8b3c0f3a4765e4 | function [f,df,x]=rsgeng(N,rL,h,lc,seed);
%RSGENG generates 1D Gaussian random rough surfaces with Gaussian Spectrum.
%
% [f,df,x]=rsgeng(N,rL,h,lc,seed)
%
% INPUT:
%
% N=total number of sample points
% rL=rough surface length
% h=rms height
% lc=correlation length
% seed=seed of random number generator
%... |
github | tisikcci/ship_sea-master | rsgeno.m | .m | ship_sea-master/rsgeno.m | 1,630 | utf_8 | f011a8529c90c8eb2a359902b4d47457 | function [f,df,x]=rsgeno(N,rL,kl,ku,us,seed);
%rsgeno generates 1D random rough surfaces with bandlimited ocean spectrum.
%
% [f,df,x]=rsgeno(N,rL,kl,ku,us,seed)
%
% INPUT:
%
% N=total number of sample points
% rL=rough surface length
% kl=lower wavenumber cutoff
% ku=upper wavenumber cutoff
% us=wind fri... |
github | oxfordcontrol/CDCS-master | cdcsTest.m | .m | CDCS-master/cdcsTest.m | 7,656 | utf_8 | eeea8b828091c9ce2859e8ceca736c6e | function cdcsTest
% CDCSTEST
%
% Run some example to test CDCS.
%
% See also CDCS
% Preliminaries
clc;
opts.maxIter = 1e+3;
opts.relTol = 1e-3;
% ---------------------------------------------------------------------------- %
% SDP with block-arrow sparsity pattern (multiple cones)
% ---------------------... |
github | oxfordcontrol/CDCS-master | factorMatrix.m | .m | CDCS-master/packages/+cdcs_hsde/private/factorMatrix.m | 4,360 | utf_8 | 8f94ccce34e4752e7e6a55206e352a4f | function [xi,solInner] = factorMatrix(At,b,c,E,D1,E2,flag)
% generate projector onto affine constraints
% The modification of H in the scaling process should be considered.
%tmpE2 = (1 - E2.^2./(1 + E2.^2)).*(E2.^2);
tmpE2 = E2.^2./(1 + E2.^2);
D = D1.*accumarray(E,tmpE2).*D1;
P = 1./(1+D);
% First factor the matrix ... |
github | oxfordcontrol/CDCS-master | clean.m | .m | CDCS-master/packages/+cdcs_utils/clean.m | 872 | utf_8 | c842450bc86814fcb31c8ac37db1b4a1 | function varargout = clean(varargin)
%clean out small values in a vector.
%
% Usage : x = clean(x), or
% x = clean(x,tol), or
% clean(x,tol);
if(nargin<2)
level = 1e-10;
nClean = 1;
else
level = varargin{end};
nClean = nargin-1;
end
%eliminate spurious values.
%nested indexing... |
github | oxfordcontrol/CDCS-master | splitBlocks.m | .m | CDCS-master/packages/+cdcs_utils/splitBlocks.m | 3,999 | utf_8 | 51164e0ccc39d628aba398f3838a8023 | function [At,b,c,K,opts] = splitBlocks(At,b,c,K,opts)
% Try to split semidefinite blocks into smaller connected components
% Return updated data with split cones and a vector of indices opts.sort such
% that
%
% x_new = x_old(opts.sort)
%
% Given x_new, the original variable x_old can be reconstructed with
%
% >> x_o... |
github | oxfordcontrol/CDCS-master | projectK.m | .m | CDCS-master/packages/+cdcs_utils/projectK.m | 2,360 | utf_8 | 1c7612792b5aeb8acc264051ff2284d6 | function X = projectK(X,K,useDual)
%project a vector onto a cone K defined in SEDUMI format
if(nargin < 3)
useDual = 0;
end
if(useDual)
dual = 1; primal = 0;
else
primal = 1; dual = 0;
end
assert(xor(primal,dual));
blockIdx = 1;
% FREE CONE PROJECTION (R^n)
if(isfield(K,'f') && K.f > 0)
if(primal)... |
github | oxfordcontrol/CDCS-master | maxSpanningTree.m | .m | CDCS-master/packages/+cdcs_utils/private/maxSpanningTree.m | 4,215 | utf_8 | 7567a9b54c0e39d180cbfffd81c487ad | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [treeValue,adjacencyMatrixT,edgeCostVectT,incidenceMatrixT,basisIdx,BInv] ...
= maxSpanningTree(clique,adjacencyMatrixC,edgeCostVectC,incidenceMatrixC,randSeed)
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
github | oxfordcontrol/CDCS-master | cliquesFromSpMatD.m | .m | CDCS-master/packages/+cdcs_utils/private/cliquesFromSpMatD.m | 8,272 | utf_8 | 763404e71a8aba3575dd5caa55bda334 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [clique] = cliquesFromSpMatD(sparsityPatternMat)
% Modified by M. Kojima,March 25, 2010
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% This file is a component of SparseCoLO
% Copyright (C) 2009
% Masaka... |
github | oxfordcontrol/CDCS-master | constructCliqueGraph.m | .m | CDCS-master/packages/+cdcs_utils/private/constructCliqueGraph.m | 3,454 | utf_8 | 081cd904c9eaf4b1eab84c1f735b3dcb | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [adjacencyMatrixC,noOfEdges,edgeCostVectC,incidenceMatrixC] ...
= constructCliqueGraph(clique)
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% This file is a component of SparseCoLO
% Copyright (C) 2009... |
github | oxfordcontrol/CDCS-master | factorMatrix.m | .m | CDCS-master/packages/+cdcs_sos/private/factorMatrix.m | 9,640 | utf_8 | 20c9eb7790d660aa08a1559f4375735f | function [eta,solInner] = factorMatrix(At,b,c,K,flag)
% generate projector onto affine constraints
% check orthogonality A = [A1 A2], A2 correponds to PSD cone variabels
if strcmpi(flag,'blk')
[A1,~,D] = diviConstraint(At,K);
Ddiag = diag(D); %% store the diagonal elements
P = 1./(1+Ddiag);
% First ... |
github | oxfordcontrol/CDCS-master | makeProjectors.m | .m | CDCS-master/packages/+cdcs_pd/private/makeProjectors.m | 7,341 | utf_8 | 57e9a03b413f288eeec0ed2333e647ee | function [projAffine,projCone] = makeProjectors(At,b,c,K,cd,E,opts)
% Create projection operators
% Import functions
import cdcs_utils.makeConeVariables
import cdcs_utils.blockify
%rho = opts.rho;
H = accumarray(E,1);
% split cost between sub-variables?
%----------------------------------
if strcmpi(opts.solver,'dua... |
github | oxfordcontrol/CDCS-master | cs_make.m | .m | CDCS-master/include/cs_make.m | 6,496 | utf_8 | 541555732b18ee9af7d696d9434a09b2 | function [objfiles, timestamp_out] = cs_make (f)
%CS_MAKE compiles CSparse for use in MATLAB.
% Usage:
% cs_make
% [objfiles, timestamp] = cs_make (f)
%
% With no input arguments, or with f=0, only those files needing to be
% compiled are compiled (like the Unix/Linux/GNU "make" command, but not
% r... |
github | williford/segmentation-caffe-master | classification_demo.m | .m | segmentation-caffe-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 | tanshen/SubCNN-master | exemplar_display_result_pascal3d.m | .m | SubCNN-master/PASCAL3D/exemplar_display_result_pascal3d.m | 6,029 | utf_8 | bc409ed4050fdd1a38f29fc2d1674963 | function exemplar_display_result_pascal3d
classes = {'aeroplane', 'bicycle', 'boat', ...
'bottle', 'bus', 'car', 'chair', ...
'diningtable', 'motorbike', ...
'sofa', 'train', 'tvmonitor'};
threshold = 0.7;
is_save = 1;
result_dir = 'results';
% read detection results
filename = sprin... |
github | tanshen/SubCNN-master | compute_recall_precision_accuracy.m | .m | SubCNN-master/PASCAL3D/compute_recall_precision_accuracy.m | 4,717 | utf_8 | 9c555c91635ecffb2820fdbd7599ac88 | % compute recall and viewpoint accuracy
function [recall, precision, accuracy, ap, aa] = compute_recall_precision_accuracy(cls, vnum_train, vnum_test)
opt = globals;
if nargin < 3
vnum_test = vnum_train;
end
azimuth_interval = [0 (360/(vnum_test*2)):(360/vnum_test):360-(360/(vnum_test*2))];
% viewpoint annotati... |
github | tanshen/SubCNN-master | voc_eval.m | .m | SubCNN-master/fast-rcnn/lib/datasets/VOCdevkit-matlab-wrapper/voc_eval.m | 1,389 | utf_8 | fd77d0da53b2585aa65e0da5edc5fe33 | function res = voc_eval(path, comp_id, test_set, output_dir, rm_res)
VOCopts = get_voc_opts(path);
VOCopts.testset = test_set;
for i = 1:length(VOCopts.classes)
cls = VOCopts.classes{i};
res(i) = voc_eval_cls(cls, VOCopts, comp_id, output_dir, rm_res);
end
fprintf('\n~~~~~~~~~~~~~~~~~~~~\n');
fprintf('Results:\n... |
github | tanshen/SubCNN-master | VOCevaldetview.m | .m | SubCNN-master/ImageNet3D/VOCevaldetview.m | 8,785 | utf_8 | 0281d634130792fb75204f5f2f944c6e | function VOCevaldetview(network, region_proposal, minoverlap)
matlabpool open;
opt = globals();
root = opt.path_imagenet3d;
result_dir = '/scail/scratch/u/yuxiang/3DVP_RCNN/fast-rcnn/output/imagenet3d/imagenet3d_test';
if exist(result_dir, 'dir') == 0
result_dir = '/home/yuxiang/Projects/3DVP_RCNN/fast-... |
github | tanshen/SubCNN-master | exemplar_display_result_nthu.m | .m | SubCNN-master/NTHU/exemplar_display_result_nthu.m | 6,781 | utf_8 | 89b89190feeacda097a7ea391b7a73c7 | function exemplar_display_result_nthu
threshold = 0.1;
threshold_people = 0.5;
is_save = 1;
result_dir = 'test_results';
root_dir = '/home/yuxiang/Projects/Driving_Events/data_result/data';
image_set = '370';
if is_save
dirname = sprintf('result_images_%s', image_set);
if exist(dirname, 'dir') == 0
mk... |
github | tanshen/SubCNN-master | compute_recall_precision_aos_box.m | .m | SubCNN-master/KITTI/compute_recall_precision_aos_box.m | 13,186 | utf_8 | fdcf27211b73698b6673ad5143489888 | function compute_recall_precision_aos_box
cls = 'car';
% evaluation parameter
MIN_HEIGHT = [40, 25, 25]; % minimum height for evaluated groundtruth/detections
MAX_OCCLUSION = [0, 1, 2]; % maximum occlusion level of the groundtruth used for evaluation
MAX_TRUNCATION = [0.15, 0.3, 0.5]; % maximum truncation lev... |
github | tanshen/SubCNN-master | exemplar_display_result_kitti.m | .m | SubCNN-master/KITTI/exemplar_display_result_kitti.m | 6,953 | utf_8 | 8752eaaad2e8b2bfa8b8bc00f523bd93 | function exemplar_display_result_kitti
threshold = 0.5;
is_save = 0;
is_train = 0;
result_dir = 'test_results_googlenet';
% result_dir = 'results_kitti_train_tf';
% read detection results
filename = sprintf('%s/detections.txt', result_dir);
[ids_det, cls_det, x1_det, y1_det, x2_det, y2_det, cid_det, score_det] = ...
... |
github | tanshen/SubCNN-master | load_off_file.m | .m | SubCNN-master/KITTI/load_off_file.m | 740 | utf_8 | 3f00e304b8e4ea1d65c6aab461491603 | % load an off file
function [vertices, faces] = load_off_file(filename)
vertices = [];
faces = [];
fid = fopen(filename, 'r');
line = fgetl(fid);
if strcmp(line, 'OFF') == 0
fprintf('Wrong format .off file %s!\n', filename);
return;
end
line = fgetl(fid);
num = sscanf(line, '%f', 3);
nv = num(... |
github | tanshen/SubCNN-master | compute_recall_simple.m | .m | SubCNN-master/KITTI/compute_recall_simple.m | 6,029 | utf_8 | 76f9965d21cce17050440d090bfb5160 | function recall_all = compute_recall_simple
classes = {'car', 'pedestrian', 'cyclist'};
MIN_OVERLAPS = [0.7, 0.5, 0.5];
% evaluation parameter
MIN_HEIGHT = [40, 25, 25]; % minimum height for evaluated groundtruth/detections
MAX_OCCLUSION = [0, 1, 2]; % maximum occlusion level of the groundtruth used for evalu... |
github | tanshen/SubCNN-master | compute_3d_points.m | .m | SubCNN-master/KITTI/compute_3d_points.m | 817 | utf_8 | 61f2ffdb21ab636a143e5ce63dcbc88b | % compute the 3D point locations of a CAD model
function x3d = compute_3d_points(vertices, object)
x3d = vertices';
% rotation matrix to transform coordinate systems
Rx = [1 0 0; 0 0 -1; 0 1 0];
Ry = [cos(-pi/2) 0 sin(-pi/2); 0 1 0; -sin(-pi/2) 0 cos(-pi/2)];
x3d = Ry*Rx*x3d;
% scaling factors
sx = object.l / (max(x... |
github | tanshen/SubCNN-master | compute_recall_precision_aos.m | .m | SubCNN-master/KITTI/compute_recall_precision_aos.m | 14,730 | utf_8 | 709bd3c02e52e214c3ab2e0620659813 | function [recall_all, precision_all, aos_all] = compute_recall_precision_aos
cls = 'car';
% evaluation parameter
MIN_HEIGHT = [40, 25, 25]; % minimum height for evaluated groundtruth/detections
MAX_OCCLUSION = [0, 1, 2]; % maximum occlusion level of the groundtruth used for evaluation
MAX_TRUNCATION = [0.15, ... |
github | tanshen/SubCNN-master | exemplar_display_result_kitti_3d.m | .m | SubCNN-master/KITTI/exemplar_display_result_kitti_3d.m | 11,474 | utf_8 | 513b6c859c7eff48832498dd656edd67 | function exemplar_display_result_kitti_3d
is_save = 0;
threshold = 0.5;
cls = 'car';
% read detection results
result_dir = 'test_results_5';
filename = sprintf('%s/dets_3d.mat', result_dir);
object = load(filename);
dets_all = object.dets_3d;
fprintf('load detection done\n');
% read ids of validation images
object ... |
github | tanshen/SubCNN-master | compute_recall_precision_aos_3d.m | .m | SubCNN-master/KITTI/compute_recall_precision_aos_3d.m | 15,479 | utf_8 | 6dac5e6a3925a60cedc1c46849955c5b | function [fppi_all, thresholds_all] = compute_recall_precision_aos_3d
cls = 'car';
% evaluation parameter
MIN_HEIGHT = [40, 25, 25]; % minimum height for evaluated groundtruth/detections
MAX_OCCLUSION = [0, 1, 2]; % maximum occlusion level of the groundtruth used for evaluation
MAX_TRUNCATION = [0.15, 0.3, 0.... |
github | tanshen/SubCNN-master | exemplar_3d_detections_light.m | .m | SubCNN-master/KITTI/exemplar_3d_detections_light.m | 12,606 | utf_8 | 293702349e336ea0eb08665271cd82aa | function exemplar_3d_detections_light
matlabpool open;
% threshold = 0.5;
cls = 'car';
is_train = 1;
% read detection results
if is_train
result_dir = 'results_kitti_train';
else
result_dir = 'test_results_5';
end
filename = sprintf('%s/detections.txt', result_dir);
[ids_det, cls_det, x1_det, y1_det, x2_det,... |
github | tanshen/SubCNN-master | exemplar_3d_detections_light_box.m | .m | SubCNN-master/KITTI/exemplar_3d_detections_light_box.m | 9,576 | utf_8 | d672af0dc15a8558641efe99deabee94 | function exemplar_3d_detections_light_box
matlabpool open;
% threshold = 0.5;
cls = 'car';
is_train = 1;
% read ids of validation images
object = load('kitti_ids_new.mat');
if is_train
ids = object.ids_val;
else
ids = object.ids_test;
end
N = numel(ids);
% read detection results
if is_train
result_dir =... |
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