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github | CohenBerkeleyLab/WRF_Utils-master | test_wrf_bl_height.m | .m | WRF_Utils-master/One-off Scripts/test_wrf_bl_height.m | 3,712 | utf_8 | 371c8d38efec5ce36408596fe62b02c5 | function [ fraction_good, fraction_ok, fraction_undefined, indices_good, indices_ok, indices_bad, indices_undefined ] = test_wrf_bl_height( z, no2, is_z_pres, pblh )
%TEST_WRF_BL_HEIGHT Plots tests of ways to find the chemical BL height from WRF profiles
% Detailed explanation goes here
E = JLLErrors;
if ndims(z)... |
github | rahafaljundi/Encoder-Based-Lifelong-learning-master | cnn_autoencoder_with_classerror.m | .m | Encoder-Based-Lifelong-learning-master/Autoencoder/cnn_autoencoder_with_classerror.m | 6,861 | utf_8 | 6e6a912f8633c3a7a031dd3fb869d0b2 | function [net, opts, imdb, info] = cnn_autoencoder_with_classerror(varargin)
%CNN_AUTOENCODER_WITH_CLASSERROR contructs an autoencoder to be trained in order to minimize:
% - the reconstruction loss (as is classicly the case for autoencoders)
% - the task loss (e.g. classification loss) when the task layers are given t... |
github | rahafaljundi/Encoder-Based-Lifelong-learning-master | cnn_train_adadelta.m | .m | Encoder-Based-Lifelong-learning-master/Autoencoder/cnn_train_adadelta.m | 22,540 | utf_8 | 02f3c27590616a1beeaa11e720632314 | function [net, stats] = cnn_train_adadelta(net, imdb, getBatch, varargin)
%CNN_TRAIN_ADADELTA An example of custom implementation of adaptive
%gradient for training CNNs, needed with matconvnet_b23 or older.
%From the version matconvnet_b24 on, the solver adadelta can be used
%instead.
%CNN_TRAIN_ADADELTA() is an exam... |
github | rahafaljundi/Encoder-Based-Lifelong-learning-master | add_new_task.m | .m | Encoder-Based-Lifelong-learning-master/LwF_with_encoder/Model_preparation/add_new_task.m | 6,061 | utf_8 | aef5b1c13f46b5114cf9430da4776951 | function [new_net, aug_imdb] = add_new_task(varargin)
%ADD_NEW_TASK modifies a model and to prepare it for training on a new task
%and modifies its corresponding data by:
% -adding the two first layers of the autoencoder of the model
% -augmenting the data if needed
% -add the task specific layers t... |
github | rahafaljundi/Encoder-Based-Lifelong-learning-master | cnn_fine_tune_freeze.m | .m | Encoder-Based-Lifelong-learning-master/LwF_with_encoder/Model_preparation/cnn_fine_tune_freeze.m | 9,697 | utf_8 | 2a13c512208940104d831be1ca2ed4fa | function [net, info] = cnn_fine_tune_freeze(varargin)
%CNN_FINE_TUNE_FREEZE finetune the last layers of the network for
%the new task before adding it to the global model
%See add_new_task for details
%
% Author: Rahaf Aljundi
%
% See the COPYING file.
%
% Adapted from MatConvNet of VLFeat library. Their copyright info... |
github | rahafaljundi/Encoder-Based-Lifelong-learning-master | cnn_imagenet_deploy.m | .m | Encoder-Based-Lifelong-learning-master/LwF_with_encoder/Model_training/cnn_imagenet_deploy.m | 6,585 | utf_8 | 2f3e6d216fa697ff9adfce33e75d44d8 | function net = cnn_imagenet_deploy(net)
%CNN_IMAGENET_DEPLOY Deploy a CNN
isDag = isa(net, 'dagnn.DagNN') ;
if isDag
dagRemoveLayersOfType(net, 'dagnn.Loss') ;
dagRemoveLayersOfType(net, 'dagnn.DropOut') ;
else
net = simpleRemoveLayersOfType(net, 'softmaxloss') ;
net = simpleRemoveLayersOfType(net, 'dropout')... |
github | rahafaljundi/Encoder-Based-Lifelong-learning-master | cnn_train_lwf_with_encoder.m | .m | Encoder-Based-Lifelong-learning-master/LwF_with_encoder/Model_training/cnn_train_lwf_with_encoder.m | 27,825 | utf_8 | 7af728d200b656511a5aa7bda59654c3 | function [net, stats] = cnn_train_lwf_with_encoder(net, imdb, getBatch, varargin)
%CNN_TRAIN_LWF_WITH_ENCODER is an example learner implementing stochastic
% gradient descent with momentum to train a CNN with the Encoder Based
% Lifelong Learning method after preparing the model(See functions under
% Model_pre... |
github | rahafaljundi/Encoder-Based-Lifelong-learning-master | cnn_lwf_with_encoder.m | .m | Encoder-Based-Lifelong-learning-master/LwF_with_encoder/Model_training/cnn_lwf_with_encoder.m | 5,108 | utf_8 | 6eb118d24d7e1561f820b348dd98a741 | function [net, info] = cnn_lwf_with_encoder(varargin)
%CNN_LWF_WITH_ENCODER Demonstrates training a CNN with the Encoder Based
%Lifelong Learning method after preparing the model(See functions under
%Model_prepapration).
%
%For more details about the model, see A. Rannen Triki, R. Aljundi, M. B. Blaschko,
%and T. Tu... |
github | rahafaljundi/Encoder-Based-Lifelong-learning-master | cnn_train.m | .m | Encoder-Based-Lifelong-learning-master/Experiments/cnn_train.m | 19,907 | utf_8 | 67356030c67680633023886d99dd8251 | function [net, new_images stats] = cnn_train(net, imdb, getBatch, varargin)
%CNN_TRAIN An example implementation of SGD for training CNNs
% CNN_TRAIN() is an example learner implementing stochastic
% gradient descent with momentum to train a CNN. It can be used
% with different datasets and tasks by providi... |
github | rahafaljundi/Encoder-Based-Lifelong-learning-master | cnn_imagenet_evaluate.m | .m | Encoder-Based-Lifelong-learning-master/Experiments/cnn_imagenet_evaluate.m | 5,272 | utf_8 | f1b153a158026e131ba92ebe7bd8bc85 | function [net,new_images,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', 'ILSVRC2012') ;
opts.expDir = fullfile('data', 'imagenet12-eval... |
github | rahafaljundi/Encoder-Based-Lifelong-learning-master | findLastCheckpoint.m | .m | Encoder-Based-Lifelong-learning-master/Experiments/findLastCheckpoint.m | 463 | utf_8 | 706404565378f9d06708c02acc71764e | % -------------------------------------------------------------------------
function epoch = findLastCheckpoint(modelDir)
% -------------------------------------------------------------------------
% Finds the network of the last epoch in the directory modelDir
list = dir(fullfile(modelDir, 'net-epoch-*.mat')) ;
token... |
github | Eden-Kramer-Lab/COMPASS-master | acf.m | .m | COMPASS-master/COMPASS_StateSpaceToolbox/acf.m | 2,458 | utf_8 | 236f4d8adcb8a89ee0851080b4ee4309 | function ta = acf(y,p)
% ACF - Compute Autocorrelations Through p Lags
% >> myacf = acf(y,p)
%
% Inputs:
% y - series to compute acf for, nx1 column vector
% p - total number of lags, 1x1 integer
%
% Output:
% myacf - px1 vector containing autocorrelations
% (First lag computed is lag 1. Lag 0 not com... |
github | Eden-Kramer-Lab/COMPASS-master | fminsearchbnd.m | .m | COMPASS-master/COMPASS_StateSpaceToolbox/fminsearchbnd.m | 8,139 | utf_8 | 1316d7f9d69771e92ecc70425e0f9853 | function [x,fval,exitflag,output] = fminsearchbnd(fun,x0,LB,UB,options,varargin)
% FMINSEARCHBND: FMINSEARCH, but with bound constraints by transformation
% usage: x=FMINSEARCHBND(fun,x0)
% usage: x=FMINSEARCHBND(fun,x0,LB)
% usage: x=FMINSEARCHBND(fun,x0,LB,UB)
% usage: x=FMINSEARCHBND(fun,x0,LB,UB,options)
% usage: x... |
github | Eden-Kramer-Lab/COMPASS-master | fminsearchcon.m | .m | COMPASS-master/COMPASS_StateSpaceToolbox/fminsearchcon.m | 11,330 | utf_8 | c52011ee59580c69f3872d1b59630088 | function [x,fval,exitflag,output]=fminsearchcon(fun,x0,LB,UB,A,b,nonlcon,options,varargin)
% FMINSEARCHCON: Extension of FMINSEARCHBND with general inequality constraints
% usage: x=FMINSEARCHCON(fun,x0)
% usage: x=FMINSEARCHCON(fun,x0,LB)
% usage: x=FMINSEARCHCON(fun,x0,LB,UB)
% usage: x=FMINSEARCHCON(fun,x0,LB,UB,A,b... |
github | cs4243-ay1718-group12/lucas-kanade-tracker-master | LK_Track_Pyramid_Iterative.m | .m | lucas-kanade-tracker-master/deprecated/LK_Track_Pyramid_Iterative.m | 2,670 | utf_8 | 891f603e5d49bda89930ac20a026c2ba | function [U, V] = LK_Track_Pyramid_Iterative(raw_img1, raw_img2, X, Y)
% Constants
win_rad = 5;
accuracy_threshold = .01;
max_iterations = 20;
max_levels = Maximum_Pyramid_Level(raw_img1, 128);
num_points = size(X,1);
% Get images for each pyramid levels
img1_pyramidized = generate_pyra... |
github | bentong95923/music_notation_printer-master | acf.m | .m | music_notation_printer-master/code/autocorrelation/sample_code/acf.m | 2,353 | utf_8 | a06fdab5c89736f28e9ce08df507dfa2 | function ta = acf(y,p)
% ACF - Compute Autocorrelations Through p Lags
% >> myacf = acf(y,p)
%
% Inputs:
% y - series to compute acf for, nx1 column vector
% p - total number of lags, 1x1 integer
%
% Output:
% myacf - px1 vector containing autocorrelations
% (First lag computed is lag 1. Lag 0 not computed)
%
%... |
github | pvarin/DynamicVAE-master | jdqr.m | .m | DynamicVAE-master/drtoolbox/techniques/jdqr.m | 73,068 | utf_8 | b45810ddb5b2767c9289909175d1dc04 | function varargout=jdqr(varargin)
%JDQR computes a partial Schur decomposition of a square matrix or operator.
% Lambda = JDQR(A) returns the absolute largest eigenvalues in a K vector
% Lambda. Here K=min(5,N) (unless K has been specified), where N=size(A,1).
% JDQR(A) (without output argument) displays the K eige... |
github | pvarin/DynamicVAE-master | lmnn.m | .m | DynamicVAE-master/drtoolbox/techniques/lmnn.m | 5,431 | utf_8 | 622ccdd8948f805d0d4552822cca46de | function [M, L, Y, C] = lmnn(X, labels)
%LMNN Learns a metric using large-margin nearest neighbor metric learning
%
% [M, L, Y, C] = lmnn(X, labels)
%
% The function uses large-margin nearest neighbor (LMNN) metric learning to
% learn a metric on the data set specified by the NxD matrix X and the
% corresponding Nx1 ... |
github | pvarin/DynamicVAE-master | d2p.m | .m | DynamicVAE-master/drtoolbox/techniques/d2p.m | 3,487 | utf_8 | 0c7024a8039ea16b937d283585883fc3 | 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 | pvarin/DynamicVAE-master | cg_update.m | .m | DynamicVAE-master/drtoolbox/techniques/cg_update.m | 3,715 | utf_8 | 1556078ae7c31950ec738949384cf180 | % Version 1.000
%
% Code provided by Ruslan Salakhutdinov and Geoff Hinton
%
% Permission is granted for anyone to copy, use, modify, or distribute this
% program and accompanying programs and documents for any purpose, provided
% this copyright notice is retained and prominently displayed, along with
% a note saying t... |
github | pvarin/DynamicVAE-master | lmvu.m | .m | DynamicVAE-master/drtoolbox/techniques/lmvu.m | 8,540 | utf_8 | c8003ed7ff0fd0e226776c42c72ad385 | function [mappedX, mapping] = lmvu(X, no_dims, K, LL)
%LMVU Performs Landmark MVU on dataset X
%
% [mappedX, mapping] = lmvu(X, no_dims, k1, k2)
%
% The function performs Landmark MVU on the DxN dataset X. The value of k1
% represents the number of nearest neighbors that is employed in the MVU
% constraints. The val... |
github | pvarin/DynamicVAE-master | cca.m | .m | DynamicVAE-master/drtoolbox/techniques/cca.m | 14,846 | utf_8 | 935e971ffe825a64e0eb80c535d71ebb | function [Z, ccaEigen, ccaDetails] = cca(X, Y, EDGES, OPTS)
%
% Function [Z, CCAEIGEN, CCADETAILS] = CCA(X, Y, EDGES, OPTS) computes a low
% dimensional embedding Z in R^d that maximally preserves angles among input
% data X that lives in R^D, with the algorithm Conformal Component Analysis.
%
% The embedding Z is co... |
github | pvarin/DynamicVAE-master | x2p.m | .m | DynamicVAE-master/drtoolbox/techniques/x2p.m | 3,597 | utf_8 | 4a102e94922f4af38e36c374dccbc5a2 | 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 | pvarin/DynamicVAE-master | sammon.m | .m | DynamicVAE-master/drtoolbox/techniques/sammon.m | 7,108 | utf_8 | 8a1fccbea9525bbebae4039127005ea6 | function [y, E] = sammon(x, n, opts)
%SAMMON Performs Sammon's MDS mapping on dataset X
%
% Y = SAMMON(X) applies Sammon's nonlinear mapping procedure on
% multivariate data X, where each row represents a pattern and each column
% represents a feature. On completion, Y contains the corresponding
% co-ordin... |
github | pvarin/DynamicVAE-master | sdecca2.m | .m | DynamicVAE-master/drtoolbox/techniques/sdecca2.m | 7,185 | utf_8 | e53979561adda6a23883da0e72af5bf6 | function [P, newY, L, newV, idx]= sdecca2(Y, snn, regularizer, relative)
% doing semidefinitve embedding/MVU with output being parameterized by graph
% laplacian's eigenfunctions..
%
% the algorithm is same as conformal component analysis except that the scaling
% factor there is set as 1
%
%
% function [P, newY, Y] ... |
github | pvarin/DynamicVAE-master | sparse_nn.m | .m | DynamicVAE-master/drtoolbox/techniques/sparse_nn.m | 972 | utf_8 | df5da172f954ec2f53125a04787cf2d3 | %SPARSE_NN
%
% This file is part of the Matlab Toolbox for Dimensionality Reduction.
% The toolbox can be obtained from http://homepage.tudelft.nl/19j49
% You are free to use, change, or redistribute this code in any way you
% want for non-commercial purposes. However, it is appreciated if you
% maintain the name of ... |
github | pvarin/DynamicVAE-master | jdqz.m | .m | DynamicVAE-master/drtoolbox/techniques/jdqz.m | 78,986 | utf_8 | be67a038982588a6ac9cbc2d36f009e8 | function varargout=jdqz(varargin)
%JDQZ computes a partial generalized Schur decomposition (or QZ
% decomposition) of a pair of square matrices or operators.
%
% LAMBDA=JDQZ(A,B) and JDQZ(A,B) return K eigenvalues of the matrix pair
% (A,B), where K=min(5,N) and N=size(A,1) if K has not been specified.
%
% [X,J... |
github | pvarin/DynamicVAE-master | lnst.m | .m | DynamicVAE-master/drtoolbox/gui/lnst.m | 866 | utf_8 | fd307c356d0eb128b0d57c9df000197e | % This file is part of the Matlab Toolbox for Dimensionality Reduction v0.7.2b.
% The toolbox can be obtained from http://homepage.tudelft.nl/19j49
% You are free to use, change, or redistribute this code in any way you
% want for non-commercial purposes. However, it is appreciated if you
% maintain the name of the or... |
github | pvarin/DynamicVAE-master | scatter12n.m | .m | DynamicVAE-master/drtoolbox/gui/scatter12n.m | 1,309 | utf_8 | 5a079c0bf3db6d26fd87f0cb3297c45b | % This file is part of the Matlab Toolbox for Dimensionality Reduction v0.7.2b.
% The toolbox can be obtained from http://homepage.tudelft.nl/19j49
% You are free to use, change, or redistribute this code in any way you
% want for non-commercial purposes. However, it is appreciated if you
% maintain the name of the or... |
github | pvarin/DynamicVAE-master | not_calculated.m | .m | DynamicVAE-master/drtoolbox/gui/not_calculated.m | 7,602 | utf_8 | 9f98d51f0c8207bd788383e580814903 | function varargout = not_calculated(varargin)
% NOT_CALCULATED M-file for not_calculated.fig
% NOT_CALCULATED by itself, creates a new NOT_CALCULATED or raises the
% existing singleton*.
%
% H = NOT_CALCULATED returns the handle to a new NOT_CALCULATED or the handle to
% the existing singleton*.
%
%... |
github | pvarin/DynamicVAE-master | choose_method.m | .m | DynamicVAE-master/drtoolbox/gui/choose_method.m | 5,336 | utf_8 | a50c15d7c51725e6e88bb12bf5be57a3 | function varargout = choose_method(varargin)
% CHOOSE_METHOD M-file for choose_method.fig
% CHOOSE_METHOD, by itself, creates a new CHOOSE_METHOD or raises the existing
% singleton*.
%
% H = CHOOSE_METHOD returns the handle to a new CHOOSE_METHOD or the handle to
% the existing singleton*.
%
% ... |
github | pvarin/DynamicVAE-master | load_data_1_var.m | .m | DynamicVAE-master/drtoolbox/gui/load_data_1_var.m | 4,776 | utf_8 | 213540e0f2d0e24db85ee4c6184178c8 | function varargout = load_data_1_var(varargin)
% LOAD_DATA_1_VAR M-file for load_data_1_var.fig
% LOAD_DATA_1_VAR, by itself, creates a new LOAD_DATA_1_VAR or raises the existing
% singleton*.
%
% H = LOAD_DATA_1_VAR returns the handle to a new LOAD_DATA_1_VAR or the handle to
% the existing singlet... |
github | pvarin/DynamicVAE-master | plotn.m | .m | DynamicVAE-master/drtoolbox/gui/plotn.m | 3,947 | utf_8 | ba3674531d91bc0b2ca405e0a9d0bc3a | % This file is part of the Matlab Toolbox for Dimensionality Reduction v0.7.2b.
% The toolbox can be obtained from http://homepage.tudelft.nl/19j49
% You are free to use, change, or redistribute this code in any way you
% want for non-commercial purposes. However, it is appreciated if you
% maintain the name of the or... |
github | pvarin/DynamicVAE-master | scattern.m | .m | DynamicVAE-master/drtoolbox/gui/scattern.m | 3,514 | utf_8 | aca2d60a4f80079c67204845b2499143 | % This file is part of the Matlab Toolbox for Dimensionality Reduction v0.7.2b.
% The toolbox can be obtained from http://homepage.tudelft.nl/19j49
% You are free to use, change, or redistribute this code in any way you
% want for non-commercial purposes. However, it is appreciated if you
% maintain the name of the or... |
github | pvarin/DynamicVAE-master | no_history.m | .m | DynamicVAE-master/drtoolbox/gui/no_history.m | 7,508 | utf_8 | d5c85b897eeca97b3e37ea41551de2b1 | function varargout = no_history(varargin)
% NO_HISTORY M-file for no_history.fig
% NO_HISTORY by itself, creates a new NO_HISTORY or raises the
% existing singleton*.
%
% H = NO_HISTORY returns the handle to a new NO_HISTORY or the handle to
% the existing singleton*.
%
% NO_HISTORY('CALLBACK',... |
github | pvarin/DynamicVAE-master | load_data_vars.m | .m | DynamicVAE-master/drtoolbox/gui/load_data_vars.m | 7,727 | utf_8 | a89e86bdd4785b42127825a4c304fb5e | function varargout = load_data_vars(varargin)
% LOAD_DATA_VARS M-file for load_data_vars.fig
% LOAD_DATA_VARS, by itself, creates a new LOAD_DATA_VARS or raises the existing
% singleton*.
%
% H = LOAD_DATA_VARS returns the handle to a new LOAD_DATA_VARS or the handle to
% the existing singleton*.
%
... |
github | pvarin/DynamicVAE-master | mapping_parameters.m | .m | DynamicVAE-master/drtoolbox/gui/mapping_parameters.m | 23,373 | utf_8 | 59c32ad869cef7d4887bd7f6bd777624 | function varargout = mapping_parameters(varargin)
% MAPPING_PARAMETERS M-file for mapping_parameters.fig
% MAPPING_PARAMETERS, by itself, creates a new MAPPING_PARAMETERS or raises the existing
% singleton*.
%
% H = MAPPING_PARAMETERS returns the handle to a new MAPPING_PARAMETERS or the handle to
% ... |
github | pvarin/DynamicVAE-master | load_xls.m | .m | DynamicVAE-master/drtoolbox/gui/load_xls.m | 4,845 | utf_8 | 98f040ec0685b024ddf99d454fea770d | function varargout = load_xls(varargin)
% LOAD_XLS M-file for load_xls.fig
% LOAD_XLS, by itself, creates a new LOAD_XLS or raises the existing
% singleton*.
%
% H = LOAD_XLS returns the handle to a new LOAD_XLS or the handle to
% the existing singleton*.
%
% LOAD_XLS('CALLBACK',hObject,eventDa... |
github | pvarin/DynamicVAE-master | drtool.m | .m | DynamicVAE-master/drtoolbox/gui/drtool.m | 52,422 | utf_8 | c15180ca46e1dcb99c001125b7429b14 | function varargout = drtool(varargin)
% DRTOOL M-file for drtool.fig
% DRTOOL, by itself, creates a new DRTOOL or raises the existing
% singleton*.
%
% H = DRTOOL returns the handle to a new DRTOOL or the handle to
% the existing singleton*.
%
% DRTOOL('CALLBACK',hObject,eventData,handles,...) ... |
github | pvarin/DynamicVAE-master | plot12n.m | .m | DynamicVAE-master/drtoolbox/gui/plot12n.m | 1,318 | utf_8 | d01421c22964c2a3a5ae9dd99d026708 | % This file is part of the Matlab Toolbox for Dimensionality Reduction v0.7.2b.
% The toolbox can be obtained from http://homepage.tudelft.nl/19j49
% You are free to use, change, or redistribute this code in any way you
% want for non-commercial purposes. However, it is appreciated if you
% maintain the name of the or... |
github | pvarin/DynamicVAE-master | not_loaded.m | .m | DynamicVAE-master/drtoolbox/gui/not_loaded.m | 7,513 | utf_8 | 749124a9066ce5eec69372e3d16cd9d1 | function varargout = not_loaded(varargin)
% NOT_LOADED M-file for not_loaded.fig
% NOT_LOADED by itself, creates a new NOT_LOADED or raises the
% existing singleton*.
%
% H = NOT_LOADED returns the handle to a new NOT_LOADED or the handle to
% the existing singleton*.
%
% NOT_LOADED('CALLBACK',... |
github | pvarin/DynamicVAE-master | load_data.m | .m | DynamicVAE-master/drtoolbox/gui/load_data.m | 6,360 | utf_8 | e87e4a8d82078cbbec95c41a786cd407 | function varargout = load_data(varargin)
% LOAD_DATA M-file for load_data.fig
% LOAD_DATA, by itself, creates a new LOAD_DATA or raises the existing
% singleton*.
%
% H = LOAD_DATA returns the handle to a new LOAD_DATA or the handle to
% the existing singleton*.
%
% LOAD_DATA('CALLBACK',hObject... |
github | pvarin/DynamicVAE-master | directCollocation.m | .m | DynamicVAE-master/MatthewPeterKelly-OptimTraj-9a33249/directCollocation.m | 17,680 | utf_8 | 99bcafa9cb7f42b2b2d8ab91d91d7e26 | function soln = directCollocation(problem)
% soln = directCollocation(problem)
%
% OptimTraj utility function
%
% This function is designed to be called by either "trapezoid" or
% "hermiteSimpson". It actually calls FMINCON to solve the trajectory
% optimization problem.
%
% Analytic gradients are supported.
%
% NOTE... |
github | pvarin/DynamicVAE-master | chebyshev.m | .m | DynamicVAE-master/MatthewPeterKelly-OptimTraj-9a33249/chebyshev.m | 10,613 | utf_8 | d197906d586572ca06c189288281ded9 | function soln = chebyshev(problem)
% soln = chebyshev(problem)
%
% This function transcribes a trajectory optimization problem Chebyshev
% orthogonal polynomials for basis functions. This is an orthogonal
% collocation method, where the entire trajectory is represented as a
% single polynomial. It is for problems where... |
github | pvarin/DynamicVAE-master | hermiteSimpson.m | .m | DynamicVAE-master/MatthewPeterKelly-OptimTraj-9a33249/hermiteSimpson.m | 11,560 | utf_8 | 690c510dbe95d1ee31b9a2c2fcda28f8 | function soln = hermiteSimpson(problem)
% soln = hermiteSimpson(problem)
%
% This function transcribes a trajectory optimization problem using the
% Hermite-Simpson (Seperated) method for enforcing the dynamics. It can be
% found in chapter four of Bett's book:
%
% John T. Betts, 2001
% Practical Methods for Optima... |
github | pvarin/DynamicVAE-master | trapezoid.m | .m | DynamicVAE-master/MatthewPeterKelly-OptimTraj-9a33249/trapezoid.m | 7,774 | utf_8 | d24e512cdcb2f48f403da6b41b881bcb | function soln = trapezoid(problem)
% soln = trapezoid(problem)
%
% This function transcribes a trajectory optimization problem using the
% trapezoid method for enforcing the dynamics. It can be found in chapter
% four of Bett's book:
%
% John T. Betts, 2001
% Practical Methods for Optimal Control Using Nonlinear Pr... |
github | pvarin/DynamicVAE-master | rungeKutta.m | .m | DynamicVAE-master/MatthewPeterKelly-OptimTraj-9a33249/rungeKutta.m | 39,686 | utf_8 | 23640bdd822c19de616e744a6202c456 | function soln = rungeKutta(problem)
% soln = rungeKutta(problem)
%
% This function transcribes a trajectory optimization problem using the
% multiple shooting, with 4th-order Runge Kutta integration
%
% See Bett's book for details on the method
%
% For details on the input and output, see the help file for optimTraj.m
... |
github | pvarin/DynamicVAE-master | getDefaultOptions.m | .m | DynamicVAE-master/MatthewPeterKelly-OptimTraj-9a33249/getDefaultOptions.m | 8,511 | UNKNOWN | b0e50d99e831c558cf728ae9bb423345 | function problem = getDefaultOptions(problem)
% problem = getDefaultOptions(problem)
%
% This function fills in any blank entries in the problem.options struct.
% It is designed to be called from inside of optimTraj.m, and not by the
% user.
%
%%%% Top-level default options:
OPT.method = 'trapezoid';
OPT.verbose = 2;
... |
github | pvarin/DynamicVAE-master | gpopsWrapper.m | .m | DynamicVAE-master/MatthewPeterKelly-OptimTraj-9a33249/gpopsWrapper.m | 5,777 | utf_8 | fb8e22a03bfa72046ab9bf5458b31b1f | function soln = gpopsWrapper(problem)
% soln = gpopsWrapper(problem)
%
% This function is a wrapper that converts the standard input for optimTraj
% into a call to GPOPS2, a commercially available transcription software
% for matlab. You can purchase and download it at http://www.gpops2.com/
%
% GPOPS2 implements an ad... |
github | pvarin/DynamicVAE-master | inputValidation.m | .m | DynamicVAE-master/MatthewPeterKelly-OptimTraj-9a33249/inputValidation.m | 4,319 | utf_8 | 394cd18a2f88465b4d3adbfe71561f8c | function problem = inputValidation(problem)
%
% This function runs through the problem struct and sets any missing fields
% to the default value. If a mandatory field is missing, then it throws an
% error.
%
% INPUTS:
% problem = a partially completed problem struct
%
% OUTPUTS:
% problem = a complete problem struc... |
github | pvarin/DynamicVAE-master | multiCheb.m | .m | DynamicVAE-master/MatthewPeterKelly-OptimTraj-9a33249/multiCheb.m | 21,236 | utf_8 | f3b52105bdd4fc219954b4149df07295 | function soln = multiCheb(problem)
% soln = multiCheb(problem)
%
% DEPRICATED
%
%
% *************************************************************************
% This file is no longer used, and is preserved for reference only. The
% numerical methods for connecting segments are not the most stable,
% particularily for l... |
github | pvarin/DynamicVAE-master | drawCartPoleAnim.m | .m | DynamicVAE-master/MatthewPeterKelly-OptimTraj-9a33249/demo/cartPole/drawCartPoleAnim.m | 2,133 | utf_8 | 2334402558a3114d7f969148319c70cd | function drawCartPoleAnim(~,p,xLow, xUpp, yLow, yUpp)
% drawCartPoleTraj(t,p,xLow, xUpp, yLow, yUpp)
%
% INPUTS:
% t = [1,n] = time stamp for the data in p1 and p2
% p = [4,n] = [p1;p2];
%
clf; hold on;
Cart_Width = 0.15;
Cart_Height = 0.05;
p1 = p(1:2,:);
p2 = p(3:4,:);
Pole_Width = 4; %pixels
%%%% Figure... |
github | pvarin/DynamicVAE-master | drawCartPoleTraj.m | .m | DynamicVAE-master/MatthewPeterKelly-OptimTraj-9a33249/demo/cartPole/drawCartPoleTraj.m | 2,226 | utf_8 | d998353b28a3858bf2e12e289f80f3a0 | function drawCartPoleTraj(t,p1,p2,nFrame)
% drawCartPoleTraj(t,p1,p2,nFrame)
%
% INPUTS:
% t = [1,n] = time stamp for the data in p1 and p2
% p1 = [2,n] = [x;y] = position of center of the cart
% p2 = [2,n] = [x;y] = position of tip of the pendulum
% nFrame = scalar integer = number of "freeze" frames to displ... |
github | pvarin/DynamicVAE-master | Derive_Equations.m | .m | DynamicVAE-master/MatthewPeterKelly-OptimTraj-9a33249/demo/fiveLinkBiped/Derive_Equations.m | 22,822 | utf_8 | db9aaefe0015ed46a21528cd1f049d49 | function Derive_Equations()
%%%% Derive Equations - Five Link Biped Model %%%%
%
% This function derives the equations of motion, as well as some other useful
% equations (kinematics, contact forces, ...) for the five-link biped
% model.
%
%
% Nomenclature:
%
% - There are five links, which will be numbered starting wi... |
github | pvarin/DynamicVAE-master | dirColGrad.m | .m | DynamicVAE-master/MatthewPeterKelly-OptimTraj-9a33249/demo/fiveLinkBiped/costOfTransport/dirColGrad.m | 11,673 | utf_8 | f7fd60b58db9ceade9467b4c0c3233f9 | function soln = dirColGrad(P, problem)
% soln = dirColGrad(P, problem)
%
% OptimTraj utility function - Direct Collocation with Gradients
%
% This function is core function that is called to run the transcription
% for both the "trapezoid" and the "hermiteSimpson" methods when they are
% running analytic gradients.
%
%... |
github | pvarin/DynamicVAE-master | Derive_Equations.m | .m | DynamicVAE-master/MatthewPeterKelly-OptimTraj-9a33249/demo/fiveLinkBiped/costOfTransport/Derive_Equations.m | 27,136 | utf_8 | 2ee06d2549cae61acad48475153b4214 | function Derive_Equations()
%%%% Derive Equations - Five Link Biped Model %%%%
%
% This function derives the equations of motion, as well as some other useful
% equations (kinematics, contact forces, ...) for the five-link biped
% model.
%
% This version of the code includes a few more complicated features for
% dealin... |
github | minitaur-users/minitaur-mainboard-code-master | open_log.m | .m | minitaur-mainboard-code-master/libraries/OpenLog/open_log.m | 1,347 | utf_8 | 66368e5a8bb5f6c5ddc3d16fdaa79511 | %{
* Copyright (C) Ghost Robotics - All Rights Reserved
* Unauthorized copying of this file, via any medium is strictly prohibited
* Proprietary and confidential
* Written by Avik De <avik@ghostrobotics.io> and Pranav Bhounsule
%}
function s = open_log(fname)
if ~exist(fname,'file')
error('File not found: %s', f... |
github | thomaskuestner/CNNArt-master | pdftops.m | .m | CNNArt-master/matlab/utils/export_fig/pdftops.m | 3,077 | utf_8 | 8dff856e4b450072050d8aa571d1a08e | function varargout = pdftops(cmd)
%PDFTOPS Calls a local pdftops executable with the input command
%
% Example:
% [status result] = pdftops(cmd)
%
% Attempts to locate a pdftops executable, finally asking the user to
% specify the directory pdftops was installed into. The resulting path is
% stored for future refere... |
github | thomaskuestner/CNNArt-master | crop_borders.m | .m | CNNArt-master/matlab/utils/export_fig/crop_borders.m | 1,669 | utf_8 | 725f526e7270a9b417300035d8748a9c | %CROP_BORDERS Crop the borders of an image or stack of images
%
% [B, v] = crop_borders(A, bcol, [padding])
%
%IN:
% A - HxWxCxN stack of images.
% bcol - Cx1 background colour vector.
% padding - scalar indicating how many pixels padding to have. Default: 0.
%
%OUT:
% B - JxKxCxN cropped stack of images.
% ... |
github | thomaskuestner/CNNArt-master | isolate_axes.m | .m | CNNArt-master/matlab/utils/export_fig/isolate_axes.m | 3,668 | utf_8 | e2dce471e433886fcb87f9dcb284a2cb | %ISOLATE_AXES Isolate the specified axes in a figure on their own
%
% Examples:
% fh = isolate_axes(ah)
% fh = isolate_axes(ah, vis)
%
% This function will create a new figure containing the axes/uipanels
% specified, and also their associated legends and colorbars. The objects
% specified must all be in the same f... |
github | thomaskuestner/CNNArt-master | im2gif.m | .m | CNNArt-master/matlab/utils/export_fig/im2gif.m | 6,168 | utf_8 | 01a5042cc084cddfe4ce631d33de7c8f | %IM2GIF Convert a multiframe image to an animated GIF file
%
% Examples:
% im2gif infile
% im2gif infile outfile
% im2gif(A, outfile)
% im2gif(..., '-nocrop')
% im2gif(..., '-nodither')
% im2gif(..., '-ncolors', n)
% im2gif(..., '-loops', n)
% im2gif(..., '-delay', n)
%
% This function converts a mu... |
github | thomaskuestner/CNNArt-master | read_write_entire_textfile.m | .m | CNNArt-master/matlab/utils/export_fig/read_write_entire_textfile.m | 924 | utf_8 | 779e56972f5d9778c40dee98ddbd677e | %READ_WRITE_ENTIRE_TEXTFILE Read or write a whole text file to/from memory
%
% Read or write an entire text file to/from memory, without leaving the
% file open if an error occurs.
%
% Reading:
% fstrm = read_write_entire_textfile(fname)
% Writing:
% read_write_entire_textfile(fname, fstrm)
%
%IN:
% fname - Pathn... |
github | thomaskuestner/CNNArt-master | pdf2eps.m | .m | CNNArt-master/matlab/utils/export_fig/pdf2eps.m | 1,471 | utf_8 | a1f41f0c7713c73886a2323e53ed982b | %PDF2EPS Convert a pdf file to eps format using pdftops
%
% Examples:
% pdf2eps source dest
%
% This function converts a pdf file to eps format.
%
% This function requires that you have pdftops, from the Xpdf suite of
% functions, installed on your system. This can be downloaded from:
% http://www.foolabs.com/xpdf ... |
github | thomaskuestner/CNNArt-master | print2array.m | .m | CNNArt-master/matlab/utils/export_fig/print2array.m | 6,273 | utf_8 | c2feb752d8836426a74edd9357f1ff17 | %PRINT2ARRAY Exports a figure to an image array
%
% Examples:
% A = print2array
% A = print2array(figure_handle)
% A = print2array(figure_handle, resolution)
% A = print2array(figure_handle, resolution, renderer)
% [A bcol] = print2array(...)
%
% This function outputs a bitmap image of the given figure, at t... |
github | thomaskuestner/CNNArt-master | append_pdfs.m | .m | CNNArt-master/matlab/utils/export_fig/append_pdfs.m | 2,010 | utf_8 | 1034abde9642693c404671ff1c693a22 | %APPEND_PDFS Appends/concatenates multiple PDF files
%
% Example:
% append_pdfs(output, input1, input2, ...)
% append_pdfs(output, input_list{:})
% append_pdfs test.pdf temp1.pdf temp2.pdf
%
% This function appends multiple PDF files to an existing PDF file, or
% concatenates them into a PDF file if the output fi... |
github | thomaskuestner/CNNArt-master | using_hg2.m | .m | CNNArt-master/matlab/utils/export_fig/using_hg2.m | 365 | utf_8 | 6a7f56042fda1873d8225eb3ec1cc197 | %USING_HG2 Determine if the HG2 graphics pipeline is used
%
% tf = using_hg2(fig)
%
%IN:
% fig - handle to the figure in question.
%
%OUT:
% tf - boolean indicating whether the HG2 graphics pipeline is being used
% (true) or not (false).
function tf = using_hg2(fig)
try
tf = ~graphicsversion(fig, 'han... |
github | thomaskuestner/CNNArt-master | eps2pdf.m | .m | CNNArt-master/matlab/utils/export_fig/eps2pdf.m | 5,009 | utf_8 | 5658b3d96232e138be7fd49693d88453 | %EPS2PDF Convert an eps file to pdf format using ghostscript
%
% Examples:
% eps2pdf source dest
% eps2pdf(source, dest, crop)
% eps2pdf(source, dest, crop, append)
% eps2pdf(source, dest, crop, append, gray)
% eps2pdf(source, dest, crop, append, gray, quality)
%
% This function converts an eps file to pdf f... |
github | thomaskuestner/CNNArt-master | copyfig.m | .m | CNNArt-master/matlab/utils/export_fig/copyfig.m | 812 | utf_8 | b6b1fa9a9351df33ae0d42056c3df40a | %COPYFIG Create a copy of a figure, without changing the figure
%
% Examples:
% fh_new = copyfig(fh_old)
%
% This function will create a copy of a figure, but not change the figure,
% as copyobj sometimes does, e.g. by changing legends.
%
% IN:
% fh_old - The handle of the figure to be copied. Default: gcf.
%
% OU... |
github | thomaskuestner/CNNArt-master | user_string.m | .m | CNNArt-master/matlab/utils/export_fig/user_string.m | 2,460 | utf_8 | e8aa836a5140410546fceccb4cca47aa | %USER_STRING Get/set a user specific string
%
% Examples:
% string = user_string(string_name)
% saved = user_string(string_name, new_string)
%
% Function to get and set a string in a system or user specific file. This
% enables, for example, system specific paths to binaries to be saved.
%
% IN:
% string_name - ... |
github | thomaskuestner/CNNArt-master | export_fig.m | .m | CNNArt-master/matlab/utils/export_fig/export_fig.m | 29,720 | utf_8 | 923dcc1ad89f1381ee70abbf422b20a5 | %EXPORT_FIG Exports figures suitable for publication
%
% Examples:
% im = export_fig
% [im alpha] = export_fig
% export_fig filename
% export_fig filename -format1 -format2
% export_fig ... -nocrop
% export_fig ... -transparent
% export_fig ... -native
% export_fig ... -m<val>
% export_fig ... -r<val... |
github | thomaskuestner/CNNArt-master | ghostscript.m | .m | CNNArt-master/matlab/utils/export_fig/ghostscript.m | 5,009 | utf_8 | e93de4034ac6e4ac154729dc2c12f725 | %GHOSTSCRIPT Calls a local GhostScript executable with the input command
%
% Example:
% [status result] = ghostscript(cmd)
%
% Attempts to locate a ghostscript executable, finally asking the user to
% specify the directory ghostcript was installed into. The resulting path
% is stored for future reference.
%
% Once ... |
github | thomaskuestner/CNNArt-master | fix_lines.m | .m | CNNArt-master/matlab/utils/export_fig/fix_lines.m | 5,759 | utf_8 | 3338572f35c4669b79cc3265892d35de | %FIX_LINES Improves the line style of eps files generated by print
%
% Examples:
% fix_lines fname
% fix_lines fname fname2
% fstrm_out = fixlines(fstrm_in)
%
% This function improves the style of lines in eps files generated by
% MATLAB's print function, making them more similar to those seen on
% screen. Grid ... |
github | thomaskuestner/CNNArt-master | fReadDICOM.m | .m | CNNArt-master/matlab/io/fReadDICOM.m | 8,286 | utf_8 | f42fb2403d27a894219274abc728ca3e | function [dImg, SInfo, SCoord] = fReadDICOM(sFolder)
% read in DICOM files
%
% (c) Christian Wuerslin, Thomas Kuestner, 2011
% ---------------------------------------------------------------------
% iMAXSIZE = 256;
if ispc, sS='\'; else sS='/'; end;
if ~nargin, sFolder = cd; end
SFolder = dir(sFolder);
SFiles = SFo... |
github | thomaskuestner/CNNArt-master | fPatchOverlay.m | .m | CNNArt-master/matlab/deepvis/fPatchOverlay.m | 4,179 | utf_8 | 9bc1e1c7e4769b84666acb79855b317d | function hfig = fPatchOverlay( dImg, dPatch, iScale, dAlpha, sPathOut, cPlotLimits, lLabel, lGray)
%FPATCHOVERLAY overlay figure
if(nargin < 8)
lGray = false;
end
if(nargin < 7)
lLabel = true;
end
if(nargin < 6)
xLimits = [1 size(dImg,2)];
yLimits = [1 size(dI... |
github | AMGoldsborough/tSDRG_PBC-master | PBC_pos.m | .m | tSDRG_PBC-master/PBC_pos.m | 149 | utf_8 | cfa59d5fd986e812e7f18499790643a4 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function x = PBC_pos(x,L)
%gives position with PBCs
x = mod(x-1,L)+1;
end |
github | jsjolund/sailoraid-master | serialRead.m | .m | sailoraid-master/Utilities/matlab/serialRead.m | 3,280 | utf_8 | c5128330d2222fc30a7bed8ecebfb4f5 | %% Start listening to sensor data
function serialRead(serialPort,callback)
if exist('s', 'var')
try
fclose(s);
catch
end
delete(s);
clear s;
end
% Supported baud rates 110, 300, 600, 1200, 2400, 4800, 9600, 14400, 19200,
% 38400, 57600, 115200, 230400, 460800, 921600
s = serial(serialPort);
... |
github | jsjolund/sailoraid-master | main.m | .m | sailoraid-master/Utilities/matlab/main.m | 1,656 | utf_8 | 17a84b2c220b6a508d528876ff8e2695 | %% Script which logs sensor values from serial port and can plot real-time
% Press Ctrl+C in the Command Window to stop reading and recording.
clear -global;
clear;
%serialPort = 'COM1'; % Windows
serialPort = '/dev/ttyACM0'; % Linux
% Can be removed for faster logging
% realTimePlot();
% Start reading from serial
s... |
github | jsjolund/sailoraid-master | serialRead.m | .m | sailoraid-master/Kalman_Filter/Serial_Read/serialRead.m | 2,808 | utf_8 | 607933805d0c2b6a07459c8e7cd4a338 | %% Start listening to sensor data
function serialRead(serialPort,callback)
if exist('s', 'var')
try
fclose(s);
catch
end
delete(s);
clear s;
end
s = serial(serialPort);
if (s.Status == 'closed')
s.BaudRate = 115200;
s.ReadAsyncMode = 'continuous';
s.InputBufferSize ... |
github | jsjolund/sailoraid-master | main.m | .m | sailoraid-master/Kalman_Filter/Serial_Read/main.m | 1,485 | utf_8 | 50d87825b14a282b6b04225f526eb936 | %% Script which logs sensor values from serial port and can plot real-time
clear -global;
clear;
serialPort = 'COM7'; % Windows
%serialPort = '/dev/ttyACM3'; % Linux
% realTimePlot();
% Start reading from serial
serialRead(serialPort, @sensorUpdateCallback);
% Plot from sensor log
global sensorLog
im... |
github | Kazell/coloring-contours-of-objects-master | fig_n.m | .m | coloring-contours-of-objects-master/fig_n.m | 3,150 | utf_8 | fb24e5717c1560f3bfecedccce8df1b2 |
function clea=fig_n(a)
%
%This function takes a picture a='name.extension' as an argument and returns a figure of external boundaries of objects
%placed on picture all colored differently (on the white background)if the background of the given picture is uniform
%and all objects don't touch the borders of t... |
github | gunpowder78/CMU-Perceptual-Computing-Lab-openpose-master | classification_demo.m | .m | CMU-Perceptual-Computing-Lab-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 | lifeng9472/IBCCF-master | tracker.m | .m | IBCCF-master/tracker.m | 8,795 | utf_8 | 12e1cf165656dd49d0a525d7b49f7501 | % Tracker: Integrating Boundary and Center Correlation Filters for Visual Tracking with
% Aspect Ratio Variation
%
% Input:
% - img_files: list of image names
% - pos: intialized center position of the target in (row, col)
% - init_target_sz: intialized target size in (Height, ... |
github | lifeng9472/IBCCF-master | test_examples.m | .m | IBCCF-master/external_libs/matconvnet/utils/test_examples.m | 1,591 | utf_8 | 16831be7382a9343beff5cc3fe301e51 | function test_examples()
%TEST_EXAMPLES Test some of the examples in the `examples/` directory
addpath examples/mnist ;
addpath examples/cifar ;
trainOpts.gpus = [] ;
trainOpts.continue = true ;
num = 1 ;
exps = {} ;
for networkType = {'dagnn', 'simplenn'}
for index = 1:4
clear ex ;
ex.trainOpts = trainOp... |
github | lifeng9472/IBCCF-master | simplenn_caffe_compare.m | .m | IBCCF-master/external_libs/matconvnet/utils/simplenn_caffe_compare.m | 5,638 | utf_8 | 8e9862ffbf247836e6ff7579d1e6dc85 | function diffStats = simplenn_caffe_compare( net, caffeModelBaseName, testData, varargin)
% SIMPLENN_CAFFE_COMPARE compare the simplenn network and caffe models
% SIMPLENN_CAFFE_COMPARE(NET, CAFFE_BASE_MODELNAME) Evaluates a forward
% pass of a simplenn network NET and caffe models stored in
% CAFFE_BASE_MODELNAM... |
github | lifeng9472/IBCCF-master | cnn_train_dag.m | .m | IBCCF-master/external_libs/matconvnet/examples/cnn_train_dag.m | 13,629 | utf_8 | 73e1103b1f7118b23a1bc12237e953ed | function [net,stats] = cnn_train_dag(net, imdb, getBatch, varargin)
%CNN_TRAIN_DAG Demonstrates training a CNN using the DagNN wrapper
% CNN_TRAIN_DAG() is similar to CNN_TRAIN(), but works with
% the DagNN wrapper instead of the SimpleNN wrapper.
% Copyright (C) 2014-16 Andrea Vedaldi.
% All rights reserved.
%
... |
github | lifeng9472/IBCCF-master | cnn_train.m | .m | IBCCF-master/external_libs/matconvnet/examples/cnn_train.m | 19,153 | utf_8 | c6c0c0c8532f9c3653af4410497f80c3 | function [net, stats] = cnn_train(net, imdb, getBatch, varargin)
%CNN_TRAIN An example implementation of SGD for training CNNs
% CNN_TRAIN() is an example learner implementing stochastic
% gradient descent with momentum to train a CNN. It can be used
% with different datasets and tasks by providing a suitable... |
github | lifeng9472/IBCCF-master | cnn_stn_cluttered_mnist.m | .m | IBCCF-master/external_libs/matconvnet/examples/spatial_transformer/cnn_stn_cluttered_mnist.m | 3,872 | utf_8 | 3235801f70028cc27d54d15ec2964808 | function [net, info] = cnn_stn_cluttered_mnist(varargin)
%CNN_STN_CLUTTERED_MNIST Demonstrates training a spatial transformer
% The spatial transformer network (STN) is trained on the
% cluttered MNIST dataset.
run(fullfile(fileparts(mfilename('fullpath')),...
'..', '..', 'matlab', 'vl_setupnn.m')) ;
opts.data... |
github | lifeng9472/IBCCF-master | fast_rcnn_train.m | .m | IBCCF-master/external_libs/matconvnet/examples/fast_rcnn/fast_rcnn_train.m | 6,399 | utf_8 | 54b0bc7fa26d672ed6673d3f1832944e | function [net, info] = fast_rcnn_train(varargin)
%FAST_RCNN_TRAIN Demonstrates training a Fast-RCNN detector
% Copyright (C) 2016 Hakan Bilen.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
run(fullfile(fileparts(m... |
github | lifeng9472/IBCCF-master | fast_rcnn_evaluate.m | .m | IBCCF-master/external_libs/matconvnet/examples/fast_rcnn/fast_rcnn_evaluate.m | 6,941 | utf_8 | a54a3f8c3c8e5a8ff7ebe4e2b12ede30 | function [aps, speed] = fast_rcnn_evaluate(varargin)
%FAST_RCNN_EVALUATE Evaluate a trained Fast-RCNN model on PASCAL VOC 2007
% Copyright (C) 2016 Hakan Bilen.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
run(fu... |
github | lifeng9472/IBCCF-master | cnn_cifar.m | .m | IBCCF-master/external_libs/matconvnet/examples/cifar/cnn_cifar.m | 5,334 | utf_8 | eb9aa887d804ee635c4295a7a397206f | function [net, info] = cnn_cifar(varargin)
% CNN_CIFAR Demonstrates MatConvNet on CIFAR-10
% The demo includes two standard model: LeNet and Network in
% Network (NIN). Use the 'modelType' option to choose one.
run(fullfile(fileparts(mfilename('fullpath')), ...
'..', '..', 'matlab', 'vl_setupnn.m')) ;
opts.... |
github | lifeng9472/IBCCF-master | cnn_cifar_init_nin.m | .m | IBCCF-master/external_libs/matconvnet/examples/cifar/cnn_cifar_init_nin.m | 5,561 | utf_8 | aca711e04a8cd82821f658922218368c | function net = cnn_cifar_init_nin(varargin)
opts.networkType = 'simplenn' ;
opts = vl_argparse(opts, varargin) ;
% CIFAR-10 model from
% M. Lin, Q. Chen, and S. Yan. Network in network. CoRR,
% abs/1312.4400, 2013.
%
% It reproduces the NIN + Dropout result of Table 1 (<= 10.41% top1 error).
net.layers = {} ;
lr = [... |
github | lifeng9472/IBCCF-master | cnn_imagenet_init_resnet.m | .m | IBCCF-master/external_libs/matconvnet/examples/imagenet/cnn_imagenet_init_resnet.m | 6,717 | utf_8 | aa905a97830e90dc7d33f75ad078301e | function net = cnn_imagenet_init_resnet(varargin)
%CNN_IMAGENET_INIT_RESNET Initialize the ResNet-50 model for ImageNet classification
opts.classNames = {} ;
opts.classDescriptions = {} ;
opts.averageImage = zeros(3,1) ;
opts.colorDeviation = zeros(3) ;
opts.cudnnWorkspaceLimit = 1024*1024*1204 ; % 1GB
opts = vl_argp... |
github | lifeng9472/IBCCF-master | cnn_imagenet_init.m | .m | IBCCF-master/external_libs/matconvnet/examples/imagenet/cnn_imagenet_init.m | 15,279 | utf_8 | 43bffc7ab4042d49c4f17c0e44c36bf9 | function net = cnn_imagenet_init(varargin)
% CNN_IMAGENET_INIT Initialize a standard CNN for ImageNet
opts.scale = 1 ;
opts.initBias = 0 ;
opts.weightDecay = 1 ;
%opts.weightInitMethod = 'xavierimproved' ;
opts.weightInitMethod = 'gaussian' ;
opts.model = 'alexnet' ;
opts.batchNormalization = false ;
opts.networkType... |
github | lifeng9472/IBCCF-master | cnn_imagenet.m | .m | IBCCF-master/external_libs/matconvnet/examples/imagenet/cnn_imagenet.m | 6,211 | utf_8 | f11556c91bb9796f533c8f624ad8adbd | function [net, info] = cnn_imagenet(varargin)
%CNN_IMAGENET Demonstrates training a CNN on ImageNet
% This demo demonstrates training the AlexNet, VGG-F, VGG-S, VGG-M,
% VGG-VD-16, and VGG-VD-19 architectures on ImageNet data.
run(fullfile(fileparts(mfilename('fullpath')), ...
'..', '..', 'matlab', 'vl_setupnn.m... |
github | lifeng9472/IBCCF-master | cnn_imagenet_deploy.m | .m | IBCCF-master/external_libs/matconvnet/examples/imagenet/cnn_imagenet_deploy.m | 6,585 | utf_8 | 2f3e6d216fa697ff9adfce33e75d44d8 | function net = cnn_imagenet_deploy(net)
%CNN_IMAGENET_DEPLOY Deploy a CNN
isDag = isa(net, 'dagnn.DagNN') ;
if isDag
dagRemoveLayersOfType(net, 'dagnn.Loss') ;
dagRemoveLayersOfType(net, 'dagnn.DropOut') ;
else
net = simpleRemoveLayersOfType(net, 'softmaxloss') ;
net = simpleRemoveLayersOfType(net, 'dropout')... |
github | lifeng9472/IBCCF-master | cnn_imagenet_evaluate.m | .m | IBCCF-master/external_libs/matconvnet/examples/imagenet/cnn_imagenet_evaluate.m | 5,089 | utf_8 | f22247bd3614223cad4301daa91f6bd7 | 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', 'ILSVRC2012') ;
opts.expDir = fullfile('data', 'imagenet12-eval-vgg-f') ;
opts.m... |
github | lifeng9472/IBCCF-master | cnn_mnist_init.m | .m | IBCCF-master/external_libs/matconvnet/examples/mnist/cnn_mnist_init.m | 3,111 | utf_8 | 367b1185af58e108aec40b61818ec6e7 | function net = cnn_mnist_init(varargin)
% CNN_MNIST_LENET Initialize a CNN similar for MNIST
opts.batchNormalization = true ;
opts.networkType = 'simplenn' ;
opts = vl_argparse(opts, varargin) ;
rng('default');
rng(0) ;
f=1/100 ;
net.layers = {} ;
net.layers{end+1} = struct('type', 'conv', ...
... |
github | lifeng9472/IBCCF-master | cnn_mnist.m | .m | IBCCF-master/external_libs/matconvnet/examples/mnist/cnn_mnist.m | 4,613 | utf_8 | d23586e79502282a6f6d632c3cf8a47e | function [net, info] = cnn_mnist(varargin)
%CNN_MNIST Demonstrates MatConvNet on MNIST
run(fullfile(fileparts(mfilename('fullpath')),...
'..', '..', 'matlab', 'vl_setupnn.m')) ;
opts.batchNormalization = false ;
opts.network = [] ;
opts.networkType = 'simplenn' ;
[opts, varargin] = vl_argparse(opts, varargin) ;
s... |
github | lifeng9472/IBCCF-master | vl_nnloss.m | .m | IBCCF-master/external_libs/matconvnet/matlab/vl_nnloss.m | 11,212 | utf_8 | e4c325752a9cddab59f01afa0d561ea1 | function y = vl_nnloss(x,c,dzdy,varargin)
%VL_NNLOSS CNN categorical or attribute loss.
% Y = VL_NNLOSS(X, C) computes the loss incurred by the prediction
% scores X given the categorical labels C.
%
% The prediction scores X are organised as a field of prediction
% vectors, represented by a H x W x D x N array... |
github | lifeng9472/IBCCF-master | vl_compilenn.m | .m | IBCCF-master/external_libs/matconvnet/matlab/vl_compilenn.m | 30,050 | utf_8 | 6339b625106e6c7b479e57c2b9aa578e | 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 options:
%... |
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