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github | Steganalysis-CNN/CNN-without-BN-master | cnn_imagenet_init.m | .m | CNN-without-BN-master/dependencies/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 | Steganalysis-CNN/CNN-without-BN-master | cnn_imagenet.m | .m | CNN-without-BN-master/dependencies/matconvnet/examples/imagenet/cnn_imagenet.m | 6,240 | utf_8 | eaead56ec3104ef8f4b39e043aaf0d41 | 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 | Steganalysis-CNN/CNN-without-BN-master | cnn_imagenet_deploy.m | .m | CNN-without-BN-master/dependencies/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 | Steganalysis-CNN/CNN-without-BN-master | cnn_imagenet_evaluate.m | .m | CNN-without-BN-master/dependencies/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 | Steganalysis-CNN/CNN-without-BN-master | cnn_mnist_init.m | .m | CNN-without-BN-master/dependencies/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 | Steganalysis-CNN/CNN-without-BN-master | cnn_mnist.m | .m | CNN-without-BN-master/dependencies/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 | Steganalysis-CNN/CNN-without-BN-master | vl_nnloss.m | .m | CNN-without-BN-master/dependencies/matconvnet/matlab/vl_nnloss.m | 11,377 | utf_8 | a070d1cf73525dca7566f6e5c0205297 | 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 | Steganalysis-CNN/CNN-without-BN-master | vl_compilenn.m | .m | CNN-without-BN-master/dependencies/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:
%... |
github | Steganalysis-CNN/CNN-without-BN-master | getVarReceptiveFields.m | .m | CNN-without-BN-master/dependencies/matconvnet/matlab/+dagnn/@DagNN/getVarReceptiveFields.m | 3,635 | utf_8 | 6d61896e475e64e9f05f10303eee7ade | function rfs = getVarReceptiveFields(obj, var)
%GETVARRECEPTIVEFIELDS Get the receptive field of a variable
% RFS = GETVARRECEPTIVEFIELDS(OBJ, VAR) gets the receptivie fields RFS of
% all the variables of the DagNN OBJ into variable VAR. VAR is a variable
% name or index.
%
% RFS has one entry for each variable... |
github | Steganalysis-CNN/CNN-without-BN-master | rebuild.m | .m | CNN-without-BN-master/dependencies/matconvnet/matlab/+dagnn/@DagNN/rebuild.m | 3,243 | utf_8 | e368536d9e70c805d8424cdd6b593960 | function rebuild(obj)
%REBUILD Rebuild the internal data structures of a DagNN object
% REBUILD(obj) rebuilds the internal data structures
% of the DagNN obj. It is an helper function used internally
% to update the network when layers are added or removed.
varFanIn = zeros(1, numel(obj.vars)) ;
varFanOut = zero... |
github | Steganalysis-CNN/CNN-without-BN-master | print.m | .m | CNN-without-BN-master/dependencies/matconvnet/matlab/+dagnn/@DagNN/print.m | 15,032 | utf_8 | 7da4e68e624f559f815ee3076d9dd966 | function str = print(obj, inputSizes, varargin)
%PRINT Print information about the DagNN object
% PRINT(OBJ) displays a summary of the functions and parameters in the network.
% STR = PRINT(OBJ) returns the summary as a string instead of printing it.
%
% PRINT(OBJ, INPUTSIZES) where INPUTSIZES is a cell array of ... |
github | Steganalysis-CNN/CNN-without-BN-master | fromSimpleNN.m | .m | CNN-without-BN-master/dependencies/matconvnet/matlab/+dagnn/@DagNN/fromSimpleNN.m | 7,258 | utf_8 | 83f914aec610125592263d74249f54a7 | function obj = fromSimpleNN(net, varargin)
% FROMSIMPLENN Initialize a DagNN object from a SimpleNN network
% FROMSIMPLENN(NET) initializes the DagNN object from the
% specified CNN using the SimpleNN format.
%
% SimpleNN objects are linear chains of computational layers. These
% layers exchange information th... |
github | Steganalysis-CNN/CNN-without-BN-master | vl_simplenn_display.m | .m | CNN-without-BN-master/dependencies/matconvnet/matlab/simplenn/vl_simplenn_display.m | 12,455 | utf_8 | 65bb29cd7c27b68c75fdd27acbd63e2b | function [info, str] = vl_simplenn_display(net, varargin)
%VL_SIMPLENN_DISPLAY Display the structure of a SimpleNN network.
% VL_SIMPLENN_DISPLAY(NET) prints statistics about the network NET.
%
% INFO = VL_SIMPLENN_DISPLAY(NET) returns instead a structure INFO
% with several statistics for each layer of the netw... |
github | Steganalysis-CNN/CNN-without-BN-master | vl_test_economic_relu.m | .m | CNN-without-BN-master/dependencies/matconvnet/matlab/xtest/vl_test_economic_relu.m | 790 | utf_8 | 35a3dbe98b9a2f080ee5f911630ab6f3 | % VL_TEST_ECONOMIC_RELU
function vl_test_economic_relu()
x = randn(11,12,8,'single');
w = randn(5,6,8,9,'single');
b = randn(1,9,'single') ;
net.layers{1} = struct('type', 'conv', ...
'filters', w, ...
'biases', b, ...
'stride', 1, ...
... |
github | gsygsy96/dense_flow-master | extractOpticalFlow_gpu.m | .m | dense_flow-master/extractOpticalFlow_gpu.m | 3,132 | utf_8 | b6bb10a83f1f67dab8c935b34f78f236 |
function [] = extractOpticalFlow_gpu(index, device_id, type)
path1 = '/media/sdg/lmwang/Data/UCF101/ucf101_org/';
if type ==0
path2 = '/media/sdb/lmwang/data/UCF101/ucf101_flow_img_farn_gpu_step_2/';
elseif type ==1
path2 = '/media/sdg/lmwang/data/UCF101/ucf101_flow_img_tvl1_gpu_size_342/';
else
path2 = '/... |
github | gsygsy96/dense_flow-master | extracOpticalFlow.m | .m | dense_flow-master/extracOpticalFlow.m | 1,032 | utf_8 | 6c684588cd7294f4b9da8c772842d1e1 |
function [] = extracOpticalFlow(index)
path1 = '/home/lmwang/data/UCF/ucf101_org/';
path2 = '/home/lmwang/data/UCF/ucf101_warp_flow_img/';
folderlist = dir(path1);
foldername = {folderlist(:).name};
foldername = setdiff(foldername,{'.','..'});
for i = index
if ~exist([path2,foldername{i}],'dir')
mkdir([p... |
github | gsygsy96/dense_flow-master | extractOpticalFlow.m | .m | dense_flow-master/extractOpticalFlow.m | 1,040 | utf_8 | cc9a423971c037f30e6cd1a2c997f820 |
function [] = extractOpticalFlow(index)
path1 = '/nfs/lmwang/lmwang/Data/UCF101/ucf101_org/';
path2 = '/media/sdb/lmwang/data/UCF101/ucf101_flow_img_TV/';
folderlist = dir(path1);
foldername = {folderlist(:).name};
foldername = setdiff(foldername,{'.','..'});
for i = index
if ~exist([path2,foldername{i}],'dir')
... |
github | MAS-dfab/T1_python-exercises-master | subsolv.m | .m | T1_python-exercises-master/07_ur_online/shifted_frames_setup/compas/src/compas/numerical/solvers/_mma/subsolv.m | 5,830 | utf_8 | c6a16954d2524b7645e6ed4ccffd8a10 | %-------------------------------------------------------------
% This is the file subsolv.m
%
% Version Dec 2006.
% Krister Svanberg <krille@math.kth.se>
% Department of Mathematics, KTH,
% SE-10044 Stockholm, Sweden.
%
function [xmma,ymma,zmma,lamma,xsimma,etamma,mumma,zetmma,smma] = ...
subsolv(m,n,eps... |
github | MAS-dfab/T1_python-exercises-master | asymp.m | .m | T1_python-exercises-master/07_ur_online/shifted_frames_setup/compas/src/compas/numerical/solvers/_mma/asymp.m | 1,193 | utf_8 | 01768c819421ea53bdf0e3d04846acc3 | %---------------------------------------------------------------------
% This is the file asymp.m. Version August 2007.
% Written by Krister Svanberg <krille@math.kth.se>.
%
% Values on the parameters raa0, raa, low and upp are
% calculated in the beginning of each outer iteration.
%
function [low,upp,raa0,raa] = ... |
github | MAS-dfab/T1_python-exercises-master | beam1.m | .m | T1_python-exercises-master/07_ur_online/shifted_frames_setup/compas/src/compas/numerical/solvers/_mma/beam1.m | 820 | utf_8 | 5aba330cf745ee4f38280eb2092fb9a4 | %---------------------------------------------------------------------
% This is the file beam1.m. Version April 2007.
% Written by Krister Svanberg <krille@math.kth.se>.
% It calculates function values (but no gradients)
% for the "beam problem" from the MMA paper.
%
% minimize 0.0624*(x(1) + x(2) + x(3) + x(4... |
github | MAS-dfab/T1_python-exercises-master | raaupdate.m | .m | T1_python-exercises-master/07_ur_online/shifted_frames_setup/compas/src/compas/numerical/solvers/_mma/raaupdate.m | 1,207 | utf_8 | 915f622afb986a8f398908bf83e38780 | %---------------------------------------------------------------------
% This is the file raaupdate.m. Version April 2007.
% Written by Krister Svanberg <krille@math.kth.se>.
%
% Values of the parameters raa0 and raa are updated
% during an inner iteration.
%
function [raa0,raa] = ...
raaupdate(xmma,xval,xmin,xmax... |
github | MAS-dfab/T1_python-exercises-master | beam2.m | .m | T1_python-exercises-master/07_ur_online/shifted_frames_setup/compas/src/compas/numerical/solvers/_mma/beam2.m | 912 | utf_8 | 2ebb5f82c752ffaa71d6789a0f6ad30d | %---------------------------------------------------------------------
% This is the file beam2.m. Version April 2007.
% Written by Krister Svanberg <krille@math.kth.se>.
% It calculates function values and gradients
% for the "beam problem" from the MMA paper.
%
% minimize 0.0624*(x(1) + x(2) + x(3) + x(4) + x... |
github | MAS-dfab/T1_python-exercises-master | gcmmasub.m | .m | T1_python-exercises-master/07_ur_online/shifted_frames_setup/compas/src/compas/numerical/solvers/_mma/gcmmasub.m | 1,891 | utf_8 | 381682bb5be9cd604f40a94b69786387 | %---------------------------------------------------------------------
% This is the file gcmmasub.m. Version Feb 2008.
% Written by Krister Svanberg <krille@math.kth.se>.
%
function [xmma,ymma,zmma,lam,xsi,eta,mu,zet,s,f0app,fapp] = ...
gcmmasub(m,n,iter,epsimin,xval,xmin,xmax,low,upp, ...
raa0,raa,f0val,d... |
github | MAS-dfab/T1_python-exercises-master | concheck.m | .m | T1_python-exercises-master/07_ur_online/shifted_frames_setup/compas/src/compas/numerical/solvers/_mma/concheck.m | 563 | utf_8 | 1af2a5bbf88f34d8f970d1818af498f4 | %---------------------------------------------------------------------
% This is the file concheck.m. Version April 2007.
% Written by Krister Svanberg <krille@math.kth.se>.
%
% If the current approximations are conservative,
% the parameter conserv is set to 1.
%
function [conserv] = ...
concheck(m,epsimin,f0app,... |
github | EhomeBurning/Machine-Learning-master | KernelDensity.m | .m | Machine-Learning-master/project/Code/KernelDensity.m | 703 | utf_8 | 53710e1313c12cb83a929cde5c53661b | %% 2-D kernel density
% last modified on 2011-09-24
function [p_Y p_grid] = KernelDensity(Y, h, is_grid, grid)
% Y: 2 by n data matrix
% h: bandwidth
% grid: 2 by k array, the (grid) points of output
% p_Y: the estimated density at the data points
% p_grid: estimated density at grid points
n =... |
github | EhomeBurning/Machine-Learning-master | conformal.m | .m | Machine-Learning-master/project/Code/Sample_Code/conformal.m | 756 | utf_8 | ea26d6ff6143e021795fa9d63636670d | %% conformal prediction region
% last modified on 2011-09-23
function conf_set = conformal(Y, h, grid, alpha)
% Y: 2 by n data matrix
% h: bandwidth
% grid: 2 by n_grid coordinate grids
% alpha: level
n = size(Y, 2);
n_grid = size(grid, 2);
conf_set = zeros(n_grid, n_grid);
p_value = con... |
github | EhomeBurning/Machine-Learning-master | KernelDensity.m | .m | Machine-Learning-master/project/Code/Sample_Code/KernelDensity.m | 703 | utf_8 | 53710e1313c12cb83a929cde5c53661b | %% 2-D kernel density
% last modified on 2011-09-24
function [p_Y p_grid] = KernelDensity(Y, h, is_grid, grid)
% Y: 2 by n data matrix
% h: bandwidth
% grid: 2 by k array, the (grid) points of output
% p_Y: the estimated density at the data points
% p_grid: estimated density at grid points
n =... |
github | JamesLinus/jbuilder-universe-master | dist.m | .m | jbuilder-universe-master/packages/gpr.1.3.1/test/dist.m | 303 | utf_8 | 1a5d8db3738957a2eba64e1597bff7bb | % Part of Ed Snelson's SPGP distribution
function D = dist(x0,x1)
% dist: compute pairwise distance matrix from two column vectors x0 and x1
warning(['To speed up gradient calculation compile mex' ...
' file dist.c'])
n0 = length(x0); n1 = length(x1);
D = repmat(x0,1,n1)-repmat(x1',n0,1);
|
github | JamesLinus/jbuilder-universe-master | oct.m | .m | jbuilder-universe-master/packages/gpr.1.3.1/test/oct.m | 4,062 | utf_8 | 52e56ba744ec1e09ea8f81bb2bcecde8 | % Octave script for testing Gaussian process regression results
%
% Copyright (C) 2009- Markus Mottl
% email: markus.mottl@gmail.com
% WWW: http://www.ocaml.info
format long
global log_sf2;
load data/inputs
load data/targets
load data/inducing_points
load data/sigma2
load data/log_ell
load data/log_sf2
global ep... |
github | JamesLinus/jbuilder-universe-master | spgp_lik.m | .m | jbuilder-universe-master/packages/gpr.1.3.1/test/spgp_lik.m | 3,008 | utf_8 | 7485b4c80e18947f8f12a41b93b8e14c | % Edward Snelson's SPGP implementation in Octave/Matlab
function [fw,dfw] = spgp_lik(w,y,x,n,del)
% spgp_lik_3: neg. log likelihood for SPGP and gradients with respect to
% pseudo-inputs and hyperparameters. Gaussian covariance with one
% lengthscale per dimension.
%
% y -- training targets (N x 1)
% x -- training in... |
github | s-guenther/hybrid-master | rand_signal.m | .m | hybrid-master/sample/rand_signal.m | 6,001 | utf_8 | be8f1061458ada6dfdbf9be2af2b302e | function [signal, seed] = rand_signal(varargin)
% RAND_SIGNAL generates a random signal satisfying energy constraints
%
% Generates a random signal of type 'fhandle', which has an integral
% function which starts at zero, never becomes negative, and ends at
% zero. Signal is reproduceable by specifying the seed o... |
github | s-guenther/hybrid-master | parse_gen_storages_input.m | .m | hybrid-master/src/_gen_storages/parse_gen_storages_input.m | 2,561 | utf_8 | df9d6a4c4d7cdd50e5b3274370b9027f | function [prices, names, opt] = parse_gen_storages_input(spec_powers, ...
varargin)
% Checks input data of gen_storages if types/classes are correct and
% consistent
if nargin == 1
opt = hybridset();
prices = make_prices(spec_powers);
names = make_na... |
github | s-guenther/hybrid-master | get_min_limits.m | .m | hybrid-master/src/_plot_eco/get_min_limits.m | 2,110 | utf_8 | e24989e79984f8fd27062a8021bd16cf | function limits = get_min_limits(ecodata, dtype, opt)
% GET_MIN_LIMITS determines power cut ranges for cheapest storage pair
%
% Determines the cheapest base/peak storage combination in power cut
% bound. For each storage pair, a tuple of [lower bound, upper bound] is
% determined. Output will be written to a cel... |
github | s-guenther/hybrid-master | verbose.m | .m | hybrid-master/src/miscellaneous/verbose.m | 1,500 | utf_8 | c349da36cf4b896a2a179c858aac8daf | function verbose(trigger, level, message)
% VERBOSE displays an information message if verbosity level is reached
%
% VERBOSE(TRIGGER, LEVEL, MESSAGE) displays the message MESSAGE if TRIGGER
% is equal or higher LEVEL. It also adds a timestamp to the message and
% indents it depending on LEVEL.
%
% With this, a simple ... |
github | s-guenther/hybrid-master | solve_fhandle_sdode.m | .m | hybrid-master/src/_hybrid/solve_fhandle_sdode.m | 2,726 | utf_8 | 02069b9840a254a1d8ec9fd952131e49 | function [tout, yout] = solve_fhandle_sdode(build, decay, opt)
% SOLVE_FHANDLE_SDODE specialized SDODE solver for function handles
%
% [TOUT, YOUT] = SOLVE_FHANDLE_SDODE(BUILD, DECAY, OPT)
%
% See also SOLVE_SDODE.
odesol = opt.continuous_solver;
ode = @(t, y) sdode(t, y, build.fcn, decay.fcn);
[tout, yout] = odesol(... |
github | s-guenther/hybrid-master | solve_discrete_sdode.m | .m | hybrid-master/src/_hybrid/solve_discrete_sdode.m | 6,206 | utf_8 | bdc51ac54646c42a9cd8ec124493d3ce | function [tout, yout] = solve_discrete_sdode(build, decay, opt)
% SOLVE_DISCRETE_SDODE specialized SDODE solver for discrete fcns
%
% [TOUT, YOUT] = SOLVE_DISCRETE_SDODE(BUILD, DECAY, OPT)
%
% See also SOLVE_SDODE.
% Allocate space for result vectors
tout = zeros(2*length(build.val), 1);
yout = zeros(2*length(build.va... |
github | s-guenther/hybrid-master | reload_factory.m | .m | hybrid-master/src/_sim_operation/reload_factory.m | 3,468 | utf_8 | dfb92060f194ada02d440d791b6fecbc | function reload = reload_factory(strategy, base, peak, opt)
% RELOAD_FACTORY builds a parameterized reload strategy
%
% The control strategy must be parameterized with a few constants, i.e.
% storage dimensions, and the backward integral. To reduce the number of
% inputs and to avoid overloading functions, this functio... |
github | s-guenther/hybrid-master | gen_sig_linear.m | .m | hybrid-master/src/_gen_signal/gen_sig_linear.m | 1,947 | utf_8 | d4a30ced2e0841bf58d9043319a0c2f8 | function signal = gen_sig_linear(time, val, opt)
% GEN_SIG_LINEAR specialized fcn of gen_signal for fhandles
%
% Generates SIGNAL struct for piecewise linear function input.
%
% SIGNAL = GEN_SIG_STEP(TIME, VAL, OPT) where TIME and VAL are vector value
% pairs describing the function and OPT the options struct obtained ... |
github | s-guenther/hybrid-master | gen_sig_fhandle.m | .m | hybrid-master/src/_gen_signal/gen_sig_fhandle.m | 2,740 | utf_8 | fb46f67dd5464efdf0e1274acd00781a | function signal = gen_sig_fhandle(fcn, period, opt)
% GEN_SIG_FHANDLE specialized fcn of gen_signal for fhandles
%
% Generates SIGNAL struct for function handle input.
%
% SIGNAL = GEN_SIG_FHANDLE(FCN, PERIOD, OPT) where FCN is a function
% handle, PERIOD the period of the function and OPT the options struct
% obtained... |
github | s-guenther/hybrid-master | gen_sig_step.m | .m | hybrid-master/src/_gen_signal/gen_sig_step.m | 1,278 | utf_8 | 6cdf2054c2c27b4040e890e51eeddb61 | function signal = gen_sig_step(time, val, opt)
% GEN_SIG_STEP specialized fcn of gen_signal for step functions
%
% Generates SIGNAL struct for step function input.
%
% SIGNAL = GEN_SIG_STEP(TIME, VAL, OPT) where TIME and VAL are vector value
% pairs describing the function and OPT the options struct obtained from
% HYB... |
github | vvanirudh/list_prediction_motion_planning-master | train_mcsvm.m | .m | list_prediction_motion_planning-master/cs_classification/utils/train_mcsvm.m | 2,278 | utf_8 | df5d75cc52cd6024ef1997a07a68f25c | function model = train_mcsvm(features,costs,varargin)
% model = TRAIN_MCSVM(features,costs)
% model = TRAIN_MCSVM(features,costs,lambda)
% model = TRAIN_MCSVM(features,costs,lambda,bw)
% features is num_instances x dim_features
% costs is num_instances x num_classes
% lambda is regularization constant
% default parame... |
github | vvanirudh/list_prediction_motion_planning-master | train_multi_linear_subgradient_primal.m | .m | list_prediction_motion_planning-master/cs_classification/utils/train_multi_linear_subgradient_primal.m | 3,321 | utf_8 | 8091a92a9dce59656ea593d60fd042ef | function [w, obj_vec] = train_multi_linear_subgradient_primal(features,costs,lambda,w0)
%TRAIN_LINEAR_SCORER
%
% weights = TRAIN_LINEAR_SCORER(features,costs,lambda)
%
% features - Cell array of length n. features{i} is L x d.
% costs - Array of size N x L.
% lambda - Regularization constant.
%
% w - d x L
% w... |
github | vvanirudh/list_prediction_motion_planning-master | train_multi_linear_primal_sg_hinge.m | .m | list_prediction_motion_planning-master/cs_classification/utils/train_multi_linear_primal_sg_hinge.m | 1,912 | utf_8 | 41e7a8a6e94d75bf6430a2ead4915adb | function [w, obj, wset] = train_multi_linear_primal_sg_hinge(features,losses,lambda, choice, w0)
%TRAIN_LINEAR_SCORER
%
% weights = TRAIN_LINEAR_SCORER(features,losses,lambda)
%
% features - Array size [d,N]
% losses - Array of size [N,L]
% lambda - Regularization constant.
%
% weights - Vector of size [dL,1]
... |
github | vvanirudh/list_prediction_motion_planning-master | train_linear_primal_sg.m | .m | list_prediction_motion_planning-master/cs_classification/utils/train_linear_primal_sg.m | 1,874 | utf_8 | 65f505389ec8269371a51099edd5abca | function [w, obj, wset] = train_linear_primal_sg(features,costs, lambda, surrogate_loss, w0)
%TRAIN_LINEAR_SCORER
%
% weights = TRAIN_LINEAR_SCORER(features,costs,lambda)
%
% features - Cell array of length N. features{i} is [L,d]
% costs - Array of size [N,L]
% lambda - Regularization constant.
%
% weights - ... |
github | vvanirudh/list_prediction_motion_planning-master | train_linear_kernelized_dual_pgd.m | .m | list_prediction_motion_planning-master/cs_classification/utils/train_linear_kernelized_dual_pgd.m | 2,318 | utf_8 | d49326557cc681dfad05808adaf1dac5 | function [predictor, obj_vec, stats] = train_linear_kernelized_dual_pgd(arg1, c, lambda, kernel_params, alpha0)
% linear kernelized dual pgd
if size(arg1,2) == length(c)
% arg1 is Q
% assumes used same kernel_params to generate Q
Q = arg1;
stats.Q_time = 0;
else
% arg1 is F_hat
F_hat = arg1;
... |
github | vvanirudh/list_prediction_motion_planning-master | createcircle.m | .m | list_prediction_motion_planning-master/matlab_environment_generation/utils/createcircle.m | 6,672 | utf_8 | 99e93d3bceb625549153e5148f5dd111 | function [Xpoints, Ypoints] = createcircle(varargin)
% CREATECIRCLE Create a circle with the mouse
% [X, Y] = createcircle(N) lets you create a circle with N points
% in the current figure. Use the mouse to indicate the center and
% adjust the radius. Press ENTER to confirm the shape and output
% the X a... |
github | vvanirudh/list_prediction_motion_planning-master | p_poly_dist.m | .m | list_prediction_motion_planning-master/matlab_environment_generation/utils/p_poly_dist.m | 3,034 | utf_8 | 122103a78032c09db450eff77e131b3d | %*******************************************************************************
% function: p_poly_dist
% Description: distance from point to polygon whose vertices are specified by the
% vectors xv and yv
% Input:
% x - point's x coordinate
% y - point's y coordinate
% xv - vector of po... |
github | vvanirudh/list_prediction_motion_planning-master | conseqopt_features.m | .m | list_prediction_motion_planning-master/conseqopt/utils/conseqopt_features.m | 3,432 | utf_8 | 1615ef706dffbc5df66cd54131212465 | function features = conseqopt_features(data,S,choices)
% data is in common format
% S is [N,K]
% choices: struct with fields
% ('append_lib_contexts','append_down_levels','append_type')
% features is cell array length N. features{i} is [L,d]
% this function is for data with environment + element features
% does not ma... |
github | vvanirudh/list_prediction_motion_planning-master | linspaceNDim.m | .m | list_prediction_motion_planning-master/chomp/utils/linspaceNDim.m | 2,585 | utf_8 | 78ae81dcc1c906b50dc5a0054ff0d366 | function y = linspaceNDim(d1, d2, n)
%LINSPACENDIM Linearly spaced multidimensional matrix.
% LINSPACENDIM(d1, d2) generates a multi-dimensional
% matrix of 100 linearly equally spaced points between
% each element of matrices d1 and d2.
%
% LINSPACENDIM(d1, d2, N) generates N points between
% each ... |
github | vvanirudh/list_prediction_motion_planning-master | v2struct.m | .m | list_prediction_motion_planning-master/chomp/utils/v2struct_2011_09_12/v2struct.m | 16,311 | utf_8 | 77b042ae8b342c50880cf0e543b7bd27 | %% v2struct
% v2struct Pack/Unpack Variables to/from a scalar structure.
function varargout = v2struct(varargin)
%% Description
% v2struct has dual functionality in packing & unpacking variables into structures and
% vice versa, according to the syntax and inputs.
%
% Function features:
% * Pack... |
github | janhon3n/PCA-ICA-master | plotMatrix.m | .m | PCA-ICA-master/src/plotMatrix.m | 598 | utf_8 | c0c7a0e3c5349d4698cf50b6c9fe466e | % Plots the individual rows of the given matrix using subplot()
%
% Parameters:
% mat - the matrix
% rowCount - the amount of rows in the subplot
% colCount - the amount of rows to draw from the matrix, each to
% different column of the subplot
% row - the row of subplot to draw the rows of the ma... |
github | janhon3n/PCA-ICA-master | calculateDifference.m | .m | PCA-ICA-master/src/calculateDifference.m | 622 | utf_8 | f41c2289cabefb09cdbfc79211aa6161 | % Calculates the difference between two vectors.
% The diffenrece is the euclidean distance between the vectors.
% It is calculated with the formula Sqrt((a1 - b1)^2 + (a2 - b2)^2 + .... + (an - bn)^2)
%
% Parameters:
% vec1 - first vector
% vec2 - second vector
%
% Returns:
% diff - The difference between the vect... |
github | janhon3n/PCA-ICA-master | findClosest.m | .m | PCA-ICA-master/src/findClosest.m | 1,177 | utf_8 | c406770a8d9e7632f9ac83852ad28656 |
% Finds the row of the given matrix that is closest to the given vector
% Also checks inversed versions of each rows (each sample *= -1)
%
% Parameters:
% mat - The matrix
% vec - The vector
%
% Returns:
% index - The index of the row that is closest to the given vector
% inverse - True if the row is inversed,... |
github | janhon3n/PCA-ICA-master | matchMatrices.m | .m | PCA-ICA-master/src/matchMatrices.m | 1,016 | utf_8 | 86f27329713114cb706c68d61f6660d7 | % Matches the rows in the second matrix with the rows of the first one
% by finding the ones that are closest to each other in terms of euclidean
% distance.
% If matrices row counts dont match, add all zero rows to mat2
%
% Parameters:
% mat1 - first matrix, the one that will be sorted
% mat2 - second matrix
% r... |
github | summitgao/SAR_Change_Detection_GarborPCANet-master | HashingHist.m | .m | SAR_Change_Detection_GarborPCANet-master/Utils/HashingHist.m | 3,013 | utf_8 | 06398464aee5f4b8b4ab10e202035881 | function [f BlkIdx] = HashingHist(PCANet,ImgIdx,OutImg)
% Output layer of PCANet (Hashing plus local histogram)
% ========= INPUT ============
% PCANet PCANet parameters (struct)
% .PCANet.NumStages
% the number of stages in PCANet; e.g., 2
% .PatchSize
% the patch size (... |
github | fspaolo/tmdtoolbox-master | tmd_mk_submodel.m | .m | tmdtoolbox-master/tmd_toolbox/tmd_mk_submodel.m | 4,488 | utf_8 | 3515cb4a6c75e450cd059cfa27bbf833 | % function to make a submodel from a model ModName (TMD format)
% calculated on bathymetry grid Gridname
%
% usage:
% ()=tmd_mk_submodel(Name_old,Name_new,limits);
%
% PARAMETERS
%
% INPUT
% Name_old - ROOT in "DATA/Model_ROOT" control file for EXISTING
% tidal model. File Model_* consists of lines:
% ... |
github | fspaolo/tmdtoolbox-master | tmd_get_ellipse.m | .m | tmdtoolbox-master/tmd_toolbox/tmd_get_ellipse.m | 1,999 | utf_8 | 64167f02369b2a1527a5cc6ef71e8b9d | % function to extract tidal ellipse grids from a model
%
% usage:
% [x,y,umaj,umin,uphase,uincl]=tmd_get_ellipse(Model,cons);
%
% Model - control file name for a tidal model, consisting of lines
% <elevation file name>
% <transport file name>
% <grid file name>
% <function to convert ... |
github | fspaolo/tmdtoolbox-master | tmd_tide_pred_par.m | .m | tmdtoolbox-master/tmd_toolbox/tmd_tide_pred_par.m | 5,370 | utf_8 | ccf2959b9011ed5fb8cad216598f7c3a | %%%% Predict tidal time series in a given locations at given times
%%%% using tidal model from a file
% USAGE:
% [TS,ConList]=tmd_tide_pred(Model,SDtime,lat,lon,ptype,Cid);
%
% PARAMETERS
% Input:
% Model - control file name for a tidal model, consisting of lines
% <elevation file name>
% <transport f... |
github | fspaolo/tmdtoolbox-master | TMD_setcaxis.m | .m | tmdtoolbox-master/tmd_toolbox/TMD_setcaxis.m | 749 | utf_8 | 3e6d6a57de0807246970f9744c3d147c | % function to set "good" caxis on current axis
% n - degree of 10 to round
% psn - -1/0/1; -1 negative, 0 symmetric, 1 positive
% ha - array to plot
% pct - % of higher amplitudes points to cut off
% set NaNs/0 in ha first for land nodes
%
% usage: TMD_setcaxis(n,psn,ha,pct);
%
function []=TMD_setcaxis(n,psn,ha,pct);
i... |
github | fspaolo/tmdtoolbox-master | TMD_cor_date.m | .m | tmdtoolbox-master/tmd_toolbox/TMD_cor_date.m | 243 | utf_8 | 7b4c460de1d19122eb4d1411596919d5 | % check/correct date
function [yy,mm,dd]=TMD_cor_date(yy,mm,dd,uT);
a=datevec(datenum(yy,mm,dd,0,0,0));
if a(1)==yy & a(2)==mm & a(3)==dd,
return;
else
yy=a(1);mm=a(2);dd=a(3);
end
for it=1:3
set(uT(it),'String',int2str(a(it)));
end
return
|
github | fspaolo/tmdtoolbox-master | tmd_tide_pred.m | .m | tmdtoolbox-master/tmd_toolbox/tmd_tide_pred.m | 5,253 | utf_8 | ba666dd4135d7166ac671817ef95090f | %%%% Predict tidal time series in a given locations at given times
%%%% using tidal model from a file
% USAGE:
% [TS,ConList]=tmd_tide_pred(Model,SDtime,lat,lon,ptype,Cid);
%
% PARAMETERS
% Input:
% Model - control file name for a tidal model, consisting of lines
% <elevation file name>
% <transport f... |
github | fspaolo/tmdtoolbox-master | TMD_changeCaxis.m | .m | tmdtoolbox-master/tmd_toolbox/TMD_changeCaxis.m | 436 | utf_8 | 674298b11c46617740fdc1afb0907c64 | % usage: TMD_changeCaxis(n12,act);
% change
% n12 - 1/2 lower/upper limit
% act '+'/'-' increase/descrease
function []=TMD_changeCaxis(n12,act,cb,CBpos);
cax=caxis;
dcax=(cax(2)-cax(1))/50;
if act=='-',
cax(n12)=floor((cax(n12)-dcax)*100)/100;
else
cax(n12)=ceil((cax(n12)+dcax)*100)/100;
end
c... |
github | fspaolo/tmdtoolbox-master | TMD_timeOnOff.m | .m | tmdtoolbox-master/tmd_toolbox/TMD_timeOnOff.m | 144 | utf_8 | 50267ad3d2c9322acef99a47279aa1ed | %
function []=TMD_timeOnOff(uTime,uT,action);
set(uTime,'Enable',action);
nt=length(uT);
for it=1:nt,
set(uT(it),'Enable',action);
end
return
|
github | fspaolo/tmdtoolbox-master | tmd_get_coeff.m | .m | tmdtoolbox-master/tmd_toolbox/tmd_get_coeff.m | 2,320 | utf_8 | 3b50e561d7d7ef05dd36e1aa43f231b1 | % function to extract amplitude and phase grids from
% a model ModName (OTIS format) calculated on bathymetry grid
%
%
% usage:
% [x,y,amp,phase]=tmd_get_coeff(Model,type,cons);
% PARAMETERS
%
% INPUT
% Model - control file name for a tidal model, consisting of lines
% <elevation file name>
% <transpor... |
github | fspaolo/tmdtoolbox-master | tmd_ellipse.m | .m | tmdtoolbox-master/tmd_toolbox/tmd_ellipse.m | 2,167 | utf_8 | 8627ba2c958574783f01ee2a0e9ede2c | % Calculate tidal ellipse parameters at given locations using a model
%
% USAGE
% [umajor,uminor,uphase,uincl]=tmd_ellipse(Model,lat,lon,constit);
%
% PARAMETERS
%
% INPUT
% Model - control file name for a tidal model, consisting of lines
% <elevation file name>
% <transport file name>
% <grid ... |
github | fspaolo/tmdtoolbox-master | tmd_extract_HC.m | .m | tmdtoolbox-master/tmd_toolbox/tmd_extract_HC.m | 5,376 | utf_8 | 868466af43a68b7f468ecc87fb72280c | % Function to extract tidal harmonic constants out of a tidal model
% for given locations
% USAGE
% [amp,Gph,Depth,conList]=tmd_extract_HC(Model,lat,lon,type,Cid);
%
% PARAMETERS
% Input:
% Model - control file name for a tidal model, consisting of lines
% <elevation file name>
% <transport file name>
%... |
github | fspaolo/tmdtoolbox-master | tmd_get_bathy.m | .m | tmdtoolbox-master/tmd_toolbox/tmd_get_bathy.m | 1,074 | utf_8 | 2a7ce7567ca7015dcb55f47d48e3c795 | %========================================================================
% tmd_get_bathy.m
%
% Gets map of bathymetry (water column thickness under ice shelves) for
% specified model.
%
% Written by: Laurie Padman (ESR): padman@esr.org
% August 18, 2004
%
% Sample call:
% [long,latg,H]=t... |
github | fspaolo/tmdtoolbox-master | BLinterp_FernandoOld.m | .m | tmdtoolbox-master/tmd_toolbox/FUNCTIONS/BLinterp_FernandoOld.m | 1,722 | utf_8 | 1922e6ca524875c2f13e9ca3c937c1d1 | % Bilinear interpolation (neigbouring NaNs avoided)
% in point xt,yt
% x(n),y(m) - coordinates for h(n,m)
% xt(N),yt(N) - coordinates for interpolated values
% Global case is considered ONLY for EXACTLY global solutions,
% i.e. given in lon limits, satisfying:
% ph_lim(2)-ph_lim(1)==360
% (thus for C-grid: x(end)-x(1)=... |
github | fspaolo/tmdtoolbox-master | BLinterp.m | .m | tmdtoolbox-master/tmd_toolbox/FUNCTIONS/BLinterp.m | 1,920 | utf_8 | 4fe0030a891fa204e40bd640d0c9d1fe | % Bilinear interpolation (neigbouring NaNs avoided)
% in point xt,yt
% x(n),y(m) - coordinates for h(n,m)
% xt(N),yt(N) - coordinates for interpolated values
% Global case is considered ONLY for EXACTLY global solutions,
% i.e. given in lon limits, satisfying:
% ph_lim(2)-ph_lim(1)==360
% (thus for C-grid: x(end)-x(1)=... |
github | fspaolo/tmdtoolbox-master | grd_out.m | .m | tmdtoolbox-master/tmd_toolbox/FUNCTIONS/grd_out.m | 879 | utf_8 | f14eb25e4e8c8441d2bd37b8ea24f018 | % outputs a grid file in matlab
% USAGE: grd_out(cfile,ll_lims,hz,mz,iob,dt);
function grd_out(cfile,ll_lims,hz,mz,iob,dt);
% open this way for files to be read on Unix machine
fid = fopen(cfile,'w','b');
[dum,nob] = size(iob);
[n,m] = size(hz);
reclen = 32;
fwrite(fid,reclen,'long');
fwrite(fid,n,'long');
fwr... |
github | fspaolo/tmdtoolbox-master | harp1.m | .m | tmdtoolbox-master/tmd_toolbox/FUNCTIONS/harp1.m | 1,023 | utf_8 | b4e5dab561d7307655a19e9b9737b508 | % function to predict tidal time series
% using harmonic constants
% INPUT: time (days) relatively Jan 1, 1992 (48622mjd)
% con(nc,4) - char*4 tidal constituent IDs
% hc(nc) - harmonic constant vector (complex)
% OUTPUT:hhat - time series reconstructed using HC
%
% Nodal corrections included
%
% u... |
github | fspaolo/tmdtoolbox-master | xy_ll_CATS2008a_4km.m | .m | tmdtoolbox-master/tmd_toolbox/FUNCTIONS/xy_ll_CATS2008a_4km.m | 308 | utf_8 | 4cbc2a473f2a81dd51203287f338c523 | % converts lat,lon to x,y(km) and back
% usage: [x,y]=xy_ll_CATS2008a_4km(lon,lat,'F'); or
% [lon,lat]=xy_ll_CATS2008a_4km(x,y,'B');
function [x2,y2]=xy_ll_CATS2008a_4km(x1,y1,BF);
if BF=='F', % lat,lon ->x,y
[x2,y2]=mapll(y1,x1,71,-70,'s');
else
[y2,x2]=mapxy(x1,y1,71,-70,'s');
end
return
|
github | fspaolo/tmdtoolbox-master | nodal.m | .m | tmdtoolbox-master/tmd_toolbox/FUNCTIONS/nodal.m | 11,697 | utf_8 | 966b2b2fe1211dc98f2557d8fb7fac9a | % ARGUMENTS and ASTROL FORTRAN subroutines SUPPLIED by RICHARD RAY, March 1999
% This is matlab remake of ARGUMENTS by Lana Erofeeva, Jan 2003
% NOTE - "no1" in constit.h corresponds to "M1" in arguments
% usage: [pu,pf]=nodal(time,cid);
% time - mjd, cid(nc,4) - tidal constituents array char*4
% pu(:,nc),pf(:,nc) - no... |
github | fspaolo/tmdtoolbox-master | xy_ll_S.m | .m | tmdtoolbox-master/tmd_toolbox/FUNCTIONS/xy_ll_S.m | 484 | utf_8 | 19e121c4e6ed5500867a96e8db934718 | % converts lat,lon to x,y(km) and back for Antarctic
% (x,y)=(0,0) corresponds to lon=135W, lon=0 points up
% usage: [x,y]=xy_ll_S(lon,lat,'F'); or
% [lon,lat]=xy_ll_S(x,y,'B');
function [x,y]=xy_ll_S(lon,lat,BF);
if BF=='F', % lat,lon ->x,y
x=-(90.+lat)*111.7.*cos((90+lon)./180.*pi);
y= (90.+lat)*111.7.*sin((... |
github | fspaolo/tmdtoolbox-master | grd_in.m | .m | tmdtoolbox-master/tmd_toolbox/FUNCTIONS/grd_in.m | 753 | utf_8 | 2f949135185e056a2a2d7a4e4977887b | % reads a grid file in matlab
% USAGE: [ll_lims,hz,mz,iob,dt] = grd_in(cfile);
% Location: C:\Toolboxes\TMD\FUNCTIONS
function [ll_lims,hz,mz,iob,dt] = grd_in(cfile);
fid = fopen(cfile,'r','b');
fseek(fid,4,'bof');
n = fread(fid,1,'long');
m = fread(fid,1,'long');
lats = fread(fid,2,'float');
lons = fread(fid,2,'f... |
github | fspaolo/tmdtoolbox-master | harp.m | .m | tmdtoolbox-master/tmd_toolbox/FUNCTIONS/harp.m | 1,089 | utf_8 | 24e7f03475cbbbd9867420ee32afa2f5 | % function to predict tidal elevation fields at "time"
% using harmonic constants
% INPUT: time (days) relatively Jan 1, 1992 (48622mjd)
% con(nc,4) - char*4 tidal constituent IDs
% hc(n,m,nc) - harmonic constant vector (complex)
% OUTPUT:hhat - tidal field at time
%
% Nodal corrections include... |
github | fspaolo/tmdtoolbox-master | xy_ll.m | .m | tmdtoolbox-master/tmd_toolbox/FUNCTIONS/xy_ll.m | 392 | utf_8 | 31802e1c496760dab7cd268c390940ed | % converts lat,lon to x,y(km) and back
% usage: [x,y]=xy_ll(lon,lat,'F'); or
% [lon,lat]=xy_ll(x,y,'B');
function [x,y]=xy_ll(lon,lat,BF);
if BF=='F', % lat,lon ->x,y
x=(90.-lat)*111.7.*cos(lon./180.*pi);
y=(90.-lat)*111.7.*sin(lon./180.*pi);
else
x=lon;y=lat;
lat=90-sqrt(x.^2+y.^2)/111.7;
lon=atan2(y,x)*18... |
github | fspaolo/tmdtoolbox-master | checkTypeName.m | .m | tmdtoolbox-master/tmd_toolbox/FUNCTIONS/checkTypeName.m | 1,407 | utf_8 | aa1bba0ee0f751a2f55a2c6845c5877b | % returns Flag=0, if ModName, GridName & type OK
% Flag>0, if they are not or files do not exist
% Flag<0, if not possible to define
% usage: [Flag]=checkTypeName(ModName,GridName,type);
function [Flag]=checkTypeName(ModName,GridName,type);
Flag=0;
% check if files exist
if exist(ModName,'file')==0,
f... |
github | fspaolo/tmdtoolbox-master | constit.m | .m | tmdtoolbox-master/tmd_toolbox/FUNCTIONS/constit.m | 3,740 | utf_8 | 5861b866d79ae0fff23d8a9db7b2e0d0 | % constit returns amplitude, phase, frequency,alpha, species for
% a tidal constituent identified by a 4 character string
% This version returns zeros for all phases
% Usage: [ispec,amp,ph,omega,alpha,constitNum] = constit(c);
function [ispec,amp,ph,omega,alpha,constitNum] = constit(c);
ispec=-1;
amp=0;ph=0;omega... |
github | fspaolo/tmdtoolbox-master | astrol.m | .m | tmdtoolbox-master/tmd_toolbox/FUNCTIONS/astrol.m | 1,101 | utf_8 | d2db9a01c06a48292ad7b7f4a26e7372 | % Computes the basic astronomical mean longitudes s, h, p, N.
% Note N is not N', i.e. N is decreasing with time.
% These formulae are for the period 1990 - 2010, and were derived
% by David Cartwright (personal comm., Nov. 1990).
% time is UTC in decimal MJD.
% All longitudes returned in degrees.
% R. D. Ray ... |
github | fspaolo/tmdtoolbox-master | rd_con.m | .m | tmdtoolbox-master/tmd_toolbox/FUNCTIONS/rd_con.m | 375 | utf_8 | ddb5daf0d42c644afb62b724e5b0e05c | % read constituents from h*out or u*out file
% usage: [conList]=rd_con(fname);
function [conList]=rd_con(fname);
fid = fopen(fname,'r','b');
ll = fread(fid,1,'long');
nm = fread(fid,3,'long');
n=nm(1);
m = nm(2);
nc = nm(3);
th_lim = fread(fid,2,'float');
ph_lim = fread(fid,2,'float');
C=fread(fid,nc*4,'uchar');
C=resh... |
github | fspaolo/tmdtoolbox-master | rdModFile.m | .m | tmdtoolbox-master/tmd_toolbox/FUNCTIONS/rdModFile.m | 1,616 | utf_8 | 70c71135dfca84f60f59ac4fa0b60603 | % function to read model control file
% usage:
% [ModName,GridName,Fxy_ll]=rdModFile(Model,k);
%
% Model - control file name for a tidal model, consisting of lines
% <elevation file name>
% <transport file name>
% <grid file name>
% <function to convert lat,lon to x,y>
% 4th line is give... |
github | fspaolo/tmdtoolbox-master | InferMinor.m | .m | tmdtoolbox-master/tmd_toolbox/FUNCTIONS/InferMinor.m | 6,069 | utf_8 | 8e1f23c7f63461086e8fbb1c99876a95 | % Lana Erofeeva, re-make for matlab OCT 2004
% usage:
% [dh]=InferMinor(zmaj,cid,SDtime);
%
% Based on Richard Ray's code perth2
% Return correction for the 16 minor constituents
% zeros, if not enough input constituents for inference
% Input:
% cid(ncon,4) - GIVEN constituents
% zmaj(ncon,... - Complex HC for GIVEN ... |
github | fspaolo/tmdtoolbox-master | TideEl.m | .m | tmdtoolbox-master/tmd_toolbox/FUNCTIONS/TideEl.m | 1,158 | utf_8 | 0b67ba4eb9e827c8d9721e6adfdcb423 | % calculates tidal ellipse parameters for the arrays of
% u and v - COMPLEX amplitudes of EW and NS currents of
% a given tidal constituent
% land should be set to 0 or NaN in u,v prior to calling tideEl
% usage: [umajor,uminor,uincl,uphase]=tideEl(u,v);
function [umajor,uminor,uincl,uphase]=tideEl(u,v);
% change to po... |
github | Xyuan13/MSRNet-master | classification_demo.m | .m | MSRNet-master/deeplab-caffe/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 | Xyuan13/MSRNet-master | MySaliencyEval.m | .m | MSRNet-master/deeplab-caffe/matlab/my_script/MySaliencyEval.m | 5,922 | utf_8 | c1fbf9a95de1539c35958d9759de81fe | %MSRAEVALSEG Evaluates a set of segmentation results.
% MSRAEVALSEG(MSRAopts,ID); prints out the per class and overall
% segmentation accuracies. Accuracies are given using the intersection/union
% metric:
% true positives / (true positives + false positives + false negatives)
%
% [ACCURACIES,AVACC,CONF] = MSRAEVAL... |
github | Xyuan13/MSRNet-master | MyVOCevalseg.m | .m | MSRNet-master/deeplab-caffe/matlab/my_script/MyVOCevalseg.m | 4,627 | utf_8 | 0e50d31b12d14678fbbb4e2e1d52f118 | %VOCEVALSEG Evaluates a set of segmentation results.
% VOCEVALSEG(VOCopts,ID); prints out the per class and overall
% segmentation accuracies. Accuracies are given using the intersection/union
% metric:
% true positives / (true positives + false positives + false negatives)
%
% [ACCURACIES,AVACC,CONF] = VOCEV... |
github | Xyuan13/MSRNet-master | MyMSRAevalseg.m | .m | MSRNet-master/deeplab-caffe/matlab/my_script/MyMSRAevalseg.m | 5,692 | utf_8 | 4ff8be0f4a6c9ea0aa622aac46821f37 | %MSRAEVALSEG Evaluates a set of segmentation results.
% MSRAEVALSEG(MSRAopts,ID); prints out the per class and overall
% segmentation accuracies. Accuracies are given using the intersection/union
% metric:
% true positives / (true positives + false positives + false negatives)
%
% [ACCURACIES,AVACC,CONF] = MS... |
github | Xyuan13/MSRNet-master | MyVOCevalsegBoundary.m | .m | MSRNet-master/deeplab-caffe/matlab/my_script/MyVOCevalsegBoundary.m | 4,415 | utf_8 | 1b648714e61bafba7c08a8ce5824b105 | %VOCEVALSEG Evaluates a set of segmentation results.
% VOCEVALSEG(VOCopts,ID); prints out the per class and overall
% segmentation accuracies. Accuracies are given using the intersection/union
% metric:
% true positives / (true positives + false positives + false negatives)
%
% [ACCURACIES,AVACC,CONF] = VOCEV... |
github | Xyuan13/MSRNet-master | My_GetDenseCRFResult.m | .m | MSRNet-master/deeplab-caffe/densecrf/my_script/My_GetDenseCRFResult.m | 952 | utf_8 | 5891b0bfcbaa14006fdd1d8aae9e451c | % compute the densecrf result (.bin) to png
%
function My_GetDenseCRFResult(stage)
addpath('/home/phoenix/deeplab/code-v2/matlab/my_script');
%SetupEnv;
load('./pascal_seg_colormap.mat');
map_folder=['/home/phoenix/Dataset/MSRA-B/SaliencyMap/' stage '_crf_bin'];
map_dir = dir(fullfile(map_folder, '... |
github | david-hofmann/EMGsim-master | active_force.m | .m | EMGsim-master/Codes/active_force.m | 558 | utf_8 | 8843bf1327aab6a84293d0f5204e418c |
function [ twitch ] = active_force( P, T, MU_spike_train, discharge_cnt )
gain_at_intersection = (1 - exp(-2 * (0.4^3)))/0.4;
if discharge_cnt == 1,
nfr = 0;
else
current_ISI = MU_spike_train(discharge_cnt)-MU_spike_train(discharge_cnt-1);
nfr = T / (current_ISI); %normalized firing rate
end
if nfr <= 0... |
github | david-hofmann/EMGsim-master | artificialEMG_onGPU.m | .m | EMGsim-master/Codes/artificialEMG_onGPU.m | 4,097 | utf_8 | 96d29b5c1677ede6ea491636457daf8a | %%% artificial EMG on GPU
function artificialEMG_onGPU(MUAP,ISIstats)
gpu = gpuDevice(1);
%seed = 12345;
%parallel.gpu.rng(seed);
timer = tic();
T = 100; % in [s]
[M, L] = size(MUAP); % number and length of MUAPs
dt = 16/L * 10^-3; % temporal resolution in [s] defined by the length of MUAP, 256 data points in x ms ... |
github | wantingallin/transferlearning-master | MyTJM.m | .m | transferlearning-master/code/MyTJM.m | 3,517 | utf_8 | ce3d34bcb6ed86fc570f1f4f818ff2aa | function [acc,acc_list,A] = MyTJM(X_src,Y_src,X_tar,Y_tar,options)
% Inputs:
%%% X_src :source feature matrix, ns * m
%%% Y_src :source label vector, ns * 1
%%% X_tar :target feature matrix, nt * m
%%% Y_tar :target label vector, nt * 1
%%% options:option struct
% Outputs:
%%% acc :f... |
github | wantingallin/transferlearning-master | MyJGSA.m | .m | transferlearning-master/code/MyJGSA.m | 6,642 | utf_8 | 09a8f009556a3e0b09d10483558976ec | function [acc,acc_list,A,B] = MyJGSA(X_src,Y_src,X_tar,Y_tar,options)
%% Joint Geometrical and Statistic Adaptation
% Inputs:
%%% X_src :source feature matrix, ns * m
%%% Y_src :source label vector, ns * 1
%%% X_tar :target feature matrix, nt * m
%%% Y_tar :target label vector, nt * 1
%%% options:option struct
% Ou... |
github | wantingallin/transferlearning-master | MyJDA.m | .m | transferlearning-master/code/MyJDA.m | 4,118 | utf_8 | 54f4173e19b0dbf7b2572a964a6a3277 | function [acc,acc_ite,A] = MyJDA(X_src,Y_src,X_tar,Y_tar,options)
% Inputs:
%%% X_src :source feature matrix, ns * m
%%% Y_src :source label vector, ns * 1
%%% X_tar :target feature matrix, nt * m
%%% Y_tar :target label vector, nt * 1
%%% options:option struct
% Outputs:
%%% acc ... |
github | wantingallin/transferlearning-master | MyGFK.m | .m | transferlearning-master/code/MyGFK.m | 2,152 | utf_8 | a01af2b801cc7b96695684ce8e803547 | function [acc,G] = MyGFK(X_src,Y_src,X_tar,Y_tar,dim)
% Inputs:
%%% X_src :source feature matrix, ns * m
%%% Y_src :source label vector, ns * 1
%%% X_tar :target feature matrix, nt * m
%%% Y_tar :target label vector, nt * 1
% Outputs:
%%% acc :accuracy after GFK and 1NN
%%% G ... |
github | wantingallin/transferlearning-master | MyTCA.m | .m | transferlearning-master/code/MyTCA.m | 2,818 | utf_8 | 7aee1d32ebfb97f5974be024ce450ce1 | function [X_src_new,X_tar_new,A] = MyTCA(X_src,X_tar,options)
% Inputs: [dim is the dimension of features]
%%% X_src:source feature matrix, ns * dim
%%% X_tar:target feature matrix, nt * dim
%%% options:option struct
% Outputs:
%%% X_src_new:transformed source feature matrix, ns * dim_new
%%... |
github | wantingallin/transferlearning-master | lapgraph.m | .m | transferlearning-master/code/MyARTL/lapgraph.m | 20,244 | utf_8 | cfed436191fe6a863089f6da80644260 | function [W, elapse] = lapgraph(fea,options)
% Usage:
% W = graph(fea,options)
%
% fea: Rows of vectors of data points. Each row is x_i
% options: Struct value in Matlab. The fields in options that can be set:
% Metric - Choices are:
% 'Euclidean' - Will use the Euclidean distance of two data... |
github | wantingallin/transferlearning-master | MyARTL.m | .m | transferlearning-master/code/MyARTL/MyARTL.m | 3,503 | utf_8 | 91802921f23d322f2ffca0e311f9372a | function [acc,acc_ite,Alpha] = MyARTL(X_src,Y_src,X_tar,Y_tar,options)
% Inputs:
%%% X_src :source feature matrix, ns * m
%%% Y_src :source label vector, ns * 1
%%% X_tar :target feature matrix, nt * m
%%% Y_tar :target label vector, nt * 1
%%% options:option struct
% Outputs:
%%% ac... |
github | AndrewSpittlemeister/Multi-contrast-MRI-Image-Registration-master | adjustgray.m | .m | Multi-contrast-MRI-Image-Registration-master/adjustgray.m | 300 | utf_8 | 8081142a8a91998df6464bc6885264cc |
function p=adjustgray(imgin)
%Pass an input image
%and output array of same size
%Output is normalized to 0-255
temp=255/double(max(max(imgin)));
aa=size(imgin);
a=zeros(aa(1),aa(2),'uint8');
for y=1:aa(1)
for z=1:aa(2)
a(y,z)=round(imgin(y,z)*temp);
end
end
p=a;
end
|
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