plateform stringclasses 1
value | repo_name stringlengths 13 113 | name stringlengths 3 74 | ext stringclasses 1
value | path stringlengths 12 229 | size int64 23 843k | source_encoding stringclasses 9
values | md5 stringlengths 32 32 | text stringlengths 23 843k |
|---|---|---|---|---|---|---|---|---|
github | jihunhamm/GRAM-master | GRAM_GeodesicRegistration.m | .m | GRAM-master/Code/Core/GRAM_GeodesicRegistration.m | 4,173 | utf_8 | 6f6579da83c3eb59501fd2641d0a7589 | %/*
% File: GRAM_GeodesicRegistration.m
% Date: $Date: $
% Version: $Revision: $
% Author: $Author: $
% ID: $Id: $
%
% File Description
% function GRAM_GeodesicRegistration(dirSubject,dirFinalField,gpath,gmean,NNiter,NNsmoothi... |
github | jihunhamm/GRAM-master | GRAM_DirectRegistrationForComparison.m | .m | GRAM-master/Code/Core/GRAM_DirectRegistrationForComparison.m | 3,075 | utf_8 | 053bc0c7a6f00c17f9e8db02df702d2f | %/*
% File: GRAM_DirectRegistrationForComparison
% Date: $Date: $
% Version: $Revision: $
% Author: $Author: $
% ID: $Id: $
%
% File Description
% function [dMSE,dHE] =GRAM_DirectRegistrationForComparison
% (dirSu... |
github | jihunhamm/GRAM-master | GRAM_GeodesicPath.m | .m | GRAM-master/Code/Core/GRAM_GeodesicPath.m | 2,108 | utf_8 | a2066c518053eeee226f731ee2234293 | %/*
% File: GRAM_GeodesicPath.m
% Date: $Date: $
% Version: $Revision: $
% Author: $Author: $
% ID: $Id: $
%
% File Description
% function [gpath,gmean,gdist] = GRAM_GeodesicPath(pair_dist,K)
% Find Geodesic P... |
github | jihunhamm/GRAM-master | GRAM_FindK.m | .m | GRAM-master/Code/Core/GRAM_FindK.m | 1,755 | utf_8 | 1d6703cabd88ae59ef7d942a181c5280 | %/*
% File: GRAM_FindK
% Date: $Date: $
% Version: $Revision: $
% Author: $Author: $
% ID: $Id: $
%
% File Description
% function [Ki tidx]= GRAM_FindK(dists,partial)
% Find connected graph
% dists: pa... |
github | jihunhamm/GRAM-master | GRAM_Montage.m | .m | GRAM-master/Code/Core/GRAM_Montage.m | 1,239 | utf_8 | ff2b8d61f658d9d27554e0203ceb0428 | %/*
% File: GRAM_Montage.m
% Date: $Date: $
% Version: $Revision: $
% Author: $Author: $
% ID: $Id: $
%
% File Description
% function GRAM_Montage(dirData,SliceNum)
% Display Montage Plot
%
% ... |
github | jihunhamm/GRAM-master | GRAM_GeodesicRegistration_temp.m | .m | GRAM-master/Code/Core/GRAM_GeodesicRegistration_temp.m | 4,255 | utf_8 | 1a5edf847e4aaff949268af99866565b | %/*
% File: GRAM_GeodesicRegistration.m
% Date: $Date: $
% Version: $Revision: $
% Author: $Author: $
% ID: $Id: $
%
% File Description
% function GRAM_GeodesicRegistration(dirSubject,dirFinalField,gpath,gmean,NNiter,NNsmoothi... |
github | jihunhamm/GRAM-master | GRAM_FineTuning.m | .m | GRAM-master/Code/Core/GRAM_FineTuning.m | 3,482 | utf_8 | fb1e645cc8e6c48e2ad84d8cde5eab34 | %/*
% File: GRAM_FineTuning
% Date: $Date: $
% Version: $Revision: $
% Author: $Author: $
% ID: $Id: $
%
% File Description
% function [gMSE,gHE] = GRAM_FineTuning(dirSubject,dirFinalField,gpath,gmean,Fineiter,Finesmoothing)... |
github | Trankwery/MR_Fin-master | New_Old_Data_Converter.m | .m | MR_Fin-master/New_Old_Data_Converter.m | 18,041 | utf_8 | 97991c387f99fde212a3fdfe35081180 | function varargout = New_Old_Data_Converter(varargin)
% NEW_OLD_DATA_CONVERTER M-file for New_Old_Data_Converter.fig
% NEW_OLD_DATA_CONVERTER, by itself, creates a new NEW_OLD_DATA_CONVERTER or raises the existing
% singleton*.
%
% H = NEW_OLD_DATA_CONVERTER returns the handle to a new NEW_OLD_DATA_CONVE... |
github | Trankwery/MR_Fin-master | changeXYR.m | .m | MR_Fin-master/changeXYR.m | 11,100 | utf_8 | 732a24e806cf4edba922de49b965a943 | function varargout = changeXYR(varargin)
%
% See also: GUIDE, GUIDATA, GUIHANDLES
% Copyright 2002-2003 The MathWorks, Inc.
% Edit the above text to modify the response to help changeXYR
% Last Modified by GUIDE v2.5 08-Jan-2008 08:45:43
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui... |
github | Trankwery/MR_Fin-master | MRfin.m | .m | MR_Fin-master/MRfin.m | 24,673 | UNKNOWN | 5173c8d9645f1c28fc71b35741dd28fe | function varargout = MRfin(varargin)
% MRFIN M-file for MRfin.fig
% MRFIN, by itself, creates a new MRFIN or raises the existing
% singleton*.
%
% H = MRFIN returns the handle to a new MRFIN or the handle to
% the existing singleton*.
%
% MRFIN('CALLBACK',hObject,eventData,handles,...) calls th... |
github | TWOEARS/localisation-training-pipeline-master | listFiles.m | .m | localisation-training-pipeline-master/Tools/listFiles.m | 4,135 | utf_8 | 3993d9476438d5ffeee195646839ee44 | %LISTFILES List all files of directory and its sub-directories.
% LISTFILES('a_directory') lists the files in a directory and its
% sub-directories up to a depth of four sub-directories. Pathnames
% and wildcards may be used.
% For example, LISTFILES('a_directory', '*.m') lists all the M-files
% in a di... |
github | TWOEARS/localisation-training-pipeline-master | spec2ceps.m | .m | localisation-training-pipeline-master/Tools/spec2ceps.m | 2,309 | utf_8 | 7f515ebc448c4d160c6694554b10c0ee | function ceps = spec2ceps(spec, kind, nc)
%SPEC2CEPS Calculate cepstra from spectra:
% ceps = spec2ceps(spec, compress, kind, nc)
%
% spec - uncompressed spectra in columns
%
% kind - cepstral parameter kind, any sensible combination of the
% following characters:
%
% '0' 0'th cepstral coef i... |
github | TWOEARS/localisation-training-pipeline-master | metrop.m | .m | localisation-training-pipeline-master/Tools/GMM_Netlab/metrop.m | 4,976 | utf_8 | 53e05637fbfd2fcd95efaadd86e97ce9 | function [samples, energies, diagn] = metrop(f, x, options, gradf, varargin)
%METROP Markov Chain Monte Carlo sampling with Metropolis algorithm.
%
% Description
% SAMPLES = METROP(F, X, OPTIONS) uses the Metropolis algorithm to
% sample from the distribution P ~ EXP(-F), where F is the first
% argument to METROP. T... |
github | TWOEARS/localisation-training-pipeline-master | hmc.m | .m | localisation-training-pipeline-master/Tools/GMM_Netlab/hmc.m | 7,683 | utf_8 | 64c15e958297afe69787b8617dc1a56a | function [samples, energies, diagn] = hmc(f, x, options, gradf, varargin)
%HMC Hybrid Monte Carlo sampling.
%
% Description
% SAMPLES = HMC(F, X, OPTIONS, GRADF) uses a hybrid Monte Carlo
% algorithm to sample from the distribution P ~ EXP(-F), where F is the
% first argument to HMC. The Markov chain starts at the poi... |
github | TWOEARS/localisation-training-pipeline-master | gtminit.m | .m | localisation-training-pipeline-master/Tools/GMM_Netlab/gtminit.m | 5,204 | utf_8 | ab76f6114a7e85375ade5e5889d5f6a7 | function net = gtminit(net, options, data, samp_type, varargin)
%GTMINIT Initialise the weights and latent sample in a GTM.
%
% Description
% NET = GTMINIT(NET, OPTIONS, DATA, SAMPTYPE) takes a GTM NET and
% generates a sample of latent data points and sets the centres (and
% widths if appropriate) of NET.RBFNET.
%
% I... |
github | TWOEARS/localisation-training-pipeline-master | mlphess.m | .m | localisation-training-pipeline-master/Tools/GMM_Netlab/mlphess.m | 1,633 | utf_8 | b91a15ca11b4886de6c1671c33a735d3 | function [h, hdata] = mlphess(net, x, t, hdata)
%MLPHESS Evaluate the Hessian matrix for a multi-layer perceptron network.
%
% Description
% H = MLPHESS(NET, X, T) takes an MLP network data structure NET, a
% matrix X of input values, and a matrix T of target values and returns
% the full Hessian matrix H corresponding... |
github | TWOEARS/localisation-training-pipeline-master | glmhess.m | .m | localisation-training-pipeline-master/Tools/GMM_Netlab/glmhess.m | 4,024 | utf_8 | 2d706b82d25cb35ff9467fe8837ef26f | function [h, hdata] = glmhess(net, x, t, hdata)
%GLMHESS Evaluate the Hessian matrix for a generalised linear model.
%
% Description
% H = GLMHESS(NET, X, T) takes a GLM network data structure NET, a
% matrix X of input values, and a matrix T of target values and returns
% the full Hessian matrix H corresponding to t... |
github | TWOEARS/localisation-training-pipeline-master | rbfhess.m | .m | localisation-training-pipeline-master/Tools/GMM_Netlab/rbfhess.m | 3,138 | utf_8 | 0a6ef29c8be32e9991cacfe42bdfa0b3 | function [h, hdata] = rbfhess(net, x, t, hdata)
%RBFHESS Evaluate the Hessian matrix for RBF network.
%
% Description
% H = RBFHESS(NET, X, T) takes an RBF network data structure NET, a
% matrix X of input values, and a matrix T of target values and returns
% the full Hessian matrix H corresponding to the second deriva... |
github | TWOEARS/localisation-training-pipeline-master | myOctaveVersion.m | .m | localisation-training-pipeline-master/Tools/DeepLearnToolbox/util/myOctaveVersion.m | 169 | utf_8 | d4603482a968c496b66a4ed4e7c72471 | % return OCTAVE_VERSION or 'undefined' as a string
function result = myOctaveVersion()
if isOctave()
result = OCTAVE_VERSION;
else
result = 'undefined';
end
|
github | TWOEARS/localisation-training-pipeline-master | isOctave.m | .m | localisation-training-pipeline-master/Tools/DeepLearnToolbox/util/isOctave.m | 108 | utf_8 | 4695e8d7c4478e1e67733cca9903f9ef | %detects if we're running Octave
function result = isOctave()
result = exist('OCTAVE_VERSION') ~= 0;
end |
github | TWOEARS/localisation-training-pipeline-master | makeLMfilters.m | .m | localisation-training-pipeline-master/Tools/DeepLearnToolbox/util/makeLMfilters.m | 1,895 | utf_8 | 21950924882d8a0c49ab03ef0681b618 | function F=makeLMfilters
% Returns the LML filter bank of size 49x49x48 in F. To convolve an
% image I with the filter bank you can either use the matlab function
% conv2, i.e. responses(:,:,i)=conv2(I,F(:,:,i),'valid'), or use the
% Fourier transform.
SUP=49; % Support of the largest filter (must be... |
github | TWOEARS/localisation-training-pipeline-master | caenumgradcheck.m | .m | localisation-training-pipeline-master/Tools/DeepLearnToolbox/CAE/caenumgradcheck.m | 3,618 | utf_8 | 6c481fc15ab7df32e0f476514100141a | function cae = caenumgradcheck(cae, x, y)
epsilon = 1e-4;
er = 1e-6;
disp('performing numerical gradient checking...')
for i = 1 : numel(cae.o)
p_cae = cae; p_cae.c{i} = p_cae.c{i} + epsilon;
m_cae = cae; m_cae.c{i} = m_cae.c{i} - epsilon;
[m_cae, p_cae] = caerun(m_cae, p_cae, x... |
github | amckay/EC702-master | tauchen.m | .m | EC702-master/code/tauchen.m | 1,626 | utf_8 | b6042f465096b896b67d70a2a673ac42 | function [Z,Zprob] = tauchen(N,mu,rho,sigma,m)
%Function TAUCHEN
%
%Purpose: Finds a Markov chain whose sample paths
% approximate those of the AR(1) process
% z(t+1) = (1-rho)*mu + rho * z(t) + eps(t+1)
% where eps are normal with stddev sigma
%
%Format: {Z, Zprob} =... |
github | icopavan/PSOAdaBoost-master | predStump.m | .m | PSOAdaBoost-master/predStump.m | 240 | utf_8 | 33aef76407d65dfa83f957307334842c | % Make prediction based on a decision stump
function label = predStump(X, stump)
N = size(X, 1);
x = X(:, stump.dim);
idx = logical(x >= stump.threshold); % N x 1
label = zeros(N, 1);
label(idx) = stump.more;
label(~idx) = stump.less;
end
|
github | Lenskiy/ESN-master | EchoStateNetwork.m | .m | ESN-master/ver0simplesESN/EchoStateNetwork.m | 2,218 | utf_8 | fde6c9c27e5a489aaebd24668d606fb3 | function EchoStateNetwork()
clc;close all;clear all
% signal
Yd = [[0.5 .* sin((1:500)/10)].^7;[0.5 .* sin((1:500)/10)]];
% The connectivity density of reservoirs. Usually the value range of D is [0.01 1]
D = 0.4;
% Number of reservoir neurons
N = 1500;
% Create echo state network
net = newESN(Yd,N,D);
% Trai... |
github | Lenskiy/ESN-master | runStackedESN.m | .m | ESN-master/ver2stackedESN/runStackedESN.m | 2,165 | utf_8 | 5be440983c6c302c9ebf6c4e55c00aa2 |
function Y = runStackedESN(initial_input, Nsamples, x, Win, W, Wout, lr, sigma, node_type, output_type, feedback_scaling )
% in generative mode no need to initialize the states i.e. x,
% they have already been initialized
% only one initial input vector is need to start generating the output
outSize ... |
github | Lenskiy/ESN-master | MinMaxNormalize.m | .m | ESN-master/ver2stackedESN/MinMaxNormalize.m | 170 | utf_8 | f3b13b44f65e7e9b37f851e0bd717417 | % Normalize data to [0,+scaleFactor]
function [ Y ] = MinMaxNormalize( X , scaleFactor)
mi = min(X);
ma = max(X);
Y = scaleFactor*(X-mi)./(ma-mi);
end
|
github | Lenskiy/ESN-master | lorenz.m | .m | ESN-master/ver2stackedESN/lorenz.m | 1,362 | utf_8 | 8dfaafdb6be202196dcb3551ca570f28 | function [x,y,z] = lorenz(rho, sigma, beta, initV, T, eps)
% LORENZ Function generates the lorenz attractor of the prescribed values
% of parameters rho, sigma, beta
%
% [X,Y,Z] = LORENZ(RHO,SIGMA,BETA,INITV,T,EPS)
% X, Y, Z - output vectors of the strange attactor trajectories
% RHO - Rayleigh number... |
github | Lenskiy/ESN-master | meanAdjSdevNorm.m | .m | ESN-master/ver2stackedESN/meanAdjSdevNorm.m | 143 | utf_8 | 1613bb97d65eea4351487a0017f5a8ca | % Centeres with mean and normalizes with sdev the input series
function [ Y ] = meanAdjSdevNorm( X )
Y = (X - mean(X))/std(X);
end
|
github | Lenskiy/ESN-master | NARMA10.m | .m | ESN-master/ver2stackedESN/NARMA10.m | 441 | utf_8 | 74ecb5cd9aab64b24aae3397dbdb70c1 | % Generates NARMA10 benchmark time-series
% First 10 y are initalized as 0 (However, not specified in paper)
function [ Y ] = NARMA10series( len )
% init Y with 0s
Y = zeros(len,1);
% init U with uniform [0,0.5]
U = 0.5*rand(len,1);
for i = 11 : len
Y(i,1) = 0.3*Y(i-1... |
github | Lenskiy/ESN-master | genStackedESN.m | .m | ESN-master/ver2stackedESN/genStackedESN.m | 2,240 | utf_8 | 06e3b18228d2b683397e92b545113c1b |
function Y = genStackedESN(initial_input, Nsamples, x, Win, W, Wout, lr, sigma, node_type, output_type, feedback_scaling, tf)
% in generative mode no need to initialize the states i.e. x,
% they have already been initialized
% only one initial input vector is need to start generating the output
outSi... |
github | Lenskiy/ESN-master | buildStackedESN.m | .m | ESN-master/ver2stackedESN/buildStackedESN.m | 1,369 | utf_8 | 03085b8651e9c9da9b5e24df8162c3c1 | % input_size defines the number of inputs
% reservoir_size defines the number of neurons in the reservoir
% connectivity is the portion of the non-zero connections in the reservoir
% sp is the spectral radius that W will have
% Win is the matrix of input weights
% W defines weights between neurons in the reservoir
fun... |
github | Lenskiy/ESN-master | gridSearchESNparamters.m | .m | ESN-master/ver2stackedESN/gridSearchESNparamters.m | 2,966 | utf_8 | c449068d948cfd6d981d3ef2e58409fa |
function [mse_results, mse_results_std, parameters_grid, best_mse, best_paramters] = gridSearchESNparamters(training_input, training_output, testing_input, testing_output,...
nTrials, numESNs, ESNtype, sParams)
feedback_scaling = 1;
stdNoise ... |
github | Lenskiy/ESN-master | homoTrans.m | .m | ESN-master/T2_MNIST/homoTrans.m | 879 | utf_8 | bde17d25ed5c319d12d7c6c52fa76ac9 | % HOMOTRANS - homogeneous transformation of points
%
% Function to perform a transformation on homogeneous points/lines
% The resulting points are normalised to have a homogeneous scale of 1
%
% Usage:
% t = homoTrans(P,v);
%
% Arguments:
% P - 3 x 3 or 4 x 4 transformation matrix
% v - ... |
github | Lenskiy/ESN-master | imTrans.m | .m | ESN-master/T2_MNIST/imTrans.m | 6,689 | utf_8 | f7ceaf2cc685b62f841c7b36d3f1d7b9 | % IMTRANS - Homogeneous transformation of an image.
%
% Applies a geometric transform to an image
%
% [newim, newT] = imTrans(im, T, region, sze);
%
% Arguments:
% im - The image to be transformed.
% T - The 3x3 homogeneous transformation matrix.
% region - An optional 4 element vector ... |
github | Lenskiy/ESN-master | Lorenz_RNN_with_diff_weights.m | .m | ESN-master/temp/Lorenz_RNN_with_diff_weights.m | 5,141 | utf_8 | a045006044647b37c197bf7e51fa6b0e | function elmanErros = trainElman(trainingData, learning_rate, netSize, N_trials)
dataLen = size(trainingData,2);
eras_count = 500;
inpSize = 3;
train_size = dataLen * 0.8;
test_size = dataLen - train_size;
seq_length = 320; % Caution, seq_length, hprev and eras_count are strongly dependent on ... |
github | Lenskiy/ESN-master | trainElman.m | .m | ESN-master/temp/trainElman.m | 5,134 | utf_8 | 442fd3c1041718b07451693b7d06a0c1 | function elmanErros = trainElman(trainingData, learning_rate, netSize, N_trials)
%delete(gcp('nocreate'));
%parpool('local',4);
dataLen = size(trainingData, 2);
inpSize = size(trainingData, 1);
numEpochs = 50000;
trainSize = dataLen * 0.8;
seq_length = 320; % Caution, seq_length, h... |
github | Lenskiy/ESN-master | gridSearchESNparamters.m | .m | ESN-master/ver3hierarhicalESN/gridSearchESNparamters.m | 2,957 | utf_8 | 937df974d66da3161d058ab2da622380 |
function [mse_results, mse_results_std, parameters_grid, best_mse, best_paramters] = gridSearchESNparamters(training_input, training_output, testing_input, testing_output,...
nTrials, ESNtype, sParams)
feedback_scaling = 1;
stdNoise = 0;
... |
github | Lenskiy/ESN-master | runStackedESN.m | .m | ESN-master/ver1basicESN/runStackedESN.m | 923 | utf_8 | 84527583370d8589d272f36507bb2694 |
function Y = runStackedESN(initial_vector, Nsamples, x, Win, W, Wout, lr, sigma)
% in generative mode no need to initialize the states i.e. x,
% they have already been initialized
% only one initial input vector is need to start generating the output
outSize = size(Wout,1);
Y = zeros(Nsamples, ou... |
github | Lenskiy/ESN-master | trainStackedESN.m | .m | ESN-master/ver1basicESN/trainStackedESN.m | 1,894 | utf_8 | 670b337b5ee1c48ade7915267d6a4a9e |
function [Wout, states, states_evolution] = trainStackedESN(input,...
target, Win, W, lr, sigma)
initLen = 0;
trainLen = size(input,1);
input_size = size(input, 2);
states_size = 0;
% allocated memory for the design (collected states) matrix
... |
github | Lenskiy/ESN-master | predictESN.m | .m | ESN-master/ver1basicESN/predictESN.m | 425 | utf_8 | 41de050a59e570d847604d0fced18673 |
function Y = predictESN(input, states, Win, W, Wout, lr)
% the states depend on the input and previous states
outSize = size(Wout,1);
Y = zeros(length(input), outSize);
u = input(1, :)';
for k = 1:length(input) - 1
states = (1 - lr) * states + lr * tanh( Win * [1; u] + W * states);
... |
github | Lenskiy/ESN-master | buildESN.m | .m | ESN-master/ver1basicESN/buildESN.m | 901 | utf_8 | dece44f89a41a8ba6c343594491b9352 | % input_size defines the number of inputs
% reservoir_size defines the number of neurons in the reservoir
% connectivity is the portion of the non-zero connections in the reservoir
% sp is the spectral radius that W will have
% Win is the matrix of input weights
% W defines weights between neurons in the reservoir
fun... |
github | Lenskiy/ESN-master | runESN.m | .m | ESN-master/ver1basicESN/runESN.m | 580 | utf_8 | e40280ee35ee8e01f8e41980dc8aed7c |
function Y = runESN(initial_vector, Nsamples, states, Win, W, Wout, lr, sigma)
% in generative mode no need to initialize the states,
% they have already been initialized
% only one initial input vector is need to start generating the output
outSize = size(Wout,1);
Y = zeros(Nsamples, outSize);
... |
github | Lenskiy/ESN-master | esn_cor_best_match.m | .m | ESN-master/ver1basicESN/esn_cor_best_match.m | 949 | utf_8 | 94d0fdbe273f3bf4017326d921830bc6 | %Find most correlated pairs of predicted and true signals
%Return lag for such signals
function [best_cor, best_lag, CorMat, LagMat] = esn_cor_best_match(Yt, Yp)
Ndim = size(Yt, 2);
CorMat = zeros(Ndim, Ndim);
LagMat = zeros(Ndim, Ndim);
for i = 1:Ndim
for j = 1:Ndim
[acor,lag] ... |
github | Lenskiy/ESN-master | trainESN.m | .m | ESN-master/ver1basicESN/trainESN.m | 1,380 | utf_8 | c14044bf6b250999de359e122359b1c3 |
function [Wout, states, states_evolution] = trainESN(input,...
target, Win, W, lr, sigma)
initLen = 0;
trainLen = size(input,1);
reservoir_size = size(W,1);
input_size = size(input, 2);
% allocated memory for the design (collected states) matr... |
github | Lenskiy/ESN-master | buildStackedESN.m | .m | ESN-master/ver1basicESN/buildStackedESN.m | 1,034 | utf_8 | de37a409191600819a2d30000be75573 | % input_size defines the number of inputs
% reservoir_size defines the number of neurons in the reservoir
% connectivity is the portion of the non-zero connections in the reservoir
% sp is the spectral radius that W will have
% Win is the matrix of input weights
% W defines weights between neurons in the reservoir
fun... |
github | Lenskiy/ESN-master | lorenz.m | .m | ESN-master/T0_chaotic/lorenz.m | 1,362 | utf_8 | 8dfaafdb6be202196dcb3551ca570f28 | function [x,y,z] = lorenz(rho, sigma, beta, initV, T, eps)
% LORENZ Function generates the lorenz attractor of the prescribed values
% of parameters rho, sigma, beta
%
% [X,Y,Z] = LORENZ(RHO,SIGMA,BETA,INITV,T,EPS)
% X, Y, Z - output vectors of the strange attactor trajectories
% RHO - Rayleigh number... |
github | Lenskiy/ESN-master | NARMA100LTdepOnly.m | .m | ESN-master/T1_NARMA/NARMA100LTdepOnly.m | 476 | utf_8 | 1845f6c34675819b506fdbb01cf0c012 | % Generates NARMA100longTerm benchmark time-series
% Task: predict y(t) given u(t)
function [ O ] = NARMA100LTdepOnly( len )
% init Y with 0s
Y = zeros(len,1);
% init U with uniform [0,0.5]
U = 0.5*rand(len,1);
for i = 101 : len
Y(i,1) = 0.3*Y(i-25,1)+0.005*Y(i-50,1)*... |
github | Lenskiy/ESN-master | NARMAvSTdepOnly.m | .m | ESN-master/T1_NARMA/NARMAvSTdepOnly.m | 466 | utf_8 | 2fe65f23fa29b642cfbb587a23569c32 | % Generates NARMA100longTerm benchmark time-series
% Task: predict y(t) given u(t)
function [ O ] = NARMAvSTdepOnly( len )
% init Y with 0s
Y = zeros(len,1);
% init U with uniform [0,0.5]
U = 0.5*rand(len,1);
for i = 101 : len
Y(i,1) = 0.3*Y(i-1,1)+0.005*Y(i-1,1)*sum(... |
github | Lenskiy/ESN-master | NARMA10series.m | .m | ESN-master/T1_NARMA/NARMA10series.m | 520 | utf_8 | b139449532b1865cb81a9938b6d4edf2 | % Generates NARMA10 benchmark time-series
% First 10 y are initalized as 0 (However, not specified in paper)
% Task: predict y(t) given u(t)
function [ O ] = NARMA10series( len )
% init Y with 0s
Y = zeros(len,1);
% init U with uniform [0,0.5]
U = 0.5*rand(len,1);
for i = 11 : len
... |
github | Lenskiy/ESN-master | NARMA100series.m | .m | ESN-master/T1_NARMA/NARMA100series.m | 461 | utf_8 | e835f1493910a10ad6c26d83e76ffe15 | % Generates NARMA100 benchmark time-series
% Task: predict y(t) given u(t)
function [ O ] = NARMA100series( len )
% init Y with 0s
Y = zeros(len,1);
% init U with uniform [0,0.5]
U = 0.5*rand(len,1);
for i = 101 : len
Y(i,1) = 0.3*Y(i-1,1)+0.005*Y(i-1,1)*sum(Y(i-100:i... |
github | bsuttonIL/StructuralConnFSL-master | display_conmat.m | .m | StructuralConnFSL-master/display_conmat.m | 2,738 | utf_8 | 3bb37a904c93d2bfdf2f8687eaec2c75 | function display_conmat(CSVmat,rot)
%DISPLAY_CONMAT displays and rotates connectivity matrix tick labels
% DISPLAY_CONMAT(H,ROT) is the calling form where H is a handle to
% the axis that contains the XTickLabels that are to be rotated. ROT is
% an optional parameter that specifies the angle of rotation. The defa... |
github | qxcv/rerank-lite-master | savejson.m | .m | rerank-lite-master/convert/jsonlab/savejson.m | 19,032 | utf_8 | fcdcba8e45230c57a5d825c60a071e81 | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | qxcv/rerank-lite-master | loadjson.m | .m | rerank-lite-master/convert/jsonlab/loadjson.m | 16,162 | ibm852 | 2bc1da81d0f677f843944b84f0181d58 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | qxcv/rerank-lite-master | loadubjson.m | .m | rerank-lite-master/convert/jsonlab/loadubjson.m | 13,317 | utf_8 | e090cd22109f32b58c8d63b948cfbfcd | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id$
%
% input:
% fname: ... |
github | qxcv/rerank-lite-master | saveubjson.m | .m | rerank-lite-master/convert/jsonlab/saveubjson.m | 17,740 | utf_8 | e8182109b9c4bc0cca50de08f1254247 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | sanghoon/pva-faster-rcnn-master | voc_eval.m | .m | pva-faster-rcnn-master/lib/datasets/VOCdevkit-matlab-wrapper/voc_eval.m | 1,332 | utf_8 | 3ee1d5373b091ae4ab79d26ab657c962 | function res = voc_eval(path, comp_id, test_set, output_dir)
VOCopts = get_voc_opts(path);
VOCopts.testset = test_set;
for i = 1:length(VOCopts.classes)
cls = VOCopts.classes{i};
res(i) = voc_eval_cls(cls, VOCopts, comp_id, output_dir);
end
fprintf('\n~~~~~~~~~~~~~~~~~~~~\n');
fprintf('Results:\n');
aps = [res(:... |
github | nspi/vbcg-master | create_video.m | .m | vbcg-master/src/utilities/create_video.m | 1,956 | utf_8 | 74a06263ec51e3095d697646e1208b0b | %CREATE_VIDEO Creates a video for testing purposes
% CREATE_VIDEO(X) creates a 90s video (.avi) and the individual frames (.png)
% on the hard disk. It shows a repeating pattern from black to white
% frames and X defines the frequency (in Hz) of this pattern. FPS defines
% the FPS of the synthetic video.
%
% The vid... |
github | VisionLearningGroup/Ask_Attend_and_Answer-master | prepare_batch.m | .m | Ask_Attend_and_Answer-master/caffe/matlab/caffe/prepare_batch.m | 1,298 | utf_8 | 68088231982895c248aef25b4886eab0 | % ------------------------------------------------------------------------
function images = prepare_batch(image_files,IMAGE_MEAN,batch_size)
% ------------------------------------------------------------------------
if nargin < 2
d = load('ilsvrc_2012_mean');
IMAGE_MEAN = d.image_mean;
end
num_images = length... |
github | VisionLearningGroup/Ask_Attend_and_Answer-master | matcaffe_demo_vgg.m | .m | Ask_Attend_and_Answer-master/caffe/matlab/caffe/matcaffe_demo_vgg.m | 3,036 | utf_8 | f836eefad26027ac1be6e24421b59543 | function scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file, mean_file)
% scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file, mean_file)
%
% Demo of the matlab wrapper using the networks described in the BMVC-2014 paper "Return of the Devil in the Details: Delving Deep into Convolutional... |
github | VisionLearningGroup/Ask_Attend_and_Answer-master | matcaffe_demo.m | .m | Ask_Attend_and_Answer-master/caffe/matlab/caffe/matcaffe_demo.m | 3,344 | utf_8 | 669622769508a684210d164ac749a614 | function [scores, maxlabel] = matcaffe_demo(im, use_gpu)
% scores = matcaffe_demo(im, use_gpu)
%
% Demo of the matlab wrapper using the ILSVRC network.
%
% input
% im color image as uint8 HxWx3
% use_gpu 1 to use the GPU, 0 to use the CPU
%
% output
% scores 1000-dimensional ILSVRC score vector
%
% You m... |
github | VisionLearningGroup/Ask_Attend_and_Answer-master | matcaffe_demo_vgg_mean_pix.m | .m | Ask_Attend_and_Answer-master/caffe/matlab/caffe/matcaffe_demo_vgg_mean_pix.m | 3,069 | utf_8 | 04b831d0f205ef0932c4f3cfa930d6f9 | function scores = matcaffe_demo_vgg_mean_pix(im, use_gpu, model_def_file, model_file)
% scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file)
%
% Demo of the matlab wrapper based on the networks used for the "VGG" entry
% in the ILSVRC-2014 competition and described in the tech. report
% "Very Deep Convo... |
github | VisionLearningGroup/Ask_Attend_and_Answer-master | classification_demo.m | .m | Ask_Attend_and_Answer-master/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 | brian-lau/MappedTensor-master | MappedTensor.m | .m | MappedTensor-master/MappedTensor.m | 86,060 | utf_8 | 75d512e5802826a3a4069f151a8bad19 | % MappedTensor - CLASS Create and map a new file to a large variable
%
% The MappedTensor class creates a large variable, and maps it directly to a
% file on disk. The variable can be passed around BY REFERENCE, indexed and
% written to without allocating space for the entire variable in matlab. Note
% that this is a... |
github | xiaozhuchacha/Kinect2Toolbox-master | plywrite.m | .m | Kinect2Toolbox-master/ColorDepth2PC/matlab_viz/plyutil/plywrite.m | 1,366 | utf_8 | 908447080fb14e25d9e6c98a2b484d49 | % ply
% format ascii 1.0
% element vertex 1280247
% property float x
% property float y
% property float z
% property uchar red
% property uchar green
% property uchar blue
% end_header
function plywrite(vertex, face, rgb, normal, filename)
fid = fopen(filename, 'w');
fprintf(fid, 'ply\n');
fprintf(fi... |
github | xiaozhuchacha/Kinect2Toolbox-master | plyread.m | .m | Kinect2Toolbox-master/ColorDepth2PC/matlab_viz/plyutil/plyread.m | 15,199 | utf_8 | 28c749daf12ab55081b862f136edf927 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [Elements,varargout] = plyread(Path,Str)
%PLYREAD Read a PLY 3D data file.
% [DATA,COMMENTS] = PLYREAD(FILENAME) reads a version 1.0 PLY file
% FILENAME and returns a structure DATA. The fields in this structure
% are defined by the P... |
github | xiaozhuchacha/Kinect2Toolbox-master | plywrite.m | .m | Kinect2Toolbox-master/Pipeline/matlab_viz/plyutil/plywrite.m | 1,366 | utf_8 | 908447080fb14e25d9e6c98a2b484d49 | % ply
% format ascii 1.0
% element vertex 1280247
% property float x
% property float y
% property float z
% property uchar red
% property uchar green
% property uchar blue
% end_header
function plywrite(vertex, face, rgb, normal, filename)
fid = fopen(filename, 'w');
fprintf(fid, 'ply\n');
fprintf(fi... |
github | xiaozhuchacha/Kinect2Toolbox-master | plyread.m | .m | Kinect2Toolbox-master/Pipeline/matlab_viz/plyutil/plyread.m | 15,199 | utf_8 | 28c749daf12ab55081b862f136edf927 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [Elements,varargout] = plyread(Path,Str)
%PLYREAD Read a PLY 3D data file.
% [DATA,COMMENTS] = PLYREAD(FILENAME) reads a version 1.0 PLY file
% FILENAME and returns a structure DATA. The fields in this structure
% are defined by the P... |
github | zsivine/TS1-algorithms-master | TS1_s1_v2.m | .m | TS1-algorithms-master/TS1_s1_v2.m | 6,343 | utf_8 | 3ecede575e4c59779e8722c3795b17dc | %--------------------------------------------------------------------------
% Threshold TS1 Method based on Approximate SVD for Matrix Completion Problem.
%
% Solves
% min rank(X)
% s.t. X_ij = M_ij , (i,j) \in \Omega
%
% Author: Shuai Zhang
% Date: Feb 2015
% version: lineartimesvd
%----------... |
github | zsivine/TS1-algorithms-master | TS1_s2.m | .m | TS1-algorithms-master/TS1_s2.m | 5,059 | utf_8 | 12855aa992618415e1ead605becdb3ea | %--------------------------------------------------------------------------
% Full Adaptive Threshold TS1 Method based on Approximate SVD
% for Matrix Completion Problem.
%
% Solves
% min rank(X)
% s.t. X_ij = M_ij , (i,j) \in \Omega
%
% Author: Shuai Zhang
% Date: April 2015
% version: lineart... |
github | zsivine/TS1-algorithms-master | TS1_s1.m | .m | TS1-algorithms-master/TS1_s1.m | 5,263 | utf_8 | ebf9e324fe83061f5aa70e10bb33bc2f | %--------------------------------------------------------------------------
% Threshold TS1 Method based on Approximate SVD for Matrix Completion Problem.
%
% Solves
% min rank(X)
% s.t. X_ij = M_ij , (i,j) \in \Omega
%
% Author: Shuai Zhang
% Date: Feb 2015
%------------------------------------... |
github | zsivine/TS1-algorithms-master | TS1_s1_v3.m | .m | TS1-algorithms-master/TS1_s1_v3.m | 5,788 | utf_8 | dbcfff63ff407ae4f5fe6f2d584ba04d | %--------------------------------------------------------------------------
% Threshold TS1 Method based on Approximate SVD for Matrix Completion Problem.
%
% Solves
% min rank(X)
% s.t. X_ij = M_ij , (i,j) \in \Omega
%
% Author: Shuai Zhang
% Date: Feb 2015
% version: random svd --- fazel
%---... |
github | vonway/teamtalk-android-master | echo_diagnostic.m | .m | teamtalk-android-master/app/src/main/jni/libspeex/echo_diagnostic.m | 2,076 | utf_8 | 8d5e7563976fbd9bd2eda26711f7d8dc | % Attempts to diagnose AEC problems from recorded samples
%
% out = echo_diagnostic(rec_file, play_file, out_file, tail_length)
%
% Computes the full matrix inversion to cancel echo from the
% recording 'rec_file' using the far end signal 'play_file' using
% a filter length of 'tail_length'. The output is saved to 'o... |
github | kirk86/LocNet-master | script_test_object_detection_pipeline_PASCAL.m | .m | LocNet-master/code/script_test_object_detection_pipeline_PASCAL.m | 14,966 | utf_8 | 4d61b8c90fa17bd8403ec8e81de7932e | function script_test_object_detection_pipeline_PASCAL(model_rec_dir_name, model_loc_dir_name, varargin)
% script_test_object_detection_pipeline_PASCAL(model_rec_dir_name, model_loc_dir_name, varargin)
% given a recognition model (model_rec_dir_name) and a localization model
% (model_loc_dir_name) it performs the object... |
github | kirk86/LocNet-master | recognize_bboxes_of_all_imgs.m | .m | LocNet-master/code/object_recognition/recognize_bboxes_of_all_imgs.m | 12,624 | utf_8 | 016305e1f3fc055090ed44d38bf8edd1 | function [abbox_scores] = recognize_bboxes_of_all_imgs(...
model, image_paths, all_bbox_proposals, dst_directory, image_set_name, varargin)
% recognize_bboxes_of_all_imgs: given a bounding box recognition model and
% a set of images with their input candidate bounding boxes, for each
% image it assigns a confiden... |
github | kirk86/LocNet-master | evaluate_average_precision_pascal.m | .m | LocNet-master/code/utils/evaluate_average_precision_pascal.m | 8,445 | utf_8 | 65fa989fbe9c782a757b53f0fb58b4be | function [ all_results, all_results_per_thr ] = evaluate_average_precision_pascal( ...
all_bbox_gt, all_detected_bbox, classes, varargin)
%
% This file is part of the code that implements the following paper:
% Title : "LocNet: Improving Localization Accuracy for Object Detection"
% Authors : Spyros Gidari... |
github | kirk86/LocNet-master | compute_ave_recall_of_bbox.m | .m | LocNet-master/code/utils/compute_ave_recall_of_bbox.m | 4,279 | utf_8 | e4f09576958e5641a17a994ee98d9c97 | function [ ave_recall, recall, thresholds] = compute_ave_recall_of_bbox( bbox_pred, bbox_gt )
% compute_ave_recall_of_bbox: given a set of predicted bounding boxes and and a set
% of ground truth bounding boxes it computes the recall for multiple IoU
% thresholds between 0.0 and 1.0 as well as the average recall (whi... |
github | kirk86/LocNet-master | get_grount_truth_bboxes_from_voc.m | .m | LocNet-master/code/data-providers/get_grount_truth_bboxes_from_voc.m | 2,815 | utf_8 | d3484aa334d56c82518bc91bb3aa8517 | function [ all_bboxes_gt ] = get_grount_truth_bboxes_from_voc( voc_path, image_set, voc_year, with_hard_samples, cache_dir )
%
% This file is part of the code that implements the following paper:
% Title : "LocNet: Improving Localization Accuracy for Object Detection"
% Authors : Spyros Gidaris, Nikos Komodaki... |
github | kirk86/LocNet-master | load_box_proposals.m | .m | LocNet-master/code/data-providers/load_box_proposals.m | 3,588 | utf_8 | 893429330940666cf21eafa2657142ba | function all_box_proposals = load_box_proposals( image_db, method )
%
% This file is part of the code that implements the following paper:
% Title : "LocNet: Improving Localization Accuracy for Object Detection"
% Authors : Spyros Gidaris, Nikos Komodakis
% Institution: Universite Paris Est, Ecole des Ponts Pa... |
github | kirk86/LocNet-master | extract_selective_search_boxes_from_dataset.m | .m | LocNet-master/code/data-providers/extract_selective_search_boxes_from_dataset.m | 3,517 | utf_8 | 080206525bafa8f5be72c19b25c863c3 | function all_bbox_proposals = extract_selective_search_boxes_from_dataset(image_db, ss_boxes_dst_file)
%
% This file is part of the code that implements the following paper:
% Title : "LocNet: Improving Localization Accuracy for Object Detection"
% Authors : Spyros Gidaris, Nikos Komodakis
% Institution: Unive... |
github | kirk86/LocNet-master | sample_bing_windows.m | .m | LocNet-master/code/data-providers/sample_bing_windows.m | 6,960 | utf_8 | 9f10dd06e3b5796231bc53173c0c9d9e | function [ candidates, scores ] = sample_bing_windows( im, num_samples)
%SAMPLE_BING_WINDOWS Will generate equaly distributed windows in space,
%following Bing sizes
% Bing uses 29 specific sizes, this method spread this sizes homogenously
% inside the image
%
% This file comes from the code that implements the pap... |
github | kirk86/LocNet-master | load_image_dataset.m | .m | LocNet-master/code/data-providers/load_image_dataset.m | 7,362 | utf_8 | 6c37b669d4f3472a95ba5384fb691157 | function image_db = load_image_dataset(varargin)
%
% This file is part of the code that implements the following paper:
% Title : "LocNet: Improving Localization Accuracy for Object Detection"
% Authors : Spyros Gidaris, Nikos Komodakis
% Institution: Universite Paris Est, Ecole des Ponts ParisTech
% ArXiv lin... |
github | kirk86/LocNet-master | post_process_candidate_detections_all_imgs.m | .m | LocNet-master/code/postprocessing/post_process_candidate_detections_all_imgs.m | 9,217 | utf_8 | 94b3903dea9a0dc51a7937a13422eac6 | function aboxes_out = post_process_candidate_detections_all_imgs(...
aboxes_in, varargin)
% post_process_candidate_detections_all_imgs performs the post-processing
% step of non-maximum-suppression and optionally of box voting on the
% candidate bounding box detections of a set of images.
%
% INPUTS:
% 1) aboxes... |
github | kirk86/LocNet-master | post_process_candidate_detections.m | .m | LocNet-master/code/postprocessing/post_process_candidate_detections.m | 9,672 | utf_8 | dd07816233ee096f7b846b25d9702355 | function bbox_detections_per_class = post_process_candidate_detections( ...
bbox_cand_dets, varargin)
% post_process_candidate_detections performs the post-processing step of
% non-maximum-suppression and optionally of box voting. For more details
% regarding the box voting step we refer to section 5 of the techni... |
github | kirk86/LocNet-master | caffe_forward_net.m | .m | LocNet-master/code/caffe-funs/caffe_forward_net.m | 3,326 | utf_8 | 355323d7d3537116623c69dc27e986d5 | function [outputs, out_blob_names_total] = caffe_forward_net(net, input, out_blob_names_extra)
%
% This file is part of the code that implements the following ICCV2015 accepted paper:
% title: "Object detection via a multi-region & semantic segmentation-aware CNN model"
% authors: Spyros Gidaris, Nikos Komodakis
% ins... |
github | kirk86/LocNet-master | decode_reg_vals_to_bbox_targets.m | .m | LocNet-master/code/object_localization/decode_reg_vals_to_bbox_targets.m | 1,739 | utf_8 | b2014fdeb579b8874559a7844f429fcf | function bbox_pred = decode_reg_vals_to_bbox_targets(bbox_init, reg_values, class_indices)
%
% The code in this file comes from the RCNN code:
% https://github.com/rbgirshick/rcnn
%
% AUTORIGHTS
% ---------------------------------------------------------
% Copyright (c) 2014, Ross Girshick
%
% This file is part of th... |
github | kirk86/LocNet-master | localize_bboxes_of_all_imgs.m | .m | LocNet-master/code/object_localization/localize_bboxes_of_all_imgs.m | 12,919 | utf_8 | 5fc8b55352476e51fc595ff619de6bb2 | function [all_bboxes_out] = localize_bboxes_of_all_imgs(...
model, image_paths, all_bboxes_in, dst_directory, image_set_name, varargin)
% localize_bboxes_of_all_imgs: given a bounding box localization model and
% a set of images with their input candidate bounding boxes, for each
% image it predicts new bounding ... |
github | kirk86/LocNet-master | decode_loc_probs_to_bbox_targets.m | .m | LocNet-master/code/object_localization/decode_loc_probs_to_bbox_targets.m | 16,783 | utf_8 | 8612d341dd1c88c529f41522388e418a | function [bbox_pred, bbox_pred_in_region, bbox_pred_in_region_quantized] = decode_loc_probs_to_bbox_targets(...
bbox_in, class_indices, loc_prob_vectors, conf)
% decode_loc_probs_to_bbox_targets: given the input bounding boxes (bbox_in),
% the category ids of each bounding box (class_indices), and the predicted
% p... |
github | kirk86/LocNet-master | GetLocNetMinibatchData.m | .m | LocNet-master/code/object_localization/GetLocNetMinibatchData.m | 6,193 | utf_8 | 955fd7628c6bde9df078fb929278a90d | function [loc_blobs, bbox_targets_loc, bbox_inits_loc, image_paths_all] = ...
GetLocNetMinibatchData(conf, model, image_roidb, sampled_regions_loc, im_scales, image_sizes)
assert(length(image_roidb) == length(sampled_regions_loc));
num_images = length(image_roidb);
bbox_inits_loc = zeros([0, 4], 'sing... |
github | kirk86/LocNet-master | localize_bboxes_of_image.m | .m | LocNet-master/code/object_localization/localize_bboxes_of_image.m | 3,956 | utf_8 | bbe3c8166dd292c69a03c1bad82756c5 | function bboxes_out = localize_bboxes_of_image(model, image, bboxes_in)
% localize_bboxes_of_image given a localization model, an image and a set
% bounding boxes with the category id of each of them, it predicts a new
% location for each box such that the new ones will be closer (i.e. better
% localized) on the actual... |
github | kirk86/LocNet-master | train_LocNet_model.m | .m | LocNet-master/code/object_localization/train_LocNet_model.m | 23,692 | utf_8 | 7119c1b0bc35ec40698185b2f09d310e | function finetuned_model_path = train_LocNet_model(...
image_db_train, image_db_val, model, conf)
% train_LocNet_model: trains the LocNet network
%
% INPUTS:
% 1) image_db_train: struct with the specifiers of the training dataset
% 2) image_db_val: struct with the specifiers of the testing dataset
% 3) model: ... |
github | kirk86/LocNet-master | encode_bbox_target_to_loc_probs.m | .m | LocNet-master/code/object_localization/encode_bbox_target_to_loc_probs.m | 9,607 | utf_8 | 0410a8d86baec5ba6bc664aeb255a3a6 | function [loc_prob_vectors, bbox_target_quantized, bbox_target_in_region] = encode_bbox_target_to_loc_probs(...
bbox_in, bbox_target, conf)
% encode_bbox_target_to_loc_probs: given input bounding boxes (bbox_in),
% target bounding boxes and the configuration parameters of LocNet (conf)
% it computes the target pro... |
github | kirk86/LocNet-master | create_regions_from_boxes.m | .m | LocNet-master/code/region-funs/create_regions_from_boxes.m | 3,486 | utf_8 | 2f5e5090d98b13fedd72e6037f862f48 | function regions = create_regions_from_boxes( region_params, boxes )
% create_regions_from_boxes: given a set of bounding boxes it creates the
% regions that will be fed to the recognition/localization network
%
% INPUTS:
% 1) region_params: (type struct) the region pooling parameters. Some of
% its fields are:
% a... |
github | kirk86/LocNet-master | extract_object_proposals.m | .m | LocNet-master/code/object_detection/extract_object_proposals.m | 4,483 | utf_8 | 0e043d831bac67b171306c868d3c62ea | function bbox_proposals = extract_object_proposals( img, conf )
% extract_object_proposals: given an image it extracts class agnostic
% bounding box proposals from it using one of the following algorithms:
% 1) Edge Box proposals (around 2k proposals) - conf.box_method = ''
% 2) Selective Search proposals (around 2k pr... |
github | kirk86/LocNet-master | LocNet_object_detection.m | .m | LocNet-master/code/object_detection/LocNet_object_detection.m | 11,387 | utf_8 | ad1250fce8246c099b098f0591568195 | function [ bbox_detections ] = LocNet_object_detection( ...
img, model_obj_rec, model_obj_loc, bbox_proposals, conf )
% LocNet_object_detection: given an image, a recognition model for
% scoring candidate detection boxes, a bounding box localilization model
% (e.g. LocNet or CNN-based bounding box regression) for ... |
github | kirk86/LocNet-master | run_region_based_net_on_img.m | .m | LocNet-master/code/generic/run_region_based_net_on_img.m | 9,635 | utf_8 | a5f0511b4ae3766fb30c3027f78cd6b3 | function [outputs, out_blob_names_total] = run_region_based_net_on_img(...
model, image, bboxes, out_blob_names_extra)
% run_region_based_net_on_img: applies on the candidate bounding boxes
% (bboxes) and on the image the provided region-based CNN network (model).
%
% INPUTS:
% 1) model: (type struct) the boundin... |
github | qiaopTDUN/bayesian-video-super-resolution-master | computeColor.m | .m | bayesian-video-super-resolution-master/celiu_optical_flow/computeColor.m | 3,142 | utf_8 | a36a650437bc93d4d8ffe079fe712901 | function img = computeColor(u,v)
% computeColor color codes flow field U, V
% According to the c++ source code of Daniel Scharstein
% Contact: schar@middlebury.edu
% Author: Deqing Sun, Department of Computer Science, Brown University
% Contact: dqsun@cs.brown.edu
% $Date: 2007-10-31 21:20:30 (Wed, 31 O... |
github | mdhumphries/NetworkNoiseRejection-master | brewermap.m | .m | NetworkNoiseRejection-master/Helper_Functions/brewermap.m | 18,422 | utf_8 | 740bdc55d43493781af29d2456ea3a02 | function [map,num,typ] = brewermap(N,scheme)
% The complete selection of ColorBrewer colorschemes (RGB colormaps).
%
% (c) 2015 Stephen Cobeldick
%
% ### Function ###
%
% Returns any RGB colormap from the ColorBrewer colorschemes, especially
% intended for mapping and plots with attractive, distinguishable colors.
%
% ... |
github | mdhumphries/NetworkNoiseRejection-master | exportfig.m | .m | NetworkNoiseRejection-master/Helper_Functions/exportfig.m | 15,275 | utf_8 | 5e71849bdb6ed7bb0a074d2279ad16f2 | function exportfig(varargin)
%EXPORTFIG Export a figure to Encapsulated Postscript.
% EXPORTFIG(H, FILENAME) writes the figure H to FILENAME. H is
% a figure handle and FILENAME is a string that specifies the
% name of the output file.
%
% EXPORTFIG(...,PARAM1,VAL1,PARAM2,VAL2,...) specifies
% parameters th... |
github | mdhumphries/NetworkNoiseRejection-master | brewermap.m | .m | NetworkNoiseRejection-master/Networks/Allen_mouse_brain_atlas/brewermap.m | 18,422 | utf_8 | 740bdc55d43493781af29d2456ea3a02 | function [map,num,typ] = brewermap(N,scheme)
% The complete selection of ColorBrewer colorschemes (RGB colormaps).
%
% (c) 2015 Stephen Cobeldick
%
% ### Function ###
%
% Returns any RGB colormap from the ColorBrewer colorschemes, especially
% intended for mapping and plots with attractive, distinguishable colors.
%
% ... |
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