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 | tanshen/SubCNN-master | compute_recall_precision_aos_3d_box_only.m | .m | SubCNN-master/KITTI/compute_recall_precision_aos_3d_box_only.m | 14,698 | utf_8 | bc46090187d1228f9797f3ee6fa45948 | function compute_recall_precision_aos_3d_box_only
cls = 'car';
% evaluation parameter
MIN_HEIGHT = [40, 25, 25]; % minimum height for evaluated groundtruth/detections
MAX_OCCLUSION = [0, 1, 2]; % maximum occlusion level of the groundtruth used for evaluation
MAX_TRUNCATION = [0.15, 0.3, 0.5]; % maximum trunca... |
github | tanshen/SubCNN-master | compute_recall.m | .m | SubCNN-master/KITTI/compute_recall.m | 5,021 | utf_8 | b70c24347c967d7d793aba585bd683c9 | function recall_all = compute_recall
cls = 'car';
% evaluation parameter
MIN_HEIGHT = [40, 25, 25]; % minimum height for evaluated groundtruth/detections
MAX_OCCLUSION = [0, 1, 2]; % maximum occlusion level of the groundtruth used for evaluation
MAX_TRUNCATION = [0.15, 0.3, 0.5]; % maximum truncation level of... |
github | tanshen/SubCNN-master | exemplar_display_result_nissan.m | .m | SubCNN-master/NISSAN/exemplar_display_result_nissan.m | 7,145 | utf_8 | 893ee1e0198a1d4ff65b627859a1cd6d | function exemplar_display_result_nissan
threshold_car = 0.1;
threshold_others = 0.5;
is_save = 1;
result_dir = '/capri5/Projects/3DVP_RCNN/fast-rcnn/output/nissan';
root_dir = '/capri5/NISSAN_Dataset';
image_set = 'autonomy_log_2016-04-11-12-15-46';
if is_save
result_image_dir = sprintf('result_images/%s', image_... |
github | tanshen/SubCNN-master | compute_recall_precision.m | .m | SubCNN-master/MOT/compute_recall_precision.m | 3,632 | utf_8 | 23bd69946ce91eaf3c39a8776e6afde7 | % compute recall and precision
function compute_recall_precision
opt = globals();
is_train = 1;
is_show = 0;
if is_train
seq_set = 'train';
N = numel(opt.mot2d_train_seqs);
else
seq_set = 'test';
N = numel(opt.mot2d_test_seqs);
end
% output dir
out_dir = 'detection_train';
% main loop
for seq_idx = ... |
github | tanshen/SubCNN-master | globals.m | .m | SubCNN-master/MOT/globals.m | 1,240 | utf_8 | 5cb54912b6542a79e1b090adcfcfd057 | % --------------------------------------------------------
% MDP Tracking
% Copyright (c) 2015 CVGL Stanford
% Licensed under The MIT License [see LICENSE for details]
% Written by Yu Xiang
% --------------------------------------------------------
function opt = globals()
opt.root = pwd;
% path for MOT benchmark
mot... |
github | hanchengge/AirCP-master | cp_apr.m | .m | AirCP-master/tensor_toolbox/cp_apr.m | 7,940 | utf_8 | 795c8cdbe723300c0e5a9a7d9a3337dd | function [M,Minit,output] = cp_apr(X, R, varargin)
%CP_APR Compute nonnegative CP with alternating Poisson regression.
%
% M = CP_APR(X, R) computes an estimate of the best rank-R
% CP model of a tensor X using an alternating Poisson regression.
% The input X can be a tensor, sptensor, ktensor, or ttensor. The
% ... |
github | hanchengge/AirCP-master | export_data.m | .m | AirCP-master/tensor_toolbox/export_data.m | 2,370 | utf_8 | 1e8dfa282d0e4b69ff77560a6f844bd7 | function export_data(A, fname)
%EXPORT_DATA Export tensor-related data to a file.
%
% EXPORT(A,FNAME) exports object A to the file named FNAME in plain ASCII
% text. Export currently supports exporting the following data types:
%
% - tensor
% - matrix
%
% In the case of a tensor, the first t... |
github | hanchengge/AirCP-master | cp_nmu.m | .m | AirCP-master/tensor_toolbox/cp_nmu.m | 4,976 | utf_8 | 633d7166c5a07d5533e57ba3ccb25162 | function [P,Uinit] = cp_nmu(X,R,opts)
%CP_NMU Compute nonnegative CP with multiplicative updates.
%
% P = CP_NMU(X,R) computes an estimate of the best rank-R PARAFAC
% model of a tensor X with nonnegative constraints on the factors.
% This version uses the Lee & Seung multiplicative updates from
% their NMF alg... |
github | hanchengge/AirCP-master | tt_combinator.m | .m | AirCP-master/tensor_toolbox/tt_combinator.m | 12,716 | utf_8 | c75d4873d79dfc2d82fa4aa65a273093 | function [A] = tt_combinator(N,K,s1,s2)
%TT_COMBINATOR Perform basic permutation and combination samplings.
% COMBINATOR will return one of 4 different samplings on the set 1:N,
% taken K at a time. These samplings are given as follows:
%
% PERMUTATIONS WITH REPETITION/REPLACEMENT
% COMBINATOR(N,K,'p','... |
github | hanchengge/AirCP-master | import_data.m | .m | AirCP-master/tensor_toolbox/import_data.m | 1,807 | utf_8 | 8b7c322f2ba9cde0561398578ec0ce13 | function A = import_data(fname)
%IMPORT_DATA Import tensor-related data to a file.
%
% A = IMPORT_DATA(FNAME) imports an object A from the file named FNAME.
% The supported data types and formatting of the file are explained in
% EXPORT_DATA.
%
% See also TENSOR, EXPORT_DATA
%
%MATLAB Tensor Toolbox.
... |
github | hanchengge/AirCP-master | create_problem.m | .m | AirCP-master/tensor_toolbox/create_problem.m | 12,899 | utf_8 | 7d998940893bb78a66078143cfa43ae5 | function [info,params] = create_problem(varargin)
%CREATE_PROBLEM Create test problems for tensor factorizations.
%
% INFO = CREATE_PROBLEM('Param',value,...) creates a tensor factorization
% test problem. It generates a solution corresponding to a ktensor or a
% ttensor, and then it generates an example dat... |
github | hanchengge/AirCP-master | renumber.m | .m | AirCP-master/tensor_toolbox/@sptensor/private/renumber.m | 1,606 | utf_8 | b0e7ca64b6641a9f0ac50bef3e9ae5ef | function [newsubs, newsz] = renumber(subs, sz, range)
%RENUMBER indices for sptensor subsref
%
% [NEWSUBS,NEWSZ] = RENUMBER(SUBS,SZ,RANGE) takes a set of
% original subscripts SUBS with entries from a tensor of size
% SZ. All the entries in SUBS are assumed to be within the
% specified RANGE. These subscripts are t... |
github | hanchengge/AirCP-master | tucker_me.m | .m | AirCP-master/tensor_toolbox/met/tucker_me.m | 4,560 | utf_8 | 3216bec3b59aecd3b4a11c8e4c559039 | function [T, max_mem, Uinit] = tucker_me(X, R, esz, opts)
%TUCKER_ME Memory-efficient Tucker higher-order orthogonal iteration.
%
% T = TUCKER_ME(X,R,ESZ) computes the best rank(R1,R2,..,Rn)
% approximation of tensor X, according to the specified dimensions
% in vector R. ESZ specifies the number of dimensions th... |
github | hanchengge/AirCP-master | tucker_me_test.m | .m | AirCP-master/tensor_toolbox/met/tucker_me_test.m | 3,079 | utf_8 | 7cd5d5bf56ea1d8a82ef189d3c71d564 | function tucker_me_test
%TUCKER_ME_TEST Very simple tests of tucker_me.
% Code by Tamara Kolda and Jimeng Sun, 2008.
%
% Based on the paper:
% T. G. Kolda and J. Sun. Scalable Tensor Decompositions for Multi-aspect
% Data Mining. In: ICDM 2008: Proceedings of the 8th IEEE International
% Conference on Data M... |
github | hanchengge/AirCP-master | ttm_me.m | .m | AirCP-master/tensor_toolbox/met/ttm_me.m | 4,085 | utf_8 | 241d426fee6a1d228fd2ca6c01db66b1 | function Y = ttm_me(X, U, edims, sdims, tflag)
%TTM_ME Memory-efficient sptensor times matrix.
%
% Y = TTM_ME(X, U, EDIMS, SDIMS, TFLAG) handles some dimensions
% elementwise and others in the standard way. Here, X is a sparse tensor
% (sptensor), U is a cell array of matrices of length ndims(X),
% EDIMS speci... |
github | hanchengge/AirCP-master | fixsigns.m | .m | AirCP-master/tensor_toolbox/@ktensor/fixsigns.m | 3,162 | utf_8 | 8a24ae047737b6b7c2b6e62347dcd2d0 | function K = fixsigns(K,K0)
%FIXSIGNS Fix sign ambiguity of a ktensor.
%
% K = FIXSIGNS(K) makes it so that the largest magnitude entries for
% each vector in each factor of K are positive, provided that the
% sign on *pairs* of vectors in a rank-1 component can be flipped.
%
% K = FIXSIGNS(K,K0) returns a vers... |
github | hanchengge/AirCP-master | datadisp.m | .m | AirCP-master/tensor_toolbox/@ktensor/datadisp.m | 3,162 | utf_8 | 0b9d29cbfc884a9dbb6eec3a8729b7b1 | function datadisp(T, dimlabels, opts)
%DATADISP Special display of a ktensor.
%
% DATADISP(T,LABELS) displays the largest positive entries of each rank-1
% factor of T using the corresponding labels. LABELS is a cell array of
% size ndims(T) such that LABELS{n} is a string cell array of length
% size(T,n).
%
%... |
github | ethz-asl/mav_control_rw-master | make_acado_solver.m | .m | mav_control_rw-master/mav_nonlinear_mpc/solver/make_acado_solver.m | 2,832 | utf_8 | 6ed89f6291bd9dd7ac815315b4f7773e | %
% This file was auto-generated using the ACADO Toolkit.
%
% While ACADO Toolkit is free software released under the terms of
% the GNU Lesser General Public License (LGPL), the generated code
% as such remains the property of the user who used ACADO Toolkit
% to generate this code. In particular, u... |
github | jolilj/SimpleMadgwickFilter-master | kalman_alg.m | .m | SimpleMadgwickFilter-master/code/kalman_alg.m | 1,109 | utf_8 | e1d07fb8c87297d7b6343514331b9255 | %% Main Kalman alg
function [mu, Sigma] = kalman_alg( mu_0, Sigma_0, z, R, Q, t )
%KALMAN Kalman algorithm
% Takes initial guess mu_0, intial variance Sigma_0, measurements z,
% covariance matrices of process noise and measurement noise R and Q
% respectively and a time vector. Runs the Kalman algorithm and retur... |
github | jolilj/SimpleMadgwickFilter-master | madgwick_simple.m | .m | SimpleMadgwickFilter-master/code/madgwick_simple.m | 1,238 | utf_8 | 65939b94e7a882791f4cc341029f119e | function [ q_est ] = madgwick_simple( q_0, omega, a, beta, t)
%MADGWICK filter algorithm without the magnetometer measurements
% q_0 - initial quaternion guess
% omega - gyroscope measurements
% a - accelerometer measurements
% beta - filter parameter
% t - time vector
%Init
%disp(omega)
N = size(omega,1);
... |
github | masbicudo/Trabalhos-UFRJ-master | l3q2.m | .m | Trabalhos-UFRJ-master/Machine Learning/lista-3/l3q2.m | 898 | utf_8 | 0b7122d96f2afd2a92635d7fcaab06d5 | clear
d1 = dlmread ('data-l3-p-1a.txt', ' ', 10, 0);
d2 = dlmread ('data-l3-p-1b.txt', ' ', 5, 0);
function retval = MLE_uniform(X)
a = min(X);
b = max(X);
retval = 1/(b-a).^length(X);
endfunction
function retval = MLE_normal(X)
N = length(X);
mu = sum(X)/N;
sigma_sq = sum((X - mu).^2)/N;
retval = ((1/s... |
github | masbicudo/Trabalhos-UFRJ-master | l3q3.m | .m | Trabalhos-UFRJ-master/Machine Learning/lista-3/l3q3.m | 1,361 | utf_8 | e961c5e9cd8cc3cb5f21cbda13109379 | clear;
d = dlmread ('data-EM.txt', ' ', 5, 0);
dh = zeros(1, length(d));
function [arg_sigma_sq, arg_mu, arg_pi] = M_step(X, Z)
A = cell(1, 2);
for i = 1:2
A{i} = X(find(Z==i));
end
N = [length(A{1}) length(A{2})];
arg_mu = [sum(A{1})/N(1) sum(A{2})/N(2)];
arg_sigma_sq = [sum((A{1} - arg_mu(1)).^2)/N(... |
github | JingWang-RU/onlinegroup-fs-master | MI.m | .m | onlinegroup-fs-master/Methods/MI.m | 4,321 | utf_8 | 3127ae99d71bdb1b3c0f8c1a104927fc | function [features] = MI(features,labels,Q,num_sel)
if any(labels==0)
labels(find(labels==0))=2;
end
if any(labels==-1)
labels(find(labels==-1))=2;
end
% function [features,weights] = MI(features,labels,Q)
% Estimates the mutual information between features and associated class labels using a quantized feature space.
%... |
github | JingWang-RU/onlinegroup-fs-master | LassoGrafting.m | .m | onlinegroup-fs-master/Methods/grafting/LassoGrafting.m | 3,552 | utf_8 | 129b002339624526c1b715d7ef3b3ce4 | function [w,wp,it] = LassoGrafting(X, y, lambda, varargin)
% function flist = LassoGrafting(X, y, lambda, varargin)
% This function computes the Least Squares parameters
% with a penalty on the L1-norm of the parameters
%
% Method used:
% The Grafting method of [Perkins et al., 2003]
% This method uses Matlab's fmi... |
github | JingWang-RU/onlinegroup-fs-master | process_options.m | .m | onlinegroup-fs-master/Methods/grafting/process_options.m | 4,394 | utf_8 | 483b50d27e3bdb68fd2903a0cab9df44 | % PROCESS_OPTIONS - Processes options passed to a Matlab function.
% This function provides a simple means of
% parsing attribute-value options. Each option is
% named by a unique string and is given a default
% value.
%
% Usage: [var1, var2, ...... |
github | JingWang-RU/onlinegroup-fs-master | my_cond_indep_fisher_z.m | .m | onlinegroup-fs-master/Methods/fast_osfs _matlab_codes/my_cond_indep_fisher_z.m | 3,736 | utf_8 | fa35dd014633a7268e7a0682876be27d | function [CI, r, p] = my_cond_indep_fisher_z(data,X, Y, S,N, alpha)
% COND_INDEP_FISHER_Z Test if X indep Y given Z using Fisher's Z test
% CI = cond_indep_fisher_z(X, Y, S, C, N, alpha)
%
% C is the covariance (or correlation) matrix
% N is the sample size
% alpha is the significance level (default: 0.05)
%
% See p133... |
github | JingWang-RU/onlinegroup-fs-master | compter_dep_2.m | .m | onlinegroup-fs-master/Methods/fast_osfs _matlab_codes/compter_dep_2.m | 2,066 | utf_8 | 51c3f0892516af9aaaca3424d14cf946 |
function [CI,dep1,p_value]=compter_dep_2(bcf,var,target,max_k, discrete, alpha, test,data)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%for a discrete data set, discrete=1, otherwise, discrete=0 for a continue data set
%test = 'chi2' for Pearson's chi2... |
github | JingWang-RU/onlinegroup-fs-master | optimal_compter_dep_2.m | .m | onlinegroup-fs-master/Methods/fast_osfs _matlab_codes/optimal_compter_dep_2.m | 2,878 | utf_8 | 33a948721e0d46ba1fa7043f334489d6 |
function [CI,dep1]=optimal_compter_dep_2(bcf,var,target,max_k, discrete, alpha, test,data)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%for a discrete data set, discrete=1, otherwise, discrete=0 for a continue data set
%if new feature X is not redunda... |
github | JingWang-RU/onlinegroup-fs-master | fast_osfs_z_k.m | .m | onlinegroup-fs-master/Methods/fast_osfs _matlab_codes/fast_osfs_z_k.m | 3,964 | utf_8 | d6d0796df3e83a779b32a67af291d3e5 | function [selected_features1,time]=fast_osfs_z_k(trainx,trainy,p1,p2)
%for continouous data,limit the size of the currrently selected feature set to k1
%input parameter:
%data1: data with all features including the class attribute
%target: the index of the class attribute (we assume the class attribute is the las... |
github | JingWang-RU/onlinegroup-fs-master | OGFS.m | .m | onlinegroup-fs-master/Methods/OGFS/OGFS.m | 3,029 | utf_8 | 568093f13ef0fdf711e626d7c552a1fa |
function [sList time_cost] = OGFS(X, Y, num_group, epsilon, gamma)
% Inputs:
%
% X - data matrix X assumed n by m, n is the number of observations, m is the
% dimension of feature space.
%
% Y - groundtruth label of data matrix X
%
% num_group - size of each group in the feature stream, assume each group
%... |
github | JingWang-RU/onlinegroup-fs-master | l2ls_learn_basis_dual.m | .m | onlinegroup-fs-master/Methods/OGFS/fast_sc/fast_sc/code/l2ls_learn_basis_dual.m | 2,282 | utf_8 | d943b19c90e15814748d824984151253 | function B = l2ls_learn_basis_dual(X, S, l2norm, Binit)
% Learning basis using Lagrange dual (with basis normalization)
%
% This code solves the following problem:
%
% minimize_B 0.5*||X - B*S||^2
% subject to ||B(:,j)||_2 <= l2norm, forall j=1...size(S,1)
%
% The detail of the algorithm is described in the... |
github | JingWang-RU/onlinegroup-fs-master | l1ls_featuresign.m | .m | onlinegroup-fs-master/Methods/OGFS/fast_sc/fast_sc/code/l1ls_featuresign.m | 7,079 | utf_8 | ed309362051a25e0af5d34273d25f81e | function Xout = l1ls_featuresign (A, Y, gamma, Xinit)
% The feature-sign search algorithm
% L1-regularized least squares problem solver
%
% This code solves the following problem:
%
% minimize_s 0.5*||y - A*x||^2 + gamma*||x||_1
%
% The detail of the algorithm is described in the following paper:
% 'Efficient Spar... |
github | JingWang-RU/onlinegroup-fs-master | sparse_coding.m | .m | onlinegroup-fs-master/Methods/OGFS/fast_sc/fast_sc/code/sparse_coding.m | 7,263 | utf_8 | 7e86eff45132381b2b55c6cca5ec72d7 | function [B S stat] = sparse_coding(X_total, num_bases, beta, sparsity_func, epsilon, num_iters, batch_size, fname_save, pars, Binit, resample_size)
% Fast sparse coding algorithms
%
% minimize_B,S 0.5*||X - B*S||^2 + beta*sum(abs(S(:)))
% subject to ||B(:,j)||_2 <= l2norm, forall j=1...size(S,1)
%
% The det... |
github | JingWang-RU/onlinegroup-fs-master | GBFS_revisit.m | .m | onlinegroup-fs-master/Methods/GBFS/GBFS_revisit.m | 1,189 | utf_8 | 63fc32d17f326104b5deefca42e22f59 | % this function is to make the GBFS to a package, and used by onlinefeature
% selection
<<<<<<< HEAD
function [feaIdx] = GBFS_jing(train_x, train_y, param_one, param_two)
=======
function [feaIdx] = GBFS_revisit(train_x, train_y, param_one, param_two)
>>>>>>> 222a11cbe2b0b8e38401a3042cd5d1e034930a93
% input data:
% xt... |
github | JingWang-RU/onlinegroup-fs-master | gbrtval.m | .m | onlinegroup-fs-master/Methods/GBFS/mex_gen/gbrtval.m | 3,338 | utf_8 | 1875b90a9fa9ac6b2058e3d3a8e21390 | %% Builds a collection of limited-depth regression trees.
%% Returns a cell array of trees and the loss value after training.
function [ensemble,loss,p] = gbrtval(X,Xv,lossNgrad,options)
% check for required arguments and outputs
if nargin < 3,
error('Too few arguments');
elseif nargin > 4,
error('Too many argu... |
github | JingWang-RU/onlinegroup-fs-master | gbrt_hack.m | .m | onlinegroup-fs-master/Methods/GBFS/mex_gen/gbrt_hack.m | 3,352 | utf_8 | 58082db15cabe724fc2b8e3699e0e4a9 | %% Builds a collection of limited-depth regression trees.
%% Returns a cell array of trees and the loss value after training.
function [ensemble,loss,p] = gbrt(X,lossNgrad,options)
% check for required arguments and outputs
if nargin < 2,
error('Too few arguments');
elseif nargin > 3,
error('Too many arguments'... |
github | JingWang-RU/onlinegroup-fs-master | softmaxloss.m | .m | onlinegroup-fs-master/Methods/GBFS/mex_gen/softmaxloss.m | 601 | utf_8 | 96123573604551b11ae284d3ab1e1d69 | %softmax generalization logistic loss for multiclass classification
function [loss,gradient] = softmaxloss(labels, preds)
% add by jing: 2015.03.16
% linearInd = sub2ind(matrixSize, rowSub, colSub)
% indices of targets
% labels: start from 1, not 0
% nsample = size(preds,1);
% nclass = length(unique(labels));
% preds ... |
github | JingWang-RU/onlinegroup-fs-master | buildtree_weight.m | .m | onlinegroup-fs-master/Methods/GBFS/mex_gen/buildtree_weight.m | 3,695 | utf_8 | 7bd73d4f6164e35eae2b43a6ed535eca | %% Greedily builds a tree layer-wise
function [tree,p] = buildtree_weight(X, Xs, Xi, y, depth, options);
% checks for required inputs.
if nargin ~= 6,
error('buildtree requires 6 arguments: X, Xs, Xi, y, depth, options');
end
% verify agreement among X, Xs, Xi, and g
% TODO
%Checks sizes of Xs, Xi, a... |
github | JingWang-RU/onlinegroup-fs-master | Demo2_CrossVal.m | .m | onlinegroup-fs-master/Methods/GBFS/mex_gen/Demo2_CrossVal.m | 528 | utf_8 | f9b87e09d0ae7d761c89f1b240915521 | %%This function finds the number of trees that minimizes the metric value.
function [trees,score] = CrossVal(X,metric,ensemble)
%Initialize score, t, and trees
score= [1:length(ensemble)];
%Minimize metric value
p = zeros(size(evalensemble(X,ensemble(1))));
for t = 1:length(ensemble);
p = evalensemble(X,ensemble(... |
github | JingWang-RU/onlinegroup-fs-master | gbrt.m | .m | onlinegroup-fs-master/Methods/GBFS/mex_gen/gbrt.m | 3,077 | utf_8 | 91b0aa685891a9f25ce5c424f7f1fba6 | %% Builds a collection of limited-depth regression trees.
%% Returns a cell array of trees and the loss value after training.
function [ensemble,loss,p] = gbrt(X,lossNgrad,options)
% check for required arguments and outputs
if nargin < 2,
error('Too few arguments');
elseif nargin > 3,
error('Too many arguments'... |
github | JingWang-RU/onlinegroup-fs-master | buildtree.m | .m | onlinegroup-fs-master/Methods/GBFS/mex_gen/buildtree.m | 3,472 | utf_8 | 5a16f431b3972610426a21b968038bc0 | %% Greedily builds a tree layer-wise
function [tree,p] = buildtree(X, Xs, Xi, y, depth, options);
% checks for required inputs.
if nargin ~= 6,
error('buildtree requires 6 arguments: X, Xs, Xi, y, depth, options');
end
% verify agreement among X, Xs, Xi, and g
% TODO
%Checks sizes of Xs, Xi, and y
... |
github | JingWang-RU/onlinegroup-fs-master | Demo2_MCL.m | .m | onlinegroup-fs-master/Methods/GBFS/mex_gen/Demo2_MCL.m | 392 | utf_8 | a3a9a2d10c4732789d60bce3e29445a5 | %softmax generalization logistic loss for multiclass classification
function [loss,gradient] = MCL(preds,labels)
% indices of targets
iy = sub2ind(size(preds),1:size(preds,1),labels')';
% loss
expreds = exp(preds); % n x k
sumexp = sum(expreds,2); % n x 1
loss = sum(1-expreds(iy)./sumexp);
% gradient
gradient = -ex... |
github | JingWang-RU/onlinegroup-fs-master | gbrtC.m | .m | onlinegroup-fs-master/Methods/GBFS/mex_gen/gbrtC.m | 3,329 | utf_8 | b2b3b7a66f990917b6ebe746d86b3795 | %% Builds a collection of limited-depth regression trees.
%% Returns a cell array of trees and the loss value after training.
function [ensemble,loss,p] = gbrt(X,lossNgrad,options)
% check for required arguments and outputs
if nargin < 2,
error('Too few arguments');
elseif nargin > 3,
error('Too many arguments'... |
github | JingWang-RU/onlinegroup-fs-master | mrmr_mid_d.m | .m | onlinegroup-fs-master/Methods/mrmr_d_matlab_src/mrmr_mid_d.m | 2,831 | utf_8 | 83cc6292ade794122c5a21bbf128e3a5 | function [fea] = mrmr_mid_d(d, f, K)
% function [fea] = mrmr_mid_d(d, f, K)
%
% The MID scheme of minimum redundancy maximal relevance (mRMR) feature selection
%
% The parameters:
% d - a N*M matrix, indicating N samples, each having M dimensions. Must be integers.
% f - a N*1 matrix (vector), indicating the class/c... |
github | JingWang-RU/onlinegroup-fs-master | mrmr_miq_d.m | .m | onlinegroup-fs-master/Methods/mrmr_d_matlab_src/mrmr_miq_d.m | 2,911 | utf_8 | 97d8e54df57f0c136815b828e147b95f | function [fea] = mrmr_miq_d(d, f, K)
% function [fea] = mrmr_miq_d(d, f, K)
%
% The MIQ scheme of minimum redundancy maximal relevance (mRMR) feature selection
%
% The parameters:
% d - a N*M matrix, indicating N samples, each having M dimensions. Must be integers.
% f - a N*1 matrix (vector), indicating the class/... |
github | JingWang-RU/onlinegroup-fs-master | nmf.m | .m | onlinegroup-fs-master/Tool/spams-matlab/src_release/nmf.m | 3,099 | utf_8 | 04975453432077d74521a3a5fe37cf74 | %
% Usage: [U [,V]]=nmf(X,param);
%
% Name: nmf
%
% Description: mexTrainDL is an efficient implementation of the
% non-negative matrix factorization technique presented in
%
% "Online Learning for Matrix Factorization and Sparse Coding"
% by Julien Mairal, Francis Bach, Jean Ponce and Guillermo Sapiro
... |
github | JingWang-RU/onlinegroup-fs-master | solverPoisson.m | .m | onlinegroup-fs-master/Tool/spams-matlab/src_release/solverPoisson.m | 1,341 | utf_8 | b2dc511094fd9dbdcc7a12a5ecb1062c | %
% Usage: [W, [optim]]=solverPoisson(Y,X,W0,param);
%
% Name: solverPoisson
%
% Description: solverPoisson solves the regularized Poisson regression
% problem for every column of Y:
%
% min_w \sum_i (x_i' w) + delta - y_i log( x_i' w + delta) + lambda psi(w) (1)
%
% delta should be positive. The method solves (... |
github | JingWang-RU/onlinegroup-fs-master | nnsc.m | .m | onlinegroup-fs-master/Tool/spams-matlab/src_release/nnsc.m | 2,793 | utf_8 | 0da189ba9a2a70fb4b6a3f1c39dc3b30 | %
% Usage: [U [,V]]=nnsc(X,param);
%
% Name: nmf
%
% Description: mexTrainDL is an efficient implementation of the
% non-negative sparse coding technique presented in
%
% "Online Learning for Matrix Factorization and Sparse Coding"
% by Julien Mairal, Francis Bach, Jean Ponce and Guillermo Sapiro
% ... |
github | JingWang-RU/onlinegroup-fs-master | nmf.m | .m | onlinegroup-fs-master/Tool/spams-matlab/build/nmf.m | 3,099 | utf_8 | 04975453432077d74521a3a5fe37cf74 | %
% Usage: [U [,V]]=nmf(X,param);
%
% Name: nmf
%
% Description: mexTrainDL is an efficient implementation of the
% non-negative matrix factorization technique presented in
%
% "Online Learning for Matrix Factorization and Sparse Coding"
% by Julien Mairal, Francis Bach, Jean Ponce and Guillermo Sapiro
... |
github | JingWang-RU/onlinegroup-fs-master | solverPoisson.m | .m | onlinegroup-fs-master/Tool/spams-matlab/build/solverPoisson.m | 1,341 | utf_8 | b2dc511094fd9dbdcc7a12a5ecb1062c | %
% Usage: [W, [optim]]=solverPoisson(Y,X,W0,param);
%
% Name: solverPoisson
%
% Description: solverPoisson solves the regularized Poisson regression
% problem for every column of Y:
%
% min_w \sum_i (x_i' w) + delta - y_i log( x_i' w + delta) + lambda psi(w) (1)
%
% delta should be positive. The method solves (... |
github | JingWang-RU/onlinegroup-fs-master | nnsc.m | .m | onlinegroup-fs-master/Tool/spams-matlab/build/nnsc.m | 2,793 | utf_8 | 0da189ba9a2a70fb4b6a3f1c39dc3b30 | %
% Usage: [U [,V]]=nnsc(X,param);
%
% Name: nmf
%
% Description: mexTrainDL is an efficient implementation of the
% non-negative sparse coding technique presented in
%
% "Online Learning for Matrix Factorization and Sparse Coding"
% by Julien Mairal, Francis Bach, Jean Ponce and Guillermo Sapiro
% ... |
github | hermanzdosilovic/coursera-machine-learning-master | submit.m | .m | coursera-machine-learning-master/ex1/submit.m | 1,876 | utf_8 | 8d1c467b830a89c187c05b121cb8fbfd | function submit()
addpath('./lib');
conf.assignmentSlug = 'linear-regression';
conf.itemName = 'Linear Regression with Multiple Variables';
conf.partArrays = { ...
{ ...
'1', ...
{ 'warmUpExercise.m' }, ...
'Warm-up Exercise', ...
}, ...
{ ...
'2', ...
{ 'computeCost.m... |
github | hermanzdosilovic/coursera-machine-learning-master | submitWithConfiguration.m | .m | coursera-machine-learning-master/ex1/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | hermanzdosilovic/coursera-machine-learning-master | savejson.m | .m | coursera-machine-learning-master/ex1/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | 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 | hermanzdosilovic/coursera-machine-learning-master | loadjson.m | .m | coursera-machine-learning-master/ex1/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | 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 | hermanzdosilovic/coursera-machine-learning-master | loadubjson.m | .m | coursera-machine-learning-master/ex1/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | 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: loadubjson.m 460 2015-01-... |
github | hermanzdosilovic/coursera-machine-learning-master | saveubjson.m | .m | coursera-machine-learning-master/ex1/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | 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 | hyichao/EgoFinger.HCII.SCUT-master | classification_demo.m | .m | EgoFinger.HCII.SCUT-master/Application/caffe-msra-demo/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 | dlut-dimt/LineMatching-master | linematch.m | .m | LineMatching-master/linematch.m | 3,496 | utf_8 | 990bec1d0d463748178c4275a778cf14 |
function linematch
clc;
clear;
close all;
img1='.\imgs\1_A.jpg';
img2='.\imgs\1_B.jpg';
pmfile=strcat('.\pts&lines\1ABpoint.txt');
ltxt1='.\pts&lines\1Aline.txt';
ltxt2='.\pts&lines\1Bline.txt';
disp(' Reading files and preparing...');
[lines1, pointlist1]=paras(img1,ltxt1);
[lines2, pointlist2]=p... |
github | dlut-dimt/LineMatching-master | sameside.m | .m | LineMatching-master/sameside.m | 406 | utf_8 | c841e76d91fe46c2dc1130652a1cb584 |
function ss=sameside(line,point1,point2)
ss = false;
if isnan(point2(1)) || isnan(point2(2))
return;
end
if (line.k~=Inf)
s1=line.k*point1(1)+line.b-point1(2);
s2=line.k*point2(1)+line.b-point2(2);
else
s1=point1(1)-line.point1(1);
s2=point2(1... |
github | dlut-dimt/LineMatching-master | charanums5.m | .m | LineMatching-master/charanums5.m | 540 | utf_8 | 98d1eed2d53c7d76b1409e2d3800b39f |
function CN=charanums5(a,b,c,d,e,f,g,h,i,j)
CN=((a*d - b*c - a*j + b*i + c*j - d*i)*(a*f - b*e - a*h + b*g + e*h - f*g)*(c*h - d*g - c*j + d*i + g*j - h*i))/...
((a*d - b*c - a*h + b*g + c*h - d*g)*(a*h - b*g - a*j + b*i + g*j - h*i)*(c*f - d*e - c*j + d*i + e*j - f*i));
end
% function CN=charanums5(p5)
% p5... |
github | dlut-dimt/LineMatching-master | getpoints.m | .m | LineMatching-master/getpoints.m | 376 | utf_8 | 8e188c7f79383dbaf893b65204ea5be8 |
function [inds]=getpoints(linenum,pointlist,except)
if~(exist('except','var'))
except=0;
end
inds=[];
len=length(pointlist);
for i=1:len
if( (~isempty(find(pointlist(i).lines==linenum)))&& ...
(isempty(find(pointlist(i).lines==except))))
... |
github | dlut-dimt/LineMatching-master | getgoodpair.m | .m | LineMatching-master/getgoodpair.m | 949 | utf_8 | ec127062cd9b736fab097c3c3fa28bf8 |
function [ind1,ind2] = getgoodpair(plines1,lines2,dist)
len1=length(plines1);
len2=length(lines2);
ind1=[];
ind2=[];
for i=1:len1
for j=1:len2
if isclose(plines1(i),lines2(j),dist)
ind1 = [ind1 plines1(i).ind];
ind2 = [ind2 j];
end
end
end
end
fu... |
github | dlut-dimt/LineMatching-master | crossproduct.m | .m | LineMatching-master/crossproduct.m | 147 | utf_8 | eb36d179cb716bcee1688ffea039c95c |
function cp=crossproduct(p1,p2,o)
% if~(exist('o','var'))
% o=[0 0];
% end
cp=(p1(1)-o(1))*(p2(2)-o(2))-(p2(1)-o(1))*(p1(2)-o(2));
end |
github | dlut-dimt/LineMatching-master | getHpoints1L.m | .m | LineMatching-master/getHpoints1L.m | 632 | utf_8 | 6b659c0ba01561bd8373d01a99be6336 |
function [p1,p2] = getHpoints1L(line1,line2,side)
minnum=10;
p1=[];p2=[];
if side == 1
[C,ind1,ind2]=intersect( line1.pleft(:,3), line2.pleft(:,3));
n=length(C);
if n>=minnum
p1= [line1.pleft(ind1,1:2)]; %line1.intsect1;line1.intsect2;
p2= [line2.pleft(ind2,1:2)];%line2.intsect1;li... |
github | dlut-dimt/LineMatching-master | intspoints.m | .m | LineMatching-master/intspoints.m | 948 | utf_8 | 93e6cd0cf86179c1dde228a22dd0b51c |
function pointlist=intspoints(lines,imsize)
len=length(lines);
k=1;
pointlist(k).point=[0 0];
pointlist(k).lines=[0 0];
for i = 1:len
for j =i+1:len
[a,b]=Itspoint(lines(i),lines(j));
if (a>0) && (a<imsize(2)) && (b>0) && (b<imsize(1))
... |
github | dlut-dimt/LineMatching-master | projline.m | .m | LineMatching-master/projline.m | 624 | utf_8 | 609679a5ca7f5e6d3608da2ab328e91a |
function [plines] = projline(H,lines)
n=length(lines);
j=1;
for i=1:n
plines(j).point1 = projpoint(H,lines(i).point1);
plines(j).point2 = projpoint(H,lines(i).point2);
if (plines(j).point2(1)~=plines(j).point1(1))
plines(j).k=(plines(j).point2(2)-plines(j).point1(2))/(plines(j).point2(1... |
github | dlut-dimt/LineMatching-master | Itspoint.m | .m | LineMatching-master/Itspoint.m | 647 | utf_8 | a1d862d6ace386a822f74e939dc5c56b |
function [X, Y]=Itspoint(line1,line2)
k1=line1.k;
k2=line2.k;
b1=line1.b;
b2=line2.b;
X=NaN;
Y=NaN;
if k1==k2
%do nothing
elseif k1~=Inf && k2~=Inf
if abs(atan((k2 - k1)/(1+ k1*k2)))> 3.14/8
X=(b2-b1)/(k1-k2);
... |
github | dlut-dimt/LineMatching-master | linegradient.m | .m | LineMatching-master/linegradient.m | 1,184 | utf_8 | afd2e6e933530ab443370939579bc357 |
function [ lines ] = linegradient( I, lines )
I = double(I);
for i=1:length(lines)
ldx=0;ldy=0;
p1=lines(i).point1;p2=lines(i).point2;
k1=lines(i).k;
b1=lines(i).b;
if k1>-1 && k1<1
for ii=min(p1(1),p2(1)):max(p1(1),p2(1))
j... |
github | dlut-dimt/LineMatching-master | addpointsnearby.m | .m | LineMatching-master/addpointsnearby.m | 2,461 | utf_8 | c4c6f21be985ba5962897fe0337360be |
function [canlines] = addpointsnearby(lines,pointlist,sublinds,charap)
canlines=lines(sublinds);
len=length(sublinds);
for i=1:len
[canlines(i).intsect1, canlines(i).intsect2] = get2imps(sublinds(i),lines,pointlist);
[canlines(i).pleft, canlines(i).pright]=addcharapsrect(lines(sublinds(i)),charap);
en... |
github | dlut-dimt/LineMatching-master | paras.m | .m | LineMatching-master/paras.m | 754 | utf_8 | 84a22eb789cfaf2b46ae1e901470a1b1 |
function [llines, pointlist]=paras(img,endpotxt)
I=imread(img);
imsize=size(I);
if numel(imsize)>2
I = rgb2gray(I);
end
points=load(endpotxt);
for i=1:length(points)
llines(i).point1=[points(i,1),points(i,2)];
llines(i).point2=[points(i,3),points(i,4)];
... |
github | RivuletStudio/rivuletpy-master | v3d2tif.m | .m | rivuletpy-master/scripts/v3d2tif.m | 5,697 | utf_8 | 2c743558719e24166ff80f037729bb0a | function v3d2tif(inpath, cropthr, rescale_ratio, nthread)
% V3DTIF Convert V3Draw files to 3D tiff. At the same time do some cropping and resizing
% v3d2tif(path2v3draw, cropthr, rescale_ratio) convert every *.v3draw in path2v3draw to .v3draw
% the boundary with voxels smaller than cropthr will be cropped
% the i... |
github | mafoti/wso2-gridlabd-master | savecase.m | .m | wso2-gridlabd-master/gridlabd/matpower/matpower40_src/savecase.m | 16,399 | utf_8 | 2fca6b1da5aeef4e7bff986e5392e3b4 | function fname_out = savecase(fname, varargin)
%SAVECASE Saves a MATPOWER case file, given a filename and the data.
% SAVECASE(FNAME, CASESTRUCT)
% SAVECASE(FNAME, CASESTRUCT, VERSION)
% SAVECASE(FNAME, BASEMVA, BUS, GEN, BRANCH)
% SAVECASE(FNAME, BASEMVA, BUS, GEN, BRANCH, GENCOST)
% SAVECASE(FNAME, BASEMVA... |
github | mafoti/wso2-gridlabd-master | qps_mips.m | .m | wso2-gridlabd-master/gridlabd/matpower/matpower40_src/qps_mips.m | 7,736 | utf_8 | 7de72ce62d90add17ac31b5d337df5a8 | function [x, f, eflag, output, lambda] = qps_mips(H, c, A, l, u, xmin, xmax, x0, opt)
%QPS_MIPS Quadratic Program Solver based on MIPS.
% [X, F, EXITFLAG, OUTPUT, LAMBDA] = ...
% QPS_MIPS(H, C, A, L, U, XMIN, XMAX, X0, OPT)
% Uses the MATLAB Interior Point Solver (MIPS) to solve the following
% QP (quadrat... |
github | mafoti/wso2-gridlabd-master | modcost.m | .m | wso2-gridlabd-master/gridlabd/matpower/matpower40_src/modcost.m | 4,331 | utf_8 | 0cc3f7cd9b98dad7570e4e5f6787f3f7 | function gencost = modcost(gencost, alpha, modtype)
%MODCOST Modifies generator costs by shifting or scaling (F or X).
% NEWGENCOST = MODCOST(GENCOST, ALPHA)
% NEWGENCOST = MODCOST(GENCOST, ALPHA, MODTYPE)
%
% For each generator cost F(X) (for real or reactive power) in
% GENCOST, this function modifies the co... |
github | mafoti/wso2-gridlabd-master | ipoptopf_solver.m | .m | wso2-gridlabd-master/gridlabd/matpower/matpower40_src/ipoptopf_solver.m | 12,302 | utf_8 | a5378b6f31541a40f193863660c64d08 | function [results, success, raw] = ipoptopf_solver(om, mpopt)
%IPOPTOPF_SOLVER Solves AC optimal power flow using MIPS.
%
% [RESULTS, SUCCESS, RAW] = IPOPTOPF_SOLVER(OM, MPOPT)
%
% Inputs are an OPF model object and a MATPOWER options vector.
%
% Outputs are a RESULTS struct, SUCCESS flag and RAW output struct.
... |
github | mafoti/wso2-gridlabd-master | qps_mips6.m | .m | wso2-gridlabd-master/gridlabd/matpower/matpower40_src/qps_mips6.m | 7,971 | utf_8 | 691254bdf566b5486b4e161dacbf23d3 | function [x, f, eflag, output, lambda] = qps_mips6(H, c, A, l, u, xmin, xmax, x0, opt)
%------------------------------ deprecated ------------------------------
% MATLAB 6.x support to be removed in a future version.
%--------------------------------------------------------------------------
%QPS_MIPS Quadratic Pr... |
github | mafoti/wso2-gridlabd-master | toggle_iflims.m | .m | wso2-gridlabd-master/gridlabd/matpower/matpower40_src/toggle_iflims.m | 12,300 | utf_8 | fc5ccd171c355820554a3888112c4473 | function mpc = toggle_iflims(mpc, on_off)
%TOGGLE_IFLIMS Enable or disable set of interface flow constraints.
% MPC = TOGGLE_IFLIMS(MPC, 'on')
% MPC = TOGGLE_IFLIMS(MPC, 'off')
%
% Enables or disables a set of OPF userfcn callbacks to implement
% interface flow limits based on a DC flow model.
%
% These callb... |
github | mafoti/wso2-gridlabd-master | loadcase.m | .m | wso2-gridlabd-master/gridlabd/matpower/matpower40_src/loadcase.m | 10,643 | utf_8 | dd17404009cad78c0fde67936ebb5f0c | function [baseMVA, bus, gen, branch, areas, gencost, info] = loadcase(casefile)
%LOADCASE Load .m or .mat case files or data struct in MATPOWER format.
%
% [BASEMVA, BUS, GEN, BRANCH, AREAS, GENCOST] = LOADCASE(CASEFILE)
% [BASEMVA, BUS, GEN, BRANCH, GENCOST] = LOADCASE(CASEFILE)
% [BASEMVA, BUS, GEN, BRANCH] =... |
github | mafoti/wso2-gridlabd-master | toggle_reserves.m | .m | wso2-gridlabd-master/gridlabd/matpower/matpower40_src/toggle_reserves.m | 19,865 | utf_8 | 13746a3a56e3e682eb2b50552782ab42 | function mpc = toggle_reserves(mpc, on_off)
%TOGGLE_RESERVES Enable or disable fixed reserve requirements.
% MPC = TOGGLE_RESERVES(MPC, 'on')
% MPC = TOGGLE_RESERVES(MPC, 'off')
%
% Enables or disables a set of OPF userfcn callbacks to implement
% co-optimization of reserves with fixed zonal reserve requirement... |
github | mafoti/wso2-gridlabd-master | t_mips.m | .m | wso2-gridlabd-master/gridlabd/matpower/matpower40_src/t/t_mips.m | 11,634 | utf_8 | 60bcf8e8ffa947c06e42ae900bd9d92c | function t_mips(quiet)
%T_MIPS Tests of MIPS NLP solver.
% MIPS
% $Id: t_mips.m 4738 2014-07-03 00:55:39Z dchassin $
% by Ray Zimmerman, PSERC Cornell
% Copyright (c) 2010 by Power System Engineering Research Center (PSERC)
%
% This file is part of MIPS.
% See http://www.pserc.cornell.edu/matpower/ for mo... |
github | mafoti/wso2-gridlabd-master | t_mips6.m | .m | wso2-gridlabd-master/gridlabd/matpower/matpower40_src/t/t_mips6.m | 11,835 | utf_8 | 34d5487abc5ace5919ae32a1037bf997 | function t_mips6(quiet)
%------------------------------ deprecated ------------------------------
% MATLAB 6.x support to be removed in a future version.
%--------------------------------------------------------------------------
%T_MIPS6 Tests of MIPS NLP solver (for MATLAB 6).
% MIPS
% $Id: t_mips6.m 4738 2... |
github | mafoti/wso2-gridlabd-master | pricelimits.m | .m | wso2-gridlabd-master/gridlabd/matpower/matpower40_src/extras/smartmarket/pricelimits.m | 2,681 | utf_8 | f4146f03c6285350a5de6e0eafd33feb | function lim = pricelimits(lim, haveQ)
%PRICELIMITS Fills in a struct with default values for offer/bid limits.
% LIM = PRICELIMITS(LIM, HAVEQ)
% The final structure looks like:
% LIM.P.min_bid - bids below this are withheld
% .max_offer - offers above this are withheld
% ... |
github | mafoti/wso2-gridlabd-master | auction.m | .m | wso2-gridlabd-master/gridlabd/matpower/matpower40_src/extras/smartmarket/auction.m | 12,390 | utf_8 | cd9ea30ef0a97c15f25dc4de3822f2f8 | function [co, cb] = auction(offers, bids, auction_type, limit_prc, gtee_prc)
%AUCTION Clear auction based on OPF results (qty's and lambdas).
% [CO, CB] = AUCTION(OFFERS, BIDS, AUCTION_TYPE, LIMIT_PRC, GTEE_PRC)
% Clears a set of BIDS and OFFERS based on the results of an OPF, where the
% pricing is adjusted for... |
github | mafoti/wso2-gridlabd-master | off2case.m | .m | wso2-gridlabd-master/gridlabd/matpower/matpower40_src/extras/smartmarket/off2case.m | 16,319 | utf_8 | 923d5a1f95b06b0f4244e870a3b62ac2 | function [gen, gencost] = off2case(gen, gencost, offers, bids, lim)
%OFF2CASE Updates case variables gen & gencost from quantity & price offers.
% [GEN, GENCOST] = OFF2CASE(GEN, GENCOST, OFFERS, BIDS, LIM) updates
% GEN & GENCOST variables based on the OFFERS and BIDS supplied, where each
% is a struct (or BIDS ... |
github | UCL-SML/gibbs-rtss-master | print_pdf.m | .m | gibbs-rtss-master/util/print_pdf.m | 7,531 | utf_8 | adf8ab6ac275952daa44c889105a7c4c | %PRINT_PDF Prints cropped figures to pdf with fonts embedded
%
% Examples:
% print_pdf filename
% print_pdf(filename, fig_handle)
%
% This function saves a figure as a pdf nicely, without the need to specify
% multiple options. It improves on MATLAB's print command (using default
% options) in several ways:
% - ... |
github | UCL-SML/gibbs-rtss-master | gibbs_smoother.m | .m | gibbs-rtss-master/filters/gibbs_smoother.m | 5,917 | utf_8 | 4cd59eca06fe5c497ea1deea776833c6 | function [m, S, mxPred, SxPred, mzPred, SzPred, J, e1, e2] = ...
gibbs_smoother(m, S, fhandle, Q, nX, NX, biX, ghandle, R, nZ, NZ, biZ, ...
meas, f_params, g_params)
%
% Gibbs-sampling based algorithm for filtering and pre-computation of
% necessary parameters for smoothing during the forward sweep of a
% forward-b... |
github | UCL-SML/gibbs-rtss-master | ekf.m | .m | gibbs-rtss-master/filters/ekf.m | 2,439 | utf_8 | 46cc978ceb3cb6a37121a1ff02c2dfb3 | function [x, P, x1, Px, mz, Pz, J] = ekf(fstate, x, P, hmeas, z, Q, R, u)
% EKF Extended Kalman Filter for nonlinear dynamic systems
% [x, P] = ekf(f,x,P,h,z,Q,R) returns state estimate, x and state covariance, P
% for nonlinear dynamic system:
% x_k+1 = f(x_k) + w_k
% z_k = h(x_k) + v_k
% where... |
github | VIP-Group/OCD_sim-master | OCD_sim.m | .m | OCD_sim-master/OCD_sim.m | 9,181 | utf_8 | 9e616908d0bb2fed70422d4377db89ef | % =========================================================================
% -- Optimized Coordinate Descent (OCD) Massive MU-MIMO Simulator
% -------------------------------------------------------------------------
% -- (c) 2016 Christoph Studer and Michael Wu
% -- e-mail: studer@cornell.edu
% ----------------------... |
github | AndyWood91/VMAC_test-retest-master | get_details.m | .m | VMAC_test-retest-master/VMAC_programs/get_details.m | 15,428 | utf_8 | af76bc7345b00ad36c26c4fb1825ff90 | %% get_details
% input arguments
% conditions: an optional cell array to set counterbalancing. Each cell
% needs to contain the range of possible values for that condition. E.g.
% {1:4 1:3} is an experiment with two conditions, the first with 4 possible
% values and the second with 3. Default is no co... |
github | AndyWood91/VMAC_test-retest-master | update_details.m | .m | VMAC_test-retest-master/VMAC_programs/update_details.m | 1,328 | utf_8 | 992cc4584616356ec0af9f87478edf5c | %% update_details
% input arguments
% experiment: Map container created by the get_details function
% bonus_session: optional float for performance bonus. Default is 0.
% outputs
% saves experiment Map to raw_data directory
%% code
function [] = update_details(experiment, bonus_session)
... |
github | AndyWood91/VMAC_test-retest-master | runTrialsSpatial.m | .m | VMAC_test-retest-master/VMAC_programs/spatialfunctions/runTrialsSpatial.m | 15,822 | utf_8 | 5bfcb73fc3d37e2a1fa06f1f4bcd5ba5 |
function totalPay = runTrials(exptPhase)
global MainWindow scrCentre DATA datafilename
global keyCounterbal starting_total exptSession
global distract_col
global white black gray yellow
global bigMultiplier smallMultiplier
global zeroPayRT oneMSvalue
global stim_size stim_pen nf
% Andy's additions
global testing
... |
github | AndyWood91/VMAC_test-retest-master | initialInstructionsSpatial.m | .m | VMAC_test-retest-master/VMAC_programs/spatialfunctions/initialInstructionsSpatial.m | 2,327 | utf_8 | 92bf34c8d0f952b7a7baf59550ed529e | % spatial
function initialInstructions()
global MainWindow white
global scrWidth scrHeigh scrCentre % Andy
instructStr1 = 'On each trial a cross will appear, to warn you that the trial is about to start. Then a set of shapes will appear; an example is shown below.';
instructStr2 = 'Each of these shapes contains a l... |
github | AndyWood91/VMAC_test-retest-master | awareInstructionsSpatial.m | .m | VMAC_test-retest-master/VMAC_programs/spatialfunctions/awareInstructionsSpatial.m | 2,271 | utf_8 | 416bd41089055baecf34a9f6c65fd3bb |
function awareInstructions()
global bigMultiplier
instructStr1 = ['During this experiment, whether each trial was a \n"', num2str(bigMultiplier),' x bonus" trial or not was determined by the colour of the coloured circle that appeared on that trial. When certain colours appeared in the display, it would be a \n"', n... |
github | AndyWood91/VMAC_test-retest-master | exptInstructionsSpatial.m | .m | VMAC_test-retest-master/VMAC_programs/spatialfunctions/exptInstructionsSpatial.m | 7,590 | utf_8 | 8497d6635252ca5a6653e90867e30236 | function exptInstructions
global MainWindow white
global oneMSvalue zeroPayRT
global bigMultiplier smallMultiplier
global centOrCents
global instrCondition
global softTimeoutDurationLate
global experiment starting_total % Andy
global scrWidth scrHeight scrCentre % Andy
instructStr1 = 'The rest of this experiment ... |
github | AndyWood91/VMAC_test-retest-master | showInstructions1.m | .m | VMAC_test-retest-master/VMAC_programs/RSVP_functions/showInstructions1.m | 2,312 | utf_8 | ea1998ddf49345f9d0943736a3a486ff | % rsvp
function showInstructions1
global MainWindow targetImages bColour white
global screenWidth
% set up instructions window
[instructions_window, ~] = Screen('OpenOffscreenWindow', MainWindow, bColour);
Screen('TextFont', instructions_window, 'Segoe UI');
Screen('TextStyle', instructions_window, 0);
image_height... |
github | AndyWood91/VMAC_test-retest-master | readInImages.m | .m | VMAC_test-retest-master/VMAC_programs/RSVP_functions/readInImages.m | 1,683 | utf_8 | 9922e047e3b8d76ea973bb858014e307 | %% Read in images
function [imageTexture, numImages, targetRotation] = readInImages(inputFoldername, readingTargetImages)
global MainWindow
inputFilenames = dir(inputFoldername);
testStringPos = zeros(length(inputFilenames), 1);
fullTargetRotation = zeros(length(inputFilenames), 1);
for ii = 1 : length(inputFilenam... |
github | AndyWood91/VMAC_test-retest-master | runTrials.m | .m | VMAC_test-retest-master/VMAC_programs/RSVP_functions/runTrials.m | 16,178 | utf_8 | 2e5c7315f86c6cd29066e01ecf902b66 | function [rewardPropCorrect, runningTotalPoints] = runTrials(exptPhase)
global DATA MainWindow
global bColour white screenWidth screenHeight
global soundPAhandle winSoundArray loseSoundArray
global datafilename
global rewardImages numRewardImages
global neutImages numNeutImages
global baselineImages numBaselineImages... |
github | old-NWTC/MATLAB_Toolbox-master | ReadSubDynSummary.m | .m | MATLAB_Toolbox-master/Utilities/ReadSubDynSummary.m | 4,674 | utf_8 | 4d38ea8de3904ead0cc56644d86c5ddf | function [data] = ReadSubDynSummary(fileName)
% [data] = ReadSubDynSummary(fileName)
% fileName is the SubDyn summary file to read
% data is a data structure containing the values from the summary file
fid = fopen( fileName );
if ( fid <= 0 )
error(['Could not open the summary file: ' fileName ]);
else... |
github | old-NWTC/MATLAB_Toolbox-master | SetFASTPar.m | .m | MATLAB_Toolbox-master/Utilities/SetFASTPar.m | 580 | utf_8 | 6d4ea195461856611eb8b6104ac1af03 | % Function for getting fast parameter
% In: FASTPar - Fast parameter structure
% Par - Parameter string
% Value - Value to set parameter to
% Out: Result - Function call result (1+ Success, -1 Failure)
%
% Knud A. Kragh %edited by Paul Fleming
function FASTParOut=... |
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