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 | phreeza/pyLaminaris-master | playRipple_set_TDT_tags.m | .m | pyLaminaris-master/stimuli/playRipple_set_TDT_tags.m | 4,027 | utf_8 | 9bef22e10070f237dabec98589ee2910 | function succeeded = playRipple_set_TDT_tags(varargin)
succeeded = 1;
switch nargin
case 3
handles = varargin{1};
wav = varargin{2};
nSamps = varargin{3};
case 4
handles = varargin{1};
wav = varargin{2};
nSamps = varargin{3};
useFrame... |
github | phreeza/pyLaminaris-master | playRipple_timer_callback_workForAll.m | .m | pyLaminaris-master/stimuli/playRipple_timer_callback_workForAll.m | 11,013 | utf_8 | 4c7f0fe77292cf797537194853ea9b0d | function playRipple_timer_callback_workForAll(varargin) %obj,event,handles,rig,useFrameClk
handles = varargin{3};
RX6 = getappdata(handles.playRipple,'RX6');
% enable = RX6.GetTagVal('enable');
% enable2 = RX6.GetTagVal('enable2');
% cnt_trg = RX6.GetTagVal('cnt_trg');
% bufPos = RX6.GetTagVal('bufPos');
% cou... |
github | phreeza/pyLaminaris-master | playRipple.m | .m | pyLaminaris-master/stimuli/playRipple.m | 58,551 | utf_8 | 18be6e0086a83683900e85d4c37a7c57 | function varargout = playRipple(varargin)
% PLAYRIPPLE MATLAB code for playRipple.fig
% PLAYRIPPLE, by itself, creates a new PLAYRIPPLE or raises the existing
% singleton*.
%
% H = PLAYRIPPLE returns the handle to a new PLAYRIPPLE or the handle to
% the existing singleton*.
%
% PLAYRIPPLE('CALL... |
github | phreeza/pyLaminaris-master | playRipple_timer_startfcn.m | .m | pyLaminaris-master/stimuli/playRipple_timer_startfcn.m | 3,833 | utf_8 | 2a3cfcc5efe4037fb0bb8f380391c7cc | function playRipple_timer_startfcn(obj,event,handles)
%
% setappdata(handles.playRipple,'waitForTrg',1);
% setappdata(handles.playRipple,'nStimPlayed',0);
% nPresent = getappdata(handles.playRipple,'nPresent');
% report_status(handles,sprintf('Waiting for trigger, stim 1 / %d... |
github | phreeza/pyLaminaris-master | playRipple_reset_atten.m | .m | pyLaminaris-master/stimuli/playRipple_reset_atten.m | 567 | utf_8 | 8147c3512178eec8dbe9fcd6f21185e7 | function succeeded = playRipple_reset_atten(handles)
% idx: which stim in stimSeq to play
succeeded = 1;
atten = 120;
PA5 = getappdata(handles.playRipple,'PA5');
for i = 1:length(PA5)
PA5(i).SetAtten(atten);
error = PA5(i).GetError();
if ~isempty(error)
PA5(i).Display(error,0);
rep... |
github | phreeza/pyLaminaris-master | dauChirpOAE.m | .m | pyLaminaris-master/stimuli/dauChirpOAE.m | 5,999 | utf_8 | 685d193a892ebc60614e58a912d73e87 | % [chirpsig] = dauChirpOAE(fs, c, a, fRange)
% OR
% [chirpsig] = dauChirpOAE(fs, c, a, fRange, plotMe)
%
% This function generates the "O-chirp" from Fobel and Dau (2004). Note
% that the equations are generalized such that users can modify the primary
% parameters, e.g. see the first footno... |
github | neilbanas/coltrane-master | coltranePopulation.m | .m | coltrane-master/coltranePopulation.m | 6,904 | utf_8 | 7d3aaa27d9a5c5d81cd9d4b1dbe27593 | function [pop,popts] = coltranePopulation(forcing,p);
% pop = coltranePopulation(forcing, p);
% [pop,popts] = coltranePopulation(forcing, p);
%
% v2.0 of the Coltrane model. This has diverged significantly from the
% Coltrane 1.0 model in Banas et al., Front. Mar. Res., 2016.
%
% forcing is a structure specifying a si... |
github | neilbanas/coltrane-master | coltraneEnsemble.m | .m | coltrane-master/coltraneEnsemble.m | 4,545 | utf_8 | 569cd578ebd7104c3a6293573addbcad | function coltraneEnsemble(outfileBasename,P,expts,p0);
% set up for saving output ------------
dirname = [outfileBasename '_output/'];
if ~exist(dirname,'dir'), mkdir(dirname); end
summaryFile = [dirname 'summary.mat'];
interimFile = [dirname 'summary-interim.mat'];
allFile = [dirname 'allCohortsAndStrategies.mat'];
... |
github | joe-of-all-trades/struct2xml-master | struct2xml.m | .m | struct2xml-master/struct2xml.m | 7,304 | utf_8 | 5a894dfd36e227395e7964f4049882a2 | function varargout = struct2xml( s, varargin )
%Convert a MATLAB structure into a xml file
% [ ] = struct2xml( s, file )
% xml = struct2xml( s )
%
% A structure containing:
% s.XMLname.Attributes.attrib1 = "Some value";
% s.XMLname.Element.Text = "Some text";
% s.XMLname.DifferentElement{1}.Attributes.attrib2 = "2";
%... |
github | nilais/CT-HMM-master | CTHMM_sim_set_syn_data_config.m | .m | CT-HMM-master/simulation/CTHMM_sim_set_syn_data_config.m | 2,128 | utf_8 | c3050a476edaf9c2b39c923054faf79e | function CTHMM_sim_set_syn_data_config(syn_config_idx)
global syn_Q_mat;
global syn_data_settings;
%% find max qi
D = -diag(syn_Q_mat);
max_qi = max(D(:));
smallest_hold_time = 1.0 / max_qi;
% find min qi
min_qi = min(D(1:end-1));
largest_hold_time = 1.0 / min_qi;
str = sprintf('max_qi = %f, min_qi = %f, small_hold_t... |
github | nilais/CT-HMM-master | num_grad.m | .m | CT-HMM-master/common/hessian/num_grad.m | 1,190 | utf_8 | 99b34460cf10da4e2554ffef7d290cc8 | % num_grad.m
%
% [df, NFV] = num_grad(func, X, NFV, h)
%
% Function to compute the numerical gradient of an arbitrary objective
% function.
%
% Inputs:
% -> func: Function handle for which numerical derivative is to
% be obtained.
% -> X: Point of interest about which derivative is to be
% obtained.
% -> NFV: Accumu... |
github | nilais/CT-HMM-master | fun.m | .m | CT-HMM-master/common/hessian/fun.m | 679 | utf_8 | bb14d9587be21eb54d2a34618c371610 | % fun.m
%
% [y, NFV] = fun(X, NFV)
%
% Test objective function for use with the numerical gradient and Hessian
% functions, and the test script sym_hess.m.
%
% Inputs:
% -> X: Point at which to evaluate the function.
% -> NFV: Accumulator to keep track of number of function
% evaluations.
%
% Outputs:
% -> y: Value of... |
github | nilais/CT-HMM-master | num_hess.m | .m | CT-HMM-master/common/hessian/num_hess.m | 1,220 | utf_8 | 0f871e6a8194999bdbcebdcab949dc46 | % num_hess.m
%
% [H, NFV] = num_hess(func, X, NFV, h)
%
% Function to compute the numerical Hessian of an arbitrary objective
% function.
%
% Inputs:
% -> func: Function handle for which numerical Hessian is to be
% obtained.
% -> X: Point of interest about which Hessian is to be obtained.
% -> NFV: Accumulator to kee... |
github | nilais/CT-HMM-master | myProcessOptions.m | .m | CT-HMM-master/common/minConf/myProcessOptions.m | 674 | utf_8 | b94d252a960faa95a3074129247619e6 | function [varargout] = myProcessOptions(options,varargin)
% Similar to processOptions, but case insensitive and
% using a struct instead of a variable length list
options = toUpper(options);
for i = 1:2:length(varargin)
if isfield(options,upper(varargin{i}))
v = getfield(options,upper(varargin{i}));
... |
github | nilais/CT-HMM-master | minConf_TMP.m | .m | CT-HMM-master/common/minConf/minConf/minConf_TMP.m | 8,255 | utf_8 | b3de93503c5d5c2f45336949325d3ab5 | function [x,f,funEvals] = minConf_TMP(funObj,x,LB,UB,options)
% function [x,f] = minConF_BC(funObj,x,LB,UB,options)
%
% Function for using Two-Metric Projection to solve problems of the form:
% min funObj(x)
% s.t. LB_i <= x_i <= UB_i
%
% @funObj(x): function to minimize (returns gradient as second argument)
%
% ... |
github | nilais/CT-HMM-master | minConf_ADMM.m | .m | CT-HMM-master/common/minConf/minConf/minConf_ADMM.m | 1,218 | utf_8 | f0b9eb7c67d5dc46fe43defd0dec51a6 | function [x,f] = minConf_ADMM(funObj,x,funProj,options)
[verbose,numDiff,optTol,progTol,maxIter,maxProject,suffDec,corrections,adjustStep,bbInit,...
SPGoptTol,SPGprogTol,SPGiters,SPGtestOpt, rho] = ...
myProcessOptions(...
options,'verbose',2,'numDiff',0,'optTol',1e-5,'progTol',1e-9,'maxIter',500,'maxPr... |
github | nilais/CT-HMM-master | minConf_ALM.m | .m | CT-HMM-master/common/minConf/minConf/minConf_ALM.m | 2,562 | utf_8 | f70558453749eb4a963babe7d3436e26 | function [x,f] = minConf_ALM(funObj,x,A,b,lb,ub,options)
% Use the augmented Lagrangian method to solve a constrained optimization
% problem, use minConf_TMP as the subroutine for the bound-constrained
% problem
% Only adopts a linear constraint of Ax <= b and box constraints x >= lb,
% x <= ub, for simplicity for now
... |
github | nilais/CT-HMM-master | minConf_PQN.m | .m | CT-HMM-master/common/minConf/minConf/minConf_PQN.m | 8,484 | utf_8 | 61b524918e153d9f1ba3c31027d346b9 | function [x,f,funEvals] = minConf_PQN(funObj,x,funProj,options)
% function [x,f] = minConf_PQN(funObj,funProj,x,options)
%
% Function for using a limited-memory projected quasi-Newton to solve problems of the form
% min funObj(x) s.t. x in C
%
% The projected quasi-Newton sub-problems are solved the spectral projecte... |
github | nilais/CT-HMM-master | WolfeLineSearch.m | .m | CT-HMM-master/common/minConf/minFunc/WolfeLineSearch.m | 11,023 | utf_8 | e78201dab344cc6fa1a101af3e1e8eec | function [t,f_new,g_new,funEvals,H] = WolfeLineSearch(...
x,t,d,f,g,gtd,c1,c2,LS,maxLS,tolX,debug,doPlot,saveHessianComp,funObj,varargin)
%
% Bracketing Line Search to Satisfy Wolfe Conditions
%
% Inputs:
% x: starting location
% t: initial step size
% d: descent direction
% f: function value at starting lo... |
github | nilais/CT-HMM-master | minFunc_processInputOptions.m | .m | CT-HMM-master/common/minConf/minFunc/minFunc_processInputOptions.m | 3,551 | utf_8 | ea7fbcf303b9cafeca4045921adad934 |
function [verbose,verboseI,debug,doPlot,maxFunEvals,maxIter,tolFun,tolX,method,...
corrections,c1,c2,LS_init,LS,cgSolve,qnUpdate,cgUpdate,initialHessType,...
HessianModify,Fref,useComplex,numDiff,LS_saveHessianComp,...
DerivativeCheck,Damped,HvFunc,bbType,cycle,...
HessianIter,outputFcn,useMex,useNegCu... |
github | nilais/CT-HMM-master | StateSequenceAnalyze.m | .m | CT-HMM-master/decode/baseline_SSA/StateSequenceAnalyze.m | 6,704 | utf_8 | be93efb998ba4635bf75d4e4e80bcb87 | function SSARes = StateSequenceAnalyze(SSAProb)
% function SSARes = StateSequenceAnalyze(SSAProb)
%
% StateSequenceAnalyze finds all non-dominated state sequences for a given
% continuous-time Markov chain, a given start state or set of start states,
% and a given final time. Optionally, it can, more generally, find t... |
github | guslott/gvision-master | gVision.m | .m | gvision-master/gVision.m | 57,224 | utf_8 | 4b88a7dbe6302cea6b5b1d657a75ba9b | function gVision
% gVision - Video Capture Tools for Ethology
% and other Machine Vision Applications
%
% Version 1.1b - Updated for Matlab 2010b - 9/14/2010
%
% gVision is distributed under the GNU Public License
% http://www.gnu.org/licenses/gpl.txt
%
% Gus K. Lott, PhD (c)2010
% lottg@janelia.hhmi.org
... |
github | guslott/gvision-master | text2im.m | .m | gvision-master/+plugins/text2im.m | 222,850 | utf_8 | 1bf1eb13f3c72b67d4f13f375ab11156 | % https://www.mathworks.com/matlabcentral/fileexchange/19896-convert-text-to-an-image
% By Tobias Kiessling
function imtext=text2im(text)
% text2im - generates an image, containing the input text
text=text+0; % converting string into Ascii-number array
laenge=length(text);
imtext=zeros(20,18*laen... |
github | Learning-and-Intelligent-Systems/mit-ros-pkg-master | agglomerative.m | .m | mit-ros-pkg-master/branches/sandbox/cardboard/matlab/agglomerative.m | 3,593 | utf_8 | e32e1c1537c2bd6a3b0e181c61e40e5c |
function [ segmentNodes ] = agglomerative(file,thresh)
%UNTITLED Summary of this function goes here
% Detailed explanation goes here
pcd = load_pcd(file);
numPoints = length(pcd.data);
clusterList = zeros(1:numPoints);
pointsList = randperm(numPoints);
numClusters = 1;
clusterList(1)= 1;
for i = 2:numPoints
... |
github | Learning-and-Intelligent-Systems/mit-ros-pkg-master | compute_swing_cost.m | .m | mit-ros-pkg-master/branches/sandbox/fastwam/matlab/compute_swing_cost.m | 4,273 | utf_8 | 62936971826aa29822cb241ac25b8dd8 | function [C, dCdU] = compute_swing_cost(q0,dq0,dt,U,X,Y, v0_des, v_des, n_des)
%[C, dCdU] = compute_swing_cost(q0,dq0,dt,U,X,Y)
if size(v0_des,1) < 3
v0_des = v0_des';
end
if size(v_des,1) < 3
v_des = v_des';
end
if size(n_des,1) < 3
n_des = n_des';
end
[Q,dQ,A,B] = simulate_lwr(q0,dq0,dt,U,X,Y);
figure(... |
github | Learning-and-Intelligent-Systems/mit-ros-pkg-master | filter_ball_trajectory.m | .m | mit-ros-pkg-master/branches/sandbox/fastwam/matlab/filter_ball_trajectory.m | 2,669 | utf_8 | 705c87b4533bf6015a20874bb90ac467 | %function [B,B_cov,B2,B2_cov,BF] = filter_ball_trajectory(X,T,x0,track_spin)
function [B,B_cov] = filter_ball_trajectory(X,T,x0,track_spin)
%[B,B_cov] = filter_ball_trajectory(X,T,x0,track_spin)
if nargin < 4
track_spin = 0;
end
% all units are SI
r = .02;
g = 9.8;
ball_coeff_rest = .88; % coefficient of restitu... |
github | Learning-and-Intelligent-Systems/mit-ros-pkg-master | arm_inverse_kinematics.m | .m | mit-ros-pkg-master/branches/sandbox/fastwam/matlab/arm_inverse_kinematics.m | 982 | utf_8 | 890f049c4c7a7c4acaa43934c1b889fa | function JA = arm_inverse_kinematics(paddle_position, paddle_normal, JA0, joint_costs, plotting)
function d = paddle_dist_fn(joint_angles, normal_weight, plotting)
if nargin < 2
normal_weight = 0;
end
if nargin < 3
plotting = 0;
end
P = arm_kinematics(joint_angles, 7, [0,0,.1... |
github | Learning-and-Intelligent-Systems/mit-ros-pkg-master | align_point_clouds.m | .m | mit-ros-pkg-master/branches/sandbox/turntable/matlab/align_point_clouds.m | 9,009 | utf_8 | e7fd6528b797d7d88cf690e5ce74349f | function [pose poses costs] = align_point_clouds(cloud1, cloud2, q0, viewpoint, range1_full)
%pose = align_point_clouds(cloud1, cloud2) -- aligns cloud2 with cloud1:
%cloud2_aligned = cloud2*R' + t, where pose = [t,q]
if nargin < 3
q0 = [];
end
range1 = [];
if nargin >= 4
range1 = cloud_to_range_image(cloud1,... |
github | Learning-and-Intelligent-Systems/mit-ros-pkg-master | align_pcds.m | .m | mit-ros-pkg-master/branches/sandbox/turntable/matlab/align_pcds.m | 10,600 | utf_8 | 6cc9a5954b3c22a5d98165b7fdc5570e | function [pose poses costs] = align_pcds(pcd1, pcd2, q0, viewpoint, range1_full)
%pose = align_pcds(pcd1, pcd2) -- aligns cloud2 with cloud1:
%cloud2_aligned = cloud2*R' + t, where pose = [t,q]
cloud1 = [pcd1.X, pcd1.Y, pcd1.Z];
cloud2 = [pcd2.X, pcd2.Y, pcd2.Z];
%rgb1 = [pcd1.R, pcd1.G, pcd1.B];
%rgb2 = [pcd2.R, pcd2... |
github | Bodeeen/PR_Reconstruction-master | GUI.m | .m | PR_Reconstruction-master/GUI.m | 54,575 | utf_8 | 2c09867bba0788c108d3de45046b736f | function varargout = GUI(varargin)
% GUI MATLAB code for GUI.fig
% GUI, by itself, creates a new GUI or raises the existing
% singleton*.
%
% H = GUI returns the handle to a new GUI or the handle to
% the existing singleton*.
%
% GUI('CALLBACK',hObject,eventData,handles,...) calls the local
% ... |
github | Bodeeen/PR_Reconstruction-master | signal_extraction.m | .m | PR_Reconstruction-master/signal_extraction.m | 5,416 | utf_8 | 80528e39e7df2189536e08270ec31ce6 | function [central_signal, peripheral_signal] = signal_extraction(data, pattern, objp, shiftp, W)
% Given the pattern period and offset as well as the output pixel length
% and the scanning pixel length it constructs the central and peripheral
% signal frames as used in the publication: 'Nanoscopy with more than a
% hu... |
github | Bodeeen/PR_Reconstruction-master | WolfeLineSearch.m | .m | PR_Reconstruction-master/Third_party/minFunc2012/minFunc/WolfeLineSearch.m | 10,590 | utf_8 | f962bc5ae0a1e9f80202a9aaab106dab | function [t,f_new,g_new,funEvals,H] = WolfeLineSearch(...
x,t,d,f,g,gtd,c1,c2,LS_interp,LS_multi,maxLS,progTol,debug,doPlot,saveHessianComp,funObj,varargin)
%
% Bracketing Line Search to Satisfy Wolfe Conditions
%
% Inputs:
% x: starting location
% t: initial step size
% d: descent direction
% f: function v... |
github | Bodeeen/PR_Reconstruction-master | minFunc_processInputOptions.m | .m | PR_Reconstruction-master/Third_party/minFunc2012/minFunc/minFunc_processInputOptions.m | 3,936 | utf_8 | 167c0b9848cba950f05d4efec5667d66 |
function [verbose,verboseI,debug,doPlot,maxFunEvals,maxIter,optTol,progTol,method,...
corrections,c1,c2,LS_init,cgSolve,qnUpdate,cgUpdate,initialHessType,...
HessianModify,Fref,useComplex,numDiff,LS_saveHessianComp,...
Damped,HvFunc,bbType,cycle,...
HessianIter,outputFcn,useMex,useNegCurv,precFunc,...
... |
github | Liusifei/caffe-lowlevel-master | dataconfig.m | .m | caffe-lowlevel-master/matlab/caffe/scripts/dataconfig.m | 700 | utf_8 | c7ae84ead900ad0fe2074688c9464e4a | % Solver:
% patchsize: width and height in caffe
% batchsize: num in caffe, can be changed accordingly
% supporting JPG and PNG only
function Solver = dataconfig( Solver, train_path )
Solver.patchsize = 64;
Solver.batchsize = 20;
tdir = dir(fullfile(train_path, '*.jpg'));
num_jpg = length(tdir);
for m = 1:num_... |
github | Liusifei/caffe-lowlevel-master | testdataconfig.m | .m | caffe-lowlevel-master/matlab/caffe/scripts/testdataconfig.m | 340 | utf_8 | d8ad50e7392a4fe85f5dbf84d6c5c66d | % parameters should be consistent with prototxt
% patchsize: width and height in caffe
% batchsize: num in caffe, can be changed accordingly
% supporting JPG and PNG only
function Solver = testdataconfig( Solver, img )
% Solver.height = size(img, 1);
% Solver.width = size(img, 2);
Solver.patchsize = 256;
Solver... |
github | Liusifei/caffe-lowlevel-master | Gen_testing_data_v1.m | .m | caffe-lowlevel-master/matlab/caffe/scripts/Gen_testing_data_v1.m | 1,180 | utf_8 | 57e4fefe0cf98c9f28adc27fdb3dea2b | % v1 support inputs with image gradients (x and y)
% use it when u use v1 for traning
function [batch, gt] = Gen_testing_data_v1( Solver, FILTER_TYPE, TEST_IMAGE)
img = imresize(im2double(TEST_IMAGE),[Solver.patchsize, Solver.patchsize]);
batch = zeros(Solver.patchsize, Solver.patchsize, 5, Solver.batchsize);
gt =... |
github | Liusifei/caffe-lowlevel-master | Gen_testing_data_v2.m | .m | caffe-lowlevel-master/matlab/caffe/scripts/Gen_testing_data_v2.m | 1,012 | utf_8 | 53749e2f749a69761cb0a0335339b06a | % v2 support inputs without image gradients
% use it when u use v2 for traning
function [batch, gt] = Gen_testing_data_v2( Solver, FILTER_TYPE, TEST_IMAGE)
img = imresize(im2double(TEST_IMAGE),[Solver.patchsize, Solver.patchsize]);
batch = zeros(Solver.patchsize, Solver.patchsize, 3, Solver.batchsize);
gt = batch;... |
github | Liusifei/caffe-lowlevel-master | Filter_Test.m | .m | caffe-lowlevel-master/matlab/caffe/scripts/Filter_Test.m | 983 | utf_8 | 5145771b90b9217f3596e8302a73104c | % Sifei Liu, 10/04/2016
% sliu32@ucmerced.edu
% Learn any type of image filters.
% FILTER_TYPE: supports the following methods:
% 'L0', 'shock', 'wls', 'WMF', 'RTV', 'RGF'
% TEST_IMAGE: an rgb image.
% Solver: solver configures and model parameters
% this version supports LRNN_v1.prototxt; in... |
github | Liusifei/caffe-lowlevel-master | L2Loss_hardsample.m | .m | caffe-lowlevel-master/matlab/caffe/scripts/L2Loss_hardsample.m | 2,031 | utf_8 | 4b3c6bef4511073256454244788edb79 | function [delta, loss] = L2Loss_hardsample(active, gt, mode, sparse)
if ~exist('sparse','var')
sparse = 0;
end
[r,c,cha,bz] = size(active);
if size(gt,1)~= r
gt = imresize(gt,[r,c]);
end
loss = zeros(2,1);
dt = active - gt;
if sparse
alpha = 0.1;
dg = zeros(r,c,cha,bz);
for kk = 1:cha
... |
github | Liusifei/caffe-lowlevel-master | Gen_training_data_v1.m | .m | caffe-lowlevel-master/matlab/caffe/scripts/Gen_training_data_v1.m | 2,776 | utf_8 | 351a78eda85c47f2ad7f3fbdefacf43f | % v1 support inputs with image gradients (x and y)
function [batch, gt] = Gen_training_data_v1( Solver, FILTER_TYPE)
batch = single(zeros(Solver.patchsize,Solver.patchsize,5,Solver.batchsize));
gt = single(zeros(Solver.patchsize,Solver.patchsize,3,Solver.batchsize));
rng('shuffle');
idpool = randperm(Solver.train... |
github | Liusifei/caffe-lowlevel-master | Gen_training_data_v2.m | .m | caffe-lowlevel-master/matlab/caffe/scripts/Gen_training_data_v2.m | 2,655 | utf_8 | b91a520ccc30cabad4e51969a706f4c5 | % v1 support inputs with rgb only, no gradient channels
function [batch, gt] = Gen_training_data_v2( Solver, FILTER_TYPE)
batch = single(zeros(Solver.patchsize,Solver.patchsize,3,Solver.batchsize));
gt = single(zeros(Solver.patchsize,Solver.patchsize,3,Solver.batchsize));
rng('shuffle');
idpool = randperm(Solver.... |
github | Liusifei/caffe-lowlevel-master | SolverParser.m | .m | caffe-lowlevel-master/matlab/caffe/scripts/SolverParser.m | 2,136 | utf_8 | 04522e95b8a7cb3a0f4b4645f20ef766 | % parse all fields of a solver proto
% transfer all FC layers in the existing model to CONV layers (equvelent)
function [ Solver ] = SolverParser( solver_def_file, resume_file )
if ~exist(solver_def_file,'file')||isempty(solver_def_file)
error('Solver definition file %s is not found.',solver_def_file);
end
if... |
github | Liusifei/caffe-lowlevel-master | Filter_Train.m | .m | caffe-lowlevel-master/matlab/caffe/scripts/Filter_Train.m | 2,704 | utf_8 | b9fbdbf3de4c7393a9e538912810e7bc | % Sifei Liu, 10/04/2016
% sliu32@ucmerced.edu
% Learn any type of image filters.
% FILTER_TYPE: supports the following methods:
% 'L0', 'shock', 'wls', 'WMF', 'RTV', 'RGF'
% TRAINI_PATH: path of the training folder, specified by users
% Solver: solver configures and model parameters
% this ver... |
github | Liusifei/caffe-lowlevel-master | bilateralFilter.m | .m | caffe-lowlevel-master/matlab/caffe/util/bilateralFilter.m | 7,009 | utf_8 | ace2ce310c584a52bd265715323494ea | % output = bilateralFilter( data, edge, ...
% edgeMin, edgeMax, ...
% sigmaSpatial, sigmaRange, ...
% samplingSpatial, samplingRange )
%
% Bilateral and Cross-Bilateral Filter using the Bilateral Grid.
%
% Bilaterally filters the image 'd... |
github | Liusifei/caffe-lowlevel-master | L0Smoothing.m | .m | caffe-lowlevel-master/matlab/caffe/util/L0Smoothing.m | 2,408 | utf_8 | 45de678c72c7cb5ed21c9a40702b39a5 | % Distribution code Version 1.0 -- 09/23/2011 by Jiaya Jia Copyright 2011, The Chinese University of Hong Kong.
%
% The Code is created based on the method described in the following paper
% [1] "Image Smoothing via L0 Gradient Minimization", Li Xu, Cewu Lu, Yi Xu, Jiaya Jia, ACM Transactions on Graphics,
%... |
github | Liusifei/caffe-lowlevel-master | tsmooth.m | .m | caffe-lowlevel-master/matlab/caffe/util/tsmooth.m | 4,591 | utf_8 | 1fea97d5068cb872b1e2a16c43c8c0ac | function S = tsmooth(I,lambda,sigma,sharpness,maxIter)
%tsmooth - Structure Extraction from Texture via Relative Total Variation
% S = tsmooth(I, lambda, sigma, maxIter) extracts structure S from
% structure+texture input I, with smoothness weight lambda, scale
% parameter sigma and iteration number maxIter. ... |
github | Liusifei/caffe-lowlevel-master | jointWMF.m | .m | caffe-lowlevel-master/matlab/caffe/util/my_mex/jointWMF.m | 5,855 | utf_8 | 00264c798e82795b1d49f94ad175f8a8 | %
% JointWMF - Joint-Histogram Weighted Median Filter
%
% O = jointWMF(I,F,r,sigma,nI,nF,iter,weightType) filter image "I" guided
% by feature map "F". The result value of each pixel is the weighted median
% of its neigbouring pixels in a local window with radius "r". The weight
% is defined as the aff... |
github | amandajshao/Slicing-CNN-master | classification_demo.m | .m | Slicing-CNN-master/caffe-multigpu-ndconv-scnn/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 | xiaolonw/py-faster-rcnn-grasp-master | voc_eval.m | .m | py-faster-rcnn-grasp-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 | s21sm/matlab-master | draw.m | .m | matlab-master/3rd_experiemtn/draw.m | 197 | utf_8 | 02893125c5272c0d766ccf5597b0f68b |
function draw(x,y,r,points)
ang=0:0.01:2*pi;
xp=r*cos(ang);
yp=r*sin(ang);
plot(x+xp,y+yp);
axis equal
points_mat=points;
hold on
text(x,y,'*')
text(points_mat(:,1),points_mat(:,2),'o')
end |
github | ZacBlanco/cloud-ksvd-master | generateNetworkInfo.m | .m | cloud-ksvd-master/matlab/mnist-sim/generateNetworkInfo.m | 1,814 | utf_8 | 6717458937a183e49e959013468b95ff | function [ adj,W ] = generateNetworkInfo( size )
%Generates a connected, undirected graph, its adjacency matrix, and the W
adj = genNodes(size); %Ardos Renier with varying connection chances
W = genW(adj); %Metropolis Hastings Weights
end
function [ adjacencymatrix ] = genNodes( size )
P = ((2/size).^0.5)... |
github | ZacBlanco/cloud-ksvd-master | MNISTload.m | .m | cloud-ksvd-master/matlab/mnist-sim/MNISTload.m | 1,612 | utf_8 | 5936786d4624f5a6d44608e5bf4aeff9 | %% MNIST Image Loading Functions
function [images,labels] = MNISTload(images_location,labels_location)
%loads mnist data from train,t10k files, and with stanford code
%load images
images = loadMNISTImages(images_location);
%load labels
labels = loadMNISTLabels(labels_location);
end
function images = loadMNISTImages(fi... |
github | ZacBlanco/cloud-ksvd-master | CloudKSVD.m | .m | cloud-ksvd-master/matlab/mnist-sim/CloudKSVD.m | 2,538 | utf_8 | 83efd6a42546d3cc2d17f8444529edaa | %==========================================================================
% Cloud K-SVD Final %
%==========================================================================
function [nodeD,nodeX,error] = CloudKSVD(cloudY,cloudD,T0,Td,Tc,Tp)
%% Prelims
D = cloudD;
Y... |
github | ZacBlanco/cloud-ksvd-master | AddData.m | .m | cloud-ksvd-master/matlab/performance-results/AddData.m | 784 | utf_8 | c7ac2466f9f52837eddbc5505220a71c | %% Used for adding new data
function [DataMatrix,DataSignal] = AddData(DataMatrix,DataSignal,Resolution,...
Classes,Amount_per_class,Signals,tD,t0,tc,tp,run_time)
%% Formatting
Pixels = Resolution(1)*Resolution(2);
%Set each variable to an index to add to the 6D matrix
res_dim= find([36,100,256]==Pixels); %make n... |
github | MMquant/BFX-lending-bot-master | urlread2.m | .m | BFX-lending-bot-master/urlread2/urlread2.m | 14,027 | utf_8 | 280fa630d402a9bc7acb5d8b45a10daf | function [output,extras] = urlread2(urlChar,method,body,headersIn,varargin)
%urlread2 Makes HTTP requests and processes response
%
% [output,extras] = urlread2(urlChar, *method, *body, *headersIn, varargin)
%
% * indicates optional inputs that must be entered in place
%
% UNDOCUMENTED MATLAB VERSION
%
%... |
github | MMquant/BFX-lending-bot-master | main_api_call_huobi.m | .m | BFX-lending-bot-master/tradesman/main_api_call_huobi.m | 4,625 | utf_8 | 34b73bd17f41c89fdf6e1a92cc06db1f | % main function HUOBI 'http://api.huobi.com/staticmarket/ticker_btc_json.js', 'http://api.huobi.com/staticmarket/depth_btc_5.js', 'http://api.huobi.com/staticmarket/btc_kline_005_json.js', 'http://api.huobi.com/staticmarket/detail_btc_json.js'
function [response,status]=main_api_call_huobi(method,params)
default_meth... |
github | MMquant/BFX-lending-bot-master | main_api_call_bitstamp.m | .m | BFX-lending-bot-master/tradesman/main_api_call_bitstamp.m | 9,196 | utf_8 | 5d88157d09634c9940cec403cc6864d5 | % main function BITSTAMP
function [response,status]=main_api_call_bitstamp(method,params)
default_method_names={'ticker','order_book','transactions','eur_usd',...
'balance','user_transactions','open_orders','cancel_order','buy',...
'sell','withdrawal_requests','bitcoin_withdrawal',...
'bitcoin_deposit_... |
github | MMquant/BFX-lending-bot-master | main_api_call_bitfinex.m | .m | BFX-lending-bot-master/tradesman/main_api_call_bitfinex.m | 25,396 | utf_8 | b1e570811ab0113c47e01f4976594b22 | % main function BITFINEX
function [response,status]=main_api_call_bitfinex(method,params)
default_method_names={'pubticker','stats','lendbook','book',...
'trades','lends','symbols','symbols_details','new_deposit','new_order',...
'multiple_new_orders','cancel_order','cancel_multiple_orders',...
'cancel_... |
github | MMquant/BFX-lending-bot-master | main_api_call_kraken.m | .m | BFX-lending-bot-master/tradesman/main_api_call_kraken.m | 2,258 | utf_8 | 21ae413c3b54b47aaecc8c5970abdd76 | % main function KRAKEN
function [response,status]=main_api_call_kraken(method,params)
default_method_names={'pubticker','getOHLC'};
default_method_types={@kraken_pubticker,@kraken_getOHLC};
method_select=find(strcmp(method,default_method_names), 1);
if isempty(method_select)
disp('wrong method name for kraken, plea... |
github | MMquant/BFX-lending-bot-master | main_api_call_btce.m | .m | BFX-lending-bot-master/tradesman/main_api_call_btce.m | 8,247 | utf_8 | b4e77927915ab48d056ca582e1b7dc9f | % main function BTC-E
function [response,status]=main_api_call_btce(method,params)
default_method_names={'info','ticker','depth','trades','getInfo','buy','sell','ActiveOrders','OrderInfo','CancelOrder','TradeHistory','TransHistory'};
default_method_types={@btce_info,@btce_ticker,@btce_depth,@btce_trades,@btce_getInf... |
github | abdolrezat/TURBN-Turbine-Design-master | Turbine_StageDesign_v2.m | .m | TURBN-Turbine-Design-master/Turbine_StageDesign_v2.m | 42,488 | utf_8 | e812373378aee73dc1180af3800824ae | function varargout = Turbine_StageDesign_v2(varargin)
addpath('functions')
% TURBINE_STAGEDESIGN_V2 MATLAB code for Turbine_StageDesign_v2.fig
% TURBINE_STAGEDESIGN_V2, by itself, creates a new TURBINE_STAGEDESIGN_V2 or raises the existing
% singleton*.
%
% H = TURBINE_STAGEDESIGN_V2 returns the handle t... |
github | abdolrezat/TURBN-Turbine-Design-master | Turbine_Results.m | .m | TURBN-Turbine-Design-master/functions/Turbine_Results.m | 9,619 | utf_8 | 7f343dd650aa333920ee0fd3a605e441 | function varargout = Turbine_Results(varargin)
% TURBINE_RESULTS MATLAB code for Turbine_Results.fig
% TURBINE_RESULTS, by itself, creates a new TURBINE_RESULTS or raises the existing
% singleton*.
%
% H = TURBINE_RESULTS returns the handle to a new TURBINE_RESULTS or the handle to
% the existing si... |
github | balusu7/Analysis-of-latent-Hand-Behavior-master | optimizeVariance.m | .m | Analysis-of-latent-Hand-Behavior-master/optimizeVariance.m | 1,063 | utf_8 | 60878ea4ccd260fd8d335e35d2a74823 | % Finds the optimal variance for a given sparsity pattern ind
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% Cop... |
github | balusu7/Analysis-of-latent-Hand-Behavior-master | computeTradeOffCurve.m | .m | Analysis-of-latent-Hand-Behavior-master/computeTradeOffCurve.m | 12,950 | utf_8 | 744298d6100a6848acd905ec484e6f5c | % Computes the sparse PCA components using the nonlinear inverse power
% method (NIPM), as described in the paper
%
% M. Hein and T. Buehler
% An Inverse Power Method for Nonlinear Eigenproblems with Applications
% in 1-Spectral Clustering and Sparse PCA
% In Advances in Neural Information Processing Systems 23 ... |
github | balusu7/Analysis-of-latent-Hand-Behavior-master | invPow.m | .m | Analysis-of-latent-Hand-Behavior-master/invPow.m | 1,831 | utf_8 | 0eb8aca2330eef2de65c36d097a3d6d9 | % Performs one run of the inverse power method for sparse PCA as
% described in the paper
%
% M. Hein and T. Buehler
% An Inverse Power Method for Nonlinear Eigenproblems with Applications in 1-Spectral Clustering and Sparse PCA
% In Advances in Neural Information Processing Systems 23 (NIPS 2010)
% Available onl... |
github | balusu7/Analysis-of-latent-Hand-Behavior-master | adjustedVariance.m | .m | Analysis-of-latent-Hand-Behavior-master/adjustedVariance.m | 742 | utf_8 | ae6fe71ff37ea95315db1ed34dd0ebf8 | % Computes the (cumulative) adjusted variance via QR decomposition
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
%... |
github | balusu7/Analysis-of-latent-Hand-Behavior-master | kernel.m | .m | Analysis-of-latent-Hand-Behavior-master/kPCA_v3-2/kPCA_v3.1/code/kernel.m | 740 | utf_8 | 93aa687ebedb5affa5cb53b6b958c0c0 | % X: data matrix, each row is one observation, each column is one feature
% type: type of kernel, can be 'simple', 'poly', or 'gaussian'
% para: parameter for computing the 'poly' kernel, for 'simple'
% and 'gaussian' it will be ignored
% K: kernel matrix
% Copyright by Quan Wang, 2011/05/10
% ... |
github | balusu7/Analysis-of-latent-Hand-Behavior-master | kPCA_PreImage.m | .m | Analysis-of-latent-Hand-Behavior-master/kPCA_v3-2/kPCA_v3.1/code/kPCA_PreImage.m | 787 | utf_8 | 3f76e26d953238e2df0fa670f6c44475 | % y: dimensionanlity-reduced data
% eigVector: eigen-vector obtained in kPCA
% X: data matrix
% para: parameter of Gaussian kernel
% z: pre-image of y
% Copyright by Quan Wang, 2011/05/10
% Please cite: Quan Wang. Kernel Principal Component Analysis and its
% Applications in Face Recognition and Active Sh... |
github | balusu7/Analysis-of-latent-Hand-Behavior-master | kernel_NewData.m | .m | Analysis-of-latent-Hand-Behavior-master/kPCA_v3-2/kPCA_v3.1/code/kernel_NewData.m | 789 | utf_8 | ff9b6e685558848cc8cfe10bd47a53f2 | % Y: new data matrix
% X: training data matrix, each row is one observation, each column is one feature
% type: type of kernel, can be 'simple', 'poly', or 'gaussian'
% para: parameter for computing the 'poly' kernel, for 'simple'
% and 'gaussian' it will be ignored
% K: kernel matrix
% Copyright by ... |
github | balusu7/Analysis-of-latent-Hand-Behavior-master | kPCA.m | .m | Analysis-of-latent-Hand-Behavior-master/kPCA_v3-2/kPCA_v3.1/code/kPCA.m | 1,369 | utf_8 | c25f786af4b7ee018effc3d7478375e5 | % X: data matrix, each row is one observation, each column is one feature
% d: reduced dimension
% type: type of kernel, can be 'simple', 'poly', or 'gaussian'
% para: parameter for computing the 'poly' and 'gaussian' kernel,
% for 'simple' it will be ignored
% Y: dimensionanlity-reduced data
% ... |
github | balusu7/Analysis-of-latent-Hand-Behavior-master | kPCA_NewData.m | .m | Analysis-of-latent-Hand-Behavior-master/kPCA_v3-2/kPCA_v3.1/code/kPCA_NewData.m | 736 | utf_8 | b6622c4d777e5834b0af443fc253d262 | % Y: new data martix
% X: training data matrix, each row is one observation, each column is one feature
% d: reduced dimension
% type: type of kernel, can be 'simple', 'poly', or 'gaussian'
% para: parameter for computing the 'poly' and 'gaussian' kernel,
% for 'simple' it will be ignored
% Z: dimens... |
github | balusu7/Analysis-of-latent-Hand-Behavior-master | distanceMatrix.m | .m | Analysis-of-latent-Hand-Behavior-master/kPCA_v3-2/kPCA_v3.1/code/distanceMatrix.m | 482 | utf_8 | 1f518f5c744a681126a0e8002674f5b9 | % X: data matrix, each row is one observation, each column is one feature
% D: pair-wise distance matrix
% Copyright by Quan Wang, 2011/05/10
% Please cite: Quan Wang. Kernel Principal Component Analysis and its
% Applications in Face Recognition and Active Shape Models.
% arXiv:1207.3538 [cs.CV], ... |
github | balusu7/Analysis-of-latent-Hand-Behavior-master | PCA.m | .m | Analysis-of-latent-Hand-Behavior-master/kPCA_v3-2/kPCA_v3.1/code/PCA.m | 820 | utf_8 | 2caaa1e1984a5e59822044db0c8f5c33 | % X: data matrix, each row is one observation, each column is one feature
% d: reduced dimension
% Y: dimensionanlity-reduced data
% Warning: This function is not optimized for very high dimensional data!
% Copyright by Quan Wang, 2011/05/10
% Please cite: Quan Wang. Kernel Principal Component Analys... |
github | balusu7/Analysis-of-latent-Hand-Behavior-master | drawFaceModel.m | .m | Analysis-of-latent-Hand-Behavior-master/kPCA_v3-2/kPCA_v3.1/demo3/drawFaceModel.m | 838 | utf_8 | 9a55780fc454769117a04df5fbf42f47 | % This functions roughly draws the human face model of BioID database
% Copyright by Quan Wang, 2011/05/10
% Please cite: Quan Wang. Kernel Principal Component Analysis and its
% Applications in Face Recognition and Active Shape Models.
% arXiv:1207.3538 [cs.CV], 2012.
function drawFaceModel(x)
hold on;... |
github | caosisi/SIFT-master | FastHessian_interpolateExtremum.m | .m | SIFT-master/FastHessian_interpolateExtremum.m | 2,345 | utf_8 | 7e3afea65eb90c0334076201435b1ad1 | function [ipts, np]=FastHessian_interpolateExtremum(r, c, t, m, b, ipts, np)
% This function FastHessian_interpolateExtremum will ..
%
% [ipts,np] = FastHessian_interpolateExtremum( r,c,t,m,b,ipts,np )
%
% inputs,
% r :
% c :
% t :
% m :
% b :
% ipts :
% np :
%
% outputs,
% ipts... |
github | caosisi/SIFT-master | SurfDescriptor_GetDescriptor.m | .m | SIFT-master/SurfDescriptor_GetDescriptor.m | 3,453 | utf_8 | 8a3b7ae401248e32da4c680c0a8ddbb3 | function descriptor=SurfDescriptor_GetDescriptor(ip, bUpright, bExtended, img, verbose)
% This function SurfDescriptor_GetDescriptor will ..
%
% [descriptor] = SurfDescriptor_GetDescriptor( ip,bUpright,bExtended,img )
%
% inputs,
% ip : Interest Point (x,y,scale, orientation)
% bUpright : If true not rotation ... |
github | caosisi/SIFT-master | FastHessian_isExtremum.m | .m | SIFT-master/FastHessian_isExtremum.m | 1,563 | utf_8 | 3018638b0d991d92cc8659eca9cc6420 | function an=FastHessian_isExtremum(r, c, t, m, b,FastHessianData)
% This function FastHessian_isExtremum will ..
%
% [an] = FastHessian_isExtremum( r,c,t,m,b,FastHessianData )
%
% inputs,
% r :
% c :
% t :
% m :
% b :
% FastHessianData :
%
% outputs,
% an :
% bounds check
layerBord... |
github | kexu935/autonomous_mobile_robot_design_course-master | unscented_kalman_filter.m | .m | autonomous_mobile_robot_design_course-master/matlab/state-estimation/unscented-kalman-filter/unscented_kalman_filter.m | 3,019 | utf_8 | 2809b098bf41c6eaa3e3dbfd9c1ac3eb | function [x,P]= unscented_kalman_filter(fstate,x,P,hmeas,z,Q,R)
%
% Syntax:
% [x,P]= unscented_kalman_filter(fstate,x,P,hmeas,z,Q,R)
% Unscented Kalman Filter implementation for nonlinear dynamic systems
% returns state estimate, x and state covariance, P
% for nonlinear dynamic system
% (for simplicity, ... |
github | kexu935/autonomous_mobile_robot_design_course-master | extended_kalman_filter.m | .m | autonomous_mobile_robot_design_course-master/matlab/state-estimation/extended-kalman-filter/extended_kalman_filter.m | 1,693 | utf_8 | e400f0f05331c86fce16a14ced478e59 | function [x,P]=extended_kalman_filter(fstate,x,P,hmeas,z,Q,R)
%
% Syntax:
% [x,P]=extended_kalman_filter(fstate,x,P,hmeas,z,Q,R)
%
% This file is inspired from: https://github.com/Piyush3dB/NN-Kalman
%
% Inputs:
% fstate : function handle for system dynamic expression f(x)
% x : "a priori" state esti... |
github | kexu935/autonomous_mobile_robot_design_course-master | rotx.m | .m | autonomous_mobile_robot_design_course-master/matlab/localization-mapping/ekf-mono-slam/ekfmonoslam/trunk/matlab_code/rotx.m | 321 | utf_8 | cfb29904a7e7c1eecb0dbc7033dea0ed | %ROTX Rotation about X axis
%
% ROTX(theta) returns a homogeneous transformation representing a
% rotation of theta about the X axis.
%
% See also ROTY, ROTZ, ROTVEC.
% Copyright (C) Peter Corke 1990
function r = rotx(t)
ct = cos(t);
st = sin(t);
r = [1 0 0 0
0 ct -st 0
0 st ct 0
0 0 0 1];
... |
github | kexu935/autonomous_mobile_robot_design_course-master | rotz.m | .m | autonomous_mobile_robot_design_course-master/matlab/localization-mapping/ekf-mono-slam/ekfmonoslam/trunk/matlab_code/rotz.m | 321 | utf_8 | 74abcbae57f9d7686e52642af857835b | %ROTZ Rotation about Z axis
%
% ROTZ(theta) returns a homogeneous transformation representing a
% rotation of theta about the X axis.
%
% See also ROTX, ROTY, ROTVEC.
% Copyright (C) Peter Corke 1990
function r = rotz(t)
ct = cos(t);
st = sin(t);
r = [ct -st 0 0
st ct 0 0
0 0 1 0
0 0 0 1];
... |
github | kexu935/autonomous_mobile_robot_design_course-master | rpy2tr.m | .m | autonomous_mobile_robot_design_course-master/matlab/localization-mapping/ekf-mono-slam/ekfmonoslam/trunk/matlab_code/rpy2tr.m | 527 | utf_8 | 6198b7af02495c30fcd9c2a29052997b | %RPY2TR Roll/pitch/yaw to homogenous transform
%
% RPY2TR([R P Y])
% RPY2TR(R,P,Y) returns a homogeneous tranformation for the specified
% roll/pitch/yaw angles. These correspond to rotations about the
% Z, X, Y axes respectively.
%
% See also TR2RPY, EUL2TR
% Copright (C) Peter Corke 1993
function r = rpy2... |
github | kexu935/autonomous_mobile_robot_design_course-master | roty.m | .m | autonomous_mobile_robot_design_course-master/matlab/localization-mapping/ekf-mono-slam/ekfmonoslam/trunk/matlab_code/roty.m | 321 | utf_8 | 1623e40d7428448c21b606b3070f767f | %ROTY Rotation about Y axis
%
% ROTY(theta) returns a homogeneous transformation representing a
% rotation of theta about the Y axis.
%
% See also ROTX, ROTZ, ROTVEC.
% Copyright (C) Peter Corke 1990
function r = roty(t)
ct = cos(t);
st = sin(t);
r = [ct 0 st 0
0 1 0 0
-st 0 ct 0
0 0 0 1];
... |
github | kexu935/autonomous_mobile_robot_design_course-master | q2tr.m | .m | autonomous_mobile_robot_design_course-master/matlab/localization-mapping/ekf-mono-slam/ekfmonoslam/trunk/matlab_code/q2tr.m | 474 | utf_8 | 1704784d7f721a73457de84aff66aeea | %Q2TR Convert unit-quaternion to homogeneous transform
%
% T = q2tr(Q)
%
% Return the rotational homogeneous transform corresponding to the unit
% quaternion Q.
%
% See also TR2Q
% Copyright (C) 1993 Peter Corke
function t = q2tr(q)
s = q(1);
x = q(2);
y = q(3);
z = q(4);
r = [ 1-2*(y^2+z^2) 2*(x... |
github | kexu935/autonomous_mobile_robot_design_course-master | tr2rpy.m | .m | autonomous_mobile_robot_design_course-master/matlab/localization-mapping/ekf-mono-slam/ekfmonoslam/trunk/matlab_code/tr2rpy.m | 670 | utf_8 | bac78cd6fa1149478a82d8b31ad19df6 | %TR2RPY Convert a homogeneous transform matrix to roll/pitch/yaw angles
%
% [A B C] = TR2RPY(TR) returns a vector of Euler angles
% corresponding to the rotational part of the homogeneous transform TR.
%
% See also RPY2TR, TR2EUL
% Copright (C) Peter Corke 1993
function rpy = tr2rpy(m)
rpy = zeros(1,3);... |
github | kexu935/autonomous_mobile_robot_design_course-master | tr2q.m | .m | autonomous_mobile_robot_design_course-master/matlab/localization-mapping/ekf-mono-slam/ekfmonoslam/trunk/matlab_code/tr2q.m | 1,213 | utf_8 | 37a09b9c332d8e25776654b3c796d711 | %TR2Q Convert homogeneous transform to a unit-quaternion
%
% Q = tr2q(T)
%
% Return a unit quaternion corresponding to the rotational part of the
% homogeneous transform T.
%
% See also Q2TR
% Copyright (C) 1993 Peter Corke
function q = tr2q(t)
q = zeros(1,4);
q(1) = sqrt(trace(t))/2;
kx = t(3,2) - t(2... |
github | kexu935/autonomous_mobile_robot_design_course-master | fast_corner_detect_12.m | .m | autonomous_mobile_robot_design_course-master/matlab/localization-mapping/ekf-mono-slam/ekfmonoslam/trunk/matlab_code/fast-matlab-src/fast_corner_detect_12.m | 126,136 | utf_8 | a893a7b42ef9722d64ec819cfb934614 | %FAST_CORNER_DETECT_12 perform an 12 point FAST corner detection.
% corners = FAST_CORNER_DETECT_12(image, threshold) performs the detection on the image
% and returns the X coordinates in corners(:,1) and the Y coordinares in corners(:,2).
%
% If you use this in published work, please cite:
% Fusing Po... |
github | kexu935/autonomous_mobile_robot_design_course-master | fast_corner_detect_9.m | .m | autonomous_mobile_robot_design_course-master/matlab/localization-mapping/ekf-mono-slam/ekfmonoslam/trunk/matlab_code/fast-matlab-src/fast_corner_detect_9.m | 177,871 | utf_8 | 23105cc2b05a91688c4a17e952fcd7e3 | %FAST_CORNER_DETECT_9 perform an 9 point FAST corner detection.
% corners = FAST_CORNER_DETECT_9(image, threshold) performs the detection on the image
% and returns the X coordinates in corners(:,1) and the Y coordinares in corners(:,2).
%
% If you use this in published work, please cite:
% Fusing Point... |
github | kexu935/autonomous_mobile_robot_design_course-master | fast_corner_detect_10.m | .m | autonomous_mobile_robot_design_course-master/matlab/localization-mapping/ekf-mono-slam/ekfmonoslam/trunk/matlab_code/fast-matlab-src/fast_corner_detect_10.m | 164,469 | utf_8 | 219987a5a165436df9ac811d9ec40fdb | %FAST_CORNER_DETECT_10 perform an 10 point FAST corner detection.
% corners = FAST_CORNER_DETECT_10(image, threshold) performs the detection on the image
% and returns the X coordinates in corners(:,1) and the Y coordinares in corners(:,2).
%
% If you use this in published work, please cite:
% Fusing Po... |
github | kexu935/autonomous_mobile_robot_design_course-master | fast_nonmax.m | .m | autonomous_mobile_robot_design_course-master/matlab/localization-mapping/ekf-mono-slam/ekfmonoslam/trunk/matlab_code/fast-matlab-src/fast_nonmax.m | 2,828 | utf_8 | d1862ded31ac358dc7d19418039cb6ea | % FAST_NONMAX perform non-maximal suppression on FAST features.
%
% nonmax = FAST_NONMAX(image, threshold, FAST_CORNER_DETECT_9(image, threshold));
% returns a list of nonmaximally suppressed corners with the X coordinate
% in nonmax(:,1) and Y in nonmax(:,2).
%
% If you use this in published work, plea... |
github | kexu935/autonomous_mobile_robot_design_course-master | fast_corner_detect_11.m | .m | autonomous_mobile_robot_design_course-master/matlab/localization-mapping/ekf-mono-slam/ekfmonoslam/trunk/matlab_code/fast-matlab-src/fast_corner_detect_11.m | 132,321 | utf_8 | ae44e523c1f863bcd9e42e13bb4612d2 | %FAST_CORNER_DETECT_11 perform an 11 point FAST corner detection.
% corners = FAST_CORNER_DETECT_11(image, threshold) performs the detection on the image
% and returns the X coordinates in corners(:,1) and the Y coordinares in corners(:,2).
%
% If you use this in published work, please cite:
% Fusing Po... |
github | BristolVisualPFT/3D_Data_Acquisition_Registration_Using_Kinects-master | distort.m | .m | 3D_Data_Acquisition_Registration_Using_Kinects-master/Double_opposing_Kinects/Registration/distort.m | 2,116 | utf_8 | 385214f0847e3a47b8963550741b1add | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% University of Bristol %%%
%%%%% Computer Science Department %%%%%
%%%=========... |
github | BristolVisualPFT/3D_Data_Acquisition_Registration_Using_Kinects-master | RGB_resize.m | .m | 3D_Data_Acquisition_Registration_Using_Kinects-master/Double_opposing_Kinects/Registration/RGB_resize.m | 2,323 | utf_8 | 35fa01029ae5f6e79ac464898e3104d9 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% University of Bristol %%%
%%%%% Computer Science Department %%%%%
%%%=========... |
github | BristolVisualPFT/3D_Data_Acquisition_Registration_Using_Kinects-master | registeration_uncoding.m | .m | 3D_Data_Acquisition_Registration_Using_Kinects-master/Double_opposing_Kinects/Registration/registeration_uncoding.m | 2,052 | utf_8 | 54a1ec425df42d7e3476601e9ec04d9f | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% University of Bristol %%%
%%%%% Computer Science Department %%%%%
%%%=========... |
github | BristolVisualPFT/3D_Data_Acquisition_Registration_Using_Kinects-master | depth2rgb.m | .m | 3D_Data_Acquisition_Registration_Using_Kinects-master/Double_opposing_Kinects/Registration/depth2rgb.m | 2,642 | utf_8 | 02f1f27efd0e1410279ee631b5559b13 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% University of Bristol %%%
%%%%% Computer Science Department %%%%%
%%%=========... |
github | BristolVisualPFT/3D_Data_Acquisition_Registration_Using_Kinects-master | Registration.m | .m | 3D_Data_Acquisition_Registration_Using_Kinects-master/Double_opposing_Kinects/Registration/Registration.m | 2,631 | utf_8 | 30d46619569d0ddaae15c9b434951d46 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% University of Bristol %%%
%%%%% Computer Science Department %%%%%
%%%=========... |
github | BristolVisualPFT/3D_Data_Acquisition_Registration_Using_Kinects-master | kinect_undistort.m | .m | 3D_Data_Acquisition_Registration_Using_Kinects-master/Double_opposing_Kinects/Registration/kinect_undistort.m | 2,316 | utf_8 | b778e8b2ea66b311d7f83c215105535d | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% University of Bristol %%%
%%%%% Computer Science Department %%%%%
%%%=========... |
github | ajuckler/AntArray-master | local_opt.m | .m | AntArray-master/src/local_opt.m | 19,704 | utf_8 | ec418077bf5dfd87537b33dd5afda17f | %LOCAL_OPT Optimizes the array arrangement using local search
%
% The behaviour of the algorithm is determined by 2 hard-coded
% parameters:
% dist_prob: distortion probability (0.01)
% th_prob: remaining probability of finding a better solution
% (0.03)
% The input arrangement i... |
github | ajuckler/AntArray-master | ga_2D.m | .m | AntArray-master/src/ga_2D.m | 28,985 | utf_8 | 4fd9b39b3bcd956bbc939998f0b4835f | %GA_2D Optimize the array arrangement using a genetic algorithm
%
% The algorithm parameters are hard-coded as follow:
% chromosome side length:
% if QUANT(see further): 1/4 of matrix size in START_POP or of
% AntArray default
% if ~QUANT: 1/2 of matrix si... |
github | ajuckler/AntArray-master | factor2.m | .m | AntArray-master/src/extern/factor2.m | 708 | utf_8 | 0ec9431d92f4836a6adb51b34f33ade8 | function a = factor2(k)
%A = FACTOR2(K)
%Returns the factors (not only the prime factors) of k, including k itself.
%This complements the usage of TMW's factor function, which returns only
%prime factors.
%E.g., >> a=factor(1365), b=factor2(1365)
% ANS: a = 3 5 7 13
% b = 1 3 5 7 13 15 2... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.