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 | kintzhao/rbpf-gmapping-master | double.m | .m | rbpf-gmapping-master/rvctools/robot/Octave/@Quaternion/double.m | 1,047 | utf_8 | 9437b4d4c74466f80c02cadf5f97b93a | % Ryan Steindl based on Robotics Toolbox for MATLAB (v6 and v9)
%
% Copyright (C) 1993-2011, by Peter I. Corke
%
% This file is part of The Robotics Toolbox for MATLAB (RTB).
%
% RTB is free software: you can redistribute it and/or modify
% it under the terms of the GNU Lesser General Public License as published by
% ... |
github | kintzhao/rbpf-gmapping-master | scale.m | .m | rbpf-gmapping-master/rvctools/robot/Octave/@Quaternion/scale.m | 1,979 | utf_8 | 727f60415a6091dddff994499196db48 |
% Ryan Steindl based on Robotics Toolbox for MATLAB (v6 and v9)
%
% Copyright (C) 1993-2011, by Peter I. Corke
%
% This file is part of The Robotics Toolbox for MATLAB (RTB).
%
% RTB is free software: you can redistribute it and/or modify
% it under the terms of the GNU Lesser General Public License as published by
%... |
github | kintzhao/rbpf-gmapping-master | mrdivide.m | .m | rbpf-gmapping-master/rvctools/robot/Octave/@Quaternion/mrdivide.m | 1,267 | utf_8 | 48371bc5e28830bbb3fa39a2da52726a | % Ryan Steindl based on Robotics Toolbox for MATLAB (v6 and v9)
%
% Copyright (C) 1993-2011, by Peter I. Corke
%
% This file is part of The Robotics Toolbox for MATLAB (RTB).
%
% RTB is free software: you can redistribute it and/or modify
% it under the terms of the GNU Lesser General Public License as published by
% ... |
github | kintzhao/rbpf-gmapping-master | qinterp.m | .m | rbpf-gmapping-master/rvctools/robot/Octave/@Quaternion/qinterp.m | 1,727 | utf_8 | 2e5be9d99ede2b9ce58a5d2b7db344fd | %QINTERP Interpolate rotations expressed by quaternion objects
%
% QI = qinterp(Q1, Q2, R)
%
% Return a unit-quaternion that interpolates between Q1 and Q2 as R moves
% from 0 to 1. This is a spherical linear interpolation (slerp) that can
% be interpretted as interpolation along a great circle arc on a sphere.
%
% If... |
github | kintzhao/rbpf-gmapping-master | char.m | .m | rbpf-gmapping-master/rvctools/robot/Octave/@Quaternion/char.m | 1,040 | utf_8 | b71a701e2d387683b513855d663de42b | %CHAR create string representation of quaternion object
% Ryan Steindl based on Robotics Toolbox for MATLAB (v6 and v9)
%
% Copyright (C) 1993-2011, by Peter I. Corke
%
% This file is part of The Robotics Toolbox for MATLAB (RTB).
%
% RTB is free software: you can redistribute it and/or modify
% it under the terms of... |
github | kintzhao/rbpf-gmapping-master | unit.m | .m | rbpf-gmapping-master/rvctools/robot/Octave/@Quaternion/unit.m | 991 | utf_8 | b080d98841b256adbca1c8a02752bd15 |
% Ryan Steindl based on Robotics Toolbox for MATLAB (v6 and v9)
%
% Copyright (C) 1993-2011, by Peter I. Corke
%
% This file is part of The Robotics Toolbox for MATLAB (RTB).
%
% RTB is free software: you can redistribute it and/or modify
% it under the terms of the GNU Lesser General Public License as published by
%... |
github | kintzhao/rbpf-gmapping-master | subsref.m | .m | rbpf-gmapping-master/rvctools/robot/Octave/@Quaternion/subsref.m | 1,930 | utf_8 | 64b5d237e94c65cd19baefc95598c973 |
% Ryan Steindl based on Robotics Toolbox for MATLAB (v6 and v9)
%
% Copyright (C) 1993-2011, by Peter I. Corke
%
% This file is part of The Robotics Toolbox for MATLAB (RTB).
%
% RTB is free software: you can redistribute it and/or modify
% it under the terms of the GNU Lesser General Public License as published by
%... |
github | kintzhao/rbpf-gmapping-master | q2tr.m | .m | rbpf-gmapping-master/rvctools/robot/Octave/@Quaternion/q2tr.m | 1,152 | utf_8 | 7b63387f00a3840099667a9f614ce068 | % Ryan Steindl based on Robotics Toolbox for MATLAB (v6 and v9)
%
% Copyright (C) 1993-2011, by Peter I. Corke
%
% This file is part of The Robotics Toolbox for MATLAB (RTB).
%
% RTB is free software: you can redistribute it and/or modify
% it under the terms of the GNU Lesser General Public License as published by
% ... |
github | kintzhao/rbpf-gmapping-master | plus.m | .m | rbpf-gmapping-master/rvctools/robot/Octave/@Quaternion/plus.m | 1,072 | utf_8 | 6fa16fb3bff1ba25f20bfea9d59927c5 | % Ryan Steindl based on Robotics Toolbox for MATLAB (v6 and v9)
%
% Copyright (C) 1993-2011, by Peter I. Corke
%
% This file is part of The Robotics Toolbox for MATLAB (RTB).
%
% RTB is free software: you can redistribute it and/or modify
% it under the terms of the GNU Lesser General Public License as published by
% ... |
github | kintzhao/rbpf-gmapping-master | mpower.m | .m | rbpf-gmapping-master/rvctools/robot/Octave/@Quaternion/mpower.m | 1,334 | utf_8 | 6bf863c884e2237c0f90c891eeed193e | % Ryan Steindl based on Robotics Toolbox for MATLAB (v6 and v9)
%
% Copyright (C) 1993-2011, by Peter I. Corke
%
% This file is part of The Robotics Toolbox for MATLAB (RTB).
%
% RTB is free software: you can redistribute it and/or modify
% it under the terms of the GNU Lesser General Public License as published by
% ... |
github | kintzhao/rbpf-gmapping-master | mtimes.m | .m | rbpf-gmapping-master/rvctools/robot/Octave/@Quaternion/mtimes.m | 2,424 | utf_8 | 35e67193c8700ca8f77d8f9b788b5005 | % Ryan Steindl based on Robotics Toolbox for MATLAB (v6 and v9)
%
% Copyright (C) 1993-2011, by Peter I. Corke
%
% This file is part of The Robotics Toolbox for MATLAB (RTB).
%
% RTB is free software: you can redistribute it and/or modify
% it under the terms of the GNU Lesser General Public License as published by
% ... |
github | kintzhao/rbpf-gmapping-master | inv.m | .m | rbpf-gmapping-master/rvctools/robot/Octave/@Quaternion/inv.m | 1,017 | utf_8 | fd6512766195091d2142c19983c3c0c0 | % Ryan Steindl based on Robotics Toolbox for MATLAB (v6 and v9)
%
% Copyright (C) 1993-2011, by Peter I. Corke
%
% This file is part of The Robotics Toolbox for MATLAB (RTB).
%
% RTB is free software: you can redistribute it and/or modify
% it under the terms of the GNU Lesser General Public License as published by
% ... |
github | kintzhao/rbpf-gmapping-master | minus.m | .m | rbpf-gmapping-master/rvctools/robot/Octave/@Quaternion/minus.m | 1,097 | utf_8 | b40b3237701218e2368ca6ab5d2e7218 | % Ryan Steindl based on Robotics Toolbox for MATLAB (v6 and v9)
%
% Copyright (C) 1993-2011, by Peter I. Corke
%
% This file is part of The Robotics Toolbox for MATLAB (RTB).
%
% RTB is free software: you can redistribute it and/or modify
% it under the terms of the GNU Lesser General Public License as published by
% ... |
github | kintzhao/rbpf-gmapping-master | display.m | .m | rbpf-gmapping-master/rvctools/robot/Octave/@Link/display.m | 992 | utf_8 | 1744ef8c7924b5841a47207003535695 | %DISPLAY display the value of a LINK object
% Ryan Steindl based on Robotics Toolbox for MATLAB (v6 and v9)
%
% Copyright (C) 1993-2011, by Peter I. Corke
%
% This file is part of The Robotics Toolbox for MATLAB (RTB).
%
% RTB is free software: you can redistribute it and/or modify
% it under the terms of the GNU Les... |
github | kintzhao/rbpf-gmapping-master | show.m | .m | rbpf-gmapping-master/rvctools/robot/Octave/@Link/show.m | 1,226 | utf_8 | c90fb8c0e8972f93ea8c30b673633d2e | %SHOW show all parameters of LINK object
%
% SHOW(link)
% Ryan Steindl based on Robotics Toolbox for MATLAB (v6 and v9)
%
% Copyright (C) 1993-2011, by Peter I. Corke
%
% This file is part of The Robotics Toolbox for MATLAB (RTB).
%
% RTB is free software: you can redistribute it and/or modify
% it under the terms of... |
github | kintzhao/rbpf-gmapping-master | subsasgn.m | .m | rbpf-gmapping-master/rvctools/robot/Octave/@Link/subsasgn.m | 2,843 | utf_8 | 9a4a1d65fc4137471a6bd218fbd2a960 | % Ryan Steindl based on Robotics Toolbox for MATLAB (v6 and v9)
%
% Copyright (C) 1993-2011, by Peter I. Corke
%
% This file is part of The Robotics Toolbox for MATLAB (RTB).
%
% RTB is free software: you can redistribute it and/or modify
% it under the terms of the GNU Lesser General Public License as published by
% ... |
github | kintzhao/rbpf-gmapping-master | char.m | .m | rbpf-gmapping-master/rvctools/robot/Octave/@Link/char.m | 2,817 | utf_8 | a0694168712bc20d14a58df87d0cce09 | % Ryan Steindl based on Robotics Toolbox for MATLAB (v6 and v9)
%
% Copyright (C) 1993-2011, by Peter I. Corke
%
% This file is part of The Robotics Toolbox for MATLAB (RTB).
%
% RTB is free software: you can redistribute it and/or modify
% it under the terms of the GNU Lesser General Public License as published by
% ... |
github | kintzhao/rbpf-gmapping-master | friction.m | .m | rbpf-gmapping-master/rvctools/robot/Octave/@Link/friction.m | 1,399 | utf_8 | fd114474c00d7a5a4850ddb1f11e11af | %FRICTION compute friction torque on the LINK object
%
% TAU = FRICTION(LINK, QD)
%
% Return the friction torque on the link moving at speed QD. Depending
% on fields in the LINK object viscous and/or Coulomb friction
% are computed.
%
% Ryan Steindl based on Robotics Toolbox for MATLAB (v6 and v9)
%
% Copyright (C) ... |
github | kintzhao/rbpf-gmapping-master | nofriction.m | .m | rbpf-gmapping-master/rvctools/robot/Octave/@Link/nofriction.m | 1,338 | utf_8 | af30540d9fe008e95184816cb1baaea8 | %NOFRICTION return link object with zero friction
%
% LINK = NOFRICTION(LINK)
%
%
% Ryan Steindl based on Robotics Toolbox for MATLAB (v6 and v9)
%
% Copyright (C) 1993-2011, by Peter I. Corke
%
% This file is part of The Robotics Toolbox for MATLAB (RTB).
%
% RTB is free software: you can redistribute it and/or mod... |
github | kintzhao/rbpf-gmapping-master | subsref.m | .m | rbpf-gmapping-master/rvctools/robot/Octave/@Link/subsref.m | 6,453 | utf_8 | 0251a70ca1dfd3e28127b24e79472d31 | % Ryan Steindl based on Robotics Toolbox for MATLAB (v6 and v9)
%
% Copyright (C) 1993-2011, by Peter I. Corke
%
% This file is part of The Robotics Toolbox for MATLAB (RTB).
%
% RTB is free software: you can redistribute it and/or modify
% it under the terms of the GNU Lesser General Public License as published by
% ... |
github | kintzhao/rbpf-gmapping-master | Link.m | .m | rbpf-gmapping-master/rvctools/robot/Octave/@Link/Link.m | 4,156 | utf_8 | 39a5ccb999393609550d38e629c54d5b | %LINK create a new LINK object
%
% A LINK object holds all information related to a robot link such as
% kinematics of the joint, rigid-body inertial parameters, motor and
% transmission parameters.
%
% LINK
% LINK(link)
%
% Create a default link, or a clone of the passed link.
%
% A = LINK(q)
%
% Compute the link tran... |
github | kintzhao/rbpf-gmapping-master | polar_sfunc.m | .m | rbpf-gmapping-master/rvctools/simulink/polar_sfunc.m | 4,897 | utf_8 | e94a62691fcfd183c87cde7beb3d91ee | function movepoint_sfunc(block)
block.NumInputPorts = 1;
block.NumOutputPorts = 1;
% Setup port properties to be inherited or dynamic
block.SetPreCompInpPortInfoToDynamic;
block.SetPreCompOutPortInfoToDynamic;
% Override input port properties
block.InputPort(1).DatatypeID = 0; % double
block.Inp... |
github | kintzhao/rbpf-gmapping-master | slplotbot.m | .m | rbpf-gmapping-master/rvctools/simulink/slplotbot.m | 1,813 | utf_8 | 59bcb601aa44c722ca1f012179a77193 | %SLPLOTBOT S-function for robot animation
%
% This is the S-function for animating the robot. It assumes input
% data u to be the joint angles q.
%
% Implemented as an S-function so as to update display at the end of
% each Simulink major integration step.
function [sys,x0,str,ts] = splotbot(t,x,u,flag, robot, fps, h... |
github | kintzhao/rbpf-gmapping-master | quadrotor_dynamics.m | .m | rbpf-gmapping-master/rvctools/simulink/quadrotor_dynamics.m | 9,700 | utf_8 | da0727924c94378980ff1f5630f5be41 |
function [sys,x0,str,ts] = quadrotor_dynamics(t,x,u,flag, quad)
% Flyer2dynamics lovingly coded by Paul Pounds, first coded 12/4/04
% A simulation of idealised X-4 Flyer II flight dynamics.
% version 2.0 2005 modified to be compatible with latest version of Matlab
% version 3.0 2006 fixed rotation matr... |
github | kintzhao/rbpf-gmapping-master | quadrotor_plot.m | .m | rbpf-gmapping-master/rvctools/simulink/quadrotor_plot.m | 6,470 | utf_8 | fc1ec32a284f9ac59ab4a09689b03a21 |
% Copyright (C) 1993-2014, by Peter I. Corke
%
% This file is part of The Robotics Toolbox for Matlab (RTB).
%
% RTB is free software: you can redistribute it and/or modify
% it under the terms of the GNU Lesser General Public License as published by
% the Free Software Foundation, either version 3 of the License, or... |
github | kintzhao/rbpf-gmapping-master | nrotor_dynamics.m | .m | rbpf-gmapping-master/rvctools/simulink/nrotor_dynamics.m | 9,893 | utf_8 | 23fda47131f8169abf23932db268fabc |
function [sys,x0,str,ts] = nrotor_dynamics(t,x,u,flag, vehicle)
% Flyer2dynamics lovingly coded by Paul Pounds, first coded 12/4/04
% A simulation of idealised X-4 Flyer II flight dynamics.
% version 2.0 2005 modified to be compatible with latest version of Matlab
% version 3.0 2006 fixed rotation matr... |
github | kintzhao/rbpf-gmapping-master | slaccel.m | .m | rbpf-gmapping-master/rvctools/simulink/slaccel.m | 1,906 | utf_8 | d3eebb9cf75ec52f5fc312348463ef87 | %SLACCEL S-function for robot acceleration
%
% This is the S-function for computing robot acceleration. It assumes input
% data u to be the vector [q qd tau].
%
% Implemented as an S-function to get around vector sizing problem with
% Simulink 4.
function [sys, x0, str, ts] = slaccel(t, x, u, flag, robot)
switch flag... |
github | saiprabhakar/DeepDriving-master | prepare_batch.m | .m | DeepDriving-master/Caffe_driving/matlab/caffe/prepare_batch.m | 1,298 | utf_8 | 68088231982895c248aef25b4886eab0 | % ------------------------------------------------------------------------
function images = prepare_batch(image_files,IMAGE_MEAN,batch_size)
% ------------------------------------------------------------------------
if nargin < 2
d = load('ilsvrc_2012_mean');
IMAGE_MEAN = d.image_mean;
end
num_images = length... |
github | saiprabhakar/DeepDriving-master | matcaffe_demo.m | .m | DeepDriving-master/Caffe_driving/matlab/caffe/matcaffe_demo.m | 3,344 | utf_8 | 669622769508a684210d164ac749a614 | function [scores, maxlabel] = matcaffe_demo(im, use_gpu)
% scores = matcaffe_demo(im, use_gpu)
%
% Demo of the matlab wrapper using the ILSVRC network.
%
% input
% im color image as uint8 HxWx3
% use_gpu 1 to use the GPU, 0 to use the CPU
%
% output
% scores 1000-dimensional ILSVRC score vector
%
% You m... |
github | metricshilab/many_IV_est_compare-master | process_options.m | .m | many_IV_est_compare-master/process_options.m | 3,812 | utf_8 | 78b80f466f80f6deb37fa92128145c4c | %% Process named arguments to a function
%
% This allows you to pass in arguments using name, value pairs
% eg func(x, y, 'u', 0, 'v', 1)
% Or you can pass in a struct with named fields
% eg S.u = 0; S.v = 1; func(x, y, S)
%
% Usage: [var1, var2, ..., varn[, unused]] = ...
% process_options(args, ...
% ... |
github | ankush-me/SynthText-master | predict_depth.m | .m | SynthText-master/prep_scripts/predict_depth.m | 3,005 | utf_8 | fefca23a5c1566fe3afcd6cfa090237f | % MATLAB script to regress a depth mask for an image.
% uses: (1) https://bitbucket.org/fayao/dcnf-fcsp/
% (2) vlfeat
% (3) matconvnet
% Author: Ankush Gupta
function predict_depth()
% setup vlfeat
run( '../libs/vlfeat-0.9.18/toolbox/vl_setup');
% setup matconvnet
dir_matConvNet='../libs/m... |
github | neerajww/sound-analysis-master | estimateLPCoeff.m | .m | sound-analysis-master/code/utilities/estimateLPCoeff.m | 475 | utf_8 | b1cd9324642f6d11f5f93b9b31fc997c | % Last edit 5 June 2019, by Neeraj Sharma (CMU)
% Modified using the template taken from Srikanth Raj (IISc)
function ret=estimateLPCoeff(sigHw)
%% input sigHw= windowed speech signal
%% output ret= LP coefficients
global params
Rm = xcorr(sigHw,sigHw,params.pAR); % symmetric autocorrelation function ... |
github | neerajww/sound-analysis-master | do_ola_frames.m | .m | sound-analysis-master/code/utilities/do_ola_frames.m | 537 | utf_8 | 6b7759c88f89046a2e0aae4993042167 | % Last edit 5 June 2019, by Neeraj Sharma (CMU)
function xola = do_ola_frames(X,len,wlen,ovlp,hop)
% ----- ola the frames
overlap = ovlp;
offset = hop;
xola = zeros(1,offset*size(X,2)+overlap);
xola(1:wlen) = X(:,1);
nframes = size(X,2);
for k = 2:nframes
xola(offset+1:... |
github | neerajww/sound-analysis-master | estimateLPCoeff2.m | .m | sound-analysis-master/code/utilities/estimateLPCoeff2.m | 488 | utf_8 | 8a58a87c80e048ea06f47cb3ac7cbabe | % Last edit 5 June 2019, by Neeraj Sharma (CMU)
% Modified using the template taken from Srikanth Raj (IISc)
function ret=estimateLPCoeff2(sigHw,pAR)
%% input sigHw= windowed speech signal
% created on 24-04-15, to take pAR as input arg
%% output ret= LP coefficients
Rm = xcorr(sigHw,sigHw,pAR); % symmetric autoco... |
github | wme7/RK-WENO-Opt-master | GetInterpOperator.m | .m | RK-WENO-Opt-master/EulerSpectrum/GetInterpOperator.m | 2,295 | utf_8 | 260212cf37762580ce3b43e327bf2743 | function Amat = GetInterpOperator( N,method,boundary )
%% GETINTERPOPERATOR Returns the matrix corresponding to an
% interpolation method that computes the
% interface fluxes from cell-centered fluxes
if (strcmp(strtrim(method),'1'))
Amat = FirstOrderUpwind(N,boundary);
else... |
github | wme7/RK-WENO-Opt-master | GetInterpOperator.m | .m | RK-WENO-Opt-master/PseudoSpectrum/GetInterpOperator.m | 2,295 | utf_8 | 260212cf37762580ce3b43e327bf2743 | function Amat = GetInterpOperator( N,method,boundary )
%% GETINTERPOPERATOR Returns the matrix corresponding to an
% interpolation method that computes the
% interface fluxes from cell-centered fluxes
if (strcmp(strtrim(method),'1'))
Amat = FirstOrderUpwind(N,boundary);
else... |
github | wme7/RK-WENO-Opt-master | switch_redraw.m | .m | RK-WENO-Opt-master/PseudoSpectrum/eigtoollib/private/switch_redraw.m | 18,650 | utf_8 | e3b797106ff2216a014fd48b9e677fc3 | function ps_data = switch_redraw(fig,cax,this_ver,ps_data)
% function ps_data = switch_redraw(fig,cax,this_ver,ps_data)
%
% Function to recompute the pseudospectra data
% Version 2.1 (Mon Mar 16 00:24:50 CDT 2009)
% Copyright 2002 - 2009 by Tom Wright; maintained by Mark Embree (embree@rice.edu)
global pause_comp
... |
github | wme7/RK-WENO-Opt-master | switch_demo.m | .m | RK-WENO-Opt-master/PseudoSpectrum/eigtoollib/private/switch_demo.m | 2,556 | utf_8 | 9044000ecc03372bac2d73a28288b0c7 | function ps_data = switch_demo(fig,ps_data,the_demo)
% function ps_data = switch_demo(fig,ps_data,the_demo)
%
% Function to get a demo matrix to compute the pseudospectra of
% Version 2.1 (Mon Mar 16 00:24:50 CDT 2009)
% Copyright 2002 - 2009 by Tom Wright; maintained by Mark Embree (embree@rice.edu)
%% Should we be... |
github | wme7/RK-WENO-Opt-master | ginput_eigtool.m | .m | RK-WENO-Opt-master/PseudoSpectrum/eigtoollib/private/ginput_eigtool.m | 6,026 | utf_8 | 8cf121adf4c962330a9d96c3bb9ce675 | function [out1,out2,out3] = ginput(arg1,ptr)
%GINPUT Graphical input from mouse.
% [X,Y] = GINPUT(N,PTR) gets N points from the current axes and returns
% the X- and Y-coordinates in length N vectors X and Y. The cursor
% can be positioned using a mouse (or by using the Arrow Keys on some
% systems). Data p... |
github | wme7/RK-WENO-Opt-master | create_3d_plot.m | .m | RK-WENO-Opt-master/PseudoSpectrum/eigtoollib/private/create_3d_plot.m | 2,947 | utf_8 | 84c67296dac3df3a6920d95083421de8 | function create_3d_plot(fig)
% function create_3d_plot(fig)
%
% Function to create a 3d plot from the current pseudospectra
% data. Also called when the plot options are changed using
% the menu in a 3D plot figure.
% Version 2.1 (Mon Mar 16 00:24:49 CDT 2009)
% Copyright 2002 - 2009 by Tom Wright; maintained by Mark... |
github | wme7/RK-WENO-Opt-master | switch_fieldofvals.m | .m | RK-WENO-Opt-master/PseudoSpectrum/eigtoollib/private/switch_fieldofvals.m | 3,760 | utf_8 | e39e7285072e4eb654199569ab8b3653 | function ps_data = switch_fieldofvals(fig,cax,this_ver,ps_data)
% Code to plot the field of values of the matrix, based on
% Nick Higham's fv.m
% Version 2.1 (Mon Mar 16 00:24:50 CDT 2009)
% Copyright 2002 - 2009 by Tom Wright; maintained by Mark Embree (embree@rice.edu)
%% This variable is used to cancel the compu... |
github | wme7/RK-WENO-Opt-master | enable_controls.m | .m | RK-WENO-Opt-master/PseudoSpectrum/eigtoollib/private/enable_controls.m | 8,187 | utf_8 | 2146decf2eb2362a8a6b276fde876df9 | function enable_controls(fig,ps_data)
% function enable_controls(fig,ps_data)
%
% Function to enable the controls on the GUI
% Version 2.1 (Mon Mar 16 00:24:49 CDT 2009)
% Copyright 2002 - 2009 by Tom Wright; maintained by Mark Embree (embree@rice.edu)
if isfield(ps_data,'projection_on'), projection_on = ps_data.p... |
github | kunegis/konect-extr-master | mkdynamic.m | .m | konect-extr-master/mkdynamic.m | 725 | utf_8 | 57c7c6bc8d5d7309f51e9fb693850733 | %if (nargin!=1)
% error ("Wrong number of arguments. Usage: octave -qf mkdynamic.m INPUTFILE");
%endif
function mkdynamic(infile)
M = load(infile); % (m*4), sorted by timestamp
n = max(max(M(:,1:2)));
timestamps = unique(M(:,4));
i=0;
c={};
for i=1:length(timestamps)
time=timestamps(i,1);
F=find(M(:,4)==time)... |
github | kunegis/konect-extr-master | clusco_single.m | .m | konect-extr-master/extr/zoo/clusco_single.m | 1,532 | utf_8 | aa7bb61840f4a355f3c55143fa81fa5d | % -*-octave-*-
function [c_vector, c] = clusco_singlw(file, a, name)
%
% Calculate clustering coefficient of graph a
%
% OUTPUT:
% c_vector: user-vector giving the clustering coefficient per user.
% c: overall coefficient
%
sum_pairs = 0;
sum_count = 0;
usum_pairs = 0;
n = size(a, 1)
c_vector = zeros(n, 1... |
github | kunegis/konect-extr-master | pred_full.m | .m | konect-extr-master/extr/zoo/pred_full.m | 1,398 | utf_8 | 50d4624c7ac36b8f41d9b5bb9bc42fd2 | % -*-octave-*-
function [] = pred_full(file, a3_test, name, u, v, threshold)
%
% Evaluation prediction accuracy
%
% file: fd to write output to
% a3_test: test matrix
% u, v: rank-reduced form of prediction matrix (u * v)
%
k_max_ = size(u, 2);
n_test = size(a3_test, 1);
count_prediction_epsilon = 0;
for k = 1:k... |
github | kunegis/konect-extr-master | troll_eval.m | .m | konect-extr-master/extr/zoo/troll_eval.m | 1,227 | utf_8 | 1f630a6bf0c3a8b4ca9703e426839aca | % -*-octave-*-
function [map] = troll_eval(file, trolls, name, trust)
%
% Evaluate a trust measure against the actual troll vector
%
nn=6;
n=size(trust, 1);
[tops, top_userids] = sort(trust);
% Since we don't know the sign of the eigenvector we calculate both the biggest and smallest
xsum=0;
xsumi=0;
troll_total ... |
github | kunegis/konect-extr-master | eval_exp.m | .m | konect-extr-master/extr/zoo/eval_exp.m | 734 | utf_8 | a566e48195ce1c5ac4b7009d16351f26 | % -*-octave-*-
%
% Evaluate parameters of exponential kernels.
%
function [] = eval_exp()
[a3_training, a, a3_test, n] = load_data();
[k_max, k_max_l, opts, opts_l] = set_sparam();
%k_max_l = 35;
file = fopen(strcat(getenv('TMP_BASE'), '.tmp'), 'w');
a_sym = a + a';
ls_sym = spdiags(abs(a_sym) * ones(n, 1), [0],... |
github | kunegis/konect-extr-master | decomp.m | .m | konect-extr-master/extr/zoo/decomp.m | 1,008 | utf_8 | 1a5b1f6df818cf815ef989ed1c929e31 | % -*-octave-*-
%
% Compute and save matrix decompositions.
%
% INPUT
% out.training
%
% OUTPUT
% out.decomp.mat
%
function [] = decomp()
ENABLE_SNORM = 0;
[k_max, k_max_sa, opts, opts_sa] = set_sparam();
[a3_training, a, a3_test, n] = load_data();
[a] = connect(a3_training, a);
a_sym = a + a';
%### SNORM
if ENA... |
github | kunegis/konect-extr-master | eval_threshold.m | .m | konect-extr-master/extr/zoo/eval_threshold.m | 630 | utf_8 | de410c922ada90a63a0e580636f01832 | % -*-octave-*-
function [] = eval_exp()
[a3_training, a, a3_test, n] = load_data();
[k_max, k_max_l, opts, opts_l] = set_sparam();
file = fopen(strcat(getenv('TMP_BASE'), '.tmp'), 'w');
eval_basic(file, a3_test);
a_sym = a + a';
[u, d] = eigs(a_sym, k_max, 'la', opts);
d = diag(exp(10^(-27/20) * diag(d)));
pr... |
github | kunegis/konect-extr-master | load_diag.m | .m | konect-extr-master/extr/zoo/load_diag.m | 200 | utf_8 | 72c88258a40a4604cd8208eb0d508c0b | % -*-octave-*-
%
% Load last last eigenvalue diagonal from logfile.
%
% Used for cases where eigs() returns a zero matrix.
%
function [d] = load_diag()
! ./save_diag
d = load('/tmp/save_diag');
|
github | kunegis/konect-extr-master | eval_basic.m | .m | konect-extr-master/extr/zoo/eval_basic.m | 158 | utf_8 | e288525927de474b8a52d5a754747fba | %# -*-octave-*-
%#
%# Basic evaluation
%#
function [] = eval_basic(file, a3_test)
fprintf (file, '#a 1 %g\n\n', sum(a3_test(:, 3)) / size(a3_test, 1));
|
github | kunegis/konect-extr-master | eval_maxit.m | .m | konect-extr-master/extr/zoo/eval_maxit.m | 731 | utf_8 | 6fdc9d8bd18ac8a4b3c91b955b799f4a | % -*-octave-*-
%
% Evaluate the maxit parameter of svds/eigs.
%
%
function [] = eval_exp()
[a3_training, a, a3_test, n] = load_data();
[k_max, k_max_l, opts, opts_l] = set_sparam();
opts.disp=1;
file = fopen(strcat(getenv('TMP_BASE'), '.tmp'), 'w');
eval_basic(file, a3_test);
a_sym = a + a';
ls_sym = spdiags(ab... |
github | kunegis/konect-extr-master | pred.m | .m | konect-extr-master/extr/zoo/pred.m | 3,312 | utf_8 | b636280bc2318721ec4433b11c1d9e9c | % -*-octave-*-
%
% Evaluate link sign prediction accuracy.
%
% Error measure is mean sign value (from -1 to +1).
%
function [] = pred()
ENABLE_BASIC = 0;
ENABLE_CONNECT = 0;
ENABLE_CONNECT2 = 0;
ENABLE_NULL = 1;
ENABLE_SNORM = 0;
ENABLE_L_SYM = 0;
ENABLE_LS_SYM = 1;
ENABLE_LS = 0;
file = fopen(str... |
github | kunegis/konect-extr-master | set_sparam.m | .m | konect-extr-master/extr/zoo/set_sparam.m | 595 | utf_8 | a2bd02d95d8f57af1d5e95ed481a9910 | % -*-octave-*-
%
% Set the parameters for eigs and svds.
%
% k: the number of dimensions
% opts: the argument to svds and eigs
%
% _sa: The variant when using the 'sa' mode of eigs/svds.
%
function [k_max, k_max_sa, opts, opts_sa] = set_sparam()
ENABLE_FAST = 0;
%#
%# Maximum reduced dimensions
%#
if ~ENABLE_... |
github | kunegis/konect-extr-master | pca_draw_one.m | .m | konect-extr-master/extr/zoo/pca_draw_one.m | 1,487 | utf_8 | 44734fed0429ab1f7a75eeaea9e38f7f | % -*-octave-*-
%
% Draw one PCA plot.
%
% PARAMETERS
% name Name of the variant
% u (n×k) Embedding to draw
% trolls Troll list
%
function pca_draw_one(name, u, trolls)
k = size(u,2)
%
% Without trolls
%
[min_x max_x] = minmax(u(:,1));
[min_y max_y] = minmax(u(:,2));
plot(u(:,1), u(:,2), '.');
axis([min_x max_... |
github | kunegis/konect-extr-master | pred_sparse.m | .m | konect-extr-master/extr/zoo/pred_sparse.m | 644 | utf_8 | 757d680a26fa04e0d2cb6dd771c9c005 | % -*-octave-*-
function [] = pred_sparse(file, a3_test, name, b)
%
% Evaluation using a sparse matrix.
%
% b is a sparse matrix that contains predictions.
%
% Evidently, methods evaluated by this function cannot be very
% sophisticated, because they give predictions only for a sparse
% subset of all user pairs.
%
sum... |
github | kunegis/konect-extr-master | sort_ud.m | .m | konect-extr-master/extr/zoo/sort_ud.m | 215 | utf_8 | 65b272437f7fac3974b143bd972ad249 | %# -*-octave-*-
%#
%# Sort result of eigs by descending absolute eigenvalues.
%#
function [u, d] = sort_ud(u, d)
[dd, permut] = sort(-abs(diag(d)));
d_diag = diag(d);
d = diag(d_diag(permut));
u = u(:, permut);
|
github | kunegis/konect-extr-master | load_data.m | .m | konect-extr-master/extr/zoo/load_data.m | 263 | utf_8 | 78fb0733ab610ea02ec21de2d97eaaa2 | % -*-octave-*-
%
% Load matrices
%
function [a3_training, a, a3_test, n] = load_data()
a3_training = load('out.training');
a3_test = load('out.test');
a = spconvert(a3_training);
[n, nr] = size(a);
n = max(n, nr);
a(n, n)= 0; % no problem because on diagonal
|
github | kunegis/konect-extr-master | lkml_labels_statistic.m | .m | konect-extr-master/extr/lkml/lkml_labels_statistic.m | 510 | utf_8 | d1b9621de20e6c652b80f478f3dc64f2 |
function labels_statistic = lkml_labels_statistic()
labels_statistic = struct();
labels_statistic.avgdegree = 'Average total degree';
labels_statistic.avgdegree1 = 'Average out-degree';
labels_statistic.avgdegree2 = 'Average in-degree';
labels_statistic.avgmultiplicity = 'Average multiplicity';
labels_statistic.avgp... |
github | kunegis/konect-extr-master | lkml_marker_styles.m | .m | konect-extr-master/extr/lkml/lkml_marker_styles.m | 93 | utf_8 | f99ef3625f1b461a82793b5726c7c730 |
function marker_styles = lkml_marker_styles()
marker_styles = { 'o'; 'x'; 's'; '^'; 'v'};
|
github | kunegis/konect-extr-master | lkml_statistic_degreediff.m | .m | konect-extr-master/extr/lkml/lkml_statistic_degreediff.m | 220 | utf_8 | adb9e866f07e51e908bffc9871918881 |
% Average indegree - outdegree
function value = lkml_statistic_degreediff(n, T, j)
d_out = sparse(T(:,1), 1, 1, n, 1);
d_in = sparse(T(:,2), 1, 1, n, 1);
d_j = d_in(j) - d_out(j);
value = mean(d_j);
|
github | kunegis/konect-extr-master | lkml_statistic_avgpagerank.m | .m | konect-extr-master/extr/lkml/lkml_statistic_avgpagerank.m | 163 | utf_8 | 148c0d1f53f071486ef3ca99667bfa1a |
% Average PageRank
function value = lkml_statistic_avgpagerank(n, T, j)
A = sparse(T(:,1), T(:,2), 1, n, n);
u = konect_pagerank(A, 0.2);
value = mean(u(j))
|
github | kunegis/konect-extr-master | lkml_statistic_avgdegree2.m | .m | konect-extr-master/extr/lkml/lkml_statistic_avgdegree2.m | 139 | utf_8 | a9e104879cd587b40789b4a77c90b31d |
% Average indegree
function value = lkml_statistic_avgdegree2(n, T, j)
d = sparse(T(:,2), 1, 1, n, 1);
d_j = d(j);
value = mean(d_j)
|
github | kunegis/konect-extr-master | lkml_statistic_avgmultiplicity.m | .m | konect-extr-master/extr/lkml/lkml_statistic_avgmultiplicity.m | 251 | utf_8 | f7919511c2f09c84087c73de63b441df |
% Average edge multiplicity between two nodes of the group
function value = lkml_statistic_avgmultiplicity(n, T, j)
jj = (j(T(:,1)) & j(T(:,2)));
T_j = T(jj,:);
A_j = sparse(T_j(:,1), T_j(:,2), 1, n, n);
[xx yy zz] = find(A_j);
value = mean(zz)
|
github | kunegis/konect-extr-master | lkml_statistic_avgdegree.m | .m | konect-extr-master/extr/lkml/lkml_statistic_avgdegree.m | 146 | utf_8 | c1f5eb310218e5a9c1c35f30f567c3ad |
% Average degree
function value = lkml_statistic_avgdegree(n, T, j)
d = sparse([T(:,1) ; T(:,2)], 1, 1, n, 1);
d_j = d(j);
value = mean(d_j)
|
github | kunegis/konect-extr-master | lkml_statistic_diameff90.m | .m | konect-extr-master/extr/lkml/lkml_statistic_diameff90.m | 267 | utf_8 | 72d18711140a20687e634e8a03126943 |
% In subgraph
function value = lkml_statistic_diameff90(n, T, j)
consts = konect_consts();
jj = (j(T(:,1)) & j(T(:,2)));
T_j = T(jj,:);
A_j = sparse(T_j(:,1), T_j(:,2), 1, n, n);
d = konect_hopdistr(A_j, consts.ASYM, [], 1);
value = konect_diameff(d, 0.9)
|
github | kunegis/konect-extr-master | lkml_labels.m | .m | konect-extr-master/extr/lkml/lkml_labels.m | 205 | utf_8 | 350ab7ce2e447aedac1a0702de70e636 |
function labels = lkml_labels()
labels = { 'Company (Most Active)'; ...
'Company (Other)'; ...
'Hobbyists'; ...
'Universities'; ...
'Research Institutions' };
|
github | kunegis/konect-extr-master | lkml_colors.m | .m | konect-extr-master/extr/lkml/lkml_colors.m | 213 | utf_8 | 5791e56d444fef53a74698b953e1c486 |
function colors = lkml_colors()
colors = { ...
[ 0 198 196 ] / 255, ...
[ 0 5 198 ] / 255, ...
[ 198 0 154 ] / 255, ...
[ 45 198 0 ] / 255, ...
[ 198 116 0 ] / 255 };
|
github | kunegis/konect-extr-master | lkml_statistic_avgdegree1.m | .m | konect-extr-master/extr/lkml/lkml_statistic_avgdegree1.m | 140 | utf_8 | f5ec2cb9ed510883b73a3092a3f290a9 |
% Average outdegree
function value = lkml_statistic_avgdegree1(n, T, j)
d = sparse(T(:,1), 1, 1, n, 1);
d_j = d(j);
value = mean(d_j)
|
github | kunegis/konect-extr-master | lkml_statistic_gini.m | .m | konect-extr-master/extr/lkml/lkml_statistic_gini.m | 163 | utf_8 | a5939ba23eb5a07b08b55cd059f78679 |
% Gini coefficient within the group
function value = lkml_statistic_gini(n, T, j)
d = sparse(T(:,2), 1, 1, n, 1);
d_j = d(j);
value = konect_gini_direct(d_j)
|
github | kunegis/konect-extr-master | readent.m | .m | konect-extr-master/extr/petster/readent.m | 717 | utf_8 | c4c572b9e2fc08b7dfedbf58c7104384 | %
% Load a text file. Returns a cell array containing all lines of the
% file as strings.
%
function [ret] = readent(filename)
% Note: It is *not* possible to do this with importdata(), as
% importdata() does not support empty lines [sic]. Also, fgetl() and
% fgets() both return -1 for empty lines, which is the s... |
github | kunegis/konect-extr-master | petster_names_feature.m | .m | konect-extr-master/extr/petster/petster_names_feature.m | 403 | utf_8 | ca18907f500f1eec8f3b8b9601b208f7 | %
% Ordered list of all features used in the paper.
%
function [names] = petster_names_feature()
names = { 'degdiff'; 'friend'; 'cn'; 'jaccard'; ...
'raceeq'; 'sexeq'; 'coloringeq'; ...
'latlongeq'; ...
'birthdaydiff'; ...
'joinedeq'; ...
'joineddiff'; ...
... |
github | kunegis/konect-extr-master | petster_line_styles.m | .m | konect-extr-master/extr/petster/petster_line_styles.m | 232 | utf_8 | 49d40d536cc379a2af60a00e72535cd7 |
UNUSED because the ':' is not recognizable in the paper.
function line_styles = petster_line_styles()
line_styles = struct();
line_styles.cat = '-';
line_styles.dog = '--';
line_styles.catdog = '-.';
line_styles.hamster = ':';
|
github | kunegis/konect-extr-master | homophily_category.m | .m | konect-extr-master/extr/petster/homophily_category.m | 725 | utf_8 | b1063ee94d30cfa55ac03e1fc9ec6aa8 | %
% PARAMETERS
% cat (n*1) Category vector, contains numbers from 1 to k,
% or 0/-1/NaN when the value is not known
% T1,T2 (e*1) The edges; contains values from 1 to n
%
% RESULT
% r Homophily value (from -1 to +1)
% err The error on it
%
% MEMORY
% O(k^2)
%
function [r err] = homophily_category(cat, T1, T2)
as... |
github | kunegis/konect-extr-master | petster_colors.m | .m | konect-extr-master/extr/petster/petster_colors.m | 201 | utf_8 | ba9311b1dae9558182eeceb568b99eee |
function colors = petster_colors()
colors = struct();
colors.cat = [ 0 0.8 0 ];
colors.dog = [ 0.5 0.5 1 ];
colors.catdog = [ 0.25 0.7 0.7 ];
colors.hamster = [ 1 0.3 0 ];
|
github | kunegis/konect-extr-master | petster_homophily_format.m | .m | konect-extr-master/extr/petster/petster_homophily_format.m | 2,712 | utf_8 | 0daa97e2ab22abb1449ebbb9272f3702 |
function [text_f text_a text_rel] = petster_homophily_format(h, feature)
frmt = '$%s%.4f%s$';
%
% Friendship
%
if ~ isfield(h.h_f, feature)
text_f = '\qquad---';
elseif strcmp(h.t_f.(feature), 'number')
rho = full(h.h_f.(feature))
p = full(h.p_f.(feature))
if rho < 0, fminus = '{-}'; else, fmi... |
github | kunegis/konect-extr-master | petster_assortativity_one.m | .m | konect-extr-master/extr/petster/petster_assortativity_one.m | 282 | utf_8 | 7a92aa5cb16f599e714e8a141b33b29a |
function petster_assortativity_one(x, y, color)
N = 10000
assert(length(x) == length(y));
n = length(x)
if n > N
ii = randperm(n);
ii = ii(1:N);
x = x(ii);
y = y(ii);
end
% subplot(2, 3, i);
plot(x, y, '.', 'Color', color, 'MarkerSize', 2);
%axis square;
|
github | kunegis/konect-extr-master | distcorr_geo.m | .m | konect-extr-master/extr/petster/distcorr_geo.m | 3,884 | utf_8 | 7f42de4762f4267182ab73e18df9bdd9 | %
% Downloaded from http://www.mathworks.com/matlabcentral/fileexchange/39905-distance-correlation
%
%
% Copyright (c) 2013, Shen Liu
% All rights reserved.
%
% Redistribution and use in source and binary forms, with or without
% modification, are permitted provided that the following conditions are
% met:
%
% * R... |
github | kunegis/konect-extr-master | pca_one.m | .m | konect-extr-master/extr/gama/pca_one.m | 260 | utf_8 | 948c04e4e7c4a53d6d6ae542b922a136 | %
% Plot an embedding.
%
% PARAMETERS
% pos,neg n×n adjacency matrix for positive and negative edges
% u (n×2) coordinates
function pca_one(pos, neg, u)
hold on;
gplot2(neg, u(:,1:2), '-or');
gplot2(pos, u(:,1:2), '-og', 'LineWidth',2);
axis square;
|
github | kunegis/konect-extr-master | pca_one.m | .m | konect-extr-master/extr/wiring/pca_one.m | 275 | utf_8 | fca59d04e8f4eb97058cc2cd10eb528e | % -*-octave-*-
%
% Plot an embedding.
%
% PARAMETERS
% pos,neg n×n adjacency matrix for positive and negative edges
% u (n×2) coordinates
function pca_one(pos, neg, u)
hold on;
gplot2(neg, u(:,1:2), '-or');
gplot2(pos, u(:,1:2), '-og', 'LineWidth',2);
axis square;
|
github | abenbihi/gtCourses-master | parseTrajectory.m | .m | gtCourses-master/CS8903/matlabCode/parseTrajectory/parseTrajectory.m | 8,121 | utf_8 | 4bbe0bd6b34a0fefda12379331ac997a | function [smoothData,numDemos] = parseTrajectory(numSet, tol, span, numPoint, plotIt)
% -----------------------------------------------------------------------
% A function to analyze raw data and output smooth data
%
% Inputs:
%
% numSet: integer used to label the plot
% endPoint: point to which all trajecto... |
github | abenbihi/gtCourses-master | jacoFK.m | .m | gtCourses-master/CS8903/matlabCode/FK/jacoFK.m | 2,137 | utf_8 | 5e35e881ec8a09a97c530c9494d15e7e | function [E,T] = jacoFK(qjaco)
%% this is the forward kinematics for Jaco2 arm
%
% Input: qjaco - joint angles in deg (size: 1,6)
%
% Output:
% T = transformation matrix from the end-effector to the base
% E = pose of the end-effector
%
% Example:
% [E,T] = jacoFK([261.74 171.01 67.60 152.93 37.12 6.... |
github | abenbihi/gtCourses-master | mdl_jaco2.m | .m | gtCourses-master/CS8903/matlabCode/jaco2Model/mdl_jaco2.m | 3,341 | utf_8 | aef7f4e8c350e0f5c6f5d767bab6c26f | %MDL_JACO2 Create model of Kinova Jaco2 manipulator
%
% MDL_JACO2 is a script that creates the workspace variable jaco which
% describes the kinematic characteristics of a Kinova Jaco2 manipulator
% using standard DH conventions.
%
% Also define the workspace vectors:
% qz zero joint angle configuration
% q... |
github | YanaLee/Relational-Knowledge-Transfer-for-ZSL-master | generateKRTdata.m | .m | Relational-Knowledge-Transfer-for-ZSL-master/code/ZSL/v-release/generateKRTdata.m | 5,046 | utf_8 | 311bb16b59b06da95800df8ab9fb4628 | function Data = generateKRTdata(Opt, LoadDataFlag) %Knowlwdge Relationship Transfer
% This function first use the spase coding of K2C matrix to learn the relation between test classes and train
% classes; then generate several virtual points to augment the whole training dataset for the
% improvement of F2K(eg. feature... |
github | YanaLee/Relational-Knowledge-Transfer-for-ZSL-master | setparamDogs.m | .m | Relational-Knowledge-Transfer-for-ZSL-master/code/ZSL/v-release/setparamDogs.m | 2,613 | utf_8 | 6596d864afbc493d860f2fa6380364e7 | function Opt = setparamDogs(datasetname, paramVersion, LoadParamFlag)
%this block used to add path ,set some params, and load the attribute data
%input:{datasetname,paramVersion,whether or not load data}
%output:parameters(include num-class,attribute data, attribute-dim)
fprintf(['Set common parameters for ',datasetnam... |
github | YanaLee/Relational-Knowledge-Transfer-for-ZSL-master | setparamCUB.m | .m | Relational-Knowledge-Transfer-for-ZSL-master/code/ZSL/v-release/setparamCUB.m | 2,721 | utf_8 | 9213483e6623935d8d3926036f14f7be | function Opt = setparamCUB(datasetname, paramVersion, LoadParamFlag)
%this block used to add path ,set some params, and load the attribute data
%input:{datasetname,paramVersion,whether or not load data}
%output:parameters(include num-class,attribute data, attribute-dim)
fprintf(['Set common parameters for ',datasetname... |
github | YanaLee/Relational-Knowledge-Transfer-for-ZSL-master | setparamAwA.m | .m | Relational-Knowledge-Transfer-for-ZSL-master/code/ZSL/v-release/setparamAwA.m | 2,717 | utf_8 | 9aa1bf1a9ac1cad67d26af4888d23bd6 | function Opt = setparamAwA(datasetname, paramVersion, LoadParamFlag)
%this block used to add path ,set some params, and load the attribute data
%input:{datasetname,paramVersion,whether or not load data}
%output:parameters(include num-class,attribute data, attribute-dim)
fprintf(['Set common parameters for ',datasetname... |
github | Tunscopi/POETS-master | parse.m | .m | POETS-master/parse.m | 10,066 | utf_8 | 0e1ec0fff5911fbb81f6d4ed4bfe9284 | %% Title: Parse.m File
% Entry point
% Description: helps parse our .xlsx data files obtained from MFS-3A GMW sensors for faster & efficient data analysis
% Author: Ayotunde Odejayi (Poets HU)
%%
function parse(filename)
% 1. Setup
%close all;
if (nargin == 0)
fprintf('Using recently used file:\n');
filepath... |
github | ZJULearning/efanna-master | fvecs_read.m | .m | efanna-master/matlab/fvecs_read.m | 1,363 | utf_8 | 267b271a3740ad6bf22d8f14965b7c4a | % Read a set of vectors stored in the fvec format (int + n * float)
% The function returns a set of output vector (one vector per column)
%
% Syntax:
% v = fvecs_read (filename) -> read all vectors
% v = fvecs_read (filename, n) -> read n vectors
% v = fvecs_read (filename, [a b]) -> read the vectors from ... |
github | jinzishuai/learn2deeplearn-master | submit.m | .m | learn2deeplearn-master/AndrewNg_ML_Courera/machine-learning-ex2/ex2/submit.m | 1,605 | utf_8 | 9b63d386e9bd7bcca66b1a3d2fa37579 | function submit()
addpath('./lib');
conf.assignmentSlug = 'logistic-regression';
conf.itemName = 'Logistic Regression';
conf.partArrays = { ...
{ ...
'1', ...
{ 'sigmoid.m' }, ...
'Sigmoid Function', ...
}, ...
{ ...
'2', ...
{ 'costFunction.m' }, ...
'Logistic R... |
github | jinzishuai/learn2deeplearn-master | submitWithConfiguration.m | .m | learn2deeplearn-master/AndrewNg_ML_Courera/machine-learning-ex2/ex2/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | 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 | jinzishuai/learn2deeplearn-master | savejson.m | .m | learn2deeplearn-master/AndrewNg_ML_Courera/machine-learning-ex2/ex2/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 | jinzishuai/learn2deeplearn-master | loadjson.m | .m | learn2deeplearn-master/AndrewNg_ML_Courera/machine-learning-ex2/ex2/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 | jinzishuai/learn2deeplearn-master | loadubjson.m | .m | learn2deeplearn-master/AndrewNg_ML_Courera/machine-learning-ex2/ex2/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 | jinzishuai/learn2deeplearn-master | saveubjson.m | .m | learn2deeplearn-master/AndrewNg_ML_Courera/machine-learning-ex2/ex2/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 | jinzishuai/learn2deeplearn-master | submit.m | .m | learn2deeplearn-master/AndrewNg_ML_Courera/machine-learning-ex4/ex4/submit.m | 1,635 | utf_8 | ae9c236c78f9b5b09db8fbc2052990fc | function submit()
addpath('./lib');
conf.assignmentSlug = 'neural-network-learning';
conf.itemName = 'Neural Networks Learning';
conf.partArrays = { ...
{ ...
'1', ...
{ 'nnCostFunction.m' }, ...
'Feedforward and Cost Function', ...
}, ...
{ ...
'2', ...
{ 'nnCostFunct... |
github | jinzishuai/learn2deeplearn-master | submitWithConfiguration.m | .m | learn2deeplearn-master/AndrewNg_ML_Courera/machine-learning-ex4/ex4/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | 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 | jinzishuai/learn2deeplearn-master | savejson.m | .m | learn2deeplearn-master/AndrewNg_ML_Courera/machine-learning-ex4/ex4/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 | jinzishuai/learn2deeplearn-master | loadjson.m | .m | learn2deeplearn-master/AndrewNg_ML_Courera/machine-learning-ex4/ex4/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 | jinzishuai/learn2deeplearn-master | loadubjson.m | .m | learn2deeplearn-master/AndrewNg_ML_Courera/machine-learning-ex4/ex4/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-... |
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