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 | caxenie/corr-learn-som-quadrotor-master | plot_attitude.m | .m | corr-learn-som-quadrotor-master/analysis/quad_data_analyzer/px4-lib/plot_attitude.m | 1,410 | utf_8 | e2c83192eaaa559a182484ec9c018727 |
function [] = plot_attitude(att, tsmin)
ts = att.ts;
if nargin == 2
t = (ts - tsmin)*1e-6;
else
t = (ts - ts(1))*1e-6;
end
r = 2;
c = 3;
p(1).h = subplot(r,c,1);
p(2).h = subplot(r,c,2);
p(3).h = subplot(r,c,3);
p(4).h = subplot(r,c,4);
p(5).h = subplot(r,c,5);
p(6).h = subplot(r,c,6);
p(1).d = att.r... |
github | caxenie/corr-learn-som-quadrotor-master | plot_acc_angle_vel.m | .m | corr-learn-som-quadrotor-master/analysis/quad_data_analyzer/px4-lib/plot_acc_angle_vel.m | 1,017 | utf_8 | 2a8f507d8bdc2396581bde63bc7c77c9 |
function [] = plot_acc_angle_vel(ld)
imu = ld.imu;
acc = ld.acc;
r = 2;
c = 1;
p(1).h = subplot(r,c,1);
p(2).h = subplot(r,c,2);
k=1;
p(k).t{1} = imu.hrt.t;
p(k).t{2} = imu.hrt.t;
p(k).d{1} = imu.xgyro;
p(k).d{2} = acc.raw.droll;
p(k).title = 'roll';
p(k).legend = {'gyro','acc'};
p(k).ylim = 'auto';
k=2;
p(k... |
github | caxenie/corr-learn-som-quadrotor-master | plot_of_weighted.m | .m | corr-learn-som-quadrotor-master/analysis/quad_data_analyzer/px4-lib/plot_of_weighted.m | 1,589 | utf_8 | eace386f1aa6e2a9bbe2377e9b43f35c |
function [] = plot_of_weighted(ld)
of = ld.of;
t = of.t;
r = 3;
c = 2;
p(1).h = subplot(r,c,1);
p(2).h = subplot(r,c,2);
p(3).h = subplot(r,c,3);
p(4).h = subplot(r,c,4);
p(5).h = subplot(r,c,5:6);
p(1).d{1} = of.flow_comp_m_x;
p(2).d{1} = of.flow_comp_m_y;
p(3).d{1} = of.vx_th;
p(4).d{1} = of.vy_th;
p(5).... |
github | caxenie/corr-learn-som-quadrotor-master | plot_rpy_mag.m | .m | corr-learn-som-quadrotor-master/analysis/quad_data_analyzer/px4-lib/plot_rpy_mag.m | 828 | utf_8 | 17ba8618913938c620d5a24fd2176fcd |
function [] = plot_rpy_mag(ld)
imu = ld.imu;
att = ld.att;
rb = ld.rb;
mag = ld.mag;
cm = colormap(jet(5));
r = 1;
c = 1;
p(1).h = subplot(r,c,1);
k=1;
p(k).t{1} = rb.t;
p(k).t{2} = att.t;
p(k).t{3} = imu.hrt.t;
p(k).d{1} = -rb.yaw;
p(k).d{2} = att.yaw - ld.yaw_off;
p(k).d{3} = mag.yaw_off - mag.yaw_f;
p(1).t... |
github | caxenie/corr-learn-som-quadrotor-master | plot_rigidBody.m | .m | corr-learn-som-quadrotor-master/analysis/quad_data_analyzer/px4-lib/plot_rigidBody.m | 1,482 | utf_8 | 846fcd445edbbee7a9b0093ef934402b |
function [] = plot_rigidBody(rb, tsmin)
ts = rb.ts;
if nargin == 2
t = (ts - tsmin)*1e-6;
else
t = (ts - ts(1))*1e-6;
end
r = 3;
c = 3;
% for k=1:(r*c)
% p(k).h = subplot(r,c,k);
% end
p(1).h = subplot(r,c,1);
p(2).h = subplot(r,c,2);
p(3).h = subplot(r,c,3);
p(4).h = subplot(r,c,4);
p(5... |
github | caxenie/corr-learn-som-quadrotor-master | simple_resampling.m | .m | corr-learn-som-quadrotor-master/analysis/quad_data_analyzer/px4-lib/simple_resampling.m | 1,142 | utf_8 | dcf9e4b36a6b180da1fde8ecd9f26f71 | function [t_new, x_new] = simple_resampling(t, x, t_start, Ts)
%**************************************************************************
%% simple resampling to constant sample rate (NO interpolation)
%**************************************************************************
%%ATTENTION: only oversampling is allow... |
github | caxenie/corr-learn-som-quadrotor-master | plot_KF_roll.m | .m | corr-learn-som-quadrotor-master/analysis/quad_data_analyzer/px4-lib/plot_KF_roll.m | 2,193 | utf_8 | 941be38d0e5af520007ffefb58374c73 |
function [] = plot_KF_roll(ld)
imu = ld.imu;
att = ld.att;
acc = ld.acc;
if(isfield(ld,'rb'))
rb = ld.rb;
else
rb.t = 0;
rb.roll = 0;
rb.pitch = 0;
rb.yaw = 0;
end
r = 3;
c = 1;
p(1).h = subplot(r,c,1);
p(2).h = subplot(r,c,2);
p(3).h = subplot(r,c,3);
% p(4).h = subplot(r,c,... |
github | caxenie/corr-learn-som-quadrotor-master | plot_manual_control.m | .m | corr-learn-som-quadrotor-master/analysis/quad_data_analyzer/px4-lib/plot_manual_control.m | 848 | utf_8 | 153889970b6daee26ded7b7318dc5e6e |
function [] = plot_manual_control(mc)
ts = mc.ts;
if nargin == 2
t = (ts - tsmin)*1e-6;
else
t = (ts - ts(1))*1e-6;
end
r = 2;
c = 2;
p(1).h = subplot(r,c,1);
p(2).h = subplot(r,c,2);
p(3).h = subplot(r,c,3);
p(4).h = subplot(r,c,4);
p(1).d = mc.x; % roll
p(2).d = mc.y; % pitch
p(3).d = mc.z; % yaw... |
github | caxenie/corr-learn-som-quadrotor-master | plot_rpy_gyro.m | .m | corr-learn-som-quadrotor-master/analysis/quad_data_analyzer/px4-lib/plot_rpy_gyro.m | 1,687 | utf_8 | 51377982a32dc121ad3b02687c6a8b01 |
function [] = plot_rpy_gyro(ld)
imu = ld.imu;
att = ld.att;
rb = ld.rb;
gyro = ld.gyro;
r = 3;
c = 1;
p(1).h = subplot(r,c,1);
p(2).h = subplot(r,c,2);
p(3).h = subplot(r,c,3);
k=1;
p(k).t{1} = rb.t;
p(k).t{2} = att.t;
p(k).t{3} = imu.hrt.t;
p(k).d{1} = -rb.roll;
p(k).d{2} = att.roll;
p(k).d{3} = gyro.raw.rol... |
github | caxenie/corr-learn-som-quadrotor-master | add_time_and_offset_precalcs.m | .m | corr-learn-som-quadrotor-master/analysis/quad_data_analyzer/px4-lib/add_time_and_offset_precalcs.m | 5,889 | utf_8 | 8c8ce5be2afb00628b6a48d3d613d958 | function [ld_out] = add_time_and_offset_precalcs(ld)
%**************************************************************************
%% find first time stamp
%**************************************************************************
k=1;
tsmin = zeros(1,7);
if(isfield(ld,'sor'))
tsmin(k) = ld.sor.ts(1);
k = k ... |
github | caxenie/corr-learn-som-quadrotor-master | plot_servo_output_raw.m | .m | corr-learn-som-quadrotor-master/analysis/quad_data_analyzer/px4-lib/plot_servo_output_raw.m | 911 | utf_8 | 430c8ba006c8eae2c44022ac17fe3ad0 |
function [] = plot_servo_output_raw(sor, tsmin)
ts = sor.ts;
if nargin == 2
t = (ts - tsmin)*1e-6;
else
t = (ts - ts(1))*1e-6;
end
r = 2;
c = 2;
p(1).h = subplot(r,c,1);
p(2).h = subplot(r,c,2);
p(3).h = subplot(r,c,3);
p(4).h = subplot(r,c,4);
k=1;
p(k).t{1} = t;
p(k).d{1} = sor.servo1_raw;
k=2;
p(... |
github | caxenie/corr-learn-som-quadrotor-master | plot_acc_filtered.m | .m | corr-learn-som-quadrotor-master/analysis/quad_data_analyzer/px4-lib/plot_acc_filtered.m | 1,295 | utf_8 | 2336ddbd42394a4f890ae127d3568411 |
function [] = plot_acc_filtered(ld)
imu = ld.imu;
acc = ld.acc;
r = 3;
c = 1;
p(1).h = subplot(r,c,1);
p(2).h = subplot(r,c,2);
p(3).h = subplot(r,c,3);
k=1;
p(k).t{1} = imu.hrt.t;
p(k).t{2} = imu.hrt.t;
p(k).d{1} = imu.xacc;
p(k).d{2} = acc.ax_f;
k=2;
p(k).t{1} = imu.hrt.t;
p(k).t{2} = imu.hrt.t;
p(k).d{1} = ... |
github | caxenie/corr-learn-som-quadrotor-master | add_KF_roll_pitch.m | .m | corr-learn-som-quadrotor-master/analysis/quad_data_analyzer/px4-lib/add_KF_roll_pitch.m | 2,286 | utf_8 | e553bcf1a605a189444b97836e7dcaf2 | function [ld_out] = add_KF_roll_pitch(ld)
if(isfield(ld,'imu') == 0)
ld_out = ld;
return;
end
%**************************************************************************
%% KALMAN filter: gyro, acc; ROLL, PITCH
%**************************************************************************
n = ld.imu.n;... |
github | caxenie/corr-learn-som-quadrotor-master | plot_acc_a_lin.m | .m | corr-learn-som-quadrotor-master/analysis/quad_data_analyzer/px4-lib/plot_acc_a_lin.m | 1,701 | utf_8 | cee5f01ff0dd1dbbb0a6ea149352ed87 |
function [] = plot_acc_a_lin(ld)
imu = ld.imu;
rb = ld.rb;
acc = ld.acc;
r = 3;
c = 1;
p(1).h = subplot(r,c,1);
p(2).h = subplot(r,c,2);
p(3).h = subplot(r,c,3);
k=1;
p(k).title = 'a_x [m/s^2]';
p(k).t{1} = rb.hrt.t;
p(k).t{2} = imu.hrt.t;
p(k).d{1} = -rb.az;
p(k).d{2} = acc.a_lin_f(1,:);
k=2;
p(k).title = '... |
github | caxenie/corr-learn-som-quadrotor-master | add_acc_a_rot.m | .m | corr-learn-som-quadrotor-master/analysis/quad_data_analyzer/px4-lib/add_acc_a_rot.m | 1,460 | utf_8 | 20ddb69a2e40fd1de3cda3cbbd5f299f | function [ld_out] = add_acc_a_rot(ld)
if(isfield(ld,'rb') == 0 || isfield(ld,'imu') == 0)
ld_out = ld;
return;
end
%**************************************************************************
%% rotational-graviational acceleration: accelerometer based
% with tracker linear acceleration as refe... |
github | caxenie/corr-learn-som-quadrotor-master | arrow.m | .m | corr-learn-som-quadrotor-master/analysis/quad_data_analyzer/px4-iface/arrow.m | 60,152 | utf_8 | da413cf51b717f1733a72aafd95155a0 | function [h,yy,zz] = arrow(varargin)
% ARROW Draw a line with an arrowhead.
%
% ARROW(Start,Stop) draws a line with an arrow from Start to Stop (points
% should be vectors of length 2 or 3, or matrices with 2 or 3
% columns), and returns the graphics handle of the arrow(s).
%
% ARROW uses the mo... |
github | caxenie/corr-learn-som-quadrotor-master | get_components.m | .m | corr-learn-som-quadrotor-master/analysis/quad_data_analyzer/px4-iface/get_components.m | 176 | utf_8 | 5aecf4199728f065e3f38be057eec3c4 | % precise atan2 computation http://en.wikipedia.org/wiki/Atan2
% prepare args as for an atan2 call
function y = get_components(c1, c2)
y=((sqrt(c1.^2+c2.^2)-c2)./c1)/2;
end |
github | caxenie/corr-learn-som-quadrotor-master | fix_singularities.m | .m | corr-learn-som-quadrotor-master/analysis/quad_data_analyzer/px4-iface/fix_singularities.m | 160 | utf_8 | 6a9716b835c4180a52d93f3ff831ee63 | % fix singularities in the atan computation
function y = fix_singularities(in)
while(any(isnan(in)))
in(isnan(in)) = in(find(isnan(in))-1);
end
y = in;
end |
github | caxenie/corr-learn-som-quadrotor-master | net_inf_engine.m | .m | corr-learn-som-quadrotor-master/analysis/quad_data_analyzer/px4-iface/net_inf_engine.m | 6,326 | utf_8 | 7410056c1d04755b48d48f2bb7529f27 | % conectivity learning mechanism using mutual information
function net_inf_engine(datafile)
close all; clc;
fprintf(1,'Running the network inference engine ...\n');
% open input data file
load(datafile);
% data props
npoints = size(x, 1); % number of points
ntotal = size(x, 2); % number of variables
% outlier detec... |
github | caxenie/corr-learn-som-quadrotor-master | estimate_joint_statistics.m | .m | corr-learn-som-quadrotor-master/analysis/quad_data_analyzer/px4-iface/estimate_joint_statistics.m | 3,752 | UNKNOWN | 96cb5773c438ad93da2a5d0db10c8c9b | %--------------------------------------------------------------------------
% obtain joint entropy and mutual information of two variables
%
% [mutinfo,fracn,H2] = f(x,y,pb,q) calculates the joint entropy
% 'H2' and mutual information 'mutinfo' of two variables 'x' and 'y',
% using the type of entropy specified... |
github | caxenie/corr-learn-som-quadrotor-master | RPYRot.m | .m | corr-learn-som-quadrotor-master/analysis/quad_data_analyzer/px4-display/RPYRot.m | 1,398 | utf_8 | 142fb98e6cfb9ef3525a6ef7b76b9122 | % FUNCTION:
% inverse RPY euler rotation (from initial cosy to current object cosy)
% col vecs of matrix are cosy axes of initial cosy on coords. of current
% object cosy
%
% [R] = RPYRot(angs)
% [R] = RPYRot(angs,mode)
% [R] = RPYRot(phi,theta,psi)
% [R] = RPYRot(phi,theta,psi,mode)
%
% ARGS:
... |
github | mpcrlab/NN-master | MPCR_LCA_Dictionary_Simple.m | .m | NN-master/MPCR_LCA_Dictionary_Simple.m | 2,916 | utf_8 | 5501adf8cb0bd801238e2595596d3d32 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%------------------------------------------------------%
%
% Machine Perception and Cognitive Robotics Laboratory
%
% Center for Complex Systems and Brain Sciences
%
% Florida Atlantic University
%
%-----------------------------------------------... |
github | mpcrlab/NN-master | MPCR_LCA_Dictionary_RGB_Butterfly1.m | .m | NN-master/MPCR_LCA_Dictionary_RGB_Butterfly1.m | 7,471 | utf_8 | 96378272151f926133d990943789d4c4 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%------------------------------------------------------%
%
% Machine Perception and Cognitive Robotics Laboratory
%
% Center for Complex Systems and Brain Sciences
%
% Florida Atlantic University
%
%-----------------------------------------------... |
github | mpcrlab/NN-master | MPCR_NN_Pedestrian_FA.m | .m | NN-master/MPCR_NN_Pedestrian_FA.m | 5,347 | utf_8 | dc482c07de1536df885f85e6436dbdea | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%------------------------------------------------------%
%
% Machine Perception and Cognitive Robotics Laboratory
%
% Center for Complex Systems and Brain Sciences
%
% Florida Atlantic University
%
%--------------------------------... |
github | mpcrlab/NN-master | MPCR_NN_2D_Landscape.m | .m | NN-master/MPCR_NN_2D_Landscape.m | 13,067 | utf_8 | f82d09947a3b7e6cc5acd2f08ff91685 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%------------------------------------------------------%
%
% Machine Perception and Cognitive Robotics Laboratory
%
% Center for Complex Systems and Brain Sciences
%
% Florida Atlantic University
%
%-----------------------------------------------... |
github | mpcrlab/NN-master | MPCR_Stochastic_Gradient.m | .m | NN-master/MPCR_Stochastic_Gradient.m | 631 | utf_8 | 81d5123f45351295dea5f2a6aec7827a | function MPCR_Stochastic_Gradient()
[X,Y] = meshgrid(-5:0.1:5,-5:0.1:5);
Z=f(X,Y);
surf(X,Y,Z)
hold on
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
x = [5 5]';
h = 0.1;
for i=1:100
xn = x - h*df(x,randi([1 2]... |
github | mpcrlab/NN-master | MPCR_ESN.m | .m | NN-master/MPCR_ESN.m | 1,204 | utf_8 | b68cdcbdaed9c3e45eae3220761d2d3f |
function MPCR_ESN
clear all
close all
clc
tic
cd timeser
data = load('LORENZ.DAT');
% data = load('ROSSLER.DAT');
% data = load('HENON.DAT');
% data = load('EXPTPER.DAT');
% data = load('EXPTQP2.DAT');
% data = load('EXPTQP3.DAT');
% data = load('EXPTCHAO.DAT');
m = [floor(0.8*size(data,1)) floor(0.1*size(data,1)) ... |
github | mpcrlab/NN-master | MPCR_NN_Pedestrian2.m | .m | NN-master/MPCR_NN_Pedestrian2.m | 4,594 | utf_8 | 3d8c5a4f251f97a1e1750236a8a97563 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%------------------------------------------------------%
%
% Machine Perception and Cognitive Robotics Laboratory
%
% Center for Complex Systems and Brain Sciences
%
% Florida Atlantic University
%
%--------------------------------... |
github | mpcrlab/NN-master | MPCR_LCA_Dictionary.m | .m | NN-master/MPCR_LCA_Dictionary.m | 2,309 | utf_8 | 4196ded047fa607bb8ac358c631c71cb | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%------------------------------------------------------%
%
% Machine Perception and Cognitive Robotics Laboratory
%
% Center for Complex Systems and Brain Sciences
%
% Florida Atlantic University
%
%-----------------------------------------------... |
github | mpcrlab/NN-master | MPCR_ELM.m | .m | NN-master/MPCR_ELM.m | 553 | utf_8 | 6d36231c63bc062a938d711d4a6b01e8 |
function MPCR_ELM
close all
clear all
clc
x=[-20:.5:20];
% % y=10.*x+0.5;
% % y=x.^2;
% % y=-abs(sin(x/8));
y=sin(.2.*x);
% y=x.^2+20.*rand(size(x));
% y=exp(-0.02.*(x-4).^2);
x=(x/norm(x))';
y=(y/norm(y))';
r=randperm(size(x,1));
x=x(r);
y=y(r);
x1=x(1:end/2);
y1=y(1:end/2);
x2=x(end/2+1:end);
y2=y(end/2+1:e... |
github | mpcrlab/NN-master | MPCR_LCA.m | .m | NN-master/MPCR_LCA.m | 502 | utf_8 | 1079c043de80317c5021b9cb6949dc8e |
function MPCR_LCA()
clear all
load patches.mat
load dict512.mat
D=Wp';
for i = 4 : 4
y=data(:,i);
yy=reshape(y,16,16);
a=LCA(y,D,0.01)
end
end
function [a, u] = LCA(y, D, lambda)
t=.01;
h=.0001;
d = h/t;
u = zeros(size(D,2),1);
for i=1:... |
github | mpcrlab/NN-master | MPCR_LCA_Dictionary_Block.m | .m | NN-master/MPCR_LCA_Dictionary_Block.m | 4,246 | utf_8 | 9145a164fab57d3d6c29be07f2746bd7 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%------------------------------------------------------%
%
% Machine Perception and Cognitive Robotics Laboratory
%
% Center for Complex Systems and Brain Sciences
%
% Florida Atlantic University
%
%-----------------------------------------------... |
github | mpcrlab/NN-master | MPCR_ALVINN.m | .m | NN-master/MPCR_ALVINN.m | 3,115 | utf_8 | f40f0fafcf64a788d8fd4e19a2cf1c71 |
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%------------------------------------------------------%
%
% Machine Perception and Cognitive Robotics Laboratory
%
% Center for Complex Systems and Brain Sciences
% Florida Atlantic University
%
%-----------------------------------------------... |
github | mpcrlab/NN-master | MPCR_NN_Pedestrian.m | .m | NN-master/MPCR_NN_Pedestrian.m | 4,592 | utf_8 | 7c8f42c167abd92c7db59889f5ebe119 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%------------------------------------------------------%
%
% Machine Perception and Cognitive Robotics Laboratory
%
% Center for Complex Systems and Brain Sciences
%
% Florida Atlantic University
%
%--------------------------------... |
github | mpcrlab/NN-master | MPCR_NN_CarDriver.m | .m | NN-master/MPCR_NN_CarDriver.m | 4,261 | utf_8 | 0f67bcd7bdbf9e83c94790537e73665d | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%------------------------------------------------------%
%
% Machine Perception and Cognitive Robotics Laboratory
%
% Center for Complex Systems and Brain Sciences
%
% Florida Atlantic University
%
%--------------------------------... |
github | winstywang/mxnet-master | parse_json.m | .m | mxnet-master/matlab/+mxnet/private/parse_json.m | 19,095 | utf_8 | 2d934e0eae2779e69f5c3883b8f89963 | function data = parse_json(fname,varargin)
%PARSE_JSON parse a JSON (JavaScript Object Notation) file or string
%
% Based on jsonlab (https://github.com/fangq/jsonlab) created by Qianqian Fang. Jsonlab is lisonced under BSD or GPL v3.
global pos inStr len esc index_esc len_esc isoct arraytoken
if(regexp(fname,'^\s*(... |
github | mhaghighat/fmi-master | fmi.m | .m | fmi-master/fmi.m | 19,650 | utf_8 | a58cb7bffb54c3581a173151d105990b | function nfmi = fmi(ima, imb, imf, feature, w)
% FMI calculates the Feature Mutual Information (FMI), the non-reference
% performance metric for fusion algorithms, proposed in:
%
% M.B.A. Haghighat, A. Aghagolzadeh, H. Seyedarabi, "A Non-Reference Image
% Fusion Metric Based on Mutual Information of Image Fea... |
github | DIDSR/iMRMC_Binary-master | iMRMC_BinaryPower.m | .m | iMRMC_Binary-master/src/iMRMC_BinaryPower.m | 2,583 | utf_8 | f4b873a7fe64aae5c2222118aefcdc46 | % function pow = iMRMC_BinaryPower(anaMethod,Nr, Nc, r, PC, nim, nexp)
% Calculation of empirical power of an analysis method (anaMethod) in a non-inferiority study
% using Monte Carlo simulations given a set of parameters and sample sizes.
%
% INPUTS:
% anaMethod, Nr, Nc, r, PC, nexp (same as for iMRMC_BinaryValidate... |
github | DIDSR/iMRMC_Binary-master | iMRMC_BinaryValidate.m | .m | iMRMC_Binary-master/src/iMRMC_BinaryValidate.m | 3,016 | utf_8 | ef21085cae801b7a50c388127edd1fae | % function prob = iMRMC_BinaryValidate(anaMethod,Nr, Nc, r, PC, nexp)
% Validates an analysis method using Monte Carlo simulation by estimating
% the coverage probability of the 95% confidence interval estimated by the analysis method.
%
% INPUTS:
% anaMethod: a character string specifying the .m file name (in th... |
github | DIDSR/iMRMC_Binary-master | iMRMC_BinaryAnalyze_OR.m | .m | iMRMC_Binary-master/src/iMRMC_BinaryAnalyze_OR.m | 4,467 | utf_8 | fc6255c0d9e6280f1c97d0447fbad285 | % function ret = iMRMC_BinaryAnalyze_OR(S1,S2)
% Analyze binary MRMC data using the Obuchowski-Rockette (1995) method
% together with the Hillis (2007) degrees of freedom.
%
% INPUTS: S1, S2 = Nc x Nr sucess matrices for each modality
%
% OUTPUT: ret = an output structure that has a field 'CI95' containing the... |
github | DIDSR/iMRMC_Binary-master | BinaryRoeMetz_v4.m | .m | iMRMC_Binary-master/src/BinaryRoeMetz_v4.m | 3,266 | utf_8 | 86057ef65370b07b49d706b1ceef0165 | % BinaryRoeMetz_v4.m
% This function creates binary MRMC data based on the continuous-valued Roe Metz model.
% For binary data, the "t" indices in the Roe-Metz model drop out.
% inputs: N_r, N_c, p=(PC1,PC2), v =(v_r,v_c,v_tr1,v_tr2,v_tc1,v_tc2,v_rc,v_e1 v_e2)
% output: S1, S2 = success matrices for modality 1 and 2... |
github | DIDSR/iMRMC_Binary-master | vc_b2c_v3.m | .m | iMRMC_Binary-master/src/vc_b2c_v3.m | 6,043 | utf_8 | b14a16a44c5a278ea3c342cf20f14066 | % function [v] = vc_b2c_v3(r,p,v_tot)
% --------------- General Version ---------------------
% allow for distinct values of r_c,r_r,v_tr, and v_tc for each modality
% inputs: r = (r_c1,r_c2,r_r1,r_r2,r_t,r_tc,r_tr)
% p = (pc1,pc2) overall percent correct for each modality
% v_tot = (v_tot1,v_tot2) su... |
github | DIDSR/iMRMC_Binary-master | iMRMC_BinarySimulate.m | .m | iMRMC_Binary-master/src/iMRMC_BinarySimulate.m | 3,832 | utf_8 | fcc66820ced2bf8aeca7e309bb31a337 | % function [S1,S2] = iMRMC_BinarySimulate(r,PC,Nr,Nc)
% Generate binary MRMC data with specified parameters and sample size.
%
% INPUTS:
% r: a vector of length 7 representing 7 correlation coefficient parameters that characterize the correlations in the binary data.
% r(1): Correlation between two cases from mod... |
github | andersonwinkler/areal-master | subdivtri.m | .m | areal-master/share/subdivtri.m | 2,936 | utf_8 | a350fbad180a5c8c35bde00977c33549 | function [vtx,fac] = subdivtri(vtx,fac,nlevels,newr)
% Subdivide progressively a triangular face into 4 subfaces, also triangular,
% using the midpoints of the edges of the previous iteraction as new vertices
% and project to the surface of a sphere of a given radius.
%
% Usage:
% [VTX,FAC] = subdivtri(VTX,FAC,NLEVELS,... |
github | andersonwinkler/areal-master | splitsrf.m | .m | areal-master/share/splitsrf.m | 7,486 | utf_8 | 28b8b9ecd3820a4dd4a336822cbd4f92 | %#!/usr/bin/octave -q
function splitsrf(varargin)
% Split a surface according to the labels given by a DPF/DPV file
%
% Usage:
% splitsrf(srffile,dpxfile,srfprefix)
%
% srffile : Surface file to be split (*.srf).
% labelfile : Labels per vertex or per face, in DPF or DPF format.
% If empty, one surface fi... |
github | andersonwinkler/areal-master | platonic.m | .m | areal-master/share/platonic.m | 13,625 | utf_8 | 145f24a60e7566421563ed17f2250d00 | % #!/usr/bin/octave -q
function platonic(varargin)
% Create one of the five Platonic polyhedra (tetrahedron, hexahedron,
% octahedron, dodecahedron and icosahedron) OR a geodesic sphere by
% progressive subdivision of the faces of the polyhedra with triangular
% faces (tetrahedron, octahedron and icosahedron). Icosahed... |
github | andersonwinkler/areal-master | smoothdpx.m | .m | areal-master/share/smoothdpx.m | 6,832 | utf_8 | 5301146e6a4202d7e841ddd1099186ec | % #!/usr/bin/octave -q
function smoothdpx(varargin)
% Smooth data per face (DPF) or data per vertex (DPV) with a Gaussian kernel
% of a specified width. The user must supply a spherical reference surface.
%
% Usage 1:
% smoothdpx -i inputdpx -o outputdpx -s srffile -f fwhm [-m matrix.mat]
%
% Usage 2:
% smoothdpx -s ... |
github | andersonwinkler/areal-master | annot2dpv.m | .m | areal-master/share/annot2dpv.m | 2,033 | utf_8 | 297a8d3b7dbe1c5c4437b85e5a974561 | % #!/usr/bin/octave -q
function annot2dpv(varargin)
% Convert an annotation file to a DPV file.
%
% Usage:
% annot2dpv(annotfile,dpvfile)
%
% Inputs:
% annotfile : Annotation file.
% dpvfile : Output DPV file.
%
% Before running, be sure that ${FREESURFER_HOME}/matlab is
% in the OCTAVE/MATLAB path.
%
% _____________... |
github | andersonwinkler/areal-master | applyolp.m | .m | areal-master/share/applyolp.m | 4,319 | utf_8 | aa46315cffe7da063699e128d18d0810 | % #!/usr/bin/octave -q
function applyolp(varargin)
% Produces an interpolated DPF file when the overlapping
% geometries source and target spheres (OLP table) are known.
% The OLP file is generated during the areal interpolation.
%
% Usage:
% applyolp(olpfile,srffile,dpffile1,dpffile2,update,reverse)
%
% Inputs:
% o... |
github | andersonwinkler/areal-master | rpncalc.m | .m | areal-master/share/rpncalc.m | 8,009 | utf_8 | c080250410047e33321ba1cbb5a38ac9 | % #!/usr/bin/octave -q
function rpncalc(varargin)
% Do some simple calculations using RPN notation.
%
% Accepted inputs are file names for DPV/DPF files, for
% CSV files, and for Matlab/Octave MAT files containing at most
% one variable inside.
%
% The current operators available are:
% Mathematical operators (binary):... |
github | andersonwinkler/areal-master | dpx2map.m | .m | areal-master/share/dpx2map.m | 15,603 | utf_8 | ded598c9c4b43bdcec76b8358233ac3d | % #!/usr/bin/octave -q
function dpx2map(varargin)
% Generate a surface map of data stored as DPV or DPF, using a custom
% colourscale. The result is saved as either OBJ/MTL pair or PLY, and
% can be imported for scene construction and rendering in 3D applications.
% In addition, saves also a PNG file represending the c... |
github | andersonwinkler/areal-master | dpf2dpv.m | .m | areal-master/share/dpf2dpv.m | 2,916 | utf_8 | 5c2a6ed0f657617646e9bd88d2224492 | % #!/usr/bin/octave -q
function dpf2dpv(varargin)
% Convert data-per-face (DPF) to data-per-vertex (DPV) files, redistributing the
% face quantities to their vertices. Assumes that the quantity is
% homogeneously distributed within face and that the redistribution is conceptually
% correct.
%
% Usage:
% dpf2dpv(srffile... |
github | andersonwinkler/areal-master | ply2idtf.m | .m | areal-master/share/ply2idtf.m | 20,888 | utf_8 | 656167b20eabb60e899d44f3a53f2bd8 | function ply2idtf(listply,idtffile)
% Convert a set of PLY files into a singe IDTF file, from
% which an U3D file can be generated.
%
% Usage:
% ply2idtf(listply,idtffile)
%
% listply : A variable of the type cell, with 3 columns and at least 1 row.
% The 1st column contains the string with the file name of... |
github | andersonwinkler/areal-master | replacedpx.m | .m | areal-master/share/replacedpx.m | 2,497 | utf_8 | e328c0223783e9d470bed3e418281247 | %#!/usr/bin/octave -q
function replacedpx(varargin)
% Replace values in a DPV/DPF file. The correspondence between
% old and new values is provided by a CSV table.
%
% Usage:
% replacedpx(olddpx,table,newdpx)
%
% olddpx : Original DPV/DPF file.
% table : A CSV file containing 2 columns. The first contain the old val... |
github | liususan091219/kdd2015-master | BFSreorder.m | .m | kdd2015-master/BFSreorder.m | 1,280 | utf_8 | a14ca1eb780dc8dddc7a461c342c87cb | % =======================================================================
% author: Xueqing Liu
% xliu93@illinois.edu
% =======================================================================
% Chi Wang et al., Towards Interactive Construction of Topical Hierarchy: A
% Recursive Tensor Decomposition Approach, KDD ... |
github | liususan091219/kdd2015-master | BFSname.m | .m | kdd2015-master/BFSname.m | 772 | utf_8 | c79d81944012b13a371e2f2107225bcf | % =======================================================================
% author: Xueqing Liu
% xliu93@illinois.edu
% =======================================================================
% Chi Wang et al., Towards Interactive Construction of Topical Hierarchy: A
% Recursive Tensor Decomposition Approach, KDD ... |
github | liususan091219/kdd2015-master | DFSprint.m | .m | kdd2015-master/DFSprint.m | 955 | utf_8 | 16e4e5aacc3b625b7a94bc2296567291 | % =======================================================================
% author: Xueqing Liu
% xliu93@illinois.edu
% =======================================================================
% % Chi Wang et al., Towards Interactive Construction of Topical Hierarchy: A
% Recursive Tensor Decomposition Approach, KD... |
github | liususan091219/kdd2015-master | decomp0_lowdim.m | .m | kdd2015-master/Library/decomp0_lowdim.m | 2,313 | utf_8 | 56d2930e6475c27e37ff610193d854d0 | % Learn the number of components and perform eigen decomposition
% use randomized linear algebra for dimensionality reduction
% Chi Wang
% chiw@microsoft.com
function [issmalldata, iseigsuccess, isasym, K,U0,D0] = ...
decomp0_lowdim(dwmat, options)
% K - number of topics
% ALPHA0 - summation of alpha_1, ... , alph... |
github | liususan091219/kdd2015-master | merge_last_case2.m | .m | kdd2015-master/Library/merge_last_case2.m | 8,891 | utf_8 | a016635e867b6168e79f04231811f2e4 | % =======================================================================
% author: Xueqing Liu
% xliu93@illinois.edu
% =======================================================================
% Chi Wang et al., Towards Interactive Construction of Topical Hierarchy: A
% Recursive Tensor Decomposition Approach, KDD ... |
github | liususan091219/kdd2015-master | maptoV.m | .m | kdd2015-master/Library/maptoV.m | 482 | utf_8 | 16c05730a83a9052e5f4b6ef2c6bbe06 | % =======================================================================
% author: Xueqing Liu
% xliu93@illinois.edu
% =======================================================================
% map a nz matrix to its original sparse matrix, size(mat,2) =
% size(voc_V_map)
% =======================================... |
github | liususan091219/kdd2015-master | merge_mid.m | .m | kdd2015-master/Library/merge_mid.m | 3,719 | utf_8 | afa79aecbec4b0e1c38f3a0457e50c57 | % =======================================================================
% author: Xueqing Liu
% xliu93@illinois.edu
% =======================================================================
% Chi Wang et al., Towards Interactive Construction of Topical Hierarchy: A
% Recursive Tensor Decomposition Approach, KDD ... |
github | liususan091219/kdd2015-master | EXP.m | .m | kdd2015-master/Library/EXP.m | 4,095 | utf_8 | 8950b6d0886d35f3e2aa604cc9320d87 | % =======================================================================
% author: Xueqing Liu
% xliu93@illinois.edu
% =======================================================================
% Chi Wang et al., Towards Interactive Construction of Topical Hierarchy: A
% Recursive Tensor Decomposition Approach, KDD ... |
github | liususan091219/kdd2015-master | decomp.m | .m | kdd2015-master/Library/decomp.m | 4,715 | utf_8 | 935b0071b78c6dba1e4f75361737a01f | % Scalable Tensor Orthogonal Decomposition for LDA
% Chi Wang
% chiw@microsoft.com
function [isnegeig, wtmat,ALPHA] = decomp(dwmat,k,ALPHA0, U0,D0,options)
% dwmat - sparse matrix of document-word matrix
% k - number of topics
% ALPHA0 - summation of alpha_1, ... , alpha_T
% options.N
% options.n
% options.lr
% Output... |
github | liususan091219/kdd2015-master | bsxrdivide.m | .m | kdd2015-master/Library/bsxrdivide.m | 652 | utf_8 | 05f3fec3241d8b3ac974a376a1f03f62 | % =======================================================================
% author: Xueqing Liu
% xliu93@illinois.edu
% =======================================================================
% re-implement bsxfun(@rdivide,...) such that there is no divided by 0 error
% ============================================... |
github | liususan091219/kdd2015-master | merge_last_case1.m | .m | kdd2015-master/Library/merge_last_case1.m | 5,102 | utf_8 | 0d82698d53dda0af9a48082be92f393a | % =======================================================================
% author: Xueqing Liu
% xliu93@illinois.edu
% =======================================================================
% Chi Wang et al., Towards Interactive Construction of Topical Hierarchy: A
% Recursive Tensor Decomposition Approach, KDD ... |
github | liususan091219/kdd2015-master | learnTopic.m | .m | kdd2015-master/Library/learnTopic.m | 2,622 | utf_8 | 379cb0d7b0055563eb120b2b8dec94ed | % =======================================================================
% author: Xueqing Liu
% xliu93@illinois.edu
% =======================================================================
% Chi Wang et al., Towards Interactive Construction of Topical Hierarchy: A
% Recursive Tensor Decomposition Approach, KDD 2015.... |
github | liususan091219/kdd2015-master | merge_first.m | .m | kdd2015-master/Library/merge_first.m | 3,636 | utf_8 | 3b1ed57c9f4bac7607d0ded8aa69d3bb | % =======================================================================
% author: Xueqing Liu
% xliu93@illinois.edu
% =======================================================================
% Chi Wang et al., Towards Interactive Construction of Topical Hierarchy: A
% Recursive Tensor Decomposition Approach, KDD ... |
github | liususan091219/kdd2015-master | MER.m | .m | kdd2015-master/Library/MER.m | 4,642 | utf_8 | 5892c7df1606ed414aa8bb09c979c218 | % =======================================================================
% author: Xueqing Liu
% xliu93@illinois.edu
% =======================================================================
% Chi Wang et al., Towards Interactive Construction of Topical Hierarchy: A
% Recursive Tensor Decomposition Approach, KDD ... |
github | liususan091219/kdd2015-master | decomp0.m | .m | kdd2015-master/Library/decomp0.m | 1,917 | utf_8 | 8c8a63679621ae0dbf10dd7bb3a48f81 | % Learn the number of components and perform eigen decomposition
% Chi Wang
% chiw@microsoft.com
function [issmalldata, iseigsuccess, isasym, K,U0,D0] = decomp0(...
dwmat, options)
% K - number of topics
% ALPHA0 - summation of alpha_1, ... , alpha_K
% options.K - the range of possible K
% options.N - number of out... |
github | liususan091219/kdd2015-master | ReadEdge.m | .m | kdd2015-master/DataProcess/readdata/ReadEdge.m | 532 | utf_8 | 3eee2ada4a60e13f7f80a952935f0825 | % author: Chi Wang
% create date: Mar 3, 2012 (3.3 Revolution)
function [edgeTriple, edgeSparse] = ReadEdge(edgeFile)
% edgeTriple: [i j A]*m
% edgeSparse: a sparse n*n matrix, A_{i,j}
% edgeFile: %d\t%d\t%d, directed network
edgeTriple = load(edgeFile);
m = size(edgeTriple,1);
if size(edgeTriple,2)<3 || tr... |
github | msyamkumar/vision-panorama-master | gamma_correction.m | .m | vision-panorama-master/gamma_correction.m | 4,651 | utf_8 | b3aa3620d120e37134c88ec0c3f4a01a | %UNTITLED2 Summary of this function goes here
% Detailed explanation goes here
% The function performs gamma correction on the input image X
%
% PROTOTYPE
% Y=gamma_correction(X, in_interval, out_interval, gamma);
%
% USAGE EXAMPLE(S)
%
% Example 1:
% X=imread('sample_image.bmp');
% Y=gamma_... |
github | msyamkumar/vision-panorama-master | homographyAlternative.m | .m | vision-panorama-master/homographyAlternative.m | 2,671 | utf_8 | d3759d78ed59d5d004221922758e263d | function H = homographyAlternative(im1, im2)
% SIFT_MOSAIC Demonstrates matching two images using SIFT and RANSAC
%
% SIFT_MOSAIC demonstrates matching two images based on SIFT
% features and RANSAC and computing their mosaic.
%
% SIFT_MOSAIC by itself runs the algorithm on two standard test
% images. Use SIFT_... |
github | msyamkumar/vision-panorama-master | cropImage2.m | .m | vision-panorama-master/cropImage2.m | 1,930 | utf_8 | 886e5dad8942a90506fddbe5403e5093 | function [ out ] = cropImage2( image )
% crops image (mosaic) to remove rows/columns on edges with black pixels
[m,n,k] = size(image);
x_min = 1;
x_max = n;
y_min = 1;
y_max = m;
x_threshold = 50;
y_threshold = 5;
function out = isBlack( pixel )
out = 1;
for c=1:3
if pixel(c) ~= 0
out = ... |
github | msyamkumar/vision-panorama-master | vl_compile.m | .m | vision-panorama-master/vlfeat-0.9.20/toolbox/vl_compile.m | 5,060 | utf_8 | 978f5189bb9b2a16db3368891f79aaa6 | function vl_compile(compiler)
% VL_COMPILE Compile VLFeat MEX files
% VL_COMPILE() uses MEX() to compile VLFeat MEX files. This command
% works only under Windows and is used to re-build problematic
% binaries. The preferred method of compiling VLFeat on both UNIX
% and Windows is through the provided Makefile... |
github | msyamkumar/vision-panorama-master | vl_noprefix.m | .m | vision-panorama-master/vlfeat-0.9.20/toolbox/vl_noprefix.m | 1,875 | utf_8 | 97d8755f0ba139ac1304bc423d3d86d3 | function vl_noprefix
% VL_NOPREFIX Create a prefix-less version of VLFeat commands
% VL_NOPREFIX() creats prefix-less stubs for VLFeat functions
% (e.g. SIFT for VL_SIFT). This function is seldom used as the stubs
% are included in the VLFeat binary distribution anyways. Moreover,
% on UNIX platforms, the stub... |
github | msyamkumar/vision-panorama-master | vl_pegasos.m | .m | vision-panorama-master/vlfeat-0.9.20/toolbox/misc/vl_pegasos.m | 2,837 | utf_8 | d5e0915c439ece94eb5597a07090b67d | % VL_PEGASOS [deprecated]
% VL_PEGASOS is deprecated. Please use VL_SVMTRAIN() instead.
function [w b info] = vl_pegasos(X,Y,LAMBDA, varargin)
% Verbose not supported
if (sum(strcmpi('Verbose',varargin)))
varargin(find(strcmpi('Verbose',varargin),1))=[];
fprintf('Option VERBOSE is no longer supported.\n');
en... |
github | msyamkumar/vision-panorama-master | vl_svmpegasos.m | .m | vision-panorama-master/vlfeat-0.9.20/toolbox/misc/vl_svmpegasos.m | 1,178 | utf_8 | 009c2a2b87a375d529ed1a4dbe3af59f | % VL_SVMPEGASOS [deprecated]
% VL_SVMPEGASOS is deprecated. Please use VL_SVMTRAIN() instead.
function [w b info] = vl_svmpegasos(DATA,LAMBDA, varargin)
% Verbose not supported
if (sum(strcmpi('Verbose',varargin)))
varargin(find(strcmpi('Verbose',varargin),1))=[];
fprintf('Option VERBOSE is no longer suppor... |
github | msyamkumar/vision-panorama-master | vl_override.m | .m | vision-panorama-master/vlfeat-0.9.20/toolbox/misc/vl_override.m | 4,654 | utf_8 | e233d2ecaeb68f56034a976060c594c5 | function config = vl_override(config,update,varargin)
% VL_OVERRIDE Override structure subset
% CONFIG = VL_OVERRIDE(CONFIG, UPDATE) copies recursively the fileds
% of the structure UPDATE to the corresponding fields of the
% struture CONFIG.
%
% Usually CONFIG is interpreted as a list of paramters with their
... |
github | msyamkumar/vision-panorama-master | vl_quickvis.m | .m | vision-panorama-master/vlfeat-0.9.20/toolbox/quickshift/vl_quickvis.m | 3,696 | utf_8 | 27f199dad4c5b9c192a5dd3abc59f9da | function [Iedge dists map gaps] = vl_quickvis(I, ratio, kernelsize, maxdist, maxcuts)
% VL_QUICKVIS Create an edge image from a Quickshift segmentation.
% IEDGE = VL_QUICKVIS(I, RATIO, KERNELSIZE, MAXDIST, MAXCUTS) creates an edge
% stability image from a Quickshift segmentation. RATIO controls the tradeoff
% bet... |
github | msyamkumar/vision-panorama-master | vl_demo_aib.m | .m | vision-panorama-master/vlfeat-0.9.20/toolbox/demo/vl_demo_aib.m | 2,928 | utf_8 | 590c6db09451ea608d87bfd094662cac | function vl_demo_aib
% VL_DEMO_AIB Test Agglomerative Information Bottleneck (AIB)
D = 4 ;
K = 20 ;
randn('state',0) ;
rand('state',0) ;
X1 = randn(2,300) ; X1(1,:) = X1(1,:) + 2 ;
X2 = randn(2,300) ; X2(1,:) = X2(1,:) - 2 ;
X3 = randn(2,300) ; X3(2,:) = X3(2,:) + 2 ;
figure(1) ; clf ; hold on ;
vl_plotframe(X... |
github | msyamkumar/vision-panorama-master | vl_demo_alldist.m | .m | vision-panorama-master/vlfeat-0.9.20/toolbox/demo/vl_demo_alldist.m | 5,460 | utf_8 | 6d008a64d93445b9d7199b55d58db7eb | function vl_demo_alldist
%
numRepetitions = 3 ;
numDimensions = 1000 ;
numSamplesRange = [300] ;
settingsRange = {{'alldist2', 'double', 'l2', }, ...
{'alldist', 'double', 'l2', 'nosimd'}, ...
{'alldist', 'double', 'l2' }, ...
{'alldist2', 's... |
github | msyamkumar/vision-panorama-master | vl_demo_ikmeans.m | .m | vision-panorama-master/vlfeat-0.9.20/toolbox/demo/vl_demo_ikmeans.m | 774 | utf_8 | 17ff0bb7259d390fb4f91ea937ba7de0 | function vl_demo_ikmeans()
% VL_DEMO_IKMEANS
numData = 10000 ;
dimension = 2 ;
data = uint8(255*rand(dimension,numData)) ;
numClusters = 3^3 ;
[centers, assignments] = vl_ikmeans(data, numClusters);
figure(1) ; clf ; axis off ;
plotClusters(data, centers, assignments) ;
vl_demo_print('ikmeans_2d',0.6);
[tree, assig... |
github | msyamkumar/vision-panorama-master | vl_demo_svm.m | .m | vision-panorama-master/vlfeat-0.9.20/toolbox/demo/vl_demo_svm.m | 1,235 | utf_8 | 7cf6b3504e4fc2cbd10ff3fec6e331a7 | % VL_DEMO_SVM Demo: SVM: 2D linear learning
function vl_demo_svm
y=[];X=[];
% Load training data X and their labels y
load('vl_demo_svm_data.mat')
Xp = X(:,y==1);
Xn = X(:,y==-1);
figure
plot(Xn(1,:),Xn(2,:),'*r')
hold on
plot(Xp(1,:),Xp(2,:),'*b')
axis equal ;
vl_demo_print('svm_training') ;
% Parameters
lambda =... |
github | msyamkumar/vision-panorama-master | vl_demo_kdtree_sift.m | .m | vision-panorama-master/vlfeat-0.9.20/toolbox/demo/vl_demo_kdtree_sift.m | 6,832 | utf_8 | e676f80ac330a351f0110533c6ebba89 | function vl_demo_kdtree_sift
% VL_DEMO_KDTREE_SIFT
% Demonstrates the use of a kd-tree forest to match SIFT
% features. If FLANN is present, this function runs a comparison
% against it.
% AUTORIGHS
rand('state',0) ;
randn('state',0);
do_median = 0 ;
do_mean = 1 ;
% try to setup flann
if ~exist('flann_search'... |
github | msyamkumar/vision-panorama-master | vl_impattern.m | .m | vision-panorama-master/vlfeat-0.9.20/toolbox/imop/vl_impattern.m | 6,876 | utf_8 | 1716a4d107f0186be3d11c647bc628ce | function im = vl_impattern(varargin)
% VL_IMPATTERN Generate an image from a stock pattern
% IM=VLPATTERN(NAME) returns an instance of the specified
% pattern. These stock patterns are useful for testing algoirthms.
%
% All generated patterns are returned as an image of class
% DOUBLE. Both gray-scale and colou... |
github | msyamkumar/vision-panorama-master | vl_tpsu.m | .m | vision-panorama-master/vlfeat-0.9.20/toolbox/imop/vl_tpsu.m | 1,755 | utf_8 | 09f36e1a707c069b375eb2817d0e5f13 | function [U,dU,delta]=vl_tpsu(X,Y)
% VL_TPSU Compute the U matrix of a thin-plate spline transformation
% U=VL_TPSU(X,Y) returns the matrix
%
% [ U(|X(:,1) - Y(:,1)|) ... U(|X(:,1) - Y(:,N)|) ]
% [ ]
% [ U(|X(:,M) - Y(:,1)|) ... U(|X(:,M) - Y(:,N)|) ]
%
% where X... |
github | msyamkumar/vision-panorama-master | vl_xyz2lab.m | .m | vision-panorama-master/vlfeat-0.9.20/toolbox/imop/vl_xyz2lab.m | 1,570 | utf_8 | 09f95a6f9ae19c22486ec1157357f0e3 | function J=vl_xyz2lab(I,il)
% VL_XYZ2LAB Convert XYZ color space to LAB
% J = VL_XYZ2LAB(I) converts the image from XYZ format to LAB format.
%
% VL_XYZ2LAB(I,IL) uses one of the illuminants A, B, C, E, D50, D55,
% D65, D75, D93. The default illuminatn is E.
%
% See also: VL_XYZ2LUV(), VL_HELP().
% Copyright ... |
github | msyamkumar/vision-panorama-master | vl_test_gmm.m | .m | vision-panorama-master/vlfeat-0.9.20/toolbox/xtest/vl_test_gmm.m | 1,332 | utf_8 | 76782cae6c98781c6c38d4cbf5549d94 | function results = vl_test_gmm(varargin)
% VL_TEST_GMM
% Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
vl_test_init ;
end
function s = setup()
randn('st... |
github | msyamkumar/vision-panorama-master | vl_test_twister.m | .m | vision-panorama-master/vlfeat-0.9.20/toolbox/xtest/vl_test_twister.m | 1,251 | utf_8 | 2bfb5a30cbd6df6ac80c66b73f8646da | function results = vl_test_twister(varargin)
% VL_TEST_TWISTER
vl_test_init ;
function test_illegal_args()
vl_assert_exception(@() vl_twister(-1), 'vl:invalidArgument') ;
vl_assert_exception(@() vl_twister(1, -1), 'vl:invalidArgument') ;
vl_assert_exception(@() vl_twister([1, -1]), 'vl:invalidArgument') ;
function te... |
github | msyamkumar/vision-panorama-master | vl_test_kdtree.m | .m | vision-panorama-master/vlfeat-0.9.20/toolbox/xtest/vl_test_kdtree.m | 2,449 | utf_8 | 9d7ad2b435a88c22084b38e5eb5f9eb9 | function results = vl_test_kdtree(varargin)
% VL_TEST_KDTREE
vl_test_init ;
function s = setup()
randn('state',0) ;
s.X = single(randn(10, 1000)) ;
s.Q = single(randn(10, 10)) ;
function test_nearest(s)
for tmethod = {'median', 'mean'}
for type = {@single, @double}
conv = type{1} ;
tmethod = char(tmethod) ;... |
github | msyamkumar/vision-panorama-master | vl_test_imwbackward.m | .m | vision-panorama-master/vlfeat-0.9.20/toolbox/xtest/vl_test_imwbackward.m | 514 | utf_8 | 33baa0784c8f6f785a2951d7f1b49199 | function results = vl_test_imwbackward(varargin)
% VL_TEST_IMWBACKWARD
vl_test_init ;
function s = setup()
s.I = im2double(imread(fullfile(vl_root,'data','spots.jpg'))) ;
function test_identity(s)
xr = 1:size(s.I,2) ;
yr = 1:size(s.I,1) ;
[x,y] = meshgrid(xr,yr) ;
vl_assert_almost_equal(s.I, vl_imwbackward(xr,yr,s.I,... |
github | msyamkumar/vision-panorama-master | vl_test_alphanum.m | .m | vision-panorama-master/vlfeat-0.9.20/toolbox/xtest/vl_test_alphanum.m | 1,624 | utf_8 | 2da2b768c2d0f86d699b8f31614aa424 | function results = vl_test_alphanum(varargin)
% VL_TEST_ALPHANUM
vl_test_init ;
function s = setup()
s.strings = ...
{'1000X Radonius Maximus','10X Radonius','200X Radonius','20X Radonius','20X Radonius Prime','30X Radonius','40X Radonius','Allegia 50 Clasteron','Allegia 500 Clasteron','Allegia 50B Clasteron','Al... |
github | msyamkumar/vision-panorama-master | vl_test_printsize.m | .m | vision-panorama-master/vlfeat-0.9.20/toolbox/xtest/vl_test_printsize.m | 1,447 | utf_8 | 0f0b6437c648b7a2e1310900262bd765 | function results = vl_test_printsize(varargin)
% VL_TEST_PRINTSIZE
vl_test_init ;
function s = setup()
s.fig = figure(1) ;
s.usletter = [8.5, 11] ; % inches
s.a4 = [8.26772, 11.6929] ;
clf(s.fig) ; plot(1:10) ;
function teardown(s)
close(s.fig) ;
function test_basic(s)
for sigma = [1 0.5 0.2]
vl_printsize(s.fig, s... |
github | msyamkumar/vision-panorama-master | vl_test_cummax.m | .m | vision-panorama-master/vlfeat-0.9.20/toolbox/xtest/vl_test_cummax.m | 838 | utf_8 | 5e98ee1681d4823f32ecc4feaa218611 | function results = vl_test_cummax(varargin)
% VL_TEST_CUMMAX
vl_test_init ;
function test_basic()
vl_assert_almost_equal(...
vl_cummax(1), 1) ;
vl_assert_almost_equal(...
vl_cummax([1 2 3 4], 2), [1 2 3 4]) ;
function test_multidim()
a = [1 2 3 4 3 2 1] ;
b = [1 2 3 4 4 4 4] ;
for k=1:6
dims = ones(1,6) ;
dim... |
github | msyamkumar/vision-panorama-master | vl_test_imintegral.m | .m | vision-panorama-master/vlfeat-0.9.20/toolbox/xtest/vl_test_imintegral.m | 1,429 | utf_8 | 4750f04ab0ac9fc4f55df2c8583e5498 | function results = vl_test_imintegral(varargin)
% VL_TEST_IMINTEGRAL
vl_test_init ;
function state = setup()
state.I = ones(5,6) ;
state.correct = [ 1 2 3 4 5 6 ;
2 4 6 8 10 12 ;
3 6 9 12 15 18 ;
4 8 12 ... |
github | msyamkumar/vision-panorama-master | vl_test_sift.m | .m | vision-panorama-master/vlfeat-0.9.20/toolbox/xtest/vl_test_sift.m | 1,318 | utf_8 | 806c61f9db9f2ebb1d649c9bfcf3dc0a | function results = vl_test_sift(varargin)
% VL_TEST_SIFT
vl_test_init ;
function s = setup()
s.I = im2single(imread(fullfile(vl_root,'data','box.pgm'))) ;
[s.ubc.f, s.ubc.d] = ...
vl_ubcread(fullfile(vl_root,'data','box.sift')) ;
function test_ubc_descriptor(s)
err = [] ;
[f, d] = vl_sift(s.I,...
... |
github | msyamkumar/vision-panorama-master | vl_test_binsum.m | .m | vision-panorama-master/vlfeat-0.9.20/toolbox/xtest/vl_test_binsum.m | 1,377 | utf_8 | f07f0f29ba6afe0111c967ab0b353a9d | function results = vl_test_binsum(varargin)
% VL_TEST_BINSUM
vl_test_init ;
function test_three_args()
vl_assert_almost_equal(...
vl_binsum([0 0], 1, 2), [0 1]) ;
vl_assert_almost_equal(...
vl_binsum([1 7], -1, 1), [0 7]) ;
vl_assert_almost_equal(...
vl_binsum([1 7], -1, [1 2 2 2 2 2 2 2]), [0 0]) ;
function te... |
github | msyamkumar/vision-panorama-master | vl_test_lbp.m | .m | vision-panorama-master/vlfeat-0.9.20/toolbox/xtest/vl_test_lbp.m | 892 | utf_8 | a79c0ce0c85e25c0b1657f3a0b499538 | function results = vl_test_lbp(varargin)
% VL_TEST_TWISTER
vl_test_init ;
function test_unfiorm_lbps(s)
% enumerate the 56 uniform lbps
q = 0 ;
for i=0:7
for j=1:7
I = zeros(3) ;
p = mod(s.pixels - i + 8, 8) + 1 ;
I(p <= j) = 1 ;
f = vl_lbp(single(I), 3) ;
q = q + 1 ;
vl_assert_equal(find(f... |
github | msyamkumar/vision-panorama-master | vl_test_colsubset.m | .m | vision-panorama-master/vlfeat-0.9.20/toolbox/xtest/vl_test_colsubset.m | 828 | utf_8 | be0c080007445b36333b863326fb0f15 | function results = vl_test_colsubset(varargin)
% VL_TEST_COLSUBSET
vl_test_init ;
function s = setup()
s.x = [5 2 3 6 4 7 1 9 8 0] ;
function test_beginning(s)
vl_assert_equal(1:5, vl_colsubset(1:10, 5, 'beginning')) ;
vl_assert_equal(1:5, vl_colsubset(1:10, .5, 'beginning')) ;
function test_ending(s)
vl_assert_equa... |
github | msyamkumar/vision-panorama-master | vl_test_alldist.m | .m | vision-panorama-master/vlfeat-0.9.20/toolbox/xtest/vl_test_alldist.m | 2,373 | utf_8 | 9ea1a36c97fe715dfa2b8693876808ff | function results = vl_test_alldist(varargin)
% VL_TEST_ALLDIST
vl_test_init ;
function s = setup()
vl_twister('state', 0) ;
s.X = 3.1 * vl_twister(10,10) ;
s.Y = 4.7 * vl_twister(10,7) ;
function test_null_args(s)
vl_assert_equal(...
vl_alldist(zeros(15,12), zeros(15,0), 'kl2'), ...
zeros(12,0)) ;
vl_assert_equa... |
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