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github | Chandan-IITI/Analysis-of-Twin-SVM-on-44-binary-datasets-master | seperate_eval.m | .m | Analysis-of-Twin-SVM-on-44-binary-datasets-master/LPP_TSVM/seperate_eval.m | 4,612 | utf_8 | cdf6ca65b7064dec5676eb185cf0d937 |
function [test_acc] = seperate_eval(name)
addpath([pwd '\lppTSVM']);
%%% datasets can be downloaded from "http://persoal.citius.usc.es/manuel.fernandez.delgado/papers/jmlr/data.tar.gz"
datapath = 'provide the path of your dataset';
train = load ([datapath name '\' name '_train_R.dat']);% for datasets where tr... |
github | Chandan-IITI/Analysis-of-Twin-SVM-on-44-binary-datasets-master | lpp_TSVM.m | .m | Analysis-of-Twin-SVM-on-44-binary-datasets-master/LPP_TSVM/lppTSVM/lpp_TSVM.m | 6,608 | utf_8 | 1c7b0a1631142a93c62f585405c02249 | % ___________________________________________________________________
%
% This is twin SVM Linear programming problem method for nonlinear case.
% The twin SVM formulation is considered and
% its penalty form in its dual is formulated in 1-norm and solved
% using Newton method
% date : Dec 20, 201... |
github | Chandan-IITI/Analysis-of-Twin-SVM-on-44-binary-datasets-master | normalize.m | .m | Analysis-of-Twin-SVM-on-44-binary-datasets-master/LPP_TSVM/lppTSVM/normalize.m | 620 | utf_8 | b69935e172ec81c7e859090cf1994fed | % -----------------------------------------------------------------------
% Time series problem whose input series is given as A matrix, with N
% attributes i.e. A(M:N). Here we normalize the data column-wise so that
% the mean of the series is zero with standard deviation equals to one.
% Input is the t... |
github | Chandan-IITI/Analysis-of-Twin-SVM-on-44-binary-datasets-master | kfold_eval.m | .m | Analysis-of-Twin-SVM-on-44-binary-datasets-master/Improved_LSTWSVM/kfold_eval.m | 5,220 | utf_8 | b5c083945e6ab761c2c080813b9ea530 |
function [mean_acc] = seperate_eval(name)
addpath([pwd '\improved_LSTWSVM'])
%%% datasets can be downloaded from "http://persoal.citius.usc.es/manuel.fernandez.delgado/papers/jmlr/data.tar.gz"
datapath = 'provide the path of your dataset';
tot_data = load([datapath name '\' name '_R.dat']);
index_tune = impo... |
github | Chandan-IITI/Analysis-of-Twin-SVM-on-44-binary-datasets-master | seperate_eval.m | .m | Analysis-of-Twin-SVM-on-44-binary-datasets-master/Improved_LSTWSVM/seperate_eval.m | 5,149 | utf_8 | a244531f9bbb7bfef82856e1b20c71eb |
function [test_acc] = seperate_eval(name)
addpath([pwd '\improved_LSTWSVM'])
%%% datasets can be downloaded from "http://persoal.citius.usc.es/manuel.fernandez.delgado/papers/jmlr/data.tar.gz"
datapath = 'provide the path of your dataset';
train = load ([datapath name '\' name '_train_R.dat']);% for datasets ... |
github | MouradGridach/Machine-Learning-Stanford-master | submit.m | .m | Machine-Learning-Stanford-master/ex4/submit.m | 17,129 | utf_8 | 7e97c75d2b70d978e93fbdb7dfa9d95b | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isem... |
github | MouradGridach/Machine-Learning-Stanford-master | submitWeb.m | .m | Machine-Learning-Stanford-master/ex4/submitWeb.m | 827 | utf_8 | bfb2fa08cac9d8d797e3071d3fdd7ca1 | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on ... |
github | MouradGridach/Machine-Learning-Stanford-master | submit.m | .m | Machine-Learning-Stanford-master/ex3/Logistic Regression/submit.m | 17,041 | utf_8 | f68fe1ee499ec01df18037b089673b0c | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isem... |
github | MouradGridach/Machine-Learning-Stanford-master | submitWeb.m | .m | Machine-Learning-Stanford-master/ex3/Logistic Regression/submitWeb.m | 827 | utf_8 | bfb2fa08cac9d8d797e3071d3fdd7ca1 | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on ... |
github | MouradGridach/Machine-Learning-Stanford-master | submit.m | .m | Machine-Learning-Stanford-master/ex1/submit.m | 17,317 | utf_8 | d91bc52d795ebec0d8a1667cd9810fea | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isem... |
github | MouradGridach/Machine-Learning-Stanford-master | submitWeb.m | .m | Machine-Learning-Stanford-master/ex1/submitWeb.m | 827 | utf_8 | bfb2fa08cac9d8d797e3071d3fdd7ca1 | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on ... |
github | xiaohuige1/udn_extend-master | fast_rcnn_get_minibatch.m | .m | udn_extend-master/functions/fast_rcnn/fast_rcnn_get_minibatch.m | 6,754 | utf_8 | 78226ab5c4a79dab0d13aac70792d9b6 | function [im_blob, rois_blob, labels_blob, bbox_targets_blob, bbox_loss_blob] = fast_rcnn_get_minibatch(conf, image_roidb)
% [im_blob, rois_blob, labels_blob, bbox_targets_blob, bbox_loss_blob] ...
% = fast_rcnn_get_minibatch(conf, image_roidb)
% --------------------------------------------------------
% Fast R-CNN
... |
github | xiaohuige1/udn_extend-master | fast_rcnn_conv_feat_detect.m | .m | udn_extend-master/functions/fast_rcnn/fast_rcnn_conv_feat_detect.m | 4,211 | utf_8 | 7757435a0286baaedd67b1aa30c1f523 | function [pred_boxes, scores] = fast_rcnn_conv_feat_detect(conf, caffe_net, im, conv_feat_blob, boxes, max_rois_num_in_gpu)
% [pred_boxes, scores] = fast_rcnn_conv_feat_detect(conf, caffe_net, im, conv_feat_blob, boxes, max_rois_num_in_gpu)
% --------------------------------------------------------
% Fast R-CNN
% Reimp... |
github | xiaohuige1/udn_extend-master | fast_rcnn_im_detect_our.m | .m | udn_extend-master/functions/fast_rcnn/fast_rcnn_im_detect_our.m | 4,807 | utf_8 | dcac6458c08d769832bb3ca5be673b93 | function [pred_boxes, scores] = fast_rcnn_im_detect_our(conf, caffe_net, im, boxes, max_rois_num_in_gpu)
% [pred_boxes, scores] = fast_rcnn_im_detect(conf, caffe_net, im, boxes, max_rois_num_in_gpu)
% --------------------------------------------------------
% Fast R-CNN
% Reimplementation based on Python Fast R-CNN (ht... |
github | xiaohuige1/udn_extend-master | fast_rcnn_train.m | .m | udn_extend-master/functions/fast_rcnn/fast_rcnn_train.m | 9,725 | utf_8 | c003ebd57a0c1417b4bbd979092a88b2 | function save_model_path = fast_rcnn_train(conf, imdb_train, roidb_train, varargin)
% save_model_path = fast_rcnn_train(conf, imdb_train, roidb_train, varargin)
% --------------------------------------------------------
% Fast R-CNN
% Reimplementation based on Python Fast R-CNN (https://github.com/rbgirshick/fast-rcnn)... |
github | xiaohuige1/udn_extend-master | fast_rcnn_im_detect.m | .m | udn_extend-master/functions/fast_rcnn/fast_rcnn_im_detect.m | 4,781 | utf_8 | 76b56954f7f1f2d32f89b7d0a00e8338 | function [pred_boxes, scores] = fast_rcnn_im_detect(conf, caffe_net, im, boxes, max_rois_num_in_gpu)
% [pred_boxes, scores] = fast_rcnn_im_detect(conf, caffe_net, im, boxes, max_rois_num_in_gpu)
% --------------------------------------------------------
% Fast R-CNN
% Reimplementation based on Python Fast R-CNN (https:... |
github | xiaohuige1/udn_extend-master | fast_rcnn_test.m | .m | udn_extend-master/functions/fast_rcnn/fast_rcnn_test.m | 8,455 | utf_8 | 1b4a7dc5b5a0d67d5458497cdc47242d | function mAP = fast_rcnn_test(conf, imdb, roidb, varargin)
% mAP = fast_rcnn_test(conf, imdb, roidb, varargin)
% --------------------------------------------------------
% Fast R-CNN
% Reimplementation based on Python Fast R-CNN (https://github.com/rbgirshick/fast-rcnn)
% Copyright (c) 2015, Shaoqing Ren
% Licensed und... |
github | xiaohuige1/udn_extend-master | fast_rcnn_prepare_image_roidb.m | .m | udn_extend-master/functions/fast_rcnn/fast_rcnn_prepare_image_roidb.m | 6,244 | utf_8 | c1d573a68d0365fd02ff3a204ccf9a3b | function [image_roidb, bbox_means, bbox_stds] = fast_rcnn_prepare_image_roidb(conf, imdbs, roidbs, bbox_means, bbox_stds)
% [image_roidb, bbox_means, bbox_stds] = fast_rcnn_prepare_image_roidb(conf, imdbs, roidbs, cache_img, bbox_means, bbox_stds)
% Gather useful information from imdb and roidb
% pre-calculate mean... |
github | xiaohuige1/udn_extend-master | fast_rcnn_generate_sliding_windows.m | .m | udn_extend-master/functions/fast_rcnn/fast_rcnn_generate_sliding_windows.m | 1,729 | utf_8 | a788da565d8e7d1810407473c3135094 | function roidb = fast_rcnn_generate_sliding_windows(conf, imdb, roidb, roipool_in_size)
% [pred_boxes, scores] = fast_rcnn_conv_feat_detect(conf, im, conv_feat, boxes, max_rois_num_in_gpu, net_idx)
% --------------------------------------------------------
% Fast R-CNN
% Reimplementation based on Python Fast R-CNN (htt... |
github | xiaohuige1/udn_extend-master | showboxes.m | .m | udn_extend-master/utils/showboxes.m | 2,624 | utf_8 | be6b3bca7e6364f27e7ac8d3f76a3628 | function showboxes(im, boxes, legends, color_conf)
% Draw bounding boxes on top of an image.
% showboxes(im, boxes)
%
% -------------------------------------------------------
fix_width = 800;
if isa(im, 'gpuArray')
im = gather(im);
end
imsz = size(im);
scale = fix_width / imsz(2);
im = imresize(im, scale);
if ... |
github | xiaohuige1/udn_extend-master | classification_demo.m | .m | udn_extend-master/external/caffe_new/matlab/demo/classification_demo.m | 5,412 | utf_8 | 8f46deabe6cde287c4759f3bc8b7f819 | function [scores, maxlabel] = classification_demo(im, use_gpu)
% [scores, maxlabel] = classification_demo(im, use_gpu)
%
% Image classification demo using BVLC CaffeNet.
%
% IMPORTANT: before you run this demo, you should download BVLC CaffeNet
% from Model Zoo (http://caffe.berkeleyvision.org/model_zoo.html)
%
% *****... |
github | xiaohuige1/udn_extend-master | script_rpn_rcnn_new.m | .m | udn_extend-master/experiments/script_rpn_rcnn_new.m | 4,036 | utf_8 | bcd6a8c3655d0587b6d7c9eca263c332 | function [train_box train_box_rcnn train_box_rpn]= script_rpn_rcnn_new()
clc;
clear mex;
clear is_valid_handle; % to clear init_key
run(fullfile(fileparts(fileparts(mfilename('fullpath'))), 'startup'));
%% -------------------- CONFIG --------------------
opts.caffe_version = 'caffe';
opts.gpu_id ... |
github | qboticslabs/Autoware-master | velCapture.m | .m | Autoware-master/ros/src/system/gazebo/catvehicle/matlab files/velCapture.m | 1,047 | utf_8 | 994f8bb886b3e3ebef61b89d3c9c00a9 | % Function to capture catvehicle velocity and plotting live graph
function velCapture(ROS_IP, roboname)
%If number of argument is not two, flag message and exit.
if nargin < 2
disp('Uage: velocityProfiler(192.168.0.32, catvehicle)');
return;
end
close all;
%rosshutdown;
modelname = strcat('/',roboname);
%Connec... |
github | qboticslabs/Autoware-master | profileByMatrix.m | .m | Autoware-master/ros/src/system/gazebo/catvehicle/matlab files/profileByMatrix.m | 1,840 | utf_8 | f81181e46cb60adc3466c4779083ce0d | %Implementation of follower algorithm
%Developed by Rahul Kumar Bhadani <rahulbhadani@email.arizona.edu>
%ROS_IP = IP Address of ROS Master
%lead = name of the model of leader AV Car
%follower = name of the model of follower car
function profileByMatrix(ROS_IP, roboname, vel_input, time_input, tire_angle)
%If nu... |
github | qboticslabs/Autoware-master | velocityProfiler.m | .m | Autoware-master/ros/src/system/gazebo/catvehicle/matlab files/velocityProfiler.m | 1,758 | utf_8 | d12bb47043d421e21fc4fd4fa8ce4b02 | %Matlab scripto to publish velocity on /catvehicle/cmd_vel topic and
%subscribe to catvehicle/speed topic
%Developed by Rahul Kumar Bhadani <rahulbhadani@email.arizona.edu>
%ROS_IP = IP Address of ROS Master
%roboname = name of the model
function velocityProfiler(ROS_IP, roboname, tire_angle)
%If number of argument... |
github | qboticslabs/Autoware-master | follower_profile.m | .m | Autoware-master/ros/src/system/gazebo/catvehicle/matlab files/follower_profile.m | 2,381 | utf_8 | ea7d00e67f5e7709a2cd7287216af4af | %Implementation of follower algorithm
%Developed by Rahul Kumar Bhadani <rahulbhadani@email.arizona.edu>
%ROS_IP = IP Address of ROS Master
%lead = name of the model of leader AV Car
%follower = name of the model of follower car
function follower_profile(ROS_IP, lead, follower)
%If number of argument is not two, f... |
github | qboticslabs/Autoware-master | plotDisvout.m | .m | Autoware-master/ros/src/system/gazebo/catvehicle/simulink/plotDisvout.m | 241 | utf_8 | 241fa676f6444e00a60b8c69a5e19efd | % Author: Jonathan Sprinkle
% plots the distance outputs from a data file
function plotData( timeseries )
% this timeseries is what we have
figure
hold on
plot(timeseries.Data);
plot(timeseries.uVelOut);
legend({'Distance','VelOut'});
end |
github | qboticslabs/Autoware-master | plotData.m | .m | Autoware-master/ros/src/system/gazebo/catvehicle/simulink/plotData.m | 350 | utf_8 | 17951edcd31fa9c02deeb1c49a2e0d7b | % Author: Jonathan Sprinkle
% plots the distance outputs from a data file
function plotData( timeseries )
% this timeseries is what we have
figure
hold on
plot(timeseries.dist);
plot(timeseries.velConverted);
plot(timeseries.vdot);
plot(timeseries.vout);
plot(timeseries.uTireAngle);
legend({'dist','velConverted','vdo... |
github | qboticslabs/Autoware-master | plotDistances.m | .m | Autoware-master/ros/src/system/gazebo/catvehicle/simulink/plotDistances.m | 288 | utf_8 | c0779b1561faff69c733c5ec8a3a9ac7 | % Author: Jonathan Sprinkle
% plots the distance outputs from a data file
function plotDistances
load distances.mat
% this timeseries is what we have
figure
hold on
plot(DistanceEstimator.Data__signal_1_);
plot(DistanceEstimator.Data__signal_2_);
legend({'Distance','Angle (rad)'});
end |
github | LucienVen/gd-master | Select.m | .m | gd-master/algorithm_demo/GA/Select.m | 256 | utf_8 | 179fd71a71f4daeb87894c1540de8ea6 | %% 选择操作
%输入
%Chrom 种群
%FitnV 适应度值
%GGAP：代沟
%输出
%SelCh 被选择的个体
function SelCh=Select(Chrom,FitnV,GGAP)
NIND=size(Chrom,1);
NSel=max(floor(NIND*GGAP+.5),2);
ChrIx=Sus(FitnV,NSel);
SelCh=Chrom(ChrIx,:); |
github | LucienVen/gd-master | PathLength.m | .m | gd-master/algorithm_demo/GA/PathLength.m | 331 | utf_8 | 13ebb52c35fa4850fcf2511f98294da0 | %% 计算各个体的路径长度
% 输入：
% D 两两城市之间的距离
% Chrom 个体的轨迹
function len=PathLength(D,Chrom)
[row,col]=size(D);
NIND=size(Chrom,1);
len=zeros(NIND,1);
for i=1:NIND
p=[Chrom(i,:) Chrom(i,1)];
i1=p(1:end-1);
i2=p(2:end);
len(i,1)=sum(D((i1-1)*col+i2));
end
|
github | LucienVen/gd-master | InitPop.m | .m | gd-master/algorithm_demo/GA/InitPop.m | 290 | utf_8 | 6b17fdfc122469cbc5d1bfb4736c2100 | %% 初始化种群
%输入：
% NIND：种群大小
% N： 个体染色体长度（这里为城市的个数）
%输出：
%初始种群
function Chrom=InitPop(NIND,N)
Chrom=zeros(NIND,N);%用于存储种群
for i=1:NIND
Chrom(i,:)=randperm(N);%随机生成初始种群
end |
github | LucienVen/gd-master | Sus.m | .m | gd-master/algorithm_demo/GA/Sus.m | 985 | utf_8 | 6df9c400c837d331ed8072eab4de995e | % 输入:
%FitnV 个体的适应度值
%Nsel 被选择个体的数目
% 输出:
%NewChrIx 被选择个体的索引号
function NewChrIx = Sus(FitnV,Nsel)
[Nind,ans] = size(FitnV);
cumfit = cumsum(FitnV);
% 平均适应度*个体行号=适应度在每行的平均比例
trials = cumfit(Nind) / Nsel * (rand + (0:Nsel-1)');
% 适应度真实累加向量，拓展为Nind*Nsel维矩阵
Mf = cumfit(:, ones(1, Nsel));
% 适应度平均比例向量，拓展为Nsel... |
github | LucienVen/gd-master | Distanse.m | .m | gd-master/algorithm_demo/GA/Distanse.m | 303 | utf_8 | 6596609d0bd418a504c5658780a7f85c | %% 计算两两城市之间的距离
%输入 a 各城市的位置坐标
%输出 D 两两城市之间的距离
function D=Distanse(a)
row=size(a,1);
D=zeros(row,row);
for i=1:row
for j=i+1:row
D(i,j)=((a(i,1)-a(j,1))^2+(a(i,2)-a(j,2))^2)^0.5;
D(j,i)=D(i,j);
end
end
|
github | LucienVen/gd-master | OutputPath.m | .m | gd-master/algorithm_demo/GA/OutputPath.m | 186 | UNKNOWN | ae0e96e563d28fa8e74cc505593c99cd | %% ���·������
%���룺R ·��
function p=OutputPath(R)
R=[R,R(1)];
N=length(R);
p=num2str(R(1));
for i=2:N
p=[p,'->',num2str(R(i))];
end
disp(p)
|
github | LucienVen/gd-master | Recombin.m | .m | gd-master/algorithm_demo/GA/Recombin.m | 1,484 | utf_8 | 3e1af8d1876ce772ba45578149206262 | %% 交叉操作
% 输入
%SelCh 被选择的个体
%Pc 交叉概率
%输出：
% SelCh 交叉后的个体
function SelCh=Recombin(SelCh,Pc)
NSel=size(SelCh,1);
for i=1:2:NSel-mod(NSel,2)
if Pc>=rand %交叉概率Pc
[SelCh(i,:),SelCh(i+1,:)]=intercross(SelCh(i,:),SelCh(i+1,:));
end
end
%输入：
%a和b为两个待交叉的个体
%输出：
%a和b为交叉后得到的两个个体
function [... |
github | LucienVen/gd-master | Reins.m | .m | gd-master/algorithm_demo/GA/Reins.m | 338 | utf_8 | 12d9f6b6178ac2b153a1fba9781b143c | %% 重插入子代的新种群
%输入：
%Chrom 父代的种群
%SelCh 子代种群
%ObjV 父代适应度
%输出
% Chrom 组合父代与子代后得到的新种群
function Chrom=Reins(Chrom,SelCh,ObjV)
NIND=size(Chrom,1);
NSel=size(SelCh,1);
[TobjV,index]=sort(ObjV);
Chrom=[Chrom(index(1:NIND-NSel),:);SelCh];
|
github | LucienVen/gd-master | Reverse.m | .m | gd-master/algorithm_demo/GA/Reverse.m | 574 | utf_8 | 9bc395d8caadb1712d5f089b36a7fc01 | %% 进化逆转函数
%输入
%SelCh 被选择的个体
%D 个城市的距离矩阵
%输出
%SelCh 进化逆转后的个体
function SelCh=Reverse(SelCh,D)
[row,col]=size(SelCh);
ObjV=PathLength(D,SelCh); %计算路径长度
SelCh1=SelCh;
for i=1:row
r1=randsrc(1,1,[1:col]);
r2=randsrc(1,1,[1:col]);
mininverse=min([r1 r2]);
maxinverse=max([r1 r2]);
SelC... |
github | LucienVen/gd-master | DrawPath.m | .m | gd-master/algorithm_demo/GA/DrawPath.m | 637 | utf_8 | b3eab77cf2d7da9806543d7d85ff2f93 | %% 画路径函数
%输入
% Chrom 待画路径
% X 各城市坐标位置
function DrawPath(Chrom,X)
R=[Chrom(1,:) Chrom(1,1)]; %一个随机解(个体)
figure;
hold on
plot(X(:,1),X(:,2),'o','color',[0.5,0.5,0.5])
plot(X(Chrom(1,1),1),X(Chrom(1,1),2),'rv','MarkerSize',20)
for i=1:size(X,1)
text(X(i,1)+0.05,X(i,2)+0.05,num2str(i),'color',[1,0,0]);... |
github | LucienVen/gd-master | Mutate.m | .m | gd-master/algorithm_demo/GA/Mutate.m | 289 | utf_8 | b34c58d441f4ceffd3fdbf23eda39b62 | %% 变异操作
%输入：
%SelCh 被选择的个体
%Pm 变异概率
%输出：
% SelCh 变异后的个体
function SelCh=Mutate(SelCh,Pm)
[NSel,L]=size(SelCh);
for i=1:NSel
if Pm>=rand
R=randperm(L);
SelCh(i,R(1:2))=SelCh(i,R(2:-1:1));
end
end
|
github | LucienVen/gd-master | Fitness.m | .m | gd-master/algorithm_demo/GA/Fitness.m | 153 | utf_8 | feee0abaf4bf254c9cbad9e0e456053d | %% 适配值函数
%输入：
%个体的长度（TSP的距离）
%输出：
%个体的适应度值
function FitnV=Fitness(len)
FitnV=1./len;
|
github | BII-wushuang/FLLIT-master | intersections.m | .m | FLLIT-master/src/intersections.m | 11,443 | utf_8 | ea1423b06fc1ebab4dbd147edf8c0a07 | function [x0,y0,iout,jout] = intersections(x1,y1,x2,y2,robust)
%INTERSECTIONS Intersections of curves.
% Computes the (x,y) locations where two curves intersect. The curves
% can be broken with NaNs or have vertical segments.
%
% Example:
% [X0,Y0] = intersections(X1,Y1,X2,Y2,ROBUST);
%
% where X1 and Y1 are equ... |
github | BII-wushuang/FLLIT-master | DataProcessing_New.m | .m | FLLIT-master/src/DataProcessing_New.m | 29,995 | utf_8 | 7ca0ff2ca117284f58bb761b4646e067 | % Processes the raw data to extract relevant parameters
function DataProcessing_New(fps,data_dir,scale, bodylength)
if (nargin < 1)
fps = 2000;
scale=11; % dimension of arena is 11mm
data_dir = uigetdir('./Results/Tracking/');
bodylength = 2.88;
end
bodylength = bodylength * 512 / scale;
try
addp... |
github | BII-wushuang/FLLIT-master | Segmentation.m | .m | FLLIT-master/src/Segmentation.m | 8,894 | utf_8 | 89de5a8eba5bbe2e49b22b847761cc93 | %Classifier for leg segmentation
function Segmentation (data_dir,score_thres,foreground_thres,load_wl)
%% locate the image folders and the output folders
if (nargin < 1)
load_wl = 1;
score_thres = 0.65;
foreground_thres = 0.1;
data_dir = uigetdir('./Data');
addpath(genpath('./KernelBoost-v0.1/'));
e... |
github | BII-wushuang/FLLIT-master | FLLIT.m | .m | FLLIT-master/src/FLLIT.m | 94,500 | utf_8 | d0a100d59fd1e1a11f9e0a06ec94cee3 | % FLLIT program: GUI incorporating the program workflow
% This GUI is created with the MATLAB GUIDE feature.
% The pushbuttons call functions to perform individual tasks such as
% segmentation, tracking or data processing.
function varargout = FLLIT(varargin)
% FLLIT MATLAB code for FLLIT.fig
% FLLIT, by itself, c... |
github | BII-wushuang/FLLIT-master | connectify.m | .m | FLLIT-master/src/connectify.m | 2,905 | utf_8 | 31614bbf2f02d96c33f38684ed9b3eb9 | function Image = connectify(img, imroi, imseg)
%CONNECTIFY Find the disconnected components and group them leg-wise.
% Convert the pixel level segmentation result to an object level.
se1 = strel('disk',5);
se2 = strel('disk',0);
% Morphological operations to estimate the body of the fly
imbody = imerode(imroi,se1);
... |
github | BII-wushuang/FLLIT-master | Segmentation_Base.m | .m | FLLIT-master/src/Segmentation_Base.m | 1,141 | utf_8 | ad553607debd1a8254b7acf918d2c9e1 | %Classifier for leg segmentation
function Segmentation_Base (data_dir)
if(nargin<1)
data_dir = uigetdir('./Data');
end
pos_bs = strfind(data_dir,'Data');
sub_dir = data_dir(pos_bs(end)+length('Data'):length(data_dir));
data_dir = ['./Data' sub_dir '/'];
output_dir = ['./Results/SegmentedImages' sub_dir '/'];
if(~... |
github | BII-wushuang/FLLIT-master | clean_skeleton.m | .m | FLLIT-master/src/clean_skeleton.m | 1,995 | utf_8 | a56e0b53522559dc4596367baa437d66 | % A function to clean skeletons.
%
function [bw_body,bw_junction,img]=clean_skeleton(seg)
%thin process
img=bwmorph(seg,'skel','Inf');
%img=bwmorph(img,'spur');
% count the neighbours of the skeletons
neighbour_count=imfilter(uint8(img),ones(3));
bw_body=neighbour_count<=3 & img;
bw_junction=neighbour_count>3 & img;
b... |
github | BII-wushuang/FLLIT-master | uipickfiles.m | .m | FLLIT-master/src/uipickfiles.m | 48,144 | utf_8 | a2c259c89144d8e1e2ce065bad663669 | function out = uipickfiles(varargin)
%uipickfiles: GUI program to select files and/or folders.
%
% Syntax:
% files = uipickfiles('PropertyName',PropertyValue,...)
%
% The current folder can be changed by operating in the file navigator:
% double-clicking on a folder in the list or pressing Enter to move further
% dow... |
github | BII-wushuang/FLLIT-master | Tracking_Base.m | .m | FLLIT-master/src/Tracking_Base.m | 8,496 | utf_8 | 72f608bee2e36e1c5b5872785e7da70c | % A very simple tracker based on the hungarian linker method
% Outputs tip positions and identities of individual legs
function Tracking_Base (data_dir)
nLegs = 6; %preassume there are 6 legs
%% Section 1: locate image folder and create output folder
if (nargin < 1)
data_dir = uigetdir('./Data');
end
pos_bs = str... |
github | BII-wushuang/FLLIT-master | findmissing.m | .m | FLLIT-master/src/findmissing.m | 1,352 | utf_8 | cd60672044a321bbf585c163a0b5d667 | function findmissing()
data_dir = uipickfiles('FilterSpec',[pwd '/Results/Tracking']);
fileID = fopen('missing_tips.csv','w');
fprintf(fileID,'%s \t %s \t %s \t %s \t %s \t %s \t %s \t %s', 'Dataset', '#frames', 'L1', 'L2', 'L3', 'L4', 'L5', 'L6');
fprintf(fileID,'\n');
fclose(fileID);
for i = 1 : length(data_dir)
... |
github | BII-wushuang/FLLIT-master | Video_base.m | .m | FLLIT-master/src/Video_base.m | 6,558 | utf_8 | bc69c4af23e5170754e8fcda84c9abc9 | %Plot the trajectory of the fly's legs
function Video(data_dir,fps,skip,startframe,endframe,bodylength)
pos_bs = strfind(data_dir,'Data');
sub_dir = data_dir(pos_bs(end)+length('Data'):length(data_dir));
data_dir = [pwd '/Data' sub_dir '/'];
if(~isempty(dir([data_dir '*.tif'])))
img_list = dir([data_dir '*.tif']);... |
github | BII-wushuang/FLLIT-master | munkres.m | .m | FLLIT-master/src/munkres.m | 8,302 | utf_8 | 05adcef4b6504b76b07ca6f59e869d20 | function [assignment,cost] = munkres(costMat)
% MUNKRES Munkres (Hungarian) Algorithm for Linear Assignment Problem.
%
% [ASSIGN,COST] = munkres(COSTMAT) returns the optimal column indices,
% ASSIGN assigned to each row and the minimum COST based on the assignment
% problem represented by the COSTMAT, where the (i,j... |
github | BII-wushuang/FLLIT-master | pdftops.m | .m | FLLIT-master/src/Export-Fig/pdftops.m | 5,994 | utf_8 | 24eb803667c83c8a28424c979311652b | function varargout = pdftops(cmd)
%PDFTOPS Calls a local pdftops executable with the input command
%
% Example:
% [status result] = pdftops(cmd)
%
% Attempts to locate a pdftops executable, finally asking the user to
% specify the directory pdftops was installed into. The resulting path is
% stored for future refere... |
github | BII-wushuang/FLLIT-master | crop_borders.m | .m | FLLIT-master/src/Export-Fig/crop_borders.m | 4,976 | utf_8 | c814ff486afb188464069b51e4b5ed8a | function [A, vA, vB, bb_rel] = crop_borders(A, bcol, padding, crop_amounts)
%CROP_BORDERS Crop the borders of an image or stack of images
%
% [B, vA, vB, bb_rel] = crop_borders(A, bcol, [padding])
%
%IN:
% A - HxWxCxN stack of images.
% bcol - Cx1 background colour vector.
% padding - scalar indicating how much... |
github | BII-wushuang/FLLIT-master | isolate_axes.m | .m | FLLIT-master/src/Export-Fig/isolate_axes.m | 4,721 | utf_8 | 253cd7b7d8fc7cb00d0cc55926f32de5 | function fh = isolate_axes(ah, vis)
%ISOLATE_AXES Isolate the specified axes in a figure on their own
%
% Examples:
% fh = isolate_axes(ah)
% fh = isolate_axes(ah, vis)
%
% This function will create a new figure containing the axes/uipanels
% specified, and also their associated legends and colorbars. The objects
%... |
github | BII-wushuang/FLLIT-master | im2gif.m | .m | FLLIT-master/src/Export-Fig/im2gif.m | 6,048 | utf_8 | 5a7437140f8d013158a195de1e372737 | %IM2GIF Convert a multiframe image to an animated GIF file
%
% Examples:
% im2gif infile
% im2gif infile outfile
% im2gif(A, outfile)
% im2gif(..., '-nocrop')
% im2gif(..., '-nodither')
% im2gif(..., '-ncolors', n)
% im2gif(..., '-loops', n)
% im2gif(..., '-delay', n)
%
% This function converts a mu... |
github | BII-wushuang/FLLIT-master | read_write_entire_textfile.m | .m | FLLIT-master/src/Export-Fig/read_write_entire_textfile.m | 924 | utf_8 | 779e56972f5d9778c40dee98ddbd677e | %READ_WRITE_ENTIRE_TEXTFILE Read or write a whole text file to/from memory
%
% Read or write an entire text file to/from memory, without leaving the
% file open if an error occurs.
%
% Reading:
% fstrm = read_write_entire_textfile(fname)
% Writing:
% read_write_entire_textfile(fname, fstrm)
%
%IN:
% fname - Pathn... |
github | BII-wushuang/FLLIT-master | pdf2eps.m | .m | FLLIT-master/src/Export-Fig/pdf2eps.m | 1,471 | utf_8 | a1f41f0c7713c73886a2323e53ed982b | %PDF2EPS Convert a pdf file to eps format using pdftops
%
% Examples:
% pdf2eps source dest
%
% This function converts a pdf file to eps format.
%
% This function requires that you have pdftops, from the Xpdf suite of
% functions, installed on your system. This can be downloaded from:
% http://www.foolabs.com/xpdf ... |
github | BII-wushuang/FLLIT-master | print2array.m | .m | FLLIT-master/src/Export-Fig/print2array.m | 10,117 | utf_8 | 826905ad12ce0de461386980b4aae89b | function [A, bcol] = print2array(fig, res, renderer, gs_options)
%PRINT2ARRAY Exports a figure to an image array
%
% Examples:
% A = print2array
% A = print2array(figure_handle)
% A = print2array(figure_handle, resolution)
% A = print2array(figure_handle, resolution, renderer)
% A = print2array(figure_handle... |
github | BII-wushuang/FLLIT-master | append_pdfs.m | .m | FLLIT-master/src/Export-Fig/append_pdfs.m | 2,678 | utf_8 | 949c7c4ec3f5af6ff23099f17b1dfd79 | %APPEND_PDFS Appends/concatenates multiple PDF files
%
% Example:
% append_pdfs(output, input1, input2, ...)
% append_pdfs(output, input_list{:})
% append_pdfs test.pdf temp1.pdf temp2.pdf
%
% This function appends multiple PDF files to an existing PDF file, or
% concatenates them into a PDF file if the output fi... |
github | BII-wushuang/FLLIT-master | using_hg2.m | .m | FLLIT-master/src/Export-Fig/using_hg2.m | 1,064 | utf_8 | a1883d15c4304cd0ac406c117e3047ea | %USING_HG2 Determine if the HG2 graphics engine is used
%
% tf = using_hg2(fig)
%
%IN:
% fig - handle to the figure in question.
%
%OUT:
% tf - boolean indicating whether the HG2 graphics engine is being used
% (true) or not (false).
% 19/06/2015 - Suppress warning in R2015b; cache result for improved per... |
github | BII-wushuang/FLLIT-master | eps2pdf.m | .m | FLLIT-master/src/Export-Fig/eps2pdf.m | 8,602 | utf_8 | a52a68e75e8696267fb74733d396a237 | function eps2pdf(source, dest, crop, append, gray, quality, gs_options)
%EPS2PDF Convert an eps file to pdf format using ghostscript
%
% Examples:
% eps2pdf source dest
% eps2pdf(source, dest, crop)
% eps2pdf(source, dest, crop, append)
% eps2pdf(source, dest, crop, append, gray)
% eps2pdf(source, dest, crop... |
github | BII-wushuang/FLLIT-master | export_fig.m | .m | FLLIT-master/src/Export-Fig/export_fig.m | 63,939 | utf_8 | d501f71f10a1918328c0e9d450cd1ed3 | function [imageData, alpha] = export_fig(varargin)
%EXPORT_FIG Exports figures in a publication-quality format
%
% Examples:
% imageData = export_fig
% [imageData, alpha] = export_fig
% export_fig filename
% export_fig filename -format1 -format2
% export_fig ... -nocrop
% export_fig ... -c[<val>,<val>,<val... |
github | BII-wushuang/FLLIT-master | ghostscript.m | .m | FLLIT-master/src/Export-Fig/ghostscript.m | 7,706 | utf_8 | 92dbafb8d4fb243cae8716c6ecb0bbe5 | function varargout = ghostscript(cmd)
%GHOSTSCRIPT Calls a local GhostScript executable with the input command
%
% Example:
% [status result] = ghostscript(cmd)
%
% Attempts to locate a ghostscript executable, finally asking the user to
% specify the directory ghostcript was installed into. The resulting path
% is s... |
github | BII-wushuang/FLLIT-master | fix_lines.m | .m | FLLIT-master/src/Export-Fig/fix_lines.m | 6,290 | utf_8 | 8437006b104957762090e3d875688cb6 | %FIX_LINES Improves the line style of eps files generated by print
%
% Examples:
% fix_lines fname
% fix_lines fname fname2
% fstrm_out = fixlines(fstrm_in)
%
% This function improves the style of lines in eps files generated by
% MATLAB's print function, making them more similar to those seen on
% screen. Grid ... |
github | BII-wushuang/FLLIT-master | train_boost_context_v4.m | .m | FLLIT-master/src/KernelBoost-v0.1/train_boost_context_v4.m | 26,156 | utf_8 | 517c845afa1d5078668d16631157513e | %
% samples_idx(:,1) => sample image no
% samples_idx(:,2) => sample row
% samples_idx(:,3) => sample column
% samples_idx(:,4) => sample label (-1/+1)
function [weak_learners] = train_boost_context_v3(params,data,samples_idx)
% Train a KernelBoost classifier on the given samples
%
% authors: Carlos Becker, Roberto R... |
github | BII-wushuang/FLLIT-master | train_admm_lat_fix.m | .m | FLLIT-master/src/KernelBoost-v0.1/train_admm_lat_fix.m | 4,714 | utf_8 | eda4fdfab5dc7b3892e6ad9bd1a84b50 | % use the mask distance as well as the main branch distance
% collect the latent label
% eavluate the effect of auto context
% includes the latent label
% discard the kernel features and adopts the new admm features
% samples_idx(:,1) => sample image no
% samples_idx(:,2) => sample row
% samples_idx(:,3) => sample co... |
github | BII-wushuang/FLLIT-master | train_boost_ctx_ac.m | .m | FLLIT-master/src/KernelBoost-v0.1/train_boost_ctx_ac.m | 5,940 | utf_8 | b79ed0d183f4ecd4875b7cac7501df6c | % use the mask distance as well as the main branch distance
% eavluate the effect of auto context
% samples_idx(:,1) => sample image no
% samples_idx(:,2) => sample row
% samples_idx(:,3) => sample column
% samples_idx(:,4) => sample label (-1/+1)
function [weak_learners,weak_learners_ctx,weak_learners_ac] = train_boos... |
github | BII-wushuang/FLLIT-master | train_boost_general_v2.m | .m | FLLIT-master/src/KernelBoost-v0.1/train_boost_general_v2.m | 6,136 | utf_8 | 1010bed3cb1e38df54bdffd912ad6ee4 | %
% samples_idx(:,1) => sample image no
% samples_idx(:,2) => sample row
% samples_idx(:,3) => sample column
% samples_idx(:,4) => sample label (-1/+1)
function [weak_learners] = train_boost_general_v2(params,data,samples_idx)
% Train a KernelBoost classifier on the given samples
%
% authors: Carlos Becker, Roberto R... |
github | BII-wushuang/FLLIT-master | train_boost_context_HRF.m | .m | FLLIT-master/src/KernelBoost-v0.1/train_boost_context_HRF.m | 29,371 | utf_8 | 4bc94478de61653c0df23f8a47a4c38d | % use the mask distance as well as the main branch distance
% samples_idx(:,1) => sample image no
% samples_idx(:,2) => sample row
% samples_idx(:,3) => sample column
% samples_idx(:,4) => sample label (-1/+1)
function [weak_learners,weak_learners_ctx1] = train_boost_context_HRF(params,data,samples_idx)
% Train a Kern... |
github | BII-wushuang/FLLIT-master | train_boost_context_v3.m | .m | FLLIT-master/src/KernelBoost-v0.1/train_boost_context_v3.m | 25,557 | utf_8 | 5aa5932a1ada7fb8cf18470eb03d4606 | %
% samples_idx(:,1) => sample image no
% samples_idx(:,2) => sample row
% samples_idx(:,3) => sample column
% samples_idx(:,4) => sample label (-1/+1)
function [weak_learners] = train_boost_context_v3(params,data,samples_idx)
% Train a KernelBoost classifier on the given samples
%
% authors: Carlos Becker, Roberto R... |
github | BII-wushuang/FLLIT-master | train_boost_ctx_latent_img_debug.m | .m | FLLIT-master/src/KernelBoost-v0.1/train_boost_ctx_latent_img_debug.m | 6,410 | utf_8 | 8229269b926bdeb54873ea52c86d242a | % use the mask distance as well as the main branch distance
% collect the latent label
% eavluate the effect of auto context
% samples_idx(:,1) => sample image no
% samples_idx(:,2) => sample row
% samples_idx(:,3) => sample column
% samples_idx(:,4) => sample label (-1/+1)
function [weak_learners,weak_learners_ctx,we... |
github | BII-wushuang/FLLIT-master | train_admm_ctx_fix.m | .m | FLLIT-master/src/KernelBoost-v0.1/train_admm_ctx_fix.m | 5,704 | utf_8 | 6693d498880a21f4f3d8fd90e3046e81 | % use the mask distance as well as the main branch distance
% collect the latent label
% eavluate the effect of auto context
% includes the latent label
% discard the kernel features and adopts the new admm features
% samples_idx(:,1) => sample image no
% samples_idx(:,2) => sample row
% samples_idx(:,3) => sample co... |
github | BII-wushuang/FLLIT-master | train_boost_latent_img_v2.m | .m | FLLIT-master/src/KernelBoost-v0.1/train_boost_latent_img_v2.m | 5,988 | utf_8 | 6d2642ea978b7da9056495d29ecf656f | % use the mask distance as well as the main branch distance
% eavluate the effect of auto context
% samples_idx(:,1) => sample image no
% samples_idx(:,2) => sample row
% samples_idx(:,3) => sample column
% samples_idx(:,4) => sample label (-1/+1)
function [weak_learners,weak_learners_ctx,weak_learners_ac] = train_boos... |
github | BII-wushuang/FLLIT-master | train_boost_ctxplus_lftrs.m | .m | FLLIT-master/src/KernelBoost-v0.1/train_boost_ctxplus_lftrs.m | 24,119 | utf_8 | aee065fc88ff5c243e5a950b83f26c06 | % use the mask distance as well as the main branch distance
% samples_idx(:,1) => sample image no
% samples_idx(:,2) => sample row
% samples_idx(:,3) => sample column
% samples_idx(:,4) => sample label (-1/+1)
function [weak_learners,weak_learners_ctx1] = train_boost_ctxplus_lftrs(params,data,samples_idx)
% Train a Ke... |
github | BII-wushuang/FLLIT-master | train_RF_ctx.m | .m | FLLIT-master/src/KernelBoost-v0.1/train_RF_ctx.m | 4,195 | utf_8 | def6c9c60931fb18d7d9eb07038a6cca | % use the mask distance as well as the main branch distance
% evaluate the effect of auto context
% takes the random forest framework
% samples_idx(:,1) => sample image no
% samples_idx(:,2) => sample row
% samples_idx(:,3) => sample column
% samples_idx(:,4) => sample label (-1/+1)
function [weak_learners,weak_learne... |
github | BII-wushuang/FLLIT-master | train_boost_context.m | .m | FLLIT-master/src/KernelBoost-v0.1/train_boost_context.m | 23,755 | utf_8 | e372e8bdd37a924891c53b0a9c01edb2 | %
% samples_idx(:,1) => sample image no
% samples_idx(:,2) => sample row
% samples_idx(:,3) => sample column
% samples_idx(:,4) => sample label (-1/+1)
function [weak_learners] = train_boost_context(params,data,samples_idx)
% Train a KernelBoost classifier on the given samples
%
% authors: Carlos Becker, Roberto Riga... |
github | BII-wushuang/FLLIT-master | train_boost_context_v8.m | .m | FLLIT-master/src/KernelBoost-v0.1/train_boost_context_v8.m | 16,247 | utf_8 | db83495a2d7559523cf4feda14816fd3 | % use the mask distance as well as the main branch distance
% samples_idx(:,1) => sample image no
% samples_idx(:,2) => sample row
% samples_idx(:,3) => sample column
% samples_idx(:,4) => sample label (-1/+1)
function [weak_learners,weak_learners_ctx1] = train_boost_context_v8(params,data,samples_idx)
% Train a Kerne... |
github | BII-wushuang/FLLIT-master | train_boost_context_v6.m | .m | FLLIT-master/src/KernelBoost-v0.1/train_boost_context_v6.m | 28,277 | utf_8 | af53953ab4065ff89b4a847d878f0501 | %
% samples_idx(:,1) => sample image no
% samples_idx(:,2) => sample row
% samples_idx(:,3) => sample column
% samples_idx(:,4) => sample label (-1/+1)
function [weak_learners] = train_boost_context_v6(params,data,samples_idx)
% Train a KernelBoost classifier on the given samples
%
% authors: Carlos Becker, Roberto R... |
github | BII-wushuang/FLLIT-master | train_LTM_validation_3D.m | .m | FLLIT-master/src/KernelBoost-v0.1/train_LTM_validation_3D.m | 7,392 | utf_8 | ed681933bec2d489d679a407dac8bb0f | %
% samples_idx(:,1) => sample image no
% samples_idx(:,2) => sample row
% samples_idx(:,3) => sample column
% samples_idx(:,4) => sample label (-1/+1)
function [weak_learners,train_scores] = train_LTM_validation_3D(params,fn,ftrs,wgt,samples_idx)
% Train a KernelBoost classifier on the given samples
% the classifier... |
github | BII-wushuang/FLLIT-master | train_boost_ctx_ac_v2.m | .m | FLLIT-master/src/KernelBoost-v0.1/train_boost_ctx_ac_v2.m | 6,845 | utf_8 | 074895ccf168b760b801b029f0a692d2 | % use the mask distance as well as the main branch distance
% eavluate the effect of auto context
% samples_idx(:,1) => sample image no
% samples_idx(:,2) => sample row
% samples_idx(:,3) => sample column
% samples_idx(:,4) => sample label (-1/+1)
function [weak_learners,weak_learners_ctx,weak_learners_ac] = train_boos... |
github | BII-wushuang/FLLIT-master | train_boost_ctx_debug.m | .m | FLLIT-master/src/KernelBoost-v0.1/train_boost_ctx_debug.m | 17,266 | utf_8 | e04fe67438959b8d920c36c60326a167 | % use the mask distance as well as the main branch distance
% samples_idx(:,1) => sample image no
% samples_idx(:,2) => sample row
% samples_idx(:,3) => sample column
% samples_idx(:,4) => sample label (-1/+1)
function [weak_learners,weak_learners_ctx1] = train_boost_ctx_debug(params,data,samples_idx)
% Train a Kernel... |
github | BII-wushuang/FLLIT-master | train_boost_ctx_latent_img.m | .m | FLLIT-master/src/KernelBoost-v0.1/train_boost_ctx_latent_img.m | 6,404 | utf_8 | 841e2c372107c2f583b349576c351b15 | % use the mask distance as well as the main branch distance
% collect the latent label
% eavluate the effect of auto context
% samples_idx(:,1) => sample image no
% samples_idx(:,2) => sample row
% samples_idx(:,3) => sample column
% samples_idx(:,4) => sample label (-1/+1)
function [weak_learners,weak_learners_ctx,we... |
github | BII-wushuang/FLLIT-master | train_boost_lat_img_node_dist.m | .m | FLLIT-master/src/KernelBoost-v0.1/train_boost_lat_img_node_dist.m | 9,152 | utf_8 | 8b1524b85dff52bd2f964deacefc5bd1 | % use the mask distance as well as the main branch distance
% collect the latent label
% eavluate the effect of auto context
% samples_idx(:,1) => sample image no
% samples_idx(:,2) => sample row
% samples_idx(:,3) => sample column
% samples_idx(:,4) => sample label (-1/+1)
function [weak_learners,weak_learners_ctx,we... |
github | BII-wushuang/FLLIT-master | train_boost_context_v2.m | .m | FLLIT-master/src/KernelBoost-v0.1/train_boost_context_v2.m | 25,632 | utf_8 | 3b1cd934b09cd47ddf2c794aab407783 | %
% samples_idx(:,1) => sample image no
% samples_idx(:,2) => sample row
% samples_idx(:,3) => sample column
% samples_idx(:,4) => sample label (-1/+1)
function [weak_learners] = train_boost_context_v2(params,data,samples_idx)
% Train a KernelBoost classifier on the given samples
%
% authors: Carlos Becker, Roberto R... |
github | BII-wushuang/FLLIT-master | train_boost_3D_CLRG_combined.m | .m | FLLIT-master/src/KernelBoost-v0.1/train_boost_3D_CLRG_combined.m | 7,462 | utf_8 | 645b471aa41ba66468a056e0ed21fb45 | %
% samples_idx(:,1) => sample image no
% samples_idx(:,2) => sample row
% samples_idx(:,3) => sample column
% samples_idx(:,4) => sample label (-1/+1)
function [weak_learners,train_scores] = train_boost_3D_CLRG_combined(params,data,ftrs,wgt,samples_idx,LTClassifier)
% Train a KernelBoost classifier on the given sampl... |
github | BII-wushuang/FLLIT-master | train_boost_ctx.m | .m | FLLIT-master/src/KernelBoost-v0.1/train_boost_ctx.m | 17,147 | utf_8 | 33c735dea4921d444df3d77daea1f804 | % use the mask distance as well as the main branch distance
% samples_idx(:,1) => sample image no
% samples_idx(:,2) => sample row
% samples_idx(:,3) => sample column
% samples_idx(:,4) => sample label (-1/+1)
function [weak_learners,weak_learners_ctx1] = train_boost_ctx(params,data,samples_idx)
% Train a KernelBoost ... |
github | BII-wushuang/FLLIT-master | train_boost_weight_3D.m | .m | FLLIT-master/src/KernelBoost-v0.1/train_boost_weight_3D.m | 7,356 | utf_8 | 838979308d182708f995a8a848328582 | %
% samples_idx(:,1) => sample image no
% samples_idx(:,2) => sample row
% samples_idx(:,3) => sample column
% samples_idx(:,4) => sample label (-1/+1)
function [weak_learners,train_scores] = train_boost_weight_3D(params,data,ftrs,wgt,samples_idx)
% Train a KernelBoost classifier on the given samples
% the classifier... |
github | BII-wushuang/FLLIT-master | train_boost_weight.m | .m | FLLIT-master/src/KernelBoost-v0.1/train_boost_weight.m | 6,870 | utf_8 | 36b46cfd41edf9337ff82b2b50fa1f21 | %
% samples_idx(:,1) => sample image no
% samples_idx(:,2) => sample row
% samples_idx(:,3) => sample column
% samples_idx(:,4) => sample label (-1/+1)
function [weak_learners,train_scores] = train_boost_weight(params,data,ftrs,wgt,samples_idx)
% Train a KernelBoost classifier on the given samples
% the classifier co... |
github | BII-wushuang/FLLIT-master | train_admm_lat_3D.m | .m | FLLIT-master/src/KernelBoost-v0.1/train_admm_lat_3D.m | 3,970 | utf_8 | 32e6c6e68884c9ef2e69079f488d343b | % use the mask distance as well as the main branch distance
% collect the latent label
% eavluate the effect of auto context
% includes the latent label
% discard the kernel features and adopts the new admm features
% samples_idx(:,1) => sample image no
% samples_idx(:,2) => sample row
% samples_idx(:,3) => sample co... |
github | BII-wushuang/FLLIT-master | train_admm_lat.m | .m | FLLIT-master/src/KernelBoost-v0.1/train_admm_lat.m | 3,783 | utf_8 | 627afc057b202ef998ede2ba0d357279 | % use the mask distance as well as the main branch distance
% collect the latent label
% eavluate the effect of auto context
% includes the latent label
% discard the kernel features and adopts the new admm features
% samples_idx(:,1) => sample image no
% samples_idx(:,2) => sample row
% samples_idx(:,3) => sample co... |
github | BII-wushuang/FLLIT-master | train_boost_context_v7.m | .m | FLLIT-master/src/KernelBoost-v0.1/train_boost_context_v7.m | 29,256 | utf_8 | 8ebd680d69ea266a4817f7d1d862a812 | % use the mask distance as well as the main branch distance
% samples_idx(:,1) => sample image no
% samples_idx(:,2) => sample row
% samples_idx(:,3) => sample column
% samples_idx(:,4) => sample label (-1/+1)
function [weak_learners] = train_boost_context_v7(params,data,samples_idx)
% Train a KernelBoost classifier o... |
github | BII-wushuang/FLLIT-master | train_RF_ctx_ac.m | .m | FLLIT-master/src/KernelBoost-v0.1/train_RF_ctx_ac.m | 5,974 | utf_8 | eb15d4fd53fd945070a6be25d7eb9b5a | % use the mask distance as well as the main branch distance
% evaluate the effect of auto context
% takes the random forest framework
% samples_idx(:,1) => sample image no
% samples_idx(:,2) => sample row
% samples_idx(:,3) => sample column
% samples_idx(:,4) => sample label (-1/+1)
function [weak_learners,weak_learne... |
github | BII-wushuang/FLLIT-master | train_admm_lat_fn.m | .m | FLLIT-master/src/KernelBoost-v0.1/train_admm_lat_fn.m | 3,832 | utf_8 | b55e33e32ebb694abb32b0e2b5b567d1 | % use the mask distance as well as the main branch distance
% train the model on the 3D training dataset
% collect the latent label
% eavluate the effect of auto context
% includes the latent label
% discard the kernel features and adopts the new admm features
% samples_idx(:,1) => sample image no
% samples_idx(:,2) ... |
github | BII-wushuang/FLLIT-master | train_boost_context_v9.m | .m | FLLIT-master/src/KernelBoost-v0.1/train_boost_context_v9.m | 16,448 | utf_8 | 320fda1a607dfc3e1e2024beb5ebcb0f | % use the mask distance as well as the main branch distance
% samples_idx(:,1) => sample image no
% samples_idx(:,2) => sample row
% samples_idx(:,3) => sample column
% samples_idx(:,4) => sample label (-1/+1)
function [weak_learners,weak_learners_ctx1] = train_boost_context_v9(params,data,samples_idx)
% Train a Kerne... |
github | BII-wushuang/FLLIT-master | train_LTM_validation_3D_v2.m | .m | FLLIT-master/src/KernelBoost-v0.1/train_LTM_validation_3D_v2.m | 8,398 | utf_8 | 8b676a7e266f354890aae827c06525c1 | %
% samples_idx(:,1) => sample image no
% samples_idx(:,2) => sample row
% samples_idx(:,3) => sample column
% samples_idx(:,4) => sample label (-1/+1)
function weak_learners = train_LTM_validation_3D_v2(params,features,wgt,samples_idx)
% Train a KernelBoost classifier on the given samples
% the classifier combine th... |
github | BII-wushuang/FLLIT-master | train_boost_context_v5.m | .m | FLLIT-master/src/KernelBoost-v0.1/train_boost_context_v5.m | 28,296 | utf_8 | 7ce667f2a31b5dc8ea96e68e951bb4b3 | %
% samples_idx(:,1) => sample image no
% samples_idx(:,2) => sample row
% samples_idx(:,3) => sample column
% samples_idx(:,4) => sample label (-1/+1)
function [weak_learners] = train_boost_context_v5(params,data,samples_idx)
% Train a KernelBoost classifier on the given samples
%
% authors: Carlos Becker, Roberto R... |
github | BII-wushuang/FLLIT-master | train_GB_ctx.m | .m | FLLIT-master/src/KernelBoost-v0.1/train_GB_ctx.m | 5,315 | utf_8 | 614ffbd7be504c723ec2cb4499eaf099 | % use the mask distance as well as the main branch distance
% evaluate the effect of auto context
% takes the gradient boost framework
% samples_idx(:,1) => sample image no
% samples_idx(:,2) => sample row
% samples_idx(:,3) => sample column
% samples_idx(:,4) => sample label (-1/+1)
function [weak_learners,weak_learn... |
github | BII-wushuang/FLLIT-master | train_boost_general.m | .m | FLLIT-master/src/KernelBoost-v0.1/train_boost_general.m | 6,090 | utf_8 | 9d6c05c3d19de9ecd4d47fac5840fc11 | %
% samples_idx(:,1) => sample image no
% samples_idx(:,2) => sample row
% samples_idx(:,3) => sample column
% samples_idx(:,4) => sample label (-1/+1)
function [weak_learners] = train_boost_general(params,data,samples_idx)
% Train a KernelBoost classifier on the given samples
%
% authors: Carlos Becker, Roberto Riga... |
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