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value | path stringlengths 12 229 | size int64 23 843k | source_encoding stringclasses 9
values | md5 stringlengths 32 32 | text stringlengths 23 843k |
|---|---|---|---|---|---|---|---|---|
github | jiangyc92/LPPLModel-master | Train.m | .m | LPPLModel-master/@LPPL/Train.m | 1,478 | utf_8 | 2dd7765bf753ce5cac8fb67fe9324762 | function Train(obj, Times, Prices, t0)
% Train function solve the optimization problem and get the optimal
% paramater
% obj: class instantce
% Times: time sequence ( suggested to be 1:N)
% Prices: prices list
% t0: the start point for the optimization
ObjFunc = @(t) Func2(t, Times, log(Prices));
OptimProblem = create... |
github | arvind96/Quantum-Mechanics-Simulations-master | ParticleInBoxWave.m | .m | Quantum-Mechanics-Simulations-master/ParticleInBoxWave.m | 365 | utf_8 | 172edcaf04f357dfbd97d910285f59be | function [z] = ParticleInBoxWave(L, c1, c2, c3, x, t)
z = c1 * (2/L)^0.5 *sin(1*pi*x / L) * exp(-1i*CalculateEnergy(1, L)*t / 1) + c2 * (2/L)^0.5 *sin(2*pi*x / L) * exp(-1i*CalculateEnergy(2, L)*t / 1) + c3 * (2/L)^0.5 *sin(3*pi*x / L) * exp(-1i*CalculateEnergy(3, L)*t / 1);
end
function e = CalculateEnergy(n, L)
... |
github | arvind96/Quantum-Mechanics-Simulations-master | StartHydrogenAtomProbabilityDensity.m | .m | Quantum-Mechanics-Simulations-master/StartHydrogenAtomProbabilityDensity.m | 13,878 | utf_8 | c8e23f45fdd7a45863bd79212f7c1dfe | function StartHydrogenAtomProbabilityDensity()
global HydrogenOrbitalGenerationRunning;
if(HydrogenOrbitalGenerationRunning == 1)
return;
end
HydrogenOrbitalGenerationRunning = 1;
global MainHandle; %stores the handle for MainGUI
set(MainHandle.uipanelTopControls, 'Title', 'HYDROGEN ATOM');
set(MainHandle.uip... |
github | arvind96/Quantum-Mechanics-Simulations-master | MainGUI.m | .m | Quantum-Mechanics-Simulations-master/MainGUI.m | 23,097 | utf_8 | b9465e2244f464f5377c58a8ad119079 | function varargout = MainGUI(varargin)
% MAINGUI MATLAB code for MainGUI.fig
% MAINGUI, by itself, creates a new MAINGUI or raises the existing
% singleton*.
%
% H = MAINGUI returns the handle to a new MAINGUI or the handle to
% the existing singleton*.
%
% MAINGUI('CALLBACK',hObject,eventData,... |
github | arvind96/Quantum-Mechanics-Simulations-master | AboutGUI.m | .m | Quantum-Mechanics-Simulations-master/AboutGUI.m | 2,805 | utf_8 | ff0e33b2df131ffeef7d28442adfc2f3 | function varargout = AboutGUI(varargin)
% ABOUTGUI MATLAB code for AboutGUI.fig
% ABOUTGUI, by itself, creates a new ABOUTGUI or raises the existing
% singleton*.
%
% H = ABOUTGUI returns the handle to a new ABOUTGUI or the handle to
% the existing singleton*.
%
% ABOUTGUI('CALLBACK',hObject,ev... |
github | arvind96/Quantum-Mechanics-Simulations-master | ParticleInBoxDiffWave.m | .m | Quantum-Mechanics-Simulations-master/ParticleInBoxDiffWave.m | 410 | utf_8 | 704d76e4545c7557b2ea35e4d41b1f78 | function [z] = ParticleInBoxDiffWave(L, c1, c2, c3, x, t)
z = c1 * (2/L)^0.5 * (1*pi / L) *cos(1*pi*x / L) * exp(-1i*CalculateEnergy(1, L)*t / 1) + + c2 * (2/L)^0.5 * (2*pi / L) *cos(2*pi*x / L) * exp(-1i*CalculateEnergy(2, L)*t / 1) + c3 * (2/L)^0.5 * (3*pi / L) *cos(3*pi*x / L) * exp(-1i*CalculateEnergy(3, L)*t /... |
github | arvind96/Quantum-Mechanics-Simulations-master | ParticleInFiniteBoxWave.m | .m | Quantum-Mechanics-Simulations-master/ParticleInFiniteBoxWave.m | 1,768 | utf_8 | 966c7dfe606ca2090b66ddd56ee0bfcc | function [z] = ParticleInFiniteBoxWave(c1, c2, c3, x, t)
%returns the wavefunction with center of box as origin and length L
%v1 = 1.28
%v2 = 2.54
%v3 = 3.73
L = 10;
if(x < -L/2) %-Inf to -L/2
z = c1 * 27.5606 * exp((2 * (0.632 - CalculateEnergy(1.28, L)))^0.5 * x) * ex... |
github | jehoons/sbie_weinberg-master | savejson.m | .m | sbie_weinberg-master/module/ifa/matlab/libs/jsonlab-1.2/jsonlab/savejson.m | 18,983 | utf_8 | 2f510ad749556cadd303786e2549f30a | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | jehoons/sbie_weinberg-master | loadjson.m | .m | sbie_weinberg-master/module/ifa/matlab/libs/jsonlab-1.2/jsonlab/loadjson.m | 16,145 | ibm852 | 7582071c5bd7f5e5f74806ce191a9078 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | jehoons/sbie_weinberg-master | loadubjson.m | .m | sbie_weinberg-master/module/ifa/matlab/libs/jsonlab-1.2/jsonlab/loadubjson.m | 13,300 | utf_8 | b15e959f758c5c2efa2711aa79c443fc | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id$
%
% input:
% fname: ... |
github | jehoons/sbie_weinberg-master | saveubjson.m | .m | sbie_weinberg-master/module/ifa/matlab/libs/jsonlab-1.2/jsonlab/saveubjson.m | 17,723 | utf_8 | 3414421172c05225dfbd4a9c8c76e6b3 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | jehoons/sbie_weinberg-master | savejson.m | .m | sbie_weinberg-master/module/attractor/fumia/matlab/libs/jsonlab-1.2/jsonlab/savejson.m | 18,983 | utf_8 | 2f510ad749556cadd303786e2549f30a | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | jehoons/sbie_weinberg-master | loadjson.m | .m | sbie_weinberg-master/module/attractor/fumia/matlab/libs/jsonlab-1.2/jsonlab/loadjson.m | 16,145 | ibm852 | 7582071c5bd7f5e5f74806ce191a9078 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | jehoons/sbie_weinberg-master | loadubjson.m | .m | sbie_weinberg-master/module/attractor/fumia/matlab/libs/jsonlab-1.2/jsonlab/loadubjson.m | 13,300 | utf_8 | b15e959f758c5c2efa2711aa79c443fc | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id$
%
% input:
% fname: ... |
github | jehoons/sbie_weinberg-master | saveubjson.m | .m | sbie_weinberg-master/module/attractor/fumia/matlab/libs/jsonlab-1.2/jsonlab/saveubjson.m | 17,723 | utf_8 | 3414421172c05225dfbd4a9c8c76e6b3 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | LucaDeSiena/MuRAT-master | Murat_test.m | .m | MuRAT-master/bin/Murat_test.m | 4,852 | utf_8 | 52d650fdb0af7a90357016b5a240ec71 | function [image, SAChdr] = Murat_test(nameWaveform,...
centralFrequencies,smoothingC,figOutput,verboseOutput)
% TEST seismogram envelopes for changes in broadening
% CREATES a figure with seismograms and envelopes for different frequencies
%
% Input Parameters:
% nameWaveform: name of the SAC ... |
github | LucaDeSiena/MuRAT-master | Murat_Qc.m | .m | MuRAT-master/bin/Murat_Qc.m | 4,811 | utf_8 | c1f465e321541a0c536c806a1c2ed599 | function [inverseQc_i, uncertaintyQc_i] = Murat_Qc(cf,sped,sp_i,...
cursorCodaStart_i,cursorCodaEnd_i,tCoda_i,srate_i,QcMeasurement)
% function [inverseQc_i, uncertaintyQc_i] = Murat_Qc(cf,sped,sp_i,...
% cursorCodaStart_i,cursorCodaEnd_i,tCoda_i,srate_i,QcMeasurement)
%
% MEASURES Qc and its uncertaint... |
github | LucaDeSiena/MuRAT-master | Murat_velocity.m | .m | MuRAT-master/bin/Murat_velocity.m | 1,488 | utf_8 | 1de5fbc525bde1a813520c7f5bac4afc | % FUNCTION Murat_velocity: It finds the velocity at the point (xx,yy,zz) by
% linear interpolation.
function v = Murat_velocity(xx,yy,zz,gridD,pvel)
%
% CALCULATES the velocity at xx, yy, and zz by linear interpolation
%
% Input parameters:
% xx: x point
% yy: y point
% zz: ... |
github | LucaDeSiena/MuRAT-master | Murat_dataParallelized.m | .m | MuRAT-master/bin/Murat_dataParallelized.m | 9,743 | utf_8 | 556beb9aa8bf6ba90035092e662c94d3 | function Murat = Murat_dataParallelized(Murat)
% MEASURES Qc, peak-delay and Q for each seismic trace located in a folder.
% This code is a collection of functions that do all the necessary.
% Inputs
listSac = Murat.input.listSac;
lengthData ... |
github | LucaDeSiena/MuRAT-master | Murat_paasschensFunction.m | .m | MuRAT-master/bin/Murat_paasschensFunction.m | 2,567 | utf_8 | 99c9b500b261025953898c086ccb52b9 | function [t0,A0,N,coda,t] = Murat_paasschensFunction(r,v,B0,Le_1,dt,T)
% function [t0,A0,N,coda,t] = Murat_paasschensFunction(r,v,B0,Le_1,dt,T)
%
% CREATES the Paasschens function for a fixed r, with constants v,B0,Le_1,
% for points in the vector t until t_max given by T.
%
% Structure:
% The Paasschens f... |
github | LucaDeSiena/MuRAT-master | Murat_inversion.m | .m | MuRAT-master/bin/Murat_inversion.m | 12,333 | utf_8 | 3cddd5208ee1f1700c76c454229961d6 | %% Peak-delay, Qc and Q TOMOGRAPHIC INVERSIONS
function Murat = Murat_inversion(Murat)
%%
% Importing all the necessary inputs and data for plotting
FLabel = Murat.input.label;
outputLCurve = Murat.input.lCurve;
tWm ... |
github | LucaDeSiena/MuRAT-master | Murat_selection.m | .m | MuRAT-master/bin/Murat_selection.m | 6,663 | utf_8 | 3670e8259ff2851b2f20fcb2bfbc3719 | %% Seismic attributesare selected and components are considered
function Murat = Murat_selection(Murat)
% SELECTS inputs and data
components = Murat.input.components;
tresholdnoise = Murat.input.tresholdNoise;
modv ... |
github | LucaDeSiena/MuRAT-master | Murat_data.m | .m | MuRAT-master/bin/Murat_data.m | 9,728 | utf_8 | cc1f113fb803fb0a712364bf20d24da9 | function Murat = Murat_data(Murat)
% MEASURES Qc, peak-delay and Q for each seismic trace located in a folder.
% This code is a collection of functions that do all the necessary.
% Inputs
listSac = Murat.input.listSac;
lengthData ... |
github | LucaDeSiena/MuRAT-master | Murat_testData.m | .m | MuRAT-master/bin/Murat_testData.m | 3,958 | utf_8 | 0754655d61be06cd08094cda36762c52 | function [muratHeader,flag] =...
Murat_testData(folderPath,originTime,PTime,STime)
% TEST all seismograms in a folder for the input parameters and
% CREATES a file storing the parameters and flagging those missing
%
% Input Parameters:
% folderPath: folder containing the SAC data
% originTime: o... |
github | LucaDeSiena/MuRAT-master | Murat_plot.m | .m | MuRAT-master/bin/Murat_plot.m | 23,641 | utf_8 | 2f3db3d30d35e77bfd856cec3e0ebb1f | %% MURAT_PLOT Creates files for visualization in Matlab and Paraview
function Murat = Murat_plot(Murat)
%%
% Importing all the necessary inputs and data for plotting
FLabel = Murat.input.label;
origin = Murat.input.origin;
ending ... |
github | LucaDeSiena/MuRAT-master | Murat_checks.m | .m | MuRAT-master/bin/Murat_checks.m | 4,102 | utf_8 | 68fdc004d6d957ab6590dbec71161ed5 | % ADDITIONAL input variables that are not set by the user.
function Murat = Murat_checks(Murat)
% INPUTS
dataDirectory = ['./' Murat.input.dataDirectory];
PTime = ['SAChdr.times.' Murat.input.PTime];
PorS = Murat.input.POrS... |
github | LucaDeSiena/MuRAT-master | checkerBoard3D.m | .m | MuRAT-master/Utilities_Matlab/GIBBON/checkerBoard3D.m | 1,470 | utf_8 | 635da4564067b7f09920be527a385cc1 | function M=checkerBoard3D(varargin)
% function M=checkerBoard3D(siz)
% ------------------------------------------------------------------------
% This function creates a checkboard image of the size siz whereby elements
% are either black (0) or white (1). The first element is white.
% example: siz=[12 12 6]; blockSiz... |
github | LucaDeSiena/MuRAT-master | inpaintn.m | .m | MuRAT-master/Utilities_Matlab/GIBBON/inpaintn.m | 11,643 | utf_8 | e056d112de8a3d4da94ffedf055d72b2 | function y = inpaintn(x,n,y0,m)
% INPAINTN Inpaint over missing data in N-D array
% Y = INPAINTN(X) replaces the missing data in X by extra/interpolating
% the non-missing elements. The non finite values (NaN or Inf) in X are
% considered as missing data. X can be any N-D array.
%
% INPAINTN (no input/o... |
github | LucaDeSiena/MuRAT-master | vtkwrite.m | .m | MuRAT-master/Utilities_Matlab/VTKWRITE/vtkwrite.m | 11,698 | utf_8 | b2d2311772bb3c962cf4c421b43f3ea2 | function vtkwrite( filename,dataType,varargin )
% VTKWRITE Writes 3D Matlab array into VTK file format.
% vtkwrite(filename,'structured_grid',x,y,z,'vectors',title,u,v,w) writes
% a structured 3D vector data into VTK file, with name specified by the string
% filename. (u,v,w) are the vector components at the points ... |
github | LucaDeSiena/MuRAT-master | colMapGen.m | .m | MuRAT-master/Utilities_Matlab/COLORMAP/colMapGen.m | 3,113 | utf_8 | a2cb6d462c88f7316457c16e3547033b |
function [colMap] = colMapGen(topCol,botCol,numCol,varargin)
% Creates a colormap using two boundary colors and one middle
% color. Both boundary colors blend into the middle color.
% By default, the middle color is white. This can be changed using
% the 'midCol' name-value pair argument. Input ... |
github | LucaDeSiena/MuRAT-master | fwrite_sac.m | .m | MuRAT-master/Utilities_Matlab/F_SAC/fwrite_sac.m | 14,136 | utf_8 | a0783d7ee600d31d1d6413745197c71f | function sac_mat = fwrite_sac(sac_mat, varargin)
% FWRITE_SAC Write a SAC struct variable into a binary file.
%#########################################
%# #
%# [Function] #
%# Write SAC-formatted files #
%# ... |
github | LucaDeSiena/MuRAT-master | fread_sac.m | .m | MuRAT-master/Utilities_Matlab/F_SAC/fread_sac.m | 12,145 | utf_8 | c6aa24aba27e817a7b2e978bb200ae22 | function varargout = fread_sac(varargin)
% FREAD_SAC Read SAC-formatted files and save as struct variables.
%#########################################
%# #
%# [Function] #
%# Read SAC-formatted files #
%# ... |
github | LucaDeSiena/MuRAT-master | Murat_test.m | .m | MuRAT-master/Utilities_Matlab/MyUtilities/Murat_test.m | 4,824 | utf_8 | 33a4360f329c71f7e4cf2e0acd3156ef | function [image, SAChdr] = Murat_test(nameWaveform,...
centralFrequencies,smoothingC,figOutput,verboseOutput)
% TEST seismogram envelopes for changes in broadening
% CREATES a figure with seismograms and envelopes for different frequencies
%
% Input Parameters:
% nameWaveform: name of the SAC ... |
github | LucaDeSiena/MuRAT-master | Murat_changeHdr.m | .m | MuRAT-master/Utilities_Matlab/MyUtilities/Murat_changeHdr.m | 1,907 | utf_8 | 6bc33f6d31d6da6139f47c74360de77d | %% CHANGES the header of sac files to include the pickings for MSH
function seism = Murat_changeHdr(newFolder)
% function seism = Murat_changeHdr(newfolder)
% CHANGES header of a file
%
% Input Parameters:
% newFolder: folder where you save the changed file
%
% ... |
github | LucaDeSiena/MuRAT-master | Murat_testAll.m | .m | MuRAT-master/Utilities_Matlab/MyUtilities/Murat_testAll.m | 3,301 | utf_8 | f45f33ba1ab04e214045c9260845080a | function [muratHeader,flag] = Murat_testAll(folderPath)
% TEST all seismograms in a folder for the input parameters and
% CREATES a file storing the parameters and flagging those missing
%
% Input Parameters:
% folderPath: folder vontaining the SAC data
%
% Output:
% muratHeader: Murat table showing... |
github | LucaDeSiena/MuRAT-master | corner.m | .m | MuRAT-master/Utilities_Matlab/regtu/corner.m | 8,828 | utf_8 | 0c1b046514caab7971fcc127355e042a | function [k_corner,info] = corner(rho,eta,fig)
%CORNER Find corner of discrete L-curve via adaptive pruning algorithm.
%
% [k_corner,info] = corner(rho,eta,fig)
%
% Returns the integer k_corner such that the corner of the log-log
% L-curve is located at ( log(rho(k_corner)) , log(eta(k_corner)) ).
%
% The vecto... |
github | LucaDeSiena/MuRAT-master | l_curve_tikh_svd.m | .m | MuRAT-master/Utilities_Matlab/regtu/l_curve_tikh_svd.m | 2,242 | utf_8 | e1bdddd1a72a141b0d242e3b03af8edb | % Parameter Estimation and Inverse Problems, 2nd edition, 2011
% by R. Aster, B. Borchers, C. Thurber
%
% return l curve parematers for Tikhonov Regularization
%
% Routine originally inspired by Per Hansen's l-curve
% program (http://www2.imm.dtu.dk/~pch/Regutools/)
%
%
% [rho,eta,reg_param] = l_curve_tikh(U,s,d,npoin... |
github | ostwaldd/Variational-Bayes-GLM-master | beh_model_experiment.m | .m | Variational-Bayes-GLM-master/beh_model_experiment.m | 50,096 | utf_8 | b884acb91730dbe9897bb719a7ee8fc5 | function beh_model_experiment(sj_id, run_id)
% This function presents the gridworld search paradigm.
%
% Inputs
% sj_id : participant ID, string
% run_id : run ID, scalar
%
% Outputs
% None, saves results file to disc
%
% Copyright (C) Lilla Horvath, Dirk Ostwald
% ------------------... |
github | yu-jiang/Squirrelbot-master | armUI.m | .m | Squirrelbot-master/armUI.m | 6,568 | utf_8 | 11e2a6de0ea54f142ddd1ee62755bc1e | classdef armUI < handle
properties
fig
axis
lines
ac %arduno controller
% Sliders
sliderMotor1
sliderMotor2
sliderMotor3
sliderMotor4
simulationOnly = false;
positionData
buttonConnect
buttonReplay
buttonAddPoint
buttonClear
%
... |
github | anduresu/NonLinearOptimizationHW-master | step.m | .m | NonLinearOptimizationHW-master/step.m | 853 | utf_8 | da0005ef212fbebf552e873e78184bf0 | function tk = step( f , x0 )
tg = 0 ;
td = 0 ;
t = 1/2 ;
tk = 0 ;
while tk ~= t
[a,b,c] = golstein_rule( f, x0, t) ;
if a == 1
tk = t ;
end
if b == 1
td = t ;
end
if c == 1
tg = t ;
end
if b == 1 || c == 1
if td == 0
t = 10*tg ;
else
t = (tg + td)/2 ;
end
end
... |
github | cszn/DnCNN-master | Cal_PSNRSSIM.m | .m | DnCNN-master/utilities/Cal_PSNRSSIM.m | 6,569 | utf_8 | c726759a14c4754004b2fbbec4ebbf36 | function [psnr_cur, ssim_cur] = Cal_PSNRSSIM(A,B,row,col)
[n,m,ch]=size(B);
A = A(row+1:n-row,col+1:m-col,:);
B = B(row+1:n-row,col+1:m-col,:);
A=double(A); % Ground-truth
B=double(B); %
e=A(:)-B(:);
mse=mean(e.^2);
psnr_cur=10*log10(255^2/mse);
if ch==1
[ssim_cur, ~] = ssim_index(A, B);
else
... |
github | cszn/DnCNN-master | DnCNN_Init.m | .m | DnCNN-master/TrainingCodes/DnCNN_TrainingCodes_DagNN_v1.1/DnCNN_Init.m | 5,836 | utf_8 | 094a3b14e2884158ae9137ec571ea0f8 | function net = DnCNN_Init()
% by Kai Zhang (1/2018)
% cskaizhang@gmail.com
% https://github.com/cszn
% Create DAGNN object
net = dagnn.DagNN();
% conv + relu
blockNum = 1;
inVar = 'input';
channel= 1; % grayscale image
dims = [3,3,channel,64];
pad = [1,1];
stride = [1,1];
lr = [1,1];
[net, inVar, blockNum] ... |
github | cszn/DnCNN-master | DnCNN_train_dag.m | .m | DnCNN-master/TrainingCodes/DnCNN_TrainingCodes_DagNN_v1.1/DnCNN_train_dag.m | 18,129 | utf_8 | 97d9e7711f5ea677436f0f6476a94755 | function [net,stats] = DnCNN_train_dag(net, varargin)
%CNN_TRAIN_DAG Demonstrates training a CNN using the DagNN wrapper
% CNN_TRAIN_DAG() is similar to CNN_TRAIN(), but works with
% the DagNN wrapper instead of the SimpleNN wrapper.
% Copyright (C) 2014-16 Andrea Vedaldi.
% All rights reserved.
%
% This file i... |
github | cszn/DnCNN-master | Cal_PSNRSSIM.m | .m | DnCNN-master/TrainingCodes/DnCNN_TrainingCodes_DagNN_v1.1/utilities/Cal_PSNRSSIM.m | 6,569 | utf_8 | c726759a14c4754004b2fbbec4ebbf36 | function [psnr_cur, ssim_cur] = Cal_PSNRSSIM(A,B,row,col)
[n,m,ch]=size(B);
A = A(row+1:n-row,col+1:m-col,:);
B = B(row+1:n-row,col+1:m-col,:);
A=double(A); % Ground-truth
B=double(B); %
e=A(:)-B(:);
mse=mean(e.^2);
psnr_cur=10*log10(255^2/mse);
if ch==1
[ssim_cur, ~] = ssim_index(A, B);
else
... |
github | cszn/DnCNN-master | Demo_DagNN_Merge_Bnorm.m | .m | DnCNN-master/TrainingCodes/DnCNN_TrainingCodes_DagNN_v1.1/utilities/Demo_DagNN_Merge_Bnorm.m | 6,930 | utf_8 | 2d9818396cfb748ea1134a8b04e11426 | function [] = Demo_DagNN_Merge_Bnorm()
% merge bnorm: 'DnCNN-epoch-50.mat' ------> 'DnCNN-epoch-0.mat'
inputfileName = 'DnCNN-epoch-50.mat';
targetfileName = 'DnCNN-epoch-0.mat';
% Merge Bnorm to (1) accelerate the testing inference; and (2) fine-tune the model with small learning rate for better PSNR.
load(input... |
github | cszn/DnCNN-master | DnCNN_init_model_64_25_Res_Bnorm_Adam.m | .m | DnCNN-master/TrainingCodes/DnCNN_TrainingCodes_v1.1/DnCNN_init_model_64_25_Res_Bnorm_Adam.m | 1,705 | utf_8 | fc6fb32e289c615f4b7d2cbedcba8be6 |
function net = DnCNN_init_model_64_25_Res_Bnorm_Adam
%%% 17 layers
b_min = 0.025;
lr11 = [1 1];
lr10 = [1 0];
weightDecay = [1 0];
meanvar = [zeros(64,1,'single'), 0.01*ones(64,1,'single')];
% Define network
net.layers = {} ;
net.layers{end+1} = struct('type', 'conv', ...
'weights', {{sqrt(2/(9*64))*randn(3... |
github | cszn/DnCNN-master | DnCNN_train.m | .m | DnCNN-master/TrainingCodes/DnCNN_TrainingCodes_v1.1/DnCNN_train.m | 12,685 | utf_8 | d664f407bd2a6d0711394366d701cebf | function [net, state] = DnCNN_train(net, varargin)
% The function automatically restarts after each training epoch by
% checkpointing.
%
% The function supports training on CPU or on one or more GPUs
% (specify the list of GPU IDs in the `gpus` option).
% Copyright (C) 2014-16 Andrea Vedaldi.
% All rights... |
github | cszn/DnCNN-master | Cal_PSNRSSIM.m | .m | DnCNN-master/TrainingCodes/DnCNN_TrainingCodes_v1.1/data/utilities/Cal_PSNRSSIM.m | 6,471 | utf_8 | 1689b76bfd626a066df745e53cf59f19 | function [psnr_cur, ssim_cur] = Cal_PSNRSSIM(A,B,row,col)
[n,m,ch]=size(B);
A = A(row+1:n-row,col+1:m-col,:);
B = B(row+1:n-row,col+1:m-col,:);
A=double(A); % Ground-truth
B=double(B); %
e=A(:)-B(:);
mse=mean(e.^2);
psnr_cur=10*log10(255^2/mse);
if ch==1
[ssim_cur, ~] = ssim_index(A, B);
else
... |
github | cszn/DnCNN-master | DnCNN_init_model_64_25_Res_Bnorm_Adam.m | .m | DnCNN-master/TrainingCodes/DnCNN_TrainingCodes_v1.0/DnCNN_init_model_64_25_Res_Bnorm_Adam.m | 1,533 | utf_8 | 5cf85b75dfccd48c4d410e7419b73e39 |
function net = DnCNN_init_model_64_25_Res_Bnorm_Adam
%%% 17 layers
lr = [1 1];
lr1 = [1 0];
weightDecay = [1 0];
meanvar = [zeros(64,1,'single'), 0.01*ones(64,1,'single')];
% Define network
net.layers = {} ;
net.layers{end+1} = struct('type', 'conv', ...
'weights', {{sqrt(2/(9*64))*randn(3,3,1,64,'single'),... |
github | cszn/DnCNN-master | DnCNN_train.m | .m | DnCNN-master/TrainingCodes/DnCNN_TrainingCodes_v1.0/DnCNN_train.m | 10,969 | utf_8 | d6a38316cf04f80bd144c2e9eb06b01f | function [net, state] = DnCNN_train(net, imdb, varargin)
% The function automatically restarts after each training epoch by
% checkpointing.
%
% The function supports training on CPU or on one or more GPUs
% (specify the list of GPU IDs in the `gpus` option).
% Copyright (C) 2014-16 Andrea Vedaldi.
% All ... |
github | cszn/DnCNN-master | Cal_PSNRSSIM.m | .m | DnCNN-master/TrainingCodes/DnCNN_TrainingCodes_v1.0/data/utilities/Cal_PSNRSSIM.m | 6,471 | utf_8 | 1689b76bfd626a066df745e53cf59f19 | function [psnr_cur, ssim_cur] = Cal_PSNRSSIM(A,B,row,col)
[n,m,ch]=size(B);
A = A(row+1:n-row,col+1:m-col,:);
B = B(row+1:n-row,col+1:m-col,:);
A=double(A); % Ground-truth
B=double(B); %
e=A(:)-B(:);
mse=mean(e.^2);
psnr_cur=10*log10(255^2/mse);
if ch==1
[ssim_cur, ~] = ssim_index(A, B);
else
... |
github | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_compile.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/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 | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_noprefix.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/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 | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_pegasos.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/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 | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_svmpegasos.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/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 | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_override.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/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 | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_quickvis.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/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 | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_demo_aib.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/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 | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_demo_alldist.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/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 | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_demo_ikmeans.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/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 | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_demo_svm.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/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 | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_demo_kdtree_sift.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/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 | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_impattern.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/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 | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_tpsu.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/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 | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_xyz2lab.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/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 | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_test_gmm.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/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 | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_test_twister.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/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 | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_test_kdtree.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/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 | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_test_imwbackward.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/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 | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_test_alphanum.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/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 | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_test_printsize.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/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 | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_test_cummax.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/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 | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_test_imintegral.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/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 | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_test_sift.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/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 | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_test_binsum.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/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 | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_test_lbp.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/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 | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_test_colsubset.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/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 | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_test_alldist.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/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... |
github | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_test_ihashsum.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/toolbox/xtest/vl_test_ihashsum.m | 581 | utf_8 | edc283062469af62056b0782b171f5fc | function results = vl_test_ihashsum(varargin)
% VL_TEST_IHASHSUM
vl_test_init ;
function s = setup()
rand('state',0) ;
s.data = uint8(round(16*rand(2,100))) ;
sel = find(all(s.data==0)) ;
s.data(1,sel)=1 ;
function test_hash(s)
D = size(s.data,1) ;
K = 5 ;
h = zeros(1,K,'uint32') ;
id = zeros(D,K,'uint8');
next = zer... |
github | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_test_grad.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/toolbox/xtest/vl_test_grad.m | 434 | utf_8 | 4d03eb33a6a4f68659f868da95930ffb | function results = vl_test_grad(varargin)
% VL_TEST_GRAD
vl_test_init ;
function s = setup()
s.I = rand(150,253) ;
s.I_small = rand(2,2) ;
function test_equiv(s)
vl_assert_equal(gradient(s.I), vl_grad(s.I)) ;
function test_equiv_small(s)
vl_assert_equal(gradient(s.I_small), vl_grad(s.I_small)) ;
function test_equiv... |
github | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_test_whistc.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/toolbox/xtest/vl_test_whistc.m | 1,384 | utf_8 | 81c446d35c82957659840ab2a579ec2c | function results = vl_test_whistc(varargin)
% VL_TEST_WHISTC
vl_test_init ;
function test_acc()
x = ones(1, 10) ;
e = 1 ;
o = 1:10 ;
vl_assert_equal(vl_whistc(x, o, e), 55) ;
function test_basic()
x = 1:10 ;
e = 1:10 ;
o = ones(1, 10) ;
vl_assert_equal(histc(x, e), vl_whistc(x, o, e)) ;
x = linspace(-1,11,100) ;
o =... |
github | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_test_roc.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/toolbox/xtest/vl_test_roc.m | 1,019 | utf_8 | 9b2ae71c9dc3eda0fc54c65d55054d0c | function results = vl_test_roc(varargin)
% VL_TEST_ROC
vl_test_init ;
function s = setup()
s.scores0 = [5 4 3 2 1] ;
s.scores1 = [5 3 4 2 1] ;
s.labels = [1 1 -1 -1 -1] ;
function test_perfect_tptn(s)
[tpr,tnr] = vl_roc(s.labels,s.scores0) ;
vl_assert_almost_equal(tpr, [0 1 2 2 2 2] / 2) ;
vl_assert_almost_equal(tnr,... |
github | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_test_dsift.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/toolbox/xtest/vl_test_dsift.m | 2,048 | utf_8 | fbbfb16d5a21936c1862d9551f657ccc | function results = vl_test_dsift(varargin)
% VL_TEST_DSIFT
vl_test_init ;
function s = setup()
I = im2double(imread(fullfile(vl_root,'data','spots.jpg'))) ;
s.I = rgb2gray(single(I)) ;
function test_fast_slow(s)
binSize = 4 ; % bin size in pixels
magnif = 3 ; % bin size / keypoint scale
scale = binSize... |
github | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_test_alldist2.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/toolbox/xtest/vl_test_alldist2.m | 2,284 | utf_8 | 89a787e3d83516653ae8d99c808b9d67 | function results = vl_test_alldist2(varargin)
% VL_TEST_ALLDIST
vl_test_init ;
% TODO: test integer classes
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_alldist2(zeros(15,12), zeros(15,0), 'kl2'), ...
... |
github | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_test_fisher.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/toolbox/xtest/vl_test_fisher.m | 2,097 | utf_8 | c9afd9ab635bd412cbf8be3c2d235f6b | function results = vl_test_fisher(varargin)
% VL_TEST_FISHER
vl_test_init ;
function s = setup()
randn('state',0) ;
dimension = 5 ;
numData = 21 ;
numComponents = 3 ;
s.x = randn(dimension,numData) ;
s.mu = randn(dimension,numComponents) ;
s.sigma2 = ones(dimension,numComponents) ;
s.prior = ones(1,numComponents) ;
s... |
github | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_test_imsmooth.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/toolbox/xtest/vl_test_imsmooth.m | 1,837 | utf_8 | 718235242cad61c9804ba5e881c22f59 | function results = vl_test_imsmooth(varargin)
% VL_TEST_IMSMOOTH
vl_test_init ;
function s = setup()
I = im2double(imread(fullfile(vl_root,'data','spots.jpg'))) ;
I = max(min(vl_imdown(I),1),0) ;
s.I = single(I) ;
function test_pad_by_continuity(s)
% Convolving a constant signal padded with continuity does not change... |
github | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_test_svmtrain.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/toolbox/xtest/vl_test_svmtrain.m | 4,277 | utf_8 | 071b7c66191a22e8236fda16752b27aa | function results = vl_test_svmtrain(varargin)
% VL_TEST_SVMTRAIN
vl_test_init ;
end
function s = setup()
randn('state',0) ;
Np = 10 ;
Nn = 10 ;
xp = diag([1 3])*randn(2, Np) ;
xn = diag([1 3])*randn(2, Nn) ;
xp(1,:) = xp(1,:) + 2 + 1 ;
xn(1,:) = xn(1,:) - 2 + 1 ;
s.x = [xp xn] ;
s.y = [ones(1,Np) ... |
github | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_test_phow.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/toolbox/xtest/vl_test_phow.m | 549 | utf_8 | f761a3bb218af855986263c67b2da411 | function results = vl_test_phow(varargin)
% VL_TEST_PHOPW
vl_test_init ;
function s = setup()
s.I = im2double(imread(fullfile(vl_root,'data','spots.jpg'))) ;
s.I = single(s.I) ;
function test_gray(s)
[f,d] = vl_phow(s.I, 'color', 'gray') ;
assert(size(d,1) == 128) ;
function test_rgb(s)
[f,d] = vl_phow(s.I, 'color',... |
github | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_test_kmeans.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/toolbox/xtest/vl_test_kmeans.m | 3,632 | utf_8 | 0e1d6f4f8101c8982a0e743e0980c65a | function results = vl_test_kmeans(varargin)
% VL_TEST_KMEANS
% 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 ;
function s = setup()
randn('sta... |
github | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_test_hikmeans.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/toolbox/xtest/vl_test_hikmeans.m | 463 | utf_8 | dc3b493646e66316184e86ff4e6138ab | function results = vl_test_hikmeans(varargin)
% VL_TEST_IKMEANS
vl_test_init ;
function s = setup()
rand('state',0) ;
s.data = uint8(rand(2,1000) * 255) ;
function test_basic(s)
[tree, assign] = vl_hikmeans(s.data,3,100) ;
assign_ = vl_hikmeanspush(tree, s.data) ;
vl_assert_equal(assign,assign_) ;
function test_elka... |
github | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_test_aib.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/toolbox/xtest/vl_test_aib.m | 1,277 | utf_8 | 78978ae54e7ebe991d136336ba4bf9c6 | function results = vl_test_aib(varargin)
% VL_TEST_AIB
vl_test_init ;
function s = setup()
s = [] ;
function test_basic(s)
Pcx = [.3 .3 0 0
0 0 .2 .2] ;
% This results in the AIB tree
%
% 1 - \
% 5 - \
% 2 - / \
% - 7
% 3 - \ /
% 6 - /
% 4 - /
%
% coded by the map [5 ... |
github | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_test_plotbox.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/toolbox/xtest/vl_test_plotbox.m | 414 | utf_8 | aa06ce4932a213fb933bbede6072b029 | function results = vl_test_plotbox(varargin)
% VL_TEST_PLOTBOX
vl_test_init ;
function test_basic(s)
figure(1) ; clf ;
vl_plotbox([-1 -1 1 1]') ;
xlim([-2 2]) ;
ylim([-2 2]) ;
close(1) ;
function test_multiple(s)
figure(1) ; clf ;
randn('state', 0) ;
vl_plotbox(randn(4,10)) ;
close(1) ;
function test_style(s)
figure... |
github | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_test_imarray.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/toolbox/xtest/vl_test_imarray.m | 795 | utf_8 | c5e6a5aa8c2e63e248814f5bd89832a8 | function results = vl_test_imarray(varargin)
% VL_TEST_IMARRAY
vl_test_init ;
function test_movie_rgb(s)
A = rand(23,15,3,4) ;
B = vl_imarray(A,'movie',true) ;
function test_movie_indexed(s)
cmap = get(0,'DefaultFigureColormap') ;
A = uint8(size(cmap,1)*rand(23,15,4)) ;
A = min(A,size(cmap,1)-1) ;
B = vl_imarray(A,'m... |
github | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_test_homkermap.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/toolbox/xtest/vl_test_homkermap.m | 1,903 | utf_8 | c157052bf4213793a961bde1f73fb307 | function results = vl_test_homkermap(varargin)
% VL_TEST_HOMKERMAP
vl_test_init ;
function check_ker(ker, n, window, period)
args = {n, ker, 'window', window} ;
if nargin > 3
args = {args{:}, 'period', period} ;
end
x = [-1 -.5 0 .5 1] ;
y = linspace(0,2,100) ;
for conv = {@single, @double}
x = feval(conv{1}, x) ;... |
github | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_test_slic.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/toolbox/xtest/vl_test_slic.m | 200 | utf_8 | 12a6465e3ef5b4bcfd7303cd8a9229d4 | function results = vl_test_slic(varargin)
% VL_TEST_SLIC
vl_test_init ;
function s = setup()
s.im = im2single(vl_impattern('roofs1')) ;
function test_slic(s)
segmentation = vl_slic(s.im, 10, 0.1) ;
|
github | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_test_ikmeans.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/toolbox/xtest/vl_test_ikmeans.m | 466 | utf_8 | 1ee2f647ac0035ed0d704a0cd615b040 | function results = vl_test_ikmeans(varargin)
% VL_TEST_IKMEANS
vl_test_init ;
function s = setup()
rand('state',0) ;
s.data = uint8(rand(2,1000) * 255) ;
function test_basic(s)
[centers, assign] = vl_ikmeans(s.data,100) ;
assign_ = vl_ikmeanspush(s.data, centers) ;
vl_assert_equal(assign,assign_) ;
function test_elk... |
github | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_test_mser.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/toolbox/xtest/vl_test_mser.m | 242 | utf_8 | 1ad33563b0c86542a2978ee94e0f4a39 | function results = vl_test_mser(varargin)
% VL_TEST_MSER
vl_test_init ;
function s = setup()
s.im = im2uint8(rgb2gray(vl_impattern('roofs1'))) ;
function test_mser(s)
[regions,frames] = vl_mser(s.im) ;
mask = vl_erfill(s.im, regions(1)) ;
|
github | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_test_inthist.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/toolbox/xtest/vl_test_inthist.m | 811 | utf_8 | 459027d0c54d8f197563a02ab66ef45d | function results = vl_test_inthist(varargin)
% VL_TEST_INTHIST
vl_test_init ;
function s = setup()
rand('state',0) ;
s.labels = uint32(8*rand(123, 76, 3)) ;
function test_basic(s)
l = 10 ;
hist = vl_inthist(s.labels, 'numlabels', l) ;
hist_ = inthist_slow(s.labels, l) ;
vl_assert_equal(double(hist),hist_) ;
function... |
github | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_test_imdisttf.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/toolbox/xtest/vl_test_imdisttf.m | 1,885 | utf_8 | ae921197988abeb984cbcdf9eaf80e77 | function results = vl_test_imdisttf(varargin)
% VL_TEST_DISTTF
vl_test_init ;
function test_basic()
for conv = {@single, @double}
conv = conv{1} ;
I = conv([0 0 0 ; 0 -2 0 ; 0 0 0]) ;
D = vl_imdisttf(I);
assert(isequal(D, conv(- [0 1 0 ; 1 2 1 ; 0 1 0]))) ;
I(2,2) = -3 ;
[D,map] = vl_imdisttf(I) ;
asse... |
github | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_test_vlad.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/toolbox/xtest/vl_test_vlad.m | 1,977 | utf_8 | d3797288d6edb1d445b890db3780c8ce | function results = vl_test_vlad(varargin)
% VL_TEST_VLAD
vl_test_init ;
function s = setup()
randn('state',0) ;
s.x = randn(128,256) ;
s.mu = randn(128,16) ;
assignments = rand(16, 256) ;
s.assignments = bsxfun(@times, assignments, 1 ./ sum(assignments,1)) ;
function test_basic (s)
x = [1, 2, 3] ;
mu = [0, 0, 0] ;
a... |
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