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values | md5 stringlengths 32 32 | text stringlengths 23 843k |
|---|---|---|---|---|---|---|---|---|
github | aamiranis/sampling_theory-master | sgwt_demo1.m | .m | sampling_theory-master/reconstruction_methods/pocs_bandlimited/sgwt_toolbox/demo/sgwt_demo1.m | 4,442 | utf_8 | 4884997e211ca4ec15e9fbd0e822a790 | % sgwt_demo1 : SGWT for swiss roll data set
%
% This demo builds the SGWT for the swiss roll synthetic data set. It
% computes a set of scales adapted to the computed upper bound on the
% spectrum of the graph Laplacian, and displays the scaling function and
% the scaled wavlet kernels, as well as the corresponding fra... |
github | aamiranis/sampling_theory-master | sgwt_soft_threshold.m | .m | sampling_theory-master/reconstruction_methods/pocs_bandlimited/sgwt_toolbox/utils/sgwt_soft_threshold.m | 1,117 | utf_8 | 2c60d2416dbd759097345f610f622d03 | % sgwt_soft_threshold : Soft thresholding operator
%
% x_t = bpdq_soft_threshold(x,tgamma)
%
% Applies soft thresholding to each component of x
%
% Inputs:
% x - input signal
% tgamma - threshold
%
% Outputs:
% x_t - soft thresholded result
% This file is part of the SGWT toolbox (Spectral Graph Wavelet Transform too... |
github | aamiranis/sampling_theory-master | argselectCheck.m | .m | sampling_theory-master/reconstruction_methods/pocs_bandlimited/sgwt_toolbox/utils/argselectCheck.m | 2,050 | utf_8 | 13096e9ec4f9fc475fb154c322cc7352 | % argselectCheck : Check if control parameters are valid
%
% function argselectCheck(control_params,varargin_in)
%
% Inputs:
% control_params and varargin_in are both cell arrays
% that are lists of pairs 'name1',value1,'name2',value2,...
%
% This function checks that every name in varargin_in is one of the name... |
github | aamiranis/sampling_theory-master | argselectAssign.m | .m | sampling_theory-master/reconstruction_methods/pocs_bandlimited/sgwt_toolbox/utils/argselectAssign.m | 1,706 | utf_8 | a225f6b476ca9762053f486bc6a2f8b9 | % argselectAssign : Assign variables in calling workspace
%
% function argselectAssign(variable_value_pairs)
%
% Inputs :
% variable_value_pairs is a cell list of form
% 'variable1',value1,'variable2',value2,...
% This function assigns variable1=value1 ... etc in the *callers* workspace
%
% This is used at beg... |
github | aamiranis/sampling_theory-master | vec.m | .m | sampling_theory-master/reconstruction_methods/pocs_bandlimited/sgwt_toolbox/utils/vec.m | 857 | utf_8 | b795ffb5f2186f33aaa81ef7daa3cac5 | % vec : vectorize input
%
% r=vec(x)
%
% returns r=x(:);
% This file is part of the SGWT toolbox (Spectral Graph Wavelet Transform toolbox)
% Copyright (C) 2010, David K. Hammond.
%
% The SGWT toolbox is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as pub... |
github | aamiranis/sampling_theory-master | sgwt_show_im.m | .m | sampling_theory-master/reconstruction_methods/pocs_bandlimited/sgwt_toolbox/utils/sgwt_show_im.m | 1,806 | utf_8 | f0aa589e604cc94d1e07c64a3eb27723 | % sgwt_show_im : Display image, with correct pixel zoom
%
% sgwt_show_im(im,range,zoom)
%
% Inputs :
% im - 2-d image
% range - 2 element vector giving display color map range,
% range(1) maps to black, range(2) maps to white
% If range not given, or empty matrix given for range, then
% the default is to set it to th... |
github | tmquan/PVR-master | resize.m | .m | PVR-master/resize.m | 6,573 | utf_8 | fc6ab29e1a4106ddb03733c0b1048169 | function x = resize(x,newsiz)
%RESIZE Resize any arrays and images.
% Y = RESIZE(X,NEWSIZE) resizes input array X using a DCT (discrete
% cosine transform) method. X can be any array of any size. Output Y is
% of size NEWSIZE.
%
% Input and output formats: Y has the same class as X.
%
% Note:
% ... |
github | tmquan/PVR-master | vec.m | .m | PVR-master/vec.m | 42 | utf_8 | 8909a59f67f977a13b68984a04173168 |
function u = vec(v)
u = v(:);
return |
github | pauloabelha/gazebo_tasks-master | GenerateBoxSDF.m | .m | gazebo_tasks-master/GenerateBoxSDF.m | 6,266 | utf_8 | 247027bd44b2954c039f37434208aca7 | % By Paulo Abelha
%
% Generates a parametrizable rectangular box for Gazebo
% I use it to generate the box that holds the grains for my
% 'scooping_grains' task
% This was very useful when I was extensively
% experimenting with different boxes
%
% task_folder is the folder with the .world, tool(s) and task code
% ... |
github | Rahmeen14/Chall-Vihaan-master | MeanVariance.m | .m | Chall-Vihaan-master/routes/codes/MeanVariance.m | 1,364 | utf_8 | 8b23466de4e94d6052135ba671cde88f | ## Copyright (C) 2017 Shreya
##
## This program is free software; you can redistribute it and/or modify it
## under the terms of the GNU General Public License as published by
## the Free Software Foundation; either version 3 of the License, or
## (at your option) any later version.
##
## This program is distributed in... |
github | Rahmeen14/Chall-Vihaan-master | probability.m | .m | Chall-Vihaan-master/routes/codes/probability.m | 1,015 | utf_8 | a28db31f8c29e452e555c6e50edd8c41 | ## Copyright (C) 2017 Shreya
##
## This program is free software; you can redistribute it and/or modify it
## under the terms of the GNU General Public License as published by
## the Free Software Foundation; either version 3 of the License, or
## (at your option) any later version.
##
## This program is distributed in... |
github | Rahmeen14/Chall-Vihaan-master | main.m | .m | Chall-Vihaan-master/routes/codes/main.m | 1,710 | utf_8 | 1f7ebc3c7c60fd3954acb86f1669cac3 | ## Copyright (C) 2017 Shreya
##
## This program is free software; you can redistribute it and/or modify it
## under the terms of the GNU General Public License as published by
## the Free Software Foundation; either version 3 of the License, or
## (at your option) any later version.
##
## This program is distributed ... |
github | Rahmeen14/Chall-Vihaan-master | predictNature.m | .m | Chall-Vihaan-master/routes/codes/predictNature.m | 1,438 | utf_8 | 0f5d425211ad4bf375290e74adbc88bc | ## Copyright (C) 2017 Shreya
##
## This program is free software; you can redistribute it and/or modify it
## under the terms of the GNU General Public License as published by
## the Free Software Foundation; either version 3 of the License, or
## (at your option) any later version.
##
## This program is distributed ... |
github | Rahmeen14/Chall-Vihaan-master | exponential.m | .m | Chall-Vihaan-master/routes/codes/exponential.m | 941 | utf_8 | 5da284170d8f5071a07b143da331d909 | ## Copyright (C) 2017 Shreya
##
## This program is free software; you can redistribute it and/or modify it
## under the terms of the GNU General Public License as published by
## the Free Software Foundation; either version 3 of the License, or
## (at your option) any later version.
##
## This program is distributed ... |
github | FanmingL/independent-analysis-master | my_RealTimeIVA.m | .m | independent-analysis-master/iva_matlab/my_RealTimeIVA.m | 5,722 | utf_8 | 5e3bd938e17b5a4c18e57b9e1f3d07c3 | %initialization
%it spends aboud 0.36 seconds to initialize
%//1 matlab clear
clear;
close all;
%//2 initialize some coeffs according to the paper
fft_length = 256; %unit is sample
window_length = fft_length; %unit is sample
shift_size = 64; ... |
github | FanmingL/independent-analysis-master | my_RealTimeIVA_GMM.m | .m | independent-analysis-master/iva_matlab/my_RealTimeIVA_GMM.m | 8,995 | utf_8 | 17b3add0f5b9b511801d4f3eeffe8ee0 | %initialization
%it spends aboud 0.36 seconds to initialize
%//1 matlab clear
clear;
close all;
load('GMM_SYS_4.mat')
%//2 initialize some coeffs according to the paper
gamma_whiten = 0.0025;
lambda = 0.95;
Ns = 4;
fft_length = 512; %unit is sample
win... |
github | jacky18008/NCCU_Coding_is_Magic-master | eval_multiclass.m | .m | NCCU_Coding_is_Magic-master/UniMiB-SHAR/code/eval_multiclass.m | 13,160 | utf_8 | 08f4ba858317dd05be872e7f3b82cb9c | % /*************************************************************************************
%
% Project Name: UniMiB SHAR: a new dataset for human activity recognition using acceleration data from smartphones
% File Name: eval_multiclass.m
% Authors: D. Micucci and M. Mobilio and P. Napoletano (pao... |
github | KaygoYM/FBMC-channel-estimation-based-on-SVR-master | FBMC_signal.m | .m | FBMC-channel-estimation-based-on-SVR-master/FBMC_signal.m | 631 | utf_8 | 5a30487cbebb6691662c236897f61d4d | %author:KAI
function [BinaryDataStream_FBMC_Aux,xP_FBMC,x_FBMC_Aux,s_FBMC_Aux]= FBMC_signal(AuxiliaryMethod,FBMC,PAM,ChannelEstimation_FBMC)
BinaryDataStream_FBMC_Aux = randi([0 1],AuxiliaryMethod.NrDataSymbols*log2(PAM.ModulationOrder),1);
xD_FBMC_Aux = PAM.Bit2Symbol(BinaryDataStream_FBMC_Aux);
xP... |
github | KaygoYM/FBMC-channel-estimation-based-on-SVR-master | FBMC_data.m | .m | FBMC-channel-estimation-based-on-SVR-master/FBMC_data.m | 414 | utf_8 | 85fbcece29580b65dd76cf907f6808e9 | %author:KAI
function [BinaryDataStream_FBMC_Aux,x_FBMC_Aux,s_FBMC_Aux]= FBMC_data(AuxiliaryMethod,FBMC,PAM)
BinaryDataStream_FBMC_Aux = randi([0 1],AuxiliaryMethod.NrTransmittedSymbols*log2(PAM.ModulationOrder),1);
xD_FBMC_Aux = PAM.Bit2Symbol(BinaryDataStream_FBMC_Aux);
x_FBMC_Aux = reshape(xD_FBMC_Aux... |
github | KaygoYM/FBMC-channel-estimation-based-on-SVR-master | svminterp_real.m | .m | FBMC-channel-estimation-based-on-SVR-master/svminterp_real.m | 393 | utf_8 | 06978ab9d707b8ba097cdc297eb53840 | %author:KAI
function h_FBMC_Aux = svminterp_real(index,hP_LS_FBMC_Aux,h,NrTime)
x=index.';
y=(hP_LS_FBMC_Aux).';
h_FBMC_Aux=nan(size(h));
model=svmtrain(y,x,'-s 3 -t 2 -c 2.2 -g 2.8 -p 0.01');
new_x=(1:NrTime).';
new_x(index)=[];
new_y=h(new_x);
[predict_real,mse_real,dec_real]=svmpredict(new_y,new_x,model);
... |
github | KaygoYM/FBMC-channel-estimation-based-on-SVR-master | make.m | .m | FBMC-channel-estimation-based-on-SVR-master/libsvm/matlab/make.m | 888 | utf_8 | 4a2ad69e765736f8cca8e3b721fb7ebd | % This make.m is for MATLAB and OCTAVE under Windows, Mac, and Unix
function make()
try
% This part is for OCTAVE
if (exist ('OCTAVE_VERSION', 'builtin'))
mex libsvmread.c
mex libsvmwrite.c
mex -I.. svmtrain.c ../svm.cpp svm_model_matlab.c
mex -I.. svmpredict.c ../svm.cpp svm_model_matlab.c
% This part is fo... |
github | KaygoYM/FBMC-channel-estimation-based-on-SVR-master | FBMC.m | .m | FBMC-channel-estimation-based-on-SVR-master/+Modulation/FBMC.m | 37,528 | utf_8 | 00e25b472f90c845f3f2e1e60aba51c5 | classdef FBMC < handle
% Ronald Nissel, rnissel@nt.tuwien.ac.at
% (c) 2016 by Institute of Telecommunications, TU Wien
% www.tc.tuwien.ac.at
properties (SetAccess = private)
Method
Nr
PHY
PrototypeFilter
Implementation
end
methods
%% Class... |
github | DrNickDMartin/VehicleDynamics-master | optimplotfval2.m | .m | VehicleDynamics-master/tyres/MF_Tire_GUI_V2a/optimplotfval2.m | 3,179 | utf_8 | e633e336e1fff27a799afa1f818057eb | function stop = optimplotfval(~,optimValues,state,varargin)
% OPTIMPLOTFVAL Plot value of the objective function at each iteration.
%
% STOP = OPTIMPLOTFVAL(X,OPTIMVALUES,STATE) plots OPTIMVALUES.fval. If
% the function value is not scalar, a bar plot of the elements at the
% current iteration is displayed. If ... |
github | DrNickDMartin/VehicleDynamics-master | MF_Tire_GUI_V2a.m | .m | VehicleDynamics-master/tyres/MF_Tire_GUI_V2a/MF_Tire_GUI_V2a.m | 42,176 | utf_8 | 083e33679131da8156761186600a9c74 | function varargout = MF_Tire_GUI_V2a(varargin)
% MF_Tire_GUI_V2a MATLAB code for MF_Tire_GUI_V2a.fig
% MF_Tire_GUI_V2a, by itself, creates a new MF_Tire_GUI_V2a or raises the existing
% singleton*.
%
% H = MF_Tire_GUI_V2a returns the handle to a new MF_Tire_GUI_V2a or the handle to
% the existing si... |
github | derpycode/muffinplot-master | plot_target.m | .m | muffinplot-master/source/plot_target.m | 10,424 | utf_8 | e6dd9d9e31a424566dcedf77d9fe96cc | % ------------------------------PLOT_TARGET-------------------------------
% ------------------------------------------------------------------------
% Description: Plots the target diagram of Jolliff et al.(2009) for
% model skill assessment.
%
% plot_target has been designed to be accomodate most uses of the t... |
github | derpycode/muffinplot-master | plot_taylordiag.m | .m | muffinplot-master/source/plot_taylordiag.m | 17,558 | utf_8 | 8b116586f635384019cd2826a13ecb9a | % TAYLORDIAG Plot a Taylor Diagram
%
% [hp ht axl] = taylordiag(STDs,RMSs,CORs,['option',value])
%
% Plot a Taylor diagram from statistics of different series.
%
% INPUTS:
% STDs: Standard deviations
% RMSs: Centered Root Mean Square Difference
% CORs: Correlation
%
% Each of these inputs are one dimensiona... |
github | derpycode/muffinplot-master | calc_allstats_target.m | .m | muffinplot-master/source/calc_allstats_target.m | 3,876 | utf_8 | 52793ea025c4e3068d275402196cf5b1 | % STATM Compute statistics from 2 series
%
% STATM = calc_allstats(Cr,Cf)
%
% Compute statistics from 2 series considering Cr as the reference.
%
% Inputs:
% Cr and Cf are of same length and uni-dimensional. They may contain NaNs.
%
% Outputs:
% STATM(1,:) => Mean
% STATM(2,:) => Standard Deviation (scale... |
github | derpycode/muffinplot-master | calc_allstats.m | .m | muffinplot-master/source/calc_allstats.m | 4,749 | utf_8 | 82a5324d8cf4ff557d819d13e12874cd | % STATM Compute statistics from 2 series
%
% STATM = calc_allstats(Cr,Cf)
%
% Compute statistics from 2 series considering Cr as the reference.
%
% Inputs:
% Cr and Cf are of same length and uni-dimensional. They may contain NaNs.
%
% Outputs:
% STATM(1,:) => Mean
% STATM(2,:) => Standard Deviation (scale... |
github | derpycode/muffinplot-master | plot_taylor.m | .m | muffinplot-master/source/plot_taylor.m | 10,556 | utf_8 | 6bdbf62fca37c526a199841f448e06c6 | % Taylor diagram for summarizing model performance
%
% Taylor, 2001 - JGR, 106(D7)
%
% Program adapted from the IDL routine from K.E. Taylor
% (simplified version including less options)
%
% ---- Call :
% plot_taylor(tsig,rsig,tcorr,out,name_experiment,title)
%
% ---- Input:
% Needed:
% tsig ... |
github | eulertech/DeepLearningCrudeOilForecast-master | jdqr.m | .m | DeepLearningCrudeOilForecast-master/drtoolbox/techniques/jdqr.m | 73,068 | utf_8 | b45810ddb5b2767c9289909175d1dc04 | function varargout=jdqr(varargin)
%JDQR computes a partial Schur decomposition of a square matrix or operator.
% Lambda = JDQR(A) returns the absolute largest eigenvalues in a K vector
% Lambda. Here K=min(5,N) (unless K has been specified), where N=size(A,1).
% JDQR(A) (without output argument) displays the K eige... |
github | eulertech/DeepLearningCrudeOilForecast-master | lmnn.m | .m | DeepLearningCrudeOilForecast-master/drtoolbox/techniques/lmnn.m | 5,431 | utf_8 | 622ccdd8948f805d0d4552822cca46de | function [M, L, Y, C] = lmnn(X, labels)
%LMNN Learns a metric using large-margin nearest neighbor metric learning
%
% [M, L, Y, C] = lmnn(X, labels)
%
% The function uses large-margin nearest neighbor (LMNN) metric learning to
% learn a metric on the data set specified by the NxD matrix X and the
% corresponding Nx1 ... |
github | eulertech/DeepLearningCrudeOilForecast-master | d2p.m | .m | DeepLearningCrudeOilForecast-master/drtoolbox/techniques/d2p.m | 3,487 | utf_8 | 0c7024a8039ea16b937d283585883fc3 | function [P, beta] = d2p(D, u, tol)
%D2P Identifies appropriate sigma's to get kk NNs up to some tolerance
%
% [P, beta] = d2p(D, kk, tol)
%
% Identifies the required precision (= 1 / variance^2) to obtain a Gaussian
% kernel with a certain uncertainty for every datapoint. The desired
% uncertainty can be specified... |
github | eulertech/DeepLearningCrudeOilForecast-master | cg_update.m | .m | DeepLearningCrudeOilForecast-master/drtoolbox/techniques/cg_update.m | 3,715 | utf_8 | 1556078ae7c31950ec738949384cf180 | % Version 1.000
%
% Code provided by Ruslan Salakhutdinov and Geoff Hinton
%
% Permission is granted for anyone to copy, use, modify, or distribute this
% program and accompanying programs and documents for any purpose, provided
% this copyright notice is retained and prominently displayed, along with
% a note saying t... |
github | eulertech/DeepLearningCrudeOilForecast-master | lmvu.m | .m | DeepLearningCrudeOilForecast-master/drtoolbox/techniques/lmvu.m | 8,540 | utf_8 | c8003ed7ff0fd0e226776c42c72ad385 | function [mappedX, mapping] = lmvu(X, no_dims, K, LL)
%LMVU Performs Landmark MVU on dataset X
%
% [mappedX, mapping] = lmvu(X, no_dims, k1, k2)
%
% The function performs Landmark MVU on the DxN dataset X. The value of k1
% represents the number of nearest neighbors that is employed in the MVU
% constraints. The val... |
github | eulertech/DeepLearningCrudeOilForecast-master | cca.m | .m | DeepLearningCrudeOilForecast-master/drtoolbox/techniques/cca.m | 14,846 | utf_8 | 935e971ffe825a64e0eb80c535d71ebb | function [Z, ccaEigen, ccaDetails] = cca(X, Y, EDGES, OPTS)
%
% Function [Z, CCAEIGEN, CCADETAILS] = CCA(X, Y, EDGES, OPTS) computes a low
% dimensional embedding Z in R^d that maximally preserves angles among input
% data X that lives in R^D, with the algorithm Conformal Component Analysis.
%
% The embedding Z is co... |
github | eulertech/DeepLearningCrudeOilForecast-master | x2p.m | .m | DeepLearningCrudeOilForecast-master/drtoolbox/techniques/x2p.m | 3,597 | utf_8 | 4a102e94922f4af38e36c374dccbc5a2 | function [P, beta] = x2p(X, u, tol)
%X2P Identifies appropriate sigma's to get kk NNs up to some tolerance
%
% [P, beta] = x2p(xx, kk, tol)
%
% Identifies the required precision (= 1 / variance^2) to obtain a Gaussian
% kernel with a certain uncertainty for every datapoint. The desired
% uncertainty can be specifie... |
github | eulertech/DeepLearningCrudeOilForecast-master | sammon.m | .m | DeepLearningCrudeOilForecast-master/drtoolbox/techniques/sammon.m | 7,108 | utf_8 | 8a1fccbea9525bbebae4039127005ea6 | function [y, E] = sammon(x, n, opts)
%SAMMON Performs Sammon's MDS mapping on dataset X
%
% Y = SAMMON(X) applies Sammon's nonlinear mapping procedure on
% multivariate data X, where each row represents a pattern and each column
% represents a feature. On completion, Y contains the corresponding
% co-ordin... |
github | eulertech/DeepLearningCrudeOilForecast-master | sdecca2.m | .m | DeepLearningCrudeOilForecast-master/drtoolbox/techniques/sdecca2.m | 7,185 | utf_8 | e53979561adda6a23883da0e72af5bf6 | function [P, newY, L, newV, idx]= sdecca2(Y, snn, regularizer, relative)
% doing semidefinitve embedding/MVU with output being parameterized by graph
% laplacian's eigenfunctions..
%
% the algorithm is same as conformal component analysis except that the scaling
% factor there is set as 1
%
%
% function [P, newY, Y] ... |
github | eulertech/DeepLearningCrudeOilForecast-master | sparse_nn.m | .m | DeepLearningCrudeOilForecast-master/drtoolbox/techniques/sparse_nn.m | 972 | utf_8 | df5da172f954ec2f53125a04787cf2d3 | %SPARSE_NN
%
% This file is part of the Matlab Toolbox for Dimensionality Reduction.
% The toolbox can be obtained from http://homepage.tudelft.nl/19j49
% You are free to use, change, or redistribute this code in any way you
% want for non-commercial purposes. However, it is appreciated if you
% maintain the name of ... |
github | eulertech/DeepLearningCrudeOilForecast-master | jdqz.m | .m | DeepLearningCrudeOilForecast-master/drtoolbox/techniques/jdqz.m | 78,986 | utf_8 | be67a038982588a6ac9cbc2d36f009e8 | function varargout=jdqz(varargin)
%JDQZ computes a partial generalized Schur decomposition (or QZ
% decomposition) of a pair of square matrices or operators.
%
% LAMBDA=JDQZ(A,B) and JDQZ(A,B) return K eigenvalues of the matrix pair
% (A,B), where K=min(5,N) and N=size(A,1) if K has not been specified.
%
% [X,J... |
github | eulertech/DeepLearningCrudeOilForecast-master | lnst.m | .m | DeepLearningCrudeOilForecast-master/drtoolbox/gui/lnst.m | 891 | utf_8 | 93ca6136f90181897631256d58517558 | % This file is part of the Matlab Toolbox for Dimensionality Reduction v0.7.2b.
% The toolbox can be obtained from http://homepage.tudelft.nl/19j49
% You are free to use, change, or redistribute this code in any way you
% want for non-commercial purposes. However, it is appreciated if you
% maintain the name of th... |
github | eulertech/DeepLearningCrudeOilForecast-master | scatter12n.m | .m | DeepLearningCrudeOilForecast-master/drtoolbox/gui/scatter12n.m | 1,348 | utf_8 | 65c091a54cbbe59f0a7ddef27fcc2c3f | % This file is part of the Matlab Toolbox for Dimensionality Reduction v0.7.2b.
% The toolbox can be obtained from http://homepage.tudelft.nl/19j49
% You are free to use, change, or redistribute this code in any way you
% want for non-commercial purposes. However, it is appreciated if you
% maintain the name of th... |
github | eulertech/DeepLearningCrudeOilForecast-master | not_calculated.m | .m | DeepLearningCrudeOilForecast-master/drtoolbox/gui/not_calculated.m | 7,818 | utf_8 | 07c7ebdd2ecb821df6d1b4ccd5f47662 | function varargout = not_calculated(varargin)
% NOT_CALCULATED M-file for not_calculated.fig
% NOT_CALCULATED by itself, creates a new NOT_CALCULATED or raises the
% existing singleton*.
%
% H = NOT_CALCULATED returns the handle to a new NOT_CALCULATED or the handle to
% the existing singleton... |
github | eulertech/DeepLearningCrudeOilForecast-master | choose_method.m | .m | DeepLearningCrudeOilForecast-master/drtoolbox/gui/choose_method.m | 5,483 | utf_8 | 7fcb2ff0eb7f662fc75d652d9c440d65 | function varargout = choose_method(varargin)
% CHOOSE_METHOD M-file for choose_method.fig
% CHOOSE_METHOD, by itself, creates a new CHOOSE_METHOD or raises the existing
% singleton*.
%
% H = CHOOSE_METHOD returns the handle to a new CHOOSE_METHOD or the handle to
% the existing singleton*.
%
... |
github | eulertech/DeepLearningCrudeOilForecast-master | load_data_1_var.m | .m | DeepLearningCrudeOilForecast-master/drtoolbox/gui/load_data_1_var.m | 4,902 | utf_8 | 5b70e8dd70f9e4386ea769372ed55ffe | function varargout = load_data_1_var(varargin)
% LOAD_DATA_1_VAR M-file for load_data_1_var.fig
% LOAD_DATA_1_VAR, by itself, creates a new LOAD_DATA_1_VAR or raises the existing
% singleton*.
%
% H = LOAD_DATA_1_VAR returns the handle to a new LOAD_DATA_1_VAR or the handle to
% the existing s... |
github | eulertech/DeepLearningCrudeOilForecast-master | plotn.m | .m | DeepLearningCrudeOilForecast-master/drtoolbox/gui/plotn.m | 4,103 | utf_8 | e9c0840dca614923d10952e9b37f06c5 | % This file is part of the Matlab Toolbox for Dimensionality Reduction v0.7.2b.
% The toolbox can be obtained from http://homepage.tudelft.nl/19j49
% You are free to use, change, or redistribute this code in any way you
% want for non-commercial purposes. However, it is appreciated if you
% maintain the name of th... |
github | eulertech/DeepLearningCrudeOilForecast-master | scattern.m | .m | DeepLearningCrudeOilForecast-master/drtoolbox/gui/scattern.m | 3,651 | utf_8 | bf506a19215a7e0b4cb62da12fa09d16 | % This file is part of the Matlab Toolbox for Dimensionality Reduction v0.7.2b.
% The toolbox can be obtained from http://homepage.tudelft.nl/19j49
% You are free to use, change, or redistribute this code in any way you
% want for non-commercial purposes. However, it is appreciated if you
% maintain the name of th... |
github | eulertech/DeepLearningCrudeOilForecast-master | no_history.m | .m | DeepLearningCrudeOilForecast-master/drtoolbox/gui/no_history.m | 7,508 | utf_8 | d5c85b897eeca97b3e37ea41551de2b1 | function varargout = no_history(varargin)
% NO_HISTORY M-file for no_history.fig
% NO_HISTORY by itself, creates a new NO_HISTORY or raises the
% existing singleton*.
%
% H = NO_HISTORY returns the handle to a new NO_HISTORY or the handle to
% the existing singleton*.
%
% NO_HISTORY('CALLBACK',... |
github | eulertech/DeepLearningCrudeOilForecast-master | load_data_vars.m | .m | DeepLearningCrudeOilForecast-master/drtoolbox/gui/load_data_vars.m | 7,943 | utf_8 | 3e892de48b2b883da0e7121eb6b7cfbc | function varargout = load_data_vars(varargin)
% LOAD_DATA_VARS M-file for load_data_vars.fig
% LOAD_DATA_VARS, by itself, creates a new LOAD_DATA_VARS or raises the existing
% singleton*.
%
% H = LOAD_DATA_VARS returns the handle to a new LOAD_DATA_VARS or the handle to
% the existing singleto... |
github | eulertech/DeepLearningCrudeOilForecast-master | mapping_parameters.m | .m | DeepLearningCrudeOilForecast-master/drtoolbox/gui/mapping_parameters.m | 24,066 | utf_8 | ddb259e8821b5440a07fc05910595864 | function varargout = mapping_parameters(varargin)
% MAPPING_PARAMETERS M-file for mapping_parameters.fig
% MAPPING_PARAMETERS, by itself, creates a new MAPPING_PARAMETERS or raises the existing
% singleton*.
%
% H = MAPPING_PARAMETERS returns the handle to a new MAPPING_PARAMETERS or the handle to
... |
github | eulertech/DeepLearningCrudeOilForecast-master | load_xls.m | .m | DeepLearningCrudeOilForecast-master/drtoolbox/gui/load_xls.m | 4,845 | utf_8 | 98f040ec0685b024ddf99d454fea770d | function varargout = load_xls(varargin)
% LOAD_XLS M-file for load_xls.fig
% LOAD_XLS, by itself, creates a new LOAD_XLS or raises the existing
% singleton*.
%
% H = LOAD_XLS returns the handle to a new LOAD_XLS or the handle to
% the existing singleton*.
%
% LOAD_XLS('CALLBACK',hObject,eventDa... |
github | eulertech/DeepLearningCrudeOilForecast-master | drtool.m | .m | DeepLearningCrudeOilForecast-master/drtoolbox/gui/drtool.m | 53,877 | utf_8 | 25abf1c6522b90b00b1c29d7d4f4c091 | function varargout = drtool(varargin)
% DRTOOL M-file for drtool.fig
% DRTOOL, by itself, creates a new DRTOOL or raises the existing
% singleton*.
%
% H = DRTOOL returns the handle to a new DRTOOL or the handle to
% the existing singleton*.
%
% DRTOOL('CALLBACK',hObject,eventData,handl... |
github | eulertech/DeepLearningCrudeOilForecast-master | plot12n.m | .m | DeepLearningCrudeOilForecast-master/drtoolbox/gui/plot12n.m | 1,356 | utf_8 | 8a16c46e9b838f4602a5af8fc8a857a8 | % This file is part of the Matlab Toolbox for Dimensionality Reduction v0.7.2b.
% The toolbox can be obtained from http://homepage.tudelft.nl/19j49
% You are free to use, change, or redistribute this code in any way you
% want for non-commercial purposes. However, it is appreciated if you
% maintain the name of th... |
github | eulertech/DeepLearningCrudeOilForecast-master | not_loaded.m | .m | DeepLearningCrudeOilForecast-master/drtoolbox/gui/not_loaded.m | 7,728 | utf_8 | a754380baacab27eac13658ea4cc21a3 | function varargout = not_loaded(varargin)
% NOT_LOADED M-file for not_loaded.fig
% NOT_LOADED by itself, creates a new NOT_LOADED or raises the
% existing singleton*.
%
% H = NOT_LOADED returns the handle to a new NOT_LOADED or the handle to
% the existing singleton*.
%
% NOT_LOADED('CA... |
github | eulertech/DeepLearningCrudeOilForecast-master | load_data.m | .m | DeepLearningCrudeOilForecast-master/drtoolbox/gui/load_data.m | 6,534 | utf_8 | ade0538cbeeb79ed3c72aea5743a2424 | function varargout = load_data(varargin)
% LOAD_DATA M-file for load_data.fig
% LOAD_DATA, by itself, creates a new LOAD_DATA or raises the existing
% singleton*.
%
% H = LOAD_DATA returns the handle to a new LOAD_DATA or the handle to
% the existing singleton*.
%
% LOAD_DATA('CALLBACK'... |
github | thomasjlew/davis_tracker-master | knnsearch.m | .m | davis_tracker-master/src/knnsearch.m | 4,157 | utf_8 | bf67a671cf817680b7a3f8a108d816e1 | function [idx,D]=knnsearch(varargin)
% TLEW Note 09/29/17: Code Taken from https://ch.mathworks.com/
% matlabcentral/fileexchange/19345-efficient-k-nearest-neighbor-search-
% using-jit?focused=5151612&tab=function
% KNNSEARCH Linear k-nearest neighbor (KNN) search
% IDX = knnsearch(Q,R,K) searches the reference ... |
github | thomasjlew/davis_tracker-master | is_in_patch.m | .m | davis_tracker-master/src/is_in_patch.m | 1,506 | utf_8 | 8c0cf739ecbaf71ac334f7dcf48440c8 | % IS_IN_PATCH - Determines if a point is inside a patch
%
% Syntax: is_in_patch(event_x, event_y, patches(patch_id), PATCH_WIDTH)
%
% Inputs:
% - pt_x: x position of the point
% - pt_y: y position of the point
% - patch: patch structure as defined in "features_main.m"
% - PATCH_WIDTH: Width of... |
github | thomasjlew/davis_tracker-master | icp.m | .m | davis_tracker-master/src/icp.m | 18,480 | utf_8 | fdad7e189d4f40abc7d4f4d67948f984 | function [TR, TT, ER, t] = icp(q,p,varargin)
% TLEW Note 09/29/17: Code Taken from https://ch.mathworks.com/
% matlabcentral/fileexchange/27804-iterative-closest-point
% Perform the Iterative Closest Point algorithm on three dimensional point
% clouds.
%
% [TR, TT] = icp(q,p) returns the rotation matri... |
github | thomasjlew/davis_tracker-master | show_frames.m | .m | davis_tracker-master/src/show_frames.m | 2,916 | utf_8 | 8747c8f29ebef9ce6102ca26116d4ad0 | % SHOW_FRAMES - Show all frames quickly from "The Event-Camera Dataset" [1]
% Syntax: show_frames
%
% Inputs:
% Frames from "The Event-Camera Dataset" [2]
%
% Outputs:
% Images shown sequentially from the set [2]
%
% Example:
% show_frames
%
% Other m-files required: none
% Subfunctions: none
% MAT-file... |
github | yilei0620/RGBD-Slam-Semantic-Seg-DeepLab-master | classification_demo.m | .m | RGBD-Slam-Semantic-Seg-DeepLab-master/deeplab/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 | yilei0620/RGBD-Slam-Semantic-Seg-DeepLab-master | MyVOCevalseg.m | .m | RGBD-Slam-Semantic-Seg-DeepLab-master/deeplab/matlab/my_script/MyVOCevalseg.m | 4,625 | utf_8 | 128c24319d520c2576168d1cf17e068f | %VOCEVALSEG Evaluates a set of segmentation results.
% VOCEVALSEG(VOCopts,ID); prints out the per class and overall
% segmentation accuracies. Accuracies are given using the intersection/union
% metric:
% true positives / (true positives + false positives + false negatives)
%
% [ACCURACIES,AVACC,CONF] = VOCEV... |
github | yilei0620/RGBD-Slam-Semantic-Seg-DeepLab-master | MyVOCevalsegBoundary.m | .m | RGBD-Slam-Semantic-Seg-DeepLab-master/deeplab/matlab/my_script/MyVOCevalsegBoundary.m | 4,415 | utf_8 | 1b648714e61bafba7c08a8ce5824b105 | %VOCEVALSEG Evaluates a set of segmentation results.
% VOCEVALSEG(VOCopts,ID); prints out the per class and overall
% segmentation accuracies. Accuracies are given using the intersection/union
% metric:
% true positives / (true positives + false positives + false negatives)
%
% [ACCURACIES,AVACC,CONF] = VOCEV... |
github | yhyoscar/sklearn-study-master | karmarkar.m | .m | sklearn-study-master/20180407_LinearProgramming/karmarkar.m | 474 | utf_8 | 508c1aae69a7d8a67e07d0308acb3575 | %% karmarkar's algorithm
function x = karmarkar(A,b,c,x)
N = size(c,1);
x0 = x;
y0 = ones(N,1);
epi = 1e-3;
alpha = 0.8;
x_c = 2*ones(N,1);
x_n = x0;
r = zeros(N,1);
Dx = diag(x_c);
flag = 0;
i = 1;
while flag == 0
x_c = x_n;
Dx = diag(x_c);
r = c - A'*inv((A*Dx*Dx*A'))*A*Dx*Dx*c;
p =... |
github | alex-parisi/Phased-Vocoder-master | changePitchLength.m | .m | Phased-Vocoder-master/changePitchLength.m | 2,028 | utf_8 | 69a7ffac541318a8da2dca0c8b1e2031 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% %%%%
%%%% INPUT: %%%%
%%%% sig - audioread(filename) %%%%
%%%% Fs - ... |
github | alex-parisi/Phased-Vocoder-master | PhaseVocoder.m | .m | Phased-Vocoder-master/PhaseVocoder.m | 7,160 | utf_8 | f0d64ec15bf41366a1148c2b9934ee37 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% %%%%
%%%% INPUT: %%%%
%%%% origSpeech - audioread(filename) %%%%
%%%% Fs - ... |
github | brightnesss/RGB-D-Tracking-master | run_tracker.m | .m | RGB-D-Tracking-master/run_tracker.m | 9,067 | utf_8 | ccd2b4a290e4a753d839215267a71a06 |
%
% High-Speed Tracking with Kernelized Correlation Filters
%
% Joao F. Henriques, 2014
% http://www.isr.uc.pt/~henriques/
%
% Main interface for Kernelized/Dual Correlation Filters (KCF/DCF).
% This function takes care of setting up parameters, loading video
% information and computing precisions. For the actua... |
github | HEVC-Projects/CPH-master | extractCUDepthGrndTruthCPHInter.m | .m | CPH-master/extractCUDepthGrndTruthCPHInter.m | 13,369 | utf_8 | d84d00f57e79881007284e2b04fa0d55 | function extractCUDepthGrndTruthCPHInter
% This program transfers raw video and label files to samples for
% training, validation and test, establishing a large-scale database
% for CU partition of inter-mode HEVC (CPH-Inter).
% The database contains 111 videos in total.
% For each video, the... |
github | HEVC-Projects/CPH-master | extractCUDepthGrndTruthCPHIntra.m | .m | CPH-master/extractCUDepthGrndTruthCPHIntra.m | 10,768 | utf_8 | 1dad2cb9720a3eb99ec39590d41811b7 | function extractCUDepthGrndTruthCPHIntra
% This program transfers raw image and label files to samples that are directly available for deep CNNs,
% in order to establish a large-scale database for CU partition of
% intra-mode HEVC (CPH-Intra).
% All images are stored in 12 YUV files, arranged by ... |
github | fangcaoxin/Myproject-master | generate_2d_points.m | .m | Myproject-master/SfM/generate_2d_points.m | 1,495 | utf_8 | fcb889bbb13927636e96ccae3687121c |
% drawmultiview();
function generate2D()
%drawmultiview();
generate_2DPoints();
end
function generate_2DPoints()
addpath('cylindrical')
load teapotMatrix1008.mat
imagePointMatrix = zeros(size(teapotMatrix,1), 2, 10);
for i = 1:10
imagePointMatrix(:,:,i) = point3d_t_2d(teapotMatrix(:,:,i));
%
plot(ima... |
github | fangcaoxin/Myproject-master | refractivetriangulateMultiview.m | .m | Myproject-master/SfM/refractivetriangulateMultiview.m | 1,296 | utf_8 | 70d34e64fe663cdb4debd9dbfb94d25d | function [points3d, errors] = refractivetriangulateMultiview(bearingVec, ...
camPoses, cameraParams)
%outputType = validateInputs(pointTracks, camPoses, cameraParams);
numTracks = numel(bearingVec);
points3d = zeros(numTracks, 3);
numCameras = size(camPoses, 1);
cameraMatrices = containers.Map('KeyType', 'uint32'... |
github | fangcaoxin/Myproject-master | refractiveBA.m | .m | Myproject-master/SfM/refractiveBA.m | 2,834 | utf_8 | b8ab627f87b89be67cecc5ca7615fedf | function [xyzPoints, camPoses] = refractiveBA(p, bearingVec, camPoses)
% p is point 1xN , Rt estimated rotation and translation mat4x3xM
% v calucated bearing vector
p_one_row = reshape(p, 1, []);
R_one_row = reshape(cell2mat(camPoses.Orientation), 1, []);
t_one_row = reshape(cell2mat(camPoses.Location), 1, []);
x0 = [... |
github | fangcaoxin/Myproject-master | sfm_one_view.m | .m | Myproject-master/SfM/cube_near_1010/sfm_one_view.m | 1,615 | utf_8 | e462e05e331c3e3369e012cc60d56f65 |
function [x_s, r_out_norm, r_in] = sfm_one_view(gg, x, K, c, w)
d_flat = gg(1);
Rc = angle2Rot(gg(2), gg(3), gg(4));
hcx = K(3,1);
hcy = K(3,2);
fx = K(1,1);
fy = K(2,2);
n1 = c(1);
n2 = c(2);
n3 = c(3);
u_v = x - [hcx hcy];
u_v(:,3) = 1;
r_in = u_v./[fx fy 1];
r_in = r_in*Rc';
r_in = r_in./sqrt(sum(r_in.*r_in,2)); % ... |
github | fangcaoxin/Myproject-master | refractive_sfm.m | .m | Myproject-master/SfM/cube_near_1010/refractive_sfm.m | 4,255 | utf_8 | 9af689c433c22f9cac2b88c5d3b07040 | % Use |imageDatastore| to get a list of all image file names in a
% directory.
clear;clc;
addpath('../common');
load corres.mat
IntrinsicMatrix = [2881.84239103060,0,0;0,2890.20944782907,0;2073.63152572517,1398.01946105023,1];
radialDistortion = [0.0424498292149281,-0.0489981810340664];
%cameraParams = cameraParameters... |
github | fangcaoxin/Myproject-master | lagrange.m | .m | Myproject-master/SfM/common/lagrange.m | 2,287 | utf_8 | a7d43423e24d7835dab7f4f602d61800 | function g=lagrange(U,g0)
l = zeros(6,1);
%l = [0.5;0.5;0.5;0.5;0.5;0.5];
gg0=[g0;l];%init
f=@(gg)Ug(gg,U);%
% [gg,fval,info]=fsolve(f,gg0,optimset("TolFun",3e-16,"TolX",3e-16,"MaxIter",1e20));
options=optimoptions('fsolve','Algorithm', 'levenberg-marquardt',...
'Display','iter',...
'FunctionTo... |
github | fangcaoxin/Myproject-master | R_t_estimator_pixel.m | .m | Myproject-master/SfM/common/R_t_estimator_pixel.m | 1,308 | utf_8 | c9c659d08f91fe8ed85c063aff0f6492 | function [R_est,t_est]=R_t_estimator_pixel(U, mark, vertical)
%load parameter.mat
addpath('common');
[R_est, t_est] = Rt_estimate(U, mark, vertical);
end
function [R_est, t_est] = Rt_estimate(U, mark, vertical)
U = cast(U, 'double');
[v,lambda]=eig(U'*U);
if (vertical)
g=v(:,2);
else
g =... |
github | fangcaoxin/Myproject-master | R_t_estimator_3d.m | .m | Myproject-master/SfM/common/R_t_estimator_3d.m | 1,290 | utf_8 | 630b96fd1b87d070ac970be92c30d654 | function [R_est, t_est] = R_t_estimator_3d(bearing_vector, d3_points, vertical)
ro = bearing_vector(:, 1:3);
xs = bearing_vector(:, 4:6);
X = d3_points;
U = [ro(:, 3).*X(:,1)-ro(:, 2).*X(:,1) ...
ro(:, 3).*X(:,2)-ro(:, 2).*X(:,2) ...
ro(:, 3).*X(:,3)-ro(:, 2).*X(:,3) ...
ro(:, 1).*X(... |
github | fangcaoxin/Myproject-master | optim_point.m | .m | Myproject-master/SfM/common/optim_point.m | 4,569 | utf_8 | cc7cc33ee2be85704f02fcdcd9c8ee1b | function [xyzPoints, view] = optim_point(view, tracks, K, step, startNum, endNum)
% p is point 1xN , Rt estimated rotation and translation mat4x3xM
% v calucated bearing vector
tracks_cell = struct2cell(tracks);
nxyzPoints = reshape(cell2mat(tracks_cell(3,:,:)), 3, []); % get pointcloud
p = nxyzPoints'; % all points no... |
github | fangcaoxin/Myproject-master | triangulateOptim.m | .m | Myproject-master/SfM/common/triangulateOptim.m | 700 | utf_8 | 42b18bf7d5fa641327bc7366f0de17b5 | function xw = triangulateOptim(vec1, vec2, R_2, t_2)
% R_2, t_2 the camera pose relative world coordinate system
r_out_w1 = vec1(:, 1:3);
xs_w1 = vec1(:, 4:6);
ro2 = vec2(:, 1:3);
xs2 = vec2(:, 4:6);
r_out_w2 = ro2*R_2';
xs_w2 = xs2*R_2'+t_2';
num = size(r_out_w1, 1);
k0 =50* ones(num, 2);
... |
github | fangcaoxin/Myproject-master | lagrange_pnp.m | .m | Myproject-master/SfM/common/lagrange_pnp.m | 1,610 | utf_8 | de38f7fca54749f8ca6f4197c58beb2c | function g=lagrange_pnp(U,g0)
l = zeros(6,1);
%l = [0.5;0.5;0.5;0.5;0.5;0.5];
gg0=[g0;l];%init
f=@(gg)Ug(gg,U);%
[gg,fval,info]=fsolve(f,gg0,optimset("TolFun",3e-16,"TolX",3e-16,"MaxIter",1e20));
%options=optimoptions('fsolve','Algorithm', 'levenberg-marquardt',...
%'Display','iter',...
% 'Func... |
github | fangcaoxin/Myproject-master | sfm_two_view.m | .m | Myproject-master/SfM/calibration/sfm_two_view.m | 1,860 | utf_8 | 9de142007a0437d9b75262532066c2c3 | function xw_est = sfm_two_view()
load imagePoints.mat
load worldPoints.mat
load historyn5.mat
in = [1 2 3 5 9];
view = [3 4]; % good result
x_best = historyn5.x(end,:);
gg1 = [x_best(view(1)*9-8:view(1)*9) x_best(end-5:end) ];
gg2 = [x_best(view(2)*9-8:view(2)*9) x_best(end-5:end) ];
x1 = imagePoints(:,:,in(view(1)));
... |
github | fangcaoxin/Myproject-master | backProjectionError.m | .m | Myproject-master/SfM/calibration/backProjectionError.m | 2,918 | utf_8 | 0d40c57c1eea57e083e2e9c610eea17b | function val = backProjectionError(x, x_w)
load res.mat
K =[590.2313 0 0; 0 559.4365 0; 369.2098 272.4348 1];
c = [1 1.49 1];
Ra = 50;
ra = 46;
r1 = [res(1) res(2) res(3)];
r2 = [res(4) res(5) res(6)];
r1 = r1/norm(r1);
r2 = r2/norm(r2);
r3 = cross(r1,r2);
Rot = [r1;r2;r3];
ts = [res(7); res(8);res(9)];
tc = [res(10); ... |
github | fangcaoxin/Myproject-master | fermat_flat.m | .m | Myproject-master/SfM/flat/fermat_flat.m | 699 | utf_8 | 3ce8b3c716837ff8e8cae3c065d94440 | % use fermat to determine the intersect point at glass
function [c]=fermat_flat(point,n1,n2,n3,w,d,cali)
if(cali > 0)
n3 = n1;
end
v = [-point(1), -point(2), -point(3)];
v = v/norm(v);
t = (d+w-point(3))/v(3);
t1 = (d-point(3))/v(3);
x0 = point(1) + v(1)*t ; % outer
y0 = point(2) + v(2)*t;
... |
github | fangcaoxin/Myproject-master | simulation_flat.m | .m | Myproject-master/SfM/flat/simulation_flat.m | 3,359 | utf_8 | 7e70c44b36a0cc4084871bcaabf1724c | %%simulate SfM
%% load 3D points
addpath('../common');
load('teapot.mat');
teapot1 = teapot(1:10:end,:) + [0 0 600]; % Z>600
%% transform 3D points to another view(R,t) external matrix
load camera_motion.mat
load parameter.mat
teapot2 = (teapot1-trans')*rotate;
teapot1 = cast(teapot1,'double');
teapot2 = cast(teapot2,'... |
github | fangcaoxin/Myproject-master | error_min.m | .m | Myproject-master/SfM/cylindrical/error_min.m | 2,822 | utf_8 | 0fa3d4b232c0453c67f09777904807d4 | function res = error_min(init, x,x_w, K, c, Ra, ra)
fun = @(gg)fun1(gg, x, K, c, Ra, ra) - x_w;
lb = [-1 -1 -1 -1 -1 -1 -500 -500 0 0];
ub = [1 1 1 1 1 1 500 500 800 45];
%options=optimoptions('lsqnonlin', 'Display','iter','FunctionTolerance',1e-10);
opts = optimset("MaxIter", 1e5, "Display", "on");
res = lsqnonlin(fu... |
github | fangcaoxin/Myproject-master | sfm_multi_view_Rt.m | .m | Myproject-master/SfM/cylindrical/sfm_multi_view_Rt.m | 3,436 | utf_8 | 0dd3f7953db9347d06b7a6a5beb704a7 | function [xw_est, R_opm, t_opm] = sfm_multi_view_Rt(imagePoints, views)
% calibration result
gg = [ -0.72005 2.06590 42.66089 -0.28110 -1.39643 -1.96133 ];
K =[590.2313 0 0; 0 559.4365 0; 369.2098 272.4348 1];
c = [1 1.49 1];
Ra = 50;
ra = 46;
n = size(imagePoints, 1);
m = size(views, 2); % the number of view
Rt ... |
github | fangcaoxin/Myproject-master | sfm_one_view.m | .m | Myproject-master/SfM/cylindrical/sfm_one_view.m | 2,595 | utf_8 | 9e4269061aa317e78af57939bcbf78e7 | function [xs_w, r_out_w] = sfm_one_view(gg, x, K, c, Ra, ra)
r1 = [gg(1) gg(2) gg(3)];
r2 = [gg(4) gg(5) gg(6)];
r3 = cross(r1,r2);
Rot = [r1;r2;r3];
ts = [gg(7); gg(8);gg(9)];
tc = [gg(10); gg(11); gg(12)];
ac = [gg(13); gg(14); gg(15)]; %rotate x, y, z
Rc = angle2Rot(gg(13), gg(14), gg(15));
hcx = K(3,1);
hcy = K(3,2... |
github | fangcaoxin/Myproject-master | sfm_one_view_Rt.m | .m | Myproject-master/SfM/cylindrical/sfm_one_view_Rt.m | 2,431 | utf_8 | b9bd89530f1ecbac0e443f30f0d64b79 | function [xs, ro] = sfm_one_view_Rt(gg, x, K, c, Ra, ra)
tc = [gg(1); gg(2); gg(3)];
ac = [gg(4); gg(5); gg(6)]; %rotate x, y, z
Rc = angle2Rot(gg(4), gg(5), gg(6));
hcx = K(3,1);
hcy = K(3,2);
fx = K(1,1);
fy = K(2,2);
n1 = c(1);
n2 = c(2);
n3 = c(3);
R = Ra;
r = ra;
u_v = x - [hcx hcy];
u_v(:,3) = 1;
r_in = u_v./[fx ... |
github | fangcaoxin/Myproject-master | fermat.m | .m | Myproject-master/SfM/cylindrical/fermat.m | 1,650 | utf_8 | da4bc1faf6de6486ebefb87c32310e6c | % use fermat to determine the intersect point at glass
function [c]=fermat(point,n1,n2,n3,R,r,d, cali)
load parameter.mat
if(cali > 0)
n3 = n1;
end
% point = double(point);
v = [point(1), point(2), point(3)-d];
% v =[0 point(2)-camera_center(2) point(3)-camera_center(3)];
v = v/norm(v);
t =( -(point... |
github | fangcaoxin/Myproject-master | error_min_2.m | .m | Myproject-master/SfM/cylindrical/error_min_2.m | 8,253 | utf_8 | e95c3c41468a15ed9c19dbe38c683124 | function res = error_min_2(init, x,x_w, K, c, Ra, ra,lb,ub, N)
historyn5.x = [];
historyn5.fval = [];
fun = @(ga)fun_total(ga, x, x_w, K, c, Ra, ra, N);
% options=optimoptions(@fmincon, 'Display','iter', 'Algorithm','sqp',...
% 'MaxIterations',3000, 'MaxFunctionEvaluations', 1e4, 'ConstraintTolerance', 1e-1);
opti... |
github | fangcaoxin/Myproject-master | error_min_1.m | .m | Myproject-master/SfM/cylindrical/error_min_1.m | 5,901 | utf_8 | 4542da36101a814cb88048da1e5029ab | function res = error_min_1(init, x,x_w, K, c, Ra, ra)
fun = @(gg)fun1(gg, x, x_w, K, c, Ra, ra);
lb = [-1 -1 -1 -1 -1 -1 -500 -500 0 0];
ub = [1 1 1 1 1 1 500 500 800 43];
options=optimoptions(@fmincon, 'Display','iter', 'Algorithm','sqp',...
'MaxIterations',3000, 'MaxFunctionEvaluations', 1e5,'ConstraintTolerance... |
github | fangcaoxin/Myproject-master | simulate_sfm.m | .m | Myproject-master/SfM/cylindrical/simulate_sfm.m | 3,750 | utf_8 | 2675724566b0af6a7d0daac5c67cfdef | %%simulate SfM
%% load 3D points
addpath('common')
load('teapot.mat');
teapot1 = teapot(1:10:end,:) + [0 0 600]; % Z>600
%% transform 3D points to another view(R,t) external matrix
load camera_motion.mat
load parameter.mat
teapot2 = (teapot1-translation')*Rotate; % transform teapot 1 to Camera 2 system according R and ... |
github | ctjacobs/plane-poiseuille-flow-master | poiseuille.m | .m | plane-poiseuille-flow-master/poiseuille.m | 3,160 | utf_8 | 46fbab88c09a6344eb5ecf19205462e9 | % Solves the equation d2u/dy2 = -G/mu to simulate plane Poiseuille flow.
% This considers the fluid between two parallel plates located at y = 0 and
% y = Ly, with both plates stationary and a constant pressure
% gradient G = -dp/dx applied in the streamwise direction. The dynamic viscosity of
% the fluid is denoted by... |
github | vkosuri/CourseraMachineLearning-master | submit.m | .m | CourseraMachineLearning-master/home/week-8/exercises/machine-learning-ex7/ex7/submit.m | 1,438 | utf_8 | 665ea5906aad3ccfd94e33a40c58e2ce | function submit()
addpath('./lib');
conf.assignmentSlug = 'k-means-clustering-and-pca';
conf.itemName = 'K-Means Clustering and PCA';
conf.partArrays = { ...
{ ...
'1', ...
{ 'findClosestCentroids.m' }, ...
'Find Closest Centroids (k-Means)', ...
}, ...
{ ...
'2', ...
... |
github | vkosuri/CourseraMachineLearning-master | submitWithConfiguration.m | .m | CourseraMachineLearning-master/home/week-8/exercises/machine-learning-ex7/ex7/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | vkosuri/CourseraMachineLearning-master | savejson.m | .m | CourseraMachineLearning-master/home/week-8/exercises/machine-learning-ex7/ex7/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | vkosuri/CourseraMachineLearning-master | loadjson.m | .m | CourseraMachineLearning-master/home/week-8/exercises/machine-learning-ex7/ex7/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | vkosuri/CourseraMachineLearning-master | loadubjson.m | .m | CourseraMachineLearning-master/home/week-8/exercises/machine-learning-ex7/ex7/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | vkosuri/CourseraMachineLearning-master | saveubjson.m | .m | CourseraMachineLearning-master/home/week-8/exercises/machine-learning-ex7/ex7/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | vkosuri/CourseraMachineLearning-master | submit.m | .m | CourseraMachineLearning-master/home/week-9/exercises/machine-learning-ex8/ex8/submit.m | 2,135 | utf_8 | eebb8c0a1db5a4df20b4c858603efad6 | function submit()
addpath('./lib');
conf.assignmentSlug = 'anomaly-detection-and-recommender-systems';
conf.itemName = 'Anomaly Detection and Recommender Systems';
conf.partArrays = { ...
{ ...
'1', ...
{ 'estimateGaussian.m' }, ...
'Estimate Gaussian Parameters', ...
}, ...
{ ...... |
github | vkosuri/CourseraMachineLearning-master | submitWithConfiguration.m | .m | CourseraMachineLearning-master/home/week-9/exercises/machine-learning-ex8/ex8/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
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