plateform stringclasses 1
value | repo_name stringlengths 13 113 | name stringlengths 3 74 | ext stringclasses 1
value | path stringlengths 12 229 | size int64 23 843k | source_encoding stringclasses 9
values | md5 stringlengths 32 32 | text stringlengths 23 843k |
|---|---|---|---|---|---|---|---|---|
github | shawnngtq/machine-learning-master | submitWithConfiguration.m | .m | machine-learning-master/andrew-ng-machine-learning/week02/Programming Assignment/machine-learning-ex1/ex1/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | 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 | shawnngtq/machine-learning-master | savejson.m | .m | machine-learning-master/andrew-ng-machine-learning/week02/Programming Assignment/machine-learning-ex1/ex1/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 | shawnngtq/machine-learning-master | loadjson.m | .m | machine-learning-master/andrew-ng-machine-learning/week02/Programming Assignment/machine-learning-ex1/ex1/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 | shawnngtq/machine-learning-master | loadubjson.m | .m | machine-learning-master/andrew-ng-machine-learning/week02/Programming Assignment/machine-learning-ex1/ex1/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 | shawnngtq/machine-learning-master | saveubjson.m | .m | machine-learning-master/andrew-ng-machine-learning/week02/Programming Assignment/machine-learning-ex1/ex1/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 | shawnngtq/machine-learning-master | submit.m | .m | machine-learning-master/andrew-ng-machine-learning/week07/Programming Assignment/machine-learning-ex6/ex6/submit.m | 1,318 | utf_8 | bfa0b4ffb8a7854d8e84276e91818107 | function submit()
addpath('./lib');
conf.assignmentSlug = 'support-vector-machines';
conf.itemName = 'Support Vector Machines';
conf.partArrays = { ...
{ ...
'1', ...
{ 'gaussianKernel.m' }, ...
'Gaussian Kernel', ...
}, ...
{ ...
'2', ...
{ 'dataset3Params.m' }, ...
... |
github | shawnngtq/machine-learning-master | porterStemmer.m | .m | machine-learning-master/andrew-ng-machine-learning/week07/Programming Assignment/machine-learning-ex6/ex6/porterStemmer.m | 9,902 | utf_8 | 7ed5acd925808fde342fc72bd62ebc4d | function stem = porterStemmer(inString)
% Applies the Porter Stemming algorithm as presented in the following
% paper:
% Porter, 1980, An algorithm for suffix stripping, Program, Vol. 14,
% no. 3, pp 130-137
% Original code modeled after the C version provided at:
% http://www.tartarus.org/~martin/PorterStemmer/c.tx... |
github | shawnngtq/machine-learning-master | submitWithConfiguration.m | .m | machine-learning-master/andrew-ng-machine-learning/week07/Programming Assignment/machine-learning-ex6/ex6/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | 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 | shawnngtq/machine-learning-master | savejson.m | .m | machine-learning-master/andrew-ng-machine-learning/week07/Programming Assignment/machine-learning-ex6/ex6/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 | shawnngtq/machine-learning-master | loadjson.m | .m | machine-learning-master/andrew-ng-machine-learning/week07/Programming Assignment/machine-learning-ex6/ex6/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 | shawnngtq/machine-learning-master | loadubjson.m | .m | machine-learning-master/andrew-ng-machine-learning/week07/Programming Assignment/machine-learning-ex6/ex6/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 | shawnngtq/machine-learning-master | saveubjson.m | .m | machine-learning-master/andrew-ng-machine-learning/week07/Programming Assignment/machine-learning-ex6/ex6/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 | shawnngtq/machine-learning-master | submit.m | .m | machine-learning-master/andrew-ng-machine-learning/week08/Programming Assignment/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 | shawnngtq/machine-learning-master | submitWithConfiguration.m | .m | machine-learning-master/andrew-ng-machine-learning/week08/Programming Assignment/machine-learning-ex7/ex7/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | 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 | shawnngtq/machine-learning-master | savejson.m | .m | machine-learning-master/andrew-ng-machine-learning/week08/Programming Assignment/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 | shawnngtq/machine-learning-master | loadjson.m | .m | machine-learning-master/andrew-ng-machine-learning/week08/Programming Assignment/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 | shawnngtq/machine-learning-master | loadubjson.m | .m | machine-learning-master/andrew-ng-machine-learning/week08/Programming Assignment/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 | shawnngtq/machine-learning-master | saveubjson.m | .m | machine-learning-master/andrew-ng-machine-learning/week08/Programming Assignment/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 | shawnngtq/machine-learning-master | submit.m | .m | machine-learning-master/andrew-ng-machine-learning/week09/Programming Assignment/machine-learning-ex8/ex8/submit.m | 2,064 | utf_8 | 7c4fcf60df3a7e09d05a74f7772fed3b | 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 | shawnngtq/machine-learning-master | submitWithConfiguration.m | .m | machine-learning-master/andrew-ng-machine-learning/week09/Programming Assignment/machine-learning-ex8/ex8/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | 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 | shawnngtq/machine-learning-master | savejson.m | .m | machine-learning-master/andrew-ng-machine-learning/week09/Programming Assignment/machine-learning-ex8/ex8/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 | shawnngtq/machine-learning-master | loadjson.m | .m | machine-learning-master/andrew-ng-machine-learning/week09/Programming Assignment/machine-learning-ex8/ex8/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 | shawnngtq/machine-learning-master | loadubjson.m | .m | machine-learning-master/andrew-ng-machine-learning/week09/Programming Assignment/machine-learning-ex8/ex8/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 | shawnngtq/machine-learning-master | saveubjson.m | .m | machine-learning-master/andrew-ng-machine-learning/week09/Programming Assignment/machine-learning-ex8/ex8/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 | shawnngtq/machine-learning-master | submit.m | .m | machine-learning-master/andrew-ng-machine-learning/week03/Programming Assignment/machine-learning-ex2/ex2/submit.m | 1,605 | utf_8 | 9b63d386e9bd7bcca66b1a3d2fa37579 | function submit()
addpath('./lib');
conf.assignmentSlug = 'logistic-regression';
conf.itemName = 'Logistic Regression';
conf.partArrays = { ...
{ ...
'1', ...
{ 'sigmoid.m' }, ...
'Sigmoid Function', ...
}, ...
{ ...
'2', ...
{ 'costFunction.m' }, ...
'Logistic R... |
github | shawnngtq/machine-learning-master | submitWithConfiguration.m | .m | machine-learning-master/andrew-ng-machine-learning/week03/Programming Assignment/machine-learning-ex2/ex2/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | 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 | shawnngtq/machine-learning-master | savejson.m | .m | machine-learning-master/andrew-ng-machine-learning/week03/Programming Assignment/machine-learning-ex2/ex2/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | shawnngtq/machine-learning-master | loadjson.m | .m | machine-learning-master/andrew-ng-machine-learning/week03/Programming Assignment/machine-learning-ex2/ex2/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | shawnngtq/machine-learning-master | loadubjson.m | .m | machine-learning-master/andrew-ng-machine-learning/week03/Programming Assignment/machine-learning-ex2/ex2/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | shawnngtq/machine-learning-master | saveubjson.m | .m | machine-learning-master/andrew-ng-machine-learning/week03/Programming Assignment/machine-learning-ex2/ex2/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | congzlwag/UnsupGenModbyMPS-master | tensor_product.m | .m | UnsupGenModbyMPS-master/matlab_code/tensor_product.m | 1,985 | utf_8 | 0aab341bb32bc59072d6bf91566b6350 | function [C cindex] = tensor_product(varargin)
% Author: Jing Chen yzcj105@126.com
% varargin is cindex
%C(cindex)=A(aindex)*B(bindex)
% the same string in index will be summed up
% A,B,C is muti dimention array
%get all the permute order
if nargin == 4
A = varargin{1};
aindex = varargin{2};
B... |
github | Moein-Khajehnejad/Automated-Classification-of-Right-Hand-and-Foot-Movement-EEG-Signals-master | mutation.m | .m | Automated-Classification-of-Right-Hand-and-Foot-Movement-EEG-Signals-master/Feature Selection by Genetic Algorithm/mutation.m | 383 | utf_8 | 693a8986fc3cce36ba68e225b78affbc | % function y = mutation(x,VarRange) %Gaussian mutation
% nVar=numel(x);
% j=randi([1 nVar]);
% Varmin=min(VarRange);
% Varmax=max(VarRange);
% sigma= (Varmax-Varmin)/10;
% y=x;
% y(j)=x(j)+sigma * randn;
% y=min(max(y, Varmin), Varmax));
% end
function y = mutation(x)
nVar=length(x);
j1=randi([1 nVar-1]);
j2=randi([j1... |
github | kul-optec/nmpc-codegen-master | compare_libs_table.m | .m | nmpc-codegen-master/old_code/demos/Matlab/compare_libs_table.m | 4,686 | utf_8 | 51be9c2e097d0c560e6afb8ad713299f | % Compare the nmpc-codegen library with alternatives for different
% obstacles. Print out a table with the timing results.
clear all;
addpath(genpath('../../src_matlab'));
% noise_amplitude=[0;0;0];
noise_amplitude=[0.1;0.1;0.05];
shift_horizon=true;
%%
names={"controller_compare_libs","demo2","demo3"};
result_mean = ... |
github | kul-optec/nmpc-codegen-master | demo_set_obstacles.m | .m | nmpc-codegen-master/old_code/demos/Matlab/demo_set_obstacles.m | 11,179 | utf_8 | bcfbf687601a2b72b0cc66b60fefac99 | function [ trailer_controller,initial_state,reference_state,reference_input,obstacle_weights ] = demo_set_obstacles( name,shift_horizon )
%DEMO_SET_OBSTACLES
if(strcmp(name,"controller_compare_libs"))
[trailer_controller,initial_state,reference_state,reference_input,obstacle_weights ] = generate_controller... |
github | kul-optec/nmpc-codegen-master | simulate_demo_trailer_OPTI_ipopt.m | .m | nmpc-codegen-master/old_code/demos/Matlab/simulate_demo_trailer_OPTI_ipopt.m | 2,641 | utf_8 | de13fac5910022b2d4c227a294a7112f | function [ state_history,time_history,iteration_history ] = simulate_demo_trailer_OPTI_ipopt( controller, simulator, ...
initial_state,reference_state,reference_input,obstacle_weights,shift_horizon,noise_amplitude)
%SIMULATE_DEMO_TRAILER_PANOC_MATLAB Summary of this function goes here
% Detailed explanation goes ... |
github | kul-optec/nmpc-codegen-master | simulate_OPTI_ipopt.m | .m | nmpc-codegen-master/old_code/demos/Matlab/quadcopter/simulate_OPTI_ipopt.m | 2,684 | utf_8 | 71726baf867043dd6698a0133bf8aee5 | function [ state_history,time_history,iteration_history ] = simulate_OPTI_ipopt( controller, simulator, ...
initial_state,reference_state,reference_input,obstacle_weights,shift_horizon,noise_amplitude)
%SIMULATE_DEMO_TRAILER_PANOC_MATLAB Summary of this function goes here
% Detailed explanation goes here
% --... |
github | kul-optec/nmpc-codegen-master | integrate.m | .m | nmpc-codegen-master/old_code/src_matlab/+nmpccodegen/+models/integrate.m | 3,294 | utf_8 | cff2851dfdb4d749cddbd4c8206cd4fb | function [ next_state ] = integrate( state,step_size,function_system,key_name)
%INTEGRATE Summary of this function goes here
% Detailed explanation goes here
next_state = integrate_lib(state,step_size,function_system,key_name);
% if(key_name=='RK44') % for now only 1 integrator available
% k1 = function_syst... |
github | kul-optec/nmpc-codegen-master | lbfgs.m | .m | nmpc-codegen-master/Matlab/lbfgs/lbfgs.m | 1,665 | utf_8 | 9b9248e31868782d232d95c5edd5f02f | % f=function
% df=gradient of function
% g_i=df(x(i))
% The buffer of length m contains 2 variables
% s_i = x_{i+1} - x_{i}
% y_i = g_{i+1} - g_{i}
function [ s,y,new_x] = lbfgs(iteration_index,buffer_size,x,df,s,y)
% if this is the first time, use the gradient descent
if(iteration_index==1)
direction... |
github | kul-optec/nmpc-codegen-master | myfun_poly.m | .m | nmpc-codegen-master/Matlab/lbfgs/lib/fminlbfgs_version2c/myfun_poly.m | 227 | utf_8 | 594dc9467dd6877b46a36211ac1475aa | % where myfun is a MATLAB function such as:
% function [f,g] = myfun(x)
% f = sum(sin(x) + 3);
% if ( nargout > 1 ), g = cos(x); end
function [f,g] = myfun_poly(x)
f =x(1)^10 + x(2)^10;
g = [10*x(1)^9; 10*x(2)^9 ];
end |
github | kul-optec/nmpc-codegen-master | myfun.m | .m | nmpc-codegen-master/Matlab/lbfgs/lib/fminlbfgs_version2c/myfun.m | 274 | utf_8 | 01f35caf22b243254ec4046505457734 | % where myfun is a MATLAB function such as:
% function [f,g] = myfun(x)
% f = sum(sin(x) + 3);
% if ( nargout > 1 ), g = cos(x); end
function [f,g] = myfun(x)
a=1;
b=100;
f =(a-x(1))^2 + b*(x(2)-x(1))^2;
g = [-2*(a-(b+1)*x(1)+b*x(2)); 2*b*(x(2)-x(1)) ];
end |
github | andersonreisoares/DivideAndSegment-master | divSeg.m | .m | DivideAndSegment-master/divSeg.m | 4,538 | ibm852 | c9126c6d7b43c01b35d43d612463aacf | % % The divide and segment method appears in
% % Divide And Segment - An Alternative For Parallel Segmentation. TS Korting,
% % EF Castejon, LMG Fonseca - GeoInfo, 97-104
% % Improvements of the divide and segment method for parallel image segmentation
% % AR Soares, TS Körting, LMG Fonseca - Revista Brasileira de ... |
github | andersonreisoares/DivideAndSegment-master | dijkstra.m | .m | DivideAndSegment-master/dijkstra.m | 1,422 | utf_8 | bcd87e1fec09ed7eecb50ee9e017bc41 | %---------------------------------------------------
% Dijkstra Algorithm
% author : Dimas Aryo
% email : mr.dimasaryo@gmail.com
%
%---------------------------------------------------
% example
% G = [0 3 9 10 10 10 10;
% 0 1 10 7 1 10 10;
% 0 2 10 7 10 10 10;
% 0 0 0 0 0 2 8;
% 0 0 4 5 0... |
github | xjsxujingsong/UberNet-master | classification_demo.m | .m | UberNet-master/caffe-fast-rcnn/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 | xjsxujingsong/UberNet-master | voc_eval.m | .m | UberNet-master/lib/datasets/VOCdevkit-matlab-wrapper/voc_eval.m | 1,332 | utf_8 | 3ee1d5373b091ae4ab79d26ab657c962 | function res = voc_eval(path, comp_id, test_set, output_dir)
VOCopts = get_voc_opts(path);
VOCopts.testset = test_set;
for i = 1:length(VOCopts.classes)
cls = VOCopts.classes{i};
res(i) = voc_eval_cls(cls, VOCopts, comp_id, output_dir);
end
fprintf('\n~~~~~~~~~~~~~~~~~~~~\n');
fprintf('Results:\n');
aps = [res(:... |
github | anmolnijhawan/Piecewise-linear-model-fitting-of-DCE-MRI-data-master | PLMmain.m | .m | Piecewise-linear-model-fitting-of-DCE-MRI-data-master/PLMmain.m | 477 | utf_8 | fe7202a2b43457fa562c7ae46e20e671 | %% function to be used in Perfusio Tool Main for PL model calculation
function [ Ct ] = PLMmain( par_PL,time )
for t = 1: length(time)
if (time(t)<=par_PL(1))
Ct(t) = par_PL(3);
elseif (time(t)<=par_PL(2))
Ct(t) = par_PL(3) + par_PL(4)*(time(t)-par_PL(1)... |
github | s0920832252/Number-Optimization-Class--master | hessian_f.m | .m | Number-Optimization-Class--master/Hw2/hessian_f.m | 425 | utf_8 | 6ab24e44ad54c197c6f3a1cb813af792 | %function h = hessian_f(X)
function hes = hessian_f( X )
% g is a function which can return gradient vector.
%idea : (g(x+h ; y)-g(x;y))/h -> g() for( delat_x )
%and (g(x ; y+h)-g(x;y))/h -> g() for( delat_y )
h=0.0001;
g=gradient_f(X)';
hes=[];
for i=1:length(X)
newX=X;
... |
github | s0920832252/Number-Optimization-Class--master | 別人的InteriorPointMethod.m | .m | Number-Optimization-Class--master/Hw4/別人的InteriorPointMethod.m | 3,691 | utf_8 | 7b22636821f0d713a1ce16cd20241b43 | %{
GNU Octave
version = 3.8.2
http://octave-online.net/
GNU Octave
version = 4.0.0
Windows XP
%}
function [outputX, outputCase] = InteriorPointMethod(c, A, b, x0, lambda0, s0);
%{
min c^T * x
s.t.
A*x >= b
%}
%{
min c^T * x... |
github | s0920832252/Number-Optimization-Class--master | main.m | .m | Number-Optimization-Class--master/Hw4/世承的作業/main.m | 412 | utf_8 | 4d7cd5aabe4a933a8f88d8a8252cd567 | % EXAMPLE
% max z=8*x1+5*x2
% s.t. 2*x1+x2<=1000
% 3*x1+4*x2<=2400
% x1+x2<=700
% x1-x2<=350
function main
A=[-2,-1;-3,-4;-1,-2;-1,1;1,0;0,1];
b=[-1000;-2400;-700;-350;0;0];
c=[-8;-5];
lambda0=[1;1;1;1;1;1];
x0=[0;0];
s0=[1000;2400;700;350;0;0];
[Z X]=interior_point_method(A, b, c, x... |
github | s0920832252/Number-Optimization-Class--master | draw_trace.m | .m | Number-Optimization-Class--master/Hw1/draw_trace.m | 1,312 | utf_8 | 3e4bf037967e1f3757b10abe04cc2f1f |
function draw_trace()
step = 0.1;
X = 0:step:9;
Y = -1:step:1;
n = size(X,2);
m = size(Y,2);
Z = zeros(m,n);
for j = 1:m
for i = 1:n
Z(j,i) = f(X(i),Y(j));
end
end
contour(X,Y,Z,50);
hold on; % this is important!! This will overlap your plots.
% plot the trace
% You can record the trace of your resu... |
github | shaform/facenet-master | detect_face_v1.m | .m | facenet-master/tmp/detect_face_v1.m | 7,954 | utf_8 | 678c2105b8d536f8bbe08d3363b69642 | % MIT License
%
% Copyright (c) 2016 Kaipeng Zhang
%
% Permission is hereby granted, free of charge, to any person obtaining a copy
% of this software and associated documentation files (the "Software"), to deal
% in the Software without restriction, including without limitation the rights
% to use, copy, modify, mer... |
github | shaform/facenet-master | detect_face_v2.m | .m | facenet-master/tmp/detect_face_v2.m | 9,016 | utf_8 | 0c963a91d4e52c98604dd6ca7a99d837 | % MIT License
%
% Copyright (c) 2016 Kaipeng Zhang
%
% Permission is hereby granted, free of charge, to any person obtaining a copy
% of this software and associated documentation files (the "Software"), to deal
% in the Software without restriction, including without limitation the rights
% to use, copy, modify, mer... |
github | kevinliu001/Android-SpeexDenoise-master | echo_diagnostic.m | .m | Android-SpeexDenoise-master/app/src/main/jni/libspeexdsp/echo_diagnostic.m | 2,076 | utf_8 | 8d5e7563976fbd9bd2eda26711f7d8dc | % Attempts to diagnose AEC problems from recorded samples
%
% out = echo_diagnostic(rec_file, play_file, out_file, tail_length)
%
% Computes the full matrix inversion to cancel echo from the
% recording 'rec_file' using the far end signal 'play_file' using
% a filter length of 'tail_length'. The output is saved to 'o... |
github | Minyu-Shen/Simulation-for-bus-stops-near-signalized-intersection-master | Simulation_far_side.m | .m | Simulation-for-bus-stops-near-signalized-intersection-master/Simulation_far_side.m | 23,064 | utf_8 | 4b73d726815eec5a3e86be60926b91a6 | clear;clc;
% rng(2);
global serving_rate;
global cs_number;
global berth_number;
global buffer_number;
global jam_spacing;
global free_speed;
global back_speed;
global moveup_speed;
global cycle_length_number;
global green_ratio;
global sim_size;
global il;
% cs_number = [0.15,0.3,0.45,0.6,0.75];
global cs;
cs = 0.6;
... |
github | erwinwu211/TS-LSTM-based-HAR-master | create_flow_images_LRCN.m | .m | TS-LSTM-based-HAR-master/Action_Recognition/create_flow_images_LRCN.m | 1,867 | utf_8 | 2c6c52d02e2a85fc153ac4e7d2711107 | function create_flow_images_LRCN(base, save_base)
%create_flow_images will compute flow images from RGB images using [1].
%input:
% base: folder in which RGB frames from videos are stored
% save_base: folder in which flow images should be saved
%
%[1] Brox, Thomas, et al. "High accuracy optical flow estimation... |
github | jlperla/continuous_time_methods-master | ValueMatch.m | .m | continuous_time_methods-master/matlab/tests/ValueMatch.m | 1,127 | utf_8 | 8638ee3b217786493d64e98e26b97168 | % this is a function take input of Delta_p and Delta_m,v and create
% v1-omega*v
function residual = ValueMatch(v,z,Delta_p,Delta_m,h_p,h_m)
I = length(Delta_p);
N = length(v)/I; % v is N*I
alpha = 2.1;
F_p = @(z) alpha*exp(-alpha*z);
eta = 1;
%Trapezoidal weights, adjusted for non-uniform t... |
github | jlperla/continuous_time_methods-master | Julia_comparison_stationary_test.m | .m | continuous_time_methods-master/matlab/tests/Julia_comparison_stationary_test.m | 6,411 | utf_8 | 3428cc8accd57af623a963ac72d17209 | % This is test function that try to replicate Julia problem with same set
% up
%Using the unit testing framework in matlab. See https://www.mathworks.com/help/matlab/matlab_prog/write-simple-test-case-with-functions.html
%To run tests:
% runtests %would run all of them in the current directory
% runtests('my_test') %... |
github | jlperla/continuous_time_methods-master | discretize_univariate_diffusion_test.m | .m | continuous_time_methods-master/matlab/tests/discretize_univariate_diffusion_test.m | 14,330 | utf_8 | 6053e25e9b61e0b069dea7d221595fe8 | %Using the unit testing framework in matlab. See https://www.mathworks.com/help/matlab/matlab_prog/write-simple-test-case-with-functions.html
%To run tests:
% runtests %would run all of them in the current directory
% runtests('my_test') %runs just the my_test.m file
% runtests('my_test/my_function_test') %runs only `... |
github | jlperla/continuous_time_methods-master | time_varying_optimal_stopping_diffusion_test.m | .m | continuous_time_methods-master/matlab/tests/time_varying_optimal_stopping_diffusion_test.m | 5,932 | utf_8 | 07eb198d7ad8da763c93e6ae5bf93526 | %Using the unit testing framework in matlab. See https://www.mathworks.com/help/matlab/matlab_prog/write-simple-test-case-with-functions.html
%To run tests:
% runtests %would run all of them in the current directory
% runtests('my_test') %runs just the my_test.m file
% runtests('my_test/my_function_test') %runs only `... |
github | jlperla/continuous_time_methods-master | discretize_nonuniform_univariate_diffusion_test.m | .m | continuous_time_methods-master/matlab/tests/discretize_nonuniform_univariate_diffusion_test.m | 9,936 | utf_8 | 730b1686969f65e6c50d1eef6d7be0f1 | %Using the unit testing framework in matlab. See https://www.mathworks.com/help/matlab/matlab_prog/write-simple-test-case-with-functions.html
%To run tests:
% runtests %would run all of them in the current directory
% runtests('my_test') %runs just the my_test.m file
% runtests('my_test/my_function_test') %runs only `... |
github | jlperla/continuous_time_methods-master | KFE_discretized_univariate_test.m | .m | continuous_time_methods-master/matlab/tests/KFE_discretized_univariate_test.m | 6,773 | utf_8 | de2022549d28f4046aae8918d000139e | %Using the unit testing framework in matlab. See https://www.mathworks.com/help/matlab/matlab_prog/write-simple-test-case-with-functions.html
%To run tests:
% runtests %would run all of them in the current directory
% runtests('my_test') %runs just the my_test.m file
% runtests('my_test/my_function_test') %runs only `... |
github | jlperla/continuous_time_methods-master | discretize_time_varying_univariate_diffusion_test.m | .m | continuous_time_methods-master/matlab/tests/discretize_time_varying_univariate_diffusion_test.m | 18,948 | utf_8 | da7ccac5bc2c633d5a41ad01ffd7f0ee | %Using the unit testing framework in matlab. See https://www.mathworks.com/help/matlab/matlab_prog/write-simple-test-case-with-functions.html
%To run tests:
% runtests %would run all of them in the current directory
% runtests('my_test') %runs just the my_test.m file
% runtests('my_test/my_function_test') %runs only `... |
github | jlperla/continuous_time_methods-master | HJBE_discretized_univariate_test.m | .m | continuous_time_methods-master/matlab/tests/HJBE_discretized_univariate_test.m | 5,791 | utf_8 | deb22a4d65b518586586c31aa32b43a9 | %Using the unit testing framework in matlab. See https://www.mathworks.com/help/matlab/matlab_prog/write-simple-test-case-with-functions.html
%To run tests:
% runtests %would run all of them in the current directory
% runtests('my_test') %runs just the my_test.m file
% runtests('my_test/my_function_test') %runs only `... |
github | jlperla/continuous_time_methods-master | simple_optimal_stopping_diffusion_test.m | .m | continuous_time_methods-master/matlab/tests/simple_optimal_stopping_diffusion_test.m | 35,987 | utf_8 | 608f184a92134e7f8d0d49679e367164 | %Using the unit testing framework in matlab. See https://www.mathworks.com/help/matlab/matlab_prog/write-simple-test-case-with-functions.html
%To run tests:
% runtests %would run all of them in the current directory
% runtests('my_test') %runs just the my_test.m file
% runtests('my_test/my_function_test') %runs only `... |
github | jlperla/continuous_time_methods-master | simple_model_test.m | .m | continuous_time_methods-master/matlab/tests/simple_model_test.m | 6,062 | utf_8 | 2bdecc69be86d7a099960399ddc228b9 |
function tests = simple_model_test
tests = functiontests(localfunctions);
end
%This is run at the beginning of the test. Not required.
function setupOnce(testCase)
addpath('../lib/');
end
function simple_v_test(testCase)
%% 1. test on v behavior for time changing u and big t grid
% this test checks when T is... |
github | jlperla/continuous_time_methods-master | HJBE_discretized_nonuniform_univariate_test.m | .m | continuous_time_methods-master/matlab/tests/HJBE_discretized_nonuniform_univariate_test.m | 7,946 | utf_8 | 24b20fb4f3cd538ecfd99916137a9388 | %Using the unit testing framework in matlab. See https://www.mathworks.com/help/matlab/matlab_prog/write-simple-test-case-with-functions.html
%To run tests:
% runtests %would run all of them in the current directory
% runtests('my_test') %runs just the my_test.m file
% runtests('my_test/my_function_test') %runs only `... |
github | jlperla/continuous_time_methods-master | optimal_stopping_diffusion.m | .m | continuous_time_methods-master/matlab/lib/optimal_stopping_diffusion.m | 6,836 | utf_8 | 2c932e105565b2a65bd30f7578635e60 | % Modification of Ben Moll's: http://www.princeton.edu/~moll/HACTproject/option_simple_LCP.m
% See notes and equation numbers in 'optimal_stopping.pdf'
% Solves the HJB variational inequality that comes from a general diffusion process with optimal stopping.
% min{rho v(t,x) - u(t,x) - mu(t,x)D_x v(t,x) - sigma(t,x)... |
github | jlperla/continuous_time_methods-master | simple_joint_HJBE_stationary_distribution_univariate.m | .m | continuous_time_methods-master/matlab/lib/simple_joint_HJBE_stationary_distribution_univariate.m | 2,898 | utf_8 | 16afd41183f79902c52da542e96c6872 | %Takes the discretized operator A, the grid x, and finds the stationary distribution f.
function [v, f, success] = simple_joint_HJBE_stationary_distribution_univariate(A, x, u, rho, settings)
I = length(x);
if nargin < 5
settings.default = true; %Just creates as required.
end
if(~isfield(setting... |
github | jlperla/continuous_time_methods-master | simple_optimal_stopping_diffusion.m | .m | continuous_time_methods-master/matlab/lib/simple_optimal_stopping_diffusion.m | 6,496 | utf_8 | e46fcf40131efca018a2a0f4ee6ef1ae | % Modification of Ben Moll's: http://www.princeton.edu/~moll/HACTproject/option_simple_LCP.m
% See notes and equation numbers in 'optimal_stopping.pdf'
% Solves the HJB variational inequality that comes from a general diffusion process with optimal stopping.
% min{rho v(x) - u(x) - mu(x)v'(x) - sigma(x)^2/2 v''(x), ... |
github | jlperla/continuous_time_methods-master | discretize_univariate_diffusion.m | .m | continuous_time_methods-master/matlab/lib/discretize_univariate_diffusion.m | 4,082 | utf_8 | 03ba4ed2a62e7d0f59f353bce684430e | % Modification of Ben Moll's: http://www.princeton.edu/~moll/HACTproject/option_simple_LCP.m
%For algebra and equation numbers, see the 'operator_discretization_finite_differences.pdf'
%This function takes a grid on [x_min, x_max] and discretizing a general diffusion defined by the following SDE
%d x_t = mu(x_t)dt + s... |
github | jlperla/continuous_time_methods-master | discretize_time_varying_univariate_diffusion.m | .m | continuous_time_methods-master/matlab/lib/discretize_time_varying_univariate_diffusion.m | 4,398 | utf_8 | fe952d4c7af49c5f6b9f9a12eb03f75c | %For algebra and equation numbers, see the 'operator_discretization_finite_differences.pdf'
%This function takes a grid on [x_min, x_max], [t_min, t_max] and discretizing a general diffusion defined by the following SDE
%d x_t = mu(t, x_t)dt + sigma(t, x_t)^2 dW_t
%Subject to reflecting barrier at x_min and x_max and ... |
github | jlperla/continuous_time_methods-master | LCP.m | .m | continuous_time_methods-master/matlab/lib/LCP.m | 5,203 | utf_8 | 8fbbd2626f905e54a8c206b3570719e0 | function [x, iter, converged] = LCP(M,q,l,u,settings)
%LCP Solve the Linear Complementarity Problem.
%
% USAGE
% x = LCP(M,q) solves the LCP
%
% x >= 0
% Mx + q >= 0
% x'(Mx + q) = 0
%
% x = LCP(M,q,l,u) solves the generalized LCP (a.k.a MCP)
%
% l < x < u => Mx + q = 0
% x = u => ... |
github | jlperla/continuous_time_methods-master | stationary_distribution_discretized_univariate.m | .m | continuous_time_methods-master/matlab/lib/stationary_distribution_discretized_univariate.m | 5,544 | utf_8 | 18fe7580476e59859ec139c501e32912 | %Takes the discretized operator A, the grid x, and finds the stationary distribution f.
function [f, success] = stationary_distribution_discretized_univariate(A, x, settings)
I = length(x);
if nargin < 3
settings.default = true; %Just creates as required.
end
%TODO: Consider adding in a 'dense' o... |
github | jlperla/continuous_time_methods-master | simple_HJBE_discretized_univariate.m | .m | continuous_time_methods-master/matlab/lib/simple_HJBE_discretized_univariate.m | 761 | utf_8 | 78aad7cc10394b8df25366a34a51a53b | %Takes the discretized operator A, the grid x, and finds the stationary distribution f.
function [v, success] = simple_HJBE_discretized_univariate(A, x, u, rho, settings)
I = length(x);
assert(I == size(A,1) && I == size(A,2)); %Make sure sizes match
if nargin < 5
settings.default = true; %Just crea... |
github | BoianAlexandrov/HNMF-master | outputGreenNMF.m | .m | HNMF-master/outputGreenNMF.m | 783 | utf_8 | b631997750825de4e8242d51f6dce3c4 | %% Output of the simulations
function [Sf, Comp, Dr, Det, Wf] = outputGreenNMF(max_number_of_sources, RECON, SILL_AVG, numT, nd, xD, t0, S)
close all
x = 1:1:max_number_of_sources;
y1 = RECON;
y2 = SILL_AVG;
createfigureNS(x, y1, y2)
[aic_values, aic_min, nopt] = AIC( RECON, SILL_AVG, numT, nd);
name1 = sprintf('R... |
github | hafezbazrafshan/LQR-OPF-master | checkPowerFlowsPerNode.m | .m | LQR-OPF-master/checkPowerFlowsPerNode.m | 3,025 | utf_8 | 66ea172afd1b9026da17bef296e26c0a |
function [checkpf, checkEqs,realGen_check, reactiveGen_check, ...
realLoad_check,reactiveLoad_check]...
= checkPowerFlowsPerNode(VS,thetaS,pgS,qgS, pdS,qdS)
% CHECKPOWERFLOWS Validates given power flow solution.
% [checkpf,checkEqs,realGen_check,...
% reactiveGen_check, realLoad_check,...
% reactiveLoa... |
github | gctronic/e-puck-library-master | OpenEpuck.m | .m | e-puck-library-master/library/matlab/matlab files/OpenEpuck.m | 553 | utf_8 | 1374131feb92d077a351f85a5740e359 | %! \brief Open the communication with the e-puck
% \params port The port in wich the e-puck is paired to.
% it must be a string like that "COM11" if e-puck is paired
% on COM 11.
%/
function OpenEpuck(port)
global EpuckPort;
EpuckPort = serial(port,'BaudRate', 115200,'inputBuffersize',4096,'OutputBufferSize',4096,'... |
github | gctronic/e-puck-library-master | CloseEpuck.m | .m | e-puck-library-master/library/matlab/matlab files/CloseEpuck.m | 153 | utf_8 | 45c5daa80365a8c5266f36e3ba47573f | %! \brief Close the communication with the e-puck
function CloseEpuck()
global EpuckPort;
fclose(EpuckPort);
clear EpuckPort;
clear global EpuckPort;
end |
github | gctronic/e-puck-library-master | two_complement.m | .m | e-puck-library-master/tool/ePic/two_complement.m | 597 | utf_8 | 824666e5ce11c268e2dcc8608edb553c | function value=two_complement(rawdata)
if (mod(max(size(rawdata)),2) == 1)
error('The data to be converted must be 16 bits and the vector does not contain pairs of numbers')
end
value=zeros(1,max(size(rawdata))/2);
j=1;
for i=1:2:max(size(rawdata))
if (bitget(rawdata(i+1),8)==1) % Negatif number -> two'comp... |
github | gctronic/e-puck-library-master | controller_pos.m | .m | e-puck-library-master/tool/ePic/controller_pos.m | 3,562 | utf_8 | f625e31e5b3d2b88fb870f79abb0c704 | % controller_pos is an exemple controller.
% ---------------------------------------------
% It drives the epuck from the current position which is define as [0 0 0]
% to a goal position which can be set by the user.
% The control uses a smooth controller which drives the e-puck along
% smooth curves from the current... |
github | gctronic/e-puck-library-master | main.m | .m | e-puck-library-master/tool/ePic/main.m | 87,934 | utf_8 | 6eeefa1ec55877edf2bdb3579b034718 | function varargout = main(varargin)
% MAIN M-file for main.fig
% MAIN, by itself, creates a new MAIN or raises the existing
% singleton*.
%
% H = MAIN returns the handle to a new MAIN or the handle to
% the existing singleton*.
%
% MAIN('CALLBACK',hObject,eventData,handles,...) calls the local
... |
github | gctronic/e-puck-library-master | two_complement.m | .m | e-puck-library-master/tool/ePic/@ePicKernel/private/two_complement.m | 597 | utf_8 | 824666e5ce11c268e2dcc8608edb553c | function value=two_complement(rawdata)
if (mod(max(size(rawdata)),2) == 1)
error('The data to be converted must be 16 bits and the vector does not contain pairs of numbers')
end
value=zeros(1,max(size(rawdata))/2);
j=1;
for i=1:2:max(size(rawdata))
if (bitget(rawdata(i+1),8)==1) % Negatif number -> two'comp... |
github | jtomelin/caixeiro-viajante-algoritmo-genetico-master | cvfun.m | .m | caixeiro-viajante-algoritmo-genetico-master/cvfun.m | 683 | utf_8 | 4aed36af92ca49b1e057789977bf50fa | %Funcao de custo para o problema do caixeiro viajante
function dist=cvfun(pop)
% Utiliza variaveis globais "x" e "y"
global x y
[Npop,Ncidade]=size(pop);
tour=[pop pop(:,1)]; % gera a matriz 20x21 da populacao onde a ultima
% coluna eh a copia da primeira coluna (o agente deve voltar a cidade inicial)
%dis... |
github | aayush-k/Local-Feature-Matching-master | get_features.m | .m | Local-Feature-Matching-master/code/get_features.m | 4,250 | utf_8 | 072ff4a20bda044515a18de8bd55e28e | % Local Feature Stencil Code
% CS 4476 / 6476: Computer Vision, Georgia Tech
% Written by James Hays
% Returns a set of feature descriptors for a given set of interest points.
% 'image' can be grayscale or color, your choice.
% 'x' and 'y' are nx1 vectors of x and y coordinates of interest points.
% The loc... |
github | aayush-k/Local-Feature-Matching-master | show_correspondence2.m | .m | Local-Feature-Matching-master/code/show_correspondence2.m | 1,618 | utf_8 | d754a0a7f9f2ca2960dfea8e0518a162 | % Automated Panorama Stitching stencil code
% CS 4476 / 6476: Computer Vision, Georgia Tech
% Written by Henry Hu <henryhu@gatech.edu> and James Hays
% Visualizes corresponding points between two images. Corresponding points
% will be matched by a line of random color.
% This function provides another method of visua... |
github | aayush-k/Local-Feature-Matching-master | show_correspondence.m | .m | Local-Feature-Matching-master/code/show_correspondence.m | 2,215 | utf_8 | 2d4943c5a3ff072fa99f194331ba8180 | % CS 4476 / 6476: Computer Vision, Georgia Tech
% Written by Henry Hu <henryhu@gatech.edu> and James Hays
% Visualizes corresponding points between two images. Corresponding points
% will have the same random color.
% You do not need to modify anything in this function, although you can if
% you want to.
function [ h... |
github | aayush-k/Local-Feature-Matching-master | get_interest_points.m | .m | Local-Feature-Matching-master/code/get_interest_points.m | 4,341 | utf_8 | 627f44b3ca3d41aa59bcc5ecfdd57455 | % Local Feature Stencil Code
% CS 4476 / 6476: Computer Vision, Georgia Tech
% Written by James Hays
% 1. Compute the horizontal and vertical derivatives of the image Ix and Iy by convolving the original image with derivatives of Gaussians (Section 3.2.3).
% 2. Compute the three images corresponding to the oute... |
github | aayush-k/Local-Feature-Matching-master | cheat_interest_points.m | .m | Local-Feature-Matching-master/code/cheat_interest_points.m | 1,111 | utf_8 | 9d93fe7e1b0c34e407f2e5d3528315bf | % Local Feature Stencil Code
% CS 4476 / 6476: Computer Vision, Georgia Tech
% Written by James Hays
% This function is provided for development and debugging but cannot be
% used in the final handin. It 'cheats' by generating interest points from
% known correspondences. It will only work for the three image pa... |
github | aayush-k/Local-Feature-Matching-master | show_ground_truth_corr.m | .m | Local-Feature-Matching-master/code/show_ground_truth_corr.m | 441 | utf_8 | 98f706af0be75ce44da3822986ef85b2 | % Local Feature Stencil Code
% CS 4476 / 6476: Computer Vision, Georgia Tech
% Written by James Hays
function show_ground_truth_corr()
image1 = imread('../data/Notre Dame/921919841_a30df938f2_o.jpg');
image2 = imread('../data/Notre Dame/4191453057_c86028ce1f_o.jpg');
corr_file = '../data/Notre Dame/92191984... |
github | aayush-k/Local-Feature-Matching-master | match_features.m | .m | Local-Feature-Matching-master/code/match_features.m | 2,023 | utf_8 | ddb965c0b19bfc79a8e1157c7ceb1e5a | % Local Feature Stencil Code
% CS 4476 / 6476: Computer Vision, Georgia Tech
% Written by James Hays
% 'features1' and 'features2' are the n x feature dimensionality features
% from the two images.
% If you want to include geometric verification in this stage, you can add
% the x and y locations of the intere... |
github | aayush-k/Local-Feature-Matching-master | collect_ground_truth_corr.m | .m | Local-Feature-Matching-master/code/collect_ground_truth_corr.m | 2,009 | utf_8 | f59689693ec3d8d895fededdbc1efe39 | % Local Feature Stencil Code
% CS 4476 / 6476: Computer Vision, Georgia Tech
% Written by James Hays
function collect_ground_truth_corr()
%An interactive method to specify and then save many point correspondences
%between two photographs, which will be used to generate a projective
%transformation. Run this bef... |
github | aayush-k/Local-Feature-Matching-master | evaluate_correspondence.m | .m | Local-Feature-Matching-master/code/evaluate_correspondence.m | 3,952 | utf_8 | 35e20889ed67de05a8a5706b3d1fcada | % Local Feature Stencil Code
% Computater Vision
% Written by Henry Hu <henryhu@gatech.edu> and James Hays
% You do not need to modify anything in this function, although you can if
% you want to.
function evaluate_correspondence(imgA, imgB, ground_truth_correspondence_file, scale_factor, x1_est, y1_est, x2_est,... |
github | akileshbadrinaaraayanan/IITH-master | imdb_cnn_train.m | .m | IITH-master/Sem6/CS5190_Soft_Computing/cs13b1042_final_code/imdb_cnn_train.m | 11,107 | utf_8 | a957bb75bebf326aa0d9d848e0c4293d | function imdb_cnn_train(imdb, opts, varargin)
% Train a CNN model on a dataset supplied by imdb
opts.lite = false ;
opts.numFetchThreads = 0 ;
opts.train.batchSize = opts.batchSize ;
opts.train.numEpochs = 25 ;
opts.train.continue = true ;
opts.train.useGpu = false ;
opts.train.prefetch = false ;
opts.train.learningRa... |
github | akileshbadrinaaraayanan/IITH-master | get_rcnn_features.m | .m | IITH-master/Sem6/CS5190_Soft_Computing/cs13b1042_final_code/get_rcnn_features.m | 2,567 | utf_8 | 8ec7919f6877980bbd15cde85e9a3ffd | function code = get_rcnn_features(net, im, varargin)
% GET_RCNN_FEATURES
% This function gets the fc7 features for an image region,
% extracted from the provided mask.
opts.batchSize = 96 ;
opts.regionBorder = 0.05;
opts = vl_argparse(opts, varargin) ;
if ~iscell(im)
im = {im} ;
end
res = [] ;
cache = struct... |
github | akileshbadrinaaraayanan/IITH-master | get_bcnn_features.m | .m | IITH-master/Sem6/CS5190_Soft_Computing/cs13b1042_final_code/get_bcnn_features.m | 5,006 | utf_8 | dc6b3d661ab0ce34e4c10321d9784263 | function [code, varargout]= get_bcnn_features(neta, netb, im, varargin)
% GET_BCNN_FEATURES Get bilinear cnn features for an image
% This function extracts the binlinear combination of CNN features
% extracted from two different networks.
% Copyright (C) 2015 Tsung-Yu Lin, Aruni RoyChowdhury, Subhransu Maji.
% Al... |
github | akileshbadrinaaraayanan/IITH-master | model_setup.m | .m | IITH-master/Sem6/CS5190_Soft_Computing/cs13b1042_final_code/model_setup.m | 4,981 | utf_8 | dd2ecf29110db93049ea41940d366eaf | function [opts, imdb] = model_setup(varargin)
% Copyright (C) 2015 Tsung-Yu Lin, Aruni RoyChowdhury, Subhransu Maji.
% All rights reserved.
%
% This file is part of the BCNN and is made available under
% the terms of the BSD license (see the COPYING file).
setup ;
opts.seed = 1 ;
opts.batchSize = 128 ;
opts.numEpoc... |
github | akileshbadrinaaraayanan/IITH-master | bcnn_asym_forward.m | .m | IITH-master/Sem6/CS5190_Soft_Computing/cs13b1042_final_code/bcnn_asym_forward.m | 3,110 | utf_8 | ae749c28e094092f12b95926a2f4f0f1 | function [code, varargout]= bcnn_asym_forward(neta, netb, im, varargin)
% BCNN_ASYM_FORWARD run the forward passing of the two networks and output the
% bilinear cnn features for batch of images. The images are pre-cropped,
% resized and mean subtracted. The function doesn't preprocess the images
% instead just get t... |
github | akileshbadrinaaraayanan/IITH-master | vl_bilinearnn.m | .m | IITH-master/Sem6/CS5190_Soft_Computing/cs13b1042_final_code/vl_bilinearnn.m | 11,132 | utf_8 | fd1ef3540067f57e543ae75b8fb91153 | function res = vl_bilinearnn(net, x, dzdy, res, varargin)
% VL_BILINEARNN is the extension of VL_SIMPLENN to suppport
% 1.vl_nnbilinearpool()
% 2.vl_nnbilinearclpool()
% 3.vl_nnsqrt()
% 4.vl_nnl2norm()
% RES = VL_BILINEARENN(NET, X) evaluates the convnet NET on data X.
% RES = VL_BILINE... |
github | akileshbadrinaaraayanan/IITH-master | bcnn_train_sw.m | .m | IITH-master/Sem6/CS5190_Soft_Computing/cs13b1042_final_code/bcnn_train_sw.m | 11,154 | utf_8 | 4537c6cb7fb3028503d506f65310b2d9 | function [net, info] = bcnn_train_sw(net, imdb, getBatch, varargin)
% BNN_TRAIN_SW training a symmetric BCNN
% BCNN_TRAIN() is an example learner implementing stochastic gradient
% descent with momentum to train a symmetric BCNN for image classification.
% It can be used with different datasets by providin... |
github | akileshbadrinaaraayanan/IITH-master | get_dcnn_features.m | .m | IITH-master/Sem6/CS5190_Soft_Computing/cs13b1042_final_code/get_dcnn_features.m | 5,754 | utf_8 | 75a8a8a1052928aa38fc0e3a6965005c | function code = get_dcnn_features(net, im, varargin)
% GET_DCNN_FEATURES Get convolutional features for an image region
% This function extracts the DCNN (CNN+FV) for an image.
% These can be used as SIFT replacement in e.g. a Fisher Vector.
%
opts.useSIFT = false ;
opts.crop = true ;
%opts.scales = 2.^(1.5:-.5:-... |
github | akileshbadrinaaraayanan/IITH-master | imdb_bcnn_train.m | .m | IITH-master/Sem6/CS5190_Soft_Computing/cs13b1042_final_code/imdb_bcnn_train.m | 17,253 | utf_8 | 0df08e6209d5478cefffaf69a7fc339d | function imdb_bcnn_train(imdb, opts, varargin)
% Train a bilinear CNN model on a dataset supplied by imdb
% Copyright (C) 2015 Tsung-Yu Lin, Aruni RoyChowdhury, Subhransu Maji.
% All rights reserved.
%
% This file is part of BCNN and is made available
% under the terms of the BSD license (see the COPYING file).
%
% T... |
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