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 | lihp11/predictability_transport-master | findlabel.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Toolboxes/OpenTSTOOL/tstoolbox/@unit/private/findlabel.m | 1,565 | utf_8 | 31c0ddaf800254da0b93f0b06880e536 | function [label, name, qeng, qger, dBScale, dBRef] = findlabel(factor, exponents)
% finds label and name for a given set of factors and exponents
%RESOURCES = get(0, 'UserData');
%TSTOOLunittab = RESOURCES{2};
load 'tstoolbox/units.mat';
if (exponents == [0 0 0 0 0 0 0 0]) | (factor == 0)
label = '';
name = '';
q... |
github | lihp11/predictability_transport-master | makemex.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Toolboxes/OpenTSTOOL/mex-dev/makemex.m | 4,543 | utf_8 | a5c5d656e3ecaf2639c8ba5655d69ed0 | function makemex(TSTOOLpath)
% compile and copy mex-files to destination directories
% Invoked by : makemex(TSTOOLpath)
% or: makemex
if nargin == 0
if which('units.mat')
TSTOOLpath = fileparts(which('units.mat'));
elseif exist(fullfile(pwd,'../tstoolbox','units.mat'))==2
TSTOOLpath=f... |
github | lihp11/predictability_transport-master | brute.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Toolboxes/OpenTSTOOL/mex-dev/NN/TestSuite/brute.m | 1,123 | utf_8 | d22cdf5bea5cc3dcbd66e4199d90d4d9 | function [indices, distances] = brute(points, refind, nnr, past)
% [indices, distances] = brute(points, refind, nnr, past)
%
% Brute force implementation of nearest neighbor search
%
% Input arguments :
%
% points - N by D matrix of N points of dimension D
% refind - integer reference indices
% nnr - number of neighb... |
github | lihp11/predictability_transport-master | test.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Toolboxes/OpenTSTOOL/mex-dev/NN/TestSuite/test.m | 6,469 | utf_8 | aeba8aef7698bae6251c4870d8a52f18 | function test(mode)
% test nearest neighbor search based mex files
% recompile
error_flag = 0;
if nargin < 1
mode = 'all';
end
disp('Fast nearest neighbor search routines test')
load points.dat
dat = points;
%dat = generate_chaotic_data(40000, 20);
%size(dat)
if strcmp(mode, 'delaunay2D') | strcmp(mode, 'all')
... |
github | lihp11/predictability_transport-master | pauswahl.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Toolboxes/OpenTSTOOL/mex-dev/Polynomauswahl/pauswahl.m | 7,307 | utf_8 | f03f7cce7ca1ea159b5f43d27e308de3 | function [pol, train_fehler, test_fehler] = pauswahl(x, y, fracref, maxgrad)
% Polynomauswahlverfahren
% Monome werden nach einer Greedy-Heuristik aus einer vorgebenen Menge ausgewaehlt. Es
% wird dasjenige Monom gewaehlt, was den Fehler im aktullen Schritt am staeksten vermindert.
% Als Grad eines Monoms wird die Sum... |
github | lihp11/predictability_transport-master | RM_information.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Toolboxes/Rudy_Moddemeijer/RM_information.m | 3,939 | utf_8 | 97af7638c51e84d7465823993914a331 | % RM_information Estimates the mutual information of two stationary signals with
% independent pairs of samples using various approaches.
% [ESTIMATE,NBIAS,SIGMA,DESCRIPTOR] = INFORMATION(X,Y) or
% [ESTIMATE,NBIAS,SIGMA,DESCRIPTOR] = INFORMATION(X,Y,DESCRIPTOR) or
% [ESTIMATE,NBIAS,SIGMA,DESCRIPTOR]... |
github | lihp11/predictability_transport-master | RM_entropy.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Toolboxes/Rudy_Moddemeijer/RM_entropy.m | 3,130 | utf_8 | 6daa29bb6da5a10361b1632d7ce683b8 | % RM_entropy Estimates the entropy of stationary signals with
% independent samples using various approaches.
% [ESTIMATE,NBIAS,SIGMA,DESCRIPTOR] = ENTROPY(X) or
% [ESTIMATE,NBIAS,SIGMA,DESCRIPTOR] = ENTROPY(X,DESCRIPTOR) or
% [ESTIMATE,NBIAS,SIGMA,DESCRIPTOR] = ENTROPY(X,DESCRIPTOR,APPROACH) or
%... |
github | lihp11/predictability_transport-master | RM_histogram2.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Toolboxes/Rudy_Moddemeijer/RM_histogram2.m | 2,490 | utf_8 | 827c5ebe29d9f1568a1cb0a5325dae0a | % RM_histogram2 Computes the two dimensional frequency histogram of two
% row vectors x and y.
% [RESULT,DESCRIPTOR] = HISTOGRAM2(X,Y) or
% [RESULT,DESCRIPTOR] = HISTOGRAM2(X,Y,DESCRIPTOR) or
%where
% DESCRIPTOR = [LOWERX,UPPERX,NCELLX;
% LOWERY,UPPERY,NCELLY]
%
% RESULT : A matr... |
github | lihp11/predictability_transport-master | RM_histogram.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Toolboxes/Rudy_Moddemeijer/RM_histogram.m | 1,615 | utf_8 | 4ae2da9d29e8e01d9bfc62a918eeb0d0 | % RM_histogram Computes the frequency histogram of the row vector x.
% [RESULT,DESCRIPTOR] = HISTOGRAM(X) or
% [RESULT,DESCRIPTOR] = HISTOGRAM(X,DESCRIPTOR) or
% where
% DESCRIPTOR = [LOWER,UPPER,NCELL]
%
% RESULT : A row vector containing the histogram
% DESCRIPTOR: The used descriptor
%
% X : T... |
github | lihp11/predictability_transport-master | SQL_FlushKeywords.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Database/SQL_FlushKeywords.m | 6,678 | utf_8 | e4f59fb4eb4e415d8daac572c2e5349c | function out = SQL_FlushKeywords(flushWhat)
% SQL_FlushKeywords
%
% Recomputes all keywords and linkage information in the database, for either
% time series ('ts') or operations ('ops').
%
% Useful for when there's a problem with the keyword relationships (e.g., when
% an SQL_add is interrupted).
% ------------------... |
github | lihp11/predictability_transport-master | TS_InspectQuality.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/PlottingAnalysis/TS_InspectQuality.m | 9,940 | utf_8 | d309c9428b8a19f0b3ed8ab7732b97df | function hadProblem = TS_InspectQuality(inspectWhat,customFile)
% TS_InspectQuality Statistics of quality labels from an hctsa analysis.
%
% This function loads the calculation quality information from HCTSA.mat,
% and plots a visualization of where different special-valued outputs are occurring.
%
% Useful for check... |
github | lihp11/predictability_transport-master | TS_TopFeatures.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/PlottingAnalysis/TS_TopFeatures.m | 15,085 | utf_8 | 0def29a3a5c411087ad85c3afc5c31ab | function [ifeat, testStat, testStat_rand] = TS_TopFeatures(whatData,whatTestStat,doNull,varargin)
% TS_TopFeatures Top individual features for discriminating labeled time series
%
% This function compares each feature in an hctsa dataset individually for its
% ability to separate the labeled classes of time series a... |
github | lihp11/predictability_transport-master | TS_SimSearch.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/PlottingAnalysis/TS_SimSearch.m | 13,980 | utf_8 | 0ace00ecc1c05f6e3fd0f338af93a625 | function TS_SimSearch(varargin)
% TS_SimSearch Nearest neighbors of a given time series from an hctsa analysis.
%
% Nearest neighbors can provide a local context for a particular time series or
% operation.
%
%---INPUTS:
%
% targetID, the ID of the target time series or operation
% numNeighbors, the number of nearest ... |
github | lihp11/predictability_transport-master | TS_cluster.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/PlottingAnalysis/TS_cluster.m | 7,465 | utf_8 | 6392b53bab112941abb2098a4f087f0d | function TS_cluster(distanceMetricRow, linkageMethodRow, distanceMetricCol, linkageMethodCol, doSave, theFile)
% TS_cluster Linkage clustering for hctsa data.
%
% Reads in normalized data from HCTSA_N.mat, clusters the data matrix by
% reordering rows and columns with linkage clustering, and then saves the result
% ... |
github | lihp11/predictability_transport-master | LempZiv.m | .m | predictability_transport-master/code_github]/LempZiv/LempZiv.m | 3,082 | utf_8 | 81faf9a6a1417ec0556b6182de46d726 | % Author: Mehedi Hasan
% Bangladesh University of Engineering and Tech.
% email: mh.eee08@gmail.com
% ref: http://www.data-compression.com/lempelziv.html
% data is a string of character or anything
% dict is a cell containing the dictionary
% comdata is the compressed form of the data
% ComdataBin is the binary r... |
github | wanweiwei07/hiromatlab-master | anargtik.m | .m | hiromatlab-master/robotsim/anargtik.m | 4,506 | utf_8 | d92f222ae86b027e5b814903d678e5ec | % ! NOT WORKING
function [isdone, q] = anargtik(rgtarm, gripper, pt, Rt, varargin)
% check whether the joint angles q are inside working range
% direct ik is implemented in this function
%
% input
% ----------
% - rgtarm - the data structure of robot links
%
% output
% ----------
% - isdone - boolean var denoting the ... |
github | wanweiwei07/hiromatlab-master | spawnag.m | .m | hiromatlab-master/robotsim/spawnag.m | 2,021 | utf_8 | e4d9ebd7a551311ed27004c37bb4e055 | % not sure if this func is still needed 20160310
function interairi = spawnag(interair, objp, objR)
% intermediate placements in the air and the associated grasps
%
% input
% ----
% - interair - the standard interair state
% - objp - 3by1 array indicating the x,y,z pos of the placement
% - objR - 3by3 array indicating... |
github | wanweiwei07/hiromatlab-master | loadwobj.m | .m | hiromatlab-master/primitives/grip/loadwobj.m | 5,151 | utf_8 | c00b75da63f466fc1f5759798b14ce43 | function [V,F3,F4]=loadwobj(modelname,opts)
% loadawobj
% Load an Wavefront/Alias obj style model. Will only consider polygons with
% 3 or 4 vertices.
% Programme will also ignore normal and texture data. It will also ignore any
% part of the obj specification that is not a polygon mesh, ie nurbs, these
% deficiencies... |
github | wanweiwei07/hiromatlab-master | loadwobj.m | .m | hiromatlab-master/primitives/push/loadwobj.m | 5,151 | utf_8 | c00b75da63f466fc1f5759798b14ce43 | function [V,F3,F4]=loadwobj(modelname,opts)
% loadawobj
% Load an Wavefront/Alias obj style model. Will only consider polygons with
% 3 or 4 vertices.
% Programme will also ignore normal and texture data. It will also ignore any
% part of the obj specification that is not a polygon mesh, ie nurbs, these
% deficiencies... |
github | wanweiwei07/hiromatlab-master | drawEdge.m | .m | hiromatlab-master/addpath/drawEdge.m | 3,305 | utf_8 | 934f6bc1e92c903cd29132474699ecf2 | function varargout = drawEdge(varargin)
%DRAWEDGE Draw an edge given by 2 points
%
% drawEdge(x1, y1, x2, y2);
% draw an edge between the points (x1 y1) and (x2 y2).
%
% drawEdge([x1 y1 x2 y2]) ;
% drawEdge([x1 y1], [x2 y2]);
% specify data either as bundled edge, or as 2 points
%
% The function support... |
github | wanweiwei07/hiromatlab-master | dpsimplify.m | .m | hiromatlab-master/addpath/dpsimplify.m | 6,351 | utf_8 | 614ccde9431c0fb088e3b7ae4242f6c0 | function [ps,ix] = dpsimplify(p,tol)
% Recursive Douglas-Peucker Polyline Simplification, Simplify
%
% [ps,ix] = dpsimplify(p,tol)
%
% dpsimplify uses the recursive Douglas-Peucker line simplification
% algorithm to reduce the number of vertices in a piecewise linear curve
% according to a specified tolerance. The a... |
github | wanweiwei07/hiromatlab-master | surface2volume.m | .m | hiromatlab-master/addpath/surface2volume.m | 9,334 | utf_8 | ee1aa6149f97815121f0e9485b38dcd3 | function outputVolume = surface2volume(inputPatches, inputGrid, verboseOutput)
%SURFACE2VOLUME convert a surface volume to a solid volume
% OV = surface2volume(fv) creates a volume block (logical) in which every
% voxel which is inside the given surface fv is set to 1. The
% structure fv defines the surface by ve... |
github | wanweiwei07/hiromatlab-master | inpolyhedron.m | .m | hiromatlab-master/addpath/inpolyhedron.m | 22,311 | utf_8 | 05e96cdeb9584d94fef4720c9f965c44 | function IN = inpolyhedron(varargin)
%INPOLYHEDRON Tests if points are inside a 3D triangulated (faces/vertices) surface
% BY CONVENTION, SURFACE NORMALS SHOULD POINT OUT from the object. (see
% FLIPNORMALS option below for details)
%
% IN = INPOLYHEDRON(FV,QPTS) tests if the query points (QPTS) are inside the
%... |
github | wanweiwei07/hiromatlab-master | meshReduce.m | .m | hiromatlab-master/addpath/meshReduce.m | 9,838 | utf_8 | cda72b8e992250f78636deaf35d499bd | function varargout = meshReduce(nodes, varargin)
%MESHREDUCE Merge coplanar faces of a polyhedral mesh
%
% Note: deprecated, should use "mergeCoplanarFaces" instead
%
% [NODES FACES] = meshReduce(NODES, FACES)
% [NODES EDGES FACES] = meshReduce(NODES, EDGES, FACES)
% NODES is a set of 3D points (as a Nn-by-3 ar... |
github | wanweiwei07/hiromatlab-master | enclosingCircle.m | .m | hiromatlab-master/addpath/enclosingCircle.m | 1,823 | utf_8 | a2c80d5c1b477d881c1a096a567df75e | function circle = enclosingCircle(pts)
%ENCLOSINGCIRCLE Find the minimum circle enclosing a set of points.
%
% CIRCLE = enclosingCircle(POINTS);
% compute cirlce CIRCLE=[xc yc r] which enclose all points POINTS given
% as an [Nx2] array.
%
%
% Rewritten from a file from
% Yazan Ahed (yash78@gmail.com)... |
github | wanweiwei07/hiromatlab-master | check_face_vertex.m | .m | hiromatlab-master/addpath/check_face_vertex.m | 669 | utf_8 | c940a837f5afef7c3a7f7aed3aff9f7a | function [vertex,face] = check_face_vertex(vertex,face, options)
% check_face_vertex - check that vertices and faces have the correct size
%
% [vertex,face] = check_face_vertex(vertex,face);
%
% Copyright (c) 2007 Gabriel Peyre
vertex = check_size(vertex,2,4);
face = check_size(face,3,4);
%%%%%%%%%%%%%%%%%%%%%%%... |
github | wanweiwei07/hiromatlab-master | meshSurfaceArea.m | .m | hiromatlab-master/addpath/meshSurfaceArea.m | 1,881 | utf_8 | cb41ab6d25b207c9d6858e95e7cb65b6 | function area = meshSurfaceArea(vertices, edges, faces)
%MESHSURFACEAREA Surface area of a polyhedral mesh
%
% S = meshSurfaceArea(V, F)
% S = meshSurfaceArea(V, E, F)
% Computes the surface area of the mesh specified by vertex array V and
% face array F. Vertex array is a NV-by-3 array of coordinates.
% Fac... |
github | wanweiwei07/hiromatlab-master | icosphere.m | .m | hiromatlab-master/addpath/icosphere.m | 4,755 | utf_8 | 03eee2ca0b4ea4f19048924fcfd14108 | function [vv,ff] = icosphere(varargin)
%ICOSPHERE Generate icosphere.
% Create a unit geodesic sphere created by subdividing a regular
% icosahedron with normalised vertices.
%
% [V,F] = ICOSPHERE(N) generates to matrices containing vertex and face
% data so that patch('Faces',F,'Vertices',V) produces a unit icosph... |
github | wanweiwei07/hiromatlab-master | mergeCoplanarFaces.m | .m | hiromatlab-master/addpath/mergeCoplanarFaces.m | 9,906 | utf_8 | c2eece3ca2be23a866477123bbf34944 | function varargout = mergeCoplanarFaces(nodes, varargin)
%MERGECOPLANARFACES Merge coplanar faces of a polyhedral mesh
%
% [NODES FACES] = mergeCoplanarFaces(NODES, FACES)
% [NODES EDGES FACES] = mergeCoplanarFaces(NODES, EDGES, FACES)
% NODES is a set of 3D points (as a Nn-by-3 array),
% and FACES is one of:
... |
github | wanweiwei07/hiromatlab-master | read_vrml.m | .m | hiromatlab-master/addpath/read_vrml.m | 7,932 | utf_8 | 33325d3b604a7c18e845f38c8748a70b | %/*********************************************************************************
% FUNCTION NAME : read_vrml
% AUTHOR : G. Akroyd
% PURPOSE : reads a VRML or Inventor file and stores data points and connectivity
% in arrays ready for drawing wireframe images.
%
% VARIABLES/PARAMETERS:
% i/p fi... |
github | zhiyishou/py-faster-rcnn-master | voc_eval.m | .m | py-faster-rcnn-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 | ChengtaoLi/dppnys_icml2016-master | gauss_dpp_judge.m | .m | dppnys_icml2016-master/gauss_dpp_judge.m | 1,955 | utf_8 | d89f093d035797ac730e6b6e89dc74ab | %% judge if prob < u^T A^{-1} u
function [flag, gauss] = gauss_dpp_judge(A, u, prob, lambdaMin, lambdaMax)
%% Gauss Quadrature results
% Gauss -> gauss(1)
% Gauss Radau Lower Bound -> gauss(2)
% Gauss Radau Upper Bound -> gauss(3)
% Initialization
K = size(A, 1);
gauss = zeros(3,1);
g = 0;
l... |
github | ChengtaoLi/dppnys_icml2016-master | gaussdpp_mc.m | .m | dppnys_icml2016-master/gaussdpp_mc.m | 1,476 | utf_8 | d835efbf2b2986418f1a017b545c62b3 | %% sampling subsets from (Gibbs) Markov chain k-DPP with Gauss quadrature
%
% -input
% L: data kernel matrix, N*N where N is number of samples
% rangeFun: range function for bounding eigenspectrum of matrices
% mixStep: number of burn-in iterations
% k: the size of sampled subset
% init_C: initialization, sho... |
github | ChengtaoLi/dppnys_icml2016-master | gauss_kdpp_judge.m | .m | dppnys_icml2016-master/gauss_kdpp_judge.m | 4,487 | utf_8 | 13945d7ce32e4aec94f5de99bf0edff3 | %% judge if tar < prob * v' * inv(L) * v - u' * inv(L) * u
function [flag] = gauss_kdpp_judge(A, u, v, prob, tar, lambdaMin, lambdaMax)
%% Gauss Quadrature results
% Gauss -> gauss_U(1) and gauss_V(1)
% Gauss Radau Lower Bound -> gauss_U(2) and gauss_V(2)
% Gauss Radau Upper Bound -> gauss_U(... |
github | prabhsuggal/Machine-Learning-By-AndrewNG-Solutions-MATLAB--master | submit.m | .m | Machine-Learning-By-AndrewNG-Solutions-MATLAB--master/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 | prabhsuggal/Machine-Learning-By-AndrewNG-Solutions-MATLAB--master | submitWithConfiguration.m | .m | Machine-Learning-By-AndrewNG-Solutions-MATLAB--master/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 | prabhsuggal/Machine-Learning-By-AndrewNG-Solutions-MATLAB--master | savejson.m | .m | Machine-Learning-By-AndrewNG-Solutions-MATLAB--master/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 | prabhsuggal/Machine-Learning-By-AndrewNG-Solutions-MATLAB--master | loadjson.m | .m | Machine-Learning-By-AndrewNG-Solutions-MATLAB--master/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 | prabhsuggal/Machine-Learning-By-AndrewNG-Solutions-MATLAB--master | loadubjson.m | .m | Machine-Learning-By-AndrewNG-Solutions-MATLAB--master/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 | prabhsuggal/Machine-Learning-By-AndrewNG-Solutions-MATLAB--master | saveubjson.m | .m | Machine-Learning-By-AndrewNG-Solutions-MATLAB--master/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 | prabhsuggal/Machine-Learning-By-AndrewNG-Solutions-MATLAB--master | submit.m | .m | Machine-Learning-By-AndrewNG-Solutions-MATLAB--master/machine-learning-ex4/ex4/submit.m | 1,635 | utf_8 | ae9c236c78f9b5b09db8fbc2052990fc | function submit()
addpath('./lib');
conf.assignmentSlug = 'neural-network-learning';
conf.itemName = 'Neural Networks Learning';
conf.partArrays = { ...
{ ...
'1', ...
{ 'nnCostFunction.m' }, ...
'Feedforward and Cost Function', ...
}, ...
{ ...
'2', ...
{ 'nnCostFunct... |
github | prabhsuggal/Machine-Learning-By-AndrewNG-Solutions-MATLAB--master | submitWithConfiguration.m | .m | Machine-Learning-By-AndrewNG-Solutions-MATLAB--master/machine-learning-ex4/ex4/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 | prabhsuggal/Machine-Learning-By-AndrewNG-Solutions-MATLAB--master | savejson.m | .m | Machine-Learning-By-AndrewNG-Solutions-MATLAB--master/machine-learning-ex4/ex4/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 | prabhsuggal/Machine-Learning-By-AndrewNG-Solutions-MATLAB--master | loadjson.m | .m | Machine-Learning-By-AndrewNG-Solutions-MATLAB--master/machine-learning-ex4/ex4/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 | prabhsuggal/Machine-Learning-By-AndrewNG-Solutions-MATLAB--master | loadubjson.m | .m | Machine-Learning-By-AndrewNG-Solutions-MATLAB--master/machine-learning-ex4/ex4/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 | prabhsuggal/Machine-Learning-By-AndrewNG-Solutions-MATLAB--master | saveubjson.m | .m | Machine-Learning-By-AndrewNG-Solutions-MATLAB--master/machine-learning-ex4/ex4/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 | prabhsuggal/Machine-Learning-By-AndrewNG-Solutions-MATLAB--master | submit.m | .m | Machine-Learning-By-AndrewNG-Solutions-MATLAB--master/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 | prabhsuggal/Machine-Learning-By-AndrewNG-Solutions-MATLAB--master | porterStemmer.m | .m | Machine-Learning-By-AndrewNG-Solutions-MATLAB--master/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 | prabhsuggal/Machine-Learning-By-AndrewNG-Solutions-MATLAB--master | submitWithConfiguration.m | .m | Machine-Learning-By-AndrewNG-Solutions-MATLAB--master/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 | prabhsuggal/Machine-Learning-By-AndrewNG-Solutions-MATLAB--master | savejson.m | .m | Machine-Learning-By-AndrewNG-Solutions-MATLAB--master/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 | prabhsuggal/Machine-Learning-By-AndrewNG-Solutions-MATLAB--master | loadjson.m | .m | Machine-Learning-By-AndrewNG-Solutions-MATLAB--master/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 | prabhsuggal/Machine-Learning-By-AndrewNG-Solutions-MATLAB--master | loadubjson.m | .m | Machine-Learning-By-AndrewNG-Solutions-MATLAB--master/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 | prabhsuggal/Machine-Learning-By-AndrewNG-Solutions-MATLAB--master | saveubjson.m | .m | Machine-Learning-By-AndrewNG-Solutions-MATLAB--master/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 | prabhsuggal/Machine-Learning-By-AndrewNG-Solutions-MATLAB--master | submit.m | .m | Machine-Learning-By-AndrewNG-Solutions-MATLAB--master/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 | prabhsuggal/Machine-Learning-By-AndrewNG-Solutions-MATLAB--master | submitWithConfiguration.m | .m | Machine-Learning-By-AndrewNG-Solutions-MATLAB--master/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 | prabhsuggal/Machine-Learning-By-AndrewNG-Solutions-MATLAB--master | savejson.m | .m | Machine-Learning-By-AndrewNG-Solutions-MATLAB--master/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 | prabhsuggal/Machine-Learning-By-AndrewNG-Solutions-MATLAB--master | loadjson.m | .m | Machine-Learning-By-AndrewNG-Solutions-MATLAB--master/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 | prabhsuggal/Machine-Learning-By-AndrewNG-Solutions-MATLAB--master | loadubjson.m | .m | Machine-Learning-By-AndrewNG-Solutions-MATLAB--master/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 | prabhsuggal/Machine-Learning-By-AndrewNG-Solutions-MATLAB--master | saveubjson.m | .m | Machine-Learning-By-AndrewNG-Solutions-MATLAB--master/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 | prabhsuggal/Machine-Learning-By-AndrewNG-Solutions-MATLAB--master | submit.m | .m | Machine-Learning-By-AndrewNG-Solutions-MATLAB--master/machine-learning-ex5/ex5/submit.m | 1,765 | utf_8 | b1804fe5854d9744dca981d250eda251 | function submit()
addpath('./lib');
conf.assignmentSlug = 'regularized-linear-regression-and-bias-variance';
conf.itemName = 'Regularized Linear Regression and Bias/Variance';
conf.partArrays = { ...
{ ...
'1', ...
{ 'linearRegCostFunction.m' }, ...
'Regularized Linear Regression Cost Fun... |
github | prabhsuggal/Machine-Learning-By-AndrewNG-Solutions-MATLAB--master | submitWithConfiguration.m | .m | Machine-Learning-By-AndrewNG-Solutions-MATLAB--master/machine-learning-ex5/ex5/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 | prabhsuggal/Machine-Learning-By-AndrewNG-Solutions-MATLAB--master | savejson.m | .m | Machine-Learning-By-AndrewNG-Solutions-MATLAB--master/machine-learning-ex5/ex5/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 | prabhsuggal/Machine-Learning-By-AndrewNG-Solutions-MATLAB--master | loadjson.m | .m | Machine-Learning-By-AndrewNG-Solutions-MATLAB--master/machine-learning-ex5/ex5/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 | prabhsuggal/Machine-Learning-By-AndrewNG-Solutions-MATLAB--master | loadubjson.m | .m | Machine-Learning-By-AndrewNG-Solutions-MATLAB--master/machine-learning-ex5/ex5/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 | prabhsuggal/Machine-Learning-By-AndrewNG-Solutions-MATLAB--master | saveubjson.m | .m | Machine-Learning-By-AndrewNG-Solutions-MATLAB--master/machine-learning-ex5/ex5/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 | prabhsuggal/Machine-Learning-By-AndrewNG-Solutions-MATLAB--master | submit.m | .m | Machine-Learning-By-AndrewNG-Solutions-MATLAB--master/machine-learning-ex3/ex3/submit.m | 1,567 | utf_8 | 1dba733a05282b2db9f2284548483b81 | function submit()
addpath('./lib');
conf.assignmentSlug = 'multi-class-classification-and-neural-networks';
conf.itemName = 'Multi-class Classification and Neural Networks';
conf.partArrays = { ...
{ ...
'1', ...
{ 'lrCostFunction.m' }, ...
'Regularized Logistic Regression', ...
}, ..... |
github | prabhsuggal/Machine-Learning-By-AndrewNG-Solutions-MATLAB--master | submitWithConfiguration.m | .m | Machine-Learning-By-AndrewNG-Solutions-MATLAB--master/machine-learning-ex3/ex3/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 | prabhsuggal/Machine-Learning-By-AndrewNG-Solutions-MATLAB--master | savejson.m | .m | Machine-Learning-By-AndrewNG-Solutions-MATLAB--master/machine-learning-ex3/ex3/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 | prabhsuggal/Machine-Learning-By-AndrewNG-Solutions-MATLAB--master | loadjson.m | .m | Machine-Learning-By-AndrewNG-Solutions-MATLAB--master/machine-learning-ex3/ex3/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 | prabhsuggal/Machine-Learning-By-AndrewNG-Solutions-MATLAB--master | loadubjson.m | .m | Machine-Learning-By-AndrewNG-Solutions-MATLAB--master/machine-learning-ex3/ex3/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 | prabhsuggal/Machine-Learning-By-AndrewNG-Solutions-MATLAB--master | saveubjson.m | .m | Machine-Learning-By-AndrewNG-Solutions-MATLAB--master/machine-learning-ex3/ex3/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 | prabhsuggal/Machine-Learning-By-AndrewNG-Solutions-MATLAB--master | submit.m | .m | Machine-Learning-By-AndrewNG-Solutions-MATLAB--master/machine-learning-ex1/ex1/submit.m | 1,876 | utf_8 | 8d1c467b830a89c187c05b121cb8fbfd | function submit()
addpath('./lib');
conf.assignmentSlug = 'linear-regression';
conf.itemName = 'Linear Regression with Multiple Variables';
conf.partArrays = { ...
{ ...
'1', ...
{ 'warmUpExercise.m' }, ...
'Warm-up Exercise', ...
}, ...
{ ...
'2', ...
{ 'computeCost.m... |
github | prabhsuggal/Machine-Learning-By-AndrewNG-Solutions-MATLAB--master | submitWithConfiguration.m | .m | Machine-Learning-By-AndrewNG-Solutions-MATLAB--master/machine-learning-ex1/ex1/lib/submitWithConfiguration.m | 3,733 | utf_8 | 4c1a23d8632614ececc696ab415b54b8 | 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 | prabhsuggal/Machine-Learning-By-AndrewNG-Solutions-MATLAB--master | savejson.m | .m | Machine-Learning-By-AndrewNG-Solutions-MATLAB--master/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 | prabhsuggal/Machine-Learning-By-AndrewNG-Solutions-MATLAB--master | loadjson.m | .m | Machine-Learning-By-AndrewNG-Solutions-MATLAB--master/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 | prabhsuggal/Machine-Learning-By-AndrewNG-Solutions-MATLAB--master | loadubjson.m | .m | Machine-Learning-By-AndrewNG-Solutions-MATLAB--master/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 | prabhsuggal/Machine-Learning-By-AndrewNG-Solutions-MATLAB--master | saveubjson.m | .m | Machine-Learning-By-AndrewNG-Solutions-MATLAB--master/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 | bastibe/Violinplot-Matlab-master | testviolinplot.m | .m | Violinplot-Matlab-master/test_cases/testviolinplot.m | 2,640 | utf_8 | 019cbab25d11199d62a308ea88e78137 | function testviolinplot()
figure();
% One could use tiled layout for better plotting them but it would be
% incompatible with older versions
subplot(2,4,1);
% TEST CASE 1
disp('Test 1: Violin plot default options');
load carbig MPG Origin
Origin = cellstr(Origin);
vs = violinplot(MPG, Origin);
plotdetails(1);
% TES... |
github | UberGames/ioef-master | echo_diagnostic.m | .m | ioef-master/code/libspeex/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 | ThomasHSimm/DPPA-master | loadb_GUI.m | .m | DPPA-master/loadb_GUI.m | 4,930 | utf_8 | 930d70c0a700bc88e55c9cdbef09e8c3 | function loadb_GUI(handles, dataorInstr)
%% load data files for dippa
mnu = menu('Choose data to load:','2-Theta','1/d');
inner(handles, mnu, dataorInstr)
end
function inner(handles, mnu, dataorInstr)
haxes = handles.axes1;
data=[];
%find file location
[file, path] = uigetfile({'*.dat';'... |
github | tzzcl/ChaLearn-APA-Code-master | vl_nnloss.m | .m | ChaLearn-APA-Code-master/video/train/vl_nnloss.m | 12,419 | utf_8 | add8960d123058c68624eeb9cbae5cb0 | function Y = vl_nnloss(X,c,dzdy,varargin)
%VL_NNLOSS CNN categorical or attribute loss.
% Y = VL_NNLOSS(X, C) computes the loss incurred by the prediction
% scores X given the categorical labels C.
%
% The prediction scores X are organised as a field of prediction
% vectors, represented by a H x W x D x N array... |
github | tzzcl/ChaLearn-APA-Code-master | train_regression_DAN_l1.m | .m | ChaLearn-APA-Code-master/video/train/train_regression_DAN_l1.m | 4,128 | utf_8 | 8b7ce1a05080b5ccaf7591c4d00935f1 | function [net, info] = train_regression_DAN_l1(varargin)
opt.model = 'vgg-face';
run('/opt/zhangcl/matconvnet/matlab/vl_setupnn.m');
net = load(['/opt/zhangcl/' opt.model '.mat']);
net = dagnn.DagNN.fromSimpleNN(net);
%net = net.net;
inputVar = 'data' ;
%[opts, varargin] = vl_argparse(opts, varargin) ;
opts.imdbPath =... |
github | tzzcl/ChaLearn-APA-Code-master | train_regression_DANplus.m | .m | ChaLearn-APA-Code-master/video/train/train_regression_DANplus.m | 4,511 | utf_8 | 6801d4cdf50fae2bdc8d4387e3c2d4ce | function [net, info] = train_regression_DANplus(varargin)
opt.model = 'vgg-face';
run('/opt/zhangcl/matconvnet/matlab/vl_setupnn.m');
net = load(['/opt/zhangcl/' opt.model '.mat']);
net = dagnn.DagNN.fromSimpleNN(net);
%net = net.net;
inputVar = 'data' ;
%[opts, varargin] = vl_argparse(opts, varargin) ;
opts.imdbPath ... |
github | NEU-Gou/kernel-metric-learning-reid-master | PCCA.m | .m | kernel-metric-learning-reid-master/PCCA.m | 5,501 | utf_8 | 4541b514112035d1358a83d259ea96f6 | % PCCA distance learning algorithm proposed in "PCCA: A New Approach for
% Distance Learning from sparse pairwise constraints" cvpr 2012
% By Fei Xiong,
% ECE Dept,
% Northeastern University
% 2013-11-04
% INPUT
% X: N-by-d data matrix. Each row is a sample vector.
% ix_pair: the index for pairwise cons... |
github | NEU-Gou/kernel-metric-learning-reid-master | compute_rank2.m | .m | kernel-metric-learning-reid-master/compute_rank2.m | 5,416 | utf_8 | 54bc3c70b464a94f02fd1ee25a33c137 | % calculate the matching characteristics.
% By Fei Xiong,
% ECE Dept,
% Northeastern University
% 2013-11-04
% Input:
% Method: the distance learning algorithm struct. In this function
% two field are used.
% P is the projection matrix. d'-by-Ntr (assume the kernel trick is used.... |
github | NEU-Gou/kernel-metric-learning-reid-master | kissme.m | .m | kernel-metric-learning-reid-master/kissme.m | 1,031 | utf_8 | 9a8ff9dfaa49403029a73cd92c154ca6 | % Wrapping data and modified the structure to fullfill the kissme* algorithm.
% *Proposed by Martin Koestinger (2011), please refer the following paper
% if you use this code please cite the following paper:
% Kostinger, M., Hirzer, M.,Wohlhart, P., Roth, P.M., Bischof, H.:
% Large scale metric learning f... |
github | NEU-Gou/kernel-metric-learning-reid-master | Vote_MultiRankingAlgo.m | .m | kernel-metric-learning-reid-master/Vote_MultiRankingAlgo.m | 1,919 | utf_8 | 937631ca5da412d10500d8c647a87886 | % voting with multiple ranking algorithm
function [MC] = Vote_MultiRankingAlgo(Method, Partition, gID, flag)
num_itr = 10;%size(Partition(1).ix_test_gallery,1);
Method = shiftdim(Method, length(size(Method))-2); % shift the algorithm matrix so that the 2-by-partition-by....
Method = reshape(Method, size(Method,1), siz... |
github | NEU-Gou/kernel-metric-learning-reid-master | ComputeFeature.m | .m | kernel-metric-learning-reid-master/ComputeFeature.m | 1,534 | utf_8 | 6ed1014f7ca5fb29cf6a2b440ff53442 | % calculate RGB, LAB, LOG-RGB and Gabor feature mapping of the input image.
% By Fei Xiong,
% ECE Dept,
% Northeastern University
% 2013-11-04
% Input:
% I is the inuput RGB image. H*W*3
% option is the struct contains the flag indicating which feature
% channel is required to be comp... |
github | NEU-Gou/kernel-metric-learning-reid-master | MFA.m | .m | kernel-metric-learning-reid-master/MFA.m | 3,264 | utf_8 | eb1b60aaf61a36f8612367fa07e067b8 | % implemented according to CVPR2013: Graph Embedding and Extensions: A General
% Framework for Dimensionality Reduction
% By Fei Xiong,
% ECE Dept,
% Northeastern University
% 2014-02-15
% INPUT
% X: N-by-d data matrix. Each row is a sample vector.
% id: (N-by-1) the identification number for each samp... |
github | NEU-Gou/kernel-metric-learning-reid-master | CalculatePUR.m | .m | kernel-metric-learning-reid-master/CalculatePUR.m | 687 | utf_8 | 3a8b44e8d7e322ce5f4dad8e120d6346 | % Calculate the "proportion of uncertainty removed" as in cvpr 2013 paper
% "Local Fisher Discriminant Analysis for Pedestrian Re-Identification"
% By Fei Xiong,
% ECE Dept,
% Northeastern University
% 2013-11-04
% r: cumulated matching propability, which represents the probability that
% the choice of ... |
github | NEU-Gou/kernel-metric-learning-reid-master | GenerateGridBBox.m | .m | kernel-metric-learning-reid-master/GenerateGridBBox.m | 1,124 | utf_8 | 5a247a3178f08921ce94eb7018210eaa | % Generate Grid Bounding Box
% calculate RGB, LAB, LOG-RGB and Gabor feature mapping of the input image.
% By Fei Xiong,
% ECE Dept,
% Northeastern University
% 2013-11-04
% INPUT
% BBoxsz: Bounding Box size [height, width]
% imsz: image size [height, width]
% OUTPUT
% region_idx: the cell structure storing... |
github | NEU-Gou/kernel-metric-learning-reid-master | ComputeKernel.m | .m | kernel-metric-learning-reid-master/ComputeKernel.m | 3,052 | utf_8 | 17ff2608e00cc3cd280032710f823b5e | % calculate the kernel matrix.
% By Fei Xiong,
% ECE Dept,
% Northeastern University
% 2013-11-04
% Input:
% Method: the distance learning algorithm struct. In this function
% two field are used.
% rbf_sigma is written while computing the rbf-chi2 kernel
% m... |
github | NEU-Gou/kernel-metric-learning-reid-master | compute_rank_svmml.m | .m | kernel-metric-learning-reid-master/compute_rank_svmml.m | 3,507 | utf_8 | d92c50b4e897120262c2730c401c353f | function [R,Alldist,ixx] = compute_rank_svmml(Method,train,test,ix_partition, IDs)
% A = Method.A;
% B = Method.B;
% b = Method.b;
% [K_test] = ComputeKernelTest(train, test, Method); %compute the kernel matrix.
% K_test = test';
for k = 1:size(ix_partition,1)
ix_ref = ix_partition(k,:) == 1;
if min(min(doubl... |
github | NEU-Gou/kernel-metric-learning-reid-master | compute_rank_oLFDA.m | .m | kernel-metric-learning-reid-master/compute_rank_oLFDA.m | 1,972 | utf_8 | 6edfb6da6cf296237906c7b15e6ef2b6 | % calculate the matching characteristics for original LFDA algorithm.
% the major difference between this and compute_rank2.m is no kernel
% technique is used. So the projection matrix is d'-by-d
% By Fei Xiong,
% ECE Dept,
% Northeastern University
% 2013-11-04
% Input:
% Method: the distance learni... |
github | NEU-Gou/kernel-metric-learning-reid-master | LFDA.m | .m | kernel-metric-learning-reid-master/LFDA.m | 2,652 | utf_8 | 054ef12ebf00aba51196cb690b7c8b1e | % implemented according to CVPR2013: Local Fisher Discriminant Analysis
% for Pedestrian Re-identification.
% By Fei Xiong,
% ECE Dept,
% Northeastern University
% 2013-11-04
% INPUT
% X: N-by-d data matrix. Each row is a sample vector.
% id: the identification number for each sample
% option: algori... |
github | NEU-Gou/kernel-metric-learning-reid-master | oLFDA.m | .m | kernel-metric-learning-reid-master/oLFDA.m | 2,547 | utf_8 | 76bb0302952cc0550cecfaad50e7b5bf | % implemented according to CVPR2013: Local Fisher Discriminant Analysis
% for Pedestrian Re-identification.
% By Fei Xiong,
% ECE Dept,
% Northeastern University
% 2013-11-04
% INPUT
% X: N-by-d data matrix. Each row is a sample vector.
% id: the identification number for each sample
% option: algori... |
github | NEU-Gou/kernel-metric-learning-reid-master | GeneratePair.m | .m | kernel-metric-learning-reid-master/Assistant Code/GeneratePair.m | 888 | utf_8 | 0e66acfd9e0232a06f6ba3039b25c321 | % given the ID set, find the positive and negative pair of samples.
% NOTE that the negative pair samples are random permutated, so that taking
% first N pairs is the same as randomly pick the negative pairs.
function [ix_pos_pair, ix_neg_pair]=GeneratePair(ID, varargin)
ID = double(ID);
R = repmat(ID.^2, length(ID), 1... |
github | NEU-Gou/kernel-metric-learning-reid-master | LGE.m | .m | kernel-metric-learning-reid-master/Assistant Code/MFA/LGE.m | 8,861 | utf_8 | 21948a1840763aa006b0e3163a7050b3 | function [eigvector, eigvalue] = LGE(W, D, options, data)
% LGE: Linear Graph Embedding
%
% [eigvector, eigvalue] = LGE(W, D, options, data)
%
% Input:
% data - data matrix. Each row vector of data is a
% sample vector.
% W - Affinity g... |
github | NEU-Gou/kernel-metric-learning-reid-master | evalData.m | .m | kernel-metric-learning-reid-master/Assistant Code/KISSME/toolbox/evalData.m | 4,143 | utf_8 | fa4260fdbaa73509795057250201aece | function [ds,rocPlot] = evalData(pairs, ds, params)
% EVALDATA Evaluate results and plot figures
%
% Input:
% pairs - [1xN] struct. N is the number of pairs. Fields: pairs.fold
% pairs.match, pairs.img1, pairs.img2.
% ds - [1xF] data struct. F is the number of folds.
% ds.method.dist is required to comp... |
github | NEU-Gou/kernel-metric-learning-reid-master | LearnAlgoLMNN.m | .m | kernel-metric-learning-reid-master/Assistant Code/KISSME/toolbox/learnAlgos/LearnAlgoLMNN.m | 2,829 | utf_8 | f833d30dfe0476ecab72fc14f7cacc8a | %LEARNALGOLMNN Wrapper class to the actual LMNN code
classdef LearnAlgoLMNN < LearnAlgo
properties
p %parameters
s %struct
available
fhanlde
end
properties (Constant)
type = 'lmnn'
end
methods
function obj = LearnAlgoLMNN(p)
if... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.