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 | xiaoxiaojiangshang/Programs-master | testall.m | .m | Programs-master/matlab/cvx/lib/@cvxtuple/testall.m | 452 | utf_8 | be9d0553e0e560f2c73fdb02a37b08b6 | function y = testall( func, x )
y = do_test( func, x.value_ );
function y = do_test( func, x )
switch class( x ),
case 'struct',
y = do_test( func, struct2cell( x ) );
case 'cell',
y = all( cellfun( func, x ) );
otherwise,
y = feval( func, x );
end
% Copyright 2005-2016 CVX Researc... |
github | xiaoxiaojiangshang/Programs-master | disp.m | .m | Programs-master/matlab/cvx/lib/@cvxtuple/disp.m | 1,366 | utf_8 | ed9c53cbe23c1f5ab4440b5d9a10cbfd | function disp( x, prefix )
if nargin < 2,
prefix = '';
end
disp( [ prefix, 'cvx tuple object: ' ] );
prefix = [ prefix, ' ' ];
do_disp( x.value_, {}, prefix, prefix, '' );
if ~isempty( x.dual_ ),
dn = cvx_subs2str( x.dual_ );
disp( [ prefix, 'dual variable: ', dn(2:end) ] );
end
function do_disp( x, f, f... |
github | xiaoxiaojiangshang/Programs-master | sparsify.m | .m | Programs-master/matlab/cvx/lib/@cvx/sparsify.m | 4,013 | utf_8 | 2eda31274fafa84387d3aad5be748107 | function x = sparsify( x, mode )
global cvx___
narginchk(2,2);
persistent remap
%
% Check mode argument
%
if ~ischar( mode ) || size( mode, 1 ) ~= 1,
error( 'Second arugment must be a string.' );
end
isobj = strcmp( mode, 'objective' );
pr = cvx___.problems( end );
touch( pr.self, x );
bz = x.basis_ ~= 0;
bs = ... |
github | xiaoxiaojiangshang/Programs-master | prelp.m | .m | Programs-master/matlab/cvx/sedumi/conversion/prelp.m | 3,887 | utf_8 | 4d1e23d094e3f1b35ec666df11a34003 | % PRELP Loads and preprocesses LP from an MPS file.
%
% > [A,b,c,lenx,lbounds] = PRELP('problemname')
% The above command results in an LP in standard form,
% - Instead of specifying the problemname, you can also use PRELP([]), to
% get the problem from the file /tmp/default.mat.
% - Also, you may type PRE... |
github | xiaoxiaojiangshang/Programs-master | feasreal.m | .m | Programs-master/matlab/cvx/sedumi/conversion/feasreal.m | 4,144 | utf_8 | 454dcb6c42c0642ed5c5a524e84bec58 | % FEASREAL Generates a random sparse optimization problem with
% linear, quadratic and semi-definite constraints. Output
% can be used by SEDUMI. All data will be real-valued.
%
% The following two lines are typical:
% > [AT,B,C,K] = FEASREAL;
% > [X,Y,INFO] = SEDUMI(AT,B,C,K);
%
% An extended version is:
% > [... |
github | xiaoxiaojiangshang/Programs-master | sdpa2vec.m | .m | Programs-master/matlab/cvx/sedumi/conversion/sdpa2vec.m | 2,352 | utf_8 | f055b4df357f6cf3b426ce27869bc738 | % x = sdpavec(E,K)
% Takes an SDPA type sparse data description E, i.e.
% E(1,:) = block, E(2,:) = row, E(3,:) = column, E(4,:) = entry,
% and transforms it into a "long" vector, with vectorized matrices for
% each block stacked under each other. The size of each matrix block
% is given in the field K.s.
% **********... |
github | xiaoxiaojiangshang/Programs-master | blk2vec.m | .m | Programs-master/matlab/cvx/sedumi/conversion/blk2vec.m | 1,653 | utf_8 | 0d48b96f66e746fce480e7a3e9c271a5 | % x = blk2vec(X,nL)
%
% Converts a block diagonal matrix into a vector.
%
% ********** INTERNAL FUNCTION OF FROMPACK **********
function x = blk2vec(X,nL)
%
% This file is part of SeDuMi 1.1 by Imre Polik and Oleksandr Romanko
% Copyright (C) 2005 McMaster University, Hamilton, CANADA (since 1.1)
%
% Copyright (C)... |
github | xiaoxiaojiangshang/Programs-master | writesdp.m | .m | Programs-master/matlab/cvx/sedumi/conversion/writesdp.m | 4,708 | utf_8 | 31f196bca4c11b9610c98de8e4d7b107 | % This function takes a problem in SeDuMi MATLAB format and writes it out
% in SDPpack format.
%
% Usage:
%
% writesdp(fname,A,b,c,K)
%
% fname Name of SDPpack file, in quotes
% A,b,c,K Problem in SeDuMi form
%
% Notes:
%
% Problems with complex data are not allowed.
%
% ... |
github | xiaoxiaojiangshang/Programs-master | frompack.m | .m | Programs-master/matlab/cvx/sedumi/conversion/frompack.m | 2,509 | utf_8 | a4730dcb4ec069944953dce753da973d | % FROMPACK Converts a cone problem in SDPPACK format to SEDUMI format.
%
% [At,c] = frompack(A,b,C,blk) Given a problem (A,b,C,blk) in the
% SDPPACK-0.9-beta format, this produces At and c for use with
% SeDuMi. This lets you execute
%
% [x,y,info] = SEDUMI(At,b,c,blk);
%
% IMPORTANT: this function assumes that th... |
github | xiaoxiaojiangshang/Programs-master | feascpx.m | .m | Programs-master/matlab/cvx/sedumi/conversion/feascpx.m | 4,385 | utf_8 | c48c6cf336cdb94ad12b04e1efd2417b | % FEASCPX Generates a random sparse optimization problem with
% linear, quadratic and semi-definite constraints. Output
% can be used by SEDUMI. Includes complex-valued data.
%
% The following two lines are typical:
% > [AT,B,C,K] = FEASCPX;
% > [X,Y,INFO] = SEDUMI(AT,B,C,K);
%
% An extended version is:
% > [AT... |
github | xiaoxiaojiangshang/Programs-master | cvx_glpk.m | .m | Programs-master/matlab/cvx/shims/cvx_glpk.m | 4,344 | utf_8 | fc695a24b6757c72ebcc0c9422e98dca | function shim = cvx_glpk( shim )
% CVX_SOLVER_SHIM GLPK interface for CVX.
% This procedure returns a 'shim': a structure containing the necessary
% information CVX needs to use this solver in its modeling framework.
if ~isempty( shim.solve ),
return
end
if isempty( shim.name ),
fname = 'glpk.m';
ps =... |
github | xiaoxiaojiangshang/Programs-master | cvx_sedumi.m | .m | Programs-master/matlab/cvx/shims/cvx_sedumi.m | 10,740 | utf_8 | 42324556d877c6a43224d410f1a12290 | function shim = cvx_sedumi( shim )
% CVX_SOLVER_SHIM SeDuMi interface for CVX.
% This procedure returns a 'shim': a structure containing the necessary
% information CVX needs to use this solver in its modeling framework.
global cvx___
if ~isempty( shim.solve ),
return
end
if isempty( shim.name ),
fname = ... |
github | xiaoxiaojiangshang/Programs-master | cvx_sdpt3.m | .m | Programs-master/matlab/cvx/shims/cvx_sdpt3.m | 12,657 | utf_8 | b42871a1a82cd274121bc52919caa778 | function shim = cvx_sdpt3( shim )
% CVX_SOLVER_SHIM SDPT3 interface for CVX.
% This procedure returns a 'shim': a structure containing the necessary
% information CVX needs to use this solver in its modeling framework.
global cvx___
if ~isempty( shim.solve ),
return
end
if isempty( shim.name ),
fname = 's... |
github | hnanhtuan/selectiveConvFeature-master | apply_mask.m | .m | selectiveConvFeature-master/utils/apply_mask.m | 926 | utf_8 | d86c8eba84407eed59d1110068a3caf0 | function [ masked_fea ] = apply_mask( filename, maskmethod )
% apply_mask
% + Load the feature map file.
% + Create the mask then apply
% + Return a set of local features
l = load(filename);
k = size(l.fea, 3);
switch maskmethod
case 'max'
mask = create_max_mask( l.fea );
case {'sum50', 'sum'}
... |
github | hnanhtuan/selectiveConvFeature-master | vecpostproc.m | .m | selectiveConvFeature-master/utils/vecpostproc.m | 354 | utf_8 | d516f36867714475f5635721288ec358 | function x = vecpostproc(x, a)
if ~exist('a'), a = 1; end
x = replacenan (yael_vecs_normalize (powerlaw (x, a)));
% replace all nan values in a matrix (with zero)
function y = replacenan (x, v)
if ~exist ('v')
v = 0;
end
y = x;
y(isnan(x)) = v;
% apply powerlaw
function x = powerlaw (x, a)
if a == 1, return;... |
github | hnanhtuan/selectiveConvFeature-master | sinkhornm.m | .m | selectiveConvFeature-master/tools/democratic/sinkhornm.m | 746 | utf_8 | 00b5563e8a441037d97fe9e0a1f3b82c | % This is a modified version of the SINKHORN algorithm (see sinhorn.m)
%
% It converts a square positive matrix to a matrix that sums to a constant C
% (rows and columns sum to one) based on the Knight variant.
%
% Usage: [kn, d1, nbiter] = sinkhornm(kn, reg, nbiter)
%
% The algorithm steps after nbiter iterations
f... |
github | hnanhtuan/selectiveConvFeature-master | qdemocratic.m | .m | selectiveConvFeature-master/tools/democratic/qdemocratic.m | 1,100 | utf_8 | 69cffbcf97d19ad0fa67caf9f827c86d | % Compute the weights to tend towards a democratic kernel
% Usage: alpha = qdemocratic (x, method, tau)
% method 'sum'
% 'sinkhorn' regular Sinkhorn
% 'zca' ZCA
%
% tau sparsificatino of the Gram matrix
%
% param1, param2, ... Method-dependent
%
function [y, alpha] = qdemocr... |
github | hnanhtuan/selectiveConvFeature-master | compute_map.m | .m | selectiveConvFeature-master/tools/evaluation/compute_map.m | 1,846 | utf_8 | 97ec2a86542fd2a35fd509732ae5f396 | % This function computes the mAP for a given set of returned results.
%
% Usage: map = compute_map (ranks, gnd);
%
% Notes:
% 1) ranks starts from 1, size(ranks) = db_size X #queries
% 2) The junk results (e.g., the query itself) should be declared in the gnd stuct array
function [map, aps] = compute_map (ranks, gnd, v... |
github | hnanhtuan/selectiveConvFeature-master | projection_learn_batch.m | .m | selectiveConvFeature-master/tools/faemb/projection_learn_batch.m | 1,767 | utf_8 | 38ada347cd49eae4254d173697dea39a | %% Function for learning projection params used for whitening stage
function [Xmean, eigvec, eigval] = projection_learn_batch (X, B, gama_all_final, k, hes)
if (~exist('hes', 'var'))
hes = 1;
end
m = size (X, 2) ; % number of input vectors
n = size(X, 1) ; % dimesion of input vectors
D = n * (n+1) * k / 2... |
github | hnanhtuan/selectiveConvFeature-master | bvecs_read.m | .m | selectiveConvFeature-master/tools/yael/bvecs_read.m | 1,520 | utf_8 | 977df50a2a45c709849888179f2f1e39 | % Read a set of vectors stored in the bvec format (int + n * float)
% The function returns a set of output uint8 vector (one vector per column)
%
% Syntax:
% v = bvecs_read (filename) -> read all vectors
% v = bvecs_read (filename, n) -> read n vectors
% v = bvecs_read (filename, [a b]) -> read the ... |
github | hnanhtuan/selectiveConvFeature-master | bvecs_size.m | .m | selectiveConvFeature-master/tools/yael/bvecs_size.m | 494 | utf_8 | f740fcd630ac853920781a4a1a4f742c | % Return the number of vectors contained in a bvecs files and their dimension
%
% Syntax: [n,d] = bvecs_size (filename)
function [n, d] = bvecs_size (filename)
% open the file and count the number of descriptors
fid = fopen (filename, 'rb');
if fid == -1
error ('I/O error : Unable to open the file %s\n', filena... |
github | hnanhtuan/selectiveConvFeature-master | fvecs_write.m | .m | selectiveConvFeature-master/tools/yael/fvecs_write.m | 635 | utf_8 | 69a30552f6ea46eddf3605642048b76b | % This function reads a vector of float vectors
%
% Usage: fvecs_write (filename, v)
% where v is a set of vector (stored columnwise)
function fvecs_write (filename, v)
% open the file and count the number of descriptors
fid = fopen (filename, 'wb');
d = size (v, 1);
n = size (v, 2);
for i = 1:n
% first write... |
github | hnanhtuan/selectiveConvFeature-master | yael_pca.m | .m | selectiveConvFeature-master/tools/yael/yael_pca.m | 1,665 | utf_8 | aa7a3cf0b3e26bc890a66a46fd9d94f1 | % PCA with automatic selection of the method: covariance or gram matrix
% Usage: [X, eigvec, eigval, Xm] = pca (X, dout, center, verbose)
% X input vector set (1 vector per column)
% dout number of principal components to be computed
% center need to center data?
%
% Note: the eigenvalues are given in ... |
github | hnanhtuan/selectiveConvFeature-master | bvecs_write.m | .m | selectiveConvFeature-master/tools/yael/bvecs_write.m | 561 | utf_8 | 6cec679cf64f52656b11ffacfefce339 | % This function reads a vector from a file in the libit format
function bvecs_write (filename, v)
% open the file and count the number of descriptors
fid = fopen (filename, 'wb');
d = size (v, 1);
n = size (v, 2);
for i = 1:n
% first write the vector size
count = fwrite (fid, d, 'int');
if count ~= 1
... |
github | hnanhtuan/selectiveConvFeature-master | yael_hamming.m | .m | selectiveConvFeature-master/tools/yael/yael_hamming.m | 1,225 | utf_8 | b630ed71a4baee7ecbfa561b2d12cee8 | % Compute the hamming distances between binary vectors in compact format
%
% Usage: D = yael_hamming(X, Y);
% Input: X and Y are set of bit vectors, 1 vector per column
% Output: Set of Hamming distances, in uint16 format
function D = yael_hamming(X, Y);
% Tabulate Hamming distance.
tabhamdis = uint16([...... |
github | hnanhtuan/selectiveConvFeature-master | yael_vecs_normalize.m | .m | selectiveConvFeature-master/tools/yael/yael_vecs_normalize.m | 797 | utf_8 | 4c8a85a6b713f2aa9654ea5e8312d7af | % This function normalize a set of vectors
% Parameters:
% v the set of vectors to be normalized (column stored)
% nr the norm for which the normalization is performed (Default: Euclidean)
% rval replace value in case the vector is 0-norm
%
% Output:
% vout the normalized vector
% vnr the norms of ... |
github | hnanhtuan/selectiveConvFeature-master | yael_kmin.m | .m | selectiveConvFeature-master/tools/yael/yael_kmin.m | 1,029 | utf_8 | 9c0a18bd3826f63bcfcce260fdfe4ae3 | % This function returns the k smallest values of a vector
%
% Usage: [val, idx] = yael_kmin (v,k)
%
% Parameters:
% v the vector to be partially ranked. If v is a matrix, the function
% returns the k largest values of each column (like min function)
% k the number of neighbors to be returned. Must ... |
github | hnanhtuan/selectiveConvFeature-master | yael_L2sqr.m | .m | selectiveConvFeature-master/tools/yael/yael_L2sqr.m | 860 | utf_8 | 81282dfbf310860445f74620ccb58948 | % Compute all the distances between two sets of vectors
%
% Usage: [dis] = dis_L2sqr(q, v)
%
% Parameters:
% q, v sets of vectors (1 vector per column)
%
% Returned values
% dis the corresponding *square* distances
% vectors of q corresponds to row, and columns for v
function dis = dis_... |
github | hnanhtuan/selectiveConvFeature-master | fvec_write.m | .m | selectiveConvFeature-master/tools/yael/fvec_write.m | 401 | utf_8 | 0eb88a1df37dd296f56f52331ed8e54d | % This function reads a vector from a file in the libit format
function fvec_write (fid, v)
% first read the vector size
count = fwrite (fid, length(v), 'int');
if (count ~= 1)
error ('Unable to write vector dimension: count !=1 \n');
end
% write the vector components
count = fwrite (fid, v, 'float');
if (count... |
github | hnanhtuan/selectiveConvFeature-master | ivecs_write.m | .m | selectiveConvFeature-master/tools/yael/ivecs_write.m | 561 | utf_8 | b6fbc8815b3445b63ff6e0650507879d | % This function writes a vector from a file in the ivecs format
function ivecs_write (filename, v)
% open the file and count the number of descriptors
fid = fopen (filename, 'wb');
d = size (v, 1);
n = size (v, 2);
for i = 1:n
% first write the vector size
count = fwrite (fid, d, 'int');
if count ~= 1
... |
github | hnanhtuan/selectiveConvFeature-master | fvecs_size.m | .m | selectiveConvFeature-master/tools/yael/fvecs_size.m | 498 | utf_8 | 6d37c7e1e4d00bd9fa8dc774eaf3ca1c | % Return the number of vectors contained in a fvecs files and their dimension
%
% Syntax: [n,d] = fvecs_size (filename)
function [n, d] = fvecs_size (filename)
% open the file and count the number of descriptors
fid = fopen (filename, 'rb');
if fid == -1
error ('I/O error : Unable to open the file %s\n', filena... |
github | hnanhtuan/selectiveConvFeature-master | uint8tobit.m | .m | selectiveConvFeature-master/tools/yael/uint8tobit.m | 326 | utf_8 | 1074f77e291a69382ecc5b7d60c2c334 | % This function translates a uint8 vector into a binary vector
% Usage: b = uint8tobit (v)
% The vectors are column-stored
function b = uint8tobit (v)
n = size (v, 2);
dbytes = size (v, 1);
d = dbytes * 8;
b = zeros(d, n, 'uint8');
for i = 1:n
for j = 1:dbytes
b((j-1)*8+1:j*8 ,i) = bitget (v(j, i), 1:8);
end... |
github | hnanhtuan/selectiveConvFeature-master | yael_kmax.m | .m | selectiveConvFeature-master/tools/yael/yael_kmax.m | 1,038 | utf_8 | fc1109c025b4398d6ac82e9a403db3cf | % This function returns the k largest values of a vector
%
% Usage: [val, idx] = yael_kmax (v,k)
%
% Parameters:
% v the vector to be partially ranked. If v is a matrix, the function
% returns the k largest values of each column (like max function)
% k the number of neighbors to be returned. Must ... |
github | hnanhtuan/selectiveConvFeature-master | siftgeo_read.m | .m | selectiveConvFeature-master/tools/yael/siftgeo_read.m | 994 | utf_8 | 65eb5ad63f3acbafad26f4c850e05a31 | % This function reads a siftgeo binary file
%
% Usage: [v, meta] = siftgeo_read (filename)
% filename the input filename
%
% Returned values
% v the sift descriptors (1 descriptor per line)
% meta meta data for each descriptor, i.e., per line:
% x, y, scale, angle, mi11, mi12, mi... |
github | hnanhtuan/selectiveConvFeature-master | fvec_read.m | .m | selectiveConvFeature-master/tools/yael/fvec_read.m | 213 | utf_8 | 363d7d860bcd190414ecad7c6a887f8d | % This function reads a vector from a file in the libit format
function [v,d] = fvec_read (fid)
% first read the vector size
d = fread (fid, 1, 'int');
% read the elements
v = fread (fid, d, 'float=>single');
|
github | hnanhtuan/selectiveConvFeature-master | gmm_read.m | .m | selectiveConvFeature-master/tools/yael/gmm_read.m | 637 | utf_8 | 4a76011f253e15048c463832a1fdf432 | % This function reads the parameters of a gmm file
%
% Usage: [w, mu, sigma] = gmm_read (filename)
function [w, mu, sigma] = gmm_read (filename)
% open the file and count the number of descriptors
fid = fopen (filename, 'rb');
if fid == -1
error ('I/O error : Unable to open the file %s\n', filename)
end
% first... |
github | hnanhtuan/selectiveConvFeature-master | yael_nn.m | .m | selectiveConvFeature-master/tools/yael/yael_nn.m | 1,908 | utf_8 | adc08b31a08f01d9b0735a94cc01fca9 | % Return the k nearest neighbors of a set of query vectors
%
% Usage: [ids,dis] = nn(v, q, k, distype)
% v the dataset to be searched (one vector per column)
% q the set of queries (one query per column)
% k (default:1) the number of nearest neigbors we want
% distype d... |
github | hnanhtuan/selectiveConvFeature-master | ivecs_read.m | .m | selectiveConvFeature-master/tools/yael/ivecs_read.m | 1,446 | utf_8 | bccc2d5437bc4c9f3372086f6f6b6d6d | % Read a set of vectors stored in the ivec format (int + n * int)
% The function returns a set of output vector (one vector per column)
%
% Syntax:
% v = ivecs_read (filename) -> read all vectors
% v = ivecs_read (filename, n) -> read n vectors
% v = ivecs_read (filename, [a b]) -> read the vectors from a ... |
github | hnanhtuan/selectiveConvFeature-master | ivecs_size.m | .m | selectiveConvFeature-master/tools/yael/ivecs_size.m | 496 | utf_8 | 10a6970b9239b5eee1b6831b3665e93a | % Return the number of vectors contained in a ivecs file and their dimension
%
% Syntax: [n,d] = ivecs_size (filename)
function [n, d] = ivecs_size (filename)
% open the file and count the number of descriptors
fid = fopen (filename, 'rb');
if fid == -1
error ('I/O error : Unable to open the file %s\n', filename... |
github | hnanhtuan/selectiveConvFeature-master | ivec_write.m | .m | selectiveConvFeature-master/tools/yael/ivec_write.m | 409 | utf_8 | cc96e4c56543e2c74419151a3823e9d4 | % This function writes a vector from a file in the libit format
function [v,d] = ivec_write (fid, v)
% first write the vector size
count = fwrite (fid, length(v), 'int');
if count ~= 1
error ('Unable to write vector dimension: count !=1 \n');
end
% write the vector components
count = fwrite (fid, v, 'int');
... |
github | hnanhtuan/selectiveConvFeature-master | yael_ivf_he.m | .m | selectiveConvFeature-master/tools/yael/yael_ivf_he.m | 8,187 | utf_8 | a3d9ade30faad13177030bc79a4fdaed | % Construct Hamming Embedding structure from a learning set
% WARNING: this is a very slow implementation of HE, for exposition purpose only
%
% Usage: ivf = ivfhe_new (k, nbits, v, quantizer, C, idx)
% ivf = vifhe_new (k, nbits, v, C)
% where
% k number of centroids (=visual words) for the inverted ... |
github | hnanhtuan/selectiveConvFeature-master | load_ext.m | .m | selectiveConvFeature-master/tools/yael/load_ext.m | 2,715 | utf_8 | 2cec807c8443df302331fc4cd049076f | % Generic way to load files depending on the type (determined by extension)
%
function [X,Y] = load_ext (filename, nrows, bounds, verbose)
% Retrieve the extension of the file
ext = regexp (filename, '\.(\w)*$');
if length(ext) == 0
error ('The filename should have an extension');
end
ext = filename (ext:end);
nmin... |
github | hnanhtuan/selectiveConvFeature-master | ivec_read.m | .m | selectiveConvFeature-master/tools/yael/ivec_read.m | 203 | utf_8 | 68cb23cdec0879a0f09f0773c5ff6c95 | % This function reads a vector from a file in the libit format
function [v,d] = ivec_read (fid)
% first read the vector size
d = fread (fid, 1, 'int');
% read the elements
v = fread (fid, d, 'int');
|
github | hnanhtuan/selectiveConvFeature-master | save_ext.m | .m | selectiveConvFeature-master/tools/yael/save_ext.m | 1,473 | utf_8 | 225eee847e71d3efc0dc3137d172cfd5 | % Generic way to save files depending on the type (determined by extension)
function save_ext (filename, X, verbose)
% Retrieve the extension of the file
ext = regexp (filename, '\.(\w)*$');
if length(ext) == 0
error ('The filename should have an extension');
end
ext = filename (ext:end);
% Default
Y = [];
if nargi... |
github | hnanhtuan/selectiveConvFeature-master | yael_fvecs_normalize.m | .m | selectiveConvFeature-master/tools/yael/yael_fvecs_normalize.m | 628 | utf_8 | 1687902f2d255497b8c79ecbdb31c6b4 | % This function normalize a set of vectors
% Parameters:
% v the set of vectors to be normalized (column stored)
% nr the norm for which the normalization is performed (Default: Euclidean)
%
% Output:
% vout the normalized vector
% vnr the norms of the input vectors
%
% Remark: the function return Na... |
github | hnanhtuan/selectiveConvFeature-master | yael_cross_distances.m | .m | selectiveConvFeature-master/tools/yael/yael_cross_distances.m | 825 | utf_8 | af5bf753183dea59cc7b4a0e4b367958 | % Compute all the distances between two sets of vectors
%
% Usage: [dis] = dis_cross_distances(q, v, distype, nt)
%
% Parameters:
% q, v sets of vectors (1 vector per column)
% distype distance type: 1=L1,
% 2=L2 -> Warning: return the square L2 distance
% ... |
github | hnanhtuan/selectiveConvFeature-master | fvecs_read.m | .m | selectiveConvFeature-master/tools/yael/fvecs_read.m | 1,457 | utf_8 | 0318bd7f465153c725687e87ddd7c3ca | % Read a set of vectors stored in the fvec format (int + n * float)
% The function returns a set of output vector (one vector per column)
%
% Syntax:
% v = fvecs_read (filename) -> read all vectors
% v = fvecs_read (filename, n) -> read n vectors
% v = fvecs_read (filename, [a b]) -> read the vectors from ... |
github | hnanhtuan/selectiveConvFeature-master | b2fvecs_read.m | .m | selectiveConvFeature-master/tools/yael/b2fvecs_read.m | 1,609 | utf_8 | 58b9445ea5835d74f5186f5fddef4b21 | % Read a set of vectors stored in the bvec format (int + n * float)
% The function returns a set of output floating point vector (one vector per column)
%
% Syntax:
% v = b2fvecs_read (filename) -> read all vectors
% v = b2fvecs_read (filename, n) -> read n vectors
% v = b2fvecs_read (filename, [a b... |
github | hnanhtuan/selectiveConvFeature-master | triemb_learn.m | .m | selectiveConvFeature-master/tools/triemb/triemb_learn.m | 1,390 | utf_8 | fc74130ad165ccb5eeca3bf1abaa78b1 | % Learn the embedding parameters for triangulation embedding
%
% Usage: [Xmean, eigvec, eigval] = triemb_learn (vtrain, C, dout)
% vtrain vector set for learning
% C centroids
% dout request output dimensionality
function [Xmean, eigvec, eigval] = triemb_learn (vtrain, C)
nlearn = size (vtrain, ... |
github | hnanhtuan/selectiveConvFeature-master | triemb_res.m | .m | selectiveConvFeature-master/tools/triemb/triemb_res.m | 413 | utf_8 | febf3e798c5d4b3f7cd8d098445e011b | % Usage: Y = triemb_res (X, C, Xm)
%
% Perform the triangulation embedding
% X input vectors
% C centroids
% Xm mean to be removed
function Y = triemb_res (X, C, Xm)
n = size(X, 2);
d = size(X, 1);
kc = size(C, 2);
D = d * kc;
Y = bsxfun (@minus, repmat(X, kc, 1), C(:));
for j = 1:kc
idxj = 1+(j-... |
github | Dicksonlab/MateBook-master | xlswriteonly.m | .m | MateBook-master/scripts/moonwalk_20130106/xlswriteonly.m | 11,637 | utf_8 | 24492874563c3aba69017ed30e90f2f6 | function [success,message]=xlswriteonly(xlshandle,data,sheet,range)
% XLSWRITE Stores numeric array or cell array in Excel workbook.
% [SUCCESS,MESSAGE]=XLSWRITE(FILE,ARRAY,SHEET,RANGE) writes ARRAY to the Excel
% workbook, FILE, into the area, RANGE in the worksheet specified in SHEET.
% FILE and ARRAY must be s... |
github | Dicksonlab/MateBook-master | xlsopen.m | .m | MateBook-master/scripts/moonwalk_20130106/xlsopen.m | 4,622 | utf_8 | 1537b40528e736ab3fb72dd4f606b876 | function xlshandle=xlsopen(file)
% XLSOPEN Opens an Excel workbook or creates it if it doesn't exist.
%==============================================================================
try
% handle requested Excel workbook filename.
if ~isempty(file)
if ~ischar(file)
error('MATLAB:xlswrite:Inpu... |
github | Dicksonlab/MateBook-master | getExcelCol.m | .m | MateBook-master/scripts/moonwalk_20130106/getExcelCol.m | 1,517 | utf_8 | 70cced69f70e99027562811ed20aa567 | function s = getExcelCol(d)
% taken from xlswrite.m:
% DEC2BASE27(D) returns the representation of D as a string in base 27,
% expressed as 'A'..'Z', 'AA','AB'...'AZ', and so on. Note, there is no zero
% digit, so strictly we have hybrid base26, base27 number system. D must be a
% negative integer bigger ... |
github | xalinchi/Deep-Learning-WiFi-CSI-master | get_scaled_csi.m | .m | Deep-Learning-WiFi-CSI-master/matlab code/data segementation/get_scaled_csi.m | 1,868 | utf_8 | 21129db6cce7a77c81c687e679b1f361 | %GET_SCALED_CSI Converts a CSI struct to a channel matrix H.
%
% (c) 2008-2011 Daniel Halperin <dhalperi@cs.washington.edu>
%
function ret = get_scaled_csi(csi_st)
% Pull out CSI
csi = csi_st.csi;
% Calculate the scale factor between normalized CSI and RSSI (mW)
csi_sq = csi .* conj(csi);
csi_pwr =... |
github | xalinchi/Deep-Learning-WiFi-CSI-master | get_total_rss.m | .m | Deep-Learning-WiFi-CSI-master/matlab code/data segementation/get_total_rss.m | 587 | utf_8 | 9c5a5f37a01f792ebe614efd783dd22a | %GET_TOTAL_RSS Calculates the Received Signal Strength (RSS) in dBm from
% a CSI struct.
%
% (c) 2011 Daniel Halperin <dhalperi@cs.washington.edu>
%
function ret = get_total_rss(csi_st)
nargoutchk(1,1,nargin);
% Careful here: rssis could be zero
rssi_mag = 0;
if csi_st.rssi_a ~= 0
rssi_mag = rss... |
github | xalinchi/Deep-Learning-WiFi-CSI-master | read_bf_file.m | .m | Deep-Learning-WiFi-CSI-master/matlab code/data segementation/read_bf_file.m | 2,573 | utf_8 | d25e3fc0ffb85dc441ad47a28aac2965 | %READ_BF_FILE Reads in a file of beamforming feedback logs.
% This version uses the *C* version of read_bfee, compiled with
% MATLAB's MEX utility.
%
% (c) 2008-2011 Daniel Halperin <dhalperi@cs.washington.edu>
%
function ret = read_bf_file(filename)
%% Input check
nargoutchk(1,1,nargin);
%% Open file
f = fopen(fi... |
github | xalinchi/Deep-Learning-WiFi-CSI-master | dbinv.m | .m | Deep-Learning-WiFi-CSI-master/matlab code/data segementation/dbinv.m | 145 | utf_8 | f3e0b99630ef3ad7fcc8ca3ac3daade3 | %DBINV Convert from decibels.
%
% (c) 2008-2011 Daniel Halperin <dhalperi@cs.washington.edu>
%
function ret = dbinv(x)
ret = 10.^(x/10);
end
|
github | wgshun/AndrewNG-Machinelearning-master | submit.m | .m | AndrewNG-Machinelearning-master/homework/machine-learning-ex2/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 | wgshun/AndrewNG-Machinelearning-master | submitWithConfiguration.m | .m | AndrewNG-Machinelearning-master/homework/machine-learning-ex2/machine-learning-ex2/ex2/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | wgshun/AndrewNG-Machinelearning-master | savejson.m | .m | AndrewNG-Machinelearning-master/homework/machine-learning-ex2/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 | wgshun/AndrewNG-Machinelearning-master | loadjson.m | .m | AndrewNG-Machinelearning-master/homework/machine-learning-ex2/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 | wgshun/AndrewNG-Machinelearning-master | loadubjson.m | .m | AndrewNG-Machinelearning-master/homework/machine-learning-ex2/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 | wgshun/AndrewNG-Machinelearning-master | saveubjson.m | .m | AndrewNG-Machinelearning-master/homework/machine-learning-ex2/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 | wgshun/AndrewNG-Machinelearning-master | submit.m | .m | AndrewNG-Machinelearning-master/homework/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 | wgshun/AndrewNG-Machinelearning-master | submitWithConfiguration.m | .m | AndrewNG-Machinelearning-master/homework/machine-learning-ex4/ex4/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | wgshun/AndrewNG-Machinelearning-master | savejson.m | .m | AndrewNG-Machinelearning-master/homework/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 | wgshun/AndrewNG-Machinelearning-master | loadjson.m | .m | AndrewNG-Machinelearning-master/homework/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 | wgshun/AndrewNG-Machinelearning-master | loadubjson.m | .m | AndrewNG-Machinelearning-master/homework/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 | wgshun/AndrewNG-Machinelearning-master | saveubjson.m | .m | AndrewNG-Machinelearning-master/homework/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 | wgshun/AndrewNG-Machinelearning-master | submit.m | .m | AndrewNG-Machinelearning-master/homework/machine-learning-ex6/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 | wgshun/AndrewNG-Machinelearning-master | porterStemmer.m | .m | AndrewNG-Machinelearning-master/homework/machine-learning-ex6/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 | wgshun/AndrewNG-Machinelearning-master | submitWithConfiguration.m | .m | AndrewNG-Machinelearning-master/homework/machine-learning-ex6/machine-learning-ex6/ex6/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | wgshun/AndrewNG-Machinelearning-master | savejson.m | .m | AndrewNG-Machinelearning-master/homework/machine-learning-ex6/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 | wgshun/AndrewNG-Machinelearning-master | loadjson.m | .m | AndrewNG-Machinelearning-master/homework/machine-learning-ex6/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 | wgshun/AndrewNG-Machinelearning-master | loadubjson.m | .m | AndrewNG-Machinelearning-master/homework/machine-learning-ex6/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 | wgshun/AndrewNG-Machinelearning-master | saveubjson.m | .m | AndrewNG-Machinelearning-master/homework/machine-learning-ex6/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 | wgshun/AndrewNG-Machinelearning-master | submit.m | .m | AndrewNG-Machinelearning-master/homework/machine-learning-ex7/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 | wgshun/AndrewNG-Machinelearning-master | submitWithConfiguration.m | .m | AndrewNG-Machinelearning-master/homework/machine-learning-ex7/machine-learning-ex7/ex7/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | wgshun/AndrewNG-Machinelearning-master | savejson.m | .m | AndrewNG-Machinelearning-master/homework/machine-learning-ex7/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 | wgshun/AndrewNG-Machinelearning-master | loadjson.m | .m | AndrewNG-Machinelearning-master/homework/machine-learning-ex7/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 | wgshun/AndrewNG-Machinelearning-master | loadubjson.m | .m | AndrewNG-Machinelearning-master/homework/machine-learning-ex7/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 | wgshun/AndrewNG-Machinelearning-master | saveubjson.m | .m | AndrewNG-Machinelearning-master/homework/machine-learning-ex7/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 | wgshun/AndrewNG-Machinelearning-master | submit.m | .m | AndrewNG-Machinelearning-master/homework/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 | wgshun/AndrewNG-Machinelearning-master | submitWithConfiguration.m | .m | AndrewNG-Machinelearning-master/homework/machine-learning-ex5/ex5/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | wgshun/AndrewNG-Machinelearning-master | savejson.m | .m | AndrewNG-Machinelearning-master/homework/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 | wgshun/AndrewNG-Machinelearning-master | loadjson.m | .m | AndrewNG-Machinelearning-master/homework/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 | wgshun/AndrewNG-Machinelearning-master | loadubjson.m | .m | AndrewNG-Machinelearning-master/homework/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 | wgshun/AndrewNG-Machinelearning-master | saveubjson.m | .m | AndrewNG-Machinelearning-master/homework/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 | wgshun/AndrewNG-Machinelearning-master | submit.m | .m | AndrewNG-Machinelearning-master/homework/machine-learning-ex3/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 | wgshun/AndrewNG-Machinelearning-master | submitWithConfiguration.m | .m | AndrewNG-Machinelearning-master/homework/machine-learning-ex3/machine-learning-ex3/ex3/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | wgshun/AndrewNG-Machinelearning-master | savejson.m | .m | AndrewNG-Machinelearning-master/homework/machine-learning-ex3/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 | wgshun/AndrewNG-Machinelearning-master | loadjson.m | .m | AndrewNG-Machinelearning-master/homework/machine-learning-ex3/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 | wgshun/AndrewNG-Machinelearning-master | loadubjson.m | .m | AndrewNG-Machinelearning-master/homework/machine-learning-ex3/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 | wgshun/AndrewNG-Machinelearning-master | saveubjson.m | .m | AndrewNG-Machinelearning-master/homework/machine-learning-ex3/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 | wgshun/AndrewNG-Machinelearning-master | submit.m | .m | AndrewNG-Machinelearning-master/homework/machine-learning-ex8/machine-learning-ex8/ex8/submit.m | 2,135 | utf_8 | eebb8c0a1db5a4df20b4c858603efad6 | function submit()
addpath('./lib');
conf.assignmentSlug = 'anomaly-detection-and-recommender-systems';
conf.itemName = 'Anomaly Detection and Recommender Systems';
conf.partArrays = { ...
{ ...
'1', ...
{ 'estimateGaussian.m' }, ...
'Estimate Gaussian Parameters', ...
}, ...
{ ...... |
github | wgshun/AndrewNG-Machinelearning-master | submitWithConfiguration.m | .m | AndrewNG-Machinelearning-master/homework/machine-learning-ex8/machine-learning-ex8/ex8/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | wgshun/AndrewNG-Machinelearning-master | savejson.m | .m | AndrewNG-Machinelearning-master/homework/machine-learning-ex8/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 | wgshun/AndrewNG-Machinelearning-master | loadjson.m | .m | AndrewNG-Machinelearning-master/homework/machine-learning-ex8/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 | wgshun/AndrewNG-Machinelearning-master | loadubjson.m | .m | AndrewNG-Machinelearning-master/homework/machine-learning-ex8/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 | wgshun/AndrewNG-Machinelearning-master | saveubjson.m | .m | AndrewNG-Machinelearning-master/homework/machine-learning-ex8/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... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.