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 | indigits/sparse-plex-master | contents.m | .m | sparse-plex-master/library/+spx/contents.m | 1,276 | utf_8 | 1120acbea277ab250216ada17e84dafd | function contents()
% Lists contents of spx package
root = fileparts(which('spx.locate'));
process_dir(root, 'spx')
end
function process_dir(directory, package_name)
packages = dir([directory, filesep '+*']);
mat_files = dir([directory, filesep '*.m']);
for i=1:numel(packages)
subpac... |
github | indigits/sparse-plex-master | synthetic.m | .m | sparse-plex-master/library/+spx/+data/synthetic.m | 467 | utf_8 | 485e7912adfecf2a1e5c91d96a80dd6a | classdef synthetic
methods(Static)
function signal = picket_fence(N)
import spx.discrete.number;
if ~number.is_perfect_square(N)
error('N must be perfect square');
end
n = sqrt(N);
signal = zeros(1, N);
signal(1:n:end) = 1;
end
function X = uniform(N, S)
% Generates uniformly dis... |
github | indigits/sparse-plex-master | mtx_mkt.m | .m | sparse-plex-master/library/+spx/+data/mtx_mkt.m | 558 | utf_8 | b11bac99173e5a5a76139d3134559321 | % Wrapper for files from matrix market
classdef mtx_mkt
methods(Static)
function A = abb313()
A = read_file('abb313.mtx');
end
function A = bfwb398()
A = read_file('bfwb398.mtx');
end
function A = cryg10000()
A = read_file('cryg10000.mtx');
end
function A = illc1850()
A = read_file('illc1850.mtx');... |
github | indigits/sparse-plex-master | func.m | .m | sparse-plex-master/library/+spx/+data/+synthetic/func.m | 532 | utf_8 | e98c7fbe21251aa1911cdf8dc7619cb2 | classdef func
methods(Static)
function [x, y, res] = sinusoid(varargin)
params = inputParser;
params.addParameter('n', 10);
params.addParameter('sigma', 0.1);
params.addParameter('min', 0);
params.addParameter('max', 1);
params.addParameter('theta', 0);
params.addParameter('f', 1);
pa... |
github | indigits/sparse-plex-master | lowrank.m | .m | sparse-plex-master/library/+spx/+data/+synthetic/lowrank.m | 278 | utf_8 | 0d62ebdec5b9767e5410ad9b1dd12cad | classdef lowrank
% Generates matrices of low rank
methods(Static)
function A = from_randn(m, n, r)
% Returns a low rank matrix constructed by randn(m, r) * randn(r, m)
X = randn(m, r);
Y = randn(r, n);
A = X * Y;
end % function
end % methods
end % classdef
|
github | indigits/sparse-plex-master | roots.m | .m | sparse-plex-master/library/+spx/+opt/roots.m | 761 | utf_8 | be3c7ec9d664f4da382081d73a4a6d99 | classdef roots
methods(Static)
% Public static methods
function [x, change, iterations] = newton(f, df, x0, tolerance, max_iters)
if nargin < 2
error("function and its derivative handles must be provided.");
end
if nargin < 3
error('Initial estimate must be provided');
end
if nargin < 4
tolerance = 1e-... |
github | indigits/sparse-plex-master | projections.m | .m | sparse-plex-master/library/+spx/+opt/projections.m | 517 | utf_8 | 2460c55ed9b2873183617c5e05d2c6a2 | classdef projections
% projections to different convex sets
methods(Static)
function x = proj_l2_ball(x, radius)
% project x to an l2 ball of given radius
if nargin < 2
radius = 1;
end
x_norm = norm(x);
if x_norm > radius
x = (radius / x_norm) * x;
end
end
function x = proj... |
github | indigits/sparse-plex-master | ls.m | .m | sparse-plex-master/library/+spx/+opt/ls.m | 1,634 | utf_8 | 1ddf981ddffba48cc0af93b019b75c2d | classdef ls
% Most of these methods are for reference only
% MATLAB provides high quality implementation of
% Least Squares problems.
methods(Static)
function x = normal_eq(A, b)
% Method of normal equations
% GVL4 algorithm 5.3.1
import spx.la.tris;
% Form the normal equations A' A x = A' b
% W... |
github | indigits/sparse-plex-master | test_cg_ls.m | .m | sparse-plex-master/library/+spx/+opt/convex_optimization/conjugate_gradient/tests/test_cg_ls.m | 930 | utf_8 | 124b223cae50a142984a52381c8598e7 | function test_suite = test_cg_ls
clear all;
initTestSuite;
end
function test_1
A = [
1 -1
1 1
2 1
];
B = [
2
4
8
];
solver = SPX_CGLeastSquare(A, B);
x = solver.solve();
%solver.printResults();
verifyTrue(testCase, solver.hasConverged());
end
function... |
github | indigits/sparse-plex-master | test_conjugate_gradients.m | .m | sparse-plex-master/library/+spx/+opt/convex_optimization/conjugate_gradient/tests/test_conjugate_gradients.m | 571 | utf_8 | 5e7f9211ff3ff1d7da43dca1895e547a | function test_suite = test_conjugate_gradients
clear all;
initTestSuite;
end
function test_1
A = [3 2; 2 6];
X = [2 1 0 4; -2 2 0 4];
B = A * X;
solver = SPX_ConjugateDescent(A, B);
x = solver.solve();
%solver.printResults();
verifyTrue(testCase, solver.hasConverged());
end
functi... |
github | indigits/sparse-plex-master | test_steepest_descent.m | .m | sparse-plex-master/library/+spx/+opt/convex_optimization/steepest_descent/tests/test_steepest_descent.m | 626 | utf_8 | 563c6559946317622b3c6016b7a6045c | function test_suite = test_steepest_descent
clear all;
initTestSuite;
end
function test_1
A = [3 2; 2 6];
X = [ 2 1 0 4;
-2 2 0 4];
B = A * X;
solver = SPX_SteepestDescent(A, B);
x = solver.solve();
%solver.printResults();
verifyTrue(testCase, solver.hasConverged());
end
... |
github | indigits/sparse-plex-master | bp.m | .m | sparse-plex-master/library/+spx/+opt/+admm/bp.m | 3,886 | utf_8 | c2da7de7a8615216f5aee5821d32b8fc | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Main problem is minimize \| A x \|_1 subject to A x = b
% We introduce z = A x - b
% x \in R^n A \in R^{m x n} b \in R^m
% m < n
% ADMM terms
% minimize f(x) + g(z) subject to A x + B z = c
% f(x) : {x \in R^n | Ax = b}
% g(z) : \| z \|_1
% minimize f(x) + \| z ... |
github | indigits/sparse-plex-master | lad.m | .m | sparse-plex-master/library/+spx/+opt/+admm/lad.m | 3,540 | utf_8 | 3a6ee0c54d1d99663a506f1845eb0a65 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Main problem is minimize \| A x - b \|_1
% We introduce z = A x - b
% Equivalent problem
% Minimize \|z \|_1 subject to Ax - z = b
% ADMM terms
% minimize f(x) + g(z) subject to A x + B z = c
% f(x) : 0
% g(z) : \| z \|_1
% A : A
% B : -1
% c : b
% Primal residu... |
github | indigits/sparse-plex-master | hessenberg.m | .m | sparse-plex-master/library/+spx/+la/hessenberg.m | 4,077 | utf_8 | bfc17fdb16dc80e843fbe14bc481d89d | classdef hessenberg
% Functions to work with Hessenberg forms
methods(Static)
function [Q, R] = qr(A)
% Givens method for computing the QR factorization A = QR of a Hessenberg matrix.
% A is modified in the algorithm
% GVL4: algorithm 5.2.5
import spx.la.givens.rotation;
[n,n] = size(A);
% Sp... |
github | indigits/sparse-plex-master | lu.m | .m | sparse-plex-master/library/+spx/+la/lu.m | 1,878 | utf_8 | ced933554c935c386448406fb956ff11 | classdef lu
methods(Static)
function [L, U] = outer(A)
% Computes the A = LU factorization without pivoting
% This is an outer product version of LU factorization
% A is modified in the algorithm
% GVL4: algorithm 3.2.1
[n,n] = size(A);
for k=1:n-1
% identify the range
rho = k+... |
github | indigits/sparse-plex-master | tris.m | .m | sparse-plex-master/library/+spx/+la/tris.m | 1,474 | utf_8 | fd96cd2c5324f850c96547c7291d18e7 | classdef tris
methods(Static)
function x = forward_row(L,b)
% Solvex L x = b problem by forward substitution row wise
% b gets overwritten in the process
% GVL4: algorithm 3.1.1
n = length(b);
% start from last row
for i=1:n
b(i) = (b(i) - L(i, 1:i-1)* b(1:i-1)) / L(i,i);
end
x... |
github | indigits/sparse-plex-master | house.m | .m | sparse-plex-master/library/+spx/+la/house.m | 8,116 | utf_8 | c6ef1a26355907a1aa32963587002405 | classdef house
methods(Static)
function [v, beta] = gen(x)
% Generates the householder reflection vector for a given vector x
% P = I - beta * v * v' is the householder projection matrix
% P is an orthogonal projector
% P *x changes x in such a way that only 1st entry is non-zero
% GVL4: algorithm... |
github | indigits/sparse-plex-master | pca.m | .m | sparse-plex-master/library/+spx/+la/pca.m | 1,602 | utf_8 | d1642f4e8187634685ca8d66d3d08953 | classdef pca
% Methods related to principal component analysis
methods(Static)
%% low_rank_approx: Computes the lower rank approximation of a data set
function Xp = low_rank_approx(X, n)
% X : the data set to be approximated
% n : number of dimensions
[M, S] = size(X);
if n == 0
Xp = X;
... |
github | indigits/sparse-plex-master | schur.m | .m | sparse-plex-master/library/+spx/+la/schur.m | 5,780 | utf_8 | 992d169ea62fd1539485467f62952b63 | classdef schur
% Methods for computing the Schur form
methods(Static)
function kappa = wilk_shift(a,b,c,d)
% Computes the Wilkinson shift for 2x2 sub-matrix
kappa = d;
% check if the matrix is 0
s = abs(a) + abs(b) + abs(c) + abs(d);
if (s == 0)
% nothing to do
return;
end
... |
github | indigits/sparse-plex-master | givens.m | .m | sparse-plex-master/library/+spx/+la/givens.m | 2,134 | utf_8 | 94911f12f8fd425d3d857aabe8e9f1ba | classdef givens
methods(Static)
function [c,s] = rotation(a,b)
% Givens rotation computation
% Determines cosine-sine pair (c,s) so that [c s;-s c]'*[a;b] = [r;0]
% G = [c s; -s c]
% x = [a; b]
% G' x = [r; 0]
% GVL4: algorithm 5.1.3
if b==0
% No rotation needed
c = 1; s =... |
github | indigits/sparse-plex-master | qr.m | .m | sparse-plex-master/library/+spx/+la/qr.m | 4,452 | utf_8 | 1f0f19f5a9af2774129a000acb6fc979 | classdef qr
% Algorithms related to QR factorization
% These are mostly for reference
methods(Static)
function [Q, R] = gram_schmidt(A)
[m, n] = size(A);
if m < n
error('Wide matrices not supported');
end
Q = zeros(m, n);
R = zeros(n, n);
for j=1:n
% pick up the j-th column f... |
github | indigits/sparse-plex-master | eig.m | .m | sparse-plex-master/library/+spx/+la/eig.m | 3,909 | utf_8 | ba10222f451e864395e949c4b5d5a51f | classdef eig
methods(Static)
function [x, lambda, details] = power(A, x, options)
% Power method for computing the largest eigen value and vector of A
% GVL4: section 7.3.1
if nargin < 3
options = struct;
end
tolerance = 1e-6;
if isfield(options, 'tolerance')
tolerance = option... |
github | indigits/sparse-plex-master | chol.m | .m | sparse-plex-master/library/+spx/+la/chol.m | 367 | utf_8 | 531982866baf3e1f2f3b625e0c6d1dbb | classdef chol
methods(Static)
function L_new = chol_update(L, atom)
n = size(L, 1);
if n == 0
L_new = sqrt(atom);
return;
end
v = atom(1:end-1);
c = atom(end);
opts.LT = true;
w = linsolve(L, v, opts);
c_new = sqrt(c - w' * w);
L_new = [L zeros(n, 1); w' c_new];
end... |
github | indigits/sparse-plex-master | lanczos.m | .m | sparse-plex-master/library/+spx/+la/+svd/lanczos.m | 25,702 | utf_8 | 3cb25bf7ca40d0983251c54bdb2c3b1c | classdef lanczos
% The code in this file is largely based on the code and papers for PROPACK
% developed by Rasmus Munk Larsen, 1998.
methods(Static)
function [U, B, V, p, details] = bd(A, k, options)
% Perform bidiagonalization using Lanczos iterations
% The U,V vectors are not re-orthogonalized
% Based... |
github | indigits/sparse-plex-master | bht.m | .m | sparse-plex-master/library/+spx/+ssp/bht.m | 4,383 | utf_8 | 7fc5948f018118c552cda89042e621d0 | classdef bht
methods (Static)
function [ R] = bayes_risk( C, PF, PD, P1)
%BAYES_RISK Computes the Bayes risk
PM = 1 - PD;
P0 = 1 - P1;
C00 = C(1,1);
C10 = C(2,1);
C01 = C(1,2);
C11 = C(2,2);
R = (1 - PF) * P0 * C00 + PM * P1 * C01 ...
+ PF * P0 * C10 + PD * P1 * C11;
end
f... |
github | indigits/sparse-plex-master | L1_ADMM_YZ.m | .m | sparse-plex-master/library/+spx/+pursuit/+single/L1_ADMM_YZ.m | 13,791 | utf_8 | 18a30bcef488c464fce9d5735873feb0 | classdef L1_ADMM_YZ < handle
% Solver for various L1 minimization problems
% based on the 2011 paper by
% Junfeng Yang and Yin Zhang
properties
% weight for the quadratic penalty term
rho
% verbosity
verbose
% Maximum number of ADMM iterations
max_iterations
% relative tolerance
t... |
github | indigits/sparse-plex-master | subspace.m | .m | sparse-plex-master/library/+spx/+cluster/subspace.m | 14,777 | utf_8 | 7f9d3b8fdb1b8b1607cdc0008cc4cfd8 | classdef subspace
methods(Static)
function result = ssc_l1_mahdi(X, options)
if nargin > 1
else
% empty options
options = struct;
end
cvx_solver sdpt3
cvx_quiet(true);
[M, S] = size(X);
% M is ambient dimension
% S is number of signals
% storage for coefficients
... |
github | indigits/sparse-plex-master | OMP_REPR_METHOD.m | .m | sparse-plex-master/library/+spx/+cluster/+ssc/OMP_REPR_METHOD.m | 1,043 | utf_8 | 4431222d5770b34058c49aa25545ddc8 | classdef OMP_REPR_METHOD
% method for computing OMP based representations
enumeration
CLASSIC_OMP_C
BATCH_OMP_C
FLIPPED_OMP_MATLAB
BATCH_FLIPPED_OMP_MATLAB
BATCH_FLIPPED_OMP_C
GOMP_C
MC_OMP
end
methods
function result = isClassicOMP_C(self)
result = (self == spx.cluster.ssc.OMP_REPR_ME... |
github | indigits/sparse-plex-master | poly_thresh_numeric.m | .m | sparse-plex-master/library/+spx/+cluster/+lrsc/poly_thresh_numeric.m | 2,374 | utf_8 | e05a2060ac783e4d147453c2b1d1a456 | %--------------------------------------------------------------------------
% [A,C] = polythreshAP(D,tau,alpha)
% Low Rank Subspace Clustering algorithm for data lying in a union of
% subspaces and contaminated with outliers
%
% min |C|_* + tau/2*|A-AC|_F^2 + alpha/2*|D-A|_F^2 s.t. C = C'
%
% C = affinity matrix
% A ... |
github | indigits/sparse-plex-master | poly_thresh_closed_form.m | .m | sparse-plex-master/library/+spx/+cluster/+lrsc/poly_thresh_closed_form.m | 2,046 | utf_8 | 71450c79ae0575d009acfbdf865a1599 | %--------------------------------------------------------------------------
% [A,C] = poly_thresh_closed_form(D,tau,alpha)
% Low Rank Subspace Clustering algorithm for data lying in a union of
% subspaces and contaminated with outliers
%
% min |C|_* + tau/2*|A-AC|_F^2 + alpha/2*|Delta-A|_F^2 s.t. C = C'
%
% C = affin... |
github | indigits/sparse-plex-master | noisy_relaxed.m | .m | sparse-plex-master/library/+spx/+cluster/+lrsc/noisy_relaxed.m | 1,268 | utf_8 | 8a94f4ea44728b00c65c9082c63c0d1d | %--------------------------------------------------------------------------
% [C,A] = noisy_relaxed(D,tau,alpha,useSampledPolynoimial)
% Low Rank Subspace Clustering algorithm for noisy data lying in a
% union of subspaces
%
% (C,A) = argmin |C|_* + tau/2*|A - AC|_F^2 + alpha/2*|D - A|_F^2 s.t. C = C'
%
% C = affinity... |
github | indigits/sparse-plex-master | clean_relaxed.m | .m | sparse-plex-master/library/+spx/+cluster/+lrsc/clean_relaxed.m | 959 | utf_8 | ab8a76e8befc68cc62e8eaa4090c3caf | %--------------------------------------------------------------------------
% C = lrsc(A,tau)
% Low Rank Subspace Clustering algorithm for clean data lying in a
% union of subspaces
%
% C = argmin |C|_* + tau/2 * |A - AC|_F^2 s.t. C = C'
%
% A: clean data matrix whose columns are points in a union of subspaces
% tau: ... |
github | indigits/sparse-plex-master | noisy_exact.m | .m | sparse-plex-master/library/+spx/+cluster/+lrsc/noisy_exact.m | 505 | utf_8 | 18657253291cb7d9a264196dfecfcadd |
function [A,C] = noisy_exact(D,alpha,~)
% Implements the solution of noisy data with exact condition A = AC
if nargin < 2
tau = 100/norm(D)^2;
alpha = 0.5*tau;
end
% eq 41, theorem 5
threshold = sqrt(2/alpha);
options = struct;
options.lambda = threshold;
options.toleran... |
github | indigits/sparse-plex-master | DFTBasis.m | .m | sparse-plex-master/library/+spx/+dict/DFTBasis.m | 934 | utf_8 | ef9ca57891fcda4068f7724ae1c97b9d | classdef DFTBasis < spx.dict.Operator
properties(SetAccess=private)
% Dimensions
N
end
methods
function self = DFTBasis(N)
if nargin < 1
error('Basis dimensions must be specified');
end
self.N = N;
end
function [mm, nn] = get_size(self)
mm = self.N;
nn = self.N;
end
function r... |
github | indigits/sparse-plex-master | rip.m | .m | sparse-plex-master/library/+spx/+dict/rip.m | 2,862 | utf_8 | b70729b3bb312bf9150cb6078adb26e3 | classdef rip
methods(Static)
function [ delta] = estimate_delta( Phi, KMax )
%ESTIMATERIPDELTA Estimates delta for the sensing matrix Phi
[M, N] = size(Phi);
if nargin == 1
KMax = N;
end
% the value of K for this test
K = 0;
delta = zeros(KMax, 2);
tic;
while K < KMax
... |
github | indigits/sparse-plex-master | PartialDCT.m | .m | sparse-plex-master/library/+spx/+dict/PartialDCT.m | 1,438 | utf_8 | fa3e56ee4e3fc0750cfb4a4492a732cd | classdef PartialDCT < spx.dict.Operator
properties(SetAccess=private)
% Dimensions
row_pics
col_perm
num_rows
num_cols
end
methods
function self = PartialDCT(row_pics, col_perm)
if nargin < 2
error('Row selection and column permutation must be specified');
end
self.row_pics =... |
github | indigits/sparse-plex-master | PartialDFT.m | .m | sparse-plex-master/library/+spx/+dict/PartialDFT.m | 1,482 | utf_8 | 7e1224d9fd5205b0db9b3b267317354f | classdef PartialDFT < spx.dict.Operator
properties(SetAccess=private)
% Dimensions
row_pics
col_perm
num_rows
num_cols
end
methods
function self = PartialDFT(row_pics, col_perm)
if nargin < 2
error('Row selection and column permutation must be specified');
end
self.row_pics =... |
github | indigits/sparse-plex-master | DCTBasis.m | .m | sparse-plex-master/library/+spx/+dict/DCTBasis.m | 904 | utf_8 | 1e1aee7144a487308122611047ee8faa | classdef DCTBasis < spx.dict.Operator
properties(SetAccess=private)
% Dimensions
N
end
methods
function self = DCTBasis(N)
if nargin < 1
error('Basis dimensions must be specified');
end
self.N = N;
end
function [mm, nn] = get_size(self)
mm = self.N;
nn = self.N;
end
function r... |
github | indigits/sparse-plex-master | Matrix.m | .m | sparse-plex-master/library/+spx/+io/+latex/Matrix.m | 2,392 | utf_8 | fd551d19fec613af70d3697f2f9c861d | classdef Matrix < handle
properties
hide_zeros = true
show_nonzero_as_x = false
end
properties(SetAccess=private)
% The matrix to be printed
A
% colors for individual cells
cell_colors
% colors for individual rows
row_colors
% colors for individual columns
col_colors
end
metho... |
github | indigits/sparse-plex-master | test_prob.m | .m | sparse-plex-master/library/+spx/+prob/tests/test_prob.m | 208 | utf_8 | 1afa1c78756e32ae9b255980d0b680a0 | function test_suite = test_prob
initTestSuite;
end
function test_is_pmf
x = [.1 .4 .3 .2];
verifyTrue(testCase, SPX_Prob.is_pmf(x));
x = [-.1 .6 .3 .2];
assertFalse(SPX_Prob.is_pmf(x));
end |
github | indigits/sparse-plex-master | test_it.m | .m | sparse-plex-master/library/+spx/+prob/tests/test_it.m | 186 | utf_8 | 4e7b2ffcaf60c0a066d7795635c9892d | function test_suite = test_it
initTestSuite;
end
function test_entropy
data = [0 0 0 1 1 1 2 2 2 3 3 3];
h = SPX_IT.entropy(data);
assertElementsAlmostEqual(h, 2.0);
end
|
github | indigits/sparse-plex-master | dct.m | .m | sparse-plex-master/library/+spx/+dsp/dct.m | 4,821 | utf_8 | 7d06aab9f71ac414a01aef8afd6f4172 | classdef dct
% Functions related to discrete cosine transform
%
%
% Influenced by:
% - WaveLab
methods(Static)
function alpha = forward_2(x)
% Forward transform for DCT type 2.
options.length_constraint = @spx.dsp.dyadic_length_constraint;
alpha = spx.dsp.apply_transform(x, @dct_2_impl... |
github | indigits/sparse-plex-master | dtmf_detector.m | .m | sparse-plex-master/library/+spx/+dsp/dtmf_detector.m | 2,936 | utf_8 | 04aa818537d19c8f33b16597693fd245 |
function [symbols, starts, durations] = dtmf_detector(signal, fs)
window_length = floor(fs * 50 / 1000);
overlap_length = floor(window_length / 2);
n_fft = 2^nextpow2(window_length);
% compute the spectrogram
[s, f, t] = spectrogram(signal,hamming(window_length),...
overlap_length,n_fft, fs, 'yaxis',...
'MinTh... |
github | indigits/sparse-plex-master | dst.m | .m | sparse-plex-master/library/+spx/+dsp/dst.m | 2,639 | utf_8 | 58e84de801027a18db880a6ade91765c | classdef dst
% Functions related to discrete sine transform
%
% Remarks:
%
%
% Influenced by:
% - WaveLab
methods(Static)
function alpha = forward_1(x)
% Forward transform for DST type 1.
options.length_constraint = @spx.dsp.dyadic_minus_one_length_constraint;
alpha = spx.dsp.apply_t... |
github | indigits/sparse-plex-master | make.m | .m | sparse-plex-master/library/+spx/+fast/private/make.m | 7,477 | utf_8 | e4a378338c63925492fad1ccb6d2b1c0 | function make(arg1)
options.clean = false;
options.filepath = [];
if nargin >= 1
if endsWith(arg1, '.cpp') | endsWith(arg1, '.c')
options.filepath = arg1;
end
if strcmp(arg1, 'clean')
options.clean = true;
end
end
compstr = computer;
is64bit = strcmp(compstr(end-1:end),'64');
% compi... |
github | indigits/sparse-plex-master | synthetic.m | .m | sparse-plex-master/library/+spx/+ecg/synthetic.m | 617 | utf_8 | 3a00ffd0c4277afd9f5ce26a22fae64f | classdef synthetic
methods(Static)
function x = simple(L)
a0 = [0, 1, 40, 1, 0, -34, 118, -99, 0, 2, 21, 2, 0, 0, 0];
d0 = [0, 27, 59, 91, 131, 141, 163, 185, 195, 275, 307, 339, 357, 390, 440];
a = a0 / max(a0);
d = round(d0 * L / d0(15));
d(15) = L;
for i = 1:14
m = d(i) : d(i+1) - 1;
slope ... |
github | indigits/sparse-plex-master | runalltests.m | .m | sparse-plex-master/library/tests/runalltests.m | 481 | utf_8 | 178adf221c2fdc074a6b81a3c974b893 |
function runalltests(all)
if nargin < 1
all = false;
else
end
import matlab.unittest.TestSuite;
import matlab.unittest.selectors.HasTag;
suite = TestSuite.fromFolder(pwd, 'IncludingSubfolders', true);
untagged_tests = suite.selectIf(~HasTag);
tagged_tests = suite.selectIf(H... |
github | indigits/sparse-plex-master | test_ssp.m | .m | sparse-plex-master/library/tests/ssp/test_ssp.m | 925 | utf_8 | 9ec28dba3886c7d188a7e3f13a3c5b87 | function tests = test_ssp
tests = functiontests(localfunctions);
end
function test_autocorr(testCase)
x = [1 2 3];
r1 = xcorr(x);
r2 = spx.ssp.auto_correlation(x);
verifyEqual(testCase, r1, r2, 'AbsTol', 1e-12);
for i=1:100
x = randn(1000, 1);
r1 = xcorr(x);
r2 = spx.s... |
github | indigits/sparse-plex-master | test_cluster_comparison.m | .m | sparse-plex-master/library/tests/clustering/test_cluster_comparison.m | 2,475 | utf_8 | c8bd6b3c764558cf5d55954b6fc81920 | function tests = test_cluster_comparison
tests = functiontests(localfunctions);
end
function test_complete_match_1(testCase)
a = [1 1 1 2 2 2];
b = [2 2 2 1 1 1];
comparer = spx.cluster.ClusterComparison(a, b);
result = comparer.fMeasure();
verifyEqual(testCase, result.fMeasure, 1.0);
verify... |
github | indigits/sparse-plex-master | test_similarity.m | .m | sparse-plex-master/library/tests/clustering/test_similarity.m | 840 | utf_8 | 6bfb44f67a66e58ffb997d9e38636d3a | function tests = test_similarity
tests = functiontests(localfunctions);
end
function test_k_nearest(testCase)
m = [1.0000 0.6000 0.2000
0.6000 1.0000 0.3000
0.2000 0.3000 1.0000];
m = spx.cluster.similarity.filter_k_nearest_neighbors(m, 2);
verifyEqual(testCase, m, [ 1.0000 ... |
github | indigits/sparse-plex-master | test_subspaces.m | .m | sparse-plex-master/library/tests/clustering/test_subspaces.m | 2,542 | utf_8 | 881aa390c7676bfe0074e182ebe75a8f | function tests = test_subspaces()
tests = functiontests(localfunctions);
end
function test_affinity(testCase)
N = 4;
theta = pi/4;
[A, B] = spx.data.synthetic.subspaces.two_spaces_at_angle(N, theta);
verifyTrue(testCase, spx.matrix.is_orthonormal(A));
verifyTrue(testCase, spx.matrix.is_orthonorma... |
github | indigits/sparse-plex-master | test_kmeans_pp.m | .m | sparse-plex-master/library/tests/clustering/test_kmeans_pp.m | 552 | utf_8 | 8731c6560b258490c935c89ff4b8b5a6 | function tests = test_kmeans_pp
tests = functiontests(localfunctions);
end
function test_1(testCase)
X = [1 2 7 8 20 21
1 2 7 8 20 21];
% Capture current random number generator state
st = rng;
% Go to default state
rng('default');
% Perform testing
[seeds, labels] = spx.clust... |
github | indigits/sparse-plex-master | test_hungarian.m | .m | sparse-plex-master/library/tests/clustering/assignment/test_hungarian.m | 2,177 | utf_8 | 3ac80febe3de46fb181d15a671d782d9 | function tests = test_hungarian
tests = functiontests(localfunctions);
end
function test_7x7(testCase)
A = [88 7 10 53 5 30 56
75 69 80 70 53 81 57
88 73 87 89 64 5 84
18 98 1 14 25 5 29
19 8 71 96 36 19 54
94 88 7 87 17 36 24
... |
github | indigits/sparse-plex-master | test_spectral_cluster.m | .m | sparse-plex-master/library/tests/clustering/spectral/test_spectral_cluster.m | 3,871 | utf_8 | e574ab61dd424ef6cdebc18fdbd189b7 | function tests = test_spectral_cluster
tests = functiontests(localfunctions);
end
function test_gaussian_values(testCase)
points_per_set = 100;
% number of clusters
num_clusters = 8;
% points
points = [];
gap = 2;
true_labels = zeros(points_per_set*num_clusters, 1);
fprintf('Creating... |
github | indigits/sparse-plex-master | test_simple_spectral_cluster.m | .m | sparse-plex-master/library/tests/clustering/spectral/test_simple_spectral_cluster.m | 1,329 | utf_8 | 93edd7392db86d3170f988ae1fedeb5d | function tests = test_spectral_cluster
tests = functiontests(localfunctions);
end
function problem = problem1()
problem.W = [ones(4) zeros(4); zeros(4) ones(4)];
problem.num_clusters = 2;
problem.labels = [1 1 1 1 2 2 2 2];
end
function verify_problem(testCase, problem, result)
verifyEqual(testCase,... |
github | indigits/sparse-plex-master | test_ssc.m | .m | sparse-plex-master/library/tests/clustering/ssc/test_ssc.m | 1,559 | utf_8 | 62a94a7c38d22daadc2f6187669ee829 | % Tests sparse subspace clustering algorithm
classdef (TestTags = {'Long'}) test_ssc < matlab.unittest.TestCase
methods (Test)
function test_1(testCase)
% dimension of ambient space
n = 30;
% number of subspaces = number of clusters
ns = 2;
% dimensions of individual subspaces 1 and 2
d1 = 1... |
github | indigits/sparse-plex-master | test_dct.m | .m | sparse-plex-master/library/tests/dsp/test_dct.m | 1,630 | utf_8 | 4241b0c5464aadac77be067725b5b669 | function tests = test_dct
tests = functiontests(localfunctions);
end
function test_dct_2_3(testCase)
n = 256;
for i=randperm(n, 40)
x = spx.vector.unit_vector(n, i);
alpha = spx.dsp.dct.forward_2(x);
tolerance = 0.0001;
verifyEqual(testCase, norm(x), norm(alpha), 'AbsTol', tol... |
github | indigits/sparse-plex-master | test_dst.m | .m | sparse-plex-master/library/tests/dsp/test_dst.m | 1,012 | utf_8 | b12942f723d2fea40edae3d3cfdfbd2b | function tests = test_dst
tests = functiontests(localfunctions);
end
function test_dst_1(testCase)
x = cos((1:15) * .1);
alpha = spx.dsp.dst.forward_1(x);
y = spx.dsp.dst.inverse_1(alpha);
tolerance = 1e-6;
verifyEqual(testCase, x, y, 'AbsTol', tolerance);
end
function test_dst_2(testCase)
... |
github | indigits/sparse-plex-master | test_statistics.m | .m | sparse-plex-master/library/tests/stats/test_statistics.m | 225 | utf_8 | e6e0657f472d6c459894e1a309ff40f4 | function tests = test_statistics
tests = functiontests(localfunctions);
end
function test_mean(testCase)
x = 1:100;
xm = spx.stats.compute_statistic_per_vector(x, 1, @mean);
verifyEqual(testCase, xm', x);
end
|
github | indigits/sparse-plex-master | test_io.m | .m | sparse-plex-master/library/tests/io/test_io.m | 1,099 | utf_8 | 666caec846afe7a793960ee11f7652f2 | function tests = test_io
tests = functiontests(localfunctions);
end
function test_bytes2str(testCase)
import spx.io;
verifyEqual (testCase, io.bytes2str(10), '10 Bytes');
verifyEqual (testCase, io.bytes2str(1024), '1.00 KB');
verifyEqual (testCase, io.bytes2str(1024*1024), '1.00 MB');
verifyEqual... |
github | indigits/sparse-plex-master | test_omp_chol.m | .m | sparse-plex-master/library/tests/pursuit/single/test_omp_chol.m | 939 | utf_8 | d5277dd9a7e6b87f2c7998bbcd7bd7cf | function tests = test_omp_chol
tests = functiontests(localfunctions);
end
function [A, x, b, k] = problem_1()
m = 100;
n = 1000;
k = 4;
A = spx.dict.simple.gaussian_dict(m, n);
gen = spx.data.synthetic.SparseSignalGenerator(n, k);
% create a sparse vector
x = gen.biGaussian();
b = A... |
github | indigits/sparse-plex-master | test_omp.m | .m | sparse-plex-master/library/tests/pursuit/single/test_omp.m | 2,329 | utf_8 | 74f615b4d58a91dd58ca8326ac15687a | function tests = test_omp
tests = functiontests(localfunctions);
end
function [A, x, b, k] = problem_1()
m = 100;
n = 1000;
k = 4;
A = spx.dict.simple.gaussian_dict(m, n);
gen = spx.data.synthetic.SparseSignalGenerator(n, k);
% create a sparse vector
x = gen.biGaussian();
b = A*x;
e... |
github | indigits/sparse-plex-master | test_cosamp.m | .m | sparse-plex-master/library/tests/pursuit/single/test_cosamp.m | 1,437 | utf_8 | ac710139fd1c50333cb9d69750f2eb18 | function tests = test_cosamp
tests = functiontests(localfunctions);
end
function [A, x, b, k] = problem_1()
m = 100;
n = 1000;
k = 4;
A = spx.dict.simple.gaussian_dict(m, n);
gen = spx.data.synthetic.SparseSignalGenerator(n, k);
% create a sparse vector
x = gen.biGaussian();
b = A*x... |
github | indigits/sparse-plex-master | test_mp.m | .m | sparse-plex-master/library/tests/pursuit/single/test_mp.m | 1,443 | utf_8 | fa16972408abc662a5ffab6ab46d801b | function tests = test_mp
tests = functiontests(localfunctions);
end
function [A, x, b, k] = problem_1()
m = 100;
n = 1000;
k = 4;
A = spx.dict.simple.gaussian_dict(m, n);
gen = spx.data.synthetic.SparseSignalGenerator(n, k);
% create a sparse vector
x = gen.biGaussian();
b = A*x;
en... |
github | indigits/sparse-plex-master | test_l1_recovery.m | .m | sparse-plex-master/library/tests/pursuit/single/long/test_l1_recovery.m | 3,280 | utf_8 | 357fa436d695b4dda025e3d48dfcdb65 | function test_suite = test_l1_recovery
initTestSuite;
end
function [A, x, b] = problem_1()
m = 100;
n = 1000;
k = 4;
A = normrnd(0, 1/sqrt(m), m, n);
% create a sparse vector
x = zeros(n, 1);
indices = randperm(n, k);
x(indices) = normrnd(0, 1, k, 1);
b = A*x;
end
function [A, X... |
github | indigits/sparse-plex-master | test_dictionary_comparison.m | .m | sparse-plex-master/library/tests/dict/test_dictionary_comparison.m | 568 | utf_8 | 1f0477dc2aa24e1d194be5253a1ce56f | function tests = test_dictionary_comparison
tests = functiontests(localfunctions);
end
function test_1(testCase)
d = spx.dict.simple.dirac_fourier_mtx(16);
ratio = spx.dict.comparison.matching_atoms_ratio(d, d);
verifyEqual(testCase, ratio, 1);
% Let's mess up with one of the atoms
d2 = d;
d... |
github | indigits/sparse-plex-master | test_dictionaries.m | .m | sparse-plex-master/library/tests/dict/test_dictionaries.m | 925 | utf_8 | d6e7d3d1ad1526fc52d1c7b829b3cb38 | function tests = test_dictionaries
tests = functiontests(localfunctions);
end
function test_gaussian(testCase)
D = 10;
N = 4;
Dict = spx.dict.simple.gaussian_dict(N, D);
verifyTrue(testCase, isa(Dict, 'spx.dict.MatrixOperator'));
verifyTrue(testCase, isa(Dict, 'spx.dict.Operator'));
% Check s... |
github | indigits/sparse-plex-master | test_operator.m | .m | sparse-plex-master/library/tests/dict/test_operator.m | 5,079 | utf_8 | f6530128d6b8fdd6d74736932651dc74 | function tests = test_operator
tests = functiontests(localfunctions);
end
function test_1(testCase)
b = [ 1 2 3; 3 4 3];
bb = spx.dict.MatrixOperator(b);
[m, n] = size(bb);
verifyEqual(testCase, [m, n], size(b));
bbb = double(bb);
verifyEqual(testCase, bbb, b);
v = [1 2 3]';
verifyEqu... |
github | indigits/sparse-plex-master | test_lang.m | .m | sparse-plex-master/library/tests/commons/test_lang.m | 242 | utf_8 | 2e9b93471be05dbc6b2dc9c5ebae696f | function tests = test_lang
tests = functiontests(localfunctions);
end
function test_1(testCase)
verifyTrue(testCase, spx.lang.is_class('spx.lang'));
verifyFalse(testCase, spx.lang.is_class('spx.lang.noop'));
spx.lang.noop;
end
|
github | indigits/sparse-plex-master | test_spx_norms.m | .m | sparse-plex-master/library/tests/commons/test_spx_norms.m | 1,690 | utf_8 | 3ffcc39c4b15fba2358b7faa1f47def9 | function tests = test_spx_norms
tests = functiontests(localfunctions);
end
function test_lp_norms(testCase)
x = [1 1 1
2 2 2
3 3 3];
verifyEqual(testCase, [6 6 6], spx.norm.norms_l1_cw(x));
verifyEqual(testCase, [3 6 9]', spx.norm.norms_l1_rw(x));
verifyEqual(testCase, sqrt([14 14... |
github | indigits/sparse-plex-master | test_spx_checks.m | .m | sparse-plex-master/library/tests/commons/test_spx_checks.m | 1,429 | utf_8 | 4bdb7de87334e2ce50bcd534b60a8df5 | function tests = test_spx_checks
tests = functiontests(localfunctions);
end
function setupOnce(testCase)
import spx.matrix;
end
function test_is_square(testCase)
import spx.matrix;
a = randn(3, 4);
verifyFalse(testCase, matrix.is_square(a));
verifyTrue(testCase, matrix.is_square(zeros(3, 3)));
... |
github | indigits/sparse-plex-master | test_spx_vector.m | .m | sparse-plex-master/library/tests/commons/test_spx_vector.m | 3,485 | utf_8 | d2ac3d3005cb569f8035fe409b99cda3 | function tests = test_spx_vector
tests = functiontests(localfunctions);
end
function test_reverse(testCase)
v1 = [1 2 3];
v2 = [3 2 1];
verifyEqual(testCase, v1, spx.vector.reverse(v2));
end
function test_reshape_as_row(testCase)
x = [1 2 3];
y = spx.vector.reshape_as_row_vec(x);
verifyEqua... |
github | indigits/sparse-plex-master | test_signals.m | .m | sparse-plex-master/library/tests/commons/test_signals.m | 542 | utf_8 | fa5b7f2c160a8270d75e563ae3da44ae |
function tests = test_signals
tests = functiontests(localfunctions);
end
function test_picket_fence(testCase)
import spx.data.synthetic.picket_fence;
import spx.commons.sparse.support;
s = picket_fence(16);
expected_s = [ 1 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 ];
verifyEqual(testCase, s, s);
fs = f... |
github | indigits/sparse-plex-master | test_basic_noise.m | .m | sparse-plex-master/library/tests/data/test_basic_noise.m | 316 | utf_8 | 0f63da46dffad64238d8260e7ae6e554 | function tests = test_basic_noise
tests = functiontests(localfunctions);
end
function test1(testCase)
N = 100;
S = 100;
gen = spx.data.noise.Basic(N, S);
sigma = 1;
X = gen.gaussian(sigma);
variance = sum(sum(X.^2)) / (N * S);
verifyEqual(testCase, sigma, variance, 'RelTol', .1);
end
|
github | indigits/sparse-plex-master | test_standard_images.m | .m | sparse-plex-master/library/tests/data/test_standard_images.m | 592 | utf_8 | 365084969dc9b7569540da8b641d249a | function tests = test_standard_images
tests = functiontests(localfunctions);
end
function setupOnce(testCase)
figure;
end
function teardownOnce(testCase)
close;
end
function test_barbara(testCase)
image = spx.data.standard_images.barbara_gray_512x512();
imshow(uint8(image));
pause(.1);
end
func... |
github | indigits/sparse-plex-master | test_yale_faces.m | .m | sparse-plex-master/library/tests/data/image/test_yale_faces.m | 983 | utf_8 | b995e2d5cb1d27022ca1963456e4f4bd | function tests = test_yale_faces
tests = functiontests(localfunctions);
end
function setupOnce(testCase)
close all;
end
function teardownOnce(testCase)
close all;
end
function test_basic(testCase)
yf = spx.data.image.YaleFaces();
yf.load();
verifyEqual(testCase, yf.ImageHeight, 192);
verify... |
github | indigits/sparse-plex-master | test_ehsan_yale_faces.m | .m | sparse-plex-master/library/tests/data/image/test_ehsan_yale_faces.m | 785 | utf_8 | 5de138b05d688c8188e8803739f3ea5f | function tests = test_ehsan_yale_faces
tests = functiontests(localfunctions);
end
function setupOnce(testCase)
close all;
end
function teardownOnce(testCase)
close all;
end
function test_basic(testCase)
yf = spx.data.image.EhsanYaleFaces();
verifyEqual(testCase, yf.image_height, 48);
verifyEqua... |
github | indigits/sparse-plex-master | test_sparse_sig_gen.m | .m | sparse-plex-master/library/tests/data/synthetic/test_sparse_sig_gen.m | 1,198 | utf_8 | eab0e496609576d4fd8faf7bf47c156f | function tests = test_sparse_sig_gen
tests = functiontests(localfunctions);
end
function test_uniform(testCase)
N = 10;
K = 4;
S = 1;
gen = spx.data.synthetic.SparseSignalGenerator(N, K, S);
a = 1;
b = 2;
result = gen.uniform(a, b);
omega = gen.Omega;
nz_part = result(omega);
... |
github | indigits/sparse-plex-master | test_dict_learn.m | .m | sparse-plex-master/library/tests/data/synthetic/test_dict_learn.m | 364 | utf_8 | 475f9de46a6e3ac39838943ee94d7620 | function tests = test_dict_learn
tests = functiontests(localfunctions);
end
function setupOnce(testCase)
figure;
end
function teardownOnce(testCase)
close;
end
function test1(testCase)
import spx.data.synthetic.dict_learn_problems;
problem = dict_learn_problems.problem_random_dict();
imagesc(pr... |
github | indigits/sparse-plex-master | test_chol.m | .m | sparse-plex-master/library/tests/la/test_chol.m | 753 | utf_8 | 3660edd7800e4243ecfebc8e4e283efb | function tests = test_spaces
tests = functiontests(localfunctions);
end
function test_chol_update1(testCase)
n = 5;
A = gallery('moler', n);
L = sqrt(A(1,1));
for c = 2:n
% pick c elements from c-th column
atom = A(1:c, c);
L = spx.la.chol.chol_update(L, atom);
estima... |
github | indigits/sparse-plex-master | test_spaces.m | .m | sparse-plex-master/library/tests/la/test_spaces.m | 2,638 | utf_8 | a0517ee1a40ad05aad1c232975ffca47 | function tests = test_spaces
tests = functiontests(localfunctions);
end
function test_orth_complement(testCase)
M = 100;
N1 = 10;
N2 = 10;
N = N1 + N2;
X = randn(M, N) ./ sqrt(M);
A = X(:, 1:N1);
B = X(:, N1+1:end);
C = spx.la.spaces.orth_complement(A, B);
D = A' * C;
verifyE... |
github | indigits/sparse-plex-master | test_svd.m | .m | sparse-plex-master/library/tests/la/test_svd.m | 937 | utf_8 | 88b5e9f662519ffd25daa9d0a67a498c | function tests = test_svd
tests = functiontests(localfunctions);
end
function test_mahdi_rank(testCase)
s = [ 1 1 1 1 0 0];
[r, g] = spx.la.svd.mahdi_rank(s);
verifyEqual(testCase, r, 4);
verifyEqual(testCase, g, 1);
s = [ 1 .9 .8 .7 .3 .2 .1];
[r, g] = spx.la.svd.mahdi_rank(s);
ver... |
github | indigits/sparse-plex-master | test_householder.m | .m | sparse-plex-master/library/tests/la/test_householder.m | 1,009 | utf_8 | e0b033e03727f0917fd662631f5b59cb | function tests = test_householder
tests = functiontests(localfunctions);
end
function [v, beta, y] = verify_house(x, testCase)
[v , beta] = spx.la.house.gen(x);
y = spx.la.house.premul(x, v, beta);
verifyEqual(testCase, norm(y), norm(x), 'AbsTol', 1e-12);
verifyEqual(testCase, y(2:3), zeros(2,1), 'Ab... |
github | indigits/sparse-plex-master | test_lansvd.m | .m | sparse-plex-master/library/tests/la/svd/test_lansvd.m | 3,422 | utf_8 | f205a455e5ac4ed6621aaed22d36846f | function tests = test_lansvd
tests = functiontests(localfunctions);
end
function A = mat_simple1_1(n)
m = 200;
if nargin < 1
n = 50;
end
U0 = orth(randn(m));
V0 = orth(randn(n));
S0 = zeros(m, n);
for i=1:n
S0(i,i) = m / (i);
end
A = U0*S0*V0';
end
function verify... |
github | indigits/sparse-plex-master | test_bdhizsqr.m | .m | sparse-plex-master/library/tests/la/svd/test_bdhizsqr.m | 4,147 | utf_8 | c62b10c9302f39aac4e0c803db68ed15 | function tests = test_bdhizsqr
tests = functiontests(localfunctions);
end
function verify_bd_full_svd(a, b, testCase)
n = numel(a);
A = full(spdiags([a [0; b]], [0 1], n, n));
[UU, SS, VV] = svd(A);
options.verbosity = 0;
[U, S, V] = spx.fast.bdhizsqr_svd(a,b, options);
verifyEqual(testCase,... |
github | indigits/sparse-plex-master | test_batch_omp.m | .m | sparse-plex-master/library/tests/fast/test_batch_omp.m | 1,029 | utf_8 | b4e735182291b81a6dcd873ef0981823 | function tests = test_batch_omp
tests = functiontests(localfunctions);
end
function [dict, reps, signals, k] = problem_2()
m = 200;
n = 1000;
k = 10;
s = 500;
dict = spx.dict.simple.gaussian_dict(m, n);
gen = spx.data.synthetic.SparseSignalGenerator(n, k, s);
% create a sparse vector
... |
github | indigits/sparse-plex-master | test_batch_omp_spr.m | .m | sparse-plex-master/library/tests/fast/test_batch_omp_spr.m | 1,799 | utf_8 | b3812e86b3989cbec10328dfc0723b9b | function tests = test_batch_omp_spr
tests = functiontests(localfunctions);
end
function test_batch_omp_spr_1(testCase)
% dimension of ambient space
n = 50;
% number of subspaces = number of clusters
ns = 2;
% dimensions of individual subspaces 1 and 2
d1 = 3;
d2 = 3;
% number of si... |
github | indigits/sparse-plex-master | test_fast_omp_chol.m | .m | sparse-plex-master/library/tests/fast/test_fast_omp_chol.m | 1,214 | utf_8 | b696e183d89a36a26fdfc4681978a709 | function tests = test_fast_omp_chol
tests = functiontests(localfunctions);
end
function [A, x, b, k] = problem_1()
m = 100;
n = 1000;
k = 4;
A = spx.dict.simple.gaussian_dict(m, n);
gen = spx.data.synthetic.SparseSignalGenerator(n, k);
% create a sparse vector
x = gen.biGaussian();
... |
github | indigits/sparse-plex-master | test_beylkin.m | .m | sparse-plex-master/library/tests/wavelet/test_beylkin.m | 215 | utf_8 | 25755340b18ab6d8fae018352ec6ddf7 | function tests = test_beylkin
tests = functiontests(localfunctions);
end
function test_qmf(testCase)
f = spx.wavelet.baylkin.quad_mirror_filter();
verifyTrue(testCase, spx.norm.is_unit_norm_vec(f));
end
|
github | indigits/sparse-plex-master | test_symmlet.m | .m | sparse-plex-master/library/tests/wavelet/test_symmlet.m | 838 | utf_8 | f2191ff433435e1cacd3a7d388e0d041 | function tests = test_symmlet
tests = functiontests(localfunctions);
end
function test_qmf(testCase)
f = spx.wavelet.symm.quad_mirror_filter(4);
verifyTrue(testCase, spx.norm.is_unit_norm_vec(f));
f = spx.wavelet.symm.quad_mirror_filter(5);
verifyTrue(testCase, spx.norm.is_unit_norm_vec(f));
f = ... |
github | indigits/sparse-plex-master | test_vaidyanathan.m | .m | sparse-plex-master/library/tests/wavelet/test_vaidyanathan.m | 225 | utf_8 | ac4e3c8c785922c1b8acf6dbae17729e | function tests = test_vaidyanathan
tests = functiontests(localfunctions);
end
function test_qmf(testCase)
f = spx.wavelet.vaidyanathan.quad_mirror_filter();
verifyTrue(testCase, spx.norm.is_unit_norm_vec(f));
end
|
github | indigits/sparse-plex-master | test_wl_transform.m | .m | sparse-plex-master/library/tests/wavelet/test_wl_transform.m | 2,161 | utf_8 | 0d4ecb6fb355d4f4f00b1efb4e7f25b4 | function tests = test_wl_transform
tests = functiontests(localfunctions);
end
function test_fwd_po_wavelet_transform(testCase)
x = [0 5 -3 3 -4 -6 -1 6 1 -7 -2 -7 1 -8 1 -3];
h = spx.wavelet.daubechies.quad_mirror_filter(20);
L = 1;
w = spx.wavelet.transform.forward_periodized_orthogonal(h, x, L);
... |
github | indigits/sparse-plex-master | test_daubechies.m | .m | sparse-plex-master/library/tests/wavelet/test_daubechies.m | 1,052 | utf_8 | eb350114088d78d235d7a1ea1eaeca5b | function tests = test_daubechies
tests = functiontests(localfunctions);
end
function test_qmf(testCase)
f = spx.wavelet.daubechies.quad_mirror_filter(4);
verifyTrue(testCase, spx.norm.is_unit_norm_vec(f));
f = spx.wavelet.daubechies.quad_mirror_filter(6);
verifyTrue(testCase, spx.norm.is_unit_norm_ve... |
github | indigits/sparse-plex-master | test_wavelet.m | .m | sparse-plex-master/library/tests/wavelet/test_wavelet.m | 6,029 | utf_8 | 4af5d2b82efd06ff363c6a99675f7e40 | function tests = test_wavelet
tests = functiontests(localfunctions);
end
function test_dyad(testCase)
verifyEqual(testCase, spx.wavelet.dyad(0) , 2:2);
verifyEqual(testCase, spx.wavelet.dyad(1) , 3:4);
verifyEqual(testCase, spx.wavelet.dyad(2) , 5:8);
verifyEqual(testCase, spx.wavelet.dyad(3) , 9:16)... |
github | indigits/sparse-plex-master | test_haar.m | .m | sparse-plex-master/library/tests/wavelet/test_haar.m | 209 | utf_8 | 95bba51b83208a69a9492f1d9e0105c1 | function tests = test_haar
tests = functiontests(localfunctions);
end
function test_qmf(testCase)
f = spx.wavelet.haar.quad_mirror_filter();
verifyTrue(testCase, spx.norm.is_unit_norm_vec(f));
end
|
github | indigits/sparse-plex-master | test_lcs_base.m | .m | sparse-plex-master/library/tests/wavelet/test_lcs_base.m | 898 | utf_8 | 3cc938348d18a8aab3ae195924c51ab2 | function tests = test_lcs_base
tests = functiontests(localfunctions);
end
function test_psi(testCase)
x = -0.5:.01:.5;
epsilon = .1;
psi_a = spx.wavelet.lcs.psi(epsilon, x);
psi_b = spx.wavelet.lcs.psi(epsilon, -x);
verifyEqual(testCase, psi_a, psi_b, 'RelTol', 0.01);
end
function test_theta(tes... |
github | indigits/sparse-plex-master | test_coiflet.m | .m | sparse-plex-master/library/tests/wavelet/test_coiflet.m | 644 | utf_8 | aaac1aa41673d8b9572a77c41a7ee948 | function tests = test_coiflet
tests = functiontests(localfunctions);
end
function test_qmf(testCase)
f = spx.wavelet.coiflet.quad_mirror_filter(1);
verifyTrue(testCase, spx.norm.is_unit_norm_vec(f));
f = spx.wavelet.coiflet.quad_mirror_filter(2);
verifyTrue(testCase, spx.norm.is_unit_norm_vec(f));
... |
github | indigits/sparse-plex-master | ar_omp.m | .m | sparse-plex-master/experiments/atom_ranking_in_greedy_pursuit/ar_omp.m | 3,836 | utf_8 | 9ae5b4cc1a812ec9faae2eef2888a7be | function result = ar_omp(Phi, K, y, matching_mode, options)
% dimensions
[n, d] = size(Phi);
% active indices
omega = [];
% residual
r = y;
% residual norm
r_norm = norm(r);
% result
z = zeros(d, 1);
atom_index_sum = 0;
matched_atoms = 0;
selected_atoms = 1:d;
if nargin == 3
matching_mode = 4;
end
if nargin < 5
... |
github | indigits/sparse-plex-master | atom_ranked_omp.m | .m | sparse-plex-master/experiments/atom_ranking_in_greedy_pursuit/atom_ranked_omp.m | 2,412 | utf_8 | 2cb2258e2eca8fc7bc676ff842c0aef6 | function result = atom_ranked_omp(Phi, y, K, atoms_to_match, matching_mode)
% dimensions
[n, d] = size(Phi);
% active indices
omega = [];
% residual
r = y;
% residual norm
r_norm = norm(r);
% result
z = zeros(d, 1);
atom_index_sum = 0;
selected_atoms = 1:d;
if nargin == 3
atoms_to_match = n;
end
if nargin < 5
m... |
github | krystophny/matclipse-master | output_xml.m | .m | matclipse-master/org.eclipselabs.matclipse.mconsole/m-files/output_xml.m | 28,832 | utf_8 | d04c088f646c959a1cbf45e2df1f1a6c | function str=output_xml(data,orient,fid,vname,xml,init,varargin)
%
% produces xml output of matlab variable data
%
% usage to write files:
% output_xml(data,orient,fid,vname,xml)
% output_xml(data) - simplest version, output to screen
% output_xml(data,[],[],1) - output to screen, only link to data
%
% usage fo... |
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