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 | chaosuo/hctsa-master | RA_keyboard.m | .m | hctsa-master/PeripheryFunctions/RA_keyboard.m | 1,653 | utf_8 | 46d3e36bbd9727be0888401b10690ace | % ------------------------------------------------------------------------------
% RA_keyboard
% ------------------------------------------------------------------------------
%
% Romesh Abeysuriya's replacement of Matlab's 'keyboard' command.
%
% Keyboard debug caller
% Provides more information including the stack... |
github | chaosuo/hctsa-master | ML_l1pwcar1.m | .m | hctsa-master/Toolboxes/Max_Little/steps_bumps_toolkit/ML_l1pwcar1.m | 2,540 | utf_8 | 9d54dd6018fa164663f21d16a1757904 | % Performs discrete correlated total variation denoising (CTVD) using a
% primal-dual interior-point solver. It minimizes the following discrete
% functional:
%
% E=(1/2)||y_0-ay_1-x||_2^2+lambda*||Dx||_1,
%
% over the variable x, given the input signal y, according to each
% value of the regularization parameter lamb... |
github | chaosuo/hctsa-master | ML_ckfilter.m | .m | hctsa-master/Toolboxes/Max_Little/steps_bumps_toolkit/ML_ckfilter.m | 2,305 | utf_8 | ce08bfcf3e205227e46cdc08b1bd0124 | % Implements the Chung-Kennedy sliding window nonlinear step filter. This
% filter is similar to a centred moving average filter of length K, but the
% centre sample in the window is replaced by a weighted sum of forward and
% backward moving average filters. The weights are inversely proportional
% to the one-step-ahe... |
github | chaosuo/hctsa-master | ML_l1pwclmax.m | .m | hctsa-master/Toolboxes/Max_Little/steps_bumps_toolkit/ML_l1pwclmax.m | 1,072 | utf_8 | e392aeba5a721f8fdc65204f75643b3c | % Calculate the value of lambda so that if lambda >= lambdamax, the TVD
% functional solved by l1pwc is minimized by the trivial constant
% solution x = mean(y). This can then be used to determine a useful range
% of values of lambda, for example.
%
% Usage:
% lambdamax = l1pwclmax(y)
%
% Input arguments:
% - y ... |
github | chaosuo/hctsa-master | ML_l1pwc.m | .m | hctsa-master/Toolboxes/Max_Little/steps_bumps_toolkit/ML_l1pwc.m | 6,386 | utf_8 | 35a2f72b14c9e9dc8e8f6be4307fd245 | % Performs discrete total variation denoising (TVD) using a primal-dual
% interior-point solver. It minimizes the following discrete functional:
%
% E=(1/2)||y-x||_2^2+lambda*||Dx||_1,
%
% over the variable x, given the input signal y, according to each
% value of the regularization parameter lambda > 0. D is the firs... |
github | chaosuo/hctsa-master | ML_kvsteps.m | .m | hctsa-master/Toolboxes/Max_Little/steps_bumps_toolkit/ML_kvsteps.m | 1,280 | utf_8 | affbbbbf7715764bb93eca5d0d45c377 | % Implements the Kalafut-Visscher step detection method, using the MEX
% wrapper of the C version.
%
% Usage:
% [y, steps] = ML_kvsteps(x)
%
% Inputs
% x - Input signal
%
% Outputs
% y - Estimated piecewise constant approximation to the input signal
% steps - Vector of estimated step-change points i... |
github | chaosuo/hctsa-master | ML_fastdfa.m | .m | hctsa-master/Toolboxes/Max_Little/fastdfa/ML_fastdfa.m | 1,237 | utf_8 | f34bf3cc4bf90912bc4a07fc585024e6 | % Performs fast detrended fluctuation analysis on a nonstationary input signal to
% obtain an estimate for the scaling exponent.
%
% Useage:
% [alpha, intervals, flucts] = fastdfa(x)
% [alpha, intervals, flucts] = fastdfa(x, intervals)
% Inputs
% x - input signal: must be a column vector
% Optional inputs
%... |
github | chaosuo/hctsa-master | TS_entropy.m | .m | hctsa-master/Toolboxes/TS_Research/TS_entropy.m | 1,287 | utf_8 | 25d6e5aba1660a87f1fdf677099f3024 | % TS_entropy
%
% TS_entropy estimates the entropy of signals:
% OUTPUT:
% entr : The entropy estimate
% INPUTS:
% q : input parameter, q >= 1;
% x : The time series to be analyzed
% q : Tsallis non-extensive parameter value, q >= 1;
% if q == 1 then Tsallis' entropy con... |
github | chaosuo/hctsa-master | likT.m | .m | hctsa-master/Toolboxes/gpml/lik/likT.m | 6,161 | utf_8 | 7ed3bbaf1c71cd2e56e1818df722f18d | function [varargout] = likT(hyp, y, mu, s2, inf, i)
% likT - Student's t likelihood function for regression.
% The expression for the likelihood is
% likT(t) = Z * ( 1 + (t-y)^2/(nu*sn^2) ).^(-(nu+1)/2),
% where Z = gamma((nu+1)/2) / (gamma(nu/2)*sqrt(nu*pi)*sn)
% and y is the mean (for nu>1) and nu*sn^2/(nu-2) is ... |
github | chaosuo/hctsa-master | likLaplace.m | .m | hctsa-master/Toolboxes/gpml/lik/likLaplace.m | 11,152 | iso_8859_13 | 64102178a902207cd938dedbe5a255d1 | function [varargout] = likLaplace(hyp, y, mu, s2, inf, i)
% likLaplace - Laplacian likelihood function for regression.
% The expression for the likelihood is
% likLaplace(t) = exp(-|t-y|/b)/(2*b) with b = sn/sqrt(2),
% where y is the mean and sn^2 is the variance.
%
% The hyperparameters are:
%
% hyp = [ log(sn) ... |
github | chaosuo/hctsa-master | likPoisson.m | .m | hctsa-master/Toolboxes/gpml/lik/likPoisson.m | 6,315 | utf_8 | 464a93907c3fe117d6e260de85e37ca3 | function [varargout] = likPoisson(kind, hyp, y, mu, s2, inf, i)
% likPoisson - Poisson likelihood function for count data y. The expression for
% the likelihood is
% likPoisson(f) = mu^y * exp(-mu) / y! with mean=variance=mu
% where mu = g(f) is the Poisson intensity, f is a
% Gaussian process, y is the non-negativ... |
github | chaosuo/hctsa-master | likLogistic.m | .m | hctsa-master/Toolboxes/gpml/lik/likLogistic.m | 8,181 | utf_8 | 3c63d63bb39e2f462465fdd0e5677a43 | function [varargout] = likLogistic(hyp, y, mu, s2, inf, i)
% likLogistic - logistic function for binary classification or logit regression.
% The expression for the likelihood is
% likLogistic(t) = 1./(1+exp(-t)).
%
% Several modes are provided, for computing likelihoods, derivatives and moments
% respectively, see... |
github | chaosuo/hctsa-master | likSech2.m | .m | hctsa-master/Toolboxes/gpml/lik/likSech2.m | 11,140 | utf_8 | cfade3d75712b667f7a97d9c1da54cf5 | function [varargout] = likSech2(hyp, y, mu, s2, inf, i)
% likSech2 - sech-square likelihood function for regression. Often, the sech-
% square distribution is also referred to as the logistic distribution not to be
% confused with the logistic function for classification. The expression for the
% likelihood is
% li... |
github | chaosuo/hctsa-master | likMix.m | .m | hctsa-master/Toolboxes/gpml/lik/likMix.m | 8,590 | utf_8 | f9f6a474ae6c850aff8793ba315a17ff | function [varargout] = likMix(lik, hyp, varargin)
% likMix - Mixture of likelihoods for regression/classification.
% The expression for the likelihood is
% log( likMix(t) ) = sum_i=1..m w_i * log( lik_i(t) ),
% where lik_i are the m individual likelihood functions combined by a weighted
% sum in the log domain wi... |
github | chaosuo/hctsa-master | lbfgsb.m | .m | hctsa-master/Toolboxes/gpml/util/lbfgsb.m | 4,993 | utf_8 | e8376ef952af2fc29fe1b08e69366bf1 | % LBFGSB Call the nonlinear bound-constrained solver that uses
% limited-memory BFGS quasi-Newton updates.
%
% The basic function call is
%
% LBFGSB(x0,lb,ub,objfunc,gradfunc)
%
% The first input argument x0 is either a matrix or a cell array of
% matrices. It declares the starting point for the s... |
github | chaosuo/hctsa-master | gauher.m | .m | hctsa-master/Toolboxes/gpml/util/gauher.m | 2,245 | utf_8 | 441ef6c145fe66f1b7ca9da6207f6003 | % compute abscissas and weight factors for Gaussian-Hermite quadrature
%
% CALL: [x,w] = gauher(N)
%
% x = base points (abscissas)
% w = weight factors
% N = number of base points (abscissas) (integrates an up to (2N-1)th order
% polynomial exactly)
%
% p(x)=exp(-x^2/2)/sqrt(2*pi), a =-Inf, b = Inf
%
% Th... |
github | chaosuo/hctsa-master | elsympol.m | .m | hctsa-master/Toolboxes/gpml/util/elsympol.m | 699 | utf_8 | 33e751b982c07eb890d26629bf71f595 | % Evaluate the order R elementary symmetric polynomial Newton's identity aka
% the Newton–Girard formulae: http://en.wikipedia.org/wiki/Newton's_identities
%
% Copyright (c) by Carl Edward Rasmussen and Hannes Nickisch, 2010-01-10.
function E = elsympol(Z,R)
% evaluate 'power sums' of the individual terms in Z
sz = si... |
github | chaosuo/hctsa-master | minimize.m | .m | hctsa-master/Toolboxes/gpml/util/minimize.m | 11,338 | utf_8 | 9125c9c5a74235bad430135b677edd29 | function [X, fX, i] = minimize(X, f, length, varargin)
% Minimize a differentiable multivariate function using conjugate gradients.
%
% Usage: [X, fX, i] = minimize(X, f, length, P1, P2, P3, ... )
%
% X initial guess; may be of any type, including struct and cell array
% f the name or pointer to the funct... |
github | chaosuo/hctsa-master | minimize_new.m | .m | hctsa-master/Toolboxes/gpml/util/minimize_new.m | 11,952 | utf_8 | d8aad9cf50639371a892fbcc202eed7c | % minimize.m - minimize a smooth differentiable multivariate function using
% LBFGS (Limited memory LBFGS) or CG (Conjugate Gradients)
% Usage: [X, fX, i] = minimize(X, F, p, other, ... )
% where
% X is an initial guess (any type: vector, matrix, cell array, struct)
% F is the objective function (function poi... |
github | chaosuo/hctsa-master | sq_dist.m | .m | hctsa-master/Toolboxes/gpml/util/sq_dist.m | 1,967 | utf_8 | 4b47740ab9df8ebf0acd5ae2d557acef | % sq_dist - a function to compute a matrix of all pairwise squared distances
% between two sets of vectors, stored in the columns of the two matrices, a
% (of size D by n) and b (of size D by m). If only a single argument is given
% or the second matrix is empty, the missing matrix is taken to be identical
% to the fir... |
github | chaosuo/hctsa-master | unwrap.m | .m | hctsa-master/Toolboxes/gpml/util/unwrap.m | 651 | utf_8 | 47d4deafec9cfdde0a4c291b3825c401 | % Extract the numerical values from "s" into the column vector "v". The
% variable "s" can be of any type, including struct and cell array.
% Non-numerical elements are ignored. See also the reverse rewrap.m.
function v = unwrap(s)
v = [];
if isnumeric(s)
v = s(:); % numeric values are re... |
github | chaosuo/hctsa-master | rewrap.m | .m | hctsa-master/Toolboxes/gpml/util/rewrap.m | 1,014 | utf_8 | 64b6d7c0f51a8c77ddd012370a288b20 | % Map the numerical elements in the vector "v" onto the variables "s" which can
% be of any type. The number of numerical elements must match; on exit "v"
% should be empty. Non-numerical entries are just copied. See also unwrap.m.
function [s v] = rewrap(s, v)
if isnumeric(s)
if numel(v) < numel(s)
error('The ... |
github | chaosuo/hctsa-master | solve_chol.m | .m | hctsa-master/Toolboxes/gpml/util/solve_chol.m | 993 | utf_8 | 50d81a361032ceb40d9102492db78fe9 | % solve_chol - solve linear equations from the Cholesky factorization.
% Solve A*X = B for X, where A is square, symmetric, positive definite. The
% input to the function is R the Cholesky decomposition of A and the matrix B.
% Example: X = solve_chol(chol(A),B);
%
% NOTE: The program code is written in the C language ... |
github | chaosuo/hctsa-master | minimize_lbfgsb_gradfun.m | .m | hctsa-master/Toolboxes/gpml/util/minimize_lbfgsb_gradfun.m | 2,390 | utf_8 | 0eca58fc12d068780d735fd5a83ebdfa | function G = minimize_lbfgsb_gradfun(X,varargin)
% extract input arguments
varargin = varargin{1}; strctX = varargin{2}; f = varargin{1};
% global variables serve as communication interface between calls
global minimize_lbfgsb_iteration_number
global minimize_lbfgsb_objective
global minimize_lbfgsb_gradie... |
github | chaosuo/hctsa-master | minimize_lbfgsb.m | .m | hctsa-master/Toolboxes/gpml/util/minimize_lbfgsb.m | 4,476 | utf_8 | 10c2d1fef0bdc071cd35d3904c88f0ed | function [X, fX, i] = minimize_lbfgsb(X, f, length, varargin)
% Minimize a differentiable multivariate function using quasi Newton.
%
% Usage: [X, fX, i] = minimize_lbfgsb(X, f, length, P1, P2, P3, ... )
%
% X initial guess; may be of any type, including struct and cell array
% f the name or pointer to th... |
github | chaosuo/hctsa-master | minimize_lbfgsb_objfun.m | .m | hctsa-master/Toolboxes/gpml/util/minimize_lbfgsb_objfun.m | 2,695 | utf_8 | d9bbd3614b193a06603c12f33f877104 | function y = minimize_lbfgsb_objfun(X,varargin)
% extract input arguments
varargin = varargin{1}; strctX = varargin{2}; f = varargin{1};
% global variables serve as communication interface between calls
global minimize_lbfgsb_iteration_number
global minimize_lbfgsb_objective
global minimize_lbfgsb_gradien... |
github | chaosuo/hctsa-master | covADD.m | .m | hctsa-master/Toolboxes/gpml/cov/covADD.m | 3,664 | utf_8 | 1b8b5711d67b327547d34b486852b399 | function K = covADD(cov, hyp, x, z, i)
% Additive covariance function using a 1d base covariance function
% cov(x^p,x^q;hyp) with individual hyperparameters hyp.
%
% k(x^p,x^q) = \sum_{r \in R} sf_r \sum_{|I|=r}
% \prod_{i \in I} cov(x^p_i,x^q_i;hyp_i)
%
% hyp = [ hyp_1
% hyp_2
% ...
... |
github | chaosuo/hctsa-master | infMCMC.m | .m | hctsa-master/Toolboxes/gpml/inf/infMCMC.m | 10,673 | utf_8 | 346201720f95a22a681c50bd2535b84c | function [post nlZ dnlZ] = infMCMC(hyp, mean, cov, lik, x, y, par)
% Markov Chain Monte Carlo (MCMC) sampling from posterior and
% Annealed Importance Sampling (AIS) for marginal likelihood estimation.
%
% The algorithms are not to be used as a black box, since the acceptance rate
% of the samplers need to be careful... |
github | chaosuo/hctsa-master | infFITC_EP.m | .m | hctsa-master/Toolboxes/gpml/inf/infFITC_EP.m | 10,312 | utf_8 | 1abd5f8600d7dd7db01a9ca3fde0e2d5 | function [post nlZ dnlZ] = infFITC_EP(hyp, mean, cov, lik, x, y)
% FITC-EP approximation to the posterior Gaussian process. The function is
% equivalent to infEP with the covariance function:
% Kt = Q + G; G = diag(g); g = diag(K-Q); Q = Ku'*inv(Kuu + snu2*eye(nu))*Ku;
% where Ku and Kuu are covariances w.r.t. to i... |
github | chaosuo/hctsa-master | infFITC_Laplace.m | .m | hctsa-master/Toolboxes/gpml/inf/infFITC_Laplace.m | 9,692 | utf_8 | d55d9988952c1a49cded6e5879266ffe | function [post nlZ dnlZ] = infFITC_Laplace(hyp, mean, cov, lik, x, y)
% FITC-Laplace approximation to the posterior Gaussian process. The function is
% equivalent to infLaplace with the covariance function:
% Kt = Q + G; G = diag(g); g = diag(K-Q); Q = Ku'*inv(Kuu + snu2*eye(nu))*Ku;
% where Ku and Kuu are covarian... |
github | chaosuo/hctsa-master | infEP.m | .m | hctsa-master/Toolboxes/gpml/inf/infEP.m | 5,964 | utf_8 | c0ad6163dfaa5740138133b31d0b2fa1 | function [post nlZ dnlZ] = infEP(hyp, mean, cov, lik, x, y)
% Expectation Propagation approximation to the posterior Gaussian Process.
% The function takes a specified covariance function (see covFunction.m) and
% likelihood function (see likFunction.m), and is designed to be used with
% gp.m. See also infFunctions.m.... |
github | chaosuo/hctsa-master | infVB.m | .m | hctsa-master/Toolboxes/gpml/inf/infVB.m | 6,054 | utf_8 | bd5ba48650a5268bf66dd60db54f4bc5 | function [post, nlZ, dnlZ] = infVB(hyp, mean, cov, lik, x, y)
% Variational approximation to the posterior Gaussian process with MKL
% covariance function hyperparameter optimisation.
% The function takes a likelihood function (see likFunction.m), and is designed
% to be used with gp.m. See also infFunctions.m.
%
% M... |
github | chaosuo/hctsa-master | infLaplace.m | .m | hctsa-master/Toolboxes/gpml/inf/infLaplace.m | 7,122 | utf_8 | 0afd81c506ee903bca07bb187d8dd1ae | function [post nlZ dnlZ] = infLaplace(hyp, mean, cov, lik, x, y)
% Laplace approximation to the posterior Gaussian process.
% The function takes a specified covariance function (see covFunction.m) and
% likelihood function (see likFunction.m), and is designed to be used with
% gp.m. See also infFunctions.m.
%
% Copyri... |
github | chaosuo/hctsa-master | MS_embed.m | .m | hctsa-master/Toolboxes/Michael_Small/MS_embed.m | 1,500 | utf_8 | 368575a3c663145eaa9cf32e6506d2eb | % [x,y] or x = MS_embed(z,lags) or MS_embed(z,dim,lag)
% embed z using given lags or dim and lag
% embed(z,dim,lag) == MS_embed(z,[0:lag:lag*(dim-1)])
% negative entries of lags are into future
%
% If return is [x,y], then x is the positive lags and y the negative lags
% Order of rows in x and y the same as sort(lags)
... |
github | chaosuo/hctsa-master | MS_nearneigh.m | .m | hctsa-master/Toolboxes/Michael_Small/MS_nearneigh.m | 2,312 | utf_8 | 2540fcab7a7b3cedf587127fe1a237f7 | % function [d,i] = MS_nearneigh(X,tau,blocksize)
%
% calculate the nearest (RMS) neighbour of each embedded point
% represented as columns of X.
% tau points either side of each point are excluded (default tau=0);
% i is the index of the nearest neighbours and d are the distances.
%
% nearest neighbours are calculat... |
github | chaosuo/hctsa-master | MS_firstzero.m | .m | hctsa-master/Toolboxes/Michael_Small/MS_firstzero.m | 1,065 | utf_8 | 27ea3a4955b3b8725386cbf98306c377 | % function tau = MS_firstzero(y);
%
% Find the first zero of the autocorrelation function of y.
%
% Michael Small
% michael.small@uwa.edu.au, http://school.maths.uwa.edu.au/~small/
% 3/3/2005
% For further details, please see M. Small. Applied Nonlinear Time Series
% Analysis: Applications in Physics, Physiology and Fi... |
github | chaosuo/hctsa-master | MS_fnn.m | .m | hctsa-master/Toolboxes/Michael_Small/MS_fnn.m | 2,010 | utf_8 | 1cd69c6e7a88f85b85b540f448e41246 | % function nfnn = MS_fnn(y,de,tau,th,kth)
%
% determine the number of false nearest neighbours for the time
% series y embedded in dimension de with lag tau.
%
% for each pair of values (de,tau) the data y is embeded and the
% nearest neighbour to each point (excluding the immediate
% neighbourhood of n points) is de... |
github | chaosuo/hctsa-master | MS_complexity.m | .m | hctsa-master/Toolboxes/Michael_Small/MS_complexity.m | 1,528 | utf_8 | 80985ea160b0be7d1b39a5f46d701e9e | % cmp = MS_complexity(x,n);
%
% calculate the Lempel-Ziv complexity of the n-bit encoding of x.
%
% cmp is the normalised complexity, that is the number of distinct
% symbol sequences in x, divided by the expected number of distinct
% symbols for a noise sequence.
%
% Algorithm is implemented in complexitybs.c
%
% Mi... |
github | chaosuo/hctsa-master | MS_nlpe.m | .m | hctsa-master/Toolboxes/Michael_Small/MS_nlpe.m | 1,456 | utf_8 | 9bbf4e92bb2d79e1758dc4df077b62cf | % function e = MS_nlpe(y,v);
%
% Compute the normalised "drop-one-out" constant interpolation nonlinear
% prediction error for embedding dimension de and lag tau or for embedding
% strategy v (v>0)
%
% Michael Small
% michael.small@uwa.edu.au, http://school.maths.uwa.edu.au/~small/
% 3/3/2005
% For further details, ple... |
github | chaosuo/hctsa-master | MS_unfolding.m | .m | hctsa-master/Toolboxes/Michael_Small/MS_unfolding.m | 2,818 | utf_8 | 3e951ae555c52a4c7e6eaf1767c2d1f5 | % function [de,nfnn] = MS_unfolding(y,th,de,tau)
%
% estimate the minimum unfolding dimension by calculating when the
% proportion of false nearest neighbours if first below th.
%
% The number of false nearest neighbours are calculated for the
% time series y embedded in dimension de with lag tau.
%
% for each pair o... |
github | chaosuo/hctsa-master | MS_rms.m | .m | hctsa-master/Toolboxes/Michael_Small/MS_rms.m | 753 | utf_8 | b8873cc9f3c7a713aad1d30c4be94cb1 | % function e = MS_rms(y);
%
% e is the l2-norm of row vector y, for a n-by-m matrix e is the n-by-1 column
% vector which is the l2-norm of the n rows of y.;
%
% Michael Small
% michael.small@uwa.edu.au, http://school.maths.uwa.edu.au/~small/
% 3/3/2005
% For further details, please see M. Small. Applied Nonlinear Tim... |
github | chaosuo/hctsa-master | PN_sampenc.m | .m | hctsa-master/Toolboxes/Physionet/PN_sampenc.m | 2,111 | utf_8 | a59dc8d5b9101daa52c1ec07a0ac69d8 | % PN_sampenc
%
% function [e,A,B]=sampenc(y,M,r);
%
% INPUTS:
%
% y input data
% M maximum template length
% r matching tolerance
%
% Output
%
% e sample entropy estimates for m=0,1,...,M-1
% A number of matches for m=1,...,M
% B number of matches for m=1,...,M excluding last point
%
% ----------------------------... |
github | chaosuo/hctsa-master | LA_permen.m | .m | hctsa-master/Toolboxes/Land_and_Elias/LA_permen.m | 980 | utf_8 | 42a048ad9704142d6f345fe4522596d3 | % ------------------------------------------------------------------------------
% LA_permen
% ------------------------------------------------------------------------------
% Originally logisticPE.m
% http://people.ece.cornell.edu/land/PROJECTS/Complexity/
% http://people.ece.cornell.edu/land/PROJECTS/Complexity/logis... |
github | chaosuo/hctsa-master | DK_lagembed.m | .m | hctsa-master/Toolboxes/Danny_Kaplan/DK_lagembed.m | 1,447 | utf_8 | 99dd3cfc35e268aaf092a57de63c5245 | % DK_lagembed(x,dim,lag) constructs an embedding of a time series on a vector
% DK_lagembed(x,dim) makes an m-dimensional embedding with lag 1
% DK_lagembed(x,dim,lag) uses the specified lag
%
% ------------------------------------------------------------------------------
% Copyright (C) 1996, D. Kaplan <kaplan@macal... |
github | chaosuo/hctsa-master | DK_theilerQ.m | .m | hctsa-master/Toolboxes/Danny_Kaplan/DK_theilerQ.m | 1,101 | utf_8 | fb5691eff7119f7a73995cacde412b97 | % DK_theilerQ
%
% theilerQ calculates Q=<(x_t + x_{t+1})^3> normalized by <x^2>^{3/2}
% on a vector x
%
% ------------------------------------------------------------------------------
% Copyright (C) 1996, D. Kaplan <kaplan@macalester.edu>
%
% This function is free software: you can redistribute it and/or modify it u... |
github | chaosuo/hctsa-master | DK_disttyp.m | .m | hctsa-master/Toolboxes/Danny_Kaplan/DK_disttyp.m | 1,762 | utf_8 | 6c4ab60376b0c767a79585ea2ed629ba | % d = DK_disttyp(z,percs)
%
% Calculates typical distances between pre-images
% z - embedded data
% percs -- percentiles to use
% returns distances at the given percentiles
% all of this is from a small sample of all pairs of distances
%
% ------------------------------------------------------------------------------... |
github | chaosuo/hctsa-master | DK_quickde.m | .m | hctsa-master/Toolboxes/Danny_Kaplan/DK_quickde.m | 1,635 | utf_8 | 83d705960a38a0434f42dc6c0ebbd801 | % DK_quickde
%
% Does a quick-and-dirty characterization of determinism using de
% ts -- the time series
% dim -- the embedding dimension
% lag -- the embedding lag
% nmin -- optional: number of points to use for delta-eps fitting
% default value: 500
%
%
% Tweaked ever so slightly by B. D. Fulcher
% -----... |
github | chaosuo/hctsa-master | DK_onedist.m | .m | hctsa-master/Toolboxes/Danny_Kaplan/DK_onedist.m | 1,172 | utf_8 | 1537fcaa0daea8e9dc0d723cc34aa4e2 | % DK_onedist(z,pt) calculates the distance between point pt and each
% row in matrix z
%
% ------------------------------------------------------------------------------
% Copyright (C) 1996, D. Kaplan <kaplan@macalester.edu>
%
% This function is free software: you can redistribute it and/or modify it under
% the terms... |
github | chaosuo/hctsa-master | DK_crinkle.m | .m | hctsa-master/Toolboxes/Danny_Kaplan/DK_crinkle.m | 1,205 | utf_8 | 2fdcefea52995a9e281e5cc479ad0728 | % DK_crinkle
%
% Calculates the "crinkle statistic" on a vector x
% <(x_{t-1}-2*x_t+x_{t+1})^4> / < ( x_t^2 ) >^2
% as proposed by James Theiler
%
% ------------------------------------------------------------------------------
% Copyright (C) 1996, D. Kaplan <kaplan@macalester.edu>
%
% This function is free softwar... |
github | chaosuo/hctsa-master | DK_findneib.m | .m | hctsa-master/Toolboxes/Danny_Kaplan/DK_findneib.m | 1,386 | utf_8 | 1d435ca2af141bf173cfd5c384efe2cc | % DK_findneib(z, pt, k, r ) finds the nearest neighbors to pt in z
% z -- matrix of points, 1 per row
% pt -- vector of a single point
% k -- number to find
% r (optional -- if specified, find all neighbors closer than this
%
% inds -- indices of the closest points to pt
% dist -- corresponding distances from pt
%
% -... |
github | chaosuo/hctsa-master | DK_getimage.m | .m | hctsa-master/Toolboxes/Danny_Kaplan/DK_getimage.m | 1,456 | utf_8 | 9b278f4da60e02ae48344c66394451e2 | % [data2, images] = DK_getimage(data,pred) finds the scalar images of
% the points in a time series <pred> time sets in the future
% data --- matrix of embedded data (from lagembed)
% pred --- look ahead time, default value 1
% Returns
% data2 --- a new embedded data matrix appropriately trimmed
% images --- the images... |
github | chaosuo/hctsa-master | DK_deltaeps.m | .m | hctsa-master/Toolboxes/Danny_Kaplan/DK_deltaeps.m | 2,058 | utf_8 | 73dd91ce5458bbec53ae08a83fdd2fc0 | % DK_deltaeps
%
% [delta,epsilon] = deltaeps(z, images)
% Delta-epsilon method
% z -- embedded data as from getimage()
% images -- as from getimage()
% lockout-- don't consider points closer in time than this
% --------------
% delta -- distances between pre-images
% eps -- distances between corresponding ima... |
github | chaosuo/hctsa-master | DK_defit.m | .m | hctsa-master/Toolboxes/Danny_Kaplan/DK_defit.m | 1,604 | utf_8 | d5fbdddf1e87b06cbbd4c649f772a6d9 | % DK_defit
%
% [a,b] = DK_defit(delta,epsilon,maxdelta)
%
% linear fitting routine for delta-epsilon
% delta -- distances between pre-images: output by delta-epsilon
% epsilon -- distances between images: output by delta-epsilon
% maxdelta-- optional - largest delta to consider.
%
% -------------------------------... |
github | chaosuo/hctsa-master | DK_timerev.m | .m | hctsa-master/Toolboxes/Danny_Kaplan/DK_timerev.m | 1,166 | utf_8 | 0af74a297dd5bcc8518a95c080f2894f | % DK_timerev
%
% Calculates a time reversal asymmetry statistic
% x --- the time series
% lag --- a time scale (in samples) default 1
%
% ------------------------------------------------------------------------------
% Copyright (C) 1996, D. Kaplan <kaplan@macalester.edu>
%
% This function is free software: you can r... |
github | chaosuo/hctsa-master | BD_hurst_exponent.m | .m | hctsa-master/Toolboxes/Bill_Davidson/BD_hurst_exponent.m | 2,011 | utf_8 | 2a959cb4c31149aac23898806cc06cfb | % BD_hurst_exponent
%
% The Hurst exponent
%--------------------------------------------------------------------------
% The first 20 lines of code are a small test driver.
% You can delete or comment out this part when you are done validating the
% function to your satisfaction.
%
% Bill Davidson, quellen@yahoo.com
... |
github | chaosuo/hctsa-master | ZG_hmm_cl.m | .m | hctsa-master/Toolboxes/ZG_hmm/ZG_hmm_cl.m | 2,918 | utf_8 | f7863d1e28a45ab314bace9d71b51d35 | % function [lik,likv] = hmm_cl(X,T,K,Mu,Cov,P,Pi);
%
% Calculate Likelihood for Hidden Markov Model
%
% X - N x p data matrix
% T - length of each sequence (N must evenly divide by T, default T=N)
% K - number of states
% Mu - mean vectors
% Cov - output covariance matrix (full, tied across states)
% P - state transi... |
github | chaosuo/hctsa-master | ZG_hmm.m | .m | hctsa-master/Toolboxes/ZG_hmm/ZG_hmm.m | 4,477 | utf_8 | 759fbe16465ef5e964624980bde37aef | % function [Mu,Cov,P,Pi,LL] = ZG_hmm(X,T,K,cyc,tol);
%
% Gaussian Observation Hidden Markov Model
%
% X - N x p data matrix
% T - length of each sequence (N must evenly divide by T, default T=N)
% K - number of states (default 2)
% cyc - maximum number of cycles of Baum-Welch (default 100)
% tol - termination toleranc... |
github | chaosuo/hctsa-master | ZG_rprod.m | .m | hctsa-master/Toolboxes/ZG_hmm/ZG_rprod.m | 1,615 | utf_8 | 14b0166c9cc9cade5ddd66d223841bd7 | % ZG_rprod
%
% row product
%
% Machine Learning Toolbox
% Version 1.0 01-Apr-96
% Copyright (c) by Zoubin Ghahramani
% http://mlg.eng.cam.ac.uk/zoubin/software.html
%
% ------------------------------------------------------------------------------
% The MIT License (MIT)
%
% Copyright (c) 1996, Zoubin Ghahramani
% ... |
github | chaosuo/hctsa-master | ZG_rdiv.m | .m | hctsa-master/Toolboxes/ZG_hmm/ZG_rdiv.m | 1,726 | utf_8 | 57343e64a66f56fee5ff32c773be0125 | % function Z = ZG_rdiv(X,Y)
%
% row division: Z = X / Y row-wise
% Y must have one column
%
% Machine Learning Toolbox
% Version 1.0 01-Apr-96
% Copyright (c) by Zoubin Ghahramani
% http://mlg.eng.cam.ac.uk/zoubin/software.html
%
% ------------------------------------------------------------------------------
% The M... |
github | chaosuo/hctsa-master | ZG_rsum.m | .m | hctsa-master/Toolboxes/ZG_hmm/ZG_rsum.m | 1,559 | utf_8 | 9134bc89fec347536ea1cff8b98506cd | % ZG_rsum(X)
% row sum
%
% Machine Learning Toolbox
% Version 1.0 01-Apr-96
% Copyright (c) by Zoubin Ghahramani
% http://mlg.eng.cam.ac.uk/zoubin/software.html
%
% ------------------------------------------------------------------------------
% The MIT License (MIT)
%
% Copyright (c) 1996, Zoubin Ghahramani
%
% Pe... |
github | chaosuo/hctsa-master | opentstool.m | .m | hctsa-master/Toolboxes/OpenTSTOOL/tstoolbox/gui/opentstool.m | 34,661 | UNKNOWN | 562f849f89e1cb9e2ba93e6ef39da6e6 | function tstool(varargin)
global TSTOOLdatapath TSTOOLpath TSTOOLfilter
% tstool is a matlab toolbox for nonlinear time series analysis
% which includes a graphical user interface (GUI)
%
% The command 'tstool' creates a GUI that allows
% the user to perform data manipulation and analysis
% with a wide range of cla... |
github | chaosuo/hctsa-master | tsplot.m | .m | hctsa-master/Toolboxes/OpenTSTOOL/tstoolbox/gui/private/tsplot.m | 3,555 | utf_8 | 3cc59c080ab7d923759639dcc211a901 | function tsplot(filename, varargin)
if nargin < 2
mode = 'large'; % im Modus 'large' wird eine eigene Figure gestartet
else
mode = varargin{1}; % im Modus 'small' wird in das Preview-Areal des tstool geplottet
end
if isunix % use greater fonts on Unix workstations
if strcmp(mode, 'small')
fontsize = 14;
else... |
github | chaosuo/hctsa-master | sortdatafiles.m | .m | hctsa-master/Toolboxes/OpenTSTOOL/tstoolbox/gui/private/sortdatafiles.m | 2,484 | utf_8 | d6d685053bc67bc69234cd4a986ea350 | function datafiles=sortdatafiles(datafiles)
% das ist ein Test
%
newdatafiles={};
new_n=0;
n=length(datafiles(:,1));
m=length(datafiles(1,:));
for i=1:n
equal_line=0;
for i1=1:i-1
equal=1;
for i2=1:m
if ~strcmp(char(datafiles(i,i2)),char(datafiles(i1,i2)))
equal=0;
end
end
% if equal... |
github | chaosuo/hctsa-master | filterbank.m | .m | hctsa-master/Toolboxes/OpenTSTOOL/tstoolbox/@core/filterbank.m | 11,025 | utf_8 | 623ac34c8f407ad76dc62ff43b93210c | function cout=filterbank(cin,h,g,order,basis)
%tstoolbox/@core/filterbank
% Syntax:
% * filterbank(cin,H,G,ORDER,BASIS)
%
% Input Arguments:
% * H - lowpass filter
% * G - highpass filter
% * ORDER - indicates the type of tree:
% + 0 - band sorting according to the filter bank
% +... |
github | chaosuo/hctsa-master | scalogram.m | .m | hctsa-master/Toolboxes/OpenTSTOOL/tstoolbox/@core/scalogram.m | 2,465 | utf_8 | 88f20c353031e76599846ca8edd42ee3 | function cout = scalogram(cin, smin, smax, sstep, tim)
%tstoolbox/@core/scalogram
% Syntax:
% * cout = scalogram(cin, smin, smax, sstep, tim)
%
% Copyright 1997-2001 DPI Goettingen, License http://www.physik3.gwdg.de/tstool/gpl.txt
x = data(cin);
lx = dlens(cin,1);
s = smin:sstep:smax;
sc = zeros(lx, length(s)... |
github | chaosuo/hctsa-master | help_mex.m | .m | hctsa-master/Toolboxes/OpenTSTOOL/tstoolbox/utils/help_mex.m | 219 | utf_8 | afa3a795ca56aa3160afd131bf079046 | function help_mex
d = dir('*.mexsg64'); % ".dll'
for i = 1:length(d)
n = d(i).name;
[path,name,ext] = fileparts(n);
myeval(name, 'disp(lasterr)');
end
function myeval(s1, s2)
disp(s1)
eval(s1, s2)
disp('')
|
github | chaosuo/hctsa-master | TSTOOLpca.m | .m | hctsa-master/Toolboxes/OpenTSTOOL/tstoolbox/utils/TSTOOLpca.m | 3,637 | utf_8 | 5eb90ef700324568d5a82672979b211f | function [rlvm, frvals, frvecs, trnsfrmd, mn, dv] = TSTL_pca(data, mode, maxpercent, sil)
% [rlvm, frvals, frvecs, trnsfrmd, mn, dv] = pca(data, mode, maxpercent, silent)
%
% principal component analysis of column orientated data set <data>
%
% input arguments :
%
% - each row of data is one 'observation', ... |
github | chaosuo/hctsa-master | pauswahl.m | .m | hctsa-master/Toolboxes/OpenTSTOOL/tstoolbox/utils/pauswahl.m | 7,308 | utf_8 | c3c8bd4889a85306b1904dfbf438dbbd | function [pol, train_fehler, test_fehler] = pauswahl(x, y, fracref, maxgrad)
% Polynomauswahlverfahren
% Monome werden nach einer Greedy-Heuristik aus einer vorgebenen Menge ausgewaehlt. Es
% wird dasjenige Monom gewaehlt, was den Fehler im aktullen Schritt am staeksten vermindert.
% Als Grad eines Monoms wird die Sum... |
github | chaosuo/hctsa-master | infodim2.m | .m | hctsa-master/Toolboxes/OpenTSTOOL/tstoolbox/@signal/infodim2.m | 1,441 | utf_8 | 3ea1dd8b9b9bb431cfdab1fe4ab767c4 | function [rs, s] = infodim2(s, n, kmax, past)
%tstoolbox/@signal/infodim2
% Syntax:
% * rs = infodim2(s, n, kmax, past)
%
% Input arguments:
% * n - number of randomly chosen reference points (n == -1 means :
% use all points)
% * kmax - maximal number of neighbors for each reference point
% ... |
github | chaosuo/hctsa-master | filterbank.m | .m | hctsa-master/Toolboxes/OpenTSTOOL/tstoolbox/@signal/filterbank.m | 3,799 | utf_8 | ff14b54270f1d4c981d3fb697209da05 | function rs = filterbank(s, depth, filterlen)
%tstoolbox/@signal/filterbank
% Syntax:
% * filterbank(s, depth, filterlen)
%
% Filter scalar signal s into 2^textdepth bands of equal bandwith, using
% maximally flat filters.
%
% Copyright 1997-2001 DPI Goettingen, License http://www.physik3.gwdg.de/tstool/gpl.... |
github | chaosuo/hctsa-master | signal.m | .m | hctsa-master/Toolboxes/OpenTSTOOL/tstoolbox/@signal/signal.m | 8,300 | utf_8 | ec2e1c14f23ebb4e243efe7f3eb44ef5 | function s = signal(argument, varargin)
%tstoolbox/@signal/signal
% Syntax:
% * s = signal(array)
% creates a new signal object from a data array array the data
% inside the object can be retrieved with x = data(s);
% * s = signal(array, achse1, achse2, ...)
% creates a new signal object fr... |
github | chaosuo/hctsa-master | view.m | .m | hctsa-master/Toolboxes/OpenTSTOOL/tstoolbox/@signal/view.m | 6,508 | utf_8 | c0172e02fb20774df5c9d0d156c46c90 | function view(s, fontsize, fhandle)
%tstoolbox/@signal/view
% Syntax:
% * view(signal) (fontsize=12)
% * view(signal, fontsize)
% * view(signal, fontsize, figurehandle)
%
% Signal viewer that decides from the signal's attributes which kind of
% plot to produce, using the signal's plothint entry to ge... |
github | chaosuo/hctsa-master | addcommandlines.m | .m | hctsa-master/Toolboxes/OpenTSTOOL/tstoolbox/@description/addcommandlines.m | 1,013 | utf_8 | bfd4fee3343b0b5120135767dc80c5a2 | function d = commandlines(d, commandname, varargin)
%tstoolbox/@description/addcommandlines
% adds new commandline to list of commands that have been applied to
% that signal
% example 1
% addcommandlines(s, 's = spec2(s', 512, 'Hanning' )) will add 's
% = spec2(s, 512, 'Hanning');' to the list... |
github | chaosuo/hctsa-master | findlabel.m | .m | hctsa-master/Toolboxes/OpenTSTOOL/tstoolbox/@unit/private/findlabel.m | 1,565 | utf_8 | 31c0ddaf800254da0b93f0b06880e536 | function [label, name, qeng, qger, dBScale, dBRef] = findlabel(factor, exponents)
% finds label and name for a given set of factors and exponents
%RESOURCES = get(0, 'UserData');
%TSTOOLunittab = RESOURCES{2};
load 'tstoolbox/units.mat';
if (exponents == [0 0 0 0 0 0 0 0]) | (factor == 0)
label = '';
name = '';
q... |
github | chaosuo/hctsa-master | makemex.m | .m | hctsa-master/Toolboxes/OpenTSTOOL/mex-dev/makemex.m | 4,543 | utf_8 | a5c5d656e3ecaf2639c8ba5655d69ed0 | function makemex(TSTOOLpath)
% compile and copy mex-files to destination directories
% Invoked by : makemex(TSTOOLpath)
% or: makemex
if nargin == 0
if which('units.mat')
TSTOOLpath = fileparts(which('units.mat'));
elseif exist(fullfile(pwd,'../tstoolbox','units.mat'))==2
TSTOOLpath=f... |
github | chaosuo/hctsa-master | brute.m | .m | hctsa-master/Toolboxes/OpenTSTOOL/mex-dev/NN/TestSuite/brute.m | 1,123 | utf_8 | d22cdf5bea5cc3dcbd66e4199d90d4d9 | function [indices, distances] = brute(points, refind, nnr, past)
% [indices, distances] = brute(points, refind, nnr, past)
%
% Brute force implementation of nearest neighbor search
%
% Input arguments :
%
% points - N by D matrix of N points of dimension D
% refind - integer reference indices
% nnr - number of neighb... |
github | chaosuo/hctsa-master | test.m | .m | hctsa-master/Toolboxes/OpenTSTOOL/mex-dev/NN/TestSuite/test.m | 6,469 | utf_8 | aeba8aef7698bae6251c4870d8a52f18 | function test(mode)
% test nearest neighbor search based mex files
% recompile
error_flag = 0;
if nargin < 1
mode = 'all';
end
disp('Fast nearest neighbor search routines test')
load points.dat
dat = points;
%dat = generate_chaotic_data(40000, 20);
%size(dat)
if strcmp(mode, 'delaunay2D') | strcmp(mode, 'all')
... |
github | chaosuo/hctsa-master | pauswahl.m | .m | hctsa-master/Toolboxes/OpenTSTOOL/mex-dev/Polynomauswahl/pauswahl.m | 7,307 | utf_8 | f03f7cce7ca1ea159b5f43d27e308de3 | function [pol, train_fehler, test_fehler] = pauswahl(x, y, fracref, maxgrad)
% Polynomauswahlverfahren
% Monome werden nach einer Greedy-Heuristik aus einer vorgebenen Menge ausgewaehlt. Es
% wird dasjenige Monom gewaehlt, was den Fehler im aktullen Schritt am staeksten vermindert.
% Als Grad eines Monoms wird die Sum... |
github | chaosuo/hctsa-master | RM_information.m | .m | hctsa-master/Toolboxes/Rudy_Moddemeijer/RM_information.m | 3,957 | utf_8 | 9203df2c57d7df96aec27c98a44e3fc8 | % RM_information Estimates the mutual information of two stationary signals with
% independent pairs of samples using various approaches.
% [ESTIMATE,NBIAS,SIGMA,DESCRIPTOR] = INFORMATION(X,Y) or
% [ESTIMATE,NBIAS,SIGMA,DESCRIPTOR] = INFORMATION(X,Y,DESCRIPTOR) or
% [ESTIMATE,NBIAS,SIGMA,DESCRIPTOR]... |
github | chaosuo/hctsa-master | RM_entropy.m | .m | hctsa-master/Toolboxes/Rudy_Moddemeijer/RM_entropy.m | 3,130 | utf_8 | 6daa29bb6da5a10361b1632d7ce683b8 | % RM_entropy Estimates the entropy of stationary signals with
% independent samples using various approaches.
% [ESTIMATE,NBIAS,SIGMA,DESCRIPTOR] = ENTROPY(X) or
% [ESTIMATE,NBIAS,SIGMA,DESCRIPTOR] = ENTROPY(X,DESCRIPTOR) or
% [ESTIMATE,NBIAS,SIGMA,DESCRIPTOR] = ENTROPY(X,DESCRIPTOR,APPROACH) or
%... |
github | chaosuo/hctsa-master | RM_histogram2.m | .m | hctsa-master/Toolboxes/Rudy_Moddemeijer/RM_histogram2.m | 2,490 | utf_8 | 827c5ebe29d9f1568a1cb0a5325dae0a | % RM_histogram2 Computes the two dimensional frequency histogram of two
% row vectors x and y.
% [RESULT,DESCRIPTOR] = HISTOGRAM2(X,Y) or
% [RESULT,DESCRIPTOR] = HISTOGRAM2(X,Y,DESCRIPTOR) or
%where
% DESCRIPTOR = [LOWERX,UPPERX,NCELLX;
% LOWERY,UPPERY,NCELLY]
%
% RESULT : A matr... |
github | chaosuo/hctsa-master | RM_histogram.m | .m | hctsa-master/Toolboxes/Rudy_Moddemeijer/RM_histogram.m | 1,615 | utf_8 | 4ae2da9d29e8e01d9bfc62a918eeb0d0 | % RM_histogram Computes the frequency histogram of the row vector x.
% [RESULT,DESCRIPTOR] = HISTOGRAM(X) or
% [RESULT,DESCRIPTOR] = HISTOGRAM(X,DESCRIPTOR) or
% where
% DESCRIPTOR = [LOWER,UPPER,NCELL]
%
% RESULT : A row vector containing the histogram
% DESCRIPTOR: The used descriptor
%
% X : T... |
github | chaosuo/hctsa-master | SQL_add_chunked.m | .m | hctsa-master/Database/SQL_add_chunked.m | 2,877 | utf_8 | 47d7748c9783aff56b5ce76916f92603 | % ------------------------------------------------------------------------------
% SQL_add_chunked
% ------------------------------------------------------------------------------
%
% Insert a large set of time series or operations into the database using
% repeated queries, adding smaller subsets over multiple iterat... |
github | chaosuo/hctsa-master | SQL_create_all_tables.m | .m | hctsa-master/Database/SQL_create_all_tables.m | 3,703 | utf_8 | 1b922594de07f213db5fd3e1a195158e | % ------------------------------------------------------------------------------
% SQL_create_all_tables
% ------------------------------------------------------------------------------
%
% Create all the tables in the database
%
% Uses SQL_tablecreatestring to retrieve the appropriate mySQL CREATE TABLE
% statements... |
github | chaosuo/hctsa-master | mysql_dbquery.m | .m | hctsa-master/Database/mysql_dbquery.m | 2,490 | utf_8 | 1e034ccac628ddd47dc12299bf123bf5 | % ------------------------------------------------------------------------------
% mysql_dbquery
% ------------------------------------------------------------------------------
% Used to retrieve data from a database connection
% ------------------------------------------------------------------------------
%---HISTOR... |
github | chaosuo/hctsa-master | mysql_dbexecute.m | .m | hctsa-master/Database/mysql_dbexecute.m | 1,350 | utf_8 | a55b8622b8ee14a0952021e7d2264ca0 | % ------------------------------------------------------------------------------
% mysql_dbquery
% ------------------------------------------------------------------------------
% Used to retrieve data from a database connection
% ------------------------------------------------------------------------------
%---HISTOR... |
github | chaosuo/hctsa-master | SQL_add.m | .m | hctsa-master/Database/SQL_add.m | 28,577 | utf_8 | 97bf39d99e077524de8b3c09a334a9b8 | %% ------------------------------------------------------------------------------
% SQL_add
% ------------------------------------------------------------------------------
%
% Adds a set of time series, operations, or master operations to the mySQL
% database.
%
%---INPUTS:
% importWhat: 'mops' (for master operation... |
github | chaosuo/hctsa-master | SQL_create_db.m | .m | hctsa-master/Database/SQL_create_db.m | 5,611 | utf_8 | abe6665d66bb3988c64e71cc749d1990 | % ------------------------------------------------------------------------------
% SQL_create_db
% ------------------------------------------------------------------------------
%
% Set up the mySQL database for use with highly comparative time-series analysis
%
% -----------------------------------------------------... |
github | chaosuo/hctsa-master | SQL_ChangeDatabase.m | .m | hctsa-master/Database/SQL_ChangeDatabase.m | 3,226 | utf_8 | 9add8407497989699508bdfed314c661 | % ------------------------------------------------------------------------------
% SQL_ChangeDatabase
%
% Write a new .conf file with the connection details for next time
%
%---HISTORY:
% Ben Fulcher, 2015-03-31
%
% ------------------------------------------------------------------------------
% Copyright (C) 2013, ... |
github | chaosuo/hctsa-master | SQL_opendatabase.m | .m | hctsa-master/Database/SQL_opendatabase.m | 3,203 | utf_8 | 5ac06408730a7c3453206733e35d0713 | % ------------------------------------------------------------------------------
% SQL_opendatabase
% ------------------------------------------------------------------------------
%
% Opens the database as dbc for use in retrieving and storing in the mySQL
% database
%
%---HISTORY
% (c) 2013
% Ben D. Fulcher <ben.d.... |
github | chaosuo/hctsa-master | SQL_GiveMeCode.m | .m | hctsa-master/Database/SQL_GiveMeCode.m | 2,709 | utf_8 | e544aa181e421926e1be319da6e23802 | % ------------------------------------------------------------------------------
% SQL_GiveMeCode
% ------------------------------------------------------------------------------
% Returns a string containing code for evaluating an operation with a given op_id.
% Can be difficult to do this manually, especially when de... |
github | chaosuo/hctsa-master | mysql_dbopen.m | .m | hctsa-master/Database/mysql_dbopen.m | 4,121 | utf_8 | 06794489089642bb363a127f757735fa | % ------------------------------------------------------------------------------
% mysql_dbopen
% ------------------------------------------------------------------------------
% Opens a connection to the database using the mySQL j-connector.
% Checks for an available database toolbox and uses that, but otherwise uses
... |
github | chaosuo/hctsa-master | SQL_closedatabase.m | .m | hctsa-master/Database/SQL_closedatabase.m | 1,413 | utf_8 | 203432cad9aaece500c24b5e3d7a522a | % ------------------------------------------------------------------------------
% SQL_closedatabase
% ------------------------------------------------------------------------------
%
% Closes the connection to database, dbc
%
% ------------------------------------------------------------------------------
% Copyrigh... |
github | chaosuo/hctsa-master | SQL_getids.m | .m | hctsa-master/Database/SQL_getids.m | 12,781 | utf_8 | 9c0c83449591617da1f8fbdb166bb859 | % ------------------------------------------------------------------------------
% SQL_getids
% ------------------------------------------------------------------------------
%
% Takes as input a set of constraints on the time series and operations to
% include then runs the appropriate mySQL commands and outputs the ... |
github | chaosuo/hctsa-master | SQL_TableCreateString.m | .m | hctsa-master/Database/SQL_TableCreateString.m | 5,919 | utf_8 | 3c0a4d3f69294bc31188f16c485b0be5 | % ------------------------------------------------------------------------------
% SQL_TableCreateString
% ------------------------------------------------------------------------------
%
% Determines the appropriate mySQL CREATE TABLE statement to use to create a given
% table, identified by the input string, WhatTab... |
github | chaosuo/hctsa-master | SQL_clear_remove.m | .m | hctsa-master/Database/SQL_clear_remove.m | 10,060 | utf_8 | bbe0374f475c0a1cc0105ea7bd251e5c | % ------------------------------------------------------------------------------
% SQL_clear_remove
% ------------------------------------------------------------------------------
%
% Either clears results or removes entirely a given set of ts_ids
% or op_ids from the database.
%
% *** Clear ***:
% The results of a p... |
github | chaosuo/hctsa-master | TSQ_plot_pca.m | .m | hctsa-master/PlottingAnalysis/TSQ_plot_pca.m | 8,056 | utf_8 | b6e4b0d553b5f98c79219088fc271bd4 | % ------------------------------------------------------------------------------
% TSQ_plot_pca
% ------------------------------------------------------------------------------
%
% Calculates then plots a lower-dimensional feature-based representation of the
% data (e.g., using PCA).
%
%---HISTORY:
% [Previously calle... |
github | chaosuo/hctsa-master | TSQ_us_cluster.m | .m | hctsa-master/PlottingAnalysis/TSQ_us_cluster.m | 20,603 | utf_8 | 38c8c928e3d0c5047d83c77aa13317fd | % ------------------------------------------------------------------------------
% TSQ_us_cluster
% ------------------------------------------------------------------------------
%
% Perform unsupervised clustering on a matrix using a given method.
%
% Loads a data matrix and clustering options and outputs a clusteri... |
github | chaosuo/hctsa-master | TSQ_plot_2d.m | .m | hctsa-master/PlottingAnalysis/TSQ_plot_2d.m | 19,615 | utf_8 | 7a2b5445a28ffa1e03822783a89e4b0e | % ------------------------------------------------------------------------------
% TSQ_plot_2d
% ------------------------------------------------------------------------------
%
% Plots the dataset in a two-dimensional space
% e.g., that of two chosen operations, or two principal components.
%
%---INPUTS:
% Features,... |
github | chaosuo/hctsa-master | TSQ_normalize.m | .m | hctsa-master/PlottingAnalysis/TSQ_normalize.m | 15,604 | utf_8 | eb53bf8872c7420d027fef73f07a9546 | % --------------------------------------------------------------------------
% TSQ_normalize
% --------------------------------------------------------------------------
%
% Reads in data from HCTSA_loc.mat, writes a trimmed, normalized version to
% HCTSA_loc_N.mat
% The normalization is all about a rescaling to the [... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.