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github | lihp11/predictability_transport-master | NW_VisibilityGraph.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Operations/NW_VisibilityGraph.m | 9,952 | utf_8 | 56626516e6cd6807ec6a6f39fdb08546 | function out = NW_VisibilityGraph(y,meth,maxL)
% NW_VisibilityGraph Visibility graph analysis of a time series.
%
% Constructs a visibility graph of the time series and returns various
% statistics on the properties of the resulting network.
%
% cf.: "From time series to complex networks: The visibility graph"
% Lac... |
github | lihp11/predictability_transport-master | MF_ExpSmoothing.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Operations/MF_ExpSmoothing.m | 9,555 | utf_8 | e29ac62c43617b9f1a035c111535fc07 | function out = MF_ExpSmoothing(x,ntrain,alpha)
% MF_ExpSmoothing Exponential smoothing time-series prediction model.
%
% Fits an exponential smoothing model to the time series using a training set to
% fit the optimal smoothing parameter, alpha, and then applies the result to the
% try to predict the rest of the time... |
github | lihp11/predictability_transport-master | SC_FluctAnal.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Operations/SC_FluctAnal.m | 13,692 | utf_8 | 541b573fa14b143ab27831ea5154f962 | function out = SC_FluctAnal(x,q,wtf,tauStep,k,lag,logInc)
% SC_FluctAnal Implements fluctuation analysis by a variety of methods.
%
% Much of our implementation is based on the well-explained discussion of
% scaling methods in:
% "Power spectrum and detrended fluctuation analysis: Application to daily
% temperatures"... |
github | lihp11/predictability_transport-master | CO_AddNoise.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Operations/CO_AddNoise.m | 7,966 | utf_8 | 271e0441e4e2ce65c6792c557408f19c | function out = CO_AddNoise(y,tau,amiMethod,extraParam,randomSeed)
% CO_AddNoise Changes in the automutual information with the addition of noise
%
% Adds Gaussian-distributed noise to the time series with increasing standard
% deviation, eta, across the range eta = 0, 0.1, ..., 2, and measures the
% mutual information... |
github | lihp11/predictability_transport-master | NL_TSTL_PoincareSection.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Operations/NL_TSTL_PoincareSection.m | 8,296 | utf_8 | 402a9b29e3d46a6d807525f48425e3bf | function out = NL_TSTL_PoincareSection(y,ref,embedParams)
% NL_TSTL_PoincareSection Poincare sectino analysis of a time series.
%
% Obtains a Poincare section of the time-delay embedded time series, producing a
% set of vector points projected orthogonal to the tangential vector at the
% specified index using TSTOOL ... |
github | lihp11/predictability_transport-master | SD_SurrogateTest.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Operations/SD_SurrogateTest.m | 12,083 | utf_8 | 8109ed148adc20f5f7355443e1e0b18e | function out = SD_SurrogateTest(x,surrMeth,numSurrs,extrap,theTestStat,randomSeed)
% SD_SurrogateTest Analyzes test statistics obtained from surrogate time series
%
% This function is based on information found in:
% "Surrogate data test for nonlinearity including nonmonotonic transforms"
% D. Kugiumtzis Phys. Rev. E... |
github | lihp11/predictability_transport-master | SY_Trend.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Operations/SY_Trend.m | 2,575 | utf_8 | d46ef4c75208bcc28e29cb9182952a34 | function out = SY_Trend(y)
% SY_Trend Quantifies various measures of trend in a time series.
%
%---INPUT:
% y, the input time series.
%
%---OUTPUTS:
% Linearly detrends the time series using detrend, and returns the ratio of
% standard deviations before and after the linear detrending. If a strong linear
% trend is pr... |
github | lihp11/predictability_transport-master | WL_DetailCoeffs.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Operations/WL_DetailCoeffs.m | 5,698 | utf_8 | 38804222795f61e518cc4f98491cf0d4 | function out = WL_DetailCoeffs(y, wname, maxlevel)
% WL_DetailCoeffs Detail coefficients of a wavelet decomposition.
%
% Compares the detail coefficients obtained at each level of the wavelet
% decomposition from 1 to the maximum possible level for the wavelet given the
% length of the input time series (computed usi... |
github | lihp11/predictability_transport-master | NL_embed_PCA.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Operations/NL_embed_PCA.m | 4,933 | utf_8 | 5b412cf7cee832e86e1315879c63d1f0 | function out = NL_embed_PCA(y,tau,m)
% NL_embed_PCA Principal Components analysis of a time series in an embedding space.
%
% Reconstructs the time series as a time-delay embedding, and performs Principal
% Components Analysis on the result using princomp code from
% Matlab's Bioinformatics Toolbox.
%
% This technique... |
github | lihp11/predictability_transport-master | SP_Summaries.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Operations/SP_Summaries.m | 16,882 | utf_8 | 9617af80206ba2404c6ef9d84f0f0c20 | function out = SP_Summaries(y,psdmeth,wmeth,nf,dologabs)
% SP_Summaries Statistics of the power spectrum of a time series
%
% The estimation can be done using a periodogram, using the periodogram code in
% Matlab's Signal Processing Toolbox, or a fast fourier transform, implemented
% using Matlab's fft code.
%
%---INP... |
github | lihp11/predictability_transport-master | SC_MMA.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Operations/SC_MMA.m | 8,770 | utf_8 | 237b3c97b17bf16e6d41c12b2cadc07c | function out = SC_MMA(y,doOverlap,scaleRange,qRange)
% SC_MMA Physionet implementation of multiscale multifractal analysis
%
% Scale-dependent estimates of multifractal scaling in a time series.
% ------------------------------------------------------------------------------
% Modified by Ben Fulcher for use in hcts... |
github | lihp11/predictability_transport-master | DN_RemovePoints.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Operations/DN_RemovePoints.m | 4,669 | utf_8 | 192ac49fd65e8e245d20363947f8b646 | function out = DN_RemovePoints(y,removeHow,p)
% DN_RemovePoints How time-series properties change as points are removed.
%
% A proportion, p, of points are removed from the time series according to some
% rule, and a set of statistics are computed before and after the change.
%
%---INPUTS:
% y, the input time series
... |
github | lihp11/predictability_transport-master | NL_TSTL_dimensions.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Operations/NL_TSTL_dimensions.m | 14,999 | utf_8 | 2d42906471557cfe45d2b0cc23b43030 | function out = NL_TSTL_dimensions(y,nbins,embedParams)
% NL_TSTL_dimensions box counting, information, and correlation dimension of a time series.
%
% Computes the box counting, information, and correlation dimension of a
% time-delay embedded time series using the TSTOOL code 'dimensions'.
% This function contains ext... |
github | lihp11/predictability_transport-master | SB_MotifTwo.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Operations/SB_MotifTwo.m | 6,309 | utf_8 | fe33ad4079fc7d7f67c9f4e54358d043 | function out = SB_MotifTwo(y,binarizeHow)
% SB_MotifTwo Local motifs in a binary symbolization of the time series
%
% Coarse-graining is performed by a given binarization method.
%
%---INPUTS:
% y, the input time series
% binarizeHow, the binary transformation method:
% (i) 'diff': incremental time-series incre... |
github | lihp11/predictability_transport-master | ST_MomentCorr.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Operations/ST_MomentCorr.m | 5,415 | utf_8 | 1756564c1c7a2e9df7eacc247c6c404d | function out = ST_MomentCorr(x,windowLength,wOverlap,mom1,mom2,whatTransform)
% ST_MomentCorr Correlations between simple statistics in local windows of a time series.
%
% Idea to implement by Nick S. Jones.
%
%---INPUTS:
% x, the input time series
%
% windowLength, the sliding window length (can be a fraction to spe... |
github | lihp11/predictability_transport-master | NL_MS_fnn.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Operations/NL_MS_fnn.m | 5,370 | utf_8 | c53c4e877995775ddeefa83943855703 | function out = NL_MS_fnn(y,de,tau,th,kth,justBest,bestp)
% NL_MS_fnn False nearest neighbors of a time series.
%
% Determines the number of false nearest neighbors for the embedded time series
% using Michael Small's false nearest neighbor code, fnn (renamed MS_fnn here).
%
% False nearest neighbors are judged usin... |
github | lihp11/predictability_transport-master | NL_crptool_fnn.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Operations/NL_crptool_fnn.m | 4,953 | utf_8 | e1a79d9e00ccf8d7487ca4f035efbf15 | function out = NL_crptool_fnn(y,maxm,r,taum,th,randomSeed)
% NL_crptool_fnn Analyzes the false-nearest neighbours statistic.
%
%---INPUTS:
% y, the input time series
% maxm, the maximum embedding dimension to consider
% r, the threshold; neighbourhood criterion
% taum, the method of determining the time delay, 'corr... |
github | lihp11/predictability_transport-master | CO_StickAngles.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Operations/CO_StickAngles.m | 10,799 | utf_8 | 0fd74672caa734070ebea06771a37e8b | function out = CO_StickAngles(y)
% CO_StickAngles Analysis of line-of-sight angles between time-series data points.
%
% Line-of-sight angles between time-series points treat each time-series value
% as a stick protruding from an opaque baseline level.
% Statistics are returned on the raw time series, where sticks pr... |
github | lihp11/predictability_transport-master | TS_subset.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/PeripheryFunctions/TS_subset.m | 6,074 | utf_8 | 9133009ebbef7e83e85f9a07bdd2e45f | function [TS_DataMat,TimeSeries,Operations] = TS_subset(whatData,ts_ids_keep,op_ids_keep,doSave,outputFileName)
% TS_subset Save a given subset of an hctsa dataset, based on time series and operation IDs
%
%---INPUTS:
% whatData, the source of the hctsa dataset (default, 'HCTSA_N.mat', cf. TS_LoadData)
% ts_ids_keep, ... |
github | lihp11/predictability_transport-master | BF_NormalizeMatrix.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/PeripheryFunctions/BF_NormalizeMatrix.m | 7,750 | utf_8 | a765f70a44bc426ea584d85aa51c851d | function dataMatrixNorm = BF_NormalizeMatrix(dataMatrix,normMethod,itrain)
% BF_NormalizeMatrix Normalizes all columns of an input matrix.
%
%---INPUTS:
% dataMatrix, the input data matrix
% normMethod, the normalization method to use (see body of the code for options)
% itrain, learn the normalization parameters ju... |
github | lihp11/predictability_transport-master | BF_pdist.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/PeripheryFunctions/BF_pdist.m | 7,653 | utf_8 | 22939b37587fd5068d115809f9a3eacd | function R = BF_pdist(dataMatrix,distMetric,toVector,opts,beSilent,minPropGood)
% BF_pdist Pairwise distances between rows of a data matrix.
%
% Same as pdist but then goes through and fills in NaNs with indiviually
% calculated values using an overlapping range of good values.
% -------------------------------------... |
github | lihp11/predictability_transport-master | TS_local_clear_remove.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/PeripheryFunctions/TS_local_clear_remove.m | 6,542 | utf_8 | 8218628ff36c294ef22f941fbd08068f | function TS_local_clear_remove(tsOrOps,idRange,doRemove,whatData)
% TS_local_clear_remove Clear or remove data from an hctsa dataset
%
% 'Clear' means clearing any calculations performed about a given time series
% or operation, but keeping it in the dataset.
% 'Remove' means removing the time series or operation f... |
github | lihp11/predictability_transport-master | BF_AnnotatePoints.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/PeripheryFunctions/BF_AnnotatePoints.m | 8,752 | utf_8 | ccc11882f29d24d44de64de8ee1a7472 | function BF_AnnotatePoints(xy,TimeSeries,annotateParams)
% BF_AnnotatePoints Annotates time series/metadata to a plot
%
%---INPUTS:
% xy, a vector (or cell) of x-y co-ordinates of points on the plot
% TimeSeries, a structure array of time series making up the plot
% annotateParams, structure of custom plotting para... |
github | lihp11/predictability_transport-master | BF_MutualInformation.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/PeripheryFunctions/BF_MutualInformation.m | 4,573 | utf_8 | 57928a4e3581df155882e6638ed89482 | function mi = BF_MutualInformation(v1,v2,r1,r2,numBins)
% BF_MutualInformation Mutual information between two data vectors using bin counting.
%
% Mutual information computed using a histogram-based, bin-counting method.
%
%---INPUTS:
% v1, the first input vector
% v2, the second input vector
% r1, the bin-partitionin... |
github | lihp11/predictability_transport-master | ML_l1pwcar1.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/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 | lihp11/predictability_transport-master | ML_ckfilter.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/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 | lihp11/predictability_transport-master | ML_l1pwclmax.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/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 | lihp11/predictability_transport-master | ML_l1pwc.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/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 | lihp11/predictability_transport-master | ML_kvsteps.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/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 | lihp11/predictability_transport-master | ML_fastdfa.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/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 | lihp11/predictability_transport-master | likBeta.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Toolboxes/gpml/lik/likBeta.m | 4,830 | utf_8 | 8e503690924874d07a77dc48bc238db1 | function [varargout] = likBeta(link, hyp, y, mu, s2, inf, i)
% likBeta - Beta likelihood function for interval data y from [0,1].
% The expression for the likelihood is
% likBeta(f) = 1/Z * y^(mu*phi-1) * (1-y)^((1-mu)*phi-1) with
% mean=mu and variance=mu*(1-mu)/(1+phi) where mu = g(f) is the Beta intensity,
% f ... |
github | lihp11/predictability_transport-master | likT.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Toolboxes/gpml/lik/likT.m | 4,776 | utf_8 | 6463e0fed8f6484854dd3dd212db5202 | 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 | lihp11/predictability_transport-master | likLaplace.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Toolboxes/gpml/lik/likLaplace.m | 6,922 | iso_8859_13 | 9673b9c57508bdbfd0dc917f10944f80 | 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 | lihp11/predictability_transport-master | likGaussWarp.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Toolboxes/gpml/lik/likGaussWarp.m | 9,118 | utf_8 | baca6bc6eb9f081dff2f85d7a4eb8318 | function [varargout] = likGaussWarp(warp, hyp, y, mu, varargin)
% likGaussWarp - Warped Gaussian likelihood for regression.
% The expression for the likelihood is
% likGaussWarp( y | t ) = likGauss( g(y) | t ) * g'(y),
% where likGauss is the Gaussian likelihood and g is the warping function.
%
% The hyperparamete... |
github | lihp11/predictability_transport-master | likWeibull.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Toolboxes/gpml/lik/likWeibull.m | 4,548 | utf_8 | 5134b34b56b016f15d716469fb93c583 | function [varargout] = likWeibull(link, hyp, y, mu, s2, inf, i)
% likWeibull - Weibull likelihood function for strictly positive data y. The
% expression for the likelihood is
% likWeibull(f) = g1*ka/mu * (g1*y/mu)^(ka-1) * exp(-(g1*y/mu)^ka) with
% gj = gamma(1+j/ka), mean=mu and variance=mu^2*(g2/g1^2-1) where mu... |
github | lihp11/predictability_transport-master | likGamma.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Toolboxes/gpml/lik/likGamma.m | 4,573 | utf_8 | 30195b20deb79baed3429087b58977a8 | function [varargout] = likGamma(link, hyp, y, mu, s2, inf, i)
% likGamma - Gamma likelihood function for strictly positive data y. The
% expression for the likelihood is
% likGamma(f) = al^al*y^(al-1)/gamma(al) * exp(-y*al/mu) / mu^al with
% mean=mu and variance=mu^2/al where mu = g(f) is the Gamma intensity, f is... |
github | lihp11/predictability_transport-master | likInvGauss.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Toolboxes/gpml/lik/likInvGauss.m | 4,679 | utf_8 | 1bffc204bfdee3ee427008906bce81ad | function [varargout] = likInvGauss(link, hyp, y, mu, s2, inf, i)
% likInvGauss - Inverse Gaussian likelihood function for strictly positive data
% y. The expression for the likelihood is
% likInvGauss(f) = sqrt(lam/(2*pi*y^3))*exp(-lam*(mu-y)^2/(2*mu^2*y)) with
% mean=mu and variance=mu^3/lam where mu = g(f) is th... |
github | lihp11/predictability_transport-master | likPoisson.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Toolboxes/gpml/lik/likPoisson.m | 4,178 | utf_8 | 9bdb4f7a4905445839d4697149efc827 | function [varargout] = likPoisson(link, 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 | lihp11/predictability_transport-master | likLogistic.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Toolboxes/gpml/lik/likLogistic.m | 6,137 | utf_8 | 0227c40f8798f8f47d1f32e9dfd6e946 | 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 | lihp11/predictability_transport-master | likSech2.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Toolboxes/gpml/lik/likSech2.m | 8,514 | utf_8 | 25a639e43b4bcdc60d8fd113ded18611 | 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 | lihp11/predictability_transport-master | likGumbel.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Toolboxes/gpml/lik/likGumbel.m | 3,976 | utf_8 | e181712e58f8360c4d43c5c354d8431a | function [varargout] = likGumbel(sign, hyp, y, mu, s2, inf, i)
% likGumbel - Gumbel likelihood function for extremal value regression.
% The expression for the likelihood is
% likGumbel(t) = exp(-z-exp(-z))/be, z = ga+s*(y-t)/be, be = sn*sqrt(6)/pi
% where s={+1,-1} is a sign switching between left and right skewed... |
github | lihp11/predictability_transport-master | priorSmoothBox1.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Toolboxes/gpml/prior/priorSmoothBox1.m | 1,572 | utf_8 | 0d5860f41982700afdaa7de2047f48ff | function [lp,dlp] = priorSmoothBox1(a,b,eta,x)
% Univariate smoothed box prior distribution with linear decay in the log domain
% and infinite support over the whole real axis.
% Compute log-likelihood and its derivative or draw a random sample.
% The prior distribution is parameterized as:
%
% p(x) = sigmoid(eta*(x-... |
github | lihp11/predictability_transport-master | logphi.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Toolboxes/gpml/util/logphi.m | 2,261 | utf_8 | 69fbcfc9d9913da15644d5f0a0368d5f | % Safe computation of logphi(z) = log(normcdf(z)) and its derivatives
% dlogphi(z) = normpdf(x)/normcdf(x).
% The function is based on index 5725 in Hart et al. and gsl_sf_log_erfc_e.
%
% Copyright (c) by Carl Edward Rasmussen and Hannes Nickisch, 2013-11-13.
function [lp,dlp,d2lp,d3lp] = logphi(z)
... |
github | lihp11/predictability_transport-master | gauher.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/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 | lihp11/predictability_transport-master | elsympol.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/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 | lihp11/predictability_transport-master | minimize.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Toolboxes/gpml/util/minimize.m | 11,338 | utf_8 | ead014bd1c8c090ceaf16b20bf0cef66 | 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 | lihp11/predictability_transport-master | minimize_v2.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Toolboxes/gpml/util/minimize_v2.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 | lihp11/predictability_transport-master | sq_dist.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Toolboxes/gpml/util/sq_dist.m | 1,967 | utf_8 | 75b906d47729b33d7567f1353ced2f83 | % 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 | lihp11/predictability_transport-master | cov_deriv_sq_dist.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Toolboxes/gpml/util/cov_deriv_sq_dist.m | 1,906 | utf_8 | 625e697b220630f920d967bce06884e7 | % Compute derivative k'(x^p,x^q) of a stationary covariance k(d2) (ard or iso)
% w.r.t. to squared distance d2 = (x^p - x^q)'*inv(P)*(x^p - x^q) measure. Here
% P is either diagonal with ARD parameters ell_1^2,...,ell_D^2 where D is the
% dimension of the input space or ell^2 times the unit matrix for isotropic
% covar... |
github | lihp11/predictability_transport-master | unwrap.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/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 | lihp11/predictability_transport-master | glm_invlink_expexp.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Toolboxes/gpml/util/glm_invlink_expexp.m | 427 | utf_8 | 99a5cdb9880a947109671401c7398199 | % Compute the log intensity for the inverse link function g(f) = exp(-exp(-f)).
%
% The function is used in GLM likelihoods such as likPoisson, likGamma, likBeta
% and likInvGauss.
%
% Copyright (c) by Hannes Nickisch, 2013-10-16.
function [lg,dlg,d2lg,d3lg] = glm_invlink_expexp(f)
lg = -exp(-f);
if nargout>1
... |
github | lihp11/predictability_transport-master | glm_invlink_logistic.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Toolboxes/gpml/util/glm_invlink_logistic.m | 686 | utf_8 | b21f086f037b6560c290e0044e0beef5 | % Compute the log intensity for the inverse link function g(f) = log(1+exp(f))).
%
% The function is used in GLM likelihoods such as likPoisson, likGamma, likBeta
% and likInvGauss.
%
% Copyright (c) by Hannes Nickisch, 2013-10-16.
function [lg,dlg,d2lg,d3lg] = glm_invlink_logistic(f)
l1pef = max(0,f) + log(1+exp(-a... |
github | lihp11/predictability_transport-master | minimize_v1.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Toolboxes/gpml/util/minimize_v1.m | 11,202 | utf_8 | cd58ba0b83b1121423ed9a53b33562a1 | function [X, fX, i] = minimize_old(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 f... |
github | lihp11/predictability_transport-master | rewrap.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/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 | lihp11/predictability_transport-master | solve_chol.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Toolboxes/gpml/util/solve_chol.m | 994 | utf_8 | f4d6cd4b9e7b0a955c2c8709a4894dd3 | % 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 | lihp11/predictability_transport-master | glm_invlink_logit.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Toolboxes/gpml/util/glm_invlink_logit.m | 786 | utf_8 | b2fc9a03b835c7f6643f37b29eac8c0b | % Compute the log intensity for the inverse link function g(f) = 1/(1+exp(-f)).
%
% The function is used in GLM likelihoods such as likPoisson, likGamma, likBeta
% and likInvGauss.
%
% Copyright (c) by Hannes Nickisch, 2013-10-16.
function varargout = glm_invlink_logit(f)
varargout = cell(nargout, 1); % allocate th... |
github | lihp11/predictability_transport-master | minimize_lbfgsb_gradfun.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/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 | lihp11/predictability_transport-master | minimize_lbfgsb.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/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 | lihp11/predictability_transport-master | minimize_lbfgsb_objfun.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/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 | lihp11/predictability_transport-master | logsumexp2.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Toolboxes/gpml/util/logsumexp2.m | 454 | utf_8 | aa7e4f12a67c8f2e12bc5d9113b9abd0 | % Compute y = log( sum(exp(x),2) ), the softmax in a numerically safe way by
% subtracting the row maximum to avoid cancelation after taking the exp
% the sum is done along the rows.
%
% Copyright (c) by Hannes Nickisch, 2013-10-16.
function [y,x] = logsumexp2(logx)
N = size(logx,2); max_logx = max(logx,[],2);
%... |
github | lihp11/predictability_transport-master | lik_epquad.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Toolboxes/gpml/util/lik_epquad.m | 1,622 | utf_8 | 9f92aef26b02e08fcee8f74617ebd05d | % Compute infEP part of a likelihood function based on the infLaplace part using
% Gaussian-Hermite quadrature.
%
% The function is used in GLM likelihoods such as likPoisson, likGamma, likBeta
% and likInvGauss.
%
% Copyright (c) by Hannes Nickisch, 2013-10-16.
function varargout = lik_epquad(lik,hyp,y,mu,s2)
n = m... |
github | lihp11/predictability_transport-master | glm_invlink_exp.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Toolboxes/gpml/util/glm_invlink_exp.m | 443 | utf_8 | af4bb74d42054f7b470ed8aecfcf4607 | % Compute the log intensity for the inverse link function g(f) = exp(f).
%
% The function is used in GLM likelihoods such as likPoisson, likGamma, likBeta
% and likInvGauss.
%
% Copyright (c) by Hannes Nickisch, 2013-10-16.
function [lg,dlg,d2lg,d3lg] = glm_invlink_exp(f)
lg = f;
if nargout>1
dlg = ones(size(f... |
github | lihp11/predictability_transport-master | covPeriodicNoDC.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Toolboxes/gpml/cov/covPeriodicNoDC.m | 3,630 | utf_8 | 32d02bd08932f22fe8302ce97b797d39 | function K = covPeriodicNoDC(hyp, x, z, i)
% Stationary covariance function for a smooth periodic function, with period p:
%
% k(x,x') = sf^2 * [k0(pi*(x-x')/p) - f(ell)] / [1 - f(ell)]
% with k0(t) = exp( -2*sin^2(t)/ell^2 ) and f(ell) = \int 0..pi k0(t) dt.
%
% The constant (DC component) has been removed and... |
github | lihp11/predictability_transport-master | covGrid.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Toolboxes/gpml/cov/covGrid.m | 7,051 | utf_8 | 45064e9c69d8e20f1122e20679e25085 | function [K,Mx,xe] = covGrid(cov, xg, hyp, x, z, i)
% covGrid - Kronecker covariance function based on a grid.
%
% The grid g is represented by its p axes xg = {x1,x2,..xp}. An axis xi is of
% size (ni,di) and the grid g has size (n1,n2,..,np,D), where D=d1+d2+..+dp.
% Hence, the grid contains N=n1*n2*..*np data point... |
github | lihp11/predictability_transport-master | covPERiso.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Toolboxes/gpml/cov/covPERiso.m | 3,145 | utf_8 | 7308c3f2001744df0d77cd5dc190c637 | function K = covPERiso(cov, hyp, x, z, i)
% Stationary periodic covariance function for an isotropic stationary covariance
% function k0 such as covMaterniso, covPPiso, covRQiso and covSEiso.
% Isotropic stationary means that the covariance function k0(x,z) depends on the
% data points x,z only through the squared dis... |
github | lihp11/predictability_transport-master | covADD.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Toolboxes/gpml/cov/covADD.m | 3,632 | utf_8 | 45875a6c0e52c3f98448f40f6b6fc599 | 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 | lihp11/predictability_transport-master | covPERard.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Toolboxes/gpml/cov/covPERard.m | 3,588 | utf_8 | 47dfb8b9857ef5e9bc3693bb6e5c0aa9 | function K = covPERard(cov, hyp, x, z, i)
% Stationary periodic covariance function for a stationary covariance function
% k0 such as covMaternard, covPPard, covRQard and covSEard.
% Stationary means that the covariance function k0(x,z) depends on the
% data points x,z only through the squared distance
% dxz = (x-z)'*... |
github | lihp11/predictability_transport-master | infMCMC.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/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 | lihp11/predictability_transport-master | infKL.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Toolboxes/gpml/inf/infKL.m | 10,925 | utf_8 | 25a0bb16b3bced105beb3520acf7f57e | function [post nlZ dnlZ] = infKL(hyp, mean, cov, lik, x, y)
% Approximation to the posterior Gaussian Process by minimization of the
% KL-divergence. The function is structurally very similar to infEP; the
% only difference being the local divergence measure minimised.
% In infEP, one minimises KL(p,q) whereas in inf... |
github | lihp11/predictability_transport-master | infFITC_EP.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Toolboxes/gpml/inf/infFITC_EP.m | 11,888 | utf_8 | 534a28c784811c4521c02312ca70c082 | 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 | lihp11/predictability_transport-master | infFITC_Laplace.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Toolboxes/gpml/inf/infFITC_Laplace.m | 11,364 | utf_8 | 1de345e37242cee549b4d7841e348f84 | 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 | lihp11/predictability_transport-master | infGrid.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Toolboxes/gpml/inf/infGrid.m | 7,627 | utf_8 | cca2d1208eb8763b1f518d5106ec6fd5 | function [post nlZ dnlZ] = infGrid(hyp, mean, cov, lik, x, y, opt)
% Inference for a GP with Gaussian likelihood and covGrid covariance.
% The (Kronecker) covariance matrix used is given by:
% K = kron( kron(...,K{2}), K{1} ) = K_p x .. x K_2 x K_1.
%
% Compute a parametrization of the posterior, the negative log ma... |
github | lihp11/predictability_transport-master | infEP.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Toolboxes/gpml/inf/infEP.m | 5,886 | utf_8 | bc70721e1eea7c28c46653d36d1cd851 | 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 covFunctions.m) and
% likelihood function (see likFunctions.m), and is designed to be used with
% gp.m. See also infMethods.m.... |
github | lihp11/predictability_transport-master | infVB.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Toolboxes/gpml/inf/infVB.m | 6,024 | utf_8 | ff46ca9c2cce23402f0058955fe60b93 | function [post, nlZ, dnlZ] = infVB(hyp, mean, cov, lik, x, y, opt)
% Variational approximation to the posterior Gaussian process.
% The function takes a specified covariance function (see covFunctions.m) and
% likelihood function (see likFunctions.m), and is designed to be used with
% gp.m. See also infMethods.m.
%
% ... |
github | lihp11/predictability_transport-master | infLaplace.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Toolboxes/gpml/inf/infLaplace.m | 7,949 | utf_8 | 297780be514bf1d87f826763094f104f | function [post nlZ dnlZ] = infLaplace(hyp, mean, cov, lik, x, y, opt)
% Laplace approximation to the posterior Gaussian process.
% The function takes a specified covariance function (see covFunctions.m) and
% likelihood function (see likFunctions.m), and is designed to be used with
% gp.m. See also infMethods.m.
%
% C... |
github | lihp11/predictability_transport-master | MS_embed.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/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 | lihp11/predictability_transport-master | MS_nearneigh.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/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 | lihp11/predictability_transport-master | MS_firstzero.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/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 | lihp11/predictability_transport-master | MS_fnn.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/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 | lihp11/predictability_transport-master | MS_complexity.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Toolboxes/Michael_Small/MS_complexity.m | 1,484 | utf_8 | e5f7f0d7194bf7f0f8b3368ba6246f31 | % 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 | lihp11/predictability_transport-master | MS_unfolding.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/Toolboxes/Michael_Small/MS_unfolding.m | 3,108 | utf_8 | a42fccf6f4203969973bea0050013570 | % 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 of... |
github | lihp11/predictability_transport-master | MS_rms.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/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 | lihp11/predictability_transport-master | ZG_hmm_cl.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/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 | lihp11/predictability_transport-master | ZG_hmm.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/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 | lihp11/predictability_transport-master | ZG_rprod.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/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 | lihp11/predictability_transport-master | ZG_rdiv.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/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 | lihp11/predictability_transport-master | ZG_rsum.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/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 | lihp11/predictability_transport-master | opentstool.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/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 | lihp11/predictability_transport-master | tsplot.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/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 | lihp11/predictability_transport-master | sortdatafiles.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/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 | lihp11/predictability_transport-master | filterbank.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/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 | lihp11/predictability_transport-master | scalogram.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/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 | lihp11/predictability_transport-master | help_mex.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/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 | lihp11/predictability_transport-master | TSTOOLpca.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/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 | lihp11/predictability_transport-master | pauswahl.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/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 | lihp11/predictability_transport-master | infodim2.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/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 | lihp11/predictability_transport-master | filterbank.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/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 | lihp11/predictability_transport-master | signal.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/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 | lihp11/predictability_transport-master | view.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/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 | lihp11/predictability_transport-master | addcommandlines.m | .m | predictability_transport-master/code_github]/nbit复杂度计算/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... |
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