[944] | 1 | /*! |
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| 2 | \page app_base bdmtoolbox - List of available basic objects |
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| 3 | |
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| 4 | Basic objects of BDM are: |
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| 5 | - <b>RV</b> decriptor of data vectors and random variables |
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| 6 | - <b>fnc</b> functions of vector arguments |
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| 7 | - <b>pdf</b> pdf with non-empty conditioning part |
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| 8 | - <b>epdf</b> pdf with empty conditioning part |
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| 9 | - <b>BM</b> Bayesian model - (approximate) calculation of specific type of the Bayes rule, |
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| 10 | - <b>DS</b> Data Sources - recursive sources of data |
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| 11 | - <b>logger</b> object storing results of all experiments |
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| 12 | |
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| 13 | See, \ref userguide_pdf and \ref userguide_sim for their introduction. |
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| 14 | |
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| 15 | |
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| 16 | |
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| 17 | \section app_base_rv Working with RV |
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| 18 | |
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| 19 | In bdmtoolbox, RV is represented by a matlab structure. It is recommended to use the following function to work with it: |
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| 20 | |
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| 21 | - <b>RV</b>, <b>rva=RV('a',1)</b>, <b>rvab=RV({'b','c'},[1,2])</b> create empty RV, RV named "a" of size 1, and vector of RV "b" and "c" of sizes 1 and 2, respectively. |
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| 22 | - <b>RVtimes(rva,-1)</b> set time (delay) of rva to -1 from current time. |
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| 23 | - <b>RVjoin([rva,rvb])</b> joins rva and rvb into a vector |
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| 24 | |
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| 25 | \section app_base_fnc Basic functions |
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| 26 | |
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| 27 | Predefined functions are: |
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| 28 | - <b>constfn</b> Class representing function , here rv is empty |
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| 29 | - <b>linfn</b> Class representing function |
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| 30 | - <b>mexFnc</b> Matlab extension of a function, calling predefined matlab functions |
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| 31 | - <b>grid_fnc</b> Function defined by values on a fixed grid and interpolated inbetween them |
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| 32 | |
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| 33 | \section app_base_sq Square root decompositions of symetric matrices |
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| 34 | |
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| 35 | For information purpose, these matrices are used in epdfs: |
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| 36 | - <b>chmat</b> Symmetric matrix stored in square root decomposition using upper cholesky |
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| 37 | - <b>fsqmat</b> Fake sqmat. This class maps sqmat operations to operations on full matrix |
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| 38 | - <b>ldmat</b> Matrix stored in LD form, (commonly known as UD) |
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| 39 | |
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| 40 | \section app_base_epdf Basic epdfs accessible from matlab are: |
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| 41 | - <b>dirac</b> Dirac delta function distribution |
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| 42 | - <b>eDirich</b> Dirichlet posterior density |
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| 43 | - <b>eEF</b> General conjugate exponential family posterior density |
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| 44 | - <b>eEmp</b> Weighted empirical density |
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| 45 | - <b>egamma</b> Gamma posterior density |
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| 46 | - <b>egiw</b> Gauss-inverse-Wishart density stored in LD form |
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| 47 | - <b>egrid</b> Discrete density defined on a continuous grid |
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| 48 | - <b>eigamma</b> Inverse-Gamma posterior density |
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| 49 | - <b>eiWishartCh</b> Inverse Wishart on Choleski decomposition |
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| 50 | - <b>enorm<ldmat></b> Gaussian density with positive definite (decomposed) covariance matrix |
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| 51 | - <b>enorm<chmat></b> Gaussian density with positive definite (decomposed) covariance matrix |
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| 52 | - <b>enorm<fsqmat></b> Gaussian density with positive definite (decomposed) covariance matrix |
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| 53 | - <b>elognorm</b> LogNormal density |
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| 54 | - <b>emix</b> Mixture of epdfs |
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| 55 | - <b>eprod</b> Product of densities, elements may be unconditional, however, the result should be unconditioned pdf. |
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| 56 | - <b>estudent< ldmat></b> Student-t density |
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| 57 | - <b>estudent< ldmat></b> Student-t density |
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| 58 | - <b>estudent< ldmat></b> Student-t density |
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| 59 | - <b>euni</b> Uniform density on rectangular support |
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| 60 | - <b>eWishartCh</b> Wishart in choleski decomposition |
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| 61 | |
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| 62 | \section app_base_pdf Basic (conditioned) pdfs accessible from matlab are: |
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| 63 | - <b>mDirich</b> Dirichlet random walk |
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| 64 | - <b>mexBM</b> BM with functions implemented in matlab |
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| 65 | - <b>mexEpdf</b> Epdf with functions implemented in matlab |
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| 66 | - <b>mgamma</b> Gamma random walk |
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| 67 | - <b>mgamma_fix</b> Gamma random walk around a fixed point |
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| 68 | - <b>mgdirac</b> Dirac delta pdf with geenral function of the support point |
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| 69 | - <b>mgnorm< ldmat ></b> Gaussian Pdf with general function for mean value |
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| 70 | - <b>mgnorm< chmat ></b> Gaussian Pdf with general function for mean value |
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| 71 | - <b>mgnorm< fsqmat ></b> Gaussian Pdf with general function for mean value |
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| 72 | - <b>mguni</b> Uniform density with geenral function of mean value |
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| 73 | - <b>migamma</b> Inverse-Gamma random walk |
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| 74 | - <b>migamma_ref</b> Inverse-Gamma random walk around a fixed point |
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| 75 | - <b>mlnorm< ldmat></b> Normal distribution with linear function with linear function of mean value; |
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| 76 | - <b>mlnorm< chmat></b> Normal distribution with linear function with linear function of mean value; |
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| 77 | - <b>mlnorm< fsqmat></b> Normal distribution with linear function with linear function of mean value; |
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| 78 | - <b>mlognorm</b> Log-Normal random walk |
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| 79 | - <b>mlstudent</b> Student distributed linear function with linear function of mean value; |
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| 80 | - <b>mmix</b> Mixture of pdfs with constant weights, all pdfs are of equal RV and RVC |
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| 81 | - <b>mprod</b> Chain rule decomposition of epdf |
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| 82 | - <b>mratio</b> Class representing ratio of two densities which arise e.g. by applying the Bayes rule. |
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| 83 | - <b>rwiWishartCh</b> Random Walk on inverse Wishart |
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| 84 | |
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| 85 | \section app_base_ds DataSources |
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| 86 | - <b>MemDS</b> Memory storage of off-line data column-wise |
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| 87 | - <b>CsvFileDS</b> CSV file data storage The constructor creates Data matrix from the records in a CSV file fname. The orientation can be of two types: 1. BY_COL which is default - the data are stored in columns; one column per time , one row per data item. 2. BY_ROW if the data are stored the classical CSV style. Then each column stores the values for data item, for ex. , one row for each discrete time instant |
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| 88 | - <b>EpdfDS</b> Simulate data from a static pdf (epdf) |
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| 89 | - <b>PdfDS</b> Simulate data from conditional density Still having only one density but allowing conditioning on either input or delayed values |
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| 90 | |
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| 91 | \section app_base_log Loggers - storage of results |
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| 92 | - <b>mexlog</b> Logger storing results into an mxArray |
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| 93 | - <b>dirfilelog</b> Logging into dirfile with buffer in memory |
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| 94 | - <b>stdlog</b> Simple logger used for debugging All data records are written out to std from where they could be send to file |
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| 95 | - <b>ITppFileDS</b> Read Data Matrix from an IT file |
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| 96 | |
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| 97 | \section app_base_merg Mergers |
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| 98 | - <b>merger_base</b> Base class for general combination of pdfs on discrete support |
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| 99 | - <b>merger_mix</b> Merger using importance sampling with mixture proposal density |
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| 100 | |
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| 101 | |
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| 102 | \section app_base_bm Bayesian Models |
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| 103 | |
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| 104 | Basic filters: |
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| 105 | |
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| 106 | - <b>ARX</b> Linear Autoregressive model with Gaussian noise |
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| 107 | - <b>ARXfrg</b> ARX with conditioned on forgetting factor |
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| 108 | - <b>ARXg</b> ARX with Non-linear transformation + Gaussian noise |
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| 109 | - <b>EKFCh</b> Extended Kalman Filter in Square root |
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| 110 | - <b>EKFfull</b> Extended Kalman Filter in full matrices |
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| 111 | - <b>KalmanCh</b> Kalman filter in square root form |
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| 112 | - <b>KalmanFull</b> Basic Kalman filter with full matrices |
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| 113 | - <b>MixEF</b> Estimator of Mixtures of Exponential Family Densities |
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| 114 | - <b>multiBM</b> Estimator for Multinomial density |
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| 115 | - <b>MultiModel</b> (Switching) Multiple Models. The model runs several BMs in parallel and evaluates thier weights (fittness) |
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| 116 | - <b>PF</b> Particle filtering: Wrapper for particles |
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| 117 | - <b>BootstrapParticle</b> Class used in PF |
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| 118 | - <b>MarginalizedParticle</b> Class used in PF |
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| 119 | |
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| 120 | \section app_base_ctrl Controllers (and related classes) |
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| 121 | - <b>LQG</b> Basic class computing LQG control |
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| 122 | - <b>LQG_ARX</b> Controller using ARX model for estimation and LQG designer for control |
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| 123 | - <b>StateFromARX</b> conversion function |
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| 124 | - <b>StateSpace< ldmat></b> Basic elements of linear state-space model |
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| 125 | - <b>StateSpace< chmat></b> Basic elements of linear state-space model |
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| 126 | |
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| 127 | \section app_base_mpdm Multiple Patricipant Decision Makers |
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| 128 | * <b>ARXAgent</b> ARX agent |
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| 129 | |
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| 130 | |
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| 131 | */ |
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