| 1 | /*! |
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| 2 | * \file |
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| 3 | * \brief Include file for the IT++ statistics module |
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| 4 | * \author Adam Piatyszek and Conrad Sanderson |
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| 5 | * |
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| 6 | * ------------------------------------------------------------------------- |
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| 7 | * |
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| 8 | * Copyright (C) 1995-2010 (see AUTHORS file for a list of contributors) |
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| 9 | * |
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| 10 | * This file is part of IT++ - a C++ library of mathematical, signal |
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| 11 | * processing, speech processing, and communications classes and functions. |
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| 12 | * |
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| 13 | * IT++ is free software: you can redistribute it and/or modify it under the |
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| 14 | * terms of the GNU General Public License as published by the Free Software |
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| 15 | * Foundation, either version 3 of the License, or (at your option) any |
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| 16 | * later version. |
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| 17 | * |
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| 18 | * IT++ is distributed in the hope that it will be useful, but WITHOUT ANY |
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| 19 | * WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS |
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| 20 | * FOR A PARTICULAR PURPOSE. See the GNU General Public License for more |
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| 21 | * details. |
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| 22 | * |
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| 23 | * You should have received a copy of the GNU General Public License along |
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| 24 | * with IT++. If not, see <http://www.gnu.org/licenses/>. |
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| 25 | * |
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| 26 | * ------------------------------------------------------------------------- |
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| 27 | */ |
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| 28 | |
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| 29 | #ifndef ITSTAT_H |
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| 30 | #define ITSTAT_H |
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| 31 | |
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| 32 | /*! |
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| 33 | * \defgroup stat Statistics Module |
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| 34 | * @{ |
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| 35 | */ |
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| 36 | |
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| 37 | //! \defgroup histogram Histogram |
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| 38 | //! \defgroup statistics Miscellaneous Statistics Functions |
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| 39 | |
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| 40 | /*! \defgroup MOG Mixture of Gaussians (MOG) |
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| 41 | \brief Classes and functions for modelling multivariate data as a Mixture of Gaussians |
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| 42 | \author Conrad Sanderson |
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| 43 | |
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| 44 | The following example shows how to model data: |
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| 45 | \code |
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| 46 | Array<vec> X; |
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| 47 | // ... fill X with vectors ... |
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| 48 | int K = 3; // specify the number of Gaussians |
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| 49 | int D = 10; // specify the dimensionality of vectors |
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| 50 | MOG_diag model(K,D); |
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| 51 | MOG_diag_kmeans(model, X, 10, 0.5, true, true); // initial optimisation using 10 iterations of k-means |
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| 52 | MOG_diag_ML(model, X, 10, 0.0, 0.0, true); // final optimisation using 10 iterations of ML version of EM |
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| 53 | double avg = model.avg_log_lhood(X); // find the average log likelihood of X |
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| 54 | \endcode |
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| 55 | |
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| 56 | See also the tutorial section for a more elaborate example. |
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| 57 | */ |
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| 58 | |
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| 59 | /*! |
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| 60 | * @} |
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| 61 | */ |
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| 62 | |
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| 63 | |
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| 64 | #include <itpp/itbase.h> |
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| 65 | #include <itpp/stat/histogram.h> |
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| 66 | #include <itpp/stat/misc_stat.h> |
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| 67 | #include <itpp/stat/mog_generic.h> |
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| 68 | #include <itpp/stat/mog_diag.h> |
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| 69 | #include <itpp/stat/mog_diag_kmeans.h> |
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| 70 | #include <itpp/stat/mog_diag_em.h> |
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| 71 | |
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| 72 | #endif // #ifndef ITSTAT_H |
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