00001 #ifndef mcal_extract_mode_h_ 00002 #define mcal_extract_mode_h_ 00003 //: 00004 // \file 00005 // \brief Functions to learn modes from subsets of data 00006 // \author Tim Cootes 00007 00008 #include <vnl/vnl_vector.h> 00009 #include <vnl/vnl_matrix.h> 00010 #include <vcl_vector.h> 00011 00012 //: Computes one mode from used elements of each \p dv 00013 // Compute a mode which only uses a subset of the elements, 00014 // all other elements are set to zero. 00015 // Effectives computes the first eigenvector of the 00016 // covariance matrix formed from selecting the used elements 00017 // of each \p dv[i], ie \p dv[i][used[j]]. 00018 // Resulting vector is returned as a full length vector 00019 // (the same size as \p dv[i]). 00020 // \param dv is assumed to be zero mean. 00021 // 00022 // The contribution of this vector is removed from each \p dv, 00023 // \p dv[i]-=mode*b, where \p b=dv[i].mode 00024 void mcal_extract_mode(vcl_vector<vnl_vector<double> >& dv, 00025 const vcl_vector<unsigned>& elements_used, 00026 vnl_vector<double>& mode, 00027 double& var); 00028 00029 //: Computes one mode by applying PCA to \p dv 00030 // Effectives computes the first eigenvector of the 00031 // covariance matrix. 00032 // The contribution of this vector is removed from each \p dv, 00033 // \p dv[i]-=mode*b, where \p b=dv[i].mode 00034 void mcal_extract_mode(vcl_vector<vnl_vector<double> >& dv, 00035 vnl_vector<double>& mode, 00036 double& var); 00037 00038 //: Compute modes and associated variance of supplied data 00039 // \param elements_used[i] indicates the set of elements to be used for 00040 // mode i. Modes beyond \p used.size() will use all elements. 00041 void mcal_extract_modes(vcl_vector<vnl_vector<double> >& dv, 00042 const vcl_vector<vcl_vector<unsigned> >& elements_used, 00043 unsigned max_modes, double var_prop, 00044 vnl_matrix<double>& modes, 00045 vnl_vector<double>& mode_var); 00046 00047 #endif //mcal_extract_mode_h_
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