Changeset 202
 Timestamp:
 Nov 1, 2004, 4:00:41 PM (19 years ago)
 Location:
 trunk/src
 Files:

 5 added
 3 edited
Legend:
 Unmodified
 Added
 Removed

trunk/src/Makefile.am
r198 r202 14 14 Kernel.cc kNNI.cc matrix.cc Merge.cc NNI.cc PCA.cc \ 15 15 PolynomialKernelFunction.cc random_singleton.cc \ 16 RegressionLinear.cc RegressionLocal.cc RegressionNaive.cc ROC.cc \ 17 Score.cc Statistics.cc \ 18 PolynomialKernelFunction.cc random_singleton.cc \ 16 19 RegressionLinear.cc ROC.cc Score.cc Statistics.cc \ 17 20 SVD.cc SVM.cc tScore.cc vector.cc WeightedAverager.cc WeNNI.cc … … 28 31 KernelFunction.h kNNI.h matrix.h Merge.h NNI.h PCA.h Pearson.h \ 29 32 PolynomialKernelFunction.h random_singleton.h \ 33 Regression.h RegressionLinear.h RegressionLocal.h RegressionNaive.h \ 34 ROC.h Score.h \ 35 PolynomialKernelFunction.h random_singleton.h \ 30 36 RegressionLinear.h ROC.h Score.h \ 31 37 Statistics.h stl_utility.h SVD.h SVM.h tScore.h vector.h WeNNI.h \ 
trunk/src/RegressionLinear.cc
r196 r202 1 1 // $Id$ 2 2 3 // Header 3 4 #include "RegressionLinear.h" 4 5 6 // C++_tools 7 #include "Regression.h" 5 8 #include "vector.h" 6 9 … … 13 16 14 17 RegressionLinear::RegressionLinear(void) 15 : cov00_(0), cov01_(0), cov11_(0), k_(0), m_(0), sumsq_(0)18 : Regression(), cov00_(0), cov01_(0), cov11_(0), k_(0), m_(0), sumsq_(0) 16 19 { 17 20 } 
trunk/src/RegressionLinear.h
r196 r202 6 6 // C++ tools include 7 7 ///////////////////// 8 #include "Regression.h" 8 9 #include "vector.h" 9 10 … … 20 21 21 22 22 class RegressionLinear 23 class RegressionLinear : public Regression 23 24 { 24 25 … … 30 31 31 32 /// 33 /// Copy Constructor. (not implemented) 34 /// 35 RegressionLinear(const RegressionLinear&); 36 37 /// 32 38 /// Destructor 33 39 /// … … 36 42 /// 37 43 /// This function computes the bestfit linear regression 38 /// coefficients (k,m) of the model \f$ y = kx + m \f$ from from44 /// coefficients (k,m) of the model \f$ y = kx + m \f$ from 39 45 /// vectors \a x and \a y, by minimizing \f$ \sum{(kx_i+mx_i)^2} 40 46 /// \f$. … … 48 54 /// This function computes the bestfit linear regression 49 55 /// coefficients (k,m) of the model \f$ y = kx + m \f$ from from 50 /// vectors \a x and \a y, by minimizing \f$ \sum w_i(kx_i+mx_i)^2 51 /// \f$. The weight \f$ w_i \f$ is the inverse of the variance for 52 /// \f$ y_i \f$ 56 /// vectors \a x and \a y, by minimizing \f$ \sum 57 /// w_i(kx_i+mx_i)^2 \f$. 53 58 /// 54 inline int fit_weigted(gslapi::vector x, gslapi::vector y, gslapi::vector w) 59 inline int fit_weighted(const gslapi::vector x, const gslapi::vector y, 60 const gslapi::vector w) 55 61 { return gsl_fit_wlinear_vector(x.gsl_vector_pointer(), 56 62 w.gsl_vector_pointer(),
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