| void Impala::Core::Training::Fisher::Train | ( | Util::PropertySet * | properties | ) | [inline] |
Definition at line 182 of file Fisher.h.
References Impala::Core::Vector::AddAssign(), Impala::Core::Vector::Avg(), Impala::Core::Array::Array2dTem< StorT, elemSize, ArithT >::CH(), CNormC(), Impala::Core::Array::Array2dTem< StorT, elemSize, ArithT >::CW(), Impala::Core::Vector::VectorTem< ElemT >::Elem(), Impala::Core::Vector::VectorSet< ArrayT >::GetVector(), Impala::Core::Matrix::MatMul(), Impala::Core::Matrix::MatPInv(), mOffset, mRotation, mTrainFeatures, mTrainLabels, Impala::Core::Vector::MulAssign(), Impala::Core::Array::Set(), Impala::Core::Vector::SetPart(), Impala::Core::Vector::VectorSet< ArrayT >::SetStorage(), Impala::Core::Array::SetVal(), Impala::Core::Array::Array2dTem< StorT, elemSize, ArithT >::SetValue(), Impala::Core::Array::Array2dTem< StorT, elemSize, ArithT >::Value(), and Impala::Core::Array::W.
00183 { 00184 int i,j; 00185 bool debug=false; 00186 int W = mTrainFeatures->CW(); 00187 int H = mTrainFeatures->CH(); 00188 00189 Matrix::Mat* tmp = 0; 00190 Matrix::Mat* tmp2 = 0; 00191 Matrix::Mat* tmp3 = 0; 00192 00193 Matrix::Mat* Targets= 0; 00194 Matrix::Mat* FeatureAverages = 0; 00195 Matrix::Mat* CenteredFeatureInverse = 0; 00196 Array::Array2dScalarReal64* CenteredFeatures = 0; 00197 Array::Array2dScalarReal64* Features = 0; 00198 Array::Array2dScalarReal64* Labels = 0; 00199 00200 Vector::VectorSet<Array::Array2dScalarReal64> vs(true, W, H); 00201 00202 Array::Set(Features,mTrainFeatures); 00203 Array::Set(Labels,mTrainLabels); 00204 00206 //Clear the transformation if it was already calculated 00207 if(mRotation!=0) 00208 delete mRotation; 00209 if(mOffset!=0) 00210 delete mOffset; 00211 00213 //Get The Average for each feature 00214 vs.SetStorage(Features); 00215 Vector::VectorTem<Real64> featureMeans=0; 00216 Avg(&featureMeans,&vs,H,0); 00217 00219 //Center the data around 0 mean: 00220 Vector::MulAssign(featureMeans,Impala::Real64(-1)); 00221 for(i=0;i<H;i++){ 00222 Vector::VectorTem<Real64> tmpVec = vs.GetVector(i,true); 00223 AddAssign(tmpVec,featureMeans); 00224 } 00225 Vector::MulAssign(featureMeans,Impala::Real64(-1)); 00226 00228 //Now The data is centered around origin 00229 //We should add a column with 1's to the end of the feature vectors 00230 00231 //If the rank of this Centered matrix with the ones column is 00232 //less than the initial number of features for each sample, 00233 //Then we should find the Pseudo Inverse of this B matrice, and multiply 00234 //it with a two column labels matrix 00235 CenteredFeatures = Array::ArrayCreate<Array::Array2dScalarReal64>(W+1,H); 00236 SetVal(CenteredFeatures,1); 00237 SetPart(CenteredFeatures,Features,0,0,W,H,0,0); 00238 00239 00240 Targets = Matrix::MatCreate<Array::Array2dScalarReal64>(H,2); 00241 FeatureAverages = Matrix::MatCreate<Array::Array2dScalarReal64>(1,W); 00242 00243 for(int i=0;i<W;i++){ 00244 FeatureAverages->SetValue(featureMeans.Elem(i),i,0); 00245 } 00246 00247 SetPart(Targets,Labels,0,0,1,H,0,0); 00248 for(int i=0;i<H;i++){ 00249 Targets->SetValue(Targets->Value(0,i),1,i); 00250 Targets->SetValue(-Targets->Value(0,i),0,i); 00251 } 00252 CenteredFeatureInverse=Matrix::MatPInv(CenteredFeatures); 00253 00254 00255 tmp = Matrix::MatMul(CenteredFeatureInverse,Targets); 00256 tmp2 = Matrix::MatCreate<Array::Array2dScalarReal64>(W,2); 00257 00258 for(int i=0;i<W;i++){ 00259 tmp2->SetValue(tmp->Value(0,i),0,i); 00260 tmp2->SetValue(tmp->Value(1,i),1,i); 00261 } 00262 tmp3 = Matrix::MatMul(FeatureAverages,tmp2); 00263 00264 mOffset = Matrix::MatCreate<Array::Array2dScalarReal64>(1,2); 00265 mOffset->SetValue(tmp->Value(0,W)-tmp3->Value(0,0),0,0); 00266 mOffset->SetValue(tmp->Value(1,W)-tmp3->Value(1,0),1,0); 00267 00268 mRotation = Matrix::MatCreate<Array::Array2dScalarReal64>(W,2); 00269 SetPart(mRotation,tmp,0,0,2,W,0,0); 00270 00271 delete tmp; 00272 delete tmp2; 00273 delete tmp3; 00274 00275 delete Targets; 00276 delete Labels; 00277 //delete Features; //will be released by the vector set 00278 delete FeatureAverages; 00279 delete CenteredFeatures;; 00280 delete CenteredFeatureInverse; 00281 CNormC(); 00282 00283 }
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