Changeset 1167 for trunk/yat/classifier/SubsetGenerator.h

Timestamp:

Feb 26, 2008, 9:02:28 PM (16 years ago)

Author:

Peter

Message:

refs #342 in KernelLookup? and DataLookup2D - nothing changed in MatrixLookups?

File:

• trunk/yat/classifier/SubsetGenerator.h (modified) (6 diffs)

trunk/yat/classifier/SubsetGenerator.h

@@ -133 +133 @@
     const Target& validation_target(std::vector<Target>::size_type i) const;
 
-    ///
-    /// @return true if weighted 
-    /// @todo remove this function
-    //bool weighted(void) const;
-
   private:
     void build(const MatrixLookup&);
@@ -168 +163 @@
     training_data_.reserve(sampler_.size());
     validation_data_.reserve(sampler_.size());
-    for (size_t i=0; i<sampler_.size(); ++i){
-      // Dynamically allocated. Must be deleted in destructor.
-      training_data_.push_back(data.training_data(sampler.training_index(i)));
-      validation_data_.push_back(data.validation_data(sampler.training_index(i),
-                                                      sampler.validation_index(i)));
-
-      training_target_.push_back(Target(target(),sampler.training_index(i)));
-      validation_target_.push_back(Target(target(),
-                                          sampler.validation_index(i)));
-      utility::yat_assert<std::runtime_error>(training_data_.size()==i+1);
-      utility::yat_assert<std::runtime_error>(training_target_.size()==i+1);
-      utility::yat_assert<std::runtime_error>(validation_data_.size()==i+1);
-      utility::yat_assert<std::runtime_error>(validation_target_.size()==i+1);
-    }
-
-    // No feature selection, hence features same for all partitions
-    // and can be stored in features_[0]
-    features_.push_back(utility::Index(data.rows()));
-
+    build(data);
     utility::yat_assert<std::runtime_error>(training_data_.size()==size());
     utility::yat_assert<std::runtime_error>(training_target_.size()==size());
@@ -196 +173 @@
   template<typename T>
   SubsetGenerator<T>::SubsetGenerator(const Sampler& sampler, 
-                                   const T& data, 
-                                   FeatureSelector& fs)
+                                      const T& data, 
+                                      FeatureSelector& fs)
     : f_selector_(&fs), sampler_(sampler)
   { 
@@ -229 +206 @@
       training_target_.push_back(Target(target(),training_index(k)));
       validation_target_.push_back(Target(target(),validation_index(k)));
-      // training data with no feature selection
-      const MatrixLookup* train_data_all_feat = 
-        ml.training_data(training_index(k));
-      // use these data to create feature selection
-      utility::yat_assert<std::runtime_error>(train_data_all_feat);
-      f_selector_->update(*train_data_all_feat, training_target(k));
+      if (f_selector_){
+        // training data with no feature selection
+        const MatrixLookup* train_data_all_feat = 
+          ml.training_data(training_index(k));
+        // use these data to create feature selection
+        utility::yat_assert<std::runtime_error>(train_data_all_feat);
+        f_selector_->update(*train_data_all_feat, training_target(k));
         // get features
-      features_.push_back(f_selector_->features());
-      utility::yat_assert<std::runtime_error>(train_data_all_feat);
-      delete train_data_all_feat;
+        features_.push_back(f_selector_->features());
+        utility::yat_assert<std::runtime_error>(train_data_all_feat);
+        delete train_data_all_feat;
+      }
+      else // no feature selection
+        features_.push_back(utility::Index(ml.rows()));
+
       
       // Dynamically allocated. Must be deleted in destructor.
@@ -256 +238 @@
       training_target_.push_back(Target(target(),training_index(k)));
       validation_target_.push_back(Target(target(),validation_index(k)));
-      // training data with no feature selection
-      const MatrixLookupWeighted* train_data_all_feat = 
-        ml.training_data(training_index(k));
-      // use these data to create feature selection
-      f_selector_->update(*train_data_all_feat, training_target(k));
-      // get features
-      features_.push_back(f_selector_->features());
-      delete train_data_all_feat;
-      
+      if (f_selector_){
+        // training data with no feature selection
+        const MatrixLookupWeighted* train_data_all_feat = 
+          ml.training_data(training_index(k));
+        // use these data to create feature selection
+        f_selector_->update(*train_data_all_feat, training_target(k));
+        // get features
+        features_.push_back(f_selector_->features());
+        delete train_data_all_feat;
+      }
+      else // no feature selection
+        features_.push_back(utility::Index(ml.rows()));
+
+
       // Dynamically allocated. Must be deleted in destructor.
       training_data_.push_back(new MatrixLookupWeighted(ml, features_.back(), 
@@ -280 +267 @@
       validation_target_.push_back(Target(target(),validation_index(k)));
 
-      if (kernel.weighted()){
-        utility::SmartPtr<const MatrixLookupWeighted> ml=kernel.data_weighted();
-        f_selector_->update(MatrixLookupWeighted(*ml,training_index(k),false), 
-                            training_target(k));
+      if (f_selector_){
+        if (kernel.weighted()){
+          utility::SmartPtr<const MatrixLookupWeighted> ml=
+            kernel.data_weighted();
+          f_selector_->update(MatrixLookupWeighted(*ml,training_index(k),false), 
+                              training_target(k));
+        }
+        else {
+          utility::SmartPtr<const MatrixLookup> ml=kernel.data();
+          f_selector_->update(MatrixLookup(*ml,training_index(k), false), 
+                              training_target(k));
+        } 
+        features_.push_back(f_selector_->features());
+        const KernelLookup* kl = kernel.selected(features_.back());
+        // Dynamically allocated. Must be deleted in destructor.
+        training_data_.push_back(new KernelLookup(kernel,training_index(k),
+                                                  training_index(k)));
+        validation_data_.push_back(new KernelLookup(kernel, training_index(k), 
+                                                    validation_index(k)));
+        utility::yat_assert<std::runtime_error>(kl);
+        delete kl;
       }
-      else {
-        utility::SmartPtr<const MatrixLookup> ml=kernel.data();
-        f_selector_->update(MatrixLookup(*ml,training_index(k), false), 
-                            training_target(k));
-      } 
-      utility::Index dummie=f_selector_->features();
-      features_.push_back(dummie);
-      //features_.push_back(f_selector_->features());
-      const KernelLookup* kl = kernel.selected(features_.back());
+      else {// no feature selection
+        training_data_.push_back(new KernelLookup(kernel, training_index(k),
+                                                  training_index(k)));
+        validation_data_.push_back(new KernelLookup(kernel, 
+                                                    training_index(k), 
+                                                    validation_index(k)));
+      }
       
-      // Dynamically allocated. Must be deleted in destructor.
-      training_data_.push_back(kl->training_data(training_index(k)));
-      validation_data_.push_back(kl->validation_data(training_index(k), 
-                                                     validation_index(k)));
-      utility::yat_assert<std::runtime_error>(kl);
-      delete kl;
     }
   }