Changeset 2138

Timestamp:

Dec 24, 2009, 9:23:49 PM (12 years ago)

Author:

Peter

Message:

merge patch release 0.5.6 into trunk. Delta 0.5.6 - 0.5.5

Location:

trunk

Files:

• NEWS (modified) (1 diff)
• m4/gsl.m4 (modified) (1 diff)
• m4/version.m4 (modified) (1 diff)
• test/ensemble_test.cc (modified) (2 diffs)
• yat/Makefile.am (modified) (2 diffs)
• yat/classifier/EnsembleBuilder.h (modified) (3 diffs)

trunk/NEWS

@@ -21 +21 @@
   A complete list of closed tickets can be found here [[br]]
   http://dev.thep.lu.se/yat/query?status=closed&milestone=yat+0.6
+
+Version 0.5.6 (released 24 December 2009)
+
+  - Fixed bugs in EnsembleBuilder::predict (bugs #567 and #579)
+  - Corrected dependency libs in libyat.la (bug #573)
+
+  A complete list of closed tickets can be found here [[br]]
+  http://dev.thep.lu.se/yat/query?status=closed&milestone=yat+0.5.6
 
 Version 0.5.5 (released 5 November 2009)

trunk/m4/gsl.m4

@@ -42 +42 @@
     GSL_LIBS=`$GSL_CONFIG --libs`
     ac_save_LIBS="$LIBS"
-    CXXFLAGS="$CXXFLAGS $GSL_CFLAGS"
     LIBS="$GSL_LIBS $LIBS"
     AC_LINK_IFELSE([AC_LANG_PROGRAM(, [return 0])],,[no_gsl=yes])  

trunk/m4/version.m4

@@ -59 +59 @@
 # yat-0.5.4  2:3:0
 # yat-0.5.5  2:4:0
+# yat-0.5.6  2:5:0
 #
 # *Accidently, the libtool number was not updated for yat 0.5

trunk/test/ensemble_test.cc

@@ -5 +5 @@
   Copyright (C) 2007 Jari Häkkinen, Peter Johansson
   Copyright (C) 2008 Jari Häkkinen, Peter Johansson, Markus Ringnér
+  Copyright (C) 2009 Peter Johansson
 
   This file is part of the yat library, http://dev.thep.lu.se/yat
@@ -114 +115 @@
   suite.err() << "build ensemble" << std::endl;
   ensemble.build();
+  utility::Vector out(target.size(),0);
+  for (size_t i = 0; i<out.size(); ++i) {
+    out(i)=ensemble.validate()[0][i].mean(); 
+  }
+  statistics::AUC roc;
+  suite.err() << roc.score(target,out) << std::endl;
+
   std::vector<std::vector<statistics::Averager> > result;
   ensemble.predict(kernel_lookup, result);
-  
-  utility::Vector out(target.size(),0);
-  for (size_t i = 0; i<out.size(); ++i)
-    out(i)=ensemble.validate()[0][i].mean(); 
-  statistics::AUC roc;
-  suite.err() << roc.score(target,out) << std::endl;
+  for (size_t i = 0; i<result.size(); ++i) {
+    for (size_t j=0; j<result[0].size(); ++j) {
+      if (!suite.add(result[i][j].variance() > 0)) {
+        suite.err() << "error: element " << i << " " << j << "\n";
+        suite.err() << "expected finite prediction varince\n";
+        suite.err() << "found: " << result[i][j].variance() << "\n";
+      }
+    } 
+  }
+
+  {
+    suite.err() << "test ensemble of SVMs with weighted kernel" << std::endl;
+    classifier::MatrixLookupWeighted wdata(data_core);
+    classifier::Kernel_SEV kernel(wdata, *kf);
+    classifier::KernelLookup wkl(kernel);
+    classifier::EnsembleBuilder<classifier::SVM, classifier::KernelLookup> 
+      ensemble(svm, wkl, sampler);
+    suite.err() << "build ensemble" << std::endl;
+    ensemble.build();
+    ensemble.validate(); 
+    std::vector<std::vector<statistics::Averager> > result;
+    ensemble.predict(wkl, result);
+  }
 
   {

trunk/yat/Makefile.am

@@ -6 +6 @@
 # Copyright (C) 2004 Jari Häkkinen, Peter Johansson, Cecilia Ritz
 # Copyright (C) 2005, 2006, 2007, 2008 Jari Häkkinen, Peter Johansson
+# Copyright (C) 2009 Peter Johansson
 # Copyright (C) 2009 Peter Johansson
 #
@@ -32 +33 @@
 nodist_EXTRA_libyat_la_SOURCES = dummy.cc
 
-libyat_la_LDFLAGS = -version-info $(YAT_LT_VERSION)
+libyat_la_LDFLAGS = -version-info $(YAT_LT_VERSION) $(AM_LDFLAGS)
 
 libyat_la_LIBADD = \

trunk/yat/classifier/EnsembleBuilder.h

@@ -193 +193 @@
       Data sub_data = test_data(data, k);
       classifier(k).predict(sub_data,prediction);
-    }
-
-    for(size_t i=0; i<prediction.rows();i++) 
-      for(size_t j=0; j<prediction.columns();j++) 
-        result[i][j].add(prediction(i,j));    
+      for(size_t i=0; i<prediction.rows();i++) 
+        for(size_t j=0; j<prediction.columns();j++) 
+          result[i][j].add(prediction(i,j));    
+    }
   }
 
@@ -233 +232 @@
     // weighted case
     if (kernel.weighted()){
-      YAT_ASSERT(false);
       // no feature selection
       if (kernel.data_weighted().rows()==subset_->training_features(k).size())
@@ -248 +246 @@
     
     // feature selection
-    return subset_->training_data(k).test_kernel(test_data(kernel.data(),k));
+    MatrixLookup ml = test_data(kernel.data(),k);
+    return subset_->training_data(k).test_kernel(ml);
   }