Merge pull request #389 from karlnapf/master

some news updates
shogun-toolbox · Mar 24, 2012 · f399095 · f399095
2 parents 0445a57 + e5ce2ef
commit f399095
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Showing 3 changed files with 12 additions and 3 deletions.
diff --git a/src/NEWS b/src/NEWS
@@ -13,14 +13,23 @@
 		  new rejection schemes concept by Sergey Lisitsyn.
 		- New regression estimation algorithms.
 		- New graphical python modular examples.
+		- Standard Cross-Validation splitting for regression problems by Heiko Strathmann
+		- New data-locking concept by Heiko Strathmann which allows to tell machines that data
+		  is not going to change during training/testing until unlocked.
+		  KernelMachines now make use of that by not recomputing kernel matrix in cross-validation.
+		- Cross-validation for KernelMachines is now parallelized.
+		- Cross-validation is now possible with custom kernels.
 	* Bugfixes:
 		- Fix for bug in the Gaussian Naive Bayes classifier, its domain was
 		  changed to log-space.
 		- Fix for R_static interface installation (thanks Steve Lianoglou).
 		- SVMOcas memsetting and max_train_time bugfix.
 		- Various fixes for compile errors with clang.
+		- Stratified-cross-validation now used different indices for each run.
 	* Cleanup and API Changes:
-		- Improvements for parameters handling and serialization by Heiko Strathmann.
+		- Parameter migration framework by Heiko Strathmann. From now on,
+		  changes in the shogun objects will not break loading old serialized
+		  files anymore
 
 2011-12-01 Soeren Sonnenburg <sonne@debian.org>
 

diff --git a/src/shogun/base/SGObject.cpp b/src/shogun/base/SGObject.cpp
@@ -906,7 +906,7 @@ TParameter* CSGObject::migrate(DynArray<TParameter*>* param_base,
 		SG_FREE(s);
 	}
 
-	SG_PRINT("leaving CSGObject::migrate\n");
+//	SG_PRINT("leaving CSGObject::migrate\n");
 
 	return result;
 }

diff --git a/src/shogun/machine/Machine.cpp b/src/shogun/machine/Machine.cpp
@@ -31,7 +31,7 @@ CMachine::CMachine() : CSGObject(), m_max_train_time(0), m_labels(NULL),
 	SG_ADD(&m_store_model_features, "store_model_features",
 	       "Should feature data of model be stored after training?", MS_NOT_AVAILABLE);
 	SG_ADD(&m_data_locked, "data_locked",
-	       "Indicates whether data is locked", MS_NOT_AVAILABLE);
+			"Indicates whether data is locked", MS_NOT_AVAILABLE);
 
 	m_parameter_map->put(
 		new SGParamInfo("data_locked", CT_SCALAR, ST_NONE, PT_BOOL, 1),