modified constructor of CResultSet; added checks in HashedMultilabelM…

…odel::argmax() method
shogun-toolbox · Jun 18, 2014 · 44f064b · 44f064b
1 parent ebb952f
commit 44f064b
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Showing 3 changed files with 18 additions and 7 deletions.
diff --git a/src/shogun/structure/HashedMultilabelModel.cpp b/src/shogun/structure/HashedMultilabelModel.cpp
@@ -262,6 +262,7 @@ CResultSet * CHashedMultilabelModel::argmax(SGVector<float64_t> w,
 
 	SGVector<float64_t> y_pred_dense(m_num_classes);
 	y_pred_dense.zero();
+	int32_t count = 0;
 
 	for (int32_t c = 0; c < m_num_classes; c++)
 	{
@@ -278,11 +279,13 @@ CResultSet * CHashedMultilabelModel::argmax(SGVector<float64_t> w,
 		{
 			y_pred_dense[c] = 1;
 			total_score += score;
+			count++;
 		}
 
 	}
 
 	SGVector<int32_t> y_pred_sparse = to_sparse(y_pred_dense, 1, 0);
+	ASSERT(count == y_pred_sparse.vlen);
 
 	CResultSet * ret = new CResultSet();
 	SG_REF(ret);

diff --git a/src/shogun/structure/StructuredModel.cpp b/src/shogun/structure/StructuredModel.cpp
@@ -14,7 +14,15 @@
 using namespace shogun;
 
 CResultSet::CResultSet()
-: CSGObject(), argmax(NULL), score(0), delta(0)
+: CSGObject(), argmax(NULL),
+	psi_computed_sparse(false),
+	psi_computed(false),
+	psi_truth(SGVector<float64_t>(0)),
+	psi_pred(SGVector<float64_t>(0)),
+	psi_truth_sparse(SGSparseVector<float64_t>(0)),
+	psi_pred_sparse(SGSparseVector<float64_t>(0)),
+	score(0),
+	delta(0)
 {
 }
 

diff --git a/src/shogun/structure/StructuredModel.h b/src/shogun/structure/StructuredModel.h
@@ -63,22 +63,22 @@ struct CResultSet : public CSGObject
 	CStructuredData* argmax;
 
 	/** whether joint feature vector is sparse or not */
-	bool psi_computed_sparse = false;
+	bool psi_computed_sparse;
 
 	/** whether joint feature vector is dense or not */
-	bool psi_computed = false;
+	bool psi_computed;
 
 	/** joint feature vector for the given truth */
-	SGVector< float64_t > psi_truth = SGVector<float64_t>(0);
+	SGVector< float64_t > psi_truth;
 
 	/** joint feature vector for the prediction */
-	SGVector< float64_t > psi_pred = SGVector<float64_t>(0);
+	SGVector< float64_t > psi_pred;
 
 	/** joint feature vector for the given truth */
-	SGSparseVector< float64_t > psi_truth_sparse = SGSparseVector<float64_t>(0);
+	SGSparseVector< float64_t > psi_truth_sparse;
 
 	/** joint feature vector for the prediction */
-	SGSparseVector< float64_t > psi_pred_sparse = SGSparseVector<float64_t>(0);
+	SGSparseVector< float64_t > psi_pred_sparse;
 
 	/** \f$ \Delta(y_{pred}, y_{truth}) + \langle w,
 	 *  \Psi(x_{truth}, y_{pred}) - \Psi(x_{truth}, y_{truth}) \rangle \f$ */