Use the built-in get_unique_labels instead of wrting a new function.

shogun-toolbox · Apr 5, 2012 · 580d1f8 · 580d1f8
1 parent 49b6e2e
commit 580d1f8
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Showing 3 changed files with 21 additions and 31 deletions.
diff --git a/src/shogun/evaluation/ClusteringEvaluation.cpp b/src/shogun/evaluation/ClusteringEvaluation.cpp
@@ -19,16 +19,6 @@
 using namespace shogun;
 using namespace std;
 
-vector<int32_t> CClusteringEvaluation::unique_labels(CLabels* labels)
-{
-	std::set<int32_t> uniq_lbl;
-	for (int32_t i=labels->get_num_labels()-1; i >= 0; --i)
-	{
-		uniq_lbl.insert(labels->get_int_label(i));
-	}
-	return std::vector<int32_t>(uniq_lbl.begin(), uniq_lbl.end());
-}
-
 int32_t CClusteringEvaluation::find_match_count(const SGVector<int32_t>& l1, int32_t m1, const SGVector<int32_t>& l2, int32_t m2)
 {
 	int32_t match_count=0;
@@ -49,36 +39,37 @@ int32_t CClusteringEvaluation::find_mismatch_count(const SGVector<int32_t>& l1,
 void CClusteringEvaluation::best_map(CLabels* predicted, CLabels* ground_truth)
 {
 	ASSERT(predicted->get_num_labels() == ground_truth->get_num_labels());
-	std::vector<int32_t> label_p=unique_labels(predicted);
-	std::vector<int32_t> label_g=unique_labels(ground_truth);
+	SGVector<float64_t> label_p=predicted->get_unique_labels();
+	SGVector<float64_t> label_g=ground_truth->get_unique_labels();
 
 	SGVector<int32_t> predicted_ilabels=predicted->get_int_labels();
 	SGVector<int32_t> groundtruth_ilabels=ground_truth->get_int_labels();
 
-	int32_t n_class=max(label_p.size(), label_g.size());
+	int32_t n_class=max(label_p.vlen, label_g.vlen);
 	SGMatrix<float64_t> G(n_class, n_class);
 	G.zero();
 
-	for (size_t i=0; i < label_g.size(); ++i)
+	for (size_t i=0; i < label_g.vlen; ++i)
 	{
-		for (size_t j=0; j < label_p.size(); ++j)
+		for (size_t j=0; j < label_p.vlen; ++j)
 		{
-			G(i, j)=find_mismatch_count(groundtruth_ilabels, label_g[i],
-				predicted_ilabels, label_p[j]);
+			G(i, j)=find_mismatch_count(groundtruth_ilabels, static_cast<int32_t>(label_g[i]),
+				predicted_ilabels, static_cast<int32_t>(label_p[j]));
 		}
 	}
 
 	Munkres munkres_solver(G);
 	munkres_solver.solve();
 
 	std::map<int32_t, int32_t> label_map;
-	for (size_t i=0; i < label_p.size(); ++i)
+	for (size_t i=0; i < label_p.vlen; ++i)
 	{
-		for (size_t j=0; j < label_g.size(); ++j)
+		for (size_t j=0; j < label_g.vlen; ++j)
 		{
 			if (G(j, i) == 0)
 			{
-				label_map.insert(make_pair(label_p[i], label_g[j]));
+				label_map.insert(make_pair(static_cast<int32_t>(label_p[i]), 
+						static_cast<int32_t>(label_g[j])));
 				break;
 			}
 		}
@@ -87,5 +78,7 @@ void CClusteringEvaluation::best_map(CLabels* predicted, CLabels* ground_truth)
 	for (int32_t i= 0; i < predicted_ilabels.vlen; ++i)
 		predicted->set_int_label(i, label_map[predicted_ilabels[i]]);
 
+	label_p.free_vector();
+	label_g.free_vector();
 	G.destroy_matrix();
 }
diff --git a/src/shogun/evaluation/ClusteringEvaluation.h b/src/shogun/evaluation/ClusteringEvaluation.h
@@ -46,13 +46,6 @@ class CClusteringEvaluation: public CEvaluation
 	 */
 	virtual float64_t evaluate(CLabels* predicted, CLabels* ground_truth) = 0;
 protected:
-	/** get a vector of unique labels occured
-	 *
-	 * @param labels labels to be investigated
-	 * @return a vector of unique labels
-	 */
-	std::vector<int32_t> unique_labels(CLabels* labels);
-
 	/** find number of matches in the two labels sequence.
 	 *
 	 * For each index i, if l1[i] == m1 and l2[i] == m2, then we get a match.

diff --git a/src/shogun/evaluation/ClusteringMutualInformation.cpp b/src/shogun/evaluation/ClusteringMutualInformation.cpp
@@ -18,12 +18,12 @@ using namespace shogun;
 
 float64_t CClusteringMutualInformation::evaluate(CLabels* predicted, CLabels* ground_truth)
 {
-	std::vector<int32_t> label_p=unique_labels(predicted);
-	std::vector<int32_t> label_g=unique_labels(ground_truth);
+	SGVector<float64_t> label_p=predicted->get_unique_labels();
+	SGVector<float64_t> label_g=ground_truth->get_unique_labels();
 
-	if (label_p.size() != label_g.size())
+	if (label_p.vlen != label_g.vlen)
 		SG_ERROR("Number of classes are different\n");
-	uint32_t n_class=label_p.size();
+	uint32_t n_class=label_p.vlen;
 	float64_t n_label=predicted->get_num_labels();
 
 	SGVector<int32_t> ilabels_p=predicted->get_int_labels();
@@ -65,5 +65,9 @@ float64_t CClusteringMutualInformation::evaluate(CLabels* predicted, CLabels* gr
 		entropy_p += -G_colsum[i] * log(G_colsum[i])/log(2.);
 	}
 
+	label_p.free_vector();
+	label_g.free_vector();
+	G.destroy_matrix();
+
 	return mutual_info / std::max(entropy_g, entropy_p);
 }