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tests/unit/preprocessor/NormOne_unittest.cc

@@ -4,27 +4,28 @@
  * Authors: Wuwei Lin
  */
 
+#include <gtest/gtest.h>
 #include <shogun/mathematics/Math.h>
 #include <shogun/preprocessor/NormOne.h>
-#include <gtest/gtest.h>
 
 using namespace shogun;
 
 class NormOne : public ::testing::Test
 {
 public:
 	NormOne()
-		: feats(Some<CDenseFeatures<float64_t>>::from_raw(nullptr))
-		, transformer(some<CNormOne>())
+	    : feats(Some<CDenseFeatures<float64_t>>::from_raw(nullptr)),
+	      transformer(some<CNormOne>())
 	{
 		SGMatrix<float64_t> m(data, num_features, num_vectors, false);
 		m = m.clone();
 		feats = some<CDenseFeatures<float64_t>>(m);
 	}
 
 protected:
-	float64_t data[6] = {1,2,3,4,5,6};
-	float64_t norm [2] = {std::sqrt(1+2*2+3*3), std::sqrt(4*4+5*5+6*6)};
+	float64_t data[6] = {1, 2, 3, 4, 5, 6};
+	float64_t norm[2] = {std::sqrt(1 + 2 * 2 + 3 * 3),
+	                     std::sqrt(4 * 4 + 5 * 5 + 6 * 6)};
 	int32_t num_vectors = 2;
 	int32_t num_features = 3;
 
@@ -35,16 +36,18 @@ class NormOne : public ::testing::Test
 TEST_F(NormOne, transform)
 {
 	transformer->fit(feats);
-	feats = wrap(transformer->transform(feats)->as<CDenseFeatures<float64_t>>());
+	feats =
+	    wrap(transformer->transform(feats)->as<CDenseFeatures<float64_t>>());
 
 	EXPECT_EQ(feats->get_num_vectors(), num_vectors);
 
 	for (auto i : range(num_vectors))
 	{
 		SGVector<float64_t> v = feats->get_feature_vector(i);
 		EXPECT_EQ(v.vlen, num_features);
-		for (auto j : range(v.vlen)) {
-			EXPECT_DOUBLE_EQ(v[j], data[num_features*i+j]/norm[i]);
+		for (auto j : range(v.vlen))
+		{
+			EXPECT_DOUBLE_EQ(v[j], data[num_features * i + j] / norm[i]);
 		}
 	}
 }
@@ -58,8 +61,9 @@ TEST_F(NormOne, apply_to_vector)
 		SGVector<float64_t> v = feats->get_feature_vector(i);
 		auto result = transformer->apply_to_feature_vector(v);
 		EXPECT_EQ(result.vlen, num_features);
-		for (auto j : range(v.vlen)) {
-			EXPECT_DOUBLE_EQ(result[j], data[num_features*i+j]/norm[i]);
+		for (auto j : range(v.vlen))
+		{
+			EXPECT_DOUBLE_EQ(result[j], data[num_features * i + j] / norm[i]);
 		}
 	}
 }