int ret = linear_sum_assignment(C, row_ind, col_ind);
EXPECT_EQ(ret, 0);
}
-
-
-TEST(Distance, consine)
-{
- Vector3d u, v;
- u << 1, 0, 0;
- v << 0, 1, 0;
- double d = distance_cosine(u, v);
- EXPECT_DOUBLE_EQ(d, 1.0);
-
- u << 100, 0, 0;
- v << 0, 1, 0;
- d = distance_cosine(u, v);
- EXPECT_DOUBLE_EQ(d, 1.0);
-
- u << 1, 1, 0;
- v << 0, 1, 0;
- d = distance_cosine(u, v);
- EXPECT_TRUE(std::abs(d - 0.2928932) < 0.0001);
-}
-
-TEST(Distance, euclidean)
-{
- Vector3d u, v;
- u << 1, 0, 0;
- v << 0, 1, 0;
- double d = distance_euclidean(u, v);
- EXPECT_TRUE(std::abs(d - 1.41421356) < 0.0001);
-
- u << 1, 1, 0;
- v << 0, 1, 0;
- d = distance_euclidean(u, v);
- EXPECT_DOUBLE_EQ(d, 1.0);
-}
-
-TEST(Distance, vector)
-{
- std::vector<int> sv = {1, 2, 3, 4, 5, 6};
- VectorXi v1;
- VectorXi b = Eigen::Map<VectorXi>(sv.data(), sv.size());
- std::cout << b << std::endl;
- std::vector<float> f1_hog = { 0.1, 0.2, 0,3};
-// Eigen::Map<Eigen::VectorXd>(f2_hog.data(), f2_hog.size())
-
- //VectorXd mf = Map<VectorXd, 0, InnerStride<2> >(sv.data(), sv.size());
- std::vector<double> sd = {1, 2, 3, 4, 5, 6};
- VectorXd mm = Map<VectorXd>(sd.data(), sd.size());
- VectorXd xd = Map<VectorXd, 0, InnerStride<2> >(sd.data(), sd.size());
- cout << Map<VectorXd, 0, InnerStride<2> >(sd.data(), sd.size()) << endl;
-
- int array[12];
- for(int i = 0; i < 12; ++i) array[i] = i;
- cout << Map<VectorXi, 0, InnerStride<2> >(sv.data(), sv.size()) // the inner stride has already been passed as template parameter
- << endl;
-
-
- //Vector3d v = Vector3d::Random();
- //std::cout << v << std::endl;
-
-}
-
-