diff options
| -rw-r--r-- | src/python/doc/wasserstein_distance_user.rst | 5 | ||||
| -rw-r--r-- | src/python/gudhi/hera.cc | 19 |
2 files changed, 8 insertions, 16 deletions
diff --git a/src/python/doc/wasserstein_distance_user.rst b/src/python/doc/wasserstein_distance_user.rst index 6cd7f3a0..355ad247 100644 --- a/src/python/doc/wasserstein_distance_user.rst +++ b/src/python/doc/wasserstein_distance_user.rst @@ -11,8 +11,9 @@ Definition Functions --------- -This implementation is based on ideas from "Large Scale Computation of Means -and Cluster for Persistence Diagrams via Optimal Transport". +This implementation uses the Python Optimal Transport library and is based on +ideas from "Large Scale Computation of Means and Cluster for Persistence +Diagrams via Optimal Transport". .. autofunction:: gudhi.wasserstein.wasserstein_distance diff --git a/src/python/gudhi/hera.cc b/src/python/gudhi/hera.cc index 898040fb..61f0da10 100644 --- a/src/python/gudhi/hera.cc +++ b/src/python/gudhi/hera.cc @@ -10,16 +10,6 @@ namespace py = pybind11; typedef py::array_t<double, py::array::c_style | py::array::forcecast> Dgm; -namespace hera { -template <> struct DiagramTraits<Dgm>{ - using PointType = std::array<double,2>; - using RealType = double; - - static RealType get_x(const PointType& p) { return std::get<0>(p); } - static RealType get_y(const PointType& p) { return std::get<1>(p); } -}; -} - double wasserstein_distance( Dgm d1, Dgm d2, double wasserstein_power, double internal_p, @@ -32,7 +22,7 @@ double wasserstein_distance( throw std::runtime_error("Diagram 1 must be an array of size n x 2"); if((buf2.ndim!=2 || buf2.shape[1]!=2) && (buf2.ndim!=1 || buf2.shape[0]!=0)) throw std::runtime_error("Diagram 2 must be an array of size n x 2"); - typedef hera::DiagramTraits<Dgm>::PointType Point; + typedef std::array<double, 2> Point; auto p1 = (Point*)buf1.ptr; auto p2 = (Point*)buf2.ptr; auto diag1 = boost::make_iterator_range(p1, p1+buf1.shape[0]); @@ -52,16 +42,17 @@ PYBIND11_MODULE(hera, m) { m.def("wasserstein_distance", &wasserstein_distance, py::arg("X"), py::arg("Y"), // Should we name those q, p and d instead? - py::arg("wasserstein_power") = 1, + py::arg("order") = 1, py::arg("internal_p") = std::numeric_limits<double>::infinity(), py::arg("delta") = .01, R"pbdoc( - Compute the Wasserstein distance between two diagrams. Points at infinity are supported. + Compute the Wasserstein distance between two diagrams. + Points at infinity are supported. Parameters: X (n x 2 numpy array): First diagram Y (n x 2 numpy array): Second diagram - wasserstein_power (float): Wasserstein degree W_q + order (float): Wasserstein exponent W_q internal_p (float): Internal Minkowski norm L^p in R^2 delta (float): Relative error 1+delta |